diff --git a/_toc.yml b/_toc.yml
index 3d0acf65483759695c47852e9bc256e4bb7f83ef..09794b616a33569683a2768d271c187dba90bc21 100644
--- a/_toc.yml
+++ b/_toc.yml
@@ -12,7 +12,7 @@
   chapters:
   - file: federated_learning/introduction.md
 
-  - file: federated_learning/FedAvg_FedProx_MNISt_iid_and_nidd.ipynb
+  - file: federated_learning/FedAvg_FedProx_MNIST_iid_and_noniid.ipynb 
     title: FedAVG and FedProx
 
   - file: federated_learning/federated_mcvae.ipynb
diff --git a/federated_learning/FedAvg_FedProx_MNIST_iid_and_noniid.ipynb b/federated_learning/FedAvg_FedProx_MNIST_iid_and_noniid.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4d8e868fced82577703d2bb704922701c083ef9d
--- /dev/null
+++ b/federated_learning/FedAvg_FedProx_MNIST_iid_and_noniid.ipynb
@@ -0,0 +1,1131 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example of FedAvg and FedProx for two datasets: MNIST iid and MNIST non-iid\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch.optim as optim\n",
+    "\n",
+    "from copy import deepcopy\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. MNIST iid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data loading and visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First of all, we load the train and test MNIST dataset and randomly split them in 3 non-overlapping datasets. We will use them for the 3 different nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from create_MNIST_datasets import get_MNIST, plot_samples\n",
+    "mnist_iid_train_dls, mnist_iid_test_dls = get_MNIST(\"iid\",\n",
+    "    n_samples_train =200, n_samples_test=100, n_clients =3, \n",
+    "    batch_size =25, shuffle =True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following cell we plot some samples from the 3 datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_samples(next(iter(mnist_iid_train_dls[0])), 0, \"Client 1\")\n",
+    "plot_samples(next(iter(mnist_iid_train_dls[1])), 0, \"Client 2\")\n",
+    "plot_samples(next(iter(mnist_iid_train_dls[2])), 0, \"Client 3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, all the digits are represented in each client. Indeed we split the dataset so to have independent and identically distributed (iid) samples."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We define a convolutional neural network (CNN) for digits classification. We also define the functions for training, and for computing loss and accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CNN(nn.Module):\n",
+    "\n",
+    "    \"\"\"ConvNet -> Max_Pool -> RELU -> ConvNet -> \n",
+    "    Max_Pool -> RELU -> FC -> RELU -> FC -> SOFTMAX\"\"\"\n",
+    "    def __init__(self):\n",
+    "        super(CNN, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(1, 20, 5, 1)\n",
+    "        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n",
+    "        self.fc1 = nn.Linear(4*4*50, 500)\n",
+    "        self.fc2 = nn.Linear(500, 10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = F.relu(self.conv1(x))\n",
+    "        x = F.max_pool2d(x, 2, 2)\n",
+    "        x = F.relu(self.conv2(x))\n",
+    "        x = F.max_pool2d(x, 2, 2)\n",
+    "        x = x.view(-1, 4*4*50)\n",
+    "        x = F.relu(self.fc1(x))\n",
+    "        x = self.fc2(x)\n",
+    "        return x\n",
+    "    \n",
+    "\n",
+    "model_0 = CNN()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def loss_classifier(predictions,labels):\n",
+    "    \n",
+    "    m = nn.LogSoftmax(dim=1)\n",
+    "    loss = nn.NLLLoss(reduction=\"mean\")\n",
+    "    \n",
+    "    return loss(m(predictions) ,labels.view(-1))\n",
+    "\n",
+    "\n",
+    "def loss_dataset(model, dataset, loss_f):\n",
+    "    \"\"\"Compute the loss of `model` on `dataset`\"\"\"\n",
+    "    loss=0\n",
+    "    \n",
+    "    for idx,(features,labels) in enumerate(dataset):\n",
+    "        \n",
+    "        predictions= model(features)\n",
+    "        loss+=loss_f(predictions,labels)\n",
+    "    \n",
+    "    loss/=idx+1\n",
+    "    return loss\n",
+    "\n",
+    "\n",
+    "def accuracy_dataset(model, dataset):\n",
+    "    \"\"\"Compute the accuracy of `model` on `dataset`\"\"\"\n",
+    "    \n",
+    "    correct=0\n",
+    "    \n",
+    "    for features,labels in iter(dataset):\n",
+    "        \n",
+    "        predictions= model(features)\n",
+    "        \n",
+    "        _,predicted=predictions.max(1,keepdim=True)\n",
+    "        \n",
+    "        correct+=torch.sum(predicted.view(-1,1)==labels.view(-1, 1)).item()\n",
+    "        \n",
+    "    accuracy = 100*correct/len(dataset.dataset)\n",
+    "        \n",
+    "    return accuracy\n",
+    "\n",
+    "\n",
+    "def train_step(model, model_0, mu:int, optimizer, train_data, loss_f):\n",
+    "    \"\"\"Train `model` on one epoch of `train_data`\"\"\"\n",
+    "    \n",
+    "    total_loss=0\n",
+    "    \n",
+    "    for idx, (features,labels) in enumerate(train_data):\n",
+    "        \n",
+    "        optimizer.zero_grad()\n",
+    "        \n",
+    "        predictions= model(features)\n",
+    "        \n",
+    "        loss=loss_f(predictions,labels)\n",
+    "        loss+=mu/2*difference_models_norm_2(model,model_0)\n",
+    "        total_loss+=loss\n",
+    "        \n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        \n",
+    "    return total_loss/(idx+1)\n",
+    "\n",
+    "\n",
+    "\n",
+    "def local_learning(model, mu:float, optimizer, train_data, epochs:int, loss_f):\n",
+    "    \n",
+    "    model_0=deepcopy(model)\n",
+    "    \n",
+    "    for e in range(epochs):\n",
+    "        local_loss=train_step(model,model_0,mu,optimizer,train_data,loss_f)\n",
+    "        \n",
+    "    return float(local_loss.detach().numpy())\n",
+    "\n",
+    "\n",
+    "def difference_models_norm_2(model_1, model_2):\n",
+    "    \"\"\"Return the norm 2 difference between the two model parameters\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    tensor_1=list(model_1.parameters())\n",
+    "    tensor_2=list(model_2.parameters())\n",
+    "    \n",
+    "    norm=sum([torch.sum((tensor_1[i]-tensor_2[i])**2) \n",
+    "        for i in range(len(tensor_1))])\n",
+    "    \n",
+    "    return norm\n",
+    "\n",
+    "\n",
+    "def set_to_zero_model_weights(model):\n",
+    "    \"\"\"Set all the parameters of a model to 0\"\"\"\n",
+    "\n",
+    "    for layer_weigths in model.parameters():\n",
+    "        layer_weigths.data.sub_(layer_weigths.data)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aggregation strategies\n",
+    "\n",
+    "Federated learning requires to define an aggregation strategy, i.e. a method to combine the local models coming from the clients into a global one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Federated averaging**\n",
+    "\n",
+    "The standard and simplest aggregation strategy is federated averaging ([FedAvg](https://arxiv.org/pdf/1602.05629.pdf)).\n",
+    "\n",
+    "The learning is performed in rounds. At each round, the server samples a set of $m$ clients (out of the total $K$ clients) which will be considered for the current iteration and sends them the current global model.\n",
+    "These clients update the parameters of their local copy of the model by optimizing the loss $F_k$ on their local training data using SGD for $E$ epochs. At the end of the round, the local parameters are sent to the server, which aggregates them by performing a weighted average. The aggregated parameters define the global model for the next round."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**FedProx**\n",
+    "\n",
+    "Another strategy is [FedProx](https://arxiv.org/pdf/1812.06127.pdf), which is a generalization of FedAvg with some modifications to address heterogeneity of data and systems.\n",
+    "\n",
+    "The learning is again performed in rounds. At each round, the server samples a set of $m$ clients and sends them the current global model.\n",
+    "Differently from FedAvg, here the clients optimize a regularized loss with a proximal term. In particular, the new function to minimize is $F_k(\\omega) + \\frac{\\mu}{2}||\\omega - \\omega^t ||^2$, where $F_k$ is the loss, $\\omega$ are the local parameter to optimize, and $\\omega^t$ are the global parameters at time $t$.   \n",
+    "Moreover we run the local optimization for a variable number of epochs according to the system resources (so that slow clients can also contribute to the training with a reduced number of epochs). \n",
+    "As for FedAvg, the local parameters are sent to the server and aggregated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE** FedAvg is a particular case of FedProx with $\\mu=0$. So, we just need to implement the code for FedProx, which we will be used also for FedAvg by setting the parameter *mu=0*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def average_models(model, clients_models_hist:list , weights:list):\n",
+    "\n",
+    "\n",
+    "    \"\"\"Creates the new model of a given iteration with the models of the other\n",
+    "    clients\"\"\"\n",
+    "    \n",
+    "    new_model=deepcopy(model)\n",
+    "    set_to_zero_model_weights(new_model)\n",
+    "\n",
+    "    for k,client_hist in enumerate(clients_models_hist):\n",
+    "        \n",
+    "        for idx, layer_weights in enumerate(new_model.parameters()):\n",
+    "\n",
+    "            contribution=client_hist[idx].data*weights[k]\n",
+    "            layer_weights.data.add_(contribution)\n",
+    "            \n",
+    "    return new_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def FedProx(model, training_sets:list, n_iter:int, testing_sets:list, mu=0, \n",
+    "    file_name=\"test\", epochs=5, lr=10**-2, decay=1):\n",
+    "    \"\"\" all the clients are considered in this implementation of FedProx\n",
+    "    Parameters:\n",
+    "        - `model`: common structure used by the clients and the server\n",
+    "        - `training_sets`: list of the training sets. At each index is the \n",
+    "            training set of client \"index\"\n",
+    "        - `n_iter`: number of iterations the server will run\n",
+    "        - `testing_set`: list of the testing sets. If [], then the testing\n",
+    "            accuracy is not computed\n",
+    "        - `mu`: regularization term for FedProx. mu=0 for FedAvg\n",
+    "        - `epochs`: number of epochs each client is running\n",
+    "        - `lr`: learning rate of the optimizer\n",
+    "        - `decay`: to change the learning rate at each iteration\n",
+    "    \n",
+    "    returns :\n",
+    "        - `model`: the final global model \n",
+    "    \"\"\"\n",
+    "        \n",
+    "    loss_f=loss_classifier\n",
+    "    \n",
+    "    #Variables initialization\n",
+    "    K=len(training_sets) #number of clients\n",
+    "    n_samples=sum([len(db.dataset) for db in training_sets])\n",
+    "    weights=([len(db.dataset)/n_samples for db in training_sets])\n",
+    "    print(\"Clients' weights:\",weights)\n",
+    "    \n",
+    "    \n",
+    "    loss_hist=[[float(loss_dataset(model, dl, loss_f).detach()) \n",
+    "        for dl in training_sets]]\n",
+    "    acc_hist=[[accuracy_dataset(model, dl) for dl in testing_sets]]\n",
+    "    server_hist=[[tens_param.detach().numpy() \n",
+    "        for tens_param in list(model.parameters())]]\n",
+    "    models_hist = []\n",
+    "    \n",
+    "    \n",
+    "    server_loss=sum([weights[i]*loss_hist[-1][i] for i in range(len(weights))])\n",
+    "    server_acc=sum([weights[i]*acc_hist[-1][i] for i in range(len(weights))])\n",
+    "    print(f'====> i: 0 Loss: {server_loss} Server Test Accuracy: {server_acc}')\n",
+    "    \n",
+    "    for i in range(n_iter):\n",
+    "        \n",
+    "        clients_params=[]\n",
+    "        clients_models=[]\n",
+    "        clients_losses=[]\n",
+    "        \n",
+    "        for k in range(K):\n",
+    "            \n",
+    "            local_model=deepcopy(model)\n",
+    "            local_optimizer=optim.SGD(local_model.parameters(),lr=lr)\n",
+    "            \n",
+    "            local_loss=local_learning(local_model,mu,local_optimizer,\n",
+    "                training_sets[k],epochs,loss_f)\n",
+    "            \n",
+    "            clients_losses.append(local_loss)\n",
+    "                \n",
+    "            #GET THE PARAMETER TENSORS OF THE MODEL\n",
+    "            list_params=list(local_model.parameters())\n",
+    "            list_params=[tens_param.detach() for tens_param in list_params]\n",
+    "            clients_params.append(list_params)    \n",
+    "            clients_models.append(deepcopy(local_model))\n",
+    "        \n",
+    "        \n",
+    "        #CREATE THE NEW GLOBAL MODEL\n",
+    "        model = average_models(deepcopy(model), clients_params, \n",
+    "            weights=weights)\n",
+    "        models_hist.append(clients_models)\n",
+    "        \n",
+    "        #COMPUTE THE LOSS/ACCURACY OF THE DIFFERENT CLIENTS WITH THE NEW MODEL\n",
+    "        loss_hist+=[[float(loss_dataset(model, dl, loss_f).detach()) \n",
+    "            for dl in training_sets]]\n",
+    "        acc_hist+=[[accuracy_dataset(model, dl) for dl in testing_sets]]\n",
+    "\n",
+    "        server_loss=sum([weights[i]*loss_hist[-1][i] for i in range(len(weights))])\n",
+    "        server_acc=sum([weights[i]*acc_hist[-1][i] for i in range(len(weights))])\n",
+    "\n",
+    "        print(f'====> i: {i+1} Loss: {server_loss} Server Test Accuracy: {server_acc}')\n",
+    "        \n",
+    "\n",
+    "        server_hist.append([tens_param.detach().cpu().numpy() \n",
+    "            for tens_param in list(model.parameters())])\n",
+    "        \n",
+    "        #DECREASING THE LEARNING RATE AT EACH SERVER ITERATION\n",
+    "        lr*=decay\n",
+    "            \n",
+    "    return model, loss_hist, acc_hist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Federated training with FedAvg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will now train the model on the 3 clients, using FedAvg aggregation strategy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE** In this notebook, during the training, we will consider the 3 clients for each round ($m=K=3$)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clients' weights: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n",
+      "====> i: 0 Loss: 2.3077298005421953 Server Test Accuracy: 7.333333333333333\n",
+      "====> i: 1 Loss: 2.2404345671335855 Server Test Accuracy: 19.666666666666664\n",
+      "====> i: 2 Loss: 1.8850175539652505 Server Test Accuracy: 45.33333333333333\n",
+      "====> i: 3 Loss: 1.0085218747456868 Server Test Accuracy: 75.33333333333333\n",
+      "====> i: 4 Loss: 0.6295895576477051 Server Test Accuracy: 79.0\n",
+      "====> i: 5 Loss: 0.5280802349249522 Server Test Accuracy: 79.33333333333333\n",
+      "====> i: 6 Loss: 0.2651289403438568 Server Test Accuracy: 90.33333333333333\n",
+      "====> i: 7 Loss: 0.2068410962820053 Server Test Accuracy: 90.33333333333333\n",
+      "====> i: 8 Loss: 0.18058088421821591 Server Test Accuracy: 91.0\n",
+      "====> i: 9 Loss: 0.15036222835381824 Server Test Accuracy: 90.33333333333333\n",
+      "====> i: 10 Loss: 0.15352871268987656 Server Test Accuracy: 93.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train with FedAvg -> FedProx with mu=0\n",
+    "\n",
+    "n_iter=10\n",
+    "\n",
+    "model_f, loss_hist_FA_iid, acc_hist_FA_iid = FedProx( model_0, \n",
+    "    mnist_iid_train_dls, n_iter, mnist_iid_test_dls, epochs =2, \n",
+    "    lr =0.1, mu=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_acc_loss(title:str, loss_hist:list, acc_hist:list):\n",
+    "    plt.figure()\n",
+    "    \n",
+    "    plt.suptitle(title)\n",
+    "\n",
+    "    plt.subplot(1,2,1)\n",
+    "    lines=plt.plot(loss_hist)\n",
+    "    plt.title(\"Loss\")\n",
+    "    plt.legend(lines,[\"C1\", \"C2\", \"C3\"])\n",
+    "\n",
+    "    plt.subplot(1,2,2)\n",
+    "    lines=plt.plot(acc_hist )\n",
+    "    plt.title(\"Accuracy\")\n",
+    "    plt.legend(lines, [\"C1\", \"C2\", \"C3\"])\n",
+    "    \n",
+    "\n",
+    "plot_acc_loss(\"FedAvg MNIST-iid\", loss_hist_FA_iid, acc_hist_FA_iid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Federated training with FedProx"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will now train the model on the 3 clients, using FedProx aggregation strategy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NOTE** We are going to consider the 3 clients in every round ($m=K=3$) and we assume that they have the same system resources (they will run the same number of epochs each round)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clients' weights: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n",
+      "====> i: 0 Loss: 2.3077298005421953 Server Test Accuracy: 7.333333333333333\n",
+      "====> i: 1 Loss: 2.2553473313649492 Server Test Accuracy: 25.666666666666664\n",
+      "====> i: 2 Loss: 2.0728208223978677 Server Test Accuracy: 48.0\n",
+      "====> i: 3 Loss: 1.344037135442098 Server Test Accuracy: 66.33333333333333\n",
+      "====> i: 4 Loss: 0.8007276654243468 Server Test Accuracy: 73.33333333333333\n",
+      "====> i: 5 Loss: 0.6816384394963582 Server Test Accuracy: 74.33333333333333\n",
+      "====> i: 6 Loss: 0.4158744712670644 Server Test Accuracy: 87.0\n",
+      "====> i: 7 Loss: 0.3552906016508738 Server Test Accuracy: 90.33333333333333\n",
+      "====> i: 8 Loss: 0.23262095948060352 Server Test Accuracy: 87.33333333333333\n",
+      "====> i: 9 Loss: 0.16984646519025165 Server Test Accuracy: 92.0\n",
+      "====> i: 10 Loss: 0.15044508377710977 Server Test Accuracy: 91.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train with FedProx, mu=1\n",
+    "\n",
+    "n_iter=10\n",
+    "\n",
+    "model_f, loss_hist_FP_iid, acc_hist_FP_iid = FedProx( model_0, \n",
+    "    mnist_iid_train_dls, n_iter, mnist_iid_test_dls, \n",
+    "    epochs =2, lr =0.1, mu =.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_acc_loss(\"FedProx mu=1. MNIST-iid\", loss_hist_FP_iid, acc_hist_FP_iid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conclusion and comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We notice that with both aggregation strategies the model converges to a high value of accuracy (consider that we are working with 600 images only). In this case where we have iid dataset across clients, the accuracy of FedAvg and FedProx are comparable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. MNIST non-iid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We split MNIST using a non-iid sampling. We assign digits 012, 345, 6789 to the 3 different clients."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "mnist_non_iid_train_dls, mnist_non_iid_test_dls = get_MNIST(\"non_iid\",\n",
+    "    n_samples_train =200, n_samples_test=100, n_clients =3, \n",
+    "    batch_size =25, shuffle =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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GEhcXR2xs7BV9a11dXXzlK1+hp6eH+fl56XVBEMjKymLz5s38/d//fVCtAMJSX48fP35ZFDJAZWUlDz30ELt27VrpZdyAsbi4SG9vL263W1qOTk5Ovuy42NhYIiIiqKmpkRI6f/jDH0rvP/TQQzzxxBPs27cvOAQG/mtpuqKigtzcXDZu3EhmZuZNn8fr9WK1Wqmvr2d4eBilUskDDzxAVVXV7TbxtrBarQwMDDAyMrJsRUSpVLJhwwaqq6t57LHHyMvLIzIy8qp2rMViYWxsjFdffZXJyUkEQaC6upqamhoSExPXXGD8K4CXCsHi4iKzs7MMDAyQlZVFdnY2giDg8Xh45plnaGxspKura9kKTGxsLMnJyXz2s5+lpKSE+Pj4oPJfjI2N0d/ff5kZ52dgYIAjR46wuLhIZGQkoaGhpKSkEBUVRXx8PKGhoWs64N0uoiheNViypaUFj8cjmYYrwYp52mJjY4mNjb0lcYElR5XJZGJsbAy73Y5OpyMvL4+MjIyVauIt4XcGms1mHA4H8F92enFxMRs2bLih9H2TycTU1BSDg4M4HA4UCgWZmZlkZGQExQ0rCAIhISGEh4ej0+mw2Wz4fD6cTicDAwOkpqYSGRkpzejOnDnDxYsXMZlMwNI0PCYmhqysLAoLC6muriYrK0sKsFtr/LEiIyMjtLe343Q6rxjuPz8/T29vL1qtFp1OR3h4OJmZmcTFxUmxSvHx8ej1+qAxnxQKBXq9HqvVis/nw2AwYLFYLvNb+nw+XC4XZrOZhYWFy84zOztLW1vbFWO4bpW1X/v9A93d3TQ1NTExMUFSUhIlJSUkJiaueYj53NwcDQ0Ny5yeMTExpKam8ud//ufk5OTc0HkaGxs5c+aMNGoqFAqqqqqCKv4FID8/nz179nDkyBEpk/inP/0phw8f5kMf+hC9vb1cvHiRpqYm6X2AqKgoPv/5z1NbW8u2bdtQq9VBszIGS6tjjY2NvPTSSxw6dAij0XjF8ICpqSlmZmZoaWmR2u/3laWmprJp0yZ27NjB+973PqKjo1e7G1dEr9dz7733cuHCBYaGhvjRj37Evffey+c+97llx1mtVmZnZ2lvb2d4ePiy89hsNjwezw2FWNwoQSEwfnuwqakJj8dDWloatbW1REZGrvlyZmxsLBs2bKCxsRGFQoFKpaKmpoaqqirS0tKu63V3OBzMzc3R2NhIfX29FC+k1WpJTk4mISFhlXpyYxQXF7N//35aW1uZnp7G4XBgsVgYHR3ljTfeYG5uTkqi8z+gW7duZd26dezatYusrKw1N/fezalTpxgcHOTs2bM0NTVhMBiumax4pfe8Xi8zMzM0NzdjsVhISkoiLy+PgoKCQDb9hggJCaG0tJSxsTEGBgbo6OjA6XQyNzdHcXExSUlJ5OTkMD4+Tn9/P9PT0ywuLhIREYHdbr8s1GIlWXOB8ceO9PT00NLSgiiKpKamsnXrVnQ63ZpPQ+Pi4qiurubEiROSabRv3z727t1LfHz8dYOvHA4Hg4ODnDt3jgsXLuD1egkJCUGv15OcnHxDkc+rSUFBAZGRkfz617/G4XDgdDqx2+3Y7fbLonIVCgVqtZodO3awa9cutm7dGnTBaD6fj5MnT9LQ0MC5c+cwm83XjLK+1nlMJhMLCwv09/dTVFSEy+UiLy9vze9Rv8A0NTWhUCgYHBxkYGCAw4cP8973vpeysjJ2795NT08PbW1tGAwGPB4PsbGxy4JCbzTV52ZYc4HxX7Bz587R1tZGQkICxcXFVFdXB4X9HhISQlJSEl/+8pdZXFxEoVBItvmNXAyDwSCtivkvZE1NDQcOHKCoqChoptl+9Ho9Wq2Wr3zlK5w6dYpnnnkGo9Eopf1fyvr16/lv/+2/UVVVRUpKStCJi9lsZnx8nGPHjtHY2HhVx+7N4PV6cTgc/OQnP6G9vZ3CwkJSU1NXzCl6K+j1evbu3YvNZiMyMpLZ2VkMBgNDQ0OcPn2aU6dO8fOf/xyXy4Xb7SY1NVWKSr+U+Ph4srKyVtQnuOYCY7Va6enpwWAw4PV6KSgoICUlJSgcn7Ck6iqV6pYSEa1WK9PT03R2drKwsIAgCOh0OjIyMqioqECn0wXdQ+lP8MvIyCApKemavpSIiAgqKipISkoKqmVoP34x8EeOr1QNF1EUMRgMUkHw2xWt28U/6BUWFrK4uIjRaMRgMDA8PMzg4CAmkwmLxSLVsvG3+d1tj4qKIjs7e0VX/dZUYERRZHp6mjfffJOZmRnCw8N58MEHg87xeSuIosjY2BgdHR28/fbbmEwm1Go1WVlZVFZWsmPHjqCYoV0JQRCkyOtr4b+pg5VLs/NXukCUz+eTSoYGSy5ZdXU11dXVwH/5/k6dOkV/fz8XL15keHiYsbExhoeHr+jITU1NZcuWLYSHh69Ym9ZUYAwGA/39/Rw9ehRRFMnJyWHXrl2kpaWtZbNWBK/XS319PefPn5cSIMPCwigrK5NC5YNpleVSXC4XP/7xj2lsbGRubu6KtW1gaUn3zJkzFBUVERMTs8qtvD7+2adGo0GpVF51phEeHk5MTAx79uyRFhVcLhculwun00ljYyOTk5NBIyQ3gj9CfNu2baxfv559+/ZJOX49PT10dHTwyiuvsLCwIF3f/Px87r///hVNF1gzgfH5fExMTDA8PMzw8DCFhYVkZmaSnZ295kvTt4t/+tnZ2Ul/fz+Li4tSXZvCwkISExODzjTyY7VamZub48yZM3R1deFwOAgJCUGhUEizAf+DarVa6e7uJjU1NSgFxu+U1+v1UpzIlWYcarUanU5HUVGRJDBWqxWHw4HJZAramea18BdeT09PX/a6fyDX6/UcP34cp9MpCUxMTMwVc+huhzUTGI/Hw3e/+10uXLgAwD333MPOnTsJCwsL2ofvRpmcnGRwcJBXXnlFijdISUmhuLiYz372s2vqELwev/vd73j11Vc5c+aM5ASsrKwkLi6O2dlZRkdHmZiYAJZihA4fPkxpaektB1gGksjISPR6PXv27CEiIoLGxkaMRuNlzk1/8FlzczNKpRJRFJmYmGB2dpbe3t4r1ue9UxEEgaKiIqxWK2VlZdetXXS7rInAmM1mpqen6e3tZX5+nuTkZIqKiiguLr7jxQWWChmdOXMGs9ks2br+lTF/FbFgw+fzYbPZpDR+/26GABs2bKC0tBRRFHnttdckgXG5XBiNxuvWsV1LFAoFW7duJS0tjeLiYs6ePUtLS4u0qAD/tatAR0eHVDzMbDZjt9uDum+3in9rmdUoE7Imd/rc3Bw9PT309/djt9spKiqitLSUdevWBa1f4kYRRZH+/n6OHz+OzWZDFEUUCgUVFRXU1tYGVV7OpfiTHf2BWoBUv2fTpk1S3M/ExARvvPGG9Bn/6kQws23bNql/YWFhGAwGTCbTMoFxu920tbXd8DkVCkVQVre7HURRRBTFFe3TmghMQ0MDv/71rzGbzRQWFvLUU0+Rl5d3x18sp9NJZ2cnDQ0N1NXV4XQ6iYuLo7Kykj179rB58+agnaFNTU3x5S9/WTJZYSno7sEHH2T9+vWo1Wqee+65ZQ9hREQE5eXld0TWsb9wWVVVFTabjYGBgas6r6+HQqGgtraWLVu2kJOTc8f7DP3MzMzQ1tZGQUHBivmdVlVgPB4PExMT9Pf309fXR3h4OKmpqRQVFQW1X+JGWVxcpL29XappIwgCERERUo2NYN2oa2xsjK6uLtra2qRtLpKTkyksLGTTpk2EhoZiNBppbm6W9kHSaDRS3EQwxsBcCZVKRXJyMgUFBWi1WskculEUCgWRkZFER0dTXV3NunXrgtbkvR5arZbExMRltXn85VLv2Fwki8XCyy+/zMmTJ+nr62Pnzp1BmfB3K4iiiMlk4vnnn6erqwtRFCUv/n333Rc0FeuuxO9+9zvOnDlDR0eHVJTowIED7Nixg/e///10dXVx8eJFXnnlFWZnZxEEgZiYGLKzs9m+fXvQpTtci7y8PKKiooiIiGB+fv6GzTt/tnl5eTk7d+7kYx/7GElJSWtePOtWiYmJYfv27TQ0NEh1cex2O3NzcysaM7RqAmMymRgeHubw4cMMDQ0RFhYmFVS+G/A7Ctvb2zEYDCiVSu655x42bdpERUVFUJsRFotlWba4z+djaGgIQRAwmUx0dXUxOjoqJcn5fUpVVVXk5+ffUSaCRqMhJiaGr3/96zQ2NvLKK68wNTUlleK4ElVVVWRmZlJSUkJxcTGlpaVBV+fmZvHXUPZXswsUqyYwRqOR8fFx2tvb8Xg8JCUlsXHjxhVfd18rFhYWMBgM0kOoVqspKyujtLT0itXFggmn07ls1cjn8zE9PY3T6WRycpL+/n4MBgNWqxWVSkVYWBjFxcUUFBTcUbMXWDJzwsLCOHDgAFFRUXR3dxMSEnLN3SorKyspKSmhurqajIyMNa9RtBI4nU7pXvXjTxO5I528x48f58yZM0xMTPDQQw/xgQ984K5ykL3xxhvL6r0olUqp5Oedhs/no7OzU9oJ4tLgtLy8PMrLy/n85z9/Wd3hOwl/fMzWrVuvu3WMf7cLlUq15pnTK8Xi4iJTU1PLikv5d1JYycDCgAuM1WplZmaGpqYmuru7KS0tpby8nNLS0jsyQvLdOJ1OzGYzra2ttLS04PP5SE9PJycnR6oCF+yEhoYSHh6+zOn57rB6f0nN6upq9u3bFxRbydwOgiCgVqvvaDPndnm3ryUkJISoqKgVXekMuMCYTCaam5s5c+YMIyMjPP7442zatImSkpJAf/WqYLfbGR4eluqNiKJIQUEB+/btC8T+vwHh0r2KrubgCw0NJScnhz179vDEE0+scgtlAsG7TaGQkJAVv18DJjCiKGI0Gqmvr+fpp58mPj6e+++/n8985jNBvaJys/j9FDabDZVKRUFBATt27ODBBx+8Y8y/Rx55hP379/OXf/mXVz1GoVBIOyDI3PnExcWxZ88eenp6mJ2dJT8/n6SkpBX/noAIjM/nw+12097eTm9vLxaLhS1btlBRUUF6enrQ1Hq5XXw+H/Pz87S3t2O1WgkJCaG6upri4mKSk5PvmPgIfyHrrKystW6KzCqh0+koKCigpqaG6Oho8vPzA5JPJlwn0OiWMrwcDgcGg4FPfepTOJ1OsrKy+NznPkdZWdlq2Lw34wK/rQw2q9XK7373O5588kkcDgfZ2dkcPnyY6OjoQIrojfbvTszOW7Vrt0YE3bW79Pm/zdWjK344IENsZ2cnLS0tREREsG7dOh566CEyMzPvmBH9RgkJCaGmpoZ/+7d/w+v1otfriYqKuqOdnzJ/XNyRyY4TExO0tbURFhZGVlYW27dvD8TXrDkqlYqcnJwb3rpERuaPjeuZSDIyMjK3zN0RNSQjIxOUyAIjIyMTMGSBkZGRCRiywMjIyAQMWWBkZGQChiwwMjIyAUMWGBkZmYAhC4yMjEzAkAVGRkYmYMgCIyMjEzBkgZGRkQkYssDIyMgEDFlgZGRkAoYsMDIyMgFDFhgZGZmAIQuMjIxMwLheRbs7sRqVXNd1ibu5b3B39++u6Zs8g5GRkUEUxWvubnmryAIjI/NHjN1up7e3l4cffphvfvObXLx4cdl+1bfL3VXmX0ZG5obx+XyMjo7S0dFBfX098fHxWK3Wy7YNvh1kgZGR+SPE5/OxuLjIz372M44cOYLRaMTlcq3496yKwPh8PmZnZ5mdnaWuro7k5GTS09MpKipCq9WuRhNkZGT+gMPhYGZmhpaWFlpaWhgdHb3qnuS3S8AFxuVyYbPZGB4eZnBwkIMHD1JRUYHH4yE3N1cWGJm7Dv/WyaIoIggCKpUKpVK51s0CltpmsVgYHR3l1KlTDAwMMDs7i0ajQaVSoVAoVnQztoAKjNVq5fz58/zkJz/hwoULGI1G4uLiqKysvKP2bpaRuVGsVivz8/McOXKEhYUFIiIiqK2tpaCgYK2bxuLiIjabjYMHD9LY2MgLL7yA0+kkLCyMPXv2sHHjxhV/LgP2hHu9Xrq6umhtbeXixYuMjY3hcDgQBIHBwUHa2tooKioiJCQkUE2QuQ2sVismk4nx8XHGx8ex2WzSe4IgoNfr0ev1JCQkkJGRQURExBq29to4nU4cDgdTU1PMzs5iNBoBSE1NZePGjSvyHW63m87OTqamppiYmGBwcBCtVkt+fj5hYWEr8h23y/T0NAMDA9TV1dHZ2YnVaiU6OpqkpCR27txJcXExer1+RWdbARMYj8fD4cOHqa+vp6WlRXp9cnKSuro6DAYD9913H5GRkYFqgswt4veZtbe38/vf/5433niD4eFh6X2FQkFBQQHZ2dns2LGD973vfUEtMCaTienpad5++23q6upobW0F4MCBAysmMHa7nVdeeYWuri66u7spKChg3bp11NbWBo3AdHd38+abb/Lqq69iNBoRBIH09HTKy8v5kz/5EyIiIlZ8X/WACExPTw9dXV389re/ZXR09LL3p6encblcAfFarxRWq5Unn3ySmZkZFhcX2bVrF9nZ2axfv56pqSnm5+epqqoiJiaGmJgYYGkK2t3dzfT0NLOzswCEhYWRmJhIYWGhdFww09TUREtLCy+99BKTk5NMTU1hMBgQBEHyKQCMjo5iMBjo7+/n3LlzFBUV8dRTTxERERF0frWOjg5aW1v50Y9+hMlkwul0snPnztvaU9xgMDAxMcHp06eZm5vDarVitVopKSnhgQceoLCwkNjYWMLDw9fc/2IymXjppZc4evQoJ0+exO12k5iYSFZWFp/+9KepqakhMjIyIC6LgAjMxMQELS0tDA0NSUp5KU6nE5PJtKLr7SuN2+2mvr6ekZERnE4nKpWKqakpAMbGxpidnUWhUBAbG0t8fDyw1K+LFy8yMTHB1NQUgiAQHh5OSkoKi4uLxMXFLfsOpVJJWFgYSUlJqNVqFIq1i3t0u91MT0/T3t5OfX099fX12O12lEol8fHxkhPQj3+AmJ2dpampCYPBwP79+8nMzCQ1NXXNH6pLMZvNknng9XpRq9XEx8cTHR190+dyu904nU46OzsZGBigvr4eq9WKz+ejrKyM3NxcKisryc7ODgrz3+12YzabOXfuHB0dHYyOjpKUlERaWhobN25k/fr1lJSUBOz7AyIwDQ0NPPvssywsLATi9KuKIAgIgsA777zD8ePH+cUvfoHH48Hr9fLjH/8YpVIpPUyiKOJyuXC73Xg8HgRBQKFQoFKp0Gg0yx46QRCIiopiw4YNfP3