examples/two_qubits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup of a two-qubit chip with $C^3$\n",
    "\n",
    "In this example we will set-up a two qubit quantum processor and define a simple gate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "# System imports\n",
    "import copy\n",
    "import numpy as np\n",
    "import time\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import tensorflow_probability as tfp\n",
    "\n",
    "# Main C3 objects\n",
    "from c3po.c3objs import Quantity as Qty\n",
    "from c3po.experiment import Experiment as Exp\n",
    "from c3po.system.model import Model as Mdl\n",
    "from c3po.generator.generator import Generator as Gnr\n",
    "\n",
    "# Building blocks\n",
    "import c3po.generator.devices as devices\n",
    "import c3po.signal.gates as gates\n",
    "import c3po.system.chip as chip\n",
    "import c3po.signal.pulse as pulse\n",
    "import c3po.system.tasks as tasks\n",
    "\n",
    "# Libs and helpers\n",
    "import c3po.libraries.hamiltonians as hamiltonians\n",
    "import c3po.libraries.fidelities as fidelities\n",
    "import c3po.libraries.envelopes as envelopes\n",
    "import c3po.utils.qt_utils as qt_utils\n",
    "import c3po.utils.tf_utils as tf_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model components\n",
    "We first create a qubit. Each parameter is a Quantity (Qty()) object with bounds and a unit. In $C^3$, the default multi-level qubit is a Transmon modelled as a Duffing oscillator with frequency $\\omega$ and anharmonicity $\\delta$ :\n",
    "$$ H/\\hbar = \\omega b^\\dagger b - \\frac{\\delta}{2}                        \\left(b^\\dagger b - 1\\right) b^\\dagger b \n",
    "$$\n",
    "The \"name\" will be used to identify this qubit (or other component) later and should thus be chosen carefully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "qubit_lvls = 3\n",
    "freq_q1 = 5e9 * 2 * np.pi\n",
    "anhar_q1 = -210e6 * 2 * np.pi\n",
    "t1_q1 = 27e-6\n",
    "t2star_q1 = 39e-6\n",
    "qubit_temp = 50e-3\n",
    "\n",
    "q1 = chip.Qubit(\n",
    "    name=\"Q1\",\n",
    "    desc=\"Qubit 1\",\n",
    "    freq=Qty(\n",
    "        value=freq_q1,\n",
    "        min=4.995e9 * 2 * np.pi,\n",
    "        max=5.005e9 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    anhar=Qty(\n",
    "        value=anhar_q1,\n",
    "        min=-380e6 * 2 * np.pi,\n",
    "        max=-120e6 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    hilbert_dim=qubit_lvls,\n",
    "    t1=Qty(\n",
    "        value=t1_q1,\n",
    "        min=1e-6,\n",
    "        max=90e-6,\n",
    "        unit='s'\n",
    "    ),\n",
    "    t2star=Qty(\n",
    "        value=t2star_q1,\n",
    "        min=10e-6,\n",
    "        max=90e-3,\n",
    "        unit='s'\n",
    "    ),\n",
    "    temp=Qty(\n",
    "        value=qubit_temp,\n",
    "        min=0.0,\n",
    "        max=0.12,\n",
    "        unit='K'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the same for a second qubit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "freq_q2 = 5.6e9 * 2 * np.pi\n",
    "anhar_q2 = -240e6 * 2 * np.pi\n",
    "t1_q2 = 23e-6\n",
    "t2star_q2 = 31e-6\n",
    "q2 = chip.Qubit(\n",
    "    name=\"Q2\",\n",
    "    desc=\"Qubit 2\",\n",
    "    freq=Qty(\n",
    "        value=freq_q2,\n",
    "        min=5.595e9 * 2 * np.pi,\n",
    "        max=5.605e9 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    anhar=Qty(\n",
    "        value=anhar_q2,\n",
    "        min=-380e6 * 2 * np.pi,\n",
    "        max=-120e6 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    hilbert_dim=qubit_lvls,\n",
    "    t1=Qty(\n",
    "        value=t1_q2,\n",
    "        min=1e-6,\n",
    "        max=90e-6,\n",
    "        unit='s'\n",
    "    ),\n",
    "    t2star=Qty(\n",
    "        value=t2star_q2,\n",
    "        min=10e-6,\n",
    "        max=90e-6,\n",
    "        unit='s'\n",
    "    ),\n",
    "    temp=Qty(\n",
    "        value=qubit_temp,\n",
    "        min=0.0,\n",
    "        max=0.12,\n",
    "        unit='K'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A static coupling between the two is realized in the following way. We supply the type of coupling by selecting int_XX $(b_1+b_1^\\dagger)(b_2+b_2^\\dagger)$ from the hamiltonian library. The \"connected\" property contains the list of qubit names to be coupled, in this case \"Q1\" and \"Q2\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "coupling_strength = 20e6 * 2 * np.pi\n",
    "q1q2 = chip.Coupling(\n",
    "    name=\"Q1-Q2\",\n",
    "    desc=\"coupling\",\n",
    "    comment=\"Coupling qubit 1 to qubit 2\",\n",
    "    connected=[\"Q1\", \"Q2\"],\n",
    "    strength=Qty(\n",
    "        value=coupling_strength,\n",
    "        min=-1 * 1e3 * 2 * np.pi,\n",
    "        max=200e6 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    hamiltonian_func=hamiltonians.int_XX\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the same spirit, we specify control Hamiltonians to drive the system. Again \"connected\" connected tells us which qubit this drive acts on and \"name\" will later be used to assign the correct control signal to this drive line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "drive = chip.Drive(\n",
    "    name=\"d1\",\n",
    "    desc=\"Drive 1\",\n",
    "    comment=\"Drive line 1 on qubit 1\",\n",
    "    connected=[\"Q1\"],\n",
    "    hamiltonian_func=hamiltonians.x_drive\n",
    ")\n",
    "drive2 = chip.Drive(\n",
    "    name=\"d2\",\n",
    "    desc=\"Drive 2\",\n",
    "    comment=\"Drive line 2 on qubit 2\",\n",
    "    connected=[\"Q2\"],\n",
    "    hamiltonian_func=hamiltonians.x_drive\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SPAM errors\n",
    "In experimental practice, the qubit state can be mis-classified during read-out. We simulate this by constructing a _confusion matrix_, containing the probabilities for one qubit state being mistaken for another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "m00_q1 = 0.97 # Prop to read qubit 1 state 0 as 0\n",
    "m01_q1 = 0.04 # Prop to read qubit 1 state 0 as 1\n",
    "m00_q2 = 0.96 # Prop to read qubit 2 state 0 as 0\n",
    "m01_q2 = 0.05 # Prop to read qubit 2 state 0 as 1\n",
    "one_zeros = np.array([0] * qubit_lvls)\n",
    "zero_ones = np.array([1] * qubit_lvls)\n",
    "one_zeros[0] = 1\n",
    "zero_ones[0] = 0\n",
    "val1 = one_zeros * m00_q1 + zero_ones * m01_q1\n",
    "val2 = one_zeros * m00_q2 + zero_ones * m01_q2\n",
    "min = one_zeros * 0.8 + zero_ones * 0.0\n",
    "max = one_zeros * 1.0 + zero_ones * 0.2\n",
    "confusion_row1 = Qty(value=val1, min=min, max=max, unit=\"\")\n",
    "confusion_row2 = Qty(value=val2, min=min, max=max, unit=\"\")\n",
    "conf_matrix = tasks.ConfusionMatrix(Q1=confusion_row1, Q2=confusion_row2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following task creates an initial thermal state with given temperature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "init_temp = 50e-3\n",
    "init_ground = tasks.InitialiseGround(\n",
    "    init_temp=Qty(\n",
    "        value=init_temp,\n",
    "        min=-0.001,\n",
    "        max=0.22,\n",
    "        unit='K'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We collect the parts specified above in the Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "model = Mdl(\n",
    "    [q1, q2], # Individual, self-contained components\n",
    "    [drive, drive2, q1q2],  # Interactions between components\n",
    "    [conf_matrix, init_ground] # SPAM processing\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further, we can decide between coherent or open-system dynamics using set_lindbladian() and whether to eliminate the static coupling by going to the dressed frame with set_dressed()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "model.set_lindbladian(False)\n",
    "model.set_dressed(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Control signals\n",
    "With the system model taken care of, we now specify the control electronics and signal chain. Complex shaped controls are often realized by creating an envelope signal with an arbitrary waveform generator (AWG) with limited bandwith and mixing it with a fast, stable local oscillator (LO)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "sim_res = 100e9 # Resolution for numerical simulation\n",
    "awg_res = 2e9 # Realistic, limited resolution of an AWG\n",
    "lo = devices.LO(name='lo', resolution=sim_res)\n",
    "awg = devices.AWG(name='awg', resolution=awg_res)\n",
    "mixer = devices.Mixer(name='mixer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Waveform generators exhibit a rise time, the time it takes until the target voltage is set. This has a smoothing effect on the resulting pulse shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "resp = devices.Response(\n",
    "    name='resp',\n",
    "    rise_time=Qty(\n",
    "        value=0.3e-9,\n",
    "        min=0.05e-9,\n",
    "        max=0.6e-9,\n",
    "        unit='s'\n",
    "    ),\n",
    "    resolution=sim_res\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In simulation, we translate between AWG resolution and simulation (or \"analog\") resolultion by including an up-sampling device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dig_to_an = devices.Digital_to_Analog(\n",
    "    name=\"dac\",\n",
    "    resolution=sim_res\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Control electronics apply voltages to lines, whereas in a Hamiltonian we usually write the control fields in energy or frequency units. In practice, this conversion can be highly non-trivial if it involves multiple stages of attenuation and for example the conversion of a line voltage in an antenna to a dipole field coupling to the qubit. The following device represents a simple, linear conversion factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "v2hz = 1e9\n",
    "v_to_hz = devices.Volts_to_Hertz(\n",
    "    name='v_to_hz',\n",
    "    V_to_Hz=Qty(\n",
    "        value=v2hz,\n",
    "        min=0.9e9,\n",
    "        max=1.1e9,\n",
    "        unit='Hz 2pi/V'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The generator combines the parts of the signal generation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "generator = Gnr([lo, awg, mixer, v_to_hz, dig_to_an, resp])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gates-set\n",
    "It remains to write down what kind of operations we want to perform on the device. For a gate based quantum computing chip, we define a gate-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "gateset = gates.GateSet()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We choose a gate time and a gaussian envelope shape with a list of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "t_final = 7e-9   # Time for single qubit gates\n",
    "sideband = 50e6 * 2 * np.pi\n",
    "gauss_params_single = {\n",
    "    'amp': Qty(\n",
    "        value=0.5,\n",
    "        min=0.4,\n",
    "        max=0.6,\n",
    "        unit=\"V\"\n",
    "    ),\n",
    "    't_final': Qty(\n",
    "        value=t_final,\n",
    "        min=0.5 * t_final,\n",
    "        max=1.5 * t_final,\n",
    "        unit=\"s\"\n",
    "    ),\n",
    "    'sigma': Qty(\n",
    "        value=t_final / 4,\n",
    "        min=t_final / 8,\n",
    "        max=t_final / 2,\n",
    "        unit=\"s\"\n",
    "    ),\n",
    "    'xy_angle': Qty(\n",
    "        value=0.0,\n",
    "        min=-0.5 * np.pi,\n",
    "        max=2.5 * np.pi,\n",
    "        unit='rad'\n",
    "    ),\n",
    "    'freq_offset': Qty(\n",
    "        value=-sideband - 3e6 * 2 * np.pi,\n",
    "        min=-56 * 1e6 * 2 * np.pi,\n",
    "        max=-52 * 1e6 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    'delta': Qty(\n",
    "        value=0,\n",
    "        min=-5,\n",
    "        max=3,\n",
    "        unit=\"\"\n",
    "    )\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we take gaussian_nonorm() from the libraries as the function to define the shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "gauss_env_single = pulse.Envelope(\n",
    "    name=\"gauss\",\n",
    "    desc=\"Gaussian comp for single-qubit gates\",\n",
    "    params=gauss_params_single,\n",
    "    shape=envelopes.gaussian_nonorm\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also define a gate that represents no driving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "nodrive_env = pulse.Envelope(\n",
    "    name=\"no_drive\",\n",
    "    params={\n",
    "        't_final': Qty(\n",
    "            value=t_final,\n",
    "            min=0.5 * t_final,\n",
    "            max=1.5 * t_final,\n",
    "            unit=\"s\"\n",
    "        )\n",
    "    },\n",
    "    shape=envelopes.no_drive\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We specify the drive tones with an offset from the qubit frequencies. As in experiment, we will later adjust the resonance by modulating the envelope function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "lo_freq_q1 = 5e9 * 2 * np.pi + sideband\n",
    "carrier_parameters = {\n",
    "    'freq': Qty(\n",
    "        value=lo_freq_q1,\n",
    "        min=4.5e9 * 2 * np.pi,\n",
    "        max=6e9 * 2 * np.pi,\n",
    "        unit='Hz 2pi'\n",
    "    ),\n",
    "    'framechange': Qty(\n",
    "        value=0.0,\n",
    "        min= -np.pi,\n",
    "        max= 3 * np.pi,\n",
    "        unit='rad'\n",
    "    )\n",
    "}\n",
    "carr = pulse.Carrier(\n",
    "    name=\"carrier\",\n",
    "    desc=\"Frequency of the local oscillator\",\n",
    "    params=carrier_parameters\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the second qubit drive tone, we copy the first one and replace the frequency. The deepcopy is to ensure that we don't just create a pointer to the first drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "lo_freq_q2 = 5.6e9 * 2 * np.pi + sideband\n",
    "carr_2 = copy.deepcopy(carr)\n",
    "carr_2.params['freq'].set_value(lo_freq_q2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instructions\n",
    "We define the gates we want to perform with a \"name\" that will identify them later and \"channels\" relating to the control Hamiltonians and drive lines we specified earlier. As a start we write down 90 degree rotations in the positive $x$-direction and identity gates for both qubits. Then we add a carrier and envelope to each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "X90p_q1 = gates.Instruction(\n",
    "    name=\"X90p\",\n",
    "    t_start=0.0,\n",
    "    t_end=t_final,\n",
    "    channels=[\"d1\"]\n",
    ")\n",
    "X90p_q2 = gates.Instruction(\n",
    "    name=\"X90p\",\n",
    "    t_start=0.0,\n",
    "    t_end=t_final,\n",
    "    channels=[\"d2\"]\n",
    ")\n",
    "QId_q1 = gates.Instruction(\n",
    "    name=\"Id\",\n",
    "    t_start=0.0,\n",
    "    t_end=t_final,\n",
    "    channels=[\"d1\"]\n",
    ")\n",
    "QId_q2 = gates.Instruction(\n",
    "    name=\"Id\",\n",
    "    t_start=0.0,\n",
    "    t_end=t_final,\n",
    "    channels=[\"d2\"]\n",
    ")\n",
    "\n",
    "X90p_q1.add_component(gauss_env_single, \"d1\")\n",
    "X90p_q1.add_component(carr, \"d1\")\n",
    "QId_q1.add_component(nodrive_env, \"d1\")\n",
    "QId_q1.add_component(copy.deepcopy(carr), \"d1\")\n",
    "\n",
    "X90p_q2.add_component(copy.deepcopy(gauss_env_single), \"d2\")\n",
    "X90p_q2.add_component(carr_2, \"d2\")\n",
    "QId_q2.add_component(copy.deepcopy(nodrive_env), \"d2\")\n",
    "QId_q2.add_component(copy.deepcopy(carr_2), \"d2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When later compiling gates into sequences, we have to take care of the relative rotating frames of the qubits and local oscillators. We do this by adding a phase after each gate that realigns the frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "QId_q1.comps['d1']['carrier'].params['framechange'].set_value(\n",
    "    (-sideband * t_final) % (2*np.pi)\n",
    ")\n",
    "QId_q2.comps['d2']['carrier'].params['framechange'].set_value(\n",
    "    (-sideband * t_final) % (2*np.pi)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The remainder of the gates-set can be derived from the X90p gate by shifting its phase by multiples of $\\pi/2$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "Y90p_q1 = copy.deepcopy(X90p_q1)\n",
    "Y90p_q1.name = \"Y90p\"\n",
    "X90m_q1 = copy.deepcopy(X90p_q1)\n",
    "X90m_q1.name = \"X90m\"\n",
    "Y90m_q1 = copy.deepcopy(X90p_q1)\n",
    "Y90m_q1.name = \"Y90m\"\n",
    "Y90p_q1.comps['d1']['gauss'].params['xy_angle'].set_value(0.5 * np.pi)\n",
    "X90m_q1.comps['d1']['gauss'].params['xy_angle'].set_value(np.pi)\n",
    "Y90m_q1.comps['d1']['gauss'].params['xy_angle'].set_value(1.5 * np.pi)\n",
    "Q1_gates = [QId_q1, X90p_q1, Y90p_q1, X90m_q1, Y90m_q1]\n",
    "\n",
    "\n",
    "Y90p_q2 = copy.deepcopy(X90p_q2)\n",
    "Y90p_q2.name = \"Y90p\"\n",
    "X90m_q2 = copy.deepcopy(X90p_q2)\n",
    "X90m_q2.name = \"X90m\"\n",
    "Y90m_q2 = copy.deepcopy(X90p_q2)\n",
    "Y90m_q2.name = \"Y90m\"\n",
    "Y90p_q2.comps['d2']['gauss'].params['xy_angle'].set_value(0.5 * np.pi)\n",
    "X90m_q2.comps['d2']['gauss'].params['xy_angle'].set_value(np.pi)\n",
    "Y90m_q2.comps['d2']['gauss'].params['xy_angle'].set_value(1.5 * np.pi)\n",
    "Q2_gates = [QId_q2, X90p_q2, Y90p_q2, X90m_q2, Y90m_q2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the single qubit gates in place, we can combine them to get all possible combinations of simultaneous gates on both qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "all_1q_gates_comb = []\n",
    "for g1 in Q1_gates:\n",
    "    for g2 in Q2_gates:\n",
    "        g = gates.Instruction(\n",
    "            name=\"NONE\",\n",
    "            t_start=0.0,\n",
    "            t_end=t_final,\n",
    "            channels=[]\n",
    "        )\n",
    "        g.name = g1.name + \":\" + g2.name\n",
    "        channels = []\n",
    "        channels.extend(g1.comps.keys())\n",
    "        channels.extend(g2.comps.keys())\n",
    "        for chan in channels:\n",
    "            g.comps[chan] = {}\n",
    "            if chan in g1.comps:\n",
    "                g.comps[chan].update(g1.comps[chan])\n",
    "            if chan in g2.comps:\n",
    "                g.comps[chan].update(g2.comps[chan])\n",
    "        all_1q_gates_comb.append(g)\n",
    "\n",
    "for gate in all_1q_gates_comb:\n",
    "    gateset.add_instruction(gate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The experiment\n",
    "Finally everything is collected in the experiment that provides the functions to interact with the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-10T13:45:05.684014Z",
