{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Neurokernel's API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook illustrates how to define and connect local processing unit (LPU) models using Neurokernel." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An LPU comprises two distinct populations of neurons [(Chiang et al., 2011)](#chiang_three-dimensional_2011): *local* neurons may only project to other neurons in the LPU, while *projection* neurons may project both to local neurons and neurons in other LPUs. All synapses between neurons are comprised by *internal connectivity* patterns. LPUs are linked by *inter-LPU connectivity* patterns that map one LPU's outputs to inputs in other LPUs. The general structure of an LPU is shown below:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Defining an LPU Interface" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Interface Ports" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All communication between LPUs must pass through *ports* that are internally associated with modeling elements that must emit or receive external data. An LPU's *interface* is defined as the set of ports it exposes to other LPUs. Each port is defined by a unique identifier string and attributes that indicate whether \n", "- it transmits *spikes* (i.e., boolean values) or *graded potentials* (i.e., floating point numbers) at each step of model execution and whether\n", "- it accepts *input* or emits *output*.\n", "\n", "To facilitate management of a large numbers of ports, Neurokernel requires that port identifiers conform to a hierarchical format similar to that used to label files or [elements in structured documents](http://www.w3.org/TR/xpath/). Each identifier may comprise multiple *levels* joined by separators (/ and []). Neurokernel also defines an extended format for selecting multiple ports with a single *selector*; a selector that cannot be expanded to an explicit list of individual port identifiers is said to be *ambiguous*. Rather than define a formal grammar for this format, the following table depicts examples of how it may be used to refer to multiple ports.\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Identifier/SelectorComments
/med/L1[0]selects a single port
/med/L1[0]equivalent to /med/L1[0]
/med+/L[1]equivalent to /med/L1[0]
/med[L1,L2][0]selects two ports
/med/L1[0,1]another example of two ports
/med/L1[0],/med/L1[1]equivalent to /med/L1[0,1]
/med/L1[0:10]selects a range of 10 ports
/med/L1/*selects all ports starting with /med/L1
(/med/L1,/med/L2)+[0]equivalent to /med/[L1,L2][0]
/med/[L1,L2].+[0:2]equivalent to /med/L1[0],/med/L2[1]
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Inter-LPU Connectivity Patterns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All connections between LPUs must be defined in inter-LPU connectivity patterns that map the output ports of one LPU to the input ports of another LPU. Since individual LPUs may internally implement multiplexing of input signals to a single destination in different ways, the LPU interface only permits fan-out from individual output ports to multiple input ports; connections from multiple output ports may not converge on a single input port. A single pattern may define connections in both directions.\n", "\n", "A connectivity pattern between two LPUs is fully specified by the identifiers and attributes of the ports in its two interfaces and the directed graph of connections defined between them. An example of such pattern defined between ports /lam[0:6] and /med[0:5] follows:\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
PortInterfaceI/OPort Type
/lam[0]0ingraded potential
/lam[1]0ingraded potential
/lam[2]0outgraded potential
/lam[3]0outspiking
/lam[4]0outspiking
/lam[5]0outspiking
/med[0]1outgraded potential
/med[1]1outgraded potential
/med[2]1outgraded potential
/med[3]1inspiking
/med[4]1inspiking
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
FromTo
/lam[0]/med[0]
/lam[0]/med[1]
/lam[1]/med[2]
/med[3]/lam[3]
/med[4]/lam[4]
/med[4]/lam[5]
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using Neurokernel's API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Setting up LPU Interfaces and Patterns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Neurokernel provides Python classes for defining LPUs and connectivity patterns that can be used to link them together. The former (``neurokernel.core.Module`` for LPUs that don't access the GPU and ``neurokernel.core_gpu.Module`` for LPUs that do) requires an LPU designer to implement all of the LPU's internals from the ground up; the latter class places no explicit constraints upon how an LPU uses GPU resources. In order to enable independently implemented LPUs to communicate with each other, each LPU must implement a method called ``run_step()`` called during each step of execution that consumes incoming data from other LPUs and produces data for transmission to other LPUs. The example below generates random data in its ``run_step()`` method:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "from neurokernel.core_gpu import Module\n", "\n", "class MyModule(Module):\n", "\n", " # Process incoming data and set outgoing data:\n", " def run_step(self): \n", " super(MyModule, self).run_step()\n", "\n", " # Display input graded potential data:\n", " self.logger.info('input gpot port data: '+str(self.pm['gpot'][self.in_gpot_ports]))\n", " \n", " # Display input spike data:\n", " self.logger.info('input spike port data: '+str(self.pm['spike'][self.in_spike_ports]))\n", "\n", " # Output random graded potential data:\n", " out_gpot_data = gpuarray.to_gpu(np.random.rand(len(self.out_gpot_ports)))\n", " self.pm['gpot'][self.out_gpot_ports] = out_gpot_data\n", " self.log_info('output gpot port data: '+str(out_gpot_data))\n", " \n", " # Randomly select output ports to emit spikes:\n", " out_spike_data = gpuarray.to_gpu(np.random.randint(0, 2, len(self.out_spike_ports)))\n", " self.pm['spike'][self.out_spike_ports] = out_spike_data\n", " self.log_info('output spike port data: '+str(out_spike_data))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that every LPU instance must be associated with a unique identifier (``id``). An LPU contains a port-mapper attribute (``pm``) that maps input and output ports to a data array that may be accessed by the LPU's internal implementation; after each step of execution, the array associated with the port-mapper is updated with input data from source LPUs while output data from the array is transmitted to destination LPUs. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One can instantiate the above LPU class as follows: " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from neurokernel.plsel import Selector,SelectorMethods\n", "\n", "m1_int_sel_in_gpot = Selector('/a/in/gpot[0:2]')\n", "m1_int_sel_out_gpot = Selector('/a/out/gpot[0:2]')\n", "m1_int_sel_in_spike = Selector('/a/in/spike[0:2]')\n", "m1_int_sel_out_spike = Selector('/a/out/spike[0:2]')\n", "m1_int_sel = m1_int_sel_in_gpot+m1_int_sel_out_gpot+\\\n", " m1_int_sel_in_spike+m1_int_sel_out_spike\n", "m1_int_sel_in = m1_int_sel_in_gpot+m1_int_sel_in_spike\n", "m1_int_sel_out = m1_int_sel_out_gpot+m1_int_sel_out_spike\n", "m1_int_sel_gpot = m1_int_sel_in_gpot+m1_int_sel_out_gpot\n", "m1_int_sel_spike = m1_int_sel_in_spike+m1_int_sel_out_spike\n", "N1_gpot = SelectorMethods.count_ports(m1_int_sel_gpot)\n", "N1_spike = SelectorMethods.count_ports(m1_int_sel_spike)\n", "\n", "m2_int_sel_in_gpot = Selector('/b/in/gpot[0:2]')\n", "m2_int_sel_out_gpot = Selector('/b/out/gpot[0:2]')\n", "m2_int_sel_in_spike = Selector('/b/in/spike[0:2]')\n", "m2_int_sel_out_spike = Selector('/b/out/spike[0:2]')\n", "m2_int_sel = m2_int_sel_in_gpot+m2_int_sel_out_gpot+\\\n", " m2_int_sel_in_spike+m2_int_sel_out_spike\n", "m2_int_sel_in = m2_int_sel_in_gpot+m2_int_sel_in_spike\n", "m2_int_sel_out = m2_int_sel_out_gpot+m2_int_sel_out_spike\n", "m2_int_sel_gpot = m2_int_sel_in_gpot+m2_int_sel_out_gpot\n", "m2_int_sel_spike = m2_int_sel_in_spike+m2_int_sel_out_spike\n", "N2_gpot = SelectorMethods.count_ports(m2_int_sel_gpot)\n", "N2_spike = SelectorMethods.count_ports(m2_int_sel_spike)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the ports in each of the above LPUs' interfaces, one can define a connectivity pattern between them as follows:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from neurokernel.pattern import Pattern\n", "\n", "pat12 = Pattern(m1_int_sel, m2_int_sel)\n", "pat12.interface[m1_int_sel_out_gpot] = [0, 'in', 'gpot']\n", "pat12.interface[m1_int_sel_in_gpot] = [0, 'out', 'gpot']\n", "pat12.interface[m1_int_sel_out_spike] = [0, 'in', 'spike']\n", "pat12.interface[m1_int_sel_in_spike] = [0, 'out', 'spike']\n", "pat12.interface[m2_int_sel_in_gpot] = [1, 'out', 'gpot']\n", "pat12.interface[m2_int_sel_out_gpot] = [1, 'in', 'gpot']\n", "pat12.interface[m2_int_sel_in_spike] = [1, 'out', 'spike']\n", "pat12.interface[m2_int_sel_out_spike] = [1, 'in', 'spike']\n", "\n", "pat12['/a/out/gpot[0]', '/b/in/gpot[0]'] = 1\n", "pat12['/a/out/gpot[1]', '/b/in/gpot[1]'] = 1\n", "pat12['/b/out/gpot[0]', '/a/in/gpot[0]'] = 1\n", "pat12['/b/out/gpot[1]', '/a/in/gpot[1]'] = 1\n", "pat12['/a/out/spike[0]', '/b/in/spike[0]'] = 1\n", "pat12['/a/out/spike[1]', '/b/in/spike[1]'] = 1\n", "pat12['/b/out/spike[0]', '/a/in/spike[0]'] = 1\n", "pat12['/b/out/spike[1]', '/a/in/spike[1]'] = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A Simple Example: Creating an LPU" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To obviate the need to implement an LPU completely from scratch, the [Neurodriver](https://github.com/neurokernel/neurodriver) package provides a functional LPU class (``neurokernel.LPU.LPU.LPU``) that supports the following neuron and synapse models:\n", "\n", "* Leaky Integrate-and-Fire (LIF) neuron (spiking neuron)\n", "* Morris-Lecar (ML) neuron (graded potential neuron), \n", "* Alpha function synapse\n", "* Conductance-based synapse (referred to as ``power_gpot_gpot``). \n", "\n", "Note that although the ML model can in principle be configured as a spiking neuron model, the implementation in the LPU class is configured to output its membrane potential.\n", "\n", "Alpha function synapses may be used to connect any type of presynaptic neuron to any type of of postsynaptic neuron; the neuron presynaptic to a conductance-based synapse must be a graded potential neuron.\n", "\n", "It should be emphasized that the above LPU implementation and the currently support models are not necessarily optimal and may be replaced with improved implementations in the future." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``LPU`` class provided by Neurodriver may be instantiated with a graph describing its internal structure. The graph must be stored in [GEXF](http://gexf.net) file format with nodes and edges respectively corresponding to instances of the supported neuron and synapse models. To facilitate construction of an LPU, the [networkx](http://networkx.github.io) Python package may be used to set the parameters of the model instances. For example, the following code defines a simple network consisting of an LIF neuron with a single synaptic connection to an ML neuron; the synaptic current elicited by the LIF neuron's spikes is modeled by an alpha function:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import networkx as nx\n", "\n", "G = nx.MultiDiGraph()\n", "# add a neuron node with LeakyIAF model\n", "G.add_node('neuron0', # UID\n", " **{'class': 'LeakyIAF', # component model\n", " 'name': 'neuron_0', # component name\n", " 'initV': np.random.uniform(-60.0, -25.0), # initial membrane voltage\n", " 'reset_potential': -67.5489770451, # reset voltage\n", " 'threshold': -25.1355161007, # spike threshold\n", " 'resting_potential': 0.0, # resting potential\n", " 'resistance': 1024.45570216, # membrane resistance\n", " 'capacitance': 0.0669810502993}) # membrane capacitance\n", "\n", "# The above neuron is a projection neuron,\n", "# create an output port for it\n", "G.add_node('neuron0_port', # UID\n", " **{'class': 'Port', # indicates it is a port\n", " 'name': 'neuron_0_output_port', # name of the port\n", " 'selector': '/a[0]', # selector of the port\n", " 'port_io': 'out', # indicates it is an output port\n", " 'port_type': 'spike'}) # indicates it is a spike port\n", "\n", "# connect the neuron node and its port\n", "G.add_edge('neuron0', 'neuron0_port')\n", "\n", "# add a second neuron node with MorrisLecar model\n", "G.add_node('neuron1',\n", " **{'class': \"MorrisLecar\", \n", " 'name': 'neuron_1',\n", " 'V1': 30.0,\n", " 'V2': 15.0,\n", " 'V3': 0.0,\n", " 'V4': 30.0,\n", " 'phi': 25.0,\n", " 'offset': 0.0,\n", " 'V_L': -50.,\n", " 'V_Ca': 100.0,\n", " 'V_K': -70.0,\n", " 'g_Ca': 1.1,\n", " 'g_K': 2.0,\n", " 'g_L': 0.5,\n", " 'initV': -52.14,\n", " 'initn': 0.03,})\n", "\n", "# add a synapse node with AlphaSynapse model\n", "G.add_node('synapse_0_1',\n", " **{'class': 'AlphaSynapse',\n", " 'name': 'synapse_0_1',\n", " 'ar': 1.1*1e2, # decay rate\n", " 'ad': 1.9*1e3, # rise rate\n", " 'reverse': 65.0, # reversal potential\n", " 'gmax': 2*1e-3, # maximum conductance\n", " })\n", "\n", "# connect presynaptic neuron to synapse\n", "G.add_edge('neuron0', 'synapse_0_1')\n", "# connect synapse to postsynaptic neuron\n", "G.add_edge('synapse_0_1', 'neuron1')\n", "\n", "# export the graph to GEXF file\n", "nx.write_gexf(G, 'simple_lpu.gexf.gz')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can prepare a simple pulse input and save it in an HDF5 file to pass to ``neuron_0`` as follows:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import h5py\n", "\n", "dt = 1e-4 # time resolution of model execution in seconds\n", "dur = 1.0 # duration in seconds\n", "Nt = int(dur/dt) # number of data points in time\n", "\n", "start = 0.3\n", "stop = 0.6\n", "\n", "I_max = 0.6\n", "t = np.arange(0, dt*Nt, dt)\n", "I = np.zeros((Nt, 1), dtype=np.double)\n", "I[np.logical_and(t>start, t" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "\n", "Image(filename = 'simple_output.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A More Complex Example: Connecting LPUs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following example demonstrates the creation and connection of two LPUs containing multiple neurons with a connectivity pattern. The number of neurons and synapses in each of the LPUs' internal populations are randomly generated: the number of neurons in each populations is randomly selected between 30 to 40. The LPUs' projection neurons - as well as populations of input neurons presynaptic to the LPUs that accept an input stimulus - are modeled as LIF neurons, while each of the local neurons is modeled as either an LIF neuron or a graded potential ML neuron. Synaptic currents are modeled with alpha functions. Synapses between the LPU's local and projection neurons, as well as synpases between the input neurons and the LPU's internal neurons, are also created randomly; roughly half of the total number of pairs of neurons are connected. \n", "\n", "To generate the LPUs and input signals used in this demo, we run the following script:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import sys\n", "sys.path.append('../examples/intro/data')\n", "\n", "from gen_generic_lpu import create_lpu, create_input\n", "\n", "for i in [0,1]:\n", " \n", " #Set the random seed\n", " np.random.seed(i)\n", " \n", " #Define file names\n", " lpu_name = 'lpu_%i' % i\n", " lpu_file_name = 'generic_lpu_%i.gexf.gz' % i\n", " in_file_name = 'generic_lpu_%i_input.h5' % i\n", " \n", " #Define variables\n", " dt = 1e-4\n", " dur = 1.0\n", " start = 0.3\n", " stop = 0.6\n", " I_max = 0.6\n", " neu_num = [np.random.randint(31, 40) for _ in range(3)]\n", " \n", " #Create the LPU and input files\n", " create_lpu(lpu_file_name, lpu_name, *neu_num)\n", " create_input(in_file_name, neu_num[0], dt, dur, start, stop, I_max)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the neurons and synapses in the generated LPUs are stored in GEXF format, they can be easily accessed using networkx and [pandas](http://pandas.pydata.org). Neurokernel provides a convenience function to convert between networkx graphs and pandas' DataFrame class:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import networkx as nx\n", "import neurokernel.tools.graph\n", "g_0 = nx.read_gexf('generic_lpu_0.gexf.gz')\n", "df_comp_0, df_conn_0 = neurokernel.tools.graph.graph_to_df(g_0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Say one wishes to explore several LIF neurons in LPU 0. Here is how to access their parameters:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
nameclassinitVreset_potentialresting_potentialthresholdresistancecapacitance
sensory_4sensory_4_sLeakyIAF-50.457-67.5490-25.13551002.450.0669811
sensory_5sensory_5_sLeakyIAF-31.5741-67.5490-25.13551002.450.0669811
sensory_6sensory_6_sLeakyIAF-46.2525-67.5490-25.13551002.450.0669811
sensory_8sensory_8_sLeakyIAF-37.314-67.5490-25.13551002.450.0669811
sensory_9sensory_9_sLeakyIAF-26.4996-67.5490-25.13551002.450.0669811
\n", "
" ], "text/plain": [ " name class initV reset_potential resting_potential \\\n", "sensory_4 sensory_4_s LeakyIAF -50.457 -67.549 0 \n", "sensory_5 sensory_5_s LeakyIAF -31.5741 -67.549 0 \n", "sensory_6 sensory_6_s LeakyIAF -46.2525 -67.549 0 \n", "sensory_8 sensory_8_s LeakyIAF -37.314 -67.549 0 \n", "sensory_9 sensory_9_s LeakyIAF -26.4996 -67.549 0 \n", "\n", " threshold resistance capacitance \n", "sensory_4 -25.1355 1002.45 0.0669811 \n", "sensory_5 -25.1355 1002.45 0.0669811 \n", "sensory_6 -25.1355 1002.45 0.0669811 \n", "sensory_8 -25.1355 1002.45 0.0669811 \n", "sensory_9 -25.1355 1002.45 0.0669811 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_comp_0[df_comp_0['class'] == 'LeakyIAF'][25:30][['name','class',\n", " 'initV','reset_potential','resting_potential',\n", " 'threshold','resistance','capacitance']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Say one wishes to find the parameters of the synapses linking neuron ``output_31_s`` to other destination neurons; these can be accessed using the following query:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
nameclassaradreversegmax
synapse_proj_31_s-local_0_gproj_31_s-local_0_gAlphaSynapse1101900103.1e-07
synapse_proj_31_s-local_12_gproj_31_s-local_12_gAlphaSynapse1101900103.1e-07
synapse_proj_31_s-local_14_gproj_31_s-local_14_gAlphaSynapse1101900103.1e-07
synapse_proj_31_s-local_17_gproj_31_s-local_17_gAlphaSynapse1101900103.1e-07
synapse_proj_31_s-local_18_gproj_31_s-local_18_gAlphaSynapse1101900103.1e-07
synapse_proj_31_s-local_21_gproj_31_s-local_21_gAlphaSynapse1101900103.1e-07
synapse_proj_31_s-local_23_gproj_31_s-local_23_gAlphaSynapse1101900103.1e-07
synapse_proj_31_s-local_25_gproj_31_s-local_25_gAlphaSynapse1101900103.1e-07
synapse_proj_31_s-local_26_gproj_31_s-local_26_gAlphaSynapse1101900103.1e-07
synapse_proj_31_s-local_2_gproj_31_s-local_2_gAlphaSynapse1101900103.1e-07
synapse_proj_31_s-local_5_gproj_31_s-local_5_gAlphaSynapse1101900103.1e-07
\n", "
" ], "text/plain": [ " name class ar ad \\\n", "synapse_proj_31_s-local_0_g proj_31_s-local_0_g AlphaSynapse 110 1900 \n", "synapse_proj_31_s-local_12_g proj_31_s-local_12_g AlphaSynapse 110 1900 \n", "synapse_proj_31_s-local_14_g proj_31_s-local_14_g AlphaSynapse 110 1900 \n", "synapse_proj_31_s-local_17_g proj_31_s-local_17_g AlphaSynapse 110 1900 \n", "synapse_proj_31_s-local_18_g proj_31_s-local_18_g AlphaSynapse 110 1900 \n", "synapse_proj_31_s-local_21_g proj_31_s-local_21_g AlphaSynapse 110 1900 \n", "synapse_proj_31_s-local_23_g proj_31_s-local_23_g AlphaSynapse 110 1900 \n", "synapse_proj_31_s-local_25_g proj_31_s-local_25_g AlphaSynapse 110 1900 \n", "synapse_proj_31_s-local_26_g proj_31_s-local_26_g AlphaSynapse 110 1900 \n", "synapse_proj_31_s-local_2_g proj_31_s-local_2_g AlphaSynapse 110 1900 \n", "synapse_proj_31_s-local_5_g proj_31_s-local_5_g AlphaSynapse 110 1900 \n", "\n", " reverse gmax \n", "synapse_proj_31_s-local_0_g 10 3.1e-07 \n", "synapse_proj_31_s-local_12_g 10 3.1e-07 \n", "synapse_proj_31_s-local_14_g 10 3.1e-07 \n", "synapse_proj_31_s-local_17_g 10 3.1e-07 \n", "synapse_proj_31_s-local_18_g 10 3.1e-07 \n", "synapse_proj_31_s-local_21_g 10 3.1e-07 \n", "synapse_proj_31_s-local_23_g 10 3.1e-07 \n", "synapse_proj_31_s-local_25_g 10 3.1e-07 \n", "synapse_proj_31_s-local_26_g 10 3.1e-07 \n", "synapse_proj_31_s-local_2_g 10 3.1e-07 \n", "synapse_proj_31_s-local_5_g 10 3.1e-07 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ind = df_comp_0['name'].str.startswith('proj_31_s-')\n", "df_comp_0[ind][['name','class','ar','ad','reverse','gmax']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the configuration and the input stimulus are ready, we execute the entire model with connections defined between the LPUs:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import random\n", "\n", "import neurokernel.core_gpu as core_gpu\n", "import neurokernel.pattern as pattern\n", "import neurokernel.plsel as plsel\n", "from neurokernel.LPU.LPU import LPU\n", "\n", "from neurokernel.LPU.InputProcessors.FileInputProcessor import FileInputProcessor\n", "from neurokernel.LPU.OutputProcessors.FileOutputProcessor import FileOutputProcessor\n", "\n", "from neurokernel.tools.logging import setup_logger\n", "\n", "#Set logger\n", "logger = setup_logger(file_name=\"neurokernel.log\", screen=False)\n", "\n", "#Set the random seed\n", "random.seed(0)\n", "\n", "#Load the manager\n", "man = core_gpu.Manager()\n", "\n", "#Define filenames\n", "lpu_file_0 = 'generic_lpu_0.gexf.gz'\n", "in_file_0 = 'generic_lpu_0_input.h5'\n", "out_file_0 = 'generic_lpu_0_output.h5'\n", "lpu_file_1 = 'generic_lpu_1.gexf.gz'\n", "in_file_1 = 'generic_lpu_1_input.h5'\n", "out_file_1 = 'generic_lpu_1_output.h5'\n", "\n", "#Extract the LPU graphs\n", "comp_dict_0, conns_0 = LPU.lpu_parser(lpu_file_0)\n", "comp_dict_1, conns_1 = LPU.lpu_parser(lpu_file_1)\n", "\n", "#Attach file processors to LPU 0\n", "fl_input_processor_0 = FileInputProcessor(in_file_0)\n", "fl_output_processor_0 = FileOutputProcessor(\n", " [('V',None),('spike_state',None)],\n", " out_file_0, sample_interval=1)\n", "\n", "lpu_0_id = 'lpu_0'\n", "man.add(LPU, lpu_0_id, dt, comp_dict_0, conns_0,\n", " input_processors = [fl_input_processor_0],\n", " output_processors = [fl_output_processor_0],\n", " device=0,\n", " debug=False, time_sync=False)\n", "\n", "#Attach file processors to LPU 1\n", "fl_input_processor_1 = FileInputProcessor(in_file_1)\n", "fl_output_processor_1 = FileOutputProcessor(\n", " [('V',None),('spike_state',None)],\n", " out_file_1, sample_interval=1)\n", "\n", "lpu_1_id = 'lpu_1'\n", "man.add(LPU, lpu_1_id, dt, comp_dict_1, conns_1,\n", " input_processors = [fl_input_processor_1],\n", " output_processors = [fl_output_processor_1],\n", " device=1,\n", " debug=False, time_sync=False)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Find all output and input port selectors in each LPU:\n", "out_ports_spk_0 = plsel.Selector(\n", " ','.join(LPU.extract_out_spk(comp_dict_0, 'id')[0]))\n", "out_ports_gpot_0 = plsel.Selector(\n", " ','.join(LPU.extract_out_gpot(comp_dict_0, 'id')[0]))\n", "\n", "out_ports_spk_1 = plsel.Selector(\n", " ','.join(LPU.extract_out_spk(comp_dict_1, 'id')[0]))\n", "out_ports_gpot_1 = plsel.Selector(\n", " ','.join(LPU.extract_out_gpot(comp_dict_1, 'id')[0]))\n", "\n", "in_ports_spk_0 = plsel.Selector(\n", " ','.join(LPU.extract_in_spk(comp_dict_0, 'id')[0]))\n", "in_ports_gpot_0 = plsel.Selector(\n", " ','.join(LPU.extract_in_gpot(comp_dict_0, 'id')[0]))\n", "\n", "in_ports_spk_1 = plsel.Selector(\n", " ','.join(LPU.extract_in_spk(comp_dict_1, 'id')[0]))\n", "in_ports_gpot_1 = plsel.Selector(\n", " ','.join(LPU.extract_in_gpot(comp_dict_1, 'id')[0]))\n", "\n", "out_ports_0 = plsel.Selector.union(out_ports_spk_0, out_ports_gpot_0)\n", "out_ports_1 = plsel.Selector.union(out_ports_spk_1, out_ports_gpot_1)\n", "\n", "in_ports_0 = plsel.Selector.union(in_ports_spk_0, in_ports_gpot_0)\n", "in_ports_1 = plsel.Selector.union(in_ports_spk_1, in_ports_gpot_1)\n", "\n", "# Initialize a connectivity pattern between the two sets of port\n", "# selectors:\n", "pat = pattern.Pattern(plsel.Selector.union(out_ports_0, in_ports_0),\n", " plsel.Selector.union(out_ports_1, in_ports_1))\n", "\n", "# Create connections from the ports with identifiers matching the output\n", "# ports of one LPU to the ports with identifiers matching the input\n", "# ports of the other LPU:\n", "N_conn_spk_0_1 = min(len(out_ports_spk_0), len(in_ports_spk_1))\n", "N_conn_gpot_0_1 = min(len(out_ports_gpot_0), len(in_ports_gpot_1))\n", "for src, dest in zip(random.sample(out_ports_spk_0.identifiers,\n", " N_conn_spk_0_1),\n", " random.sample(in_ports_spk_1.identifiers,\n", " N_conn_spk_0_1)):\n", " pat[src, dest] = 1\n", " pat.interface[src, 'type'] = 'spike'\n", " pat.interface[dest, 'type'] = 'spike'\n", "for src, dest in zip(random.sample(out_ports_gpot_0.identifiers,\n", " N_conn_gpot_0_1),\n", " random.sample(in_ports_gpot_1.identifiers,\n", " N_conn_gpot_0_1)):\n", " pat[src, dest] = 1\n", " pat.interface[src, 'type'] = 'gpot'\n", " pat.interface[dest, 'type'] = 'gpot'\n", "\n", "man.connect(lpu_0_id, lpu_1_id, pat, 0, 1)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MPI_RUN_SUCCESS: MPI_Function\n", "\n" ] } ], "source": [ "from neurokernel.tools.mpi_run import mpi_run_manager\n", "\n", "dt = 1e-4\n", "dur = 1.0\n", "steps = int(dur/dt)\n", "\n", "#Use mpi_run_manager to execute the manager in notebook\n", "from subprocess import CalledProcessError\n", "try:\n", " output = mpi_run_manager(man, steps = steps, log = True)\n", "except CalledProcessError:\n", " with open('neurokernel.log', 'r') as f:\n", " print(f.read())\n", "print(output)\n", "\n", "#This should be replaced by the following in regular code\n", "#man.spawn()\n", "#man.start(steps=steps)\n", "#man.wait()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we generate plots that depict the model execution" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import matplotlib as mpl\n", "mpl.use('agg')\n", "import matplotlib.pyplot as plt\n", "\n", "import neurokernel.LPU.utils.visualizer as vis\n", "import networkx as nx\n", "import h5py\n", "\n", "# Temporary fix for bug in networkx 1.8:\n", "nx.readwrite.gexf.GEXF.convert_bool = \\\n", " {'false':False, 'False':False,\n", " 'true':True, 'True':True}\n", "\n", "V = vis.visualizer()\n", "\n", "# create a plot for current input injected to 'neuron0'\n", "V.add_LPU('generic_lpu_0_input.h5', LPU='Input', is_input = True, dt = dt)\n", "V.add_plot({'type': 'waveform', 'uids': [['sensory_0']], 'variable': 'I', 'title': 'Input'}, 'input_Input')\n", "\n", "for i in [0,1]:\n", " lpu_file_name = 'generic_lpu_%i.gexf.gz' % i\n", " out_file_name = 'generic_lpu_%i_output.h5' % i\n", " \n", " G = nx.read_gexf(lpu_file_name) \n", " neu_proj = sorted([k for k, n in G.node.items() if \\\n", " n['name'][:4] == 'proj' and \\\n", " n['class'] == 'LeakyIAF'])\n", " N = len(neu_proj)\n", " \n", " # create a plot for membrane potential output from 'neuron1'\n", " V.add_LPU(out_file_name,\n", " gexf_file= lpu_file_name, LPU='Generic LPU %i' % i, dt = dt)\n", " #neu_proj = [s for s in V._uids[\"Generic LPU 0\"][\"spike_state\"] if s.startswith('proj')]\n", " V.add_plot({'type': 'raster', 'uids': [neu_proj], 'variable': 'spike_state', 'title': 'Output - LPU %i' % i,\n", " 'yticks': range(1,1+N),\n", " 'yticklabels': neu_proj},\n", " 'Generic LPU %i' % i)\n", "\n", "V._update_interval = None\n", "V.rows = 3\n", "V.cols = 1\n", "V.fontsize = 15\n", "\n", "V.dt = 0.01\n", "V.xlim = [0, 1.0]\n", "V.figsize = (16, 12)\n", "\n", "V.run('intro_output.png')\n", "\n", "# Don't automatically display the output image:\n", "plt.close(plt.gcf())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output rendered to an image for two connected LPUs is shown below:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAEsAAAA4QCAYAAADmR3Z/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAuIwAALiMBeKU/dgAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3WtslvX9P/DP3aMoIGBlsqqInObAI4rK/E2cQyFuonMeIgydY2xREw+bEM86jbgMp1Gnw0lAUafLPCVTQTctTFGhoKjooEULiihUASsqh3L/Hyz/xnJor7u01z3h9Ur64Hv1c2qyPSNvM9lsNhsAAAAAAAAAAAAAAAAAAAAAAACQkoJ8HwAAAAAAAAAAAAAAAAAAAAAAAMDORQAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJAqAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJAqAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJAqAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJAqAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJAqAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJAqAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJAqAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJAqAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJAqAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJAqAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJAqAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAA8D+soqIiMplM4p/rrrsu3ycDAAAAAAAAAAA0SwAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAADQYPHhwZDKZRD81NTX5PhcAAAAAAAAAgG8oAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJAqAVgAAAAAAAAAAAAAAAAAAAAAAACkSgAWAAAAAAAAAAAAAAAAAAAAAAAAqRKABQAAAAAAAAAAAAAAAAAAAAAAQKoEYAEAAAAAAAAAAAAAAAAAAAAAAJCqonwfAAAAAAAAAAAAwDfXm2++Gf/85z/jjTfeiCVLlkRdXV3stttuUVZWFt26dYtjjz02fvjDH0anTp3yfWpks9moqKiIp556KubMmRNVVVWxevXq2LhxY3Ts2DF69OgRhx12WAwbNixOOumkKC4uzvfJAAAAAAAAAACwwyrI9wEAAAAAAAAAAACk77rrrotMJpPoZ8qUKY16N23aFJMnT46DDz44DjrooLj00ktjypQp8cILL0RlZWXMmDEjHn300bjzzjvj9NNPj7Kyshg5cmQsWbJku25Oeu9+++23Re/UqVOjb9++8YMf/CBuueWWmDlzZixfvjy+/PLL2LBhQ3zyySdRWVkZ99xzT5x66qmxzz77xO233x4bN25s8b2DBw9OfHNFRUVOsysqKhLPPvfcc3OaMWPGjMR39OjRo8X/OwIAAAAAAAAAYOcmAAsAAAAAAAAAAIDE5s+fH4ceemicd9558cYbbyTqqa+vjwcffDD69u0bkyZNauMLG1uxYkUcf/zxMWrUqKiqqkrc9/HHH8dFF10URx99dFRXV7fhhQAAAAAAAAAAsHMSgAUAAAAAAAAAAEAi9913Xxx55JGJg682t27duhg9enRMmDChlS/bukWLFsWAAQPi+eefb/GMysrKOProo2POnDmteBkAAAAAAAAAACAACwAAAAAAAAAAgGb94Q9/iHPPPTfWrVu33bPGjh27XaFUSdTU1MTgwYPjgw8+2O5ZtbW1ceKJJ8bbb7/dCpcBAAAAAAAAAAARArAAAAAAAAAAAABoxuTJk2Ps2LGtNi+bzcYvfvGLWLt2bavN/LqvvvoqfvzjH8fy5ctbbeaqVavi5JNPjrq6ulabCQAAAAAAAAAAOzMBWAAAAAAAAAAAADRp5syZrT6zpqYmpk6d2upzIyI+/vjjeOutt1p97uLFi+Piiy9u9bkAAAAAAAAAALAzEoAFAAAAAAAAAABAXkycODHVfSUlJbHvvvtGnz59okOHDi2aMXny5HjllVda+TIAAAAAAAAAANj5FOX7AAAAAAAAAAAAAL5ZBg4cGL/+9a9j8ODB0a1bt1izZk28+OKL8fvf/z7mzJmTeM7rr78eixYtij59+rThtRGHHnpoXHPNNTF06NDYZZddGr7Pnj077rzzzpg6dWriWdlsNq6//vp45pln2uLUVLVv3z4OPvjgLb5XV1fH2rVrE8044IADoqSkJFFtly5dcroPAAAAAAAAAIAdmwAsAAAAAAAAAAAAErvpppti3LhxUVBQ0PBtl112idNOOy2GDx8eo0aNir/+9a+J582ePbtNA7B++ctfxl133RVFRVv+c7mBAwfG/fffHyeddFKcffbZsWnTpkQzp02bFgsXLoy+ffu29rmpOvzww+P111/f4vvgwYNjxowZiWY8/fTTsd9++7XyZQAAAAAAAAAA7AwKmi8BAAAAAAAAAACAiGuvvTYuv/zyRuFXX1dUVBSTJk2KvfbaK/HMysrK1jpvCyeccEL8+c9/3mr41dedeeaZMX78+JxmT506dXtOAwAAAAAAAACAnZ4ALAAAAAAAAAAAAJrVv3//uPrqq5uta9euXZxxxhmJ51ZXV2/PWdtUVFQUt99++zbDujZ3ySWXRJ8+fRLPf+qpp1p6GgAAAAAAAAAAEAKwAAAAAAAAAAAASOCqq66KwsLCRLWHHHJI4rlr1qxp6UlNGjJkSPTt2zdxfXFxcYwZMyZx/fz582PVqlUtOQ0AAAAAAAAAAAgBWAAAAAAAAAAAADSjffv2cfLJJyeu33vvvRPXrl69uiUnNeu0005r055sNhvz58/PeQcAAAAAAAAAAPBfArAAAAAAAAAAAABo0sCBA6Ndu3aJ63fffffEtZ9//nlLTmrWEUcckXPPfvvtF126dElcv3Dhwpx3AAAAAAAAAAAA/yUACwAAAAAAAAAAgCYdfPDBOdXvtttuiWuz2Wyu5zSroKAgvvOd77So94ADDkhcu2zZshbtAAAAAAAAAAAABGABAAAAAAAAAADQjG7duuVUX1xc3EaXJNO+ffsoKSlpUW+XLl0S165atapFOwAAAAAAAAAAAAFYAAAAAAAAAAAANKNjx4451RcWFrbRJcl06NAhld6vvvqqxXsAAAAAAAAAAGBnJwALAAAAAAAAAACAJpWWluZUn+8ArPr6+lR6M5lMi/dsLpvN5lS/YcOGVtsNAAAAAAAAAAD5IAALAAAAAAAAAACAHUpdXV0qve3atWvxns3lGtq1Zs2aVtsNAAAAAAAAAAD5IAALAAAAAAAAAACAHcratWvj888/b1HvihUrEteWlZW1aMfWrF+/Pqf62traVtsNAAAAAAAAAAD5IAALAAAAAAAAAACAHc6CBQty7slms/HOO+8kri8vL2/y95lMJvGsL774InFtRMSbb76ZUz0AAAAAAAAAAPyvEYAFAAAAAAAAAADADufll1/OuefNN9+MtWvXJq7v169fk79v165d4llr1qxJXBsR8corr+RUDwAAAAAAAAAA/2sEYAEAAAAAAAAAALDD+dvf/pZzz9///vfEtcXFxXHggQc2WbP77rsnnvf+++8nrl20aFHMmzcvcX2uiouLE9du3Lixze4AAAAAAAAAAGDHJgALAAAAAAAAAACAHc7LL78cL774YuL6urq6mDhxYuL6o446Knbdddcma3IJwJo/f37i2gkTJiSubYkOHTokrv3oo4/a8BIAAAAAAAAAAHZkArAAAAAAAAAAAADYIV1wwQXx+eefJ6odN25crFixIvHs4cOHN1vTvXv3xPOeffbZWL16dbN1L730UkyaNCnx3JbIJbhr+vTpbXgJAAAAAAAAAAA7MgFYAAAAAAAAAAAA7JDeeOONGD58eNTW1m6zpr6+Pq6++uq4++67E88tKiqKESNGNFt3xBFHJJ75xRdfxAUXXBDZbHabNa+99lqcfPLJsWnTpsRzW6K8vDxx7YQJE2LSpEmxdu3aNrwIAAAAAAAAAIAdUVG+DwAAAAAAAAAAAIC28vzzz8cBBxwQF154YQwfPjx69OgRpaWl8eGHH8bzzz8fd999d8ybNy+nmSNGjIi99tqr2brDDz88MplMk6FWX/fQQw/F6tWrY/z48XHQQQc1fF+8eHFMmjQpbrnllli/fn1Ot7bEYYcdlrj2q6++itGjR8eYMWNi7733jo4dO0ZhYeEWdZdddlmi0DAAAAAAAAAAAHYeArAAAAAAAAAAAADYodXW1sZ1110X11133XbPKi0tjauvvjpRbadOneLYY4+NioqKxPOffvrpePrpp2PPPfeMsrKyWLlyZdTW1rbw2pYZOHBgzj2bNm2KpUuXbvP3K1eu3J6TAAAAAAAAAADYARXk+wAAAAAAAAAAAAD4prjiiiuiZ8+eievHjBnToj0rV66Md955J/Xwq4iIvffeOwYPHpz6XgAAAAAAAAAAdi4CsAAAAAAAAAAAANihlJeXt0mA0zHHHBNXXnllTj0/+clPYv/992/1W/r06dPqM7/uwgsvbNP5AAAAAAAAAAAgAAsAAAAAAAAAAIAdSlFRUTz66KPRv3//VpvZu3fvePzxx6OwsDCnvtLS0pg8eXIUFLTeP9cbNmxY3HHHHa02b2tOO+20+OlPf9qmOwAAAAAAAAAA2LkJwAIAAAAAAAAAAGCH06VLl6ioqIiBAwdu96x+/frFCy+8EGVlZS3q//73vx833HDDdt8RETFy5Mh48skno6SkpFXmNWXSpElxwgkntPkeAAAAAAAAAAB2TgKwAAAAAAAAAAAA2CHtscceMXPmzLjggguioKBl/1xu5MiR8fLLL0d5efl23XLFFVfEHXfc0eI7dt111/jTn/4UU6dOjeLi4u26JamOHTvGtGnT4rbbbovu3bunshMAAAAAAAAAgJ2HACwAAAAAAAAAAAB2WKWlpXHnnXfGq6++GieffHLiAKrjjjsuXnjhhZg6dWp06NChVW658MILo7KyMoYMGZK4p6SkJM4999z4z3/+E+eff36r3JGLTCYTF110Ubz77rsxbdq0uOqqq2LYsGHRu3fv6Nq1a5SWlqZ+EwAAAAAAAAAAO4ZMNpvN5vsIAAAAAAAAAAAAaE4mk0lU171796ipqdnq7z7++OOYPn16zJkzJ6qrq2P16tWxcePG6NChQ/To0SMGDBgQQ4cOjf33378VL9/SG2+8Ec8880z861//iiVLlkRtbW2sWbMmdt111ygvL48DDzwwjj/++Dj11FOja9eubXoLAAAAAAAAAADkgwAsAAAAAAAAAAAAvhFaIwALAAAAAAAAAAD431CQ7wMAAAAAAAAAAAAAAAAAAAAAAADYuQjAAgAAAAAAAAAAAAAAAAAAAAAAIFUCsAAAAAAAAAAAAAAAAAAAAAAAAEiVACwAAAAAAAAAAAAAAAAAAAAAAABSJQALAAAAAAAAAAAAAAAAAAAAAACAVAnAAgAAAAAAAAAAAAAAAAAAAAAAIFUCsAAAAAAAAAAAAAAAAAAAAAAAAEhVUb4PALZu3rx5UVVVFcuWLYuIiPLy8ujTp08ceuihrbpn9erVMWvWrFi2bFnU1tZGWVlZlJeXx6BBg6JTp06tugsAAAAAAAAAAAAAAAAAAAAAACIEYLETevfdd2POnDlRWVkZc+bMiXnz5kVdXV3D77t37x41NTV5uW3Dhg1xyy23xL333huLFy/eak2vXr1i9OjRcemll0ZxcXGLd7322mvxu9/9Lp5++ulYv379Fr8vLS2NYcOGxbXXXhuHHHJIi/cAAAAAAAAAAAAAAAAAAAAAAMDmMtlsNpvvI6CtVVRUxPjx46OysjI+/fTTJmvzFYBVVVUVZ511VsybNy9R/YABA+Lhhx+OXr165bzr5ptvjmuuuSY2bNjQbG1JSUnccMMNMXbs2Jz3AAAAAAAAAABAa8pkMonq8vkfwQMAAAAAAAAAAJIpyvcBkIbXX389nn322XyfsU0fffRRDBkyJJYsWdLoe69evaJfv36RzWZjwYIFsXjx4obfzZ07N0444YR45ZVXomvXrol33XTTTXHllVc2+tauXbs44ogjolu3bvHhhx/GnDlz4quvvoqIiPXr18e4ceMik8nEZZddth1/JQAAAAAAAAAAAAAAAAAAAAAA/FdBvg+AfCotLY2ePXvm9YZNmzbFKaec0ij8qlu3bjF9+vSoqqqKJ554Ip588smorq6OZ555Jvbaa6+Guvfeey9OPfXUyGaziXb94x//iKuuuqrRtzFjxsTSpUtjxowZ8fDDD8fMmTNj6dKlMXr06EZ148aNi2nTpm3HXwoAAAAAAAAAANsnm80m+qmpqcn3qQAAAAAAAAAAQDMEYLHTKC4ujkMOOSRGjx4dEydOjLlz50ZdXV3ce++9eb3rwQcfjFdffbXh3aVLl5g1a1accMIJW9QOHTo0Zs2aFZ07d274NmvWrHjkkUea3VNfXx+//e1vG4VlXXLJJTFx4sQoKytrVLvnnnvGX/7yl7j44osbvmWz2fjNb34T9fX1Of19AAAAAAAAAAAAAAAAAAAAAACwuUz262k4sINatWpVtGvXLnbZZZctfldRURHHHXdcw7t79+6p/Rcg6+vro3fv3vHee+81fJsyZUqcc845TfZNmTIlfv7znze8e/bsGYsWLYqCgm1n2k2ePDnOO++8hnffvn1j/vz5UVpaus2edevWxcEHHxwLFy5s+HbffffFqFGjmrwPAAAAAAAAAAAAAAAAAAAAAACasu20HNiBdO7ceavhV/n24osvNgq/Ki8vj5EjRzbb97Of/SzKy8sb3osXL45Zs2Y12XP//fc3el9yySVNhl9FRJSWlsZFF13U5BwAAAAAAAAAAAAAAAAAAAAAAMiVACzIo8cff7zRe9SoUVFYWNhsX2Fh4RZBWY899tg26z/55JP497//3fAuKSmJs88+O9GNI0aMiOLi4ob3jBkz4tNPP03UCwAAAAAAAAAAAAAAAAAAAAAAWyMAC/Jo2rRpjd6DBw9O3Lt57TPPPLPN2ueeey7q6+sb3gMGDIgOHTok2tOxY8c47LDDGt4bN26M5557LvGdAAAAAAAAAAAAAAAAAAAAAACwOQFYkCfr1q2L6urqRt+OOuqoxP2DBg1q9K6qqor169dvtfatt95q9D766KMT79nargULFuTUDwAAAAAAAAAAAAAAAAAAAAAAXycAC/Jk4cKFUV9f3/Du2rVrdOzYMXF/x44do6ysrOFdX18fixYt2mrt22+/3ejdq1evnG7t2bNnk/MAAAAAAAAAAAAAAAAAAAAAACAXArAgT6qrqxu9991335xnbN5TVVXVJruS7gEAAAAAAAAAAAAAAAAAAAAAgCQEYEGerF69utG7a9euOc/YvGfNmjVtsivpHgAAAAAAAAAAAAAAAAAAAAAASKIo3wfAzurzzz9v9G7Xrl3OMzbvqaura5NdSffkasWKFbFy5cqcej777LOorKyMjh07RqdOnWKfffaJ0tLSVrkHAAAAAAAAAAAAAAAAAAAAACCpdevWxfvvv9/wPvbYY6NTp055vOibRQAW5MnmoVS77LJLzjM2D6bafGZr7Uq6J1d33XVXXH/99a0yCwAAAAAAAAAAAAAAAAAAAAAgn5544okYPnx4vs/4xijI9wHAf2UymVR6WtLX0j0AAAAAAAAAAAAAAAAAAAAAALA1ArAgT9q3b9/o/eWXX+Y8Y/OezWe21q6kewAAAAAAAAAAAAAAAAAAAAAAIImifB8AO6u0A7BWrVrV4l1tFYB1/vnnx+mnn55Tz9tvvx1nnHFGw/uJJ56IXr16tco9AAAAAAAAAAAAAAAAAAAAAABJVVdXxymnnNLw3mefffJ4zTePACzIk913373Re+XKlTnPWLFiRaN3p06dtrnr/fffb/GupHty1bVr1+jatet2zejVq1f069evVe4BAAAAAAAAAAAAAAAAAAAAAGip0tLSfJ/wjVKQ7wNgZ9W7d+9G7yVLluQ8Y/OezWe21q6kewAAAAAAAAAAAAAAAAAAAAAAIAkBWJAnffv2jcLCwob3ihUroq6uLnH/Z599FrW1tQ3vwsLCbQZTHXDAAY3e1dXVOd367rvvNjkPAAAAAAAAAAAAAAAAAAAAAAByIQAL8qS0tDR69uzZ6NvLL7+cuH/WrFmN3r17947S0tKt1vbv37/FeyIiXnrppSbnAQAAAAAAAAAAAAAAAAAAAABALgRgQR4NHTq00buioiJx7+a1w4YN22btkCFDorCwsOE9d+7cqKurS7Snrq4u5s2b1/AuKiqKIUOGJL4TAAAAAAAAAAAAAAAAAAAAAAA2JwAL8ujUU09t9J46dWrU19c321dfXx8PPPBAk7O+rqysLI455piG9/r16+Ohhx5KdOODDz4YGzZsaHh///vfjy5duiTqBQAAAAAAAAAAAAAAAAAAAACArRGABXn0f//3f9GjR4+G9wcffLBFsNXWPPDAA7Fs2bKGd8+ePeN73/tekz2jRo1q9L711ltj3bp1TfasW7cubrvttkbfzjnnnGbvAwAAAAAAAAAAAAAAAAAAAACApgjAglaUyWQa/VRUVDRZX1hYGNdff32jb5deemnU1NRss6empiYuueSSRt9uvPHGKCho+v/O55xzTvTt27fhvXDhwrjiiiua7Ln88stj4cKFDe/vfve7MWLEiCZ7AAAAAAAAAAAAAAAAAAAAAACgOQKw2Gl88MEHUVNTs8XPRx991Khu48aNW62rqamJ2traVr9rxIgRceSRRza8P/300xg0aFA8++yzW9ROnz49jj766Fi1alXDt0GDBsWZZ57Z7J7CwsKYMGFCZDKZhm9//OMf41e/+lV88sknjWpra2tjzJgxceuttzZ8y2Qyccstt0RhYWFOfx8AAAAAAAAAAAAAAAAAAAAAAGyuKN8HQFqOOeaYWLJkSbN1y5Ytix49emz1d+ecc05MmTKlVe8qKCiIxx9/PI466qhYunRpREQsX748TjzxxOjdu3f069cvstlsLFiwIKqrqxv17rfffvHYY481CrVqyo9+9KO48cYb48orr2z4ds8998TUqVPjyCOPjL322iuWL18es2fPji+//LJR78033xxDhw7dzr8WAAAAAAAAAAAAAAAAAAAAAAAEYMH/hG7dusVzzz0XZ511Vrz22msN36uqqqKqqmqrPYcddlg88sgj8a1vfSunXVdccUVkMpm49tprY8OGDRER8eWXX0ZFRcVW64uLi+OGG26IsWPH5rQHAAAAAAAAAAAAAAAAAAAAAAC2pSDfBwD/1adPn3j11Vdj/Pjxsf/++2+zrmfPnjF+/Ph45ZVXolevXi3adfnll8err74aw4cPj5KSkq3WlJSUxPDhw2P27Nkxbty4Fu0BAAAAAAAAAAAAAAAAAAAAAICtyWSz2Wy+jwC2NHfu3Fi0aFF8+OGHERHx7W9/O/r06RMDBgxo1T2rVq2KWbNmxbJly+KTTz6JPfbYI8rLy2PQoEHRuXPnVt3VGhYsWBD9+/dveL/11lvRr1+/PF4EAAAAAAAAAAAAAAAAAAAAAOyMZKFsn6J8HwBs3YABA1o97GprOnfuHCeddFKb7wEAAAAAAAAAAAAAAAAAAAAAgP+vIN8HAAAAAAAAAAAAAAAAAAAAAAAAsHMRgAUAAAAAAAAAAAAAAAAAAAAAAECqBGABAAAAAAAAAAAAAAAAAAAAAACQKgFYAAAAAAAAAAAAAAAAAAAAAAAApEoAFgAAAAAAAAAAAAAAAAAAAAAAAKkSgAUAAAAAAAAAAAAAAAAAAAAAAECqBGABAAAAAAAAAAAAAAAAAAAAAACQKgFYAAAAAAAAAAAAAAAAAAAAAAAApEoAFgAAAAAAAAAAAAAAAAAAAAAAAKkSgAUAAAAAAAAAAAAAAAAAAAAAAECqBGABAAAAAAAAAAAAAAAAAAAAAACQKgFYAAAAAAAAAAAAAAAAAAAAAAAApEoAFgAAAAAAAAAAAAAAAAAAAAAAAKkSgAUAAAAAAAAAAAAAAAAAAAAAAECqBGABAAAAAAAAAAAAAAAAAAA/yWdjAAAgAElEQVQAAACQKgFYAAAAAAAAAAAAAAAAAAAAAAAApEoAFgAAAAAAAAAAAAAAAAAAAAAAAKkSgAUAAAAAAAAAAAAAAAAAAAAAAECqBGABAAAAAAAAAAAAAAAAAAAAAACQKgFYAAAAAAAAAAAAAAAAAAAAAAAApEoAFgAAAAAAAAAAAAAAAAAAAAAAAKkSgAUAAAAAAAAAAAAAAAAAAAAAAECqBGABAAAAAAAAAAAAAAAAAAAAAACQKgFYAAAAAAAAAAAAAAAAAAAAAAAApEoAFgAAAAAAAAAAAAAAAAAAAAAAAKkSgAUAAAAAAAAAAAAAAAAAAAAAAECqBGABAAAAAAAAAAAAAAAAAAAAAACQKgFYAAAAAAAAAAAAAAAAAAAAAAAApEoAFgAAAAAAAAAAAAAAAAAAAAAAAKkSgAUAAAAAAAAAAAAAAAAAAAAAAECqBGABAAAAAAAAAAAAAAAAAAAAAACQKgFYAAAAAAAAAAAAAAAAAP+PvbuPzbK8+wb+u6AtVlAECxu68SICipgIyxCqZizEybK4sgnp1DjBNixbshHjFsy2xJVlm3MZmYuZLlux6Hzj1fgwpVETDJtsYRMjUFC0fXC3TEA6neW9va/7j/vZ9XABhb6eZ18+n4Q/fkfP4zi+kPjPkfgtAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkCgFWAAAAAAAAAAAAAAAAAAAAAAAACRKARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkCgFWAAAAAAAAAAAAAAAAAAAAAAAACRKARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkCgFWAAAAAAAAAAAAAAAAAAAAAAAACRKARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkCgFWAAAAAAAAAAAAAAAAAAAAAAAACRKARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkCgFWAAAAAAAAAAAAAAAAAAAAAAAACRKARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkCgFWAAAAAAAAAAAAAAAAAAAAAAAACRKARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkCgFWAAAAAAAAAAAAAAAAAAAAAAAACRKARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkCgFWAAAAAAAAAAAAAAAAAAAAAAAACRKARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkCgFWAAAAAAAAAAAAAAAAAAAAAAAACRKARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkCgFWAAAAAAAAAAAAAAAAAAAAAAAACRKARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkCgFWAAAAAAAAAAAAAAAAAAAAAAAACRKARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkCgFWAAAAAAAAAAAAAAAAAAAAAAAACRKARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAAAAAAAAAAAAAAAAAkKiCtANAmhoaGuL111+PvXv3RlNTU4waNSrGjBkTpaWlUVhYmHY8AAAAAAAAAAAAAAAAAAAAAADokxRg0S+tXr06li1bFps3bz7jz4cPHx7l5eWxdOnSKCkp6bYcY8eOjT179nTJWXfeeWfU1NS0+vOamppYuHBhh8//3Oc+Fxs3buzwfgAAAAAAAAAAAAAAAAAAAAAA+I8BaQeAJDU1NcWtt94a8+fPb7X8KiKisbExHn744ZgyZUrU1tYmmLDjiouL044AAAAAAAAAAAAAAAAAAAAAAABtUpB2AEhKS0tLlJeXx/PPP5+3PmLEiJg6dWoMHTo03nnnndi6dWtks9mIiNi3b1+UlZXFSy+9FNdff30asdvslltuSTsCAAAAAAAAAAAAAAAAAAAAAAC0iQIs+o177703r/yqsLAwli1bFosWLYqioqLcel1dXVRWVsbmzZsjIuLYsWMxd+7c2LZtW4waNapLM/3pT3+K5ubmdu976KGH4pe//GVuHjt2bMyePbtdZ/ziF7+IefPmtfn78847r13nAwAAAAAAAAAAAAAAAAAAAABAaxRg0S/U19fHgw8+mLe2atWqKCsrO+3byZMnx8svvxyzZ8/OlWAdPHgwqqqq4pFHHunSXJ/61Kc6tO+Pf/xj3nzXXXdFJpNp1xklJSUxduzYDt0PAAAAAAAAAAAAAAAAAAAAAACdMSDtAJCEqqqqOHHiRG5esGDBGcuv/qO4uDhqamqiqKgot1ZdXR319fXdmrMt/vznP8euXbty84ABA2LBggXpBQIAAAAAAAAAAAAAAAAAAAAAgHZSgEWfd+TIkVi9enXe2pIlS865b+LEiTF37tzc3NzcHE8++WSX52uv5cuX581f+MIX4tOf/nRKaQAAAAAAAAAAAAAAAAAAAAAAoP0UYNHn1dbWxuHDh3PzzJkz44orrmjT3oULF+bNa9eu7dJs7dXU1BQrV67MW6uoqEgpDQAAAAAAAAAAAAAAAAAAAAAAdIwCLPq8DRs25M2zZs1q894bbrghCgoKcvPWrVtj3759XRWt3Z555ploamrKzSNGjIiysrLU8gAAAAAAAAAAAAAAAAAAAAAAQEcowKLP2759e948c+bMNu8dPHhwXH311XlrO3bs6JJcHbF8+fK8+Y477ojCwsKU0gAAAAAAAAAAAAAAAAAAAAAAQMcowKLP27lzZ958+eWXt2v/+PHj8+a6urpOZ+qIXbt2xauvvpq3VlFR0eHz1qxZE1/60pdizJgxcf7558fgwYNj9OjRMXPmzLjnnntiw4YNkc1mOxsbAAAAAAAAAAAAAAAAAAAAAABOU5B2AOhOjY2N0djYmLc2evTodp1x6ve7d+/udK6OqK6uzptnzJgRkydP7vB569evP23t8OHD8Y9//CP+8pe/xLJly2LSpElRVVUV5eXlHb4HAAAAAAAAAAAAAAAAAAAAAABONSDtANCdPvzww7z5/PPPj8GDB7frjJEjR+bNH330UadztVdzc3M8/vjjeWuVlZXdfu+bb74ZX/va1+Kuu+6KY8eOdft9AAAAAAAAAAAAAAAAAAAAAAD0DwVpB4Du1NTUlDcXFxe3+4xT93z88cedytQR69evj3379uXmwYMHR3l5eYfOKikpiTlz5kRpaWlMnjw5SkpKoqioKBobG2Pbtm1RW1sb69ati5aWltyeRx99NI4ePRpPPPFEZDKZTv99/mP//v1x4MCBdu15++23u+x+AAAAAAAAAAAAAAAAAAAAAADSoQCLPu3UAqzzzjuv3WecWoB16plJqK6uzpvLy8tjyJAh7Tpj4sSJsXr16igrK4uCgjP/p3/ttddGZWVl7N69O26//fbYsmVL7mdPPfVUzJgxI77zne+0/y/Qit/85jdRVVXVZecBAAAAAAAAAAAAAAAAAAAAANA7DEg7ACQpk8kksqcr/fOf/4wNGzbkrVVUVLT7nNLS0rjllltaLb862YQJE2LTpk1x3XXX5a0vXbo0/v3vf7f7bgAAAAAAAAAAAAAAAAAAAAAAOJkCLPq0IUOG5M1Hjhxp9xmn7jn1zO62YsWKaG5uzs1XXnlllJaWdvu9gwYNipUrV0ZxcXFu7eDBg7Fy5cpuvxsAAAAAAAAAAAAAAAAAAAAAgL6tIO0A0J36QgHW8uXL8+aKiorE7r7kkkvi61//evz2t7/NrW3YsCEqKyu75PxvfetbMX/+/Hbtefvtt2Pu3Lldcj8AAAAAAAAAAAAAAAAAAAAAAOlQgEWfNnTo0Lz58OHDcejQoRg8eHCbz9i/f3/efNFFF3VJtrbYtGlT7N69OzcXFhbGHXfckdj9ERFz5szJK8B64403uuzskSNHxsiRI7vsPAAAAAAAAAAAAAAAAAAAAAAAeocBaQeA7nTxxRfHsGHD8tbefffddp2xZ8+evHnChAmdztVW1dXVefPNN9+ceGHU2LFj8+YDBw4kej8AAAAAAAAAAAAAAAAAAAAAAH2PAiz6vCuvvDJvfvvtt9u1v76+/qzndZePP/44Vq1albdWUVGRyN0nKy4uzpuPHDmSeAYAAAAAAAAAAAAAAAAAAAAAAPoWBVj0eVOmTMmbN2/e3Oa9hw4dijfeeOOs53WXp59+Og4fPpybL7300rjpppsSuftkH3zwQd5cUlKSeAYAAAAAAAAAAAAAAAAAAAAAAPoWBVj0eXPmzMmbN27c2Oa9mzZtiubm5tw8derU+MQnPtFV0c6quro6b164cGEMHDgwkbtP9te//jVvvuSSSxLPAAAAAAAAAAAAAAAAAAAAAABA36IAiz7vpptuiuLi4ty8efPm2LVrV5v21tTU5M1f+cpXujJaq+rq6vKKpzKZTCxcuDCRu0/11FNP5c2zZs1KJQcAAAAAAAAAAAAAAAAAAAAAAH2HAiz6vPPPPz/mzZuXt/bzn//8nPveeuutWLduXW4uKCiI2267rcvznUl1dXXe/PnPfz4uu+yyRO4+2YoVK+Jvf/tb3lpZWVniOQAAAAAAAAAAAAAAAAAAAAAA6FsUYNEv/OhHP4rCwsLcXFNTE88991yr3x89ejQWLlwYx48fz61VVFTE+PHjz3pPJpPJ+7Nx48Z2Zz1x4kQ8/vjjeWsVFRXtPudka9eujfr6+nbtWb9+fXzjG9/IW5s9e3Zcd911ncoCAAAAAAAAAAAAAAAAAAAAAAAKsOgXLrvssli8eHHe2rx58+Khhx7KK7mKiNi5c2fMnj07Xn311dzaxRdfHPfdd18iWZ977rk4cOBAbh42bFh89atf7fSZkyZNinnz5sXKlSvjo48+avXbXbt2RWVlZXz5y1+OY8eO5dYvvPDCePDBBzuVAwAAAAAAAAAAAAAAAAAAAAAAIiIK0g4ASbn//vtjx44d8cILL0RExIkTJ+Lb3/52/PjHP45p06bFBRdcEPX19fHaa69FNpvN7SsqKop169bFqFGjEsm5fPnyvPn222+P8847r9PnNjc3x5o1a2LNmjURETFu3Li4/PLLY+jQoVFUVBT/+te/YseOHfHuu++etre4uDjWrVsXV111VadzAAAAAAAAAAAAAAAAAAAAAACAAiz6jYEDB8bKlSujsrIynnnmmdz6/v37Y8OGDWfcM3LkyFixYkXccMMNiWR87733ora2Nm+toqKiW+5qaGiIhoaGc343derU+MMf/hCTJ0/ulhwAAAAAAAAAAAAAAAAAAAAAAPQ/A9IOAEkaMmRIPP3007Fq1aqYMWNGq98NHz48vvnNb8b27dtjzpw5ieWrqamJlpaW3Dxt2rS45pprOn3unXfeGXfddVdMnDgxMpnMOb8fNGhQ3HjjjbF27drYsmWL8isAAAAAAAAAAAAAAAAAAAAAALpUJpvNZtMOAWlpaGiI1157Lfbu3RuHDh2KT37ykzFmzJi47rrroqioKO143aKpqSnq6upiz5498f7778ehQ4eiubk5Lrzwwhg2bFhMmDAhpk2b1mP//jt27IgpU6bk5u3bt8dVV12VYiIAAAAAAAAAAAAAAAAAAAAAoD/ShdI5BWkHgDSNGzcuxo0bl3aMRA0ZMiSmT58e06dPTzsKAAAAAAAAAAAAAAAAAAAAAAD91IC0AwAAAAAAAAAAAAAAAAAAAAAAANC/KMACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAAAAAAAAAAAAAAAAASJQCLAAAAAAAAAAAAAAAAAAAAAAAABKlAAsAAAAAAAAAAAAAAAAAAAAAAIBEKcACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAAAAAAAAAAAAAAAAASJQCLAAAAAAAAAAAAAAAAAAAAAAAABKlAAsAAAAAAAAAAAAAAAAAAAAAAIBEKcACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAAAAAAAAAAAAAAAAASJQCLAAAAAAAAAAAAAAAAAAAAAAAABKlAAsAAAAAAAAAAAAAAAAAAAAAAIBEKcACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAAAAAAAAAAAAAAAAASJQCLAAAAAAAAAAAAAAAAAAAAAAAABKlAAsAAAAAAAAAAAAAAAAAAAAAAIBEKcACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAAAAAAAAAAAAAAAAASJQCLAAAAAAAAAAAAAAAAAAAAAAAABKlAAsAAAAAAAAAAAAAAAAAAAAAAIBEKcACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAAAAAAAAAAAAAAAAASJQCLAAAAAAAAAAAAAAAAAAAAAAAABKlAAsAAAAAAAAAAAAAAAAAAAAAAIBEKcACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAAAAAAAAAAAAAAAAASJQCLAAAAAAAAAAAAAAAAAAAAAAAABKlAAsAAAAAAAAAAAAAAAAAAAAAAIBEKcACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAAAAAAAAAAAAAAAAASJQCLAAAAAAAAAAAAAAAAAAAAAAAABKlAAsAAAAAAAAAAAAAAAAAAAAAAIBEKcACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAAAAAAAAAAAAAAAAASJQCLAAAAAAAAAAAAAAAAAAAAAAAABKlAAsAAAAAAAAAAAAAAAAAAAAAAIBEKcACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAAAAAAAAAAAAAAAAASJQCLAAAAAAAAAAAAAAAAAAAAAAAABKlAAsAAAAAAAAAAAAAAAAAAAAAAIBEKcACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAAAAAAAAAAAAAAAAASJQCLAAAAAAAAAAAAAAAAAAAAAAAABKlAAsAAAAAAAAAAAAAAAAAAAAAAIBEKcACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAAAAAAAAAAAAAAAAASJQCLAAAAAAAAAAAAAAAAAAAAAAAABKlAAsAAAAAAAAAAAAAAAAAAAAAAIBEFaQdAAAAAAAAoLd75a0D8VLdvvjnR0fTjgIAAAAAEBERhQMzMW30sPjqtEvj4iGD0o4DAAAAAAAAcBoFWAAAAAAAAJ3w6J8bour/1KUdAwAAAADgNC9sfz9W/u0f8dSiGVGiBAsAAAAAAADoYQakHQAAAAAAAKC3Ot783/GL2jfTjgEAAAAA0Krd+5tizd//K+0YAAAAAAAAAKdRgAUAAAAAANBBb77/cRw+3pJ2DAAAAACAs/r7nn+lHQEAAAAAAADgNAqwAAAAAAAAOuh4y3+nHQEAAAAA4JxOeMsEAAAAAAAAeiAFWAAAAAAAAB2WTTsAAAAAAMA5eckEAAAAAAAAeqKCtAMAAAAAAAD0RQtKx8aATCbtGAAAAABAP7Ll/zbGtvc+SjsGAAAAAAAAQJsowAIAAAAAAOgGP/jSlVE4cEDaMQAAAACAfuRnL+xUgAUAAAAAAAD0Gv6vCwAAAAAAgA7KZtNOAAAAAABwbt4yAQAAAAAAgJ5IARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAADQQdmz/CyTWAoAAAAAgP+VaeVl8mxvmQAAAAAAAABpUYAFAAAAAAAAAAAAAAAAAAAAAABAohRgAQAAAAAAdINMJpN2BAAAAACgn/EsCQAAAAAAAPQmCrAAAAAAAAA6KJtNOwEAAAAAwLllPWYCAAAAAAAAPZACLAAAAAAAgG6QSTsAAAAAANDveJcEAAAAAAAAehMFWAAAAAAAAB2UzWbTjgAAAAAAAAAAAAAAANArKcACAAAAAAAAAAAAAAAAAAAAAAAgUQqwAAAAAAAAukEmk3YCAAAAAKC/8S4JAAAAAAAA9CYKsAAAAAAAADoom3YAAAAAAIA2yHrMBAAAAAAAAHogBVgAAAAAAADdIJPJpB0BAAAAAOhnMuFdEgAAAAAAAOg9FGABAAAAAAB0UDabdgIAAAAAgHPLhsdMAAAAAAAAoOdRgAUAAAAAAAAAAAAAAAAAAAAAAECiFGABAAAAAAB0UDayaUcAAAAAAMjJZM68nvWUCQAAAAAAAPRACrAAAAAAAAAAAAAAAAAAAAAAAABIlAIsAAAAAACALpbJpJ0AAAAAAOiPPE0CAAAAAAAAvYkCLAAAAAAAgI7Kph0AAAAAAODcst4yAQAAAAAAgB5IARYAAAAAAAAAAAAAAAAAAAAAAACJUoAFAAAAAADQQdlW1jOJpgAAAAAA+H8yZ36dzLb6mgkAAAAAAACQHgVYAAAAAAAAAAAAAAAAAAAAAAAAJEoBFgAAAAAAQBfLZDJpRwAAAAAA+iEvkwAAAAAAAEBvogALAAAAAACgg7LZtBMAAAAAAJybt0wAAAAAAACgJ1KABQAAAAAA0MUyaQcAAAAAAAAAAAAAAADo4RRgAQAAAAAAdFA2smlHAAAAAADIybTSzu8lEwAAAAAAAOiJFGABAAAAAAAAAAAAAAAAAAAAAACQqIK0A0CaGhoa4vXXX4+9e/dGU1NTjBo1KsaMGROlpaVRWFiYdrxEfPjhh/Hqq6/Ge++9Fx988EGUlJTEpZdeGqWlpXHRRRelHQ8AAAAAoFfKZNJOAAAAAAD0R5nwOAkAAAAAAAD0Hgqw6JdWr14dy5Yti82bN5/x58OHD4/y8vJYunRplJSUdGuWWbNmxSuvvNLh/Y8++mgsWLCg3fu2bt0aS5cujeeffz6OHz9+2s8HDRoUX/ziF+O+++6La665psP5AAAAAAD6smw27QQAAAAAAG3gLRMAAAAAAADogQakHQCS1NTUFLfeemvMnz+/1fKriIjGxsZ4+OGHY8qUKVFbW5tgwmTcf//9ce2118azzz57xvKriIhjx47Fs88+G9dee2088MADCScEAAAAAOjdMpFJOwIAAAAAAAAAAAAAAECPVpB2AEhKS0tLlJeXx/PPP5+3PmLEiJg6dWoMHTo03nnnndi6dWtks//7a6727dsXZWVl8dJLL8X111+fRuwu99Of/jR+8IMf5K0VFxfHZz/72Rg1alTs3bs3tmzZEkePHo2IiOPHj8eSJUsik8nE9773vTQiAwAAAAD0WNm0AwAAAAAAnCTTSjd/1msmAAAAAAAA0AMpwKLfuPfee/PKrwoLC2PZsmWxaNGiKCoqyq3X1dVFZWVlbN68OSIijh07FnPnzo1t27bFqFGjuj1nQ0NDu74vKSlp87fr16+PH/7wh3lrixYtip/85Cd55xw4cCC+//3vx+9///vc2pIlS+Lqq6+OOXPmtCsfAAAAAAAAAAAAAAAAAAAAAACcSgEW/UJ9fX08+OCDeWurVq2KsrKy076dPHlyvPzyyzF79uxcCdbBgwejqqoqHnnkkW7POnbs2G45t6WlJb773e9GNvv/f4PX3XffHcuWLTvt2xEjRsTvfve7GDJkSPzqV7+KiIhsNhv33HNP3HjjjTFw4MBuyQgAAAAA0Gdk0g4AAAAAAPRHniYBAAAAAACA3mRA2gEgCVVVVXHixIncvGDBgjOWX/1HcXFx1NTURFFRUW6turo66uvruzVnd3rsscfizTffzM2TJk2Kn/3sZ2fdc//998ekSZNyc11dXTzxxBPdlhEAAAAAoLc5+ZcOAAAAAAD0VJ4yAQAAAAAAgJ5IARZ93pEjR2L16tV5a0uWLDnnvokTJ8bcuXNzc3Nzczz55JNdni8pjz32WN589913x6BBg866Z9CgQbF48eKzngMAAAAAwOkyaQcAAAAAAAAAAAAAAADo4RRg0efV1tbG4cOHc/PMmTPjiiuuaNPehQsX5s1r167t0mxJOXjwYGzatCk3FxUVxW233damvbfffnsUFhbm5ldeeSUaGxu7PCMAAAAAQG+UTTsAAAAAAMBJMq2083vLBAAAAAAAAHoiBVj0eRs2bMibZ82a1ea9N9xwQxQUFOTmrVu3xr59+7oqWmJefPHFaGlpyc2f+cxn4oILLmjT3gsvvDCmTZuWm5ubm+PFF1/s8owAAAAAAAAAAAAAAAAAAAAAAPQfCrDo87Zv3543z5w5s817Bw8eHFdffXXe2o4dO7okV5I6828QEVFaWpo398Z/AwAAAACAbpE983Imk2wMAAAAAICIiEwrj5PZbCuPmQAAAAAAAAApUoBFn7dz5868+fLLL2/X/vHjx+fNdXV1nc50NosXL47p06fHyJEjo6ioKIYPHx4TJkyIm2++OR544IF466232n3mqZl7+r8BAAAAAAAAAAAAAAAAAAAAAAB9mwIs+rTGxsZobGzMWxs9enS7zjj1+927d3c619n8+te/ji1btsSBAwfixIkT/zrblkQAACAASURBVMPe3QZZWdZ/AP/dsiwsoAhsJGoiID4gNkKFz/1RfCB1QnMdSifNQk0bc3pU84WQvtDRNMvsYUaFKTVQIR1LbBKZHCU10QFF0A1Si2wRfNplgYXu/wvryME9y9mzZ899dvl8ZnhxXXtf1/09Z4Y31+z13Xj77bejsbExHn744bjiiivikEMOiS984Qvxt7/9reg9Gxsb88bV/h0AAAAAAPR0SSRZRwAAAAAAAAAAAAAAAKhqCrDo1d5555288YABA2LgwIGd2mP48OF543fffbfLubriP//5TyxYsCAmTpwYDzzwQFFrdvwedvxMO1Nt3wEAAAAAQLVII806AgAAAADATjnJBAAAAAAAAKpRTdYBoDs1Nzfnjevq6jq9x45r3n///S5lKuSwww6Lz33uc3H44YfHAQccEHvuuWds3rw5mpqaYsmSJTF37txYvnx57vn33nsvpk+fHg899FCceuqpHe7d1e+hu76DpqamWLduXafWNDY2luXdAAAAAAAAAAAAAAAAAAAAAABkRwEWvdqOxU/9+/fv9B47lj/tuGdXnXPOOfGzn/0sDj300ILPnHDCCXH11VfH3XffHZdcckmugGrbtm0xffr0WLlyZeyzzz4F13f1e+iu7+D222+PWbNmlWUvAAAAAIAspGn780lS2RwAAAAAABGFzyYLnWUCAAAAAAAAZGm3rANAJSUl3DgqZU1nXHTRRR2WX23v3HPPjcceeywGDBiQm2tubu50iVRnP1N3fwcAAAAAAAAAAAAAAAAAAAAAAOxaFGDRqw0aNChv3Nra2uk9dlyz456V9pnPfCauu+66vLk5c+ZES0tLwTVd/R6q7TsAAAAAAKh2/qwAAAAAAJCFxOkkAAAAAAAA0IPUZB0AulNvLMCKiLj00ktj5syZ8d5770VExJYtW+Lxxx+P008/vd3nBw0aFG+//XZuXC0FWJdeemmcffbZnVrT2NgYZ5xxRlneDwAAAADQVWmadQIAAAAAgJ1zlAkAAAAAAABUIwVY9GqDBw/OG2/cuDFaWlpi4MCBRe/R1NSUN95zzz3Lkq0r+vXrF8cff3w8+OCDublly5YVLMAaPHhwvPHGG7nxunXrOvW+7voOhg8fHsOHDy/LXgAAAAAA1SRJkqwjAAAAAAAAAAAAAAAAVLXdsg4A3WnYsGExZMiQvLnXX3+9U3u89tpreeOxY8d2OVc57L///nnjjkqtdsy842famWr9DgAAAAAAspZmHQAAAAAAYDsFu/lTp5kAAAAAAABA9VGARa93yCGH5I0bGxs7tX716tUd7peVurq6vHFra2vBZ3vrdwAAAAAAAAAAAAAAAAAAAAAAQM+kAIteb/z48XnjJUuWFL22paUlli1b1uF+WXnrrbfyxvX19QWf7cp3EBHx5JNPdrgfAAAAAAD5kqwDAAAAAAC7JGeTAAAAAAAAQE+iAIteb+rUqXnjxYsXF732iSeeiK1bt+bGEyZMiI9//OPlitYlTz/9dN547733LvjsSSedFH369MmNn3vuuXj//feLes/7778fS5cuzY1ramripJNO6mRaAAAAAIDeKU3TrCMAAAAAAOyUk0wAAAAAAACgGinAotc75ZRToq6uLjdesmRJrFy5sqi1s2fPzhufeeaZ5YxWsuXLl8fy5cvz5iZPnlzw+fr6+jj22GNz4y1btsQ999xT1LvuvvvuaGtry40/+9nPxtChQzsXGAAAAABgV5NkHQAAAAAAAAAAAAAAAKC6KcCi1xswYEA0NDTkzd1www07XffKK6/EggULcuOampo455xzyp6vs7Zt2xbf+ta38uYOOOCAGDduXIfrzjvvvLzxLbfcEps3b+5wzebNm+PHP/5x3tz555/fibQAAAAAAL1bmnUAAAAAAIDtJAXK+VOHmQAAAAAAAEAVUoDFLmHmzJnRt2/f3Hj27Nnx0EMPFXx+06ZNccEFF8SWLVtyc1/72tdizJgxHb4nSZK8f4sXL+7w+Z/+9KexadOm4j5ERGzZsiUuvPDCeOyxx/Lmr7nmmp2uPf/88+Oggw7KjVetWhU/+MEPOlxz1VVXxapVq3LjcePGxbnnnlt0XgAAAAAAAAAAAAAAAAAAAAAAaI8CLHYJo0ePjssvvzxvrqGhIW677ba8kquIiJdffjmmTJkSTz31VG5u2LBhRZVMddY3v/nNGDVqVHzve9+Lp59+OrZu3druc1u3bo0HH3wwjjjiiLjrrrvyfnbiiScWVUrVp0+fuOmmmyLZ7k973XzzzXHxxRfH+vXr855966234qKLLopbbrklN5ckSfzoRz+KPn36dOYjAgAAAADskpKdPwIAAAAAUHaJ00kAAAAAAACgB6nJOgBUyvXXXx8vvfRSPPLIIxER0dbWFpdddllce+21MXHixNh9991j9erVsXTp0kjTNLeutrY2FixYECNGjOiWXG+++WbcdNNNcdNNN0W/fv3i0EMPjREjRsTgwYOjra0tmpqa4rnnnovm5uaPrP30pz8d8+fPzyu16sjpp58e1113XVx99dW5uV/96lfx61//Oo444ojYa6+94l//+lc888wz0dramrf2+uuvj6lTp3btwwIAAAAA9DLbHScDAAAAAFStNBxmAgAAAAAAANVHARa7jD59+sS8efNixowZMXfu3Nx8U1NTLFy4sN01w4cPjzlz5sRxxx1XkYybN2+OpUuX7vS5JEnisssuixtuuCH69+/fqXf84Ac/iCRJ4pprrom2traIiGhtbY3Fixe3+3zfvn3j2muvje9///udeg8AAAAAwK6s2D9cAAAAAAAAAAAAAAAAsKvaLesAUEmDBg2K3/72t3HffffFkUceWfC5oUOHxiWXXBIvvvhiTJ06tdvy3HjjjXHqqafGsGHDinr+Yx/7WHzjG9+IFStWxK233trp8qv/ueqqq+Lpp5+OadOmRW1tbbvP1NbWxrRp0+KZZ56JK664oqT3AAAAAAD0fmnWAQAAAAAAcgp186eOMgEAAAAAAIAqVJN1AMhCQ0NDNDQ0xJo1a2Lp0qWxdu3aaGlpib322itGjhwZxxxzTMFiqI6knfztgO9+97vx3e9+NyIi/vGPf8SqVaviH//4R6xfvz5aW1ujT58+MWTIkKivr4/DDz88xowZ0+lMhUyYMCF+97vfxdtvvx1PPfVU/POf/4z169fHsGHDYp999omjjz46hgwZUrb3AQAAAAAAAAAAAAAAAAAAAADA/yjAYpc2atSoGDVqVNYxIiJi3333jX333bfi7x0yZEicdtppFX8vAAAAAEBvUOjvIiRJZXMAAAAAAHSkk3/jFQAAAAAAAKAidss6AAAAAAAAAAAAAAAAAAAAAAAAALsWBVgAAAAAAABllmQdAAAAAAAAAAAAAAAAoMopwAIAAAAAAChRmnUAAAAAAIDtJEn79fzOMgEAAAAAAIBqpAALAAAAAAAAAAAAAAAAAAAAAACAilKABQAAAAAAUKI0bX8+SZLKBgEAAAAAiIhCJ5NpocNMAAAAAAAAgAwpwAIAAAAAAAAAAAAAAAAAAAAAAKCiFGABAAAAAACUWZJ1AAAAAAAAAAAAAAAAgCqnAAsAAAAAAKBEaaRZRwAAAAAAyEm08wMAAAAAAAA9iAIsAAAAAACAMnPJDAAAAAAAAAAAAAAAoGMKsAAAAAAAAEqUplknAAAAAAD4UKFufmeZAAAAAAAAQDVSgAUAAAAAAAAAAAAAAAAAAAAAAEBFKcACAAAAAAAouyTrAAAAAAAAAAAAAAAAAFVNARYAAAAAAECJ0qwDAAAAAABsJ0naL+dPnWYCAAAAAAAAVUgBFgAAAAAAQJkVuGMGAAAAAAAAAAAAAADAfynAAgAAAAAAKFGapllHAAAAAADIKVTO7ygTAAAAAAAAqEYKsAAAAAAAAAAAAAAAAAAAAAAAAKgoBVgAAAAAAABllmQdAAAAAADYJTmbBAAAAAAAAHoSBVgAAAAAAAAAAAAAAL1YmnUAAAAAAAAAgHYowAIAAAAAACizJMk6AQAAAAAAAAAAAAAAQHVTgAUAAAAAAFCiNM06AQAAAADAdgq086cOMwEAAAAAAIAqpAALAAAAAAAAAAAAAAAAAAAAAACAilKABQAAAAAAUKI00nbnk0gqnAQAAAAAIAqeTLZ/kgkAAAAAAACQLQVYAAAAAAAAAAAAAAAAAAAAAAAAVJQCLAAAAAAAgDJLkqwTAAAAAAAAAAAAAAAAVDcFWAAAAAAAACVK06wTAAAAAAB8qGA5v7NMAAAAAAAAoAopwAIAAAAAAAAAAAAAAAAAAAAAAKCiFGABAAAAAACUKE3bn08qGwMAAAAAICIikgKnkwWOMgEAAAAAAAAypQALAAAAAAAAAAAAAAAAAAAAAACAilKABQAAAAAAUGZJkmQdAQAAAAAAAAAAAAAAoKopwAIAAAAAAChRmnUAAAAAAIDtFOrmT1OnmQAAAAAAAED1UYAFAAAAAAAAAAAAAAAAAAAAAABARSnAAgAAAAAAKFGapllHAAAAAADISQrMO8kEAAAAAAAAqpECLAAAAAAAAAAAAAAAAAAAAAAAACpKARYAAAAAAECZJUnWCQAAAAAAAAAAAAAAAKqbAiwAAAAAAIASpVkHAAAAAADYTqFy/tRhJgAAAAAAAFCFFGABAAAAAACUWaFLZgAAAAAAAAAAAAAAAHxAARYAAAAAAECp0qwDAAAAAAB8KIn22/lTh5kAAAAAAABAFVKABQAAAAAAAAAAAAAAAAAAAAAAQEUpwAIAAAAAACizJJKsIwAAAAAAAAAAAAAAAFQ1BVgAAAAAAAAlSiPNOgIAAAAAwIcKdPOnjjIBAAAAAACAKqQACwAAAAAAoMySApfMAAAAAAAAAAAAAAAA+IACLAAAAAAAgBKladYJAAAAAAA+VKib31kmAAAAAAAAUI0UYAEAAAAAAAAAAAAAAAAAAAAAAFBRCrAAAAAAAABKlBaYTyqaAgAAAAAAAAAAAAAAoOdRgAUAAAAAAAAAAAAA0AskiXp+AAAAAAAAoOdQgAUAAAAAAFBmLpkBAAAAAAAAAAAAAAB0TAEWAAAAAABAidI06wQAAAAAAB8qVM2fOswEAAAAAAAAqpACLAAAAAAAAAAAAAAAAAAAAAAAACpKARYAAAAAAECJ0kjbnU8qnAMAAAAAICIiKXA42f5JJgAAAAAAAEC2FGABAAAAAAAAAAAAAAAAAAAAAABQUQqwAAAAAAAAyi3JOgAAAAAAAAAAAAAAAEB1U4AFAAAAAABQojTNOgEAAAAAwIeSAuX8zjIBAAAAAACAaqQACwAAAAAAoMwK3DEDAAAAAAAAAAAAAADgvxRgAQAAAAAAlCjNOgAAAAAAwHaSAvX8qdNMAAAAAAAAoAopwAIAAAAAAAAAAAAAAAAAAAAAAKCiFGABAAAAAACUWZIkWUcAAAAAAAAAAAAAAACoagqwAAAAAAAASpWmWScAAAAAAMgp1M3vKBMAAAAAAACoRgqwAAAAAAAAyqzAHTMAAAAAAAAAAAAAAAD+SwEWAAAAAABAidKsAwAAAAAAFMFZJgAAAAAAAFCNFGABAAAAAAAAAAAAAAAAAAAAAABQUQqwAAAAAAAASpSm7c8nSWVzAAAAAAB0pNBZJgAAAAAAAECWFGABAAAAAAAAAAAAAPQCiXZ+AAAAAAAAoAdRgAUAAAAAAFBmSbhkBgAAAAAAAAAAAAAA0BEFWAAAAAAAACVK0zTrCAAAAAAAOYWr+Z1lAgAAAAAAANVHARYAAAAAAAAAAAAAAAAAAAAAAAAVpQALAAAAAACgRGmB+SSpaAwAAAAAgA6lhQ4zAQAAAAAAADKkAAsAAAAAAAAAAAAAoBdQzg8AAAAAAAD0JAqwAAAAAAAAAAAAAAAAAAAAAAAAqCgFWAAAAAAAACVK06wTAAAAAAB8KImk3XlHmQAAAAAAAEA1UoAFAAAAAABQZknS/iUzAAAAAAAAAAAAAAAAPqAACwAAAAAAoERp1gEAAAAAAIqQpk4zAQAAAAAAgOqjAAsAAAAAAAAAAAAAoBdIkqwTAAAAAAAAABRPARYAAAAAAECZuWMGAAAAAAAAAAAAAADQMQVYAAAAAAAAJUrTNOsIAAAAAAA5hcr5nWQCAAAAAAAA1UgBFgAAAAAAQJklhW6ZAQAAAAAAAAAAAAAAEBEKsAAAAAAAAAAAAAAAerU0zToBAAAAAAAAwEcpwAIAAAAAAAAAAAAA6AWSJOsEAAAAAAAAAMVTgAUAAAAAAFBmLpkBAAAAAAAAAAAAAAB0TAEWAAAAAABAidI06wQAAAAAANtrv50/dZgJAAAAAAAAVCEFWAAAAAAAAGWWFLhkBgAAAAAAAAAAAAAAwAcUYAEAAAAAAJQojTTrCAAAAAAAO+UkEwAAAAAAAKhGCrAAAAAAAAAAAAAAAHqBJMk6AQAAAAAAAEDxarIOAFlas2ZNvPDCC7F27dpobm6OESNGxMiRI+Poo4+Ovn37VjxPa2trvPzyy7Fy5cpYt25dNDc3x6BBg2Lo0KExfvz4OOyww6Kmxn9bAAAAAIBqkabtz7tkBgAAAABUlQJnmQAAAAAAAABZ0qTDLun++++Pm2++OZYsWdLuz4cOHRrTp0+PH/7wh1FfX9+tWZYuXRq/+93vYtGiRfHMM89EW1tbwWcHDhwY06dPj8svvzw++clPduo9s2fPjgsuuKDknP/3f/8XixcvLnk9AAAAAAAAAAAAAN1LNz8AAAAAAADQk+yWdQCopObm5vjSl74UZ599dsHyq4iIDRs2xM9//vMYP358PProo92SZdOmTTFmzJj41Kc+Fddee208+eSTHZZfRUS0tLTEnXfeGRMnTowrr7xyp88DAAAAAJANl8wAAAAAAAAAAAAAAAA6VpN1AKiUbdu2xfTp0+MPf/hD3vzHPvaxmDBhQgwePDj+9re/xfPPPx9pmkZExL///e+YNm1a/OlPf4pjjz22rHm2bt0aq1ev/sh8kiRx0EEHxX777Rf19fXR3NwcL774Yt6z27ZtixtuuCFeffXVmDt3btTU+K8MAAAAAJCFNOsAAAAAAADbSZL26/mdZQIAAAAAAADVSGsOu4wrr7wyr/yqb9++cfPNN8dFF10UtbW1ufkVK1bEjBkzYsmSJRERsXnz5jjjjDNi+fLlMWLEiG7J1qdPnzj55JPj/PPPjylTpkR9ff1Hnnnuuefi29/+dvz5z3/Ozc2fPz9mzpwZ1113XaffeeONN0ZDQ0PRz/fv37/T7wAAAAAAAAAAAAAAAAAAAAAAgPYowGKXsHr16rj11lvz5u67776YNm3aR54dN25cPPbYYzFlypRcCdb69etj1qxZ8Ytf/KKsufr16xczZsyIK6+8Mvbdd98On/3Upz4VixYtii9/+ctx77335uZvvPHGuPDCC2PkyJGdend9fX3sv//+pcQGAAAAAOC/0rTAD5KkojkAAAAAADqSFjzMBAAAAAAAAMjOblkHgEqYNWtWtLW15cZf+cpX2i2/+p+6urqYPXt21NbW5ubuuOOOWL16ddky9e/fPxobG+O2227bafnV//Tp0yfuuOOO+MQnPpGb27JlS8ybN69suQAAAAAAAAAAAADomVTzAwAAAAAAAD2JAix6vdbW1rj//vvz5q644oqdrjvwwAPjjDPOyI23bt0a99xzT9ly1dTUFF18tb26urq44IIL8uYef/zxcsUCAAAAAKAMXDIDAAAAAAAAAAAAAADomAIser1HH300Nm7cmBsfddRRcfDBBxe1dseiqfnz55c1W6kmTJiQN167dm1GSQAAAAAAdm1ppFlHAAAAAADISQq08zvJBAAAAAAAAKqRAix6vYULF+aNJ0+eXPTa4447LmpqanLj559/Pv7973+XK1rJts8UEbFly5aMkgAAAAAA0J5Cl8wAAAAAAAAAAAAAAAD4gAIser0XX3wxb3zUUUcVvXbgwIFx2GGH5c299NJLZcnVFY2NjXnjESNGZJQEAAAAAGDXlqZZJwAAAAAA2DlnmQAAAAAAAEA1UoBFr/fyyy/njQ844IBOrR8zZkzeeMWKFV3O1FX3339/3njSpEmd3uOBBx6I0047LUaOHBkDBgyIgQMHxn777RdHHXVUfOc734mFCxdG6rcdAAAAAAAAAAAAAHqMJMk6AQAAAAAAAEDxarIOAN1pw4YNsWHDhry5/fbbr1N77Pj8q6++2uVcXfHss8/Gk08+mTd35plndnqfhx9++CNzGzdujDfeeCP+8pe/xM033xwHHXRQzJo1K6ZPn15yXgAAAACAXZE7ZgAAAAAAAAAAAAAAAB3bLesA0J3eeeedvPGAAQNi4MCBndpj+PDheeN33323y7lK1dbWFhdffHHe3HHHHReTJk3qlvetWrUqvvjFL8ZXv/rV2Lx5c7e8AwAAAAAAAAAAAIDySArU86eRVjgJAAAAAAAAwM7VZB0AulNzc3PeuK6urtN77Ljm/fff71Kmrvje974Xzz//fG7ct2/f+MlPftKpPerr62Pq1Klx9NFHx7hx46K+vj5qa2tjw4YNsXz58nj00UdjwYIFsW3bttyau+66KzZt2hR33313JEn7vxhRiqampli3bl2n1jQ2Npbt/QAAAAAA3aWcZ6kAAAAAAAAAAAAAAAC9kQIserUdC7D69+/f6T12LMDacc9KufPOO+PWW2/Nm5s5c2YcfvjhRa0/8MAD4/77749p06ZFTU37//WPOOKImDFjRrz66qtx7rnnxrPPPpv72b333htHHnlkfPOb3yz9Q+zg9ttvj1mzZpVtPwAAAACASkvTNOsIAAAAAAA75SgTAAAAAAAAqEa7ZR0AKilJkoqsKbeFCxfG17/+9by5008/Pa666qqi9zj66KPjrLPOKlh+tb2xY8fGE088Ecccc0ze/A9/+MN47733in4nAAAAAAAAAAAAABWU/a+9AgAAAAAAABRNARa92qBBg/LGra2tnd5jxzU77tndnnzyyTjrrLOira0tN3fsscfG3Llzu7Wcq1+/fjFv3ryoq6vLza1fvz7mzZvXbe8EAAAAAOgt3DEDAAAAAAAAAAAAAADoWE3WAaA79fQCrOeeey5OO+202LhxY25u0qRJ8fvf/z4GDBjQ7e/fe++947zzzotf/vKXubmFCxfGjBkzyrL/pZdeGmeffXan1jQ2NsYZZ5xRlvcDAAAAAHRVmmadAAAAAADgQ4XK+R1lAgAAAAAAANVIARa92uDBg/PGGzdujJaWlhg4cGDRezQ1NeWN99xzz7Jk25lly5bFySefHO+++25ubsKECfHoo4/GHnvsUZEMERFTp07NK8BatmxZ2fYePnx4DB8+vGz7AQAAAABUi6TQLTMAAAAAAAAAAAAAAAAiImK3rANAdxo2bFgMGTIkb+7111/v1B6vvfZa3njs2LFdzrUzK1asiBNPPDE2bNiQmxs/fnz88Y9/rFgB1//sv//+eeN169ZV9P0AAAAAANUszToAAAAAAEAxHGYCAAAAAAAAVUgBFr3eIYcckjdubGzs1PrVq1d3uF+5rVq1KqZMmZJXNHXwwQfHn/70p6ivr+/Wd7enrq4ub9za2lrxDAAAAAAAAAAAAADsXJIkWUcAAAAAAAAAKJoCLHq98ePH542XLFlS9NqWlpZYtmxZh/uVU2NjY5xwwgnx5ptv5ubGjh0bixYtio9//OPd9t6OvPXWW3njLEq4AAAAAACqVZq2P5+ES2YAAAAAQPVIo8BhJgAAAAAAAECGFGDR602dOjVvvHjx4qLXPvHEE7F169bceMKECd1WRLVmzZo44YQTYu3atbm50aNHx6JFi2LEiBHd8s5iPP3003njvffeO6MkAAAAAAAAAAAAAHRENT8AAAAAAADQkyjAotc75ZRToq6uLjdesmRJrFy5sqi1s2fPzhufeeaZ5YyW8/rrr8cJJ5wQb7zxRm5u5MiRsWjRoth333275Z3Fuvfee/PGkydPziYIAAAAAEBP4pYZAAAAAAAAAAAAAABAhxRg0esNGDAgGhoa8uZuuOGGna575ZVXYsGCBblxTU1NnHPOOWXPt3bt2pgyZUr8/e9/z83ts88+sWjRohg5cmTZ39cZc+bMib/+9a95c9OmTcsoDQAAAABA9UkjzToCAAAAAMBOpY4yAQAAAAAAgCqkAItdwsyZM6Nv37658ezZs+Ohhx4q+PymTZviggsuiC1btuTmvva1r8WYMWM6fE+SJHn/Fi9e3OHzTU1NMWXKlGhsbMzNjRgxIh5//PEYPXr0Tj5V8ebPnx+rV6/u1JqHH344Lr744ry5KVOmxDHHHFO2XAAAAAAAAAAAAACUT5JknQAAAAAAAACgeAqw2CWMHj06Lr/88ry5hoaGuO222/JKriIiXn755ZgyZUo89dRTublhw4bFNddcU9ZM77zzTpx00kmxcuXK3NzAgQPjjjvuiL59+8bf//73Tv3ryEMPPRQHHXRQNDQ0xLx58+Ldd98t+OzKlStjxowZ8fnPfz42b96cm99jjz3i1ltv7fLnBgAAAADoTdK0/Xl3zAAAAACAalLgKBMAAAAAAAAgUzVZB4BKuf766+Oll16KRx55JCIi2tra4rLLLotrr702Jk6cGLvvvnusXr06li5dGul2N5Zqa2tjwYIFMWLEiLLmeeGFF2LZsmV5cy0tLXHqqaeWtF9a6JbVf23dujUeeOCBeOCBByIiYtSoUXHAAQfE4MGDo7a2Nt5+++146aWX4vXXX//I2rq6uliwYEEceuihJWUDAAAAAAAAAAAAoPsl6vkBAAAAAACAHkQBFruMPn36xLx582LGjBkxd+7c3HxTU1MsXLiw3TXDhw+POXPmxHHHHVepmBWzZs2aWLNmzU6fmzBhQvzmN7+JcePGVSAVAAAAAEDvkLhjBgAAAAAAAAAAAAAA0KHdsg4AlTRo0KD47An3PwAAIABJREFU7W9/G/fdd18ceeSRBZ8bOnRoXHLJJfHiiy/G1KlTK5iwe5x//vnx1a9+NQ488MBIirh11a9fvzjppJNi/vz58eyzzyq/AgAAAAAoIM06AAAAAABAEdLUaSYAAAAAAABQfWqyDgBZaGhoiIaGhlizZk0sXbo01q5dGy0tLbHXXnvFyJEj45hjjona2tpO79uZXw6YPHlyxX6Z4Pjjj4/jjz8+IiKam5tjxYoV8dprr8Wbb74ZLS0tsXXr1thjjz1iyJAhMXbs2Jg4cWJJnx8AAAAAgA8ksfM/RgAAAAAAUG5F/J1UAAAAAAAAgKqhAItd2qhRo2LUqFFZx6ioQYMGxaRJk2LSpElZRwEAAAAA6Pkq9IcOAAAAAAC6wkkmAAAAAAAAUI12yzoAAAAAAAAAAAAAAABdl2QdAAAAAAAAAKATFGABAAAAAACUWeKWGQAAAAAAAAAAAAAAQIcUYAEAAAAAAJQozToAAAAAAMD2CpTzpw4zAQAAAAAAgCqkAAsAAAAAAKDMkgKXzAAAAAAAAAAAAAAAAPiAAiwAAAAAAIASpWnWCQAAAAAAAAAAAAAAAHomBVgAAAAAAAAAAAAAAL1AEknWEQAAAAAAAACKpgALAAAAAACgzFwyAwAAAAAAAAAAAAAA6JgCLAAAAAAAgBKlkWYdAQAAAAAgJ+mgmz9NnWcCAAAAAAAA1UUBFgAAAAAAQJl1dMkMAAAAAAAAAAAAAAAABVgAAAAAAAAlS9OsEwAAAAAAFMd5JgAAAAAAAFBtFGABAAAAAAAAAAAAAPQCSdYBAAAAAAAAADpBARYAAAAAAECJ0qwDAAAAAAAUyXkmAAAAAAAAUG0UYAEAAAAAAAAAAAAA9AJJkmQdAQAAAAAAAKBoCrAAAAAAAADKzCUzAAAAAAAAAAAAAACAjinAAgAAAAAAKFGaZp0AAAAAAKA4qQNNAAAAAAAAoMoowAIAAAAAAAAAAAAA6AWSJOsEAAAAAAAAAMVTgAUAAAAAAFCiNNJ2590xAwAAAACqTfunmQAAAAAAAADZUYAFAAAAAAAAAAAAANALKOcHAAAAAAAAehIFWAAAAAAAAGWWuGUGAAAAAAAAAAAAAADQIQVYAAAAAAAApUqzDgAAAAAAUJzUeSYAAAAAAABQZRRgAQAAAAAAlFmSdQAAAAAAYJeUOJwEAAAAAAAAehAFWAAAAAAAACVKsw4AAAAAAFCk1IkmAAAAAAAAUGUUYAEAAAAAAAAAAAAA9ApJ1gEAAAAAAAAAiqYACwAAAAAAoMySxCUzAAAAAAAAAAAAAACAjijAAgAAAAAAKFGapllHAAAAAAAoiuNMAAAAAAAAoNoowAIAAAAAACizJOsAAAAAAMAuKXE4CQAAAAAAAPQgCrAAAAAAAABKlKZZJwAAAAAAAAAAAAAAAOiZFGABAPD/7N1rjFbV/S/w32a4iAjKgKOAR+VeBVsv7RnASz1tpFrTAyYaWrWgp0TTpo2ibcFLgtjGS/+KWnuJsT2hVq23VGsalWq9VB3EFmiU0Y4zgFCBily0CMhl2OdVn/N/cGZ4Znhm77l8Pgkv1nrW2us7O+HNguc7AAAAAAAAAABAF5DkHQAAAAAAAACgFRRgAQAAAAAAlFniW2YAAAAAAAAAAAAAAAAtUoAFAAAAAADQRmneAQAAAAAASpS60AQAAAAAAAA6GAVYAAAAAAAAZZfkHQAAAAAA6IaSxN0kAAAAAAAA0HkowAIAAAAAAGijNM07AQAAAABAadJwoQkAAAAAAAB0LAqwAAAAAAAAAAAAAAC6gCTvAAAAAAAAAACtoAALAAAAAACgjdJIm5xPfMsMAAAAAOhg0qavMwEAAAAAAAByowALAAAAAAAAAAAAAAAAAAAAAACATCnAAgAAAAAAKLMk7wAAAAAAQLeUuJwEAAAAAAAAOhEFWAAAAAAAAG2UpnknAAAAAAAojetMAAAAAAAAoKNRgAUAAAAAAAAAAAAA0AUkkeQdAQAAAAAAAKBkCrAAAAAAAADKLPEdMwAAAACgg0nTNO8IAAAAAAAAAEUUYAEAAAAAAAAAAAAAdAHK+QEAAAAAAIDORAEWAAAAAABAmSXhW2YAAAAAAAAAAAAAAAAtUYAFAAAAAADQRmma5h0BAAAAAKAkbjMBAAAAAACAjkYBFgAAAAAAQJklSd4JAAAAAAAAAAAAAAAAOjYFWAAAAAAAAG2U5h0AAAAAAKBEqQtNAAAAAAAAoINRgAUAAAAAAAAAAAAA0AUkSd4JAAAAAAAAAEqnAAsAAAAAAKDMfMkMAAAAAAAAAAAAAACgZQqwAAAAAAAA2ihN804AAAAAAFAi95kAAAAAAABAB6MACwAAAAAAoMySSPKOAAAAAAB0Q+4mAQAAAAAAgM5EARYAAAAAAEAbpZHmHQEAAAAAoCTuMwEAAAAAAICORgEWAAAAAAAAAAAAAEAXkCR5JwAAAAAAAAAonQIsAAAAAACANkrTZj7wJTMAAAAAoINp9j4TAAAAAAAAICcKsAAAAAAAAAAAAAAAAAAAAAAAAMiUAiwAAAAAAIAyS/IOAAAAAAB0S4nLSQAAAAAAAKATUYAFAAAAAADQRmneAQAAAAAASuQ+EwAAAAAAAOhoFGABAAAAAAAAAAAAAHQBSSR5RwAAAAAAAAAomQIsAAAAAACANkrTpueTxJfMAAAAAICOJW3uQhMAAAAAAAAgJwqwAAAAAAAAAAAAAAAAAAAAAAAAyJQCLAAAAAAAgDJL8g4AAAAAAHRLictJAAAAAAAAoBNRgAUAAAAAANBmad4BAAAAAABK4jYTAAAAAAAA6GgUYAEAAAAAAAAAAAAAdAFJ3gEAAAAAAAAAWkEBFgAAAAAAQBuladPziW+ZAQAAAAAdTHP3mQAAAAAAAAB5UYAFAAAAAAAAAAAAAAAAAAAAAABAphRgAQAAAAAAlFmSdwAAAAAAoFtKXE4CAAAAAAAAnYgCLAAAAAAAgDZK07wTAAAAAACUJg0XmgAAAAAAAEDHogALAAAAAACgzJIkyTsCAAAAANAtuZsEAAAAAAAAOg8FWAAAAAAAAG2URpp3BAAAAACA0rjOBAAAAAAAADoYBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAABAmSV5BwAAAAAAuqXE5SQAAAAAAADQiSjAAgAAAAAAaKM0zTsBAAAAAEBpXGcCAAAAAAAAHY0CLAAAAAAAgHJL8g4AAAAAAHRHriYBAAAAAACAzkQBFgAAAAAAQBuleQcAAAAAAChR6kITAAAAAAAA6GAUYAEAAAAAAAAAAAAAdAFJkuQdAQAAAAAAAKBkCrAAAAAAAADaKE2bnk/Cl8wAAAAAgI4ljWYuNAEAAAAAAAByogALAAAAAAAAAAAAAAAAAAAAAACATCnAAgAAAAAAKLMkyTsBAAAAANAduZoEAAAAAAAAOhMFWAAAAAAAAG2URpp3BAAAAACAkqSuMwEAAAAAAIAORgEWAAAAAAAAAAAAAEAXkCR5JwAAAAAAAAAonQIsAAAAAACAtkqbnvYdMwAAAACgo2nmOhMAAAAAAAAgNwqwAAAAAAAAAAAAAAAAAAAAAAAAyJQCLAAAAAAAgDJLkrwTAAAAAADdURIuJwEAAAAAAIDOQwEWAAAAAABAG6V5BwAAAAAAKFGautEEAAAAAAAAOhYFWAAAAAAAAGWWRJJ3BAAAAACgG0pcTQIAAAAAAACdSM+8A0CeVq1aFX//+99j3bp18fHHH8eQIUPimGOOiUmTJkWvXr1yzbZ06dKor6+PtWvXRkTEsGHDYsyYMXHSSSeV9ZwPP/wwampqYu3atbFx48YYPHhwDBs2LCZNmhSHHXZYWc8CAAAAAOhq0jTNOwIAAAAAQElcZwIAAAAAAAAdjQIsuqXHHnss5s+fH4sWLWry88rKypg2bVrceOONMXjw4Mxy7d69O26//fb41a9+FStWrGhyzahRo2LmzJlx1VVXHVBJ17Jly+LGG2+Mp556Knbt2vWpz/v06RPnnHNOzJ07N0488cQ2nwMAAAAAAAAAAAAAAAAAAAAAAPvqkXcAyNLHH38c3/jGN+KCCy5otvwqImLz5s3xy1/+MsaPHx8LFy7MJFt9fX1MmDAhrrnmmmbLryIiGhoaYs6cOTFx4sRoaGho01m33HJLVFdXxxNPPNFk+VVExM6dO+OJJ56I6urq+MlPftKmcwAAAAAAuqskyTsBAAAAAAAAAAAAAABAx9Yz7wCQlcbGxpg2bVo89dRTRfOHH354nHTSSXHooYfGihUrYtmyZZGmaUREvP/++zFlypR47rnn4rTTTmu3bP/617/irLPOitWrVxfNjxo1KsaNGxdpmkZtbW1RMdaSJUti8uTJ8dprr0VVVVXJZ910001x3XXXFc317ds3vvCFL8SQIUNi3bp18de//jU++eSTiIjYtWtXzJ49O5IkiR/84AcH8FMCAAAAAHQ9ad4BAAAAAAAAAAAAAAAAOqkeeQeArMyZM6eo/KpXr15x9913x3vvvRcLFy6MRx55JJYsWRLLly+PiRMnFtbt3Lkzpk6dGuvXr2+XXHv37o2pU6cWlV8NGTIkFi5cGPX19fHEE0/EH/7wh2hoaIinn346jjzyyMK6VatWxXnnnVco7NqfP/7xj3H99dcXzV122WWxZs2aeOmll+Khhx6Kv/zlL7FmzZqYOXNm0brZs2fHM888cwA/KQAAAABA95EkeScAAAAAALojd5MAAAAAAABAZ6IAi25h5cqVcddddxXNPfroo/Hd7343evfuXTR//PHHx5///OeiEqxNmzbFvHnz2iXbAw88EIsXLy6MKysro6amJiZPnvyptWeffXbU1NTEwIEDC3M1NTXx8MMP7/ecxsbG+P73v19UljVr1qy45557YvDgwUVrDz/88Lj33nvjyiuvLMylaRpXX311NDY2turnAwAAAADoykr8/QQAAAAAALlznwkAAAAAAAB0NAqw6BbmzZsXu3fvLowvueSSmDJlSrPr+/btGwsWLCgqx/r1r38dK1euLGuuxsbGmDt3btHc/Pnz49hjj212z/Dhw2P+/PlFc9dff33s3bu3xbPuu+++qKurK4zHjh0bN998c4t7brnllhg7dmxh/NZbb8UDDzzQ4h4AAAAAAAAAAAAAAAAAAAAAANgfBVh0eTt27IjHHnusaG727Nn73TdmzJiYOnVqYbxnz5548MEHy5rtlVdeiVWrVhXGw4YNi4svvni/+775zW/GsGHDCuMVK1ZETU1Ni3vuu+++ovGsWbOiT58+Le7p06dPXHHFFS0+BwAAAACApiR5BwAAAAAAuqEkcTcJAAAAAAAAdB4KsOjyFi5cGNu3by+MJ06cGJ/5zGdK2nvppZcWjX//+9+XNdvjjz9eNJ4+fXpUVFTsd19FRcWnirJayrZp06Z4+eWXC+PevXvHhRdeWFLGiy66KHr16lUYv/TSS7F58+aS9gIAAAAAdHVp3gEAAAAAAEqUutEEAAAAAAAAOhgFWHR5zzzzTNH4zDPPLHnv6aefHj179iyMly1bFu+//365oh1Qtn3XPv30082uffbZZ6OxsbEwPuWUU6J///4lnTNgwIA4+eSTC+M9e/bEs88+W3JOAAAAAIDuKEnyTgAAAAAAdEeuJgEAAAAAAIDORAEWXd7y5cuLxhMnTix5b79+/eKEE04omqutrS1Lrp07d0ZDQ0PR3IQJE0reP2nSpKJxfX197Nq1q8m1B/IOmjqrXO8AAAAAAKCzS9M07wgAAAAAACVxnQkAAAAAAAB0NAqw6PLefvvtovGoUaNatX/kyJFF47feeuuAM0VE1NXVRWNjY2FcVVUVAwYMKHn/gAEDYvDgwYVxY2NjvPPOO02u3TdzR3kHAAAAAAAAAAAAAAAAAAAAAAB0Tz3zDgDtafPmzbF58+aiuaOPPrpVz9h3fX19/QHniohoaGho8ZxSHH300bFx48bCuL6+PsaPH1/2s9rrHZTDjt17YvuuPXnHAAAAAAC6qca9aZPzScY5AAAAAAAiIpIWLid37G70fy4BAAAAAACgzHbs9m9wB0IBFl3ahx9+WDQ++OCDo1+/fq16RlVVVdH4o48+OuBcEZ/Otu85pSg124Ge1V7voBz+992vRu/D38s7BgAAAAAAAAAAAECHds5dL+cdAQAAAAAAALqcXR+szjtCp6YAiy7t448/Lhr37du31c/Yd8/WrVsPKNN/ZJntQM9qr3ewYcOG+OCDD1q1p6GhoSxnAwAAAAC0pyTJOwEAAAAA0B0l4XISAAAAAAAA6DwUYNGl7Vv8dNBBB7X6GfuWP+37zLbKMtuBntVe7+AXv/hFzJs3ryzPAgAAAAAAAAAAAAAAAAAAAACg8+iRdwDIUpK0/rdatWVPW2SZrbX7snoHAAAAAABdxZBD++5/EQAAAABAmR12cK/o09N/EQcAAAAAAAA6B/+6SZd2yCGHFI137NjR6mfsu2ffZ7ZVltkO9Kz2egcAAAAAAF1RRY8kvvSZqrxjAAAAAADd0EG9KuJ/jXU/CQAAAAAAAHQOPfMOAO1JAdb/n9+yZUubz2qvd/Cd73wnLrjgglbtaWhoiKlTp5blfAAAAACAcht4cK/48dQT4rghA/KOAgAAAAB0U/91wWdj++7GeLn+g0jTvNMAAAAAAAAANE8BFl3aoYceWjTevn17bNu2Lfr161fyMzZs2FA0Puyww9ol2wcffNDqZ5Sa7dBDD41//vOfbT6rvd5BVVVVVFUd2G8Zu/sbJ8WosceVJQ8AAAAAwIE4qFdFjK46JHr0SPKOAgAAAAB0Y/0P6hX3/Z//GZu37Yp/bt6edxwAAAAAAADo0hrqKuP8/5t3is5LARZd2qBBg2LgwIGxZcuWwtyaNWviuONKL0xavXp10Xj06NFlybbvc/Y9pxSlZhs9enQsX768zWe11zsoh88MGRDj/kd5CrkAAAAAAAAAAAAAuorKfr2jsl/vvGMAAAAAAABAl9bz3wPyjtCp9cg7ALS3fcuuGhoaWrV/5cqVLT6vrcaOHRsVFRWF8YYNG2Lr1q0l7//3v/8dGzduLIwrKiqaLabqqO8AAAAAAAAAAAAAAAAAAAAAAIDuSQEWXd748eOLxosWLSp577Zt2+KNN95o8Xlt1adPnxg5cmSbs9XU1BSNR48eHX369Gly7YG8g4iIV199tcXnAQAAAAAAAAAAAAAAAAAAAABAayjAoss7++yzi8YvvvhiyXtffvnl2LNnT2F80kknxRFHHFGuaAeUbd+155xzTrNrzzrrrKioqCiMlyxZElu3bi3pnK1bt8bSpUsL4549e8ZZZ51Vck4AAAAAAAAAAAAAAAAAAAAAANiXAiy6vK985SvRt2/fwnjRokXxj3/8o6S9CxYsKBqfd9555Yz2qef99re/jcbGxv3ua2xsjPvvv7/kbIMHD47TTjutMN61a1c8+OCDJWV84IEHYvfu3YXxGWecEZWVlSXtBQAAAAAAAAAAAAAAAAAAAACApijAoss7+OCD4/zzzy+au/XWW/e775133onHH3+8MO7Zs2dceOGFZc12+umnx/Dhwwvj995771PFVk25//77Y+3atYXxyJEj49RTT21xz/Tp04vGd9xxR+zcubPFPTt37ow777yzaG7GjBn7zQcAAAAAAAAAAAAAAAAAAAAAAC1RgEW3cMMNN0SvXr0K4wULFsSTTz7Z7PpPPvkkLr300ti1a1dh7lvf+laMHDmyxXOSJCn68+KLL7a4vqKiIubNm1c0d9VVV8W7777b7J533303Zs2aVTT34x//OHr0aPmv84wZM2Ls2LGFcV1dXVx77bUt7rnmmmuirq6uMD7++OPjoosuanEPAAAAAAAAAAAAAAAAAAAAAADsjwIsuoURI0bEFVdcUTR3/vnnx89+9rOikquIiLfffju+/OUvR01NTWFu0KBBMXfu3HbJdtFFF0V1dXVhvHnz5pg0aVL86U9/+tTahQsXxsSJE2PLli2FuUmTJsW0adP2e05FRUXcdtttkSRJYW7+/Plx+eWXx6ZNm4rWbty4MS677LK44447CnNJksTtt98eFRUVrfr5AAAAAAAAAAAAAAAAAAAAAABgX0mapmneISALjY2N8bWvfS2efvrpovmqqqo4+eSTo3///rFy5cpYunRp/Pe/Fr17947nnnsuTj/99P2e8d/LpSIiXnjhhTjzzDP3u2/9+vUxYcKEWLNmTdH86NGjY9y4cZGmadTW1kZDQ0PR58cee2y89tprccQRR+z3jP+46aab4rrrriua69u3b1RXV8eRRx4Z69evj9dffz127NhRtObWW2+NH/7whyWf015qa2tj/PjxhfHy5ctj3LhxOSYCAAAAAAAAAAAAAAAAAAAAALojXSgHpmfeASArFRUV8cgjj8TMmTPj4YcfLsxv2LAhnnnmmSb3VFVVxW9+85uSyq8OxJAhQ+LZZ5+Nr3/967Fs2bLCfH19fdTX1ze55+STT46HH364VeVXERHXXnttJEkSc+fOjd27d0dExI4dO+LFF19scn2vXr3iRz/6UYcovwIAAAAAAAAAAAAAAAAAAAAAoGvokXcAyNIhhxwSDz30UDz66KMxYcKEZtdVVlbGt7/97Vi+fHmcffbZmWQbM2ZMLF68OG6++eYYMWJEs+tGjhwZN998c7z22msxatSoNp11zTXXxOLFi2PKlCnRu3fvJtf07t07pkyZEq+//nrMnj27TecAAAAAAAAAAAAAAAAAAAAAAEBTkjRN07xDQF5WrVoVS5cujXXr1sW2bdviyCOPjGOOOSZOPfXUZouhsrJkyZJ45513Yt26dRERMXTo0BgzZkyccsopZT1ny5YtUVNTE2vXro1NmzbFoEGDYtiwYTFp0qQYOHBgWc8qh9ra2hg/fnxhvHz58hg3blyOiQAAAAAAAAAAAAAAAAAAAACA7kgXyoHpmXcAyNPw4cNj+PDhecdo0imnnFL2squmDBw4MM4999x2PwcAAAAAAAAAAAAAAAAAAAAAAP6jR94BAAAAAAAAAAAAAAAAAAAAAAAA6F4UYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAD9DaWaAAAgAElEQVQAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJApBVgAAAAAAAAAAAAAAAAAAAAAAABkSgEWAAAAAAAAAAAAAAAAAAAAAAAAmVKABQAAAAAAAAAAAAAAAAAAAAAAQKYUYAEAAAAAAAAAAAAAAAAAAAAAAJCpnnkHgLy8/fbbUVtbG2vXro1du3bF0KFDY8SIEVFdXR09euTTDbd169Z46623oq6uLjZt2hTbt2+PAQMGxKBBg+LEE0+M4447LpIkySUbAAAAAAAAAAAAAAAAAAAAAACUiwIsupU0TePee++Nn//85/HGG280uWbo0KExffr0uP7666Nfv37tmmfv3r3x6quvxpNPPhkvvPBCLFu2LPbu3dvs+srKypg+fXp873vfixEjRrTqrBtuuCHmzZvX5qwzZsyIBQsWtHk/AAAAAAAAAAAAAAAAAAAAAAD8R4+8A0BW3n///Zg8eXJcfvnlzZZfRUSsW7cubrnllvjc5z4Xf/vb39otz+rVq+Ooo46KM844I2677bZYsmRJi+VXERGbN2+OO++8M8aNGxfz589vt2wAAAAAAAAAAAAAAAAAAAAAANCeeuYdALKwbdu2+OpXvxpLly4tmj/qqKPis5/9bBx00EFRV1cXtbW1hc9WrFgRkydPjkWLFsXYsWPLnmnr1q2xfv36T81XVFTEuHHjYujQoVFZWRkfffRRLFu2LNatW1dY88knn8TVV18dq1evjrvuuqvs2QAAAAAAAAAAAAAAAAAAAAAAoD0pwKJbuOSSS4rKr/r37x/33HNPTJs2LXr06FGYX7x4ccyYMSPq6uoiImLLli1x7rnnxptvvhl9+/Ztt3x9+vSJKVOmxMUXXxxf/OIXY8CAAZ9a8/zzz8eVV14Zb775ZmHupz/9aZxwwgkxc+bMVp/5u9/9LiZMmFDy+kMOOaTVZwAAAAAAAAAAAAAAAAAAAAAAQFMUYNHlvfLKK/HYY48Vxr17947nn38+Pv/5z39qbXV1dbz66qtRXV0dK1asiIiIFStWxF133RVz5swpe7b+/fvHFVdcEbNmzYrKysoW137pS1+KxYsXxznnnBMvvfRSYX7OnDn/j517jc26Pv8HfrUg2nIoh1qh4CgwRRBBHeIJJwYBp5ODEd2YE52JGmdwbokQMcPD4mEPFtHpdOpCZAsmUzFuKmgVZSpDUQcrTKCMIhaFWi3HKkr7e/L3/u+uHHrb3t9by+uV9MF13Z/D9f0k8PAdP/rRjzIOqOrZs2eUlZV9nbEBAAAAAAAAAAAAAAAAAAAAAKBF8nM9AGTbzJkz0+obb7xxr+FXX+rRo0c8/PDDab277rortm3b1qpz9e3bN6qqquK22247YPjVlwoKCmLevHlpYVe1tbXx3HPPtepsAAAAAAAAAAAAAAAAAAAAAACQTQKwaNM2bNgQixcvTtUFBQUxbdq0A+4bNWpUjBgxIlXX1dXF008/3aqzde7cudnBV/+rV69eccEFF6T1Fi1a1FpjAQAAAAAAAAAAAAAAAAAAAABA1gnAok2bP39+Wj1x4sTo1q1bs/ZefvnlafWTTz7ZanO11AknnJBWb9q0KUeTAAAAAAAAAAAAAAAAAAAAAABA5gRg0aYtWLAgrR41alSz9zZd+/zzz0dDQ0MrTNVy7du3T6t3796do0kAAAAAAAAAAAAAAAAAAAAAACBzArBo0yoqKtLqU089tdl7jznmmOjevXuq3rlzZ1RVVbXWaC1SWVmZVvfq1StHkwAAAAAAAAAAAAAAAAAAAAAAQOYEYNFmbdu2Laqrq9N6AwYMyOiM/v37p9WrVq1q8Vwt1dDQEPPnz0/rjRgxIuNzHnzwwTj77LOjd+/ecdhhh0Xnzp2jrKwszjzzzJg5c2b84x//aK2RAQAAAAAAAAAAAAAAAAAAAAAgjQAs2qzKysq0uri4OAoLCzM64zvf+U5avXbt2hbP1VJPPPFEvPfee6m6Xbt2cf7552d8zmOPPRYvvvhibNq0KT777LPYsWNHbNiwIRYvXhy33357fP/734+TTjopysvLW3N8AAAAAAAAAAAAAAAAAAAAAAAQgEXbVVdXl1aXlJRkfEbTPVu3bm3RTC21devW+OUvf5nW+8lPfhKlpaVZuW/ZsmUxduzYmDlzZjQ2NmblDgAAAAAAAAAAAAAAAAAAAAAADj7tcz0AZMuOHTvS6oKCgozPaLpn+/btLZqpJRobG+Oyyy6L999/P9UrKiqKO+64I6NzevfuHeeee26MGDEiBg0aFN27d4/8/Pyora2Nt99+O/7+97/HwoUL0+69/fbbo6GhIeO7DmTLli1RU1OT0Z7KyspWnQEAAAAAAAAAAAAAAAAAAAAAgOQJwKLNahqAddhhh2V8RtMArKZnJmnWrFnx1FNPpfXuv//+KC0tbdb+ESNGxMKFC2PMmDGRl5e31zWnnXZaXHvttbFs2bKYMmVKrF27NvXbnXfeGaecckpMmDDh639EE/fff3/ccsstrXYeAAAAAAAAAAAAAAAAAAAAAADfDvm5HoC269prr428vLys/918883NmmdfoU+tvScbHnnkkbjtttvSetdcc01MmTKl2Wece+65MXbs2GZ90/Dhw+Of//xnHH300Wn9GTNmxJ49e5p9JwAAAAAAAAAAAAAAAAAAAAAA7I0ALNqsTp06pdX19fUZn9F0T9Mzk/DEE0/EVVddldabPHly3HPPPVm9t3v37jFv3ry0wKx33303Fi1alNV7AQAAAAAAAAAAAAAAAAAAAABo+9rnegDIlrYQgPXMM8/ElClTYs+ePane+eefH3/5y1+iXbt2Wb//xBNPjLFjx8bChQtTvQULFsTZZ5/dKudfc801MXny5Iz2VFZWxsSJE1vlfgAAAAAAAAAAAAAAAAAAAAAAckMAFlkzYcKE6NOnT9bvGTly5F77RUVFaXVNTU3GZ2/ZsiWt7tq1a8ZnfF3l5eVx4YUXxu7du1O9cePGxV//+tc45JBDEpvjnHPOSQvAWrFiRaudXVJSEiUlJa12HgAAAAAAAAAAAAAAAAAAAAAA3w4CsMiaMWPGxJgxY3J2/1FHHZVW19TUxK5du6KwsLDZZ2zYsGG/Z2bLK6+8EhMmTIhPP/001TvrrLNi/vz5ceihhyYyw5fKysrS6q8TJAYAAAAAAAAAAAAAAAAAAAAAAP8rP9cDQLZ06dIlSktL03rr1q3L6Iz169en1YMGDWrxXAfy2muvxQ9/+MPYtWtXqjdy5Mj429/+FgUFBVm/v6mmd9bX1yc+AwAAAAAAAAAAAAAAAAAAAAAAbYsALNq0IUOGpNVLlixp9t533303amtrU3VhYWH069ev1Wbbm6VLl8YPfvCD2LFjR6p3yimnxLPPPhsdO3bM6t378tFHH6XVxcXFOZkDAAAAAAAAAAAAAAAAAAAAAIC2QwAWbdo555yTVr/88svN3tt07bhx4yI/P3v/ZN56660YN25cbN++PdUbPnx4LFiwIDp37py1ew9k6dKlaXVpaWmOJgEAAAAAAAAAAAAAAAAAAAAAoK0QgEWbNmnSpLT6qaeeirq6umbtnTNnzn7Pak3Lly+PsWPHxtatW1O9448/Pp5//vkoKirK2r0H8umnn8aTTz6Z1hs1alRuhgEAAAAAAAAAAAAAAAAAAAAAoM0QgEWbVlZWFmeccUaqrq+vj9mzZx9w3yuvvBJLly5N1V27do3x48dnZcZVq1bFmDFj4uOPP071jjvuuCgvL49u3bpl5c7muuuuu6K6ujpVt2vXLs4777wcTgQAAAAAAAAAAAAAAAAAAAAAQFsgAIs27/bbb/9KvWzZsn2u//jjj+OKK65I602fPj2Kior2e09VVVXk5eWl/VVVVe13z9q1a2P06NFRU1OT6g0aNCjKy8ujR48e+92biblz58bmzZsz2vPQQw/FLbfckta77LLLom/fvq02FwAAAAAAAAAAAAAAAAAAAAAABycBWLR5I0eOjAsvvDBV7969O0aPHh2PPfZYNDQ0pK1dunRpnHbaabFu3bpUb8CAATFt2rRWn+u9996L0aNHx4cffpjqHXHEEfGnP/0pdu3aFVVVVc3+e//99/d71yOPPBL9+vWLqVOnxjPPPBM7d+7c59ply5bFBRdcEFdeeWU0Njam+r17947f/OY3Lf9wAAAAAAAAAAAAAAAAAAAAAAAOeu1zPQAkYc6cObFu3bp45513IiJi27Zt8eMf/zhuuOGGGDZsWHTo0CHWrFkTFRUVafu6desWzzzzTBQWFrb6TC+99FJs3Lgxrbd58+Y49dRTMz6rb9++UVVVtd819fX18eijj8ajjz4a+fn5cdRRR0VZWVkUFRVFu3btora2NpYvXx6bN2/+yt7u3bvHggULomfPnhnPBgAAAAAAAAAAAAAAAAAAAAAATQnA4qDQsWPHePbZZ+OSSy6JF198MdXfuHHjV0KovjRgwICYN29eDBw4MKkxE9PQ0BCrV6+O1atXH3Dt6NGjY86cOdGnT58EJgMAAAAAAAAAAAAAAAAAAAAA4GCQn+sBICk9e/aMF154IR544IE47rjj9rmuV69eMX369Fi+fHmcdNJJCU6YPdddd11MmTIl+vbt26z1HTt2jEmTJkV5eXmUl5cLvwIAAAAAAAAAAAAAAAAAAAAAoFXlNTY2NuZ6CMiFVatWRUVFRWzatCl2794dpaWl0b9//zjllFMiP7/tZsPV1dXFypUrY+PGjbF58+bYtWtXNDQ0RNeuXaNbt24xaNCgGDp0aLRr1y7Xo+7VypUrY8iQIam6oqIijj322BxOBAAAAAAAAAAAAAAAAAAAAAAcjGShtEz7XA8AuTJ48OAYPHhwrsdIXNeuXeP000/P9RgAAAAAAAAAAAAAAAAAAAAAABzE8nM9AAAAAAAAAAAAAAAAAAAAAAAAAAcXAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiRKABQAAAAAAAAAAAAAAAAAAAAAAQKIEYAEAAAAAAAAAAAAAAAAAAAAAAJAoAVgAAAAAAAAAAAAAAAAAAAAAAAAkSgAWAAAAAAAAAAAAAAAAAAAAAAAAiWqf6wEgV/7zn//EypUro7q6Onbv3h2lpaXRv3//OPnkkyM//+DJhvvwww/jzTffjOrq6qirq4sjjjgi+vTpE6effnoUFhbmejwAAAAAAAAAAAAAAAAAAAAAANogAVgcVBobG+Ohhx6K++67L1asWLHXNaWlpXHppZfGTTfdFB07dsz6TGVlZbFhw4avvX/RokUxatSojPe99NJLcccdd8SiRYtiz549X/m9U6dOMXHixLj11lujX79+X3s+AAAAAAAAAAAAAAAAAAAAAABoKj/XA0BSNm/eHGPHjo2rrrpqn+FXERGbNm2KO++8M4YNGxbLli1LcMJkfPHFF/GLX/wizj777CgvL99r+FVExI4dO+LPf/5zDBs2LObOnZvwlAAAAAAAAAAAAAAAAAAAAAAAtGXtcz0AJGHnzp1x7rnnxttvv53W79OnTwwdOjQOO+ywWL16daxcuTL127p162Ls2LGxZMmSGDhwYNIjZ83Pf/7z+OMf/5jWKyoqiu9973tRXFwc7733Xrz55pupYKzt27fH1KlT49BDD42LLrooFyMDAAAAAAAAAAAAAAAAAAAAANDGCMDioHDZZZelhV917tw5Hnzwwbj44osjPz8/1V+6dGlMnTo1Vq9eHRERn3zySZx33nnx73//OwoKCrI+Z+/evePVV1/NaE/Pnj2bvfa+++5LC7/Ky8uLm266KW644Ybo1KlTqr9hw4aYNm1aPP300xER0djYGFOnTo1jjjkmhg4dmtF8AAAAAAAAAAAAAAAAAAAAAADQlAAs2rxXX301Hn/88VTdoUOHeOmll2L48OFfWXvyySfHa6+9FieffHKsW7cuIiLWrVsXs2fPjhkzZmR91vbt20dZWVlWzt66dWv8+te/TuvdfffdMW3atK+s7du3b8yfPz8uuuiieOKJJyIi4tNPP40bbrghFixYkJX5AAAAAAAAAAAAAAAAAAAAAAA4eOTnegDItpkzZ6bVN954417Dr77Uo0ePePjhh9N6d911V2zbti0r8yXld7/7XXz88cep+qyzztpr+NWX8vPz44EHHogePXqkegsXLozFixdndU4AAAAAAAAAAAAAAAAAAAAAANo+AVi0aRs2bEgLbCooKNhv6NOXRo0aFSNGjEjVdXV18fTTT2dlxqTMnTs3rZ4+ffoB9xQXF8cVV1yR1nv00UdbdS4AAAAAAAAAAAAAAAAAAAAAAA4+ArBo0+bPn59WT5w4Mbp169asvZdffnla/eSTT7baXEn717/+FevXr0/VpaWlMXbs2GbtbfoOTz/9dOzZs6dV5wMAAAAAAAAAAAAAAAAAAAAA4OAiAIs2bcGCBWn1qFGjmr236drnn38+GhoaWmGq5DV9hzPPPDPy8vKatfeYY46Jnj17puqamppYtmxZq84HAAAAAAAAAAAAAAAAAAAAAMDBRQAWbVpFRUVafeqppzZ77zHHHBPdu3dP1Tt37oyqqqrWGi1RLXmHva1fuXJli2cCAAAAAAAAAAAAAAAAAAAAAODgJQCLNmvbtm1RXV2d1hswYEBGZ/Tv3z+tXrVqVYvn2p9t27bF1VdfHccff3wUFxdHhw4dori4OAYOHBiTJ0+Oe+655yvf1BxN5/7ud7+b0f6m75btdwAAAAAAAAAAAAAAAAAAAAAAoG0TgEWbVVlZmVYXFxdHYWFhRmd85zvfSavXrl3b4rn255NPPokHH3wwli9fHrW1tfH5559HbW1trFmzJh5//PG47rrron///nH55ZfHli1bmn1u07do+l0HkvQ7AAAAAAAAAAAAAAAAAAAAAADQtgnAos2qq6tLq0tKSjI+o+merVu3tmim1rB79+6YM2dOHH/88bF48eIDrm9oaIjt27en9TJ9i2/iOwAAAAAAAAAAAAAAAAAAAAAA8O3VPtcDQLbs2LEjrS4oKMj4jKZ7mgZJtZb8/PwYPnx4jBs3LoYNGxb9+vWLLl26xK5du2LTpk3x+uuvx9y5c6Oqqiq154MPPohzzz03Fi9eHCeeeOI+z276DhGZv0W23mHLli1RU1OT0Z7KyspWuRsAAAAAAAAAAAAAAAAAAAAAgNwRgEWb1TT46bDDDsv4jKbBT3sLk2qp6667LiZNmhRlZWV7/X3o0KFxzjnnxKxZs+Kee+6J6dOnx+effx4RETt37ozx48fHmjVrorCwcK/79zZzpm+RrXe4//7745ZbbmmVswAAAAAAAAAAAAAAAAAAAAAA+PbIz/UAtF3XXntt5OXlZf3v5ptvbtY8eXl5GX/D19mTqeuvv36f4Vf/q127dnH99dfH448/Hvn5//+fbnV1ddx9990Z3ZnpdyXxDgAAAAAAAAAAAAAAAAAAAAAAHDwEYNFmderUKa2ur6/P+Iyme5qemQvjx4+Pa6+9Nq33hz/8YZ/r9zZzpm/xTXwHAAAAAAAAAAAAAAAAAAAAAAC+vdrnegDIlrYagBURMWPGjLj33nujsbExIiLef//9qKioiCFDhnxl7b4CsDL5lmy9wzXXXBOTJ0/OaE9lZWVMnDixVe4HAAAAAAAAAAAAAAAAAAAAACA3BGCRNRMmTIg+ffpk/Z6RI0futV9UVJRW19TUZHz2li1b0uquXbtmfEY29OrVK4YOHRrLly9P9VasWLHXAKz8/Pzo1KlT7NixI9WrqamJww8/vNn3ZesdSkpKoqSkpFXOAgAAAAAAAAAAAAAAAAAAAADg20MAFlkzZsyYGDNmTM7uP+qoo9Lqmpqa2LVrVxQWFjb7jA0bNuz3zFwqKytLC8DaX8DXUUcdFe+8806q3rBhQwwePLjZd32T3wEAAAAAAAAAAAAAAAAAAAAAgG+f/FwPANnSpUuXKC0tTeutW7cuozPWr1+fVg8aNKjFc7WWgoKCtLq+vn6fa5vOXVlZmdFd//3vf/d7HgAAAAAAAAAAAAAAAAAAAAAAZEIAFm3akCFD0uolS5Y0e++7774btbW1qbqwsDD69evXarO11EcffZRWFxcX73NtS94hIuL111/f73kAAAAAAAAAAAAAAAAAAAAAAJAJAVi0aeecc05a/fLLLzd7b9O148aNi/z8b8Y/mT179sRbb72V1istLd3n+qbvsHjx4mhsbGzWXe+++258+OGHqbq4uDiGDx+ewbQAAAAAAAAAAAAAAAAAAAAAAJDum5HmA1kyadKktPqpp56Kurq6Zu2dM2fOfs/Kpeeeey4++eSTVN2+ffsYOXLkPtefcMIJUVZWlqqrq6vj+eefb9ZdTd9h/Pjx0a5du4zmBQAAAAAAAAAAAAAAAAAAAACA/yUAizatrGnBWCYAACAASURBVKwszjjjjFRdX18fs2fPPuC+V155JZYuXZqqu3btGuPHj8/KjJnauXNnzJgxI603atSo6NKly373/fSnP02r77rrrgPeVVtbGw8//HBa79JLL23mpAAAAAAAAAAAAAAAAAAAAAAAsHcCsGjzbr/99q/Uy5Yt2+f6jz/+OK644oq03vTp06OoqGi/91RVVUVeXl7aX1VV1T7Xf/TRRzF37tzYs2fPgT/i/9m+fXtMnjw5Vq5cmdafNWvWAff+6le/iu7du6fqRYsWxb333rvP9Q0NDXH11VdHbW1tqjdu3Lg488wzmz0vAAAAAAAAAAAAAAAAAAAAAADsjQAs2ryRI0fGhRdemKp3794do0ePjsceeywaGhrS1i5dujROO+20WLduXao3YMCAmDZtWqvPtWPHjrj00kvj6KOPjptvvjlWrFjxlXm+9Omnn8ajjz4axx9/fDz33HNpv/3sZz+LkSNHHvC+oqKiuPXWW9N61113XcyaNSt27NiR1n/vvfdi0qRJ8fjjj6d6hx56aPz2t79t7ucBAAAAAAAAAAAAAAAAAAAAAMA+5TU2NjbmegjItp07d8YZZ5wR77zzTlr/yCOPjGHDhkWHDh1izZo1UVFRkfZ7t27dYsmSJTFw4MAD3lFVVRX9+vVL661fvz7Kysqavb5jx44xZMiQKCkpiS5dukR9fX188MEH8fbbb8dnn332lTPOO++8eOqpp6J9+/YHnO9LV155ZTz00ENpva5du8bw4cOjR48esXHjxnjjjTfiiy++SP2el5cX8+bNi4svvrjZ92TLypUrY8iQIam6oqIijj322BxOBAAAAAAAAAAAAAAAAAAAAAAcjGShtEzzU3PgW6xjx47x7LPPxiWXXBIvvvhiqr9x48bYuHHjXvcMGDAg5s2b16zwq9ayc+fOWLp06QHXHXLIITFr1qyYMWNGtGvXLqM77r///igoKIh77703vsy/q6uri/Ly8r2u79SpU/z+97//RoRfAQAAAAAAAAAAAAAAAAAAAADQNuTnegBISs+ePeOFF16IBx54II477rh9ruvVq1dMnz49li9fHieddFLW5jn88MPj1ltvjdGjR0fnzp2btefII4+MGTNmRGVlZcycOTPj8KuIiPbt28fs2bOjvLw8Ro8eHfn5e/9voGPHjnHJJZfEihUrYurUqRnfAwAAAAAAAAAAAAAAAAAAAAAA+5LX2NjYmOshIBdWrVoVFRUVsWnTpti9e3eUlpZG//7945RTTtlnKFS2NDY2xvr162Pt2rVRXV0dn3zySdTX10eHDh2iW7duUVJSEsOHD4/evXu3+t0ffPBBvPHGG1FdXR1bt26NkpKSOPLII+P000+Pjh07tvp9LbVy5coYMmRIqq6oqIhjjz02hxMBAAAAAAAAAAAAAAAAAAAAAAcjWSgt0z7XA0CuDB48OAYPHpzrMSIiIi8vL/r37x/9+/dP/O5evXrFhAkTEr8XAAAAAAAAAAAAAAAAAAAAAICDV36uBwAAAAAAAAAAAAAAAAAAAAAAAODgIgALAAAAAAAAAAAAAAAAAAAAAACARAnAAgAAAAAAAAAAAAAAAAAAAAAAIFECsAAAAAAAAAAAAAAAAAAAAAAAAEiUACwAAAAAAAAAAAAAAAAAAAAAAAASJQALAAAAAAAAAAAAAAAAAAAAAACARAnAAgAAAAAAAAAAAAAAAAAAAAAAIFECsAAAAAAAAAAAAAAAAAAAAAAAAEiUACwAAAAAAAAAAAAAAAAAAAAAAAASJQALAAAAAAAAAAAAAAAAAAAAAACARAnAAgAAAAAAAAAAAAAAAAAAAAAAIFECsAAAAAAAAAAAAAAAAAAAAAAAAEiUACwAAAAAAAAAAAAAAAAAAAAAAAASJQALAAAAAAAAAAAAAAAAAAAAAACARAnAAgAAAAAAAAAAAAAAAAAAAAAAIFECsAAAAAAAAAAAAAAAAAAAAAAAAEiUACwAAAAAAAAAAAAAAAAAAAAAAAASJQALAAAAAAAAAAAAAAAAAAAAAACARAnAAgAAAAAAAAAAAAAAAAAAAAAAIFECsAAAAAAAAAAAAAAAAAAAAAAAAEiUACwAAAAAAAAAAAAAAAAAAACA/2Pn3mO9rusHjr/O4SLnAAFHJAKMA0QIEmzFTbSglKODhWAag2RQh3ILh/3RgGQVOoe6NZctmpqcEGzgRpqWDhQESQcnTjq5JckRiGAhYYc7UZzz++M3v+uLIOd7Lu/vOjwe2/nj9T6fz/vzOkz/fQIAkJQAFgAAAAAAAAAAAAAAAAAAAAAAAEkJYAEAAAAAAAAAAAAAAAAAAAAAAJCUABYAAAAAAAAAAAAAAAAAAAAAAABJCWABAAAAAAAAAAAAAAAAAAAAAACQlAAWAAAAAAAAAAAAAAAAAAAAAAAASQlgAQAAAAAAAAAAAAAAAAAAAAAAkJQAFgAAAAAAAAAAAAAAAAAAAAAAAEkJYAEAAAAAAAAAAAAAAAAAAAAAAJCUABYAAAAAAAAAAAAAAAAAAAAAAABJCWABAAAAAAAAAAAAAAAAAAAAAACQlAAWAAAAAAAAAAAAAAAAAAAAAAAASQlgAQAAAAAAAAAAAAAAAAAAAAAAkJQAFgAAAAAAAAAAAAAAAAAAAAAAAEkJYAEAAAAAAAAAAAAAAAAAAAAAAJCUABYAAAAAAAAAAAAAAAAAAAAAAABJCWABAAAAAAAAAAAAAAAAAAAAAACQlAAWAAAAAAAAAAAAAAAAAAAAAAAASQlgAQAAAAAAAAAAAAAAAAAAAAAAkJQAFgAAAAAAAAAAAAAAAAAAAAAAAEkJYAEAAAAAAAAAAAAAAAAAAAAAAJCUABYAAAAAAAAAAAAAAAAAAAAAAABJCWABAAAAAAAAAAAAAAAAAAAAAACQlAAWAAAAAAAAAAAAAAAAAAAAAAAASQlgAQAAAAAAAAAAAAAAAAAAAAAAkJQAFgAAAAAAAAAAAAAAAAAAAAAAAEkJYAEAAAAAAAAAAAAAAAAAAAAAAJCUABYAAAAAAAAAAAAAAAAAAAAAAABJCWABAAAAAAAAAAAAAAAAAAAAAACQlAAWAAAAAAAAAAAAAAAAAAAAAAAASQlgAQAAAAAAAAAAAAAAAAAAAAAAkJQAFgAAAAAAAAAAAAAAAAAAAAAAAEkJYAEAAAAAAAAAAAAAAAAAAAAAAJCUABYAAAAAAAAAAAAAAAAAAAAAAABJCWABAAAAAAAAAAAAAAAAAAAAAACQlAAWAAAAAAAAAAAAAAAAAAAAAAAASQlgAQAAAAAAAAAAAAAAAAAAAAAAkJQAFgAAAAAAAAAAAAAAAAAAAAAAAEkJYAEAAAAAAAAAAAAAAAAAAAAAAJCUABYAAAAAAAAAAAAAAAAAAAAAAABJCWABAAAAAAAAAAAAAAAAAAAAAACQlAAWAAAAAAAAAAAAAAAAAAAAAAAASQlgAQAAAAAAAAAAAAAAAAAAAAAAkJQAFgAAAAAAAAAAAAAAAAAAAAAAAEkJYAEAAAAAAAAAAAAAAAAAAAAAAJCUABYAAAAAAAAAAAAAAAAAAAAAAABJCWABAAAAAAAAAAAAAAAAAAAAAACQlAAWAAAAAAAAAAAAAAAAAAAAAAAASQlgAQAAAAAAAAAAAAAAAAAAAAAAkJQAFgAAAAAAAAAAAAAAAAAAAAAAAEkJYAEAAAAAAAAAAAAAAAAAAAAAAJCUABYAAAAAAAAAAAAAAAAAAAAAAABJCWABAAAAAAAAAAAAAAAAAAAAAACQlAAWAAAAAAAAAAAAAAAAAAAAAAAASQlgAQAAAAAAAAAAAAAAAAAAAAAAkJQAFgAAAAAAAAAAAAAAAAAAAAAAAEkJYAEAAAAAAAAAAAAAAAAAAAAAAJCUABYAAAAAAAAAAAAAAAAAAAAAAABJCWABAAAAAAAAAAAAAAAAAAAAAACQlAAWAAAAAAAAAAAAAAAAAAAAAAAASQlgAQAAAAAAAAAAAAAAAAAAAAAAkJQAFgAAAAAAAAAAAAAAAAAAAAAAAEm1zvcCkC9//vOfY8eOHXHgwIE4e/Zs9OjRI/r27RsjR46MwkJtOAAAAAAAAAAAAAAAAAAAAAAAaC4qP1xW6urq4oknnoihQ4fGoEGD4o477ojvfe97MXfu3Ljzzjtj9OjRcfXVV8cPfvCDOHnyZLPvU1BQ0GQ/Cxcu/NhvLVy4sFH3z5w5s9n/PQAAAAAAAAAAAAAAAAAAAAAAuDwIYHHZOHToUJSVlcVdd90VW7duvehzBw8ejIceeiiGDh0aVVVVCTdsnKKionyvAAAAAAAAAAAAAAAAAAAAAAAA9dI63wtACidPnozx48fHm2++mXXeq1evGDJkSLRr1y527doVO3bsyPyuuro6ysrKYtOmTTFgwIDUK+fstttuy/cKAAAAAAAAAAAAAAAAAAAAAABQLwJYXBZmzpyZFb/q2LFjPP744zFlypQoLCzMnFdWVsaMGTNi165dERHxz3/+MyZMmBDbtm2LoqKiJt9rz549DXpv/vz58cwzz2TmMWPGRP/+/XO6Y8WKFTFq1Kh6P9+hQ4ec7gcAAAAAAAAAAAAAAAAAAAAAgIsRwKLFe/3112PVqlWZuW3btvHqq6/GsGHDPvLsyJEj44033oiRI0dGdXV1RERUV1fHo48+GvPnz2/y3UpLS3N+58yZM7FmzZqss/Ly8pzv6d69e4O+DwAAAAAAAAAAAAAAAAAAAAAAjVWY7wWguS1YsCBrvvfeey8Yv/rQlVdeGU8++WTW2cMPPxzHjh1rlv1y9Zvf/CZqamoyc6dOneL222/P40YAAAAAAAAAAAAAAAAAAAAAAJAbASxatH379sXGjRszc1FRUcyZM+eS740dOzZGjBiRmWtqauKFF15olh1zVVFRkTVPnTo1ioqK8rQNAAAAAAAAAAAAAAAAAAAAAADkTgCLFu25557LmidNmhRdunSp17vf/OY3s+Znn322yfZqqD179sT69euzzsrLy/O0DQAAAAAAAAAAAAAAAAAAAAAANIwAFi3a6tWrs+axY8fW+93zn3355Zejtra2CbZquIqKiqirq8vMQ4YMiWHDhuVxIwAAAAAAAAAAAAAAAAAAAAAAyJ0AFi3a9u3bs+brrruu3u9ec801UVJSkplPnjwZe/fubarVclZbWxtPPfVU1tmsWbPytA0AAAAAAAAAAAAAAAAAAAAAADScABYt1rFjx+LAgQNZZ/369cvpjr59+2bNO3fubPReDfXyyy/H/v37M/MVV1wR3/jGNxp83+OPPx433XRT9OzZM9q1axcdO3aM0tLSGDNmTCxYsCD+8Ic/NMXaAAAAAAAAAAAAAAAAAAAAAADwEQJYtFi7d+/Omrt27RrFxcU53fHpT386a3733XcbvVdDLVmyJGuePHlylJSUNPi+lStXxrp16+LgwYPxr3/9K06cOBH79u2LjRs3xqJFi+JLX/pSDB8+PNauXdvY1QEAAAAAAAAAAAAAAAAAAAAAIIsAFi1WTU1N1tytW7ec7zj/naNHjzZqp4Y6cuRIvPDCC1lns2bNavbvVlVVRVlZWSxYsCDq6uqa/XsAAAAAAAAAAAAAAAAAAAAAAFweWud7AWguJ06cyJqLiopyvuP8d44fP96onRpq+fLlcfbs2cxcWloaX/nKVxp0V8+ePWP8+PExYsSIGDhwYJSUlERhYWEcOXIk3nzzzfj9738fa9asyTxfV1cXixYtitra2njwwQcb/bf8t/fffz8OHz6c0zu7d+9u0h0AAAAAAAAAAAAAAAAAAAAAAEhPAIsW6/wAVrt27XK+4/wA1vl3plJRUZE1f+tb34qCgoKc7hgxYkSsWbMmxo0bd9F3R48eHXfffXdUVVXFtGnT4t1338387qGHHopRo0bFrbfemvsfcBG/+MUv4r777muy+wAAAAAAAAAAAAAAAAAAAAAA+N9QmO8FaLnuvvvuKCgoaPafhQsX1mufXINRDX2nqW3ZsiW2bduWmQsLC2PmzJk53zN+/PgoKyur1980bNiw2Lx5c3z2s5/NOp8/f36cO3cu528DAAAAAAAAAAAAAAAAAAAAAMB/E8CixerQoUPWfPr06ZzvOP+d8+9MYcmSJVlzWVlZXH311c3+3ZKSklixYkVWMOudd96J9evXN/u3AQAAAAAAAAAAAAAAAAAAAABo2VrnewFoLi0hgHX69OlYuXJl1tmsWbOSff/zn/98lJWVxZo1azJnq1evjptuuqlJ7v/ud78bd9xxR07v7N69OyZNmtQk3wcAAAAAAAAAAAAAAAAAAAAAID8EsGg2t956a/Tq1avZv3PDDTdc8LxTp05Z8+HDh3O++/3338+aO3funPMdjbFq1ao4evRoZr7qqqti4sSJSXe45ZZbsgJYW7dubbK7u3XrFt26dWuy+wAAAAAAAAAAAAAAAAAAAAAA+N8ggEWzGTduXIwbNy5v3+/fv3/WfPjw4Th16lQUFxfX+459+/Z97J3NbcmSJVnz9OnTo02bNkl3KC0tzZobEhIDAAAAAAAAAAAAAAAAAAAAAID/VpjvBaC5fOITn4gePXpknVVXV+d0x549e7LmgQMHNnqv+qquro6NGzdmnZWXlyf7/oeKioqy5tOnTyffAQAAAAAAAAAAAAAAAAAAAACAlkUAixZt8ODBWfOmTZvq/e4777wTR44cyczFxcXRp0+fJtvtUioqKqKuri4zX3fddTFo0KBk3//QP/7xj6y5a9euyXcAAAAAAAAAAAAAAAAAAAAAAKBlEcCiRbvllluy5g0bNtT73fOfvfnmm6OwMM3/MufOnYunnnoq66y8vDzJt89XWVmZNffo0SMvewAAAAAAAAAAAAAAAAAAAAAA0HIIYNGiTZ48OWv+7W9/GzU1NfV6d+nSpR97V3NavXp1HDhwIDO3b98+pkyZkuz7Hzpz5kw8++yzWWdjx45NvgcAAAAAAAAAAAAAAAAAAAAAAC2LABYtWmlpaXzxi1/MzKdPn45HH330ku+99tprUVlZmZk7d+4cEydObJYdL6SioiJrnjJlSnTo0CHZ9z/08MMPZ4W4WrVqFRMmTEi+BwAAAAAAAAAAAAAAAAAAAAAALYsAFi3eokWLPjJXVVVd9PkPPvggysvLs87mzZsXnTp1+tjv7N27NwoKCrJ+9u7dm/O+hw8fjt/97ndZZ+fvk6vly5fHoUOHcnrnl7/8Zdx3331ZZzNnzozevXs3ahcAAAAAAAAAAAAAAAAAAAAAABDAosW74YYb4vbbb8/MZ8+ejRtvvDFWrlwZtbW1Wc9WVlbG6NGjo7q6OnPWr1+/mDNnTrJ9ly1bFv/+978z88CBA2P06NGNunPJkiXRp0+fmDFjRrz44otx8uTJiz5bVVUVt912W3znO9+Jurq6zHnPnj3jgQceaNQeAAAAAAAAAAAAAAAAAAAAAAAQEdE63wtACkuXLo3q6up46623IiLi2LFjMXXq1Jg7d24MHTo02rZtG3/5y19i+/btWe916dIlXnzxxSguLk62a0VFRdZcXl7eJPeePn06li1bFsuWLYvCwsLo379/lJaWRqdOnaJVq1Zx5MiRePvtt+PQoUMfebekpCRWr14d3bt3b5JdAAAAAAAAAAAAAAAAAAAAAAC4vAlgcVlo3759vPTSS3HnnXfGunXrMuf79++P/fv3X/Cdfv36xYoVK2LAgAGp1ozNmzfHzp07M3ObNm1i+vTpTf6d2tra2LVrV+zateuSz954442xdOnS6NWrV5PvAQAAAAAAAAAAAAAAAAAAAADA5akw3wtAKt27d49XXnklHnvssfjc5z530ec+9alPxbx58+Ltt9+O4cOHJ9wwYsmSJVnzV7/61ejWrVuj773nnnti2rRp0bt373o93759+5g8eXKsXbs21q5dK34FAAAAAAAAAAAAAAAAAAAAAECTKqirq6vL9xKQDzt37ozt27fHwYMH4+zZs9GjR4/o27dvjBo1KgoLW24brqamJnbs2BH79++PQ4cOxalTp6K2tjY6d+4cXbp0iYEDB8aQIUOiVatW+V71gnbs2BGDBw/OzNu3b49rr702jxsBAAAAAAAAAAAAAAAAAAAAAJcjLZTGaZ3vBSBfBg0aFIMGDcr3Gsl17tw5rr/++nyvAQAAAAAAAAAAAAAAAAAAAADAZaww3wsAAAAAAAAAAAAAAAAAAAAAAABweRHAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACApASwAAAAAAAAAAAAAAAAAAAAAAACSEsACAAAAAAAAAAAAAAAAAAAAAAAgKQEsAAAAAAAAAAAAAAAAAAAAAAAAkhLAAgAAAAAAAAAAAAAAAAAAAAAAICkBLAAAAAAAAAAAAAAAAAAAAAAAAJISwAIAAAAAAAAAAAAAAAAAAAAAACCp1vleAPio2traqKysjPfeey8OHjwYbdu2jZ49e8a1114bAwcObNJv/f3vf48tW7bEgQMHoqamJj75yU9Gr1694vrrr4/i4uIm/RYAAAAAAAAAAAAAAAAAAAAAAEQIYHEZqauri127dsWWLVtiy5YtUVVVFW+99VacOXMm88yYMWNiw4YNedvxxIkT8cADD8Ty5cvj4MGDF3xmyJAhMXv27Pj2t78dBQUFDf7Wq6++Gg8++GCsX78+zp0795Hfd+jQISZNmhT3339/9OnTp8HfAQAAAAAAAAAAAAAAAAAAAACA8wlg0eKtWrUqFi9eHH/605/i+PHj+V7nov74xz/G1KlT47333vvY57Zu3Rp33XVXrFq1Kp5++uno1q1bTt/5z3/+E9///vfjZz/7WdTV1V30uRMnTsTTErPTrAAAIABJREFUTz8dzz//fCxevDimT5+e03cAAAAAAAAAAAAAAAAAAAAAAOBiBLBo8V5//fXYsGFDvtf4WDt37oybb745ampqss4HDx4cAwYMiFOnTsXWrVvjwIEDmd+98sorMWHChHjttdeiuLi43t+aPXt2PPHEE1lnnTp1ii984QvRtWvX+Otf/xpbtmyJc+fORUTE8ePHY8aMGXHFFVfE17/+9Ub8lQAAAAAAAAAAAAAAAAAAAAAA8P8K870A5Ev79u2jtLQ032vEyZMnY8KECVnxq2uuuSYqKytj27ZtsWrVqnjppZdi37598etf/zo6duyYea6qqipmzZpV728tXrw4K35VUFAQP/zhD+Nvf/tbrFu3Lp555pnYtGlTVFdXx8SJEzPP1dXVxYwZM2Lr1q2N/GsBAAAAAAAAAAAAAAAAAAAAAEAAi8tEu3btYuTIkTF79uz41a9+Fdu2bYtjx47Fj3/843yvFo888kjs3bs3M3/mM5+JN954I0aMGJH1XKtWrWLatGmxbt26aNOmTeZ8xYoVsWnTpkt+5+jRo/GjH/0o6+ynP/1p3H///dGhQ4es8969e8dzzz0XX/va1zJnZ86ciblz5+bypwEAAAAAAAAAAAAAAAAAAAAAwAUJYNHiLViwII4fPx6bN2+On//85zFz5swYPHhwFBbm/z//mpqa+MlPfpJ19uSTT0ZJSclF3xk+fHjce++9WWcLFiy45LceeeSR+OCDDzLzl7/85ZgzZ85Fny8sLIzHHnssrrzyyszZmjVrYuPGjZf8FgAAAAAAAAAAAAAAAAAAAAAAfJz8F4CgmV111VXRunXrfK9xQc8//3wcO3YsM48aNSrGjBlzyffuueeeaNeuXWZev3597N+//2PfWb58edY8b968S36na9euUV5ennW2bNmyS74H/B97dx5ddX3mD/wJYTWgiAiyaNhcWaYoIiJUBUWdtgpTHZeq1GrVVkerdaxLW9C61I560GKt4zJ4tCpq1dp2RKWorQou4AaIghAREEUxSNgD+f3hr3f8hoQk5ubeb+LrdU7O8fnk8/l+nvxzPU/IfV8AAAAAAAAAAAAAAAAAAAAAYFsEYEEePfroo4n6tNNOq9W5HXfcMY455phtPuvLXn/99Vi0aFGm7tq1a4waNapWd1Xu6fHHH4/NmzfX6iwAAAAAAAAAAAAAAAAAAAAAAFRFABbkyZYtW+Lpp59OrB1yyCG1Pl957xNPPFHt3ilTpiTqgw8+OAoKCmp1z1577RW77LJLpl6xYkW8+uqrte4TAAAAAAAAAAAAAAAAAAAAAAAqE4AFebJw4cJYu3Ztpu7QoUPssccetT4/dOjQRD1nzpxq986ePTtRH3jggbW+p6r927oLAAAAAAAAAAAAAAAAAAAAAABqIgAL8mTu3LmJuk+fPnU637t370T9wQcfxOrVq3NyV+XnAQAAAAAAAAAAAAAAAAAAAABAXQjAgjxZsGBBot5tt93qdL6oqCg6dOiwzWdm667K++fPn1+n8wAAAAAAAAAAAAAAAAAAAAAA8GUCsCBPSktLE3WnTp3q/IzKZ1atWrXVni1btsTq1avrdVdt7gEAAAAAAAAAAAAAAAAAAAAAgNpqnu8G4OuqrKwsUbdp06bOz6h8pnLQVVX3fJW7anPPV/Hxxx/HihUr6nRm7ty5iXrBggVZ6QUAAAAAAAAAAAAAAAAAAAAAoC4qZ59s2LAhT500TgKwIE8qB1O1bt26zs+oHExVVdhVVWt1vas293wVv/vd7+KKK66o1zNGjx6dlV4AAAAAAAAAAAAAAAAAAAAAAOrjgw8+iH333TffbTQazfLdAE3XueeeGwUFBQ3+NX78+Hz/qFlRUFCQkzNf5dxXvQcAAAAAAAAAAAAAAAAAAAAA4OuitLQ03y00KgKwIE/atm2bqNetW1fnZ1Q+U/mZ1a3V9a7a3AMAAAAAAAAAAAAAAAAAAAAA8HX2+eef57uFRqV5vhuAr6t8B2DVJcSqoQKwfvzjH8dxxx1XpzOvv/56nHzyyZn6wQcfjH322Scr/QAAANBwFixYEKNHj87Ujz32WPTp0yePHQEAAFAb5jkAAIDGyTwHAADQ+JjlAAAAGqe5c+fGv//7v2fqQYMG5bGbxkcAFg3mmGOOie7duzf4PcOGDWvwOxrCDjvskKhXrFhR52d8/PHHibp9+/Zb7WnWrFm0bds2ysrKEnftvPPOWb3nq+jUqVN06tSpXs/YZ599om/fvlnpBwAAgNzp06ePeQ4AAKARMs8BAAA0TuY5AACAxscsBwAA0Dhtv/32+W6hURGARYM5/PDD4/DDD893G6m1++67J+r333+/TufXrl0bn376aWKtujT33XffPV577bXEXfvss0+t76rcW+XeAQAAAAAAAAAAAAAAAAAAAACgLprluwH4utp7770T9XvvvVen85X3d+/ePdq1a1eruxYsWFCnuxYuXLjN5wEAAAAAAAAAAAAAAAAAAAAAQF0IwII86dWrV2y33XaZ+tNPP41333231udfeOGFRN2vX79q91b+3vTp02t9T0TEiy++WOu7AAAAAAAAAAAAAAAAAAAAAACgJgKwIE8KCwvjsMMOS6w9++yztT5fee9RRx1V7d4jjzwyUf/973+PioqKWt0zb968WL58eabu2LFjDBo0qNZ9AgAAAAAAAAAAAAAAAAAAAABAZQKwII/GjBmTqP/nf/6nVuc+++yzePzxxxNro0ePrnb/wIEDo0ePHpl66dKl8dRTT9XqrkmTJiXqo48+OgoLC2t1FgAAAAAAAAAAAAAAAAAAAAAAqiIAC/Jo9OjRsf3222fqGTNmxHPPPVfjuZtvvjnWrVuXqQ899NDYbbfdtnnmlFNOSdTXXXddjfd8+umncccddyTWTj311BrPAQAAAAAAAAAAAAAAAAAAAADAtgjAgiwpKSmJgoKCxFdJSck2z7Rv3z4uuuiixNoZZ5wRn332WbVnXnnllbjmmmsSa1dffXWN/f30pz+NDh06ZOpnnnkmfvvb31a7f8uWLXH22WfHp59+mlk74ogj4uCDD67xLgAAAAAAAAAAAAAAAAAAAAAA2BYBWHwtlJSUVPn1ySefJPatX7++2r2lpaUN0tuFF14YPXr0yNQLFiyIoUOHxiuvvJLYt2XLlrj//vtj5MiRsXHjxsz6iSeeGAceeGCN9+ywww5x5ZVXJtbOP//8GDduXJSVlSXWFy9eHGPGjImHH344s9aqVav4zW9+U5cfDQAAAAAAAAAAAAAAAAAAAAAAqtQ83w1ALvTs2bNW+1566aVq944bNy7Gjx+fxa6+UFRUFH/9619j6NChsWrVqoiImDdvXgwePDj69+8fe+yxR6xfvz7eeOONWLJkSeLsoEGD4o477qj1Xeecc0688cYbcfvtt0dEREVFRVx55ZVx8803x6BBg2KnnXaKDz74IF5++eUoLy/PnCsoKIi77747BgwYkIWfGAAAAAAAAAAAAAAAAAAAAACArzsBWJAC++yzTzz55JNx0kknxcKFCzPrb731Vrz11ltVnjnssMPiD3/4Q2y33XZ1uut3v/tdtGnTJn77299GRUVFRESUlpbG1KlTq9zftm3bmDhxYhx//PF1ugcAAAAAAAAAAAAAAAAAAAAAAKrTLN8NAF844IAD4o033oif/exn0aVLl2r39e/fP2677bZ46qmnolOnTnW+p3nz5nHTTTfF1KlTY+TIkdGsWdUvA0VFRXHyySfHm2++GWPHjq3zPQAAAAAAAAAAAAAAAAAAAAAAUJ3m+W4AcqGioqLB7+jRo0e972nbtm38+te/jmuuuSZmzJgRCxcujGXLlkXLli2ja9eu0a9fv9hnn32y0u+IESNixIgR8eGHH8bLL78cS5cujVWrVkWnTp1i1113jYMOOiiKioqyclc27bzzzjFu3LhEDQAAQPqZ5wAAABon8xwAAEDjZJ4DAABofMxyAAAAjZN5rn4KKnKRDAQAAAAAAAAAAAAAAAAAAAAAAAD/X7N8NwAAAAAAAAAAAAAAAAAAAAAAAMDXiwAsAAAAAAAAAAAAAAAAAAAAAAAAckoAFgAAAAAAAAAAAAAAAAAAAAAAADklAAsAAAAAAAAAAAAAAAAAAAAAAICcEoAFAAAAAAAAAAAAAAAAAAAAAABATgnAAgAAAAAAAAAAAAAAAAAAAAAAIKcEYAEAAAAAAAAAAAAAAAAAAAAAAJBTArAAAAAAAAAAAAAAAAAAAAAAAADIKQFYAAAAAAAAAAAAAAAAAAAAAAAA5JQALAAAAAAAAAAAAAAAAAAAAAAAAHJKABYAAAAAAAAAAAAAAAAAAAAAAAA5JQALAAAAAAAAAAAAAAAAAAAAAACAnGqe7waApm3RokXx+uuvx7Jly6KsrCy6dOkSxcXFMXTo0GjRokVee5s1a1bMnz8/li5dGhER3bp1iz322CMGDhyY174AAADSIG3z3Lp16+Ltt9+OefPmxYoVK6KsrCzatm0bHTp0iH79+kX//v2jeXO/6gIAAEjbPAcAAEDN0j7LlZeXx6xZs2LOnDmxYsWK2LhxY7Rt2zbzd5d9+/b1b3UAAMDXUlrnuZUrV8arr74aixYtitLS0qioqIgddtghunfvHvvvv3/ssssueesNAACA6n1dc1D8SyPQIB5++OG48cYbY/r06VV+v0OHDnH88cfHlVdeGR07dsxZX5s2bYobbrgh7rjjjnjvvfeq3NOnT58444wz4sILL0zFH44AAADkUprmuVmzZsVjjz0W06ZNi5dffjk2bdpU7d6ioqI4/vjj4/zzz48BAwY0aF8AAABplKZ5rjbWrl0b/fv3j4ULFybWx44dG5MmTcpPUwAAADmW9llu/vz5cf3118fkyZNj1apV1e5r06ZNDBs2LH70ox/FmDFjctghAABAfqRxnquoqIjJkyfHLbfcEs8///w29w4cODDOPvvs+MEPfiDQGAAA+FpYuHBhvPLKK/Hqq6/GK6+8ErNmzYrVq1dnvl9cXBwlJSV56U0OSkRBRUVFRb6bAJqOsrKy+OEPfxgPPPBArfZ37tw57r777jjiiCMauLMv/hDjhBNOiFmzZtVq/3777RcPPPBA9OnTp4E7AwAAyL80zXPr16+Pvn37bvUm6NooLCyMiy66KH71q181yV/mAQAAVJamea4uLrjggpgwYcJW6wKwAACAr4O0z3Ll5eVx5ZVXxrXXXhvl5eW1Pnf88cfX+mcCAABojNI6zy1fvjxOOumkeOaZZ+p0zvvnAACApuzZZ5+Na6+9Nl599dVYuXLlNvfmKwBLDsoXBGABWbN58+Y4+uij43//938T6zvvvHMMHDgwdthhh3jvvffitddeiy+/9LRq1SqmTp0aw4YNa7Deli9fHkOGDIn3338/sd6nT5/o27dvVFRUxJw5c7ZKQ+zZs2fMmDEjOnXq1GC9AQAA5Fva5rmysrJo167dVusFBQWx5557xm677RYdO3aMsrKymD17dpVBWf/2b/8WkydP9slkAABAk5a2ea62ZsyYEQcddFBs2bJlq+8JwAIAAJq6tM9y69ati2OPPXar/goKCqJv376x2267Rfv27aOsrCwWLlwY8+bNy4RkCcACAACasrTOcytWrIhvfvObMW/evMR6ixYtYuDAgVFcXBzNmjWLJUuWxMyZM2P9+vWJfd27d4/nn38+iouLG6Q/AACAfJkwYUJccMEFtdqbjwAsOSj/xzsAgay55JJLEr/Aa9GiRdx4441x5plnRsuWLTPrc+fOjTPOOCOmT58eEREbNmyI0aNHx1tvvRVdunTJel9btmyJ0aNHJ170u3TpEpMmTYpRo0Yl9k6ZMiVOO+20WL58eURELFq0KMaMGRPPP/98FBQUZL03AACANEjrPBcRUVhYGKNGjYqxY8fGyJEjo2PHjlvtmTlzZlx44YXx97//PbP2yCOPxPjx4+Oqq65qkL4AAADSIM3zXHU2btwYp59+eib8ql27drF69eqc9gAAAJBPaZ7lKioq4oQTTkj017p167j44ovjzDPPjG7dum11Zu3atfH000/HAw88kOgfAACgqUnrPPeTn/xkq/Crs88+O6644oqt3gxdWloa1113XfzmN7/J/HvdkiVL4qyzzoopU6ZkvTcAAIA0atWqVXTv3n2rYKlckoOSVFDx5ShpgK9o4cKFsddee8WmTZsya4899lgcc8wxVe5ft25djBw5MvOLvIiIs846K37/+99nvbd77rknTj311EzdoUOHmDlzZvTo0aPK/YsWLYr99tsvPvvss8za/fffHyeccELWewMAAMi3NM5zZWVl0bFjxzjjjDPikksuie7du9d4ZvPmzXHKKafE/fffn1lr2bJlvPvuuz6VDAAAaJLSOM/Vxs9//vO4+uqrI+KLT0w77rjj4vrrr898f+zYsTFp0qSc9gQAAJAraZ/lbrnlljj33HMzdZcuXeJvf/tb7L333rU6X15eHs2b+3xmAACg6UnrPFdSUhI9e/ZMrF166aVxzTXXbPPcxIkT4z/+4z8SazNmzIgDDjggq/0BAADk04QJE+Liiy+Ovn37xqBBg2L//fePQYMGRf/+/eOFF16IQw89NLO3uLg4SkpKctabHJSkZvluAGgarrjiisQv8L7//e9X+wu8iIg2bdrEpEmTEun2d955ZyxcuDCrfW3evDnGjRuXWLvxxhurfdGPiOjZs2fceOONibWf//znmVR7AACApiSN81zr1q1jwYIFMXHixFqFX0VEFBYWxp133hm77rprZm3jxo3x4IMPZq0vAACANEnjPFeTN954I6677rpMfeutt0ZRUVHO7gcAAMi3NM9yixcvjksuuSRTt27dOqZOnVrr8KuIEH4FAAA0WWmd5/785z8n6s6dO2/1XrqqnHPOOTFgwIBtPgsAAKCxGzt2bHz++efx2muvxe233x5nnnlm7LvvvtGiRYu89iUHZWsCsIB6W7duXTz88MOJtZ/97Gc1nttjjz1i9OjRmbq8vDzuu+++rPb2/PPPx6JFizJ1t27d4uSTT67x3CmnnBLdunXL1O+99168+OKLWe0NAAAg39I6zzVv3rzWwVdf1qZNmzjttNMSa88880y22gIAAEiNtM5z21JeXh4/+MEPory8PCIiTjzxxDjqqKNycjcAAEAapH2Wu/rqq6OsrCxTX3755bHPPvtk/R4AAIDGJs3zXOVArVGjRkWrVq1qPFdQUBDf+c53Emvz58/Pam8AAAD5tuOOO0br1q3z3cZW5KBsTQAWUG9PPvlkrF27NlMfeOCBsddee9XqbOU3Jj/yyCNZ7e3RRx9N1KeeemoUFhbWeK6wsHCr/0FkuzcAAIB8S/M891UNHDgwUS9btixPnQAAADScxjjP/dd//VfMmjUrIiI6dOgQEyZMyMm9AAAAaZHmWW716tWJN2EXFRXF+eefn9U7AAAAGqs0z3Nr1qxJ1HX58NFdd901UX/22WdZ6QkAAIBtk4OyNQFYQL1NmTIlUR9yyCG1Pjt8+PBo3rx5pn7ttdfio48+ylZr9eqt8t4nnngiCx0BAACkR5rnua/qyz1FRGzcuDFPnQAAADScxjbPvfPOO3HFFVdk6htuuCE6derUoHcCAACkTZpnucmTJ0dZWVmm/u53vxvt2rXL2vMBAAAaszTPc7vsskuiXr9+fa3PVt7boUOHrPQEAADAtslB2ZoALKDeZs+enagPPPDAWp8tKiqK/v37J9bmzJmTlb42bNgQCxYsSKwNGTKk1ueHDh2aqOfPn++N0wAAQJOS1nmuPirPgV26dMlTJwAAAA2nMc1zW7ZsidNPPz02bNgQEREjRoyI73//+w12HwAAQFqleZZ75plnEvXhhx+etWcDAAA0dmme54YPH56oZ82aVeuzM2fOTNT7779/VnoCAACgenJQqiYAC6i3t99+O1H36dOnTud79+6dqOfOnVvvniK++CTpzZs3Z+pOnTrF9ttvX+vz22+/fXTs2DFTb968Od59992s9AYAAJAGaZ3n6uPhhx9O1IMHD85TJwAAAA2nMc1zEydOjBdeeCEiItq0aRO33XZbg90FAACQZmme5V5++eVE/c83c69bty7uu+++OProo6N3797Rpk2baN++ffTp0yeOO+64+O///u9YvXp11voAAABIozTPcyNHjow999wzU//jH/+IN998s8ZzS5cujT/+8Y+ZukWLFnHiiSdmrS8AAACqJgelagKwgHpZuXJlrFy5MrG222671ekZlffPnz+/3n1FxFaph3Xtq6oz2eoNAAAg39I8z31Vr7zySuZN1f80ZsyYPHUDAADQMBrTPFdSUhKXXXZZph43blyd3xAAAADQFKR5listLU38vWXLli2jV69e8dxzz0Xfvn3je9/7Xvz5z3+OhQsXxvr162PVqlXx3nvvxcMPPxxnnXVW9OzZM26++eas9AIAAJA2aZ7nIiKaNWsWd911V7Rq1SoiIrZs2RLHHntslJSUVHvmo48+itGjR8fatWszaz//+c+ja9euWesLAACAqslBqZoALKBeSktLE/V2220XRUVFdXpGp06dEvWqVavq3VfE1r1Vvqc2Gqo3AACAfEvzPPdVbNq0Kc4666zE2vDhw2Pw4MF56ggAAKBhNKZ57oc//GGsWbMmIiL+5V/+JX760582yD0AAABpl+ZZbvny5Ym6a9eu8cgjj8SIESNi0aJFNZ7/9NNP4/zzz49TTjklysvLs9ITAABAWqR5nvunoUOHxl/+8pfYeeedI+KLNz4PGDAgLrjggnjyySdj3rx58c4778Tf/va3uOyyy6Jv377x6quvZs6fddZZ8Ytf/CKrPQEAAFA1OShVa57vBoDGraysLFG3adOmzs+ofGb16tX16umf0twbAABAvjW1mek///M/47XXXsvULVq08EnTAABAk9RY5rk777wzpk6dGhFffPL07bffHs2b+xMFAADg6ynNs1zlP7IvKyuLk08+ObZs2RIREcXFxXHOOefEsGHDYqeddoqVK1fG888/H7fcckuUlJRkzt17773RuXPnuP7667PSFwAAQBqkeZ77ssMOOyzefvvtmDBhQvzhD3+IRYsWxYQJE2LChAnVntlrr73iyiuvjOOOOy7r/QAAAFC1xjJn5pq/LgXqpfKLa+vWrev8jMovrpWf+VWluTcAAIB8a0oz01133RU33XRTYm38+PHxjW98Iy/9AAAANKTGMM8tW7YsLrrookx93nnnxf7775/VOwAAABqTNM9ylQOwPvnkk8x/H3fccXH33XdvdfeQIUPi3HPPjVNPPTUeeuihzPoNN9wQxxxzTAwfPjwrvQEAAORbmue5ysrLyyMiolWrVjXuHTp0aIwfPz4OO+ywBukFAACAqjWmOTOXmuW7AaBpKSgoyMmZryLNvQEAAORbY52ZpkyZEmeffXZi7dvf/nZceumleeoIAAAgt9I4z/34xz/OvIG6uLg4rrrqqga9DwAAoLFJ0yy3ZcuWKtf333//uO+++6r91OnWrVvHfffdt1XgsRkQAABoytI0z33Z7bffHr17946rrroq5s2bV+P+F198MUaNGhUDBgyIF154ocH7AwAAoGppnTNzTQAWUC9t27ZN1OvWravzMyqfqfzMryrNvQEAAORbU5iZXnjhhfjud78bmzZtyqwNGzYsJk+e3CR/kQcAABCR/nnugQceiD/96U+Z+tZbb42ioqKsPR8AAKAxSvMsV91zrr/++mjevPk2zzZv3jxuvPHGxNpTTz0VH3/8cVZ6AwAAyLc0z3P/dPXVV8eZZ54Za9asyawNGjQo7rrrrpg/f36sWbMm1q1bF4sWLYr7778/Dj300My+2bNnx8EHHxx33313VnsCAACgao1hzswHAVhAvaT5xTXNvQEAAORbY5+ZZs6cGd/61rdi7dq1mbXBgwfHX//619huu+1y1gcAAECupXme++STT+K8887L1CeeeGIcddRRWXk2AABAY5bmWa6q5xQXF8c3v/nNWp0fNmxY9OrVK7H23HPPZaU3AACAfEvzPBcRMW3atPjFL36RWBs/fny8/PLLcdppp0WfPn1iu+22i9atW0ePHj3ihBNOiGnTpsVtt92W+aDRzZs3x+mnnx4vvPBC1voCAACgammfM/NFABZQLzvssEOiXrt2bSItvjYqf9JX+/bt691XxNa9rVixos7PaKjeAAAA8i3N81xN3nzzzRg1alSsWrUqszZw4MB48sknY/vtt89JDwAAAPmS5nnuvPPOy/ybXIcOHWLChAlZeS4AAEBjl+ZZrqrnDBkypE7POOCAAxL122+/Xa+eAAAA0iLN81xExOWXXx4VFRWZeuzYsTFu3LhMuFV1zjzzzLj88ssz9ebNm+P888/PWl8AAABUTQ5K1QRgAfWy0047xY477phYW7x4cZ2e8f777yfq3Xffvd59VfWcyvfURkP1BgAAkG9pnue2Ze7cuXHYYYfFypUrM2v9+vWLp556qkn8sg4AAKAmaZ3n3nnnnbj//vsz9U9+8pNYu3ZtlJSUbPOrtLQ08ZyysrLE9+v6swEAAKRRWme5iIji4uJo1apVYq1Lly51ekbXrl0T9aefflrvvgAAANIgzfPc0qVLY8aMGYm1cePG1fr8JZdcEm3atMnUM2fOjDfffDMrvQEAAFA1OShVE4AF1Nvee++dqBcsWFCn8wsXLtzm876qPffcMwoLCzP1xx9/HKtXr671+c8//zw++eSTTF1YWNgkXvgBAAD+Ka3zXHXeeeedGDlyZCLZfq+99oqpU6dGx44dG/RuAACANEnjPLdu3bpE/ctf/jJ69uxZ49dNN92UOPfHP/4x8f0BAwbUuzcAAIA0SOMsF/HF30buueeeibXKgVg1qbx//fr19e4LAAAgLdI6z73++uuJulevXtGzZ89any8qKoohQ4Yk1l6J/CGGAAAgAElEQVR66aWs9AYAAEDV5KBUTQAWUG/9+vVL1NOnT6/12TVr1myVDF/5eV9Vq1atonfv3l+5txdffDFR77777nX+ow4AAIA0S+s8V5UFCxbEiBEjYvny5Zm13XffPaZNmxadO3dusHsBAADSqDHNcwAAAHwhzbNc5fDh0tLSOp2vvH+nnXaqd08AAABpkdZ5rvIstssuu9T5GZXPfPlN1AAAAGSfHJSqCcAC6u3II49M1M8++2ytz/7jH/+I8vLyTD1w4MCsvnG5Pr1V3nvUUUdloSMAAID0SPM892WLFi2KESNGxLJlyzJrvXr1imnTpkWXLl0a5E4AAIA0ayzzHAAAAP8nzbPcv/7rvybqOXPm1On87NmzE3X37t3r3RMAAEBapHWea9++faJes2ZNnZ9RVlaWqNu2bVuvngAAAKiZHJStCcAC6u2II46INm3aZOrp06fHvHnzanV20qRJiXrMmDHZbG2r591zzz2xefPmGs9t3rw57r333gbtDQAAIN/SPM/90+LFi2PEiBHxwQcfZNaKi4tj2rRp/nAeAAD42krjPPeNb3wjKioq6vw1bty4xHPGjh2b+H7lT64GAABorNI4y/3Tt7/97cQnQ7/yyiuxcuXKWp397LPP4uWXX06sDR8+PKv9AQAA5FNa57muXbsm6nfeeSfWrl1bp2fMmjUrUe+yyy717gsAAIBtk4OyNQFYQL1tt912ceyxxybWrrvuuhrPvfvuu/Hoo49m6ubNm8dJJ52U1d6GDx8ePXv2zNRLlizZ6gW9Kvfee28sXbo0U/fu3TsOOuigrPYGAACQb2me5yIili1bFiNHjoySkpLMWrdu3WLatGlRXFyc9fsAAAAai7TPcwAAAGwtzbNcu3btEr1t2LAhJk6cWKuzEydOjPXr12fq4uLi6NevX1b7AwAAyKe0znMDBgyIHXfcMVOvX78+7rnnnlqf/8tf/pJ4/1xExLBhw7LWHwAAAFWTg7I1AVhAVowfPz5atGiRqSdNmhSPP/54tfvXr18fp512WmzcuDGzdvrpp0fv3r23eU9BQUHi69lnn93m/sLCwrjiiisSaxdeeGHizdOVlZSUxAUXXJBYu+qqq6JZMy+ZAABA05PWee7jjz+OkSNHxoIFCzJrXbp0iWeeeSZ69epVw08FAADQ9KV1ngMAAKB6aZ7lfvWrX0XLli0z9TXXXBPTp0/f5pnp06fHVVddlVi79NJLo6CgoMb7AAAAGpM0znOFhYVbBXNdcsklMXv27Bp+mojFixfH2WefnVg76KCDokuXLjWeBQAAIEkOSv01jZ8CyLtevXrF+eefn1g79thjY+LEiYlf1EVEvP322zFy5Mh48cUXM2s77bRTjBs3rkF6+973vhcHHHBApl65cmUMHTo0nnrqqa32Pvnkk3HggQfGZ599llkbOnRoHH/88Q3SGwAAQL6lcZ4rLS2Nww8/PObNm5dZKyoqijvvvDNatGgRJSUldfoCAABoitI4zwEAALBtaZ7levbsGRdffHGm3rBhQ4waNSpuvfXW2LRpU2JveXl53HbbbTFq1KhE34MHD47TTjutQfoDAADIp7TOc7/85S+jTZs2mbq0tDSGDh0aEydOjLVr1261f+PGjXH33XfHfvvtF0uXLk1879prr816fwAAAPm2ZMmSKt9vtnz58sS+8vLyat+b9sknn2S9LzkoSQUVFRUV+W4CaBo2b94c3/nOd+KJJ55IrHfq1Cn23XffaNeuXSxcuDBmzZoVX37padmyZUydOjWGDx9e4x2VPxXsmWeeiUMOOaTGcx9++GEMGTIkFi9enFjffffdo2/fvlFRURFz5syJBQsWJL7fo0ePmDFjRnTu3LnGOwAAABqrtM1zzz77bBx66KF1/0Gq4ddfAABAU5W2ee6rGD9+fOKTzMaOHRuTJk3K2vMBAADSJs2zXEVFRRx//PHx0EMPJdbbt28fQ4YMiQ4dOsTKlStjxowZUVpamtjTrVu3mDFjRnTv3r3GewAAABqjtM5zjz76aBx33HGxefPmxHqbNm1iv/32i65du0azZs1i+fLl8eqrr0ZZWdlWz7j66qvjsssuq7E/AACAxqZHjx7x/vvv1+sZNf1doxyU+mue7waApqOwsDAefPDBOOOMM2Ly5MmZ9Y8//jimTJlS5ZlOnTrF3XffXatf4NVHly5d4umnn44TTjghXnvttcz6/PnzY/78+VWe2XfffWPy5MlN6kUfAACgKmme5wAAAKieeQ4AAKDxSfMsV1BQEPfcc0906NAhbrvttsx6aWlptb1FRAwePDgeffTR6Nq1a4P2BwAAkE9pnefGjBkTf/rTn+L000+Pjz76KLO+bt26eP7557d5tqioKH7961/Hueee22D9AQAAUDU5KP+nWb4bAJqWtm3bxgMPPBAPPfRQDBkypNp9HTp0iB/96Ecxe/bsOPLII3PS2x577BEvvfRSXHvttdGrV69q9/Xu3TuuvfbamDFjRvTp0ycnvQEAAORbmuc5AAAAqmeeAwAAaHzSPMu1atUqfv/738fUqVPj8MMPj8LCwmr39uvXLyZNmhQvvvii8CsAAOBrIa3z3Le+9a2YO3duXHPNNdG7d+8a93fu3DkuuuiimDNnjvArAACAPJKD8oWCioqKinw3ATRdixYtilmzZsWyZctizZo1scsuu0RxcXEcdNBB0bJly7z2NnPmzHj33Xdj2bJlERHRtWvX2GOPPWK//fbLa18AAABpkOZ5DgAAgOqZ5wAAABqfNM9yK1asiBkzZsSHH34Yn3zySbRr1y46d+4cQ4cOje7du+e1NwAAgHxL6zy3ZMmSmDlzZnz44YdRWloaFRUVscMOO8TOO+8cAwcObJJvlgYAAGgKvq45KAKwAAAAAAAAAAAAAAAAAAAAAAAAyKlm+W4AAAAAAAAAAAAAAAAAAAAAAACArxcBWAAAAAAAAAAAAAAAAAAAAAAAAOSUACwAAAAAAAAAAAAAAAAAAAAAAABySgAWAAAAAAAAAAAAAAAAAAAAAAAAOSUACwAAAAAAAAAAAAAAAAAAAAAAgJwSgAUAAAAAAAAAAAAAAAAAAAAAAEBOCcACAAAAAAAAAAAAAAAAAAAAAAAgpwRgAQAAAAAAAAAAAAAAAAAAAAAAkFMCsAAAAAAAAAAAAAAAAAAAAAAAAMgpAVgAAAAAAAAAAAAAAAAAAAAAAADklAAsAAAAAAAAAAAAAAAAAAAAAAAAckoAFgAAAAAAAAAAAAAAAAAAAAAAADklAAsAAAAAAAAAAAAAAAAAAAAAAICcEoAFAAAAAAAAAAAAAAAAAAAAAABATgnAAgAAAAAAAAAAAAAAAAAAAAAAIKcEYAEAAAAAAAAAAAAAAAAAAAAAAJBTArAAAAAAAAAAAAAAAAAAAAAAAADIKQFYAAAAAAAAAAAAAAAAAAAAAAAA5JQALAAAAAAAAAAAAAAAAAAAAAAAAHJKABYAAAAAAAAAAAAAAAAAAAAAAAA5JQALAAAAAAAAAAAAAAAAAAAAAACAnBKABQAAAAAAAAAAAAAAAAAAAAAAQE4JwAIAAAAAAAAAAAAAAAAAAAAAACCnBGABAAAAAAAAAAAAAAAAAAAAAACQUwKwAAAAAAAAAAAAAAAAAAAAAAAAyCkBWAAAAAAAAAAAAAAAAAAAAAAAAOSUACwAAAAAAAAAAAAAAAAAAAAAAABySgAWAAAAAAAAAAAAAAAAAAAAAAAAOSUACwAAAAAAAAAAAAAAAAAAAAAAgJwSgAUAAAAAAAAAAAAAAAAAAAAAAEBOCcACAAAAAAAAAAAAAAAAAAAAAAAgpwRgAQAAAAAAAAAAAAAAAAAAAAAAkFMCsAAAAAAAAAAAAAAAAAAAAAAAAMgpAVgAAAAAAAAAAAAAAAAAAAAAAADklAAsAAAAAAAAAAAAAAAAAAAAAAAAckoAFgAAAAAAAAAAAAAAAAAAAAAAADklAAsAAAAAAAAAAAAAAAAAAAAAAICcEoAFAAAAAAAAAAAAAAAAAAAAAABATgnAAgAAAAAAAAAAAAAAAAAAAAAAIKcEYAEAAAAAAAAAAAAAAAAAAAAAAJBTArAAAAAAAAAAAAAAAAAAAAAAAADIKQFYAAAAAAAAAAAAAAAAAAAAAAAA5JQALAAAAAAAAAAAAAAAAAAAAAAAAHJKABYAAAAAAAAAAAAAAAAAAAAAAAA5JQALAAAAAAAAAAAAAAAAAAAAAACAnBKABQAAAAAAAAAAAAAAAAAAAAAAQE4JwAIAAAAAAAAAAAAAAAAAAAAAACCnBGABAAAAAAAAAAAAAAAAAAAAAACQUwKwAAAAAAAAAAAAAAAAAAAAAAAAyCkBWAAAAAAAAAAAAAAAAAAAAAAAAOSUACwAAAAAAAAAAAAAAAAAAAAAAABySgAWAAAAAAAAAAAAAAAAAAAAAAAAOSUACwAAAAAAAAAAAAAAAAAAAAAAgJwSgAUAAAAAAAAAAAAAAAAAAAAAAEBOCcACAAAAAAAAAAAAAAAAAAAAAAAgpwRgAQAAAAAAAAAAAAAAAAAAAAAAkFMCsAAAAAAAAAAAAAAAAAAAAAAAAMgpAVgAAAAAAAAAAAAAAAAAAAAAAADklAAsAAAAAAAAAAAAAAAAAAAAAAAAckoAFgAAAAAAAAAAAAAAAAAAAAAAADklAAsAAAAAAAAAAAAAAAAAAAAAAICcEoAFAAAAAAAAAAAAAAAAAAAAAABATgnAAgAAAAAAAAAAAAAAAAAAAAAAIKcEYAEAAAAAAAAAAAAAAAAAAAAAAJBTArAAAAAAAAAAAAAAAAAAAAAAAADIKQFYAAAAAAAAAAAAAAAAAAAAAAAA5JQALAAAAAAAAAAAAAAAAAAAAAAAAHJKABYAAAAAAAAAAAAAAAAAAAAAAAA5JQALAAAAAAAA/h97dxplZWHeAfyZXXZEUAKiTABxBwXSuhQQDWJwQWngmMgRjFq3VFL3NkFEm0TSnqpxSz0CJq5IFCsgaBRw16iAUUERGQWClE0FZoQZZvohiU0Mwfe93AWY3++c+2GGZ7u8h+HLPf8BAAAAAAAAAAAAAADySgAWAAAAAAAAAAAAAAAAAAAAAAAAeSUACwAAAAAAAAAAAAAAAAAAAAAAgLwSgAUAAAAAAAAAAAAAAAAAAAAAAEBeCcACAAAAAAAAAAAAAAAAAAAAAAAgrwRgAQAAAAAAAAAAAAAAAAAAAAAAkFcCsAAAAAAAAAAAAAAAAAAAAAAAAMgrAVgAAAAAAAAAAAAAAAAAAAAAAADklQAsAAAAAAAAAAAAAAAAAAAAAAAA8koAFgAAAAAAAAAAAAAAAAAAAAAAAHklAAsAAAAAAAAAAAAAAAAAAAAAAIC8EoAFAAAAAAAAAAAAAAAAAAAAAABAXgnAAgAAAAAAAAAAAAAAAAAAAAAAIK8EYAEAAAAAAAAAAAAAAAAAAAAAAJBXArAAAAAAAAAAAAAAAAAAAAAAAADIKwFYAAAAAAAAAAAAAAAAAAAAAAAA5JUALAAAAAAAAAAAAAAAAAAAAAAAAPJKABYAAAAAAAAAAAAAAAAAAAAAAAB5JQALAAAAAAAAAAAAAAAAAAAAAACAvBKABQAAAAAAAAAAAAAAAAAAAAAAQF4JwAIAAAAAAAAAAAAAAAAAAAAAACCvBGABAAAAAAAAAAAAAAAAAAAAAACQVwKwAAAAAAAAAAAAAAAAAAAAAAAAyCsBWAAAAAAAAAAAAAAAAAAAAAAAAOSVACwAAAAAAAAAAAAAAAAAAAAAAADySgAWAAAAAAAAAAAAAAAAAAAAAAAAeSUACwAAAAAAAAAAAAAAAAAAAAAAgLwSgAUAAAAAAAAAAAAAAAAAAAAAAEBeCcACAAAAAAAAAAAAAAAAAAAAAAAgrwRgAQAAAAAAAAAAAAAAAAAAAAAAkFcCsAAAAAAAAAAAAAAAAAAAAAAAAMgrAVgAAAAAAAAAAAAAAAAAAAAAAADklQAsAAAAAAAAAAAAAAAAAAAAAAAA8koAFgAAAAAAAAAAAAAAAAAAAAAAAHklAAsAAAAAAAAAAAAAAAAAAAAAAIC8EoAFAAAAAAAAAAAAAAAAAAAAAABAXgnAAgAAAAAAAAAAAAAAAAAAAAAAIK8EYAEAAAAAAAAAAAAAAAAAAAAAAJBXArAAAAAAAAAAAAAAAAAAAAAAAADIKwFYAAAAAAAAAAAAAAAAAAAAAAAA5JUALAAAAAAAAAAAAAAAAAAAAAAAAPJKABYAAAAAAAAAAAAAAAAAAAAAAAB5JQALAAAAAAAAAAAAAAAAAAAAAACAvBKABQAAAAAAAAAAAAAAAAAAAAAAQF4JwAIAAAAAAAAAAAAAAAAAAAAAACCvBGABAAAAAAAAAAAAAAAAAAAAAACQVwKwAAAAAAAAAAAAAAAAAAAAAAAAyCsBWAAAAAAAAAAAAAAAAAAAAAAAAOSVACwAAAAAAAAAAAAAAAAAAAAAAADySgAWAAAAAAAAAAAAAAAAAAAAAAAAeSUACwAAAAAAAAAAAAAAAAAAAAAAgLwSgAUAAAAAAAAAAAAAAAAAAAAAAEBeCcACAAAAAAAAAAAAAAAAAAAAAAAgrwRgAQAAAAAAAAAAAAAAAAAAAAAAkFcCsAAAAAAAAAAAAAAAAAAAAAAAAMgrAVgAAAAAAAAAAAAAAAAAAAAAAADklQAsAAAAAAAAAAAAAAAAAAAAAAAA8koAFgAAAAAAAAAAAAAAAAAAAAAAAHklAAsAAAAAAAAAAAAAAAAAAAAAAIC8EoAFAAAAAAAAAAAAAAAAAAAAAABAXgnAAgAAAAAAAAAAAAAAAAAAAAAAIK9KC30AAAAAAAAAAADsyrZu3Rrz58+PV199NebNmxdLly6Njz76KNatWxfV1dXx+eefR0VFRTRt2jRat24dnTp1is6dO0ePHj3iG9/4RvTq1SsqKioK/TYAAAAAAAAAAAAgr4oaGhoaCn0EAAAAAAAAAADsShoaGuKpp56K+++/P6ZNmxZr167NeFbz5s1j0KBBMXz48BgyZEiUlvqddkDuzZ8/P6ZOnZq4fsiQIdGzZ88cXvSXxo4dG9ddd13W5lVUVETr1q2jVatW0bp169hvv/2id+/e0atXr+jdu3e0bt06K3umTp0a8+fPT1w/evTojHd/8skncdNNNyWu79mzZwwZMiSjXfm2du3amD17djz77LPxzjvvxAcffBDr16+PjRs3RklJSTRr1iz23nvv6Nq1axx++OHRv3//6Nu3r0BJAAAAAAAAANjFCMACAAAAAAAAAICE6uvr45e//GWMHz8+Fi5cmPX5nTp1itGjR8cll1wS5eXlWZ8P8CeTJk2KUaNGJa6fOHFijBw5MncHfUm2A7C2p6SkJE466aQ477zzYvDgwVFSUpLxrJEjR8Y999yTuH7p0qXRuXPnjHZVVVVFZWVl4vqzzz47Jk2alNGufJk9e3bccsstMX369KitrU3V27p16xg+fHhcfvnl0bVr1xxdCAAAAAAAAABkU3GhDwAAAAAAAAAAgF3BK6+8Er17945Ro0blJPwqImLZsmVx2WWXxcEHHxwzZszIyY5dRVFRUaJXpsExFI5ny85m69atMW3atDjttNOic+fO8cgjjxT6pEZn8eLFccIJJ8SAAQNi6tSpqcOvIiI++eST+MUvfhEHHXRQXHTRRbFhw4YcXAoAAAAAAAAAZJMALAAAAAAAAAAA2I6Ghob4yU9+Escee2zMmzcvLzuXLFkSgwcPjtGjR8eWLVvyshOAiOXLl8fQoUNj+PDhsXr16kKf0yhMnDgxevbsGU8//XRW5tXV1cUdd9wRhx9+eLz++utZmQkAAAAAAAAA5IYALAAAAAAAAAAA+Btqa2vjrLPOin/913+Nurq6vO+/+eab46STTooNGzbkfTdAYzZ58uQ46qijoqqqqtCn7Nauu+66OOecc6K6ujrrs6uqqqJfv34xa9asrM8GAAAAAAAAALJDABYAAAAAAAAAAGxDXV1dnHHGGXH//fcX9I5nnnkmjj/++Ni4cWNB7wBobJYsWRIDBw6M9evXF/qU3dJ//Md/xNixY3O6Y9OmTXH66afHSy+9lNM9AAAAAAAAAEBmBGABAAAAAAAAAMA2XHDBBTFt2rRCnxEREb/97W9j2LBhUVdXV+hTABqVxYsXx3nnnVfoM3Y7M2fOjCuvvDIvu2pqauK0006Ljz/+OC/7AAAAAAAAAIDkBGABAAAAAAAAAMCX3HnnnXH33XcX+oy/8MQTT8SYMWMKfQZAo/PrX/86Zs6cWegzdhtr166NkSNHRkNDQ6q+srKy+PrXvx4dO3aM4uJ0H39dvXp1jBo1KlUPAAAAAAAAAJB7ArAAAAAAAAAAAODPLF68OC677LKMektKSqJ///5x2223xWuvvRYrVqyIzZs3x8qVK2PevHkxYcKEOOmkk6K8vDyj+TfeeGM8//zzGfUCkLnrr7++0CfsNsaMGROrVq1KXN+zZ8945JFHoqamJpYsWRLLly+Pzz77LG677bZo37594jkzZ86MqVOnZnIyAAAAAAAAAJAjArAAAAAAAAAAAODPjB49Oqqrq1P3/d3f/V3MmzcvZs+eHRdddFH06tUrOnToEOXl5dG+ffvo2bNnjBo1KmbMmBGLFi2KgQMHpt5RX18fF198cdTX16fuBdidzZ49OxoaGrb52rBhQ7z//vvx4IMPxpAhQ6K4OP1HJ1988cV47733cnB547JkyZL47//+78T1AwcOjBdeeCFOP/30KCkp+eL7zZo1i4suuihefPHFqKysTDzvqquu8n8oAAAAAAAAAOxEBGABAAAAAAAAAMAfzZ07N2bMmJG675//+Z/jxRdfjMMOOyxRfWVlZcyaNSt+8pOfpN715ptvxn333Ze6D6Cxat68eXTp0iWGDx8ejz76aDz77LPRvn371HOmTZuWg+sal5tuuinq6uoS1bZt2zYeeOCBaNq06d+sqaysjEmTJiXe/95778Xjjz+euB4AAAAAAAAAyC0BWAAAAAAAAAAA8Efjx49P3XPOOefETTfdFMXF6T+Kc/XVV8ePfvSj1H2Z3EnuVVdXx+TJk+N73/te9OnTJ9q2bRsVFRWxxx57RIcOHaJfv35xzTXXxEsvvVToU6FRO+aYY2LWrFlRVlaWqu+FF17I0UWNQ3V1daqwqjFjxkSbNm2+sq5v375x+umnJ557++23J64FAAAAAAAAAHJLABYAAAAAAAAAAEREVVVVzJw5M1VP9+7d484774yioqKM944bNy769u2bquett96K5557LlFtUVFR4ldaY8eOTTz7b4We/K0ZSX344Yep3mNVVdU258yZMyfxjJEjR/5Fb3V1dVx77bWx7777xvDhw2PChAnx2muvxdq1a2PLli2xefPmWLlyZTz77LPx05/+NI4++ujo0aNHzJgxI/H73BbPNtmzhW05/PDDY9SoUal63nvvvRxd0zg88cQTsXHjxkS1paWlMWLEiMSz0zzLp59+OtasWZO4HgAAAAAAAADIHQFYAAAAAAAAAAAQEQ888EDU19en6rnllluirKxsh3ffeuutUVpamqrnvvvu2+G97LjXXnstDjnkkBg3blysX78+cd+bb74ZgwcPjhEjRiQOhAGy69RTT01Vv2LFihxd0jg8+uijiWuPOuqoaN26deL6E044IfH/x1u3bo3/+Z//STwbAAAAAAAAAMgdAVgAAAAAAAAAABAR06ZNS1V/9NFHx8CBA7Oy+7DDDouhQ4em6pk+fXpWdpO5qVOnxrHHHhtVVVUZz7j33nujf//+sXr16uwdBiRSWVmZql5Y3Y6ZO3du4tq///u/TzW7SZMmcfjhhyeunzNnTqr5AAAAAAAAAEBuCMACAAAAAAAAAKDR27BhQ7z88supes4666ys3jBixIhU9cuXL4+FCxdm9QaSmzZtWgwbNiw2b968w7Nef/31OOmkk4TrQJ7V19cX+oRGo6qqKpYvX564/tBDD029I03PCy+8kHo+AAAAAAAAAJB9ArAAAAAAAAAAAGj0Xn/99VRBKCUlJTFs2LCs3nDiiSfGXnvtlarnt7/9bVZvIJmFCxfGmWeeGbW1tVmb+frrr8c555yTtXnAV1u6dGmq+ubNm+fokt3fggULUtV37do19Y40PR988IHQQQAAAAAAAADYCQjAAgAAAAAAAACg0XvjjTdS1Xfv3j11WNVXKS0tjT59+qTqSXs32fHqq6/mJDjl4Ycfjl/96ldZnwts22OPPZaqvmPHjjm6ZPe3aNGiVPX77rtv6h1pn0/amwAAAAAAAACA7BOABQAAAAAAAABAo/fBBx+kqu/Zs2dO7kg7N+3d5FazZs2iW7du0blz56ioqMhoxhVXXBGfffZZli8DvmzBggXxy1/+MlVP9+7dc3TN7i/t/1dt27ZNvaNdu3ap6pcsWZJ6BwAAAAAAAACQXaWFPgAAAAAAAAAAAApt2bJlqeoPP/zwnNyRdm7au3dG7du3jx49evzV9xcsWJCov6ysLA4++ODE+8rLyxPXJvXtb387Lr300jj66KOjqKgoIiK2bNkSTz31VNxwww3x8ssvJ561atWquOOOO+Kqq67K+p35tjs8W3ZPL774YgwdOjRqa2tT9R199NE5umj3t2rVqsS15eXl0bRp09Q72rRpk6o+zU0AAAAAAAAAQG4IwAIAAAAAAAAAoNH7+OOPU9W3bds2J3e0a9cuVX3au3dGF1xwQVxwwQV/9f0/BUl9lQ4dOsT8+fOzfVYiRUVFMWHChBg5cuRf/Vl5eXkMHjw4TjrppPjBD34Qt9xyS+K5P//5z+OKK66I4uLiLF6bf7vys2X3smnTpli1alW89tpr8eCDD8Zjjz0W9fX1qeeccsopObiucfjf//3fxLXNmjXLaEfa0CwBWAAAAAAAAABQeAKwAAAAAAAAAABo9Kqrq1PVt9Ejbc8AACAASURBVGzZMid3pJ2b9m6ya9y4cdsMv/pzxcXFcdNNN8WSJUti+vTpieauWLEinnnmmTjhhBOycCU0Dscdd1xO5x9zzDHRrVu3nO7YnW3cuDFxbZMmTTLakbZv06ZNGe0BAAAAAAAAALJn1/4VgQAAAAAAAAAAkAU1NTWp6neWAKy0d5M9Xbp0iSuvvDJRbVFRUdx0001RVlaWeH7SsCwgP374wx8W+oRd2ubNmxPXlpSUZLSjtDTd74T9/PPPM9oDAAAAAAAAAGSPACwAAAAAAAAAAEipqKhop5jb0NCQkzv4aueff36Ul5cnru/atWuceOKJievnzJmTwVXkw6RJk6KoqCgrr/79+xf67ZDA0KFDY9CgQYU+Y5dWW1ubuLa4OLOPtqbt27JlS0Z7AAAAAAAAAIDsEYAFAAAAAAAAAECj16RJk1T1n332WU7u+PTTT1PVN23aNCd38NWGDh2a0563335bOAvsBLp16xZ33XVXoc/Y5ZWVlSWura+vz2jH1q1bU9WnCTEEAAAAAAAAAHJDABYAAAAAAAAAAI1e2iCpXAVgpZ2bNriL7Nhzzz2jS5cuqfuOPPLIxLW1tbWxdOnS1DuA7OnSpUvMmjUr9txzz0KfssurqKhIXFtXV5fRjrR9aW4CAAAAAAAAAHJDABYAAAAAAAAAAI3ePvvsk6p+9erVObljzZo1qerbt2+fkzvYvoMPPjijvgMPPDCKiooS169YsSKjPcCO+8d//Md46aWXorKystCn7BaaN2+euLampiajHWn70twEAAAAAAAAAOSGACwAAAAAAAAAABq9Tp06pap/8803c3LHggULUtWnvZvs2GuvvTLqKy8vTxW4sn79+oz2AJnr2LFjTJkyJR5++OFo165d6v40IXc7qqGhIVV9Pm/7sr333jtx7aZNmzLakbYvbfglAAAAAAAAAJB9ArAAAAAAAAAAAGj0vv71r6eqnz9/fk7uSDs37d1kR4sWLfLS+/nnn2e8B0iupKQkBg8eHFOnTo2qqqoYOnRoxrNKS0tT1dfU1GS8q7q6OlV9WVlZxrt2VJqwqdra2ti4cWPqHevWrUtVLwALAAAAAAAAAAov3SctAAAAAAAAAABgN3TkkUemqn/33Xdj3bp10aZNm6zdUFdXF6+99lqqnrR3f5WGhoYoKipKXF9bW5vV/buKrVu35qU3zbP4Kp4tjV1FRUW0atXqi9f+++8fvXr1it69e0fv3r1jzz33zMqePfbYI1X9pk2bMt6VtrdJkyYZ79pRaQMbV69eHc2bN0/dk4YQSQAAAAAAAAAoPAFYAAAAAAAAAAA0er169Yri4uKor69PVL9169aYPHlyXHDBBVm74cknn4w1a9ak6unTp0/W9kf84X2Vlib/SNGnn36a1f27ig0bNuSlN5thNZ4tu7vZs2dH//79C31GtGrVKlV92tCmHelt2bJlxrt21IEHHpiqftmyZVFZWZm6J420NwEAAAAAAAAA2Vdc6AMAAAAAAAAAAKDQWrZsGd/4xjdS9dx7771ZveFXv/pVqvqOHTvGwQcfnNUbtmzZkqo+bWDX7mLVqlUZ9W3cuDGqq6sT17dt2zajPdvi2UJ+dOjQIVX9okWLMt6Vtrdjx44Z79pRPXr0SFX//vvvp96xePHixLWVlZXRokWL1DsAAAAAAAAAgOwSgAUAAAAAAAAAABFxyimnpKp/4YUX4je/+U1Wdr/11lsxZcqUVD2DBw/Oyu4/lyacKSLid7/7XdZv2BW88847UV9fn7rv7bffTlWfzbAazzZ7Ro4cGQ0NDVl5zZkzp9Bvhyzbf//9U9XPnz8/410LFixIVb/ffvtlvGtHde7cOdXPtLfeeiv1jjQ9xx57bOr5AAAAAAAAAED2CcACAAAAAAAAAICIOPPMM6OoqChVz/e///2ora3d4d0XX3xx1NXVper57ne/m6iuSZMmiWd++umniWvXr18f7777buL63Ul1dXW8+eabqfteeeWVxLVNmzaNzp07b7fGs4Wdz2GHHZaqfvr06bF169bUe+rq6mL69Ompenr06JF6Tzb1798/ce3LL7+cavaGDRtShQz269cv1XwAAAAAAAAAIDcEYAEAAAAAAAAAQERUVlbGiSeemKpn0aJFceGFF0ZDQ0PGe8eMGRPPPvtsqp5DDjkk+vbtm6i2VatWiecuW7Ysce3DDz+cUWhLUqWlpYnq0gaHZcvkyZNT90yZMiVx7RFHHBHFxdv/eJdnCzuf/fbbL/bee+/E9WvXrk31s+FPpkyZEuvWrUtc3759++jYsWPqPdk0ZMiQxLWvvvpqqvf35JNPJv65VVJSEqeeemri2QAAAAAAAABA7gjAAgAAAAAAAACAP7ryyitT99x9990xevToqK+vT93705/+NK6//vrUfVdccUXi2jQhSQsWLEhUV1dXFzfffHPiuZlo0aJForo1a9YUJCjprrvuik8++SRx/csvvxzPP/984voBAwZ8ZY1nCzungQMHpqq/8sorU4U9rVu3Lq666qqc3pQL3/rWt6J58+aJardu3Rr33HNP4tkTJkxIXDtgwIBo165d4noAAAAAAAAAIHcEYAEAAAAAAAAAwB8dd9xxMWjQoNR9t9xySxx99NHxu9/9LlF9VVVVDBo0KK655prUuw477LAYMWJE4vr9998/ce3kyZMT1f3sZz+Ld955J/HcTCQNd9q8eXPMnTs3p7dsy5o1a+Kyyy5LVFtTUxOXXHJJNDQ0JJ5/2mmnfWWNZws7pyFDhqSq/+ijj+KEE06IDz/88Ctrq6qq4vjjj4+PPvoopzflQtOmTWPkyJGJ62+44YZYs2bNV9Y99dRTMWPGjMRzL7744sS1AAAAAAAAAEBuCcACAAAAAAAAAIA/c/PNN0eTJk1S973yyitxxBFHxIABA+KOO+6IN954I1auXBlbtmyJVatWxYIFC2LixIkxePDg6N69e8yaNSv1juLi4rj11lujuDj5x3769OmTuPbFF1+MX/ziF9utmTRpUvzbv/1b4pmZ6tixY+La8847L5566qnYunVrDi/6axMmTIjLL788amtr/2bNJ598EkOHDo3XX3898dxDDz00evXq9ZV1ni35NGrUqCgqKsrqa3d16qmnRrt27VL1zJs3L7p37x4XXnhhTJs2LZYvXx41NTVRU1MTy5Yti8cffzwuuOCCOPDAA2P+/PmpZu+zzz5x8sknp+rJlUsvvTRKS0sT1a5bty6+853vxKZNm/5mzeLFi+N73/te4v3dunWLU045JXE9AAAAAAAAAJBbyT5FAAAAAAAAAAAAjcQBBxwQP/vZz+KSSy5J3bt169aYPXt2zJ49OweXRVx++eXRt2/fVD1pQpIiIi688MJ4//3341/+5V/ia1/72hfff/nll+O//uu/YvLkyanmZerII4+MF154IVHt0qVLY+DAgbHHHnvEvvvuG02bNt1muM5DDz0U3bt3z+qd//mf/xmzZs2Kiy66KL75zW9Ghw4doq6uLj788MOYNm1a/PznP4+VK1emmnnZZZclqvNs/18uni1kqqysLEaPHp06UG7z5s1x5513xp133pnVe37wgx9EWVlZVmdmqmvXrnH++efH7bffnqj+qaeeimOOOSbGjh0bp5xySpSUlERExKZNm2LixIkxbty4WL16deL9N954Y6oQSQAAAAAAAAAgt4oaGhoaCn0EAAAAAAAAAADsbEaOHBn33HNPoc/4wje/+c2YMWNGlJam+513GzdujA4dOsSGDRtS9RUXF0eHDh2iadOmsXLlytT9XzZx4sQYOXJk4vr77rsvzjrrrB3a+WXz5s2Lnj17bvPP5syZE8cdd1xW92XioIMOigULFiQKq/Fs/9/2ni3bNmnSpBg1alRBb9jexxfHjh0b1113XeJZs2fPjv79+2fhquzYtGlTdO/ePVasWFHQOzp16hTvvvtuNGnSpKB3/Lk1a9bEoYceGqtWrUrVV15eHp06dYrNmzfH73//+6ivr0/VP2jQoHjiiSdS9QAAAAAAAAAAueXXWAEAAAAAAAAAwDbcddddMWjQoEKfERERRx55ZEyZMiV1+FVERPPmzePMM89M3VdfXx/Lly+P9957b4cDkjJx2mmnRYsWLfK+t5CKiori9ttvTxR+FeHZws6sWbNmMWHChCgqKirYDUVFRTFp0qSdKvwqIqJt27YxadKk1H83W7ZsiSVLlsTy5ctTh1+1a9cuJk6cmKoHAAAAAAAAAMg9AVgAAAAAAAAAALANZWVl8dhjj8WwYcMKeke/fv1i9uzZ0bJly4xnXHrppYlDldLo1q1b1mf+SfPmzePss8/O2fwdcfzxx0f79u2zPvfqq6+O/v37p+rxbGHnNXDgwPjxj39csP3jx4+PAQMGFGz/9gwaNCjGjx+fl11NmjSJxx57LCc/twEAAAAAAACAHSMACwAAAAAAAAAA/oby8vJ44IEHYty4cVFSUpL3/RdddFHMmjVrh8KvIiIOPvjgGDNmTJau+oPrr78+vvOd72R15pf9+7//e3Tp0iWnOzKx7777xuOPPx6tWrXK2swzzjgjbrjhhtR9ni3s3K6++uq49tpr8773+uuvj8svvzzve9O4/PLLc/5306xZs3jkkUfiqKOOyukeAAAAAAAAACAzArAAAAAAAAAAAGA7iouL40c/+lE899xz0aNHj7zsrKysjMcffzxuu+22qKioyMrMa665Jr71rW/t8JySkpK49dZb44c//GEWrtq+li1bxmOPPbZTBiX17t07fvOb38Tee++9w7OGDRsWDzzwQBQXZ/ZxLs8Wdm5jx46N+++/P1q3bp3zXW3atImHHnooL/+Os2Hs2LFx9913R5MmTbI+e//99485c+bEoEGDsj4bAAAAAAAAAMgOAVgAAAAAAAAAAJDAUUcdFW+88UbcddddccABB+RkR8eOHWP8+PGxcOHCOPnkk7M6u6SkJKZOnRrf/e53M56x3377xTPPPBMXX3xxFi/bvkMOOSTeeOON+P73vx8tW7bM294kevfuHW+88UYcd9xxGfWXl5fHjTfeGA8++GCUl5dnfIdnCzu/M888M955550455xzorS0NOvzy8rK4txzz4233347hg0blvX5uXTOOefE/PnzM/5Z+mUlJSXxT//0T/Hmm29G7969szITAAAAAAAAAMgNAVgAAAAAAAAAAJBQcXFxnHvuubFo0aJ44oknYsSIEdGmTZsdmtmsWbM444wz4qGHHoqqqqq44ooroqKiIksX/6WysrK4995749e//nWqEK+99torrrvuuli4cGH07ds3J7dtT8uWLeOWW26J3//+93HffffFpZdeGv/wD/8QnTt3jjZt2kRZWVneb/qTjh07xjPPPBMPPfRQ9OrVK1FPRUVFjBgxIhYuXBhXXnllFBUV7fAdni3s/L72ta/F3XffHYsXL44xY8ZEt27ddnjmAQccENdee228//77cdddd0X79u2zcGn+HXDAAfHMM8/E008/HaeeempG//ZbtWoV559/fixcuDDuvPNOwXoAAAAAAAAAsAsoamhoaCj0EQAAAAAAAAAAsKuqq6uLefPmxauvvhrz5s2LpUuXxrJly2Lt2rVRU1MTmzdvjvLy8mjSpEm0bt06OnXqFJ07d44ePXpEnz59ok+fPrHHHnsU5O7nnnsuZs6cGc8//3ysXLky1qxZEzU1NdGiRYuorKyMI444IgYNGhQnn3xyQW7Mpzlz5sRxxx2XqPbss8+OSZMmbfPPFi1aFE8++WQsWLAgqqqqYsOGDVFUVBR77rlnHHDAAXHUUUfFiSeeuMPBadvj2cKuY+nSpTF37txYsGBBLFy4MFasWBGrVq2KTZs2xebNmyPiD6F5zZs3j3322Sc6duwYBx10UPTo0SP69esXnTt3LuwbyJE1a9bE008/Hc8991y888478cEHH8T69eujuro6iouLo1mzZrH33ntHly5dvvi76Nevn59nAAAAAAAAALCLEYAFAAAAAAAAAAA0etkKwAIAAAAAAAAAACCZ4kIfAAAAAAAAAAAAAAAAAAAAAAAAQOMiAAsAAAAAAAAAAAAAAAAAAAAAAIC8EoAFAAAAAAAAAAAAAAAAAAAAAABAXgnAAgAAAAAAAAAAAAAAAAAAAAAAIK8EYAEAAAAAAAAAAAAAAAAAAAAAAJBXArAAAAAAAAAAAAAAAAAAAAAAAADIKwFYAAAAAAAAAAAAAAAAAAAAAAAA5JUALAAAAAAAAAAAAAAAAAAAAAAAAPJKABYAAAAAAAAAAAAAAAAAAAAAAAB5JQALAAAAAAAAAAAAAAAAAAAAAACAvBKABQAAAAAAAAAAAAAAAAAAAAAAQF4VNTQ0NBT6CAAAAAAAAAAAAAAAAAAAAAAAABqP4kIfAAAAAAAAAAAAAAAAAAAAAAAAQOMiAAsAAAAAAAAAAAAAAAAAAAAAAIC8EoAFAAAAAAAAAAAAAAAAAAAAAABAXgnAAgAAAAAAAAAAAAAAAAAAAAAAIK8EYAEAAAAAAAAAAAAAAAAAAAAAAJBXArAAAAAAAAAAAAAAAAAAAAAAAADIKwFYAAAAAAAAAAAAAAAAAAAAAAAA5JUALAAAAAAAAAAAAAAAAAAAAAAAAPJKABYAAAAAAAAAAAAAAAAAAAAAAAB5VVroAwDS+OSTT2Lu3LlffN2pU6eoqKgo4EUAAAAAAAAAAAAAAAAAAAAAQGO0efPmWLZs2Rdf9+vXL1q3bl3Ai3YtArCAXcrcuXNjyJAhhT4DAAAAAAAAAAAAAAAAAAAAAOAvTJ06NU477bRCn7HLKC70AQAAAAAAAAAAAAAAAAAAAAAAADQuArAAAAAAAAAAAAAAAAAAAAAAAADIq9JCHwCQRqdOnf7i66lTp0bXrl0LdA0AAAAAAAAAAAAAAAAAAAAA0Fi9//77MWTIkC++/nI2CtsnAAvYpVRUVPzF1127do1DDjmkQNcAAAAAAAAAAAAAAAAAAAAAAPzBl7NR2L7iQh8AAAAAAAAAAAAAAAAAAAAAAABA4yIACwAAAAAAAAAAAAAAAAAAAAAAgLwSgAUAAAAAAAAAAAAAAAAAAAAAAEBeCcACAAAAAAAAAAAAAAAAAAAAAAAgrwRgAQAAAAAAAAAAAAAAAAAAAAAAkFcCsAAAAAAAAAAAAAAAAAAAAAAAAMgrAVgAAAAAAAAAAAAAAAAAAAAAAADklQAsAAAAAAAAAAAAAAAAAAAAAAAA8qq00AdALkyaNClGjRqVqPbaa6+NsWPH5vagPNm0aVO8/fbbX7xWrlwZGzZsiI0bN8aGDRuitLQ0WrRoES1atIiWLVtGly5d4tBDD41DDjkkunTpEsXFMvEAAAAAAAAAAAAAAAAAAAAAAMg9AViwi1u3bl08+uijMWXKlHj66aejtrY2oznt27ePoUOHxre//e3o27dvFBUVZflSAAAAAAAAAAAAAAAAAAAAAAD4g+JCHwBkpqamJn784x9H586d49xzz42ZM2dmHH4VEfHxxx/HbbfdFv37948+ffrEs88+m8VrAQAAAAAAAAAAAAAA/o+dO42uujDzB/4kgUAE2csiWNGKyhIdthFKLZYjLlXc0FppKbihQNWxnmmtS7Wix6K1ijAO7h1GOyhzKnE/9uCAg4MIlIqyiVQsqBBQMBAChHD/L/5nHEW0v3u5NzcJn885eZHcZ/neJPf38gEAAAAAgP/jABbUQ/PmzYujjz46brjhhti6dWvW5y9atCgGDx4cP/7xj2P79u1Znw8AAAAAAAAAAAAAAAAAAAAAwIHNASyoZ1566aX43ve+F2vXrs35rieeeCKGDBmSkyNbAAAAAAAAAAAAAAAAAAAAAAAcuBzAgnpk+fLlcd5558XOnTtrbef8+fNj5MiRtbYPAAAAAAAAAAAAAAAAAAAAAICGzwEsqEfGjBkTlZWVtb63rKwspk+fXut7ObCtWbMmCgoK9vm1Zs2afMcDDjCeSUBd47kE1CWeSUBd47kE1CWeSUBd4pkE1DWeS0Bd4pkE1DWeS0Bd4pkE1CWeSUBd47kE1CWeSUBd47kEJOUAFtQT//Vf/xVz587N2/5bb701b7sBAAAAAAAAAAAAAAAAAAAAAGhYHMCCeuKJJ55Iu2fQoEHxH//xH/H+++/Hjh07YuvWrfHWW2/FnXfeGZ06dUpr1vLly2PRokVpZwAAAAAAAAAAAAAAAAAAAAAAgL01yncAyIXRo0fH6NGj8x0jq2bPnp1W/fXXXx+33XZbFBQUfPazJk2aRK9evaJXr15x0UUXxWmnnRYLFy5MPHPWrFnRt2/ftHIAAAAAAAAAAAAAAAAAAAAAAMDeCvMdAPj7du7cGe+9917i+iFDhsTtt9/+heNXe2vXrl3853/+ZxQXFyeeu3z58sS1AAAAAAAAAAAAAAAAAAAAAADwVRzAgnrgk08+iT179iSuv+qqqxLVHXbYYXHGGWcknlteXp64FgAAAAAAAAAAAAAAAAAAAAAAvkqjfAdoKN5+++0oKyuL1157LVasWBEbN26MqqqqaNasWXTp0iVKS0vjpJNOiuHDh0fr1q3zmjWVSsXcuXOjrKwsFi5cGO+8805s2bIlqqqqoqioKA455JCYOXNm9OnTJ6P5FRUVsWDBgli8eHGsXr061q1bF59++mlUVVVFRERJSUm0atUqunTpEkceeWT06dMn+vfvH82aNcvm2zygDRw4MHHt8ccfH3/84x8T1VZXV2caCQAAAAAAAAAAAAAAAAAAAAAAPuMAVkSceOKJMWfOnES17733XnTt2vWz7+fNmxe//OUvv7K/oqIili1bFsuWLYsnn3wyrrrqqrjsssviV7/6VbRt2zbtrGvWrInDDz88Ue3gwYNj9uzZX/jZ008/HTfddFMsXbp0nz01NTWxdu3aqKioSCtXZWVlTJ8+PZ544omYO3du2oeSmjRpEoMHD46RI0fGeeedF02bNk2rf2+zZs2Ke+65J1HtiBEjYsSIEfu1L9fSPZrWpk2bnNS2atUqrRwAAAAAAAAAAAAAAAAAAAAAALAvDmBlqLq6On7+85/HpEmTIpVKJe6rqqqK++67L6ZPnx7Tpk2LU045JYcp/8+OHTvikksuiT/84Q9ZnVtVVRX33ntv3HnnnbFly5aM5+zcuTNefvnlePnll+Paa6+NG264IcaOHRuNGzfOaN7atWvj+eefT1Tbr1+/jHbUpqZNm8ahhx4aa9euTVS/devWxEeztm7dmjjHYYcdlrgWAAAAAAAAAAAAAAAAAAAAAAC+SmG+A9RHlZWVceqpp8a9996b1vGrzysvL4/TTz89Hn744Syn+7Jt27bF0KFDs378at68eXHcccfF9ddfv1/Hr/ZWXl4eV199dfTv3z/eeuutrM2t70444YTEtYsXL85J7aBBgxLXAgAAAAAAAAAAAAAAAAAAAADAV3EAK027du2K008/PV555ZX9nlVTUxNjxozJ+mGqvXdceOGFMXfu3KzOfeyxx2Lw4MGxatWqrM79vDfffDMGDhwYZWVlOdtRn4waNSpx7aOPPpqobvPmzYl/v61atYqhQ4cmzgAAAAAAAAAAAAAAAAAAAAAAAF/FAaw0jRs3LubMmZO1ealUKi666KJYvHhx1mZ+3h133BHPPfdcVmc+8MADcfHFF0d1dXVW5+5LZWVlDB8+PJ5++umc76rrhg4dGscff3yi2j/84Q8xffr0r63ZvXt3XHzxxVFRUZFo5tixY6NZs2aJagEAAAAAAAAAAAAAAAAAAAAA4Os4gJWmWbNmZX3mrl27YsSIEbFr166szl29enXceuutWZ35wgsvxLhx47I68++pqamJCy+8MBYtWlSre+uagoKCeOyxxxIdoUqlUjFixIj46U9/GitXrvzCa9XV1fHyyy/Hd77znZg5c2ai3UceeWTcdNNNGeUGAAAAAAAAAAAAAAAAAAAAAIC9OYCVBYWFhdGxY8fo3r17tG3bNqMZK1asiHvuuSerudatWxfV1dVZm7dhw4YYNWpU7NmzJ2szk9q5c2dccMEFsX379lrfXZd07949nnnmmSgpKfm7talUKv7lX/4ljjnmmGjXrl306NEjunXrFi1btoxTTjkl5s+fn2hn+/bt47nnnku0EwAAAAAAAAAAAAAAAAAAAAAAknAAaz+0b98+7r///tiwYUN89NFHsWzZsti0aVO89957cfPNN0fTpk3TmnfXXXfV6QNP1113XWzatCmtnmOPPTamTJkSK1eujK1bt0ZFRUUsW7Ysfve730W3bt3SmrV69eqYOHFiWj0N0ZAhQ+L111+PY445JnHPxx9/HMuXL4933303qqqqEveVlpbGq6++GkcffXQmUQEAAAAAAAAAAAAAAAAAAAAAYJ8a5TtAfdWvX7946aWXom3btl96rWvXrnHLLbfED37wg/je974X5eXliWZ+/PHH8fjjj8eYMWOyHfczPXv2jB//+Mdx0kknxaGHHhpt27aN7du3x/vvvx+LFy+Ol19+OcrKyr7Ut2rVqpg2bVpau2688ca4+eabo1GjL/6bde/ePbp37x5jx46Nq6++Oh588MHEM+++++645pprolWrVmllaWiOPfbYePPNN2Pq1Klxxx13xPr167M6v0WLFvGzn/0sfvGLX6R9yC0d5eXlsXHjxrR63n333RylAQAAAAAAAAAAAAAAAAAAAACgtjiAlYGOHTvGiy++uM/jV5/Xo0ePKCsri0GDBsWePXsSzZ42bVpODmAVFxfHvffeG2PGjImioqIvvNaiRYsoLS2N0tLS+MlPfhLbtm2LmpqaL9RMnjw58XuIiLj22mtjwoQJX1vTtGnTmDp1alRUVMT06dMTza2srIxHHnkkrr322sRZGqri4uK46qqrYty4cXHnnXfGLbfcEtXV1fs1s3v37nH55ZfHyJEjo02bNllK+tXuv//+XEyU5gAAIABJREFU+PWvf53zPQAAAAAAAAAAAAAAAAAAAAAA1C2F+Q5QH912223Rrl27RLUDBgyIUaNGJZ49b9682Lx5c6bR9qm4uDieffbZGDt27JeOX+1L8+bNo2XLlp99v2fPnpgxY0bifV27do3bbrstUW1BQUFMnjw5WrVqlXj+k08+mbi2Idu1a1c88MADUVpaGjfccMN+H7+KiPjrX/8as2fPjjlz5kQqlcpCSgAAAAAAAAAAAAAAAAAAAAAA+DIHsNLUqlWrGDlyZFo948ePT1y7Z8+eePXVV9ON9bVuuummOPnkkzPuX7RoUaxfvz5x/TXXXBNNmzZNXN+uXbu49NJLE9cvXLgwysvLE9c3RLNmzYqjjjoqrrjiilixYkXW5u7cuTNmzpwZ5557bpSWlsbzzz+ftdkAAAAAAAAAAAAAAAAAAAAAAPC/HMBK0xlnnBHFxcVp9fTt2zcOO+ywxPWLFy9ON9ZX6tKlS1x33XX7NWPevHmJawsKCuL8889Pe8cPf/jDxLWpVCrmz5+f9o6GYsKECTF06NB4//33c7pn6dKlccYZZ8TYsWOjuro6JzvGjRsXb7/9dlpfM2fOzEkWAAAAAAAAAAAAAAAAAAAAAABqT6N8B6hv+vfvn1Ffnz59Eh8sWrlyZUY79uXSSy+NRo3278/81ltvJa494ogjolOnTmnv6N27d5SUlERVVVWi+iVLlsSwYcPS3lPf3XbbbfGrX/2qVndOnTo11q9fH0899VQ0btw4q7Pbt28f7du3z+pMAAAAAAAAAAAAAAAAAAAAAADqvsJ8B6hvevTokfO+Dz74IKMd+3L66afv94w1a9Ykri0tLc1oR2FhYXTv3j1xfdJjYg3J7Nmz0zp+VVxcHBdffHE8//zz8eGHH8bOnTujoqIili1bFg888EAcd9xxiWfNnDkzfvGLX2QSGwAAAAAAAAAAAAAAAAAAAAAAvsQBrDS1bds2o742bdokrt28eXNGO/bWpEmTOPbYY/d7zsaNGxPXdujQIeM96fSWl5dnvKc+2rNnT1x++eWRSqUS1Xft2jUWLFgQjzzySHz/+9+PTp06RXFxcRx88MHRvXv3GDNmTPz5z3+Om266KXGGSZMmxeuvv57pWwAAAAAAAAAAAAAAAAAAAAAAgM84gJWmgw8+OOd9O3bsyGjH3rp06RLFxcX7PaeysjJxbaa/n4iIFi1aJK7dvn17xnvqoxdffDHeeeedRLVNmzaN55577u8ePyssLIxbb701Lr744kRz9+zZExMnTkxUCwAAAAAAAAAAAAAAAAAAAAAAX8cBrDTV1NTkvK+goCCjHXtr1apVVuakUqnEtYWFmf9LFRUVJa5NJ1ND8OSTTyauvfjii6Nnz56J62+77bbEh9KeffbZ2LRpU+LZAAAAAAAAAAAAAAAAAAAAAACwLw5gpWnr1q057yspKclox96aN2+elTnp5Nm2bVvGeyoqKhLXZut3VF+89tpriWuHDx+e1uxOnTrF8ccfn6i2pqYm/ud//iet+QAAAAAAAAAAAAAAAAAAAAAAsDcHsNK0YcOGjPrKy8sT17Zr1y6jHbnStm3bxLUff/xxxnvS6W3Tpk3Ge+qb3bt3x1//+tfE9ccee2zaO0pLSxPXvvnmm2nPBwAAAAAAAAAAAAAAAAAAAACAz3MAK01vv/12zvs6d+6c0Y5c+eY3v5m4NtPfT0TEsmXLEtcedthhGe+pbzZv3pxWfatWrdLekU7Ppk2b0p4PAAAAAAAAAAAAAAAAAAAAAACf5wBWmubNm5d2T01NTSxcuDBxfc+ePdPekUs9evRIXLty5crYunVr2jtWrVoVn376aU4y1Xe7du1Kq37btm1p70inp7KyMu35AAAAAAAAAAAAAAAAAAAAAADweQ5gpemll16KioqKtHrmzJkTmzZtSlzft2/fdGPl1IABAxLX7t69O8rKytLeMWPGjLTqjz/++LR31FcHHXRQWvXvvvtu2jvS6Uk3DwAAAAAAAAAAAAAAAAAAAAAA7M0BrDTt3LkzJk+enFbPXXfdlbi2SZMmMWjQoHRj5dTAgQOjRYsWiesnT54cqVQqcf2OHTvioYceSlx/1FFHRdeuXRPX13etW7dO6+jUCy+8kNb8ysrK+O///u/E9R07dkxrPgAAAAAAAAAAAAAAAAAAAAAA7M0BrAz85je/ieXLlyeqfeqpp+Kll15KPHvo0KFRUlKSabScaNKkSZx55pmJ69944414+OGHE9ffcccdsWbNmsT15513XuLahuKYY45JXDtlypT45JNPEtffe++9sXXr1sT1Rx11VOJaAAAAAAAAAAAAAAAAAAAAAADYFwewMrBt27Y4+eSTY8mSJV9bV1ZWFqNGjUpr9ujRo/cjWe6MHz8+7fqZM2f+3boHH3wwJkyYkHhuo0aN4oorrkgrS0NwwgknJK7duHFjDB8+PLZs2fJ3a2fOnBm33HJLWlkGDRqUVj0AAAAAAAAAAAAAAAAAAAAAAOzNAawMrVu3Lvr16xdjx46N2bNnR3l5eezatSs+/PDDeOaZZ+Lcc8+Ns88+O3bs2JF45hFHHBFnn312DlNnbsCAATF06NDE9dXV1XHOOefEyJEjY968eZFKpT57bc+ePTFnzpw466yz4vLLL//Ca3/PqFGj4tBDD00re0MwfPjwtOpnz54dxx13XPzrv/5rbNiw4Quv1dTUxMKFC+Oiiy6Kc889N3bv3p147oABA6JTp05pZQEAAAAAAAAAAAAAAAAAAAAAgL01yneA+qy6ujqmTp0aU6dOzcq8W2+9NYqKirIyKxcmTZoUvXv3jp07dybuefzxx+Pxxx+PZs2aRadOnSKVSsVHH30U27dvT3t/69at4/bbb0+7ryH4zne+E8cdd1y8+eabiXv+9re/xbhx42LcuHHRsWPHaNOmTezcuTPWr18flZWVGeUYO3ZsRn0AAAAAAAAAAAAAAAAAAAAAAPB5DmDVEUOGDIkf/ehH+Y7xtbp37x6//e1v48orr0y7t7KyMt5999392v/II49Ehw4d9mtGfVVQUBC/+c1v4rTTTsuof/369bF+/fr9ylBaWlrn/0dpWLp27RqpVCrfMQAiwjMJqHs8l4C6xDMJqGs8l4C6xDMJqEs8k4C6xnMJqEs8k4C6xnMJqEs8k4C6xDMJqGs8l4C6xDMJqGs8l4CkCvMdoL7JxQGgdu3axbRp07I+Nxd++tOfxvjx42t97+233x7nnHNOre+tS0499dQYO3ZsXnaXlJTEtGnToqioKC/7AQAAAAAAAAAAAAAAAAAAAABoWBzAStOECROyegSrpKQknnnmmejcuXPWZuba5MmT46qrrqqVXQUFBTFhwoS4/vrra2VfXXfffffFmWeeWas7i4uL48knn4x/+Id/qNW9AAAAAAAAAAAAAAAAAAAAAAA0XA5gpamgoCCmTZsWl1122X7PatmyZbz44osxcODALCSrPQUFBTFp0qR46KGHonnz5jnb06ZNm5gxY0bceOONOdtR3zRq1Cj++Mc/xj/90z9FQUFBzvd16tQp/vSnP8WwYcNyvgsAAAAAAAAAAAAAAAAAAAAAgAOHA1gZKCwsjAcffDAeeOCBOPjggzOa0a9fv1iwYEEMHjw4y+lqz6WXXhpLliyJ888/P6vHmIqKimL06NGxdOnSGD58eNbmNhRFRUVxzz33xCuvvBJ9+vTJyY7GjRvH2LFj4+23347vfve7OdkBAAAAAAAAAAAAAAAAAAAAAMCBywGs/TBmzJhYsWJFjB8/Ppo3b56o55hjjolHHnkk5s+fH926dctxwtw7/PDD46mnnoolS5bE+PHjo127dhnP6tixY/zsZz+L5cuXx2OPPRYdO3bMeFZNTU3i2uLi4oz35NOJJ54YCxcujD/96U9xwQUXZHyM7fOOOuqouOmmm2L16tVx//33R5s2bbKQFAAAAAAAAAAAAAAAAAAAAAAAvqhRvgPUd4ccckhMmTIlJk6cGK+88kq89tprsWLFiti0aVPs2LEjDjrooOjcuXOUlpbG0KFDo3///vu1r2vXrpFKpbKUPnt69eoVU6ZMifvuuy/eeOONmDt3bixevDhWr14dH3zwQWzZsiWqqqoiIqKkpCRat24dnTt3jm7dukXv3r3jhBNOiL59+0ZBQUFW8mzfvj1xbevWrbOyMx8KCgripJNOipNOOil27doVCxYsiNdffz2WLl0a7777bqxfvz42bdoU27dvj+rq6igoKIgmTZpEixYt4hvf+EYceuihn/0NBg0aFEceeWS+3xIAAAAAAAAAAAAAAAAAAAAAAAcAB7CypFmzZjFs2LAYNmxYvqPkVWFhYQwYMCAGDBiQ1xwbN25MXFufD2B9XnFxcQwaNCgGDRqU7ygAAAAAAAAAAAAAAAAAAAAAAPC1CvMdAHJhxYoViWs7deqUwyQAAAAAAAAAAAAAAAAAAAAAAMDeHMCiwamuro5Zs2Ylqi0qKoq+ffvmOBEAAAAAAAAAAAAAAAAAAAAAAPB5DmDR4EycODE++eSTRLW9evWK5s2b5zgRAAAAAAAAAAAAAAAAAAAAAADweY3yHQD21+7du2Pz5s3xl7/8JR599NGYPn164t4zzzwzh8kAAAAAAAAAAAAAAAAAAAAAAIB9cQCLeuvss8+OsrKyjPsbN24cY8eOzWIiAAAAAAAAAAAAAAAAAAAAAAAgicJ8B4B8GT16dHTq1CnfMQAAAAAAAAAAAAAAAAAAAAAA4IDjABYHpG9961tx99135zsGAAAAAAAAAAAAAAAAAAAAAAAckBzA4oDTtm3bmDFjRhx88MFfWTN79uwoKCjI2xcAAAAAAAAAAAAAAAAAAAAAADRkDmBxQDn66KPj9ddfj969e+c7CgAAAAAAAAAAAAAAAAAAAAAAHLAcwOKA8I1vfCPuvPPO+POf/xxHHnlkvuMAAAAAAAAAAAAAAAAAAAAAAMABrVG+A0C2tWjRItq2bRsdOnSIgQMHxpAhQ2LIkCFx0EEH5TsaAAAAAAAAAAAAAAAAAAAAAAAQDmBFRMTs2bPzHYEMzJw5M98RAAAAAAAAAAAAAAAAAAAAAACADBTmOwAAAAAAAAAAAAAAAAAAAAAAAAAHlkb5DgB10YknnhipVCrfMQAAAAAAAAAAAAAAAAAAAAAAoEEqzHcAAAAAAAAAAAAAAAAAAAAAAAAADiwOYAEAAAAAAAAAAAAAAAAAAAAAAFCrHMACAAAAAAAAAAAAAAAAAAAAAACgVjmABQAAAAAAAAAAAAAAAAAAAAAAQK1yAAsAAAAAAAAAAAAAAAAAAAAAAIBa5QAWAAAAAAAAAAAAAAAAAAAAAAAAtcoBLAAAAAAAAAAAAAAAAAAAAAAAAGqVA1gAAAAAAAAAAAAAAAAAAAAAAADUKgewaJB+//vfR0FBQaKvW265Jd9xAQAAAAAAAAAAAAAAAAAAAADggOIAFtQj6Rz2ytZXr1698v22AQAAAAAAAAAAAAAAAAAAAABoYBzAAgAAAAAAAAAAAAAAAAAAAAAAoFY5gAUAAAAAAAAAAAAAAAAAAAAAAECtcgALAAAAAAAAAAAAAAAAAAAAAACAWuUAFgAAAAAAAAAAAAAAAAAAAAAAALXKASyABmrNmjVRUFCwz681a9bkOx7UKT4vkF8+g5AbPltQe3zeIH0+N5DMgfZZaSjvt76/j/qav77mjpA9X2TPj/qavb7mjpA9X+pr9vqaO0L2fKivuSNkz5f6mr2+5o6QPV9kr331NXdE/c7+VRrie6L+8X8I6fGZgdrj8wb55TMItcNnDb6ezwjkn88h1A6fNdLlABYAAAAAAAAAAAAAAAAAAAAAAAC1ygEsAAAAAAAAAAAAAAAAAAAAAAAAalWjfAeAXBg9enSMHj063zEAAAAAAAAAAAAAAAAAAAAAAIB9cAALGqBUKpXvCAAAAAAAAAAAAAAAAAAAAAAA8JUK8x0AAAAAAAAAAAAAAAAAAAAAAACAA0ujfAdoKN5+++0oKyuL1157LVasWBEbN26MqqqqaNasWXTp0iVKS0vjpJNOiuHDh0fr1q3zmjWVSsXcuXOjrKwsFi5cGO+8805s2bIlqqqqoqioKA455JCYOXNm9OnTJ6P5FRUVsWDBgli8eHGsXr061q1bF59++mlUVVVFRERJSUm0atUqunTpEkceeWT06dMn+vfvH82aNcvm2wQAAAAAAAAAAAAAAAAAAAAAAOooB7Ai4sQTT4w5c+Ykqn3vvfeia9eun30/b968+OUvf/mV/RUVFbFs2bJYtmxZPPnkk3HVVVfFZZddFr/61a+ibdu2aWdds2ZNHH744YlqBw8eHLNnz/7Cz55++um46aabYunSpfvsqampibVr10ZFRUVauSorK2P69OnxxBNPxNy5c6O6ujqt/iZNmsTgwYNj5MiRcd5550XTpk3T6t/brFmz4p577klUO2LEiBgxYsR+7QMAAAAAAAAAAAAAAAAAAAAAAJJzACtD1dXV8fOf/zwmTZoUqVQqcV9VVVXcd999MX369Jg2bVqccsopOUz5f3bs2BGXXHJJ/OEPf8jq3Kqqqrj33nvjzjvvjC1btmQ8Z+fOnfHyyy/Hyy+/HNdee23ccMMNMXbs2GjcuHFG89auXRvPP/98otp+/fpltAMAAAAAAAAAAAAAAAAAAAAAAMhMYb4D1EeVlZVx6qmnxr333pvW8avPKy8vj9NPPz0efvjhLKf7sm3btsXQoUOzfvxq3rx5cdxxx8X111+/X8ev9lZeXh5XX3119O/fP956662szQUAAAAAAAAAAAAAAAAAAAAAAOqGRvkOUN/s2rUrTj/99JgzZ85+z6qpqYkxY8bEQQcdFCNGjMhCun3vuPDCC2Pu3LlZnfvYY4/F5ZdfHtXV1Vmd+3lvvvlmDBw4MJ544ok466yzcranIVq2bFksWLAgFi5cGB9++GFs3rw5KioqonHjxnHQQQdFp06d4tBDD43S0tLo379/dOvWLd+RAQAAAAAAAAAAAAAAAAAAAAA4gDiAlaZx48Zl5fjV/0qlUnHRRRdF9+7do3fv3lmb+7/uuOOOeO6557I684EHHogrrrgiqzO/SmVlZQwfPjxmzJgR55xzTq3sbAh69uyZVv1hhx0WP/jBD+KSSy6Jo48+OkepAAAAAAAAAAAAAAAAAAAAAADg/yvMd4D6ZtasWVmfuWvXrhgxYkTs2rUrq3NXr14dt956a1ZnvvDCCzFu3Liszvx7ampq4sILL4xFixbV6t4Dyfvvvx933XVX9OjRIy644IJYs2ZNviMBAAAAAAAAAAAAAAAAAAAAANCAOYCVBYWFhdGxY8fo3r17tG3bNqMZK1asiHvuuSerudatWxfV1dVZm7dhw4YYNWpU7NmzJ2szk9q5c2dccMEFsX379lrffSDZs2dPPPXUU9GrV694+OGH8x0HAAAAAAAAAAAAAAAAAAAAAIAGygGs/dC+ffu4//77Y8OGDfHRRx/FsmXLYtOmTfHee+/FzTffHE2bNk1r3l133VWnDzxdd911sWnTprR6jj322JgyZUqsXLkytm7dGhUVFbFs2bL43e9+F926dUtr1urVq2PixIlp9ZCZysrKuOyyy+Lqq6/OdxQAAAAAAAAAAAAAAAAAAAAAABqgRvkOUF/169cvXnrppWjbtu2XXuvatWvccsst8YMf/CC+973vRXl5eaKZH3/8cTz++OMxZsyYbMf9TM+ePePHP/5xnHTSSXHooYdG27ZtY/v27fH+++/H4sWL4+WXX46ysrIv9a1atSqmTZuW1q4bb7wxbr755mjU6Iv/Zt27d4/u3bvH2LFj4+qrr44HH3ww8cy77747rrnmmmjVqlVaWcjMfffdF40bN47f/va3OZlfXl4eGzduTKvn3XffzUkWAAAAAAAAAAAAAAAAAAAAAABqjwNYGejYsWO8+OKL+zx+9Xk9evSIsrKyGDRoUOzZsyfR7GnTpuXkAFZxcXHce++9MWbMmCgqKvrCay1atIjS0tIoLS2Nn/zkJ7Ft27aoqan5Qs3kyZMTv4eIiGuvvTYmTJjwtTVNmzaNqVOnRkVFRUyfPj3R3MrKynjkkUfi2muvTZyF/XP33XdH375948ILL8z67Pvvvz9+/etfZ30uAAAAAAAAAAAAAAAAAAAAAAB1W2G+A9RHt912W7Rr1y5R7YABA2LUqFGJZ8+bNy82b96cabR9Ki4ujmeffTbGjh37peNX+9K8efNo2bLlZ9/v2bMnZsyYkXhf165d47bbbktUW1BQEJMnT45WrVolnv/kk08mriU7rrzyyvj444/zHQMAAAAAAAAAAAAAAAAAAAAAgAbCAaw0tWrVKkaOHJlWz/jx4xPX7tmzJ1599dV0Y32tm266KU4++eSM+xctWhTr169PXH/NNddE06ZNE9e3a9cuLr300sT1CxcujPLy8sT1DV1RUVF8+9vfjmuuuSZ+//vfx6JFi2Lt2rWxdevW2LlzZ6xfvz4WLFgQkyZNiu9+97sZ7fj444/jrrvuynJyAAAAAAAAAAAAAAAAAAAAAAAOVI3yHaC+OeOMM6K4uDitnr59+8Zhhx0W77//fqL6xYsXx1lnnZVJvC/p0qVLXHfddfs1Y968eYlrCwoK4vzzz097xw9/+MP47W9/m6g2lUrF/PnzY9iwYWnvaSgaN24cw4YNi+HDh8epp54abdq0+craDh06RIcOHaJfv35x1VVXxRtvvBGjR4+O5cuXp7Vz6tSpcfPNN0dJScn+xv/MuHHj0v5/effdd+Pss8/OWgYAAAAAAAAAAAAAAAAAAAAAAGqfA1hp6t+/f0Z9ffr0SXwAa+XKlRnt2JdLL700GjXavz/zW2+9lbj2iCOOiE6dOqW9o3fv3lFSUhJVVVWJ6pcsWXJAHsDq3Llz3HnnnTFq1Kho3759RjP+8R//MRYtWhSnnXZazJkzJ3Hfp59+Gi+++GKce+65Ge3dl/bt22f8PgAAAAAAAAAAAAAAAAAAAAAAqL8K8x2gvunRo0fO+z744IOMduzL6aefvt8z1qxZk7i2tLQ0ox2FhYXRvXv3xPVJj4k1NEOHDo1//ud/3u+jUSUlJVFWVhZdunRJq+9Pf/rTfu0FAAAAAAAAAAAAAAAAAAAAAIAIB7DS1rZt24z62rRpk7h28+bNGe3YW5MmTeLYY4/d7zkbN25MXNuhQ4eM96TTW15envEe/r+WLVvGhAkT0uqZP39+jtIAAAAAAAAAAAAAAAAAAAAAAHAgcQArTQcffHDO+3bs2JHRjr116dIliouL93tOZWVl4tpMfz8RES1atEhcu3379oz38H/OP//8aNKkSeL6VatW5TANAAAAAAAAAAAAAAAAAAAAAAAHCgew0lRTU5PzvoKCgox27K1Vq1ZZmZNKpRLXFhZm/i9VVFSUuDadTHy1Zs2aRZ8+fRLXb9u2LSoqKnKYCAAAAAAAAAAAAAAAAAAAAACAA4EDWGnaunVrzvtKSkoy2rG35s2bZ2VOOnm2bduW8Z50Ditl63dERMeOHdOqr6yszFESAAAAAAAAAAAAAAAAAAAAAAAOFA5gpWnDhg0Z9ZWXlyeubdeuXUY7cqVt27aJaz/++OOM96TT26ZNm4z38EWNGzdOqz6VSuUoCQAAAAAAAAAAAAAAAAAAAAAABwoHsNL09ttv57yvc+fOGe3IlW9+85uJazP9/URELFu2LHHtYYcdlvEevijdo27NmjXLURIAAAAAAAAAAAAAAAAAAAAAAA4UDmClad68eWn31NTUxMKFCxPX9+zZM+0dudSjR4/EtStXroytW7emvWPVqlXx6aef5iQTX626ujr+/Oc/J64/6KCDomXLljlMBAAAAAAAAAAAAAAAAAAAAADAgcABrDS99NJLUVFRkVbPnDlzYtOmTYnr+/btm26snBowYEDi2t27d0dZWVnaO2bMmJFW/fHHH5/2jvps+/btsWvXrqzPffHFF9M6WHbkkUdmPQMAAAAAAAAAAAAAAAAAAAAAAAceB7DStHPnzpg8eXJaPXfddVfi2iZNmsSgQYPSjZVTAwcOjBYtWiSunzx5cqRSqcT1O3bsiIceeihx/VFHHRVdu3ZNXN8QvPPOO9GjR494+umnszZz9+7dcf3116fV8+1vfztr+wEAAAAAAAAAAAAAAAAAAAAAOHA5gJWB3/zmN7F8+fJEtU899VS89NJLiWcPHTo0SkpKMo2WE02aNIkzzzwzcf0bb7wRDz/8cOL6O+64I9asWZO4/rzzzktc25CsXr06zj333Ojfv388++yzaR0Z21sqlYqLL744li5dmlbfqaeemvFOAAAAAAAAAAAAAAAAAAAAAAD4Xw5gZWDbtm1x8sknx5IlS762rqysLEaNGpXW7NGjR+9HstwZP3582vUzZ878u3UPPvhgTJgwIfHcRo0axRVXXJFWloZm4cKFceaZZ0b37t3jvvvui02bNqXVX15eHmeccUb8+7//e1p97dq1i+9///tp9QAAAAAAAAAAAAAAAAAAAAAAwL44gJWhdevWRb9+/WLs2LExe/bsKC8vj127dsWHH34YzzzzTJx77rlx9tlnx44dOxLPPOKII+Lss8/OYerMDRgwIIYOHZq4vrq6Os4555wYOXJkzJs3L1Kp1Gev7dmzJ+bMmRNnnXVWXH755V947e8ZNWpUHHrooWllb6hWrlwZV199dRxyyCFx0kknxe9+97t4/fXXY/v27V+qraqqildffTWuvPLK+Na3vhUvvPBC2vuuueaaaNy4cTaiAwAAAAAAAAAAAAAAAAAAAABwgGuU7wD1WXV1dUydOjWmTp2alXm33nprFBUVZWVWLkyaNCl69+4dO3fuTNzz+OOPx+OPPx7NmjWLTp06RSqVio8++mifR5r+ntatW8ftt9+edl9DV11dHbNmzYpZs2Z99rMOHTpE69Zwd7BhAAAgAElEQVSto7CwMDZv3hwbN26M3bt3Z7zj8MMPj6uvvjobcQEAAAAAAAAAAAAAAAAAAAAAIApSqVQq3yHy7cQTT4w5c+bkNcOQIUO+cMDoq6xZsyYOP/zwRDMHDx4cs2fP3s9kXzRlypS48sorszozqT/+8Y9xzjnnJKr9/e9/HxdddFGi2ptvvjluueWW/UiWe3/5y1+id+/eedndqFGjmDVrVnz3u9/Ny/69LV26NHr16vXZ92+//Xb07Nkzj4kAAAAAAAAAAAAAAAAAAAAAgAORWyj7pzDfAeqbH/3oR1mf2a5du5g2bVrW5+bCT3/60xg/fnyt77399tsTH78iewoKCmLq1Kl15vgVAAAAAAAAAAAAAAAAAAAAAAANgwNYaZowYUJWj2CVlJTEM888E507d87azFybPHlyXHXVVbWyq6CgICZMmBDXX399rezj/zRu3DgeffTRuOSSS/IdBQAAAAAAAAAAAAAAAAAAAACABsYBrDQVFBTEtGnT4rLLLtvvWS1btowXX3wxBg4cmIVktaegoCAmTZoUDz30UDRv3jxne9q0aRMzZsyIG2+8MWc72LeuXbvGq6++GqNHj853FAAAAAAAAAAAAAAAAAAAAAAAGiAHsDJQWFgYDz74YDzwwANx8MEHZzSjX79+sWDBghg8eHCW09WeSy+9NJYsWRLnn39+FBQUZG1uUVFRjB49OpYuXRrDhw/P2tz6rLS0NF544YX4yU9+Ei1atMjZnpYtW8bNN98cy5YtiwEDBuRsDwAAAAAAAAAAAAAAAAAAAAAABzYHsPbDmDFjYsWKFTF+/Pho3rx5op5jjjkmHnnkkZg/f35069Ytxwlz7/DDD4+nnnoqlixZEuPHj4927dplPKtjx47xs5/9LJYvXx6PPfZYdOzYMeNZNTU1iWuLi4sz3lNbioqK4rTTTot/+7d/iw0bNsTMmTPjyiuvjOOOOy4KC/f/Y3z88cfHpEmT4m9/+1vccsstUVJSkoXUAAAAAAAAAAAAAAAAAAAAAACwb43yHaC+O+SQQ2LKlCkxceLEeOWVV+K1116LFStWxKZNm2LHjh1x0EEHRefOnaO0tDSGDh0a/fv33699Xbt2jVQqlaX02dOrV6+YMmVK3HffffHGG2/E3LlzY/HixbF69er44IMPYsuWLVFVVRURESUlJdG6devo3LlzdOvWLXr37h0nnHBC9O3bNwoKCrKSZ/v27YlrW7dunZWdtaVp06Zx1llnxVlnnRUREVu2bIn58+fHypUrY9WqVbFq1apYt25dbN26NbZu3Rrbtm2LmpqaaNKkSbRo0SI6dOgQ3/zmN6Nnz57Rp0+fOPHEE6N9+/Z5flcAAAAAAAAAAAAAAAAAAAAAABxIHMDKkmbNmsWwYcNi2LBh+Y6SV4WFhTFgwIAYMGBAXnNs3LgxcW19O4C1t1atWsUpp5wSp5xySr6jAAAAAAAAAAAAAAAAAAAAAABAIoX5DgC5sGLFisS1nTp1ymESAAAAAAAAAAAAAAAAAAAAAABgbw5g0eBUV1fHrFmzEtUWFRVF3759c5wIAAAAAAAAAAAAAAAAAAAAAAD4PAewaHAmTpwYn3zySaLaXr16RfPmzXOcCAAAAAAAAAAAAAAAAAAAAAAA+LxG+Q4A+2v37t2xefPm+Mtf/hKPPvpoTP9/7Nx7lJUFuT/wZ2a4ym1gEBEw0DQDRAKh4JgLI1BXyMXECkIhMww4aR5OaSVJXlLxhsJygUqy5lhxOR0dMyXOwQMuPJhCpOIIKYdhARkDJA4Mw21mfn+cVT8voO/es/dsYD6fteYP3M/lu9+Z1z+fBQsS944YMSKLyQAAAAAAAAAAAAAAAAAAAAAAgCNxAIvj1qhRo6KkpCTt/saNG8ekSZMymAgAAAAAAAAAAAAAAAAAAAAAAEgiP9cBIFcmTJgQp556aq5jAAAAAAAAAAAAAAAAAAAAAABAg+MAFg3Spz/96bjvvvtyHQMAAAAAAAAAAAAAAAAAAAAAABokB7BocIqKimLx4sXRqlWro9YsX7488vLycvYDAAAAAAAAAAAAAAAAAAAAAAAnMgewaFDOPvvseOmll6JPnz65jgIAAAAAAAAAAAAAAAAAAAAAAA2WA1g0CCeffHLMmDEj/vjHP8aZZ56Z6zgAAAAAAAAAAAAAAAAAAAAAANCgNcp1AMi01q1bR1FRUZxyyikxcODAGDx4cAwePDhOOumkXEcDAAAAAAAAAAAAAAAAAAAAAADCAayIiFi+fHmuI5CGp556KtcRAAAAAAAAAAAAAAAAAAAAAACANOTnOgAAAAAAAAAAAAAAAAAAAAAAAAANS6NcB4Bj0YUXXhi1tbW5jgEAAAAAAAAAAAAAAAAAAAAAACek/FwHAAAAAAAAAAAAAAAAAAAAAAAAoGFxAAsAAAAAAAAAAAAAAAAAAAAAAIB65QAWAAAAAAAAAAAAAAAAAAAAAAAA9coBLAAAAAAAAAAAAAAAAAAAAAAAAOqVA1gAAAAAAAAAAAAAAAAAAAAAAADUKwewAAAAAAAAAAAAAAAAAAAAAAAAqFcOYAEAAAAAAAAAAAAAAAAAAAAAAFCvHMACAAAAAAAAAAAAAAAAAAAAAACgXjmAxQlp/vz5kZeXl+hn+vTpuY4LAAAAAAAAAAAAAAAAAAAAAAANSqNcBwCyb/fu3fHggw9GbW1tSn3f//73o7CwMEupAAAAAAAAAAAAAAAAAAAAAABoqBzAggbg+uuvj+Li4pT7JkyY4AAWAAAAAAAAAAAAAAAAAAAAAAAZl5/rAEB2Pf3002kdvwIAAAAAAAAAAAAAAAAAAAAAgGxxAAtOYLt27YqJEyfmOgYAAAAAAAAAAAAAAAAAAAAAAHyAA1hwAps8eXJs37491zEAAAAAAAAAAAAAAAAAAAAAAOADHMCCE9SiRYti0aJFuY7BMaysrCzy8vKO+FNWVpbreEAGeM/h2OKdhNzzHsLxx3sLJwbvcsPld/9Rnsn/8Rw8gwjPIMIziPAMIjyDCM8gwjNo6N//7zwHz6Chf/+/8xw8g4b+/f+uoT+Hhv79IzyD45XfG2SHdwuOP95bOP55j+HE5N2G44N3FXLPewjHPu/piccBLDgBbd++PaZMmZLrGAAAAAAAAAAAAAAAAAAAAAAAcEQOYMEJ6Nprr42dO3fmOgYAAAAAAAAAAAAAAAAAAAAAABxRo1wHgGyYMGFCTJgwIdcxcqK4uDhKSkpyHQMAAAAAAAAAAAAAAAAAAAAAAI4qP9cBgMzZtm1bXH/99bmOAQAAAAAAAAAAAAAAAAAAAAAAH8sBLDiBXHPNNbF79+6jfn7xxRfXYxoAAAAAAAAAAAAAAAAAAAAAADiyRrkOcKJYt25dlJSUxIsvvhjr16+PHTt2RFVVVbRo0SK6dOkSvXr1iiFDhsTll18ebdu2zWnW2traWLlyZZSUlMTq1avjz3/+c+zevTuqqqqioKAgOnXqFE899VT07ds3rfkVFRXxyiuvxNq1a2Pjxo2xdevWeO+996KqqioiIpo3bx6FhYXRpUuXOPPMM6Nv377Rv3//aNGiRSa/ZoPz6KOPxpIlS476eWFhYcybNy+6dOlSj6kAAAAAAAAAAAAAAAAAAAAAAOCjHMCKiAsvvDBWrFiRqHbTpk3RrVu3f/x71apV8aMf/eio/RUVFVFaWhqlpaWxcOHCuO666+I73/lO/PSnP42ioqKUs5aVlcXpp5+eqHbQoEGxfPnyD/y3J598MqZNmxZvvPHGEXuqq6tjy5YtUVFRkVKuysrKWLBgQfzyl7+MlStXxqFDh1Lqb9q0aQwaNCiuvPLKGD16dDRr1iyl/g9btmxZPPDAA4lqx44dG2PHjq3TvlzbvHlzTJ069WNrZs2aFZ07d66nRAAAAAAAAAAAAAAAAAAAAAAAcHQOYKXp0KFD8cMf/jAefPDBqK2tTdxXVVUVDz30UCxYsCCKi4vj4osvzmLK/2///v3x7W9/O371q19ldG5VVVXMnDkzZsyYEbt37057zoEDB2Lp0qWxdOnSmDp1avzkJz+JSZMmRePGjdOat2XLlvjd736XqLZfv35p7ThW1NbWxtVXXx179uw5as1ll10W48aNq8dUAAAAAAAAAAAAAAAAAAAAAABwdPm5DnA8qqysjEsuuSRmzpyZ0vGr9ysvL49hw4bFY489luF0H7V3794YOnRoxo9frVq1Knr37h0//vGP63T86sPKy8vj+uuvj/79+8frr7+esbknqtmzZ8fzzz9/1M9PPvnkmDt3bj0mAgAAAAAAAAAAAAAAAAAAAACAj+cAVooOHjwYw4YN+9iDQ0lVV1fHxIkTM36Y6sM7xowZEytXrszo3McffzwGDRoUb731Vkbnvt+rr74aAwcOjJKSkqztON69/fbbcdNNN31szdy5c+Pkk0+up0QAAAAAAAAAAAAAAAAAAAAAAPDJHMBK0eTJk2PFihUZm1dbWxvf+ta3Yu3atRmb+X533nlnPPPMMxmdOXfu3Lj66qvj0KFDGZ17JJWVlXH55ZfHk08+mfVdx5uampqYMGFC7Nu376g148aNi8suu6weUwEAAAAAAAAAAAAAAAAAAAAAwCdzACtFy5Yty/jMgwcPxtixY+PgwYMZnbtx48a49dZbMzrz2WefjcmTJ2d05ieprq6OMWPGxJo1a+p177Hu/vvvjxdffPGon3fu3DlmzZpVj4kAAAAAAAAAAAAAAAAAAAAAACAZB7AyID8/Pzp27Bjdu3ePoqKitGasX78+HnjggYzm2rp1axw6dChj87Zv3x7jx4+PmpqajM1M6sCBA/H1r3899u3bV++7j0VvvvlmTJs27WNr5s2bF4WFhfWUCAAAAAAAAAAAAAAAAAAAAAAAknMAqw46dOgQDz/8cGzfvj3eeeedKC0tjZ07d8amTZvilltuiWbNmqU075577jmmDzzddNNNsXPnzpR6zj333Jg9e3Zs2LAh9uzZExUVFVFaWhr3339/nHXWWSnN2rhxY9x9990p9ZyIDh8+HOPHj4/9+/cftebaa6+Niy++uB5TAQAAAAAAAAAAAAAAAAAAAABAco1yHeB41a9fv1iyZEkUFRV95LNu3brF9OnT42tf+1p86UtfivLy8kQzd+3aFU888URMnDgx03H/oWfPnjFu3LgYMmRInHbaaVFUVBT79u2LzZs3x9q1a2Pp0qVRUlLykb633noriouLU9p18803xy233BKNGn3wz6x79+7RvXv3mDRpUlx//fXxyCOPJJ553333xQ033BCFhYUpZTmR3HXXXfHKK68c9fMzzjgj7r333npMlL7y8vLYsWNHSj1vv/12ltIAAAAAAAAAAAAAAAAAAAAAAFBfHMBKQ8eOHeO555474vGr9+vRo0eUlJTE+eefHzU1NYlmFxcXZ+UAVpMmTWLmzJkxceLEKCgo+MBnrVu3jl69ekWvXr3iqquuir1790Z1dfUHambNmpX4O0RETJ06NW677baPrWnWrFnMmTMnKioqYsGCBYnmVlZWxrx582Lq1KmJs5xIXn311bj11luP+nl+fn7Mnz8/WrZsWY+p0vfwww/Hz372s1zHAAAAAAAAAAAAAAAAAAAAAACgnuXnOsDx6Pbbb4/27dsnqh0wYECMHz8+8exVq1bFu+++m260I2rSpEn89re/jUmTJn3k+NWRtGzZMtq0afOPf9fU1MTixYsT7+vWrVvcfvvtiWrz8vJi1qxZUVhYmHj+woULE9eeSA4dOhTjx4+PQ4cOHbXm+9//flxwwQX1mAoAAAAAAAAAAAAAAAAAAAAAAFLnAFaKCgsL48orr0ypZ8qUKYlra2pq4oUXXkg11seaNm1aXHTRRWn3r1mzJv76178mrr/hhhuiWbNmievbt28f11xzTeL61atXR3l5eeL6E8Wtt94ar7766lE/7969e9xxxx31mAgAAAAAAAAAAAAAAAAAAAAAANLTKNcBjjeXXnppNGnSJKWe8847L7p27RqbN29OVL927doYOXJkOvE+okuXLnHTTTfVacaqVasS1+bl5cUVV1yR8o5vfOMbce+99yaqra2tjT/84Q8xfPjwlPccr1avXh133XXXUT9v1KhRFBcXp3R47FgwefLklP9e3n777Rg1alSWEgEAAAAAAAAAAAAAAAAAAAAAUB8cwEpR//790+rr27dv4gNYGzZsSGvHkVxzzTXRqFHdfs2vv/564tozzjgjTj311JR39OnTJ5o3bx5VVVWJ6l977bUGcwDrwIEDcdVVV8Xhw4ePWvOjH/0o+vXrV4+pMqNDhw7RoUOHXMcAAAAAAAAAAAAAAAAAAAAAAKCe5ec6wPGmR48eWe/btm1bWjuOZNiwYXWeUVZWlri2V69eae3Iz8+P7t27J65PekzsRHDzzTfHm2++edTP+/TpE9OmTavHRAAAAAAAAAAAAAAAAAAAAAAAUDcOYKWoqKgorb527dolrn333XfT2vFhTZs2jXPPPbfOc3bs2JG49pRTTkl7Tyq95eXlae85npSVlcX9999/1M+bNm0axcXF0bhx43pMBQAAAAAAAAAAAAAAAAAAAAAAdeMAVopatWqV9b79+/entePDunTpEk2aNKnznMrKysS16T6fiIjWrVsnrt23b1/ae44nu3fvjpqamqN+/rOf/SzOOeecekwEAAAAAAAAAAAAAAAAAAAAAAB15wBWiqqrq7Pel5eXl9aODyssLMzInNra2sS1+fnp/0kVFBQkrk0l04nspptuiry8vJR+UnH66acfccaECROy84UAAAAAAAAAAAAAAAAAAAAAAGgQHMBK0Z49e7Le17x587R2fFjLli0zMieVPHv37k17T0VFReLaTD0jAAAAAAAAAAAAAAAAAAAAAACg/jmAlaLt27en1VdeXp64tn379mntyJaioqLEtbt27Up7Tyq97dq1S3sPAAAAAAAAAAAAAAAAAAAAAACQWw5gpWjdunVZ7+vcuXNaO7LlU5/6VOLadJ9PRERpaWni2q5du6a9BwAAAAAAAAAAAAAAAAAAAAAAyC0HsFK0atWqlHuqq6tj9erViet79uyZ8o5s6tGjR+LaDRs2xJ49e1Le8dZbb8V7772XlUwAAAAAAAAAAAAAAAAAAAAAAMCxxQGsFC1ZsiQqKipS6lmxYkXs3Lkzcf15552XaqysGjBgQOLaw4cPR0lJSco7Fi9enFL9F77whZR3AAAAAAAAAAAAAAAAAAAAAAAAxwYHsFJ04MCBmDVrVko999xzT+Lapk2bxvnnn59qrKwaOHBgtG7dOnH9rFmzora2NnH9/v3749FHH01c/5nPfCa6deuWuB4AAAAAAAAAAAAAAAAAAAAAADi2OICVhrvuuivefPPNRLWLFi2KJUuWJJ49dOjQaN68ebrRsqJp06YxYsSIxPUvv/xyPPbYY4nr77zzzigrK0tcP3r06MS1AAAAAAAAAAAAAAAAAAAAAADAsccBrDTs3bs3Lrroonjttdc+tq6kpCTGjx+f0uwJEybUIVn2TJkyJeX6p5566hPrHnnkkbjtttsSz23UqFF897vfTSkLAAAAAAAAAAAAAAAAAAAAAABwbHEAK01bt26Nfv36xaRJk2L58uVRXl4eBw8ejL/85S/x9NNPx1e/+tUYNWpU7N+/P/HMM844I0aNGpXF1OkbMGBADB06NHH9oUOH4rLLLosrr7wyVq1aFbW1tf/4rKamJlasWBEjR46Ma6+99gOffZLx48fHaaedllL249nnPve5qK2tzehPKjZt2nTEGfPnz8/OFwYAAAAAAAAAAAAAAAAAAAAAoEFolOsAx7NDhw7FnDlzYs6cORmZd+utt0ZBQUFGZmXDgw8+GH369IkDBw4k7nniiSfiiSeeiBYtWsSpp54atbW18c4778S+fftS3t+2bdu44447Uu4DAAAAAAAAAAAAAAAAAAAAAACOLXm1tbW1uQ6RaxdeeGGsWLEipxkGDx4cy5Yt+8S6srKyOP300xPNHDRoUCxfvryOyT5o9uzZ8b3vfS+jM5P6j//4j7jssssS1c6fPz++9a1vJaq95ZZbYvr06XVIdnzJy8tLXLtp06bo1q1b9sKk4Y033ohzzjnnH/9et25d9OzZM4eJAAAAAAAAAAAAAAAAAAAAAICGyC2UusnPdYDjzTe/+c2Mz2zfvn0UFxdnfG42/PM//3NMmTKl3vfecccdiY9fAQAAAAAAAAAAAAAAAAAAAAAAxzYHsFJ02223ZfQIVvPmzePpp5+Ozp07Z2xmts2aNSuuu+66etmVl5cXt912W/z4xz+ul30AAAAAAAAAAAAAAAAAAAAAAED2OYCVory8vCguLo7vfOc7dZ7Vpk2beO6552LgwIEZSFZ/8vLy4sEHH4xHH300WrZsmbU97dq1i8WLF8fNN9+ctR0AAAAAAAAAAAAAAAAAAAAAAED9cwArDfn5+fHII4/E3Llzo1WrVmnN6NevX7zyyisxaNCgDKerP9dcc0289tprccUVV0ReXl7G5hYUFMSECRPijTfeiMsvvzxjcwEAAAAAAAAAAAAAAAAAAAAAgGODA1h1MHHixFi/fn1MmTIlWrZsmajns5/9bMybNy/+8Ic/xFlnnZXlhNl3+umnx6JFi+K1116LKVOmRPv27dOe1bFjx/iXf/mXePPNN+Pxxx+Pjh07pj2ruro6cW2TJk3S3gMAAAAAAAAAAAAAAAAAAAAAAKSuUa4DHO86deoUs2fPjrvvvjuef/75ePHFF2P9+vWxc+fO2L9/f5x00knRuXPn6NWrVwwdOjT69+9fp33dunWL2traDKXPnHPOOSdmz54dDz30ULz88suxcuXKWLt2bWzcuDG2bdsWu3fvjqqqqoiIaN68ebRt2zY6d+4cZ511VvTp0ycuuOCCOO+88yIvLy8jefbt25e4tm3bthnZeby45ZZbEtcWFhZmMQkAAAAAAAAAAAAAAAAAAAAAAA2VA1gZ0qJFixg+fHgMHz4811FyKj8/PwYMGBADBgzIaY4dO3Ykrm1oB7CmT5+e6wgAAAAAAAAAAAAAAAAAAAAAADRw+bkOANmwfv36xLWnnnpqFpMAAAAAAAAAAAAAAAAAAAAAAAAf5gAWJ5xDhw7FsmXLEtUWFBTEeeedl+VEAAAAAAAAAAAAAAAAAAAAAADA+zmAxQnn7rvvjr/97W+Jas8555xo2bJllhMBAAAAAAAAAAAAAAAAAAAAAADv1yjXAaCuDh8+HO+++2786U9/il/84hexYMGCxL0jRozIYjIAAAAAAAAAAAAAAAAAAAAAAOBIHMDiuDVq1KgoKSlJu79x48YxadKkDCYCAAAAAAAAAAAAAAAAAAAAAACSyM91AMiVCRMmxKmnnprrGAAAAAAAAAAAAAAAAAAAAAAA0OA4gEWD9OlPfzruu+++XMcAAAAAAAAAAAAAAAAAAAAAAIAGyQEsGpyioqJYvHhxtGrV6qg1y5cvj7y8vJz9AAAAAAAAAAAAAAAAAAAAAADAicwBLBqUs88+O1566aXo06dPrqMAAAAAAAAAAAAAAAAAAAAAAECD5QAWDcLJJ58cM2bMiD/+8Y9x5pln5joOAAAAAAAAAAAAAAAAAAAAAAA0aI1yHQAyrXXr1lFUVBSnnHJKDBw4MAYPHhyDBw+Ok046KdfRAAAAAAAAAAAAAAAAAAAAAACAcAArIiKWL1+e6wik4amnnsp1BAAAAAAAAAAAAAAAAAAAAAAAIA35uQ4AAAAAAAAAAAAAAAAAAAAAAABAw9Io1wHgWHThhRdGbW1trmMAAAAAAAAAAAAAAAAAAAAAAMAJKT/XAQAAAAAAAAAAAAAAAAAAAAAAAGhYHMACAAAAAAAAAAAAAAAAAAAAAACgXjmABQAAAAAAAAAAAAAAAAAAAAAAQL1yAAsAAAAAAAAAAAAAAAAAAAAAAIB65QAWAAAAAAAAAAAAAAAAAAAAAAAA9coBLAAAAAAAAAAAAAAAAAAAAAAAAOqVA1gAAAAAAAAAAAAAAAAAAAAAAADUKwewAAAAAAAAAAAAAAAAAAAAAAAAqFcOYJHY/PnzIy8vL9HP9OnTcx0XAAAAAAAAAAAAAAAAAAAAAAA4RjXKdQA41mzfvj3WrVsX69ati40bN8a2bdti69atsX379qisrIyqqqqoqqqK/Pz8aNKkSbRq1SratWsXnTp1im7dukXPnj2jT58+MWDAgGjWrFm9ZC4rK4s1a9bE22+/HZs2bYr//d//ja1bt8bevXtj7969UVlZGXl5edGmTZto3bp1tGnTJs4888zo27dv9OnTJz7/+c9HmzZt6iUrAAAAAAAAAAAAAAAAAAAAAAA4gEWD9+qrr8by5cvjhRdeiBdffDG2b9+eqK+mpiYOHz4c+/bti+3bt8ebb775gc+bNWsWQ4YMiXHjxsXll18ejRpl5nWrqamJ1atXx+9///t46aWX4pVXXokdO3Yk6i0vL4/y8vKIiFizZk0sXLgwIiKaNm0aI0eOjAkTJsRFF10UBQUFGckKAAAAAAAAAAAAAAAAAAAAAABH4gAWDd6Xv/zl2LVrV8bn7t+/P5555pl45pln4rTTTos77rgjrrzyyrTnlZaWxp133hm///3vEx+8SurAgQOxaNGiWLRoUfTq1SvmzJkT//RP/5TRHQAAAAAAAAAAAAAAAAAAAAAA8Hf5uQ4ADcGWLVviqquuipEjR8aePXvSmvHyyy/HE088kfHjVx/2+uuvxxe/+MWYNGlSHDhwIKu7AAAAAAAAAAAAAAAAAAAAAABomBzAgnr09NNPx5AhQ6KysjLXUT5WbW1tzJkzJy655JKoqqrKdRwAAAAAAAAAAAAAAAAAAAAAAE4wDmBBPXv55Zdj0qRJuY6RyPLly2PMmDG5jgEpKSsri7y8vCP+lJWV5ToeNEjeS8gt7yAcO7yP0DB412nZH74AACAASURBVKFh8u5nh+f6QZ6HZ9DQv//feQ6eQUP//n/nOXgGDf37/11Dfw4N/ftHeAYRnkFD//4RnkGEZxDhGTT07/93nkPD5PcO9cs7BycW7zTwfv6fAMc27ygcG7yLkDveP+BE5wAW5MC//du/xcqVK3MdI5GSkpKYM2dOrmMAAAAAAAAAAAAAAAAAAAAAAHACcQALPkF+fn4UFhZG165d45xzzolPf/rTUVhYWOe5M2fOzEC6I2vcuHF07do1evbsGV27do1mzZrVad60adNiz549GUoHAAAAAAAAAAAAAAAAAAAAAEBD1yjXATh+TJgwISZMmJDrGFnXvXv3uPjii6Nfv35xzjnnxGc/+9lo2rTpR+r++te/xn//93/H/PnzY+nSpSnvee655+LAgQNHnJ2q5s2bx0UXXRRf+cpX4otf/GKcffbZUVBQ8I/PDx8+HH/84x9j/vz58fjjj8f+/ftTmr9z58749a9/HRMnTqxzVgAAAAAAAAAAAAAAAAAAAAAAcAALIqJ3795x9dVXx1e/+tXo0qVLop6OHTvGmDFjYsyYMfG73/0uxo4dGxUVFYl37tu3L0pLS6NPnz7pxo7evXvH5MmTY8yYMdGqVauj1jVq1Cg+//nPx+c///n43ve+F6NHj47S0tKUdv37v/+7A1gAAAAAAAAAAAAAAAAAAAAAAGSEA1g0eCtWrIiePXvWacawYcPiySefjCFDhkRtbW3ivs2bN6d1AGvgwIExffr0uOiii1Lu7d69eyxbtiwGDBgQmzdvTty3cuXKqKmpifz8/JR3AgAAAAAAAAAAAAAAAAAAAADA+x3TB7DWrVsXJSUl8eKLL8b69etjx44dUVVVFS1atIguXbpEr169YsiQIXH55ZdH27Ztc5q1trY2Vq5cGSUlJbF69er485//HLt3746qqqooKCiITp06xVNPPRV9+/ZNa35FRUW88sorsXbt2ti4cWNs3bo13nvvvaiqqoqIiObNm0dhYWF06dIlzjzzzOjbt2/0798/WrRokcmveUKq6/Grvxs8eHAMHTo0li5dmrhn7969Ke0466yz4t57740RI0akGu8DOnbsGLNmzUppTlVVVWzZsiW6du1ap90AAAAAAAAAAAAAAAAAAAAAAJC1A1gXXnhhrFixIlHtpk2bolu3bv/496pVq+JHP/rRUfsrKiqitLQ0SktLY+HChXHdddfFd77znfjpT38aRUVFKWctKyuL008/PVHtoEGDYvny5R/4b08++WRMmzYt3njjjSP2VFdXx5YtW6KioiKlXJWVlbFgwYL45S9/GStXroxDhw6l1N+0adMYNGhQXHnllTF69Oho1qxZSv0ftmzZsnjggQcS1Y4dOzbGjh1bp33Ho8GDB6d0AKtly5aJa4cOHRpjx46NJk2apBPtI4YPHx5du3aNzZs3J+7ZsWOHA1gAAAAAAAAAAAAAAAAAAAAAANRZ1g5gpePQoUPxwx/+MB588MGora1N3FdVVRUPPfRQLFiwIIqLi+Piiy/OYsr/b//+/fHtb387fvWrX2V0blVVVcycOTNmzJgRu3fvTnvOgQMHYunSpbF06dKYOnVq/OQnP4lJkyZF48aN05q3ZcuW+N3vfpeotl+/fmntON4VFhamVH/KKackru3cuXOqcT7RoEGDori4OHH9gQMHMp4BAAAAAAAAAAAAAAAAAAAAAICGJz/XAf6usrIyLrnkkpg5c2ZKx6/er7y8PIYNGxaPPfZYhtN91N69e2Po0KEZP361atWq6N27d/z4xz+u0/GrDysvL4/rr78++vfvH6+//nrG5vJB27ZtS1zbuHHj+NznPpfFNJ+sU6dOKdW3adMmS0kAAAAAAAAAAAAAAAAAAAAAAGhIjokDWAcPHoxhw4bF888/X+dZ1dXVMXHixIwfpvrwjjFjxsTKlSszOvfxxx+PQYMGxVtvvZXRue/36quvxsCBA6OkpCRrOxqyZ555JnHtl770pWjevHkW03yygwcPJq7Ny8uLLl26ZDENAAAAAAAAAAAAAAAAAAAAAAANxTFxAGvy5MmxYsWKjM2rra2Nb33rW7F27dqMzXy/O++8M6VDR0nMnTs3rr766jh06FBG5x5JZWVlXH755fHkk09mfVdDMm/evJT+5q677rospkmmtLQ0ce2ZZ54ZhYWFWUwDAAAAAAAAAAAAAAAAAAAAAEBDcUwcwFq2bFnGZx48eDDGjh0bBw8ezOjcjRs3xq233prRmc8++2xMnjw5ozM/SXV1dYwZMybWrFlTr3tPRAcPHowZM2bEtddem7hn5MiRMWzYsCym+mQVFRUpHZ679NJLs5gGAAAAAAAAAAAAAAAAAAAAAICGpFGuAxxNfn5+dOjQIdq2bRvl5eWxa9eulGesX78+Hnjggbjxxhszlmvr1q0ZmxURsX379hg/fnzU1NRkdG4SBw4ciK9//evx2muvxUknnVTv+49HBw8ejH379sVf/vKX2LBhQyxfvjx+85vfxLZt2xLP6N27d/ziF7/IYspkZs+eHVVVVYnrx48fn8U0AAAAAAAAAAAAAAAAAAAAAAA0JPm5DvBhHTp0iIcffji2b98e77zzTpSWlsbOnTtj06ZNccstt0SzZs1SmnfPPffEvn37spS27m666abYuXNnSj3nnntuzJ49OzZs2BB79uyJioqKKC0tjfvvvz/OOuuslGZt3Lgx7r777pR6GoLdu3dHXl7eR36aNm0abdu2jZ49e8ZXv/rVeOihh1I6fjVs2LBYtmxZtGvXLovpP1lZWVn8/Oc/T1w/atSo6N27dxYTAQAAAAAAAAAAAAAAAAAAAADQkDTKdYD369evXyxZsiSKioo+8lm3bt1i+vTp8bWvfS2+9KUvRXl5eaKZu3btiieeeCImTpyY6bj/0LNnzxg3blwMGTIkTjvttCgqKop9+/bF5s2bY+3atbF06dIoKSn5SN9bb70VxcXFKe26+eab45ZbbolGjT74q+vevXt07949Jk2aFNdff3088sgjiWfed999ccMNN0RhYWFKWUiuR48e8ZOf/CTGjh2b6yhRVVUVo0ePjsrKykT1zZo1i7vuuisrWcrLy2PHjh0p9bz99ttZyQIAAAAAAAAAAAAAAAAAAAAAQP05Zg5gdezYMZ577rkjHr96vx49ekRJSUmcf/75UVNTk2h2cXFxVg5gNWnSJGbOnBkTJ06MgoKCD3zWunXr6NWrV/Tq1Suuuuqq2Lt3b1RXV3+gZtasWYm/Q0TE1KlT47bbbvvYmmbNmsWcOXOioqIiFixYkGhuZWVlzJs3L6ZOnZo4C8l85StfiRtvvDEuuOCCyMvLy3WcOHjwYIwePTrWrFmTuOf++++Ps88+Oyt5Hn744fjZz36WldkAAAAAAAAAAAAAAAAAAAAAABy78nMd4O9uv/32aN++faLaAQMGxPjx4xPPXrVqVbz77rvpRjuiJk2axG9/+9uYNGnSR45fHUnLli2jTZs2//h3TU1NLF68OPG+bt26xe23356oNi8vL2bNmhWFhYWJ5y9cuDBxLck9++yzMWXKlLjvvvti7969Oc1SWVkZw4cPj2effTZxz8SJE2PSpElZTAUAAAAAAAAAAAAAAAAAAAAAQEN0TBzAKiwsjCuvvDKlnilTpiSurampiRdeeCHVWB9r2rRpcdFFF6Xdv2bNmvjrX/+auP6GG26IZs2aJa5v3759XHPNNYnrV69eHeXl5YnrSW7dunXxgx/8ILp27Rpz5szJSYZ33nknLrjggli6dGninpEjR8bDDz+cxVQAAAAAAAAAAAAAAAAAAAAAADRUjXIdICLi0ksvjSZNmqTUc95550XXrl1j8+bNierXrl0bI0eOTCfeR3Tp0iVuuummOs1YtWpV4tq8vLy44oorUt7xjW98I+69995EtbW1tfGHP/whhg8fnvIekvnb3/4WkyZNiiVLlsSvf/3raN68eb3sff3112PYsGGxZcuWxD2XXHJJLFy4MAoKCrKYLGLy5Mkp/22//fbbMWrUqCwlAgAAAAAAAAAAAAAAAAAAAACgPhwTB7D69++fVl/fvn0TH8DasGFDWjuO5JprrolGjer26F5//fXEtWeccUaceuqpKe/o06dPNG/ePKqqqhLVv/baaw5g1YOSkpIYMWJEPPPMM9G0adOs7vrP//zPGD16dFRUVCTuGTFiRCxatCjr2SIiOnToEB06dMj6HgAAAAAAAAAAAAAAAAAAAAAAji35uQ4QEdGjR4+s923bti2tHUcybNiwOs8oKytLXNurV6+0duTn50f37t0T1yc9JtYQFBQURO/evT/yc9ZZZ0WnTp2iSZMmdZr/X//1XzFt2rQMpT2yefPmxVe+8pWUjl+NGzcufvOb39TL8SsAAAAAAAAAAAAAAAAAAAAAABquRrkOEBFRVFSUVl+7du0S17777rtp7fiwpk2bxrnnnlvnOTt27Ehce8opp6S9J5Xe8vLytPecaFq1ahV/+tOfjvp5dXV1bN68OV5++eV49tlnY/HixbF///6Udtx3330xbty4jPw9vV9tbW3cfPPN8fOf/zylvn/913+NGTNmRF5eXkbzAAAAAAAAAAAAAAAAAAAAAADAh+XnOkDE/x0bynZfqseJjqZLly7RpEmTOs+prKxMXJvu84mIaN26deLaffv2pb2noSkoKIgzzjgjvvGNb0RxcXFs2rQpvvnNb6Y0o6amJmbOnJnRXAcOHIhvfvObKR2/KigoiNmzZ8c999zj+BUAAAAAAAAAAAAAAAAAAAAAAPXimDiAVV1dnfW+TB32KSwszMic2traxLX5+en/mgoKChLXppKJD+rYsWM88cQT8YMf/CClvoULF8bhw4czkmHXrl0xZMiQ+PWvf524p0WLFvHUU0/FlClTMpIBAAAAAAAAAAAAAAAAAAAAAACSOCYOYO3Zsyfrfc2bN09rx4e1bNkyI3NSybN3796091RUVCSuzdQzasjuvPPO6N69e+L6ffv2xdq1a+u8d+PGjTFw4MBYuXJl4p5OnTrFCy+8EJdeemmd9wMAAAAAAAAAAAAAAAAAAAAAQCqOiQNY27dvT6uvvLw8cW379u3T2pEtRUVFiWt37dqV9p5Uetu1a5f2Hv5PQUFBXHXVVSn1lJaW1mnn//zP/8SAAQPirbfeStxz7rnnxksvvRR9+/at024AAAAAAAAAAAAAAAAAAAAAAEjHMXEAa926dVnv69y5c1o7suVTn/pU4tp0n09EaseVunbtmvYe/r/u3bunVF+XA2eLFy+OL3/5y7Fz587EPRdffHGsXLkyTjvttLT3AgAAAAAAAAAAAAAAAAAAAABAXRwTB7BWrVqVck91dXWsXr06cX3Pnj1T3pFNPXr0SFy7YcOG2LNnT8o73nrrrXjvvfeykomjy89P7bWqqqpKa8+MGTPi61//euzfvz9xz8SJE+OZZ56JVq1apbUTAAAAAAAAAAAAAAAAAAAAAAAy4Zg4gLVkyZKoqKhIqWfFihWxc+fOxPXnnXdeqrGyasCAAYlrDx8+HCUlJSnvWLx4cUr1X/jCF1LewUetX78+pfqWLVumVH/48OH47ne/GzfeeGPU1tYm6snLy4u77ror5s6dG40aNUppHwAAAAAAAAAAAAAAAAAAAAAAZNoxcQDrwIEDMWvWrJR67rnnnsS1TZs2jfPPPz/VWFk1cODAaN26deL6WbNmJT52FBGxf//+ePTRRxPXf+Yzn4lu3bolrj8R1NTUZGVmcXFxSj2f+tSnEtfu2bMnhg8fHnPnzk3c06xZs1i4cGHceOONKeUCAAAAAAAAAAAAAAAAAAAAAIBsOSYOYEVE3HXXXfHmm28mql20aFEsWbIk8eyhQ4dG8+bN042WFU2bNo0RI0Ykrn/55ZfjscceS1x/5513RllZWeL60aNHJ649UZx//vlx6623Rnl5ecZmTps2LdatW5dSz7nnnpuobtu2bXHBBRek9Lffvn37eP755+OKK65IKRMAAAAAAAAAAAAAAAAAAAAAAP+PnTuPrro+8wf+JBAWAUkIsggqi1QBkbJ1YPx5oAxozzAiFrsIKmARBVq6ON2wVge0DioFpNODC9WiVZSxgqXKocWC4kERRBFZqhQUKGVTtiSEkOT3xxytC+q9yb25Cbxe59w/yH0+z/P+fpMPfz6kU7VZgHX48OG46KKLYu3atZ9Zt2DBghgxYkRSvUeOHFmJZOkzfvz4pOvnz5//uXX33ntvTJ48OeG+tWvXjuuvvz6pLCeCnTt3xs033xxnnnlmDBs2LH7/+99HQUFBhXrt27cvrrnmmvjFL36R1Ll27dpF+/btE6q977774rXXXkuq/969e+Nf//VfIysrKyWf6nqXAAAAAAAAAAAAAAAAAAAAAACoWarNAqyIiO3bt0fPnj1j7NixsXTp0ti9e3ccPXo0/v73v8dTTz0VX/3qV2PIkCFx5MiRhHu2a9cuhgwZksbUFde7d+8YOHBgwvUlJSVx2WWXxVVXXRUrVqyI8vLyD74rKyuLZcuWxaWXXhrXXXfdR777PCNGjIgzzjgjqewnkuLi4nj00Udj6NCh0bRp07jkkkviF7/4RTzzzDPxj3/841PP7dy5MxYuXBjXX399tG3bNh544IGkZ48aNaoy0QEAAAAAAAAAAAAAAAAAAAAAoEaqnekAH1dSUhKzZs2KWbNmpaTfpEmTolatWinplQ4zZsyIbt26RXFxccJnHn744Xj44YejQYMG0bJlyygvL4+dO3dGYWFh0vPz8vLitttuS/rcierIkSOxcOHCWLhw4Qc/y8nJidzc3MjLy4ucnJwoKCiIffv2xaFDhyo1Ky8vL8aOHVvZyAAAAAAAAAAAAAAAAAAAAAAAUONUuwVYqdS/f/8YPnx4pmN8po4dO8Zdd90V3/nOd5I+W1BQEG+99Val5s+ePTuaN29eqR4nupKSktizZ0/s2bMnpX2nTp0a+fn5Ke0JRLRp0ybKy8szHQP4EPcSMssdhOrDfYSTg7sOJyd3Pz2814/yPryDk/353+c9eAcn+/O/z3vwDk7253/fyf4eTvbnj/AOIryDk/35I7yDCO8gwjs42Z//fd7DycnvHaqWOwcnFnca+DD/J0D15o5C9eAuQua4f8CJLjvTASIiLUuqmjZtGnPmzEl533T49re/HePHj6/yubfddltcdtllVT6XiPHjx8eoUaMyHQMAAAAAAAAAAAAAAAAAAAAAADKiWizAmjx5ckqXYNWvXz+eeuqpaNWqVcp6ptvMmTNjwoQJVTIrKysrJk+eHBMnTqySeXzU9773vZg5c2amYwAAAAAAAAAAAAAAAAAAAAAAQMZUiwVYWVlZMWfOnLj22msr3atx48bxzDPPRJ8+fVKQrOpkZWXFjBkz4r777ouGDRumbU6TJk1i3rx58bOf/SxtMzi+Zs2axWOPPRbTpk2LrKysTMcBAAAAAAAAAAAAAAAAAAAAAICMqRYLsCIisrOz495774177rknGjVqVKEePXv2jJdffjn69u2b4nRVZ/To0bF27dr42te+ltIlSbVq1YqRI0fGG2+8EUOHDk1Z35osPz+/Subk5ubGjTfeGJs2bYqvf/3rVTITAAAAAAAAAAAAAAAAAAAAAACqs2qzAOt9Y8aMiY0bN8b48eOjYcOGCZ0599xzY/bs2fHSSy9Fhw4d0pww/dq2bRuPP/54rF27NsaPHx9NmzatcK8WLVrED37wg9iwYUM88MAD0aJFiwr3Ki0tTbi2Tp06FZ5TVVavXh1r166NO++8My666KLIzc1NWe9GjRrF4MGD46GHHort27fHrbfemtL+AAAAAAAAAAAAAAAAAAAAAABQk2WVl5eXp6Nxv379YtmyZQnVbtmyJdq0afOJnxcUFMSzzz4bL7zwQmzcuDH27t0bR44ciVNOOSVatWoVXbp0iYEDB0avXr1SnL56KSsri5UrV8by5ctjzZo1sXnz5tixY0fs378/ioqKIiKifv36kZeXF61atYoOHTpEt27d4sILL4wePXpEVlZWSnLMnDkzJkyYkFDtr3/96xg7dmxK5lalzZs3xyuvvBLr16+Pt99+O955553YsWNHHDhwIAoLC6OwsDCOHTsWOTk5Ua9evcjNzY38/Pxo3bp1tG3bNjp16hQ9evSIrl27Rk5OTqYf54T0xhtvxHnnnffBv9etWxedO3fOYCIAAAAAAAAAAAAAAAAAAAAA4GRkF0rl1M50gM/SoEGDuOSSS+KSSy7JdJSMys7Ojt69e0fv3r0zmmPPnj0J1+bl5aUxSfq0b98+2rdvn+kYAAAAAAAAAAAAAAAAAAAAAABwQsvOdABqjo0bNyZc27JlyzQmAQAAAAAAAAAAAAAAAAAAAAAAajILsEhISUlJLFmyJKHaWrVqRY8ePdKcCAAAAAAAAAAAAAAAAAAAAAAAqKkswCIhU6ZMiXfffTeh2vPOOy8aNmyY5kQAAAAAAAAAAAAAAAAAAAAAAEBNVTvTAaiejh07Fu+99168+uqr8Zvf/Cbmzp2b8NnBgwenMRkAAAAAAAAAAAAAAAAAAAAAAFDTWYDFRwwZMiQWLFhQ4fM5OTkxduzYFCYCAAAAAAAAAAAAAAAAAAAAAABONNmZDsCJZeTIkdGyZctMxwAAAAAAAAAAAAAAAAAAAAAAAKoxC7BImfbt28fUqVMzHQMAAAAAAAAAAAAAAAAAAAAAAKjmLMAiJfLz82PevHnRqFGjT61ZunRpZGVlZewDAAAAAAAAAAAAAAAAAAAAAABUDxZgUWnnnHNOvPjii9GtW7dMRwEAAAAAAAAAAAAAAAAAAAAAAGoAC7CosNNOOy3uuOOOeOWVV+Lss8/OdBwAAAAAAAAAAAAAAAAAAAAAAKCGqJ3pANQMp556auTn50fz5s2jT58+0b9//+jfv3+ccsopmY4GAAAAAAAAAAAAAAAAAAAAAADUMGlbgLV06dJ0tSaN5s+fn+kIAAAAAAAAAAAAAAAAAAAAAADACS470wEAAAAAAAAAAAAAAAAAAAAAAAA4udTOdABOHv369Yvy8vJMxwAAAAAAAAAAAAAAAAAAAAAAADIsO9MBAAAAAAAAAAAAAAAAAAAAAAAAOLlYgAUAAAAAAAAAAAAAAAAAAAAAAECVsgALAAAAAAAAAAAAAAAAAAAAAACAKmUBFgAAAAAAAAAAAAAAAAAAAAAAAFXKAiwAAAAAAAAAAAAAAAAAAAAAAACqlAVYAAAAAAAAAAAAAAAAAAAAAAAAVCkLsAAAAAAAAAAAAAAAAAAAAAAAAKhSFmABAAAAAAAAAAAAAAAAAAAAAABQpSzAImEPPvhgZGVlJfS55ZZbMh0XAAAAAAAAAAAAAAAAAAAAAACopmpnOgBUN7t27Yp169bFunXrYvPmzbFjx47Yvn177Nq1KwoKCqKoqCiKiooiOzs76tSpE40aNYomTZrE6aefHm3atInOnTtHt27donfv3lGvXr0qz19WVhYbNmyIlStXxjvvvBPl5eUJnRs5cmS0adMmveEAAAAAAAAAAAAAAAAAAAAAACAswIJ47bXXYunSpfHcc8/FCy+8ELt27UroXFlZWRw7diwKCwtj165dsWHDho98X69evRgwYEBceeWVMXTo0KhdOz3X7Z133omVK1d+8Fm9enUcPnw46T79+vWzAAsAAAAAAAAAAAAAAAAAAAAAgCphARYnvX/7t3+Lffv2pbzvkSNHYuHChbFw4cI444wz4rbbbourrrqq0n3Xr18fTzzxxAcLr3bv3p2CtAAAAAAAAAAAAAAAAAAAAAAAUHWyMx0ATgbbtm2Lq6++Oi699NI4dOhQpXo9/vjj8fOf/zwWLlxo+RUAAAAAAAAAAAAAAAAAAAAAADWSBVhQhZ566qkYMGBAFBQUZDoKnPC2bt0aWVlZx/1s3bo10/GAcE/hw9wHSB33CaoXdxKqB3eRjzuR/yZOhGer6c9QU/PX1NwRsmdCTc0dIXum1NTsNTV3hOyZUFNzv6+m5q+puSNkzxTZq15Nzf2+mpq/puaOkD1TZD8xeTfwUe4E1DzuLSTGXYFP535A9eE+Qs3k7sJHuRNAOlmABVVs5cqVMXbs2EzHAAAAAAAAAAAAAAAAAAAAAACAjLEACzLgoYceiuXLl2c6BgAAAAAAAAAAAAAAAAAAAAAAZIQFWPA5srOzIzc3N84666w477zzon379pGbm1vpvtOnT09BOgAAAAAAAAAAAAAAAAAAAAAAqHlqZzoANcfIkSNj5MiRmY6Rdh07doyLL744evbsGeedd16ce+65Ubdu3U/U/eMf/4i//OUv8eCDD8bixYuTnvPMM89EcXHxcXtXVuvWraNXr16xZMmSOHjwYMr7AwAAAAAAAAAAAAAAAAAAAABAZViABRHRtWvXuOaaa+KrX/1qtG7dOqEzLVq0iCuuuCKuuOKK+OMf/xjDhg1LatlUYWFhrF+/Prp161bR2BER0aRJk+jZs2d86Utfil69esWXvvSlaNGiRUREtGnTxgIsAAAAAAAAAAAAAAAAAAAAAACqHQuwOOktW7YsOnfuXKkegwYNiieffDIGDBgQ5eXlCZ97++23k16A1aFDh/je9773wcKrs88+O9m4AAAAAAAAAAAAAAAAAAAAAACQUdV6Ada6detiwYIF8cILL8TGjRtjz549UVRUFA0aNIjWrVtHly5dYsCAATF06NDIy8vLaNby8vJYvnx5LFiwIFatWhV//etfY//+/VFUVBS1atWK008/PebPnx/du3evUP+DBw/Gyy+/HGvWrInNmzfH9u3b48CBA1FUVBQREfXr14/c3Nxo3bp1nH322dG9e/fo1atXNGjQIJWPeUKq7PKr9/Xv3z8GDhwYixcvTvjM4cOHk54zfPjwGD58eNLnAAAAAAAAAAAAAAAAAAAAAACgukjbAqx+/frFPDAC7AAAIABJREFUsmXLEqrdsmVLtGnT5oN/r1ixIn76059+6vmDBw/G+vXrY/369fHYY4/FhAkT4tprr42f//znkZ+fn3TWrVu3Rtu2bROq7du3byxduvQjP3vyySfjpptuijfeeOO4Z0pLS2Pbtm1x8ODBpHIVFBTE3Llz43e/+10sX748SkpKkjpft27d6Nu3b1x11VVx+eWXR7169ZI6/3FLliyJadOmJVQ7bNiwGDZsWKXm1UT9+/dPagFWw4YN05gGAAAAAAAAAAAAAAAAAAAAAACqp7QtwKqIkpKS+NGPfhQzZsyI8vLyhM8VFRXF3XffHXPnzo05c+bExRdfnMaU/3TkyJH41re+FY888khK+xYVFcX06dPjjjvuiP3791e4T3FxcSxevDgWL14cN9xwQ9x4440xduzYyMnJqVC/bdu2xR//+MeEanv27FmhGTVdbm5uUvXNmzdPUxIAAAAAAAAAAAAAAAAAAAAAAKi+sjMd4H0FBQXxla98JaZPn57U8qsP2717dwwaNCjuv//+FKf7pMOHD8fAgQNTvvxqxYoV0bVr15g4cWKlll993O7du+O73/1u9OrVK15//fWU9eWjduzYkXBtTk5OfPGLX0xjGgAAAAAAAAAAAAAAAAAAAAAAqJ6qxQKso0ePxqBBg+LZZ5+tdK/S0tIYM2ZMyhdTfXzGFVdcEcuXL09p3wceeCD69u0bb775Zkr7fthrr70Wffr0iQULFqRtxsls4cKFCdd++ctfjvr166cxDQAAAAAAAAAAAAAAAAAAAAAAVE/VYgHWuHHjYtmyZSnrV15eHqNGjYo1a9akrOeH3X777UktOkrEPffcE9dcc02UlJSktO/xFBQUxNChQ+PJJ59M+6yTyezZs5P6m5swYUIa0wAAAAAAAAAAAAAAAAAAAAAAQPVVLRZgLVmyJOU9jx49GsOGDYujR4+mtO/mzZtj0qRJKe359NNPx7hx41La8/OUlpbGFVdcEatXr67SuSeio0ePxh133BHXXXddwmcuvfTSGDRoUBpTAQAAAAAAAAAAAAAAAAAAAABA9VU70wE+TXZ2djRr1izy8vJi9+7dsW/fvqR7bNy4MaZNmxY//vGPU5Zr+/btKesVEbFr164YMWJElJWVpbRvIoqLi+Mb3/hGrF27Nk455ZQqn18THT16NAoLC+Pvf/97bNq0KZYuXRpPPPFE7NixI+EeXbt2jd/85jdpTAkAAAAAAAAAAAAAAAAAAAAAANVbdqYDfFyzZs3i17/+dezatSt27twZ69evj71798aWLVvi5ptvjnr16iXV784774zCwsI0pa28n/zkJ7F3796kzpx//vnxq1/9KjZt2hSHDh2KgwcPxvr16+OXv/xldOjQIalemzdvjilTpiR15mSwf//+yMrK+sSnbt26kZeXF507d46vfvWrcffddye1/GrQoEGxZMmSaNKkSRrTAwAAAAAAAAAAAAAAAAAAAABA9VY70wE+rGfPnrFo0aLIz8//xHdt2rSJW265Jb7+9a/Hl7/85di9e3dCPfft2xcPP/xwjBkzJtVxP9C5c+e48sorY8CAAXHGGWdEfn5+FBYWxttvvx1r1qyJxYsXx4IFCz5x7s0334w5c+YkNetnP/tZ3HzzzVG79kd/dR07doyOHTvG2LFj47vf/W7ce++9CfecOnVqfP/734/c3NykspC4Tp06xY033hjDhg3LdJRqZffu3bFnz56kzrz11ltpSgMAAAAAAAAAAAAAAAAAAAAAQFWpNguwWrRoEc8888xxl199WKdOnWLBggVxwQUXRFlZWUK958yZk5YFWHXq1Inp06fHmDFjolatWh/57tRTT40uXbpEly5d4uqrr47Dhw9HaWnpR2pmzpyZ8DNERNxwww0xefLkz6ypV69ezJo1Kw4ePBhz585NqG9BQUHMnj07brjhhoSzkJh///d/jx//+Mdx4YUXRlZWVqbjVDu//vWv47/+678yHQMAAAAAAAAAAAAAAAAAAAAAgCqWnekA77v11lujadOmCdX27t07RowYkXDvFStWxHvvvVfRaMdVp06d+MMf/hBjx479xPKr42nYsGE0btz4g3+XlZXFvHnzEp7Xpk2buPXWWxOqzcrKipkzZ0Zubm7C/R977LGEa0nc008/HePHj4+pU6fG4cOHMx0HAAAAAAAAAAAAAAAAAAAAAACqhWqxACs3NzeuuuqqpM6MHz8+4dqysrJ47rnnko31mW666aa46KKLKnx+9erV8Y9//CPh+u9///tRr169hOubNm0ao0ePTrh+1apVsXv37oTrSdy6devihz/8YZx11lkxa9asTMcBAAAAAAAAAAAAAAAAAAAAAICMq53pABER//Ef/xF16tRJ6kyPHj3irLPOirfffjuh+jVr1sSll15akXif0Lp16/jJT35SqR4rVqxIuDYrKyu+9rWvJT3jm9/8Ztx1110J1ZaXl8dLL70Ul1xySdJzSMy7774bY8eOjUWLFsWjjz4a9evXz3SkjBs3blzSf9tvvfVWDBkyJE2JAAAAAAAAAAAAAAAAAAAAAACoCtViAVavXr0qdK579+4JL8DatGlThWYcz+jRo6N27cq9utdffz3h2nbt2kXLli2TntGtW7eoX79+FBUVJVS/du1aC7CqwIIFC2Lw4MGxcOHCqFu3bqbjZFSzZs2iWbNmmY4BAAAAAAAAAAAAAAAAAAAAAEAVy850gIiITp06pf3cjh07KjTjeAYNGlTpHlu3bk24tkuXLhWakZ2dHR07dky4PtFlYieDWrVqRdeuXT/x6dChQ5x++ulRp06dSvX/85//HDfddFOK0gIAAAAAAAAAAAAAAAAAAAAAQM1SO9MBIiLy8/MrdK5JkyYJ17733nsVmvFxdevWjfPPP7/Sffbs2ZNwbfPmzSs8J5mzu3fvrvCcE02jRo3i1Vdf/dTvS0tL4+23346VK1fG008/HfPmzYsjR44kNWPq1Klx5ZVXpuTvCQAAAAAAAAAAAAAAAAAAAAAAapLsTAeI+L9lQ+k+l+xyok/TunXrqFOnTqX7FBQUJFxb0fcTEXHqqacmXFtYWFjhOSebWrVqRbt27eKb3/xmzJkzJ7Zs2RLDhw9PqkdZWVlMnz49TQkBAAAAAAAAAAAAAAAAAAAAAKD6qhYLsEpLS9N+Lisrq0IzPi43NzclfcrLyxOuzc6u+K+pVq1aCdcmk4mPatGiRTz88MPxwx/+MKlzjz32WBw7dixNqQAAAAAAAAAAAAAAAAAAAAAAoHqqFguwDh06lPZz9evXr9CMj2vYsGFK+iST5/DhwxWec/DgwYRrU/WOTma33357dOzYMeH6wsLCWLNmTRoTAQAAAAAAAAAAAAAAAAAAAABA9VMtFmDt2rWrQud2796dcG3Tpk0rNCNd8vPzE67dt29fheckc7ZJkyYVnsP/qVWrVlx99dVJnVm/fn2a0gAAAAAAAAAAAAAAAAAAAAAAQPVULRZgrVu3Lu3nWrVqVaEZ6XLmmWcmXFvR9xOR3HKls846q8Jz+KeOHTsmVV+ZBWcAAAAAAAAAAAAAAAAAAAAAAFATVYsFWCtWrEj6TGlpaaxatSrh+s6dOyc9I506deqUcO2mTZvi0KFDSc94880348CBA2nJxKfLzk7uWhUVFaUpCQAAAAAAAAAAAAAAAAAAAAAAVE/VYgHWokWL4uDBg0mdWbZsWezduzfh+h49eiQbK6169+6dcO2xY8diwYIFSc+YN29eUvX/8i//kvQMPmnjxo1J1Tds2DBNSQAAAAAAAAAAAAAAAAAAAAAAoHqqFguwiouLY+bMmUmdufPOOxOurVu3blxwwQXJxkqrPn36xKmnnppw/cyZM6O8vDzh+iNHjsR9992XcP0XvvCFaNOmTcL1J4KysrK09JwzZ05SZ84888yU5wAAAAAAAAAAAAAAAAAAAAAAgOqsWizAioj47//+79iwYUNCtY8//ngsWrQo4d4DBw6M+vXrVzRaWtStWzcGDx6ccP3KlSvj/vvvT7j+9ttvj61btyZcf/nllydce6K44IILYtKkSbF79+6U9bzpppti3bp1SZ05//zzUzYfAAAAAAAAAAAAAAAAAAAAAABqgmqzAOvw4cNx0UUXxdq1az+zbsGCBTFixIikeo8cObISydJn/PjxSdfPnz//c+vuvffemDx5csJ9a9euHddff31SWU4EO3fujJtvvjnOPPPMGDZsWPz+97+PgoKCCvXat29fXHPNNfGLX/wiqXPt2rWL9u3bV2gmAAAAAAAAAAAAAAAAAAAAAADUVLUzHeDDtm/fHj179oxvfetb8Y1vfCM6deoUubm5sXfv3li1alU8+OCD8eSTTybVs127djFkyJA0Ja6c3r17x8CBA+NPf/pTQvUlJSVx2WWXxZVXXhnjxo2L3r17R1ZWVkRElJWVxfPPPx+//OUv46mnnkoqx4gRI+KMM85IOv+Jori4OB599NF49NFHo169ejFgwIDo06dPdOvWLbp16xYtWrQ47rmdO3fG6tWrY+HChfHII4/EoUOHkp49atSopM+sWrUqRo8enVDt3//+94T7jh49Oho2bPi5dYMHD45JkyYl3BcAAAAAAAAAAAAAAAAAAAAAAD6uWi3Aivi/JU+zZs2KWbNmpaTfpEmTolatWinplQ4zZsyIbt26RXFxccJnHn744Xj44YejQYMG0bJlyygvL4+dO3dGYWFh0vPz8vLitttuS/rcierIkSOxcOHCWLhw4Qc/y8nJidzc3MjLy4ucnJwoKCiIffv2VWjh1Yfl5eXF2LFjkz53+PDheO211yo1+3g2b96cUN0Xv/jFlM8GAAAAAAAAAAAAAAAAAAAAAODkUu0WYKVS//79Y/jw4ZmO8Zk6duwYd911V3znO99J+mxBQUG89dZblZo/e/bsaN68eaV6nOhKSkpiz549sWfPnpT2nTp1auTn56e0J/BPbdq0ifLy8kzHAD6Dewr/5D5A6rhPUL24k1A9uIt83In8N3EiPFtNf4aamr+m5o6QPRNqau4I2TOlpmavqbkjZM+Empr7fTU1f03NHSF7pshe9Wpq7vfV1Pw1NXeE7Jki+4nJu4GPcieg5nFvITHuCnw69wOqD/cRaiZ3Fz7KnQDSKTvTASIiLUuqmjZtGnPmzEl533T49re/HePHj6/yubfddltcdtllVT6XiPHjx8eoUaMyHQMAAAAAAAAAAAAAAAAAAAAAADKiWizAmjx5ckqXYNWvXz+eeuqpaNWqVcp6ptvMmTNjwoQJVTIrKysrJk+eHBMnTqySeXzU9773vZg5c2amYwAAAAAAAAAAAAAAAAAAAAAAQMZUiwVYWVlZMWfOnLj22msr3atx48bxzDPPRJ8+fVKQrOpkZWXFjBkz4r777ouGDRumbU6TJk1i3rx58bOf/SxtMzi+Zs2axWOPPRbTpk2LrKysTMcBAAAAAAAAAAAAAAAAAAAAAICMqRYLsCIisrOz495774177rknGjVqVKEePXv2jJdffjn69u2b4nRVZ/To0bF27dr42te+ltIlSbVq1YqRI0fGG2+8EUOHDk1Z35osPz+/Subk5ubGjTfeGJs2bYqvf/3rVTITAAAAAAAAAAAAAAAAAAAAAACqs2qzAOt9Y8aMiY0bN8b48eOjYcOGCZ0599xzY/bs2fHSSy9Fhw4d0pww/dq2bRuPP/54rF27NsaPHx9NmzatcK8WLVrED37wg9iwYUM88MAD0aJFiwr3Ki0tTbi2Tp06FZ5TVVavXh1r166NO++8My666KLIzc1NWe9GjRrF4MGD46GHHort27fHrbfemtL+AAAAAAAAAAAAAAAAAAAAAABQk9XOdIDjOf300+NXv/pVTJkyJZ599tl44YUXYuPGjbF37944cuRInHLKKdGqVavo0qVLDBw4MHr16lWpeW3atIny8vIUpU+d8847L371q1/F3XffHStXrozly5fHmjVrYvPmzbFjx47Yv39/FBUVRURE/fr1Iy8vL1q1ahUdOnSIbt26xYUXXhg9evSIrKyslOQpLCxMuDYvLy8lM9OtS5cu0aVLl/jP//zPiIjYvHlzvPLKK7F+/fp4++2345133okdO3bEgQMHorCwMAoLC+PYsWORk5MT9erVi9zc3MjPz4/WrVtH27Zto1OnTtGjR4/o2rVr5OTkpCVzv379quXfKwAAAAAAAAAAAAAAAAAAAAAAJKpaLsB6X4MGDeKSSy6JSy65JNNRMio7Ozt69+4dvXv3zmiOPXv2JFxbUxZgfVz79u2jffv2mY4BAAAAAAAAAAAAAAAAAAAAAAAntOxMB6Dm2LhxY8K1LVu2TGMSAAAAAAAAAAAAAAAAAAAAAACgJrMAi4SUlJTEkiVLEqqtVatW9OjRI82JAAAAAAAAAAAAAAAAAAAAAACAmsoCLBIyZcqUePfddxOqPe+886Jhw4ZpTgQAAAAAAAAAAAAAAAAAAAAAANRUtTMdgOrp2LFj8d5778Wrr74av/nNb2Lu3LkJnx08eHAakwEAAAAAAAAAAAAAAAAAAAAAADWdBVh8xJAhQ2LBggUVPp+TkxNjx45NYSIAAAAAAAAAAAAAAAAAAAAAAOBEk53pAJxYRo4cGS1btsx0DAAAAAAAAAAAAAAAAAAAAAAAoBqzAIuUad++fUydOjXTMQAAAAAAAAAAAAAAAAAAAAAAgGrOAixSIj8/P+bNmxeNGjX61JqlS5dGVlZWxj4AAAAAAAAAAAAAAAAAAAAAAED1YAEWlXbOOefEiy++GN26dct0FAAAAAAAAAAAAAAAAAAAAAAAoAawAIsKO+200+KOO+6IV155Jc4+++xMxwEAAAAAAAAAAAAAAAAAAAAAAGqI2pkOQM1w6qmnRn5+fjRv3jz69OkT/fv3j/79+8cpp5yS6WgAAAAAAAAAAAAAAAAAAAAAAEANk7YFWEuXLk1Xa9Jo/vz5mY4AAAAAAAAAAAAAAAAAAAAAAACc4LIzHQAAAAAAAAAAAAAAAAAAAAAAAICTS+1MB+Dk0a9fvygvL890DAAAAAAAAAAAAAAAAAAAAAAAIMOyMx0AAAAAAAAAAAAAAAAAAAAAAACAk4sFWAAAAAAAAAAAAAAAAAAAAAAAAFQpC7AAAAAAAAAAAAAAAAAAAAAAAACoUhZgAQAAAAAAAAAAAAAAAAAAAAAAUKUswAIAAAAAAAAAAAAAAAAAAAAAAKBKWYAFAAAAAAAAAAAAAAAAAAAAAABAlbIACwAAAAAAAAAAAAAAAAAAAAAAgCplARYAAAAAAAAAAAAAAAAAAAAAAABVqnamA1BzPPjggzFq1KiEam+++ea45ZZb0huI49q5c2esWLEi1qxZE1u3bo2dO3dGQUFBlJSUxCmnnBKNGzeOtm3bxjnnnBP/7//9vzj//PMjKysr07EBAAAAAAAAAAAAAAAAAAAAADiJWIAFx1FYWBhr1qyJN954I7Zs2RJ/+9vfYsuWLfHuu+/G4cOHo6CgIIqKiqJBgwbRuHHjaNy4cZx22mnRtWvX6N69e3Tv3j06d+5cZXkPHDgQv/3tb2POnDnxyiuvRHl5ecJnmzdvHsOGDYvrrrsuzjnnnDSmBAAAAAAAAAAAAAAAAAAAAACA/2MBFkTE3r17Y/HixfGXv/wlXn755Vi3bl2UlpZ+7rmDBw/GwYMHY9u2bRER8Ze//OWD784555wYMWJEXH311dGqVau05C4uLo5f/vKXMWXKlDhw4ECFeuzatSumTZsWd999d1x55ZVxxx13RLNmzVKcFAAAAAAAAAAAAAAAAAAAAAAA/ik70wEgk6ZOnRq9e/eO5s2bx/Dhw+P++++P1157