rXyclJQW9Xr9W3WR6eppvfvOb1NXV0dbWhtvtJiEhgdLSUh577DEKCwuJi4uTZjJ9fX309fVx5MgRmpubaWho4Itf/CKPPfYYH//4x4mOjg4qkbkUhUJBWlqaNDDcDFNTU/T29vLf//t/Z2BggIWFBaqrq6moqOCv//qv0el00j0TDExOTtLZ2clbb73F3NwcSqWSHTt2UFtby2c+85mAX6MVFRiz2czbb7/NhQsXmJubw+fzkZCQQHFxMfPz85jNZkZHR4mKiiI5ORm1Wr2SXx8w/A+Vx+PB5/NJeRtOp/Oym8nr9eLz+fD5fAiCgM/nw+v1SoJzKW63m5aWFr7zne9QWlpKdnY2u3fvXtXfRRRFWltb6ejo4PTp04yPj+Pz+cjJyaGiooKHH36Y8vJy4uLiJF+CKIqEhISQlJREbGwsUVFR9Pb2MjU1RUNDA3q9nsceeyxoTUKPx8ORI0dwOp0UFhaSlJREaGjoVY93u92YTCYOHjxIf38/AwMDjI6OolKp2LBhA+9973vZuHEjoaGhazoLvRT/Uvnrr79OfX09BoOB8PBw0tPT2blzJ6Wlpauyirui37CwsMChQ4dob2/HYrEgCAJxcXHs2LGDrq4uxsbGGBsbIyoqiszMzKBephYEAY1Gg1arXWbKXZoUdiUTT6FQLPt3KX7h8X/e7XbjcDj41a9+xebNm6msrGT79u2rJjCiKOLz+Whra+Ps2bOSM16r1VJYWMjmzZt5+OGHCQ0NvWyki46OJi0tjaysLBwOB2FhYQwODnLx4kVcLhf33Xcf0dHRQTGSq1Qq1Go1SqVSEvy6ujpUKhW7du0iLCzsshnmpTidTmZnZ3nhhRfo7e1lZGQEtVpNWloa1dXV7N27lw0bNqxyr66N3zR65513OHbsGAsLCyQmJpKXl0dNTQ2ZmZmr0o4VfcKdTicdHR3Mz88vMwGeeuopvvnNbzI+Po4gCCQmJlJSUhJ0zsBLCQkJ4fOf/zy9vb20tbUBSyOfP0hpenr6ss+o1WrS09OlUf1KvqfW1lYcDoeUUObxeFhYWODs2bPMzs7yt3/7t9ccTVcSt9uNzWbjhRde4OTJk9LMpaCggK997WtkZmYSHh5+VZHQaDTExsbykY98hL179xIdHU1raystLS1MTEwQFRVFVFTUqvTlWmzdupX4+Hjq6+vp6elhbGwMWHJof+ELX+DJJ59k06ZNbNiw4YozkIGBAVpbW7lw4QILCwsoFAr279/Pxo0b+fjHP05sbOxqd+m6dHZ28rOf/YympiZMJhMqlYqioiLe8573kJWVtWrXZUUFRhRFFhcX8Xq9kp2bnJyMXq+X/BOwNMprNJqgGN2uhkqloqysjLi4OBITE4GlGYvJZGJsbExy+F6KRqMhIyODmJiYKzoQFxcXSUhIwGQyYTKZaG9vx+Fw4PP5cDqdWK3WgKTMXwmv18v09DQtLS2MjY1hsVgQRZHc3Fx27NhBRkbGDc1AlEoler0eQRCoqKhgamqK5uZmFhcXcbvdq9KX66HT6UhOTmbHjh2oVCo8Hg8mkwmXy8X4+Djnzp3D5XIhiiLp6ekkJycDS9fLaDTS1NTEhQsXsNls6HQ64uLi2LRpE+Xl5SQkJATVTFwURcmh3draislkQqlUkpubS3FxMcXFxatqyq24wPhRKpWsX7+enJwcHA4HVqsVu92+ag/Q7aJSqdi8efOy13w+HzabjZmZGebn5y/7jFqtJiEhgYiICHQ63WXvu91u+vv7GR8fZ2hoiK9//euMj48HrA/XwuVy0dbWxne+8x1GR0el61JbW8unP/1pIiMjb+omDA0N5Z577qGvrw+PxyM5u4MBrVZLWloan/nMZ4iLi0OlUnH+/HkWFhaw2+0cPHiQ06dPc/HiRR555BEeeOABYMmn2NTUxG9+8xsaGxvxeDxkZGSwZcsWPvjBD5KWlrbicSO3i9frpbm5mbNnz9LQ0MDi4iJRUVHs27ePe+65h23btq1qe1ZMYEZGRujp6WFiYgKr1YogCERHRxMZGYlSqUStVktKb7FYGBgY4Cc/+Qler5eJiYll5/KrbW1t7RUf1LVCoVAQGhpKcnLyZUvOsOS30Wq1Vx3RVCoVaWlpKJVKPB7Pmjm5fT4fRqORkZERmpqasFqt6HQ6KisrycnJITw8/IbEZWFhgeHhYbq6upiZmZES6KKiooiIiAiaADNYunYRERG85z3voaqqivPnz3P27Fl++ctfYrfbJb9MZmYmOTk55OXlMTw8zA9/+EM6OjrweDxs3ryZ/fv38/DDDwflIoXT6WR+fp7/+I//oLu7G6fTSWxsLHl5eTz22GOr5ne5lBUTGIvFgtFoZGFhAbfbjVKplKb98/Pz2O12aUSzWq1MTU1JsRaXRokCjI+PY7VapenqrcQrBAqVSnVbU2KFQoHH45FMIz+hoaFERESsitkoiiILCwsYjUbm5uYAiIiIoKysTIrJud7nPR4PZrOZgYEBzp8/z/DwMEajEafTSVpaGjqdLqh8bH6nfUZGBmlpaYSEhGC1WgkNDWVxcRGHw8Hk5CS9vb20tLSg0WgYHBykubkZs9mMRqOhsrKSyspKSktL17o7l+GPvva3eXJyEkEQSEhIkPxqVxqs/U5vm80mLWD4Qy+u5X+7UVZMYBYXF7Hb7bhcLmmp9uWXX6atrY2BgQFOnz7NwMAAoigyMjKyLML33WbTwMAAb775Jm1tbWzdupWnnnpqpZq5piwuLnLu3DlOnTrFO++8g8FgkN5bv3491dXVqzIq+nw+BgcHlzmqY2Ji+NjHPkZWVtY1byqv1ysF5XV0dPDyyy9z4sQJKeX/fe97Hx/96EfJzMxcNWf1zaJQKCgtLWV2dpaqqira29ulwfE3v/kNr7zyCvfcc48UVuGPxP7GN76xprFKV8Pr9WI2m3n++ed54YUXpEUWpVLJ+9//fnbt2iVZEpciiiI2m435+XkOHTok+U+jo6NJSUlh27ZtaLXa24qVWTGBSUlJIScnh9TUVAwGA1arFYvFwtDQEKIoMjMzg8fjWbbM63c2XSk+xOVy0drailqtpqWlhczMzKBYkbhVLBYLU1NTvPzyy3R2dtLX14fL5ZIc3sXFxWzcuHFVgtNEUcTtdi9bZler1aSkpFzXJB0eHqa3t5djx44xPDwsrRrC0j2QnZ19RySxKhQKsrOz+chHPsL58+cZGBjg+PHjeL1eFhcX6ejokBy/ERERUjRzsMS5XMqlvsGxsTE8Hg9xcXGUlJRQUVFBbm7usnb7Tamuri6ampoYHR2lp6cHr9crxThlZGTg8XiorKwkJSXlltu2YgKTnJyM3W4nOzsbn8+H3W7H6XQyMTHB5OTkslmKX12jo6OvaG74M3m7urpQKpU0NzcTERFxRwuM2WxmZGSEgwcPSrkrsPRg63Q6CgoKWL9+/aqtSLy7yLNSqSQ2Nva6wjA8PMzx48f58Y9/vMzRrdVqSU1NJSsri5ycnKBaWbkaGRkZPP7442RkZNDU1MSpU6ekWKWBgQFg6XfxryQG66qn/3kzGo3MzMwAEB8fz/bt21m3bh3p6enSsaIoSgP/kSNHePnll+np6Vl2PlEUyczMRK/Xk5KSEhwCA0si841vfIO6ujqamppoa2vD4XDg8XiYnp7GbrcDUFxcTFlZGV/4wheuGK797LPP8t3vfheTyYTZbOb06dMUFxeTnZ29ks1dFURRxOv14nA4sNvtywLtAGJjY7n33nupqKggLS1tVUZIpVJJQUEB7e3t0mtms5mDBw+yfv36a9YumZ2dpaur67JEVVEUcTgcuFwuqY/B+kD6UalU6HQ6EhMTSU5OJiwsDJvNJvkK9Xo9WVlZfPKTn2Tr1q1Bt2J0LVJTU3nwwQelEAtYWjlcWFjgq1/9Kh0dHXR0dEjpPCqVipCQELRarVTOYiVYUYHRaDRkZ2fjcDgIDw8nMTERp9OJy+Xi1KlTkvM2MTGRsrIy8vLyrhhOHhsbi1KpRBAEXC4X09PTOJ3OlWzqquF0Opmenub8+fOS4PpNE7VaTXR0NKWlpcTFxa3aqoQgCFI8R2pqKnNzc9hsNinT1uVySdfA5/NJTnqLxSJNqd1uNwqFArVaLZlbZrMZg8HAzMwMSUlJd8QsRqFQEBMTQ1JSEnFxcYiiiNlsBpbiZwoLC8nKyiI5OTkozSNYcinMzMxgt9ulBNvo6GgSExMlR7vH42FoaIienh7a2toYGhpifn4ejUZDVFQUZWVlOJ1OzGbziuYQrugdoFKpSExMJDExkd27dwNIKyZf/vKXOXbsGF1dXeTl5bF3717Cw8Ove06n08nIyMiygkd3EgaDgRMnTvDtb3+bCxcuLHtPp9ORkZHBgQMHbmsaerMoFAqioqKk3KfDhw8zOTnJd7/7Xc6fP091dTW7du1Co9Hg8Xg4ffo0/f39tLa2YjAYpAdQq9USExPD/Pw8DoeDoaEhqchYZGRkUDpEr0RmZiYKhYKSkhI6Ojqk/sXHx3PfffeRlZUVVOES78bhcHD+/HmmpqZQKBSkpqaSmpoq+Y38Ppq33nqL5557jqamJimPLjIykqysLL72ta/R0tLCsWPHmJycXLG2BXyIUSqVhIaGEh4eLi17jY2N0dDQQF5e3hWXMjMyMti5cydvvfVWUJd0uBaiKDI6OkpjYyPPPPOM5OyGpTQEnU7Hk08+SUVFBRkZGau+4hISEsK6dev4xCc+QUREBBcvXuTEiRP09vYyNzdHY2MjKpUKn8+HwWDAZrNhMpmIiYkhLy+PPXv2oFQqMRqNvPXWWwwODiKKIiaTiYGBATZt2rSq/bkdzGazVJTeP5D5fD50Oh0lJSVBXUwLltpqNpul9JOQkBDJ3FEoFJjNZl599VVOnz5Nd3c3LpeLuLg4qqqquOeee0hOTub48eM0NzfT2NiI1+slNjaWsrKy2w4RCbjA+IseazQayQQwGAwMDg5eNdIzKiqKrKws1Gr1HS0w/f39XLx4kQsXLiwz8UJDQ4mPj6e2tnbNbmCVSkV8fDx6vZ7h4WFUKhUXL17E6/UyOzvL2NiYFKfjDx70+yQKCwu555578Pl8dHV1UVdXByxd64WFBSYmJvB4PKvep1vFLzD+YlR+tFrtqpqut4PfpeD/21/AG5asgKamJvr6+qQlbL1eT15eHgUFBURGRnLw4EEpUDY5OZnk5GQyMjJuyMq4FmtiJM/NzdHZ2XnVHeS0Wu0V1+3vJFwuF1/72tfo6OiQnNt+srOz2b59O0VFRVLey1rgd3I+/vjj7Nq1C71eL1Vmq6urkx62srIyMjIy2LVrF3l5eWRlZRESEiItWV/qzB0fH6e+vp5PfepTa9Wtm6a5uZn6+noGBgaW5csFu5Paj16v595776Wrq4uzZ89K5UF8Ph8KhQKbzcbRo0elZOPo6GgpX+7f//3fGR4eZnx8HK/Xi0ql4qMf/Sg1NTVs2bLltv1OqyYwcXFxJCUlAWA0GhkeHr7qKDc5Ocn58+clp9WlahzMeL1eDAYDFy9epKWlheHhYWnKHRoaSmRkJLt376akpISysjIiIyOD4ib2O5t37dqFy+VicXGR0tJSaYbpj6bOzc0lLi5OMnUvLbblx79qdifknJlMJkZGRjh9+jQXLlyQ7kdBEFCr1VKJh2C/9xQKBWFhYajVainmrKenh3feeQedTieVePUPGP6Fh7q6OgYHB6Wqk7m5uRQUFFBbW0t+fv6KDPCrJjD+zdYUCoUUUu4PZLr0IfP7Lk6dOoXNZkOv1wdFXdPr4c8kHx4e5rXXXuP5559nfn5eumkjIiLIzs7mM5/5jJQCEUxTb51Ox549e27qM/5VpGB/AK+GwWCgrq6Od955h87OzmWpG2FhYYSFhS0zPYIVQRAICQlBrVYjCAKTk5O0tLTw2muvkZiYiNVqlZ45URSx2+3Y7XbGx8dRKBRSRnx5eTkPPPAA27dvX7GYs1UTmM2bN6PX63n55ZelB+/8+fN4PB6pJqjL5aKzs5Ph4WHsdjuhoaGUlZXx9NNPU1RUtFpNvSVGRkbo7u7mq1/9KmNjYxgMBrxeL1qtlujoaD796U+zZcsWKisrJZ9GsN+41yMuLo57772Xl156aa2bckt0dHTwP//n/8RoNC4z15VKJTt37qS2tvaOWG7XaDSkp6dTVVXFwMAAZ86cYXR0lOeee05y1DscjmWzSrVaTXh4OFVVVeTn5/PYY4+RnJxMUlLSiq7+rdov51+Xj42NxeFwYDab6ezsJCwsjKysLGmV4sKFC4yOjuLxeEhJSSEjI4P8/PygXPL07++7sLBAS0sLra2t9Pb2YrPZ8Hg8REZGEhcXx7p16ygvL6ewsHBFEsiCBbVaTUREhDQTuxPMIj9ms5m5ubnLymWEhoai1+upqqqiqKgoqGaZV8OfxZ+Xl8e2bdukWKS5ubnLzFWFQkFKSgrR0dFkZGSwfv168vLyKC0tRa/Xr3iKx6oJjE6nIz4+npycHGw2GwaDgd/+9rdMTk5SXFxMR0cH/f39PPfcc0xOTuLxeKipqWHTpk2S7ybYWFxclHZQ+N73vkdzczNGoxFRFFEoFBQVFVFdXc0nPvEJsrKy7to9oC5NO/CX4Qxm/JsCjoyMXPZefHw8hYWFfOxjHyMtLW0NWndrKBQK7r33Xnbs2EFSUhJnz57l1VdflQY7P0qlkvvvv5/169ezf/9+4uPjA1pWY1XnfjqdjgMHDhAaGipteXHq1CmcTidTU1OS8zcqKopNmzbx4IMPUlxcvJpNvCE8Hg9Op5NXXnlFKpbd29sr5Relp6dL/pbc3FwyMzODqjZKILh036Rg9sn4/RE/+tGPlm0I6C/nUFxcfMNBoMGISqXinnvuobKykvvvv19aTfKjUCjIzc0lOjqa2NjYgJfUWFWBCQkJoaKigtnZWaanp2lqamJycpJDhw5htVrxeDyEhIRI+1f783OCDX+JywsXLnDhwgVOnTq17P2YmBgKCwul0SSYH7jbxV+EKzw8/I6ItvYXw66rq2N4eFgyV/39yMzMpKKiIqhq2dwMCoWC/Px88vPz17opwBoJTH5+Ph/+8If5zne+Q3NzM2+//TaiKBIeHs6BAwfYv38/DzzwwFWzrdcar9eLy+ViaGjoirV5ExMTV7326VrgD77bvXs3KpWKF198ca2bdF18Ph8ejwer1bosqE6j0ZCfn8+GDRuk2r0yt8+q/4r+3Qz9W0bk5uZKq0ghISGUlJRQUlIS1IF2KpUKrVYrLQ36UavVZGZmUlZWxoYNG4K+Jsrt4o+FSUpKkkIQgh2VSkVYWBjJyck4HA7m5+fR6/UkJiZy4MABioqKZHFZQdbkl/TnJ913331r8fW3jVqtJiws7LK6s/4Z2pYtW9i+ffsatnD1EASBpKQkKWHQ/1qwotVqiYiIICcnB6fTicViIS4ujvz8fD7ykY/c0m6PMldHluoVRKPRXLUExd1MWVkZOp2O1NRUoqKigjp2RKvVEhsbyze+8Q2cTicOhwONRkNoaCjp6elB2+47FfnXvEUEQSAiImLZaoM/ZPuP7Sb1F22qra0lLCyMlJSUoDUP/WkAOTk5a92UPwr+uJ6EFUSpVFJeXo7RaOT48eNr3Zw1JyEhgZ/+9KfS/4PZTJJZPYTrRF/eOaGZ/8XN3Nm33D9/Zf7JyUm6u7uBpSjQkpISkpOTl5UqXGFutH/ytQs+/uiunSwwdx5/dDfpVbib+3fX9O16AiMjIyNzywR/4IKMjMwdiywwMjIyAUMWGBkZmYAhC4yMjEzAkAVGRkYmYMgCIyMjEzBkgZGRkQkYssDIyMgEDFlgZGRkAoYsMDIyMgFDFhgZGZmAIQuMjIxMwJAFRkZGJmDIAiMjIxMwZIGRkZEJGLLAyMjIBIzr1eS9E6tRyVXRlrib+wZ3d//umr7JMxgZGZmAIQuMjIxMwJAFRkZGJmDIAiMjIxMwZIGRkZEJGLLAyMjIBIyAbR0riiKLi4t4PB5cLhewtJ2oSqVCpVKhVqv/6PZwvhsQRRFRFHG5XHg8HjweD/69tZRKpXRt1Wr1Grf05nG73cv+9vl8+Hw+BEFAEATCwsJQKOQx+WYIyBPudrux2+38+te/5vz58xw6dAhY2lq1tLSULVu2sGXLFjZu3EhoaGggmiCzwvgHjIWFBUwmE4cOHaKnp4e6ujqMRiM+n4/CwkI2b97Mzp07qa6uRqfTrXWzbxi73U59fT1erxev10tdXR2Tk5MMDAwQHR1NWloaX/rSl4iKiiIsLGytm3vHEBCBGRwcpKenhzNnztDV1cXExASCIKDRaFAoFIiiiMlkwuv1kpKSQmZmJmq1Wh4dggxRFPF4PPT39zM3N8fMzAzz8/MYDAbOnz/P+Pg4g4ODWK1WaRajUCjweDyUlJQEtcCIoojVamV6epqRkRGmp6c5f/68NGvp7Oxkfn6e6elpwsPDMZvNzMzMoFarg05g/DPKlpYWaZ90P6GhoYSEhBAfH09ERAR6vZ6EhATUajWCcDNxjbdGQASmvr6eF198kaNHj2K326WOuN1u+vr66Ovr49ChQ0xOTlJTU8Ojjz5KREQEGo0mEM2RuUU8Hg92u5233nqLpqYmmpqamJqaYnZ29orHj4yMMDk5SWNjIx/5yEdITExc5RbfOB6Ph6mpKU6cOMFvf/tbOjs7GR4e5mpbKZtMJoaGhggLCyM+Pn6VW3ttvF4vFouFZ599lu9973sAUj+Sk5NJSEhg8+bNFBQUkJ+fz9atW9Hr9atixq6owJhMJt544w1ef/116uvrcTqdaLVaIiIiyM7ORqVS0d7ejtPpxOVy8dZbb9Hc3MyZM2f45Cc/SVVVFXq9/qrndzgcHD58mPn5eUwmE8XFxSQnJ1NeXr6S3bghPB4Pb731FlNTU4yNjdHW1obBYLjsOK1WS2RkpNS/6OjoVRk5bher1UpzczMvvPAC9fX1TExMsLCwIPnT7lTMZjPz8/M899xzdHV10djYyOzsLDab7ariAkuDY39/f1CKpsPh4Pz580xPT0uvCYKAKIrMz89jtVoxGAwcPXoUnU7HgQMHKC4uZteuXcTExBASEhKwtq2owDgcDlpbWxkcHGRubg5BENDr9eTn51NaWkpISAgej4fx8XGmpqaYnJzEbDazsLBAdXU14eHh5OXlERoailarBZaU2OfzSVP0hoYGZmdnMZlMuN1unE7nqguMxWLBaDRy/vx5ZmZmWFhYYGpqCovFsuw4v4Pb37+oqCiqqqqCXmB8Ph+zs7MMDAxQV1dHf3+/1Lfw8HCio6OXOTytViuLi4s4nU7UajUajSZo+zg7Oyv5jvr6+ujt7SUmJoaEhARCQ0MxmUyYzWYWFxclwQkJCSE8PFzyzwQbTqeT9vZ26ZmDpedGEARcLhdutxubzQYsOeJjY2Ox2WzExcWRl5dHXFycNLD7++z1erHb7TgcDtxuN6IootfriY6Ovqm2rajA2Gw2Tpw4wejoqNTRdevW8Q//8A+UlpYSGhpKZ2cnv/zlL3nuuecwmUw4HA6Ghob453/+Z9LT0/niF79IaWkpJSUlwNJDajab+dGPfsTx48c5ffo0Ho8HQRDo6OigtraWBx98cFVv6BMnTnDkyBGeffZZMjMzeeKJJ/jyl79Mdna2dIzP52N6epo333yTP//zP+fHP/4xjY2N/PSnPw16x7bH4+HQoUOcPHmS5ubmZSN7RUUF27dvZ9OmTZIv4ujRo/T09NDZ2YkgCISGhgbtCuGhQ4f4xS9+QWtrKz6fD71ez0c+8hHWr19PRUUFL7/8Mr/97W8ZGBjA6XTi8/kk06KgoCDozCOAubk5vv3tbzM/P3/dY71eL++88w719fUcPHiQD33oQ1RXV3PfffdJjnxBEJifn+f8+fM0NTUxNjbG4uIi73nPe3jiiSduqm0rehf4Zxv+pcxLZzB+my8zM5OHH36YvLw8Tpw4wdDQEG1tbVgsFkZHRzl06BBOp5OIiAjm5+cZGRnhzTffpLm5mZGREVwuF0qlktDQUDZu3EhlZeWqiYvJZOL111/nxIkTNDU1sW/fPkpKSti5cyeJiYmX2bQxMTGkp6dTXl6OxWJheHg4KEfAd+P1emlvb1/mk/ALR2FhIbt37yYnJ0fymYWGhrJp0yZGR0eBpVEyJiZmzdp/LTQajSSMERERFBUVUVNTQ2VlJQkJCezfv5+MjAzOnDnD8PAwzc3NGI1GOjs7+c1vfkN6ejrp6enS8X7Tfy1nbKIo4vV6lw0EkZGRpKenYzabcTgcy8x3URRxOp3Mzc1x5MgRxsbGKCkpYXh4mPb2dmw2G0ajkZ6eHqanp7FarYSEhDAzM3PTbVtRgREEAYVCsWyaptVql3mtExISSEhIYPv27YSFhXH27Fn6+vqw2WzMzc1x+vRpIiIiyMjIYGRkhObmZr773e9K59doNISHhxMVFcWGDRtWzTzy+XzMz8/z8ssvc/HiRaanp/nEJz5BZWUlGzduvOJndDodcXFx5Obm0tjYyNzc3Kq09XYQRVFyxo+Pj0uvKxQKwsPDycrKorq6moiICMlESk9Px+v1Mjc3J8XHKJVK7HY7SqVSiiNZ6wcRlsQwMjIShUKBTqcjLy+PdevWUVxcDMCWLVuoqakhPj6e5uZmyTSfmJjAYDCg1+uJiYnBZrNRVFRETEwMer1eMunXikufO1i693Jzc5mfn8dsNmO325fFLXk8HhYWFmhoaGBsbIzHH3+cxsZG3nzzTebm5lhYWGBychIAlUpFcnIydrv9ptu1ogKj0WjIzs5mfn6eqakpBEFgYGCAH/7wh7z3ve8lMzPzv75YpeKxxx4jLy+P+fl56uvrmZycZHh4mGeffZYXX3zxsiC9qKgo/uZv/oacnByys7PJzc1dtaXQ/v5+mpubeeutt9i/fz9/9Vd/xcMPP0xkZOQ1P2exWGhrayMqKors7GyUSuWqtPdWGR0dlVb6LnUaarVaSkpKyMzMXCYufhwOBydPnqS+vp7Tp0+jUCiIiYmhqqqKxMRE4uPj2b59OxEREWu6zFtQUMCePXs4deoUdrudsbExnE7nsmOUSiX33HMP27dv5+Mf/zh9fX2Mjo7y1ltvMT4+zsTEBP/6r/+KXq+ntLSUT3/609TW1l5zgSKQaLVacnNz6evrk0TA5XJhNBr53Oc+R3Z2NidOnKCuro6GhgYmJyeXBRXOzc3xhS98gYWFBSmm6dKZdlRUFP/7f/9vSktLb7ptKyowISEhlJWVMTo6Kq3H+2claWlpOJ1OkpOTCQkJQaPRoNfrSU9PZ+vWrYyNjWGxWLDb7ZjNZkwmE4IgoFariY6OJisri8zMTKqrq0lJSSExMZHIyMhVs/VbWlo4f/48Wq2WzMxMKisriYyMvOrI5fP5aGlpoa2tDaPRyO7du1m/fn3QC4w/SNIfhe0nNDSUyspKkpOTLxOXlpYWBgcHOXHiBBcvXqS/vx9RFImIiMDr9ZKUlERqaio1NTVrbiLGxMSQlZVFaGgodrudiYkJxsbGyMjIICkpSTouLCyMsLAwoqKiUCgUREdH43Q6mZ2dlZa3FxYWuHjxIl1dXSQnJ1NaWrom11en07F7926USiU2mw2LxYLT6ZRWOCMjIykrK5P6de7cOQwGgzQJ8C+8uN1uaUBXqVTo9XqysrLIycmhpKRk2e9zo6zo0xkZGclDDz1Ed3c3J0+eRBRFRkZGGBkZAaCqqooDBw6QlJREXFwcAGlpaXz0ox+lp6eHubk5hoaGJFtSFEVCQkLIycnhiSeeYMuWLWzYsGFNLuKLL75IY2Mjubm5rF+/npqammse7/F4+NnPfsb58+cxGAzs27ePBx54IOhD6P0rJV6vF5/PByxNvyMiInjooYfIzc1ddrzP5+MXv/iFFNl7qYAYjUbGxsaIi4sjOzubJ554QjrnWpGUlIRCoSAqKgqTyURXVxfnzp0jLCyMffv2XTHYMyUlhZSUFCoqKoCla/v000/T0NDAoUOHOHXqFACFhYVrcm8mJCTwpS99ibCwMObm5ujq6sJisWCxWHj++efZsGEDTz/9NCUlJezbt4+f/vSntLS0MDU1JflN/atMfrRaLWlpaXzwgx9k69atlJSU3NJgvqICo1arSUtL49FHHyUtLY1nn32Wubk57HY7J0+epK2tjSNHjpCbm0t+fr5kIzY0NHDy5ElpSu63JZOSkigrK+Opp54iLy+PhISENYv2NZvNeL1eHnzwQfLy8q557MjICL29vZw4cQKVSsXf/u3fsnHjRmJiYtbcB3E9UlJSpCm3f7laEARp5hYVFQUshdaPjIzwzjvvUFdXx+jo6DJxEQSB7OxsPvShD7Fu3ToyMjJITU0NaMzFjRAWFkZiYiJf/OIXOXHiBD/72c/4zW9+Q0dHB+Xl5ddNBTCZTExPT9PZ2cnQ0NDqNfwa+H2T/pn14OAgLpcLURTp6enBarXy05/+lLm5OSk8xO+wfff9qFAo2LNnj2RKFhYWkpSUdMvCuaIC43ecFRcXo9FoaGpqYmhoiLGxMclZNjg4yMTEhBQRajQaOXXqFOPj49hsNqnDgiAQFxdHTk4OmzdvJjw8fE0jfb1eL4IgkJqaelVb2+fzYbfbGR4epq2tDY/HQ3JyMjt37pQe3GBHp9OhUChISkpibm5Oitr1er0sLCxIAjE0NERXVxenTp1ibGwMq9UKLF03pVIpxVjs2LGD0tLSW5peBwKlUklYWBjV1dUYDAZCQ0MZGhrC6/XS3d0txWH5Z3Ber5fFxUUplmR6eprR0VFGR0ex2WzExsYSGxtLVFTUmg4eCoWCxMRECgsLiY6OluJYTCYTPp+P+vp65ubmmJ6eRqVSYbFYpPYqFApCQ0MJCwtDr9dTVVXFunXr2Lp1K5GRkbc1KATEgZGZmUlGRgbbtm2joaGB733ve5w9e5apqSkcDgednZ10d3fzu9/9DkDK/wCW5bTU1NSwcePGmw7uCQQxMTFERUUxOzt7VW+6zWbj7bff5o033uDYsWN89rOfpby8nJ07d95ReVZKpZLq6mo8Ho9k9kxMTPDFL36RPXv2sHnzZv7xH/+RwcFBZmZmlpk9/kC8p556isrKyqDsu0KhkPwKFRUVUr7cZz/7Wf7iL/6CT37yk8zPz2O32zEYDLS2tjI0NMSRI0eYnZ1lbm4Op9NJeno6n/jEJ3j44YcpKytb8wFk8+bNrFu3jsnJSS5cuMCZM2cAWFhY4NVXX5XCR4BlS9ohISFs3ryZbdu2sXv3bkpLS1dsQA+IwPiXJbVaLdnZ2Tz22GOIosjFixfp7Oy8bN1eEATi4+OJiYlh48aNkg/g3nvvva45slpUV1ej1Wp56623pPZXVFRIN1VnZyf9/f08//zzdHd3Mzs7K8XvHDt2jAcffJDCwsKgTgD0o1QqWb9+PSaTiSNHjuB0OllcXKSvrw+v10tXVxeDg4NSwios2ezr1q0jLy+PwsJCtmzZQkpKStCJix+NRkNubi4f/ehH+dGPfkRXVxfT09McOXJEWmhwOBxYLBYpSttisaDX60lOTiY7O5usrCzuueceMjMzgyJ4UqVSERYWxrZt23C73ZLAAJc51/1hA2VlZeTk5HDfffeRm5tLdnY24eHhK+YrDOgSjCiKJCcnc99999HV1cXCwgJdXV3LhMVPQkICeXl5fPjDH8bj8eB0OtmxY4dk8681NTU1KBQK/u7v/g61Wo1SqSQzM1OKqWhra6O+vp7f/va3+Hw+FAoFR48elWIOkpKSiI+PJzw8POj9MEqlkoqKCiYmJggPD5fCBS512F+KP0Zm8+bNbNq0ia1bt5KWlhbUyasqlYqsrCw+8IEP8Oabb9LX14fRaOTYsWOcP38em80mpaIolUo0Gg2pqamkp6dTWFjInj17yMzMZN26dZfFoKwV/kG9urpaimG52nF+Mdq0aRObN2/m4YcfJjQ0dMWd1AEtODU6OsrZs2f5yU9+QldXFwaDQVJSfzKW/++oqCjS09PZvHkzarUaURSDqsDPxo0bSUhIoKOjg7a2Nv7pn/6J7373u6SmplJZWcmxY8cYHR3F4/FQWVlJdXU1Dz74IDMzM/znf/4nb775Jm1tbXzrW99ac0fn9RAEgdjYWFJTU8nLy6Ozs/OaSY7bt2+nsrKSz33uc0RHR6/oCBgofD4fU1NTUg0Yf36OzWbD4XDg8/mIjo6msrKS/fv3U1RURFFREXq9Hp1OR2hoqDTQBBN+X5I/qvrdCIJAZmYmBQUFPPTQQ9TW1pKRkRGwgS8gAjM3N8fc3BwNDQ1cuHCB9vZ2DAYDi4uLaDQakpOTSUxMpK+vD4fDgdPpxGKxMD8/j9frXXOH7pUIDQ0lPj6e2tpaQkND0el0mM1maWqckJCARqNBq9VSVVXFhg0bKCsrw2AwMDc3x9tvv01PTw92u12q6hfMKJXKZVG4V8K/IuPvb3JyslTzJ5jxer0MDAzQ1dXF6dOnMRgM+Hw+1Gq1FO0KoNfr2bBhA+vXryc/P5+MjAw0Gk3QXjv/KtHZs2fp7u6W0nUu9bf4Y8tiYmJYt24dycnJREREBKxNAfmlWlpaOHHiBM888wwGg2FZ8E5ERAT33Xcf999/P//yL//C4OAg4+Pj9Pb2sri4yOjoKGlpaUGZVBYTE8PHPvYx3ve+92E0GmlpaWFxcRGA2tpa1Go1iYmJZGRkkJaWBiw5vKuqqqivr5eyr5VK5XUjgIMBj8eD1WpdFnB3KYmJiTzwwAN86EMfoqysLOgGhSvhT+h75ZVXOHPmDK+88go6nQ61Wk14eLg04MFSjNaf/umfkpubGzSm+rVobm7mnXfe4Qc/+AFGoxH4r6xqP35Hr16vp6SkJODRxysqMP6AnYaGBn7zm99IYccajYYtW7ZQVFQkJZMlJSXx2c9+lvr6er7//e/j9XoxGo0cOnSIbdu2BaXA+AkLC0OtVl8WmapQKAgJCbmiwy8iIgKVSsVzzz3Hzp072bNnz2o2+aZwuVx873vfo7GxkZGRERwOx2XHREZGkpeXxwMPPEBaWlrQm0R+rFYrMzMz/P73v6e/vx+NRsPf/M3fUFVVBcCFCxc4d+4cR48eZXh4mO985zv82Z/9GevXrw9qB73b7WZ0dJSLFy/icrmkmYvf33KpCTg5OUlrayu//e1v2bt3L1lZWQFr14oJjM/nw+l0Mjw8TE9PD93d3QiCQEhIiGTLVldX88ADD6BUKhFFkc2bN2Oz2QgJCcFms+F0Ounv75cSz4IVf1Hrm8mp8efgNDQ0LCvrEGz4M2+PHTtGZ2cnZrP5ioWY/P1PSkoiLCwsKJycN4K/Pk9fXx9zc3NERUVRW1vLvffeC0B0dDQKhYLGxkbMZjNnzpxh79695ObmBq2D3ufz4Xa7MRgMywIe/dUM9Ho9ubm5jI+PMzY2hs1mY2JiggsXLrBx48Y7Q2CsVis9PT186Utfoq+vT7oQOTk5vP/97+eDH/wg+fn5klNMEATS09OlxMXBwcE1DyMPJBUVFSwsLPDCCy+wfv36tW7OFfH5fJw8eZJjx45x+vRpjEbjVau8GQwGent7OXToEA899NCaJfrdDklJSezcuXPZbHnLli2UlZXx1ltv0dXVRV9fH83NzVJRqmD0v3i9XgwGAwMDA7S3ty/bCeGhhx5i48aNPPDAAzz33HP84he/YGhoCKPRyMmTJ3n44YfxeDwB69eKeeOsVivz8/MMDQ1hNpsRBIHk5GSpNF9iYuJlnfCXdwh2p+BKUFhYSGVl5bJgp2DCX6O2ra2N06dPY7PZJMH3+yfS09Ml35EoilK6wK2k8QcD/qjjS+8/f62hbdu2UVFRgc/nY2ZmhrGxsaC8brA0MCwsLEgFsmApzic2Npb169ezadMm4uPjSUlJITs7G7VazeLiolS1sL+/P2CD+4o92Tabjfn5eSYmJrBardJyWFlZmVT7891cWqDK//+7leLiYqqrq6VdFYJttuZyuRgfH6elpYVTp05Jjk5BEAgPDycmJoa8vLxlUdUOh+OqPpo7GaVSye7du6murgaQBCbYrpkffxrHpSUYtFot8fHxbNiwgZqaGnQ6HSkpKRQWFkqla+fm5ujp6aGrqytgfVvxeZF/aqbRaHjsscfYsGHDFY/z50fU19czNDSE3W4P+viQ2yEqKoq4uDgUCgWDg4O8/fbbbNmyJWgch34n+6ViERsbS2FhIe9///tJTEzk9OnTmM3moEnyCxQKhYKUlBQSEhLWuik3hFqtvmaOnJ/q6moyMzOZnp6W4rna29sJDQ3lvvvuC4iZtGIzGIVCIU03/f4Xf92Xd+N2u1lYWKCxsZG2tjbsdjuiKKLRaEhPT78jlgRvFqVSiVqtRqvV4nK5mJ6evury71rgX4VYWFiQXvPvp5OXl0dOTg5Wq3VZwJ1/1SzYgs2uhUKhkOKVRFGUqvBdin/fLv9v4b+3g9HBC0vtCwsLQ6fTodPpEARBWnRZWFiQElH1ej0pKSlkZWWRkJCAKIpSXRh/Ye+VZsUky//waLVaPB4PXq+X8fFx0tPTLzvWbDYzOjrKv/3bvzE8PCzNeKKjo3nve997xc/cDfizjAVBWBbVHAwsLCxw5MiRZbMTrVZLamqqFGBWX19/WZW7hISENU/yuxlCQkKkspfj4+PU19dLMSN+vF4v9fX1tLa2Akv+jGDINboafoFJTEwkOzubrq4uFhcXmZ+fZ2BggLS0NMrKylCpVISGhlJSUsL8/DyiKDI5OUlERAQOhwONRrPisUwrJjBxcXFkZWWxceNGenp6mJiY4PDhw4yNjTE/P09sbCxqtRqbzUZLSwtNTU3LChHfd999VFdXk5GRcUeuSNwIarWanJwclEol/f39UpBeMBAeHs7WrVuZnZ2lt7cXWCoY1dDQgNvtRqFQMD09jcPhQBAEkpKSqKio4LHHHiM1NXWNW3/jaDQaIiMj2blzJ83NzdTV1dHe3k5sbCy5ubkoFArcbjeNjY20t7cDkJubK+UcBSP+6Ny0tDRKS0sZGBjAbrdjs9l48cUX6e7u5vOf/zwRERGo1WrGxsaYmZmRlrGjo6MDlvawYgITFhZGTEwMxcXFkrO3p6cHh8MhFQ3WarWYzWbOnj1LY2MjLpcLrVYrhWRv27aN6OjoO2rKfTOoVCpSUlKkGdylTrm1RqvVUlBQQGxsLAqFQgqaHBwclPZEWlhYQBRFlEoliYmJ5OTkUFVVdUfNYPyrRKWlpczPz3P8+HF6e3uJjIyUzFi3283AwACzs7NotVoSExNJTU0NWoEBpGtSUFBATEyMVJuopaUFk8nEvn37iI2NJSQkRBr0BUGQasAEygRcUa9OXFwcn/jEJ/B4PAwNDbGwsEB7eztdXV1SB/xFfDweD1qtloqKCp588klqa2tJT0+/a8UFlkbP0tJSjhw5wvHjx5f5O9YarVZLXl4eqampxMXFSb6J+fn5ZWHnWq0WnU7Hzp07qampCbp9mm8ElUrF/v37cTqdPPvsszzzzDP84he/ICoqStr9YnR0FJ1OR3V1NSUlJWRlZQW1wMBSDE9paSk6nU7avtlfweDP//zPUalUKJVKDAaDNCuNi4sjJSUlYDlkKyow/pnK9u3bpRIGBoOByclJbDYbHo8HjUZDQkICcXFxlJWVUVxcTEVFBbGxsUEZxLSSKJVKydsfbFuwKpVKoqOj2b59O2q1mubmZiYmJujt7ZWikOPj40lMTCQ5OZkdO3aQk5Oz1s2+JQRBIDIykqKiIj7wgQ/Q3t4uFZLyh9anp6eTl5fHvn37yM7OviNWODUaDTqdjqSkJGmhxJ+iculgtri4iEqlIiYmhurqarZu3RqwgX3FBSYhIYEDBw6wadMmXnrpJbq6ujh79iwTExM4nU4iIyPJz8+nvLycD33oQ6SmpkqJgXc7arWarKwsKZbEHwMUDKsT/s3S7r//fnbu3MlLL70kbXGRnp5OQkIClZWVFBcXU1hYSEVFxR3x0F2NsLAwysrK+PznP8/Pf/5zLly4IO326PF4qKioYOvWrXzyk58M6p0qL8UvjnFxcZKrwe+E7+3tXVbIPTQ0lOTkZO69996A5sUJ11mauqV1K78JZDQacTqdUkauz+eTbODQ0FCio6PRarUrnSh3M0/rqkb2iaKIzWbjW9/6Fv/jf/wP/vqv/5pNmzbxyCOP3MxpbrR/t9Q3fxCg0WiUdvjTaDSo1Wrpumm12kDl5azqtfMXNjMYDNjt9mURyVFRUeh0OuLj41fSdAjotfNjsViYnJykra2NpKQk5ufneeqpp0hKSpL2JktPT2fv3r3SDpUrwBX7FhBZ9tcSCZZCz8GCIAjodDop6M7v6Q8mLi3aHRcXt2yzvLsNlUolxY7cTfjNIq1WS3R0NAaDgS1btpCUlERGRgawtHtEaWlpQGvBQIBLZspcmYyMDHbt2kV5ebl0wWVkVpKwsDApSzoyMpJf/epXwOXblAQaWWDWgPXr1xMZGUlKSsodUXhK5s5nrfx8AfHBrDFB64NZIVbFjl8j5Gu3xF3Tt+sJjIyMjMwtE9yRQzIyMnc0ssDIyMgEDFlgZGRkAoYsMDIyMgFDFhgZGZmAIQuMjIxMwJAFRkZGJmDIAiMjIxMwZIGRkZEJGLLAyMjIBAxZYGRkZAKGLDAyMjIBQxYYGRmZgCELjIyMTMCQBUZGRiZgXK+i3Z1YLEYuWrTE3dw3uLv7d9f0TS6ZKSPD0m4KTqdT2igwKSmJpKQkSktL17ppdzSywMjcNKIoLtvPKRj2dbpdvF4vZrOZgwcP8uMf/5ht27ZRW1srC8xtIguMzA0hiiIul4vjx49z7NgxGhoaiI+PZ//+/ezevfuO397E6XRy7tw5BgYGMBqNzMzMYDab17pZdzyywMhcF6/Xy+LiIpOTk3R0dFBfX8/Zs2dJT0+npKQEh8Ox1k28LaxWK9PT07S2tjI+Po7H4yExMZGYmJi1btodjywwMtfFbDYzNTXFb37zG44ePcrp06clE8n/706mubmZCxcu8M///M84HA7UajV/8Rd/QUlJyVo37Y5HFhiZ6zIxMUFnZyeHDx9meHhYEpfw8HDS0tIIDQ1d6ybeEgsLC4yNjXHw4EHOnTuH2+1m3bp1lJeXk56eHvBdD/8YkAVG5pqIosjk5CS9vb2cP38ej8eDSqVCr9cTFxdHamoqISEha93Mm8br9WI0Guns7OTEiRO0tLQQGhpKcXExe/fuJSEh4Y7rl9frxev14nK5EEURt9uNUqm86kxTFEW0Wi0qlWol999ehiwwMlfF7XZjtVp5++23OXXqFF6vl4SEBNLT0/nGN75BXl4eKSkpqNXqtW7qTeHz+Whra6OhoYFf/OIX9Pb2EhkZyVNPPcXWrVuprq6+42ZlHo+Hrq4u+vr6OHXqFH19fTQ2NpKTk0N0dDRKpZLo6GhiY2MRRRG73c7U1BQf+chHqKysJCcnJyCmriwwK4g/lmJ8fJyJiQkmJibw+Xyo1Wri4+OJjo6muLg4oCPGreBwOBBFkbCwMOk1n8/H2NgYp0+fpqurC4PBQFRUFKWlpdTU1FBQUEBCQgJarXYNW37zOBwOFhYWOHPmDM3NzYyOjpKQkEBaWhobN24kMzNz2e8Q7JjNZhYWFpienubcuXO0tLTQ0dHB5OQkExMTiKJIeHg4SqUSvV6PTqdDFEUWFxcxmUwcO3aM6elpNmzYQHJyMklJSajV6hUTG1lgVhCPx4PBYOD48eMcOnSIw4cPs7i4SEREBFu2bKG8vJwvfOEL6HQ6NBrNWjdXwmQy4fV6lz1YLpeL1tZWnn76aUwmE6IokpaWxoEDB/jIRz5CbGwsSqVyDVt9a5hMJoaHh/nVr37F0NAQs7OzPPLII2zatIndu3ejUt1Zj8To6ChjY2OcOHGCo0eP0tDQIL2nUCiYnp6+5ucnJiaIiYnh/vvvZ9euXdxzzz1ERESs2LVd01/T6XQyMTFBfX09R44cYWRkBFgaPQESEhIoLi7mU5/6FNHR0UE5bV1cXMRsNlNfX09PTw+HDh1iamqK2dlZbDYboihiMpk4c+YMQ0ND5OXlUVlZSXl5+Vo3XcI/qvnx+Xz09vbS09PD+Pg40dHRJCUl8ad/+qfU1NRIU+47CZ/Ph81m47XXXuPgwYP09vYSHR3Ntm3b+JM/+RPKysruyD699dZbnDt3jrNnzzI/P3/T53A4HMzMzHDw4EFaWlp4/fXXefTRR8nOzqaoqOi227hqAuPxePB4PFLMhCiKTE9P09/fz9mzZzl58iT9/f3Se4IgkJCQgMlkYseOHZSUlASdwPh8PsxmMz09PZw7d46LFy9y+vRplEolGo2GhIQEBEGQokRHRkZobm4mLi6OkpKSoBktNRqNJDB+52BXVxeDg4M4HA7S0tJITk6moqLijvS5wNKMbHBwkPb2dpqbm9FoNKSmprJp0yaKi4tJT09f6ybeFIuLiywsLNDd3U17eztDQ0PLoqthKcI6NDQUtVqNRqNBoVCgUCjQ6/WSie7z+fB4PExPT+NyuZibm6OgoACv10taWhohISG3dZ+u2h0+NzfH3NwcTU1N+Hw+vF4vb7zxBoODg7S2tuL1eqVj/T/S7OwsDQ0NfOUrX+GrX/0q+/btW63m3hA2m41z587xrW99i7a2NgwGAz6fj3Xr1lFWVsb69etRKBRYLBZeeukl+vv7+eUvf4lGo6Gqqoq4uLigEJlL/SiLi4vMzc3xzW9+k4GBAQDy8/Opqalh06ZNQWXa3QzT09P8r//1v2hubsZgMPCpT32KrVu38uijj95xMxeA8fFxTp48yZkzZ+js7Fw2A4WlZ0ilUrFu3TpSUlLIy8sjPDwcnU7H7t27CQ8PRxRFPB4PMzMzfPWrX2VkZISBgQF+/vOfU15ejl6vp7i4mISEhFtuZ8DublEUmZ+fZ2BggJMnTzI/P4/FYmFgYACfz4coivT19WGxWCRxuZJjyel0Mjo6Sk9PD5mZmeTn56+5g9Tn87G4uMihQ4c4e/YsPT09WK1WwsPD2bFjBzU1NWzYsIGEhAQ8Hg9zc3OcOHGCgYEBrFYrNpsNu90umYLBgD8VoKGhgcbGRqampvB6vcTFxbF161Zqa2uDQgxvhenpaQYHB+ns7MTj8ZCRkcHOnTspKSm5I8UFliwCu92Ox+O54n2UmprKli1b2LFjB2lpaURFRUkzmfT0dGnWKooiUVFRPPnkkzQ0NHD69GnGx8fp6OjghRde4M/+7M+CT2A8Hg9ut5vJyUnOnTvHM888g81mw+FwMDc3d9XPCYJASEiIZE75b/qZmRkGBgYYGBggJydnzQXG6/XicDg4evQoTU1NjI2NSSbR3r172bp1Kxs3bgSWwtCnpqakoC2n04nD4cDpdF426qwlPp8Ph8NBc3MzBw8exGAwoNVqSUhIYMOGDVRVVa35734riKLI+Pg4fX19DA4OkpiYSFZWFhs3biQlJWWtm3dbiKIomT3vvpeSkpLYu3cv+/btu675Fx0dzeOPP05kZCQmk4n+/n4GBgZwOBzs37+fDRs23PKq0ooKjN9+P3HiBPX19bz99ttMTEwwPDwszVquRVxcHE8//TR1dXW8/vrrzM/P4/V6cbvdOJ1OyWm61hgMBoaGhjh8+DDj4+MoFAppJeKjH/0o4eHh0rFut5uZmRksFgtOp3MNW31t/HZ4Z2cnjY2NuFwuCgoKePzxx8nJyVnWpzsFt9uNw+Hg3/7t3zh9+jQ2m40NGzbwwAMPEBsbe0f6kvykp6fz4IMPcurUKZxOJ0NDQ8veDwkJISUl5aaCBUNCQoiKikKhUOD1erFYLExPTzM1NUVSUtIticyKCYzP58Nut9Pd3c358+c5e/asZAK53W4UCgVqtZqoqChphmO1WqXpXWxsLJmZmZSWljIyMoJGo1nWoRsRqNViamqKjo4OzGYzoigSFxdHeXk569evJzIyEp/Ph9PpRKvV4nA4GBsbw263A0v+jpCQkMv6t9b4B4fFxUUWFxeBpRsuKSmJkJCQoGrrjXKpj2F+fp6srCwKCgooLi6+JV+Sf+a6uLi4LBdrLcIONBoNMTExFBQUYDAYpBQOP34zfmFhAbVajc/nIzQ09IoLJT6fD5PJxNTUFCMjI7jdbqmvVquVhYUFEhMT11Zg/L6G//zP/+To0aOcO3du2fsajQa9Xk9FRQVWqxWLxUJPTw8ulwuAgoIC1q9fT1lZGc3NzSsa7LPSdHZ2cujQIRwOB3q9nsLCQnbt2sXmzZsBsNvtWK1W4uPjsVgsNDc3YzabEQSBiIgI6V8w2/9+czUuLu6OdeyKoojX68Vms6FUKtm5cye1tbVs2LDhls7lcrmYnZ1lfHwcWIozUSqV5OXlERsbu9LNvyZKpZKwsDBqa2vRaDQcPXp02UKJ0+lkenqa0dFRLBYLLpfrqnljbrebvr4+zp8/z/Hjx7HZbNLvZjQaMRgMt+yauG2BWVhYYHJykmPHjtHV1cVrr72GwWBYOrlKRXR0NJmZmdTW1pKTk0N1dTWHDx/mzTffXNbg2NhYYmJipCU3i8WCz+eT8l6ys7PJy8tb04fSH0sxNDREW1ub5DB89NFHSUpKYmFhgSNHjtDU1ER3dzf/5//8H0wmE11dXVitVjQaDcXFxWRmZkpT0WDBZrNx5swZxsfHUSqVkoOwpqaGqKiotW7eLTE1NUVDQwMmk4nIyEgeffTRG47t8Hq9zM7OStdyenqa6elpDAYDc3Nz+Hw+yf9RUlJCbm4un/jEJ9Dr9asqyDk5OVgsFvR6PTabDbfbDUB/fz/f//730el00gympqaGmpoaNm/eTEREBFqtlu7ubvr6+vje977H0NAQCwsLeL1etFot0dHR5OXlUVBQcMvP3W0LjNlspquri/r6ejo7OxkcHEStVkvJcMnJyZSWllJbW0teXh7l5eV0d3dfNkNRKBS43W7a2toYHR3F6XSiVCoJDQ0lNTWVpKQk4uLi1nRW4zcDjUYjc3NzCIJAbGwsZWVlAExOTtLQ0CD9FlarFYfDwfT0NE6nE7VaTUZGRlDOChwOB52dnRgMBhQKBampqaSnp9/WCsJa4vP5mJ+f5+LFizidTsLCwigoKCA6Ovq6n/N6vUxOTjI0NERjYyPNzc1MTEwwOTmJ2WzGYrFIxysUCmZnZ5mYmGD//v2kpqYSFxcX6O5JREREEBUVhUajwel0SgJjsVhob29f9rx4vV5p1Sg6Oprw8HBaW1tpb2+nrq4Op9MpDerh4eHS9Y+Kilo7J29zczP/+I//yNDQkOSEzc3NpaioiCeeeIKcnByKiopQKpUoFIorNlShUDA4OMjs7Cz/8R//gcvlwufzkZSURH5+Pu9///vZvHkzaWlpayowXq9XGsGMRiPx8fFkZmZSXV3N66+/zrlz5/j2t7/N4uIiWq0Wl8slCbDVaiU6Oprdu3eTl5e3Zn24Ev5+Pf/881gsFgRBIDEx8boP45UQRVF6SBUKxZosbYuiiMVi4eLFi/z85z/H4/GQlpZGdHT0dZ2eCwsLzM7O8o//+I90dnbS0dEh+f/8/y7FH/U8NTXFD37wA/bt28fDDz8cwN4tR6lUolKppKxpP37z8NL/19XV0djYyAsvvEBYWBh6vZ6RkRFMJpO0agtLiy0FBQV8+MMfJj8//7ashlu++l6vl87OTrq6upicnJSWXbVaLfn5+Wzfvp2ioiLi4+PRaDT09/djMplwu9309/ezsLAgOXhFUWRqakpSYa1Wi06nY8OGDaxbt44tW7YQHx+/5j4Z/8MDSI7czs5OfvjDH9LQ0MDg4CCLi4vEx8eTmppKc3MzQ0NDUsyLSqWSYhKCDYVCgVarlW7UsrIycnJyrvkZj8eDzWajv78fg8GA2WyWQgycTicxMTHU1NQQGxu7qlHYoiguGwjS09NJTk6+ZlSqx+NhdnaWM2fOcPr0aVpbWyXTqrS0lKSkJNLT0yWz1m63Y7FYGB4epqOjA5PJRGtrK7m5uZjNZnQ63aqY89PT00xMTCwzj96NXzh8Ph9utxuj0YjVapUSJT0ej3SsQqEgISGBnJwc6drdDrctML29vVJsi/8mzczMpKqqiuTkZLRaLU6nk46ODoaHh3E6nfT09GAymZYJjN/kUKlUREZGkpiYSHV1NZWVlbfklAsEgiAs85v4zYof/OAHjI2NYbPZEASBpKQkysvLaWpqYmhoiMXFRZRKJSEhISQnJwddISP/CO1/AL1eL+vWrSM7O/uKx/tXnOx2OzMzMzQ0NNDX1yetQPhXL3Jzc6WM69UUGL95ND8/z8LCAlFRUSQmJqLVaq/q93K73QwPD3P48GF+9atf4XK50Ov1pKens2fPHiorK9m0aZMkGvPz81I0rdFoZH5+ns7OTtavX4/RaCQ0NHRVBMYfBuJ3zF6Jdw/M/hXNKx0nCAIxMTGkp6dTWlp62324rfnru5eO/T6KV155hdOnTy+bjk5NTWG32/F6vTidzmX2IiBtE7Fv3z5KSkooLy8nOTk5qPKPFAoFOp1OSnu32+2YTCasVitutxuVSkV8fDz79u3jkUce4cknn5TC7cvLy6moqCAzMzPoYkrOnj3L+fPnGR0dxeVyXbNcgcPhwGKx8K//+q8MDAwwPDzM1NSUNIL67wev18vIyAh2u53Pfe5za+4/ux6zs7N8/etfp6urC5fLxfvf/37Wr1/Pgw8+SExMjLTE6++DXq8nKSmJnJwcFhYWUKlUUrrI7OwsCQkJAfWz+Xw+XC4Xzz77rFSrZ6XO297eDsDLL7/Mpk2byMjIuOXz3bLA+JMR/T/+4uIiPp8Pn8+HwWDAZrMtO97/EF4qSIIgoNfrycjIoKCggLS0NDZt2kR2djY5OTmEhYUF1UqLQqEgPDyc/Px8tm3bRm9vLx6PB6VSKQlPfn4+ycnJzM7OYjAYWFhYACAzM5OioqJrjqJrhc1mk+KV/NdwcnKS2NhYyQ/jr3XT399PT08PFy5cYHR0lJmZmWXTc6VSiVKplKbi3d3dUrzQWgmMzWaTTIEr+QGNRiMTExP09fUBkJeXx6ZNmygrKyM7O/uK9Xv8//ffo6sdo+V0OjEYDExOTjI9PS19vyAIaLVaKS3A6XTi8Xik2CZ/39VqNTqdTpqVR0dH4/V68fl8WCwWZmZmqKurIyEhAb1ef8uO3lsWGJVKxfr16xkfHycxMZGpqSnJC2232686DbsUtVpNbm4uH/vYx9i6dSsZGRnExsYG7UinVCqJjY3l0Ucfpba2lmeffRan04lOp6OgoICMjAz27t3Lyy+/zC9/+UspEhmgpqaGvXv3BmU+j9vtXjab9Hq91NfXIwiC5JD2B6y99tprPP/888timC7F7z+bn5/HZrNx8eJFjEajtKy7Wlz6wE9OTjI6OorD4ZACPi+lu7ubc+fOMTIywubNm9m9ezdPPPHEdf0PHo8Hk8kkRbuqVCpCQkKWzXQChclk4uLFi8zOzi571vwrmxEREcTFxTE1NYXZbGZmZmbZ5/V6vbRkHxYWRk1NjeRTe/3115mYmOCZZ54hMjISjUZDdXX1Ld27tzWDCQ8Pp7Kyks9//vOMjY0xPT1NXV0daWlppKamkpGRsWyaaDQaeemllzCbzdhsNsLDw8nIyODAgQPExsYSFhYWtOJyKVFRUWi1Wj7+8Y/j9XqlWJ3Q0FAUCgXDw8OcOXMGh8Mh+V7i4+ODwlF9JSorK1EqlcTExEjOd5fLJZmAw8PDDA8P87Of/YyBgQFGRkbweDzS6FdYWEhKSgplZWWkpKSQkJDA//2//1daoVgLLv2d/aUy3n77bUpKSsjOzkar1V7xWoSEhBAREXFNX43T6eTtt99mYGCAs2fPcuHCBWw2GxUVFaxbt46MjIyAV/qz2+1MTExcJvJarZa//Mu/JCsri6SkJN588006Ojo4fPgwGo2G8PBwysvLKSkp4b3vfS+AdO39TuD4+HhaW1s5ePAgr7zyCn19fXz7299Gr9fftE/mtoZTtVpNSkoKO3bskMpEWiwW8vPzyc3NpbCwcNloMTU1xdtvv43D4cBut6PRaIiKiiI3N/d2mrHqaLVatFrtZatB/mjP+fl5xsbGAAgNDSU2NpaoqCgiIiKCUmCSk5MxmUzExsbidrux2WzMz89LZkNXVxfd3d289dZbkikMS79DTEwMJSUl5OfnU1tbS3x8PJGRkURFRTE9Pb0mEdmCIKBWq1Gr1SiVShYXFzEajZw7d05yZEZGRqJSqVCpVCwsLEh1ihYXF7FarczMzFxxhma326UCY52dnZw+fVq6l/2z2NVw4ns8HqxWqyT0/n5rNBo2bdpEUVERSUlJGI1GVCoVvb29hIWFERUVRU1NDeXl5ezcufOy8/pLpfrP3d3dLaVH6HS6m27nbc/Xo6OjiYqKoqysDFEU+ehHPypFOL57FBgaGgq6cpEricvlorOzk9nZWem1pKQkHnjgAfLy8oJu9ciPUqkkKiqKHTt2UFdXR1tbG88//zyvvfYa3//+96WAwXdvsJafn89nPvMZ9uzZQ1ZWFkqlkgsXLnDw4EH6+vqwWq1kZ2dfc0YQCBQKBZmZmWRkZJCQkCA5Xv/1X/+VmJgYKYcnLi6O9PR0RkdHmZiYwOv1cvr0aRobG2loaLjiAzU+Ps7w8DAGg0HK2YmIiCAtLY0/+7M/u+rKW6C4VLxDQ0OJiooiJiZGavsDDzzAvffey4c+9CHJl+IX3qsRFhYmOfoXFxex2+1XjAG6EW5bYPwjgv8GulaG6tUC7e4GRFHEbDbz+uuv09PTI12M6OhoamtrVz1X5Wbx2+H+WYvT6cRqtUrbYFxplcJms9HT04PT6SQ8PFyKC+nv78dmsxETE8OOHTtuOVHuVvFXcsvNzeWhhx6irq6OyclJZmZmMJlMLC4u4na7CQsLkxJyFxYWpDgnp9NJV1cXarX6MmE0m80YjUbJ4R8WFsaePXuoqKggKytr1WKc/L4kQRCke02n05GQkEBYWJj0HKpUKsLCwqQ4oKuZhpfi3/7k0iqHt0rweRzvUPz77Pz2t79ldHQUWLoJ4uLi2L59+y1Fxa4m4eHh1NbW0t7ezoULF5idncXj8UirYFfCZDLR2NjI0aNHsdlsjI6OSisR/pt67969pKSkrPrAEhISQmFhIR/60IdwOp2oVCqMRiMul0symd6NfwAURVEKL7hUYPx/KxQKoqKi0Ov1xMfH89hjj7F79+5bMiFulUsDIxUKBT6fD51OR2Ji4jKBubS918Mf/et0OqVVJ//rtyoyssCsEL29vTQ3N9PX14fdbkepVLJhwwbWr19PTExM0Nce0Wg0ZGVl8Rd/8Re85z3v4fvf/z6Dg4OMjo5iNpuv6I+wWCy0tLRIS9v+ZeDQ0FA+/elPs2HDBvbu3btm24DExsZKlQUnJiY4fPgwXV1dDA8PYzKZWFhYkIJEdTod27Ztw263s7CwgMlkkvLg/DEw/qzpuLg40tLS0Ol0xMTEEB8fv+p9TEtL4/777+f06dN4vV56enqkYM+ZmRn0ej16vf6mztna2kp9fT0vvPCCVIAf/stKuRVkgVkB/PkobW1tOBwOaWWpoqKCoqKiK061gw2/gzAlJQWdTsfWrVuJi4tDq9XS2dl5RYHxj3b+WChYWmGLj4+nurqawsJCIiMjV7srEv6SBn7Hq8ViIS4ujqysLCwWC1arVarEHx4eTk1NjVQDxWKxSPVw/JHImZmZ0uZlcXFxhIaGruqs5VK0Wi2xsbEUFhZiMBjo7e2VHNTvDmK9Fi6XC4fDQW9vLxcuXODs2bMMDAws+10iIyNv2b0hC8xt4vdRvPLKK7zzzjuSr0KlUvHxj3+c4uLioBeXS/GP1l/4whfo7e3l9ddf50c/+tEVl5v90/TNmzeTlZUFQFVVFVu3biUzMzNonPn+Pj300ENr3ZQVw79Stn//fmJjY3njjTekyHr/qs+N4K+b/Xd/93cMDw8zPj6+LEI/LS3ttgZJWWBuA79jd2hoiLGxMebm5hBFkezsbAoLCyWH251KUlIS9913H7m5uctKFPgRBAGlUklSUpKU/hATExM0uyX8MZCYmEhJSQmPPfYY58+fZ2BggB/84AeUlpZy7733UlRUdJn/ZXZ2VqqX7c+X6+7uXlZh0h/p+/DDD0u7EAS9wPhvSH9otd9udzgcaDSaoK7wdiU8Hg9Go5H29vZlEZUpKSlUVVURERER9L6Xa+Gv1ldYWLjWTZG5Cnq9npSUFGprazGZTExOTnL69GlMJhMxMTFoNBqSkpKk4/1xLn19fRw9epT6+nrGxsZwuVzSrEWj0aDT6UhNTWXjxo1s2bLllmejqyowKpWK1NRUKYfCbDYzMDDAm2++yaZNm0hOTl7N5twW/o3j3n77bf76r/9aKuitVqvZsmULn/nMZ4iOjr5rl+VlggOdTkdoaCgf/vCHiY2NJT09nRdffJGTJ09SX1+PVqtdNpv01yn2F0T3r/r5EQRB2tfrT/7kTygpKblpZ/GlrKrAhIaGsmHDBoxGIwMDA1Ldz4mJiaCuuH8lfD4fQ0NDjI6OSmUaIiIipNT+qKioO25GJnNnolQqCQ8Pp6SkBEEQaGhoYGxsDIPBgN1uv2yQ81sPfmHxv6/T6YiOjmb//v1SGdDbDQ5dVYEJCwtj586dDA0NUV9fLyVF+pfX1jLj9mbx+Xz09fVJKQH+RMiPfexjrFu3bs1WF2T+OFGpVJSWlpKTk8Orr76K1+u9ah6Y31Xx7gEwNjaW/Px8aaualbiHV1VgQkJCWLduHevXr6erq4uGhgZGR0f5xS9+gU6nY3Z2lo0bNxISEhL0vguVSsWuXbtwOp28+uqrlJeXU1paytatW29rSikjczuEhITw9NNPXzG143poNBq0Wi3p6ekrlqy56k5enU5HZmYmlZWV9Pb2Mj8/z+TkJC0tLajVarKysu6ITbEEQSAuLo7c3Fy2bdvGunXryMvLIzo6WjaNZNYMhUKx6vlQ10K4TghwQKro+AOcnnzySWknAlhaFv33f/93SktLb3h7iStwMzbWivTv0mI/q8CNfklw7FJ3c6z6tVtl/uiu3ZoIjL8odF1dHUajUcp3CQsLY/PmzcTExNxOBKh8ky5xN/cN7u7+3TV9WxOBCTDyTbrE3dw3uLv7d9f07XoCIyMjI3PL3DlJMjIyMnccssDIyMgEDFlgZGRkAoYsMDIyMgFDFhgZGZmAIQuMjIxMwPj/nXEYAyjsMz0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_samples(next(iter(mnist_non_iid_train_dls[0])), 0, \"Client 1\")\n",
+    "plot_samples(next(iter(mnist_non_iid_train_dls[1])), 0, \"Client 2\")\n",
+    "plot_samples(next(iter(mnist_non_iid_train_dls[2])), 0, \"Client 3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Federated training with FedAvg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clients' weights: [0.29, 0.2733333333333333, 0.43666666666666665]\n",
+      "====> i: 0 Loss: 2.30640879313151 Server Test Accuracy: 5.493723798307226\n",
+      "====> i: 1 Loss: 2.1980189021428425 Server Test Accuracy: 25.296830811393917\n",
+      "====> i: 2 Loss: 1.7439398292700448 Server Test Accuracy: 45.54914137438409\n",
+      "====> i: 3 Loss: 1.227887612779935 Server Test Accuracy: 52.50069193148895\n",
+      "====> i: 4 Loss: 1.0358077690998713 Server Test Accuracy: 69.34269491889722\n",
+      "====> i: 5 Loss: 0.8220566266775131 Server Test Accuracy: 64.55666752776709\n",
+      "====> i: 6 Loss: 0.5739711784323056 Server Test Accuracy: 77.7559790542405\n",
+      "====> i: 7 Loss: 0.5648151334126791 Server Test Accuracy: 78.26422426038592\n",
+      "====> i: 8 Loss: 0.40358308206001914 Server Test Accuracy: 83.93029290297295\n",
+      "====> i: 9 Loss: 0.46662271827459334 Server Test Accuracy: 78.71991250677183\n",
+      "====> i: 10 Loss: 0.33874771108229956 Server Test Accuracy: 81.94775222275786\n",
+      "====> i: 11 Loss: 0.3785162182648977 Server Test Accuracy: 83.32578141474053\n",
+      "====> i: 12 Loss: 0.3105277146647374 Server Test Accuracy: 81.09443695219264\n",
+      "====> i: 13 Loss: 0.3969525445004304 Server Test Accuracy: 75.00452993069837\n",
+      "====> i: 14 Loss: 0.26835320631663007 Server Test Accuracy: 84.14531781725279\n",
+      "====> i: 15 Loss: 0.21986659824848173 Server Test Accuracy: 88.70465340029575\n",
+      "====> i: 16 Loss: 0.21281323460241158 Server Test Accuracy: 86.37012401880318\n",
+      "====> i: 17 Loss: 0.18938758075237275 Server Test Accuracy: 88.0659826681502\n",
+      "====> i: 18 Loss: 0.15882468936343988 Server Test Accuracy: 89.17662254713508\n",
+      "====> i: 19 Loss: 0.16109553222854933 Server Test Accuracy: 89.13432285299524\n",
+      "====> i: 20 Loss: 0.20892317460228998 Server Test Accuracy: 85.58936086685014\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_iter=20\n",
+    "\n",
+    "model_f, loss_hist_FA_niid, acc_hist_FA_niid = FedProx( model_0, mnist_non_iid_train_dls, \n",
+    "    n_iter, mnist_non_iid_test_dls, epochs=2, lr=0.1, mu=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_acc_loss(\"FedAvg MNIST non-iid\", loss_hist_FA_niid, acc_hist_FA_niid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Federated training with FedProx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clients' weights: [0.29, 0.2733333333333333, 0.43666666666666665]\n",
+      "====> i: 0 Loss: 2.306407473882039 Server Test Accuracy: 5.493723798307226\n",
+      "====> i: 1 Loss: 2.2260446576277415 Server Test Accuracy: 19.66966966966967\n",
+      "====> i: 2 Loss: 2.0079417979717253 Server Test Accuracy: 40.9804435277454\n",
+      "====> i: 3 Loss: 1.5390093358357746 Server Test Accuracy: 56.72841434796729\n",
+      "====> i: 4 Loss: 1.1283928259213765 Server Test Accuracy: 62.0299323934456\n",
+      "====> i: 5 Loss: 0.9241881344715754 Server Test Accuracy: 71.65633413951318\n",
+      "====> i: 6 Loss: 0.8157852756977081 Server Test Accuracy: 69.72263141443543\n",
+      "====> i: 7 Loss: 0.5624533195296924 Server Test Accuracy: 81.53064466588964\n",
+      "====> i: 8 Loss: 0.5735172559817632 Server Test Accuracy: 78.94570931283508\n",
+      "====> i: 9 Loss: 0.4567745459079743 Server Test Accuracy: 82.46855932473453\n",
+      "====> i: 10 Loss: 0.7236683595180511 Server Test Accuracy: 70.28451639718291\n",
+      "====> i: 11 Loss: 0.3859462860226631 Server Test Accuracy: 81.24697445243842\n",
+      "====> i: 12 Loss: 0.3846401866277058 Server Test Accuracy: 83.71827980611002\n",
+      "====> i: 13 Loss: 0.3171018890539805 Server Test Accuracy: 84.80821733903693\n",
+      "====> i: 14 Loss: 0.26964718714356417 Server Test Accuracy: 86.1797082700221\n",
+      "====> i: 15 Loss: 0.24281563197573025 Server Test Accuracy: 86.45824985066349\n",
+      "====> i: 16 Loss: 0.22772537445028623 Server Test Accuracy: 87.33918610414432\n",
+      "====> i: 17 Loss: 0.2068032720685005 Server Test Accuracy: 87.93555714215005\n",
+      "====> i: 18 Loss: 0.19739950825770694 Server Test Accuracy: 87.09692058280905\n",
+      "====> i: 19 Loss: 0.18340067078669864 Server Test Accuracy: 88.70164158287888\n",
+      "====> i: 20 Loss: 0.17671960825721422 Server Test Accuracy: 88.7253557463537\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_iter=20\n",
+    "\n",
+    "model_f, loss_hist_FP_niid, acc_hist_FP_niid = FedProx( model_0, mnist_non_iid_train_dls, \n",
+    "    n_iter, mnist_non_iid_test_dls, epochs=2, lr=0.1, mu=.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_acc_loss(\"FedProx mu=1 MNIST non-iid\", loss_hist_FP_niid, acc_hist_FP_niid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conclusion and comparison\n",
+    "\n",
+    "Also in the non-iid case both aggregation methods yields good results in term of accuracy. FedProx is performing slighly better, since it is able to compensate for the heterogeneity of the data across the different clients"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extra: Synthetic MNIST non-iid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We create a synthetic dataset of noisy digits with 3 channels and a different rotation for every channel. We split it across clients using a non-iid sampling, assigning digits 012, 345, 6789 to the 3 different clients."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C= {\n",
+    "     'n_samples_train': 200,\n",
+    "     'font':'Inconsolata',\n",
+    "     'tilt': [0, 45, 90],\n",
+    "     'std_tilt': 10, #std on the tilt,\n",
+    "     'seed':0\n",
+    "     }\n",
+    "C['n_samples']= int(1.5 * C['n_samples_train']) #20% more for the testing set\n",
+    "\n",
+    "C1 =deepcopy(C)\n",
+    "C1['numbers'] = [0, 1, 2]\n",
+    "\n",
+    "C2=deepcopy(C)\n",
+    "C2['numbers'] = [3, 4, 5]\n",
+    "\n",
+    "C3=deepcopy(C)\n",
+    "C3['numbers']= [6, 7, 8, 9]\n",
+    "\n",
+    "clients = [C1, C2, C3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from create_synthetic_MNIST_datasets import get_synth_MNIST\n",
+    "custom_mnist_train, custom_mnist_test = get_synth_MNIST(\n",
+    "    clients, batch_size =10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FYkxPTzM9Pc0nPvEJ9u3bx4YNG+RZfnh4WL6gz50MbDYbH/nIR2htbaWpqYmNGzfK+Yp8XsCvBkEQMJvNc5ohtVotlZWVJBIJotGoXOo9F7VandcZ/5Wi0+lYunQpL774IrlcDkEQKCkp4cCBA3z5y1/mzJkz6HQ6Pv7xj+Pz+YhEInPGZbfbKS4uXlwejNfrZWJigv3793PixAmmp6dJp9Mv66EUeg5mNqFQCJ/Px4kTJ+jv76e3t1ceI4Db7aa2tha9Xo/BYOD666+npKSkYC7Kc0mlUsTjcQYHB+nu7qa/vx9BEDh+/Dj9/f2yd3mh0FZCrVbjdDqJxWIcP36cqqoqbDYbbre7IEVVp9PNyQ8ZDAZWrlyJz+fD4/EwNjZGNptFq9XOCTNKS0vlMLkQEAQBvV5PXV2dfH9J3mRHRwdmsxmVSoXJZGJ8fJzp6ek5Hk95eTkVFRV5O0d5EZiTJ0+ya9cu/vSnP+HxeC7pucBfXL3FwNDQEMePH+fJJ59kZGSE6enpOe93dHTw5je/GbPZjNPpzGdX77wQjUaZnJzk+eef549//CNbt259Vb9HrVZz7NgxDh48yMaNG2ltbaWiomJ+jZ0HBEHAYrFgNBrl14qKirjzzjvp6+vjxIkTZDIZstksVqtVTuqbTCaWLFlCU1MTer1+AUcwl9nVPonly5ezfPlyJicniUQiRCIRent7GRoaIpPJYDAYqKysZPny5bS3ty8ugenu7uapp55ienpaFpdrr72W4uJiBgYGGB8fZ3JyEoDi4mJWrFjBe9/7XlavXl2QVYcLkc1mmZ6elkvXsxkaGmL37t1cf/31GI3Ggl2XFI/HCQQC7Nixg+7ubh555BHGx8fPO85isVBcXMwdd9zBvn372Lt375z3y8rKqKqqIh6Pk06n5Rm/ED0XmLkhi4qK5ghMJpNhamqKVCqFzWbjPe95D+Xl5axYsUJeBqJWqykqKsJutxdc2DebXC6Hz+ejt7eX3/72t5w6dYpwOIzX65Un/OLiYhobG6mqqqKkpGRxCIxUMZqYmGBwcJBEIiGHPdXV1dTU1JDJZIhEIrLAGAwGqqqqWLJkCUuWLCnYi3I2Wq0Wk8mE2+2WZ45IJEIymSQcDuP3++Ukok6nw2KxoNFoCi5ESiQSTE1N0dXVxZEjR+ju7pbPl8lkwmAwYLVaMZlM8gV55syZ835PZWUldXV1chuC3W4v2JtQaps/t8M6k8kwNDSESqXCYrHQ3t5OfX09a9asKbjzdiHS6bT8o9Pp8Hg8HDhwgB07dnDy5Ekikch5nxEEgXg8TjQaJRaL5WWZx7z+tnA4zAsvvEBvby+pVGpOzsXtdlNXV0csFmNiYkJ+XZodpHzFYqCurg63201TUxOZTAa1Ws0f/vAHDh8+zB//+EcmJibweDz867/+Kxs2bODBBx+ktLS0IMqas/F6vezbt49HH32UU6dOyeKiVqtpb2/n6quv5v3vf7/sde7Zs4fR0dHzfs+73/1u2tvb+e53v8uSJUu46aabuOGGG7DZbFd6SJckGo3i9/uJRCIkEgn59enpab7//e9z4403ctNNN9HR0YHT6SQej6PX6wsm53IhUqkUY2Nj9Pb2Mj4+zpo1a9i6dSsf+9jH5LVH5zI5OcmTTz7J+Pg4LS0tqFQq6uvrqa6unlfb5k1gxsfHGRoa4vnnn+fUqVOEQiFyuRwOh4OGhgZaW1uprq5maGhoTodnLpcjnU6TSqVIJpPkcjk0Gk1Bh0parRZBEKioqJArQ9deey2lpaXy32F8fJyBgQFKSkoYGhrCarUWnMBks1ni8TipVOq8/JfRaMRms1FVVUUqlSIajXLkyJE5k4PBYKC4uBi1Wk0qlZqzBYe0Ur7QSCaTBAIB9uzZw8mTJ+e8l8lk6OnpIZfLMTAwQE1NDWvXrqWtrQ2Xy1WwncmSN2YymbBarbJXIhUdYKYEv2TJEpqbm9m+fTuhUIh0Os3IyAjJZJInnniC9evXIwgCZWVl83b/zZvAjIyMcPz4cbZu3cr4+DihUAiYKV92dHRQW1tLcXGxnJWXyOVyclt9MBgkk8lgsVgKWmBUKhU6nQ6n0ym/dtVVV1FTU0N3dzeZTIaxsTFGR0cpKSlhZGSExsbGBbT4wkjifi6zvcqioiJsNhs6nY4TJ07M6f8wm800NjYiiiKhUEjONxXiYlWJRCJBMBhk3759F8w39fX10dfXx7PPPktbWxvJZFIOE2dfk4UWNqlUKoqKiuZ0HkuJaCkJ3NHRwc0330xXV5ecL5ucnJR7gQRBoLq6GpfLVXgCs3v3bnbv3i2HRxLBYJDOzk4mJydRq9WcOnUKj8cjvz81NcXTTz/N3r17sVgstLW1cddddy26tTtGoxGHw8GyZcs4ffq0/Ho6ncbn8xVMY9ZsdDodDodDXgEtJayz2SxHjhxhenqa3t5eAAKBwHlezpIlS/ja177Gr371K3bt2sU73/lOWltbaWlpKcj8C8xUiySvS/K4pAlPaqWQQoq+vj6+9a1vEQwGueqqq+TeHlEUKSkpKRgh1Wq1cjOj5JXabDaWLl0qb4im0WgoLy/HZrMhiuKchtdUKsXp06e56qqr0Ol08yqe8/YXSqfTJJNJeY8XiWQyicfjIRqNIooiHo+HWCwmv5/NZuUyml6vx2KxyN5PoTIyMoLX66WlpQWDwSDv6KbRaOQ9USSy2ay8mK7QkBYmNjc3k0gkOH78uLxkIxKJMDY2Jo9LCl9nYzAYKCsrQxRFEokERUVFBRkKzkZKuptMJrlUvXTpUpxOJ5lMBp/Ph9frpa+vj1QqhdfrlXtjTp8+jdlsltsPCkVggDm2SGGOtJZK8kg1Gg2ZTOaCy3KkCXC+mwjn7S9ktVovmNST9lG5HKRmrkJvuNu2bRvPPfccn/3sZ3G73XK5U6PRUFJSMucGS6VSBAKBC4YiC43b7cbtduPxeHC73Zw4cUK+8OLxOPF4fE7O5Vyk2V5qUZdeK2QkT7O0tBSNRoPT6eRLX/oSGzZsQBRFdu/ezY4dO/j617+O1+sFZsIqj8dDT08PtbW1NDc3U11dXVC9MLORtmx1OBxzXp+enmZ0dPSi95e0dmw+c2fzJjBNTU0kEgmOHTvG8PAwU1NTc96XtjZYt24d+/fv58CBA8Bf4n2n04ndbqeurg673T5fZs0rkUiEAwcOsGfPHvbv388///M/YzKZ5JXV2WyWQCDAkSNH5M9kMhkCgUBBhkgSV199NQ0NDTQ3N3Py5El6e3sZHBxEo9Hgcrno7OzE5/Od97l0Oo3f7+etb30rgiBQV1dX0N6LhMFg4BOf+ATRaBRBEHjppZfYu3cvDz30EG1tbdjtdsbGxjh69CgvvfQSO3bsoL+/n7e+9a20tLSwatWqOT00hUI6nZbXwUnh7YsvvojP56OhoYH169fT0dHBP/7jP7Jr1y5+9rOfyZ+VOoItFsu8VszmTWDcbjfRaJRly5bJq6hnbwbd3NxMTU0NGzduZGJiQhYYaUuHuro6KioqaG5upri4eL7MmlekvU+Gh4cZGRkhGAzKDVjSCmtBEOSZT4rzpdcLlcrKSpxOpxw6qNVqcrkcOp2OkpISTp8+fUGBiUaj9Pb2snr1asrLywvyprsQKpWK2tpaIpEI6XSa/fv3k8lkSCQScpjb1tYmb30wPDxMPB7HbDbLizcLkWg0SjAYZGpqisnJSXp7e+ns7MTj8ZBKpWhoaEAQBBoaGuZEFVIYrNPpCjdEam1tpba2ltraWnp6ehgeHsZms2EwGLBYLLjdbgwGA5FIZI6HYjKZqK2t5Z/+6Z/YtGmTvECuUMnlcnIH7Oy9N2afFCnuLSkpobGxkQ0bNsypOBUaarWabDbLyZMn8fv96HQ61q9fTzKZxO/3X/SCO3z4MH/913/NZz7zGW688UbWrl1b0OdOIhaL8aEPfYiRkRFyuRw//OEP2bBhA8eOHaOzs5Ouri7q6+vPK0sX+vanBw4cYOvWrfz0pz/FbDazbt063v/+99PQ0IBer2dycpLHHnuML3zhC4yNjQF/ERe3243T6Zz3SWLeBEbyRMrLy9HpdFRWVsrJzmw2y+nTp0mlUrS3t885cVLeRafTFfwMqNPpaGxsZOnSpUxMTMx5qsC5uQeNRsOqVavkLSYLselsNlIXdiQSIRqNUlVVxdjYGIcPH5aT7tLjWTQajdylLeVqZjetFSLS0g5A3pOotraWzZs309zcTFFREeXl5fLGaPv27ZP3XdZqtXKeqZDbJyQvTHqszvHjx1Gr1bhcLlQqFaFQiOnpabxeL+l0Go1GQ3FxMWVlZbzjHe9gw4YN827TvKbB1Wo1xcXFc0KcWCyGx+PhyJEjeL1e1q1bN+ckLZbELszE7m1tbYyOjhKPxxkaGrqg3YIgoNPp2LhxI+vWrWPNmjULYO0rQxRFksmkLDLFxcUMDw+zb98++RhBEOTqi7Q9qJR7KsQq2WzS6TTj4+NkMhmi0SgqlYr29nb+8R//Ue7NqqurIx6PE4vF+P73vy/3yUh7plit1oLuNp+9rWcwGOTYsWMcPXr0vONm9znV1NSwYsUKPvGJT+RlbHmvs+n1ekpLS7nnnnuIx+PY7Xb0er3cGGS1WiktLS3YjPxspKTnddddR2trK8uWLSOZTMo5p1wuJ3ckZ7NZ3vOe91BWVrbQZl8WRqORjo4OYMabOXPmzAU3XnrnO9/Je9/7Xj7zmc9gMBi45ZZbuP7666mvry/o8CGRSNDd3c2LL77Inj17OHPmDJWVlfh8Plwul5x7kLyV2WHhli1buOqqq7j++usLOontdrtpb2/n9ttvZ2JiguHhYYaHh+XtQE0mE2azmfLycqxWK5WVldx77710dHTk7f7Lu8BIXa8VFRWk02mMRiOtra3cdNNN8mxYWlpa0DmK2UilTZ1ORzKZlFcPSwIj9QPlcjnKysoK+oKcjVRir6ioIBqNyht5G41GeTyiKGK326mtrZWfpiB1fhYVFRV0IlutVstbSEajUZLJJGNjY7zwwgvy4syOjg5GRkY4evTonC1GrFar/KjjQg6RHA4H9fX1LF26FKvViiAIiKJIOBwml8tht9txOp2sXLkSp9NJSUkJS5YsoaqqKm825V1gJHfM5XLJrz3wwAM88MAD+f7qvGEymTCZTAW1DeRrRXpKgl6vp6ysjK1btzI5OUlFRQXj4+Ny52cqlSISiWAymeScmbQfcSFTVFTEli1bCIVC8or/vXv3yltPNDY28sc//pHHHnuM73znOwts7aujrq6O6upqzGYzg4ODlJSU0NraKj8TqbKykurqau69994r1gpSOK2ICgWBtO7oH/7hH+QnBX7ta19jdHSUD3zgAwA88sgjtLW1UVFRIc+WiwW73S7fhJIHCjOriz/5yU/S09MjH6tWqzEajdjtdmw2W0F7aLPJZrOYTCYaGxu57rrrMJlM8mtSmHSlUARGYQ5SF+h1110nPxnwz3/+MwaDgdtvv529e/fKm2lJvUuFnPg8F7vdTlVVFQ6Hg3Q6TSgUQhRFYrEYf/7zn+XjpC0lXS4XxcXF2O32RSMwWq1W3lCrvb19QdMPwiVauwu77/vCKA9Qn2HexjY+Pk4qlcLlchEIBAgEAlitVnlNzjzeeHk/d9lslmg0yre//W1OnDghN9klk0kmJibkBZBVVVU0NjayZcsWbrvtNpqamuZj7dEVOXezN2WXGkCvABf8EkVgFh9XXGCknhfpuU/JZBKdTifP8vPIFTl32WyWbdu2MT4+zuDgINlsFp/Px29+8xt0Oh1FRUXccMMNcnv9kiVL5uQQXwNX/NxdQRSBuQCv5/G9nscG8zy+gYEB3vve92IymSgpKeFDH/qQnDSdR95w504RmMXHG+4ivQjzOr5UKkVfX5+8dcjQ0BDl5eXcdNNN8/k1b7hzpyR5FRSYWQbS1tZGOByW15ktpuR1oaJ4MIuPN9wseBFez+N73YytcHu7FRQUFj2KwCgoKOSNS4VICgoKCq8axYNRUFDIG4rAKCgo5A1FYBQUFPKGIjAKCgp5QxEYBQWFvKEIjIKCQt5QBEZBQSFvKAKjoKCQNxSBUVBQyBuKwCgoKOQNRWAUFBTyhiIwCgoKeUMRGAUFhbyhCIyCgkLeUARGQUEhbygCo6CgkDcuten3YtyNStnXdYbX89jg9T2+183YFA9GQUEhbygCo6CgkDcUgVFQUMgbisAoKCjkjSsqMKIoEolESCaT5HK5K/nVCgoKC8AVFZhMJoPX6yUUCpFKpVAemaKg8PrmigrM+Pg4f//3f8+Pf/xjurq6CIfDpNPpK2mCgoLCFeRSfTCvmampKY4dO4bf7ycQCNDQ0EAymWT//v24XC5cLhdarTbfZigoXJR4PE40GqW3txedTofBYMBisWA0GrHZbGg0GgThlbToFA6hUIh4PI7f7yeTyZDNZslkMuRyObLZLGq1Gp1OR01NDdFolHA4TENDA3q9fl6+P+8Cc+rUKb7+9a9z5MgRLBYLv/zlL3nhhRd47LHHWLp0KVqtFqvVmm8zFBQuit/vZ3BwkJ///Oc4HA5KS0upr6/H7XazbNkyTCYTGk3eb5W8MDExwfj4OJ2dncRiMRKJBNFolHQ6TTweR6/X43A4uOuuuxgaGuLMmTO8733vWzwCk06nCQaDJBIJAoEADz30EBs3buRv//ZvcTgcZDIZAoEAZrN5UXoy6XSaSCTC+Pg4kUiEVCpFJpMhk8mQSCTIZrOk02nS6TQmk4nbbrtt0V6sL4coisTjcQKBANPT07hcLkwmEzabbaFNuyRGoxFBEHjmmWcQRRGtVovBYKChoYFbb72Vm2++mfr6+oU281Vx4sQJDh06xO9+9zvS6TTZbJZcLif/6HQ6nE4narWampoaVq5cicFgmLfvz9uVLooifr+f6elpIpEI6XSaWCzGgQMH6OjooKamhlAoRCKRIJVKUVFRsSgFJplM4vP5OH78OJOTk7KYZDIZkskk6XSaVCpFOp3G4XBwyy23vO4ERhRF0uk04+PjDA8PMzw8zNKlSykuLl4UAhMIBOSZPplMyq8Hg0FcLhfl5eUkEgl0Oh1qtRqNRkNJSQk6na7gQ6dQKMTk5CRnzpwhk8mc975OpyMcDnP8+HFMJhNNTU2oVPOXms3blZ5Op3nsscfYvXs3AwMDxONx+b14PI7H4+GZZ55BpVLR1NTEbbfdhsViyZc5eSGTyTA5OcmuXbv41re+xdGjRwEuWh2rr6/nE5/4xLy5n4VCOp3G6/Xyq1/9im3btrFz504+9alPsX79empraxfavEvyyCOP8NJLL51XcBgeHuYXv/gFTzzxBFarlZqaGux2O2VlZXz0ox+lqqoKk8m0QFZfHiqVCrVafdH3U6kUgUCArVu3kkqlMBqNLF26dN7GNa8Ck81m5WRSPB5nbGwMr9crhwoSiUSCYDCI0Wgkm83i8XjmzByLhXg8zpkzZ/j1r3/N8PDwBWeI2SQSCbq7u6mtraW0tPQKWfnayWaz+Hw+Hn30UZLJpBz+pdNpEomEfL47OzsZHR2Vw8ZoNLrQpl8WPp+PyclJ4C83pNVqJZPJEAwGZU87kUhgMBgoKipicnISu91e8AJjt9uprq5mzZo1CIKARqNBq9Wi0WjQaDQcOnSIqakpwuEwk5OTDAwMzGtld14FJpPJ4PP5iMfjRCIRpqam8Pv9pFKpOcfFYjECgQAWi0UWm0vdnIWGKIqkUimGhoZ46qmnLniMIAhzvJlUKsXx48fR6/WLSmBisRijo6P813/9F5FIhEQiQTgcJh6PEwqF5hyrVqvRarVyqLgYCIfDBINBRFFEpVKh0WgoLy8nlUrJP8lkknA4jEqlwmAwMD09PccrL1RcLhfV1dWsXbtWHpvVapUFZmhoiMnJSZLJJKFQiKmpKdkheDnP53KZV4FJpVL09fUxPj7O2NgY3d3dTExMnHecx+Ohu7ub+vp6OcG22PISgiBgt9spKio67z3p5BkMBnK5nDzbT09P8/nPf54HH3yQ9vb2BbD61fGDH/yA7du309XVJScKRVGc042tUqkoKiqisbGRpUuXsm7dOhobGxfQ6svHbrfjcrno7++Xx/alL30Jq9XKmTNn+N73vkdnZycwE+YuW7YMh8MxLzdgvlm9ejUrVqzg1ltvZXBwkJGREdra2vB6vWzfvh2YmRTKysqor6+nubmZAwcOMDk5yVVXXfWav39e72op+RUMBgHwer0EAoHzjvN6vZw+fRoAt9vNihUrSCQSDA8PU1JSglarXRQnT61WU15ezk033YRKpZLda61Wi1arxWQy4fP5OHToED6fj2QyicfjIRKJLLTprwi/34/H45HDIQCtVotOp5P/q9frcbvdrFq1ig0bNtDc3FzwXlosFsPj8RAOh+d0lmezWY4ePYrD4SCRSNDW1obFYiEej9PQ0MCSJUsoLi7GbDYv8AgujV6vR6/XY7FYGBsbIx6PYzQayWQydHV1EQqFUKlUWCwWrFYrdrsdr9c7b5WkeRUYo9HI2rVrgZnM/OjoKF6v97zjBgYGGBwcZHh4mJtuuon/83/+D9u3b+fMmTP81V/9FUVFRYsm4dve3s5XvvIV9Ho9Op0Oo9GITqdDp9NhNpvZt28fn//85zl69KjsySzGdVjneixmsxmTyYTT6cTpdGKz2ViyZAmbN2/m5ptvXkBLLx+Px8P27dsZGhoiHA7Lr6fTaT73uc9RXFzMypUrufPOO2loaGBoaIiSkhIqKytl73sx4fP56O3tZdWqVQwNDfHII4+QTqfR6XQ4HA6Ki4spLi7m5MmT8zbB5yUuKSoqoqSk5GXLXaIoMjo6yvDwMFNTUxw6dIj+/n5UKhVtbW0sX748H6bNOxaLhebmZlQqFZlMhrGxMXbt2sX+/fu58cYb8Xq96HQ6+W8hdVEuJhwOByUlJajVakRRRKPR8JGPfITGxkbq6urkpKHFYqG4uHihzb1sioqKaGlpwel0XnDGDofDdHV14XA4GBoaYvny5VRWVtLa2lrwlUApES+KItlsVvZcWltbGR0dZWRkhEwmI3ttUrn93P9/rcyrwEj9EFIiSbqpVCoVpaWlaDQaxsfHyeVyiKJINBolGAzi8/nk5KHX610UyTMJKRwC5ErY9PQ0fX19NDY2Eg6HSSQSsqiIorjoFnm63W7q6upwuVyEQiGy2SyNjY0sW7aMhoYGuWlLaiocGRlBrVZjMBhwOBwLbf5FMRgMlJaW4nA4KCoqQq/Xy41o2WyWVCqFz+eTe0hqamrIZrMYDIaC739JJBJMTU0hCILcTjE9PU04HMbj8TA4OEgul0MQBNRqNUajEaPRiFarxWg0FmYnbyqVor+/X87CS8lOo9HIP/3TP1FRUcH9998vx7ww08zU2dnJ6tWr2bBhA4lEYtG5nhJarZbKykre/OY3s3LlSjo7OxkcHOTQoUOLpmR7Ie655x42bdqEwWBg+/btHDx4kN27dzMyMkJpaSl+v59oNIrX6yUcDhMKhXA6naxYsYKPfOQjC23+RTGZTNTW1rJu3ToEQSAUChGNRuWJTlq3c+TIEU6ePEl/fz933HGH7LXOZ8frfDM0NMSjjz6K0+kklUrx3HPP0dPTw9DQEIA84Wm1Wmw2Gxs2bKCtrY2qqiqMRiNOp3Ne7JhXgQkGgzz++ONUV1djt9vlQaRSKVQqFUajkerqallNpSaf/fv309TUhM1mIxKJEA6HGRoaYvXq1Vit1oLvNZCIRCI888wz+Hw+/H4/x44dY3BwkHg8Ti6Xw2q18u53v5sNGzYstKmvCMkbsVqt6PV6crkc+/bto7u7G7PZTCwWk9e2JJNJUqkUlZWVBZ/khZlQoLS0lIaGBtasWUNpaSlutxuv10ssFiMYDLJ161ZGRkYYGxvj9OnTHD58mOrq6oIWmJKSEq677joOHjxIb28vPT09eL3e81pG9Ho9VquVtrY2GhoaqK6upqSkpDCTvOFwmGeffZYVK1bIrrMoinJzllqtlkuXqVRKXmF98OBBcrkcJSUlsneTy+UoLS2lpqamoAVGSn6qVCoikQhPPvmkHO719vbKQgpgtVr5u7/7u0XR3XouUqVBp9ORy+U4ePDgRY8VBIFcLievnNdqtfPafj7flJSUkEgkSCaTrF+/nvXr1xMIBAgGg4yMjNDX18fQ0BAej4e+vj46Ozu56aabsNvtC236RSkpKeGGG27g0KFDjI+PMzIycp64wIwHYzabqa2tpba2lsrKynkN/+ZVYGpqavj5z3/Or371Kx5++GECgYDcwTs0NERlZSVf+MIX2L9/Pzt37uSRRx4hFArR2dkpZ64lUVKr1VRUVLBu3bqCrkqMjIywa9currnmGsLhME888QTxeFzOR83uYFapVNjt9kUZAkoCcznrxURRZHBwkOeee47/9b/+Fw888MC89FTki7a2NhwOB3v37uXkyZOk02nWrVuH0+mkqqpqTriQyWQW1WZp9957L+vXr2dgYECu6s62PRwOMzw8zH/9139x3XXXsXnzZsrKyuZtXeC8CoxWq5XXZ0jVErPZTElJCYIg4PP5iMVi9Pb2yjGu1IiWSCTm/C61Ws3o6Cg1NTXzaeK8k0qlZC8lk8nISd0LkUwmOXToEO3t7SxduvQKW/raEARBrhYJgoDb7cZqtc7pW9JqtfT19XHq1Cl5ucDw8DCxWGyhzT8PafGtz+fDaDQSi8XIZrNyR+vx48dxuVw0NjbOKdnmcrmCrgTmcjlOnDiBTqeTz1FdXR1/9Vd/hcfjwefzsXPnTvmcSPvfmEwmIpEIp0+fxm63F6bASFitVtxuNydOnKC8vJxbb70Vs9nM6dOn+frXv35ZywJEUeTMmTOUl5fnw8R5I5vNyg1ol5rV/H4/X/nKV/i7v/u7RSkwer0etVqNSqVi48aNrFy5kltvvRWbzYbRaMRut/ONb3yDz33uc8BfwsdCm+1FUSQcDtPf38/27dupqqqasygwmUzywx/+kObmZj7+8Y/PEROpulSopFIpfvSjH+FyuXjrW99KQ0MDbrebT3/60wiCQCKRYPPmzfT19QHgdDqpra3lhhtuYGxsjCeeeIIlS5bMWx/avApMMplkcHCQ0dFR/H6/vJDxxRdfRKVSkUwm54QM5xmj0VBbW0tjYyOtra1UV1fT1NSEKIoFWxasqKjgjjvuwO12EwgEeNe73iX3iuj1egKBAKdPn6avr49QKMTg4CA+n6+gx3Qh9Ho9y5Yt45577uGqq66io6ODZDLJiy++SFdXFwD//M//LLed+3w+stmsvCZtfHwct9tdMGOORqN0d3fzs5/9DKPRiNVqZfXq1VRWVrJq1SqKiorkVovZNkuVpUJFo9GwZcsWLBaLXBFSq9U4HA6eeeYZXnjhBR544AFOnz7Nz372M6anpwF49tlnWbNmDffdd9+8thbMq8Ck02lGR0eZmpoiGAySy+WIRqPydg2SuEjla61WKzcCJZNJOc6vrKxk+fLluN1uiouL8fv9GAyGOT0nhYLFYqGpqQmYGf/GjRvlHJJKpZIXj42Pj8uJQ6mqtBiWQ0hoNBrcbrfc02SxWBgfH5f3GsnlcsRiMaxWK62trRw8eHCOwExMTFBaWlowY85mswQCAXp6ekgmk1itVmpra4lEImQyGZqamlCr1Xi93vNW+heiVwYz+ZRwOIzdbsdms1FUVCTnNQOBAN3d3ezYsYOvfOUrWCwWfvazn8mbhPX393PVVVfN+xq5eRWYWCzG3r17OXz4MCdOnJBdy3PX3lRUVFBTU0NjYyOpVIqJiQm6urrw+XwMDg6i1+vRaDQ89NBDZLNZfvSjH9HR0UFtbS0tLS0Fc5Gei8Ph4P7775c32jpy5AixWIzGxkZ5loeZCzSdTp83OxYyarWakpIShoaGOHbsGN/61reorKzk7//+77nnnntwOBzE43He9ra3cffdd3P99ddz4sQJTpw4wTPPPEMwGKStra1gEtwWi4Xq6mo2bNjA0aNH8fl8/OIXv+B3v/sddrudn/70pySTSR577DG6u7vlz0k5mEITGFEU+f3vf88f/vAHRFHk6quvpqWlBZjZbP+Tn/wkx48fZ3h4GJvNJlfApPFIizznm3kVGKnXRaVSnZcEM5vN6PV6zGYzDQ0NNDY20tbWBsz0z0h7x8TjcUZHR4GZypNGo2F6eprBwcE5M0uhInll8Xhc9lrOLdGeu65nsSAldzs6Orjnnnuw2Wy0tLQwNjbGiRMnmJ6elsudUp5C6vItpBtSEARMJhPV1dVs2rQJv99PMpkkEonIHsz//M//kEqlOHToEIFAAEEQMBgMOJ1OysvLC8qTzuVycu/Y8ePH5b6lZ555hnXr1qHVaqmtrUWv19PQ0MCBAwfo6ekBZoS2tLSU66+/Pi+r3+dVYNRqNTabTd5fd/bs7HK55OTvbIExGo2IosjWrVs5deoUyWSS0dFRJicn6evrw2KxEIvFGB4elvtjCo3ZN5BarZYrESMjIyQSifNW3c6+6RaLByNRXl5OSUkJTU1N8o360ksvcfDgQbxeL0uXLqW9vV2upEmt6IW2HYfJZKKuro6bb76ZI0eO4PP5iEajZLNZYrEYv//97+W1ZdI+MTabjbKyMmpra9HpdAs9BJlMJoPf72dkZIQzZ86g0WjIZDKYzWY5Wli6dCnNzc2k02m2bt3KmTNnALDZbFRXV3PrrbdSXV0977bN61k3m81ce+21NDU18cADD1BWViavLNZoNPKFdvz4cY4fP87jjz9ObW0td999Nw6HA6PRKK9DkhZDtrS08I53vINIJIJGoym4hq3h4WF27dqFz+fDbDZz7733YrPZqK2t5fbbb2dsbEzeS2Q2hSiUL4fU1/Piiy/ywgsv8MILL1BRUcHdd99NWVkZt9xyC5FIhF27dvHZz36WyclJDAYDbrcbh8OBRqNhZGSE4uLiglmfZLPZWLFiBXfeeSc1NTU8/PDD8mM+JC86l8vJnsv73vc+rr76aq699tqCarLLZDJMTU3JpedMJsP4+DjPPvssBoOBmpoa+Ymq0WiU7du3y8lds9mM0+mksbExLzsYzLsH43K5MJvN1NTU4HQ60ev157mTgUCA8fFxuXfCZrPR2Ngo78wv/S4pk19bWysvpy8UgRFFkampKXp6eti7d68sMCtXriSVShGNRpmensbn8zExMbEotwSdTTqdpq+vjxMnTtDZ2cmJEyfwer3U1tZis9nQ6XQkk0lGRkYYGRnBYDBgt9upra2VL1ypo7tQkBb5tbS0IAgCExMT8k59yWQSr9fLyZMnqaqqorGxkTVr1tDc3IzL5Vpo0+cgbdjmdDqprKyUrzefz8exY8fkRY+pVIpEIiGHhNIePtKyh3x4mfMuMJczO5WXl7NmzRrUajVutxuXy8Vdd93FunXrCIfD8ipP6dk0hfjIiEwmw9atW9mxYwe//OUvicVimM1mAoGAPNtL24EODg5ecF+cxUQwGOQ3v/mNvBVFIpFgYGCA73znOxc8vqSkhNraWq6//nrgL6vICykXI7F27VrWrl3Le97zHrkMPTY2xjPPPMODDz7IHXfcwebNmwv2iRDSRm/r1q0jlUrx8MMPy0K5Y8eOC37GYDDgcrm45ppr2LhxY97GtSB/Lbvdjl6vx263YzAYUKlUtLa2UlVVNSdDLz1drxCRkn5arVbOp8RiMXbv3i0ncaXtC6LRKKlUCpvNxv3338/mzZvlrUIXC1arlbvuukvezzUUCuH1ejl16tSc40wmE1arlbVr11JaWoogCDQ0NFBRUUFlZWXB7wKnUqnQarWUlJSwYcMGPve5z7F582bq6+sLtrig0WhwuVy0t7djMplIpVJ4PB7ZA5N2ldTr9RiNRtrb23G5XJSVlbFp0yaWLFmSt7EtiMAYDIbz9gopKSmhpKRkIcx51RQVFWG32+UHyEmz+oWwWCyUl5ezZcsWWlpaCqoKcTno9XpWrFghX7hTU1MMDw8zNjYmHyMIgrw5VWtrKzabDUEQqKiooL6+HrvdXjAh7sUQBAFBELBYLNTV1XHHHXfQ0NBQUDmXc5GS7VVVVej1evx+PxMTEwwPDzM9PS1vvi6J/7JlyygvL6eqqoqWlhaqqqryZ9slXNbC82cvzStxC17T+DKZDKOjoxw4cIAvf/nLHDp06KLHPvTQQ1x//fXccsstcnflq+Ryx5eXcyc9ouSxxx6TdySUkvjSPkBVVVXyBk4Wi4Xa2lqcTufliMsVO3eX9QWzVsrPk7eZ13MnhaDpdJpAIMDAwABPP/00AwMDZLNZeUvXDRs2UF9fz+rVq+dzpfsFx1Z4AeUiQqPR4HA4WLp0KW9961tZsWKFvKZF2gRcEARUKhU33ngjLS0tr1VcCgZpH5UlS5bI1T3peULSYjlpuYTZbC54z+VCSCX2xYLkfen1ern8fM0117B06VJ5DZW0X4/0XOp8owjMa8RqtWKxWLj//vuJx+MYDAa5HC/txSvtvL+YLtaLkUqliEQi5HI5ysrKuP322xfaJIULYDAYqKiooKKiQn5NWhO2bds2/H7/FbFDCZHmCelpAdIsMvsH5rW8vqAhkpS8DoVCaDSa+X72dEGFSHlgwcPbdDpNOBxGq9XOdz/SBcemCMziY0Ev0jyjnLsZXjdjW3yBsYKCwqJBERgFBYW8oQiMgoJC3rhUDkZBQUHhVaN4MAoKCnlDERgFBYW8oQiMgoJC3lAERkFBIW8oAqOgoJA3FIFRUFDIG4rAKCgo5A1FYBQUFPKGIjAKCgp5QxEYBQWFvKEIjIKCQt5QBEZBQSFvKAKjoKCQNxSBUVBQyBuKwCgoKOQNRWAUFBTyxqUeW7IYd6NSNo6e4fU8Nnh9j+91MzbFg1FQUMgbisAoKCjkDUVgFBQU8oYiMAoKCnlDERgFBYW8oQiMwhsO5VE9V45LlanzSiaTmXOyBUFAo1lQk14VoijK45j9kPtUKiWPSRBeSQW2sMhms/JD7yVUKhUqlWrRna9kMkkymSSbzb6s0EjnS61WYzKZFt04M5kMsVjsvDEKgiBfk0ajMe92LNhfLZfLcfDgQWKxGLlcDo1Gg8lkYs2aNXNu0kInlUoRiUTw+/0ANDQ0AJBOp3nyyScxGAysXLkSh8OBXq9fSFNfNb29vYyPj9Pd3U0ulwOgtLSUqqoqNmzYsMDWvTKefPJJnnrqKZ5//nlisdhFj1Or1ZjNZsrLy/nCF77AtddeewWtfO3s3r2br3zlK0SjUTKZjDwZqNVqnE4n69ev5xOf+ETe7VgQgQmFQvh8PrZu3Yrf7yeVSqHX6ykpKWHlypXodLqFMOtVkUqlCAaD9Pf3k0gkiMfj5HI5EokEO3fuRKPR4Pf7sVgsaLVakskk6XSaTCaD0+mkuLiYJUuWoNVqUavVCz0cYMYjm5iYQBAEDAYDg4OD9PX1sW3bNrLZLIIg4Ha7aWhowGKxUFtbS1FR0UKbfVlEIhEmJycZGxt7WYFRqVQYDAYikQhPPPEE/f39qNVqmpubWbJkCSaTqSAnwmw2y+7du9m5cyenTp0iHo+TzWZlj1OtVlNUVIRKpeLZZ5+lpaUFs9nM6dOnqaiooK6ubl7tWRCBGR0d5ejRo/zkJz9hdHSUeDyOwWCgqamJD3zgA4tOYKampjh06BAej0c+qZFIhN/+9reIokh9fT0wc/J9Ph+RSIRoNMrq1atZu3Yt//AP/4Ddbi8Ygclms3R1daFSqSgrK+PYsWMcPXqURx55ZI7ANDc3k0gkeNvb3rZoBCaVShGPxy+Zh8nlcsRiMWKxGP/6r/8qi+0DDzzAxz72MfR6fUEKTCqV4oc//CFHjx5lcHBwTlgroVKp8Hq9eL1ePvCBD1BbW8tPfvITbr755sUtMJFIhN27d/Pcc8/xwgsvMD4+TjKZBJDVdbFhtVpZsmQJKpWKkydP8uUvf5lUKkU2m2VqagpRFAmHw8CMZ5BOp8lms2SzWQ4dOsT4+DiBQID777+f9evXL/BoZhBFkVAoRDAY5PTp0xw+fJgzZ87IN6Uoivh8PkRRJJfLsWHDBlpaWhbY6ssjlUoRjUblUA+grq4Ou92O2WwmnU6TSqVIJBKk02kSiQSJRIJcLoder8dut+N0Ogv2WhVFkUwmQyaTuaC4wIx4BgIBOjs7+fnPf05bWxt/8zd/Q21t7bzbc8UEJhAIMDk5yf79+zl8+DDHjx8nkUjMSY4W6kl7OTQaDVarFbfbzeTkJP39/SSTSQRBoKqqCpgR1kQiIScWpYSp3+8nl8tx7NgxAoHAwg7kLMlkkmg0ilarJZ1OMzw8zOjoKF6vd86sn0qlCIVCjI2NkUgkFtDiV0YmkznP3pqaGmpqanC5XKRSKdLpNPF4XBYaKczQ6XRUVVWh1+sLOmkvCAIWi4W6ujoMBsOcBLUoivT09JBKpfD7/fT19WEwGGhubsZut8+7LVdMYJ5++mn27NnDT37yExKJBJlMZq4hGs2iy9TPRqvVzkni6nQ6vvCFLyCKIs8//zynTp2Sb9JgMFgwgnIufX199Pf309raCsCOHTs4ffo0Y2Nj54UVkljO9gYKnVQqJRcWJN785jezefNmVq5ceV7Yc+6Y1Wp1QYZGEoIgYDab2bBhA5s2bWLp0qU4nU65kplIJLjmmmvo7e0ll8sRj8dJJBJYrVYMBsO825P3OzqbzRKPx9m7dy/btm2TZ/LzDNFo0Ol0BX3yXg6LxUJzczNf+cpXyGQyaDQa1q9fL3tpq1evJhQKcebMGbq6umSBEQRBTsAVAuFwmPHxceLxOP39/UxMTMzxNGcz2x0PhUK88MILlJaWUlNTQ1lZGVqtdgFG8PJIYc/s8Zw6dQqTycT09DR6vR69Xk9DQwNmsxmLxbKA1r5ytFott99+uzyGgwcPkkwmedOb3kRRUREGg2GO9yVNEvli3gVG6pnI5XJotVri8Th+v5/Ozk4OHTp0cUM0moK8IC8Xs9mM2Wzmox/96JzXQ6GQ7J0lEgm2bt3K9PQ0R44cAWYERqvVFoTLLc1owWCQyclJhoaGmJqaIpVKXfB4URTl8x2JRNi2bRutra3o9XpcLldBns8LCYwUMoyMjMiiYjQaKS0txWw2F8S5uVw0Gg2bNm1CpVKh1+v56U9/yuDgIMuXLyeTyaDT6a6oxzmvAhOJROjs7JRnvltuuYX+/n5+85vfcOrUqZf9rF6vz4uLdqW42EUYjUY5c+YMFRUVCIIgJ3klCqWRK5FIcObMGcrKytiyZQs/+tGPOHXqFCdPniSdTl/wM7lcjlQqRTKZRKfT8fa3vx273U5JSUlBigvMCEw0Gp0jMAcOHKCzs1MOf7RaLWNjY3R0dHDzzTdjs9kWVWVTqugJgoBOp2NiYoI777xTfm1sbEz+t81mo7i4OG8iOq9XtXSzAMRiMXbt2kV/fz/Hjh2TKykXQ8phLKbZ4mJMTU0xNTVFPB5ncnKSU6dOyTmM48ePMzU1BcwktnU6HXa7fcFvyFQqRW9vL0VFReh0OkKhEJFIhFQqJbvWRqORYDBIMBgE/lIVkwSoqqoKo9GI2WwumJBPIhQK8eKLL9Ld3U0qlZojMOcmfdVqNZ2dnQiCQGtrK/X19djt9gWfBC6X2feQ5B2Pjo6e57kIgkBdXR3Nzc0Eg0FEUZTv3/liXv9iOp2OhoYGAoEAXq+XH//4x4yOjjIxMXHJz+r1+ivSupxvpB6Sbdu2MTQ0RDQaxe/34/f7iUQijIyMyKV5jUaDxWKhqqpq3k/sKyUajbJnzx7MZjNGoxGv10s8HgegsrKSsrIyKioqOHbsmCwwuVxObr3P5XLU1dUV7AQxNDTEAw88QCgUkv/+FyObzfL8888zMTFBZWUlBoMBg8Gw6PIxAAaDAZPJdN55kXJ/V199Nddddx0DAwNUVlYWtsCo1WosFguCIBCNRhkfH5db6Ovq6igtLaW1tZWjR4/KOQgJo9Eof3YxEg6HmZyc5Ne//rVcho/FYmSzWXmWlxLeRqMRl8vFW97yFpYtW8aWLVuoqKhYUPudTif33Xcf+/bt49ChQ3R1deHz+YC/5FrOXb+Ty+Xm9IoUMnV1dTz88MP89Kc/5cknn+SLX/widrudaDRKKBQiFosxOTnJvn37OHjwIACDg4P88Ic/xOl0YrFYFl0+BmZyg1arFZvNhlqtllMRiUSCsbExfvnLX/Lkk09SVlbGnXfeyT333DOv3z+vApNOp/H5fExOTjIxMUEkEiGXy+FwOGhpaaG6upqmpiY5BpyNTqdb1DmYeDyO1+tl+/bt9PT0MDQ0dNFkmk6no7i4mBUrVrBy5Ura2tqusLXno9fraWlp4cSJE0SjUaanp4lGo3LyXavVzlnUKZHL5fD5fIyMjMjdrgaDoeDCJIvFwpve9Ca6uroYGhpi8+bN6PV6+vv7icVipFIpamtrGR0dlQUmEolw8uRJ/H6/vDB3sQlMcXExdXV1tLW1yfklu92Ox+NhcnKS06dP09vbS1VVFVddddW8f/+8CUwul2NsbIx/+Zd/Yf/+/Zw4cYJUKkVTUxM33XQT99xzD+Xl5bz44osXFBKLxYLNZlt0J1BienqagYEBduzYIa+iVqlUF7wpS0tL6ejooLGxkdLS0gWyeC6iKMotBJIwGI1GiouLaWhowOl0kk6nLygajz76KNu3b8doNLJ8+XLWrFnDjTfeiNVqvdLDuCQPPfQQH/zgB+Wy+pe+9CVaW1tpbm7mfe97H/v27TvvM3a7HZfLtSivzbvuuou3vOUtcpgeCoWoq6tjx44dPPjggwSDQbLZrNy5PN/Mm8D8+c9/5siRI+zbt4+xsTG5tKnX6ykrK8Nut2MymQiHw3NiYKvVSkNDAxs2bGDVqlWLspsXZmaK5cuX8+lPf1qurEh9P3q9noGBAQKBAMlkEp/PR1dXF6FQKC8n9dUQjUbp7Oxk//79HDhwgGQyidFoxGq1Ul9fj9vtxuv10tvbe95nx8bGCAaDaDQaDAYDbre7YMYlkclk8Pv96HQ6tFotZrOZ2tpaNm/ezPj4OJOTk6TT6TmTgVqtlpPWF8pjLAYEQSCVSnH48GHGx8fxer1YrVYymQxGo5FIJEImkyGZTJ7X/DofzJvAPPnkk7z00kt0d3fPOUkajYaioiI0Gg2ZTIbp6Wk5eQgzAtPe3s6qVatYvnw5wKJ1RSWRicViBINB0uk04XCYoqIiueU8mUzi8XgIBAKEQqGL9phcaZLJJEePHuXgwYNyiGCxWLBYLFRWVlJZWUkmk7mg9+nxePB4PMDM36GlpSUvF+urRfLOhoaGsFgsFBUVUVJSQnV1NZs3b+a3v/0tXq/3vA5zKQlvNpsXbfieyWQIBoPs3buXwcFBpqamWLduHfF4HJ1Oh1qtRhRFef3cfDNvAhMKhQiFQueFAydPnuSrX/0q3/nOd1CpVASDwTnL5L1eLy+88AKnT5+mtraW+++/n6amJnlflcWI0WhEr9fzf//v/5W3Zzh58iTHjx+fc5zb7aakpGSBrJyLWq2muLh4TiVPWhDX29uLRqORe0hejnQ6LefeCoWenh66urr41Kc+hU6nw+l08o1vfIPe3l6++c1volKpKC0tJR6Pz/G8pHzFYilPX4i9e/fS09PD6OgojY2N3HjjjYyNjcnNhVLiPhaLFXaIpNVqMRgMOBwOeaGYVMb0eDwEg0FUKtV5PQjpdBq/3y9XWyKRSMG51+citcfH43HZvTQYDOh0OqxWK4IgyO51JBLh+PHjcqwLyO8XFRUVTOlTsmd2P460r83lVIhUKhVFRUUYjUbUajWTk5NoNJqCENCRkRFOnz7N0NAQGo2GUCiEIAgUFRVRXV2NwWDA5XKRy+XmXJtSN+xiDNulRZ1HjhzhyJEj9PX1kclk5EWqw8PDcpVTp9OxdOlSysvL592OeRMYh8NBRUUFxcXFjI6OMjk5Ocf9t9vt6HQ6fD6fvOESzPQcRKNRubtyMeyXKrXTDw8PEwwG8Xq9lJeX43K5WLlyJYIgIIoiw8PD7Ny5k69+9atMTEzIY5PKhaWlpTgcjgUezQxqtRqXy/Wq+yC0Wi2NjY1UVlZiNpvZt28fdXV1bNmyZZ4tfeUcOHCAPXv2yEtYcrkcLpeL2tpanE4n4XBYrhLN9rykSWIxCkw8HmdkZITf/e53bNu27WWPLS0t5cMf/jCrV6+edzvmTWDuu+8+3va2t5HNZnnqqafYtm2b7MWIoshdd91FUVER3/3ud2WPBv5yEtPpNDqdDpvNVtDxriiKPP744/z5z3+WRTSZTNLc3Ex1dTXhcJj6+nrKy8sZHh5mcHBQXjAIM3F9RUUFDQ0NBTVOjUZDcXHxywqMWq2WxfPceF2j0VBdXc3SpUtZvXo1AwMDhEKhfJt9WbztbW+jrq6O7du3E4vF8Pv9fPrTn8Zut2MwGHA6nRgMBtRqtdxlDX/ZI3qx5QMBDh8+zL/8y79w4sSJSx6r0Wioq6vD6XTOux3zJjDt7e3yvwOBAOFwWC59xeNxNm7cSFFRET/72c/mxLRGo5GqqipEUaSsrKygBUZKhp08eZKnn36aYDAoz3h+v5+JiQmqqqrkhK7klkt5C5VKhdVqpaamhvb29oLqXJZCJIfDQXFxMSqVSt4gWlrtrdPpSCQSxGIxQqGQPHYp5LPb7bjdburq6vB4PAu+/EFCSjpbLBaSySSxWIznn39etreyshKr1YpKpZKbC2FxC0wkEuHMmTMkk0m0Wi0ajUbec0lacyX9t7y8/JKTy6slL9mrO+64g1tvvRWYiX/37NlDc3Mz0WgUn883J8nb1tbGZz7zGZYtWyavXi2kBq3ZpNNpRkZGmJyclDuUJfr6+hgeHqa7u1uOdc/NzJtMJrZs2cIdd9zBnXfeWVCbgEtJ3jvuuIPq6mrMZjN6vR6z2SyvRbJarWzdupUXXniB5557Tl5fJh1nMplwu90sXbqU1tbWgrox9Xo99fX1ckju8Xjksvvs6+1cz6yQxvBKuOmmmzh48CAf+chH2Lt3LxUVFTidThwOB+Xl5RQVFVFcXIzdbsdut1NbW7t49oORtifIZDKk02k8Ho98s52bqQ+Hwxw9epTGxkZMJlNBx7tSaFBTU8PatWs5duyY3NMjLfwLBoNyElgURbRardxq7nQ6WbZsmby+pdCQFr8ZDAZ5E3JpfxQpid/R0YFarSYajTI4OMipU6coKSmhtraWG2+8kZaWloLa30bC4XDw3ve+l8cffxy/3y9vzg7ni4p0fFVVFcuWLSvIhsFLodVqsVqtvOUtb6GjowOr1YrJZMJkMmG1WuVJwWg0yt3X+aiW5bX+lslkiEQi8gZGoiiSTCYRRVEWkmAwyO7du7nuuusWRYIXoKmpic2bNzMwMEAul5NnOanfQkIQBIxGIxUVFZSXl1NeXs6KFStwu90LZfolqaqqkrf6vBDt7e1UVVURDoc5ePAgfX19VFZWsmzZMm677baCFE6YWWv1wAMPMDk5SWdn55y1VbN/AHmz84aGBtasWVMwifhXikql4q677lpQG4RL3NSv6Y6XbrhgMIjRaMTn8/HVr35V3kQZZkKke++9l+Li4vlaTPZKfsErGp/kpUxNTeHz+eSyp81mI5fLMTo6yqc+9SnUarW8+nbp0qW8733vw+12Y7Va5dnjNYRHlzu+vKh1Lpcjk8nQ29vL1NSUvIdMSUkJa9eufa0eaN7OnURPTw99fX10d3fj9XoZHh5mYGBAHovZbMbhcHDfffexYsUKNm3aJD9yZh5Y0HOXZy44trx6MNIMbjQaSaVS8l6hUnUJZla5zvejEvKFtIGPVHWQ9nGxWq3kcjlKS0vZtGkTarUanU6HxWKhsbGRJUuW4HA4CnZ2fyVIyd6ysjIsFossmoX6nKBzcbvdcvv/9PQ01dXV1NTUyK0GUlVzxYoVNDc3L1rvpVDIqwezQOR9Flxg3nCz4EWY1/FlMhkmJiYYGxtjamqKG264IR9NkG+4c6cIzOLjDXeRXoR5HZ8UzsdiMeLxOG63Ox9JzzfcuVMEZvHxhrtIL8LreXyvm7EVftCsoKCwaFEERkFBIW8oAqOgoJA3FIFRUFDIG4rAKCgo5A1FYBQUFPKGIjAKCgp5QxEYBQWFvHGpRjsFBQWFV43iwSgoKOQNRWAUFBTyhiIwCgoKeUMRGAUFhbyhCIyCgkLeUARGQUEhbygCo6CgkDcUgVFQUMgbisAoKCjkDUVgFBQU8oYiMAoKCnlDERgFBYW8oQiMgoJC3lAERkFBIW8oAqOgoJA3LvXousW4WYzy8K4ZXs9jg9f3+F43Y1M8GAUFhbyhCIyCgkLeUARGQUEhbygCo6CgkDcUgVFQUMgbl6oiKbxKcrkcW7duZWpqivHxca699lrq6uooLi5GEF5JsURhPkin00xMTJDNZhEEgcrKSjQa5fLPN8pfOA9kMhlisRi7du2ip6eHEydO4HQ6sdlsuFwuRWAWgEwmw9TUFMlkklwuh91uR6/XIwjCnB+VSiX/W+G1owhMHti1axdbt27l4YcfZmpqikgkwtjYGMFgcKFNe8OiUqkwGo309fXR29vLgQMH0Ov1mM1mrFYrRqMRl8uF0+nEbrdjt9tRqZQMwmtlwQRGCiEsFgvr169fKDPyQigUYnR0FL/fTyQSIZ1Ok0wmSSaTKA+6WxjUajV2ux2NRkMsFuPIkSOIoojBYMBkMqHX6ykqKqKkpAS3280tt9xCUVHRQpu96FkwgUkmk/z7v/87dXV1rzuBiUajTE5OEovFSKfTAKRSKZLJ5AJb9sZFo9FQUVGBxWIhnU7z1FNPEQgEzjuuvLyc+vp61q5di9lsVryY10heBUYURU6fPs1TTz3F/v37mZqaQq1WIwgCoihy4MABenp6eP/738+dd95JVVUVO3bsYM2aNVx77bX5NC0vTE9P86tf/YoXX3yRnTt3Eo/H5fcSiQSxWGzRezCDg4MMDw/T0dFBKBTi0KFDpFIpstkser2e5uZmlixZstBmXpS6ujo2btzItm3bGBgYYGxsbM77Pp+PRCLBV7/6VTZu3Mj73//+BbL09UHeBEYURYLBIENDQ+zatYudO3cyMTEhC4wgCCSTSaLRKC+99BK1tbWEw2FCodCinOm9Xi+Dg4Ps3r2b7u5uvF7vnPfT6TSpVGqBrJs/gsEgw8PDlJeXMzU1xYEDB4jH42SzWYxGI0ajsaAFxm63U1NTQ0tLi3wNZrNZRFEkk8mQSqUIBoPs27cPnU7Hpk2bKCkpwWg0LrTpl4UoioyOjpJOpxFFEZVKdd6PWq1GrVaj0+kwGo2o1eq82ZMXgcnlcqRSKXbs2MHzzz/Po48+Si6XAyCbzc45Nh6PMzg4yH/+53/S2trK73//e6xWaz7MyiuPPPII27dv5/e//z2ZTOa896PRKOFweNF7MIFAgN7eXsbGxjh9+jQPP/wwqVSKXC6HwWBArVZz0003LbSZF6WkpASHw8Hf/u3f0tvby549ewgGg8TjcXmSGB4e5siRI/h8PkKhEB/+8IdZs2bNQpt+WSQSCb70pS8xNjZGMpmkqKgIo9GIzWbDaDRiMpkoKirC5XLJ3qbL5cqbPXkTmHg8zhNPPEFnZ6csLi9HIBBgenoajUaTV0XNB7lcjnQ6TTqdlm80aWaQZsREIjEnZFqsWCwWSktL2blzJ729vSQSCdkDSCQSRKNRIpEIJpOpYPMXarWaxsZGjEYjgiDg9/vJZDK4XC7+53/+h+npaWKxGNFolIGBAWKx2EKbfFns2LGD3bt3c+DAAQKBAOl0Gp1Oh0ajQa/Xo9Fo0Gq16HQ6nE4nzc3NxGIxmpubqaury0tpPq8ezMGDB+nt7QWQ3bPZrqYUu0t9I8FgkEgkgk6nQ6/X58O0eSeTyZBIJEgmk3JC12Aw4HA40Ov1RCIRgsEgqVSKeDy+KD0YURQRRZFcLodOp8Nut3Pq1CkGBwfneGvS3yISiWAwGApWYARBoLy8HKPRiEqlYmJiAkEQWLduHcPDw2zbtk0+p2NjY4tmYjhw4AC//OUv6e/vl4X/YthsNoaHhykuLkaj0VBTU5OXiT0vAqPT6SguLubxxx/nz3/+M//7f/9v9Ho99fX1/OQnP8FoNJLL5fj1r3/NoUOHePzxxwEYGRnh6quv5sEHH+STn/xkPkybVxKJBEePHuU3v/kNTz/9NAMDA2SzWZxOJ42Njeh0OqamphgaGiIajRIMBhelwExOTjI9Pc2pU6eIRCIEAgGGh4fxeDznHevz+Th9+jQWiwWLxbIA1l4+mUyGSCTCU089hcfjYWxsjMnJSWprawkGg8RisUXlwcTjcTmH+XLiAhAOhzl27BjFxcUEAgGuueaaxSMwMOOGVlRU0NHRwZ133olOp8NisdDV1YVarSabzTI0NITP55M/k8lkGB4evmD5sNDIZrN0d3dz+PBhDh48yMTEBIlEQm7aUqvVeL1eeSzJZJJEIrGwRr9KpLA1Go0yMTHB6OgosVjsghdxLBbD4/FcVli8UIiiOOfcVFZWYjAYCAaDBAIBgsHgnMRvPB4nHo8XfKK3paWFLVu24PP5yGQy5HI5enp6CIfDOBwOuRdrYmJCTmhHo9G8emh574NZt24d69atA+DUqVNcc801RKNRcrmcfBLnGKTRFKxrPZtMJsMf/vAH9u7dy7Zt24AZz212r8XRo0fl2S8ejy/aJK/dbgdmJo3+/n5279590YsyFAoxODhY0BWzXC5HV1cX2WwWk8nErbfeSiKR4IUXXmBsbIxTp07NOT4UCuH3+zEYDAW9hOCOO+7g5ptvJh6Py1XLb3/72/T09NDR0YHP58Pj8fDUU08RiUQA5KUR+eKKNtpVVlby85//nEwmgyiKhMNhDh48yLe//W1g5kJ+4IEHuO66666kWa+YSCTC9PQ0fr+faDQKQGlpKRUVFdx1110YDAZEUeTIkSOywMRiMSKRyKIUGLVajdVq5frrr+fo0aP09fWh1+tZtWoV9913Hz/4wQ/o7OwEwO/3MzAwUNACk0ql+PnPf87w8DDxeByNRoMoivINKFFWVsZVV11FdXX1oig8aLVaufws5cz++q//mlAohN1u5/Dhw+zfv3/OIs9cLpfXa/KKCoxer6e9vZ1YLEY8Hpf7ZCS0Wi3Nzc24XC6y2WzBntRAIMDo6Cgej4dwOAyA0WjEbrdTVVWFIAjE4/E5nlgsFsPv9zM0NITJZEKtVlNaWrooVvRKiwCl6mAoFMJms1FWVsab3vQm/vu//1s+Nh6P4/F4LpkDWEiy2Sx9fX2cPn2aqakp+XWpARRmrkWXy8XKlStxOp1otdqFMveyEQRB7nFJJBIEAgE0Gg1ms5lsNit3k58rKPkUmSt6dfv9fr73ve+xc+dODhw4IKusRCqVoqurC6fTSV1dHVarFY1GU3BuaWdnJ8899xxPP/30nHxRPB7n8OHD+P1+PB6P7IbCTAJ7enqa++67D4fDQXFxMV/+8pepqqpagBG8csbHx3nooYc4fvw4gFwZKysrw2AwyMdNT0/T09NT0M2SarWa1tZW4vH4HIGRbjJBEKipqWHlypXcfvvtNDc343A4FsrcyyaXy5HL5VCpVBw5coTvf//7vPTSS0xMTKBSqeT7bbZ3mclk8joZXFGBmb0eJJPJnNeQlkgk2LVrF2NjY+zbt4+77rqLqqoqKioqrqSZl0SKW2fnkAKBgHyypGUBs09kOp0mGo0yODjI9PQ0wWCwoG/C2Rw7doyuri5OnjyJz+dDEATcbjdOp5N4PI7JZMJutxMIBOR1WBdqNiwUNBoNb37zmyktLSWRSDA4ODhnMhBFEb/fTyKRoKysjDNnzpDL5Vi7dm3BetUAJ0+epLe3l6qqKkZGRlCpVHLrwOxJWhRFLBYLVVVVrFixgsrKSn7729/S3t7OqlWr5tWmKyowOp2Ouro6SktLsVgshMPhOeqZTCY5cOAABw4cQKvV0tDQIFejCgkpzp190oLBIMFgkNHR0Qt+JpvNks1mGR8fJxQKyYm4xcCxY8fYvXs3fX19ZDIZVCoVZWVlWK1WAoEAer0eu90ul3al8m8ymTzv77SQzPaYb7rpJhwOBwMDA4TD4TlLBkRRZHp6mng8jtVqZefOnUxPT7Nq1aqCFpienh5eeuklVq9eTTgcxmw2yxHA7PAPwGw209LSQltbG06nk8cffxxBEBa3wJjNZjZv3kxZWRkbN27km9/8Jj6fD1EUZY/A7/fLF8LAwABlZWUF16at1WpfU0UhmUwSDocLupQLM6IobdB0bmVPr9czPj7Oj370I44fPy4nu2HGW7vrrru4+uqr+fKXvyxPKAtJMpmkr6+PEydOMDw8zH333Ud7ezsf/ehH2bJlC4ODgzz33HPyUgGAM2fO8LWvfU3OxxQ6N954Ixs3bkSv18uJ9iVLlmA2mxEEQe4mn5ycxOv18vzzzzM9Pc3SpUv593//d4qLi+fdpisqMIIgYLFYqKmpAeBNb3qTXLPXaDTE43GeffZZORE1PT1dkD0xFosFt9vNsmXLiEQiqNVqeRGZlGiTEqM+n0/OWxgMBtavX49er8doNGI2mxd4JC+PFO4NDw/T398vz4CiKBIIBMhms0QiEbxeL4lEYs77UjL76aefZtOmTTQ1NS1oQlsQBDQajZyDmH0tptNpDAYDPT09cnjr9/sJBoMcOHCAVatWYTKZFsz2y2V2c6PT6aS6ulr2UHQ6HbFYjFAoxLZt24hGo0SjUfx+P+FwWO5snm8W5IyXl5dTXl5OR0cHiUSCUChEUVERk5OTdHV1MTk5STQaZXh4mPr6+oUw8WWpqKhAFEX0ej25XA6TyYTJZEKn02EymdBoNOh0OrRaLdu3b+djH/sY2WyW4uJivvnNb2Kz2dBqtbjd7oUeysuSyWQIhUI8++yzvPjii/Lroiie1ytyIU6ePMmDDz7Ij370IyoqKigqKlqwcEmr1VJVVcX09DSZTAa1Wo1er6esrAxRFDGZTPT396PT6TCbzezbtw+Px8Ozzz7L8uXLcbvdi6I/S8LpdLJ27VpcLhexWAyXy0UkEsHj8dDZ2Sl7nPluAF3QGmkwGOTgwYN85zvf4YYbbsBms1FRUUEqlSISiTA+Ps7k5ORCmnhBXC4XZrOZsrIyALk0KHkx0rorQRDo7e3FaDTKZWuz2YzNZpOFqJDR6/WUlpai0+le9jidTodKpZIvVEEQaGxsxOVyUV1dTVVV1YKXrSORCLt27SIYDM5JQOdyOUZHR+nv72d0dBSr1crq1as5evSo3Ew4OjpKb29vwYe08XhcXqAZCATweDzy1g11dXVYLJbzNtFKp9NzvM/5ZkGu8FwuJ2e3R0dHee6557BardTV1cnLygHZTS00pH1PLqd0abFY0Ol0JJNJ2U2XQqRCR61WYzKZKC4ulmfwc38AuVoxODgo59MqKiqora1l1apVlJaWLniiN5FI0NXVJYfjkUhEXl08OTnJ2NiY7HWe290aDAblXGGhksvlCAaDeDwejhw5It87kUgEQRDkXhij0ThnHBeq5s4nCyIwwWCQ6elpeTl5fX09qVQKv9+P3W6X/wjDw8Pn7Ti22JBccWk3u2QyWdAl3AvxwQ9+kLe//e3YbDb5Z3aT4PHjx+nq6uKhhx6SS6Lt7e1cddVVvOtd7yqI5R9+v59f//rX8upol8vFihUraG9vZ9u2bYyOjvJv//ZvfPvb3+Yb3/iGnKfRaDSyR12oSMWR3bt3s3v3bn7xi1+QyWQoLi7mmmuuwe12MzIywsmTJwkEAnJzKPxlp8V8sSACc/r0afbs2cPY2BjZbJa3v/3tGAwGkskkzz33nNz8JDUOLWaqqqp43/veRywWo6ioCLvdfsmQo9CQWgsMBoO8143FYpFFw+l04nA45JnS4XDQ3NxMTU1NwYxVpVLJDYHRaJQ//vGPTE1NYbfbWbNmDStXrsRms2G1WrFarUxPT5PNZsnlcnR0dLBp06aCLVGLokg2myUajRIIBIjH43LYc/ToUQYGBuS1SbFYbI43vX79+rz29yyIwJw4cYJHH32UgwcPsmbNGr75zW8yODjImTNnOHbs2KJpQLsc6urqePDBB+VSr9PpLPjcy7lUV1e/7PuS4AiCgNVqpaamhra2Nmpra6+QhZdGWk+l1+tJp9M89thjeDweli9fztVXX43L5UKtVmOxWCgrK5NXVGezWTZs2MAtt9xS0OdNWvmdTCZRqVSyV3Pw4MELHq/VarFYLNxwww3ccMMNeRvbgvzFpEdEqFQqDh8+zN13300qlZJ/JCoqKiguLiaZTMqx8WLDYDDIVSdp1ljofMR8IwkMzCR8bTYbdXV1BdUgqVarcblcc5Y1dHd388UvfhGHw4HBYCCXyzE4OMjo6OicMHZ8fJz+/n6ampoK8hrUaDQUFxdz2223sWHDBhobG9m/fz9PPPHEBY9XqVSsWbOGz33ucyxdupTS0tLXlwej1+vlUm0oFOLMmTPAzMClmVClUtHa2kpDQ8OiviEFQVgUC+VeCzqdjqKiItrb2ykpKaGpqYmioqKCGrdOp6OhoYGJiQk8Ho/8zCqpyiet1ZH2R5FK1263m6KiIrm3qVDRaDQ4nU7MZjNr165FpVIxPj5+Xo+W9O/a2lr8fj9AXsPYBREYh8NBfX09RUVFJJNJuYSr1+tpaWmR/yjvf//7Wbp0acHE8QoXxmg0UlNTw2c/+1msVislJSU4nc6FNmsOdrudd7zjHVgsFrRaLVu3bpVXfl+Mqqoq7r77btasWSOvki9kpKrfm9/8ZtauXcu1114rP1TObDaj1+vl7WhffPFFPvShD/Gd73wHp9OZt4fMLYjALFu2DJvNxsjICENDQ/KeqKWlpXz84x+Xnxnc3NyMzWZbCBMVXgHShd3R0SEvoygk7wVmZunq6mrWr1+P2WzGbrfLfTtTU1P4/X66u7vlULa1tZVVq1Zx7bXXUlZWVnDjuRQ2m42lS5fKPVpSJU/yYNatW8f3vvc91q5dm9eWiQURmLKyMpxOJ6tWrcLhcMiJz7KyMrZs2TInTlYofKQwsJByLueiUqmw2+00NDSg0+nmLBno6elhbGyMkZERcrkcGo2GxsZGWlpaaGpqwmq1FmTu5eWQmiQvRlVV1RXZKkS4RPNQXjuLEonEnFK0SqWajzUfr8SPLdzOqYtzueN7PY8NXuX4pOtt9lYbp0+fZmhoiKeeegq1Wo3RaKSjo0N+rPE8issb7twtaN1N8VQUrjRSmDC7LFteXo7BYCCbzaJSqdBqtVRWVuJwOBad51JoLKgHkycUD2aG1/PY4PU9vtfN2BR5VlBQyBuKwCgoKOQNRWAUFBTyhiIwCgoKeUMRGAUFhbxxqSqSgoKCwqtG8WAUFBTyhiIwCgoKeUMRGAUFhbyhCIyCgkLeUARGQUEhbygCo6CgkDf+/4OzpPFUzkRrAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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1axeJRIJTp07x3HPPMT09zSc+8QlKSkowm80X1b4FFZh0Os2pU6fo6elRb6gSNBobGyMUCuH3+9/StRTxOfO1YDBILBZbyGa/aRsymQyRSES9eTAnADU1NdTX12O32zGZTHg8Hmw2GzDnauh0Oqqrq2lqaqKtrU213CYmJkgkEmQyGUKhED6fj+HhYZxOp9pfSZKWTGAUt2BgYIDx8XFyuRxr1qzBYrFgMpnI5XJEIhFmZmZIJBL09/fj8Xhwu91UVVWRzWZJJpOqa6jX67Farej1evU1xf0oNrlcjkQigd/vZ2JiAgCj0cjs7CyZTIbZ2Vm6uroYHx8nGo3S29vL0aNHOXDgAAcOHCCZTKLX61WLO5/PMz4+zsTERMF4WLVqFW63m8rKSiRJIpVKLZpFo8RPFDcPKFjY52c8Jycn8fl8AGi1Wkwmk5qWrq+vx2KxoNPpOHbsmDrue3t70el0xGIxLBbL0gqMXq+nqqqKXbt2sXfv3oL3ziUYF4PypSlCtRjEYjHGxsbYt29fgeorN29kZIRsNksoFOLkyZMEg0FkWcbhcODxePjSl75EIpHgkUceYf369bS1taHRaOjv76e/vx+AcDjMnj17iEQiVFRUoNPpuOqqq2hoaFiUPp5Jf38/L7zwAi+++CLJZBKTycTq1auprKykrq6OdDpNNptl//79TE9Pc+jQIaqqqqipqaG2tpa+vj4OHDjAxMQEBoOBhoYGbrzxRhobG5mamkKr1WIwGBYlgCzLsioOClqtFqPRiMFg4NixY3zuc5/jb/7mb6iuruYb3/gGJ0+eZHh4WD3nTHfiXOP4wQcf5Omnn+bVV19ldnaWvr4+tmzZQklJSVH7B5BMJkkkEurfkiRRXl6OxWLB6XQyPj5OPB5X3TllcXa73Vx11VW8973vpa6ujv3796shi1WrVjEyMkJXVxfd3d2EQiEikQhOp/Oi27egAmMwGGhqasLhcBTc1HMdZzab2bZtG3a7XfV3M5mMuqKMj48D0NDQwIc//GFVcdetW0djY+NCNvuc5PN5QqEQExMTnDx5suAmKoN02bJlRCIRotEo8XicSCQCQGtrK5s3b6a9vR1JkrDb7VRWVqLT6ZAkSXU1hoeHicfj9PX1qebnLbfcQn19fdH7dz7cbjdNTU1YrVZSqRTxeJyBgQGmpqYYGhrCbDZjMpnQ6/WUlJTQ0dGhBhJPnDjB8PAwfr+fN954g2w2S09PD729vZSWlpLNZmlvb+eqq67CYrEsmsDMR6vVotPp1HvodDqRJIl0Ok00GiWZTL7p2AXOct0BMpkM3d3dJJNJpqenF62yO5VKFYigRqOhrq6OiooKampq6O3tJR6PU1NTw7Fjxzh27BgwNwfdbjcajYZ4PE5vby8ul4uKigrV5YXflWkonsjFsuAC09DQgNvtxmAwqGp/5petrGC33XYblZWVGI1GLBYLiUSCxx9/nH379qkC09jYyBe+8AX1ptvt9kXx82VZJhQKMTU1xcjICOl0Wv2C9Xo9ZrOZ5cuX4/f71aBaMplEo9HQ3t7OLbfcQnV1NXa7nZUrV5LL5dSsDMxlynw+H9FolKGhIWpqaqiurmbDhg2LGsA+k7KyMlpbWyktLVWzMPMD8ApGo5E1a9Zw8803qyncY8eOqVmNI0eOEA6HsdvtvPzyy2g0GjweD7fccguNjY2Ul5erVmmx7uebCYxGo8FqtVJbW0s+nyccDp+15UOj0aiZpvmvKZknJZam1WrJ5/McPXoUvV5/VsyumCjFc4rgaTQa6uvraWxspL29HZ1ORyKRYO3atUSjUVVgdDodTqeTbDZLMBjk+PHjLF++nKamJux2e4ErpLi6Sx7kNRgM1NbWcu+999Le3k40GuXEiRM899xzzM7Oqrn2WCxGJpPh6aefxmKxkEqlKCsrw+l0snr1anw+H/v37wfmUo0DAwP8x3/8B4ODg/z4xz9W1bWYKJZHXV0dmzdvxmAwEAgEMBgMahbFYDBQU1PD+973PgCOHDnC3r17cbvdzMzM8O1vfxu73c5VV13F2rVrsVqtHD58WA2eKqlRr9dLQ0PDkgQJz8TlctHa2sqf/dmfsXv3bv7jP/7jnMel02kOHz7M5z73OfW1fD7PihUrWLVqFZIkkc1mmZ2dZdmyZTQ3N/PAAw/gdrsxm808//zzGI1GNm7cqG6zWGiU2MN8a0On02EymdBoNKxfv57vfOc7fPnLX2bHjh0F2Re9Xs/VV1/NF7/4RcrKyjCbzWoRYiKR4C/+4i/o7e0lEAgQj8eZmpriySef5N3vfjcf+chHsNvtC96fcxGNRlXLGebGrcvlwmAwMDMzg9lsRq/XE41G1fkHc3O1tLSUkZERQqEQ3d3dVFVV4XA4iMfjBUkXpcL3UsooFjxNrdfr1cKxgYEB4vE4ra2tJJNJ4vE4ExMTJJNJMpkMAwMDGAwG0uk009PTqs8aDAbV62UyGQKBAC6Xa9GCg/C7TY16vZ7q6mqi0SixWAyr1UpbWxuVlZXqalhSUsLy5csxm804HA7MZjN+v1/dLqAEOZUg9czMDMFgkFwuh9FoxOVy0dzcTHt7+5IXcCkBalmWKS8v55ZbbuHgwYNMT08XTFTl+1GChgpKYVcmkwFQM4TBYJD+/n6mp6ex2+1qdfZiWDDz2630T6PRYDKZqKysZHZ2lvHxcdXV0Gg0bNmyhZaWFkZGRojFYphMJtVSSyaTrF69GrPZzEsvvaR+TjKZRKvVXlKs4lJJJBLqnjgFs9msBmb1er1qxcwXGMWCS6VSqjVjNpvV+zVfYIArw4JRaGxspKamhqmpKWpra3n3u9+N3W4nHo/z6KOPMjY2xuTkpGquAeouzr179xZ0LpVKMTo6ykc/+tFFDXzKsszk5CSyLLNs2TKamprQarWUlZVRU1NDeXl5wfGbN29m8+bNAPzmN7/h6aefZuPGjTQ2NrJhwwY1ezE9Pc3IyAgnT54E5jJS9fX13HjjjVx33XWL1r/zoVT0HjhwgObmZn7yk5/wkY98hO3bt6ui8Wb09vbS29tb8Nrg4CCDg4Ps2LGD2tpa2trauOaaa3C5XDgcjqJtED1XkFexYCRJIp/Pk0qlSCQSBTE2g8HAP/3TPzE4OMinP/1pVQT1ej2ZTAZZlnnssccYHx/npZdeAn634fVisyyXSywWK0hASJKE0+lEr9czMTGB1+tVfz+zjy6Xi1wuh9VqZdOmTVRVVRGPx5meni5Y5BUL5lI2IBet0E6j0VBZWUkul1NXrfLycj71qU+xe/duXn/9dfr7+1VVVfzZ2dnZAv81mUwyOTl5lkoXG61WS0dHhzo48/k8kiRhNBovOIjWr19PRUUFTz/9NDMzM2i1WjKZDMFgkIMHD6opU5hLpcbj8SvmcQ9OpxOj0cgf//EfY7VasdvtlJSUYLfbCQQCBcd6PB5Wr15NeXk5Go2GnTt3EgwGmZ2dPe/1lX1aq1atYvny5ao1UQyUeqr56PV6jEajGqjNZrPU1dXR0tLCiRMn1M2CVqsVp9OJ2+3m/vvvp7Ozk2QyyYEDB3jjjTfIZDJnuRElJSVFcfXejEgkQjgcLqiOP3ToEF6vF4/HQ2trq1px3tHRoQa029vbqa+vp7KyEpPJxJYtW9i9ezfPPvusWlkPc0H/mpqaS7asiyYwkiThdrvV/LnVaqW0tJSOjg5SqRSRSIRUKqUGRkOhEMlkssCMA1Q//szXi40kSXg8nks6t7KyEo/Hw3PPPacGSmdmZpicnGRoaEhNs89/9saVUqBmNBoxGo2sXbtWfa2mpoaWlhY1KB2Px9XNnFVVVWpF89DQkLrdIJ1Oq4NeMdMNBgMlJSU4nU5qa2upqqoqqsurZEDOzPgoGUKtVksqlaK0tJTq6mp6e3uRZRmNRoMsy1gsFlpbW9m2bRubNm0iHA6j1WrVuMt8IdVoNFgslkXf7hGPxwvqwvL5PENDQ+j1elpbW6msrMThcKhzyGAwkEgk1EB7XV2dGpo4evQofX19hEIhNamhWJyXep+KasG0t7dTUVFBVVWV2rlly5ZRVlbGtm3b1Ilms9n453/+Z3bu3MnAwEDBgMhkMkxNTS26BXO56PV6/uRP/oRsNksul+PZZ59lbGyM48ePq76+0+lUb/JSZo4uxN///d+TSCQYHR3lySef5De/+Q0HDhwgEonQ1dXFxMQEpaWl3HPPPZw4cYKDBw9y9OhR1SRvbW2lpqaG1tZW2traWLVqFR0dHUUPhCoWzPzxFA6HGRkZ4aGHHqK0tFRd+NauXcsrr7yCTqfDbDZz/PhxOjo6+PnPf65aPFqtlsbGRrZu3crevXs5ceKEel2NRqNebzEJhUJq/RXMWcSHDh3C4/GwdetWWlpa1HICJcOnxJ+8Xm+Be5pOp4nFYmqdkNFo5POf/zx33333Jbt+Rd2LpKh6RUUFmUxGDYoqfrfT6VRTge9+97tZsWIFoVCI/fv3s2vXLnXFUx5x8HbDZrOpmYy6ujpisRgbN24kFoupK2djYyO33347VVVVS93c87Jv3z5mZmaorKykvb1dDXgqFbKhUIhwOMz111+vWqvz79fmzZtZv3494+PjeL1empqa1DhIMVFcoPkCo1iUO3bsUC0OZa+YYu0kEgleeeUV0uk0K1euBOYWuomJCQ4cOMALL7zA+Pg4U1NTAFRXV1NbW8uaNWuorq4uap/OpKmpiVAohMvlYnR0VN0yoNVqKS0txWg0qoWhx48f55VXXiGfz9Pa2sr73//+gufHrFq1ivvvv191K41GI52dnZcVVyr6Zkej0ajWfiiYzeazGn3PPfeoWZcHH3yQ3t5erFYrXq8Xm822GE+KW3Dmm8tNTU0YjUaCwSDBYJBIJILdbmf58uXcc889S9jKC/Pyyy8zMDDAHXfcQVNTE1u2bGFwcJCDBw/y6quvAnOWgXL/5pfJazQarr/+eu644w5+9KMf4fF4Fv3xFMoEy+fzJJNJkskkO3bsOO/x8Xic559/Hq1Wy0c+8hHgd/vgdu3axSOPPFJwfENDA2vWrGHDhg2X7FZfKitWrMBkMjE+Ps7OnTuZmZlBlmVMJhOlpaUFgdn9+/fzb//2b6RSKW666Sbuvvvugnm1fv16Ojo61LiYwWC47Hl3xcza+c/Y+MAHPsCNN96oRu7NZvOipv6KQU1NDV6vl7a2NrWeQqkmvdL54z/+Y8bGxnjkkUcYGxtTnzQ4360LBoP84z/+I5lMhmQySSwWw+12s3HjRqqrqzEYDKxcuXJRV/iOjg7+4i/+Arvdzv79+3n22Wff0nYVJRMzv94ql8sxMzNzVvoW5lzAjRs3qiX6i8m2bdtYtWoVb7zxBqFQiEAgQCqVwuv1FmzHkGWZO+64g2XLlpHL5fB6vWftl1I2OyoWjbJv7HK4YgRmPqWlpUv66IJioNfr1dX97UZZWRk6nY6amho8Hg92u522tjb1SX9+v594PK5WXyuUlJSwceNG3G43Wq2WqqqqRdmfo2A2m6mqqmLt2rUYDAY1Wzd/86LyM39nv1arZdmyZQViqNSNKOX0RqMRk8mE2+2ms7OThoYGtYBvMVEK4ILBIFVVVWzbto1EIkFHR8dZmR+v16sWDJ5rM6YSZ1pIrqiHfi8Q4sHRcxS9b729vTzyyCM89dRT9Pb2nrUJ9brrruOxxx67mPRtUe6d8siNYDCo1rzE43H1we3JZFI9RhGbyspKysvLWb58OfA7F+nll19m+/btavbs5ptvpqysDLvd/lbqRBb83k1OTtLX18dXv/pV7r//ft73vvcRi8XUep9F5Mp86Lfg7UtlZSX33HMPJpOJI0eO8MILLxAOh1U3QqvVqo8AWEqUeIJS2KfsC1Mqc8+0ZAB1U6eCVqulvLyczZs3U11djdPpxOFwUFVVhdFoXLI+lpSU0N7ezuc//3mampqQJGlRAuhvFSEwgkvGbrfT2dnJ9PS0+oDzQCDA7OwsGo0Gp9OpPth8KVFM/8vJhkiShM1mo7GxkYqKCrUcf6m3dphMJkwmE9u2bVNfW2pBn49wkd5+XDEukkI2myUcDrN9+3a1wE6JVaxfv/5iLvW2uHeX8Z8DXnH3bgERLpKgOOh0Omw2W8HWCqvVuii73peCK8X9eDsgLJi3H793q+B5eCf37x3Tt7dfeaxAIHjbIARGIBAUDSEwAoGgaAiBEQgERUMIjEAgKBoXyiIJBALBJSMsGIFAUDSEwAgEgqIhBEYgEBQNITACgaBoCIERCARFQwiMQCAoGkJgBAJB0RACIxAIioYQGIFAUDSEwAgEgqIhBEYgEBQNITACgaBoCIERCARFQwiMQCAoGkJgBAJB0RACIxAIisaF/l+kt+PTqMR/fTHHO7lv8M7u3zumb8KCEQgERUMIjEAgKBpCYAQCQdEQAiMQCIqGEBiBQFA0hMAIBIKiIQRGIBAUDSEwAoGgaAiBEQgEReNClbyCdwjZbJbZ2Vm2b9/OwYMH0el0eL1e2tvbWb9+PaWlpUvdRME7kCUVmGAwSDabJZfLodFo0Gg0WK1WJGmu6jidTiNJEgaDAb1ej0YjDK5LRZZl0uk0vb29vPbaa+j1eurr65EkiebmZgwGAwBGoxG9Xk8ul0P5f8u1Wq16T95OZDIZ4vE4mUwGSZIoKSlRx5lgcVgygclms/zmN79hamqKaDSK0WjEZrNxzTXXoNfrkSSJ4eFhDAYDtbW1VFdX43A4lqq5b3skSUKr1XLq1Cl2794NQHNzMxqNBq/XS2VlJalUitbWVmpra1Xxl2WZ0tJSjEbjEvfg4hkfH2fv3r2MjY1hNpu58847KSkpwWKxLHXTfm9YVIGRZZlMJsPw8DCnTp3it7/9LVNTU0QiEXQ6HXa7HVmWkSSJdDrNG2+8gclkYt26dWzbto2WlhZMJtNiNvmyyOfzyLJMPp9Hp9ORTqc5cuQIqVSKfD7PunXrsNlsi9IWjUaD2WxGp9Oplkk6nSYajarW4cDAAJlMhkAggM/nY2RkhIMHD1JSUoLZbMblcmGz2bBarbhcLsxmMw6Hg4qKCux2O2azeVH68laZnp5m9+7d9PT0YDQaKSkpYc2aNbS3t78tLbK3I0URGGVSKRNMIZ/PE4/H6e3tZffu3ezcuZPp6WlisRgAdrsdj8dDNpslFovx8ssvYzabicfjNDQ04PV6MRqNSzI48vk8wHnNa6WfStsUMU2n08RiMYxGI8lkkkOHDhGPx8nlcrS1tWEymdDpiq/zkiSd9Vm5XI5YLKa6oBMTEyQSCcbHxxkfH+fIkSP87Gc/w2q1YrVaqa2txePx4PF4qKurw+l0UlFRwapVq6isrMRkMl0xEzeVSjE1NcXBgwc5dOgQOp2O2tpaysvLaW9vP+c58xcEvV6/yC0uPrlcjnw+r7qJkiQVuMLKvZMkSf25XIoysoeGhhgZGeHkyZOq/ytJErIsEw6HeeWVV3j11VeJRqPkcjn1vGg0yq9+9StkWUaWZRKJBLFYjCeffFJdYT/5yU8uqhWTz+fJZDKMjo4iyzItLS0F78uyTC6Xw+/3k81m8Xq9xONxYrEYAwMD7Nu3j29961tIkoTVauXGG2/EbrfjcDh47rnnqK2t5cYbbyx6PyRJOiuOlUqlCAaDuN1umpubWbduHY899hjPPPMMuVyOdDoNQDweJ5FIEAqF0Gq1aLVadDodGo0GrVbLu9/9bjZs2MBHPvIRzGYzWq226P15MxKJBN/97nfZt28fR44cIRqNAvCzn/2M5cuXn/P7Vu7x8PAwPp+P22+/HbvdvthNLypHjhzh8OHDrF69mrKyMmpqajh06BATExNoNBosFgs2mw2n04nJZFIt18tZAC/5zHw+z9jYGJlMpkAkAHp6eujr6+P48eOkUqkCgUkkEvT29hIKhc66pizL6mBQUFbZaDRKPB6/1OZeMrlcTm1zKpVSA4WKuufzedLpNMeOHSORSLBu3Tp6e3s5ceIEExMTdHd3Mzw8DIDZbKanp4fa2lqMRiN2u33RB7EiCrlcjmw2SzQaVQPpTqeTbDaL3+8vOEcRfEVwzqSrq4t8Ps+KFStoaGigrq5uMbpyXnK5HD09PQwODhKLxdTJ09LSQjqd5sSJEzQ1NZHP5wmHw/h8PoLBIIODg4yPj+P3+7FardhsNnK5HCUlJdjtdlpbW6/oALGy2PX09BAMBpmenlYXAp1ORyAQIJFIIEkS8Xicnp4e9u7dy+DgIJlMBqPRiNlsxm63YzQasVqtbNmyhcbGxkteNC5ZYLLZLLt27WJ2dpZkMllgZh05coSuri4OHjxIKpW61I8obKhOtySBxlQqxezsLC+++CKhUAiPx6NmVbRaLZlMhlgsxi9+8QuCwSBarZaHH36Yn/70p2ddK5FI8OKLL7JhwwbKyspYvXo1VVVVi9ofnU6HwWAgmUySSqUIhUKk02n0ej2lpaWXZB3u3buX7u5uJEnijjvuuCIE5tixYwwMDJBKpbDZbFRXV3PfffcRjUZ54okn+OQnP0k6neb48eM8++yz9Pf309/fTzKZJJvNsn//fiRJIplMsmLFCtrb2/nzP/9zNdt2JZLP50kkEjz66KO88cYbvPbaa5jNZjWBsmXLFrZs2YLL5WJmZobf/va3/PrXv6a3t5dgMFgQzoC5xeiHP/wh1dXVlxxfu2SBSaVS/OY3v2FoaIhAIFDwXjgcJhqNkslkLunaisWjxD0ADAbDksRflODg2rVr6e7u5q/+6q8KxNRgMGC1Wlm7di21tbX8wz/8g2qxaLVa1WJQ0vH5fF69ocePH6ehoYEbbriBDRs2UFtbW7R+hEIhXnvtNYaHh1WTt7Kyki1btuB0OkkkEgwODp7TsvzEJz5BW1sb4XCYcDhMKBTi5ZdfJhgMEg6HgTk3avv27QSDQQYGBrjvvvvweDxF68+bkc/n8fv9RCIRADweDxUVFVitVgKBAJOTk7z66quMjo7y1FNPcfLkSUKhEPF4XI0dKmM6n88zMTHB7t27efHFF2lsbKS1tZVbbrkFh8OBLMu43e4lzUzlcjmCwSA7d+7k9ddf54knnmBqaopEIkE6nUaj0RAKhdixYwdDQ0Ps3buXSCTCsWPHGB0dJRKJnCUuMNf3Bx98kN27d/NP//RPOByOi7ZkLstFGhoaor+/H5/P95bPU8w1rVZLPp9XTfX52O12XC6XOhFyuRwVFRU4nc5FFRhZlslms2rWJxaLsXv37gLhM5lMlJaW0trait1u54033lDjTopPW15eTjQaJZFIMDExwezsLJFIBJ/Px+joKFVVVSxbtqyofUkkEnR3dxMIBNTBZLFYqKurUwPQfX195xSYFStWcM011xCJRAiFQvj9foaGhtDr9WQyGTKZDPl8ntHRUSwWCyaTiT/8wz8san/ORy6XI5VKkUwmVZdOiSu4XC4mJiZIJpMcPnxYjZEpVtx85rvjiogeP36clpYWfD4f1dXVeL1eLBYLVqt1SQUmnU4zODjIwYMHee211zh58iSpVKrAFU4mk4yNjRGJRJiamiIejzM2Nqb2u6ysTA1wZ7NZMpkMiUSCo0ePEggE6Ovro76+Hq/Xe1Ftu2SBUbISFxNt12g02O12ysrK8Hg8RKNRwuGwuuIrvOtd7+K+++6jtbUVvV5PPB7H4XBgtVoXNbqfTCYZHBxk7969fOMb36Cnp6dAXJRjJiYm+Pa3v40kSWQyGTQaDQaDgVWrVrFlyxb+8i//ku7ubo4fP87nPvc5EokE2WyWcDispoQTiURR+xKPxzl06BAjIyNqnEun02Gz2ZAkCZ/Px9e+9rVzCozZbKa8vJyrrroKmHOPLRYLx48f59VXX2V4eJhQKEQ0GiWZTBIIBM6Kyy0W09PTnDp1ilQqhSzLaLVaPB4PjY2NXHfddWQyGUZGRvjud79LMBgkmUye8zrzV+r52dCTJ08yODjIgQMHaGpq4q677sLhcCxpJbTP5+PLX/4y3d3dDA4Oks/nMZvNlJaWkk6nyWQy6v2JxWJMTEwAv8t8ut1u/sf/+B9kMhlmZ2eZmJhgdHSUHTt2kEgkGBoa4o/+6I+47777+MIXvnBRbbtkgdHpdKxduxaNRkMkEiEajZ5liZzrnMrKSrZu3co111xDOBzm+PHj/OQnPyGZTKqD8uTJkzz11FN86EMfwmQycerUKaqqqvB4PJSWli5KliKfzzMzM0NXVxe//OUv8fl85HI59Ho9drsdq9WqKr5yY+avmKWlpWzduhWLxcI3v/lNNBoNuVyO97///Rw+fJhDhw4BcytuPB6/4Hd3uShV0vMzAj6fj+3bt1NXV4dWq0Wv1xd8t7W1tWzevJmmpqaC7JBGo2Ht2rVUVFRQVVXF4cOHGRwc5OWXXyYcDjM0NMSTTz7JqlWruP7664varzN55ZVXeO6554jFYpSUlFBTU0N7ezsWi4Xvfve7dHd3c+LECcLhMLlcDrPZzLZt22hoaKC8vJxUKkUqlSKXyzE1NcWhQ4eYnJxUXX4lkOpyuaioqKCiomLJ63+UwHwsFlMXwObmZj70oQ8RjUaZmpriZz/7GfF4XB2jWq0Wk8mkuuaBQEAN9Dc0NNDX18eOHTsAzuk+vVUuS2DWr19PPp9ncHBQzbbMz6uf65zy8nK2bNnCRz7yEcLhMC+++CLPPvss09PT6ire19fH1NQU119/PTabjWPHjqnxnNbW1ktt8kUhyzKhUIienh6eeOIJYG5iGY1GysvLqaysVFO5SsB0vsBUVFSwefNmTpw4wZe+9CU6OztpbW3l/vvvR6PRFAiM8r0VE61Wq2YHFHw+Hz6fj+XLl6uFc/MFqLa2lve///3U1dVhMBgKrLcVK1bQ3NzMsmXLcLlcHDp0iNdee02diM899xzRaHTRBea1117jP/7jP4A5s7+jo4PW1lay2Sw/+MEPCAQCRCIRkskkRqMRl8vFbbfdxpYtW1i1ahWhUIhgMEg0GuXEiROkUim1nkkZg5IkUVFRQXV1NVVVVUsuMLlcjlAoVGCNtba28ulPfxqfz0dfXx+vvvoqk5OT6hhVCluvv/56WlpaePrpp3G73bhcLtatW1cwTi7FW1G4ZIHR6/XcdttttLe309zczODgIJOTk+zbtw+fz3dW4BdQ4xgTExP09vZiNptZuXIlP//5z/niF7/IK6+8ovrPSvyjtLSUW265hdLSUux2+5KmCfV6PTU1Ndx///3ceeedTExMqPGHv/3bv2X//v1AYdGd0t5sNks2m1X3+igomxAvNSB+MW33er3nnAz/9V//hVarVVd1BSXguWfPHnQ6HW1tbWp/HA4HwWCQrq4uHnroIQ4fPkw6ncblcuH1ennPe97DypUri9qnM8lmswUimMlkiEQi6n37whe+wIsvvsju3bsZHBzk2muv5atf/SpPPPEEjz76KCtWrOC1117j17/+Na+//jpr167lG9/4Bn/1V3/FCy+8QDweL1g8tVrtFVH3o9TwKAu01WrFYDCQSqX47ne/y/DwMN/85jd5+OGH+clPfqIe09zcjNPpJJfLsX37diorK1m+fLm6VURBo9GoNTEXy2XFYOx2O1VVVaxcuRK9Xo/FYmFqagqLxYLL5SKXy6kbziKRiBrtPnLkCBaLRb05er2ecDis3jzFDFXK291ut5puW8wg75liJkkSRqMRi8WCw+HgyJEjGI1G6uvrz5neVVLCyg1X3KT5kyCfzxe4h8VCp9Phdrtxu92Ulpaq4pHP51WXQNl7pBCJRDh+/Dgmkwmz2YzZbFa/E5PJRCwWU+NIyoBU4k+1tbVUVlYWtU/zOdf3qMTHurq68Pl86HQ6/H6/uoBpNBpVfJTxpmx9CAaDxONxKioq0Ol0Bd+NJElUVlZSXV2Ny+Va0tS1YkErC50yL/V6vZpMSCQS2Gy2gnYaDAY8Hg+yLJNMJtV+NzQ0qNXnCpIkYTabF9eCUSgrK6OsrAyYU0WYS2Fns1kSiQQzMzOcOnWKnp4eQqEQAwMDDAwMnLNO5EyUeEdDQ8PlNvOSOJ+YRSIRhoeH+f73v09TUxOf/vSnC95XYjNKNWRNTQ0OhwOj0UgsFiuoDcrlcm8pfnW5GI1GmpqaaGtrIxQKqfdIGUyJREINzioTaXBwkP/8z//kxhtvpKamhmAwqApMNBrFYDDgcrnOCnxLkkRjY2NR0+5nksvliEQiBdmgQCBAIBBQ3dEzmZqaYseOHdxxxx3U19cDcPXVV9PS0sL27dsxmUyq1a1kkmBORLds2cK6detobm4uar8uxOzsLMFgsGAri+K2DQ0NYbfbKS8vVwPxCiaTifr6enU8XnvttVx99dVcf/317Nmzh5mZGfVYjUaD0+m8pEzZgm0VsFgsuN1uVq9ejdvtpqSkhKGhIcbGxtS//X4/MzMzBINBZmdnL3hNvV6/pHtClBoWBVmWicViTE9PMz4+rvrgys1Stgt4PB51hWtqasLr9RKNRvH7/Xz7299mcHAQmLMqlE2ExV4FLRYLK1euxOPxcNdddxVYMNlslmAwyPbt29m7dy9dXV3AnAUwOTmJ3++npKSE5uZmJiYmmJycZPXq1UxMTPDQQw8VZAGVlOjlBAYvBa1Wi81m473vfS8NDQ28/vrrDA0Ncfz48fOeMzY2xs9+9jPWr1+vCoxicSsxB+VxD2ei7N9aavx+P1NTU+rfivWvxA9bWlrUMez1euns7CQajdLc3Exraytr1qyhrKyMmZkZAoEAe/bs4ZlnnmFgYEC9pkajweVyLa3AWK1W8vk8ZWVlVFdX4/F41JqXcDiMTqcjGAxy6tQpgIKA8JkroIKy72UpUCp19Xo9JpOJdDqtmuEzMzOMj49TV1dHTU0NZrOZpqYmNdirZBicTidlZWXU1dUxPj5OT08Px44dIx6PYzab1RqahoYG1forFjqdrsDaPBOljDwajRIKhQoESLkPpaWlajDe7XYzOTnJkSNHCq6jZNUWW2AU97Wzs1Mt8LPb7QVFZLIsMzs7q6bpw+EwPT09BdtTlLonpYp3eHi4IHhqs9lwu91XzO5xJfhssVjUOZVMJtWwRFtbGyUlJcTjcerq6li9ejXhcJiamhrq6+tpaWnB6/WSTqfZuXMnfX199PT0qLVtJpMJh8OBx+O5pJ3/CyYw87M7ShVuJpNBq9VSXl7ODTfcgNFoZNeuXbz++uvs37+fYDBIIpFgdnb2rJ3XMGfBLMZO43MhSRI2m43a2lo2btzIkSNHCIVCjI2N8eKLLzIwMMCPf/xjqqur0el0fPaznyUajTI4OEhlZSVVVVUFVoli4a1fv14dwE6nkzVr1vCFL3xhyUvQnU4nf/iHf8jWrVuZmpoiFAqp90bZl7JixQpgzrLxer2MjY2ddR3F5TrfolEslKrq+vp6ampqaG1tJR6PEwqF1P1ymUyG7373uzz88MPIsoxOp8NisRSMsWw2SzweZ3p6mr6+PgYHB+nv71c/44477uATn/gEq1atuiI2QzqdTurq6njXu97FkSNHOHnyJH19fbhcLmpqatiwYQPV1dXIssytt96qxqgkSVILXhVx9vl87NixQ93DBbB+/XpWrlzJzTffrFp5F8OCzd4zA6LKg4xsNhsej0fd/xKPx1m1ahVr1qwhkUiocZr5ZllHRwdXXXUV1dXVS1YhqQhMW1sb99xzD/l8nlOnTjE9PY3D4VCrOBUzWXmGTU9PDyaTicbGxoLrmUwmvF4vH/zgB8lms2rgTHGxlholoF5eXo7NZiORSKjVnErg1uPx0NHRoWaKHA5HgXWn1WpZvnw511xzTcGzfRYTZeLM37CnWMq5XI477riDiooK8vk8BoMBm81GTU2Nev782iYl1ja/CNJms1FZWXnZu4wXCiXGd80111BeXk5bWxuyLLN8+XKam5ux2+1v2Qtob2/n/e9/P+vWrVN30K9evZqmpibq6uooKSm56PYV7RuSJKlgI19/fz9+v59wOMy2