     "start_time": "2020-06-10T13:45:04.441825Z"
    }
   },
   "outputs": [],
   "source": [
    "exp = Exp(model=model, generator=generator, gateset=gateset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simulation\n",
    "With our experiment all set-up, we can perform simulations. We first decide which basic gates to simulate, in this case only the 90 degree rotation on one qubit and the identity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp.opt_gates = ['X90p:Id', 'Id:Id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual numerical simulation is done by calling exp.get_gates(). \n",
    "*WARNING:* This is resource intensive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function tf_dU_of_t at 0x7efb466b61f0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Constant'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function tf_dU_of_t at 0x7efb466b61f0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: module 'gast' has no attribute 'Constant'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    }
   ],
   "source": [
    "gates = exp.get_gates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After this step the unitaries or process matrices are stored in the exp object. We can look at their names and matrix representations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'X90p:Id': <tf.Tensor: shape=(9, 9), dtype=complex128, numpy=\n",
       " array([[ 5.39825284e-01+5.54517419e-02j, -1.21231172e-05-5.60980520e-05j,\n",
       "          4.38215312e-08+4.13467618e-07j, -1.29299892e-01-8.22161934e-01j,\n",
       "         -1.80728035e-04-5.35242904e-05j,  8.98184408e-07-7.04714994e-07j,\n",
       "          1.04613741e-01+4.36000398e-02j,  4.93043540e-05-1.39851579e-04j,\n",
       "         -7.74202362e-07+1.23705675e-06j],\n",
       "        [ 7.98476205e-06-7.23397310e-05j,  5.41160291e-01+3.44797285e-02j,\n",
       "         -1.37214003e-03+6.02440007e-04j, -1.66046688e-04+1.03408081e-04j,\n",
       "         -1.27492879e-01-8.23225445e-01j,  3.45381964e-04+6.12323933e-04j,\n",
       "          4.31572117e-05+3.03419465e-05j,  1.00251291e-01+4.39340994e-02j,\n",
       "         -4.32578847e-03+2.60902726e-04j],\n",
       "        [ 4.72922878e-08-3.42862493e-07j, -5.83580511e-04-1.51315995e-03j,\n",
       "         -2.23528244e-01-4.93786725e-01j,  8.72353899e-08-1.00071172e-07j,\n",
       "         -1.29724115e-03-9.35841645e-06j, -6.24353912e-01+5.51696354e-01j,\n",
       "          7.54826280e-06+2.38541508e-06j,  2.01328317e-04+1.55353026e-04j,\n",
       "         -1.49815752e-02-1.08596916e-01j],\n",
       "        [-2.14245546e-01-8.03084039e-01j,  6.75291157e-05+8.72417146e-05j,\n",
       "          4.46535372e-08+1.30575211e-07j,  4.77289095e-01-2.75981622e-01j,\n",
       "         -4.65951479e-06-2.80491348e-05j, -7.13098786e-07-4.39096556e-07j,\n",
       "         -1.88381108e-02-6.94925754e-02j, -1.31426804e-05-1.72906166e-04j,\n",
       "         -1.38592878e-06-1.73406019e-07j],\n",
       "        [ 1.45440512e-05-9.29213835e-05j, -2.14257090e-01-8.03921809e-01j,\n",
       "          2.26616170e-04+1.48313010e-03j, -1.64587328e-05-1.36540595e-05j,\n",
       "          4.87521619e-01-2.55321069e-01j,  5.85768716e-04-6.57228447e-04j,\n",
       "          2.13187535e-05-9.74723054e-06j, -1.37135962e-02-6.84238771e-02j,\n",
       "         -4.05568165e-03-6.92925035e-03j],\n",
       "        [-1.01683267e-06+1.32660308e-06j, -4.33915629e-04+6.30538355e-04j,\n",
       "         -5.64001032e-01+6.11829786e-01j,  6.64551443e-07+2.99933232e-07j,\n",
       "          2.66993556e-04+9.55202731e-04j, -4.97198961e-01-2.35735247e-01j,\n",
       "          1.00224389e-05-3.26955153e-06j, -2.55214787e-04+8.39624582e-06j,\n",
       "         -5.08659636e-02+4.68839276e-02j],\n",
       "        [ 1.17801477e-01-2.81318874e-02j,  1.68281222e-05-2.30935257e-05j,\n",
       "         -1.05406555e-05+2.15484800e-06j, -4.31206387e-02-3.87440969e-02j,\n",
       "         -3.44120397e-05-3.18706751e-05j, -7.38994288e-06+2.23481110e-06j,\n",
       "         -9.87099259e-01+8.72120049e-02j,  1.41924571e-05-1.49096093e-04j,\n",
       "         -8.36691272e-07-2.41767185e-07j],\n",
       "        [ 1.72568002e-04+6.15617549e-05j,  1.14320996e-01-2.58564036e-02j,\n",
       "          3.96769637e-05-1.85459394e-04j,  1.46044405e-04+3.54355812e-05j,\n",
       "         -3.90825177e-02-3.98188064e-02j,  9.94983860e-05+2.96227547e-04j,\n",
       "         -1.16640418e-05-1.29700232e-04j, -9.91427008e-01+1.47090732e-02j,\n",
       "         -1.95315912e-03+3.63162940e-04j],\n",
       "        [ 6.50172527e-07-8.68913657e-07j, -1.07060815e-03-4.25024069e-03j,\n",
       "         -8.18098176e-02-8.41345157e-02j,  1.03733205e-06+3.54605097e-07j,\n",
       "          6.50627042e-03-4.60223994e-03j, -1.40476528e-02+5.32405211e-02j,\n",
       "          5.82923255e-07+1.47782853e-07j, -8.61318505e-04-1.90071477e-03j,\n",
       "          5.47504106e-01+8.26634707e-01j]])>,\n",
       " 'Id:Id': <tf.Tensor: shape=(9, 9), dtype=complex128, numpy=\n",
       " array([[ 9.99998623e-01+1.65973895e-03j, -2.77614020e-19-3.72525981e-19j,\n",
       "         -5.92176505e-20+5.39588293e-20j,  7.14217182e-24-4.15979218e-22j,\n",
       "         -4.60377183e-19-6.75942850e-19j, -3.93900555e-22+3.69500596e-22j,\n",
       "          4.75585150e-20-3.41597285e-21j,  3.32354855e-23-2.59009946e-23j,\n",
       "          1.02945987e-21+1.02399900e-21j],\n",
       "        [ 2.68505811e-19-3.79143482e-19j,  9.99664791e-01-2.58902721e-02j,\n",
       "         -3.33462500e-16+4.17061206e-17j, -8.04465478e-16+1.09939164e-15j,\n",
       "          3.53347577e-19-2.12567606e-17j,  1.02315589e-17-1.43042811e-18j,\n",
       "          2.39953294e-16+1.44762527e-16j,  1.12832081e-17-5.35902994e-19j,\n",
       "          5.41688989e-19+3.08979933e-18j],\n",
       "        [ 2.39328347e-20-1.15971147e-19j, -1.42710457e-16-3.04253784e-16j,\n",
       "         -5.11658270e-01-8.59189029e-01j,  4.48760909e-17-2.01608146e-16j,\n",
       "          1.45384352e-15+3.47670659e-15j, -1.37969871e-18+8.48990269e-19j,\n",
       "         -1.01782902e-15-1.33222032e-16j, -5.46773044e-19+2.70997471e-19j,\n",
       "         -1.06498548e-16-1.60373852e-16j],\n",
       "        [ 7.14217182e-24-4.15979218e-22j,  7.68801623e-16+1.06574676e-15j,\n",
       "         -1.54807815e-16+1.36726912e-16j,  9.99466188e-01+3.26701520e-02j,\n",
       "          3.47328300e-18+5.27355763e-18j,  4.14483575e-16-3.76896891e-16j,\n",
       "          1.65184290e-17-9.29239451e-19j,  1.12582803e-16-8.49647138e-17j,\n",
       "         -2.38425428e-17-2.44632561e-17j],\n",
       "        [ 5.25100022e-19-6.78380125e-19j,  3.53347577e-19-2.12567606e-17j,\n",
       "          1.59384461e-15-1.30843692e-16j, -3.94214788e-18+4.93290736e-18j,\n",
       "          9.98251993e-01+5.91010954e-02j,  1.47122743e-18-1.42253610e-19j,\n",
       "          2.94248327e-15+1.95041320e-15j, -1.57223778e-20+7.77569629e-23j,\n",
       "         -8.02966396e-18-6.09311608e-17j],\n",
       "        [ 1.01485242e-22-5.30461199e-22j,  6.12025274e-18+1.25713798e-17j,\n",
       "         -1.37969871e-18+8.48990269e-19j, -1.13770119e-16+5.48448343e-16j,\n",
       "          5.89925499e-19+1.35526165e-18j, -5.36324248e-01-8.44010951e-01j,\n",
       "         -9.67923384e-17-1.12458380e-17j, -3.42697891e-16+1.76080924e-16j,\n",
       "         -2.57086872e-16-3.75234811e-16j],\n",
       "        [ 5.70205604e-20-4.09560066e-21j,  2.11826990e-16-1.83475063e-16j,\n",
       "          7.42954320e-16+7.03977471e-16j,  1.65184290e-17-9.29239451e-19j,\n",
       "          2.84906797e-15-2.26314740e-15j,  7.16965089e-17+6.59911831e-17j,\n",
       "         -9.89496229e-01+1.44558658e-01j, -4.73955286e-18-5.23923175e-18j,\n",
       "         -3.21732416e-16+3.74776956e-16j],\n",
       "        [ 3.49036396e-23+2.36049776e-23j,  1.13437079e-17-5.38776469e-19j,\n",
       "          8.87717476e-20+6.03754891e-19j,  1.15887419e-16+8.10206378e-17j,\n",
       "         -1.57223778e-20+7.77569629e-23j,  6.92588678e-17+4.27879193e-16j,\n",
       "          3.51820312e-18-6.12659428e-18j, -9.97617990e-01+6.89719171e-02j,\n",
       "          2.92448707e-17-5.77843453e-18j],\n",
       "        [-1.87965382e-21-2.92586018e-22j, -2.77118268e-18+1.46997734e-18j,\n",
       "         -1.06431610e-16-1.60273051e-16j,  3.36681634e-17+5.77689490e-18j,\n",
       "          5.57864547e-17-2.66175286e-17j, -2.57086872e-16-3.75234811e-16j,\n",
       "          4.00068838e-17-4.92412150e-16j,  1.45327799e-17+2.60278903e-17j,\n",
       "          5.93399036e-01+8.04892781e-01j]])>}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.unitaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate dynamics, we define an initial state with finite temperature we set earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "psi_init = [[0] * 9]\n",
    "psi_init[0][0] = 1\n",
    "init_state = tf.transpose(tf.constant(psi_init, tf.complex128))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(9, 1), dtype=complex128, numpy=\n",
       "array([[1.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j],\n",
       "       [0.+0.j]])>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we stored the process matrices, we can now relatively inexpesively evaluate sequences. We start with just one gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "barely_a_seq = ['X90p:Id']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and plot system dynamics. The \"debug\" options shows the plot in this notebook. Otherwise the plot is stored as a file in a given directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WAe4unXHjxnkf6wSG3w5NZfmd45lzTTZJMRZu/+gXymrar+Wvo6MVmnWACyGMwIvARCAPWCuEWCilzK2z20zgIynly0KIfsAXQHpLX+uhz7eQe7DhYZBOpxOjseX52Pt1jeaBi/o3ut1ms7F7927S09MByM/PJy3t+CiQ1NRU8vPzm3yNgoICunTpAkBycjJFRUXebdnZ2fzwww/87ne/a7F7e6HS0LjWutYO+ewcaWbaSyt5dNFWnpg+0M929VGpXkEtX5VcQc3hnGcAO6WUuwGEEPOAi4G6gV8C0Z77MYDfh1G0Juj7wpEjR4iNjfVbeUKIehdvExMTOXgwuEeVjBw5MtAKPtNW10Fpsdw49jReXroLu8tFn+QoLjujG1EW/8+pUKleQS1flVxBO18tA38KUDfRRB4w/IR9HgS+FkL8BYgAzmmooEOHDtG7d2/A/Q04Y8YMxo0bR3l5OUajkfsv7FevLz8qKorKykpcLhcul4vIyEjsdjt2u91bhhDCOzTSZDJhsVi8ZQghiIyM9JYBEBERgc1m85ZhNBqpqamhvLwcgISEBFauXOl9vGfPHiZOnEhFRQW1GVAjIiKwWq04HO7cMImJiezbt4/4+HgOHz5M586dcblcVFZWUlpa6p2oVbcMKSW5ubkUFhYCMGDAAKxWKzt27AAgLS2NpKQkarOZRkdHk5WVxYoVK7yvO2bMGLZs2UJxsbvPetCgQZSXl7N7t3vB8vT0dOLj472LQMTFxTFo0CCWLVuGlBIhBGPHjmXJkiXekUhZWVmUlJSwd+9eAHr06EFUVBQbNmwAoFOnTvTv39+7eLTJZGLUqFGsX7/eO2ktOzubgoICb36SzMxMzGYzmzdv9tZXr169vP2eZrOZkSNHkpOT4z13w4cPJy8vz/trq3fv3hiNRnJycggPDyc5OZmMjAxWrVoFQFhYGMOHD2f16tXei+sjR45kz549HD58GIB+/frhdDrJCj3EmFQTX28+xCfr8/nox+3cc4aFmOgosrOzWbVqFVarFYBRo0axffv2Vp2nsrIywsPD/XaeNmzY4L3epMV5MhgM9OvXzy/nKTfX3S5sy3natm0bACkpKaSmprJ69WoAb5ZLu93ul/Pk77+nhs5Tbm4u4eHhPp+nmTNnMn/+fDw0nn5WSqnJDZgOvFHn8VXACyfsczvwN8/9kbh/DRhOLGvo0KHyRHJzc096riHKysp82q81pKamyurqaimllMXFxTI9PV2WlJTIkpISmZ6eLouLi6WUUl511VVy9erVJx3/97//XT7++ONSSikff/xxeeutt3q3PfXUU95tdfH1fbcH33//faAVfMbfrp+sPyC737VIvr58l1/LlVKtepVSLV+VXKVsmy+QIxuJz1pe3M0H6k59TPU8V5frgY8ApJSrAAtNfUsFGeeee663VRMfH899993HsGHDGDZsGPfffz/x8fEAbNy4ka5du550/N13380333xDZmYmS5Ys4a9//at32/fff8/kyZPb543otJipg1M4u08iT3+9nX3FlYHW0dFpGY19I7T1hrsbaTeQAYQCG4D+J+zzJXCt535f3H384sSy2tLidzqdLfiObBnr1q2TV155ZZP7HDt2TE6fPt2n8mpdDx8+LM8+++wG9wmmFn9VVVWgFXxGC9f80io54P7FMvuf38hr31wtv9x0yC/lqlSvUqrlq5KrlG3zJRAtfimlA7gF+ArYinv0zhYhxMNCiCme3f4G/EEIsQH4wPMl4NclwWw2mz+Lq0dWVhbjx4/3TuBqiOjo6Lp9bk1S67p//36efvppvzhqSV5eXqAVfEYL166xYbxzw3AGp8Wyq6iSP727jh93HWlzuSrVK6jlq5IraOer6Th+KeUXUspeUsrTpJSPep67X0q50HM/V0p5lpRykJRysJTya3871F6M1YrrrrvObyOHal2HDRvG4MGD/VKmljQ3XDWY0Mp1cFosr1+dzVe3jaFbfDj3/XczVkfjDQFfUKleQS1flVxBO98OP3NXR6c9CAs18vDF/dlVVMnLS/VFXXSCG3UymLUSlSZsqOQKeIfYqkB7uI7rncjFg7sye8kOlmwtYEhaHPdc0Ifw0Jb9malUr6CWr0quoJ1vhw/8wZbRsilUcgXtJsdpQXu5PvHbgaTEhrEx7xjvrd5HhdXBs78f3KIyVKpXUMtXJVfQzrfDd/WolL9eJVfAO9lGBdrL1RJi5M5JfXj3huHcMr4nn/6c3+LkbirVK6jlq5IraOfb4QO/lrQ1LfP8+fPp378/BoPBOzMQYNOmTVx77bVaaeu0E38e35O0+DDu+2wzNocr0Do6Ol46fODXciGWtqZlHjBgAJ988gljxoyp53r66aeTl5fH/v37NXP3B8nJyYFW8JlAuFpCjDw8ZQA7CyuY9O/l3PzeerYXlDd7nEr1Cmr5quQK2vl2+MCv5QXTtqZl7tu3b72LN3VdL7roIubNm6eNuJ8IlrV/fSFQruP7JDL794PpGhPGip1HuOKN1c2mdlapXkEtX5VcQTvfjnFx98u74fCmBje5nA4Mxla8zeTT4fxZjW72R1rmE6msrPQutp6dnc2sWbO48847W+7eTqxatUqZhasD6Tp1SApTh6SwKe8YU15cwexvdnD/Rf0a3V+legW1fFVyBe18O3yLXyv8nZb5RFRIy6zTMk5PjeGyM7oxd9Veth1uvstHR0crOkaLv4mWeU1FhTcdqz8JCwurNwonJSWFpUuXeh/n5eW1+Ju67nDOmpoawsLC2qqpKcHuV5dgcb3j3N58sekQ1721ljNP68S0ISmc2bN+XsJgcfUVlXxVcgXtfDt8i1+LoA/unNpOp9Mb/M877zy+/vprSktLKS0t5euvv+a8884D4Oqrr2bNmjUtct2+fTsDBgzQxN1fDB9+4vIKwUuwuMZFhLqXcow283VuAVe9uYZ1++oPAggWV19RyVclV9DOt8MHfi0XW29rWuZPP/2U1NRUVq1axeTJkznnnOPr0KiQlrl2gQsVCCbXod3j+eTPZ7HirvF0jgzloc+34HIdz00YTK6+oJKvSq6gnW+HD/x+TvZZj5tvvrneeP3rrruOnTt3snPnTmbMmAFAWVkZmZmZpKamnnT8tGnTyMvLw2q1UlBQwKeffgqA1WolJyeH888/XzN3f1C7EpIKBKNrlCWEu8/vw8a8YyxYdzwLYzC6NoVKviq5gna+HaOPP0DUTcvc2NTqlqRlrmX//v3MmjVL0zkIOsHB1MEpvLNqH499uZXiShtZ3WIDraRzCiC0bBH7i+zsbFl3ZivA1q1b6du3b7PHulwuDAY1ftj44urr+24PrFarMonlgtl1z5FKbpi7ll1F7pW8Hpvaj8tHqDPePJjr9kRUcgWoKS/H4hni3VKEEOuklNkNbVMjIraB2kWVVUAlV3AvKK8Kweya0TmCJbeP5ef7JjI8I57Hv/yVY1XariPhT4K5bk8kmF2llNRs3cqxRf+j5P33OXDzLez5/aWadFd3+L4Eh8MRaAWfUckV4PDhw/Tp0yfQGj4R7K5CCOIiQnngov5Mfu4H/v1t05O8golgr9u6BJOry2ajev3POAoO4zhyhLL/fUFNnaRsxk6dqMzORtpsCD//SunwgV9HRyX6dY1mbKqJt1ft5czTOjG4WyydI9XpmtBpHEdpKTWbN+M8egzrtl85+ul/cRYfz9xq7tOH5AfuJ3zYMAxR0Zg6d2LZDz9g0KBrqsMHfovFEmgFn1HJFaBfPzVapKCW6z8uGsD1H27jhrdzMAi4c1If/jT2tEBrNYpKdduerlJKnMXFOI4c4din/6X0ww+RtZM+TSYiR48m9pJLMPfIwBAVhalTp3bz7fB9/FpevG5rWuY77riDPn36MHDgQKZNm+bN5qlKWuamFpkPNlRyjQo18MX/jeb1q7OZ0DeJJxb/ytZDZYHWahSV6rY9XKWUHFu4kJ1jxrJj1Gj2TJ1GybvvEn3eeXR/5216fPkFvVb9SNrLLxF19nhC09MbDPpa+nb4wK/lBdO2pmWeOHEimzdvZuPGjfTq1YvHH38cUCct87Zt2wKt4DOquUZZQpjYL4l/TR9ItCWEx77Yqmkjpi2oVrda4Dx6lNIPP6Jw9mz2XXUVB++8i5CuXUn6xz/o+vRT9FzyDV2fmEX4sGGYMzIw+jhSRyvfDh/4taStaZnPPfdc71j9ESNG1MvmqUJaZh3tiQ0P5bZzMvlhxxG+2lIQaB2dBqhYsZLdF03h8AMPUPzqaziKikiaOZPu779H/NVXETN5MiFdugRasx4doo//iTVP8GvJrw1ua+04/j7xfbjrjLsa3e7vtMy1vx5qUSEtc0pKSqAVfEZl1ytHdOfDtQe45f31pHeOYNqQFP487rSgWaNZ5bptDdJmo/jNN6lY/gOO4iPY9+0ntOdppL/4ApbTT/fredGqbjtE4G8KrSZv+TMt86OPPorJZOLqq6/2PqdCWuaG0lAEKyq7hhgNvH39Gby+fDeb8o/xr6+2kRBl5nfZaY2U0L6oXLe+4rLZsOflY8/Po/CZZ7Fu3UrYkCFYevUm/oorif3dJRg0GJyhVd12iMDfVMu8vLzcu7iJP/FXWua33nqLRYsW8e2331JVVeV1VSEt8+rVq5VZ1EJ118QoC/dO7oeUkumvrOLJxduYNCCZaEtIYCTroHrdNoWUkpL/vMWRV1/FdewYAKaEBFJfeJ6oOkkVtUKrutX7+FuJP9IyL168mCeffJKFCxcSHh5eb5sKaZl12h8hBA9c1I/iSiuzv9kRaJ0OifPoUUo/+IDCZ55l35VXUfjkk4QNHkTXJ58g7fXX6PHll+0S9LWkQ7T4m0LLPD21aZnPOeecemmZAZ/SMt9yyy1YrVYmTpwIwNChQ5kzZw6gRlpmrdY60IKO5DowNZbLzujGf37cw66iCgakRHPL+EzCQhtOFKg1Haluq9auJe+Wv+A8dgxMJkyJCSQ/8jCx06cH5JqKVnXb4ZO0acn69et59tlneeeddxrdp6ysjOuvv75FGTqtVitjx45lxYoVJ2XoDIb3rRN4qm1OHvtiK2v3lvDr4XKmDOrKc5cNCbSWklSsXEnF0mU4Dh+m/LvvCO3WjZRnnsbcp0/QXEBvDad0kjYtF2Kpm5a5MVqSlrnWVZW0zKtWrQq0gs90NNewUCOPTB3A4tvGcOuETBZuOMiPu460g93JqFy3xXPmcOD6Gzj68cdYd+4k9jfTSP/oQyx9+wZF0NeqboM7svgBrX/RXHfddX4rq9a1dvZvsKNSNtGO7HrTuNP45Oc8HvhsC1/cOpoQY/u251SrW0dxMfb8fCqWLuXISy8TfcH5dJ01CxEaGmi9k9Cqbn0K/EIIM/BbIL3uMVLKh5s5bhLwb8AIvCGlPGlVdCHE74AHAQlskFJe7qO7jo4OYAkx8sCF/bnh7RyufGM1g9JiuebMdFJig3tUWCAIW7qUHbf8BTyZcKMvuoiujz2KCAn86Kj2xKc+fiHEYuAYsA7w9mtIKZ9u4hgjsB2YCOQBa4HLpJS5dfbJBD4CzpZSlgohEqWUhSeW1ZY+fillUPxk8wVfXIOpj9/hcAR9d1Qtp4LrGz/s5t2f9pF/tJrEKAtf/3UMEWbt37MqdXv00/9y6J57iBw3jthLf4+pUycsAwYEdXxoS9021cfva4mpUspJLXzdM4CdUsrdHol5wMVAbp19/gC8KKUsBWgo6LcVFcbD16KSK7iHnKqSmfFUcL1hdA9uGN2DtXtLuOSVVbz4/U7unKR97vlgrtuKZcsoeeddnGVl1GzciBw4kJR/z9Yk1bEWaFW3vnYG/iiEOL2FZacAB+o8zvM8V5deQC8hxEohxE+eriG/otLiJiq5AhQW+v17WjNOJddh6fFMG5LCGz/sYc+RSj9ZNU6w1m3Z4q848KebsO3diyEinPjrr6PwDzcoE/RBu7r1tcU/CrhWCLEHsAICkFLKgX54/UxgHJAKLBdCnC6lPFp3p0OHDtG7d28AzGYzM2bMYNy4cZSXl2M0GgkLC6s3eicqKorKykpcLhdOpxOn04ndbsdut3vLEEJ4J1+ZTCYsFou3DCEEkZGR3jIAIiIisNls9cqoqanhggsuYNGiRZjNZqZOncrq1asZMWIECxYsIDIykoqKCu9F24iICPfFJU+AF0IwY8YM1q1bR3x8PG+++SYDBgxgzZo1PP/887z22mtERETUK0NKSW5urvcDMWDAAKxWKzt2uCfzpKWlkZSURG3XWHR0NFlZWaxYscL7umPGjGHLli0UexaBGDRoEOXl5ezevRuA9PR04uPjWb9+PeCerDZo0CCWLVvm7Y4aO3Ys1dXV3tnKWVlZlJSUsHfvXgB69OhBVFQUGzZsAKBTp07079+f5cuXe+t81KhRrF+/nrIyd8rh7OxsCgoKOHDA3V7IzMzEbDazefNmwJ3GolevXqxYscJ7DkaOHElOTo733A0fPpy8vDxvnqTevXtjNBqpqKhg6dKlJCcnk5GR4R0tERYWxvDhw1m9ejXV1dUAjBw5kj179nD48GHAnRPd6XR6MyWmpKSQmprK6tWrAfdY6+zsbFatWuW9GDdq1Ci2b9/eqvNU69qW8zQ21sli4eKmd9cxNFHQN8pOapRBk/Nks9k4cuSIX85TrmcFqtaeJ9vu3eQvWYLh6FEiv1yMeeBA9l9/HYSGusfEV1T47Tz5++9pw4YN3oy+teep9rPg63maOXNm3VGEnWkMKWWzN6B7Q7dmjhkJfFXn8T3APSfs8wowo87jb4FhJ5Y1dOhQeSK5ubknPdcQNpvNp/1awwsvvCBnz57tfbxkyRK5cOFCOXnyZJ+Of/HFF+WNN94opZTygw8+kNOnT/dumzBhgty3b99Jx/j6vtuDoqKiQCv4zKnouiT3sMz+5zey+12LZK97v5Cb8o76pdwTCZa6Pbpwoczt11/m9u4jc3v3kftmXCcd5eX19gkWV19piy+QIxuJzz519Ugp9wGxwEWeW6znuaZYC2QKITKEEKHApcDCE/b5L+7WPkKIzri7fnb74uQrUsPhnHXTMgNMmDChRXmBPvvsM6655hoApk+fzvfff+/1VSEts2rD+FTBX64T+iax9t5zWP2PCURZTDy4cIsmfw+BrFuX1YrjyBGOfvIpB+++h/ChQzntq8X0XL6MtDlvYDxh5qtKnwMI/HDOW3FfiP3E89S7QojXpJTPN3aMlNIhhLgF+Ar3cM43pZRbhBAP4/4mWujZdq4QIhf3aKE7pJTFjZXZGIcfewzr1obTMjucTkzGlk9lN/ftQ/I//tHo9hPTMreGuqmcTSYTUVFRFBcX07lzZyXSMu/YsUOZlLynsmtStIW/ndubez7ZxMINB7l4sH/rIVB1e/TT/3L44YeRnq6f8DPOIPWlF08K9nVR6XMA2vn62sd/PTBcSlkJIIR4AlgFNBr4AaSUXwBfnPDc/XXuS+B2z00p/JmWuSFUSMusow6/y05j3pr9PLIoF4dTMiAlht7J/s9a216UL13KoXvvJTw7m6jzzsUYHUPUeediCMJJWMGIr4FfUGf8vud+0Ax+baplXlNTo8ki5iemZW4NKSkpHDhwgNTUVBwOB+Xl5XTyrL2pwtDOugvPBDunuqvRIHhy+iCumrOav813XyB86pJBTB/a9nzv7VW3trx8yhZ9jvPoMUo/+ABL376kvfwShogIn8tQ6XMA2vn6Gvj/A6wWQnzqeTwVmKOJkZ8J0WhGXt20zM19sdxzzz2cccYZTJs2rd7zU6ZMYe7cuYwcOZIFCxYwfvx472QSFdIyJyUlBVrBZ3RX6J0cxQ93jWdfcRX3f7aZBxduYXRmZ5Ki29Ywao+6teXls/e3v3VnzTQYCM/KIuX551oU9EGtzwFo5+vrxd1ngBlAiec2Q0o5WxMjP1NVVaVZ2bVpmWsZPXo0l1xyCd9++y2pqal89dVXAGzatInk5OSTjr/++uspLi6mZ8+ePPPMM9x3333ebSqkZT5xNnUwo7u6MZuM9EqKYtZvBmJzuHjsi61tLlPrupU2Gwf/9jek00mPL76gb+4Wur/7Dqa4uBaXpdLnALTzbbLFL4SIllKWCSHigb2eW+22eClliSZWinDzzTfz7LPPco5nUYYffvihwf3sdjsjR4486XmLxVIvc2d5eTngvpKfk5PD7Nmz/S+towOkd47gT2N78Nx3OxnaPY4haXEMSIkOqvQFVWvXUpWTQ9XPP1O9YQMps2dj7pERaK0OQXNdPe8DF+LO0VN3HJjwPO6hkZff0HIhlrppmY1NjByqbfk3R62rKmmZo6OjA63gM7rryfx5fE+W7TjC/Z9tAeDcfkm8cuVQDIaWBX8tfI99/jkH7/CMaAsJIeH224medF6by1XpcwDa+eoLsSjGqfq+dbTB4XSx5WAZ3/1ayL+/3cEjUwdw1YjuAXWy7d/Pnmm/wdy3D91efRURFobQsAHXUWnzQixCiG99eS4Yqe0+UQGVXIF61zeCHd21YUxGA4PSYrntnExGZ3bmiS9/Ja+0ZdfF/OErnU7Kv/uO0nnzOPDnP4PJRMoTT2CIiPBr0FfpcwDa+TbXx28BwoHOQog4jg/hjObkhGs6pxgqJZXTXZtGCMFj005n0uzl3DA3h0kDkjmrZ2eGpcc3e2xbfaWUHLz7Hso+/9ztEh5O6nPPEaLBxCWVPgegnW9zX6U34u7f7+P5v/b2GfCCJkY6OjoBIS0+nOcvH8Kxajuzl+zgd6+u4stNhzR/3aMfzafs88/pdNOf6LlsKb1+WE7kqLM0f91TGV8XYvlLU+kZtEZfiOU4wdTH73K5NL147k9015ZRZXNw2Ws/sb+kiq/+OobEqMbH+rfG115QQPk3S3CWHePIy68QccYZpL3+muZ9+cFQty2hLb5t7uOXUj4vhBgghPidEOLq2lurbNqZ2hSuWpU9duxY72LrkyZNIjY2lgsvvNCn45cvX05WVhYmk4kFCxZ4XYuKipg0ye9LE/idLVu2BFrBZ3TXlhEeauLp3w2iyubkzgUbsTtdje7bUl9bXh57pk6j4J//5Mhzz2PO7EnK00+1ywXcYKjblqCVr68Xdx/AnZfneWA88CQwRRMjP1MblLXgzTff5De/+Y13KOcdd9zBO++84/Px3bp146233uLyy93LDNe6JiQk0KVLF1auXOl/aT9Sm39cBXTXltMzMYp7J/dl6bYi+t63mAlPL2