{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7ec8afb5",
   "metadata": {},
   "source": [
    "# Real Time Predistortions\n",
    "This tutorial demonstrates the use of real time predistortions feature available on a Qblox QCM.\n",
    "Please familiarize yourself with the feature by going through our user guide on [Real Time Predistortions](https://docs.qblox.com/en/main/cluster/real_time_predistortions.html).\n",
    "\n",
    "## Setup\n",
    "First, we are going to import the required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2a3ecd66",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:42.190585Z",
     "iopub.status.busy": "2024-11-13T02:44:42.190425Z",
     "iopub.status.idle": "2024-11-13T02:44:42.882520Z",
     "shell.execute_reply": "2024-11-13T02:44:42.881733Z"
    }
   },
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "from typing import TYPE_CHECKING, Callable\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from qcodes.instrument import find_or_create_instrument\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "from qblox_instruments import Cluster, ClusterType\n",
    "\n",
    "if TYPE_CHECKING:\n",
    "    from qblox_instruments.qcodes_drivers.module import Module"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4daddbf5",
   "metadata": {},
   "source": [
    "### Scan For Clusters\n",
    "\n",
    "We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://docs.qblox.com/en/main/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a611f06d",
   "metadata": {},
   "source": [
    "`!qblox-pnp list`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e97d899b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:42.885253Z",
     "iopub.status.busy": "2024-11-13T02:44:42.884909Z",
     "iopub.status.idle": "2024-11-13T02:44:42.888342Z",
     "shell.execute_reply": "2024-11-13T02:44:42.887682Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad5f3a0",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e3d1ee8a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:42.890217Z",
     "iopub.status.busy": "2024-11-13T02:44:42.890057Z",
     "iopub.status.idle": "2024-11-13T02:44:43.658340Z",
     "shell.execute_reply": "2024-11-13T02:44:43.657553Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster = find_or_create_instrument(\n",
    "    Cluster,\n",
    "    recreate=True,\n",
    "    name=cluster_name,\n",
    "    identifier=cluster_ip,\n",
    "    dummy_cfg=(\n",
    "        {\n",
    "            2: ClusterType.CLUSTER_QCM,\n",
    "            4: ClusterType.CLUSTER_QRM,\n",
    "            6: ClusterType.CLUSTER_QCM_RF,\n",
    "            8: ClusterType.CLUSTER_QRM_RF,\n",
    "        }\n",
    "        if cluster_ip is None\n",
    "        else None\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae27b92c",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7eb6aa44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:43.660649Z",
     "iopub.status.busy": "2024-11-13T02:44:43.660453Z",
     "iopub.status.idle": "2024-11-13T02:44:43.664277Z",
     "shell.execute_reply": "2024-11-13T02:44:43.663526Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_connected_modules(cluster: Cluster, filter_fn: Callable | None = None) -> dict[int, Module]:\n",
    "    def checked_filter_fn(mod: ClusterType) -> bool:\n",
    "        if filter_fn is not None:\n",
    "            return filter_fn(mod)\n",
    "        return True\n",
    "\n",
    "    return {\n",
    "        mod.slot_idx: mod for mod in cluster.modules if mod.present() and checked_filter_fn(mod)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b85cef46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:43.666124Z",
     "iopub.status.busy": "2024-11-13T02:44:43.665965Z",
     "iopub.status.idle": "2024-11-13T02:44:43.691539Z",
     "shell.execute_reply": "2024-11-13T02:44:43.691017Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: <Module: cluster0_module2 of Cluster: cluster0>}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# QCM baseband modules\n",
    "modules = get_connected_modules(cluster, lambda mod: mod.is_qcm_type and not mod.is_rf_type)\n",
    "modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "442b8a4a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:43.693329Z",
     "iopub.status.busy": "2024-11-13T02:44:43.693166Z",
     "iopub.status.idle": "2024-11-13T02:44:43.696135Z",
     "shell.execute_reply": "2024-11-13T02:44:43.695479Z"
    }
   },
   "outputs": [],
   "source": [
    "module = modules[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c50837d",
   "metadata": {},
   "source": [
    "### Reset the Cluster\n",
    "\n",
    "We reset the Cluster to enter a well-defined state. Note that resetting will clear all stored parameters, so resetting between experiments is usually not desirable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1154f4b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:43.697933Z",
     "iopub.status.busy": "2024-11-13T02:44:43.697774Z",
     "iopub.status.idle": "2024-11-13T02:44:46.145588Z",
     "shell.execute_reply": "2024-11-13T02:44:46.144958Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, Flags: NONE, Slot flags: NONE\n"
     ]
    }
   ],
   "source": [
    "cluster.reset()\n",
    "print(cluster.get_system_status())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "382a4da5",
   "metadata": {},
   "source": [
    "### Upload a sequence\n",
    "\n",
    "Note that we are using a sequence that plays a square pulse once and uses no waveform memory, only voltage offsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ad960cc3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.147725Z",
     "iopub.status.busy": "2024-11-13T02:44:46.147536Z",
     "iopub.status.idle": "2024-11-13T02:44:46.199292Z",
     "shell.execute_reply": "2024-11-13T02:44:46.196049Z"
    }
   },
   "outputs": [],
   "source": [
    "# Sequence program.\n",
    "seq_prog = f\"\"\"\n",
    "set_mrk       {0b1111}          #Set all marker outputs to 1.\n",
    "set_awg_offs  1638, 1638      #Set the offset of the AWG to about 0.3 of the max.\n",
    "upd_param     1000              #Update parameters and wait 1 us.\n",
    "set_mrk       {0b0000}          #Set all marker outputs to 0.\n",
    "set_awg_offs  0, 0              #Set the offset of the AWG to zero.\n",
    "upd_param     1000              #Update parameters and wait 1 us.\n",
    "stop                            #Stop the sequence.\n",
    "\"\"\"\n",
    "\n",
    "# Add sequence to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": {},\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": {},\n",
    "    \"program\": seq_prog,\n",
    "}\n",
    "module.sequencer0.sequence(sequence)\n",
    "module.disconnect_outputs()\n",
    "module.sequencer0.connect_sequencer(\"out0_1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac6ade83",
   "metadata": {},
   "source": [
    "Now let's start the sequence. If you want to observe the sequence, this is the time to connect an oscilloscope to marker output 1 and one or more\n",
    "of the four outputs. Configure the oscilloscope to trigger a *single* time on the marker output 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "17aa2a53",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.206804Z",
     "iopub.status.busy": "2024-11-13T02:44:46.206638Z",
     "iopub.status.idle": "2024-11-13T02:44:46.219847Z",
     "shell.execute_reply": "2024-11-13T02:44:46.219220Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start both sequencers.\n",
    "module.arm_sequencer(0)\n",
    "module.start_sequencer()\n",
    "\n",
    "# Print status of both sequencers.\n",
    "print(module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8864054b",
   "metadata": {},
   "source": [
    "The following is data acquired from a scope.\n",
    "\n",
    "<img src=\"./rtp_figs/before_rtp.svg\" alt=\"drawing\" width=\"50%\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d691b3bb",
   "metadata": {},
   "source": [
    "## Apply Predistortions\n",
    "Each output of a QCM module has the following filters:\n",
    "\n",
    "1. 4 x Exponential overshoot filter\n",
    "    This filter corrects for an exponential overshoot or undershoot.\n",
    "    It is characterized by two coefficients, the time constant and the relative amplitude of the overshoot/undershoot.\n",
    "\n",
    "2. 1 x FIR filter\n",
    "    This filter may be used to correct for arbitrary distortions.\n",
    "    It provides 32 configurable coefficients corresponding to 32 ns.\n",
    "\n",
    "Each of the above filters can be in one of three states in a QCM:\n",
    "\n",
    "1. `enabled`: The filter is enabled. Note that the playback is delayed in this case.\n",
    "\n",
    "2. `bypassed`: The filter is disabled.\n",
    "\n",
    "3. `delay_comp`: The filter is disabled but is delayed as if it were enabled.\n",
    "\n",
    "In a QRM, QCM-RF or QRM-RF there are no filters, and so the `enabled` mode doesn't exist."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30bd6623",
   "metadata": {},
   "source": [
    "### Exponential overshoot filter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4b7ab36",
   "metadata": {},
   "source": [
    "In the scope data shown previously, if we zoom in to the maximum value of the square pulse, we see that there is an exponential overshoot.\n",
    "The fit shows the same, characterized by a relative amplitude and a time constant which we can use to correct the overshoot.\n",
    "\n",
    "The fit function used is:\n",
    "$$ f(t) =\n",
    "\\begin{cases}\n",
    "A_p(1 + Ae^{-t/\\tau}) \\text{ if } t > 0\\\\ % # noqa: ERA001\n",
    "0 \\text{ if } t < 0\n",
    "\\end{cases}$$\n",
    "where,\n",
    "\n",
    "$A_p$ is the pulse amplitude\n",
    "\n",
    "$A$ is the amplitude of the exponential overshoot\n",
    "\n",
    "$\\tau$ is the time constant of the exponential overshoot\n",
    "\n",
    "<img src=\"./rtp_figs/before_rtp_zoom.svg\" alt=\"drawing\" width=\"50%\"/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "06b55756",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.222045Z",
     "iopub.status.busy": "2024-11-13T02:44:46.221871Z",
     "iopub.status.idle": "2024-11-13T02:44:46.274193Z",
     "shell.execute_reply": "2024-11-13T02:44:46.272854Z"
    }
   },
   "outputs": [],
   "source": [
    "module.out0_exp0_time_constant(2019)  # ~2 us time constant\n",
    "module.out0_exp0_amplitude(0.01044)  # relative amplitude of 0.01\n",
    "# Note that a negative amplitude corrects for an undershoot.\n",
    "module.out0_exp0_config(\"enabled\")  # enable the exponential overshoot correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "325fe378",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.278232Z",
     "iopub.status.busy": "2024-11-13T02:44:46.277852Z",
     "iopub.status.idle": "2024-11-13T02:44:46.296350Z",
     "shell.execute_reply": "2024-11-13T02:44:46.293661Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start both sequencers.\n",
    "module.arm_sequencer(0)\n",
    "module.start_sequencer()\n",
    "\n",
    "# Print status of both sequencers.\n",
    "print(module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5545ccc",
   "metadata": {},
   "source": [
    "The following is data acquired after correction with one exponential overshoot filter.\n",
    "\n",
    "<img src=\"./rtp_figs/after_one_exp.svg\" alt=\"drawing\" width=\"50%\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d7b878b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.304069Z",
     "iopub.status.busy": "2024-11-13T02:44:46.303298Z",
     "iopub.status.idle": "2024-11-13T02:44:46.368999Z",
     "shell.execute_reply": "2024-11-13T02:44:46.365809Z"
    }
   },
   "outputs": [],
   "source": [
    "module.out0_exp1_time_constant(313)  # 313 ns time constant\n",
    "module.out0_exp1_amplitude(0.00617)  # relative amplitude of 0.006\n",
    "# Note that a negative amplitude corrects for an undershoot\n",
    "module.out0_exp1_config(\"enabled\")  # enable the exponential overshoot correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b7e67225",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.377957Z",
     "iopub.status.busy": "2024-11-13T02:44:46.377065Z",
     "iopub.status.idle": "2024-11-13T02:44:46.399271Z",
     "shell.execute_reply": "2024-11-13T02:44:46.396410Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start both sequencers.\n",
    "module.arm_sequencer(0)\n",
    "module.start_sequencer()\n",
    "\n",
    "# Print status of both sequencers.\n",
    "print(module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d166a5ac",
   "metadata": {},
   "source": [
    "The following is data acquired after correction with two exponential overshoot filters.\n",
    "\n",
    "<img src=\"./rtp_figs/after_two_exp.svg\" alt=\"drawing\" width=\"50%\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bb6dba6",
   "metadata": {},
   "source": [
    "### FIR filter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "715467ac",
   "metadata": {},
   "source": [
    "We can zoom in even more to see that there are short timescale distortions.\n",
    "The prediction is a convolution of the data using a best fit of 32 FIR coefficients.\n",
    "\n",
    "<img src=\"./rtp_figs/after_two_exp_zoom.svg\" alt=\"drawing\" width=\"50%\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4fe0b590",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.409012Z",
     "iopub.status.busy": "2024-11-13T02:44:46.408230Z",
     "iopub.status.idle": "2024-11-13T02:44:46.462412Z",
     "shell.execute_reply": "2024-11-13T02:44:46.459201Z"
    }
   },
   "outputs": [],
   "source": [
    "module.out0_fir_coeffs(\n",
    "    [  # 32 FIR coefficients (corresponding to 32 samples or ns)\n",
    "        # may be used. Each with a range of [-2,2).\n",
    "        7.48915982e-01,\n",
    "        9.69579219e-02,\n",
    "        9.52478332e-02,\n",
    "        9.81260837e-02,\n",
    "        -3.27428212e-02,\n",
    "        6.12058070e-03,\n",
    "        -1.29196458e-02,\n",
    "        6.58390575e-03,\n",
    "        -3.33153314e-03,\n",
    "        -3.51480767e-03,\n",
    "        9.60311013e-04,\n",
    "        -2.87579262e-03,\n",
    "        6.52713771e-03,\n",
    "        -2.56164552e-03,\n",
    "        -3.57635616e-04,\n",
    "        6.04179726e-04,\n",
    "        -1.12007036e-03,\n",
    "        6.47927968e-04,\n",
    "        -7.30273082e-04,\n",
    "        1.47880116e-04,\n",
    "        8.68155082e-05,\n",
    "        -3.04216668e-04,\n",
    "        -2.92916380e-04,\n",
    "        4.34019812e-04,\n",
    "        -8.44964139e-04,\n",
    "        8.17907868e-04,\n",
    "        -8.49444212e-04,\n",
    "        7.95641409e-04,\n",
    "        6.26446369e-05,\n",
    "        -6.65652499e-04,\n",
    "        1.23404188e-03,\n",
    "        -1.01509375e-03,\n",
    "    ]\n",
    ")\n",
    "module.out0_fir_config(\"enabled\")  # enable the exponential overshoot correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "17d738dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.470403Z",
     "iopub.status.busy": "2024-11-13T02:44:46.469617Z",
     "iopub.status.idle": "2024-11-13T02:44:46.492883Z",
     "shell.execute_reply": "2024-11-13T02:44:46.490118Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start both sequencers.\n",
    "module.arm_sequencer(0)\n",
    "module.start_sequencer()\n",
    "\n",
    "# Print status of both sequencers.\n",
    "print(module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2cf87dd",
   "metadata": {},
   "source": [
    "Here is the scope acquisition after the FIR coefficients are applied to the device.\n",
    "\n",
    "<img src=\"./rtp_figs/after_fir.svg\" alt=\"drawing\" width=\"50%\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c968c05",
   "metadata": {},
   "source": [
    "## Filter latency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b7c795",
   "metadata": {},
   "source": [
    "Looking at output 2 on the scope we see that the distortion corrected pulse is delayed.\n",
    "\n",
    "<img src=\"./rtp_figs/nd_9.svg\" alt=\"drawing\" width=\"50%\"/>\n",
    "\n",
    "Note that the two pulses have been plotted with offsets to make them both visible.\n",
    "\n",
    "We can also get the delay caused by the filters using `module.out{x}_latency()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e328823",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.501496Z",
     "iopub.status.busy": "2024-11-13T02:44:46.500679Z",
     "iopub.status.idle": "2024-11-13T02:44:46.520762Z",
