Repository: fzenke/spytorch Branch: main Commit: d363dbf93a04 Files: 17 Total size: 1.3 MB Directory structure: gitextract_9f1972op/ ├── .gitignore ├── README.md ├── notebooks/ │ ├── .gitignore │ ├── SpyTorchTutorial1.ipynb │ ├── SpyTorchTutorial2.ipynb │ ├── SpyTorchTutorial3.ipynb │ ├── SpyTorchTutorial4.ipynb │ ├── SpyTorchTutorial5.ipynb │ ├── figures/ │ │ ├── .gitignore │ │ ├── mlp_sketch/ │ │ │ ├── Makefile │ │ │ └── mlp_sketch.tex │ │ ├── snn_graph/ │ │ │ ├── Makefile │ │ │ └── snn_graph.tex │ │ └── surrgrad/ │ │ ├── Makefile │ │ └── surrgrad.gnu │ └── utils.py └── requirements.txt ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ # ---> Python # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *,cover # Translations *.mo *.pot # Django stuff: *.log # Sphinx documentation docs/_build/ # PyBuilder target/ ================================================ FILE: README.md ================================================ # SpyTorch A tutorial on surrogate gradient learning in spiking neural networks Version: 0.4 [![DOI](https://zenodo.org/badge/170391179.svg)](https://zenodo.org/badge/latestdoi/170391179) This repository contains tutorial files to get you started with the basic ideas of surrogate gradient learning in spiking neural networks using PyTorch. You find a brief introductory video accompanying these notebooks here https://youtu.be/xPYiAjceAqU Feedback and contributions are welcome. For more information on surrogate gradient learning please refer to: > Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks. IEEE Signal Processing Magazine 36, 51–63. > https://ieeexplore.ieee.org/document/8891809 > preprint: https://arxiv.org/abs/1901.09948 Also see https://github.com/surrogate-gradient-learning ## Copyright and license Copyright 2019-2022 Friedemann Zenke, https://fzenke.net This work is licensed under a Creative Commons Attribution 4.0 International License. http://creativecommons.org/licenses/by/4.0/ ================================================ FILE: notebooks/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ ================================================ FILE: notebooks/SpyTorchTutorial1.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 1: Training a spiking neural network with surrogate gradients\n", "\n", "Friedemann Zenke (https://fzenke.net)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> For more details on surrogate gradient learning, please see: \n", "> Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks.\n", "> https://arxiv.org/abs/1901.09948" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction \n", "\n", "The last months have seen a surge of interest in training spiking neural networks to do meaningful computations. On the one hand, this surge was fueled by the limited accomplishment of more traditional, and often considered more biologically plausible, learning paradigms in creating functional neural networks that solve interesting computational problems. This limitation was met by the undeniable success of deep neural networks in acing a diversity of challenging computational problems. A success that has raised both the bar and the question of how well this progress would translate to spiking neural networks.\n", "\n", "The rise of deep learning over the last decade is in large part due to GPUs and their increased computational power, growing training data sets, and --- perhaps most importantly --- advances in understanding the quirks and needs of the error back-propagation algorithm. For instance, we now know that we have to avoid vanishing and exploding gradients, a feat that can be accomplished by choice of a sensible nonlinearity, proper weight initialization, and a suitable optimizer. Powerful software packages supporting auto-differentiation have since made mangling with deep neural networks a breeze in comparison to what it used to be. This development begs the question of how much of this knowledge gain from deep learning and its tools we can leverage to train spiking neural networks. Although a complete answer to these questions cannot be given at the moment, it seems that we can learn a lot.\n", "\n", "In this tutorial, we use insights and tools from machine learning to build, step-by-step, a spiking neural network. Explicitly, we set out with the goal of building networks that solve (simple) real-world problems. To that end, we focus on classification problems and use supervised learning in conjunction with the aforementioned back-propagation algorithm. To do this, we have to overcome a vanishing gradient problem caused by the binary nature of the spikes themselves.\n", "\n", "In this tutorial, we will first show how a simple feed-forward spiking neural network of leaky integrate-and-fire (LIF) neurons with current-based synapses can be formally mapped to a discrete-time recurrent neural network (RNN). We will use this formulation to explain why gradients vanish at spikes and show one way of how the problem can be alleviated. Specifically, we will introduce surrogate gradients and provide practical examples of how they can be implemented in PyTorch." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mapping LIF neurons to RNN dynamics\n", "\n", "The de-facto standard neuron model for network simulations in computational neuroscience is the LIF neuron model which is often formally written as a time continuous dynamical system in differential form:\n", "$$\\tau_\\mathrm{mem} \\frac{\\mathrm{d}U_i^{(l)}}{\\mathrm{d}t} = -(U_i^{(l)}-U_\\mathrm{rest}) + RI_i^{(l)}$$\n", "where $U_i$ is the membrane potential of neuron $i$ in layer $l$, $U_\\mathrm{rest}$ is the resting potential, $\\tau_\\mathrm{mem}$ is the membrane time constant, $R$ is the input resistance, and $I_i$ is the input current. The membrane potential $U_i$ characterizes the hidden state of each neuron and, importantly, it is not directly communicated to downstream neurons. However, a neuron fires an action potential or spike at the time $t$ when its membrane voltage exceeds the firing threshold $\\vartheta$. After having fired a spike, a neurons membrane voltage is reset $U_i \\rightarrow U_\\mathrm{rest}$. We write\n", "$$S_i^{(l)}(t)=\\sum_{k \\in C_i^l} \\delta(t-t_j^k)$$ \n", "for the spike train (ie. the sum of all spikes $C_i^l$ emitted by neuron $i$ in layer $l$). Here $\\delta$ is the Dirac delta function and $t_i^k$ are the associated firing times of the neuron.\n", "\n", "Spikes travel down the axon and generate a postsynaptic currents in connected neurons. Using our above formalism we can thus write\n", "$$\\frac{\\mathrm{d}I_i}{\\mathrm{d}t}= -\\frac{I_i(t)}{\\tau_\\mathrm{syn}} + \\sum_j W_{ij} S_j^{(0)}(t) + \\sum_j V_{ij} S_j^{(1)}(t)$$\n", "where we have introduced the synaptic weight matrices $W_{ij}$ (feed-forward), $V_{ij}$ (recurrent), and the synaptic decay time constant $\\tau_\\mathrm{syn}$.\n", "\n", "To link to RNNs apparent, we will now express the above equations in discrete time. In the interest of brevity we switch to natural units $U_\\mathrm{rest}=0$, $R=1$, and $\\vartheta=1$. Our arguments remain unaffected by this choice, and all results can always be re-scaled back to physical units. To highlight the nonlinear character of a spike, we start by noting that we can set\n", "$$S_i^{(l)}(t)=\\Theta(U_i^{(l)}(t)-\\vartheta)$$\n", "where $\\Theta$ denotes the Heaviside step function.\n", "\n", "Assuming a small simulation time step of $\\Delta_t>0$ we can approximate the synaptic dynamics by\n", "$$I_i^{(l)}(t+1) = \\alpha I_i^{(l)}(t) + \\sum_j W_{ij} S_j^{(l-1)}(t) +\\sum_j V_{ij} S_j^{(l)}(t)$$\n", "with the constant $\\alpha=\\exp\\left(-\\frac{\\Delta_t}{\\tau_\\mathrm{syn}} \\right)$. Further, the membrane dynamics can be written as\n", "$$U_i^{(l)}(t+1) = \\underbrace{\\beta U_i^{(l)}(t)}_{\\mathrm{leak}} + \\underbrace{I_i^{(l)}(t)}_{\\mathrm{input}} -\\underbrace{S_i^{(l)}(t)}_{\\mathrm{reset}}$$\n", "with the output $S_i(t) = \\Theta(U_i(t)-1)$ and the constant $\\beta=\\exp\\left(-\\frac{\\Delta_t}{\\tau_\\mathrm{mem}}\\right)$. Note the distinct terms on the right-hand-side of the equation which are responsible individually for i) leak, ii) synaptic input, and iii) the spike reset.\n", "\n", "\n", "\n", "These equations can be summarized succinctly as the computational graph of an RNN with a specific connectivity structure. \n", "\n", "Time flows from left to right. Inputs enter the network at each time step from the bottom of the graph ($S_i^{(0)}$). These inputs sequentially influence the synaptic currents $I_i^{(1)}$, membrane potentials the $U_i^{(1)}$, and finally the spiking output $S_i^{(1)}$. Moreover, dynamic quantities have direct input on future time steps. We have suppressed the indices $i$ in the figure for clarity.\n", "\n", "The computational graph illustrates a concept which is known as unrolling in time, which emphasizes the duality between a deep neural network and a recurrent neural network, which is nothing more but a deep network in time (with tied weights). Due to this fact, we can train RNNs using the back-propagation of error through time (BPTT). We will discuss problems arising from the binary character of the spiking nonlinearity later. For now, let us start by implementing the above dynamics in a three-layer spiking neural network in PyTorch." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start with a simple multilayer network model with a single hidden layer, as shown below. For simplicity, we will not use recurrent connections $V$ for now, keeping in mind that they can be added later should the need arise.\n", "\n", "\n", "\n", "For the sake of argument, we set the numbers for the input, hidden and output neurons as follows:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "nb_inputs = 100\n", "nb_hidden = 4\n", "nb_outputs = 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we have seen above, we are technically simulating an RNN. Thus we have to simulate our neurons for a certain number of timesteps. We will use 1ms timesteps, and we want to simulate our network for say 200 timesteps. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "time_step = 1e-3\n", "nb_steps = 200" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To take advantage of parallelism, we will set up our code to work on batches of data like this is usually done for neural networks that are trained in a supervised manner.\n", "To that end, we specify a batch size here." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "batch_size = 256" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With these basic design choices made, we can now start building the actual network. Here we will be using PyTorch, but you will be able to reproduce these results in most common machine learning libraries.\n", "\n", "We start by importing the libraries we need." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.gridspec import GridSpec\n", "import seaborn as sns\n", "\n", "import torch\n", "import torch.nn as nn" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "dtype = torch.float\n", "device = torch.device(\"cpu\")\n", "\n", "# Uncomment the line below to run on GPU\n", "# device = torch.device(\"cuda:0\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A simple synthetic dataset \n", "\n", "We start by generating some random spiking data set, which we will use as input to our network. In the beginning, we will work with a single batch of data. It will be straight forward to expand later what we have learned to larger datasets.\n", "\n", "Suppose we want our network to classify a set of different sparse input spike trains into two categories. \n", "\n", "To generate some synthetic data, we fill a tensor of (batch_size x nb_steps x nb_inputs) with random uniform numbers between 0 and 1 and use this to generate our input dataset:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "freq = 5 # Hz\n", "prob = freq*time_step\n", "mask = torch.rand((batch_size,nb_steps,nb_inputs), device=device, dtype=dtype)\n", "x_data = torch.zeros((batch_size,nb_steps,nb_inputs), device=device, dtype=dtype, requires_grad=False)\n", "x_data[mask" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data_id = 0\n", "plt.imshow(x_data[data_id].cpu().t(), cmap=plt.cm.gray_r, aspect=\"auto\")\n", "plt.xlabel(\"Time (ms)\")\n", "plt.ylabel(\"Unit\")\n", "sns.despine()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we assign a random label of 0 or 1 to each of our input patterns. Our network's task will be to differentiate these patterns." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "y_data = torch.tensor(1*(np.random.rand(batch_size)<0.5), device=device)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that there is no structure in the data (because it is entirely random). Thus we won't worry about generalization now and only care about our ability to overfit these data with the spiking neural network we are going to build in a jiffy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup of the spiking network model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now is the time to implement our LIF neuron model in discrete time.\n", "We will first do this step by step before we wrap all the steps into a function later on.\n", "But first, we fix several model constants such as the membrane and the synaptic time constant. Moreover, we define some essential variables, including our $\\alpha$ and $\\beta$ as described above. We do this now because we will use some of these variables to scale our weights to meaningful ranges." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "tau_mem = 10e-3\n", "tau_syn = 5e-3\n", "\n", "alpha = float(np.exp(-time_step/tau_syn))\n", "beta = float(np.exp(-time_step/tau_mem))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we set up our weight matrices, which connect the input and the hidden layer, as well as the matrix connecting the hidden layer with the output layer. Moreover, we initialize these weights randomly from a normal distribution. Note that we scale the variance with the inverse square root of the number of input connections. Moreover, for the sake of simplicity, we ignore Dale's law in this tutorial. Thus weights can be either excitatory or inhibitory. This choice is prevalent in artificial neural networks." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "init done\n" ] } ], "source": [ "weight_scale = 7*(1.0-beta) # this should give us some spikes to begin with\n", "\n", "w1 = torch.empty((nb_inputs, nb_hidden), device=device, dtype=dtype, requires_grad=True)\n", "torch.nn.init.normal_(w1, mean=0.0, std=weight_scale/np.sqrt(nb_inputs))\n", "\n", "w2 = torch.empty((nb_hidden, nb_outputs), device=device, dtype=dtype, requires_grad=True)\n", "torch.nn.init.normal_(w2, mean=0.0, std=weight_scale/np.sqrt(nb_hidden))\n", "\n", "print(\"init done\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A spiking neuron model in discrete time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first thing we need to do to implement our spiking neuron is to multiply all input spikes with the weight matrix. We have to do this for each time step in each input example in the batch. Because we have stored our input spikes in a rank three tensor we can express this operation in a single line:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "h1 = torch.einsum(\"abc,cd->abd\", (x_data, w1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These \"weighted\" input spikes will now feed into our synaptic variable and, ultimately, the membrane potential. To trigger a spike, we need to define moreover a threshold or spike function, which we do in the following. We will later have to alter this definition to train the network, but more about that later." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The spiking nonlinearity (the naive way)\n", "\n", "In discrete-time, as explained earlier, we can formulate our spiking nonlinearity as a Heaviside step function. So let's begin with defining a Heaviside function. One way of implementing it is the following:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def spike_fn(x):\n", " out = torch.zeros_like(x)\n", " out[x > 0] = 1.0\n", " return out" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each trial, we initialize the synaptic currents and membrane potentials at zero.\n", "Next, we need to implement a loop that simulates our neuron models over time. \n", "Moreover, we will record the membrane potentials and output spikes of all trials and all neurons." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "syn = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n", "mem = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n", "\n", "# Here we define two lists which we use to record the membrane potentials and output spikes\n", "mem_rec = []\n", "spk_rec = []\n", "\n", "# Here we loop over time\n", "for t in range(nb_steps):\n", " mthr = mem-1.0\n", " out = spike_fn(mthr)\n", " rst = out.detach() # We do not want to backprop through the reset\n", "\n", " new_syn = alpha*syn +h1[:,t]\n", " new_mem = (beta*mem +syn)*(1.0-rst)\n", " \n", " mem_rec.append(mem)\n", " spk_rec.append(out)\n", " \n", " mem = new_mem\n", " syn = new_syn\n", "\n", "# Now we merge the recorded membrane potentials into a single tensor\n", "mem_rec = torch.stack(mem_rec,dim=1)\n", "spk_rec = torch.stack(spk_rec,dim=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And that's it. The above loop has now simulated our neurons for '''nb_steps''' and stored their membrane traces and output spikes. Let us take a look at those membrane potentials in which we directly \"paste\" the spikes for visual inspection. We will directly plot multiple trials at once and define a little helper function for this purpose." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def plot_voltage_traces(mem, spk=None, dim=(3,5), spike_height=5):\n", " gs=GridSpec(*dim)\n", " if spk is not None:\n", " dat = 1.0*mem\n", " dat[spk>0.0] = spike_height\n", " dat = dat.detach().cpu().numpy()\n", " else:\n", " dat = mem.detach().cpu().numpy()\n", " for i in range(np.prod(dim)):\n", " if i==0: a0=ax=plt.subplot(gs[i])\n", " else: ax=plt.subplot(gs[i],sharey=a0)\n", " ax.plot(dat[i])\n", " ax.axis(\"off\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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jbX+optrC5vKO8EROHDnHYrEAKDDVl+dbBvTw5flu9uX5ardPsmEpV5OuBZge7UmlaqGzvBEAVa1TGacBA1Jbh76JhAzvSwZ16eqjOh258xS3xulxnuj0OB/camTvtkrd5XWDAfrJy+kpBTCmLr0cSJJU1d8+HD4HONyX5yv35fmKfHk+hfoFEbts95pt80+EQ+YTgN9vd/RVo8kfy+ZSsxJJUyMVPXMLxn6sABcYkgMfAUgSMvUz1lN3mHjDc3zc/ofa9hiby2vY0ck7wubyXtuKortfNT1HP3lF3fFyNekNYNQxNU8PHFDz+vXAfJvLm7lUaXtTQGmpLlPa8F1qKpXmKjKiUTKj9QtBM6JCKZhZoXLJL/XHIwZecS72L73yB+V1gDbFIKtiFlhhIQz8Ejv3mhsNp+/2WziwtOu2TuWx96Pk/aJw5Q/KrafMi24C5l/wRvfrqW/HiwP5uQpCKAI7HYzs6Df55/uPGf3qfRet6Or6+h+by3vaf2VIc8A6Wvu3CjFQvA64TzvWg+0F/y7RVOJHUT/ofHiK5Vbn1UYvt5k+xyGtDiHa/Lc2l/ekPU2/4YSycISYh1A5UF1swvlVMtd5oxgj9c6DKhKypZAjU3+YtOKx7I6B/FwlkJ/7ECh3Gq1z686TDFXdnR5nM8m0Jd+YvnBVapcnmxuS1n+LcA6L3a9uNPxLagoVshw/Nb/P6XE+DTwHEA213lKz6aTpcb+PYPsZl4n6yjMnlpUdPGtnhNe4A7itr2v8LzaXd8BuvafGo7u2XYKYwcZ7bL6rbb9GmARaNStT7/DbHcf67Y56Zwu3NRW39bCtHKrcVslvd/S6bt7EzztHN1CipkWXK60jABFVDgEX4g6efUeL5rFZLCvMpntGvuV4dttRqM3l7WRzef+1ub79TFWlFwCqZPmMIlmej9sa37HGE7PJLUaoFiNAK8Bmc3m7Ar/NVzpPmpMkVlmoyFNtLu/Acn9+a5vL21irCwBwepzdnB7nY/nZmResMBn5Shm2rlDNujWqSuo6NZvnImevUlXhL6FEeKy6yDwSQAnLpnCV8dfa25vtcmmI0+M8HeFI4gb+6v1ev242l/eGo2v+75v4876z3EP7jD+MALeWlPb4bu2Gz30rV1+9TXIxydDd6XG+4vQ4xzs9zp2p2R4ELIh28C/QcnNyxidG67zTAOTSbr9OfG3a1X67o2ryxNtn3zR1gUlWVIAOad0e6iJb1keBrjaXt/WunrEBGKZtL0SolsuATOCw3blYswW/OtowhWSplqgqrfgt2i/4t+JgZM0TG3EHv1mptrkYocLuALx4Qu1T7U9IPVc6p32zyAM5ognlBct5b8NGzisr54XCzbyztsKbqihbzp6mYImI1SjvHp3BlcfcP1W79VahHGZ1lbjiFoPptisM+bFjlUmMd3qcb2gDs7ucHmf63r+mRqXd8XMVZBVqtaFQ7nQsGZUqZlV96EfzHcM+Nj1KK4r+xW1tPcH8kH2c8U2aUbatqTGe2G8Bbdv7eMOc8dcZv+l0q/HzgY8a334Ht/Ul3NadtaNLECavJQhTEpKx5BjAFnfOudtfVo/f7sjw2x3/57c7XvfbHck2l1dG9P2/x/K21HLRqhSppk77+qX5QRfCfJTE1k57u0VDdlrDgPba/q2b5mU822KTTItClTZFUe6/2ACSRLlB5ox2bQA6ZEajK1q80eO9fy3mv9N6GB+S5AgoRhTFXC4bq9KBy9O6Pn0CUKfuU1X5TKfHafBBz+piU+bq+dZoSbk5MuswyTJ0s8KRVSE+7ZSMMQopNdzmbw9IEuHgYa3CxUPzLS1+iM/ztrZVO0LFXQbEhss7GrhsJcxt0sZj56tdZ2kON9cBFwA1wGuB/Nx79uJd7hGa8IvNSv715flCTo/zZYQtXfHl+VYB+PJ8NU6P88HsMvXdce9FHwIoS2aN3+7o7Dh//XDgC0THE8Vt/RUoCRWbjkJWc1JWhORCMvim//B/no+cOeM0w983b1Cb/f6H0tcXAFaaTa0AMqLRaJnBYFhnMt6SFY12cHqcl/pWrjYAt99sOLvP89GzuiV3eLubJNWPGWYlWUwnVVX/hNtagFC5jsUdXK79XDfCxh2sxm2dgbCVn27K/DtbMpbJyeGl/K4J5erVV14NxDq15TaXt1tjLM/RtCyzgIxJVjMbzc2oqGj5eufHljx34z33/exVjpihIneJYFhyh2kCFRuSjlVqZSRZZKW23JgcKjFNNLutp+IORv12hwQ0A0ocBX5Fu0cH6gdUAC1RjDMkYzDLpmzYKj/FskxRcgkgcUJlVazOPo7bOgv4GXdQpV4InBh3aXNgKzWb3+44n/o6fjlQAqzKqS5zZFcbKEpOK/d45vxE/Xu2H7NAtadXKTxzljw0aqwemtLphSiK5SXJUFOztd9kw7L2wS5DXjB1G/hmJBef2nl6ID83YnN5pyAcSI9FlNFW2FzeRxFmnfsD+blbDKlLz5QMFRxRKyYFBkl9/LLwnQaEueRkm8tbCzwal8RFxrRFFyW1mkRU60MNqqoGDfLILuFI5j1bStrVYLYkS8GnhsxLb37CXHUFkPzI+TLRrCSK11sfs7m83snbCOU/nNIqVZY6FmapdStOtGZyFfVq2HZsbccEt3UoYMEd/HUvX+M+Y6lVOxyxRGT20QsMXPpzlM4b4fpvFV4429S8k7yO7qzjd/l/o4Heg+QlwwbJSwiSNkR0lzskNlOeixCksYkHNxgngai7NyAG6rdofkcnAG0QJpSYoHwHrR4Yktf13+Yeo50e572+PN92fYRok+qXIB0HsDE1K4LQHAGoBqL/Pmh8/xeTpCzY6kI5fNdxHe5w/xk+wmfOnDV5T+t/Q6qvT1GAckn62P9pm3erNtf7SvVYB31XbP3MqYqilBoM8r8W8+XAG5IcSQY4qrIWQzQ5Zo97Cq0DUcKZmwEkSUkDOlRstFyy+tdslEKzwVqF5ZxpKjdPUjjiZzPPvhnlqXeiPPRRlBPnqKiKQY2U9wYMhEu3KpPubE0/bbsA4eABMAC3NRnAb3ek+e2O91f/3uzOL82p3N+8GdWSRD95OYaUFSS1+XiWOfv3KxBLJrLYo6Bme4cmkF+SVDWl0waV516PDPXbHY+8/HLkS+Bp4AS/3XGu3+6Y7bc71n2Sr1x56+fmaKawEpBRTXtJVsdHQvJXWp5rEHbnE1SV0ZsXpbVEkWSA4MoUFm9sP7WK5F/GR4/hD6XvSKDY5vrmQ1UxDAfoGI6cM7a4tFBSVUoMhrNSFWXtl2mps1W4d6zxq9Oy0mdiTF221TPMT6ob6NoRDelBIKYNic2UF2nbTwCKZfkyS0vvnZacX5nUZg1lBgNJ4SSiVbbOiEYaBGY14nrZpxHqewCWm8zMVrt9BfDS448uVJEfBngresopJWoawZXCn0Uyqs+bUiM/A1SsSxoJuP12x2iEM9EWYOGUIxxX93mv9yvAKkSZ/AO0V1WWSIaaLHP27/SUhPVnrtJ1EXDfnCQLiiTRvaaWzLDpfURHBsJ+F3OUi38XVdr2WKfHWWc+8Nsd51G/DLEAWOAo8K8uNyUvBhi4VOXEBRXe1NpoPkDzXuVqy/5BFIPKwGUqx88Vt5AkDJKh5kpEWTQObmuzdtKWaaMMf0n3GD+uRbwv0NS/OVUlZ/rtjgHagAcAm8vbDrgX4e28uNP9H7ya1OaTZ5Pbjufl1hWxF/Q9QtsUAQ6nXiB/oV2HKUusMlMV43tA/6gk9bzjplU/4g5OkB8K/l/yQ5vH4Q5GrvpB6SOrJG/OgEUdJYZH16lJ1PRGzOLiy2PaP92kswBUieaxg+mbbFcB0+LOy3N6nFlOj/N6p8f5xZjXu4/UnveXbbVNfruj2V681b2i+zq1Z0otlCfBknbwcq6BsAH6r1Dpu1RigbAukSSFbwPqVLqnGaY3v+Keh7c3pbmtSQj1MggNbB3zlc7bruu+ecw9TyxC9Lc/IAayi4BcIxF+Md/WeYXlwuc6S+vDctKamKZhAlCOmJht60cU44SYQAZoWVNy3VXpT3NUh7u5OfX5v5YnXRy5xPjT9aWyzE8pycrZbVtNvz0nm7Patmo9M9Xwujlz1j0DqkNjd5L2TmkQoez0ONstNxnPuKFlDsM7tjt7dlfpElSJDVnw3UDRHkZPVUitVum8QaW2aNgfr23c1PzOopI/O9eK99s/FOK5ws2MK1rHu0WLf9vmFidWLnP1VmqFmqhXcaszgyuSb1UiMstawzsnyPjbQaWFItmkLI6/8PwpBA3/XiKptVnVwFuhDWdTFbg29vNQp8dZ5/233GQcclz7NhzVoa1zYlpqW4BNBsMJr2dmfPFRbs8WiEK8uHJjUvNVCzOZmJ7Gl+mptM6YqqR0fAOTdQGWFt9jtM66A9GY326I97sLLgCuv/xHhSfei9KmGBdwX04Zv04YF3l7wrhIJqIC9gfaGFRlaPfCagPAt4dLKBKoinTWcm8La3WRKYBQ+/VRIvxv9a/ZiyvWJQMqptQIqBJnzZ7iQFT89xEzqHQ5ec2FkhxtpirGGl+S5burg2X9nt+0ZVa7cJhKWc54MCe784PNmyFLCp2afasCHF8R4pHNRQBMTEubp72vf7VnOge31YpQ/aQAoUGhlxWby3v/8JrnnKpKaIoxuU9aTW28lzzylmGAoRwxk46Nohscp8eZiaqeAfDq+i1BgCKjjLnz8yvjTnsWmF2DmStrbqNkk3CFUGrl92vLTQ8AlK1Opmx10tWgfkr94MPRIsjrd49XrmtdpILQ2pzvy/OtDZcc+QGAKWOBeovxMz9AX2nZe7iDjz3VLOslgE41EqNrHwi+Ehk1vlK1KABVqvn6mA0+jvMQPhageRP77Y6BiHIF+AwYefIZT59uc3l/Gd/92J4AJ85RuPTnaN161Gb2CqlZ98qZ6wbVhADO/EthwPrIWMRM+x5fnq/RAv+EVcMdAKUrk8n+psg8+evbVvrtjuOALyyRGuWpqS8fiZghxTv7DI3bzzGmL7xWNlZJAL4kC69lZqzDHVwXyM9dQ72XNQh/jHMD+bmvI4WXGFICAEhy5Alfnm+uL89XsJNsHglQ0F4CSaJXuEKaZL4Pm7Th6Wmte8d8PpBNymfzLl84B/gwPujSFd8bHp4wLvL96y9E3nninUjo1q+i6af/pRRnVKovA2ctspi/m5CeFhvVPuK3Oyx+u2Ow3+6Yjlj/vF8Ec8fNhn4Aa1qCKkmsaSExq0tqJUDHTSqvW5vxbuSERSBGIvPN5pmLDOlqW6mIswx/7kib2Ashn4qAdbGDCxUbp9c++vkFtfd+MjD0at3JR8qL45djLW7Dlpp7jR8yxfK/aBd5wzWypA691PxlqTmrLp7QD9QvxT1qR89kSo1cD5DZpRJDUhQpguSLbmJOqsqCnJVDwtBrndHACe3bRP/XMkf+12we8kNaKmtNJjKjUfqGahhSHUrbUdr/haSqezaReP66Yc9mlFWMqUxNmXbjG9PPcnqcaQhhVcfYr6NLhy9Wu/3UT+LzYTLPvhElJc7d5ktHnx/u/Wr8SIDqhzJPKZHlT1pFoxmKKmOUFIC7nZ06dAWuQNi0BvryfGrP107abEhe3/z2BWrRIG80W0XlgYuNLGknoapcX/HvQz4Uy7//LL3msWhYurJ0RQpqRIw7SrPTSjOLKpqffMbTt4Oan9o1X5VNQQk4zZfn+xbA9XLn1d601PYARlVVflqzTn4ouxm/p6Zw3Fzll2u+V+pGTWEDuC80sLGVSrlhu7HNO7483xV79GJ3k6dvPOb0juuK37VuCadNGiTNmdJHPsIUUXn32WjYHGFbd/f1CFUOwDtRSXrjjd6jvnIUr2odlo2z3hgd6GffVGJ0f1WLocKAKTUyq+vspYcD+O2Ox4G7QVVa9i+Tk7NrCfyUgwpRCRyOAv9SzZHm8KQ2n7xuss7vEw72JbT+grxAfu77uK1SqSzf+EFG+jNvZ2aYopLEcZVV/J6STFSS8K5ZTwiZs9vXrZp5BFXt+8ymLX1PrKruCNyDENKfF6npKwbUvN4WYePkxeBzS9pNX9OjVpb49kIDNclVRCRpo3f9g0fURLIrA/m5RY3x7mM8caWzujBVSVrfLcInWzYytEO7WB3o5cvz1Q0KNUeTUYPXL+z6wMz33LWyUTUrEQsQQVJ/R5Xi12DOBc4tTeXGjEpukYFCK7VjR538ann5ce2BVhAdktrtcWRjJY9tLmJURWUpYMcdLHR6nBOB02s35lJTMjwKGFKpZo7lWixSmLvDVzzwbfefDkMsEUF7l+ciHKNm+PJ8R/rtjh8R6r+vgbNPPuPpG9C8yNPVdbw3+dmt2nH7o4pIa13zC3DCiNZtX3K/p17fugRMWeEqa5tQq5wv1u5zdKMd8fplAxeaayM5mYbqZkpmjbHXVDNqdcynS10LUoc3ep+27OqF33QGKEzOKr30pHu/QMiD/kB/icgbt2c+Oui11qF+AC3CUniTSTVZFDVUI0spMXWm5tBzLPBUID+3wm93JE92Zj3w9YgyV1G6vEWS1BY7Un0C+O2OGxFLP3n7BJkfBsp8sWZDsHskbPVGBzF57eFFN/z1VTZA55M3hS3WyOHOTh1KpSi+8U9G/tN2vCELPj4ZKpDZnAneknWUzU+LFC1OjzdJqsCpjgL/d3v1onfCS1cP/ja1svpwVZLDkmK+Ibt3yferfIbQ8XNVvjlC4oNjRVk8+0bkybZF3DndIfHcGQaUcMa0Hzb6X30xK/Ph79JSOwO8t76QATV1qxZ/A07AHYzitl4GvKOo0m8LVdvMPvLKuwAurb2T35V+1yG0mdPON/xKvuktlipt159Q+9QFQFUgaUyNojJVlrDG5/uxrJYVn2Za0pTarArZXJKDGKy9gFid0tOX56urr367I1U2KZuVsJzc5oiSXzYuz7QpW6QuHx0t8/VgGVNY5cgClQWdJIJp27sd3VhSevQ1pWVnAA/iDu7RwHSPZ8qpVdWnHzm7psWIaSVnzrqj+yPUr4esI6tCLE9Y2laiJF3ip/5bZ/os/4KT/HbHfX6749zAJ61t1ZOyX8/fcv4Pr68/BSUsAYybvGbdD2ZVvQ+4sK7SyxEfQOdFNdkA/3aUlixpJwG8XVGQ3wHFMhVYdnH3e2tb9S+jzaDSuntmFlVkGizRy4CXQJoVKe8lASg1zZ+3ubwtR7/Rwzg1Oalu5BqRJHlyaiq/p6aAqjK4QK0TyHM7S5iicMtXKlWaH1izaJQXCjfHnMMuf/b5jssKH2nW4AEUTNHwcb39tVntN6umk2cpRwAMWqKu0gTyWsRaufOANdQL5LXAdaee/lT5pC7DWz9x+EWhDkcWnT8kvLl2STuJeafUqgDhSuMAv90xym93tEK4+1OZxlVp3Wo2RZoZWdjCVisJ1fZ0v93xzeSJt3dPd7gWmqzzmwFEynsB3GNzeSXcQTXzgZIXLy6JZMubj56uqjK/pKYQlSTahyOh6aFhnBd8tsYYVmMV9n4kadR9OdmZmr5zJJqjx6/RwzoghEi1KRomdVZxj6QaiYxquHBlEU9uLor836YtI5Y8esnqyRNvT/LbHWf77Y49ia+729hcXjl3WiTpqh8U7nxXYtrfdvovqlO2bOXUMXni7SdOnnj7Qw/MfM8NsDSznXTyGU8bHAV+FVW6UTYq2qOqKpDnKPAvv3qswXn/xQaqzURbBjGfP73yZuAsYAgYUIoHRwDym2Vxe072YmenDjFHvb4A0UjmdLT1opUkM0OxbwQYJvvuUxXTxljeNK/0mdq//Wb1dZyEEMgR4JaTz3i6I2LtNcBLoWaBd74YVt9dpLYOlae1rgF4G3dQLUkyrHn6bANVZgiXmFJCJaZG86WwrazsdeTsmhb2mbKx54/JcQIZQGoHDDhz+R8x8xMtq0sys0JlVyDWAveXTFvo1PmB033FVf1O/0tBjiqqZ+PaEqOqUiNLSQhnLgAC+blTAvm5D2oC2QEsPdlX4nrs/SgtgsqM/xDII9AEMsC/op8iBeVEgFzDTB61vFvnZGnOiJiAa315vlWhVVfeFjs+vXVv5uR0W4zQajwyz65ODqZC6xK47WN48GOFBz5SKKi1sI1A/gZo29ACGSC7qOLYI2fXtBg8q7rtkXOCEzt/IpfGzBa1GUostsSKtkUiklybDVZFVaWIbCobenL7th/GBDLA1KStJpLHUK9K7gvwUfS4w9+Pnljn0DtF6QNCXf8XMO8fRWi4u0rrmgWSxswJJI2ZF6mRP5PEcqu5CFPYFSrwR6ohDaBm08i0cn/+ezWbToxV6HbAcqfH2RHAb3eYkNQvlLCcbEiKktq65pm/DD1nAHTdoGKIqtz1ucLYbxUe+SCKOayCMBwnAY8Dp11zy5opuIO37qlAhr0QyqEky3wAUxTWhpTbqHc5p0M4vBZVXdxhsyoBrMqR6FJb+0xNj9CMsFHdsk1SjyDUqi/XlhvvOPPnP046duocCudnqADtItEJswNrmvvyfAV+uyPbb3ecf7t3o7HrOpXMNaIRfjDCsAVAqWm+ArE8CiC9QO1w/XE1T2FuH6X9iCJScsRITAnL4yZPvD3l/6a8OOezN6eR+7eKbNnS2dz8p0l+izlcZjBIABZFeQjgyWzhhDy4QKVPQKXWAHdcbuD/zpQJJRHOKVfot6hlBGBsSal6TFV1/0u0MngnM6PL2JY5t+zp+90VEdmwcHUr0QBshZBelnzBDT9FYvbWzxwF/oijwD8BsW75J+2421Hgr0Vb7mUgOuMMw7T3+tdWpgC81PwwaXV6i+8QnuZfAxsA0+ocyi4ba3r2pJRzPjix5knecIyaj7B7Nkc4lr2OEN7tVJUNkYoeIYQdKBaNjX41bw4s33TCkMj6OtuuutpoPuvRqouqnvr1dcsbLypSRqUKYvkN1bJsXS7Wtx8Ry+9itaMRmAKkjSyctKl1ecwcCs02GADuxR1c6rc7DNozfw5s8NsdjRGi1ezvLtdWpEhqcqVEdqCM676rIKtcBejitzvMmg3/bS0fdc6EBc06graO31Hg97U/quiRVgNLaX9Usc9R4Pd99kzbkUjSccvbQkmb6DiAYesXgNAYnA2c/HXFNy/3CdVQbpD5IS11CPC+0+O0onmUGjN8o6lfOvbyAHlpLsCJzDKP/G3wCV1XJX9CvS/FMlS1FLAokmbHh9cdBf4AcBNiIPQLcFNSq29mfTNIYmovaQWSWpZtr0hHxGyOdfqvrsmRHjN2q14KECo2XRJvy21INraxFC/sYQhvbibV+WxM6S2xrGvdv//kVAe7qaAEzcJsMGrFH5+bcyZPSO36+IaMzk9x/g/hljdPUrjwd4X8aVWF7SLRFp1rw7E6WucR7Lc7ZL/dcZLf7ngEsQKgLUB2OVz7nfJfS66uj9v/X6AF1wPntbuveCaajdTasor0XtXVLfoFX9GCH56H22qurena5an+F/BynzOLHzniUu4dek3Pk894+mXH+ev/uqDfhiGHHVtIcouaus6+RRBmLWlWNzj4arD0B3COo8C/tTdgA1FutSz/o7eEr6PItCFMXayBSMgwBngI0f/MALCVlkrd1yunUe9FXcd4S7fQq5HTtGtlKjZYbvXb7bnF/6aet/r3Ziyu7JD2TXSw8k5k5NpTah5foyI/GsjPXZrucLU1pC3OXaG2vrNStayXJJKAS1ZPafbJ0q9a9Vg3PatGCUsn+j9t85j/0zZLlpYkr1pvMiKrkhqpcACcV1s8In7pZg4wRQuL+iCqdJJkUGg3tKTQmKT8NL2NWEnSeYPKuPeiv/YJiNfdqhSefiu6Mf+dyI++PF+NL893b0zzurfssVC+8fXpZ063m0sBVgVNyQCHhUJlvpWr8a7d8L/2m/k2o1q4/69rzrrlZvN9D9694shRpz4z9OTTnyL39CerI5J8FWLk9yeis62jdFmqtP7vzC01ZQZUlSv/7dP9RmAZ8MngJcrwx9+PYoxKVCQbov+2FZ1L9frzHtOe5TfgdGD1crUtw2ue54Hml9F2RHGJOT2CqkjNAb+jZNXVJiVK3m9ROm1QseT8ElubTG5FZUmNLL9HnINK7kzR2L8+UmJVS4kas8S/rdsWA4ycYVkG9B9VXtkDuP624tIHziqvmJCqKIWpihJbF9xg3PbylDf+HTroCSwKMvDloo0vWFQ1tgB/Ysx26Cjwb0I4VGQ7Cvwx2/ZAgFuMX1QBw7rVhqsByiwVjD3qFgPCBl7XuCcMlzOAjE3Zi/+3QU5laVaHeYgO61HEiYNNEVUsNZC4CdXyo3ZpTEXKVb5Jz3z57T084p23AuHle/TCS+dPPnfpbyVtK7eQVqOmn/j94OXl/vwT0BxaFlvMpQiBMBLAr3YEGJfucKkjV/2dAbDOKvqBTSutq6iPmHUCYpkaCAepPf2azy4J5OeGzv16kSWtSk0FRgPIKhxZIEzlYZl3EYPNWICWOiPW1DZ9IC5YQkpO+O2srlWkta7pg9t6Vo3EFwD9amoYclhxl6gk0aK6lKPWzr0/kJ/7ZSBpjK+7tPnmdzYWckR16Msea1XGfh0dcMx85XwtyTUFYz9aF8jPvQhI++Lbe99aPb7ZQ4GZzTet/iGbK2b+2vWeLyKjX3wufTVuq9W3cvXEIdWhzOygSlpIjYUKfFYzS4zW/n9Oc5briiTx4ijD1/bRG2altqwF+Bh3MAjgy/MFfXm++xy2so8kg0IkZGjDTmx1+0rex/Oyz/16oXnE9MUGYPB0u/TRx8dK0U8Gbr2YRILb0murngHorE450dJ8ymjZVNb6rGkqwxfVT3B7raAVQJtIZJN2KH597LsIx6/7AMIGKsYPF92mY426w7X+frvDCpyh/TvAUeB/dsFlC1/15fkmaMfGKiqqgsSTPcbMzLZX3oQYCGchfEQG/tphAN92HupCC5gzTPY9pKpMBKy1KeYl1wy9o+Lk059ifZrwNRy6UKjsXjhN5pOjDcPPuGaEw+byNoo9+ep3ZjlfOs3geGSMgStvlaluJbrKxe3loPvNRdN8eT63L89Xog0KlgLSY+9HDYgAL/0R/dIggIrU9UkvttvC2I1XsfTrlqyZkj0KpG8L51hbVW5Motv8NdRgHnL5o+Pbr7G/1j/d4fI4Pc5rgDUp7d//Ic1xz0upUs0TAJWF5pcrNySdA1C+Jtmy5IvWvyM+evRn5IfMjn1XKHSprd2Mahbri1VjasVSV91ztd2idnz1ecMWhCMgbY4oJSWn9ipb6GNlWht7r4gMOWVg28SxiH7yOSDSqpRWnQsZ7bc7uvjtjl2Hct4Fe+XotTFTmQnQQQuvcVZ5ZSygx6yRs5UtABuzoNYkeXx5vpD2W18kCUWSFzr9i95yFPhPdxT4hzsK/EcjFlr3RRMIwUBK85U/5Cirfs1Oi9YaXgQyQa1f0Q/M7GKUkaQcgFg0IeD/Avm5kxCFvmIzmXypjHjbaFJfazu0GINZqUAshajjwU+iWCvqG+htxSW/+PJ8AeAa4LeL59f80329MKT+2F+8rrfWFypftD2pJUDvLYHWE8ZFFprdwaW4g6/K7uAjD9248rxKWW49KznpSRqBkYZZ9xtaiDjGoUXGnGjIALJK9zM3DAdqcVvPA3AU+FVHgb8Y6gLCHyOjMMbwix2gTSTyvKqiGpI2EkkpPenkM57+AmhZlCYNu+5aS83MHuJ5ZVOpZLLOAliiNbQHgCIJjB02kYZYPvYFIpQpwDXd7vh65MvHj1l61vI/DjOqCr2LVnaZMC7SY8K4SDuA49bMrlMvt6kIxjwgFwBMSUleEv+8fqXDZuAXx2r1BNsmNQng5T7nTosiKYZqpaP/0zaxaGYXa9s3EQ42/x1bdx9wFPirHQX+z4BbAI5aqNB5g3qqSSEWxvV34FJHgf9IYNBXXYa/sETMlO+si1XtDq6jPvraF7OTklIAhleFSLaEL0jNFBoe1+IPM3Bbz0KYIbCocPsLhrce+SDK8MUqIxaqsVnu/Fj+Jk+8vSolUvMecGr1CnOL2jLhbpBWU22w2TbNqNpsWlbkTxt1bEk13darSEgUJWWsPvmMp+2IMIitgVKE9zZoEbM6hMNFklTXKT2w7Xsxp0enWDtVa/+pjR7/2lHg//uKiYsvuqOy+P+kVmHmddJmb5nhVRffZvj4iXMlO8DAZWrG8IVK+d3jo3+O/lMMstPaVs8CqNpsRo2CLAb1AEc4Pc6cO8b2Go/wko5GZT557zh5803XGNLmdBH3MCr02Yk2YDBiULmCei/4etzBP5+PnH3jqNrHmKQM7Yg7GEX7mIGq8uZow2+xAdIsxIBTucDwy5GShNmndFrRv+b1bkvUDm2QpJUbUloWxye9ob0CIKnR1E+ABTaXN/4rSw3JMqCmLEnmmguzv77odgMPXShtZ8oE/tC2w315vqjmFPej9mx+AEPyOvqs9IG6/atsXV5UC8x0epzdtHsupT4IUm9E4Jg3a4LGuRtnx0zIKipqBOEs1hxAQuLC3xTOLi+PBPJzn06vrbwpvbaS9sUhQhvFTP2iXxWyq2qsAKWdwvzYLYueoXf+Bu6uTi/v8+PWZtj3HQX+W4GHtf8/0vI3x2937NNkYK+EcqCV8gNA541giKoMra42AMVAYNhi1QiwOkcCmGhzeQ1aYI3HtcvnbJueo8Bf6SjwL0DYfL4BUKOyXL3ZApJKZpfKv+yjN6S1G1GEikpxGnw1LCKJ85JAsajAyYH83G8BAvm5BYH83C6INYRXA7OTMiN0Oa1wM6ixDv8boDClBpwrxAj7tY2beC005jiby3ueL8/3zoRxkdGnTZY7AhjbhxhOVeDrtesxV7dbPSvbTqUxqcaAamWbdZ4AE8ZFhk0YF2mUJVHdHl4cLqzOygWo3qwtNcgMY7Coj2rP/Clu67ZrsB80EO35tOk1NVsq7wSEHLXh/5Mk4YFrafkNSLW3nnzG01WXn3fyI0VZUYsSziRUKNbYGdMKQOv0tU/JzQbovFEF+FyzrX0KLDcqkTa3zJ0w+di1c7cdNb4CfOS3O67NDpXVLfvoVrIGxCzeB/BbSnLpOjW7GOCPqJMgaf8XyM+NtC1SH5RVKEoj+NWb9wwzoMbUtB/57Y6jqF9/+76jwD/DUeDf9utIjcFHKtR03gj57wkz5tSeEqPvNj7vKPB7ABwF/n/ecJ7+P4RmKAUtoI3G3WjL7xabzRGApJrMlQDJOcKrKrN11UuIQU+Ml1RFqvv0X6/VamtrhYopoi7w2x3pfrujDbCJ+hnfBoMl+o5ql6sBqv2mvqt+yWm+aX4G0vRuhW01t7glLdp1QHikxvI3LpCfG3Pt6g1wQVlFLKrYVNzBmP0wnunNulXGBMWodSd13C9BdU6rrPrwrcJN6sejVN48Seaqiywda8zS43O7yrn/dJOQVRj7jZJ+2Ap1GECzHhW0HVJyG6ib1IhM0ZK0J6akJMfWXZ9iiKoTT5irjAb4ZpAkXXCXsfV3g+ScIqvE2hzWq0KTFgsmsi0xoTptZ59dfD569seLVBuIIDg5iDXff0gSpnuNH6WAWgssCuTnLpdRbh0oi4UJD4cv7hzGKCMc9PrKqlr3ZaLCHJWRSgUAsmWjAzEBKdnrl/ofaBEC/QAhc2X/WpOEKknrd3BqLFDKVloTrb+4HSCnVGXwalGVMo+u+oq4GAbpyhY53eH6VEtnK8ctgLQq9Qz/p21uXjE5p0dtmQnJoNbMOlwZPvZa46ZnT5e5/yIDV401UG0G2yYY+Y/aOjC0S9fx3z348ITvHuT1X59msC+V6KI76LtS3PeTky1cfV4SD7UxoXZ4f1NKx1cfTe30IuNHyMzuIm1EfDDpf1oWXkZoOeqyzA7kwZ6wV0J5djfpq4okMEfAsVEhR4SW+8cW+thaJWcMBkgK83W5P389IjziLMRIWyHO+WFbHAX+dxwF/lGACdQTrLaqpR2P20Lrw4ODJZkW6W1qVrQbUnL/zWO6bS7MEqMWJZwJcFsgP/f7bdML5OdGtZBu3wNlBpPaqcupm6ZmdKxyIb6aNAHAudDBtFVrGFxVw2fREVloYT+B+1GlFqbUSHW3gSWM21xk6xyO8GTk/OaKJFOUlBGb5TzptztSoc4G9RBihNhoM7Xhf/79c7XBXGczTW6+XTTJ22M7Npd3FKgPPml6g7MMf0oIIXAJ7uBm4BFVpdaYthRz9h8nGNIWl5qz/zgGQAm1/MGYuvQEAEPyWpBD8QOqeKH8HQjV7uD1vtFPTn2l6Ni1c4hKkrqwme0e6mNWx3gVYEuStVJBomV1KYdtWjLksh+j0eSQSlQ1DT265plml9beye3ha5cCL951Y69Bp/yjiFG/xGQtHRdi9tgNMTNtTv1XfPYLjgL/Fql+wAnAz4fJEBcpyOlxmtIdLjPa5zSRqwd2y39AqD7dQR9wfKksf7TeJOLSPlF+q/nh8MUsTu74B0B1kdmCeK6PANeqX7LfAPoqEF2nKSjP+Fvh9ReiFyM64RVQt9b1KUeBv033+f9ecX7X+2//19ZuqwmJUprasp1YekUks64OpSGiID2r5b890BlVVc4or4hpIz5kR7iDtRZr5N7mvcppN6K4Kr1taMEOz2to3MEFyarqOLa26vaf+ssUp8sgVm/wwbHyA4ilXmsN5uictsOKadGvrFg2MB0kL8DmBRmdo5K0ANFfycMXqUPab4GyZPhiqCwT5zsTNkrnS9oAkm2EjebXEAv/GB9BcCsC+bmliHcMMAh3MIIwv2CVqvjANC4USBojA6xIumhSS6mUsGpgkWr7BTglkJ97cSA/t7xFVUmdUJ7VYwCDqoV2xZCyAlAeCuTn+vfsRe4RsfXTsaBROxLKMe3DIL/d0Tz+B1+e77ueq5TjX341ikGFOTldGZz5ShpGzoqdk10ZMaZWq6MRH6bYgOizP0L039z8tXIJMA6kFOCPalnq/eTxlvs2ZUlt/uoplyxpLz0ZTJNemt1V+hKgdHmqpKqMlcTSTwBunvcZI1YvrTUqsD4LvuobAc3Ib0xdgSFFLH+vtkg8MdrwgaPAf3ZMA6ltByPs6LH3sbPohLvFXgnlOVcsXBloJUUARgVEJahRTd8D04st6acBOFdKHyA6YztiVLkEuCOQn7toh4nGIZyVCn5uc2Rpn5Tm4Zhd5ULAnvFO4aMlFcfeFg2JpTTRqs6V1Kszdow7WIkmaM1p0SvbDi7Nd5y/fjDCqYkRa+YrKVUSBWoHysRX0+wXXPLEMIQKm5QWtXcZzGoYYInS7p+/lF5pwJqsmvKxiBmJE/jcb3fcgmiIMbVeo4Z5TI7WXlRpTJoeNKdGUrtrzmyqFGvovaDu+7JvjJKnc7ZhKoiyOAd38DMAX55vtiQJYWHJ+ZmU9u8bJUMINZq82Ji+5Dxj2r9/qCq1kiFEeg93nY1qVY5YU9x9nQowW5uh3fzATM+XjpLV2UCVQVVPOXf65HGIjySAECx12oPmoeAHRUkZ8wEen/7mqSfPVl+/7jslIsmRtGhSIfZ5K7/0THxsw+SJt79y9p/Kj+2KoDyJmuxyYfNxFPjXs/W6U4AVjgJ/FfuXRxAj55oKC/8WiC7qCACnxykj4vyuSetxb4lk3lyc1u0xi6XlpNVOj/Oo3u/1fdbZqcOnwzu2uxBAVQxF5dGWbd+JnlyyYX32xQA1pSY2L0zrjzt4Ee7gE1WbLUMBIgZmTjpSNOHcf1TSamiP8Ly2AMsRdb7OC7rMmP7Ozf1uWXz6qfncdNTNAPQoWUMPbRXowk5bTXyfCeTnhgG61tbeAXBYTY2cpqpmhAPbztfgu4Ov5TjLD09vU3NFxjuF2wZ6aDzcwSW33rzqmb6hmrryl1VVSU8LP+Mo8Oc5Cvztu59V+HtGuxCSxHeaIHwBUSfPnTAu0h9RjrVH+dQagNI0XqpKkuKXdl3qy/NNpX6N97ZhGp9BhPeMUh/feWfENDnHaPmvDSgtlwMMNyzMoH6d9CUAJik6Y3H+2ccH8nNjg1KOn/3nhogkn/6K84x/ZuYchrOmBouiIhsrSe36ZGNH+Ppim/+3E8qOAv9qxCBZZpuPhPjtDunBj5W6icvP3WxR4ISTc5/5aNRp41iTKcatTuFUdTHQxZfnO8+X57sImNBmi7qib0CN9bE3AMdccrtxAMJ8GQKG+vJ8d/nyfGOHLVYvkwxKpLbcSKjYfJN2zZvAipRIDTfP+9wMMKu7WE9+75ZiJWX9iTdGyh1fqlFL/AD07x084ypHgd9NfZCZE/fFyXGvg4e0qjZ8BnDkaqGyO7bmaZesKo4O5YUAPHDk5Ych3MTDwBGB/Fx7ID/3/3aS3I5xB0O4g0/gDl6LO/gx7mAYYPkDt3xQtXLs/RVL715UUzjqrEB+bvWukkKo5C5GLEYHMYr+FZhhUFR5/YxMVoear0Qb3WbUVj4BWGpk06pR3R9/E+i/Rsl56Pza+2Lu/M8O8s1Zj/CKjSCckp5FdMY1wBWOAv92NreGxFHgVwYunDt0QfMulgtN9xU+EM7jheiZsRX1sZjGbisVLR81vRMThg/jDk7cJqm30GY+qoqiqtILkqF6gOa8UytJdRF16r7b+9ZIQ60CtN8CE8ZFjkTYep5DhERdBRzlKPB/r+VzGsJb+0hEB7QJEXHruecPO/fGJZmxgTYMXKoaZEUlPeP3olErpp2GiFF+SXoI68ZMeONk+Q5Hgb/Ov0Br9HVaAeojf+03NNv9s0DWY+cbjlPFKLuT9tGPkYhnyJbk6NXmZtNWSnIESY42VyJpv0iScgtxn6dTanNi6sbJF079ejXasqUtCzOu1AJDGNBmCeYo3n+6S58UaStaVeHUMhohFLo5CvwPOQr8dQ6LgfzcEHB02GB8ZFlm20dqZeOSpGiY5kExU16WE0EyBosR6XwCcNsrXUasNprGApxVXglCkFyCO/jfphl3cBbu4Kd78z73lfuKiuuChZhVVf5y3UahTnRb7WgDbWAigKPAPw8xyAB4xZfn++nSn6Jdeq5WDQAdNvOiL8+X4cvzSYDsy/N5tHNjTlsj/XbHYVAXDe1m7fhFjgL/1qHrtifmpXuhzeW1AdwSvmFh3O+v4LYOQnM8Yusvj9Xh9C+a9E2XYceXkva2CTiyuloFkE2lZ+zi/vvK72wdbWxHM2Wo/xLgTVr9jXGupGkafuonMaNPSexDOslhg4k5mvFrzO/KL74834cTxkXMfrvjCb/dMWXCuEjPkbOVFQArW7LFUeB/ZfTdRgktxjXwjC/PV6clcBT4yzI6hN6O+bHKRuVrRLjSrT4otKKjWnhHUQnnl1fMnHH3My/7b5xw9sLLZ12M6PvuQJvI7YQpQDVCc3Ca3+541G93mP/j/B2y10K5eWHkfYDoFhPPRc5iHTktm1eVYlEihGUD81p0j1WkhwL5uQ2uTgzkj3p05aNjegfyc3/c9dmAO6jiDn5I/ch2sKPAr8om5SFQqdyQhO27dck9i1Y+DZBTXToE4I+2fTtuSc58Endw4fDa51NLyMhGCKBXABwF/j8RtvAqhKH/cURwjXca8nn/i6t/Ha8sULtMej96Eu9HTowt58i56Z57+wHXjDV+RYZULSNUbvnbXu/L86m+PN/FQFdJImfhpQtujnPQg/r4wXVCeUk7ybGoY91g8CfqBcu9QF9HgX+rmMOOAr/XUeCf6Sjw/4FYVpLjKPAvmd3SPvd/I27kxqNvRYUao4J08iwV108LmklizTVVZn7zHi5xT54hMsMuv8/2xC99WriD3/cLjgJ/9fI20jrEwE9G2JbeiztlgCljfp2OWDZW1HVQ0VCrMlUxfhZaf14skkKsXsc6tBsQs7O3EU5xIWDCX9cuHPPTYfKgLencL8FwR4H/M0eBf97ObJmB/NzNgfzcB1Y+cdoDZiVyc/xvG5pJWLs8OXe4vKB/IGnMuODDWe6lJtP3tbLE0KpqRlVUTgJOxx3cnUHwAcN+z5aPY/vOmlqASbitaQjhm4pQqU6Mu+R2xORhkN/uyDtlljpcElouv6PAH4syF7ODAuAo8C9G1HsjMM1vd7xBvUboGUeBf3cGJN+ifSAGEaf98Hlq174jap5FVetWQcxAxOP3Inw2dkggP7fsR8tdVwOhMyoqJQCjqt55+8udR+5GPvYK7X2chVg29y9xjobb8ClCG2FGPGuMWwCm9pL+fvNkA4as+RvRVlxIhnJm20VTaVlKf7/dYUSYBO9EDHKnjJyjHg8wzSFnOz3OXtr9D0e0jW1NZrQ5onRsp5GbV3U8bgvdz9r4u+P89bX20es7mtMisYhhha+xfsElZeWwjRbAl+eb7cvzPe3L80W3TTeGo8BfTf0X+r5G9IV7HFVwX8JszgAIVxh5t3IkwCv9Ni/9AGBTclYsXPIDxD69mDj8g5jZdsBt7dHj7I0p7Y8qRjYpqhqRWz0z9eWBwD3dS9cAsDSrHdTbiI7RtvcH8nPrwtBoDj0ZjgJ/N0eB/15HgT8+3OL+YjxAMRkXVamWEoAI8l0dpELTpYYfYg389pi2YUf48nzLfXm+4h38tJ1QBo6eMEJGqY8RXgXYHQX+xx0F/uB/ZVRbSx0GCOTnViqyYd3yzLasSm+5AiDvF4V+K+ssn49eeptxgud4AxUp0p++PN92aWtq7BuAj9mVKaOR0TqqmC3xG7ZeljVIMlQ7AWqLhtXN9mu2HEPVylsyKpY8mqLUtO6FqJ8xH4kJ1Ht4ngjkIYb7lzoK/EsBHnpj0T/D//E/uhszs235CbH2FiUnXCvJEJWjx2W3e+s1BW58qlnmgyvNpmRrNKoOrQ4dIbuDp2t+CE2BodmR6F+PbKkL7uZHmHQ2AmM0j2cAHAX+NQiHHRA+L7H2HlNR74yLEb4VyYjoUCZEee3Wt5y1PiT24Q8ZIdRtq9WWxSWk3R136nLgWu2jIjtHaC9WHltVzYiqanKiUUPXcLjj7uRlb/Hl+Tb58nzHA3Zfnq9iR+c4CvxR6kNltgfQlg4NBhTv4XLsPXdBaC+Rk9fwb1uoSCIqCYe6LcStH0ccIyKj/NNdkhCD8Vio2od9eb4122XEHQwnZUY+TcmpRZLpChwhydzV6aTNbdPahF4xpUbOkWRiUfa8212/e9yDmLSB8Lzf7oMou2KvhbKjwF+Cpip88bdnrwrk595w67zPfgYIy4Y1QJ9Afu4jmqNV4uAOVlAfVOMO4My01jVk2yti7vz3TJ54+52HF4pQtkuyOoDwkGwLxDxPt4qvDXUV70DyK2J0Zp6ldM8CaC9tGn6n8VOMkiIBP+AO7p5WYXtiFWuA0+OUtXjhRyxpJ/FnL+kshB1ugKPAv2TnSfwncwDe7D2qR/zBr4+QPkYM7GKDgWnsBEeB/xVHgf9CrYM90EyO24+ZOCoQX4FKAyrM2X/2AM4HJkYq7DG7b+xzMl8E8nMLoU41/iBi0LEBoT4+01HgH88+on2NahhwT4ce5a9cpgW++SU1ZdQxHdrydXoakqoyMFRz9cX/WzvzPxNLMHx5vum/X7F4SNtINLYssR1i4Dgad3DjDi65A/Ht63TqP1v02w7Oq8NR4C9EqF/vQgyKbwDG7ElfEMjPnYXQtEH9BxheaPbQuicQy9KOA3rhDq7dzSR9MvD45iI+Xrfx72tLy97Y3bzsCzuLbBZHzGHBpm2v07Y/rWgtxZaNddH8GM42WWcvUWSJuZ2l2Ow75nn9EeJDMGcBY787XLp2Q7YUe9/lwBm+PN9/TQRjArM7cCuAbFJpP6L4+q6nbZqK8MdYh/gYyx6jLRnthYjuN9BR4J+6i0u2Y18dkb4CeuWEgtdqEYyEBCsv/CmQn+v770sPKE8hvkZ0HpqRIbNz1bObfRldEZ8KzNTO+/LfzPatgCEIFVfM0WnVfs3tbhDIz1VtLu85wBOzlB7/G2Hwca7hj7Zd5fWoKook1X2Qe2/wI2wlaYj30woxK1j70ijDN9d8scsGuSt+AE6b07KHfO/gqziudPzSz46t7FaUIVW6PD4VjzMmlGfv4332Fx8h6lYYuNKX51vl9DjPQjgrAvyqLSkZD4zXBnzxHtzbmT4cBf5X0EwmDYk2uB6H25o0tiR0RcBkSv8pNYVigwFUtUaVpGueu365Z5cJJS73I+puF+A53MEd1iFHgT/itzsmI4IPgegXtnPq2cF1ldSHI91bvkT4dcQQqlcxeNjRAOK/eBMYbVUUgPt2ObvefyxBOGXa/XZHJmKpKgg7eUxr1NnpcUqBfN+XTo8rH+DdE+VxwxdH+yJ8ZF4F/tD8JL4CESnoQ49zPsKk86Uvz7crLWVMLp3wH+d49uW9aVrAvdaW7qtQfgExShyAqPQxVUnCCa1t+APhbBSLOlJkTFamImwVAxBqlTWAB0nS4g4L+wewKZCf22hfvtkXAvm5EeC28+9ZNAA4qqss/C4kCa+29Gav8OX5Ik6PcwrC3jMdMZsA+G03Rsi7w0eIJQXZc1r2qC0YnPqswVL1CtBHc5aKqaX2WBV0INC+jHT0NocfQNjdi6h3RgEgkJ+7zubyLkI8Zxjt04P7FXcwJLmt3+Rv2jLGaU1nvsXy0y+pKZf58nzrdn1xAuMO1lK/xHFXvEG9UF64KzNMQxHIzy2xubznIfwH3gvk5+6pIK7HHfwZt/VKhO38gH1feQfEtItOhKo/TTv2PWKAryDW8LdyepzNEcscIxXJ0o+OAv/nO0ivDl+ebyb1cdx3xbZ278cRXtMjEeaIAnYQFGd/sk9C2VHg3+y3O6YhOqCTaCpCWXyF5FHEoALga9zBqMNNEcLBpl7N6/J+TX28Z6iPUJOw/K30umR6tOe8IYbFWYpKVJbEetN95B1ExY0JZIUG+jRlID+31ObyHofwiP/eYNkcUzH1A0Yh6uk6tIhWTRFfnm8F/z06Pwth13wzkJ97oEwhj5jhlMuC5SEoP2dvguk3ZRwF/u/8dscdiDr39K7Ob0gC+bkTqPfo3jfcwf3xydg9JRZu9jjqg6s87Sjwqz6odXqcaxDyowv1YXP/avBPf7qDNbitkxGa0rWAW/Oz+Yr6iIQHlD3+dOO2+O0OF8KZayYi6k8KcISjwJ/YNigRI/okhB3n4/9yYLG5vOcinDCCwMhAfu72ofMSDbfVgni+pbiD+xxAwOlxSghV4AmIGevzWjjSBke7V4CtoyU968vz/W/HV+g0GG5rM0DFHWyUSFA6hyba0qBN1NuGNwI2R4G/BsDpcf6MENjfIpZPKohP9jZ8X+u2dkfY/5/GHUwEH5StaAih3JV64zkI9cBhO1uS0VSxubxmQI0FVNBpXJwe511svXxrgC/Pt98ideno6DQsfrvjQepNCbc6CvzPxX5zepxPs7VZ5zFfnu++/Ze7xGGfI045CvzL/HbHr4gPgQM8f7AJZIC4GMA6+4dnEGquU4AndYGso9PkeQTx0ZkI9YFTYvxIvVBeCjzGIco+z5QB/HaHHeFFV4L4WHvNLi7R0dktnB6n7MvzJdayOh0dnQZFM1ldgViL/Iwvz5fYfkmNSIMIZR0dHR0dHZ19Z18ieuno6Ojo6Og0ILpQ1tHR0dHRSRB0oayjo6Ojo5Mg6EJZR0dHR0cnQdCFso6Ojo6OToKgC2UdHR0dHZ0EQRfKOjo6Ojo6CYIulHV0dHR0dBIEXSjr6Ojo6OgkCLpQ1tHR0dHRSRB0oayjo6Ojo5Mg6EJZR0dHR0cnQdCFso6Ojo6OToKgC2UdHR0dHZ0EQRfKOjo6Ojo6CYIulHV0dHR0dBIEXSjr6Ojo6OgkCLpQ1tHR0dHRSRB0oayjo6Ojo5Mg6EJZR0dHR0cnQdCFso6Ojo6OToKgC2UdHR0dHZ0EQRfKOjo6Ojo6CYIulHV0dHR0dBIEXSjr6Ojo6OgkCLpQ1tHR0dHRSRB0oayjo6Ojo5Mg6EJZR0dHR0cnQdCFso6Ojo6OToKgC2UdHR0dHZ0EwdgQidhc3r5AUiA/d8Z/nui2GoFTgL9wBzc3xL13ka/U4cYZF7ZrOd72W5rxyb8vW1S6r2k6Pc7+gOTL883e9xzqNAROj/NwIOzL88070HnZE5we5wAAvS41DH67IxU4AfjRUeCv2h/3dHqcTiDNl+f7K+5YEjAS+NWX5ytrxHvvl/vsirj3/pOjwF/ZmPeyubztgd7A94H8XHUf0xoKlATycxfvzfVOj7MfYPbl+WbuSz62ZZ9nyjaXVwLmAX/bXN5muzj9JuBrYH91Qp7Dcjyvf5thvtuoqv89YNgNtEYwG5jl9DhT9j17OvuK0+O0AjOBuU6P03Cg87O7OD3OZGAWel1qSN4HvgLe2o/3XABMd3qcLeOOPavl44tGvvf/aff5qpHvsyve1fLw7n6412rgO+DUfUnE5vJ2AP4EFu3N9VpfMxeY4fQ4M/clL9vSEOrr+I6w5U7PEpytbds3wH13h7O/SUsFIGgwdG+A9FLj9tMbID2dfad53H6DaH72E2lx+3pdahjO0rYXHIB7t4vbv1LbHt/I97xC2x7byPfZFedus90fHL2P13fZx+vj+5qcfUxrKxpCKEuNdG6DsE/6je3Z7/nX2SVNtUyaar51NJwe587KcH+VrV6H9p59fXeN9u4bQijHp9HAMjDhiC+Ig/1ZmyJNtZPS61LTRO8PDhwH+n03Wtk3tFDW0dnfxDcOvS7q7E/0+tZ02dcBfKOV/f4Wyvtffd2wd9RHxolHUxXKTXVWr1NPvD9NfH+gq68Tn319dzsr+31mf6uvm3olaur5P9jRy0dnf7Kzvk+vh43PgZ4UNZrZVldf7xlNdVZ2qNCUykTayb5O06Ep1TednaAt691TdPX13tLAwyl5J/s6B46mOlDS61LTZ2flpquvE599HRQ3GaF8oFUKjU38s+oNIvFoSmWi16Wmz6HU9yUaDfm+D2qhfLCjz24Sm6ZUJvGOIk0p3zr16OV2cLA35dhog+qGFsq7ypyuvtZpaHT1tc6B4kCXm65h2Xv2td9otPa7v4VyU+dQetamQlMtk6aab516dPX1gSOR1NcJPVM+0CPHxuZQetamQlMtk6aab516DvTASh/MNQwH9Ux5V+np6mudhqaplklTzbdOPQe6DA9lobyvXbuuvj5IOJSetanQVMukqeZbpx69DA8cuvp6N9M42Ef8h9KzNhWaapk01Xzr1KOX4cHBIT1Tburqa30ZS+LRVDvGpppvnXr0mXLTpSHV1/pM+QCiN8LEo6mWSVPNt049h1Lfl2gkkvo6oWfKB3vFPJSetanQVMukqeZbpx69DA8OdPX1/kRtPM2C3ggTg6ZaJk013zr16NqOpsu+tj9dfZ0g6I0w8WiqZdJU861Tz6HU9yUa+6q+3tf212Rmygd7xTyUnrWp0FSd7/S61PTRy7Dp0pAz5YQWygmnvm5g9EaYeDTVMmmq+dapR9d2NF32tf3FTwZ09fXu0+DhaPVGmHg01TJpqvnWqSeB+76DHl19vZtpJFTFlBtXKCfUsx7CNNUyaar51qlHL8P9iM3lbcjBq66+3s3fGxQZBbVh76g3wsSjqZZJU823Tj26tmP/0pDtRPe+PhA08kxZb4SJQVMtk6aab516ErbvO0iJf8e6+no309hVevu185F09fWhQFMtk6aab516mqrnf1MlUWfKCS2UE0993dBJ7nhf58DRVMukqeZbpx5d27F/Mez6lN1GV18fCHT19SFBUy2TpppvnXoStu87SNHV13uRRkJ1Lo0glHV1VeLRVDvGpppvnXoStu87SElU9XWTninvZ5uyrr4+BGiqZdJU861Tj16G+5dEFcoJPVNOqIqpq68PCZpqmTTVfOvUk7B930GKrr7eizQSqnORUfWZ8sFPUy2TpppvnXoStu87SEnUmbKuvt5d9IhehwRNtUyaar516tHLcP/SkO94X/2DmsxMOaEqpoTS0EnqI+PEo6mWSVPNt049Cdv3HaQ0ZJvR1dcHAn2mfEjQVMukqeZbp56E7fsOUhqyzejqa439rr7WbcoHPU21TJpqvnXq0ctw/5KoQjmhZ8oJVTFlXX19KNBUy6Sp5lunnoTt+w5SdPX1XqSRUJ2LJOnq60OAphrQRa9LTZ+E7fsOUhJ1pqyrr3cXXX19SNBUy6Sp5lunHr0M9y+JKpQTeqacUBVTV18fEjTVMmmq+dapJ2H7voOURFJfN5qG7qBWX+ve14cETbVMmmq+depJ2L7vICVRZ8q6+np3kVBRG/aWTdV+eTDTVIVbU823Tj16Ge5fElUoN2jZGxsgjZ2PGNxWI5CJO7ilAe6zx0hEqJUbRig7PU4T8MZWyScYTo9T8uX5Glw9kOA01dlKg+Tb5vK2AJoD/kB+7n+Wvc3lzQFuApIAdyA/t3Jv75to+O2O+L6sQduAzeXtC6wK5OeWbvNTU617TQaby5sFfAH0B36N++lAq68brewbWijX77ut7YDfgK64reOBy4GcuN8l3MHdajxOj7MT4AYqAbcvz7dpd66Lpq7endN2lwHb/J9QI2Onx3k+8JrT49wAXOvL803Z33mwubx9gCHAe4H83NB+um1G3H5ClckusMTt/2e+nR7nyUChL883J/64zeUdAXwPJAMTbC7vJYH83JodpWFzeSVgEnCkdihZK6++wLPAw4H83O2cMGwubwai7s9McCF+ftx+gzV8m8t7GfAOoi87dpufu8XtywBOj3OrsrS5vJcDTsT7LdmbPDg9TgnxfKovz/fp3qSRyGgDSzdCHn0DTI0bAD0NHKPtnxl32b629Tb7mNbA2I4SSUu1uby/AUXAubsaHO+KhhDKzeP24x/uUaCrtn8eYqRTd+6yb1qkhz91fAz0Bo53FPiX7Shxp8dpASYDPbRDbYAzdpUpm8trP6LFAmJTdIui1O76Uf6TPtv8nzACwOlxZgKvAFbt72enx3mML8/35/7Kg83lbQlMA9KAETaX98J9rZy7yVFx+wlTJrvBYXH7O8230+M8DvgOCDo9zl4IE0oQqID85xECGWA0UAJcu5OkhlMvkAFuiNt/EEgB7owdsLm8ucCbQGvt0LfAaf/9SAeU8+L2qxoiQZvLKwPPaP8eY3N5BwXyc2fGnXJy3H6sDE+PHVBVKhDaNQPQwebynrOXbeJmxMAJp8e52Zfn+8Xpcebs4pr9gt/uiDfpRff0+t7v9utgSM2bGK3sHmsPVwGFNpf3MITMuFw7/jaijNO0//e1rR8Tt783aR0X2wmXHn4mcLT277HAL3ufrYYRys64fTGNd1uTgHO1Y/cDD7H1qJKVcqvX27ElF2BOF+np0WI2cKMvz/fmNumfRL1ABjjF6XG2Q7zUgC/PNxXA5vIagduAYcAWoGOyZW3dRSqSpM0sgsDCQH7udhXI6XEmAYOBOb48X3Cbn3ts838iqatuA7KA9cBsROf5kdPj7LOD59h7hDniPmAo8DnwJu5gbHZ1NfUN5gJgLvDUniSvDcBuQGhUnvPl+Qp3cf4xwPFxhxKpTHZFvOblv/J9vba1AnUVWlWMS5DCPVBNYUTH9T5wjc3l/SOQn/sxgM3lNQMfACOAmObiI0Qb6aj9/zLind9hc3nfCuTn/qupuT/S7hnjVJvLawzk50Z25+FsLu9RwOxAfm7F7py/tzg9zueNEfXEDyXayfXiToI6lfYwYLqjwL83g/J+iHYV4zY04e/0OM0IoVF3T6fHOQz4su6IaoxQ74dyFnA7e94mTIAr7tAtiE4/foD0n+2kkYnv11ftyYVOj7MdkrQopcM7adVrL1KjodarJSnSUalt2RLRn5yonfpSID93rM3lvQ94DlEG+2LySQO6xx3aaVpOj9OAKLP+wChfnq/M6XEOBOyxc5Ta7JPEXhTJWHET+yiU92m0oen7z9lBeoMRI+8NwGMIFUQdqgLW2tJ4ddPpgBl4Q6vsbPMbwIvAIsAErEF0Qj9qs0QQwj8fOBW4tK+07Jhic307VJFNwBRgHkKwbIXT4zQCvyPsFuudHuep2z7uNv/XvTuby3uTzeUtsLm8X9lc3mbbpr0fiM0S/gdcCKwAOgAv7HYKbqsTt/U73NavcFvTd3LW3YhZ1fHAa8BLADaXNwm4TjvnD207ThsE1WFzeSWby+u23fOF6nhpdHHvt48o6vX2EdN6v9evi3bK64iZiatVJPIubusA3Nb/qqPbzgqbxExZU3HGdwo7zLfWIZy4o98kOdIjrbsbyVg6K5Cf+yHwiPbTqzaXt5W2f2sGlaOvNUxq1U1aa9OO/YyoI4uBRwL5uTciVOAgBlMghIdVOyd+BvrO7jyfzeXNRmi3NthcXtsuTt9rnB5nH+CmvitVu6zWDQih/n0+iFA7L/DbHZ324hZDte0abXuuzeU9Q9vvytYmCAPw6taXy9tOep60ubx372EeBgEt4/4/xelx3o3QYtTdaA/TbEjiNYh7qgW4SZLUNIDkdh9KaV2f6pja5VmSWo8npeOr7rTuDwxJav2ZKpm2vAoQyM/diJABsG/PfDJbC+Kdtb88IALcitDIxWTdLfHnSajISasXpHbNJ63buFG93+v7+j7kbZ8LczQQ34HH0otV5ima3fiBb6JHzr0rfBUAS8uTSa+pH3B3W6eCWleedepITUCP0v6dZI1G60ehgiTgWJvL2x+4Szv2PcBphj9ZY6pvE9vUlhs01VTsPhLwJHCEdigFYeOI/W5HjHTjkQFsLu8pwPOImfQZwDSby/uSzeVtzn7A6XGmU28m+NWX5ysHLgYU4BKnx3nuTi+O4bamId7byYhn+N8OzhkIPKz9NwlAVbmq4sGWWaA+j1BzbgBOQMzODMD4OAEBwib0YFKrrzGm+7MkY1Uz2Vg1RI2k/uz0OLsDebETS2T55KAszZptsUzr917vj50e52tOj/MwAJvLO8jm+vY7VTHHbEyxWWCTEMpAO+rVzrDzfPdCaB8UhGBYCXRGzCKQ5CgpHd7qqLWTR4BZCBv7XTaXV+orLbspP/NOaPkDH1oeoqu0FuCvQH7utEB+bq9Afu4D2n0+0bYX21ze0dSX/32B/NwJwJi43+PV7lthc3kNNpf3VYSmKhkxc9qj2dMecjbAEUtE615ZL7pkTa16s/Z/D2CF3+54yG937MkMK6Yd+wihPpWAz7U2332bc/sjTHFViNk5qHWayEeBj2P7Npe33R7kITaw/RyYiKgrj5M4nt/xQnlP87FtnwqAKXMuhpRVSIZaTJmzpbSuT4+O+znWle/LM9+4zf/bpeX0ODsDb21z+ExNVowEUFWpFABJiaTYXquRTeXiX0m52ulxXu70OD+JmzTuNvtamCdt87+kVfoh2v9/AeAO1owN31TqjQqZN786BYDFbVKiIYOJ9BB0XptUql1zdlx6JwLNcyIR5qxc/dMPa9bff0JlFdmRKGZVXa+dczJiNm4BfgBOASbaLIuJSNu1v8FADUI9Gq/2vAwxGoL6Dqqv0+NM1tRH32jHQsD82LNq29u07W9ALUKtcQNCLbg/cGp5We/L820G8OX5pgPjtN/fdXqc2zqpbctFbO34cPNWM1S3tQPwJ0ClalnTJfRB9wKlPZuMBuNJbboWp3R89WqkMMDYdIcrPbXLE+8hV/mBVojZ0oc2l/d54BHJGMSUMReAYVtagmJANpXZgCUALSKRed1raqmRZV7MymRsy5wjo5J0AXANMKvnq7m5wE+SMXiyJNeaVFVSgJgD1IFXX7utnXFbT/Hef6Jsc3l72FzeW20u752aSjhG/LsmWVFMO0kt1vHPRNSr3r4830pfnu/NqlVX/KVGzciWLW2AN9MdLgW4Vzv/Bgu1o29KfbXNXa3T+ciazpR0iffN+YsD+blLdnCfL4FSoAswHmHWmoKwIxPIz/2E+nbxtOY0tiOuoV57UYsQ6o3pVzAou0zlKG3BwbzOIluqqAcD2HrCAPDAT+0HXmtzeX+zubx+m8v79C40W7H3/y/iuT5FDDa9taUD79zm3Fhf+DNQLHalmOp6JWLAuQbR5/5pc3mn2Vze3bHR9wFoG46seH3DpnzqZ4qfUT+BOZD1fivzkd/uSPXbHY5dDX6cHmdroIuqSlStugpVJYoY0JyuqoZAyxKVVsV1Vedep8fZT9uPmctOtbm8F+5pZv0P9M592vTqiGaUAWxCqiGp7QcnOD3Oa50eZ2rcqZcg2sG/iEkGiDI+HsgGQmpt9noAU+asoCQph6uqHI1U2cSLUNS306vU86kfGO42ey2UtYY5TPt3fZfStXzy3YP3g7olVGKMzZSnx11iV7TbVZRpfZBc+8dya9sVAKf80Sr9kp+jvPZi5Jo/BzqKj3mh99kGVR0JcGxVNSYgVVX5v01b+H3NOh7ZXLQGQFWlMyRD+UjtHvcF8nPVd0xPXuPLEKOWtLAYrKqSqgbyc/8GvtLOnWRzeZ2a1/JL2rEXgCsQjcrYZb3a764J0Zeyy9SuwCZExxhTZck2lzeTeoeBy9larTGS/UM/bTtvm+MPAT8BqcB3To/z0mPe7pmM23oHbusM3NbnlXszm/vtjntW/97sgWitBEI9HULY0boKD3nrQ4jZjiWiyouPqnk2M4rBPik6mKebZVKWVIYhZTWWlt98n2Z3mYENsrnkl7TuD3e2tJwURq4CoS69CejZL30ySNAnVMOr5f9wz5YyVY3rti8Llve+sEyU3fiMdMoNMt1qa2kTjpQCsqqaHgcy5KQNACi1Oaqq1s+UnR5nvDd2o+K3O9L9dsfZfrvjBMdtX4x65b6LXlFVCgDvejV7CVAA/B/wBPC75vcAW6sjqZblNx98qdN5bE97bbvGl+er8uX56hyYolXd2levuxBVFRoR4O0U2wu/Al8Dpv7y0k+nWesd4Menp9FSKu64xNmju9/uuM1vd8z02x0f+O2ONoH83Ip0Km+zUGfu+Qw4PpA0piVu6/O4rYMQ5qEQwpHlc5vLG29vjvUHsQ7oHsAayM+duPtvc8/w2x3SmdOiQ159OYoErM5ozj9dhOWrwmLIRtN6bUqzzH32+M5zlmS3WADQrmLTC5KqHI1oy7cBBTaXd5TN5bXs4DZ1QlmzpV8LojeXTUWDtzm3rbb9kzrBUS+UtetjmqCOZsJDiHOs+w8GAtxdVDx2SCg05ZWNm85FOAmeB2zUzjkgM2W/3dGSeu0iCBPfRoTZ4yft951xPIBSm0O0qssCkI8ABvvyfJMef7/m2GffiIaefz1ae2SB8gdCOL6naYRiQrk18KHN5d22HHaO2yrZpTWTzjFMJd/0ZjlS7eoU22uYMhb9D2F6uCvu7Nhg4ylfnu8SYAbCdDpZO/6jEk23AhiS12QDKDUtPqzZJHz/Tpup8sJr0fCDH0U373b+NPalMDsAOUjhyNHZryjj5j5LZm1lG5CabVmcngFUA/OdHqfUdZy7JdBaBb5JSyGpRNTVvq1LDj+207xCgBGrlxtO/UelWQVkV5DVtkj9XNHslP1DNSAE5hC0RnFCZdVhRE1lkqQ2T+n8HIbUfxVgIcAA04IBE9OFiclRIjRFcf3+VYgZvMXScuIjiBlAMkKo3elbubqmWTRaCHD75PBHA5arV1/znQIw2ZfnW8XW6pORiFHqskB+bgBhZ30dqQaksMnm8u5sBtQg9HFNuEiqbnkRwBWlweNwW2+J/ebL84URNpB5QAvg3VqJ1WuMxicRdqqbipakTgceq9yY1Lp0eSoIB4X5ANGwNGjLorSnVIWYirPq4cgl07aQmQ7Mn5OUdMovqSl1eTFlzhwhSXyMqLjVkoTF3Gy6Ka3rU4op8++pCPUbWamL1JxSlas+o/Lfr1oyeBLSCfP6F4ZLB3BcqVE9q7zC2KNa8sU/5/XFQeXeouJMgGTzhj5HZXlIae8BQAm1NagRa2wW+hEQvPvlzssVt/Vm3NZTGuI97wi/3dEpLBmWac/142u/Pv31SdP/vm7dtCzTlkVpnBj9pyvCG/V37ZKewOM2l3dZaMPp47dNb7XROG7bY9QL5a2W+Gg223bRyh6okfSrtPtcakhe/7ul1RePAJsHSgX8llJfPv9azLxU3Tw1Gpb8CB+Pw4GLUluHJuK2futLuuq5+ZarQv2kZYuA/wWSxkiIdnIT8HogP3c5oj1GEGrHj7ep3x0RQiyMcMxp1CVx67M5/4I/VDEAk1UUh4naMqEQqrYomautVhfAb4eFD/vr8NX9nz23rE/IKOMoWW30FN4Z+cX8vxkXGH5ZZ6E2BzGQCdpc3rpO2ebyplLvDPcvQCA/Nwgc0bqZ921j6koAlEhaTGMX4zu2V7EGtO3vwOpR8jQWWS7nBsPEYTaXt9/OntHpcXYDukqqqvYP1SQDluHVoVHl/nxnuT9/cWXguge1UzM0ter+Zkd1NmbbPw7YuMje8zjc1jTc1k64rSao86m4FyBS3gtg/MJL58/25fkWAHRbzx1GhSQJzLd+pRzWrEIJAn37hmomplC5rcxysft0kCRRJkPlhUZzs2nNDdrgXuNU3NZWqx/NvgVVja1U+FnbfqttDVnlKu028SbRpK1MlLKp9EmlumOJYeUozvtDUVJrMPVare5xO9gXoXw4qKS3f7N6vWFVu/RgfZ2o3GhBVfjH2anD2cCq5DZfbExq/RlS1ozSe3Ka026LqLOtraG0rC5Vg82tI2GAtemZdTMB+xoVVZJkAH+oz5SPfznqbv+nbfr6J7TuDyw1gfmoNQPToqFWyMZKUjq8Ix9tu2XVYe/1/uKB5s1cxQYDqVG1tm1FTDsl7ql5g35jSPNjyvo75kT2PnCkb+XqLOCv3IpKR/e1Ktmb5E4A/VaqfPB0+Fy/3THPWqEaAFTF8AxEY2qNb7S0VeB6U+Y/1Wk9HkxO6fx0o60pvO/eW8Z8Z7nrg5bGDYMBDgvVWIBncVvrbJW+PF8ZYib/NUCZwdD8thbNCcryRCVKuHRZap3nZDCQrNZWGBYgvLcpnG3N3+zLuK2oII0Zin08kPZ+9MRe2umvLerwe6uwJNGpNky/UI0iScQkQBQx0x4JLJQM1XJS64lHpjtcE46W5x1VLFdx/ydRWq41pEZrDFRvtnDNz39nDljUY9NzJSukZEVVr696WFXV+vr0VtkNco9qGUlVCZuqmNPKX/ebUt0WU216F+L4Ni218xPNsp4jzi+godmcbP3KpEZbANQajbSoLqV2vYnytcls9mWgTlU4jtlXBvJzj0EINoA7gC5JxtLtZmWFRkPHWKcVR91MeZvjgwBSqV66aO2iwDllFeNkVa0EhqZZZ077JfVSjkn7li1GAyaFGuBGSVHp97sZCUkOmSgCNQgQrZEPB3KB9CQpnDTR8kBqIGlMlXYsZvvsh9vaO5Cf+x71ntynAN9qTn5Qv9xqbiA/t3zP3uaekyQrjwH81Uelx9kbGGhbRt9llhsAWpSpdAgG29Qa4I/eoh4VZ0X4WptTBYok440dpCNat/i87SdpN1XlUFIMWCRVedzm8sbq+GXathDqVlYSSBpjsmf8dDlAuPgIlFBsggzAdxPGRazv/V/klV6rFCRDiKS2H6sjs18pwm19LpA0Zt4DRs+tL5hfxiRFucM0AQPRn20u7w6d+dA0b4OrQ0q6pk4KVLc4q2fRyvdRVbsaSYvZ+mXgY80xcL/gtztORHtHprRIrB+kNEvh86H1bVeWlJ83zs4oj4alFcDfuK0Sou70UKMWpbboaIC/49JNR2h+AJAgPf/PSivA/CTLyUe1eDnevAkwyubynslusExpU/eeU6Wa5OT0ec0BwsG+c7XDhxUaDH9NyEh7FkkyoKqLfHm+gPbbzwBtt6i8+GpUHfcuT171+wbLQx9EYmr2MskQKgD+HrpYxRRFRkxw3t2dvMWzV0JZOEmptxhSl0Lq6vR+K0SFWdIOZLOCEpZ5q6R7GsK5oT2AKXM2hlaTM5NDKs21JitLnCbJrO48fJNJPd7Adce4kkvNqTcBONaINM2KypuVVx6VFSqfAbyKIr2pqNI7AOdG/HJV4IbFFkUtBpidnNQiIkln/ZqaMgKgV7U8WxIyFEWSJG1JD6D4LTk/opmc3zq7rOIK38rVdwDLgUGXl5ZxxoytV0xZwlIK0HfoYtUOIMnRDHPOj0ZQf6Pe85VAfq5iTPevlSSFaFWnrZaBNRhuq3yp4YeXVqRUsdEkkx5VGBCqixkRwG2NmRXw5flKfXm+M14s3Hy9NRrFbzEzrEPb7gvXpc+OhAxIRgVQqQmapOXftnQAs6q2mAgGUtoCbF6Qwfm19+V2qv7w4pMCM4YMKCwA8KL5DQysqln57MbNcv9QKOa5l+/L89X48nw/INRs4xGz5wmru3z0Sc+FBqlVKSgShVZb1a1JzWpRI7LFNfPD9JoyAwvUztIqtVWfzFX9OWGOglTapfjvaP+FY2oeYnBVmMwKlVNnKDzzdTWvfl3JzE1v4TQsr5sSJtVkAvCxNZ0v07TpTAPzybDT7DnVwb6g0iW3EOcZa2g1sJRI7yo+P8oQrTVA9RYLT61+rS1Aatf8902ZM2aYs3/HkLKCw0xztwvSscZkknPbtf5VcyqMsUOhfLQ87+RA0hgWJV3RDfjlwaLi+75duz61S20ttbJkeSE7I+e7NPFKLChf+/J8L1/7W2RciyBUJME1N8nNMkcWfQsQKjURrZVcCHvwBoQK8iLgUhDT3m/SUlhgMT8AEMjP/QuhgakCTjQ3/+XV3u85x6d0ev5Vc/OfAWVGA7zi/+SPQQ5LaqkYMC/qnoMqy+RIQa4rmVwX60ABPjpGZoOhp8+i8F27cEQxthTq+U6FKmtNJt7MtHJFO2vK2BzXJ+cs/W3W15Pulh/+663f3jh2dBL1zkCPx9vF1xkNN85OskgAtwQ3ANHs2G8tS9QXgE9SajjuwY8VWhepmDIWSJkp/m8Qqv0+lxt/+CL+WTpLG7LZgf+Jtg75coArgmViIhCFmt8Mg56Z+jJjlvzENqbk8xGayb3xMt9jjCmRhwCyulaSfURFnQr7i4FGJgyXuTvPwO9OCVSJkqVpLP+2BSt/aN6/YELrdYf/q1wNEA4OkFEssLXp7VwgVZLVVW0Gl6gAzRZauHO5WNk5O7OkD3JdXxeLsHgbO8Hm8tpiTr2FamadiWiDwYCSXJimqhI1m045zBzKqgAYn5Fmm6BpWe8sLl0Yl9QsgFEzFMxRJIsScZy8cAuOteIY8IQvz6ek1VbNOWNFXXgIj6PAv8c+FXsslF+7/PAFH/56e/TFmXcM7Wl+mxPmKFzyqxBgs7rKlLYXfXP1pmh/RK3x1JYOLI5dP3iVOFc2KeXJL275FjhKkqFn8zW0kEulV/ucGQSwr4XMCpWsiIGOxZuxlW/srSVxzL1ll9kA+srLyVBrr7+xpHTuUVXV3FRcSrtwWKSvqlgKj/HE5/3qVjkTAFK7PHWCIWkDqmJCiaS53EXFDyAEawqwqGhVq+P6LRMVfvxwmRVx/sPd16udY/uW5lNIs9+tpDtcWy3jMiSvMgFIkvLhnr7fXfHCNcMfnzq5TbTIq2atmJ5NarVKbmUlafWG2RbAD9suJTq6qnrEqxs3k6wo1UhSz7/K0zsAWDtWk96uTsOSt3FOxqJVv9ZrZRQkLJFw2uANizy3zPuMh/96m8kTb+9nrVD7X/V9lLNeT2oT9GZH3lu7yfjyxk3XIpahAODL80WAizqGw+8YVFXZbDS06asN4GSVx9ocWfp8+xHFMw2WKMaQkrziu5b414rJ2d3eVUVX/aDw6atLMiZPvH3DU5NenjLmw5QFz78ZiVzyq0L7xSayF1vYODmbU78yctsXUY4oUNi84o7ZtcVi0vZA8+btG0Ot12/LMj/ApjYqE9sk82e6hWW9o+Sdms6EIZJhcQdxy2iNfIbT4zxeNpUuS2r91RGWFt+T1v4tJdlcuMM8rTaZhgF/Oj1OK4Ckqh20n7YSytcaJ8XPFtYBc9pHoqVPbC7CoKr8mprC+Azh41Qhy58CHDNTygH4xwHVZlla1NpwocESBUXixkjbvs5OHS4/sX2bminJSQBXArlVksQZ7VovvienOde3zDnnyHd7ZQIE8nO9wA2mzL+x5Px0qSQx2pC0IdOS8zMpnZ/r5/Q4u2lLDBsHAyFLGErSYKoyZomM4gdIaxs6PHbKnz1SPprY6dinQoW5A2etWj1t8tr18t3RzUsAmpeBbaN6ZZtwZHlEkvjKnH5Nnt9rN6lRDi8syPmnpaMK4XldQfxMx21Nm2+xjFYlia61tVzLn5hTVtRpPV58LdoGYdYD4Lj5Yuw1O8kyYmc9c08pANBZW1MezxlAUnYkuv7wUA1/RXtStjYJpUxUnQuW/Bw0KNstGe9lrE5+xm93nOO3O0b47Y7Dtz2hIfhwdN/SSJXxSIDMrpUkSeE6T/R/O6orTguqa5e3kXjlVAOzhlunlplTiNYYCJWYURWp9ai/lRMBIhV2EOFLhXxwW2VQLwPI7FJZmtG+WkrKqq1QwjIDP0upvX1SBCmsGI2pdb6KDyHGX0NtLm9vtsHm8p4FrGxG2XO4rScPNSw+DiCqStEfNdNbtMqGIZLK2OCmNIA3M61UyzLtqyJcWFp+Jm6rBcC3cnXUtaW4sN/K7UvyiAKVF16NPA3wwIx327Wr2EzIYlK6nbFxFG7riO0u2AV7LJRNESU1uwy6rocn341y1Q8KBkVicYd0vh8g8U1fMTMdUlCNKWSiYuldRTUbzmoW2ngaLYoM3mu+UxQANSoJu5o7GEBEguIow3wO67h8VHJ2LbIKh/+rMGJ+ZvC5P17cKg/rtzS7pkhNJ1OqZEHSVbmXlpX3fKlwM1cFyx59feNmjq+s4pXCzXO+qz3xi7XR9nXXzbNYcp0e58myueR6AGnLUC5a0bcTwjEF4MbnImc5f1w94CGjorKsFXwxTMZ1mZF2R2/xAxy+XFFkRSVa3RZVRZEkjkOsjwbA6XFaJTliAzBlzt5j1cWuiBpJaR6EjEqJI5aoPO6JckKwijI1ZSz1LvwpxAc2ECqjY5y1tVxVWnYjQNvNwgM4JacWa6c6q8GNJf+mPYwSp35CZcS6+aELC36s+x+Y9OaL0f4nzFUxKFhqy0zGyk0WRlSHHL4831YqBt/K1Wd8u3bDxd+vWS9fuzFIj3V1lXoq7qBqTFLy2g4pqZYMogPrOXNl6VW+SX93LtuYDSAJJ48TkqO1R7UoDvVJDkmxzv5pJPUvVZHoE1A54l+Vm79WQlcs/O4iZdPwzcZQMkp1+82IWXqDEtVez/j+Rh5p3owbWrXg8tYtCQvVy5aV2iBuQ9jUX1LVr4DmLSLCRKPIijxXm2kptVkRgKRQZl3v2rpIzb57fPTlcVc5HlElqbVBVXEVFVfaXF6LzeU9PPJg1nlHygVtANYoOT8ANtzBAUCzHrXh9h1qIrdQH1mpivp140MAwi0iywF+S01mVQdRFj3WqBcAR2wwGm03tmrBX0kW51KTyXhau9Y1q02mngBBg0E6qqr68Vg+U7s+NtXSapIKUFsyiNriobWqimqwbBqOsMFucHqcdQKqIanWxOCyThZ1k9LhDEkSATvS21ffAmoVqEvzbKtu+bf8/Q9WGm7yIlZnYLCojyPW8PPku9HV9xcVj0pSFIb/LRmNSv065+HrF0gSCvcaP5wQSBrTN+7WrllJlkyAtqHorNLlKVy6xEhOqcpxc5V8tJUjimZD7rJeVJSNRiMrTcb5COekAUAztNlxbzkQQfTDHZ0eZy+nx/mV0+OcgTB1MLxCrZWAqYqTwmWZdY3HqCrWcxfPCasRM0mKWntsUcaTANf8GDoT4ag3Bfjbb3c0fBQ2RUkCWNoGQpnq0vXNoNYImzNgZY4h9/GStWc4QzVBgCeHVb53x7Dr/1nYrhMpOWKG23ETFqIyUeGp7MVtNeO2vldbbigHaRioanbPir6SDDnO8jzgD1TJPGgRjPldwZjuB3gzkJ+7Hs00h3D6Otzm8va0ubxna46HdzQnyNfm+8cibP1EVYkpSt+n/xaDT9QKu/qy6YWpF1VtwKEp+1oXqTz+usrKyS3MgV+yH/XbHR+v+D7n/wZ/ntQyK84ws6FrlOI0SA9Bq1J+9dsdcs/iwGCAtD41sjFJOZqtI/ftFnsslIusGbcsHmH8VUqt73vT2lZPHX90j2FRE0zqbqY4Q1VTa2DQvLaokaz/gUxSYb9/X3qjpqsUkmUkdZGqSLfEJesFGGP4hauM3jPT2oqZW+78Zhz7Z3VdhzWvuViOe1jhMv5Sev6lHb4DUdlrgHEdIpFbnt205fuh1aELA/m5W36NDmrpWVt8a5KiUC3LZrTCOa+snBfL5jNEXnRZdbHJsPbPrC3+T9v8b8jX80uPXzN7GMDP3cSkOBpq/VtyZmSUbFYw1UpyzxlnUxUYG0KxxAJm9Ix7ltjyo0BsiVJDUmMyv/rziKz3PjrePD9sQG1dAgVLhjCo5pUNuINXxZ4PYROM0QPh8Ru6Klj2cVq1OreTFgNINqoXpLWpuRfUCQgBeBLAs6fLfHS0qB63zp2Q1LlMOET82VMiotWaLemoquYEU74uCeAo3FYxKnNbO+C2PoKw15vS1hj/OM6TssYiFBnLgAXivGBBasvaod1OL5wgGZQNpkg086zlf8Tsk3MQwWDy0NbmanzlKPDf4fAXDAHuikpSAYBRIemcZVP++mLik5nvP1fJ457wEF+eb1/Dq27Hr0c1e8uTm/XCH72kmHF7LWLEXggcUZgp8rOmLElWJSntsFBInbxmvamfZmKolsULjFbbrqpY6gqHVt5ofH7jZvI2lPPY+1EOW6FemDud+9pvVrk8WMb5wcpTAW8zymbWYPpUVeGHTf15yXfaVbiDEb/dYfV/2uZG/6dtfnniWemuWyZGr0irUq8HnL48X5Hf7shCq6OrsuRxAJPS03h3gJicHeNTI4ctU+5Gc/J7M9PKNa1y2GQ0WoA1Z8+vLbnp6ygV5cbRINb1y6byhyRJkaLV7UprNp45t6bwtB6SxEnAn223qDzwcbT5hb9GY6saGpTFPXMe+fGY7BdXt2+eEcjPLUCsA55ntKjNu5+1ManHORtbJWWFNyPqWMyLtgzhlDdF+/+sYXcVLh6wUll20hwh694+UZTLgMKC6B/KzZOvMn53OfAbbuu5uK1ZwK0ztM78VK+hZMM/mYycWMXLr0a55nvFhdbmZM2XocNGM7aQWg1wVasWLZ2dOtzn7NRhGe5gCZrK9ghZRBozNZtyEqK+nwEMklS1G6rKyMqqHIDicJo3ukUIeUtmeCHAJf6fTe+/GObLr8rMjwaW3Nlms5GjFofr3lPIYOKBI69o8I/KF7ZsdtPdeQYeGmPgnqThEW92Ctdfb+Cuyw0/+fJ8BbiDs31JltiEpM/qjFYX3jHwBhaP6IQqqySFIWdTDqjmCYhANXcAeZWbLCkAKTm1kilZAfghrU3NV8DR5gwR5OLoBSrNjHMqU7uOizmg3owIL9sXsXRwEaKcrzMS6feC6UXay5spUjPUadFejIuMUZ609M+flWRRAT6NeAInGWYNNwIDNzinRCo7h0dNM2MJSdSWG6nebLkduKCm1HRLTYloL1Km+nPWyZUcNaAQuQVva/kYClxmUNXuAJ3bF1KpWtRjap6JecjvNnsslO988bdvzm6z+viuJ26e13pQCe2GFRW3HVx6zoR7PpjWs6Z2sSpJ/NjPIAGcMXfTWoTA/e7pqS8vQwiHCKo0xlHgjw9u/66qEnbKAdKkkGxqHS0BaLOxiOxQeTbAo4df8uT47scqAMetmVV72Iwl1yDsld8BE4A83MEq3MHncQdP9n/aZqPf7sgK5Odu6h+ueOPc8oq6UYRBVbm+JMjRhvn0KlpxceCn5pSvTW4OdE4PV2cAVJiSyqelnPdc1aorqVp1tbMrH65Ozq4tBhi1cQ7AVMlQM09LMhYfGDQnHEQFaXBcz/2yZOwb0y979KX5/UxREcxDXqUSwhxTaU7UtvHrB4/Wtn/hDobOnar8IquwyYoyZHiLL6WHgo+DdAmajabWQGh2N4nvBkpsjltgNN8m8cLpBq682cBdlxq49WrDRAnGAgQDyYSr5X5ABLd1BWIZ1X1A8qb56YvX/NFsqKpI7RHq1oscBf76zsIdnGt4vPQ8NSofjijPYkSwhuMcBX6vo8D/vqPA/yZiTfoP1AcxwVHgf7K3f7EDMSItAjJNatRkVBVUSVJtLm+DO7/c9Nq0q/KfmX6zJEu9gWa+PF97xIy8jS/Pt8KocL0CdN4IOaUqdxWVSmaYudZodMenY7LO/VGNZPYqJ432lc04+R+VNM2SYIrCzV9HuXBLBcvUtmcCx11j/IYUalg8ux0dft3Ipf7vZ/jtjm8QtuAXEN7PrYf41ffeeT761IRxkZjqLBZAZvlPncx1JpWFNolgCgXmCMa7P1NuHLBUuUNWVP5JTmKz0YhBVQuefSNy6+jvpIxhi1Uu/8SQ/eNQx9kIR5wLAQzJa08L5J/aP5CfG/Dl+X7yrVx9431/Vtf0XqVy+gz1NL/dsZ1acV+59ZU/Hrj51T9vuu//fhYhPN3BmOPZFINZlWWjuqOIdE/gDlZRv956tN/uaHX5N2TJKvzRS+KHATIbs1VkMKRvqD5ZswgZEf1L8XqjIWW1yYQprEQ7BKT+O7gHCMH6aVSS1fRwiLM3VRgBNhmNrRDxC27XzvsJoLe0MqUFJZgyZ18JmG/4Jlr2xguRyJvPR3nxtWjFkdWlqYoqkb0sOANVkgxJUTocs2U5Ys00yaEwFf+mUjg9i7Nn12eioFl7XMOulf9p5aj3imwgbnt5yhtLrF2m15okfk1NdryV0oGyVImKZCleMzhL2x4fyM9dChTcFb2GYJZQtR23Mai0pPi6QNKYZDQP6soNlhKA5JxaEEtQz8AdVB0FfjWjfehtc0ZYMUfBvk5NlU3B+3BbjwwkjbnPZfz4fOCbbtJaxpsf5hXTc2RQ8fKTpjeShhgWU6laGF17v3Rh+F7eiua+ta79z2eHZFnKikbpUxuK2eAfvtM94Wir/+wzBy8RXZMxKYpkUKIASc1qI2ltQrQ8LOg+ZcSTj09N7YtBgiMPX1+OmBCCpqmUU5Qig1nlF6W/tFJtHR9pbrfYO+9rd1A1WpTTMztXP5Lermag/GjpJoAFSZYLAKb2EiO6DqVlbSdPvP2OyRNv/7xD+aYTtKvvcRT4F2yT3kZJqnd2+DJ9RJYveyt/hQ8/+uDuu/zNOrYOGUwzjapiLlud8jDu4Pm4g7n+T9us93/axuO3O7712x3n++2OGxCj/oV+uyMFd7AqJxq90qi1srxgGc0UBVWFivlJ6agSy7LaqI8MyuOeIVdz1XF3vpEWDrXdkpx9X7SqawQluTnQy5we+QPAXrQaxPKh2HKI1rGvw3QMh88CaBGJFOzVu90zPgDot2kp2dWlZ9pc3pxN89L/rSw0AxypReqC+q+r/A5w0mzVCTCjhySjrTV3FPhrHAX+a4CT3Oelra01SVSWD9v8wihDqFRzo/rLIZUAmVVJ0qCVraVeNWbpXETnMkONyGxZWNcXdkK4u/+2+vdmHxX503tqazbHA05HgX+HzkCOAv86R4E/11Hgz3YU+K90FPhLt/n9e0eBf6SjwD9vB9fOQwjma4Ejfmk/YIC9ZPUxO4px3lD48nyKL89XErevADzz/KLfQmYROOfGX8OLetXWDgKGDJgvjXeuVOKj123QOqwHp9X2wvivmIW9frJMaQp02AybPmuJvCByeA4l5Ck/RNZObYa8rG480xqhSUhGrA29m/qQmanAO36743zq4y2/4cvz1SAidlWpknSOtYoTEOrWtnd9rvzoeSaqtt8k8nfPJ8obbYuYIGnrbVNqoH0Rnx+2TInZk+7b6qMnbmuPmqDxt+ylRguICFsbLi/ZrS+67TPu4AbcwaMRNl0Hwi6+CKEmnoAI0wsijO5yhAp5Q3o12RVJVE45Ur0eYKpDdIkb/skk8HPzxYhofQDMSBLlc9x8VkpI2YakaNg+ej0vnnk2f7Ttuy6KVARc5jh/vbk4NT0CcMbqKpOrqHg9qhoTjpc4PU4Jd3AV8LcswfCk35DNm/oe/q/CUQvVjMxKjBnV0LKUtFCRmRVqa/W4JbMiACnNazFa1MMc568fE04zvG7JFDPjcIWR4XPFiO6ZUzOIHGfk41aPLw0kjalfF9eASMbyW1RVwpSxCNUcRFWlEFqkPw0vIoBML6fH6QT+KiOVxc1TJIDhm7bIM5Ju/ALtIxOqyqLytUlBECY1YJw22AIg54u1qiUjUgjQZYOKpKrXzbZY/gKuvtb47YOBpDEXfGu+968j5AJOMczkPuNHnGUQVXOmYr99udr2IeDylE7P3Ylmzji/rKJUFpOH89G0G2/8/GR6SqSGzclWOo3arNjP3WhwnL+eTiduMbYfUexv1qMyX5ENvX5WhEJUkhiV3LwmFrQHUGvaDSoOARSqWW+yv7yvAXAHV+MOPoA7WOfhqq0zG745U5pYlM5cSTh6LUbEzDUBEx0F/p0FZL8jqKa87VM6Rd6NjmROi62+//AFwJKnz9qUFA1fjlgreYbf7jjTb3c8i1g6YEGojz5BBAPpgIic1A/gslvXvnf/luKTv1+zLnprSVDxKZ1+KpybQXWRUEm87DxLmt7Gyf+3d57hUVRtA75ntqRnSYAAoS1NMsAiTbEi2DV2fO0ae+819rUSe/e1fhorYvdlVVAUUBQEaQvM0hcCoZNsetmd+X6c2WQJoScQ5dzXxcXulDOzJzPnOeepszIOeHZVSsZtWkAvtUrVRdVdg1S7+QVAQnEVbYziuQh1pYGYTbeveqTVWRWKchDArZuL+1i23GZDC+hLTZhiw2T4qlnxHUvXn70pkPLZyl/bUFoYZwdOwus6A5EZLQJ8pWdpbVRrFf3TQBXECqOOc+611yzuUtUDoLZ48O8LOyv333ytjTuusPHrgcrr/hx/yJ/jn+7P8S/w5/gjlnfh3QDFSxONTQuTzkDEKLbXR2f+r3xtfDTrzoPA+VpAL2rG/ijQAvqbWkD/68afPpqpBfT5Oz6reUis4WkAbaHaUx+dOQ9vKHLFeOORB0cbnDBTpJUdMyrc1kqw8N3CVR0xalWUpAh/9zN4+UylxFDMCoBwQLW9NvcFs+CH1vaywmgEEncivKRvRWhn+mkBPU8L6Cch8shPQLx/nyJSb65AJPPHn+N/AUj25/i/1AL6KkR++Y0AcWGUu76McNJ0Y7JnhfkEYoz4cOpF5aHo5KxPgZmCSIxS50uB19UtXK3+UjA5PY2wyoYMw9RPr1heriitmqF7t403VIA3FMAbehdvqB/e0I14Q+fiDZUCaAE9gui3qPNpTXIVp39yl/5fYP0vB6qYVvhk1SZnH3105pOIQbvqz4R4A2DEHLF6SmpfPUNR4RT7NEYddHHHU8545htrwTG2Z5s1DoDazU4uLCl7FEUZjCg925V6bdpnAK2MaTz0aYS7vqybbM2OfihfG0conDgnIVJzHkBC65oIYmzL6n9KwaLuJ25AGVA/zKxJw5zRp5z0lJm0Vkp7As0ilBdc/ct0M5zyXwDTJKIo5r3+HH+d9tOf4y+m3pT2thq/ah6Y6J3FHLltoYppMtw0eR2gdFX8T6C4wayuLbN1wRt6seE1bfHGXwAHr4pgKop6bfu2rLHZQPhLlMUptXWJRM6xi2E7YipfjHhs8nPBvGxvMC/7PVv82pEIU97S9TZbJtANb+izaBlhhxm5CODnzkMYYwxv6NXlxRuqBvr9ZngIm2oE6N7xsKJnUcShyZnVjyS1r+kIVF1l//72xsqh7ogmzwTjz/H/7s/xn9m6lNOoT0m5GaGi2HYeZm8o7HpkzZWn1jzRaaXZ7sLiuOSTEUJvNTA+epg10D5vff2K+ixaP1KfbjGW1/UsbZmepV141p2FP3YMR4YAA1f90OrvokViIbnG1ZpAWlcDyArmZd8VzMuOLf0WneH2/Np+xHeoJmatyuiSxw6xEnQsBmgfDh/8Y1LiuxvsdlIiBsdUVJ5Nfd7uZkOBfIBjV87gpODUC7CyRYWWJYKYDH0GUFupvqyPzvRg5aUuTWD52nQF4Bwr5R2efM/ZpsnXioJSUzwEozpzmj/H/3xVnHJkQYZys6kojzd2D1pAnwyMB0VdP8t1EN7QL/rozPOo/zs9ogX0x3cnPOAfzLcITUoccKiepXVG5IrnivEG7z8fqUXYotd8/82d486dKQrLxLePLP5g7fqz53WxtVZNJU3JMGsAUpZVKOFKG6hmCDhbC+jPaQH9Yy2gv6QF9OmxfasF9FpEoo85CA/i34FzYisl+XP8scfPRWg3zgKM9sVw2c/GMMQK/BfgsrMjJX9POlyML1kiXPFuf44/gtd1OV7X18CydTNTM2vL7aCYK/p2KD3wjpLinqfetWZRU3fsnqIF9KkI4Xgs0EsL6NGqPos3pSqMOsd2HfUqyfv00Znz1y5JOmBcUuImW8SkywYRqmazmy8BnGz7i/HOu3jfkXc+XtdhwLCE1qKryzc4q4BP/Tn+SupXktFczp9uXhVfeerXETwr6v4cS5entj98ap8+fwFsXphE8tgyGyLdZklq18polsRjsMLWPu12NJH2Yihf3DeyLGJTsByZ/os31CxmNADVUXIDcJCi0N6f43+xkUPuRzx/QxPdr1/iaDWdOT0sxdVaJ4HPMlkzrRXVJfaidTNdVjUxZXzax2saxuWL69nMzwG6rIWe1TVmlapyQWb7hRtsamUEeLJ1Wui4TpmLC+z1jv82xXyiQTPRRciH3puWV0aFMYCepR2L5RcwqdPA0CPhS2xBo91ohGy5DuFAB9CvgnjWmOl+AEeiQacjNtP5qE10HrY5mjd+HN7QblVIU7bIcdjEWPlP04GQFtB3quRbg/NTAFML6GUNtichHDeik4qRWkD/Kuaaz7J1UYVyQNMCeoGepQ004W8FlHf6ZvN1z6MwFHVSMC97eMN7cOf6bkGsML4AvKN/fXieK1RO5mGba1xdqk66tl3bT6ckJmSMLC37e77TOTgQ5+T6ouKa64pLnMBPeEPbSgzQJOhZWqsIynob5hZexqrTiBxwxlpb2Zo4Ni9MXlexPq4u5V1EUXlm8Nnr/j7mfw5FrU1HhKM+hlDhqJGq9lQEr9uEGdfPqsyyM/dxDmICsAaRWStqO3sCeHA/E8gA6Fna+wgnte8Q9t6s7Z4APD34/Hvf+/ihPBCpK8+t/aX4Vv3z1HCVDZvTmFFSEJ+dNX9hs6mE9SztEKI564X3dj8toC/H63q1stx2Q/B/7TDBUKCHdl5h90itMsEMK1RudrDqt9aIGnDKoVpAbzZh0Fx48j1fIUw9148ZFV5NvWcvFU4mXXqH/aislWbNox9HnAj/hQztvMKnaSROdt6mLth+CiOStCjpWkA3PPmeMxBpfgsA95hR4aEm5hTFypig2Ez+dve+6QHPVad8bT5wQryvEjNct26qAk7Uzis8iZh0kKZJ8XE1z7z3nO318j61Kx6Y2DrOuL1dW7VnTQ1fr17bA29oWTN01U7jyfcMRWht6vJKP5KvRLTC2hhfD7MWlDUIDcDtWkB/obG29CzNCWY5KPZfT6y99r8DE14C4mymOT3BNDeVqeqJAJ2qnKs/KVzbIWB0eerQx/6MRtZEKwFuQmiOhvpz/H/FtN0KkRGyI/DWSWc82w6hQXospnBLNJ1sEeC62jb2pPscnwxFZPu6kPr6CQBH4w39uus91sw5U7WAbmoBfdPuCGTr/NKGAtnaXo5YhUYQAdpfxewztYB+BzHFxi2SqBcUIxRQ1ie0mvNlz+EYInFYo6tArBAKxEqizx9pwm+lfE280wgz4aSC6gzFNPk+KXFwIM6Japo1/aprTkLYtM7eRptNhhbQiysc8dFBFFOEwISMGtW21JfxZ8Fvrc1YgfxHh77GLUfdzKROB7erWHlF1GbjQDhPqeFSrbAieAOYca/urEC2+A6x8utAfT8/zn4qkC2ipQ5Poz5veleENuMYRF7jujjGkDORiZ0GuWPOz/rMMSL1hYHn1LY6svLszB9XHNScAhnqVpFtEaucU7WAHjVPzU9IihCfXrNZARXMCct/avP9ku/aseR/7Vj1W7q1slSe/ScKZItotESGFtC/I6bEa2INR/1ncoTDdGOTtekny1nxLuDIryOHb1HT+PeU/lTY4wxQXNSPRT8CpUDnIYuME4FPFBRl2gEKb1yrkvWfNawd0OaV7krhCQMSltNmUN3QV41YeEyiPsQNAEXhtp9HXX37gfbl7zgSDGNwVbUKsMTpxNOtS0lTdczu4s/xT0MUAloc3fa/gx2vIxy5LBQH9fHdW/y+WISmR/kbYMSPjmqsVW1EUQ4qU9UTM4pMBi82WO2s7ji8W9phsQLZwoMQyCHqHdGivIwQyEsQC7poKtzbrKIyURtBJ0RZ0/BbkVN+wRt6BG/ob4QpZypi8ffQ7gpk+OeUutsKLaD7EB101TYOmRjzOao2inokDwRoUxn6BkV5BLg+mJf9M42z2vq/I6BN6DwEgNDyRJaMbccBXyVz8QSjLsylTTHj2ryXvtSyae2Vl8IZEWnulrg6cs3Rd706u02PCQC15fZDFVBWJ7Xhp85DuGH4bTw29DJ1aatOiwCMSndm9frj68K2jNrUHytXXdzBWnTnN3atbaEF9CpEnOYcRMWnS7SAvj8L5Kha/2Xr6yLgWC2gr9QC+notoP+iBfS/tYD+G5Yj1thuh2MqyjnuXF+0oMswUBgTGTEl5ZG1XzZyiea6741aQH9SC+i/xGz+BaD9kFCKyC2ldK/a5IwzalVMQwGUOISpx7u37rMZiAqKtgBaQH8CMYi/DvCfKSYnzjSjkRY/AeANmXhDv99We8Nz/ave5t3wSRtmGj3/96ZxCr92GhgdO17Xs7QUf46/qm2x+eN/Jke4+TvjS8C9MYXKN05WCYXdiwHOsv3GPfbRBQAV7oS5iDE6XQvoUftsw3HqS+s+VgBPpBvGynbhcLG1b5cTVzQH/hy/HxGSdz5w0Qyt5g7EeBqHqEwW7ffl1Js8t8VP1v8j/Tn+CQiT6GJXufnnC29FNt/zhcEpf5kYijKikXOjoZbTok6ZAFYsd125W2vR9zki5XAywgx3pHV4NP3q7GBedn24pTe0Hm/oULyhZLyhugyPu0Ozqq/3NXqWdhXCE/NFhKOLgag9PA/hADFCC+gTt9eGO9fXDlH5xESons569dfn5/QIFdYlFShJoPTKW+0pHTaZPPtupMwRwQS6aQF90zaabVL0LE19/OBLZszI6D2w2h6H3Qhzz/SP/Z3K1nf9vtuhqWO7HYap1M2/1iDsU3cDd6HU4Gw9ETOS7K8tOjQBlJ7Ap8G87Au2cTnJLmLZk9drAb16W8eMHzKs97VH3zXRUNVo/rjTELHZpwDeYF72I3vhVreNcFqcChxcvs5ZWFYYn2nUKoQrbc+VrYkvQLxb+VpA3+ers93Fk++5GeFt/bk/x19Xw1fP0tRJ/ZRFR80zoznWTaCrFtDrbJ9Wxbg1iBrvbwLXJNRWffmV74FBCC3bncDzRUnMTisX5RgNhQ23Xm1LXpuuJHSvNg75cnXhRLti1HnyAYfhDf1JQ7yusxEpjD/EG7qikd/xGnA98LI/x7/LpQP3NnqW5kIslGbu6PnRs7TeCCdDE1Fv/EyEh/PzCIc8CtPhrwOUWWdMNXO0gF5X3MaT7/kaEQf+qD/H/3BMm9MQznfPaAG9rnKXO9eXivDUbwM8jfCLWoPQdJ0XzMveqrBMU/CvFsqx6FnaAoSAjjIbGLSjlZyVN7UKoeItAVIzyzaMfPfnpw5EFF64CWBDKgtbl9BLrdc+nKsF9DFN/DO2d59XUZ8LNpYKREKTzohwoYeCednzrQo/tyMe0kNiji8ADgzmZTebl7Skcdy5vkGI4iaZDXb1DuZl73uHKa/rTIRzZZQH8IYaOtL8Y7HKuH4KTPTn+EfEbE8FCrqvMVOvHxvJ77KRsVpA/6Lh+e5c3y/Ul3IFeOyHb+4sQ5TuXI9wSv0EoMIJz4xUme9WQag8Xf7lK9+mfiX2Md7QRdu8Wa8rFSiNdVSKud+of8ccf45/wM7+/n8KepY2i/qStSAmhFtpfU1YqsABlj2/N0KTowBH+3P8v1ptDQGmI8K3OmsBfQvzkDvXdx1CU/I9wrFuvWiapGBedmUT/zRo7If8i3mowfdnd0a1arm0L7W+pgIUJredqQX0h7WAfjNWScK2JfRWt+zPY5rgnneFD7DSlcZQAZwfzMsOBPOyfwrmZY8M5mXPBwjmZdcG87KfCuZlH4oYNEDYfY6XAnnfEMzLnomY/a+O2TypRQhkwTeIBBg+4JJ/k0C2qLMpN9h+OZC6rIOi33mV/fLGBLJFw0n4FwjzRcRq81WAgja8cekd9rAlkAE+tNLT3o8IwZzLlrV9t8YbKmlMIFtE7bL9PfmetO2288/k/xp8j3bkFmUcFaEajxbnOVdsYjJbmjajzlljGgpki+hKewgi+RXAyuYSyLAfCWXrRRpnfV3M1i/Q9ohNBFLBlvVtbwV+s9q8gvowqKv1LG2vqY6CednViMxd3RFx04cDvYJ52d9t7zzr3FyE7STLSlso2UcE87KLEQNANDFNs6Sq3C2E/fR1vKFT8IY+3PEJ/zi2sCkDWOUQb7K+vhRri2yE94FoyJI3mJc91/K1iJYGTAdqO2/kAYST0rvAWf4cv0jXG02A4g0diDe0mt3En+Nfi/DrULBynv/LeMP69yMizebHQI4W0J8CRtWqlK20/oKGwqXW3zBqjnsvGg6oZ2kHIuzcAI16fCNW0ZWISVX02GbNf9B8lVxaJiMRNrpfrVjOnWUOQs0L8EdsQLgW0FcT41Ah3PYJIkrgDWYvEszLDiOcJaB+cNjZc8t3fJRkbxDMy17rzvX1BrRgXvb0fX0/+xFRodzGk++xW1XOTkZMdIuwMuhti2BedpU71zcM6BTMy14Rs+s7xEQL4HstoG/yi9wL47dqpOm4EpHnYcmODvynEROH39i++07P6/dQQrW58eFPDZcJ56WWmxtKkpTeQDHwtZ6lqYhQzejKeowW0BvLcUEwL7vaneubiVjkXG9tbtbogv1KKFtedbtjnP8t5nNDFXHDa9ToWdowhDPV3O0dK5Fsi2Bedhlili7Ze2xAxOw7EHb9ldQnJ3rbn+Ov2MZ5dVhpXVc02Pw6wp8jk/pKbs3KFulP9zO+zZ0X7v9+v6/XteLSdsUk9Fhj3j2rpwJwvz/HH9JHaSOoF8hFbBlf3BjzEEI5yk/bOrAp2G/U13vIJER8IcQkFNgWVrpHX6x3pkQiadlYqumoaaqrJ98zADgaYRN+bVvn7QgrCqMfMFAL6GP39D4lO8ZUlLeCGSK0uF0xIEL63rB2e2IOPUgL6IVsn9hc/UHqk+s0C1Io7wSWWngAMDyYlz1rB4dLJJJ/LtFVbnesnO7AGH+Of+U2jt8ptIBe1FghFUmz8fc6y8WtfZEJcHKMP0A0CudJLaAv3frUrRgX8/nbYF52s4YsSaG8kwTzspcF87In7fhIiUTyD2aB9f/7CMceE5G2V/IPwp/jr1nZVqyU+60wq6zqaFG6W//vlL09mJddiIjeCbBth7AmY7+yKUskEskOaKgJe8mf42/UCUjSslmbptxsKLzcZQPxepb2gBbQo6mUo0J5+bbObUgwL/sxRH2AZkeulCUSiaSeWCeer6jP4y75h/Hl/fNeUc26le2jepbWVc/SbIj881Bf16BFIYWyRCKRWPhz/AXAfYj48HOtpB6Sfy7RohQKcCrCA96B8LLf7Vjw5mS/SbMpkUgkkv0PPUu7C5G72kCExJ4PLNUCes99emPbQK6UJRKJRPJv5jVETnmV+qxcLTapihTKEolEIvnXogX0CkRN69gayrP3zd3sGCmUJRKJRPKvxio+FFtA5fttHbuvkTZliUQikewX6FnakYCpBfQWm4ZUCmWJRCKRSFoIUn0tkUgkEkkLQQpliUQikUhaCFIoSyQSiUTSQpBCWSKRSCSSFoIUyhKJRCKRtBCkUJZIJBKJpIUghbJEIpFIJC0EKZQlEolEImkhSKEskUgkEkkLQQpliUQikUhaCFIoSyQSiUTSQpBCWSKRSCSSFoIUyhKJRCKRtBCkUJZIJBKJpIUghbJEIpFIJC0EKZQlEolEImkhSKEskUgkEkkLQQpliUQikUhaCFIoSyQSiUTSQpBCWSKRSCSSFoIUyhKJRCKRtBCkUJZIJBKJpIVgb9LWvK5LgZHAeXhD5XvSlDvX1wl4B3g5mJf9fRPcnUTyj0fP0jTgJcB70hnPHgEcCFwczMs2ANy5vm7AG8Azwbzsn/f0ep58z1NAoj/Hf1Mj+14Gqv05/rv29Dr/ZDz5HhX4AJjnz/Hn7cP76AW8Bjzhz/FP2lf3sbfw5HtuAo4Czvfn+Gt34bwHge7A5cAz9rDp/OSZSHtgihbQX4o57jWgzJ/jv6eJb327NPVK+T3gFOC2Jmjrv8AJgK8J2pJI/i18BxwHTAGeAi6wvkf5ADge+GlPL+TJ98QBdwM3evI9nRrsywRuAu705HsS9/Ra/3COBi4ERu3j+xiDeBYm7uP72Fu8jFgEnruL5z0KXAqMAO44eJF5E/Af4MXoAZ58T1fgeuBuT77H2RQ3u7M0l/o6rQna6NAEbUgk/za6NrItVih2bMJr2WI+Oxrsix2olCa85j+R5H19Axad9/UN7CNSdvO8ZICE6kb3xcV83qvPd3MJZbOZ2pVIJFuzN9637V1Dvu+Sfcm/6vmTjl4SiWRb7OwKYX9fKUv+3exVOdmShbJ80SWSrdnRe9GU7832xofY67TkcWRv0FLGqpZyH/8ottFp++z5luprieSfT3O9b7HjQ8NrKNv4LJHsbXb6+ffke3bnuf1X2JQlEsm+YW+tlHfnOIlkX7PVCljZsUj/V6yUmwI5+5ZItqax98Lcwf7dRaqvd46WMla1lPtoyajb+NwQqb6WSCQ7xY6EclOys+ODFAaSfcmuPP+xz7RUX0skkn8UsXHKDQcmuVKuR05K/jnEPtMt0jzTkl8m+aBLJFuzr7yvG44VUijX01LGqpZyHy2ZrZ7pbdiUpfpaIpHsNntDfd1wwN8dNeC/ngbevZK9Q3Oor/dZdMH+PsOVSCTbZnsr5Z11mNnfkH3RstlvHb2aAjnjlEi2pqWor6VQrqelqPLlmLljtlopb6PT9tnzLdXXEsk/H6m+bjnIvtj7NIf6ep8931IoSySSbSFXyruO7Iu9wB7Y7mP/PrZtHvUvXCk3BXLGKZHsOlJ9vfdpKSlH96cxsymEsh3Y1hLyXyeU5UpZItl7SPV1y2F/n6DsLbaXl31nz9vZlfK/Qn0tkUj++ciV8q4j+2LvsLv9vPVKOQY9S1MaOe5fsVJuCuTsWyLZdfaW+np3MiP9W5Hq671PkwllpfH9/zqhLNXXEsk/H6m+3nX29wnK3qLJ1NcNMno1tlKW6muJRLJLNNd7LNXXu46MU947NIVQ3kp9zb94pdwU7E8PmESyJzTXrF4K5Z1Dqq/3Ps1iU+ZfLJSl+loi2Xs0l1CQ6utdZ3+foOwtmsv7WqqvJRLJHiPV1/uWlpJmc39CrpR3iNfV1DcuZ98Syc4h1df7lpaiNdifxsym8742G93/LxDKu69OkEgke0ZzDcZSfb1zyAnK3qcphPK/Xn0tH0aJZN8g1df7FtkXe5/d7fP9SH3d9De+v8++JZKdZW84ekmhvG1aitZgfxozm0wo/5uTh0j1tUSyb9gbK2Wpvt42coKy99nd50+qryUSSbMj1df7FtkXex+pvt7LbYGcfUskO0tzqa+3l99aCqJ6WorWYH8aM5tOfS29ryUSSRMj1df7FjlB2fvs7vNna/h5G7mvbY1s2yu05JWyRCLZOfaF+lpWiapnnw3g+zFNtlJW95OVclMgH26JZOeQ3tf7lpbSF/vTmCmF8l5uSyKR7DxSfb1vaSlCeX+iybyv/82lG+WDKZHsG6T39b5FTlD2PtLRaxfb2t9fUolkbyLV1/uWltIX+9OEoAnV12Zj+/91Qnl/ejgkkn2NVF/vW1qKUN6fkOrrXWxLPpgSyd6juQYQuVLeOeQEZe8j1de72FZTtCsfbolk55Dq631LS+mL/WnMlN7Xu9jW/vRwSCT7Gqm+3re0FKG8PyHV17vYlnwwJZK9h/S+3rfICcreR6qvd7Etqb6WSPYeUn29b2kpfbE/jZn/WvV1Y1Uydpd/5WzRnes7A9CA94N52Wv28e1IJI0h1df7lr02gOtZWn+gRgvogea8zj8Aqb7elbYWGR27NmG7dXjyPQM8+Z4ezdF2Y7hzfQOBr4EngeXuXN957lxfU05kJJKdRs/StvW+iu1eV4dLbT+2Sqdkp9v05Hv6ePI9Ez35nl88+R5Po+02+OzJ96jAWds4bn/EEfO52QZwPUvLA+YAC/Qs7YLmus4/hN2dCNX9rUwTe+uQySnT66XyKweedWxj7btzfWnuXN/hu3Wnu0iTCZha09bVoUQAmGr0Oe34XJ87mJcd3NF5nnzPUKALMNaf46+M2aXEHHM28H9ACmB68j3PAx/6c/xzmur+t8EVMZ/jgE+BK925vuOCedn/zEpYXtcFwFPAw3hD/7evb2dn0bO0/wD3AbOBa7WAXr1v76j5cOf6FOBq4BhgPPBhMC+72voOQK2yRX12Ba/LGTbVpV7HB7RXisgLn1+305PvORC4CfjYv3zl74ADb6gi65ULVEcqn7UuMft13mCid1YmW+/jWqACaBdzjdhB6jTg6G3s++fjddmB3sACvKGdec+PiPncLH2hZ2lZwN3WVwV4V8/SpmsBfTGAJ9+j0LSaz5ZOZszn7fa51TenAScDl0a3mzVt+l3947otjg2kdX3BnesrStFIqjvOcDqB34C+7lzfScG87B/3/Pa3zZ4/QF6Xgtd1j0OJfN+g3bt2dKon33M5MBUYA3zQYHcKgC1xCcDn0e+IB/IOYLYn33P1nt38Djne+v8mhDAAMTCe5s71ZbhzfT53rm+xO9d3fTPfR9PgdR0SrlY+rtjg7GSaPGsNPk2OO9enuHN9jh0fuXPoWVoH4GNgAOKleqCp2m6hXAi8AfwHeBsxGcT6DkCpMzH2eBU4za4YCQB9lCAIoRodkMYBVzhM8wvTZL1pEnr3gXO/qd18WEWvVWa/l96McN8Yg2fejbTqsMlcCISA5cD90QtcUxS6CK8r1fp6ToP7Vdy5vjh3ru8fKZzduT6bNRECrysNmALMA77E64rb3rmefE87IHYF1Vwr5XOstn8EJgHxxCwa+lVXHxxzbCUtEa8rAa8rvolaOzTm8476/HLgG8RE1xndmBwpdhy4rFHd9ftGOOnc6Lba4kGHAH2trz9YGtRmoyleosuBvC0bNWDLmfRWePI9ccComE2ne/I9sSNNezBwtv0p+n0W8BFCgEd505Pved+T70nY0U168j0dPfmenjs6Loo719cK6GV9HR3Myx4Yc7/PAd8jZl49gdfcub69plbfXcLVyt3B8W1ZMaENhVNbpZkGJ+B1nY/X9Tpe12l4XXs8oLhzfXHABGClO9c3eCfPUdy5vkPcub7+dYPjllzNlirC2/Qsrale7n1GrBBz5/r6uXN9F7lzfQcB/7U2/4KoTX6mO9fnJuadUqyS5dnqVCY7b7kWMXEFIFPZBPXv9utYK95aRUlf5bC3UhTsZ9l+P92ZtCju4l8jOMPiwPbFcP9nEVxlJkAnIDnaZsdw+DjgNuvrCOt/A8CodaUAi8Eo7Pbgh8ftSZ/sbdy5vuuAauBRa9OLQFTAnQm8tYMmhrKlUGiuicmZ1v9jgDetzycCTH7wiGNGlpT/GnNsTTPdw47xuu7G6/oKr2t4g+3ZwAZAx+tKbuzUXaRvzOdt9rllank4ZlNh9IO2ugYVWNUaqhxsBrCZkVJb8gJUe/mB0eMiVZ3PbtDs3+5c36l7dPfbYc8eIDGI3wHwv8ghaz8IH2c1agL0cuf6tjdwDgMygHUIdZkDOATAnetzATjSpmFPXIFpEg6X9bqiVM/7tGzR/c8b1W1GUj8I5UTvYVt48j29gAAQ8OR7tjtZiCH6Ry8I5mVvtD4/D5QBPYDBqJVlzrY/Vtld0wFO2cl2mxQ9S7PrWdrxepZ2pJ6lNS5UvS4nXpcrtCzx5NpysTguWZFI6er4scAnwHXAt0A+Xpet0TYQAted6zvHnevzunN9Q7Zx2IWIQbs98KM719d7e/dvCeH/A/5E2MvGunN96TG/z4EQytG2VwFJQIse/D35nkRPvmeyJ98z1pPvqZudu3N9qe5cX74712cCEXeu7zd3ru9BhCbmQ+AvhDD8CzgVIZhpX77pJ8RzB4h8vSeo03nF8Qpd1A3u2Gt3VtajYqiefM8A4NrYfVPjkikyk0lTyuhjzCzNWgURBZ4afMGjVQ5WZ4Qg9/OIDvwde561nhhqrQzbmyZmpLptCCBc0v8oxbGxc1KvJ9oldnl7bN93hybxD8Cd60sAXncQtt1q/+J+vC4TuMTa/SwQAS7B69reu93w+W5yoaxnaekILRHAWGCi9dlz9FPDL3u8S8nPj7RNr1uYmKYa58n39PPke9o29b1sF9FPTyEmED68rl7W9kTgPcR766a+j/eErOiHmk3D7nTn+kLuXN+vjcicU4HOQCnQCnAbtSnfA7gtzfXiTAXTesQHbFj8oDP9N7HDNGlXZBJfTRfExO0RYAGgqEbkzib4DY2ypw9Q90iNom0KJEWmz+vVpqRMPBd2ItUIz7as7Zw7XDFMhvmN2cPnGPohusHbL4XH6FnaxcAglDDRzqnZdPTKyoIrZgI+M5IytXzZnV+WLXrQH9PW+Vu17nW1xeu6Bq/rzSMrKn9CDHQ24AtPvmdnVnDRVfWi6AZLOD8ivpkk9XhucVybifEJmV+iODYdu3UTzYslhL9EqCcnA8/jdcXhdV2I1/UmXtfymjvTa2pKbdVAcemqBKGKU8wIQPGyRIBahEosAlwMeBtex53rG+bO9d0E/J5GyWeP8e7DD5vv/9U394vsBsc5gNiHtQ0w3p3r67ydnzGcejtPBKF9+DRm/2EI+9Em4AuE4x009jdvWVwJHAlkAw/GbH+RLQelIxCrtIaToduCedkViAkLB69d0BOg0ibkezw1vOl8AQWT0lBczcb5ycz/oSPr5qfgNMJ0YJMCXADQu8Acd9qKmgUA45yZlJoJ7wFkFm1SAFantGJi50H3je550kMAPdbS+9xJkRtTIkZ59GZMoNBmGwTcCGDUtFXMsCsNIFLVMcfRajqqvRzVWeQE25uefM8llmNmS1ZpDwd4wP4ht9q/ip3QTgbuQUzCAcbgdR1B4xzQ4HtzqK+j6vElWkDfoAX0NQjzgppZvvHtNc4GZm9DjUstNT8AVnjyPdnsPR6J+ZwIfIzX5QAuM6Ht2KRERqckU6oqTz3+svvWvu8e9skBz924yxpGT76nNULQAmCaihNIRfw9z4s5LgN41/r6pT/HH/Ln+GuN6sz+AJ03in5b1VbBtFRPB5X8stCWuBxHrcn9n6i88kaEjz4fwxGr58wN5mV7B6xffIb3z3fxfXfPsAVZWqWepc3Ts7SX9SytySaie/TChKvUESt+acP62S7b+foE+6BpARPgHPukuOPV6QANvTnrL2yYQ+8dY3DjWOOE6783Rtz+jYGrgtbAB/83flR+YspvqM7NGOFEajYO7x49L7mmgi4lazEjSY9WFFwSncH26fd+/7KJT7W/Aq/rVbyup4AVwBsVinL1rPi4WG/wNOBFvK5WeF2n4HVdgtf1Il7X43hdWsxx0c9LGtz688CVce2/eUu1l9XZFlTH5sO2oXptTs5AODBYmDf/UZw858n0tI9ebuW6etFfae6lY9s5lvrasWxcGyo3iQE9pVPVQwDla+JZPzflMryhkxAaB4AH8LoGRFu0hPEk4GUVY8gHm0cZB/vmc6hvrjJqxVvf9Lj7u/7WcVci1GYaYgzvAyxEOPGNd+f6Yp2GYom+RG8iVIEAx7lzfRkANTZORjT4oxbQa6j3PThLz9J2aLbYF1immRtjNl3qzv22rzvX91/gMmvbrwjtSnRFWmRLXjAzvsPn9E7+hSVxF0UdWT4HVgxbLXwaf+oiFBRxNbWUrYljxm89WPVDa+cGfypqyGSzP4UVv7bmYNNvA84cMcfgsY8iJ5w/RtGSK0wCCWZtF3XDuCpFodVmJRlgU3yrVYD9s97HPF1pc04F1JF/mJf9UrB6zfDyCgC8bVtzQpeO7bDs+bXFB6MoNUXiFk3syYvrfqxqL70QyEeYnDKaplebhSPbUsyFtgkAlJiJNcBLeENH4Q0ZCCGzHEgAfsPruqqRNhoK5ct2xUy2k9xg/f9DzLYpAL1XGzbbllWO+M8fNco7r0YGnjcx4oirMf9q4ntpHK+rGzAICAMDgWLgIOAqIO/j1BTuzWjDE23Sye6UmfyZK+UF1V56vi1+re7O9bUHcOf6erlzfdnuXN/d2/N0Ng3nYbHfFXv5CmCu9fVWd67P5sn32BGLjdYIH4m7AAbe+umR9qqETpgmvVeJfgtmQI2DeIBPjw7/oGCSM85pHriyFoC4SISzF08EYNQfb946dJ0urivs+n0RPkdP7n7nbckeCeVNC5JzqovrTX2tisqUSI2QS285X+B824Tzoc7ZpA5PvueQ4XPNYwYsNzHFSm0LOlRs6nzEpt8BqFl/EphOgDOenPLGSZ99/1Dpm788y4X6ONLWZ441St0AKIqZNDY58R3EA3w3kLDQ6Vh3aOeOGDUKHWvD5riC1dFLHFGiKquA/yEGj1sQTi1T8boyAQ4oWnnqE1Pe5ITgtLqTAIJ52UaKlrvUmTYt1jMb1VmUTox6sbmxwmOetr4+DnwOiuoal9q79aREChenEFkWlVmmWV0U1aCaa0oLEkY5ksJ/AWxakOIGwBv6GLESBUtd7M71jQBethFhkLKId3iGuKlVqlGrYoZVes0qsL8644lZJ9x76WOo1S9F7601oTeC8RdcEojLKX/T8XzlCepfWXYiU925vn6xv8GT71HsyfOPc6RNQY0r/C2Yl/03MBOx2sj25HtuKGgrPE7fO0491JPvaYUQYoUIb/hY5xase04fcvPH5z590hVn6Fnadp10mpGLgF6mqZRa3zupcRvmUa9Knh7Myz46mJftQ0ysRsVlfH9vYucPBjla/Y3a3oddMT7H6zo+mJdd+8M3d2p9NgdrAdYlpn8cvUjBpNYkF1ZigFkUl1yysFVnInbFqNwYxylFX6m2iNnzgomGCWALK8rBiwxCceWOt1ypa2bGx9F1vWinz6ZVbwLTgdaPHHKZNYE1r4qscfTcaoAwwQgnEQ4N+EtNWDkdQLGXVNniCxse+TcwxZ/jX7unnbldvK4ueF1X4HUdi9e1q5O0gSfYpmNXDGYZPelf/Y7TXfXJn/Vth8rZ0qnt4kbaaKi+vhgY68n3bNMMBODO9Tndub7/uHN9J2zvuL/7a62MelPNK9Htf2T2LgPQCkwe27iJW9fXvnbj5uK1qmFy5p8GAGf9ado/fD588vbab0IutP6fhjc0u0pR3p+cEM/3SYmvhVQl+dU0VyR6oFmpcsYfBl3XmdiTFzpsiUtedef6XkdoJcciVODj3bm+No1dKFx2wBaqY2erGZHkAx6a5Wz9SxVqxYFxGb63gBmIycFmYLg/x79Rz9Kcr/36/HfPfTGPHmugdSlU2yHQWaHKIfwn4mpNrhxncLy/UgH4pb8QXb1CBe0sh9NrAPKGXMhTgy8wlqV2iGr1Lm6q8WaPhHLZuriBAJMPPJBNcSklClCxoc58htf+wYknvKM9DoQ8+Z4HwDK8m+aLJ88QD44C9wJHLM6k/Lc+CjWWP/Bx80owDRu1Jf0Bll+oj/tu4IYlj6mWF/ZFC3/iw/GP8+IH63HUihnP7Pg4lhrtJwOz/46Lq7ioTbt078cGb78cYWSwclpmOEJGOFwNMM8ZlwQEEbbM9xCzqVTgDHeuL+O+vz7sM2jDYm6e/UXsigdPvqcrYvViQ6wg3wNQnRuJ7/ix15PvWdPvvcH3u3N9zW1XG4BQsZcDT1XbuWlpJzOimjBsvknOBNG/S9vzMSidrHuuBuVRLaCbteX2r6x2+sS0+SaAYSoXH5T78c/AL0lU8p3zgZqv4rx4liwlUmNDdRjr/xpiFNbawF1Yoj7z3bQHTq4clWiLX75mSJdHcqbarztiUyApd+MfyYP6z1qc8HThm0zgdveVNt/MVx+45A28rv4ApuF8NqHzh93i2/+PRPfr11gD2TfiVoz7u6w3X+1hDelTs5TuwKVaQDcR4QlQ73BEv/cPzDro5RFj0xN/WtWpeunoU5f/8bWBMr+J+3xnOQWgZuMxjnCpsODYU/0gBpwHEEIbgGBe9qpgXvZ9ztaT6yZ5qxwONqoqCHsYwMGK8LkovGr+2KhDUh0/uA/98YKTvIFbh9/C3217jxcNOxmy2MRVUa9OPWil+P+VNNeTD7RpbXZdL96buEh4hnXPS+e07dVhYefOpaAoa2e6WG22qRtMb9lczC3LulG+9A7MSMqXiiIcveIyxv0GYBrOxeXLb6Si4BLO/e6wVldPOrAw6h/SLHhd1yNsfO8AP0VMZQNe11N4XY2Oa558TwdPvucFT74n+swPPEQVq54lRmZ0Rfm+O9dX/054QzMQ2h6AI/C62gHoWdqQXw/Rnu8XNNphmrUIzVyU3ggH2Nhr9/Hke261tCjYI+FXEE5b37tzfd229RNnd1fOVkFd1wrOuddeN6n99IiEswD6roQ+3yZy+vTSbw//Ni54/VgDh/UXs2dW0yN7/exttd0kiOibB4HHaitVjAhfevI9yimdOhx8Q/sM7slow7mZHShXVRugX/QzPZ79vwgXTDLIey/CiTMMnEnzRiL8WgCWWf8nAjfqWVobPUt7WM/ShoKYzCi2ikMBTNNWYR3bXbHV5MRljI9P6f0ozta/XQ5EHbVe8ef4ZwPUqPZ+adVlrToXVTEqX3TSrB4KtXYFw3pLHvrU4PhZ4r0YN0hZ9v6xKhEFVJMu5XHcihj3p03qNPDjiZ0HqTeNuO28UkdCOUIDe1G3Bz+My3rlgo+1V86/E69rixX9zrLbQnnliG4ZNSF7EsAPHQ4pS62p+AKgfK2YLFSaTjY6DGWd3X4vQpA+5sn39AOO77fCHNplA5gibONdLaBPuT/HnvvK6TZuu8pGWIV+K0wOnp8IZhzAhIsW/jQMGIJw94+qKuhUUsLZfhYArLPbOSHu0mHuqo82XtamU9Gt35iOrNXgiECPvzqUAQyuqo4D+CMxfjWQhTd0GN7Q5RWK8mIYMEzl8LYVRc+1qyyyOsjMaOBA9TLQRjXMmcBJiJUdztaTcaT6L0Q4ON0BbHbn+l7c3f7dCaIz4J+1gF526+3Kkfde7LA9fImC4TRmANTY4Y2TbZu0gF6oBfRzgCQtoL9hnRcAKHEkDnXn+m505/ouHFL1+pywqeqqYiYfbZt1DBB5zvHf9X3VFU4zQrhokXCa/MFj+/zZ45yZuZfZWNvaxBGBm34o4z8b/tuhprQif96Edp71s12UFiRQvCSJwqlpVHxr5+Lff3ScMW3yNetmp8y68bWeRylqze3RH6Oo4SOBbxM6/99XwOp0dX6P3M/Fi7OwIxQnK1j9DWIyBHC7nqX17vPGSd17rlL9r//f2uz/GzMu4cRNowFwZITrHMb2Fp58T3fTFH+bcHmv+NrQIACcaVMqU7Tcs4N52U8E87IXNTinL3CQappG67D4zXPi4wBO3DCyk0J9CNivCLt7HUtTM/ko6/h1iFUB09tq7wC0L1AZOUVMzJypQhnVodAKblCUw0OmqmRurmtmTjAvez1wwjHq30UnD5meAlBT4mBVZdu6Fd/x5RWcpszBYTgBfFje14piiJWcUvOtUdVp+rmFqxm5YFKPY/+c8Z/8Px+P9ZJtMiIPt7oaeA1IWmx0pNBMx6aYSQgt2THbOO1l4Nbsv4y/Z/Tvu7RX0cp2Q9SFAJxum5KLUHfGI5wN6ycT3lABInRTQXj+JwPftS/mtoc+NTh6jrkQaJgw6SVrvItO5CcBLzhrzSf1LG386B8evnLQuoUgxuARbIO2Jeb1AEs6KACve/I9rXo++cTha7vrGXPdYliq2eBkw9yUD21rHYcMmy8Eiv8Ak55HbsKZHGkebZEQxpdEapUFJSvjH13xS2uWfNuehZ93eObNl8P/26Da6gTSakdd5OWorsu7XptqeSrYTLj8J4PciX+CWg2QF8zL7oEV6qWYxtGIiYsXmKpnaXOOW/nntbb41ZZ6NhIdBwC+N022yFthhJN/rFx10cvR7ytS2m3lHDo1S/Rh/Fb6WpblH6uWVMUpLO4oNiRV18WJ/wJcD3xmKKoy+oBjkgDK7PEP2JP0Xxyp/gvsqfOeKVWUKXhdu+ylvctC+bkbhl/x2lWH/Dy/TdJUULAlR5gT3+Mdhxn5CqBkZQKmAV9HDhub27YNEQX1Ol+Ex/PDDF0UuQg481BhekaBD7WAXmw1/RqQu6GVwv+Gio66+Ydibpk1hgenvZ9GvV3la4TtsQvWyi5HL026vkg0E9f+G5ytJx974pzqjoNiYtDWVbY5dqnRgQFVIufEpykpa/GGqi0v2VcO7dop97CunfgyKf3UIesCdSsZix4AVjaxU+8ZE+HTpyL9x4wKf92z0NxKPaeoNWmKvdiJUJ00KU/ffMyg16465GcTHgN4/1j1UE++551SVf0Y4MCUipKMXuUHv3+s+uT9l9hY0U45Kmo+0AJ67ICuAzjNih7JPZ54JanHMx+FWi1c/9/IaRrAMerMsitsvqNPtM1Iq1IUvl/c9p1IjUpYZdN7x6nnAxS0VR4aohXVrfByJhg89V6E+CIbtXaz3BTC5FkwdUyFinVxlBYksDmQohYWx08EqC3xULUu+1uEPTrbnrxobmL35yb/Z/Eko00JrE+xVU30qEdZlzjKcvL4EOGpnQLcklip/HTXN1X25CqIC8NR88TfvaS1bbsqxN3Bk++xv33Fob77bjn2rz6j7v64kXjsxxQFZ7iiK0Zl53VGbVp702SVYqtOIGaF3IAzAY4pqjKGl4sQ0z8S4sNAz+qQfRRwLFCFsHHWPdTrB6Utu/Ho2ymOTzkLsZL+a2z3wycG0rqgmuC21NNPDxDJn9oVVZBR0PUk4L5TgtVLVRNUu1GJiH4gGH/B8tccLxc74wxqUsRA2isUrLvJzrXhdRlKMXmOtz4M5mXPj70XAEVh3O9xN196/eJvDEwFWwez8qgRC1btSv/uDK9ef9iHszckvbl5WQJfLjmcUyof57DqV/gyciQAC41OtzY8xzJ9nN1lvUnOBCM+qcbofs+sfDKMIoCwU4lMQzgcBoFuwNsNfESiNsNbVYfxFdAhuuPMP4106u26KxEJXxIQWkAQjn5tFMPkpv8ZtwPHJYWr1YemvUfXkjVgOZw1RM/S+vYqZKABfH+QCmLCf70at/ZtRYnw/unOmvj0aPSTsoXPxuSOPehX/W7hDTU3z9pmR+4mL1w/7FHfunbL1qxMzF86NiNr9R/pVKyPyn7FllZOds9CaFsVI+pMk4+eDlccuHHpXQCtPaVmeh9h3Tl4cYRB4U/WAS9YR88FGLp2QT+2nLD0P3jDrMsVWzWmaQsrCmFbxOTw+QbvPR+eOvrp8MNmlevC2tCBZsWKq6hceO+J4dJ+f7hzfal4XY4kZ1U2wPxOdvPLwxR8QxSm9RZ/4vSyLX7iucBxYZsyAGBa763E5B/BvOySYF72ecARP3c5aGa1aic5XOW+/e8fDkusEs/Cz4mJBlsm4Nkpdlkou8pK7z/6t9Axbn+kG8DCzPiIiXofMF61G9WRahvl6+IY3XXh97Pj4+ixPsKIuSYHFMLl4827HWHztD4r6h7gn6Mf/Dl+05/jf6pUz3sl330pf3e318ZFTE5c8ReHrZk3kvrECbO0gF6lBfQCLMeHys3OrpeGSok3jHmqvYLk9B/4z+9ilVCQnPEZQJ9NQa6tuoVNJQeDiVGjKoOttIJfAjcaihJXqap8aiSkXD0/6uBbx9kADtM8x1VmKoOXmigie84J93weqZsJmaaCabIUwJa4HIRXdJMSV1t96dG/hY6JjhZTeysZwBU1iuJMNgwuDZU80PbLVeb3B6kvrGinVCHUOMMA3Lm+Q6MOVOed9HB8rQrxYWhbWYLq3ERcxveMN4Rv3vG2v5MfdHw8qVJRHBdktC9PCjquBRg/SIk3VCUdIRSfS+lU9Z5y7saC2HvcnAzX3mBPOvde+w3n3Gs/8px7HaE7rrAFPj7KVjdJ6R80MU2Fmo0jqN185K0Ip5AJgNqjaP35JwaCKsAbQw9yfhM/yo4IGYoD7tICehnWgFejqueOnL2ye3oZGA2EhL916+ZwvLMf+kfxyRePW33QsRt8F9zlejRgPOx6PfJwqyc/e8Y91DSF41r12tMA9aFl3mvXKQrR2fo7nnyPz5Pv6QBgqeauOmWaccbI3w2uetNhP+tjR+3dn0fo8WOCurkwnooNzqg24TEre1Pdb1zYoety62M0qce3QPjXrvVmTj290+Zx6UPnb4xPxYbJqx8tLffn+EddNa12PEBC65oE7bzCtXhd64BQvFLbzTCVshmpWWUAB6yqm9csUxQRpz/S9vvplu9FOLpzUFVVwdzlK99sX7N5funqeBXA3MAwvKGVTdHpsWTNK7oo8ZdU1v2VRp8Zy3lyylt/g3LAlEjf3wAylOKTB+SO/sOd63vPCnuid3XNxQDnTjaqou10LAmxaWEywCy8oYpgXvY6xMTJQIw3Q2MuOxaYVFNqizNq1eMA49VT1BW1NmhXTGanjXXHfY81YQZOskLILgQ4dZoZHLqw/hGNM8Icu3IGwFFWrL7izvUN8z14/KvGw66NyVllP4BQsS7uqDwNYJrqmY5kXQO4tGKDM/2AOgd5qhx8saAzfH64wo+ZR4XKScj0GYc0eahm+3VFd3b/1eYu/qMVkWobYK4GnkFMHgHou8KkoOikugnrheOTws6I8FkJKypr26d/165/KRU9qmsArpsyv90A1z1Rn5xFAMNXzUqzvr+MpS3qXr5WONaFE9Zimuat3xrc8p1BUjWP2gy++fyFTUeO/KX12Dsn/cmXvge4deaY3seumb52RW3Gip5K4ZEAf/axVX12lI3842wVEZvSMKb7PS2gj9HOK6xz4Au5t1pGT41+eLHNVT0v7XpX/LTuB1QCDPdXcZyl/s5zdYn0Tj3/o13s3l0XyrV22/pVGbCsPSzoDB8MVQqDedmVWkCvVWzmGIApi9LXLHc6Xge4ZkZ9NsS0UpSn342077iZqIPXlEYukVVd2ZeH+z1x3Ue9j/tuSgfPdFMIt1KguN3g4nWWY4cC/AJmpLbMjrnesbhKVY8CPui33AwnV8HGhCSuPebOczfGu3AaYbqsWjfhueoriVR1jg4UcxEB+BXAacOLayK3fmUQHzZY2h4+GqGGAUKJ5G70pl09sKr6ngsmGlvcrKuCS7L/EtuMqkxal2esAmib8ZWZ2P25LdSUTYGhqBtWZUBhhsmMgyJk2tVgr5oaFNPkxLKKKR0f2PwKgD/Hv5F6T+X8A5699a6knnl/JLpfXXnAczf9XJJcPWeVFcV40a/GY6apVCq2apZ0nvR02FTHRq/3bHqr8h4LbEntioWw/WyYmoTwqj7Sn+OvwBsyeyg1o98/RmV1OhR0Nsxxw8zJpYlKOWI1MRQ4pCBDyfr2MCX97RPEI3fIUhtVqy/EqM5cEMzLDvpz/HP9Of5jE6rN+277JkJ8LczrojC9zWAVbA9SH6p1lyff0//iO2xHVDgVw2kY6WdMEwoAFUYiBk8A5rd1BJu6//05/iqbNa4OXGrye+ui7tWqcp1NMe+1wZ+Kghqp6oBR3fEd4G3LvvkCMM5Ra3LepMjJp0wzftSztOcQDmtvXfKLMfjc38Qz5AjZHEOWmAxcirpucjqRapsDxaxGJAGhTd/SoKtbRU1bTwkl8UmfNbi934J52aHfB4QmrkmDWhVD27zqTODAlJrKaMa9gwDKCuPtAHFpYRAe0hlYyUJUxXwutbLsGYCh8xOqTZMfECrh1xHx06mIhEFv2kxzybW/1Zjez8KdjVqle8V6JxgKis1Y0tu/cEbT9r5gc5pau7gDLLfWIH02rxj8wzd39k9XSo9fZbapSFPKuNv+2aGIle9LeF23aTU1LztqTQYuNe0AEw4U87WpG1NZ5rDX+R4E87KnQHiMYi8GsWISiHSbn4SCwgRQ6eD3yR6168KOopzByCnG2wgt3t2IQbsIYWf8HIi3Rcy/L5wo9P6bUuD9o8Vk56B1AROh9esDfAZM6qMEbwiX2VqXLkrqDDCnO5uBVwEUxRiixq/DbpqcWF5OSufKKWD+BcyNr+VS70X25z4fZnsyXK7lIsLymnxhUJFgKyu0xOWGhBTuPuL6s7WAfvdJZzy7/osDDvkdYOAyg3BZb46YOOTn88elcvrMEjvAj10PNh869Mr89umh84F5/fsVOcuSTDptgmP18KMAwbzskq4lazccUVgX9Zrf4eCiIED74vIER9jkmOqN6W99V1E1dKGJiWmimDOtY6+9KDD+1OGrZxMfqeWElX9xx7TPEmrH0SG6mj8trngjYkE3BGEG/H5lG25COP3ebOVqePO1tes5sqLSfLRqA+UZ4TDA+IHKKi2gbwS47r89c0a1Tnv/vbTUPtOz5yZs6CmEd7/CCPERGxVx5Q7FXrrVCm9H7HKaxeve/esQEBmygFVgdPTke1L8Of7SSLXtSQMu6LJG6XDmHwaJtUaw2zy72wRKMsOkFtrpaK2VFLHiqau65M71DUd4QycDRFSb/sS3L0djzNCzNOWAkWu62xzmPITdp70W0J9YMaz7xor1ce1WTGjTa8yE8KXAryacALSb3SZrvaGoGX+11zg5OJX/LP7VOaHLQVQV/mdTUo/nXYiXBuDZUj3Pd9ryPLNtyUZKkok8dr7NFlGxnzUFXBW47jY6vGkWKIzwG5iYdB62mYr1cWwOJJMzwWBJV9hYM4Anza+Puj3RTpmjVnFF1n3JDjKb7Sq3vT75MeCx3x48PHCxbV7vi9fhBgjDh/b6sCbcub4MZ+vjn43LGH800DOu7QThqe0ojrMlrDrGFreW+V0Uuq0zOUw3M148w8wFXrInBs/9xJU04pKS0rT1Npv7q8Sktq/9IQTGF0eoVMYpAGf5c/xRz2Ic8HDwwNoh9w5xDDqlrHLYozcF536d70lCrNLbIGxxlUDFCY6S8yD5hi5rarEX9SJsZ1rs73v/+UipAhQlwbNnqVXhFR0dwPBS/YkHk7Me+E1RzCOBOdVOYeY49zchIQ2FGtXEl9q1YuHaTfGnlNhVZrvX/tGUfR/DEcDvWoHJ804nh3XpSt+qMHGKCD2IlPcKA/cF4y84BPjJv3zlAgO+GT83I6HrAvswMPsD/Rs2mti9wqxeFf/E7A62DhtcXN5vhamEbWAMrIg7rm3oGbyuG9p66IRIFVhzJ5/nvxo5szfCh6EU4UFNbYeZ1XdfbiO1gvt+uWXe5CCgf3Pn74gB6GAAM6L0AShfE3cLA/iV+r9REd7Qxn6jtbbAI51LQnGf53GeFtBFlQuv60ZgGnCxf/nKv2sr1F+W/N6+ZxXxrPot/R0jrLQHTjEj6vhm6XngnG/nOz35nuHAr1f+GIk65lz3wBOvf7nioV4XlKvKN2c4fuWl8Fl41OXnAqmLnA4GLTWxG9jL7fElXxxRm3rMnAjd18DXampSNPuQJ9+jJPVM7aU6Sqhac+a17lxeDOZlrwAwIowLBYWD93SPuRwYtrItq/utpNPhuplw5df+uiIdnnzPt4hJwZEAI6cY3yrwqAmbb7wqKTneqHJe8gtm19J1SrvyTaxLaj0PIENZG+mirrMVzk5DMRTK42B1D2OFP2dBwYD3+/0dUZTBAJ7qalyGCTbu0AKBunfIv2WegGbhio9mZ/R73+PrUGSevLn8IDaV9XjUneubAIz6vl8H5YwlkLUKbpjx84/ZwaknRs8rdSTw2oFnjVzy9Olfw23gdZ3uSDDeK8uqHpb8dzxd1nI4XldcaEXCdy/rGa1spkFShyqzy1Gb7zJNRm7wpxKutNF/uYknvSoxY1XcOTVARv9SpU2fso366Mxs4GaEuXFBUvuqXvZ4QwsFE6ktE6LOkRhmWGJRZ//yot54Q5UIM96E+pET8LpOALoMq6wqGlZZdQCwuuyIYueD9gz0bnSc+HbvfkuczsHx8XHvVAmHTKYlxlM12OD+JQa9gkpR5eaBXxyyZmHOH+1TGk6cd8huO3r5c/yrER6HKtaLrgX0wOweSiHA+ZMMTv8DtxlRUR3GX0WHdo6kWKqWiMJPCBtyLB9Qn9YvjMg9W4cW0E2bw3wMIZABHsfrOrr94FBqzGHPAe8pQo9flRiuuggoH9f14DBAl9L1Q1OryzFqMrRIddujge8QM8kXgAPjltbaAXodUGwcFql8qdqpMKWvSFl38XiTy36yVjNp4c9TMqvJOLCExLZCE/D61BATw//lsMha3ly7nvbhMNcUh7bIitSUPB6+6PkCQyx1TZPQqdWjxrurPrnBStX4f8Dqmk1HTw6X9TrNqGm9Vdkge0qAeV3rtLvHILzIg0DXZ1qnLfN06/LdMZ0zV/ddiTOtHMIqxRP7K/OAU/05/gVbNOYNVX529aKjp182v9XDNwXnAvhz/OX+HP8f/hz/d/4c/7f+HP94f47/9xOSS750JIVRTPBsXAbCuQYQYV6KeKlYkaG8VpGgHI/p/FDstX1avfb0utR3pqnwec8RLGvVeg2AapKvBfSajocWZ+vnlHPb1TbWxdmay9FrBlDlqoDO69kYVg26zYYR/3OQUmnQr9yhB+MvKEM8j0nAQYrJEwesNodFG4goRIDz3jtWvTKswoY0k86Di2cfMHfhg+eMW3DlG9m242++zjbz1mvsPDgkhUpFuRIR8zm4VFF4Nr3VBk+3LvemaLn3ItSGRwfzsqOq2f7VToUNrZRYR5jp1v+H61maByt7XnXIMQ5vyI83NBdvaDHe0EYALaBvoD4lYaw38nSEqhLgxcpNzrr88xXr4/pXbXZGbWj1oUXNgD/HPxG44ochdUPYEXqWlnBKj/jfT+ycWXtOx/Y8nPgiDzvfSY0AFSV2rvlBvL+/de6fXFB8BQVtRC3d6tXOWCetvqqjZDCAs82EeJTqaJQCCz/PdNSW21FUk2mHGAcArGqrRFWZJzZI/fp+zOeis/4wo/Ha31XXupXyBIU1afZVAIesFa+TPXV2pLr3i+a17dpSukEI/5dOV9HUKhvA7ZtKN0QbVGpTdEDFG9piUru3UBQ+XZuuUNVhBmAch9CcKEUdlq0Ze7AYV2IFMkBhUpufhEC28IaW4Q0dVdLGeA7AvVpJKJzu+qjwz7TjncW1DoDWWWUKcJ6i4HB0ilQAjJhr0j6oTqspcaDaDdJ6lQMcr51XGNbOKzxdO6/weu28wmldhm/WMg8ppvtJ6zc6ksIzwFyVMaCkSBG39/h28ppHJ1efWu/DD25nDXSspUZVlSVOpx94v0pV7YOqqri0uOSrBMNA76xQayOcWEPa5+9MveoOX5Hzy3cn7XIe8j3NthNdiRwO4Mn3JLx/rNp29ZZDYaVRq54zm15rOw0K0f70ULBdn9ITtIBeZ49y5/p6EZOhBfgumJe9pSDxutoi1JMgnCkAHoxzhRN6nLKuDMxnEJ7QfyIcYvpd8esXPwHdVqS07wTMVMB566wxFTfM+TLx+beNp8eMClePGRVmzKiwOXhd4Jy2ZSFMBVzdKh0vrN94y/RgAdd3XetUHQbti6FtCUQUqsJFjusATVGY4upWUQxQtlq8j6YJnqraUl9BIZeWlDZbmruFZpcfT615nOdrz+bS2rs362bXDxFxjB8iElTYgfaVBVfcXL70ztTypbfXVm8ccSwxyUb0Lgqm8ObtOWZUOA24rOs6k9P/NEisMp9CUQ4+Yr5ZA2A3+HTW5fM8/hz/2K3vZpeYm9ROTGQuXDJ+IvWx0SDCSHoBpQOWm7n+5SuDE5x3VFqlCDvVhgamxpd1Ri3tQeWqi2eVF510TpnN1RmxGonmZb6kI2EiNgUUpVnixq0KVZMBnno/8kKfFcYbF0wyGLzU5OVvqvik9oOeiPSx0aT5z5UEE6gOOTBspnndDTZuvtZWoAX0z344SG19x5U2fhxZg2qrT23pz/H/bCjKQXbTXF2uqvyUlAgipnnwo23SyXeldkSo9K8P5mVPCOZlzwDw5HvaUO+EFBsSNgURPtcJ+AkR2vGVFtAXbuenRifG/UBoq/Qs7TJ9dIcgIhlDYcV6Z+zxByPU47Fha83J9NWtoSgJE+FvcBhwVbHN5ljhcHBP1xqeaOdk6apkHv7QJLkK4lvX0K3/OtUs7/l5anq1AeDaqMYK5brVruooIaHTR4NiMtIdAeBMr2VeimMAwO99lU+BAoRG6AE9S3tUz9ImfzYqfBhWYhjFMJ9WTc4A8Lfu/mekwu0A+Ku3EQI4etXUYkf65NKEjqNthoJ9dVk8Sg1UOWBuN4UhFfTvlfvN/51RUn3UoCox75qREHfNTlawai5GA6WqvQI1fk05og+89pT5G0YfpRLoxBjqfQ7+BM7sXVwwsrGG1mSajwU6iSiZ0NKkujzTlZlhPk0/ZjYi491jD3f+zxyAgxeZdP4lcTOAEVY/tTnMaLW7cYjMf+OBJ6xt3rgXNrXt+ffig7RAoHNql6r3re23A6V4XYXREM0YoolLooUX7lRgdt6GjXSuFSrqjHCYWzYX8+q6DWPvuGXFyEpVTa5xKD0dEb6MaWcTMQUwdpY9rRI0CZHu8FRExx29Nl2Jv+0aewHQdcyo8HHAGi2gr7g098NZN9m/7piWUO7Gw21YKeysXKXvWO2NB3KBxVtcRcQG/h/iB85ADEZjsbwWncmRv7RA4G4aIZiXvQFAH3vfI8C3h66dHy16UeceH3ImvXLd3G/OA1if2GpJH0ehARwQb5oQb9Lx0KKZBZPTy0AJ2UzytIC+CdHhR6zJ0tLAXF8dctg3+FPYvDCJOFf4Z/dxG6cgHJeahWBe9kp3rm/uy5Gz+iO8RauB3xH2Ph2xQhsJXAsKRk3GZ4vueHmCR6iVAaiMU4pMWKiIVdO1Y0aFPwqrVNgNEjtsVngj27buUN2sQUyYvmuSG/eGNiXktF9bvCypvRZa0T2Yl20A6FlaZ6hziHpSC+hleF2f9lDXnDIz/lrGhI+irVJcMWLDnMQKM25tovLrIeSFakQWS0SmGeF81C+rusZ0RSJPhGy2ZtNUIGyFx9sNbnj4EyMaW0lS0ElNJ0dCXKcICK/mS/TRmXPAvBxIa+UpVTalpgO4LQGqrWmt0KWoFkQGrDr8OX7Dk+95A3jsufRWHF5ROcRlGMrkxC1yZJwOvBTzPZpFb3msiUEL6JV6lvYhIoFJdDX7BNtnHqJSWj8rWc0LwM2goI/O7KnYjHwzoiwGGsYiv60F9BUNG2sGFqIotX634rBCgY4lJgf9ZeMjDF3oIFLmIAnYnKwy9MjNdI6bZp7CtMDyNolqeFEcmRto68n3xPtz/FVYRR4QmrtL7MmLcbSadgFkP4XlMLk+06BSVRNMw7mxylnjQ6hLnyamqhZw2Nk/tR02+tD0kz56c8FERDGb8qeHXFgYLqskLmMcEwYo/c6cCt1D61qltfJRaYWUawVC1i7sqGCoCgNrSxhp++2yVKWS59YYHBN/5o3+3Kf3xqRnm/hz/GFPvmcqcFyi+5XbygJ576RoufHAg4ai8NDF9jvHjArfi8h1/auVX6BRrr1nZehCQyu85TMyU6qgJNnk8f/YWZlhI7Tw4uQrnxj9sDvX18aWueCOeV0U+q00cYbrwiN/RoRNxSPSysbmh/ic+kIjUe5BTDKPQ0QsdADm4HV9hkh/m0R93QOx6PSGlgADO3ld7b5ZtWbeKoe9TZfaMHZRIOlmqz/KgaX6KM2LiLGeBryqBfTQrvXsnq+Uv0E4bA3x5Htepb5c4w/+HL+pBfTxWkD3A6wjfcFL4bpJ6O14XX0tJ5iHEQ97OeAN5mXPCuZlCwd1r+sivK7ZiMHtZMTM6y6EyjM29/UOU8lpAf074Mpq1TF/iasjv2X2J6KoZQCumvKLO5ZvdAAYpu1FhLAPIIREZnJm9RAtEDhKC+inaQH9jwbtFoHyfwAb56dghFUqNznP1EdnFuujM2PvsTm4D9H/i4ABwbzsY4N52QcH87JzEF6ksff6GtQ9PHcgTA/D1foB/V5gvt0gEWDEXLPglf+G+zkjdRqMJkvXl9C6diyAUat20bO06EwyDxFG8jvwNF5XD2IG2HPskxhhm5MIkKhUtweq8bpuqWtU5Nj9EqBDJDLj98sXPOjP8X/TVPfcCB8DS4FMpb6ebhBgw7zkjyLVyhUrJ6afpY/OnAo8DkqaMyVcnnlAGa0ikagvxZmIQYtOtWGwYt4b8BymqW+22fgxObHVEqfjmAp1i9d2hCff0ynme1Qoz2VrYgeoAA0mAY0QbeNEhEbj5tjrmhH1B1BciHf3HITWpYytB8JmwZ/jrwHmzu4uhFm1nUttEbMfUHNuoOrpk/4260Jd5roVnjjH/l5RXMoku2IodsV4sEsroVnssRa1R6F5llVtLlpo5dFIRZfFAM42P+fefHvf87ByiU/vJvq/tmhom1I9rzuivOYSgFBi0nIABWznr1w1pc2Kkx+Yk97nYavNHzYluDKN6vYYNWml69MUVrUGuwFHzDdRTPO1aX+vqbxyvFCzBzorOAzMjEiE++0ikdsKo0tRqPio15upS3cVHUBROMCqL98foYHZAKzSAvoyLaD/sj2BHGVupv3zW6+x8c2Jxpp7L7YTbC8mJGD0dOf6DgAetcWtT1yxdYDRz3hDZXhDFyJS/B6GSK15InDhVtoEb6iWxjOznYt4H6NpfxfjDa1vcO46JwzrXhseZYeD8IYuxhsqij1EC+gBS048sTsCGfZQKPtz/OuoH9RvAKLxpJMaOXzmmMhwIqYC0BGY5zfcjwN3gclJ6rQ7gvEX1Lma43XlIlSxUTviTOAwvKGJeEMRhCpvvPXvBXYCLaC/O2DB3H43jbjt2ycPvoTTTx2VPLHjAELORPS0rsUTOg06++iZv7+GN7QGb0jDG7rF+ryjhyqvkW3vAF81sr3JsNI0dgT6BvOyAw32VSEmFycBQ4N52XV968/xP+/P8bv9Of65CI/PUdSnO10IoEDndsV1qu7FWkBvsphrZ3L4FUUVXWqLj3TRszQNMeCZwC1aQDfYMr3hWkR1qJfZstznwzE1fi/CspPCFiqkZkEL6JUIh8LPEWEw5yBsyFQXO9ss+rrDxeVr4/8AShBesKRnleUrKpxdWhZdwd6HaR4OkBkOGzQiSP05/koU5R2Ab5OTebBN6+iu8VCXLGFQzClRobzVhNByrByC0DpdsxODZdRZqzdiwApTbzp6GxiMyHF8ghbQP0eYHvprAX01e4+xf/VWqHBSGxem/YOfRkipMN8f+XW9EvDBi2w8fp5KQTvj67ZKKBtLrexMiVDU2qhxROAov3Ebom8ciOdtWU3RIc+YporqKG3VfxlvAnGlKYkrvukl5pHhst4AR1m5Fg64fdiN6WXZyZndT1oPiklkvY2L142jtRE6HkB1GH+84nj5opttX2GW9JsJ8NNAMQRfNc7g09dq2q38pXWdGmSeWyHJjKxSgRRFTCASqXrHEoAtgajpI1onIPr+zfDn+Hf1Ht8vTVT4ZKCzw4ZW9ZGMqnMDavzKtxO7vXBdXMaPsX4wAC9pAb0+5M4bCuAN/Yk3tBlvaJwlgLfGG1qHmGB+ifCXiPpbfIAV5UB94ZuG5+p4Q/dZmd6ahaYocp8LbGRLwdSYamVSMSnmI+FLlEcd+dbFjWsAW67907nX2se+AZxipWzLon7wfR54BW9MFgMQTgJiUNwdHgCOjai2pKcOugjgjGBe9re72RZaQF+uZ2U9A8pdYNaCEo3PG727be4sUfX8NvZFK0BtE2tgvk/P0p5BJCcIIOyPh1JfYaVJnXYUlXn2xIhZW2ZX4lvVDixfaxtu7fpWC+jR1WJ0QnAt3tCbWzTgdb2LWJmkITxcX6a+vONyRNm9ZkcL6EuJmTzoWdoA6+OJDQ5VAF9aj4q3gOsvDZW2f8eVWoSiuFEUUiIGPWrDi/CGKmic74Dn9LgtzFP/Q9hwD0TYcaPmhah9rLGVMlpA/xsrY9JO/L41epY2lnqNxSWIePG5iLHDBC7XAvoU6/jljbXTzPxe41B49VTVcfeXBn0K4K2XI1lYK95nzlJZ2LluIJ+EN1SO13U0EFIUqO5WM4tN8UOPm20OaVcceTzvHBVTUX735/hNd67v/ypq2tzVufVrvYYuMlIB3hhyTOp6xw+opkmksjPR62gB3dTuy33wWNusuEiqgq2zWRpZqaQctnm+4SiuVQ1UOh+16e7etrXtscEJoS6tr0ozXv15oBJ/6c8MVmCgWmo7OyrJCtrAokyIQ5mHeC8dgF9TCx6m5RDVtBxkJSiKaox2WbXuz/HP9uR7nqRe++cAUBNW4UybOswWL+os/t1L5b8nc/V13xuTiangt8t4Q68QzSXudR2HUDf3RjzTX9OEBSZ2lT1VX+PP8Uf8Of6nqLcLT/fn+AsaHhfMy14L/PxB5AROrn7yM4DeSkF6P2W5cY1tbHR2fwpCIEQF8tN4Q3dsJZD3kGBe9jzEYHYdcOCeCOR6lHuA/qAkI1YVl7IXhHJToQX0Ii2g65aQ/iBm1ybqkyE0Dd6QYY+PVAGYhjIUSy1I1CPf6xpK/ax7a8cyb2gp9SrSAXhd/azjwwhtSmSrc/YOcxEr+sa4H+F4VeYyjNScktL7gAl9qmvmvb9mHS7D2Kb925/jX4JVV9liOeJ9iybfuc6T7znWUmNHi3Q0lenkRuvazwCjtYCuI1SE9wEHawF9l+Mwm5hpADMOUHnpNDGc2UyGAYkmTJvZU4nKuTX+HL8V1hUqQazunrRl1ly6IRVsBgxcZg6zEhv9DhDMy44YVV3OPWZ6MnYDVrWKXz01My0NwF0bJllUQD3enetT3bm+nn3V5dcALDY7zUhSqvIBbIsjqlGjothMEtJr20dvuq+6st8fK1edN7Og4C4FjlWdxkfx6TVmm76lpAwrOuP+S2yYqkKVqpYgwiqvB46ywnhaCjMRviwZCA3NAGv7bpm6/Dn++xEx2+2wZEBC5ufYErYQJ+t+PVD9QgvoC3dGLb5TeEMhhNbnVKA33tBIa9s+oSlWylFuRuTm/Xk7xzwJHLfAdJ82KdI/cpRtrm1s3P2xE4MQwmnEjQi+b1phEEMwL3spwibYJFgPSHQg/Kap2t1HvItQffcGcrSA3rB85R7jTImsr9xI16rNjksRxcfXAb9atuG3rcM+xBvalio02tce6msr+/CGmrcq0XbQArphaRyiJp1chDPVe1pAF6pmr+t74Jw7Nxf/987NxcOAWxGOJ43Zk2O5DKFaCwE5lqPN58BDCEejn2KOrWLrkqO7+5tW0CCXtBbQp1Ov8tun+HP8pZ58z2jgvCl91atu+c4oRsTrT1Pg+YhN6YGY7G2pPfGGAsD9w4DjX+j340tvRk4EkXN/vru+L4N52bO+GPFoNRD33YD2HcMJ67ABWk0NFcqm0sVmYmfEiuv6oaqwIKUrJa+b8eafKOaNCHMdtZ3sZqUSd/XhVS8/8ZTjrYzjhQ9iG+AKLaA/h9e1CKFRmYQ39G1VvicHUf1tFN7QHKLOjC0If46/2pPv+QFhSryX+ip5uz0hjC7orL/pvTG7nkSYXdb4c/zb0ijtPqIi2J5GljQJimnuPfOEO9dnQwwW7kHKIsY4H8WuGCBWY8cjkrpH7bBevKFH9trNSfYqG8/pOHrD3NRzYza9rgX0G/C6bkBkL9qEKBiysdEGvK6uCMeqCLAe4UV5Ad7Qp40ev5ewipdcBTi0gP6anqWlASV1ece9roOoX0lMRUxA2wMj8IYm7ur1rGIHnyBWryBUfzf7c/xvbPusfxeefE880MGf498t9bkn39N3mN+YfuNYIyGUSJmrApfl14CepSnVNrUsLmIk3nOpjeWiMAS5mzYzY+PFP35tHHkiQAoVzIm7ClUszDPxhtasHNGtoHxNfCcjXuHS4feyIT79UeAhMCv0uMseSlBqnkXEgp+LCOdJBC7BG/pwT/tkbxFN4hKzab0/x7/L+Z630fZtCJPJ50CeP8dv7OCUfwV7VSgDuHN9ByIM7D3OUH9/9UXn64uAr/GGVlnpzSYjnJcG4Q01eUEHScug+vb0I5b9kPFbdCUBDNAC+hy8rsmIuOPb8Ya278DndS2kvsi8UKMJ1WTLxus6lC0949cAPbdjU94unnyPA2GDX41wsgnv4BRJA667u2+nG/9n6KpJMkJdPBGhuleBFyMKXHynjbBdwVNVvfmdtevTx9Qe94E3fOklAMPVWbzvfAbTZInySKgXQPl1bTtWbnZecWrmE1lr4tqeH3O5d4LxF9yMMHf0jNleBbTFG9qyPEILxnr2iqgPRZrgz/Efu51TJDtgrwvlKO5cX8dgXvbWqkmR01rBG9ovZkX7LV6XslFPXrBhTkpWcmZ1Zedhm5OBdIQaWwXceEPbj3WtX1UDfIM3dGaz3nNT4nU9gbDLbgJOxhtqspAzye6hZ2lvIIrY64jY1zqnKgPmnnev/WFgmX/5yjuAS5Yb7Z8dUfP8nQDvOp7xH2Ob5QHewRu6KrZdd66vLyK/QjzCTDEimJddgtc1CLFAcVuH/oA3dDL/MDz5nk+oNyE9admGJbvJPhPKEglelytSo2xW7aaqqFyISFKSh6jaM2gHZ2PFud+EWG08boU6/HMQyU6Kd3eFLGla9CytB0J4tmqwywTO0wL6GAC8rkcR5RjfdVd9ElQwOiyLu+gURaELcArekK9h2+5cXxbC/+GHujwMoi0VYTs9DThnhxPRFogn39MbYfN2AYP9Of7mzs/wr0YKZcm+xet6DBGiNhHhEdsOyMEb+mB7p0kkzYGepY1E1GmvQeRJmAosq3PUA/C6RiKSqczGGxqI16UBCxAmlPT9cZLlyfekAon+HP8+c7T8tyCFsmTf4nUNRqxOoiwG+m4z8F8iaWb0LE3ZbriN0HCsRGSvegRhU30RkV3quG2eJ5HsBHscpyyR7CEzqXd6MoDrpUCW7Et2GP/qDRUiwnNA2J2j4VaN1YeXSHYJKZQl+xaRwvR0hG14ON7Q9uLcJZKWQmwBnGi+h+1V3JJIdgqpvpZIJJLdwevqy5Z13w/EG2o0valEsrPIlbJEIpHsHguoL1zwLk2X2lSyHyNXyhKJRLK7iLwKbbcq8yeR7CZSKEskEolE0kKQ6muJRCKRSFoIUihLJBKJRNJCkEJZIpFIJJIWghTKEolEIpG0EKRQlkgkEomkhSCFskQikUgkLQQplCUSiUQiaSFIoSyRSCQSSQtBCmWJRCKRSFoIUihLJBKJRNJCkEJZIpFIJJIWghTKEolEIpG0EKRQlkgkEomkhSCFskQikUgkLQQplCUSiUQiaSFIoSyRSCQSSQvh/wF8+02YkCKycQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig=plt.figure(dpi=100)\n", "plot_voltage_traces(mem_rec, spk_rec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, our random initialization gives us some sporadic spiking. Thus far, we have only an input layer and a spiking layer, which should become our hidden layer. Next, we will have to add a readout layer to our network." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding a readout layer\n", "\n", "To use our network as a classifier, we need to have a readout layer on whose output we can define a cost function. There are several possibilities for doing this. For instance, we could count output layer spikes, or we could directly define an objective function on the membrane potential of the output neurons. Here we will follow the latter approach, but keep in mind that there are many other possibilities of defining an output layer and respective cost functions on them.\n", "\n", "In the following, we will build the output layer as a population of leaky integrator neurons. The reason for this choice is that leaky integration is the natural way of how neurons receive the spiking output of their brethren. Moreover, because we will need this code again, we combine our code from above plus the added readout layer into a single function." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def run_snn(inputs):\n", " h1 = torch.einsum(\"abc,cd->abd\", (inputs, w1))\n", " syn = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n", " mem = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n", "\n", " mem_rec = []\n", " spk_rec = []\n", "\n", " # Compute hidden layer activity\n", " for t in range(nb_steps):\n", " mthr = mem-1.0\n", " out = spike_fn(mthr)\n", " rst = out.detach() # We do not want to backprop through the reset\n", "\n", " new_syn = alpha*syn +h1[:,t]\n", " new_mem = (beta*mem +syn)*(1.0-rst)\n", "\n", " mem_rec.append(mem)\n", " spk_rec.append(out)\n", " \n", " mem = new_mem\n", " syn = new_syn\n", "\n", " mem_rec = torch.stack(mem_rec,dim=1)\n", " spk_rec = torch.stack(spk_rec,dim=1)\n", "\n", " # Readout layer\n", " h2= torch.einsum(\"abc,cd->abd\", (spk_rec, w2))\n", " flt = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\n", " out = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\n", " out_rec = [out]\n", " for t in range(nb_steps):\n", " new_flt = alpha*flt +h2[:,t]\n", " new_out = beta*out +flt\n", "\n", " flt = new_flt\n", " out = new_out\n", "\n", " out_rec.append(out)\n", "\n", " out_rec = torch.stack(out_rec,dim=1)\n", " other_recs = [mem_rec, spk_rec]\n", " return out_rec, other_recs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now run this code and plot the output layer \"membrane potentials\" below. As desired, these potentials do not have spikes riding on them." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "out_rec,other_recs = run_snn(x_data)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig=plt.figure(dpi=100)\n", "plot_voltage_traces(out_rec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By preventing the output neurons from spiking themselves, we can define a relatively smooth objective on their membrane voltages directly. Specifically, we use the maximum voltage over time of each output unit\n", "$$\\hat U^\\mathrm{out}_i=\\max_t U^\\mathrm{out}_i(t)$$\n", "and then use this vector as input for either an argmax to compute the classification accuracy or as we will see below as input for a standard softmax function in conjunction with a negative log-likelihood loss for optimizing the weights in the network. \n", "\n", "Let us first compute the classification accuracy of this random network. We will see that this accuracy is somewhere around 50% as it should be since that corresponds to the chance level of our synthetic task." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy 0.516\n" ] } ], "source": [ "def print_classification_accuracy():\n", " \"\"\" Dirty little helper function to compute classification accuracy. \"\"\"\n", " output,_ = run_snn(x_data)\n", " m,_= torch.max(output,1) # max over time\n", " _,am=torch.max(m,1) # argmax over output units\n", " acc = np.mean((y_data==am).detach().cpu().numpy()) # compare to labels\n", " print(\"Accuracy %.3f\"%acc)\n", " \n", "print_classification_accuracy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Supervised learning\n", "\n", "So far, we have built the infrastructure to simulate our spiking neural network, but we have worked with purely random network weights thus far.\n", "The vanilla method to adjust network weights to decrease the specified objective is gradient descent. \n", "Machine learning libraries like Tensorflow and PyTorch make implementing gradient descent a breeze.\n", "We first perform gradient descent on the correct gradient and use this as a motivation for introducing surrogate gradients.\n", "Here we go." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Supervised learning with the true gradient" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "params = [w1,w2] # The paramters we want to optimize\n", "optimizer = torch.optim.Adam(params, lr=2e-3, betas=(0.9,0.999)) # The optimizer we are going to use\n", "\n", "log_softmax_fn = nn.LogSoftmax(dim=1) # The log softmax function across output units\n", "loss_fn = nn.NLLLoss() # The negative log likelihood loss function\n", "\n", "# The optimization loop\n", "loss_hist = []\n", "for e in range(1000):\n", " # run the network and get output\n", " output,_ = run_snn(x_data) \n", " # compute the loss\n", " m,_=torch.max(output,1)\n", " log_p_y = log_softmax_fn(m) \n", " loss_val = loss_fn(log_p_y, y_data)\n", "\n", " # update the weights\n", " optimizer.zero_grad()\n", " loss_val.backward()\n", " optimizer.step()\n", " \n", " # store loss value\n", " loss_hist.append(loss_val.item())\n", " \n", "loss_hist_true_grad = loss_hist # store for later use" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(loss_hist)\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "sns.despine()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy 0.512\n" ] } ], "source": [ "print_classification_accuracy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We appreciate that loss decreases over iterations and converges towards a steady state. The classification accuracy, however, does not seem to improve dramatically throughout the optimization. What a shame! \n", "\n", "The underlying reason is that the nonlinearity of the hidden units have zero derivatives everywhere except at threshold crossings, where they become infinite. In practice that means that weight updates in the hidden layer vanish and the weights remain unmodified. By plotting the hidden layer activations and comparing them with what we have plotted before, we will see that these activations have not changed at all. Thus no learning happens in the hidden layer. The reason why the loss decreased initially during optimization is that the output layer weights could still change and allow for some improvement (even if it was very little).\n", "\n", "To improve performance, we need to get the hidden layer units to take part in learning. To achieve this, we will introduce a surrogate gradient in the next section." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "output,other_recordings = run_snn(x_data)\n", "mem_rec, spk_rec = other_recordings" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig=plt.figure(dpi=100)\n", "plot_voltage_traces(output)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Supervised learning with surrogate gradients\n", "\n", "In the last section, we saw that the hidden layer units did not participate.\n", "The underlying reason is that the partial derivative of the step function we used has a vanishing derivative everywhere (except at zero where it becomes infinite).\n", "\n", "Most conventional neural networks avoid this problem by choosing a nonlinearity with non-zero partial derivative. For instance, sigmoidal or tanh units were standard during the beginnings of neural networks research. Today, ReLUs are more common. Importantly, all these activation functions have substantial non-zero support, which allows gradients to flow (to a greater or lesser extent).\n", "\n", "What do we if we want to stick to our binary nonlinearity? There have been several approaches to tackle this problem. Here we use one such strategy which has been applied successfully to spiking neural networks: We use a surrogate gradient approach.\n", "\n", "The idea behind a surrogate gradient is dead simple. Instead of changing the nonlinearity itself, we only change the gradient. Thus we use a different \"surrogate\" gradient to optimize parameters that would otherwise have a vanishing gradient.\n", "\n", "\n", "Specifically, we use the partial derivative of a function which to some extent approximates the stepfunction $\\Theta(x)$.\n", "In what follows, chiefly, we will use (up to rescaling) the partial derivative of a fast sigmoid function $\\sigma(x)$. \n", "While $\\Theta$ is invariant to multiplicative rescaling, $\\sigma$ isn't. Thus we have to introduce a scale parameter." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "class SurrGradSpike(torch.autograd.Function):\n", " \"\"\"\n", " Here we implement our spiking nonlinearity which also implements \n", " the surrogate gradient. By subclassing torch.autograd.Function, \n", " we will be able to use all of PyTorch's autograd functionality.\n", " Here we use the normalized negative part of a fast sigmoid \n", " as this was done in Zenke & Ganguli (2018).\n", " \"\"\"\n", " \n", " scale = 100.0 # controls steepness of surrogate gradient\n", "\n", " @staticmethod\n", " def forward(ctx, input):\n", " \"\"\"\n", " In the forward pass we compute a step function of the input Tensor\n", " and return it. ctx is a context object that we use to stash information which \n", " we need to later backpropagate our error signals. To achieve this we use the \n", " ctx.save_for_backward method.\n", " \"\"\"\n", " ctx.save_for_backward(input)\n", " out = torch.zeros_like(input)\n", " out[input > 0] = 1.0\n", " return out\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " \"\"\"\n", " In the backward pass we receive a Tensor we need to compute the \n", " surrogate gradient of the loss with respect to the input. \n", " Here we use the normalized negative part of a fast sigmoid \n", " as this was done in Zenke & Ganguli (2018).\n", " \"\"\"\n", " input, = ctx.saved_tensors\n", " grad_input = grad_output.clone()\n", " grad = grad_input/(SurrGradSpike.scale*torch.abs(input)+1.0)**2\n", " return grad\n", " \n", "# here we overwrite our naive spike function by the \"SurrGradSpike\" nonlinearity which implements a surrogate gradient\n", "spike_fn = SurrGradSpike.apply" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "init done\n" ] } ], "source": [ "# The following lines will reinitialize the weights\n", "torch.nn.init.normal_(w1, mean=0.0, std=weight_scale/np.sqrt(nb_inputs))\n", "torch.nn.init.normal_(w2, mean=0.0, std=weight_scale/np.sqrt(nb_hidden))\n", "print(\"init done\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "params = [w1,w2]\n", "optimizer = torch.optim.Adam(params, lr=2e-3, betas=(0.9,0.999))\n", "\n", "log_softmax_fn = nn.LogSoftmax(dim=1)\n", "loss_fn = nn.NLLLoss()\n", "\n", "loss_hist = []\n", "for e in range(1000):\n", " output,_ = run_snn(x_data)\n", " m,_=torch.max(output,1)\n", " log_p_y = log_softmax_fn(m)\n", " loss_val = loss_fn(log_p_y, y_data)\n", "\n", " optimizer.zero_grad()\n", " loss_val.backward()\n", " optimizer.step()\n", " loss_hist.append(loss_val.item())" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(3.3,2),dpi=150)\n", "plt.plot(loss_hist_true_grad, label=\"True gradient\")\n", "plt.plot(loss_hist, label=\"Surrogate gradient\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.legend()\n", "sns.despine()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "output,other_recordings = run_snn(x_data)\n", "mem_rec, spk_rec = other_recordings" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig=plt.figure(dpi=100)\n", "plot_voltage_traces(mem_rec, spk_rec)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig=plt.figure(dpi=100)\n", "plot_voltage_traces(output)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy 0.859375\n" ] } ], "source": [ "output,_ = run_snn(x_data)\n", "m,_=torch.max(output,1)\n", "\n", "# Compute training accuracy\n", "_,am=torch.max(m,1)\n", "acc = np.mean((y_data==am).detach().cpu().numpy())\n", "print(\"Accuracy %f\"%acc)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Creative
This work is licensed under a Creative Commons Attribution 4.0 International License." ] } ], "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": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: notebooks/SpyTorchTutorial2.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 2: Training a spiking neural network on a simple vision dataset\n", "\n", "Friedemann Zenke (https://fzenke.net)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> For more details on surrogate gradient learning, please see: \n", "> Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks.\n", "> https://arxiv.org/abs/1901.09948" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Tutorial 1, we have seen how to train a simple multi-layer spiking neural network on a small synthetic dataset. In this tutorial, we will apply what we have learned so far to a slightly larger dataset.\n", "Concretely, we will use the [Fashion MNIST dataset](https://github.com/zalandoresearch/fashion-mnist). " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.gridspec import GridSpec\n", "import seaborn as sns\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torchvision" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.7.0'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.__version__" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# The coarse network structure is dicated by the Fashion MNIST dataset. \n", "nb_inputs = 28*28\n", "nb_hidden = 100\n", "nb_outputs = 10\n", "\n", "time_step = 1e-3\n", "nb_steps = 100\n", "\n", "batch_size = 256" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "dtype = torch.float\n", "\n", "# Check whether a GPU is available\n", "if torch.cuda.is_available():\n", " device = torch.device(\"cuda\") \n", "else:\n", " device = torch.device(\"cpu\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Here we load the Dataset\n", "root = os.path.expanduser(\"~/data/datasets/torch/fashion-mnist\")\n", "train_dataset = torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=True)\n", "test_dataset = torchvision.datasets.FashionMNIST(root, train=False, transform=None, target_transform=None, download=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Standardize data\n", "# x_train = torch.tensor(train_dataset.train_data, device=device, dtype=dtype)\n", "x_train = np.array(train_dataset.data, dtype=np.float)\n", "x_train = x_train.reshape(x_train.shape[0],-1)/255\n", "# x_test = torch.tensor(test_dataset.test_data, device=device, dtype=dtype)\n", "x_test = np.array(test_dataset.data, dtype=np.float)\n", "x_test = x_test.reshape(x_test.shape[0],-1)/255\n", "\n", "# y_train = torch.tensor(train_dataset.train_labels, device=device, dtype=dtype)\n", "# y_test = torch.tensor(test_dataset.test_labels, device=device, dtype=dtype)\n", "y_train = np.array(train_dataset.targets, dtype=np.int)\n", "y_test = np.array(test_dataset.targets, dtype=np.int)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-0.5, 27.5, 27.5, -0.5)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Here we plot one of the raw data points as an example\n", "data_id = 1\n", "plt.imshow(x_train[data_id].reshape(28,28), cmap=plt.cm.gray_r)\n", "plt.axis(\"off\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we are working with spiking neural networks, we ideally want to use a temporal code to make use of spike timing. To that end, we will use a spike latency code to feed spikes to our network." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def current2firing_time(x, tau=20, thr=0.2, tmax=1.0, epsilon=1e-7):\n", " \"\"\" Computes first firing time latency for a current input x assuming the charge time of a current based LIF neuron.\n", "\n", " Args:\n", " x -- The \"current\" values\n", "\n", " Keyword args:\n", " tau -- The membrane time constant of the LIF neuron to be charged\n", " thr -- The firing threshold value \n", " tmax -- The maximum time returned \n", " epsilon -- A generic (small) epsilon > 0\n", "\n", " Returns:\n", " Time to first spike for each \"current\" x\n", " \"\"\"\n", " idx = x0.0] = spike_height\n", " dat = dat.detach().cpu().numpy()\n", " else:\n", " dat = mem.detach().cpu().numpy()\n", " for i in range(np.prod(dim)):\n", " if i==0: a0=ax=plt.subplot(gs[i])\n", " else: ax=plt.subplot(gs[i],sharey=a0)\n", " ax.plot(dat[i])\n", " ax.axis(\"off\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now run this code and plot the output layer \"membrane potentials\" below. As desired, these potentials do not have spikes riding on them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training the network" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "class SurrGradSpike(torch.autograd.Function):\n", " \"\"\"\n", " Here we implement our spiking nonlinearity which also implements \n", " the surrogate gradient. By subclassing torch.autograd.Function, \n", " we will be able to use all of PyTorch's autograd functionality.\n", " Here we use the normalized negative part of a fast sigmoid \n", " as this was done in Zenke & Ganguli (2018).\n", " \"\"\"\n", " \n", " scale = 100.0 # controls steepness of surrogate gradient\n", "\n", " @staticmethod\n", " def forward(ctx, input):\n", " \"\"\"\n", " In the forward pass we compute a step function of the input Tensor\n", " and return it. ctx is a context object that we use to stash information which \n", " we need to later backpropagate our error signals. To achieve this we use the \n", " ctx.save_for_backward method.\n", " \"\"\"\n", " ctx.save_for_backward(input)\n", " out = torch.zeros_like(input)\n", " out[input > 0] = 1.0\n", " return out\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " \"\"\"\n", " In the backward pass we receive a Tensor we need to compute the \n", " surrogate gradient of the loss with respect to the input. \n", " Here we use the normalized negative part of a fast sigmoid \n", " as this was done in Zenke & Ganguli (2018).\n", " \"\"\"\n", " input, = ctx.saved_tensors\n", " grad_input = grad_output.clone()\n", " grad = grad_input/(SurrGradSpike.scale*torch.abs(input)+1.0)**2\n", " return grad\n", " \n", "# here we overwrite our naive spike function by the \"SurrGradSpike\" nonlinearity which implements a surrogate gradient\n", "spike_fn = SurrGradSpike.apply" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def run_snn(inputs):\n", " h1 = torch.einsum(\"abc,cd->abd\", (inputs, w1))\n", " syn = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n", " mem = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n", "\n", " mem_rec = []\n", " spk_rec = []\n", "\n", " # Compute hidden layer activity\n", " for t in range(nb_steps):\n", " mthr = mem-1.0\n", " out = spike_fn(mthr)\n", " rst = out.detach() # We do not want to backprop through the reset\n", "\n", " new_syn = alpha*syn +h1[:,t]\n", " new_mem = (beta*mem +syn)*(1.0-rst)\n", "\n", " mem_rec.append(mem)\n", " spk_rec.append(out)\n", " \n", " mem = new_mem\n", " syn = new_syn\n", "\n", " mem_rec = torch.stack(mem_rec,dim=1)\n", " spk_rec = torch.stack(spk_rec,dim=1)\n", "\n", " # Readout layer\n", " h2= torch.einsum(\"abc,cd->abd\", (spk_rec, w2))\n", " flt = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\n", " out = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\n", " out_rec = [out]\n", " for t in range(nb_steps):\n", " new_flt = alpha*flt +h2[:,t]\n", " new_out = beta*out +flt\n", "\n", " flt = new_flt\n", " out = new_out\n", "\n", " out_rec.append(out)\n", "\n", " out_rec = torch.stack(out_rec,dim=1)\n", " other_recs = [mem_rec, spk_rec]\n", " return out_rec, other_recs" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def train(x_data, y_data, lr=2e-3, nb_epochs=10):\n", " params = [w1,w2]\n", " optimizer = torch.optim.Adam(params, lr=lr, betas=(0.9,0.999))\n", "\n", " log_softmax_fn = nn.LogSoftmax(dim=1)\n", " loss_fn = nn.NLLLoss()\n", " \n", " loss_hist = []\n", " for e in range(nb_epochs):\n", " local_loss = []\n", " for x_local, y_local in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs):\n", " output,_ = run_snn(x_local.to_dense())\n", " m,_=torch.max(output,1)\n", " log_p_y = log_softmax_fn(m)\n", " loss_val = loss_fn(log_p_y, y_local)\n", "\n", " optimizer.zero_grad()\n", " loss_val.backward()\n", " optimizer.step()\n", " local_loss.append(loss_val.item())\n", " mean_loss = np.mean(local_loss)\n", " print(\"Epoch %i: loss=%.5f\"%(e+1,mean_loss))\n", " loss_hist.append(mean_loss)\n", " \n", " return loss_hist\n", " \n", " \n", "def compute_classification_accuracy(x_data, y_data):\n", " \"\"\" Computes classification accuracy on supplied data in batches. \"\"\"\n", " accs = []\n", " for x_local, y_local in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs, shuffle=False):\n", " output,_ = run_snn(x_local.to_dense())\n", " m,_= torch.max(output,1) # max over time\n", " _,am=torch.max(m,1) # argmax over output units\n", " tmp = np.mean((y_local==am).detach().cpu().numpy()) # compare to labels\n", " accs.append(tmp)\n", " return np.mean(accs)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1: loss=0.89463\n", "Epoch 2: loss=0.53745\n", "Epoch 3: loss=0.48071\n", "Epoch 4: loss=0.44689\n", "Epoch 5: loss=0.42401\n", "Epoch 6: loss=0.40686\n", "Epoch 7: loss=0.39227\n", "Epoch 8: loss=0.37885\n", "Epoch 9: loss=0.36825\n", "Epoch 10: loss=0.36175\n", "Epoch 11: loss=0.35250\n", "Epoch 12: loss=0.34323\n", "Epoch 13: loss=0.33708\n", "Epoch 14: loss=0.33294\n", "Epoch 15: loss=0.32420\n", "Epoch 16: loss=0.31920\n", "Epoch 17: loss=0.31117\n", "Epoch 18: loss=0.30753\n", "Epoch 19: loss=0.30294\n", "Epoch 20: loss=0.29746\n", "Epoch 21: loss=0.29385\n", "Epoch 22: loss=0.28760\n", "Epoch 23: loss=0.28413\n", "Epoch 24: loss=0.27772\n", "Epoch 25: loss=0.27499\n", "Epoch 26: loss=0.27133\n", "Epoch 27: loss=0.26617\n", "Epoch 28: loss=0.26376\n", "Epoch 29: loss=0.26041\n", "Epoch 30: loss=0.25375\n" ] } ], "source": [ "loss_hist = train(x_train, y_train, lr=2e-4, nb_epochs=30)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(3.3,2),dpi=150)\n", "plt.plot(loss_hist)\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "sns.despine()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training accuracy: 0.912\n", "Test accuracy: 0.863\n" ] } ], "source": [ "print(\"Training accuracy: %.3f\"%(compute_classification_accuracy(x_train,y_train)))\n", "print(\"Test accuracy: %.3f\"%(compute_classification_accuracy(x_test,y_test)))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def get_mini_batch(x_data, y_data, shuffle=False):\n", " for ret in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs, shuffle=shuffle):\n", " return ret " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "x_batch, y_batch = get_mini_batch(x_test, y_test)\n", "output, other_recordings = run_snn(x_batch.to_dense())\n", "mem_rec, spk_rec = other_recordings" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig=plt.figure(dpi=100)\n", "plot_voltage_traces(mem_rec, spk_rec)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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QnspTifz8fIwxZbZProwTOU4Bl6cS5bcT+fn5zm1PidJZK1HZNqj8tut41XZ+CRo1zpO+EvRDHTp04Pbbb+f00093DouOjuaGG25gxIgRFT7ERkREcPXVV3PllVdWenNZkbpg/fr1vPjii6xfv56CggI++OADZs6cSU5ODsYYwsLC+O6773j++efZvXu3Mys7d+5k8uTJfP/990DRXpPyOSpZ9q+//lrl+kvmUwMpwSAkJISwsDC+/vprXnzxRQ4ePOjMxaZNm3jppZdYtWpVmXnCwsKcTeLKlSt59tlnSU5OrjBOpK4qvZ04dOgQr732GgsWLCgzTb169Spsg1avXs2LL77I5s2bncNKtmuVNYCHDx9mypQpzJs3r8paqptfxBVqIv1Qt27deOaZZxgyZIhzWMOGDXnggQe45ZZbKjSRkZGR/OMf/+DOO++kfv363i5XxC8sX76cu+++m+XLl5OXl8e///1vnn76abKyspzTLFiwgHvuuYeNGzc6h23atIl77rmHTz/9tMplr1ixgrvvvpsffvjBo+9BxN98+OGH3HvvvezYscM57JdffuGuu+5i8eLFVc63ePFi7r77btauXeuFKkUCz/79+3nwwQeZMWMGxzoq8HjzdODAAR5++GGmT59+zGWL1JS+FhSRoDB06FDeeecdzjjjDMLDw3n00UfJy8srcw7JlVdeSY8ePejWrZtzWNeuXZkxYwadO3euctlDhgzhnXfeoVevXh59DyL+5q9//Stnn302HTp0cA7r3bs37777Lt27d69yvosuuojWrVvTp08fb5QpEnASExN57bXXaNas2TGPYDnePLVo0YJXXnnFpWWL1JTOiXQjd50TmZ+fT2ZmJpGRkc5bBVhrSU9Pd17UoDKFhYVkZGQQFhami3tIbfjNFsddmRLxMb/IlPIkQUJ5EnEfnRMZTBYvXszAgQOZOXOmc9j+/fu57LLL+Pvf/17h8uklfvvtN8455xyefvppb5UqIiIiIiJ1jJpIP1RyQYPSJzuXnABd3YUJKptPRESkpnbs2MHatWvLnFssIiKicyL90ODBg1m6dKnzUFYourfQrFmzqr1EeufOnfn88891BTwREXGLZ555hg8++IAFCxZw2mmn+bocERHxE+o2/EhaWhoLFiygRYsWDB48mNWrV7Nu3TrOPvts4uLimD9/PrGxsZxzzjmsX7+eVatWMXjwYBITE1mwYAH16tVj+PDhzibyhx9+YPPmzQwfPpz4+PgK67PW8tVXX3H48GEuuOAC3V9SRETKOO2008jPz6dx48a+LkVERPyIDmf1I3v37mXMmDG8/PLLWGuZNWsWN910E+vWrePQoUPcddddPP300xQUFLBgwQKuv/56VqxYQWZmJuPHj+eRRx4hNzfXuby3336bm2++mT/++KPS9TkcDp599lnGjRvHwYMHvfU2RUQkQFx33XVMnTqVdu3a+boUERHxI9oT6UeaN2/O008/TatWrTDGcPHFF9OmTRu6dOlCo0aNePTRR4mLiyM0NJSzzz6bqKgoevbsSVRUFA8++CD16tUrcwhsye0M2rZtW+n6QkJC+L//+z9SU1P1LbOIiIiIiLgkIG/xYYwZDFR9l+P/edha+0jxPBOAh6uZ9ilr7b21rEuXe5aAs2TJEoYMGeLKpF7NU/F6apypvLw8rLVlvlgR8QZ/zZS2URKIlCcRj6rxLT4CdU9kCvBWFeNCgT8X//ubSsZ/B2yuZPhqN9QlEnASEhK49tprna/feuut0tkKyDzl5uYyZswYMjMzmTp1KrGxsb4sR+qYYMyUiK8oTyL+KSCbSGvt78B1lY0zxpxH0R+UncCSSiZ53Vo73VO1iQSazp07M336dOfr6dOnX1fy70DK09GjR9m/fz/NmjWjfv36pKamcuTIETZv3kxiYiIJCQm+LlHqiGDJlIg/UJ5E/FMwXlin5Bup/9pAPFZXxL8ETJ7mzZtHv379mDdvHhEREUybNo1JkyZx5ZVXcv/99+Pn5UvdETCZEgkAypOIjwRVE2mMaQBcUvxyhi9rEQl0gZan5s2bc+aZZ9K8eXMAGjVqRMuWLenTpw+dO3fGmBof9i/iFoGWKRF/pjyJ+FZAHs5ajT8BDYCfrLUbqphmqDGmBxAB7AIWWmt1bLxIRQGVp7PPPpthw4YREvK/78ZatWrF9OnT1UCKvwioTIn4OeVJxIeCrYksOayhum+k/lLu9aPGmI+A66y1GZ4pSyQgBVyeSjeQ1Q0T8ZGAy5SIH1OeRHwoaJpIY0wLYBhQCLxbySSbgbuBhcB2oBEwEHgauIyiK3xd6uK6qrqe84nHV7WIf/JmnorXp0xJUNM2SsR9lCcR3wuaJhK4iqI/Cp9Za1PKj7TWziw3KBN4xxizGPgVGGGM6WOtXe75UkX8nvIk4l7KlIj7KE8iPhZMTaQrhzVUYK3da4x5k6JvrIYDx/yDYq09ubLhxd9WJR3P+kX8lNfyVDyfMiXBTtsoEfdRnkR8LChOFjLGdAFOBTKAOTVYxKbi5xbuqkkkUClPIu6lTIm4j/Ik4h+CoonkfydOf2ytzarB/I2KnzPdVI/bWGv55ZdfWL58Obm5udVOm56ezrfffktycrKXqpMgFdB5Sk5O5ttvvyU9Pd0XqxepTEBnSsTPKE/oM5/4XsA3kabo2v2ji18e932CiucvObl6jbvqcpfCwkLGjx/PVVddxYEDB6qdNjk5mYsuuojnnnvOS9VJsCm+V3NA5+mFF17goosuYuPGjb5YvUgZwZApEX+hPP2PPvOJrwXDOZEDgBOA3cCiyiYwxsQDI4G3rbXppYZHA/8CegMpwMcer9YFmzZtYsGCBZx55pn07NmTCy+8kE6dOvH+++/Trl07RowY4bxtwYIFC9ixYwcjR46kWbNm3HzzzfTs2bPCMo8cOcL7779Py5YtufDCC739liRAfPPNNxDgeRo8eDAxMTE0a9bMF6sXKSMYMiXiL+p6nlz9zCfiDcHQRJacXP2OtdZRxTQNgMnAk8aYlcBeIB7oCTQBjgCX1/CwCLf7+eefGTt2LE8++SSnn346f/vb39i1axf9+vXjpJNO4qKLLnI2kW+99RZffPEFgwYNokuXLjz11FOVLnP//v088MADDBgwgAsuuEA3X5dKzZzpvKBdwOZp5MiRjBw50herFqkgGDIl4i/qep5c/cwn4g0B3UQaY8KBy4tflr+cc2mpwFNAH6Aj0I+iewttBaYDz1trd3uu0uNzxhln8N///pcePXo4hzVp0oQXX3yR2NhY6tX733/b7bffzhVXXEHLli2rXWaLFi2YOnUqzZo1UwMplcrNzWXWrFklL4MmTyK+okyJuE+w5WnPnj088sgjJCUlcccdd7g0j6uf+US8wRQfXy5uYIxZn5SUlLR+fVX3pRUJCH7zLYMyJUHCLzKlPEmQCIo8JScnc+655zJw4ECmT5+uL/jFV2r8ixfQeyJFRERERALNCSecwPz584mOjlYDKQFJTaSIiIiIiBeFh4eTlJTkfL137172799P+/btiYmJ8WFlIq4J+Ft8iIiIiIgEsldeeYUhQ4awcuVKX5ci4hI1kSIiIiIiPnTyySczYsQI3Z5KAoYOZxURERER8aErrriCK664wtdliLhMeyL9nMPhYMqUKTz00EMcOXLE1+WI+L333nuPu+66i+3bt/u6FBEREafKtk9r167ljjvu4KuvvvJhZSLHT02kn3M4HCxcuJD//ve/ZGRk+LocEb+3bNky3nzzTQ4cOODrUkRERJwq2z798ccfvPbaa6xdu9Z3hYnUgA5n9XOhoaFMmjSJrKwsHScv4oJx48bx5z//mc6dO/u6FBEREafKtk8DBw7kyy+/pG3btr4rTKQG1ET6OWMMXbt29XUZIgGjQ4cOdOjQwddliASFAwcOkJGRQWJiIiEhIezevZvIyEiaN2/OoUOHOHLkCC1atCAyMhKA7Oxs9u7dS1xcHI0bN650menp6ezfv5/4+HhiY2O9+XZEfKphw4aEhoYSFhZGfn6+M0/9+/d3TrN3717y8vJo2bIl9eoVfUw/cuQIqampJCQk0KBBAwByc3PZs2cP0dHRxMfHA2CtZc+ePTgcDlq2bElISNUHHJZktWHDhjRp0sSD71qClQ5nFRERkUo9+eSTDBkyhA0bNrBt2zaGDx/O+PHjsdYyZcoU+vfvz48//uicftWqVQwcOJCXX365ymXOnTuXfv36MXv2bG+8BRG/UV2eAAoKChg7diwjRoxg//79zvlmzpzJmWeeyddff+0c9ttvvzF06FAmTZrkHJabm8tf//pXRo4ceczraKxevZqBAwfy0ksvufdNSp2hPZF+aO/evaxatYouXbpU2KNy8OBBli9fzoknnkiXLl18VKGIiNQF7dq1o1evXkRHRxMZGUnPnj3p2LEjxhjatGlD7969adiwoXP62NhYzjjjDNq0aVPlMps3b07v3r1p3ry5N96CiN+oLk9QdPRZ586dadCgAfXr13fOl5iYSO/evcvs3Y+OjqZXr160b9/eOSwkJISTTz6ZjIwMwsLCqq2lJKsnnHCCm9+l1BWm5NsPqT1jzPqkpKSk9evX12o5s2bNYtSoUTzxxBPcc889ZcZ9/fXXXHDBBYwdO5Ynn3yyVusRqYLxdQEl3JUpER/zi0zVJE/WWqy1zsPiHA4HxhiMMRXGlSg9TVUcDke1h9qJVCMo81TVNFUNO575q+JKViXo1fg/X3/B/ZTD4aCqBr+6cSIiIu5ijCnzYTQkJMT5gXPRokU8/vjjbNmypcw8paepihpIqYuMMRQWFvL6668zefJkcnNzK2SlfOaqGgaVZ62qaSvjSlZFquLVv+LGmC7GmMuNMb29ud5AU/IHoLI/AtWNExERcSdrLQ6Ho9JxX3/9NY8//jh//PGHl6sSCVz5+flMmzaNKVOmkJGRoZ0CErDcfk6kMWYUcAtwn7X2x1LDnwHGlXo9D7jcWlvo7hoCXb9+/Zg7d26l5zx269aNjz/+uMwx8CIiIp7w8ssvs3jxYiZNmkTHjh3LjLvmmmvo378/p556qo+qEwk84eHhPPPMM2zbto2//e1v9OrVi3vvvdfXZYkcN09cWOfPQA/gp5IBxph+wF3AUWA+0Ae4GLgaeNsDNQScQ4cOYa2lcePGNG7cmF69ehETE+Mc73A4OHToEKGhoZx//vll5rXWcujQIYwxNGrUSIcmiIiIW2zdupU1a9aQmZlZYVznzp11P1aR4xQaGkr//v1p3rw5jz/+OI0bN8Zaq89uEnA8cUxkV+AXa21eqWF/ASww0lp7NdALyABu8sD6A05OTg433XQTV111FWlpaSxatIh+/frx9tv/66/379/PiBEjuOOOOygoKCgz/9GjRxk9ejQ33ngj2dnZ3i5fRESC1H333cfixYtJSkrydSkiQaVt27Z89tlnPP7442ogJSB5Yk9kM+D7csOGAPuttV8AWGsPGWOWAad5YP0BxxhDs2bNiI6OJiQkhMzMTP74448y9/gpKChgx44dhIeHVzh+3uFwsHPnTqKjo3VsvYiIuE3Tpk1p2rSpr8sQCTphYWG6vYYENE80kdlAbMkLY0wLoCPwQbnpjgCNPLD+gBMeHs5LL72EtbbMfYFERERERET8jSeayD+AAcaYOGvtEYrOe7TAF+WmSwD2e2D9Aan0TWE7dOjAnXfeyemnn+4cFhMTww033ECzZs0IDQ0tM29ERAR/+ctfqF+//jFvLisiIiIiIlIbnmgipwOTgdXGmLXABRSd/zi3ZAJjTBhwOrDKA+sPeD169OCFF14oM6xhw4ZMmDCh0ukjIyO57777PF+YiIiIiIjUeZ64sM5rwEdAO+BSIB+4xVqbWmqaC4GGwCIPrF9ERETcbO/evdx2221Mnjz5mNN++umnXHvttaxYscILlYnUDZMnT+bWW29lz549lY7Py8vjkUce4Z577iEjI8PL1Uld4/Ym0lqbb629AmgP9AYSrbXvlZtsK0UN5hvuXr+I1E35+fkcPXqU/Px85zCHw0F6evpxXbU4Ozub9PT0Km+wLlJXpaens3DhQlauXHnMi7ht2bKFuXPnsnv3bi9VJxL8Vq1axYIFC0hPT690fGFhIUuWLOHLL78kLy+v0mlE3MUTeyIBsNZus9autNZW+E231q611s611u7z1PpFpG755JNPGDRoEJ988olz2Pbt27nooot46KGHXF7OhAkTuPDCC9m2bZsHqhQJXCeccAKffPIJEydOPOYtCa666iqWLl3KkCFDvFSdSPCbOHEi8+fPp23btpWOj4iI4JVXXuGdd96hYcOG3i1O6hy3nxNpjCkEpltrbzzGdK8B11trPXFepojUQZXtPTze295Ya3WrHJFquHJPu2bNmtGsWTMvVCNSd5xwwgkUFhayefNmQkJCOPHEEwkJ+d/+IGMMJ510kg8rlLrEEw2cKX64Oq2ISK1ddNFFnHXWWURGRjqHnXDCCXz66afUq+f6n7qJEydSUFBAgwYNPFGmSMAq2bM/cOBApk+frhuki/hAeno611xzDVFRUcyfP5+oqChflyR1lC/3AjYEcn24fhEJImFhYRVucRMSEkJMTMxxLad0Eyoi/xMdHc3w4cNJSkpSAyniI2FhYQwZMoTw8PAKt3wT8Sa3NJHGmDblBkVXMqz0OjsB5wBb3LF+ERER8azExET+85//+LoMkTqtQYMGPPnkk74uQ8RtF9bZRtEVV7cWv76s1Ovyj03Ap0AsRbcDqRFjzBJjjK3mMbyK+a4zxqwwxmQYYw4ZYxYYY/rVtA5vSUtLY8KECUyZMkVXjRS3Gzx4MMaYkkdA5umHH37gzjvv5IcffnAO27dvH+PHj+fNN9885vxff/01Y8eOZe3atR6sUuqKQM9U6Tzl5+fz3HPP8cQTT5CVleWc5pNPPmHcuHFs3LjROSw5OZlx48aVucBViR07dvDPf/6T994rf8F2keoFep5Kq22esrOzmTRpEs8++2yFK7Dm5+fz/PPP89hjj5GZmen5NyN1mruayGWlHgD7yw0r/fgKeBu4zFr7ohvW/RHwViWPCtcVN8a8ALwJdC2uYwVwNrDMGDPCDbV4TEZGBjNmzOCTTz5REykec9lll0GA5um3335j6tSp/Pbbb85hR44c4c033+TLL7885sVy1q5dy9SpU9m8ebOnS5U6JFAzVTpP+fn5fPzxx7z//vvk5OQ4p/nxxx959dVX2blzp3PY7t27efXVV8t8mVPiwIEDvP766yxdutQr70GCT6DmqbTa5ik3N5f333+fjz76qMwtrQAKCgqYPXt2hWWLeIJx91UIjTEOiq7OeoNbF1xxPUuAQUA7a+02F6Y/C/gSSAX6Wms3FQ/vCywBsoqXdaQWNa1PSkpKWr9+fU0XUaW8vDx++uknoqKi6Nq1q85HEbcaPHgwS5cuZevWrbRt2/aYv1zeyFPx8lzOVEpKCsnJyXTs2JGEhASg6BvbtWvXEhcXR5cuXaqdf9euXWzdupUuXbrQtGnT2pQt4peZqmmemjVrxs8//0xhYSE9evRwXqhq69at7Nq1i65du9KoUSOg6IubdevW0bJlS9q1a1dmmRkZGaxdu5bmzZvrCpJyXAI9T6U5HI5a5amgoICff/6ZkJAQunfvXubqrA6Hg19++YWCgoIyyxapRo0bCk/8drUDMjyw3NoaV/z8WMkfEwBr7Q/GmKnAHcCNwLO+KO5Y6tevT+/evX1dhkgJv8tTQkKCs3ksERkZSd++fV2av1WrVrRq1coTpYm4wq8yVT5Pp556aoVp2rVrV6FRjIuLo3///pUuMzo6uspxIm7mV3kqLSQkpFZ5qlevHqeddlqVy+7Ro4fbahWpjrsOZ3Wy1m631qa6e7m1YYyJBIYWv5xVySQlwy7yTkUigUt5EnEvZUrEfZQnEe+o9Z5IY8w1xf+cba1NL/XaJdbat2tZwo3GmCaAA0gG5lhrd5SbphMQDhyw1u6qZBlrip+71bIWkYA2bdo0HnvssSkoTyJuoUyJuI/yJOI/3HE463TAAsuB9FKvj8UUT1fbJvKBcq//ZYx51Fr7aKlhJbcbqeyPCdbaTGPMEaCRMSbGWpte3QqNMVUdAH+iKwWL+KvHHnsM4NZSgzyeJ1CmJHj5IlPKkwQr5UnEf7ijiXyEombwYLnXnrYMeB34HtgLtAYup6ipfMQYc9Ra++/iaaOLn7MqLOV/MoE4IIaiZlikzhg4cCA33XQT/fr148QTT4wiAPO0Zs0aPv30Uy688EJOOeUU3n77bXJzc7n++uuJjIwEim7j8cMPPzB69Gjat2/vjbKkjgqGTJXIz8+vNE8i3hJMeSrvt99+Y9asWQwZMkTnDEtgsdYG1QM4h6Im9jAQWTxsdPGwb6uZb1fxNIm1WPf6pKQkKxLg/CJP9jgz9eqrr1rAvvrqqzYzM9P26dPHdunSxaamplprrS0sLLT33nuvDQ8Pt1988YU7fk4irvKLTNV0G1VZnkR8KKDzVN6HH35oQ0JC7JNPPlnrZYnUQI0/owXdtX+ttV8YY1YBpwO9KbqUc8nVYqOqmbVB8bP2QooUC9Q8hYeH89RTT1FQUEBMTAzff/89Tz/9NKeffjoffvihrl4nPhOImSqfJxF/EYh5Kq9fv37Mnj37mLehEvE3QddEFttE0R+UFsWvS066rvT6/caYBhQd1nDYunD+lkgdExB5ioyMpFmzZkRGRhIaGsrAgQOd41JTU1m+fDnDhg3joot0QT7xuYDIVInyeRLxMwGVp/ISExO5+OKL3brMwsJCDh8+TFhYGA0bNnTrskVKeKSJNMaEA1cBAykKdXgVk1pr7TAPlNCo+Dmz+HkjkAvEG2NaWmt3l5u+Z/HzLx6oRSTQBUSeLr74Yvr27Ut8fHyFcUOHDuW7776jSZMm3ixJpCoBkSmRAKE8lbNjxw5GjhxJr169ePnllzGmxveTF6mS25tIY0xL4GvgJIquwFodt1+AxxgTDwwofrkGwFqbbYxZBJwHXAG8UG62y4ufP3F3PZ6ye/dutm7dSufOnWnatKmvy5EgFUh5io2NJTY2ttJxDRo04MQTdSE98b1AypSIv1OeKlevXj3i4+OJi4vzdSkSxEI8sMxngI7ADxQFtRvQropHjS6PaIzpZ4wZYYwJLTe8LTCbomPd59my9wd6rvj5AWPMSaXm6QvcAhwBptWkHl94//33Ofvss1m6dKmvS5EA9/333zNnzhwKCwvLDK9LeRJxJ2VKxH2Up+PXqlUrZs+ezcSJE7UXUjzGE4eznkvR8ehnWWtzPLB8KGpS3wRSjDFrKPpjcAJwGhABrAf+WnoGa+1Xxph/A3cCa40xXwL1gbMp2mN6vbX2iIfqdbtu3bpx00030a5dO1+XIgEuOTmZ66+/noSEBHr27MmCBQv+Sx3Lk4g7KVMi7qM8HT9jDOHhVZ1JJuIenmgiw4GvPNhAAvwI/IeiK3H1ouh4+ExgLfAh8B9rbXb5may1Y40xa4ExFP0hyQO+Ah611n7vwXrd7qyzzuKss87ydRkSBHr37s2tt97Kjz/+yMqVKwFGUsfyJOJOypSI+yhPIv7JWOve0xKNMT8AWR66YI5fM8asT0pKSlq/fr2vSxGpDb859qUmmZo+fTrff/899957L+3b1+iIeRF384tMuWsbtXLlSqZMmcJll13GhRde6KbqRFwWVHkS8bEa58kT50Q+BQw0xpzhgWWLiFRrzZo1zJ07l8OHD/u6FJGgtHv3bj7++GOSk5N9XYqIiPiIJw5nXUPRCc1fG2OeA74EdgGOyia21u6obLiISE384x//4MYbb+Skk0469sQictwGDx7MkiVLSExM9HUpIiLiI55oIrdRdOsOAzxQ/KiK9VANAS0tLY0dO3aQkJBQ6T3vRKRqrVu3pnXr1r4uQyRoxcXFceqpp5KSksK6deto27Yt0dHRvi5LJKDk5uayZcsWoqOjadOmja/LETlunjicdVnxY2mpf1f1+MYD6w94S5YsYeDAgfz3v//1dSkiIiKVeuWVVxgyZAgrVqzwdSkiAWf79u1ccMEFPPTQQ7j7+iQi3uD2vYDW2sHuXmZd06JFCy644ALdHF1ERPxW586dOe+883TEjEgNREdHc+6555KUlKR7OUpA0qGkfuiMM85g5syZvi5DRESkSqNGjWLUqFG+LkMkICUmJjJ16lRflyFSY7VuIo0xD7kwmaXonj67gO+stbtru14RERERERHxPnfsiZxAUZPoKocx5iPg/6y1qW5Yv4iIiIiIiHiJO5rItzl2E2mAKKA90AO4AuhsjOlrrc12Qw0iIiIiIiLiBbVuIq211x3P9MaY1sAbwFDgNuDZ2tYgIiIi7peamkpmZiYJCQnUr18fgKysLA4cOEBcXBwNGzYsM31OTg779u0jNjaWRo0a+aJkEb9mrSUlJQVrLQkJCYSElL1Rwr59+8jPz6dFixaEhoZWuZy8vDxSUlJo0KABTZo08XTZIhV44hYf1bLW7gQuB44WP4uIiIgfevLJJxk8eDAbNmxwDlu0aBF9+/ZlxowZFaZfuXIlAwYM4OWXX/ZmmSIBIycnhxtvvJGrrrqKtLS0MuMKCgoYO3YsF198Mfv27at2Ob/99htDhgzhiSee8GS5IlXyydVZrbVpxphvgf6+WL+IBJ+dO3eybt06unbtSuvWrQHIzMxk+fLlNGnShB49elQ6X2pqKqtWraJt27Z06tTJixWL+L/WrVvTvXt3oqKinMPi4uLo0aMHzZs3rzB9TEwM3bt3JzEx0ZtligSMkJAQOnXqRFZWFvXqlf0YbozhxBNPJCwszLnnvypRUVF0796dNm3aeLJckSoZX93g1BgzE7jCWhvukwI8wBizPikpKWn9+vW+LkWkNvzmhlXHk6nXX3+dW265hVdeeYWbbroJgN9//51BgwYxZMgQ3n333UrvxbVo0SIuuOACxo4dy6RJk9z+HkTwk0zVZBvlcDiw1pY5rM5ai8PhICQkpNJMFRYWVjlOxA384herNp/5HA4HQIVDWUvGlc9cVQoLCzHGVLocERfVOE++vE9kO+CgD9cvIkHk1FNPZfz48Zx66qnOYU2bNmXs2LG0b9++yg+0bdu25Z577qFfv37eKlUkYFT24dQYU+0HXFc+/IrUZdU1fcfTECpr4ks+aSKNMWcAvYF5vli/iASf0047jdNOO63MsKZNm3LfffdVO1/79u2ZMGGCBysTERERCS5eayKNMREU3eLjAuBeinafTvXW+kVERERERKT2at1EGmMKazIb8JS19ovarl9ERERERES8xx17Io/nhMxs4Dvg39ba+W5Yt4iIiIiIiHiRO5rIdi5MY4Es4JC11uGGdYqIiIiHbdmyhYMHD3LyyScTHR3t63JERMRP1LqJtNZud0chIiIi4l+ef/553n33XT7//HNOP/10X5cjIiJ+wpe3+BARERE/1q9fP8LCwoiPj/d1KSIi4kfURIqIiEilRo8ezejRo31dhoiI+BnX72gqIiIiIiIidZ6ayACQk5NDVlYW1tpqp3M4HGRmZpKbm+ulykREREREpK5RE+nnCgsLufvuu7n88svZv39/tdP+/vvvnHvuuTz99NNeqk5EREREROoaNZEBIC8vj5ycHJf2RObm5lJQUOClykREREREpK7RhXX8XGhoKM899xwOh4OYmJhqp+3SpQtfffUVYWFhXqpORERERETqmoDcE2mMiTLGjDDGTDPGbDTG5BhjMo0xPxtjHjLGVLgjsjFmgjHGVvN40hfvxRXR0dHExsZijKl2utDQUBo2bEhUVJSXKpNgkJWVxZw5c7jxxhvp1KkTwZ4nEU8qn6eIiAiUJ5Ga0zZKxD8F6p7I0cBrxf/+DZgHxAL9gInAVcaYQdbayk4i/A7YXMnw1Z4oVMTfvfPOO/z1r38FivZmozyJ1Fj5PF188cV8+OGH36A8idSItlEi/ilQm8h84FXgBWvtbyUDjTEtgPnAqcALFDWb5b1urZ3uhRpFAkJYWBg333wzY8eOLdlAjwTlSaQmKskTwPBAzdOsWbNYs2YNt956K61btwbgl19+YcaMGZx//vkMGTKk0vkWL17Mp59+yjXXXEP37t29WbIEmWDbRuXn5/Of//yHnJwcxowZU+boMYfDwbRp00hJSeGOO+6gYcOGQPV5WrFiBe+//z6XX345ffv29ep7kbotIA9ntda+Za29pXQDWTx8L/B/xS//ZIyp7/3qRALLtddeyyuvvFL6Ay+gPInURLDl6euvv2bKlCmkpKQ4h23atIkXX3yRVatWVTnf6tWrmTx5Mps2bfJGmRLEgi1T+fn5vPfee8ycOZOcnJwy4xwOB7Nnz+aNN94gPT3dOby6PK1fv56XXnqJX3/91eO1i5QWqHsiq/Nz8XM40ATY68NaRAKd8iTiPgGXpzvvvJMrr7ySTp06OYcNHDiQzz77jPbt21c538iRI+nZsyddu3b1RplSdwVcpiIiInjxxRcpLCwkNja2zLjQ0FCeeOIJMjIyiI+Pdw6vLk/Dhw/ns88+K5NREW8IxiayZKuWDxyqZPxQY0wPIALYBSy01urYeJHKKU8i7hNweercuTOdO3cuMyw+Pr7Kw1hLtGnThjZt2niyNBEIwEyFhIRw+umnU1BQQEpKChERETRt2hQAYww9evSoME91eWrRogUtWrTwZMkilQrGJvLO4ufPrLW5lYz/S7nXjxpjPgKus9ZmuLICY8z6Kkad6GKNIoHC43kCZUrqDOVJxL0C9jPftm3buPTSS+nXrx9Tp0495hX4RfxNQJ4TWRVjzPnAjRR9I/VgudGbgbuBk4FooDVwNbAbuAyY4b1KRfyf8iTiPsqTiHsFeqbCw8Pp0qULrVu3VgMpAclYa31dg1sYYzoD3wONgLHW2n+7OF8L4FeKjqXva61dXosa1iclJSWtX1/Vl1YiAcH4Q56Kl6dMScAzxnRBeRJxF7/YRrkjT4WFhRhjCAkJqn06Elhq/A1GUPzWGmNaAp9R9MfkOVf/mIDz6l5vFr8c7oHyRAKK8iTiPrt374YAzNPatWuZNGkSa9eupaCggLfeeotXX32V7Oxsfv/9dyZNmsQPP/yAw+HgvffeY/LkyWWuJrl8+XImTZrEb7/9VmHZP//8M5MmTeKnn36qcv1Lly7lqaee4o8//qi2ztzcXF577TXefPNN8vPza/6GJWAE8jZq4cKF/Otf/2Lv3r2kpqby0ksvMXfuXACWLFnCU089xdatW0lPT2fy5Mm8++67OByOSvO0d+9e/vWvf7FgwYIK6zl06BAvvPACH3/8cY3qTElJ4dlnn6102SIlAr6JNMY0Br4ATqDoD8PdNVhMyTWTdWay1GmHDh2CIMjTsY6wsNa6ZRp3C5YjQ6TIoUOHOOeccyAA87Rq1SrGjx/PypUryc3N5T//+Q8vvPAC2dnZrFu3jvHjx7N06VIcDgfTp0/n6aefJi0tzTn/N998w/3338+6deuqXPaKFSuqXP/nn3/Ogw8+eMxbhGRnZ/Piiy8yefJkcnMrOyVOgkmgb6PmzJnDhAkT2LVrFwcOHODxxx/n3XffxVrLF198wUMPPcSmTZs4evQozzzzDG+88QYFBQUsW7asQp527drFhAkT+Oijjyqs5+DBgzzxxBPMnDmTwsLC465zz549TJw4sdJli5QI6AvrGGOigYVAEvAx8Fdbs09hjYqfM91Vm0igycjI4LzzzoMAz9PUqVNZtmwZjzzyCB06dKgwvrCwkCeeeIJdu3bxxBNP0KRJkwrTZGZmcv/99xMREcHEiRMJDw/3eN2vvfYaixcv5uGHH9al2oNASZ42bNgAAZinYcOGMWvWLFasWMGNN97IDTfcQNu2bYmOji4zXWhoKA888ABHjx4tk6VLLrmE9u3b07t37yqXfeqpp1a5/tGjR3P66adXeqXK0ho0aMBzzz1HaGgoERERx/cmJaAEwzbqlltu4bzzzqNDhw7Uq1ePN954g2bNmmGMYfTo0fTq1YsePXoQExPD1KlT2bp1K3/+85/p1KkTH374YaV5qkxiYiJvvPEGW7ZsYeTIkVx99dX86U9/crnO9u3bM2PGDFq1alXTtyp1QMA2kcaYcGAucAbwOXCVtfa4v24xRWczX1r8co37KhQJHLm5uVxyySUlewYCOk/Jycl8++23HD16tNLx1lp++eUXNm7cWOWei/z8fFasWEGDBg1q9C1uTWzatIlly5ZVWbcEjtJ5Ovfcc/n8888DLk/t2rWjXbt2fPfdd3z//ff885//pGfPngDUr1+fpk2bEhUVhTGG/v37V5i/Y8eOdOzYsdJlt23blrZt21a7/q5du7p0j8mwsDDOPvvsY78hCWjBso3q2bOnM0cAF110kfPf5X/nzzvvPJYtW8ajjz5K165dueyyy8osKywsjCZNmlS41yRAdHQ0F154IZ9++imPP/44AwYMOK464+LiuOSSS45rHqmDSg7ZCqQHEErRt1AWWAZEHWP6eOD/gJhyw6OBqcXL2Xus5bhQ1/qkpCQrEkgKCgrspZdeagE7YMAA6y95sjXMVEpKik1OTrbZ2dlVTrNr1y67ZcsWm5+fX+n4wsJCu3XrVrt9+3brcDiOa/01tW/fvmPWLf6vfJ4yMzOtDbI8paen240bN9rU1NQa/YxEjoe/bqO88ZkvMzPTJicn2wMHDlQYl5OTYzdt2mRTUlKqnF9ZFRfUOAOBuidyDP/7JukgMKWKyyPfba09CDQAJgNPGmNWUvTHIx7oSdEVuo4Al1trszxct4jfmTx5MrNnzwYoueFxQOepefPmNG/evNppWrZsWe34kJCQY+4pcbdmzZrRrFkzr65T3K98nm677Tbeeuut6ZVMGrB5io6OrnIvo4i7Bds26nhERUVx0kknVTouPDy80lM2SlNWxZMCtYlsVOrfl1Y5FUygqMlMBZ4C+gAdgX5AIbAVmA48b63d7YlCRfzd4cOHnf8u3lBfW8WkE1CeRKpVSZ6g8kxNQHkSOSZto0T8U9DcJ9IfeOMeXLt372b+/Pl069aNPn36eGw9Uqf5zV2PdV87CRJ+kSnlSYKE8iTiPnX7PpF1ye+//86YMWNKf8MtIiIiIiLiNYF6OGud1aVLF1555RWXrlonIiIiIiLibmoiA0xiYiLXX3+9r8sQ8Vt5eXkUFBQQERFBSIgOthDxBmstOTk5GGN0v0YRD8nNzaWwsJDIyEiquLhQpfLz88nPzyc8PJzQ0FAPVih1iT5hiUhQ+de//sXw4cNLbvIuIl5w4MABRo4cybhx4ygoKPB1OSJBaeLEiVx44YVs27btuOZ77bXXGDZsGCtXrvRMYVInqYn0I3l5eSQnJ7Nr1y5flyISsHJzc8nIyEAXDRPxHmstmZmZ5OTk+LoUkYBQk898OTk5ZGdn43A4jntdmZmZ+oJH3EpXZ3Wj2l6pa9OmTQwfPpxBgwYxbdq04zpUQcSN/OYXryaZysrKIjc3l9jYWB22I/7CLzLlyatJOhwOjh49SmhoKDExMW5fvkgpQZGnmnzmy8jIoKCggNjY2OM6XSM7O5ucnByio6MJCwurUb0StGqcJ50T6UeioqIYNGgQp5xyihpIkRqKiooiKirK12WI1CkhISHExcX5ugyRgFGTz3zR0dE1WldkZCSRkZE1mlekKmoi/UjLli154403fF2GiIiIiHiQPvNJoNM5kX7o119/5d5772XJkiW+LkUk4Hz88cc8+OCD7Ny5s8K4zz77jPHjx7Np0yYfVCYiIiISHNRE+qHk5GSef/55XUVLpAa+/PJLXnrpJVJSUiqMW7ZsGc8///xxX9lORERERP5Hh7P6oQEDBjB//nw6dOjg61JEAs4dd9zBFVdcQadOnSqMu+GGGxg2bBg9evTwfmEiIiIiQUJNpB9q1qwZZ511lkvTpqamkpOTQ/PmzalXT/+dIl26dKFLly6VjuvQoYO+nBERERGpJR3OGuAeeOABzj33XLZu3errUkREREREpA5QE+nnrLWsWbOGb775htzc3ArjW7ZsSceOHQkPD/dBdSL+Y/fu3Xz55Zfs3r0bh8PBihUr+P7778nPz3dOs3nzZr7++mtSU1MrzL99+3a++uor9u3b582yRUSkDqlq+yQSaNRE+rnCwkIeeOABrrnmGg4cOFBh/H333ccHH3xAmzZtfFCdiP/47LPPOP/88/nss8/Iycnhzjvv5JZbbiE9Pd05zRtvvMGFF17ImjVrKsz/4Ycfcv755/PNN994s2wREalDqto+iQQanUTn50JCQrj88svZv38/MTExFcaHhob6oCoR/9OtWzfuvvtuunXrRlhYGFdffTV79uzhlVdeoUuXLowYMYKBAwcC0LZt2wrzOxwO8vPzcTgcXq5cRETqipLtU15eHhEREWzcuJE5c+YwaNAg+vTp4+vyRFymJtJPWWsxxhASEsINN9xQ6TgR+Z9evXrRq1cv5+sxY8bw+++/M2DAAIYMGcIll1zC8OHDGT58eJXLMMYoWyIi4jFhYWGMGTPG+frXX3/lvvvuY9KkSWoiJaDocFY/tHz5ckaNGsWcOXMqjPv1118ZPXo0M2fO9H5hIgGmZcuWTJs2jXHjxh2zObzkkkv44IMP6Nu3r5eqExGRuq5Pnz588MEHjBgxwteliBwX7Yn0Q/v372fx4sWceeaZFcYdPnyYxYsX07lzZx9UJuL/MjMzyc3NJTY2lpiYGC6++GKX5uvUqVOl95YUERHxlFatWnH55Zf7ugyR46Ym0g8NGTKEb775hvj4+ArjTj/9dJYsWUKTJk18UJmI/3v66aeZN28eb7/9NqeccoqvyxEREREJOmoi/VBMTEyVexqjoqK0F1KkGhEREcTGxuqiUyIiIiIeonMiRSSo3HXXXSxcuFBftoiIiIh4iPZEikhQqV+/PvXr1/d1GSIiIiJBS3siRURERERExGVqIoNAVlYWEydOZNKkSeTl5fm6HBERCRIzZ87kjjvuYNu2baSkpDBu3DheffVV53iHw8GLL77Ivffey+HDh53Dv/jiC/72t7+xevXqapefn5/PU089xcMPP0xmZqbH3oeIPyidpxJr1qzhb3/7G59//nmF6X///Xduu+02Pv74Y+ew6j7zuZKnI0eOMH78eJ5//nkKCwvd88akTlIT6YcKCwvJysqioKAAgNzcXLKzs7HW4nA4yMrKIj8/H4C8vDyOHDnCnDlzmD9/PmlpaRX+qOTk5JCTk1PpukovW0REpLTly5fz7rvvcvDgQdLS0vjwww9ZtmyZc5vhcDj46quv+Pjjj8nKynLOt379embOnMn27durXX5BQQELFy5k7ty5VW6nRIJF6TyV2L59OzNnzmTdunUVpk9JSeGdd97hp59+cg7Ly8tj7ty5LFy40PlZsIQrecrKymL27Nl8+eWXOBwON70zqYuMmgf3McasT0pKSlq/fn2tlrNo0SLGjx/PbbfdxtVXX83f//53tm7dyrRp09i+fTt33nkno0ePZsyYMYwfP54ffviBv//97+Tl5fHvf/+b8847j/HjxwNFTeKtt95Keno6r7/+Og0bNnSup7CwkHHjxrFlyxZef/11EhISalW3BA3j6wJKuCtTIj7mF5mqSZ62bdvGwYMH6dKlC6GhoWzYsIHY2Fg6dOjgnGbjxo1kZ2eTlJTkPB9579697Ny5kw4dOtC4ceMql+9wOPj9998pKCggKSmJevV0qQY5pqDIU4MGDQA4dOgQmzdvpnXr1rRo0aLM9EePHmXjxo0kJCTQunVroOiz24YNGzDGkJSUREjI//YHuZKn/Px8NmzYQHh4OJ06dcIYv/hxiu/U+Begzv21NsZEAvcBVwJtgEPAZ8CD1trdvqytRH5+PmlpaeTm5gJFN09PT0/H4XBQUFBAenq68xum7OxssrKyOPnkk8nOziYjI4Ps7Gznsqy1ZGZmkpGRUenexqysLOeyRY6XP+UpLS2NlJQUEhISnF+W5OXlsWPHDqKiokhMTPRmOSI14k+ZAmjbti1t27Z1vu7Zs2eFaTp16lRhWIsWLSp8IK5MSEgISUlJtapRpCr+nieAxo0bc8YZZ1Q6fWxsLL169SozLDQ0tMp7ILuSp7CwMLp37+560SJVqFN7Io0xEcBioA+wF/gGaAucARwA+lhr/6jF8t2y1yQvL4+MjAyioqKIiIggPT2dwsJCYmNjKSwsJD09nYiICKKiosjIyCA/P5/Y2Fig6IN0eHi48xsuKPomy+Fw0LBhwwrfOKWnp1NQUEDDhg3LfJsldZpL30p5Ok/F63A5UzNmzGDs2LG88MIL/OUvfwFg06ZNnH/++QwYMIBp06bpG1fxFb/IlPbsS5BQnkTcp8YfjOpa1/AARX9MfgA6WmtHWWt7A3cB8cAbviyuxP79+1m0aBHbt2/HWsvatWv59ttvyc3NJSwsjMaNG7Nz505mzZrFkSNHaNSoEaGhoRw9epSlS5fy+++/l1lebGwscXFxGGPYvXs3s2bNck4TExNDo0aN1EBKTfhVnhITExk6dCh79uxh9uzZHDp0iKioKAYMGEBcXByzZs1i48aNFebbv38/H330Eb/++qs3yxWpjF9lSiTAKU8iHlRnOgdjTH1gTPHL/7PWZpSMs9Y+B/wCDDLGnOaL+kpbvnw5I0eOZM6cORQWFvL4449z2223kZqa6pxm3rx5jBw5kh9++ME5bMuWLVxzzTVMnTq1ymWvWLGCkSNHMnv2bI++Bwlu/pinYcOG8cEHH7Blyxb+8pe/sHnzZlq2bMm0adPo27cvo0aNYs6cORXm+/XXXxk9ejT//e9/vVWqSAX+mCmRQKU8iXheXTon8kygIbDFWvtTJeNnAd2Ai4Dqr0nuYaeccgqTJk1i0KBBhISEcP3115OamlrmojgDBw5k0qRJZY6Lb9WqFRMnTqRLly5VLvvkk09m0qRJDBw40KPvQYKeX+bJGMOf/vQnOnXq5LwIgTGGbt26OTNVXocOHXjssccqnHci4mV+mSmRAKU8iXhYXWoiS84iXlPF+JLh3bxQS7U6derEPffc43w9atSoCtP07t2b3r17lxmWkJDAuHHjql12x44dyyxbpIb8Nk/Dhw9n+PDhZYaVz1RpJ5xwAv/4xz+8UZpIdfw2UyIBSHkS8bC61ES2KX7eVcX4kuEnHGtBxpiqzqI+8XiLEglQbssTKFMiaBsl4k7Kk4iH1aUmMrr4OauK8ZnFzzG1WEfYli1bOPnkk2uxCBHf2rBhwzxr7cXHmMwbeQJlSoKAH2VKeZKApzyJuI+LeapUXWoi3cZaW+lfDGNMSm5ubvyGDRvygS1eLqsuKfn2Tz/jIKFM+ZTyFGSUJ59SnoKM8uRzypSfqktNZMmVuaKqGF9yY8X0mq7AWptQcthDVX90pPb0M/YLHs8TKFPeoJ+v39A2Kgjo5+s3lKcgoZ+x/6ozt/gAdhQ/t6pifMnw7V6oRSTQKU8i7qVMibiP8iTiYXWpify5+LlnFeNLhv/ihVpEAp3yJOJeypSI+yhPIh5Wl5rI74A04ERjTI9Kxl9e/PyJ1yoSCVzKk4h7KVMi7qM8iXhYnWkirbV5wOTily8bY0qOh8cYM46iewUttdbqprMix6A8ibiXMiXiPsqTiOfVpQvrADwGnAX0AzYZY76h6B5BvYEDwA0+rE0k0ChPIu6lTIm4j/Ik4kHGWuvrGrzKGBMJ3AeMBloDh4DPgAettVXdlFZEKqE8ibiXMiXiPsqTiOfUuSZSREREREREaq7OnBMpIiIiIiIitacmUkRERERERFymJlJERERERERcpiZSREREREREXKYmUkRERERERFymJlJERERERERcpibSTYwxkcaYR4wxycaYHGPMHmPMG8aYlr6uLVAYY5YYY2w1j+FVzHedMWaFMSbDGHPIGLPAGNPP2/WL+yhPtac8SWnKVO0pU1JCeao95Snw1fN1AcHAGBMBLAL6AHuBuUBb4HrgQmNMH2vtH76rMOB8BGRUMnx3+QHGmBeAO4Fs4AsgAjgbOMcYc7m1do7nyhRPUJ7cTnmq45Qpt1Om6jDlye2Up0BlrdWjlg/gMcAC3wPRpYaPKx6+xNc1BsIDWFL882rr4vRnFU9/EDip1PC+QC5wGIjz9fvS47h/D5Qn9/wclSc9Sv4PlSn3/ByVKT2UJ/f9HJWnAH/ocNZaMsbUB8YUv/w/a63z2xRr7XPAL8AgY8xpvqgvyI0rfn7MWrupZKC19gdgKhAH3OiDuqSGlCefUp6CkDLlU8pUkFGefEp58jNqImvvTKAhsMVa+1Ml42cVP1/kvZKCnzEmEhha/HJWJZPo5x6YlCcfUJ6CmjLlA8pU0FKefEB58k86J7L2uhc/r6lifMnwbl6oJVjcaIxpAjiAZGCOtXZHuWk6AeHAAWvtrkqWoZ97YFKe3E95qtuUKfdTpuou5cn9lKcApSay9toUP1f2S116+AleqCVYPFDu9b+MMY9aax8tNazan7u1NtMYcwRoZIyJsdame6BOcT/lyf2Up7pNmXI/ZaruUp7cT3kKUDqctfaii5+zqhifWfwc44VaAt0y4C/AiUAURd883Q8UAI8YY+4sNe2xfu6gn30gUp7cR3kSUKbcSZkS5cl9lKcApyZS/Ia19iFr7Uxr7R/W2mxrbbK19glgRPEkE4qPixeRY1CeRNxLmRJxH+Up8KmJrL2SK3NFVTG+QfGzdq3XkLX2C2AVRVfe6l08+Fg/d9DPPhApTx6mPNU5ypSHKVN1ivLkYcpT4FATWXslJ/+2qmJ8yfDtXqglmJVczrlF8XO1P3djTAOK/gAd1rHxAUV58g7lqe5QprxDmaoblCfvUJ4CgJrI2vu5+LlnFeNLhv/ihVqCWaPi55Jj3jdSdHPZeGNMy0qm1889MClP3qE81R3KlHcoU3WD8uQdylMAUBNZe98BacCJxpgelYy/vPj5E69VFGSMMfHAgOKXawCstdnAouJhV1Qym37ugUl58jDlqc5RpjxMmapTlCcPU54Ch5rIWrLW5gGTi1++XLxLHQBjzDiK7lmz1Fq72hf1BQpjTD9jzAhjTGi54W2B2RQd6z6v3P2Bnit+fsAYc1KpefoCtwBHgGmerFvcS3lyD+VJSihT7qFMCShP7qI8BQdjrfV1DQHPGBMBLKHoBOC9wDcU3SOoN3AA6GOt/cNnBQYAY8x1wJtACkXfPB2h6Gd4GhABrAeGWmv3l5vvBeBOii77/CVQHzgbMMDl1to53qhf3Ed5qj3lSUpTpmpPmZISylPtKU/BQU2kmxRfhvg+YDTQGjgEfAY8WO6bFKmEMaYLcDtFf4RbU3Q8fCbwG/Ah8J/iwxkqm/c6YAzQBcgDlgOPWmu/93zl4gnKU+0oT1KeMlU7ypSUpjzVjvIUHNREioiIiIiIiMt0TqSIiIiIiIi4TE2kiIiIiIiIuExNpIiIiIiIiLhMTaSIiIiIiIi4TE2kiIiIiIiIuExNpIiIiIiIiLhMTaSIiIiIiIi4TE2kiIiIiIiIuExNpIiIiIiIiLhMTaSIiIiIiIi4TE2kiIiIiIiIuExNpIiIiIiIiLhMTaS4jTHGHudjW/F8S4pft/XtOxARERERkWOp5+sCJKi8Vcmw/sCJwM/A2nLjDnq6IBERERERcS9jrfV1DRLEjDHTgWuBidbaCVVM0waIArZYa/O9V52IiIiIiBwv7YkUn7PW7vB1DSIiIiIi4hqdEyk+V9U5kSXnTRpj6hljHjTGbDbGZBtjfjPGXF9quqHGmMXGmKPGmMPGmLeNMU2qWFc9Y8ytxpgfiqfPNsasNcaMNcboSxURERERkWPQh2YJBB8AQ4HFwBZgEPCGMQYgHXgXWA58DvQF/gK0M8YMtKWO1zbGRALzgSHAoeJ5coDewPPAEGPMpdZah5fel4iIiIhIwFETKf7uBIoaxZOstQcAjDFDgEXA40B9YIS1dn7xuFjge4ou6DOYosazxL8oaiDfB26x1qYVzxMDvAdcDNwMTPX4uxIRERERCVA6nFUCwdiSBhLAWrsY+AloASwsaSCLxx0FXi1+OahkuDGmGfBXYCdwfUkDWTxPOnAjkAfc6sH3ISIiIiIS8NREir/LB5ZUMvyP4ucvqhnXotSwwUAY8Jm1Nrv8DNbaFGATcErxYa8iIiIiIlIJNZHi71KstYWVDM8oft5dzbjwUsPaFj//tfiCPRUewMmAARq7o3ARERERkWCkcyLF3x3rIjeuXgSn5AuTtcDPx5g218VlioiIiIjUOWoipa7YVfz8rbX2dp9WIiIiIiISwHQ4q9QVi4FC4EJjTJivixERERERCVRqIqVOsNbuBt6g6NzId40xzctPY4zpYIy5zNu1iYiIiIgEEh3OKnXJnRQ1kZcBw40xa4EdQAMgCegAzAU+8lF9IiIiIiJ+T02k1BnW2mxjzHnA1cC1QA/gDOAAsB2YAbznswJFRERERAKAsdb6ugYREREREREJEDonUkRERERERFymJlJERERERERcpiZSREREREREXKYmUkRERERERFymJlJERERERERcpiZSREREREREXKYmUkRERERERFymJlJERERERERcpiZSREREREREXKYmUkRERERERFymJlJERERERERcpiZSREREREREXKYmUkRERERERFymJlJERERERERcpiZSREREREREXKYmUkRERERERFymJlJERERERERc9v/BhiofXBN34AAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Let's plot the hiddden layer spiking activity for some input stimuli\n", "\n", "nb_plt = 4\n", "gs = GridSpec(1,nb_plt)\n", "fig= plt.figure(figsize=(7,3),dpi=150)\n", "for i in range(nb_plt):\n", " plt.subplot(gs[i])\n", " plt.imshow(spk_rec[i].detach().cpu().numpy().T,cmap=plt.cm.gray_r, origin=\"lower\" )\n", " if i==0:\n", " plt.xlabel(\"Time\")\n", " plt.ylabel(\"Units\")\n", "\n", " sns.despine()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In conclusion, we see that already this simple spiking network solves the classification problem with ~85% accuracy, and there is plenty of room left for tweaking. However, the hidden layer activities do not look very biological. Although the network displays population sparseness in that only a subset of neurons are active at any given time, the individual neurons' firing rates are pathologically high. This pathology is not too surprising since we have not incentivized low activity levels in any way. We will create such an incentive to address this issue by activity regularization in one of the next tutorials." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Creative
This work is licensed under a Creative Commons Attribution 4.0 International License." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: notebooks/SpyTorchTutorial3.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 3: Training a spiking neural network on a simple vision dataset\n", "\n", "Friedemann Zenke (https://fzenke.net)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> For more details on surrogate gradient learning, please see: \n", "> Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks.\n", "> https://arxiv.org/abs/1901.09948" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Tutorial 2, we have seen how to train a simple multi-layer spiking neural network on the [Fashion MNIST dataset](https://github.com/zalandoresearch/fashion-mnist). However, the spiking activity in the hidden layer was not particularly plausible in a biological sense. Here we modify the network from this previous tutorial by adding activity regularizer, which encourages solutions with sparse spiking." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.gridspec import GridSpec\n", "import seaborn as sns\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torchvision" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# The coarse network structure is dicated by the Fashion MNIST dataset. \n", "nb_inputs = 28*28\n", "nb_hidden = 100\n", "nb_outputs = 10\n", "\n", "time_step = 1e-3\n", "nb_steps = 100\n", "\n", "batch_size = 256" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "dtype = torch.float\n", "\n", "# Check whether a GPU is available\n", "if torch.cuda.is_available():\n", " device = torch.device(\"cuda\") \n", "else:\n", " device = torch.device(\"cpu\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Here we load the Dataset\n", "root = os.path.expanduser(\"~/data/datasets/torch/fashion-mnist\")\n", "train_dataset = torchvision.datasets.FashionMNIST(root, train=True, transform=None, target_transform=None, download=True)\n", "test_dataset = torchvision.datasets.FashionMNIST(root, train=False, transform=None, target_transform=None, download=True)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Standardize data\n", "# x_train = torch.tensor(train_dataset.train_data, device=device, dtype=dtype)\n", "x_train = np.array(train_dataset.data, dtype=np.float)\n", "x_train = x_train.reshape(x_train.shape[0],-1)/255\n", "# x_test = torch.tensor(test_dataset.test_data, device=device, dtype=dtype)\n", "x_test = np.array(test_dataset.data, dtype=np.float)\n", "x_test = x_test.reshape(x_test.shape[0],-1)/255\n", "\n", "# y_train = torch.tensor(train_dataset.train_labels, device=device, dtype=dtype)\n", "# y_test = torch.tensor(test_dataset.test_labels, device=device, dtype=dtype)\n", "y_train = np.array(train_dataset.targets, dtype=np.int)\n", "y_test = np.array(test_dataset.targets, dtype=np.int)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-0.5, 27.5, 27.5, -0.5)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Here we plot one of the raw data points as an example\n", "data_id = 1\n", "plt.imshow(x_train[data_id].reshape(28,28), cmap=plt.cm.gray_r)\n", "plt.axis(\"off\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we are working with spiking neural networks, we ideally want to use a temporal code to make use of spike timing. To that end, we will use a spike latency code to feed spikes to our network." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def current2firing_time(x, tau=20, thr=0.2, tmax=1.0, epsilon=1e-7):\n", " \"\"\" Computes first firing time latency for a current input x assuming the charge time of a current based LIF neuron.\n", "\n", " Args:\n", " x -- The \"current\" values\n", "\n", " Keyword args:\n", " tau -- The membrane time constant of the LIF neuron to be charged\n", " thr -- The firing threshold value \n", " tmax -- The maximum time returned \n", " epsilon -- A generic (small) epsilon > 0\n", "\n", " Returns:\n", " Time to first spike for each \"current\" x\n", " \"\"\"\n", " idx = x0.0] = spike_height\n", " dat = dat.detach().cpu().numpy()\n", " else:\n", " dat = mem.detach().cpu().numpy()\n", " for i in range(np.prod(dim)):\n", " if i==0: a0=ax=plt.subplot(gs[i])\n", " else: ax=plt.subplot(gs[i],sharey=a0)\n", " ax.plot(dat[i])\n", " ax.axis(\"off\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training the network" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "class SurrGradSpike(torch.autograd.Function):\n", " \"\"\"\n", " Here we implement our spiking nonlinearity which also implements \n", " the surrogate gradient. By subclassing torch.autograd.Function, \n", " we will be able to use all of PyTorch's autograd functionality.\n", " Here we use the normalized negative part of a fast sigmoid \n", " as this was done in Zenke & Ganguli (2018).\n", " \"\"\"\n", " \n", " scale = 100.0 # controls steepness of surrogate gradient\n", "\n", " @staticmethod\n", " def forward(ctx, input):\n", " \"\"\"\n", " In the forward pass we compute a step function of the input Tensor\n", " and return it. ctx is a context object that we use to stash information which \n", " we need to later backpropagate our error signals. To achieve this we use the \n", " ctx.save_for_backward method.\n", " \"\"\"\n", " ctx.save_for_backward(input)\n", " out = torch.zeros_like(input)\n", " out[input > 0] = 1.0\n", " return out\n", "\n", " @staticmethod\n", " def backward(ctx, grad_output):\n", " \"\"\"\n", " In the backward pass we receive a Tensor we need to compute the \n", " surrogate gradient of the loss with respect to the input. \n", " Here we use the normalized negative part of a fast sigmoid \n", " as this was done in Zenke & Ganguli (2018).\n", " \"\"\"\n", " input, = ctx.saved_tensors\n", " grad_input = grad_output.clone()\n", " grad = grad_input/(SurrGradSpike.scale*torch.abs(input)+1.0)**2\n", " return grad\n", " \n", "# here we overwrite our naive spike function by the \"SurrGradSpike\" nonlinearity which implements a surrogate gradient\n", "spike_fn = SurrGradSpike.apply" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def run_snn(inputs):\n", " h1 = torch.einsum(\"abc,cd->abd\", (inputs, w1))\n", " syn = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n", " mem = torch.zeros((batch_size,nb_hidden), device=device, dtype=dtype)\n", "\n", " mem_rec = []\n", " spk_rec = []\n", "\n", " # Compute hidden layer activity\n", " for t in range(nb_steps):\n", " mthr = mem-1.0\n", " out = spike_fn(mthr)\n", " rst = out.detach() # We do not want to backprop through the reset\n", "\n", " new_syn = alpha*syn +h1[:,t]\n", " new_mem = (beta*mem +syn)*(1.0-rst)\n", "\n", " mem_rec.append(mem)\n", " spk_rec.append(out)\n", " \n", " mem = new_mem\n", " syn = new_syn\n", "\n", " mem_rec = torch.stack(mem_rec,dim=1)\n", " spk_rec = torch.stack(spk_rec,dim=1)\n", "\n", " # Readout layer\n", " h2= torch.einsum(\"abc,cd->abd\", (spk_rec, w2))\n", " flt = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\n", " out = torch.zeros((batch_size,nb_outputs), device=device, dtype=dtype)\n", " out_rec = [out]\n", " for t in range(nb_steps):\n", " new_flt = alpha*flt +h2[:,t]\n", " new_out = beta*out +flt\n", "\n", " flt = new_flt\n", " out = new_out\n", "\n", " out_rec.append(out)\n", "\n", " out_rec = torch.stack(out_rec,dim=1)\n", " other_recs = [mem_rec, spk_rec]\n", " return out_rec, other_recs" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def train(x_data, y_data, lr=1e-3, nb_epochs=10):\n", " params = [w1,w2]\n", " optimizer = torch.optim.Adamax(params, lr=lr, betas=(0.9,0.999))\n", "\n", " log_softmax_fn = nn.LogSoftmax(dim=1)\n", " loss_fn = nn.NLLLoss()\n", " \n", " loss_hist = []\n", " for e in range(nb_epochs):\n", " local_loss = []\n", " for x_local, y_local in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs):\n", " output,recs = run_snn(x_local.to_dense())\n", " _,spks=recs\n", " m,_=torch.max(output,1)\n", " log_p_y = log_softmax_fn(m)\n", " \n", " # Here we set up our regularizer loss\n", " # The strength paramters here are merely a guess and there should be ample room for improvement by\n", " # tuning these paramters.\n", " reg_loss = 1e-5*torch.sum(spks) # L1 loss on total number of spikes\n", " reg_loss += 1e-5*torch.mean(torch.sum(torch.sum(spks,dim=0),dim=0)**2) # L2 loss on spikes per neuron\n", " \n", " # Here we combine supervised loss and the regularizer\n", " loss_val = loss_fn(log_p_y, y_local) + reg_loss\n", "\n", " optimizer.zero_grad()\n", " loss_val.backward()\n", " optimizer.step()\n", " local_loss.append(loss_val.item())\n", " mean_loss = np.mean(local_loss)\n", " print(\"Epoch %i: loss=%.5f\"%(e+1,mean_loss))\n", " loss_hist.append(mean_loss)\n", " \n", " return loss_hist\n", " \n", " \n", "def compute_classification_accuracy(x_data, y_data):\n", " \"\"\" Computes classification accuracy on supplied data in batches. \"\"\"\n", " accs = []\n", " for x_local, y_local in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs, shuffle=False):\n", " output,_ = run_snn(x_local.to_dense())\n", " m,_= torch.max(output,1) # max over time\n", " _,am=torch.max(m,1) # argmax over output units\n", " tmp = np.mean((y_local==am).detach().cpu().numpy()) # compare to labels\n", " accs.append(tmp)\n", " return np.mean(accs)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1: loss=2.10319\n", "Epoch 2: loss=1.64978\n", "Epoch 3: loss=1.30702\n", "Epoch 4: loss=1.08484\n", "Epoch 5: loss=0.95575\n", "Epoch 6: loss=0.87375\n", "Epoch 7: loss=0.81585\n", "Epoch 8: loss=0.77363\n", "Epoch 9: loss=0.73897\n", "Epoch 10: loss=0.70913\n", "Epoch 11: loss=0.68416\n", "Epoch 12: loss=0.66362\n", "Epoch 13: loss=0.64633\n", "Epoch 14: loss=0.63024\n", "Epoch 15: loss=0.61729\n", "Epoch 16: loss=0.60596\n", "Epoch 17: loss=0.59438\n", "Epoch 18: loss=0.58355\n", "Epoch 19: loss=0.57630\n", "Epoch 20: loss=0.56730\n", "Epoch 21: loss=0.56177\n", "Epoch 22: loss=0.55338\n", "Epoch 23: loss=0.54661\n", "Epoch 24: loss=0.54120\n", "Epoch 25: loss=0.53651\n", "Epoch 26: loss=0.53226\n", "Epoch 27: loss=0.52634\n", "Epoch 28: loss=0.52089\n", "Epoch 29: loss=0.51909\n", "Epoch 30: loss=0.51457\n" ] } ], "source": [ "loss_hist = train(x_train, y_train, lr=2e-4, nb_epochs=30)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(3.3,2),dpi=150)\n", "plt.plot(loss_hist)\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "sns.despine()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training accuracy: 0.848\n", "Test accuracy: 0.827\n" ] } ], "source": [ "print(\"Training accuracy: %.3f\"%(compute_classification_accuracy(x_train,y_train)))\n", "print(\"Test accuracy: %.3f\"%(compute_classification_accuracy(x_test,y_test)))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def get_mini_batch(x_data, y_data, shuffle=False):\n", " for ret in sparse_data_generator(x_data, y_data, batch_size, nb_steps, nb_inputs, shuffle=shuffle):\n", " return ret " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "x_batch, y_batch = get_mini_batch(x_test, y_test)\n", "output, other_recordings = run_snn(x_batch.to_dense())\n", "mem_rec, spk_rec = other_recordings" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Let's plot the hiddden layer spiking activity for some input stimuli\n", "\n", "nb_plt = 4\n", "gs = GridSpec(1,nb_plt)\n", "fig= plt.figure(figsize=(7,3),dpi=150)\n", "for i in range(nb_plt):\n", " plt.subplot(gs[i])\n", " plt.imshow(spk_rec[i].detach().cpu().numpy().T,cmap=plt.cm.gray_r, origin=\"lower\" )\n", " if i==0:\n", " plt.xlabel(\"Time\")\n", " plt.ylabel(\"Units\")\n", "\n", " sns.despine()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compared to the hidden layer activity in our previous Tutorial 2, we can now appreciate that spiking in the hidden layer is much sparser.\n", "\n", "In the next tutorial notebook, we will apply the same training paradigm to the Heidelberg Digits, a realistic speech dataset generated from spoken digits processed through a plausible cochlea model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\"Creative
This work is licensed under a Creative Commons Attribution 4.0 International License." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: notebooks/SpyTorchTutorial4.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 4: Training a spiking neural network on a spiking dataset (Spiking Heidelberg Digits)\n", "\n", "Manu Srinath Halvagal (https://zenkelab.org/team/) and Friedemann Zenke (https://fzenke.net)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more details on surrogate gradient learning, please see: \n", "\n", "> Neftci, E.O., Mostafa, H., and Zenke, F. (2019). Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks. IEEE Signal Process Mag 36, 51–63.\n", "> https://ieeexplore.ieee.org/document/8891809 and https://arxiv.org/abs/1901.09948\n", "\n", "> Cramer, B., Stradmann, Y., Schemmel, J., and Zenke, F. (2020). The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning Systems 1–14. \n", "> https://ieeexplore.ieee.org/document/9311226 and https://arxiv.org/abs/1910.07407\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Tutorials 2 and 3, we have seen how to train a multi-layer spiking neural network on the [Fashion MNIST dataset](https://github.com/zalandoresearch/fashion-mnist) along with an activity regularizer. However, the spiking activity input to the network was generated using a simple time-to-first-spike code. Here we apply the model to train a spiking network to learn the Spiking Heidelberg Digits dataset (https://compneuro.net/posts/2019-spiking-heidelberg-digits/). This dataset uses a more sophisticated cochlear model to generate the spike data corresponding to audio recordings of spoken digits (examples below)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Image of Yaktocat](https://compneuro.net/img/hdspikes_digits_samples.png)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import h5py\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.gridspec import GridSpec\n", "import seaborn as sns\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torchvision\n", "from torch.utils import data\n", "\n", "from utils import get_shd_dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# The coarse network structure and the time steps are dicated by the SHD dataset. \n", "nb_inputs = 700\n", "nb_hidden = 200\n", "nb_outputs = 20\n", "\n", "time_step = 1e-3\n", "nb_steps = 100\n", "max_time = 1.4\n", "\n", "batch_size = 256" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "dtype = torch.float\n", "\n", "# Check whether a GPU is available\n", "if torch.cuda.is_available():\n", " device = torch.device(\"cuda\") \n", "else:\n", " device = torch.device(\"cpu\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup of the spiking dataset" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "File shd_train.h5.gz decompressed to:\n", "/home/zenkfrie/data/hdspikes/shd_train.h5.gz\n", "File shd_test.h5.gz decompressed to:\n", "/home/zenkfrie/data/hdspikes/shd_test.h5.gz\n" ] } ], "source": [ "# Here we load the Dataset\n", "cache_dir = os.path.expanduser(\"~/data\")\n", "cache_subdir = \"hdspikes\"\n", "get_shd_dataset(cache_dir, cache_subdir)\n", "\n", "train_file = h5py.File(os.path.join(cache_dir, cache_subdir, 'shd_train.h5'), 'r')\n", "test_file = h5py.File(os.path.join(cache_dir, cache_subdir, 'shd_test.h5'), 'r')\n", "\n", "x_train = train_file['spikes']\n", "y_train = train_file['labels']\n", "x_test = test_file['spikes']\n", "y_test = test_file['labels']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code for learning the SHD dataset is nearly identical to what we have seen for the FashionMNIST dataset in the last two tutorials. An important difference is that, now, we have the input data already in the form of spikes. This is reflected in the sparse_data_generator below. \n", "\n", "In order to use the data for learning with our spiking network, we also need to discretize the spike times into n_steps bins. Note the additional max_time argument (~1.4 for SHD) that forms the upper limit of the bins." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def sparse_data_generator_from_hdf5_spikes(X, y, batch_size, nb_steps, nb_units, max_time, shuffle=True):\n", " \"\"\" This generator takes a spike dataset and generates spiking network input as sparse tensors. \n", "\n", " Args:\n", " X: The data ( sample x event x 2 ) the last dim holds (time,neuron) tuples\n", " y: The labels\n", " \"\"\"\n", "\n", " labels_ = np.array(y,dtype=np.int)\n", " number_of_batches = len(labels_)//batch_size\n", " sample_index = np.arange(len(labels_))\n", "\n", " # compute discrete firing times\n", " firing_times = X['times']\n", " units_fired = X['units']\n", " \n", " time_bins = np.linspace(0, max_time, num=nb_steps)\n", "\n", " if shuffle:\n", " np.random.shuffle(sample_index)\n", "\n", " total_batch_count = 0\n", " counter = 0\n", " while counter