X9/tKT9muJr8tqJf+vtyPtdjI+/YTMVT+SsWABRg3zXtUlWOrWV7Ty9XWg+HRgEPCzlHKGECIJeFcTI4V47733eP/9972PJ0yY4F2YxBdqM3s29FNu6tSpvPfee5x1lt7XqRM4rh6ZTmpcGGv2lLJo40FufGcd3/x1DLHhvidDk1Jy7OOPqd68GevOndRs2kTK889hCZIuy1MRXwN/tZTSJYRwCCGigUIgaLId/fDRdo4cqGhwW2v7+DunRTL6d70a3e6PtMwnUjcpW3Z2NjNnzvRb2VowaNCgQCv4jO7aes7uk8TZfZK4aFAXLn5hJfd9toXnLh3s/btqzvfICy9y5MUXMURHI0JCSLpvJtETJ7aH+kkEW902h1a+vgb+HCFELPA67lE9FbjTMgc9EonQIJGoFmmZnU6nd7auCmmZy8vLiWtFvpRAoLu2nf5dY7jtnEye+no7kWYjQ7rFMWlAcpO+VTk5HHn5ZWIunkKXWbMCPtAiWOu2MbTy9SnwSyn/7Ln7ihBiMRAtpdzod5tW0lTLvLy8vEWrYvmKP9Iyn4jNZsPsWQhahbTMu3fvplu3boHW8And1T/cNK4nu4sq+XDtAT5Yc4BXlu3i7wNlPV9XdTVHP/0U55FiSj/6iJC0VJLuuy/gQR+Cu24bQivf5iZwZTW1TUq53u9GiuCPtMxNoUJaZp1TD6NB8MzvBzPrtwNZuesI17+1lvd/NXGBp+dGSkneLX+h0jMwwdS1C6nPPdfkqlg67U9zLf6nm9gmgbP96KIJoRquyFOblrk2O+fo0aP59ddfqaioIDU1lTlz5nDeeeexadMmpkw5eRDU2rVrmTZtGqWlpXz++eckJSWRm5sLqJGW2Z/XN7RGd/UvoSYD43sncvP4njz/3U6+2nKY8/onc+yTT6hcuZKke+8l7orLQYigaOnXokLd1kUr3yYDv5RyvCav2o5omeGyrWmZhw0bRl5envdx3aGnCxcu5LPPPvOzsX+Jj29+On+woLtqw1/OzuSbLYf407vrGGCq4Z+fP0Z0djZxV1welInVVKpb0M7X13H8Vzd008TIz2i5EEvdtMxN4Wta5lrXoqIibr/99qC/CLV+vTo9fbqrNoQY4IGtH7Jo4d08OX8mLrudeWOvDsqgD2rVLWjn62tzeFid+xZgArAeeNvvRopx3XXX+b3MhIQEpk6d6vdydXT8zbFPPiF2+VKiL7yQ0PR05pszeHWXjSxP149OcOLrqJ6/1H3sGdo5Twshf9PUAinBhkquQND/IqmL7up/HEVFFPzrKVx9+9L1X08ihOAGh4svXl7JnQs2sv1wOZlJkUzsl4yxhYu7aIUqdVuLVr6tWohFCBECbJZS9va/0snoC7Ec51R93zrBQ8WKldRs3kTF90up+fVXMj79tN6SiHuOVPLHt3PYUeieVHluvyRevnJo0AT/UwV/LMTyuRBioef2P2Ab8Kk/JbVCpcVNVHIFWLZsWaAVfEZ39Q+l8+dz4IYbKJr9b2q2biX5gQf46cD+evtkdI7gm9vHkvvwedxzfh++zi3gqa+3Bci4PsFctw2hla+vffxP1bnvAPZJKfMa21nn1ECFZTtr0V3bjr2ggMJZTxA+YgRpL72ICA1FmEzIRvJThYea+OOYHuwtruLlpbuosjrolRzFhD5JJMc0PfdFK4K1bhtDK19f0zIvw93KjwHicQf/U562pmV+5pln6NevHwMHDmTChAns3+9uOamSljmYxmc3h+7aepzl5dgPHuTQ/fcjHQ66PPwQhvBwhGeodFO+QggemtKfyQO78PZP+7j3081MfHZZg1k+24Ngq9vm0MrX166eG4A1wG9wZ+r8SQjh/+EsGqBFuoZa2pqWeciQIeTk5LBx40amT5/Oww8/DKiTlnns2LGBVvAZ3bV1lLz3HttHnsnOsydQuWw5iXfcQegJKQSa8w01GXjx8iy2PXI+X902hviIUK6Zs4aNeUc1NG+YYKpbX9DK19fBtncAQ6SU10oprwGGAndpYuRntBzH/95773HxxRd7H0+YMKFFXzTjx48nPDwcgBEjRnhb/HA8LXMws2HDhkAr+Izu2nJqcnMpeOxxIkaMIPnBB+j25hzir7zipP189Q01GeidHMX7fxhBTHgIV7yxmhvm5vDi9zuxOrRbN6MuwVK3vqKVr699/MVA3SuP5Z7ngoLv33qNwn27G9zmdDgxmlo+TDKxew/GX/vHRrf7Oy3znDlzvDOAQY20zKWlgfm53hp015bhstk4OHMmxvg4Up5+CmNMTKP7ttQ3JTaMD/4wgocX5bKvuJIlWwtYvaeE164aiiVE2yHNwVC3LUErX18D/05gtRDiM9w5ei4GNgohbgeQUj6jiV0Q48+0zO+++y45OTksWrTI+5wKaZl1OhZSSkre/A9HP/kEV1kZjqIiUl98ocmg31rS4sN5/Wr3SMOP1h7gzo83ctO763jlqqGYW9FQ02kZvgb+XZ5bLbVJZLTrQG8BTbXMnU6nJhOj/JWWecmSJTz66KMsW7as3mQNFdIyZ2U1mrw16NBdm6f8yy8p/Ne/CBs6FPNppxF13rlETZjQ7HFt9f3dsDScUnLPJ5sY8MBXpMWFc/f5fThXg5m/Kn0OQDtfX2fuPgQghIj0PG54uasTEEJMAv4NGIE3pJSzGtnvt8ACYJiUMqehfVqLw+HQJPD7Iy3zzz//zI033sjixYtJTEzEarV6XVVIy1xSUkJ0dHSgNXxCd20aR0kJhx99DMuAAXR/ey6iBX8z/vC97IxuJMdYWL27hOXbi/jze+t589phjOmV0KZyT0SlzwFo5+vrqJ4BQoifgS3AFiHEOiFE/2aOMQIvAucD/YDLhBD9GtgvCrgVWN1SeV+w2WxaFAscT8tcy+jRo7nkkkv49ttvSU1N9SZn27RpE8nJJ7de7rjjDioqKrjkkksYPHhwvS8GFdIy7927N9AKPqO7Nkz1xo2UzptH/q234Soro8ujj7Yo6IP/fMf3TuTu8/vw4Y0jyEyK4qZ31/G/jYfYXlCOy+Wf8ewqfQ5AO19fu3peA26XUn4PIIQYh3sZxjObOOYMYKeUcrfnmHm4rw3knrDfI8ATuEcOKUVb0zIvWbKk3uO6M3dVSMusozbH/vc/Dv7t7+4HRiPJ99+PpXfjq9m1F1GWEN6aMYxLXlnFze+7s1OenhLDm9cOIyHKHGC7joFPuXqEEBuklIOae+6E7dOBSVLKGzyPrwKGSylvqbNPFnCvlPK3QoilwN8b6upJSUmRkZ4VfMxmMzNmzGDcuHH07NkTo9FIWFgYFRXHe5+ioqKorKzE5XLhcrmIjIzEbrdjt9u9ZQghvH30JpMJi8XiLUMIQWRkpLcMgIiICGw220llvP7661x++eWYzWbMZjOVlZX1yqioqPDOvouIiMBqteJwuOe/WSwWpJRYrVYADAYDYWFh7Nu3j59++okpU6YQERFRr4wDBw4AUFhYCMCAAQOwWq3s2LEDgLS0NJKSkqjNbRQdHU1WVhYrVqzwvu6YMWPYsmULxcXugVmDBg2ivLyc3bvdI6PS09OJj4/3poSNi4tj0KBBLFu2zLt4/dixY1m5cqW3PrKysigpKfG2UHr06EFUVJR3OFqnTp3o378/y5cv99b5qFGjWL9+PWVlZYB7JFNBQYH3PWZmZmI2m9m8eTPgvuDdq1cv768ss9nMyJEjycnJ8Z674cOHk5eXR35+PgC9e/fGaDTyyy+/EBoaSnJyMhkZGaxa5V4yOiwsjOHDh7N69Wqqq6sBGDlyJHv27OHw4cMA9OvXD6fTybZt22o/j6SmprJ6tftHamRkJNnZ2axatcp7LkeNGsX27dtbdZ6qqqoIDQ3123nasGGDd3RI7Xnat2ULnR94EFNaGp0ee5Qte/ciw8JadZ7i4uJISUnxy3mqXYgoOTmZ5JRuzPtmFQcrXHy4zUaPxGj+nmXC6LS2+jwlJiaSn5/vl/Pk77+nhs7T9u3bCQ0N9fnvaebMmcyfPx+A7du375NSptMAvgb+T3GnYa6dnXQlMFRK2ehags0FfiGEAfgOuFZKubepwN+WJG0Oh0PTxVj8iS+uwZSkrbS0VJlsh7prfQ7ddz9HP/6YjI8XYGnj56k9fH/YUcT1b+WQFh/GyNM6MaBrDNOHpmIytizvv0qfA2ibb5uTtAHXAQnAJ8DHQGfPc02RD6TVeZzqea6WKGAAsFQIsRcYASwUQjQo2lpqW3EqoJIrqDUZRncFe0Ehhx/5J/tvvJGj8+cTf+21bQ760D51OzozgVc9Qz0/33CIuz/ZxBVvrOZoVcuu4an0OYAATeASQliAPwE9gU3A36SUdh/LXgtkCiEycAf8S4HLazdKKY/h/gKpfa2lNNLi19HRaRvSZuPADddj27uPkO7diJk6lYT/+0vzBwYR4/skMr5PIgAfr8vjnk828duXf+SK4d3pEmNhbO8EwkPV+HUfaJqrpbmAHfgB9+icvsBtvhQspXQIIW4BvsI9nPNNKeUWIcTDQI6UcmGrrVuASoubqOQK7n5GVTjVXUveex/rjp2kvvQSUWf7dyntQNTtb4em0jU2jFvn/czDi9zXBVJiw3j+8iFkdWu8a0SlzwFo59tkH78QYpOU8nTPfROwRkrZ7jMg2tLHX3vxRAV8cQ2mPn6Xy4UhSNdWPZFT2dV+6BC7L5pCWNYQur32mt/KrSWQdet0SY5W2dhysIx7/7uJgmNWrhzRna6xFs48rTP9utYfA6/S5wDa5tuWPn5vt46UUslUzHVH+/ibummZf/nlF0aOHEn//v0ZOHAgH374YbPHW61Wfv/739OzZ0+GDx/Oli1bAPe4/2uvvVYzb39RO6JABU41VyklRc+/wPbRo9k58VxwuUj+xz/8YHcygaxbo0HQKdLMmF4JLLx5FBP6JvLuT/v45/+2cuHzP/Cvr37FWWcOgEqfA9DOt7munkFCiDLPfQGEeR4LQEop1ZkCpwF10zKHh4fz9ttvk5mZycGDBxk6dCjnnXdek/l85syZQ1xcHDt37mTevHk88MADfPzxx5x++unk5eWxf/9+up2QAldHxxfKFi3iyIsvEjFmNOaMHkRPuYhQPyUUDFbiIkJ5+cqh2J0ujlbZ+ddXv/Li97tYtauYYRnxnNY5kng/TQRTnSYDv5RSrU7ndua9997j/fffB6BXr+MTX7p27UpiYiJFRUVNBv7PPvuMBx98EIDp06dz8803e7t7LrroIubNm8edd96p5VtoE6oMk4VTy9VZUUHBk09iOf100l5+ucUzcVtKsNVtiNFAQpSZJ6cPYki3OF5bvps3V+zB7pSkxxjoPbiKtPjwQGv6hFZ1G1xnrJUc/XwXtoOVjW5vzSDJ0K4RxF50WqPbm0rLvGbNGmw2G6ed1vjxAPn5+aSluUe8mkwmYmNjKS4upnPnzmRnZzNr1qygDvyjRo0KtILPnAqutrx8rNt+5dhnC3EeKSbtpZc0D/oQ3HV72RnduOyMbkgp+WrLYe5csJELn1/BhL6JJESa+d2wNE5LiAy0ZqNoVbfqXOVoJS6XNgs8NJaW+dChQ1x11VX85z//afFFmboX2lVIy1w7E1EFOrpr9eYt7L7gAvJuvoXyr78m4dZbCTv9dA3sTkaFuhVCMGlAFx4fH8cZGfH8uLOY/6zcy/mzf2DOij1BuxavVnXbIVr8TbXMy8vLNVl+saG0zGVlZUyePJlHH32UESNGNFtGSkoKBw4cIDU1FYfDwbFjx7zDt1RIy1w7hV8FOrKry2rl4N13YYyNJeXZZzAlJRGamqqR3cmoVLcRsorXrx4HQGF5Df/4ZDOPLMpl7o97SYsPo3/XGG4YnUFiVGAWgz8Rreq2w7f4taJuWmZwd/1MmzaNq6++munTp9fb95577uHTTz89qYwpU6Ywd+5cABYsWMDYsWO9wzlVSMusExwUPfcctp276PLoPwkfOrRdg77KJEZZeP3qofxr+kD6d42myuZkzoo9nP3UMv69ZAdLcgv4eX+p3zKDBhMdosXfFLVr2mpBbVrmc845h48++ojly5dTXFzMW2+9BcBbb73F4MGD2bRpE1OmTDnp+Ouvv56rrrqKnj17Eh8fX2+NXRXSMmdn+zW7hqZ0NFfr7j0UPDELR0Eh1l9/JfbS3xM5enQ72J2MynUrhOCS7DQuyXZfa9tdVMHDi3J5dsl27z4ZnSO4dUImY3olEBMWgtHQfvOCtKrbDh/47Xa7ZjNi66ZlvvLKK7nyyisbdWgoLbPFYvFm0gO8vx6sVis5OTnMnj1bE29/UVBQQG3W1GCnI7lKm438227DfvAgYUOGEDl2LAk3/7kdDevTkeq2R0Ikb804g6JyK4eOVbOrqIJXl+3mtg9/8e4zPCOem8f3ZHRmZ80nh2pVt6dE4G9uhazWkpWVxfjx45td3rF2QZbmqHXdv38/s2bNCrphcidy4MCBZkcuBQsdybXo5Zexbt9O6ssvETXev+kXWkNHqttaEqLMJESZGZgay8WDUli56wjbCyo4UmFl4S8HufrNNaTEhpEUbea0hEiuPSud/l39vzaxVnUb3JFFAa67rrkkpS0nMzOTzMxMv5eroy7S5cK2dy81uVspfu11YqZNC4qgfypgMAhGZyYwOtO9DORt52SyYF0eP+0uoaTSypebDzN/XR7jeifQOzmKzhFmzj89mdS44J0r0OEDv9mszoo9KrkCSn05qezqqqnhwB9vpGrNGgDMmT1J+sc9gVBrEJXrtjWYTUauGN6dK4Z3B6Csxs4bP+xh4S/5/LizGJvTxazFv3LhwC4Mz+hEdJiJ/l1jyOgcERDfhujwgV+VBG2gliuo9UWlsuuRV1+las0aEv52O5bevQkfNgxDEA31Vblu/UG0JYTbJ/bi9onu2ft5pVXM/XEv76/ez2e/HJ+Lc3pKDGN6dSbaEkJitJlRPROaXUpSq7rt8IG/pqaGkJCQQGv4hEquAJs3b2bcuHGB1vAJVV1t+/ZR8sYcoi+6iM5/+ENgxRpB1brVitS4cO6d3I+7JvWhsNzKsWo7K3ce4b+/5PPKst31ksZlJkbSvVMEMWEhnJYYwZjMBPp1icbgGTmklW+HD/w6OipSvWUL1b/8wtGPP0aEhJD4978HWkmnhZiMBrrGhtE1Noy+XaK5YXQPnC5Jjd3JniOVLN9RxNo9JeQfrWbLwWN8vD6PJxdvI9RkIC48hC4xYZxmsXHWaBchLVxislk3v5YWhGg5Mqa6uppJkybx3XffsWnTJm666SbKysowGo3ce++9/P73v2/y+OXLl3PbbbexceNG5s2b5x23X1RUxFVXXcXixYs1c/cHiYmJgVbwGaVcCwrYe9OfQUoMERF0fepfhCQFr79SdRtgV6NBEGE2MSAlhgEpMTDu+LbCshqW7zjC9oJyjlbZ2F5QweoCMGkwb6DDB36thnJC29Myd+vWjbfeeounnnqqnmtCQgJdunRh5cqVnHXWWZr5t5W6GUmDHVVcXVVVhLz+OqJbGt3ffBNTYiIiyLv/VKlbCG7XxGgL04fWn3VdXmXV5Npfh0/ZoOVCLO+99x4XX3wx4P5A1V6Br5uWuSnS09MZOHCgN5lbXdepU6fWm8kbjKxYsSLQCj6jimvh7Nk48vLp+uijhKSkBH3QB3XqFtRyBVi3ZpUm5XaIFv+XX37J4cOHG9zW3OSqxkhOTub8889vdLs/0jI3RXZ2NjNnzmz18TrqYC8s5MjLL2Pft4/KH1dRNX4c4QqlQdBRjw4R+ANBc2mZ586d2+K0zHV/0qmQlvlUH8bnD6TTSf5tf6V60yYsmZnEX3M1R4M4v31DBGvdNoRKrqAP52ySplrmWuGPtMwnUjcnhwppmRvKPxSsBKtryTvvUL1+PV1mPU7s1KkAJAVWqcUEa902hEquoJ1vh+/jr6xsfGWutuCPtMxNuaqQljknJyfQCj4TbK7Oo0cpX7KEomdnEzluHDGea0UQfK7NoZKvSq6gnW+HD/wul0uzsmvTMgPetMy1qZgHDx7ML7/8AsCmTZtITk4+6fi1a9eSmprK/PnzufHGG+ulYFUhLbOWF879TbC4Sik59MCDbB8xkrxb/oIxLo7khx+q180XLK6+opKvSq6gnW+H6OoJFG1Nyzxs2DDy8vK8j8vLy733Fy5cyGeffeZ/aZ2AcuzT/3L0ww+Jmf5bos87j7DBgzFqsEKcjk5TdPjAHxHR8sRIvuLvtMy1rkVFRdx+++3ExcX5xVMrhg8fHmgFnwkGV/vBgxQ89hhh2UPp8tBDjS6EHgyuLUElX5VcQTvfDt/VY7PZNC3/uuuu89tCL7WuCQkJTPVc6Atm6v5aCXYC6Vr+/fcUzp7N/j/+Eely0fXxxxsN+qBWvYJaviq5gna+HT7w2+32QCv4jEquAPn5+YFW8JlAuR77fBF5N/2Z4ldeRVptpD73HKFpaU0eo1K9glq+KrmCdr4dvqtHRydQ2A8d4vDDDxM2eDDd356LCA0NtJKODnAKtPhVmrChkitA7969A63gM+3pWrtaVv4ddyCdTro++USLgr5K9Qpq+arkCtr5dvgWv0qLm6jkCmi2iL0WtJdr+fffc+ief+A8ehSArk/MIrRbtxaVoVK9glq+KrmCdr6atviFEJOEENuEEDuFEHc3sP12IUSuEGKjEOJbIUR3fzucOLvWn1RXVzN27FicTie//PILI0eOpH///gwcOJAPP/yw2eOfeeYZ+vXrx8CBA5kwYQLbt28H3KN6Jk2apJm3v8jNzQ20gs+0h6v94EEO3nkXpsREkh95mIyFn9WbmOUrKtUrqOWrkito56tZ4BdCGIEXgfOBfsBlQoh+J+z2M5AtpRwILACe1MpHCxpKy7xlyxYWL17MbbfdxlFPq68xhgwZQk5ODhs3bmT69Oncf//9QP20zDpqIJ1ODt51N7hcpL74AnGXXIIliFMA65zaaNniPwPYKaXcLaW0AfOAes0fKeX3Usoqz8OfgFT8jJYLsbQ1LfP48eMJDw8HYMSIEfWSsqmQlrmh2cjBilau1l27KPjXv9h76WVUrV1L0syZzY7aaQ6V6hXU8lXJFbTz1bKPPwU4UOdxHtDUbITrgS9b80Lbtz9CecXWhjdKCa3oO4+K7EuvXvc1ut3faZnnzJnDBRdc4H2sQlrmjIyMQCv4jBautrw89l56Ga6aGiy9epF4913ETG15186JqFSvoJavSq6gnW9QXNwVQlwJZANjG9p+6NAh79Vts9nMjBkzGDduHOXl5RiNRiTgdDq8+xuNJpxOJyCR0n2BREqJlO68PQZhAHE8j48QAoPB4DkGwP1FUVlZ6d0nIiICm83mHWtfXFxMTEyMN82CyWTCbDaza9currjiCl599VUMBgMVFRVIKb1lWK1WHA63q8ViQUrJ3LlzWb16Nf/73/9wuVxUVlYSFhbm/QVQtwwpJbm5uRQWFgIwYMAArFYrO3bsACAtLY2kpCRvcqfo6GiysrJYsWKF93XHjBnDli1bKC4uBmDQoEGUl5eze/duwL1ATHx8POvXrwfcCekGDRrEsmXLkFIihGDs2LF899133gyiWVlZlJSUsHfvXgB69OhBVFQUGzZsAKBTp07079+f5cuXe+tr1KhRrF+/nrKyMsD9ZVdQUMCBA+72QmZmJmazmc2bNwPuVNW9evXy5kcym82MHDmSnJwcb06T4cOHk5eX5x3/3Lt3b4xGI2vWrCEyMpLk5GQyMjJYtcq9wEVYWBjDhw9n9erVVFdXA+6MiHv27PGu8dCvXz+cTifbtm0DICUlhZSkJHb84Y+Y7HZqZj1O38mTWbVqFdZlywAYNWoU27dvb9V5Onr0KJGRkX47Txs2bKC0tFSz82Sz2cjKyvLLeart0/bXeUpNTWX16tWAO/ttRUUFZrMZq9Xa5vPk77+nhs7T5s2biYyM9Pk8zZw5k/nz5+OhM43hDoj+vwEjga/qPL4HuKeB/c4BtgKJjZU1dOhQeSK5ubknPdcQZWVlPu3XUkpKSmT37t3rPXfs2DE5ZMgQOX/+fJ/L+eabb2SfPn1kQUFBPdeysjKZkpJy0v6+vu/24Pvvvw+0gs/4y9VZUyNrdu6UBx94QOb27iOP/e9/fim3LirVq5Rq+arkKmXbfIEc2UhM1bLFvxbIFEJkAPnApcDldXcQQgwBXgUmSSkLtZDQaohk3bTMFoul2bTMZ5xxBtOmTav3/M8//8yNN97I4sWLSUxMrJeJT4W0zMG+XkBd/OFq3bGDfTOuw3nkCADx11xDdJ3uOX+hUr2CWr4quYJ2vppd3JVSOoBbgK9wt+g/klJuEUI8LISY4tntX0AkMF8I8YsQYqG/PeoubuJv2pqW+Y477qCiooJLLrmEwYMHc/nlx78XVUjLrFLCq7a6uqqqyLvtrwB0mfU46QsWkHj3Xf5QOwmV6hXU8lXJFTT0beynQDDd2tLVU15e7tN+rWHdunXyyiuvbHa/c88916fy6rqOHj1alpSUnLRPMHX1/PTTT4FW8Jm2uLqcTpl/550yt09fWfHjj360ahiV6lVKtXxVcpWybb4EqKsnKJCei6Ja4O+0zLWuqqRlrr3IpgKtca1a/zMFjz+Obe9eXOXldL7lFiLaYek+leoV1PJVyRW08+3wgV9rrrvuOr+XqUpa5o6MLS+PAzfdhDEigugLJxOena1Jf76OTiDo8IFfy4VY/I1KrqDWwtUtcXXZbOTf9leQkm5v/afFuXbaikr1Cmr5quQK+mLrDeJLN07teF0VaM5Vy26r1rBnz55AK/iML65lX3/NgRv/xO7JF1KzeTNdHv1nuwd9UKteQS1flVxBO19lA7/FYqG4uLjZYFg7yUIFmnKVUlJcXIzFYmlHo6apnTijAs25li9ZQv6tt2HduRNzjx6k/PvfRE+c2E529VGpXkEtX5VcQTtfZbt6UlNTycvLazYfTu04exVoztVisZCa6vd0Rqc81l27OHjnXVhOP53ub8/FoMjnRUentSgb+ENCQnzKY1FYWEhiYmI7GLUdlVzBPUVeFU50lVJSMmcOlat+onrzZkRYGKnP/Tsogr5K9Qpq+arkCtr5KtvV4yvH8+8EPyq5glq+dV2llBTOmkXhU0/jKC4m8qyz6P7O24QESeZGleoV1PJVyRW08+3wgb82YZMKqOQKavnWukqXi8KnnqJk7tvEXXUVGZ9+QsozT2Pu0SPAhsdRqV5BLV+VXEE7X2W7enR0WoJp3z52/+Y32Pftx1VZSexll5L0j3uUW+5SR8cfdPjAn5KSEmgFn1HJFdTxrf7lFzo99zzO6Ghipk4lPHsoUZMmBW3QV6Vea1HJVyVX0M63wwd+lUbBqOQKwe3rqqmhJjeXmq1bKXr6GUydOpE+9y1CunYNtFqzBHO9NoRKviq5gna+Hb6Pv3YRBhVQyRWC19e2fz+7J1/IvsuvoOCRfxKans6hm/+sRNCH4K3XxlDJVyVX0M63w7f4dU4t7AcPsu/aa5FV1aTMfhZz796Edu/OPs9KRTo6OqdA4NcyH7+/UckVgsfXWV7OkRdfombLFmq2bgUh6D73LSx1xkAHi6svqOQKavmq5Ara+Ypgy//SENnZ2bJ2zUsdnbo4jx5l/w1/oGbrVsIGDiQ0I4P4a67B0rtXoNV0dAKKEGKdlDK7oW0dvo+/drFmFVDJFQLnKx0OKn/6idKPPmLftTOwbttG6gvPk/7B+3R97NEGg75KdauSK6jlq5IraOfb4bt6OlJ2zmAjEL6O0lLy/nwz1T//DIAxLo7Ul14kcvToJo9TqW5VcgW1fFVyBe18O3zg11EfKSX2Awew7T9AwaOPYs/Pp8uj/yRixAhMycmIJlY/09HROZkO38fvcDgwmdT4flPJFdrHtyY3l/y/34Ft924AjDExpL74AuHZDXZdNopKdauSK6jlq5IrtM33lO7j3759e6AVfEYlV9DO11FcTMXKlRx55VX2XnY5rqoqkh98gG7/eZPTvvm6xUEf1KpblVxBLV+VXEE73w4f+AsLCwOt4DMquYL/fV01NRx+5J/sOGsUB66/gaLZs4k480wyPl5A3KWXEjFyJMbo6KBw1RKVXEEtX5VcQTtfdX7z6HRI7Pn5lH/7Lba8PCqWLsO+fz9xl19O1HnnEdIlOSBLH+rodHQ6fOAfMGBAoBV8RiVXaJuvtNsp/WAehc8+i6yuxhAeTmjPnnR58AEizjzTj5ZuVKpblVxBLV+VXEE73w4f+FUavqWSK7Tc156fT9Fzz1O1di2OkhJkTQ0Ro0aRfP99mrfsVarb9naVLgkShNGdrdReUIn9UCXGeAumGDPWvWXU7CjFYDERmhaFs8JGzbZSpNWJOSOaGkcVxaWF2IuqCOkaiTkjBkdJDbZ9ZRjCTJgzYhChRmwHynHVOAjtEkFIl0gcJTU4iqsxxVsw94wFIXAUVmIICyE0PRpDqNHtBgiDfzKpqvQ5AH04Z6vZsWOHMqlYVXIF33ytu3ZRtTYH2549lM6fD0DUuLGYEpOIOHMkEaNHt0t6ZJXq1p+u9qIqnMdsmOItYBRU/1KEbX8ZhsgQTInh2PaVUb2lGKQkJCkCaXPiKK45qRxDuAmXzQUr8gEwJYRhCA+hfHkeuMAWYyakawTWHaVU/1IERkFoSiSOompqtpZ4yzBEhFCTWwyewYTCYkTWNLDKlMmAKd6M86gV6ZSEpkVh6RUHLomz3IYxzoI5PRpjnAVXpR0RYsDUKazZLwiVPgegnW+HD/w67Yurqgp7QQHWnTs5+sE8Kn/80b3BYCDqvHNJ+vvfCVHoDy+YcdmcOIqqEaEGDKFGqn8toXrzEXBKjPEWHIVV2PaXn3ScqXMYzp02pNWJIdxExBnJGMxGbPkViBALkaNSCE2PwXGkClelnZDkCEK7RYNLYi+swhBmwhTnXpvYZXWwcukKRp07DCEE0ilxVtgwhJkwhLrnVzjLbEiXC2OMGSEEzko7zpIajLFmjFGhOI5Zse0+BgaBKSEMV6Wdmq0lOMusWDLjwGigZlsJZV/vA0CEmZDVjpPelyHchCkhHOlwgVMS0iXi+C8Hp+f11Vp5UTM6fOBPS0sLtILPqOQKbl/pcOAoLqH65585+tFHVK5aBZ65IabERBJuu5XoCy/E1LlzQBcyV6lua11dNQ4QIEKN2A9WUr2xCGeFHUNkCK4KO9WbjyCt9SOZKSEMQ5iJml9LMMaaibkgg5CUSBxFVUinxNIz1t2yd0lcnrIaayWHdomo/4RBENq1ftIwg9lE155p3l9twigwxZjr7WOMDq3/OCIEY0TIcecYM6YhifX2sWTG1X/tCzJwWZ0Ig0CEGHBW2rHtOYaz0o4xIgRXjQPbvnLsR6oxRoaAENRsL6Xq5/qjYnoYjBxevxYpwRBqJLR7FKHdohGhBoTJgDEylJDkcERIcEwK1Opz2+EncFVUVCiTkS/YXaXdjqOkFGfxESpWrOToos+x79h5PNB36ULMlCmYe2Rg6tKF8CFDECEhzZTaPgRz3bpqHNRsLcFeWIUIMWCrseLcXYE9r8K9g1GAU4JRYIwMwVlhR4QaCesTj6VvPNLhQlY7CO0eTUhKZLuvLBasdSulxFlqRbokwiBwFFVRvq0IQ6VECHBWObDtKzvpyxMDGGMtiBCDuwspxowpKRxjRAgizOT+xdMpDGOs2b2PhvXdlrptagJXh2/x5+TkMG7cuEBr+EQwuUopse3Zg7O4GNu+fZR/v5TKFSuQdS422Xr0oOtNf8KUkEBo9+6EDx8etOkTAlm3jpIabPvLkA538LbtL6Pm1xJclQ5EqAFXlR1cgAAkSCShKVFET+yOMBlwVtoISQzH0rcTxogQ9wVPQdAsHRlMn9u6CCHc1zY8mOIt/HRoI+OmjPM+J50SR0k1OCXS7sJ5zIrtYAWOkhqk3YW0u7AXVlFd57pEPYwCQ5gJY6yZkIRwDBEhGCLdv2iM0aEYokIxhBoRZiOGiMZ/XTWGVnXb4QO/TvM4y8qo2byZmm3bcR47im3vPqrWrMFZUuLdx5ScTOxvf4u5VybGuHgs/fvz484dDArCP/j2xFlhw36wEme5DWESOIqqse4rw1lmQxgFrmoHztL6IzNEiAFzZhymODPS5sIQGYKld5y7H90pWb5sOWPPGdLoa/prhIuOu2sqJCH8+BNpUYQN6HzSftLpwlXjRNY4cFU53BfNy2zIageuageOkhqse47hqrQj7a6GX8wAxshQhMWICDViMBsxhIdgCDMhTAb3c2EmDOEmhMWEwWzEfFSb961p4BdCTAL+DRiBN6SUs07YbgbeBoYCxcDvpZR7/ekQ3cqZnoFAC1dXdTX2/HxseXm4ystxlpVh27sP2+7d2AsO46qoxHH48PEDjEZMSYlEjDqL8GHDCE1JwZSURGiPHie1MKMLC/zuqxW+1K2UElntwFlpB6d0/0EXVeMo9bT+HC4cR6qxF1QhrQ53y9txQjNQQEhyBKZOYeCSmDqHEXpWCpaesYgQA9IpMcVbEKZGJs0bBFHx6nxm4dT4GxNGA8YIA0SEQCcITYtqdF+XzYmrwo6zzIqrwo7L5kRanTjLbO4vC5sTaXPiqnFiP1yJq8bh/nzZXOCq/3lKMRthaquUm34/WvXxCyGMwHZgIpAHrAUuk1Lm1tnnz8BAKeWfhBCXAtOklL8/saxTfSEWl82Gq7ISV2WV+/+q4/edJcXYCwtxHj3qfnz0KI7DBTiKinBVVyNrTh6aJ8LDMWdkENK1K4bISEK7d8Ny+ulY+vXDGBsbNF0IzSGldP9Ed0pwupBOiXS6wCHdf2w1DncrzeFy/6FV2nFWOcDhDuLOcjuuchsumxM8o1EaHFpoEO4WmUlgjLcQkhSBIdwEBnefe0jXSEyxZqTDPXLEYNF/SOu0HCkl0ubCVWV3f26tDvcF+dNiW1VeoPr4zwB2Sil3eyTmARcDuXX2uRh40HN/AfCCEEJIP30bffTmhYRGlzS/o8qEACme20mEeW4e6sXzg56bh8OemwaIk1/cp23uLU18CbXmC6rOR0tGABG1jyWy9l95/LH7/wY+jtWe/ysBdX746ChGVVkMV572td/L1TLwpwAH6jzOA4Y3to+U0iGEOAZ0Ao7U3enQoUP07t0bALPZzIwZMxgyxN0H2qlTJ/r3789yz2LaJpOJUaNGsX79esINEUhDmf/fmU7Lkd5/Gt7U6LZGAm+dMk/aetLz8qR/dXRUYenSpfTo0YOoqCg2bNgANB73Zs6cyXzPREng5IsVHrTs6pkOTJJS3uB5fBUwXEp5S519Nnv2yfM83uXZp17gb21XT2XOr2zauInTB57uaR2K46MhvDe827wNyHrbmzvO+16ObzMYECEh7ltDrdITn/I8/mnVT4wYObLRfU8qSTT6oPFGdFON5CZa0A1tWrFyJaPOOuvkjXWdjQYwioBfkFy6dGlQjjxpCJVcQS1flVyhbb5NdfVoGfhHAg9KKc/zPL4HQEr5eJ19vvLss0oIYcLd2ZBwYldPW/r4XS4XBoMa2adVcgW1fHVX7VDJVyVXaJtvoBZiWQtkCiEyhBChwKXAwhP2WQhc47k/HfjOX/37tWzZssWfxWmKSq6glq/uqh0q+arkCtr5atbH7+mzvwX4CvdwzjellFuEEA8DOVLKhcAc4B0hxE6gBPeXg18pLi72d5GaoZIrqOWru2qHSr4quYJ2vpqOO5NSfgF8ccJz99e5XwNcoqWDjo6Ojk591OnsaiWDBg0KtILPqOQKavnqrtqhkq9KrqCdb4cP/K+++mqgFXxGJVdQy1d31Q6VfFVyBe18O3x2zt69e7Nt2zY/G2mDSq6glq/uqh0q+arkCm3zDdSoHh0dHR2dIESJFr8QogjY18rDO3PCTOAgRiVXUMtXd9UOlXxVcoW2+XaXUiY0tEGJwK+jo6Oj4z/0rh4dHR2dUww98Ovo6OicYnTYwC+EmCSE2CaE2CmEuDvQPk0hhHhTCFHoSVoX1Agh0oQQ3wshcoUQW4QQtwbaqSmEEBYhxBohxAaP70OBdmoOIYRRCPGzEGJRoF2aQwixVwixSQjxixAiqBfNEELECiEWCCF+FUJs9eQTCzqEEL099Vl7KxNC3ObX1+iIffy+LAITTAghxgAVwNtSygGB9mkKIUQXoIuUcr0QIgpYB0wN4roVQISUskIIEQKsAG6VUv4UYLVGEULcDmQD0VLKCwPt0xRCiL1A9okZdYMRIcRc4Acp5Rue/GHhUsqjAdZqEk8sy8edtbi1A1xOoqO2+L2LwEgpbUDtIjBBiZRyOe5cRUGPlPKQlHK95345sJVGloEJBqSbCs/DEM8taFs7QohUYDLwRqBdOhJCiBhgDO78YEgpbcEe9D1MAHb5M+hDxw38DS0CE7TBSVWEEOnAEGB1gFWaxNN18gtQCHwjpQxm39nAnUAjK3YHHRL4WgixTgjxx0DLNEEGUAT8x9ON9oYQIiLQUj5wKfCBvwvtqIFfR2OEEJHAx8BtUsqgXuZMSumUUg4GUoEzhBBB2Z0mhLgQKJRSrgu0SwsYJaXMAs4HbvZ0WwYjJiALeFlKOQT3opnBfu0vFJgCzG9u35bSUQN/PpBW53Gq5zkdP+DpK/8YeE9K+UmgfXzF89P+e2BSgFUa4yxgiqfffB5wthDi3cAqNY2UMt/zfyHwKe5u1mAkD8ir82tvAe4vgmDmfGC9lNLvqzp31MDvyyIwOq3Ac7F0DrBVSvlMoH2aQwiRIISI9dwPw33B/9eASjWClPIeKWWqlDId92f2OynllQHWahQhRITnAj+ebpNzgaAcmSalPAwcEEL09jw1AQjKAQl1uAwNunlA43z8gaKxRWACrNUoQogPgHFAZyFEHvCAlHJOYK0a5SzgKmCTp98c4B+etReCkS7AXM/oCAPwkZQy6IdJKkIS8KlnXWkT8L6UcnFglZrkL8B7nsbgbmBGgH0axfNFOhG4UZPyO+JwTh0dHR2dxumoXT06Ojo6Oo2gB34dHR2dUww98Ovo6OicYuiBX0dHR+cUQw/8Ojo6OqcYeuDX0dHROcXQA79Oh0YI0alOetvDQoh8z/0KIcRLGrzeW0KIPUKIP7Xy+O89bg0ukq2j4w865AQuHZ1apJTFwGAAIcSDQIWU8imNX/YOKeWC1hwopRwvhFjqZx8dnXroLX6dUxIhxLjahU6EEA8KIeYKIX4QQuwTQvxGCPGkZ4GRxZ7cRAghhgohlnkyUX7lWZugudd5SwjxnBDiRyHEbiHEdM/zXYQQyz2/PjYLIUZr+451dI6jB34dHTenAWfjzob4LvC9lPJ0oBqY7An+zwPTpZRDgTeBR30suwswCrgQmOV57nLgK0/W0EHAL/55Gzo6zaN39ejouPlSSmkXQmzCnd+pNufMJiAd6A0MAL7x5KYxAod8LPu/UkoXkCuESPI8txZ40/OF8l8p5S9+eRc6Oj6gt/h1dNxYATwB2i6PJ7Fy4W4gCWCLlHKw53a6lPLclpTtQXheZznuFaHygbeEEFf7403o6PiCHvh1dHxjG5BQu0C3ECJECNG/tYUJIboDBVLK13EvsxjsueF1OhB6V4+Ojg9IKW2eC7PPedZvNeFeJrG16b7HAXcIIexABaC3+HXaDT0ts46OHxFCvAUsau1wTk8ZS4G/Sylz/OWlo1MXvatHR8e/HAMeacsELqAHYPerlY5OHfQWv46Ojs4pht7i19HR0TnF0AO/jo6OzimGHvh1dHR0TjH0wK+jo6NziqEHfh0dHZ1TjP8HxWXVc4vTLcMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.plot_dynamics(init_state, barely_a_seq, debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a bad, un-optimized gate. The labels indicate qubit states in the product basis. Next we increase the number of repetitions of the same gate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id',\n",