     "shell.execute_reply": "2024-11-13T02:44:46.518011Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4e-07 0.0\n"
     ]
    }
   ],
   "source": [
    "print(module.out0_latency(), module.out1_latency())  # Compare latencies of the two outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7204cd6c",
   "metadata": {},
   "source": [
    "### Delay compensation\n",
    "In order to have the two outputs be synchronized in time, we can enable delay compensation on output 2 for the 3 filters that we enabled.\n",
    "\n",
    "**Note**: Using delay compensation increases the output path latency, and so also increases the time of flight. This means you may haev to adjust your acquisition delay accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e68f9a14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.529112Z",
     "iopub.status.busy": "2024-11-13T02:44:46.528332Z",
     "iopub.status.idle": "2024-11-13T02:44:46.596057Z",
     "shell.execute_reply": "2024-11-13T02:44:46.592789Z"
    }
   },
   "outputs": [],
   "source": [
    "module.out1_exp0_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on output 2 for the exponential overshoot correction 0\n",
    "module.out1_exp1_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on output 2 for the exponential overshoot correction 1\n",
    "module.out1_fir_config(\"delay_comp\")  # enable delay compensation on output 2 for the FIR filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0fce2ff0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.603902Z",
     "iopub.status.busy": "2024-11-13T02:44:46.603734Z",
     "iopub.status.idle": "2024-11-13T02:44:46.616109Z",
     "shell.execute_reply": "2024-11-13T02:44:46.615465Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start both sequencers.\n",
    "module.arm_sequencer(0)\n",
    "module.start_sequencer()\n",
    "\n",
    "# Print status of both sequencers.\n",
    "print(module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ad51a81",
   "metadata": {},
   "source": [
    "After delay compensation, here are the two waveforms.\n",
    "\n",
    "<img src=\"./rtp_figs/nd_10.svg\" alt=\"drawing\" width=\"50%\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbbdd6ec",
   "metadata": {},
   "source": [
    "## Marker and non-QCM outputs\n",
    "\n",
    "All marker channels on the Cluster and outputs that do not belong to a QCM (i.e. QRM, QCM-RF and QRM-RF outputs), do not have predistortion filters. However, they might need to be synchronized with QCM outputs.\n",
    "\n",
    "To do this on a non-QCM output, you may use the same function as in a QCM output, i.e. `module.out{x}_exp0_config(\"delay_comp\")`.\n",
    "To do this on a marker output, you may use the `module.marker{x}_exp0_config(\"delay_comp\")`.\n",
    "\n",
    "For the above example, to ensure that pulses from output 1 of a QCM-RF in slot 6 and marker 3 of the QCM coincide with output from QCM, use the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e6aaacba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.617951Z",
     "iopub.status.busy": "2024-11-13T02:44:46.617784Z",
     "iopub.status.idle": "2024-11-13T02:44:46.735493Z",
     "shell.execute_reply": "2024-11-13T02:44:46.732275Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster.module4.out0_exp0_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on output 1 for the exponential overshoot correction 0\n",
    "cluster.module4.out0_exp1_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on output 1 for the exponential overshoot correction 1\n",
    "cluster.module4.out0_exp2_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on output 1 for the exponential overshoot correction 2\n",
    "cluster.module4.out0_fir_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on output 1 for the FIR filter\n",
    "\n",
    "module.marker2_exp0_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on marker 3 for the exponential overshoot correction 0\n",
    "module.marker2_exp1_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on marker 3 for the exponential overshoot correction 1\n",
    "module.marker2_exp2_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on marker 3 for the exponential overshoot correction 2\n",
    "module.marker2_fir_config(\"delay_comp\")  # enable delay compensation on marker 3 for the FIR filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b449175e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.744707Z",
     "iopub.status.busy": "2024-11-13T02:44:46.743849Z",
     "iopub.status.idle": "2024-11-13T02:44:46.769619Z",
     "shell.execute_reply": "2024-11-13T02:44:46.766467Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start both sequencers.\n",
    "module.arm_sequencer(0)\n",
    "module.start_sequencer()\n",
    "\n",
    "# Print status of both sequencers.\n",
    "print(module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "138c7c45",
   "metadata": {},
   "source": [
    "## Simulator Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80649e84",
   "metadata": {},
   "source": [
    "The filters mentioned above may be simulated using functions from `qblox_instruments.simulations.predistortions`. The functions listed here allow you to predict the behaviour of the filters given the coefficients that are applied to them, and can be used to determine the correct coefficients.\n",
    "\n",
    "This can be done by measuring the impulse response $h$ of the path to the DUT perhaps using an oscilloscope, or a technique that is specific to your system (like the Cryoscope). The impulse response can then be inverted to get $h_{inv}$.  The simulation functions can then be used to validate these filter coefficients and, together with an iterative optimization scheme, to find the values that best match the inverse impulse response.\n",
    "\n",
    "We demonstrate the utility of these functions through a straightforward example where we apply the simulator function to visualize the effects of a filter designed to correct for exponential overshoot. We follow these steps:\n",
    "\n",
    "1. Play a square waveform as done in the previous sections without applying any predistortions.\n",
    "2. Obtain the waveform samples from the oscilloscope (or any equivalent measurement device such as a Cluster QRM).\n",
    "3. Fit the waveform for an exponential overshoot/undershoot to the form $f(t) =A_p(1 + Ae^{-t/\\tau})$ and thereby obtain the filter coefficients $AA_p$ and $\\tau$.\n",
    "4. Pass these filter coefficients and the waveform samples obtain to the simulator function to see the corrected waveform as seen by the measurement device (oscilloscope/QRM)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f39c3f6",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Plotting the Obtained Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2340c73b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.779169Z",
     "iopub.status.busy": "2024-11-13T02:44:46.778316Z",
     "iopub.status.idle": "2024-11-13T02:44:46.796169Z",
     "shell.execute_reply": "2024-11-13T02:44:46.793164Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Define t(time samples) and waveform_captured\n",
    "# Here we use a simulated trace\n",
    "def func(x: np.ndarray, a_p: float, a: float, tau: float) -> np.ndarray:\n",
    "    return a_p * (1 + a * np.exp(-x / tau))\n",
    "\n",
    "\n",
    "t = np.arange(1000)\n",
    "noise = np.random.normal(0, 2e-4, size=(t.size,))\n",
    "waveform_captured = func(t, a_p=9.4e-2, a=-3.5e-2, tau=200) + noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "33a27ba6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.804180Z",
     "iopub.status.busy": "2024-11-13T02:44:46.803350Z",
     "iopub.status.idle": "2024-11-13T02:44:46.981633Z",
     "shell.execute_reply": "2024-11-13T02:44:46.981105Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(t, waveform_captured, \"ko\", label=\"Original Captured Waveform\")\n",
    "plt.xlabel(\"Time (in ns)\")\n",
    "plt.ylabel(\"Amplitude (in V)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca5be80",
   "metadata": {},
   "source": [
    "We can see that there is an exponential undershoot as captured by the oscilloscope. We will now try to fit these samples to the form $ f(t) =A_p(1 + Ae^{-t/\\tau})$ using the `scipy.optimize.curve_fit` module."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fec6fd4c",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Curve Fitting to an Exponential Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f1f10919",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.983664Z",
     "iopub.status.busy": "2024-11-13T02:44:46.983435Z",
     "iopub.status.idle": "2024-11-13T02:44:46.988599Z",
     "shell.execute_reply": "2024-11-13T02:44:46.987954Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a_p, a, tau are : [ 9.40101392e-02 -3.50471288e-02  2.02794862e+02]\n"
     ]
    }
   ],
   "source": [
    "# Fit curve using initial guess p0\n",
    "popt, pcov = curve_fit(func, t, waveform_captured, p0=(1, 0.4, 400))\n",
    "\n",
    "print(f\"a_p, a, tau are : {popt}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b5298f45",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:46.990492Z",
     "iopub.status.busy": "2024-11-13T02:44:46.990305Z",
     "iopub.status.idle": "2024-11-13T02:44:47.126031Z",
     "shell.execute_reply": "2024-11-13T02:44:47.125300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(t, waveform_captured, \"ko\", label=\"Original Data\")\n",
    "plt.plot(t, func(t, *popt), \"r-\", label=\"Fitted Curve\")\n",
    "plt.xlabel(\"Time (in ns)\")\n",
    "plt.ylabel(\"Amplitude (in V)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0956ae4c",
   "metadata": {},
   "source": [
    "The `a` and `tau` values correspond to the $A$ and $\\tau$ filter coefficients for the exponential overshoot filter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb15785",
   "metadata": {},
   "source": [
    "### Simulate the Corrections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5638a530",
   "metadata": {},
   "source": [
    "We now import the utility functions from the qblox-instruments driver and pass these filter coefficients to validate the distortion corrections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c7371a15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:47.128133Z",
     "iopub.status.busy": "2024-11-13T02:44:47.127924Z",
     "iopub.status.idle": "2024-11-13T02:44:47.291680Z",
     "shell.execute_reply": "2024-11-13T02:44:47.290974Z"
    }
   },
   "outputs": [],
   "source": [
    "from qblox_instruments.simulations.predistortions import exponential_overshoot_correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7c015e5a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:47.294017Z",
     "iopub.status.busy": "2024-11-13T02:44:47.293749Z",
     "iopub.status.idle": "2024-11-13T02:44:47.297658Z",
     "shell.execute_reply": "2024-11-13T02:44:47.297008Z"
    }
   },
   "outputs": [],
   "source": [
    "amp = popt[1]\n",
    "tau = popt[2]\n",
    "\n",
    "# Pass the captured waveform and filter coefficients to the simulator functions\n",
    "corrected_waveform = exponential_overshoot_correction(sig=waveform_captured, amp=amp, tau=tau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0f64d801",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:44:47.299391Z",
     "iopub.status.busy": "2024-11-13T02:44:47.299228Z",
     "iopub.status.idle": "2024-11-13T02:44:47.410425Z",
     "shell.execute_reply": "2024-11-13T02:44:47.409804Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8bjtV9EGn3xjpjd3J22g6v+ih4KWjd/TRWvr6k04Sf//669r/990H9Ounha6u5bKMuRV68bjWqdb3SXMQJ16EnpPCuiJyfW63ZKFnFf6ai9/ipmB6YyVKoUJPbw+9Vt97DzjhBC2CoWNH4KKLtLGHs2a5246Xc9gqC2iOE/v4SAc71ZyTsdBzkraTimKr8zZXjh5ZjS8J4Npr2bA+u5eNDz7YqHUSz++7BI+PACYc62DmeBxxPzTx2tRj9ARCL9lgHM9ozInQq8r8HeeFXmWlNrSjutqdIMrB/S7aYDyTgm6lx+TJiFAtSu4nvNDbEN2M0ycBF+4F1Hpx5885R/vfaQ3GFoASeormQ22tlo5cD0FsrjRFeMdzz2mFs887T7vJffWV3Oly+zAXha7QTqhboScSC146GHrq+meesZ6PrpuGNgHuhd7RRwPbbKOdh8CWEydXXgmcf774Oy8OabYD53NFSxZ62eLW0aP7auVK4KCDgM8+0z5T0ePg/lOVB/eOnhOR79Up9oJVMhYv95NNm+SrSxChl3Bxf6+pAT78ECkAi9qDc7pc1j9zgiwsjd4jqNDjr7lGCN1MVlUCl1+u/dOxEnrr12sv7445ptHGPi8tcD5GOdZQhwFnAOOOA/D551o4Y2ORSpmuS2aMnuAeGSEOXTRqH13T0GBc0/E4t38POwyYOVMr0SF7aSAiB30eRuh5sHaj1CQm+ynBCb3nkgtxzxjgP+OAdxoWut5Os0lklkOU0FM0Hy6/XEtHPmZMU7fEGtoh2lJjuGiH7IkngJ120hKviHD7MBelFndbu8fOAWzM1Pp03XzYkluhpxcbvvZa7f8tIU4aGrRQtFtu0cba8NCOwZNPGp+ffx7o1k1c+5A6el72fWkp8N133p1Bna1Z6NlhFbp57LHaWNtd02kKqdCzOSbX7AqUXQK8MNij0GvsMV5OsQonc9uORx4B2rYFbrpJ+DUNcXPl6E2ZAuy5J67cDRh6GnDBXuS7ZBLv9gVuGgekoun70nPPaaGvv/zirv06MqFHry0qxvl7dy6OXzzOOHpJUTihldCrNoSLp1D6K680kmRJcf47F1b9jj/bAF91B1IHHaQlKFm1yl27nJBKaXUiR49mjpedoxepN67RWMw+LLeeCL1Ygnv+vfOO9v+LLwL19VjaBvi9LWzvzcuKYjjwEGBeN9vNS6FCL2DXbYpGtZeuJLFcjJ5SpL0x7lBXJY3zsSbuXrQlfcB9o4HPerhetNmihJ6i+fD++9r/bmu51dZqY4o+/jj3bRJhl2GysXniCe3/b74Rf+92jB6fWnzxYmDZMmOakzEfbkM9vXD44dq4Kd6poJ+zFXr8fFtCyNM2is4n+vuOOsp4WB96qNYhOfBA8zLZOnpVVcCoUcC4cdmtZ2sWem6zbtLzgB+TR8c82Qi9/0u//zl5istkLM1N6OUydFNPJnHRRcKvE6QAuOW4Jp5PPgEAXDNe+3jrWPJdMomJRwIX7QW8U7xGu6ccdpgW+nr99eZ1ff65lgHRSgTSc0T2wpGOqbZ6Sec1OVI8zjh6CZEDapX5UzRUwCmzZ2svxfbYw3I2N2dHMmbs04wj2xhhe8mklvjku+/MkSc6IkePCKSGhL2jR5MJxS3c6WhtFfqdBQw4E2iIWN9TjpiWwMtDgLEn2G5eO+f23BO49FJ2ezSRTMrmufrQQ1o/Z+ZMAEBdCPipI12ZccziXDKWKBG3iaR7J/I/Y4FTpwB7HuV60WaLEnqK5kNxsbflrr9eyxIoc7hyTVN0XmnHpqjI+bxOoL9h7Vpg4EBgu+2MaU6SsTRG6CalpgZ49lmtCPXKlex3jSn0tsTxpftL1EHiO2S0qDQgDqfzIvREy3jJPEY7nVuz0LPDytHjz2O6H7N1WSl0O06c+1wIPacCI9ehmxbQDqEp3M0r5DpYGq7VOvk6r72m1cHTX24CwC67aPPsv798nQ4cvVR9HR4ZqRUoN93/6H7z+hKLd/SSLq/rbJKf0BeQOSJFRH5c7xE3RikYyf2PCd0UHBMailmdcuDoRajQk1/Tm6vXZ/6uqt9suc5f29lu1uD114EPPzS9zKBCL2F3znBj7fc/jP06Rfokce5WEEkSEehB6F2cduWj7itANFuU0FM0H7wKPZqJbEvQFI4efUAXFjqf1wn0N4iKPDuJWafraAxHj3YOqqrk39kJPafHqzGFXiqlZS389lvts93+4jvG/PEVdZzdhG7q40ayeYst60DyQm/VKi08266YeGsgmzp6/HlM7zkOhZ4jOSV6QeNVRKVSWjkWuyLFyaTmzIwZYz0e2yp0M8cdceriCR0qL5DrIJqMsWHZlZWaaNl7b/NyVvvEgdB7ObQUx++vFSi3FO9eEy8lEsxYxCTZXwkfUB+EtYjMRuhZiX+CV0evUYWe5LfS61QUNhwhwq06YP88oo5ezELoRUlIqN+q3h7IfnGCpL8QoUIv5e65+kFf9jNNUMO3LZKlo9caUUJPseWwu6nbOVUyrMJEGgP6gGyKxCyN6eiJHv51dZqr9/LL8rp1bsboeQkZom3cZhu2ViDtVOTK0dP3Q2MIvWee0bIWbr+99pm2UdRBsnvzLvqeunN2+3u//bQwTacuYCym1VqbO1e8DSuhN3Omluhm9Ghn28oV776rhXy5OfdE8/LTHHY8heTS0fv6a2DJEvF2/vhDy0hLxrsI25Ct0HvxRa3kSU+bnPb6efDNN8BZZ8nn24JZN6m4y5mjR+4d0VTMWWQEoM3Xo4e4HqoDofd9cL14foDdb16fXfE4Pifjl6hIHnUyUHIpUPXyM9pwCtG5Knt+/vabdl94+WX5th1fb87PDyr0YnpXorEdPcl9KCl4ycAIPcvCeBr1JDNnPMk+z7/rDEw5AvipA5vkJWEz9s+V0JP8Nuro2brAqRR+6gBszhd/nSClFuJ+dpvRFLmW3Qq9Vhp1ooSeYsvwyCOaY0dDVXioo+emQ2Yl9N5+2z5ro1ua2tGjQk+0n55+2lmRcwD43//YZB4yoXfZZdpYsMmTtYQgfAiNmzF6XvYZ3yk56CDx+lpC6OZ777Gfs637I1rGqdBLpYA33wS+/17754T77gNuvBHYbTf79vBC77vvtL+3dGaziRO1JA5WnUgrbr1VGx85fDi7b63KHtiFyVkJPauMnHzn+c8/gR13BPr3ZyanfNCy6+28s1ZjcuZMc/F3t0mXrM7PDz+Ufycbl0UTc/BswRd4VKxYhbu5gnH04s6FHqC5evQel1lRFLOHAbseC6xObBZuK9hgHY77cS/gz3Lxd054t2QtjplONk3OoQWdgIQf+PzN+7ThFKLSODKhd8IJwPz54jHHOnZCb8MGIBKxlnm1tcApp2TO1yRpT0bQuHmR8NZbQN++2hhLK6ShmwbJuPmYUCfMmdAjIog7l8cdB7w5ANjjKKCOlmGIWT+z3Qq9hM8cURCNGvd8u9DN+anVGHYq0EfyHoi6kbEAmPMoQsSta6HXFC/utwBK6Cm2DMcfr3Vop0+Xz0OFnpuOoFWHYNIk4Igjchvb39TJWGjopqzzMGyY/Xo2bwZ22EHLHGe1vvp6o1Dxl19qHd7jjjO+/+UXdkyfnaPn5WZqtZ8bw9ETFY7PFXxoG23jeeeZawHahW6K2kjFgNVv8HIui14iOA3dtHJptgQy10sE/U3nnae94PjpJ6PsB+Bc6InOu1gM1+8M9DobWFUC6xckVqGb/JhNyqOPsiG5hxzCnn9uHb1cXA/0/LByTp5+Wp6YJOeOnsdkLFZQoQeXQk9GNIqZBwKf9QT+s+JZYPBgTRyR4xKojzLzU76JLcfuxwB9zobnTu2ccvb+JNpfmayKehZjikzoWZS/yEDvH/y5uH69loV46FCmXIaJf/8buP9+LWEIgHjceGa4EjQ6kydrrrnsxVdm5XEsKwd+q2An0zF6on1JE6VUh8zlGUzzbzYc3Rjn6DWkdfK6YjY7ZzyHjl5togF9zwIO595TUKGXtAndfMP3OwBgU4H4+whtux/GOTV3LiKrjfub7VhAHiX0FIocYDXugnaaZCGCImSdBfogEKWt90q2DoyMDRvk41tox4bup8pK79sTdRBFnZG6OnPH6uOPDWfjWK4qrZ2j5+VNstUNmH7Ht99qrJMTvBzfWAxYulT+vZXQe+kl4Mgj2e/tQjftMnVa/Qa7jJ9Ot0dpzkIvHteumWyumw0bjL/9fi3kzK6+nD4+8emnjX0Sj+PSPYHl5cDlu8P5eCpe6DlwChhoVuNcCj2rDihdJ12H1bbWrdMKtdutLwckSJsaxdFLJXLiYmfKNAAIVtUCv/4KnHsus0+D9fKsm19Fl0q/c0oc7P1I5EL5rbSITOhZ9Q10rArD//mndv9fuhS+zeT65s/ZxYvZ5hChd90uwLn7kGXc3Cdsni2peBy9zwYGnQFURoz1MkJP5OgRgVQdhvi4bdwIXHIJ8NNPqP/LyFlglXWzvsFw0+Mx65cQbsrezamdj+XlwHPbgLkGIjHq6Fk/01I2I4xpyYmEH8a+3203pt6eW0cv5XXcajNHCT3FliUY1DKOHXOMucPi9u2ejszRi0rebN51F3Dnnc7Xz9NYQq9dO218yz//mL+jHRv6W/QHkZdxb6IUz6JSCnV1YjH96adaUpGvv2anN4ajZ7UM7WTnytHT8ZKZ7sADgX79tOxjIqyEHqAVyU6lDNHqxdGjWP2GbIWeaBlZ6GYyae2A2bFggeasubk38DQ0AF27atea16RB69YZf69fDwwapI2B4+HP+W22AWbMMO495Fz8pxDW7bESerIkD7LOGT0f3Aq9XLzxpueH17FQOR5DlSQdwqwTOBx5pBbGR45ZFImcOHp/p4zOeU9dKxQXI5VI4KBDgJOnAMF6cg/kzinG6fJ4LONgr/lkIoYVpcBfZcY0Ruhx959UNIqphwOHHcS1wcn4OyoGeaFHzivmDOZ/J/c5Stysu3YAbtsJ+HnzYi2xUHm55gDmADoOblWN4bKnyP09KTj3qECqzoP4PnHaacANNwDDhqHeYdbJughxxQ49RHue54CUJNlONEYdPRdCb/FidKhnr3dacgIAUjdcD5xxhvYd6Q66dfQSDgrSt0SU0FNsWQIBLX30448D//kP+x3tzLhx9JwIPV0ArFoFnHmmlgTAa5ryxhJ6OqKMhLTzRTsMutDz8iZKJPRkoZuijtVvv2lJRXhEDyJ688+1o0cf+F6F3t13a4WMebwcX13g3X67+Hur+mn6Nk88ESgo0N4+uxHxNKSWrk+GrC6XFXQf8hlQAWtHT9aZW79ePJ0yYoQ2Vm633ZzNL2LtWu26j8fF2Q3nzNHuTXbr4OFcAhOJhHFPe/tt7X+yb6pkHTgdq9BNifCWnjUyoffPP8ADD7COpdWyLqhPRrHPTOCOHbh1eHXm9OVuuUVrc5awyViyfKv/1FNaqYROnTKTor6kN6HHXZO/BoyXHBkxNWQI/qj5Cy8NAR4YDdZv469p2bF3QYLrpNcihh7nAj3PMaYF6MnHteGvmlV4faDm+NRffYXxDHPg6NXH6zH1cODB7WB20WW/xyq7LYBo3HxcGmL1Ru3FK66wbpTDBDEJmuRH0laho0cEUiQA8ctYMs6+nuzG2C8/a88SgZtcH+USmhxwgLzxLkilxPd/KqgTKetzj1nHwIFoV82eczR0EwDiN96gPcMBRMjvd+voxXmh57XWZDNDCT3FloXezFetYr+jN+Bly7SaaU6KdbsRegsXGtOsEgFYQR/YjSH0RJ0fO6HnxZ349VfzNKehm4BcGIjaQvdTrsfo0Qc+/0CzE3rz52uFWc84wyyw58/PzsFwmkyCP8cTCeDhh7W/b7nF3tGj3HyzeRq/7zZuBO64QxMsdP84DS2j143IXXMaunnmmdr/t90GdOigZeN0woIFWg0ynt9/t38rTd1yPpw7ldKyRh5zDLB8uXwd1NGzgh6nE0ilYf28IOdWZT6MY8Ef70MOAZ5/3vhs5eg5Eeuy9PZ1dcDJJ4vvCzpOQzdvuUVLUpHmwdS3eK8fcPa+sHX0qvKAD/qAqdVmwufTxluef77W5mRSG4/oNAkVByP0klkKPQGWQs9KPHMvNH4PkQQa+q4LhRAk9Q4aqF7iHT3ZtekCPnRzTdh8v2ccPa4NdExc8qWXgHPSCtGB0Hug6mO8PhA4aSqsHT26fTtHT5Bl1a7cAENpqfX36eMej4pfEFP3SpR1s4GEVUaCEPeJyHGtJ7ozXrlJKz5+772mRUxCj16XK1dq15OT/hfflJT4ZUKU/I6kbegm95mvlRdhRb6eLfWt/lqymczmXZZxiPFCr5Vk4VRCT7FlsbqZ0xvwzJla4pZLLrFfpyyMh96k9M4RHZfmpag0v95cDd51c0OhHQbdUfHy0Ba5pjKhJ9rHso6LSOhZdUZXr9YyOdJwR/5B4DR0k3/Layf0Ro8Gjj5avN5sSwA4KX4OmNvIOx5O3yoGApoLyKOfW/pvP/lk4OyztURF33xjzOdU6FHHZ9ddtfbTNltl3aRC7667tHPo3HO1z6ef7mz7gNiNHzAAGD/eEOx//qltkx5z2nFesUL7/quvtN/Au1synAo9uk9oqnyB0KvKg7Gv+N/2wgtaEiQdK0eP7G9p6KZA6Dnu1lpdh3S955+vJalIU52SvBwTvLTY60hgr6OAW8ZatMPnY8X4s89qbraTJFQCGqWOHiHqT8mvL6vnEOce04LZMf2WnEgwDlod6einuI66rxEcvajAnfFbOHr0fhf3w3g5Q/sGL74ofNlSlbB4qefU0eOetdG4Wcz43RjXpaXy6+fpp7V78qOPsolWSBuSdmP0iOMYCcA2YqaWCj39HOFfqgOoI9k5E7zQ22EH7XpyErYai7HClbQlRdpK93Piow+1bM8yuGeeqVZetE74/eQZ7Hx2ziGPKSmNxwiG5oYSeootC72Z8x1YkVh56CH7dTpx9PSH6YIF5mluyYWj99RT2ngjvY30IWUXzpSr0E3RA0PUGZGFbsrC5+yEHv9A7toVOPVULYHJ1KnaNF5EOhV6bh29bFi3zloQiM5L2h79ezs3ln/YyM6PQEA8Bi6Z1EIL27QBLr9cGyMLaKUOaCpzp0KP/uZVq4APPmDb6CYZi1dXHdBSpN92m3n6999rZRT69NFEPN2/VKStWAFcdx2w007a+UfPOasHvCh0U4Ts3qBfS599lplUSUM3ReGwFCtHz4mzv3Chce+Nx/HEtkDHC4Cvutkv6qQWGEO6rSnZi4D0vkj4gG+6ANEA8L90O54cbrNu+sKBlojxAC3gbOnozZunhYq6DOmydPSsEn7QjKngaoTpt+R4nBERVOiZQtESNo7eTz/ZJi7jw+GiMJ/nVo5eiuwHpgYaDYE8+GCgVy/TesO0UnuOxujFEtk5eq8OTKHz+cBHvWE+L2aklcdxxzHHIkVeJtA7jSjrZoQIpGiQ3X/GSoy1/GBEDBvniOC5UB/nio7Ta1Q/B0S1HJmV1GtlXcaPz0xKkXFxdFwiFaxJH9hs3xx0L9aEBUKPc/Qu2hM4QlCVI241Rq+yUiu9RfqBsaj8nGrJKKGnyB3vvgvsvruWaliGVTib6MHj5I0KXSe90YqEHn1LmAtHz+uN4MgjtfFGl1+ufbYTavR30Ru9/rcXoSdaRuSUyEI3afY+iltHT4SdYOPbJ/pbtFyubtwNDUDHjkD79uxvo8dJdK7TY6e/9LDrnItCN0UdzWBQ+45/qCcSwKWXauf7tdcCZWXmZfm2WcGL24KC3Ai9fEl1XBn332+4gZR43BhXM3s2u3/py4mVK4Hrr9f+fvhh5y9wnDp6snUEAprIfPbZzCQmdNNufLJVMXXynbSreuqpwL/+pf0dj+Po6cD6ImCGkyE6Vo6Q6JzUx0HS7wTlFf69KzDmJOC4/Y2vQvytn67D52PPQ74tn32mJahw+CKBuniWjt7YsZojblUzUEDUn5JfX1bHmxtHGk0Jar7F40ybaehmlOsQU0cvk2Hwp580Abt8ueaIdu1qeU/iw+FE4XFM2C13v0/UG88Y2oFfm5/AcfsDX3eVbhohp0JPFLr5999AdTXW+uuw/YnpcX4AogJx5UsmHYv56Tsuw9piYI+jYZl9N05CRFPkfE2QxgoLpifY6z0WMW/jxzZRDDgDeGAUK/QyBeAF2Y7rifDa4URgUbnD/gBl7lztvPnii8z+T9EMthHjGDGOno3yoGP0Si4F/mzDfh+Jsu367/bAMwIzP2EVunnEEdq/E0/UPp95JuIXnMfMkow0tApXTwk9Re6YOFFLu3/UUfJ5rEI3cyH0aOeKPqz0Bz69ETelo6ejJ6Sxc5roduj29X1mJxb++svcVtH+lhVMFzl6XoUe/Vs2BoB/iFs9cGSO3qZNZiGQK0ePdvZlQtPv1/b54YcbiVno79XPW7txEKIOh+g46evjkwMkEuw6ysvF23Hi6KVS2hg/fv1OQzd5EUrdK72O5saNZrfYjYOSSLAigO5fOqZwxQqgSxfjs+gFiginzqeV0ONehiX8MK4bu6yi/PXltN2Uq6/W/ifXg6MU6rJjK0PPMCs7PzZvBp58Etfton2cTVy8EL/7eCeQ3n/4tuy6qzYu6cor7dsINtOmowQOv/9uOwtjaqWF3sYC4KnhnENBHD3TWf7nn8xH6uhlOvGc0KNjtGimQwDwEacqk2Fwr72ACRO0TrtORYW0ADjfeY4KHg3PbgMcP1WcZCj24fuZv+l+OLXPIjw6EtjxROFmgVQKoRrj96Q4UdUQrcPOxwGnTubO5VhMu1936QK0b49L+y7Dt13T4/wARAWOXiIR85aIwyKREU3GQkM040ToiUI3G7hkMZF688uLI/bYjN8rgJP3A5Jkn24oSAs/gQCtS7DrPXia4LldW2vdB6OOc7o/lSLCOUbG5dGxkEmbe03Kpl/VwDtvEqRj9M45xxhD/Oyz2ku/u+5C/KsvmdkSgwZo5kULRwk9hXfuugs47DBz55kLN2GgQo93iUSdcCc3Wyr0RAlYAEPU0Rue17Cxxsi66VXo6ctZdbpefVUr23DEEex0py5gQ4NY6MlC2KJRrRN94olG8huZoyfr1LoRenRe/fhu3qy9eODJldCT1QWjYVipFPDGG9qDRE84QM8d/QHqxdETdej164B/mcK/nc5G6PGiEdDaT/eBm6yb9BosLtbmqajQkrPQ32h1/FNcEeEbb2SFuGz/btggF3o5qHlmGbopalMs3bl0K/QkWWfdCrc8J5fGt98afzu5ltJCzyer65h+MSjq+JkcvWgUa4s016I6lGKfM7J7mVViGQKTjMVJHb30/fCbLsCx+4tnob9JH6O3y7HAkQcAD21HZkzfM57dBqi4CJjbi3y3bBmzzggNMZWEbtIxWtEIdx6TazMWi2if16zRzinqLNbUaJlDBc9ffoxeRPDu9t4xwCPbATMPgDnL5WdzjTYEkNnGr3k2L17POw+h9z4wlq1jn98La//AFz2A+7YH3u9L2huNGOdtJIKNQa49SZHQi2NR2wR2PAF4ux+cuzr8SzACdfTo39T9FJZXMAk9836qNl0sGo+NBEaeAnxWZ74O6pPsC8bVxcbff5YDd41J19qzej7RCKn0SzvqVtJwVfrS4dadtG3ISNmURYjEshR6fEbs9HUW47o5iQ3/AJ980uKzbyqhp/DOmWcCzz3HZFcDoF0Ushuj2zF6bh09uo6W4Ojp8PXGPv2UzQJJt0M7dk6E3k03af/TzH12y1BkY/Rkgj4a1cYmPPSQUV9M5ujJHox8R9tKlIsctTPOAP73P/O82Qg9PnRMh+5HKvRqa82/g39JkEq5H6PHr0fHytGj65CFbjoRN6L9F4nkJnSzqIgVOdTVswpvq69n286XOpDt30gE6NzZ+ExfXNgVQHeClaMnuPck1vytjSs8+WTr9fLXrRdHT4cczzy3t7JYTHOc3njDLLZ1dEdPJvTSiERpmJ8tFsNeR2muxRk7bWTHksnuZZWVjsIsE0wdPQc7In2tjTlJ60yLYIReAPgTm7Cog/b537sChxwM/NwemXP78IOATQXAZPo+jnf0IB6jR0P+KkkEdIwrhE3r6MWi9fZ1NAXjRfk6evUWQTqvD4TZ0SOPEjpGT6JVDG67jTkn6mo3s+2SCPT42tXMPZHPGhoVjMlMJGI4fGItvu4GTJoJyxdNeVSpWTl6F18kbGvCxtGLcMliIhFzW+xkyIsBgdCLsfc4Gk653cnAmZOA/xsbtb6nLF6Mt/sBX3ZH5lyh52KMEXrG39V5wHBBZSYdO0cvYlPgXYd/KYHKSi0RGbTMvoNPAz7rAWCvvfBNF+CLHtzy+qFt4WP1lNBTeIN2VHhn7s8/tc7kyy9rn+lD3m3oppM3KVSEyIqkixy9phyjx0M70IsXa4ObaV03u9BNK9Em2+duHD3RGD3Z/otGjeymfMZHfrtOHT2rY0WPh77cU0+J581G6DkJT6Qdo9paNlSRFkHXlxs2TBtLJMPnE7dZ9PDVj7NI6FFkjp4TkUDbsu222v+80LMK3eTbRo9/cTEbcikT0DzDh2sJAWTIQmMjEbY9v/xi/O3F0Xv1VfazldATdByjP/+ovVm2SYZh6eiRY+joHTQVem4vjVhME6b77Qe89554Hj3bH01uIXhxIRqzYwrdPO00LOyo/fly91o2AYvsXvbll8CeeyIFYFm5fJ9QcRdPxrSMgFa1ER0UbOeF3ufFxkutVaXAC0OBXY6D6dyuCwOzhwEbC2By9GjiE5p1kwrVSnLLiXIhbnSMXryhTvzSkMLfd1MpxLm92GDxSAdguv5i5L0sDd0MJezPWDr2rp4LYRSJJACI7b0nc73FuXFwMYFATCTiWF1ETkCLBEllcbIDuBeXK0uBC/cClpcB8V9+NtpA3T2yDxICR6+Bc974YuGAfShkXr35N9KC6fw6NqeTN78xAJZDC/5c9TMmzQTGHY/MPoqR30aTm/DirEaQNyzTFpvMt54dvQsvBO64A4vaa5l9f20P7H40UJNswJiTgKOns7OfMQm4dhd4LkXSXFBCT+ENOkhcVDC0psbI6EcfII0xRk/mIIhCN+mDLReOXi5CAcePZ1KRMx1O0XZE5R2sXCHZPndae6+hwZ2gjUbN54RbR48XHU6PlS7ki4rE369apWV008s4uEEmVul+pJ22mhpW6NXXmx+aP/8MW/h9n23opqzAr0zc6NdXPM62paJC+9+No8e/MNAdHwAoLGSFHn0pY+XoLV0qF0cFBdaOHj0edNycF0dv+nS2DIJV6KZI6Dksu2jp6Lmte5WNo0evB5F7DgA//KCJlZTkRYAFjMNTV6cl1knjS4F9SWBzH751J6D32cCle4i/Zxy9n3/WMgIOHMjOZJdoiV8nff8YYJNf6GwqgPAlxswD087eunVs0Wki9GrDWuhoMh5jQk+poxflOtc0JC52xGFGxAUg3oe6437TTcB22wFr1pjqG9ZLbic6iTr23k0dvQ/6AIm0wxZ0cFpQUVTXwIovqaPnB1NfkRmDmUoJy0MkYhFWzlq8aCqjP4hz9KYeDvxnHLDPkWzb42RcIBO6KTgGEW4MIV9aAGBr8YkQCb16TiyJ6lbWhsHcX/iyE98FjaiLyGbt3k2FczxGyis4dOEAICYZJxtMb593OWWYHL30fWooebcaDwBL2oqXf3QkcPkeUEJPsZVCO2h2b7/p97IH5Ndfi1NkO3H0ZAlY+NDNVErs6CUS9uNiKLQzZRGT75hPP2ULuYvErRdHT19PLhw9Nx3IaJTd5v33a51x0XZl+z1boSdIzQ1AS87w4otaGQeRoLZCJlZlzlNNDSuqams9FaCVhkvyOE3GIjvuMnHzzz9aKYMOHdgxGYWF2v9uxujxnXy6vkSCDdek9w27TJQy2rVzLvTotr2GbtIxbDahm/ydzbHQc+roORmjl62jp1NUJL5Xf/cd0Ls3U0/LsdDTZ0smTcLY9NNs7mXn76P9f8Mu4u+T1NFbtUI8E/0NHhy9+oSkoys5t7/qnv6DHN+Iz7i27h+thY4+WPwbM0aPcfS4zjUVhLEA2OvPytG76CKtbMkVV5hS3ds5elFe6JHz/IxJwC1DNcHmxNGj265vqNHa/+STQCwmdMOAtLAk50c8xd67haGb1ZyDJ3P0kkmURo0Dnbz9NmCPPbSxpwC+T0eG/9aOFVLxjz8ELrtMG19p4+hFuPbxmVQBe/c+LkheUh9glxK56jVhIFFvbC/IdU1WBYzrsnKzFvpOk9tQR49PKiPk9NOBiy5CJCW+nvPTjXS0LpjDjFFfj2Xl5vmYMbMCEhGXIfHNDCX0FN6gQs+uU0Q77bIH5I47iqdn4+jxoZsy8TBxItC2rfOOP13P1KnaYF032IlX+r1eo8xLMpalS436dCLcjNFzM/YnGmXHYp1yCpvRzYmjxwsZp0JP7xQ5aa/LFOnMMeBrl335pfZ2/I03jOk1NeyxrKtzL/REoZsyR08X17yw54WeTPjI9lnHjppY37QJuOYaow16cXY3oZtWQi8alTt6VqGbVpSWWoduyoSe12QsTsK606GbfLiVZ6EncfSchG6mSLib7uj9XQwccKjmtlhCf2tRkfW9mj4v3Dp6iYTp+velAPTubYS3ZxlZQUM3mQ4vFZiijLkW8EKvQZDdEYD9uU3OxajPvO/ubvM7I1StxujFRXX4MhMsHD2dn3821zSz2RWxermjBwAPDNS+DxKhd9nuwOc9zG1iHb1qLWT7qKOABx5AXBa6GQDzQpEJPY3FxI5eFXdMZEJv7FiUbTb2ceXyxcBHH2mZGi3aHv/yC62G55NPsqGbAleyLsU5eiKhZ/NSZzPM9/Y67n1g0gfgq6+MkjPQhF6kZnPmM6cN8XuRsd6qqnXAO+8g+qVRHzRWX6Nlv128GDUpwX3444+1OqYLFmhjpO+5B7jpJqGYBYD8dEpRvuSEjDjn6L3afgN6n22e754x1usRlbRoSSihp/CGU0cvmWS/b4xBrTJHjz6Ya2vNglTvPHyQzuT1yCPOtse7UJdc4mw5Hbt9QDtMe+2lvbH0IvSmTgXuu09e19Cp0IvH3YW5ikI3ZduVOXp859xikDuDfoydCCq7wtQ8Vo7errtqYWpPPGFMr6lhf2uuHL2kpPiy02QsssQ2TsSNnoAnGDTCUt2Eblpl6LUSeitX2rdNRDJp7ejJaux5dfScOFdphyrGdZA9h25mkXWzgXSYdEfvtMnAK4O1MSw8Gwu0yOc3BoC9douKrO9r9Pp1mMEw4+glEmJHr7zcOAf588wlTHkF2itauFATYvE4e79w4OgleEcvKJHedkJPPxdTKUR95nWMqCs1O3X6drkQN1pAmhdcwnvK6tXsWPE//zSdt3ahm1EuOyY/v/6LQnHjt123a3r8ItcmxtGL1hnH/fPPpY5e3A9gzRrcN1rLoMkc32hULPTqOcdd9KxoaAC+/popDv9PIfmeO2fpC4RMG95/n8u6aT5363xs+0Tj0+yuqM0+83NnUwH7OeGHJrouvTQzLRYAGqqN6zyYhPHSMJXCb6XESa7+B9h3X+Y+Fn/8UW0M+sCBYqG3++6auNxvP+ZlpEzoFWSEnrNhJwluz1w7fLOj5XhMhdRbGHbDaBUKMU7ffq9fLw41BHKXspY+2IcN09qTl8d2uurrxUKPOkpOw0T1BAM6bjPduRF6AHDMMUZRT357VnX07FKLu4k7dzMvH7rJ42WMHl9XTUYkou1fffmPP9Y6K6+9pmWIpbh1iWRCjw9d1OHH5NXWuj9XAPO63Qo9foxeOqxI2F47REIvGmXP2dde0xKkAPaOHl/vTib0uOyDjonHvYVuenX0nAi9WAyorTU5Izlx9FwmY6lJGvPrGQ2Xk6SsOx0PHP4T8FcZMHQd8G0X4MWh2r8UPVbBoLsXRw7IOHqCF02+FLTkPfo26Tr1seEuSCQTQHr/M8dlp500UTdyJBte70BMmhw92S3RIiw5BcCnn4vxuPAc6dmQLy3yHo1xQk9UnkFH9HJj5kz289q1pt9hG7p53lnAqWQzJmGonanBhECuRCJGfU1wjh4NhS0vl47Ri/mBH2qX4tQp2ufR9PEdiyEmEHrxWJR5UZKsrTG7IukXHVT4bigE+uuPNO4eTcMDM7/jhx8Qp6UgBK5krZ8TegLRYevohZLAqaci0N4QnKtKrJfRadjAlVFKJrVnTX09s46qWu2H0/0Rm/9N5u8an4U4W72auX+IfiMA5Ke0lTsVevHaau0cSj+r6vlBhg6J6WVKvv1W++0DBsjzADRDlKOn8Aaf7l9WPHv5crbT1BiDWmknM5UC3n9fSxZz3HFsG/kH2U8/GQkl9GXffJMZ9G/axpo15o6K206hXUdH9Mb7wQeNv93W0RPBuzy5xM7Ro79f5pzwzhcv9MJhLeOfCDqmsFs3rX6g6Le6GZcJyEMSrfY97cR5cfREoZuJhNiVswrddHJ+ODmP9es8GDTCc3VxrXPFFVooDmAv9KjTE43KXbVshJ6X0E0njl5+vnmak9DNWAyoqTE5KjkZo+fy/KohGf30DiDtM37VHThrX+CWscBx07RskRno/orHnd+DHLYxk0o/Hhc7eiUlYkdvzhxn7SDQIuCmpBTJJDB/PrvfLe7hk2YA7/UFPu1pTIv5LcoQWLxw2lgAJkpBdI4kUgmpmxXlUvHTRCS8M4f6enzUG1jUXtocAGZhZ1VeAQBikQYmWyZfP03/KhAX3KPJs+7TnsCFextf1SVZoScTu3E/sKZuHfPZaFyMSXCjk+AEck2t4FmRvrfT9W2gLhl3P71ve7ZNAIBffmGcPpOjl0qhzs9OEzl6di91NucDuO8+5jisKZbOztBwzx2Zv+N+GPe12lrUkBEalXXaPqLnaCxgXEy1Put+TyoaxfRDtdqLIpcVAPLT3lREUPtQRKKuFpgwIfO5LuBR6OnCc8YMLSnR/Pme1tNUKKGn8AbtOK9dy9ajomzeLHf0RCn7vcB3qIJBrY4apaHB3HmjdeoA7cY8ZYr2FpO+rV60SBvDd+21bMiqjtswLzuh5zQbJl2XW6GXjeCm7qIIN6Gbss6wndBbtUpLDiCirs445/SOoKgz5TaRjpWjJ4M6EV7G6PHbArSO7NFHm+ezCt10co46cRv19ViFbgLaOBXAXujRbfKhm7Sj1BiOXjzOboOeI3ai1+9nnIYMToVeNo4e3acff8w6tG6SsSxahJqUMb8uPAXRgRmY7/hSGA7vKalIBH+0Ae4fZf2bQ1ToyRw9/WVDlmVTEr8Z0Q8mp0uExfbe7q9lWTzgMGNawi93vpKVm6VZgNcUwzgXo1HheLh4KikM+QOAWOUmbl5z6Oaycq2e32/xtdjjaC0j4f+6itsKmH9H0mZ/RQPsefOv3STz+ayF3vhj2a+Y8gpt2kjF7tPDgOAGYz8w7Y/FEBUU1U7EY8y5WVVH9uOcOdq483TdTfrChgndtLif6udYCmxIZ0as1tdnXtDVcftbVEPOLuvm5vQ7KRpmanfcdBp+XmC0zwfj3K+pQTVJ/FPVUIlf22kvhXSiAS1cdn0hUOM3H5+3+wFDTgP+1yWFv6tX49XBwOzhwDqJWaY7eg0ph46eH1poaE0N8Pjjti8lZGQcPf3FJDUIWgBK6Cm8QTt2fMF0Cu10A94eyHxIJd+B4j8XFJjHpfFFlUWQFMxMm484QusEXn65WOjlOnTTzfqc1NGj6PvSq9BbuVIrB2GFG0dPdj7YCb127bREGwUFMFFba/w+3XURCT23jp7e1vXrtSx0Olb7kjoRXsfo8efLF18Yjt5++xnTrUI3nbh1bpxpTug1II4bdgYWpgtCZ8KGZUKPvGXNEImwbdBFZTJpqifmGCuhB8jHK9oJ42BQfI67CN10O0bvl3Zpx4BuY9IkdiY359fQoaghHSZ9+1b6kPmOCj0Xjl4y2oB+ZwKn7AfcNE4+H5OMReToFRSwLxucwj9PbroJiSW/Zz6Ksg+acPkci/vlY9mq1v6ljacW8HcJjHPxnXeE50g8lZCHblZxQk8wFnHXY4HtTwK+DhglSnY4EbhhZ3F73XaWYwHrlw4pH4BUiskomsHiuNZVkWiA/Hxp6ObVE4AVJDtkFS1tGo0KE9xE4xGttECaynqyH6dN05JTXXcdAPbFwP+6aiGa1WFY3k9nD9fq6/EvFRLJtHtdUaHVKa2ry7QjkN6JojIFfGInvgalSOiJuHUnYPRJ7LRact7G/WCEHuPoRSox4Rh22YeHxTBpJrDtLKBGMEZ10kzgl/bAlMOS8L1mvOz4WxJWqjt6UYmo58ns31mzgGOOsR1PKiMWbQCSSbzXZiPe7wNsLm5Zo96U0FN4g3ZirMaC1dWxnSkvY/ToMqtXa1kATzlF3BZ9vaKEBYLaVQw//CBe5wLjjRZTP5Cu2w12nQQ3nRa3jp5bYcjTtav1+DugcRw9mSgTuSp03sZw9I47ji0FYiUkqGDIVeimzn33aSm6daxCN504em5CebkxejcMXI9L9gSG62NxfvtN+1+WjKVdO/M6o1F2/+ht3rjRnctNsQrdBLwLvVhMfB3Q5RIJLK5gO5aZZWtq7B29e+/NOLe/twWGnA60vxDGvvj5Z/NLIbdj9GDsG114Wjl6DHTMazyORCyKO3YAfuhkvViioT7T8Z/bSz7f112BtUWQO3oFBYaj5+YezJ/nl1wiTpRhhcsarAmf3NHbnJA/l/4qg/HbZswQCz0kmNBTSrR6MzsvdfQCmkBYUaaJ0E1J9py/ZE/xvrAtkC6Y39ZdjsXE67V46VlfQ+7ziQSTOZWHhhzT6zERbRCGbvL7orJB8PxYuBCvDQS+62JMuncMcOJUYPDpQMriHvL6QK2+3uMj2OnJaAS46y7tmP/8M3DuuZkxjW1SWsMjgtIC/P7N45SfnnjFTuidtw8wvws7je6vpB9IpmvjRas2MS+rqqo3YC33OH5pgHa//7sEjHDm2VgAxG79T+azbPxgQSoduikpv8DzXj/gzh0APPUUAOuXFBUW6WOjsQZg82actm8Kex8FLIyvks7bHGlRQm/z5s248MILMWzYMHTu3BmdOnVCp06dMGTIEMyaNQtr1661Xcf8+fMxdepU9OnTJ7N8165dMWbMGDz00ENIcB3PVCqF2bNnY/To0ejSpUtmmc6dO2PQoEG4/PLLUWsnINJ8/PHHGDfO4hVmS8Jpp/DCC9m3lV4EBr1hvv66Zp/ff7+xLr4t0ah4O3YOjkiQ8utessS6fTKWLgV22UVLv59LR89JwXTRurMJ3bQScXpbsnH0Egmjc26XxtxO6Fk5em6Fnn7cPvuMnW6173lHz0syFpnQ69xZyzyok23ophu4MXrftOE62roDJ3P08nj1A7nQ8+KC6tg5erLMqzbC4a/SFPbadz3e6s99Qe4hCwL/YOAZQJ+zuHl0R89O6E2YANx9N4B0qnmkO3SxGPDYY8A225gblsUYPVMWRgGMCKTRDfE4Hmm/AmfvC4w8xbQYA61JZdXx/Ko70OU8yMfo5eUZ55EboSe49zE1zpz0is4/H7jtNsebjFuM0dPdFhF/tAFz7bp29GoqTfPqxPzs+hIx83VSyV2mKQANLl0RO2GYAoBIBBEroSd4ZtbVVeKPNsDDI4FYLMIWQufnJW2uoXUGG2oRE+Ss3Aj2flkVqdJeqpG+THLTRux/uHh7q0qB2rrN0vbovNeX/Zy46042i/ejj2YctbbQ1JqoWDh/GfE1MSNB4MBDgDoLsSVjAffiRj9Paqr+YaZX1rCfeWpstk3vP1HBueBLAWHooZvWL8uv7GwcmLP21fbPTx3E6wWAorgfbWLyEzUWawA2bsSGdGhuu1LJUKVmSosReolEAnvssQeKioowd+5crF69GmvWrMGaNWswb948DBo0COPGjUOdRYdm/vz5OPjgg3HKKafgl19+ySy/YsUKPPnkk3jjjTdwEQ3JAjBnzhzcdttteOKJJ7Bq1arMMn///Te+/vprVFdX49xzz7Vt/8qVK3HUUUdhFZ+xsaXiNIU1/3u9hG7ShziNjdYHxPJtiUTEQsZNx15vJ12mb1/zuD7R9kXMmAF8/rkWame3D7yEbr7/vrP5sxF6Zel0fHZC76+/2Lp5PHaOXlERcPXV2t+dbOwBO6GntzWXjh4/ttRqPdmGbuqp3UUEg2zmr2xDN93Aj9Hjuxq6uKK/307o8clR9Ht5Ni8l7Bw9WVSBjTA+firwQZcGTJ7BfUGE3huFmvu/oZCbRzJGz5QcIxTKuIZheplEo8CxZMDSqFHa2GLAdXmFSNT4nRlHz2J+6XfxOOYXOstim4g6E3pAehyRpI5eKj8Pp3X+DpftDqCuzpGDqbc1Q/pewYyTstgB/7cbsOMJadHm4LmfWafFGD0rofdnOYxrt0cPoRiKI4mkLBlLXRVzjseJqIlxmUDjAgGhj8H6qLdW286tmwc4DPWMRITrTum/XXAN1yci6HsWcML+wH+jX0jH6AFpZ1hALNogHBtYzZUCqIxWaaGaZCxlpVUWSQD1H75r+T0AzOf0QnKtOaldxtHThZ6ghpxJ6MH8RuDlIbbNEXLePuzneDp0tKaaLXlUlbR+xlgJvRQE9z+OQBII+tLlFSA/1iVRH4YVswr6+l2AYadKFgBQGg8gZHHni0UbEF+/NuOMVhSqMXqNwiuvvIJevXrhyiuvREVFBXyks1VWVoazzjoLkyZNwv333y9dx5VXXok77rgDkyZNQh7paPj9fgwcOBCzZ8/GCy+8wDiDDzzwAG6++WYMGTKE2aa+3ZtvvhmvvfYaohZvjaPRKA4++GBcSuqTtHi8Zmz06uglk8A777B1t3RnhRcLMkdPH0hb4iCvsN4ZoC5fIqGFUwBAe5vUZHSbd94JfP01ux4r3IZurlkDvPCCs/mzEXp6enG70E077By9SMTonNgJPVGKY13o5ecbomzgQPN8LsOvpELPKpKACgYvyVieegr48Ufxd6EQUEhUhH5Neg3ddAMv9HjBpG/vd2PsEyP0RBkrYzF2/5AkFJ6xc/RkyEI60ywrt1/Ol5RID6dj9MLhzLHMp5cMlxEQgYAh7l06xvEGQ4g7ScYiFUGxmONnAnX0HIWJShy9n/OrcW/pb7huV2Ctrw49ztHGPZk47DD2M73nfPstAHaMk5Wjd8144Otu2hgrNyR88jF6jh29REISupmUhi3GEnGge/fM84E6evscCexJaiXGBKn9q9Od8z2O1mrb3TzWNIstduOiMo6e4LdFGtL3aMF5TQXkvORyS0dPlmEyFq0XCr1Ikt0XdRvN93i+Dp2pff+9y3oGAH+0ZT/z40OTPsNBbevXnnWiZCz8GL38PPuUmnkJHw5q6Gs7H0/G0athX3Dy7i+P1XmQ9APn7iP/HgCCKSDk00M35cc6mPIhGGA3dtke1usuTAYQSskv/FisAZvWGREMbQvaSudtjrQYobdw4ULstpskXVOaPffcEwtprRuX6ygqKsKoUaPwmz6+BMCff/6JgaJOYppQKIQePXrgL1GSjjTnnHMOtt9+exx+uMTnb0ksXAhcdZVtR0gK7XS9/74zV6W2FrjnHmDffYEzzzSmr0unTBYJPZGDoQs93ZmyQhdC9HeuWKG11+dji8haccUVwFlc7FauHT1ZaQurdXvp/Hbvrv1v5+jZ4aa4sSybq46Vo0fFBF9Dzwt6W/liyaL9r4c1ZjtGz4pQiBW6+rpFoZuN6ehFo+ZXyrW12rVDC50nk9aOHmDOUgpk7+iJzvWwTRyRTd1GqbNBQ0FlwictaK3G6P1WAYx5Y3/MWfIGACCPXCa1Pm5/0MQwLh29eINxXjhJxiJNVBKPI5VyKvSMbdo5evq6RY7e6rBxn7xpVANWlmnjnkzwGfLo+fSNVueLClgnyVgchXcSkhahm1Yd5D/awLh2JXX04qmkvLxCAFpUzSGHpOdl77d0PFZcUJusmmvbh5KKNlY4dfREbmWDnllTcN+kL0raJPMsx+jJhF40Wo+ooLZalBsDVvePcY9vCGq1JjdZCHTAfdIawDgP79wBeGkwG3Lapkg7jyMb1pmW46/1kIOufYdYGG2TNupMQDyeFnpcyQmm9Eoax+N9oWWrtSKY9CEYSo9TTMqfo6GkD8Gguz5KAYIIWuyzWKwB/zz7MACgPB5E0K+SsTQKK1euRGebTl+XLl0sBVdlZSWKRR1Di3XU19ejQJTZj1BQUIAaiTvwxBNPYP78+bj55pst19FiGD4c+Ne/gLff9rY8FTErVgA77GC/TFWVlniCR7/58x2q2lpxEVpdAJQK7kg8MkcPAHr0MAuMRx8Fnn+ezdwJGGnmKbkeo+emE6/vs2w6z43t6FE6dLD+3kroUTHRvz/wyCPO2icjHtc6TRvYkBXhywrd8aVCr7o6t0KPjpMDjPOGPz5VVc4TH3nZtih0Mx5nC0wD9qGbACuU9H2XraMn2ud2rv769ZYOlVTo0fuFbJ+nSxFYjdGbeQDwzfofMO256UAwyAgiUz0qKvQE947VJVptN9N4QgBxIrrsQqcAC0cvHkfKYn/RPZGMGsfDUYEdSdbNf4KkwDJ3POb2Ak6ekk4k0ZZ7+07vOf/7n7YJPhmLzcssRwKVQw9dK0yx67Zy9NYVA7G69DklE3oWjh4z/7p1TOgmT0yQtbIyj3ValraRt1XGbTtZf59CCojHhddURugJzmt6/bT57FvE/xCMn08jy+KohW6aDybvGNVVG/f40ScBvc4BPrARvV4yPCZ9WqmLs/YFDjoU+JYI8fJ22ovWCF/AHOb3bPU2NesAoCgVEgR42hNPh17XcGMQfxeYXG6Enh3BFBDK08NX5f2XYMqHQMDdzi9MhWC1N2JffYENX34IAGiXtFH4zZAWI/QSiQQCNokZgsEg4tnU0xGso7Cw0HLcH6CJQRELFizAFVdcgRdeeAFhuzfIaSKRCKqqqph/rQq+0yVKbsJTWSl+w97QoLl6b77JTpc5XHonrJAfNCNAJPR02rQxJwk57jjg0EOBYcPYDuLOghzVuXT04nGjQ2yXuISuuzGTsdjhxtGzc1+dOnqAs+NuRTyuFWDnEd0f9KyStIM6f763ZCwyQiE2jFRfN398ZPUGs8EudBMwF5WlWTdlQo8PdQWyO1dTKfE+t3nhh2RS/LIojTBxBKCFlqfbaxm6GY1aOnor6buoYJDp1JrGutDQTe7+urADcMoU7W355BnaGDOmKXFDRDsK3bRw9Oh9b0lbYMPMA4GeWtVwGlqWIEIvG0fvn4CxHn5f7nYM8MDo9O+1EnrpF3OmZCxdLQrJQRsv5Bb9uJWCPfethB4A1NUbQk9YR8+p0Jszx+ToUephvs7uH80KtZUOgmF45nU3/hbut3SWbNFva7juai37rKCE00by/r28AUgs/d00j45V6GZMcMLzBbtpxsif0+8e792emQW7dd8Vt+59K3pHtSgLL47et12AUSeTdR5j/J3fXhvGEI3UGdFMaXhHr46MYeNLLegUIIgAH/PpAD10vCadiXRQvBwAsMbBiJhsCKZ8COZrz/CGgPzGEUz5EAy6yzpT4AtZh27O+yJTI7GixuOwpSakxQi9pqJPnz745ZdfpN9Ho1EsXbrUNH3Tpk04+OCD8dBDD6F79+6CJcVcf/31KCsry/xzs2yrpbKSrdmk09CgCSu+QyZKvAEYnQXR+CAeK6FXWGgtquiYKn48V1lZbssrxGJGh9iJU5lroefwBQbD//2f4YLZ7QuaVVKE1Rg9XkzYOPO2yNrKC738fGNbvNCjY0yzhRd0MqHnNczaCpPQE8zDl11x4uhRcuHo0fVQsjwXGsKSR2cspmXYBeDjHS49hDst9KzG6DFCMhhk5q3lI3ODAUzr+LGWlIQTtcNP1VK561wzXqvHp0MFkpNkLFaOHhV6/c8EOvefAzz5JABe6HkYo1dTw2zbB2C13xB/soyhiytgFnr03pd+dvDJWL4bWGoq2kybmo2jVwL2nmkv9NIve2WOni+J5F/LhMsy59hDD1k6enWCBBdzBlm3zS15ss50LCYO3fxnDXDaacDpp5u+W02ERThhHU4rC2OOvvW6cJ9GNrH9jboQ8Gs74NOexrSN3C3kgX3vxTk7nYOCdK03L47e/+0uf4kUzkuP0QvAyBWQhtdrQ3xGDoGwRMzlI2hbaF1EJnRz9XIAQLdgW7RLiO/n7mWknEDKh+IC7U2D1TUTSvkQCLhT2YW+MEI+eZ9uc9V6TE8P9W1X0fL65C1W6P3222/o06cPFi9e7HkdN9xwA6ZKCpXqnHrqqTj33HPx7bffIkYeEKlUCn///TfOOOMMU5KWZDKJI488EkcffTT22ks0OlzOJZdcgsrKysy/FaK6ba0Nu7Ay2Zt13dHjkSXZ0Du8Tjp5ojF6OnZCj2bA5Dup9fWNF7rpJMlMLoQeDQ10sk3R/v78c+1/L45euvMIQOzM6CIy146e6FwDzKGzFRXiMXqpFPDBB9m1gcKHaOovCGShtYMH53bbZIyeT3QN8y/A7JKx8Ogvd0Tn6sMPA8cc46ytIqHHC03BC4Ovumnp/Z+hVQyOPBJYuBAxUXFnHT0zL79P9PHh0SgQj1s6eoy7EQpZOnofta3EnILluG5XOHpJVJWHzO+nbYgGtBTkVi/5pY6eIBlLLBXPuCWMkOKybtp2NSsrgVWrGNHiSwGrOxrHTBZ2Oq8bMGMzF7JNX9ikI2aoiPy2CzBqpx+x/YnsYvQ3WBgKUjKOns+do1cf0Z5niURMy0LKEa+vQ2Lel8JlI4ceCMyZo3343/+YUF2eOkFB61yTlzIfqBRSSEYjwmNoleWTCr243zpbqozYG68JhR4/rS6k1cYbT5Ld1nC3kFBYO5AF0BTeSzm83QJAXlDbYEMQpj4Rf+R29PXAq4e+ioWzFiIkUbl5viCSFsJfhu7oVS/TclkUt+2M/oly4bweDEMpwZQPFSUdAcD0EoaZD34Eg+7GHhb4w5Zj9B7azvg7v3+OD+wWoMUKvYEDB+KPP/7AgAEDPK/j4osvxmuvvWY5z6RJk3DVVVfh3HPPRd++fTN19Hr06IGjjjoKu+22G4ZyiTmuvfZapFIpXELroTgkLy8PpaWlzL9Wj53okDl0skyTuRB6Vo5eUZG1OKVOhi70/u//jM92GRC9hm66cfSycUmoY+RE6ImKY+v7z87R44XeWWcBM2can52O0QNYoccnVHFCOqGBCf54tm0rFnqiz9nAO3eyZCw6L70EnHyy+Ds7CguBhx4yPnNj9IRvhv/4Q/tfF1GbN7tz9JYv184T0bkaCjl35USh9XT7eXlaOLZO+iXO9EO1sT1HHESWC4fF9esoaaFncvR0Zyn98s5qjB7v6FFBxhcerg2Rfe9A6CV9yFxXdL2rSrUU5PMsXli7HaO3MrbBtBwVej92BA47iF+KY6+9gIULmf3j694dK2KG6yJz9DYXAE9v+sTUVgBMGR4q4j5PuzZ/lbOLUfHtxdHTRZrr0M200OPDCXWsRM6mHh202m/psc5WQs+LA+WWsMDRSwGIkOyvFF3oiX7f35zQc5sgB9CuOaGjxwnMfxy8I9TFRYFfu0DvHeO+PVZ0LdHCif9oA9M9kddyAX8A+w/aH9t02AYhidrK84U8Dd1OxGPA6tWoSY9bLO7YDR0hjo3lhfqhJTYDNi0IpnxoV64NWuRFNiWUcp+MpRDWjt43JJJ7Tb11oq7mSIsRek7G38XjcQSzTBQhWse0adPw6aef4q+//mJq773//vs4jEvd/O677+Kxxx7Dk08+Cb+XzuTWCJ8VjccqfT1FP2660OvcWRvbN348Oz0XoZtWqcRpZ0sXsTR8SCZcRcvbsaVCN4eTfOJuHT3R8dVDGu0cvYICVrjwTqpI6OnJUvjjTNfDh3Nlg0jo6UKiVtCBGTTIyGCaDU5DNwFNpA4e7OzcF3HxxWybnYRu6o5el3RGgSeeMAqpOxF61dWaaNfPVRomHAo5Dxu2E3rhMCv0BmnxaqIOoKNERGmBaxqjx4UhWzl6jHPGhW7yjl48IBinaUHKh8y9wm3nmLaL6T/efrtw28sia03L0cLcf7QFnrfRzTqPjTD+Xl69Ap8u/zTz2dXv0M8nMv5dJpQyYbJ77sl0/r2M0dMpBXsN2gq9WB2QSiEicZHjfrnTurY2/exMX7tW+6luCwg9UX23FICGOvEzURcKonBG+sLDah9YEZMJPW7abw7KpoXS48J0oeeGoIPzaVjHYQC0lyP885u//QaIaBGJawDI94eQ8uLoxaPAH39k7kPFhW2QL/nNvEs7qWQ74XxOCKZ8jsImg/Aj4GWMnkUyFlpK4//G/5+rdTcHWowS6datG9bYpJFfvXo1evToIf2+vLxcmh1Tto7zzz8fmy0G5QNAQ0NDJtnKbbfdho0bN2LIkCEZ90//179/f6xYsQKdOnXCXKuC0i2J/jY5cZ1gV8/szz+drUd3bPT1hcOam6S/+den5yJ006nQ09+8FRUZHVOb88l1Hb3GDN08+WQtRO7VV41pVEg4EZfU0dOTHOjiyM7Ry89nxQnf0XYj9GiINe3YZwsv9Gjopkjo7bxz9plLAfM69H0pWreeREYkAmfPtt9WOMyKIyehm/o1IsqWbCf09OOzbJlxrlJHls84aoUTR49eO4MsBiY5SUSk32f4ewTXXts6ejqBAONY8WP04kHjy+Qm+3I1yWyEHrmEZC4aZbku9CTJWJwSCWiZCClJUs7BScZQAPAnYVwnutArLpaKhIe2Az676HC8eNMxuG+0Md1J6QoZIkfvO4uE4nW//QTU1WG9xFWK++Uhcutq0+HmzUXoCUI3ASBSYy307Aq1e3X0Yn7xucNfi78KglJ49JT7BX73JQvCophcwhW7XoFtOmhvRNaUAOvr2czP/PEP+I0fIEsyku/PQ9KDpRePR4CGBvySHgbYtbQr8gPO7sWBLMoSBOFDRXt5/z4zX8pvqqNnR54vaOnoAYAPPqw9fy327ru3q3U3B1qM0Bs2bBg+/PBDy3k+/PBDDBs2zPM6amtrMX/+fKZu3q+//oqf+JT5hHg8jmXLlqFXr14AgHfeeQebNm3KOH/03zfffINhw4ZhzZo1mDBhguVvaTFcfTWwYIG4IHWukI2N4uEFnd4p06fnKnTTi6MXChmdSTuh5wYauulE6K1ezbbLjm231UpH9O5tTHMbunnKKeZlnTp6eXnWQk/kUOkdOF5M0Hmd1FJ0Ci8kaOimLqypg9irl7MMqXbIRIdoui6SRNu1y0AJaL+HChUq9BoarEOZdUePYucs6i+Qli0zXpZQoRcKOXMFAXbMrA4v9CgWLwvdCD0qflOCZa0cPYZk0tLRoyIl9vtvsCMboSdK3qIjEj/L6v82tTHpQeiZMo1C61jrzoUT0QmkQy55R6+kROronb0vsGvBMzj4tZm4nBRd9iIqdHiht7qEzbTIU1e1AXjxRUw7TPy9Vejm2pq0o5c+p63a7SVLpFvCIkfPB0RqsxN6NWHxb+tV3styOVnoJj+twcFlH/JrMxUE3EdN5FkMZvv1tF9x9W5XozhcjD4R7R74U/1yY4ZkUhi6mWmXpJuf5w97TsaSamjAZ+nb5M49dkZ+wNm92O/3/twLpvxoVyZ4lnCE4M/U23O8bl8AQZ/1STalbHt0KLIp99RMaTFCb9q0aVi5ciWuvPJKbNiwASnyEK2qqsJdd92FN998EyeddJJ0HVdffTXOPvtsvPnmm4iQzngymcSSJUswc+ZMHHTQQejYsSOz3auuukroJtbX1+OKK67A2LFjUZhtooeWSiCghfU57XTZccstxt96p96meHEGXujpHVO9U6l3unMxRo8KvXQtpgw0hEnvpIbDjSP0aOimk9+l1z90KvRE7hCdZnfeX3EFMH268Vnv7MocPT7Lpp3Qs3J1eDExdKjmTl58cfYZOCm8ozdkiLldtCxDz55bXujp4+RE4eTkfidFJPR08frPP+6Fnt39Qn+xsGSJca7SYxYK2b8ksIJuv6yMvZ6tXgI4cWJ1oUdCN5M+mI5LLI9dl1TocTX3+DF6iYDxpRNna/yxwOfFmvPnVrB8S8aqxPxaVsvnhmpCVnQGrIhoL+kYRy/mTuglfWKh17dN34zLQX+HVVIOfwrme3tpqeukEV6FXp4vhKIAe+9ZZ/OepS4EYMUKLJL0Ma3CFt04evx5lS0ilypPcMmmAETrBM9ZOBd6/xkHXL+Lefr/Tvgf2vvkmTukoZseRG/G0fMi9CxS+xeHjROkU0I7dzbHSNmtWMws9GjopizrZrgASS9CLxbF8pqVWF2qFTEf03UM8oPsbz6iRlxk0G02TGZZ+FBRaB9Dq4VuunP0An57R2//Dru6WmdzosUIvUAggA8++AD19fUYP348unTpkgmJHDNmDH766Sd8/vnnKEp3aObOnYt+/foxdehGjhyJl19+Gffffz8GDx6cWb5bt244/PDDse++++Kmm25itnvcccdh4sSJGD9+vCkUc8iQIdi0aRMefPBBR78hPz8fbXM5NmhLI+rM6R3HXI1HpMl19A6o0zF6vHOnd0yduEA8bkI3+Y6hzNHTnZNcO3q6o+Skw/7559pvcir0RKKBTrNzyXbckT039Hlkjh5/rHmhxwskK4eFF3M+n+ZOXn+997FqIqijd9xxWjpwXsjQuly5cvRkokM0XRd6dLu33w489xwwcqS9YBcJvW7dtN8pyLiYwecTn5f8/rnhBvbzqFHa/59+Knf0vv3Wus1W0O0feCDbfqt9wZ1vwrFa+rlN1pnwa6LniW0N9ykecib0Ttu5Eu/0Mz5bjdGTikWOCdtpJWCycaZiAWDgGcBhBwNvSnKiPbLsFbw8WD5Gzwlxv1iElOSVIJwOGaO/g2ZG5PGnoGVsXbnScPRKS12P7/K630qDRQiN2t5+RkJdCEBdHTqnH0Xbdx7NfG/l6FVGKrHPU/vgs7Y1mXktt5NDCnzmFYZj5gsmFgBizz4tXMe5+wA37Aw8MtJbG4L+ICr88qiTk/YTO9Hz7Y0jE6F0uGBByP1LRFlIKwAUhQ2hGk6XbojGSR9DMPyBhkjKsm7mbzOSMUyckkjEsKJWc+p7N+SjMFTICL3imB/tN5uv8Tx/GH6f8wune2l3PN79zMznYMqPigIHQs8XcO3oBXx+hGzCSstL2lt+35zZAmZ97igrK8NNN91kEmMiJkyYgCWCYtwjR460zbRJ8fv9OO+883Deeee5aquITp064aOPPsp6PU2GaKC/3nHk68V5hXbAOnUCVq2yH8Ono3fQlqfDGvROWXvuAs1F6CYVKPw4NdEYPero6VkhcwEN3Swr04rFd+oknz+R0LL+Oc26KXJeqJCgf//9t7mTrJ8f//oXsHixJg6+/dZoMy/09CLg+gMoG0dve4sOVS6Fnt6hnz5d60SK2kXHqTWX0M0zzzSu27FjjdIPs2ZpLtwnnxjTRGP0/H6gTx/Aos4oSkvFx4g/rwYM0NapX3d77w1ceCHw8cfa3/Q36Ns/9FAtLHPXXTVB6Ibx44FnntEE8PHHG043IK7NCC20rT5Zi9Kk0bHKFw0xFYzRi/uBoc9PQPV0YG0RcMGXQCzMngO6SOM74/duy16r/Bi9GBF6ds6Hji5sshJ6ZNnvOyGdjdQ8zOHAQ4Hp5BRJrF0DuMhQHvcDNQN7A2DHapeESxBLai+sqJP5hUXkbcIP4LHHtON9440AgK+6JPEfl0KiymMAS2moGP58d0JAF3q6cXvh2Atw8EuHZr63GqMHAO8tfQ/v4T2k4E7oDW0/FD+v/1k8swMKgwWoTLDubV7ULPSiASD22y/A7uZ1/F0CXLKnu+2GEUAU2nMl6A+iXagUSIjzO6yyGWIeSvoQc5hiVXfRCsKFgDiJqJQ8i654UYgIvfQ2GKEneGnraIxeQQkqPYZuNkS0H1iQ0tpNhV5B0o9glbnPlh8qcDVG77BtDkOIlJ0NwoeCUAGKQkWojcl3cAh+BFyO0Qv6gwjZhG6WlDjIyNNMaTGOnqIZIBJ6MkcvENDSOrvB52M7qR1cxkPzAk7vYJ52mnm6nQP5/ffAddcBImHOO3pOhZ4+n6j4u1do6GZhoeae2ImYtWu1VPtOEAk9eoxkf+vowuzKK7WkH7qrqbse/NtIn888fooeV17oWY0X1AWCiFwKPR3aNl7c9CGhLJ07s4Jr8GCgb1/329N/+4gR2v/6Cw2noZv05QzNqrr99sDll7PCSuToAfbtLixEtT9mzizIn1d5eew+GT5cS8hSVwcsXJhZF7P9Y44BPvwQeOMNY7pdBl+dk0/WXnj8/rsWSkv3mUDoLegI9DsTqMi/A8s3G+NjhEJv8WLgmWfgqzful3E/UB3TOj/vpXdZPCQWenYigne3Goit6FTo0XZ5hS5bHAVSQfnK3iOnSWLxr9L5ZNupGW5OkFOSV4K89Nggp2P0YoF0iOnatZk6jHsO/8FVewDggr2B/4x1vRhKQw7Gw6YpCmr3vfoggLq6zP4uCLPnp9OMk/8Uuhuj99HR2b2ULswz/9a8iDl2MxpwnkzHCR39xvM46A+iIlzueV2lSeeCQa+nXNC1l+vthC0EUIiIFl2MxBJE3NkIPVnoZl4wz9UYPT0zaDweRSSmRbHoWVTzQ8a9uQBBxEcMNy2fH8yH38ELznGRjnhwvwdx1YSrmDIJep27HmXWCVmC8OLoBTKhtzKKldBTbBVYCT3e0QsEtEKtbpzQQIDtLLvNjCgTen36ADRJTyBgn1ThzjuByy4zPlMxV1TEOlH8dmWhm+mEPfj9d+ttu4GGburtoO6EiM8/ZzvHVogEEd139HiJbuL8NL2zbpWMhW7TjaNHQ0fbtWPDgK22kSushF5pqZYIZ+1abZ/Q/fLhh8CeLl9bA8ZxmDMHOOkkzYHj26FjNUYPYPedvjz/e0RCrx+JKRSQKixA6aYL0eZiriPJ7/9wmN0nPp8xXlMPdaYCzOfT5t99d03gf/opsO++wPPPW7aHoVs3w2m9+27tGN1yi1DojZgFrE7fAt5Z8k5m+qZ8YPsTgTt34BY44ghgo5EB878k2k4vti1z9OyEHi9q6kn17ob27u6Z2Qg9KiqLYuxYQR4+Fb4bEj6gptB8TheHizNFpN2MqeL7vbUBb2M9L/SQfK80XAIfnEW/lAQ1oVQXAlBbm9lv/Hgop8XCXx7sztErzy931E4ZBUGzcxkWpL2PBpwLdSd0CnBCr8D7cJnSlPuBiwXt3cd9ysoT8IR95tDNRMTcL6POWUAWuhnMdxW6WZTQblDxRAwNUe3lcn46PDc/bBzrwlQIK/qYRdEZY85w5Oj1TJXihO1OQEGoACFS+FwXep8c84lsUW0+n4dkLP6gbehmSVnLDd1UQk/hHKvQTb4DqQs/NyFqfj/bueQTc9jBCy6ZIAkGnWXPo1DRwDt6vMiVOXp6p/hXd2+0LeEdPQCYMEFziXj08aGff+58/Xahm3zn3GpewOhE88lYDjgA0EOqeXfFSujRealY6dLFOpzYjdA75hhgn33s57MSesGgJipELrVdYpKnnhJP1/d9jx7A/fcbx9xt1k2A3Xf6PFZCT7/erTJUAogVGdfkCjqE087RA4zrWR9LZTV2bpddgLfeEp/3TthuOy2k+txzpaGbOpkEF9CKYH/b1Zz6n+eivYy/9UiwHyrYN/G60Ntkc2ry4/DqiaMXaVduvTDltNOyEnobyOEoijrvrPM1yuyI+4GaQvNCBcGCzBg9N+PLqHvkti3ZUprnoBxNmpJ0yJ4eumkp9AT7fqcw67bfu731MYry79AsUuYPzOsKv033sVAg9PJG8W9EtDFyuSzW3ilQnvk74A+gTRZCr8SFo6fDHx8n5LkVegkjnDsaqTPN50RQHTzkYCR7OK/nWpBOrqM5elpfMC/dHuoyF/jDWF75F7Ps0RV74KKdL3KUjIWGUAbJi4FA+nxrX2QtuEK+IAIhdwI9MHI7W6FXrISeYqvAjaOn40bo8Y5etkKPL7Cs40Xo0RT0ovIKU6YYf8scPV3oWZVmcIruDtIxenxoG4+eEOS775xvRyRC6DG1cs34eQHW0UskDEfvqquA/fYzvtNp3946GYvM0bNLTONG6AWDzsZ10n3O7zdZzTtA+w1Wb1ZFY9yCQfk158XRo461zNGjv0k/h/nxrxyxYmO/MUNd+P3DO3qAcYz0cbJOMhtLEtQsrgDesDlVM/vGRuhlilDzcOHQsnFTgaSWUv+hAew4lozQsznVTKnf/SR0s8JF2ZATT8wqZI7WdfOnnIffuc1oGPcDNfnm8zY/mJ8J3XQj9Ki4/dFB/qpcUpZflgnxA4CCuPxlVEmIOHp1dZn9yyf7WFIhHse2X8EI3D/lfsw+YDZ88GFBJ23MmxtOGXWKcPquQyfh/fHWSegKBIInr424IB0/7jQbdsjvix267oDJ/SfD7/OjpLDcdplwHJhz2BzTdE+OnpdkLEFnDpRewoGGbsZqq0zzBWzKGHxzyAfoX9EfKYsXdT7ukVSYHo8Xb6hDQzwduplx9IybQYkv35Qdc9+2YxD0B4XlFa7Z7RrM3unmzGcq9ESOnh1Bn58RiDK222wcp2Bpm8y+lVFS1HITKSqhp3COqPCwTOjpoqa5CL1sHT0qDPjyCoDmRn3zjfa3naOXC/QxVaLQTcBa6NExgjNnWm9HJIjose7VSwub08dR8fDHX+9Ev/eednz1Gol0PlquIBRyHrpJxUo7cYciA11nmzbA66/L5w0E3As9kaNHoSGrdo6eSOhZnb9uyysAYkePimXe0dPbn97PfKdAJ1Zo7GdLoZeXZ95Hbhw9vu0cA88A9jsC+LSn/So8C73992c+ysZN+VPAUkGEZTSghUNutBN6O+/IfK6nQm/ENtYLU4qKsnL01pPdFAsAUYdJKzw5emGzIMoP5mc6yFY14PbssydeOPgFo63kN3/dPUdJxBxSWlDOfLaqn1YSZoWezNGTEfAHcNKok3DEsCPQKU/reK91PkQQAHDflPuE08OBMPw9ewu/0xEJHplLmMuMn2F/CPOOn4fXD9fu6U466EkfMHXgVFyetxczvQjuGyYKWbWjJGh9z9HRx/JFk+l+xfz5iH1orhPKCj3zddmlRAsvTUL+0jnIfVWY3heJ+tqMo5cJ3cw32l8WLMQ9k+5hlg2lQylpkpQZvabi4nEX49JdLkW/iv7GvMRZo4ItaFP+ILM8gq4Lsw9uPxhBG6FHy1y0NJTQUzjHaejmJZdoHXn6vRP4sUtOhB6d3yp0k/7tZIwe5cgj2Y6pyNHz+YzOskjohUJsQo5s0ZNgxGKGA8ann+fh65ldfz0wbZr1duxESCCghc1tI+lg8p132kaaTdWqPpmX0E2bzjqzzr59rYWhz+fMAaRt4cUZfzyoo2dXm80ufNaqHTpuQjdF4+/CYXa9nKMnGQaCWJFDoWfl6OlCjy7jxs0kfN0VhnMsoCHeIKzZRllTI87gx7dfNm4qkGLHtw2o0KzGP9oABZcDBx9ivf1o+7Zafco09X7jpUHDYQdbL0xp2zY7oUcu5bgfiMnUPocXR09UXsGpo9envA8OHHwgsz6d/x0uro1VlufCGXVBaUEbZoxevkUWlWyFHk1l37Ooq8Wc7vH7/Lad6cKQ+cUMk0SEoB/f4mT2ieCDgRB8Pl/GOS0ptk+ioR+GACcmCjwkpu9Y7N4mLnOYpCecDvGMJmLAvHnA6NGI/d9lpvnsjo0/neQkmXIh9HzpUib1tYikxwjqIaf5JPFOabAY/dr2w8UFhmgOpc9Z6uhduM3JuH7P6+H3+ZFfaFjNVOiFwsTRI6Jvrz6sIGfa7Q8gWOwsRPqzYz/DHRPvwH4D9mOS3ojIc1gUvjmihJ7COU5DN6+7zhAVVACKwvxowhMvjh6fuIOSi9DNt94C7r2XFZEioUfbIgrdDIdzU6Rbdz5oEfK/0zmIRZ11SlfuYd+jh31mUzuhN2SI9fcyR89uPopTR4/uXzdCj3/BIGpLrh09KvTsSpO4dfSsQjdFxcsBcegmL/Totcw5ejKoo8eYF/xvEgk93tGjy8iEt43Qi516Eo46qgRHvnKkMBFBz9t7ouTl0ZbCgY7Rs0LWhw8k2Vp4l+2i3QN/szGhdaKJKJPttN5nCL2I30VYeNu2iG83wvn8HP/wQq8xHb2gtu5zdzw3Mz0/mG+M0bMQ520L2sLn82XG+NAQ0+/iK4TLdCvt5q6RDiktZK1ca0dP6/zWhYDUd99lzqeScAkOHnIwepX3stwWI/RKcvt76mP1tjXR9PZTNjWISwu9kH6M0DFxe/Tew1PbeJEjypYYTPlw1LZHZT7rL6r4Za3KHgAQ7oMJvSY4bKlBWdiZMAmnxUgsFQNefln7W3AY7MbC6QXFDxh0gHwePnTTn058tHI5Gj6fCwDIT19/+flE6KXHoeYTRzeUFmm0XUEyji6/wPj9tBxEkIZulpZn/n72oGfx793+jSd2v9vU7qDPmaOXArBzj51x5g5nwufzIWRVowRgQq5bGkroKZzjpryCDu289RaEetAOdCrFdtQKC63rpAFsh48PLc02dDM/X8vkV1ysJeRo104LZ+vVS5wtUhdF0agx5oo6ej6fvXtDOfRQ87SXX9aK/dJ0+Js2afuZii7Rdvj9372797FsixZpWR7tXEqnQk/UXv2cclownX7nVujR7Rdzb1edOnpeQzcB92P0rM7f9APpl3bAK3pWen1/HHywVj/vuefYZUShm1To8e3nHD3GzBlkpMKPFRoPasbhCgbZdRYUmO8hvKMXCmllOo4+WkueIsImgmBdRQGe/PlpPPXjU1hVvYr5LpFMZETcLxaia22NJHSTw8rR0x2Mvfvu7Tq7YTQRZX5nA3X04oJ7tAXxvtbhd1bcSsoLxBrR0Uv4gZp0ZkwaPkUdPSv08UKhdCp46uitj5vHNwFGaFuu4UM38y3SZbZJnxe/tmPFadAfxPMHP493ZrxjWoZmWKTuSc8SNumGVxGl05BosB0HNqgt+2L35r1uxsb6jcJ530lH7pWQMXH3T7kfYZtwOhFBzpkpKTLHSe9QX4HHpz1ums7/poAvgJBFUtZiQchlYagQfdu4K5cjS9LDhwvq48iiyXimbyEaG2sndPQQykOGHoK5R8/FkjPMNad5R684oPXVav73eeYazjh6AqFXQBzdUNjs6FERl19IhZ5xDodCxnM3UGYcx7YFbXHZrpdheM8xpnaH/AFbd05EKGYc6DxviXibLUroKZwjGqNnVzDdrvNNO9C80MvPt+9g0+9pEg/AfejmsccCY8iNg4YZ7rOPVox8+XLxGD2Adb90gUcdPb4dlLPOMk8T/Xa9Vh7f8R40yH6MXv/+5s92Qk/m6A0erBWqtkM27opH1EHXxQfdD7wDKcoESZeV4VboOQlBpuvgC9JbOXp2yJKxyFi/HgAw5HTggMOAj3uBDd284w7gEC4+kDp6ooya/LWvn/+icXP3GeN6YgVE6NGnjShMU3au6CI4FAL+9S+t4LXH0M31deszf6+q0oReIqk91SOkuLPVy12rYr0UqzF6uqNXFCqyzG4oIpqIMr+TcfRoIWUb/vvtfxFPujgPLYj7kSlSbQefTMbJuvX9ZRJ6DpJYtE1nXdTH+FAHpCpeI1rEVsR4he/Q51kIvSl9JqLEl49FHYDXiWbS632J6n7RmmmMo1fODk69ee+bsUPEoYUswImjN7yDUdLo6glX47yx50mFnk4JjGvB7/PbRzsIMDl6grFVQUnbA1yXOOgLMMKDp0Qybuvbk75FOzgYU5ymNF/8vPr+5O+Zz/q9IpqMZSKH/iw3L0edM9HrFz100+fzYXyv8ehaag7tDXBdnNJ0uY/qsBF6rl9/NPRSd60L8oz+ni7Y6LEJhsVCL0jO4WDCaET7zuaXyuWl5oikoD+EcCCMt2fYlJniIc/s4lzW+2gGtK5fo2hcsnX0RJ182uFOJp0JPbpO+v1OO7HzuQ3dfOQRVrzwndhAwBA+dkIvEtE6qE6F3u23m2vbiTrR+m/n1zNyJPtZ1OHVyyvodOxoLvbOYxe6aQffoe/VS9vusGFsaK6ovbpYo6KJd3LofvD7gTPO0JzLE0+0bpdV6CZfeB1wlimV/gaa8IZvJ+BO6NkVrU+zpmYNpj07De9Us52D7zrDXqhSYayLumAQOP98rfD8GO7NqcjR1iFiPFZgXINxXujRfVBQYJSw0Iuei2rt2SG7D6WhYZfLK5fjzcVvouT6Esz+cTbjhtlE8ThC1of3p4wsg8XhYtdCL5aIsUIPxn504+jNenNWToVezOcsbNRT6GZIW7cXR08XevrYH/08jAbk+6s2yor5bGvK6RSHi9kQMP6eTOhQ3BH7Fm4LQHP1dCyFHgl7o+5Jx5JOzHxBfzDjcHqhPm4v9LqWGS7iX+l0+5UNlZbLFBOhZzV+zIogF7YoCiENpcS/PZBkZVHA57cM6ZMl6CjPL8eRvhE2LSXryTc/hy/GLujXlk3gpt8rYqk4qqr/wS7HAtMPM6/PqaOnI7qOeINeF7VVeUbyI328KA29LEsLvfywWejRcyZI3Doa5pkixz3U2/j9I3ty/TtoWWx5goVaOyf2m2jpxvLUxYwkcMWxlhumKUIJPYVz3NTR478HxMLFytHji2Xr0Icj/X7kSMZN8FRHj6/hJkPU0aUd0UiE7cjq63UztkokjEU1zgCgJ5dOUCSc+Gk+H/vGdL/9NIFy0knGtGwLi4vKKyxfDnz9tTwk85JLtP/vTsff//KL8R0/Jozv/N95J7B0qZZJ0wq3jp5boUcTzfDfAY0Suvl/H/8f5vw2B/s2PASMHm3TWA6ZM/6f/wDvvisP3UzDvPQm+y+Wb7SdET4iR+/mm7Xt6dlrrZIryRA4AHS7VOj9VfkXZr4yE/Xxeu3/mOFaunWdAOD4Occj8fRsYNIkAPLi1HSMXraO3g+dgI9Tf2S+cx26mSOhFwsAMYsMfhQvyViqk9rvoh3rfm37uXP00p1fPdSt2mK310SN67d7aXd8d5KLkjQWFIQK2DTu7eWuWiAYzpQooMlonAo9mlgkL499loUDYQQtnCo76mP1psQlPCFStFo/To9NewwBXyAzjm1MV/YFUqkvH0Xp+oFex0nyIqYkzyz0gpKi9Xy4YsAXYPYpTzuLmm664HBCYYFZ6IUFQlwf6xZNxXFv4Dt83hOoFlZAshlbyH0vGn/GX6f6frx1J+DOdOJfXSBSR053JxlHLx26GSD7nRY1pwmGEinj2RgsMNaxbadtTW0UhbzSRCz88bSilgg9sQ/aclFCT+GcbOvo2Tl6TkM3aVIRGl4WCAC77WZ89pKMhR8jKEPU8ff7jfVGIqwTZefo8dsGrN1MPxfWYle3TTaN8s8/mpuiu5CAMxeFwotj0Tbz8rRzgZ4bdL5rrwU2bAAmT9Y+DxsmXxfdn7p4chLuYyX0hg1j5/Ui9M44Q/4d0Cihm0xYlC6WAPjOO89+G3Sf8SHQIuj+ePxxVoCTcyAaNo5xwg9U5gF37gCsrvmbXZ/frwnE8883xpJy5391KIm5y+ZmQi15FqxZgDcXvwlAe0x/1U1zzmiHhXH0Ni9nsitSkeQl3fsjPzyCF4b5gTffBNq1cxS66cXRo0Lv0IPY79wKvfq4IBzfA25CN906esvLgEXrFwEAOhV3wjsz3sFt+9yGCb0mONp3uhunCyxdgFcVan+IskPWxmrx8NSHMbn/ZCw6bRF6t/E+lpGSF8jDrO1noV/bfrho3EWW3clAMJT5fbTOnHNHz/g7L18g9DwYZnrHfteeu9o6esFQHuYcNgeT+k/KJBya2G8iai6twcdHf4yfZv2ET475BKUwrvOwL4j1F6xH9SXVQhE/ZcAU2/GTvKMnct1k4Zh8uKJd6ObVu1+DYR2G4aY9bzKva7hZmMgoLBQIFoEYzYRupuJYGxUntgHMYtf8vf3bFv461ceXVpHbsu7EMUJPH6OXTzJp6mP0yAlPhR59+ZEgjh5NmLVNB3Nmb9E52LbMcK8DFscu1bkz87m2wmgvfyezS3zU3HGfO1ax9bLzzsAJJwAPPWRMcxO6KRJOVkkr8vNZcdiunbaOJ54AfvoJmDsX2GMP4O23jTbQzqEsGYtVeQWnQm/YMK0NPHl5mlBatoxNeOHE0ePDKK0cPX1dNNkLRSb0rrlGS81+++3m79NjuxgRYhMKZ+Lrr9lSC1Yhg/wx0fH5WNf2ssu0cMrDDzevQ1TbzQlWoZtHHaWJjf/7P6M9Vo6bDv093bsDa9caYyB58WnVVn57Dhy9Kz66Ai/9YhTsZsKeZJk2JSS3H23/BpAKvaOOgi/0ArA4HXpMHb2UcS4lBg/ErDa/4ZlhwL2P745f7bbBnf/71PwX8x6/EKM6j8Jj0x4zPfhH3D8CAPBDR+CbrsCJU4Hd/wBefN6YhyZSWV65HN1Ku2F55XIAnNAbMwLAD3YtNPF3dVrAxmLS0M0EKRdQFPbo6AUCWF4GLOYMITrO0AlVEXEyErfE/UAsxZ7Tsw+YjYJgPg54/kBmultH76BDAaQT53Qv645B7QZhn35amK+T0E29ox/0s2P0qkrCABpQmlfKhG0Bmvg7buRxOG7kce4aa0N+MB/l+eX4/YzfAQBv/v6mdN5AIIRwWuzo50sgabgvIqEXkiRjCYfZZ1leIM/S0Tt8G8G9FsCi0xbhrd/fwgnbnYA/NhlO8uRoL7wZXsbMGwzlYerAqZg6cCozXXdvhnYYCgCoCBSjKqFdeyFfkKm/Rzv63530HUZ2HonqSDVKb5APOQhwCVxEoZtBiUtnCt30B5gskDzdS7vjx1k/itfloGi3ThGXjRUAU4ZDRz8fYqkEovVs1Eh+woeGdKpM29BNBzXporyjxyUSAoC8dPglTcZS0lPLBVBQYEwLhdPHlOzfYNh4BlNHkb7IG9RuEI4dcSz6tukrfCEjolMHYyxf0OqxXcaeQ7VD+gMLvwQAxIkifemQl7Bzj50dbbu5ohw9hXO23Ra44AJ2mt5BvuEG7X/Z94BYONHOsZ2jd8opWtjfsGFap//++80ddlkqfjtHTxdlTkM377oLOO004Ntv2em6s7bLLpo7wW/fSui158JA7IQeFQB2ddsA7XdfeqkWCnnmmcb0a67R/tfDXt24TTxDhwK33SZuL4/M0eMpL9dEF59MBhA7ek6g56IodJPUKQPgzNHj9zkdg8gnM+H3MRV2duGpgm39+7N/M5/X166Ha1atwqdv3Yc270zAo98/aj2vxb5OkbbRzn+itARvpg/hbxt+s28P5+jNi2kdy/l/z8ew+4aJlgAA/NQBuCU9nOOjPmwYZop4KBvrNzJJCGh9vLpzzgDalNu3kSOaSL94qayUOnoxf/aO3pLYWpw41fydW0fPbsyUU2J+VtQDWuHojsWdTPM2SC7107Y/DUvOWIJ3pjwr3U73UjZ7pF3oZsgfyoQA6uOEdEevskTb7zT8q2tJVwysGIhHpj5iuV4R5+x4DhacssByHiehpjqBYBh5aVGkO8xUnNk6esTtCHPb1Rw9s5AYVNIHC2ctxBPTnxC2qU+bPjh9zOnID+ZjcLvBOHuHs3Hf5PvQKy5IeOJQ6FQExTXUeEZ2Hplpu46oDmEwyN4bi8Lm57jc0WOVgeboybvJVuJDdHyk6ykuN01LCfzeUPo4RiN1qPWz11vXiHGMbZOx2LixIkTZS/N0R4+I8/wy7fmVTxy9cJ72PR1/R4UehYZu+nw+PLL/I7hsV3OtQBmduxiZi0TnuM6MYTOYzz0rDIFIX9IdMPgAdCiyKUPVzFFCT+EOvtOpOz7bbad1Zm/iQhjsQjdph9VO6Ik6vHQaL/Rk2+GF3jXXGFkInTp6FRXaGLJRo9jpsuLW+hsrN0LPLgmHldATCadAQDtegwaxDtPll2tjyvbcU/tMQze94FTA2dWuc4KotpsTqIjnhR5/3L2EbgLsMeHH7FmJ6bfeYtvgtmA6kHGpXNGlC/b9/jxURapw3Gs2TobF/kiSrgUv9MJu0lbb1C687rPr8MzCZ0zT4362xpvMQYokIkzI0M/rf878XedPuHezwTpqMkcvGjBC8azG6D01/Sn8a/y/zMsnohj6w0l4P53BfbtQD+w3QCsC71roRXIj9OJ+LZyMkhfMEzoHsvGP+cF89G3bl+k08vCd9l167GLZrk0XbcrsXz1UbEOh1vmtKtZODCr0pgyYgl9P/zUjKij/nfxfy211KOqA4R2HW87DFzsX1XLUCQRDCIfYgvA03NJNMhb+HJOFbk7rtQ+26bCNI5Hi8/lw28TbcMroU0w112TtE1ERMsKnQ5wbd81u2kvIk7Yzxo3T1Pn5AhHGn3MiUSN1M7n7WsAfQMiim1xgca46cc10CottxpSn0c+HaH0N/ua0dVHC2J7tGD0PQq+02DyeND/t1NHzWj/XCmgylvR8SeLWyV4EJByO9ZXRqZ0RZk3PywGRYrx66KvYeOFGvD3jbZyz4znMcuftdB7OGHMGPj76Y8QdloppKSihp3AHL1RoZ0gksuj3duPGUimtUztokJadsbzcuiA63x4+dJPetK2SschCPO1qsYkQtVE2VpCnqIjt4JeWmssuUFfLar1ux+jR35qNo8eTC0fPKdkIPSvn2ecDZs40r+Pcc5macZa/gQ+ftNrHo0cDP/xgfHZbRw9Glju38CFsUqyEHnlry4Rulha7E3o2iYAu++gyHPHyEabpa4qBf8jhlQmLSDzCCDMaiuZ4PwjWqSN19ALOHL0RnUbggnEXmKZHE1FEU8bLmKAvkFlHUzl6cYGjlxfIE3YoZWP09N/AJ4qwYmC7gZjRbnfhd4EkKwx1gbDfEcD5ewNVhWahZ7X/Th59Mm7rf7r0eydhpLzQsyLgC2QcvVqnjl7SmdDLC+YJw9raCDrzTvBnI/TyDJHDO3oXjLsAC05ZgHsm32Nsi5xTBT7ztRNM2HfSZUIvlogynwO+AFOygsfK0ZPVxhOup7DcNE3k6IXT50PMD6ziVk/H9DFCr2cP8Hgp/l1Saj438tLXFxXouuteQMKFM0KPOnqS8yORZSKUzqXGs5Ye5wHxMuw/aH+0KWiDif0mmkqoFIWLcOe+d2JCrwnSRFotlVb2cxSNDt/ptHNi6PeiN+S0c5xMavN8/71WkDsYdCf0AgH5mC2rOnoywWHl6Mmwa6Nd5kBaJy4Y1MbSbZse1N2+Pbt+t46eUzGVraNHj7NToefBPTHhRqDyQs8qsY3Pp5Xu+P134L/krf6NN7JhlqL9O3cucM892vhWip0olZ27VtMIdCyaaKyHE5ZtXqal8hdh0X4aehMLkLEXJUXCQrRfdQPGPjwWX6/8mv3CxtGj0A7ExXux38mERSQRMUItwSZqqYvVedpvThy9mMMxeuFA2ORwAGDaDKTDy9Iixk0dPUDcmfTCrWOBf+o3MNPygnnCenQyh1XfD3aJJHhCkv3HO1a0Y3nrWGDGLtrxph1yu5T+JYPkCTachGXyYtBq/wf9QYRDXOimjaMnG6PHty3kDwkdvcICm/qjEkRJLxwLvQJjPDZ/vvt9fgzvOFy6roISsxMWiDt4myTZ7fEE+wzRylDIn01W4v6E7U5gxnbRYvA8RXnOMnTqmUyjAWAVN/QwSNrJXEN0CEEWlJSba+7qjp7P58NXx3+FD478AB2Ltfny2xlh26F8rS+VbFdhtFdyTOPbjXDVrg+O/ID53K7QeCbTLJ/5gpcCMpTQU2zdyEI3ZdiF5/GOHsAmYaGdPSdCj3bYqetgFbrJJzjRyZXQc+roAWz4pt6uRx8FZswA5s93vt5shF62jh49Bk5DN3OBV0fP52NfKMiKrffrx56PgQBbikH0W8ePB0491T4TKB/C1b+/JvAnTPB0LGk2RS9vbwGg9x29se/sfYXffdqTFUaUZCqphQMPHIjYREN1JYqLGEfv+3Q/YOzxwLyV8zBx9kR2ReSYWMmRVColF6SQO3oN8QZGGNFi6nWxOlsRJHpjn1nfXntl7eiFA2GhUIol2d8a9AczHeSGhDtHT8frywAr8oP5wvA1mfDWO81+SbHyZw8Uj90LS0LAeCEjc19C/hD+s9d/0KOsB67e7Wpx4/R1CoR3ph0Oxlq6cvT8gYzQW5LWQvQ3idxSdoye2NELJ7R7glDoFXkTej5BCKpToVdKknyEXIp80f4UCb0fT/kRD+73YOaz7NqOQxC6KXH0Dt/mcMt7a1G4CJ8d+1nm86n5u+DPs/4Uzus00Yh+PvxTCGzm3oPRc5MKPavwYDeUtu1smpZH8jnu0G0H7NFnD6M9JSQkN531lWa6lIWPJgqdv+ADgD367IHr97heuF7q6OX7nQs92Uu6looSegp35Fro8WP0eNw6ehSr0E1+bJ9ovi0dugmYHT1Aqw/41FNaJkfZepuTo0cffk6zbuYCr8lYIhHtWH/4IfDxx+LQTdnftLh6Ln9PIAB89x3w0UeIpxI4/8IReLsf+d7mPKI14bLhwz8/ZCe8+irePXAExvf9FCP+O0K4TCKZ0Ma9/vorYgXG9ZBoV8EIve1O0R6o+rN4c8NmdkVkfzaccoK0jVd8fAUu+uAi4XfFyZB8jB4Xusk7elaE/CFh5ywT+vfCC0jsPkG4LE3GYjVGLxQIwe/zmzpEfO27gC+QEXpUuD4xTZxQAzAXAC8gHbYb9rgBF469ULqsU7qXds+Zo3f4Nofj0G0OFS7n1NGThdL9sOYHnD/2fCw/e7ltGnUr8eJE6PHOmuUYPV8AeWnHZF36fRL9TT6fD0FOSNMww4AkdFO/BoVCT1IA3I6UwAl1KvSKSLFwNwlMAC3hj2m7BebfMKzjMBy17VGZz1Kht8s45nPAJx+j9/SBT7tpKnwjR0qFvlDobTfKNCmcHhP4myDCloZr0r9FyWi8IKpHmBeTO+D0WOp/2znmgBaO7ZbDttGqxo/vOZ5tAxF6eRYvaXiUo6fYuskmdNOpo0exS8bCj9GjyMLf+JBQmdDjsx86QeQCbiL1brwIPRluHT2nDlouHT2noZu5wI3Qo86cXh9y9901B41HJvQAe0cvG9K1Eh/+7mHcUvgDJtFhgnZCz0N9NEfj0vbfHy8frRU5/puvhZeGPsxpmGFil3EIF7OOAS32q4uVyoZKrSYgqUO5+TouCyrh2s+uxR1f3yH8rl0qXz5GLxFhhJEboRfwB4Sd0ppYOulOWRkSXcxvwAEguu1Q1BVrP7wwVGjp6AHmcDYeGrqpC82eZT1x5LZHSpeh9QMBIJ8Ive27bp8pMp4NHYo6uHL0MmP0BNeR1XincEgcPsfXRON/s84evfcQThdhJUS8jNGzco0D/gDCYVbImAp6c+1xkoxFz0ToF/S59WLlbklm4egVFRLnx2UG2gKRozdqe+G89DpKyoReIbu+oD9omXXTFYGgK6GXKjELq1BYO8foy5LSvFLMOWwOggGxo3fPpHswoMLIROkVUZmKvN6CTNhpaKZK/XcPaT9EOv9nx36Gi8ddjDN3OFM6j4xe5b2w8cKN+PAo9sUkDSnOd3B96iRbmTJqZT9H0ejw4ZG5DN0UkY2jRzv+fM02mdCj6+vVy7ptIooFb0Rp4XS6/gMP1EL7PvnEmEaLeNoJIS9ZN52wNQg92sYGm3A3K6GXK0fP4s3+ss3LzBNttuUlmUimBpwNdiGNTDIWEmaY8PuQN2goM281OW2LwkVIJBMov7EcFTdVINJgZCrdHHdQxF1AUSokH6PHOXq0JEVd3Hr/xRIxYUeW1qXjnbfMsoX5iJZp94m8YJ5UyGWyRdp0mIP+oCkZi+7mPHfQcxjbfaxpmbJ8VvTQEKyAL+AqxFCnX9t+zGefzyd09N6S9Dl151IUuinqZOqEJB04J45er/Je+Pfu/zZNl28ru9BNN45VwBcw1b8LWYw7BCzq6JG26XMkBdkNnYYQ8iRS5nuvY6FXVJ75O+QiUyUgDt0MSo4RDbPU72D/t6tWK3XW6FkABG55IIhcJmCUXVdO9xV/Phze/wBsumgTpg6cypTQCJASE/3a9sNvp/+G9m4LWHK0LWhrcsySHeVlBwpDhfj9jN+x9Mylmd9XUViBZWctw/oLzOV/du6xM67f83pP9x4AaFPQxnS/oeMWva63NaCEnsI9VuKKJ1tHj4o7URY+WQgmYD1Gz4mjl63QGzlSK7x9DknjS9ux7bZaso5ddzWmDR4sbosIK0dP5Pg4HauVy9BNq202pdCjRFwksLASenZurUeEwspmWzVRQyQ5HX/lVBxSIScKxWGSsZCxc4lUwtQZriKXd1GoCJsaDPd7XVvjGvVaBiDuSzoeo0fbbZeMJZFK2Ao9UecX0MSv7nSGA2H4fD6h2HMj9DJj9NJCTxdNhww9BM8f9LxpGV705PuIG+APOK73RsNKpw2cZvreTYp5vSMuCt304ujxQk/k6N26962oKKwwTZdhdSx26LaD4/Xo9G8rd0QC/gDy8tiOfZA7pfj2UFHCJGMRiOG44Nr1KvREDpnTFP5FpD4bL2TtEHXenYQH6vfUKydcie9P/h537nsnAJjG+gb8AeaWr4cIHjL0EHcNhXZ+W70M2H/g/hhYYR22GOZqz/XpNDizn0uChhsruobys7SpfD4fPj76Y1RdbNzj7IYI9GvbD33a9GGm9SzvySRMaUyYZCwuHD097P22fW6zmbNlkONYI8VWQShkuFS5HKMnStmuj0srKQF22816eb4tI0aI57MSelTkZCv0Jk4ErruO/Z62Q+RQDiGhDW6EntXYyXRiDMdkm6XLqaBsLkLPztGj7L239v/wdL2sxgzdTCMcx2NzjKjQs+PiDy7Gmpo1jkNmqICpilShPL+caaPU0UtaC73icDH+qfvHWHbnnYB//QsYOdI8fs8hcaSkY8JSSKE2JnYKnSRjsRV6SYnQS7BCT/+fT7Kiize7BBVBfzAzjx6ySwWWqMPNt42Gbgb98hAznsJQYeZcyw/mo2NRR6ytXYueZT21dkgSq4jIOHqC8gqi8UE6eoIKngAn1EViUS+m7hSRcB1QMQDvzXwPXUq6CJaw5r9T/ovCdwsxqvMoXPgBOy4y4AsgzAu9pPU5merbB9ikhSBToSc6hxIwn5+ehV4WCT+KSGkBWeITGcPaDcUbf7FZF6lLL0O/X/l9fozoNCIznXf0gggwL3zunXQvjhp+FMb3Yp0tJwxpP8RS/L5y6CtIIYXA1dpxG91ltGmeEBfKS0VUWV4pkH5XFwgKhF4q++etz+djrsX+FfIXFc2BIAm7tarPyXPktkdi2qBplvedloQSegr3WIkrHjflFUQPixNP1LIdjh0rTo7Ch2QCwIIFwBdfaJkqZfPJhN7q1cbfXgQPdXhE7aX7TpQ+ntZl27jReltWjh4VW+ecA7R1MebmvvuAI44ALvSYkKG1CT36e9q1A6qrjfPHa+jmU09ptfmefNJ2VqHg6NTJPI3gVOilUinc+MWNAIAJvSY4Woau+7Pln2G/gfsx4o6KCPqGPJ6Mm5yFzeQyLAoXMUKvNlYHXHmlNt9P4oyLdsQhd/QAeR05J+6mW0evLK8MlZFKk6On/8+LTl0k2Tl6Ab+RjOW7v78zfS/qXPK/L99j6GZRqIgRem8e8Sau+/w63LDHDZl1OUXvULsO3ZQ5ep1Y4cWHqwLwJM542he2R8/ynp6W7VLSBc8epJ3bRww7At1uM4RnwB9AOI8bo2d3iysuAdKmON2PQrHftg0AtiSG59DNLIpc09ICIQc18ABg7tFzMW/lPEzpuAuu/44dn+ukxIhsK93L2IRnAfgYl7QkrwT79hdnIpYx7/h5+OKvL3DEMHPNT4rP54MPPiw6dREWrF2ASf0nmebhQzcHtTP6C2X5ZYbQEzl6Nt3940Ych0d+eMRyHp1Fpy7C2tq1pnDt5gatLehG6AHWL5daGip0U+EekbiSYVdTzYlrtc8+bIeaIhKSw4cDs2ax23bq6NEELF7S0lOHx4vQo8lc7JLBWDl6TsfJiejbF/j6a20MoRec7remzLpJcSP0AO0Y62336ujNmKFtVy/EbvFGXOjodRYn+tBxKvToG2ynmTqpGJv67FT8uelPaTgn4+gJQjdXEpOlKFTEjpMjYsTp+EGeuC8lHaMHsMKMUhuttQ15FQmw1dWrM+KWd83+O0WrwVgfq8+Idyr03GyH/T5kWn5trVFHUeSq8aKSD9104+hl1hHMx6guo/DSIS9l3vS7cfQy2xeIQ+vQTeM+2jZkiLlgPtspFq3DbQiZKCzQy28U0bW0K7teXyBTkDoDdy8whZ2Se5WsTIVOfKg5MYZ3Ry8LoUcSwIQc3sLH9xqPi3e+GCGBm2tV9F5H5tafNOok7NfXEFhB+Jl96ubFhc6O3XbEeWPPcxzKOrj9YBy2zWHC0g1hLqMoDfUsKzBCYIWOno3Qu3vS3Xjl0FewfRdxMhu+jU5fDDYlATpGT+L8bw0ooadwj5tC125CN71Aty8K/RRtJxhkwyZpB33mTOCSS7Sxc16gHX9RYhY7oQcA33wD3H+/OFSV0lhCL1tai6PXMV0gdto0+bLZJGOh5+AFF2j/H3usaTYrR686Ui1cdXVUPJ2HZsWUJQ/hoUIPAH5c+yPjXknH6CXN49qWlRt/+3w+Zt1U6P25WVx/yo6YjaMnG0e3sd7GTYdYgEUTUfy+8XfhunVRsap6VWaaE6FHBaNovqA/yKwTYEtVNKqjR4SIsKaZhzF6ovZavV2nnf3pnY17Ji/ARGP0nI5F1BEJGrclAZwS8AeYBBuAOe1752L2hQ+tWy4TerpDlQiY79NeU/FnJfTINkNxdyGgwZD5eqBFymWkRMMmoF1f90y8M/M5kGJf93itSZorQhVGnd12he0YoV9aZETtiBLS2Am9glABpg2ahmcOfAYjOo3A0we4Kx/RHGGSsXh8idEaUEJP4Z5cCr1sXR1RyQURfB09maMXDmvj6sa7j8EHYO/o0XbIhN7o0cBJJ9kLJqehm0roWSMTeosXAwsXamHDMnKVdXPIEKCuDnj4YdNXwk5Up0648fMbUXpDKV7+5WXT10wyFovjwZQ/kIgeHl7oleaVOnb0+N9ChV4kHmGKllPXyavQiyMpHaNnxZqaNZbfl+aVSjv4C9cuBGB29CoKzEk/nAg9eoxEYiXoD1oKU5FwOnG7E5nPdo7egYMPxPEjjzeth9YxEwo9D2P0RMu0yW9jmqZDE1QUkKLI/PGxcgWdIroWcxH+KcLv85uEqEnolXBCj/xtJ7JF17v30M0sxuhRR2/bEa6WDZKw3S+mvY6/z/vbtE9EpLYZKv0uSF4c8OM8mxp6n+ATt5SVGO50IGi+nxT4nPWV+rbti+9P/h6HDzs8cw11L+1us1TzhHH0wkroKRTOsQvHpDS20Csv18bxHXssW4OOh2+zTOhlSy4cPac4TcaihJ41sqybpaXANttYL5vLZCwFBcJ9Jwzd7NQJF394MQDguDnHmb6mQs+qKLNbRy+VSpmEXiQRkQo9RkgmE6bOJQ3djCaickdvk0ehZ5F104raWK0w/HVo+6HYp+8++PCoD+VCb11a6HG/VeSW6GPrrIQeFcuijnjQF8AVu14hrVEl6vBfusuluHyXyzOfGaEncPS6lnTFQ1MfwtHbHs2ILj5008m2ZWTG6AmEqVXSFOro0c4sf3x4V9CuPqEIkdDrVdbL9Xpk8PuQPy94odepiB2ryzh6MpGSniy63kX7RM8wuVefvcTrA5Ac4D0pB70ugr36ulo2mG8sW9q5FzoVW49d1knlWYRKE5Hkh0++H5sAej7wx6Os1Oj/CEM3PZzv846fh8n9J+PtGW+7XrY5QB09PoPt1oQSegr3UBHR1I4eADzwAPCIzSBi2oG2cvSyJdsxem5oro7etGnafpg40Xq+XI3R08/BceO8Le8m6yZPrhw9C2jo5ilT0p09PawU4tBOGtJp5dRRIeZkjF4ylTR1ECPxCONeyZKxJFIJk8u1vp1xDUQSrKOnC71UKpWdoyc4/YvDgpcwHKLsfaO6jMI7M9/B6C6jpULvr8q/AJgdPb4THfKHMm6rpdAj+3B55XLT9wGfH8M6DsPPp/4sdJdEwikcCGdSxQNAfqkR9hXwB0xJc3QB8ti0x/DUAU9lptuGbnrJuilob8fijqZpOrSouJWjx3/2UldLJPR6t+ntej0y2he2Zz7bCT0r90r6ZE6LAFFWWJH7/9B+D+GJaU/g+YPNZTp0kt28u5r0ZYHT8HEdekzdCHerl19MhtJUynF5mi0B3VdHbnsk810pdfQESW0K/O7ClAEt8+cbR7yBoR3kDmhzhgndzLO/57dWlNBTuKc5jdFzSlMIvaZ09NwUtc815eXAhg3AW29Zz5er/f7rr8ANNwDXXutuucmTtf9PPNF6Piu2RB090im5fzTw1HAw4/tEHTbqRll9zwi9uL3QE3XErBw9vrwCv/z6DoZQiMQj2FBnZAHUhd6mhk2eCsAD8vIKXsP4aGdSJvT0fcsLbD7FPe3EO3X0RASJa9axyCyIZEkgqNgNd++V+Vvk6LUtMIQg/S5Xjl7v8t44eOjB0mWsxsGFyL4r9pOwO249vCvoReiJau71Ku/lej0y2hexQo8X3LE8dj8cOvRQ5rMjRy/9AtJpqHZJXgmO3PZIlOeXS+eRlRJxAj2H6P3ICfS8cPNSwap0Cl1nNmUjGoPy/HLcP+V+PLb/Y6b6dGXtScbWdubopvxtRjR285odAR8Vet7Gn7YGlNBTuMdOvMnmtSuv0Ji0RkeP7jsrR68pBpCHw/bbzdV+798fuOgisbC24plngFdeAW6/3fu23W7TA3ynZN2/LmA+i8QXXYZ3IV74+QWUXF+CSz+8FBNnG66rE0ePbktPehCJs0LPqmA637mkoZrRRJQRdLVRbYyekyx6MmKS0E3e0bMSWhRXQo/r/PLboJ+tauXZuRxU6HUoMnfwZEKPukF07B9fR+/kUSfjxFHGyxBXQs9B5/vWYRdg6ZlLM8fEaXZCHbof2/cfkfmbP9c6FHXAp8d8atleO3brtRsuGncRMy2nQs/O0Wtbznzu27YvVp6zMvOZ3imkQs+vTXfrnlmRTTIWeryzEXpunDdLR49c4ykkm5Gfp3HSqJNw9IijTdNLiRAPhM3uXX5xuWlaa4dx9PKV0FMonJPL0M0tFVbIhzK2BkeP/iYrR6+5sqVDSnlKSrQw02yOA3X06p2VJxDx4qIXMfvH2cLvTJ0SruyGXYeN7/Ae+qLmAlz/+fVYsnFJZroT14xuS0+iEElEmG1YOXpWb/4jiQgTLqm3x23nj5JCCvXnnG6aTpOI+H1+poNtNc6HdixlQkEXevxx4UPLnDp6FFEtLir0LhirvQTYu+/emWkysRUOhLHmvDVYc94aFJASBXwylvN2Ok/q6NFEGl4dvaA/wIQMuhV6VCS3LTUcTVHpjF167pL5u8BlXS1AC228Yc8bmN/KJ6rIJtSPd/RMQs9nFihMWQa6HyXdO7192bhwPPSan9R/Eu4kmSvd4PZap+e2m4yYwzsOl35Hr3ErQdjcoC+vRNeQlxcbLZ1Ag/E8ye/Wq+ka0sQooadwT65CN/fZp2kcvcYUerQOXmM7erL1Ai1D6Lkp4t5coQLb41i/aCKKg184GDNfmWlKdAJYhxkB9iFYiWQC0UQUt867FT+t+0kaguU2dFMfn8U7erSzxmfd1JfnQ9L09dBix7rQo66gF+qS5s4jzWaYF8hjhIwsqQnAigpZJshch25SHtv/MTw1/SlmGhV6e/TZA0vPXIrXD389M81KOHUs7oiOxR3ZEDgudJM//+h3XrNu+kmMIZ8Knl+GbkME44wSMV3ZUGm5nCiDqVOoy8wf18VnLMYdE+/gF3EE7+iZsm7avNQZ3H5w5m+7zh29Zvu37Y+rJlzlrJEC6Ln+5hFv4owdzvC0HrdCj57bTgT2tyd+iyt2vQKX7HyJdB7m/EtlJ9ydYFdI3Sn0vi665vUw3x5lPXKyvZaAb73xPC2qsM/G2lpRQk/hnmwcPTr/229vOUHChzI2ltCjgksk9Gg7tnZH78ortdIFDz7Y1C3Jjp12AioqtP89IBI3FD4syqrjcfKok03TEqkE7vz6Tpz33nkYdt8wtCkQCxSZ0EulUrj6k6sx5J4hmbIDPvgyHXB+jN6oB0Zl3BS+jp7eIZwyYIppO9FElHH09PIK2Th6gPh3UaGZF8xjQhB7l8uTa1AhQcUhZUXVCny54ktT1k5TMhYiEByHjgZCmDF8Bm7e6+bMNF4Y9WnTh1mfk44qdd54R48XF1R42YVuykRmHt0e5/rxnfd5x8+zbDv9rXRf0FqClHsn3YsORR3w4H6Nc9/p17YfztzhTNP0aYOm2S7LF3A3OXoSoffFcV/gX+P/hVO3Py0zLWDTvbt38r0ozSvFzXvdjMVnLMb/jf8/2/bJyCZ0kzK2u0UpGwEFwQLs2WdPjO0+Fj3Le9rOP6rLKFy929WO3dzGDN3MC+Th2xO/xWP7P5aT9RWFi3D/lPtxx8Q7hPemnbrvhJ9P/Rk/zfopJ9trCXzfxzjOAyoGNGFLmpYtZKcoWhVUGNkJCj7N/7HHarXCdtmlacVIYwm9Hj2AI47QwjZFQo6WAMilo8f/hi2dgMULFRXAF180dSuy5/PPgViMLYDuAup6icZ9uQkfum2f23D//PuZaYlkAvNWGp1lmRMlG6N31//uwpVzrwQA/Puzf2faqYslPusmADz303M4cdSJJkdPn08kCiIJsaOXrdATjfHjHT3qWnUt6WqaX4cJE5QIvc0NmzHuEXMGWCsx51To6dAQv2A36zf0TkLa6DwBX4DZP7yj6maMnox8hFAPTbTw5zwVfm/NeAvbdtrWcl1UQNNlRVlTAWDW9rNwyuhTclL8WpT8hifgC2DuMXMxqvMo23n5kELe+ZYJvbHdx2Js97FYWWWM17MrCzCi0whsvHCjqyQmMrIVesvPXo5f//kVE3pNcLWcz+fDezPfy/yda5L5+fANHgKstq6r6YVupd0wqov9OeGGk0adZPm9VbRCa2RJgfFMs0ro1NppAb1BRbPDjTDiHb077wRmzwbmzMl9u6zgHwKNJfR8Pu333X+/+Ps4eVDnN2LMfEsQeq0Fv9+zyAPshYxd6GamGT4/8oP5JgeH74S5dfTOeueszN/6mL6gP5gRA7yjBxi15GR19ESiIJlKMm3IhG4Ksk5a1fTiEf0uuv28YB4jpq1KL9DfIxN6Ik7f/nT4fD78dfZfmWl0n1mJSxG00xJ0KRJF0HMm4A/A7/Ojd3lvlIRLMKjdIGZeuu+oQHUj9PJg3HP5Dlg2yVicipZciQJmfJwEv8+PnXvs7MhFmtx/Mq7Z7RrMOUx7PvJhoXahm1ToOqn/lguRBwDHbHsMAGC7ztt5Wr5HWQ9mXKkbfD5fo4g8AEiNHs2Ow84Bnx37GXbrtRtePezVnK5XYea+yffB7/PjtcNea+qmNClbr8RVeMeNiOCFXmGh5nhtaayE3paECr1sxZjVw60lhG4qALChm6I347yjJ+vUBHxaUotwIMy4GYlUglmHdIyeg6ybq6pWATA7eny7f1qnhQfxWTetxugBbP0/K0fv0KGHok+bPib3UoQoHJZuf3C7wdhQb5R1sBJ6yzYvy/ztVOjdvs/tOG2MFlJHU/zTfTa2+1jc9b+7HK0P4IReDt5U844eoI01iyfjpnFiMkHnTuiR9pez+9HtuCsqhtwUaM8FIzuNtJ3Hjdvl8/lw+a5GIXte9NqNx2WSk3Rx9/IgG6YNmoYFpyxAv7b9ttg2twSpgD/nInLnHjvjo6M/yuk6FWJOGX0KjhlxzFaZiIaiXvsr3ONV6DWly8QnK6EOTCy7ZA+uiOcupbUlSugxVDZU5jSdeLZ8ueLLzHg3KmTeXfKuKVSOd/RknV+9w8+7AIlkglmHLEzQSTKWv2v+zmyLOnp8B1R3/jY1bGLaYRW6CQDVUUPo6WP0RMlYQoGQVCzyfPSnuVNFt3/k8CMZIUyLgPP8semPzN+80Ht35rvCZXbotkPm2NBOIy/03JBzocc5evp6RceJTqNuq2dHr4RNiuLWZeIdPTeFs73y7IHPYt9+++I/e/3Hdl6njnwuoEI31b6dxZy5xefzYXjH4Uwob2ugJWXdVIjZ2kUeoISewgvZhG42FZMmAWPGAKeeqn2mjl5LFXrK0XPEX5V/ofzGcuzz1D5N3RQAwNxlczHukXHY5t5tALBC74TXT8DlH12O2mgtfl73MwDnnQ29g8x3dHlHT5bF0omjp2Pn6OmO4m///JaZFk/GM4KQd4lEbKrXRKLI0Qv5Q47WIePwbQ5H7/Le2LPPnjhg8AHM2BUrR+/E7Yx6clToLTljiTT0TOYy0X3Wo6wHxnQd47j9XgtFA+KEPSJHTwadl7rRbo4H4+hZhG46cVP4MXp8QpPG4NBtDsVbM96ShkEDwOguowEAhww9JKttrTnP+fgwei7w940Lx14IALh94u1ZtWdrIoWU4xdKCkVzRYVuKtyTTehmUxEOA19/bXymZR1aqtCzQgm9DE8ueBKA2NmxoyHegCcXPImJ/Saie1l3+wUc8MzCZwAgEy7IJ42463934eVfX8aSjUvw4VEfCh0BUTiY3kG3c/REY94AZ3X0dOzG6DXEG7ChbgMTEukkdJPy5+Y/ARhCryBYkHEdQ4GQ6wQmlMkDJmPygMmZz7fsfQv8Pj9O2O6ETKF2yqT+k3DFrlcwYowKPauC5zIhxu+zecfPQ+Bq+T1SVqzdjaM3rCof/53yX8t53KyPhqK6WY46erywpJ+dhG7yjt4rh76CKc9MwU173uS4PY3B2zPexqu/vppJa++VjsX2CV90GEePu2/csOcNOG/seehQ1CGr9mxNlIRLcOOeN+KrlV/hrB3Osl9AoWiGKKGncE9LdPSsaKlCz0rMlXmvEdXaoOGDbrlq7lW44Ysb0L6wPdZdsC4n7VlRtYL5zDtWiVQiE/r47E/Pmssr+HyMk6KTCd3kHD1+eVnyFyehm3RbGUcvYc66WROtwW8bfmOmOQndpGxu2IyN9RszwrQoXGQIPX8o62yclPZF7fHYtMcAAJ//9bnp+8JQIXbstiMzjQo9q7BgJ44eIE9C0qm4E9bUrMHuvXfPTPMq9IJJ8T2DCionyVA+POpD/FX5F44cfiSe/ulp9Crr5bgNAJCfCkDfZNaOHjdGb3S30Vh3/rpGS9DhlHaF7XDCdifkZF07ddsJ81bOw269drOcz8rd9fl8SuQ55M6Jd+LDPz/EjOEzEA6EsezsZU3dJIXCM0roKdyTa0evVy9g2TJg4MBsWuWevn2BpUuBnXfectvcUgXiZ84EXnsN2H13+3lbORvrN3pe9o3f3wAArK9bn6vmmIQeL9qoaPLBJ3T0RCUDMqGbvKPHhW7y9d10XIduBuWhm8lUEgvWLDC1wyrrJqU0rxRVkSos3bg0I+jo+J9QIIRvV3/ruL1usArdpJSEjWx8FQUV0vmcOnoyPjv2Mzzy/SM4Z8dzjHUS8ZjrZCxOBBIVnR8e9aHr7eV17g6s0a4Dvv1MW1w6evqyTS3ycs2rh72KJxc8iaO2PcpyPsbRU+PLPHPGDmd4LvquUDQ31Bg9hXu8OnoygfjBB8Bpp2kF1Lckv/wCVFYCbeTjLHLO5ZcDAwYA/7EfxG/LoEHy78Jh4NVXgTPNhXu3NrJx9HinKhesqLR39HR8Pp856yZ8QqGnd5j5jjMfuvnp8k+F7XITukmToYhCNwHg139+NbVD359247n0MXN/bPojM6awKGQkSQn5Qzh2xLEAgH377ZuZTot5e4VuR0ckOHw+H5acsQQLZy1EWb7cQZc5ek474v3a9sN1e1zH1s5rxGQsW4K8kvLM39m2nwq9XBXubm50KOqA88aex5wDIpgxelswCYxCoWi+KKGncI8boUfFnewta9++wN13A717Z9cut4RCQGnplt1m587Ab78B55+f/bqOPhq45hrgU3HHXaGRjaNnl87cC5WRSuaz2zp6Pp9Y6GXG6AmSscgSsFhtxwre0RPtp6WbljKfX1v8WiaTpt0YvYEVmru/cN1CoaMX9AcxY/gMzDt+Hl485EW8ecSbGNlpJF465CXHv0GGU0cPAPq27YttOmyT+XzvpHtNtcTcOHrHjTgOAHDCSOuQPyYZSw5KCmxpB4wefyuh5zYZS2sVek5Rjp5CoeBRQk/hHq+hm4rcEghoDuEuuzR1S5o1evZGLzSGo8fDJ2PhEXVe3YZuunHrnMCP0RO1URd6+li2ZZuXYV2tNs7RLnRTH4v0xuI3pELP7/Njx247ojBUiEn9J+G7k7/DqC6jsvxl4vIKToXQrO1nYf5J85nxe23yxREDon127+R78dFRH+HuSXdbbsezoydJub/FHb2gQ6HnMnRzaxd69DxVjp5CoQCU0FN4wWvopkLRBGTj6OW64yh6y27n6IkSfViFboqSsbhJtOIEWmNNNEYPMGrOdSnpYvqOD92k488AYMqAKfD7/FiwdkEm+yYVYFZF42U8Pu1xHDPiGHx7ovXYPqehm1bQlwuydP+ifZYXzMNuvXezDW31KvR87cShf03p6FklEBncfrDtuujyW7vQoyhHT6FQAEroKbygHD1FCyKrMXoeQjffWfIODnr+IKyvNSdwEYk2K6Hng8+x0LMqr9Aojl5QnnUTMH5X5+LOpu94R2+nbjsxgqWisAJdS7oC0OogAnBUjNlK9PQs64lH93/U1vUTCQ+7JBg81E2RiahsHBevQk/W+d/Sjh49/qL2rzxnJRadugidiju5Wi9fxH5rRA89HtZxWBO3RKFQNAdU1k2Fe9wIPTfzKhSNABU5yVTSMn38u0vexTWfXoOHpz6Mge0Gegrd3He2lhykLK8MD+//MPMdDdPUO7iiUgkUXujZJWMRjdFrzNDNhniDpZMicvR4oRcOhBHwBRCH8VsLQlpiFX1Mo8hpE7VLhlWtOxkDKgbglUNfYQqq54ps3KdcJ2PZ0jCOnsCF7VraFV3R1fH6Xj7kZaytXYsBFQNy0r6WzLrz1yGSiKA0bwuPP1coFM2SlveEUDQ9blw6Wk5gSyc+USg4EskE/AG50Js4eyIA4LCXDsP3J3+fVTKW5ZXLTdOoQNM7uG5DN1NIWY7RE2XdzIXQC/lDmZp2duUVKMLQTS4ZS14wT2s/2d16Bs3KhkrmMyB3oKxEjxdBdOaYM7MSeVZjEXMl9KxCH3maS9kBGprqpv0ypg+envU6WgsFoYLMSxKFQqFw/eRLpVKYN28eVq5cidWrV6OqqsrZhoJBdO7cGV27dsWYMWNQXl7udtOK5oIbly4YBF58EaivB9pbp4ZWKBqbeDLuyNlZVbUKQHbJWETOIe/efff3d/jln18s18MLvWQqiZVVK03zSUM3UwlXNfJEDKwYiGt3vxYHvXAQAEHBdIkgDvgCaF9ovu75MWh5gTyTENM7q1UR7RnjpMi6paPnd+7ovTfzPSxYuwCztp/leBkRY7uPNU3rUdYDf1X+hT377Ol5vTkP3WzCMXpbOmxUoVAotiZcCb3HHnsM//73v7Fs2TJ06dIF7du3R0lJiaOHRCwWw7p167B69Wr4/X6cfPLJuOGGGxBQY7haHm6P2YEHNk47FAob+I6taLybCH2+bBw9Xei9s+Qd9CjrgSHthzChm5FEBKMesM8SyYvNZ396FgvXLQQAbNtxWyxYqxUml4Zu5sDRO2LYEUxSkaA/mEmOUtlQKRXEJXklQiEiC92kZBy9dOgmza7YoaiDcHtWYbluQjf36rsX9uq7l+P5eT4/9nPc+b87cevet5q++/SYT/H4gsdx6vanel5/S6+jR108q2OmUCgUiuxw/IR44403cNxxx+Hcc8/Feeedh86dzQPsndDQ0ICnnnoKp59+OvLy8vDvf//b03oUTYgS54oWgh5qqONU6OnLZePoBfwBLFizIDNmL3WlOOTSDr7N36z+BgDQtaQrHt3/UWz3wHaZ7QFmQVMfr8+6HmA4EGaEVtAfRK/yXvD7/KiOVkuXKwoVCUPzpKGbBN7RCwVCeP3w17G2Zi36V/QXbs/qpaMbRy9bxvUYh3E9xgm/61neE/83/v+yWr/nrJuS/eOmdmAuoOKuuYSTKhQKRWvE8RPi8ssvx7Rp03DzzTdntcH8/HyccMIJWLNmDa666ipcccUVyMuzTiWtaGaoBCuKFgI//s2x0EsXGHc7jooWJvfBh0XrFzHf2yVe4fH5xFk3AeC2fW5jsgzKHD2+DV4IB8Km2mf5wXz0Ku+VKaMgojBU6MjRywvkSR09XeiFA2FMGTDF82/wkoyluZLr0M2jRxyNF395EXv32TvrtsnwwZfJNEodRBW6qVAoFI2Hox57IpHAr7/+ir33zt1DYPfdd0cikcDixYtztk7FFkI5eooWgmehpzt6Lp0wXZQAmkij4iiVyo2jpxMOhBknRu/E84JGNJ7PLbyjp4vJgRUDLZcrDBUKQ/NEoZu8YOHnydaR25KOXmND91UuhFJ+MB/vH/k+Lhh3QdbrkiGrDagcPYVCoWg8HAm9WCyGUaNGYfz48Tnb8LBhw7DjjjsqN68looSeooXgVejpTp5V6OZHf36ET5d/ykzTx5MBWoZNGqIYSUSYMXpOkbU5FAgxRcR1ESlzeI4YdoRw+r93sw+fDwfCzG/Rt2En9IrCRYzLqRMKhBiBkhcUJGMJspkDqdD0Qmty9LxmqmxKUUUdW9oONUZPoVAoGg9Hd9j8/HzMnj0bS5cuRSKR3VgPnZKSEnz55ZcYMEDVvWlxqNBNRQuBD5V0KvR0ZI5eZUMl9nhiD4x/bDyzDb0UAADURGsYcVIbrXUdugnI20wzXwKG0JM5VwcNPghn7XCWafrAdtZiDRCHbgJA7za9LZcrDBWiPm7O+BnwBRixUhAsMK2LTxGfrdBrifXmZHj9LbLQzS0Bn4BFP097lPVoqiYpFApFq8dxj/2DDz7A1KlT0atXL9xwww34559/GrNdiubMsGFN3QKFwhFeHT0d2Rg93rkTTa+OsElKamO1OQ3dDPlDjDNiJfRGdBqBqQOnCr/bpsM2zOdRnUdh+y7bY/su22em5QXyGKGld9pFpRMohaFCac0/6pYWhArw6P6PYmK/iXj/yPe1aZyj59aRmzpwqjDctDXQEkUrde5SqRQ2X7wZVRdXOSqboVAoFApvOBZ6J5xwAn755RdMmzYN1113Hbp3744TTjgBP/74Y2O2T9EcOfVU4F//Ar78sqlbolAA0MSQSFDwQo/PwmnFDZ/fIBV6tNNKXT86Rq86Ws1sryZa4zp0875v78uUUuDhhU9G6AkE0bju4xDwB4QhfwMrBqJPmz6Zz6eMPgX/O/F/6FraNTOND93Uwy5lZQ50CkOFwhp+QX8wk5gD0ERdr/JeeHvG25n6ctk6et1KuuH4kcdnPrem0E2vQq+5hG4mU0kUhgpRklfSZO1RKBSKrQFXMXgDBw7EXXfdhZUrV+KGG27AJ598gpEjR2LChAl45ZVXkEy6y1CnaKGEQsCVVwI77dTULVEoAAAj7x+JipsqTPXinDp681fPx9WfXM1Mu+TDS6TboyFwdBt86CbdntfQTRm8Q2Xl6BWFtLF8VKC+ePCL+OzYz+Dz+bB7r90z0/UOORUTfDIWXdzaCb2iUJHY0eMybIqEC+/ouRU3Pp+PGQfYmhw9fv85pSlDN+m55zabrUKhUCi84WmwVWlpKc466ywsXrwYr732GvLz83HggQeid+/e+M9//oONGzfmup0KhULBcNMXN2HovUOxvnY9flr3E+pidfju7++YeZwKvdEPjsaVc690vG26nlgihlgihnkr5uGfOiOkvTpSzcxXE63xFLopw42jVxgqBMAKhP0H7Y+de+wMAOhU3CkzXVSmgR+jp3fURULvmBHHMNsVjdHjE3CInCbe0auJ1pjmcUNLDHeU0RIzVVI3WQk9hUKh2DJklVXD5/Nh8uTJeOedd7Bo0SLst99+uPrqq9G9e3ecfPLJ+Omnn3LVToVCoWC46IOLsGj9Itz0xU2ZabyQynaMngy6nmgiigvfvxBjHxmLc987NzM9loyhNlqb+Vwbq/WUdVMGL1wsHb2w2dGjoq88v9yYnu6Ql+WVZaaFA2Fmfn18XUVhBbOdr0/4GheNuyjzuTBUiNFdRpva40So8I7ehroNtstYbacliiMn8MfAiqbcB36fH7v12g0AcOS2RzZZOxQKhWJrImfpEwcNGoS7774bq1atwrXXXouPPvoI2267LXbffXe89tprKqxToVA0CtQx4oUeL6x4oXfV3Ksw7pFxjrZDw954oXf717cLl9nUsCnzd020plFDN/VwSpFzpYduUleFdvrL8g1Rpy8/oMLIiBwOhJn5Zdsa0WkESsLGuKvCUCGOHH4kHtrvIabmnxP4JB1eREprLsb93EHP4Y6Jd2BQu0FN3RRHBHwBfHDUB9h00Sb0a9uvqZujUCgUWwU5z5NfWlqKs88+G4sXL8acOXMQCoUwffp09OvXDw888ECuN6dQKLZyqABz6+j965N/4csVzpIK0XAzmmSF3waFcfSi3rJuypAlFxFN1x092dgu6uhlauSRsgt8sWtZ6B1fxD3kDyHgD+D47Y7HuO7OBLUODd3sVd4Lp4853dXyQOt18QDgkKGH4MwdzmzqZjjG7/PD7/Mz55pCoVAoGpdGK4jm8/kwZcoU3Hjjjdhll12wbNkyXHXVVY21OYVCsZVCs17yjlkuQzfpsryjJ6M2Zgg9L1k3rZAlF3GajIVCwzR1MUidIl4gUqE3c/hMAMDLh7ysbYsUcafztS1oK9y2DCrgfzzlR9fLdy7u3KodvZbCpP6TAEBYw1GhUCgUjUujCL1kMolXXnkFEyZMwKhRo7Bs2TLcdNNN+PnnnxtjcwqFYiuGigleSOVS6FFBySRjsSjZYBqjl8vQTS+OnqC8AiB29HqW9cxM4wvH031+/5T78fsZv2P64OnM8vx8boUaPXZuwj5fPPhFzBw+E+fsdE6rdvRaCi8d8hK+PuFrnLWjEnoKhUKxpclpGrLNmzfjoYcewj333IPly5dj/PjxeOmll7DffvshEPCWDlqhUGydLNm4BLd8eQsuHHcherfpLZ2PionGTMbCZ9qUbYNCHb1ch27Kskh6cvQEY/QC/gDunHgnFq1fhFGdRzHz02LnhaFC6ZgrWiuvosB50hCAFe1uBNuBQw7EgUMO1JZTjl6Tkx/Mx5iuY5q6GQqFQrFVkhOht2jRItx555146qmnkEgkMGPGDJx55pkYPnx4LlavUCi2QvZ6ci8s27wMn/71KX4+9Wec+uapiMQjeHj/h5mwvqYQes0pdPOeSffgtLdOw1UTtNB4p+UVKNTRo6LqjB3OEM7vND1+No7exH4TAQBD2g9xtRxFOXoKhUKh2JrxLPRSqRTefPNN3HHHHfjoo4/QpUsXXH755TjxxBNRUeHuza1CoVDwLNu8DACwaP0i1EZrcd+39wEAztnpHPRv2z8zHw0r3BJCrzZai4e+fygzvT5mrhOnQ0M3l2xaYltg3A26oDt1+1MxfdD0TC08YdbNdOjmIUMPwbnvnYtde+7KfE/H6PFF50XwoZwyaAKWvfvuLZxHJj67lHTBPxf8g5K8EuH3TlCOnkKhUCi2ZlwLvaqqKjzyyCO4++678ccff2DcuHF49tlnMX36dASDracgrUKhaD5QZ2jYfcNw3+T7hN/xY+D4z7kQeue8ew6e+vGpzPR1tetM84b8Ia2OHnH0PvjjA4zvOd7z9kXb0Olc0lk4XUcP3exa2hVVF1cxCVMANqumk8Lkdo7e0jOXYtH6Rdir716ZaYPbD8b3J3+PjkUd2d8hGWsIuKsRp5CTH8xHQ7wBE3pOaOqmKBQKhWIL4liZ/f3337juuuvw+OOPIxqN4rDDDsPzzz+P7bbbrjHbp1AoFCYHadabszJ/0/Fydo4endctutB77ufnmOlra9ea5m1f1B6rq1czjl40EcX7f7zvefs8XpKxALB1yKwcSh06Rk9EnzZ90KdNH9P0EZ1GmKbJsofmggm9JuDWr25ttPW3FBadugivL34dJ2x3QlM3RaFQKBRbEMdZN1999VW89tpruOCCC/DXX3/hscceUyJPoVBsEaycOBpqSP9eXb0aF35woWk9Szcu9ZQURW8Dn9BkTc0a07x6iCZ19HR6lfdCr/JerrdP8cEnTaxiVTDdipO2OwndS7vjkKGH2M7rNHTTCVaOXrZMGTAFcw6bgz/P+rPRttES6N2mN87c4czMWE2FQqFQbB04dvRmzZqFWbNm2c+oUCgUAlZUrkD7ovbID+a7XtbKQZIJvcs/utw071tL3sIxc45Bx6KO+Py4z121QSb0KiOVzOdwIIzSvFIA7Bg9nZ177IyvV37teLuD2w3GL//8wkyzEkeVDZWmaeFA2HY79+93P1KplKMEJvp4wFwgyx6aC3w+H6YOnNpo61coFAqFojnTaAXTFQqFQufndT+jx+09MPw+b5l4HTt6ce3vX9b/gkd/eNQ07/M/Pw9AC7d8d8m7rtqgi00+wQefvKRtQduMsBIlNulZ1lNaz07EpbtcappmFe74d83fpmlOs0/azffG4W9gUv9JuH2f2x2tzwmNKfQUCoVCodiaUU9YhULR6Ly46EUAwO8bf/e0vNvQzeNeO852nZsaNnlqA+/o8WPayvPLkRfQkpvooZsdizpmxvL1LOspzTQpQuTGWTl6Bww+AFd8fAW27bgthnYYim3ab+N4W3ZMHjAZkwdMztn6gMYdo6dQKBQKxdaMEnoKhaLRybaemZXQ44uSA8BXK7+yXeeGug2u2/Duknexvm49M93K0dMzWPYo62EIvXLW0Xv6gKfx9pK38eSPTwq3q4tGipU4GtJ+CFacswLtC9sz2TSbK11KujR1ExQKhUKhaJWo0E2FQtHoZFvPzFLokXFwuuga3tE+RPSf+n9ct2Hi7Imm6bzQa5PfJiOw9DIE3cu6Z77nHb1de+6KgmCBdLtuHT0A6FbardmLvNcPfx3juo/DE9OfaOqmKBQKhULRKlGOnkKhyAmnv3U6NjVswlPTn8raweNxG7rppIyCW0cvkogIp5uEXkEbU3hnj9Iemb+7l3Vn9o+dIBMJvdYwrm3KgCmYMmBKUzdDoVAoFIpWS8vvLSgUiiYnkUzgnm/uAQD8a/y/0L+if07X71bo6eUTHpjyABauW4ivVn6Fb1Z/wyy3od6d0Pu72pzkhN8+oDl6fPmGLiVd8OB+D6IgWIDCUCGTRTQvkIcUUtLtioSgGtemUCgUCoXCDhW6qVAosoYKsVjS7KY15hg9uj19vJ7uvo3uMhp37nsnCkLm0Ei3jt7iDYuF03mhd/g2h5tcuFAghBO2OwEzhs8AwP4eu9IHXkI3FQqFQqFQKJTQUygUWUOFi6jmndsxerywc1qgm3f0dDdMVFtuY/1GV226/GNzXT66zbHdx+L7k7/HTt13MiVQ4UMteaGXSlk4ei6TsSgUCoVCoVAAOQrdfPnllzF37lz8+eefqKw0d6h0evXqhSeeUAPvFYrWBhUuegKSbOBLFkTi4vFxPLzQ04uz80XNAfflFWToLmL7wvYY0WkEALMLZyX07NxO5egpFAqFQqHwQlZCLxaLYe+998Ynn3yCUaNGoWfPnujbt690/h49eki/UygULRfG0RO4b1TMpFIpW3FTH2eFnl6mwI5oIop4Mp4RhrrQq4pUOVreC7qwpWKOH1fHO3BOHUqg9SZjUSgUCoVC0bhk1Vu45ZZb8OWXX2Lu3LnYddddc9UmhULRwqBCTzSejoZuJlIJBH3srefzvz7HES8dgTv3vRPTBk0zOXrV0WrHbamOVGeElB72KArdzDXUZbNz9EThrTJUMhaFQqFQKBReyGqM3iuvvIJDDz10i4i8zZs348ILL8SwYcPQuXNndOrUCZ06dcKQIUMwa9YsrF271nYd8+fPx9SpU9GnT5/M8l27dsWYMWPw0EMPIZFgO1+pVAqzZ8/G6NGj0aVLl8wynTt3xqBBg3D55ZejtraWWaa6uhrnnHMOs41OnTqhe/fumDRpEn744Ydc7haFollAxV00EXU8LwCsq12HXR7dBSuqVmD6c9MBeHf0AHbsne7oXTn+SsfLe4Vx9FyM0ePhxzOq0E2FQqFQKBReyEroLV26FIMGDcpVW6QkEgnsscceKCoqwty5c7F69WqsWbMGa9aswbx58zBo0CCMGzcOdXV10nXMnz8fBx98ME455RT88ssvmeVXrFiBJ598Em+88QYuuugiZpk5c+bgtttuwxNPPIFVq1Zllvn777/x9ddfo7q6Gueeey6zzEUXXYSGhgYsWLAgM/+aNWuwfPlynHnmmZg+fbrlOEaFoiVCQxFFQo+GavI17jre3NE0v8nRizh39KjQ092wy3a9DGftcJbjdXiBijlR1k2KldA7cMiBzGeh0FOOnkKhUCgUChuyEnobN25EWVlZrtoi5ZVXXkGvXr1w5ZVXoqKiguk0lpWV4ayzzsKkSZNw//33S9dx5ZVX4o477sCkSZOQl2e8bff7/Rg4cCBmz56NF154gXEGH3jgAdx8880YMmSIaUxRWVkZbr75Zrz22muIRo2O7bvvvotrrrkGJSUlzPx+vx8TJ07EzjvvjE8//dTzvlAomiN2jh51qUTlF3h4R89N6KYu9AK+QEZ8+X1+7NxjZ8fr8AINR+XDLe0cPVpH76H9HsLp259urEuUdVM5egqFQqFQKGzISuiVlZVh06bcZK6zYuHChdhtt90s59lzzz2xcOFCz+soKirCqFGj8Ntvv2Wm/fnnnxg4cKB0mVAohB49euCvv/7KTLv55pvRrl076TJdu3Z1FGaqULQkqHARZcikQsZJmCfv6PGhmzfueSM+PvpjlITZFyqAIfT0sE2dwlChrPk5gYo5PvOoXTIWWl6hLL8MZ+94trFsIIQDBh8g3ZZCoVAoFAqFiKx6C3379sWKFSty1RYpK1euxPDhwy3n6dKlCyO4eCorK1FcXOxqHfX19SgoMBdaphQUFKCmxuiETp8+3XL+lStX4pBD/r+9O4+zqf7/AP66y+yrYcw+ZsY22WUXjS1FYYRUZA1RyJKU3TcplL4oRIXwJRSSFKJFFMq+72bG2Gdf773n94ffPXPO3ZfZ5/V8PObRved8zud87rjN3Pe835/P5wWLbYjKGmvBm3TxEWnp5sNs038o0m94rpeSkyJ7PrH1RCgVSvi4+Rhl+2wJ9NxUbkb3cJY0y2a4Gbs9c/QAIKZSDPrV74dK7pWgVqqxJn4Novyi8PGhjx/di6WbREREZIVTGb3nn38eO3bsgE7n/L5Zlmi1WqhUKott1Go1NBrLH56sMezD09PT4rw/4FEwaIu8vDz88MMPuHLlisWgNTc3F2lpabIvotLOWqAnPS8t3TS3l51hVvBO5h3Zc6Xi0Y8ub1fjP96YC/SkzwM8Akze15T+DfqjW61uVttJg7l7WffMngOsB3oKhQJrn1+LxV0XAwC8XL1kc/dYuklERETWOBXojRs3DgEBAZg8eXJhjadUiYmJwdmzZ82ez8vLw+XLl82enzhxIoKDgxEUFARPT0+89NJLWLZsGdRq84nUuXPnws/PT/yKiIhw6jUQOepI0hH8eu1Xm9rKSjdNZMrMbb9gLqOn3/Bc73am6XJnU6Wb+uDRcJ6cPjgE7Av01Eo1PFwsZ/b17fReqv+S2XOA9UDPFOn4/d387b6eiIiIKhanSjfnzp2LatWq4aOPPsLmzZvRqlUri5m3qKgozJ4925lbAgDOnz+PLl26YNeuXahVq5ZDfXzwwQf4888/sX37drNtRo0ahfHjx+OLL75Aw4YN4eLy6K/ogiAgOTkZM2fOtLjx84IFC7BgwQIAQE5ODg4ePIhBgwbhm2++QY0aNUxe884778hW8kxLS2OwR0XucOJhJKYnIj42XjzWbEUzAMDtibdR1auqxetvphaUcFvN6GltyOgZBIu3M0wHevZk9KQLwlTyqGSyP1PUCjU81PYFes3DmuPYiGNotLwRAOMFaAzn8EnnMJqjUhT8bA32DrbanoiIiCo2pwK9GzduIDMzU9xHLykpyWJ7a+WXtqpduzauXLniVB+2ZCG7du2KvLw8jB8/HteuXRNX13RxcUFsbCyGDh0qW7zFEnd3d7Rv3x5jx47FJ598giVLlphs5+bmJlsVlKg4NF/ZHABwauQp1K1aVxaI3Ei9YTHQ+/bst+j1TUFZoT2lm9KtEKQMSzfNZvTcbF+MpW7VugCAQM9AeLl4mezPFJVSZdPiJ4bz5hoEFZRo27MPoKVx6AV5G29JQURERCTlVKC3atWqQhqGZbbMv9NoNBZLIm1hqo/4+HjEx8ebvWbZsmV23aNhw4b44osvHBkeUZG7cP8C6latK8u6WdsAfdq+abLndmX0bFyMxVygZGnVTcNtCTxdPJE2OQ0uKhe8uPlFk/2ZooDC7tJNQL53oGEGzxHM6BEREZE9nJqjV1zCw8ORnJxssU1SUhIiIyPNnvf395etjmlLHxMnTkRKSorFa3JycuDq+mhD4x07dmDGjBkW2yuVSiiVZeLbThWQftl/W7ZA0JOWRAKmt1cwO0fPTOmm4Rw9c+wp3QQeZQDd1e42BW56CoXC6DWaYirrN6vdLHSK6YTutbvbfD9zpHP0GOgRERGRNWUi4qhfvz727t1rsc3evXtRv359h/vIzMzE0aNHZfvmnTt3DqdOnTJ7jUajwbVr1xAVFQXg0b6CR44csTjO06dPo3r16hbbEJUUfRBmLtATBAHfnv0Wt9JvAQBm7JuB03dPy/qwa9VNcxm9/w8Wq3ia35MSMJ3Ru5/9aGsDU4GenqVzhhRQ2JSRMxXoTY+bjt2v7IarytXm+5kjLd1koEdERETWlIlALz4+HgkJCZgxYwbu378v21w4LS0Nixcvxg8//IDhw4eb7WP27Nl488038cMPPyA3tyDjoNPpcOnSJfTv3x+9e/dGUFDB3Jf4+HjMmjXLZDYxOzsb06ZNQ+vWreHp+Wh/rqZNm+Ly5ctYu3atUampIAj4559/MGvWLIwaNcrh7wVRUdLvd2cY6GXlZ+Hr419j/p/z0eubXui58dF+kbN/M15cKU+bh1xNLraf34603DSj/qSlm4Z74OnpSzdDfUItjtdURu9u5l0AxqtuSkkXV/Fy8cKOl3aYbatQKGxaLKWotzxIzy34XgV5cY4eERERWWbzpLYZM2bgwIED6Nu3L4YNGwYAOHPmjNWSSqng4GDUqVPH7kGqVCrs2bMHc+bMQVxcnCzY8/f3R1xcHP744w94eT1aYGH//v149dVX8c8//8DX1xcA0LhxY3z77beYMWMGRo8eLe6Pp1QqERYWhmHDhmHo0KGy+w4ZMgSpqamIi4tDamqq7JyHhweefvpprFixQnbs+++/xzvvvIN3331XXLxF/xqioqLw3//+F02aNLH7e0BUHExl9HI0OXjr57fw2ZHPxGN/Jf6Fyw9Mby2Sq83FO3vfwcJDC9EhugP2DtgLjWC6dFNfKip18f5FpOY8+v8txDsEJ26fMDteU4ux6IMyWzN6tavUlmXIvF29ZXMCFVDI/rhkji0LtkjbanQasSS0ml81q9cEegWKj+0pPSUiIqKKyeZPJnPnzoVGo8Hdu3fFQK9jx464fdv0animBAUF4datW/aPEo/KIufNm4d58+ZZbduuXTtcunTJ6Hjjxo0tbqdgSKlUYsKECZgwYYLN19SsWRObN2+2uT1RSZMGMRqdBjmaHGRrssVjmXmZ+N+p/xldt/PiTpP95Wnz8PWJrwEAv1z9BfnafLOlm6b2k6u1pGDLFFOlmxG+BduNmCrd1AvxDjF7ThroqRQqWVmkj6uP0eIvjpZumvPboN8w7qdx+OSZTwAAbz3xFm5l3ELP2J5mr4mpFIMdL+1g2SYRERHZxOZPJkuXLsWff/6Jnj0LPoicO3fO6mIlUv7+/vaMjYiKgTSISc9LR8CHAbJMW1Z+liwQ0ktISzDZX542T7bVgN8HfrLAUVq6aW3jcH93f9nzRsGN8MuAX8TnlrJ2bSLbmD0nLd003D7BsBxUoTA9R++Hl3/Agj8XYN+1fQDsC/RaRbTCoVcPic89XTyx7DnrK/g+W+tZm+9BREREFZvNn0yGDh1qVNro5+cHPz+/Qh8UERUfaVB3JOmILCgDgMz8TNmKj3rm9rbL1ebKFh8x7E+a0dPPCTTHMNBrG9lWttm5pUVOLAV6hhk9aZD2bM1n8clfn4jPFVAYbdUAAJ1iOuHzo5+Lzw330SMiIiIqSTYvxmLLHBV7FUWfRGQfabBlasXMrPws2R5ueskZpufn5mnzLC5MYm6rBVMMAz3DrJk00JO2DfUJtbjJu3SOm1qplvXbPro9Dgw5IGs/6YlJqBlQE7UqF5SVuihdZHME7cnoERERERU1mwK9zMxM+Pv74+DBg4V243v37sHPzw///PNPofVJRPaTZvSk2Ta9rPwsuzJ6hqWbhqSlm6YWY5HydvWW3dtSoOfnVlBdYG1VSllGz6B008vFC60jWovPFQoFgryDcGH0BUxpO0V23NfV1+zYiIiIiEqSTYGep6cndDod/v7770K78enTp5GZmQlvb+Pl0Ymo+FjL6GXmZZqco2cpo2eppNLaYixS7mp3WVBmGExJM4fSjF6Qt+VATzZHT6GSZSy9XL3MXme4cTozekRERFRa2RToKRQKNGjQAN99912hlVtu27YNHh4e3DycqITJMnpaOzJ6GWbm6GlybS7dtDZHz03lJgvKbC3dDPQMhCXWMnpS0uBOoZAHer5uzOgRERFR6WTzHL2FCxfi4MGDePHFF3H06FHodNaXGzclMTER06ZNw3//+1/Mnz8fKpVxpoCIio/VOXoa04GeuU3ErWb07Fh1003tZjSfTkpWuuleULppaiN1KWmfhouxGGb0pMGdUUZPsr1DUW+YTkRERGQPm/8E3bx5c+zcuRPvvfcemjVrBoVCgYCAAHh7exv9lduU/Px83Lt3D3l5eYiIiMCSJUswcuRIpwZPRM6TZvRyNDlG5zPzMk0uxmKO1Tl6dpRuuqncLJZuSgM9aXAnzQKaIs3aqZQqWSBraoVNvYbBDWXPmdEjIiKi0squTyYdO3ZEx44dkZiYiJs3byIpKQnp6em23UitRnBwMMLCwlCjRg2o1fxQRFTSPvrzIySlJ4nPTQV65ko3zcnV5lrMqMlKN60sxuKudjeaTyclDfSkgZani6fFfqVZO8MN04320ZNk8epVrYc9r+xBmG8YAM7RIyIiotLLoU8mYWFhCAsLK+yxEFExSkhLwMTdE2XHDPe8Ax7to2dqMRZz7Fl105bSTVszetJg1FrppvS8SqmCv7s/5nacC5VCJSsBNaVjTEfxsbR0k4EeERERlSa2/5meiEq1Y8nH8N5v75nMyply4f4Fo2PZ+caBXnZ+ttVFU6SszdF795d3xfvYtBiLjXP0VAoVhj8+HCHeIXj18Vct9isr3fz/LOHkNpPx1hNvGbW1VJouLd3khulERERUmjDQIyonGi9vjGn7pmHegXk2tb/y8IrRscz8TKNj2Zps5GpzbR7HhfsX8OOlHy222XlxJwDjjN6nXT+VPXdTW151UxpcKRVKLO+2HAnjE1DZs7LF+0szejrB8sJShguwSLF0k4iIiEorBnpE5cyx5GM2tbv84LLRsYy8DKNj2fnZNmcJbaUPigzn6I1qNkpWqmltHz3DjB4Am+YTSufoWXtttmb07ClvJSIiIipqDPSIyhlbVsEFgIsPLhodM7W9QrYm22RJpzP0mUNTc/Skq166q91tLt20h/Q6a4FeqE+o2XPSOXr2lLcSERERFTXWGhGVM5ZKDaVuZ5re8NxQdn621UVTLPF180VabprsWGae+UBPuv1CFc8qNmf0rK3gaY65QG/LC1uw8+JOjGxqfhsYaQmoqYVsiIiIiEoKM3pE5YytGT3p6peWGM7R83OzvCqlocoeBfPlYqvEAigoETWVBcvKzxIfe7p4yrdXUJrfXsHRjJq5AO35x57Hyu4r4aY2v6+edDz2fl+IiIiIihIDPaJyxtaMnqkyTVOki5XUDKiJ9zu+b9d4pAuj6Pe3M1W62a1WN5PXSwO9ql5VZedcVAWLsWgEx7KOzs4/3NRnE+Z1mme0mToRERFRSWLpJlE5Y3NGT2dbRk/q1KhT+PGi5RU1DUkzevoMnL50U19ueXDoQbQIa2HyegGC+LhBUAPZOenCKw5n9Jycf9i7Tm+nriciIiIqCoUS6N27dw8HDx7EtWvXcPfuXbi7u+ONN96Ar68vNmzYgNDQUDzxxBNQqbgqHVFRszWjZ2vpppSL0kWWRbOFv7u/7HqgoHRTn9HzUHuYDVCl20AEeQWZvY+j8wgLe0VRIiIiotLA6dLNBQsWoHr16ujRowemTp2Kr776CtOmTcOBAwcAAEuXLkW7du3w1FNPISPDeOl2IipcG09vxOHEw1bbOZLRUygUdm8MPqTxEPFxncA6AApKN/VZOFN70OkXOpGO01K20tp+eOZwERUiIiIqj5wK9H788UdMmjQJo0aNwt27d5GamoqDBw9CEAQIwqNyq19//RW//PIL/v77byxZsqRQBk1EljVf2dxqG1vn6BmyZ2PwmXEz0bl6ZxwbcQy7X9mNuoF1ARjP0TO1B51+j7oPOn6A2Cqx+F+v/1m8V2GvuklERERUljkV6H300Udo37495s6di8qVH83DMfUX93bt2qF3797YuHGjM7cjIgD3s+5j8LbB+O36b071Y2/pZqvwVgDkC6DoF1cxp0loEwBAw+CG6BTTSdyo/JvT3yBoQRAe5jwEYDp4bBvZFgBQP6g+zr5+Fi/We9HivRwt3SzsPQKJiIiISgOnAr0LFy6gdevWNrWNjY3FzZs3nbkdUYWWo8mBIAh4d++7WHVsFeJWxTnVn72lm78NfhRYSks3pRuGm6JSyDN1Xi5e4uM7mXfEx9JA75/h/2BM8zH4tOundo3P3sVYnqnxDAB5aSkRERFReeHUYiwJCQkICjK/OIKUt7c3Hj586MztiCqsu5l3EfJRCDrGdJStNGmL9Nx0fHTwI/Su0xv1qtYTj9ub0dMHY9KMno+bj8WN1w1LMvUZPaN2koCwcUhjNA5pbNfYAPszeht7b8Tuy7vRpWYXu+9FREREVNo5vRiLUmlbF7Yu+U5Exjaf2QytoMXPl38WFymRspTNmrZvGmb9Ogv1l9aXHXd0jp40o6efR2eOYUmmqbGbaucIe+fo+br5oledXlbLT4mIiIjKIqcCPbVajfx827ICGo2G2ysQOUj6hxJTwZKloO1wkukVOB1ZdROQZ/SkpZimMo2WSjdl7UwsxmIvR/fRIyIiIiqPnAr0oqKikJiYaFPb27dvo1q1as7cjqjCku6NZ2penKWgzdR2CDpB5/B2BNLsm4eLh/j4/qT7FtsCgLva3WqfjnJ01U0iIiKi8sipQK9u3brYvn07NBrLc2MEQcDOnTtRr149i+2IyDRpRk+aFdOvGGlpvp2pDc4d2Sxd7E8SOHqoCwI9V5WrUVvDTF1MpRgEeQWhftX6qOpVtaCdghk9IiIiosLk1J/RZ82ahSZNmqBDhw4YO3YsoqOjkZ396IPnrVu3cObMGdy8eROfffYZTp8+jTVr1hTKoIkqMmlWLCUnBR4uHnZn9BydnwfIA0dpRs/UfQwzdV6uXrj25jUIgoCai2uabecIR7dXICIiIiqPnPp01aBBA+zatQvjx49Hnz59ZFmHYcOGQaFQQBAE1K1bFz/++CMaNGjg9ICJKiJp6aa05DIlJwUhPiEWAzdTQZSj8/MAeUDnpnKzeB9TmTp9oCrN9rF0k4iIiKhwOf3pqmPHjjh+/DhSUlJw7do1pKamiud8fHwQHR2NSpUqOXsbogpN+kcUaeZKv+G4uVLMjLyMQindjI+NFx9L+5MGaKZW1rW0yIr02sJYjCXAI8DpPoiIiIjKC+f/jP7//P390ahRo8LqjogkpBk9aTYuJSfF6JjeuhPr0P+7/ib7szWjN6HVBET4RmBAwwHiMWlGL6ZSjMXrLWXqZIGeE3P0tvbdio8OfoTlzy13uA8iIiKi8qbQAj0iKjrSbFmuJld8/DD7UUbPVOmmuSDPXHtTwn3DMbblWNkxaYBWvVJ17Hx5Jyp7VjZ5vaUAzlo20FY9YnugR2wPh68nIiIiKo8Y6BGVMTmaHPFxWm4aANtLMQVBgEKhsLm9qYyctHRTpVShS80udl0vXlsIK20SERERkWk2BXo///wzEhISnL5ZeHg4Onfu7HQ/RBWNtHQzR1sQ6Onn69laipmnzYOb2s3m9qYCNenG6NYWUbE0964w5uURERERkWk2BXqjRo3ClStXjI7rV9W0Rl+WFRMTg4sXL9o5RCKSljbq984DCgI8W0sxszXZjwI9JzJ6UtaycrbO0SMiIiKiwmXThumXLl2CTqcz+hIEAUuWLDF5Tv/177//olWrVujZsyfOnTtX1K+HqFySZfQkpZv6gM3WwE1/ra2BoXT7BFN83Xxlz5uHNZc9txQIsnSTiIiIqOjYFOg5o2HDhti2bRt27tyJlStXFvXtiMolAQWZc2mgZ6l0Uxoc6umzgbaWbrqpTQd6H3b6EIMbDcaT1Z6UHd/x0g7Uq1pPfG7r9gpEREREVLiKPNADgMqVK6Nr165Yv359cdyOqNyR7p0ny+jpzGf0TK1kqb/W1gyguYzepCcm4cseXxrdI9ArEMMeHyY+t7gYC+foERERERWZYgn0ACA2NhZnzpwprtsRlStanVZ8nK0pmKOnDwDtmaMH2J7Rc1e72zpEkTSTaOv2CkRERERUuIot0AsMDMTDhw+L63ZEZY4gCLLMnZRWKAj0TM7Rs7F00+45emZKNy2RZvm4GAsRERFRyXAq0FMqldBqtdYbwrkNkYkqgg5rOiD6v9GyDdH1pBk9W0s3pdsg6Ilz9Jws3bREltGztL0CF2MhIiIiKjJOBXoRERG4deuWTW3v3LmDiIgIZ25HVK7tv7YfCWkJ+Dvxb6Nz5jJ6lko3Lc7RK8rSTWb0iIiIiEqcU4FebGwsfvnlF5va7tmzB7Vr13bmdkQVlrSkU7aPnoXSTVPEOXq2ZvQcKN2UZhItbq/AxViIiIiIioxTgd4777yDv//+G0OGDMGpU6eQlpYm20MvLS0NJ06cwODBg3H48GFMmTKlsMZNVK7oBJ3F8+ZKN/UB4JGkI0bXFMYcPWcXY7FUss2MHhEREVHRceqT1pNPPokvv/wS77zzDlavXm2yjSAIqFq1Kr744gu0bdvWmdsRlVvWMmxmF2PR5eNe1j188e8XRteYCrJupN4Qr7OFQ3P0bJyPyzl6REREREXH6T+pDxo0CH379sX58+dx7do1pKamiuf8/PwQFRWF2rVrw8PDw9lbEZVbhqttjt45GrUq18LoFqMByDN60qAvX5ePxLREk32ayhIuP7ocY1uMxYgdI2wal0OrbprIJJrCjB4RERFR0SmUT1oeHh5o1KgRGjVqVBjdEVU40kDvYMJBLDm8BAAKAj3B9Oq2Gp1Gtq+elKnyzBupN/Dt2W9tHpezi7FY4u/ub3ffRERERGSbYttHj4jMk5ZSZuRliI83ntqIO5l3zJZ25mvzkZWfBQDwcvGy6V6m5vOZ40jppqltHUyZETcDzcOaY+mzS+2+BxERERFZxtopolLA3EbpL2550ep1+lU4fdx8kJmfafVeh5MOi4+/6f0NXtj8AgCgSUgTXHpwCfm6guDRkfJKW0s3A70C8derf9ndPxERERFZ53RGT7rKprUvIjJNmrGztgKn7DpJUObr5muxrberNwCI+/S92eJNPFntSfH8MzWewYO3H6BH7R7iMVvLMKUcuYaIiIiICpdTgV716tXh4uJi81eNGjUKa9xEZdr7v7+PLuu64NSdUwDkGT1BEGzuR1q6aS3Qi/SLBFAw3y/MNwyBXoHi+buZd6FUKM1mF23VtWZXAEC9qvWc6oeIiIiIHOdU6eby5cuRmGh6xT8A0Gg0+P3337FhwwYMHz4c3bp1c+Z2ROXGnN/nICs/C/uv7Uf2lGzZHD0Btgd60sVYrM3Ri/SLxJm7Z8Tn4b7hsvl0tzNvi306o6pXVaROToWni6dT/RARERGR45wK9Dp16mS1zdChQ/Hcc89h6NChmDRpkjO3Iyo39Fm4HE0O/nvov+L+doDtm5kD8tJNHzcfi20jfSNlz8N8wmTP9YGerXvsWWItu0hERERERatYVt3s3bs3YmJiMG/evOK4HVGZ8uZPb+LjQx+Lz6UbolsjXYwl2CsYk5+YbLZtdKVo2fOYSjGy5x5qD7FPIiIiIirbim17hfbt22PXrl3FdTuiIiUIgl1z6exhT6AnnaPn6eKJuZ3m4tdBv5psWyewjux5iE8IAODHfj+iTWQbcZsDBnpEREREZV+xBXrBwcEW5/MRlRX52nw0XNYQz3/zfJH0n6vNtX0sOnmgBwBPVnsS9avWN2ob7B0se66fn/dMjWfw++DfUbtK7Ud9mtmzj4iIiIjKjmLbR8/DwwM5ObZnKohKqz9v/omTd07i5J2TRdK/vhTTFtLFWDxcPMTjpva/c1G62NwnEREREZVtxZbRu3fvHkJDQ4vrdkRFpqgDIWdKN/VcVMZBnVqpRoBHAACgYVBDs30y0CMiIiIq+4ot0Dt06BD30aNywdlVKa1tiG73Yiz6jJ7ackZPrVRj9yu7ER8bj//1+p/FPomIiIiobCvy0s2MjAwsXLgQe/bswebNm4v6dkRFThoI6QSdbC86W2h1WovnnZ2jB5gP9B4PeRzf9f3OYp9DGw/F0VtH0Tqitc3jICIiIqLSxalALy4uDlevXjV7PiMjA6mpqXB1dcW0adPQs2dPZ25HVCpIAz2NTgNXlatd12sFy4GePXP0zJZumpiPp1KqbOpzRNMRaBjc0GJ5JxERERGVbk4FeoMGDcKNGzfMnvf19UVUVBRatGjB+XlUbkgDPa1OC9gWP5m83hRH99GzthiLqWOmKBVKZvOIiIiIyjinAr3BgwcX1jiIygzp9gPWsnOmWAv0irJ0k4iIiIgqhmJbjIWovDAs3bSXtTl69mb00nLTAABeLl7icXOrbhIRERFRxeBUoJeVlVWk7YlKI2kWz1rQZkphlm7maHJwI/VR+XSNgIJVbZnRIyIiIqrYnAr0wsPDsX79epvafv3114iMjHTmdkSlQp42T3zsSOmmtWtsCfRqBtQUHwsQUMWzCgK9AsVjDPSIiIiIKjanAr2UlBSkpKTY1PbChQvw8vKy3pColMvVFMyhc6R0szAyenUC61h8bmrVTQZ6RERERBVHsc3R279/P2rWrGm9IVEpJ10spShKN20JHh+r8pjF58zoEREREVVsdn/y0+l0AABBEMTn+mOGsrKycPPmTSxZsgR//vkndu3a5cRQiUoHacbNntLNW+m3EOwd7FBwaKhu1bqy51W9qsqemwrqVAo794EgIiIiojLLrozeqlWroFar4eLiAlfXR5tEjx07Fi4uLia//Pz8UK9ePWzYsAFLlizBU089VSQvgqg4SUs3bQ3avj37LUI/DsXbe952qNxT76V6L2Fgw4HoUbuH7Ljhpu2mSjeVCi6yS0RERFRR2JXR69KlC1atWgVBECAIAoYMGYJ+/fqhY8eOJtt7eXkhKioKsbGx8Pb2LpQBE5U0aUbP1qDtjZ1vAADm/zkfrzR4xeF7v9LgFXSp2UXMqOsZBnqmMnoKhcLh+xIRERFR2WJXoBcUFIQBAwaIz4cMGYKWLVti4MCBhT4wotJKNkfPxtLN1NxUu68xRaV8VH6pUCigVqrFQNMwgycN9H7u/zOqB1R3+J5EREREVPY4tTrD7Nmz0alTp8IaC1GZ4EjpZlZ+wR6SzpRuSufZuancxL4sZfQaBjc0msNHREREROWbU4He1KlTC2scRGVGjtb20k2NToNTd07JjjmzGIs0gHNVuSIzPxMA4KKSZ/T0mT+Ai7AQERERVURcnYHITrKMnpUyzM+Pfo7GyxvLjjmV0ZMEcNIsnmFGTwGFyWuIiIiIqGKwOaM3Y8YMHDhwAH379sWwYcMAAGfOnEFycrLNNwsODkadOnWsNyQqxWTbK1jJzn137jujY7YGepF+kbiRekN2TFa6qXYTHxsFepKFV5jRIyIiIqp4bA705s6dC41Gg7t374qBXseOHXH79m2bbxYUFIRbt27ZP0qiUkS6GMsz657BudfPIcg7yGTbWgG1sOfKHtmxbE22Tfd5stqTWHtireyYuYye4WIs0q0UmNEjIiIiqnhsDvSWLl2KP//8Ez179hSPnTt3DikpKTbfzN/f356xEZVK0tLNlJwUjP5xNL7p843Jtvm6fADAa01ew7KjywAAD7Mf2nQfD7WH0TFpds7m0k1m9IiIiIgqHJsDvaFDh2Lo0KGyY35+fvDz8yv0QRGVZtLSTQC49OCS2bZ52jwAQHSlaHi6eCIrPwsPcxwP9KSLsbipCko3DRdjkZVuMqNHREREVOFwMRYiO0lLNwGIK1+aog/0XFWu8HXzBQA8yH5g0308XTyNjun70Pdp6jFgULrJjB4RERFRhVNsgV5SUhLWrl1rvSFRKZeemy57Lt0jz5C+dNNF6WJ3oOeudjc6FuUfJT62tXRTmt0jIiIiooqh2AK9jRs3YtSoUcV1O6IiIQgCktKTZMcy8+zL6Nlaumkq0JMGbdJVNw0XY2FwR0RERFSxFVugd/jwYURHRzt8fUpKCiZNmoT69esjJCQEwcHB4nYNI0eOtGn1z6NHj6J79+6IiYkRrw8LC0Pz5s2xcuVKaLXypfIFQcC6devQtGlThIaGiteEhIQgNjYWU6dORWam/EN+YmIiXnnlFURGRortg4ODERkZiW7duuHMmTMOfw+o5KXnpRuVauqf52vzka/Nl53TP5cGevez7hv1G+gZiCqeVWTHfN18MSNuBoK9g+Hp4ondr+yWnbe1dJOIiIiIKh6bFmNZuXIlbty4Yb2hCbm5uTh16hR27dqFFStWONSHVqtFx44d0b17d+zfvx8BAQFixiI1NRWrVq3CE088gRMnTsDT03heE/AoyOvTpw+WLFmCjh07ws3tUTZEp9Ph4sWLePvtt3Hu3DksWLBAvGbbtm1YuHAh1qxZg8cee0yWJUlNTcX06dMxfvx4LF++XDzev39/dO3aFV988QVcXQs+fGu1WuzcuRPdu3fH8ePH4eXl5dD3gkpWYlqi0bE8bR5SclJQY1ENNAhqgL0D9orvFX1Gz0Xlgki/SADAuXvnjPq4PfE29l3bh45rOorH1Eo1ZrabiZntZkIn6IyCN4uLsYAZPSIiIqKKzKZAb926dbh27ZpDN3B3d0d0dDQ2bNiAPn36ONTHd999h6ioKMyYMcPonJ+fH8aOHYvLly9j+fLlGDdunMk+ZsyYgf/+97/o2rWr7LhSqUTt2rWxbt061KlTB2+99RaCgh7tifb5559jwYIFJjd59/Pzw4IFCxAZGYnFixfD1dUVmZmZOH/+PH755Rej0jmVSoVu3bph/fr1+O2339ClSxeHvhdUsgzLNvU+P/o57mffx75r+/DL1V/QMeZRwCYt3axftT4A4OKDi0bXKxQKBHgEyI5JV8s0laGzOEePpZtEREREFZpNgd6+ffuKehwWnTx5Eu3bt7fYplOnTti6davDfXh5eaFJkyY4f/68GOhdvXoVtWvXNnuNi4sLIiMjcePGDdSoUQMKhQLTp0+3+CE7IiICN2/etPhaqPRKTDfO6AHA23veFh9vObtFDPSki7E0CGpgse8Q7xDZc+lWCqbYuhgLEREREVU8ZWIiT0JCAkJCQiy2CQ0NtVhempqaCm9vb7v6yM7OhoeH8V5mUh4eHsjIyAAAeHp64rXXXrPY/tSpU6hRo4bFNlR6mcvoSaXnFazKaSqjZ04VzyqyrRCsBXqy0k0uxkJEREREEmUi0NNqtVCpLO8FplarodFonLqPYR+enp7IyjK/dD7wKBi0hSAIWLp0KZKTk9GmTRuz7XJzc5GWlib7opJ3O+M2dIIO11OuW22bqynYZ0+/GIuLygVVvapavE6lVMnaWAv0pPPymNEjIiIiIqkyEeiVlJiYGJw9e9bs+by8PFy+fNlqPwkJCXjuuefwxRdfYOvWrbJFWgzNnTsXfn5+4ldERIRDY6fCs//afgR/FIzRO0fj0sNLVtvnaHLEx9KMnkKhsLp5eYhPQebaWltpIGi4GAtX3SQiIiKq2Gyao2dNYmIi/vrrL1y9ehWpqanicT8/P0RHR6NFixYICwsrjFsBAM6fP48uXbpg165dqFWrlkN9fPDBB/jzzz+xfft2s21GjRqF8ePH44svvkDDhg3h4vLow7QgCEhOTsbMmTMtlshptVosXboU77//PkaNGoWJEyfC3d14bzSpd955B+PHjxefp6WlMdgrYeN+erTAz2dHPkO0/6MtQgI8AsxufJ6rLcjoSQM9/X+zNeazwNJ5etYyetKsHRdjISIiIiIppwO9adOmYcGCBcjNzUVAQAB8fHzEc+np6Xjw4AHc3Nzw1ltvYfbs2c7eDgBQu3ZtXLlyxak+Jk+ebLVN165dkZeXh/Hjx+PatWvIy/v/pfJdXBAbG4uhQ4fi/PnzJq89deoUBgwYgJiYGBw6dAiRkZE2jcvNzU3c+oGKjiAINgdDF+8XrJJ5PfVR6WbtyrVxMOGgyfbSjJ50MRbgUebNUqAX7B0sPrYW6EkZBnrWsoFEREREVL45Feht2bIFc+bMwcyZMzFu3DhZkKeXkZGBjz/+GLNmzULDhg3Rq1cv+wdpw/w7jUYDtdq5uNVUH/Hx8YiPjzd7zbJly4yO/fDDDxg5ciQWL16MHj16ODUmKlw6QYcOqztAoVDglwHG22CYIt0gXSfo4OniKQvIDEnn6Blm9AwXTTFUI6BgoR6rGT3J2A37HdRoEBYeWoguNbiNBxEREVFF5NREnsWLF6NTp06YPn26ySAPALy9vTF9+nR06tQJixcvdug+4eHhSE5OttgmKSnJYtbM399fXB3T1j4mTpyIlJQUi9fk5OTI5txdvXoVQ4cOxZ49exjklUKXH1zGr9d/xf5r+/Ew56HV9vpATSrcNxwCBPG5l4uX7LypOXr6OXSGc+kMNQ9rLj62p3TTMGD1c/fD1bFXsfS5pRb7ICIiIqLyyalA79KlS2jZsqVNbVu2bIlLl6wvZGFK/fr1sXfvXott9u7di/r1zS9fb62PzMxMHD16VLZv3rlz53Dq1Cmz12g0Gly7dg1RUVHisenTp2P69OkOzx2konU15ar4OCPPcuAPAA+zjYNBb1dv6ASd+Pz4a8dl56Vz9PSrbkrn6FnSNLSp+Phe1j2r47OE8/SIiIiIKi6nAr2kpCRUrWp5yXi9KlWq4NatWw7dJz4+HgkJCZgxYwbu378PQSjIpqSlpWHx4sX44YcfMHz4cLN9zJ49G2+++SZ++OEH5OYWfBDX6XS4dOkS+vfvj969e4ubpevvO2vWLJPZxOzsbEybNg2tW7eGp6enePzo0aN49tlnHXqdVPTO3yuYU5mWa33rCumeeHpeLl6yQM9w2wRzq24C1ks3fd18xRUz61ata7EtAzkiIiIiMsepSW1qtRr5+fk2tVUqHY8pVSoV9uzZgzlz5iAuLk4W7Pn7+yMuLg5//PEHvLweldDt378fr776Kv755x/4+voCABo3boxvv/0WM2bMwOjRo8X98ZRKJcLCwjBs2DAMHTpUdt8hQ4YgNTUVcXFxstVEgUcbpT/99NNYsWKF7PiDBw/QvHlzix/CBw0ahA8++MDh7wfZLjUnFXnaPAR6BQIALty/IJ5Lz30UxOkEHdJy0+Dv7o95B+bhXtY9zHtqHgDTWT9PF09ZoOft6i07rw/0BEEwuRiLNYnjE3Ej9QbqVa1nsR33yiMiIiIic5wK9KpUqYLbt28X1lgs8vPzw7x58zBv3jyrbdu1a2eyTLRx48YWt1MwpFQqMWHCBEyYMMHma6zNJaTiFTAvADpBh9TJqfB188XFBwUraOozes+ufxa7Lu3C0eFH8faetwEAY1uMRZhvmBgMSnm6eMpWzjQM6vWLsWh0BQsIWSrd7Fy9s+x5sHewxcVezN2XiIiIiEjPqdLN5s2b459//imssRAVKkEQxMzb6TunAQApOSnieX2gt+vSLgAQgzwA4mIr5jJ6Wp1WdmxK2ykI9w0HUJDRky7kIi7GIindfLPFm9j24jZs7rPZgVfHjB4RERERmedUoDd8+HDs3bsXZ8+eLazxEBUafdkkUBB8ZeVniccM59+du3eu4Nr/X0TF1Bw9w9JNAHivw3v4+9W/ATxajEVatglI5uhJSje9XL3QvXZ3+LiZXrGWiIiIiMhRTpVutm7dGgMGDEDnzp0xZ84ctGrVCiqV6Y2a7969C+DR9gPSxVT8/f0REBDgzDCITJLuZ6cP9KQll4aLsSSkJYiP9UGauYyedHsFPXe1O4BHc/40Oo08o6c0zujZsyG6KSzdJCIiIiJznPqkGRsbizt37gB4tMCIpQ+e+uCuRo0asmNVq1blvDYqEtJAy2RGLzcddzLvmLxWP7/O3Bw9w9JNoCDQA4AbqTew6cwmAI8COv3/G9I5eiqF6T+K2OqN5m9g4aGFeLn+y071Q0RERETlj1OB3r59+8RAz1G2bs9AZEl6bjq8Xb1lf2yQBnqZ+ZkAgOz8goze8dvHEbSgYDsNKX3ppi2rbuq5qd3Exx3XdMT11OsA5MGdtHTT2p561kT6RSLjnQyn+yEiIiKi8sepQO+xxx7DY489VlhjIXLIv7f+xeOfP44BDQdgdfxq8bh043J9maY0o7fl7BazfYoZPRvn6AGAUqGEi9IF+bp8McgD5OWa0sceLh4WX5ctpMElEREREZGeU4uxEJUGHx74EACw5vga2XFpRi8tNw1anVYW/FlibY6eqUAPMB14mcvoeaidD/SIiIiIiExhoEdlnrlFTaSBXnpuujhPzxaOZPQA+Tw9PXPlmoWR0SMiIiIiMoWBHpV5KqXpRU0MM3rSsk1rHJmjBwBuKisZPSUzekRERERU9JwO9A4cOIDnn38eNWvWhJeXF1QqldmvWrVqFcaYiWTMrV4p3V4hLc++QM/aqpv2ZPSk4yjsOXpERERERKY4tRjL3r178dRTTyEqKgovvPACwsPD4eFh/sNreHi4M7cjMslcoGeY0ZPuoWeNfo7eg+wHRuc8XTyhFYy3VwBMz9HrGdtTfMw5ekRERERUHJwK9GbOnIl69erh77//hru7cSaDqDjYMkfP0dLNm2k3AQBVvaqKe+7ZmtF7KuYpLOm6BLUqF2SyOUePiIiIiIqDU6Wb586dw/PPP88gj0qUuTl60hU2U3NSZXvoWaPRaXAk6YgY3FXzqyae83TxhCAIJq+TBnrV/KrJgjyAc/SIiIiIqHg4Fejdv38fgYGBhTUWIofYUrqZlZ9lV0Zv16VdaLaiGYBHgV0VzyriuUi/SIT4hJi8zsfVR3zs5epldF5WusmMHhEREREVEacCPQ8PD6SnGy9WQVScbFl1M1uTLQZ6Ciis9rny35Xi46z8LIxsOhJNQ5tizyt74OvmixXdVuCZGs/gp/4/ya7zcZMEei4mAj1m9IiIiIioGDg1R6927do4c+ZMYY2FyCG2ZvT0i7H4ufshJSfFrnt0q90N3Wp3E59H+kXix34/GrWzltGTBqXM6BERERFRUXEqozd+/Hhs2LABv//+e2GNh8hu0uBJOndOuq2BtHTTz83Prv6HNh5qc1tfN1/xsamMnlZXsFonM3pEREREVFScyuj1798fWq0WHTt2ROPGjREdHQ1PT0+z7atVq4YZM2Y4c0siI9KMXr4uX1zZUla6mV9Quunn7gek2tb3ym4r0aduH5vHIs3oebt6G53X788HMKNHREREREXHqUDv7t27+OSTT6DRaHDjxg1otVqL++hpNBqz54gcJc3o5Wnz4KpyxeUHl/HGj2+Ix7WCFmm5aQDkwZg1vev0lmXprJFl9EyUbkoDPaXCqYQ6EREREZFZTgV6EyZMwMWLF/Hzzz+jU6dOhTUmIrtIM3r6LN6YXWOM2ukDPVMBmDmeLuYz1KZYW4xFGugRERERERUVp1IKP/30E4YMGcIgj0qUQlGwiqY+0LudcduoXXruoxVibZ0b56J0kW2HYAtri7FoBa3RMSIiIiKiwuZUoHf37l3UrFmzsMZC5BCdoBMfS+flGUrLe5TRs3VunL3ZPMD6YizM6BERERFRcXAq0FOr1dDpdNYbEhUh6UqWe6/sNdvO3oyeI4GetHTT2mIsRERERERFxalALyYmBjdu3CissRA5RFoOOWT7EFy8fxECBKN26XnFEOhZKd1koEdERERExcGpQG/48OHYvHkzsrOzC2s8RHaTlm4CwNl7Z00GVGJGrwhLN6XBnanSzbEtxgIA4mPj7e6biIiIiMhWTq262blzZ2zfvh2PP/44Xn/9dURFRVncRy8oKAh169Z15pZERqSlmwDgpnLDw+yHRu30q266qlyhUqisLoziSKDnoixYvMXU9Q2DG+LBpAeP9vIjIiIiIioiTgV6nTp1wp07dwAAY8YYL2dvKCgoCLdu3XLmlkRGDAM2V5Ur7mXdM2qnD/TUSjVcVC7QauTX9a3bF8kZyfj1+q8A7NuGQS+6UjRaR7SGl4uX2UCxkkclu/slIiIiIrKHU4He+fPnkZKSYnN7f39/Z25H5dyF+xcwaOsgTGk7Bc/Wetbm6wwzeul56cjWGJcT6+foqRQquChdkIMc2fkNvTdg9q+zxUDPkYyeUqHEH4P/ACDf9oGIiIiIqDg5Fej5+fnBz48laFQ4RuwYgYMJB/Hc/56DMMN4MRVzDOfomdpDDwAy8jIAPMrouapcTbZRKwv+l3Ak0AMY4BERERFRyXNqMRaiwqRfLMVehqWb+swdAEx+YjK61uwqO68v3TTF2hw7IiIiIqKyoFgCvezsbKxevRpTpkwpjttRGeXoAiVGpZv/HzD6uflhbqe5uJEq3wJErVTLAjrDc3qeagZ6RERERFQ2FWmgd/r0aYwZMwahoaEYMmQILl26VJS3ozLO183XoevMZfT05ZnNQpvJzquUKrOlm9JMHzN6RERERFRWFXqgl5OTgzVr1qBNmzZo0KAB1q9fj+HDh+Py5cvYuHFjYd+OyhFpoCcIgs2lnIaBnn4unj6Ye6/De7LztpZuert623R/IiIiIqLSptACvTNnzuDNN99EaGgoBg0ahIMHD2LKlClISEjAhx9+iKioqMK6FZVTfm4FpZvT9k2D7we+2HJmi9XrDBdj0Qd6+mAu1CcUTUKaiOdtLd0M8QmxffBERERERKWIU4Febm4u1q5di7Zt26J+/fpYsWIFevfujW3btkEQBLRs2RLu7u6FNVYq56RB1pzf5wAABm0bZPU6U9srAJCVZ0ozeKZW3fyp/09G7cJ9w20cORERERFR6eLQ9grnzp3D559/jjVr1uDBgweoXbs2Fi5ciIEDB8LPzw+JiYmFPU6qAPK1+UbHDLN1ppgr3ZRm7aRBpGHp5ux2s9G5emejdgz0iIiIiKissjnQy8vLw+bNm7F8+XL88ccfUCqV6NmzJ0aOHIn27dsX5RipgsjXGQd6htk6AEjLTYO72l3MyplbdVOW0ZMEffoN08XnSpXJ+zHQIyIiIqKyyuZAr0GDBrh48SLc3NwwY8YMDBs2DCEhnMNEhUej0xgdM8zW3Ui9gRqLaqB9dHv81P8nHEs+hk1nNsnaGC7GAlgu3VQpCgK9O5l3xMeV3Cs5+EqIiIiIiEqWzXP0unXrBnd3d+Tk5GDt2rXYuHEjHj58WJRjowrGlozeXwl/IV+Xj58v/4yjSUfReHlj8VwVzyoAgFsZtwDIgzvDMk7pOWlGT7rZukKhcPSlEBERERGVKJsDvfnz5yMpKQmLFi2Cu7s7xo8fj/DwcLz66qv4559/inKMVEGYmqMnQJA9lwZf285vk53zUHsAAFJyUgBYzugZlnLqjWgyAqE+oXi3zbsOvAIiIiIiotLBrlU3/fz88MYbb+DEiRM4cOAA+vTpg/Xr16NZs2Zo0aIF1qxZg5ycnKIaK5VzpjJ6hrLzs8XH+hJNPQ8XD9lzs3P0DDZMl2b0QnxCkDAuAXM6zrF94EREREREpYzD2yu0atUKq1atQlJSEhYuXIisrCwMGjQI4eHhmD59OsveyG6m5ugZytYUBHrSoA8APF08Zc+lwZ1RRk9lOqMHsGSTiIiIiMo+pzdM9/f3x5gxY3Dy5En8/vvv6Nq1K9avXw9BEDB79mxs2rQJGo31D/BEpko3DUmDu2VHl8nOGQZ65jJ6RouxKOWBHhERERFRWed0oCf1xBNPYM2aNWKWLz09HX379kVkZCRmzZqFpKSkwrwdlTPWSje1Oq0so2dIP0dPz1KgJ21rmNEjIiIiIirrCjXQ06tUqRLGjh2L06dP49dff0WHDh3wwQcfoEGDBkVxOyonLGX0jicfR6UPK2H2r7PNtjEq3VSZL92UBXrM6BERERFROVMkgZ5U27ZtsXbtWiQmJmL37t1FfTsqwyxl9F79/lWk56VbzOgZlW4qzW+Y7q52F5+rlTZvJ0lEREREVCYU2yfcgIAABAQEFNftqAwytxiLIAh4kP3A6vWGq25azOi5sHSTiIiIiMqvIs/oERlad2Iduv2vG9Jy02THzZVu5mhyxL3xLDHcXN3SHD1pRo+lm0RERERU3jDQo2LX/7v+2HFhB97//X3Z8Vxtrsn2WflZSM1JtdqvYVmnpQ3TuRgLEREREZVnDPSoWBxPPo6BWwfiesp18di9rHvi49d/eB2n7pwyeW1mfia0gtbkOSk3lZvsuWEWT0+lVMlLN5nRIyIiIqJyhqtQULFovLwxBAi4cP+CyfOfHfnM7LWZeZk23ePFei/i78S/cfHBRQB2lG4yo0dERERE5QwzelQsBAgAgNN3Ttt9rS0LsQCP9tH7ZeAv4nObSzeZ0SMiIiKicoaBHhUrR4Kqyw8v29ROqVDKAjilouDtbbRhOlfdJCIiIqJyjIEeFSvpXDlBEGy6Jik9yew5H1cf8bFKKd8fTzqvzzCjx1U3iYiIiKg8Y6BHxcqR7Nmt9Ftmz/m4SQI9hXyRFel2C4YbpnPVTSIiIiIqzxjoUbGSZs8UCoVN19zKMB/oSVfaVClVsnJNSxk9rrpJREREROUZAz0qVtLsma2lm8kZyWbPSUswpUEeAGh0GvGxAgVBJVfdJCIiIqLyjoEeFSvpHD1bWQr03NSSjJ5BwCYt3ZRmDw1X3XRkTEREREREpRkDPSpWjpRJ2lO6KSUt3ZRm9Aw3TDfMBBIRERERlXX8hEvFypGgKiMvw+w5WzN60vsalm5KSzyJiIiIiMoDBnpUrGRz9GB5jl5Vr6pW+zM1R6+yR2UAwHO1nhPPWSrdzNPmWb0PEREREVFZwkCPipU9pZu2BHqdYzob9X1h9AX89epfaB/dXjwnLd1UKpSyVTjzdfk2j4mIiIiIqCzgKhRU5KSra9qz8Ekl90pmzyWNT8L11OuyvvXZwgCPADQPay5rb2krB2b0iIiIiKi8YUaPipw0kDK1lYG5bRYqeZgO9DzUHgjxCUHL8JayzJylbKE0o6cX5hMGAGgV3srsdUREREREZREzelTksjXZ4mNpMKafo6cTdCavM5fR83HzER+7qlzFx5YWejGV0bs85jKyNdnwd/c3ex0RERERUVnEQI+KXI4mR3xsKrMm3QZBylyg5+vmKz52UUoyehY2Pjd1Xze1m2zVTiIiIiKi8oKlm1TkpIGedOGTVcdWYfv57bJtEKTBnblM2/iW48XH0oyepdLNOoF17BozEREREVFZxkCPipws0NPKV7jssaEHFv21SHy+ofcG8bG3q7ds8ZZ+9fvh4NCDGNF0hHhMNkfPQkavbtW6+OHlH3BsxDGHXgMRERERUVnC0k0qcuYyenqT904WH7upCkop3dXu8FB7ID0vHcCjDF/L8Jaya6WBoLXN2LvW7GrfwImIiIiIyihm9KjIZecXLMaSq8m12FZaiumudoeHS8HG5tIgUE+axbO0hQIRERERUUXCQI8KlU7QITkjGXN+m4PkjGQA8oye9LEp0sVR3NRu8FAXBHrSIFBPmsWTzvUjIiIiIqrIWLpJhWbZkWV4e8/bcFe7407mHWw+uxn/jvjXrkDPYkbPxAqZfu5+4mPpapxERERERBUZAz0qNCN/GAkASMtNAwAcSz4GAMjVFpRrSh8bUilUsjl3+jl6eqZKN11Vrrg4+iJ0gk4WFBIRERERVWQM9KjISUsqLWX0VEqVbF88w4yeqdJNAKgRUKMQRklEREREVH5wjh4VqfP3zmPz2c3ic52gM9vWakaPm5sTEREREdmEGT0qUrGfxtrcVqWUB3puKjerq24SEREREZExZvSo1DDM6Nmy6iYRERERERljoEelhmFGz0XpYnXVTSIiIiIiMsZAj0oNpUIJF1XBYixqpdrqqptERERERGSMgR6VGkqFEiqFSnyuVqplq3BynzwiIiIiItsw0KMS9UqDV8THgiDI5uEFegXizL0z4vM2kW2KdWxERERERGUVAz1y2F8Jf2H1sdVO9RHhL4g4UgAAPdtJREFUGyE+FiDAReWC06NO4/hrx+Ht6o3Xm70OAHiv/Xuco0dEREREZCNur0AOa/lFSwBA7Sq10TK8pUN9uKvdxceCIAAA6gTWEY89/9jzuD/pPgI8ApwYKRERERFRxVKmM3opKSmYNGkS6tevj5CQEAQHByM4OBh16tTByJEjcfv2bat9HD16FN27d0dMTIx4fVhYGJo3b46VK1dCq9XK2guCgHXr1qFp06YIDQ0VrwkJCUFsbCymTp2KzMxMk/f67rvvUK9evUJ57SVNuvH59ZTrDvcjDfTMbabOII+IiIiIyD5lNtDTarXo2LEjvLy8sH//fiQlJSE5ORnJyck4ePAgYmNj8cQTTyArK8tsH0ePHkWfPn3w2muv4ezZs+L1N2/exNdff40dO3bg7bffll2zbds2LFy4EGvWrEFiYqJ4za1bt/DXX38hPT0d48ePN7rXN998g0mTJiEhIaHQvxcl4V7WPfGxp4sn2q1q51A/soweBGeHRUREREREKMOB3nfffYeoqCjMmDEDlStXhkKhEM/5+flh7Nix6Nq1K5YvX262jxkzZuC///0vunbtCje3gvlfSqUStWvXxrp167Bp0yZZZvDzzz/HggULUKdOHdk99fddsGABtm/fjry8PPH45s2bMWXKFOzevbswXnqpcCv9lvj4UMIh/Hr9V4f6kW6noC/dJCIiIiIi55TZQO/kyZNo3769xTadOnXCyZMnHe7Dy8sLTZo0wfnz58VjV69eRe3atc1e4+LigsjISNy4cUM8VqlSJfzyyy+IioqyON6y5FZGQaB34s4Jh/uRbpDOjB4RERERUeEos4uxJCQkoEGDBhbbhIaGygIuQ6mpqfD29rarj+zsbHh4eFi4AvDw8EBGRob4vGPHjhbbl0XSjN6J24UT6Jmbo0dERERERPYps4GeVquFSqWy2EatVkOj0Th1H8M+PD09kZWVBX9/f7PXZGdnO3y/3Nxc5Obmis/T0tIc7qsoJWcki49vpJoPpq2RbojO0k0iIiIiosJRZks3S0pMTAzOnj1r9nxeXh4uX77scP9z586Fn5+f+BUREWH9ohIgDfRs1Ti4sdExlm4SERERERW+chPonT9/HjExMbhw4YLDfXzwwQfo3r27xTajRo3C+PHjceTIEeTn54vHBUHArVu3MHr0aKNFWuzxzjvvIDU1Vfy6efOmw30VpWyN/VlLU9skyAI9ZvSIiIiIiApFmS3dNFS7dm1cuXLFqT4mT55stU3Xrl2Rl5eH8ePH49q1a+Lqmi4uLoiNjcXQoUNli7fYy83NTbYCaGnlyHw6L1cvo2Oco0dEREREVPjKbKBny/w7jUYDtdq5l2iqj/j4eMTHx5u9ZtmyZU7dsyywJSh7pcErWHdyndjW08XTqI1sewWWbhIRERERFYoyW7oZHh6O5GTL88SSkpIQGRlp9ry/v79sdUxb+pg4cSJSUlIsXpOTkwNXV1eLbco6raC12mZV/CpMaTtFfO6pNg70WLpJRERERFT4ymygV79+fezdu9dim71796J+/foO95GZmYmjR4/K9s07d+4cTp06ZfYajUaDa9eulas980yxltFTK9VQKpRQKQpWRjWV0eNiLEREREREha/MBnrx8fFISEjAjBkzcP/+fVk2KC0tDYsXL8YPP/yA4cOHm+1j9uzZePPNN/HDDz/ItjTQ6XS4dOkS+vfvj969eyMoKEh231mzZpnMJmZnZ2PatGlo3bo1PD2Ng5ryRKuznNFzUz2aZ6hSWg70uL0CEREREVHhK7OBnkqlwp49e5CdnY24uDiEhoYiODgYwcHBaN68OU6dOoU//vgDXl6PFgDZv38/atSoIduXrnHjxvj222+xfPlyPPbYY+L14eHheOmll9ClSxfMmzdPdt8hQ4bgmWeeQVxcnNhe/1WnTh08fPgQK1asMDvu8PDwovmGFDNbMnoAZBk9d7W72Xa29ElERERERLYps4uxAICfnx/mzZtnFIyZ0q5dO1y6dMnoeOPGjbF9+3ab76lUKjFhwgRMmDDBrrHqWSr7LEusBWX6LSakGT3pYz2WbhIRERERFb4ym9GjkmVtMRYF/j/Qk2T0pI/1pIEeEREREREVDgZ65BBbyyytZfSk2ysQEREREVHhYKBHDpEuxmJq7l2ARwAAZvSIiIiIiEoCAz2ymyAIYkZvZbeVyHw3Ew2CGsjaDGg4AIB9c/SIiIiIiKhwMNAjuyz5ewnCF4bjzN0zAAr2y1MqCt5Knat3xsTWEwFYz+hJt1cgIiIiIqLCwXQK2WX0j6Nlz/UBnjTQ+6n/T+JjZvSIiIiIiIofM3rkFFOBnhTn6BERERERFT8GeuQUfZbObKAnyeKZasNAj4iIiIio8DHQI6dYy+hJAzlur0BEREREVDyYTiGn6MsxWbpJRETlRX5+PrRarfWGREROUKlUcHEpuqQHP2WTU/QBnqkgDuBiLEREVHakpaXh3r17yM3NLemhEFEF4ebmhipVqsDX17fQ++anbHIKF2MhIqLyIC0tDYmJifD29kaVKlXg4uIChUJR0sMionJKEATk5+cjNTUViYmJAFDowR4/ZZNT7FmMxTCjd2joIbPXERERFad79+7B29sb4eHhDPCIqFh4eHjAx8cHCQkJuHfvXqEHevyUTU5xJqPXIrxF0Q2MiIjIRvn5+cjNzYWfnx+DPCIqVgqFAn5+fsjNzUV+fn6h9s1Aj2ymE3RGx/TBm6+b6b9AWJujBwAjmowAALzR7A1nh0hERGQ3/cIrRbkoAhGROfqfPYW9CBRLN8lmuRrjyen6TN7Cpxfi8sPLeLPFm7Lzhhk9BRQQIMjaLO6yGAMaDkCz0GaFP2giIiIbMZtHRCWhqH72MKNHNsvR5Bgd0wd61fyr4fhrxzG48WDZecMN04c3GQ4A6BDdQTzuonJB64jW3FOPiIiIiKiQMKNHNjMV6JkrxxTPK+SlmwufXohOMZ3wVMxThT4+IiIiIiJ6hIEe2cxSRs8c2Rw9hQoeLh7oXad3oY+NiIiIiIgKsHSTbGYyo2dmo3RT561l/4iIiIiIqHAw0CObFUZGj4iIiIgcV6dOnZIeQqlw8OBBdOrUCREREZg6dWpJD6dUYqBHNnMo0GNGj4iIqFwQBAFbtmxB27ZtERERgeDgYPErNDQUjRs3xrJly6DTGW/HRIUnKSnJ4vmnn34ad+/eNXt+48aNiIuLs9jHqFGjcODAAYfGVxxu3LiBF198EVOmTMH169fx3nvvlfSQSiUGemQzhxZjYUaPiIioXFi0aBEWLVqExYsX49q1a0hOTha/EhMTsWXLFmzatAnLly8v6aEWm549e+LChQslPQyZmJgYbN261ez5NWvW4O+//8bVq1dNns/OzsaWLVvQsGHDIhqh8/73v/9hyJAhaN++PZRKhjPm8DtDNmNGj4iIqGLSaDT48MMPsWHDBjRq1Agqlfx3ukKhQExMDBYtWoTVq1eX0CiL38OHD5GVlVXSw5Dp06cPNm3aZPJceno6jh07hlGjRmHbtm0m2/z0009o06YNvL29i3KYTrl27Rpq1apV0sMo9bjqJtnMkcVY1MqCtxgzekREVOYIAlDKPsjbxdMTKITNmG/evInQ0FCEhIRYbFezZk1kZGQ4fT9y3JNPPon+/fvj/v37qFy5suzcrl278NRTT6F3795499138eabbxpdv3nzZvTp06eYRuuY7OxseHh4lPQwSj1m9MhmTi/GwoweERGVNVlZgLd32f0qpCA1ISEBwcHBVtu5urri5MmTRsd3796NJ598EuHh4eK8vrCwMDRr1gwbNmwwar9lyxbMmTMHmZmZmDlzJurUqYPg4GCxb2vnAeDIkSPo0qULqlWrJt4zJCQEdevWxfz585GXl2d03/Pnz6N3796IiooSr6lcuTKqV6+O2bNni/MPFyxYgODgYPz555/o2LGj2Pbrr782+bojIyNlr/vxxx/HypUrIQiC0Rj045ZeI/1KTU21+G+gVqvRrVs3k+WbW7duRXx8PFq0aIFLly7h/v37svM5OTnYvXs3nn32WfGYIAhYvnw5mjZtitDQUAQFBSE4OBjR0dHo378/rl+/LrY9duwYBg8ebHF8+/btkwWYOTk5eO+991CnTh2EhoaKr7NatWro3r07jh07JrbVarWoVasWNm7ciEGDBoltp0yZYtTfY489hpCQELFNVFQU+vTpY7LUtmfPnrh+/TqOHz+Ol156CREREejUqZPs/MGDB9GrVy/ExMQgODgYtWrVwtChQ/Hw4UP8+uuv6Nq1K6KjoxEcHIyIiAgMHz4c6enpFr8XRU6gUi01NVUAIKSmppb0UIQv//lSwEzIvk7dPmXxmkv3L4lt/731b/EMlIiIyA7Z2dnCmTNnhOzsbOOTGRmC8CivVza/MjIK5Xu0b98+oUePHg5d+++//wrVq1cXDh06JGi1WvG4TqcTTp06JTRu3FjYsWOH7JqvvvpKePPNN4XOnTsLH3/8sZCcnCzodDqbzycmJgo1a9YUfv/9d9k9BUEQkpKShF69egmzZs2SHU9PTxeio6OFDRs2CHl5eeJxrVYrXLlyRXjuueeE5cuXy66Ji4sT/v33X7Ovu379+sKxY8dkY9PpdMLFixeFtm3bCqtXr5Zdk5iYKERGRgo//fSToNFoTPbr5+dn8rjU7t27hWeeeUZ2LDc3VwgJCREyMzMFQRCEESNGCKtWrZK12bZtmxAfHy87tn79eqFevXrCiRMnZK8jOztb+OSTT4THHntM/H7l5+cLMTExwpkzZ0yOS6fTCa1atRJ27dolHpswYYLw+uuvCw8ePJC11Wg0wvbt24XIyEjh3r17snMDBw4UvvvuO5P3mDBhgtC/f3/hzp07suN5eXnC+vXrhZiYGPF7oBcXFyesWLFCaN++vbBnzx4hw+D/m7i4OKFVq1bC4cOHxe9BXl6eMGHCBKFjx45Cu3bthHPnzonnMjIyhBdffFGYOnWqyTEasvgzyIA9sQEDvVKuNAV6n/39mVGgd/buWYvXXH14VWx7IvlEMY2UiIjIdhY/ZOl0j4Klsvol+WDuDGcCvddff1344osvzJ7fvXu38Nxzz8mOffXVV0JAQICwe/duk9dYOz9//nxh3rx5Zu+ZnJws1KxZU3bsm2++EQYOHGj2mhMnTggrVqyQHbMU6L3++uvCN998Y7a/Q4cOCU899ZTRuKdPn272GkGwLdDLz88XQkJChPv374vHfv75Z9m/4Y8//ij07NlTdl3//v2FdevWyY6NGTNG2Lx5s9l76YMjvc8++8zs9/Gnn34SWrduLQZEOp1OCAsLkwXWhl577TWjMZkL9HQ6nRAYGGgUqEn169fP6PXoAzlz18XFxQlfffWV0fGMjAxBpVIJ+/btMzp37tw5oVGjRmbHIVVUgR5LN8lmgV6BCPMJkx3jYixERFSuKRSAl1fZ/SqE+XnOunLlCmJjY82er1OnDq5cuWJ03LB8zp7zV65cQXR0tNlrq1atarQFwdWrV1G7dm2z19SvXx+vvvqq2fP2jiEmJsZo5Utr3ytbqdVqPPfcc9i+fbt4TF+2qde+fXscOnQI2dnZAIDc3Fz8/PPPeO6552R9zZ8/H88//7zZezVo0ED27zdo0CDs3bsXN27ckLUTBAEzZszArFmzoPj/9+Xdu3fh7+8PFxcXs/2b+j6Zo+/Py8vLbBtz77cXXnjB4nWm/l28vLwQGBho8lxUVJTVrTCKGhdjIZv1rtMbbSPbIvijghp9bphORERElmRlZcHT09PseU9PT2RmZhodtxbwWDqflZWFV199FW+88YbZNu7u7kbX+Pv7W7ynPbKysvD000+bDWIEQUBUVJTRNYW1yEjv3r2xaNEiDBo0CDqdDjt27MCsWbPE825uboiLi8Pu3bvRvXt37N69Gy1btoSvr6+sn/z8fEyePBk//PCDyfmBGRkZsn3sPDw8MHr0aHz00Uf473//Kx7/6aefoFar0bFjR9nrPXfunMX5n3l5eZg5c6ZNr9naew149H5LSUkxOm7t/Wb4ftFzc3Mzec7NzQ25ubkW+yxqDPTILq4qV9lza8EbM3pERERln0qlgkajsamtIAhixsYZ5j5Y23r+q6++Qs+ePZ0ehzP27NmDxo0bl8i927dvj0GDBiElJQUXLlxAdHQ0qlSpImsTHx+PrVu3onv37ti0aZPJ1TZnzpyJ7OxsHD582CgIBGBys/LXXnsNjz32GKZOnYrAwEAIgoCZM2di7ty5Ru+NunXr4vjx406+WudZez+VRSzdJLu4qd1kz+3J6FlrS0RERKVTeHg4kpOTrbbLy8tDvXr1ZMc8PDzE8kBTsrKyLJbMOcLDwwNpaWkW2/zxxx+y556enhbHaYq7u7vZrI21Meh0Ohw4cMCuMQiCAK1Wa9PYXFxc0LVrV3z//fdGZZt6Xbp0wc8//4zs7Gzs2rUL3bp1M2rzyy+/4O233zYZ5OlfhyFfX18MHjwYixcvBvBoWwd3d3e0b99e1s6Wf6cbN27IVva0xNp7DSia91tpxU/eZBejjJ6VLB3LNYmIiMq+iIgIJCUl4datWxbbXbx4ET4+PrJjMTExOH/+vNlrzp49i5iYmEIZp150dLTJbR707ty5gyFDhhhdY2mcJ0+exJdffik7FhUVhcuXLzs0hn/++ceoJDE6Ohrnzp0ze82ZM2fs2qewd+/e2LRpE7Zu3YoePXoYnff19UWDBg0wa9YsNG/eHH5+fkZttFotlErTIYMgCPj1119Nnhs7diy+/PJLpKenY+bMmbKyUb2qVasiJSUFDx48MPsa5s2bZxSUm1O1alWrm9gXxfuttGKgR3ZRK9VQoCDlbk9Gj4iIiMomtVqNyZMno2/fvjh27JhRVkkQBFy9ehVjxozBgAEDZOeGDh2KOXPm4PDhw7J94wRBwNmzZzFp0iSMGDGiUMf70ksvYe3atdi+fbtRyendu3cxceJEvPDCC7LjXbp0wW+//YZvvvlGdo1Op8P169cxZcoUo74GDRqE9957DydOnDDaE2/o0KH48MMP8fvvvxtlvW7cuIF3330Xffv2lR1/+eWXsWrVKuzevVv2Pc7KysJ3332HsWPH2lVi2LFjRxw6dAiurq5mF4aJj4/HggULzG6S3qZNGyxatAj5+fniMUEQcP78eQwfPtxskBYYGIhevXrhhRdegLe3N+Li4ozaKBQKDBw4EMOGDTPKGOt0OuzevRs7d+60uCiPYX/9+vXDyJEjce/ePdk5jUaDDRs24M8//8QzzzxjU39lHefokd1cVC7I0z7aZNSeVTcNfwASERFR2TF69GiEhYXhjTfewPXr12Uf/BUKBapWrYrXXnvNKGh7/PHHsWTJEowbNw5Xr14VAxilUonQ0FBMmDDBaKVHHx8fk9klW89HRERg27ZtmDp1KkaOHCm7p6+vL/r374/JkyfLrvH29sbOnTvxzjvvYOLEieKG6nl5efD398eAAQOMVt1s2bIlPvzwQwwfPhw3b97EtGnT8NprrwEAmjRpghUrVmDy5Mm4cuWK+DlIpVKhSpUqGDFiBIYOHSrrLywsDJs3b8bUqVMxZMgQ5Ofnw83NDaGhoWjcuDFWrVqFp59+2uzrNuTi4oKXX34ZkZGRZtv06NED8+fPR/fu3U2ef++99zB+/HhUr14deXl5UCqVqFKlCmrXro0hQ4agXr168Pb2NnntxIkT0bp1a2zcuNHs/efOnYsPP/wQcXFxssVeXFxc0LBhQ2zatAlBQUGya3x9fc3e8/3338e8efPwxBNPIC0tTfy+u7m5oWnTpti5c6dR6aa/v7/Z/vTnzS3yUqVKFbML7oSGhprtszgoBH76LtXS0tLg5+eH1NRUs7XRxc3rfS9k5T9Kid+ZeAeBXoFm2+ZocuAx59HqUZdGX0L1gOrFMkYiIiJb5eTk4OrVq4iOji6XCzIQUelmz88ge2IDlm6S3dTKgkSwPRk9w4VciIiIiIioaLB0k+xmz5YJLioXzOkwBxl5GQj3DS/qoRERERERERjokQOkWTxbtkx4t+27RTkcIiIiIiIywNJNshv3xiMiIiIiKt34KZ3sJivd5D55RERERESlDgM9shszekREREREpRs/pZPd7FmMhYiIiIiIih8DPbIbM3pERERERKUbP6WT3aQZPQUUJTgSIiIiIiIyhYEe2U2axVMoGOgREREREZU2DPTIbmolt18kIiIiIirNGOiR3bgACxERERFR6cZAj+zGBViIiIiIiEo3fmInu3GTdCIiIiKi0o2BHtmNpZtEREREck2bNkVmZmZJD6NUyMrKwrRp01C7dm1Uq1YNt2/fLukhVUgM9MhuzOgRERFVPIIgYMuWLWjTpg0iIiIQHBwsfoWFhaFp06ZYvHgx8vPz7er34cOH8PHxwbRp05weY3h4OF555RWn+3HEvXv3rL72HTt2YPr06WbP63Q61K9fH7t37zbb5vLly+jVq5fD4ywOb7/9NhITE3Hw4EFcv34dQUFBJT2kComBHtmNGT0iIqKKZ/HixVi0aBE+/fRTXL16FcnJyeLXzZs38c033+DgwYN47bXX7OrX1dUVsbGxCA0NdXqMjz32GKpVq+Z0P0WlSZMmWLVqFXQ6ncnzR48exaVLl7Bu3TqzfWzYsAF169YtqiE6TRAEbNiwAUuWLEFAQEBJD6dC4zr5ZDdm9IiIiCoWjUaDDz/8EEeOHEFISIjReaVSiZiYGKxatQr16tXDpUuXUKNGDZv69vLywuHDhwtlnJYyYaVBSEgIoqKicOjQIbRu3dro/NatW/H2229j+fLl0Gg0UKuNP6pv2rQJX3/9dXEM1yF37txBQEAAPD09S3ooFR4zemQ3ZvSIiIgqloSEBISGhpoM8qRcXV3Rpk0bnD59uphGVvb06dMHmzdvNnlu69atGDhwIBo0aIADBw4Ynb948SJycnJQr169oh6mw7Kzs+Hh4VHSwyAw0CMHcHsFIiKqKARBQGZeZpn9EgShUL4PCQkJNs+zCg0Nxc2bN2XHevbsievXr+P48eN46aWXEBERgU6dOonnBw4caJTV02q1+Oyzz9CgQQOEhoaK8wFr1KiBPn36YOPGjRgzZozsmunTp2PLli2yY2PGjMHu3buxevVqxMfHIzY2FsHBwQgPD8eTTz6Jn3/+2eg1aLVavP/++6hXrx5CQkIQFBSEkJAQ1KhRA6+//jru3btn0/fClOeffx7ffvut0b/NxYsX4eLigujoaPTs2RNbt241unbz5s3o3bs3FAqFeOzKlSsYMGAAqlWrJn6PQkND8fjjj2PVqlWy+wwcOBAnTpwwOzZBENCtWzfcuHFDPHbkyBF06dJF1n9ISAjq1q2L+fPnIy8vT2y7dOlSNGvWDKdOnRLbRkZGyhZj0fcXGRkpG2+DBg3w2WefQavVysZ0+PBhDB8+HBqNBgsXLsTjjz+OqlWrYteuXeL5gQMHYvXq1WjSpAlCQ0MRGhqKJ598Eps2bUJ2djbeeustNG7cWPy3bNy4scl/9/KGpZtkN5ZuEhFRRZGVnwXvud4lPQyHZbyTAS9XL6f7MVdGaIparYZGo5Ede/jwIXbv3o3169djypQpWLlyJby8CsaVmppqtGLl0qVL8f333+P7779HZGSkGNzodDr89ttvGDBgADp06CC7Ji0tDenp6UbHpkyZgri4OHzyySeoVq0aFAoFdDodDh8+jH79+mHz5s1o1KiReM28efOwZ88ebN++HTExMeLx9PR0vPfee3jppZfw888/ywIuW4WFhSE8PBx///03WrRoIR7funUr4uPjAQDdu3dH27Zt8fHHH8vusWnTJnz11Vfic0EQ0KVLFwwbNgxLly4Vv6eCIODs2bN45ZVX4OLign79+gEA4uLiMHfuXPzvf/8zObbdu3fj3r17iIiIAAAkJSXh5ZdfxpdffonWrVtDqSz4Y/+tW7cwevRofPDBB+ICMyNHjkSXLl0QHx+PY8eOGfWflJSEXr16YcWKFejYsSNUKpU43hs3bmDYsGEQBAGvv/66eE1mZiZSU1PxyiuvoGbNmti2bRvCwsLEsWRmZmLnzp0IDw/Hr7/+Cm/vR/+/nj59Gl26dMHq1avxzDPP4NChQ3Bzc4MgCPjzzz/Rq1cvnD17FpUqVbLhX61sYmqG7MbSTSIiIrLXl19+ie+//x4dO3aUBXnmLFu2DEuXLhUDMz2lUol27drJggFr6tevj/nz5yMqKkrsS6lUokWLFpg8eTK++OILWftDhw5h3rx5siAPAHx8fDB37lxcvXoVV65csfn+hvr06YNNmzbJjkkDvdDQUAQGBuLkyZPi+cuXLyMjIwMNGjQQjz18+BCurq6YOHGi7HuqUChQp04dLFy4EMuWLROP9+/fHwcPHsTly5eNxiQIAmbOnInZs2eL36P169dj2LBhaNOmjSzIAx7NN/z000+xdu1am1/3+vXrMXDgQHTu3FkM8vTjrVatGj777DPZePV2796Np556CrNnz0ZERITRWHx8fPCf//xHDPIAoG7duujXrx8yMjLwxhtvwM3NTbzXE088gfbt2+O3336zeexlETN6ZDdm9IiIqKLwdPFExjsZJT0Mh3m6lJ4FMV544QWbAjy9pKQkREdHmz1fo0YNnD171qa+nnnmGbPn6tSpg++++052bNOmTXB1dTXZXqlUol69erhy5QqqV69u0/0N9erVC08++STmz58PhUKB5ORk3Lp1Cw0bNhTb6Ms39YHdpk2b0KdPH1nQGxAQgKNHj5q9T4MGDWQBqaurK958803MmzcPy5cvl7X9+eefoVQqZSW1V65cMcqaSlWtWhV37961+XVfuXIFbdq0MXs+JiYGiYmJRsd1Oh0GDx5s9rpatWoZBX8AEBUVhYcPH5q8JioqCklJSTaMuuxioEd2Y0aPiIgqCoVCUSiljwTExsba1V6n01ksjZRmhKxxcXExe87T09OobDQlJQXvvPMO9u/fb3IT9NTUVAwaNMjm+xsKDw9HcHAwjh49iqZNm2L79u3o3r277PX26NEDL7/8slgWuXnzZqxYscKorwMHDmDmzJm4fPmyUcmsIAjIzc2VHRs2bBhq166NW7duiYvrCIKAGTNmYM6cObIxZGVl4dVXX8Ubb7xh9rW4u7vb/LqzsrIsrsapVCpNbj0RGxtr8b1gbgxubm4Wzxl+b8obBnpkN2b0iIiIKhaVSmUURJhjbj6fPQFBSXv99dfRsGFDLFmyxOQKkq+++qrT99CXbzZt2hRbt27FpEmTZOdjY2ORk5OD69evQ6fTITU1VTaPEACSk5MxaNAgbN68GU2aNDGZ1apSpYrsuZeXF0aMGIGFCxdi3rx5AIBdu3bBzc3NZPbuq6++Qs+ePZ18tc4pS++d0oRz9MhuzOgRERFVLOHh4UhOTrapbVJSkriYhzOUSqXFVUMNV2csTL/99hsmT55sdpsAcxue26NXr17YsmUL0tLScOzYMZMljfHx8di+fTs2bdpktNom8GjFyfbt26NZs2YmgzxBEEx+D9944w2sW7cOKSkp4ty8WbNmGfXv4eGBtLQ0i6/jjz/+sOXliv1lZ2ebPa/T6Uy+DnIMv5NkN26vQEREVLFEREQgKSkJt27dstguLy8Pf/zxB+rWrev0PUNCQnD9+nWz5w8dOuT0PcyxVDaq1Wrx+++/Gx339va2GMQYioyMRJUqVfD++++jc+fOJrOg8fHx2Lp1qxjomRqLpcDo999/NxkQV6pUCf369cOnn36KH3/8EZ6enmjXrp1Ru+joaNmCMIbu3LmDIUOGmD1vKCYmBufPnzd7/urVqwgLC7O5P7KMn9jJbmolK36JiIgqErVajbfffht9+/bF8ePHjYIHQRBw7do1DBkyBG3atEGNGjWcvufw4cMxcuRI2Z58giDg5s2bmD17triPWlFo0aIFlixZIsvc6XQ6/Pvvv3jxxRfFFRylmjVrhrVr19q1d2GfPn2wYMECcbVNQ02bNsWlS5fw4MEDPP744ybP7927F+fOnZMdz8rKwubNmzF37lyzgeC4ceOwbNkyTJs2DbNmzTLZ5qWXXsLatWuxfft2o9Ldu3fvYuLEiXjhhRdseKUF/X311VfYs2eP0Xvoxo0bGDVqFEaMGGFzf2QZP7GT3ThHj4iIqOIZM2YMwsLCMGrUKFy/fl32wV+pVCI4OBgDBw7EqFGjjK719/eXLX1vyM/Pz2hFzjfeeANKpRJdunTB/fv3IQgCfH19Ub16dXTp0gUzZszAjh07ZNf4+PjAx8dHdszX19fivT09PeHv7y87tmzZMowdOxbz58+HRqOBSqVCUFAQ6tSpgwkTJuCbb74x6nPu3LkYOXIkIiIiMGvWLAwdOtTsPfX69OmD5cuX46mnnjJ5XqlU4uWXX4a3t7fJDGN4eDgWL16Ml156Cbdv34ZOp4OHhwciIyPRqlUrrFu3zmSmDniUMX3llVdw4sQJPPnkkybbREREYNu2bZg6dSpGjhwpBmdKpRK+vr7o378/Jk+eLLvG3d0dAQEBJvsLDw/Hpk2bMHXqVAwePBj5+fkAHi16VKVKFYwYMQIjR46UXePt7W1xrzsvLy/4+fmZPOft7W30ftAz9V4pbxSCPX92oGKXlpYGPz8/pKamwtfXt6SHAwAYsm0Ivjr2aLNOYQbfPkREVLbl5OTg6tWriI6O5qIPZcjWrVuxdetWrFq1qqSHQuQUe34G2RMbsHST7OaiNL9EMREREVFhWLNmjcUyyPz8fLN73RERAz1ywPS46Qj2Dsa0J6eV9FCIiIionFq4cCG2bNlicoVLjUaD3bt3o379+iUwMqKygXP0yG5hvmFIGp9kceNKIiIiImcsW7YMI0eOxIgRI4w2PFer1ejYsSOGDRtWQqMjKv0Y6JFDGOQRERFRUWrRogX++eefkh4GUZnF0k0iIiIiIqJyhoEeERERERFROcNAj4iIiIiIqJxhoEdEREQEWFzKn4ioqBTVzx4GekRERFShKZWPPg5ptdoSHgkRVUT6nz36n0WFhYEeERERVWguLi5QqVTIzs4u6aEQUQWUnZ0NlUpltI2IsxjoERERUYWmUCjg6emJ1NRUZvWIqFhptVqkpqbC09Oz0Lcv4z56REREVOFVrVoV165dw/Xr1xEQEAA3NzfuGUtERUYQBOTm5uLBgwfQ6XSoWrVqod+DgR4RERFVeK6urggPD8e9e/dw69atkh4OEVUQXl5eCA4Ohqura6H3zUCPiIiICICnpyciIyOh0Wig0WhKejhEVM6p1Wqo1UUXjjHQIyIiIpIo6g9fRETFgYuxEBERERERlTMM9IiIiIiIiMoZBnpERERERETlDAM9IiIiIiKicoaBHhERERERUTnDQI+IiIiIiKicYaBHRERERERUznCTmFJOEAQAQFpaWgmPhIiIiIiISpI+JtDHCJYw0Cvl0tPTAQARERElPBIiIiIiIioN0tPT4efnZ7GNQrAlHKQSo9PpkJSUBB8fHygUipIeDtLS0hAREYGbN2/C19e3pIdDZQDfM2QvvmfIXnzPkL34niF7lZb3jCAISE9PR2hoKJRKy7PwmNEr5ZRKJcLDw0t6GEZ8fX35g5HswvcM2YvvGbIX3zNkL75nyF6l4T1jLZOnx8VYiIiIiIiIyhkGekREREREROUMAz2yi5ubG2bMmAE3N7eSHgqVEXzPkL34niF78T1D9uJ7huxVFt8zXIyFiIiIiIionGFGj4iIiIiIqJxhoEdERERERFTOMNAjIiIiIiIqZxjoERERERERlTMM9MiilJQUTJo0CfXr10dISAiCg4MRHByMOnXqYOTIkbh9+3ZJD5GK2Nq1a9GoUSPZv39ISAjq1KmDjz/+GBqNRtZ+79696NSpE6KiosT2ERERaNu2Lb799luYWv/p6NGj6N69O2JiYsRrwsLC0Lx5c6xcuRJarba4Xi4VEa1Wi+effx6bNm2SHef7hYBH74M2bdogLCxM/DfVvxe6d++OM2fOyNpfvHgRL7/8MmrWrCm2DQ0NxeOPP44PP/wQubm5Rve4desWRowYgccee0z8eRYSEoIGDRpg8uTJSEtLK66XS4Xg9u3bGDZsGKKjo2XvmejoaPTr1w83b96UtXfk80x2djbmzp2Lxo0bIzQ0VLymVq1a6NevHy5fvlxcL5cclJGRgWeffRYrVqyQHXfk90iZ/LkjEJmh0WiExx9/XJg5c6Zw7949QafTiedSUlKETz75RKhevbqQmZlZgqOkorRv3z6hbt26wvnz543OJScnC926dRMWLVokHvvuu++E+vXrC7///ruQl5cnHs/PzxeOHj0qtGnTRli8eLGsnyNHjgjR0dHCDz/8IOTk5IjHtVqtcO7cOaFHjx7ChAkTiuDVUXF69913BVdXV+Grr74Sj/H9QoIgCBcuXBCqVasmHDp0SPZ7RhAe/R7auHGjULNmTfHf++bNm0JERISwbt062e8fnU4n3LhxQxg2bJjQq1cvWT/p6elCTEyMsHjxYiEtLU12zZ07d4SpU6cKzZo1E7RabRG+UipMnTt3Fv7zn/8I2dnZsuP5+fnCF198Ifv3dPTzTI8ePYTXXntNSEhIkF2TkZEhrF69WoiIiBASExOL8FWSM1JTU4U2bdoI1apVExYuXCged+T3SFn9ucNAj8zatGmT8Pzzz1tsM3r0aOHjjz8uphFRcZs0aZKwbNkys+fPnTsnNG/eXHxet25d4fjx42bbJyQkCKGhoYJGoxGPPfvss8L27dvNXpORkSFERkYKycnJdo6eSoutW7cKjRo1EoYPHy4L9Ph+IUEQhDlz5ghz5syx2KZDhw7C0aNHBUEQhNdff132ByZDWq1WaNiwofDPP/+Ix+bPny+MHz/e4j26desmfPvtt3aMnEpKSkqKEBkZafSHAaknnnhCOHPmjCAIjn2eOXz4sNCkSROLH8I/+ugjYcyYMXaOnopDVlaW0KJFC2HWrFnCjBkzZIGeI79HyurPHZZuklknT55E+/btLbbp1KkTTp48WUwjouLWqlUrPPXUU2bPR0REiOUx+fn5uHfvHho0aGC2fVhYGIKCgpCYmCges/Y+8/LyQpMmTXD+/HkHXgGVtIsXL2Ls2LHYvHkzPDw8xON8v5DetWvXUL16dYtt4uPjoVQ++shi7T2gVCrRoUMH2e8m/j4rX+7cuYOQkBAoFAqzbcLDw8XfT478+588eRLt2rUT33e2XEOlhyAIGD9+PKZPn250zpHfI2X15w4DPTIrISEBISEhFtuEhobixo0bxTQiKm7x8fGIiYkxe/7UqVOoUaMGACA5ORmBgYFW+zR8z6SmpsLb29uua6hsyMzMRK9evbBo0SKjD/J8v5BeXl4e3NzcLLYZPXo0GjVqBMCx3038fVa+REdH48GDB9DpdGbb3Lx5U/z9xfdMxePp6YkXXnjB5DlHfo+U1fcQAz0yS6vVQqVSWWyjVquNFuOgiiExMREjRozAmDFjANj2fgEce8/wfVb2CIKA4cOH49lnn0X37t2NzvP9QlIpKSkYPXo0GjduLFtYo27duhg+fDiSkpLEto78buLvs/JFrVajefPmmDJlCh4+fChbtCk3Nxdff/01rl27JgZ6fM+QvQrj/VAa3kMM9IjILoIgYNWqVWjUqBEGDRqE3r17l/SQqBRasmQJbt26hf/85z8lPRQqAz744AN07twZBw4cQHJyMpKTk3Hr1i0cOHAAdevWxTPPPMMP1CQzdepULF26FAEBAahSpQqCg4MRFBQET09PDBs2DJs2bbJYdklUEfD/ALLZ+fPnERMTgwsXLpT0UKiEXLhwAZ07d8by5cvx008/YezYsRbbv/7663j99dftuscHH3xgMgNEZceBAwfw0Ucf4X//+x/UarXN1/H9UnH17dsX3bp1g6enp3hMoVDA398fY8eORXh4OH777TeT165duxaNGze263779+9HjRo1uKVCGXX37l10794dK1asQEZGBu7fv4/k5GTcvn0baWlp+Prrr/HBBx8gLy/P5PWOfJ5JTU1FjRo1sH///kJ6FVRaOPJ7pKz83LH9NzBVeLVr18aVK1dKehhUArRaLebMmYOVK1di9uzZGDBggE1/Kf3000/tvtfkyZMdGSKVEsnJyXj55Zexfv16BAUF2XUt3y8VV2xsrMXz9erVw+XLl9GhQwejc/3790f//v3tul+7du1w6dIlu66h0mPDhg3o0aMH+vTpY3TOy8sLffr0wV9//YVNmzahX79+Rm0c+Tzj5+fH90w55cjvkbLyc4eBHpllS92wRqOx6y/2VPZkZGTg5ZdfhlqtxokTJ+Dv72+yna115o68Z/g+Kxs0Gg369u2LcePG4YknnrDYlu8XkpKuyGqKp6cnMjMzATj2u4m/z8qXc+fOoWXLlhbb1K9fH4cPH0a/fv34niG7Fcb7oTS8h/juJLPCw8ORnJxssU1SUhIiIyOLaURUEiZOnIjw8HB8+umnFpeyDg4Oxr1796z2Z/ie8ff3R0ZGhsUVsPg+KxsOHDiAo0eP4ty5c/jggw+MzqempsLV1RWbN2/G1q1b+X4hAI8yMDk5ORbbZGdnIzQ0FEDB76YqVaqYbZ+UlCSu0im9xhK+b8oOWz4YKxQK8UO2I//+4eHhOHXqlF3XUNngyO+Rsvpzh3P0yKz69etj7969Ftvs3bsX9evXL6YRUXG7ePEidu3ahY8++shikAc8+sUbGBiIY8eOmW2TlJSE27dvIywsTDxm7X2WmZmJo0ePonbt2naPn4pXXFwcMjIycPv2bXFBDelX3759sXr1auzYsYPvFxLFxMTg4sWLFtucPXtWXEHR2ntAp9Nh3759st9N/H1WvsTExODMmTMW25w5cwbR0dEAHPv3r1+/Pvbt22dxCwe+Z8omR36PlNmfO0W2FTuVeRqNRmjatKkwffp04d69e4JOpxPPpaamCosWLRKqV68uZGRklOAoqSht3LhRGDRokM3tt23bJtSrV0/4/fffhby8PPG4RqMR/v33X6FNmzbCokWLZNf8888/QlRUlLBjxw4hJydHPK7VaoWLFy8K8fHxwvjx451/MVTiBg4cKHz33Xfic75fSBAEISkpSYiOjhb27dsnaLVa2bm8vDzhyy+/FGJjY8V/74SEBCEiIkJYu3atkJmZKbbV6XRCQkKCMGzYMOH555+X9ZORkSHExMQIixYtEtLS0mTX3Lt3T5g2bZrQtGlTo/tT6XT37l2hWrVqwq5duwSNRiM7p9FohB9//FEICwsTkpKSxGOOfJ7p2bOnMHz4cCEhIUF2TWZmpvD1118LERERQmJiYhG+UioMM2bMEBYuXCg+d+T3SFn9uaMQBMnmI0QGUlNTMWfOHOzcuRP3798X96rx9/dHXFwcZs2aheDg4BIeJRWVpUuX4q233rJY3uDi4oK//vpLLKv65Zdf8P777+PixYvIzc0V21SrVg3jxo3D888/b5Qd/PfffzFjxgycOnUKWVlZAAClUomwsDAMGzYMQ4cOtWnPNSrdxo4di27duqFTp07iMb5fCACOHj2Kd999F2fOnEF+fr543M3NDa1atcL7778vZvQA4NKlS5g2bRqOHDmC9PR0AI9K9YKCgvDiiy/izTffhLu7u+weycnJmDFjBn799VekpqZCEAQoFApUrlwZzz77LKZMmQJfX9/iecHktOPHj2Py5Mk4deqU7D2jVqtRt25dvPfee2jWrJl43JHPMzk5OVi4cCE2btyI27dvi9f4+PigWbNm+M9//oPq1asXw6slZ3z88cfw8/PD0KFDxWOO/B4piz93GOgRERERERGVM5yjR0REREREVM4w0CMiIiIiIipnGOgRERERERGVMwz0iIiIiIiIyhkGekREREREROUMAz0iIiIiIqJyhoEeERERERFROcNAj4iIiIiIqJxhoEdERERERFTOMNAjIiIqQ/Lz85GTk1PSwygR6enpEAShpIdBRFQmMNAjIqJSZd26dVAoFHZ/vf/++2IfgwcPhpubG3bu3FmCr6RwrV69GnXr1oWnpyd69eolHi+Pr9WU8+fPw9fXF4GBgYiPj0dSUlJJD4mIqFRTl/QAiIiIpDp06IAlS5YYZW7u3r2L2bNnY8yYMahZs6bRdZ07dxYf//rrr8jLy8ORI0fQtWvXIh9zUfvPf/6D6dOn45VXXsGYMWPQoUMH8Vx5e63m1KhRA99//z1OnjyJFStWoFGjRjhx4gSCg4NLemhERKWSQmANBBERlQGXLl1CzZo1sW/fPrRr185i29mzZ2PLli1Yvnw5WrZsWTwDLCKpqamIiIjAe++9hzFjxhidL0+v1VYpKSlo0qQJ+vfvj1mzZpX0cIiISiUGekREVCbYE+iVJ2vXrsWYMWOQmJgIDw+Pkh5OqTFv3jx8/vnnuHTpUkkPhYioVOIcPSIiolLs5s2bqFevHoM8A02aNEFCQgIXZyEiMoOBHhERlTt79uxB9erVodPpjM7VqFEDP/30E9asWYOGDRvC3d0dCoUClStXRnx8vJgh2rVrF1q3bg0fHx8oFAr4+fmhWbNm2LBhg9n7CoKArVu3ol27dggPDxcXinFxcUGtWrXQu3dvnD171q7XkpSUhMDAQIdf665du3Dz5k288cYbqFWrFjw9PaFQKBAeHo527drh22+/tTtYSklJQVBQEE6dOoXTp09j8ODBiIqKgru7O1QqFaKiovD0009j//79Zvs4fPgw2rdvD39/f6OFdXx9fdGuXTv89ddfZq8PDAxEbm4uHj58aNfYiYgqCi7GQkRE5U5CQgKuXLkCnU4HpVL+N83Lly/j/fffx8mTJ/Hyyy/jlVdegaurK27cuIFVq1YhLi4Oy5YtQ8+ePdG9e3c888wzqFSpElJSUrBz50689NJL0Gg06N+/v9F9Z8+ejZkzZyIuLg4DBw5EUFAQlEol8vPzkZiYiO+//x4NGzbE3r170bZtW5teS3p6Ory9vR1+rdu2bcMLL7yAatWqoUuXLggJCYGnpyfu3LmDv/76C7169cK0adMwe/Zsm8YDPAr07ty5gy+//BJLlixBkyZN0KtXLwQHB8PV1RXJycn45Zdf0L59e6xcuRJDhw6VXf/w4UM8/fTTiI6OxqRJk+Dr6ys7f+vWLWzbtg1du3bF+fPnUaVKFaMx+Pj4AADS0tIQEBBg89iJiCoMgYiIqAy4ePGiAEDYt2+f1bZfffWVAEDIz883OgdAqFSpkpCcnGx07uzZs4JCoRCUSqXw1VdfGZ3XaDTCE088ITRs2NDoXGJiouDu7i5MnDhR0Ol0JseVm5srtG3bVmjevLnV16A3cOBAYeDAgWbPW3utLi4uwoYNG8yOadasWYKbm5tw8+ZNm8d09epVAYDg6+sr7N+/32QbnU4nDB48WAgMDBQyMjJk59auXSsolUrhzp07Zu9x+/ZtQaFQCOvWrbM4hqtXr9o8biKiioSlm0REVOHos22GYmNj0axZM1SuXBkDBgwwOq9SqdCvXz+cPn3aqFTyr7/+Qk5ODt566y0oFAqT93V1dcWYMWPw999/IyUlpVBeizUvv/wy+vbta3ZM48aNQ25uLg4dOmR33+PGjUNcXJzJcwqFAuPHj8fdu3eNylVv3ryJgIAAiyWpVatWxUcffYQGDRrYPS4iIuIcPSIiqoCio6PNngsPD0dUVJRRGaT0vEajwd27d2XHr127Bi8vL1StWtXivWNiYgAA169ft3PUjmnVqpXF8z4+PggICMDVq1cLve9q1aoBgFHfAQEByMjIgEajsXj9uHHjUK9ePbvHRUREnKNHREQVkKenp9lzHh4eVs8DQHZ2tux4amoqvL29kZCQYPHeubm5AGAUKBYVlUpltY2Pjw9SU1MLvW/9PDrDvlu3bo3c3Fy8++67GDJkiNEcRC8vL1SqVMnu8RARUQEGekRERIXk9u3biIiIsKmtu7t7EY+m9KpXrx4++OADTJ06FfPnzzfZpnLlynjrrbcwadIks2WnRERkHgM9IiKiQhIQEIDVq1dbbefu7o6WLVsWw4hKr0mTJmHw4ME4d+6cUcYvOzsbBw4cwOTJk1G9enX07t27hEZJRFR2MdAjIiIqBPr99p577rmSHkqZERgYaHZBlj59+uDIkSPYtm0bAz0iIgdwMRYiIqJCEB0djfv37yMrK8tiu1u3bqFatWq4cuVKMY2s9Nm1axf27dtntV1ERASSk5OLYUREROUPAz0iIqJC0Lx5c7i5ueHTTz+12G758uW4c+cOgoODi2lkpc+mTZswbtw4CIJgto0gCLh48SLCw8OLcWREROUHSzeJiIgKQXh4ON5++21MmjQJ+/btE/fjUyqV0Gq1SE1NxV9//YWdO3fi3Xfftbiyp5RarTZa4bOs69OnD7788ks89dRTePLJJxEQECA7n52djYMHD+Lo0aN4//33TfaRn58P4NH3h4iIjPGnIxERlQmVK1dGaGgoQkJCrLYNCwtDdHS0yb3wqlevjrCwMLPXVqtWzWLwEBwcjKpVq8Lf39/o3MyZM9GgQQMsWrQIK1euRFJSEgBAqVQiMDAQtWrVwldffYWBAwdafQ3S+/39999mz1t6rTExMTZlxKKiosQ972zh7++PqlWr2pSVNDWGZ555BqtWrcKCBQswd+5c5OTkyM57eHigRo0aWL16NTp37myy3zt37kChUJjc+J6IiACFYKlugoiIiErU0qVLsWDBAly6dInbDEisWbMGkyZN4hw+IiIzOEePiIioFGvfvj2uXr2KPXv2lPRQSg1BELBixQp06NChpIdCRFRqsXSTiIioFIuNjUXv3r3Rq1cv/Oc//0HLli1Rr149eHl5lfTQit3169dx9uxZLF68GAcOHMA///xT0kMiIiq1WLpJRERUymVmZmL8+PH4/PPPAQBdunTBzp07S3hUxev8+fOIjY0FAISGhmL16tXo1KlTCY+KiKj0YqBHRERURmRmZuL8+fPw9fVFjRo1Sno4xUqr1eLPP/9EREQEIiMjTS4+Q0REBRjoERERERERlTP8cxgREREREVE5w0CPiIiIiIionGGgR0REREREVM4w0CMiIiIiIipnGOgRERERERGVMwz0iIiIiIiIyhkGekREREREROUMAz0iIiIiIqJy5v8AVfi+KNsVEN4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(t, corrected_waveform, \"r-\", label=\"Corrected Waveform\")\n",
    "plt.plot(t, waveform_captured, \"g-\", label=\"Original Waveform\")\n",
    "plt.xlabel(\"Time (in ns)\")\n",
    "plt.ylabel(\"Amplitude (in V)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "743dd5ab",
   "metadata": {},
   "source": [
    "**PLEASE NOTE** : The argument `sig` of the simulator function takes in waveform samples only at 1 GSPS (1 Gigasample per second) owing to the sampling rate of the Digital-to-Analog converters present in the Cluster QCM modules."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e053768",
   "metadata": {},
   "source": [
    "Following the procedure above, you can also simulate the behaviour for FIR coefficients using `qblox_instruments.simulations.predistortions.fir_correction`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "338cc012",
   "metadata": {},
   "source": [
    "## Things to note:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b4e5b74",
   "metadata": {},
   "source": [
    "When the filters are enabled, they are applied on a continuous data stream.\n",
    "When filter parameters are changed, the sequencer is force stopped, and the data stream is changed to all zeros.\n",
    "This means that when changing filter parameters, there might be transient effects.\n",
    "These can be avoided by stopping the sequencer connected to the output before changing the filter parameters.\n",
    "All active sequencers of a certain can be stopped using `module.stop_sequencer()`"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:percent"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}