LaHlV59n06ZNMXHixGjbtm385Cc/icLCwhQk/qfXX389evbsGRMnTqzw8qsPKy0tjd/+9rfRuXPn+MMf/pCChAAAAAAAAAAAAAAAAAAAAAAAcHwWYHFSmzlzZrz00ktRVlaWthklJSUxZcqU6Ny5czz//PMp6blo0aK44IILYt26dSnp92F79+6NIUOGxMyZM1PeGwAAAAAAAAAAAAAAAAAAAAAAIizAgiqzdevWGDBgQMyfP79Sff785z/HkCFD4tChQylK9kllZWUxYcKEuOeee9I2A9Jl69atkZWVddzP1q1bMx0PTiruI3w69wOqjvsGmeP+Qea4f3yeE/lvpKY/m/yZUVNzv68m56+p2Wtq7gjZM6WmZq+puSNkz4SamjtC9kyRPTNqavaamjtC9kyoqbkjZM+Umpq9puZOJ+8EPsqdgMxx/6By3CFInnsD1Ys7CZnlDsLnc0+A6sgCLKhCR48ejW9+85uxYsWKCp3fvHlzXH755VFcXJziZMc3bty4+POf/1wlswAAAAAAAAAAAAAAAAAAAAAAOHlYgAVVrLi4OIYPHx5FRUVJnSsvL4+rrroqDhw4kKZkn1RWVhZXX3117Nu3r8pmAgAAAAAAAAAAAAAAAAAAAABw4rMACxKUn58fX/jCF+Lcc8+NZs2aVarXli1bYsaMGUmdeeihh2LFihUJ19eqVSuuu+66eO6552Lv3r1RXFwc27Zti9/97nfRq1evhPvs3LkzJk6cmFRWAAAAAAAAAAAAAAAAAAAAAAD4LBZgkbCRI0dGeXl5Qp9bbrkl03Er7Zxzzokf/ehHsWjRonj33Xdj7969sWnTptiwYUPs2rUrdu7cGQ888EB07dq1Qv3/53/+J8rKyhKqLS8vj9tvvz3h3g0aNIglS5bErFmz4sILL4z8/PyoU6dOtG7dOoYNGxYvvvhifOc730m43+zZs2P9+vUJ1wMAAAAAAAAAAAAAAAAAAAAAwGexAAs+pH79+jFmzJhYuXJlbNy4MaZMmRIXX3xx5OXlfaK2RYsWMXLkyHjllVdi0qRJSc/avn17vPjiiwnVPv/887Fx48aEe0+bNi369u37qd9nZ2fHjBkz4oILLkioX2lpadx1110JzwcAAAAAAAAAAAAAAAAAAAAAgM9iARZERMOGDePGG2+Md955J+65557o1atXwmezs7Pjpptuittvvz3puc8991xCdU888UTCPc8666wYPXr059ZlZWXF5MmTE+77yCOPxL59+xKuBwAAAAAAAAAAAAAAAAAAAACAT1M70wE+y7p162LBggXxwgsvxMaNG2PPnj1RVFQUDRo0iNatW0eXLl1iwIABMXTo0MjLy8to1vLy8li+fHksWLAgVq1aFX/9619j//79UVRUFLVq1YrTTz895s+fH927d69Q/4MHD8bLL78ca9asic2bN8f27dvjwIEDUVRUFBER9evXj9zc3GjdunWcffbZ0b179+jVq1c0aNAglY95wqldu3Zcf/31MWnSpDjttNMq1evHP/5xPPHEE7Fq1aqEz2zatCmhukQXZUVEDB06NLKyshKq7du3bzRr1ix27979ubXFxcXx+9//Pq699tqEswAAAAAAAAAAAAAAAAAAAAAAwPGkbQFWv379YtmyZQnVbtmyJdq0afPBv1esWBE//elPP/X8wYMHY/369bF+/fp47LHHYsKECXHttdfGz3/+88jPz08669atW6Nt27YJ1fbt2zeWLl36kZ89+eSTcdNNN8Ubb7xx3DOlpaWxbdu2OHjwYFK5CgoKYu7cufG73/0uli9fHiUlJUmdr1u3bvTt2zeuuuqquPzyy6NevXpJnf+4JUuWxLRp0xKqHTZsWAwbNqxS86rCn/70p4R/958nKysrxo0bF9dcc03CZ/bs2fO5NaWlpbFu3bqEe/bs2TPh2uzs7OjevXssWrQoofr//d//tQALAAAAAAAAAAAAAAAAAAAAAIBKS9sCrIooKSmJH/3oRzFjxowoLy9P+FxRUVHcfffdMXfu3JgzZ05cfPHFaUz5T0eOHIlvfetb8cgjj6S0b1FRUUyfPj3uuOOO2L9/f4X7FBcXx+LFi2Px4sVxww03xI033hhjx46NnJycCvXbtm1b/PGPf0yoNpklTJmUquVX7+vXr19S9cXFxZ9bs23btjh27FjCPdu1a5dUhvbt2ydc+/zzz0dJSUmF/4YAAAAAAAAAAAAAAAAAAAAAACAiIjvTAd5XUFAQX/nKV2L69OlJLb/6sN27d8egQYPi/vvvT3G6Tzp8+HAMHDgw5cuvVqxYEV27do2JEydWavnVx+3evTu++93vRq9eveL1119PWV8+qmXLlknVN27c+HNr9u7dm1TPRo0apa2+qKgoVq9enVR/AAAAAAAAAAAAAAAAAAAAAAD4uGqxAOvo0aMxaNCgePbZZyvdq7S0NMaMGZPyxVQfn3HFFVfE8uXLU9r3gQceiL59+8abb76Z0r4f9tprr0WfPn1iwYIFaZtxMispKUmq/swzz/zcmqKioqR6ZmVlpbX+1VdfTaoeAAAAAAAAAAAAAAAAAAAAAAA+rloswBo3blwsW7YsZf3Ky8tj1KhRsWbNmpT1/LDbb789Fi5cmNKe99xzT1xzzTVJL1CqiIKCghg6dGg8+eSTaZ91slm/fn1S9T179vzcmpycnKR6Hjp0KKn6gwcPJlW/YcOGpOoBAAAAAAAAAAAAAAAAAAAAAODjqsUCrCVLlqS859GjR2PYsGFx9OjRlPbdvHlzTJo0KaU9n3766Rg3blxKe36e0tLSuOKKK2L16tVVOvdE99RTTyVcm5OTExdffPHn1jVu3DipDH/729/SWv/WW28lVQ8AAAAAAAAAAAAAAAAAAAAAAB9XLRZgHU92dna0aNEiOnbsGPn5+RXqsXHjxpj2/9m50+gqC3N/2HcCBMI8yyiDIyBYJhtED2oBXYIITnVCsAepSFuq/ttaWwsVXZZSLQj14AiltgdhaQnUI7XFAqKIYCmICFUEjiAQQDBAACPJ++G8Wgeszw47JMB1rZUPYf/uYT/Zm4/3r3+d1r02btwYhYWFaeu3devWGDQmyqKwAAAgAElEQVRoUBQVFaWtZ1IHDhyIb37zm1FQUHDEZx+Ldu/eHQ8//HDifN++fRN9tk888cSU9kjlqFlxcXEsW7Yspf5btmxJKQ8AAAAAAAAAAAAAAAAAAAAAAJ9X7g5gNWzYMB566KHYunVrbN68OVatWhXbt2+PdevWxciRI6NKlSop9Rs7dmy5PvB0xx13xPbt21Oq6dChQ0ycODHWrFkTu3fvjvz8/Fi1alU88MADccopp6TUa+3atTFmzJiUaji0O+64I3bs2JEom5GRET/72c8SZatVqxYnnXRS4j2efvrpKC4uTpRdsGBBygettm7dmlIeAAAAAAAAAAAAAAAAAAAAAAA+r2JZL/BpXbp0iTlz5kS9evW+8FrLli1j1KhRcdVVV8X5558feXl5iXru2LEjnnzyyRg6dGi61/1Eu3bt4vrrr4+ePXtG8+bNo169elFQUBAbNmyIZcuWxfPPPx+5ublfqHvrrbdi6tSpKc366U9/GiNHjoyKFT/7p2vTpk20adMmhg0bFiNGjIhHHnkkcc/7778/br311qhdu3ZKu/Av06dPj4ceeihxfvDgwfG1r30tcb5Hjx6xdu3aRNl169bF5MmT41vf+tZXZpMe4fq0PXv2pFzzZfLy8mLbtm0p1bz99ttpmw8AAAAAAAAAAAAAAAAAAAAAQNkoNwewGjVqFM8999whj199Wtu2bSM3Nze6d+8eRUVFiXpPnTq1VA5gZWVlxbhx42Lo0KFRoUKFz7xWs2bNaN++fbRv3z5uuOGG2LNnTxw8ePAzmQkTJiR+DxERt99+e4wePfrfZqpUqRKTJk2K/Pz8mDZtWqK+e/fujccffzxuv/32xLvwL3Pnzo0bbrghcf7kk0+OcePGpTTj8ssvjyeeeCJxfsSIEXHqqafGOeecc8jXi4uL47bbbosFCxaktEdExP79+1Ou+TIPPfRQ/PznP09bPwAAAAAAAAAAAAAAAAAAAAAAjg6ZZb3Ax+65556oX79+omxOTk4MGjQoce9FixbFzp07S7raIWVlZcXs2bNj2LBhXzh+dSjVq1ePWrVqffJ7UVFRzJgxI/G8li1bxj333JMom5GRERMmTIjatWsn7v/UU08lzvIvs2fPjr59+8aBAwcS5WvVqhWzZs2KmjVrpjTnwgsvjNatWyfO79mzJ84///y45ZZbYuHChbFz58748MMPY9OmTTFt2rTo1q1byke4PlZYWFiiOgAAAAAAAAAAAAAAAAAAAAAA+Fi5OIBVu3btGDhwYEo1w4cPT5wtKiqKBQsWpLrWv3XXXXdF7969S1z/2muvxZYtWxLnb7311qhSpUrifP369WPIkCGJ80uXLo28vLzEeSJ+85vfxIABA2L//v2J8tnZ2TF79uxo06ZNyrMqVKgQd911V0o1H330UfzXf/1XnHvuuVG3bt2oXLlyNGvWLK655ppYvHhxyjt8LCsrq8S1AAAAAAAAAAAAAAAAAAAAAAAQEVGxrBeIiOjbt2/KR3U6d+4cLVq0iA0bNiTKL1u2LC699NKSrPcFzZo1izvuuOOweixatChxNiMjI6688sqUZ1x99dXxq1/9KlG2uLg4Fi9eHJdccknKc443RUVF8YMf/CAeeOCBxDVVqlSJmTNnxrnnnlviuYMGDYopU6bE/PnzS9wjHVI5xPZVbrnllpQ/22+//Xb0798/bTsAAAAAAAAAAAAAAAAAAAAAAHDklYsDWF27di1RXadOnRIfwFqzZk2JZhzKkCFDomLFw3t0r7/+euJs69ato3HjxinP6NixY2RnZ8e+ffsS5VesWOEA1lfYt29fXH/99fHMM88krqlatWrk5uZGz549D2t2RkZG/P73v4+uXbvG5s2bD6vX4ahRo0baejVs2DAaNmyYtn4AAAAAAAAAAAAAAAAAAAAAABwdMst6gYiItm3blnrdpk2bSjTjUPr06XPYPdavX5842759+xLNyMzMjDZt2iTOJz0mdrzKy8uL888/P6XjV3Xq1Im//OUvh3386mNNmzaN559/Pho0aJCWfiXRpEmTMpsNAAAAAAAAAAAAAAAAAAAAAMCxoVwcwKpXr16J6urWrZs4u3PnzhLN+LzKlStHhw4dDrvPtm3bEmdPOOGEEs9JpTYvL6/Ec451q1evjpycnFi8eHHimubNm8eLL74YZ599dlp3OeOMM2LRokUlPhx3KHXq1EmcdQALAAAAAAAAAAAAAAAAAAAAAIDDVS4OYNWoUaPU6/bv31+iGZ/XrFmzyMrKOuw+e/fuTZwt6fOJiKhZs2bibEFBQYnnHMvmz58fZ599dqxbty5xTceOHWPx4sXRrl27UtnppJNOiqVLl8b/+3//77A+j5mZmTFixIj47W9/m7jm1FNPLfE8AAAAAAAAAAAAAAAAAAAAAACIKCcHsA4ePFjqdRkZGSWa8Xm1a9dOS5/i4uLE2czMkv+ZKlSokDibyk7HiyeffDJ69+4dO3fuTFzTp0+fWLBgQTRu3LgUN4vIzs6OsWPHxpo1a+LWW2+NBg0aJK6tUaNGDB8+PFatWhXjxo2LjRs3Jq7t0KFDSdYFAAAAAAAAAAAAAAAAAAAAAIBPVCzrBSIidu/eXep12dnZJZrxedWrV09Ln1T22bNnT4nn5OfnJ86m6xkdK+6+++4YOXJkSjXDhw+P8ePHp3R47HC1bNkyHnjggfjVr34Vr7zySixatChWrVoV7777buTn58fBgwejTp06Ua9evTj99NPjP/7jPyInJ+czf+/ly5cnnte5c+fSeBsAAAAAAAAAAAAAAAAAAAAAABxHysUBrK1bt5aoLi8vL3G2fv36JZpRWurVq5c4u2PHjhLPSaW2bt26JZ5zLCksLIyhQ4fGlClTEtdkZmbG2LFj47bbbiu9xRLscPbZZ8fZZ5+dcu2zzz6bKNe0adM47bTTUu4PAAAAAAAAAAAAAAAAAAAAAACfllnWC0RErFy5stTrmjZtWqIZpeXEE09MnC3p84mIWLVqVeJsixYtSjznWLFr16646KKLUjp+VbVq1Xj66afL9PjV4ViyZEls3LgxUbZXr16lvA0AAAAAAAAAAAAAAAAAAAAAAMeDimW9QETEokWLUq45ePBgLF26NHG+Xbt2Kc8oTW3btk2cXbNmTezevTtq1KiR0oy33norPvjgg1LZ6Vi0YcOGuPjii1M6GnbCCSfE7Nmzo2vXrqW4Wen6xS9+kTh7zTXXlOImAAAAAAAAAAAAAAAAAAAAAAAcLzLLeoGIiDlz5kR+fn5KNfPnz4/t27cnznfu3DnVtUpVTk5O4uxHH30Uubm5Kc+YMWNGSvmvf/3rKc84VixdujRycnJSOn7Vtm3bWLx48VF9/GrJkiXxxz/+MVG2SZMm0bNnz1LeCAAAAAAAAAAAAAAAAAAAAACA40G5OIB14MCBmDBhQko1Y8eOTZytXLlydO/ePdW1SlW3bt2iZs2aifMTJkyI4uLixPn9+/fHo48+mjh/6qmnRsuWLRPnjyW5ubnRo0eP2LJlS+Kab3zjG/Hyyy9HixYtSnGz0rVjx4646qqrEn+uRowYEZmZ5eK/DAAAAAAAAAAAAAAAAAAAAAAAjnLl5prNL37xi3jzzTcTZadPnx5z5sxJ3LtXr16RnZ1d0tVKReXKlaNfv36J86+++mo89thjifP33XdfrF+/PnH+iiuuSJw9ljz44INx2WWXRUFBQeKaG2+8MZ577rmoVatWKW52aDt27IiNGzcedp+tW7dG3759E39G6tatG7fccsthzwUAAAAAAAAAAAAAAAAAAAAAgIhydABrz5490bt371ixYsW/zeXm5sagQYNS6j148ODD2Kz0DB8+POX8zJkzvzL3yCOPxOjRoxP3rVixYtx8880p7XKsGDFiRBQVFaVUM3ny5MjKyoqMjIy0/KRyqOytt96Kli1bxoABA+K5556LwsLCFN9xxOzZs6NTp07xyiuvJK659957o3r16inPAgAAAAAAAAAAAAAAAAAAAACAQyk3B7AiIjZu3BhdunSJYcOGxbx58yIvLy8+/PDDeO+992LWrFlx2WWXRf/+/WP//v2Je7Zu3Tr69+9filuXXE5OTvTq1StxvrCwMAYMGBADBw6MRYsWRXFx8SevFRUVxfz58+PSSy+Nb3/725957asMGjQomjdvntLulJ2DBw/GzJkz4+KLL44GDRrENddcE48//ni89tprceDAgS/ki4qKYvny5TF+/Pj42te+Fv369Yv33nsv8bxu3brF0KFD0/kWAAAAAAAAAAAAAAAAAAAAAAA4zlUs6wU+r7CwMCZNmhSTJk1KS7+77747KlSokJZepWH8+PHRsWPHQx4u+jJPPvlkPPnkk1GtWrVo3LhxFBcXx+bNm6OgoCDl+XXq1Il777035TrKhw8++CCmTZsW06ZNi4iIChUqRO3ataNOnTqRlZUV77//frz//vvx4Ycflqh/3bp147//+78jM7Nc3coDAAAAAAAAAAAAAAAAAAAAAOAol1FcXFxcGo3PO++8mD9/fmm0TuyCCy6IuXPnfmVu/fr10apVq0Q9e/ToEfPmzTvMzT5r4sSJ8d3vfjetPZN65plnYsCAAYmyU6ZMiRtvvDFRduTIkTFq1KjD2OzIyMjIKOsVYt26ddGyZctE2VdeeSW6detWugv9/7KysuJPf/pT9OrV64jMS+qNN96IM84445PfV65cGe3atSvDjQAAAAAAAAAAAAAAAAAAAACA45FbKIcns6wXiIi47rrr0t6zfv36MXXq1LT3LQ3f+c53Yvjw4Ud87r333pv4+BXHl0qVKsX06dPL3fErAAAAAAAAAAAAAAAAAAAAAACODeXiANbo0aPTegQrOzs7Zs2aFU2bNk1bz9I2YcKE+N73vndEZmVkZMTo0aPjzjvvPCLzOLrUrVs35syZE5deemlZrwIAAAAAAAAAAAAAAAAAAAAAwDGqXBzAysjIiKlTp8ZNN9102L1q1aoVzz33XHTr1i0Nmx05GRkZMX78+Hj00UejevXqpTanbt26MWPGjPjpT39aajM4ep177rmxZMmSuOCCC8p6FQAAAAAAAAAAAAAAAAAAAAAAjmHl4gBWRERmZmY88sgj8fDDD0eNGjVK1KNLly6xZMmS6NGjR5q3O3KGDBkSK1asiCuvvDIyMjLS1rdChQoxePDgeOONN+Lyyy9PW1+ODSeeeGI8+uijMX/+/GjdunVZrwMAAAAAAAAAAAAAAAAAAAAAwDGu3BzA+tjQoUNj9erVMXz48KhevXqimtNPPz0ef/zxWLx4cZxyyimlvGHpa9WqVUyfPj1WrFgRw4cPj/r165e4V6NGjeK2226LN998MyZPnhyNGjUqca+DBw8mzmZlZZV4Dl+uU6dO8eyzz8awYcOiZcuWh9UrMzMzvvGNb8TUqVPj7bffjiFDhqT16BoAAAAAAAAAAAAAAAAAAAAAAHyZimW9wKE0adIkJk6cGGPGjIkXXnghXnrppVi9enVs37499u/fH1WrVo2mTZtG+/bto1evXtG1a9fDmteyZcsoLi5O0/bpc8YZZ8TEiRPjwQcfjFdffTUWLlwYy5Yti7Vr18amTZti165dsW/fvoiIyM7Ojjp16kTTpk3jlFNOiY4dO8a5554bnTt3TttRo4KCgsTZOnXqpGVmaSuPf/d/JysrKy6++OK4+OKLIyJi8+bNsWTJkvj73/8e77zzTmzYsCHefffd2L17dxQUFMT+/fsjKysrqlWrFg0aNIhWrVrF6aefHt27d4//+I//iAYNGpTxOwIAAAAAAAAAAAAAAAAAAAAA4HhULg9gfaxatWpxySWXxCWXXFLWq5SpzMzMyMnJiZycnDLdY9u2bYmzR8sBrKNd48aNo1+/ftGvX7+yXgUAAAAAAAAAAAAAAAAAAAAAABLLLOsFOHqsXr06cbZx48aluAkAAAAAAAAAAAAAAAAAAAAAAHA0cwCLRAoLC2Pu3LmJshUqVIjOnTuX8kYAAAAAAAAAAAAAAAAAAAAAAMDRygEsEhkzZky8//77ibJnnHFGVK9evZQ3AgAAAAAAAAAAAAAAAAAAAAAAjlYVy3oByqePPvoodu7cGf/4xz/iiSeeiGnTpiWu7devXyluBgAAAAAAAAAAAAAAAAAAAAAAHO0cwOIz+vfvH7m5uSWur1SpUgwbNiyNGwEAAAAAAAAAAAAAAAAAAAAAAMeazLJegGPL4MGDo3HjxmW9BgAAAAAAAAAAAAAAAAAAAAAAUI45gEXanHTSSXH//feX9RoAAAAAAAAAAAAAAAAAAAAAAEA55wAWaVGvXr2YMWNG1KhR40sz8+bNi4yMjDL7AQAAAAAAAAAAAAAAAAAAAAAAygcHsDhsp512WrzyyivRsWPHsl4FAAAAAAAAAAAAAAAAAAAAAAA4CjiARYk1aNAgfvnLX8bf//73OPnkk8t6HQAAAAAAAAAAAAAAAAAAAAAA4ChRsawX4OhQs2bNqFevXpxwwgnRrVu3uOCCC+KCCy6IqlWrlvVqAAAAAAAAAAAAAAAAAAAAAADAUabUDmDNmzevtFpTimbOnFnWKwAAAAAAAAAAAAAAAAAAAAAAAMe4zLJeAAAAAAAAAAAAAAAAAAAAAAAAgONLxbJegOPHeeedF8XFxWW9BgAAAAAAAAAAAAAAAAAAAAAAUMYyy3oBAAAAAAAAAAAAAAAAAAAAAAAAji8OYAEAAAAAAAAAAAAAAAAAAAAAAHBEOYAFAAAAAAAAAAAAAAAAAAAAAADAEeUAFgAAAAAAAAAAAAAAAAAAAAAAAEeUA1gAAAAAAAAAAAAAAAAAAAAAAAAcUQ5gAQAAAAAAAAAAAAAAAAAAAAAAcEQ5gAUAAAAAAAAAAAAAAAAAAAAAAMAR5QAWAAAAAAAAAAAAAAAAAAAAAAAAR5QDWCQ2ZcqUyMjISPQzatSosl4XAAAAAAAAAAAAAAAAAAAAAAAopyqW9QJQHhUUFMSyZcvijTfeiHXr1sU777wT69ati/fffz/27NkTe/fujX379kW1atWiVq1aUatWrWjQoEGceeaZ0alTp+jUqVO0a9eurN9G7N+/P1555ZVYuHBhrFixIt55553YtGlT7NmzJ/bt2xeVKlWKatWqRbVq1aJx48bRsmXLaNWqVZx55plx1llnRevWrcv6LQAAAAAAAAAAAAAAAAAAAAAAcAxyAAsiYvv27fH888/H3/72t1iyZEmsXLkyDh48+JV1+fn5kZ+fH++++25ERPztb3/75LXTTjstBg0aFDfccEM0bdq01HY/lAULFsRjjz0WM2fOjN27d39p7uDBg7F///7YsWNH/O///m8sXrz4M6/Xr18/evfuHX379o2LLroo6tSpU9qrAwAAAAAAAAAAAAAAAAAAAABwHMgs6wWgLN1///2Rk5MTJ5xwQlx33XXx2GOPxfLlyxMdv/oqa9asiTvvvDNatWoVd9xxRxQUFKRh43/v5ZdfjnPPPTd69OgRv/vd7/7t8asktm/fHn/4wx/i2muvjVtvvTVNWwIAAAAAAAAAAAAAAAAAAAAAcLxzAIvj2oQJE2Lx4sVRVFRUajMKCwtjzJgx0a5du3jxxRdLZUZBQUEMGzYszjnnnFi4cGGpzAAAAAAAAAAAAAAAAAAAAAAAgHRxAAuOkPXr10fPnj1j5syZae27YcOG6N69e0yaNCmKi4vT2psjb/369ZGRkXHIn/Xr15f1enBc8D2Eo5fvLxzf/B8ARzffYSg/fB8pKZ+dL/JM/s/x/hyO9/f/Mc/BMzje3//HPAfP4Hh//x/zHDyD4/39R3gGEZ7B8f7+P3a8P4fj/f1HeAYRnkG6eI5wdPGdheOD7zqUT76bULZ8B6Hs+R7Cscv3G/D/wNHNASw4gj788MO4+uqrY9GiRWnpt3bt2jjnnHPiH//4R1r6AQAAAAAAAAAAAAAAAAAAAADAkeAAFhxhBw4ciOuuuy727dt3WH02b94c559/fmzcuDFNmwEAAAAAAAAAAAAAAAAAAAAAwJHhABYkVK9evTj11FPj9NNPj4YNGx5Wr3Xr1sX48eNLXF9YWBhXXHFFvPvuuyWqz8jIiFq1akXr1q2jTZs20bhx48jOzi7xPgAAAAAAAAAAAAAAAAAAAAAAkIqKZb0AR4/BgwfH4MGDy3qNI+a0006LSy+9NC644II466yzok6dOp95fcuWLTFnzpwYN25cLF++POX+v/nNb+KHP/xhZGamfodu1KhR8fLLL6dUU7Nmzbjxxhujd+/ecc4550TNmjW/kFm3bl0sX748Fi1aFLNmzYrVq1envBsAAAAAAAAAAAAAAAAAAAAAAHyV1C/vwDEsOzs7hg4dGq+++mqsXr06xowZExdeeOEXjl9FRDRq1CgGDx4cf//73+Puu+9OedbGjRvjlVdeSbnujTfeiLFjxybOZ2RkxA9+8IPYsGFDjBs3Li6++OJDHr+KiGjVqlX0798/xowZE2+++Wa89dZb8ZOf/CQaN26c8p4AAAAAAAAAAAAAAAAAAAAAAPBlKpb1AlAeVK9ePUaMGBHf//73o379+inVZmZmxl133RWVKlWKH//4xynVLliwIM4+++yUan70ox9FYWFhomx2dnZMnz49+vbtm9KMj5188slxzz33xKhRo+Ltt98uUQ8AAAAAAAAAAAAAAAAAAAAAAPi8cn0Aa+XKlZGbmxsvvfRSrF69OrZt2xb79u2LatWqRbNmzaJ9+/bRs2fPuPzyy6NOnTplumtxcXEsXLgwcnNzY+nSpfHPf/4zdu3aFfv27YsKFSpEkyZNYubMmdGpU6cS9c/Pz48lS5bEsmXLYu3atbFx48b44IMPYt++fRHxf4eOateuHc2aNYuTTz45OnXqFF27do1q1aql820ecypWrBg333xz3H333dGgQYPD6vWjH/0onn766Vi6dGnimjVr1qQ047XXXotnn302cX7KlCklPn71aRUrVozTTz/9sPsAAAAAAAAAAAAAAAAAAAAAAEBEKR7AOu+882L+/PmJsuvWrYuWLVt+8vuiRYvixz/+8ZfW5+fnx6pVq2LVqlXx1FNPxfe+97246aab4mc/+1nUq1cv5V3Xr18frVq1SpTt0aNHzJs37zP/9sc//jHuuuuueOONNw5Zc/DgwXj33XcjPz8/pb327t0b06ZNi9///vexcOHCKCwsTKm+cuXK0aNHjxg4cGBcccUVUaVKlZTqP2/u3Lnx61//OlH22muvjWuvvfaw5h0Jf/nLXxL/7b9KRkZG3HLLLfGtb30rcc22bdtSmjFhwoTE2aFDh8ZVV12VUn8AAAAAAAAAAAAAAAAAAAAAADgSSu0AVkkUFhbGD3/4wxg/fnwUFxcnrtu3b188+OCDMW3atJg6dWpceOGFpbjlv+zfvz/+8z//M/7whz+kte++ffti3Lhx8ctf/jJ27dpV4j4HDhyI559/Pp5//vm4/fbb4yc/+UkMGzYsKlWqVKJ+7777bjz77LOJsl26dCnRjCMtXcevPnbeeeellD9w4EDi7N69e+Ppp59OlK1SpUqMHDkypV0AAAAAAAAAAAAAAAAAAAAAAOBIySzrBT62d+/euOiii2LcuHEpHb/6tLy8vOjTp0889thjad7ui/bs2RO9evVK+/GrRYsWxZlnnhl33nnnYR2/+ry8vLwYMWJEdO3aNV5//fW09eWzGjdunFK+Vq1aibNz5syJPXv2JMpeffXV0aRJk5R2AQAAAAAAAAAAAAAAAAAAAACAI6VcHMD68MMPo0+fPvHCCy8cdq+DBw/G0KFD036Y6vMzrrnmmli4cGFa+06ePDl69OgRb731Vlr7ftry5cujW7dukZubW2ozjmeFhYUp5U888cTE2T//+c+Js5deemlKewAAAAAAAAAAAAAAAAAAAAAAwJFUsawXiIi45ZZbYv78+WnrV1xcHDfeeGO0adMmOnbsmLa+H7vvvvviT3/6U1p7Pvzww3HzzTenteeX2bt3b1x++eUxY8aMGDBgwBGZebxYtWpVSvkuXbokzs6bNy9RrkKFCtGrV69Pft+1a1f8+c9/jhdffDEWL14ceXl5sWPHjjhw4EBUrVo1GjVqFK1bt44uXbpEz54945xzzokKFSqk9D4AAAAAAAAAAAAAAAAAAAAAACAV5eIA1ty5c9Pe88MPP4xrr702li9fHllZWWnru3bt2nj55ZfT1i8i4n/+53/illtuSWvPr3Lw4MG45ppr4qWXXorOnTsf0dnHslmzZiXOVqpUKS688MJE2T179sTbb7+dKNuiRYuoVq1avP3223H//ffH7373u9i7d+8hs/n5+ZGfnx///Oc/Y86cOXHPPfdEo0aN4uabb47vf//7UatWrcTvBwAAAAAAAAAAAAAAAAAAAAAAksos6wW+TGZmZjRq1CjatGkT9erVK1GP1atXx69//eu07rVx48YoLCxMW7+tW7fGoEGDoqioKG09kzpw4EB885vfjIKCgiM++1i0e/fuePjhhxPn+/btm/izvXLlyiguLk6UPemkk+K+++6L9u3bx6RJk770+NWX2bJlS4waNSpOOumkmDp1akq1AAAAAAAAAAAAAAAAAAAAAACQRLk7gNWwYcN46KGHYuvWrbF58+ZYtWpVbN++PdatWxcjR46MKlWqpNRv7Nix5frA0x133BHbt29PqaZDhw4xceLEWLNmTezevTvy8/Nj1apV8cADD8Qpp5ySUq+1a9fGmDFjUqrh0O64447YsWNHomxGRkb87Gc/S9x73bp1ibN//etf484774z9+/cnrjmUHTt2xKBBg2Lw4MFpPfoGAAAAAAAAAAAAAAAAAAAAAAAVy3qBT+vSpUvMmTMn6tWr94XXWrZsGaNGjYqrrroqzj///MjLy0vUc8eOHfHkk0/G0KFD073uJ9q1axfXX3999OzZM5o3bx716tWLgoKC2JCAmWEAACAASURBVLBhQyxbtiyef/75yM3N/ULdW2+9FVOnTk1p1k9/+tMYOXJkVKz42T9dmzZtok2bNjFs2LAYMWJEPPLII4l73n///XHrrbdG7dq1U9qFf5k+fXo89NBDifODBw+Or33ta4nz7733XuJscXFx4mwSv/3tb2P79u3xxz/+MSpVqpTW3nl5ebFt27aUat5+++207gAAAAAAAAAAAAAAAAAAAAAAwJFXbg5gNWrUKJ577rlDHr/6tLZt20Zubm507949ioqKEvWeOnVqqRzAysrKinHjxsXQoUOjQoUKn3mtZs2a0b59+2jfvn3ccMMNsWfPnjh48OBnMhMmTEj8HiIibr/99hg9evS/zVSpUiUmTZoU+fn5MW3atER99+7dG48//njcfvvtiXfhX+bOnRs33HBD4vzJJ58c48aNS2nG1q1bU10rrZ599tkYPnx4SofVknjooYfi5z//eVp7AgAAAAAAAAAAAAAAAAAAAABQ/mWW9QIfu+eee6J+/fqJsjk5OTFo0KDEvRctWhQ7d+4s6WqHlJWVFbNnz45hw4Z94fjVoVSvXj1q1ar1ye9FRUUxY8aMxPNatmwZ99xzT6JsRkZGTJgwIWrXrp24/1NPPZU4y7/Mnj07+vbtGwcOHEiUr1WrVsyaNStq1qyZ0pyCgoKSrJdWjz76aEqfWQAAAAAAAAAAAAAAAAAAAAAA+DLl4gBW7dq1Y+DAgSnVDB8+PHG2qKgoFixYkOpa/9Zdd90VvXv3LnH9a6+9Flu2bEmcv/XWW6NKlSqJ8/Xr148hQ4Ykzi9dujTy8vIS54n4zW9+EwMGDIj9+/cnymdnZ8fs2bOjTZs2Kc9KOuPfqVKlSrRq1SpOPfXUlA9wfezWW29Nyy4AAAAAAAAAAAAAAAAAAAAAABzfysUBrL59+0ZWVlZKNZ07d44WLVokzi9btizVtb5Us2bN4o477jisHosWLUqczcjIiCuvvDLlGVdffXXibHFxcSxevDjlGcejoqKiuP322+M73/lOHDx4MFFNlSpVYubMmXHuueeWaGZxcXGJ6iIi2rdvH7NmzYpdu3bFO++8E2vWrIldu3bFq6++Gv369Uup16ZNm2Ly5Mkl3uXzbrnllli5cmVKPzNnzkzbfAAAAAAAAAAAAAAAAAAAAAAAykbFsl4gIqJr164lquvUqVNs2LAhUXbNmjUlmnEoQ4YMiYoVD+/Rvf7664mzrVu3jsaNG6c8o2PHjpGdnR379u1LlF+xYkVccsklKc85nuzbty+uv/76eOaZZxLXVK1aNXJzc6Nnz54lnlu5cuUS1Z1//vnx7LPPRnZ29mf+PSMjI7p27Rq5ubnxox/9KH75y18m7vnEE0/EsGHDSrTP5zVs2DAaNmyYll4AAAAAAAAAAAAAAAAAAAAAABw9Mst6gYiItm3blnrdpk2bSjTjUPr06XPYPdavX5842759+xLNyMzMjDZt2iTOJz0mdrzKy8uL888/P6XjV3Xq1Im//OUvh3X8KiKiSpUqKddUr149fve7333h+NXn3XfffdGpU6fEfZcuXRqbN29OeR8AAAAAAAAAAAAAAAAAAAAAAPhYuTiAVa9evRLV1a1bN3F2586dJZrxeZUrV44OHTocdp9t27Ylzp5wwgklnpNKbV5eXonnHOtWr14dOTk5sXjx4sQ1zZs3jxdffDHOPvvsw56fymf9Y1dffXU0bdr0K3OZmZnx/e9/P6XeL7/8csr7AAAAAAAAAAAAAAAAAAAAAADAx8rFAawaNWqUet3+/ftLNOPzmjVrFllZWYfdZ+/evYmzJX0+ERE1a9ZMnC0oKCjxnGPZ/Pnz4+yzz45169YlrunYsWMsXrw42rVrl5YdSnIErU+fPomzffv2Tan3mjVrUl0HAAAAAAAAAAAAAAAAAAAAAAA+US4OYB08eLDU6zIyMko04/Nq166dlj7FxcWJs5mZJf8zVahQIXE2lZ2OF08++WT07t07du7cmbimT58+sWDBgmjcuHHa9mjevHnKNR06dEicrVOnTjRr1ixx/r333kt5HwAAAAAAAAAAAAAAAAAAAAAA+Fi5OIC1e/fuUq/Lzs4u0YzPq169elr6pLLPnj17SjwnPz8/cTZdz+hYcffdd8fAgQPjww8/TFwzfPjwyM3NTdvn5GOnn356yjUNGzZMKd+gQYPE2b1796a6DgAAAAAAAAAAAAAAAAAAAAAAfKJiWS8QEbF169YS1eXl5SXO1q9fv0QzSku9evUSZ3fs2FHiOanU1q1bt8RzjiWFhYUxdOjQmDJlSuKazMzMGDt2bNx2222lslOLFi2ievXqKR1DS/WgWbVq1RJnK1YsF/91AAAAAAAAAAAAAAAAAAAAAABwlMos6wUiIlauXFnqdU2bNi3RjNJy4oknJs6W9PlERKxatSpxtkWLFiWec6zYtWtXXHTRRSkdv6patWo8/fTTpXb8KiIiIyMjcnJyUqrZt29fSvm9e/cmzlavXj2l3gAAAAAAAAAAAAAAAAAAAAAA8Gnl4gDWokWLUq45ePBgLF26NHG+Xbt2Kc8oTW3btk2cXbNmTezevTvlGW+99VZ88MEHpbLTsWjDhg3RvXv3eOGFFxLXnHDCCTFv3rzo379/KW72f3r06JFSPi8vL6X8tm3bEmdTOeAGAAAAAAAAAAAAAAAAAAAAAACfVy4OYM2ZMyfy8/NTqpk/f35s3749cb5z586prlWqcnJyEmc/+uijyM3NTXnGjBkzUsp//etfT3nGsWLp0qWRk5MTq1atSlzTtm3bWLx4cXTt2rUUN/uXSy65JKX88uXLE2d37twZGzduTJw//fTTU9oFAAAAAAAAAAAAAAAAAAAAAAA+rVwcwDpw4EBMmDAhpZqxY8cmzlauXDm6d++e6lqlqlu3blGzZs3E+QkTJkRxcXHi/P79++PRRx9NnD/11FOjZcuWifPHktzc3OjRo0ds2bIlcc03vvGNePnll6NFixaluNlnnXnmmXHaaaclzj/77LOJs3/6058SZzMzM6Nbt26J8wAAAAAAAAAAAAAAAAAAAAAA8Hnl4gBWRMQvfvGLePPNNxNlp0+fHnPmzEncu1evXpGdnV3S1UpF5cqVo1+/fonzr776ajz22GOJ8/fdd1+sX78+cf6KK65InD2WPPjgg3HZZZdFQUFB4pobb7wxnnvuuahVq1YpbnZoQ4cOTZx96qmnYtOmTV+ZKyoqivHjxyfu271796hdu3biPAAAAAAAAAAAAAAAAAAAAAAAfF65OYC1Z8+e6N27d6xYseLf5nJzc2PQoEEp9R48ePBhbFZ6hg8fnnJ+5syZX5l75JFHYvTo0Yn7VqxYMW6++eaUdjlWjBgxIoqKilKqmTx5cmRlZUVGRkZaflI5VDZkyJDEh7f27NkTAwcOjH379v3b3J133hmvvfZa4h1uuOGGxFkAAAAAAAAAAAAAAAAAAAAAADiUcnMAKyJi48aN0aVLlxg2bFjMmzcv8vLy4sMPP4z33nsvZs2aFZdddln0798/9u/fn7hn69ato3///qW4dcnl5OREr169EucLCwtjwIABMXDgwFi0aFEUFxd/8lpRUVHMnz8/Lr300vj2t7/9mde+yqBBg6J58+Yp7U7ZqFmzZvzkJz9JnP/b3/4WX//612P27Nlx4MCBT/69uLg4lixZEv37948xY8Yk7te4ceMYOHBgSjsDAAAAAAAAAAAAAAAAAAAAAMDnVSzrBT6vsLAwJk2aFJMmTUpLv7vvvjsqVKiQll6lYfz48dGxY8fPHCf6Kk8++WQ8+eSTUa1atWjcuHEUFxfH5s2bo6CgIOX5derUiXvvvTflOsrOiBEjYvLkyfHmm28myr/++uvRr1+/yM7OjiZNmkTFihVjy5Yt8cEHH6Q8e8yYMVG5cuWU6wAAAAAAAAAAAAAAAAAAAAAA4NMyiouLi0uj8XnnnRfz588vjdaJXXDBBTF37tyvzK1fvz5atWqVqGePHj1i3rx5h7nZZ02cODG++93vprVnUs8880wMGDAgUXbKlClx4403JsqOHDkyRo0adRibHRkZGRllvUKsW7cuWrZsmVLNihUr4qyzzkrpcNrhuvTSS2PmzJlHbN6XeeONN+KMM8745PeVK1dGu3btynAjAAAAAAAAAAAAAAAAAAAAAOB45BbK4cks6wUiIq677rq096xfv35MnTo17X1Lw3e+850YPnz4EZ977733Jj5+RfnSoUOHmDJlyhE74NWhQ4ej5vsEAAAAAAAAAAAAAAAAAAAAAED5Vy4OYI0ePTqtR7Cys7Nj1qxZ0bRp07T1LG0TJkyI733ve0dkVkZGRowePTruvPPOIzKP0nH11VfHww8/HBUqVCjVOWeeeWb89a9/jZo1a5bqHAAAAAAAAAAAAAAAAAAAAAAAjh/l4gBWRkZGTJ06NW666abD7lWrVq147rnnolu3bmnY7MjJyMiI8ePHx6OPPhrVq1cvtTl169aNGTNmxE9/+tNSm8GRc9NNN0Vubm7UqVOnVPpfddVV8dJLL0WDBg1KpT8AAAAAAAAAAAAAAAAAAAAAAMencnEAKyIiMzMzHnnkkXj44YejRo0aJerRpUuXWLJkSfTo0SPN2x05Q4YMiRUrVsSVV14ZGRkZaetboUKFGDx4cLzxxhtx+eWXp60vZa9Pnz6xcuXKuOyyy9LWs0WLFjFt2rR46qmnolq1amnrCwAAAAAAAAAAAAAAAAAAAAAAEeXoANbHhg4dGqtXr47hw4dH9erVE9Wcfvrp8fjjj8fixYvjlFNOKeUNS1+rVq1i+vTpsWLFihg+fHjUr1+/xL0aNWoUt912W7z55psxefLkaNSoUYl7HTx4MHE2KyurxHNIXZMmTeLpp5+OJUuWxDXXXBPZ2dkl6nPWWWfFY489Fv/85z/jm9/8Zpq3BAAAAAAAAAAAAAAAAAAAAACA/1OxrBc4lCZNmsTEiRNjzJgx8cILL8RLL70Uq1evju3bt8f+/fujatWq0bRp02jfvn306tUrunbteljzWrZsGcXFxWnaPn3OOOOMmDhxYjz44IPx6quvxsKFC2PZsmWxdu3a2LRpU+zatSv27dsXERHZ2dlRp06daNq0aZxyyinRsWPHOPfcc6Nz586RkZGRln0KCgoSZ+vUqZOWmaWtPP7dD0eXLl3iD3/4Q+Tn58cLL7wQL774Yrz++uuxdu3a2LFjR+zduzeKi4sjOzs76tWrF82bN4927dpF165d48ILL4xmzZqV9VsAAAAAAAAAAAAAAAAAAAAAAOA4UC4PYH2sWrVqcckll8Qll1xS1quUqczMzMjJyYmcnJwy3WPbtm2Js0fLAaxjVc2aNaN///7Rv3//sl4FAAAAAAAAAAAAAAAAAAAAAAC+ILOsF+DosXr16sTZxo0bl+ImAAAAAAAAAAAAAAAAAAAAAADA0cwBLBIpLCyMuXPnJspWqFAhOnfuXMobAQAAAAAAAAAAAAAAAAAAAAAARysHsEhkzJgx8f777yfKnnHGGVG9evVS3ggAAAAAAAAAAAAAAAAAAAAAADhaVSzrBSifPvroo9i5c2f84x//iCeeeCKmTZuWuLZfv36luBkAAAAAAAAAAAAAAAAAAAAAAHC0cwCLz+jfv3/k5uaWuL5SpUoxbNiwNG4EAAAAAAAAAAAAAAAAAAAAAAAcazLLegGOLYMHD47GjRuX9RoAAAAAAAAAAAAAAAAAAAAAAEA55gAWaXPSSSfF/fffX9ZrAAAAAAAAAAAAAAAAAAAAAAAA5ZwDWKRFvXr1YsaMGVGjRo0vzcybNy8yMjLK7AcAAAAAAAAAAAAAAACA/4+dO42usjD3h30nzINADKNiRQoqUrQgLEHbBVKnFmdtq1gGK0WFVqusWup8UJdFpaCoVevQQ7UHtVWibfXQWgFpI8pwREUQEPyDRQIIBpJAYpL3w7tsFVGfvbN3BriutfIBcg+/5Nl5Pt4AAAAAUD84gEWNHXbYYfHyyy9H37596zoKAAAAAAAAAAAAAAAAAAAAAADQADiARdo6dOgQt912WyxevDh69OhR13EAAAAAAAAAAAAAAAAAAAAAAIAGonFdB6BhaNOmTeTn50enTp1i0KBBMXTo0Bg6dGi0bNmyrqMBAAAAAAAAAAAAAAAAAAAAAAANTNYOYM2ZMydbo8miWbNm1XUEAAAAAAAAAAAAAAAAAAAAAABgL5db1wEAAAAAAAAAAAAAAAAAAAAAAADYtzSu6wDsO4YMGRLV1dV1HQMAAAAAAAAAAAAAAAAAAAAAAKhjuXUdAAAAAAAAAAAAAAAAAAAAAAAAgH2LA1gAAAAAAAAAAAAAAAAAAAAAAADUKgewAAAAAAAAAAAAAAAAAAAAAAAAqFUOYAEAAAAAAAAAAAAAAAAAAAAAAFCrHMACAAAAAAAAAAAAAAAAAAAAAACgVjmABQAAAAAAAAAAAAAAAAAAAAAAQK1yAAsAAAAAAAAAAAAAAAAAAAAAAIBa5QAWAAAAAAAAAAAAAAAAAAAAAAAAtcoBLBL77W9/Gzk5OYm+brzxxrqOCwAAAAAAAAAAAAAAAAAAAAAA1FON6zoA1EelpaWxZMmSePPNN2PNmjXxzjvvxJo1a+KDDz6IHTt2RElJSZSVlUWrVq2ibdu20bZt2+jQoUMcddRR0a9fv+jXr1/07t07K9m6desW7777blZmf54nn3wyzj333FrdCQAAAAAAAAAAAAAAAAAAAADA3ssBLIiIzZs3x+zZs+PFF1+MV199Nd54442orKz80r7i4uIoLi6OdevWRUTEiy+++O/vHXbYYTFq1KgYOXJkHHjggVnLDgAAAAAAAAAAAAAAAAAAAAAADU1uXQeAujRlypQYOHBgdOrUKS644IJ48MEH47XXXkt0/OrLrFixIq6++uo45JBDYuLEiVFaWpqBxAAAAAAAAAAAAAAAAAAAAAAA0PA5gMU+bfr06bFgwYKoqqrK2o6KioqYPHly9O7dO1566aWs7QEAAAAAAAAAAAAAAAAAAAAAgIbCASyoJWvXro0TTjghZs2aVddRgCxau3Zt5OTk7PFr7dq1dR0PqAXeA8CeeDcA6fL+AFLlvUFD5bPbMHlu9Z9n1HB4VvWfZ9RweFb1n2fUMHhODYPn1DB4Tg2D59QweE57F88TGg5/r4D3ALA77wUgVd4bQE14hwCfx/sB9h4OYEEtKi8vj/POOy8KCwvrOgoAAAAAAAAAAAAAAAAAAAAAANQZB7Cglu3atSsuuOCCKCsrq+soAAAAAAAAAAAAAAAAAAAAAABQJxzAgoTy8/Pj0EMPjcMPPzw6duxYo1lr1qyJO++8M0PJAAAAAAAAAAAAAAAAAAAAAACgYXEAi8RGjx4d1dXVib5uvPHGuo5bY4cddlhcddVV8fzzz8cHH3wQmzdvjhUrVsRbb70VGzdujA0bNsQjjzwSRx11VFrz77nnnqiqqspwagAAAAAAAAAAAAAAAAAAAAAAqP8cwIJPaNGiRYwdOzZeeeWVWL58eUyePDlOPvnkyMvL+0xt586dY/To0bF48eKYNGlSyrvWr18fL7/8ciZif8YNN9yQ+FhZkq9zzz03KzkBAAAAAAAAAAAAAAAAAAAAANg3Na7rAFAftG7dOi6//PL46U9/Gu3bt0+pNzc3N6677rpo0qRJ/OIXv0ipd968eXHsscem1AMAAAAAAAAAAAAAAAAAAAAAAA1dvT6A9cYbb0RBQUH84x//iOXLl8emTZuirKwsWrVqFV27do0+ffrECSecEOecc07k5eXVadbq6uqYP39+FBQUxMKFC+Ptt9+Obdu2RVlZWTRq1CgOOOCAmDVrVvTr1y+t+cXFxfHqq6/GkiVLYvXq1bF+/fr48MMPo6ysLCIiWrRoEe3atYuuXbtGjx49ol+/fjFgwIBo1apVJn/MvU7jxo3jkksuiUmTJkWHDh1qNOvnP/95/PGPf4yFCxcm7lmxYkWNdgIAAAAAAAAAAAAAAAAAAAAAQEOUtQNYQ4YMiblz5yaqXbNmTXTr1u3f/y4sLIxf/OIXn9tfXFwcy5Yti2XLlsXjjz8el112WfzoRz+K66+/PvLz81POunbt2jjkkEMS1Q4ePDjmzJnzqf97+umn47rrros333xzjz2VlZWxbt26KC4uTilXSUlJzJw5Mx577LGYP39+VFRUpNTfrFmzGDx4cIwYMSLOPffcaN68eUr9u3vhhRdi6tSpiWqHDx8ew4cPr9G+2vDXv/418bP/Mjk5OTFu3Lj44Q9/mLhn06ZNGdkNAAAAAAAAAAAAAAAAAAAAAAANSdYOYKWjoqIirrrqqrjzzjujuro6cV9ZWVncddddMXPmzJgxY0acfPLJWUz5Hzt37oyLLroofv/732d0bllZWUybNi1uu+222LZtW9pzdu3aFbNnz47Zs2fHhAkT4pprrolLL700mjRpkta8devWxZ///OdEtf37909rR23L1PGrjw0ZMiSl+l27dmV0PwAAAAAAAAAAAAAAAAAAAAAANAS5dR3gYyUlJXHKKafEtGnTUjp+9UlFRUUxbNiwePDBBzOc7rN27NgRJ554YsaPXxUWFsZRRx0VV199dY2OX+2uqKgoLr/88hgwYEC8/vrrGZvLp3Xp0iWl+rZt22YpCQAAAAAAAAAAAAAAAAAAAAAA1F+N6zpARER5eXkMGzYs5s6dW+NZlZWVMXbs2GjZsmUMHz48A+n2vOP888+P+fPnZ3TuI488EhdffHFUVFRkdO4nvfbaazFo0KB47LHH4owzzsjann1Vqs/uK1/5SlZyFBcXx9/+9rd45ZVXYtmyZbF169bYunVrVFRURPPmzSMvLy8OPPDAOPTQQ+Ooo46KgQMHRsuWLbOSBQAAAAAAAAAAAAAAAAAAAAAAdlcvDmCNGzcuI8evPlZdXR0XXnhh9OrVK/r27ZuxuR+79dZb409/+lNGZ95///1xySWXZHTm5ykpKYlzzjknnnzyyTjrrLNqZee+YtmyZSnV9+/fPys5pk6dGlOnTk1c36xZsxgyZEiMGjUqzjnnnGjatGlWcgEAAAAAAAAAAAAAAAAAAAAAQEREbl0HiIh44YUXMj6zvLw8hg8fHuXl5Rmdu3r16pg0aVJGZ/7lL3+JcePGZXTml6msrIzzzz8/Fi1aVKt793bPPPNM4tomTZrEySefnMU0ye3atSv+93//N4YPHx49evSIhx56KKqrq+s6FgAAAAAAAAAAAAAAAAAAAAAAe6l6cQBrT3Jzc6Nz587Rq1evyM/PT2vG8uXLY+rUqRnNtX79+qioqMjYvI0bN8aoUaOiqqoqYzOT2rVrV3z/+9+P0tLSWt+9N9q+fXvcf//9ietPPfXUtD/b2bRu3boYM2ZMHH/88bFhw4a6jgMAAAAAAAAAAAAAAAAAAAAAwF6o3h3A6tixY9x7772xcePG2LBhQyxbtiw2b94ca9asiRtuuCGaN2+e0rzbb7+9Xh94mjhxYmzevDmlniOPPDLuvvvuWLFiRWzfvj2Ki4tj2bJl8atf/Sp69uyZ0qzVq1fH5MmTU+phzyZOnBhbtmxJVJuTkxPXX399lhPVzNy5c6N///7x5ptv1nUUAAAAAAAAAAAAAAAAAAAAAAD2Mo3rOsAn9e/fP55//vnIz8//zPe6desWN954Y3zve9+L448/PoqKihLN3LJlSzz66KMxduzYTMf9t969e8cPfvCDOOGEE+Kggw6K/Pz8KC0tjXfffTeWLFkSs2fPjoKCgs/0rVy5MmbMmJHSrmuvvTZuuOGGaNz404+uV69e0atXr7j00kvj8ssvjwceeCDxzClTpsQVV1wR7dq1SykL//HEE0/Evffem7h+9OjR8fWvfz2LiTLjX//6VwwdOjQKCwuje/fuGZ9fVFQUmzZtSqln1apVGc8BAAAAAAAAAAAAAAAAAAAAAEDtqjcHsDp37hzPPffcHo9ffdIRRxwRBQUFcdxxx0VVVVWi2TNmzMjKAaymTZvGtGnTYuzYsdGoUaNPfa9NmzbRp0+f6NOnT4wcOTJ27NgRlZWVn6qZPn164p8hImLChAlx0003fWFN8+bN47777ovi4uKYOXNmorklJSXx0EMPxYQJExJn4T9eeOGFGDlyZOL6Hj16xLRp07KYKLOKiori7LPPjgULFkSzZs0yOvvee++N//qv/8roTAAAAAAAAAAAAAAAAAAAAAAA6r/cug7wsZtvvjnat2+fqHbgwIExatSoxLMLCwtj69at6Ubbo6ZNm8azzz4bl1566WeOX+1J69ato23btv/+d1VVVTz55JOJ93Xr1i1uvvnmRLU5OTkxffr0aNeuXeL5jz/+eOJa/uPZZ5+NU089NXbt2pWovm3btvHMM89EmzZtspwss1577bX45S9/WdcxAAAAAAAAAAAAAAAAAAAAAADYS9SLA1jt2rWLESNGpNQzfvz4xLVVVVUxb968VGN9oeuuuy5OOumktPsXLVoU77//fuL6K664Ipo3b564vn379jFmzJjE9QsXLoyioqLE9UTcc889cdZZZ8XOnTsT1bdo0SKeffbZ6NWrV8azdOrUKc4///yYPHlyPP/88/H222/Hpk2bory8PD788MN455134qmnnorLLrss8vLy0tpx2223xZYtWzKcHAAAAAAAAAAAAAAAAAAAAACAfVHjug4QEXHqqadG06ZNU+o5+uij4+CDD4533303Uf2SJUvijDPOSCfeZ3Tt2jUmTpxYoxmFhYWJa3NycuK73/1uyjvOO++8uOOOOxLVVldXx4IFC+K0005Lec++pqqqKn72s5/Fr371q8Q9zZs3j1mzZsU3v/nNjOXo3r17jBgxIoYNGxb9+/ePnJycPdY1adIk2rRpE4ccckicddZZceutt8Ytt9wSv/zlL6OqqirxvtLS0rj//vvj6quvztSPEOPGjUv5s71q1ao4DbmUJQAAIABJREFU88wzM5YBAAAAAAAAAAAAAAAAAAAAAIDaVy8OYA0YMCCtvn79+iU+gLVixYq0duzJmDFjonHjmv3qXn/99cS13bt3jy5duqS8o2/fvtGiRYsoKytLVL906VIHsL5EWVlZ/OAHP4innnoqcU/Lli2joKAgTjjhhIxkOPfcc+Pb3/52DB069HOPXn1ZnltuuSWOOeaYOOecc+Kjjz5K3Dtz5syMHsDq2LFjdOzYMWPzAAAAAAAAAAAAAAAAAAAAAABoGHLrOkBExBFHHJH1vvfeey+tHXsybNiwGs9Yu3Zt4to+ffqktSM3Nzd69eqVuD7pMbF9VVFRURx//PEpHb/Ky8uLv/71rxk7fhURcccdd8S3vvWttI5ffdLpp58ekydPTqnn9ddfj40bN9ZoLwAAAAAAAAAAAAAAAAAAAAAA1IsDWPn5+Wn17b///olrt27dmtaO3TVr1iyOPPLIGs/ZtGlT4tpOnTqlvSeV3qKiorT37O2WL18eAwcOjAULFiTuOeigg+Kll16KY489NovJauayyy6LHj16pNSTyu8AAAAAAAAAAAAAAAAAAAAAAAD2pF4cwNpvv/2y3rdz5860duyua9eu0bRp0xrPKSkpSVyb7u8nIqJNmzaJa0tLS9PeszebO3duHHvssbFmzZrEPX379o0FCxZE7969s5is5ho3bhzf+973UupZuXJlltIAAAAAAAAAAAAAAAAAAAAAALCvqBcHsCorK7Pel5OTk9aO3bVr1y4jc6qrqxPX5uam/5gaNWqUuDaVTPuKRx99NE466aTYunVr4p5hw4bFvHnzokuXLllMljmDBw9Oqf69997LUhIAAAAAAAAAAAAAAAAAAAAAAPYV9eIA1vbt27Pe16JFi7R27K5169YZmZNKnh07dqS9p7i4OHFtpn5He4tJkybFiBEjory8PHHP+PHjo6CgIGOfk9rQuXPnlOpLSkqylAQAAAAAAAAAAAAAAAAAAAAAgH1F47oOEBGxcePGtPqKiooS17Zv3z6tHdmSn5+fuHbLli1p70mld//99097z96koqIixo4dG7/97W8T9+Tm5sbtt98eV155ZfaCZUmTJk1Sqq+urs5SEgAAAAAAAAAAAAAAAAAAAAAA9hW5dR0gIuKNN97Iet+BBx6Y1o5s+cpXvpK4Nt3fT0TEsmXLEtcefPDBae/ZW2zbti1OOeWUlI5ftWzZMv74xz82yONXEakfoGvVqlWWkgAAAAAAAAAAAAAAAAAAAAAAsK9oXNcBIiIKCwtT7qmsrIyFCxcmru/du3fKO7LpiCOOSFy7YsWK2L59e+y3334p7Vi5cmV8+OGHWcm0N3r33XfjO9/5TkpHwzp16hTPPvtsDBgwIIvJsuvll19Oqb5Lly5ZSgIAAAAAAAAAAAAAAAAAAAAAwL4it64DREQ8//zzUVxcnFLP3LlzY/PmzYnrjz766FRjZdXAgQMT13700UdRUFCQ8o4nn3wypfpjjjkm5R17i4ULF8bAgQNTOn51xBFHxIIFC2rt+NXWrVuzMjfVz0nPnj2zkgMAAAAAAAAAAAAAAAAAAAAAgH1HvTiAtWvXrpg+fXpKPbfffnvi2mbNmsVxxx2XaqysGjRoULRp0yZx/fTp06O6ujpx/c6dO+M3v/lN4vpDDz00unXrlrh+b1JQUBCDBw+O999/P3HPt771rfjnP/8ZBx98cBaTfdqYMWPi4osvjo0bN2Zs5v/8z//E4sWLU+oZNGhQxvYDAAAAAAAAAAAAAAAAAAAAALBvqhcHsCIifvnLX8Zbb72VqPaJJ56I559/PvHsE088MVq0aJFutKxo1qxZnH766YnrX3nllXjwwQcT1996662xdu3axPXnnntu4tq9yV133RVnn312lJaWJu658MIL47nnnou2bdtmMdlnVVZWxgMPPBDdu3ePn/3sZykd7NqTJUuWxMUXX5xSz9e//vXo3LlzjfYCAAAAAAAAAAAAAAAAAAAAAEC9OYC1Y8eOOOmkk2Lp0qVfWFdQUBCjRo1Kafbo0aNrkCx7xo8fn3L9rFmzvrTugQceiJtuuinx3MaNG8cll1ySUpa9xeWXXx5VVVUp9TzyyCPRtGnTyMnJychXKofKIiJKS0vjjjvuiIMPPjhGjhwZ8+bNi+rq6pRm/P73v4/jjz8+tm/fnlLfyJEjU6oHAAAAAAAAAAAAAAAAAAAAAIA9qTcHsCIi1q9fH/37949LL7005syZE0VFRVFeXh7/+te/4plnnomzzz47zjzzzNi5c2fimd27d48zzzwzi6nTN3DgwDjxxBMT11dUVMRZZ50VI0aMiMLCwk8dPaqqqoq5c+fGGWecERdffHFKB5FGjRoVBx10UErZqXvl5eXxu9/9LgYPHhwHH3xwjBs3Lv7whz/Eu+++u8fnv2bNmnj44YdjwIABccEFF8SHH36Y0r78/PwYM2ZMpuIDAAAAAAAAAAAAAAAAAAAAALAPa1zXAXZXUVER9913X9x3330ZmTdp0qRo1KhRRmZlw5133hl9+/aNXbt2Je559NFH49FHH41WrVpFly5dorq6OjZs2BClpaUp78/Ly4tbbrkl5T7ql3Xr1sWvf/3r+PWvfx0REc2bN48DDjggWrZsGaWlpbFly5aUD17t7qabbor99tsvE3EBAAAAAAAAAAAAAAAAAAAAANjH1bsDWJk0dOjQuOCCC+o6xhfq1atX3HHHHfGTn/wk5d6SkpJYtWpVjfY/9NBD0alTpxrNoP7ZuXNnvPPOOxmbd/LJJ8cll1ySsXmwN+vWrVtUV1fXdQygDnkPAHvi3QCky/sDSJX3Bg2Vz27D5LnVf55Rw+FZ1X+eUcPhWdV/nlHD4Dk1DJ5Tw+A5NQyeU8PgOe1dPE9oOPy9At4DwO68F4BUeW8ANeEdAnwe7wfYe+TWdYCIyMqRqvbt28eMGTMyPjcbfvzjH8f48eNrfe8tt9wSZ511Vq3vpWHp06dPPP7445GTk1PXUQAAAAAAAAAAAAAAAAAAAAAA2EvUiwNYN910U0aPYLVo0SKeeeaZOPDAAzM2M9umT58el112Wa3sysnJiZtuuimuvvrqWtlHw3XMMcfEiy++GG3btq3rKAAAAAAAAAAAAAAAAAAAAAAA7EXqxQGsnJycmDFjRvzoRz+q8ay2bdvGc889F4MGDcpAstqTk5MTd955Z/zmN7+J1q1bZ23P/vvvH08++WRce+21WdtBw5eTkxM/+clPYu7cuZGfn1/XcQAAAAAAAAAAAAAAAAAAAAAA2MvUiwNYERG5ubnxwAMPxP333x/77bdfWjP69+8fr776agwePDjD6WrPmDFjYunSpfHd7343cnJyMja3UaNGMXr06HjzzTfjnHPOydhcas/06dPjtttui379+mV1zze+8Y0oLCyMu+66K5o1a5bVXQAAAAAAAAAAAAAAAAAAAAAA7JvqzQGsj40dOzaWL18e48ePj9atWyfqOfzww+Ohhx6KBQsWRM+ePbOcMPsOOeSQeOKJJ2Lp0qUxfvz4aN++fdqzOnfuHFdeeWW89dZb8cgjj0Tnzp3TnlVZWZm4tmnTpmnvYc8OOuig+NnPfhaLFi2Kt99+O6ZMmRKnn3565OXl1Xh2Xl5eXHTRRfHPf/4zXnrppTjmmGMykBgAAAAAAAAAAAAAAAAAAAAAAPascV0H2JMDDjgg7r777pg8eXL8/e9/j3/84x+xfPny2Lx5c+zcuTNatmwZBx54YPTp0ydOPPHEGDBgQI32devWLaqrqzOUPnO+9rWvxd133x133XVXvPLKKzF//vxYsmRJrF69Ot57773Ytm1blJWVRUREixYtIi8vLw488MDo2bNn9O3bN775zW/G0UcfHTk5ORnJU1pamrg2E0eZakN9fO5J9OzZM6688sq48soro7q6Ot5444147bXXYuXKlbFq1apYtWpVfPDBB7Fjx47Yvn17lJWVRePGjaN58+bRvn37OOCAA6Jnz57Rp0+fOO644+Loo4+ORo0a1fWPBQAAAAAAAAAAAAAAAAAAAADAPqJeHsD6WKtWreK0006L0047ra6j1Knc3NwYOHBgDBw4sE5zbNq0KXFtQzmAtTfIycmJPn36RJ8+feo6CgAAAAAAAAAAAAAAAAAAAAAAJJJb1wFoOJYvX564tkuXLllMAgAAAAAAAAAAAAAAAAAAAAAANGQOYJFIRUVFvPDCC4lqGzVqFEcffXSWEwEAAAAAAAAAAAAAAAAAAAAAAA2VA1gkMnny5Pjggw8S1X7ta1+L1q1bZzkRAAAAAAAAAAAAAAAAAAAAAADQUDWu6wDUTx999FFs3bo1/u///i8efvjhmDlzZuLe008/PYvJAAAAAAAAAAAAAAAAAAAAAACAhs4BLD7lzDPPjIKCgrT7mzRpEpdeemkGEwEAAAAAAAAAAAAAAAAAAAAAAHub3LoOwN5l9OjR0aVLl7qOAQAAAAAAAAAAAAAAAAAAAAAA1GMOYJExX/3qV2PKlCl1HQMAAAAAAAAAAAAAAAAAAAAAAKjnHMAiI/Lz8+PJJ5+M/fbb73Nr5syZEzk5OXX2BQAAAAAAAAAAAAAAAAAAAAAA1A8OYFFjhx12WLz88svRt2/fuo4CAAAAAAAAAAAAAAAAAAAAAAA0AA5gkbYOHTrEbbfdFosXL44ePXrUdRwAAAAAAAAAAAAAAAAAAAAAAKCBaFzXAWgY2rRpE/n5+dGpU6cYNGhQDB06NIYOHRotW7as62gAAAAAAAAAAAAAAAAAAAAAAEADk7UDWHPmzMnWaLJo1qxZdR0BAAAAAAAAAAAAAAAAAAAAAADYy+XWdQAAAAAAAAAAAAAAAAAAAAAAAAD2LY3rOgD7jiFDhkR1dXVdxwAAAAAAAAAAAAAAAAAAAAAAAOpYbl0HAAAAAAAAAAAAAAAAAAAAAAAAYN/iABYAAAAAAAAAAAAAAAAAAAAAAAC1ygEsAAAAAAAAAAAAAAAAAAAAAAAAapUDWAAAAAAAAAAAAAAAAAAAAAAAANQqB7AAAAAAAAAAAAAAAAAAAAAAAACoVQ5gAQAAAAAAAAAAAAAAAAAAAAAAUKscwAIAAAAAAAAAAAAAAAAAAAAAAKBWOYBFYr/97W8jJycn0deNN95Y13EBAAAAAAAAAAAAAAAAAAAAAIB6qnFdB4D6qLS0NJYsWRJvvvlmrFmzJt55551Ys2ZNfPDBB7Fjx44oKSmJsrKyaNWqVbRt2zbatm0bHTp0iKOOOir69esX/fr1i969e9dJ9srKyigsLIw5c+bE4sWLY9WqVbFhw4YoKSmJ8vLyf2c+5JBD4vDDD49vfOMb8a1vfSsOOOCAOskLAAAAAAAAAAAAAAAAAAAAAMC+xwEsiIjNmzfH7Nmz48UXX4xXX3013njjjaisrPzSvuLi4iguLo5169ZFRMSLL7747+8ddthhMWrUqBg5cmQceOCBWcv+sQ0bNsTUqVPjd7/7Xbz//vuJMs+bNy8eeOCByM3NjaFDh8a4cePirLPOynpWAAAAAAAAAAAAAAAAAAAAAAD2bbl1HQDq0pQpU2LgwIHRqVOnuOCCC+LBBx+M1157LdHxqy+zYsWKuPrqq+OQQw6JiRMnRmlpaQYSf1Z5eXlce+218dWvfjVuv/32Lzx+9Xmqqqrib3/7W5x99tnRv3//WLRoURaSAgAAAAAAAAAAAAAAAAAAAADA/88BLPZp06dPjwULFkRVVVXWdlRUVMTkyZOjd+/e8dJLL2V09urVq6N///5xyy23RFlZWUZmLlq0KAYOHBiTJ0/OyDwAAAAAAAAAAAAAAAAAAAAAANidA1hQS9auXRsnnHBCzJo1KyPzFi5cGIMGDYrXX389I/M+6aOPPoqJEyfGxRdfHNXV1RmfDwAAAAAAAAAAAAAAAAAAAADAvs0BLKhF5eXlcd5550VhYWGN5qxYsSJOOeWU2LRpU4aS7dkDDzwQV155ZVZ3sHdYu3Zt5OTk7PFr7dq1dR0PqCXeBcAX8Y4AasI7BEiFdwZkhr+lhs3zaxg8p4bBc2oYPKeGwXOq/zyjhsFzahg8p4bBc2o4PKv6zzNqODwrqDl/R7Bv8w4APsk7AUjK+wLIBO8S4It4RwANnQNYUMt27doVF1xwQZSVlaXVv2PHjjjttNNiy5YtGU62Z9OmTYuHH364VnYBAAAAAAAAAAAAAAAAAAAAALBvcAALEsrPz49DDz00Dj/88OjYsWONZq1ZsybuvPPOtHp/+tOfxsqVK1Pu69ixY/Ts2TOaN2+ecu9ll10Wq1evTrkPAAAAAAAAAAAAAAAAAAAAAAD2xAEsEhs9enRUV1cn+rrxxhvrOm6NHXbYYXHVVVfF888/Hx988EFs3rw5VqxYEW+99VZs3LgxNmzYEI888kgcddRRac2/5557oqqqKqWeV155JR5++OHE9a1atYqbb745Nm3aFBs3boy33347SktLY86cOTF06NDEc0pKSmLChAkpZQUAAAAAAAAAAAAAAAAAAAAAgM/jABZ8QosWLWLs2LHxyiuvxPLly2Py5Mlx8sknR15e3mdqO3fuHKNHj47FixfHpEmTUt61fv36ePnll1Pq+fnPfx7V1dWJavfff/948cUX45prron27dv/+/9zcnJi8ODBMXv27LjooosS7y4oKIj58+enlBcAAAAAAAAAAAAAAAAAAAAAAPbEASyIiNatW8c111wT/+///b+4//77Y8CAAYl7c3Nz47rrrotbb7015b3z5s1LXLt48eKYM2dO4vqpU6d+4c/RqFGj+PWvfx2HH3544plTpkxJXAsAAAAAAAAAAAAAAAAAAAAAAJ+ncV0H+CJvvPFGFBQUxD/+8Y9Yvnx5bNq0KcrKyqJVq1bRtWvX6NOnT5xwwglxzjnnRF5eXp1mra6ujvnz50dBQUEsXLgw3n777di2bVuUlZVFo0aN4oADDohZs2ZFv3790ppfXFwcr776aixZsiRWr14d69evjw8//DDKysoiIqJFixbRrl276Nq1a/To0SP69esXAwYMiFatWmXyx9zrNG7cOC655JKYNGlSdOjQoUazfv7zn8cf//jHWLhwYeKeFStWJK695557EtceffTRMXLkyC+ta9KkSUyePDnOOOOMRHOfeeaZWL9+fXTt2jVxFgAAAAAAAAAAAAAAAAAAAAAA2F3WDmANGTIk5s6dm6h2zZo10a1bt3//u7CwMH7xi198bn9xcXEsW7Ysli1bFo8//nhcdtll8aMf/Siuv/76yM/PTznr2rVr45BDDklUO3jw4JgzZ86n/u/pp5+O6667Lt5888099lRWVsa6deuiuLg4pVwlJSUxc+bMeOyxx2L+/PlRUVGRUn+zZs1i8ODBMWLEiDj33HOjefPmKfXv7oUXXoipU6cmqh0+fHgMHz68Rvtqw1//+tfEz/7L5OTkxLhx4+KHP/xh4p5NmzYlqvvoo4/i6aefTjz3wgsvTFw7bNiw6NChQ6IsVVVV8Yc//CF++tOfJp4PAAAAAAAAAAAAAAAAAAAAAAC7y9oBrHRUVFTEVVddFXfeeWdUV1cn7isrK4u77rorZs6cGTNmzIiTTz45iyn/Y+fOnXHRRRfF73//+4zOLSsri2nTpsVtt90W27ZtS3vOrl27Yvbs2TF79uyYMGFCXHPNNXHppZdGkyZN0pq3bt26+POf/5yotn///mntqG2ZOn71sSFDhqRUv2vXrkR1c+bMia1btyae++1vfztxbaNGjeKkk06Kxx57LFH9U0895QAWAAAAAAAAAAAAAAAAAAAAAAA1klvXAT5WUlISp5xySkybNi2l41efVFRUFMOGDYsHH3www+k+a8eOHXHiiSdm/PhVYWFhHHXUUXH11VfX6PjV7oqKiuLyyy+PAQMGxOuvv56xuXxaly5dUqpv27Ztoro5c+YkntmhQ4fo3r17SjkGDhyYuHbBggWxc+fOlOYDAAAAAAAAAAAAAAAAAAAAAMAn1YsDWOXl5TFs2LD4+9//XuNZlZWVMXbs2Iwfptp9x/nnnx/z58/P6NxHHnkkBg8eHCtXrszo3E967bXXYtCgQVFQUJC1HfuyioqKlOq/8pWvJKpL5bP2ta99LaUMqfaUl5fHq6++mvIOAAAAAAAAAAAAAAAAAAAAAAD4WL04gDVu3LiYO3duxuZVV1fHhRdeGEuWLMnYzE+69dZb409/+lNGZ95///3xwx/+MOUDSukoKSmJc845J55++ums79rXLFu2LKX6/v37J6pbunRp4pk9evRIKUM6Pa+99lrKOwAAAAAAAAAAAAAAAAAAAAAA4GP14gDWCy+8kPGZ5eXlMXz48CgvL8/o3NWrV8ekSZMyOvMvf/lLjBs3LqMzv0xlZWWcf/75sWjRolrdu7d75plnEtc2adIkTj755C+t27hxY2zdujXx3K5duyau/ViXLl0iNzf562D58uUp7wAAAAAAAAAAAAAAAAAAAAAAgI/ViwNYe5KbmxudO3eOXr16RX5+flozli9fHlOnTs1orvXr10dFRUXG5m3cuDFGjRoVVVVVGZuZ1K5du+L73/9+lJaW1vruvdH27dvj/vvvT1x/6qmnJvpsv/POOynlaN++fUr1ERGNGjWKvLy8xPWrV69OeQcAAAAAAAAAAAAAAAAAAAAAAHys3h3A6tixY9x7772xcePG2LBhQyxbtiw2b94ca9asiRtuuCGaN2+e0rzbb7+9Xh94mjhxYmzevDmlniOPPDLuvvvuWLFiRWzfvj2Ki4tj2bJl8atf/Sp69uyZ0qzVq1fH5MmTU+phzyZOnBhbtmxJVJuTkxPXX399otqNGzemlCOVQ1aftP/++yeuTTUTAAAAAAAAAAAAAAAAAAAAAAB8Ur06gNW/f/9YtmxZXHrppdG+fftPfa9bt25x4403xqJFi6Jjx46JZ27ZsiUeffTRTEf9lN69e8ett94ar776arz//vtRUVERH374YSxdujT++7//Oy644IJo3br1Z/pWrlwZM2bMSGnXtddeG4sWLYrx48fHoYceGq1bt4799tsvevXqFVdccUUsXbo0xo4dm9LMKVOmxLZt21Lq4dOeeOKJuPfeexPXjx49Or7+9a8nqi0qKkopS6tWrVKq/1jLli0T1zqABQAAAAAAAAAAAAAAAAAAAABATTSu6wAf69y5czz33HORn5//hXVHHHFEFBQUxHHHHRdVVVWJZs+YMSPlo1BJNG3aNKZNmxZjx46NRo0afep7bdq0iT59+kSfPn1i5MiRsWPHjqisrPxUzfTp0xP/DBEREyZMiJtuuukLa5o3bx733XdfFBcXx8yZMxPNLSkpiYceeigmTJiQOAv/8cILL8TIkSMT1/fo0SOmTZuWuH7Hjh0p5WnRokVK9en0lZSUpLVjd0VFRbFp06aUelatWpWR3QAAAAAAAAAAAAAAAAAAAAAA1J16cwDr5ptvjvbt2yeqHThwYIwaNSoeeeSRRPWFhYWxdevWyMvLq0nET2natGk8++yzcdJJJyWqb9269af+XVVVFU8++WTifd26dYubb745UW1OTk5Mnz49nn/++di2bVuinscff9wBrDQ8++yz8b3vfS927dqVqL5t27bxzDPPRJs2bRLvSDr7Y7sfY0uqcePkr4OdO3emtWN39957b/zXf/1XRmYBAAAAAAAAAAAAAAAAAAAAANBw5NZ1gIiIdu3axYgRI1LqGT9+fOLaqqqqmDdvXqqxvtB1112X+PjVnixatCjef//9xPVXXHFFNG/ePHF9+/btY8yYMYnrFy5cGEVFRYnribjnnnvirLPOSnwMqkWLFvHss89Gr169UtpTUVGRUn1ubnp/1qn0lZeXp7UDAAAAAAAAAAAAAAAAAAAAAAAi6skBrFNPPTWaNm2aUs/RRx8dBx98cOL6JUuWpBrrc3Xt2jUmTpxYoxmFhYWJa3NycuK73/1uyjvOO++8xLXV1dWxYMGClHfsi6qqqmLChAnx4x//OCorKxP1NG/ePGbNmhXf/OY3U97XpEmTlPOlI+nPEhEp/70CAAAAAAAAAAAAAAAAAAAAAMAnNa7rABERAwYMSKuvX79+8e677yaqXbFiRVo79mTMmDHRuHHNfnWvv/564tru3btHly5dUt7Rt2/faNGiRZSVlSWqX7p0aZx22mkp79mXlJWVxQ9+8IN46qmnEve0bNkyCgoK4oQTTkhrZ7NmzVKq/+ijj9Lak0pfqpk+z7hx41I+7rZq1ao488wzM7IfAAAAAAAAAAAAAAAAAAAAAIC6US8OYB1xxBFp9z399NOJat977720duzJsGHDajxj7dq1iWv79OmT1o7c3Nzo1atXLF68OFF90mNi+6qioqI4/fTTY8GCBYl78vLy4k9/+lMce+yxae9t3bp1SvVJD57VpC/VTJ+nY8eO0bFjx4zMAgAAAAAAAAAAAAAAAAAAAACg4agXB7Dy8/PT6tt///0T127dujWtHbtr1qxZHHnkkTWes2nTpsS1nTp1SntPKr1FRUVp79nbLV++PL7zne/EmjVrEvccdNBB8dxzz0Xv3r1rtDvVA1ElJSVp7UmlryafSQAAAAAAAAAAAAAAAAAAAAAAyK3rABER++23X9b7du7cmdaO3XXt2jWaNm1a4zmpHBtK9/cTEdGmTZvEtaWlpWnv2ZvNnTs3jj322JSOX/Xt2zcWLFhQ4+NXEakfm/rggw/S2pNKnwNYAAAAAAAAAAAAAAAAAAAAAADURL04gFVZWZn1vpycnLR27K5du3YZmVNdXZ24Njc3/cfUqFGjxLWpZNpXPProo3HSSSfF1q1bE/cMGzYs5s2bF126dMlIhu7du6dUv2nTppR3VFRUxLZt2xLXp5oJAAAAAAAAAAAAAAAAAAAAAAA+qV4cwNq+fXvW+1q0aJHWjt21bt06I3NSybNjx4609xQXFyeuzdTvaG8xadIPna20AAAgAElEQVSkGDFiRJSXlyfuGT9+fBQUFGTscxIR0alTp8jLy0tcv27dupR3vPfeeykdQOvVq1fKOwAAAAAAAAAAAAAAAAAAAAAA4GP14gDWxo0b0+orKipKXNu+ffu0dmRLfn5+4totW7akvSeV3v333z/tPXuTioqKuPDCC+OGG25I3JObmxtTpkyJu+++Oxo1apTxTEceeWTi2lWrVqU8f+XKlSnVp5IHAAAAAAAAAAAAAAAAAADg/2Pn3oOrLs88gD8JIRCuRlBAsMYrAqLlEhtqnTgo6tSKeGm3ylrQKhVwZaudrlptXSm1aK1ctOMFCmWwgzJWgra47mLBgpSLoigYFAqOtCqgQAQChJD9o9PWC+rvHM7hJOTzmckfcJ73eb7Jm7x/PgAA8En1YgHWa6+9lvVznTt3TmtGtnzpS19KXJvuzyciYtWqVYlrjznmmLTnHCq2bt0a559/fkydOjXxmRYtWsQTTzwRN954Y9Zyfe1rX0tcu3LlypT7p/I7VlhYGKWlpSnPAAAAAAAAAAAAAAAAAAAAAACAf6gXC7AWLVqU8pna2tpYtmxZ4voePXqkPCObunfvnrh29erV8eGHH6Y8480334xt27ZlJdOh6K233oozzjgjnnvuucRnOnToEPPmzYtBgwZlMVnEWWedlbj2gw8+iDfeeCOl/qn8DZ5++ulRVFSUUn8AAAAAAAAAAAAAAAAAAAAAAPioerEA65lnnomqqqqUzsyfPz82b96cuL5Pnz6pxsqqsrKyxLV79+6NioqKlGfMnDkzpfqvfOUrKc84VCxbtizKyspi1apVic907949Fi9eHKWlpVlM9nfl5eVRXFycuH7OnDmJa2tqauL//u//EtdfcskliWsBAAAAAAAAAAAAAAAAAAAAAGB/6sUCrN27d8fEiRNTOnPPPfckrm3WrFmcccYZqcbKqn79+kWbNm0S10+cODHq6uoS1+/atSseeeSRxPUnnXRSlJSUJK4/lFRUVER5eXm8++67ic+cffbZ8cILL8QxxxyTxWT/0rRp07j44osT10+dOjXx78usWbNiy5YtiWrz8/PjsssuS5wDAAAAAAAAAAAAAAAAAAAAAAD2p14swIqI+PnPfx6vv/56otrHH388nnnmmcS9BwwYEEVFRelGy4pmzZrFwIEDE9cvWbIkJk2alLj+rrvuivXr1yeub6xLjSZMmBCXXHJJ7Ny5M/GZq666KubMmRNt27bNYrJPGzFiROLal19+OaZOnfqFdbt27Ypbbrklcd+BAwfG0UcfnbgeAAAAAAAAAAAAAAAAAAAAAAD2p94swNq+fXuce+65sWLFis+tq6ioiCFDhqTUe+jQoQeQLHtGjhyZcv2sWbO+sO7hhx+O0aNHJ+5bUFAQ1113XUpZDhWjRo2Kffv2pXRmypQpUVhYGHl5eRn5SrqorE+fPlFeXp4450033RSLFy/+zM/37t0b1157baxduzZxzxtvvDFxLQAAAAAAAAAAAAAAAAAAAAAAfJZ6swArImLDhg3Rt2/fGD58eMybNy82btwYe/bsib/97W8xe/bsuOSSS2LQoEGxa9euxD2PO+64GDRoUBZTp6+srCwGDBiQuL6mpiYuvvjiuPLKK2PRokVRV1f3z8/27dsX8+fPj4suuii+973vfeyzLzJkyJA4+uijU8pObtx9992Rl5eXqHbLli1x9tlnx5gxY+L999//2Gd//OMf4+yzz47p06cnnj1w4MA488wzU8oLAAAAAAAAAAAAAAAAAAAAAAD7U5DrAJ9UU1MTDz74YDz44IMZ6XfnnXdGkyZNMtIrG8aPHx+9evWK3bt3Jz4zffr0mD59erRs2TI6deoUdXV18c4778TOnTtTnl9cXBxjxoxJ+Ry5cfrpp8fVV18dkydPTlS/Y8eOuO222+K2226LDh06RNu2bePtt9+O6urqlOa2bNkyfvnLX6YTGQAAAAAAAAAAAAAAAAAAAAAAPiU/1wGyqX///jF48OBcx/hc3bp1i1/84hdpnd2xY0esWbMm1q5dm9byq4iIyZMnR4cOHdI6S26MGzcuTjjhhJTPvffee/HGG2+kvPwqImLChAlx/PHHp3wOAAAAAAAAAAAAAAAAAAAAAAD2pyDXASIiBg8eHI8++mhGe7Zv3z6mTZuW0Z7Zcv3110dlZWU88MADB3XumDFj4uKLLz6oMzlwrVq1iqeffjrOOOOMeP/997M+b9SoUXH11VdnfQ4NW0lJSdTV1eU6BpBj3gLg83gjgAPhDQFS4c2AzPC31LC5v4bBPTUM7qlhcE8Ng3uq/9xRw+CeGgb31DC4p4bDXdV/7qjhcFdw4PwdQePmDQA+ypsAJOW9ADLBWwJ8Hm8E0NDl5zpARMTo0aNj8ODBGetXVFQUs2fPjs6dO2esZ7ZNnDgxbrjhhoMyKy8vL0aPHh233nrrQZlH5nXt2jXmzJkT7du3z+qca6+9Nu67776szgAAAAAAAAAAAAAAAAAAAAAAoPGpFwuw8vLyYtq0aXHttdcecK+2bdvGnDlzol+/fhlIdvDk5eXF+PHj45FHHolWrVplbc7hhx8eM2fOjNtuuy1rMzg4SktL44UXXogePXpkvHdBQUH87Gc/i4cffjjy8vIy3h8AAAAAAAAAAAAAAAAAAAAAgMatXizAiojIz8+Phx9+OB566KFo3bp1Wj369u0bS5cujfLy8gynO3iuueaaWLFiRXzzm9/M6OKhJk2axNChQ2PlypVx6aWXZqwvuXXiiSfGiy++GLfccks0b948Iz179+4dL7zwQtxyyy0Z6QcAAAAAAAAAAAAAAAAAAAAAAJ9UbxZg/cOwYcOisrIyRo4cGa1atUp05uSTT47JkyfH4sWL48QTT8xywuw79thj4/HHH48VK1bEyJEjo3379mn36tixY9x4443x+uuvx5QpU6Jjx45p96qtrU1cW1hYmPYcUtOsWbP42c9+FmvXro0f/OAH0aFDh5R75OXlRf/+/eOJJ56IZcuWRWlpaRaSAgAAAAAAAAAAAAAAAAAAAADA3xXkOsD+HHXUUXH//ffH2LFj47nnnouFCxdGZWVlbN68OXbt2hUtWrSIzp07R8+ePWPAgAEHvKynpKQk6urqMpQ+c0455ZS4//77Y8KECbFkyZJYsGBBLF++PNauXRt//etfY+vWrVFdXR0REUVFRVFcXBydO3eOE088MXr16hVnnnlm9OnTJ/Ly8jKSZ+fOnYlri4uLMzIz2+rjvafrqKOOinvuuSfuuuuuWLhwYcyfPz9eeumlWLNmTbz77ruxffv22Lt3b7Ro0SLatm0bJSUlcfLJJ8fXvva1OOecc6Jz5865/hYAAAAAAAAAAAAAAAAAAAAAAGgk6uUCrH9o2bJlXHjhhXHhhRfmOkpO5efnR1lZWZSVleU0x6ZNmxLXNpQFWIeigoKCKC8vj/Ly8lxHAQAAAAAAAAAAAAAAAAAAAACA/crPdQAajsrKysS1nTp1ymISAAAAAAAAAAAAAAAAAAAAAACgIbMAi0Rqampi7ty5iWqbNGkSffr0yXIiAAAAAAAAAAAAAAAAAAAAAACgobIAi0TGjh0bH3zwQaLaU045JVq1apXlRAAAAAAAAAAAAAAAAAAAAAAAQENVkOsA1E979+6NLVu2xMsvvxy//vWvY8aMGYnPDhw4MIvJAAAAAAAAAAAAAAAAAAAAAACAhs4CLD5m0KBBUVFRkfb5pk2bxvDhwzOYCAAAAAAAAAAAAAAAAAAAAAAAONTk5zoAh5ahQ4dGp06dch0DAAAAAAAAAAAAAAAAAAAAAACoxyzAImOOP/74uPfee3MdAwAAAAAAAAAAAAAAAAAAAAAAqOcswCIj2rVrFzNnzozWrVt/Zs28efMiLy8vZ18AAAAAAAAAAAAAAAAAAAAAAED9YAEWB6xr167x5z//OXr16pXrKAAAAAAAAAAAAAAAAAAAAAAAQANgARZpO+KII+Luu++Ol156KU444YRcxwEAAAAAAAAAAAAAAAAAAAAAABqIglwHoGFo06ZNtGvXLjp06BD9+vWL/v37R//+/aNFixa5jgYAAAAAAAAAAAAAAAAAAAAAADQwWVuANW/evGy1JotmzZqV6wgAAAAAAAAAAAAAAAAAAAAAAMAhLj/XAQAAAAAAAAAAAAAAAAAAAAAAAGhcCnIdgMbjrLPOirq6ulzHAAAAAAAAAAAAAAAAAAAAAAAAciw/1wEAAAAAAAAAAAAAAAAAAAAAAABoXCzAAgAAAAAAAAAAAAAAAAAAAAAA4KCyAAsAAAAAAAAAAAAAAAAAAAAAAICDygIsAAAAAAAAAAAAAAAAAAAAAAAADioLsAAAAAAAAAAAAAAAAAAAAAAAADioLMACAAAAAAAAAAAAAAAAAAAAAADgoLIACwAAAAAAAAAAAAAAAAAAAAAAgIPKAiwAAAAAAAAAAAAAAAAAAAAAAAAOKguwOCRNnTo18vLyEn3dcccduY4LAAAAAAAAAAAAAAAAAAAAAACNSkGuAwAHZufOnbF8+fJYuXJlrFu3Lv7yl7/EunXr4oMPPojt27fHjh07orq6Olq2bBlt27aNtm3bxhFHHBGnnXZa9O7dO3r37h09evTI9bcBAAAAAAAAAAAAAAAAAAAAAEAjYgEWNDCbN2+OZ599Nv74xz/G0qVL47XXXova2tovPFdVVRVVVVXx9ttvR0TEH//4x39+1rVr1xgyZEh85zvfic6dO2ctOwAAAAAAAAAAAAAAAAAAAAAARETk5zoAkMy9994bZWVl0aFDhxg8eHBMmjQpXnnllUTLr77I6tWr49Zbb41jjz02br755ti5c2cGEgMAAAAAAAAAAAAAAAAAAAAAwP5ZgAUNxMSJE2Px4sWxb9++rM2oqamJsWPHRo8ePeJPf/pT1uYAAAAAAAAAAAAAAAAAAAAAANC4WYAFfMr69evjnHPOiVmzZuU6CgAAAAAAAAAAAAAAAAAAAAAAhyALsID92rNnT3z729+ORYsW5ToK/NP69esjLy9vv1/r16/PdTwgh7wPQLq8H0CqvBvAZ/E+wN/5W2iY3FvD4a4aBvfUMLin+s8dNQzuqWFwTw2De2oY3FPD4J4aBvfU8LgzyB1/f8A/eA+ApLwXQCZ4S4DP440A9sfbAKTLAizgM+3evTsGDx4c1dXVuY4CAAAAAAAAAAAAAAAAAAAAAMAhxAIsOAS1a9cuTjrppDj55JPjyCOPPKBe69ati/Hjx2coGQAAAAAAAAAAAAAAAAAAAAAAWIDFIWro0KFRV1eX6OuOO+7IddwD1rVr1/jhD38YzzzzTHzwwQexefPmWL16dbz++uvx3nvvxTvvvBNTpkyJ0047La3+DzzwQOzbty/DqQEAAAAAAAAAAAAAAAAAAAAAaKwswIIGqqioKIYNGxZLliyJysrKGDt2bJx33nlRXFz8qdqOHTvG0KFD46WXXoo777wz5VkbNmyIP//5z5mIDQAAAAAAAAAAAAAAAAAAAAAAUZDrAEBqWrVqFaNGjYr//M//jPbt26d0Nj8/P26//fZo2rRp3HLLLSmdff755+OrX/1qSmcAAAAAAAAAAAAAAAAAAAAAAGB/LMDKkNdeey0qKipi4cKFUVlZGZs2bYrq6upo2bJldOnSJXr27BnnnHNOXHrppVFcXJzTrHV1dbFgwYKoqKiIZcuWxRtvvBFbt26N6urqaNKkSRx11FExa9as6N27d1r9q6qqYunSpbF8+fJYu3ZtbNiwIbZt2xbV1dUREVFUVBSHHXZYdOnSJU444YTo3bt3lJaWRsuWLTP5bR5yCgoK4rrrros777wzjjjiiAPq9V//9V/xxBNPxLJlyxKfWb169QHNBAAAAAAAAAAAAAAAAAAAAACAf7AAKyLOOuusmD9/fqLadevWRUlJyT//vWjRorjllls+83xVVVWsWrUqVq1aFY899ljccMMNce2118aPf/zjaNeuXcpZ169fH8cee2yi2vLy8pg3b97H/u/JJ5+M22+/PVauXLnfM7W1tfH2229HVVVVSrl27NgRM2bMiEcffTQWLFgQNTU1KZ1v1qxZlJeXx5VXXhmXXXZZNG/ePKXznzR37ty47777EtVeccUVccUVVxzQvIPhf//3fxPf/RfJy8uLESNGxNVXX534zKZNmzIyGwAAAAAAAAAAAAAAAAAAAAAALMBKU01NTfzwhz+M8ePHR11dXeJz1dXVMWHChJgxY0ZMmzYtzjvvvCym/Jddu3bFd7/73fjtb3+b0b7V1dUxbty4uPvuu2Pr1q1p99m9e3c8++yz8eyzz8ZNN90UP/rRj2L48OHRtGnTtPq9/fbb8fvf/z5Rbd++fdOacbBlavnVP5x11lkp1e/evTuj8wEAAAAAAAAAAAAAAAAAAAAAaLzycx2gIdqxY0ecf/75MW7cuJSWX33Uxo0b44ILLohJkyZlON2nbd++PQYMGJDx5VeLFi2K0047LW699dYDWn71SRs3boxRo0ZFaWlpvPrqqxnry8d16tQppfq2bdtmKQkAAAAAAAAAAAAAAAAAAAAAAI2NBVgp2rNnT1xwwQXx3HPPHXCv2traGDZsWMYXU31yxuWXXx4LFizIaN8pU6ZEeXl5vPnmmxnt+1GvvPJK9OvXLyoqKrI2ozGrqalJqf5LX/pSlpIAAAAAAAAAAAAAAAAAAAAAANDYWICVohEjRsT8+fMz1q+uri6uuuqqWL58ecZ6ftRdd90VTz/9dEZ7PvTQQ3H11VenvEApHTt27IhLL700nnzyyazPamxWrVqVUn3fvn2zlAQAAAAAAAAAAAAAAAAAAAAAgMbGAqwUzZ07N+M99+zZE1dccUXs2bMno33Xrl0bd955Z0Z7/uEPf4gRI0ZktOcXqa2tjcsvvzxefPHFgzr3UDd79uzEtU2bNo3zzjsvi2kAAAAAAAAAAAAAAAAAAAAAAGhMLMDKgPz8/OjYsWN069Yt2rVrl1aPysrKuO+++zKaa8OGDVFTU5Oxfu+9914MGTIk9u3bl7GeSe3evTv+7d/+LXbu3HnQZx+KPvzww3jooYcS13/jG99I+3cbAAAAAAAAAAAAAAAAAAAAAAA+yQKsA3DkkUfGr371q3jvvffinXfeiVWrVsXmzZtj3bp18ZOf/CSaN2+eUr977rmnXi94uvnmm2Pz5s0pnTn11FPj/vvvj9WrV8eHH34YVVVVsWrVqvjlL38ZJ554Ykq91q5dG2PHjk3pDPt38803x/vvv5+oNi8vL3784x9nOREAAAAAAAAAAAAAAAAAAAAAAI2JBVhp6tu3b6xatSqGDx8e7du3/9hnJSUlcccdd8SLL74YRx55ZOKe77//fkyfPj3TUT+mR48ecdddd8XSpUvj3XffjZqamti2bVusWLEifvOb38TgwYOjVatWnzr35ptvxrRp01Kaddttt8WLL74YI0eOjJNOOilatWoVrVu3jm7dusX3v//9WLFiRQwbNiylnvfee29s3bo1pTN83OOPPx6/+tWvEtcPHTo0vvzlL2cxEQAAAAAAAAAAAAAAAAAAAAAAjU1BrgM0RB07dow5c+ZEu3btPreue/fuUVFREWeccUbs27cvUe9p06alvBQqicLCwhg3blwMGzYsmjRp8rHP2rRpEz179oyePXvGd77zndi+fXvU1tZ+rGbixImJv4eIiJtuuilGjx79uTXNmzePBx98MKqqqmLGjBmJ+u7YsSMmT54cN910U+Is/MvcuXPjO9/5TuL6E044IcaNG5e1PBs3boxNmzaldGbNmjVZSgMAAAAAAAAAAAAAAAAAAAAAwMFiAVYafvrTn0b79u0T1ZaVlcWQIUNiypQpieoXLVoUW7ZsieLi4gOJ+DGFhYXx1FNPxbnnnpuovlWrVh/79759+2LmzJmJ55WUlMRPf/rTRLV5eXkxceLEeOaZZ2Lr1q2Jzjz22GMWYKXhqaeeim9961uxe/fuRPVt27aN2bNnR5s2bbKW6Ve/+lX893//d9b6AwAAAAAAAAAAAAAAAAAAAABQP+XnOkBDc9hhh8WVV16Z0pmRI0cmrt23b188//zzqcb6XLfffnvi5Vf78+KLL8a7776buP773/9+NG/ePHF9+/bt45prrklcv2zZsti4cWPieiIeeOCBuPjii2PXrl2J6ouKiuKpp56Kbt26ZTkZAAAAAAAAAAAAAAAAAAAAAACNkQVYKfrGN74RhYWFKZ3p06dPHHPMMYnrly9fnmqsz9SlS5e4+eabD6jHokWLEtfm5eXFN7/5zZRnfPvb305cW1dXF4sXL055RmO0b9++uOmmm+L666+P2traRGeaN28es2bNijPPPDPL6QAAAAAAAAAAAAAAAAAAAAAAaKwKch2goSktLU3rXO/eveOtt95KVLt69eq0ZuzPNddcEwUFB3bNr776auLa4447Ljp16pTyjF69ekVRUVFUV1cnql+xYkVceOGFKc9pTKqrq+Pf//3f43e/+13iMy1atIiKioo455xzspjsX0aMGJHywrQ1a9bEoEGDspQIAAAAAAAAAAAAAAAAAAAAAICDwQKsFHXv3j3tc08++WSi2r/+9a9pzdifCy644IB7rF+/PnFtz54905qRn58f3bp1i5deeilRfdJlYo3Vxo0bY+DAgbF48eLEZ4qLi+Ppp5+Or371q1lM9nFHHnlkHHnkkQdtHgAAAAAAAAAAAAAAAAAAAAAA9YMFWClq165dWucOP/zwxLVbtmxJa8YnNWvWLE499dQD7rNp06bEtR06dEh7TipnN27cmPacQ11lZWV8/etfj3Xr1iU+c/TRR8ecOXOiR48eWUwGAAAAAAAAAAAAAAAAAAAAAAB/l5/rAA1N69ats35u165dac34pC5dukRhYeEB99mxY0fi2nR/PhERbdq0SVy7c+fOtOccyubPnx9f/epXU1p+1atXr1i8eLHlVwAAAAAAAAAAAAAAAAAAAAAAHDQWYKWotrY26+fy8vLSmvFJhx12WEb61NXVJa7Nz0//V6pJkyaJa1PJ1FhMnz49zj333NiyZUviMxdccEE8//zz0alTpywmAwAAAAAAAAAAAAAAAAAAAACAj7MAK0Uffvhh1s8VFRWlNeOTWrVqlZE+qeTZvn172nOqqqoS12bqZ3SouPPOO+PKK6+MPXv2JD4zcuTIqKioyNjvCQAAAAAAAAAAAAAAAAAAAAAAJFWQ6wANzXvvvZfWuY0bNyaubd++fVozsqVdu3aJa99///2056Ry9vDDD097zqGkpqYmhg0bFlOnTk18Jj8/P+6555648cYbsxcMAAAAAAAAAAAAAAAAAAAAAAA+R36uAzQ0r732WtbPde7cOa0Z2fKlL30pcW26P5+IiFWrViWuPeaYY9Kec6jYunVrnH/++Sktv2rRokU88cQTll8BAAAAAAAAAAAAAAAAAAAAAJBTBbkO0NAsWrQo5TO1tbWxbNmyxPU9evRIeUY2de/ePXHt6tWr48MPP4zWrVunNOPNN9+Mbdu2ZSXToeitt96Kr3/96yktDevQoUM89dRTUVpamsVkAAAAAAAAAAAAAAAAAAAAAADwxfJzHaCheeaZZ6KqqiqlM/Pnz4/Nmzcnru/Tp0+qsbKqrKwsce3evXujoqIi5RkzZ85Mqf4rX/lKyjMOFcuWLYuysrKUll917949Fi9ebPkVAAAAAAAAAAAAAAAAAAAAAAD1ggVYKdq9e3dMnDgxpTP33HNP4tpmzZrFGWeckWqsrOrXr1+0adMmcf3EiROjrq4ucf2uXbvikUceSVx/0kknRUlJSeL6Q0lFRUWUl5fHu+++m/jM2WefHS+88EIcc8wxWUwGAAAAAAAAAAAAAAAAAAAAAADJWYCVhp///Ofx+uuvJ6p9/PHH45lnnknce8CAAVFUVJRutKxo1qxZDBw4MHH9kiVLYtKkSYnr77rrrli/fn3i+ssuuyxx7aFkwoQJcckll8TOnTsTn7nqqqtizpw50bZt2ywmAwAAAAAAAAAAAAAAAAAAAACA1FiAlYbt27fHueeeGytWrPjcuoqKihgyZEhKvYcOHXoAybJn5MiRKdfPmjXrC+sefvjhGD16dOK+BQUFcd1116WU5VAxatSo2LdvX0pnpkyZEoWFhZGXl5eRr1QWlQEAAAAAAAAAAAAAAAAAAAAAwGexACtNGzZsiL59+8bw4cNj3rx5sXHjxtizZ0/87W9/i9mzZ8cll1wSgwYNil27diXuedxxx8WgQYOymDp9ZWVlMWDAgMT1NTU1cfHFF8eVV14ZixYtirq6un9+tm/fvpg/f35cdNFF8b3vfe9jn32RIUOGxNFHH51SdgAAAAAAAAAAAAAAAAAAAAAAoH4pyHWAhqympiYefPDBePDBBzPS784774wmTZpkpFc2jB8/Pnr16hW7d+9OfGb69Okxffr0aNmyZXTq1Cnq6urinXfeiZ07d6Y8v7i4OMaMGZPyOQAAAAAAAAAAAAAAAAAAAAAAoH7Jz3UA/q5///4xePDgXMf4XN26dYtf/OIXaZ3dsWNHrFmzJtauXZvW8quIiMmTJ0eHDh3SOgsAAAAAAAAAAAAAAAAAAAAAANQfBbkO0NAMHjw4Hn300Yz2bN++fUybNi2jPbPl+uuvj8rKynjggQcO6twxY8bExRdffFBnAvVPSUlJ1NXV5ToGUA95H4B0eT+AVHk3gM/ifYC/87fQMLm3hsNdNQzuqWFwT/WfO2oY3FPD4J4aBvfUMLinhsE9NQzuqeFxZ5A7/v6Af/AeAEl5L4BM8JYAn8cbAeyPtwFIV36uAzQ0o0ePjsGDB2esX1FRUcyePTs6d+6csZ7ZNnHixLjhhhsOyqy8vLwYPXp03HrrrQdlHgAAAAAAAAAAAAAAAAAAAAAAkH0WYKUoLy8vpk2bFtdee+0B92rbtm3MmTMn+vXrl4FkBz2PgQwAACAASURBVE9eXl6MHz8+HnnkkWjVqlXW5hx++OExc+bMuO2227I2AwAAAAAAAAAAAAAAAAAAAAAAOPgswEpDfn5+PPzww/HQQw9F69at0+rRt2/fWLp0aZSXl2c43cFzzTXXxIoVK+Kb3/xm5OXlZaxvkyZNYujQobFy5cq49NJLM9YXAAAAAAAAAAAAAAAAAAAAAACoHyzAOgDDhg2LysrKGDlyZLRq1SrRmZNPPjkmT54cixcvjhNPPDHLCbPv2GOPjccffzxWrFgRI0eOjPbt26fdq2PHjnHjjTfG66+/HlOmTImOHTum3au2tjZxbWFhYdpzAAAAAAAAAAAAAAAAAAAAAACA1BXkOkBDd9RRR8X9998fY8eOjeeeey4WLlwYlZWVsXnz5ti1a1e0aNEiOnfuHD179owBAwZEaWnpAc0rKSmJurq6DKXPnFNOOSXuv//+mDBhQixZsiQWLFgQy5cvj7Vr18Zf//rX2Lp1a1RXV0dERFFRURQXF0fnzp3jxBNPjF69esWZZ54Zffr0iby8vIzk2blzZ+La4uLijMzMtvp47wAAAAAAAAAAAAAAAAAAAAAAkA4LsDKkZcuWceGFF8aFF16Y6yg5lZ+fH2VlZVFWVpbTHJs2bUpc21AWYAEAAAAAAAAAAAAAAAAAAAAAwKEiP9cBIBsqKysT13bq1CmLSQAAAAAAAAAAAAAAAAAAAAAAgE+yAItDTk1NTcydOzdRbZMmTaJPnz5ZTgQAAAAAAAAAAAAAAAAAAAAAAHyUBVgccsaOHRsffPBBotpTTjklWrVqleVEAAAAAAAAAAAAAAAAAAAAAADARxXkOgAcqL1798aWLVvi5Zdfjl//+tcxY8aMxGcHDhyYxWQAAAAAAAAAAAAAAAAAAAAAAMD+WIBFgzVo0KCoqKhI+3zTpk1j+PDhGUwEAAAAAAAAAAAAAAAAAAAAAAAkkZ/rAJArQ4cOjU6dOuU6BgAAAAAAAAAAAAAAAAAAAAAANDoWYNEoHX/88XHvvffmOgYAAAAAAAAAAAAAAAAAAAAAADRKFmDR6LRr1y5mzpwZrVu3/syaefPmRV5eXs6+AAAAAAAAAAAAAAAAAAAAAADgUGYBFo1K165d489//nP06tUr11EAAAAAAAAAAAAAAAAAAAAAAKDRsgCLRuGII46Iu+++O1566aU44YQTch0HAAAAAAAAAAAAAAAAAAAAAAAatYJcB4BMa9OmTbRr1y46dOgQ/fr1i/79+0f//v2jRYsWuY4GAAAAAAAAAAAAAAAAAAAAAACEBVgRETFv3rxcRyANs2bNynUEAAAAAAAAAAAAAAAAAAAAAAAgDfm5DgAAAAAAAAAAAAAAAAAAAAAAAEDjUpDrAFAfnXXWWVFXV5frGAAAAAAAAAAAAAAAAAAAAAAAcEjKz3UAAAAAAAAAAAAAAAAAAAAAAAAAGhcLsAAAAAAAAAAAAAAAAAAAAAAAADioLMACAAAAAAAAAAAAAAAAAAAAAADgoLIACwAAAAAAAAAAAAAAAAAAAAAAgIPKAiwAAAAAAAAAAAAAAAAAAAAAAAAOKguwAAAAAAAAAAAAAAAAAAAAAAAAOKgswAIAAAAAAAAAAAAAAAAAAAAAAOCgsgALAAAAAAAAAAAAAAAAAAAAAACAg8oCLBKbOnVq5OXlJfq64447ch0XAAAAAAAAAAAAAAAAAAAAAACopwpyHQAagi1btsTSpUtj+fLlUV1dnehMSUlJDB06NLvBvsCHH34YCxcujIULF8bKlSvjL3/5S7zzzjuxY8eOqK6ujubNm0eLFi2iVatW0blz5ygpKYnjjjsuevXqFaeffnp07tw5p/kBAAAAAAAAAAAAAAAAAAAAADg0WYAFn1BdXR0vv/xyLFmy5J9fa9asSblPeXl5ThZg1dXVxe9///uYMmVK/OEPf4hdu3Z9Zu3OnTtj586dsXnz5li/fn0sXLjwY5936dIlzj///PjGN74R55xzTrRs2TLb8QEAAAAAAAAAAAAAAAAAAAAAaAQswIKI+PWvf/3PZVevvvpq7N27N9eR0vL000/HrbfeGq+++mpG+m3YsCEmTZoUkyZNip/85Cdxxx13ZKQvAAAAAAAAAAAAAAAAAAAAAACNmwVYEBHf/e53cx3hgLz//vvxve99L5544olcRwEAAAAAAAAAAAAAAAAAAAAAgC9kARY0cK+88koMGjQo1q9fn+soAAAAAAAAAAAAAAAAAAAAAACQSH6uAwDpW7JkSZSXl1t+xSFp/fr1kZeXt98vv/Nw6PMGAB/lTQCS8l4AmeAtgc/nb6ThcncNh7tqGNxTw+CeGgb31DC4p4bBPTUM7qlhcE8Ng3tqGNxTw+K+4ODx9waNmzcA8A4An8X7AKTL+wGkwpsB1DcWYEEDtXLlyjjvvPNi27ZtuY4CAAAAAAAAAAAAAAAAAAAAAAApKch1ACB127Zti0GDBsXWrVvTOp+fnx+HHXZYHH744VFQUBBbtmyJLVu2xJ49ezKcFAAAAAAAAAAAAAAAAAAAAAAAPs0CLBIbOnRoDB06NNcxcqJZs2bx5S9/OQoLC+NPf/pTruPEiBEjYs2aNSmdOfLII+Oaa66Js88+O/r16xdFRUUf+3zfvn3x5ptvxiuvvBJ/+tOf4qmnnoq33nork7EBAAAAAAAAAAAAAAAAAAAAACAiLMCCT8nPz49u3brF6aefHqWlpXH66afHqaeeGk2bNo2pU6fmfAHWs88+G7/97W8T1zdt2jTGjBkT119//aeWXn1Ufn5+dO3aNbp27Rrf+ta3YuLEibFixYqYPHlyTJs2LRPRAQAAAAAAAAAAAAAAAAAAAAAgIizAgoiI+Na3vhWlpaVRWloaffv2jZYtW+Y60n7V1dXFD37wg8T1RxxxRMyePTvKysrSmnfqqafG+PHj4+c//3n87W9/S6sHAAAAAAAAAAAAAAAAAAAAAAB8Ur1egPXaa69FRUVFLFy4MCorK2PTpk1RXV0dLVu2jC5dukTPnj3jnHPOiUsvvTSKi4tzmrWuri4WLFgQFRUVsWzZsnjjjTdi69atUV1dHU2aNImjjjoqZs2aFb17906rf1VVVSxdujSWL18ea9eujQ0bNsS2bduiuro6IiKKiorisMMOiy5dusQJJ5wQvXv3jtLS0nq7yKm+eeyxx3IdIZEnn3wyXn311US1hYWF8bvf/S7t5VcfVVRUFMcff/wB9wEAAAAAAAAAAAAAAAAAAAAAgIgsLsA666yzYv78+Ylq161bFyUlJf/896JFi+KWW275zPNVVVWxatWqWLVqVTz22GNxww03xLXXXhs//vGPo127dilnXb9+fRx77LGJasvLy2PevHkf+78nn3wybr/99li5cuV+z9TW1sbbb78dVVVVKeXasWNHzJgxIx599NFYsGBB1NTUpHS+WbNmUV5eHldeeWVcdtll0bx585TOf9LcuXPjvvvuS1R7xRVXxBVXXHFA8/i0CRMmJK6944474mtf+1oW0wAAAAAAAAAAAAAAAAAAAAAAQHqytgArHTU1NfHDH/4wxo8fH3V1dYnPVVdXx4QJE2LGjBkxbdq0OO+887KY8l927doV3/3ud+O3v/1tRvtWV1fHuHHj4u67746tW7em3Wf37t3x7LPPxrPPPhs33XRT/OhHP4rhw4dH06ZN0+r39ttvx+9///tEtX379k1rBp9t/fr18fzzzyeq7dixY4waNSrLiQAAAAAAAAAAAAAAAAAAAAAAID35uQ7wDzt27Ijzzz8/xo0bl9Lyq4/auHFjXHDBBTFp0qQMp/u07du3x4ABAzK+/GrRokVx2mmnxa233npAy68+aePGjTFq1KgoLS2NV199NWN9OXieeOKJxH8b1113XbRo0SLLiQAAAAAAAAAAAAAAAAAAAAAAID31YgHWnj174oILLojnnnvugHvV1tbGsGHDMr6Y6pMzLr/88liwYEFG+06ZMiXKy8vjzTffzGjfj3rllVeiX79+UVFRkbUZZMf//M//JK696KKLspgEAAAAAAAAAAAAAAAAAAAAAAAOTL1YgDVixIiYP39+xvrV1dXFVVddFcuXL89Yz4+666674umnn85oz4ceeiiuvvrqqKmpyWjf/dmxY0dceuml8eSTT2Z9FplRW1ubeOFax44d48tf/vI///3ee+/Fb37zm7jmmmuiV69e0aVLl2jRokUUFhZGcXFx9OjRIy666KIYM2ZMLF26NOrq6rL1bQAAAAAAAAAAAAAAAAAAAAAAQEREFOQ6QETE3LlzM95zz549ccUVV8Qrr7wShYWFGeu7du3aeOGFFzLWLyLiD3/4Q4wYMSKjPb9IbW1tXH755bFw4cLo06fPQZ1N6lavXh3V1dWJart37x4REcuXL49f/OIXMXPmzM9crLZ169bYunVrrFq1KmbPnh233XZbHHvssfEf//EfMXz48GjevHnGvgcAAAAAAAAAAAAAAAAAAAAAAPiH/FwH+Cz5+f/Pzr0GV1lf+wNfO8FAJCCQyEVQUysqKLYIVDi2A6Xi6ani4KjVai20IC1gPVXPtNZ6K2itWq9Qj6KoMLQH5cwI9oLTVgaFDpWLaNAIBSodQeWiIBDCLcn/xRn9KxV89s7e2Tvw+czkBez1W+ub35Nnv1xF0blz5+jRo0eUl5dn1GPFihVx3333ZTXXunXrDrhMKBMbNmyI4cOHR319fdZ6JrV79+645JJLYufOnU0+m/RUVVUlrj322GPjuuuui759+8Zvf/vbtP9e33zzzbj22mvjpJNOijlz5qQbFQAAAAAAAAAAAAAAAAAAAAAAPlPBLcDq2LFjPPTQQ7Fhw4Z45513orq6OjZv3hxvvvlm3HLLLdGqVau0+t19990FveDp+uuvj82bN6d15vTTT49JkybFypUrY/v27bFt27aorq6Oe++9N7p3755WrzVr1sSdd96Z1hma3ptvvpm4dtq0aXHvvfc2eqnaW2+9Fd/4xjfiZz/7WTQ0NDSqFwAAAAAAAAAAAAAAAAAAAAAAfFxBLcDq27dvVFdXx5gxY6KiouITn1VWVsatt94aS5cujY4dOybu+d5778X06dOzHfUTTj311Ljjjjti8eLF8e6778bevXvjgw8+iKqqqpg6dWpcfvnlUVZW9i/nVq1aFdOmTUtr1o033hhLly6NcePGxUknnRRlZWXRpk2b6NGjR1xzzTVRVVUVo0ePTqvnPffcE1u3bk3rDE3r7bffTlyb7WVVv/jFL2LMmDFZ7QkAAAAAAAAAAAAAAAAAAAAAwOGtRb4DfKhz584xZ86cKC8vP2hdz549Y/bs2XHWWWdFfX19ot7Tpk1LeylUEiUlJXH//ffH6NGjo7i4+BOftW3bNnr16hW9evWK73znO7Fjx46oq6v7RM3EiRMT/w4REdddd11MmDDhoDWtWrWKhx9+OLZt2xYzZsxI1LempiamTJkS1113XeIsNK0NGzbkdf4jjzwSxx13XNxwww1Z7btx48bYtGlTWmdWr16d1QwAAAAAAAAAAAAAAAAAAAAAADS9glmAddttt0VFRUWi2v79+8fw4cPjiSeeSFS/cOHC2LJlS7Rv374xET+hpKQkfve738U555yTqL6srOwT/66vr4+ZM2cmnldZWRm33XZbotpUKhUTJ06M5557LrZu3ZrozFNPPWUBVgHbuXNnviPEzTffHIMHD47+/ftnredDDz0UP//5z7PWDwAAAAAAAAAAAAAAAAAAAACA5qEo3wEiItq1axdXXHFFWmfGjRuXuLa+vj5efPHFdGMd1E033ZR4+dWnWbp0abz77ruJ66+55ppo1apV4vqKiooYNWpU4volS5bExo0bE9fTtHbt2tXoHq1bt47u3bvHiSeeGK1bt077fF1dXVx11VWNzgEAAAAAAAAAAAAAAAAAAAAAAAWxAOu8886LkpKStM706dMnjj/++MT1y5YtSzfWAXXr1i2uv/76RvVYuHBh4tpUKhUXX3xx2jMuvfTSxLUNDQ3x0ksvpT2DptHQ0JDx2S9/+csxd+7c2Lp1a/z973+PVatWxdatW2Pu3Lnx5S9/Oa1eS5cujTlz5mScBQAAAAAAAAAAAAAAAAAAAAAAIiJa5DtARES/fv0yOnfGGWfEP//5z0S1K1euzGjGpxk1alS0aNG4q1u+fHni2hNOOCG6dOmS9ozevXtHaWlp1NbWJqqvqqqKoUOHpj2H3GvZsmVG5y677LKYNm1aFBcXf+L/W7RoEV/96ldj3rx5ccUVV8T//M//JO75+OOPx3/8x39klGd/Y8eOTXu52+rVq2PYsGFZmQ8AAAAAAAAAAAAAAAAAAAAAQH4UxAKsnj17ZnzumWeeSVS7fv36jGZ8mnPPPbfRPdauXZu4tlevXhnNKCoqih49esTLL7+cqD7pMjGaXqtWrdI+061bt5g8efK/LL/6uOLi4pg8eXLMnz8/1q1bl6jvnDlzYt++fY1eAhcR0bFjx+jYsWOj+wAAAAAAAAAAAAAAAAAAAAAA0LwU5TtARER5