       " 'X90p:Id']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barely_a_seq * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GjHDpWBESwrCH/054/iwq//Vv6j/4oMt2pWLrP0vYumEymSgtLaW9vf+C0BaLZVDSzUaoJC+cnUp4azm7drln4TCQ1tDQUNLT09Fqte7K9Co6nWdZKtbWVhq/WkbjV1+iiogg7vLLCZs6VZHRsada3eGrHeXERYQwa2Scy8cOhV5nGJcaxer91UgpHefJV7X2hTf1WhoaKLvrLgBGffuNW20IrZb0J57g8HXXU3bPvYRkZRE+fbrXtXbGbzv+0tJSIiMjyczM7LejaGpqGrSKO6qyRrRqFaOT9G4d359WKSU1NTWUlpaSleUbXu47duxg3rx5Lh8npaThk08dfzAdNH35FSI8nIxn/0fEMcd4SaUNd7V6ws6jDczNSUSnUbt87FDodYYpGTGs2FPF5pI6po+wfaD5qta+8KbehiWfOX4OSXctc6sz6shI0p98gqKzzqbk2t+Q8fRTRBx7rGKx9dupnvb2duLj431q7lSv09BqNGO2WL3ethCC+Pj4Ab/h+DpWo5EDJ57UpdOPu+ZqEm/+A6G5ucjWVkquuJKGJUuGUKXn7KtooqLRwPhhrk/z+DK/mmmbyth6OLhuxdrWRsVDDwEw4p23PW5Pk5DAiLfeQhMXR8n1N9Dw+ecet9kXftvxA051+oNpyBQTbpuCaTe51/EPpNWXPuQAkpKSXNpfSsm+WfmYjtpuDkYceyw5GzeQfPvtJPz2t2R99CEj3ngdVUQER/98BzUvvzJkWj3lw802G+NTxju/Wrczg63XWRIjdcRHhLC7/OcpSV/V2hfe0lv+wIOOn8OmTPFKm7qRWYx4601Chg/n6J9uI2ll0JbZLUJDQxVru62tjblz52KxL8B4/503Oev46UwaP5bXXnttwONra2tZsGAB2dnZLFiwgDb7Kr6lS5dy7733KqbbW+Tk5Li0f8XDD2NtbQUg9e9/Z/jLL6HuNrUVPmMG2Wt/JOLYfCoffZTatz0fSbmj1VMKyxrJTY0iI861NM4OBluvK4xLjeLTLUcxmm0DHF/W2hve0CulpOHjjwEY8eYbXh2UaVNSyProQyLmziFk3TqsLS1ea7uDgO/4m5ubFWv75Zdf5rzzzkOtVlNbW8tDDz7IO0u/4/0vvuf++++nrq7/DJ9HHnmEE088kX379nHiiSfy4IO2EcQZZ5zBZ599Rqu9k/RV1qxZ4/S+rZs3U/f6GwAk/vGPxJx3bp/7qkJCSHv8ccLz8qh44EGaVzv/Pt7Q6ilSSn4qqXMrm6eDwdTrKqeMT8ZgtrLjqG26x5e19oY39FY/+ZTj57BJkzxurzsqnY6Mp57iyB9+jyrCOUdXl9r3eoudEEKcKoTYI4TYL4S4s5ftw4UQy4UQPwkhtgkhTldSj7d56623OOeccwBYtmwZCxYsIDMtGV1EFCeddBJfffVVv8d/+umnXHnllQBceeWVLF26FLBN6cybN8/x3N8xFhdz6NJf2Z4IQfxvrh3wGHVkJOlPP4UmMZGjt92GoahIYZXe4+vCCprazRyTFTvUUhRhTk4iYLuP8Uul+plnAEh/5hlESIgi7yG0WmRYmCJtKzYBLoRQA08DC4BSYKMQYomUsrDTbncD70sp/yeEyAW+ADJdfa/7P9vZI7e4A4vFglrtelZF7rAo/npW3/UujUYjRUVFZGZmAnDkyBEyMjIID1FTDaQMS+PIkb7L1YFtOXZqaioAKSkpVFX9bICVl5fH6tWrufDCC13WPlg4k2pmPHSIA6ee5ng+6puvESrnxhvq6GjSn3mGkquu4sitfyLzrTfdHv0MZsphR32Gc6e6n+XhyymSGbHhhGpVbC1t4KIZvq21NzzV27Jhg+Pn8BnK+hQpFVslR/zHAPullEVSSiPwLnBOt30k0PF9OBrw+pJAdzp9Z6iuriYmJqbH66Fa2/t1zH86S4eDXwdJSUkcPerbKyTz8/P73W5pbuHAKac6nqf+7UGXU97CJk5g2CMPY9izh7J7/9qrLbA3tHqTPeXN5CTrCdG4/+c1mHpdRaUSTE6P4bOtR7FYpU9r7Q1P9ZZcYfuWnvC73/a4R+VtlIqtkikvaUBno4lSYGa3fe4DvhZC/B6IAE7qraGysjLGjBkD2D4Br7rqKubNm0dTUxNqtZp7z8ztMpcfGRlJS0sLVqsVq9WKXq/HZDJhshc/0Ol0CCEcqZEajYbQ0FBHG0II9Hq9ow2AiIgIjEajow21Wk17eztNdk/txMREfvjhB4xtLQig+FAJk8efTHNzs6OzioiIwGAwYDabAVvnfujQIeLi4igvLychIQGr1UpLSwt1dXWOhVqd25BSUlhYSGWlrSjGhAkTMBgM7LNX9snIyCA5OZmCggIAoqKimDZtGmvWrHG875w5c9i5cyc1NbaFZpMnT6apqYki+3RKZmYmcXFxjiIQsbGxTJ48mZUrVzoW7sydO5dvv/3WkYk0bdo0amtrKS4uBmDkyJGYH/w568E6aRKR557LihUrHDGfPXs2mzdvdixay8vLo6KiwuFPkp2djU6nY4dGg/6Uk+HzzwmZMJ6d9nUMOp2O/Px8CgoKHOdu5syZlJaWOr5tjRkzBrVaTUFBAeHh4aSkpJCVlcXatWsBCAsLY+bMmaxfv95xcz0/P5+DBw9SXm5beZubm4vFYmHPnj0ApKWlkZ6ezvr16wHQ6/Xk5eWxdu1a6prbWbO3lQXjUzw6T62trZx66qleOU9bt2513G/q7TxFRkaydetWAOLj4xk/fryjyHdf52l2egjrD5r54MvlpEZqyM3NZceOHY7rOicnxzGX7sp5Kiy0TQgoeZ6++uorR9LH7Nmz2bt3r9PnKbq2lo50kT0xsexcscJrf0+9nafCwkLCw8OdPk933303ixcvtiskgb6QUiryABYBL3Z6fjnwVLd9bgX+ZP85HygEVN3bmj59uuxOYWFhj9d6o7Gx0an93CE9PV22tbVJKaWsqamRmZmZsra2Vu4oKpVpGcNldXW1lFLKyy+/XK5fv77H8bfddpt8+OGHpZRSPvzww/Lmm292bPvXv/7l2NYZZ39vb2C1WKSptlZa2tt73b58+fI+j234/HNZOGas7TF+grQajZ5pMRhk8eVXyMIxY2Xjd9+7fHx/Wr3Jp1uOyBF3LJXLd1d41M5g6XWXDQdr5Ig7lsrvd1f4vNbueKL34EUXy8IxY+XBiy72nqB+8EQrUCD76J+VnOo5AmR0ep5uf60z1wDvA0gp1wKh9Pcp5WOcfPLJjlFNXFwc99xzDzNmzODMk+Zw3c1/JirGdnNv27ZtDBs2rMfxd955J9988w3Z2dl8++23/PGPf3RsW758OWecccbg/CLdMFVUUPKb69idO559+ceyZ/IUR93Q8r//HUtD34t3rK2tVD/3PEdu/ZPjtezVqxAe2kyIkBAynn8OXU4OR++4w7EWwNc4VG1LvZs1Mn6IlShLpr128I8eWpH7Ey0//kjbli0ARC9cOKRaPEXJjn8jkC2EyBJChAAXA92XY5YAJwIIIcZh6/i9WuInQoFUqA5uvPHGLvn6V199Nfv372fbzt0svOhXNBvMNDY2kp2dTXovc9vx8fF899137Nu3j2+//ZaMDNvnZEVFBW1tbUwcwNPb20gpqXn5FfbPnUfL6tW97lP3+hvsnTmLI7f/mdyjZbTv2uU4tuHTT9kzbTpV//mPY/+0/zyGJtY72S2q0FDSn3wCaTZT+oebkUaj08fOnNl9llEZimtaSY7SOe71uMtg6XWXxEgd00fEsnRbmc9r7Y47eq1tbZRcfY3jeezFF3lTUp8oFVvFOn4ppRm4CVgG7MKWvbNTCPGAEOJs+25/An4jhNgKvAP82v4VxWsYXegcXGXatGnMnz/fsYCrg/AQ27x3U5uZqKioznNu/dKhtaSkhH//+9/eFTsA0mKh/L77qXzU5guecOONjNnyE9lrVpPx/HPEdLvQGz/7jJoHHuDgueexa+w4do/LdRSi6CBizvFEnXYa3iRkxAiG/f0h2nfs4Oidd/ZbvagzpaWlXtXRG80GM18XljMmxXObhsHQ6yknjE2irKGdvUUlQy3FJdyJbX2nv+Fhj/7Dm3L6RanrQNE8finlF1LKHCnlKCnlQ/bX7pVSLrH/XCilPE5KOVlKOUVK+bW3NZicrGbvLldffXWPzCG1ShAVqqXd7Fyn1EGH1hkzZjDFS0vAnUFKSdnd91D/3nuE5uaSvfZHEn9/E6rQUDQJCejnzCH1vvsYW7iTqLPOcqrNiOOOY/jzzyuiN+q000j4/U00fvElR++4E2m/ad0fA6XWeoOdRxpoajdzgYtlFntjMPR6Ssd0z9YDvv8h1RlXY2tpaKDi7w87nkeffXY/e3sXpa6DgF+5O1TotCoMZqvb6YeDSfXTz9Dw8cfEXnopmR9+0OfUjFCpSPvno2Qt+RTduHF9tpf+7P8Y/pKyRSUSfvc7Em+5hcalSym77z4MBw8ird43x3OFklrbSutJ6a6VWfRXRibaOv5t1a4NcPyNyv/+1/Gzu9bLvkbAd/xDtbhEp1EjpaTV6PwfxVBobfxqGdVPPYV+/nyS//J/TnmOhObkMPLjj4j+/jsyXnqRkFGjQK0m4Q+/Z+zOHUQOgkWvEIKEG64n5uKLaPjgQ4pOO53iCy7s86ZvRzqwkhyqaUUlYFiM56stB0Ovp4xNiSQ7Sc+Oet+oD+EsrsTWWFpKvb2sYtRZZ3lkvewOSl0HfuvH7yxD5Wip19lC29huIkLnXJgHW2vL+g0cveMOQidNIu2xfyNcXOym0WjQH3cc+s+Hzloi9b77iL3wQlo3/0TlP/9J8aW/IvOtN9GmpXXZT6mFfJ35ZMsRcpIj0ao9H08Nhl5PEUIwOzuBdzeUYLVKVCrfco/tC2djayor48BJCxzPhz38d6Uk9YlS10HAj/iHyr8+RKMiVKvG4IJF82BqNVdXU/aXv6DS60n/739QueEJ0rHYZqgJzc0l7rJfMeKN17E2NlJy9TWY7AtyOlBaa12LkdK6Nk6fmOqV9nwltgMxKlFPm8lKeaP/1IlwJrZN3y9n//wTHM9THrgfMYgW7x0odR0EfMevJN1tmV977TWys7PJzs7mtddeQ6exzfP3xeLFixk/fjwqlcqxMhBg+/bt/PrXv1ZUe/lDD2EqK2PYP/6Btpc1Bv5I2KRJZDz/HKbycg5fdz2WpsEzESuy5+9PSAuswisDkZNssyxYtrN8iJV4j/qPP6H0d7/r8lrMeecNkRplCPiOX8lCLN1tme+//37Wr1/Phg0buP/++2lrbsBgtmDqoyLXhAkT+Oijj5gzZ04XrRMnTqS0tJSSEmXS5Bo+/ZSmL78i9pJL0M8+zu12UlLcKzKiJOF5eaQ/8TiG3bspv+9+R7qn0lqLqmxWBB2ZLp7ii7HtjekjYonQqlhX5F6d6aGgr9iaKio4dPkVPUqCZq/9cUhG+6DcdRDwHb+SN0x7s2WOi4sjNjaWBQsWsHbF9wA0tfeeUjpu3LguN286az3rrLN49913va659aefOPp/f0E3dizJd/zZo7Z8pfZvd/Rz5xJ7+eU0fv45jfbydUpr/WxbGeEhakZ4qeP31dh2R60S5I+K50CV94uFKEX32LZu3syu8RPYP3cerRs3Ol7X5Y5jzKYCry1AdAelroPAuLn75Z1Qvr3XTVaLGZXajV8zZSKc9kifm/uyZe4gPT2d6soyhBD9Tvd0pqWlxVFsPS8vj0ceeYQ//9mzzrkz1tZWjtzyR9RRUQx/5WWPbRTWrl3rs0W2k++6k+YVKyh/8G/o581j7ebNimmVUrKlpI6Tc5NRe+kGpy/Htjs6Qz1FVSZMFqtXbmwrTUdszbW1HL7+Btq39+w7Mp57Fv3cuUOgritKXQe+f5Z8lL5smTsjhCBErXLZohm8b8sspaT05lswV1Qw7NF/DOkoZjAQKhXJd92FtamJ2lcHLoPpCfWtJhrbzUxI+2Xk73dnmF5glbBij1fdVpTDYqHs3r+y79jjenT60eefx+jl3/tEp68kgTHi72dk3t7cjF6v9/pbhoWFdcnCSUtLc1gOg22p9bx58wjRqGg3WR32q/3ReXt7ezthXqy+0/Dhh7SsXk3C729Cf/zxXmnTm/qUQD9/HqETJ1L97LOEP/5fxd7nYI1tmsNb0zzg+7HtzOzMSF7YXsOPB6pZkJs81HL6xdLcTNIfbqa+k9VH4i23EH/tNUM2j98fSl0HAT/iV6LTB5untsVicXT+p5xyCl9//TV1dXXU1dXx9ddfc8opp6DXabjtpt+w6oe1Lmndu3cvEyZM8IrW9r17qfjHo+hycoi/5pqBD3ASXzfnEkKQ+tDfwGolxUm/JHf4trACgJxk711rvh7bzsw9bhYT0qLYX6lcfWtvYCwpYW/eDIS908944XnG7iok4YbrfbLTBz80afMVlCy23pct84wZM7j33nuJi4sjOkzLvl07iUvsORL6+OOPSU9PZ+3atZxxxhmcdNLPdWi8Zctsrqri8G+uQ6hUDPvXP1GFhg58kJN0FLjwZUJzcohedD4tq1bTai+E4W3WFdUwNiXSqyN+f4htB+vXr2d0op4dR/q26x5qjCUlHDj5FNvP8+czdlch+uOPH7IFns6i1HUQ8B2/kl45fdky79+/n6uuugqAtpYmhmeNIj65Z678ueeeS2lpKQaDgYqKCj7++GMADAYDBQUFnOahs6WpvJySa67FXF1N2uOPE5qT41F73emohOTrJP7+90i1uovRljc5VNPK1OExXm3TX2ILNq1jU6OoazVRUFw71HJ6YK6ro/TGmwBI+eu91F10oc93+B0odR0EfMevJH3ZMncmOjqap156gzYXPHtKSkp45JFHPFqDYG1p4fD1N2AsKSH9ySeImOU/UwfeRpucTPPZZ9O+YweN33jXZKup3URNi9Gro31/5NypNouMLYfrh1ZIN6TZzJE/3Ixh3z5S7vsrsZdcMtSSfIKA7/iVLMQCvdsyd0enUdFiNGMewD2yQ2t2drZHKVymikpKfnMdhj17SH3wASJPOGHgg9zAn4psT7ztT6iio6l78y2vtltQbKuRmpXg3evMn2Kbn59PclQoMeFaDlb7Vj5/5X/+Q+vGjSTecjOxF18M+F9slSDgO36DwTDUEoiNCAEY0LfHU63G0iMUX3Ip++fPp337dlIefIBoJ/3z3eHgwYOKte1tDlVVEbPwHFrXr8dgL6TtDZbtLEetEszK8m6pRX+KbYfWEfERPnWDt/HLL6l96WX0J55Iwg03OF73x9h6m4Dv+M1OFOlQGp3GFuaBFnJ5otXS3MyhKy6nfccO4i6/nJFLPyP2ggvcbs8Zysv9x5+lvLycGHs86t5732vtFte0MCUjhuhw71oT+1tsASamRbH+YC0Hqoa+8zcUHeTI7X8mZMQI0h7rWs3OH2PrbQK+4/cFQtQqBAKDSZmCFVajkSO33or5aBlpTzxO8l13EjJihCLv5c/oRo8mfNYs6t9/36V6vf1xqKaVEfHhXmnL37kiPxOA7aVDm90jTSZKf/tbhFZLxksvohqimhy+TMB3/KFeTF90FyEEIRoVjX149nTgrtaqxx+nZdVqEv/4RyLnz3erDXfIzc0dtPfylA6tkSeeiDQaafjM8xoCTe0myhravWbM1hl/jG1mfARqlRjyEX/1c89jPHSIYX9/qNfCKf4YW28T8B2/kumcA9kydyYqTIPBbO1yg/f2229n7NixTJo0iXPPPZe6OtuNQldsmVvWraP2pZeJPOUUEq6/zju/mJP0l83ka3Rojb3sV6hjY6l/3/Ppns+2lgEwJSPG47a644+xDdGoGBEXPqROne1791Lz4ouEz5xJ5Mkn97qPP8bW2wR8x6/kzd2BbJk7OnKA8BBbamZn354FCxawY8cOtm3bRk5ODg8/bMszd9aWWVqtVD3xJOrYWNsK1UFmz549g/6e7tKhVQhBeN502rZupd3DIhd7K5oI1ao4PjvBGxK74I+xBRiTEknBoToaWvv/dqsEVqOR0pt+j9DpSLnvr31WlPPX2HqTgO/4lWQgW+avvvrKsW/HDd7OHf/JJ5/syNWfNWsWR44ccWxzxpa5efly2jZvJvaSi1ErZE0RiCTffTcA9R997FE7h2paGJmg95vFQIPBhTMykBL2Vg5eERywfbOveOjvmEpKSL3/fnR+Yms9VPimQYWL/GPDP9hdu7vXbVarFZXK9c+3sXFjueOYO/rc7owtc+eOPMRuV1vfaiImPKRHex3fHjoYyJZZms1U/uc/qKOjSehWLWiwSOtW19aX6axVm5xMSFYWdW++SeLNf0Btt8J2lUO1rYxJdu/YgfDX2I5OtA1A9lc2MyMzbtA0NHz4IfXvvUfMBYuIOvWUfvf119h6k4Af8bvT6TuDM7bMXXUIwkM0tPWS2fPQQw+h0Wi44oorHK8NZMvctHw5xv0HiL/hhiEzmErv5caZr9Jda+LNfwCg7aef3GrvcG0rRVUtjE5S5puWv8Y2LSaMUK2KHw8M3jy/6ehRyv/2EKHjx5Py178OuL+/xtabBMSIv7+ReVNTk6O4iTdx1pa5M1GhGsob27FYpaNgx6uvvsrSpUv57rvvaG1tdWjtz5ZZWixUPPwwqqgoYn91qXd/MRdYv3693xQL6a41YrbNmrr8wb8x+puvXW5vrb1jO2W8MqXx/DW2KpUgNzWKVXursFolKi8VpukLaTJRdvfdSKOR1If+5tQgyF9j600CfsSvFM7aMgNcccUVbNiwodM8v23U/9VXX/Hoo4+yZMkSwsO75oL3Z8vc+MUXmI+WkfTHW1CF9Jw2CjIwan0EkQsWYDp8GEuD63nnJbWtqFWCsSnKTPX4M+dNS6ehzURZY/vAO3tIzauv0vLjWpJu/SOhY8cq/n6BQsB3/EpN9YBztswA27ZtY9iwYYRobFkGje22Fbo33XQTTU1NLFiwgClTpnDLLbc42u7PlrnmhRcBiD77bKV+NadQqtaBEvSmNeaCRQC0beu9bGd/HKptJS0mDI1CpQb9ObYd019K2zc0r1pF1eNPEDZ5MnEu1Jnw59h6DSmlzz+mT58uu1NYWNjjtcFm06ZN8rLLLut3n4aGBrlo0SIppZQWq1VuL62XByqb+j2mvb1dzpw5U5pMph7bdmzeLAvHjJU1r7/hvvAgUkopjeUVsnDMWHnwootdPnbeP5fLy15cp4Aq/6eqqV2OuGOpvG/JDsXeo2XTJlk4YaLcd9ICaaqtVex9/BmgQPbRpwb8iF/JQizO2DJHRUWx2F79SSUE0WHaPj17OrT2Z8tsbW5GpdcTdabnRVo8Ze3agauK+Qq9adUmJxFx3HG079qFHMA5tTO7yxs5WN3C9BHK1S3259jGR4SQGR/Oyr3K1OBtLyyk9Kbfo46KYsSbb7hcP9qfY+stAr7jlwqu3AXnbJk7o9OoMFmsWKw9dXVo7cuWWVosSKORmPPP94li6b7gfOosfWmNPPlkpMGA8cABp9s6WGWzHj5pnHL1Zf05tkIIFk5N42B1i0t1KJzBVFnJ4Rt+C8Dwl19Cm+z6OfDn2HoLpzp+IYROCHGpEOL/hBD3djycOO5UIcQeIcR+IcSdfexzoRCiUAixUwjxtqu/gL/RcYO3xeC6E6e5yjaCipg926uafsmE2r1Qqp5+xuljDte1ApARFzRn64sxyZFICfu8tJCrfdcuSn//Bw6eex6WxkbSn3yC0DFjvNL2LxFn0zk/BRqATYBTH0FCCDXwNLAAKAU2CiGWSCkLO+2TDdwFHCelrBNCJLki3hl87UZOuM4W8iaDmaiwrla+A2m1traCWk3E7OMU0+cKs/3oA6gvraETxhMyciQGF5bGH6ppJSpUQ3SYd62YO+PvsR2bGgXAm+sO8eiiGI/aN5aUUHzprxBCEDF7NvHXXkPYpElut+fvsfUGzk71pEspL5JSPiql/HfHY4BjjgH2SymLpJRG4F3gnG77/AZ4WkpZByClrHRJvRN0zrX3BbRqFeEhGtp7WcjVn1ZrWxvW1lZUoaE+YxGwd+/eoZbgNH1pFUIQdeopGA8exFQ58OVnsUq+21VJjkIrdjvw99hmJUQwNiWSHUcaPWrb2tZGya9t9auzPv2E9Cce96jTB/+PrTdwtuP/UQgx0cW204DDnZ6X2l/rTA6QI4T4QQixTghxqovvMSC+UIilOzqNqotnTwf9ae3INVeF+870QqUTHaWv0J/WsKlTAah9+ZUB2zla30Z5Yzsnj1dufh8CI7ZzchLZX9WM2eL8jfPuHL3zLkxHj5Lx9FOEdLJE8YRAiK2nODvVMxv4tRDiILapHgFIKaVnH722988G5gHpwCohxEQpZX3nncrKyhhjn8/T6XRcddVVzJs3j6amJtRqNWFhYV2ydyIjI2lpacFqtWKxWLBYLJhMJkwmk6MNIYRjhK3RaAgNDXW0IYRAr9c72gBbPVyj0diljfb2dk4//XSWLl2KTqdj4cKFrF+/nlmzZvHBBx+g1+tpbm523LSNiIiw3ayxmjBZJPWNTfzmmqvZtGkTcXFxvPzyy0yYMIENGzbw5JNP8vzzzxMREUFzczPq2lpkiA7MJgoLCx0XxIQJEzAYDOyzlxPMyMggOTmZgoICwJZVNG3aNNasWeP4YJkzZw47d+6kpsa2+nTy5Mk0NTVRVFQEQGZmJnFxcWzevBmwLVabPHkyK1euREqJEIK5c+fS1tbmWK08bdo0amtrKS4uBmDkyJFERkaydetWAOLj4xk/fjyrVq1yxHz27Nls3ryZxkbbqDAvL4+KigoOH7aNF7Kzs9HpdOzYsQOw2Vjk5OQ41k7odDry8/MpKChwnLuZM2dSWlrq8EkaM2YMarWa5uZmVqxYQUpKCllZWY5sibCwMGYefzzWxETKf/yRXStWkJ+fz8GDBx3Vj3Jzc7FYLOzZs4fCGts3tZyEMMfvrtfrycvLY+3atY6bcbNnz2bv3r1un6fm5masVqtXztPWrVsdTrFKnCej0Uh1dXWP8yTrSjGarby4dDU3nDPXqfNUaHdMTUlJIXH3bpqWLaN1zhx2qNXMxLaSta2tDaDf8wS21fTp6emsX7++y3lqaWlxnDtPzxN47++pt/PUcd06e57uvvtuRxYh0LdtbF95np0fwIjeHgMckw8s6/T8LuCubvs8C1zV6fl3wIzubXmSx280Gp3azx2eeuop+d///tfx/Ntvv5VLliyRZ5xxRr/HNbYZ5dbDdfIfjz0ur7/+eimllO+8844j319KKU888UR56NAhKaWU5oYG2bp9uzSWlfnE+oUOqqqqhlqC0wyk9ejd98jCMWOlsby83/3e3XBIjrhjqSypafGmvB4EQmzLG9rkiDuWyrs/3u5ym1arVe474US5/4wzpLmp2VOJXQiE2DoDnubxSykPATHAWfZHjP21/tgIZAshsoQQIcDFwJJu+3yCbbSPECIB29RPkTOanEUqmM7Z2ZYZ4MQTT3TKF0iv06BWCT7/bAlXXnklAIsWLWL58uUOvZ1tmS32UZImyev3vj0ikNLi9PPnAdCwpPsl2pWtpQ2EqFWkRitb2S0QYpscFcoxWXEUlrk+z1/70kuYjhwh/tprUeu9W+EsEGLrKU5N9QghbsZ2I/Yj+0tvCiGel1I+2dcxUkqzEOImYBmgBl6WUu4UQjyA7ZNoiX3byUKIQsAC3C6ldNnWr/zvf8ewq3dbZrPFgsaFPPsOdOPGkvJ//9fn9u62zK4ghECnUVNWdtRh5azRaIiMjKSmpoaEhASHLfPtf/wjltpaVOERCAXtJ9xh3759fmNxO5DWyBNOQDMsFcPuvrN7pJR8ub2MKRkxilk1dBAosR2XEslraw+5ZNgmrVZq33yLsOnTiT6nez6I5wRKbD3B2av3GmCmlPJeKeW9wCxsHwT9IqX8QkqZI6UcJaV8yP7avfZOH/s3klullLlSyolSyv4rj/gQrtoyd0enUWGVfX8j6bBltrbacsY18YPnbf5LRTd6NI2ff46lqffc8/pWE3WtJsVv7AYS4+xpnct2ljt9TN2bb2IuLyfmvPN8JoMt0HD25q7ANiLvwGJ/zSfob2Te3t6uSMH17rbMrqLTqEhKTmVfUTEZGRmYzWaampqIj48HfrZlNtfWghCoFLCW9pQML2VZDAbOaNUfP4eWVatp+eEHok7tmWBWXGNbsTtCgeLq3QmU2C6cmsadH21n06E6TpuYOmBb0mik9vU30GVnE33eud6U6SBQYusJzo74XwHWCyHuE0LcB6wDXlJEkZfRapVZZNPdlrk/7rrrLj7+uGuZv9iIEOYtOJXXXn8dgA8++ID58+c7Rjh79+5lfG4u1pYW1Hq9z03zACS7sVx+qHBGa8yFF4AQGPb3bt/Q4TaZGa98Sm2gxDZUq2ZSejS7y51bwVv/0UeYSkuJvfwyxUb7gRJbT3D25u5jwFVArf1xlZTyv4oo8jKt9qkSJehsywxw/PHHc8EFF/Ddd9+Rnp7OsmXLANi+fTspKV0LdmjVKhZdegU11TWMHj2axx57jHvuucexffny5Zx20kkAqBO8X8zbG3SkuPkDzmhV6XRokpNp3bix1+1Lth4lPERNZoLyI/5Aiu2Y5EjW7K8e0LfH2tZGzYsvEZKZScwFF3hTYhcCKbbu0m/HL4SIsv8bBxQDb9ofh+yv/aK58cYbee211xzPV69eTVVVFW1tbZSWljoKsZhMJvLz83scH6WP4D/Pv8b+/fvZsGEDWfYC0QaDgYKCAk6ecQxg65CCDA66UaNoXb8eU0VFj227yhpZkJuMVuEbu4HG7GzbwGXl3v4XIzV9/z2m0lLirr4qOLevMANdwR2maZuAgk6Pjuc+j5KFWJyxZQYcI//u6DQq2kwWTPaVjR1aS0pKePjhh1G1tyFUqiGrqTsQUVFRQy3BaZzVGnfF5YDNFKwzzQYz1c1GxgxSxa1Aiu0p41PQqMSA9g1NX3+DKjqamPPP96a8HgRSbN2l3x5FSnmm/d8sRd59EIiIUPZr+dVXX+32sbHhIdS1Gmk1WogOUzm0ZmdnMyozE8OePWhTB74hNlRMmzZtqCU4jbNaQyfanElMJSVdXi+utt3YzRyEG7sQWLEN1aoZlahn/cG+M7Utzc00ff89Meefh3Aj/doVAim27uKsLfN3zrzmizT1kZrnC4SF2C7wDsO2zlqtdm8eoUBGkrfofH/D13FWqzo2FhEeTt3b73R5/fPtZQCDNuIPtNiOS41kY3EdR+vbet3e9O23YDIRfdZZ3pbXg0CLrTsMNMcfap/LTxBCxAoh4uyPTHoargVxEbVKEKJW0dhu6rHNYcoWFjbYspzGFw3w+sJZrUIIwqdMwVhcjKW+3vH6TyV1ZCfpGZU4ODbfgRbbq2fbJg02Ftf22CalpOqJJ9AkJxM2ZYq35fUg0GLrDgON+K/HNp8/1v5vx+NT4ClFFP3C0GnVtBktmDuV/pNSYm032EafPpjGGejEXXkFAIaig47XDtW0MjE9eqgk+T25qVGEalW9lmM0FhVhPlpGwu9+p/g0TxAb/fYqUsrH7fP7t0kpR0ops+yPyVJKv+j4fa0QS3cS9CEAtJusDq3WpiaQVp+yYO6NOXPmDLUEp3FFa8iIEQC0rLM5eLabLJQ1tA/a/D4EXmw1ahXjh0WzdGtZj7TOxi++BCBs6hQl5PUg0GLrDs7m8T8phJhgL5N4RcdDEUVepsPCVam2586d68jqOfXUU4mJieHMM8906vhVq1YxJ38m0zITeH/xYofW8gMHOPuGG1ApfGPaU3bu3DnUEpzGFa3atDRQqah/9z0A1uyrBhi0aR4IzNj+5vgsjBYrW0vrHa9JKalfvJjQ8ePRjR6tkMKuBGJsXcXZm7t/BZ60P+YDjwJnK6LIywyUaukJL7/8Muedd56j2Prtt9/OG2+84fTxw4cP59VXX+H0hYtobjc7tMZHRDAsNZW1fSwk8hU6/Mf9AVe0Cq2W6HMXYq6sxNrWxgp7/vncMYlKyetBIMZ2ZpbNjuT9jT/XZzIdOoS5spKYRecP2rRmIMbWVZyN9CLgRKBcSnkVMBn4xU94umvL3EFmZiaTJ09Gp9VgskisUmJta0MajZx95lm89dZbSsgO4gR6e61T46FDtvn9tGj0Ot9cT+EvxEaEMDYlku/3VGK12swJq198EYCwadOHUtovDmev5DYppVUIYbav5q0EfMbpaPX7e6k+3NzrNmmvcOMqCRl6jr8wp8/tntgyd8e2ElSi1oZirrXd/Dpm9nHc969/ety2kkyePHmoJTiNq1pD7Kuoq59+mpLh5zEhbXDHOYEa22uPH8lti7eyv6qZnORIWlatRj9/PqFj+v5b8zaBGltXcHbEXyCEiAFewJbVsxlYq4giLyNRphCLp7bMnVHbfcqb2k1IgwFVeDgpw4dz9OhRr7SvFL68RqI7rmrV5eQgQkNp3bWHQzWtjBwEf57OBGpsp4+IBeCDTaW0bNiAubKSiF7sTJQkUGPrCk6N+KWUv7P/+KwQ4isgSkq5TRFFbtDfyLypqcml6Rdn8dSWuTO2jl/Q3GYkprUVdWycw5bZlykqKmL48OFDLcMpXNUqVCriLr+MqpdfQWW1MHv04BrlBWpsM+PDGZ2kZ9nOcn5d8zWoVESdeYbCCrsSqLF1hYEWcE3r/gDiAI39518sntoyd0cfqiG0zWYLoNZHsHfvXiZMmOAVrUHcI2TECFQWC5Obj3BM1i/ek9ArCCG4MC+dQzWtNG/dRsTxs9HEBWM72Aw04v93P9skcIIXtShCSEiIYm132DKfZLdPPv7449m9ezfNzc2kp6fz0ksvccopp7B9+3bOPrtnEtTGjRs599xzqaurY8lnn/FYXDxblnyKOjqa5cuXc8YZgzsSchVv3N8YLNzRGmr/4F14pGDQ3SIDObbTR8QxrXIPloNFhJ/tXOqzNwnk2DrLQCZt8xV510FEo6Cz5Y033sh//vMfR8e/evXqXvfry5Z5xowZlJaWAtBqNNNWdBCLfb5/yZIlfPrppwop9w5xfjRSc0erbswYiuIyyGjpudpUaQI5ttOGxzC7+RCAor77fRHIsXUWZ/P4r+jtoYgiL6NkIRZPbZk7o5MWws0GDGotVVVV3HrrrcTGxnpLqiJs3rx5qCU4jTtaf9hfwz59KvFN1Qoo6p9Ajq0QgummaoqiUmkJH/ys8ECOrbM4m9Uzo9PjeOA+/GQBl9JcffXVjgVcniBbbPP7DdowEhISWLhwocdtBvGM3eWNlEfEEdJQ12thliDuYSorI2nXZvbHpPHimqKhlvOLxFnLht93evwGmAb4tgmOHW90yoOBNBoBQYsqBIPZOuD+voCvfyPpjDtaD1S1UJQ+FoCGjz7ytqR+CeTYtqxbD0D5xJl8t6v/qlxKEMixdRZ310i3AH5RnCXcx43OOrA0NkKIFgnUtxqHWo5TBPJCGLPFyso9lTBhEpphqV2cOgeDQI6tYe9eREgIw09fQGFZIz/uH9yptECOrbM4O8f/mRBiif3xObAH6D8/0Ufwh8Ua1vZ2pNGIVaslTKumdYCi1L7CypUrh1qC07iqtbCskaMN7Zw4NomQ9AxM9pvwg0WgxlaazdS98w667GyuPH4UIRoVn24Z3IWKgRpbV3A25eVfnX42A4eklIP7lxDASIMBAGtUFOEqDTUtBqxWiUrl2wWnpVRmVbQSuKp1X4XNAuSk3GS06ek0fPQRVoNh0ArfB2ps2wsLke3thE2fRniIhgXjkvlkyxGunzuSkYPkfhqosXUFZ+f4V2Ib5UdjW8DlPyVsFMRTW+bHHnuM3NxcpuTnc/q111Jy9CgROjW1NdUsOOUUJaV7hcHObfcEV7XuPNpIiFrFiLhwQnNzAXt5wEEiUGPbvmcPAHGX24ra33HqWEwWK+91cuxUmkCNrSs4O9VzLbABOA+bU+c6IYT7VcYHESXsGjrw1JZ56tSpbNy4kQ3vv8+5p5zCA3/7G9FhWpISk4hPTOaHH35QSrpXmDt37lBLcBpXtJotVj7YdJgJaVFo1CpiLrwAhMB4YPAyUAI1trUvv4IqIsJW8wAYHh/OMVlxvLTm4KDd2wrU2LqCszd3bwemSil/LaW8EpgO3KGIIi+jZB6/p7bM8+fPJ0yrBWDWccdRUlKCEIIInZrjTjyNN9980+uavcnWrVuHWoLTuKK1pLaVxnYzC6faOidVSAja1FSMpYM3Kg3E2Jrr6jAePIhu7Ngu3vu/nTcas1XyxfZypSR2IRBj6yrOzvHXAJ3vkjbZX/MJlr/6PJWHeh+NWcwW1BrXUzqTRoxk/q+v63O7t2yZO+b3X/vwQ8cK4MhQLbmTpvDcY3/3qG2lqaurG2oJTuOK1n2Vtvn9yekxjte0GRk0fbUM+fDDg1IXNhBja9i1C4DEm27s8vqc7ATSYsL4z7d7uTAvHY1a2YIsgRhbV3E2wvuB9UKI++zVuNYBe4UQtwohblVEmY/jLVtmc0UF73z2GZu2bOHmm28GICZcS0JiEmVlZR63H8R1VuypQiVgdNLPNxtDx45BGo20+dFo0deofvY5AHRjx3Z5XQjBDfNGUdVkYPUgp3b+UnF2xH/A/uigw0RGuQl0F+hvZG6xWBRZxOUNW2YpJd+uXMmjL77IqrVrHYs1VEKgliZCdKFYpUTlozejpk3zH4NWZ7VKKfliexkzMuOI6FRxK/ZXv6L2tdcxFhURPgi/d6DFVlqttG3ZgiYxEU0vi5IWTUvn0a928+hXe5g/JkkJmQ4CLbbu4GxWz/1SyvuxuXX+u+N5p9d7RQhxqhBijxBivxDizn72O18IIYUQea7/Cv1jNiuTgOQNW+bNGzfy+/vu4+N33iEpKamL1qrSYkaPGUeLwXcTqGpra4dagtM4q7Wq2UBDm4nTJqR0eV07bBhoNBhLBmeeP9Biazp8GGk0knjzH3rdHhai5vcnjGZXWSNLtymb1x9osXUHZ7N6JgghfgJ2AjuFEJuEEOMHOEYNPA2cBuQClwghcnvZLxK4GVjvqnhnMBqVyxTosGXu4Pjjj+eCCy7gu+++Iz093WHOtn37dlJSUnoc/+c//5mW1lYuufZapkyZwrnnnuvYtmndGo4/8WSf7viLi4uHWoLTOKt1d5ntVtbopK5fZoVGg3bYMOrefntQ8sADLbYNSz4DQDdmbJ/7XDpzBKMSI7jzw+3UNBu8Ja8HgRZbd3B2jv954FYp5Qgp5QjgT9jKMPbHMcB+KWWRlNIIvAuc08t+DwL/ALxTzmoQufHGG3nttdccz1evXk1VVRVtbW2UlpZyij0Xvy9b5i9efpniVav46aef2LJlC++9955j29LPPuP8Cy+mtsXoVwtO/J13N5agEjAhLarHtrCJE7E2N2M64tslMX2Rxi++QJ2QQOjYMX3uo9dp+PeFU2g2mPnb57sGUd0vD2fn+COklMs7nkgpVwghBipCmgZ0/l5cCszsvIO9ileGlPJzIcTtfTVUVlbGmDG2C0an03HVVVcxb948mpqaUKvVhIWF0dz8c7H1yMhIWlpasFqtWK1WLBYLJpMJk8nkaEMI4Zim0Wg0hIaGOtoQQqDX6x1tAERERGA0Gru0MXHiRPLz86mvr0en06HT6Wixu2x2tNHc3MwHH3xAU1MTERERGAwGx5SOxmBAREXRbE85ValUWK1WDh06xG9/+1vSEmKpbrNQ19iMVuWIPYWFhVRW2sytJkyYgMFgYN++fQBkZGSQnJxMQUEBAFFRUUybNo01a9Y43nfOnDns3LmTmhpbYtbkyZNpamqiqMiWGZWZmUlcXJzDEjY2NpbJkyezcuVKR/H6uXPnotVqWbFiBWCbi6ytrXWMUEaOHElkZKQjHS0+Pp7x48ezatUqR8xnz57N5s2baWxsBCAvL4+KigoOH7ZdNtnZ2eh0Onbs2AFAUlISOTk5jm9ZOp2O/Px8CgoKHOdu5syZlJaWcuTIEQDGjBmDWq3GaDSyYsUKUlJSyMrKYu1aW8nosLAwZs6cyfr162lra2Pdvlbm5iRQXlLElnJbemFubi4Wi4XiMTnEff45B3/8geFnncX69bYvqXq9nry8PNauXYvBnqU1e/Zs9u7d6/Z5MhqNWK1Wr5ynrVu3OrJDlDhPsbGxVFdX932eNBpiDh/GeNpprLSvTenrPNUfKOSE4Ro+/ukIJ+TEE9lwoNfzBJCfn8/Bgwcp73ae9tgXiaWlpZGent7jPFmtVsd16+l5Au/9PfV2njquW2fP0913383ixYux03e9UCnlgA9svjz3AJn2x93AxwMcswh4sdPzy4GnOj1XASuATPvzFUBeb21Nnz5ddqewsLDHa71hMpmc2m+wsZrNsnX7dmmsrHS81l1ru9Estx6uk+UNbY7XnP29B4Pa2tqhluA0zmitbzXKEXcslc8s39/rdmNZmSwcM1bWvvOOt+X1IJBiayguloVjxsq6xYudaq+hzSgXPLZCTn/wa1nd1O4NiV0IpNj2B1Ag++ifnZ3quRpIBD4CPrR/kgy0cvcIkNHpebr9tQ4igQnACiFEMTALWOLtG7wdowNfw2IfPak6lYbsrjVEoyJUq6am2TenewJtIczn22zps+NSe09W0yQlIUJCqP9QeYvmQIpt9Qu2WWHd2HFOtRcVquWBcyZQ3Wzk71/sxmr17rUfSLF1l36neoQQocANwGhgO/AnKaXJybY3AtlCiCxsHf7FwKUdG6WUDXT6KiKEWAHcJqUscOUX8Fes9ikhVUTfM2ZCCBL0OkrrWmk3WQgLUa6MZBD4ckcZcREhHDuq92/IQqUibMoUWjduRJpMCPuq6yD9YzpUgjo6mtDxPXI7+mTWyHh+NXM4b60vITZcy1/OGOdXHju+zkAj/teAPGyd/mnAP51tWEppBm4ClgG7gPellDuFEA8IIQatepevFmKRBgOqiAhEp5rAvWmNDNWgEoKj9b537zs+Pn6oJTjNQFqllOw40sCCccmEaPr+s4heuBCkxHRU2Ru8gRRbw4ED6Bec5HLH/beFEzhlfDIvrjnII1/u9tq33kCKrbsMNITMlVJOBBBCvITNqM1ppJRfAF90e+3ePvad50rbzhIWFqZEsx4hpUQaDKi7rfztTatWrSIpUkd5YzttPubTP358vxm9PsVAWr/dVUldq4mpw2P63U+bbvPvMZaWEjJihLfk9SBQYtu6aROW2lpCs7NdblcIwdOXTuPm97bw3Koi9lQ0cdnMEcwbk+iRrUOgxNYTBoqeY1rHPoL3Ozpn+3ibzrbMW7ZsIT8/n/HjxzNp0qQuqZndsTY2Iq1WjMBFF13E6NGjmTlzJjt37gRsef+//vWvHfvHhNumFCqbfGvU35FR4A8MpPXL7WWoVcJhzNYXIenpALSsXtPvfp4SKLFt+vY7APRuukxq1CqeumQqfzwphx8P1HDt6wWc/J9VbD1c71Z7EDix9YSBOv7JQohG+6MJmNTxsxCiURFFfkRnW+bw8HBef/11du7cyVdffcUtt9xCfX19r8dZW203cV/98ENiY2PZv38/f/zjH/nrX/8KwMSJEyktLaWkpASAEI2a+IgQGtpMWLx8oyuIjTX7qzlxbBKh2v6nBjUpKWhSUmj2o85jKDHs24cudxwhHpgZCiG4+aRstv31ZJ64ZCrNBjOXvLCO1fuqvCf0F0a/Hb+UUi2ljLI/IqWUmk4/91zh8gujsy1zTk4O2favs8OGDSMpKYmqqt4vTGk0oAoNZcnSpVx55ZUALFq0iBUrVjjmMc866yzeffddxzGxEbbsn2YfWsmr0fjPzeb+tH61o4zKJgPHZ/ed9tyBUKmIPvMMjIcPIy3KTb0FQmytRiMta9agGz3aK+8TqlVz9uRhfHrTcaTHhnHVKxv5crvrRoaBEFuP21Wk1UGm/rMDGI+29LndnYTOkGERxJw1qs/t/dkyb9iwAaPRyKhRPY+XUmJpbkYdGcWRI0fIyLBlvGo0GmJiYqipqSEhIYG8vDweeeQR/vznPwMQHqJBr9NQbTTTZrQQFjL0N61nz5491BKcpj+taw/UoBI2ywBn0KZngMmEubISbWqqtyR2IRBi2/TllwCETZjo1fdLjQ7jvevy+fWrG7nx7c08e9l0Th7f0xKlLwIhtp6irPG1D2C1KjMq68uWuaysjMsvv5xXXnkFlapneK1NTSAlIrRn7dbOWQtJSUkc7ZY5EhcRgsUK720s8fwX8AIdKxH9gb60Sin54UANM7PiUTtZ41hrn+dv27LFW/J6EAixbd9tW0Ebe+klXn/P2IgQ3rp2JjnJkfzxvS2U1jlfcCkQYuspATHi729k3tTUpEj5xd5smRsbGznjjDN46KGHmDVrVq/HWTtsIuLjSUtL4/Dhw6Snp2M2m2loaHCkb7W3t/fI8okJD0GrFrz9YwkXzsggfIjz+juW8PsDfWndU9HE/spmLj1muNNt6XJsU3qNy74m6rTTvKKvO4EQ2+bVqwjNze2SsuxN9DoN/714Cmc9uYarXtnIFzcfj9aJbJ9AiK2nBPyIXym62zIbjUbOPfdcrrjiChYtWtRl3862zNJoRGi1CLWas88+22Hy9sEHHzB37lxHrvPevXuZMGFCj/eNDNWyt6KZ1348pOSv94vh+902j5YzJjk/ZaNNSkI/fz4Gu59LkJ60796Ncf8BdNnemd/vi7EpUfz3oqnsq2zmX8v2KPpegUTAd/zh4eGKtd3Zlvn9999n1apVvPrqq0yZMoUpU6awxT4V0GHLLC0WLPX1CJ1tmueaa66hpqaG0aNH89hjj/GPf/zD0fby5cs544wzev4+IWryRsTy76/3sKtsaEcueXleL5+gGL1pbTWaeWn1QSakRZEcFepSeyGZmZiOHFHMSsPfY9tuN2yL65SWrBRnTErl/GnpvLjmIJsODVyq0N9j6w0CvuPvcNNUgs62zJdddhkmk4ktW7Y4HlOmTHFoyM/Px2r34lHbbRpCQ0NZvHgx+/fvZ8OGDY4bvQaDgYKCAk7rYxrh6V9NI1Sr5sa3NmO2WBX7/QaioqJiyN7bVXrT+lNJPTUtRn43z/VRqTY9DdnejumQMt+8/D227Xv3IsLD0Y3p24bZm9x9xjiSI3Xc8eE22k3939fz99h6g2DH7wHTpk1j/vz5WAZI6+soyNJRWF3VR63eDq0lJSU88sgjfaZyJUeFctfpYymqbuHJ7/e7qd5zOmx5/YHetH629ShCwGwn0ji7EzrOZjhW/1HPymrewJ9ja21vp+71NwjNyUH0kuCgBLERIfzt3Ansr2zmqQH+Jvw5tt4i4Dt+pbn66qud9gOytrUhVKoBb3ZlZ2czb968fve5ZMZwThqXxOPf7WN9UY2zcoPYqW818n7BYWaPTiAq1HWztfBp09COGB6c5+8Fwz5bxxtx/OCmTZ4wNpmzJg/j+VVFlNQ4n+XzSyTgO36drmfa5FDgmN8PDe3TrMoVrSqV4NFFk4kO0/LnD7fR1K7cN5u+yHbDf2Wo6K71tR8PYZVwy0k5brepGzUaU6kyIzJ/jq3hgK3jjzrt9EHXcuuCHIwWK29v6Dvl2Z9j6y0CvuP3FStXaa/9q46O7nMfV7XGRYTwrwsmc6imlSte3kBD6+B2/r7yoeoMnbWaLVZe+fEgGXFhTMmIcbvNkIwMDMWHkFbv32fx19gCNH39DUKrJWR4Rh9HKEdWQgTHjornxdVFlDX0vnTTn2PrLQK+4++eaz9UWDvm9/vx33dH64LcZP62cAI/ldRz/rM/9nmxK0FHqT1/oLPWz7Ydpb7VxF9OH+f0oq3eCM0dByYTjZ9/7g2JXfDX2EqzmZbVq9GOGK5Y/v5A3H1GLmar5L2NvX8b89fYepOA7/h9hY7CK6JTxS1vcdmsEbxwRR4Hq1tY+PQPPPHdPnYcafD6+wQKz60sYlh0KPPHJnnUTtSZZ4JKRfvu3V5S5v8YS0qQJhPx1147ZBrGpUYyIzOWp77fT2Wjbwz8fI2A7/iVNGRy1pZZSom1oRFVaGiXLIdVq1Yxbdo0NBoNH3zwgUNrVVUVp556qktaFuQm8/a1M9HrNDz2zV7OfHINty/eisGsnJFYUpJnHedg0qF1zb5qdpc38dt5o9BpPPM7Emo1IRkZmA6XekNiF/wxtvDzjV3d6KGbRxfCdv/LbJW8tra4x3Z/ja03CfiOPzTUtYU5ruC0LbPZjLRaehReGT58OK+++iqXXnppF62JiYmkpqbyww8/uKRn5sh4vvvTPDbdfRJX5I9g8aZSLn1hPTXNBk9/1V7JyXH/xuhg06H12ZUHSIzUsWi6d+afQ7KyMOzd65W2OuOPsQWo/t//QK1GN7pvG5XBICshglPGJ/PKD8U9Eh/8NbbeJOA7fiULsThry2y139gVuq4fQpmZmUyaNMlh5tZZ68KFC3nrrbfc0hWv1/HAORN4/OIpbCut54Ln1lJc3bd7qbt0rFr2B9asWcPHP5WyZn81l88a4TV307ApkzEWF9Na4N1S0f4WW8BWXKikhIiZx6BScMDlLL+bN5pWo4U313XN8PHH2HqbgDBp+/LLLykvL+91m8VicavubkpKSp8rZ8E1W2ZLg22+vTdHzr7Iy8vj7rvvdk10N86ZkkZ8hI7fvrWJi59fx/vX5zM8XjkLC1/GbJU88d1+MuPDuWGu90aj0QsXUvXfx2nfu5dwP7ICUALT0TJkayuRp7g2TakUkzNiOHZUPC//cJBrj89yysDtl0IwEm7iii2ztakJlU6HStv/QqHO6Zy92TK7w+zsBN69bhYtRjPXv7mJFi8WcvGntLjlRyQHq1u49viR/RZTdxVNUhJCp/P6PL8/xbZDa739vlboGN+ZSrnquCyqmgx8vu3ngi3+GFtvExAj/v5G5krhrC2ztFiQJhPqpOQB29Tr9Y6fe7Nldpfxw6L5z4VT+M0bBTy4tJBHzp/klXbz8/O90o7SHKxu4YM9BqaPiOWyWd4tkC5UKkJGjMCwx7vOkP4SW/hZa/MPaxChoYT6UDHz+WMSSY0O5fPtZY56yv4YW28T8CP+lhbvz22D87bM0mjk3v/+lyVfL3NJa1+2zO5yUm4y1xyXxbsbD/PZVs+/SQAUeHleWwnMFiu3vLcFabXy7wsmK/IeYdOm0vLjjxhLj3itTX+IbQcFBQVIkwnDvv3EXX4ZYoBvtoOJRq3i+OwEVu2tciQ5+FtslSDgO36rAqsqO3DGltlSX8+OfftIyeiZRbJx40bS09NZvHgx119/fRcL1r5smT3htlPGkJUQwf99tN0r+c1K3jj3Fo8u28PWw/X8eoKOzIS+F895QvTZthv8xgPeM8zzh9h20NzcTOumTWAyETJyaLN5euOSY4ZjMFsdN3n9LbZKEPAdv5I4Y8tsbWnBLCXHzZ3b4/gZM2ZQWlpKS0sLNTU1bNiwwbFtyZIlXHbZZV7VG6pV8+QlU2k1WfjtW5sV85L3Fd5eX8Lzq4q4KC+DY4cpN6vZYU1gPOQbJTGHgvqPPkLodOjnHD/UUnowdXgsOcl61gXNDB0EfMcf0Y9FgqcMZMsspcRqMPBFp8Vc/dGhtaqqiltvvZXY2Fivae1gQlo0fzl9HJsO1fHSmoMetTVz5kwvqfI+Ww7Xc8+nO5icHs1fz85VVKs6Ph5VVBRtO7Z7rU1fjm13Zs6cSfvOQiKOOw6NvXSor3HC2GTWFtXw44Fqv4utEgR8x2+059ArRX+2zLK93VZY3ck78x1aExMTWbhwobck9uDKYzOZnBHDMysOeJTlU1rq/RWr3uC7XRVc8dJ6kiJ1PH9FHuEhGkW1CiGIyM+n8YsvHdYcnuKrse2Nw3v2YDxwAN0o35vm6eD3J4wmIkTNJz8d8avYKqU14Dt+JQuxDISlrg4QqDpl6/THYGlVqwS3nJRNbYuRx75xf9XpkSPeu5npDQxmC/d8soNrXisgJTqU967Ld5RUVFpr1Ckng9nstRu8vhbb/qj/4AMAIo47boiV9E2ETsNJucl8vq2M/Yf8p+NX6joI+I5/KLG2t6MKD0OlgDGbp8wfk8TpE1N45YeDg+roqQRN7SbWFdVw8fPreGPdIc6bmsZHvztuUBeraUfY0kSNxcWD9p6+grboINr0dCJm+fYUygXTM2gxWthZrZx/lb8Q8B3/UC7WkAaj09M8MPhafzt3NFYJL6xyb65/zCDVU+2LdpOF+5bsZOoD33Dx8+soqmrhH+dP5LGLpqDXdb2Zq7RW3ciRoFLR4qUl9kMdW1cIr61FN9r1usWDzTFZcYRqVWyu958FXEpdBwHf8Q9VIRZLczPSYkalc96zZLC1TkyPZk5OIi//cJCDbnj5uGOF4S2O1LdxyQvrePXHYk6dkMKzl03jxztP4KIZw3vdX2mtqrAw9PPn0/jFF17JlhrK2LpC27ZtWIuLbbUJfJwQjYp5OUn8WNxIeYN/2DUrdR0o2vELIU4VQuwRQuwXQtzZy/ZbhRCFQohtQojvhBDeXVaJsoVY+rNlfvett0AI1LExfR7/2GOPkZuby6RJkzjxxBPZa3d5dMeW2V3uOm0swIAFqnujsLDQ23Kc4mh9Gxc+u5bdZU08umgST14ylVMnpBKh6ztlczC0Rsw8BmtLC5baWo/bGqrYukrL2nUAxHo59VgpbjrB9s1kbVH1ECtxDqWuA8U6fiGEGngaOA3IBS4RQuR22+0nIE9KOQn4AHhUKT1K0J8t82333EOjwYDo5xN76tSpFBQUsG3bNhYtWsS9994LuG/L7A7jUqM4OTeZDzeXKuLg6W2qmgxc9tJ6jja08dKVeVyYl+Ez5TVDskYC0L5z5xArGTwMe/diiYtDExc31FKcYlxqFJFaeGb5gaGWMqQoOeI/BtgvpSySUhqBd4FzOu8gpVwupWy1P10HpHtbhJKFWPqyZU5NSSExLo7qAVbdzZ8/n/Bw2w3IWbNmdTFl88SW2VXuO9vmrfKvr13zm0lJSVFCTp9YrJIb397MoZpWnrtsOseOTnD62MHQGjo+F9Rq6hcv9ritwY6tO1iam2leswZ1VtZQS3EatUqwcHws+yqbqW1RNtXbGyh1HShp0pYGdC56WQr0d9v/GuBLd95o794HaWre1ftGKcGNEWGkfhw5Off0ub0/W+Z1K1diNBrJdsFr56WXXuL00093PPeGLbOzDIsJY25OIp9vL+O+ZgMJeudufmUN4h+8lJJ7P93BhoO13Logh5PHu/YHMRhaNXFxRC5YQOsmz/1VBjO27tJaUIC1oYHkKy4faikuceq0kbyxZROfbzvK5fmZQy2nX5S6DnzCnVMIcRmQB/T0NcBmddxxd1un03HVVVcxb948mpqaUKvVSMBi+Xkhklqtsa+mlUhpu0EipURKm2+PSqhA/OzjI4RApVJ1WoFr+6BoaWlx7BMREYHRaHTk2tfU1BAdHU1TUxNg+2ah0+k4cOAAV157LS88+CDa6Giam5sdN/siIiIwGAyYzTatoaGhSCl57bXXWL9+PZ9//jlWq5WWlhbCwsIc3wA6tyGlpLCwkMrKSgAmTJiAwWBg3759AGRkZJCcnOwwd4qKimLatGmsWbPG8b5z5sxh586d1NTYlrBPnjyZX02KYuXeKv782gruPWMMcXFxbN68GbAZ0k2ePJmVK1cipUQIwdy5c/n+++8dDqKT08fRtLmc2pZ6WhIlGZNGEhkZydatWwGIj49n/PjxrFq1yhGv2bNns3nzZhobGwHbh11FRQWHD9vGC9nZ2eh0Onbs2MH3JSbeKjSycMowJqpKWbHiCDqdjvz8fAoKChyeJjNnzqS0tNSR/zxmzBjUajUbNmxAr9eTkpJCVlYWa9euBWwuqzNnzmT9+vW0tdnSWvPz8zl48KCjxkNubi4Wi4U9dgfOtLQ00tPTWb9+PWBzVc3Ly2Pt2rWow8OJrKrGUFPDgYoKt89Tc3Mzp59+eo/z1NTURFFREWAr5OPMedq6dSt1dXWAbbV5bW0txfa005Ej3T9PtV98gR7YZDAwtbraURg8KSmJnJwch4+VK+epY05byfNUs2cTqRGC/y4r5OK8NPbt2+f1vydvnacdO3ag1+udPk933303i3/+xtn3V2Jbh+j9B5APLOv0/C7grl72OwnYBST11db06dNldwoLC3u81huNjY1O7ecqtbW1csSIEV1ea2hokFOnTpXvPPOMbNu926l2vvnmGzl27FhZUVHRRWtjY6NMS0vrsb+zv7erWCxWeeK/V8hJ9y2TZovVqWOWL18upZSyeXOFPHzHqi6Pmvec+/2dYXtpvRxz9xfy5MdWynaT2a02OrQqTePy5bJwzFjZsGyZR+0Mll53sVqtsuj8RXLf/BN8Xmt3li9fLt9ef0iOuGOp3F2mTP/gLTyJLVAg++hTlZzj3whkCyGyhBAhwMXAks47CCGmAs8BZ0spK5UQodSNv75smS+/7DLOOf74LqXn7rrrLj7++OMebfz0009cf/31LFmyhKSkpC5avW3LPBAqleAPJ2bT0GZy2rY5LCwMa7uZuvd63hto3VxJ44rDvRzlGlJKfvfWZkLUKp6/YrrbBdK9VdtgIMLzZqCOjqZhyZKBd+6HwdLrLqbDh2nfsYPoc8/1ea3dCQsLY96YRAA+/qnvlbHSImn4upjSO1dz5L4fadtZjbQOrrGhUrFVrOOXUpqBm4Bl2Eb070spdwohHhBCnG3f7Z+AHlgshNgihPDsr6UX9E7aJbhDb7bMr736KjMXLWLGmWeyZcsWALZv397rTZrbb7+d5uZmLrjgAqZMmeIoug7K2DIPxLwxiYRqVbzyY7FT+8+cOZOjf1vveK6OC0WE/jx72PhVMdLsmS32i6sPUlLbyr1njWdEvPuGe4NlzKXWRxB+zAyMB4o8asfXjcTa7VMy+vnzfV5rd2bOnElqdBjHZMWxam9Vn/vVL9lP0/e2wYtst1Dzxi7K/7kRa7v3qtgNhF+atEkpv5BS5kgpR0kpH7K/dq+Ucon955OklMlSyin2x9n9t+g6Snpv92bLXPD996z/4AO2bN7MlClTAJsHT2+VdL799lsqKiocNs5vv/22Y5sStswDERWq5bdzR7P1cD27yxsH3H/zsrVg79gTrp1A6p9nkHZfPqn3zHLsU/m/rW7rOVTTwlPL9zMhLYrzp6W53Q7gmOcdDHTZORiLizHb52vdYTD1ukPTN9+CSoVu9Cif19qdDr2zsuIoLGukopfaFIbiBlrW96zjbakzUP3SDsU1dqBUbAN+5a5U0HO+N1tmc20tQqvtUoVo2bKBq2/Bz1qVtGUeiLMmpwLw7IqB85yTlttGPuFTkwgd/bNWdYSWxN/aql2ZjjRjNbrnjfKvr/fSYjDz8LmTPJ6y67ghOBjo584BKanr9EHuKoOp11WsRiONX39N1GmnoQoN9WmtvdGhd/7YJABe/qGrZYnVaKHq2W2O54nXTSTq1EzHc+PhJlo29vxQ8DZbD9ez6mCTIm0HfMevNJ1tmaXZjDQanXbj7AulbZn7Y2SinvOmpvHJlqP95jm3bv/5K3L0aZk9tutGRBE61raop/bt3S7rWF9Uw2dbj3L+tHQmpke7fPxQEjZ5MiFZWRh2u/57+wOGPXvBZCJywYKhluIRU4fHMiMzlrUHuhZoqftwn+PnYffOQjcyhqh5GaQ9dBxCp3bsIy3KVPczmq089vUezvvfj7xRaPTIOr0vAr7jV7IQS3es9hu96mj3OqrB1Nofi6bb1tG91s9cf+1btk4tZuFo1FG95/1HzrO107671qV50RaDmbs/2UF4iJq7z/SOB8xgF9jWjR1D29ZtSDdLf/pyQfAme/3o0HE2uw9f1tobnfXOyIxjW2kD20rrAbC2m2nbahvUxF+Riyr852/uQq0i9c5jHM9bNlV4Xdu+iibOeGI1T3y/n/ljEln6+9n9WpG4i193/M5M4xgMhkFQYsNin9PtnNHjCgNpVXLaqjPHjk5g2vAYXlxdRLup5zRN286fR0gRM5L7bEeXGU3YBFtFpsavDzn9/u8XHGZfZTMPnDOByFDvFO4+eNCzamOuoj/uOMyVlTR+4daaxEHX6woNSz8nLG862uE2Qzxf1tobnfVenm+zB1u6rQyAo/fZ1g1o0/SE5fasJqYK0xCz0FZwpnn1Ea9m+Ww6VMdFz6/jSH0bj188hReuyKO1xrkMO1fx244/NDSUmpqaATvDjkUWSiOlxNrSgio0FOGmTUR/WqWU1NTUEOrmh4qrnDV5GC1GC8t29pzLbP7BlgJXPtmKUPd/Cenn2Eb9zT8eRZoGHv22Gs089vVeshIiPL6h25mORT6DRfR556GKjKR1/Tq3jh9svc5irqnBXFZG5IknOe67+KrWvuisNzU6jLwRsby57hCG1p8LISVeN7HP4/WzhgFgrmrDWDJwEoQzbDlczzWvbUSrFnz0u2M5Z0oaQgjFYusTK3fdIT09ndLSUqqq+k7HAps752B0ltJiwVxRgTo6GtWuPuwjBmAgraGhoaSne93OqFeuzM/k8e/28dKag5wz5ecO2FzThqGoAW26nubUhgHb0Q2PQpMcjrmildYtlUTM6N9q4ZUfimkymHn8kik+Y77mDkKlImzaVJpXrnKszgwEGpZ8BkDoON+3YXaW+WOTKDhUR+kTm9EBsReNQTXA9ErUghE0fnOIhq+KSbphskfvv+NIA1e+vAGdRsXrV89kTEqkR+05g992/Fqt1ikfi8rKSpKSkhTXU/XU01Q/9RSZi98nzM0/isHS6gwqlWBGZhzfFFawp7zJcTG2brfZ2YaOjiU317kRedxFY6h84icMxY39dvwtBjOPf7ePvBGxzMvxbhxyc7sbwyqP/rjjaFm5iraCAsJnzHDp2KHQ6wxN335LSFYW4TPyHK/5qta+6K73mtlZPP7tPnT1tmSGsHEDO41Gzs+g8ZtDGIsbMde0oYl3b6HVgapmLnlhHVq1ijeu6dnpKxVbv53qcZbOqZZK0rxqFZqUFELHj3e7jcHS6iz32107P9r8c43SlrVHEVoVUaeMcFpvyDA9CGjdVIGpqrXP/f7zzV6MZiu3npyDSuXdEfJQxDb6bNuylNaftrh8rK9dC2D7VmvYtYuI/PwuduO+qLU/uusN1aq5KdLW4eoWDEcVOvB4WKgE+tm2gU/j9+6tUG8xmLnsxfVYrJK3f9P7SF+p2AZ8x99h2KQk1vZ22nftIvqsMxEq90M6GFpdYVhMGCPiw3lpzUHaTRYMJY1YGoxoEsIQQrikN+oke03aw73nJZssVt7ZUMLYlEhmZvW8qeYpQxFbdUwM2uHD3arK5WvXAkDDJ59gbW0lbPq0Lq/7otb+6E3vqdLW2e8Mc37AEXWy7Zpu3VTh8gp1KSXXvlZAWUM7j5w/ibEpUU5r9QYB3/EPBvXvLwaTibDp04daite5cd5ozFZJQXEdxmLbjazYC12vA9qR2ln34b5eO8F/fLmbFqOF208Zg9rLo/2hJOqUUzDs3o1h376Bd/ZxmleuQh0fT9Qppwy1FK9ibjAQ3mDiS2Hi35tKnD5OFaImfLotq81wyLWbvE99v5+1RTX8dt4ozp48zKVjvUHAd/xpad7LDOmL5h/WoI6ORn/88R61MxhaXeXk8cmEqFX8e9luGr6wpcGFpNrWG7iiV6hVqCK0YJGYa7oukW9qN/HGukPkjYjlhLHK3OMYqtjGXLAIgLZNm1w6zteuBWk207p5M+EzZvTIWvM1rQPRXW/zj7aUybqkULYfaXCpQEvkHFtbNa85X3Vtf2Uz//5mL/PHJHLrghyXtHqLgO/4lc6CsRoMtPzwI5Enn9xvmUVnGKyMHVeICQ/h8vwRtJbapmi06T+vSnZVb/yVthtVptKu0z3//novBrOVm0/KViz7Zahiq83IQJOYSM1LL7s03eNr10LDks+wVFejn9ezZIavaR2IznqtBjPNK0vRpkQw5xzbN9nPt5c53ZYmKRxUAmm0OpWuXN9q5Pfv/ESIRsV9Z49HO0A6tFKxDfiOX2kDqfoPPgCzGf38+R635atmV+dNS+MubFkL8Zf/nGXgqt6QYbYPjdp39zg6weLqFl5bW8y8MYkcn53oJcU9GarYCiGI+/WvMZWWYtzvfEF7X7sW2n7ajCoqiuhzzumxzde0DkRnvS0bbatvQ8fHMzk9hjCtmhdWOe+sKoQg7hLbCmZnbMhvW7yVXWWN/HPRJKfcZoMmbT5K6/oNqOPjiTzB847fVxkbFcZo1LQhMYe7nwEsNCqHf4+lzrZK+Z0NJUgJN5+Y7RWtvkjkKScDUP/JJ0MrxE0sTU00ffsdYZM8N8vzJaRV0viNbUV51InDCQtR86eTcyipbaWoynlXX12W7cZs247qfvf7YFMp3+6q5MK89C5rY4aCgO/4lfTjN1VW0vT11+hnz/ZKe0pq9YQOJ8I/08ojX/5sPOaO3qiTbMv86z87QFFVM8+tKuK40fFMHa6sE+lQxlablkbEccdR9/Y7WJ10svSla6F5+XIsdXXEXnJxr9t9SaszdOg17KtDGiyETUxA2BMK5ubYvnX+c5nz2TRqfQj6OWmYK1qxGnpPvzxU08Jti7eSEhXK3xb2vSq4L63eJuA7/ry8vIF3cpOmb78FIOaiC73SnpJa3UVKSdMqm0WDTIngky1HMNldCd3Rq02xfb1t31fPa3Y73JvmKz/aH8rYCiGIv/YaZFsbDZ9+6tQxvnQttPy4FhEejn7evF63+5JWZ+jQ27rNNkKPWTjasS07OZIzJ6Xy5Y7yXn2q+iIk3ZaDX/9ZTztzKSW3f2CzeX7k/ImEaJzvdpWKbcB3/B3Fmr2NlJLmb79DO2wYYVOneqVNpbR6gqW2HdlmJmrBCM6ZkU59q4lX7B22O3qFRkXshTlgtnJ03VEmpUeTP8r7efvdGerYhufloR0+nLq333Fq/6HW24G5tpaGpUt7LNrqjK9odZa1a9dibjDQuqkCbZoedURXI8BTJ9hWl//LhVF/2IQERIgKU1lLj23Ldlaw4WAt88ckMm+Ma1lrSsU24Dt+pdw52zZvpuXHH4leuNBr856D6STqLHUf225IhubGc/msESToQ3hmxQGMZqvbenUjYwC4AJ1jdbDSDHVshVZL7MUXY9i7l9bNmwfcf6j1dtDyw49gNhN/zTV97uMrWp3FYDBgLLb5TEUtGNFj++kTUhmTHMkb6w45PeoXKoH+2DRMR5q7zPW3myz85ePtxIZr+d9lrq/zUSq2Ad/xK0XzqtUgBHFXXjHUUhRDWiXG0mZCMqMISY1Ao1ZxyTHDqW818fFPpQM30AeVWHkHA7momZyovCGVrxB91pmI8HBqnn9hqKU4hbRaqXrqSdSJCYRNnjTUcryHhPpPDyB0akKze95bUqkEF+SlYzBbeX1tsdPNRsy0fVNo3fazceTv3tpMTYuRe87MJVTrWbq3Nwn4jn+2l268dsba0kLdm28SNnmy20VXekMJrZ7QUlCObDcTkfezsdqtC3JIjNRxx4fbmXbMrH6O7ptrXitgM7aRVMNXg+Pl7gux1SQmEnvBBTSvWEH73r397usLeg27d2M6VELSzTf3u0bFF7S6wszRU7G2mtHPTkOoe/+2fvVxWaREhfL3L3bT2G7qdZ/uaGJDCZuUQNu2akzlLXy0uZTvd1dyUV4G501zLx9fqdgGfMe/d4A/MHdo/PobrC0tJNx4o1fbVUKrJ7RurkSEqAmbmOB4TQjBlfbiFU9/+ZPLbX665Qi7yhrRj4tDmxKO4eDA1s7ewFdiG3flFaDRUPPsc/3u5wt6q//3LAARx8/pdz9f0OoKFR/ZbNMjjunbKValElw9OxOADzc5/+1Wn2+zXzj8Yym3vr8VgL94UEVOqdgGfMdfWVnp1fas7e1UPPIIIVlZRBzr3ZJz3tbqCabyFozFjUTMSkGl6zra+9280SRH6XhuYy0Nrc6NhsDmRnjzu1sA+Pt5kwiblIi5so22nf3nP3sDX4mtdtgwYs47j8Yvvuh3rn+o9Vqammj67juiF52PNrn/G5JDrdUVTFWtaMpMRMxIQRPde8nQDq6bM4qRiRHc/1khFY3t/e7bgS4rGvWwCNQbKtAD7103iygPqsgpFduA7/i9TcMnn2BtaCD5//7PY4sGX6bx+xIQoJ+Z2mObSiX4v9Nto5gb3nTeg+a2xbYR0GMXTiYxUof+2GGgErRs8p+Owxsk3fYnVHo9FQ8/gtVHb4zWvPAiWK3ELFw41FK8Sv1nRUgk+rnOTb3ccpLNS+f3bzv37VZKyXvtragR/HPKCGaOVD5jzR0CvuOfMGGC19qSFgvVzz2PNiODiFkzvdZuB97U6gmWRgNtO2uIyEvps8DE2ZOHkRkXytqiGt5cN3A93U9+OsKXO8rJTY3i3Km2VYuqUA0Recm0F9Zgquzbp98b+EpsAdRRUST96Vbat2+n+ulnet1nKPVKq5X6jz4ibPp0pxxne9MqpaRtZzXljxVQeudqSu9czZF7f6Dq5R20bq1y2cbYG5jKWzDsrUMzLQ5tgnOFU86ePIyRiRFsKK7l0y1HBtz/L5/s4PHaBlpVMPWoweOavEpdB35bgctZvJkOVf2/ZzGXlZH23/8gtN4pAm6ubafxuxJaN9k8Q0oBVbgGbXok+lmpvRZ8VpqGZYdAgn5O38vKhRA8sXAUZ7+8k7s/2cGEtGimZMT0uu+OIw3c8t4WAF65akaX9NfIOem0FFTQ+O0h4i9Vrpyfr6Ucxl5yCc0//EDNCy8QedKJhE3qmjUzlHprX3kVS3U1cf93l1Opyp21SouVtsJaat/qWX5UGq0Y9tZh2FuHKlyD/rg0IuelD1i32VvULy0CtcAyybVMsnevm8UxD33Hze9uYXhceJ+rzB9cWsjb60sI06qJyUvFuLYMY0kjukz3E0CC6Zxuss9LPuiW+npqXnqJiOOOI/Lkkz1qS0pJy6YKjj6wlvJHNzo6/Q6srWYMe+uoeb2Q0rtW07T6iFPOf96gfU8trZsqiMhLRpsY3u++tUeLef3qYwBY+PQP7DjS80btjweqOfPJNQC8/ZuZJEd1rSmsSQgjIi+Ztm3VGLxUuLo3BroOpFVirm3HcLCB1m1VtO2uxdJgcLmAiisM+9vfUEVFcfi3v8NcU+OSXqWwGo3UvvoqurFjiTztNKeO6dBqaTBw9MH1jk5fFaEl/spcUu+ZxbD78km5YwYJV08gNDcea6uZxm8OUfbwBlq3VykaZ7DZjhj21xN10nD2l7uWSZYUGcpTl9oWaZ77zI98sKm0i942o4ULn1vLS2ts7X5/21wSFowAYavO5cnvptR1EPAjfm9R8fAjyLY2Em78nUdVtkxVrVQ9uxVri9nxmv64YUTMTGXt1g0cN+tYpMmKuabNVtOzpImGz4to/LoY/ew0IudnoApR5t6CNFmo/7wIVYSGmLNHOXXMnJxEHrtwMre+v5Uzn1zD2ZOHcf3ckRytb+flNQdZW2Tr0B49fxLHjkrotY2oBSNoKSin8dsSEn493uGbojTWVhPNPx6lee3RLuejN/Rz04k8dhjqAW4IuoI6Jobhzz1L8WWXc+SWPzL89deG3ASt5tnnMFdVkXzvPS5paSkop+4DWyelTY0g7tKxPQYOqlANmthQQnNisTQbadlYQeM3xdS+tRttmp64C3Iclh7exNpu+5DRpoQTOScdVjvvvtnBmZOGUddi5J5Pd3Lb4q3ctngrJ41L4qeSemrs/v2ROg1r7jiB6HDbbEDEjBRaNpRjKGogdFSMN38ljwn4jj8jI8PjNuo/+ICGTz8l9le/InzatIEP6AVpttL4fQlNnepzJt04BW263vEHljYyA3VkCACauFBCs2OxGi20rCujac0RmpYfpm1bFVGnZNqMpbzcSTStPoK5so3Y87IRTviJdMT2vGnpxEaEcNUrG1my9ShLth7tst8rv57B/H4KrKgjQ4icP5ym70poLajoN83OXTq0SouV1p+qaF53FFNpVwdGVWQIWKyoIrRY28xYm3/OWGpeWUrzylLUUSHEXjgGXVZ0nzngrhA2ZQpJt/2Jykf+wZFbbyX1wQdR6/VeuW5dpX3PXqqffZaIOccTtWCBU8dIs5WsPRHUHdwHGkH85bmEjRm4WLlaH0LU/Az0xw6jaflhmlaXUvn0FiJPyECfP8ypurdO6bNI6j7Zj6XRSOwFOQi1yu3YXp6fSf6oBE56bCUA3+76OSlhQW4yz142vUv1uOjTsmjdUkndh/tIuS3PrQGNUteBUPorljfIy8uTBQUFbh3b3NzskcNd608/cejSXxE2aRLDX38Nlc71EZ+hpJHqV3Yi22yjythF2V0WRTmjVVolLevKaPz2ENZWM9rUCCLnphM2KdErI2RDUT1Vz28ndFwc8VfkOvWh0l1vRWM7/1txgCVbjxKmVXP17CyuyB8xYLEJsP1+FY9vxlJnIOWOGT38UzylqakJ1Z4WGpYdwtpkG6GFZEUTlhtPxPQkVOG9v5/VYMFU1kzTylLad9V22RY2OZGYM0c6PqzdRUpJ9VNPU/3004SMHEnaf/6DOW3YoLpeWtvbKb7kUoxFRYz8/HNC0ge2Dba0mKh+eQemI82EDI8k4aoJqMLc67BNla3UfbgP46FGRJiGqHnpRMwa1iOV2FXqlxbRvOYI+jnpxJyeBXjeJ1itkv1VzXy7q4KRCXpOHJfU5zXevKGM+o/2o5+TRszpI11+L0+0CiE2SSl7dXkL+I5/xYoVzOvDVXAgWjdvpuSqq1FFRpK1+H20qT1TG/vDarTQuKyY5h9sI+DwaUn9jqad0Wo1mGlZV07z2qNY6g2o40OJPmmE7RuAC65/nTFVtFD5zFaEWpByW16fnaA7el3BeLiJyqe32D58Ls/1ygeaubadlvVl1K09jMYoUMfq0B+bhn5WCsLFJfRSStoLa2laeRhjyc9VxMKnJqE/dhghGZ7ZTzSvXMnRO+/C2txM49lnM+OB+wctZbjs/vupf+ddUh64n9gLB3abNZQ0UvPmLqyNRmpHWZn0m56VuVxFSomhqIGm5Ycx7K9HpdcSeXw64XnJLg8EpNlK/WcHaFlfTvi0JOI61Yn29nXbrw6rpPrVnRj215N47QSHT5WzeKK1v44/4Kd63KVlwwZKb7wJdXQ0I958w6VOX0pJa0EFDcuKsTabCBkeSdzFY9HEhQ588ACodBoi56ajPz6Ntm1VNC4/TO17e1B9XkTomDhCc2JQx4aiiQtFFaHtd+QupaRtWzX1n+4HIUi8YbLTnb4ShGREEnXyCBq/PkT9ZweIOWuUW52/pcWEYX89rVuraN9dAxKMcZB00hjCJiW4nUUihCBsfDxh4+OxGi20/lRJ4zeHaP2pktafKlFFhhAxLYnwaUlok12fq9bPnUvWxx9R9pe7kR9+SNHWLST85jdEnnwyqjDn0g/dof6DD6h/512iTj99wE7fajDTtKKUppWHUYVqSLh6AvuPbvWKDiEEoaNi0I2MxrC/nsbvSmj48iANXx0kZHgU2mERqKN0aBLCCBkWgToutMf1LU1W2nbX0vhNMebKNiLyU4k5y7n7VUogVILY87Op+t9Wql8vJPn3U/tMkR5MFO34hRCnAo8DauBFKeUj3bbrgNeB6UANcJGUstibGqKiolza31JfT82LL1Lz6mtoU1IY/tKLhAwf7tSx0mSldVsVTSsOY65qQxWpJfaCHMKnJTk1deKKVqEShE9JImxSIu27amjdUkXbzuouGULqaB0hwyPRDotAkxBu+zAI02BtM2M62kzLhnKMh5vQJIcT/6txaJP6z+LxRK+zRM7LwNJgoGVtGcbiRmIX5RCS1sf0l5RY6gyYqlqx1BswV7VhKG5wzN2rwjXoZ6ehn5nKtkO7CJ/qvULuqhA1+pmp6GemYqpooWVDOW2FNTStLKVpZamtcxoRhW5UNKGjYpy+KaxNSSHjxRfY9vTThHz+BUfvuBPV3x4i8sQT0Z94AuF5eWhivVO0RkpJ/bvvUv63hwidNIlhjzzc974WSevmChq/OYSl0Ujo+Hhiz8tGHaElqtm714EQgtDsWHSjYzCVtdC2o5r2vXW0/lSFbP/5JrzQqlDH6FBHhiB0aqytZkzlLUiDBVWklrhfjSV8Ys9ynkpct/2hidYRf3kulc9soeaNQuKvGO/0IFAprYpN9Qgh1MBeYAG29PSNwCVSysJO+/wOmCSlvEEIcTFwrpTyou5teTLV4wzSaKRt61bqP/qYxi+/RLa3E3X2WaTccw/qyL6/vkurxNpswljaRPueWtp2VGNtMaOODkF/7DD0x6W5Pf3i1u9hsWKqaMXSYMBc3Y6xpBFjaZOjzGF31FEh6I9LQ3/sMITWdzJ7O74x1S8tQhos6EZGo8uORRWuAavEXNOOqawZU1kL1tZO2TgaFSFpekKzY9BlxxKSHumVG7CuYDxqs+U1HLR9AHWk4aqjQ9Cm6tGmRKBNCUeTGI4mVocI0/Q5KJBS0rp2LQ2ffkrTN99ibbUtclPHxaEbORJ1bCzq2FhCMjMJycxEN2ok2vR0p6aHDPv3U/H3h2n58UfCZ84k/emnUHebS5Zm2/XUvquGlo0VWBoMaFPCiT4tC11O7JBkIFkNZsxVbRiPNGOubMVcb8DaaMRqtKAK1aBNDkeXHUNYbsKgn/uBaNtVY6s3bbHa+odj09DEdB0QWI0WTEebMZY2Yyptwmq0knBFbh8t9s+QzPELIfKB+6SUp9if3wUgpXy40z7L7PusFUJogHIgUXYT5W7H/9Fj12GxWlCrVNC5RQkqqwSLRHT8C0gBMkSDDNUiNSpsl83P/1dJFWqpQS3VaOz/Cvt2iaRN1U6ruplWtXurUE0mE1ovLQzrjAoVGqlBY9WgQoUVK2aVGaMwetSuUno7UEkVUZYowi0RaOTPX04lEpMwYlSZMKoMmIQJi7BgFn2nZCqttTcEEGLVobPqCJEhaK0haGVXDRKJRViwIgErViEBiUVaQWXbbvvPChYrwmK1XbNWCVZAWhHd/oSlWgUqASqBFOLnDloCVitYJcJsBSGQOg3WEHXHZY5K2q4VrdSg7hTzdlU7zeqmXq/toYitJwylXo3UEGOKJdxq+3ZtEiaswgoSNKi7xNwiLBikmdP+8IhbU55DNcefBnQuO18KdPc5cOwjpTQLIRqAeKCLa1dZWRljxthuzuh0Oq666iqm2qtexcfHM378eFatWgWARqNh9uzZbN68Gf3ElajV/edne0L3JVU6+0PZ6rG/TLqXw1ADYfaHP9FbWQ+B7feh07++0I121qrFdl0Hr23v0BFbFV1X0Xa/PoQhlJWrTmPkyJFERkaydavtfkpf/d7dd9/N4sWLOw7vfeEMyo74FwGnSimvtT+/HJgppbyp0z477PuU2p8fsO/TpeN3d8T/7QsPUd9YT3RsjO0FlQqEsEVaq3GMcuj2YSp7eybAqpY99vUmVVXVJCb2ea58Dn/S609awUt6JWA0gcn887UfGuL1a/gXGdtBoqa6jl/9pu97L/0xVCP+I0Dn1Qfp9td626fUPtUTje0mr1c46Td/wWq1ovJgpe1g4k9awb/0+pNW8C+9/qQV/Euv1aqMVYuSv/1GIFsIkSWECAEuBpZ022cJcKX950XA993n9z1l586d3mxOUfxJK/iXXn/SCv6l15+0gn/pVUqrYiN++5z9TcAybFOXL0spdwohHgAKpJRLgJeAN4QQ+4FabB8OXqWmxmtfIBTHn7SCf+n1J63gX3r9SSv4l16ltCqaxy+l/AL4ottr93b6uR24QEkNQYIECRKkK/4x0eUBkydPHmoJTuNPWsG/9PqTVvAvvf6kFfxLr1JaA77jf+65/ota+xL+pBX8S68/aQX/0utPWsG/9CqlNeBN2saMGcOePXu8rEgZ/Ekr+Jdef9IK/qXXn7SCf+n1RGt/6ZwBP+IPEiRIkCBd8YsRvxCiChi4onfvJNBtJbAP409awb/0+pNW8C+9/qQV/EuvJ1pHSCl7utThJx1/kCBBggTxHsGpniBBggT5hRHs+IMECRLkF0bAdvxCiFOFEHuEEPuFEHcOtZ6BEEIUCyG2CyG2CCGUKz7gJkKIl4UQlXZjvY7X4oQQ3wgh9tn/9Qnzxj603ieEOGKP7xYhxOlDqbEDIUSGEGK5EKJQCLFTCHGz/XVfjW1fen0uvkKIUCHEBiHEVrvW++2vZwkh1tv7hvfsljK+qvVVIcTBTnGd4pX3C8Q5fmeKwPgaQohiIK+7M6mvIISYAzQDr0spJ9hfexSolVI+Yv9wjZVS3jGUOu26etN6H9AspfzXUGrrjhAiFUiVUm4WQkQCm4CFwK/xzdj2pfdCfCy+wlaIIEJK2SyE0AJrgJuBW4GPpJTvCiGeBbZKKf/no1pvAJZKKT/w5vsF6oj/GGC/lLJISmkE3gXOGWJNfo2UchU2P6XOnAO8Zv/5NWwdwJDTh1afREpZJqXcbP+5CdiFrU6Fr8a2L70+h7TRbH+qtT8kcALQ0ZH6RGz70aoIgdrx91YExicvzk5I4GshxCYhxHVDLcZJkqWUZfafy4HkoRTjBDcJIbbZp4J8YuqkM0KITGAqsB4/iG03veCD8RVCqIUQW4BK4BvgAFAvpeyo0OQzfUN3rVLKjrg+ZI/rf4StTrnHBGrH74/MllJOA04DbrRPV/gNdjttX543/B8wCpgClAH/HlI13RBC6IEPgVuklI2dt/libHvR65PxlVJapJRTsNUDOQYYO7SK+qa7ViHEBOAubJpnAHGAV6b7ArXjd6YIjE8hpTxi/7cS+BjbRerrVNjnfDvmfiuHWE+fSCkr7H9YVuAFfCi+9jndD4G3pJQf2V/22dj2pteX4wsgpawHlgP5QIy98BP4YN/QSeup9qk1KaU0AK/gpbgGasfvTBEYn0EIEWG/UYYQIgI4GdjR/1E+QedCOlcCnw6hln7p6ETtnIuPxNd+U+8lYJeU8rFOm3wytn3p9cX4CiEShRAx9p/DsCV77MLWqS6y7+YTse1D6+5OH/4C270Ir8Q1ILN6AOzpZP/l5yIwDw2tor4RQozENsoHW42Et31NrxDiHWAetiXkFcBfgU+A94Hh2Cw1LpRSDvlN1T60zsM2DSGBYuD6TnPoQ4YQYjawGtgOdNTZ+z9s8+a+GNu+9F6Cj8VXCDEJ281bNbZB7vtSygfsf2/vYps6+Qm4zD6iHjL60fo9kIitUvIW4IZON4Hdf79A7fiDBAkSJEjvBOpUT5AgQYIE6YNgxx8kSJAgvzCCHX+QIEGC/MIIdvxBggQJ8gsj2PEHCRIkyC+MYMcfJEiQIL8wgh1/kIBGCBHfydK2vJN1cLMQ4hkF3q/DRvcGN49fbtfWa5HsIEG8gWbgXYIE8V+klDXYFhYNpjXz7e7a6Eop5wshVnhZT5AgXQiO+IP8IhFCzBNCLLX/fJ8Q4jUhxGohxCEhxHlCiEeFrTDOV3ZvGoQQ04UQK+0Oqsu62RT09T6vCiGeEEL8KIQoEkIssr+eKoRYZf/2sUMIcbyyv3GQID8T7PiDBLExCptP+9nAm8ByKeVEoA04w975PwksklJOB14GnLXVSAVmA2cCj9hfuxRYZndjnIxtOX6QIINCcKonSBAbX0opTUKI7dj8Ur6yv74dyATGABOAb2x+Waix2Q87wyd218pCIUSHr/5G4GX7B8onUsotXvktggRxguCIP0gQGwYAewdtkj+bWFmxDZAEsFNKOcX+mCilPNmVtu0I+/usAuZgswR+VQhxhTd+iSBBnCHY8QcJ4hx7gEQhRD7YPOmFEOPdbUwIMQKokFK+ALwITPOOzCBBBiY41RMkiBNIKY32G7NPCCGisf3t/BfY6WaT84DbhRAmbIXhgyP+IING0JY5SBAvIoR4FVjqbjqnvY0VwG1SygJv6QoSpDPBqZ4gQbxLA/CgJwu4gJGAyauqggTpRHDEHyRIkCC/MIIj/iBBggT5hRHs+IMECRLkF0aw4w8SJEiQXxjBjj9IkCBBfmEEO/4gQYIE+YXx/wMxgDty9y7uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.plot_dynamics(init_state, barely_a_seq * 5, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gi5XyfO4pq65beuuGeKCUguVLOurmjxTqU+fkIOcHP4Dz8GFY3347aXocHh9eq2rD9Yvy/YmKiVKeb0GPzQ272yfJ8aRgynf8KpX4iNW5eRZ0Wl2wOsfnBMWiNVk09Q3D7WNDzjNLoXdahgHtfNlGOZmIbRuJ2kEWJZlG5KfqJT1uMf+UJeWTbSJt6+Tr0Krz88fkKaTedit05eXofPr/gXUn5nIZSCS9753ohMfHJmRCGMzMLG6KNJ4MXrmu2ynf8a9atUr0tgVp3JesyzY+0QSxaE0WJ9u50Ziw+B2IFHpnZBpR3yN/GG08WhtuutkfXujii4AnA5alqBmEqOzQWLHo1Mg2a3FSwsS5RK6D/pf+DgDIfGBsAT6iUCDja/eDHRrCwL9ejvszgomk990THcgwaiR90prHP2U19Mb+lCVXnzDlO36hsr0YZvF35mPjFHoVi9ZkcabTBo1SgdlBC7uANHrn5Jrh9rHoHJL3ZhurVusHH8B9+rT/78abbwHrSM76T0OvHXa3DxXTE1tYDEdZrhltA9L9XxK5DmwffAAASLn55pDvmy+9FMaLVqDn17+G62xN3J8TSDi9Q04vdtb04JqFeWGL2sRDfqoeGpUC9d2xRwzK1SdM+Y7fahU/fzwn1wylgoxbSGcsWpNFXbcNxZkGqELYAEuhtyCVKwDePijvdE+sWtu/+S0AQN6GZ5DxpS8CAPpfeklyXaH4tJmLLEs0oiQcRekGNPdL1/FLcR2Es6MgCgXyntkAhU6H1vvvh12Cxc5wej+q7oKPpVg/Py/hzwhEqSAozTbhZBwhnXL1CVO+448FhYIgy6RFl1W6+cTJzukOG+bkjp3mkYrpGVzHXzeBkoqcAdmSqTfeiOz/+R+YL78MfX/+C3x98tt6nO6wQavEKHsMKZmVZcKgwyv7zTYazBDXEabecUfE7dQ52Sj83XMgSiVav3o/bNu3y6Ln47PdSDOoZZliK802JWUtSyxTvuOvrKyMafvSHNO4mbXFqlVuXF4G54acKMkK3QFJobcwTQ+9WokGmZ+yYtHadMedAIDshx/2v5b1ne+CdTrR/X//J7m2YE62D2FOnkXS+P1AZmZz03bnJOr4470OhjZtBgB429qibmuorMSMt96EprgYHT94DN5z8ddsDqWXZSn21PZi5axMSad5BIozjWgfdMYc2SNXnzDlO/6urtjqjM7h5z/HI6QzVq1y09znAKVAcZiwNin0EkKQl6JDV5xeJmIRq5X6Rr6Y6V/4vP93bckMWK6+GtbNb8MnY41ZSinOdtkw3SzfV7OQD2KQKnEu3uuAsXNPebk/fFzU9kqTCQW//AWYgQG0f+e7cX0mEFpvXY8dQ04v1pRKmzchIDw113TF9mQrV58w5Tv+1tbYKt1nmrRweVnYxiHmNlatciPEes/OCe0QKJXeonQDarrkjSsXq9URsJhGFKO/Hpn3fxXU48Hgxv9Iqi2QTqsLNpcPZsi3kFyYpodaSVAtUf5EvNdB729+CwBQ5Yqv8aArL4f5iivgPHYMzhMn4/rcUHoPNnKVriqmp8Z1zGgIU5qxPmXJ1SdM+Y4/Vmbxj8FShrtNVlr4BcBp/EUrF9PSDWjsHU5aNa5IDL76GgAg/2c/HfOetrQUhuXL0ffnP4Oxy3OjEpLZii3yfTW1KiWK0gyyR1KJRaGLXMkqmJxHuCm4nud+K5mGY62DSDWo/TH3UiN0/PFaN0jNlO/4S0tLY9peGN22Shj1IJZYtcpNc98w0o0amLShk0ik0luebwHD0rita8UgVqv1nXcAAIZly0K+n/WNB8HabBh64w3JtAXS0MOtdaxcMEuW4wvkp+rRJtEcfzzXgX+6LI4EJXV+PrRz5mB45y7/AnEshNL7afMAFhSkJFzsJhwGjQrTMwwxW2XI1SdMrnTGONBqtTFtn2Hi7Ff7hqWtUiSGaFrdtbVouO56mC65BIW/+TWIzNmo9T12/xNQKGJt23AU8Nmp7QNOFKbJ83QhRiv1jtRcVueFDukzVFZCv3gx+v/5L6TdfTeIMnGf/ECqO6zIMmuRnSpPRI/A9AwDXj/cFneBkEDiuQ68LS0AAIUmPrvj7O98G61fvR+9f3je/wQAAF6vF21tbXBFsHRmGGZUmCRLKR69yAKLXoXTAbkbUvO/69LAUhrTZwRrDYVOp0NhYSHUavH1mKd8x3/y5EmsW7dO9PYGjQppBvW4eJlE0urt6kLDddcDAOwff4wz8xdg7hn5LlIAaBtw4qKZmWHfj7VtwzEtnevsm/sdWC6xN42AGK2BfvCRSP/C59H+ne/C9uFHsFx1pQTqRqjvtmNOrlmytg3HzCwTXF4Wg04v0uP0mheIR+vgG28CAIr++Hxcn2lcswYA0P/Pf47q+Nva2mA2m1FcXBx29G6z2UZVtrK5vGB6h1GcYYRFH18xezG0DzoxMOxBWb4FCpFPFsFag6GUoq+vD21tbZgxQ3yhmCk/1RMPxZlGnOmYGHNxAHdy69auG/P60KZNsn2m28egy+ry21jIifAZUoUXxksbHymS9b3IESPmyy6DOj8f3T//uaQeMpRSNPQO+11L5SQ/lZtXH682H3z1VQCAMjP8wCIShBCkf/E+wOeD68wZ/+sulwsZGRkxTdk4PQwAwKCR9uktGJ1KAZZS+BjpIgYJIcjIyIj4hBOKKd/xZ2dnx7zPvHwLzg0l/wsRTqt182b/77N27kDZ8WOAQoGu//eMbFWKmnodYCkwM0wMPxBf24ZCrVQg06SRNXFOjFbBosFQURFxO6JWI/epJ+Ftb0fnE0+AeqSZFmwbcMLm8qEs1yxZ24ZDmFKTwp45Hq0KCxfeqCkujvtzU2+5FQAw+Npro16P1ukHG585PAzUSkXI7HQp0ai443tiCGIQY9IWz7rElO/4Z8+O3VM7L0WPQYfXPxJIFuG0nnv4EQCcn4k6JwcKjQYFv34WzNAQht58UxYt7YPc4nZRevg593jaNhzZZp2kHvHBxKLVsHRp1G1Mq1cj5ZabMbRpM849+gNJ8j6EKKoZmUZJ2zYUwtqNFLH8sWpl3W6/FXMii6maGcUAAOfxEzHtpwuKInL5GElH+06nE2vXrgXDcP3HSy+9hNLSUixZUI7NG/8Nlzdyv9Lf34/LL78cpaWluP766zHAL4Rv2bIFTzzxhCQap3zHv2fPnpj38S82DiY3sieUVnZ4JKM178kf+X83X3YZNMXF6HvxRVBG+htU+yD3JFEQwRY4nrYNR1G6XtZIqmha3Q0NMR8z/+mnkfn1r8H6zjsY3rM3Xml+BI+o6RlGSds2FDq1Eil6NbptiT9lxao1nkicUBBCYLrkErhOnozJQM8eEIrrY1l4fKwkxewF/va3v+Hmm2+GUqlEf38/nnrqKRw4cACHDh7E88/+FN29kW0/NmzYgEsvvRS1tbVYtWoVNmzYAAC45ppr8Pbbb8MhgVmgrB0/IeQqQshZQkgdIeSREO9PI4RsJ4QcIYQcJ4RcLacesRSlc51d6wTw1mi6627/7yQgAoIQgswHvg5vcwuGP/lE8s+t77bDoFEiyyRN5E40SrPN6LK6xi2Wv/tXvwIA6BYtjGm/jPvvhyo/D10/3ZDwDbiu2w6jRon8lNji2uOlIFUfl1VwonjbuEX0/F/+IuFjmfhFXnucN0q3l7vepBzx/+tf/8INN9wAANi6dSsuv/xypKenIy0tDSvXXIKPPtgacf9NmzbhC1/4AgDgrrvuwltvvQWA+86vW7cOW7ZsSVijbFE9hBAlgN8BuBxAG4BDhJDNlNJAY/PHAbxGKf0DIaQcwLsAiqXUEU+oWbaZ++L1JNmsLVgr9XjgPnsWQOiEIvMVV0Dx1I9h3fw2TCtXSqqlfdCJwjR9xFA/qcI5ASAvVQeWAt02t+TFR4DoWj119QCA3B/G9iit0GiQ/b3v4dz3/gfW995HyrXXxK2xbcCJonQDCCGStm04CtL0OC6BL1WsWtu/9z0AAHUl/v0yX3YpOp98Eq7jx2G54opR7z319ilUnxsbCskwDJR8GK6PpXB7Geg1SlGRNuX5FvzouvB1cD0eDxoaGlDMr120t7ejqKjI/35BYQHaokSPdXV1IY8PJ87NzR1l21BZWYndu3fj9ttvj6o1EnKO+C8AUEcpbaCUegC8AuCGoG0oAMH6MQVA/M5LYVixYkXM+2SZuQs52Qu8wVq7f/1r/+8p118/ZnuFVgvzVVfCunUrmMFBSbV0DrmQlxK5A46nbcMhLDbKNd0TTaunqQkAoJtXHvOxLevXQzNzJrp/9ct4pPlpG3D4p9akbNtwzMu3oMvqhtuX2JNKrFq1JSUAAOOFyxP6XABQZWZCPW0a+v7yV9H7KANyL1iWAgSiwyuj0dvbi9TU1LDvqxQEDAvRa0Jms3nUOkh2djbOJWBQ59eR8BHCUwAg0GiiDUDwmX4SwAeEkG8AMAK4LNSBOjo6UFZWBoAbXdx7771YsmQJACAjIwPz5s3zFyVWqVRYtWoVDh8+DKvVCofDgTVr1qCrq8vve1FaWgqtVouTJzmvj+zsbMyePds/V6nVapFl1uJwTSt2qLhGXr58Odra2tDO363LysqgVCpRzVdmys3NxYwZM7B//34AgF6vx/Lly3HgwAE4ndwNZMWKFWhsbERnZycAoLy8HAzD4Cw/qqeU4sILL8SBAwcAADl//RsAgDUYsGPHDgBcRZ6amhp0d3dzOq5aD/qf1/HpT38Kx5VXoqioCDk5OaiqqgIAWCwWVFRUYM+ePfDxBmRr1qzBqVOn0MdbDC9atAg2mw0N/Dz39OnT0do/jHQyjB07diAtLQ2LFi3Czp07QSkFIQRr167FRx995I86qKioQH9/P5r4DrSkpARmsxnHeIvjaOepc5h75D5S0wxnywnR52nFihWoqqryz9uGO09VVVUwGAwhz9OyBQv819rOnTujnqeCggIUFhb6z5PJZMK0yy5D3x//iN2vvQYmO3vMeZo/fz7cbjdqa2sBYMx5MpvNaOwdxgyDGzt27IDD4cBVV10V8TwVFxcjPT3dX6wj3Hk6duyYf4Ew8DzZOrmEtZrWbvQ1nxV1ngBu1Bn4fVIoFCgvLxd9nnL2cushTQ4H2vnrOpHvk8tggBZAV2srGIaBzcaFYj98+UxoNBoM8+tkCoUCRqMRQ0NDUPA+TL1uBTxeBvkmrnPV6XSglMLNh+mq1Wqo1Wr/vLqwn/AZwvl3Op1gGAY+nw8ulwtutxsejwfp6enYt28fGIaBw+HAubZWLFi2Ej6WwuUYmWYzm81wOBxgGAZZWVloa2tDVlYWmpqakJmZCbfbDaVSiYGBAahUKjgcDuj1ev91L4RzPv7449i4caNw2PCxspRSWX4A3ArgLwF/fw7Ac0HbfBfA9/jfVwCoBqAIPtbSpUtpvGzfvj2u/W75/V565x/3x/25YmDdburt6/P/HajV095Oq8vm0OqyOdTb2xvxOI13303rrriSsgwjia4hp4dOf3gLfX5HXcTt4m3bUDjcPjr94S30uY9rJTtmIJG0Dr71lr+t48Xd3Eyry+bQ3r/+La79e2wuOv3hLfTFvY2UUmnbNhx7a3vo9Ie30N01PQkdJ1atibZ1MML5G3p/K62uro66vdVq9f9+psNKG3vskmmhlNLCwkLqdDoppZT29fXR4uJi2t/fT/v7++n06cV01/EGanV66Oc+9zl64MCBMfv/z//8D33mmWcopZQ++eST9KGHHvK/94tf/ML/XiCh/t8AqmiY/lnOqZ52AEUBfxfyrwXyRQCvAQCldD8AHSLdpZJIllmLbhlr79o+3o4zCxeh9qKVOD1nLtq//33/e5RlUXfJpf6/VRmRs1lTb70VnuZmOA4ekkRbF2/elZukRUYA0Gu4KJOOccifEMJlTZddGmXL8GimTYN2zhzYP/44Pg18IpUc6xvhEAqvtyTRl4qy3JMdicFeIBpCFq8YX/9AWErh9jHQqqXtBq+44gr/0056ejp++MMfYtmyZVi2bBkee/xxpKSlweNjcfz4ceTn54/Z/5FHHsGHH36I0tJS7NixA488MhIXs337dlxzTfzrSAJydvyHAJQSQmYQQjQA7gSwOWibFgCXAgAhZC64jr9HShHLl8c3j5ht1uLcoEsWX353YyPavj66uLR189vIuf9rcDc0oPGmkfqjKbfeEvV4liuuANFqYYsSLSAWwSwt1xK544+3bcPBhXTK0/GH0yp0RACQ+9hjCX2G+ZKL4aiqgjcOD/UmPp5e8MqXum1DkW3WQqkgCWfvxqJ14B//AMAFJkiFKi0NUCgwODLFERGjkbvheX3cuddJGMoJAA888ABeCijTed9996Gurg51dXX44hfvAwAMDA6htLQUhYWFY/bPyMjAtm3bUFtbi23btiE9nasI1tXVBafTiQUBU5PxIlvHTyn1AXgQwFYAp8FF75wihPyYECKsVH4PwJcJIccA/BvAPVTinrYtxlGAwLQMI5xeBj126SN7mj9zFwAg9c47kPn1r416r+Hqa/yRPACQ9Y1vRj2ewmCAae1aDP7ndUlKA7YNiBt9xtu24chP0cs24g+n1dPc7P9dFad9gIDQmVnjCLcTFrWFojdSt20oVEoFci26hEswxqLVxc/hS20wqNDpwHrEfVc9fKa1m+/41UppHTkrKipw8cUX+xO4AlEQArVSAbXeGDgXH1UrALS0tOCXv0wsgMCvQ5KjhIFS+i6ldDaldCal9Gn+tScopZv536sppSsppYsopYsppR9IraFdpPFWMEJ0RbfEIZ3OEyfBDA5CW1qKvCefRNY3v4npL78ccltVVhbUOeLS4bO+8SCox4P+v/8jYY3nBp1QkOgdf7xtG47cFJ1sHvHhtJ57aGSKLdHpB92cOSA6XVznoGPIiVSDGno+nlzqtg1HcaYh4bKXsWgVyi3mPJ7Y01Uwlhuuh+9cByBi3OjlXViFaCadWnqPnvvuu29U9FAgerXSf9OJhjfAMXbZsmVYvHixFPKmfuZuvGSZuWSpXolH/AP/5DqFaS++4H/NULEEuT/58ZhtZ+3cIfq42tJSmC6+GP0vvjhqFBsPXVYXsvhpgGSSm6KD1eWDw5O86mcuPhIlJ8FpHgHzFZfD19UVczJXx6Ar6tSaHBSmGsbFqE1hkrbgiaZoGoDR1trR8PhYKBVEdo+eYFRKMu5Fh6Z8xy+EgcZKlon7Eib6GByIp60NQ1veQdrdd49ZsE277TZYNo+4bZYdPTKm9F80ch7+PijLoumuu+Ht6IhbZ5fV7U9ii0S8bRsOIZa/TYaM6VBaA+vrptx0oySfY6jgfH4cBw/GtF8jbwssIHXbhiM/VY8eW2Kx/GK1BnbKUhc8EXICxNxwhYQzt4+FOsmdPgBoVQr4WAqviM5frkS+Kd/xh3vcikZBmh4qBZE04mHw9dcBSpF+770h31enpmLumdOYe+Z0zOXoAM7pcPqLL4B1OtH51NgnCLG0DTj8tr2RiLdtw5HNJ871SOAfE0worS1f+vLI+xKNQI0rLwIAuE6dEr0PpRQdQ6MtsKVu23AIi8mJLKqL1WrfLZ//kJrPjg30tgqHcNNx+1hZpnmioec/M5pZGyD9DVJgynf8QkJIrCgVBDkWnWSdEKUU1i3vwLhiBTSFBSG3iVdrIIalS5H+uc/BvnMn3A2NcR2jY8iFgtTolbCk0BuIkDEt9fQaMFarr78fDt7jyFBZKdnnqPkojYFXXhW9j9Xlg9PLjJrqkbptw1GcyWdMD8Q/wBGrlfq4Eb/lauktuZRCsRIRc/wulwss5UbcWlXyu0DBnlnMiD9Wn32xTPmOPxEyTRr02aXxWncdOwZvayss114ryfEikXbH7SBqNXp+85uY97W7fXB4GGRbkmPOFojQ8TdLYBUcjdqLRryN8n/xc8mOSwiBtqwsppjyNr7TzRPxlCU1wgJ+Mub527/5LQBA1je/Icvx9RUVouf4hU5XJXFEDxDelrm0tBQvvfQS1EoFCCFweUN3/Bs3bsS8efOgUCj8WdkAcOLECdxzzz2SaJzyHX9ubm7c+2aYtJLN8Q+9vQVEo4H58pCuFAAS0xqIOj8faXfdBduHH8Yc3tnNx/CLceWUSq+ARadGpkmDdhnm+AO1Drw6unCHWuL/h/mKywGM+P9EIziUE5C+bcORbdYlHMsfq1ZFSkrcnxUJdQH3JB2t81epVH5XTintmAXC2TIfPHgQTz31FAYHB6FTKcJG9syfPx9vvPEG1qxZM2oabcGCBWhra0MLX684EaZ8xx9LHcpgss1atPY7Ek7iYp1ODG3aBNMll0ScS05EazApN94AMIw/fE4swo1OzOhTSr0CORadLLkTgtaOp55C549G6hqU7kvcRz8YI29aJtYuW6g8FpgpLUfbhkKpIChM06MlgTl+MVoDq5Sp0tLi/qxImC/lMq8DF+1DodVq/SN+OaZ6ItkyX3755Xj//fehVirCTvXMnTvXv2CuCSpGf9111+GVV15JWOOUL7a+f//+uItWz8wywe1jYXP7YNHFH+Nt+/hjsHY70u68M+J2iWgNRjdnDjQlJRjevx8Z94VeTA5FBx9HX5QWfY5fSr0CmSatLIu7+/fvxxKbHYP/HvnS5PzgB1DxWZFSopszBwBg++CDqOcc4MJnVQqCdMPIl1yOtg1HjkWHLmv8c8litMaTzRwr6oICoLsLjNUKhV4PvPcI0Dm2OhfL+GCGAiU+FiqtEkAM0z25C4D1G8K+Hc2WubCwEO3t7dCqFLC5fWApjegMKhjSCVRWVmLDhg34foDFSzxM+RF/ImSYuC9iovP8A//8F1S5uTBcsEwKWaIxXnQRHFVVYGOoCSt0uplJKsASTI5Fi+a+xBKKQqGur8e5hx7y/226+GKkf/5zkn8OwGVSq7Kz4TwlbtGzbcCJ3BRdxNoHcpJjkS9xTqD3938AIM/CroB2Jmf3HG3ED3A+PYQAJJZOXwTRbJkFtGolKBUX0hnIZLBlnhDo9fGbXgmdX+eQCzMywxcdj4SnpQXOI0eQ+c1vRI3LT0RrKIwXrcDAP/8J55GjMC6/QNQ+PTY3zFqVP4M0ElLrBYAZmSZYXT5YXd6EnrICoQyD9J+PVHsqO3okrnDZWNAvXgzbBx+AdTq50WcEzg06xzxhydG24chP1WHLcQdnWBbHnLcYrUJtaGH9Qw4UBgOgVI5E9oQZmbvsdnQ6AAUBSrKkTSTT6/WjInEKCgr8luoAZ2+xbt06v02Ej6HQRuiFg8M5XS6XJNfGlB/xJ2J2NY0vNN7SH/8IdOjttwFCkHrTTVG3ldqYy3AB19n3/elPovfpGHKKjuiRw0gsN4UP6ZRwuqePr2sAAHNOnpC90wcAA3+j9fKe/pHotrnHtHkyTNoEZmaaQGn89iSxaDWuWh3XZ4iGUjBDY6tuBWIymeBl5EneSktLA8Mw/s7/yiuvxAcffICBgQEMDAzggw8+wJVXXgmNSoHHvn0/9u2PvA5kMIweENTU1GD+/PkJ65zyHb9QLCMehMW2eOecqc+Hwddfh+GCC6DmS6lFIhGtoVCaTDAsXw5Po/h4/nODLhSlR5/fB6TXC4w8ZUlRBBzgzkEPX093dlWV5OZg4dCWlgIA7Dt3RtyOUopum8ufvCYgR9uGI4e/zuOd54+m1V1X5/9daYrvyVksRKEAaOTpE7vdDh9LZQnlBCLbMj/xxBNIT0+HRqlA7elTSMvKGbP/m2++icLCQuzfvx9XX301rrzySv97k8GWeUIQvDgSCzq1EmatCr1xzvHbPvgAvnMdSL3tNlHbJ6I1HOZLL4H33DlRI0+AS54SW2BdDr2CVcSZjsijNrH0Pv9HAID9uutk73QC0ZVzJRyZ3t6I2w04vHB5WeQGlbmUo23DIdx04s1Sj6b13KM/AABo586N6/ixQHRcOwbabQfjYVhQSmWza4hky3wvn7Vvs9lQXDILWblj/fhvuukmtLW1we12o76+Hlu3cnbrbrcbVVVVWL9+fcIap3zHnygZJk3c4YX9f/8H1EVFsKy/SmJV4tEv5bxjBjf+J+q2LEvRa3cj0zw+C7sAMDOL65zjvdkGY33vPQDAcJLPgdJkgio7G76eyB2/ED9fkMQCLMEI+QNNCbp0hsN1gousyf9/T8ty/EAIv0YRaYFXWE+VK2s3ki2zgMViwR9e+GfYJK5QtLS0YMOGDf5yp4kw5Tv+RItW56bo4ioAbvv4YziPHkX6PV8AEellIkeBbd3cuYBCAa+ISIABhwdehooe8cuhV6VUIMei9ReDSQTniZPw1Ncj46tfxYqVK6PvIDHU48HQpk0RtxEGFVlBN9tkFFsX0GuUyLXocC7OyJ5IWgMjynRJGPGDD6CI5Nmj0nBtLadBWyRbZgHOnpkBGyFPSCgaA3A1qKUK8Z3yHX9jDPPboZiWboh57nP44EG0f/s70JaVIfXmm6PvwJOo1lAQhQK6efP8URWREFwxC9PEjT7l0Atwlb8SiSsXGHztNUCpRPo9X5BNayR0fKWkSAmAwvpR8Bx/svXmpMTf5pG09v7hD/FKigv/Gk6EEb/Lw2X2qsYpfFZgJLIn/KhfKPouNVO+4+8UObcdjgyTFv3DHtHZu5Rl0fnUj6FMSUHRH5+PGsoXSKJaw2GoqADAZRBHQjBHyxbpCy+X3vxUfcJWGdTng23bNpivuByqtDTZtEbCtIp7yvBFSF4S4ueDR/zJ1luUpkdtlz2ufSNp7fvD8wC4ZLlkIDxdR5rj9/oYEJCk15sIRnDpdHjCTwn5ROQkxMOU7/gTJcOogZehsLrEnQDbtm3w1Ncj69vfktwDJl5087iFRqHoSDhGkrc0EbeTm/xUfcIJRY5Dh8D098MiwUJYvKinccVB7AFx3MG0DziRZdaOiz1wIMUZRnTbXGBY6SqfetpGKnOlfSZ6BrMk8HHvrM0WdhMfy4225bI8FotwzsVW45KSKd/xl/PRFfEijMTEGofZ3t8KZVoaUnivjlhIVGs49Hy5NufJyB7xwohfbNauXHqzzFo4PAyG3fGPdqzvvQ9iMMC0Zg0A+bRGwv+k5Qh/7fTa3SHbO9l6c1N0YCnQbYv9hhtOa/v3vuv/PdGylrHCRrAzZoki6VW3QqFQcPV33REWeHUy5ZyM//9eZiKtrIthOh/xIMa9kHo8sO/cCdOll8QVL56o1nAIRSqGd++OuF37oBMZRo3o0adceoWOMN45Z+rxwLZ1K8wXX+xP1pJLayQUFgsAoPePfwy7Ta/dPWaaB0i+XiF343QcYbShtFJK4Tp2HAAw84OtiYmLEWVKCogi/DXsZSg0MsXwA9FtmQMxaJRweEcPcB566CHMmTMHCxcuxK233orBwUEA522ZY+Ls2bMJ7S9Me/QNR19kGT5wAKzdDnOcK++Jag0HIQS6BQvgPHo04nbdVjdyYqj7KpfeIn5xuTHO8ELbRx+BGRqC5bqR2gdyaY2EMJVAHeGjwqo7rCGn1pKtd14+d5OKpxZCKK22Dz/0/67hp7ySBVGrQSkbcl1O8MfRyGDHLBDNlnlgYMC/rUapgI+ho7RefvnlOHnyJI4fP46SkhI888wzAM7bMieVDKNQFSp6XLl9+3YAgOHC5IXiiUW/YAFYhyPiotd4x/ALTMvgRp9dcVoIDG15B6qcHJhWy2wPIILU22+HQqgOFYTN5YWXoTBqxt8yK8OogUalkMysTSi6UvTH5yU5XkwoFACloCGme3z8GoZcWbuAOFtmAa1aCZbSUWUYr7jiCn+s/rJly9AWUNTnvC2zSAoKQpc5FIteo4RRo0THUPSpHusHH8JQWRl3hmiiWiMhLPAO794N09q1IbfptXswM1u8aZVcekdutrF3/MzgIOy7dyP9s58dlT8hZ9tGQpliAdPfD29HxxjbDmEwUTE9dcx+ydZLCEFeig71PbE/ZQVr9XZ3+38Pd63JiTC999NPf44a++hQU5ZSOD0MtGplXOGcc9Ln4OELHg77vlhbZgHBDNHtY6EPEVPxr3/9C5/5zGf8f5+3ZRZJIV8DNRGmZxj9Me7h8A0MgOnthXb27Lg/Rwqt4RCKg3g7Qofe+RgW3TZXTFM9cunVqBRINaj9tQFiYfD11wGvF5ZrR/uZyNm2kRCuB09z85j3hBtbunHsU9Z46C3NNsdliR2stfMJrtBN9sPhO0g5IVq+PUNO9fDbyPTZYm2ZBYRYfk+IyJ6nn34aarUad999t/+187bMIjlw4EDC2W65Kbqo0Q62D7g5zURsZ6XQGg5VDmcG1f/SS0i7844x7/cNc1m7sVgHyKmXqwoVeyc0tGkzdAsW+L1yBOTUGgmhKEuoEpgDw9yIP8M4dqg3HnoL0/Q40BBbqU5gtFbKMP7w1fQvfF5CdeIRAiv+Z/bXoM7JHvVer92Nc4NOzM2zyJK5K9aWWUClUECrUsLu9iFQ6YsvvogtW7bgrbfeGhV2et6WOYlkGKMXXbd//DGAESvkiQZRKrlap2Fil8e7AEswRWmGmOf4XWfOwF1TA8v69eMeoy2gzMwEAAzv3jPmvUEHl0GaakhuqGM48lJ0sLm5WgjxIriRpt9zT9T6E7LBf66vb6xPko9hQSBf1q5YW2YA+PznP4+DBw/CoFGOiuV///338bOf/QybN28+b8scL6YINW7FkmHSomPIBTZCcovr9GloZsxI6GKXQmskzJdcAm9HR8j3+vjRZyzJW3LqzTLHXoJxaPPbgFKJlJtuHPOe3G0bDiX/2O+qGRv50hMhb2I89ArFhmLN4A3U2s0XvMn46lekExYjwk0/VO6Aj6FQKuRN3hJjywwAx48fR35+PnRqrv6uYN3w4IMPwmaz4fLLL8fKlStx//33+48tlS3zlJ/qqaysTPgYRenco1XbgNMfcRKIt7MTvu5uZAeU9osHKbRGQpmeBup0wtvZOSaruK6b+7LHMuKXU2+WSYshpxdODyOqGhjrcGDwP/+Bac2akMW85W7bcBBCoF+8GCREIk631QWLThUyb2I89BbzHX/7oBNLp4sviC5oZezD8DQ2wnDBBbIVVBeL0mwG9Y59cvGy8tkxCzzwwAP4v//7P1x22WUAOMO2++67b9Q2VqsVpaWlKCwshI1/wnL5WJiUCtQF1C8IRLBlfvbZZxPWOOVH/Pv370/4GMK8d4899Dy/k09U0c6amdDnSKE1EsK8tzcgqkBASJbKj2GOX069QidU3yNu9Dm0aRNYqxUZX/5yyPflbttIKDMzwISYdugJk7wFjI/ePL4gS0eMPkmCVuvbmwEA6ffdK62weFAqQUMklnl8LBSQ1yJBrC3zxo0bAQA6PqfAGcKzx24fuf7P2zLHgBTudsIouMcWep7fdeY0AMCQ4ChNLic+Ae0sripUqI6/z+5BrkUHTQwe5XLqFRxCxVgIsB4P+l54Ebr586FfsjjkNnK3bTTctXVjbAQ6h1xhO/7x0GvWqZFqUKO2O7apHkGr7cOPAMBvkzHeUK93TOfvY1nIGMLvR4wts4BKSaBSKkbF8gsEJnYl3ZaZEKIlhNxFCPkBIeQJ4UfEflcRQs4SQuoIIY+E2eZ2Qkg1IeQUIeTlWP8DySCahYDz08NQpKRAYUxehad4UBdw1X6GNm0e817fcPjR53ggOIR2DkXuAKnPh/bvfBfelhZkPvD1CbOoG4iWj+n2BVXjahtw+us6TxSKM4xxJXH5BgYwvG8fUm66afwWdQMQ5vcDC7KwlIJhaVI6/lgghECvVmLY4xPtApwoYs/QJgA3APABGA74CQshRAngdwDWAygH8BlCSHnQNqUAHgWwklI6D8C3YxEvhlWrViV8jCyzFhqVImxpOl9XF/S893oiSKE1EsoIC4YDDi/SQoQVRkJOvUIxmIYoUz39f/8H7Nu2IfPBB2G++OKw28ndtpHQV3BV0JiAVH2Wpegf9oRdUxkvvbkWXcyW2KtWrYJt6wcAgLS77pJDVswohGiYgBG/j+E6VYNeHuOzRDBqlfD42DHuqHIt8ovt+AsppXdQSn9GKf2l8BNlnwsA1FFKGyilHgCvgLt5BPJlAL+jlA4AAKW0GxJTU1OT8DGUCoL8FF3IAuCsywVPc7Mk1YWk0BoN7dy5GN67d8zrgw4PUvSxhRXKqVejUqAwTY/WgfDeMZRSDLzyCnQLFyLrwQciHi8ZbRsOVRYX0uk8ftz/mtXlhY+lYTv+8dI7v8CCxt5hf46BGGpqamD78EOop02Dbv48GdWJx+/LH9DxC1MpLCOPx30ihPPmd0VwGU0EsR3/PkJIrEPaAgCtAX+38a8FMhvAbELIXkLIJ4QQyQujdndLcy/JNGnRG6Ljd/NfUHV+3pj3YkUqrZEQOqHg+eY+uydmH3659c7INEaM5XdVV8Pb0oLUm2+KeqxktG04BJMy56ef+l8TsnYzwrT5eOmdmcWNMGPJmu49cwbD+/bBfNllE2eqje/4A4sPCT49Spp8p9ZoCH5NwR2/XIVYxC4PrwJwDyGkEYAbXMYzpZQulODzSwGsA1AIYBchZAGldDBwo46ODpSVlQEAtFot7r33XixZsgQAkJGRgXnz5mHXrl3cAVUqrFq1CocPH4bVaoXdbofdbkdXVxdaW7n7UGlpKbRaLU7yhUmys7Mxe/Zsf+ytVqvFihUrUFVV5V9VT9UbcLZjyJ+FV1ZWBqVSiaaNG2EG0JmRAYPb7Y9w0Ov1WL58OQ4cOAAnf/GtWLECjY2N/opF5eXlYBjG727odrvhdDpx4MABANxjXmVlJfbv3+9fQFu1ahVqamr8HcP8+fPhdrtRW1sLACgqKkJOTg6qqqoAcNEDFRUV2LNnD3w+H/SFRbAAOLVnD/r46IDZc+fD7vbB1tOOHTt6UFxcjPT0dBw+fBgAl5SyaNEi7Ny5E5RSEEKwdu1aOJ1Of3tUVFSgv78fTU1NAICSkhKYzWYcO3ZM1HkCuLDAwPNkUmpwstfu/4zg85Ty5pvQqVSoz8nxZ4suX74cbW1tfj8U4TzZ7dxxcnNzMWPGjITOU0FBAQoLC2M6T7lpaXAC/v9LjZtzw2yvP4Mdg7VjzpPdbgfLsjh16hT6+KzfRYsWwWazoaGhAQBEn6djx475HSGjnScX4QzlNu04iO58lajzpPn0MEApaqYV4fSOHaK+T+HOU3V1NddeCZwnm80GsCxUAFi7HTY+09XJP8QQsNw2/LlzuVz+Dlan04FS6j+ParUaarUaDt5hVaFQwGg0+vcXjuF0OkdF8Vx99dV4++23oVQqccstt+DgwYO48MILsXHjRiiVShgMhlHHMJvN0CgJrE43VF47vv71r+PTTz9FWloaXnzxRZSWlqK6uhq/+MUv8Pzzz0OpVEKv1/vbU3gyePzxx/3RQgAyEQ5KadQfANND/UTZZwWArQF/Pwrg0aBtngdwb8Df2wAsCz7W0qVLabz09PTEvW8gT24+SUsfe5cyDDvq9fZHf0Cry+ZQxu1O+DOk0hoJ2549tLpsDh3YuNH/WlOvnU5/eAvdWNUa07Hk1vvcx7V0+sNb6JDTE/L92iuuoM1f/JKoYyWjbSNRd/U1tOlzn/f//eLeRjr94S202+oKuf146fX6GFr62Lv0x2+fEr1P7R130rprrpFRlXiqq6v9vzurq6m7deSaPjfooCfaBqnHE/p6kornnnuOPvvss/6/P/roI7p582Z6TZQ2OjfooMdbB+lvn3uOfvWrX6WUUvqPf/yD3n777f5tLr30Utrc3Dxm38D/twCAKhqmfxY11UMpbQaQCuA6/ieVfy0ShwCUEkJmEEI0AO4EEBxO8ha40T4IIZngpn4axGgSi1RhcUVpBnh8LIaco5NCPC3N0JbPhUKTeLnCZITwCWsR7vqRZu6Os+Si3HqFiJdQUSbe7m54m1v85nPRGO9wTl9nJxwHD/r/7rO7oSBAepgF9fHSq1IqkBdmPSsUvr4+eI8fh+WKK2VWFjtEpQYCCpl7+axdKnPkTKAtMwBceumlMIex5g7EqFWBguKttzbhC1/4AgDg5ptvxrZt2/yak2rLTAj5FriF2Df4l/5JCPkTpfS34fahlPoIIQ8C2ApACeBvlNJThJAfg7sTbebfu4IQUg2AAfAQpTR2l6gI1NbWSmJxK8zF9trd/ugX6vPBWfUpLFdLU9dVKq2RUKWnQ5mSAtY1MvfZykcrxRpaKLdewSm0Y8iF2TmjvzhOfipLrDdSMto2EoYVF8L+0TZQhgFRKtHPL6aHK/g9nnpnZZlwrHVQ1La2Dz8CWBbmK6+QV1Qc9P7lL/DU10MhTPV4GRAArQpAJTLGPhjt3DnIjVA4PtiWORYMfIZ6W4CVM8MwSElJQV9fHzIzM5Nuy/xFAMsppU9QSp8AcCG4G0FEKKXvUkpnU0pnUkqf5l97gu/0wT+RfJdSWk4pXUApTfxWJhNZ/iSukZGQl7dH1cyaNS6a4oV1uzH475GmFv5PsVgyJ4MCPokrVEino+pTKAwG6ObOSbasuDDyNyiGnycfdHiRZhjfovbhWFqchpZ+x5in21AMvPIKfDk5CdmRywbDgLIshPE9pZB98TlWW+ZAVAoF9GplRE+wZNsyE3AjcgEG8llaS0pgEYREyOXT2QN9+YVSholm7ApIpTUa6rw8eBpHClT02NzQq5UwamNLBZdbb36KDlqVImShe/uuXZwHjsj09WS1bTiUaZwxl7etHaq0NPQPeyLmTYyn3tJs7umqvseOimnhPXc8TU1wnzkD7X33TZxongCyvvVNMEND0JWXgygUONU+hBSjBuk6IlsR82Bb5lgx69TIzMlDQ2MzCgsLQQjB0NAQMjIyACTflvkFAAcIIU8SQp4E8AmAvyb86Ukgh/ehTxShGPW5gEpcbj66QltaKslnSKU1GparrwYwktUYyTMmEnLrFapCdQZlTHtaWuBta4M2htF+sto2HGp+2sZx4BMA3M02K4Ih3njqncVXYauPYt0wuGkTQAiyRYTTjgdCEhdlGC5rl3Jz/OoQrp1SEWzLHIlHH30Ub7755qjXUvQqrLv8KvztxRcBAJs3b8Yll1ziv7Em1ZaZUvorAPcC6Od/7qWUPpvwpycBIawxUdRKripUYDlAX3cPVNnZkjkRSqU1GsoMbvTp6+sHALQPOP1PNLGQDL05Ft2Y6mf2nVxIqOUq8WkfyWrbcPinQgj3leuNcrMdT71FaXqolSRiGUbq9XJuqGvX4mhATdiJhP9pkGH8Fa5UCuIPzZSLQFtmAFi9ejVuu+02bNu2DYWFhdi6dSsA4MSJE8gNcsnVqZW447NfQHdPL2bNmoVf/vKX2LBhg//9pNgyE0IslFIrISQdQBP/I7yXTintT1jBJCLLNNojfnjPnpCe3xMdVQZfHGTfPqTedCM6rS4sK04fZ1WhKcky4t0To8tFus6eATBS3WoyoDAaAJUKzOAgPD4WAw7vhPJGCkSlVKA4w+i36g6FbdvHYHp6kRqimtuEQcEncQ0Pw2dMAQBoVQrQ+OvMiCLYlnn37t0ht/N6vVgRFJVGCEF2mhkbfv8C5uSa4XY6/BFBybRlFkzTPgVQFfAj/D3hsVgskh0rN0WH5r6R0YKvpwdEgjBOASm1RkK/kEvCpm43KKXotbtjDuUEkqO3KN2AIacXw+6RDMbhPXuhr1wa0003WW0bDkII4PPBceSw32o60hz/eOudlW2K6JM08OorUOfnw7R69bhrDQfR8tF3Xh98LDfiVyoVUMhsIifGlhmAf+QfjFCKc8DhHaU1abbMlNJr+X9nUEpLAn5mUEpLEv70JFBRUSHZsWZkGnGON7BiHQ6AUqTceKNkx5dSaySU/EIRM9APu9sHl5eNa/SZDL1+j3g+lp91u+Hr7IQmxlDHZLVtNJxVn/rXLGZmhndzHW+9M7NMaOgdhts3tvNyNzbCsf8TpN5+O4hSOe5aw+EfGBD4zc9UCgJjElx0Y7FlDkajUsKoUaF/2DOq9OJ42DJvE/PaRCRwri1Rss1aWF0+uLyMv4ShKjs7yl7ikVJrJIRks/5//svvvZ5tjn2OPxl6hdyC2i4uvd3LzycbV66M6TjJattImC67FAqTCf18/eZIRW/GW+/cPG4UX9U0MOa9/hdfAlQqpN5yM4Dx1xoOQghn1sYwIz49CjLKKmGikmHSwMuw6OiXR2vEjp8QouPn9zMJIWmEkHT+pxhjDdcmJFKaHAmdY7fVDRfv86PKzJDs+HIZMoWD+nz+rNhQJSWjkQy9s/jQQsGlc5j3xxGMz8SS7LYNhWbadFCvFwMOruOPNNUz3norpqcCGLnhCvT97QUMvvoqUm+5BaqsLADjrzUilDMk9LFcRI9iAoadhiJFr4ZOpcSgm8qSaRxtxP9VcPP5c/h/hZ9NAJ6TXM0EJ9vCTYfU9djg7ewCIF0Mf7JJueF6KI1G/2J1UdrEKggikKJXw6xV+SN7fLzpmU6CkLZko0xJAXW70d9vg1JBYNFN3JLXuRYdClL12FM3UjzG29GB7l/+EvqlS5HzaMi6ShMOyjJgHQ74GBaqCVAgRiyEEOSl6pCqledGFfHKo5T+GsCvCSHfiGTPMJFZI2EZuLJcbvTZMeRCKe9mqJAgmUJASq3RUKamwXvuHPpsLpAInjGRSJbe6ZkGnO3kRp6Og4egLigQnbglkMy2DQflfeCdNbXItaRHTHoab72EEMwvsPjbHQAGN/4HYFnk/3QDFAEJUOOtNRJKsxmMzQYfQ6HiS2/JVdxEasw6NUxalSzJcWLj+H9LCJnPl0n8vPAjuRoZOHXqlGTHyjJpQQjQZXXDzdsgS4mUWqNB+C+up6YGFl14z5hIJEtvSabJvyDq7ezwL07HQjLbNhwG3kqc6e5CQZSi9hNB7wUzMtDU50BDjx2UUgxt3gzjihXQFBaO2m4iaA2HcJ27vAzU/Ijf6RybCS4lTqcTa9eu9Uf1XHXVVUhNTcW1114rav9du3ahoqICKpUKL788Uo22p6cHV8WQuxIJsYu7PwLwW/7nYgA/A3C9JApkRvAylwKVUoEskxbNfcNghoaQwi9uSYWUWqNhvHA5AMDW0Y3iCNElkUiW3rxUHToGXXA6XPCd64ApjrKEyWzbcKiLuHUJxmqN+oQ1EfSuLuXyPY62DsJx6BC8bW2wXHfdmO0mgtZwED6Wn7IshIFztDDLRPnb3/6Gm2++2R/V89BDD+Ef//iH6P2nTZuGF198EXfddRdYdsRdNCsrC3l5edgbooJerIid9LoVwKUAOiml9wJYBCAl4U+fhMzMMqGzsx+s1QpVZtZ4y4kbVQ6XMUj7+5A3wczZgllUmAoPw+Lsx/sAAKq83Ch7TEyUqdxXRmkbirm+8XgwM8sEo0aJY62DsG/bBqjVsFxx+XjLig3+SVZJGb/7pdzEa8ssUFxcjIULF4bMN7jxxhvxr3/9K2GNYidKnZRSlhDiI4RYAHQDGF/XK5EsWrRI0uPlp+oxeJKreKSKY8ohElJrjYQ6hwtDXXZqFw5deXVcx0iWXiGk01bfBC0Aw9KlMR8jmW0bDgUfP77uzG58arwn4rYTQa9SQbCgMAVH24bg+PQwDEuW+P8PgUwEraHY/VoNehoHQd1ueJQ2qDUavx9/vPPmmUUmrL49vBNpIrbModAEJYhWVlbi8ccfT/i4Ykf8VYSQVAB/BhfVcxjA/oQ/PQlIHbObY9ECfVykgzGOKYdIJDO+WPgCKz1uf6ZgrCRLrxBxxOzjUt81cThXToTYbaGzcak0KM6IPL02EfQCwKKiVDS29MB1+jT0S0Mnak0UraEgyoAuju/rKeQrxJKILXMoAqd6gCTbMlNKv87/+jwh5H0AFkrp8YQ/PQk0NDRgWowx35EoSNOD9rcAkDZ5C5BeazTIsuVg6joiJhJFIll6UwxqlOWY4T3UB6LTxeWPlOy2DYdv+UowZ5qi1j6YKHoXF6biQE8jwDAwVIR+0pooWoNZfftsUJ8PrjNn0KNPRc60PGjVSthstpimXmIhUVvmYIJzJKSyZY5m0hY2F5sQUkEpPZywgklGSaYJjINbzFIYJ2bsu1g8DMWcgRb0x1h5azyYnWuGub/Lbyk9WXHpjDB7HP6KbhOdNbOz8OlgE1hCoF+yeLzlxA6/wKqgLDQq+eP4A22Zo3n+P/roo7jgggtw003iba2lsmWONuL/ZYT3KIBLElYgM1LNtQnkWLQ453ViuHyR5PG1UmuNhgcEagAZNL76rsnUO8fAIsVpBYnTpz7ZbRsOz/AwcpwDsGgiXzsTRa9Rq8KqwTo0phWhPIzHzUTRGgrhO2r2Ov2/B8+bS41gyyy4c65evRpnzpyB3W5HYWEh/vrXv+LKK6/EiRMncP31Y4MjDx06hJtuugkDAwN4++23sWHDBn/IbFJsmSmlFyf8CeNMerq0dsM5Fh1S3XZYDdMlPS4gvdZonFu6BqWHDyDVOYR4HDiSqXe2m3MAH0zNRl4c+ye7bcPRp01BOoD0oV4gM7yr5UTRywwOIrurBbtnrISppgfrysZOb04UrZHQMCNezFK4W0YiEVtmAFi2bBnaeE8qhmFGmb1t3rwZmzZtSlij2Dj+z4f6SfjTk8Dhw9LORulZL/KH+0bZBEuF1Fqj0cZyFhRGV/iCG5FIpt4CL1ertiUzvrnkZLdtOM4W85bYQ0MRt5soeocPHARhfDgxbSE2Hwu9qDhRtIbDpTXAqxoZ5ctdiCVRW+ZAArX29PTgu9/9LtIkKPwkdtJrWcDPagBPYpIkcEmNUGC9zSTtwu54IHT8TEvzOCuJTmont6B+xjsxi5eIpQtcB8QMDY6vEJF4GuoBADMuWooPq7tC2jRPdHwgUFI2+oYSkogtcziysrJwo0Q28GItG74R8PNlABUAJoXhhRR3x0B8vdzC7pFU6aMYpNYajUYFdwqdvNNorCRTL9vEFYc/ZY0vFC/ZbRuOdobr+F2nT0fcbqLoddfVQ5Wfh6svKIHN5cP7JzvHbDNRtIaCUgofCEhAWKTUHbKcyKU13mXuYQAzpBQiF1Inl7jr6wAAtW41XF5pRz/JToSp86jg0psANr7ONJl6fQP9aC8qw7G2wbj2nwhJRpRSnHBxoaiepqaI204EvQDgrjkL7cxZWD0rEyWZRvxuex18zOjR80TRGgqGpSCUglAWlJ96CSxuMtGRS6vYOf63CSGb+Z93AJwF8Ga0/SYCO3fulPR43pZWAECXPhXtg9KaPUmtNRI2lxc9Njd8KWlgBgfjOkYy9TqrPoUmPQ1dVjdsrtiLpiZTazisTh/sUMGVmYNoOUQTQS9jH4a7tg7qgnwoFATfvLQUNV12/PbjulEe8RNBazgYlsIn+PW4uei1iZxwFggzPAx7Z6csfvxil7d/EfC7D0AzpbRNcjUyIHWj+fr6wGZkwaHWo33AiZlZ0s14yXGCwyEUYNF6XLB9+GFcx0iWXnaYW3y26LnR8t66Xlw1P7bYnmS2bTh67HzYrNkCxhp5cXci6PXyaz96Pm78hsX5eP1wG369rRZatQJfXzcLwMTQGg4vw8Kp0gGwgjLJnedPFF9XF5RuNxBnCHMkxM7x7wQ3yk8BkA6u858USB1r7+vqgoY3CTt1zirpseXw3Q5HN1+ARalRAXF+cZOl19vFFb2ZdtO10KkV+KShP+ZjJLNtw9HHd/wKiwXsUORrZyLodTdw6yrasjkAOE1/u2cZrpyXg2c/qkU3b5U9EbSGw8OwYAR9bHIWphO1Zf7Vr36F8rlzUXnVVVj/pS+hpYULbBgPW+YvATgI4GZwTp2fEELuk0SBzKxdu1bS4zkOHgQZHECORYs6vl6tVEitNRL1PZx27fU3AuDK08VKsvQ6qqoAALr8PCwsTMXR1sGYj5HMtg1Hcx8XmqfxeeA8ejTithNBr4tPGtLOmul/Ta1U4LGry8GwFL/extWkmAhaw+FjKVjCdXOshyt5KZddg0CitsxLlizB/i1bcPCNN3D7Zz6D73//+wDGx5b5IQBLKKX3UEq/AGApgIcT/vQkcIyvlCUFLB9Ta7jgAszMMvk7T6mQUms02gedUCsJMgq4x8h45vmTpVcIodWWlWFJUSqqz1ljDitMZtuGo9vG3VwN+dwTIxuhIMhE0Otta4Nm5swxVeamZRhwe2URNla1odvmmhBaw8EwFJSf4xeucbnj+BO1ZV63bh20Ph8UZjMWV1T4k7mA5Nsy9wEIXBGx8a9NeAYGBiQ7lo8vOGGorMRMhQlvHW1PyOI1GCm1RqNzyIXcFB2UKdyXwtveDnVubD73ydLr6+2FKisLSosFS6YN44+7WFSfs2LJNPFhhMls23D02j0wa1WwLF4Jx4cfgrHawpbunAh6bR9/DOPKi0K+99U1JXjlUAte2NuE5brx1xqK7S/+Ca11dWBYiuM+F4hSBaLTgvExUKriC5PMnl6Ci+/5Stj3pbBlpm43qNcLVVYWXnzxRaxfv97/XrJtmesAHCCEPMlX4/oEQA0h5LuEkO8mrGKS4D57FgCgykjHzCwjbC7fyILdJKN9wIksk9bvMOo6c2acFYXH+s67/nKLi4u4zv5Iy+A4KoqPvmEPMkwaKFM4qwY2ygLveMLYhwGGgTovP+T7xZlGrJqVifdOdEzoxV2WUq7ylkKZFJ1S2DIz/PrPy5s348iRI3jooYf87yXVlhlAPf8jIJhFyDtZJgEVFWENRmPG08wtsmhLSzFzmIswaegZRrZZmgpWUmqNRmPvMNbOzoKunFu4Y+2x2zYkQy+lFNTphELLZezmpuiQn6LDoaZ+3LdKfCpJMts2HE29w8g0aaEwc0lc3u5uaEtLQ2473nrdp6sBAMblF4Td5qr5uXjszZOwFI1/24bi4nu+gjMdVhi1KuTYekC9XmhnzRrjfyMlUtgys3Y7th8+jGd++lN8/PHH0GpHstWlsmUWG9XzFKX0KXBunb8U/g54PSSEkKsIIWcJIXWEkEcibHcLIYQSQipj/y9Epr8/9giQcPj6+kA0Gqjy8vxhnFLO80upNRJuH4O+YQ+mZxih0OlADAYwcXx2MvSydq59zVdc4X/twpkZ2FvXC28M4XnJattIdFpdmJFphCqDMzVznQxfpHy89brO1gBA2BsTAFxengNCgLePtiZLVkxQSuFjKVQKAsow/gCGYI97KQm0ZY7Go48+ijffHJ0ORRkGh48cxjeeeAKbN28eY4AnlS2z2Kie+YSQIwBOAThFCPmUEDIvyj5KAL8DsB5AOYDPEELKQ2xnBvAtAAdiFS+GpigZkrHgOHAA1OMBIQS5Fh0MGiXqu+MzOAuFlFoj0WvnohtyLNxIgjocGHj55ZiPkwy9nhah6M1IfePL5ubA6vJhX734ZaZktW04WJaif9iDHIsO2tlc6T7KR5mEYrz1es+dA9FooJk5M+w22WYdlhWn4/3q3iQqEw9LuakepZIAfP1ayjDwRGh3KRBsmQVWr16N2267Ddu2bUNhYaHfnO3EiRPIDVpXY51OPPaLX8DucOC2227DsmXLRlk3J8WWOYA/AfgupXQ7ABBC1oErwxh65YfjAgB1lNIGfp9XANwAoDpou58A+Cm4yKEJDWOzgfCPXQoFQUmWEbXdkyMLMBAhDDU3hZuiIno9VBPUWtd9hltXCVx4vmRONgwaJd4/2Ym1sydHwftBpxcMS5Fh0oAolVCYzWCs0uaBSInj4EGo8/JAQhT8DuSqebn4cWM/2gYcKEybWFYIHh/3RKhSKKBMSQFrt4PKONoXSMSWmXU48M5f/gLdnDkgKtWYamFS2TKL7fiNQqcPAJTSHYSQyEVDOYP3wGfANgDLAzfgK3wVUUrfIYSE7fg7OjpQVlYGANBqtbj33nuxZMkSAEBGRgbmzZuHXbt2cf8hlQqrVq3C4cOHYbVa4fF4YLfb0dXVhdZWTk5paSm0Wi1O8uZk2dnZmD17tv8urdVqsWLFClRVVcHOTzUsX74c3p4eOCuXYseOHSgrK0OhWYVDTX3YsWMHcnNzMWPGDOzfz5Ui1uv1WL58OQ4cOAAnH7a3YsUKNDY2orOTM7oqLy8HwzA4yy8aG41GOJ1OHDjAPfyYTCZUVlZi//79cPPp5qtWrUJNTQ26u7sBAPPnz4fb7UZtLRdTXVRUhJycHFTxse8WiwUVFRXYs2eP/xG3TVsMAOhrOIkdHQoUX3UVnB9/jB07dgDgCmukp6f77XbT0tKwaNEi7Ny50x/FtHbtWqjVav8+FRUV6O/v949US0pKYDab/aF+0c4TwEUsBJ8nhh/xHxwYQHZ1NWbPno1P9u7B/HTgnWOt+N8b5+PI4U9Hnae2tja0t7cDAMrKyqBUKuHxeCQ7TwUFBSgsLIzpPLHpXP2GnpZ67PA2I8dggLuvz99+wefJ4/GAZVmcOnUKfXw02aJFi2Cz2dDQ0BDTeTp27Jg/SkjseRpubweTkYE9e/ZEPE9aO9e5vrKvDpVGbnpK7Pcp1HmqrubGhYmcJ8GSwUP5ME6PCy6PB0oAYFlQSv3bmEwmuFwu/3dDp9OBUuo/j2q1Gmq12h8CqlAoYDQaR9k+mEwmOJ1Of8LWwoULsWrVKgwODkKpVEKj0UClUvmPoVQqYTAY8J///Md/HLPZzL1vtQFqNVhC4HO7wbIsbDYbNBoN+vv78bWvfc1/LL1e729PYWrp8ccfx8aNGwVpmQgHpTTqDzhfnh8CKOZ/HgfwZpR9bgXwl4C/PwfguYC/FQB2ACjm/94BoDLUsZYuXUrjpb+/P+59A2E9HlpdNod2//Y5/2v/9+FZWvzIFupw+yT5DKm0RuMXW8/QGY9soV4fQymltOvZZ2n13HLKMkxMx0mG3o6nn6anFywc8/rmo+10+sNb6IGGPlHHSVbbhmMTr/d0xxCllNLqsjm0umxO2O3HUy/r9dLqueW069lno2/LsnThj96nn/3LJ0lQFp3q6mr/7312Fz3WOkDdXh/12e3UceIE9dls1Ov1jqPC8LAsS53V1dTd1uZ/TazWwP+3AIAqGqZ/FhvOeR+ALABvAHidv5NEy9xtB1AU8Hch/5qAGcB8ADsIIU0ALgSwWeoFXqmSS7xd3MhNGRCqVZ5nAaWI2zEymGQlwjTw0SUqJXf6lSYzwLLwnuuI6TjJ0Gv/eHvIufCL52RDo1Rgy3FxoW3jnWQk2BvkpXARGZoZM6CI4Lw4nno9ra0Ay0KdEz2vgxCChRnA7treMa6d442P4cI3VQoFCB/Fw9rs/ieGiQZ1u0EZZlRuh1xaI3b8hBAdIeTb4ObhTwFYTildSin9NqU0WtbGIQClhJAZhBANgDsBbBbepJQOUUozKaXFlNJicLkB11NKqxL4/8iGu4aLclBlZvhfWzwtFQBwumPiztWGor7bjrLckXlDYeHU09w0TooiQCm0pbPGvGzSqnBZeTZe/7QtLrfOZNM24IRWpYBFx82umi+7FNTrnZAx8J5mzpxNyPGIxqw0rhs5KbF3VaK4fSyUhEChIP61uai2qOOIkMmtCFPbWEqijfhfAlAJ4AS46Jyfiz0wpdQH4EEAWwGcBvAapfQUIeTHhJCkVe/KyMiIvpEImAFu/lI3f4H/tWyzDpkmDaoluuCl0hoJSinaglxFdfO4AC2mP7YMzGToZQYGYAhRlxQAvrpmJoY9DP68qyHqcZKhNRINvcMoSNP7s7yVKSlcxx9mRDeeen38061u7hxR26+axU0ln5DoyVcqvAwLjYrr4gghICoVKMtO2EIs1O0GCAEJKAYvl9Zoi7vllNIFAEAI+Ss4ozbRUErfBfBu0GtPhNl2XSzHFsu8eRGjTkXj4+OqVemjbQLm5lmwo6ZHks+QSmskemxu2N0+FKSOPE4K01ex+vXIrZexWsE6HKOm1wJZVJSKi8uy8PyuBqyZnYXK4vCRSclo20h0DDpRmj1ysyX8NI+3sxPakpIx24+nXtfpaoAQqDLDrw0Gsm7ZQmR82ItjbUP4nMzaYoFhKdTKgLGtUgkwjCQJUHLAulxQaLWjLGDk0hptxO9/huZH8JMOIYokUZxHjgLAmHnZmVkm9Njc/jncRJBKaySq+Wmp8nyL/zWlhfvdvju2z5dbr6uaK08YKdT06ZsWwKhR4q6/HMCnzeGfWJLRtpHotLqQaxnJ8FalcyN6d319yO3HU6+3rR1EpwNRq0Vtv3v3biwsTMGRlonl2eNjKZSKAB8tnw+M1eqPhJGLQFvmo0ePYsWKFZg3bx4WLlyIV199Nex+1O0G0Wrhdrtxxx13YNasWVi2bJk/AuvEiRO45557JNEYreNfRAix8j82AAuF3wkhE2tCT2a8naEXPq9ewBUEOd42cX1XAmnq5RLOZgWOPlUq7ouumFiPwL5eLjHIsGxZ2G3yU/XY+p01sOjU+N93qifknPnAsAc2lw/ZAR2/js++ZCdgLL+vpwfGC8JbNYRi2Yx01PcMo39Y3uQosTAshZdhR4/4k1Q3INCW2WAw4O9//ztOnTqF999/H9/+9rcxGOLJmjIMqNcLotXhr3/9K9LS0lBXV4cHHngADz/MGSEvWLAAbW1tfn/+RIjY8VNKlZRSC/9jppSqAn63RNp3oqBSiU1ViAJLYbr44jEvz8u3QKNU4JOGxM1KJdMageoOK/RqJbJM2lGv6xcvjnmqR269bj43QRVlvjvbrMO3LivFkZZBvHtibDFwIDltG47GvrE325HptdADhvHU6z57Fqp88RXOVCoVFhWmApg4gQ6CnYdWNdLFKZOUpBhoyzx79myU8rYX+fn5yM7ORk/P2KlhoSykQqfFpk2b8IUvfAEAZ8O8bds2/4DmuuuuwyuvvJKwxvG7upLEqlWrJDmO+8wZ6PgkskCMWhX3mBtHcZBgpNIaiaZeB+blW6BQjB79KNNS4T4dm0On3Ho9fOk/RUpK1G3vumAa/rq7Ac9tr8P6+blj/n/JaNtwCGUuC9NG5msVRm7K0N0YemF6vPR6eO93pUl8SdFVq1b5R/rV56xYOUvc2oCcMCyFYfc5uAY96BbsGrxeUI8HCkMDnHGO/jX5RqReF97GIpIt88GDB+HxeDAzhA2GUOuDaLVob29HUREXCZ+WloaUlBT09fUhMzMTlZWV2LBhg784S7yIjeOftAhZjYngE7zRw1wsi4tScbJ9KCbTsFBIoTUSlFLUdNswPWNsuJjSZIInRn8YufUyvX3Qls4SVe9AqSD46tqZON1hxY6a7jHvy601Ei393Jc6cI5f+D85Dh4Kuc946fW2cak2xpUrRe9z+PBhpBs1yEvRYVetNIEOiSLkFIy6dvwVGOUrwRjOlrmjowOf+9zn8MILL0ARwgZDMJALjOgBgOHh0V5gybZlnrRYJZhD9XVw8/umdaFLzC2eloq/7GnE8bZBLJ0e/+OkFFoj0W1zY9DhHTXlIKBM5aKVfL29oqM55NbrPHECxlXiO6BbKgrxzLunseV4By6ZM7pAtdxaI9HYMwyzVoV04+gvtW7ePFAaerAwXnqdfOJYLEV5BK2zc8yS16GOF5ePhWN1PorzLP5ERcZqhaelBUxGBkx54qeyYiGULbPVasU111yDp59+GhdeeGHI/ajbDYWeC/ctKChAa2srCgsL4fF4MDQ05A/vTaot8387wiKjKky1+4tmZkKpINh2euxIcyIh1HwNjOgR0C3k8hN83RPj/8AMDYG6XFBotNE35tGoFLhyXi62nuzEsHviBKF121yYlmEY8+SiKSkBa5M3wiRWvG2cT5I6P3QBlkhcMCMdvXY3HJ7xb3svw0KpIP5OHwiIyGPlyzAOtmX2eDy46aab8PnPfx633nrrqG0FW2bKsmCdThAd16Fff/31eOmllwAAb731Fi655BL/tZNUW+bJTGVl4g4QjqpPAQCqrNCZjOlGDZZOS8POBOP5pdAaiZPt3EJiccZYqwBVGj/ij6Hkn5x6vbzxlvGi0Mlb4bhxSQGGPcwYy2a52zYSHUMuZJvH3sCUKSlghkIv7o6XXm9nF7Rz546ZcoiEoLWYn0I82Dj+tQ/GxPADXBw/AI3MCVyBtsyvvfYadu3ahRdffBGLFy/G4sWLcfToUQAjtsyCJQnRcOGzX/ziF9HX14dZs2bh97//PTZs2OA/tlS2zFO+4+/q6kr4GKydc9BTR4h0WFuWhVPnrKjpit+mWQqtkajrscOoUYae4xc6/g7xfj1y6vX1cE9Zmhhrly4rTodRo8SH1aOje+Ru23B4GRb1PfaQlsVKixms1QomRFz5eOl1nT4d82hf0LqsmLuGhJDh8cTtY6EKWuAXRs1MghWyovHAAw/4R+yf/exn4fV6cfToUf/P4sWLAYzYMgsdv/BEotPpsHHjRtTV1WH37t0o4RP83G43qqqqRtXgjZcp3/ELFr+J4O3iSuRF8ia/bWkhCAE2HW0Pu000pNAaiTMdVpTmhK6WKfiyOGMwB5NTr/sMn7yVFZvfvkalwCVzc7Dp6Dm4vCOLeHK3bTh67W54GTrKG0lAxc8ze5qax7w3Hnqp1wumtzdsAfhwCFqzzFqYtSo0jnPHTynlfHoUYYIChuXVV1FRgYsvvthv0xwOoSCLENGj0I59KvR6R3yoWlpasGHDBklCfad8xy8FnsZGf7HvcGRbdFhRkoH3TnROyCQiH8Pi1Dkr5uaF7viVZjMUBgOoZ2IYnglhhepp02Le9+YlBXD7WFQ1jX8mqRDKmZcyti6zdiZnPsfEML0mJ4IDrSFCnd1IEEIwI8uIhnHu+FneetigGTulozAa/dW45OS+++4T7bNDfT4QpRIkSodeWlqKdevWSaDuv6DjL41QM1QMlFJ4GhpELQitX5CHht7huCMbEtUaiboeO9w+NmLUkbp4etg551DIqZcZGISmpERUKGcwF8xIh0pBsKdupCSgnFojIayrTA+xrqJM5fITQiXOjYdeXzc3ZaPOjS3iJVDrjEwjznaOb1U6L2/HPGaOH/yoeoINzKjHG3ZNRRviKUAKpnzHn2jDCcW+9YsWRt32ynk50KgU+JMIt8hQyHWSgZEFt/kF4ROuValpcJ0KXwA8GDn1Oo8e9XsIxYpRq8LS6WnYfmYkQklOrZFo7HVAo1SMckMVELJ3h/fuHfPeeOj1NDYBgL8YvFgCtc7MMqHb5kav3S2ltJhwergpFlWokb1CyVXhkjGyJ1ZYpwNEFdoXKZ6BjximfMcvlFeMFx+fXi0UyI5EtlmHu5dPwzsnOuIa9SSqNRLbz3Qjw6hBWZg5fg4KX0+P6KkqOfX6urrADscf6rhiZgZqum2w82GdcmqNxMn2IRRnjg3lBAKK+oTooMZDr+DDr501tv5BJAK1zuNDhcfTusHLd+o6daiOnzsPVOYFXrFQhgEoBdGFvtEH5wRIxZTv+BNFiGuP5hcj8JU1JTBolPjWK0dwbnDiVPo53WFDWa454gjCcCEXOsnK7F4YDcbOzRFbrou/bMOiwlRQOjLVMl409A4jK0QoJwAQhQLauXPB9I9/+CPAXeuq3NyYQjmDmV/ATV+NZ2SPj6FQktEx/AKCFUUyiq6LQdBBkvyEN+U7/myRVYTCIURciA1xy0vR42e3LERDzzBu+v3emB55E9Uajua+YXRaXbhkTuTjCxE0YjsiufQKN1t1buiEOTEsLOQ6oO1nuWPJpTUSdrcPvXZ3RO8aZWpKyDn+8dA7fPBAxHKQ4QjUmm3WwqBRjusCr8PjC9npAwD4BVQqo21DLLbMLB9hFGiBvWvXLlRUVEClUuHtt9/2v97T04OrrrpKEo1TvuOfLWKKJhK+nh6AEKgLC0Xvs35BHl796oXosbnx7Ec1ovdLVGs4DvM+6YuLUiNuJxSZ8YqM5ZdLr5A9KuQWxEOGSYviDANOtfN2AjJpjYTgTz89PXwpPYVGCyef0BPIeOj1nevwl/+LhUCthBDMyDSOa0inj6FQK0M/2QbW3pWLWGyZqY+7AQWG0E6bNg0vvvgi7rrrLqgDbghZWVnIy8vD3hBrQrEy5Tt+IYMuXnx9vVCmpkYNtQpmybQ0fOaCafjnJy04JtK5M1Gt4TjeNgStShG94+dHbu660MVBgpFLr/CUJWZdJRLLZ2TgcMsAPD5WNq2RaOjhOr+IC+p8vePgDjfZeikfL556yy0x7xusdUamEfvr+8YlrJlSLqpHHyKUE0DAeop82mKxZaYeNxfKGbDOU1xcjIULF0KhUIyZ47/xxhvxr3/9K2GNU96kLVHcZ2tEm5YF88j6Odh87Bye/agGL9wbX2y0FOyq6eFCHMM9/vJo+Qs0Vl9+qfH19ABqdczJW8EsLU7Dq1WtqO0en/DC+h47zFoVpqWHnz7RL16CwY3/ga+vD5oYniqlxsMX91DlJD7FVJJlgtvXgR67G9nmsfkLcsKwLCg4u4b33nsPnZ1j6zOwTidXg1cXu7bc3NyImbOx2jKzww7/9JMYKisr8fjjj8ekORRTfsSfaFicr6sLysz4Cl+bdWp8fsV0bD/bg1bemjcScoTwtfY7UN8zjHVl0b/QRKWCIiVF9By/XCGHjkOHoM7KipgpLYaKadxU0Y6zPeMSHlnfY0dJtinigrownRWcxJVsve4abkpSnRe7OVuw1vl8ZI+QvJZMfCw3ktdEGuQQIls4Z8y2zCwTMmNXIPjaOW/LLJIVK2Iz+QqEUgpfXx/MV14Z9zHuXDYNv9tej5f2NeHxa8sjbpuI1nD88xNu2iTawq6AwmiAQ6QXvBx6AYCx2bgMywSZmWWERqVAfbcdD9whj9ZI1HbZsao08tOisK7iqj4N/YIF/tflattweHm/Hf382Iu8B2st4AvOfNLQh4V8Za5kwbAUSgB6tTLsyNzT3g5mYAC68vKEBxfBxGLLTBkGlGFAIlhk6IKeSs7bMoukqqoq7n3Z4WFQtxuqOEf8AFCUbsCiolS8frgNLBt5XjERraGglOLdkx1YXZqJGZniOlJ1fj4Yq7gQSKn1Cvi6u2FYvjzh4xBCsHRaGvY39MmmNRydQy5029wRp3kAQD19OgCA6R/tJppsvY79n4Co1aKqnQUTrHVOLjfi7xuH+rvCiF8VZnEXAHxCRS6P9PpisWV+5Pvfx6Zt2yKuHwbfRM7bMovEnkBMuqu6GgCi+vRE485lRRhwePFJY+S6vIloDcWxtiG09jtxzQLxKfi6sjmiIx6k1gtw6wuszeZfaE6UonQ9Oq0u9A8ld56/uoO7eS6ZFjkySZWWBoXZDF/f6Ok1Odo2Er7BAUCtjitTNFirUkFQlK5Hx2Dyp3q8vhCVt4Jg+cgeIaJGasTaMp88eRK5GRljnm4PHTqEwsJCbNy4Ed/85jcxb97IU9h5W+Yk4M/anZWYb8q1C/OgUhBsOpL43FwsvHqolXvkjaHjV2akg7XZZBkNicF15iyA2F05w7FyViYoBbocyY0w2V3L+QQtKIg+glalp48Z8ScbX0cnLAlMaQaTZ9HjUFPyE9MYSpGiD21/IECFkM4EMsMjIdqW2e3G8sWLx4z4ly1bhra2NgwPD6O5uRmnAmxUNm/ejM9+9rMJa5zyHf/yBKYMmD7uy6guiH3BKxCzTo1L52bjP4fb4PGFX1RKRGswQw4vNh1tx/r5uVG/CIGo0jmfFq8IP3gp9Qr4eriEKzHeSGIQprjcliJJjieW+p5hpBrUY8othoLodLC++96o1+Ro23CwHg983d1Qx1mOMJTWkiwj+uyeqNObUuJjWHiZEAVYgjAIi68yLfCKtWV++8UXQRSKiOsMxoCngZ6eHnz3u99FWgL5LQJTvuNv4+1948HT2gYQAmUc857BXFGeC4al2N8QfmSXiNZgntteC4eHwT0ri2PaT13EdZDu+uix/FLqFRByCKSa6pmfnwIFAc60Jq8IuNvHYFdNj+gFdV15OUhQxqwcbRsO91kuokeZHl+96FBa5+Sa4WFY9DuS9+RY282N4KN1/F4v54Ypp22DKFtmrxcK41jzvkA8AU/eWVlZuPHGGyVQ91/Q8be3x18YxX32LGegJMHK/9oyburiYIR5/kS0BnK6w4q/7mnETUsKYo6q0EwvBgAwfdEf06XSG4i7vg5Eo/F7qiSKQkEwK9uEE0n07BH8gZZOFzcy0xQXgzocYAMW8uRo23B4O7lMbUPFkrj2D6W1gK84VpNEi+YWPmTaqI3c4Xq9XhCFIiYLcjlgPV4QdeTAysBCLFIy5Tv+RGAGB2G69FJJjpVp0qI8z4Ktp+QtqTfk8OIHb56AWafGY9fMjXl/wZLX1zc+c87e9nOSRPQEMi8/BeeGk2fDe4gvAHPlvFxR2/vbvHe82pzruFW54vSKoZyP5W/qi56/IhW1fNlTrSp6t0Y0Wi6ef5y8+VmvF6AsoByfiPop3/GXlZXFva+vtzfurN1QzMu3oL7HPqokYCCJaAWAum47Vv/sYxxrHcSPb5iHTFPsSUAKvR4KgwGu09VRt01UbzCUUnhbWqDhQxylYm6eGTYPkuYR/+6JDhRnGES3vzCtFVgLQeq2jYS7thZAgE10jITSmmvRQaUgaO5PnmfPifYhqBQEyihP6FqtFgqjgfN3kNGsLRJCpTuFPnL28PlCLHEitvxZMMzgIJj+fijTE19IEbhyXi4oBT4OKBASSLxaAYBlKR55/TgoBTbefxFuWFwQ97FU2dlgRIw+E9EbCmZgAOzwMDTTpF2InZ/PrdEcaRmU9Lih6LG5cbxtKGoYZyC6ci6xz9c7sg4hddtGwtPYBP2SJXFPaYbSqlQQFKbpUR1nNbp4ONluhUbMaJ8QfyRNPKZ0kuDjOv5oFtiTshALIeQqQshZQkgdIeSREO9/lxBSTQg5TgjZRgiRdqgHoLo6+sg1FB6+gLSmKPaar+EQsjgF18Zg4tUKAO+c6EBV8wAevXqu6LnlcGjnzIGvtzfqdonoDYVQCCSeOruREKYdarrkn2/ey5d7vG/lDNH7KNPSAKXSHz4MSN+2kfA0NUE7a2b0DcMQTuucXAvaBpLTsfYPe9A+6AzryhmIy+Xy+/TIEbYsxpaZCWHHLPCrX/0K5eXlWLhwIS6//HI089+LSWHLTAhRAvgdgPUAygF8hhAS7FlwBEAlpXQhgP8A+JlcemLF18N9gbUS1j7VqZVYVJiCPXXSz+X+ZXcDSrKMuGNZ4qNlVUaG3xM/mXiFm63EHX+qQQO9aqT8pJzsqu1BmkHtr0QlBqJUQqHXw75tm4zKQsMMDYHp74cmhKlYokzPMKCxdxhMEkI6hcX7iB49ARCNhpvjl2HxVJQtM8NwrpwhnpaWLFmCqqoqHD9+HDfccAO+//3vA5g8tswXAKijlDZQSj0AXgFwQ+AGlNLtlFJh9ecTAJLbE+bGuWAleMKrsqSb4weA1aVZON1hxUCIdPZ4te6p7cWxtiHcc1ExlIrEHw1V2dlgh4fhi2LWFq/ecHhaWmOufSCW+TkG2SuisSzFx2e6sXJWJhQxngfd/PkARvaRum3DITxlaWaIf0IJJpzW3BRuVJ2MMoxCJJVaxFSPSqXyT/fIEdkjxpaZuj1h3UEvvvhiGPjw3gsvvHBUuOxksGUuANAa8HcbgEjhGl8E8F6E9+NiRpwXtD+eXKIMUoEFfGWoo62DuDgozjterb/bXodMkxa3VEjTYWpnlgDgoj1UEWK749UbDk9LM1R5uVAkUPovHPOmZeLF/S3wMmzUOO94+aC6E4MOLy4vj71ymGZGMWxnzvj/lrptw+FpbOQ+P4ERfzity4q5a6dtwOkvySgXJ9qGMD3DAEXAnHhNzU9gs58euzGl3Gjf5QJlWSiGxVcdM5vmYvbsH4Z9X4wtc0lJCTynT4vKD/rnP/85ymxOKlvmCeHOSQj5LIBKAGtDvd/R0eGPHNBqtbj33nuxZAkXc5yRkYF58+Zh165dALi7+apVq3D48GFYrVbY7XasW7cOXV1daOWnEkpLS6HVav1ForOzszF79my/v4ZWq0VhTw+YadOwc/duAFx2Yltbmz9muaysDEql0j+/mZubixkzZmD//v0AOJe+5cuX48CBA3DyC0grVqxAho+b5vnHtiOYl74EDMPg7FnOpsDtdmP1ilVo/fOncKVQeBcZULlsGfbv3w+3m4tIWbVqFWpqatDNT8WkFc7C/oY+XFuixqH9e1BUVIScnBy/cZbFYkFFRQX27NkDH5+wsmbNGpw6dQp9fMjmokWLYLPZ0NDQAACYxmc0Ht32Mdx9fUhLS8OiRYuwc+dOUEpBCMHatWvx8ccf+50CKyoq0N/fj6amJgBASUkJzGYzjh07Juo8AUBRUxOYzCzs2LFD9HlasWIFqqqq/H4x4c6TvasZAMHWT07g8so5Uc9TY2Oj38u9vLx81HkqKChAYWEhDhw4AAAwmUyorKzE77ceR6qWwDxQA58vZ9R5mj9/PtxuN2r5CJrg85Tu9UE9OIg9H30En0oFu92Oq6++OuJ5Ki4uRnp6Og7zbqrhztOxY8cwwNs+B5+nwmPHAYUC+xsagJYWUeepsrJy1PfJ4/GgoqJizHlqOMXpev/AKVw1P1fUeYr1+yScp0P1DiyZlgqGYWCzcWs5PsYHUAqGFSJ3CJRKJXwMA0IAEAJCKViGAeULsygUCoACLBU8fxQghIDlj+HlF2WFzxDOv9PpBMMw6OjoQGpqKtxutz/5SqPRoKenB3fffTeef/55uIeHQQB4CYGbP47ZbIbD4fBn+wpTRAcPHsR7770Ht9sNpVIJvV6P9vZ2OBwO6PV6f3sKZm6PP/44Nm7cKEgLP11BKZXlB8AKAFsD/n4UwKMhtrsMwGkA2eGOtXTpUhov27dvj2u/+ptuos1f/FLcnxuJJT/+gH72L5+MeX379u209eFd/p+uPxyNeqw/7Kij0x/eQuu7bZLp83R10eqyObT3z3+OuF28bRuOsxetpOcef1zSYwq8uOkjOv3hLfTd4+dkOf6JtkE6/eEt9Gfvn45r/4H//IdWl82hw58eppRK37bhaP32t2nt5VckdIxwWlmWpdMf3kK/8fLhhI4fja4hJ53+8Bb6/I46Wl1dHXV7q9VKKaXUNzREHSdOUGZ4WDIt/f39dPr06aNeGxoaokuWLKEbN27kPtdqpY4TJ6iP1xGKDz/8kM6ZM4fW19eP0V5QUDBm+1D/bwBVNEyfKucc/yEApYSQGYQQDYA7AWwO3IAQsgTAHwFcTymVZTUxHu9q6vPBXX0a6oL4QyIjsW52FvbW9Y7xMdEHzfl5mqyg3siJR+8c78D8AgtKsqTJdAW46S2FxQI3Pw0QDil8wQUY+zCYvj6oJYyiCqSIzyT9JMAyw9s5jLZHdqPtkd2gTGIJXn/c1QCTVoWvrIkvOkbHe/H7+CxaKds2Ep6mZmhmFCd0jHBaCSG4oDhd9oIsO85y0VBiQ2iFEEnCx8izbunyO8TYMlP+vcd+8hO8+eabY45x5MgRfPWrX8XmzZtHFbIHJoEtM6XUB+BBAFvBjehfo5SeIoT8mBByPb/ZzwGYAGwkhBwlhGwOc7i4icfsyscblOnmyJNEMyPTCJYCzUFVucp9YyNyXGfDL7BWn7PiRPsQLp8r7UIgIQSa6dPh64p8L5bSSMzbypX+kzqiR+CS1SuQoleP8ojvenak4Ez7Y/FHSpxoG8Lbx87h7uXTYjLEC0Sdw60LOE9ySVzJMGmjLMuFciYY0RNJa36qDsfaBhM6fjReq2qFVqVApcgwZhNvByLE0Eu9wBvNlvnIkaOAQoGT1dUhF8Yfeugh2O123HbbbVi1ahWuv/56/3uTwpaZUvoupXQ2pXQmpfRp/rUnKKWb+d8vo5TmUEoX8z/XRz5i7AjzsLHgaePmHaXOIBVYPZtbMD4QZNg2+C43d2u6KB/G5dwF0ffPEItTPO+d5EaHN1dI/2Siys6GN4pRWDxtGw5PixDKKY+L5oEDB7CoKNXfCTH2sVFVrCP20D6nh8F3XzuKVIMaX1lTErc+hcUCqFT+Eb+UbRsOX3c3qNOZcChnJK2pBg3cPhZDcbStGFiWor7Hjnn5FtGRVMK8OCEECq0OkNiXP5ot86Ky2VCo1fB6vSErrX300Ufo6urC0aNHsWfPHmzePDIePm/LLBJnHJl5nhYhkUiejn9unhlKBcGZIAMrws82pFw9A5bLo3/2ttPdMGtVKIpS5SkeNIWF8DQ3R3wMjqdtwzHS5vKM+J1OJ2ZlmdDa74TLy8C2c+xNbeCtupiOWddtwwMvH0Zttx2/vnMJMuKwyBAghMBQWQnvuQ6/Xrnx8Au8iYRyApG1XjCDj+zptaPzF1UYeL02oc8K5nj7EAYcXnz2QvHfVRrgz6MwGcF63JJ69kSzZWY9HhCtFlu3bo1J63lbZpnxtrQAajXUefLEUmtVSiwoSBn1CMxYR0agRKWA0jQS0si6x9rHdg65UN1hxW2V8oyQtWVlAKX+aS+58ba0QpmeLpkrZyjmF3BJVW0DDth3c0916XfNQerNswAAzuPRs5UFWvsduPF3+7C7tgc/uq4ca2cnHvarzsuD89ixpBmHSRHKGQ2h9KTqn2fh63Vi+FAn+l87K9nxt53ugoIAF5fFZ+NNtFqAZSVP5Apny0wpBfV4olo1hOK8LXMMxFO02tPcAk1hYcisOqlYWJiC421D8PKLisOHuQ5WXTDS8SnM3Hyxt2usw+HOGm7+/Yp5sceMi0Gdyx3XG8EeWMqC4J7WVmiK5CuWsmLFCszK5tr2aIBnj35+JozLYrvBe3wsvvavT0EAfPCdtbg3BnuGSKjz87mbbWdnUoqte5qaQPT6hGsfRNJammOCFoDROtKxOg53g/UkPr3CsBQbq9pQOT0daSIK3ggEFjchWt66IUmePdTl4qzeRZqvGYPKMkrFlO/4G6NEpoTC09Ii2yKjwNLpaWBYiqOtgwAA6/tNAICMz424WqTdwI1E+146Fbw7PjjVhWyzVvSCVqwI6xuCc2Mo4mnbcHhammWb5gE4rfN4szbCj+yVKRoQBRllhCVmnv+57XU42W7FT26cL7qIvRj0vB++p7VV0rYNh7upCZrp0xOuNxFJq1alxPe1Y9to4NXER/2fNPSh0+rClfNH37ijPTG5A6YvFTot581vS07dAKHmgiJM1m4wbhERR/E8IU75jl9IwhELpRSelhaop8vb8Qu1WE8GFQhRpoyMXLSzUgEArHP0VI/bx2BvfS/K8y1QyZSFqsrPB9Hp/HPOoYi1bcPBejzwdXTKOuLv7OyEUkFQkmVESgtnkJV284gPk/ECrvMY3NIQ8ThOD4M/72rARTMzcOMSaRfVhcGG89gxydo2Ep6mpoRDOYHo18GVbu7JOe3W2f61K+epxP2q3jrSDr1aibsuGPmu6nQ69PX1RewMfQGVt4hSCYXRCNaRnLoB1O3hEsdEdvy+KFXCKKXo6+uDTuTxBCZE5u5EguntBXU4oJFpYVdgRqYRFp0KnzT04R7+wnVZ6KjRp0LHn56gEPMzHTa4vCwumyvPNA/ALTaq8/PhaWmR7TMEvG1tAKXQyHyzBYCCVD3m9HDrKbqyETsK89pCDB/shLc7cgfwwr5GOL0MHrxkluTaNEVFUBiN8DQ2ARKaA4aCejzwtrXDcvXVsn5OYPSUdnEWVCoFrB9yC/mUpSBxekt1WV3YdPQcrluUD71mZEq2sLAQbW1t6OkJX2rT5XKN6igZmw2szQYVX5lLTnz9/aBeH9QB9hyRCNYaCp1Oh8IY/a2mfMdfXh5sCBoZoaOTK5RTgBCCbIsOtV12eDu5zkZvHJsIoytLg+vsAFi3Dwotd7p213IXtRQLipHQTJ8Od4QLNNa2DYfQ5nIlbwEjWpfPSAdqx45QVRlc23vb7GGP4fIyeH5HPSqmpWJFSYYsOrVz5sB15jTKv/0tWY4v4GlrAxgm4Rh+IPJ14PiUW4v6GF5c1O/wr7MAgPNkLwwL47uG/7SrAQyl+Nalo2+QarU6qs9Rd3f3qMSo4U8+QcsDD6Lg2f+DRSLb43A03HAj1Lm5KPrj86K2D9YqFVN+qidapftgPM1Cxy//6PPSOdlo6B2Gq5OPK1491lpDM42LRLHvG5ly+eh0NwpS9bKEcQainT0b3q6usEWpY23bcHhljuEHRrTO4v3Ph0rHGmQpDNyNlfpCZ/G+tK8JVpcPD1w8S7YCGbqyMnhq6+CTwSc+EH8opwQdf6TrwLaTO7cvwI1j/HpW+p1cYqS7Mb7EqbOdNrywtxE3LM7HtIzYvwPBeg2VlYBKhWGZcycoy8J99iw0xeIHlVJ9x4KZ8h2/YKwlFk9LM6BScREWMiMsDPZv5R59G7qax2xjWMyNiITOiFKKmi4b5uSaZdenKSoEGMafXBVMrG0bDtfp01CYzVBmyDOKBka0zvNxl3y3bmzHrc7nRqPO6tDzz3/e3QCDRhl36KAYdPPmgXq9qOfNAeXCXcvlLEjR8Ue6DlgHN2hoBItOK7ewKUyxDe8Pv34UDi/D4odvnYRWpcTDV82JQ+1YvUSlgqGyEsN798V1PLG4TnEGdKq8PNH7SPUdC2bKd/yx4m1pgbog31+aTU5WzOQ6OpuOOw2+EJYnynRufm/4EDc9cbhlAA4Pg3Vz5Ot8BLSzZwMA3GfCZw9LgevkSRgqKmQbRQeib+WiN14ZHlsLNuWqYgCjcyoETndY0Wv34JaKwpi99mNBN4+bNlE3yBvV46iqgqakRJQ1cLwEhmyatCq8cZhLmlPoR75bsXgkceVFT+BgUz8ev3YuciyxLWhGwrR6NbwtLf6sfTkQbEmMKy6S7TPEMuU7/oIYjdY8zS2yL+wKFKYZoCQEab1uEI0ipFahM2T5zmgvX71rbam88/sAoCnhDMc8raGtG2Jt23B4u7qgLpD3CUvQ6qrmvI863GPDNlXZ3LTBUIjInpf2NQEA7l1ZLI9AHm1pKYhGg9ShQVk/x9PSDG3ZbEmOFe46cNcNAgBSb5yFvBTdqEpcRMstyHq7xcXP76vrxdW/2Y3XD7fha+tm4u7l8X9HQ+k1rloFALDv2hn3caPhrqsDFIqYpjSl+o4FM+U7/lhWuyml8DQ3yx7DL6BUEKwpTOV+Tw2/Mm9cnguouBvAvnouBj2euc2Y9ZmMUGVlwXXyRMj3Y40kCIW3sxOs1SprDD/AaRVC/LqNShxvt44J+VPw0SFEP/ppj1KK/3zahgyjRlIX1FAQpRLa0lKo6yOHlSYCY7PB29IKTaE0ayrhrgNXDVcDQFNgwiVzs9HU54CHn7JM/ww3TTN8MPp0z6Gmftz74iEMOrx4/Jq5+P6ViZknhtKrnV0KVV4ebB9+mNCxI+E6fQaakhlQxOC8KsV3LBRTvuOPxezK09AA1m6XtM5uNC7mI3lUawvCalWYNICPYrjNik8a+mUxZQuHbt48uMN0QlIYiQlzzTqJIoTCceDAAfh6uNGlMNoM9koCAFWOAdTpGzUF8fGZbvhYiq+ti78geSzol1bAffo0qEwLe+6zZwFKYbhgmSTHC3cdDH/CdeqaIjMKUrnr/HDLyM0A4LJ4I9Fnd+O+Fw4h06TF5m+sxJdWlyQ8JRhKLyEElvXr4dj/Cbwy2ZS4zpyBriy2dQm5zPqmfMcfCy4+dFHPV/dKBov5xcYGRfi5Tg2/6NhwlpvmWShzGbtAtLNnczdEmVLa/dElMo1sAhGiSHTruM+qPje2FqwQ1unrHfn//vsgt7j9mQuS8ySoX7gIhGXhPHJEluMLbZ6MAAaB1fzU5Fn+Zqs0c4mK1B355va/75yGze3D859dimyzdHP6oUi96UYAwNAbb0h+bGZwEL6ODujmxrcgLTVTvuM3xWD65eXnsuUyZwtF+pAXLCheq+sJq1UzjYvgsR/gFngvi6Oma7zo5s8DwC3ABhNL24bDVV0NZVpaTJEO8WAymeA4yo0uC0u5qJK99WNN2cyruKcp3yCXKu/2MfjodBcK0/QwapOT9mJccSEAyBZe6Dh8BMrU1IRdOQVCXQfuJu4ma1rD3WQL07gb6q6asYlV1Bu6899+phtvHmnHV9eW+GtVS0G461ZbWgrdwoUY/M/roGxihXmCcfElJbVz5sa0nxTfsVBM+Y6/srJS9LbOI0egmTEDSotFRkVB9DjhBFDbZQurVWHiYs8Ndi8UhFsUThZ6vjKU89RYv6BY2jYcnoYGbkFT5oieyspKMFYPFGYNzGl6lGabcLpj7FSPMpUzz7Lt4Eb5+/jF9DtkckENhSojA5pZM2ULL/Q0NUEzc6ZkWaqhrgNPK5ebYljEjfTVSgVmZhnR0DsSTZV2CzelGmqB1+Vl8J3XjkKpIPj2pdIsQkfS69d0+23wtrdjeN9+ST/TvmcviEYD/eJFMe0nxXcsFFO+4xeKNYvB09IC7SzpU/HDwfKPuftTlTjWNoQ9Yb7ohBCoS1KQwRKskzlbNxhVbi6UmZlwHj025r1Y2jYUrMcD15kz0MyMv4CJWPbv3QemzwV1DnfTnJllwukOK1xBo00VHz4rZEn/+yAXgnfX8uRM8whYZ86C88gR+Pgi6VJBGQau6mpoS6W7zkNdB0L4sTp3xKDtghnpaOwdhs3FRVQpLNx0j33/uTH7/3lXAwYdXvz0loWjLBnk0itgufZaEL0eA//4h6Sf6a6vg6a4OGbb8US/Y+GY8h2/GHc7gJuD40ZC8ndCAsLjcE4R94RR1xt+Hr2Tj/W/sXhsdq+cEEKgX7QIrhMnxkTBiG3bcHgam0Ddbi5zUmbYAd6fZw43zbOqlGvH0x1j5/kVBhVcZ/rhY1jsrOlBeZ4loSIr8eCYVw5QimG+hJ9UeJqbQV0u6BfGNvKMRPB1QCmFr8cBda4BRDnyJLe4KBUAUNvNPQ3oZnPOsoHbAIDN5cUvP6wBANwiQyBDpOtWodMh86tfgX3nTgx/8okkn0cphev4CX9eTCwk+h0Lx5Tv+MXirqvjIh2WLk3aZ/p4Q7CS5dwi28ne8AtdRzTcnOMFluRN8wjoFy2Ct70dvu7wxlfx4K7hvtzJCJ/V93Odi6aIWy9ZOYvr+LeeGhvBoV/Avbf/RCfcPhY3LE7eIqiAd+ZMKMxm2Ldvl/S4zmPHASCuTkgszJAHoBhT5+BC3t/oVX6xnBACKAiGD4z2TvrJFm4+/JmbFyQlqS+Y9HvugSo/D50/ehJUAusMb3s7mIEBv+32RGDKd/yr+MSMaAhfCDmrEQXjabGBqBUom8V9Id5tDr+g9LtjfB3gxrEjVLkx8Bes8/Cno14X27bhcJ44DqLTQTc3tgWveJhu4haPhYVywS7j+Z31Y7bVzeXOx8mPOQuNO5MUzRPIqrVrYVq3DrbtO8BK6NvjOl0NotdLGl0SfB14WrlrVPCZEpiewbX5q1UjFiDqPO414WnS7vbhtSouyOLOZfKsq0S7bhU6HfJ+9CN4mpvR+5e/JPx5QnSWPo6Q5US/Y+GY8h1/DT+qjIbz+HGop02T1RM+GHfjkD+m/ILidDjCVCXqHHKhh7LwkrHe/MlAv3AhoFbD8enhUa+LbdtwuE5VQ1tSkhR7DHtNL9S5xlEjSKGIDcuOnsLSFnMdlrV7GGadCil6tez6gqmpqYHl6vWgTifs27ZJdlznkaPQzpolaXW54OvA02oHlMTfqQdyEW9TIlSe08/n/hayfH/yNjfaf+6uJbKN9sVct8Y1a2C+8kr0/v4PsO/aldDnOY8cBdFqoeMDJWIh0e9YOKZ8x9/dHTlBBOBc8xxVVdBJlMIuBuplwNq9/sfhaxdxI9KdIcLdXuc9TryFJrhORS4yIQdEo4FhyZIxXwAxbRsO6vPBdfIk9JXyT61RHwvlIAOFcfQN5rZKLtTwg+rRUw0KnQpOvRJXQh23EViidHd3w7RmDZQZGeh/8SVJzjn1+eA6fVry6czg68BdPwh1vglENbZ7uaWCa/PXP+WuacOCLP8+Li/jfxq4dqF802tirltCCPJ+/BS0JSVo++a30P2LX4AZis9N1FFVBd2C+XHdbBP5jkViynf8YvA0N4Pp64NxzZqkfaa7iXscFqJIbua/EPtCxJb/YQc3HZE9MxUAwNqkLQwtBtPaNZyJVfNYB9F4cJ09C+p2QzdX3oxdAHDVcpExwXPO6xdwN9sDjf1j9mlxemAGwW1L5U8sCwdRKpF5//1wHjsGhwQx/c4TJwCfD7ry0FNrrIdJuBYuY/fA226HriR03P1VfJnEffVcmKwyg7v+7fvO4Zl3OTPAf3zxgoQ0SIUyJQVFf/oj9AsXou8vf0Xz5z4PX//YayUSjNUKd00NDMukyZKWiinf8c+fPz/qNsO7ucgJQxIzdoXSc7q5XJSJSavC/Dwj/rizYZSZVWu/A3a3D0umpUJXyk1NuGpiu/ikwHz55QAA63vv+V8T07bhcB45CgARvxBDHzaj7ZHdaHtkN8795JOwiT5RP8vf1qNtny06NZZMS8ULe5tGTfccax3E+/BCCwJFvyuuz0wUoW1Tb78NyqxM9P7u9wkf03mYm2s2LL9w1OuUUnT95jDOPbEP557Yh7ZHdsPTKr4GbeB14DjKPbEK13UwRq0K68qysPnYOdhcXhBCoJlmBvWweGk/N6hYOVPayDVKKbydwxg+1AlPuz2m61adm4vpf38JRX/5CzxNTWi5976Y6vMKSXjG5ctH9HgZ9P2jGm2P7Ia7JfKaXSLfsUhM+Y5fTDjU8MEDUGZlRo3hpwyLdv6L0fbIbnT95jAoE98juKfVBmWKFkrTSI3dpYVcjO/LB0ZG1X/gFx+/d3kZtPzCZCxfSqnQTJsGbflcDL29xf9aIqFmjoMHoUxPD+vK2fvCSdi2jZR9ZIe9aP/hPlBv7BmVnhYrkKGGQjv2UXsNbyXw70Mjn3X7H/fjDLibjJCIlGyEtlVotcj88lfgOHQI9gQ9+ocPHoAqNxeq7NG5IJ0/OwTvudE21d2/Oxq1UwrWCvBz9QpAMz18EuTlfOb5jzZxSYEa/ulgFhT49Z2LJbW9ZmwetD+6B13PHsbA67Xo/u0RuH5xGqwjtqdm06qVKPzdc3DX1aHzxz8Rvd/wnr2AWg394sUAAFf9INp/uM8/GOn5/TGce/qTsFN558M546S2tjbi+5RhMLx7D8zrLo64nW/QjfbH9oIGPAp7zw2j/bE9YN2xLbgyNu5x2LBk9BdwmZnr0H+46RQopbC6vHj5ANchrZyVAaJWQjPNDMfxsdNBYqEsheecHdZtLXCejq3gdeqNN8JTXw/nCc6+IVrbRsJRVQXjihUhF/Cs21vgOstNz6TfUYbsb4w8ibX/cG9MI3/fgAu+bid6M0J/ge65qBgA8NibJ+FjWGw+dg5uH4tqMCBqBZwn42/rRAhs29Q774B62jR0PP7DmKcaBKjXC8fBQzCuWjmqza3bWsAMcG2T98MLUfDMKqRcw1k59Pz+GHx90T2aBK3Uy8JVOwDDkpyIC7NCFvQbR9qx6Wg7vnmcu8bXQI0bFksXt+841o2Op0NPkZ378Sd+Ww6xmFavRsZXvgzr22/DtmNH1O0ppbDv3gXjsmVQ6HTw9bvQ++cRp1v9Qu7JhrV50fXs4ZCdfyLfsUhM+Y4/GsP79oG63TBetCLsNtTHonPDQf/f2Q8uRvYDi/1/n/vRfjA28SF3QqcWPPVg0hB/1MO8H23Fwic/AAB857LZ/i+SdlYqqNM3ykRMLO6GIbT/YA+6f3ME1g+b0fcS97g5GMJ/PhSW664DlEoMbtwY82cH4qquBtPfH3KaxzfkhpWvSJb9rQoYlmRDU2BCwTOroOZj8Hv+dlL0YqfzJHdzG84OvX2aUYMLZnDTErMeew/f/Dc3HfLGAyuhyjbA3SxN+Czr8sHTZsPQh80Y/rQLnjYbKCvu/6DQaFDwi5+DGRxE2wMPxhXe6Tx+HNTphPHCkevcN+jyFz7Pf3IFlEY1CCEwry702yn0/Om46But83QfwFB/HkQ4VEoFHlnPLZp/65Wj2NvPPVV9aY50HlnDVZ3o//dI9aq8x5ajcMNq5D858v/v3HAwbJnNcGR+7WvQlJSg44c/jJpV7a6phe9cBwwXLANlKHr/xg2YUq4tQeGG1ci4ay4K/t8qKNN18HU5YN8jXxGYYKZ8x18UJTxz6O23QdRqmC4OP+LvenYkjLHg/62CptDMWc0+swpaPvuw+/dHRVcTclb3QWFS+5OJArX+5jPc6DYwtPPBS0amoIxLucdkR4wj0cG369Hzp+Mh37PvaUfbI7uj6lelpcFy5RUY2rQJnrb2qG0bDusH3A3NdMnoNqeUovev3Igo7bbZ0ASEAxJCkPPAYujnZ8DTaIV939g0/1A4jnRBlW1A7pzwi7Qv3Tt6MfG2pYVYVJQK/YJMUKcPnvb4pntYN4Pu3x3l1iie3I/u547Ctq0FAxtr0P3cUbT/YA/aHtmN4SNjIzeC21a/cCHy/vd/4TxyBG0PPBizlYPj0CEAGDXA6fsnt5iaed98KHSjI56My3JhXlcEZsiDgbfG5jqE0uo43A2FSe3PyI3E/WtnYj2/0MsAYEtTwZ4ZiLkjDsXAm7UY+A83UlZl6LjOlXcDVehUcH85H3q+pGn3c0djiphSaDTI/9nPwAwMoudX/xdxW9sHHwCEIPXWWzH0fiN8vU6Y1hT4jQABgCgIcr9XCc0MC4beaRzzhBnvdyzq/0OWo04gcnLCO1ky9mHYtn4AyzXXQKELbfk68Fadf3Rd8PRKkID5R0IIsu6bD9PaQjADbvT983TUC5d1eOE62w/9vIxRxxK0Zpq0o6IaTv/4KigDtlNl6KHON8J1Wvwjv3VbC+x7uY4y5eoZKNyw2v+Tet2IRUX7Y3ujPv5mf+97AICu//f/kJ0de/lHSims770H/dKlUAft7zjcDV+3E0St8N/ggkm/ay60s9Mw9HYDhg9H9k33tNngPTcM4/Jc5OSGH03qNUqc+clVuKOyCC/cuww/v42zMzAs5vQ5jseWsUwpxcBbdTj3o32i1mMGXj2Ltkd2j2r7UNdtynXXIvdHT8DxySeov/IqtH3zW36L5WhYt34A3fz5UKVzTzfO6j542+zQlqSE7ahTriqGcUUeHJ92YejD8NFcOTk58LTb4TrTD+Oy3DHXdTj+8NmlaNpwDZo2XIM0vg6vM4brOhhKKbp+fdifCawrz0DuQ8tCfs8y7pwDXXkGvJ3DGNwc+cYWjH7+PKTd9RkMvv46l/EfBtsHW2GorIS3h8C+ux2aGSlIvXqsJQxREmTeMx/qAhP6Xjkz6ikzUv+VCFO+46+qqgr73tAbb4C63Ui9/baQ77tqB/zFJPIeXw6iDN1cqetnwHLZNLhO96P3pVMRO//hqi6AoTCE6NgEratLs/xfiFAGVbq5GfA0W6N20pRhMbilwf84n/fDC2FeM3rka1pZgLzHRyIOOjcc9FdOCoW6oACZX/867B9/jFO/fS7i54fCeeQovM0tsFy9ftTrrIfB0LuNfp3hIAqCjM/OhabYgoGNNRE7f+u2FhCNAoZFWRGvAwDQqZX46a0LRxVSV6VqoZ2ZAuexHtGjQtbhRfvje/3XDQBkfnnBqJut8JP/5AqYVo+M/jo3HIR9L/e4H05v2mc+g+LXXoX5kkswvG8fGq6/Af0vRY7zd9fWwn36NFKuvw4AF3I58EYtlGlaZN4bOWok9bqZMFRkw7atBUPvNYacnqo6VIXBTXVQGFQwr45vjt64jPs+xLum4m4aQvuje+Dt4BapMz43F5mfDx0qLLRtxmfnQjcnHcP7O0IaxUUi8/77oTAa0fWzn4XWU18Pd20djGuuRf8rZ6HONSDz3nlhj6fQKpHxublQWrTo/esJfwhytOs2XqZ8xx8OxmpF7x//CMOyZTBUVIx5n3X50PtXbk4u+8HFo6JvQmG5bDpSr58Jd+0guv9wDK6asY+tzLAXtp2t0M5KhXZa/NbPBn5RKNJ0B2N1o+/v1bDvaYehIhsFP1kJpTF0BqrSpEHhhtXQlXPrC71/O4mu3x4JOwed8aUvQl9RAcu//w373r0xaR96800QjQaWq6/2v0YpxdCWBrDDXqTdPttfAjEcCo0SmffOg2a6BQOv1aD3xVOw7z83KlLDfqADrtP9MK8tinruImFYkg1m0A13ffTkHefJXnT+ogrgI71yH1nGtSuffzHm/6FTIfWaEhQ8swrmtdwNefDtBgy8WQsSYVpdN3cu8jc8g5J334HxoovQ9cwGdPzwh2Erdg1teQdQKGBZvx7Ux6L/5TNgnT5k3D0XRB25CyAKgrRbZ8NQmQPbzjZ0//4orNtb4Wm1gfpYMHYPsk4ReFpssFxVDIUhvixnhVYF4wW5cJ7ogW9I/KIrY/Wg+3dH0fP8yDRm7sPLoJ8XPSSUKAgyPjcX2tJUDG6qh3V7i+gbvCo9HZlf+xqGd+0eFekmMPD6G1AVLIO7tYDr1O+ZF/W6VqXqkH3/IijTdOj920kMvl0PlUOUnJiRNVeeEHIVgF8DUAL4C6V0Q9D7WgB/B7AUQB+AOyilTVJqsITw1qcsi87//V8wAwPIfuh/xrzP2D3+Tj/lmhJoCs1jtgmF6aJ8KMwaDG6uQ+/fToLolDBW5EA3Nx1ErcDgpnqwTgYp60MXwAilNRTqHCMMS7Jh39sOw8JMvz7KUi4D8uwAhg90gLIUqdeVwLRS3Cgs8/PlcJ7pR9+Lp+Btt+PcTz5Bxp1l0M5OGxWlQZRKFP72Nzh7+x1o/fJXkPGVLyPzy1+Gwjg2RT8Qb3s7hrZsgWX9eqjSRqYX7LvaMXywE6ZVBTBWiHu0VWhVyPrSAth2tsG+/xxcZ/oxuKWBswEmgLfNDs2MFH+HKrZtg9HPy8TQu42wbmuGdmbKmGgVSim8bXbYdrTCeaoPynQdMu8og64sdBx7KAghSFk/A6ZVBRjcXI/hA52YblbBmd8H3Zz0sBEy6uxsFP7h9+j5v2fR96c/wdfTg4Jf/gpK08h5YIaGMPDyyzCtXQuFJQ19/zwNd8MQ0m4tFX1dEwVB2i2l0M5IgW17K6xbm2DdOvJ+ChTcuVuW2OKseW0hhg92wr73HFKvjlwkxtfnhP1gJ+w72/yvpd0+W9T1E3gtEKUCmV+Yh/7XzsK6tRnuRissl06DZpo5bLuzTh+8HcPQzLwUxku9GHy3H87aQ1x9A6UC1OeDp2Me9MsuhCrLgIy750KVKq56mNKiQfbXFmHw7QbY953DNJUS9HI26g06Vohc6f+EECWAGgCXA2gDcAjAZyil1QHbfB3AQkrp/YSQOwHcRCm9I/hYlZWVVKpHHm9XFzp+8BiG9+5F5te/jqxvfsP/HmUpXNV9GNxcD8buQfrtZf553lhgPQzcdYNwHO3m4nWFWH8lQfrts2FYFPsxg2FsHi4EzMfCvKYQrIeB80g3/n975x4jV1nG4ec3M3vv0tqlSEO5VclCsbRcIhBrUxCRW/jD9A8vUUMQNREDiWIgEIMi8RKDgFFDwFqDBA2IlBCkIlIxSlCorW2BpRTQFkrvt21353Ze/zjfbqfrtJ3uznTO2X2f5GTO+ebszDNnv3nnnO+c877l3QUQdMyZxuSPnzxcSvBIsHLEzifWMbBqK9G+Ei3Hd9J53vG0nz6VXE/78Bei3N/Ppju+y64lS8h0ddF53nm0zTqD7gULaJ89+4BCH1E+z/ovXsfA6tXMXPI4rSedRDRYYs+y9exZtoH2M6bS87lZNY8Pj6Twbj8DK7dQWL+HqFCm44weuuefgFrGnpOm/4V32blkHZOvOJXu+TPiS2LX72Hfy5vIv7mL0tYB1Jqle/4JdC84sWqqgiNh4LXtcR/cPkjL8V10zJlG60nd5Ho64qO2nMDiG4EsHxHlS+xa8iTbFz9EW+8spl77ZTIt7VgpYvdTSxlYuYrJCz9DYX2ZaG+ByVfOpLvGnYFqlPsL5NftHK5h3N479f8uVBgt2x58hYHXtnPc9WcfcHLfihGFd/bE5xL6dpBfuwMsLpxzzCUn0XnuoS8hPRwWGf0vvMvuZ/6LDZbIdLeSO7Zj+N4PK5SJ8mXKO/NEe/cfWapDFDe8TnZyBx2zz4LIyK9dS+Ht15n6+cs55tLZo+7TpZ15Spv31XSyvBqSXjazqjnPGxn4LwRuN7NPhOVbAMzsexXrLA3rvCApB7wHTLMRUqMN/I/d9SWicplMJgMGmVKEwg1A1tmKtbcAImMZ2qI22qI2hCipxPaWbQxmxn7XZoYM7eV2QOQzg5QPcQxfLBZpaan9ULnFcvQUjqXV4lzxA5kB9mb7GcwMEmnsV0dkyDCpPInOUhetFg+XlFWmpBJlykRWjoN7VIZCGcrxZAAZQS4L4byIihEqG9bRguWyZC1Le9SOEP3Zfna0bKORGYiOdNtWIqCncBydUQcFFcmSIWtZDGMwM8hAdoB9mb112eZDlIolpmSmMKk0afj/OxYMI58ZZFduF/lMfW8KGsu2HUnWshxfmI6APdk9GEZb1D7cVwBKKrIvO0B/dg8lHXnSwkP5ZsjQWe6kLWojZy3I4vc0GRFR6P9FiipSyBQoq0x29yDZHfuIOlogmyHTnyea1Eap59BHwLW5Rlz7tdEVhTlU4G/kUM8JwPqK5Q3A+Qdbx8xKknYBPcABZ3g2btxIb28vAG1tbVxzzTWcHdIr9PT0cOaZZ/J8SCCWy+WYN28ey5cvZ9Lsv5DN1t4xhr62ChJpYeinpDVMjXwPiDtNPTrO0PbuCFPSKROPWQ7Nw/5tXr+KsNXftx60AEe3jM/oMaCyVlXlT6qArjCNdwr5dpYtW8bMmTPp7u5m5cq4Et7B4t5tt93GI/vvsznov7uRe/wLgcvM7Ith+XPA+WZ2fcU6q8M6G8LyurDOAYF/tHv8f7r/Tnbs3sWUnimQyUBrbvh09v5PbVjGsPpWdxsVW7ZsZdq0tHw1a/A1YCAPUQRdHXD0a2oMM+627aEwYO8AZHNwFFJKN2rbqgwyEeXqG6Ma1heG0onUcTx+29YdfPa67x1+xSo0a4//HaDy7oMZoa3aOhvCUM9k4pO8deGS624liqJ4qCcFpMkV0uWbJldIl2+aXCFdvlFUv+HDShr56f8JnCbpVEmtwKeAJ0as8wTwhTC/EPjzyPH9sbJmzZp6vlxDSZMrpMs3Ta6QLt80uUK6fBvl2rA9/jBmfz2wlHhodJGZrZH0HeAlM3sC+AXwoKQ3gO3EPw51Zdu2uh1ANJw0uUK6fNPkCunyTZMrpMu3Ua4NvY7fzJ4CnhrR9q2K+UGg+m2zjuM4TkNIx0DXGJgzZ06zFWomTa6QLt80uUK6fNPkCunybZTruA/89913X7MVaiZNrpAu3zS5Qrp80+QK6fJtlGvDLuesJ2O5c7e3t5e+vr7Dr5gA0uQK6fJNkyukyzdNrpAu37G4HupyznG/x+84juMcSCr2+CVtAQ6eEPzQHMuIO4ETTJpcIV2+aXKFdPmmyRXS5TsW15PNbFq1J1IR+B3HcZz64UM9juM4EwwP/I7jOBOMcRv4JV0mqU/SG5JubrbPSCQtkrQ5JKobapsq6RlJa8Pj6BJx1xlJJ0p6TtIrktZIuiG0J9W3XdI/JK0Mvt8O7adKejH0id+GVCKJQFJW0r8kPRmWk+z6tqRVklZIeim0JbUvTJH0qKTXJL0q6cIEu/aGbTo07ZZ0YyN8x2XgD0VgfgpcDswCPi2pegHO5rEYuGxE283As2Z2GvBsWE4CJeDrZjYLuAD4atieSfXNAxeb2RxgLnCZpAuAHwA/NrMPAjuAa5un+H/cALxasZxkV4CLzGxuxeWCSe0L9wBPm9npwBzibZxIVzPrC9t0LnFVwn3A72mEr5mNuwm4EFhasXwLcEuzvap4ngKsrljuA6aH+elAX7MdD+K9hLiyWuJ9gU5gOXEtiK1ArlofabLjjPCFvhh4kjiBdSJdg8/bwLEj2hLXF4iz/b5FuIglya5V3C8F/tYo33G5x0/1IjCjrzV39Hi/mW0M8+8BtRWgPYpIOgU4G3iRBPuGoZMVwGbgGWAdsNPMhirzJKlP3A18k/31RnpIrivEGf//KOllSV8KbUnsC6cCW4BfhmG0ByR1kUzXkXwKeDjM1913vAb+1GPxz3uirrWVNAn4HXCjme2ufC5pvmZWtviQeQbwYeD05hpVR9JVwGYze7nZLkfAPDM7h3go9auS5lc+maC+kAPOAX5uZmcDexkxTJIg12HC+ZyrgUdGPlcv3/Ea+GspApNENkmaDhAeNzfZZxhJLcRB/yEzeyw0J9Z3CDPbCTxHPFwyJRT8geT0iY8AV0t6G/gN8XDPPSTTFQAzeyc8biYeg/4wyewLG4ANZvZiWH6U+Icgia6VXA4sN7NNYbnuvuM18NdSBCaJVBam+QLxWHrTkSTi2gmvmtldFU8l1XeapClhvoP4fMSrxD8AC8NqifA1s1vMbIaZnULcT/9sZp8lga4AkrokdQ/NE49FryaBfcHM3gPWS+oNTR8DXiGBriP4NPuHeaARvs0+idHAkyNXAK8Tj+3e2myfKn4PAxuBIvGeybXEY7vPAmuBPwFTm+0ZXOcRH17+G1gRpisS7HsW8K/guxr4VmifCfwDeIP4MLqt2a4jvBcATybZNXitDNOaoe9WgvvCXOCl0BceB96XVNfg20VcfnZyRVvdfT1lg+M4zgRjvA71OI7jOAfBA7/jOM4EwwO/4zjOBMMDv+M4zgTDA7/jOM4EwwO/4zjOBMMDvzOukdRTkeb2PUnvhPl+ST9rwPstlvSWpK+M8u+fC25Vi2Q7Tj3IHX4Vx0kvZraN+CYeJN0O9JvZjxr8tjeZ2aOj+UMzu0jSsjr7OM4B+B6/MyGRtKCi6Mntkn4l6a+S/iPpk5J+GIqNPB3yFCHpXEl/CVkplw7lTznM+yyWdK+kv0t6U9LC0D5d0vPh6GO1pI829hM7zn488DtOzAeIE6RdDfwaeM7MZgMDwJUh+P8EWGhm5wKLgDtrfO3pxGkvrgK+H9o+Q5xjfy5xgZAV9fkYjnN4fKjHcWL+YGZFSauALPB0aF9FXDCnF/gQ8Eycs44sca6lWnjczCLgFUlDudT/CSwKPyiPm9mKunwKx6kB3+N3nJg8QAjQRdufxCoi3kESsMZCaTwzm21mlx7JawcU3ud5YD5xuuXFkj5fjw/hOLXggd9xaqMPmCbpQojrE0g6c7QvJulkYJOZ3Q88QJwn3nGOCj7U4zg1YGaFcGL2XkmTib87dxOnJh4NC4CbJBWBfsD3+J2jhqdldpw6ImkxcU79UV3OGV5jGfANM3upXl6OU4kP9ThOfdkF3DGWG7iIi50U62rlOBX4Hr/jOM4Ew/f4HcdxJhge+B3HcSYYHvgdx3EmGB74HcdxJhge+B3HcSYY/wPEH8SYoui7jQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.plot_dynamics(init_state, barely_a_seq * 10, debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that at this point, we only multiply already computed matrices. We don't need to solve the equations of motion again for new sequences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Open-loop optimal control\n",
    "\n",
    "In order to improve the gate, we create the optimizer object for open-loop optimal control. We notice some leakage into the $|2,0>$ state and enable a DRAG option. Details on DRAG can be found here: https://arxiv.org/abs/1809.04919 The main principle is adding a phase-shifted component proportional to the derivative of the original signal. With automatic differentiation, our AWG can perform this operation automatically for arbitrary shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.devices['awg'].options = 'drag_2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the moment there are two implementations of DRAG, variant 2 is independent of the AWG resolution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To define which parameters we optimize, we write the gateset_opt_map, a nested list of tuples that identifies each parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_gates = [\"X90p:Id\"]\n",
    "gateset_opt_map=[\n",
    "    [\n",
    "      (\"X90p:Id\", \"d1\", \"gauss\", \"amp\"),\n",
    "    ],\n",
    "    [\n",
    "      (\"X90p:Id\", \"d1\", \"gauss\", \"freq_offset\"),\n",
    "    ],\n",
    "    [\n",
    "      (\"X90p:Id\", \"d1\", \"gauss\", \"xy_angle\"),\n",
    "    ],\n",
    "    [\n",
    "      (\"X90p:Id\", \"d1\", \"gauss\", \"delta\"),\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the two parameters this opt_map specified with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(), dtype=float64, numpy=0.5>,\n",
       " <tf.Tensor: shape=(), dtype=float64, numpy=-333008821.28051805>,\n",
       " <tf.Tensor: shape=(), dtype=float64, numpy=-4.440892098500626e-16>,\n",
       " <tf.Tensor: shape=(), dtype=float64, numpy=0.0>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.gateset.get_parameters(gateset_opt_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More human friendly output is generated by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X90p:Id-d1-gauss-amp        : 500.000 mV \n",
      "X90p:Id-d1-gauss-freq_offset: -53.000 MHz 2pi \n",
      "X90p:Id-d1-gauss-xy_angle   : -444.089 arad \n",
      "X90p:Id-d1-gauss-delta      : 0.000  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(exp.gateset.print_parameters(gateset_opt_map))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from c3po.optimizers.c1 import C1\n",
    "import c3po.libraries.algorithms as algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The C1 object will handle the optimization for us. As a fidelity function we choose average fidelity as well as LBFG-S (a wrapper of the scipy implementation) from our library. See those libraries for how these functions are defined and how to supply your own, if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = C1(\n",
    "    dir_path=\"/tmp/c3log/\",\n",
    "    fid_func=fidelities.average_infid_set,\n",
    "    fid_subspace=[\"Q1\", \"Q2\"],\n",
    "    gateset_opt_map=gateset_opt_map,\n",
    "    opt_gates=opt_gates,\n",
    "    algorithm=algorithms.lbfgs,\n",
    "    options={\"maxfun\" : 10},\n",
    "    run_name=\"better_X90\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we supply our defined experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt.set_exp(exp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything is in place to start the optimization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opt.optimize_controls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After a few steps we have improved the gate significantly, as we can check with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0006394 "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt.current_best_goal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And by looking at the same sequences as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.plot_dynamics(init_state, barely_a_seq, debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp.plot_dynamics(init_state, barely_a_seq * 5, debug=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}