bdvYtm0bMOcqTU9PMzQ0xOjoKJIksX79ej7+8Y9TV1dXdNfhzbDZbCxfvpyqqipGRkbQaDREo1G8Xi/Nzc0FA0zZOHjgwAHKy8tZt25dwbVMJhMVFRXcd999i9yLt45Op8PpdOJ0Os97jNvtZuXKlYRCIVwuFy6Xi0gkQiaTQa/Xc9VVV/HRj34Up9O5JAKjYDAYzmkV3nPPPdxxxx0FQfX5i5jiFiqxGGXLgLLKKy6S0Wi8InZWO51ODAYD1113HcuXLycQCJDJZKiqqqK9vf2i3JpVq1bR3t5OIBAgFosRDAbVTJndbr+keyldwFdeMEc6nU6TTqeJx+PqHh343X6fkydP4vf7iUaj1NTU0NDQoKZFL7JjF3PwBfunrHynTp1iYGCA//qv/+L222/nhhtuoKysTF0dFBM8EAgU+zEMb7V/RQ2C5PN5otEoY2NjxONxUqkUgUCAuro6GhsbsVqtlzIBF/TenfOkefEYhfntnJiYYHBwkI997GOMjo4Cc4tDTU0NDz74IDU1NVRWVl5qbLAo905JNSsbaue7fxc7f5T9gcq/SnX3W7iX5/yQRbPxlBXlTEVVHoTU2NhIdXU1sVhMfRbHlYBicldWVqLX67n66qtZtmwZFRUVBccpgdDFfvzCUqHUjDgcDnXxCIVC6iM1r4TV/VzMf2rbuTAajTidTq699lr1uThGo5HKyko6OzsLHkp/paC42wvBQj8UfdEsmEWk6KvgEnNFWDBFQty7Od4xfbsylxmBQPCOQAiMQCAoGkJgBAJB0RACIxAIioYQGIFAUDSEwAgEgqIhBEYgEBQNITACgaBoCIERCARFQwiMQCAoGhfaKiAQCASXjLBgBAJB0RACIxAIioYQGIFAUDSEwAgEgqIhBEYgEBQNITACgaBoCIERCARFQwiMQCAoGkJgBAJB0RACIxAIioYQGIFAUDSEwAgEgqIhBEYgEBQNITACgaBoCIERCARFQwiMQCAoGroLvP92fBqV+P+N53gn9w3e2f17x/RNWDACgaBoCIERCARFQwiMQCAoGkJgBAJB0RACIxAIioYQGIFAUDSEwAgEgqIhBEYgEBQNITACgaBoCIERCARF40JbBQSCtz3Dw8PMzs4SCATwer14PB5KS0vRaObW156eHkKhELIsU1tbS3V1NcPDw4yMjLBnzx4kSUKj0aDVatHpdGi1WkwmE+Xl5WzcuBG73Y7BYFjiXl6ZLLjA5PN5AoEAs7OzyLKMLM9tq1D+1Wg0lJaWUlpaWnBeIpEgmUwyNTWFxWLBbrdjt9vRarUL3cS3TC6XUwfXfJS+SNLFbJ0RLBUnT55kdHSUyclJWlpaaGpqwmKxIEkSiUSCiYkJZmZmyOVy5HI5MpkMvb299PX1sXPnTnUM6HQ69cdkMlFXV0dlZSU1NTU4nU50OrFen8mCfiP5fJ5IJMLDDz/Mr371KzKZDLlcjnw+Ty6XA8BkMvHxj3+c++67r+Dcrq4ujh07xj//8z9z1VVXcdNNN3H77bfjdDoXsolvGVmWCYfD6HQ6bDabKib5fJ5sNks+n8doNAqReRvw/e9/n1deeYWpqSm2bt3Kli1b+OAHP0g8HufQoUNEIhFSqRTRaJSXX36ZmZkZhoeH8fv9nDp16rzXra2tZXJykjvvvJNVq1bh8XjEeDiDBZdcSZJYvnw56XRaXekVNBoNRqORzs7Os86bmZlhdHQUn8/H4cOHyeVydHZ2IkkSJSUlC93MC5JOp3niiSdwu91s2LABo9GILMsEAgEGBweZmprijjvuwGazLXrblop8Pq/+Pt+qSyaTZDIZkskkBoMBs9mMXq9f8skmyzK5XI5QKEQoFCKbzdLX10cul+M973kPGo0Gk8mEz+djenqa7u5ugsEgoVCISCRCIpEo6POZBAIBXn31VRwOB+FwmBtuuAGLxbJk7pLiMciyfE7LeylYMIFRTMtEIkF1dTVWqxWtVoskSWi1WtWHNZlM2O12otFowflTU1OMj48TDAZJJBKEQiHe+973YjabMRgMGAyGRXWX0uk0O3fupLq6msrKSiwWC7lcjomJCQ4dOsTQ0BA333wzFoulwFKbj0ajQaPRYDAYroib/WbIskwymVQtzTPfA8hmswUugiIg0WiUSCRCIBDA6XRSVlaGTqdbcoHJ5/PqmIzH4wCMjo4SCoXIZDJYrVYcDgfxeJzJyUmOHDlCJBJRj5UkSR13kiSRzWZVNwogEolw6NAhPB4PmUyGVatWqecsBZlMhnQ6TTqdRpKkApdNkiSMRiN6vR6Y+25SqRR6vV7tXzFYMIHZvXs3J06c4Be/+IU6UPP5vKqo8yef0iGNRoMkSUiShM/nU1eZaDRKMpnkW9/6Fm63m9LSUh544AE2bty4UM19UxKJBJlMhj/7sz/jxRdf5N577yWdTmMwGGhubuYDH/gAH/3oR3E6nfT29vL444+zc+dOfD4fJpMJvV6PwWDAZDKxbNky7r33XhobG5fEEnurjI6O8pWvfIXR0VGmp6fV+6e4hIo1cMstt3Drrbeybds21Xp77LHH2LFjBzt37uTuu+/mj/7oj+jo6MBkMi1pnxKJBDMzM6TTafU1g8GA3W6nvLycmpoampqa+OUvf8muXbvw+/0F47Suro7bb7+ddevW4XA42LVrF6+//jp79uwp+JydO3fS3d2N2Wxm48aNXHvttVit1kVbVGRZJp1Os2/fPg4ePMgPfvADZmdnVdHQ6XRUVFTwwAMPqKGJ48eP873vfY+7776bjo4OKisriyIyCyYwPp+PkydP0t3dXTA4zwz0zg+QKh2SJIl4PF7gVmWzWQYGBpiamqKkpISenh48Hg8NDQ1FvXG5XI5Tp04xPDzM8PAwhw4dYmRkhHQ6jV6vR5ZlBgYGGBwcZGZmhq6uLvbu3cvx48eZmZnBYDCg0+lUkUmn07S2tqouVigUwmAwYDQacTqdSxrEno/BYKC+vh6LxUJ5ebl6r3K5XMEi0dTUhNvtLlgdA4EAY2NjjI2NMTs7u4S9KCSbzRKJRMhms+prRqMRq9Wqrty5XI5oNEooFFItE41Gw5o1a/B6veh0OgKBAOFwmJmZGXQ6HdXV1djtdpLJJIODgyQSCQKBAPv378fr9bJ58+azwgPFIp/Pk0wmGRoa4tChQ+zYsYOTJ08Si8XUY/R6PdlsltHRUUZHRzl06BDd3d0cPHgQm81Gf38/brebZcuWsXLlygVt34IJzODgIN3d3QwPD7+p3/pWkWWZoaEhNcj68ssvk0ql+OhHP4rRaFyAFp+NYja+9tprPPPMMzz33HMkk0n1/XQ6zcmTJ3n22WeZmJigoaGB3t5e9bhz9Xu+OZ5KpTh8+DAlJSV4vV5Wr16NxWIpSl8uFo/Hw3//7/9dFRPFuoTfxVskSUKv16upWoV4PE4sFkOWZfR6vZqhWWpSqdRZFozNZsPj8aDRaEin02p8JhKJqMfodDruv/9+AHbt2sW+ffsIBoPMzs5SUVHBxo0bWbZsGRMTE/zf//t/gTlr6emnn6a+vn5R+57JZAgEArz88ss8/vjjvPDCC+c9dnx8nN27d/P5z3+eiYkJstks+/btQ5IkZFnms5/9rBr3XCgW1IIZHx9fcOVWVpjp6WkmJycXRLzOx+zsLC+//DIvvfQSe/bsKRiY8xkYGCAQCKjBvfOJC0A4HObgwYM899xz9Pf3o9Vq0Wq1VFVVXRGTEOaEdXBwkM997nPqaj/fupxvbSrt12g0dHR08MADDzAzM6NaLq+99hrBYBCbzcbKlSv5zGc+szSdYk5gpqenCyyYTCZDNBrlmWeeoba2Fq/XSyqVUt9XEhErVqygrKyM9vZ2dfw98sgjeL1eGhoacLlcBIPBgvM8Hg8ulwuLxbJolunQ0BBdXV1873vfY2xsDEB1TWVZJpPJkM1mmZqa4plnnmHfvn1MTU2RyWRUL0PhN7/5DT6fj6985SvU1NQsSPsWRGBkWVYLmRZaYGRZJpvNkslkyGQyC3rtM0mn06qpPzk5ed7jZmdniUajGAwGNWVts9nUIJriTigxpZmZGXp6ekilUnR0dKiZlitFYJRV8IUXXlC/4/kCo/yr/CjB65mZGa699lqmp6eJx+PIsszo6Cizs7NoNBrC4TB/+Id/iNvtXhJLLZPJqC6SEuS0Wq3YbDb8fj9GoxGTyVQgQDqdDrPZjMfjoba2Fo/HUzBBXS4XZWVlZ907jUaD3W7HbDYvaj3M7OwsPp+Prq4ucrkcGo2GiooKTCYTJpOJgYEBwuEw8Xic4eFhRkdHC/o7f76eOnWKcDjM5z//+QVr34IJzOjoKCMjIwtxuXPicrmoqKgoavxFo9GoKdY3Q8kkzLdwbrrpJtrb26mvr1drKh566CEmJyeJRCLs3buXkZERPvjBD7Js2TJaWlquGIGZmZlhcnJSDW6/VXbv3s2nP/1pJiYmiEQiyLJMLBYjHo8jSRI7d+7k05/+NJ/97GfZtm1b8TpwHhKJBD6fj3Q6jclkYvny5axatYqOjg7WrVtHPp9nbGyswA12OBxUVVXhcDgoKSmhpKREnYTRaBS9Xk9VVRVdXV34/X71PI1Gg9frxeFwLGofY7FYQUbWaDTymc98hjVr1rB+/Xruuecenn32WXXRezMPIJ1OE4vFFtRLuGyBURp+9913s2LFCiKRCHv27OHIkSNnHavRaLBYLKxevZrOzk6y2SzxeJypqSmOHDlCMBikqqqKsrIyPB4P6XRajYusWLGC2traoguM0WgsMG+bmprYsmWLugKHQiEOHTrEiRMngLkBWVNTg8vlQq/Xk0ql8Hq9tLW18dRTTxGJRIhEIgU3d77LsZQoQdyZmRkCgQB6vb6gKPJCJJNJVZjmD0oljpNMJvH5fAUTeDFRYizZbBatVktJSQl6vZ5kMsnx48eJRCIMDw8TCATUc8xmMy6Xq2AMKDGKYDCILMs4nc6zJrZGo8HhcGA2mxe1j6lUikQiUfCazWbDZrNhMpnQaDSqQLrdbsrKyrj++uvV8azRaIhEIvz85z8nmUySSqWuPIGRZZk777yTaDSK3+8nmUzS09NzVgxDo9Fgs9nYuHEj9957L9lsFr/fz7Fjx9RVsK6ujo6ODjo6OkgkEqTTaeLxOJ2dnUXPIEmShMlkKhhcjY2NfOhDH8LlcgGoK958gVmxYoWagg6Hw7S1tbFixQrcbjc+n6/gu1Iya1cCuVyOZDKJ3+8nEAhgtVrPKTBKwFep75ifFYzFYgVxG8V9UmpGotFogUm+mGQyGWZnZ1XXwWazkc/nmZ2dZWhoiOnpaXp7ewsExmg04nA4VCtVq9WqqXolYG+xWIhEIoTDYfU8jUZDSUlJ0RIQ5yOVShUIuHKfFDdtvli43W6WL1/OAw88QHl5ufr62NgYL7zwAlNTUwXxqIXgsgVGGXzT09OcOnWKp59+mrq6Or7yla/w9a9/veDmKSm1kydPsnPnTj74wQ/icrno7+8vEI6qqio2bdpEXV0dBoMBWZYxmUxFL7bTarVnbVzTarUYjUaeffZZAO6//35++9vfqu9bLBbq6urI5XL4/X76+/tVH95gMBTEHmRZvmg3pJhMTEywf/9++vr6iEQifOpTn2LHjh3s37+faDSqCklHRwcrVqzgc5/7nFpzkUgk1In6ox/9iO7ubsrLy1m1ahVr1qzhN7/5DaWlpXzsYx+jo6NjSfqXSCSYmpoinU4TjUZ58cUX0el0aDQaVTTOjO1NTU2RzWZ58MEHWb9+Pe973/sIhUL4fD78fj8vvvgiIyMjdHd3EwqF1PM0Gg3l5eWLXuuklBEoJREwZ4WdS+iUYlclk2kymejp6WFgYIDp6WkSiUSBxbMQLEgMRln5lcClTqfDarVy7bXXMjU1hd/vZ2JiQq11mZycpKenh66uLtVMTSQS6nUsFgsOh4PS0tJFXRGU0vH5IhYOh+nr6yMajWK323E4HGe1KZ/Pqze1rKwMp9OJzWbDarUWiJXiNizVin4mBoNB3aSnuBBVVVU0NjYyNDREKpVSq0NDoRB9fX1q31paWtQAqsViUVfw+vp61qxZw9jYGKWlpXR2di7ZfrL5dTD5fL6gNuR8pFIpwuEwU1NTamYsHo+rlphSjDczM6NaDqWlpWrh3mILTEVFBQ0NDVRXVzM9PU0mk1Frd1555RUqKipYt24dhw4dUs9RXDtFgKenp9VwhEajUUX3QrHIt8KChburqqrQ6/UsW7aMQCDAyMgIX/nKV5iZmeGll17ikUceob+/n3g8Tm9vLzMzM0xMTJBKpRgaGmJychKNRoPT6cRut6v+42JyLgvm1KlTPPzww1xzzTVqIdr8LEE6nSYYDLJmzRrcbjednZ2sXr2alpYW3G53wV4lZTPoUsUkzsTr9eL1egkEAgwPD5PJZGhtbaWyspLnn3+eQCBAKBTi5MmT9Pb28vzzz7NhwwY2b97MX//1X2M0GtVtEDqdjsrKSjo7O7n++uspLy/HZrOxYcOGJetfMpkkEAhclMWYSqVU90hxL6LRKMFgUH1SwHyrHOYsvJUrV7Jx40bq6uoWtA8X4pprrqG6upqdO3eyb98+RkZGMJlMHDt2jG9/+9v86Z/+KTfffDN/9Ed/BPzOLYa58RiNRonH46oIw9wWiFgstiALw4IJjOIWXH/99Wruvbu7m3A4jNVqpaOjA4vFwtGjR4lGo2QyGTViHYvFSCaTuN1u3vve97J8+XK1inIxUTIEGzZsYGZmhh07dhAKhejq6uJd73oXlZWVaLVa6urqWL9+PfF4XI3NdHZ20traCoDT6USSJEpLS7Hb7er1lYCqstflSuHqq6+mvr6eZ599FrPZjNPp5K//+q8ZHx9Xy+N9Ph/ZbJb+/n6i0SgdHR04HA5mZ2dJJpPqYzicTicul4uVK1cu+eMLlHhQTU0Nsixjs9lUS1OxLk0mk1peYDQa1UD/xo0bqa+vB+buZ2NjI/feey9dXV309PRw++23Y7fbSafTbNq0idbWVjo6OpZk86siFIqQGo1Gli9fzqc//Wk2bdqEwWDgb/7mb8jn8+ojKlKpFFqtlqGhIU6dOqWKiyzLRCIRotHolSUwWq0Wi8WiBmJzuRw9PT2qL19VVYVGo8Hv96s7Vc/c8GgwGOjs7KSqqmpJ6ia0Wi0Oh4OWlhbWrFnD4cOHmZ2dZXp6Gq1Wq7qA1dXVrF27lkgkgslkoqysjOrqanX1UrJjSkWsQj6fVwvzriRqamowmUw4nU7Vn9+0aRM+n49YLMbg4KBaTh8MBonFYuzbtw+3243BYFAFxmKxYLFY1BqMpcZisVBbWwvM3Vun04nValVdcEVolA21Sg2Lsm1CmWAWiwWPx8OmTZuAObf5mmuuwePxkEgkWL16NbW1teesj1kMFPe2rKxMnYdutxu73U5tbS1arZY/+IM/ULOFIyMjqoUWDAaZmZkpiLvE4/GzMlOXyoIuMVqtVl3R8/k8N954I0ePHuXXv/41bW1tXHXVVdx77708//zzPP/884yPjxfEI7RaLV6vt2DVX0yUSXLdddfR1tbGwMAA3d3d9PT0EAgE8Pv9yLLMXXfdxX/7b/+t4KbMj8uMjIzQ29vLnj176O/vV1/P5XLqbt4rDbfbzSc/+UmeffZZ9uzZQzgcxuVycdtttxGJRHC73bz66quk02mSySSPPvooer0evV5PMBjEaDTi8XiwWq1L3RWVrVu3cvXVV6t/n1k0eOZr84+bf4ySfr755ptpb2/nhhtu4LbbblPjLcrm3aUqPfB6vXzpS19idnaWRCJBe3u7GktUFrirr75azXwpG1qVkIUyrhWUbRELQdFsWI1Gg8vlora2ltWrVzM7O0swGFSzDLFYjGeeeYZwOKyu9MomwaXeAGi1WikvL+fGG2/E4XCo9Q+K5aE8skBBef6GgrLilZWVMT09DaBmlM4M/F4pKOnNtrY2DAaD2vaDBw+qsaXBwUF145/yBEKtVksmk8FsNl9xfVOszoW6lsvlUse1zWZbkCDoQqDT6dR4XzabxWQynfW4DMWyUbK+NpsNWZZpbm5WU9Mej4fq6moaGhoWLDBfVCdZySrkcjmeeOIJwuEwtbW16pewe/du9WFFig+spBGXEsUnv+2227BYLHR3d6PT6c6b/Tlz5XI4HKrL5Pf7kSQJs9mM3W5X96pcqSxbtoxly5YBc5bYrl272LRpE+Xl5fT19TE0NKTuCldSpDD3HVxpArOQKFmykpKSRQ/kXgilyO9CKM9VcjgcamHoqlWr1IeDrVixgrVr19LW1vb2EBiYm2wdHR34/X41Mr1q1Sqam5vp6+tjfHycaDSKJElUVFQseWBQQaPRUFtby3ve8x6uv/56dd/KWxE/5Zkwn/rUp9i7dy+BQIBcLofX6+VP//RPKSsrW4QeXD7Lly/nb//2b3nkkUcIBoP83d/9nVqt/LnPfY7e3l6Gh4eB38UBFruSVXBpWCwWmpqauPvuu9myZQvxeJy6ujpaWloWNINb9NmsmKnV1dXqE7SUIOBVV11FQ0ODmlW5kp6PAr9TfJfL9ZbL5wG1FqiyspK2tjY1s+ZyuaiqqroiAqBvBavVitFopLa2FrvdTlNTEyaTCVmW2bp1K9XV1YyNjZHJZNQ9PEsVPxNcHEq2TNl3FY/HKSsrW/D7J12gau/KqGm/OC4m0vZO7l/R+6ZsfQgEAqRSKXX/y2UIqLh3c7xj+iYE5u3HFTVIla0D+XxejZ9dhhUq7t0c75i+CYF5+/F7N0jPwzu5f++Yvl3Zj7oXCARva4TACASCoiEERiAQFA0hMAKBoGgIgREIBEVDCIxAICgaQmAEAkHREAIjEAiKxoUK7QQCgeCSERaMQCAoGkJgBAJB0RACIxAIioYQGIFAUDSEwAgEgqIhBEYgEBQNITACgaBoCIERCARFQwiMQCAoGkJgBAJB0RACIxAIioYQGIFAUDSEwAgEgqIhBEYgEBQNITACgaBoXOj/pn47PixG/Oddc7yT+wbv7P69Y/omLBiBQFA0hMAIBIKiIQRGIBAUDSEwAoGgaAiBEQgERUMIjEAgKBpCYAQCQdEQAiMQCIqGEBiBQFA0hMAIBIKiIQRGIBAUjQvtRfq9J5/Pk8vlkCQJSZLQarXnPVaWZWKxGMlkknQ6jdlsRq/XY7FYyGQyyLKMyWRaxNYL3gqZTIZ8Pk8+n0en06HX64G5e5/JZEilUuTzeUwmEzqdDp1OTJu3ivimLkAymWRmZgatVovBYMDj8Zz32EQiwf79+zl+/Dh9fX2sWbOG+vp6Nm7cyOjoKOl0mvb2dnQ6HZJ0Mfv6BMVkcnKSSCRCIpGgvLycyspKtFot8Xic4eFh+vv7icfjdHZ2Ul5eTnl5+VI3+W1DUQQml8uRy+XQaDTqz9sRWZbp7u7msccew+v1UlNTw/ve975z9qevr4+hoSGefPJJTp06xdjYGKlUikQiQWdnJ3v27MHv91NeXo7D4XhbWjL5fF615M713uzsLCaTCbPZvASte3Py+TzxeBy9Xo9OpyuwRGVZJh6Pc+DAASorK5mcnGTXrl3E43Hy+TzHjx8nGAxSVlaG1WrFbrezadMm3G43Ho9HXXxqa2vftmO9WBRFYOLxOPF4HKvVil6vR6/XI8tzO9A1Gs3bZvXO5XIMDQ3x+OOP097ezooVK7jnnnvU9mcyGVVMe3p6OHr0KM8//zyTk5MEg0FMJhNWq5VYLEZXVxcjIyPcddddmEymK1ZglP4ABW6hJEnqe/l8Xl04FHchk8ng8/mw2+04nU71PkuShE6nW9KFRpZl0uk0fr8fo9GI0WjEYDAAoNVqSaVSRKNRjh8/zszMDD6fj//8z/9ULZqjR4/i9/vV70Sv1/Oxj32MlpYWWltbMZvNWK1WqqurhcCcgaRM/PNwSc+l+M53vsPPf/5zvvjFL+L1erFYLMzOziLLMl6vl5KSEux2+6W1+MIs2DNF4vE4s7OzTE5OotFo0Ov1uN1udDodsizzxBNPcPToUV588UVSqRTJZJLx8XGy2SyyLNPW1saqVau499570ev1mEwm2traKCkpoaSk5FKFtqjPFHn11VfZv38/BoMBu92O1+vlqquuwuVyMT09zZNPPskzzzxDa2srzc3NbNu2Da1Wy9TUFH/2Z38GgMlkorKyEpvNRmlpKdu2bWP58uU0NjZeaAIW5XkwQ0NDDAwM8L3vfY+hoSEmJyfVcVhZWUlVVRVms5lIJMLBgwc5dOgQ4XAYWZbRarWk02ny+fzvGilJVFZWUldXxy233MKyZcuoq6tj48aNF4rP/N49D+ayLRhZlslmsxw+fJhAIEA2m2Xv3r309vby/PPP43K5MJlMRKNRzGYzN998M5lMhkgkQiAQIJPJkE6nSafTSJKEx+NRf5aKo0ePMjAwQDgcJpvNks1myefzyLKsruiyLLNr1y76+/vp7+9Ho9GQz+dJp9PA3CCcnZ3l1KlTvPzyy5SUlGA2m+nr61NXvM7OTtxuN16vd8n6ei5kWWZqagq/34/P56OmpgaTyYTBYMBms+F0OkmlUoRCIcbHx0kkEoyPjzM0NEQ2m8VoNFJZWYlOpyOZTJJIJEgkElxgMStaXw4cOMDhw4fp6upiYmKCYDDI7OwsNpuNyclJJiYmsFgs5PN5hoeHVWsF5iwzg8GAXq/HaDSq/QkGgxgMBnp7e/F6vVRUVCx63y6WfD5PNpslHA6TTqdJJpMkk0kymQzxeJx0Ok0mk6GiooKSkpIFscguW2Cy2SyxWIzvfe977N+/n2g0yszMDKFQiK9//esFx9bV1bFixQo0Gg2pVIr9+/cTDAbx+/2EQiE0Gg3XXnstW7ZsYevWrZfbtEvmpz/9KT/84Q/x+/0FK9fFIMsyPp8Pn8/H/v37z3pfp9PxP//n/2TDhg3ccsstl9vkBaO0tJT6+np27typTr62tjYcDge1tbVcddVVWK1Wuru7yefz9PT0MDAwwMjICOFwmEwmg06nY82aNXg8HqamptDpdKRSqUXviyzL5HI5fvrTn/LKK68wNTWlvhcMBgkGg4yMjFzwOna7XbXmfD4fY2NjJBIJfD4fO3fupKGhgfr6+iUR0IshnU4Ti8Xo6ekhEAio4zMUCjEyMkIwGCQUCnHrrbeycuVK7r77bvR6/WWJzGULTDKZZHp6msHBQQYGBsjlcuoqfiaZTEaNynd3d9Pf308ikSCTyZDJZNBoNOqgnpmZ4YYbbsDhcCx6zCaTyZBMJos+YLRa7ZumvReTRCKhxommp6dZtmwZK1euxGq1snr1aiRJ4hvf+AaxWIx0Oo3L5cLlclFXV0c0GlVdYCXdOzExgSRJ2O12HA4Hdrt9Ue9jMpkkGAwyOjrK1NQUsVjsLZ8rSRLNzc3U1tayceNGgsEgkiSxZcsWYrEYfr+f73znO0xPTzM1NcWRI0cwGo3ccMMNaor7SiKXyzE9Pc3rr7/Onj172LNnD5FIRHXrs9msaslks1keffRRDhw4QD6fZ/PmzdTX11/yZ1+WwGQyGWZnZxkeHmZmZoZIJPKmx6fTaU6ePEl3dzeHDx9mYmKCbDarvq9YNjabDYvFQkdHB1qtFpvNdjnNvGhyuVxBu4rFlSQw2WyWQCDA1NQUk5OTOJ1OdDodFouFUChELBZj586dyLKMXq+nubkZWZax2WxEIhHi8bgqyPl8nrGxMWRZxu12q6K0WCu8Uo80Pj7OwYMHmZmZIZPJqO9LkoTL5VKzScrESiQSavystLSUhoYGNm3aRH9/P8lkkpqaGnWMms1m8vk8yWRSdSWV4PhiIcuy+p0qvysir4QuZFkmkUjQ39/PwYMH2bFjB2+88QaJRKLgWhqNRj1fWSxOnjzJypUrL6uNlyww+XyeoaEh9uzZw2OPPcbo6OgFz5mZmeGb3/xmwZdw5jVDoRA7duxg//79WK1W1q9fz6233nqpzbxkFmMymM3mKyabpNVqcblcvPbaa2zfvp1jx46pcTGbzYZer1dTvYlEQs0QzS8+VCZxPp/niSeeUN9/3/vex7XXXktra+uiCGoul6Orq4tnnnmGb37zm2qRo4Jer+ejH/0o1dXVeDwehoaG8Pl87Nixg8nJSWZmZtDpdDidTtrb25mdnWVkZIRf/epXrFixghUrVhT0I5FIEI1Gi96v+ShWhzKX0uk0qVRKjbEkEgnC4bDqCj3yyCOMjIwwMTFxltsvSZJaDLrQruxlCYzP52NwcJDu7m5isZg6SDs7O2ltbVVV8KWXXlLPy2azVFRUUFVVRWlpKSaTCZvNxpEjR/D5fASDQbLZrFoJa7FYFqSjC0lbWxvNzc20tbWh0WjIZrOkUinC4TC7d+8mGAwSDocBMBgMOBwOLBYLGo2GRCKhruYWiwWj0bjEvZnDYDBQXV3NypUr1ZSt4urm83k1Va2k5udzrtKD+ccoFsJiWjDpdFqtxZIkSf3sNWvWsHr1arZu3YperycajdLU1ER5eTn5fJ69e/fi9/sZHx+nr6+PvXv3qgHhjo4OKioqKC8v55577uHQoUM8//zzqit26NAhGhoaqKmpKVrfFOvwxIkTvPrqq2oVslJCkMvlSKVSqpufTCaJRqMMDQ0RDodVcamqquLaa6/F5XKh0Wg4efIkU1NT6k80GqWrq4umpiY0Gg3t7e2XtDhclsBMTk4yOjpKf38/AEajkfLycm688UZuvfVWcrkc27dvLxAYgJqaGtavX099fT1Op5Oqqip1kgaDQWBukDgcjkV3jwA1payYmWe6S83NzWofTSaTmiUZHx9XVwhFYIxGI16vl/LyckwmE9PT02rE3mazqfUYS41Op6OqqorOzk5kWeanP/2p2gclRjYfRVCUFfRMzgwMXmqw/FKRZRmdTofD4SAYDKqfv2rVKu655x7Wrl1LKBTi6NGjauWuXq9nfHycQ4cO4fP5MBgM7Nmzh+rqampqarjxxhuBOVf/7rvvpqysjOeff14Nkh48eBCdTldUgcnlcvT397N9+3b+5V/+5awU+puhFARqNBoaGxv5wAc+QG1tLZIk8dRTT9HX14dOpyMYDBKNRjl69Ch1dXXo9XpaWloWV2Dm77tR0Ov1VFVV0dLSwooVKxgcHDxrAun1et71rnfxJ3/yJ/z0pz9Fq9Vy9dVX8/Of//ysQJzX612SdPUHPvAB1q1bRyKRYOfOnfzgBz8oeH/v3r2cPHmSJ598Ui3a+tCHPoTT6cRisRQE+txuNzfeeCPvete7qKurIxwOY7fbcblceDyeK8aCyefzRCIRNV5SUlJCPB4/Z1xNkiRaW1vJZrOMj4+fNcg1Gg2bN2/GaDTi9/upqKjAarUuWpBXr9ezZcsWKisraW5u5hvf+AY9PT0A7Nq1i0AgwBe+8AVCoRB79uxRK3StVqtq6aRSKfx+P0ePHuWOO+7gmmuuIZVK8fTTT/P444/z2c9+Vt0yEI1GyWazPPzwwwBs2rSpKP1KpVJMT0/zl3/5lwwPD5NKpS7KKrz99tu54447WLFiBR6Ph6qqKn784x/T29tLe3u7WvfT39/P7OwsfX19jIyMXFaG7LKDvPNNYY1Go1apmkyms+IskiSptRQul4vR0VEymQx2u10NmCkrj8ViweFwYLVaL6eJl0R1dTV6vZ433ngDt9vNtddeq6r60NAQ0WhUreXR6/UYDAaOHz+Ox+MhEokUZNEMBgMul0utak4kElRUVNDY2Ljo/XozlO9/cnKSsbExtmzZwszMDMPDw2pNkIJiMlutVrLZLLOzs0QiEQYHB4lEIiSTSZqbmyktLSWfz7N8+fJFL6O3WCw4nU48Hk9B8VsgEGBgYIATJ06QTqdV61SpSnY4HFRVVVFXV4fdbsftdlNWVkZJSQmzs7M4nU6qq6s5fvw4AwMDwNy41mg0OJ3Oom6TUNLMExMTBAIBAHU+tbW1FWzEnJqa4vjx4wXnh8NhxsfHuf766ykvL8dmsxEOh/H7/axZswaj0ai6wkqKP5fLqe6WVqu96I2el2XBKIU5CkoZtVKIdub7Go2mIBaxfft2li9fjslkIh6PEwqFALBarXi9XrxeLy6X61KbeMl4vV6sVivf/OY3qays5H//7//N66+/zvHjx/nBD35AKpVSYy4Kv/rVr7DZbKqvq6D02e/3EwwGeemll7jppptob29f9H69GblcjtnZWbq6uti9ezcPPvggqVSKnTt38o1vfIPDhw+rx2q1Wm699VbWrVvHpk2b1BKFf/3Xf6W7u5uBgQHWr1/PmjVrWLVqFRqNRnVBFrtPZ67ygUCAcDjML37xCzUWWFVVhd1uJ5vN0tDQQCaT4eMf/zhmsxm/368WnLlcLm677TY2bdrEPffcQ1dXF/C7ReTWW29lxYoVRetPPB4vcPcAXC4Xra2t/N3f/R12ux2j0YhGo+HZZ5/lM5/5TMH5r7zyCq+//jpXX321KsDJZJJYLIbZbEaj0aiLvGIsKHM5FAohSRIOh+Oi2nxZFoyyLwVQVf/WW29VS8Jra2u56aabMBgM/PrXv8bn87F8+XKqq6vRarV84AMfIBgM8vnPf56jR4+i0Wiw2+3cddddfOhDH1LTgkuBTqdj8+bNeL1e2traMBqNtLW1UVlZyZEjR+jr6+PEiROqgA4PD6PT6YjH4wWiqtPpMJvNtLa24vV6aWlpueIqd+F37u22bduoqqrihz/8IVVVVWzdupUHHniA7u5udu3axcTEBH6/n+9///u4XC7Ky8tZtmwZdrud8vJyxsbGyOVyPPLII/T09KhV3ADXXnvtoj7qIJfLkUgkzgpK53I5Dh48yKpVq9i0aRPr1q2jrKwMWZbp6OggEAjgcrkIBoMMDw+zevVq9VzlfipxN5hLXGg0GrZt20ZtbW3R+hOLxQiFQgWCWVZWhtfrJZPJsHv3boaHh3nPe96DxWKhpaWF8fFx4vF4wXXmxzbvvvtuVq9ezXPPPUdfXx+9vb3EYjGqqqp497vfTX19PQ6Hg4GBAerq6hZXYHQ6HSaTSa3ybG1tpaOjA4/HgyRJlJSU0NLSgtVqZXR0lMHBQXXLO0B9fT3hcJinnnqKyclJJEnCarWybNkybr755stp2mWj1WppbW3F7XZTWlqKJEm43W7sdruq9rOzs4TDYWKxGJFI5Lx+qsFgoLS0lJqamqIGAC8HjUaDzWajubkZi8XCzp07MRqNeDweVq5cic1mw+/3I8uymmFQeNe73kVDQ4P6t7ILXZmIikW32JWuSgrd6XRSWlpKLpcjmUySSqXw+Xw0NDTgdDqpqKigsrISmLNeleLR2dlZNb6ioNFoMBgMeL1eQqGQKl7l5eU0NDRQUlJStP4oLtJ8C8Zms2Gz2dR9cD09PSQSCaxWq2olh0IhdcOpxWJRLR1AXRx+9KMfMTY2RiAQwGQyUVFRwYYNG7BarZhMpgJ38mK4ZIHRarU0NzernVy3bh2NjY1s2rRJjTYbDAZ1gn79619XTS9l9+qrr77K0aNH6e3tJZ/PqxNxKeIuZ6LX69m6dasamHS5XKr/XVlZycaNG7nttts4evQov/71r5mcnDyreAlQ40lXSkHdhXC73TidTr70pS8RiUTo7e1lenoanU7HRz/6Ud544w3279/PwMAAwWCQiYkJ9uzZw9GjR7Hb7WpswGw2U1FRwebNm1VhWewHNblcLlatWsUnPvEJJicn1dL+gwcPqpP0zBS7su8I5upbysrKChIVyiR9+OGHiUQi6s555WFiyn61YhCNRgkEAmcJjNFoVBMqLS0tpNNpWltb+Yd/+AeOHDnC7OwsFRUVuFwu3G43ra2tav2V0WjEbrdTXV2tipLD4aC+vp4VK1bQ2NioLrCX0q/LEpimpiZ10lVXV6vVkfNRGqZ8+alUSk1tl5WVqfUHer0eq9VKbW1tUVeBi+FMUVBWRGVHtcvlwmKxMDMzo25v6O3tPWfw8O2yjX/+qq/T6chkMurq39DQgNVqpampiVOnTtHV1cVjjz2mbvdQNgIq91vx4ZcKk8mE1+tl3bp1zM7OqlsGlJ/y8nI1ZjEfSZLUkou1a9fidDrPurbD4cBoNGI2mykvL1czMMXMlCk1LfMtwcHBQXK5nDoXvV4ver0es9lMdXW1uv/q1KlTlJSUUFNTc1YszGw2c+utt6rbBcxmszqvbTbbZS0Ml3ymRqOhublZtWLeCkolaH9/P7t376auro54PK5Wi7rdblpaWnC73ZfarEXB6XTidDqpr6/H5XKRyWQYHx9nbGyMyclJtaBJsdiuxP0pb4ZS2ansEFdobW2lra0NWZYZHBzkt7/9Lb/85S/VKlKlzEARmKVOwSsi5/V61a0QoVBI3clfXV2N2Ww+pwgqAnO+p9cpzzlazDotxUWab0309/cTiUS47rrrqKqqUgXEZDLh8XgoKysjHA5z/PhxWltbz1n2YbPZuOuuu4rS5kW1WWVZJplMUlZWVrAyXHXVVfz5n/85GzZsUHeuvl2oqqrizjvvJJPJMDk5Ccw9gtHv95PJZNQ4zlJPtktBicEombz5D9r6+te/Tjab5V/+5V/493//94IsE1DsZ/5cNDqdjtLSUt773vdyyy23kMvlMBqNOByOK/IJfOfCZDLhdDq55pprmJ6eZnp6msnJSXQ6HR6Ph/b2djo6OtSsHcxVnTc0NHD11VdfdIB2IVhUgVHqYNxut5oGy2azbNu2jTVr1lxxqdu3gl6vV4XSYDCwYcMGZmZmCIfDpFIpqqqqKCsre1sKjPI0unOZyA6Hg0wmg8ViobOzE6vVSjweVyuf6+vrKSsrW4JWnx9FZEpLS5e6KZeEy+WioaGBzZs34/P5GBgYUGODlZWVuFyusywqs9msujxLQVGeaPdWUTbTLbALUZSnol1BLPlT0WRZZmhoiJ6eHh5//HHuuusuGhsbGRwcxO/3EwgE1KzZtm3bLubS4t7NccG+DQ0NsW/fPoLBIGazmW3btuF0Opdka81pztm3JRWYN3uI9GUgBukcRe1bNBolEokwMjJCbW2t+uzhVCpFOp3GYDBgsVgu9gn84t7NccG+xeNxZmZmSKfTaLVaNbi7hP+lypUnMEVCDNI53sl9g3d2/94xfXt75E4FAsHbEiEwAoGgaAiBEQgERUMIjEAgKBpCYAQCQdEQAiMQCIqGEBiBQFA0hMAIBIKicaFCO4FAILhkhAUjEAiKhhAYgUBQNITACASCoiEERiAQFA0hMAKBoGgIgREIBEXj/we7JN/wQieULAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 288x288 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "custom_mnist_train[1].dataset.plot_samples(0, \"Client 1 custom channel 1\")\n",
+    "custom_mnist_train[1].dataset.plot_samples(1, \"Client 1 custom channel 2\")\n",
+    "custom_mnist_train[1].dataset.plot_samples(2, \"Client 1 custom channel 3\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CNN(nn.Module):\n",
+    "\n",
+    "    \"\"\"ConvNet -> Max_Pool -> RELU -> ConvNet -> \n",
+    "    Max_Pool -> RELU -> FC -> RELU -> FC -> SOFTMAX\"\"\"\n",
+    "    def __init__(self):\n",
+    "        super(CNN, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(3, 20, 5, 1)\n",
+    "        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n",
+    "        self.fc1 = nn.Linear(4*4*50, 500)\n",
+    "        self.fc2 = nn.Linear(500, 10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = F.relu(self.conv1(x))\n",
+    "        x = F.max_pool2d(x, 2, 2)\n",
+    "        x = F.relu(self.conv2(x))\n",
+    "        x = F.max_pool2d(x, 2, 2)\n",
+    "        x = x.view(-1, 4*4*50)\n",
+    "        x = F.relu(self.fc1(x))\n",
+    "        x = self.fc2(x)\n",
+    "        return x\n",
+    "    \n",
+    "\n",
+    "model_1 = CNN()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Federated training with FedAvg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clients' weights: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n",
+      "====> i: 0 Loss: 2.302333037058512 Server Test Accuracy: 12.0\n",
+      "====> i: 1 Loss: 2.272705078125 Server Test Accuracy: 12.0\n",
+      "====> i: 2 Loss: 2.106392423311869 Server Test Accuracy: 21.333333333333332\n",
+      "====> i: 3 Loss: 1.6988290150960286 Server Test Accuracy: 47.99999999999999\n",
+      "====> i: 4 Loss: 1.215367039044698 Server Test Accuracy: 53.666666666666664\n",
+      "====> i: 5 Loss: 1.1272305647532144 Server Test Accuracy: 58.66666666666666\n",
+      "====> i: 6 Loss: 0.8320387800534566 Server Test Accuracy: 68.33333333333333\n",
+      "====> i: 7 Loss: 0.6955994764963785 Server Test Accuracy: 69.33333333333333\n",
+      "====> i: 8 Loss: 1.1870109736919403 Server Test Accuracy: 62.0\n",
+      "====> i: 9 Loss: 0.6495806078116099 Server Test Accuracy: 68.0\n",
+      "====> i: 10 Loss: 1.100920557975769 Server Test Accuracy: 68.66666666666666\n",
+      "====> i: 11 Loss: 0.5672701994578043 Server Test Accuracy: 73.66666666666666\n",
+      "====> i: 12 Loss: 0.6078662524620692 Server Test Accuracy: 66.66666666666666\n",
+      "====> i: 13 Loss: 0.7772290756305058 Server Test Accuracy: 64.33333333333333\n",
+      "====> i: 14 Loss: 1.3320786456267038 Server Test Accuracy: 68.0\n",
+      "====> i: 15 Loss: 0.8158280352751413 Server Test Accuracy: 65.33333333333333\n",
+      "====> i: 16 Loss: 0.6301640470822651 Server Test Accuracy: 68.0\n",
+      "====> i: 17 Loss: 0.5881374652187029 Server Test Accuracy: 75.99999999999999\n",
+      "====> i: 18 Loss: 0.7799253811438878 Server Test Accuracy: 67.33333333333333\n",
+      "====> i: 19 Loss: 0.7908426423867543 Server Test Accuracy: 58.66666666666666\n",
+      "====> i: 20 Loss: 0.6535146832466125 Server Test Accuracy: 66.66666666666666\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_iter=20\n",
+    "\n",
+    "model_f, loss_hist_FA_niid, acc_hist_FA_niid = FedProx( model_1, custom_mnist_train, \n",
+    "    n_iter, custom_mnist_test, epochs=2, lr=0.1, mu=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_acc_loss(\"FedAvg Synthetic MNIST non-iid\", loss_hist_FA_niid, acc_hist_FA_niid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Federated training with FedProx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Clients' weights: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n",
+      "====> i: 0 Loss: 2.302333037058512 Server Test Accuracy: 12.0\n",
+      "====> i: 1 Loss: 2.2694875399271646 Server Test Accuracy: 11.0\n",
+      "====> i: 2 Loss: 2.205150802930196 Server Test Accuracy: 22.333333333333332\n",
+      "====> i: 3 Loss: 1.9699933926264444 Server Test Accuracy: 38.666666666666664\n",
+      "====> i: 4 Loss: 1.629088004430135 Server Test Accuracy: 46.0\n",
+      "====> i: 5 Loss: 1.3054340283075967 Server Test Accuracy: 51.33333333333333\n",
+      "====> i: 6 Loss: 1.2765230337778726 Server Test Accuracy: 48.66666666666666\n",
+      "====> i: 7 Loss: 1.1138799786567688 Server Test Accuracy: 59.0\n",
+      "====> i: 8 Loss: 0.8983448147773743 Server Test Accuracy: 68.0\n",
+      "====> i: 9 Loss: 0.842544396718343 Server Test Accuracy: 63.33333333333333\n",
+      "====> i: 10 Loss: 0.8188411394755045 Server Test Accuracy: 62.66666666666666\n",
+      "====> i: 11 Loss: 0.7055371999740601 Server Test Accuracy: 70.66666666666666\n",
+      "====> i: 12 Loss: 0.6621905962626139 Server Test Accuracy: 73.0\n",
+      "====> i: 13 Loss: 0.5331746935844421 Server Test Accuracy: 76.0\n",
+      "====> i: 14 Loss: 0.49626907706260676 Server Test Accuracy: 77.33333333333333\n",
+      "====> i: 15 Loss: 0.4725141525268554 Server Test Accuracy: 76.33333333333333\n",
+      "====> i: 16 Loss: 0.4512250522772471 Server Test Accuracy: 78.0\n",
+      "====> i: 17 Loss: 0.47632099191347754 Server Test Accuracy: 77.66666666666666\n",
+      "====> i: 18 Loss: 0.41631517807642615 Server Test Accuracy: 78.33333333333333\n",
+      "====> i: 19 Loss: 0.3920407692591349 Server Test Accuracy: 79.66666666666666\n",
+      "====> i: 20 Loss: 0.6105889678001404 Server Test Accuracy: 72.66666666666666\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_iter=20\n",
+    "\n",
+    "model_f, loss_hist_FP_niid, acc_hist_FP_niid = FedProx( model_1, custom_mnist_train, \n",
+    "    n_iter, custom_mnist_test, epochs=2, lr=0.1, mu=.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_acc_loss(\"FedProx mu=1 Synthetic MNIST non-iid\", loss_hist_FP_niid, acc_hist_FP_niid)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/federated_learning/FedAvg_FedProx_MNISt_iid_and_nidd.ipynb b/federated_learning/FedAvg_FedProx_MNISt_iid_and_nidd.ipynb
deleted file mode 100644
index 2de3e9e632610a7a7347054c7af8251e62801d80..0000000000000000000000000000000000000000
--- a/federated_learning/FedAvg_FedProx_MNISt_iid_and_nidd.ipynb
+++ /dev/null
@@ -1,884 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Example of FedAvg and FedProx for two datasets: MNIST iid and MNIST non-iid\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! curl -o create_MNIST_datasets.py \"https://gitlab.inria.fr/ssilvari/flhd/-/raw/master/federated_learning/create_MNIST_datasets.py?inline=false\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "import torch.optim as optim\n",
-    "\n",
-    "from copy import deepcopy\n",
-    "\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. MNIST iid"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data loading and visualization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First of all, we load the train and test MNIST dataset and randomly split them in 3 non-overlapping datasets. We will use them for the 3 different nodes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from create_MNIST_datasets import get_MNIST_iid\n",
-    "mnist_iid_train_dls, mnist_iid_test_dls = get_MNIST_iid(\n",
-    "    n_samples_train =200, n_samples_test=100, n_clients =3, \n",
-    "    batch_size =25, shuffle =True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following cell we plot some samples from the 3 datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 288x288 with 20 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 288x288 with 20 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 288x288 with 20 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mnist_iid_train_dls[0].dataset.plot_samples(0, \"Client 1\")\n",
-    "mnist_iid_train_dls[1].dataset.plot_samples(0, \"Client 2\")\n",
-    "mnist_iid_train_dls[2].dataset.plot_samples(0, \"Client 3\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, all the digits are represented in each client. Indeed we split the dataset so to have independent and identically distributed (iid) samples."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Classification"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We define a convolutional neural network (CNN) for digits classification. We also define the functions for training, and for computing loss and accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class CNN(nn.Module):\n",
-    "\n",
-    "    \"\"\"ConvNet -> Max_Pool -> RELU -> ConvNet -> \n",
-    "    Max_Pool -> RELU -> FC -> RELU -> FC -> SOFTMAX\"\"\"\n",
-    "    def __init__(self):\n",
-    "        super(CNN, self).__init__()\n",
-    "        self.conv1 = nn.Conv2d(3, 20, 5, 1)\n",
-    "        self.conv2 = nn.Conv2d(20, 50, 5, 1)\n",
-    "        self.fc1 = nn.Linear(4*4*50, 500)\n",
-    "        self.fc2 = nn.Linear(500, 10)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = F.relu(self.conv1(x))\n",
-    "        x = F.max_pool2d(x, 2, 2)\n",
-    "        x = F.relu(self.conv2(x))\n",
-    "        x = F.max_pool2d(x, 2, 2)\n",
-    "        x = x.view(-1, 4*4*50)\n",
-    "        x = F.relu(self.fc1(x))\n",
-    "        x = self.fc2(x)\n",
-    "        return x\n",
-    "    \n",
-    "\n",
-    "model_0 = CNN()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def loss_classifier(predictions,labels):\n",
-    "    \n",
-    "    m = nn.LogSoftmax(dim=1)\n",
-    "    loss = nn.NLLLoss(reduction=\"mean\")\n",
-    "    \n",
-    "    return loss(m(predictions) ,labels.view(-1))\n",
-    "\n",
-    "\n",
-    "def loss_dataset(model, dataset, loss_f):\n",
-    "    \"\"\"Compute the loss of `model` on `dataset`\"\"\"\n",
-    "    loss=0\n",
-    "    \n",
-    "    for idx,(features,labels) in enumerate(dataset):\n",
-    "        \n",
-    "        predictions= model(features)\n",
-    "        loss+=loss_f(predictions,labels)\n",
-    "    \n",
-    "    loss/=idx+1\n",
-    "    return loss\n",
-    "\n",
-    "\n",
-    "def accuracy_dataset(model, dataset):\n",
-    "    \"\"\"Compute the accuracy of `model` on `dataset`\"\"\"\n",
-    "    \n",
-    "    correct=0\n",
-    "    \n",
-    "    for features,labels in iter(dataset):\n",
-    "        \n",
-    "        predictions= model(features)\n",
-    "        \n",
-    "        _,predicted=predictions.max(1,keepdim=True)\n",
-    "        \n",
-    "        correct+=torch.sum(predicted.view(-1,1)==labels.view(-1, 1)).item()\n",
-    "        \n",
-    "    accuracy = 100*correct/len(dataset.dataset)\n",
-    "        \n",
-    "    return accuracy\n",
-    "\n",
-    "\n",
-    "def train_step(model, model_0, mu:int, optimizer, train_data, loss_f):\n",
-    "    \"\"\"Train `model` on one epoch of `train_data`\"\"\"\n",
-    "    \n",
-    "    total_loss=0\n",
-    "    \n",
-    "    for idx, (features,labels) in enumerate(train_data):\n",
-    "        \n",
-    "        optimizer.zero_grad()\n",
-    "        \n",
-    "        predictions= model(features)\n",
-    "        \n",
-    "        loss=loss_f(predictions,labels)\n",
-    "        loss+=mu/2*difference_models_norm_2(model,model_0)\n",
-    "        total_loss+=loss\n",
-    "        \n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "        \n",
-    "    return total_loss/(idx+1)\n",
-    "\n",
-    "\n",
-    "\n",
-    "def local_learning(model, mu:float, optimizer, train_data, epochs:int, loss_f):\n",
-    "    \n",
-    "    model_0=deepcopy(model)\n",
-    "    \n",
-    "    for e in range(epochs):\n",
-    "        local_loss=train_step(model,model_0,mu,optimizer,train_data,loss_f)\n",
-    "        \n",
-    "    return float(local_loss.detach().numpy())\n",
-    "\n",
-    "\n",
-    "def difference_models_norm_2(model_1, model_2):\n",
-    "    \"\"\"Return the norm 2 difference between the two model parameters\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    tensor_1=list(model_1.parameters())\n",
-    "    tensor_2=list(model_2.parameters())\n",
-    "    \n",
-    "    norm=sum([torch.sum((tensor_1[i]-tensor_2[i])**2) \n",
-    "        for i in range(len(tensor_1))])\n",
-    "    \n",
-    "    return norm\n",
-    "\n",
-    "\n",
-    "def set_to_zero_model_weights(model):\n",
-    "    \"\"\"Set all the parameters of a model to 0\"\"\"\n",
-    "\n",
-    "    for layer_weigths in model.parameters():\n",
-    "        layer_weigths.data.sub_(layer_weigths.data)\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Aggregation strategies\n",
-    "\n",
-    "Federated learning requires to define an aggregation strategy, i.e. a method to combine the local models coming from the clients into a global one."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Federated averaging**\n",
-    "\n",
-    "The standard and simplest aggregation strategy is federated averaging ([FedAvg](https://arxiv.org/pdf/1602.05629.pdf)).\n",
-    "\n",
-    "The learning is performed in rounds. At each round, the server samples a set of $m$ clients (out of the total $K$ clients) which will be considered for the current iteration and sends them the current global model.\n",
-    "These clients update the parameters of their local copy of the model by optimizing the loss $F_k$ on their local training data using SGD for $E$ epochs. At the end of the round, the local parameters are sent to the server, which aggregates them by performing a weighted average. The aggregated parameters define the global model for the next round."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**FedProx**\n",
-    "\n",
-    "Another strategy is [FedProx](https://arxiv.org/pdf/1812.06127.pdf), which is a generalization of FedAvg with some modifications to address heterogeneity of data and systems.\n",
-    "\n",
-    "The learning is again performed in rounds. At each round, the server samples a set of $m$ clients and sends them the current global model.\n",
-    "Differently from FedAvg, here the clients optimize a regularized loss with a proximal term. In particular, the new function to minimize is $F_k(\\omega) + \\frac{\\mu}{2}||\\omega - \\omega^t ||^2$, where $F_k$ is the loss, $\\omega$ are the local parameter to optimize, and $\\omega^t$ are the global parameters at time $t$.   \n",
-    "Moreover we run the local optimization for a variable number of epochs according to the system resources (so that slow clients can also contribute to the training with a reduced number of epochs). \n",
-    "As for FedAvg, the local parameters are sent to the server and aggregated."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**NOTE** FedAvg is a particular case of FedProx with $\\mu=0$. So, we just need to implement the code for FedProx, which we will be used also for FedAvg by setting the parameter *mu=0*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def average_models(model, clients_models_hist:list , weights:list):\n",
-    "\n",
-    "\n",
-    "    \"\"\"Creates the new model of a given iteration with the models of the other\n",
-    "    clients\"\"\"\n",
-    "    \n",
-    "    new_model=deepcopy(model)\n",
-    "    set_to_zero_model_weights(new_model)\n",
-    "\n",
-    "    for k,client_hist in enumerate(clients_models_hist):\n",
-    "        \n",
-    "        for idx, layer_weights in enumerate(new_model.parameters()):\n",
-    "\n",
-    "            contribution=client_hist[idx].data*weights[k]\n",
-    "            layer_weights.data.add_(contribution)\n",
-    "            \n",
-    "    return new_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def FedProx(model, training_sets:list, n_iter:int, testing_sets:list, mu=0, \n",
-    "    file_name=\"test\", epochs=5, lr=10**-2, decay=1):\n",
-    "    \"\"\" all the clients are considered in this implementation of FedProx\n",
-    "    Parameters:\n",
-    "        - `model`: common structure used by the clients and the server\n",
-    "        - `training_sets`: list of the training sets. At each index is the \n",
-    "            training set of client \"index\"\n",
-    "        - `n_iter`: number of iterations the server will run\n",
-    "        - `testing_set`: list of the testing sets. If [], then the testing\n",
-    "            accuracy is not computed\n",
-    "        - `mu`: regularization term for FedProx. mu=0 for FedAvg\n",
-    "        - `epochs`: number of epochs each client is running\n",
-    "        - `lr`: learning rate of the optimizer\n",
-    "        - `decay`: to change the learning rate at each iteration\n",
-    "    \n",
-    "    returns :\n",
-    "        - `model`: the final global model \n",
-    "    \"\"\"\n",
-    "        \n",
-    "    loss_f=loss_classifier\n",
-    "    \n",
-    "    #Variables initialization\n",
-    "    K=len(training_sets) #number of clients\n",
-    "    n_samples=sum([len(db.dataset) for db in training_sets])\n",
-    "    weights=([len(db.dataset)/n_samples for db in training_sets])\n",
-    "    print(\"Clients' weights:\",weights)\n",
-    "    \n",
-    "    \n",
-    "    loss_hist=[[float(loss_dataset(model, dl, loss_f).detach()) \n",
-    "        for dl in training_sets]]\n",
-    "    acc_hist=[[accuracy_dataset(model, dl) for dl in testing_sets]]\n",
-    "    server_hist=[[tens_param.detach().numpy() \n",
-    "        for tens_param in list(model.parameters())]]\n",
-    "    models_hist = []\n",
-    "    \n",
-    "    \n",
-    "    server_loss=sum([weights[i]*loss_hist[-1][i] for i in range(len(weights))])\n",
-    "    server_acc=sum([weights[i]*acc_hist[-1][i] for i in range(len(weights))])\n",
-    "    print(f'====> i: 0 Loss: {server_loss} Server Test Accuracy: {server_acc}')\n",
-    "    \n",
-    "    for i in range(n_iter):\n",
-    "        \n",
-    "        clients_params=[]\n",
-    "        clients_models=[]\n",
-    "        clients_losses=[]\n",
-    "        \n",
-    "        for k in range(K):\n",
-    "            \n",
-    "            local_model=deepcopy(model)\n",
-    "            local_optimizer=optim.SGD(local_model.parameters(),lr=lr)\n",
-    "            \n",
-    "            local_loss=local_learning(local_model,mu,local_optimizer,\n",
-    "                training_sets[k],epochs,loss_f)\n",
-    "            \n",
-    "            clients_losses.append(local_loss)\n",
-    "                \n",
-    "            #GET THE PARAMETER TENSORS OF THE MODEL\n",
-    "            list_params=list(local_model.parameters())\n",
-    "            list_params=[tens_param.detach() for tens_param in list_params]\n",
-    "            clients_params.append(list_params)    \n",
-    "            clients_models.append(deepcopy(local_model))\n",
-    "        \n",
-    "        \n",
-    "        #CREATE THE NEW GLOBAL MODEL\n",
-    "        model = average_models(deepcopy(model), clients_params, \n",
-    "            weights=weights)\n",
-    "        models_hist.append(clients_models)\n",
-    "        \n",
-    "        #COMPUTE THE LOSS/ACCURACY OF THE DIFFERENT CLIENTS WITH THE NEW MODEL\n",
-    "        loss_hist+=[[float(loss_dataset(model, dl, loss_f).detach()) \n",
-    "            for dl in training_sets]]\n",
-    "        acc_hist+=[[accuracy_dataset(model, dl) for dl in testing_sets]]\n",
-    "\n",
-    "        server_loss=sum([weights[i]*loss_hist[-1][i] for i in range(len(weights))])\n",
-    "        server_acc=sum([weights[i]*acc_hist[-1][i] for i in range(len(weights))])\n",
-    "\n",
-    "        print(f'====> i: {i+1} Loss: {server_loss} Server Test Accuracy: {server_acc}')\n",
-    "        \n",
-    "\n",
-    "        server_hist.append([tens_param.detach().cpu().numpy() \n",
-    "            for tens_param in list(model.parameters())])\n",
-    "        \n",
-    "        #DECREASING THE LEARNING RATE AT EACH SERVER ITERATION\n",
-    "        lr*=decay\n",
-    "            \n",
-    "    return model, loss_hist, acc_hist"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Federated training with FedAvg"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will now train the model on the 3 clients, using FedAvg aggregation strategy."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**NOTE** In this notebook, during the training, we will consider the 3 clients for each round ($m=K=3$)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Clients' weights: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n",
-      "====> i: 0 Loss: 2.3026487827301025 Server Test Accuracy: 6.0\n",
-      "====> i: 1 Loss: 2.0403614044189453 Server Test Accuracy: 56.0\n",
-      "====> i: 2 Loss: 0.9044532378514607 Server Test Accuracy: 67.0\n",
-      "====> i: 3 Loss: 0.6221261421839396 Server Test Accuracy: 74.66666666666666\n",
-      "====> i: 4 Loss: 0.2780721187591553 Server Test Accuracy: 84.66666666666666\n",
-      "====> i: 5 Loss: 0.13878133396307626 Server Test Accuracy: 90.0\n",
-      "====> i: 6 Loss: 0.09306075672308603 Server Test Accuracy: 91.0\n",
-      "====> i: 7 Loss: 0.06332090869545937 Server Test Accuracy: 90.33333333333333\n",
-      "====> i: 8 Loss: 0.04245084896683693 Server Test Accuracy: 90.66666666666666\n",
-      "====> i: 9 Loss: 0.031204679359992344 Server Test Accuracy: 90.0\n",
-      "====> i: 10 Loss: 0.02209764036039511 Server Test Accuracy: 90.33333333333333\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Train with FedAvg -> FedProx with mu=0\n",
-    "\n",
-    "n_iter=10\n",
-    "\n",
-    "model_f, loss_hist_FA_iid, acc_hist_FA_iid = FedProx( model_0, \n",
-    "    mnist_iid_train_dls, n_iter, mnist_iid_test_dls, epochs =3, \n",
-    "    lr =0.1, mu=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_acc_loss(title:str, loss_hist:list, acc_hist:list):\n",
-    "    plt.figure()\n",
-    "    \n",
-    "    plt.suptitle(title)\n",
-    "\n",
-    "    plt.subplot(1,2,1)\n",
-    "    lines=plt.plot(loss_hist)\n",
-    "    plt.title(\"Loss\")\n",
-    "    plt.legend(lines,[\"C1\", \"C2\", \"C3\"])\n",
-    "\n",
-    "    plt.subplot(1,2,2)\n",
-    "    lines=plt.plot(acc_hist )\n",
-    "    plt.title(\"Accuracy\")\n",
-    "    plt.legend(lines, [\"C1\", \"C2\", \"C3\"])\n",
-    "    \n",
-    "\n",
-    "plot_acc_loss(\"FedAvg MNIST-iid\", loss_hist_FA_iid, acc_hist_FA_iid)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Federated training with FedProx"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will now train the model on the 3 clients, using FedProx aggregation strategy."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**NOTE** We are going to consider the 3 clients in every round ($m=K=3$) and we assume that they have the same system resources (they will run the same number of epochs each round)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Clients' weights: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n",
-      "====> i: 0 Loss: 2.302648862202962 Server Test Accuracy: 6.0\n",
-      "====> i: 1 Loss: 2.256343364715576 Server Test Accuracy: 49.33333333333333\n",
-      "====> i: 2 Loss: 1.960483193397522 Server Test Accuracy: 42.666666666666664\n",
-      "====> i: 3 Loss: 1.4760499000549316 Server Test Accuracy: 53.99999999999999\n",
-      "====> i: 4 Loss: 0.8978065252304077 Server Test Accuracy: 67.0\n",
-      "====> i: 5 Loss: 0.7698407967885335 Server Test Accuracy: 71.0\n",
-      "====> i: 6 Loss: 0.4063149690628052 Server Test Accuracy: 84.33333333333333\n",
-      "====> i: 7 Loss: 0.3871262570222218 Server Test Accuracy: 86.33333333333333\n",
-      "====> i: 8 Loss: 0.34678515791893005 Server Test Accuracy: 84.33333333333333\n",
-      "====> i: 9 Loss: 0.20760734379291534 Server Test Accuracy: 88.0\n",
-      "====> i: 10 Loss: 0.19162407517433167 Server Test Accuracy: 88.33333333333333\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Train with FedProx, mu=1\n",
-    "\n",
-    "n_iter=10\n",
-    "\n",
-    "model_f, loss_hist_FP_iid, acc_hist_FP_iid = FedProx( model_0, \n",
-    "    mnist_iid_train_dls, n_iter, mnist_iid_test_dls, \n",
-    "    epochs =3, lr =0.1, mu =1.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_acc_loss(\"FedProx mu=1. MNIST-iid\", loss_hist_FP_iid, acc_hist_FP_iid)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Conclusion and comparison"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We notice that with both aggregation strategies the model converges to a high value of accuracy (consider that we are working with 600 images only). In this case where we have iid dataset across clients, the accuracy of FedAvg and FedProx are comparable."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. MNIST non-iid"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We create a dataset of noisy digits and split it using a non-iid sampling. We assign digits 012, 345, 678 to the 3 different clients."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "C= {\n",
-    "     'n_samples_train': 200,\n",
-    "     'font':'InconsolataN',\n",
-    "     'tilt': [0, 45, 90],\n",
-    "     'std_tilt': 10, #std on the tilt,\n",
-    "     'seed':0\n",
-    "     }\n",
-    "C['n_samples']= int(1.5 * C['n_samples_train']) #20% more for the testing set\n",
-    "\n",
-    "C1 =deepcopy(C)\n",
-    "C1['numbers'] = [1, 2, 3]\n",
-    "\n",
-    "C2=deepcopy(C)\n",
-    "C2['numbers'] = [4, 5, 6]\n",
-    "\n",
-    "C3=deepcopy(C)\n",
-    "C3['numbers']= [7, 8, 9]\n",
-    "\n",
-    "clients = [C1, C2, C3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "from create_MNIST_datasets import get_MNIST_niid\n",
-    "custom_mnist_train, custom_mnist_test = get_MNIST_niid(\n",
-    "    clients, batch_size =10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 288x288 with 20 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 288x288 with 20 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 288x288 with 20 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "custom_mnist_train[1].dataset.plot_samples(0, \"Client 1 custom channel 1\")\n",
-    "custom_mnist_train[1].dataset.plot_samples(1, \"Client 1 custom channel 2\")\n",
-    "custom_mnist_train[1].dataset.plot_samples(2, \"Client 1 custom channel 3\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Federated training with FedAvg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Clients' weights: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n",
-      "====> i: 0 Loss: 2.2996485233306885 Server Test Accuracy: 10.333333333333332\n",
-      "====> i: 1 Loss: 2.1265652179718018 Server Test Accuracy: 40.0\n",
-      "====> i: 2 Loss: 1.5005696614583333 Server Test Accuracy: 50.0\n",
-      "====> i: 3 Loss: 0.8817243576049804 Server Test Accuracy: 68.33333333333333\n",
-      "====> i: 4 Loss: 0.7961297035217285 Server Test Accuracy: 73.0\n",
-      "====> i: 5 Loss: 0.47359542051951087 Server Test Accuracy: 80.0\n",
-      "====> i: 6 Loss: 0.5291634003321329 Server Test Accuracy: 75.66666666666666\n",
-      "====> i: 7 Loss: 0.3741661409536997 Server Test Accuracy: 82.33333333333333\n",
-      "====> i: 8 Loss: 0.7093196461598077 Server Test Accuracy: 67.66666666666666\n",
-      "====> i: 9 Loss: 0.8421186978618304 Server Test Accuracy: 65.33333333333333\n",
-      "====> i: 10 Loss: 0.7133570685982704 Server Test Accuracy: 65.66666666666666\n",
-      "====> i: 11 Loss: 1.4599915978809197 Server Test Accuracy: 55.33333333333333\n",
-      "====> i: 12 Loss: 0.6197358941038449 Server Test Accuracy: 78.33333333333333\n",
-      "====> i: 13 Loss: 0.2078131486972173 Server Test Accuracy: 91.0\n",
-      "====> i: 14 Loss: 0.1419557308157285 Server Test Accuracy: 90.0\n",
-      "====> i: 15 Loss: 0.11648576582471529 Server Test Accuracy: 91.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_iter=15\n",
-    "\n",
-    "model_f, loss_hist_FA_niid, acc_hist_FA_niid = FedProx( model_0, custom_mnist_train, \n",
-    "    n_iter, custom_mnist_test, epochs=3, lr=0.1, mu=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_acc_loss(\"FedAvg MNIST non-iid\", loss_hist_FA_niid, acc_hist_FA_niid)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Federated training with FedProx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Clients' weights: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]\n",
-      "====> i: 0 Loss: 2.2996485233306885 Server Test Accuracy: 10.333333333333332\n",
-      "====> i: 1 Loss: 2.1950867970784502 Server Test Accuracy: 23.0\n",
-      "====> i: 2 Loss: 1.8597861528396606 Server Test Accuracy: 30.0\n",
-      "====> i: 3 Loss: 1.4968410730361938 Server Test Accuracy: 39.0\n",
-      "====> i: 4 Loss: 1.1217716534932454 Server Test Accuracy: 54.33333333333333\n",
-      "====> i: 5 Loss: 1.190993030865987 Server Test Accuracy: 54.33333333333333\n",
-      "====> i: 6 Loss: 0.8669322927792866 Server Test Accuracy: 66.66666666666666\n",
-      "====> i: 7 Loss: 0.714597225189209 Server Test Accuracy: 70.66666666666666\n",
-      "====> i: 8 Loss: 0.6316446661949158 Server Test Accuracy: 73.66666666666666\n",
-      "====> i: 9 Loss: 0.5291367868582407 Server Test Accuracy: 75.0\n",
-      "====> i: 10 Loss: 0.4781126379966736 Server Test Accuracy: 78.0\n",
-      "====> i: 11 Loss: 0.4492119451363881 Server Test Accuracy: 76.0\n",
-      "====> i: 12 Loss: 0.3932275573412577 Server Test Accuracy: 79.33333333333333\n",
-      "====> i: 13 Loss: 0.3988556365172068 Server Test Accuracy: 77.66666666666666\n",
-      "====> i: 14 Loss: 0.38684090971946716 Server Test Accuracy: 76.66666666666666\n",
-      "====> i: 15 Loss: 0.3316544542709986 Server Test Accuracy: 80.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "n_iter=15\n",
-    "\n",
-    "model_f, loss_hist_FP_niid, acc_hist_FP_niid = FedProx( model_0, custom_mnist_train, \n",
-    "    n_iter, custom_mnist_test, epochs=3, lr=0.1, mu=.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_acc_loss(\"FedProx mu=1 MNIST non-iid\", loss_hist_FP_niid, acc_hist_FP_niid)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Conclusion and comparison\n",
-    "\n",
-    "Also in the non-iid case both aggregation methods yields good results in term of accuracy. FedProx is performing slighly better, since it is able to compensate for the heterogeneity of the data across the different clients"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/federated_learning/create_MNIST_datasets.py b/federated_learning/create_MNIST_datasets.py
index be42fe285269503b934605f459b3c9917697a976..e0d49b052dd8180e4c982a1558acdae216147a41 100644
--- a/federated_learning/create_MNIST_datasets.py
+++ b/federated_learning/create_MNIST_datasets.py
@@ -1,358 +1,94 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-"""This code to create a custom MNIST dataset was made possible thanks to
- https://github.com/LaRiffle/collateral-learning . 
- 
-Important to know that aside the tampering I did on the build_dataset function
-for my own application, I also had to change rgba_to_rgb. Indeed, the function
-was working as desired on Jupyter but not on Spyder. Do not ask me why !
-"""
-
-
-
-import matplotlib.pyplot as plt
-import numpy as np
-from scipy.ndimage.interpolation import map_coordinates
-from scipy.ndimage.filters import gaussian_filter
-import pickle
-from torch.utils.data import Dataset, DataLoader
 import torch
-import math
-import os
-
-
-import torchvision.datasets as datasets
-import torchvision.transforms as transforms
-from torch.utils.data import Dataset,DataLoader
-
-class MNISTiidDataset(Dataset):
-    """Convert the MNIST pkl file into a Pytorch Dataset"""    
-   
-    def __init__(self, lower_bd_idx:int, upper_bd_idx:int):
-    
-        self.dataset =datasets.MNIST(root='./data', train=True, 
-            download =True, transform=transforms.ToTensor())
-        self.lower_bd_idx = lower_bd_idx
-        self.upper_bd_idx = upper_bd_idx
-       
-   
-    def __len__(self):
-        return (self.upper_bd_idx - self.lower_bd_idx )
-   
-    def __getitem__(self, sample_idx):
-        
-        #3D input 1x28x28
-        img =torch.Tensor(np.array(self.dataset[self.lower_bd_idx 
-            +sample_idx][0]))
-        multi_channel_img = torch.cat((img, img, img), 0)
-
-        label =torch.ones((1,), dtype=torch.long)
-        label =label.new_tensor(self.dataset[self.lower_bd_idx 
-            +sample_idx][1])
-
-
-        return multi_channel_img, label
-    
-    
-    def plot_samples(self, channel:int, title=None, plot_name="", 
-        n_examples =20):
-    
-        n_rows = int(n_examples / 5)
-        plt.figure(figsize=(1* n_rows, 1*n_rows))
-        if title: plt.suptitle(title)
-            
-        for idx in range(n_examples):
-            
-            X, y = self[idx]
-
-            ax = plt.subplot(n_rows, 5, idx + 1)
-
-            image = 255 - X[channel].view((28,28))
-            ax.imshow(image, cmap='gist_gray')
-            ax.axis("off")
-
-        if plot_name!="":plt.savefig(f"plots/"+plot_name+".png")
-
-        plt.tight_layout()
-   
-   
-def get_MNIST_iid(n_samples_train:int, n_samples_test:int, n_clients:int,
-    batch_size:int, shuffle:bool):
-    """Returns the dataloader of each client"""
-
-    list_dls_train, list_dls_test =list(), list()
-    low_bd =0
-    for k in range(n_clients):
-        dataset_object =MNISTiidDataset(low_bd, low_bd +n_samples_train)      
-        dataset_dl = DataLoader(dataset_object, batch_size=batch_size,
-            shuffle=shuffle)  
-        list_dls_train.append(dataset_dl)
-    
-        low_bd +=n_samples_train
-        
-        dataset_object =MNISTiidDataset(low_bd, low_bd +n_samples_test)      
-        dataset_dl = DataLoader(dataset_object, batch_size=batch_size,
-            shuffle=shuffle)  
-        list_dls_test.append(dataset_dl)
-    
-        low_bd +=n_samples_test
-
-    return list_dls_train, list_dls_test
-
-
-
-
-
-"""PLOT FUNCTIONS TO VISUALIZE THE FONTS AND DATASETS"""
-def show_original_font(family:str):
-    """Plot the original numbers used to create the dataset"""
-    
-    plt.figure()
-    plt.title(family)
-    plt.text(0, 0.4, '1234567890', size=50, family=family)
-    plt.axis("off")
-    plt.tight_layout()
-    plt.savefig(f"plots/{family}_original.png") 
-    
-    
-def convert_to_rgb(data):
-    
-    def rgba_to_rgb(rgba):
-        return rgba[1:]
-
-    return np.apply_along_axis(rgba_to_rgb, 2, data) 
-
-
-
-def elastic_transform(image, alpha, sigma, random_state=None):
-    """Elastic deformation of images as described in [Simard2003]_.
-    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
-       Convolutional Neural Networks applied to Visual Document Analysis", in
-       Proc. of the International Conference on Document Analysis and
-       Recognition, 2003.
-    """
-    if random_state is None:
-        random_state = np.random.RandomState(None)
-
-    shape = np.array([28, 28, 3], dtype =int)
-    dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
-    dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
-
-    x, y, z = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), np.arange(shape[2]))
-    #print(x.shape, y.shape, z.shape)
-    #print(dx.shape, dy.shape)
-    #x, y, z = x[:28, :28, :3], y[:28, :28, :3], z[:28, :28, :3]
-    #dx, dy = dx[:28, :28, :3], dy[:28, :28, :3]
-    indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))
-
-    distored_image = map_coordinates(image, indices, order=1, mode='reflect')
-    return distored_image.reshape(shape)
+from torchvision import datasets
+from torchvision import transforms
+import matplotlib.pyplot as plt
 
+def non_iid_split(dataset, nb_nodes, n_samples_per_node, batch_size, shuffle, shuffle_digits=False):
+    assert(nb_nodes>0 and nb_nodes<=10)
 
+    digits=torch.arange(10) if shuffle_digits==False else torch.randperm(10, generator=torch.Generator().manual_seed(0))
 
-def center(data):
-    # Inverse black and white
-    wb_data = np.ones(data.shape) * 255 - data
-    
-    # normalize
-    prob_data = wb_data / np.sum(wb_data)
-    
-    # marginal distributions
-    dx = np.sum(prob_data, (1, 2))
-    dy = np.sum(prob_data, (0, 2))
+    # split the digits in a fair way
+    digits_split=list()
+    i=0
+    for n in range(nb_nodes, 0, -1):
+        inc=int((10-i)/n)
+        digits_split.append(digits[i:i+inc])
+        i+=inc
 
-    # expected values
-    (X, Y, Z) = prob_data.shape
-    cx = np.sum(dx * np.arange(X))
-    cy = np.sum(dy * np.arange(Y))
-    
-    # Check bounds
-    assert cx > X/4 and cx < 3 * X/4, f"ERROR: {cx} > {X/4} and {cx} < {3 * X/4}"
-    assert cy > Y/4 and cy < 3 * Y/4, f"ERROR: {cy} > {Y/4} and {cy} < {3 * Y/4}"
-    
-    # print('Center', cx, cy)
-    
-    x_min = int(round(cx - X/4))
-    x_max = int(round(cx + X/4))
-    y_min = int(round(cy - Y/4))
-    y_max = int(round(cy + Y/4))
-    
-    return data[x_min:x_max, y_min:y_max, :]
-   
+    # load and shuffle nb_nodes*n_samples_per_node from the dataset
+    loader = torch.utils.data.DataLoader(dataset,
+                                        batch_size=nb_nodes*n_samples_per_node,
+                                        shuffle=shuffle)
+    dataiter = iter(loader)
+    images_train_mnist, labels_train_mnist = dataiter.next()
 
+    data_splitted=list()
+    for i in range(nb_nodes):
+        idx=torch.stack([y_ == labels_train_mnist for y_ in digits_split[i]]).sum(0).bool() # get indices for the digits
+        data_splitted.append(torch.utils.data.DataLoader(torch.utils.data.TensorDataset(images_train_mnist[idx], labels_train_mnist[idx]), batch_size=batch_size, shuffle=shuffle))
 
-def create_transformed_digit(digit:int, size:float, rotation:float, family:str):
-    
-    fig = plt.figure(figsize=(2,2), dpi=28)
-    fig.text(0.4, 0.4, str(digit), size=size, rotation=rotation, family=family)
-
-    # Rm axes, draw and get the rgba shape of the digit
-    plt.axis('off')
-    fig.canvas.draw()
-    data = np.frombuffer(fig.canvas.tostring_argb(), dtype=np.uint8)
-    data = data.reshape(fig.canvas.get_width_height()[::-1] + (4,))
-
-    # Convert to rgb
-    data = convert_to_rgb(data)
+    return data_splitted
 
-    # Center the data
-    data = center(data)
 
-    # Apply an elastic deformation
-    data = elastic_transform(data, alpha=991, sigma=9)
 
-    # Free memory space
-    plt.close(fig)
+def iid_split(dataset, nb_nodes, n_samples_per_node, batch_size, shuffle):
+    # load and shuffle n_samples_per_node from the dataset
+    loader = torch.utils.data.DataLoader(dataset,
+                                        batch_size=n_samples_per_node,
+                                        shuffle=shuffle)
+    dataiter = iter(loader)
     
-    return data
+    data_splitted=list()
+    for _ in range(nb_nodes):
+        data_splitted.append(torch.utils.data.DataLoader(torch.utils.data.TensorDataset(*(dataiter.next())), batch_size=batch_size, shuffle=shuffle))
 
-    
+    return data_splitted
 
-def save_dataset(dataset_name:str, array_X:np.array, array_y:np.array):
-    
-    with open(f'{dataset_name}.pkl', 'wb') as output:
-        dataset = array_X, array_y
-        pickle.dump(dataset, output)
-        
-        
-        
-def build_dataset(C:dict, std_size=2.5):
-    """build a dataset with `dataset_size` according to the chosen font
-    and deformation. Only digits in `datasets_digits` are in the created 
-    dataset."""
-    
-    numbers_str="".join([str(n) for n in C['numbers']])
-    file_name=f"{C['font']}_{numbers_str}_{C['n_samples']}_{C['tilt']}_{C['seed']}"    
-    
-    if os.path.isfile(f"{file_name}.pkl"):
-        return pickle.load(open(f"{file_name}.pkl", "rb"))
-    
-    
-    if C['seed']: np.random.seed(C['seed'])
-    
-    #Make a plot of each original digit to know what they look like
-#    show_original_font(C['font'])
-    
-    list_X = []
-    list_y= []
-    
-    for i in range(C['n_samples']):
-        
-        if i%10 == 0: print(round(i / C['n_samples'] * 100), '%')
-        
-        X = np.zeros((3, 28, 28 ))
-        #Choosing a number at this step and its transformation characteristics
-        digit = C["numbers"][np.random.randint(len(C["numbers"]))]
 
-        for j, tilt in enumerate(C['tilt']):
-        	rotation = tilt + np.random.normal(0, C['std_tilt'])
-        	size = 60 + np.random.normal(0, std_size)         	
+def  get_MNIST(type="iid", n_samples_train=200, n_samples_test=100, n_clients=3, batch_size=25, shuffle=True):
+    dataset_loaded_train = datasets.MNIST(
+            root="./data",
+            train=True,
+            download=True,
+            transform=transforms.ToTensor()
+    )
+    dataset_loaded_test = datasets.MNIST(
+            root="./data",
+            train=False,
+            download=True,
+            transform=transforms.ToTensor()
+    )
 
-        	X_tilt=create_transformed_digit(digit, size, rotation, C['font'])
+    if type=="iid":
+        train=iid_split(dataset_loaded_train, n_clients, n_samples_train, batch_size, shuffle)
+        test=iid_split(dataset_loaded_test, n_clients, n_samples_test, batch_size, shuffle)
+    elif type=="non_iid":
+        train=non_iid_split(dataset_loaded_train, n_clients, n_samples_train, batch_size, shuffle)
+        test=non_iid_split(dataset_loaded_test, n_clients, n_samples_test, batch_size, shuffle)
+    else:
+        train=[]
+        test=[]
 
-        	X[j] = X_tilt[:, :, j]
+    return train, test
 
-        # Append data to the datasets
-        #list_X.append(X[:,:,0])
-        list_X.append(X)
-        list_y.append([digit])
-    
-    #save the dataset
-    dataset = (np.array(list_X), np.array(list_y))
-    pickle.dump(dataset, open(f'{file_name}.pkl', 'wb'))
-    
-    return np.array(list_X), np.array(list_y)
 
- 
-class Ds_MNIST_modified(Dataset):
-    """Creation of the dataset used to create the clients' dataloader"""
-    
-    def __init__(self, features, labels):
-        self.features = features
-        self.labels = labels
     
-    def __len__(self): return len(self.features)
+def plot_samples(data, channel:int, title=None, plot_name="", n_examples =20):
 
-    def __getitem__(self,idx):
+    n_rows = int(n_examples / 5)
+    plt.figure(figsize=(1* n_rows, 1*n_rows))
+    if title: plt.suptitle(title)
+    X, y= data
+    for idx in range(n_examples):
         
-        #3D input 1x28x28
-        sample_x = torch.Tensor(self.features[idx])
-        sample_y = self.labels[idx]
-        
-        return sample_x, sample_y
-
-
-    def plot_samples(self, channel:int, title=None, plot_name="", 
-        n_examples =20):
-    
-        n_rows = int(n_examples / 5)
-        plt.figure(figsize=(1* n_rows, 1*n_rows))
-        if title: plt.suptitle(title)
-            
-        for idx in range(n_examples):
-            
-            X, y = self[idx]
-
-            ax = plt.subplot(n_rows, 5, idx + 1)
-
-            image = 255 - X.view((-1, 28, 28))[channel]
-            ax.imshow(image, cmap='gist_gray')
-            ax.axis("off")
-
-        if plot_name!="":plt.savefig(f"plots/"+plot_name+".png")
+        ax = plt.subplot(n_rows, 5, idx + 1)
 
-        plt.tight_layout()
+        image = 255 - X[idx, channel].view((28,28))
+        ax.imshow(image, cmap='gist_gray')
+        ax.axis("off")
 
-    
-    
-
-def get_MNIST_niid(clients, batch_size:int, shuffle=True):
-    """function returning a list of training and testing dls."""
-    
-    list_train, list_test = [], []
-    
-    for C in clients:
-        X, y = build_dataset(C)
-        X = (255 - X) /255
-
-        X_train, y_train = X[:C['n_samples_train']], y[:C['n_samples_train']]
-        X_test, y_test = X[C['n_samples_train']:], y[C['n_samples_train']:]
-            
-        train_ds = Ds_MNIST_modified(X_train, y_train)         
-        train_dl = DataLoader(train_ds, batch_size = batch_size, shuffle = shuffle)
-        list_train.append(train_dl)
-         
-        test_ds = Ds_MNIST_modified(X_test, y_test)         
-        test_dl = DataLoader(test_ds, batch_size = batch_size, shuffle = shuffle)  
-        list_test.append(test_dl)
-        
-    return list_train, list_test
-          
-    
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
-        
+    if plot_name!="":plt.savefig(f"plots/"+plot_name+".png")
 
+    plt.tight_layout()
+   
\ No newline at end of file
diff --git a/federated_learning/create_synthetic_MNIST_datasets.py b/federated_learning/create_synthetic_MNIST_datasets.py
new file mode 100644
index 0000000000000000000000000000000000000000..82f9b2d91afe258bb4b0ec49e64523a48b4ce43e
--- /dev/null
+++ b/federated_learning/create_synthetic_MNIST_datasets.py
@@ -0,0 +1,253 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+"""This code to create a custom MNIST dataset was made possible thanks to
+ https://github.com/LaRiffle/collateral-learning . 
+ 
+Important to know that aside the tampering I did on the build_dataset function
+for my own application, I also had to change rgba_to_rgb. Indeed, the function
+was working as desired on Jupyter but not on Spyder. Do not ask me why !
+"""
+
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.ndimage.interpolation import map_coordinates
+from scipy.ndimage.filters import gaussian_filter
+import pickle
+import torch
+import math
+import os
+
+
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+from torch.utils.data import Dataset,DataLoader
+
+
+"""PLOT FUNCTIONS TO VISUALIZE THE FONTS AND DATASETS"""
+def show_original_font(family:str):
+    """Plot the original numbers used to create the dataset"""
+    
+    plt.figure()
+    plt.title(family)
+    plt.text(0, 0.4, '1234567890', size=50, family=family)
+    plt.axis("off")
+    plt.tight_layout()
+    plt.savefig(f"plots/{family}_original.png") 
+    
+    
+def convert_to_rgb(data):
+    
+    def rgba_to_rgb(rgba):
+        return rgba[1:]
+
+    return np.apply_along_axis(rgba_to_rgb, 2, data) 
+
+
+
+def elastic_transform(image, alpha, sigma, random_state=None):
+    """Elastic deformation of images as described in [Simard2003]_.
+    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
+       Convolutional Neural Networks applied to Visual Document Analysis", in
+       Proc. of the International Conference on Document Analysis and
+       Recognition, 2003.
+    """
+    if random_state is None:
+        random_state = np.random.RandomState(None)
+
+    shape = np.array([28, 28, 3], dtype =int)
+    dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
+    dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha
+
+    x, y, z = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), np.arange(shape[2]))
+    #print(x.shape, y.shape, z.shape)
+    #print(dx.shape, dy.shape)
+    #x, y, z = x[:28, :28, :3], y[:28, :28, :3], z[:28, :28, :3]
+    #dx, dy = dx[:28, :28, :3], dy[:28, :28, :3]
+    indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))
+
+    distored_image = map_coordinates(image, indices, order=1, mode='reflect')
+    return distored_image.reshape(shape)
+
+
+
+def center(data):
+    # Inverse black and white
+    wb_data = np.ones(data.shape) * 255 - data
+    
+    # normalize
+    prob_data = wb_data / np.sum(wb_data)
+    
+    # marginal distributions
+    dx = np.sum(prob_data, (1, 2))
+    dy = np.sum(prob_data, (0, 2))
+
+    # expected values
+    (X, Y, Z) = prob_data.shape
+    cx = np.sum(dx * np.arange(X))
+    cy = np.sum(dy * np.arange(Y))
+    
+    # Check bounds
+    assert cx > X/4 and cx < 3 * X/4, f"ERROR: {cx} > {X/4} and {cx} < {3 * X/4}"
+    assert cy > Y/4 and cy < 3 * Y/4, f"ERROR: {cy} > {Y/4} and {cy} < {3 * Y/4}"
+    
+    # print('Center', cx, cy)
+    
+    x_min = int(round(cx - X/4))
+    x_max = int(round(cx + X/4))
+    y_min = int(round(cy - Y/4))
+    y_max = int(round(cy + Y/4))
+    
+    return data[x_min:x_max, y_min:y_max, :]
+   
+
+
+def create_transformed_digit(digit:int, size:float, rotation:float, family:str):
+    
+    fig = plt.figure(figsize=(2,2), dpi=28)
+    fig.text(0.4, 0.4, str(digit), size=size, rotation=rotation, family=family)
+
+    # Rm axes, draw and get the rgba shape of the digit
+    plt.axis('off')
+    fig.canvas.draw()
+    data = np.frombuffer(fig.canvas.tostring_argb(), dtype=np.uint8)
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (4,))
+
+    # Convert to rgb
+    data = convert_to_rgb(data)
+
+    # Center the data
+    data = center(data)
+
+    # Apply an elastic deformation
+    data = elastic_transform(data, alpha=991, sigma=9)
+
+    # Free memory space
+    plt.close(fig)
+    
+    return data
+
+    
+
+def save_dataset(dataset_name:str, array_X:np.array, array_y:np.array):
+    
+    with open(f'{dataset_name}.pkl', 'wb') as output:
+        dataset = array_X, array_y
+        pickle.dump(dataset, output)
+        
+        
+        
+def build_dataset(C:dict, std_size=2.5):
+    """build a dataset with `dataset_size` according to the chosen font
+    and deformation. Only digits in `datasets_digits` are in the created 
+    dataset."""
+    
+    numbers_str="".join([str(n) for n in C['numbers']])
+    file_name=f"{C['font']}_{numbers_str}_{C['n_samples']}_{C['tilt']}_{C['seed']}"    
+    
+    if os.path.isfile(f"{file_name}.pkl"):
+        return pickle.load(open(f"{file_name}.pkl", "rb"))
+    
+    
+    if C['seed']: np.random.seed(C['seed'])
+    
+    #Make a plot of each original digit to know what they look like
+#    show_original_font(C['font'])
+    
+    list_X = []
+    list_y= []
+    
+    for i in range(C['n_samples']):
+        
+        if i%10 == 0: print(round(i / C['n_samples'] * 100), '%')
+        
+        X = np.zeros((3, 28, 28 ))
+        #Choosing a number at this step and its transformation characteristics
+        digit = C["numbers"][np.random.randint(len(C["numbers"]))]
+
+        for j, tilt in enumerate(C['tilt']):
+        	rotation = tilt + np.random.normal(0, C['std_tilt'])
+        	size = 60 + np.random.normal(0, std_size)         	
+
+        	X_tilt=create_transformed_digit(digit, size, rotation, C['font'])
+
+        	X[j] = X_tilt[:, :, j]
+
+        # Append data to the datasets
+        #list_X.append(X[:,:,0])
+        list_X.append(X)
+        list_y.append([digit])
+    
+    #save the dataset
+    dataset = (np.array(list_X), np.array(list_y))
+    pickle.dump(dataset, open(f'{file_name}.pkl', 'wb'))
+    
+    return np.array(list_X), np.array(list_y)
+
+ 
+class Ds_MNIST_modified(Dataset):
+    """Creation of the dataset used to create the clients' dataloader"""
+    
+    def __init__(self, features, labels):
+        self.features = features
+        self.labels = labels
+    
+    def __len__(self): return len(self.features)
+
+    def __getitem__(self,idx):
+        
+        #3D input 1x28x28
+        sample_x = torch.Tensor(self.features[idx])
+        sample_y = self.labels[idx]
+        
+        return sample_x, sample_y
+
+
+    def plot_samples(self, channel:int, title=None, plot_name="", 
+        n_examples =20):
+    
+        n_rows = int(n_examples / 5)
+        plt.figure(figsize=(1* n_rows, 1*n_rows))
+        if title: plt.suptitle(title)
+            
+        for idx in range(n_examples):
+            
+            X, y = self[idx]
+
+            ax = plt.subplot(n_rows, 5, idx + 1)
+
+            image = 255 - X.view((-1, 28, 28))[channel]
+            ax.imshow(image, cmap='gist_gray')
+            ax.axis("off")
+
+        if plot_name!="":plt.savefig(f"plots/"+plot_name+".png")
+
+        plt.tight_layout()
+
+    
+    
+
+def get_synth_MNIST(clients, batch_size:int, shuffle=True):
+    """function returning a list of training and testing dls."""
+    
+    list_train, list_test = [], []
+    
+    for C in clients:
+        X, y = build_dataset(C)
+        X = (255 - X) /255
+
+        X_train, y_train = X[:C['n_samples_train']], y[:C['n_samples_train']]
+        X_test, y_test = X[C['n_samples_train']:], y[C['n_samples_train']:]
+            
+        train_ds = Ds_MNIST_modified(X_train, y_train)         
+        train_dl = DataLoader(train_ds, batch_size = batch_size, shuffle = shuffle)
+        list_train.append(train_dl)
+         
+        test_ds = Ds_MNIST_modified(X_test, y_test)         
+        test_dl = DataLoader(test_ds, batch_size = batch_size, shuffle = shuffle)  
+        list_test.append(test_dl)
+        
+    return list_train, list_test
+    
\ No newline at end of file