eXlG5zp06JC4dsuWLRnN2F/Lli3j9NNPb3SfTZs2Ja7t1KlTxnPSObtx48aM55Bb6fytf+gHP/hBtG7d+jPrysrKYvTo0Yn71tTUxKuvvpp2HgAAAAAAAAAAAAAAAAAAAAAA+FBBLMBq06ZNzs/t2rUroxn769atW5SUlDS6T01NTeLaTO8nIqJt27aJa3fu3JnxHHIrkyVo5557buLa8847L63eK1euTDcOAAAAAAAAAAAAAAAAAAAAAAB8pCAWYNXV1eX8XCqVymjG/tq1a5eVPg0NDYlri4oyf0zFxcWJa9PJRNM69thj06ovLi6Onj17Jq7v2bNnWn8rb7/9dlp5AAAAAAAAAAAAAAAAAAAAAADg4wpiAdb27dtzfq60tDSjGfsrKyvLSp908uzYsSPjOdu2bUtcm607IvtOOeWUtOrbtWsXJSUlietbtmwZbdu2TVxfU1OTVh4AAAAAAAAAAAAAAAAAAAAAAPi4gliAtWHDhozObdy4MXFtRUVFRjNypby8PHHte++9l/GcdM526NAh4znk1qmnnppWfSbLzFq3bp24tkWLFmn3BwAAAAAAAAAAAAAAAAAAAACADxXEAqzXXnst5+e6du2a0YxcOe644xLXZno/ERHV1dWJa48//viM55BbRx99dJx44omJ62tra9OeUVNTk7i2rKws7f4AAAAAAAAAAAAAAAAAAAAAAPChgliAtXDhwrTP1NXVxZIlSxLXn3rqqWnPyKWePXsmrl25cmVs37497RmrVq2KDz74ICeZaHoDBw5MXLt169bYs2dP4vrdu3fHtm3bEtens8ANAAAAAAAAAAAAAAAAAAAAAAD2VxALsJ577rm0lu9ERLzwwguxefPmxPV9+vRJN1ZO9e/fP3Htvn37Yvbs2WnPmDlzZlr1Z555ZtozaDpDhw5NXFtXVxevv/564vrq6uqoq6tLXH/KKackrgUAAAAAAAAAAAAAAAAAAAAAgP0VxAKs3bt3x8SJE9M6c/fddyeubdmyZZx11lnpxsqpAQMGRNu2bRPXT5w4MRoaGhLX79q1Kx599NHE9SeddFJUVlYmrqfpff3rX0/rb+YPf/hD4trf//73iWs7dOhgARYAAAAAAAAAAAAAAAAAAAAAAI1SEAuwIiJ++ctfxhtvvJGo9umnn47nnnsuce8hQ4ZEaWlpptFyomXLlnH++ecnrl+0aFE89thjievvuOOOWLt2beL6iy66KHEt+dGyZcsYPnx44vrJkydHTU3NZ9bV1NTE5MmTE/c999xzI5VKJa4HAAAAAAAAAAAAAAAAAAAAAID9FcwCrB07dsQ555wTVVVVB62bPXt2WkuAIiJGjBjRiGS5M27cuLTrZ82a9Zl1kydPjgkTJiTu26JFi/jBD36QVhby40c/+lEUFxcnqn3rrbdi9OjRUV9ff8Ca+vr6GD16dKxbty5xhu985zuJawEAAAAAAAAAAAAAAAAAAAAA4NO0yHeAj1u3bl307ds3Ro4cGZdcckn07Nkz2rVrF5s3b44lS5bEk08+Gc8880xaPU844YQYNmxYjhI3Tv/+/WPIkCHx5z//OVH93r1744ILLohvf/vbMXbs2Ojfv3+kUqmI+L9FRvPnz4977703nn322bRyDB8+PI499ti08x9KRo0aFUuWLPnMuvfffz9xzyVLlsQXv/jFRLV//OMf45hjjvnMuhNOOCG+//3vx0MPPZSo729/+9t46623Yvz48fGVr3zlo+VZdXV1MX/+/Lj55ptj/vz5iXpFRPTu3TvOPvvsxPUAAAAAAAAAAAAAAAAAAAAAAPBpCmoBVsT/LXl6+OGH4+GHH85Kv/Hjx3+09KcQPfDAA9G7d+/YvXt34jPTp0+P6dOnR+vWraNLly7R0NAQ77zzTuzcuTPt+e3bt4/bb7897XOHmtWrV8err76a1Z41NTWJe+7Zsydx3/Hjx8f//u//xsaNGxPVz58/P7761a9GWVlZHHPMMdHQ0BBvv/121NTUJJ75ofvuuy/tMwAAAAAAAAAAAAAAAAAAAAAAsL+ifAfIpcGDB8fll1+e7xgH1aNHj/jVr36V0dmamppYvXp1rFmzJqPlVxERU6ZMiU6dOmV0lvwoLy+PqVOnRiqVSuvcjh074u9//3usWrUqo+VXV199dQwcODDtcwAAAAAAAAAAAAAAAAAAAAAAsL8W+Q4QEXH55ZfHb37zm6z2rKioiGnTpmW1Z65cddVVsWLFivj1r3/dpHNvv/32uOCCC5p0Jtnx9a9/PX75y1/GT37ykyaZd/bZZ2e8qA0yVVlZGQ0NDfmOAeSJ7wDg43wnAEn5vgCywXcJHJx3pPny7JoPz6p58JyaB8+pefCcmgfPqXnwnJoHz6l58JyaB8+pefCcmhfPC5qO9w0Ob74DAN8DwIH4fgAy5fsDSIfvDKDQFOU7QETEhAkT4vLLL89av9LS0nj22Weja9euWeuZaxMnToyrr766SWalUqmYMGFC3HDDDU0yj9z48Y9/HDfddFPO55x99tkxe/bsOOKII3I+CwAAAAAAAAAAAAAAAAAAAACAw0NBLMBKpVIxbdq0uPLKKxvd66ijjoo5c+bEgAEDspCs6aRSqXjggQfi0UcfjbKyspzN6dChQ8ycOTNuvPHGnM2g6YwfPz6mTJkSpaWlWe+dSqXihz/8YcyZMyeOPPLIrPcHAAAAAAAAAAAAAAAAAAAAAODwVRALsCIiioqKYvLkyfHII49EmzZtMurRt2/fWLx4cQwcODDL6ZrOqFGjoqqqKi6++OJIpVJZ61tcXBwjRoyI119/PS688MKs9SX/vve978WyZcuy+nd/6qmnxp///Od48MEHo0WLFlnrCwAAAAAAAAAAAAAAAAAAAAAAEQW0AOtDo0ePjhUrVsS4ceOirKws0ZlTTjklpkyZEi+99FJ07949xwlz73Of+1w8/fTTUVVVFePGjYuKioqMe3Xu3DmuvfbaeOONN+KJJ56Izp07Z9yrrq4ucW1JSUnGc0jfySefHPPmzYu//OUvcd5552W0tKqoqCgGDx780d/e1772tRwkBQAAAAAAAAAAAAAAAAAAAACAiPS35DSBY445JiZNmhR33nlnzJ07N/7617/GihUrYvPmzbFr16448sgjo2vXrtGrV68YMmRI9OvXr1HzKisro6GhIUvps+e0006LSZMmxYMPPhiLFi2KBQsWxLJly2LNmjWxfv362Lp1a9TW1kZERGlpabRv3z66du0a3bt3j969e8dXvvKV6NOnT6RSqazk2blzZ+La9u3bZ2VmU5k3b16+I2TF1772tfja174Wmzdvjr/85S+xYMGCqK6ujn/84x/x/vvvx86dOyOVSsWRRx4ZRx99dBx33HFx2mmnxZlnnhnnnHNOHH300fn+FQAAAAAAAAAAAAAAAAAAAAAAOAwU5AKsD7Vu3TqGDh0aQ4cOzXeUvCoqKor+/ftH//7985pj06ZNiWub2wKsQ01FRUVceumlcemll+Y7CgAAAAAAAAAAAAAAAAAAAAAA/IuifAeg+VixYkXi2i5duuQwCQAAAAAAAAAAAAAAAAAAAAAA0JxZgEUie/fujeeffz5RbXFxcfTp0yfHiQAAAAAAAAAAAAAAAAAAAAAAgObKAiwSufPOO+P9999PVHvaaadFWVlZjhMBAAAAAAAAAAAAAAAAAAAAAADNVYt8B6Aw7du3L7Zs2RKvvPJKPP744zFjxozEZ88///wcJgMAAAAAAAAAAAAAAAAAAAAAAJo7C7D4hGHDhsXs2bMzPn/EEUfEmDFjspgIAAAAAAAAAAAAAAAAAAAAAAA41BTlOwCHlhEjRkSXLl3yHQMAAAAAAAAAAAAAAAAAAAAAAChgFmCRNZ///OfjnnvuyXcMAAAAAAAAAAAAAAAAAAAAAACgwFmARVaUl5fHzJkzo02bNgesmTdvXqRSqbz9AAAAAAAAAAAAAAAAAAAAAAAAhcECLBrt5JNPjr/97W/Ru3fvfEcBAAAAAAAAAAAAAAAAAAAAAACaAQuwyNjRRx8dd911V7z88stx4okn5jsOAAAAAAAAAAAAAAAAAAAAAADQTLTIdwCah7Zt20Z5eXl06tQpBgwYEIMHD47BgwfHkUceme9oAAAAAAAAAAAAAAAAAAAAAABAM5OzBVjz5s3LVWtyaNasWfmOAAAAAAAAAAAAAAAAAAAAAAAAHOKK8h0AAAAAAAAAAAAAAAAAAAAAAACAw0uLfAfg8DFo0KBoaGjIdwwAAAAAAAAAAAAAAAAAAAAAACDPivIdAAAAAAAAAAAAAAAAAAAAAAAAgMOLBVgAAAAAAAAAAAAAAAAAAAAAAAA0KQuwAAAAAAAAAAAAAAAAAAAAAAAAaFIWYAEAAAAAAAAAAAAAAAAAAAAAANCkLMACAAAAEOB2eQAAIABJREFUAAAAAAAAAAAAAAAAAACgSVmABQAAAAAAAAAAAAAAAAAAAAAAQJOyAAsAAAAAAAAAAAAAAAAAAAAAAIAmZQEWAAAAAAAAAAAAAAAAAAAAAAAATcoCLBJ78sknI5VKJfq59dZb8x0XAAAAAAAAAAAAAAAAAAAAAAAoUC3yHQCagy1btsTixYtj2bJlUVtbm+hMZWVljBgxIrfB0jB16tR488030zozaNCgGDRoUG4CAQAAAAAAAAAAAAAAAAAAAABw2LIAC/ZTW1sbr7zySixatOijn9WrV6fdZ+DAgQWzAGvu3Lnx3e9+NxoaGtI+awEWAAAAAAAAAAAAAAAAAAAAAADZZgEWRMTjjz/+0bKr5cuXx759+/IdKWu2b98e3/ve9zJafgUAAAAAAAAAAAAAAAAAAAAAALlgARZExMiRI/MdIWeuueaa+Oc//5nvGAAAAAAAAAAAAAAAAAAAAAAA8JGifAcAcmfOnDkxZcqUfMcAAAAAAAAAAAAAAAAAAAAAAIBPsAALDlFbt26NUaNG5TsGNJm1a9dGKpX61J+1a9fmOx7wGbzD0Px4b6H58x5D8+TdhcLgXcwP9/7/uQt3EOEOItxBhDuIcAcR7iDCHUS4gwh3EOEOItxBhDuIcAeH++8f4Q4i3EGEO4hwB2SHvyMoPN5LOLR5x6GweCehcHk/4dDjvYbC5f0EmoIFWHCI+uEPfxhvv/12vmMAAAAAAAAAAAAAAAAAAAAAAMC/sAALDkGzZs2K6dOn5zsGAAAAAAAAAAAAAAAAAAAAAAB8qhb5DkDzMWLEiBgxYkS+Y+RFy5Yt44tf/GKUlJTE/Pnz8x3noDZv3hzf//738x0DAAAAAAAAAAAAAAAAAAAAAAAOyAIs2E9RUVH06NEjvvSlL0W/fv3iS1/6Upx++ulxxBFHxJNPPlnwC7DGjh0bGzduzHcMAAAAAAAAAAAAAAAAAAAAAAA4IAuwICK++c1vRr9+/aJfv37Rt2/faN26db4jZWTGjBkxc+bMA37eunXr6N27dyxYsKAJUwEAAAAAAAAAAAAAAAAAAAAAwCcV9AKs1157LWbPnh1//etfY8WKFbFp06aora2N1q1bR7du3aJXr15x9tlnx4UXXhjt27fPa9aGhoZYsGBBzJ49O5YsWRJ///vfY+vWrVFbWxvFxcVxzDHHxKxZs+KMM87IqP+2bdti8eLFsWzZslizZk2sW7cuPvjgg6itrY2IiNLS0mjXrl1069YtTjzxxDjjjDOiX79+zXaRU1N76qmn8h2h0d5999246qqrDlpz1113xaJFiyzAAgAAAAAAAAAAAAAAAAAAAAAgr3K2AGvQoEHxwgsvJKp98803o7Ky8qN/L1y4MH76058e8Py2bduiuro6qqur46mnnoqrr746rrzyyrj55pujvLw87axr166Nz33uc4lqBw4cGPPmzfvE/z3zzDNx0003xeuvv/6pZ+rq6uKtt96Kbdu2pZWrpqYmZsyYEb/5zW9iwYIFsXfv3rTOt2zZMgYOHBhXXHFFXHTRRdGqVau0zu/v+eefj/vuuy9R7WWXXRaXXXZZo+aRntGjR8d77713wM/PPvvsGDNmTCxatKgJUwEAAAAAAAAAAAAAAAAAAAAAwL/K2QKsTOzduzd+/OMfxwMPPBANDQ2Jz9XW1saDDz4YM2bMiGnTpsW///u/5zDl/7dr164YOXJk/Pa3v81q39ra2rj//vvjrrvuiq1bt2bcZ/fu3fGnP/0p/vSnP8V1110XP/vZz2LMmDFxxBFHZNTvrbfeij/84Q+Javv27ZvRDDLz5JNPxu9+97sDfn7UUUfF448/HqlUqglTAQAAAAAAAAAAAAAAAAAAAADApyvKd4AP1dTUxNe//vW4//7701p+9XEbN26Mc889Nx577LEsp/tXO3bsiCFDhmR9+dXChQvjC1/4Qtxwww2NWn61v40bN8Z//ud/Rr9+/WL58uVZ60v+rVu3Ln70ox8dtOaBBx6IY489tokSAQAAAAAAAAAAAAAAAAAAAADAwRXEAqw9e/bEueeeG3Pnzm10r7q6uhg9enTWF1PtP+Nb3/pWLFiwIKt9n3jiiRg4cGCsWrUqq30/7tVXX40BAwbE7NmzczaDpjVy5Mj44IMPDvj5+eefH8OHD2/CRAAAAAAAAAAAAAAAAAAAAAAAcHAFsQBr7Nix8cILL2StX0NDQ3z3u9+NZcuWZa3nx91xxx3x+9//Pqs9H3nkkfje974Xe/fuzWrfT1NTUxMXXnhhPPPMMzmfRW498sgj8ac//emAn1dUVMTkyZObMBEAAAAAAAAAAAAAAAAAAAAAAHy2gliA9fzzz2e95549e+Kyyy6LPXv2ZLXvmjVrYvz48Vnt+cc//jHGjh2b1Z6fpa6uLr71rW/F0qVLm3Qu2bN27dr4r//6r4PW/Pd//3d06tSpiRIBAAAAAAAAAAAAAAAAAAAAAEAyBbEA69MUFRVF586do0ePHlFeXp5RjxUrVsR9992X1Vzr1q2LvXv3Zq3fhg0bYvjw4VFfX5+1nknt3r07Lrnkkti5c2eTz6ZxGhoa4rvf/W7s2LHjgDXf+ta34qKLLmrCVAAAAAAAAAAAAAAAAAAAAAAAkEzBLcDq2LFjPPTQQ7Fhw4Z45513orq6OjZv3hxvvvlm3HLLLdGqVau0+t19990FveDp+uuvj82bN6d15vTTT49JkybFypUrY/v27bFt27aorq6Oe++9N7p3755WrzVr1sSdd96Z1hny78EHH4x58+Yd8PMuXbrEpEmTmi4QAAAAAAAAAAAAAAAAAAAAAACkoaAWYPXt2zeqq6tjzJgxUVFR8YnPKisr49Zbb42lS5dGx44dE/d87733Yvr06dmO+gmnnnpq3HHHHbF48eJ49913Y+/evfHBBx9EVVVVTJ06NS6//PIoKyv7l3OrVq2KadOmpTXrxhtvjKVLl8a4cePipJNOirKysmjTpk306NEjrrnmmqiqqorRo0en1fOee+6JrVu3pnWG/Fm1alX89Kc/PWjNY489Fh06dGiiRAAAAAAAAAAAAAAAAAAAAAAAkJ4W+Q7woc6dO8ecOXOivLz8oHU9e/aM2bNnx1lnnRX19fWJek+bNi3tpVBJlJSUxP333x+jR4+O4uLiT3zWtm3b6NWrV/Tq1Su+853vxI4dO6Kuru4TNRMnTkz8O0REXHfddTFhwoSD1rRq1Soefvjh2LZtW8yYMSNR35qampgyZUpcd911ibOQH/X19TF8+PCora09YM2oUaPiG9/4RhOmytzGjRtj06ZNaZ1ZvXp1jtIAAAAAAAAAAAAAAAAAAAAAANBUCmYB1m233RYVFRWJavv37x/Dhw+PJ554IlH9woULY8uWLdG+ffvGRPyEkpKS+N3vfhfnnHNOovqysrJP/Lu+vj5mzpyZeF5lZWXcdtttiWpTqVRMnDgxnnvuudi6dWuiM0899ZQFWM3Ar371q1i4cOEBP6+srIx77723CRM1zkMPPRQ///nP8x0DAAAAAAAAAAAAAAAAAAAAAIAmVpTvABER7dq1iyuuuCKtM+PGjUtcW19fHy+++GK6sQ7qpptuSrz86tMsXbo03n333cT111xzTbRq1SpxfUVFRYwaNSpx/ZIlS2Ljxo2J62l61dXVcfPNNx/w81QqFU888US0adOmCVMBAAAAAAAAAAAAAAAAAAAAAED6CmIB1nnnnRclJSVpnenTp08cf/zxieuXLVuWbqwD6tatW1x//fWN6rFw4cLEtalUKi6++OK0Z1x66aWJaxsaGuKll15KewZNY9++fTF8+PDYvXv3AWuuvvrqGDRoUNOFAgAAAAAAAAAAAAAAAAAAAACADLXId4CIiH79+mV07owzzoh//vOfiWpXrlyZ0YxPM2rUqGjRonFXt3z58sS1J5xwQnTp0iXtGb17947S0tKora1NVF9VVRVDhw5New6594tf/CKWLFlywM9PPvnkuOOOO5owUXaMHTs27eVuq1evjmHDhuUoEQAAAAAAAAAAAAAAAAAAAAAATaEgFmD17Nkz43PPPPNMotr169dnNOPTnHvuuY3usXbt2sS1vXr1ymhGUVFR9OjRI15++eVE9UmXidG0XnnllbjtttsO+HlxcXFMnTo1SktLmzBVdnTs2DE6duyY7xgAAAAAAAAAAAAAAAAAAAAAADSxonwHiIgoLy/P6FyHDh0S127ZsiWjGftr2bJlnH766Y3us2nTpsS1nTp1ynhOOmc3btyY8Rxy59prr429e/ce8POf/OQnceaZZzZhIgAAAAAAAAAAAAAAAAAAAAAAaJyCWIDVpk2bnJ/btWtXRjP2161btygpKWl0n5qamsS1md5PRETbtm0T1+7cuTPjOeTO+++/f8DPTj/99LjllluaMA0AAAAAAAAAAAAAAAAAAAAAADReQSzAqqury/m5VCqV0Yz9tWvXLit9GhoaEtcWFWX+mIqLixPXppOJwlBVVRUtW7aMVCqV+Gfq1KmJ+//85z8/YB8AAAAAAAAAAAAAAAAAAAAAAMhUQSzA2r59e87PlZaWZjRjf2VlZVnpk06eHTt2ZDxn27ZtiWuzdUcAAAAAAAAAAAAAAAAAAAAAAAAHUxALsDZs2JDRuY0bNyauraioyGhGrpSXlyeufe+99zKek87ZDh06ZDwHAAAAAAAAAAAAAAAAAAAAAAAgqYJYgPXaa6/l/FzXrl0zmpErxx13XOLaTO8nIqK6ujpx7fHHH5/xHAAAAAAAAAAAAAAAAAAAAAAAgKQKYgHWwoUL0z5TV1cXS5YsSVx/6qmnpj0jl3r27Jm4duXKlbF9+/a0Z6xatSo++OCDnGQCAAAAAAAAAAAAAAAAAAAAAADIVEEswHruuedi27ZtaZ154YUXYvPmzYnr+/Tpk26snOrfv3/i2n379sXs2bPTnjFz5sy06s8888y0ZwAAAAAAAAAAAAAAAAAAAAAAAKSrIBZg7d69OyZOnJjWmbvvvjtxbcuWLeOss85KN1ZODRgwINq2bZu4fuLEidHQ0JC4fteuXfHoo48mrj/ppJOisrIycT0AAAAAAAAAAAAAAAAAAAAAAECmCmIBVkTEL3/5y3jjjTcS1T799NPx3HPPJe49ZMiQKC0tzTRaTrRs2TLOP//8xPWLFi2Kxx57LHH9HXfcEWvXrk1cf9FFFyWuBQAAAAAAAAAAAAAAAAAAAAAAaIyCWYC1Y8eOOOecc6KqquqgdbNnz47hw4en1XvEiBGNSJY748aNS7t+1qxZn1k3efLkmDBhQuK+LVq0iB/84AdpZQEAAAAAAAAAAAAAAAAAAAAAAMhUi3wH+Lh169ZF3759Y+TIkXHJJZdEz549o127drF58+ZYsmRJPPnkk/HMM8+k1fOEE06IYcOG5Shx4/Tv3z+GDBkSf/7znxPV7927Ny644IL49re/HWPHjo3+/ftHKpWKiIj6+vqYP39+3HvvvfHss8+mlWP48OFx7LHHpp3/UDJq1KhYsmTJZ9a9//77iXsuWbIkvvjFLyaq/eMf/xjHHHPMp372yiuvJJ6ZxIgRI2Lq1KmJam+55Za49dZbszofAAAAAAAAAAAAAAAAAAAAAAAKagFWxP8teXr44Yfj4Ycfzkq/8ePHR3FxcVZ65cIDDzwQvXv3jt27dyc+M3369Jg+fXq0bt06unTpEg0NDfHOO+/Ezp07057fvn37uP3229M+d6hZvXp1vPrqq1ntWVNTk7jnnj17sjobAAAAAAAAAAAAAAAAAAAAAAAKWcEtwMqmwYMHx+WXX57vGAfVo0eP+NWvfhU//OEP0z5bU1MTq1evbtT8KVOmRKdOnRrVA6AQVFZWRkNDQ75jABnyDkPz472F5s97DM2TdxcKg3cxP9z7/+cu3EGEO4hwBxHuIMIdRLiDCHcQ4Q4i3EGEO4hwBxHuIMIdHO6/f4Q7iHAHEe4gwh2QHf6OoPB4L+HQ5h2HwuKdhMLl/YRDj/caCpf3E2gKRfkOEBE5WVJVUVER06ZNy3rfXLjqqqti3LhxTT739ttvjwsuuKDJ5wIAAAAAAAAAAAAAAAAAAAAAAIe3gliANWHChKwuwSotLY1nn302unbtmrWeuTZx4sS4+uqrm2RWKpWKCRMmxA033NAk8wAAAAAAAAAAAAAAAAAAAAAAAD6uIBZgpVKpmDZtWlx55ZWN7nXUUUfFnDlzYsCAAVlI1nRSqVQ88MAD8eijj0ZZWVnO5nTo0CFmzpwZN954Y85mAAAAAAAAAAAAAAAAAAAAAAAAHExBLMCKiCgqKorJkyfHI488Em3atMmoR9++fWPx4sUxcODALKdrOqNGjYqqqqq4+OKLI5VKZa1vcXFxjBgxIl5//fW48MILs9YXAAAAAAAAAAAAAAAAAAAAAAAgXQWzAOtDo0ePjhUrVsS4ceOirKws0ZlTTjklpkyZEi+99FJ07949xwlz73Of+1w8/fTTUVVVFePGjYuKioqMe3Xu3DmuvfbaeOONN+KJJ56Izp07Z9yrrq4ucW1JSUnGcwAAAAAAAAAAAAAAAAAAAAAAgENbi3wH+DTHHHNMTJo0Ke68886YO3du/PWvf40VK1bE5s2bY9euXXHkkUdG165do1evXjFkyJDo169fo+ZVVlZGQ0NDltJnz2mnnRaTJk2KBx98MBYtWhQLFiyIZcuWxZo1a2L9+vWxdevWqK2tjYiI0tLSaN++fXTt2jW6d+8evXv3jq985SvRp0+fSKVSWcmzc+fOxLXt27fPysymMm/evHxHaDLDhg2LysrKRLWDBg3KaRYAAAAAAAAAAAAAAAAAAAAAAA5PBbkA60OtW7eOoUOHxtChQ/MdJa+Kioqif//+0b9//7zm2LRpU+La5rYA63AybNiwGDZsWL5jAAAAAAAAAAAAAAAAAAAAAABwGCvKdwCajxUrViSu7dKlSw6TAAAAAAAAAAAAAAAAAAAAAAAAzZkFWCSyd+/eeP755xPVFhcXR58+fXKcCAAAAAAAAAAAAAAAAAAAAAAAaK4swCKRO++8M95///1EtaeddlqUlZXlOBEAAAAAAAAAAAAAAAAAAAAAANBctch3AArTvn37YsuWLfHKK6/E448/HjNmzEh89vzzz89hMgAAAAAAAAAAAAAAAAAAAAAAoLmzAItPGDZsWMyePTvj80cccUSMGTMmi4kAAAAAAAAAAAAAAAAAAAAAAIBDTVG+A3BoGTFiRHTp0iXfMQAAAAAAAAAAAAAAAAAAAAAAgAJmARZZ8/nPfz7uueeefMcAAAAAAAAAAAAAAAAAAAAAAAAKnAVYZEV5eXnMnDkz2rRpc8CaefPmRSqVytsPAAAAAAAAAAAAAAAAAAAAAABQGCzAotFOPvnk+Nvf/ha9e/fOdxQAAAAAAAAAAAAAAAAAAAAAAKAZsACLjB199NFx1113xcsvvxwnnnhivuMAAAAAAAAAAAAAAAAAAAAAAADNRIt8B6B5aNu2bZSXl0enTp1iwIABMXjw4Bg8eHAceeSR+Y4GAAAAAAAAAAAAAAAAAAAAAAA0MzlbgDVv3rxctSaHZs2ale8IAAAAAAAAAAAAAAAAAAAAAADAIa4o3wEAAAAAAAAAAAAAAAAAAAAAAAA4vLTIdwAOH4MGDYqGhoZ8xwAAAAAAAAAAAAAAAAAAAAAAAPKsKN8BAAAAAAAAAAAAAAAAAAAAAAAAOLxYgAUAAAAAAAAAAAAAAAAAAAAAAECTsgALAAAAAAAAAAAAAAAAAAAAAACAJmUBFgAAAAAAAAAAAAAAAAAAAAAAAE3KAiwAAAAAAAAAAAAAAAAAAAAAAACalAVYAAAAAAAAAAAAAAAAAAAAAAAANCkLsAAAAAAAAAAAAAAAAAAAAAAAAGhSFmABAAAAAAAAAAAAAAAAAAAAAADQpCzAIrEnn3wyUqlUop9bb70133EBAAAAAAAAAAAAAAAAAAAAAIAC1SLfAaA52LJlSyxevDiWLVsWtbW1ic5UVlbGiBEjcpqrtrY23njjjXjttddixYoV8dZbb8X69etj/fr1sW3btti5c2fU1tZGXV1dlJSURGlpabRv3z46duwYxx9/fHTv3j2+8IUvxFlnnRVdunTJaVYAAAAAAAAAAAAAAAAAAAAAAPiQBViwn9ra2njllVdi0aJFH/2sXr067T4DBw7M+gKsjRs3xty5c2P+/Pnx4osvRnV1ddTX1yc6u2vXrti1a1ds2bIl/vGPf8Tf/va3T3zeq1ev+OY3vxkjR460DAsAAAAAAAAAAAAAAAAAAAAAgJyyAAsi4vHHH/9o2dXy5ctj3759+Y70qe6666645557ctJ7+fLlsXz58pgwYUJceeWVMX78+OjQoUNOZgEAAAAAAAAAAAAAAAAAAAAAcHizAAsiYuTIkfmOUDD27NkTv/71r2PWrFnx9NNPx7/927/lOxIAAAAAAAAAAAAAAAAAAAAAAIeYonwHAArT+vXrY8iQIfHiiy/mOwoAAAAAAAAAAAAAAAAAAAAAAIcYC7CAA9q5c2dcdNFFsWHDhnxHgYK2du3aSKVSn/qzdu3afMcD8sj3A/BxvhOAD/k+AHwPgPegufG8mg/PqvB5Rs2HZ9U8eE6FzzNqPjyrwucZNR+eVeHzjJoPz6p58bzg8OTdBz6L7wng0/huAPbnewH4kO8DwPcAHJ4swAIOatOmTfGzn/0s3zEAAAAAAAAAgP/H3p2HWV2XfwO/Z9hlQDbZk0VQASGRRdzSUKl+pmGkKC6QICqUlpqaS4rLz0dTxLRyCfcUtCdFTclEJSFUNkVFDBBMSQFFlmFf5vnDy3k8gHoOc2bO98y8XtfFdfX5zGd548S5rzlzzn0AAAAAAAAAAACgEtEACyqZunXrRosWLWLfffeNfffdN1q2bBmFhWX7p/7QQw/Fp59+mqWEAAAAAAAAAAAAAAAAAAAAAABUddVzHYD8MWTIkBgyZEiuY+RErVq1Yv/994+aNWvGyy+/nOs4pRo1ahRHHnlkHH744dGlS5fYb7/9okmTJjusW7duXcyePTsee+yx+NOf/hRr167N6J6NGzfGs88+G6eeemq2ogMAAAAAAAAAAAAAAAAAAAAAUIVpgAXbKSwsjE6dOkXv3r2jV69e0bt37+jWrVvUqFEj7rvvvpw3wGrYsGEMGjQoTj311Ojdu3cUFhZ+457ddtstDjnkkDjkkEPi4osvjhNPPDGmTJmS0b3Tp0/XAAsAAAAAAAAAAAAAAAAAAAAAgKzQAAsi4sQTT4xevXpFr169omfPnlG3bt1cR9qpc845J6677rqoVavWLp/RokWLeO655+KAAw6IefPmpb3v/fff3+U7AQAAAAAAAAAAAAAAAAAAAADgyxLdAOutt96KCRMmxNSpU2PevHmxfPnyWL9+fdStWzdat24dXbt2jaOOOioGDBgQDRs2zGnWkpKSmDJlSkyYMCFmzJgR//73v2PlypWxfv36qFatWrRs2TKeeOKJOOCAA3bp/NWrV8f06dNj9uzZsXDhwvjwww9j1apVsX79+oiIqFOnTjRo0CBat24dHTp0iAMOOCB69eqV2EZOSTN+/PhcR0jLXnvtlZVz6tSpE7/5zW9i0KBBae8pLi7Oyt0AAAAAAAAAAAAAAAAAAAAAAFBuDbCOOOKImDx5clprFy1aFG3bti0dT5s2LX79619/5f7Vq1fH3LlzY+7cuTF+/Pg499xz48wzz4zf/OY30bhx44yzLl68ONq1a5fW2sMPPzxeeumllLnHH388rrjiinj77bd3umfr1q3xwQcfxOrVqzPKtXbt2hg3blz8+c9/jilTpsTmzZsz2l+rVq04/PDD47TTTouf/OQnUbt27Yz2b2/SpElxyy23pLV20KBBGTVXouL17ds3o/VFRUXllAQAAAAAAAAAAAAAAAAAAAAAgKqm3Bpg7YrNmzfHRRddFLfeemuUlJSkvW/9+vXxu9/9LsaNGxcPPPBAfO973yvHlP/fhg0bYujQofHwww9n9dz169fHmDFj4sYbb4yVK1fu8jkbN26M5557Lp577rm44IIL4rLLLotzzjknatSosUvnffDBB/G3v/0trbU9e/bcpTuoOA0aNMhofbNmzcopCQAAAAAAAAAAAAAAAAAAAAAAVU1hrgN8Ye3atfH9738/xowZk1Hzqy9btmxZHHPMMfGnP/0py+l2VFxcHEcffXTWm19NmzYtvv3tb8ell15apuZX21u2bFmcd9550atXr3jzzTezdi75a8mSJRmt7927dzklAQAAAAAAAAAAAAAAAAAAAACgqklEA6xNmzbFMcccEy+88EKZz9q6dWsMHz48642ptr/j5JNPjilTpmT13HvvvTcOP/zwmD9/flbP/bI33ngjDjrooJgwYUK53UF+ePrpp9NeW61atejXr185pgEAAAAAAAAAAAAAAAAAAAAAoCpJRAOsESNGxOTJk7N2XklJSfz0pz+N2bNnZ+3ML7v++uszah6UjjvvvDPOOOOM2Lx5c1bP3Zm1a9fGgAED4vHHHy/3u0imjz/+OK699tq01x9//PHxrW99qxwTAQAAAAAAAAAAAAAAAAAAAABQlSSiAdakSZOyfuamTZti0KBBsWnTpqyeu3Dhwrj66quzeuYzzzwTI0aMyOqZ32Tr1q1x8sknx8yZMyv0XnJv6tSpcfDBB8fy5cvTWl+/fv24+eabyzkVAAAAAAAAAAAAAAAAAAAAAABVSSIaYO1MYWFhNG/ePDp16hSNGzfepTPmzZsXt9xyS1Zzffjhh7F58+asnbd06dIYPHhwbNu2LWtnpmvjxo0xcODAWLduXYXfTfnbtm1bFBcXxwcffBAvvPBC3HjjjXHooYfGoYceGosWLUrrjFq1asUjjzwSe+65ZzmnBQAAAAAAAAAAAAAAAAAAAACgKklcA6ymTZvGH/7wh1i6dGnlRcd6AAAgAElEQVR89NFHMXfu3Pjkk09i0aJFceWVV0bt2rUzOu+3v/1tohs8XXLJJfHJJ59ktKdbt25x++23x7vvvhtr1qyJ1atXx9y5c2P06NHRsWPHjM5auHBh3HDDDRntIXn69+8fBQUFKX+qVasW9erViz333DOOPPLIuPjii2Pq1Klpn9myZct45pln4n/+53/KMTkAAAAAAAAAAAAAAAAAAAAAAFVRohpg9ezZM+bOnRvnnHNONGnSJOVrbdu2jauuuipmzpwZTZs2TfvMTz/9NB566KFsR03RpUuXuP7662P69Onx8ccfx+bNm2PVqlUxZ86cuP/+++OUU06JoqKiHfbNnz8/HnjggYzuuvzyy2PmzJkxcuTI2HvvvaOoqCjq1asXnTp1il/+8pcxZ86cGD58eEZn3nzzzbFy5cqM9lB5FRUVxYUXXhhvvfVW9O3bN9dxAAAAAAAAAAAAAAAAAAAAAACohKrnOsAXmjdvHs8++2w0btz4a9d17tw5JkyYEIccckhs27YtrbMfeOCBjJtCpaNmzZoxZsyYGD58eFSrVi3la/Xr14+uXbtG165d4/TTT4/i4uLYunVryprbbrst7b9DRMQFF1wQ11xzzdeuqV27dtxxxx2xevXqGDduXFrnrl27NsaOHRsXXHBB2lmofFq1ahXXXHNN/PjHP47dd9+9Qu5ctmxZLF++PKM9CxYsKKc0AAAAAAAAAAAAAAAAAAAAAABUlMQ0wLr22mujSZMmaa3t06dPDB48OO6999601k+bNi0+++yzaNiwYVkipqhZs2Y89dRT0a9fv7TWFxUVpYy3bdsWjz32WNr3tW3bNq699tq01hYUFMRtt90WEydOjJUrV6a1Z/z48RpgVXFLliyJCy+8MKZOnRrnn39+dO7cudzv/MMf/hCjRo0q93sAAAAAAAAAAAAAAAAAAAAAAEiWwlwHiIho0KBBnHbaaRntGTlyZNprt23bFv/85z8zjfW1rrjiirSbX+3MzJkz4+OPP057/S9/+cuoXbt22uubNGkSw4YNS3v9jBkzYtmyZWmvp3JasWJFjB07Nvbbb7845ZRTYsWKFbmOBAAAAAAAAAAAAAAAAAAAAABAJZSIBlg//OEPo2bNmhnt6dGjR7Rp0ybt9bNnz8401ldq3bp1XHLJJWU6Y9q0aWmvLSgoiBNOOCHjO0466aS015aUlMSrr76a8R1UTiUlJfHwww9Ht27dYvr06bmOAwAAAAAAAAAAAAAAAAAAAABAJVM91wEiInr16rVL+w444IB4//3301r77rvv7tIdOzNs2LCoXr1s/+nefPPNtNe2b98+WrRokfEd3bt3jzp16sT69evTWj9nzpw49thjM76HymvJkiXRr1+/eP7556NHjx5ZP3/EiBEZN3dbsGBB9O/fP+tZAAAAAAAAAAAAAAAAAAAAAACoOIlogNW5c+dd3vf444+ntXbJkiW7dMfOHHPMMWU+Y/HixWmv7dq16y7dUVhYGJ06dYpZs2altT7dZmIkT7t27eLb3/52ytzmzZujuLg4Vq1aFatWrdrls1euXBknnHBCzJkzJ4qKisoaNUXTpk2jadOmWT0TAAAAAAAAAAAAAAAAAAAAAIDkS0QDrMaNG+/SvkaNGqW99rPPPtulO7ZXq1at6NatW5nPWb58edprmzVrtsv3ZLJ32bJlu3wPuXXLLbd87dc/+eSTePvtt+Pvf/97jB8/Pt57772Mzl+0aFGMGjUqfvvb35YlJgAAAAAAAAAAAAAAAAAAAAAAREREYa4DRETUq1ev3Pdt2LBhl+7YXuvWraNmzZplPmft2rVpr93V/z4REfXr10977bp163b5HpKtSZMmcfjhh8f//u//xr///e+4++67o27duhmdcdddd8WaNWvKKSEAAAAAAAAAAAAAAAAAAAAAAFVJIhpgbd26tdz3FRQU7NId22vQoEFWzikpKUl7bWHhrn+bqlWrlvbaTDKRv6pVqxbDhg2LyZMnR+3atdPet3r16njqqafKMRkAAAAAAAAAAAAAAAAAAAAAAFVFIhpgrVmzptz31alTZ5fu2F5RUVFWzskkT3Fx8S7fs3r16rTXZuu/EfmhR48eccUVV2S0Z+rUqeWUBgAAAAAAAAAAAAAAAAAAAACAqiQRDbCWLl26S/uWLVuW9tomTZrs0h3lpXHjxmmv/fTTT3f5nkz2NmrUaJfvIT+dccYZGa2fO3duOSUBAAAAAAAAAAAAAAAAAAAAAKAqSUQDrLfeeqvc97Vq1WqX7igve+65Z9prd/W/T0RmDYvatGmzy/eQn5o3bx4NGjRIe31ZmrEBAAAAAAAAAAAAAAAAAAAAAMAXEtEAa9q0aRnv2bp1a8yYMSPt9V26dMn4jvLUuXPntNe+++67sWbNmozvmD9/fqxatapcMlF5FBam/zCwfv36ckwCAAAAAAAAAAAAAAAAAAAAAEBVkYgGWBMnTozVq1dntGfy5MnxySefpL2+R48emcYqV3369El77ZYtW2LChAkZ3/HYY49ltP7AAw/M+A7y2/Lly2PFihVpry8qKirHNAAAAAAAAAAAAAAAAAAAAAAAVBWJaIC1cePGuO222zLa89vf/jbttbVq1YpDDjkk01jl6qCDDor69eunvf62226LkpKStNdv2LAh7r777rTX77333tG2bdu011Oxtm3bVi7n3nvvvRmt33PPPcslBwAAAAAAAAAAAAAAAAAAAAAAVUsiGmBFRPyf//N/4p133klr7aOPPhoTJ05M++yjjz466tSps6vRykWtWrXiuOOOS3v9a6+9Fn/605/SXn/99dfH4sWL017/k5/8JO21VLzrr78+TjrppJg2bVrWznzjjTdi1KhRGe3p1q1b1u4HAAAAAAAAAAAAAAAAAAAAAKDqSkwDrOLi4ujXr1/MmTPna9dNmDAhBg8enNHZQ4YMKUOy8jNy5MiM1z/xxBPfuO6uu+6Ka665Ju1zq1evHmeffXZGWahYmzdvjvHjx8fBBx8cvXr1iltuuSWjBmdfVlJSEg899FAcdthhsW7duoz2fu9739ulOwEAAAAAAAAAAAAAAAAAAAAA4Muq5zrAl3344YfRs2fPGDp0aAwcODA6d+4cDRo0iE8++SRmzJgR9913Xzz++OMZndm+ffvo379/OSUumz59+sTRRx8d//jHP9Jav3nz5jj++OPj1FNPjREjRkSfPn2ioKAgIiK2bdsWL7/8cowePTqefPLJjHIMHjw4vvWtb2WcvzIZNmxYzJgx4xvXrVixIu0zZ8yYEfvvv39aa5955plo2bJl2ufOmDEjzj///Nh///2jb9++0b179+jevXvss88+Ub36jv+s165dG3Pnzo3nnnsuHn744Zg7d27af48vdOjQIQ455JCM9wEAAAAAAAAAAAAAAAAAAAAAwPYS1QAr4vMmT3fccUfccccdWTnv6quvjmrVqmXlrPJw6623Rvfu3WPjxo1p73nooYfioYceirp160aLFi2ipKQkPvroo1i3bl3G9zds2DCuu+66jPdVNgsWLIg33ngjq2euXbs27TM3bdq0S3e8/vrr8frrr5eOCwoKon79+tGwYcMoKiqKDRs2xJo1a2LZsmVRUlKyS3d84dJLLy1tuAYAAAAAAAAAAAAAAAAAAAAAAGWRuAZY2dS3b9845ZRTch3ja3Xq1Cluuumm+PnPf57x3rVr18aCBQvKdP/YsWOjWbNmZTqD5CgpKYlVq1bFqlWrsnruEUccEUOGDMnqmVCZtG3btsxN5oDKyeMD8GUeE4AveDwAPA6Afwf5xvcrf/heJZ/vUf7wvcoPvk/J53uUP3yvks/3KH/4XiWf71H+8L3KL75fUDX5tw98E48TwM54bAC253EB+ILHA8DjAFRNhbkOEBHl0qSqSZMm8cADD2T93PLws5/9LEaOHFnh91533XVx/PHHV/i95Jf27dvH+PHjo6CgINdRAAAAAAAAAAAAAAAAAAAAAACoJBLRAOuaa67JahOsOnXqxJNPPhmtWrXK2pnl7bbbbotzzz23Qu4qKCiIa665Ji699NIKuY/8td9++8XkyZOjadOmuY4CAAAAAAAAAAAAAAAAAAAAAEAlkogGWAUFBfHAAw/EmWeeWeazdt9993j22WfjoIMOykKyilNQUBC33npr3H333VFUVFRu9zRq1Cgee+yxuPzyy8vtDvJfYWFhnHXWWfHqq69G69atcx0HAAAAAAAAAAAAAAAAAAAAAIBKJhENsCI+b7hz1113xZ133hn16tXbpTN69uwZ06dPj8MPPzzL6SrOsGHDYs6cOXHCCSdEQUFB1s6tVq1aDBkyJN5+++0YMGBA1s6lYjRs2DCr/3/4KgUFBXHsscfGa6+9FnfccUfstttu5X4nAAAAAAAAAAAAAAAAAAAAAABVT2IaYH1h+PDhMW/evBg5cmQUFRWltWffffeNsWPHxquvvhodO3Ys54Tlr127dvHoo4/GnDlzYuTIkdGkSZNdPqt58+Zx/vnnxzvvvBP33ntvNG/efJfP2rp1a9pra9asucv3sKPzzjsvli5dGn/+859j8ODB0b59+6ydXVhYGD169Iirr746FixYEE8++WT06NEja+cDAAAAAAAAAAAAAAAAAAAAAMD2quc6wM60bNkybr/99rjhhhvihRdeiKlTp8a8efPik08+iQ0bNsRuu+0WrVq1iq5du8bRRx8dvXr1KtN9bdu2jZKSkiylz5799tsvbr/99vjd734Xr732WkyZMiVmz54dCxcujCVLlsTKlStj/fr1ERFRp06daNiwYbRq1So6duwY3bt3j8MOOyx69OgRBQUFWcmzbt26tNc2bNgwK3dWlJdeeinXEb7RHnvsEYMGDYpBgwZFRMTKlStj1qxZ8frrr8eiRYvigw8+iP/85z/x6aefxtq1a2PdunWxYcOGqFatWtSqVSuKioqicePG0axZs2jXrl107Ngx9t9//+jdu3c0atQox387AAAAAAAAAAAAAAAAAAAAAACqkkQ2wPpC3bp149hjj41jjz0211FyqrCwMPr06RN9+vTJaY7ly5envTbfGmDlowYNGkTfvn2jb9++uY4CAAAAAAAAAAAAAAAAAAAAAAAZKcx1APLHvHnz0l7bokWLckwCAAAAAAAAAAAAAAAAAAAAAADkMw2wSMvmzZtj0qRJaa2tVq1a9OjRo5wTAQAAAAAAAAAAAAAAAAAAAAAA+UoDLNJyww03xIoVK9Jau99++0VRUVE5JwIAAAAAAAAAAAAAAAAAAAAAAPJV9VwHIJm2bNkSn332Wbz++utxzz33xLhx49Lee9xxx5VjMgAAAAAAAAAAAAAAAAAAAAAAIN9pgEWK/v37x4QJE3Z5f40aNeKcc87JYiIAAAAAAAAAAAAAAAAAAAAAAKCyKcx1ACqXIUOGRIsWLXIdAwAAAAAAAAAAAAAAAAAAAAAASDANsMiavfbaK26++eZcxwAAAAAAAAAAAAAAAAAAAAAAABJOAyyyonHjxvHYY49FvXr1vnLNSy+9FAUFBTn7AwAAAAAAAAAAAAAAAAAAAAAAJEP1XAcg/+2zzz7x9NNPR4cOHXIdhSpg48aNKeMFCxbkKAkAAAAAAAAAAAAAAAAAAAAAUJVt3/tk+94ofD0NsNhle+yxR/zqV7+KkSNHxm677ZbrOFQRH3zwQcq4f//+OUoCAAAAAAAAAAAAAAAAAAAAAPD/ffDBB3HAAQfkOkbe0ACLtNSvXz8aN24czZo1i4MOOij69u0bffv21fiKCrdy5cpcRwAAAAAAAAAAAAAAAAAAAAAA2IHeKJkptwZYL730UnkdTTl64oknch0Bvtbq1atzHQEAAAAAAAAAAAAAAAAAAAAAYAd6o2Sm3BpgAZSHnj17powfffTR6Ny5c47SAEB2LViwIPr37186fuKJJ6JDhw45TAQA2aPOAVCZqXMAVGbqHACVmToHQGWlxgFQmalzAFRm6hwAlZk6B0BlNnfu3DjxxBNLx9v3RuHraYBFhTniiCOipKQk1zHIc/Xr108Zd+7cObp06ZKjNABQvjp06KDOAVBpqXMAVGbqHACVmToHQGWmzgFQWalxAFRm6hwAlZk6B0Blps4BUJlt3xuFr1eY6wAAAAAAAAAAAAAAAAAAAAAAAABULRpgAQAAAAAAAAAAAAAAAAAAAAAAUKE0wAIAAAAAAAAAAAAAAAAAAAAAAKBCaYAFAAAAAAAAAAAAAAAAAAAAAABAhdIACwAAAAAAAAAAAAAAAAAAAAAAgAqlARYAAAAAAAAAAAAAAAAAAAAAAAAVSgMsAAAAAAAAAAAAAAAAAAAAAAAAKpQGWAAAAAAAAAAAAAAAAAAAAAAAAFQoDbAAAAAAAAAAAAAAAAAAAAAAAACoUBpgAQAAAAAAAAAAAAAAAAAAAAAAUKE0wAIAAAAAAAAAAAAAAAAAAAAAAKBCVc91AIBM7LHHHnHllVemjAGgslDnAKjM1DkAKjN1DoDKTJ0DoDJT5wCorNQ4ACozdQ6AykydA6AyU+cAqMzUubIpKCkpKcl1CAAAAAAAAAAAAAAAAAAAAAAAAKqOwlwHAAAAAAAAAAAAAAAAAAAAAAAAoGrRAAsAAAAAAAAAAAAAAAAAAAAAAIAKpQEWAAAAAAAAAAAAAAAAAAAAAAAAFUoDLAAAAAAAAAAAAAAAAAAAAAAAACqUBlgAAAAAAAAAAAAAAAAAAAAAAABUKA2wAAAAAAAAAAAAAAAAAAAAAAAAqFAaYAEAAAAAAAAAAAAAAAAAAAAAAFChNMACAAAAAAAAAAAAAAAAAAAAAACgQmmABQAAAAAAAAAAAAAAAAAAAAAAQIXSAAsAAAAAAAAAAAAAAAAAAAAAAIAKpQEWAAAAAAAAAAAAAAAAAAAAAAAAFUoDLAAAAAAAAAAAAAAAAAAAAAAAACpU9VwHACq3RYsWxeuvvx7//e9/o7i4OFq0aBFt2rSJgw8+OGrUqJHTbLNmzYr58+fHkiVLIiKiVatWsffee0f37t1zmguA/JHkOgcAZZW0Ord+/fp45513Yt68ebF8+fIoLi6OoqKiaNSoUey3337RtWvXqF7dU10AfLOk1bgVK1bEvHnz4oMPPoilS5fG2rVrIyJi9913j2bNmkX37t2jffv2FZ4LgPyUtDoHANmkzgFQmSW9zm3ZsiVmzZoVb7/9dixfvjw2bdoURUVFpa+77NKli9/VAfCVkl7nAKAsklrnVqxYETNmzIhFixbFypUro6SkJHbfffdo3bp19OrVK5o3b56zbADkh6TWuCVLlsS0adNi6dKlsWrVqmjSpEm0bNkyDjzwwNhjjz1ylgsAsqWq9kHxm0agXPzlL3+J0aNHx7Rp03b69UaNGsXAgQPj6quvjiZNmlRYrs2bN8fNN98cf/rTn2LhwoU7XdOhQ4cYNmxYnH/++X6hBsBOJbHObdmyJd5+++2YPn16zJgxI6ZPnx5vvvlmbN68uXTN4MGD47777quQPADkryTVuVmzZsUTTzwRL7zwQrz22mspdW17devWjYEDB8Z5550X3bp1K9dcAOSnpNS44uLiuP3222PatGkxffr0+Oijj75xT+vWreP000+Pc889N5o1a1Zu2QDIX0mpc+lat25ddO3aNd57772Uec9hArAzSapzRxxxREyePHmX9997770xZMiQ7AUCIO8lqc7tzPz58+Omm26K8ePHx6pVq75yXZ06deLQQw+Nc845J44//vgKTAhAkiWlzrVt2zbef//9rJzlOUwAvpCUOvdlJSUlMX78+Pj9738fU6ZM+dq13bt3j7PPPjvOOOMMDY0BSJHEGrdt27Z45JFH4sYbb4w5c+bsdE21atWib9++cdFFF8VRRx1VIbkAyC/vvfdeynuwZ82aFWvWrCn9eps2bWLx4sU5yaYPSkRBSUlJSa5DAJVHcXFxnHnmmTFu3Li01jdr1izuv//++N73vlfOyT5/IcZJJ50Us2bNSmt9jx49Yty4cdGhQ4dyTgZAvkhinfvjH/8YDz74YLz++uuxfv36r13rhRcAfJ0k1bkNGzZEly5ddngjdDqqVasWF154YVxzzTWV8sk8ADKXpBoXEbFgwYLo2LHjLu3dfffdY8yYMd4sDUCppNW5dP3yl7+MMWPG7DDvOUwAviyJdU4DLACyJYl17su2bNkSV199dVx//fWxZcuWtPcNHDgw7b8TAJVX0upcNhtgnX322fHHP/4xK2cBkJ+SVue+8PHHH8egQYPixRdfzGif988B8IUk17gBAwbEv/71r7T3nHXWWXHrrbdGrVq1yjEZAPngpZdeiuuvvz5mzJgRK1as+Nq1uWqApQ/K5zTAArJm69atcdxxx8UzzzyTMr/HHntE9+7dY/fdd4+FCxfG7Nmz48sPPbVq1Yrnn38+Dj300HLL9vHHH0efPn12+MVVhw4dokuXLlFSUhJvv/32Dt0Q27VrF6+88ko0bdq03LIBkB+SWuf69+8fEyZMSGutN48B8FWSVueKi4ujXr16O8wXFBTEPvvsE3vuuWc0adIkiouL46233tppo6wf//jHMX78eJ9MBlDFJa3GRey8AVajRo2iY8eO0bx58ygqKoqNGzfGxx9/HG+88UbKp8p84aabbooLLrgg69kAyC9JrHPpeOWVV+KQQw6Jbdu27fA1z2EC8IWk1jkNsADIhqTWuS+sX78+fvKTn+yQr6CgILp06RJ77rlnNGjQIIqLi+O9996LefPmlTbJ0gALgCTWuWw2wPrHP/4RRx11VFbOAiD/JLHORUQsX748vvOd78S8efNS5mvUqBHdu3ePNm3aRGFhYXz44Ycxc+bM2LBhQ8q61q1bx5QpU6JNmzblkg+A5Etqjfvvf/8bffr0iQ8++CBlvkGDBnHggQdGo0aNYsWKFfHKK6/EqlWrUtaceOKJMW7cuCgoKCiXbADkhzFjxsQvf/nLtNbmogGWPij/n3cAAllzySWXpPxwU6NGjRg9enQMHz48atasWTo/d+7cGDZsWEybNi0iIjZu3Bj9+/ePN998M1q0aJH1XNu2bYv+/funPOi3aNEi7rvvvujXr1/K2okTJ8ZPf/rT+PjjjyMiYtGiRXH88cfHlClT/JADUMUltc59lQYNGkTdunVjyZIlFXYnAPkryXWuWrVq0a9fvxg8eHAceeSR0aRJkx3WzJw5M84///z45z//WTr317/+Na666qq49tpryyUXAPkhqTWuadOmccwxx8TRRx8dBx988Fe+gHDz5s3x5JNPxsUXX5zyS6uLLrooDj300DjwwAOzng2A/JHUOvd1Nm3aFEOHDi1tflWvXr2dNnsEgHypc4sWLcpo/c6e3wSg6klynSspKYmTTjopJV/t2rXjoosuiuHDh0erVq122LNu3br4xz/+EePGjUvJD0DVlMQ6N2XKlNJmjZm4/fbb4+abby4dt23bNo488shsRgMgzySxzkVE/OIXv9ih+dXZZ58do0aN2uHN0CtXrowbbrghbrzxxtLf2X344Ydx1llnxcSJE7OeDYD8kMQat3nz5hgwYEBK86uioqK48cYbY9iwYVGjRo2UtXfffXdcdNFFsXbt2oiIePTRR2OfffaJq6++Oqu5AKgcatWqFa1bt96hsVRF0gclVUHJl9tsAuyi9957L/bdd9/YvHlz6dwTTzwRP/rRj3a6fv369XHkkUeW/pATEXHWWWfFHXfckfVsDz74YJx++uml40aNGsXMmTOjbdu2O12/aNGi6NGjR3z22Welc4888kicdNJJWc8GQH5Icp3r379/TJo0Kbp37x49e/aMXr16Rc+ePaNDhw4xatSoGDVqVOnawYMHx3333Zf1DADktyTWueLi4mjSpEkMGzYsLrnkkmjduvU37tm6dWucdtpp8cgjj5TO1axZM/7973/7VDKAKiqJNS7i85pVUFAQhYWFae/57LPP4jvf+U689dZbpXPf//7349lnn81qNgDyR1Lr3De5/PLL47rrrouIzz8t7YQTToibbrqp9OuewwQgItl17ogjjojJkyeXjr30DoBMJbnORUT8/ve/j5/97Gel4xYtWsSkSZOiU6dOae3fsmVLVK/u85kBqqqk17lMderUKaWZyNVXXx1XXHFFDhMBkEtJrXOLFy+Odu3apcz9+te/jv/93//92n233357/PznP0+Ze+WVV3wYG0AVlNQad+edd8bZZ59dOq5Vq1a8+OKLcdBBB33lnilTpsSRRx4ZmzZtKt0zb968r3w/OQCV35gxY+Kiiy6KLl26pLwHu2vXrjF16tT47ne/W7q2TZs2sXjx4grLpg9KqvTfXQHwNUaNGpXyw82QIUO+8oebiIg6derEfffdl9L5d+zYsfHee+9lNdfWrVvjyiuvTJkbPXr01/6w0q5duxg9enTK3OWXX17a1R6AqiepdS7i8yfzVq1aFf/85z9j9OjRcfLJJ0fHjh0rTcdeAMpfEutc7dq1Y8GCBXH77ben1fwqIqJatWoxduzY+Na3vlU6t2nTpnj00UezlguA/JLEGhfxec3KpPlVRETDhg3j1ltvTZl7/vnnY82aNdmMBkAeSWqd+zpvvPFG3HDDDaXjP/7xj1G3bt0Kux+A/JGPdQ4A0pXkOvef//wnLrnkktJx7dq14/nnn0+7+VVEaH4FUMUluc5laurUqSnNrwoLC2PIkCG5CwRAziW1zj311FMp42bNmu3wXrqdGTlyZHTr1u1rzwKgakhijSspKUl5jUnE5+/z/rrmVxERhx56aFx66aWl440bN8ZvfvObrOUCIP8MHjw4Vq9eHbNnz4677747hg8fHgcccEDUqFEjp7n0QdmRBlhAma1fvz7+8pe/pMxdfPHF37hv7733jv79+5eOt2zZEg8//HBWs02ZMiUWLVpUOm7VqlWceuqp37jvtNNOi1atWpWOFy5cGKXaeigAACAASURBVP/617+ymg2A/JDkOhfx+S+oMn3jNAB8Ial1rnr16mk3vvqyOnXqxE9/+tOUuRdffDFbsQDII0mtcWVxxBFHRJ06dUrHW7Zsiffffz+HiQDIlXysc1u2bIkzzjgjtmzZEhERJ598cvzgBz+okLsByC/5WOcAIF1Jr3PXXXddFBcXl44vu+yy6Ny5c9bvAaBySnqdy9Q999yTMu7Xr1/Kh7IBULUkuc5t32ykX79+UatWrW/cV1BQEMcee2zK3Pz587OaDYDkS2qNe+utt1LeG167du342c9+ltben//851G7du3S8WOPPZbyvCcAVUvDhg1T6kJS6IOyI++UB8rs73//e6xbt650fNBBB8W+++6b1t7t35j817/+NavZHn/88ZTx6aefHtWqVfvGfdWqVduhQGQ7GwD5Icl1DgDKqjLWue7du6eM//vf/+YoCQC5VBlrXGFhYTRo0CBlbs2aNTlKA0Au5WOd++1vfxuzZs2KiIhGjRrFmDFjKuReAPJPPtY5AEhXkuvcmjVrUt6gVrdu3TjvvPOyegcAlVuS61ymiouL49FHH02ZGzp0aI7SAJAESa5za9euTRln8uGj2zd3/Oyzz7KSCYD8kdQa989//jNlfOCBB+7w+smv0qhRo+jVq1fpeMOGDfG3v/0ta9kAIBv0QdmRBlhAmU2cODFlfMQRR6S997DDDovq1auXjmfPnh1Lly7NVrQyZdt+7bPPPpuFRADkmyTXOQAoq8pY576cKSJi06ZNOUoCQC5Vxhq3bt26WL58ecpcy5Ytc5QGgFzKtzr37rvvxqhRo0rHN998czRt2rRc7wQgf+VbnQOATCS5zo0fPz6Ki4tLxwMGDIh69epl7XwAKr8k17lMbV8X99hjj/jRj36UszwA5F6S61zz5s1Txhs2bEh77/ZrGzVqlJVMAOSPpNa4Dz/8MGW83377ZbS/a9euKWMNsABIGn1QdqQBFlBmb731Vsr4oIMOSntv3bp1d/hB4u23385Kro0bN8aCBQtS5vr06ZP2/oMPPjhlPH/+fG+cBqiCklrnACAbKmOd2/7nwBYtWuQoCQC5VBlr3COPPBJbtmwpHbdr1y7atGmTw0QA5Eo+1blt27bF0KFDY+PGjRER0bdv3xgyZEi53QdA/sunOgcAmUpynXvxxRdTxkcffXTWzgagakhyncvUPffckzI+7bTTokaNGjlKA0ASJLnOHXbYYSnjWbNmpb135syZKeNevXplJRMA+SOpNW7FihUp4wYNGmS0f/v1b775ZpkzAUC26IOycxpgAWX2zjvvpIw7dOiQ0f699torZTx37twyZ4r4/NOkt27dWjpu2rRp1K9fP+399evXjyZNmpSOt27dGv/+97+zkg2A/JHUOgcA2VAZ69xf/vKXlHHv3r1zlASAXKpsNW7q1Klx4YUXpsxtPwag6sinOnf77bfH1KlTIyKiTp06ceedd5bbXQBUDvlU5yIizjvvvOjdu3c0bdo0atasGY0aNYqOHTvGscceGzfeeKPXmQCQIsl17rXXXksZf/FGt/Xr18fDDz8cxx13XOy1115Rp06daNCgQXTo0CFOOOGEuOuuu2LNmjVZywFA/kpyncvEvHnz4l//+lfK3NChQ3OSBYDkSHKdO/LII2OfffYpHb/88ssxZ86cb9y3ZMmS+L//9/+WjmvUqBEnn3xy1nIBkB+SWuNq1qyZMv7ig9fStf36efPmxbZt28qcCwCyQR+UndMACyiTFStW7NBJd88998zojO3Xz58/v8y5ImKHroeZ5trZnmxlAyA/JLnOAUBZVcY6N3369NI3Vn/h+OOPz1EaAHKlMtS4jRs3xocffhhPPfVUnHrqqfGd73wnVq5cWfr1Y489Ns4555wKzQRAMuRTnVu8eHFceumlpeMrr7wy4xdKAlC15FOd+8Lvfve7mD59eixfvjw2b94cn332WSxYsCCefvrpuPjii6NTp07x4x//OBYuXFiuOQBIviTXuZUrV6a83rJmzZrRvn37mDx5cnTp0iVOOeWUeOqpp+K9996LDRs2xKpVq2LhwoXxl7/8Jc4666xo165d/O53v8tKFgDyU5LrXKbGjh2bMu7Tp0907tw5J1kASIak17nCwsK45557olatWhERsW3btvjJT34Sixcv/so9S5cujf79+8e6detK5y6//PJo2bJl1nIBkHxJrnGNGzdOGX/00UcZ7d9+/YYNG+I///lPmXMBQDbog7Jz1XMdAMhvX37TVUTEbrvtFnXr1s3ojKZNm6aMV61aVeZcETtm2/6edJRXNgDyQ5LrHACUVWWrc5s3b46zzjorZe6www6L3r175ygRALmSjzVu//33jzfeeOMb1xUUFMSIESNi9OjRUVBQUK6ZAEimfKpzZ555ZqxduzYiIr797W/HBRdcUC73AFB55FOdS9e2bdvi8ccfj0mTJsU999wTAwYMyGkeAHInyXXu448/Thm3bNky/vrXv8aJJ54Y27Zt+8b9n376aZx33nkxffr0uPfee6N6dS9PB6hqklznMrFly5Z48MEHU+aGDRtW4TkASJZ8qHMHH3xwPP300zFo0KBYvnx5zJ8/P7p16xZDhw6N73//+9GmTZsoKCiIDz/8MCZNmhR33XVXfPrpp6X7zzrrrLjiiiuymgmA5EtyjevUqVPK+JVXXslo/87W5/r3hgDwBX1Qds5vGIEyKS4uThnXqVMn4zO237NmzZoyZfpCkrMBkB/UEgAqs8pW5371q1/F7NmzS8c1atTwSdMAVVRlq3ERETVr1owzzzwzRowY4ROmAaq4fKlzY8eOjeeffz4iPv/U6bvvvtsboAH4RvlS5yIiunbtGj/4wQ9i//33jw4dOkSDBg1i48aNsWzZspg2bVqMHz8+3nzzzdL1q1evjoEDB8aTTz4Z//M//1MumQBItiTXue1fZF9cXBynnnpqafOrNm3axMiRI+PQQw+Nxo0bx4oVK2LKlCnx+9//PhYvXly676GHHopmzZrFTTfdlJVcAOSPJNe5TDz99NOxdOnS0nHdunVj4MCBFZ4DgGTJlzp31FFHxTvvvBNjxoyJP//5z7Fo0aIYM2ZMjBkz5iv37LvvvnH11VfHCSeckPU8ACRfkmvc4YcfnjJeuHBhTJs2LQ466KBv3DtlypR47733dpjP9etAAeALSa7BueQVpkCZbP/gWrt27YzP2P7Bdfszd1WSswGQH9QSACqzylTn7rnnnrj11ltT5q666qrYf//9c5IHgNyqTDXuC5s2bYqHHnooNm3aFBdffHHstddeOc0DQO7kQ53773//GxdeeGHp+Nxzz41evXpl9Q4AKqd8qHODBg2K3//+99GlS5evXNO3b9+47LLL4s9//nOcc845pS8y3Lp1awwcODDmzZsXrVq1ymouAJIvyXVu+wZYn3zySen/PuGEE+L+++/f4e4+ffrEz372szj99NPjscceK52/+eab40c/+lEcdthhWckGQH5Icp3LxNixY1PGAwcOjKKiogrPAUCy5FOd27JlS0RE1KpV6xvXHnzwwXHVVVfFUUcdVS5ZAEi+JNe45s2bx5FHHhmTJk0qnbvgggvi5ZdfjmrVqn3lvi1btsQFF1yw069VhsYgAFQOSa7BuVSY6wBA5VJQUFAhe3ZFkrMBkB/UEgAqs3ytcxMnToyzzz47Ze6HP/xh/PrXv85RIgCSJh9q3DPPPBOLFi0q/TNnzpx47rnn4tprr41OnTpFRMSqVavi7rvvjm7dusV9991XofkASK4k1rkRI0aUvnm6TZs2ce2115brfQBUXkmsc8OHD//a5ldfdsopp8SkSZNit912K50rLi6OUaNGlVc8APJIkurctm3bdjrfq1evePjhh7/yU6dr164dDz/88A5Nj/0cCECS6ly6Pvroo5g4cWLK3NChQ3OUBoAkS2qdu/vuu2OvvfaKa6+9NubNm/eN6//1r39Fv379olu3bjF16tRyzwdA8iWtxl122WUp42nTpsVpp50WGzdu3On6DRs2xCmnnBKvvfbaTr9eWKitBgDJlLQanCsqNVAm23+iyfr16zM+Y/s92fqUlCRnAyA/qCUAVGaVoc5NnTo1BgwYEJs3by6dO/TQQ2P8+PGV8ok8ANKTjzWuZcuW0bZt29I/Xbt2jaOPPjouu+yymDt3btx1112ln+yybt26OOOMM+KBBx4o10wAJFPS69y4ceNiwoQJpeM//vGPUbdu3aydD0DllvQ6tyt69eq1QxOQ+++/P9auXZujRADkSpLr3Fedc9NNN0X16tW/dm/16tVj9OjRKXPPPfdcLFu2LCvZAMgPSa5z6br//vtjy5YtpeNOnTrFwQcfXKEZAEimfKhz1113XQwfPjzleceePXvGPffcE/Pnz4+1a9fG+vXrY9GiRfHII4/Ed7/73dJ1b731Vhx++OFx//33ZzUTAMmX9Br33e9+N84999yUuUceeSQ6deoUN998c7z66qsxf/78ePXVV+Omm26KTp06xaOPPhoRnzfvb9y4ccreBg0aZC0bAJRF0mtwrmiABZRJkh9ck5wNgPyglgBQmeV7nZs5c2Ycc8wxsW7dutK53r17x9/+9rfYbbfdKiwHAMmT7zVuZ84888wYP3586bikpCRGjBgRS5YsyWEqAHIhyXXuk08+SXnh4cknnxw/+MEPsnI2AFVDkutcWYwYMSLq169fOt60aVO8+OKLOUwEQC4kuc7t7Jw2bdrEd77znbT2H3roodG+ffuUucmTJ2clGwD5Icl1Ll333HNPynjo0KEVej8AyZX0OvfCCy/EFVdckTJ31VVXxWuvvRY//elPo0OHDrHbbrtF7dq1o23btnHSSSfFCy+8EHfeeWfpB41u3bo1hg4dGlOnTs1aLgCSL+k1LuLzJv0nn3xyytyiRYviwgsvjD59+sTee+8dffr0iV/96lexePHiiIgoLCyMBx98cIcsGmABkBT5UINzQQMsoEx23333lPG6desy/pTK7T/pK1s/RGyfbfny5RmfUV7ZAMgPSa5zAFBW+Vzn5syZE/369YtVq1aVznXv3j3+/ve/p7yRDICqKZ9r3Nc57rjj4vjjjy8dr127Nv7whz/kMBEAuZDkOnfuueeW/j6uUaNGMWbMmKycC0DVkeQ6Vxa1atWK7373uylzc+bMyVEaAHIlyXVuZ+f06dMnozMOPPDAlPE777xTpkwA5Jck17l0vPzyyzF//vzScY0aNeK0006rsPsBSLak17nLLrssSkpKSseDBw+OK6+8srS51VcZPnx4XHbZZaXjrVu3xnnnnZe1XAAkX9JrXMTnP589/PDDccstt0SjRo2+cX27du1i0qRJMWDAgPjoo49SvtasWbOsZgOAXaUPys5pgAWUSePGjaNhw4Ypc//5z38yOuP9999PGXfs2LHMuXZ2zvb3pKO8sgGQH5Jc5wCgrPK1zs2dOzeOOuqoWLFiRencfvvtF88991yleLIOgLLL1xqXju0/yWzixIk5SgJAriS1zr377rvxyCOPlI5/8YtfxLp162Lx4sVf+2flypUp5xQXF6d8PdO/GwD5Lal1Lhvatm2bMt6VFy8CkN+SXOfatGkTtWrVSplr0aJFRme0bNkyZfzpp5+WORcA+SPJdS4dY8eOTRkfe+yx0bRp0wq7H4BkS3KdW7JkSbzyyispc1deeWXa+y+55JKoU6dO6XjmzJma9wNUIUmucdv7xS9+EYsWLYo77rgjfvzjH0f79u2jXr16Ubt27Wjfvn388Ic/jAcffDD+H3v3HSVVneaP/+kmN7SSRIKIgIhZhFEJOiCKusiorIlVMTGuuuo4Y1p1goiK4zruWcOZHdOC6NExopgVkRkDzRhQooIBlSQSWjJNqN8f87V/FtBd1d3V1dXt63VO/3FvPffzeW4dTj8HuPWuWbNmxYABA+Lzzz+PkpKS0uv33HNPnzUAIGfIQdkxAVhAle2zzz5Jx5999lmFrv/iiy/KXa+yunfvHvXq1Ss9Xrp0aaxevTrt61etWhXLli0rPa5Xr16d+MUPQMXk6pwDgEyobXPu008/jaOOOirpw2F77713TJw4MVq3bl2tewNQu9S2GZeu7t27Jx1X9L4AqBtycc6tX78+6fgPf/hDdO7cOeXPnXfemXTd008/nfT6gQceWOXeAKhdcnHOZcKPP0QWsf3sBOCnIVfnXL169bb7t8dtA7FS2bZ+w4YNVe4LgNolV+dcKqtXr44nn3wy6dyIESOysjcAtUeuzrmPPvoo6bhLly7RuXPntK9v2rRp9O7dO+nc1KlTM9IbALVDrs64Hdlpp53iwgsvjKeffjo+//zzWLVqVaxfvz4+//zzeP755+Oss84q/T+5KVOmJF172GGHVVtfAFBRclB2TAAWUGX7779/0vG2fzEoz9q1a7dLht92vcpq1KhRdO3atdK9vfvuu0nH3bp1q/BDHQDUfrk65wAgE2rTnPvss89i4MCBsWTJktJz3bp1i0mTJsWuu+5abfsCUDvVphlXEQ0aNEg63rhxYw11AkBNqqtzDgAi6u6c+/GDhxEh0B/gJyqX59y2AcTFxcUVun7b+latWlW5JwBql1yec+X561//GuvWrSs97tChQxx77LFZ2RuA2iNX59y2fxdr27ZthdfY9ppt/y0TgLotV2dcVb3xxhtJxwMGDKiZRgBgB+Sg7JgALKDKjjvuuKTjyZMnp33tW2+9FZs3by49PvjggzP6weWq9LZt7b/8y79koCMAaptcnnMAUFW1Zc59+eWXMXDgwFi0aFHpuS5dusSkSZOiXbt21bInALVbbZlxFbVgwYKk41zpC4DsqqtzDgAi6u6cmzp1atJx+/bta6gTAGpSLs+5wYMHJx3PmjWrQtfPnDkz6Xi33Xarck8A1C65POfK8+CDDyYdn3feeVGvXr2s7A1A7ZGrc6558+ZJx2vXrq3wGmvWrEk6btasWZV6AqB2ydUZVxWrV6+Op556qvS4WbNmcfrpp9dgRwCwPTko2xOABVTZscceG02aNCk9njJlSnzyySdpXTt27Nik46FDh2ayte3We/jhh2PLli0pr9uyZUs88sgj1dobALVDLs85AKiq2jDnvv766xg4cGB88803pec6deoUkyZN8uA8AGWqDTOuMl577bWk427dutVQJwDUpFyccz169IhEIlHhnxtuuCFpnXPOOSfp9W2/tRqAui8X51xVzZgxI2bMmJF0zrdMA/w05fKcGzJkSNI3Q7/33nuxYsWKtK5duXJl/OMf/0g6d8QRR2S0PwByXy7PubLMnj07KbA4Ly8vzjvvvKzsDUDtkqtzbtug/U8//TTWrVtXoTU+/PDDpOO2bdtWuS8Aao9cnXFVcddddyWFQp5xxhlRWFhYgx0BwPbkoGxPABZQZQUFBXHKKacknbvttttSXjd37twYP3586XH9+vXjjDPOyGhvRxxxRHTu3Ln0eMGCBdv9Qt+RRx55JBYuXFh63LVr1+jXr19GewOgdsjlOQcAVZXrc27RokVx1FFHxfz580vPdejQISZNmhSdOnXK+H4A1B25PuMqY/HixXHfffclnTvxxBNrqBsAalJdnHMA8IO6Nue2bNkSv/nNb5LO7bnnnrHvvvvWUEcA1KRcnnOFhYVJvW3cuDHuueeetK695557YsOGDaXHnTp1iv333z+j/QGQ+3J5zpXlwQcfTDo+8sgjo0uXLlnZG4DaJVfn3IEHHhgtWrQoPd6wYUM8/PDDaV//wgsvJH1+LiLi8MMPz1h/AOS+XJ1xlfXJJ5/ELbfcUnrcrFmzuP7662uwIwDYMTko2xOABWTEyJEjo0GDBqXHY8eOjQkTJpRZv2HDhjjvvPOipKSk9NyIESOia9eu5e6Tl5eX9DN58uRy6+vVqxc33nhj0rkrrrgi6cPT25o/f/52Dx/efPPNkZ/vVybAT1WuzjkAyIRcnXNLly6No446Kj777LPSc+3atYs333zTw4YApCUXZ9zatWvjv//7v2P9+vXp30hEfPfdd3H88cfHqlWrSs+1bNky/u3f/q1C6wBQd+TinAOATMnVOXf33XcnhXukUlJSEhdccEG88cYbSedvuOGGtNcAoO7J1TkXEXHTTTdFw4YNS49Hjx4dU6ZMKfeaKVOmxM0335x07rrrrou8vLyU+wFQ9+TynNvWpk2btgsIGTFiRIXXAeCnIxfnXL169bYLLbn22mtj5syZKe4m4uuvv46LLroo6Vy/fv2iXbt2Ka8FoG7JxRn3g82bN6e+gf/n008/jaOPPjrp+cxbb73VF28DkBVyUKqubtwFUOO6dOkSl19+edK5U045Je65556kv8RERMyZMyeOOuqoePfdd0vPtWrVqtoe8DvzzDPjsMMOKz1esWJF9O3bN1577bXtal999dXo06dPrFy5svRc37594/TTT6+W3gCoHXJ5zm3evDnmz5+/w5/i4uKk2jVr1pRZu2bNmmrpD4Dcl4tzrri4OAYNGhSffPJJ6bmmTZvGgw8+GA0aNChznpX1A8BPUy7OuE2bNsWVV14ZXbp0iSuuuCKmTJmyXS8/9u2338Ydd9wR++yzT0ybNi3ptdtvvz1at26d0f4AqD1ycc4BQKbk6pz71a9+FZ07d46rr746pk6dWuYD95s3b47nnnsuDjvssBgzZkzSa0cffXSceeaZGe8NgNojV+dcRETnzp3jmmuuKT3euHFjHHPMMfG///u/sWnTpqTazZs3x7333hvHHHNMUt+HHnponHfeedXSHwC5L5fn3LYmTJgQ3333XelxixYt4l//9V+zsjcAtVOuzrk//OEP0aRJk9Lj4uLi6Nu3b9xzzz2xbt267epLSkrioYceil69esXChQuTXrv11lsz3h8AuS9XZ1xExLnnnhtnnnlmvPDCC2V+8eiiRYti5MiRcfDBByfNtuOPPz4uueSSaukLgNplwYIFO/y82ZIlS5Lqyvu89rJlyzLelxyUZHmJRCJR000AdcOWLVviF7/4Rbz88stJ59u0aRM9e/aMwsLC+OKLL+LDDz+MH//qadiwYUycODGOOOKIlHts+61gb775ZgwYMCDldYsXL47evXvH119/nXS+W7dusd9++0UikYhZs2bFZ599lvT6HnvsEUVFRbHrrrum3AOAui1X59z8+fOjc+fO6d9IGcaMGRPnnntuldcBoHbKtTk3efLkOPLIIyt+I2Xwz18AP125NuOKi4ujRYsWSecaNmwY++67b7Rr1y6aN28eiUQivv/++5g7d2588cUXO5xjt9xyS1x//fUpewOgbsu1OVcZI0eOTPoWs3POOSfGjh2bsfUBqL1ycc5tW9+oUaPYb7/9ol27drHzzjvHpk2bYunSpfHBBx/s8Mtnfvazn8WkSZOisLAwZW8A1G25OOd+kEgk4vTTT48nn3wy6Xzz5s2jd+/e0bJly1ixYkUUFRVt98VsHTp0iKKiothtt91S7gNA3ZXLc+7Hjj/++HjppZdKjy+99NK4++67K7QGAD89uTrnxo8fH6eeemps2bIl6XyTJk2iV69e0b59+8jPz48lS5bE+++/v8N/v/QsCsBPW67OuFNOOSWefvrpiIho0KBB7LPPPrHHHntEYWFhFBcXx9dffx0zZ87c7jnLY445JsaPHx8FBQUp+wKg7ttjjz3iq6++qtIaqZ5tlINSdfVrugGg7qhXr1488cQT8ctf/jIef/zx0vNLly6NV155ZYfXtGnTJh566KG0/nJTFe3atYvXX389hg0bFtOmTSs9P2/evJg3b94Or+nZs2c8/vjjdeqXPgCVl8tzDgCqypwDoK6qDTOupKQkPvroo/joo49S1u62225x1113xdChQ7PQGQC5rjbMOQCorNow5zZu3Bgffvhhyrq8vLy47LLL4rbbbovGjRtnoTMAcl0uz7m8vLx4+OGHo2XLlnHvvfeWni8uLi6zt4iIQw89NMaPHx/t27ev1v4AyH25POd+sHDhwnj11VeTzo0YMSIrewNQu+XqnBs6dGg899xzMWLEiPj2229Lz69fvz7efvvtcq9t2rRp/PGPf4xLL7202voDIPfl6oz7sU2bNsX06dNj+vTpZdbUr18/rr766hg1alTUry9GA4DcJwfl/5df0w0AdUuzZs3ir3/9azz55JPRu3fvMutatmwZF198ccycOTOOO+64rPS21157xdSpU+PWW2+NLl26lFnXtWvXuPXWW6OoqCj23HPPrPQGQO2Qy3MOAKrKnAOgrsqlGbfTTjvFhAkT4j/+4z9i3333jfz81P9NU79+/TjiiCPivvvuizlz5gi/AiBJLs05AMi0XJtzt99+ewwePDhatWqVVv0uu+wSl1xyScyePTvuvPNO4VcAJMm1OfdjjRo1ir/85S8xceLEGDRoUNSrV6/M2v333z/Gjh0b7777rvArAErl8pyLiBg7dmxs2bKl9Lhnz57Ro0ePrO0PQO2Wq3Pu+OOPj9mzZ8fo0aOja9euKet33XXXuOqqq2LWrFnCrwCIiNycccOGDYuBAwem/H+2wsLCGDFiRMycOTNGjx4t/AqAWkUOyj/lJRKJRE03AdRdX375ZXz44YexaNGiWLt2bbRt2zY6deoU/fr1i4YNG9Zobx988EHMnTs3Fi1aFBER7du3j7322it69epVo30BUHvk8pwDgKoy5wCoq3Jpxq1evTpmz54d8+fPjyVLlsTatWsj4p9BWTvvvHN07949DjzwQB+SBiBtuTTnACDTcmnOLViwID799NNYsGBBLF++PNavXx/16tWLFi1aROvWraNHjx5pfcgMAH6QS3NuW999910UFRXF4sWLY9myZVFYWBi77rpr9O3bN3bbbbca7Q2A2iGX5xwAVFWuzrkFCxbEBx98EIsXL47i4uJIJBKx8847xy677BIHH3xwnfywNACZlUszrqSkJKZPzuMa/gAAIABJREFUnx7z5s2LxYsXx7p166J+/frRpk2b2HfffaNXr17RoEGDrPYEANXlp5qDIgALAAAAAAAAAAAAAAAAAAAAAACArMqv6QYAAAAAAAAAAAAAAAAAAAAAAAD4aRGABQAAAAAAAAAAAAAAAAAAAAAAQFYJwAIAAAAAAAAAAAAAAAAAAAAAACCrBGABAAAAAAAAAAAAAAAAAAAAAACQVQKwAAAAAAAAAAAAAAAAAAAAAAAAyCoBWAAAAAAAAAAAAAAAAAAAAAAAAGSVACwAAAAAAAAAAAAAAAAAAAAAAACySgAWAAAAAAAAAAAAAAAAAAAAAAAAWSUACwAAAAAAAAAAAAAAAAAAAAAAgKwSgAUAAAAAAAAAAAAAAAAAAAAAAEBWCcACAAAAAAAAAAAAAAAAAAAAAAAgqwRgAQAAAAAAAAAAAAAAAAAAAAAAkFUCsAAAAAAAAAAAAAAAAAAAAAAAAMgqAVgAAAAAAAAAAAAAAAAAAAAAAABklQAsAAAAAAAAAAAAAAAAAAAAAAAAskoAFgAAAAAAAAAAAAAAAAAAAAAAAFklAAsAAAAAAAAAAAAAAAAAAAAAAICsEoAFAAAAAAAAAAAAAAAAAAAAAABAVgnAAgAAAAAAAAAAAAAAAAAAAAAAIKsEYAEAAAAAAAAAAAAAAAAAAAAAAJBVArAAAAAAAAAAAAAAAAAAAAAAAADIKgFYAAAAAAAAAAAAAAAAAAAAAAAAZJUALAAAAAAAAAAAAAAAAAAAAAAAALJKABYAAAAAAAAAAAAAAAAAAAAAAABZJQALAAAAAAAAAAAgx0yePDny8vLS+oF0vP/++5Gfn1/un6WXXnqpptuskBkzZqS8p2eeeaam2wQAAAAAAAAAoAwCsAAAAAAAAAAAAKAOSyQScdlll0UikSizpnfv3jF48OAsdlV1BxxwQJx66qnl1lx55ZWxYcOGLHUEAAAAAAAAAEBFCMACAAAAAAAAAADqvAEDBkReXl5O/owcObKm3x7quIcffjiKiorKrbnpppuy1E1mjRw5MvLzy34Ucv78+fGnP/0pix0BAAAAAAAAAJAuAVgAAAAAAAAAAABQR61bty6uvfbacmv69u0bRx99dJY6yqx99tknTjvttHJrbr311li0aFGWOgIAAAAAAAAAIF0CsAAAAAAAAAAAAKCOuueee2Lx4sXl1lx55ZVZ6qZ6XHXVVeW+vm7duhg9enSWugEAAAAAAAAAIF0CsAAAAAAAAAAAAKAOWr16dfzXf/1XuTVdunSJk046KUsdVY9evXrFz3/+83Jr7r///vj666+z1BEAAAAAAAAAAOkQgAUAAAAAAAAAAAB10J133hnLly8vt+byyy+P/Pza/yjhFVdcUe7rJSUlcdNNN2WpGwAAAAAAAAAA0lH7n1oBAAAAAAAAAAAAkmzYsCHuvPPOcmsaN24cZ599dpY6ql5DhgyJdu3alVszbty4WLx4cZY6AgAAAAAAAAAgFQFYAAAAAAAAAAAAOWbAgAGRSCTS+oEdeeSRR2LZsmXl1gwdOjSaN2+epY6qV7169WL48OHl1pSUlMSf//znLHUEAAAAAAAAAEAqArAAAAAAAAAAAACgjvmf//mflDXnn39+FjrJnvPOOy9lzV/+8pfYsGFDFroBAAAAAAAAACAVAVgAAAAAAAAAAECdN3ny5EgkEhX+Oeecc9LeY8yYMZXaY+TIkdV34/wk/e1vf4tZs2aVW9OmTZsYOHBgljrKjr333jt69OhRbs2yZcviiSeeyFJHAAAAAAAAAACURwAWAAAAAAAAAAAA1CEPP/xwypoTTzwx8vPr3iOEJ510UsqadN4fAAAAAAAAAACqX/2abgAAAAAAAAAAAIBkkydPjiOPPDKt2kQiUc3dRHzwwQfx+OOPx9tvvx1z586N77//Ppo0aRJt27aNXr16xQknnBAnnXRSNGnSJK315s6dG4899lj8/e9/j9mzZ8eKFSuiUaNG0aZNm+jYsWMcddRRMWTIkOjRo0e13tcXX3wREydOjPfffz/mzJkT33zzTaxYsSLWr18feXl50bRp02jfvn107do1DjnkkBg4cGD06dMnp4OjNmzYEE899VTKuqFDh2Z032nTpsWbb74Zc+bMiU8++SQWLVoUa9asiTVr1sTGjRujSZMmUVBQEM2bN4+OHTvG7rvvHl26dImDDjooevToER07dsxIH0OHDo2RI0eWWzNp0qRYuHBhdOjQISN7AgAAAAAAAABQOXmJbDz9BAAAAAAAAAAAUAude+658dBDD6VVO2bMmDj33HMzsm+mA7Aqu97MmTPj8ssvj0mTJqW8rl27dnHzzTfH+eefX2bNl19+GVdffXU888wzafU9ZMiQuP3222PvvfdOq/d0rF27NsaMGRP33XdfzJgxo8LXt2/fPi6++OK45JJLokWLFhnrK1OeeeaZOPnkk8utady4cRQXF0ejRo2qtNeqVavijjvuiHHjxsX8+fOrtFarVq3i8MMPj/79+0f//v2jZ8+elV5r9913j2+++abcmjvuuCOuuOKKSu8BAAAAAAAAAEDV5e7X0AEAAAAAAAAAAFBj7rvvvujZs2da4VcREYsXL44RI0bEsGHDoqSkZLvXH3nkkTjwwAPj6aefTiv8KiLihRdeiJ49e8bLL79cod7L8sADD0TXrl3jsssuq1T4VUTEokWL4ve//31069YtHnjggYz0lUkvvvhiypo+ffpUOfzqiSeeiD333DNGjRpV5fCriIjly5fHc889F1dccUX06tWrSmv1798/Zc1LL71UpT0AAAAAAAAAAKg6AVgAAAAAAAAAAAAkGT16dFx44YWxadOmCl/7+OOPx2mnnZYUcnXbbbfF8OHDY82aNRVeb/369XHiiSfG66+/XuFrf7By5coYPHhwXHDBBfHtt99Wep0fW758eVxwwQVx2mmnxfr16zOyZia88sorKWvSCYgqz1133RXDhg2L7777rkrrVJd07u+tt96KtWvXZqEbAAAAAAAAAADKIgALAAAAAAAAAACAUg899FD89re/rdIazz33XNx+++0REfHAAw/EtddeW6X1Nm3aFOeff36sWrWqwtcuWLAg+vbtGy+//HKVeijLk08+GYMGDapUuFemzZgxIxYtWpSy7vDDD6/0Hu+++278+te/Tgo4yzVHHHFEypqSkpKYPHly9TcDAAAAAAAAAECZBGABAAAAAAAAAABQ6qKLLsrIOjfeeGO88sorcemll2ZkvQULFsSoUaMqdM3y5ctj0KBB8cknn2Skh7K88847ceqpp8aWLVuqdZ9UioqK0qo76KCDKr3HRRddlNPhVxER3bp1i4KCgpR16b5fAAAAAAAAAABUDwFYAAAAAAAAAAAAlNqwYUNG1lm3bl0cf/zxsXHjxoysFxExZsyYtNdLJBJxzjnnVHv41Q9eeeWVuOWWW7KyV1nee++9lDVt27aN1q1bV2r9adOmxYwZMyp1bTbl5+fHfvvtl7IunfcLAAAAAAAAAIDqU7+mGwAAAAAAAAAAACA3FRYWxqmnnhoHHXRQNGnSJGbNmhXjxo2LlStXpnX91q1btzvXq1evOOGEE6JDhw6xcuXKePbZZ+Odd95Ja70VK1bEs88+G6effnrK2nHjxsWLL76Y1roREfXq1YtBgwZFv379om3btlFSUhJz5syJ8ePHx8KFC9Na46abbopTTz019tlnn7T3zaQPPvggZc0BBxxQ6fVfeOGFtGt79+4d/fr1i06dOkVhYWFs3rw5iouLo7i4OObNmxezZs2KuXPnxqZNmyrdT3kOPPDAlAFX77//frXsDQAAAAAAAABAegRgAQAAAAAAAAAAsJ0jjzwyHnvssdh1112Tzl955ZXRu3fvWLRoUYXWa9y4cdx///1x1llnJZ2/6qqr4ve//33cfPPNaa3z9ttvpwzA2rhxY1x33XVp9zZw4MC4//77o0uXLtu99qc//SlGjRoVo0ePTrnO5s2b4/rrr4/x48envXcmzZs3L2VNp06dKr3+l19+mbKmTZs2MWHChDjssMNS1paUlMSUKVNi4sSJMXHixJg6dWokEolK9/dj6dzn8uXLY8WKFdGyZcuM7AkAAAAAAAAAQMXk13QDAAAAAAAAAAAA5Jb99tsvJkyYsF34VUREx44d45prrqnwmvfdd9924Vc/uPHGG2OvvfZKa533338/Zc2jjz4aixcvTmu9k046KV5//fUdhl9FRDRq1ChuueWWGDVqVFrrTZgwIT7//PO0ajNp+fLlsXr16pR1HTp0qPQe3377bcqakSNHphV+FRHRsGHD6N+/f9x0000xZcqU+Oqrr+KPf/xjHHDAAZXu8Qfp3udXX31V5b0AAAAAAAAAAKgcAVgAAAAAAAAAAAAkufvuu6NZs2Zlvn7MMcdUaL0BAwbE8OHDy3w9Pz8/Tj/99LTWmjVrVsqasWPHprVWu3btYty4cZGfn/pRut/97ndx0EEHpazbunVrjBs3Lq39MyndIKeqBGAlEomUNZ07d670+h07doz//M//jOnTp1d6jR8IwAIAAAAAAAAAyH0CsAAAAAAAAAAAACjVo0ePOPLII8ut6d69ezRs2DDtNa+44oqUNb169UprrdWrV8eWLVvKfH358uXx9ttvp7XWVVddFYWFhWnV5uXlxaWXXppW7YQJE9Kqy6Tly5enVbfzzjtXeo/WrVunrHn88ccrvX4mpXufy5Ytq+ZOAAAAAAAAAAAoiwAsAAAAAAAAAAAASp144okpa/Lz82OXXXZJa70mTZrE0UcfnbJu9913T2u9iIjvv/++zNeKiopi69ataa1zxhlnpL1nRET//v3Tqps+fXqsWbOmQmtX1dq1a9Oqa9KkSaX36N69e8qasWPHxnHHHRdPPfVULFmypNJ7VVVBQUFadevWravmTgAAAAAAAAAAKIsALAAAAAAAAAAAAEodcsghadW1bt06rbr9998/rdClZs2apbVeRJQbLjVt2rS01ujcuXO0bds27T0jIjp06JBW3datW2PGjBkVWruq0g3ASjcYakfSCTKLiHj11Vfj1FNPjXbt2kXLli3j0EMPjbPOOitGjRoVjz32WEyfPj1KSkoq3Uc60r3PdN83AAAAAAAAAAAyr35NNwAAAAAAAAAAAEDu2HvvvdOqa9SoUVp13bt3T6uuQYMGadVF/DNgqizz589Pa40vv/wy8vLy0t6zoubPnx99+vSptvUrK5FIVPraQw89NA466KD4+OOP075m5cqV8d5778V7772XdL5hw4bRs2fP6N+/fwwePDgOP/zwyM/P3Hd6pnufVXk/AAAAAAAAAAComsw9LQIAAAAAAAAAAECt16pVq7Tq0g3ASne9TFm6dGlW9yvLt99+m9X9CgoK0qpbv359pffIy8uL22+/PSPBYSUlJVFUVBS33XZb9O/fPzp16hSjR4+ONWvWVHntiIh169alVZfu+wYAAAAAAAAAQOYJwAIAAAAAAAAAAKBUs2bN0qrLz0/v8bN018uUqgQ8ZVK6AUyZ0rRp07Tqqvr+DBo0KO6+++4qrbEjCxYsiN/+9rfRvXv3eOutt6q8Xrr3me77BgAAAAAAAABA5gnAAgAAAAAAAAAAoFT9+vVzer3aYtOmTVndr2XLlmnVFRcXV3mvSy65JJ555pno0KFDldfa1qJFi2Lw4MHx3nvvVWmddO8z3fcNAAAAAAAAAIDME4AFAAAAAAAAAABAnVFQUFDTLURERCKRyOp+nTp1SqtuwYIFGdlv6NCh8dlnn8W9994bhx12WOTl5WVk3YiINWvWxJlnnhmbN2+u9Brp3me67xsAAAAAAAAAAJknAAsAAAAAAAAAAIA6o3Xr1jXdQo3YZZdd0gr/WrhwYcb2bNy4cfz7v/97FBUVxYIFC2LcuHFx8cUXR58+faJFixZVWnvevHnxyCOPVPr6dO9zjz32qPQeAAAAAAAAAABUTf2abgAAAAAAAAAAAAAyJd1Ao6OOOiomTpxYvc1kWbdu3eLjjz8ut2b+/PnVsnf79u1j+PDhMXz48NJzK1asiM8//zy++OKLmDt3bkybNi0mT54cK1euTGvNCRMmxLnnnlupfr766quUNc2bN//JBqYBAAAAAAAAAOQCAVgAAAAAAAAAAADUGQceeGBadR9++GFs3rw56tevO4/R9erVK2UA1syZM7PUTUTLli2jZcuWccghh5Se27RpU/z5z3+O3/zmN5FIJMq9/u9//3ul954+fXrKml69elV6fQAAAAAAAAAAqi6/phsAAAAAAAAAAACATOnTp0/k5eWlrFu5cmU8//zzWegoe372s5+lrFm6dGksXbo0C93sWIMGDeLyyy+Pk08+OWXt8uXLo6SkpMJ7bN26NWbPnp2yLp33CwAAAAAAAACA6iMACwAAAAAAAAAAgDqjTZs20bt377Rqr7/++tiwYUPG9p48eXKccMIJGVuvotK9748//rhS669duza2bNlSqWu31bVr17Tqli9fXuG1P/3001i/fn3KunTfLwAAAAAAAAAAqocALAAAAAAAAAAAAOqUs88+O626Tz75JIYNGxYbN26s9F6rVq2Khx56KHr27BlHHnlkPP/885Veq6p69OgRu+66a8q6t956q1LrT5kyJTp16hQ33HBDLFiwoFJr/OCdd95Jq65Zs2YVXjud+6tfv34MHDiwwmsDAAAAAAAAAJA5ArAAAAAAAAAAAACoU84+++xo3bp1WrXPPfdcHHzwwfHCCy/Eli1b0rpm/vz5MWbMmBgyZEjssssuce6558a0adOq0nJG5OXlxbHHHpuy7m9/+1ul91i4cGGMGjUqOnXqFH369Ikbb7wx/vGPf8TmzZvTun7t2rVx2WWXxdtvv52ytmnTplFYWFjhHtO5v379+sVOO+1U4bUBAAAAAAAAAMic+jXdAAAAAAAAAAAAAGRSQUFB3HzzzXHRRRelVT9nzpz4xS9+EW3bto2f//znccABB0SrVq2ioKAgVq1aFStXrozly5fH7Nmz48MPP4wVK1ZU8x1U3vHHHx/jxo0rt2bq1KmxYcOGaNy4caX32bp1axQVFUVRUVGMHDkyGjZsGN27d4999903unTpEjvttFPstNNO0axZs9i4cWN8++23MWPGjHjttdeiuLg4rT0OOOCASvWWTgDW4MGDK7U2AAAAAAAAAACZIwALAAAAAAAAAACAOueCCy6IJ598Mt544420r1myZEk88cQT8cQTT1RjZ9VryJAhUVhYGKtXry6zZuPGjTFx4sQYMmRIxvYtKSmJGTNmxIwZMzK25mmnnVbha6ZNmxYLFy4styYvLy+GDRtW2bYAAAAAAAAAAMiQ/JpuAAAAAAAAAAAAADItPz8/Hn300ejatWtNt5JVBQUFcfLJJ6ese/bZZ7PQTeW1bNkyzjzzzApfl859DRgwIHbffffKtAUAAAAAAAAAQAYJwAIAAAAAAAAAAKBOatOmTUyaNCm6detW061k1TnnnJOy5vnnn4+tW7dmoZvKueuuu6JNmzYVvm78+PEpa4YPH16ZlgAAAAAAAAAAyDABWAAAAAAAAAAAANRZu+++e0ydOjUGDx5c061kzYABA2Lfffctt2bp0qXxxhtvZKmj9OXl5cWtt94aZ555ZoWvnT17dsyYMaPcmlatWsWwYcMq2x4AAAAAAAAAABkkAAsAAAAAAAAAAIA6rUWLFvHiiy/GuHHjomPHjtWyR4cOHeKqq66Kjz/+uFrWr6hf//rXKWv+7//+r0JrFhQURP369SvbUkodO3aMp556Kq699tpKXT9mzJiUNRdeeGE0adKkUusDAAAAAAAAAJBZ1fckCgAAAAAAAAAAQC130kknxR577JFWbY8ePTK274ABAyKRSGRsPf5p+PDhcfrpp8ejjz4aDz74YLzzzjuVfp/z8/OjZ8+eMXDgwDjuuOOif//+kZ+fO99JOXz48Pjtb38b3333XZk1zz77bBQXF0fz5s3TWrNv376xYsWKePPNN+P111+PyZMnx5w5c2LLli1V6rVHjx5xzjnnxMUXXxyNGjWq1BqbN2+Ohx9+uNyaBg0axCWXXFKp9QEAAAAAAAAAyLy8hKekAAAAAAAAAAAA+AlaunRpTJw4MT744IOYOXNmfPPNN7FkyZJYt25dbNq0KQoKCqKwsDAKCwujbdu20b1799h7771jn332iT59+qQdHFVTbrnllvjd735Xbs2dd94Zv/rVryq9x7p16+Kjjz6KadOmxWeffRbz58+Pr776Kr777rtYu3ZtrF27NrZs2RJNmzaNpk2bxk477RRdunSJvffeO/bbb78YNGhQ7L777pXe/wfPPPNMnHzyyeXWjBgxIh544IEq7wUAAAAAAAAAQGYIwAIAAAAAAAAAAIA6aM2aNdG5c+dYtmxZmTVdunSJefPmRX5+fhY7y7zDDz883nnnnTJfb9iwYcydOzc6deqUxa4AAAAAAAAAAChP7X5iBQAAAAAAAAAAANihZs2axTXXXFNuzRdffBHPPvtsljqqHu+991654VcREb/85S+FXwEAAAAAAAAA5Ji8RCKRqOkmAAAAAAAAAAAAgMxbv3597LnnnrFo0aIya/r27ZsyQCqXDRs2LB5//PEyXy8oKIh58+ZF+/bts9gVAAAAAAAAAACp5Nd0AwAAAAAAAAAAAED1aNKkSdx2223l1rz77rsxceLELHWUWbNnz44nn3yy3JrrrrtO+BUAAAAAAAAAQA7KSyQSiZpuAgAAAAAAAAAAAKgeiUQi+vXrF1OmTCmzpnfv3uW+nqtOO+20cgOw9thjj5gzZ040btw4i10BAAAAAAAAAJCO/JpuAAAAAAAAAAAAAKg+eXl5cdddd0VeXl6ZNUVFRfHSSy9lsauqmzlzZjz11FPl1txxxx3CrwAAAAAAAAAAclReIpFI1HQTAAAAAAAAAAAAAAAAAAAAAAAA/HTk13QDAAAAAAAAAAAAAAAAAAAAAAAA/LQIwAIAAAAAAAAAAAAAAAAAAAAAACCrBGABAAAAAAAAAAAAAAAAAAAAAACQVQKwAAAAAAAAAAAAAAAAAAAAAAAAyCoBWAAAAAAAAAAAAAAAAAAAAAAAAGSVACwAAAAAAAAAAAAAAAAAAAAAAACySgAWAAAAAAAAAAAAAAAAAAAAAAAAWSUACwAAAAAAAAAAAAAAAAAAAAAAgKwSgAUAAAAAAAAAAAAAAAAAAAAAAEBWCcACAAAAAAAAAAAAAAAAAAAAAAAgqwRgAQAAAAAAAAAAAAAAAAAAAAAAkFUCsAAAAAAAAAAAAAAAAAAAAAAAAMgqAVgAAAAAAAAAAAAAAAAAAAAAAABklQAsAAAAAAAAAAAAAAAAAAAAAAAAskoAFgAAAAAAAAAAAAAAAAAAAAAAAFklAAsAAAAAAAAAAAAAAAAAAAAAAICsEoAFAAAAAAAAAAAAAAAAAAAAAABAVgnAAgAAAAAAAAAAAAAAAAAAAAAAIKsEYAEAAAAAAAAAAAAAAAAAAAAAAJBVArAAAAAAAAAAAAAAAAAAAAAAAADIKgFYAAAAAAAAAAAAAAAAAAAAAAAAZJUALAAAAAAAAAAAAAAAAAAAAAAAALJKABYAAAAAAAAAAAAAAAAAAAAAAABZJQALAAAAAAAAAAAAAAAAAAAAAACArBKABQAAAAAAAAAAAAAAAAAAAAAAQFYJwAIAAAAAAAAAAAAAAAAAAAAAACCrBGABAAAAAAAAAAAAAAAAAAAAAACQVQKwAAAAAAAAAAAAAAAAAAAAAAAAyCoBWAAAAAAAAAAAAAAAAAAAAAAAAGSVACwAAAAAAAAAAAAAAAAAAAAAAACySgAWAAAAAAAAAAAAAAAAAAAAAAAAWSUACwAAAAAAAAAAAAAAAAAAAAAAgKwSgAUAAAAAAAAAAAAAAAAAAAAAAEBWCcACAAAAAAAAAAAAAAAAAAAAAAAgqwRgAQAAAAAAAAAAAAAAAAAAAAAAkFUCsAAAAAAAAAAAAAAAAAAAAAAAAMgqAVgAAAAAAAAAAAAAAAAAAAAAAABklQAsAAAAAAAAAAAAAAAAAAAAAAAAskoAFgAAAAAAAAAAAAAAAAAAAAAAAFklAAsAAAAAAAAAAAAAAAAAAAAAAICsEoAFAAAAAAAAAAAAAAAAAAAAAABAVgnAAgAAAAAAAAAAAAAAAAAAAAAAIKsEYAEAAAAAAAAAAAAAAAAAAAAAAJBVArAAAAAAAAAA+P/Yu/Mwretyf+D3DMwMKOAIpCggEAICIiqQqSWoIRBWXGJ6XPi5pCZqipGmmUhoi3RKwCW7TCVzAaQjJCZYKrjhArKo4IKBIiGbIAMMMDDz+6OTRw31+314nnkY5vW6rvljmHt5Dx+dv7juAQAAAAAAAAAAAACoVg5gAQAAAAAAAAAAAAAAAAAAAAAAUK0cwAIAAAAAAAAAAAAAAAAAAAAAAKBaOYAFAAAAAAAAAAAAAAAAAAAAAABAtXIACwAAAAAAAAAAAAAAAAAAAAAAgGrlABYAAAAAAAAAAAAAAAAAAAAAAADVygEsAAAAAAAAAAAAAAAAAAAAAAAAqpUDWAAAAAAAAAAAAAAAAAAAAAAAAFQrB7AAAAAAAAAAAAAAAAAAAAAAAACoVnXzHQAAAAAAAAAAAGqy7du3x9y5c+PFF1+MOXPmxOLFi+Pdd9+NDz74IDZt2hSbN2+OkpKS2GOPPaK0tDRatmwZrVu3jq5du8ZXvvKV6NatW5SUlOT72wAAAAAAAAAAAIBqVVBVVVWV7xAAAAAAAAAAAFCTVFVVxd/+9re4//77Y8qUKbFmzZqMZzVo0CD69u0bp556agwYMCDq1vU77YDcmzt3bkyaNClx/YABA+LQQw/NYaJPGj58ePzsZz/L2rySkpIoLS2NvfbaK0pLS+OAAw6I7t27R7du3aJ79+5RWlqalT2TJk2KuXPZa6eYAAAgAElEQVTnJq4fMmRIxrvXrVsXo0aNSlx/6KGHxoABAzLaVd02bNgQs2bNitmzZ0dZWVmintLS0hgyZEiOkwEAAAAAAAAA2eQAFgAAAAAAAAAAJFRZWRn33HNPjBw5MhYuXJj1+S1btowhQ4bEJZdcEsXFxVmfD/BvY8eOjXPOOSdx/d133x1nn3127gJ9SrYPYH2eOnXqRL9+/eL888+P/v37R506dTKedfbZZ8cf//jHxPWLFy+O1q1bZ7RryZIl0aZNm8T1Z511VowdOzajXblUUVER8+fPj5deeilefPHFePHFF2PhwoVRWVmZak6rVq1iyZIluQkJAAAAAAAAAOSEXxcJAAAAAAAAAAAJvPDCCzF48OCYM2dOznYsXbo0hg4dGrfddluMGTMmvvnNb+Zs166uoKAgUZ2DJzWPt2VXs3379pgyZUpMmTIlWrRoEaNHj46TTjop37F2a3/+85/jqaeeihdffDHmzp0bmzdvznckAAAAAAAAACAPCvMdAAAAAAAAAAAAdmVVVVXxy1/+Mr72ta/l9PjVx7399tvRv3//GDJkSGzdurVadgIQ8d5778XAgQPj1FNPjVWrVuU7zm5r6NChMWbMmHj++ecdvwIAAAAAAACAWswBLAAAAAAAAAAA+AwVFRVx5plnxk9+8pPYtm1bte8fPXp09OvXL8rKyqp9N0BtNmHChDjyyCNjyZIl+Y4CAAAAAAAAALDbcgALAAAAAAAAAAB2YNu2bXHSSSfF/fffn9ccTzzxRBx//PGxYcOGvOYAqG3efvvtOOGEE2Lt2rX5jgIAAAAAAAAAsFtyAAsAAAAAAAAAAHbgwgsvjClTpuQ7RkREvPTSS3HKKafEtm3b8h0FoFZ566234vzzz893DAAAAAAAAACA3ZIDWAAAAAAAAAAA8Cm333573HnnnfmO8QmPPvpoDBs2LN8xAGqdP//5zzF16tR8xwAAAAAAAAAA2O04gAUAAAAAAAAAAB/z1ltvxdChQzPqrVOnTvTq1StuvfXWmDVrVixbtiy2bNkSy5cvjzlz5sRdd90V/fr1i+Li4ozm33jjjfHMM89k1AtA5q6//vp8R6hV6tatG4ceemj06dMn31EAAAAAAAAAgByqm+8AAAAAAAAAAACwKxkyZEhs2rQpdd8RRxwRd9xxR3Tp0uU/vtasWbNo1qxZHHrooXHOOefE4sWL48ILL4zHHnss1Y7Kysq4+OKLY86cOVFY6HffAfzbk08+Gb169drh1zZs2BArVqyIWbNmxbhx4+Ivf/lLVFZWppr/3HPPxZtvvhnt27fPQlo+rqCgIA488MDo0aNHfOUrX4kePXrEYYcdFvXr14/p06fHtGnT8h0RAAAAAAAAAMgRB7AAAAAAAAAAAOB/zZgxI/7617+m7rv00kvjpptuSnyUqk2bNjFt2rT41a9+FVdffXWqXfPnz4/77rsvBg0alDonQG3UoEGDaNCgQbRt2zZOPfXUePbZZ+Pkk0+O999/P9WcKVOmxA9/+MMcpaxd+vXrFy1atPjo4FVpaWm+IwEAAAAAAAAAeeDXQAIAAAAAAAAAwP8aOXJk6p5zzz03Ro0alfj41cddddVVce2116buyyQnubdp06aYMGFCfO9734sePXpE06ZNo6SkJOrVqxf7779/9OzZM66++uqYOXNmvqNCrXb00UfHtGnToqioKFXfs88+m6NEtc/vfve7uOaaa6J3796OXwEAAAAAAABALeYAFgAAAAAAAAAARMSSJUti6tSpqXo6dOgQt99+exQUFGS8d8SIEXHMMcek6nn11Vfj6aefTlRbUFCQ+COt4cOHJ549duzYVDOSeuedd1J9j0uWLNnhnOnTpyeecfbZZ3+id9OmTXHddddFixYt4tRTT4277rorZs2aFWvWrImtW7fGli1bYvny5fHUU0/Fr371qzjqqKOia9eu8de//jXx97kj3jbZ28KOHHLIIXHOOeek6nnzzTdzlAYAAAAAAAAAoHZyAAsAAAAAAAAAACLigQceiMrKylQ9Y8aMiaKiop3efcstt0TdunVT9dx33307vZedN2vWrOjcuXOMGDEi1q5dm7hv/vz50b9//xg0aFBs2LAhhwmBz/Ltb387Vf2yZctylAQAAAAAAAAAoHZyAAsAAAAAAAAAACJiypQpqeqPOuqoOOGEE7Kyu0uXLjFw4MBUPY888khWdpO5SZMmxde+9rVYsmRJxjPuvffe6NWrV6xatSp7wYBE2rRpk6resToAAAAAAAAAgOxyAAsAAAAAAAAAgFqvrKwsnn/++VQ9Z555ZlYzDBo0KFX9e++9FwsXLsxqBpKbMmVKnHLKKbFly5adnjV79uzo16+f4zpQzSorK/MdAQAAAAAAAACgVnMACwAAAAAAAACAWm/27NmpDqHUqVMnTjnllKxm6NOnTzRp0iRVz0svvZTVDCSzcOHCOO2006KioiJrM2fPnh3nnntu1uYBX2zx4sWp6hs0aJCjJAAAAAAAAAAAtZMDWAAAAAAAAAAA1Hovv/xyqvoOHTqkPlb1RerWrRs9evRI1ZM2N9nx4osvxoYNG7I+98EHH4w//elPWZ8L7NjkyZNT1Tdv3jxHSQAAAAAAAAAAaicHsAAAAAAAAAAAqPX+8Y9/pKo/9NBDc5Ij7dy0ucmtPffcM9q1axetW7eOkpKSjGZcccUVsX79+iwnAz5t3rx5cc8996Tq6dChQ47SAAAAAAAAAADUTnXzHQAAAAAAAAAAAPJt6dKlqeoPOeSQnORIOzdt7l1Rs2bNomvXrv/x5/PmzUvUX1RUFJ06dUq8r7i4OHFtUt/97nfjsssui6OOOioKCgoiImLr1q3xt7/9LW644YZ4/vnnE89asWJF/O53v4sf//jHWc9Z3XaHt2X39Nxzz8XAgQOjoqIiVd9RRx2Vo0QAAAAAAAAAALWTA1gAAAAAAAAAANR677//fqr6pk2b5iTHl770pVT1aXPvii688MK48MIL/+PP/31I6ovsv//+MXfu3GzHSqSgoCDuuuuuOPvss//ja8XFxdG/f//o169fXH755TFmzJjEc2+++ea44oororCwMItpq19Nflt2Lxs3bowVK1bErFmzYty4cTF58uSorKxMPedb3/pWDtIBAAAAAAAAANReDmABAAAAAAAAAFDrbdq0KVV9o0aNcpIj7dy0ucmuESNG7PD41ccVFhbGqFGj4u23345HHnkk0dxly5bFE088Ed/4xjeykBJqh2OPPTan848++uho165dTncAAAAAAAAAANQ2NftXBAIAAAAAAAAAQBaUl5enqt9VDmClzU32tG3bNq688spEtQUFBTFq1KgoKipKPD/psSygevz0pz/NdwQAAAAAAAAAgN2OA1gAAAAAAAAAAJBSQUHBLjG3qqoqJzn4YhdccEEUFxcnrj/wwAOjT58+ieunT5+eQSqqw9ixY6OgoCArH7169cr3t0MCAwcOjL59++Y7BgAAAAAAAADAbscBLAAAAAAAAAAAar369eunql+/fn1Ocnz44Yep6vfYY4+c5OCLDRw4MKc9r732WmzdujX1DiC72rVrF3fccUe+YwAAAAAAAAAA7JYcwAIAAAAAAAAAoNZLe0gqVwew0s5Ne7iL7Nh7772jbdu2qfsOP/zwxLUVFRWxePHi1DuA7Gnbtm1MmzYt9t5773xHAQAAAAAAAADYLTmABQAAAAAAAABArbfvvvumql+1alVOcqxevTpVfbNmzXKSg8/XqVOnjPoOOuigKCgoSFy/bNmyjPYAO+/kk0+OmTNnRps2bfIdBQAAAAAAAABgt+UAFgAAAAAAAAAAtV7Lli1T1c+fPz8nOebNm5eqPm1usqNJkyYZ9RUXF0eDBg0S169duzajPUDmmjdvHhMnTowHH3wwvvSlL6XuT3PkbmdVVVWlqq/ObAAAAAAAAAAASdTNdwAAAAAAAAAAAMi3L3/5y6nq586dm5McaeemzU12NGzYcKd6y8rKEtVu3rw54z1AcnXq1Im+ffvG+eefH/3794+6dTP/p5Vpe8vLyzPetWnTplT1RUVFGe8CAAAAAAAAAMgFB7AAAAAAAAAAAKj1Dj/88FT1b7zxRnzwwQfRuHHjrGXYtm1bzJo1K1VP2txfpKqqKgoKChLXV1RUZHV/TbF9+/Zq6U3zFl/E21LblZSUxF577fXRR6tWraJbt27RvXv36N69e+y9995Z2VOvXr1U9Rs3bsx4V9re+vXrZ7wLAAAAAAAAACAXHMACAAAAAAAAAKDW69atWxQWFkZlZWWi+u3bt8eECRPiwgsvzFqGxx57LFavXp2qp0ePHlnbH/Gv76tu3eT/pOjDDz/M6v6aoqysrFp6s3msxtuyu3vyySejV69e+Y4Re+21V6r6VatWZbwrbW+jRo0y3gUAAAAAAAAAkAuF+Q4AAAAAAAAAAAD51qhRo/jKV76Squfee+/NaoY//elPqeqbN28enTp1ymqGrVu3pqpPe7Brd7FixYqM+jZs2BCbNm1KXN+0adOM9uyIt4Xqsf/++6eqf/311zPelba3efPmGe8CAAAAAAAAAMgFB7AAAAAAAAAAACAivvWtb6Wqf/bZZ+Pvf/97Vna/+uqrMXHixFQ9/fv3z8ruj0tznCki4pVXXsl6hppgwYIFUVlZmbrvtddeS1WfzWM13jZ7zj777KiqqsrKx/Tp0/P97ZBlrVq1SlU/d+7cjHfNmzcvVf0BBxyQ8S4AAAAAAAAAgFxwAAsAAAAAAAAAACLitNNOi4KCglQ9P/jBD6KiomKnd1988cWxbdu2VD1nnHFGorr69esnnvnhhx8mrl27dm288cYbiet3J5s2bYr58+en7nvhhRcS1+6xxx7RunXrz63xtrDr6dKlS6r6Rx55JLZv3556z7Zt2+KRRx5J1dO1a9fUewAAAAAAAAAAcskBLAAAAAAAAAAAiIg2bdpEnz59UvW8/vrrMXjw4Kiqqsp477Bhw+Kpp55K1dO5c+c45phjEtXutddeiecuXbo0ce2DDz6Y0dGWpOrWrZuoLu3hsGyZMGFC6p6JEycmrj3ssMOisPDz/3mXt4VdzwEHHBD77LNP4vo1a9ak+tnwbxMnTowPPvggcX2zZs2iefPmqfcAAAAAAAAAAOSSA1gAAAAAAAAAAPC/rrzyytQ9d955ZwwZMiQqKytT9/7qV7+K66+/PnXfFVdckbg2zZGkefPmJarbtm1bjB49OvHcTDRs2DBR3erVq/NyKOmOO+6IdevWJa5//vnn45lnnklcf9xxx31hjbeFXdMJJ5yQqv7KK69Mdczqgw8+iB//+Mc5zQQAAAAAAAAAUB0cwAIAAAAAAAAAgP917LHHRt++fVP3jRkzJo466qh45ZVXEtUvWbIk+vbtG1dffXXqXV26dIlBgwYlrm/VqlXi2gkTJiSq+/Wvfx0LFixIPDcTSY87bdmyJWbMmJHTLDuyevXqGDp0aKLa8vLyuOSSS6Kqqirx/O985ztfWONtYdc0YMCAVPXvvvtufOMb34h33nnnC2uXLFkSxx9/fLz77rs5zQQAAAAAAAAAUB0cwAIAAAAAAAAAgI8ZPXp01K9fP3XfCy+8EIcddlgcd9xx8bvf/S5efvnlWL58eWzdujVWrFgR8+bNi7vvvjv69+8fHTp0iGnTpqXeUVhYGLfccksUFib/Zz89evRIXPvcc8/F73//+8+tGTt2bFxzzTWJZ2aqefPmiWvPP//8+Nvf/hbbt2/PYaL/dNddd8WPfvSjqKio+MyadevWxcCBA2P27NmJ5x588MHRrVu3L6zztlSnc845JwoKCrL6sbv69re/HV/60pdS9cyZMyc6dOgQgwcPjilTpsR7770X5eXlUV5eHkuXLo2HH344LrzwwjjooINi7ty5qWbvu+++ceKJJ6bqAQAAAAAAAACoDnXzHQAAAAAAAAAAAHYl7du3j1//+tdxySWXpO7dvn17PPnkk/Hkk0/mIFnEj370ozjmmGNS9aQ5khQRMXjw4Fi0aFH88Ic/jP322++jP3/++efjpptuigkTJqSal6nDDz88nn322US1ixcvjhNOOCHq1asXLVq0iD322GOHx3XGjx8fHTp0yGrO3/zmNzFt2rS46KKLonfv3rH//vvHtm3b4p133okpU6bEzTffHMuXL081c+jQoYnqvO3/ycXbQqaKiopiyJAhqQ/KbdmyJW6//fa4/fbbs5rn8ssvj6KioqzO3FnDhg2Lv/zlL19Yt2HDhsQz//nPf8ahhx6aqPYPf/hDdO/ePfFsAAAAAAAAACA3HMACAAAAAAAAAIBPufjii+Oll16KP/7xj/mO8pHevXvHz3/+89R9xx9/fDRs2DDKysoS1VdVVcV///d/x29/+9vYf//9Y4899ojly5cn7s+WI444Im6++eZUPZs3b45FixZ95tfLy8t3NtYOvfrqq3HRRRdlZVbHjh3jjDPOSFTrbf9Prt4WMnXZZZfFbbfdFsuWLctrjpYtW8all16a1ww78u6778a8efOyOrOioiLxzDSHtQAAAAAAAACA3CnMdwAAAAAAAAAAANgV3XHHHdG3b998x4iIiMMPPzwmTpwYdeum/313DRo0iNNOOy11X2VlZbz33nvx5ptvVvuBpIiI73znO9GwYcNq35tPBQUFcdttt0VRUVGiem8Lu64999wz7rrrrigoKMhbhoKCghg7dmzUr18/bxkAAAAAAAAAAD6PA1gAAAAAAAAAALADRUVFMXny5DjllFPymqNnz57x5JNPRqNGjTKecdlllyU+qpRGu3btsj7z3xo0aBBnnXVWzubvjOOPPz6aNWuW9blXXXVV9OrVK1WPt4Vd1wknnBC/+MUv8rZ/5MiRcdxxx+VtPwAAAAAAAADAF3EACwAAAAAAAAAAPkNxcXE88MADMWLEiKhTp06177/oooti2rRpO3X8KiKiU6dOMWzYsCyl+pfrr78+Tj/99KzO/LSf//zn0bZt25zuyESLFi3i4Ycfjr322itrM0866aS44YYbUvd5W9i1XXXVVXHddddV+97rr78+fvSjH1X7XgAAAAAAAACANBzAAgAAAAAAAACAz1FYWBjXXnttPP3009G1a9dq2dmmTZt4+OGH49Zbb42SkpKszLz66qvjm9/85k7PqVOnTtxyyy3x05/+NAupPl+jRo1i8uTJu+ShpO7du8ff//732GeffXZ61imnnBIPPPBAFBZm9s+5vC3s2oYPHx73339/lJaW5nxX48aNY/z48dXy/zEAAAAAAAAAwM5yAAsAAAAAAAAAABI48sgj4+WXX4477rgj2rdvn5MdzZs3j5EjR8bChQvjxBNPzOrsOnXqxKRJk+KMM87IeMYBBxwQTzzxRFx88cVZTPb5OnfuHC+//HL84Ac/iEaNGlXb3iS6d+8eL7/8chx77LEZ9RcXF8eNN94Y48aNi+Li4oxzeFvY9Z122mmxYMGCOPfcc6Nu3bpZn19UVBTnnXdevPbaa3HKKadkfT4AAAAAAAAAQC44gAUAAAAAAAAAAAkVFhbGeeedF6+//no8+uijMWjQoGjcuPFOzdxzzz3jpJNOivHjx8eSJUviiiuuiJKSkiwl/qSioqK49957489//nOqI15NmjSJn/3sZ7Fw4cI45phjcpLt8zRq1CjGjBkT//znP+O+++6Lyy67LL7+9a9H69ato3HjxlFUVFTtmf6tefPm8cQTT8T48eOjW7duiXpKSkpi0KBBsXDhwrjyyiujoKBgp3N4W9j17bfffnHnnXfGW2+9FcOGDYt27drt9Mz27dvHddddF4sWLYo77rgjmjVrloWkAAAAAAAAAADVo6Cqqqoq3yEAAAAAAAAAAKCm2rZtW8yZMydefPHFmDNnTixevDiWLl0aa9asifLy8tiyZUsUFxdH/fr1o7S0NFq2bBmtW7eOrl27Ro8ePaJHjx5Rr169vOR++umnY+rUqfHMM8/E8uXLY/Xq1VFeXh4NGzaMNm3axGGHHRZ9+/aNE088MS8Zq9P06dPj2GOPTVR71llnxdixY3f4tddffz0ee+yxmDdvXixZsiTKysqioKAg9t5772jfvn0ceeSR0adPn50+nPZ5vC3UHIsXL44ZM2bEvHnzYuHChbFs2bJYsWJFbNy4MbZs2RIR/zqa16BBg9h3332jefPm0bFjx+jatWv07NkzWrdund9vAAAAAAAAAABgJziABQAAAAAAAAAA1HrZOoAFAAAAAAAAAABAMoX5DgAAAAAAAAAAAAAAAAAAAAAAAEDt4gAWAAAAAAAAAAAAAAAAAAAAAAAA1coBLAAAAAAAAAAAAAAAAAAAAAAAAKqVA1gAAAAAAAAAAAAAAAAAAAAAAABUKwewAAAAAAAAAAAAAAAAAAAAAAAAqFYOYAEAAAAAAAAAAAAAAAAAAAAAAFCtHMACAAAAAAAAAAAAAAAAAAAAAACgWjmABQAAAAAAAAAAAAAAAAAAAAAAQLVyAAsAAAAAAAAAAAAAAAAAAAAAAIBq5QAWAAAAAAAAAAAAAAAAAAAAAAAA1coBLAAAAAAAAAAAAAAAAAAAAAAAAKpVQVVVVVW+QwAAAAAAAAAAAAAAAAAAAAAAAFB7FOY7AAAAAAAAAAAAAAAAAAAAAAAAALWLA1gAAAAAAAAAAAAAAAAAAAAAAABUKwewAAAAAAAAAAAAAAAAAAAAAAAAqFYOYAEAAAAAAAAAAAAAAAAAAAAAAFCtHMACAAAAAAAAAAAAAAAAAAAAAACgWjmABQAAAAAAAAAAAAAAAAAAAAAAQLVyAAsAAAAAAAAAAAAAAAAAAAAAAIBq5QAWAAAAAAAAAAAAAAAAAAAAAAAA1coBLAAAAAAAAAAAAAAAAAAAAAAAAKpV3XwHAEhj3bp1MWPGjI8+b9myZZSUlOQxEQAAAAAAAAAAAAAAAAAAAABQG23ZsiWWLl360ec9e/aM0tLSPCaqWRzAAmqUGTNmxIABA/IdAwAAAAAAAAAAAAAAAAAAAADgEyZNmhTf+c538h2jxijMdwAAAAAAAAAAAAAAAAAAAAAAAABqFwewAAAAAAAAAAAAAAAAAAAAAAAAqFZ18x0AII2WLVt+4vNJkybFgQcemKc0AAAAAAAAAAAAAAAAAAAAAEBttWjRohgwYMBHn3/6NgqfzwEsoEYpKSn5xOcHHnhgdO7cOU9pAAAAAAAAAAAAAAAAAAAAAAD+5dO3Ufh8hfkOAAAAAAAAAAAAAAAAAAAAAAAAQO3iABYAAAAAAAAAAAAAAAAAAAAAAADVygEsAAAAAAAAAAAAAAAAAAAAAAAAqpUDWAAAAAAAAAAAAAAAAAAAAAAAAFQrB7AAAAAAAAAAAAAAAAAAAAAAAACoVnXzHQByYezYsXHOOeckqr3uuuti+PDhuQ1UTTZu3BivvfbaRx/Lly+PsrKy2LBhQ5SVlUXdunWjYcOG0bBhw2jUqFG0bds2Dj744OjcuXO0bds2CgvdxAMAAAAAAAAAAAAAAAAAAAAAIPccwIIa7oMPPoiHHnooJk6cGI8//nhUVFRkNKdZs2YxcODA+O53vxvHHHNMFBQUZDkpAAAAAAAAAAAAAAAAAAAAAAD8S2G+AwCZKS8vj1/84hfRunXrOO+882Lq1KkZH7+KiHj//ffj1ltvjV69ekWPHj3iqaeeymJaAAAAAAAAAAAAAAAAAAAAAAD4Pw5gQQ00c+bM6NChQ1xzzTVRVlaW9fmzZ8+Onj17xplnnhmbNm3K+nwAAAAAAAAAAAAAAAAAAAAAAGo3B7Cghpk6dWoce+yxsXTp0pzvuu++++K4447LyZEtAAAAAAAAAAAAAAAAAAAAAABqLwewoAZZuHBhnHzyybFly5Zq2/nCCy/EoEGDqm0fAAAAAAAAAAAAAAAAAAAAAAC7PwewoAa54IILYuPGjdW+d/LkyTFu3Lhq3wsAAAAAAAAAAAAAAAAAAAAAwO7JASyoIZ588sl45pln8rZ/xIgRedsN7NiSJUuioKBghx9LlizJdzygGvl5AHwRPyeAneXnCJCEnxXki//2ahbvVTN4p5rBO9Uc3mrX541qDm+16/NGNYN3qhm8U83gnWoG71R7eXvYvfh/GojwswD4fH5GADvDzxAgDT8zgM/i5wNkxgEsqCHuu+++1D1HH310PPDAA/HOO+/E5s2bo6ysLF555ZUYOXJk7LfffqlmLVy4MGbPnp06AwAAAAAAAAAAAAAAAAAAAAAAfFrdfAeAXDj77LPj7LPPzneMrJo+fXqq+p/85Cdxww03REFBwUd/VlJSEgcffHAcfPDBcc4550S/fv1i1qxZiWc+/vjj0a1bt1Q5AAAAAAAAAAAAAAAAAAAAAADg0wrzHQD4Ylu2bInFixcnrj/uuOPi5z//+SeOX31a06ZNY+LEiVFcXJx47sKFCxPXAgAAAAAAAAAAAAAAAAAAAADAZ3EAC2qADz74ICorKxPXX3rppYnqWrVqFSeeeGLiuStXrkxcCwAAAAAAAAAAAAAAAAAAAAAAn6VuvgPsLl599dWYPHlyPPvss/H666/HqlWrory8PPbcc89o0aJFdOnSJb7xjW/EwIEDY++9985r1qqqqnjmmWdi8uTJMWvWrHjzzTdj3bp1UV5eHnXq1In9998/Jk2aFIcffnhG89evXx8vvfRSzJkzJ95+++1477334sMPP4zy8vKIiKhfv36UlpZGixYt4sADD4zDDz88evToEXvuuWc2v81a7cgjj0xce8QRR8T//M//JKqtqKjINBIAAAAAAAAAAAAAAAAAAAAAAHzEAayI6NWrV8yYMSNR7eLFi6N169YffT5z5sy4+uqrP7N//fr1sWDBgliwYEGMHz8+Lr300jj//PNj2LBh0aRJk9RZlyxZEm3atElU27Nnz5g+ffon/uyhhx6Ka6+9Nl577bUd9mzfvj2WLsoBaxMAACAASURBVF0a69evT5Vr48aNMW7cuLjvvvvimWeeSX0oqaSkJHr27BmDBg2Kk08+OerVq5eq/9Mef/zxuOmmmxLVnn766XH66afv1L5cS3s0rXHjxjmpLS0tTZUDAAAAAAAAAAAAAAAAAAAAAAB2xAGsDFVUVMSVV14Zo0ePjqqqqsR95eXlMWbMmBg3blzcc8890adPnxym/D+bN2+O733ve3H//fdndW55eXmMGjUqRo4cGevWrct4zpYtW+Kxxx6Lxx57LIYOHRrXXHNNDB48OIqKijKat3Tp0njkkUcS1Xbv3j2jHdWpXr160bJly1i6dGmi+rKyssRHs8rKyhLnaNWqVeJaAAAAAAAAAAAAAAAAAAAAAAD4LIX5DlATbdy4Mfr27RujRo1Kdfzq41auXBn9+/ePP/zhD1lO9582bNgQvXv3zvrxq5kzZ0bXrl3jJz/5yU4dv/q0lStXxmWXXRY9evSIV155JWtza7qvf/3riWvnzJmTk9qjjz46cS0AAAAAAAAAAAAAAAAAAAAAAHwWB7BS2rp1a/Tv3z+eeOKJnZ61ffv2uOCCC7J+mOrTO0477bR45plnsjr37rvvjp49e8Zbb72V1bkfN2/evDjyyCNj8uTJOdtRk5x11lmJa++6665EdWvXrk3891taWhq9e/dOnAEAAAAAAAAAAAAAAAAAAAAAAD6LA1gpXXTRRTFjxoyszauqqopzzjkn5syZk7WZH/fLX/4ypkyZktWZv//97+Pcc8+NioqKrM7dkY0bN8bAgQPjoYceyvmuXV3v3r3jiCOOSFR7//33x7hx4z63Ztu2bXHuuefG+vXrE80cPHhw7LnnnolqAQAAAAAAAAAAAAAAAAAAAADg8ziAldLjjz+e9Zlbt26N008/PbZu3ZrVuW+//XaMGDEiqzP/+te/xkUXXZTVmV9k+/btcdppp8Xs2bOrde+upqCgIO6+++5ER6iqqqri9NNPj0suuSTeeOONT3ytoqIiHnvssfja174WkyZNSrT7wAMPjGuvvTaj3AAAAAAAAAAAAAAAAAAAAAAA8GkOYGVBYWFhNGvWLDp27BhNmjTJaMbrr78eN910U1Zzvffee1FRUZG1eStWrIizzjorKisrszYzqS1btsSpp54amzZtqvbdu5KOHTvGX/7yl6hfv/4X1lZVVcWtt94aBx10UDRt2jQ6deoU7dq1i7322iv69OkTL7zwQqKd++yzT0yZMiXRTgAAAAAAAAAAAAAAAAAAAAAASMIBrJ2wzz77xG233RYrVqyI5cuXx4IFC2L16tWxePHiuO6666JevXqp5v3617/epQ88XXXVVbF69epUPYccckjccsst8cYbb0RZWVmsX78+FixYEL/97W+jXbt2qWa9/fbbceONN6bq2R0dd9xx8fzzz8dBBx2UuGfNmjWxcOHCWLRoUZSXlyfu69KlSzz11FPRoUOHTKICAAAAAAAAAAAAAAAAAAAAAMAOOYCVoe7du8eCBQti8ODB0bRp0098rXXr1jF8+PCYPXt27LPPPolnrlmzJu69995sR/2Ezp07xy9/+ct46aWX4v3334+Kior48MMPY/78+fHHP/4xzjjjjGjQoMF/9L311ltxzz33pNr105/+NGbPnh0XX3xxtG/fPho0aBANGzaMjh07xuWXXx7z58+PCy64INXM3/zmN7Fu3bpUPbujQw45JObNmxejR4+OZs2aZX1+o0aNYvjw4fHiiy86fgUAAAAAAAAAAAAAAAAAAAAAQNbVzXeAmqhZs2bx6KOPRpMmTT63rlOnTjF58uQ4+uijo7KyMtHse+65J/VRqCSKi4tj1KhRccEFF0SdOnU+8bVGjRpFly5dokuXLvH//t//iw0bNsT27ds/UXPzzTcn/h4iIoYOHRrXX3/959bUq1cvbr/99li/fn2MGzcu0dyNGzfGnXfeGUOHDk2cZXdVXFwcl156aVx00UUxcuTIGD58eFRUVOzUzI4dO8b3v//9GDRoUDRu3DhLST/bypUrY9WqVal6Fi1alKM0AAAAAAAAAAAAAAAAAAAAAABUFwewMnDDDTdE06ZNE9V+9atfjbPOOivuvvvuRPUzZ86MtWvXxt57770zET+huLg4Hn744TjhhBMS1Tdo0OATn1dWVsaDDz6YeF/r1q3jhhtuSFRbUFAQN998c0ydOjXWrVuXqGf8+PEOYEXE1q1b4+67745Ro0bF66+/npWZ//jHP2L69OlxwAEHxIABA6KgoCArcz/LbbfdFj/72c9yugMAAAAAAAAAAAAAAAAAAAAAgF1PYb4D1DSlpaUxaNCgVD0XX3xx4trKysp46qmn0sb6XNdee23i41c7Mnv27Hj//fcT119++eVRr169xPVNmzaN8847L3H9rFmzYuXKlYnrd0ePP/54tG/fPi688MKsHb+KiNiyZUtMmjQpTjrppOjSpUs88sgjWZsNAAAAAAAAAAAAAAAAAAAAAAD/5gBWSieeeGIUFxen6unWrVu0atUqcf2cOXPSxvpMLVq0iKuuumqnZsycOTNxbUFBQXz3u99NveO//uu/EtdWVVXFCy+8kHrH7uL666+P3r17xzvvvJPTPa+99lqceOKJMXjw4KioqMjpLgAAAAAAAAAAAAAAAAAAAAAAape6+Q5Q0/To0SOjvsMPPzzxwaI33ngjox07ct5550Xdujv3zK+88kri2i9/+cux3377pd5x2GGHRf369aO8vDxR/fz58+Nb3/pW6j013Q033BDDhg2r1p233357vP/++zFhwoQoKirK6uyLLroo9cG0RYsWxYABA7KaAwAAAAAAAAAAAAAAAAAAAACA6uUAVkqdOnXKuO+hhx5KVLts2bKMduxI//79d3rGkiVLEtd26dIlox2FhYXRsWPHePnllxPVJz0mtjuZPn16quNXxcXFceaZZ8bAgQPjsMMOiyZNmsSWLVvivffei6effjpuu+22mDdvXqJZkyZNih//+Mfx29/+NtP4O7TPPvvEPvvsk9WZAAAAAAAAAAAAAAAAAAAAAADs+grzHaCmadKkSUZ9jRs3Tly7du3ajHZ8WklJSRxyyCE7PWfVqlWJa/fdd9+M96TpXblyZcZ7aqLKysr4/ve/H1VVVYnqW7duHS+99FLceeed8c1vfjP222+/KC4ujoYNG0bHjh3jggsuiJdffjmuvfbaxBlGjx4dzz//fKbfAgAAAAAAAAAAAAAAAAAAAAAAfMQBrJQaNmyY877NmzdntOPTWrRoEcXFxTs9Z+PGjYlrM/37iYho1KhR4tpNmzZlvKcmevTRR+PNN99MVFuvXr2YMmXKFx4/KywsjBEjRsS5556baG5lZWXceOONiWoBAAAAAAAAAAAAAAAAAAAAAODzOICV0vbt23PeV1BQkNGOTystLc3KnKqqqsS1hYWZ/ydVp06dxLVpMu0Oxo8fn7j23HPPjc6dOyeuv+GGGxIfSnv44Ydj9erViWcDAAAAAAAAAAAAAAAAAAAAAMCOOICVUllZWc776tevn9GOT2vQoEFW5qTJs2HDhoz3rF+/PnFttv6Oaopnn302ce3AgQNTzd5vv/3iiCOOSFS7ffv2eO6551LNBwAAAAAAAAAAAAAAAAAAAACAT3MAK6UVK1Zk1Ldy5crEtU2bNs1oR640adIkce2aNWsy3pOmt3HjxhnvqWm2bdsW//jHPxLXH3LIIal3dOnSJXHtvHnzUs8HAAAAAAAAAAAAAAAAAAAAAICPcwArpVdffTXnfc2bN89oR64ccMABiWsz/fuJiFiwYEHi2latWmW8p6ZZu3ZtqvrS0tLUO9L0rF69OvV8AAAAAAAAAAAAAAAAAAAAAAD4OAewUpo5c2bqnu3bt8esWbMS13fu3Dn1jlzq1KlT4to33ngjysrKUu9466234sMPP8xJpppu69atqeo3bNiQekeano0bN6aeDwAAAAAAAAAAAAAAAAAAAAAAH+cAVkpTp06N9evXp+qZMWNGrF69OnF9t27d0sbKqa9+9auJa7dt2xaTJ09OvePBBx9MVX/EEUek3lFT7bHHHqnqFy1alHpHmp60eQAAAAAAAACA/8/OnUZXXZj5A39uAiSsJkBltSyCFYEiW4Wh/kEK6pQBUewCFkHrFmnp4rGLrZWptSrqiOJx3FCLG0hPFQeX0mLB4qAoUhECKigUqKyKQBKSAPm/6Gmn7vcm93ID+XzOyYskz/LN7+belw8AAAAAAAAAAADwYQ5gpai8vDymT5+eUs8NN9yQdG1eXl4MGjQo1VgZNXDgwGjWrFnS9dOnT4+qqqqk6/ft2xd333130vXHHXdcdOzYMen6w11hYWFKR6eeeuqplOaXlJTEn//856TrW7dundJ8AAAAAAAAAAAAAAAAAAAAAAD4MAewquG6666L1atXJ1X76KOPxjPPPJP07OHDh0fDhg2rGy0j8vLyYtSoUUnXL126NO65556k66+99tpYv3590vVnn3120rVHiuOPPz7p2ttuuy3efffdpOunTZsWe/bsSbr+uOOOS7oWAAAAAAAAAAAAAAAAAAAAAAA+jgNY1bB379449dRTY8WKFZ9aN3fu3JgwYUJKsydOnFiDZJkzadKklOsff/zxz6y766674uqrr056br169eKSSy5JKcuR4OSTT066dvv27TFmzJjYtWvXZ9Y+/vjjMWXKlJSyDBo0KKV6AAAAAAAAAAAAAAAAAAAAAAD4MAewqmnTpk3Rr1+/KCoqioULF8a2bduioqIi/va3v8UTTzwRZ511VowePTr27duX9MzOnTvH6NGjM5i6+gYMGBDDhw9Pur6ysjLOPPPMGD9+fCxZsiSqqqr++buDBw/GokWL4owzzoiLL774A7/7LBMmTIhjjjkmpexHgjFjxqRUv3DhwujVq1f893//d2zduvUDvztw4EC8/PLLcd5558VZZ50V+/fvT3rugAEDok2bNillAQAAAAAAAAAAAAAAAAAAAACAD0tUpXJ96Ag1ZMiQWLRoUbZjxIMPPhjnnHPOp9asX78+OnXqlNS8wYMHx8KFC9OQ7O9Wr14dvXv3jvLy8pR7GzduHG3atImqqqp45513orS0NOUZhYWFsXr16mjVqtVn1t5///1x3nnnJTX3qquuiilTpqSc51CqqqqK3r17x6uvvlqt/tatW0fz5s2jvLw8tmzZEiUlJdWa85vf/CbOPffcavWmy6pVq6JHjx7//H7lypXRvXv3LCYCAAAAAAAAAAAAAAAAAAAAAOoit1BqJifbAfi7oUOHfubxq2zr1q1b3HjjjdXqLSkpibVr18a6deuqdfwqImLGjBlJHb86EiUSibjuuuuq3b9ly5YoLi6OdevWVfv4Vc+ePWv9/ygAAAAAAAAAAAAAAAAAAAAAAIcHB7BSlIkDQC1btoyZM2emfW4mfOc734lJkyYd8r3XXHNNnHnmmYd8b21y+umnR1FRUVZ2N2zYMGbOnBm5ublZ2Q8AAAAAAAAAAAAAAAAAAAAAwJHFAawUXX311Wk9gtWwYcN44oknol27dmmbmWnTp0+PyZMnH5JdiUQirr766rjiiisOyb7a7tZbb41Ro0Yd0p0NGjSI2bNnx4knnnhI9wIAAAAAAAAAAAAAAAAAAAAAcORyACtFiUQiZs6cGRdeeGGNZx111FHx9NNPx8CBA9OQ7NBJJBJxyy23xN133x1NmjTJ2J7mzZvHnDlz4uc//3nGdhxu6tWrF7/73e/i+9//fiQSiYzva9OmTfzhD3+IkSNHZnwXAAAAAAAAAAAAAAAAAAAAAAB1hwNY1ZCTkxN33XVX3HnnndG0adNqzejXr1+89NJLMXjw4DSnO3QuuOCCWLFiRXzta19L6zGm3NzcmDhxYqxatSrGjBmTtrlHitzc3Lj55pvj2WefjT59+mRkR/369aOoqChWrlwZ/+///b+M7AAAAAAAAAAAAAAAAAAAAAAAoO5yAKsGLrroolizZk1MmjQpmjRpklTP8ccfHzNmzIgXX3wxunbtmuGEmdepU6d49NFHY8WKFTFp0qRo2bJltWe1bt06fvjDH8bq1avjvvvui9atW1d71oEDB5KubdCgQbX3ZNOQIUPi5Zdfjj/84Q/xjW98o9rH2P7VcccdF1deeWWsW7cubr/99mjevHkakgIAAAAAAAAAAAAAAAAAAAAAwAfVy3aAw13btm3jtttui+uvvz6effbZeP7552PNmjWxY8eO2LdvXzRq1CjatWsXPXv2jOHDh0f//v1rtK9jx45RVVWVpvTp06NHj7jtttvi1ltvjaVLl8bixYtj+fLlsW7duti8eXPs2rUrysrKIiKiYcOGUVhYGO3atYuuXbtG79694+STT46+fftGIpFIS57S0tKkawsLC9OyMxsSiUQMGzYshg0bFhUVFfHSSy/FCy+8EKtWrYq1a9fGli1bYseOHVFaWhqVlZWRSCQiLy8vmjVrFp/73OfimGOO+edrMGjQoOjSpUu2/yQAAAAAAAAAAAAAAAAAAAAAAOoAB7DSpHHjxjFy5MgYOXJktqNkVU5OTgwYMCAGDBiQ1Rzbt29PuvZwPoD1rxo0aBCDBg2KQYMGZTsKAAAAAAAAAAAAAAAAAAAAAAB8qpxsB4BMWLNmTdK1bdq0yWASAAAAAAAAAAAAAAAAAAAAAADgwxzA4ohTWVkZCxYsSKo2Nzc3+vbtm+FEAAAAAAAAAAAAAAAAAAAAAADAv3IAiyPO9ddfH++++25StT169IgmTZpkOBEAAAAAAAAAAAAAAAAAAAAAAPCv6mU7ANTU/v3747333ou//OUvce+998asWbOS7h01alQGkwEAAAAAAAAAAAAAAAAAAAAAAB/HASwOW6NHj465c+dWu79+/fpRVFSUxkQAAAAAAAAAAAAAAAAAAAAAAEAycrIdALJl4sSJ0aZNm2zHAAAAAAAAAAAAAAAAAAAAAACAOscBLOqkY489Nm666aZsxwAAAAAAAAAAAAAAAAAAAAAAgDrJASzqnBYtWsScOXOiadOmn1izcOHCSCQSWfsCAAAAAAAAAAAAAAAAAAAAAIAjmQNY1Clf+MIX4oUXXojevXtnOwoAAAAAAAAAAAAAAAAAAAAAANRZDmBRJ3zuc5+LqVOnxiuvvBJdunTJdhwAAAAAAAAAAAAAAAAAAAAAAKjT6mU7AKRbs2bNokWLFtGqVasYOHBgDB06NIYOHRqNGjXKdjQAAAAAAAAAAAAAAAAAAAAAACAcwIqIiIULF2Y7AtXw+OOPZzsCAAAAAAAAAAAAAAAAAAAAAABQDTnZDgAAAAAAAAAAAAAAAAAAAAAAAEDdUi/bAaA2GjJkSFRVVWU7BgAAAAAAAAAAAAAAAAAAAAAAHJFysh0AAAAAAAAAAAAAAAAAAAAAAACAusUBLAAAAAAAAAAAAAAAAAAAAAAAAA4pB7AAAAAAAAAAAAAAAAAAAAAAAAA4pBzAImn3339/JBKJpL6mTJmS7bgAAAAAAAAAAAAAAAAAAAAAAEAtVS/bAaC22bp1a6xcuTJWrlwZ69ati82bN8emTZti69atUVJSEmVlZVFWVhY5OTnRoEGDaNq0aTRv3jzatm0bHTt2jO7du0fv3r1jwIABkZ+fn/Z8HTt2jA0bNqR97qeZM2dOnH322Yd0JwAAAAAAAAAAAAAAAAAAAAAARy4HsKjzXn311Vi4cGE899xz8fzzz8fWrVuT6jt48GDs378/SktLY+vWrbF69eoP/D4/Pz+GDRsW3/rWt2LMmDFRr563GwAAAAAAAAAAAAAAAAAAAAAARDiABfGVr3wldu7cmfa5+/bti3nz5sW8efPimGOOiWuuuSbGjx+f9j0AAAAAAAAAAAAAAAAAAAAAAHC4ycl2AKgLNm7cGOeee26cccYZsWfPnmzHAQAAAAAAAAAAAAAAAAAAAACArHIACw6hJ554IoYNGxYlJSXZjgIAAAAAAAAAAAAAAAAAAAAAAFnjABYcYkuXLo2ioqJsxwAAAAAAAAAAAAAAAAAAAAAAgKxxAAuy4IEHHojFixdnOwZwmFu/fn0kEomP/Vq/fn224wGHiM8C4NP4jABqwmcIkAqfGWSL/73Di9fr8OG1qv28RocPr1Xt5zU6fHitaj+v0eHB63R48DodHrxOhwevU93ltYcji/c04HMA+Dg+G4BU+dwAqsvnB/BJfD5AejiABZ8hJycnCgoKokOHDtGjR4849thjo6CgoMZzp02bloZ0AAAAAAAAAAAAAAAAAAAAAABw+KmX7QAcPiZOnBgTJ07MdoyM69atW5x22mnRr1+/6NGjRxx//PGRl5f3kbotW7bEn/70p7j//vtj/vz5Ke95+umno7y8/GNnAwAAAAAAAAAAAAAAAAAAAADAkcwBLIiIXr16xfnnnx9nnXVWtG/fPqme1q1bx9ixY2Ps2LHx5JNPxrhx42L37t1J7ywtLY3i4uLo3bt3dWN/oquuuiqmTJmS9rkAAAAAAAAAAAAAAAAAAAAAAJAODmBR5y1atCi6d+9eoxkjRoyIxx57LIYNGxZVVVVJ923YsCEjB7AAAAAAAAAAAAAAAAAAAAAAAKA2q9UHsFauXBlz586N559/PtasWRPbt2+PsrKyaNy4cbRv3z569uwZw4YNizFjxkRhYWFWs1ZVVcXixYtj7ty58fLLL8cbb7wRu3btirKyssjNzY22bdvG448/Hn369KnW/N27d8dLL70Uy5cvj3Xr1sWmTZvi/fffj7KysoiIaNiwYRQUFET79u2jS5cu0adPn+jfv380btw4nX/mEammx6/+YejQoTF8+PCYP39+0j179+5Ny24AAAAAAAAAAAAAAAAAAAAAADicZOwA1pAhQ2LRokVJ1b799tvRsWPHf36/ZMmS+OlPf/qJ/bt3747i4uIoLi6O2bNnx+TJk+PCCy+MX/ziF9GiRYuUs65fvz46deqUVO3gwYNj4cKFH/jZY489FldeeWWsWrXqY3sOHDgQGzdujN27d6eUq6SkJGbNmhUPPfRQLF68OCorK1Pqz8vLi8GDB8f48ePj7LPPjvz8/JT6P2zBggVx8803J1U7bty4GDduXI32HY6GDh2a0gGsJk2aZDANAAAAAAAAAAAAAAAAAAAAAADUThk7gFUdlZWV8aMf/ShuueWWqKqqSrqvrKwsbr311pg1a1bMnDkzTjvttAym/D/79u2Lb3/72/Hwww+ndW5ZWVlMmzYtpk6dGrt27ar2nPLy8pg/f37Mnz8/LrvssvjZz34WRUVFUb9+/WrN27hxYzz55JNJ1fbr169aOw53BQUFKdW3atUqQ0kAAAAAAAAAAAAAAAAAAAAAAKD2ysl2gH8oKSmJ008/PaZNm5bS8at/tW3bthgxYkTcc889aU73UXv37o3hw4en/fjVkiVLolevXnHFFVfU6PjVh23bti2+973vRf/+/eO1115L21w+aPPmzUnX1q9fP0488cQMpgEAAAAAAAAAAAAAAAAAAAAAgNqpXrYDRERUVFTEiBEjYtGiRTWedeDAgbjooouiUaNGMW7cuDSk+/gdY8eOjcWLF6d17n333RcXX3xxVFZWpnXuv3r11Vdj4MCB8dBDD8UZZ5yRsT111bx585KuPeWUU6Jhw4YZybF79+744x//GEuXLo3i4uJ477334r333ovKysrIz8+PwsLCaNeuXRx33HHRq1evGDBgQDRq1CgjWQAAAAAAAAAAAAAAAAAAAAAA4MNqxQGsSy+9NC3Hr/6hqqoqzjvvvOjWrVv07t07bXP/4dprr03p0FEy7rzzzrjkkkvSOvOTlJSUxJgxY2LOnDlx5plnHpKddcGMGTNi+fLlSddPnjw5Y1luvvnmuPnmm5Ouz8vLiyFDhsSECRNizJgx0aBBg4xlAwAAAAAAAAAAAAAAAAAAAACAnGwHiIhYsGBB2mdWVFTEuHHjoqKiIq1z161bF7/85S/TOvOpp56KSy+9NK0zP8uBAwdi7NixsWzZskO690hUUVERU6dOjYsvvjjpnjPOOCNGjBiRwVSpKS8vj9///vcxbty46NKlS8yYMSOqqqqyHQsAAAAAAAAAAAAAAAAAAAAAgCNUrTiA9XFycnKidevW0a1bt2jRokW1ZqxZsyZuvvnmtObatGlTVFZWpm3e1q1bY8KECXHw4MG0zUxWeXl5fOMb34jS0tJDvvtwVVFREbt27Yri4uJ47LHH4nvf+1507tw5fvzjH8eBAweSmtGrV6+49957M5y0+jZu3BgXXHBBnHLKKfHOO+9kOw4AAAAAAAAAAAAAAAAAAAAAAEegWncA6+ijj47bb789tm7dGu+8804UFxfHjh074u23346rrroq8vPzU5p3ww031OoDTz/5yU9ix44dKfV88YtfjNtuuy1ef/312LNnT+zevTuKi4vjv/7rv6Jr164pzVq3bl1cf/31KfXUBbt27YpEIvGRr7y8vCgsLIzu3bvHWWedFbfeemts3rw56bkjRoyIBQsWRPPmzTOYPj0WLVoU/fr1i1WrVmU7CgAAAAAAAAAAAAAAAAAAAAAAR5hadQCrX79+UVxcHEVFRdGyZcsP/K5jx44xZcqUWLZsWRx99NFJz9y5c2c8+OCD6Y76Ad27d49rr702XnrppdiyZUtUVlbG+++/HytWrIjf/OY3cc4550STJk0+0vfmm2/GzJkzU9r185//PJYtWxaTJk2K4447Lpo0aRJNmzaNbt26xQ9+8INYsWJFXHTRRSnNvOmmm2LXrl0p9ZCaE044IR566KGYN29etGjRIttxkva3v/0thg4dGm+99Va2owAAAAAAAAAAAAAAAAAAAAAAcASpl+0A/9C6det4+umnP/M40AknnBBz586NQYMGxcGDB5OaPXPmzJSPQiWjQYMGMW3atLjooosiNzf3A79r1qxZ9OzZM3r27Bnnnntu7N27qeiKCwAAIABJREFUNw4cOPCBmunTpyf9N0REXHbZZXH11Vd/ak1+fn7ccccdsXv37pg1a1ZSc0tKSmLGjBlx2WWXJZ2F5Hz1q1+NH//4x3HyySdHIpHIdpxq2bZtW5x11lnx4osvRl5eXtpnb9++PaWetWvXpjUDAAAAAAAAAAAAAAAAAAAAAACHXq05gPWrX/0qWrZsmVTtgAEDYsKECXHfffclVb9kyZJ47733orCwsCYRP6BBgwbxP//zP3HqqacmVd+kSZMPfH/w4MGYM2dO0vs6duwYv/rVr5KqTSQSMX369HjmmWdi165dSfXMnj3bAawMeOqpp+Kvf/1rTJgwIS655JKP/B8cLl599dW47rrr4qqrrkrr3Ntvvz3+8z//M60zAQAAAAAAAAAAAAAAAAAAAACo/XKyHSAioqCgIMaPH59Sz6RJk5KuPXjwYDz33HOpxvpUV155ZdLHrz7OsmXLYsuWLUnX/+AHP4j8/Pyk61u2bBkXXHBB0vUvv/xybNu2Lel6krdy5cq4/PLLo0OHDnHHHXdkZEerVq1i7Nixcf3118czzzwTb7zxRmzfvj0qKiri/fffj7feeit+97vfxeTJk6t9CG7q1Kmxc+fONCcHAAAAAAAAAAAAAAAAAAAAAKAuqhUHsP7jP/4jGjRokFJP3759o0OHDknXL1++PNVYn6h9+/bxk5/8pEYzlixZknRtIpGIr33taynv+OY3v5l0bVVVVbz44osp7yB57777bhQVFcXo0aOjrKysxvM6d+4cV111VSxdujTeeeedePjhh+NHP/pRnHbaadG1a9do2bJl1K9fP5o1axadOnWKM888M2655ZbYtGlTXHHFFZGTk9rbv7S0NO68884a5wYAAAAAAAAAAAAAAAAAAAAAgHrZDhAR0b9//2r19enTJzZs2JBU7euvv16tHR/nggsuiHr1avboXnvttaRrO3fuHG3atEl5R+/evaNhw4ZJH1tasWJFjBw5MuU9pGbu3LkxatSomDdvXuTl5aXcf/bZZ8e///u/x9ChQyORSKTc36hRo7jmmmvipJNOijFjxsT+/fuT7p01a1ZcccUVKe/8JJdeemnKx93Wrl0bo0ePTlsGAAAAAAAAAAAAAAAAAAAAAAAOvVpxAOuEE06odt9jjz2WVO3mzZurtePjjBgxosYz1q9fn3Rtz549q7UjJycnunXrFq+88kpS9ckeE6sLcnNzo1evXh/5eWlpaZSUlMSOHTuioqKi2vP/+Mc/xpVXXhlTp05NuffGG2+s9t5/NWrUqLj++uvjsssuS7rntddei61bt0arVq3SkuHoo4+Oo48+Oi2zAAAAAAAAAAAAAAAAAAAAAAA4fORkO0BERIsWLarV17x586Rr33vvvWrt+LC8vLz44he/WOM527dvT7q2JseGUundtm1btfccaZo2bRp/+ctfPvL1xhtvxObNm6O0tDTWrVsXjzzySIwfPz7y8/NT3nHTTTfFihUrMpA+eZMnT44uXbqk1PPiiy9mKA0AAAAAAAAAAAAAAAAAAAAAAHVFrTiA1bRp04z37du3r1o7Pqx9+/bRoEGDGs8pKSlJura6zyciolmzZknXlpaWVntPXZObmxudO3eOb37zmzFz5sx4++2345xzzklpxsGDB2PatGkZSpicevXqxde//vWUet58880MpQEAAAAAAAAAAAAAAAAAAAAAoK6oFQewDhw4kPG+RCJRrR0fVlBQkJY5VVVVSdfm5FT/ZcrNzU26NpVMfFDr1q3jwQcfjMsvvzylvtmzZ8f+/fszlCo5gwcPTql+8+bNGUoCAAAAAAAAAAAAAAAAAAAAAEBdUSsOYO3ZsyfjfQ0bNqzWjg9r0qRJWuakkmfv3r3V3rN79+6ka9P1jOqya6+9Nrp165Z0fWlpaSxfvjyDiT5b69atU6ovKSnJUBIAAAAAAAAAAAAAAAAAAAAAAOqKWnEAa+vWrdXq27ZtW9K1LVu2rNaOTGnRokXStTt37qz2nlR6mzdvXu09/F1ubm6ce+65KfUUFxdnKE1y6tevn1J9VVVVhpIAAAAAAAAAAAAAAAAAAAAAAFBX1IoDWCtXrsx4X7t27aq1I1M+//nPJ11b3ecTkdpxpQ4dOlR7D/+nW7duKdXX5MBZOqR6gK5x48YZSgIAAAAAAAAAAAAAAAAAAAAAQF1RKw5gLVmyJOWeAwcOxMsvv5x0fffu3VPekUknnHBC0rWvv/567NmzJ+Udb775Zrz//vsZycQny8lJ7W1VVlaWoSTJeeGFF1Kqb9OmTYaSAAAAAAAAAAAAAAAAAAAAAABQV9SKA1jPPPNM7N69O6WeRYsWxY4dO5Ku79u3b6qxMmrAgAFJ1+7fvz/mzp2b8o45c+akVH/SSSelvIOPWrNmTUr1TZo0Saruvffeq06cz5Tq/0nXrl0zkgMAAAAAAAAAAAAAAAAAAAAAgLqjVhzAKi8vj+nTp6fUc8MNNyRdm5eXF4MGDUo1VkYNHDgwmjVrlnT99OnTo6qqKun6ffv2xd133510/XHHHRcdO3ZMuv5IcPDgwYzMnDlzZko9n//855Oqu+CCC+Liiy+OrVu3Vifax3rkkUfilVdeSaln4MCBadsPAAAAAAAAAAAAAAAAAAAAAEDdVCsOYEVEXHfddbF69eqkah999NF45plnkp49fPjwaNiwYXWjZUReXl6MGjUq6fqlS5fGPffck3T9tddeG+vXr0+6/uyzz0669kgxaNCg+OUvfxnbtm1L28wrr7wyVq5cmVLPF7/4xaTqDhw4EHfddVd07tw5Lr/88tiyZUt1Iv7T8uXL4+KLL06p58QTT4zWrVvXaC8AAAAAAAAAAAAAAAAAAAAAANSaA1h79+6NU089NVasWPGpdXPnzo0JEyakNHvixIk1SJY5kyZNSrn+8ccf/8y6u+66K66++uqk59arVy8uueSSlLIcCd5555246qqr4vOf/3yMGzcufve730VJSUm1Zu3cuTPOP//8+PWvf51SX+fOnePYY49Nqae0tDRuvPHG6NChQ5x77rnx3HPPRVVVVUozHn744TjllFNiz549KfWde+65KdUDAAAAAAAAAAAAAAAAAAAAAMDHqTUHsCIiNm3aFP369YuioqJYuHBhbNu2LSoqKuJvf/tbPPHEE3HWWWfF6NGjY9++fUnP7Ny5c4wePTqDqatvwIABMXz48KTrKysr48wzz4zx48fHkiVLPnD06ODBg7Fo0aI444wz4uKLL07pINKECRPimGOOSSn7kaS8vDweeeSRGDNmTLRs2TJGjhwZv/71r+Ppp5+OLVu2fGLfO++8E/PmzYtLLrkkOnXqFPfdd1/Ku88777xq566oqIgHHnggBg8eHB06dIhLL700fvvb38aGDRs+9vV/++234957743+/fvHOeecE++//35K+1q0aBEXXHBBtfMCAAAAAAAAAAAAAAAAAAAAAMA/JKpSuZSUgiFDhsSiRYsyMTolDz74YJxzzjmfWrN+/fro1KlTUvMGDx4cCxcuTEOyv1u9enX07t07ysvLU+5t3LhxtGnTJqqqquKdd96J0tLSlGcUFhbG6tWro1WrVp9Ze//99yd9sOmqq66KKVOmpJznUOrYsWNs2LDhM+vq168fBQUFUVhYGPXr14+SkpLYuXNn7Nmzp0b7CwsL480334wWLVokVT969OiYO3duUrX5+fnRtm3baNSoUZSWlsbOnTtTPnj1YbfffnsUFRXVaEY6rFq1Knr06PHP71euXBndu3fPYiIAAAAAAAAAAAAAAAAAAAAAoC5yC6Vm6mU7QCYNHTr0M49fZVu3bt3ixhtvjO9+97sp95aUlMTatWtrtH/GjBlJHb+qyyorK2P79u2xffv2tM696aabkj5+lap9+/bFW2+9lbZ5p512WlxyySVpmwcAAAAAAAAAAAAAAAAAAAAAQN2Wk+0AEZGRI1UtW7aMmTNnpn1uJnznO9+JSZMmHfK911xzTZx55pmHfC8RkyZNivPOOy/bMZLSs2fPmD17diQSiWxHAQAAAAAAAAAAAAAAAAAAAADgCFErDmBdffXVaT2C1bBhw3jiiSeiXbt2aZuZadOnT4/Jkycfkl2JRCKuvvrquOKKKw7JPj7o+9//fkyfPj3bMZJy0kknxZ/+9Kc46qijsh0FAAAAAAAAAAAAAAAAAAAAAIAjSK04gJVIJGLmzJlx4YUX1njWUUcdFU8//XQMHDgwDckOnUQiEbfcckvcfffd0aRJk4ztad68ecyZMyd+/vOfZ2wHH+/oo4+O2bNnx8033xyJRCLbcT5VIpGI7373u7Fo0aJo0aJFtuMAAAAAAAAAAAAAAAAAAAAAAHCEqRUHsCIicnJy4q677oo777wzmjZtWq0Z/fr1i5deeikGDx6c5nSHzgUXXBArVqyIr33ta2k9kpSbmxsTJ06MVatWxZgxY9I293B2qA47FRQUxM9+9rN4/fXX4+tf/3q150yfPj2mTp0affr0SWO6j/ryl78cS5YsiVtvvTXy8vIyugsAAAAAAAAAAAAAAAAAAAAAgLqp1hzA+oeLLroo1qxZE5MmTYomTZok1XP88cfHjBkz4sUXX4yuXbtmOGHmderUKR599NFYsWJFTJo0KVq2bFntWa1bt44f/vCHsXr16rjvvvuidevW1Z514MCBpGsbNGhQ7T2HyrJly2LFihVxww03xKmnnhoFBQVpm920adMYNWpUPPDAA7Fp06b41a9+VeP5xxxzTFx++eWxbNmyeOONN+Kmm26KUaNGRWFhYY3zFhYWxre//e343//93/jzn/8cJ510Uo1nAgAAAAAAAAAAAAAAAAAAAADAJ0lUVVVVZWLwkCFDYtGiRUnVvv3229GxY8eP/LykpCSeffbZeP7552PNmjWxY8eO2LdvXzRq1CjatWsXPXv2jOHDh0f//v3TnL52OXjwYCxdujQWL14cy5cvj3Xr1sXmzZtj165dUVZWFhERDRs2jMLCwmjXrl107do1evfuHSeffHL07ds3EolEWnJMnz49Jk+enFTt7bffHkVFRWnZeyitW7cuXnnllSguLo4NGzbEX//619i8eXO8//77UVpaGqWlpbF///6oX79+5OfnR0FBQbRo0SLat28fnTp1ihNOOCH69u0bvXr1ivr16x+SzFVVVbFy5cp49dVX480334y1a9fG2rVr49133429e/fGnj17oqysLOrVqxf5+fnRsmXLaNu2bXTt2jV69uwZgwYNir59+0Zubu4hyVtTq1atih49evzz+5UrV0b37t2zmAgAAAAAAAAAAAAAAAAAAAAAqIvcQqmZetkO8GkaN24cI0eOjJEjR2Y7Slbl5OTEgAEDYsCAAVnNsX379qRrCwsLM5gkc4499tg49thjsx0jJYlEInr27Bk9e/bMdhQAAAAAAAAAAAAAAAAAAAAAAEhKTrYDcPhYs2ZN0rVt2rTJYBIAAAAAAAAAAAAAAAAAAAAAAOBw5gAWSamsrIwFCxYkVZubmxt9+/bNcCIAAAAAAAAAAAAAAAAAAAAAAOBw5QAWSbn++uvj3XffTaq2R48e0aRJkwwnAgAAAAAAAAAAAAAAAAAAAAAADlf1sh2A2mn//v3x3nvvxV/+8pe49957Y9asWUn3jho1KoPJAAAAAAAAAAAAAAAAAAAAAACAw50DWHzA6NGjY+7cudXur1+/fhQVFaUxEQAAAAAAAAAAAAAAAAAAAAAAcKTJyXYAjiwTJ06MNm3aZDsGAAAAAAAAAAAAAAAAAAAAAABQizmARdoce+yxcdNNN2U7BgAAAAAAAAAAAAAAAAAAAAAAUMs5gEVatGjRIubMmRNNmzb9xJqFCxdGIpHI2hcAAAAAAAAAAAAAAAAAAAAAAFA7OIBFjX3hC1+IF154IXr37p3tKAAAAAAAAAAAAAAAAAAAAAAAwGHAASyq7XOf+1xMnTo1XnnllejSpUu24wAAAAAAAAAAAAAAAAAAAAAAAIeJetkOwOGhWbNm0aJFi2jVqlUMHDgwhg4dGkOHDo1GjRplOxoAAAAAAAAAAAAAAAAAAAAAAHCYydgBrIULF2ZqNBn0+OOPZzsCAAAAAAAAAAAAAAAAAAAAAABwhMvJdgAAAAAAAAAAAAAAAAAAAAAAAADqlnrZDkDdMWTIkKiqqsp2DAAAAAAAAAAAAAAAAAAAAAAAIMtysh0AAAAAAAAAAAAAAAAAAAAAAACAusUBLAAAAAAAAAAAAAAAAAAAAAAAAA4pB7AAAAAAAAAAAAAAAAAAAAAAAAA4pBzAImn3339/JBKJpL6mTJmS7bgAAAAAAAAAAAAAAAAAAAAAAEAtVS/bAaC22bp1a6xcuTJWrlwZ69ati82bN8emTZti69atUVJSEmVlZVFWVhY5OTnRoEGDaNq0aTRv3jzatm0bHTt2jO7du0fv3r1jwIABkZ+ff0gyr1+/PpYtWxZr166Nt99+O956663YtGlT7N27N/bu3RslJSWRSCTiqKOOimbNmsVRRx0VXbp0iT59+kTv3r3jS1/6Uhx11FGHJCsAAAAAAAAAAAAAAAAAAAAAADiARZ336quvxsKFC+O5556L559/PrZu3ZpU38GDB2P//v1RWloaW7dujdWrV3/g9/n5+TFs2LD41re+FWPGjIl69dLzdjt48GC8/PLL8fvf/z5eeOGFeOmll2L79u1J9W7bti22bdsWERHLli2L2bNnR0REXl5enHHGGTFx4sQ49dRTIzc3Ny1ZAQAAAAAAAAAAAAAAAAAAAADg4ziARZ33la98JXbu3Jn2ufv27Yt58+bFvHnz4phjjolrrrkmxo8fX+15xcXFce2118bvf//7pA9eJau8vDweffTRePTRR6Nnz55xxx13xL/927+ldQcAAAAAAAAAAAAAAAAAAAAAAPxDTrYDQF2wcePGOPfcc+OMM86IPXv2VGvG0qVL48EHH0z78asPe+211+LLX/5yFBUVRXl5eUZ3AQAAAAAAAAAAAAAAAAAAAABQNzmABYfQE088EcOGDYuSkpJsR/lUVVVVcccdd8Tpp58eZWVl2Y4DAAAAAAAAAAAAAAAAAAAAAMARxgEsOMSWLl0aRUVF2Y6RlIULF8bYsWOzHQMAAAAAAAAAAAAAAAAAAAAAgCOMA1iQBQ888EAsXrw42zGSMnfu3LjjjjuyHeOQWr9+fSQSiY/9Wr9+fbbjAUnyXoYjh/cz1A7ei1D7eF/Ckcl7m9rC/+In82z+znPwDCI8gwjPIMIziPAM6vrfH+EZRHgGdf3vj/AMIjyDCM8gwjOI8Azq+t8f4RlEeAbp4BlC8rxfoHby3oQjk/c21F7en3Dk8v6G2sv7E2oP70eOBA5gwWfIycmJgoKC6NChQ/To0SOOPfbYKCgoqPHcadOmpSHdx6tfv3506NAhunfvHh06dIj8/Pwazbvyyitjz549aUoHAAAAAAAAAAAAAAAAAAAAAEBdVy/bATh8TJw4MSZOnJjtGBnXrVu3OO2006Jfv37Ro0ePOP744yMvL+8jdVu2bIk//elPcf/998f8+fNT3vP0009HeXn5x85OVcOGDePUU0+Nr371q/HlL385vvCFL0Rubu4/f79///545ZVX4v7774/77rsv9u3bl9L8HTt2xCOPPBIXXXRRjbMCAAAAAAAAAAAAAAAAAAAAAIADWBARvXr1ivPPPz/OOuusaN++fVI9rVu3jrFjx8bYsWPjySefjHHjxsXu3buT3llaWhrFxcXRu3fv6saOXr16xaWXXhpjx46Npk2bfmJdvXr14ktf+lJ86Utfiu9+97tx9tlnR3FxcUq7fvvb3zqABQAAAAAAAAAAAAAAAAAAAABAWjiARZ23aNGi6N69e41mjBgxIh577LEYNmxYVFVVJd23YcOGah3AGjhwYEyZMiVOPfXUlHu7desWCxYsiAEDBsSGDRuS7lu8eHEcPHgwcnJyUt4JAAAAAAAAAAAAAAAAAAAAAAD/qlYfwFq5cmXMnTs3nn/++VizZk1s3749ysrKonHjxtG+ffvo2bNnDBs2LMaMGROFhYVZzVpVVRWLFy+OuXPnxssvvxxvvPFG7Nq1K8rKyiI3Nzfatm0bjz/+ePTp06da83fv3h0vvfRSLF++PNatWxebNm2K999/P8rKyiIiomHDhlFQUBDt27ePLl26RJ8+faJ///7RuHHjdP6ZR6SaHr/6h6FDh8bw4cNj/vz5Sffs3bs3pR1du3aNG2+8MUaNGpVqvA9o3bp1TJ8+PaU5ZWVlsXHjxujQoUONdgMAAAAAAAAAAAAAAAAAAAAAQMYOYA0ZMiQWLVqUVO3bb78dHTt2/Of3S5YsiZ/+9Kef2L979+4oLi6O4uLimD17dkyePDkuvPDC+MUvfhEtWrRIOev69eujU6dOSdUOHjw4Fi5c+IGfPfbYY3HllVfGqlWrPrbnwIEDsXHjxti9e3dKuUpKSmLWrFnx0EMPxeLFi6OysjKl/ry8vBg8eHCMHz8+zj777MjPz0+p/8MWLFgQN998c1K148aNi3HjxtVo3+Fo6NChKR3AatKkSdK1w4cPj3HjxkWDBg2qE+0jRo4cGR06dIgNGzYk3bN9+3YHsAAAAAAAAAAAAAAAAAAAAAAAqLGMHcCqjsrKyvjRj34Ut9xyS1RVVSXdV1ZWFrfeemvMmjUrZs6cGaeddloGU/6fffv2xbe//e14+OGH0zq3rKwspk2bFlOnTo1du3ZVe055eXnMnz8/5s+fH5dddln87Gc/i6Kioqhfv3615m3cuDGefPLJpGr79etXrR2Hu4KCgpTqW7VqlXRtu3btUo3zmQYPHhwzZ85Mur68vDztGQAAAAAAAAAAAAAAAAAAAAAAqHtysh3gH0pKSuL000+PadOmpXT86l9t27YtRowYEffcc0+a033U3r17Y/jw4Wk/frVkyZLo1atXXHHFFTU6fvVh27Zti+9973vRv3//eO2119I2lw/avHlz0rX169ePE088MYNpPlvbtm1Tqj/qqKMylAQAAAAAAAAAAAAAAAAAAAAAgLqkVhzAqqioiBEjRsSzzz5b41kHDhyIiy66KO2HqT68Y+zYsbF48eK0zr3vvvti8ODB8eabb6Z17r969dVXY+DAgTF37tyM7ajL5s2bl3TtKaecEg0bNsxgms9WUVGRdG0ikYj27dtnMA0AAAAAAAAAAAAAAAAAAAAAAHVFrTiAdemll8aiRYvSNq+qqirOO++8WL58edpm/qtrr702pUNHybjzzjvj/PPPj8rKyrTO/TglJSUxZsyYeOyxxzK+qy6ZMWNGSv9zkydPzmCa5BQXFydd26VLlygoKMhgGgAAAAAAAAAAAAAAAAAAAAAA6opacQBrwYIFaZ9ZUVER48aNi4qKirTOXbduXfzyl79M68ynnnoqLr30/7Nzp9FZFmb+gO8krBIwIZFFUFnc2GQQmMJYDw6K7RRB3FoVFbWKIi11GUet+teKomIRFMdxKWIRW5FpFcuI46gFD5a6gIqsBUZQKLIJAkkgZPl/6LEjbjxPFhLgus7Jh+T93cv78L58vK+q0p57UlpaGuedd17MnTt3r87dHxUXF8fo0aPjiiuuSFxz+umnR//+/atxqz3bunVrqsNzp512WjVuAwAAAAAAAAAAAAAAAAAAAADAgaROTS/wTTIzM6NZs2aRm5sb69evj02bNqXusWTJkhg7dmzccMMNVbbX6tWrq6xXRMS6detiyJAhUVZWVqV9k9i5c2f86Ec/ivnz58dBBx201+fvi4qLi6OwsDD++te/xtKlS2PmzJnxu9/9LtasWZO4R9euXeOJJ56oxi2Teeihh6KoqChxfsiQIdW4DQAAAAAAAAAAAAAAAAAAAAAAB5LMml7gy5o1axYPP/xwrFu3LtauXRuLFi2KjRs3xocffhi33XZbNGjQIFW/++67LwoLC6tp28q78cYbY+PGjalqjjvuuHjooYdi6dKlsW3btti6dWssWrQo7r///jjqqKNS9VqxYkXce++9qWoOBFu2bImMjIyv/NSvXz9yc3OjU6dOceaZZ8aDDz6Y6vhV//7949VXX42mTZtW4/Z7tnLlyhg1alTi/KBBg6Jr167VuBEAAAAAAAAAAAAAAAAAAAAAAAeSWnUAq0ePHrFo0aIYNmxY5Ofn7/ZamzZt4vbbb4+5c+dGs2bNEvfctGlTTJ48uapX3U2nTp3i7rvvjrfffjs++eST2LVrV3z22Wcxf/78+PWvfx2DBw+O7Ozsr9QtW7YsJk2alGrWLbfcEnPnzo3hw4fH0UcfHdnZ2dG4cePo0KFDXHPNNTF//vwYOnRoqp5jxoyJLVu2pKohnY4dO8bTTz8d06dPj7y8vBrdpaioKM4+++woKChIlG/QoEHcc8891bwVAAAAAAAAAAAAAAAAAAAAAAAHkjo1vcDnWrRoETNmzNjjcaCOHTvGtGnT4oQTToiysrJEvSdNmpT6KFQS9erVi3HjxsXQoUMjKytrt9eaNGkSXbp0iS5dusRFF10U27dvj9LS0t0y48ePT/weIiKuu+66GDly5LdmGjRoEI888khs3bo1nnnmmUR9CwoKYsKECXHdddcl3oVkfvCDH8QNN9wQJ554YmRkZNT0OlFcXBxnn312zJ07N3HN/fffH8ccc0y17LN+/frYsGFDqprly5dXyy4AAAAAAAAAAAAAAAAAAAAAAOw9teYA1p133hn5+fmJsr169YohQ4bExIkTE+XnzJkTmzdvjtzc3MqsuJt69erFH/7whzj11FMT5bOzs3f7vaysLKZOnZp4Xps2beLOO+9MlM3IyIjx48fHSy+9FFu2bElUM2XKFAewqsGLL74YH330UQwZMiSuvPLKr3wO9qaCgoI488wz4+WXX05cM3To0Bg2bFi17fTwww/HL37xi2rrDwAAAAAAAAAAAAAAAACefp2hAAAgAElEQVQAAABA7ZRZ0wtEROTk5MSFF16Yqmb48OGJs2VlZfH666+nXetb3XrrrYmPX32duXPnxieffJI4f80110SDBg0S5/Pz8+Oyyy5LnH/nnXdi/fr1ifMkt2DBgrj++uvjiCOOiEceeaRGdli7dm2ceOKJqY5fnX766fHwww9X41YAAAAAAAAAAAAAAAAAAAAAAByoasUBrNNOOy3q1auXqqZ79+5xxBFHJM6/++67adf6Rq1bt44bb7yxUj3mzJmTOJuRkRHnnHNO6hnnnntu4mx5eXm8+eabqWeQ3KeffhrDhg2LQYMGRVFR0V6b+8EHH8R3vvOdVN+B73//+zFlypTIysqqxs0AAAAAAAAAAAAAAAAAAAAAADhQ1anpBSIievbsWaG6448/PlatWpUou3Tp0grN+DqXXXZZ1KlTuUf3wQcfJM62a9cuWrZsmXpGt27domHDhomPLc2fPz8GDBiQeg7pTJs2LQYOHBjTp0+P+vXrV+us//mf/4mzzz47tm7dmrhm4MCB8eyzz1b7bhERV111VerjbsuXL49BgwZV00YAAAAAAAAAAAAAAAAAAAAAAOwNteIAVseOHStc99xzzyXKrlmzpkIzvk7//v0r3WPlypWJs126dKnQjMzMzOjQoUPMmzcvUT7pMbEDQVZWVnTt2vUrfy8sLIyCgoLYuHFjFBcXV7j/K6+8ErfeemuMHj26Mmt+qwkTJsSVV14ZJSUliWsuuOCCmDhxYqUPvCXVrFmzaNas2V6ZBQAAAAAAAAAAAAAAAAAAAABA7VErDmDl5eVVqK5p06aJs5s3b67QjC+rX79+HHfccZXus2HDhsTZ5s2bV3hOmtr169dXeM7+pnHjxvHee+994+ulpaWxatWqeOutt+LFF1+MqVOnxo4dO1LNGDNmTFxwwQVV8nn6ovLy8rjlllti1KhRqer+9V//NUaPHh0ZGRlVug8AAAAAAAAAAAAAAAAAAAAAAHxZZk0vEPG3Y0PVXZf2ONE3ad26ddSrV6/SfQoKChJnK/p8IiKaNGmSOFtYWFjhOQearKysaNeuXZx77rkxadKk+PDDD2Pw4MGpepSVlcW4ceOqdK+dO3fG4MGDUx2/ysrKioceeijuu+8+x68AAAAAAAAAAAAAAAAAAAAAANgrasUBrNLS0mqvq6rDPjk5OVXSp7y8PHE2M7Pi/0xZWVmJs2l2YnctWrSIyZMnx/XXX5+qbsqUKVFSUlIlO2zatClOOeWU+O1vf5u4plGjRvH888/H8OHDq2QHAAAAAAAAAAAAAAAAAAAAAABIolYcwNq2bVu11zVs2LBCM74sOzu7Svqk2Wf79u0VnrN169bE2ap6Rgeyu+++Ozp06JA4X1hYGO+++26l565YsSJ69+4ds2fPTlxz6KGHxuuvvx6nnXZapecDAAAAAAAAAAAAAAAAAAAAAEAateIA1rp16ypUt379+sTZ/Pz8Cs2oLnl5eYmzmzZtqvCcNLVNmzat8Bz+JisrKy666KJUNYsWLarUzD/96U/Rq1evWLZsWeKa4447Lv785z/H8ccfX6nZAAAAAAAAAAAAAAAAAAAAAABQEbXiANaCBQuqva5Vq1YVmlFdDj/88MTZij6fiHTHlY444ogKz+H/dOjQIVW+MgfOpk6dGieffHJs3Lgxcc33vve9mD17dhx22GEVngsAAAAAAAAAAAAAAAAAAAAAAJVRKw5gzZkzJ3VNaWlpvPPOO4nznTp1Sj2jOnXs2DFxdunSpbFt27bUM5YtWxafffZZtezEN8vMTPe1KioqqtCc0aNHx49+9KPYsWNH4pqhQ4fG9OnTo3HjxhWaCQAAAAAAAAAAAAAAAAAAAAAAVaFWHMB66aWXYuvWralqZs2aFRs3bkyc7969e9q1qlWvXr0SZ0tKSmLatGmpZ0ydOjVV/jvf+U7qGXzVkiVLUuWzs7NT5UtKSuLKK6+MG264IcrLyxPVZGRkxD333BOPPvpo1KlTJ9U8AAAAAAAAAAAAAAAAAAAAAACoarXiANbOnTtj/PjxqWruu+++xNn69evHCSeckHatatW7d+9o0qRJ4vz48eMTHzuKiNixY0c8/vjjifNHH310tGnTJnF+f1BWVlYtPSdNmpSq5vDDD0+c3bZtWwwYMCAeffTRxDUNGjSIKVOmxA033JBqLwAAAAAAAAAAAAAAAAAAAAAAqC614gBWRMQ999wTixcvTpR99tln46WXXkrcu1+/ftGwYcOKrlYt6tevHwMHDkycf+utt+JXv/pV4vzdd98dK1euTJw/++yzE2f3FyeccELccccdsX79+irreeutt8aCBQtS1Rx33HGJcmvWrIkTTzwx1Wc/Pz8/XnvttTjnnHNS7QQAAAAAAAAAAAAAAAAAAAAAANWp1hzA2r59e5x66qkxf/78b81NmzYthgwZkqr3xRdfXInNqs/w4cNT559//vk95h577LEYOXJk4r516tSJK6+8MtUu+4O1a9fGbbfdFocffnicf/758fvf/z4KCgoq1GvTpk1x6aWXxqhRo1LVtWvXLtq3b58o+/jjj8f777+fqv/GjRvjn/7pnyIjI6NKfmrrdwkAAAAAAAAAAAAAAAAAAAAAgH1LrTmAFRGxevXq6NGjRwwbNixmzpwZ69evj+Li4vjrX/8aL7zwQpx55pkxaNCg2LFjR+Ke7dq1i0GDBlXj1hXXq1ev6NevX+L8rl274owzzogLL7ww5syZE+Xl5X9/raysLGbNmhWnn356XHHFFbu9tidDhgyJww47LNXu+5OdO3fGb3/72zjrrLMiPz8/BgwYEKNGjYoZM2bEJ5988o11a9eujenTp8eVV14Zbdu2jYkTJ6aefckll1RmdQAAAAAAAAAAAAAAAAAAAAAA2CdllKe5lJTCSSedFLNmzaqO1qlMnjw5Bg8e/K2ZlStXRtu2bRP169OnT8ycObMKNvubxYsXR7du3WLnzp2paxs1ahQtW7aM8vLyWLt2bRQWFqbukZubG4sXL47mzZvvMfvkk08mPth02223xe233556n72pTZs2sWrVqj3m6tatGzk5OZGbmxt169aNgoKC2LRpU2zbtq1S83Nzc2PZsmWRl5eXKH/77bfHL37xi0rNrKwhQ4bEk08+WaM7LFy4MDp37vz33xcsWBCdOnWqwY0AAAAAAAAAAAAAAAAAAAAAgAORWyiVU6emF6hOffv23ePxq5rWoUOH+OUvfxk//elPU9cWFBTE8uXLKzV/woQJiY5fHch27doVGzZsiA0bNlRp3zFjxiQ+fgUAAAAAAAAAAAAAAAAAAAAAAPuTzJpeICKq5UhVfn5+TJo0qcr7Voef/OQnMXz48L0+96677oozzjhjr88lYvjw4XHJJZfU9BoAAAAAAAAAAAAAAAAAAAAAAFAjasUBrJEjR1bpEayGDRvGCy+8EK1ataqyntVt/PjxMWLEiL0yKyMjI0aOHBk///nP98o8dnf11VfH+PHja3oNAAAAAAAAAAAAAAAAAAAAAACoMbXiAFZGRkZMmjQpLr/88kr3Ovjgg2PGjBnRu3fvKths78nIyIgHHnggHn/88cjOzq62OU2bNo2pU6fGLbfcUm0z+HrNmjWLKVOmxNixYyMjI6Om1wEAAAAAAAAAAAAAAAAAAAAAgBpTKw5gRURkZmbGY489Fo8++mg0bty4Qj169OgRb7/9dvTp06eKt9t7Lrvsspg/f36cc845VXokKSsrKy6++OJYuHBhnHXWWVXWd1+Wl5e3V+bk5OTEzTffHEuXLo0f/vCHe2UmAAAAAAAAAAAAAAAAAAAAAADUZrXmANbnhg4dGkuWLInhw4dHdnZ2oppjjz02JkyYEG+++WYcddRR1bxh9Wvbtm08++yzMX/+/Bg+fHjk5+dXuFeLFi3i2muvjcWLF8fEiROjRYsWFe5VWlqaOFuvXr0Kz9lb5s6dG/Pnz4/77rsvTj311MjJyamy3o0bN46BAwfGU089FatXr44777yzSvsDAAAAAAAAAAAAAAAAAAAAAMC+LKO8vLy8OhqfdNJJMWvWrETZDz/8MNq0afOVvxcUFMRrr70Wb7zxRixZsiQ2btwYO3bsiIMOOihatWoVXbp0iX79+kXPnj2rePvapaysLN56662YPXt2vPvuu7FixYpYs2ZNbNmyJYqKiiIiomHDhpGbmxutWrWKo446Krp16xYnnnhidO/ePTIyMqpkj/Hjx8eIESMSZR9++OEYNmxYlczdm1asWBHz5s2LRYsWxapVq+Kjjz6KNWvWxGeffRaFhYVRWFgYJSUlUbdu3WjQoEHk5OREXl5etG7dOtq2bRsdO3aM7t27R9euXaNu3bo1/Xb2SwsXLozOnTv//fcFCxZEp06danAjAAAAAAAAAAAAAAAAAAAAAOBA5BZK5dSp6QW+TaNGjWLAgAExYMCAml6lRmVmZkavXr2iV69eNbrHhg0bEmdzc3OrcZPq0759+2jfvn1NrwEAAAAAAAAAAAAAAAAAAAAAAPu1zJpegH3HkiVLEmdbtmxZjZsAAAAAAAAAAAAAAAAAAAAAAAD7MgewSGTXrl3x6quvJspmZWVF9+7dq3kjAAAAAAAAAAAAAAAAAAAAAABgX+UAFonce++98emnnybKdu7cObKzs6t5IwAAAAAAAAAAAAAAAAAAAAAAYF9Vp6YXoHYqKSmJzZs3x3vvvRdPPPFEPPPMM4lrBw4cWI2bAQAAAAAAAAAAAAAAAAAAAAAA+zoHsNjNoEGDYtq0aRWur1u3bgwbNqwKNwIAAAAAAAAAAAAAAAAAAAAAAPY3mTW9APuXiy++OFq2bFnTawAAAAAAAAAAAAAAAAAAAAAAALWYA1hUmfbt28eYMWNqeg0AAAAAAAAAAAAAAAAAAAAAAKCWcwCLKpGXlxdTp06Nxo0bf2Nm5syZkZGRUWM/AAAAAAAAAAAAAAAAAAAAAABA7eAAFpV2zDHHxJ///Ofo1q1bTa8CAAAAAAAAAAAAAAAAAAAAAADsAxzAosIOOeSQGD16dMybNy+OPPLIml4HAAAAAAAAAAAAAAAAAAAAAADYR9Sp6QXYNzRp0iTy8vKiefPm0bt37+jbt2/07ds3DjrooJpeDQAAAAAAAAAAAAAAAAAAAAAA2MdU2wGsmTNnVldrqtHzzz9f0ysAAAAAAAAAAAAAAAAAAAAAAAD7ucyaXgAAAAAAAAAAAAAAAAAAAAAAAIADS52aXoADx0knnRTl5eU1vQYAAAAAAAAAAAAAAAAAAAAAAFDDMmt6AQAAAAAAAAAAAAAAAAAAAAAAAA4sDmABAAAAAAAAAAAAAAAAAAAAAACwVzmABQAAAAAAAAAAAAAAAAAAAAAAwF7lABaJPfnkk5GRkZHo5/bbb6/pdQEAAAAAAAAAAAAAAAAAAAAAgFqqTk0vALXNunXrYsGCBbFgwYJYsWJFrFmzJlavXh3r1q2LgoKCKCoqiqKiosjMzIx69epF48aNo2nTpnHooYdGmzZtolOnTtGtW7fo1atXNGjQYK/vX1paGnPmzImZM2fGvHnzYvny5bF27dooKCiI4uLiaNSoURx88MHRtm3bOPbYY+O73/1unHzyyXHooYfu9V0BAAAAAAAAAAAAAAAAAAAAADgwOYDFAe/999+PmTNnxuuvvx5vvPFGrFu3LlFdWVlZlJSURGFhYaxbty4WL1682+sNGjSIU045JS644II466yzok6d6v26rV27NsaOHRtPPfVUfPLJJ9+Y27p1a2zdujU+/vjjeP311+Oxxx6LzMzM6Nu3b1x11VVxxhlnVOueAAAAAAAAAAAAAAAAAAAAAADgABYHvJNPPjk2bdpU5X137NgR06dPj+nTp8dhhx0Wd911V1x44YVVPqe4uDjuuOOOuP/++6OoqKhCPcrKyuKVV16JV155Jbp37x6PPvpodO/evYo3BQAAAAAAAAAAAAAAAAAAAACAv8ms6QXgQPDxxx/HRRddFKeffnps27atyvquWLEievToEXfddVeFj1992dy5c6NXr15x7733Vkk/AAAAAAAAAAAAAAAAAAAAAAD4MgewYC964YUX4pRTTomCgoJK93rnnXeid+/e8cEHH1TBZrsrKSmJG2+8Ma644oooLy+v8v4AAAAAAAAAAAAAAAAAAAAAABzYHMCCveytt96KYcOGVarH0qVL4/vf/35s2LChirb6eo899lhce+211ToDAAAAAAAAAAAAAAAAAAAAAIADjwNYUAOeeuqpmD17doVqt2/fHgMGDIhNmzZV8VZfb9y4cfHEE0/slVn7ipUrV0ZGRsbX/qxcubKm1wP2wHcY9l++33Bg8F2H2sf3EmqW7yA1xWfvqzyTv/EcPIMD/f1/7kB/Dgf6+4/wDCI8gwjP4EB//5870J/Dgf7+IzyDCM8gwjM40N9/hGcQ4RlEeAZVxXOE2s13FPYdvq9Q83wPoeb5HsL+x/ca9l++37B/8F3mmziABXuQmZkZOTk5ccQRR0Tnzp2jffv2kZOTU+m+48aNq1Dd1VdfHcuWLUtd16xZszjqqKOiQYMGqWtHjBgRK1asSF0HAAAAAAAAAAAAAAAAAAAAAABfxwEsErv44oujvLw80c/tt99e0+tWWIcOHeLqq6+OyZMnx3vvvReFhYWxefPmWLlyZXzwwQexfPny2Lx5c6xduzZ+85vfxKmnnlqhOTNmzIidO3emqnnrrbfiiSeeSJxv1KhR3HnnnbFhw4ZYt25d/OUvf4nCwsKYOXNm9O3bN3GfgoKCuO6661LtCgAAAAAAAAAAAAAAAAAAAAAA38QBLIiIrl27xgMPPBAff/xxLFq0KMaOHRuDBw+Orl27Rv369b+2pkWLFnHeeefFf//3f8f06dOjSZMmqWYWFhbGokWLUtXccMMNUV5enijbtGnT+OMf/xg333xz5Ofn//3vGRkZ0adPn3j55Zfjxz/+ceLZ06ZNi9mzZ6faFwAAAAAAAAAAAAAAAAAAAAAAvo4DWBzwZs2aFe+9916MGDEiWrduXaEe/fv3j+eeey4yMjJS1a1atSpxdt68eTFz5szE+bFjx0bPnj2/8fWsrKz4j//4jzj22GMT9xwzZkziLAAAAAAAAAAAAAAAAAAAAAAAfJM6Nb3At1mwYEFMmzYt3njjjViyZEls2LAhioqKolGjRtG6devo0qVLnHLKKXHWWWdFbm5uje5aXl4es2fPjmnTpsU777wTf/nLX2LLli1RVFQUWVlZceihh8bzzz8fxx9/fIX6b926Nd5+++149913Y8WKFbF69er47LPPoqioKCIiGjZsGDk5OdG6des48sgj4/jjj4+ePXtGo0aNqvJt7pc6depUJX369u0b/fr1i5dffjlxzfbt2xNn//3f/z1xtnv37nHRRRftMVe3bt2499574/TTT0/U94UXXojVq1dX+FAYAAAAAAAAAAAAAAAAAAAAAABEVOMBrJNOOilmzZqVKPvhhx9GmzZt/v77nDlz4qabbvrG+q1bt8aiRYti0aJFMWXKlBgxYkRcfvnl8f/+3/+LvLy81LuuXLky2rZtmyjbp0+fmDlz5m5/e+655+LWW2+NhQsXfm1NaWlpfPzxx7F169ZUexUUFMQzzzwTTz/9dMyePTt27dqVqr5+/frRp0+fuPDCC+Pss8+OBg0apKr/sldffTXGjh2bKHv++efH+eefX6l5+6K+ffumOoCVnZ2dKFdSUhLPPfdc4r6XXHJJ4mz//v3jkEMOiQ0bNuwxW1ZWFv/5n/8ZV199deL+AAAAAAAAAAAAAAAAAAAAAADwZdV2AKsidu3aFf/2b/8WDzzwQJSXlyeuKyoqigcffDCeeeaZmDRpUnzve9+rxi3/z44dO+LHP/5x/OY3v6nSvkVFRTFu3LgYPXp0bNmypcJ9du7cGS+//HK8/PLLcd1118XNN98cw4YNi7p161ao38cffxz/9V//lSjbo0ePCs3Y1+Xk5KTKN2/ePFFu5syZsXnz5sR9/+Vf/iVxNisrK0499dR4+umnE+V///vfO4AFAAAAAAAAAAAAAAAAAAAAAEClZNb0Ap8rKCiI73//+zFu3LhUx6++aP369dG/f//41a9+VcXbfdX27dujX79+VX78as6cOdG1a9f4+c9/XqnjV1+2fv36+NnPfhY9e/aMDz74oMr6srs1a9YkztatWzf+4R/+IVF25syZifsecsgh0a5du8T5iIhevXolzr755puxY8eOVP0BAAAAAAAAAAAAAAAAAAAAAOCLasUBrOLi4ujfv3+89tprle5VWloaQ4cOrfLDVF+ecd5558Xs2bOrtO/EiROjT58+sWzZsirt+0Xvv/9+9O7dO6ZNm1ZtMw5k06dPT5z953/+52jYsGGibJrPWufOnRNnK1JTXFwcb7/9duoZAAAAAAAAAAAAAAAAAAAAAADwuVpxAOuqq66KWbNmVVm/8vLyuOSSS+Ldd9+tsp5fdPfdd6c6dJTEo48+Gpdeemns2rWrSvt+nYKCgjjrrLPiueeeq/ZZB5IJEyak+syNGDEicXb+/PmJs0ceeWTibEVr3n///dQzAAAAAAAAAAAAAAAAAAAAAADgc7XiANarr75a5T2Li4vj/PPPj+Li4irtu2LFirjjjjuqtOeLL74YV111VZX23JPS0tI477zzYu7cuXt17v6ouLg4Ro8eHVdccUXimtNPPz369++fKLtu3brYvHlz4t6tW7dOnP1cy5YtIzMz+X8HS5YsST0DAAAAAAAAAAAAAAAAAAAAAAA+VysOYH2dzMzMaNGiRXTo0CHy8vIq1GPJkiUxduzYKt1r9erVsWvXrirrt27duhgyZEiUlZVVWc+kdu7cGT/60Y+isLBwr8/eVxUXF8eWLVti0aJF8dxzz8XPfvazaNeuXdxwww1RWlqaqEfXrl3jiSeeSDzzf//3f1PtmJ+fnyofEZGVlRW5ubmJ8ytWrEg9AwAAAAAAAAAAAAAAAAAAAAAAPlfrDmA1a9YsHn744Vi3bl2sXbs2Fi1aFBs3bowPP/wwbrvttmjQoEGqfvfdd1+tPvB04403xsaNG1PVHHfccfHQQw/F0qVLY9u2bbF169ZYtGhR3H///XHUUUel6rVixYq49957U9UcCLZs2RIZGRlf+alfv37k5uZGp06d4swzz4wHH3ww1qxZk7hv//7949VXX42mTZsmrlm3bl2q3dMcsvqi6twJAAAAAAAAAAAAAAAAAAAAAAC+qFYdwOrRo0csWrQohg0bFvn5+bu91qZNm7j99ttj7ty50axZs8Q9N23aFJMnT67qVXfTqVOnuPvuu+Ptt9+OTz75JHbt2hWfffZZzJ8/P37961/H4MGDIzs7+yt1y5Yti0mTJqWadcstt8TcuXNj+PDhcfTRR0d2dnY0btw4OnToENdcc03Mnz8/hg4dmqrnmDFjYsuWLalqSKdjx47x9NNPx/Tp0yMvLy9V7fr161PlGzVqlCr/uYMOOihx1gEsAAAAAAAAAAAAAAAAAAAAAAAqo05NL/C5Fi1axIwZM/Z4HKhjx44xbdq0OOGEE6KsrCxR70mTJqU+CpVEvXr1Yty4cTF06NDIysra7bUmTZpEly5dokuXLnHRRRfF9u3bo7S0dLfM+PHjE7+HiIjrrrsuRo4c+a2ZBg0axCOPPBJbt26NZ555JlHfgoKCmDBhQlx33XWJdyGZH/zgB3HDDTfEiSeeGBkZGRXqsX379lT5hg0bVmhOmrqCgoIKzfiy9evXx4YNG1LVLF++vEpmAwAAAAAAAAAAAAAAAAAAAABQc2rNAaw777wz8vPzE2V79eoVQ4YMiYkTJybKz5kzJzZv3hy5ubmVWXE39erViz/84Q9x6qmnJspnZ2fv9ntZWVlMnTo18bw2bdrEnXfemSibkZER48ePj5deeim2bNmSqGbKlCkOYFWDF198MT766KMYMmRIXHnllV/5HCSxc+fOVPkvH2NLqk6d5P8d7Nixo0Izvuzhhx+OX/ziF1XSCwAAAAAAAAAAAAAAAAAAAACAfUdmTS8QEZGTkxMXXnhhqprhw4cnzpaVlcXrr7+edq1vdeuttyY+fvV15s6dG5988kni/DXXXBMNGjRInM/Pz4/LLrsscf6dd96J9evXJ86T3IIFC+L666+PI444Ih555JHU9bt27UqVz8ys2Nc6TV1xcXGFZgAAAAAAAAAAAAAAAAAAAAAAQEQtOYB12mmnRb169VLVdO/ePY444ojE+XfffTftWt+odevWceONN1aqx5w5cxJnMzIy4pxzzkk949xzz02cLS8vjzfffDP1DJL79NNPY9iwYTFo0KAoKipKXFe3bt1Uc8rKytKuFhERpaWlibNpv68AAAAAAAAAAAAAAAAAAAAAAPBFdWp6gYiInj17Vqju+OOPj1WrViXKLl26tEIzvs5ll10WdepU7tF98MEHibPt2rWLli1bpp7RrVu3aNiwYeJjS/Pnz48BAwaknkM606ZNi4EDB8b06dOjfv36e8wnyXxRSUlJhfZKU5d2p29y1VVXpT7utnz58hg0aFCVzAcAAAAAAAAAAAAAAAAAAAAAoGbUigNYHTt2rHDdc889lyi7Zs2aCs34Ov379690j5UrVybOdunSpUIzMjMzo0OHDjFv3rxE+aTHxA4EWVlZ0bVr16/8vbCwMAoKCmLjxo1RXFxc4f6vvPJK3HrrrTF69Og9ZrOzs1P1TnrwrDJ1aXf6Js2aNYtmzZpVSS8AAAAAAAAAAAAAAAAAAAAAAPYdteIAVl5eXoXqmjZtmji7efPmCs34svr168dxxx1X6T4bNmxInG3evHmF56SpXb9+fYXn7G8aN24c77333je+XlpaGqtWrYq33norXnzxxZg6dWrs2LEj1YwxY8bEBRdcsMfPU9oDUQUFBanyFamrzGcSAAAAAAAAAAAAAAAAANT3LvgAACAASURBVAAAAAAya3qBiL8dG6ruurTHib5J69ato169epXuk+bYUEWfT0REkyZNEmcLCwsrPOdAk5WVFe3atYtzzz03Jk2aFB9++GEMHjw4VY+ysrIYN27cHnNpj019+umnqfIVqXMACwAAAAAAAAAAAAAAAAAAAACAyqgVB7BKS0urvS4jI6NCM74sJyenSvqUl5cnzmZmVvyfKSsrK3E2zU7srkWLFjF58uS4/vrrU9VNmTIlSkpKvjXTrl27VD03bNiQKh8RsWvXrtiyZUvifNqdAAAAAAAAAAAAAAAAAAAAAADgi2rFAaxt27ZVe13Dhg0rNOPLsrOzq6RPmn22b99e4Tlbt25NnK2qZ3Qgu/vuu6NDhw6J84WFhfHuu+9+a6Z58+aRm5ubuOfHH3+cOPu5NWvWpDqAluY9AgAAAAAAAAAAAAAAAAAAAADAl9WKA1jr1q2rUN369esTZ/Pz8ys0o7rk5eUlzm7atKnCc9LUNm3atMJz+JusrKy46KKLUtUsWrRoj5njjjsucb/ly5enmh8RsWzZslT5NPsAAAAAAAAAAAAAAAAAAAAAAMCX1YoDWAsWLKj2ulatWlVoRnU5/PDDE2cr+nwikh1X+twRRxxR4Tn8nw4dOqTKJzlS9t3vfjdxv4ULF6aaH5HuM1avXr3o2bNn6hkAAAAAAAAAAAAAAAAAAAAAAPC5WnEAa86cOalrSktL45133kmc79SpU+oZ1aljx46Js0uXLo1t27alnrFs2bL47LPPqmUnvllmZrqvVVFR0R4zJ510UuJ+n376afzlL39JtUOa7+A//uM/RsOGDVP1BwAAAAAAAAAAAAAAAAAAAACAL6oVB7Beeuml2Lp1a6qaWbNmxcaNGxPnu3fvnnatatWrV6/E2ZKSkpg2bVrqGVOnTk2V/853vpN6Bl+1ZMmSVPns7Ow9Zvr06RO5ubmJe86YMSNxdteuXfHKK68kzp955pmJswAAAAAAAAAAAAAAAAAAAAAA8HVqxQGsnTt3xvjx41PV3HfffYmz9evXjxNOOCHtWtWqd+/e0aRJk8T58ePHR3l5eeL8jh074vHHH0+cP/roo6NNmzaJ8/uDsrKyauk5adKkVDWHH374HjN169aNM844I3HPJ598MvHn5fnnn4/NmzcnymZmZsbZZ5+deA8AAAAAAAAAAAAAAAAAAAAAAPg6teIAVkTEPffcE4sXL06UffbZZ+Oll15K3Ltfv37RsGHDiq5WLerXrx8DBw5MnH/rrbfiV7/6VeL83XffHStXrkycPxCPGp1wwglxxx13xPr166us56233hoLFixIVXPcccclyl111VWJe7733nvx5JNP7jG3Y8eOuOmmmxL3HThwYBx22GGJ8wAAAAAAAAAAAAAAAAAAAAAA8HVqzQGs7du3x6mnnhrz58//1ty0adNiyJAhqXpffPHFldis+gwfPjx1/vnnn99j7rHHHouRI0cm7lunTp248sorU+2yP1i7dm3cdtttcfjhh8f5558fv//976OgoKBCvTZt2hSXXnppjBo1KlVdu3bton379omy3bt3jz59+iTufd1118Wbb775ja+XlJTE5ZdfHitWrEjc89prr02cBQAAAAAAAAAAAAAAAAAAAACAb1JrDmBFRKxevTp69OgRw4YNi5kzZ8b69eujuLg4/vrXv8YLL7wQZ555ZgwaNCh27NiRuGe7du1i0KBB1bh1xfXq1Sv69euXOL9r164444wz4sILL4w5c+ZEeXn5318rKyuLWbNmxemnnx5XXHHFbq/tyZAhQ+Kwww5Ltfv+ZOfOnfHb3/42zjrrrMjPz48BAwbEqFGjYsaMGfHJJ598Y93atWtj+vTpceWVV0bbtm1j4sSJqWdfcsklqfKjR4+OjIyMRNnNmzfHySefHHfddVds2rRpt9f++Mc/xsknnxyTJ09OPHvgwIFx4oknptoXAAAAAAAAAAAAAAAAAAAAAAC+TkZ5mktJKZx00kkxa9as6midyuTJk2Pw4MHfmlm5cmW0bds2Ub8+ffrEzJkzq2Czv1m8eHF069Ytdu7cmbq2UaNG0bJlyygvL4+1a9dGYWFh6h65ubmxePHiaN68+R6zTz75ZOKDTbfddlvcfvvtqffZm9q0aROrVq3aY65u3bqRk5MTubm5Ubdu3SgoKIhNmzbFtm3bKjU/Nzc3li1bFnl5eanqLrvsspgwYULqec2bN4+DDz44Pv744ygqKkpV26hRo3j//fejffv2qedWtYULF0bnzp3//vuCBQuiU6dONbgRAAAAAAAAAAAAAAAAAAAAAHAgcgulcurU9ALVqW/fvns8flXTOnToEL/85S/jpz/9aeragoKCWL58eaXmT5gwIdHxqwPZrl27YsOGDbFhw4Yq7TtmzJjUx68iIsaNGxezZs1K/W+/bt26WLduXep5EREPPvhgrTh+BQAAAAAAAAAAAAAAAAAAAADA/iGzpheIiGo5UpWfnx+TJk2q8r7V4Sc/+UkMHz58r8+966674owzztjrc4kYPnx4XHLJJRWqzc7OjunTp1foeFZF/OxnP4tLL710r8wCAAAAAAAAAAAAAAAAAAAAAODAUCsOYI0cObJKj2A1bNgwXnjhhWjVqlWV9axu48ePjxEjRuyVWRkZGTFy5Mj4+c9/vlfmsburr746xo8fX6kexxxzTMyYMSPy8/OraKuvd/nll8fYsWOrdQYAAAAAAAAAAAAAAAAAAAAAAAeeWnEAKyMjIyZNmhSXX355pXsdfPDBMWPGjOjdu3cVbLb3ZGRkxAMPPBCPP/54ZGdnV9ucpk2bxtSpU+OWW26pthl8vWbNmsWUKVNi7NixkZGRUel+PXv2jD/96U/RqVOnKthud3Xq1IlRo0bFY489ViW7AgAAAAAAAAAAAAAAAAAAAADAF9WKA1gREZmZmfHYY4/Fo48+Go0bN65Qjx49esTbb78dffr0qeLt9p7LLrss5s+fH+ecc06VHh7KysqKiy++OBYuXBhnnXVWlfXdl+Xl5e2VOTk5OXHzzTfH0qVL44c//GGV9j7qqKNi7ty5cdNNN0WDBg2qpOfxxx8ff/rTn+Kmm26qkn4AAAAAAAAAAAAAAAAAAAAAAPBlteYA1ueGDh0aS5YsieHDh0d2dnaimmOPPTYmTJgQb775Zhx11FHVvGH1a9u2bTz77LMxf/78GD58eOTn51e4V4sWLeLaa6+NxYsXx8SJE6NFixYV7lVaWpo4W69evQrP2Vvmzp0b8+fPj/vuuy9OPfXUyMnJqbLejRs3joEDB8ZTTz0Vq1evjjvvvLNK+39R/fr1Y9SoUbFixYr413/912jevHnqHhkZGdG3b9/43e9+F++880707NmzGjYFAAAAAAAAAAAAAAAAAAAAAIC/ySgvLy+vjsYnnXRSzJo1K1H2ww8/jDZt2nzl7wUFBfHaa6/FG2+8EUuWLImNGzfGjh074qCDDopWrVpFly5dol+/fvv9sZ6ysrJ46623Yvbs2fHuu+/GihUrYs2aNbFly5YoKiqKiIiGDRtGbm5utGrVKo466qjo1q1bnHjiidG9e/fIyMiokj3Gjx8fI0aMSJR9+OGHY9iwYVUyd29asWJFzJs3LxYtWhSrVq2Kjz76KNasWROfffZZFBYWRmFhYZSUlETdunWjQYMGkZOTE3l5edG6deto27ZtdOzYMbp37x5du3aNunXr1sh7KCkpiTfeeCNmzZoV8+bNi+XLl8cnn3wS27dvj5KSkjjooIPi4IMPjjZt2sSxxx4b3/3ud+OUU06JVq1a1ci+aS1cuDA6d+78998XLFgQnTp1qsGNAAAAAAAAAAAAAAAAAAAAAIADkVsolVOnphf4No0aNYoBAwbEgAEDanqVGpWZmRm9evWKXr161egeGzZsSJzNzc2txk2qT/v27aN9+/Y1vUal1KlTJ/r06RN9+vSp6VUAAAAAAAAAAAAAAAAAAAAAAOBrZdb0Auw7lixZkjjbsmXLatwEAAAAAAAAAAAAAAAAAAAAAADYlzmARSK7du2KV199NVE2KysrunfvXs0bAQAAAAAAAAAAAAAAAAAAAAAA+yoHsEjk3nvvjU8//TRRtnPnzpGdnV3NGwEAAAAAAAAAAAAAAAAAAAAAAPuqOjW9ALVTSUlJbN68Od5777144okn4plnnklcO3DgwGrcDAAAAAAAAAAAAAAAAAAAAAAA2Nc5gMVuBg0aFNOmTatwfd26dWPYsGFVuBEAAAAAAAAAAAAAAAAAAAAAALC/yazpBdi/XHzxxdGyZcuaXgMAAAAAAAAAAAAAAAAAAAAAAKjFHMCiyrRv3z7GjBlT02sAAAAAAAAAAAAAAAAAAAAAAAC1nANYVIm8vLyYOnVqNG7c+BszM2fOjIyMjBr7AQAAAAAAAAAAAAAAAAAAAAAAagcHsKi0Y445Jv785z9Ht27danoVAAAAAAAAAAAAAAAAAAAAAABgH+AAFhV2yCGHxOjRo2PevHlx5JFH1vQ6AAAAAAAAAAAAAAAAAAAAAADAPqJOTS/AvqFJkyaRl5cXzZs3j969e0ffvn2jb9++cdBBB9X0agAAAAAAAAAAAAAAAPD/2bnz6KrrM3/gT8IatoBBlgKKFrWAoLKMoD8LWkDHpWLdcEHQKi3QU2pRi7uiPRZxAVGn7h5GW9SxErGVwUEBtYiiDCgIIgIFRwkoEAghxCS/P3pcQKrfG+4lAV6vc/jD5v15njeX3P75AAAAAACwh8nYAawZM2ZkajQZNHny5KquAAAAAAAAAAAAAAAAAAAAAAAA7OWyq7oAAAAAAAAAAAAAAAAAAAAAAAAA+5aaVV2AfUfv3r2joqKiqmsAAAAAAAAAAAAAAAAAAAAAAABVLLuqCwAAAAAAAAAAAAAAAAAAAAAAALBvcQALAAAAAAAAAAAAAAAAAAAAAACA3coBLAAAAAAAAAAAAAAAAAAAAAAAAHarmlVdgD3H448/HhdffHGi7I033hg33XRTZguxUytWrIiFCxfGwoULY8mSJbFx48bYvHlzbNq0KUpKSqJBgwbRsGHDaNCgQTRr1iw6duwYHTt2jMMPPzxyc3Oruj4AAAAAAAAAAAAAAAAAAAAAAPsAB7BgJ7Zs2RLz5s2LhQsXxvLly+Ojjz6K5cuXx+effx6bN2+OoqKiKC4ujvr160dubm7k5ubG/vvvH0cccUR06dIlunTpEh07dtxtfd9444145pln4tlnn42VK1dWakZ2dnb8+Mc/jrPPPjvOOuusaNasWZpbAgAAAAAAAAAAAAAAAAAAAADAPzmABRGxbt26mDZtWrzyyivx1ltvxXvvvRdlZWXf+66wsDAKCwtj1apVERHxyiuvfPWzww47LAYNGhQXXXRRtGrVKiO933rrrRg5cmS8+uqruzyrvLw8ZsyYETNmzIiRI0fG5ZdfHldffXU0bNgwDU0BAAAAAAAAAAAAAAAAAAAAAOBr2VVdAKrSnXfeGT169IjmzZvHBRdcEA8//HDMnz8/0fGr77NkyZK45ppr4qCDDopRo0bFli1b0tD4n7Zt2xbDhg2Lo48+Oi3Hr3a0devWuO222+KQQw6JadOmpX0+AAAAAAAAAAAAAAAAAAAAAAD7Ngew2KdNmDAh5syZE+Xl5RnbUVpaGmPGjImOHTum5VhVSUlJnHbaafEf//EfUVFRkYaG/9qaNWvi5JNPjj//+c8Z3QMAAAAAAAAAAAAAAAAAAAAAwL7FASzYTVasWBF9+vSJyZMn79KcYcOGxbRp09LU6vuVlZXFoEGDYs6cObttJwAAAAAAAAAAAAAAAAAAAAAAezcHsGA32rZtWwwYMCBmz55dqffTp0+PRx99NM2tvl9paWn8/Oc/j7Kyst2+GwAAAAAAAAAAAAAAAAAAAACAvY8DWLCblZSUxAUXXBDFxcUpvx09enQGGiWzcOHCePbZZ6tsP3xpxYoVkZWVtdM/K1asqOp6QCX5bkP143sJVcf3D6of30vYd/i+Vw/+Hbbn8/AZRPgMvrSvfw77+t8/wmcQ4TOI8BlE+AwifAYRPoMIn0GEzyDCZxDhM4jwGezrf/8In8GXfA4k4fcEdh/fN6hefCdh3+C7Dvsm332onnw3oer5HgJ7EgewIKG8vLw49NBD40c/+lE0a9Zsl2YtX748xo8fn9KbVatWxaxZs1J607Bhwxg1alS88cYbsWHDhti2bVusWbMm/vrXv8YZZ5yR0qyIiP/8z/9M+Q0AAAAAAAAAAAAAAAAAAAAAAOyoZlUXYM8xePDgGDx4cFXX2G0OO+ywOP300+OEE06If/u3f4smTZps9/NPP/00pk6dGuPGjYv58+enPP++++6Lq666KrKzk92hmzlzZkrz27RpEy+//HK0a9duu/+9WbNmcfLJJ8fJJ58cjz/+eFxyySVRUVGRaOaMGTOivLw8cWcAAAAAAAAAAAAAAAAAAAAAANgZV2zgG3JycmLIkCHx5ptvxuLFi2PMmDFx4oknfuv4VUREixYtYvDgwfHOO+/E6NGjU961evXqeOONNxLn33///ZTmP/HEE986frWjwYMHx2WXXZZ45ubNm2PVqlUp9QAAAAAAAAAAAAAAAAAAAAAAgB05gAUR0aBBg7j22mvjH//4RzzwwAPRvXv3xG+zs7Pj+uuvj9tuuy3lvbNmzUqcXbduXeLskUceGT/+8Y8TZX/1q18lnhsRUVBQkFIeAAAAAAAAAAAAAAAAAAAAAAB2VLOqC3yX9957L/Lz8+P111+PxYsXx9q1a6O4uDjq168frVu3jk6dOkWfPn3izDPPjCZNmlRp14qKinjttdciPz8/5s6dGx988EFs2LAhiouLo0aNGvGDH/wgJk+eHF26dKnU/MLCwnjrrbdi3rx5sWzZsli9enVs3LgxiouLIyIiJycnGjduHK1bt4527dpFly5donv37lG/fv10/jX3OjVr1oxf/vKXMXr06Nh///13adbvfve7ePbZZ2Pu3LmJ3yxZsmSXdv4rPXv2TJw9/PDDo169erFly5ZE+dLS0srWAgAAAAAAAAAAAAAAAAAAAACAiMjgAazevXvHzJkzE2WXL18ebdu2/eq/Z8+eHVdfffW/fF9YWBiLFi2KRYsWxVNPPRW//vWv47LLLosbbrgh8vLyUu66YsWKOOiggxJle/XqFTNmzNjuf3vuuefi+uuvj4ULF+70TVlZWaxatSoKCwtT6lVUVBSTJk2KJ598Ml577bWUDw/VqVMnevXqFQMHDoyzzjor6tatm9L7HU2fPj3uvvvuRNnzzz8/zj///F3atzu89NJLif/tv09WVlYMGzYsLrnkksRv1q5dmzibypG3/fbbL3E2KysrmjRpkvgAVuPGjRPPBgAAAAAAAAAAAAAAAAAAAACAncnYAazKKC0tjauuuirGjx8fFRUVid8VFxfHPffcE5MmTYqJEyfGiSeemMGWX9u6dWv8/Oc/jz/96U9pnVtcXBzjxo2L22+/PTZs2FDpOSUlJTFt2rSYNm1ajBw5Mq699toYOnRo1KpVq1LzVq1aFX/9618TZbt161apHbtbuo5ffal3794p5UtKShJnDz300MTZTZs2pdQjaT47OzvatGmT0mwAAAAAAAAAAAAAAAAAAAAAANhRdlUX+FJRUVGcdNJJMW7cuJSOX31TQUFBnHLKKfHwww+nud23bd68Ofr27Zv241ezZ8+OI444Iq655ppdOn61o4KCghgxYkR079493n333bTNZXstW7ZMKZ+bm5s4e9xxxyXOzps3L3H2o48+isLCwkTZTp06RcOGDRPPBgAAAAAAAAAAAAAAAAAAAACAnakWB7C2bdsWp5xySrz88su7PKusrCyGDBmS9sNUO+4477zz4rXXXkvr3Mceeyx69eoVS5cuTevcb5o/f3707Nkz8vPzM7ZjX1ZaWppS/oADDkicPeSQQ6Jnz56Jsq+//nosWbIkUfaxxx5L3OGcc85JnAUAAAAAAAAAAAAAAAAAAAAAgH+lWhzAGjZsWMycOTNt8yoqKuLiiy+OefPmpW3mN912223xwgsvpHXmAw88EJdccknKB5Qqo6ioKM4888x47rnnMr5rX7No0aKU8t26dUspf/311yfKlZeXx0UXXRSbN2/+ztzrr78eY8eOTTSzUaNGMWTIkERZAAAAAAAAAAAAAAAAAAAAAAD4LtXiANb06dPTPnPbtm1x/vnnx7Zt29I6d9myZTF69Oi0zvzb3/4Ww4YNS+vM71NWVhbnnXdevP3227t1797u+eefT5ytVatWnHjiiSnN//d///e45JJLEmXffPPN6Nq1azz99NOxZcuW7X728ccfx8033xw/+clPoqSkJNG8O++8M5o2bZpSXwAAAAAAAAAAAAAAAAAAAAAA2JmaVV3gX8nOzo5mzZpFkyZNoqCgID777LOUZyxevDjuvvvu+N3vfpe2XqtXr07brIiINWvWxKBBg6K8vDytc5MoKSmJc889NxYsWBD16tXb7fv3Nps2bYoHHnggcf7UU0+NvLy8lPf88Y9/jLVr18aUKVO+N/vBBx/EueeeGzVr1ow2bdpEvXr14vPPP49PPvkkpZ0jRoyISy+9NOWuAAAAAAAAAAAAAAAAAAAAAACwM9lVXWBHzZo1i/vvvz/WrFkTn3zySSxatCjWrVsXy5cvjxtvvDHq1q2b0ryxY8fGli1bMtR2140aNSrWrVuX0pvOnTvHvffeG0uWLIlNmzZFYWFhLFq0KO6666445JBDUpq1bNmyGDNmTEpv2LlRo0YlPtSWlZUVN9xwQ6X21KpVK5577rm49tprIzs72Vf4iy++iOXLl8fChQtTOn5Vo0aNuOWWW2LcuHGV6goAAAAAAAAAAAAAAAAAAAAAADtTs6oLfFO3bt1i6tSpkZeX962ftW3bNm666aY455xz4vjjj4+CgoJEMz/77LN44oknYsiQIemu+5WOHTvGhRdeGH369Ik2bdpEXl5ebNmyJVauXBnz5s2LadOmRX5+/rfeLV26NCZOnJjSruuuuy5uvPHGqFlz+3+69u3bR/v27WPo0KExYsSIePDBBxPPvPPOO+Pyyy+Pxo0bp9SFrz399NNx//33J84PHjw4jjzyyErvq1GjRtx6661x7rnnxnXXXRdTpkyJioqKSs/bmeOOOy7Gjh0bRx99dFrnflNBQUGsXbs2pTcffvhhhtoAAAAAAAAAAAAAAAAAAAAAALC7VJsDWC1atIgXX3xxp8evvqlDhw6Rn58fxx57bJSXlyeaPXHixIwcwKpdu3aMGzcuhgwZEjVq1NjuZ40aNYpOnTpFp06d4qKLLorNmzdHWVnZdpkJEyYk/jtERIwcOTJuueWW78zUrVs3/vjHP0ZhYWFMmjQp0dyioqJ45JFHYuTIkYm78LXp06fHRRddlDjfrl27GDduXFp2d+rUKfLz82PRokVx4YUXxrx583ZpXq1ateKCCy6Iiy++OH784x+npeN3uf/+++Pmm2/O+B4AAAAAAAAAAAAAAAAAAAAAAKqX7Kou8KVbb701mjZtmijbo0ePGDRoUOLZs2fPjvXr11e22k7Vrl07pkyZEkOHDv3W8audadCgQeTm5n713+Xl5fHMM88k3te2bdu49dZbE2WzsrJiwoQJ0bhx48Tzn3rqqcRZvjZlypQ49dRTo6SkJFE+Nzc3nn/++WjUqFFa9n95+Kpbt267fPwqIqK0tDRef/31eOmll2L16tVpaAgAAAAAAAAAAAAAAAAAAAAAAN9WLQ5gNW7cOAYOHJjSm+HDhyfOlpeXx6xZs1Kt9Z2uv/766NevX6Xfv/322/Hpp58mzl9++eVRt27dxPmmTZvGpZdemjg/d+7cKCgoSJwn4r777oszzjgjtm7dmiifk5MTU6ZMifbt2+/y7qKiohgyZEh06tQpnnzyySguLt7lmV9aunRp3HrrrfHDH/4whg8fHhs2bEjbbAAAAAAAAAAAAAAAAAAAAAAAiIioWdUFIiJOPfXUqF27dkpvunbtGgceeGCsXLkyUX7evHlx+umnV6bet7Ru3TpGjRq1SzNmz56dOJuVlRVnn312yjsGDBgQd9xxR6JsRUVFzJkzJ0477bSU9+xrysvL48orr4y77ror8Zu6devG5MmT47jjjtvl/cuXL48TTzwxli5dusuzvsu2bdvi/vvvjxdeeCH+/Oc/xzHHHJP2HcOGDUv5d/vDDz+M/v37p70LAAAAAAAAAAAAAAAAAAAAAAC7T7U4gNW9e/dKvevSpUviA1hLliyp1I6dufTSS6NmzV376N59993E2YMPPjhatmyZ8o6jjjoqcnJyori4OFF+wYIFDmB9j+Li4rjwwgvjL3/5S+I39erVi/z8/OjTp88u71+9enUcf/zxiX/v0+Ef//hH9O3bN/72t79Fr1690jq7WbNm0axZs7TOBAAAAAAAAAAAAAAAAAAAAACg+suu6gIRER06dMj4u48//rhSO3bmlFNO2eUZK1asSJzt1KlTpXZkZ2dH+/btE+d351GlPVFBQUEcf/zxKR2/atKkSbz00ktpOX5VUVERgwYNSunfqX379jFhwoR49913Y8OGDbFt27ZYs2ZNTJs2LYYOHRp169ZNNGfLli1x+umnx6pVqypbHwAAAAAAAAAAAAAAAAAAAAAAvlItDmDl5eVV6t1+++2XOLt+/fpK7dhRnTp1onPnzrs8Z+3atYmzzZs3r/SeVN4WFBRUes/ebvHixdGjR4+YM2dO4jdt2rSJV199NY455pi0dHjyySfj5ZdfTpy/8sor4913341f/epXcfjhh0dubm7UqlUrmjVrFn379o37778/3nnnnWjXrl2ieRs3boxf/OIXla0PAAAAAAAAAAAAAAAAAAAAAABfqRYHsBo2bJjxd1u3bq3Ujh21bt06ateuvctzioqKEmcr+/lERDRq1Chxvwe0ZQAAIABJREFUdsuWLZXeszebOXNmHHPMMbF8+fLEb4466qiYM2dOdOzYMW09xo8fnzg7cODAuP3226NGjRrfmWvfvn288MILkZOTk2juiy++GPPnz0/cAwAAAAAAAAAAAAAAAAAAAAAAdqZaHMAqKyvL+LusrKxK7dhR48aN0zKnoqIicTY7u/L/TN93AOmbUum0r3jiiSeiX79+sX79+sRvTjnllJg1a1a0bNkybT0++uijmDt3bqJsrVq14rbbbks8+7DDDotLL700cf7RRx9NnAUAAAAAAAAAAAAAAAAAAAAAgJ2pFgewNm3alPF3OTk5ldqxowYNGqRlTip9Nm/eXOk9hYWFibPp+oz2FqNHj46BAwfGtm3bEr8ZPnx45Ofnp+335Euvv/564myPHj2iVatWKc3v379/4uyrr76a0mwAAAAAAAAAAAAAAAAAAAAAANhRzaouEBGxZs2aSr0rKChInG3atGmldmRKXl5e4uxnn31W6T2pvN1vv/0qvWdvUlpaGkOGDInHH3888Zvs7OwYO3Zs/Pa3v81Ipw8++CBxtnPnzinP79SpU+LsggULoqKiIrKyslLeAwAAAAAAAAAAAAAAAAAAAAAAERHZVV0gIuK9997L+LtWrVpVakemHHDAAYmzlf18IiIWLVqUOHvggQdWes/eYsOGDXHSSSeldPyqXr168eyzz2bs+FVExOeff54427hx45Tnp/KmrKwsNmzYkPIOAAAAAAAAAAAAAAAAAAAAAAD4UrU4gDV79uyU35SVlcXcuXMT5zt27Jjyjkzq0KFD4uySJUti06ZNKe9YunRpbNy4MSOd9kYrV66MY489Nl5++eXEb5o3bx4zZsyI/v37Z7BZxLZt2xJnN2/enPL8VN8UFRWlvAMAAAAAAAAAAAAAAAAAAAAAAL5ULQ5gTZ06NQoLC1N6M3PmzFi3bl3ifNeuXVOtlVE9evRInP3iiy8iPz8/5R3PPPNMSvmjjz465R17i7lz50aPHj1i0aJFid906NAh5syZE927d89gs3+qV69e4uyHH36Y8vxU36TSBwAAAAAAAAAAAAAAAAAAAAAAdlQtDmCVlJTEhAkTUnozduzYxNk6derEsccem2qtjOrZs2c0atQocX7ChAlRUVGROL9169Z46KGHEucPPfTQaNu2beL83iQ/Pz969eoVn376aeI3P/nJT+Lvf/97HHjggRls9rVWrVolzs6aNSuKiopSmj916tTE2Tp16kTjxo1Tmg8AAAAAAAAAAAAAAAAAAAAAAN9ULQ5gRUT84Q9/iPfffz9R9umnn07pYE/fvn0jJyenstUyok6dOvHTn/40cf7NN9+Mhx9+OHH+tttuixUrViTOn3XWWYmze5N77rknfvazn8WWLVsSv7n44ovjxRdfjNzc3Aw221779u0TZzdt2hR333134vz69evj3nvvTZxv165dZGdXm//rAAAAAAAAAAAAAAAAAAAAAABgD1Rtrths3rw5+vXrFwsWLPjOXH5+fgwaNCil2YMHD96FZpkzfPjwlPOTJ0/+3tyDDz4Yt9xyS+K5NWvWjF/+8pcpddlbjBgxIsrLy1N689hjj0Xt2rUjKysrLX+SHCo75phjUjo6dfPNN8d//dd/fW+usLAwzjrrrCgoKEg8+//9v/+XOAsAAAAAAAAAAAAAAAAAAAAAADtTbQ5gRUSsXr06unXrFkOHDo0ZM2ZEQUFBbNu2Lf7v//4vnn/++fjZz34W/fv3j61btyaeefDBB0f//v0z2LryevToEX379k2cLy0tjTPOOCMGDhwYs2fPjoqKiq9+Vl5eHjNnzozTTz89fvGLX2z3s+8zaNCgaNOmTUrd2b3y8vKid+/eifNffPFFnH322XHBBRfE7Nmz44svvtju5+vWrYuHH344jjzyyHj55ZdT6nLGGWeklAcAAAAAAAAAAAAAAAAAAAAAgB1lVaRyKSkFvXv3jpkzZ2ZidEqeeOKJuOCCC74zs2LFijjooIMSzevVq1fMmDEjDc3+6f3334+jjjoqSkpKUn5bv379aNmyZVRUVMQnn3wSW7ZsSXlGkyZN4v3334/mzZt/b/bxxx+Piy++ONHcG2+8MW666aaU++xuWVlZVV0hli9fHm3btv3e3HPPPRc/+9nPKrUjJycnWrZsGTk5ObF+/fr4v//7v0rNadeuXSxevDhq1KhRqffpsHDhwjj88MO/+u/33nsvOnbsWGV9AAAAAAAAAAAAAAAAAAAAAIB9k1souya7qgtk0gknnPC9x6+qWvv27eOOO+6o1NuioqL48MMPY9myZZU6fhUR8cgjjyQ6fkXV69+/f/Ts2bNSb4uLi+Ojjz6KhQsXVvr4VUTE73//+yo9fgUAAAAAAAAAAAAAAAAAAAAAwN6hWhzAysSRqqZNm8bEiRPTPjcTfvWrX8Xw4cN3+97f//73ccYZZ+z2vVROVlZWPP7445Gbm1sl+wcMGBDnnHNOlewGAAAAAAAAAAAAAAAAAAAAAGDvUi0OYN1yyy1pPYKVk5MTzz//fLRq1SptMzNtwoQJ8etf/3q37MrKyopbbrklrrnmmt2yj/Q59NBD47nnnov69evv1r3HH398PPbYY7t1JwAAAAAAAAAAAAAAAAAAAAAAe69qcQArKysrJk6cGJdddtkuz8rNzY0XX3wxevbsmYZmu09WVlaMHz8+HnrooWjQoEHG9uy3337xzDPPxHXXXZexHWTW8ccfHzNnzoyDDjpot+y7+OKL48UXX4y6devuln0AAAAAAAAAAAAAAAAAAAAAAOz9qsUBrIiI7OzsePDBB+OBBx6Ihg0bVmpGt27d4q233opevXqlud3uc+mll8aCBQvi7LPPjqysrLTNrVGjRgwePDgWLlwYZ555ZtrmUjW6du0aCxYsiKuuuipycnIysqN9+/YxZcqUePTRR6NOnToZ2QEAAAAAAAAAAAAAAAAAAAAAwL6p2hzA+tKQIUNi8eLFMXz48GjQoEGiNz/60Y/ikUceiTlz5sQhhxyS4YaZd9BBB8XTTz8dCxYsiOHDh0fTpk0rPatFixbx29/+Nt5///147LHHokWLFpWeVVZWljhbu3btSu8hmQYNGsSYMWNixYoVcdttt0Xnzp13eWZOTk789Kc/jcmTJ8e7774bp556ahqaAgAAAAAAAAAAAAAAAAAAAADA9mpWdYGd+cEPfhD33ntvjBkzJl5++eV4/fXXY/HixbFu3brYunVr1KtXL1q1ahWdOnWKvn37Rvfu3XdpX9u2baOioiJN7dPn8MMPj3vvvTfuueeeePPNN+O1116LefPmxbJly+Ljjz+ODRs2RHFxcUT883BRkyZNolWrVnHIIYfEUUcdFccdd1x07do1srKy0tJny5YtibNNmjRJy85Mq47/7qlq1qxZjBo1KkaNGhWrVq2K119/Pd55551YsmRJrFy5MtauXRsbN26MkpKSKCsri1q1akVOTk7k5eVFixYt4uCDD44OHTrE0UcfHT179oycnJyq/isBAAAAAAAAAAAAAAAAAAAAALCXq5YHsL5Uv379OO200+K0006r6ipVKjs7O3r06BE9evSo0h5r165NnN1TDmDtbdq0aRMDBgyIAQMGVHUVAAAAAAAAAAAAAAAAAAAAAAD4l7KrugB7jsWLFyfOtmzZMoNNAAAAAAAAAAAAAAAAAAAAAACAPZkDWCRSWloa06dPT5StUaNGdO3aNcONAAAAAAAAAAAAAAAAAAAAAACAPZUDWCQyZsyY+PzzzxNlDz/88GjQoEGGGwEAAAAAAAAAAAAAAAAAAAAAAHuqmlVdgOrpiy++iPXr18f//u//xqOPPhqTJk1K/PanP/1pBpsBAAAAAAAAAAAAAAAAAAAAAAB7Ogew2E7//v0jPz+/0u9r1aoVQ4cOTWMjAAAAAAAAAAAAAAAAAAAAAABgb5Nd1QXYuwwePDhatmxZ1TUAAAAAAAAAAAAAAAAAAAAAAIBqzAEs0uaHP/xh3HnnnVVdAwAAAAAAAAAAAAAAAAAAAAAAqOYcwCIt8vLy4plnnomGDRv+y8yMGTMiKyuryv4AAAAAAAAAAAAAAAAAAAAAAADVgwNY7LLDDjss3njjjTjqqKOqugoAAAAAAAAAAAAAAAAAAAAAALAHcACLStt///3j9ttvj3feeSfatWtX1XUAAAAAAAAAAAAAAAAAAAAAAIA9RM2qLsCeoVGjRpGXlxfNmzePnj17xgknnBAnnHBC1KtXr6qrAQAAAAAAAAAAAAAAAAAAAAAAe5iMHcCaMWNGpkaTQZMnT67qCgAAAAAAAAAAAAAAAAAAAAAAwF4uu6oLAAAAAAAAAAAAAAAAAAAAAAAAsG+pWdUF2Hf07t07KioqqroGAAAAAAAAAAAAAAAAAAAAAABQxbKrugAAAAAAAAAAAAAAAAAAAAAAAAD7FgewAAAAAAAAAAAAAAAAAAAAAAAA2K0cwAIAAAAAAAAAAAAAAAAAAAAAAGC3cgCLxB5//PHIyspK9Oemm26q6roAAAAAAAAAAAAAAAAAAAAAAEA1VbOqC0B1tGXLlpg3b14sXLgwli9fHh999FEsX748Pv/889i8eXMUFRVFcXFx1K9fP3JzcyM3Nzf233//OOKII6JLly7RpUuX6NixY9r6PP7443HxxRenbd6uqKioqOoKAAAAAAAAAAAAAAAAAAAAAADs4RzAgohYt25dTJs2LV555ZV466234r333ouysrLvfVdYWBiFhYWxatWqiIh45ZVXvvrZYYcdFoMGDYqLLrooWrVqlbHuAAAAAAAAAAAAAAAAAAAAAACwp8mu6gJQle68887o0aNHNG/ePC644IJ4+OGHY/78+YmOX32fJUuWxDXXXBMHHXRQjBo1KrZs2ZKGxgAAAAAAAAAAAAAAAAAAAAAAsOdzAIt92oQJE2LOnDlRXl6esR2lpaUxZsyY6NixY7z66qsZ2wMAAAAAAAAAAAAAAAAAAAAAAHsKB7BgN1mxYkX06dMnJk+eXNVVAAAAAAAAAAAAAAAAAAAAAACgSjmABbvRtm3bYsCAATF79uyqrgIAAAAAAAAAAAAAAAAAAAAAAFXGASzYzUpKSuKCCy6I4uLiqq4C1d6KFSsiKytrp39WrFhR1fVgp/zeQub5nkH6+D6xJ/B7CpnlOwbpsy99n/aEv2t17ajXntlHpz2zj057Zh+d9NlbO1W3PjrtmX2qY6fq1qc6dqpufXTaM/votGf2qY6d9NFpd9jT+uLfDJLwPYHdz/eOPY3fWfhuviNQNXz32Fv4XWZf5vcfqobvXtVwAAsSysvLi0MPPTR+9KMfRbNmzXZp1vLly2P8+PFparb79O3bt6orAAAAAAAAAAAAAAAAAAAAAACwF3AAi8QGDx4cFRUVif7cdNNNVV13lx122GFx1VVXxdSpU+Pzzz+PdevWxZIlS+L999+PNWvWxCeffBKPPfZYHHHEEZWaf99990V5eXmaW2fWFVdcUdUVAAAAAAAAAAAAAAAAAAAAAADYCziABd+Qk5MTQ4YMiTfffDMWL14cY8aMiRNPPDGaNGnyrWyLFi1i8ODB8c4778To0aNT3rV69ep44403EmVTOT6Wyp8LL7wwcd/OnTtHv379Uv57AgAAAAAAAAAAAAAAAAAAAADAjmpWdQGoDho0aBAjRoyI3/zmN9G0adOU3mZnZ8f1118ftWrViquvvjqlt7NmzYpjjjkmpTfpsnr16njqqacS56+44ooMtgEAAAAAAAAAAAAAAAAAAAAAYF9SrQ9gvffee5Gfnx+vv/56LF68ONauXRvFxcVRv379aN26dXTq1Cn69OkTZ555ZjRp0qRKu1ZUVMRrr70W+fn5MXfu3Pjggw9iw4YNUVxcHDVq1Igf/OAHMXny5OjSpUul5hcWFsZbb70V8+bNi2XLlsXq1atj48aNUVxcHBEROTk50bhx42jdunW0a9cuunTpEt27d4/69eun86+516lZs2b88pe/jNGjR8f++++/S7N+97vfxbPPPhtz585N/GbJkiW7tHNXjB8/PkpLSxNlW7duHQMGDMhwIwAAAAAAAAAAAAAAAAAAAAAA9hUZO4DVu3fvmDlzZqLs8uXLo23btl/99+zZs+Pqq6/+l+8LCwtj0aJFsWjRonjqqafi17/+dVx22WVxww03RF5eXspdV6xYEQcddFCibK9evWLGjBnb/W/PPfdcXH/99bFw4cKdvikrK4tVq1ZFYWFhSr2Kiopi0qRJ8eSTT8Zrr72W+FjRl+rUqRO9evWKgQMHxllnnRV169ZN6f2Opk+fHnfffXei7Pnnnx/nn3/+Lu3bHV566aXE//bfJysrK4YNGxaXXHJJ4jdr165Ny+5UFRYWxoMPPpg4P2LEiKhVq1YGGwEAAAAAAAAAAAAAAAAAAAAAsC/J2AGsyigtLY2rrroqxo8fHxUVFYnfFRcXxz333BOTJk2KiRMnxoknnpjBll/bunVr/PznP48//elPaZ1bXFwc48aNi9tvvz02bNhQ6TklJSUxbdq0mDZtWowcOTKuvfbaGDp0aKUPGa1atSr++te/Jsp269atUjt2t3Qdv/pS7969U8qXlJSkdX9SDz74YOKDbI0aNYohQ4ZkuBEAAAAAAAAAAAAAAAAAAAAAAPuS7Kou8KWioqI46aSTYty4cSkdv/qmgoKCOOWUU+Lhhx9Oc7tv27x5c/Tt2zftx69mz54dRxxxRFxzzTW7dPxqRwUFBTFixIjo3r17vPvuu2mby/ZatmyZUj43NzdDTf610tLSGD9+fOL8kCFDolGjRhlsBAAAAAAAAAAAAAAAAAAAAADAvqZaHMDatm1bnHLKKfHyyy/v8qyysrIYMmRI2g9T7bjjvPPOi9deey2tcx977LHo1atXLF26NK1zv2n+/PnRs2fPyM/Pz9iOfVlpaWlK+QMOOCBDTf61SZMmxerVqxNla9WqFSNGjMhwIwAAAAAAAAAAAAAAAAAAAAAA9jXV4gDWsGHDYubMmWmbV1FRERdffHHMmzcvbTO/6bbbbosXXnghrTMfeOCBuOSSS1I+oFQZRUVFceaZZ8Zzzz2X8V37mkWLFqWU79atW4aa/Gt33HFH4uyAAQOidevWGWwDAAAAAAAAAAAAAAAAAAAAAMC+qFocwJo+fXraZ27bti3OP//82LZtW1rnLlu2LEaPHp3WmX/7299i2LBhaZ35fcrKyuK8886Lt99+e7fu3ds9//zzibO1atWKE088MYNtvm3atGmxYMGCxPkrrrgig20AAAAAAAAAAAAAAAAAAAAAANhXVYsDWDuTnZ0dLVq0iPbt20deXl6lZixevDjuvvvutPZavXp1lJaWpm3emjVrYtCgQVFeXp62mUmVlJTEueeeG1u2bNntu/dGmzZtigceeCBx/tRTT63073Zl3XHHHYmz/fr1i86dO2ewDQAAAAAAAAAAAAAAAAAAAAAA+6pqdwCrWbNmcf/998eaNWvik08+iUWLFsW6deti+fLlceONN0bdunVTmjd27NhqfeBp1KhRsW7dupTedO7cOe69995YsmRJbNq0KQoLC2PRokVx1113xSGHHJLSrGXLlsWYMWNSesPOjRo1Kj777LNE2aysrLjhhhsy3Gh78+fPj5deeilx/oorrshgGwAAAAAAAAAAAAAAAAAAAAAA9mU1q7rAN3Xr1i2mTp0aeXl53/pZ27Zt46abbopzzjknjj/++CgoKEg087PPPosnnngihgwZku66X+nYsWNceOGF0adPn2jTpk3k5eXFli1bYuXKlTFv3ryYNm1a5Ofnf+vd0qVLY+LEiSntuu666+LGG2+MmjW3/6dr3759tG/fPoYOHRojRoyIBx98MPHMO++8My6//PJo3LhxSl342tNPPx33339/4vzgwYPjyCOPzGCjb7vjjjsSZ4844ojo27dvBtv8U0FBQaxduzalNx9++GGG2gAAAAAAAAAAAAAAAAAAAAAAsLtUmwNYLVq0iBdffHGnx6++qUOHDpGfnx/HHntslJeXJ5o9ceLEjBzAql27dowbNy6GDBkSNWrU2O5njRo1ik6dOkWnTp3ioosuis2bN0dZWdl2mQkTJiT+O0REjBw5Mm655ZbvzNStWzf++Mc/RmFhYUyaNCnR3KKionjkkUdi5MiRibvwtenTp8dFF12UON+uXbsYN25cBht92+rVq+Opp55KnL/iiisy2OZr999/f9x88827ZRcAAAAAAAAAAAAAAAAAAAAAANVHdlUX+NKtt94aTZs2TZTt0aNHDBo0KPHs2bNnx/r16ytbbadq164dU6ZMiaFDh37r+NXONGjQIHJzc7/67/Ly8njmmWcS72vbtm3ceuutibJZWVkxYcKEaNy4ceL5qRxH4mtTpkyJU089NUpKShLlc3Nz4/nnn49GjRpluNn2xo0bF6WlpYmyrVu3jnPPPTfDjQAAAAAAAAAAAAAAAAAAAAAA2JdViwNYjRs3joEDB6b0Zvjw4Ymz5eXlMWvWrFRrfafrr78++vXrV+n3b7/9dnz66aeJ85dffnnUrVs3cb5p06Zx6aWXJs7PnTs3CgoKEueJuO++++KMM86IrVu3Jsrn5OTElClTon379hlutr3CwsJ46KGHEud/85vfRK1atTLYCAAAAAAAAAAAAAAAAAAAAACAfV3Nqi4QEXHqqadG7dq1U3rTtWvXOPDAA2PlypWJ8vPmzYvTTz+9MvW+pXXr1jFq1KhdmjF79uzE2aysrDj77LNT3jFgwIC44447EmUrKipizpw5cdppp6W8Z19TXl4eV155Zdx1112J39StWzcmT54cxx13XAab7dwDDzwQhYWFibKNGjWKyy67LMONvjZs2LCUf7c//PDD6N+/f4YaAQAAAAAAAAAAAAAAAAAAAACwO1SLA1jdu3ev1LsuXbokPoC1ZMmSSu3YmUsvvTRq1ty1j+7dd99NnD344IOjZcuWKe846qijIicnJ4qLixPlFyxY4ADW9yguLo4LL7ww/vKXvyR+U69evcjPz48+ffpksNnOlZaWxj333JM4/4tf/CIaNWqUwUbba9asWTRr1my37QMAAAAAAAAAAAAAAAAAAAAAoHrIruoCEREdOnTI+LuPP/64Ujt25pRTTtnlGStWrEic7dSpU6V2ZGdnR/v27RPnkx4T21cVFBTE8ccfn9LxqyZNmsRLL71UJcevIiL+/Oc/x+rVqxNla9WqFSNGjMhwIwAAAAAAAAAAAAAAAAAAAAAAiKhZ1QUiIvLy8ir1br/99kucXb9+faV27KhOnTrRuXPnXZ6zdu3axNnmzZtXek8qbwsKCiq9Z2+3ePHiOPnkk2P58uWJ37Rp0yZefPHF6NixYwabfbc777wzcfa8886LVq1aZbANAAAAAAAAAAAAAAAAAAAAAAD8U3ZVF4iIaNiwYcbfbd26tVI7dtS6deuoXbv2Ls8pKipKnK3s5xMR0ahRo8TZLVu2VHrP3mzmzJlxzDHHpHT86qijjoo5c+ZU6fGr//7v/44FCxYkzo8cOTKDbQAAAAAAAAAAAAAAAAAAAAAA4GvV4gBWWVlZxt9lZWVVaseOGjdunJY5FRUVibPZ2ZX/Z6pRo0bibCqd9hVPPPFE9OvXL9avX5/4zSmnnBKzZs2Kli1bZrDZ97vjjjsSZ/v16xedO3fOYBsAAAAAAAAAAAAAAAAAAAAAAPhatTiAtWnTpoy/y8nJqdSOHTVo0CAtc1Lps3nz5krvKSwsTJxN12e0txg9enQMHDgwtm3blvjN8OHDIz8/P22/J5U1f/78+J//+Z/E+SuvvDKDbQAAAAAAAAAAAAAAAAAAAAAAYHs1q7pARMSaNWsq9a6goCBxtmnTppXakSl5eXmJs5999lml96Tydr/99qv0nr1JaWlpDBkyJB5//PHEb7Kzs2Ps2LHx29/+NnPFUjB27NjE2SOPPDL69OmTwTYAAAAAAAAAAAAAAAAAAAAAALC97KouEBHx3nvvZfxdq1atKrUjUw444IDE2cp+PhERixYtSpw98MADK71nb7Fhw4Y46aSTUjp+Va9evXj22WerzfGrVatWxVNPPZU4f8UVV2SwDQAAAAAAAAAAAAAAAAAAAAAAfFvNqi4QETF79uyU35SVlcXcuXMT5zt27Jjyjkzq0KFD4uySJUti06ZN0bBhw5R2LF26NDZu3JiRTnujlStXxsknn5zS0bDmzZvHlClTonv37hlslppx48bFF198kSjbpk2bOPfcczPcCAAAAAAAAAAAAAAAAAAAAAAAtpdd1QUiIqZOnRqFhYUpvZk5c2asW7cucb5r166p1sqoHj16JM5+8cUXkZ+fn/KOZ555JqX80UcfnfKOvcXcuXOjR48eKR2/6tChQ8yZM6daHb/auHFjPPzww4nzv/nNb6JmzWpxBw8AAAAAAAAAAAAAAAAAAAAAgH1ItTiAVVJSEhMmTEjpzdixYxNn69SpE8cee2yqtTKqZ8+e0ahRo8T5CRMmREVFReL81q1b46GHHkqcP/TQQ6Nt27aJ83uT/Pz86NWrV3z66aeJ3/zkJz+Jv//973HggQdmsFnqHnjggcTH5HJzc+Oyyy7LcCMAAAAAAAAAAAAAAAAAAAAAAPi2anEAKyLiD3/4Q7z//vuJsk8//XRMnTo18ez/z869RlldkPsDf/YwXEbAAQcFBA3yioDKrQOVf9BAXXnDWykmg6Yo0JKSpZE3EHIZmopAlrf0kBXiKhmtNDooKIYKhSLXlBgPqHFRcYQZhnFm/i/OyrREfntm79kDfD5r7Rfwey7fPXvvt8+QIUOioKCgrtGyonnz5nHmmWcmrn/55ZfjgQceSFx/6623RmlpaeL68847L3Ht3mTatGlxzjnnRHl5eeKeSy65JJ566qkoLCzMYrL0VVVVxbRp0xLXX3HFFdG6dessJgIAAAAAAAA1kgQ6AAAgAElEQVQAAAAAAAAAAAAAgM/WaA5gbdu2LU4++eRYtmzZ59aVlJREcXFxWrNHjBhRj2TZM2bMmLTr58yZs9u6++67LyZPnpx4bn5+flx55ZVpZdlbjB07NmpqatLqeeihh6JZs2aRSqUy8krnUNnn+dWvfhVvvfVWotqmTZvGVVddlZG9AAAAAAAAAAAAAAAAAAAAAACQrkZzACsiYsOGDdG3b98YNWpUzJ8/PzZt2hQ7d+6Mt99+O5544ok455xzYujQobFjx47EM7/4xS/G0KFDs5i67vr37x9DhgxJXF9VVRVnn312XHzxxbFo0aKora39+FlNTU0sWLAgzjrrrLjiiis+9Wx3iouL45BDDkkrO43PHXfckbj2wgsvjE6dOmUxDQAAAAAAAAAAAAAAAAAAAAAA7FqqNp1LSWkYNGhQLFiwIBuj0/LII4/ERRdd9Lk1paWl0bVr10TzBg4cGPPnz89Asv+zatWq6NWrV1RWVqbd27Jly+jYsWPU1tbGO++8E+Xl5WnPaNu2baxatSrat2+/29qHH344LrnkkkRzJ0yYEBMnTkw7T0NLpVK5jhDr1q2LLl261GvGH//4xzj11FMT1y9btix69uxZr525smLFiujRo8fH/16+fHl07949h4kAAAAAAAAAAAAAAAAAAAAAgH2RWyj1k5frANl00kkn7fb4Va5169YtfvzjH9epd/v27fHGG2/E2rVr63T8KiLiwQcfTHT8isbt9ttvT1x7yimn7LHHrwAAAAAAAAAAAAAAAAAAAAAA2Ds0igNY2ThS1a5du5g5c2bG52bDd77znRgzZkyD773lllvi7LPPbvC9ZNYrr7wS8+bNS1x/zTXXZDENAAAAAAAAAAAAAAAAAAAAAADsXqM4gDV58uSMHsEqKCiIJ554Ijp16pSxmdk2ffr0uOqqqxpkVyqVismTJ8d1113XIPvIrttvvz1x7fHHHx9f+9rXspgGAAAAAAAAAAAAAAAAAAAAAAB2r1EcwEqlUjFz5sy4/PLL6z2rsLAwnnrqqRgwYEAGkjWcVCoVd999d9x///3RqlWrrO054IAD4rHHHosbbrghaztoOOvXr4/Zs2cnrr/mmmuymAYAAAAAAAAAAAAAAAAAAAAAAJJpFAewIiLy8vLivvvui3vvvTdat25dpxl9+/aNxYsXx8CBAzOcruFcdtllsWzZsjj//PMjlUplbG6TJk1ixIgRsWLFijj33HMzNpfcuuuuu+Kjjz5KVHvooYfGN77xjSwnAgAAAAAAAAAAAAAAAAAAAACA3Ws0B7D+aeTIkbF69eoYM2ZMtGrVKlHP0UcfHQ8++GC89NJLccQRR2Q5YfZ17do1Zs+eHcuWLYsxY8ZEu3bt6jyrQ4cOcfXVV8eqVavioYceig4dOtR5VnV1deLaZs2a1XkPyXzwwQfxwAMPJK4fO3Zs5OfnZzERAAAAAAAAAAAAAAAAAAAAAAAk0yiv4Rx88MExY8aMmDJlSjzzzDPxwgsvxOrVq2PLli2xY8eO2G+//aJTp07Rs2fPGDJkSPTr169e+7p06RK1tbUZSp85PXr0iBkzZsS0adPi5ZdfjoULF8bSpUtj7dq18dZbb8XWrVujoqIiIiIKCgqibdu20alTpzjiiCOiV69eccIJJ0SfPn0ilUplJE95eXni2rZt22ZkZ7Y1xs89qcLCwigrK8t1DAAAAAAAAAAAAAAAAAAAAAAASFujPID1Ty1btowzzjgjzjjjjFxHyam8vLzo379/9O/fP6c5Nm/enLh2TzmABQAAAAAAAAAAAAAAAAAAAAAANLy8XAdgz7F69erEtR07dsxiEgAAAAAAAAAAAAAAAAAAAAAAYE/mABaJVFVVxbx58xLVNmnSJPr06ZPlRAAAAAAAAAAAAAAAAAAAAAAAwJ7KASwSmTJlSrz33nuJanv06BGtWrXKciIAAAAAAAAAAAAAAAAAAAAAAGBPlZ/rADROH330Ubz//vvxyiuvxM9//vOYNWtW4t4zzzwzi8kAAAAAAAAAAAAAAAAAAAAAAIA9nQNYfMrQoUOjpKSkzv1NmzaNUaNGZTARAAAAAAAAAAAAAAAAAAAAAACwt8nLdQD2LiNGjIiOHTvmOgYAAAAAAAAAAAAAAAAAAAAAANCIOYBFxhx22GFxxx135DoGAAAAAAAAAAAAAAAAAAAAAADQyDmARUYUFRXFY489Fq1bt95lzfz58yOVSuXsBQAAAAAAAAAAAAAAAAAAAAAANA4OYFFvRx11VLz44ovRq1evXEcBAAAAAAAAAAAAAAAAAAAAAAD2AA5gUWcHHnhg3HbbbfHXv/41Dj/88FzHAQAAAAAAAAAAAAAAAAAAAAAA9hD5uQ7AnmH//fePoqKiaN++fQwYMCBOOumkOOmkk2K//fbLdTQAAAAAAAAAAAAAAAAAAAAAAGAPk7UDWPPnz8/WaLJozpw5uY4AAAAAAAAAAAAAAAAAAAAAAADs5fJyHQAAAAAAAAAAAAAAAAAAAAAAAIB9S36uA7DvGDRoUNTW1uY6BgAAAAAAAAAAAAAAAAAAAAAAkGN5uQ4AAAAAAAAAAAAAAAAAAAAAAADAvsUBLAAAAAAAAAAAAAAAAAAAAAAAABqUA1gAAAAAAAAAAAAAAAAAAAAAAAA0KAewSOzhhx+OVCqV6DVx4sRcxwUAAAAAAAAAAAAAAAAAAAAAABqp/FwHgMaovLw8li5dGitWrIh169bF3//+91i3bl289957sW3btti+fXtUVFREy5Yto7CwMAoLC+PAAw+M4447Lnr37h29e/eO7t275/ptfEpJSUksXbo0rZ7jjz8+hg4dmqVEAAAAAAAAAAAAAAAAAAAAAADsqxzAgojYsmVLzJ07N5599tlYvHhxLF++PKqrq3fbV1ZWFmVlZbF+/fqIiHj22Wc/fnbUUUdFcXFxDB8+PDp16pS17EmsWrUqLrjggtixY0dafcXFxQ5gAQAAAAAAAAAAAAAAAAAAAACQcXm5DgC5dMcdd0T//v2jffv2cdFFF8UDDzwQr776aqLjV7uzZs2auO6666Jr164xfvz4KC8vz0Di9FVXV0dxcXHax68AAAAAAAAAAAAAAAAAAAAAACBbHMBinzZ9+vR46aWXoqamJms7qqqqYsqUKdG9e/d4/vnns7ZnV2699dZYvHhxg+8FAAAAAAAAAAAAAAAAAAAAAIBdcQALGkhpaWkMHjw45syZ02A7X3311Zg0aVKD7QMAAAAAAAAAAAAAAAAAAAAAgCQcwIIGtHPnzrjgggti0aJFWd9VVVUVxcXFUVVVlfVdAAAAAAAAAAAAAAAAAAAAAACQDgewoIFVVlbGRRddFBUVFVndM2nSpHj11VezugP2RaWlpZFKpT7zVVpamut4sFu+w+yJfG8hOb8XGhvfSfZVvvvsK3zX9xyN8bPKdaZ9dX8u3/e+9p7ttdfePXu3vfbaa6+99u7Le3O12/vdu9+vvfbau2fvbaw59mT+huzNfL/ZG/le05j5fkJ2+G2xJ/K9ZU/luwvp8ZuhMfA9ZF+3q99Ajx49ch1tj+YAFiRUVFQURx55ZBx99NFx0EEH1WvWunXr4u67785Qsv+0ZMmS+NGPfpS1+QAAAAAAAAAAAAAAAAAAAAAAUB8OYJHYiBEjora2NtFr4sSJuY5bb0cddVRce+218fTTT8d7770XW7ZsiTVr1sSqVati48aN8c4778RDDz0Uxx13XJ3m/+QnP4mampoMp46orKyM4uLi+OijjzI+GwAAAAAAAAAAAAAAAAAAAAAAMsEBLPiEgoKCGDlyZLz88suxevXqmDJlSpxyyinRtm3b/6jt0KFDjBgxIv7617/GpEmT0t61YcOGePHFFzMR+1NuuOGGWLlyZcbnAgAAAAAAAAAAAAAAAAAAAABApjiABRHRqlWruP766+N///d/4957741+/fol7s3Ly4sbb7wxbr311rT3Pvfcc2n3fJ4///nPceedd+7yeV5eXgwePDijOwEAAAAAAAAAAAAAAAAAAAAAIF35uQ7weZYvXx4lJSXxwgsvxOrVq2Pz5s1RUVERLVu2jM6dO0fPnj1j8ODBce6550bbtm1zmrW2tjYWLlwYJSUlsWTJkvjb3/4WW7dujYqKimjSpEkcfPDBMWfOnOjdu3ed5peVlcXixYtj6dKlsXbt2tiwYUN88MEHUVFRERERBQUF0aZNm+jcuXMcfvjh0bt37+jXr1+0bNkyk29zr5Ofnx9XXnllTJo0KQ488MB6zfr+978fv/nNb2LJkiWJe9asWVOvnZ9UXl4eI0aMiJqaml3WjBs3Lvbbb7/4n//5n4ztBQAAAAAAAAAAAAAAAAAAAACAdGXtANagQYNiwYIFiWrXrVsXXbp0+fjfixYtih/84Ae77C8rK4uVK1fGypUr49FHH42rrroqLr/88rjpppuiqKgo7aylpaXRtWvXRLUDBw6M+fPnf+r/Hn/88bjxxhtjxYoVn9lTXV0d69evj7KysrRybd++PWbNmhW//OUvY+HChVFVVZVWf/PmzWPgwIFx8cUXx3nnnRctWrRIq//fzZs3L+66665EtcOGDYthw4bVa19D+NOf/pT4s9+dVCoVo0ePjksvvTRxz+bNmzOyOyJi/Pjx8frrr+/yeffu3WPy5Mlx6623ZmwnAAAAAAAAAAAAAAAAAAAAAADURdYOYNVFVVVVXHvttXH33XdHbW1t4r6KioqYNm1azJo1K2bOnBmnnHJKFlP+y44dO+Lb3/52/OpXv8ro3IqKipg6dWrcdtttsXXr1jrPqaysjLlz58bcuXNj3Lhxcf3118eoUaOiadOmdZq3fv36+P3vf5+otm/fvnXa0dAydfzqnwYNGpRWfWVlZUb2PvvsszFjxoxdPs/Pz4+ZM2dG8+bNM7IPAAAAAAAAAAAAAAAAAAAAAADqIy/XAf5p+/btceqpp8bUqVPTOn71SZs2bYrTTjstHnjggQyn+0/btm2LIUOGZPz41aJFi+K4446L6667rl7Hr/7dpk2bYuzYsdGvX7947bXXMjaXT+vYsWNa9YWFhfXe+eGHH8all176ub+bG264IXr37l3vXQAAAAAAAAAAAAAAAAAAAAAAkAmN4gDWzp0747TTTotnnnmm3rOqq6tj5MiRGT9M9e87Lrzwwli4cGFG5z700EMxcODAeP311zM695NeffXVGDBgQJSUlGRtx76sqqoqrfpDDz203jvHjRsXpaWlu3zep0+fuP766+u9BwAAAAAAAAAAAAAAAAAAAAAAMqVRHMAaPXp0LFiwIGPzamtr45JLLomlS5dmbOYn3XrrrfG73/0uozPvvffeuPTSS9M+oFQX27dvj3PPPTcef/zxrO/a16xcuTKt+r59+9Zr3x//+Me4//77d/m8efPmMXPmzMjPz6/XHgAAAAAAAAAAAAAAAAAAAAAAyKRGcQBr3rx5GZ+5c+fOGDZsWOzcuTOjc9euXRuTJk3K6Mw//OEPMXr06IzO3J3q6uq48MIL4y9/+UuD7t3bPfHEE4lrmzZtGqecckqdd23dujW+/e1vf27ND3/4wzjmmGPqvAMAAAAAAAAAAAAAAAAAAAAAALKhURzA+ix5eXnRoUOH6NatWxQVFdVpxurVq+Ouu+7KaK4NGzZEVVVVxuZt3LgxiouLo6amJmMzk6qsrIxvfvObUV5e3uC790Yffvhh3HvvvYnrTz/99Dp/tyMirrrqqnjrrbd2+fyrX/1qXH311XWeDwAAAAAAAAAAAAAAAAAAAAAA2dLoDmAddNBBcc8998TGjRvjnXfeiZUrV8aWLVti3bp1MWHChGjRokVa826//fZGfeBp/PjxsWXLlrR6jj322JgxY0asWbMmPvzwwygrK4uVK1fGnXfeGUcccURas9auXRtTpkxJq4fPNn78+Hj33XcT1aZSqbjpppvqvKukpCR+8Ytf7PJ5y5Yt4+GHH468vEb3EwcAAAAAAAAAAAAAAAAAAAAAgMjPdYBP6tu3bzz99NNRVFT0H8+6dOkSEydOjG984xtx4oknxqZNmxLNfPfdd+ORRx6JkSNHZjrux7p37x7f+ta3YvDgwXHIIYdEUVFRlJeXx5tvvhlLly6NuXPnRklJyX/0vf766zFz5sy0dt1www0xYcKEyM//9EfXrVu36NatW4waNSrGjh0b9913X+KZd9xxR3zve9+LNm3apJWFf5k9e3bcc889ietHjBgRxx9/fJ12vfvuu3HFFVd8bs3tt98ehx12WJ3mN6RNmzbF5s2b0+p54403spQGAAAAAAAAAAAAAAAAAAAAAICG0mgOYHXo0CGeeuqpzzx+9UnHHHNMlJSUxFe+8pWoqalJNHvmzJlZOYDVrFmzmDp1aowcOTKaNGnyqWf7779/9OzZM3r27BnDhw+Pbdu2RXV19adqpk+fnvg9RESMGzcuJk+e/Lk1LVq0iJ/97GdRVlYWs2bNSjR3+/bt8eCDD8a4ceMSZ+Ff5s2bF8OHD09cf/jhh8fUqVPrvG/06NGxcePGXT4fMmRIjBo1qs7zG9I999wTN998c65jAAAAAAAAAAAAAAAAAAAAAADQwPJyHeCffvjDH0a7du0S1fbv3z+Ki4sTz160aFG8//77dY32mZo1axZPPvlkjBo16j+OX32WVq1aRWFh4cf/rqmpicceeyzxvi5dusQPf/jDRLWpVCqmT58ebdq0STz/0UcfTVzLvzz55JNx+umnR2VlZaL6wsLCeOKJJ2L//fev077Zs2fH7NmzP3f+z3/+8zrNBgAAAAAAAAAAAAAAAAAAAACAhtIoDmC1adMmLr744rR6xowZk7i2pqYmnnvuuXRjfa4bb7wxTj755Dr3/+Uvf4l//OMfieu/973vRYsWLRLXt2vXLi677LLE9UuWLIlNmzYlrifiJz/5SZx99tmxY8eORPUFBQXx5JNPRrdu3eq0b+PGjbv93k+bNi06d+5cp/kAAAAAAAAAAAAAAAAAAAAAANBQ8nMdICLi9NNPj2bNmqXV06dPn/jCF74Qb775ZqL6pUuXxllnnVWXeP+hc+fOMX78+HrNWLRoUeLaVCoV559/fto7Lrjggvjxj3+cqLa2tjZeeumlOOOMM9Les6+pqamJa665Ju68887EPS1atIg5c+bECSecUOe9V1xxRWzZsmWXz88666wYPnx4nefnwujRo9P+br/xxhsxdOjQLCUCAAAAAAAAAAAAAAAAAAAAAKAhNIoDWP369atTX+/evRMfwFqzZk2ddnyWyy67LPLz6/ene+211xLXfvGLX4yOHTumvaNXr15RUFAQFRUVieqXLVvmANZuVFRUxLe+9a347W9/m7hnv/32i5KSkhg8eHCd986cOTNKSkp2+bxdu3Zx33331Xl+rhx00EFx0EEH5ToGAAAAAAAAAAAAAAAAAAAAAAANLC/XASIijjnmmKz3vfXWW3Xa8VlOO+20es8oLS1NXNuzZ8867cjLy4tu3bolrk96TGxftWnTpjjxxBPTOn7Vtm3b+NOf/lSv41dvvfVWjB079nNrfvrTnzokBQAAAAAAAAAAAAAAAAAAAADAHiM/1wEiIoqKiurUd8ABBySuff/99+u04981b948jj322HrP2bx5c+La9u3b13lPOr2bNm2q85693erVq+PrX/96rFu3LnHPIYccEk899VR07969XrtvvPHG2Lp16y6fDxs2LM4777x67QAAAAAAAAAAAAAAAAAAAAAAgIaUl+sAERGtW7fOet+OHTvqtOPfde7cOZo1a1bvOdu3b09cW9e/T0TE/vvvn7i2vLy8znv2ZgsWLIgvf/nLaR2/6tWrV7z00kv1Pn4VEfHee+/t8tnBBx8cM2bMqPcOAAAAAAAAAAAAAAAAAAAAAABoSI3iAFZ1dXXW+1KpVJ12/Ls2bdpkZE5tbW3i2ry8un9MTZo0SVybTqZ9xSOPPBInn3xyvP/++4l7TjvttHjuueeiY8eOWUz2f95+++044IADIpVKJX7dfPPNief/93//9y7nlJaWZu+NAQAAAAAAAAAAAAAAAAAAAACwV2sUB7A+/PDDrPcVFBTUace/a9WqVUbmpJNn27Ztdd5TVlaWuDZTf6O9xaRJk+Liiy+OnTt3Ju4ZM2ZMlJSUZOx7AgAAAAAAAAAAAAAAAAAAAAAAe6P8XAeIiNi4cWOd+jZt2pS4tl27dnXakS1FRUWJa999990670mn94ADDqjznr1JVVVVjBw5Mh5++OHEPXl5eXH77bfH1Vdfnb1gAAAAAAAAAAAAAAAAAAAAAACwl8jLdYCIiOXLl2e9r1OnTnXakS2HHnpo4tq6/n0iIlauXJm49gtf+EKd9+wttm7dGqeeempax6/222+/+M1vfuP4FQAAAAAAAAAAAAAAAAAAAAAAJJSf6wAREYsWLUq7p7q6OpYsWZK4vnv37mnvyKZjjjkmce2aNWviww8/jNatW6e14/XXX48PPvggK5n2Rm+++WZ8/etfT+toWPv27ePJJ5+Mfv36ZTEZAAAAAAAAAAAAAAAAAAAAAADsXfJyHSAi4umnn46ysrK0ehYsWBBbtmxJXN+nT590Y2VV//79E9d+9NFHUVJSkvaOxx57LK36//qv/0p7x95iyZIl0b9//7SOXx1zzDHx0ksvOX4FAAAAAAAAAAAAAAAAAAAAAABpahQHsCorK2P69Olp9dx+++2Ja5s3bx5f+cpX0o2VVQMGDIj9998/cf306dOjtrY2cf2OHTvi/vvvT1x/5JFHRpcuXRLX701KSkpi4MCB8Y9//CNxz9e+9rX485//HF/4wheymAwAAAAAAAAAAAAAAAAAAAAAAPZOjeIAVkTEj370o1i1alWi2tmzZ8fTTz+dePaQIUOioKCgrtGyonnz5nHmmWcmrn/55ZfjgQceSFx/6623RmlpaeL68847L3Ht3mTatGlxzjnnRHl5eeKeSy65JJ566qkoLCzMYjIAAAAAAAAAAAAAAAAAAAAAANh7NZoDWNu2bYuTTz45li1b9rl1JSUlUVxcnNbsESNG1CNZ9owZMybt+jlz5uy27r777ovJkycnnpufnx9XXnllWln2FmPHjo2ampq0eh566KFo1qxZpFKpjLzSOVQGAAAAAAAAAAAAAAAAAAAAAAB7g0ZzACsiYsOGDdG3b98YNWpUzJ8/PzZt2hQ7d+6Mt99+O5544ok455xzYujQobFjx47EM7/4xS/G0KFDs5i67vr37x9DhgxJXF9VVRVnn312XHzxxbFo0aKora39+FlNTU0sWLAgzjrrrLjiiis+9Wx3iouL45BDDkkrOw1nzpw5UVtbm7HXhAkTEu8uLi7e5ZwuXbpk700DAAAAAAAAAAAAAAAAAAAAALBXS9WmcykpDYMGDYoFCxZkY3RaHnnkkbjooos+t6a0tDS6du2aaN7AgQNj/vz5GUj2f1atWhW9evWKysrKtHtbtmwZHTt2jNra2njnnXeivLw87Rlt27aNVatWRfv27Xdb+/DDD8cll1ySaO6ECRNi4sSJaedpaKlUKtcRYt26dQ16TGrixIlx8803J6otLi6Ohx9+OLuB0rRixYro0aPHx/9evnx5dO/ePYeJAAAAAAAAAAAAAAAAAAAAAIB9kVso9ZOX6wDZdNJJJ+32+FWudevWLX784x/XqXf79u3xxhtvxNq1a+t0/Coi4sEHH0x0/AoAAAAAAAAAAAAAAAAAAAAAACBTGsUBrGwcqWrXrl3MnDkz43Oz4Tvf+U6MGTOmwffecsstcfbZZzf4XgAAAAAAAAAAAAAAAAAAAAAAYN/WKA5gTZ48OaNHsAoKCuKJJ56ITp06ZWxmtk2fPj2uuuqqBtmVSqVi8uTJcd111zXIPgAAAAAAAAAAAAAAAAAAAAAAgE9qFAewUqlUzJw5My6//PJ6zyosLIynnnoqBgwYkIFkDSeVSsXdd98d999/f7Rq1Sprew444IB47LHH4oYbbsjaDgAAAAAAAAAAAAAAAAAAAAAAgM/TKA5gRUTk5eXFfffdF/fee2+0bt26TjP69u0bixcvjoEDB2Y4XcO57LLLYtmyZXH++edHKpXK2NwmTZrEiBEjYsWKFXHuuedmbC4AAAAAAAAAAAAAAAAAAAAAAEC6Gs0BrH8aOXJkrF69OsaMGROtWrVK1HP00UfHgw8+GC+99FIcccQRWU6YfV27do3Zs2fHsmXLYsyYMdGuXbs6z+rQoUNcffXVsWrVqnjooYeiQ4cOdZ5VXV2duLZZs2Z13gMAAAAAAAAAAAAAAAAAAAAAAOzd8nMd4LMcfPDBMWPGjJgyZUo888wz8cILL8Tq1atjy5YtsWPHjthvv/2iU6dO0bNnzxgyZEj069evXvu6dOkStbW1GUqfOT169IgZM2bEtGnT4uWXX46FCxfG0qVLY+3atfHWW2/F1q1bo6KiIiIiCgoKom3bttGpU6c44ogjolevXnHCCSdEnz59IpVKZSRPeXl54tq2bdtmZGe2NcbPPdsGDRqUuPb444/PXhAAAAAAAAAAAAAAAAAAAAAAAPZZjfIA1j+1bNkyzjjjjDjjjDNyHSWn8vLyon///tG/f/+c5ti8eXPi2j3lANa+aNCgQWkdwQIAAAAAAAAAAAAAAAAAAAAAgEzLy3UA9hyrV69OXNuxY8csJgEAAAAAAAAAAAAAAAAAAAAAAPZkDmCRSFVVVcybNy9RbZMmTaJPnz5ZTgQAAAAAAAAAAAAAAAAAAAAAAOypHMAikSlTpsR7772XqLZHjx7RqlWrLCcCAAAAAAAAAAAAAAAAAAAAAAD2VPm5DkDj9NFHH8X7778fr7zySvz85z+PWbNmJe4988wzs5gMAAAAAAAAAAAAAAAAAAAAAADY0zmAxacMHTo0SkpK6tzftGnTGDVqVAYTAQAAAAAAAAAAAAAAAAAAAAAAe4rQLEsAACAASURBVJu8XAdg7zJixIjo2LFjrmMAAAAAAAAAAAAAAAAAAAAAAACNmANYZMxhhx0Wd9xxR65jAAAAAAAAAAAAAAAAAAAAAAAAjZwDWGREUVFRPPbYY9G6detd1syfPz9SqVTOXgAAAAAAAAAAAAAAAAAAAAAAQOPgABb1dtRRR8WLL74YvXr1ynUUAAAAAAAAAAAAAAAAAAAAAABgD+AAFnV24IEHxm233RZ//etf4/DDD891HAAAAAAAAAAAAAAAAAAAAAAAYA+Rn+sA7Bn233//KCoqivbt28eAAQPipJNOipNOOin222+/XEcDAAAAAAAAAAAAAAAAAAAAAAD2MFk7gDV//vxsjSaL5syZk+sIAAAAAAAAAAAAAAAAAAAAAADAXi4v1wEAAAAAAAAAAAAAAAAAAAAAAADYt+TnOgD7jkGDBkVtbW2uYwAAAAAAAAAAAAAAAAAAAAAAADmWl+sAAAAAAAAAAAAAAAAAAAAAAAAA7FscwAIAAAAAAAAAAAAAAAAAAAAAAKBBOYAFAAAAAAAAAAAAAAAAAAAAAABAg3IAi8QefvjhSKVSiV4TJ07MdVwAAAAAAAAAAAAAAAAAAAAAAKCRys91AGiMysvLY+nSpbFixYpYt25d/P3vf49169bFe++9F9u2bYvt27dHRUVFtGzZMgoLC6OwsDAOPPDAOO6446J3797Ru3fv6N69e9Zz7ty5M9asWRPLly+PVatWxfr162PDhg3x1ltvxdatW6OioiLKy8vjo48+iqZNm0aLFi2ibdu2ceCBB8ahhx4ahx12WBx33HExYMCA6Nq1a9bzAgAAAAAAAAAAAAAAAAAAAABAhANYEBERW7Zsiblz58azzz4bixcvjuXLl0d1dfVu+8rKyqKsrCzWr18fERHPPvvsx8+OOuqoKC4ujuHDh0enTp0ykvODDz6IZ599Np577rl4/vnn45VXXomPPvooUW9lZWVUVlbGBx98EKWlpbF48eJPPT/ssMPi/PPPj8suuywOO+ywjOQFAAAAAAAAAAAAAAAAAAAAAIDPkpfrAJBLd9xxR/Tv3z/at28fF110UTzwwAPx6quvJjp+tTtr1qyJ6667Lrp27Rrjx4+P8vLyes/8xS9+EWeffXbcddddsWTJksTHr5JYu3Zt/OhHP4ojjzwyhg0bFhs2bMjYbAAAAAAAAAAAAAAAAAAAAAAA+CQHsNinTZ8+PV566aWoqanJ2o6qqqqYMmVKdO/ePZ5//vms7cmUmpqa+PWvfx09evSIkpKSXMcBAAAAAAAAAAAAAAAAAAAAAGAv5AAWNJDS0tIYPHhwzJkzJ9dREvnggw/i3HPPjUcffTTXUQAAAAAAAAAAAAAAAAAAAAAA2Ms4gAUNaOfOnXHBBRfEokWLch0lkerq6rjkkktixYoVuY4CAAAAAAAAAAAAAAAAAAAAAMBexAEsaGCVlZVx0UUXRUVFRa6jJFJRURFjx47NdQz2MqWlpZFKpT7zVVpamut4sEfyu4KG5TcH6fGbgcbFbxLqx2+Ixm5f+I7uDe9xT34PsueG7A1vT80dIXuuyJ4be2r2PTV3hOy5Intu7KnZ99TcEbLniuy5sadm31NzR8i+t/C3YF/i+w4Nw28N0ud3A5nlNwW553cI2eG3BZnj9wSZ4/fUeDmABQkVFRXFkUceGUcffXQcdNBB9Zq1bt26uPvuuzOU7NMKCgqiQ4cOceSRR0a3bt2ic+fOkZ+fX6+Z8+bNi+XLl2coIQAAAAAAAAAAAAAAAAAAAAAA+7r6XcVhnzJixIgYMWJErmM0mKOOOirOOuusOOmkk+JLX/pStG3b9lPP//GPf8TTTz8dU6dOjVdffTXt+T/5yU/i2muvjby8ut+ha9WqVQwaNChOPPHE6NmzZ3Tv3j0OPvjg/6jbuXNnvPbaa/H444/Hz372s3j33XfT3jVnzpzo0aNHnbMCAAAAAAAAAAAAAAAAAAAAAMA/OYAFn1BQUBAXX3xxXHbZZdGvX7/Pre3QoUOMGDEihg8fHrfcckvcdNNNae3asGFDvPjii/HlL385rb6WLVvGeeedF8XFxfHVr341mjZtutueZs2aRZ8+faJPnz5xzTXXxKWXXhq//e1v09q7ePHitOoBAAAAAAAAAAAAAAAAAAAAAGBXHMCCiGjVqlWMHTs2vvvd70a7du3S6s3Ly4sbb7wxmjZtGj/4wQ/S6n3uuefSOoB15plnRnFxcbRu3TqtPZ9UWFgYjz32WAwePDieffbZxH1vvvlmnXcCAAAAAAAAAAAAAAAAAAAAAMAnNeoDWMuXL4+SkpJ44YUXYvXq1bF58+aoqKiIli1bRufOnaNnz54xePDgOPfcc6Nt27Y5zVpbWxsLFy6MkpKSWLJkSfztb3+LrVu3RkVFRTRp0iQOPvjgmDNnTvTu3btO88vKymLx4sWxdOnSWLt2bWzYsCE++OCDqKioiIiIgoKCaNOmTXTu3DkOP/zw6N27d/Tr1y9atmyZybe518nPz48rr7wyJk2aFAceeGC9Zn3/+9+P3/zmN7FkyZLEPWvWrElrx6GHHppurM+Ul5cXt9xyS1rHt7Zt25aR3QAAAAAAAAAAAAAAAAAAAAAAkLUDWIMGDYoFCxYkql23bl106dLl438vWrQofvCDH+yyv6ysLFauXBkrV66MRx99NK666qq4/PLL46abboqioqK0s5aWlkbXrl0T1Q4cODDmz5//qf97/PHH48Ybb4wVK1Z8Zk91dXWsX78+ysrK0sq1ffv2mDVrVvzyl7+MhQsXRlVVVVr9zZs3j4EDB8bFF18c5513XrRo0SKt/n83b968uOuuuxLVDhs2LIYNG1avfQ3hT3/6U+LPfndSqVSMHj06Lr300sQ9mzdvzsjuuhgwYEAUFBR8fERtd1q1apXlRAAAAAAAAAAAAAAAAAAAAAAA7CuydgCrLqqqquLaa6+Nu+++O2praxP3VVRUxLRp02LWrFkxc+bMOOWUU7KY8l927NgR3/72t+NXv/pVRudWVFTE1KlT47bbboutW7fWeU5lZWXMnTs35s6dG+PGjYvrr78+Ro0aFU2bNq3TvPXr18fvf//7RLV9+/at046GlqnjV/80aNCgtOorKyszuj9dhYWFiQ9gtW/fPstpAAAAAAAAAAAAAAAAAAAAAADYV+TlOsA/bd++PU499dSYOnVqWsevPmnTpk1x2mmnxQMPPJDhdP9p27ZtMWTIkIwfv1q0aFEcd9xxcd1119Xr+NW/27RpU4wdOzb69esXr732Wsbm8mkdO3ZMq76wsDBLSXZv586dsXnz5sT1X/rSl7KYBgAAAAAAAAAAAAAAAAAAAACAfUmjOIC1c+fOOO200+KZZ56p96zq6uoYOXJkxg9T/fuOCy+8MBYuXJjRuQ899FAMHDgwXn/99YzO/aRXX301BgwYECUlJVnbsS+rqqpKq/7QQw/NUpLde/rpp6O6ujpx/de//vUspgEAAAAAAAAAAAAAAAAAAAAAYF/SKA5gjR49OhYsWJCxebW1tXHJJZfE0qVLMzbzk2699db43e9+l9GZ9957b1x66aVpH1Cqi+3bt8e5554bjz/+eNZ37WtWrlyZVn3fvn2zlOTzbd++Pb7//e8nru/bt28MGDAgi4kAAAAAAAAAAAAAAAAAAAAAANiXNIoDWPPmzcv4zJ07d8awYcNi586dGZ27du3amDRpUkZn/uEPf4jRo0dndObuVFdXx4UXXhh/+ctfGnTv3u6JJ55IXNu0adM45ZRTspjmsy1fvjxOOOGEWL16daL6/Pz8+OlPf5rlVAAAAAAAAAAAAAAAAAAAAAAA7EsaxQGsz5KXlxcdOnSIbt26RVFRUZ1mrF69Ou66666M5tqwYUNUVVVlbN7GjRujuLg4ampqMjYzqcrKyvjmN78Z5eXlDb57b/Thhx/Gvffem7j+9NNPr/N3O4na2trYvn17vP322/H888/H9OnT49RTT41jjz02li5dmmhGKpWKe+65J/r27Zu1nAAAAAAAAAAAAAAAAAAAAAAA7Hsa3QGsgw46KO65557YuHFjvPPOO7Fy5crYsmVLrFu3LiZMmBAtWrRIa97tt9/eqA88jR8/PrZs2ZJWz7HHHhszZsyINWvWxIcffhhlZWWxcuXKuPPOO+OII45Ia9batWtjypQpafXw2caPHx/vvvtuotpUKhU33XRTxnZ/97vfjVQq9alXXl5etGrVKjp16hT/7//9v7jqqqvij3/8Y9TW1iaaWVhYGL/+9a/j8ssvz1hOAAAAAAAAAAAAAAAAAAAAAACIiMjPdYBP6tu3bzz99NNRVFT0H8+6dOkSEydOjG984xtx4oknxqZNmxLNfPfdd+ORRx6JkSNHZjrux7p37x7f+ta3YvDgwXHIIYdEUVFRlJeXx5tvvhlLly6NuXPnRklJyX/0vf766zFz5sy0dt1www0xYcKEyM//9EfXrVu36NatW4waNSrGjh0b9913X+KZd9xxR3zve9+LNm3apJWFf5k9e3bcc889ietHjBgRxx9/fBYT1V2zZs1i+PDhMWHChOjcuXNWd23atCk2b96cVs8bb7yRpTQAAAAAAAAAAAAAAAAAAAAAADSURnMAq0OHDvHUU0995vGrTzrmmGOipKQkvvKVr0RNTU2i2TNnzszKAaxmzZrF1KlTY+TIkdGkSZNPPdt///2jZ8+e0bNnzxg+fHhs27YtqqurP1Uzffr0xO8hImLcuHExefLkz61p0aJF/OxnP4uysrKYNWtWornbt2+PBx98MMaNG5c4C/8yb968GD58eOL6ww8/PKZOnZrFRHXTpk2bmDBhQgwbNiwOOuigBtl5zz33xM0339wguwAAAAAAAAAAAAAAAAAAAAAAaDzych3gn374w//Pzp2GV1mY+eO/T0AgEAgICggWtG6AyABSg9bipaJWBcGltSoFK6UiHZ3q2DpWKwN13K0I9QKX4nDRFqEVonZwGS1YLLKJBURQKXEbZVEQCCEEkv+L39+2rn3O4RxOQj6f68oLyL1885w8eXn/PNq0aZOotqSkJIYOHZp49vz582PTpk2ZRvtcjRo1iscffzxGjhz5meNXn6eoqCiKi4v/9u/q6uqYMWNG4n2dO3eOn//854lqU6lUjB8/Plq2bJl4/iOPPJK4lr97/PHH4+yzz47KyspE9cXFxfHYY49FixYtcpwsfZs3b44bbrghrrzyyliwYEG+4wAAAAAAAAAAAAAAAAAAAAAAsA+rFQewWrZsGUOGDEmrZ9SoUYlrq6ur4/nnn0831pe68cYb47TTTsu4f8mSJfH+++8nrv/Rj34UTZo0SVzfpk2bGD58eOL6xYsXx/r16xPXE/HLX/4yBg8eHDt27EhUX1hYGI8//nh06dIlx8kyV15eHo888kiUlJTEN7/5zXjrrbfyHQkAAAAAAAAAAAAAAAAAAAAAgH1Qw3wHiIg4++yzo1GjRmn19O7dOzp16hRvvvlmovqlS5fGOeeck0m8z+jYsWNcd911ezRj/vz5iWtTqVRccMEFae+48MIL484770xUW1NTEwsWLIgBAwakvae+qa6ujmuvvTbuvvvuxD1NmjSJWbNmxYknnpjDZNn15JNPRo8ePeLXv/51nHnmmTnZccUVV6T9u/3GG2/EoEGDcpIHAAAAAAAAAAAAAAAAAAAAAIC9o1YcwOrTp09Gfb169Up8AGv16tUZ7fg8w4cPj4YN9+zRLV++PHHtoYceGu3bt097R8+ePaOwsDAqKioS1S9btswBrH+ioqIiLrnkknj00UcT9zRt2jRKS0vj1FNPzWGy3Ni8eXMMHjw4Zs2aFd/85jezPv/AAw+MAw88MOtzAQAAAAAAAAAAAAAAAAAAAACo3WrFAayuXbtm3Ddz5sxEte+++25GOz7PWWedtcczysrKEtd27949ox0FBQXRpUuXeOmllxLVJz0mVl+tX78+Bg4cGAsWLEjc06pVq3jiiSfi+OOPz2GyiA4dOkSPHj0+8X+7du2K8vLy2LJlS3z44YcZz965c2dcdNFFsWLFiujQocOeRgUAAAAAAAAAAAAAAAAAAAAAgNpxAKt169YZ9e2///6Jazdt2pTRjk9r3LhxHHPMMXs8Z8OGDYlr27Ztm/GedHrXr1+f8Z593apVq+LMM8+MtWvXJu45+OCDY/bs2dGtW7ccJvt/rr322rj22mu/8PsfffRRvPrqq/HMM8/EjBkzYvny5WnN37x5c/zbv/1bzJgxY0+jAgAAAAAAAAAAAAAAAAAAAABAFOQ7QERE8+bNc963Y8eOjHZ8WseOHaNRo0Z7PKe8vDxxbabPJyKiRYsWiWu3b9+e8Z592dy5c+P4449P6/hVz549Y8GCBXvl+FUSxcXFUVJSEjfeeGP85S9/iZkzZ8YBBxyQ1oxHH300rWcAAAAAAAAAAAAAAAAAAAAAAABfpFYcwNq9e3fO+1KpVEY7Pq1ly5ZZmVNTU5O4tqAg84+pQYMGiWvTyVRfTJ06NU477bTYtGlT4p6zzjornn/++Wjfvn0Ok2UulUrFoEGDYtGiRWkdwaquro7f/va3OUwGAAAAAAAAAAAAAAAAAAAAAEB9USsOYG3dujXnfYWFhRnt+LSioqKszEknz7Zt2zLes2XLlsS12XpG+4oxY8bEkCFDYufOnYl7Ro0aFaWlpVn7PcmlTp06xT333JNWzwsvvJCjNAAAAAAAAAAAAAAAAAAAAAAA1Ce14gDWunXrMupbv3594to2bdpktCNXWrdunbj2gw8+yHhPOr37779/xnv2JVVVVXHppZfGTTfdlLinoKAg7rrrrpgwYUI0aNAgh+my6/zzz48WLVokrl+5cmUO0wAAAAAAAAAAAAAAAAAAAAAAUF/UigNYK1asyHlfhw4dMtqRK1/5ylcS12b6fCLSO1jUqVOnjPfsKzZv3hxnnHFGPPzww4l7mjZtGr///e/j6quvzl2wHGnUqFEceuihiev35BgbAAAAAAAAAAAAAAAAAAAAAAB8rGG+A0REzJ8/P+2e3bt3x+LFixPXd+vWLe0dudS1a9fEtatXr46tW7dG8+bN09rx+uuvx0cffZSTTPuiN998M84888y0joa1bds2Hn/88ejTp08Ok+VWQUHyO3gVFRU5TAIAAAAAAAAAAAAAAAAAAAAAQH2R/PJNDj355JOxZcuWtHrmzp0bGzduTFzfu3fvdGPlVElJSeLaXbt2RWlpado7ZsyYkVb9cccdl/aOfcXixYujpKQkreNXXbt2jQULFtTp41dVVVWxZs2axPVFRUU5TAMAAAAAAAAAAAAAAAAAAAAAQH1RKw5gVVZWxvjx49PqueOOOxLXNm7cOE444YR0Y+VU3759o0WLFonrx48fHzU1NYnrd+zYEQ888EDi+iOOOCI6d+6cuH5fUlpaGv369Yv3338/cc8pp5wSf/7zn6NTp045TPZ31dXVOZk7c+bM+OijjxLXf+UrX8lJDgAAAAAAAAAAAAAAAAAAAAAA6pdacQArIuLWW2+NV199NVHt9OnT48knn0w8u3///lFYWJhptJxo3LhxDBw4MHH9woUL48EHH0xcf8stt0RZWVni+vPPPz9x7b7k3nvvjXPPPTe2b9+euOfSSy+N2bNnR3FxcQ6TfdLUqVPjzDPPjKeeeiqtQ2hf5u23344rr7wyrZ5jjjkmK7sBAAAAAAAAAAAAAAAAAAAAAKjfas0BrG3btsVpp50Wy5Yt+9K60tLSGDp0aFqzhw0btgfJcmfUqFFp18+aNeuf1t1///0xduzYxHMbNmwYl19+eVpZ9hVXXXVVVFdXp9UzefLkaNSoUaRSqax8JTlUVl1dHbNnz44zzjgjunbtGjfffHOsXLkyw586Yvbs2fG1r30t1q1bl1bf6aefnvFOAAAAAAAAAAAAAAAAAAAAAAD4WKqmpqYmF4NPOumkmDt3btp9++23X1x22WXx7W9/O7p27RotW7aMjRs3xuLFi+Phhx+OmTNnpjXv0EMPjddeey0aNGjwhTVlZWVxyCGHJJrXr1+/mDNnTloZvsxpp50WzzzzTFo9l1xySVxxxRVRUlISqVQqIv7fgaQ//elPcffdd8djjz2W1rzLLrssHnzwwX9a9/DDD8ell16aaOZNN90Uo0ePTitHPnz8/PJp7dq10blz5y+t+aJnf8QRR0T//v2jV69e0bNnz+jWrVs0atToM3WVlZWxevXqePbZZ2PatGmxcOHCtHMWFxfHO++8E0VFRWn3ZtMrr7wSRx999N/+vWLFiujWrVseEwEAAAAAAAAAAAAAAAAAAAAA9ZFbKHumYb4DfFpVVVVMnDgxJk6cmJV5Y8aM+dLjV/k2bty46NmzZ1RWVibumTp1akydOjWaNWsW7du3j5qamnjvvfdi+/btae9v1apV3HzzzWn3UTu89tpr8dprr33i/5o3bx6tWrWKFi1axM6dO2Pbtm3x/vvvR3V19R7tuuqqq/J+/AoAAAAAAAAAAAAAAAAAAAAAgH1DrTuAlU0nn3xyXHzxxfmO8aW6dOkSd955Z/zrv/5r2r3l5eXxxhtv7NH+hx56KNq2bbtHM6hdtm7dGlu3bs3qzKOOOiquu+66rM4EAAAAAAAAAAAAAAAAAAAAAKD+Ksh3gIjIyZGqNm3axJQpU7I+Nxd++MMfxqhRo/b63ptvvjkGDx681/dSt7Ru3TpmzpwZhYWF+Y4CAAAAAAAAAAAAAAAAAAAAAMA+olYcwBo7dmxWj2AVFhbGY489Fh06dMjazFwbP358XHnllXtlVyqVirFjx8b111+/V/ZRdx188MExZ86cOOqoo/IdBQAAAAAAAAAAAAAAAAAAAACAfUitOICVSqViypQp8f3vf3+PZxUXF8fs2bOjb9++WUi296RSqRg3blw88MADUVRUlLM9+++/f8yYMSNuuOGGnO1g33DeeefF0qVL4+ijj853FAAAAAAAAAAAAAAAAAAAAAAA9jG14gBWRERBQUHcf//9MWnSpGjevHlGM4499thYtGhR9OvXL8vp9p7hw4fHsmXL4oILLohUKpW1uQ0aNIhhw4bFK6+8Euedd17W5rJ3FBcXR8OGDffKrm984xvxv//7v/G73/0uWrduvVd2AgAAAAAAAAAAAAAAAAAAAABQv9SaA1gfGzFiRKxatSpGjRoVRUVFiXqOOuqoeOihh2LBggVx+OGH5zhh7h1yyCExffr0WLZsWYwaNSratGmT8ax27drF1VdfHa+++mpMnjw52rVrl/Gs3bt3J65t1KhRxnv4rMGDB8cHH3wQjz76aPzgBz+ILl26REFB9l7frl27xo9//ONYtmxZzJ07N0455ZSszQYAAAAAAAAAAAAAAAAAAAAAgE9rmO8An+eggw6KCRMmxG233RbPPfdcvPDCC7Fq1arYuHFj7NixI5o2bRodOnSI7t27R//+/aNPnz57tK9z585RU1OTpfTZc/TRR8eECRPi3nvvjYULF8a8efNi6dKlsWbNmnj33Xdj8+bNUVFRERERhYWF0apVq+jQoUMcfvjh0bNnzzjxxBOjd+/ekUqlspJn+/btiWtbtWqVlZ25Vhs/9y/SokWLGDx4cAwePDgiIsrLy2Pp0qWxdOnS+Otf/xpvvfVWvP3227F+/fooLy+P7du3x44dOyKVSkWjRo2iqKgo9t9//2jbtm107tw5DjvssOjRo0f06dMn2rdvn+efDgAAAAAAAAAAAAAAAAAAAACA+qRWHsD6WLNmzWLAgAExYMCAfEfJq4KCgigpKYmSkpK85tiwYUPi2rpyAKsua9asWXz961+Pr3/96/mOAgAAAAAAAAAAAAAAAAAAAAAAaSnIdwDqjlWrViWubd++fQ6TAAAAAAAAAAAAAAAAAAAAAAAAdZkDWCRSVVUVzz77bKLaBg0aRO/evXOcCAAAAAAAAAAAAAAAAAAAAAAAqKscwCKR2267LT788MNEtUcffXQUFRXlOBEAAAAAAAAAAAAAAAAAAAAAAFBXNcx3AGqnXbt2xaZNm+Lll1+OX/3qVzFt2rTEvQMHDsxhMgAAAAAAAAAAAAAAAAAAAAAAoK5zAItPGDRoUJSWlmbcv99++8XIkSOzmAgAAAAAAAAAAAAAAAAAAAAAANjXFOQ7APuWYcOGRfv27fMdAwAAAAAAAAAAAAAAAAAAAAAAqMUcwCJrvvrVr8Zdd92V7xgAAAAAAAAAAAAAAAAAAAAAAEAt5wAWWdG6deuYMWNGNG/e/Atr5syZE6lUKm9fAAAAAAAAAAAAAAAAAAAAAABA7eAAFnvsyCOPjBdffDF69uyZ7ygAAAAAAAAAAAAAAAAAAAAAAEAd4AAWGTvggAPi9ttvj5deeikOO+ywfMcBAAAAAAAAAAAAAAAAAAAAAADqiIb5DkDd0KJFi2jdunW0bds2+vbtGyeffHKcfPLJ0bRp03xHAwAAAAAAAAAAAAAAAAAAAAAA6picHcCaM2dOrkaTQ7Nmzcp3BAAAAAAAAAAAAAAAAAAAAAAAYB9XkO8AAAAAAAAAAAAAAAAAAAAAAAAA1C8N8x2A+uOkk06KmpqafMcAAAAAAAAAAAAAAAAAAAAAAADyrCDfAQAAAAAAAAAAAAAAAAAAAAAAAKhfHMACAAAAAAAAAAAAAAAAAAAAAABgr3IAi8QefvjhSKVSib5Gjx6d77gAAAAAAAAAAAAAAAAAAAAAAEAt1TDfAaA22r59eyxdujReeeWVWLt2bfz1r3+NtWvXxocffhjbtm2L8vLyqKioiGbNmkVxcXEUFxfHAQccED169IhevXpFr169olu3bnstb1lZWSxZsiTeeOONv+V95513Ytu2bX/Lm0qlori4OFq0aBHFxcVx2GGHRa9evaJnz57xta99LYqLi/daXgAAAAAAAAAAAAAAAAAAAAAA6jcHsCAiNm7cGE8//XT8hC6OlgAAIABJREFU8Y9/jEWLFsWKFSti9+7d/7Rvy5YtsWXLlnj77bcjIuKPf/zj37535JFHxtChQ+O73/1udOjQIWtZq6urY/HixfHUU0/Fiy++GIsWLYoNGzYk6l2/fn2sX78+IiKWLFkSjzzySERENG7cOM4555wYNmxYnHbaadGgQYOs5QUAAAAAAAAAAAAAAAAAAAAAgE9zAIt67a677ooZM2bEokWLorq6OquzV69eHddff33cdNNNcfXVV8fPfvazaNq0acbzVq5cGbfccks89dRTiQ9eJVVZWRnTp0+P6dOnR/fu3WPixIlx/PHHZ3UHAAAAAAAAAAAAAAAAAAAAAAB8rCDfASCfxo8fHwsWLMj68at/VFVVFbfddlt069Yt/vSnP2U8Z+HChTF16tSsH7/6tOXLl8fXv/71GDlyZFRWVuZ0FwAAAAAAAAAAAAAAAAAAAAAA9ZMDWLCXlJWVxamnnhqzZs3Kd5R/qqamJiZOnBhnnHFGVFRU5DsOAAAAAAAAAAAAAAAAAAAAAAD7GAewYC/auXNnXHjhhTF//vx8R0lkzpw58Z3vfCffMQAAAAAAAAAAAAAAAAAAAAAA2Mc4gAV7WWVlZVx88cVRUVGR7yiJlJaWxsSJE/MdI6vKysoilUp97ldZWVm+4wFp8k5D3eX9hdrD+wh1m3cY6hbvLLWd39Ev5/l4BhGeQYRnEOEZRHgGEZ5BhGdQ33/+CM8gwjOI8AwiPIMIz6C+//wRnkGEZ1Dff/4Iz2BPeHbwxbwfUPd5j6Hu8L5C7eX9hH2bdxxqF+8k1C3eWfYVDmBBQq1bt44jjjgijjrqqDjwwAP3aNbatWtj3LhxWUr2Wfvtt1906tQpunXrFp06dYomTZrs0bwbb7wxtm7dmqV0AAAAAAAAAAAAAAAAAAAAAADUdw5gkdiwYcOipqYm0dfo0aPzHXePHXnkkfHjH/84nnzyyfjwww9j48aNsXr16nj11Vdj3bp18d5778XkyZOjR48eGc3/5S9/GdXV1VnJWlhYGOecc05MmjQpXnnllaioqIiysrJYsWJFlJWVxdatW2PBggUxcuTIjI5hbdy4MX77299mJSsAAAAAAAAAAAAAAAAAAAAAADiABf+gsLAwRowYEQsXLoxVq1bFbbfdFqeffnq0atXqM7Xt2rWLYcOGxUsvvRRjxoxJe9c777wTL7744h7l7dGjR0yaNCnWrVsXs2bNihEjRkTXrl2jQYMGn6hr2LBhfO1rX4v77rsvXnrppejatWvau373u9/tUVYAAAAAAAAAAAAAAAAAAAAAAPiYA1gQEUVFRfHTn/403nrrrZg0aVL06dMncW9BQUHceOONccstt6S99/nnn0+7JyKib9++8dRTT8XLL78cI0aMiObNmyfu7dKlSzz77LPRqVOntHbOmzcvqqur040KAAAAAAAAAAAAAAAAAAAAAACf0TDfAb7MihUrorS0NF544YVYtWpVbNiwISoqKqJZs2bRsWPH6N69e5x66qlx3nnnRatWrfKataamJubNmxelpaWxePHieO2112Lz5s1RUVERDRo0iIMOOihmzZoVvXr1ymj+li1bYtGiRbF06dJYs2ZNvPPOO/HRRx9FRUVFREQUFhZGy5Yto2PHjnHYYYdFr169ok+fPtGsWbNs/pj7nIYNG8bll18eY8aMiQMOOGCPZv3kJz+J3//+97F48eLEPatXr05rx+GHHx533nlnDBw4MN14n9CuXbsYP358WnMqKiri7bffTvtwFgAAAAAAAAAAAAAAAAAAAAAAfFrODmCddNJJMXfu3ES1a9eujc6dO//t3/Pnz4//+I//+ML+LVu2xMqVK2PlypXxyCOPxJVXXhnf//7342c/+1m0bt067axlZWVxyCGHJKrt169fzJkz5xP/N3PmzLjxxhvjlVde+dye3bt3x9tvvx1btmxJK1d5eXlMmzYtfv3rX8e8efOiqqoqrf7GjRtHv379YsiQIXH++edHkyZN0ur/tGeffTZ+8YtfJKq96KKL4qKLLtqjfXvDM888k/iz/2dSqVRcccUV8b3vfS9xz4YNGxLX9u/fPy666KJo1KhRJvE+Y8CAAdGpU6d48803E/ds2LDBASwAAAAAAAAAAAAAAAAAAAAAAPZYzg5gZaKqqip+/OMfx7hx46KmpiZxX0VFRdx7770xbdq0mDJlSpx++uk5TPl3O3bsiMsuuyx+85vfZHVuRUVF3HPPPXH77bfH5s2bM55TWVkZTz/9dDz99NNxzTXXxE9/+tMYOXJk7LfffhnNe/vtt+MPf/hDotpjjz02ox17W7aOX33spJNOSqu+srIycW2HDh3STPPP9evXL6ZMmZK4Pp28AAAAAAAAAAAAAAAAAAAAAADwRQryHeBj5eXlccYZZ8Q999yT1vGrf7R+/fo466yz4sEHH8xyus/atm1b9O/fP+vHr+bPnx89evSI66+/fo+OX33a+vXr46qrroo+ffrE8uXLszaXT2rfvn1a9cXFxTlKksxBBx2UVn2+8wIAAAAAAAAAAAAAAAAAAAAAsG+oFQewdu7cGWeddVY899xzezxr9+7dMWLEiKwfpvr0ju985zsxb968rM6dPHly9OvXL15//fWszv1Hf/nLX6Jv375RWlqasx31WVVVVVr1X/nKV3KUJJmdO3cmrk2lUtGxY8ccpgEAAAAAAAAAAAAAAAAAAAAAoL6oFQewrrjiipg7d27W5tXU1MSll14aS5cuzdrMf3TLLbfEE088kdWZkyZNiu9973tpH1DKRHl5eZx33nkxc+bMnO+qb1auXJlW/bHHHpujJMmkk/ewww6Lli1b5jANAAAAAAAAAAAAAAAAAAAAAAD1Ra04gPXss89mfebOnTvjoosuip07d2Z17po1a2LMmDFZnfk///M/ccUVV2R15j+ze/fu+M53vhNLlizZq3v3dY899lji2v322y9OP/30HKb5clu2bEnr8NzZZ5+dwzQAAAAAAAAAAAAAAAAAAAAAANQnteIA1ucpKCiIdu3aRZcuXaJ169YZzVi1alX84he/yGqud955J6qqqrI2b926dTF06NCorq7O2sykKisr49vf/nZs3759r+/eF23dujUmTZqUuP7ss8/O+Hc7GyZMmBAVFRWJ64cOHZrDNAAAAAAAAAAAAAAAAAAAAAAA1Ce17gDWgQceGPfdd1+sW7cu3nvvvVi5cmVs3Lgx1q5dGzfddFM0adIkrXl33HFHrT7wdN1118XGjRvT6jnmmGNiwoQJsXr16ti6dWts2bIlVq5cGXfffXccfvjhac1as2ZN3HbbbWn18Pmuu+66+OCDDxLVplKp+NnPfpbjRF+srKws/uu//itx/aBBg6JHjx45TAQAAAAAAAAAAAAAAAAAAAAAQH1Sqw5gHXvssbFy5coYOXJktGnT5hPf69y5c4wePTqWLFkSBx54YOKZH3zwQUydOjXbUT+hW7duccstt8SiRYvi/fffj6qqqvjoo49i2bJl8d///d9x8cUXR1FR0Wf6Xn/99ZgyZUpau2644YZYsmRJjBo1Ko444ogoKiqK5s2bR5cuXeJHP/pRLFu2LEaMGJHWzLvuuis2b96cVg+fNH369LjvvvsS1w8bNiz+5V/+JYeJvlhFRUWcf/75UV5enqi+SZMmceutt+Y4FQAAAAAAAAAAAAAAAAAAAAAA9UnDfAf4WLt27WL27NnRunXrL63r2rVrlJaWxgknnBDV1dWJZk+ZMiXto1BJNGrUKO65554YMWJENGjQ4BPfa9GiRXTv3j26d+8e3/3ud2Pbtm2xe/fuT9SMHz8+8c8QEXHNNdfE2LFjv7SmSZMmMXHixNiyZUtMmzYt0dzy8vJ46KGH4pprrkmchb979tln47vf/W7i+sMOOyzuueeeHCb6Yjt37ozzzz8/lixZkrjn7rvvjiOPPDInedavXx8bNmxIq+eNN97ISRYAAAAAAAAAAAAAAAAAAAAAAPaeWnMA6+c//3m0adMmUW1JSUkMHTo0Jk+enKh+/vz5sWnTpmjVqtWeRPyERo0axeOPPx6nnXZaovqioqJP/Lu6ujpmzJiReF/nzp3j5z//eaLaVCoV48ePjyeffDI2b96cqOeRRx5xACsDjz/+eHzrW9+KysrKRPXFxcXx2GOPRYsWLXKc7LPKy8vj3HPPjaeffjpxz4gRI2LkyJE5y3TffffFf/7nf+ZsPgAAAAAAAAAAAAAAAAAAAAAAtVNBvgNERLRs2TKGDBmSVs+oUaMS11ZXV8fzzz+fbqwvdeONNyY+fvV5lixZEu+//37i+h/96EfRpEmTxPVt2rSJ4cOHJ65fvHhxrF+/PnE9Eb/85S9j8ODBsWPHjkT1hYWF8fjjj0eXLl1ynOyz3nvvvTjxxBPTOn51zjnnxH333ZfDVAAAAAAAAAAAAAAAAAAAAAAA1Fe14gDW2WefHY0aNUqrp3fv3tGpU6fE9UuXLk031hfq2LFjXHfddXs0Y/78+YlrU6lUXHDBBWnvuPDCCxPX1tTUxIIFC9LeUR9VV1fHNddcEz/84Q9j9+7diXqaNGkSs2bNihNPPDHH6T5r+fLlcdxxx6X1DpxxxhnxyCOPRIMGDXKYDAAAAAAAAAAAAAAAAAAAAACA+qphvgNERPTp0yejvl69esWbb76ZqHb16tUZ7fg8w4cPj4YN9+zRLV++PHHtoYceGu3bt097R8+ePaOwsDAqKioS1S9btiwGDBiQ9p76pKKiIi655JJ49NFHE/c0bdo0SktL49RTT81hss/3zDPPxPnnnx9btmxJ3DNw4MCYPn16NG7cOIfJ/p8rrrgi7eNub7zxRgwaNChHiQAAAAAAAAAAAAAAAAAAAAAA2BtqxQGsrl27Ztw3c+bMRLXvvvtuRjs+z1lnnbXHM8rKyhLXdu/ePaMdBQUF0aVLl3jppZcS1Sc9JlZfrV+/PgYOHBgLFixI3NOqVat44okn4vjjj89hss/30EMPxeWXXx67du1K3HPJJZfE5MmT9/jAW1IHHnhgHHjggXtlFwAAAAAAAAAAAAAAAAAAAAAAtUdBvgNERLRu3Tqjvv333z9x7aZNmzLa8WmNGzeOY445Zo/nbNiwIXFt27ZtM96TTu/69esz3rOvW7VqVZSUlKR1/Orggw+OP/3pT3v9+FVNTU389Kc/jeHDh6d1/Orf//3fY8qUKXvt+BUAAAAAAAAAAAAAAAAAAAAAAPVXrbh007x585z37dixI6Mdn9axY8do1KjRHs8pLy9PXJvp84mIaNGiReLa7du3Z7xnXzZ37twYPHhwWkfUevbsGX/4wx+iffv2OUz2WZWVlXHppZfGb3/728Q9DRo0iHHjxsWoUaNymAwAAAAAAAAAAAAAAAAAAAAAAP6uVhzA2r17d877UqlURjs+rWXLllmZU1NTk7i2oKAg4z0NGjRIXJtOpvpi6tSpcdlll8XOnTsT95x11lkxbdq0KCoqymGyz/rggw9i0KBBMW/evMQ9zZo1i2nTpsXZZ5+dw2QAAAAAAAAAAAAAAAAAAAAAAPBJmV9WyqKtW7fmvK+wsDCjHZ+WraNG6eTZtm1bxnu2bNmSuDZbz2hfMWbMmBgyZEhax69GjRoVpaWle/341Zo1a6Jv375pHb866KCD4vnnn3f8CgAAAAAAAAAAAAAAAAAAAACAva5hvgNERKxbty6jvvXr1yeubdOmTUY7cqV169aJaz/44IOM96TTu//++2e8Z19SVVUVI0aMiIcffjhxT0FBQdxxxx1x9dVX5y7YF/jzn/8c55xzTmzcuDFxzzHHHBNPPPFEHHzwwTlMBgAAAAAAAAAAAAAAAAAAAAAAn68g3wEiIlasWJHzvg4dOmS0I1e+8pWvJK7N9PlERKxcuTJxbadOnTLes6/YvHlznHHGGWkdv2ratGn8/ve/z8vxqxkzZsQpp5yS1vGr008/PebNm+f4FQAAAAAAAAAAAAAAAAAAAAAAeVMrDmDNnz8/7Z7du3fH4sWLE9d369Yt7R251LVr18S1q1evjq1bt6a94/XXX4+PPvooJ5n2RW+++WaccMIJ8dxzzyXuadu2bcyZMycGDRqUw2Sf7/bbb49vf/vbsWPHjsQ9I0aMiCeeeCKaN2+ew2QAAAAAAAAAAAAAAAAAAAAAAPDlasUBrCeffDK2bNmSVs/cuXNj48aNiet79+6dbqycKikpSVy7a9euKC0tTXvHjBkz0qo/7rjj0t6xr1i8eHGUlJTEypUrE/d07do1FixYEH369Mlhss/atWtXXH755fGTn/wkampqEvWkUqm49dZbY9KkSdGwYcMcJwQAAAAAAAAAAAAAAAAAAAAAgC9XKw5gVVZWxvjx49PqueOOOxLXNm7cOE444YR0Y+VU3759o0WLFonrx48fn/jYUUTEjh074oEHHkhcf8QRR0Tnzp0T1+9LSktLo1+/fvH+++8n7jnllFPiz3/+c3Tq1CmHyT5r69atMWDAgJg0aVLiniZNmsQjjzwSP/nJT3KYDAAAAAAAAAAAAAAAAAAAAAAAkqsVB7AiIm699dZ49dVXE9VOnz49nnzyycSz+/fvH4WFhZlGy4nGjRvHwIEDE9cvXLgwHnzwwcT1t9xyS5SVlSWuP//88xPX7kvuvffeOPfcc2P79u2Jey699NKYPXt2FBcX5zDZZ7377rtx4oknpvW736ZNm3juuefiggsuyGEyAAAAAAAAAAAAAAAAAAAAAABIT605gLVt27Y47bTTYtmyZV9aV1paGkOHDk1r9rBhw/YgWe6MGjUq7fpZs2b907r7778/xo4dm3huw4YN4/LLL08ry77iqquuiurq6rR6Jk+eHI0aNYpUKpWVr6SHyh544IH4y1/+klbWjRs3xvHHH5+1rLX1XQIAAAAAAAAAAAAAAAAAAAAAoG6pNQewIiLeeeedOPbYY2PkyJExZ86cWL9+fezcuTP+7//+Lx577LE499xzY9CgQbFjx47EMw899NAYNGhQDlNnrqSkJPr375+4vqqqKgYPHhxDhgyJ+fPnR01Nzd++V11dHXPnzo1zzjknfvCDH3zie//M0KFD4+CDD04rOwAAAAAAAAAAAAAAAAAAAAAAQKYa5jvAp1VVVcXEiRNj4sSJWZk3ZsyYaNCgQVZm5cK4ceOiZ8+eUVlZmbhn6tSpMXXq1GjWrFm0b98+ampq4r333ovt27envb9Vq1Zx8803p90HdVnnzp3TOhIH1G7eaai7vL9Qe3gfoW7zDkPd4p2ltvM7+uU8H88gwjOI8AwiPIMIzyDCM4jwDOr7zx/hGUR4BhGeQYRnEOEZ1PefP8IziPAM6vvPH+EZ7AnPDr6Y9wPqPu8x1B3eV6i9vJ+wb/OOQ+3inYS6xTvLvqIg3wFy6eSTT46LL7443zG+VJcuXeLOO+/MqLe8vDzeeOONWLNmTUbHryIiHnrooWjbtm1GvQAAAAAAAAAAAAAAAAAAAAAAAJmoFQewcnGkqk2bNjFlypSsz82FH/7whzFq1Ki9vvfmm2+OwYMH7/W9AAAAAAAAAAAAAAAAAAAAAABA/VYrDmCNHTs2q0ewCgsL47HHHosOHTpkbWaujR8/Pq688sq9siuVSsXYsWPj+uuv3yv7AAAAAAAAAAAAAAAAAAAAAAAA/lGtOICVSqViypQp8f3vf3+PZxUXF8fs2bOjb9++WUi296RSqRg3blw88MADUVRUlLM9+++/f8yYMSNuuOGGnO0AAAAAAAAAAAAAAAAAAAAAAAD4MrXiAFZEREFBQdx///0xadKkaN68eUYzjj322Fi0aFH069cvy+n2nuHDh8eyZcviggsuiFQqlbW5DRo0iGHDhsUrr7wS5513XtbmAgAAAAAAAAAAAAAAAAAAAAAApKvWHMD62IgRI2LVqlUxatSoKCoqStRz1FFHxUMPPRQLFiyIww8/PMcJc++QQw6J6dOnx7Jly2LUqFHRpk2bjGe1a9curr766nj11Vdj8uTJ0a5du4xn7d69O3Fto0aNMt4DAAAAAAAAAAAAAAAAAAAAAADs2xrmO8DnOeigg2LChAlx2223xXPPPRcvvPBCrFq1KjZu3Bg7duyIpk2bRocOHaJ79+7Rv3//6NOnzx7t69y5c9TU1GQpffYcffTRMWHChLj33ntj4cKFMW/evFi6dGmsWbMm3n333di8eXNUVFRERERhYWG0atUqOnToEIcffnj07NkzTjzxxOjdu3ekUqms5Nm+fXvi2latWmVlZ67Vxs/9i4wePTpGjx6d7xgAAAAAAAAAAAAAAAAAAAAAALDHauUBrI81a9YsBgwYEAMGDMh3lLwqKCiIkpKSKCkpyWuODRs2JK6tKwewAAAAAAAAAAAAAAAAAAAAAACAva8g3wGoO1atWpW4tn379jlMAgAAAAAAAAAAAAAAAAAAAAAA1GUOYJFIVVVVPPvss4lqGzRoEL17985xIgAAAAAAAAAAAAAAAAAAAAAAoK5yAItEbrvttvjwww8T1R599NFRVFSU40QAAAAAAAAAAAAAAAAAAAAAAEBd1TDfAaiddu3aFZs2bYqXX345fvWrX8W0adMS9w4cODCHyQAAAAAAAAAAAAAAAAAAAAAAgLrOASw+YdCgQVFaWppx/3777RcjR47MYiIAAAAAAAAAAAAAAAAAAAAAAGBfU5DvAOxbhg0bFu3bt893DAAAAAAAAAAAAAAAAAAAAAAAoBZzAIus+epXvxp33XVXvmMAAAAAAAAAAAAAAAAAAAAAAAC1nANYZEXr1q1jxowZ0bx58y+smTNnTqRSqbx9AQAAAAAAAAAAAAAAAAAAAAAAtYMDWOyxI488Ml588cXo2bNnvqMAAAAAAAAAAAAAAAAAAAAAAAB1gANYZOyAAw6I22+/PV566aU47LDD8h0HAAAAAAAAAAAAAAAAAAAAAACoIxrmOwB1Q4sWLaJ169bRtm3b6Nu3b5x88slx8sknR9OmTfMdDQAAAAAAAAAAAAAAAAAAAAAAqGNydgBrzpw5uRpNDs2aNSvfEQAAAAAAAAAAAAAAAAAAAAAAgH1cQb4DAAAAAAAAAAAAAAAAAAAAAAAAUL80zHcA6o+TTjopampq8h0DAAAAAAAAAAAAAAAAAAAAAADIs4J8BwAAAAAAAAAAAAAAAAAAAAAAAKB+cQALAAAAAAAAAAAAAAAAAAAAAACAvcoBLAAAAAAAAAAAAAAAAAAAAAAAAPaqhvkOQN3x8MMPx6WXXpqo9qabborRo0fnNhCfq6ysLF588cV4+eWX46233or3338/ysvLo7q6Opo1axYtW7aMQw89NLp06RLf+MY34sgjj8x3ZAAAAAAAAAAAAAAAAAAAAAAA6hkHsCCBTZs2xaJFi2Lp0qVRUVGRqKdz584xbNiw3Ab7/61bty4efPDB+PWvfx2vvvpqWr2dO3eOIUOGxIgRI6Jjx445SggAAAAAAAAAAAAAAAAAAAAAAH/nABZ8SkVFRbz88suxcOHCv3298cYbac/p169fzg9gbdmyJcaOHRsTJkyIHTt2ZDSjrKwsxo4dG7fffntcccUVMWbMmCgqKspyUgAAAAAAAAAAAAAAAAAAAAAA+DsHsCAifvWrX/3t2NXy5ctj165d+Y70Tz3//PMxZMiQeOutt7Iyr7KyMn7xi1/EzJkz4ze/+U307ds3K3MBAAAAAAAAAAAAAAAAAAAAAODTHMCCiLjsssvyHSEtkydPjh/84AdRVVWV9dllZWVx8sknx9SpU+O8887L+nwAAAAAAAAAAAAAAAAAAAAAACjIdwAgPVOmTInLLrssJ8evPrZjx4648MIL4w9/+EPOdgAAAAAAAAAAAAAAAAAAAAAAUH85gAV1yPz582P48OFRU1OT8127du2Kb33rW7FixYqc7wIAAAAAAAAAAAAAAAAAAAAAoH5xAAvqiIqKirj44oujqqpqr+3cvn17XHTRRVFZWbnXdmZTWVlZpFKpz/0qKyvLdzxgL/G3APiYvwdAUv5eANngbwnwMX8P6gefc93hs6obfE51g8+pbvA51Q0+p7rB51Q3+JzqBp9T3eBzqht8TnWDz4nayO8l1F/ef6jf/A0A/pG/CUBS/l4A2eBvCfBF/H0gwgEsqDPuuOOOWLt2beL6Jk2axLXXXhsLFy6MTZs2RUVFRfz1r3+NBx54II444ojEc5YvXx533313JpEBAAAAAAAAAAAAAAAAAAAAAOBzOYBFYsOGDYuamppEX6NHj8533Kxq3LhxHHfccXHiiSfmZX95eXmMGzcucX3btm1j8eLFcfvtt0efPn3+P3buPLjq+twf+JOwJbIIJiIIrQEFy1pZcg0/6+Bliu0dCyMXrXUFFJkKvXYU7VWrlRY7VFFcUFt3pbaD0oqxrTJ2pFDxohJEEVkECq04KiBgWAKGJL8/OraVon7P4RzOCbxeM/kj5v15nndiwp9PtG3bNoqKiqJLly4xduzYePPNN2PEiBGJ502ZMiU2bdqUTnUAAAAAAAAAAAAAAAAAAAAAAPg3DmDBPgoLC6NXr14xZsyYuPfee6Oqqiq2b98eL7/8clx88cU56fTb3/42tmzZkjg/Y8aM6NWr12d+vXnz5vHrX/86unbtmmje9u3b4+c//3ni/QAAAAAAAAAAAAAAAAAAAAAA8Hma5roA5INvf/vbUV5eHuXl5TFw4MBo2bJlrit9ym9/+9vE2VNPPTVOP/30L8wVFRXF9ddfn/io189//vO47rrromlT/2wAAAAAAAAAAAAAAAAAAAAAAHBg8vqSzbJly6KysjJeeumlWLlyZWzatClqamqiZcuW0blz5+jTp098/etfj5EjR0a7du1y2rWhoSEWLFgQlZWVUVVVFW+//XZs27YtampqokmTJnHsscfG008/Hf37909rfnV1dSxatCiWLFkSa9eujQ0bNsRHH30UNTWSERjzAAAgAElEQVQ1ERFRXFwcbdu2jc6dO8cJJ5wQ/fv3j/Ly8rw75JSvnnjiiVxX+Eyf/G4ldfbZZyfOjhw5Mi699NKoq6v7wuz7778ff/rTn2Lo0KGJ5wMAAAAAAAAAAAAAAAAAAAAAwP5k7QDWaaedFvPnz0+UXbduXZSVlf3j84ULF8a11177me+rq6tj+fLlsXz58njiiSfi8ssvj0svvTR+9KMfRUlJScpd169fH126dEmUHTx4cMybN+9T/2327Nlxww03xFtvvbXfN3V1dfHOO+9EdXV1Sr127twZM2fOjF/96lexYMGCqK2tTel9ixYtYvDgwXHhhRfGWWedFUVFRSm939cLL7wQt99+e6LseeedF+edd94B7ePv/va3v8WWLVsS5wcOHJg426ZNm+jWrVusXLkyUf43v/mNA1gAAAAAAAAAAAAAAAAAAAAAABywrB3ASkdtbW384Ac/iDvvvDMaGhoSv6upqYm77rorZs6cGTNmzIhvfOMbWWz5T7t3745LLrkkfv3rX2d0bk1NTdxxxx1xyy23xLZt29Kes2fPnnj++efj+eefj4kTJ8YPf/jDuOyyy6JZs2ZpzXvnnXfiD3/4Q6JsKkeY+Hzr1q1LKd+1a9eU8scff3ziA1hz585NaTYAAAAAAAAAAAAAAAAAAAAAAOxPYa4LfGLnzp3xzW9+M+64446Ujl/9q40bN8YZZ5wRDz74YIbb/bsdO3bE0KFDM378auHChfHVr341rrvuugM6frWvjRs3xve///0oLy+PN998M2Nzyb7NmzenlG/dunXW8mvWrIn3338/pfkAAAAAAAAAAAAAAAAAAAAAALCvvDiA9fHHH8cZZ5wRc+fOPeBZdXV1MW7cuIwfptp3x7nnnhsLFizI6NxHHnkkBg8eHKtXr87o3H/1xhtvxKBBg6KysjJrO8ismpqalPIFBQVZzb/++usp5QEAAAAAAAAAAAAAAAAAAAAAYF95cQBr/PjxMX/+/IzNa2hoiDFjxsSSJUsyNvNfTZkyJX7/+99ndOZ9990XF198cdTW1mZ07v7s3LkzRo4cGbNnz876Lg5cs2bNUspv3749pXx1dXVK+RUrVqSUBwAAAAAAAAAAAAAAAAAAAACAfeXFAawXXngh4zM//vjjOO+88+Ljjz/O6Ny1a9fGT37yk4zOfPbZZ2P8+PEZnflF6urq4txzz43Fixcf1L2k7sgjj0wp/5e//CWr+TVr1qSUBwAAAAAAAAAAAAAAAAAAAACAfeXFAaz9KSwsjA4dOkSPHj2ipKQkrRkrV66M22+/PaO9NmzYELW1tRmb98EHH8SoUaOivr4+YzOT2rNnT5xzzjmxa9eug76b5I477riU8qkcNdu+fXu8/fbbKc1///33U8oDAAAAAAAAAAAAAAAAAAAAAMC+8u4AVvv27ePee++NDz74IN57771Yvnx5bN68OdatWxc33nhjFBUVpTRv6tSpeX3g6ZprronNmzen9KZv375x9913x6pVq2L79u1RXV0dy5cvj2nTpkW3bt1SmrV27dq4+eabU3rDwdWtW7coLi5OnJ81a1bi7FNPPRV1dXUp9fnggw9SygMAAAAAAAAAAAAAAAAAAAAAwL7y6gDWwIEDY/ny5XHZZZdFaWnpp75WVlYWkyZNisWLF0f79u0Tz/zwww/j8ccfz3TVT+nVq1dMmTIlFi1aFO+//37U1tbGRx99FEuXLo3HHnsszj///GjVqtW/vVu9enXMmDEjpV3XX399LF68OCZMmBDdu3ePVq1aRevWraNHjx5xxRVXxNKlS2PcuHEpzbztttti27ZtKb3h4GnWrFkMGjQocX7+/Pkxd+7cL8zt2bMnfvrTn6bcZ8eOHSm/AQAAAAAAAAAAAAAAAAAAAACAf9U01wU+0aFDh3juueeipKTkc3M9e/aMysrKOOWUU6K+vj7R7BkzZqR8FCqJ5s2bxx133BHjxo2LJk2afOprbdq0iT59+kSfPn3ioosuih07dkRdXd2nMtOnT0/8PURETJw4MSZPnvy5maKiovjFL34R1dXVMXPmzERzd+7cGQ899FBMnDgxcRcOrpEjRyY6avWJCy64IObOnRtf+cpX9vv12trauOCCC2L16tUpd9m9e3fKbz7Lxo0bY9OmTSm9WbNmTcb2AwAAAAAAAAAAAAAAAAAAAACQG3lzAOumm26K0tLSRNmKiooYNWpUPPLII4nyCxcujK1bt0a7du0OpOKnNG/ePH73u9/F6aefnijfqlWrT31eX18fs2bNSryvrKwsbrrppkTZgoKCmD59esyZMye2bduW6M0TTzzhAFYeu+CCC+Laa6+N6urqRPn33nsv+vfvH5dffnmcddZZ0a1btygqKor33nsv5s6dG1OnTo2VK1em1aW2tjatd/tz7733xo9//OOMzQMAAAAAAAAAAAAAAAAAAAAAoHEozHWBiIi2bdvGhRdemNKbCRMmJM7W19fHn//851Rrfa4bbrgh8fGr/Vm8eHG8//77ifNXXHFFFBUVJc6XlpbG2LFjE+erqqpi48aNifMcXG3atIkrrrgipTc1NTVx8803R3l5ebRt2zaKioqiS5cucckll6R9/Cri78ffAAAAAAAAAAAAAAAAAAAAAADgQOTFAaxvfetbKR/VGTBgQBx33HGJ80uWLEm11mfq3LlzXHPNNQc0Y+HChYmzBQUFcfbZZ6e84zvf+U7ibENDQ7zyyisp7+Dg+d///d/o3r17rmukdIgNAAAAAAAAAAAAAAAAAAAAAAD2p2muC0RElJeXp/Wuf//+8de//jVRdtWqVWnt2J+xY8dG06YH9qN78803E2e7du0aHTt2THlHv379ori4OGpqahLlly5dGsOGDUt5DwdHcXFxzJo1K0455ZTYsWNHznq0bt06Y7PGjx+f8nG3NWvWxJlnnpmxDgAAAAAAAAAAAAAAAAAAAAAAHHx5cQCrZ8+eab+bPXt2ouy7776b1o79OeOMMw54xvr16xNn+/Tpk9aOwsLC6NGjR7z22muJ8kmPiZE7ffv2jWeeeSa+9a1vxa5du3LS4dhjj83YrPbt20f79u0zNg8AAAAAAAAAAAAAAAAAAAAAgMahMNcFIiJKSkrSenfUUUclzm7dujWtHftq0aJF9O3b94DnbNq0KXH2mGOOSXtPKm83btyY9h4Onv/8z/+MefPmRefOnTM2s127domzmTyABQAAAAAAAAAAAAAAAAAAAADA4SkvDmC1bt066+92796d1o59de7cOZo3b37Ac3bu3Jk4m+7PJyKiTZs2ibO7du1Kew8HV3l5eSxdujTGjBkThYXp/xm3aNEiJk+eHD/72c8Sv+nevXva+wAAAAAAAAAAAAAAAAAAAAAAICJPDmDV1dVl/V1BQUFaO/bVtm3bjMxpaGhInD2QA0dNmjRJnE2lE7nXrl27ePjhh+ONN96IsWPHpnTsrLS0NK699tpYs2ZNXH/99bF27drEb/v27ZtOXQAAAAAAAAAAAAAAAAAAAAAA+IemuS4QEbF9+/asvysuLk5rx75atWqVkTmp9NmxY0fae6qrqxNnM/Uz4uDq3bt3PPDAA3HPPffEggUL4pVXXomVK1fGhg0bYseOHdHQ0BBHHXVUlJaWRp8+feLUU0+N8vLyaNas2T9mvPHGG4l2NW3a1AEsAAAAAAAAAAAAAAAAAAAAAAAOWF4cwPrggw/Serdx48bE2dLS0rR2ZEtJSUni7Icffpj2nlTeHnXUUWnvIfeaN28eQ4YMiSFDhqT0bseOHTFv3rxE2f/4j//I2BE4AAAAAAAAAAAAAAAAAAAAAAAOX4W5LhARsWzZsqy/69SpU1o7suXLX/5y4my6P5+IiOXLlyfOHnfccWnvofF67rnnYs+ePYmyp59+epbbAAAAAAAAAAAAAAAAAAAAAABwOMiLA1gLFy5M+U1dXV1UVVUlzvfq1SvlHdnUs2fPxNlVq1bF9u3bU96xevXq+Oijj7LSiUPHLbfckjj7ne98J4tNAAAAAAAAAAAAAAAAAAAAAAA4XOTFAaw5c+ZEdXV1Sm/mz58fmzdvTpwfMGBAqrWyqqKiInF27969UVlZmfKOWbNmpZQ/+eSTU95B4/ab3/wm8SG5ioqKOPHEE7PcCAAAAAAAAAAAAAAAAAAAAACAw0FeHMDas2dPTJ8+PaU3U6dOTZxt0aJFnHLKKanWyqpBgwZFmzZtEuenT58eDQ0NifO7d++OBx54IHG+e/fuUVZWljhP47dmzZoYN25c4vwVV1yRxTYAAAAAAAAAAAAAAAAAAAAAABxO8uIAVkTEz372s1ixYkWi7JNPPhlz5sxJPHvo0KFRXFycbrWsaNGiRQwfPjxx/tVXX40HH3wwcX7KlCmxfv36xPmzzjorcZbc2bBhQ3z44YcHPGf16tVxxhlnxNatWxPlTzzxRL8jAAAAAAAAAAAAAAAAAAAAAABkTN4cwNqxY0ecfvrpsXTp0s/NVVZWxqhRo1KaPXr06ANolj0TJkxIOf/0009/Ye7++++PyZMnJ57btGnT+O53v5tSF3JjwYIF0blz57joooviz3/+c9TX16f0vq6uLh577LEoLy+Pt99+O/G722+/PQoL8+afCwAAAAAAAAAAAAAAAAAAAAAAGrmmuS7wrzZs2BADBw6MSy65JM4555zo2bNntG3bNjZv3hxVVVXx6KOPxuzZs1Oa2bVr1zjzzDOz1PjAVFRUxNChQ+OPf/xjonxtbW2MGDEiLrjgghg/fnxUVFREQUFBRETU19fHiy++GNOmTYtnnnkmpR6jRo2KL33pSyn3P5SMHTs2qqqqvjC3ZcuWxDOrqqripJNOSpR99tln49hjj02U3b17d/zyl7+MX/7yl3H00UfHsGHD4rTTTouTTjopevToEU2bfvrPeu/evbF48eL405/+FA888ED85S9/Sfw9REScc8458V//9V8pvQEAAAAAAAAAAAAAAAAAAAAAgM9T0NDQ0JCNwaeddlrMnz8/G6NT8vjjj8f555//uZn169dHly5dEs0bPHhwzJs3LwPN/m7FihXRr1+/2LNnT8pvW7ZsGR07doyGhoZ47733YteuXSnPaNeuXaxYsSKOOeaYL8w++uijMWbMmERzb7zxxpg0aVLKfXIl17+v69ati7Kysi/MzZw5M84999zP/HrTpk2jXbt20a5duygsLIwtW7bEli1bYu/evWn1Ov7446Oqqiratm2b1vtseOutt6J3797/+HzZsmXRq1evHDYCAAAAAAAAAAAAAAAAAAAAAA5HbqEcmKa5LpBNQ4YM+cLjV7nWo0ePuPXWW+N//ud/Un67c+fOWLNmzQHtf+ihhxIdv6Jx2Lt3b2zatCk2bdp0wLOOPPLIeOqpp/Lq+BUAAAAAAAAAAAAAAAAAAAAAAIeGwlwXiIisHKkqLS2NGTNmZHxuNnzve9+LCRMmHPS9P/3pT2PEiBEHfS/5r3Xr1jFnzpzo27dvrqsAAAAAAAAAAAAAAAAAAAAAAHAIyosDWJMnT87oEazi4uJ45plnolOnThmbmW3Tp0+Pyy+//KDsKigoiMmTJ8d11113UPbRuBx33HHx4osvRkVFRa6rAAAAAAAAAAAAAAAAAAAAAABwiMqLA1gFBQUxY8aMuPTSSw941pFHHhnPPfdcDBo0KAPNDp6CgoK4884744EHHohWrVplbc9RRx0Vs2bNiuuvvz5rO2i8RowYEYsWLYqvfvWrua4CAAAAAAAAAAAAAAAAAAAAAMAhLC8OYEVEFBYWxv333x/33XdftG7dOq0ZAwcOjEWLFsXgwYMz3O7gGTt2bCxdujTOPvvsKCgoyNjcJk2axOjRo+Ott96KkSNHZmwuh4aePXvGU089FU899VQcffTRua4DAAAAAAAAAAAAAAAAAAAAAMAhLm8OYH1i3LhxsXLlypgwYUK0atUq0ZuvfOUr8dBDD8Urr7wS3bp1y3LD7OvSpUs8+eSTsXTp0pgwYUKUlpamPatDhw5x5ZVXxooVK+KRRx6JDh06pD2rrq4ucbZ58+Zp7+GzDRs2LGbNmhWjR48+oP+XEX//fzR8+PCYPXt2LFu2LEaMGJGhlgAAAAAAAAAAAAAAAAAAAAAA8Pma5rrA/hx77LFx9913x8033xxz586Nl156KVauXBmbN2+O3bt3xxFHHBGdOnWKPn36xNChQ6O8vPyA9pWVlUVDQ0OG2mdO79694+6774677rorXn311ViwYEEsWbIk1q5dG++++25s27YtampqIiKiuLg42rVrF506dYpu3bpFv3794tRTT40BAwZEQUFBRvrs2rUrcbZdu3YZ2XmwzJs3L9cVEmnZsmWcddZZcdZZZ0VExPr162PRokXx+uuvx7p16+Kvf/1rvPvuu7Fjx47YtWtX7NmzJ1q0aBEtW7aMY445Jrp27Rq9evWKU045JU499dQ48sgjc/wdAQAAAAAAAAAAAAAAAAAAAABwOMrLA1ifaNmyZQwbNiyGDRuW6yo5VVhYGBUVFVFRUZHTHps2bUqcbWwHsBqrsrKyKCsri7PPPjvXVQAAAAAAAAAAAAAAAAAAAAAAILHCXBeg8Vi5cmXibMeOHbPYBAAAAAAAAAAAAAAAAAAAAAAAaMwcwCKR2traeOGFFxJlmzRpEgMGDMhyIwAAAAAAAAAAAAAAAAAAAAAAoLFyAItEbr755tiyZUuibO/evaNVq1ZZbgQAAAAAAAAAAAAAAAAAAAAAADRWTXNdgPy0d+/e2Lp1a7z++uvx8MMPx8yZMxO/HT58eBabAQAAAAAAAAAAAAAAAAAAAAAAjZ0DWHzKmWeeGZWVlWm/b9asWVx22WUZbAQAAAAAAAAAAAAAAAAAAAAAABxqCnNdgEPL6NGjo2PHjrmuAQAAAAAAAAAAAAAAAAAAAAAA5DEHsMiY448/Pm677bZc1wAAAAAAAAAAAAAAAAAAAAAAAPKcA1hkRElJScyaNStat279mZl58+ZFQUFBzj4AAAAAAAAAAAAAAAAAAAAAAID84AAWB+zEE0+Ml19+Ofr165frKgAAAAAAAAAAAAAAAAAAAAAAQCPgABZpO/roo+OWW26J1157LU444YRc1wEAAAAAAAAAAAAAAAAAAAAAABqJprkuQOPQpk2bKCkpiWOOOSYGDRoUQ4YMiSFDhsQRRxyR62oAAAAAAAAAAAAAAAAAAAAAAEAjk7UDWPPmzcvWaLLo6aefznUFAAAAAAAAAAAAAAAAAAAAAADgEFeY6wIAAAAAAAAAAAAAAAAAAAAAAAAcXprmugCHj9NOOy0aGhpyXQMAAAAAAAAAAAAAAAAAAAAAAMixwlwXAAAAAAAAAAAAAAAAAAAAAAAA4PDiABYAAAAAAAAAAAAAAAAAAAAAAAAHlQNYAAAAAAAAAAAAAAAAAAAAAAAAHFQOYJHYo48+GgUFBYk+Jk2alOu6AAAAAAAAAAAAAAAAAAAAAABAnmqa6wLQGGzdujUWLVoUS5YsiZqamkRvysrKYvTo0RnZP2nSpPjxj3+ckVkH4rjjjov169fnugYAAAAAAAAAAAAAAAAAAAAAAI2cA1iwj5qamnj99dfj1Vdf/cfHmjVrUp4zePDgjB3AAgAAAAAAAAAAAAAAAAAAAACAQ4kDWBARDz/88D+OXb355puxd+/eXFcCAAAAAAAAAAAAAAAAAAAAAIBDlgNYEBGXXHJJrisAAAAAAAAAAAAAAAAAAAAAAMBhozDXBQAAAAAAAAAAAAAAAAAAAAAAADi8OIAFAAAAAAAAAAAAAAAAAAAAAADAQeUAFpBYQUFBrivAZ1q/fn0UFBTs92P9+vW5rgeHNH9/5Cu/m5CcvxfIL/4myXd+R2H/Dte/jcbwfedrx3zspZNOh2qffOykj06HYh+dGmeffOyUb33ysVO+9dGpcfbJx0766HQo9tGp8XbSJ32NqSuHBr9z5Du/o7B//jYgP/hbpDHz+wt/528B8oO/RQ5Ffq85nPh9zz0HsIDEhg4dmusKAAAAAAAAAAAAAAAAAAAAAAAcAprmugCNx+jRo2P06NG5rpETLVq0iJNOOimaN28eL774Yq7r5ERBQUFMnDgx1zUAAAAAAAAAAAAAAAAAAAAAADgEFOa6AOSbwsLC6NWrV4wZMybuvffeqKqqiu3bt8fLL78cF198cU46TZo0KRoaGjL+8bWvfS1xh2HDhsWJJ56Yxe8SAAAAAAAAAAAAAAAAAAAAAIDDRdNcF4B88O1vfzvKy8ujvLw8Bg4cGC1btsx1pax79dVXY8GCBYnzV111VRbbAAAAAAAAAAAAAAAAAAAAAABwOMnrA1jLli2LysrKeOmll2LlypWxadOmqKmpiZYtW0bnzp2jT58+8fWvfz1GjhwZ7dq1y2nXhoaGWLBgQVRWVkZVVVW8/fbbsW3btqipqYkmTZrEscceG08//XT0798/rfnV1dWxaNGiWLJkSaxduzY2bNgQH330UdTU1ERERHFxcbRt2zY6d+4cJ5xwQvTv3z/Ky8sPi0NOmfDEE0/kusJBN3Xq1MTZk08+OU499dQstgEAAAAAAAAAAAAAAAAAAAAA4HCStQNYp512WsyfPz9Rdt26dVFWVvaPzxcuXBjXXnvtZ76vrq6O5cuXx/Lly+OJJ56Iyy+/PC699NL40Y9+FCUlJSl3Xb9+fXTp0iVRdvDgwTFv3rxP/bfZs2fHDTfcEG+99dZ+39TV1cU777wT1dXVKfXauXNnzJw5M371q1/FggULora2NqX3LVq0iMGDB8eFF14YZ511VhQVFaX0fl8vvPBC3H777Ymy5513Xpx33nkHtI/s+ctf/hJPPfVU4vxVV12VxTYAAAAAAAAAAAAAAAAAAAAAABxusnYAKx21tbXxgx/8IO68885oaGhI/K6mpibuuuuumDlzZsyYMSO+8Y1vZLHlP+3evTsuueSS+PWvf53RuTU1NXHHHXfELbfcEtu2bUt7zp49e+L555+P559/PiZOnBg//OEP47LLLotmzZqlNe+dd96JP/zhD4myAwcOTGsHB8e0adOivr4+UbZr167x3//931luBAAAAAAAAAAAAAAAAAAAAADA4aQw1wU+sXPnzvjmN78Zd9xxR0rHr/7Vxo0b44wzzogHH3www+3+3Y4dO2Lo0KEZP361cOHC+OpXvxrXXXfdAR2/2tfGjRvj+9//fpSXl8ebb76Zsbk0Ph9++GE88sgjifNXXnllFBbmzT8VAAAAAAAAAAAAAAAAAAAAAAAcAvLiqs3HH38cZ5xxRsydO/eAZ9XV1cW4ceMyfphq3x3nnntuLFiwIKNzH3nkkRg8eHCsXr06o3P/1RtvvBGDBg2KysrKrO0gv917772xa9euRNmSkpIYM2ZMlhsBAAAAAAAAAAAAAAAAAAAAAHC4yYsDWOPHj4/58+dnbF5DQ0OMGTMmlixZkrGZ/2rKlCnx+9//PqMz77vvvrj44oujtrY2o3P3Z+fOnTFy5MiYPXt21neRX3bv3h1333134vz48ePjiCOOyGIjAAAAAAAAAAAAAAAAAAAAAAAOR3lxAOuFF17I+MyPP/44zjvvvPj4448zOnft2rXxk5/8JKMzn3322Rg/fnxGZ36Rurq6OPfcc2Px4sUHdS+5NWPGjNi4cWOibFFRUXzve9/LciMAAAAAAAAAAAAAAAAAAAAAAA5HeXEAa38KCwujQ4cO0aNHjygpKUlrxsqVK+P222/PaK8NGzZEbW1txuZ98MEHMWrUqKivr8/YzKT27NkT55xzTuzateug7+bga2hoiGnTpiXOX3TRRdG+ffssNgIAAAAAAAAAAAAAAAAAAAAA4HCVdwew2rdvH/fee2988MEH8d5778Xy5ctj8+bNsW7durjxxhujqKgopXlTp07N6wNP11xzTWzevDmlN3379o277747Vq1aFdu3b4/q6upYvnx5TJs2Lbp165bSrLVr18bNN9+c0hsap2eeeSZWrVqVKFtQUBBXXnlllhsBAAAAAAAAAAAAAAAAAAAAAHC4yqsDWAMHDozly5fHZZddFqWlpZ/6WllZWUyaNCkWL14c7du3Tzzzww8/jMcffzzTVT+lV69eMWXKlFi0aFG8//77UVtbGx999FEsXbo0HnvssTj//POjVatW//Zu9erVMWPGjJR2XX/99bF48eKYMGFCdO/ePVq1ahWtW7eOHj16xBVXXBFLly6NcePGpTTztttui23btqX0hsbn1ltvTZwdPnx4nHjiiVlsAwAAAAAAAAAAAAAAAAAAAADA4axprgt8okOHDvHcc89FSUnJ5+Z69uwZlZWVccopp0R9fX2i2TNmzEj5KFQSzZs3jzvuuCPGjRsXTZo0+dTX2rRpE3369Ik+ffrERRddFDt27Ii6urpPZaZPn574e4iImDhxYkyePPlzM0VFRfGLX/wiqqurY+bMmYnm7ty5Mx566KGYOHFi4i40Lq+88kosWLAgcf6qq67KYpt/2rhxY2zatCmlN2vWrMlSGwAAAAAAAAAAAAAAAAAAAAAADpa8OYB10003RWlpaaJsRUVFjBo1Kh555JFE+YULF8bWrVujXbt2B1LxU5o3bx6/+93v4vTTT0+Ub9Wq1ac+r6+vj1mzZiXeV1ZWFjfddFOibEFBQUyfPj3mzJkT27ZtS/TmiSeecADrEDZ16tTE2ZNPPjm+9rWvZbHNP917773x4x//+KDsAgAAAAAAAAAAAAAAAAAAAAAgfxTmukBERNu2bePCCy9M6c2ECRMSZ+vr6+PPf/5zqrU+1w033JD4+NX+LF68ON5///3E+SuuuCKKiooS50tLS2Ps2LGJ81VVVbFx48bEeRqPtWvXxuzZsxPnr7766iy2AQAAAAAAAAAAAAAAAAAAAACAPDmA9a1vfSuaN2+e0psBAwbEcccdlzi/ZMmSVGt9ps6dO8c111xzQMUCpm0AACAASURBVDMWLlyYOFtQUBBnn312yju+853vJM42NDTEK6+8kvIO8t+0adOivr4+Ufb444+PESNGZLkRAAAAAAAAAAAAAAAAAAAAAACHu6a5LhARUV5enta7/v37x1//+tdE2VWrVqW1Y3/Gjh0bTZse2I/uzTffTJzt2rVrdOzYMeUd/fr1i+Li4qipqUmUX7p0aQwbNizlPeSvDz/8MB599NHE+SuvvDIKCw/eXbzx48enfNxtzZo1ceaZZ2apEQAAAAAAAAAAAAAAAAAAAAAAB0NeHMDq2bNn2u9mz56dKPvuu++mtWN/zjjjjAOesX79+sTZPn36pLWjsLAwevToEa+99lqifNJjYjQe99xzT+zatStRtqSkJEaPHp3dQvto3759tG/f/qDuBAAAAAAAAAAAAAAAAAAAAAAg9wpzXSDi74d30nHUUUclzm7dujWtHftq0aJF9O3b94DnbNq0KXH2mGOOSXtPKm83btyY9h7yz+7du+Oee+5JnJ8wYUIcccQRWWwEAAAAAAAAAAAAAAAAAAAAAAB/lxcHsFq3bp31d7t3705rx746d+4czZs3P+A5O3fuTJxN9+cTEdGmTZvE2V27dqW9h/zz2GOPJT5qVlRUFBMmTMhyIwAAAAAAAAAAAAAAAAAAAAAA+Lu8OIBVV1eX9XcFBQVp7dhX27ZtMzKnoaEhcbawMP3/TU2aNEmcTaUT+a2hoSGmTZuWOH/RRRdF+/bts9gIAAAAAAAAAAAAAAAAAAAAAAD+KS8OYG3fvj3r74qLi9Pasa9WrVplZE4qfXbs2JH2nurq6sTZTP2MyL3Kysp4++23E2ULCwtj4sSJWW4EAAAAAAAAAAAAAAAAAAAAAAD/lBcHsD744IO03m3cuDFxtrS0NK0d2VJSUpI4++GHH6a9J5W3Rx11VNp7yC+33npr4uywYcOie/fuWWwDAAAAAAAAAAAAAAAAAAAAAACflhcHsJYtW5b1d506dUprR7Z8+ctfTpxN9+cTEbF8+fLE2eOOOy7tPeSPl19+OV566aXE+auvvjqLbQAAAAAAAAAAAAAAAAAAAAAA4N/lxQGshQsXpvymrq4uqqqqEud79eqV8o5s6tmzZ+LsqlWrYvv27SnvWL16dXz00UdZ6UT+mjp1auJsRUVFnHLKKVlsAwAAAAAAAAAAAAAAAAAAAAAA/y4vDmDNmTMnqqurU3ozf/782Lx5c+L8gAEDUq2VVRUVFYmze/fujcrKypR3zJo1K6X8ySefnPIO8svatWvj6aefTpy/+uqrs9gGAAAAAAAAAAAAAAAAAAAAAAD2Ly8OYO3ZsyemT5+e0pupU6cmzrZo0SJOOeWUVGtl1aBBg6JNmzaJ89OnT4+GhobE+d27d8cDDzyQON+9e/coKytLnCc/3XbbbVFfX58oe8IJJ8SZZ56Z5UYAAAAAAAAAAAAAAAAAAAAAAPDv8uIAVkTEz372s1ixYkWi7JNPPhlz5sxJPHvo0KFRXFycbrWsaNGiRQwfPjxx/tVXX40HH3wwcX7KlCmxfv36xPmzzjorcZb8tHnz5njssccS56+88sooLMybfwIAAAAAAAAAAAAAAAAAAAAAADiM5M31mx07dsTpp58eS5cu/dxcZWVljBo1KqXZo0ePPoBm2TNhwoSU808//fQX5u6///6YPHly4rlNmzaN7373uyl1If/cc889sWvXrkTZ0tLSvP27AAAAAAAAAAAAAAAAAAAAAADg0Nc01wX+1YYNG2LgwIFxySWXxDnnnBM9e/aMtm3bxubNm6OqqioeffTRmD17dkozu3btGmeeeWaWGh+YioqKGDp0aPzxj39MlK+trY0RI0bEBRdcEOPHj4+KioooKCiIiIj6+vp48cUXY9q0afHMM8+k1GPUqFHxpS99KeX+h5KxY8dGVVXVF+a2bNmSeGZVVVWcdNJJibLPPvtsHHvssYln72v37t1xzz33JM6PHz8+iouL094HAAAAAAAAAAAAAAAAAAAAAAAHoqChoaEhG4NPO+20mD9/fjZGp+Txxx+P888//3Mz69evjy5duiSaN3jw4Jg3b14Gmv3dihUrol+/frFnz56U37Zs2TI6duwYDQ0N8d5778WuXbtSntGuXbtYsWJFHHPMMV+YffTRR2PMmDGJ5t54440xadKklPvkSq5/X9etWxdlZWVpv7/vvvviu9/9bqJsUVFR/O1vf4ujjz467X259NZbb0Xv3r3/8fmyZcuiV69eOWwEAAAAAAAAAAAAAAAAAAAAAByO3EI5MIW5LpBNQ4YM+cLjV7nWo0ePuPXWW9N6u3PnzlizZk2sXbs2reNXEREPPfRQouNX5K/6+vqYNm1a4vyoUaMa7fErAAAAAAAAAAAAAAAAAAAAAAAODXlxACsbR6pKS0tjxowZGZ+bDd/73vdiwoQJB33vT3/60xgxYsRB30tmPfPMM/H2228nyhYWFsaVV16Z5UYAAAAAAAAAAAAAAAAAAAAAAPD58uIA1uTJkzN6BKu4uDieeeaZ6NSpU8ZmZtv06dPj8ssvPyi7CgoKYvLkyXHdddcdlH1k19SpUxNnhw8fHt27d89iGwAAAAAAAAAAAAAAAAAAAAAA+GJ5cQCroKAgZsyYEZdeeukBzzryyCPjueeei0GDBmWg2cFTUFAQd955ZzzwwAPRqlWrrO056qijYtasWXH99ddnbQcHz8KFC+P//u//EuevvvrqLLYBAAAAAAAAAAAAAAAAAAAAAIBk8uIAVkREYWFh3H///XHfffdF69at05oxcODAWLRoUQwePDjD7Q6esWPHxtKlS+Pss8+OgoKCjM1t0qRJjB49Ot56660YOXJkxuaSW1OnTk2cHTRoUPy///f/stgGAAAAAAAAAAAAAAAAAAAAAACSyZsDWJ8YN25crFy5MiZMmBCtWrVK9OYrX/lKPPTQQ/HKK69Et27dstww+7p06RJPPvlkLF26NCZMmBClpaVpz+rQoUNceeWVsWLFinjkkUeiQ4cOac+qq6tLnG3evHnae0hmzZo1UVlZmTh/1VVXZbENAAAAAAAAAAAAAAAAAAAAAAAk1zTXBfbn2GOPjbvvvjtuvvnmmDt3brz00kuxcuXK2Lx5c+zevTuOOOKI6NSpU/Tp0yeGDh0a5eXlB7SvrKwsGhoaMtQ+c3r37h1333133HXXXfHqq6/GggULYsmSJbF27dp49913Y9u2bVFTUxMREcXFxdGuXbvo1KlTdOvWLfr16xennnpqDBgwIAoKCjLSZ9euXYmz7dq1y8jOg2XevHm5rpCyE044IaWjZAAAAAAAAAAAAAAAAAAAAAAAkC/y8gDWJ1q2bBnDhg2LYcOG5bpKThUWFkZFRUVUVFTktMemTZsSZxvbASwAAAAAAAAAAAAAAAAAAAAAAODgKcx1ARqPlStXJs527Ngxi00AAAAAAAAAAAAAAAAAAAAAAIDGzAEsEqmtrY0XXnghUbZJkyYxYMCALDcCAAAAAAAAAAAAAAAAAAAAAAAaKwewSOTmm2+OLVu2JMr27t07WrVqleVGAAAAAAAAAAAAAAAAAAAAAABAY9U01wXIT3v37o2tW7fG66+/Hg8//HDMnDkz8dvhw4dnsRkAAAAAAAAAAAAAAAAAAAAAANDYOYDFp5x55plRWVmZ9vtmzZrFZZddlsFGAAAAAAAAAAAAAAAAAAAAAADAoaYw1wU4tIwePTo6duyY6xoAAAAAAAAAAAAAAAAAAAAAAEAecwCLjDn++OPjtttuy3UNAAAAAAAAAAAAAAAAAAAAAAAgzzmARUaUlJTErFmzonXr1p+ZmTdvXhQUFOTsAwAAAAAAAAAAAAAAAAAAAAAAyA8OYHHATjzxxHj55ZejX79+ua4CAAAAAAAAAAAAAAAAAAAAAAA0Ag5gkbajjz46brnllnjttdfihBNOyHUdAAAAAAAAAAAAAAAAAAAAAACgkWia6wI0Dm3atImSkpI45phjYtCgQTFkyJAYMmRIHHHEEbmuBgAAAAAAAAAAAAAAAAAAAAAANDJZO4A1b968bI0mi55++ulcVwAAAAAAAAAAAAAAAAAAAAAAAA5xhbkuAAAAAAAAAAAAAAAAAAAAAAAAwOGlaa4LcPg47bTToqGhIdc1AAAAAAAAAAAAAAAAAAAAAACAHCvMdQEAAAAAAAAAAAAAAAAAAAAAAAAOLw5gAQAAAAAAAAAAAAAAAAAAAAAAcFA5gAUAAAAAAAAA/5+dO4+usjDzB/4krAFkC0U2a0RtDYpVloq1FHRE7VAtikvFBRTkJ9Bqree0arWi6FjrLh7qLlLbQemMhLbKqCgoShUcFBSwkAJFawkoEAgBQpLfHz06FlHfe7k3N5HP55z8EfIsX/Lmvn8+AAAAAAAAAAAAAECdcgCLxCZPnhx5eXmJvsaPH5/ruAAAAAAAAAAAAAAAAAAAAAAAQD3VONcBoCHYsGFDzJ8/PxYuXBiVlZWJeoqKimLEiBHZDRYRtbW18Ze//CUWLlwYpaWlsXLlyvjrX/8af//732PLli1RUVERFRUV0bhx42jdunW0adMm2rZtG1//+tejV69e0atXr+jTp0+0aNEi61kBAAAAAAAAAAAAAAAAAAAAACDCASz4lMrKynjjjTfitdde+/hrxYoVKc8ZMGBAVg5gVVVVxSuvvBLPPPNMvPrqq7FgwYLYtGlTor7KyspYu3ZtRES89tpr8Zvf/CYiIvbZZ58444wzYsSIEdG/f/+MZwYAAAAAAAAAAAAAAAAAAAAAgE9yAAsi4uGHH/742NXixYtj586duY70KS+//HLccccd8eyzz0Z5eXlGZ2/evDkefvjhePjhh6N///5x7733Ro8ePTK6AwAAAAAAAAAAAAAAAAAAAAAAPuIAFkTEyJEjcx3hCz377LPxX//1X1nf89JLL8URRxwRV155ZYwfPz7y8vKyvhMAAAAAAAAAAAAAAAAAAAAAgL1Lfq4DAPVPVVVVXH/99XHOOedETU1NruMAAAAAAAAAAAAAAAAAAAAAAPAl4wAW8Jn+8z//My677LJcxwAAAAAAAAAAAAAAAAAAAAAA4EvGASzgc919990xc+bMXMcgy1atWhV5eXm7/Vq1alWu4wF1zDsBiPAuALwHYG/nHQBfLj7TDZ9n2DB4Tg2D59QweE71n2fUcHhW9Z9n1HB4Vg2D59QweE4Ng+fUMHhOsOd8joBP8k4APuJ9AHgPwN7L5x/wHgB25b2QfQ5gwZdQ8+bNo3v37nHooYdGt27domnTpns07yc/+UnU1NRkKB0AAAAAAAAAAAAAAAAAAAAAAHs7B7BIbMSIEVFbW5voa/z48bmOm1HNmjWLo446Kvr375/rKLvVpk2bOPvss+PRRx+NFStWxNatW6O0tDTeeuutWLNmTWzatCleeOGFGDZsWDRq1Cjl+UuXLo3nnnsuC8kBAAAAAAAAAAAAAAAAAAAAANgbNc51AKhv8vPzo7i4OL75zW9G375945vf/GYcfvjh0aRJk5g8eXK89NJLuY74sf79+8fFF18cp512WjRv3vwz65o3bx4DBw6MgQMHxrhx4+LMM8+M9957L6Vdv//97+OEE07Y08gAAAAAAAAAAAAAAAAAAAAAAOAAFkREnHnmmdG3b9/o27dv9OnTJ1q2bJnrSJ/rpJNOivHjx8dRRx2Vcu+3vvWtePbZZ+Nb3/pWbNy4MXHfiy++mPIuAAAAAAAAAAAAAAAAAAAAAADYnXp9AOutt96KkpKSePnll2PZsmWxbt26qKysjJYtW0a3bt2iZ8+ecfzxx8fQoUOjXbt2Oc1aW1sbc+fOjZKSkliwYEH85S9/iY0bN0ZlZWU0atQounTpEtOnT49evXqlNb+8vDzmz58fCxcujNLS0nj33Xdj06ZNUVlZGRERBQUF0bZt2+jWrVscdNBB0atXr+jbt2+9P+RUXzz++OO5jpBI796944477oj+/fvv0Zzi4uK4/vrr45JLLkncU1paGjt37ozGjev1awMAAAAAAAAAAAAAAAAAAAAAgAYga5dsBg4cGHPmzElUu3LlyigqKvr4+3nz5sWVV175mf3l5eWxZMmSWLJkSTz++ONxySWXxEUXXRS/+MUvorCwMOWsq1atigMOOCBR7YABA2L27Nn/8m9PPvlkXHPNNfH222/vtqe6ujrWrFkT5eXlKeWqqKiIqVOnxm9/+9uYO3duVFVVpdTfrFmzGDBgQJx33nlx+umnR/PmzVPq39WsWbPijjvuSFQ7bNiwGDZs2B7t419dcMEF8Ytf/CLy8/MzMm/kyJHxs5/97OMjal9k586dsXHjxujQoUNG9gMAAAAAAAAAAAAAAAAAAAAAsPfK2gGsdFRVVcVPf/rTuOuuu6K2tjZxX2VlZdx9990xderUmDJlSpx44olZTPl/tm3bFiNHjozf/e53GZ1bWVkZd955Z/zqV7+KjRs3pj1n+/bt8cwzz8QzzzwTl19+efz85z+PMWPGRJMmTdKat2bNmvjTn/6UqLZPnz5p7eCz7b///hmd16JFi+jbt2+8+OKLiXu2b9+e0QwAAAAAAAAAAAAAAAAAAAAAAOyd8nMd4CMVFRVx0kknxZ133pnS8atPKisri8GDB8eDDz6Y4XSftmXLlhg0aFDGj1/NmzcvvvGNb8RVV121R8evdlVWVhaXXnpp9O3bNxYvXpyxuTRsXbp0Sam+TZs2WUoCAAAAAAAAAAAAAAAAAAAAAMDepF4cwNqxY0cMHjw4nn/++T2eVV1dHaNHj874Yapdd5x99tkxd+7cjM595JFHYsCAAbF8+fKMzv2kN998M44++ugoKSnJ2g4ajh07diSubdeuXbRq1SqLaQAAAAAAAAAAAAAAAAAAAAAA2FvUiwNYY8eOjTlz5mRsXm1tbVxwwQWxcOHCjM38pJtuuin++Mc/ZnTmfffdFxdeeGFUVVVldO7uVFRUxNChQ+PJJ5/M+i7qtyVLliSu7dOnTxaTAAAAAAAAAAAAAAAAAAAAAACwN6kXB7BmzZqV8Zk7duyIYcOGxY4dOzI6t7S0NK6//vqMznzqqadi7NixGZ35Raqrq+Pss8+O119/vU73Un8sX748li1blrj+e9/7XhbTAAAAAAAAAAAAAAAAAAAAAACwN6kXB7B2Jz8/Pzp16hTFxcVRWFiY1oxly5bFHXfckdFc7777blRVVWVs3tq1a2P48OFRU1OTsZlJbd++Pc4666zYunVrne8m92655ZbEtc2aNYsf/OAHWUwDAAAAAAAAAAAAAAAAAAAAAMDepN4dwOrYsWNMmjQp1q5dG++//34sWbIk1q9fHytXroxrr702mjdvntK8W265pV4feLriiiti/fr1KfUcfvjhcc8998Q777wTmzdvjvLy8liyZEncfvvtcfDBB6c0q7S0NG6++eaUemj4Xn311XjooYcS148ZMyY6duyYxUQAAAAAAAAAAAAAAAAAAAAAAOxNGuc6wCf16dMnZs6cGYWFhZ/6WVFRUYwfPz7OPPPMOPbYY6OsrCzRzA8++CAee+yxGD16dKbjfuzQQw+Nc889N44//vjYb7/9orCwMLZu3RqrV6+OhQsXxjPPPBMlJSWf6lu+fHlMmTIlpV1XX311XHvttdG48b8+uuLi4iguLo4xY8bEpZdeGvfff3/imbfddltcdtll0bZt25Sy0DCtW7cuzjrrrKipqUlU/5WvfCV+/vOfZyVLWVlZrFu3LqWeFStWZCULAAAAAAAAAAAAAAAAAAAAAAB1p94cwOrUqVM8/fTTuz1+9Uk9evSIkpKSOOaYYxIf8JkyZUpWDmA1bdo07rzzzhg9enQ0atToX37WunXr6NmzZ/Ts2TPOP//82LJlS1RXV/9LzcSJExP/HyIiLr/88pgwYcLn1jRv3jzuvffeKC8vj6lTpyaaW1FREQ899FBcfvnlibPQMG3atCm++93vxurVqxPV5+XlxSOPPBIdOnTISp5JkybFddddl5XZAAAAAAAAAAAAAAAAAAAAAADUX/m5DvCRG264IfGRnX79+sXw4cMTz543b15s2LAh3Wi71bRp0/jDH/4QY8aM+dTxq91p1apVtGnT5uPva2pqYtq0aYn3FRUVxQ033JCoNi8vLyZOnBht27ZNPP/xxx9PXEvDVFZWFscee2y8/vrriXtuvPHGGDx4cBZTAQAAAAAAAAAAAAAAAAAAAACwN6oXB7Datm0b5513Xko948aNS1xbU1MTL774YqqxPtc111wTJ5xwQtr9r7/+evzjH/9IXH/ZZZdF8+bNE9d36NAhRo0albh+wYIFUVZWlriehmXZsmXRr1+/WLhwYeKecePGxZVXXpnFVAAAAAAAAAAAAAAAAAAAAAAA7K0a5zpARMT3vve9aNq0aUo9vXv3jv333z9Wr16dqH7hwoXx/e9/P514n9KtW7e44oor9mjGvHnzEtfm5eXFGWeckfKOH/zgB3Hrrbcmqq2trY1XX301Tj755JT3UL/Nnj07Tj311Ni4cWPinpEjR8bEiROzmOqfxo4dm/Lf9ooVK2LIkCFZSgQAAAAAAAAAAAAAAAAAAAAAQF2oFwew+vbtm1Zfr169Eh/Aeuedd9LasTujRo2Kxo337Fe3ePHixLXdu3ePzp07p7zjyCOPjIKCgqisrExUv2jRIgewvmR+85vfxKhRo2LHjh2Jey6++OKYNGlS5OXlZTHZP3Xs2DE6duyY9T0AAAAAAAAAAAAAAAAAAAAAANQv+bkOEBHRo0ePrPe99957ae3YncGDB+/xjFWrViWu7dmzZ1o78vPzo7i4OHF90mNiNAzXXXddnH/++Skdv/rZz34Wv/71r+vk+BUAAAAAAAAAAAAAAAAAAAAAAHuvxrkOEBFRWFiYVl/79u0T127YsCGtHbtq1qxZHH744Xs8Z926dYlr991337T3pNJbVlaW9h7qj6qqqrjooovi0UcfTdyTn58ft99+e1x66aVZTAYAAAAAAAAAAAAAAAAAAAAAAP9ULw5g7bPPPlnv27ZtW1o7dtWtW7do2rTpHs+pqKhIXJvu7ycionXr1olrt27dmvYe6oeNGzfGaaedFi+88ELinoKCgvjtb38bp556ahaTAQAAAAAAAAAAAAAAAAAAAADA/6kXB7Cqq6uz3peXl5fWjl21bds2I3Nqa2sT1+bn56e9p1GjRolrU8lE/bNq1aoYPHhwLFmyJHFPx44dY8aMGXHUUUdlMRkAAAAAAAAAAAAAAAAAAAAAAPyrenEAa/PmzVnvKygoSGvHrlq1apWROank2bJlS9p7ysvLE9dm6ndE3Zs/f36cfPLJsXbt2sQ9xcXF8ac//SkOOOCALCYDAAAAAAAAAAAAAAAAAAAAAIBPy891gIhI6WjPJ5WVlSWu7dChQ1o7sqWwsDBx7QcffJD2nlR627dvn/Yecmf69OkxcODAlD5Hxx57bLzyyiuOXwEAAAAAAAAAAAAAAAAAAAAAkBP14gDWW2+9lfW+rl27prUjW7761a8mrk339xMRsWTJksS1+++/f9p7yI277rorhg4dGlu3bk3cM3z48Pif//mfaNu2bRaTAQAAAAAAAAAAAAAAAAAAAADAZ6sXB7DmzZuXck91dXUsWLAgcf2hhx6a8o5s6tGjR+Lad955JzZv3pzyjuXLl8emTZuykoncqqmpiUsuuSR+/OMfR01NTeK+6667LiZPnhxNmjTJYjoAAAAAAAAAAAAAAAAAAAAAAPh89eIA1syZM6O8vDylnjlz5sT69esT1/fu3TvVWFnVr1+/xLU7d+6MkpKSlHdMmzYtpfqjjjoq5R3Uva1bt8app54aEydOTNzTtGnTeOyxx+IXv/hFFpMBAAAAAAAAAAAAAAAAAAAAAEAy9eIA1vbt21M65hMRccsttySubdasWRxzzDGpxsqqo48+Olq3bp24fuLEiVFbW5u4ftu2bfHAAw8krv/a174WRUVFievJjX/84x/xne98J2bMmJG4p3379vHss8/GOeeck8VkAAAAAAAAAAAAAAAAAAAAAACQXL04gBUR8ctf/jKWLl2aqPaJJ56ImTNnJp49aNCgKCgoSDdaVjRr1ixOOeWUxPWvvfZaPPjgg4nrb7rppli1alXi+tNPPz1xLbnx9ttvR79+/eL1119P3NO9e/d45ZVX4jvf+U4WkwEAAAAAAAAAAAAAAAAAAAAAQGrqzQGsLVu2xAknnBCLFi363LqSkpIYPnx4SrNHjBixB8myZ9y4cSnXT58+/Qvr7r///pgwYULiuY0bN46LL744pSzUvVtuuSVWr16dUs9f//rXOOSQQyIvLy8jX+PHj8/Ofw4AAAAAAAAAAAAAAAAAAAAAgL1K41wH+KR33303+vTpEyNHjoyzzjorevToEW3bto3169fHggULYvLkyfHkk0+mNLN79+4xZMiQLCXeM/369YtBgwbFs88+m6i+qqoqTj311Dj33HNj7Nix0a9fv8jLy4uIiJqamnjppZfi9ttvjxkzZqSUY/jw4bHffvulnP/LZNSoUbFgwYIvrPvwww8Tz1ywYEEcccQRiWqfeuqp6NKlS+LZAAAAAAAAAAAAAAAAAAAAAADQkNWrA1gR/zzydO+998a9996bkXnXX399NGrUKCOzsuGuu+6KI488MrZv356457HHHovHHnssWrZsGZ07d47a2tp4//33Y+vWrSnvb9euXdx4440p933ZrFixIt58882MzqyoqEg8c8eOHRndDakqKiqK2traXMcA6gnvBCDCuwDwHoC9nXcAfLn4TDd8nmHD4Dk1DJ5Tw+A51X+eUcPhWdV/nlHD4Vk1DJ5Tw+A5NQyeU8PgOcGe8zkCPsk7AfiI9wHgPQB7L59/wHsA2JX3Qvbl5zpANh133HFxzjnn5DrG5youLo5bb701rd6KiopYsWJFlJaWpnX8KiLioYcein33VnweSQAAIABJREFU3TetXgAAAAAAAAAAAAAAAAAAAAAAgHTUiwNY2ThS1aFDh5gyZUrG52bDD3/4wxg3blyd773xxhvj1FNPrfO9AAAAAAAAAAAAAAAAAAAAAADA3q1eHMCaMGFCRo9gFRQUxIwZM6Jr164Zm5ltEydOjEsuuaROduXl5cWECRPiqquuqpN9AAAAAAAAAAAAAAAAAAAAAAAAn1QvDmDl5eXFlClT4qKLLtrjWW3atImnn346jj766Awkqzt5eXlx1113xQMPPBCtWrXK2p727dvHtGnT4uqrr87aDgAAAAAAAAAAAAAAAAAAAAAAgM9TLw5gRUTk5+fH/fffH/fdd1/ss88+ac3o06dPzJ8/PwYMGJDhdHVn1KhRsWjRojjjjDMiLy8vY3MbNWoUI0aMiLfffjuGDh2asbkAAAAAAAAAAAAAAAAAAAAAAACpqjcHsD4yevToWLZsWYwbNy5atWqVqOeQQw6Jhx56KF599dU4+OCDs5ww+w444IB44oknYtGiRTFu3Ljo0KFD2rM6deoUP/nJT2Lp0qXxyCOPRKdOndKeVV1dnbi2adOmae8BAAAAAAAAAAAAAAAAAAAAAAC+3BrnOsDudOnSJe655564+eab4/nnn4+XX345li1bFuvXr49t27ZFixYtomvXrtGzZ88YNGhQ9O3bd4/2FRUVRW1tbYbSZ85hhx0W99xzT9x9993x2muvxdy5c2PhwoVRWloa7733XmzcuDEqKysjIqKgoCDatWsXXbt2jYMPPjiOPPLI6N+/f/Tu3Tvy8vIykmfr1q2Ja9u1a5eRnXVl9uzZuY7whSZPnhyTJ0/OdQwAAAAAAAAAAAAAAAAAAAAAANhj9fIA1kdatmwZJ598cpx88sm5jpJT+fn50a9fv+jXr19Oc6xbty5xbUM7gAUAAAAAAAAAAAAAAAAAAAAAANSd/FwHoOFYtmxZ4trOnTtnMQkAAAAAAAAAAAAAAAAAAAAAANCQOYBFIlVVVTFr1qxEtY0aNYrevXtnOREAAAAAAAAAAAAAAAAAAAAAANBQOYBFIjfffHN8+OGHiWoPO+ywaNWqVZYTAQAAAAAAAAAAAAAAAAAAAAAADVXjXAegftq5c2ds2LAh3njjjXj44Ydj6tSpiXtPOeWULCYDAAAAAAAAAAAAAAAAAAAAAAAaOgew+BdDhgyJkpKStPubNGkSY8aMyWAiAAAAAAAAAAAAAAAAAAAAAADgyyY/1wH4chkxYkR07tw51zEAAAAAAAAAAAAAAAAAAAAAAIB6zAEsMubAAw+M2267LdcxAAAAAAAAAAAAAAAAAAAAAACAes4BLDKisLAwpk2bFvvss89n1syePTvy8vJy9gUAAAAAAAAAAAAAAAAAAAAAANQPDmCxx77+9a/Hn//85zjyyCNzHQUAAAAAAAAAAAAAAAAAAAAAAGgAHMAibV/5ylfiV7/6Vfzv//5vHHTQQbmOAwAAAAAAAAAAAAAAAAAAAAAANBCNcx2AhqF169ZRWFgY++67bxx99NFx3HHHxXHHHRctWrTIdTQAAAAAAAAAAAAAAAAAAAAAAKCBydoBrNmzZ2drNFk0ffr0XEcAAAAAAAAAAAAAAAAAAAAAAAC+5PJzHQAAAAAAAAAAAAAAAAAAAAAAAIC9S+NcB2DvMXDgwKitrc11DAAAAAAAAAAAAAAAAAAAAAAAIMfycx0AAAAAAAAAAAAAAAAAAAAAAACAvYsDWAAAAAAAAAAAAAAAAAAAAAAAANQpB7AAAAAAAAAAAAAAAAAAAAAAAACoUw5gkdjkyZMjLy8v0df48eNzHRcAAAAAAAAAAAAAAAAAAAAAAKinGuc6ADQEGzZsiPnz58fChQujsrIyUU9RUVGMGDEiu8F2Y/v27fHSSy/F7Nmz480334zS0tIoKyuLLVu2RHV1dbRs2TLat28f3bt3j+Li4vjOd74Txx13XBQWFtZ5VgAAAAAAAAAAAAAAAAAAAAAA9k4OYMEuKisr44033ojXXnvt468VK1akPGfAgAF1egBrxYoVcdttt8XUqVNj48aNn1m3adOm2LRpU6xcuTJmzZoV99xzTzRp0iT+/d//PS699NI49thj6ywzAAAAAAAAAAAAAAAAAAAAAAB7JwewICIefvjhj49dLV68OHbu3JnrSImVl5fHz372s3jwwQfTzl1VVRUlJSVRUlISxx9/fPz617+Ogw46KMNJAQAAAAAAAAAAAAAAAAAAAADgnxzAgogYOXJkriOkZf78+XH66afH3/72t4zNfO655+KII46ISZMmxfnnn5+xuQAAAAAAAAAAAAAAAAAAAAAA8JH8XAcA0vPUU0/FwIEDM3r86iMVFRUxfPjwuPHGGzM+GwAAAAAAAAAAAAAAAAAAAAAAHMCCBmju3LkxdOjQ2Lp1a1b3XH311XHXXXdldQcAAAAAAAAAAAAAAAAAAAAAAHsfB7Cggfn73/8eQ4YMiW3bttXJvssuuyyee+65OtkFZMaqVasiLy9vt1+rVq3KdTygnvLuANLh3QF8Hu8IYFfeC9Q1f3MNh2fVMHhODYPn1DB4Tg2D59QweE4Ng+fUMHhODYPn1DB4Tg2D57R38txh7+CzDiThXQHsKe8R4It4TwCfxfsB+CTvBPgnB7CggRk+fHh88MEHKfXk5eVFly5d4sADD4ymTZum1FtbWxvDhw+PDRs2pNQHAAAAAAAAAAAAAAAAAAAAAACfxQEsEhsxYkTU1tYm+ho/fnyu42ZUs2bN4qijjor+/fvnNMe0adPiueeeS1zfsWPHuPvuu2PTpk3x3nvvxYoVK6KysjJKSkqiV69eief8/e9/j+uuuy6dyAAAAAAAAAAAAAAAAAAAAAAA8CkOYMEu8vPz49BDD40LLrggJk2aFAsWLIjNmzfHn//857jwwgtzlqu6ujquvPLKxPVFRUXxyiuvxI9+9KPYZ599Pv73/Pz8OOWUU2Lu3Lnx3e9+N/G8SZMmxcqVK1PKDAAAAAAAAAAAAAAAAAAAAAAAu9M41wGgPjjzzDOjb9++0bdv3+jTp0+0bNky15E+Zfr06VFaWpq4fvLkyXHggQd+5s8LCgrisccei69//euxfv36L5xXVVUVd999d9xxxx2JMwAAAAAAAAAAAAAAAAAAAAAAwO7U6wNYb731VpSUlMTLL78cy5Yti3Xr1kVlZWW0bNkyunXrFj179ozjjz8+hg4dGu3atctp1tra2pg7d26UlJTEggUL4i9/+Uts3LgxKisro1GjRtGlS5eYPn169OrVK6355eXlMX/+/Fi4cGGUlpbGu+++G5s2bYrKysqI+Ocxo7Zt20a3bt3ioIMOil69ekXfvn3r5SGn+ujxxx/PdYQvNGnSpMS1p512WgwYMOAL69q3bx/XXHNNXHrppYnmPvzww3HTTTdF8+bNE2cBAAAAAAAAAAAAAAAAAAAAAIBdZe0A1sCBA2POnDmJaleuXBlFRUUffz9v3ry48sorP7O/vLw8lixZEkuWLInHH388LrnkkrjoooviF7/4RRQWFqacddWqVXHAAQckqh0wYEDMnj37X/7tySefjGuuuSbefvvt3fZUV1fHmjVrory8PKVcFRUVMXXq1Pjtb38bc+fOjaqqqpT6mzVrFgMGDIjzzjsvTj/99D0+WjRr1qy44447EtUOGzYshg0btkf7+D9lZWWJP08RERdeeGHi2vPOOy8uv/zy2Llz5xfWlpeXx8yZM2PIkCGJ5wMAAAAAAAAAAAAAAAAAAAAAwK6ydgArHVVVVfHTn/407rrrrqitrU3cV1lZGXfffXdMnTo1pkyZEieeeGIWU/6fbdu2xciRI+N3v/tdRudWVlbGnXfeGb/61a9i48aNac/Zvn17PPPMM/HMM8/E5ZdfHj//+c9jzJgx0aRJk7TmrVmzJv70pz8lqu3Tp09aO9i9GTNmRHV1daLapk2bxnHHHZd4drt27eKoo46Kl19+OVH9f//3fzuABQAAAAAAAAAAAAAAAAAAAADAHsnPdYCPVFRUxEknnRR33nlnSsevPqmsrCwGDx4cDz74YIbTfdqWLVti0KBBGT9+NW/evPjGN74RV1111R4dv9pVWVlZXHrppdG3b99YvHhxxuZSN2bPnp249hvf+EYUFBSkNL9fv35ZyQIAAAAAAAAAAAAAAAAAAAAAALtTLw5g7dixIwYPHhzPP//8Hs+qrq6O0aNHZ/ww1a47zj777Jg7d25G5z7yyCMxYMCAWL58eUbnftKbb74ZRx99dJSUlGRtB5mXyt/aYYcdlvL8VHrWrFkTa9asSXkHAAAAAAAAAAAAAAAAAAAAAAB8pF4cwBo7dmzMmTMnY/Nqa2vjggsuiIULF2Zs5ifddNNN8cc//jGjM++777648MILo6qqKqNzd6eioiKGDh0aTz75ZNZ3sefKy8tj9erViesPOuiglHek2vPmm2+mvAMAAAAAAAAAAAAAAAAAAAAAAD5SLw5gzZo1K+Mzd+zYEcOGDYsdO3ZkdG5paWlcf/31GZ351FNPxdixYzM684tUV1fH2WefHa+//nqd7iV1S5cuTam+W7duKe/o2rVrSvXLli1LeQcAAAAAAAAAAAAAAAAAAAAAAHykXhzA2p38/Pzo1KlTFBcXR2FhYVozli1bFnfccUdGc7377rtRVVWVsXlr166N4cOHR01NTcZmJrV9+/Y466yzYuvWrXW+m+T++te/plTfoUOHlHd85StfSam+tLQ05R0AAAAAAAAAAAAAAAAAAAAAAPCRencAq2PHjjFp0qRYu3ZtvP/++7FkyZJYv359rFy5Mq699tpo3rx5SvNuueWWen3g6Yorroj169en1HP44YfHPffcE++8805s3rw5ysvLY8mSJXH77bfHwQcfnNKs0tLSuPnmm1PqoW6tXbs2pfp27dqlvKNVq1bRpEmTxPWpZgIAAAAAAAAAAAAAAAAAAAAAgE9qnOsAn9SnT5+YOXNmFBYWfupnRUVFMX78+DjzzDPj2GOPjbKyskQzP/jgg3jsscdi9OjRmY77sUMPPTTOPffcOP7442O//faLwsLC2Lp1a6xevToWLlwYzzzzTJSUlHyqb/ny5TFlypSUdl199dVx7bXXRuPG//roiouLo7i4OMaMGROXXnpp3H///Yln3nbbbXHZZZdF27ZtU8pC3Uj6t/6Rli1bprWnRYsWsWnTpkS1mTqAVVZWFuvWrUupZ8WKFRnZDQAAAAAAAAAAAAAAAAAAAABA7tSbA1idOnWKp59+erfHrz6pR48eUVJSEsccc0zU1NQkmj1lypSsHMBq2rRp3HnnnTF69Oho1KjRv/ysdevW0bNnz+jZs2ecf/75sWXLlqiurv6XmokTJyb+P0REXH755TFhwoTPrWnevHnce++9UV5eHlOnTk00t6KiIh566KG4/PLLE2eh7mzZsiWl+oKCgrT2FBQUJD6AVVFRkdaOXU2aNCmuu+66jMwCAAAAAAAAAAAAAAAAAAAAAKDhyM91gI/ccMMN0aFDh0S1/fr1i+HDhyeePW/evNiwYUO60XaradOm8Yc//CHGjBnzqeNXu9OqVato06bNx9/X1NTEtGnTEu8rKiqKG264IVFtXl5eTJw4Mdq2bZt4/uOPP564lrq1ffv2lOqT/D3uTuPGye/hbdu2La0dAAAAAAAAAAAAAAAAAAAAAAAQUU8OYLVt2zbOO++8lHrGjRuXuLampiZefPHFVGN9rmuuuSZOOOGEtPtff/31+Mc//pG4/rLLLovmzZsnru/QoUOMGjUqcf2CBQuirKwscT11p6qqKqX6/Pz0Ptap9O3YsSOtHQAAAAAAAAAAAAAAAAAAAAAAEBHRONcBIiK+973vRdOmTVPq6d27d+y///6xevXqRPULFy6M73//++nE+5Ru3brFFVdcsUcz5s2bl7g2Ly8vzjjjjJR3/OAHP4hbb701UW1tbW28+uqrcfLJJ6e8h+xq0qRJSvU1NTVp7amurk5cm+rn9bOMHTs25b/tFStWxJAhQzKyHwAAAAAAAAAAAAAAAAAAAACA3KgXB7D69u2bVl+vXr0SH8B655130tqxO6NGjYrGjffsV7d48eLEtd27d4/OnTunvOPII4+MgoKCqKysTFS/aNEiB7DqoWbNmqVUv3PnzrT2pNKXaqbP0rFjx+jYsWNGZgEAAAAAAAAAAAAAAAAAAAAA0HDk5zpARESPHj2y3vfee++ltWN3Bg8evMczVq1albi2Z8+eae3Iz8+P4uLixPVJj4lRt1q1apVSfdKDZ3vSl2omAAAAAAAAAAAAAAAAAAAAAAD4pHpxAKuwsDCtvvbt2yeu3bBhQ1o7dtWsWbM4/PDD93jOunXrEtfuu+++ae9JpbesrCztPWRPx44dU6qvqKhIa8/WrVsT1+7J3yQAAAAAAAAAAAAAAAAAAAAAANSLA1j77LNP1vu2bduW1o5ddevWLZo2bbrHc1I5UpTu7ycionXr1olrUzmARN1J9djUhx9+mPKO8vLy2LlzZ+J6B7AAAAAAAAAAAAAAAAAAAAAAANgT9eIAVnV1ddb78vLy0tqxq7Zt22ZkTm1tbeLa/Pz0H1OjRo0S16aSibrTvXv3lOrXrVuX8o5Ue1LNBAAAAAAAAAAAAAAAAAAAAAAAn1QvDmBt3rw5630FBQVp7dhVq1atMjInlTxbtmxJe095eXni2kz9jsisQw45JKX6NWvWpLwj1Z7i4uKUdwAAAAAAAAAAAAAAAAAAAAAAwEfqxQGstWvXptVXVlaWuLZDhw5p7ciWwsLCxLUffPBB2ntS6W3fvn3ae8ieNm3axP7775+4fsWKFSnvWL58eUr1hx9+eMo7AAAAAAAAAAAAAAAAAAAAAADgI/XiANZbb72V9b6uXbumtSNbvvrVryauTff3ExGxZMmSxLWpHFmibn37299OXJvO30sqPfvtt19Kf78AAAAAAAAAAAAAAAAAAAAAALCrenEAa968eSn3VFdXx4IFCxLXH3rooSnvyKYePXokrn3nnXdi8+bNKe9Yvnx5bNq0KSuZqFsDBw5MXLt48eKoqKhIaX4qn8EBAwakNBsAAAAAAAAAAAAAAAAAAAAAAHZVLw5gzZw5M8rLy1PqmTNnTqxfvz5xfe/evVONlVX9+vVLXLtz584oKSlJece0adNSqj/qqKNS3kHdOOWUUyI/P9nHdefOnfHcc88lnr1u3bp4/fXXE9efdtppiWsBAAAAAAAAAAAAAAAAAAAAAGB36sUBrO3bt8fEiRNT6rnlllsS1zZr1iyOOeaYVGNl1dFHHx2tW7dOXD9x4sSora1NXL9t27Z44IEHEtd/7Wtfi6KiosT11K2OHTvGwIEDE9c//PDDiWsnT54cNTU1iWpbt24dJ510UuLZAAAAAAAAAAAAAAAAAAAAAACwO/XiAFZExC9/+ctYunRpotonnngiZs6cmXj2oEGDoqCgIN1oWdGsWbM45ZRTEte/9tpr8eCDDyauv+mmm2LVqlWJ608//fTEteTG2LFjE9fOmDEjXnjhhS+sKysri//4j/9IPPfCCy+sd58lAAAAAAAAAAAAAAAAAAAAAAAannpzAGvLli1xwgknxKJFiz63rqSkJIYPH57S7BEjRuxBsuwZN25cyvXTp0//wrr7778/JkyYkHhu48aN4+KLL04pC3VvyJAhceCBByauv+CCC2L58uWf+fOKiooYNmxYbNy4MdG8Jk2axI9+9KPE+wEAAAAAAAAAAAAAAAAAAAAA4LM0znWAT3r33XejT58+MXLkyDjrrLOiR48e0bZt21i/fn0sWLAgJk+eHE8++WRKM7t37x5DhgzJUuI9069fvxg0aFA8++yzieqrqqri1FNPjXPPPTfGjh0b/fr1i7y8vIiIqKmpiZdeeiluv/32mDFjRko5hg8fHvvtt1/K+b9MRo0aFQsWLPjCug8//DDxzAULFsQRRxyRqPapp56KLl26fG5No0aN4qabboozzzwz0czVq1fHMcccE9dcc01ccMEF0apVq4j459/KH/7wh7j22mvjzTffTDQrImLMmDHRvXv3xPUAAAAAAAAAAAAAAAAAAAAAAPBZ8mpra2uzMXjgwIExZ86cbIxOyWOPPRbnnHPO59asWrUqDjjggETzBgwYELNnz85Asn9aunRpHHnkkbF9+/aUe1u2bBmdO3eO2traeP/992Pr1q0pz2jXrl0sXbo09t133y+snTx5clxwwQWJ5l577bUxfvz4lPPkSq7/XleuXBlFRUWJagcNGhTPPfdcSvPz8/Ojc+fOUVBQEH/7299ix44dKfV36dIlFi9eHO3bt0+pLxvefvvtOOywwz7+/q233opDDz00h4kAAAAAAAAAAAAAAAAAAAAAgL2RWyh7Jj/XAbLpuOOO+8LjV7lWXFwct956a1q9FRUVsWLFiigtLU3r+FVExEMPPZTo+BX1x6OPPhqFhYUp9dTU1MR7770XK1asSPn4VV5eXjz66KP14vgVAAAAAAAAAAAAAAAAAAAAAABfDvXiAFY2jlR16NAhpkyZkvG52fDDH/4wxo0bV+d7b7zxxjj11FPrfC97pkuXLjF9+vRo3rx5ney7/fbb4/jjj6+TXQAAAAAAAAAAAAAAAAAAAAAA7B3qxQGsCRMmZPQIVkFBQcyYMSO6du2asZnZNnHixLjkkkvqZFdeXl5MmDAhrrrqqjrZR+Z9+9vfjt///vfRokWLrO65/vrr48c//nFWdwAAAAAAAAAAAAAAAAAAAAAAsPepFwew8vLyYsqUKXHRRRft8aw2bdrE008/HUcffXQGktWdvLy8uOuuu+KBBx6IVq1aZW1P+/btY9q0aXH11VdnbQd1Y/DgwfHCCy/Efvvtl/HZLVq0iMmTJ8c111yT8dkAAAAAAAAAAAAAAAAAAAAAAFAvDmBFROTn58f9998f9913X+yzzz5pzejTp0/Mnz8/BgwYkOF0dWfUqFGxaNGiOOOMMyIvLy9jcxs1ahQjRoyIt99+O4YOHZqxueTWN7/5zVi8eHFcdNFF0ahRo4zMPO644+KNN96I4cOHZ2QeAAAAAAAAAAAAAAAAAAAAAADsqt4cwPrI6NGjY9myZTFu3Lho1apVop5DDjkkHnrooXj11Vfj4IMPznLC7DvggAPiiSeeiEWLFsW4ceOiQ4cOac/q1KlT/OQnP4mlS5fGI488Ep06dUp7VnV1deLapk2bpr2H1LRp0ybuv//+WLJkSfy///f/ok2bNinPaNKkSZxyyikxa9asmDVr1pficwQAAAAAAAAAAAAAAAAAAAAAQP3VONcBdqdLly5xzz33xM033xzPP/98vPzyy7Fs2bJYv359bNu2LVq0aBFdu3aNnj17xqBBg6Jv3757tK+oqChqa2szlD5zDjvssLjnnnvi7rvvjtdeey3mzp0bCxcujNLS0njvvfdi48aNUVlZGRERBQUF0a5du+jatWscfPDBceSRR0b//v2jd+/ekZeXl5E8W7duTVzbrl27jOysK7Nnz851hD32ta99Le6999648847Y86cOTFnzpx48803o7S0NMrKyqKioiKqq6ujZcuW0a5du+jevXv06NEj+vfvH//2b/+2R4fWAAAAAAAAAAAAAAAAAAAAAAAgFfXyANZHWrZsGSeffHKcfPLJuY6SU/n5+dGvX7/o169fTnOsW7cucW1DO4D1ZdK8efM48cQT48QTT8x1FAAAAAAAAAAAAAAAAAAAAAAA2K38XAeg4Vi2bFni2s6dO2cxCQAAAAAAAAD/n717j9N6zvsH/p7prCSVzquQpVIrirIsctjd2yIbcq5VIlluaxfrsM63m602ZJ22xFoK9xKWbqt1KqGUY7KVsnKo6Dg11UzN74/9mdtV4bpmrpnre03P5+Mxj4fPdz6HV+nxfT/mmuv7vgAAAAAAAAAAAAAgn2mARVpKSkpi8uTJac2tVatW7LvvvlWcCAAAAAAAAAAAAAAAAAAAAAAAyFcaYJGWm266KZYtW5bW3L322isaNWpUxYkAAAAAAAAAAAAAAAAAAAAAAIB8VTvXAUim0tLSWL58ebz55psxduzYGD9+fNprjznmmCpMBgAAAAAAAAAAAAAAAAAAAAAA5DsNsEjRt2/fmDhxYoXX16lTJ4YOHZrFRAAAAAAAAAAAAAAAAAAAAAAAQE1TmOsA1CwDBw6M1q1b5zoGAAAAAAAAAAAAAAAAAAAAAACQYBpgkTW77bZbjBgxItcxAAAAAAAAAAAAAAAAAAAAAACAhNMAi6xo1qxZPPLII7H99tt/45wXXnghCgoKcvYFAAAAAAAAAAAAAAAAAAAAAAAkgwZYVNoee+wRr776anTv3j3XUQAAAAAAAAAAAAAAAAAAAAAAgDygARYVttNOO8XNN98cM2fOjI4dO+Y6DgAAAAAAAAAAAAAAAAAAAAAAkCdq5zoA+aFx48bRrFmzaNmyZfTu3Tv69OkTffr0ie222y7X0QAAAAAAAAAAAAAAAAAAAAAAgDxTZQ2wXnjhharamir0+OOP5zoCAAAAAAAAAAAAAAAAAAAAAABQwxXmOgAAAAAAAAAAAAAAAAAAAAAAAADbltq5DsC245BDDomysrJcxwAAAAAAAAAAAAAAAAAAAAAAAHKsMNcBAAAAAAAAAAAAAAAAAAAAAAAA2LZogAUAAAAAAAAAAAAAAAAAAAAAAEC10gALAAAAAAAAAAAAAAAAAAAAAACAaqUBFjXSuHHjoqCgIK2vq6++OtdxAQAAAAAAAAAAAAAAAAAAAABgm1I71wGA7Fq+fHlMnz49Zs2aFcXFxWmt6dChQwwcOLBqgwEAAAAAAAAAAAAAAAAAAAAAwP+nARbkseLi4njzzTfj9ddfL/+aN29exvscfPDBGmABAAAAAAAAAAAAAAAAAAAAAFBtNMCCPDN27NjyZlfvvPNOlJaW5jr8OQZqAAAgAElEQVQSAAAAAAAAAAAAAAAAAAAAAABkRAMsyDODBg3KdQQAAAAAAAAAAAAAAAAAAAAAAKiUwlwHAAAAAAAAAAAAAAAAAAAAAAAAYNuiARYAAAAAAAAAAAAAAAAAAAAAAADVSgMsACARFi5cGAUFBVv9WrhwYa7jAdsY9yQgadyXgCRxTwKSxn0JSBL3JCBJ3JOApHFfApLEPQlIGvclIEnck4AkcU8CksZ9CUgS9yQgadyXqCgNsAAAAAAAAAAAAAAAAAAAAAAAAKhWtXMdAKrCwIEDY+DAgbmOkRP16tWLvffeO+rWrRsvv/xyruMAAAAAAAAAAAAAAAAAAAAAAMAWNMCCPFZYWBidOnWK/fbbL3r27Bn77bdfdOvWLerUqRPjxo3TAAsAAAAAAAAAAAAAAAAAAAAAgETSAAvyzIknnhg9e/aMnj17Ro8ePaJhw4a5jgQAAAAAAAAAAAAAAAAAAAAAABnRACtL3n333Zg4cWJMnTo15syZE0uXLo3i4uJo2LBhtGvXLrp27RqHH3549OvXL3bcccecZi0rK4spU6bExIkTY8aMGfHPf/4zVqxYEcXFxVGrVq1o06ZNPP7447HPPvtUaP9Vq1bF9OnTY9asWTF//vxYtGhRrFy5MoqLiyMiokGDBtGkSZNo165ddOzYMfbZZ5/o2bOnRk5pmjBhQq4jAAAAAAAAAAAAAAAAAAAAAABApWiAFRGHHHJIvPjii2nNXbBgQXTo0KF8PG3atPjtb3/7jetXrVoVs2fPjtmzZ8eECRPi/PPPj7POOit+97vfRbNmzTLOunDhwthll13SmnvwwQfHCy+8kHLtscceiyuvvDLee++9ra7ZuHFjfPzxx7Fq1aqMcq1ZsybGjx8ff/nLX2LKlClRUlKS0fp69erFwQcfHKeffnocf/zxUb9+/YzWb27y5Mnxhz/8Ia25p5xySpxyyimVOg8AAAAAAAAAAAAAAAAAAAAAAEifBlgVVFJSEhdffHHccsstUVZWlva64uLiuPXWW2P8+PFx//33x49//OMqTPl/1q1bF4MGDYoHH3wwq/sWFxfHqFGj4uabb44VK1ZUeJ/169fHs88+G88++2xcdNFFcfnll8fQoUOjTp06Fdrv448/jr/97W9pze3Ro0eFzgAAAAAAAAAAAAAAAAAAAAAAACqmMNcB8tGaNWviJz/5SYwaNSqj5ldft2TJkjjqqKPiT3/6U5bTbamoqCiOOOKIrDe/mjZtWvzgBz+Iyy67rFLNrza3ZMmSuOCCC6Jnz57xzjvvZG1fAAAAAAAAAAAAAAAAAAAAAAAgGTTAytCGDRviqKOOin/84x+V3mvjxo0xZMiQrDem2vyMk08+OaZMmZLVfe+99944+OCDY+7cuVnd9+veeuut6N27d0ycOLHKzgAAAAAAAAAAAAAAAAAAAAAAAKqfBlgZOvfcc+PFF1/M2n5lZWXxi1/8ImbNmpW1Pb/uxhtvjKeeeiqre951111x5plnRklJSVb33Zo1a9ZEv3794rHHHqvyswAAAAAAAAAAAAAAAAAAAAAAgOqhAVaGJk+enPU9N2zYEKecckps2LAhq/vOnz8/rr322qzu+fTTT8e5556b1T2/y8aNG+Pkk0+ON954o1rPBQAAAAAAAAAAAAAAAAAAAAAAqoYGWFlQWFgYrVq1ik6dOkWzZs0qtMecOXPiD3/4Q1ZzLVq0KEpKSrK23+LFi2PAgAGxadOmrO2ZrvXr10f//v1j7dq11X42AAAAAAAAAAAAAAAAAAAAAACQXRpgVUKLFi3ij3/8YyxevDg+++yzmD17dnzxxRexYMGCuOqqq6J+/foZ7ff73/8+0Q2eLr300vjiiy8yWtOtW7cYPXp0fPDBB7F69epYtWpVzJ49O0aOHBm77757RnvNnz8/brrppozWAAAAAAAAAAAAAAAAAAAAAAAAyVM71wHyVY8ePWLSpEnRrFmzLb7XoUOHuPrqq+PEE0+MQw89NJYsWZLWnl9++WU88MADMWTIkGzHLdelS5c47bTT4vDDD4/vfe970axZs1i7dm189NFHMWvWrHj22Wdj4sSJW6ybO3du3H///RmddcUVV8RVV10VtWun/jPr1KlTdOrUKYYOHRoXXHBB3H333WnvOWLEiLjwwgujSZMmGWUhmZYsWRJLly7NaM28efOqKA0AAAAAAAAAAAAAAAAAAAAAANVFA6wKaNWqVTzzzDNbbX71dZ07d46JEyfGD3/4w9i0aVNae99///1V0gCrbt26MWrUqBgyZEjUqlUr5XuNGzeOrl27RteuXeOMM86IoqKi2LhxY8qc2267Le0/Q0TERRddFNddd923zqlfv37ceeedsWrVqhg/fnxa+65ZsybGjBkTF110UdpZSK4//vGPcc011+Q6BgAAAAAAAAAAAAAAAAAAAAAA1aww1wHy0fXXXx/NmzdPa26vXr1iwIABae89bdq0WL58eUWjbVXdunXjySefjKFDh27R/GprGjVqFDvssEP5eNOmTfHII4+kfV6HDh3i+uuvT2tuQUFB3HbbbdGkSZO0958wYULacwEAAAAAAAAAAAAAAAAAAAAAgOTRACtDTZo0idNPPz2jNcOGDUt77qZNm+Kll17KNNa3uvLKK+PII4+s8Po33ngjPv/887TnX3jhhVG/fv205zdv3jwGDx6c9vwZM2bEkiVL0p4PAAAAAAAAAAAAAAAAAAAAAAAkS+1cB8g3P/vZz6Ju3boZrdl3332jffv28dFHH6U1f9asWXHsscdWJN4W2rVrF5deemml9pg2bVracwsKCuKEE07I+IyTTjophg8fntbcsrKyeO211+Loo4/O+ByS5dxzz83438u8efOib9++VZQIAAAAAAAAAAAAAAAAAAAAAIDqoAFWhnr27Fmhdfvss0/aDbA++OCDCp2xNYMHD47atSv3v/mdd95Je+6uu+4arVu3zviM7t27R4MGDaK4uDit+W+//bYGWDVAixYtokWLFrmOAQAAAAAAAAAAAAAAAAAAAABANSvMdYB807lz5ypf98knn1TojK056qijKr3HwoUL057btWvXCp1RWFgYnTp1Snt+us3EAAAAAAAAAAAAAAAAAAAAAACA5NEAK0PNmjWr0LqmTZumPXf58uUVOmNz9erVi27dulV6n6VLl6Y9t2XLlhU+J5O1S5YsqfA5AAAAAAAAAAAAAAAAAAAAAABAbmmAlaHtt9++ytetW7euQmdsrl27dlG3bt1K77NmzZq051b07ycionHjxmnPXbt2bYXPAQAAAAAAAAAAAAAAAAAAAAAAcksDrAxt3LixytcVFBRU6IzNNWnSJCv7lJWVpT23sLDi/6Rq1aqV9txMMgEAAAAAAAAAAAAAAAAAAAAAAMmiAVaGVq9eXeXrGjRoUKEzNteoUaOs7JNJnqKiogqfs2rVqrTnZuvvCAAAAAAAAAAAAAAAAAAAAAAAqH4aYGVo8eLFFVq3ZMmStOc2b968QmdUlWbNmqU998svv6zwOZmsbdq0aYXPAQAAAAAAAAAAAAAAAAAAAAAAcksDrAy9++67Vb6ubdu2FTqjquy8885pz63o309ExOzZs9Oe2759+wqfAwAAAAAAAAAAAAAAAAAAAAAA5JYGWBmaNm1axms2btwYM2bMSHt+ly5dMj6jKnXu3DntuR988EGsXr064zPmzp0bK1eurJJMAAAAAAAAAAAAAAAAAAAAAABAsmiAlaFJkybFqlWrMlrz4osvxhdffJH2/H333TfTWFWqV69eac8tLS2NiRMnZnzGI488ktH8/fffP+MzAAAAAAAAAAAAAAAAAAAAAACAZNAAK0Pr16+P2267LaM1v//979OeW69evfjhD3+Yaawq1bt372jcuHHa82+77bYoKytLe/66devinnvuSXv+97///ejQoUPa8wEAAAAAAAAAAAAAAAAAAAAAgGTRAKsC/vu//zvef//9tOY+/PDDMWnSpLT3PuKII6JBgwYVjVYl6tWrF8ccc0za819//fX405/+lPb8G2+8MRYuXJj2/OOPPz7tuQAAAAAAAAAAAAAAAAAAAAAAQPJogFUBRUVFceSRR8bbb7/9rfMmTpwYAwYMyGjvgQMHViJZ1Rk2bFjG8x9//PHvnHf33XfHddddl/a+tWvXjnPOOSejLAAAAAAAAAAAAAAAAAAAAAAAQLLUznWAfLVo0aLo0aNHDBo0KPr37x+dO3eOJk2axBdffBEzZsyIcePGxWOPPZbRnrvuumv07du3ihJXTq9eveKII46Iv//972nNLykpieOOOy5OO+20OPfcc6NXr15RUFAQERGbNm2Kl19+OUaOHBlPPPFERjkGDBgQ3/ve9zLOX5MMHjw4ZsyY8Z3zli1blvaeM2bMiL333jutuU8//XS0adMm7b0BAAAAAAAAAAAAAAAAAAAAAGBzGmBVQklJSdx5551x5513ZmW/a6+9NmrVqpWVvarCLbfcEt27d4/169enveaBBx6IBx54IBo2bBitW7eOsrKy+Oyzz2Lt2rUZn7/jjjvGDTfckPG6mmbevHnx1ltvZXXPNWvWpL3nhg0bsno2wFc6dOgQZWVluY4BEBHuSUDyuC8BSeKeBCSN+xKQJO5JQJK4JwFJ474EJIl7EpA07ktAkrgnAUningQkjfsSkCTuSUDSuC9RUYW5DsC/9enTJ0499dRcx/hWnTp1iuHDh1do7Zo1a2LevHkxf/78CjW/iogYM2ZMtGzZskJrAQAAAAAAAAAAAAAAAAAAAACA5NAAK0NV0aSqefPmcf/992d936pw3nnnxbBhw6r93BtuuCGOO+64aj8XAAAAAAAAAAAAAAAAAAAAAADIPg2wMnTddddltQlWgwYN4oknnoi2bdtmbc+qdtttt8X5559fLWcVFBTEddddF5dddlm1nAcAAAAAAAAAAAAAAAAAAAAAAFQ9DbAyVFBQEPfff3+cddZZld5rhx12iGeeeSZ69+6dhWTVp6CgIG655Za45557olGjRlV2TtOmTeORRx6JK664osrOAAAAAAAAAAAAAAAAAAAAAAAAqp8GWBVQWFgYd999d9x1112x/fbbV2iPHj16xPTp0+Pggw/OcrrqM3jw4Hj77bfjhBNOiIKCgqztW6tWrRg4cGC899570a9fv6ztCwAAAAAAAAAAAAAAAAAAAAAAJIMGWJUwZMiQmDNnTgwbNiwaNWqU1po999wzxowZE6+99lrsvvvuVZyw6u2yyy7x8MMPx9tvvx3Dhg2L5s2bV3ivVq1axa9+9at4//334957741WrVpVeK+NGzemPbdu3boVPgcAAAAAAAAAAAAAAAAAAAAAAMhc7VwHyHdt2rSJ0aNHx0033RT/+Mc/YurUqTFnzpz44osvYt26dbHddttF27Zto2vXrnHEEUdEz549K3Vehw4doqysLEvps2evvfaK0aNHx6233hqvv/56TJkyJWbNmhXz58+PTz75JFasWBHFxcUREdGgQYPYcccdo23btrH77rtH9+7d46CDDop99903CgoKspJn7dq1ac/dcccds3JmdXnhhRdyHQEAAAAAAAAAAAAAAAAAAAAAACpFA6wsadiwYRx99NFx9NFH5zpKThUWFkavXr2iV69eOc2xdOnStOfmWwMsAAAAAAAAAAAAAAAAAAAAAADId4W5DgBVYc6cOWnPbd26dRUmAQAAAAAAAAAAAAAAAAAAAAAANqcBFjVOSUlJTJ48Oa25tWrVin333beKEwEAAAAAAAAAAAAAAAAAAAAAAF+nARY1zk033RTLli1La+5ee+0VjRo1quJEAAAAAAAAAAAAAAAAAAAAAADA19XOdQCorNLS0li+fHm8+eabMXbs2Bg/fnzaa4855pgqTAYAAAAAAAAAAAAAAAAAAAAAAGyNBljkrb59+8bEiRMrvL5OnToxdOjQLCYCAAAAAAAAAAAAAAAAAAAAAADSoQEW26yBAwdG69atcx2DDK1fvz5lPG/evBwlAQAAAAAAAAAAAAAAAAAAAAC2ZZv3Ptm8NwrfTgMstkm77bZbjBgxItcxqICPP/44Zdy3b98cJQEAAAAAAAAAAAAAAAAAAAAA+D8ff/xx7LPPPrmOkTcKcx0AqluzZs3ikUceie233/4b57zwwgtRUFCQsy++2YoVK3IdAQAAAAAAAAAAAAAAAAAAAABgC3qjZEYDLLYpe+yxR7z66qvRvXv3XEehglatWpXrCAAAAAAAAAAAAAAAAAAAAAAAW9AbJTO1cx0AqsNOO+0Uv/nNb2LYsGGx3Xbb5ToOldCjR4+U8cMPPxydO3fOURoAyK558+ZF3759y8ePP/54dOzYMYeJACB71DkAajJ1DoCaTJ0DoCZT5wCoqdQ4AGoydQ6AmkydA6AmU+cAqMlmz54dJ554Yvl4894ofDsNsKhxGjduHM2aNYuWLVtG7969o0+fPtGnTx+Nr2qIxo0bp4w7d+4cXbp0yVEaAKhaHTt2VOcAqLHUOQBqMnUOgJpMnQOgJlPnAKip1DgAajJ1DoCaTJ0DoCZT5wCoyTbvjcK30wArIl544YVcR6ACHn/88VxHAAAAAAAAAAAAAAAAAAAAAAAAKqAw1wEAAAAAAAAAAAAAAAAAAAAAAADYttTOdQBIokMOOSTKyspyHQMAAAAAAAAAAAAAAAAAAAAAAGqkwlwHAAAAAAAAAAAAAAAAAAAAAAAAYNuiARYAAAAAAAAAAAAAAAAAAAAAAADVSgMsAAAAAAAAAAAAAAAAAAAAAAAAqpUGWAAAAAAAAAAAAAAAAAAAAAAAAFQrDbAAAAAAAAAAAAAAAAAAAAAAAACoVhpgAQAAAAAAAAAAAAAAAAAAAAAAUK00wAIAAAAAAAAAAAAAAAAAAAAAAKBaaYAFAAAAAAAAAAAAAAAAAAAAAABAtaqd6wAAmdhpp53iqquuShkDQE2hzgFQk6lzANRk6hwANZk6B0BNps4BUFOpcQDUZOocADWZOgdATabOAVCTqXOVU1BWVlaW6xAAAAAAAAAAAAAAAAAAAAAAAABsOwpzHQAAAAAAAAAAAAAAAAAAAAAAAIBtiwZYAAAAAAAAAAAAAAAAAAAAAAAAVCsNsAAAAAAAAAAAAAAAAAAAAAAAAKhWGmABAAAAAAAAAAAAAAAAAAAAAABQrTTAAgAAAAAAAAAAAAAAAAAAAAAAoFppgAUAAAAAAAAAAAAAAAAAAAAAAEC10gALAAAAAAAAAAAAAAAAAAAAAACAaqUBFgAAAAAAAAAAAAAAAAAAAAAAANVKAywAAAAAAAAAAAAAAAAAAAAAAACqlQZYAAAAAAAAAAAAAAAAAAAAAAAAVCsNsAAAAAAAAAAAAAAAAAAAAAAAAKhWGmABAAAAAAAAAAAAAAAAAAAAAABQrWrnOgBQsy1YsCDefPPN+PTTT6OoqChat24d7du3jwMOOCDq1KmT02wzZ86MuXPnxieffBIREW3bto3vf//70b1795zmAiB/JLnOAUBlJa3OFRcXx/vvvx9z5syJpUuXRlFRUTRq1CiaNm0ae+21V3Tt2jVq1/ZSFwDfLWk1btmyZTFnzpz4+OOPY/HixbFmzZqIiNhhhx2iZcuW0b1799h1112rPRcA+SlpdQ4AskmdA6AmS3qdKy0tjZkzZ8Z7770XS5cujQ0bNkSjRo3K33fZpUsXv6sD4Bslvc4BQGUktc4tW7YsZsyYEQsWLIgVK1ZEWVlZ7LDDDtGuXbvo2bNntGrVKmfZAMgPSa1xn3zySUybNi0WL14cK1eujObNm0ebNm1i//33j5122ilnuQAgW7bVPih+0whUiUcffTRGjhwZ06ZN2+r3mzZtGv37949rr702mjdvXm25SkpKYsSIEfGnP/0p5s+fv9U5HTt2jMGDB8evfvUrv1ADYKuSWOdKS0vjvffei+nTp8eMGTNi+vTp8c4770RJSUn5nAEDBsS4ceOqJQ8A+StJdW7mzJnx+OOPxz/+8Y94/fXXU+ra5ho2bBj9+/ePCy64ILp161aluQDIT0mpcUVFRTF69OiYNm1aTJ8+PT777LPvXNOuXbs444wz4vzzz4+WLVtWWTYA8ldS6ly61q5dG127do0PP/ww5brXMAHYmiTVuUMOOSRefPHFCq+/9957Y+DAgdkLBEDeS1Kd25q5c+fG8OHDY8KECbFy5cpvnNegQYM48MADY+jQoXHcccdVY0IAkiwpda5Dhw7x0UcfZWUvr2EC8JWk1LmvKysriwkTJsTtt98eU6ZM+da53bt3j3POOSfOPPNMDY0BSJHEGrdp06Z46KGH4uabb4633357q3Nq1aoVffr0iYsvvjgOP/zwaskFQH758MMPU57BnjlzZqxevbr8++3bt4+FCxfmJJs+KBEFZWVlZbkOAdQcRUVFcdZZZ8X48ePTmt+yZcu477774sc//nEVJ/v3GzFOOumkmDlzZlrz99133xg/fnx07NixipMBkC+SWOfuuOOO+POf/xxvvvlmFBcXf+tcb7wA4Nskqc6tW7cuunTpssWD0OmoVatW/PrXv47rrruuRr6YB0DmklTjIiLmzZsXu+++e4XW7rDDDjFq1CgPSwNQLml1Ll0XXnhhjBo1aovrXsME4OuSWOc0wAIgW5JY576utLQ0rr322rjxxhujtLQ07XX9+/dP+88EQM2VtDqXzQZY55xzTtxxxx1Z2QuA/JS0OveVzz//PE455ZR4/vnnM1rn+TkAvpLkGtevX7945ZVX0l5z9tlnxy233BL16tWrwmQA5IMXXnghbrzxxpgxY0YsW7bsW+fmqgGWPij/pgEWkDUbN26MY445Jp5++umU6zvttFN07949dthhh5g/f37MmjUrvn7rqVevXjz33HNx4IEHVlm2zz//PHr16rXFL646duwYXbp0ibKysnjvvfe26Ia4yy67xKuvvhotWrSosmwA5Iek1rm+ffvGxIkT05rr4TEAvknS6lxRUVFsv/32W1wvKCiIPfbYI3beeedo3rx5FBUVxbvvvrvVRlk///nPY8KECT6ZDGAbl7QaF7H1BlhNmzaN3XffPVq1ahWNGjWK9evXx+effx5vvfVWyqfKfGX48OFx0UUXZT0bAPkliXUuHa+++mr88Ic/jE2bNm3xPa9hAvCVpNY5DbAAyIak1rmvFBcXx/HHH79FvoKCgujSpUvsvPPO0aRJkygqKooPP/ww5syZU94kSwMsAJJY57LZAOvvf/97HH744VnZC4D8k8Q6FxGxdOnS+NGPfhRz5sxJuV6nTp3o3r17tG/fPgoLC2PRokXxxhtvxLp161LmtWvXLqZMmRLt27evknwAJF9Sa9ynn34avXr1io8//jjlepMmTWL//fePpk2bxrJly+LVV1+NlStXpsw58cQTY/z48VFQUFAl2QDID6NGjYoLL7wwrbm5aIClD8r/8QQgkDWXXnppyg83derUiZEjR8aQIUOibt265ddnz54dgwcPjmnTpkVExPr166Nv377xzjvvROvWrbOea9OmTdG3b9+Um37r1q1j3LhxceSRR6bMnTRpUvziF7+Izz//PCIiFixYEMcdd1xMmTLFDzkA27ik1rlv0qRJk2jYsGF88skn1XYmAPkryXWuVq1aceSRR8aAAQPisMMOi+bNm28x54033ohf/epX8dJLL5Vf++tf/xpXX311XH/99VWSC4D8kNQa16JFizjqqKPiiCOOiAMOOOAb30BYUlISTzzxRFxyySUpv7S6+OKL48ADD4z9998/69kAyB9JrXPfZsOGDTFo0KDy5lfbb7/9Vps9AkC+1LkFCxZkNH9rr28CsO1Jcp0rKyuLk046KSVf/fr14+KLL44hQ4ZE27Ztt1izdu3a+Pvf/x7jx49PyQ/AtimJdW7KlCnlzRozMXr06BgxYkT5uEOHDnHYYYdlMxoAeSaJdS4i4j//8z+3aH51zjnnxDXXXLPFw9ArVqyIm266KW6++eby39ktWrQozj777Jg0aVLWswGQH5JY40pKSqJfv34pza8aNWoUN998cwwePDjq1KmTMveee+6Jiy++ONasWRMREQ8//HDssccece2112Y1FwA1Q7169aJdu3ZbNJaqTvqgpCoo+3qbTYAK+vDDD2PPPfeMkpKS8muPP/54HHvssVudX1xcHIcddlj5DzkREWeffXbceeedWc/25z//Oc4444zycdOmTeONN96IDh06bHX+ggULYt99943ly5eXX3vooYfipJNOyno2APJDkutc3759Y/LkydG9e/fo0aNH9OzZM3r06BEdO3aMa665Jq655pryuQMGDIhx48ZlPQMA+S2Jda6oqCiaN28egwcPjksvvTTatWv3nWs2btwYp59+ejz00EPl1+rWrRv//Oc/fSoZwDYqiTUu4t81q6CgIAoLC9Nes3z58vjRj34U7777bvm1n/zkJ/HMM89kNRsA+SOpde67XHHFFXHDDTdExL8/Le2EE06I4cOHl3/fa5gARCS7zh1yyCHx4osvlo+99Q6ATCW5zkVE3H777XHeeeeVj1u3bh2TJ0+OTp06pbW+tLQ0atf2+cwA26qk17lMderUKaWZyLXXXhtXXnllDhMBkEtJrXMLFy6MXXbZJeXab3/72/iv//qvb103evTo+OUvf5ly7dVXX/VhbADboKTWuLvuuivOOeec8nG9evXi+eefj969e3/jmilTpsRhhx0WGzZsKF8zZ86cb3yeHICab9SoUXHxxRdHly5dUp7B7tq1a0ydOjUOPfTQ8rnt27ePhQsXVls2fVBSpf90BcC3uOaaa1J+uBk4cOA3/nATEdGgQYMYN25cSuffMWPGxIcffpjVXBs3boyrrroq5drIkSO/9YeVXXbZJUaOHJly7Yorrijvag/AtiepdS7i3y/mrVy5Ml566aUYOXJknHzyybH77rvXmMsJKr4AACAASURBVI69AFS9JNa5+vXrx7x582L06NFpNb+KiKhVq1aMGTMmvve975Vf27BhQzz88MNZywVAfklijYv4d83KpPlVRMSOO+4Yt9xyS8q15557LlavXp3NaADkkaTWuW/z1ltvxU033VQ+vuOOO6Jhw4bVdj4A+SMf6xwApCvJde5f//pXXHrppeXj+vXrx3PPPZd286uI0PwKYBuX5DqXqalTp6Y0vyosLIyBAwfmLhAAOZfUOvfkk0+mjFu2bLnFs3RbM2zYsOjWrdu37gXAtiGJNa6srCzlPSYR/37O+9uaX0VEHHjggXHZZZeVj9evXx+/+93vspYLgPwzYMCAWLVqVcyaNSvuueeeGDJkSOyzzz5Rp06dnObSB2VLGmABlVZcXByPPvpoyrVLLrnkO9d9//vfj759+5aPS0tL48EHH8xqtilTpsSCBQvKx23bto3TTjvtO9edfvrp0bZt2/Lx/Pnz45VXXslqNgDyQ5LrXMS/f0GV6YPTAPCVpNa52rVrp9346usaNGgQv/jFL1KuPf/889mKBUAeSWqNq4xDDjkkGjRoUD4uLS2Njz76KIeJAMiVfKxzpaWlceaZZ0ZpaWlERJx88snx05/+tFrOBiC/5GOdA4B0Jb3O3XDDDVFUVFQ+vvzyy6Nz585ZPweAminpdS5TY8eOTRkfeeSRKR/KBsC2Jcl1bvNmI0ceeWTUq1fvO9cVFBTE0UcfnXJt7ty5Wc0GQPIltca9++67Kc+G169fP84777y01v7yl7+M+vXrl48feeSRlNc9Adi27Ljjjil1ISn0QdmSJ+WBSvvf//3fWLt2bfm4d+/eseeee6a1dvMHk//6179mNdtjjz2WMj7jjDOiVq1a37muVq1aWxSIbGcDID8kuc4BQGXVxDrXvXv3lPGnn36aoyQA5FJNrHGFhYXRpEmTlGurV6/OURoAcikf69zvf//7mDlzZkRENG3aNEaNGlUt5wKQf/KxzgFAupJc51avXp3ygFrDhg3jggsuyOoZANRsSa5zmSoqKoqHH3445dqgQYNylAaAJEhynVuzZk3KOJMPH928uePy5cuzkgmA/JHUGvfSSy+ljPfff/8t3j/5TZo2bRo9e/YsH69bty7+9re/ZS0bAGSDPihb0gALqLRJkyaljA855JC01x500EFRu3bt8vGsWbNi8eLF2YpWqWybz33mmWeykAiAfJPkOgcAlVUT69zXM0VEbNiwIUdJAMilmljj1q5dG0uXLk251qZNmxylASCX8q3OffDBB3HNNdeUj0eMGBEtWrSo0jMByF/5VucAIBNJrnMTJkyIoqKi8nG/fv1i++23z9r+ANR8Sa5zmdq8Lu60005x7LHH5iwPALmX5DrXqlWrlPG6devSXrv53KZNm2YlEwD5I6k1btGiRSnjvfbaK6P1Xbt2TRlrgAVA0uiDsiUNsIBKe/fdd1PGvXv3Tnttw4YNt/hB4r333stKrvXr18e8efNSrvXq1Svt9QcccEDKeO7cuR6cBtgGJbXOAUA21MQ6t/nPga1bt85REgByqSbWuIceeihKS0vLx7vssku0b98+h4kAyJV8qnObNm2KQYMGxfr16yMiok+fPjFw4MAqOw+A/JdPdQ4AMpXkOvf888+njI844ois7Q3AtiHJdS5TY8eOTRmffvrpUadOnRylASAJklznDjrooJTxzJkz0177xhtvpIx79uyZlUwA5I+k1rhly5aljJs0aZLR+s3nv/POO5XOBADZog/K1mmABVTa+++/nzLu2LFjRut32223lPHs2bMrnSni358mvXHjxvJxixYtonHjxmmvb9y4cTRv3rx8vHHjxvjnP/+ZlWwA5I+k1jkAyIaaWOceffTRlPF+++2XoyQA5FJNq3FTp06NX//61ynXNh8DsO3Ipzo3evTomDp1akRENGjQIO66664qOwuAmiGf6lxExAUXXBD77bdftGjRIurWrRtNmzaN3XffPY4++ui4+eabvc8EgBRJrnOvv/56yvirB92Ki4vjwQcfjGOOOSZ22223aNCgQTRp0iQ6duwYJ5xwQtx9992xevXqrOUAIH8luc5lYs6cOfHKK6+kXBs0aFBOsgCQHEmuc4cddljsscce5eOXX3453n777e9c98knn8T//M//lI/r1KkTJ598ctZyAZAfklrj6tatmzL+6oPX0rX5/Dlz5sSmTZsqnQsAskEflK3TAAuolGXLlm3RSXfnnXfOaI/N58+dO7fSuSJii66Hmeba2ppsZQMgPyS5zgFAZdXEOjd9+vTyB6u/ctxxx+UoDQC5UhNq3Pr162PRokXx5JNPxmmnnRY/+tGPYsWKFeXfP/roo2Po0KHVmgmAZMinOrdw4cK47LLLysdXXXVVxm+UBGDbkk917iu33nprTJ8+PZYuXRolJSWxfPnymDdvXjz11FNxySWXRKdOneLnP/95zJ8/v0pzAJB8Sa5zK1asSHm/Zd26dWPXXXeNF198Mbp06RKnnnpqPPnkk/Hhhx/GunXrYuXKlTF//vx49NFH4+yzz45ddtklbr311qxkASA/JbnOZWrMmDEp4169ekXnzp1zkgWAZEh6nSssLIyxY8dGvXr1IiJi06ZNcfzxx8fChQu/cc3ixYujb9++sXbt2vJrV1xxRbRp0yZruQBIviTXuGbNmqWMP/vss4zWbz5/3bp18a9//avSuQAgG/RB2brauQ4A5LevP3QVEbHddttFw4YNM9qjRYsWKeOVK1dWOlfEltk2PycdVZUNgPyQ5DoHAJVV0+pcSUlJnH322SnXDjrooNhvv/1ylAiAXMnHGrf33nvHW2+99Z3zCgoK4txzz42RI0dGQUFBlWYCIJnyqc6dddZZsWbNmoiI+MEPfhAXXXRRlZwDQM2RT3UuXZs2bYrHHnssJk+eHGPHjo1+/frlNA8AuZPkOvf555+njNu0aRN//etf48QTT4xNmzZ95/ovv/wyLrjggpg+fXrce++9Ubu2t6cDbGuSXOcyUVpaGn/+859Trg0ePLjacwCQLPlQ5w444IB46qmn4pRTTomlS5fG3Llzo1u3bjFo0KD4yU9+Eu3bt4+CgoJYtGhRTJ48Oe6+++748ssvy9efffbZceWVV2Y1EwDJl+Qa16lTp5Txq6++mtH6rc3P9e8NAeAr+qBsnd8wApVSVFSUMm7QoEHGe2y+ZvXq1ZXK9JUkZwMgP6glANRkNa3O/eY3v4lZs2aVj+vUqeOTpgG2UTWtxkVE1K1bN84666w499xzfcI0wDYuX+rcmDFj4rnnnouIf3/q9D333OMBaAC+U77UuYiIrl27xk9/+tPYe++9o2PHjtGkSZNYv359LFmyJKZNmxYTJkyId955p3z+qlWron///vHEE0/Ef/zHf1RJJgCSLcl1bvM32RcVFcVpp51W3vyqffv2MWzYsDjwwAOjWbNmsWzZspgyZUrcfvvtsXDhwvJ1DzzwQLRs2TKGDx+elVwA5I8k17lMPPXUU7F48eLyccOGDaN///7VngOAZMmXOnf44YfH+++/H6NGjYq//OUvsWDBghg1alSMGjXqG9fsueeece2118YJJ5yQ9TwAJF+Sa9zBBx+cMp4/f35MmzYtevfu/Z1rp0yZEh9++OEW13P9PlAA+EqSa3AueYcpUCmb31zr16+f8R6b31w337OikpwNgPyglgBQk9WkOjd27Ni45ZZbUq5dffXVsffee+ckDwC5VZNq3Fc2bNgQDzzwQGzYsCEuueSS2G233XKaB4DcyYc69+mnn8avf/3r8vH5558fPXv2zOoZANRM+VDnTjnllLj99tujS5cu3zinT58+cfnll8df/vKXGDp0aPmbDDdu3Bj9+/ePOXPmRNu2bbOaC4DkS3Kd27wB1hdffFH+3yeccELcd999W5zdq1evOO+88+KMM86IRx55pPz6iBEj4thjj42DDjooK9kAyA9JrnOZGDNmTMq4f//+0ahRo2rPAUCy5FOdKy0tjYiIevXqfefcAw44IK6++uo4/PDDqyQLAMmX5BrXqlWrOOyww2Ly5Mnl1y666KJ4+eWXo1atWt+4rrS0NC666KKtfq8mNAYBoGZIcg3OpcJcBwBqloKCgmpZUxFJzgZAflBLAKjJ8rXOTZo0Kc4555yUaz/72c/it7/9bY4SAZA0+VDjnn766ViwYEH519tvvx3PPvtsXH/99dGpU6eIiFi5cmXcc8890a1btxg3bly15gMguZJY584999zyh6fbt28f119/fZWeB0DNlcQ6N2TIkG9tfvV1p556akyePDm222678mtFRUVxzTXXVFU8APJIkurcpk2btnq9Z8+e8eCDD37jp07Xr18/HnzwwS2aHvs5EIAk1bl0ffbZZzFp0qSUa4MGDcpRGgCSLKl17p577onddtstrr/++pgzZ853zn/llVfiyCOPjG7dusXUqVOrPB8AyZe0Gnf55ZenjKdNmxann356rF+/fqvz161bF6eeemq8/vrrW/1+YaG2GgAkU9JqcK6o1EClbP6JJsXFxRnvsfmabH1KSpKzAZAf1BIAarKaUOemTp0a/fr1i5KSkvJrBx54YEyYMKFGvpAHQHrysca1adMmOnToUP7VtWvXOOKII+Lyyy+P2bNnx913313+yS5r166NM888M+6///4qzQRAMiW9zo0fPz4mTpxYPr7jjjuiYcOGWdsfgJot6XWuInr27LlFE5D77rsv1qxZk6NEAORKkuvcN+0zfPjwqF279reurV27dowcOTLl2rPPPhtLlizJSjYA8kOS61y67rvvvigtLS0fd+rUKQ444IBqzQBAMuVDnbvhhhtiyJAhKa879ujRI8aOHRtz586NNWvWRHFxcSxYsCAeeuihOPTQQ8vnvfvuu3HwwQfHfffdl9VMACRf0mvcoYceGueff37KtYceeig6deoUI0aMiNdeey3mzp0br732Wgwf/v/Yu/PoKstrcfw7YQ5EmWQUgSAijgitMmgZFGuBqtSqtJaipbZatVqHXqttRVSs13pvHVZvtVoQXFqcUBzqgEjrAFQRZVRQQWWSMTIThvz+6Nf8PECSk+Tk5CR+Pmvlj+c5+93Pfs9yZa/ge/b5Y3Tt2jUeffTRiPjP8P5mzZolXNu4ceOU1QYAFZHpPbiqGIAFVEgm/3LN5NoAqB70EgBqsure52bNmhWDBw+OrVu3Fu0df/zx8dxzz0VOTk7a6gAg81T3Hrc/F154YUycOLFoXVhYGL/4xS9i+fLlVVgVAFUhk/vc2rVrEx48/MEPfhDf+c53UpIbgK+HTO5zFfGLX/wiDjjggKJ1QUFBvPrqq1VYEQBVIZP73P7ytG/fPr71rW8ldf2JJ54YeXl5CXv//Oc/U1IbANVDJve5ZP3tb39LWI8cOTKt5wOQuTK9z02dOjV+97vfJeyNGjUq/v3vf8cFF1wQhx56aOTk5ET9+vWjQ4cOMWzYsJg6dWrce++9RV80unv37hg5cmS88cYbKasLgMyX6T0u4j9D+n/wgx8k7C1ZsiSuvvrq6NmzZxx22GHRs2fPuOaaa2Lp0qUREZGdnR0TJkzYpxYDsADIFNWhB1cFA7CACjnwwAMT1lu3bi3zt1Tu/U1fqfojYu/a1qxZU+YclVUbANVDJvc5AKio6tzn5syZE6eeemp88cUXRXvHHXdcvPjiiwkfJAPg66k697iSnH766TF06NCi9ZYtW+LPf/5zFVYEQFXI5D73y1/+suj/xzVt2jT+9Kc/pSQvAF8fmdznKqJevXrRv3//hL05c+ZUUTUAVJVM7nP7y9OzZ88y5TjhhBMS1gsXLqxQTQBUL5nc55Lx2muvxeLFi4vWderUieHDh6ftfAAyW6b3ueuvvz4KCwuL1iNGjIgbbrihaLhVcX72s5/F9ddfX7TevXt3XH755SmrC4DMl+k9LuI/f589/PDD8b//+7/RtGnTUuM7duwYr7zySpx11lmxcuXKhNdatmyZ0toAoLzMQdk/A7CACmnWrFk0adIkYe/TTz8tU45PPvkkYd25c+cK17W/PHufk4zKqg2A6iGT+xwAVFR17XMLFiyIU045JdavX1+0d9RRR8VLL71UI/6xDoCKq649Lhl7f5PZCy+8UEWVAFBVMrXPffDBB/HII48Ura+44orYunVrLF26tMSf/Pz8hDybN29OeL2s9wZA9ZapfS4VOnTokLAuz8OLAFRvmdzn2rdvH/Xq1UvYa926dZlytGnTJmG9bt26CtcFQPWRyX0uGQ888EDC+rvf/W60aNEibecDkNkyuc8tX748ZsyYkbB3ww03JH39tddeGw0aNChaz5o1y/B+gK+RTO5xe7viiitiyZIl8Ze//CW+973vRV5eXuTm5kb9+vUjLy8vhgwZEhMmTIj58+dHv3794qOPPoqCgoKi6w899FCfNQAgY5iDsn8GYAEV1rVr14T1hx9+WKbrP/744xLzlVeXLl2iVq1aRevVq1fHpk2bkr5+48aNsXbt2qJ1rVq1asQvfgDKJlP7HACkQnXrcx988EGcfPLJCR8OO/zww2PKlCnRvHnzSj0bgOqluvW4ZHXp0iVhXdb7AqBmyMQ+t23btoT173//++jYsWOpP3feeWfCdU888UTC68ccc0yFawOgesnEPpcKX/0QWcS+vROAr4dM7XO1atXa598e9x6IVZq947dv317hugCoXjK1z5Vm06ZN8dhjjyXsjRw5Mi1nA1B9ZGqfe/fddxPWeXl50bFjx6Svb9iwYfTs2TNhb+bMmSmpDYDqIVN73P4ccMAB8fOf/zyeeOKJ+Oijj2Ljxo2xbdu2+Oijj+KZZ56JH/3oR0X/T2769OkJ155wwgmVVhcAlJU5KPtnABZQYUcddVTCeu8/DEqyZcuWfSbD752vvOrVqxedOnUqd21vvvlmwrpz585lfqgDgOovU/scAKRCdepzH374YQwYMCBWrVpVtNe5c+eYOnVqtGzZstLOBaB6qk49rizq1KmTsN6xY0cVVQJAVaqpfQ4AImpun/vqg4cRYaA/wNdUJve5vQcQ5+fnl+n6veObNWtW4ZoAqF4yuc+V5O9//3ts3bq1aN22bdv49re/nZazAag+MrXP7f23WKtWrcqcY+9r9v63TABqtkztcRX1yiuvJKz79etXNYUAwH6Yg7J/BmABFXbaaaclrKdNm5b0ta+99lrs2rWraH3cccel9IPLFalt79jvfOc7KagIgOomk/scAFRUdelzS5YsiQEDBsSKFSuK9vLy8mLq1KnRunXrSjkTgOqtuvS4slq2bFnCOlPqAiC9amqfA4CImtvnZs6cmbBu06ZNFVUCQFXK5D43aNCghPX8+fPLdP28efMS1gcffHCFawKgesnkPleSBx54IGF9wQUXRK1atdJyNgDVR6b2ucaNGyest2zZUuYcmzdvTlg3atSoQjUBUL1kao+riE2bNsXjjz9etG7UqFGce+65VVgRAOzLHJR9GYAFVNi3v/3taNCgQdF6+vTp8f777yd17bhx4xLWQ4cOTWVp++SbMGFC7N69u9Trdu/eHQ899FCl1gZA9ZDJfQ4AKqo69LlPP/00BgwYEJ999lnRXvv27WPq1KkenAegWNWhx5XHSy+9lLDu3LlzFVUCQFXKxD7XrVu3KCwsLPPPDTfckJBnxIgRCa/v/a3VANR8mdjnKmru3Lkxd+7chD3fMg3w9ZTJfW7IkCEJ3wz91ltvxfr165O6dsOGDfHvf/87Ye+kk05KaX0AZL5M7nPFWbBgQcLA4qysrLjgggvScjYA1Uum9rm9B+1/8MEHsXXr1jLleOeddxLWrVq1qnBdAFQfmdrjKuKuu+5KGAr5wx/+MHJzc6uwIgDYlzko+zIAC6iwnJyc+P73v5+wd9ttt5V63aJFi2LSpElF69q1a8cPf/jDlNZ20kknRceOHYvWy5Yt2+cX+v489NBDsXz58qJ1p06dok+fPimtDYDqIZP7HABUVKb3uRUrVsTJJ58cS5cuLdpr27ZtTJ06Ndq3b5/y8wCoOTK9x5XHypUr47777kvYO+OMM6qoGgCqUk3scwDwpZrW53bv3h2/+tWvEvYOPfTQOOKII6qoIgCqUib3udzc3ITaduzYEffcc09S195zzz2xffv2onX79u3jqKOOSml9AGS+TO5zxXnggQcS1v3794+8vLy0nA1A9ZKpfe6YY46JJk2aFK23b98eEyZMSPr6Z599NuHzcxERJ554YsrqAyDzZWqPK6/3338/brnllqJ1o0aN4rrrrqvCigBg/8xB2ZcBWEBKjBo1KurUqVO0HjduXEyePLnY+O3bt8cFF1wQBQUFRXsjR46MTp06lXhOVlZWws+0adNKjK9Vq1bceOONCXtXXnllwoen97Z06dJ9Hj68+eabIzvbr0yAr6tM7XMAkAqZ2udWr14dJ598cnz44YdFe61bt45XX33Vw4YAJCUTe9yWLVvif/7nf2Lbtm3J30hErFmzJgYPHhwbN24s2mvatGn84Ac/KFMeAGqOTOxzAJAqmdrn7r777oThHqUpKCiICy+8MF555ZWE/RtuuCHpHADUPJna5yIibrrppqhbt27ResyYMTF9+vQSr5k+fXrcfPPNCXu/+c1vIisrq9TzAKh5MrnP7W3nzp37DAgZOXJkmfMA8PWRiX2uVq1a+wwtufbaa2PevHml3E3Ep59+GhdddFHCXp8+faJ169alXgtAzZKJPe5Lu3btKv0G/p8PPvggTjnllITnM2+99VZfvA1AWpiDUnE14y6AKpeXlxeXX355wt73v//9uOeeexL+iImIWLhwYZx88snx5ptvFu01a9as0h7wO++88+KEE04oWq9fvz569+4dL7300j6xL774YvTq1Ss2bNhQtNe7d+8499xzK6U2AKqHTO5zu3btiqVLl+73Jz8/PyF28+bNxcZu3ry5UuoDIPNlYp/Lz8+PgQMHxvvvv1+017Bhw3jggQeiTp06xfaz4n4A+HrKxB63c+fOuOqqqyIvLy+uvPLKmD59+j61fNXnn38ed9xxR3Tt2jVmz56d8Nrtt98ezZs3T2l9AFQfmdjnACBVMrXP/fKXv4yOHTvGNddcEzNnziz2gftdu3bF008/HSeccEKMHTs24bVTTjklzjvvvJTXBkD1kal9LiKiY8eO8etf/7povWPHjjj11FPj//7v/2Lnzp0Jsbt27Yp77703Tj311IS6jz/++LjgggsqpT4AMl8m97m9TZ48OdasWVO0btKkSXzve99Ly9kAVE+Z2ud+//vfR4MGDYrW+fn50bt377jnnnti69at+8QXFBTEgw8+GD169Ijly5cnvHbrrbemvD4AMl+m9riIiPPPPz/OO++8ePbZZ4v94tEVK1bEqFGj4rjjjkvobYMHD45LLrmkUuoCoHpZtmzZfj9vtmrVqoS4kj6vvXbt2pTXZQ5KoqzCwsLCqi4CqBl2794d3/3ud+Mf//hHwn6LFi2ie/fukZubGx9//HG888478dVfPXXr1o0pU6bESSedVOoZe38r2Kuvvhr9+vUr9bqVK1dGz54949NPP03Y79y5cxx55JFRWFgY8+fPjw8//DDh9Q4dOsSMGTOiZcuWpZ4BQM2WqX1u6dKl0bFjx+RvpBhjx46N888/v8J5AKieMq3PTZs2Lfr371/2GymGf/4C+PrKtB6Xn58fTZo0SdirW7duHHHEEdG6deto3LhxFBYWxhdffBGLFi2Kjz/+eL997JZbbonrrruu1NoAqNkyrc+Vx6hRoxK+xWzEiBExbty4lOUHoPrKxD63d3y9evXiyCOPjNatW8eBBx4YO3fujNWrV8esWbP2++Uz3/jGN2Lq1KmRm5tbam0A1GyZ2Oe+VFhYGOeee2489thjCfuNGzeOnj17RtOmTWP9+vUxY8aMfb6YrW3btjFjxow4+OCDSz0HgJork/vcVw0ePDief/75ovWll14ad999d5lyAPD1k6l9btKkSXH22WfH7t27E/YbNGgQPXr0iDZt2kR2dnasWrUq3n777f3++6VnUQC+3jK1x33/+9+PJ554IiIi6tSpE127do0OHTpEbm5u5Ofnx6effhrz5s3b5znLU089NSZNmhQ5OTml1gVAzdehQ4f45JNPKpSjtGcbzUGpuNpVXQBQc9SqVSseffTR+OlPfxoTJ04s2l+9enW88MIL+72mRYsW8eCDDyb1x01FtG7dOl5++eUYNmxYzJ49u2h/8eLFsXjx4v1e071795g4cWKN+qUPQPllcp8DgIrS5wCoqapDjysoKIh333033n333VJjDz744Ljrrrti6NChaagMgExXHfocAJRXdehzO3bsiHfeeafUuKysrLjsssvitttui/r166ehMgAyXSb3uaysrJgwYUI0bdo07r333qL9/Pz8YmuLiDj++ONj0qRJ0aZNm0qtD4DMl8l97kvLly+PF198MWFv5MiRaTkbgOotU/vc0KFD4+mnn46RI0fG559/XrS/bdu2eP3110u8tmHDhvGHP/whLr300kqrD4DMl6k97qt27twZc+bMiTlz5hQbU7t27bjmmmti9OjRUbu2MRoAZD5zUP5/2VVdAFCzNGrUKP7+97/HY489Fj179iw2rmnTpnHxxRfHvHnz4rTTTktLbYcddljMnDkzbr311sjLyys2rlOnTnHrrbfGjBkz4tBDD01LbQBUD5nc5wCgovQ5AGqqTOpxBxxwQEyePDl+8YtfxBFHHBHZ2aX/b5ratWvHSSedFPfdd18sXLjQ8CsAEmRSnwOAVMu0Pnf77bfHoEGDolmzZknFH3TQQXHJJZfEggUL4s477zT8CoAEmdbnvqpevXrxl7/8JaZMmRIDBw6MWrVqFRt71FFHxbhx4+LNN980/AqAIpnc5yIixo0bF7t37y5ad+/ePbp165a28wGo3jK1zw0ePDgWIpiLgAAAH/xJREFULFgQY8aMiU6dOpUa37Jly7j66qtj/vz5hl8BEBGZ2eOGDRsWAwYMKPX/s+Xm5sbIkSNj3rx5MWbMGMOvAKhWzEH5j6zCwsLCqi4CqLmWLFkS77zzTqxYsSK2bNkSrVq1ivbt20efPn2ibt26VVrbrFmzYtGiRbFixYqIiGjTpk0cdthh0aNHjyqtC4DqI5P7HABUlD4HQE2VST1u06ZNsWDBgli6dGmsWrUqtmzZEhH/GZR14IEHRpcuXeKYY47xIWkAkpZJfQ4AUi2T+tyyZcvigw8+iGXLlsW6deti27ZtUatWrWjSpEk0b948unXrltSHzADgS5nU5/a2Zs2amDFjRqxcuTLWrl0bubm50bJly+jdu3ccfPDBVVobANVDJvc5AKioTO1zy5Yti1mzZsXKlSsjPz8/CgsL48ADD4yDDjoojjvuuBr5YWkAUiuTelxBQUHMmTMnFi9eHCtXroytW7dG7dq1o0WLFnHEEUdEjx49ok6dOmmtCQAqy9d1DooBWAAAAAAAAAAAAAAAAAAAAAAAAKRVdlUXAAAAAAAAAAAAAAAAAAAAAAAAwNeLAVgAAAAAAAAAAAAAAAAAAAAAAACklQFYAAAAAAAAAAAAAAAAAAAAAAAApJUBWAAAAAAAAAAAAAAAAAAAAAAAAKSVAVgAAAAAAAAAAAAAAAAAAAAAAACklQFYAAAAAAAAAAAAAAAAAAAAAAAApJUBWAAAAAAAAAAAAAAAAAAAAAAAAKSVAVgAAAAAAAAAAAAAAAAAAAAAAACklQFYAAAAAAAAAAAAAAAAAAAAAAAApJUBWAAAAAAAAAAAAAAAAAAAAAAAAKSVAVgAAAAAAAAAAAAAAAAAAAAAAACklQFYAAAAAAAAAAAAAAAAAAAAAAAApJUBWAAAAAAAAAAAAAAAAAAAAAAAAKSVAVgAAAAAAAAAAAAAAAAAAAAAAACklQFYAAAAAAAAAAAAAAAAAAAAAAAApJUBWAAAAAAAAAAAAAAAAAAAAAAAAKSVAVgAAAAAAAAAAAAAAAAAAAAAAACklQFYAAAAAAAAAAAAAAAAAAAAAAAApJUBWAAAAAAAAAAAAAAAAAAAAAAAAKSVAVgAAAAAAAAAAAAAAAAAAAAAAACklQFYAAAAAAAAAAAAAAAAAAAAAAAApJUBWAAAAAAAAAAAAAAAAAAAAAAAAKSVAVgAAAAAAAAAAAAAAAAAAAAAAACklQFYAAAAAAAAAAAAAAAAAAAAAAAApJUBWAAAAAAAAAAAABlm2rRpkZWVldQPJOPtt9+O7OzsEv9bev7556u6zDKZO3duqff05JNPVnWZAAAAAAAAAAAUwwAsAAAAAAAAAAAAqMEKCwvjsssui8LCwmJjevbsGYMGDUpjVRV39NFHx9lnn11izFVXXRXbt29PU0UAAAAAAAAAAJSFAVgAAAAAAAAAAECN169fv8jKysrIn1GjRlX120MNN2HChJgxY0aJMTfddFOaqkmtUaNGRXZ28Y9CLl26NP74xz+msSIAAAAAAAAAAJJlABYAAAAAAAAAAADUUFu3bo1rr722xJjevXvHKaeckqaKUqtr165xzjnnlBhz6623xooVK9JUEQAAAAAAAAAAyTIACwAAAAAAAAAAAGqoe+65J1auXFlizFVXXZWmairH1VdfXeLrW7dujTFjxqSpGgAAAAAAAAAAkmUAFgAAAAAAAAAAANRAmzZtiv/+7/8uMSYvLy/OPPPMNFVUOXr06BHf+ta3Soz561//Gp9++mmaKgIAAAAAAAAAIBkGYAEAAAAAAAAAAEANdOedd8a6detKjLn88ssjO7v6P0p45ZVXlvh6QUFB3HTTTWmqBgAAAAAAAACAZFT/p1YAAAAAAAAAAACABNu3b48777yzxJj69evHj3/84zRVVLmGDBkSrVu3LjFm/PjxsXLlyjRVBAAAAAAAAABAaQzAAgAAAAAAAAAAyDD9+vWLwsLCpH5gfx566KFYu3ZtiTFDhw6Nxo0bp6miylWrVq0YPnx4iTEFBQXx5z//OU0VAQAAAAAAAABQGgOwAAAAAAAAAAAAoIb505/+VGrMT37ykzRUkj4XXHBBqTF/+ctfYvv27WmoBgAAAAAAAACA0hiABQAAAAAAAAAA1HjTpk2LwsLCMv+MGDEi6TPGjh1brjNGjRpVeTfO19I///nPmD9/fokxLVq0iAEDBqSpovQ4/PDDo1u3biXGrF27Nh599NE0VQQAAAAAAAAAQEkMwAIAAAAAAAAAAIAaZMKECaXGnHHGGZGdXfMeITzzzDNLjUnm/QEAAAAAAAAAoPLVruoCAAAAAAAAAAAASDRt2rTo379/UrGFhYWVXE3ErFmzYuLEifH666/HokWL4osvvogGDRpEq1atokePHnH66afHmWeeGQ0aNEgq36JFi+KRRx6Jf/3rX7FgwYJYv3591KtXL1q0aBHt2rWLk08+OYYMGRLdunWr1Pv6+OOPY8qUKfH222/HwoUL47PPPov169fHtm3bIisrKxo2bBht2rSJTp06xTe/+c0YMGBA9OrVK6MHR23fvj0ef/zxUuOGDh2a0nNnz54dr776aixcuDDef//9WLFiRWzevDk2b94cO3bsiAYNGkROTk40btw42rVrF4ccckjk5eXFscceG926dYt27dqlpI6hQ4fGqFGjSoyZOnVqLF++PNq2bZuSMwEAAAAAAAAAKJ+swnQ8/QQAAAAAAAAAAFANnX/++fHggw8mFTt27Ng4//zzU3JuqgdglTffvHnz4vLLL4+pU6eWel3r1q3j5ptvjp/85CfFxixZsiSuueaaePLJJ5Oqe8iQIXH77bfH4YcfnlTtydiyZUuMHTs27rvvvpg7d26Zr2/Tpk1cfPHFcckll0STJk1SVleqPPnkk3HWWWeVGFO/fv3Iz8+PevXqVeisjRs3xh133BHjx4+PpUuXVihXs2bN4sQTT4y+fftG3759o3v37uXOdcghh8Rnn31WYswdd9wRV155ZbnPAAAAAAAAAACg4jL3a+gAAAAAAAAAAACoMvfdd1907949qeFXERErV66MkSNHxrBhw6KgoGCf1x966KE45phj4oknnkhq+FVExLPPPhvdu3ePf/zjH2WqvTj3339/dOrUKS677LJyDb+KiFixYkX87ne/i86dO8f999+fkrpS6bnnnis1plevXhUefvXoo4/GoYceGqNHj67w8KuIiHXr1sXTTz8dV155ZfTo0aNCufr27VtqzPPPP1+hMwAAAAAAAAAAqDgDsAAAAAAAAAAAAEgwZsyY+PnPfx47d+4s87UTJ06Mc845J2HI1W233RbDhw+PzZs3lznftm3b4owzzoiXX365zNd+acOGDTFo0KC48MIL4/PPPy93nq9at25dXHjhhXHOOefEtm3bUpIzFV544YVSY5IZEFWSu+66K4YNGxZr1qypUJ7Kksz9vfbaa7Fly5Y0VAMAAAAAAAAAQHEMwAIAAAAAAAAAAKDIgw8+GNdff32Fcjz99NNx++23R0TE/fffH9dee22F8u3cuTN+8pOfxMaNG8t87bJly6J3797xj3/8o0I1FOexxx6LgQMHlmu4V6rNnTs3VqxYUWrciSeeWO4z3nzzzbjiiisSBpxlmpNOOqnUmIKCgpg2bVrlFwMAAAAAAAAAQLEMwAIAAAAAAAAAAKDIRRddlJI8N954Y7zwwgtx6aWXpiTfsmXLYvTo0WW6Zt26dTFw4MB4//33U1JDcd544404++yzY/fu3ZV6TmlmzJiRVNyxxx5b7jMuuuiijB5+FRHRuXPnyMnJKTUu2fcLAAAAAAAAAIDKYQAWAAAAAAAAAAAARbZv356SPFu3bo3BgwfHjh07UpIvImLs2LFJ5yssLIwRI0ZU+vCrL73wwgtxyy23pOWs4rz11lulxrRq1SqaN29ervyzZ8+OuXPnluvadMrOzo4jjzyy1Lhk3i8AAAAAAAAAACpP7aouAAAAAAAAAAAAgMyUm5sbZ599dhx77LHRoEGDmD9/fowfPz42bNiQ1PV79uzZZ69Hjx5x+umnR9u2bWPDhg3x1FNPxRtvvJFUvvXr18dTTz0V5557bqmx48ePj+eeey6pvBERtWrVioEDB0afPn2iVatWUVBQEAsXLoxJkybF8uXLk8px0003xdlnnx1du3ZN+txUmjVrVqkxRx99dLnzP/vss0nH9uzZM/r06RPt27eP3Nzc2LVrV+Tn50d+fn4sXrw45s+fH4sWLYqdO3eWu56SHHPMMaUOuHr77bcr5WwAAAAAAAAAAJJjABYAAAAAAAAAAAD76N+/fzzyyCPRsmXLhP2rrroqevbsGStWrChTvvr168df//rX+NGPfpSwf/XVV8fvfve7uPnmm5PK8/rrr5c6AGvHjh3xm9/8JunaBgwYEH/9618jLy9vn9f++Mc/xujRo2PMmDGl5tm1a1dcd911MWnSpKTPTqXFixeXGtO+ffty51+yZEmpMS1atIjJkyfHCSecUGpsQUFBTJ8+PaZMmRJTpkyJmTNnRmFhYbnr+6pk7nPdunWxfv36aNq0aUrOBAAAAAAAAACgbLKrugAAAAAAAAAAAAAyy5FHHhmTJ0/eZ/hVRES7du3i17/+dZlz3nffffsMv/rSjTfeGIcddlhSed5+++1SYx5++OFYuXJlUvnOPPPMePnll/c7/Coiol69enHLLbfE6NGjk8o3efLk+Oijj5KKTaV169bFpk2bSo1r27Ztuc/4/PPPS40ZNWpUUsOvIiLq1q0bffv2jZtuuimmT58en3zySfzhD3+Io48+utw1finZ+/zkk08qfBYAAAAAAAAAAOVjABYAAAAAAAAAAAAJ7r777mjUqFGxr5966qllytevX78YPnx4sa9nZ2fHueeem1Su+fPnlxozbty4pHK1bt06xo8fH9nZpT9K99vf/jaOPfbYUuP27NkT48ePT+r8VEp2kFNFBmAVFhaWGtOxY8dy52/Xrl3813/9V8yZM6fcOb5kABYAAAAAAAAAQOYzAAsAAAAAAAAAAIAi3bp1i/79+5cY06VLl6hbt27SOa+88spSY3r06JFUrk2bNsXu3buLfX3dunXx+uuvJ5Xr6quvjtzc3KRis7Ky4tJLL00qdvLkyUnFpdK6deuSijvwwAPLfUbz5s1LjZk4cWK586dSsve5du3aSq4EAAAAAAAAAIDiGIAFAAAAAAAAAABAkTPOOKPUmOzs7DjooIOSytegQYM45ZRTSo075JBDksoXEfHFF18U+9qMGTNiz549SeX54Q9/mPSZERF9+/ZNKm7OnDmxefPmMuWuqC1btiQV16BBg3Kf0aVLl1Jjxo0bF6eddlo8/vjjsWrVqnKfVVE5OTlJxW3durWSKwEAAAAAAAAAoDgGYAEAAAAAAAAAAFDkm9/8ZlJxzZs3TyruqKOOSmroUqNGjZLKFxElDpeaPXt2Ujk6duwYrVq1SvrMiIi2bdsmFbdnz56YO3dumXJXVLIDsJIdDLU/yQwyi4h48cUX4+yzz47WrVtH06ZN4/jjj48f/ehHMXr06HjkkUdizpw5UVBQUO46kpHsfSb7vgEAAAAAAAAAkHq1q7oAAAAAAAAAAAAAMsfhhx+eVFy9evWSiuvSpUtScXXq1EkqLuI/A6aKs3Tp0qRyLFmyJLKyspI+s6yWLl0avXr1qrT85VVYWFjua48//vg49thj47333kv6mg0bNsRbb70Vb731VsJ+3bp1o3v37tG3b98YNGhQnHjiiZGdnbrv9Ez2PivyfgAAAAAAAAAAUDGpe1oEAAAAAAAAAACAaq9Zs2ZJxSU7ACvZfKmyevXqtJ5XnM8//zyt5+Xk5CQVt23btnKfkZWVFbfffntKBocVFBTEjBkz4rbbbou+fftG+/btY8yYMbF58+YK546I2Lp1a1Jxyb5vAAAAAAAAAACkngFYAAAAAAAAAAAAFGnUqFFScdnZyT1+lmy+VKnIgKdUSnYAU6o0bNgwqbiKvj8DBw6Mu+++u0I59mfZsmVx/fXXR5cuXeK1116rcL5k7zPZ9w0AAAAAAAAAgNQzAAsAAAAAAAAAAIAitWvXzuh81cXOnTvTel7Tpk2TisvPz6/wWZdcckk8+eST0bZt2wrn2tuKFSti0KBB8dZbb1UoT7L3mez7BgAAAAAAAABA6hmABQAAAAAAAAAAQI2Rk5NT1SVERERhYWFaz2vfvn1SccuWLUvJeUOHDo0PP/ww7r333jjhhBMiKysrJXkjIjZv3hznnXde7Nq1q9w5kr3PZN83AAAAAAAAAABSzwAsAAAAAAAAAAAAaozmzZtXdQlV4qCDDkpq+Nfy5ctTdmb9+vXjZz/7WcyYMSOWLVsW48ePj4svvjh69eoVTZo0qVDuxYsXx0MPPVTu65O9zw4dOpT7DAAAAAAAAAAAKqZ2VRcAAAAAAAAAAAAAqZLsQKOTTz45pkyZUrnFpFnnzp3jvffeKzFm6dKllXJ2mzZtYvjw4TF8+PCivfXr18dHH30UH3/8cSxatChmz54d06ZNiw0bNiSVc/LkyXH++eeXq55PPvmk1JjGjRt/bQemAQAAAAAAAABkAgOwAAAAAAAAAAAAqDGOOeaYpOLeeeed2LVrV9SuXXMeo+vRo0epA7DmzZuXpmoimjZtGk2bNo1vfvObRXs7d+6MP//5z/GrX/0qCgsLS7z+X//6V7nPnjNnTqkxPXr0KHd+AAAAAAAAAAAqLruqCwAAAAAAAAAAAIBU6dWrV2RlZZUat2HDhnjmmWfSUFH6fOMb3yg1ZvXq1bF69eo0VLN/derUicsvvzzOOuusUmPXrVsXBQUFZT5jz549sWDBglLjknm/AAAAAAAAAACoPAZgAQAAAAAAAAAAUGO0aNEievbsmVTsddddF9u3b0/Z2dOmTYvTTz89ZfnKKtn7fu+998qVf8uWLbF79+5yXbu3Tp06JRW3bt26Muf+4IMPYtu2baXGJft+AQAAAAAAAABQOQzAAgAAAAAAAAAAoEb58Y9/nFTc+++/H8OGDYsdO3aU+6yNGzfGgw8+GN27d4/+/fvHM888U+5cFdWtW7do2bJlqXGvvfZaufJPnz492rdvHzfccEMsW7asXDm+9MYbbyQV16hRozLnTub+ateuHQMGDChzbgAAAAAAAAAAUscALAAAAAAAAAAAAGqUH//4x9G8efOkYp9++uk47rjj4tlnn43du3cndc3SpUtj7NixMWTIkDjooIPi/PPPj9mzZ1ek5JTIysqKb3/726XG/fOf/yz3GcuXL4/Ro0dH+/bto1evXnHjjTfGv//979i1a1dS12/ZsiUuu+yyeP3110uNbdiwYeTm5pa5xmTur0+fPnHAAQeUOTcAAAAAAAAAAKlTu6oLAAAAAAAAAAAAgFTKycmJm2++OS666KKk4hcuXBjf/e53o1WrVvGtb30rjj766GjWrFnk5OTExo0bY8OGDbFu3bpYsGBBvPPOO7F+/fpKvoPyGzx4cIwfP77EmJkzZ8b27dujfv365T5nz549MWPGjJgxY0aMGjUq6tatG126dIkjjjgi8vLy4oADDogDDjggGjVqFDt27IjPP/885s6dGy+99FLk5+cndcbRRx9drtqSGYA1aNCgcuUGAAAAAAAAACB1DMACAAAAAAAAAACgxrnwwgvjsccei1deeSXpa1atWhWPPvpoPProo5VYWeUaMmRI5ObmxqZNm4qN2bFjR0yZMiWGDBmSsnMLCgpi7ty5MXfu3JTlPOecc8p8zezZs2P58uUlxmRlZcWwYcPKWxYAAAAAAAAAACmSXdUFAAAAAAAAAAAAQKplZ2fHww8/HJ06darqUtIqJycnzjrrrFLjnnrqqTRUU35NmzaN8847r8zXJXNf/fr1i0MOOaQ8ZQEAAAAAAAAAkEIGYAEAAAAAAAAAAFAjtWjRIqZOnRqdO3eu6lLSasSIEaXGPPPMM7Fnz540VFM+d911V7Ro0aLM102aNKnUmOHDh5enJAAAAAAAAAAAUswALAAAAAAAAAAAAGqsQw45JGbOnBmDBg2q6lLSpl+/fnHEEUeUGLN69ep45ZVX0lRR8rKysuLWW2+N8847r8zXLliwIObOnVtiTLNmzWLYsGHlLQ8AAAAAAAAAgBQyAAsAAAAAAAAAAIAarUmTJvHcc8/F+PHjo127dpVyRtu2bePqq6+O9957r1Lyl9UVV1xRaszf/va3MuXMycmJ2rVrl7ekUrVr1y4ef/zxuPbaa8t1/dixY0uN+fnPfx4NGjQoV34AAAAAAAAAAFKr8p5EAQAAAAAAAAAAqObOPPPM6NChQ1Kx3bp1S9m5/fr1i8LCwpTl4z+GDx8e5557bjz88MPxwAMPxBtvvFHu9zk7Ozu6d+8eAwYMiNNOOy369u0b2dmZ852Uw4cPj+uvvz7WrFlTbMxTTz0V+fn50bhx46Ry9u7dO9avXx+vvvpqvPzyyzFt2rRYuHBh7N69u0K1duvWLUaMGBEXX3xx1KtXr1w5du3aFRMmTCgxpk6dOnHJJZeUKz8AAAAAAAAAAKmXVegpKQAAAAAAAAAAAL6GVq9eHVOmTIlZs2bFvHnz4rPPPotVq1bF1q1bY+fOnZGTkxO5ubmRm5sbrVq1ii5dusThhx8eXbt2jV69eiU9OKqq3HLLLfHb3/62xJg777wzfvnLX5b7jK1bt8a7774bs2fPjg8//DCWLl0an3zySaxZsya2bNkSW7Zsid27d0fDhg2jYcOGccABB0ReXl4cfvjhceSRR8bAgQPjkEMOKff5X3ryySfjrLPOKjFm5MiRcf/991f4LAAAAAAAAAAAUsMALAAAAAAAAAAAAKiBNm/eHB07doy1a9cWG5OXlxeLFy+O7OzsNFaWeieeeGK88cYbxb5et27dWLRoUbRv3z6NVQEAAAAAAAAAUJLq/cQKAAAAAAAAAAAAsF+NGjWKX//61yXGfPzxx/HUU0+lqaLK8dZbb5U4/Coi4qc//anhVwAAAAAAAAAAGSarsLCwsKqLAAAAAAAAAAAAAFJv27Ztceihh8aKFSuKjendu3epA6Qy2bBhw2LixInFvp6TkxOLFy+ONm3apLEqAAAAAAAAAABKk13VBQAAAAAAAAAAAACVo0GDBnHbbbeVGPPmm2/GlClT0lRRai1YsCAee+yxEmN+85vfGH4FAAAAAAAAAJCBsgoLCwuruggAAAAAAAAAAACgchQWFkafPn1i+vTpxcb07NmzxNcz1TnnnFPiAKwOHTrEwoULo379+mmsCgAAAAAAAACAZGRXdQEAAAAAAAAAAABA5cnKyoq77rorsrKyio2ZMWNGPP/882msquLmzZsXjz/+eIkxd9xxh+FXAAAAAAAAAAAZKquwsLCwqosAAAAAAAAAAAAAAAAAAAAAAADg6yO7qgsAAAAAAAAAAAAAAAAAAAAAAADg68UALAAAAAAAAAAAAAAAAAAAAAAAANLKACwAAAAAAAAAAAAAAAAAAAAAAADSygAsAAAAAAAAAAAAAAAAAAAAAAAA0soALAAAAAAAAAAAAAAAAAAAAAAAANLKACwAAAAAAAAAAAAAAAAAAAAAAADSygAsAAAAAAAAAAAAAAAAAAAAAAAA0soALAAAAAAAAAAAAAAAAAAAAAAAANLKACwAAAAAAAAAAAAAAAAAAAAAAADSygAsAAAAAAAAAAAAAAAAAAAAAAAA0soALAAAAAAAAAAAAAAAAAAAAAAAANLKACwAAAAAAAAAAAAAAAAAAAAAAADSygAsAAAAAAAAAAAAAAAAAAAAAAAA0soALAAAAAAAAAAAAAAAAAAAAAAAANLKACwAAAAAAAAAAAAAAAAAAAAAAADSygAsAAAAAAAAAAAAAAAAAAAAAAAA0soALAAAAAAAAAAAAAAAAAAAAAAAANLKACwAAAAAAAAAAAAAAAAAAAAAAADSygAsAAAAAAAAAAAAAAAAAAAAAAAA0soALAAAAAAAAAAAAAAAAAAAAAAAANLKACwAAAAAAAAAAAAAAAAAAAAAAADSygAsAAAAAAAAAAAAAAAAAAAAAAAA0soALAAAAAAAAAAAAAAAAAAAAAAAANLKACwAAAAAAAAAAAAAAAAAAAAAAADSygAsAAAAAAAAAAAAAAAAAAAAAAAA0soALAAAAAAAAAAAAAAAAAAAAAAAANLKACwAAAAAAAAAAAAAAAAAAAAAAADSygAsAAAAAAAAAAAAAAAAAAAAAAAA0soALAAAAAAAAAAAAAAAAAAAAAAAANLKACwAAAAAAAAAAAAAAAAAAAAAAADSygAsAAAAAAAAAAAAAAAAAAAAAAAA0soALAAAAAAAAAAAAAAAAAAAAAAAANLKACwAAAAAAAAA/r927lgAAAAAYJC/9Sx2FUgAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAACsBFgAAAAAAAAAAAAAAAAAAAAAAAKsAoKLE7Z/E7pUAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "\n", "Image(filename = 'intro_output.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is the output of the LPUs with synaptic connections from neurons in LPU 0 to LPU 1 rendered to video:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/jpeg": "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\n", "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import IPython\n", "IPython.display.YouTubeVideo('U2FGNbQ5ibg')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To experiment with running LPUs unconnected please see neurodriver/examples/intro/intro_demo.py. The below video shows the output of two unconnected LPUs. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/jpeg": "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\n", "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import IPython\n", "IPython.display.YouTubeVideo('FY810D-hhD8')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If one compares the two videos, one will observe that the output of LPU 0 in both videos remains the same while that of LPU 1 exhibits changes when connected to LPU 0. This confirms that the one-way connectivity pattern defined earlier is transmitting data from one LPU to the other during model execution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### References" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Chiang, A.-S., Lin, C.-Y., Chuang, C.-C., Chang, H.-M., Hsieh, C.-H., Yeh, C.-W., et al. (2011), Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution, Current Biology, 21, 1, 1–11, doi:10.1016/j.cub.2010.11.056" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }