From 952197f52e9a8ebbee7546990f8dd78fe78ac4b0 Mon Sep 17 00:00:00 2001 From: Joe Date: Fri, 28 Jun 2024 08:33:37 -0700 Subject: [PATCH] sphinx docs --- .github/workflows/sphinx_docs_to_gh_pages.yml | 29 +++ binomial_cis/__init__.py | 4 +- binomial_cis/conf_intervals.py | 116 ++++++--- binomial_cis/mixed_monotonic.py | 80 +++++-- binomial_cis/plotting.py | 87 ++++--- binomial_cis/volume.py | 221 +++++++++++------- docs/Makefile | 20 ++ docs/_include/api_reference.rst | 7 + docs/_include/background.rst | 165 +++++++++++++ docs/_include/community_guidelines.rst | 5 + docs/_include/notebooks.rst | 31 +++ docs/_include/potential_pitfalls.rst | 30 +++ docs/_include/tests.rst | 50 ++++ docs/conf.py | 32 +++ docs/index.rst | 126 ++++++++++ docs/make.bat | 35 +++ joss/paper.bib | 20 ++ joss/paper.md | 5 +- requirements.txt | 126 +--------- setup.py | 5 +- tests/2_side_validation.ipynb | 37 +-- tests/conf_set_validation.ipynb | 71 ++++-- 22 files changed, 986 insertions(+), 316 deletions(-) create mode 100644 .github/workflows/sphinx_docs_to_gh_pages.yml create mode 100644 docs/Makefile create mode 100644 docs/_include/api_reference.rst create mode 100644 docs/_include/background.rst create mode 100644 docs/_include/community_guidelines.rst create mode 100644 docs/_include/notebooks.rst create mode 100644 docs/_include/potential_pitfalls.rst create mode 100644 docs/_include/tests.rst create mode 100644 docs/conf.py create mode 100644 docs/index.rst create mode 100644 docs/make.bat diff --git a/.github/workflows/sphinx_docs_to_gh_pages.yml b/.github/workflows/sphinx_docs_to_gh_pages.yml new file mode 100644 index 0000000..bfc463c --- /dev/null +++ b/.github/workflows/sphinx_docs_to_gh_pages.yml @@ -0,0 +1,29 @@ +name: Sphinx docs to gh-pages + +on: [push, workflow_dispatch] + +jobs: + sphinx_docs_to_gh-pages: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: ["3.10"] + + name: Sphinx docs to gh-pages + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: Setup Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Installing the Documentation requirements + run: | + python -m pip install --upgrade pip + pip install .[docs] + - name: Installing the library + run: | + python setup.py install + - name: Running the Sphinx to gh-pages Action + uses: uibcdf/action-sphinx-docs-to-gh-pages@v2.1.0 \ No newline at end of file diff --git a/binomial_cis/__init__.py b/binomial_cis/__init__.py index 80739df..c607684 100644 --- a/binomial_cis/__init__.py +++ b/binomial_cis/__init__.py @@ -1,6 +1,6 @@ from .binomial_helper import binom_coeff, binom_pmf, binom_cdf -from .conf_intervals import accept_prob, llc_accept_prob, binom_ci, CDF -from .conf_intervals import accept_prob_cp, llc_accept_prob_cp, get_ps_cp +from .conf_intervals import accept_prob, llc_accept_prob, binom_ci +from .conf_intervals import accept_prob_cp, get_ps_cp from .conf_intervals import accept_prob_2_sided, llc_accept_prob_2_sided, max_accept_prob_2_sided, UMAU_lb, UMAU_ub from .mixed_monotonic import Interval, mmp_solve from .plotting import plot_expected_shortage_mixed_monotonic, plot_shortage_curve diff --git a/binomial_cis/conf_intervals.py b/binomial_cis/conf_intervals.py index 879dcb5..b3b32b7 100644 --- a/binomial_cis/conf_intervals.py +++ b/binomial_cis/conf_intervals.py @@ -102,16 +102,27 @@ def F(t): return CDF(t, p_0, n) def binom_ci(k, n, alpha, side, verbose=True, randomized=True): - """ - Inputs - k: number of observed successes in n trials - n: number of trials - alpha: miscoverage rate, P(p in CI) = 1-alpha - verbose: whether to print intermediate updates - randomized: if true solves for the UMA bounds, if false then solves for (less efficient) non-randomized bounds + """ Compute a binomial confidence interval. + + Parameters + ---------- + k: int + Number of observed successes in `n` trials. + n: int + Number of trials (samples). + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + side: {'lb', 'ub', 'lb,ub'} + Selection of lower, upper, or 2-sided bounds. + verbose: bool, default True + Whether to print intermediate updates. + randomized: bool, default True + If True solves for the UMA bounds, if False then solves for (less efficient) non-randomized bounds. Returns - CI: either a lower bound, upper bound, or simultaneous lower & upper bounds + ------- + CI: float + Either a lower bound, upper bound, or simultaneous lower & upper bounds (returned as a tuple) """ if side == "lb": print("Comuting lower confidence bound") if verbose else None @@ -271,8 +282,6 @@ def accept_prob_cp(num_args, args): return accept_prob -llc_accept_prob_cp = LowLevelCallable(accept_prob_cp.ctypes) # SciPy LowLevelCallable version of accept_prob - def binom_bisection(k, n, alpha): @@ -337,13 +346,21 @@ def F(p): return -binom_cdf(k, n, p) # negative so now monotonically increasing def get_ps_cp(p, n, alpha): """ - Inputs - p: true success probability - n: number of samples - alpha: miscoverage rate, P(p_ <= p) >= 1-alpha + Compute all possible Clopper-Pearson lower bounds given `n` trials. + + Parameters + ---------- + p: float + True success probability. + n: int + Number of trials (samples). + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. Returns - ps: array of possible lower bounds from CP that are cutoff points in the expected shortage integral + ------- + ps: array_like + Array of possible Clopper-Pearson lower bounds that are cutoff points in the expected shortage integral. """ ps = np.array([0.]) @@ -416,13 +433,21 @@ def get_lb_ub(num_successes, n, alpha): def UMAU_lb(t_o, n, alpha, tol=1e-6): """ - Inputs - t_o: observed test statistic - n: number of trials - alpha: miscoverage rate + Computes the lower bound for a 2-sided UMAU CI. + + Parameters + ---------- + t_o: float + Observed test statistic (number of successes + uniform random number). + n: int + Number of trials (samples). + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. Returns - p_lb: lower bound of UMAU confidence interval for p + ------- + p_lb: float + Lower bound of UMAU confidence interval for p """ # find smallest value of p, such that t_o in [t_lo, t_up] @@ -471,13 +496,21 @@ def UMAU_lb(t_o, n, alpha, tol=1e-6): def UMAU_ub(t_o, n, alpha, tol=1e-6): """ - Inputs - t_o: observed test statistic - n: number of trials - alpha: miscoverage rate + Computes the upper bound for a 2-sided UMAU CI. + + Parameters + ---------- + t_o: float + Observed test statistic (number of successes + uniform random number). + n: int + Number of trials (samples). + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. Returns - p_ub: upper bound of UMAU confidence interval for p + ------- + p_ub: float + Upper bound of UMAU confidence interval for p """ # find largest value of p, such that t_o in [t_lo, t_up] @@ -703,21 +736,30 @@ def accept_prob_2_sided(num_args, args): def max_accept_prob_2_sided(alpha, n, p, n_grid): """ - Inputs - alpha: miscoverage rate, P(p_ <= p) >= 1-alpha - n: sample size - p: true probability of success - n_grid: size of discrete grid to search over + Grid search to find the parameter which has the highest probability of being in the 2-sided CI. - Returns - max_ap: maximum acceptance probability - max_p_0: location of maximum acceptance probability + Parameters + ---------- + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + p: float + True probability of success. + n_grid: int + Size of discrete grid to search over. - It is unclear whether we can rigorously solve for this using mixed monotonic programming - accept_prob_2_sided is mixed monotonic in p_0, but I'm not sure if we can define the function - with split p_0 inputs. + Returns + ------- + max_ap: float + Maximum acceptance probability. + max_p_0: float + Parameter that attains the maximum acceptance probability. """ - + # It is unclear whether we can rigorously solve for this using mixed monotonic programming + # accept_prob_2_sided is mixed monotonic in p_0, but I'm not sure if we can define the function + # with split p_0 inputs. + p_0s = np.linspace(0,1,num=n_grid, endpoint=True) aps = [accept_prob_2_sided(5, [p_0, alpha, n, p, p]) for p_0 in p_0s] diff --git a/binomial_cis/mixed_monotonic.py b/binomial_cis/mixed_monotonic.py index 42de648..22c5357 100644 --- a/binomial_cis/mixed_monotonic.py +++ b/binomial_cis/mixed_monotonic.py @@ -5,31 +5,73 @@ class Interval: """ Class for storing and performing common operations on intervals. + + Parameters + ---------- + x_min: float + Lower bound of interval + x_max: float + Upper bound of interval """ def __init__(self, x_min, x_max): self.x_min = x_min self.x_max = x_max def get_midpoint(self): + """ + Computes the midpoint of the interval. + + Returns + ------- + float + Midpoint of interval + """ return self.x_min + (self.x_max - self.x_min)/2 def volume(self): + """ + Computes the width of the interval. + + Returns + ------- + float + Width of interval + """ return self.x_max - self.x_min def get_feasible(self, F): + """ + Finds a feasible point in the interval. + + Parameters + ---------- + F: function + Mixed monotonic function (increasing in first arg, decreasing in second arg) + + Returns + ------- + x_feas: float + Midpoint of interval + x_feas_val: float + Value of midpoint on the function `F` + """ x_feas = self.get_midpoint() x_feas_val = F(x_feas, x_feas) return x_feas, x_feas_val def get_ub(self, F): """ - Gets a certified upper bound of the function F over the interval. + Computes a certified upper bound of the function `F` over the interval. - Inputs - F: F(x1, x2) mixed monotonic function (increasing in x1, decreasing in x2) + Parameters + ---------- + F: function + Mixed monotonic function (increasing in first arg, decreasing in second arg) - Outputs - ub: certified upper bound of max(F) over Interval + Returns + ------- + ub: float + Certified upper bound of max(F) over Interval """ ub = F(self.x_max, self.x_min) return ub @@ -37,6 +79,11 @@ def get_ub(self, F): def split(self): """ Split along longest axis, else split along first axis. + + Returns + ------- + I1, I2: Interval + Intervals resulting from splitting the orignal in half. """ midpoint = self.get_midpoint() I1 = Interval(self.x_min, midpoint) @@ -48,16 +95,23 @@ def mmp_solve(F, I, tol=1e-3, max_iters=1000, verbose=True): """ Uses mixed-monotonic programming to solve for a certified maximum of the function F over the interval I. - Inputs - F: F(x1, x2) mixed monotonic function (increasing in x1, decreasing in x2) - I: Domain of maximization. Interval object - tol: maximum solve tolerance. terminate when ub - lb <= tol + Parameters + ---------- + F: function + Mixed monotonic function (increasing in first arg, decreasing in second arg) + I: Interval + Domain to maximize over. Returns - ub: certified upper bound of max F over Interval R within tol of global max - lb: certified lower bound of max F over Interval R within tol of global max - x_lb: x that achieves lb - num_iters: number of iterations taken for the solve + ------- + ub: float + Certified upper bound of max `F` over `I` within `tol` of global max. + lb: float + Certified lower bound of max `F` over `I` within `tol` of global max. + x_lb: float + Input that achieves `lb`. + num_iters: int + Number of iterations taken for the solve. """ # initialize set of intervals and associated ubs in each intervals = [I] diff --git a/binomial_cis/plotting.py b/binomial_cis/plotting.py index 8a6b869..9c099ab 100644 --- a/binomial_cis/plotting.py +++ b/binomial_cis/plotting.py @@ -6,17 +6,25 @@ def plot_expected_shortage_mixed_monotonic(alpha, n, p1s, p2s, verbose=True, randomized=True): """ - Contour plot of the mixed-monotonic form of expected shortage. - - Inputs - alpha: miscoverage rate - n: number of samples - p1s: prob. of success parameter for limit of integration - p2s: prob. of success parameter for CDF - randomized: if False then use Clopper-Pearson + Generate a contour plot of the mixed-monotonic form of expected shortage. + + Parameters + ---------- + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + p1: float + True probability of success input as limit of integration. + p2: float + True probability of success input as paremter of CDF in integrand. + randomized: bool, default True + If False then use Clopper-Pearson CI. Returns - Contour plot showing expected shortage for each parameter combination. + ------- + plot + Contour plot showing expected shortage for each parameter combination. """ # given grid of p1s and p2s p1s2D, p2s2D = np.meshgrid(p1s, p2s, indexing="ij") @@ -47,14 +55,21 @@ def plot_shortage_curve(alpha, n, num_p=21, verbose=True, randomized=True): """ Plot expected shortage as a function of true prob of success p. - Inputs - alpha: miscoverage rate - n: number of samples - num_p: number of values of p to compute shortage for - randomized: if False then use Clopper-Pearson + Parameters + ---------- + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + num_p: int + Number of values of p to compute shortage for. + randomized: bool, default True + If False then use Clopper-Pearson CI. Returns - Plot of curve which visualizes expected shortage as a function of true prob of success p. + ------- + plot + Plot of curve which visualizes expected shortage as a function of true prob of success p. """ ps = np.linspace(0,1,num=num_p) exp_shortages = np.zeros(num_p) @@ -93,16 +108,25 @@ def plot_shortage_curve(alpha, n, num_p=21, verbose=True, randomized=True): def plot_expected_width_mixed_monotonic(alpha, n, p1s, p2s, verbose=True): """ - Contour plot of the mixed-monotonic form of expected width. - - Inputs - alpha: miscoverage rate - n: number of samples - p1s: prob. of success parameter for first CDF tern - p2s: prob. of success parameter for second CDF term + Generate a contour plot of the mixed-monotonic form of expected width. + + Parameters + ---------- + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + p1: float + True probability of success input as limit of integration. + p2: float + True probability of success input as paremter of CDF in integrand. + randomized: bool, default True + If False then use Clopper-Pearson CI. Returns - Contour plot showing expected width for each parameter combination. + ------- + plot + Contour plot showing expected width for each parameter combination. """ # given grid of taus and alphas p1s2D, p2s2D = np.meshgrid(p1s, p2s, indexing="ij") @@ -130,13 +154,20 @@ def plot_width_curve(alpha, n, num_p=21, verbose=True): """ Plot expected width as a function of true prob of success p. - Inputs - alpha: miscoverage rate - n: number of samples - num_p: number of values of p to compute width for + Parameters + ---------- + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + num_p: int + Number of values of p to compute width for. + Returns - Plot of curve which visualizes expected width as a function of true prob of success p. + ------- + plot + Plot of curve which visualizes expected width as a function of true prob of success p. """ ps = np.linspace(0,1,num=num_p) exp_widths = np.zeros(num_p) diff --git a/binomial_cis/volume.py b/binomial_cis/volume.py index b790f4b..74afa88 100644 --- a/binomial_cis/volume.py +++ b/binomial_cis/volume.py @@ -7,16 +7,23 @@ def expected_shortage(accept_prob, alpha, n, p): """ - Computes the expected shortage of the lower bound CI - - Inputs - accept_prob: function that takes in p_0 and outputs acceptance prob for lb - alpha: miscoverage rate - n: number of samples - p: true probability of success + Computes the expected shortage of a lower confidence bound. + + Parameters + ---------- + accept_prob: function + Function that takes in p_0 and outputs acceptance prob for lb. + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + p: float + True probability of success. Returns - exp_shortage: the expected shortage of the CI + ------- + exp_shortage: float + The expected shortage of the CI. """ # numerically integrate CDF to solve for shortage @@ -28,17 +35,24 @@ def expected_shortage_mixed_monotonic(accept_prob, alpha, n, p1, p2): """ Implements the mixed-monotonic form of expected shortage for the lower bound CI. - Inputs - accept_prob: function that takes in p_0 and outputs acceptance prob - alpha: miscoverage rate - n: number of samples - p1: true probability of success input as limit of integration - p2: true probability of success input as paremter of CDF in integrand + Parameters + ---------- + accept_prob: function + Function that takes in p_0 and outputs acceptance prob for lb. + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + p1: float + True probability of success input as limit of integration. + p2: float + True probability of success input as paremter of CDF in integrand. Returns - exp_shortage_mm: the expected volume of the conf set below p + ------- + exp_shortage_mm: float + The expected shortage of the CI. """ - # numerically integrate CDF to solve for exp_shortage_mm exp_shortage_mm, tolerance = integrate.quad(accept_prob, eps, p1, args=(alpha, n, p2)) return exp_shortage_mm @@ -46,16 +60,23 @@ def expected_shortage_mixed_monotonic(accept_prob, alpha, n, p1, p2): def expected_excess(accept_prob, alpha, n, p): """ - Computes expected excess of the upper bound CI. - - Inputs - accept_prob: function that takes in p_0 and outputs acceptance prob for lb - alpha: miscoverage rate - n: number of samples - p: true probability of success + Computes the expected excess of an upper confidence bound. + + Parameters + ---------- + accept_prob: function + Function that takes in p_0 and outputs acceptance prob for lb. + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + p: float + True probability of success. Returns - exp_excess: the expected excess of the CI + ------- + exp_excess: float + The expected excess of the CI. """ # convert probability of success to probability of failure q = 1-p @@ -67,18 +88,24 @@ def expected_excess(accept_prob, alpha, n, p): def expected_width(accept_prob, alpha, n, p): """ - Computes expected width of the 2-sided CI - - Inputs - accept_prob: function that takes in p_0 and outputs acceptance prob for 2-sided bound - alpha: miscoverage rate - n: number of samples - p: true probability of success + Computes the expected width of a 2-sided CI. + + Parameters + ---------- + accept_prob: function + Function that takes in p_0 and outputs acceptance prob for lb. + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + p: float + True probability of success. Returns - exp_width: the expected width of the 2-sided CI + ------- + exp_width: float + The expected width of the CI. """ - # numerically integrate accept prob to solve for width exp_width, tolerance = integrate.quad(accept_prob, eps, 1., args=(alpha, n, p, p)) return exp_width @@ -88,17 +115,25 @@ def expected_width_mixed_monotonic(accept_prob, alpha, n, p1, p2): """ Implements the mixed-monotonic form of expected width for the 2-sided CI. - Inputs - accept_prob: function that takes in p_0 and outputs acceptance prob - alpha: miscoverage rate - n: number of samples - p1: true probability of success input for CDF at t_u - p2: true probability of success input for CDF at t_l + Parameters + ---------- + accept_prob: function + Function that takes in p_0 and outputs acceptance prob for lb. + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + p1: float + True probability of success input for CDF at t_u. + p2: float + True probability of success input for CDF at t_l. + Returns - exp_width_mm: the expected volume of the conf set below p + ------- + exp_width_mm: float + The expected width of the CI. """ - # numerically integrate CDF to solve for exp_width_mm exp_width_mm, tolerance = integrate.quad(accept_prob, eps, 1. - eps, args=(alpha, n, p1, p2)) return exp_width_mm @@ -108,18 +143,23 @@ def max_expected_shortage(alpha, n, tol=1e-3, verbose=True, randomized=True): """ Computes maximum expected shortage (MES) for the lower bound. - Inputs - alpha: miscoverage rate - n: number of samples - tol: maximum solve tolerance. terminate when ub - lb <= tol - verbose: boolean for whether to print out progress - randomized: if False then compute MES for Clopper-Pearson - + Parameters + ---------- + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + Returns - ub: an upper bound on max expected shortage - lb: a lower bound on max expected shortage - p_lb: the argument that achieves lb - num_iters: number of iterations taken for the solve + ------- + ub: float + An upper bound on max expected shortage. + lb: float + A lower bound on max expected shortage. + p_lb: float + The parameter that achieves lb. + num_iters: int + Number of iterations taken for the solve. """ I = Interval(0,1) if randomized: @@ -135,17 +175,23 @@ def max_expected_excess(alpha, n, tol=1e-3, verbose=True): """ Computes maximum expected excess (MEE) for the upper bound. - Inputs - alpha: miscoverage rate - n: number of samples - tol: maximum solve tolerance. terminate when ub - lb <= tol - verbose: boolean for whether to print out progress - + Parameters + ---------- + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + Returns - ub: an upper bound on max expected excess - lb: a lower bound on max expected excess - p_lb: the argument that achieves lb - num_iters: number of iterations taken for the solve + ------- + ub: float + An upper bound on max expected excess. + lb: float + A lower bound on max expected excess. + p_lb: float + The parameter that achieves lb. + num_iters: int + Number of iterations taken for the solve. """ # solve for prob of failure that achieves the MES # then convert to prob of success that achieves the MEE @@ -155,19 +201,25 @@ def max_expected_excess(alpha, n, tol=1e-3, verbose=True): def max_expected_width(alpha, n, tol=1e-3, verbose=True): """ - Computes maximum expected width (MEW) for the 2-sided bound - - Inputs - alpha: miscoverage rate - n: number of samples - tol: maximum solve tolerance. terminate when ub - lb <= tol - verbose: boolean for whether to print out progress - + Computes maximum expected width (MEW) for the 2-sided bound. + + Parameters + ---------- + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + Returns - ub: an upper bound on max expected width - lb: a lower bound on max expected width - p_lb: the argument that achieves lb - num_iters: number of iterations taken for the solve + ------- + ub: float + An upper bound on max expected width. + lb: float + A lower bound on max expected width. + p_lb: float + The parameter that achieves lb. + num_iters: int + Number of iterations taken for the solve. """ I = Interval(0,1) # expected width is increasing in p2 and decreasing in p1 @@ -194,17 +246,22 @@ def F(p2, p1): expected_width_mixed_monotonic(llc_accept_prob_2_sided, alpha, n, def expected_shortage_cp(alpha, n, p): """ - Computes the expected shortage of the lower bound CI - - Inputs - alpha: miscoverage rate - n: number of samples - p: true probability of success + Computes the expected shortage of a Clopper-Pearson lower confidence bound. + + Parameters + ---------- + alpha: float + Miscoverage rate, P(p in CI) = 1-alpha. + n: int + Number of trials (samples). + p: float + True probability of success. Returns - exp_shortage: the expected shortage of the CI + ------- + exp_shortage: float + The expected shortage of the CI. """ - ps = get_ps_cp(p, n, alpha) z = len(ps) diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d4bb2cb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/_include/api_reference.rst b/docs/_include/api_reference.rst new file mode 100644 index 0000000..d464c9a --- /dev/null +++ b/docs/_include/api_reference.rst @@ -0,0 +1,7 @@ +API Documentation +================= +This page displays the functions which are exported by the binomial_cis package. + +.. automodule:: binomial_cis + :members: + :imported-members: \ No newline at end of file diff --git a/docs/_include/background.rst b/docs/_include/background.rst new file mode 100644 index 0000000..150caba --- /dev/null +++ b/docs/_include/background.rst @@ -0,0 +1,165 @@ +Background +########## + + +What is a binomial confidence interval? +*************************************** +The binomial distribution represents the likelihood of observing :math:`k` successes in :math:`n` trials where the probability of success for each trial is :math:`p`. +One often does not know the true value of :math:`p` and wishes to estimate this value. +After observing :math:`k` successes in :math:`n` trials with unknown probability of success :math:`p`, a confidence interval (CI) is constructed in such a way that it contains the true value of :math:`p` with some high probability. + +In constructing confidence intervals one has to tradeoff between three quantities: + +#. Confidence: The probability that the CI contains the true parameter. Often written as :math:`1-\alpha` where :math:`\alpha` is small. +#. Volume: The length of the CI. If the CI is constructed as a lower bound on :math:`p` (i.e. :math:`[\underline{p}, 1]`), then we care about the length of the CI which is below :math:`p`. This is known as the `shortage`. +#. Number of samples: In general, with more samples one can construct CIs with higher confidence and smaller volume. + + + +Why does this package exist? +**************************** +Existing implementations of binomial CIs do not optimally control the tradeoffs between confidence, volume, and number of samples. +In practice, this means that if a user specifies :math:`k` and :math:`n`, existing implementations return CIs with higher/lower confidence than desired and/or with higher volume than necessary. +CIs which optimally trade-off between these quantities have the property of being `uniformly most accurate` (UMA) or `uniformly most accurate unbiased` (UMAU). +Methods for constructing UMA and UMAU binomial confidence intervals has existed since the mid-20th century, but until now have not been implemented in an open-source software package. +We implement UMA and UMAU binomial confidence intervals using methods from + +* `On the treatment of discontinuous random variables `_ by Eudey, +* `Testing Statistical Hypotheses `_ by Lehmann and Romano, +* `Table of Neyman-shortest unbiased confidence intervals for the binomial parameter `_ by Blyth and Hutchinson. + + +Existing software implementations for binomial CIs include: + +* `statsmodels.stats.proportion.proportion_confint `_ (Python) +* `scipy.stats._result_classes.BinomTestResult.proportion_ci `_ (Python) +* `astropy.stats.binom_conf_interval `_ (Python) +* `scipy.stats.binom `_ (Python) +* `EBCIC `_ (Python) +* `binom.test `_ (R) +* `binom.confint `_ (R) +* `BinomCI `_ (R) +* `HypothesisTests.jl `_ (Julia) +* `RobustStats.jl `_ (Julia) +* `ClinicalTrialUtilities.jl `_ (Julia) +* `binofit `_ (Matlab) + +The methods these packages implement include: + +* Wald/Normal [1] +* Agresti-Coull [1] +* Clopper-Pearson (*) [1] +* Wilson [1] +* Modified Wilson [1] +* Wilson with continuity correction (*) [1] +* Jeffreys [1] +* Bayesian uniform prior +* Inverting the binomial test (*) [3] +* Arcsine [1] +* Logit [1] +* Probit [8] +* Complementary log [8] +* Likelihood (Profile) [1] +* Witting (*) [4] +* Pratt [5] +* Mid-p [7] +* Blaker (*) [6] +* Second-order corrected [2] + +The reference of each method points to a survey paper (if possible) rather than the original derivation of the method. + +Only the methods marked with (*) are guaranteed to provide at least as much confidence as desired. +However, none of these methods provide exactly the confidence level desired (Witting might, but the listed reference is not freely available online and is only published in German). + +It is also worth noting the `ump `_ R package. +This package implements UMP and UMPU hypothesis tests for the binomial distribution. +Such tests could be leveraged to construct UMA and UMAU confidence intervals, but this doesn't appear to be implemented based on the documented functions. +In addition, the authors of this package note that their implemetation has issues with numerical stability. + +Existing software implementations for computing binomial CIs with exact confidence levels are then either non-existent or unsatisfactory. +Unsurprisingly then, there is also no open-source implementation for computing the expected shortage (or expected excess or expected width) of binomial CIs, and no implementation for computing the worst-case values of these quantities. +This package exists to fill this gap. + + + + + + + + + +What exactly does this package do? +********************************** +This package constructs optimal confidence intervals for the probability of success parameter of a binomial distribution. + +Lower Bounds +============ +Given user specified miscoverage rate (:math:`\alpha`) and maximum expected shortage (:math:`\text{MES}`), return a lower bound on :math:`p` that satisfies the following requirements: + +#. achieves exact desired coverage: :math:`\mathbb{P}[\underline{p} \le p] = 1-\alpha`, +#. :math:`[\underline{p}, 1]` is uniformly most accurate, +#. achieves exact desired maximum expected shortage: :math:`\max_p \ \mathbb{E}_p[\max (p - \underline{p}, 0)] = \text{MES}`, +#. uses the minimum number of samples :math:`n` to achieve requirements 1,2,3. + + +Upper Bounds +============ +Given user specified miscoverage rate (:math:`\alpha`) and maximum expected excess (:math:`\text{MEE}`), return an upper bound on :math:`p` that satisfies the following requirements: + +#. achieves exact desired coverage: :math:`\mathbb{P}[p \le \overline{p}] = 1-\alpha`, +#. :math:`[0, \overline{p}]` is uniformly most accurate, +#. achieves exact desired maximum expected excess: :math:`\max_p \ \mathbb{E}_p[\max (\overline{p} - p, 0)] = \text{MEE}`, +#. uses the minimum number of samples :math:`n` to achieve requirements 1,2,3. + + + + +Simultaneous Lower and Upper Bounds +=================================== +Given the user specified miscoverage rate (:math:`\alpha`) and maximum expected width (:math:`\text{MEW}`), return simultaneous lower and upper bounds on :math:`p` that satisfy the following requirements: + +#. achieves exact desired coverage: :math:`\mathbb{P}[\underline{p} \le p \le \overline{p}] = 1-\alpha`, +#. :math:`[\underline{p}, \overline{p}]` is uniformly most accurate unbiased, +#. achieves exact desired maximum expected width: :math:`\max_p \ \mathbb{E}_p[\overline{p} - \underline{p}] = \text{MEW}`, +#. uses the minimum number of samples :math:`n` to achieve requirements 1,2,3. + + + + + +Relevant Literature +=================== +For more information on the mathematics behind these confidence intervals see our affiliated paper `How Generalizable Is My Behavior Cloning Policy? A Statistical Approach to Trustworthy Performance Evaluation `_ + +Below are some of the resources that we found most useful for understanding binomial confidence intervals. + +* `Testing Statistical Hypotheses `_ by Lehmann and Romano +* `On the treatment of discontinuous random variables `_ by Eudey +* `Table of Neyman-shortest unbiased confidence intervals for the binomial parameter `_ by Blyth and Hutchinson +* `Length of Confidence Intervals `_ by Pratt +* `More on length of confidence intervals `_ by Madansky +* `Binomial confidence intervals `_ by Blyth and Still +* `Smallest confidence intervals for one binomial proportion `_ by Wang +* `Fuzzy and randomized confidence intervals and p-values `_ by Geyer and Meeden +* `Nonoptimality of Randomized Confidence Sets `_ by Casella and Robert + + + + + + + + + +References +========== + +#. `Interval Estimation for a Binomial Proportion `_ by Brown, Cai, and DasGupta +#. `One-sided confidence intervals in discrete distributions `_ by Cai +#. `Some Remarks on Confidence or Fiducial Limits `_ by Sterne +#. `Mathematische Statistik I. `_ by Witting +#. `Binomial Confidence Intervals `_ by Blyth and Still +#. `Confidence Curves and Improved Exact Confidence Intervals for Discrete Distributions `_ by Blaker +#. `Comment: Randomized Confidence Intervals and the Mid-P Approach `_ by Agresti and Gottard +#. `binom `_ by Sundar Dorai-Raj +#. `Fuzzy and randomized confidence intervals and p-values `_ by Geyer and Meeden. \ No newline at end of file diff --git a/docs/_include/community_guidelines.rst b/docs/_include/community_guidelines.rst new file mode 100644 index 0000000..c040828 --- /dev/null +++ b/docs/_include/community_guidelines.rst @@ -0,0 +1,5 @@ +Community Guidelines +==================== + +* If you'd like to contribute please fork the repository and submit a pull request on Github. +* For issues with the software or for further support please file an issue on Github. diff --git a/docs/_include/notebooks.rst b/docs/_include/notebooks.rst new file mode 100644 index 0000000..2b8ecb6 --- /dev/null +++ b/docs/_include/notebooks.rst @@ -0,0 +1,31 @@ +Notebooks +========= + +The ``notebooks/`` directory in the Github repository has notebooks which explore different aspects of the code. +To run the notebooks, first clone the repository + +.. code-block:: + + gh repo clone TRI-ML/binomial_cis + + +Then, create a virtual environment and load the dependencies (these commands are for Unix/macOS) + +.. code-block:: + + python -m venv .venv + source .venv/bin/activate + pip install -r requirements.txt + + + +tradeoff_table.ipynb +******************** +This notebook is used to computes maximum expected shortage (MES) vs miscoverage rate (alpha) and number of samples :math:`n`. +Precomputed values have been stored in ``MES_table.csv`` which is visualized in a plot from the last cell of the notebook. + + +conf_set_validation.ipynb +************************* +This notebook is used to visualize the mixed-monotonic forms of expected shortage and expected width. +Also visualized is how these functions vary with :math:`p` and their maxima. \ No newline at end of file diff --git a/docs/_include/potential_pitfalls.rst b/docs/_include/potential_pitfalls.rst new file mode 100644 index 0000000..72b150b --- /dev/null +++ b/docs/_include/potential_pitfalls.rst @@ -0,0 +1,30 @@ +Potential Pitfalls +================== + + +Randomized Confidence Intervals +******************************* +The methods used in this package to construct CIs are based on the inversion of randomized hypothesis tests. +This means that calling ``binom_ci()`` with the same ``k,n,alpha`` will result in different CIs. +For the guarantees of the CI to hold it is critical that the user only construct one CI for the experiment they have. +Constructing multiple CIs and choosing the best one invalidates the guarantees of the CI. + +For the 1-sided bounds there is the option to get less efficient but non-randomized CIs: + +.. code-block:: + + lb = binom_ci(k, n, alpha, 'lb', randomized=False) + ub = binom_ci(k, n, alpha, 'ub', randomized=False) + +These non-randomized 1-sided bounds are equivalent to 1-sided Clopper-Pearson bounds. +We currently don't have an implementation of non-randomized 2-sided bounds. +Randomization allows the CIs to be UMA. +Although randomization has been a point of debate amongst statisticians, we take the view (first given by Mark Eudey) that insofar as construction of confidence intervals can be treated as a (von Neumann) game, randomization merely allows the statistician to employ a mixed strategy. + + + +Multiple Tests +************** +As with all CIs one must take special care when interpreting the results of multiple CIs constructed from independent tests. +If one constructs :math:`m` CIs where the probability of each CI containing the true parameter is 1-alpha, then the probability that **all** :math:`m` CIs contain their respective parameters is less than 1-\alpha. +For more explanation, see the `Wikipedia article `_ on the multiple comparisons problem. diff --git a/docs/_include/tests.rst b/docs/_include/tests.rst new file mode 100644 index 0000000..30a1c29 --- /dev/null +++ b/docs/_include/tests.rst @@ -0,0 +1,50 @@ +Tests +===== +A variety of tests have been implemented to check the validity of the provided functions. +Each test file is located in the ``tests/`` directory of the Github repository. +To run the tests, first clone the repository + +.. code-block:: + + gh repo clone TRI-ML/binomial_cis + + +Then, create a virtual environment and load the dependencies (these commands are for Unix/macOS) + +.. code-block:: + + python -m venv .venv + source .venv/bin/activate + pip install -r requirements.txt + + +binom_ci_test.py +****************** +This file contains functions to test the correctness of the lower, upper, and 2-sided bounds. +The lower/upper bound tests ensure the bounds we compute are better than Clopper-Pearson bounds (without being too optimistic). +The 2-sided test ensures that our 2-sided bounds agree with those computed by Blyth and Hutchinson in their 1960 paper titled "Table of Neyman-Shortest Unbiased Confidence Intervals for the Binomial Parameter". +We use a Github Action to automatically run the tests in this file via ``pytest``. +To run the tests yourself simply navigate to the ``tests/`` directory and run + +.. code-block:: + + pytest -v + + + + +2_side_validation.ipynb +*********************** +This notebook can be used to more easily inspect any differences between our 2-sided bounds and those from the Blyth paper. + + +binom_helper_validation.ipynb +***************************** +This notebook is used to validate the binomial helper functions from ``binomial_cis/binomial_helper.py```. +Accuracy and speed of our implementation is tested against the SciPy implementation. + + +conf_set_validation.ipynb +************************* +This notebook is used to validate the probabilistic guarantees of the confidence intervals using Monte Carlo simulation. + diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..d83b909 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,32 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +import os +import sys +sys.path.insert(0, os.path.abspath('../')) + +project = 'binomial_cis' +copyright = '2024, Joe Vincent' +author = 'Joe Vincent' +release = '0.0.12' + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = ['sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx.ext.mathjax'] + +templates_path = ['_templates'] +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'sphinx_rtd_theme' +html_static_path = ['_static'] diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..9652e8a --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,126 @@ +.. binomial_cis documentation master file, created by + sphinx-quickstart on Thu Jun 27 09:55:48 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +binomial_cis +======================================== + +This is a Python package for computing confidence intervals for the probability of success parameter, :math:`p`, of a binomial distribution. +The binomial distribution represents the likelihood of observing :math:`k` successes in :math:`n` trials where the probability of success for each trial is :math:`p`. +For example, :math:`p` may be the probability of a coin flip landing on heads, and :math:`k` the number of heads we observe after :math:`n` flips. +One often does not know the value of :math:`p` and wishes to estimate it. A confidence interval is a set, constructed based on :math:`k, n`, that covers the unknown parameter :math:`p` with some user-specified probability. +The binomial_cis package computes confidence intervals that lower and/or upper bound :math:`p` with a user-specified probability. + + + +Source Code +=========== +The source code for this package is available at `https://github.com/TRI-ML/binomial_cis/ `_. + + +Installation +============ +Install the package with pip: + +.. code-block:: + + pip install binomial_cis + + + +Example Usage +============= + +Lower Bounds +************ +Find a lower bound on :math:`p`: + +.. code-block:: + + from binomial_cis import binom_ci + + k = 5 # number of successes + n = 10 # number of trials + alpha = 0.05 # miscoverage probability + + lb = binom_ci(k, n, alpha, 'lb') + + +Find maximum expected shortage given miscoverage rate and number of samples: + +.. code-block:: + + mes_ub, mes_lb, p_lb, num_iters = max_expected_shortage(alpha, n, tol=1e-3) + + + + +Upper Bounds +************ +Find an upper bound on :math:`p`: + +.. code-block:: + + from binomial_cis import binom_ci + + k = 5 # number of successes + n = 10 # number of trials + alpha = 0.05 # miscoverage probability + + ub = binom_ci(k, n, alpha, 'ub') + + +Find maximum expected excess given miscoverage rate and number of samples: + +.. code-block:: + + mee_ub, mee_lb, p_lb, num_iters = max_expected_excess(alpha, n, tol=1e-3) + + + + + +2-Sided Bounds +************** +Find simultaneous lower and upper bounds on :math:`p`: + +.. code-block:: + + from binomial_cis import binom_ci + + k = 5 # number of successes + n = 10 # number of trials + alpha = 0.05 # miscoverage probability + + lb, ub = binom_ci(k, n, alpha, 'lb,ub') + + +Find maximum expected width given miscoverage rate and number of samples: + +.. code-block:: + + mew_ub, mew_lb, p_lb, num_iters = max_expected_width(alpha, n, tol=1e-3) + + + + +More Resources +============== +.. toctree:: + :maxdepth: 1 + + _include/background + _include/notebooks + _include/tests + _include/api_reference + _include/potential_pitfalls + _include/community_guidelines + + + + +Index +================== + +* :ref:`genindex` diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..32bb245 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/joss/paper.bib b/joss/paper.bib index 0fc4bbe..e379891 100644 --- a/joss/paper.bib +++ b/joss/paper.bib @@ -33,3 +33,23 @@ @article{vincent2024generalizable journal={arXiv preprint arXiv:2405.05439}, year={2024} } + +@book{altman2013statistics, + title={Statistics with confidence: confidence intervals and statistical guidelines}, + author={Altman, Douglas and Machin, David and Bryant, Trevor and Gardner, Martin}, + year={2013}, + publisher={John Wiley \& Sons} +} + + +@article{cameron2011estimation, + title={On the estimation of confidence intervals for binomial population proportions in astronomy: the simplicity and superiority of the Bayesian approach}, + author={Cameron, Ewan}, + journal={Publications of the Astronomical Society of Australia}, + volume={28}, + number={2}, + pages={128--139}, + year={2011}, + publisher={Cambridge University Press} +} + diff --git a/joss/paper.md b/joss/paper.md index 4701bfd..740cc29 100644 --- a/joss/paper.md +++ b/joss/paper.md @@ -12,7 +12,7 @@ authors: affiliations: - name: Department of Aeronautics and Astronautics, Stanford University index: 1 -date: 11 June 2024 +date: 28 June 2024 bibliography: paper.bib --- @@ -25,6 +25,7 @@ bibliography: paper.bib # Statement of Need Constructing confidence intervals for an unknown probability success given samples of successes and failures is one of the most fundamental problems in statistical inference. +Confidence intervals for success probabilities are used in many disciplines including medicine[@altman2013statistics], astronomy[@cameron2011estimation], and robotics[@vincent2024generalizable]. Research into this question dates back at least to the 1930s with the work of Clopper and Pearson [@clopper_pearson]. A foundational result for constructing binomial confidence intervals of minimal width was given by [@eudey1949] and is formalized in [@lehmann_textbook]. We refer to these intervals as *optimal binomial confidence intervals* and they have the property of being uniformly most accurate (UMA) and uniformly most accurate unbiased (UMAU). @@ -51,7 +52,7 @@ binomial_cis has been used to compute confidence intervals for the success rate # Acknowledgements -Financial support was provided by Toyota Research Institute. +Financial support was provided by Toyota Research Institute, where the author began development of the software during an internship. diff --git a/requirements.txt b/requirements.txt index a316b78..74daf2e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,115 +1,11 @@ -anyio==3.7.1 -argon2-cffi==23.1.0 -argon2-cffi-bindings==21.2.0 -arrow==1.2.3 -asttokens==2.2.1 -async-lru==2.0.4 -attrs==23.1.0 -Babel==2.12.1 -backcall==0.2.0 -beautifulsoup4==4.12.2 -bleach==6.0.0 -build==1.2.1 -certifi==2023.7.22 -cffi==1.15.1 -charset-normalizer==3.2.0 -comm==0.1.4 -contourpy==1.1.0 -cycler==0.11.0 -debugpy==1.6.7.post1 -decorator==5.1.1 -defusedxml==0.7.1 -exceptiongroup==1.1.3 -executing==1.2.0 -fastjsonschema==2.18.0 -fonttools==4.42.0 -fqdn==1.5.1 -idna==3.4 -ipykernel==6.25.1 -ipython==8.14.0 -ipython-genutils==0.2.0 -ipywidgets==8.1.0 -isoduration==20.11.0 -jedi==0.19.0 -Jinja2==3.1.2 -json5==0.9.14 -jsonpointer==2.4 -jsonschema==4.19.0 -jsonschema-specifications==2023.7.1 -jupyter==1.0.0 -jupyter-console==6.6.3 -jupyter-events==0.7.0 -jupyter-lsp==2.2.0 -jupyter_client==8.3.0 -jupyter_core==5.3.1 -jupyter_server==2.7.1 -jupyter_server_terminals==0.4.4 -jupyterlab==4.0.5 -jupyterlab-pygments==0.2.2 -jupyterlab-widgets==3.0.8 -jupyterlab_server==2.24.0 -kiwisolver==1.4.4 -llvmlite==0.40.1 -MarkupSafe==2.1.3 -matplotlib==3.7.2 -matplotlib-inline==0.1.6 -mistune==3.0.1 -nbclient==0.8.0 -nbconvert==7.7.4 -nbformat==5.9.2 -nest-asyncio==1.5.7 -notebook==7.0.2 -notebook_shim==0.2.3 -numba==0.57.1 -numpy==1.24.4 -openpyxl==3.1.4 -overrides==7.4.0 -packaging==23.1 -pandas==2.1.0 -pandocfilters==1.5.0 -parso==0.8.3 -pexpect==4.8.0 -pickleshare==0.7.5 -Pillow==10.0.0 -platformdirs==3.10.0 -prometheus-client==0.17.1 -prompt-toolkit==3.0.39 -psutil==5.9.5 -ptyprocess==0.7.0 -pure-eval==0.2.2 -pycparser==2.21 -Pygments==2.16.1 -pyparsing==3.0.9 -pyproject_hooks==1.1.0 -python-dateutil==2.8.2 -python-json-logger==2.0.7 -pytz==2023.3.post1 -PyYAML==6.0.1 -pyzmq==25.1.1 -qtconsole==5.4.3 -QtPy==2.3.1 -referencing==0.30.2 -requests==2.31.0 -rfc3339-validator==0.1.4 -rfc3986-validator==0.1.1 -rpds-py==0.9.2 -scipy==1.11.2 -Send2Trash==1.8.2 -six==1.16.0 -sniffio==1.3.0 -soupsieve==2.4.1 -stack-data==0.6.2 -terminado==0.17.1 -tinycss2==1.2.1 -tomli==2.0.1 -tornado==6.3.3 -traitlets==5.9.0 -typing_extensions==4.7.1 -tzdata==2023.3 -uri-template==1.3.0 -urllib3==2.0.4 -wcwidth==0.2.6 -webcolors==1.13 -webencodings==0.5.1 -websocket-client==1.6.1 -widgetsnbextension==4.0.8 +build +ipykernel +matplotlib +numba +numpy +openpyxl +pandas +pytest +scipy +sphinx +sphinx-rtd-theme \ No newline at end of file diff --git a/setup.py b/setup.py index 6a7823e..5a0a403 100644 --- a/setup.py +++ b/setup.py @@ -2,11 +2,12 @@ setup( name="binomial_cis", - version='0.0.11', + version='0.0.12', author="Joe Vincent", description="Confidence intervals for binomial distributions.", packages=find_packages(), install_requires=['numpy', 'numba', 'scipy', 'matplotlib'], + extras_require={'docs': ['sphinx', 'sphinx-rtd-theme']}, long_description=open('README.md').read(), long_description_content_type='text/markdown' -) +) \ No newline at end of file diff --git a/tests/2_side_validation.ipynb b/tests/2_side_validation.ipynb index 528330b..2aa2d1d 100644 --- a/tests/2_side_validation.ipynb +++ b/tests/2_side_validation.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -217,7 +217,7 @@ "0.4 28.0 0.0 25.0 0 23 " ] }, - "execution_count": 164, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -281,9 +281,20 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_8475/3073144996.py:4: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'nan' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " my_df.loc[:, :] = np.nan\n", + "/tmp/ipykernel_8475/3073144996.py:4: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'nan' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " my_df.loc[:, :] = np.nan\n" + ] + } + ], "source": [ "# these values are generated using our code\n", "our_df = fill_our_df(df, alpha)" @@ -298,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1655,7 +1666,7 @@ "5.5 NaN NaN 0.0 0.0 " ] }, - "execution_count": 167, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1666,23 +1677,23 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1.0" + "np.float64(1.0)" ] }, - "execution_count": 168, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# this is the value of the maximum absolute discrepency between the tables\n", - "# we consider discrepancies on the order of 1.0 or 2.0 to be acceptable\n", + "# we consider discrepancies on the order of 1.0 to be acceptable\n", "(our_df - df).abs().max().max()" ] } @@ -1703,7 +1714,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.12.3" }, "orig_nbformat": 4 }, diff --git a/tests/conf_set_validation.ipynb b/tests/conf_set_validation.ipynb index 143c668..a7c2e5f 100644 --- a/tests/conf_set_validation.ipynb +++ b/tests/conf_set_validation.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": { "metadata": {} }, @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": { "metadata": {} }, @@ -62,11 +62,38 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": { "metadata": {} }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/joe/Documents/binomial_cis/tests/../binomial_cis/volume.py:23: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n", + " If increasing the limit yields no improvement it is advised to analyze \n", + " the integrand in order to determine the difficulties. If the position of a \n", + " local difficulty can be determined (singularity, discontinuity) one will \n", + " probably gain from splitting up the interval and calling the integrator \n", + " on the subranges. Perhaps a special-purpose integrator should be used.\n", + " exp_shortage, tolerance = integrate.quad(accept_prob, eps, p, args=(alpha, n, p))\n", + "/home/joe/Documents/binomial_cis/tests/../binomial_cis/volume.py:64: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n", + " If increasing the limit yields no improvement it is advised to analyze \n", + " the integrand in order to determine the difficulties. If the position of a \n", + " local difficulty can be determined (singularity, discontinuity) one will \n", + " probably gain from splitting up the interval and calling the integrator \n", + " on the subranges. Perhaps a special-purpose integrator should be used.\n", + " exp_excess, tolerance = integrate.quad(accept_prob, eps, q, args=(alpha, n, q))\n", + "/home/joe/Documents/binomial_cis/tests/../binomial_cis/volume.py:83: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n", + " If increasing the limit yields no improvement it is advised to analyze \n", + " the integrand in order to determine the difficulties. If the position of a \n", + " local difficulty can be determined (singularity, discontinuity) one will \n", + " probably gain from splitting up the interval and calling the integrator \n", + " on the subranges. Perhaps a special-purpose integrator should be used.\n", + " exp_width, tolerance = integrate.quad(accept_prob, eps, 1., args=(alpha, n, p, p))\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -174,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "metadata": { "metadata": {} }, @@ -182,16 +209,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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g1KhRePXVV/HBBx8422zatAkAMHnyZJfHvvjiiwDgMrzVXU8++aTLcK1u3brBbrfj/PnzAICff/4ZFosFzz//vEu7iRMnFrm+c+fO4dy5c7d8XqPRCF9f32LFmB/DxIkTIcvXPi7jxo2Dn59foe338fHBv/71L+dtrVaLDh064MyZM85lDzzwANRqNdasWeNc9s8//+DYsWMYMWKEc9l3332Hbt26ITAw0Ln/U1NT0adPH9jtduzcudPluR988EGEhIQ4b9vtdvz8888YOnQoIiMjncsbNGiAAQMGuDx2/fr1UBQFw4cPd3mu8PBwNGzYsNCQ4eJs5/fff4/g4GA8//zzhV7X/P353Xffwd/fH/fcc4/L87Zt2xY+Pj5FDlXOl5CQgJiYGIwdOxZBQUHO5S1btsQ999zjfO+WxLfffgtZlvHggw86l40aNQqbN292Dm9r1KgRWrdu7bIf7XY71q1bh8GDBzvPSXN3G+vWrYt+/foViqngOW6ZmZlITU1Fjx49cObMGWRmZgIAtm/fDpvNhmeeecblsUXtA3ffXwXlf7cV93O0adMmdOjQwWUIr4+PD5588kmcO3fOOayzoj4bAKBSqZznfyqKgitXrsBms6Fdu3Y4ePCgs92WLVug0Wgwbtw45zJZlgudA3zlyhXs2LEDw4cPR1ZWljOmtLQ09OvXD3Fxcbh06VKxXi+qfJjzmfOZ85nzq2vOBxzD9RcvXoy6devihx9+wJQpU9C0aVP07t27yNxW3M97cT9rOp0Ojz322E1jvL59/nrtdjvS0tLg4+ODxo0bF8rxUVFRGDJkiHOZXq93yflAyb6jy1rlmyXoNpGSkgKTyYTGjRsXuq9p06ZQFAUXLlxA8+bNnUl5165dqFmzJg4dOoSZM2ciJCQEc+fOdd7n5+eHVq1aAQDi4uIAAGPGjLlhDJmZmQgMDHTerlu3rlvb0KJFC5dJNoYPH47MzExMnToVDz/8MEJCQnD+/HnIsowGDRq4PDY8PBwBAQHOD19J1KpVy+V2/rbkf0Hmr7thw4Yu7UJCQly2211+fn7IysoqVtv8GK7fz1qtFvXq1Su0/TVr1ix03lhgYCD+/vtv5+3g4GD07t0ba9euxdtvvw0AWLNmDdRqNR544AFnu7i4OPz999+Fio18+ROt5Lt+/ycnJyM3N7fQvgNQaFlcXByEEIVe63wajcbt7Tx9+jQaN25808nI4uLikJmZWeT5UPnbcCM32jeA4zP4008/FWtypqJ89dVX6NChA9LS0pCWlgYAaNOmDSwWC7777js8+eSTAIARI0bglVdewaVLlxAVFYVff/0VycnJLj/E3N3GG32O9+zZgzfffBN79+6FyWRyuS8zMxP+/v7O1+T6/RsUFFToM+Pu+6sgPz8/AHDrc9SxY8dCy5s2beq8/4477qiwz0a+L774AvPmzcOJEydgtVqLbH/+/HlEREQUmvDm+tf41KlTEELg9ddfx+uvv37DuKKiooq8jyo35nzmfOZ85vzqmvOBawern332WaSlpWHPnj1YtmwZNm/ejJEjRzoPSOYr7ue9uJ+1qKgotyZaVBQFH3zwAZYuXYqzZ8/Cbrc776tRo4bz/+fPn0f9+vULvb+L+swA7n1HlzUW7RUgMjISdevWxc6dO1GnTh0IIdCpUyeEhIRgwoQJOH/+PHbt2oXOnTs7jwrlH615//330bp16yLX6+Pj43K7LGab7N27N3788Ufs27cPgwYNci4vagKT4ir4QSlIpVIVuVwUmKinPDRp0gQxMTGwWCxlPtNqcbdp5MiReOyxxxATE4PWrVtj7dq16N27N4KDg51tFEXBPffcg3//+99FrrNRo0Yut0uz/xVFgSRJ2Lx5c5HbcP17raz2naIoCA0NveG1gm+UXMpTXFwc9u/fD6Dwj0fAcUmYggl82rRp+O677zBx4kSsXbsW/v7+6N+/v7O9u9tY1H48ffo0evfujSZNmmD+/PmIjo6GVqvFpk2bsGDBghId3XX3/VVQ/mROR44cwdChQ91+7pupqM/GV199hbFjx2Lo0KF46aWXEBoaCpVKhdmzZxdrcqvr5e+DKVOmFNlrAhT+EUBVE3M+cz5z/o2flzn/9sv516tRowaGDBmCIUOG4O6778Zvv/2G8+fPo3bt2s42Zf15d/f9PmvWLLz++ut4/PHH8fbbbyMoKAiyLGPixIklfv0A976jyxqL9hIKCQmBwWBwDn8r6MSJE5BlGdHR0c5l3bp1w86dO1G3bl20bt0avr6+aNWqFfz9/bFlyxYcPHjQZXbR+vXrA3AcIa7IS07YbDYAQHZ2NgCgdu3aUBQFcXFxzl4xwDEjakZGhssHNDAwsNCMjxaLBQkJCSWKJX/dcXFxLsMRU1JSij3LaFEGDx6MvXv34vvvv8eoUaOKFUNsbKxLDBaLBWfPni3xvhk6dCjGjx/vHGZ18uRJTJs2zaVN/fr1kZ2dXeLnCA0NhV6vx6lTpwrdd/2y+vXrQwiBunXruvXFfTP169fHn3/+CavVWuiofcE2P//8M7p06eL2F3LBfXO9EydOIDg4uERH3FevXg2NRoNVq1YVSjq7d+/Ghx9+iPj4eNSqVQt169ZFhw4dsGbNGjz33HNYv349hg4dCp1OVybbmO+///0vzGYzNm7c6HL0+vphdvmvyalTp1yO3qelpRX6zJTm/dW1a1cEBgbim2++wSuvvHLD5Fwwrhvtp4JxAxXz2QCAdevWoV69eli/fr1LgfLmm28Wiv2XX34pdHmZ6z9D+d8PGo2G1/atgpjzmfOZ82+OOb/025ivsuX8m2nXrh1+++03JCQkuHw/3EpZfdZudIBx3bp16NmzJz799FOX5RkZGS4Hy2rXro1jx45BCOGyrqI+M0DFf0cXxHPaS0ilUqFv3774z3/+43JOVVJSEr7++mt07drVOYQUcCTwc+fOYc2aNc6hc7Iso3Pnzpg/fz6sVqtzOQC0bdsW9evXx9y5c53JtKDyuibijz/+CADOIXsDBw4E4LgOY0Hz588HAJcj8/Xr1y90TszHH398w6Put9KnTx9oNBosWrTI5cjc9bHkK+7lX5566ilERETgxRdfxMmTJwvdn5ycjJkzZzpj0Gq1+PDDD11i+PTTT5GZmemy/e4ICAhAv379sHbtWnz77bfQarWFeiyHDx+OvXv34qeffir0+IyMDOePrRtRqVTo06cPNmzYgMuXLzuXnzp1Cps3b3Zp+8ADD0ClUmHGjBmFjoIKIZzDxdzx4IMPIjU11eVyNgXXCTi20W63O4cMFmSz2Qr9ICwoIiICrVu3xhdffOHS7p9//sHWrVud7113rV69Gt26dcOIESMwbNgwl7+XXnoJAPDNN984248YMQJ//PEHPvvsM6SmproMkyvtNubL/yFRcN9kZmbi888/d2nXu3dvqNXqQpeFKWoflOb9ZTAY8PLLL+P48eN4+eWXizxy/tVXX2Hfvn0AHN8j+/btw969e5335+Tk4OOPP0adOnXQrFkz5/KK+GwARb+mf/75p0uMANCvXz9YrVaX6zgrioIlS5a4tAsNDcXdd9+N5cuXF1m0eOI6tlR2mPOZ85nzb445H6XexnyVLecnJiY6554pyGKxYPv27UWeUnMrZfVZ8/b2LvI1ValUhd7b3333XaHz7/v164dLly65XJo1Ly/PJecDnvuOLog97bfw2WefYcuWLYWWT5gwATNnzsS2bdvQtWtXPPPMM1Cr1Vi+fDnMZnOh61fmJ+fY2FjMmjXLubx79+7YvHkzdDod2rdv71wuyzI++eQTDBgwAM2bN8djjz2GqKgoXLp0Cb/88gv8/Pzw3//+t1TbtmvXLuc1KK9cuYKNGzfit99+w8iRI53DX1u1aoUxY8bg448/RkZGBnr06IF9+/bhiy++wNChQ12uWfnEE0/gqaeewoMPPoh77rkHhw8fxk8//eRyRMsdISEhmDJlCmbPno17770XAwcOxKFDh7B58+Yi19m7d28AuOXENIGBgfjhhx8wcOBAtG7dGv/617/Qtm1bAMDBgwfxzTffoFOnTs4Ypk2bhhkzZqB///4YMmQIYmNjsXTpUrRv395lYhZ3jRgxAv/617+wdOlS9OvXDwEBAS73v/TSS9i4cSPuvfdejB07Fm3btkVOTg6OHDmCdevW4dy5c7d8badPn46tW7eiS5cuePrpp2G327F48WLccccdiImJcbarX78+Zs6ciWnTpuHcuXMYOnQofH19cfbsWfzwww948sknMWXKFLe2b/To0fjyyy8xefJk7Nu3D926dUNOTg5+/vlnPPPMM7jvvvvQo0cPjB8/HrNnz0ZMTAz69u0LjUaDuLg4fPfdd/jggw9crn16vffffx8DBgxAp06d8H//93/Izc3FokWL4O/vX+T1g2/lzz//xKlTp/Dcc88VeX9UVBTuvPNOrF69Gi+//DIARyKcMmUKpkyZgqCgoEJHYEu7jQDQt29faLVaDB48GOPHj0d2djZWrFiB0NBQlwIxLCwMEyZMwLx58zBkyBD0798fhw8fdn5mCh5FLu3766WXXsLRo0cxb948/PLLLxg2bBjCw8ORmJiIDRs2YN++ffj9998BAFOnTsU333yDAQMG4IUXXkBQUBC++OILnD17Ft9//73LJDRAxXw27r33Xqxfvx73338/Bg0ahLNnz2LZsmVo1qyZS0IeOnQoOnTogBdffBGnTp1CkyZNsHHjRly5cgWA61H+JUuWoGvXrmjRogXGjRuHevXqISkpCXv37sXFixeLvD4sVS7M+cz5zPnM+fmY8x0uXryIDh06oFevXujduzfCw8ORnJyMb775BocPH8bEiRPd/tyX1Wetbdu2+OijjzBz5kw0aNAAoaGh6NWrF+6991689dZbeOyxx9C5c2ccOXIEq1evdunVB4Dx48dj8eLFGDVqFCZMmICIiAisXr0aer0ewLUcXxHf0bdU7vPT36byL1lwo78LFy4IIYQ4ePCg6Nevn/Dx8REGg0H07NlT/P7770WuMzQ0VAAQSUlJzmW7d+8WAES3bt2KfMyhQ4fEAw88IGrUqCF0Op2oXbu2GD58uNi+fbuzTf7lX1JSUoq1bUVd/kWr1YomTZqId955p9B1Mq1Wq5gxY4aoW7eu0Gg0Ijo6WkybNs3lskZCOC6D9PLLL4vg4GBhMBhEv379xKlTp254+ZfrL62TH1fBayLa7XYxY8YMERERIby8vMTdd98t/vnnn0LrFKL4l3/Jd/nyZTFp0iTRqFEjodfrhcFgEG3bthXvvPOOyMzMdGm7ePFi0aRJE6HRaERYWJh4+umnXa5jKoTjkhTNmzcv9DxjxowpMi6j0Si8vLwEAPHVV18VGWNWVpaYNm2aaNCggdBqtSI4OFh07txZzJ0717mf8i//8v777xe5ju3bt4s2bdoIrVYr6tevLz755BPx4osvCr1eX6jt999/L7p27Sq8vb2Ft7e3aNKkiXj22WdFbGxsibbTZDKJV1991fneCQ8PF8OGDXO5ZJIQQnz88ceibdu2wsvLS/j6+ooWLVqIf//73+Ly5ctFblNBP//8s+jSpYvw8vISfn5+YvDgweLYsWMubYp7+Zfnn39eACgUX0H5l1I5fPiwc1mXLl2KvEySu9tYu3ZtMWjQoCIfv3HjRtGyZUuh1+tFnTp1xLvvvuu8xE7+9WmFcFzK7PXXXxfh4eHCy8tL9OrVSxw/flzUqFFDPPXUUy7rLM7761bWrVsn+vbtK4KCgoRarRYRERFixIgR4tdff3Vpd/r0aTFs2DAREBAg9Hq96NChg/jxxx+LXGdFfDYURRGzZs0StWvXFjqdTrRp00b8+OOPRb6PU1JSxMMPPyx8fX2Fv7+/GDt2rNizZ48AIL799ttC2zl69GgRHh4uNBqNiIqKEvfee69Yt25dsV5P8gzm/GuY85nzmfOvYc53vHc/+OAD0a9fP1GzZk2h0WiEr6+v6NSpk1ixYoXLJdDc+bwLUbrPmhCOy94NGjRI+Pr6CgDOy7/l5eWJF1980fld0qVLF7F3717Ro0ePQpeIO3PmjBg0aJDw8vISISEh4sUXX3Re7vWPP/5waVuc7+jyIglRzjOAEFGlM3ToUBw9etQ5GyZVbRkZGQgMDMTMmTPx6quvejqcKmHDhg24//77sXv3bnTp0sXT4RAR3RBzfvXCnF96CxcuxKRJk3Dx4sVKc9UXntNOVMXl5ua63I6Li8OmTZtw9913eyYgKlfX72/g2jmh3Oclc/1rarfbsWjRIvj5+eHOO+/0UFRERIUx51cvzPmld/1rmJeXh+XLl6Nhw4aVpmAHeE47UZVXr149jB071nndy48++gharfaGl/2g29uaNWuwcuVKDBw4ED4+Pti9eze++eYb9O3blz3CJfT8888jNzcXnTp1gtlsxvr16/H7779j1qxZZXLZLSKissKcX70w55feAw88gFq1aqF169bIzMzEV199hRMnTtzwsn2ewqKdqIrr378/vvnmGyQmJkKn06FTp06YNWtWkdcjpdtfy5YtoVar8d5778FoNDonqsmfHZnc16tXL8ybNw8//vgj8vLy0KBBAyxatOiGkxcREXkKc371wpxfev369cMnn3yC1atXw263o1mzZvj2228LXRnA03hOOxEREREREVElxXPaiYiIiIiIiCopFu1ERERERERElVS1O6ddURRcvnwZvr6+kCTJ0+EQERFBCIGsrCxERkZClnk8vSww3xMRUWVSmlxf7Yr2y5cvIzo62tNhEBERFXLhwgXUrFnT02FUCcz3RERUGZUk11e7ot3X1xeA48Xy8/PzcDRERESA0WhEdHS0M0dR6THfExFRZVKaXF/tivb8IXJ+fn5M4kREVKlwGHfZYb4nIqLKqCS5nifOEREREREREVVSLNqJiIiIiIiIKikW7URERERERESVVLU7p52IqLqw2+2wWq2eDoMAqFQqqNVqnrNORFSFCSFgs9lgt9s9HQp5iEajgUqlKvP1smgnIqqCsrOzcfHiRQghPB0KXWUwGBAREQGtVuvpUIiIqIxZLBYkJCTAZDJ5OhTyIEmSULNmTfj4+JTpelm0ExFVMXa7HRcvXoTBYEBISAh7dz1MCAGLxYKUlBScPXsWDRs2hCzz7DQioqpCURScPXsWKpUKkZGR0Gq1zL3VkBACKSkpuHjxIho2bFimPe4s2omIqhir1QohBEJCQuDl5eXpcAiAl5cXNBoNzp8/D4vFAr1e7+mQiIiojFgsFiiKgujoaBgMBk+HQx4UEhKCc+fOwWq1lmnRzkP9RERVFI/yVy7sXSciqtr4PU/l9duL7ywiIiIiIiKiSopFOxEREREREVElxaKdiIiqtJUrVyIgIMCtx4wdOxZDhw4tl3iIiIhuF5IkQZKkYufRkuTc28Xdd9/tfD1iYmIq9LlZtBMRUaWxc+dODB48GJGRkZAkCRs2bPB0SERERFXW2LFjnYVowb/+/fs723z++ec4efKkB6N08PQBgfXr12Pfvn0eeW7OHk9ERJVGTk4OWrVqhccffxwPPPCAp8MhIiKq8vr374/PP//cZZlOp3P+PyAgAKGhoRUdlgur1erR5weAoKAgGI1Gjzw3e9qJiKoJxaJAsSgQQjiXCbtwLLOJotsqJW9bEgMGDMDMmTNx//33F/sx8+fPR4sWLeDt7Y3o6Gg888wzyM7OvmH76dOno3Xr1li+fLnz8jzDhw9HZmZmobZz585FREQEatSogWeffdblR8OqVavQrl07+Pr6Ijw8HA8//DCSk5Pd22AiIqrSKjL3lpROp0N4eLjLX2Bg4A3bHz58GD179oSvry/8/PzQtm1b/PXXXy5tfvrpJzRt2hQ+Pj7o378/EhISrsWuKHjrrbdQs2ZN6HQ6tG7dGlu2bHHef+7cOUiShDVr1qBHjx7Q6/VYvXo1HnvsMWRmZjpHA0yfPh1A8fLxxo0b0bBhQ+j1evTs2RNffPEFJElCRkaGs83u3bvRrVs3eHl5ITo6Gi+88AJycnJK8cqWHRbtRETVxMVF53Fx0XkouYpzmfGvTFxcdB7pO9Jc2l5aFo+Li87DnmVzLsuKMeLiovNI25ri0vbyJxdwcdF52K5cK2hzjmaV01YUJssyPvzwQxw9ehRffPEFduzYgX//+983fcypU6ewdu1a/Pe//8WWLVtw6NAhPPPMMy5tfvnlF5w+fRq//PILvvjiC6xcuRIrV6503m+1WvH222/j8OHD2LBhA86dO4exY8eWwxYSEdHtqiJzb0V55JFHULNmTezfvx8HDhzA1KlTodFonPebTCbMnTsXq1atws6dOxEfH48pU6Y47//ggw8wb948zJ07F3///Tf69euHIUOGIC4uzuV5pk6digkTJuD48ePo2bMnFi5cCD8/PyQkJCAhIcG5zlvl47Nnz2LYsGEYOnQoDh8+jPHjx+PVV191ea7Tp0+jf//+ePDBB/H3339jzZo12L17N5577rlyeAXdx+HxRER0W5s4caLz/3Xq1MHMmTPx1FNPYenSpTd8TF5eHr788ktERUUBABYtWoRBgwZh3rx5CA8PBwAEBgZi8eLFUKlUaNKkCQYNGoTt27dj3LhxAIDHH3/cub569erhww8/RPv27ZGdnQ0fH59y2FIiIqKy9+OPPxbKW6+88gpeeeWVItvHx8fjpZdeQpMmTQAADRs2dLnfarVi2bJlqF+/PgDgueeew1tvveW8f+7cuXj55ZcxcuRIAMC7776LX375BQsXLsSSJUuc7SZOnOhyqpy/vz8kSXLm6Xy3ysfLly9H48aN8f777wMAGjdujH/++QfvvPOO83GzZ8/GI4884vxN0bBhQ3z44Yfo0aMHPvroI+j1+pu8guWPRTsRUTVR8/naAABJIzmX+bXzh28bP0iy5NI26qlajrbqa8t9W/vBp4VvobaRT0QXauvd3Ldsgwcwa9YszJo1y3n72LFjqFWrFn7++WfMnj0bJ06cgNFohM1mQ15eHkwmEwwGQ5HrqlWrlrNgB4BOnTpBURTExsY6fww0b94cKpXK2SYiIgJHjhxx3j5w4ACmT5+Ow4cPIz09HYri6EWJj49Hs2bNynTbiYjo9lSRubekevbsiY8++shlWVBQ0A3bT548GU888QRWrVqFPn364KGHHnIW6ABgMBhcbkdERDiHqxuNRly+fBldunRxWWeXLl1w+PBhl2Xt2rUrVvy3ysexsbFo3769y2M6dOjgcvvw4cP4+++/sXr1aucyIQQURcHZs2fRtGnTYsVSXjg8noiompC1MmStDEm6luAlleRYdl3Sd7aVS962rD311FOIiYlx/kVGRuLcuXO499570bJlS3z//fc4cOCA8yi9xWIp1fMVHOoHOC57k/9DICcnB/369YOfnx9Wr16N/fv344cffiiT5yUioqqjInNvSXl7e6NBgwYufzcr2qdPn46jR49i0KBB2LFjB5o1a+bMgUDR+bPgOf3uxHUrZZWPs7OzMX78eJffGYcPH0ZcXJzLAQhPYU87ERHdFoKCggr9iDhw4AAURcG8efMgy47j0GvXrr3luuLj43H58mVERkYCAP744w/IsozGjRsXK5YTJ04gLS0Nc+bMQXS0o7fj+kl4iIiIqqpGjRqhUaNGmDRpEkaNGoXPP/+8WJPI+vn5ITIyEnv27EGPHj2cy/fs2VOo9/t6Wq0WdrvdZVlx8nHjxo2xadMml2X79+93uX3nnXfi2LFjaNCgwS23wRPY005ERJVGdna28wg34Jg8JiYmBvHx8UW2b9CgAaxWKxYtWoQzZ85g1apVWLZs2S2fR6/XY8yYMTh8+DB27dqFF154AcOHDy90ntyN1KpVC1qt1vm8GzduxNtvv13s7SQiIqoszGYzEhMTXf5SU1OLbJubm4vnnnsOv/76K86fP489e/Zg//79bg0ff+mll/Duu+9izZo1iI2NxdSpUxETE4MJEybc9HF16tRBdnY2tm/fjtTUVJhMpmLl4/Hjx+PEiRN4+eWXcfLkSaxdu9Y5sWz+CIiXX34Zv//+O5577jnExMQgLi4O//nPfyrNRHQs2omIqNL466+/0KZNG7Rp0waA47y5Nm3a4I033iiyfatWrTB//ny8++67uOOOO7B69WrMnj37ls/ToEEDPPDAAxg4cCD69u2Lli1b3nTiuuuFhIRg5cqV+O6779CsWTPMmTMHc+fOLfbjiYiIKostW7YgIiLC5a9r165FtlWpVEhLS8Po0aPRqFEjDB8+HAMGDMCMGTOK/XwvvPACJk+ejBdffBEtWrTAli1bnJdku5nOnTvjqaeewogRIxASEoL33nuvWPm4bt26WLduHdavX4+WLVvio48+cs4en389+pYtW+K3337DyZMn0a1bN+dvj/wReZ4miZKcYHAbMxqN8Pf3R2ZmJvz8/DwdDhFRmcvLy8PZs2dRt25dj892WhlNnz4dGzZscPbmV5Sb7RfmprLH15SIKkpVzruSJOGHH37A0KFDPR1KmXrnnXewbNkyXLhwwa3HnTt3DnXr1sWhQ4fQunXrQveXV65nTzsREREREREVadSoUahZs6anwyiVpUuXYv/+/c5T6d5//32MGTPGrXUMGDAAzZs3L6cIb44T0REREREREVEhcXFxAOByCdTbUVxcHGbOnIkrV66gVq1aePHFFzFt2jS31vHJJ58gNzcXgGNum4rEop2IiKqV6dOnY/r06Z4Og4iIqNKrrLOpu2vBggVYsGBBqdYRFRVVRtG4j8PjiYiIiIiIiCopjxbtO3fuxODBgxEZGQlJkrBhw4ZbPubXX3/FnXfeCZ1OhwYNGjin6yciIlfVbJ7RSq+67g/meiKqLqrr9zxdU17vAY8W7Tk5OWjVqhWWLFlSrPZnz57FoEGD0LNnT8TExGDixIl44okn8NNPP5VzpEREt4/8884sFouHI6GCTCYTAECj0Xg4korFXE9EVV3+93r+9zxVX/m/vcp6DgCPntM+YMAADBgwoNjtly1bhrp162LevHkAgKZNm2L37t1YsGAB+vXrV15hEhHdVtRqNQwGA1JSUqDRaCDLPBPKk4QQMJlMSE5ORkBAwG0/mY+7mOuJqKpTqVQICAhAcnIyAMBgMECSJA9HRRVNURSkpKTAYDBArS7bMvu2mohu79696NOnj8uyfv36YeLEiTd8jNlshtlsdt42Go1lFs/mg7n4/YQZLWtr8VAXQ5mtl4ioNCRJQkREBM6ePYvz5897Ohy6KiAgAOHh4Z4Oo9IrSa4HyjffExHdSv73e37hTtWTLMuoVatWmR+0ua2K9sTERISFhbksCwsLg9FoRG5uLry8vAo9Zvbs2ZgxY0a5xJNiVJCYoSAxIw8PdPKCSuYRNSKqHLRaLRo2bMgh8pWERqOpdj3sJVWSXA+Ub74nIrqV/APmoaGhsFqtng6HPESr1ZbLCMfbqmgviWnTpmHy5MnO20ajEdHR0WWy7h7NdNh1zHFUn/NOEFFlI8sy9Hq9p8MgqhDlme+JiIpLpVLxIC2VuduqaA8PD0dSUpLLsqSkJPj5+d3wyLtOp4NOpyuXeEL8eZ4oERFRWSpJrgfKN98TERF50m1VdXbq1Anbt293WbZt2zZ06tTJI/EUPFeBPe1ERESlV9lyPRERkad5tGjPzs5GTEwMYmJiADgu8xITE4P4+HgAjqFuo0ePdrZ/6qmncObMGfz73//GiRMnsHTpUqxduxaTJk3yRPgoeAY7a3YiIqLCbvdcT0RE5GkeLdr/+usvtGnTBm3atAEATJ48GW3atMEbb7wBAEhISHAmdQCoW7cu/ve//2Hbtm1o1aoV5s2bh08++cRzl4ApULWzp52IiKiw2z7XExEReZgkRPUqN41GI/z9/ZGZmQk/P79SrctsFXhuRToAYPG4QOg0nD2eiIjcV5a5iRz4mhIRUWVSmrx0W53TXplVqyMfREREREREVCFYtJeCxJPaiYiIiIiIqByxaC+F0tTsJrOClTuy8dIX6biQaivLsIiIiIiIiKiKuK2u017puExEJ1wX3ERcghUrtuYgPUcBAMResiE6mLuCiIiIiIiIXLFSLIWSTDv350kzVu7IgU25tizPyrH1REREREREVBiL9lKQ3Lzk259xZnz6cw4EgDvraeCjl7HzmJlFOxFRJSCEgM0OWGwCZpvjX6tNwGITsFy9rVFJaBylhiwBNgWwK4CeVw4hIiKicsSivRTcOaf9+EUrPt/uKNi7N9PhkR4G/Hd/LgDAbGHRTkRVh80uYLbm/zlGE1lsAnkFljnvtzn+BYDGURr4eUnOAjn/fosVV/+9etsGQAANItQI8JavFtUC1quPK1x0X1tusV69bS/w/wKPL+63sSQ5DtbWrKHCmyP8y+21JCIiImLRXhrF7Gk3mhR8si0bdgXo0FCLR3oYIEsSvLSOFeSyaCeqkhRFINciYDIL+Blk6CqwR9auCORZHIVyrqWo/+Pa/6/e5/zXKuDrJeGOWlrIUoEC2+oohs1X21is1xXiVwttu3Lr+Iqy44jZrfa7jrvX3h0qGdCqJWjV+f9KUKmAC6l2CHHtO99q5/c3ERERla9qW7QrFgVCCEhXx7gLu4CwC0iyBEktubQDAEktQZJd215/UntRbRWbglU/ZyPbpCCyhhpjenpDliQoVgV6yfHLL394vFAEhE0AMiCrr03sr1gVQFwXw43a2hRAKYO2EiBrHG2NJgVHz1mglgXaNdRDUt24LQAIm4BQBCSVVLgtAFlbNm0VRSA5047LyTZcumLHpUw7EjIEooJU+L9eBkgKIKkk5NocQ159vSSIq6+1pJFc9r3dpiDDJJCSI5CSaUdypoLUKzaoZODhnt7w9lKV+H1SqG1R+9OdtkXsT7siYMyyQ6+RoNfLZbbv3W1bUfsecAxlzt+fBdvarQpy8xRkW4AcK5BrUVArWAWfq69r/r632QWyTQpMJgU5VgGTDcjJcxTYphwbciwCuTagbQMdooJUyMlVkJurwGQFcm2AySJgMivIM9mRaxHItgImC2AyC+SZFeTlKci1AcrV95lKBtpEq2GxATlWAYNeRqu6GuhkIDfv6nrtQK7ZUVibcxXkWgRyrvYa1w5RoVG4GuY8BXm2q22vFtmWPAV5V9vmXe3ZtloFVMLRc2yXr72nVIqABMAuASL/HB8hoC6ibex5a7HbykJAFoAiwXnukFoS8FJL0GskqHUSdBoJOrUEvQrQqwGNVoZO5yiIzyRYkZJudxTHWhk6jaNY1qsAnQrQamVotI7Ha9VAfKINCel2qLQytFfb6iRApwY0GgkarQytWoJGJa4ul6DRSdBqri6XBLTy1Rj00tW2gBaAWgVodHKR+cEsHN+JWrUEjVCgVkuO9+atPsu3+I4gIiIiupFqW7RfWh4P30nNoTI4ijHjX5nI3J0Onxa+COobfK3dsngIq0DkEzWh9tcAALJijMj49QoMTb2R/xIKAVz+5AKUXAURY6KgCdYCAI79moGWv6chyKBF95FR0F79EZe48hJCUiyo4eXrLNpNsTlI25QCfW09QodFOGNIWn0Z1jQrQoeHQx/tBQDIPWNC6n+SoYvUIWxUpLNt8ppEWBLNCLk/DF71DAAA84U8JK9LhCZUi4hHo5xtU75PgvliHoLvDYWhsTcAwJJgRtK3CbD7qHCyQwgOnbXiTKINPVOMqJlrxbnBIajbJcCx3hQLkr+6DJWPClHjaznXm7Y5BaaTOQjsVQO+bfwAALZMGxI+uwhZJ6Pmc7Wdba/8nIqco9kI6B4Iv/aO9dpz7Lj88QVABmpNqut4bcwK4v+XCtuxLFyI8sYBHy8kpNsBi4KRF9MRAOC/0UEQkoTLV+yIPpeF4Is5OOqrxx5vAyQAL93nA+23l2C1CaTfG4EEE5CYbofhhBF1EnJw3FuPfUHeztj+FZ8GGcDvARG4p7OPY98fzETGznR4N/dBjf4hzraXP74Axawg4vGa0ARefZ/8bUTGjiswNPJG8OBQZ9uEzy7Cnm1H+KORkGtocSVbgf1kNqw7r8CrrhdCHgh3tk1cdQm2dBv0Q8Jg9NHgSraC3JM58P8rHeneGvxexx8ZOQoyTQKDLmciyGqDun8IcgL1SM9RIC7kou7RdGjDdGj1bPS1ff9dIsyXzQi+LxSGBo5tNl/KQ/LaRGhqaBAxtqazbeqGJOSdz0ONgSHwbup4HSzJFiStvgy1nxqR466tN/V/ycg9ZUJAnxpAQx8YcxUYL1sg/TcBdq0KDSfUdvY2p/2Uguzj2VB1DIS5gQ+ycgVyUi3w/ykJNknCXx1DkZWrICtXoNE5IxrkmKHrEIDMej7IzhPINdpQ99ck2BVge+tQZOcpyM4TaJqQjaZZefjbzwsxAY7PgFoReCwpHYoANjaoAaPFUQi3zjChpTEXx3312B94bd+Pjk9DIIA1UYHYe9IKAGiRmYs2mSbE+eiwN8jH2fbhC1egFgLrIwOQrXZ8nzQ15qJ9hglnDDrsDna0tStAvT0p0CsK/hPhj0yNGkfirWiYnYdOV3KQ6aXFLyG+zvU+cDkdPjYF/wvzR5pOjbQsBelHstEtLRvZeg1+DfVzth2SkIEAqx0/hfrBqHe8/6JzreiZmoVUvRq7agfASytBr5XQ+XQ6/E02nGkeCEu4DnqthMBMC8L+SoMI0MDcNxwWm8DRC1bUi7mCgGwL4psFwByph1YjwTfbiog/UiF81MgbFO4oxDUS1L+mApdy4d0rGL53+ECnkaCkWZC46up3xNhr3xGp/02G6Zjrd4Q13Vrkd0TalpRC3xG2LBsu70xy+Y4AgCvbU5EdkwX/TgHw7xwIAFDy7Li4JN7xmkys4zzok/7bFWT9lQnfdv4I7BEEwFFEX1h4DgBQ89lakPRX88OfGcjcmwGf1r4I6h0Mvb9jefyC84ACRD4ZDbWvIxe48x2R/XcW0nekwdDIG9oeehAREREVpdoW7RVBUQR+j7WgKYBaISpE1XB9ufN7cWIv2bAvzozmHoixoBSjHTHHzPBNtyMtXWCDPbdQm2OXrDjwuwl/n7fAnGzBY2YB32v1C7LzFCSk26DkKLAZ7fAttIZbc57Lagc+2ZaNcyk2JGUo6HDFjCbZCk4n2nDe3w4AMFwdwqpRA8M6eeGfizYcv2jD+RQ7fCwCeRYA3o5ewQ9/zMLwFBsA4NtfcmCRHb2zrXIUCAGoJCA8QEaIvwqhfjJ8UmSYchX8+FceurQ1wKCTC8Vqtjp6+7NzFdjyFOzek4OLVhkpRjsiknJxj1mBbBGIS7Ai1agg1aggNM0OKdeOVRuycN4uQwigfrYZA80K0q7YsfsPE65kK7iSraB1vBX6PDs2/5iFFJ3jh35tkxk9chWkKQrOJdsLxbT5oBkJekevXlSuFREWgUvJNtRItUGSgPQcBUqGHZJJwaGYPFw4qSA9R4FPhgUDLAJqs8DBMxZoVMAdtTQu67baBDJMCjJSbLCbBSxZdvz5hwmZJseBg7qxZtTIsGHNT9k4ucfxWvtbbbjPqCBPBj5YkY6aNVTIylXQ6kIe6uTYsX+PCcePOA5c+djseCBHgU2SsC/O4nze6KtDt/ceMeNIvGM/6OwKRuQ5Hncq0VboddCogCAfGVeyFee+AoD0bAW2qz2bEgBZArz1EuqEqmDQSfDWyfBNkSFJjscn5AJaNeClBTQqCUHeMlrU0sBLJ8GgkxCYLkMtBB7s5AV9oBYGnQT9KQD7zajfWIdH7g2EySywL86C0J8yobYC97X3woE0x+elpk4FQ46EyCAZfVrq4KWV4KWTEbEjC+pcO0b39MZlSYXYy1aEptnhkyOjZg0VBrfTQ691nGYTtCMb6ixgTE8DvGp5Qa+RIF/MRdbmXDSK1OH+UYHO1yVxdQ4siQLN23s5D+zlnZeQfFSGxl+FiAaOA46dm+iQdCUL5ot2NG3t5TywZ76Uh6TDMtR+MiIb6pzrTfGSkKuS4KOX4K137KNre5CIiIiISkoSojjznlcdRqMR/v7+SE9Jh38N/1INj5dkCeM/TocAMHdsAK52tDjbHjxjwbLNWfDRSnj7YX94e6uurdeqIO6SFXP/lw1IEvy8JMwdE1BoKPOeE2b8ejgXwzt5oUFNrdvDnjNyFOw+lofaQTKa19YWaptnEThwxoo/Tllw8rLNOfRVloH6NTVoU0+LVnW0+O1wLrYeyis0TLZdHQ3636nHkUt2/HPeijPJNkiKY5hszVAVXh0e4BovCg97NubYcSrJjrgkG+ISbLiQaoPq6jmxtgLDRkN9JNSqoUJEDRWiQjSIqqFCiK8E6WrdKmkkJGcq+N+BXPjqJET6ywivoYbZDiz4b5Zz2ww6CaFBKoQHqREeICPcT0a4v4xgPxVUBWIz5dgxdVUGcu1AiL8KD3c3IDHNjuQMG5IyFSQahbMgVCuObbMVGBosCcfwZEW6NkQauDY82Z22QgUE+KhQw1dGkLeEIC8Jgb4qBPipEOAtI8BbRlKqDd/9boJGJyHAW4UAHwkBegmHTzv2S0mGSAf5yAj3lWA0KUjPFcjJr8JuMUTafnXbfLwk+OolyIpjVIOtiLYaLeBjUMH3als/LeCrl+Htq4KvlwxfLwnHz1txLN4KnRYwGFTw0Uvw0Unw0TgKbh8f9dViUYK3WoJBB2g1MiSVhORMO45fsMBLBXjrZBi8ZXjrZXjrJOjVcJxGcaPPvUaCXQHUKqlsTo0o7WkUlfQUGuAWpztUktMorj8tRtjdbFuO+z7LlAV/f39kZmbCz+/aKAoqufx8z9eUiIgqg9LkpWrb0y5rr52vCMDlh9v17a7n0lYCcHVSouvbbovJg5AkdGuhdynYAccP2OAaamfRZsy9+iPv6uR0NrvA2j0m/PKPY6Kl/edsaFjrWq9WwbYu6736gzsjR8HmgybsPGaGzQ74GyTMrX9t+GVyph2/HMnDnhMW50R4EoAm0Rp0bKRD6zoaZ28ZALSsr8Pmw2Z46yXcUUuDIF8Zmw7k4a/zNvx1PtslBi+9DJNZ4Fyqgte/zsDQjga0ra91xms0KTh2wYqTl204mWBFUsb1s1ZJ8PaRUCdUffVPhdohavh6Fd4XAIACL21YgAqP9/Yp1OSVB/1gUwTCA1Q3Xs91DN4q9L7TCz/+lYcUo4IPfswusp23TkJ4gBqhATJC/VUI8ZcR7KvCJz9nI9WoQJaAGj4ygv1kBPs6Dg4E+8kI8XP832IT+GhLNoSAoyj3kRGU/6+PjCBfFQIMEuRbnPca4K3Fa7W1hZY3rW3Du+uNsNscsQZ4ywj0ka8W+xICvWWoVBK+2ZVz9b1yrXfa0ePvuj61CvA3qBBgkOHvLcHfIDv+vGX4G6Sr/8rw0UtQFYj51NXRBj5eEvy8ZPh4yfDVS9Cob75dAHBHrcLbVVyh/iqE+nu59ZiCn+WrI95L/h1R0raaItre6HPvTlt1+bSV1BKk6yb6qBRtpRu0LeJ1d6ctUH77noiIiKigalu0l5WrNXshiRl2nEq0QZaAni2KPlexhq8KT/XzwbKfsqGS4ZwYz2ITWPZTNo6ctzrbZuVee5bYS1as/d2ENnW1uLedazFitgpsjcnDT4dyYS4wYjjT5BhefOmKDf87kId/zludcYf6y+jaVIeOjXQI8im6oK0frsaicYHQqABZlpBrEdjxdx7yrI6hw01ratCytuZqQa/Cy19m4Eq2gsQMBZsP5sJHL+HoBSuOxlsRn+o6pFsCEBmkQqNINRpGqFE/QnPDOEqqbljJ3ur3dTAgI0dgX5wZIX4qhAXICAtQISxAhfCr//fRFx3rW6P8YTQpCPCWXYrXorwxvPwuGVUrWI35jwVCkuCcU6EonRtrIQDIkgS7IvDbUTOsNlGgIHcU5Qad5HLAq7gaRGjQIOLW7YiIiIiI6BoW7aUkFehpL+iPWEcPefNoDQK8b1yANo92nDNsVwCr3VG4L9qUhdhLNmhUQNv6Wvxx0oKsPAWKENh8MA8b/nSca34lK8+laD9w2oJvd+cgI8cRTN0wFe7vaMDHW7ORnScw9z9GnE+5VjC3qKVBr5Y6NIvWQC5GEVbwclVeWglTH/SD0STQIFxdqLf0//p4Y/dxM/bGWnA+xY65/8lyub9WiApNozRoGKlGg3C1S69+ZTOmpzfG9PS+dcPraFQSaviqbt2wAhTnUmOSdK2PUiVL6HWDg01ERERERFRxWLSXUn6Rc/3UAPtPOU7+vavxzYf1ajXXeuuz8wS++jUHsZds0GuA5wb6wq4Af5y04EqWgsWbXHvfs/MEhBAw5gp8vTMHB8847gv2k/HAXV5oV18LSZIQ7CcjO8+O8yl2qGSgU2Md+rfRIyygdAVlVJAaUUFF39coUoOGEWpcSLXjYpodPnoJzaM1aF5Lg+bRGvgZKm+RTkREREREVFmwaC+t/Dm8CixKvnqdb5UMtCjiHOOCZMlxGaZci8An27IRl2CDVg1MHOyL+uEaxKc6xrinGBWkGBVoVMDwLgas3mkCAPx+woLvfjchxyygkoH+bfQY1NbLpee7VR0tLqTmolNjHQbeqUeIf8X0/kqShCn3+SLDpCAiUFWs3nwiIiIiIiK6hkV7KRVVhh694Ojxrh+uhlcRkxpdz+tq0R6X4Lgk17h7fFA/3DFs3rfAsHFfLwkvDPJFnVA1/rMvF9l5Ait/yQEA1ApWYUwvb9QKLrxL723nhQF36m95XnV58NbLlXroOxERERERUWXGor2UClwty+lovKNozz9f/VbyZ+oGgIc6G9C67rXeeX+D4/rRNjvwTH8fZy+5v8Ex5B0A+rTU4cFOBqhvMguxJwp2IiIiIiIiKh0W7WUkv2YXQuBMkmNIe+Oo4hXttUNUOJ9iR3iAjD4tdS73ybKEVx50XMev4Izd7RtoYTpqxoiujsupERERERERUdXDor2UnGX01ar9SraCrFzH+eW1got37viIrgYcPmvFwLb6Ii+lVdSyQe28MKide9eeJiIiIiIiotsLi/bSum4iunPJjiHrUUGqQpdBu5GGERo0jCherzwRERERERFVH5whrJSuL8vPJTuGxtcJ5fEQIiIiIiIiKh0W7aWUP3Q9fyK6+BRH0V47tGIuq0ZERERERERVF4v2UpKuGx6flOmYCT4ykEU7ERERERERlQ6L9jIiBGC1C1zJchTtof4s2omIiIiIiKh0WLSXUsHZ41MzFQgAeg3g68XrohMREREREVHpcLa0Uio4PD7F6Jg5PtRfVeRl2oiIiIiIiIjcwZ72MiIgkJRxrWgnIiIiIiIiKi0W7aXk7FAXQEpm/vnsfFmJiIiIiIio9FhdlhEBIPXqJHQh7GknIiIiIiKiMsCivZQKTkSXaXIU7f4Gns9OREREREREpceivZQKTkRnvFq0+xn4shIREREREVHpsbosI4oAsnIFAMDPiy8rERERERERlR6ry1LK72nPzhVQHDU7r9FOREREREREZYJFeylJV89qzz+f3VsnQa1i0U5ERERERESlx6K9lPJ72o25+eezs2AnIiIiIiKissGivYxkchI6IiIiIiIiKmOsMEvJ2dNu4iR0REREREREVLZYYZaRzBwOjyciIiIiIqKyxaK9lPJ72p3D49nTTkRERERERGWEFWYp5ferZ+c5hsf78HJvREREREREVEZYtJeRXIujaDdo+ZISERERERFR2WCFWUr5w+PtjtHx8NKyp52IiIiIiIjKBov2UpKuq9ENOhbtREREREREVDZYtJfS9SU6e9qJiIiIiIiorLBoL2PsaSciIiIiIqKywqK9lK4fHs+ediIiIiIiIiorLNpL7VqRrlYBGjWLdiIiIiIiIiobLNpLqWBPu17Dgp2IiIiIiIjKDov2UipYputYtBMREREREVEZYtFehrQcGk9ERERERERliEV7KRUcHq/TeC4OIiIiIiIiqnpYtJeSS9HOnnYiIiIiIiIqQyzaS6lgma7lOe1ERERERERUhli0lxZ72omIiIiIiKiceLxoX7JkCerUqQO9Xo+OHTti3759N22/cOFCNG7cGF5eXoiOjsakSZOQl5dXQdEW5jp7vMfCICIiqtRu93xPRETkKR4t2tesWYPJkyfjzTffxMGDB9GqVSv069cPycnJRbb/+uuvMXXqVLz55ps4fvw4Pv30U6xZswavvPJKBUdeNA6PJyIiKqyq5XsiIqKK5NGiff78+Rg3bhwee+wxNGvWDMuWLYPBYMBnn31WZPvff/8dXbp0wcMPP4w6deqgb9++GDVq1C2P1pcnTkRHRER0c1Uh3xMREXmKx4p2i8WCAwcOoE+fPteCkWX06dMHe/fuLfIxnTt3xoEDB5xJ+8yZM9i0aRMGDhx4w+cxm80wGo0uf2XJdXg8i3YiIqKCqkq+JyIi8hS1p544NTUVdrsdYWFhLsvDwsJw4sSJIh/z8MMPIzU1FV27doUQAjabDU899dRNh8vNnj0bM2bMKNPYXRSo07UeezWJiIgqpyqT74mIiDzE4xPRuePXX3/FrFmzsHTpUhw8eBDr16/H//73P7z99ts3fMy0adOQmZnp/Ltw4UKZxlSwb12jYk87ERFRaVXGfE9EROQpHusbDg4OhkqlQlJSksvypKQkhIeHF/mY119/HY8++iieeOIJAECLFi2Qk5ODJ598Eq+++ipkufAxCJ1OB51OV/YbcJVUoGxXq8rtaYiIiG5LVSXfExEReYrHetq1Wi3atm2L7du3O5cpioLt27ejU6dORT7GZDIVStQqlaNSFkKUX7A3UXAiOjV72omIiFxUlXxPRETkKR49C3vy5MkYM2YM2rVrhw4dOmDhwoXIycnBY489BgAYPXo0oqKiMHv2bADA4MGDMX/+fLRp0wYdO3bEqVOn8Prrr2Pw4MHOZF7hCtTpHB5PRERUWJXI90RERB7i0aJ9xIgRSElJwRtvvIHExES0bt0aW7ZscU5WEx8f73Kk/bXXXoMkSXjttddw6dIlhISEYPDgwXjnnXc8tQku57RzeDwREVFhVSHfExEReYokqtk4M6PRCH9/f2RmZsLPz6/U63t/gxEnL9sAABPv9UXzWppSr5OIiKqXss5NxNeUiIgql9Lkpdtq9vjKjj3tREREREREVJZYtJeS6/B4ntNOREREREREZYdFe2kVnIjOozMEEBERERERUVXDor2UCvata2T2tBMREREREVHZYdFeSq7XafdcHERERERERFT1sGgvQxo1e9qJiIiIiIio7LBoL0PsaSciIiIiIqKyxKK9lGz2a//n7PFERERERERUlli0l5JNEc7/a9jTTkRERERERGWIRXsp2Qv0tKs4ezwRERERERGVIRbtpWRXPB0BERERERERVVUs2ktJEeLWjYiIiIiIiIhKgEV7KbGnnYiIiIiIiMoLi/ZSYkc7ERERERERlRcW7aXEnnYiIiIiIiIqLyzaS4nntBMREREREVF5YdFeSqzZiYiIiIiIqLywaC8lhUU7ERERERERlRMW7aWk8Jx2IiIiIiIiKics2kuJPe1ERERERERUXli0l5LCqp2IiIiIiIjKCYv2UrKzZiciIiIiIqJywqK9lDh7PBEREREREZUXFu2lxInoiIiIiIiIqLywaC8ldrQTERERERFReWHRTkRERERERFRJsWgnIiIiIiIiqqRYtBMRERERERFVUizaiYiIiIiIiCopFu1ERERERERElRSLdiIiIiIiIqJKqkRF++nTp/Haa69h1KhRSE5OBgBs3rwZR48eLdPgiIiIiIiIiKozt4v23377DS1atMCff/6J9evXIzs7GwBw+PBhvPnmm2UeIBEREREREVF15XbRPnXqVMycORPbtm2DVqt1Lu/Vqxf++OOPMg2OiIiIPMdisSA2NhY2m83ToRAREVVbbhftR44cwf33319oeWhoKFJTU8skKCIiIvIck8mE//u//4PBYEDz5s0RHx8PAHj++ecxZ84cD0dHRERUvbhdtAcEBCAhIaHQ8kOHDiEqKqpMgiIiIiLPmTZtGg4fPoxff/0Ver3eubxPnz5Ys2aNByMjIiKqftwu2keOHImXX34ZiYmJkCQJiqJgz549mDJlCkaPHl0eMRIREVEF2rBhAxYvXoyuXbtCkiTn8ubNm+P06dMejIyIiKj6cbtonzVrFpo0aYLo6GhkZ2ejWbNm6N69Ozp37ozXXnutPGK8LQT58Op5RERUNaSkpCA0NLTQ8pycHJcinoiIiMqf25WmVqvFihUrcPr0afz444/46quvcOLECaxatQoqlao8YqzUXr7fF01rqvHCvT6eDoWIiKhMtGvXDv/73/+ct/ML9U8++QSdOnXyVFhERETVkrqkD6xVqxZq1apVlrHclhpEaDB5iMbTYRAREZWZWbNmYcCAATh27BhsNhs++OADHDt2DL///jt+++03T4dHRERUrbhdtE+ePLnI5ZIkQa/Xo0GDBrjvvvsQFBRU6uCIiIio4nXt2hUxMTGYM2cOWrRoga1bt+LOO+/E3r170aJFC0+HR0REVK1IQgjhzgN69uyJgwcPwm63o3HjxgCAkydPQqVSoUmTJoiNjYUkSdi9ezeaNWtWLkGXhtFohL+/PzIzM+Hn5+fpcIiIiJibygFfUyIiqkxKk5fcPqf9vvvuQ58+fXD58mUcOHAABw4cwMWLF3HPPfdg1KhRuHTpErp3745Jkya5u2oiIiKqBIxGY5F/WVlZsFgsng6PiIioWnG7pz0qKgrbtm0r1It+9OhR9O3bF5cuXcLBgwfRt29fpKamlmmwZYFH3omIqLKpbLlJluWbzhJfs2ZNjB07Fm+++SZkuXJePaWyvaZERFS9lSYvuX1Oe2ZmJpKTkwsV7SkpKTAajQCAgIAAHoknIiK6Ta1cuRKvvvoqxo4diw4dOgAA9u3bhy+++AKvvfYaUlJSMHfuXOh0OrzyyisejpaIiKhqc7tov++++/D4449j3rx5aN++PQBg//79mDJlCoYOHQrAkdgbNWpUpoESERFRxfjiiy8wb948DB8+3Lls8ODBaNGiBZYvX47t27ejVq1aeOedd1i0ExERlTO3i/bly5dj0qRJGDlyJGw2m2MlajXGjBmDBQsWAACaNGmCTz75pGwjJSIiogrx+++/Y9myZYWWt2nTBnv37gXgmGE+Pj6+okMjIiKqdtw+Ec3HxwcrVqxAWloaDh06hEOHDiEtLQ0ff/wxvL29AQCtW7dG69atyzpWIiIiqgDR0dH49NNPCy3/9NNPER0dDQBIS0tDYGBgRYdGRERU7bjd057Px8cHLVu2LMtYiIiIqBKYO3cuHnroIWzevNl5Ktxff/2FEydOYN26dQAcp8aNGDHCk2ESERFVCyUq2v/66y+sXbsW8fHxhSacW79+fZkERkRERJ4xZMgQxMbGYvny5YiNjQUADBgwABs2bECdOnUAAE8//bQHIyQiIqo+3C7av/32W4wePRr9+vXD1q1b0bdvX5w8eRJJSUm4//77yyNGIiIiqmB16tTB7NmzPR0GERFRted20T5r1iwsWLAAzz77LHx9ffHBBx+gbt26GD9+PCIiIsojRiIiIvIAk8lU5Kg6nh5HRERUcdwu2k+fPo1BgwYBALRaLXJyciBJEiZNmoRevXphxowZZR4kERERVZyUlBQ89thj2Lx5c5H32+32Co6IiIio+nJ79vjAwEBkZWUBAKKiovDPP/8AADIyMmAymco2OiIiIqpwEydOREZGBv788094eXlhy5Yt+OKLL9CwYUNs3LjR0+ERERFVK273tHfv3h3btm1DixYt8NBDD2HChAnYsWMHtm3bht69e5dHjERERFSBduzYgf/85z9o164dZFlG7dq1cc8998DPzw+zZ892jrgjIiKi8ud20b548WLk5eUBAF599VVoNBr8/vvvePDBB/Haa6+VeYBERERUsXJychAaGgrAMcIuJSUFjRo1QosWLXDw4EEPR0dERFS9uDU83maz4ccff4RKpXI8WJYxdepUbNy4EfPmzUNgYKDbASxZsgR16tSBXq9Hx44dsW/fvpu2z8jIwLPPPouIiAjodDo0atQImzZtcvt5iYiIqGiNGzd2XuqtVatWWL58OS5duoRly5aVeNJZ5nsiIqKScaunXa1W46mnnsLx48fL5MnXrFmDyZMnY9myZejYsSMWLlyIfv36ITY21nmEvyCLxYJ77rkHoaGhWLduHaKionD+/HkEBASUSTxEREQETJgwAQkJCQCAN998E/3798fq1auh1WqxcuVKt9fHfE9ERFRykhBCuPOAu+++G5MmTcJ9991X6ifv2LEj2rdvj8WLFwMAFEVBdHQ0nn/+eUydOrVQ+2XLluH999/HiRMnoNFoSvScRqMR/v7+yMzMhJ+fX6niJyIiKguVPTeZTCacOHECtWrVQnBwsNuPZ74nIqLqrjR5ye3Z45955hlMnjwZixcvxt69e/H333+7/BWXxWLBgQMH0KdPn2vByDL69OmDvXv3FvmYjRs3olOnTnj22WcRFhaGO+64A7NmzbrppWfMZjOMRqPLHxEREd3YW2+95XJFGIPBgDvvvBPe3t5466233FoX8z0REVHpuF20jxw5EmfPnsULL7yALl26oHXr1mjTpo3z3+JKTU2F3W5HWFiYy/KwsDAkJiYW+ZgzZ85g3bp1sNvt2LRpE15//XXMmzcPM2fOvOHzzJ49G/7+/s6/6OjoYsdIRERUHc2YMQPZ2dmFlptMJsyYMcOtdTHfExERlY7bs8efPXu2POIoFkVREBoaio8//hgqlQpt27bFpUuX8P777+PNN98s8jHTpk3D5MmTnbeNRiMTORER0U0IISBJUqHlhw8fRlBQULk/P/M9ERHRNW4X7bVr1y6TJw4ODoZKpUJSUpLL8qSkJISHhxf5mIiICGg0Gufs9QDQtGlTJCYmwmKxQKvVFnqMTqeDTqcrk5iJiIiqssDAQEiSBEmS0KhRI5fC3W63Izs7G0899ZRb62S+JyIiKh23h8cDwKpVq9ClSxdERkbi/PnzAICFCxfiP//5T7HXodVq0bZtW2zfvt25TFEUbN++HZ06dSryMV26dMGpU6egKIpz2cmTJxEREVFkAiciIqLiW7hwIebPnw8hBGbMmIEFCxY4/5YtW4bdu3djyZIlbq2T+Z6IiKh03O5p/+ijj/DGG29g4sSJeOedd5yTwgQEBGDhwoVuzSo/efJkjBkzBu3atUOHDh2wcOFC5OTk4LHHHgMAjB49GlFRUZg9ezYA4Omnn8bixYsxYcIEPP/884iLi8OsWbPwwgsvuLsZREREdJ0xY8bAZrNBkiT06tWrzIaXM98TERGVnNtF+6JFi7BixQoMHToUc+bMcS5v164dpkyZ4ta6RowYgZSUFLzxxhtITExE69atsWXLFudkNfHx8ZDla4MBoqOj8dNPP2HSpElo2bIloqKiMGHCBLz88svubgYREREVQa1W4+mnn8bx48fLbJ3M90RERCXn9nXavby8cOLECdSuXRu+vr44fPgw6tWrh7i4OLRs2RK5ubnlFWuZ4HVbiYiosqlsuenuu+/GxIkTMXToUE+HUmKV7TUlIqLqrTR5ye2e9rp16yImJqbQhHRbtmxB06ZN3V0dERERVTLPPPMMXnzxRVy8eBFt27aFt7e3y/0tW7b0UGRERETVj9tF++TJk/Hss88iLy8PQgjs27cP33zzDWbPno1PPvmkPGIkIiKiCjRy5EgAcDmHXJIk56Xg8uezISIiovLndtH+xBNPwMvLC6+99hpMJhMefvhhREZG4oMPPnAmeSIiIrp9nT171tMhEBER0VVuF+0A8Mgjj+CRRx6ByWRCdnY2QkNDyzouIiIi8pDrT4EjIiIiz3H7Ou0zZ850HoE3GAws2ImIiKqg06dP4/nnn0efPn3Qp08fvPDCCzh9+rSnwyIiIqp23C7av/vuOzRo0ACdO3fG0qVLkZqaWh5xERERkYf89NNPaNasGfbt24eWLVuiZcuW+PPPP9G8eXNs27bN0+ERERFVK25f8g0Ajh49itWrV+Pbb7/FxYsXcc899+CRRx7B0KFDYTAYyiPOMsNLwBARUWVT2XJTmzZt0K9fP8yZM8dl+dSpU7F161YcPHjQQ5EVX2V7TYmIqHorTV4qUdFe0J49e/D111/ju+++Q15eHoxGY2lWV+6YxImIqLKpbLlJr9fjyJEjaNiwocvykydPomXLlsjLy/NQZMVX2V5TIiKq3kqTl9weHn89b29veHl5QavVwmq1lnZ1RERE5GEhISGIiYkptDwmJoZz2RAREVWwEs0ef/bsWXz99df4+uuvERsbix49emDGjBkYNmxYWcdHREREFWzcuHF48skncebMGXTu3BmAY2Tdu+++i8mTJ3s4OiIiourF7aL9rrvuwv79+9GyZUs89thjGDVqFKKiosojNiIiIvKA119/Hb6+vpg3bx6mTZsGAIiMjMT06dPxwgsveDg6IiKi6sXtor1379747LPP0KxZs/KIh4iIiDxMkiRMmjQJkyZNQlZWFgDA19fXw1ERERFVT24X7e+8847z//lz2EmSVHYRERERUaXBYp2IiMizSjQR3ZdffokWLVrAy8sLXl5eaNmyJVatWlXWsREREZEHJCUl4dFHH0VkZCTUajVUKpXLHxEREVUct3va58+fj9dffx3PPfccunTpAgDYvXs3nnrqKaSmpmLSpEllHiQRERFVnLFjxyI+Ph6vv/46IiIiOKKOiIjIg9wu2hctWoSPPvoIo0ePdi4bMmQImjdvjunTp7NoJyIius3t3r0bu3btQuvWrT0dChERUbXn9vD4hIQE5+VfCurcuTMSEhLKJCgiIiLynOjoaOe8NURERORZbhftDRo0wNq1awstX7NmDRo2bFgmQREREZHnLFy4EFOnTsW5c+c8HQoREVG15/bw+BkzZmDEiBHYuXOn85z2PXv2YPv27UUW80RERFT5BQYGupy7npOTg/r168NgMECj0bi0vXLlSkWHR0REVG25XbQ/+OCD+PPPP7FgwQJs2LABANC0aVPs27cPbdq0Kev4iIiIqAIsXLjQ0yEQERFRESRRzU5aMxqN8Pf3R2ZmJvz8/DwdDhEREXNTOeBrSkRElUlp8lKxz2m/fPkypkyZAqPRWOi+zMxMvPTSS0hKSnLryYmIiKjysNlsMJvNLsuSkpIwY8YM/Pvf/8bu3bs9FBkREVH1Veyiff78+TAajUUeFfD390dWVhbmz59fpsERERFRxRk3bhxeeOEF5+2srCy0b98eS5YswU8//YSePXti06ZNHoyQiIio+il20b5lyxaXa7Nfb/To0fjxxx/LJCgiIiKqeHv27MGDDz7ovP3ll1/CbrcjLi4Ohw8fxuTJk/H+++97MEIiIqLqp9hF+9mzZ1GrVq0b3l+zZk1eGoaIiOg2dunSJZfLt27fvh0PPvgg/P39AQBjxozB0aNHPRUeERFRtVTsot3Ly+umRfm5c+fg5eVVFjERERGRB+j1euTm5jpv//HHH+jYsaPL/dnZ2Z4IjYiIqNoqdtHesWNHrFq16ob3f/nll+jQoUOZBEVEREQVr3Xr1s5cv2vXLiQlJaFXr17O+0+fPo3IyEhPhUdERFQtFfs67VOmTME999wDf39/vPTSSwgLCwPgmFX2vffew8qVK7F169ZyC5SIiIjK1xtvvIEBAwZg7dq1SEhIwNixYxEREeG8/4cffkCXLl08GCEREVH1U+yivWfPnliyZAkmTJiABQsWwM/PD5IkITMzExqNBosWLXI5Gk9ERES3lx49euDAgQPYunUrwsPD8dBDD7nc37p1a46qIyIiqmCSEEK484BLly5h7dq1OHXqFIQQaNSoEYYNG4aaNWuWV4xlqjQXtSciIioPzE1lj68pERFVJqXJS8Xuac8XFRWFSZMmufswIiIiIiIiInJTsSeiIyIiIiIiIqKKxaKdiIiIiIiIqJJi0U5ERERERERUSbFoJyIiokIyMjLwySefYNq0abhy5QoA4ODBg7h06ZKHIyMiIqpe3J6IDnAk8nXr1uH06dN46aWXEBQUhIMHDyIsLAxRUVFlHSMRERFVoL///ht9+vSBv78/zp07h3HjxiEoKAjr169HfHw8vvzyS0+HSEREVG243dP+999/o1GjRnj33Xcxd+5cZGRkAADWr1+PadOmlXV8REREVMEmT56MsWPHIi4uDnq93rl84MCB2LlzpwcjIyIiqn7cLtqZyImIiKq2/fv3Y/z48YWWR0VFITEx0QMRERERVV9uF+1M5ERERFWbTqeD0WgstPzkyZMICQnxQERERETVl9tFOxM5ERFR1TZkyBC89dZbsFqtAABJkhAfH4+XX34ZDz74oIejIyIiql7cLtqZyImIiKq2efPmITs7G6GhocjNzUWPHj3QoEED+Pr64p133vF0eERERNWK27PHz5s3D8OGDXNJ5ImJiejUqRMTORERURXg7++Pbdu2Yffu3fj777+RnZ2NO++8E3369PF0aERERNWO20U7EzkREVH10LVrV3Tt2tXTYRAREVVrJbpOO8BETkREVFV9+OGHRS6XJAl6vR4NGjRA9+7doVKpKjgyIiKi6sftop2JnIiIqGpbsGABUlJSYDKZEBgYCABIT0+HwWCAj48PkpOTUa9ePfzyyy+Ijo72cLRERERVm9tFOxM5ERFR1TZr1ix8/PHH+OSTT1C/fn0AwKlTpzB+/Hg8+eST6NKlC0aOHIlJkyZh3bp1Ho6WiIioanN79vhZs2ahffv2iIuLQ1paGtLS0nDy5El07NgRH3zwAeLj4xEeHo5JkyaVR7xERERUzl577TUsWLDAWbADQIMGDTB37lxMmzYNNWvWxHvvvYc9e/Z4MEoiIqLqwe2e9tdeew3ff/99kYn8wQcfxJkzZ/Dee+/x8m9ERES3qYSEBNhstkLLbTYbEhMTAQCRkZHIysqq6NCIiIiqHbd72pnIiYiIqraePXti/PjxOHTokHPZoUOH8PTTT6NXr14AgCNHjqBu3bqeCpGIiKjacLtoZyInIiKq2j799FMEBQWhbdu20Ol00Ol0aNeuHYKCgvDpp58CAHx8fDBv3jwPR0pERFT1uT08/tNPP8Wjjz6Ktm3bQqPRAHD0svfu3ZuJnIiIqAoIDw/Htm3bcOLECZw8eRIA0LhxYzRu3NjZpmfPnp4Kj4iIqFqRhBCiJA+8WSKvzIxGI/z9/ZGZmQk/Pz9Ph0NERMTcVA74mhIRUWVSmrzkdk97viZNmqBJkyYlfTgRERFVYhcvXsTGjRsRHx8Pi8Xict/8+fM9FBUREVH1U6KinYmciIio6tq+fTuGDBmCevXq4cSJE7jjjjtw7tw5CCFw5513ejo8IiKiasXtop2JnIiIqGqbNm0apkyZghkzZsDX1xfff/89QkND8cgjj6B///6eDo+IiKhacXv2+PxEfuTIEej1enz//fe4cOECevTogYceeqg8YiQiIqIKdPz4cYwePRoAoFarkZubCx8fH7z11lt49913PRwdERFR9eJ20V4eiXzJkiWoU6cO9Ho9OnbsiH379hXrcd9++y0kScLQoUNL9LxERERUmLe3t/P0t4iICJw+fdp5X2pqaonWyVxPRERUMm4X7WWdyNesWYPJkyfjzTffxMGDB9GqVSv069cPycnJN33cuXPnMGXKFHTr1s3t5yQiIqIbu+uuu7B7924AwMCBA/Hiiy/inXfeweOPP4677rrL7fUx1xMREZWc20V7WSfy+fPnY9y4cXjsscfQrFkzLFu2DAaDAZ999tkNH2O32/HII49gxowZqFevntvPSURERDc2f/58dOzYEQAwY8YM9O7dG2vWrEGdOnXw6aeflmh9zPVEREQl4/ZEdPPnz0d2djYARyLPzs7GmjVr0LBhQ7dnjrdYLDhw4ACmTZvmXCbLMvr06YO9e/fe8HFvvfUWQkND8X//93/YtWvXTZ/DbDbDbDY7bxuNRrdiJCIiqk7sdjsuXryIli1bAnCMsFu2bFmJ11cRuR5gvicioqrLraK9rBN5amoq7HY7wsLCXJaHhYXhxIkTRT5m9+7d+PTTTxETE1Os55g9ezZmzJhR4hiJiIiqE5VKhb59++L48eMICAgo9foqItcDzPdERFR1uTU8Pj+Rp6enl1c8N5WVlYVHH30UK1asQHBwcLEeM23aNGRmZjr/Lly4UM5REhER3d7uuOMOnDlzxiPPXZJcDzDfExFR1eX28Pj8RF63bt1SP3lwcDBUKhWSkpJcliclJSE8PLxQ+9OnT+PcuXMYPHiwc5miKAAcM9nHxsaifv36Lo/R6XTQ6XSljpWIiKi6mDlzJqZMmYK3334bbdu2hbe3t8v9fn5+xV5XReR6gPmeiIiqLreL9rJM5FqtFm3btsX27dudl3JRFAXbt2/Hc889V6h9kyZNcOTIEZdlr732GrKysvDBBx8gOjra3c0hIiKi6wwcOBAAMGTIEEiS5FwuhIAkSbDb7cVeF3M9ERFR6bhdtJdlIgeAyZMnY8yYMWjXrh06dOiAhQsXIicnB4899hgAYPTo0YiKisLs2bOh1+txxx13uDw+/3y765cTERFRyfzyyy9luj7meiIiopJzu2gv60Q+YsQIpKSk4I033kBiYiJat26NLVu2OCesiY+Phyy7fWU6IiIiKqEePXqU6fqY64mIiEpOEkIITwdRkYxGI/z9/ZGZmenWUH4iIqLyUhlz065du7B8+XKcOXMG3333HaKiorBq1SrUrVsXXbt29XR4t1QZX1MiIqq+SpOXSnRYe9euXfjXv/6Fzp0749KlSwCAVatWYffu3SVZHREREVUi33//Pfr16wcvLy8cPHjQef3zzMxMzJo1y8PRERERVS9uF+1M5ERERFXbzJkzsWzZMqxYsQIajca5vEuXLjh48KAHIyMiIqp+3C7amciJiIiqttjYWHTv3r3Qcn9/f2RkZFR8QERERNWY20U7EzkREVHVFh4ejlOnThVavnv3btSrV88DEREREVVfbhftTORERERV27hx4zBhwgT8+eefkCQJly9fxurVqzFlyhQ8/fTTng6PiIioWnH7km/5ifyzzz5zJvK9e/diypQpeP3118sjRiIiIqpAU6dOhaIo6N27N0wmE7p37w6dTocpU6bg+eef93R4RERE1YrbRTsTORERUdUmSRJeffVVvPTSSzh16hSys7PRrFkz+Pj4eDo0IiKiaqfE12m3WCy3ZSLndVuJiKiyqWy56auvvsIDDzwAg8Hg6VBKrLK9pkREVL1V6HXav/rqK5hMJmi1WjRr1gwdOnS4bQp2IiIiurVJkyYhNDQUDz/8MDZt2gS73e7pkIiIiKott4t2JnIiIqKqLSEhAd9++y0kScLw4cMRERGBZ599Fr///runQyMiIqp23C7amciJiIiqNrVajXvvvRerV69GcnIyFixYgHPnzqFnz56oX7++p8MjIiKqVtyeiC4/kd97770wmUz44Ycf8PXXX6Nnz56oWbMmTp8+XR5xEhERkQcYDAb069cP6enpOH/+PI4fP+7pkIiIiKoVt4v2gpjIiYiIqqb8A/OrV6/G9u3bER0djVGjRmHdunWeDo2IiKhaKVHRzkRORERUdY0cORI//vgjDAYDhg8fjtdffx2dOnXydFhERETVkttFOxM5ERFR1aZSqbB27Vr069cPKpXK5b5//vkHd9xxh4ciIyIiqn7cLtqZyImIiKq21atXu9zOysrCN998g08++QQHDhzglWOIiIgqkNuzx69evRoDBw50FuxZWVn4+OOP0aFDB7Rq1arMAyQiIiLP2LlzJ8aMGYOIiAjMnTsXvXr1wh9//OHpsIiIiKqVEk9Et3PnTnz66af4/vvvERkZiQceeABLliwpy9iIiIiogiUmJmLlypX49NNPYTQaMXz4cJjNZmzYsAHNmjXzdHhERETVjls97YmJiZgzZw4aNmyIhx56CH5+fs5EPmfOHLRv37684iQiIqJyNnjwYDRu3Bh///03Fi5ciMuXL2PRokWeDouIiKhaK3bRzkRORERUtW3evBn/93//hxkzZmDQoEGF5q4hIiKiilfsop2JnIiIqGrbvXs3srKy0LZtW3Ts2BGLFy9Gamqqp8MiIiKq1opdtDORExERVW133XUXVqxYgYSEBIwfPx7ffvstIiMjoSgKtm3bhqysLE+HSEREVO0Uu2hnIiciIqoevL298fjjj2P37t04cuQIXnzxRcyZMwehoaEYMmSIp8MjIiKqVty+5BsTORERUfXRuHFjvPfee7h48SK++eYbT4dDRERU7bhdtBfERE5ERFQ9qFQqDB06FBs3bvR0KERERNVKqYr2fEzkRERERERERGWvTIp2IiIiIiIiIip7LNqJiIiIiIiIKikW7URERERERESVFIt2IiIiIiIiokqKRTsRERERERFRJcWinYiIiIiIiKiSYtFOREREREREVEmxaCciIiIiIiKqpFi0ExEREREREVVSLNqJiIiIiIiIKikW7URERERERESVFIt2IiIiIiIiokqKRTsRERERERFRJcWinYiIiIiIiKiSYtFOREREREREVEmxaCciIiIiIiKqpFi0ExEREREREVVSLNqJiIiIiIiIKikW7URERERERESVFIt2IiIiIiIiokqKRTsRERERERFRJcWinYiIiIiIiKiSYtFOREREREREVEmxaCciIiIiIiKqpFi0ExEREd2EEAJ2RXg6DCIiqqZYtBMRERHdwJHzFoxflo45642eDoWIiKopFu1EREREN6CSJQgBWG2ejoSIiKorFu1EREREN6BRO/612jk8noiIPEPt6QCIiIiIKiuNSgIA2OweDuQmEtLt+HhrNgBg8hBf+HrdvE9GCIG4BBty8gT8vWXUClZBkgBZAiRJqoiQiYjIDSzaiYiIqMpSLAqEEM5iVNgFhF1AkiVIasmlHQBIagmSfK2tWlEgC+HS0359W0UISAqKXq9VAcR161UEhE0AMiCr5Vu2VawKTibYceSSDfXD1VCEQEycBRACZgEcPn917L4QWLEpC0/284aPz7WfeIpNAZRr690ba8Hn27OhFoAAYL/6XLWCVejWWIu7Gmmh08qQVNfFKwGy5lq8wiYgFAFJJRVuC0DWlmFbISCsjraSRiq8P91pW8x9X+q2Re1Pd9re6H1y3f4scdvr9qc7bT2170v9PrnR/iyD94lQAXY7oFFLLm0hAdl5AqmZdljyFBjzBFJNAoE+MtSyBFOO4/ObnicASYJGJUEtCWgkwKoAQpZgzBWQJcc2Z+bYkZ4noJIl5FoEzBYFplyB7DwBRZaQf9xNLQQkAHYJEJIEb50EH50Ew9VtssmAn5cMP4OMAB1gtQnIagmySoLVDtisCmxWAaNJQKOXoVVLyLMI2KwKtDJgE0COFTBZBEx5CuxWAbUsQdJIUKskaNSATnYUm5IKsEOCSgWoJCAnW4G3XkZggAp+BgkGnQyTyY4sk4I8G2AVgNXueJ/464AAgwwfXzVUMiDLgKwAGllAo5FhFYAiAI1KwG4VUMmApJJhtgEWm4DdJqCCgK9BBZ1egixJEEKBTgK8dBK0OkdbtUqCsAnYbArMdsDHoIJGDRh0EnRw5JDSfkfkv+dLolIU7UuWLMH777+PxMREtGrVCosWLUKHDh2KbLtixQp8+eWX+OeffwAAbdu2xaxZs27YnoiIiDzPU7n+0vJ4+E5qDpVBBQAw/pWJzN3p8Gnhi6C+wdfaLYuHsApEPlETan8NACArxgjL9jR0zlPjL63PtbYrLkDkKYgYE4X9qQKrd+ZgZJiC2qey4NXAgJD7wpxtE1degs1oQ9gjkdCF6wAAptgcpG1KgTZaj5zuwYgIVMGgk5G0+jKsaVaEDg+HJkqPrTF5MMXlICwmHUlaNbaF+WPbYcd6ByZmIthiw/YQX8BLCwCIMNvQfv8V7D6qRotnoxFVw/EzL2FtIlJP5eJkA3+kBOpw6KwVIRYbBiQZYVSrsCEyAAAQn2pHwvEkHDBb4XV3DbS5JxCSJMGaakHiqstQ+agQNb6Wc9vSNqfAdDIHgb1qwLeNHwDAlmlDwmcXIetk1HyutrPtlZ9TkXM0GwHdA+HX3vF89hw7Ln98AZCBWpPqOtum/5aG7Jgs+HcKgH/nQACAMCu4uCQeABA9sQ7g2J3I2J2OrL8y4dvOH4E9ghwLFeDiovMAgJrP1oKkv7rv/8xA5t4M+LT2RVDva/v+4pLzgAJEPhkNta/jNcs6mImMnenwbu6DGv1DnG0vf3wBillBxOM1oQl0vE+y/85C+o40GBp5I3hwqLNtwmcXYc+2I/zRSGhDHfs+53g2rvyUCq+6Xgh5IPza+2TVJdjSbQgbGQFdlB4AkBtnQuqPydDV1CNsRISzbdI3CbAmWxA6LBz62l4AgLxzuUj5IQnacB3CH4l0tk35LhHmy2YE3xcKQwNvRwwX8pC0JgFWHzUSuodCo3IUfz67UqBLNSOxeQCuhHoh1ahAl2lB6+PpMGtlHLozGL56Gb4GCVGH02FIykNsbV8kBHvBagOi1Qpa/p0GlZcKVwZFwK4IWKxA+D/pMFzKRY1eQfBvd3XfZ9lw+ZOLkDQSol+oc23f70hD9pEseHcKQMBdAZAlIM9oR8LH8RACqPFsHahkRzFm3HkFuTFGpNXxwZWGvtCqJajsCqJ+SoQkARn3RSLFJCAE4HvCiIAz2bgYasDJKB+YrQKKAO45nAJJAva2DEaOJMFkFmhyJReNknNwMVCPPYHe8L5a6PU6kgK1ENjZOAg2gxrZuQL10k1okZyDswYddtbwgRCAj17CkLNXoLUr+F+UPzLUaljtQMPsPHS6koMLXlr8EuLr3OYHLqfDx6Zgb5g/0nSO91/dHDO6pWUjRa/BtlA/Z9shCRloZLXjp1A/JOkd779okwVDUrOQrFNjS5i/40gcgH6JRud3xCUvLTJNAoYrFnRKNuKKRo0fI/wBOIYQ9UsyIsxsxW/BPjhvcLxXQ8zWQt8RANArxYiauVbsCfLBaR9H20CLDUMSM2FSyVgXFehs2yM1C7VNFvwZ6I1YX8f72tdqx/0JGbDIEr6tGeRs2yUtG/VzzDgQYMBRP8f72mBTMOxyOhQAX9Wq4Wzb4UoOmmTn4S9/Lxz2NwAAtIqCkRfTYQWwKjoI4urRi7bpJjTPysVfvl44EOhoKwmBRy9cAQB8WzMQFtlxMKhVpgmtMnNxwkePfUHezud79EIaagaqUOvpWqX6jrDXVFBSHi/a16xZg8mTJ2PZsmXo2LEjFi5ciH79+iE2NhahoaGF2v/6668YNWoUOnfuDL1ej3fffRd9+/bF0aNHERUV5YEtICIiopu5rXP91Q4U69Xh8bGXrLhw2YZwL0DOtOPzHbkAgL2xFkTLAIRw9uzn5CmIT7UhAEX3rpxOtGHl+iwAQINwNYaZBTQADpy2YOV/HeuNNtkQet3DA7wlSNetq2tTHe4O0uLS2iyYrcDb3xnRorYGeRaB2rEWhJkVxF624nyG48epj05CrWA1VAFq1OjuDR+9hM+35wAAFAXYGmPGCb0JQzt6OX8sWmwCvx3Ng9UGdG2mw5kkK6xpNpyMM6NJtA1RNVRIz7ZDEWU/aZLN7hjtYLUDl9JtiKqhdnsof65FIM8qIOUJBBYYfVESSoH9XBxmq0B6jgKNVcCYq+BSkg37DuRCApCWpaB2gg0Gix2bf8mBvYYVYQEqRBotCDAryMy0I+64GXqNhBq+MiwmBcIisP3vPFw4YUNGjkB0ngXNsxWIdDu27MhGfKod3joJnRJt0GfZ8fs+E+IO2pBjFtCmmXHPFTsysoCNO03OGO9JtSMiTyDmnBVnUxx7sIbZjjvsArlWgWMXrs3G2DPFjmizwIVUO+LyHMtTrDZEZyvIMwFrd+Q423ZNtaKeyY4NP+cg7oACWQIitQJ3p9lghYQ5H19x9OJaBXpm5SLqig0HfjPhSIyAJAFam4IRaY4P4JwvMpzrbZ+eh6ZZCv4+b0VMZh4AQK0IPJztKIy+/tUE29WeztYZNrQ0CyRl2HFSurYdtqsjaE4n2WBWObY5PEeB1ebYT9lejh5sABDC8ZdiVJBtcsRjyhNQFMCuOO4DHL3qNjugVgCLDbA6OtvhrXP0QPsZJHRoqEVmjgKbHfBNl6E3C7Str4UlQONYlmCHIVtCqL+MDg218DfIEBAIy1XByyQwpIMXlFAdtCoJhuRcqHaaUCdSi94PBDjiBJC1zgR7isBT/Xwg1fRCnkUg64wJ6l9yEOCnwlP9fJCdpyDTJBC0Jxs+mTJa19GibqgOGpUEX6OMgGwZfj4qDOvkBasd8NJKqLEvF+pEO+6+Q4deDb1h0EnwyrFBbMwGDCq8fL+vo6feLiDtNEN1wYZed+jQvZE3bHZAybQieEcW7CoJ97bTIytXwGQWqC2r4A8ZHRtq0aqRARq1BDnXDt//ZcImgPYNtLArjoMtEUKG3iIh1F+FZjXVjhfYrECf7Jg4tFGkGlqNDI0aqBWvgsEiIcRfRq0QFRQFkCGgvuwYJaVRATq9BLsCaNUSZNmxr7y00tXvHce+lT08E5wkhCh5P30Z6NixI9q3b4/FixcDABRFQXR0NJ5//nlMnTr1lo+32+0IDAzE4sWLMXr06Fu2NxqN8Pf3R2ZmJvz8/G7ZnoiIqLxV9dxU0bkeuPaapqekw7+Gf4mHvmbl2PHSqkwokoTlTwVi+hojUtIcP/r9/WSkZV8diisEHu9pwPd7c+Hnq8Kd9bQ4nWjDsXMWSAAe7+eN9g0dPU1CEUhKs2H6GqNzaDoAqBSBRpFqHE+wIX+MqyQEVAIIDZDx+qgAqK8OAy44lDkrTzh6AwFkZdvxyfYcHE+wu6xXAiCrgZAANWqHqtC1iRb1g9Uuw56FEMjKVrDj71xsijFDSBJUMhDiK0GxOYrLgvHKQkAW14bfXl0JvFRADV8ZKq2MTo11OHTGggyjHe3qaQCVBKuQ4OclITnDjrQMOww6Cdk2IM8i0LyWBrALJKXbUStMDZ1WRsxZC04nWGG3Op7CJgE6jYR64Wq0iNagUwMNhCwhOUuBRiUhM8eOxFQb/jplhcFbhsXuGLZ8PskKKIAiOYYcN4lSo4avCgnJVrSorUHHpjoocPzov5xig04F6PUy0nIchY3RpCApzYa0LAUXM+zQa2U0jFSjYZgKOhWQniNwJVfgSpaCK9kKsrPt0GskBAWocC7FDrtybX8qEqBIUqF9ZJNQaN8XPIWhpG2v30c6CdBrJfj4qOBvkOCtl2FQAbIkoNfLUKklBPnI8NJIMKgAuxDIskrIylWQlScg2QX89IC3QQUbHAd60rPtSLliR5pRgU0lIdBbhiQBmUY7Eq7YYVZcY1BfrUBsRbynXF4fIaCXAI3G8T4RAlDJgLcaCPaREV5DBa1WRoZJgV4N2K2OotmsCPj7qKDTSNCpgCAvCTqtDKGSoNdI0GkAvSSQaxHIscHxGugkWC0CdrsCAQn+vipk5wnYFYFAnaOoSzUJmCyO97gMAVkR0OlkaHUyVDJgzFVgzVOglgGNToYsSwjwlqGWyu8Umoo8jeKmpztUktMoyvIUGqtNwJhlR4BBgqyRSzU8Pis7CwE1AkqU6z3a026xWHDgwAFMmzbNuUyWZfTp0wd79+4t1jpMJhOsViuCgoKKvN9sNsNsNjtvG428zioREVFFqYhcD9w438ta2aU3tOAPt4IK/iAs2FbrpXIWD1a7o1jILzLyC3bAUYx8+qujdzzDbEd8au7VFTvafrzNhCY1tcizCPz3r1zsjbU47/P1kpCVK2CXJRxPtDsLsd4tdKgTpkaLWhoYdJLLdhT8we1nuLbcz0+NF4b4YWtMHhLS7TibbINGJeG5gT6o4au64esHOCah8/NVYWgXH9SO0OLb3SZcyVaQmClctiWfIklQJECnBsw250qQqwAXMwUAO86nXOvJ/d/flps+PwCcTb52sGH/uYLX2ZOg0wGB3jJSjArMNuD4RRuOX7Rh7d7cG6/wSsHhqBKC/GRk5CgQwvF4wPEcp9PM2HDQXOQqiiYhxywQc9aKmLPWG7bJNQPpSde2SUgSggJkNIt29KgqQsDfICPETwVFCHhpJaRlKUjPVpB2tfjXqq/15KZmOXpnvbQSWtfVIiJQBR+9Y1j36URHT3qtYBWig9XIswokZ9qhU0tQBFArRAWV7Cg8G4SrIcuFPwflxa44imOz1dFzmZJph49eho+XBEUBTGYBrdrRi+1nkBEeIDsutWgHtGrAoHO83xXlagFXgbFfr/DYIFc3mwiyyO8eTRHfPbIESVvKturyaSupJVw/1qdStJVu0LaI73x32mrUEmoEFi6Z3c0lksq1uHeXR4v21NRU2O12hIWFuSwPCwvDiRMnirWOl19+GZGRkejTp0+R98+ePRszZswodaxERETkvorI9UD55XtNgTrXZhfOS8DlC/KRMeFeX7z5beYt1zX58wyoZEcBls9bJ2H+Y4HYdSwPX/5qglYNjO7pjY4NdSWOWa2SMLCtV4kfDwBt6mnRuq4GW2PycCTeiubRGrStr0Wgj4zkDDuuZCtoWlPj7Pm/mGbDxTQ7WtTSID7VDptd4FyyHUfjrfDWO4b5ZpoE1CrHTPwWm0BYgAo1fGUoCmBTHMOQ/z5nhVbtGFYcESgj1yLQOFKDhpFq1A9XOyff+vOkGcZcgUNnLLiYdq0o1lzt5ff1cvR61gtTo2YNFSAB0TXUCA9UwWITSDUqiDlrgdnmmGjrVIIViRkKJDhPCYavlwSzVSAiUIUQP0ePtE4jITpYjYggGXkW4FSCFacSbRACCPSREeQjO/8N8JaRlqUgId2OxlFqhPmrkGsRznOky1rPFmW+yjKjkiX46CX4OAabIDyg6ANIUTWKXOzkyWKdyJM8fk57acyZMwfffvstfv31V+j1+iLbTJs2DZMnT3beNhqNiI6OrqgQiYiIqBSKk+uB8sv3KllyFtpWO6C9rmcl2E9GZJAKfl6OGZ4LCvKR8cIgH6zYloNLVxyFZcGC3d8g4f/Zu/M4G+v3j+Pvc2Y5s8/YhhnGvu+7EBIlSSkV6pelaFOJVLSQylJZvxFKX0p8SUlaiKSiZKeULfs69tnNdj6/P6Y5HDM0Z8zMOczr+XjMg/O5P/d9X/d97nOuc93r2z3DJEmtavrpxho2JaUYx1FFd7NYLOrQwF8dGjjvAChdzDtLcVWmmLfK/HPjuxplMuKvU07q3MS1nQf3Nv/3Pv6+Ft1UO2Nb6NzY75+j0RlFYWbcV+LrbVFkUS9FFr0Qm91udCrOrhIhVtlNxvueE5VKeavDFYaHh3qpRhkfx+tgf4pOAK5za9FevHhxeXl5KTo62qk9OjpapUqVusxYGcaOHasxY8bo+++/V926dS/bz2azyWbL/d5qAACQewWR66X8zffeXv8U7WlGKelZC3NJiijqpdgjGadaP3tHsGpGXbhR2kv3hmj84jjtOZ4x/J1eYTobb1dYoNXpyKHFYlGAjaLOFRaL5V9P+88JqzXjplZSxiOpAMCTuHVXrq+vrxo1aqQVK1Y42ux2u1asWKHmzS+/q/Xtt9/WG2+8oaVLl6px48YFESoAAMiF6yHX+/xTxR0/l659F12fLGUcaZek1jVt8vGSapTxdirYpYwju/c291fRIKt6tw1UWKBVFUp6q0iQZxxRBwB4NrefHj9o0CD16tVLjRs3VtOmTTVx4kQlJCSoT58+kqSePXuqdOnSGj16tCTprbfe0rBhwzR37lyVL19ex48flyQFBQUpKCjosvMBAADuca3n+oyi3ei3XVlvpFY0KOPobNMqNjWq5CuLJfvTsytH+Oitf06FBwDAFW4v2rt166aTJ09q2LBhOn78uOrXr6+lS5c6blhz8OBBWS96MN7UqVOVkpKie++912k6w4cP12uvvVaQoQMAgBy41nN95s3njvxzw7Oyxb108FTG/4sFX4g7p9dBAwDgCrc/p72gXe/PwgUAXHvITXkvL9fpa/NidORMuuPO4g+2DtCcnzMeZfZatxCVLub2YyAAAA93NXmJLAMAAHAF3v/c5yzzKEfpYl7qUN9PsUl2RRa9+pugAQBwJRTtAAAAV+B9ye3EA3wturdFgJuiAQAUNty2FAAA4Aq8L/m15OvDtesAgIJD0Q4AAHAFXpecAW/zpmgHABQcinYAAIAr8L7krvC+FO0AgAJE0Q4AAHAF3pccaffljkAAgAJE0Q4AAHAFl96Izsrz2AEABYiiHQAA4Aq8+LUEAHAj0hAAAMAVXHqkHQCAgkTRDgAAcAWXPvINAICCRBoCAAC4Ao60AwDciaIdAADgCijZAQDuRNEOAABwBXZjHP8PtFHCAwAKFkU7AADAFVxUs+u17qHuCwQAUChRtAMAAFyB/aL/hwXy0wkAULDIPAAAAFdg7P/eBwCA/ELRDgAAcAV28+99AADILxTtAAAAV2Ao2gEAbkTRDgAAcAV2qnYAgBtRtAMAAFwBp8cDANyJoh0AAOAKGlfylSQVC+ZnEwCg4Hm7OwAAAABPVqecj17qGqKSYRTtAICCR9EOAABwBRaLRRVK8pMJAOAe7DIGAAAAAMBDUbQDAAAAAOChKNoBAAAAAPBQFO0AAAAAAHgoinYAAAAAADwURTsAAAAAAB6Koh0AAAAAAA9F0Q4AAAAAgIeiaAcAAAAAwENRtAMAAAAA4KEo2gEAAAAA8FAU7QAAAAAAeCiKdgAAAAAAPBRFOwAAAAAAHoqiHQAAAAAAD0XRDgAAAACAh6JoBwAAAADAQ1G0AwAAAADgoSjaAQAAAADwUBTtAAAAAAB4KIp2AAAAAAA8FEU7AAAAAAAeiqIdAAAAAAAPRdEOAAAAAICHomgHAAAAAMBDUbQDAAAAAOChKNoBAAAAAPBQFO0AAAAAAHgoinYAAAAAADwURTsAAAAAAB6Koh0AAAAAAA9F0Q4AAAAAgIeiaAcAAAAAwEN5RNE+ZcoUlS9fXn5+fmrWrJnWrVt3xf4LFixQ9erV5efnpzp16ujbb78toEgBAEBukOsBAMgdtxft8+fP16BBgzR8+HBt2rRJ9erVU4cOHXTixIls+//666/q0aOHHnnkEW3evFldunRRly5dtG3btgKOHAAA5AS5HgCA3LMYY4w7A2jWrJmaNGmiyZMnS5LsdruioqL09NNPa8iQIVn6d+vWTQkJCfr6668dbTfccIPq16+vadOm/ev8YmNjFRoaqpiYGIWEhOTdggAAkEvXe24q6FwvXf/rFABwbbmavOTWI+0pKSnauHGj2rdv72izWq1q37691qxZk+04a9asceovSR06dLhs/+TkZMXGxjr9AQCAglEQuV4i3wMArl9uLdpPnTql9PR0lSxZ0qm9ZMmSOn78eLbjHD9+3KX+o0ePVmhoqOMvKioqb4IHAAD/qiByvUS+BwBcv9x+TXt+Gzp0qGJiYhx/hw4dcndIAAAgj5HvAQDXK293zrx48eLy8vJSdHS0U3t0dLRKlSqV7TilSpVyqb/NZpPNZsubgAEAgEsKItdL5HsAwPXLrUfafX191ahRI61YscLRZrfbtWLFCjVv3jzbcZo3b+7UX5KWL19+2f4AAMB9yPUAAFwdtx5pl6RBgwapV69eaty4sZo2baqJEycqISFBffr0kST17NlTpUuX1ujRoyVJAwYMUJs2bTRu3Dh16tRJ8+bN04YNG/T++++7czEAAMBlkOsBAMg9txft3bp108mTJzVs2DAdP35c9evX19KlSx03oDl48KCs1gsnBLRo0UJz587VK6+8opdeeklVqlTRokWLVLt2bXctAgAAuAJyPQAAuef257QXNJ7bCgDwNOSmvMc6BQB4kmv2Oe0AAAAAAODyKNoBAAAAAPBQFO0AAAAAAHgoinYAAAAAADwURTsAAAAAAB6Koh0AAAAAAA9F0Q4AAAAAgIeiaAcAAAAAwENRtAMAAAAA4KEo2gEAAAAA8FAU7QAAAAAAeCiKdgAAAAAAPBRFOwAAAAAAHoqiHQAAAAAAD0XRDgAAAACAh6JoBwAAAADAQ1G0AwAAAADgoSjaAQAAAADwUBTtAAAAAAB4KIp2AAAAAAA8FEU7AAAAAAAeiqIdAAAAAAAPRdEOAAAAAICHomgHAAAAAMBDUbQDAAAAAOChvN0dQEEzxkiSYmNj3RwJAAAZMnNSZo7C1SPfAwA8ydXk+kJXtMfFxUmSoqKi3BwJAADO4uLiFBoa6u4wrgvkewCAJ8pNrreYQrZb32636+jRowoODpbFYrmqacXGxioqKkqHDh1SSEhIHkVYsK71ZSB+9yJ+9yJ+98rL+I0xiouLU2RkpKxWrlzLC+T7C4jfvYjfvYjfva71+KW8W4aryfWF7ki71WpVmTJl8nSaISEh1+xGmOlaXwbidy/idy/id6+8ip8j7HmLfJ8V8bsX8bsX8bvXtR6/lDfLkNtcz+58AAAAAAA8FEU7AAAAAAAeiqL9KthsNg0fPlw2m83doeTatb4MxO9exO9exO9e13r8yLlr/b0mfvcifvcifve61uOXPGMZCt2N6AAAAAAAuFZwpB0AAAAAAA9F0Q4AAAAAgIeiaAcAAAAAwENRtAMAAAAA4KEo2gEAAAAA8FAU7VdhypQpKl++vPz8/NSsWTOtW7fO3SFJkn7++Wd17txZkZGRslgsWrRokdNwY4yGDRumiIgI+fv7q3379tq9e7dTnzNnzujBBx9USEiIwsLC9Mgjjyg+Pr5A4h89erSaNGmi4OBghYeHq0uXLtq5c6dTn/Pnz6t///4qVqyYgoKC1LVrV0VHRzv1OXjwoDp16qSAgACFh4fr+eefV1paWr7HP3XqVNWtW1chISEKCQlR8+bNtWTJkmsi9kuNGTNGFotFzz77rKPN0+N/7bXXZLFYnP6qV69+zcQvSUeOHNH//d//qVixYvL391edOnW0YcMGx3BP/gyXL18+y/q3WCzq37+/JM9f/+np6Xr11VdVoUIF+fv7q1KlSnrjjTd08YNWPHn9I++R6/MHud79ueZi11q+J9dnINfnzjWZ6w1yZd68ecbX19f897//NX/++afp16+fCQsLM9HR0e4OzXz77bfm5ZdfNgsXLjSSzBdffOE0fMyYMSY0NNQsWrTIbN261dx5552mQoUKJikpydHntttuM/Xq1TO//fabWbVqlalcubLp0aNHgcTfoUMHM3PmTLNt2zazZcsWc/vtt5uyZcua+Ph4R5/HH3/cREVFmRUrVpgNGzaYG264wbRo0cIxPC0tzdSuXdu0b9/ebN682Xz77bemePHiZujQofke/+LFi80333xjdu3aZXbu3Gleeukl4+PjY7Zt2+bxsV9s3bp1pnz58qZu3bpmwIABjnZPj3/48OGmVq1a5tixY46/kydPXjPxnzlzxpQrV8707t3brF271uzdu9d899135u+//3b08eTP8IkTJ5zW/fLly40ks3LlSmOM56//kSNHmmLFipmvv/7a7Nu3zyxYsMAEBQWZSZMmOfp48vpH3iLX5x9yvWfkemOuzXxPrs9Ars+dazHXU7TnUtOmTU3//v0dr9PT001kZKQZPXq0G6PK6tJEbrfbTalSpcw777zjaDt37pyx2Wzmf//7nzHGmL/++stIMuvXr3f0WbJkibFYLObIkSMFFnumEydOGEnmp59+csTr4+NjFixY4Oizfft2I8msWbPGGJPxY8ZqtZrjx487+kydOtWEhISY5OTkgl0AY0yRIkXMjBkzrpnY4+LiTJUqVczy5ctNmzZtHEn8Woh/+PDhpl69etkOuxbif/HFF82NN9542eHX2md4wIABplKlSsZut18T679Tp07m4Ycfdmq75557zIMPPmiMufbWP64Oub7gkOvdE/u1mu/J9Z71GSbXZ8jP9c/p8bmQkpKijRs3qn379o42q9Wq9u3ba82aNW6M7N/t27dPx48fd4o9NDRUzZo1c8S+Zs0ahYWFqXHjxo4+7du3l9Vq1dq1aws85piYGElS0aJFJUkbN25Uamqq0zJUr15dZcuWdVqGOnXqqGTJko4+HTp0UGxsrP78888Ciz09PV3z5s1TQkKCmjdvfs3E3r9/f3Xq1MkpTunaWfe7d+9WZGSkKlasqAcffFAHDx68ZuJfvHixGjdurPvuu0/h4eFq0KCBPvjgA8fwa+kznJKSok8++UQPP/ywLBbLNbH+W7RooRUrVmjXrl2SpK1bt2r16tXq2LGjpGtr/ePqkOsLFrnePbFfy/meXO8Zn2FyfcGsf+88n2IhcOrUKaWnpzttaJJUsmRJ7dixw01R5czx48clKdvYM4cdP35c4eHhTsO9vb1VtGhRR5+CYrfb9eyzz6ply5aqXbu2Iz5fX1+FhYU59b10GbJbxsxh+e2PP/5Q8+bNdf78eQUFBemLL75QzZo1tWXLFo+Pfd68edq0aZPWr1+fZdi1sO6bNWumWbNmqVq1ajp27JhGjBihVq1aadu2bddE/Hv37tXUqVM1aNAgvfTSS1q/fr2eeeYZ+fr6qlevXtfUZ3jRokU6d+6cevfu7YjL09f/kCFDFBsbq+rVq8vLy0vp6ekaOXKkHnzwQacYroX1j6tDri845PqCj126tvM9ud5zPsPk+gvyc/1TtMOj9e/fX9u2bdPq1avdHYpLqlWrpi1btigmJkafffaZevXqpZ9++sndYf2rQ4cOacCAAVq+fLn8/PzcHU6uZO4llaS6deuqWbNmKleunD799FP5+/u7MbKcsdvtaty4sUaNGiVJatCggbZt26Zp06apV69ebo7ONR9++KE6duyoyMhId4eSY59++qnmzJmjuXPnqlatWtqyZYueffZZRUZGXnPrH7hWkOsL3rWe78n1noNcXzA4PT4XihcvLi8vryx3QYyOjlapUqXcFFXOZMZ3pdhLlSqlEydOOA1PS0vTmTNnCnT5nnrqKX399ddauXKlypQp42gvVaqUUlJSdO7cOaf+ly5DdsuYOSy/+fr6qnLlymrUqJFGjx6tevXqadKkSR4f+8aNG3XixAk1bNhQ3t7e8vb21k8//aT//Oc/8vb2VsmSJT06/uyEhYWpatWq+vvvvz1+/UtSRESEatas6dRWo0YNx2l/18pn+MCBA/r+++/Vt29fR9u1sP6ff/55DRkyRN27d1edOnX00EMPaeDAgRo9erRTDJ6+/nH1yPUFg1zvntivt3xPrs9Ars+ZazHXU7Tngq+vrxo1aqQVK1Y42ux2u1asWKHmzZu7MbJ/V6FCBZUqVcop9tjYWK1du9YRe/PmzXXu3Dlt3LjR0eeHH36Q3W5Xs2bN8j1GY4yeeuopffHFF/rhhx9UoUIFp+GNGjWSj4+P0zLs3LlTBw8edFqGP/74w+nDtHz5coWEhGT5kiwIdrtdycnJHh97u3bt9Mcff2jLli2Ov8aNG+vBBx90/N+T489OfHy89uzZo4iICI9f/5LUsmXLLI892rVrl8qVKyfp2vgMS9LMmTMVHh6uTp06OdquhfWfmJgoq9U5NXp5eclut0u6dtY/rh65Pn+R690b+/WW78n1Gcj1OXNN5vo8v7VdITFv3jxjs9nMrFmzzF9//WUeffRRExYW5nQXRHeJi4szmzdvNps3bzaSzPjx483mzZvNgQMHjDEZjzAICwszX375pfn999/NXXfdle0jDBo0aGDWrl1rVq9ebapUqVJgj4F54oknTGhoqPnxxx+dHieRmJjo6PP444+bsmXLmh9++MFs2LDBNG/e3DRv3twxPPNRErfeeqvZsmWLWbp0qSlRokSBPEpiyJAh5qeffjL79u0zv//+uxkyZIixWCxm2bJlHh97di6+m6wxnh//c889Z3788Uezb98+88svv5j27dub4sWLmxMnTlwT8a9bt854e3ubkSNHmt27d5s5c+aYgIAA88knnzj6ePpnOD093ZQtW9a8+OKLWYZ5+vrv1auXKV26tOMxMAsXLjTFixc3L7zwgqOPp69/5B1yff4h13tWrjfm2sr35PoM5PrcuRZzPUX7VXj33XdN2bJlja+vr2natKn57bff3B2SMcaYlStXGklZ/nr16mWMyXiMwauvvmpKlixpbDabadeundm5c6fTNE6fPm169OhhgoKCTEhIiOnTp4+Ji4srkPizi12SmTlzpqNPUlKSefLJJ02RIkVMQECAufvuu82xY8ecprN//37TsWNH4+/vb4oXL26ee+45k5qamu/xP/zww6ZcuXLG19fXlChRwrRr186RxD099uxcmsQ9Pf5u3bqZiIgI4+vra0qXLm26devm9NxTT4/fGGO++uorU7t2bWOz2Uz16tXN+++/7zTc0z/D3333nZGUJSZjPH/9x8bGmgEDBpiyZcsaPz8/U7FiRfPyyy87PYLG09c/8ha5Pn+Q692fay51LeV7cn0Gcn3uXIu53mKMMXl//B4AAAAAAFwtrmkHAAAAAMBDUbQDAAAAAOChKNoBAAAAAPBQFO0AAAAAAHgoinYAAAAAADwURTsAAAAAAB6Koh0AAAAAAA9F0Q4AAAAAgIeiaAcgSTp58qSeeOIJlS1bVjabTaVKlVKHDh30yy+/SJIsFosWLVrk3iABAECukeuBa5O3uwMA4Bm6du2qlJQUffTRR6pYsaKio6O1YsUKnT592t2hAQCAPECuB65NFmOMcXcQANzr3LlzKlKkiH788Ue1adMmy/Dy5cvrwIEDjtflypXT/v37JUlffvmlRowYob/++kuRkZHq1auXXn75ZXl7Z+wTtFgseu+997R48WL9+OOPioiI0Ntvv6177723QJYNAACQ64FrGafHA1BQUJCCgoK0aNEiJScnZxm+fv16SdLMmTN17Ngxx+tVq1apZ8+eGjBggP766y9Nnz5ds2bN0siRI53Gf/XVV9W1a1dt3bpVDz74oLp3767t27fn/4IBAABJ5HrgWsaRdgCSpM8//1z9+vVTUlKSGjZsqDZt2qh79+6qW7eupIy96F988YW6dOniGKd9+/Zq166dhg4d6mj75JNP9MILL+jo0aOO8R5//HFNnTrV0eeGG25Qw4YN9d577xXMwgEAAHI9cI3iSDsASRnXuR09elSLFy/Wbbfdph9//FENGzbUrFmzLjvO1q1b9frrrzv23gcFBalfv346duyYEhMTHf2aN2/uNF7z5s3Z+w4AQAEj1wPXJm5EB8DBz89Pt9xyi2655Ra9+uqr6tu3r4YPH67evXtn2z8+Pl4jRozQPffck+20AACAZyHXA9cejrQDuKyaNWsqISFBkuTj46P09HSn4Q0bNtTOnTtVuXLlLH9W64Wvl99++81pvN9++001atTI/wUAAABXRK4HPB9H2gHo9OnTuu+++/Twww+rbt26Cg4O1oYNG/T222/rrrvukpRxV9kVK1aoZcuWstlsKlKkiIYNG6Y77rhDZcuW1b333iur1aqtW7dq27ZtevPNNx3TX7BggRo3bqwbb7xRc+bM0bp16/Thhx+6a3EBACh0yPXAtYsb0QFQcnKyXnvtNS1btkx79uxRamqqoqKidN999+mll16Sv7+/vvrqKw0aNEj79+9X6dKlHY+B+e677/T6669r8+bN8vHxUfXq1dW3b1/169dPUsbNaaZMmaJFixbp559/VkREhN566y3df//9blxiAAAKF3I9cO2iaAeQr7K7Ey0AALh+kOuB/MU17QAAAAAAeCiKdgAAAAAAPBSnxwMAAAAA4KE40g4AAAAAgIeiaAcAAAAAwENRtAMAAAAA4KEo2gEAAAAA8FAU7QAAAAAAeCiKdgAAAAAAPBRFOwAAAAAAHoqiHQAAAAAAD0XRDgAAAACAh6JoBwAAAADAQ1G0AwAAAADgoSjaAQAAAADwUBTtAAAAAAB4KIp2AAAAAAA8FEU7Cg2LxaLXXnvN3WHgGrJ06VLVr19ffn5+slgsOnfunLtDAgDkADkfriLnu8drr70mi8WiU6dOuTsUj1ZoivY9e/boscceU8WKFeXn56eQkBC1bNlSkyZNUlJSkqNf+fLlZbFYHH/h4eFq1aqVvvjii3+dx/W80c2aNctpvWSum7Zt22rJkiXuDq/A5HQ7wrXv9OnTuv/+++Xv768pU6Zo9uzZCgwM/Nfx3nvvPVksFjVr1qwAorz2pKena+bMmbrppptUtGhR2Ww2lS9fXn369NGGDRvcHR6uE+T8q0POz0DOLzzI+XkvJSVFkyZNUoMGDRQSEqKwsDDVqlVLjz76qHbs2OG2uEaNGqVFixa5bf655e3uAArCN998o/vuu082m009e/ZU7dq1lZKSotWrV+v555/Xn3/+qffff9/Rv379+nruueckSUePHtX06dN1zz33aOrUqXr88cfdtRge4fXXX1eFChVkjFF0dLRmzZql22+/XV999ZXuuOMOd4eXr1zdjnBtW79+veLi4vTGG2+offv2OR5vzpw5Kl++vNatW6e///5blStXzscory1JSUm65557tHTpUrVu3VovvfSSihYtqv379+vTTz/VRx99pIMHD6pMmTLuDhXXMHJ+3iHnk/MLC3J+3uvatauWLFmiHj16qF+/fkpNTdWOHTv09ddfq0WLFqpevbpb4ho1apTuvfdedenSxS3zzzVzndu7d68JCgoy1atXN0ePHs0yfPfu3WbixImO1+XKlTOdOnVy6nPs2DETGBhoqlatesV5DR8+3EgyJ0+ezJvgC1h8fPxlh82cOdNIMuvXr3dqP3PmjPHx8TEPPPBAfod31SSZ4cOH52pcV7eja1FSUpJJT093dxge46OPPsp2m7+SvXv3Gklm4cKFpkSJEua1117Lxwizl56ebpKSkgp8vjnRv39/I8lMmDAhy7C0tDTzzjvvmEOHDhV8YP+Cz8a1g5yfc+T8yyPnFz7k/Ly1bt06I8mMHDkyy7C0tDRz6tQpx+uC+C612+0mMTHRGGNMYGCg6dWrV77NK79c96fHv/3224qPj9eHH36oiIiILMMrV66sAQMGXHEapUqVUo0aNbRv3748iemHH35Qq1atFBgYqLCwMN11113avn27Y/jvv/8ui8WixYsXO9o2btwoi8Wihg0bOk2rY8eOWU7JWbJkiWP6wcHB6tSpk/7880+nPr1791ZQUJD27Nmj22+/XcHBwXrwwQddXpawsDD5+/vL29v5pI2EhAQ999xzioqKks1mU7Vq1TR27FgZYxx99u/fL4vFolmzZmWZ7qXXomWehvj333+rd+/eCgsLU2hoqPr06aPExESncZOTkzVw4ECVKFFCwcHBuvPOO3X48OFs49+xY4cOHjz4r8vp6naUlpamN954Q5UqVXKc/vvSSy8pOTnZabzy5cvrjjvu0OrVq9W0aVP5+fmpYsWK+vjjjx19NmzYIIvFoo8++ijLfL/77jtZLBZ9/fXXjrYjR47o4YcfVsmSJWWz2VSrVi3997//dRrvxx9/lMVi0bx58/TKK6+odOnSCggIUGxsrCRpwYIFqlmzpvz8/FS7dm198cUX6t27t8qXL+80HbvdrokTJ6pWrVry8/NTyZIl9dhjj+ns2bMuL2emc+fOaeDAgSpfvrxsNpvKlCmjnj17Op2CmpycrOHDh6ty5cqy2WyKiorSCy+8kGX9Xs6CBQvUqFEj+fv7q3jx4vq///s/HTlyxDH8pptuUq9evSRJTZo0kcViUe/evf91unPmzFGRIkXUqVMn3XvvvZozZ45jWGpqqooWLao+ffpkGS82NlZ+fn4aPHiwy8tosVj01FNPac6cOapVq5ZsNpuWLl0qSRo7dqxatGihYsWKyd/fX40aNdJnn32WZf5JSUl65plnVLx4ccdn5siRI9leE5qT7Ss7hw8f1vTp03XLLbfo2WefzTLcy8tLgwcPdjrKvnnzZnXs2FEhISEKCgpSu3bt9NtvvzmGF/Rn48yZMxo8eLDq1KmjoKAghYSEqGPHjtq6dWuW+R84cEB33nmnAgMDFR4eroEDBzpi+vHHH536rl27VrfddptCQ0MVEBCgNm3a6JdffvnXdYqsyPnkfHI+Of9S5HxnBZHz9+zZI0lq2bJllmFeXl4qVqxYlvZz58796+fd1c/ad999p8aNG8vf31/Tp0+XxWJRQkKCPvroI8elP5nv9YEDB/Tkk0+qWrVq8vf3V7FixXTfffdp//79WWL9/fff1aZNG/n7+6tMmTJ68803NXPmTFksliz9c/IdnSPu3muQ30qXLm0qVqyY4/7Z7XVPSUkxJUuWNKVKlbriuDnZU7R8+XLj7e1tqlatat5++20zYsQIU7x4cVOkSBGzb98+Y0zGXrOwsDDz3HPPOcabMGGCsVqtxmq1mpiYGEe/kJAQM3jwYEe/jz/+2FgsFnPbbbeZd99917z11lumfPnyJiwszDF9Y4zp1auXsdlsplKlSqZXr15m2rRp5uOPP75s3Jl73b///ntz8uRJc+LECbNt2zbz2GOPGavVapYtW+boa7fbzc0332wsFovp27evmTx5suncubORZJ599llHv3379hlJZubMmVnmp0v2kGeu2wYNGph77rnHvPfee6Zv375GknnhhRecxv2///s/I8k88MADZvLkyeaee+4xdevWzXavuyTTpk2byy53Jle3o169ehlJ5t577zVTpkwxPXv2NJJMly5dnPqVK1fOVKtWzZQsWdK89NJLZvLkyaZhw4bGYrGYbdu2OfpVrFjR3H777Vnm06dPH1OkSBGTkpJijDHm+PHjpkyZMiYqKsq8/vrrZurUqebOO+/McnRz5cqVRpKpWbOmqV+/vhk/frwZPXq0SUhIMF9//bWxWCymbt26Zvz48ebVV181RYoUMbVr1zblypVzmn/fvn2Nt7e36devn5k2bZp58cUXTWBgoGnSpIkjJleWMy4uztSuXdt4eXmZfv36malTp5o33njDNGnSxGzevNkYk7Hd33rrrSYgIMA8++yzZvr06eapp54y3t7e5q677vrX9yZzW27SpImZMGGCGTJkiPH39zfly5c3Z8+eNcYYs2zZMvPoo48aSeb11183s2fPNr/++uu/Trt69ermkUceMcYY8/PPPxtJZt26dY7hDz/8sAkLCzPJyclO4126h9+VZZRkatSoYUqUKGFGjBhhpkyZ4lhXZcqUMU8++aSZPHmyGT9+vGnatKmRZL7++munadx///1GknnooYfMlClTzP3332/q1auX5TOT0+0rO++//76RdMXvmYtt27bNBAYGmoiICPPGG2+YMWPGmAoVKhibzWZ+++03R7+C/GysX7/eVKpUyQwZMsRMnz7dvP7666Z06dImNDTUHDlyxDGN+Ph4U7FiRePv72+GDBliJk6caJo2bepYpytXrnT0XbFihfH19TXNmzc348aNMxMmTDB169Y1vr6+Zu3atTlaV7iAnE/OJ+eT8y9GzndPzv/111+NJNOvXz+Tmpp6xb6ufN5d+axVrlzZFClSxAwZMsRMmzbNrFy50syePdvYbDbTqlUrM3v2bKf3esGCBaZevXpm2LBh5v333zcvvfSSKVKkiClXrpxJSEhwTPvw4cOmaNGiplixYmbEiBFm7Nixpnr16o51ePF3b06/o3Piui7aY2JijKQcfagzlStXztx6663m5MmT5uTJk2br1q2me/fuRpJ5+umnrzhuThJ4/fr1TXh4uDl9+rSjbevWrcZqtZqePXs62jp16mSaNm3qeH3PPfeYe+65x3h5eZklS5YYY4zZtGmTkWS+/PJLY0zGl19YWJjp16+f0zyPHz9uQkNDndozN/ohQ4bkYK1c+NK79M9ms5lZs2Y59V20aJGRZN58802n9nvvvddYLBbz999/G2Nyl8Affvhhp3533323KVasmOP1li1bjCTz5JNPOvV74IEHcp3AXd2OMmPo27evU/vgwYONJPPDDz842sqVK2ckmZ9//tnRduLECWOz2Zx+wA0dOtT4+PiYM2fOONqSk5NNWFiY0zp55JFHTEREhNNpR8YY0717dxMaGuo4NSgzgVesWNHRlqlOnTqmTJkyJi4uztH2448/GklOCXzVqlVGkpkzZ47T+EuXLs3SntPlHDZsmONUs0vZ7XZjjDGzZ882VqvVrFq1ymn4tGnTjCTzyy+/ZBk3U0pKigkPDze1a9d2Op3s66+/NpLMsGHDHG2XOz30cjZs2GAkmeXLlzviLVOmjBkwYICjz3fffWckma+++spp3Ntvv93pB6IryyjJWK1W8+eff2aJ6dL3NiUlxdSuXdvcfPPNjraNGzdm+XFtjDG9e/fO8pnJ6faVnYEDBxpJjh8X/6ZLly7G19fX7Nmzx9F29OhRExwcbFq3bu1oK8jPxvnz57OcTrpv3z5js9nM66+/7mgbN26ckWQWLVrkaEtKSjLVq1d3KtrtdrupUqWK6dChg2P7NibjfatQoYK55ZZbcrSukIGcn4GcT84n52cg57sv59vtdtOmTRsjyZQsWdL06NHDTJkyxRw4cCBLX1c/76581pYuXZplfpc7PT675VmzZk2WAw5PP/20sVgsTr9nTp8+bYoWLepUtLvyHZ0T1/Xp8Zmn/QQHB7s03rJly1SiRAmVKFFC9erV04IFC/TQQw/prbfeuqp4jh07pi1btqh3794qWrSoo71u3bq65ZZb9O233zraWrVqpU2bNikhIUGStHr1at1+++2qX7++Vq1aJUlatWqVLBaLbrzxRknS8uXLde7cOfXo0UOnTp1y/Hl5ealZs2ZauXJllpieeOIJl5ZhypQpWr58uZYvX65PPvlEbdu2Vd++fbVw4UJHn2+//VZeXl565plnnMZ97rnnZIy5qjvPXnpToFatWun06dOO9zpzHV467+xOx5UkY0yWU1Uv5ep2lBnDoEGDnNozb3T0zTffOLXXrFlTrVq1crwuUaKEqlWrpr179zraunXrptTUVKf1vGzZMp07d07dunVzLMvnn3+uzp07yxjjtA106NBBMTEx2rRpk9O8e/XqJX9/f8fro0eP6o8//lDPnj0VFBTkaG/Tpo3q1KnjNO6CBQsUGhqqW265xWlejRo1UlBQUJbtLSfL+fnnn6tevXq6++67s6xXi8XimG+NGjVUvXp1p/nefPPNkpTtdp5pw4YNOnHihJ588kn5+fk52jt16qTq1atneW9cMWfOHJUsWVJt27Z1xNutWzfNmzdP6enpkqSbb75ZxYsX1/z58x3jnT17VsuXL3e8j7lZxjZt2qhmzZpZYrr4vT179qxiYmIc3y2ZMk+re/LJJ53Gffrpp51e52b7upgrn6P09HQtW7ZMXbp0UcWKFR3tEREReuCBB7R69WrH9ArqsyFJNptNVqvVEePp06cVFBSkatWqZVmnpUuX1p133ulo8/PzU79+/Zymt2XLFu3evVsPPPCATp8+7YgpISFB7dq1088//yy73f6v6wsZyPnk/EzkfHK+RM53Z863WCz67rvv9Oabb6pIkSL63//+p/79+6tcuXLq1q1bto/Ty+nnPaeftQoVKqhDhw6XjfFSF6+/1NRUnT59WpUrV1ZYWFiWddi8eXPVr1/f0Va0aNEslxzl5jv6Sq7ru8eHhIRIkuLi4lwar1mzZnrzzTdlsVgUEBCgGjVqKCws7KrjOXDggCSpWrVqWYbVqFFD3333nRISEhQYGKhWrVopLS1Na9asUVRUlE6cOKFWrVrpzz//dErgNWvWdPwY2L17tyQ5PuSXylwfmby9vV2+S3PTpk3VuHFjx+sePXqoQYMGeuqpp3THHXfI19dXBw4cUGRkZJaEV6NGDaf1kBtly5Z1el2kSBFJGV9OISEhOnDggKxWqypVquTUL7t1nlOubkeZMVx6B9FSpUopLCwsy/JfukxSxnJdfI1YvXr1VL16dc2fP1+PPPKIJGn+/PkqXry44/0+efKkzp07p/fff/+yd7Q9ceKE0+sKFSpkiV1Stnc/rVy5stOX1u7duxUTE6Pw8PAczSsny7lnzx517do12+ldPN/t27erRIkSOZrvxa70GaxevbpWr159xXlfTnp6uubNm6e2bds6XQfbrFkzjRs3TitWrNCtt94qb29vde3aVXPnzlVycrJsNpsWLlyo1NRUpwTu6jJe+j5m+vrrr/Xmm29qy5YtTtd7Zf4Yki5sr5dO49JtIDfb18Vc+RydPHlSiYmJl/2utNvtOnTokGrVqlVgnw0p43rOSZMm6b333tO+ffscP8wkOV2fd+DAAVWqVMlpPUtZ12nmd3bmtZTZiYmJcXzP4crI+c7I+blDzpejjZyfPXJ+9nFdymaz6eWXX9bLL7+sY8eO6aefftKkSZP06aefysfHR5988olT/5x+3nP6WbvcerqcpKQkjR49WjNnztSRI0ec7skRExPj+P+BAwfUvHnzLONfLsfn9Dv631z3RXtkZKS2bdvm0njFixd36XEP+aFx48by8/PTzz//rLJlyyo8PFxVq1ZVq1at9N577yk5OVmrVq1y2juZeURm9uzZKlWqVJZpXnrjmIuPGuWW1WpV27ZtNWnSJO3evVu1atXK8biX/qDNdPEP4Ut5eXll237xByuv5XY7utzyXSqny9StWzeNHDlSp06dUnBwsBYvXqwePXo43tfM9////u//LlsE1K1b1+n1pUcSXWG32xUeHu5045WLXZp88uq9s9vtqlOnjsaPH5/t8KioKJemlxd++OEHHTt2TPPmzdO8efOyDJ8zZ45uvfVWSVL37t01ffp0LVmyRF26dNGnn36q6tWrq169eo7+ri5jdu/jqlWrdOedd6p169Z67733FBERIR8fH82cOVNz5851eRlzs31dLPPRLn/88YfT3um8UFCfjVGjRunVV1/Vww8/rDfeeENFixaV1WrVs88+m6sj4pnjvPPOO5ddJxcf/cKVkfOdkfNzh5yfPXL+BeT8DFfK+ZeKiIhQ9+7d1bVrV9WqVUuffvqpZs2a5fQ9ldNtJqefNVe396efflozZ87Us88+q+bNmys0NFQWi0Xdu3e/qhyf0+/of3NdF+2SdMcdd+j999/XmjVrst0rUpDKlSsnSdq5c2eWYTt27FDx4sUVGBgoSfL19VXTpk21atUqlS1b1nGKUatWrZScnKw5c+YoOjparVu3dkwjc09zeHh4gf4ASUtLkyTFx8dLyljO77//XnFxcU573nfs2OEYLl3Yg3bpKTJXs1e+XLlystvt2rNnj9Oe1ezWuStc2Y4yY9i9e7fjSIMkRUdH69y5c47ld1W3bt00YsQIff755ypZsqRiY2PVvXt3x/DMO+emp6fn+v3PjO3vv//OMuzStkqVKun7779Xy5Ytr+qHwKXT/LcfSpUqVdLWrVvVrl27HH9xZ7r4M3jpns+dO3fm+r2ZM2eOwsPDNWXKlCzDFi5cqC+++ELTpk2Tv7+/WrdurYiICM2fP1833nijfvjhB7388stO41zNMmb6/PPP5efnp++++042m83RPnPmTKd+mdvrvn37VKVKFUf7pe/31W5fHTt2lJeXlz755BM99NBDV+xbokQJBQQEXPa70mq1Ov2IKYjPhiR99tlnatu2rT788EOn9nPnzql48eKO1+XKldNff/0lY4zT+5fdZ0jKKBLcXTReL8j5+Y+cn30M5HzXkfMzXI85/3J8fHxUt25d7d69W6dOncq2mL2cvPqsXW4df/bZZ+rVq5fGjRvnaDt//nyW76xy5crl+DMj5d139HV9TbskvfDCCwoMDFTfvn0VHR2dZfiePXs0adKkAoklIiJC9evX10cffeS0AWzbtk3Lli3T7bff7tS/VatWWrt2rVauXOlI4MWLF1eNGjUc19pdfL1Qhw4dFBISolGjRik1NTXL/E+ePJnny5Samqply5bJ19fX8QG6/fbblZ6ersmTJzv1nTBhgiwWizp27Cgp44dq8eLF9fPPPzv1e++993IdT+a0//Of/zi1T5w4Mdv+OX38iyvbUeb7eOk8M/egdurU6V/nl50aNWqoTp06mj9/vubPn6+IiAinH3BeXl7q2rWrPv/882yTYE7e/8jISNWuXVsff/yx4weZJP3000/6448/nPref//9Sk9P1xtvvJFlOmlpadler/Rvunbtqq1bt+qLL77IMixzT+v999+vI0eO6IMPPsjSJykpyXFNaHYaN26s8PBwTZs2zenUsSVLlmj79u25em+SkpK0cOFC3XHHHbr33nuz/D311FOKi4tzPM7JarXq3nvv1VdffaXZs2crLS3N6TS5q13GTF5eXrJYLE5Hsfbv369FixY59cu83uvSz927776bZXpXs31FRUWpX79+WrZsWZZpSxl7pMeNG6fDhw/Ly8tLt956q7788kunR6dER0dr7ty5uvHGG51OKyuIz0bmdC7d479gwQKnRwdJGev0yJEjTo/wOn/+fJb3s1GjRqpUqZLGjh3r9HlzNS5cQM6/gJyfFTnfGTmfnJ8pr3P+7t27s/2snTt3TmvWrFGRIkUueznA5eTVZy0wMDDb7TW7HP/uu+9mORuoQ4cOWrNmjbZs2eJoO3PmTJazUPL6O/q6P9JeqVIlzZ07V926dVONGjXUs2dP1a5dWykpKfr111+1YMGCHD2L0RXjx49XQECAU5vVatVLL72kd955Rx07dlTz5s31yCOPKCkpSe+++65CQ0OzPBuxVatWGjlypA4dOuSUqFu3bq3p06erfPnyTtenhYSEaOrUqXrooYfUsGFDde/eXSVKlNDBgwf1zTffqGXLllmSqquWLFni2Ht+4sQJzZ07V7t379aQIUMcP6I7d+6stm3b6uWXX9b+/ftVr149LVu2TF9++aWeffZZp2vP+vbtqzFjxqhv375q3Lixfv75Z+3atSvX8dWvX189evTQe++9p5iYGLVo0UIrVqzIdo+YlJEU27Rp8683pnFlO6pXr5569eql999/X+fOnVObNm20bt06ffTRR+rSpYvjpiW50a1bNw0bNkx+fn565JFHspzqOGbMGK1cuVLNmjVTv379VLNmTZ05c0abNm3S999/rzNnzvzrPEaNGqW77rpLLVu2VJ8+fXT27FlNnjxZtWvXdkrqbdq00WOPPabRo0dry5YtuvXWW+Xj46Pdu3drwYIFmjRpku69916Xlu/555/XZ599pvvuu08PP/ywGjVqpDNnzmjx4sWaNm2a6tWrp4ceekiffvqpHn/8ca1cuVItW7ZUenq6duzYoU8//dTxTM7s+Pj46K233lKfPn3Upk0b9ejRQ9HR0Zo0aZLKly+vgQMHuhSvJC1evFhxcXFONx272A033KASJUpozpw5jkTdrVs3vfvuuxo+fLjq1KnjtMdY0lUtY6ZOnTpp/Pjxuu222/TAAw/oxIkTmjJliipXrqzff//d0a9Ro0bq2rWrJk6cqNOnT+uGG27QTz/95PgcXrxH+mq3r3HjxmnPnj165plnHD96ihQpooMHD2rBggXasWOH40jSm2++qeXLl+vGG2/Uk08+KW9vb02fPl3Jycl6++23s0y7ID4bd9xxh15//XX16dNHLVq00B9//KE5c+Y43SxPkh577DFNnjxZPXr00IABAxQREaE5c+Y4boSUuU6tVqtmzJihjh07qlatWurTp49Kly6tI0eOaOXKlQoJCdFXX331r3HhAnI+OZ+cT87PRM53X87funWrHnjgAXXs2FGtWrVS0aJFdeTIEX300Uc6evSoJk6ceNnT4S8nrz5rjRo10vfff6/x48crMjJSFSpUULNmzXTHHXdo9uzZCg0NVc2aNbVmzRp9//33WZ4p/8ILL+iTTz7RLbfcoqefflqBgYGaMWOGypYtqzNnzjjWYZ5/R7t0r/lr2K5du0y/fv1M+fLlja+vrwkODjYtW7Y07777rjl//ryjX3bPbM2pzEcWZPfn5eXl6Pf999+bli1bGn9/fxMSEmI6d+5s/vrrryzTi42NNV5eXiY4ONikpaU52j/55BOjf56vmJ2VK1eaDh06mNDQUOPn52cqVapkevfubTZs2ODo06tXLxMYGJjjZcvu8S9+fn6mfv36ZurUqU6PKzIm4zEHAwcONJGRkcbHx8dUqVLFvPPOO1n6JSYmmkceecSEhoaa4OBgc//995sTJ05c9vEvlz5aJzOui591mJSUZJ555hlTrFgxExgYaDp37mwOHTqU68e/XCyn21FqaqoZMWKEqVChgvHx8TFRUVFm6NChTn2Mufz21qZNm2zj2r17t2P9r169OtsYo6OjTf/+/U1UVJTx8fExpUqVMu3atTPvv/++o0/m418WLFiQ7TTmzZtnqlevbmw2m6ldu7ZZvHix6dq1q6levXqWvu+//75p1KiR8ff3N8HBwaZOnTrmhRdeMEePHs3Vcp4+fdo89dRTpnTp0sbX19eUKVPG9OrVy+mRIykpKeatt94ytWrVMjabzRQpUsQ0atTIjBgxwvFM4yuZP3++adCggbHZbKZo0aLmwQcfNIcPH3bqk9PHv3Tu3Nn4+fk5PcPzUr179zY+Pj6OZbDb7SYqKirbxyS5uoySTP/+/bOdxocffmiqVKlibDabqV69upk5c6bjs3SxhIQE079/f1O0aFETFBRkunTpYnbu3GkkmTFjxjj1zcn2dSVpaWlmxowZplWrViY0NNT4+PiYcuXKmT59+mR5HNymTZtMhw4dTFBQkAkICDBt27a97LNzC+Kzcf78efPcc8+ZiIgI4+/vb1q2bGnWrFmT7Xa8d+9e06lTJ+Pv729KlChhnnvuOfP5558bSU7PmTfGmM2bN5t77rnHFCtWzNhsNlOuXDlz//33mxUrVuRgjSI75HxyPjmfnJ+JnF/wOT86OtqMGTPGtGnTxkRERBhvb29TpEgRc/PNN5vPPvvMqa8rn/er/awZY8yOHTtM69atjb+/v5HkePzb2bNnTZ8+fUzx4sVNUFCQ6dChg9mxY4cpV65clkfEbd682bRq1crYbDZTpkwZM3r0aPOf//zHSDLHjx936puT7+icsBiTj3fzAHDdqF+/vkqUKKHly5e7OxQUgC1btqhBgwb65JNPsjzGBLkzceJEDRw4UIcPH1bp0qXdHQ4AXBY5v3Ah51+9Z599VtOnT1d8fLzLZxHkxHV/TTsA16SmpjpuNJTpxx9/1NatW3XTTTe5Jyjkq6SkpCxtEydOlNVqdbqGEjl36To9f/68pk+fripVqlCwA/AY5PzCh5x/9S5dh6dPn9bs2bN144035kvBLhWCa9oBuObIkSNq3769/u///k+RkZHasWOHpk2bplKlSunxxx93d3jIB2+//bY2btyotm3bytvbW0uWLNGSJUv06KOPuuVxOteDe+65R2XLllX9+vUVExOjTz75RDt27Ljs45IAwB3I+YUPOf/qNW/eXDfddJNq1Kih6Ohoffjhh4qNjdWrr76ab/Pk9HgATmJiYvToo4/ql19+0cmTJxUYGKh27dppzJgxTjcUwvVj+fLlGjFihP766y/Fx8erbNmyeuihh/Tyyy+7/BxRZJg4caJmzJih/fv3Kz09XTVr1tQLL7yQ5a7BAOBO5PzCh5x/9V566SV99tlnOnz4sCwWixo2bKjhw4fn6+M3KdoBAAAAAPBQXNMOAAAAAICHomgHAAAAAMBDFboLF+x2u44eParg4GBZLBZ3hwMAgIwxiouLU2RkpKxW9qfnBfI9AMCTXE2uL3RF+9GjR7kzIgDAIx06dEhlypRxdxjXBfI9AMAT5SbXF7qiPTg4WFLGygoJCXFzNAAASLGxsYqKinLkKFw98j0AwJNcTa4vdEV75ilyISEhJHEAgEfhNO68Q74HAHii3OR6LpwDAAAAAMBDUbQDAAAAAOChKNoBAAAAAPBQhe6adgDIqfT0dKWmpro7DFwHfHx85OXl5e4wAAD5jN8OhZuvr2++PLqVoh0ALmGM0fHjx3Xu3Dl3h4LrSFhYmEqVKsXN5gDgOsRvB0iS1WpVhQoV5Ovrm6fTpWgHgEtkJt3w8HAFBARQZOGqGGOUmJioEydOSJIiIiLcHBEAIK/x2wF2u11Hjx7VsWPHVLZs2TzdBijaAeAi6enpjqRbrFgxd4eD64S/v78k6cSJEwoPD+dUeQC4jvDbAZlKlCiho0ePKi0tTT4+Pnk2XW5EBwAXybwOLSAgwM2R4HqTuU1xrSMAXF/47YBMmafFp6en5+l0KdoBIBuc1oa8xjYFANc3vueRX9sARTsAAAAAAB6Koh0A4FF++eUX1alTRz4+PurSpYt+/PFHWSyWK96Rd9asWQoLCyuwGAEAKAwsFossFkuOc+z1nI9vuukmx/rYsmVLgc6boh0AriPHjx/X008/rYoVK8pmsykqKkqdO3fWihUrHH3Kly/vSDqBgYFq2LChFixY8K/T/vzzz3XTTTcpNDRUQUFBqlu3rl5//XWdOXMmT5dh0KBBql+/vvbt26dZs2apRYsWOnbsmEJDQ/N0PgAAFHa9e/d2/Ca4+O+2225z9Jk5c6Z27drlxigzuHuHwMKFC7Vu3Tq3zJuiHQCuE/v371ejRo30ww8/6J133tEff/yhpUuXqm3bturfv79T39dff13Hjh3T5s2b1aRJE3Xr1k2//vrrZaf98ssvq1u3bmrSpImWLFmibdu2ady4cdq6datmz56dp8uxZ88e3XzzzSpTpozCwsLk6+vL880BAMgnt912m44dO+b097///c8xPCwsTOHh4W6M0DNu4lq0aFGVKFHCLfOmaAeAHLKn2GVPscsY42gz6SajLc1k39ee+76uevLJJ2WxWLRu3Tp17dpVVatWVa1atTRo0CD99ttvTn2Dg4NVqlQpVa1aVVOmTJG/v7+++uqrbKe7bt06jRo1SuPGjdM777yjFi1aqHz58rrlllv0+eefq1evXo6+U6dOVaVKleTr66tq1aplKegtFotmzJihu+++WwEBAapSpYoWL14sKWOng8Vi0enTp/Xwww/LYrFo1qxZ2Z4eP2vWLJUtW1YBAQG6++67dfr06Sxxf/nll2rYsKH8/PxUsWJFjRgxQmlpaTmKJdOff/6pO+64QyEhIQoODlarVq20Z88ex/AZM2aoRo0a8vPzU/Xq1fXee+/9y7sEAChMCvK3Q27ZbDaVKlXK6a9IkSKX7b9161a1bdtWwcHBCgkJUaNGjbRhwwanPt99951q1KihoKAgx04BR+x2u15//XWVKVNGNptN9evX19KlSx3DM38PzJ8/X23atJGfn5/mzJmjPn36KCYmxnE2wGuvvSZJmj17tho3buz4bfPAAw/oxIkTTvEsXrxYVapUkZ+fn9q2bauPPvooy2+L1atXq1WrVvL391dUVJSeeeYZJSQkXMWazTsU7QCQQ4ffPaDD7x6QPcnuaIvdEKPD7x7Q2R+ci8Yj0w7q8LsHlB53oUiM2xKrw+8e0OllJ536Hp1xSIffPaC0Mxf2Iif8GedSbGfOnNHSpUvVv39/BQYGZhl+pdPJvL295ePjo5SUlGyHz5kzR0FBQXryySezHZ457S+++EIDBgzQc889p23btumxxx5Tnz59tHLlSqf+I0aM0P3336/ff/9dt99+ux588EGdOXNGUVFROnbsmEJCQjRx4kQdO3ZM3bp1yzK/tWvX6pFHHtFTTz2lLVu2qG3btnrzzTed+qxatUo9e/bUgAED9Ndff2n69OmaNWuWRo4cmaNYJOnIkSNq3bq1bDabfvjhB23cuFEPP/ywo/CfM2eOhg0bppEjR2r79u0aNWqUXn31VX300UeXXdcAgMKlIH87FJQHH3xQZcqU0fr167Vx40YNGTLE6ZnkiYmJGjt2rGbPnq2ff/5ZBw8e1ODBgx3DJ02apHHjxmns2LH6/fff1aFDB915553avXu303yGDBmiAQMGaPv27Wrbtq0mTpyokJAQx9kAmdNMTU3VG2+8oa1bt2rRokXav3+/evfu7ZjOvn37dO+996pLly7aunWrHnvsMb388stO89qzZ49uu+02de3aVb///rvmz5+v1atX66mnnsqHNZgLppCJiYkxkkxMTIy7QwHggZKSksxff/1lkpKSsgw7MHavOTB2r0lLSHO0nfvtrDkwdq85/d1Jp74HJ+0zB8buNannUhxtMRvOmQNj95qT30Q79T00Zb85MHavSTmZ7GiL2+rad9TatWuNJLNw4cJ/7VuuXDkzYcIEY4wxycnJZtSoUUaS+frrr7Pt37FjR1O3bt1/nW6LFi1Mv379nNruu+8+c/vttzteSzKvvPKK43V8fLyRZJYsWeJoCw0NNTNnznS8XrlypZFkzp49a4wxpkePHk7TNMaYbt26mdDQUMfrdu3amVGjRjn1mT17tomIiMhxLEOHDjUVKlQwKSkpJjuVKlUyc+fOdWp74403TPPmzbPtf6Vti9yU91inAAqKp/x2yI1evXoZLy8vExgY6PQ3cuRIY0xGrvziiy+cxgkODjazZs3KdnozZ840kszff//taJsyZYopWbKk43VkZKRj+pmaNGlinnzySWOMMfv27TOSzMSJE7NM++Jcfznr1683kkxcXJwxxpgXX3zR1K5d26nPyy+/7PTb4pFHHjGPPvqoU59Vq1YZq9Xq9L5mxrZ58+Zs551fud67wPcSAMA1qszT5SRJFp8L11aHNA5VcIMQWazO11uXfrxsRl/vC+3B9UMUVCc4S9/IvlFZ+gbWCnYpNmNcOz3uxRdf1CuvvKLz588rKChIY8aMUadOna5q2tu3b9ejjz7q1NayZUtNmjTJqa1u3bqO/wcGBiokJCTLaWz/Np+7777bqa158+ZOp9Zt3bpVv/zyi9OR9fT0dJ0/f16JiYkKCAj411i2bNmiVq1aOR09yJSQkKA9e/bokUceUb9+/RztaWlp3DAPAOBQkL8dcqtt27aaOnWqU1vRokUv23/QoEHq27evZs+erfbt2+u+++5TpUqVHMMDAgKcXkdERDhya2xsrI4ePaqWLVs6TbNly5baunWrU1vjxo1zFP/GjRv12muvaevWrTp79qzs9oyzGg4ePKiaNWtq586datKkidM4TZs2dXq9detW/f7775ozZ46jzRgju92uffv2qUaNGjmKJb9QtANADll9s15RZPGyyOKVNWHmRV9XVKlSRRaLRTt27MhR/+eff169e/dWUFCQSpYsecWbvFWtWlWrV69WampqtgWsqy6dhsVicSTYvBIfH68RI0bonnvuyTLMz88vR7H4+/tfcfqS9MEHH6hZs2ZOw7y8vHIdNwDg+lKQvx1yKzAwUJUrV85x/9dee00PPPCAvvnmGy1ZskTDhw/XvHnzHDvUs8utrh5cyIzr3yQkJKhDhw7q0KGD5syZoxIlSujgwYPq0KHDZS/7y058fLwee+wxPfPMM1mGlS1b1qW48wPXtAPAdaBo0aLq0KGDpkyZku1NUy59xnnx4sVVuXLlHN2V/YEHHlB8fPxlb7KWOe0aNWrol19+cRr2yy+/qGbNmjlfkByoUaOG1q5d69R26Y32GjZsqJ07d6py5cpZ/qzWnKW+unXratWqVdnesbZkyZKKjIzU3r17s0y/QoUKuV84AACuAVWrVtXAgQO1bNky3XPPPZo5c2aOxgsJCVFkZGSufi/4+voqPT3dqW3Hjh06ffq0xowZo1atWql69epZzt6rVq1alhvlrV+/3ul1w4YN9ddff2X7u8HX1zdHy5afKNoB4DoxZcoUpaenq2nTpvr888+1e/dubd++Xf/5z3/UvHnzXE+3WbNmeuGFF/Tcc8/phRde0Jo1a3TgwAGtWLFC9913n+PGa88//7xmzZqlqVOnavfu3Ro/frwWLlzodPOZvPDMM89o6dKlGjt2rHbv3q3Jkyc7nRovScOGDdPHH3+sESNG6M8//9T27ds1b948vfLKKzmez1NPPaXY2Fh1795dGzZs0O7duzV79mzt3LlTUsZN7EaPHq3//Oc/2rVrl/744w/NnDlT48ePz9PlBQAgPyUnJ+v48eNOf6dOncq2b1JSkp566in9+OOPOnDggH755RetX7/epdPHn3/+eb311luaP3++du7cqSFDhmjLli0aMGDAFccrX7684uPjtWLFCp06dUqJiYkqW7asfH199e6772rv3r1avHix3njjDafxHnvsMe3YsUMvvviidu3apU8//VSzZs2SJMeBixdffFG//vqr4ya3u3fv1pdffukxN6KjaAeA60TFihW1adMmtW3bVs8995xq166tW265RStWrMhyrZqr3nrrLc2dO1dr165Vhw4dHI+Sq1u3ruORb126dNGkSZM0duxY1apVS9OnT9fMmTN100035cHSXXDDDTfogw8+0KRJk1SvXj0tW7YsSzHeoUMHff3111q2bJmaNGmiG264QRMmTFC5cuVyPJ9ixYrphx9+UHx8vNq0aaNGjRrpgw8+cJz217dvX82YMUMzZ85UnTp11KZNG82aNYsj7QCAa8rSpUsVERHh9HfjjTdm29fLy0unT59Wz549VbVqVd1///3q2LGjRowYkeP5PfPMMxo0aJCee+451alTR0uXLnU8ku1KWrRooccff1zdunVTiRIl9Pbbb6tEiRKaNWuWFixYoJo1a2rMmDEaO3as03gVKlTQZ599poULF6pu3bqaOnWq4+7xNptNUsbZdT/99JN27dqlVq1aqUGDBho2bJgiIyNzvFz5yWJyc4HBNSw2NlahoaGKiYlRSEiIu8MB4GHOnz+vffv2qUKFCk7XPgNX60rbFrkp77FOARSU6/m3g8Vi0RdffKEuXbq4O5Q8NXLkSE2bNk2HDh1yabz9+/erQoUK2rx5s+rXr59leH7leo60AwAAAACy1aNHD5UpU8bdYVyV9957T+vXr9fevXs1e/ZsvfPOO44zBXOqY8eOqlWrVj5FeGXcPR4AAAAAkMXu3bslXftPRtm9e7fefPNNnTlzRmXLltVzzz2noUOHujSNGTNmKCkpSVLB31Geoh0AAAAAkIUrj4LzZBMmTNCECROuahqlS5fOo2hcx+nxAAAAAAB4KLcW7T///LM6d+6syMhIWSwWLVq06F/H+fHHH9WwYUPZbDZVrlzZcbt+AMhLhewenSgAhXWbItcDKCwK6/c8LsivbcCtRXtCQoLq1aunKVOm5Kj/vn371KlTJ7Vt21ZbtmzRs88+q759++q7777L50gBFBaZj/NKTEx0cyS43mRuU5nbWGFBrgdwveO3AzKlpKRIyvt7ALj1mvaOHTuqY8eOOe4/bdo0VahQQePGjZMk1ahRQ6tXr9aECRPUoUOH/AoTQCHi5eWlsLAwnThxQpIUEBAgi8Xi5qhwLTPGKDExUSdOnFBYWNg1fzMfV5HrAVzv+O0ASbLb7Tp58qQCAgLk7Z23ZfY1dSO6NWvWqH379k5tHTp00LPPPnvZcZKTk5WcnOx4HRsbm2fxrPzjvDbsScmz6eWnG6r6qlXN6+u5kUB+KVWqlCQ5ki+QF8LCwhzbFi4vN7leyt98DwD/ht8OkCSr1aqyZcvm+U6ba6poP378uEqWLOnUVrJkScXGxiopKUn+/v5Zxhk9erRGjBiRL/GcjLVr19G0fJl2Xjt8Op2iHcghi8WiiIgIhYeHKzU11d3h4Drg4+NT6I6w51Zucr2Uv/keAP4Nvx0gSb6+vrJa8/4K9GuqaM+NoUOHatCgQY7XsbGxioqKypNpN6/mq0qlPHsVptmNZixPUGKyUWKyXQG23G1E9n9uqmAtBKf62I1Rul0Zf+kZ/0+zS+l2o/T0f/69uM0upaVnHUcWqXZZHwX5eeZDGowxspuMmO12yWqVfL2v//fXFV5eXhRawDUiP/M9AOQUvx2QHzy74rxEqVKlFB0d7dQWHR2tkJCQy+55t9lsstls+RJPVHFvRRXPl0nnqXmrEhV/3ujY2XSFBRolnDeKP28Uf96uhPNGceeNEs/blZRqdD7FKCnlon//aTufKhUNsmrY/SEKLIAi1G43SkmTktOMklONUlLNP/+XUtIy/p+allEsp6ZLqWnmwv/TjdL++depT7btGW0XF972PLzpY80ob3Vq5P9PQZ9Z4Gf8Py09m7bL7CjI/P9lx/mnzf7PjoPMHQ/2i4eZjP9f6OMcq8Ui3dXEXzWjfJRuNwr0syqiiHuSjrlox4ndZGwPWZbBZCyf3W4c/0+3O++IsP+zY6JiSW8F++ffdpu5A+RCXBe9vvT9MBnxhwRYFRrgnh069n9isJsL6zczXqfXF7ddZhzzz2vzTx+n13bJKId9L2rPbvyMfy/09fKS6pbzUZEgL8f6d0zHLtl1+Vgc03KaZkb/S+eTZdqXxB5gs6hplfzJMYVVbnK9lL/5HgAAd7qmivbmzZvr22+/dWpbvny5mjdv7vK07Cl2GWMc1xuYdCOTbmSxWmS56GijPcUuSbJ4W2Sx5mHf1IxfjE597UYmzUhWyeptzV3fNLtkd+5bPNii84l2vfNZrNKtF2LwshtZJKVbJJN5BN0Yef/zw/XivlZjFBubrp2HU9Wwss05Botk9bHKmIziPiEhXUnJdp1Pl5JSpaQUk/E6ya6kZKNEuyWjLcUoOdmu1BSjpDSjZLtFyalGyal26Z+rDtIuicFqJLtFsl8SrySlWZRReeZFX0ne/1S1aRbJy8siL6vka5W8LRlHpS3eVnlZJS+rRb4y8rZK8rY4+u44lKpdB1K142Cq03Qz1/vFMViMkVc2MVyp76Xvkat9vS95743d6OvfEvXVbxf6Nq/mq4rFvWRPN0o1Uros/5xNYFd6akaxnGaxOM5AsKcZ2dON0oyUJkvGjoV0u0xaxvCUi/qatIwiMNUYpZkL01VaRjHk0nv/L30txqhpRZ+M6Uqy/7McJs3uKLTTZckoro1kScsoslNlZP8nNmO3y6RnxJYqi2NngTUto6jLyft58TYli0XVS3srzN8i2Y3ssmTE9k9haFJNRjwWKd1YMtrTjZRulK6M9ZtZPFvSM2JINZJd/xSd9owXdrtRqsXimK7Scv65d/U7wmquvm9O3s/MvsvWJ7n9OyKiqJeaVPK96vyAC/Iy1wMAcD1wa9EeHx+vv//+2/F637592rJli4oWLaqyZctq6NChOnLkiD7++GNJ0uOPP67JkyfrhRde0MMPP6wffvhBn376qb755huX531k+kEFD6wlr4CMI4mxG2IUs/qsguoEq+itFw6fH5l2UCbVKLJvGXmHZjzOIW5LrM79eEYBNQJV/PZwR9+jMw7JnmRXRK/S8inuK0lK+DNOZ5afln/lAJW468I1esdnHVFabJpKPhgpW6mMIjhxZ4JOf3tSfuX8FH5vhKNv9JyjSj2dqvD7S8kvKuMoQ9LeRJ368oRskTaV7BHp6Hti/nGlHE9WibtLyr9igCSpUYBR68NndMbHW0tLhyrQz6IgP6taHjynogmpOlyriNLL+snf16LguBQV++WUFOIj610R8vO1yN/Xom0zj8rn+HntWp2uQ2eCFJ9klHY6RTU3n1ail1VflS+i+PMZhU6bU3Eql5iitUUCtTM44zr64NR03X3snHysFn1bpqgj3pan41UpIVkbwwL0Z0jGsgWkGd179KzskuaULSZfH8nmbVGj04mqeO689kUE6EjpIPl6W+Rn7Gq8/qQsFumvNqXk7WuRj5dFJXbHKWx/vBIrB+l83VB5e1nkYzEKWXhUFkmme2n5+Fvl7WWRtsYobVOMbLWDFdSm2D+FuHRyygHJSJH9ysgnJOO9j11/Tud+PqvAWkEqdtuF5Tg8+YDsyXZFPFxGPkUy+i6ff1KBv8foeIivtpYLlbc14+hgqz9Pyy/Vri21iyo52EdeVin85HlV2BOjuKI2Ha5XVF5eGTsDKv56Qj5J6YpuXlz2Yr7ysloUeDxJoRvOKK24TedvLiEva8ZOAr/vomU5lyq1LyFraT9ZLRZZjiTJ/v1JWUv4KuDuCFn/WbbERceVdjxZoZ3C5V85UGnpRnMXnFGdHWcVZ/PWZyVCJUlrdqYoaFWsIs6n6rdiQdoXmLGtFktOU6foGMV7W7UwsohjPbQ9GaeopBStKRqo3UEZ731oapruOhaj81arPi1zoe+Np+JVMTFZ68MCtP2f9z4oza57jp5TmsWiuVEX1m+zswmqEp+sLWEB+quIv7wskr+Ruhw4I0n6sloJx7LVjo5XhTPntTc8QHsjAnXgZLq8jFTtp4wjd3PLFHUUevXPJapubJK2B/tpfZFAx/x6HjwtSZpfuoiSvTJ2itWJSVKDmETtDrJpTdEgR98HDp+VtzFaGBmmeO+M75PqcefV5Fyi9gbYtCY8SF5WyWqR7j5wTja7XV+UDFWMj7d2HElTlfjzan4mQYf8fbWyRLBjuvccPaugNLu+KRmq07aMr+oKCclqdTpex/x8tDw8xNH3zmPnFJaaru/CQxTtl7H9RSWmqO2pOJ2weWtpyVBH39tPxKp4SppWlAjWEf+M76mI5DTdciJWZ3y89W1kqKz/7JhqfyxO4edT9VtEsI4G22S1SsWT0tX6UIwSfK36qUpRWSwZl800PhCjEnEp+jMqWMeLZWx/wUmparLzrFJ8rFpXv7gskqxWi6r/fU7FziRrf4VgnYzIuMOu//k01dl0SuneVv3RIjwjBotFUTvPqUj0eUVXCtLZckGyWCWf83aVXhmt5HSj/5Ur9k8MUuPTiaoce15/FQ3Q9mIBslokX7tR5z1nJUlfVS+WUUxbpJrRCap0Okn7SvhrV0RQxvxk1P73U7JYpFV1isvua5VFUvljCSp3LEHHwv21v1xIxvysUrPfouV92qL0hCB5B2e8R3GbYi76jijhWO9H3z+U5Tsi/vc4nf3htAKqBsq3zfV7zxF35noAAK4Hbi3aN2zYoLZt2zpeZ16L1qtXL82aNUvHjh3TwYMHHcMrVKigb775RgMHDtSkSZNUpkwZzZgxg0fA/Iubavvp2F9eqhjuqzt7FnGcXRA9P1HJh+2qUc9PAdUyCvzkIxZFb7DKO8CiyNIXniUcGmBVoqT9J9K1J/G8JKlISpoqphqdtxvFJF441zrzB39ogEXlSnjJ39eiIukWBZ61KNDHqjsaZ+wg8Pe1KmxLinwPpKljQz91qR8im7dF3slpip8TK6vVoulPXIj3zIp0xW9JVY0G/gptkVGw2M+n6/DOjMLtgdaBsnhl9D2bfF5xJ6wqE+mjIrUyfgybdKNDvhnDy0R4y+qXUWDFBFgV42VRgM2isMALZy1YLMo4wyGX1/HfUNWms4e9VLWqn+7pfKFoOnIqRunx6erVNlC+4RlFcPw26cyJeEWV8VGbNheKx6M7vJR21qhaDZtspTOWI9EvXae2WWUL81LJi07LPRZoVWqCReElvOUXkfHeJSV66aS3Rb42q0pddKp7mrdFxmpx7JSRLOrVNlAnTsbJp5iPWj9QRPNXJyj+vFHx81YFmIyjwpGl/tlxkGBVcJxVAX5e6tTIz7HjoMSmJPlHp6l1TZuaVwyQl9Uin/hUBS2Ll8XPqmc6BTn6Wn9Jkfal6e5m/upWN0ReVoss8WlK/F+cLD5WTXw4LOOsBYsUsyJdidvSVffGQIU2C5MkpSem68jUjLtDv90rzLFsZ1emK25Tqqo39FPYjaE6fjZd2/Ymq8iSjAKseyt/WX285GWV/P9Kk9+OZDWvalOThkGOwt9n7jlZJA24I0hWf295WSXzu1H6pmSVreanTm1CM/papJgZsVK6XSO6h8onzEdWi5S0xarYn1NUo4afet1+0c6d9+JkT7Jr5AOh+v2cdCrOrqBDdhX5I0kBpbwVcUOAo1gt8l2crElp6trCXyrmK6vFIt+DFvn8lqTipXxUq12wrP/sDLB+lSDFSA+3C5RXpJ+8LJL9YKJSv09UVElftb47oxD3sloU91mS0k4aDbgjSP4VAmWxSKmHknTmi0RVLuGru3peiDd6frKSDxs16BCsgGoZ22XykfOKnhcv7yLeav/AhfV+cmGSkvalq1bLAAXVztj5kHIiWcejY+UV5KUb77ywk+HUV0lKTE9Ttfr+Cm6Q0Tf1bKqO/X1WVptVTdpd2ClyOjlRCQkpqljNppAmGTGkxaXp6BYvySpNffxCvGdW2BW/JU11mgcotEXGDiL7+XQdnpKxnYx6MOzCd8RPdsVtSFG1Bv7q0Sbj82nSjQ4dyijwB94ZfOE74td0xSSeV6UaNt3Z7sKOlYM7TmWc2oArItcDAHB1LMYY8+/drh+xsbEKDQ3V2ZNnFVostFCcHp+rvpaMU94zxcWn69PVCUozFgUFWBXkb1GQzaIgHynY36qgIC8F+VkU6GeVryVjOhYvi+MHsmO6kqy+F6Zr0szV9TVGJjWjr8XHkvX9dKWvO9/7vNhO8um9d6XvVb+fuXzvr3o7udz76WnbiQd/R1zx/fSQ7cSTvyPiEuMUGhqqmJgYhYRc2MGB3MvM96xTAIAnuJq8VGiLdpI4AMBTkJvyHusUAOBJriYveeazqAAAAAAAAEU7AAAAAACeiqIdAAAAAAAPRdEOAAAAAICHomgHAAAAAMBDUbQDAAAAAOChKNoBAAAAAPBQFO0AAAAAAHgoinYAAAAAADwURTsAAAAAAB6Koh0AAAAAAA9F0Q4AAAAAgIeiaAcAAAAAwENRtAMAAAAA4KEo2gEAAAAA8FAU7QAAAAAAeCiKdgAAAAAAPBRFOwAAAAAAHoqiHQAAAAAAD0XRDgAAAACAh6JoBwAAAADAQ1G0AwAAAADgoSjaAQAAAADwUBTtAAAAAAB4KIp2AAAAAAA8FEU7AAAAAAAeiqIdAAAAAAAPRdEOAAAAAICHomgHAAAAAMBDUbQDAAAAAOChKNoBAAAAAPBQFO0AAAAAAHgoinYAAAAAADwURTsAAAAAAB6Koh0AAAAAAA9F0Q4AAAAAgIeiaAcAAAAAwENRtAMAAAAA4KEo2gEAAAAA8FAU7QAAAAAAeCiKdgAAAAAAPBRFOwAAAAAAHoqiHQAAAAAAD0XRDgAAAACAh6JoBwAAAADAQ1G0AwAAAADgoSjaAQAAAADwUBTtAAAAAAB4KIp2AAAAAAA8FEU7AAAAAAAeiqIdAAAAAAAPRdEOAAAAAICHomgHAAAAAMBDUbQDAAAAAOChKNoBAAAAAPBQbi/ap0yZovLly8vPz0/NmjXTunXrrth/4sSJqlatmvz9/RUVFaWBAwfq/PnzBRQtAADIDfI9AAC549aiff78+Ro0aJCGDx+uTZs2qV69eurQoYNOnDiRbf+5c+dqyJAhGj58uLZv364PP/xQ8+fP10svvVTAkQMAgJwi3wMAkHtuLdrHjx+vfv36qU+fPqpZs6amTZumgIAA/fe//822/6+//qqWLVvqgQceUPny5XXrrbeqR48e/7q3HgAAuA/5HgCA3HNb0Z6SkqKNGzeqffv2F4KxWtW+fXutWbMm23FatGihjRs3OpL23r179e233+r222+/7HySk5MVGxvr9AcAAAoG+R4AgKvj7a4Znzp1Sunp6SpZsqRTe8mSJbVjx45sx3nggQd06tQp3XjjjTLGKC0tTY8//vgVT5cbPXq0RowYkaexAwCAnCHfAwBwddx+IzpX/Pjjjxo1apTee+89bdq0SQsXLtQ333yjN95447LjDB06VDExMY6/Q4cOFWDEAADAVeR7AAAucNuR9uLFi8vLy0vR0dFO7dHR0SpVqlS247z66qt66KGH1LdvX0lSnTp1lJCQoEcffVQvv/yyrNas+yBsNptsNlveLwAAAPhX5HsAAK6O2460+/r6qlGjRlqxYoWjzW63a8WKFWrevHm24yQmJmZJ1F5eXpIkY0z+BQsAAHKFfA8AwNVx25F2SRo0aJB69eqlxo0bq2nTppo4caISEhLUp08fSVLPnj1VunRpjR49WpLUuXNnjR8/Xg0aNFCzZs30999/69VXX1Xnzp0dyRwAAHgW8j0AALnn1qK9W7duOnnypIYNG6bjx4+rfv36Wrp0qeNmNQcPHnTa0/7KK6/IYrHolVde0ZEjR1SiRAl17txZI0eOdNciAACAf0G+BwAg9yymkJ1nFhsbq9DQUMXExCgkJMTd4QAAQG7KB6xTAIAnuZq8dE3dPR4AAAAAgMKEoh0AAAAAAA9F0Q4AAAAAgIeiaAcAAAAAwENRtAMAAAAA4KEo2gEAAAAA8FAU7QAAAAAAeCiKdgAAAAAAPBRFOwAAAAAAHoqiHQAAAAAAD0XRDgAAAACAh6JoBwAAAADAQ1G0AwAAAADgoSjaAQAAAADwUBTtAAAAAAB4KIp2AAAAAAA8FEU7AAAAAAAeiqIdAAAAAAAPRdEOAAAAAICHomgHAAAAAMBDUbQDAAAAAOChKNoBAAAAAPBQFO0AAAAAAHgoinYAAAAAADwURTsAAAAAAB6Koh0AAAAAAA9F0Q4AAAAAgIeiaAcAAAAAwENRtAMAAAAA4KEo2gEAAAAA8FAU7QAAAAAAeKhcFe179uzRK6+8oh49eujEiROSpCVLlujPP//M0+AAAAAAACjMXC7af/rpJ9WpU0dr167VwoULFR8fL0naunWrhg8fnucBAgAAAABQWLlctA8ZMkRvvvmmli9fLl9fX0f7zTffrN9++y1PgwMAAO6TkpKinTt3Ki0tzd2hAABQaLlctP/xxx+6++67s7SHh4fr1KlTeRIUAABwn8TERD3yyCMKCAhQrVq1dPDgQUnS008/rTFjxrg5OgAACheXi/awsDAdO3YsS/vmzZtVunTpPAkKAAC4z9ChQ7V161b9+OOP8vPzc7S3b99e8+fPd2NkAAAUPi4X7d27d9eLL76o48ePy2KxyG6365dfftHgwYPVs2fP/IgRAAAUoEWLFmny5Mm68cYbZbFYHO21atXSnj173BgZAACFj8tF+6hRo1S9enVFRUUpPj5eNWvWVOvWrdWiRQu98sor+REjAAAoQCdPnlR4eHiW9oSEBKciHgAA5D+Xi3ZfX1998MEH2rNnj77++mt98skn2rFjh2bPni0vL6/8iBEAABSgxo0b65tvvnG8zizUZ8yYoebNm7srLAAACiXv3I5YtmxZlS1bNi9jAQAAHmDUqFHq2LGj/vrrL6WlpWnSpEn666+/9Ouvv+qnn35yd3gAABQqLhftgwYNyrbdYrHIz89PlStX1l133aWiRYtedXAAAKDg3XjjjdqyZYvGjBmjOnXqaNmyZWrYsKHWrFmjOnXquDs8AAAKFYsxxrgyQtu2bbVp0yalp6erWrVqkqRdu3bJy8tL1atX186dO2WxWLR69WrVrFkzX4K+GrGxsQoNDVVMTIxCQkLcHQ4AAOSmfMA6BQB4kqvJSy5f037XXXepffv2Onr0qDZu3KiNGzfq8OHDuuWWW9SjRw8dOXJErVu31sCBA12dNAAA8ACxsbHZ/sXFxSklJcXd4QEAUKi4fKS9dOnSWr58eZaj6H/++aduvfVWHTlyRJs2bdKtt96qU6dO5WmweYE97wAAT+NpuclqtV7xLvFlypRR7969NXz4cFmtLu//LxCetk4BAIXb1eQll69pj4mJ0YkTJ7IU7SdPnlRsbKwkKSwsjD3xAABco2bNmqWXX35ZvXv3VtOmTSVJ69at00cffaRXXnlFJ0+e1NixY2Wz2fTSSy+5OVoAAK5vLhftd911lx5++GGNGzdOTZo0kSStX79egwcPVpcuXSRlJPaqVavmaaAAAKBgfPTRRxo3bpzuv/9+R1vnzp1Vp04dTZ8+XStWrFDZsmU1cuRIinYAAPKZy0X79OnTNXDgQHXv3l1paWkZE/H2Vq9evTRhwgRJUvXq1TVjxoy8jRQAABSIX3/9VdOmTcvS3qBBA61Zs0ZSxh3mDx48WNChAQBQ6Lh8IVpQUJA++OADnT59Wps3b9bmzZt1+vRpvf/++woMDJQk1a9fX/Xr18/rWAEAQAGIiorShx9+mKX9ww8/VFRUlCTp9OnTKlKkSEGHBgBAoePykfZMQUFBqlu3bl7GAgAAPMDYsWN13333acmSJY5L4TZs2KAdO3bos88+k5RxaVy3bt3cGSYAAIVCror2DRs26NNPP9XBgwez3HBu4cKFeRIYAABwjzvvvFM7d+7U9OnTtXPnTklSx44dtWjRIpUvX16S9MQTT7gxQgAACg+Xi/Z58+apZ8+e6tChg5YtW6Zbb71Vu3btUnR0tO6+++78iBEAABSw8uXLa/To0e4OAwCAQs/lon3UqFGaMGGC+vfvr+DgYE2aNEkVKlTQY489poiIiPyIEQAAuEFiYmK2Z9VxeRwAAAXH5aJ9z5496tSpkyTJ19dXCQkJslgsGjhwoG6++WaNGDEiz4MEAAAF5+TJk+rTp4+WLFmS7fD09PQCjggAgMLL5bvHFylSRHFxcZKk0qVLa9u2bZKkc+fOKTExMW+jAwAABe7ZZ5/VuXPntHbtWvn7+2vp0qX66KOPVKVKFS1evNjd4QEAUKi4fKS9devWWr58uerUqaP77rtPAwYM0A8//KDly5erXbt2+REjAAAoQD/88IO+/PJLNW7cWFarVeXKldMtt9yikJAQjR492nHGHQAAyH8uF+2TJ0/W+fPnJUkvv/yyfHx89Ouvv6pr16565ZVX8jxAAABQsBISEhQeHi4p4wy7kydPqmrVqqpTp442bdrk5ugAAChcXDo9Pi0tTV9//bW8vLwyRrZaNWTIEC1evFjjxo1TkSJFXA5gypQpKl++vPz8/NSsWTOtW7fuiv3PnTun/v37KyIiQjabTVWrVtW3337r8nwBAED2qlWr5njUW7169TR9+nQdOXJE06ZNy/VNZ8n3AADkjktH2r29vfX4449r+/bteTLz+fPna9CgQZo2bZqaNWumiRMnqkOHDtq5c6djD//FUlJSdMsttyg8PFyfffaZSpcurQMHDigsLCxP4gEAANKAAQN07NgxSdLw4cN12223ac6cOfL19dWsWbNcnh75HgCA3LMYY4wrI9x0000aOHCg7rrrrqueebNmzdSkSRNNnjxZkmS32xUVFaWnn35aQ4YMydJ/2rRpeuedd7Rjxw75+Pjkap6xsbEKDQ1VTEyMQkJCrip+AADygqfnpsTERO3YsUNly5ZV8eLFXR6ffA8AKOyuJi+5fPf4J598UoMGDdLkyZO1Zs0a/f77705/OZWSkqKNGzeqffv2F4KxWtW+fXutWbMm23EWL16s5s2bq3///ipZsqRq166tUaNGXfHRM8nJyYqNjXX6AwAAl/f66687PREmICBADRs2VGBgoF5//XWXpkW+BwDg6rhctHfv3l379u3TM888o5YtW6p+/fpq0KCB49+cOnXqlNLT01WyZEmn9pIlS+r48ePZjrN371599tlnSk9P17fffqtXX31V48aN05tvvnnZ+YwePVqhoaGOv6ioqBzHCABAYTRixAjFx8dnaU9MTNSIESNcmhb5HgCAq+Py3eP37duXH3HkiN1uV3h4uN5//315eXmpUaNGOnLkiN555x0NHz4823GGDh2qQYMGOV7HxsaSyAEAuAJjjCwWS5b2rVu3qmjRovk+f/I9AAAXuFy0lytXLk9mXLx4cXl5eSk6OtqpPTo6WqVKlcp2nIiICPn4+DjuXi9JNWrU0PHjx5WSkiJfX98s49hsNtlstjyJGQCA61mRIkVksVhksVhUtWpVp8I9PT1d8fHxevzxx12aJvkeAICr4/Lp8ZI0e/ZstWzZUpGRkTpw4IAkaeLEifryyy9zPA1fX181atRIK1ascLTZ7XatWLFCzZs3z3acli1b6u+//5bdbne07dq1SxEREdkmcAAAkHMTJ07U+PHjZYzRiBEjNGHCBMfftGnTtHr1ak2ZMsWlaZLvAQC4Oi4faZ86daqGDRumZ599ViNHjnTcFCYsLEwTJ0506a7ygwYNUq9evdS4cWM1bdpUEydOVEJCgvr06SNJ6tmzp0qXLq3Ro0dLkp544glNnjxZAwYM0NNPP63du3dr1KhReuaZZ1xdDAAAcIlevXopLS1NFotFN998c56dXk6+BwAg91wu2t9991198MEH6tKli8aMGeNob9y4sQYPHuzStLp166aTJ09q2LBhOn78uOrXr6+lS5c6blZz8OBBWa0XTgaIiorSd999p4EDB6pu3boqXbq0BgwYoBdffNHVxQAAANnw9vbWE088oe3bt+fZNMn3AADknsvPaff399eOHTtUrlw5BQcHa+vWrapYsaJ2796tunXrKikpKb9izRM8txUA4Gk8LTfddNNNevbZZ9WlSxd3h5JrnrZOAQCF29XkJZePtFeoUEFbtmzJckO6pUuXqkaNGq5ODgAAeJgnn3xSzz33nA4fPqxGjRopMDDQaXjdunXdFBkAAIWPy0X7oEGD1L9/f50/f17GGK1bt07/+9//NHr0aM2YMSM/YgQAAAWoe/fukuR0DbnFYnE8Ci7zfjYAACD/uVy09+3bV/7+/nrllVeUmJioBx54QJGRkZo0aZIjyQMAgGvXvn373B0CAAD4h8tFuyQ9+OCDevDBB5WYmKj4+HiFh4fndVwAAMBNLr0EDgAAuI/Lz2l/8803HXvgAwICKNgBALgO7dmzR08//bTat2+v9u3b65lnntGePXvcHRYAAIWOy0X7ggULVLlyZbVo0ULvvfeeTp06lR9xAQAAN/nuu+9Us2ZNrVu3TnXr1lXdunW1du1a1apVS8uXL3d3eAAAFCouP/JNkv7880/NmTNH8+bN0+HDh3XLLbfowQcfVJcuXRQQEJAfceYZHgEDAPA0npabGjRooA4dOmjMmDFO7UOGDNGyZcu0adMmN0WWc562TgEAhdvV5KVcFe0X++WXXzR37lwtWLBA58+fV2xs7NVMLt+RxAEAnsbTcpOfn5/++OMPValSxal9165dqlu3rs6fP++myHLO09YpAKBwu5q85PLp8ZcKDAyUv7+/fH19lZqaerWTAwAAblaiRAlt2bIlS/uWLVu4lw0AAAUsV3eP37dvn+bOnau5c+dq586datOmjUaMGKF77703r+MDAAAFrF+/fnr00Ue1d+9etWjRQlLGmXVvvfWWBg0a5OboAAAoXFwu2m+44QatX79edevWVZ8+fdSjRw+VLl06P2IDAABu8Oqrryo4OFjjxo3T0KFDJUmRkZF67bXX9Mwzz7g5OgAACheXi/Z27drpv//9r2rWrJkf8QAAADezWCwaOHCgBg4cqLi4OElScHCwm6MCAKBwcrloHzlypOP/mfews1gseRcRAADwGBTrAAC4V65uRPfxxx+rTp068vf3l7+/v+rWravZs2fndWwAAMANoqOj9dBDDykyMlLe3t7y8vJy+gMAAAXH5SPt48eP16uvvqqnnnpKLVu2lCStXr1ajz/+uE6dOqWBAwfmeZAAAKDg9O7dWwcPHtSrr76qiIgIzqgDAMCNXC7a3333XU2dOlU9e/Z0tN15552qVauWXnvtNYp2AACucatXr9aqVatUv359d4cCAECh5/Lp8ceOHXM8/uViLVq00LFjx/IkKAAA4D5RUVGO+9YAAAD3crlor1y5sj799NMs7fPnz1eVKlXyJCgAAOA+EydO1JAhQ7R//353hwIAQKHn8unxI0aMULdu3fTzzz87rmn/5ZdftGLFimyLeQAA4PmKFCnidO16QkKCKlWqpICAAPn4+Dj1PXPmTEGHBwBAoeVy0d61a1etXbtWEyZM0KJFiyRJNWrU0Lp169SgQYO8jg8AABSAiRMnujsEAACQDYspZBetxcbGKjQ0VDExMQoJCXF3OAAAkJvyAesUAOBJriYv5fia9qNHj2rw4MGKjY3NMiwmJkbPP/+8oqOjXZo5AADwHGlpaUpOTnZqi46O1ogRI/TCCy9o9erVbooMAIDCK8dF+/jx4xUbG5vtXoHQ0FDFxcVp/PjxeRocAAAoOP369dMzzzzjeB0XF6cmTZpoypQp+u6779S2bVt9++23bowQAIDCJ8dF+9KlS52ezX6pnj176uuvv86ToAAAQMH75Zdf1LVrV8frjz/+WOnp6dq9e7e2bt2qQYMG6Z133nFjhAAAFD45Ltr37dunsmXLXnZ4mTJleDQMAADXsCNHjjg9vnXFihXq2rWrQkNDJUm9evXSn3/+6a7wAAAolHJctPv7+1+xKN+/f7/8/f3zIiYAAOAGfn5+SkpKcrz+7bff1KxZM6fh8fHx7ggNAIBCK8dFe7NmzTR79uzLDv/444/VtGnTPAkKAAAUvPr16zty/apVqxQdHa2bb77ZMXzPnj2KjIx0V3gAABRKOX5O++DBg3XLLbcoNDRUzz//vEqWLCkp466yb7/9tmbNmqVly5blW6AAACB/DRs2TB07dtSnn36qY8eOqXfv3oqIiHAM/+KLL9SyZUs3RggAQOGT46K9bdu2mjJligYMGKAJEyYoJCREFotFMTEx8vHx0bvvvuu0Nx4AAFxb2rRpo40bN2rZsmUqVaqU7rvvPqfh9evX56w6AAAKmMUYY1wZ4ciRI/r000/1999/yxijqlWr6t5771WZMmXyK8Y8dTUPtQcAID+Qm/Ie6xQA4EmuJi/l+Eh7ptKlS2vgwIGujgYAAAAAAFyU4xvRAQAAAACAgkXRDgAAAACAh6JoBwAAAADAQ1G0AwCALM6dO6cZM2Zo6NChOnPmjCRp06ZNOnLkiJsjAwCgcHH5RnRSRiL/7LPPtGfPHj3//PMqWrSoNm3apJIlS6p06dJ5HSMAAChAv//+u9q3b6/Q0FDt379f/fr1U9GiRbVw4UIdPHhQH3/8sbtDBACg0HD5SPvvv/+uqlWr6q233tLYsWN17tw5SdLChQs1dOjQvI4PAAAUsEGDBql3797avXu3/Pz8HO233367fv75ZzdGBgBA4eNy0U4iBwDg+rZ+/Xo99thjWdpLly6t48ePuyEiAAAKL5eLdhI5AADXN5vNptjY2Cztu3btUokSJdwQEQAAhZfLRTuJHACA69udd96p119/XampqZIki8WigwcP6sUXX1TXrl3dHB0AAIWLy0U7iRwAgOvbuHHjFB8fr/DwcCUlJalNmzaqXLmygoODNXLkSHeHBwBAoeLy3ePHjRune++91ymRHz9+XM2bNyeRAwBwHQgNDdXy5cu1evVq/f7774qPj1fDhg3Vvn17d4cGAECh43LRTiIHAKBwuPHGG3XjjTe6OwwAAAq1XD2nXSKRAwBwvfrPf/6TbbvFYpGfn58qV66s1q1by8vLq4AjAwCg8HG5aCeRAwBwfZswYYJOnjypxMREFSlSRJJ09uxZBQQEKCgoSCdOnFDFihW1cuVKRUVFuTlaAACuby4X7SRyAACub6NGjdL777+vGTNmqFKlSpKkv//+W4899pgeffRRtWzZUt27d9fAgQP12WefuTlaAACuby7fPX7UqFFq0qSJdu/erdOnT+v06dPatWuXmjVrpkmTJungwYMqVaqUBg4cmB/xAgCAfPbKK69owoQJjoJdkipXrqyxY8dq6NChKlOmjN5++2398ssvbowSAIDCweUj7a+88oo+//zzbBN5165dtXfvXr399ts8/g0AgGvUsWPHlJaWlqU9LS1Nx48flyRFRkYqLi6uoEMDAKDQcflIO4kcAIDrW9u2bfXYY49p8+bNjrbNmzfriSee0M033yxJ+uOPP1ShQgV3hQgAQKHhctFOIgcA4Pr24YcfqmjRomrUqJFsNptsNpsaN26sokWL6sMPP5QkBQUFady4cW6OFACA65/Lp8d/+OGHeuihh9SoUSP5+PhIyjjK3q5dOxI5AADXgVKlSmn58uXasWOHdu3aJUmqVq2aqlWr5ujTtm1bd4UHAEChYjHGmNyMeKVE7sliY2MVGhqqmJgYhYSEuDscAADITfmAdQoA8CRXk5dcPtKeqXr16qpevXpuRwcAAB7s8OHDWrx4sQ4ePKiUlBSnYePHj3dTVAAAFD65KtpJ5AAAXL9WrFihO++8UxUrVtSOHTtUu3Zt7d+/X8YYNWzY0N3hAQBQqLhctJPIAQC4vg0dOlSDBw/WiBEjFBwcrM8//1zh4eF68MEHddttt7k7PAAAChWX7x6fmcj/+OMP+fn56fPPP9ehQ4fUpk0b3XffffkRIwAAKEDbt29Xz549JUne3t5KSkpSUFCQXn/9db311ltujg4AgMLF5aI9PxL5lClTVL58efn5+alZs2Zat25djsabN2+eLBaLunTpkqv5AgCArAIDAx2Xv0VERGjPnj2OYadOncrVNMn1AADkjstFe14n8vnz52vQoEEaPny4Nm3apHr16qlDhw46ceLEFcfbv3+/Bg8erFatWrk8TwAAcHk33HCDVq9eLUm6/fbb9dxzz2nkyJF6+OGHdcMNN7g8PXI9AAC553LRnteJfPz48erXr5/69OmjmjVratq0aQoICNB///vfy46Tnp6uBx98UCNGjFDFihVdnicAALi88ePHq1mzZpKkESNGqF27dpo/f77Kly+vDz/8MFfTI9cDAJA7Lt+Ibvz48YqPj5eUkcjj4+M1f/58ValSxeU7x6ekpGjjxo0aOnSoo81qtap9+/Zas2bNZcd7/fXXFR4erkceeUSrVq264jySk5OVnJzseB0bG+tSjAAAFCbp6ek6fPiw6tatKynjDLtp06blenoFkesl8j0A4PrlUtGe14n81KlTSk9PV8mSJZ3aS5YsqR07dmQ7zurVq/Xhhx9qy5YtOZrH6NGjNWLEiFzHCABAYeLl5aVbb71V27dvV1hY2FVPryByvUS+BwBcv1w6PT4zkZ89eza/4rmiuLg4PfTQQ/rggw9UvHjxHI0zdOhQxcTEOP4OHTqUz1ECAHBtq127tvbu3euWeecm10vkewDA9cvl0+MzE3mFChWueubFixeXl5eXoqOjndqjo6NVqlSpLP337Nmj/fv3q3Pnzo42u90uKeNO9jt37lSlSpWcxrHZbLLZbFcdKwAAhcWbb76pwYMH64033lCjRo0UGBjoNDwkJCTH0yqIXC+R7wEA1y+Xi/a8TOS+vr5q1KiRVqxY4XiUi91u14oVK/TUU09l6V+9enX98ccfTm2vvPKK4uLiNGnSJEVFRbm6OAAA4BK33367JOnOO++UxWJxtBtjZLFYlJ6enuNpkesBALg6LhfteZnIJWnQoEHq1auXGjdurKZNm2rixIlKSEhQnz59JEk9e/ZU6dKlNXr0aPn5+al27dpO42deb3dpOwAAyJ2VK1fm6fTI9QAA5J7LRXteJ/Ju3brp5MmTGjZsmI4fP6769etr6dKljhvWHDx4UFary0+mAwAAudSmTZs8nR65HgCA3LMYY4y7gyhIsbGxCg0NVUxMjEun8gMAkF88MTetWrVK06dP1969e7VgwQKVLl1as2fPVoUKFXTjjTe6O7x/5YnrFABQeF1NXsrVbu1Vq1bp//7v/9SiRQsdOXJEkjR79mytXr06N5MDAAAe5PPPP1eHDh3k7++vTZs2OZ5/HhMTo1GjRrk5OgAACheXi3YSOQAA17c333xT06ZN0wcffCAfHx9He8uWLbVp0yY3RgYAQOHjctFOIgcA4Pq2c+dOtW7dOkt7aGiozp07V/ABAQBQiLlctJPIAQC4vpUqVUp///13lvbVq1erYsWKbogIAIDCy+WinUQOAMD1rV+/fhowYIDWrl0ri8Wio0ePas6cORo8eLCeeOIJd4cHAECh4vIj3zIT+X//+19HIl+zZo0GDx6sV199NT9iBAAABWjIkCGy2+1q166dEhMT1bp1a9lsNg0ePFhPP/20u8MDAKBQcbloJ5EDAHB9s1gsevnll/X888/r77//Vnx8vGrWrKmgoCB3hwYAQKGT6+e0p6SkXJOJnOe2AgA8jaflpk8++UT33HOPAgIC3B1KrnnaOgUAFG4F+pz2Tz75RImJifL19VXNmjXVtGnTa6ZgBwAA/27gwIEKDw/XAw88oG+//Vbp6enuDgkAgELL5aKdRA4AwPXt2LFjmjdvniwWi+6//35FRESof//++vXXX90dGgAAhY7LRTuJHACA65u3t7fuuOMOzZkzRydOnNCECRO0f/9+tW3bVpUqVXJ3eAAAFCou34guM5HfcccdSkxM1BdffKG5c+eqbdu2KlOmjPbs2ZMfcQIAADcICAhQhw4ddPbsWR04cEDbt293d0gAABQqLhftFyORAwBwfcrcMT9nzhytWLFCUVFR6tGjhz777DN3hwYAQKGSq6KdRA4AwPWre/fu+vrrrxUQEKD7779fr776qpo3b+7usAAAKJRcLtpJ5AAAXN+8vLz06aefqkOHDvLy8nIatm3bNtWuXdtNkQEAUPi4XLSTyAEAuL7NmTPH6XVcXJz+97//acaMGdq4cSNPjgEAoAC5fPf4OXPm6Pbbb3cU7HFxcXr//ffVtGlT1atXL88DBAAA7vHzzz+rV69eioiI0NixY3XzzTfrt99+c3dYAAAUKrm+Ed3PP/+sDz/8UJ9//rkiIyN1zz33aMqUKXkZGwAAKGDHjx/XrFmz9OGHHyo2Nlb333+/kpOTtWjRItWsWdPd4QEAUOi4dKT9+PHjGjNmjKpUqaL77rtPISEhjkQ+ZswYNWnSJL/iBAAA+axz586qVq2afv/9d02cOFFHjx7Vu+++6+6wAAAo1HJctJPIAQC4vi1ZskSPPPKIRowYoU6dOmW5dw0AACh4OS7aSeQAAFzfVq9erbi4ODVq1EjNmjXT5MmTderUrdw1agABAABJREFUKXeHBQBAoZbjop1EDgDA9e2GG27QBx98oGPHjumxxx7TvHnzFBkZKbvdruXLlysuLs7dIQIAUOjkuGgnkQMAUDgEBgbq4Ycf1urVq/XHH3/oueee05gxYxQeHq4777zT3eEBAFCouPzINxI5AACFR7Vq1fT222/r8OHD+t///ufucAAAKHRcLtovRiIHAKBw8PLyUpcuXbR48WJ3hwIAQKFyVUV7JhI5AAAAAAB5L0+KdgAAAAAAkPco2gEAAAAA8FAU7QAAAAAAeCiKdgAAAAAAPBRFOwAAAAAAHoqiHQAAAAAAD0XRDgAAAACAh6JoBwAAAADAQ1G0AwAAAADgoSjaAQAAAADwUBTtAAAAAAB4KIp2AAAAAAA8lLe7AwAAAPBU8eftOnw6Xd5WqXKEj7vDAQAUQhxpBwAAuIx90Wka92Wc5q1OdHcoAIBCiqIdAADgMny9LZKklDTj5kgAAIUVRTsAAMBlZBbtyaluDgQAUGhRtAMAAFyG7z+XsXOkHQDgLhTtAAAAl8Hp8QAAd+Pu8QAAAJdhcxTtkt0YWS0WN0eUVcJ5u77ddF7/z959h0dRtW0Av2e2pxfSSaFJ6L0EEEQiCIiiIsUCqGADaTYQpVjAivgKUizAh6iACDZAuoqC1KD03tJJSE+2zfn+WLKwJGD6bsj9u64VM3Nm5pmye84zc2ZGowLubWeALLtejEREVHZM2omIiOiWpZgUCCEgXUm2hVVAWAUkWYKklhzKAYCkliDJV8uqISALAUWSYLYAOg1gNVohSVKRssXO16wA4rr5KgLCIgAZkNVyicoKSSA9DwjwVkEIgfRMK3LzFcRnKFi5Mx/Z+QIQAr564PYmesf5WhRAuTrfArPAT7vykHbZCne9DK1BgkaWUD9EjTq1VHDXSpBUto9DvBIga67OV1gEhCKKLwtA1lZgWSEgzLaykkYquj9LU7aE+77cZYvbnxVxnFy3P8tc9rr9WZqyztr35T5ObrQ/XeA4ESpAlm1xXLs/JZUEowVQywAsCjJzFWRbALVKgtEMmEwKcvKsyC4AoJKgU0tw10uQrAKAAGQJkCXIkm0eaiGgUknQ6mW46WT4uEuAVZRo3wshYDYKCAhYhQSjFSgwCVisChSLgAAAlQwhAAFAKwmoAAiVBFkl2YYLgdwcK/RaCf6+ahi0tnW2mhVk5ykwWgFFklBgElDJAv4GCQatfEv8Rohy9Nhi0k5ERES3rPgF5+E5vglUbioAQNaeTGRuvwyPZp7w61nrarn55yHMAqEjakPtbbuRPTsuCxnb0tEpV4XttTxgsggkpFtx5pNzCDIAtz0djr+SFaz5Ox9PRgh4HciEob4bAu4Lss83aXE8LFkWBD0SCl2wDgCQdywXaWtToYvQI6NTLXi7yQj0kZG8LAHmNDMCBwZDH27A+VQLCk7lQtmahrNCxiofL3gaJHi7yWh+KB21TBZsDvBEtkELAAgxWiB/E48jETo0eS7CHkPqqmTkXyiA/z0B0Nd3x7p9+di/Kwe9k7OQpVZhTaiPreB+4M7ULNSzWBB+bwBC2nkDAMyXTEhamgCVhwphT1+db9q6VOQdz4Xvnf7wbOUFALBkWpD45UXIOhm1R0fay6ZvuoTcQznw6eoLr3a25VlzrUhYeAGQgYjxdexlL/+Whpy4bHjH+MC7ky8AQBgVXJx7HgAQPi4KsO1OZGy/jOw9mfBs6w3fbn62gQpw8ZNzAIDaoyIg6a/s+78zkLkjAx4tPeHX4+q+vzj3HKAAoU+FQ+1paxpn78tExu+X4d7EA/53B9jLJiy8AMWoIOSJ2tD42o6TnH+ycXlLGtxuc0etfoH2solfXoQ1x4rgx0KhDbTt+9wjOUj/9RIMdQwIeCD46nGyNB6WyxYEDQ6BLkwPAMg/kYdLP6dAV1uPoEEh9rLJ3yTCnGJC4IBg6CMNAICCs/lIXZ0MbbAOwY+EXt33K5NgTDCi1n2BcKvvDgAwxhcgZUUSNP4ahAyvbS97aU0yCs4VwL9PANwbedjmm2RE4rIEqL3UCH8q3J7cXPolBbkncuF1Zy14NPeEVQHUWWYkLomHbJBR+7mr+z5tQyryjuTC5w4/eLWxHVPWbAsSPr8ISSMhfEzU1X2/JQ05/2bDu4svvDvYjhMlX0H8PNu+j3jh6nGS8Uc6svdlwauDN3y62Pa9MIur+/75SEhaW7yZOy7j8o4MqJt6QtPRDxargEYtIWfhOQgBeA6rDatWBaNZQD6YCfOeTEi3eQAxvhACUARgWXoBsAgo94dA9tSgwCygO5ENaW8GcoINSG3hC7NFwNdDRq11SZBNVqTfGQiThwbZ+QLaUzkIPJqJBHctNvp5wssgQa2S0OVwGgxmBT8FeiFdp4ZOI6F+vhFtE3MQr1NjW4gXTBbbOt+bmAEfsxW/BnohWW87/sLzTOh+KRuXdWqsD/K2b58+SZn234j4wt+IAjPuSslCukaNn0NsZdUqoO+lLATkWxAX4YU0Px3UKgl++WY0P3IZmSoZayNs38N8k0C35CzUzjfjTz8PnPKwHde+Jgv6JWUiTyXjuzBfewzdLmUjMs+Ev33dcczTdlx7mq24PzEDJlnCt7X9oJIBg1ZCy4Rs1M0xYq+PGw552Y5rN4uCAQmXISRgZV1/yLIEqyLQLi0XDbIKcDzAHaeC3KAIQG1VcPexdADA+ib+KLBIMFkEmqbmolFWAc4HGnAh0hOyBEAR6LovFZCAuA6BMEoStGoJtS/mIPh8DhJqGZDY0BuyBHi5yYjemghPnYTaT0eU6zfCWltBWTFpJyIiIroJ1ZULPCaLwLLfc9FBAdKzFWz8Jx+rj1oBAGv3FWCwqui0irjxlZXTSRZ8+UM2AMDTIOFZo4AOgNkCHDxpwoINObYGeaYVuTpb8pGdL5Cdb0Xz6+bVLFKD4DwBpAAnE634d3suHoxxQ55R4NAFMzSXLPjq52ycczMBAAIAGHQStO4yvN0k+HrIOJtiWxejWeCbP/LQO8QNjWprHJaTnqMg36QgzE+NnHwFeQUKzJlWeFzTm4FuTgAwWQUUxXYVMyNXICdfQDILHEsww1urRrCPCleuW95QrlFBYrIFBWYBQ6YVuHKsGc0CKZlWqGXJdgUXtquhiUkWmK0CWefMMOQpyBNWfL8uG7IEXM5V0PyECX7ZFvy8NQen/zYj3yTgmWtB3zQrcjIF3p5/GW46CQathFbnChCcbcWOTTk4sdOWUUZqFPTOsgJGgQ2bc2CyACazQLMLZgTkK0iKN8PiboRKBnyEAmESUCwCfx83QqO2XYXVX7ZCKlBw5rQJmXI+ZAkwZVsQmWWFVQE2bsqBRmVLxkJOGeGXbsWvf+Xj4DFbbG4y0CvDCkkCVv2cjRwrkJ0nUCchD00zrTiyrwC7z2Tat+HQdNsxP3tlFoxXvujNMgvQKtOKE4cKsCMx21724QwFaiHw/aZc5KhtX/ZGWQVol6XgtMWC7QX59rID063QKwp++DsfmRrbqyca5JgRUyCQJQnkuQvkGW37y2QV0CrCfnIg3ySQUyBgvTKsMGG/lodeQp5BglqW4K+Soc2UUMtTRpt6GhjNtmPDI0OCVpEQ4itD66WCIgDvTCvUaRK0GttvTp5RwGIFCky237fULCvOWWzbJMBoQUOzgEkAOQXFH4uyBOi1EjxVElQyoFFJ8POQUfhToLlsu8Kv09gScwDQKLZeAtYr87AqtvkX/lSqVYCbToJeI0FttCW5QgAFZgBXvhMmS+F0CpIybGW0igKL1TY+8bICcSUIswWwWgUycgSOJ9g2piQE2l+50h53xgSTbNv3pkwLPAsEEjMU7Dphsq9nYLYCd20xP/BVSBLiJrXJLSgrKwve3t7IzMyEl5eXs8MhIiJi3VQJCrfp5dTL8Pb3Llf3xxeXZCDbDLwx2BsLN+YgKdXW8LNIQGHrVBICbw3ywqUcAX8fFYJ8VDibYsEH32WiWaQGw2I9oNdd6WKqCChmBZOWZeJywdWYVYrAQzEGbD5sRGq2sM9XdSXxuq+TG6wKoNdIqFtLhsUi4OmhQr4FCPaRoVMB32zLxfajJlivua9dpQhIAKwS7A3Z2n4yJt/vCVmW7F1fky5b8feRAvxz1owLl62QZAnt6mvRtbEWh8+asOuEGal5trgCvGSkZVohC9t8Q/3VCPaVYTQJ+BkkhPmpoDWo0KqOBn+fMCEtw4r29TSQVBI0WhnuOgkFJgUmowJ/TxUskoRco0CQtwxYAatVgUotQ8hAfJoVF1ItSLxkwaUsBd5eMoJ91YgMUCPMV4YaKNL11WxUcDbZgqRsBTlGIC1bQXK6BQlpVrjpJIQEqFHbXwU/DxkJqWY0C9ciOlIDWSVDCIG8fAW6K92VTcJ2S0GBSSAx1YzEy1YkZikw6GQ0DtfgtmA1dLJAvhlIyxOIT7cizyhgMlqhWAF3DxnnLylIzbLCQwMcPm+2dwFWybbko3AfXXtMuWsBD60Es1XAJCToNBJ83WXk5VlhsQIpOYrD8acB4G6QkFEAe7pf3L6HEFBfOab+6zjBlfkqwrGsLIR9318/XwCwlLOsItm2T7nKXrMt5SvfI0l9dbvLkgSdJKCSgYwCAb1Whk4jwWpRoMKVrupqCcI2W/jpJOi1Ei4XKMg3Az5uMiwWBW5qCT6eMiS1BJUkIStfgTDbkm61VoIsS/AwyPDQAHoV4OOpQkSQGjlGAbNFQC8LSJDg4yVDpZJhtAgUGBVYTAJe7hKgkuFlkGFVBKwmBQadBLVGrpDbKCxWgcu5CtIvW2GyKJBUEixCgsUKWCwKVELA11OG3qCCBMBNJ0Mr2W7FUWtkaLXXdecv5W0UFlmynfwzChjUErwMgFotO5TNy7Mi3ySgqGQoCiDLgMUsYDYrMCuAFRIkCZAgAIuAVQGglqDTyNCqAUkAefkKCiwC+ZYr3w0hIFkFLAqQZwE0GgkmM2C1KBBWATeDjHyL7bjPzhfIzrbg0TvcodXK5eoen52TDR9/nzLV9UzaiYiInIx1U8WryG36yv9lID1HweQBXvjmj1ycTrYWW657Ux22HjQCAFrX1eDQBbP9/e7dm+rwcFd3ZOQq2PxPAX47ZES+ydYE69JIhx3HjLbG5jW0aqB+sBqNwzWIrq1BZMB/d5AUQmDfaTMWbc6B8ZordHc00SHUXwV/Dxn5ZoFGYRp4uRX/EiGzRWDRllzsPmkqdnxFk678RwjA112G2Wq70ljb39ZdOTXrxl1K3XQS6gSqIUlASqYVsgTkGQWy8svWvHXT2a74Fu6bwqT6ZlSy7VPcFdGSkADU8rLtC71WQlq2Yr8K+190GttJnMLkopCbztaNuPD4AwAfdwnKlauc9YJt3bD1GsDHXUaAlwoqlW1dPfS2q+l6re1fg9aWxOUZBfJMtti0aglBPipAABZFQFGAi2lWJGVYcSlLgQTA30u2JcS5CpIzFJgstqu6ZqtAaqYCD4MED71t3iaLgFolId8k4KGXEOyjgiQBimJ77WKAlwoeegnpOQqsiu27oVHbbhUJ9JYhSxLSsm1X2PONAiaLLbnz0Evw0Nt6k3i52eIRQJEHSl773AuiylKeeond44mIiIhuQnultWSyXO3CWSjMT4XWdTX4aU+BPWEHgH2nzQ7lth40wtdDxi978h2SaQAY1t0d9UPUWLwlFwBweyMderbS25OR0pAkCW3qadEkwheJ6VYkZ1hxW6gafp4l79qpUUt4qqcHujcz4+vf83AxzYpAbxmRAbYTCPkmgfOpFnS8TYeIABUKzAIb4gpwPtWCesEapOcoyMlXEJ9utXerNWglGM0CGrUtMbzSA/dqUnxlu17OvZohX0yzFdKoAF8PGdFhGgR4y0jNVHDkohlZeYq9+/+NhNdSoba/Cv6eMmp5qRDur0Ku0fZsgnOpFqTnKMjKs3ULLkxMr1WYsEuSLTl200mIDFAhyFuFfLPA4QtmpGQq9nKeBgmhfip4GWxX+QqunHRoGKqBj7sEsxWIClCjYZj6StdkAS83GWrVNVfqhC3ZTrpsu2Kv1QAmsy2GjFzbstx1EhpHaKDXXJ0u6bIVWfkKQnxV8DTIUISt2736SjLuaSj7m55VMmxX+ot9W7QthsZutp4HzhLqV7JjvLhvFBN2cnVM2omIiIhuovBd7YcvmHEmxfEqu5+njB7N9fhpT0Fxk6JesBqnkmxZ+vc784uMn/W4DwCgc7QOretqkZZlRe1a5W+e6TUS6gSpUSeo7PNqEKLBqwO8kJGjIMD7xgmRpwF4pKt7keFCCGTmCbjrJWhUtm7esgSYrYDFKmDQ2u53Tc5QoFLZYj543my/au2ms3VjbhCihpuuaLKoKAJHLlpw+KIZ7jrbE7PVKgn+njICvVVQy7hhb4Lr79XPyFVwLN6MYB8VrALQqW37Pd8s4Odh685/o8QuLdt2v7WPu2w/VkrCy634srIkwaBFqfddsK8Kwb5X95MsSTdcBhFVL0zaiYiIiG7iyjOn8Mveoom5v4cMd73ttUkZuVev0qpl4LE73NEpWocNcflY+dfVhH3aIC+cv2SFv6fscPXToJUqJGGvSBqVdNOE/WYkSbK9TuqaeQFXHux3zRXiaxPNmIa6Es9fliU0idCgSUT5r+76uMvocFvJl30t/1L0YiAiKgvXqhmIiIiIXIxKvvHVysKEM8BLhYxc2xX1iQ94oV7w1SZWz5YGGM3Aj7vzERmgQpi/GmH+bIIREVHJsMYgIiIiugn1dRdSe7fSY91+21X3kCtJe7sGWpxItMDHXUKdoKJXXnu21MPTYHsaOxERUWkwaSciIiK6ieuvtMc01NmT9sKHX3Vvqkegl+0VYsU9PE6nkXBHU33lB0tERLecsj9GsgLNnTsXUVFR0Ov16NChA3bt2nXDsp999hluv/12+Pr6wtfXF7GxsTctT0RERM5Xnet61XWtJb1WwqsPeuGF+zzh4351ZJMIDUJK+ARrIiKiknJ60r58+XJMmDABU6dOxb59+9CiRQv06tULKSkpxZbftm0bhgwZgq1bt2LHjh0IDw9Hz549ER8fX8WRExERUUlU97r++u7xOo3tyd7RYc57vRUREdUckhDXv3G0anXo0AHt2rXDnDlzAACKoiA8PBzPP/88Jk6c+J/TW61W+Pr6Ys6cORg6dOh/li/PS+2JiIgqw61eN1V1XQ9U7Db9bGMOdp0w2f+e97Svw3u1iYiI/kt56iWnXmk3mUzYu3cvYmNj7cNkWUZsbCx27NhRonnk5eXBbDbDz8+v2PFGoxFZWVkOHyIiIqoaVVHXA5Vb31/fPZ4JOxERVSWnJu2XLl2C1WpFUFCQw/CgoCAkJSWVaB6vvPIKQkNDHRoD15o5cya8vb3tn/Dw8HLHTURERCVTFXU9ULn1PZN0IiJyJqff014e77zzDr799lusXr0aen3xT2SdNGkSMjMz7Z8LFy5UcZRERERUViWp64HKre/V1bq1RERE1Z1TX/lWq1YtqFQqJCcnOwxPTk5GcHDwTaf94IMP8M4772DTpk1o3rz5DcvpdDrodLoKiZeIiIhKpyrqeqBy6/vru8cTERFVJadWQ1qtFm3atMHmzZvtwxRFwebNmxETE3PD6d577z28+eabWL9+Pdq2bVsVoRIREVEZ3Ap1vYrd44mIyImceqUdACZMmIBhw4ahbdu2aN++PWbPno3c3Fw8/vjjAIChQ4ciLCwMM2fOBAC8++67mDJlCr7++mtERUXZ74fz8PCAh4eH09aDiIiIilfd63qZOTsRETmR05P2QYMGITU1FVOmTEFSUhJatmyJ9evX2x9Yc/78ecjy1Q4B8+bNg8lkwoABAxzmM3XqVEybNq0qQyciIqISqO51vXLNy3G1Tm85ERFRTeP097RXtVv9XbhERFT9sG6qeBW5TVf+mYcNBwoAAO885g1/T1VFhEhERDVItX1POxEREZGrU665vsGEnYiIqhqTdiIiIqKbUGpUn0QiInI1TNqJiIiIboJJOxEROROTdiIiIqKbUBRnR0BERDUZk3YiIiKim+CVdiIiciYm7UREREQ3odSsF+0QEZGLYdJOREREdBORAXw5OxEROQ9rISIiIqKb6NZEB0UBomuz2URERFWPtQ8RERHRTahkCbEt9M4Og4iIaih2jyciIiIiIiJyUUzaiYiIiIiIiFwUk3YiIiIiIiIiF8WknYiIiIiIiMhFMWknIiIiIiIiclFM2omIiIiIiIhcFJN2IiIiIiIiIhfFpJ2IiIiIiIjIRTFpJyIiIiIiInJRTNqJiIiIiIiIXBSTdiIiIiIiIiIXxaSdiIiIiIiIyEUxaSciIiIiIiJyUUzaiYiIiIiIiFwUk3YiIiIiIiIiF8WknYiIiIiIiMhFMWknIiIiIiIiclFM2omIiIiIiIhcFJN2IiIiIiIiIhfFpJ2IiIiIiIjIRTFpJyIiIiIiInJRTNqJiIiIiIiIXBSTdiIiIiIiIiIXxaSdiIiIiIiIyEUxaSciIiIiIiJyUUzaiYiIiIiIiFwUk3YiIiIiIiIiF8WknYiIiIiIiMhFMWknIiIiIiIiclFM2omIiIiIiIhcFJN2IiIiIiIiIhfFpJ2IiIiIiIjIRTFpJyIiIiIiInJRTNqJiIiIiIiIXBSTdiIiIiIiIiIXxaSdiIiIiIiIyEUxaSciIiIiIiJyUUzaiYiIiIiIiFwUk3YiIiIiIiIiF8WknYiIiIiIiMhFMWknIiIiIiIiclFM2omIiIiIiIhcFJN2IiIiIiIiIhfFpJ2IiIiIiIjIRTFpJyIiIiIiInJRTNqJiIiIiIiIXBSTdiIiIiIiIiIX5RJJ+9y5cxEVFQW9Xo8OHTpg165dNy2/cuVKREdHQ6/Xo1mzZli7dm0VRUpERERlwbqeiIiobJyetC9fvhwTJkzA1KlTsW/fPrRo0QK9evVCSkpKseX/+usvDBkyBE8++ST279+P/v37o3///jh48GAVR05EREQlwbqeiIio7CQhhHBmAB06dEC7du0wZ84cAICiKAgPD8fzzz+PiRMnFik/aNAg5Obm4ueff7YP69ixI1q2bIn58+f/5/KysrLg7e2NzMxMeHl5VdyKEBERldGtXjdVdV0P3PrblIiIqpfy1EtOvdJuMpmwd+9exMbG2ofJsozY2Fjs2LGj2Gl27NjhUB4AevXqdcPyRqMRWVlZDh8iIiKqGlVR1wOs74mI6Nbl1KT90qVLsFqtCAoKchgeFBSEpKSkYqdJSkoqVfmZM2fC29vb/gkPD6+Y4ImIiOg/VUVdD7C+JyKiW5fT72mvbJMmTUJmZqb9c+HCBWeHRERERBWM9T0REd2q1M5ceK1ataBSqZCcnOwwPDk5GcHBwcVOExwcXKryOp0OOp2uYgImIiKiUqmKuh5gfU9ERLcup15p12q1aNOmDTZv3mwfpigKNm/ejJiYmGKniYmJcSgPABs3brxheSIiInIe1vVERETl49Qr7QAwYcIEDBs2DG3btkX79u0xe/Zs5Obm4vHHHwcADB06FGFhYZg5cyYAYOzYsejWrRs+/PBD9O3bF99++y327NmDhQsXOnM1iIiI6AZY1xMREZWd05P2QYMGITU1FVOmTEFSUhJatmyJ9evX2x9Ac/78ecjy1Q4BnTp1wtdff43XXnsNr776Kho0aIA1a9agadOmzloFIiIiugnW9URERGXn9Pe0VzW+t5WIiFwN66aKx21KRESupNq+p52IiIiIiIiIboxJOxEREREREZGLYtJORERERERE5KKYtBMRERERERG5KCbtRERERERERC6KSTsRERERERGRi2LSTkREREREROSimLQTERERERERuSgm7UREREREREQuikk7ERERERERkYti0k5ERERERETkopi0ExEREREREbkoJu1ERERERERELopJOxEREREREZGLYtJORERERERE5KKYtBMRERERERG5KCbtRERERERERC6KSTsRERERERGRi2LSTkREREREROSimLQTERERERERuSgm7UREREREREQuikk7ERERERERkYti0k5ERERERETkopi0ExEREREREbkoJu1ERERERERELopJOxEREREREZGLUjs7gKomhAAAZGVlOTkSIiIim8I6qbCOovJjfU9ERK6kPHV9jUvas7OzAQDh4eFOjoSIiMhRdnY2vL29nR3GLYH1PRERuaKy1PWSqGGn9RVFQUJCAjw9PSFJUrnmlZWVhfDwcFy4cAFeXl4VFGHVqu7rwPidi/E7F+N3roqMXwiB7OxshIaGQpZ551pFYH1/FeN3LsbvXIzfuap7/EDFrUN56voad6VdlmXUrl27Qufp5eVVbQ/CQtV9HRi/czF+52L8zlVR8fMKe8VifV8U43cuxu9cjN+5qnv8QMWsQ1nrep7OJyIiIiIiInJRTNqJiIiIiIiIXBST9nLQ6XSYOnUqdDqds0Mps+q+DozfuRi/czF+56ru8VPJVfd9zfidi/E7F+N3ruoeP+Aa61DjHkRHREREREREVF3wSjsRERERERGRi2LSTkREREREROSimLQTERERERERuSgm7UREREREREQuikl7OcydOxdRUVHQ6/Xo0KEDdu3a5eyQAAC///47+vXrh9DQUEiShDVr1jiMF0JgypQpCAkJgcFgQGxsLE6cOOFQJj09HY888gi8vLzg4+ODJ598Ejk5OVUS/8yZM9GuXTt4enoiMDAQ/fv3x7FjxxzKFBQUYNSoUfD394eHhwcefPBBJCcnO5Q5f/48+vbtCzc3NwQGBuKll16CxWKp9PjnzZuH5s2bw8vLC15eXoiJicG6deuqRezXe+eddyBJEsaNG2cf5urxT5s2DZIkOXyio6OrTfwAEB8fj0cffRT+/v4wGAxo1qwZ9uzZYx/vyt/hqKioIttfkiSMGjUKgOtvf6vVitdffx116tSBwWBAvXr18Oabb+LaZ7a68vanise6vnKwrnd+XXOt6lbfs663YV1fNtWyrhdUJt9++63QarXiyy+/FIcOHRIjR44UPj4+Ijk52dmhibVr14rJkyeL77//XgAQq1evdhj/zjvvCG9vb7FmzRpx4MABce+994o6deqI/Px8e5m7775btGjRQuzcuVP88ccfon79+mLIkCFVEn+vXr3EokWLxMGDB0VcXJzo06ePiIiIEDk5OfYyzzzzjAgPDxebN28We/bsER07dhSdOnWyj7dYLKJp06YiNjZW7N+/X6xdu1bUqlVLTJo0qdLj//HHH8Uvv/wijh8/Lo4dOyZeffVVodFoxMGDB10+9mvt2rVLREVFiebNm4uxY8fah7t6/FOnThVNmjQRiYmJ9k9qamq1iT89PV1ERkaK4cOHi7///lucPn1a/Prrr+LkyZP2Mq78HU5JSXHY9hs3bhQAxNatW4UQrr/93377beHv7y9+/vlncebMGbFy5Urh4eEhPv74Y3sZV97+VLFY11ce1vWuUdcLUT3re9b1Nqzry6Y61vVM2suoffv2YtSoUfa/rVarCA0NFTNnznRiVEVdX5EriiKCg4PF+++/bx+WkZEhdDqd+Oabb4QQQhw+fFgAELt377aXWbdunZAkScTHx1dZ7IVSUlIEAPHbb7/Z49VoNGLlypX2MkeOHBEAxI4dO4QQtsaMLMsiKSnJXmbevHnCy8tLGI3Gql0BIYSvr6/4/PPPq03s2dnZokGDBmLjxo2iW7du9kq8OsQ/depU0aJFi2LHVYf4X3nlFdGlS5cbjq9u3+GxY8eKevXqCUVRqsX279u3r3jiiScchj3wwAPikUceEUJUv+1P5cO6vuqwrndO7NW1vmdd71rfYdb1NpW5/dk9vgxMJhP27t2L2NhY+zBZlhEbG4sdO3Y4MbL/dubMGSQlJTnE7u3tjQ4dOthj37FjB3x8fNC2bVt7mdjYWMiyjL///rvKY87MzAQA+Pn5AQD27t0Ls9nssA7R0dGIiIhwWIdmzZohKCjIXqZXr17IysrCoUOHqix2q9WKb7/9Frm5uYiJiak2sY8aNQp9+/Z1iBOoPtv+xIkTCA0NRd26dfHII4/g/Pnz1Sb+H3/8EW3btsVDDz2EwMBAtGrVCp999pl9fHX6DptMJnz11Vd44oknIElStdj+nTp1wubNm3H8+HEAwIEDB7B9+3b07t0bQPXa/lQ+rOurFut658Renet71vWu8R1mXV81219d4XOsAS5dugSr1epwoAFAUFAQjh496qSoSiYpKQkAio29cFxSUhICAwMdxqvVavj5+dnLVBVFUTBu3Dh07twZTZs2tcen1Wrh4+PjUPb6dShuHQvHVbZ///0XMTExKCgogIeHB1avXo3GjRsjLi7O5WP/9ttvsW/fPuzevbvIuOqw7Tt06IDFixejYcOGSExMxPTp03H77bfj4MGD1SL+06dPY968eZgwYQJeffVV7N69G2PGjIFWq8WwYcOq1Xd4zZo1yMjIwPDhw+1xufr2nzhxIrKyshAdHQ2VSgWr1Yq3334bjzzyiEMM1WH7U/mwrq86rOurPnagetf3rOtd5zvMuv6qytz+TNrJpY0aNQoHDx7E9u3bnR1KqTRs2BBxcXHIzMzEd999h2HDhuG3335zdlj/6cKFCxg7diw2btwIvV7v7HDKpPAsKQA0b94cHTp0QGRkJFasWAGDweDEyEpGURS0bdsWM2bMAAC0atUKBw8exPz58zFs2DAnR1c6X3zxBXr37o3Q0FBnh1JiK1aswLJly/D111+jSZMmiIuLw7hx4xAaGlrttj9RdcG6vupV9/qedb3rYF1fNdg9vgxq1aoFlUpV5CmIycnJCA4OdlJUJVMY381iDw4ORkpKisN4i8WC9PT0Kl2/0aNH4+eff8bWrVtRu3Zt+/Dg4GCYTCZkZGQ4lL9+HYpbx8JxlU2r1aJ+/fpo06YNZs6ciRYtWuDjjz92+dj37t2LlJQUtG7dGmq1Gmq1Gr/99hv+97//Qa1WIygoyKXjL46Pjw9uu+02nDx50uW3PwCEhISgcePGDsMaNWpk7/ZXXb7D586dw6ZNmzBixAj7sOqw/V966SVMnDgRgwcPRrNmzfDYY49h/PjxmDlzpkMMrr79qfxY11cN1vXOif1Wq+9Z19uwri+Z6ljXM2kvA61WizZt2mDz5s32YYqiYPPmzYiJiXFiZP+tTp06CA4Odog9KysLf//9tz32mJgYZGRkYO/evfYyW7ZsgaIo6NChQ6XHKITA6NGjsXr1amzZsgV16tRxGN+mTRtoNBqHdTh27BjOnz/vsA7//vuvw5dp48aN8PLyKvIjWRUURYHRaHT52Hv06IF///0XcXFx9k/btm3xyCOP2P/fleMvTk5ODk6dOoWQkBCX3/4A0Llz5yKvPTp+/DgiIyMBVI/vMAAsWrQIgYGB6Nu3r31Yddj+eXl5kGXHqlGlUkFRFADVZ/tT+bGur1ys650b+61W37Out2FdXzLVsq6v8Efb1RDffvut0Ol0YvHixeLw4cPiqaeeEj4+Pg5PQXSW7OxssX//frF//34BQMyaNUvs379fnDt3Tghhe4WBj4+P+OGHH8Q///wj7rvvvmJfYdCqVSvx999/i+3bt4sGDRpU2Wtgnn32WeHt7S22bdvm8DqJvLw8e5lnnnlGREREiC1btog9e/aImJgYERMTYx9f+CqJnj17iri4OLF+/XoREBBQJa+SmDhxovjtt9/EmTNnxD///CMmTpwoJEkSGzZscPnYi3Pt02SFcP34X3jhBbFt2zZx5swZ8eeff4rY2FhRq1YtkZKSUi3i37Vrl1Cr1eLtt98WJ06cEMuWLRNubm7iq6++spdx9e+w1WoVERER4pVXXikyztW3/7Bhw0RYWJj9NTDff/+9qFWrlnj55ZftZVx9+1PFYV1feVjXu1ZdL0T1qu9Z19uwri+b6ljXM2kvh08++UREREQIrVYr2rdvL3bu3OnskIQQQmzdulUAKPIZNmyYEML2GoPXX39dBAUFCZ1OJ3r06CGOHTvmMI+0tDQxZMgQ4eHhIby8vMTjjz8usrOzqyT+4mIHIBYtWmQvk5+fL5577jnh6+sr3NzcxP333y8SExMd5nP27FnRu3dvYTAYRK1atcQLL7wgzGZzpcf/xBNPiMjISKHVakVAQIDo0aOHvRJ39diLc30l7urxDxo0SISEhAitVivCwsLEoEGDHN576urxCyHETz/9JJo2bSp0Op2Ijo4WCxcudBjv6t/hX3/9VQAoEpMQrr/9s7KyxNixY0VERITQ6/Wibt26YvLkyQ6voHH17U8Vi3V95WBd7/y65nrVqb5nXW/Dur5sqmNdLwkhRMVfvyciIiIiIiKi8uI97UREREREREQuikk7ERERERERkYti0k5ERERERETkopi0ExEREREREbkoJu1ERERERERELopJOxEREREREZGLYtJORERERERE5KKYtBMRERERERG5KCbtRAQASE1NxbPPPouIiAjodDoEBwejV69e+PPPPwEAkiRhzZo1zg2SiIiIyox1PVH1pHZ2AETkGh588EGYTCYsWbIEdevWRXJyMjZv3oy0tDRnh0ZEREQVgHU9UfUkCSGEs4MgIufKyMiAr68vtm3bhm7duhUZHxUVhXPnztn/joyMxNmzZwEAP/zwA6ZPn47Dhw8jNDQUw4YNw+TJk6FW284JSpKETz/9FD/++CO2bduGkJAQvPfeexgwYECVrBsRERGxrieqztg9nojg4eEBDw8PrFmzBkajscj43bt3AwAWLVqExMRE+99//PEHhg4dirFjx+Lw4cNYsGABFi9ejLffftth+tdffx0PPvggDhw4gEceeQSDBw/GkSNHKn/FiIiICADreqLqjFfaiQgAsGrVKowcORL5+flo3bo1unXrhsGDB6N58+YAbGfRV69ejf79+9uniY2NRY8ePTBp0iT7sK+++govv/wyEhIS7NM988wzmDdvnr1Mx44d0bp1a3z66adVs3JERETEup6omuKVdiICYLvPLSEhAT/++CPuvvtubNu2Da1bt8bixYtvOM2BAwfwxhtv2M/ee3h4YOTIkUhMTEReXp69XExMjMN0MTExPPtORERUxVjXE1VPfBAdEdnp9XrcdddduOuuu/D6669jxIgRmDp1KoYPH15s+ZycHEyfPh0PPPBAsfMiIiIi18K6nqj64ZV2Irqhxo0bIzc3FwCg0WhgtVodxrdu3RrHjh1D/fr1i3xk+erPy86dOx2m27lzJxo1alT5K0BEREQ3xbqeyPXxSjsRIS0tDQ899BCeeOIJNG/eHJ6entizZw/ee+893HfffQBsT5XdvHkzOnfuDJ1OB19fX0yZMgX33HMPIiIiMGDAAMiyjAMHDuDgwYN466237PNfuXIl2rZtiy5dumDZsmXYtWsXvvjiC2etLhERUY3Dup6o+uKD6IgIRqMR06ZNw4YNG3Dq1CmYzWaEh4fjoYcewquvvgqDwYCffvoJEyZMwNmzZxEWFmZ/Dcyvv/6KN954A/v374dGo0F0dDRGjBiBkSNHArA9nGbu3LlYs2YNfv/9d4SEhODdd9/FwIEDnbjGRERENQvreqLqi0k7EVWq4p5ES0RERLcO1vVElYv3tBMRERERERG5KCbtRERERERERC6K3eOJiIiIiIiIXBSvtBMRERERERG5KCbtRERERERERC6KSTsRERERERGRi2LSTkREREREROSimLQTERERERERuSgm7UREREREREQuikk7ERERERERkYti0k5ERERERETkopi0ExEREREREbkoJu1ERERERERELopJOxEREREREZGLYtJORERERERE5KKYtBMRERERERG5KCbtRERERERERC6KSTtVO4sXL4YkSTh79qyzQ6FqwmKx4OWXX0Z4eDhkWUb//v2dHRIREVUgtg2otNg2oOqESXsxpk2bBkmScOnSpWLHN23aFHfccUfVBlWB7rjjDkiSZP9otVrUqVMHTz31FC5cuODs8KrM6tWr0bt3b9SqVQtarRahoaEYOHAgtmzZ4uzQqIJ9+eWXeP/99zFgwAAsWbIE48ePL9F07du3hyRJmDdvXiVHWD0lJyfjxRdfRHR0NNzc3ODu7o42bdrgrbfeQkZGhrPDI6pQbBvUDGwb1BxsG1Ssbdu2OfyGXP/59ttvnR1itaZ2dgDkHLVr18bMmTMBACaTCYcPH8b8+fPx66+/4siRI3Bzc3NyhJVHCIEnnngCixcvRqtWrTBhwgQEBwcjMTERq1evRo8ePfDnn3+iU6dOzg6VKsiWLVsQFhaGjz76qMTTnDhxArt370ZUVBSWLVuGZ599thIjrH52796NPn36ICcnB48++ijatGkDANizZw/eeecd/P7779iwYYOToySi0mDbgG2DmoRtg8oxZswYtGvXrsjwmJgYJ0Rz62DSfgtSFAUmkwl6vf6GZby9vfHoo486DKtTpw5Gjx6NP//8E3fddVdlh+k0H374IRYvXoxx48Zh1qxZkCTJPm7y5MlYunQp1Orq/dXIzc2Fu7u7s8NwGSkpKfDx8SnVNF999RUCAwPx4YcfYsCAATh79iyioqIqJb4bcdX9mJGRgfvvvx8qlQr79+9HdHS0w/i3334bn332mZOiuzlX3aZElY1tg5tj26DmYdugctx+++0YMGCAs8O45bB7fAUo7A6yfPlyvPrqqwgODoa7uzvuvffeIl3K7rjjDjRt2hR79+5Fp06dYDAYUKdOHcyfP7/IfI1GI6ZOnYr69etDp9MhPDwcL7/8MoxGo0M5SZIwevRoLFu2DE2aNIFOp8P69etLvR7BwcEAUKRS2r9/P3r37g0vLy94eHigR48e2Llzp0OZwm6D1yvuHrOoqCjcc8892L59O9q3bw+9Xo+6devi//7v/4pMf+jQIdx5550wGAyoXbs23nrrLSiKUqRcZmYmjh49iszMzJuuY35+PmbOnIno6Gh88MEHxcb82GOPoX379va/T58+jYceegh+fn5wc3NDx44d8csvvzhMU3gMrFixAm+//TZq164NvV6PHj164OTJk/Zyo0ePhoeHB/Ly8oosd8iQIQgODobVarUPW7duHW6//Xa4u7vD09MTffv2xaFDhxymGz58ODw8PHDq1Cn06dMHnp6eeOSRR+zrO2bMGNSqVQuenp649957ER8fD0mSMG3aNIf5xMfH44knnkBQUBB0Oh2aNGmCL7/8skzrWejvv/9Gnz594OvrC3d3dzRv3hwff/yxQ5mjR49iwIAB8PPzg16vR9u2bfHjjz8WmVdxcnNz8cILLyA8PBw6nQ4NGzbEBx98ACEEAODs2bOQJAlbt27FoUOH7F20tm3b9p/z/vrrrzFgwADcc8898Pb2xtdff20f991330GSJPz2229FpluwYAEkScLBgwdLtY6F35XffvsNzz33HAIDA1G7dm0AwLlz5/Dcc8+hYcOGMBgM8Pf3x0MPPVTsvZv//PMPunXr5vCdWbRoUbH3epbk+CrOggULEB8fj1mzZhVJ2AEgKCgIr732msOwTz/91P77FBoailGjRjl0oa/q78Yff/yBhx56CBEREfbf1/HjxyM/P7/I8leuXInGjRtDr9ejadOmWL16NYYPH16koaYoCmbPno0mTZpAr9cjKCgITz/9NC5fvvyf25RuPWwbsG3AtgHbBoVqQtugpAqXe/1xNGPGDEiShLVr19qHZWRkYPz48YiKioJOp0Pt2rUxdOhQh9uUSvqbuHHjRnTp0gU+Pj7w8PBAw4YN8eqrrzqU+eSTT9CkSRO4ubnB19cXbdu2ddjHTiWoiKlTpwoAIjU1tdjxTZo0Ed26dbP/vXXrVgFANGvWTDRv3lzMmjVLTJw4Uej1enHbbbeJvLw8e9lu3bqJ0NBQERgYKEaPHi3+97//iS5duggA4osvvrCXs1qtomfPnsLNzU2MGzdOLFiwQIwePVqo1Wpx3333OcQDQDRq1EgEBASI6dOni7lz54r9+/ffcP26desmoqOjRWpqqkhNTRUJCQli8+bNokmTJqJ+/frCaDTayx48eFC4u7uLkJAQ8eabb4p33nlH1KlTR+h0OrFz584i2+x6ixYtEgDEmTNn7MMiIyNFw4YNRVBQkHj11VfFnDlzROvWrYUkSeLgwYP2comJiSIgIED4+vqKadOmiffff180aNBANG/evMg8C5ezaNGiG663EEJs2LBBABBvvPHGTcsVSkpKEkFBQcLT01NMnjxZzJo1S7Ro0ULIsiy+//57e7nCY6BVq1aiTZs24qOPPhLTpk0Tbm5uon379vZyv//+uwAgVqxY4bCc3Nxc4e7uLkaNGmUf9n//939CkiRx9913i08++US8++67IioqSvj4+Dis+7Bhw4ROpxP16tUTw4YNE/Pnzxf/93//J4QQYuDAgQKAeOyxx8TcuXPFwIEDRYsWLQQAMXXqVIf1rF27tggPDxdvvPGGmDdvnrj33nsFAPHRRx+Vej0Lt7VWqxWRkZFi6tSpYt68eWLMmDEiNjbWXubgwYPC29tbNG7cWLz77rtizpw5omvXrkKSJIftWxxFUcSdd94pJEkSI0aMEHPmzBH9+vUTAMS4ceOEEELk5OSIpUuXiujoaFG7dm2xdOlSsXTpUpGUlHTTee/cuVMAEH/88YcQQognnnhCNG7c2D4+Ly9PeHh4iOeee67ItN27dxdNmjQp9ToWHsONGzcW3bp1E5988ol45513hBBCrFy5UrRo0UJMmTJFLFy4ULz66qvC19dXREZGitzcXPs8Ll68KPz8/IS/v7+YPn26+OCDD0R0dLR9n1973JT0+CpOp06dhMFgcPituJnC34fY2FjxySefiNGjRwuVSiXatWsnTCaTEKLqvxvPP/+86NOnj5gxY4ZYsGCBePLJJ4VKpRIDBgxwWP7PP/8sJEmy/7a//vrrwtfXVzRt2lRERkY6lB0xYoRQq9Vi5MiRYv78+eKVV14R7u7uDutJ1RfbBmwbFGLbgG0DIdg2uF7hcfDll1/af0eu/SiKYi97zz33CG9vb3H+/HkhhBD//POP0Gq14sknn7SXyc7OFk2bNhUqlUqMHDlSzJs3T7z55puiXbt29t+ykv4mHjx4UGi1WtG2bVvx8ccfi/nz54sXX3xRdO3a1V5m4cKFAoAYMGCAWLBggfj444/Fk08+KcaMGXPT9a4qTNqLUdaKOSwsTGRlZdmHr1ixQgAQH3/8sX1Yt27dBADx4Ycf2ocZjUbRsmVLERgYaG/YLV26VMiybP9hKDR//nwBQPz555/2YQCELMvi0KFDJVq/whiu/zRq1EicPn3aoWz//v2FVqsVp06dsg9LSEgQnp6eDgd6aStmAOL333+3D0tJSRE6nU688MIL9mHjxo0TAMTff//tUM7b27vMFfPHH38sAIjVq1fftNz1MVy7H7Kzs0WdOnVEVFSUsFqtQoirx0CjRo0cGjaFy/v333+FELbKJCwsTDz44IMOyyk8Vgq3SXZ2tvDx8REjR450KJeUlCS8vb0dhg8bNkwAEBMnTnQou3fvXodKqtDw4cOLVMxPPvmkCAkJEZcuXXIoO3jwYOHt7W1vXJZ0PS0Wi6hTp46IjIwUly9fdpjntT/aPXr0EM2aNRMFBQUO4zt16iQaNGggbmbNmjUCgHjrrbcchg8YMEBIkiROnjxpH9atWzeHyvK/jB49WoSHh9tjLWzQXdvgHTJkiAgMDBQWi8U+LDExUciy7NDwK+k6Fh7DXbp0cZinEMKhcV9ox44dAoC9ESaELRGVJMkhzrS0NOHn5+fwnSnN8VUcX19f0aJFi5uWKZSSkiK0Wq3o2bOn/fsihBBz5syxV+5CVO13Q4jit+nMmTOFJEni3Llz9mHNmjUTtWvXFtnZ2fZh27ZtEwAckvY//vhDABDLli1zmOf69euLHU7VD9sGV7FtwLYB2wZsG1yv8Di40ScxMdFhm/j5+Ym77rpLGI1G0apVKxERESEyMzPtZaZMmSIAFHuipnAflPQ38aOPPrrp77cQQtx3332lOh6qGrvHV6ChQ4fC09PT/veAAQMQEhLi0M0DsHUxe/rpp+1/a7VaPP3000hJScHevXsB2LpjNmrUCNHR0bh06ZL9c+eddwIAtm7d6jDPbt26oXHjxiWONSoqChs3bsTGjRuxbt06zJ49G5mZmejduzdSU1MBAFarFRs2bED//v1Rt25d+7QhISF4+OGHsX37dmRlZZV4mddq3Lgxbr/9dvvfAQEBaNiwIU6fPm0ftnbtWnTs2NGhO1pAQIC9e9e1hg8fDiEEhg8fftPlFsZ77X66mbVr16J9+/bo0qWLfZiHhweeeuopnD17FocPH3Yo//jjj0Or1dr/LlzHwvWSJAkPPfQQ1q5di5ycHHu55cuXIywszL6cjRs3IiMjA0OGDHHY/yqVCh06dCiy/wEUeRhKYTfI5557zmH4888/7/C3EAKrVq1Cv379IIRwWF6vXr2QmZmJffv2lWo99+/fjzNnzmDcuHFF7hcr7HaYnp6OLVu2YODAgcjOzrYvMy0tDb169cKJEycQHx9fZD0LrV27FiqVCmPGjHEY/sILL0AIgXXr1t1w2puxWCxYvnw5Bg0aZI/1zjvvRGBgIJYtW2YvN2jQIKSkpDh0p/vuu++gKAoGDRpU5nUcOXIkVCqVwzCDwWD/f7PZjLS0NNSvXx8+Pj4O+2b9+vWIiYlBy5Yt7cP8/PyKfGfKcnxdKysrq8TfoU2bNsFkMmHcuHGQ5atVzsiRI+Hl5WXvTlqV3w3AcZvm5ubi0qVL6NSpE4QQ2L9/PwAgISEB//77L4YOHQoPDw97+W7duqFZs2YO81u5ciW8vb1x1113OcTVpk0beHh4/Oc2pVsX2wYlx7YB2wZsG1TftkGhKVOm2H9Hrv34+fnZywQHB2Pu3LnYuHEjbr/9dsTFxeHLL7+El5eXvcyqVavQokUL3H///UWWUbgPSvqbWHi8/fDDD8XeSlNY5uLFi9i9e3eJ1rOqVe8najhRcfc7NWjQoEiZ+vXrF7lXJDQ0tMgDJG677TYAtvtsOnbsiBMnTuDIkSMICAgodvkpKSkOf9epU6dU8bu7uyM2Ntb+9913340uXbqgbdu2eOedd/Dhhx8iNTUVeXl5aNiwYZHpGzVqBEVRcOHCBTRp0qRUywaAiIiIIsN8fX0d7v08d+4cOnToUKRccfGUVOGPQXZ2donK3yiGRo0a2cc3bdrUPvz69fL19QUAh/UaNGgQZs+ejR9//BEPP/wwcnJysHbtWjz99NP24+rEiRMAYP/RudF6FFKr1fZ7nK6NXZblIsdG/fr1Hf5OTU1FRkYGFi5ciIULFxa7vOuPt/9az1OnTgGAw7a53smTJyGEwOuvv47XX3/9hssNCwsrdty5c+cQGhpapJF17b4piw0bNiA1NRXt27d3uBeve/fu+Oabb/Duu+9ClmXcfffd8Pb2xvLly9GjRw8AtgZWy5Yt7d/nsqxjcd/lwvstFy1ahPj4ePt9eQAc7tU8d+5csU9nvX6fl/b4Km58ab5DQNHvrVarRd26dR32U1V9NwDg/PnzmDJlCn788cci95wXbtPC2K7ffoXDrm0UnThxApmZmQgMDCw2ruu/Q3RrYtuAbYPC8WwbsG1Q09oGhZo1a+bwO3IjgwcPxldffYVffvkFTz31lH17FTp16hQefPDBm86jpL+JgwYNwueff44RI0Zg4sSJ6NGjBx544AEMGDDAfkHhlVdewaZNm9C+fXvUr18fPXv2xMMPP4zOnTuXZLUrHZP2YhQ+WbW4BxIBQF5e3k2fvloRFEVBs2bNMGvWrGLHh4eHO/x97dm2smrTpg28vb3x+++/l3ra4hoqABwenHKt688WFrr2B6cyFD40699//0X//v0rfP4lWa+OHTsiKioKK1aswMMPP4yffvoJ+fn59jOwAOxnAZcuXWp/CNC1rn8gkE6nc7iKWRqFy3r00UcxbNiwYss0b97c4e+K2H+Fy33xxRfRq1evYssUlyxVtsIz5gMHDix2/G+//Ybu3btDp9Ohf//+WL16NT799FMkJyfjzz//xIwZM+xly7KOxX2Xn3/+eSxatAjjxo1DTEwMvL29IUkSBg8efMMzxjdT2uPretHR0YiLi4PJZHK4qlJeVfXdsFqtuOuuu5Ceno5XXnkF0dHRcHd3R3x8PIYPH17mbXr9FZdr3ahBQdUH2wZsG5QV2wZsG9SEtkFppaWlYc+ePQCAw4cPQ1GUUh+vJf1NNBgM+P3337F161b88ssvWL9+PZYvX44777wTGzZsgEqlQqNGjXDs2DH8/PPPWL9+PVatWoVPP/0UU6ZMwfTp08u3shWASXsxIiMjAQDHjh0rUgHm5eXhwoUL6NmzZ5HpCs9QFRJC4OTJk0V+1BISEoq8ruH48eMAYH8acb169XDgwAH06NHjhpVeZbBarfauWQEBAXBzc8OxY8eKlDt69ChkWbZvn8KzqRkZGQ5dnsp6RhOw7YfrtymAYuMpqS5dusDX1xfffPMNXn311RtWMNfGcKP1LxxfFgMHDsTHH3+MrKwsLF++HFFRUejYsaN9fL169QAAgYGBJTpbWZzIyEgoioIzZ844XOm5/kmuAQEB8PT0hNVqLfOyrlcY/8GDB284z8JulRqNpkzLjYyMxKZNm5Cdne1wRr08+yY3Nxc//PADBg0aVOzrSsaMGYNly5ahe/fuAGxnbpcsWYLNmzfjyJEjEEI4NLDKu46FvvvuOwwbNgwffvihfVhBQYHD09cB2zoX96Te64eV9/jq168fduzYgVWrVmHIkCE3LXvt7+m1XWlNJhPOnDlTZPlV8d34999/cfz4cSxZsgRDhw61D9+4cWOxsZd0m27atAmdO3eukESJXA/bBmwbXBsD2walx7aBza3aNiitUaNGITs7GzNnzsSkSZMwe/ZsTJgwwSGea5+2X5zS/CbKsowePXqgR48emDVrFmbMmIHJkydj69at9vV1d3fHoEGDMGjQIJhMJjzwwAN4++23MWnSpEo/KftfeE97MXr06AGtVot58+YVOVO1cOFCWCwW9O7du8h0//d//+fQteq7775DYmJikbIWiwULFiyw/20ymbBgwQIEBASgTZs2AGw/3PHx8cW+6zg/Px+5ubnlWsfibN26FTk5OWjRogUA2xnTnj174ocffnDoxpecnIyvv/4aXbp0sXeVKfyiX3smPjc3F0uWLClzPH369MHOnTuxa9cu+7DU1NRir2SV9LUubm5ueOWVV3DkyBG88sorxZ79/eqrr+zL7NOnD3bt2oUdO3bYx+fm5mLhwoWIiooq1b2C1xo0aBCMRiOWLFmC9evXFzlz26tXL3h5eWHGjBkwm81Fpi+8t/BmCs/efvrppw7DP/nkE4e/VSoVHnzwQaxatarYH8eSLOt6rVu3Rp06dTB79uwilUfhNg8MDMQdd9yBBQsWIDExsdTL7dOnD6xWK+bMmeMw/KOPPoIkScV+R//L6tWrkZubi1GjRmHAgAFFPvfccw9WrVplf41IbGws/Pz8sHz5cixfvhzt27d36MJW3nUspFKpihyrn3zySZGrVb169cKOHTsQFxdnH5aenl7kO1Pe4+uZZ55BSEgIXnjhBXtSca2UlBS89dZbAGzbSKvV4n//+5/DOnzxxRfIzMxE3759Haatiu9GYYP82niEEEVeORQaGoqmTZvi//7v/xzuM/3tt9/w77//OpQdOHAgrFYr3nzzzSLLs1gsRb4HVP2wbcC2AdsGpVvW9dg2QIWsYyFXaxuUxnfffYfly5fjnXfewcSJEzF48GC89tprDm2KBx98EAcOHMDq1auLTF+43iX9TUxPTy8yvvAe/8L9lpaW5jBeq9WicePGEEIUuz2qGq+0FyMwMBBTpkzBa6+9hq5du+Lee++Fm5sb/vrrL3zzzTfo2bMn+vXrV2Q6Pz8/dOnSBY8//jiSk5Mxe/Zs1K9fHyNHjnQoFxoainfffRdnz57FbbfdhuXLlyMuLg4LFy6ERqMBYHsf6IoVK/DMM89g69at6Ny5M6xWK44ePYoVK1bg119/Rdu2bcu8jpmZmfjqq68A2BoKx44dw7x582AwGDBx4kR7ubfeesv+XsPnnnsOarUaCxYsgNFoxHvvvWcv17NnT0RERODJJ5/ESy+9BJVKhS+//BIBAQE4f/58mWJ8+eWXsXTpUtx9990YO3Ys3N3dsXDhQkRGRuKff/5xKLt69Wo8/vjjWLRo0X8+cOall17CoUOH8OGHH2Lr1q0YMGAAgoODkZSUhDVr1mDXrl3466+/AAATJ07EN998g969e2PMmDHw8/PDkiVLcObMGaxatarM3c5at26N+vXrY/LkyTAajQ5nYAHbfUPz5s3DY489htatW2Pw4MH2bfnLL7+gc+fORSqk67Vp0wYPPvggZs+ejbS0NHTs2BG//fab/Qfx2jOS77zzDrZu3YoOHTpg5MiRaNy4MdLT07Fv3z5s2rSp2B+7m5FlGfPmzUO/fv3QsmVLPP744wgJCcHRo0dx6NAh/PrrrwCAuXPnokuXLmjWrBlGjhyJunXrIjk5GTt27MDFixdx4MCBGy6jX79+6N69OyZPnoyzZ8+iRYsW2LBhA3744QeMGzfO3lgsjWXLlsHf3x+dOnUqdvy9996Lzz77DL/88gseeOABaDQaPPDAA/j222+Rm5uLDz74oMg05VnHQvfccw+WLl0Kb29vNG7cGDt27MCmTZvg7+/vUO7ll1/GV199hbvuugvPP/883N3d8fnnnyMiIgLp6en2fV7e48vX1xerV69Gnz590LJlSzz66KP2pGLfvn345ptv7PfPBQQEYNKkSZg+fTruvvtu3HvvvTh27Bg+/fRTtGvXDo8++qjDvKviuxEdHY169erhxRdfRHx8PLy8vLBq1api36c+Y8YM3HfffejcuTMef/xxXL58GXPmzEHTpk0dEvlu3brh6aefxsyZMxEXF4eePXtCo9HgxIkTWLlyJT7++ONir9BQ9cG2AdsGbBuwbVActg0c/fHHHygoKCgyvHnz5mjevDlSUlLw7LPPonv37hg9ejQAYM6cOdi6dSuGDx+O7du3Q5ZlvPTSS/juu+/w0EMP4YknnkCbNm2Qnp6OH3/8EfPnz0eLFi1K/Jv4xhtv4Pfff0ffvn0RGRmJlJQUfPrpp6hdu7b9QY89e/ZEcHAwOnfujKCgIBw5cgRz5sxB3759S/yQykpVuQ+nr96++uor0bFjR+Hu7i50Op2Ijo4W06dPd3g9gxBXX3HwzTffiEmTJonAwEBhMBhE3759HV4dJMTV10vs2bNHxMTECL1eLyIjI8WcOXOKLN9kMol3331XNGnSROh0OuHr6yvatGkjpk+f7vBKBAAO7/D8L9e/1kWSJOHn5yfuvfdesXfv3iLl9+3bJ3r16iU8PDyEm5ub6N69u/jrr7+KlNu7d6/o0KGD0Gq1IiIiQsyaNeuGr3Xp27dvsXFd+7ocIWzvbezWrZvQ6/UiLCxMvPnmm+KLL74o82tdrvXdd9+Jnj17Cj8/P6FWq0VISIgYNGiQ2LZtm0O5U6dOiQEDBggfHx+h1+tF+/btxc8//+xQpvAYWLlypcPwM2fO3DCuyZMnCwCifv36N4xx69atolevXsLb21vo9XpRr149MXz4cLFnzx57mWHDhgl3d/dip8/NzRWjRo0Sfn5+wsPDQ/Tv318cO3ZMALC/57NQcnKyGDVqlAgPDxcajUYEBweLHj16iIULF5Z5Pbdv3y7uuusu4enpKdzd3UXz5s3FJ5984lDm1KlTYujQoSI4OFhoNBoRFhYm7rnnHvHdd9/dcLsUys7OFuPHjxehoaFCo9GIBg0aiPfff9/h1TFClOy1LsnJyUKtVovHHnvshmXy8vKEm5ubuP/+++3DNm7caP8eXbhwodjpSrKOhcfw7t27i0x/+fJl8fjjj4tatWoJDw8P0atXL3H06FERGRkphg0b5lB2//794vbbbxc6nU7Url1bzJw5U/zvf/8TAIq8g7Ykx9fNJCQkiPHjx4vbbrtN6PV64ebmJtq0aSPefvtth98oIWyveIuOjhYajUYEBQWJZ599tsgrfwpVxXfj8OHDIjY2Vnh4eIhatWqJkSNHigMHDhR7HH/77bciOjpa6HQ60bRpU/Hjjz+KBx98UERHRxeZ78KFC0WbNm2EwWAQnp6eolmzZuLll18WCQkJN1wXql7YNrBh24Btg7KuJ9sGJV/H6tQ2+K9XvhW+TvCBBx4Qnp6e4uzZsw7T//DDDwKAePfdd+3D0tLSxOjRo0VYWJjQarWidu3aYtiwYQ6vISzJb+LmzZvFfffdJ0JDQ4VWqxWhoaFiyJAh4vjx4/b5LFiwQHTt2lX4+/sLnU4n6tWrJ1566aUi7RlnkYSo5Kd71ADbtm1D9+7dsXLlyv+8knLHHXfg0qVL/3mPBlFliYuLQ6tWrfDVV18V+4ocuvWMGzcOCxYsQE5Ozn/eq0kl07JlSwQEBBS5D56oENsGVJ2wbVDzsG1QvfCedqJbWHFPOZ49ezZkWUbXrl2dEBFVtuv3eVpaGpYuXYouXbqwUi4Ds9kMi8XiMGzbtm04cOAA7rjjDucERURUDmwb1DxsG1R/vKed6Bb23nvvYe/evejevTvUajXWrVuHdevW4amnniry9GO6NcTExOCOO+5Ao0aNkJycjC+++AJZWVk3fBcs3Vx8fDxiY2Px6KOPIjQ0FEePHsX8+fMRHByMZ555xtnhERGVGtsGNQ/bBtUfk3aiW1inTp2wceNGvPnmm8jJyUFERASmTZuGyZMnOzs0qiR9+vTBd999h4ULF0KSJLRu3RpffPEFr56Uka+vL9q0aYPPP/8cqampcHd3R9++ffHOO+8UedgPEVF1wLZBzcO2QfXHe9qJiIiIiIiIXBTvaSciIiIiIiJyUUzaiYiIiIiIiFxUjbunXVEUJCQkwNPTE5IkOTscIiIiCCGQnZ2N0NBQyDLPp1cE1vdERORKylPX17ikPSEhgU/GJCIil3ThwgXUrl3b2WHcEljfExGRKypLXV/jknZPT08Ato3l5eXl5GiIiIiArKwshIeH2+soKj/W90RE5ErKU9fXuKS9sIucl5cXK3EiInIp7MZdcVjfExGRKypLXc8b54iIiIiIiIhcFJN2IiIiIiIiIhfFpJ2IiIiIiIjIRdW4e9qJiGoKRVFgMpmcHQYB0Gg0UKlUzg6DiIhcmNVqhdlsdnYYVA5arbZSXt3KpJ2I6BZkMplw5swZKIri7FDoCh8fHwQHB/Nhc0RE5EAIgaSkJGRkZDg7FConWZZRp04daLXaCp0vk3YioluMEAKJiYlQqVQIDw+vlDO+VHJCCOTl5SElJQUAEBIS4uSIiIjIlRQm7IGBgXBzc+PJ3WpKURQkJCQgMTERERERFbofmbQTEd1iLBYL8vLyEBoaCjc3N2eHQwAMBgMAICUlBYGBgewqT0REAGxd4gsTdn9/f2eHQ+UUEBCAhIQEWCwWaDSaCpsvL78QEd1irFYrAFR41ywqn8ITKLxfkYiIChXWCTzJfmsobHsVtsUqCpN2IqJbFLvXuRbuDyIiuhHWEbeGytqPTNqJiIiIiIiIXBSTdiIiuqUtXrwYPj4+pZpm+PDh6N+/f6XEQ0REVBNIkgRJkkpdB7uqO+64w75OcXFxVbpsJu1EROQyfv/9d/Tr1w+hoaGQJAlr1qxxdkhERERUjOHDh9uT2Gs/d999t73MokWLcPz4cSdGWXG+//577Nq1yynL5tPjiYjIZeTm5qJFixZ44okn8MADDzg7HCIiIrqJu+++G4sWLXIYptPp7P/v4+ODwMDAqg6rUvj5+SErK8spy+aVdiIichm9e/fGW2+9hfvvv7/E08yaNQvNmjWDu7s7wsPD8dxzzyEnJ+eG5adNm4aWLVtiwYIFCA8Ph5ubGwYOHIjMzMwiZT/44AOEhITA398fo0aNcnjy+9KlS9G2bVt4enoiODgYDz/8sP1d7ERERDWBTqdDcHCww8fX1/em0/zwww9o3bo19Ho96tati+nTp8NisQAA3njjDYSGhiItLc1evm/fvujevTsURQEAHDp0CPfccw+8vLzg6emJ22+/HadOnbKX//zzz9GoUSPo9XpER0fj008/tY8zmUwYPXo0QkJCoNfrERkZiZkzZwIAhBCYNm0aIiIioNPpEBoaijFjxlTYtioPJu1ERDWEYlKgmBQIIezDhFXYhllE8WWVspetKrIs43//+x8OHTqEJUuWYMuWLXj55ZdvOs3JkyexYsUK/PTTT1i/fj3279+P5557zqHM1q1bcerUKWzduhVLlizB4sWLsXjxYvt4s9mMN998EwcOHMCaNWtw9uxZDB8+vBLWkIiIaqKqrLeryh9//IGhQ4di7NixOHz4MBYsWIDFixfj7bffBgBMnjwZUVFRGDFiBABg7ty5+Ouvv7BkyRLIsoz4+Hh07doVOp0OW7Zswd69e/HEE0/Yk/5ly5ZhypQpePvtt3HkyBHMmDEDr7/+OpYsWQIA+N///ocff/wRK1aswLFjx7Bs2TJERUUBAFatWoWPPvoICxYswIkTJ7BmzRo0a9asyrdRsUQNk5mZKQCIzMxMZ4dCRFQp8vPzxeHDh0V+fr7D8HMfnBbnPjgtLLkW+7CMnZfFuQ9Oi7RfUx3Knv/4jDj3wWlhzjDZh2XuyRDnPjgtUn9Jdih7Ye5Zce6D08KUarQPyz5Q/t9YAGL16tWlnm7lypXC39/f/veiRYuEt7e3/e+pU6cKlUolLl68aB+2bt06IcuySExMFEIIMWzYMBEZGSkslqvb6qGHHhKDBg264XJ3794tAIjs7Oxix99ovwjBuqkycJsSUXVws7qhKuvtshg2bJhQqVTC3d3d4fP2228LIYqvx3v06CFmzJjhMGzp0qUiJCTE/vepU6eEp6eneOWVV4TBYBDLli2zj5s0aZKoU6eOMJlMojj16tUTX3/9tcOwN998U8TExAghhHj++efFnXfeKRRFKTLthx9+KG677bYbzlsIIc6cOSMAiP379xc7vrLqel5pJyKiamHGjBnw8PCwf86fPw8A2LRpE3r06IGwsDB4enriscceQ1paGvLy8m44r4iICISFhdn/jomJgaIoOHbsmH1YkyZNoFKp7H+HhIQ4dH/fu3cv+vXrh4iICHh6eqJbt24AYI+LiIjoVte9e3fExcU5fJ555pkblj9w4ADeeOMNh/p85MiRSExMtNfbdevWxQcffIB3330X9957Lx5++GH79HFxcbj99tuh0WiKzDs3NxenTp3Ck08+6TD/t956y959fvjw4YiLi0PDhg0xZswYbNiwwT79Qw89hPz8fNStWxcjR47E6tWr7VfwnY0PoiMiqiFqPx8JAJA0kn2YV1tveLbygiRLDmXDnomwlVVfHe7Z0gsezTyLlA0dEV6krHsTz4oNHsAzzzyDgQMHXl1uaCjOnj2Le+65B88++yzefvtt+Pn5Yfv27XjyySdhMpng5uZW5uVd3yCQJMl+P11ubi569eqFXr16YdmyZQgICMD58+fRq1cvmEymMi+TiIioUFXW22Xl7u6O+vXrl7h8Tk4Opk+fXuzDZvV6vf3/f//9d6hUKpw9exYWiwVqtS1tNRgMN503AHz22Wfo0KGDw7jCk/CtW7fGmTNnsG7dOmzatAkDBw5EbGwsvvvuO4SHh+PYsWPYtGkTNm7ciOeeew7vv/8+fvvtt2JPElQlJu1ERDWErC3auUpSSZBURSvtiihb0fz8/ODn5+cwbO/evVAUBR9++CFk2RbHihUr/nNe58+fR0JCAkJDQwEAO3fuhCzLaNiwYYliOXr0KNLS0vDOO+8gPNzW+NmzZ09pVoeIiOimqrLeriqtW7fGsWPHbproL1++HN9//z22bduGgQMH4s0338T06dMBAM2bN8eSJUtgNpuLJNJBQUEIDQ3F6dOn8cgjj9xw/l5eXhg0aBAGDRqEAQMG4O6770Z6ejr8/PxgMBjQr18/9OvXD6NGjUJ0dDT+/fdftG7dumI2QBkxaSciIpeRk5ODkydP2v8+c+YM4uLi4Ofnh4iIiCLl69evD7PZjE8++QT9+vXDn3/+ifnz5//ncvR6PYYNG4YPPvgAWVlZGDNmDAYOHIjg4OASxRkREQGtVotPPvkEzzzzDA4ePIg333yz5CtKRER0CzAajUhKSnIYplarUatWrWLLT5kyBffccw8iIiIwYMAAyLKMAwcO4ODBg3jrrbdw8eJFPPvss3j33XfRpUsXLFq0CPfccw969+6Njh07YvTo0fjkk08wePBgTJo0Cd7e3ti5cyfat2+Phg0bYvr06RgzZgy8vb1x9913w2g0Ys+ePbh8+TImTJiAWbNmISQkBK1atYIsy1i5ciWCg4Ph4+ODxYsXw2q1okOHDnBzc8NXX30Fg8GAyMjIqtiUN8V72omIyGXs2bMHrVq1QqtWrQAAEyZMQKtWrTBlypRiy7do0QKzZs3Cu+++i6ZNm2LZsmX2V7fcTP369fHAAw+gT58+6NmzJ5o3b+7wSpj/EhAQgMWLF2PlypVo3Lgx3nnnHXzwwQclnp6IiOhWsH79eoSEhDh8unTpcsPyvXr1ws8//4wNGzagXbt26NixIz766CNERkZCCIHhw4ejffv2GD16tL38s88+i0cffRQ5OTnw9/fHli1bkJOTg27duqFNmzb47LPP7FfdR4wYgc8//xyLFi1Cs2bN0K1bNyxevBh16tQBAHh6euK9995D27Zt0a5dO5w9exZr166FLMvw8fHBZ599hs6dO6N58+bYtGkTfvrpJ/j7+1f+hvwPkhCi6p/170RZWVnw9vZGZmYmvLy8nB0OEVGFKygowJkzZ1CnTh2H+8PIZtq0aVizZg3i4uKqdLk32y+smyoetykRVQe3cp0tSRJWr16N/v37OzuUCnP27FnUqVMH+/fvR8uWLYuMr6y6nlfaiYiIiIiIqMINGTIEtWvXdnYYFaJ3795o0qSJU5bNe9qJiIiIiIioQp04cQIAHF6fWp19/vnnyM/PB4Bin7NTmZi0ExFRjTJt2jRMmzbN2WEQERHd0krzKrjqICwszGnLZvd4IiIiIiIiIhfl1KT9999/R79+/RAaGgpJkrBmzZr/nGbbtm1o3bo1dDod6tevj8WLF1d6nERE1VENe86oy6up+4N1PRHRf6updcStprL2o1OT9tzcXLRo0QJz584tUfkzZ86gb9++6N69O+Li4jBu3DiMGDECv/76ayVHSkRUfRTeO2YymZwcCV0rLy8PAOyvpakpWNcTEd1YYZ1QWEdQ9VbY9qro+/idek9779690bt37xKXnz9/PurUqYMPP/wQANCoUSNs374dH330EXr16lVZYRIRVStqtRpubm5ITU2FRqOBLPNOKGcSQiAvLw8pKSnw8fG5ZR7IU1Ks64mIbkylUsHHxwcpKSkAADc3N0iS5OSoqCwURUFqairc3NygVldsml2tHkS3Y8cOxMbGOgzr1asXxo0bd8NpjEYjjEaj/e+srKwKi+evo0b8c67kV7KiAtW4u5WhwpZPRFQcSZIQEhKCM2fO4Ny5c84Oh67w8fFBcHCws8NweWWp64HKre+JiCpTYd1QmLhT9SXLMiIiIir8xEu1StqTkpIQFBTkMCwoKAhZWVnIz8+HwVA0IZ45cyamT59eKfFcTLNi7ylzicvvPWVGx9t08HHnVS8iqlxarRYNGjRgF3kXodFoatwV9rIqS10PVG59T0RUmQpPtgcGBsJsLnluQa5Hq9VWSg/HapW0l8WkSZMwYcIE+99ZWVkIDw+vkHm3rqtBgFfJdsp3O/JgsgAFJgG4V8jiiYhuSpZl6PV6Z4dBVCUqs74nIqoKKpWKJ3ipWNUqaQ8ODkZycrLDsOTkZHh5ed3wzLtOp4NOp6uUeOqHaFA/pGQPFPplbz5MFgGTlU+GJCIiupGy1PVA5db3REREzlSt+mnHxMRg8+bNDsM2btyImJgYJ0VUclq17b4Gs4VJOxER0Y1U57qeiIioMjg1ac/JyUFcXBzi4uIA2F7zEhcXh/PnzwOwdXUbOnSovfwzzzyD06dP4+WXX8bRo0fx6aefYsWKFRg/frwzwi8VtcqWtJssTg6EiIioCtWkup6IiKgyODVp37NnD1q1aoVWrVoBACZMmIBWrVphypQpAIDExER7pQ4AderUwS+//IKNGzeiRYsW+PDDD/H5559Xi1fAaK/ciMAr7UREVJPUpLqeiIioMkhCiBqVRWZlZcHb2xuZmZnw8vKqsuW+uzoLJxMteKaXB9rU096wnCIE0rMV+LjL9qvzRER0a3NW3XQr4zYlIiJXUp56qVo9iK460155EKTpBlfas/MVbD9ixLaDRqTnKOjVSo8BMW5VGCERERERERG5GibtVURT+CC6654en5Wn4Oc9+dh+xAiz9erws8m8+Z2IiIiIiKimY9JeRa4+Pd72t6IIbDxQgJ/25MNotg2LCFAh2EeFXSdMyDXWqLsWiIiIiIiIqBhM2quI5sqWNlkE0rKt+HxTLk4m2jL4qEAVHujohugwNc6mWLHrhAl5TNqJiIiIiIhqPCbtVUR75aFyxxMs2HigANn5AjoNMKizG7o00kGSbOPd9bZ/c42K02IlIiIiIiIi18CkvYoU3tN+8LytL3x4LRWevdsDAV4qh3JuOls5oxmwWAWfIE9ERERERFSDOfU97TVVo9pqvNzfq0jCDgBu2qtJemoWr7YTERERERHVZEzaq0iwj21T+3nIGNXbE3pt8VfQZfnq8OXb8244P7NFYOu/BXhjRSa2HSyo2GCJiIiIiIjIJdTY7vGKSYEQwn4vubAKCKuAJEuQ1JJDOQCQ1BIkuexlY+prEeCtQr1gNbRqCYpZAcR1ZRUBYRFwkwXyFAkWxfYwumvLWgXw+2Ej1u/NQ3aOgADwhwTc0VRvK2tRAAXItwr8ddyM+DQLHuhggIdGAmRAVl89T1NYtrgYIAGy5mpZYREQioCkkiCpylEWgKytwLJCQJhtZSWNVHR/XilrsggkXrYg+ZIFyZetcHNX4c7mekiS5LA/8ywCyZkKkjOsSL9sQZMIDSKDNeXa90XKFrfvS1O2cPuUZn+WY99X2HFSifu+3MfJjfZnacqW8zeiXPveBY6T6v4bUaKylbjviYiIiG6kxibt8QvOw3N8E6jcbF3Us/ZkInP7ZXg084Rfz1pXy80/D2EWCB1RG2pvDQAgOy4LGdvS4dbIHbX6BNrLJnx+AUq+gpBhYdDU0gIAcg9lI31jGgz13dDoviB72aTF8bBkWRD0SCh0wToAQN6xXKStTcUzvlrMKjCgwGRrPCYvS4A5zYycbrXw7QkrEi8rCM8z4b60bCRp1fjL3cc+3wtfJeDyOSPW+rjjjMYWQ7jZggYHL0MTqEXIY2HIMyrYdcIE/JqCMLMFYfcHwa2hOwDAlGhE8reJUPuqEfpEuH2+l35MRv6ZfPj1qgWPpp4AAPMlE5KWJkDloULY0xH2smnrUpF3PBe+d/rDs5UXAMCSaUHilxch62TUHh1pL5u+6RJyD+XAp6svvNrZ1sOaa0XCwguADESMr3N1m226hOy4bHh19EHwHX4AAGFUcHHuedt6jotCnkVBQroVmX9chvpoNs7WMmCnlxvSshRACDx2IR0+AL6t7Ys8EyBJgPRPJmqdycFRdx22e7nbl/fo+TQkyBJO9guGzluNLo10yN6XiYzfL8O9iQf87w64uu8XXoBiVBDyRG1ofG3HSc4/2bi8JQ1ut7mjVr+rx0nilxdhzbEi+LFQaANt+z73SA7Sf70EQx0DAh4IvrrOS+NhuWxB0OAQ6MJsJ2byT+Th0s8p0NXWI2hQiL1s8jeJMKeYEDggGPpIAwCg4Gw+UlcnQxusQ/AjofayqSuTYEwwotZ9gXCrb1tnY3wBUlYkQeOvQcjw2lf3/ZpkFJwrgH+fALg38rAdJykmJC9LgNpLjdCR1xwnv6Qg/2Qe/O7yh0fzK/s+3YzEJfGQDTJqP3d136dtSEXekVz43OEHrzbetn2fbUHC5xchaSSEj4myl728JQ05/2bDu4svvDvYjhMlX0H8PNu+j3jh6nGS8Uc6svdlwauDN3y6XDlOzAIXPzkHAKj9fCSkKz1dMndcRtbfmfBs7QXf7v72eRSWDXs2okp/IwJK+Buhj9QjcMA1+/7Kb0TgwGDow237Pv90Hi79kAJdqA5BQ67u+5TlSTAlGRFwfxAMdd1s+/5CAVK+S7L/RhRKXZUM48UC1LonsFr8Rlz+LQ05cdnwjvGBdydf276/7jcCV+5Kyth+Gdl7MuHZ1hu+3WzHCZSr+772qAhI+iv7/u8MZO7IgEdLT/j1uLrvL849ByhA6FPhUHvaqtOy/kZou+lBREREVJwam7S7MvWVvVL4rnarAlzKtuL73/OQrNfA0yAhtoEOwXF5SMoBsvMFkjOs+HlPPvxPm+FvUmDyAGBrF+JyjhUAkJOv4PONOdh32gSzFeiVpcBDKChsopstAuk55buPXgiBnHwFFkv5XlmnCAGjCfh5Tz7OplhwLtWC2y4UIDrHil3/FOCRbgKXcxScTzBDzlNgtgjM/zoDqdm25ba5bEKTPAWJlxVcgm2dPHQSdBoJxitX0n7cnQ8AaJFpgZdZwGR7Ax983CUEeasgXwCsisBPuwuQp5aRkG5F4EUTIhXHdTNbBIyWq1f+ipNvEkjNtCI1S4EqS4HIVbB+aw5y3Yx4/E4PqISAVbH1nLgRi9W2zmmXLFAKFCSlWrBuSw68DDLu62Ao45YmIiIiIiJXJgkhatQLwbOysuDt7Y3LqZfh7e/tkl1fkzOtmLIyGwathDF9PbBoQw7SsxVYZeDOZnrc294Ag0aC1axg7BcZMEGCLAGKAFSKQHSYGrGt9MgqABZtyUWApwQvrYSzqVZYryzLx11CdraCWl4yRvT0wJ/HTPj7hAn5RgVP3emGNvV1Je7Omm9ScDjJisMXLDh03ozMHCtkAbwywAtRwRqHskDR7qxWq4KkTAWnL1lxOsmC00kWXEq3QgCwXNNtVCUEZAFYJUCvl23vshcC6itHsEUCIEnwcZcQ5i0jxEdGkJ8aIf5qhPiq4KEHYAGSM634fGsutGoZgT4yAj1lBHvJCPBRIchPDZ3Gtsw//snHjqMm5FkF4i/b9q18JYauTXVIylKQnKkgPVuB6koiH9tajxA/NVKzrEi9bEVaphWp2QoyjVePQZUiIF0TLwCoJQFYgehwDXq00iM9W8GlbAWXM6xIz1aQkmtFZh4gAEhCQCVs/2+9Zvt4aYE+rfTo0kwP3ZVtfH235wKzQHq2gvQMCy7nWJGeJ5CeZzsZkJmroHGoCg91cmf3+Jt0j4cEmCAhJ19Bdr5Abq4F4f5qeHmqSvwbYZUAzZXtxu7x7B4vyRKy87Lh7e2NzMxMeHl5gcqvsL7nNiUiIldQnnqpxibtrlyJZ+crmLAoAwCgkm1X2mt5yXiyhzvqh2gcyr7yfxn2q+ONa6txf0c3RAXaLtWfSDTjvdXZ9rJqFRBzmw63N9bBz1PGi4szil1+52gtht/pcdMYM3IVxJ0xYf8ZE47FW2At5gJ9hwZajLir6HyEEEi4bMWxeAuOxZtxPMGCnIKih2EtLxl1g9SoE6hGZKAK4f5qfLYxB/+cM9u3TYivCuG1VAivpbb966+Cu75in6+YkatgydZcZOQquJhmLfN8PPQSArxlBHipEOgtIy1bwY5jplLNQ6MC/D1l+Huq4O8p48hFc7FvGXi0mxtyC2w9J9JzFFy+8m+e8b+/7r1a6dGrpR6ehorbjooikJUvkJlrO1F0/T6yWAWy8hRk5gnkGQVuC1NDc83rDk0W27SSBNQq5q0LBWbb9IXzyM5XUDdIjYgANYQQyDcJZOfbhmdd+Tc7XyArX0F23tXhihBo30AHT4OEnHyB7AIFOfkCOQUKcgps5XIKBCzFHAZhfirkGhVoVBLaN9BCkoDcAoGcAtv0hf+fW6DAaAECvWU0DtdcGa4g1yjg6y6jWaQGZguQa7RNk2sUyC0QKDAJRASqEBWgRp5RINeooJaXCk0jNFfKKVDLEqICVfZkk4qnKAIFZttxkW8UyDPZ/r+g8N8r4zz1MuoFq2GyXB1ecM14o0nAwyDjnrbl7+lSHeqm6obblIiIXEl56iV2j3dBhe9qB2wJe7v6Wjx2hzsMxTxx/u5Wevxzzoy7WujRONwxoQ+vpYafhwyzVaB7Uz3uaKqzJ2JCCHi7ScjME1DLQKu6Whi0En4/bETC5eIT0+x8BXtO2q7In06yOHTlDvaR0TRSgybhGpxPtWL13/n4+4QJvdtYEOanRna+goPnzfj3nBlH483IzndMHnVqoE6QGnWD1KgbbEvUvdyKJo3DurvjeIIFgd4yQvxUDoldZfFxlzH2Hts9upsOFOBEohlB3ioE+qgQ5CMjyFsFAeDDH7JgNAMB3jICvVS2f71VCPCSEeCtKnb/dW9qgdkqUMtTxppd+Thw1gxfd/lKYi47JOj+njI8DZJDQqYIgcMXzJAgYfHWHGTk2rbrV7/d+M0DBq0EPw/Z9vGU4eshw9ddxtF4M3YcM+HX/QX4dX8BJj7giXrBGtutCmZAr0GxyaDFKpCZd/XkgP2TaxuWkWtLpK89Pdiuvhb5JnFlnFLkeFDJQL1gtT0JzzddHV8vSA0vNwlZebakOzNPsd/acD1Pg4R8o4ClFHd9/HTlton/olEBngbZftIsPr3weyPwy97/fqNDSqaClGu7YAA4n2rFgbPmG05zKtkCwHjD8dfGFeanQv0QNZpGauDtJtsT+9wCgWBfFYJ9VPYTGrlGgbwrJwjcdRIiAmyJv9lqO4mSWyCQZ1TsJxDyjAJaNdDhNp29Z0ppKOJKsnwlYc678v/BPjJC/VQwWXBlnIICk0Bt/6s9YBTlSrJ9/cdo+zfvur+LllFQcONNXGohvqoKSdqJiIiIboRX2l3U5GUZSMlU0L+9AX3a6Mt85UwI233ScjHTn02xID7dipZRGrjrZcSnWzDt2yzoNcD/RvjaG+3/nDVj53Ej/j1ndriiXidIhVZ1tGhVR4tg36tXPzPzFPtV/CbhGuSZFJxNtjok+Vq1LSlrGKZBw1A1ogLVUFdBAn4rE0Lg0AUzVu/Mh0HnmJj7ediScz+P4k8eAECeUcHcdTk4nnA1A/b3lJGZp8BiBSICVGhfX+uQkF/OUZCVd/N78QtJEnCzXxuVDOg0Uol6AxRHqwa83WR4uck4lVQ0i9drbMmsl0GGp5sET4MMT/2Vf90kqGUJvx0qgFWx9Yrw0MvwMEjwvPKvh/7a/5ftSWRWnoJjCWa4aW0nyHYcM0KSbOXdr0zjri/8W4aHXkJ6joIDZ03QayS462zjcwpsJ2CsioC7zlbO7Uoc7joJF9IsOJNshU4DuOtk5BoVnEy0wGyxnejTqGE/aVMSOg1gtthuq7meWgXIEm54MqRQRIAKob6qK1f+bYl9nlHAqthOwnkZpKsnBa75FJhKdsxcy8fdNi9jBSbcapXtJFbhx00nQa+RoNdKsCrA6WQLLFYBvcY2Xq+9Ot5w5f99PWT72zvKo7rUTdUJtykREbkSdo8vhepSiadnW5FrFAivVXWdISxWgdGfXYZVAV653xP/njfjj8NGh6ugEQEqxNymQ5t6Wvh63Lj79Nq9+Vj9t+MVy9r+KjSP1KBJhAZ1gtRVcpWcSu9kohnvXnNbRUmoZdiu2F+5au/rcfVEgY+77eNlkJCUoeDPI0a46SX4uMnwdi/815a4ypKEo/FmXLhkhZebbZyXmwxvNwlatYRN/xTAaBa2YQYZXm4SvK6U0V9zxddoFjidbIFBK8HTYEvMtepb73izWAVk+epJubRsK5IzFFzKtuJEggVnUyxIylAgwZbYu+ttJ0Wuvx1Fq77aw+f6xL9w2sLp3XS2Ew2HLpjLfILl+uW66WQYzQJp2VfPCqpkW0Jd3K0zQPEJt/1vnQSDVoab9vphjn+70m9QdambqhNuUyIiciVM2kuBlfjNTfkmE4nXdY/3cZfQ8TYdOjbUIsyvZCcRLucoWPBrDjzdJDSL1KBphBZ+N0nyybVk5Co4kWCGt7st+f7zqBEnEizwuS4hL0zSPQxSsb05yPkKzLau7Nfun8R0KxQh4K6X4aaTHE5oXMqyIrdA2JN0vbb4fZt02Yo9p0zQqK4m3oVJ/cVLVsSnW+GmtfUWcLuSVLvp5CtJf/FJc0auAiFwJSbYe/ucTbFAo3LdhLsisG6qeNymRETkSpi0lwIr8Zv7YlMOdh63PRytYaga3Zvp0bKOBir51mogExG5EtZNFY/blIiIXAkfREcV5oGObogMUKNRbTXC/Hl4EBEREREROROzMnLg6yEjtkX5H6pERERERERE5cebjImIiIiIiIhcFJN2IiIiIiIiIhfFpJ2IiIiIiIjIRTFpJyIiIiIiInJRTNqJiIiIiIiIXBSTdiIiIiIiIiIXxaSdiIiIiIiIyEUxaSciIiIiIiJyUUzaiYiIiIiIiFwUk3YiIiIiIiIiF8WknYiIiIiIiMhFMWknIiIiIiIiclFM2omIiIiIiIhcFJN2IiIiIiIiIhfFpJ2IiIiIiIjIRTFpJyIiIiIiInJRTNqJiIiIiIiIXBSTdiIiIiIiIiIXxaSdiIiIiIiIyEUxaSciIiIiIiJyUUzaiYiIiIiIiFwUk3YiIiIiIiIiF8WknYiIiIiIiMhFMWknIiIiIiIiclFM2omIiIiIiIhcFJN2IiIiIiIiIhfFpJ2IiIiIiIjIRTFpJyIiIiIiInJRTNqJiIiIiIiIXBSTdiIiIiIiIiIXxaSdiIiIiIiIyEUxaSciIiIiIiJyUUzaiYiIiIiIiFwUk3YiIiIiIiIiF8WknYiIiIiIiMhFMWknIiIiIiIiclFOT9rnzp2LqKgo6PV6dOjQAbt27bpp+dmzZ6Nhw4YwGAwIDw/H+PHjUVBQUEXREhERUVmwviciIiobpybty5cvx4QJEzB16lTs27cPLVq0QK9evZCSklJs+a+//hoTJ07E1KlTceTIEXzxxRdYvnw5Xn311SqOnIiIiEqK9T0REVHZOTVpnzVrFkaOHInHH38cjRs3xvz58+Hm5oYvv/yy2PJ//fUXOnfujIcffhhRUVHo2bMnhgwZ8p9n64mIiMh5WN8TERGVndOSdpPJhL179yI2NvZqMLKM2NhY7Nixo9hpOnXqhL1799or7dOnT2Pt2rXo06fPDZdjNBqRlZXl8CEiIqKqwfqeiIiofNTOWvClS5dgtVoRFBTkMDwoKAhHjx4tdpqHH34Yly5dQpcuXSCEgMViwTPPPHPT7nIzZ87E9OnTKzR2IiIiKhnW90REROXj9AfRlca2bdswY8YMfPrpp9i3bx++//57/PLLL3jzzTdvOM2kSZOQmZlp/1y4cKEKIyYiIqLSYn1PRER0ldOutNeqVQsqlQrJyckOw5OTkxEcHFzsNK+//joee+wxjBgxAgDQrFkz5Obm4qmnnsLkyZMhy0XPQeh0Ouh0uopfASIiIvpPrO+JiIjKx2lX2rVaLdq0aYPNmzfbhymKgs2bNyMmJqbYafLy8opU1CqVCgAghKi8YImIiKhMWN8TERGVj9OutAPAhAkTMGzYMLRt2xbt27fH7NmzkZubi8cffxwAMHToUISFhWHmzJkAgH79+mHWrFlo1aoVOnTogJMnT+L1119Hv3797JU5ERERuRbW90RERGXn1KR90KBBSE1NxZQpU5CUlISWLVti/fr19ofVnD9/3uFM+2uvvQZJkvDaa68hPj4eAQEB6NevH95++21nrQIRERH9B9b3REREZSeJGtbPLCsrC97e3sjMzISXl5ezwyEiImLdVAm4TYmIyJWUp16qVk+PJyIiIiIiIqpJmLQTERERERERuSgm7UREREREREQuikk7ERERERERkYti0k5ERERERETkopi0ExEREREREbkoJu1ERERERERELopJOxEREREREZGLYtJORERERERE5KKYtBMRERERERG5KCbtRERERERERC6KSTsRERERERGRi2LSTkREREREROSimLQTERERERERuSgm7UREREREREQuikk7ERERERERkYti0k5ERERERETkopi0ExEREREREbkoJu1ERERERERELopJOxEREREREZGLYtJORERERERE5KKYtBMRERERERG5KCbtRERERERERC6KSTsRERERERGRi2LSTkREREREROSimLQTERERERERuSgm7UREREREREQuikk7ERERERERkYti0k5ERERERETkopi0ExEREREREbkoJu1ERERERERELopJOxEREREREZGLKlPSfurUKbz22msYMmQIUlJSAADr1q3DoUOHKjQ4IiIiIiIiopqs1En7b7/9hmbNmuHvv//G999/j5ycHADAgQMHMHXq1AoPkIiIiIiIiKimKnXSPnHiRLz11lvYuHEjtFqtffidd96JnTt3VmhwRERERERERDVZqZP2f//9F/fff3+R4YGBgbh06VKFBEVEREREREREZUjafXx8kJiYWGT4/v37ERYWViFBEREREREREVEZkvbBgwfjlVdeQVJSEiRJgqIo+PPPP/Hiiy9i6NChlREjERERERERUY1U6qR9xowZiI6ORnh4OHJyctC4cWN07doVnTp1wmuvvVYZMRIRERERERHVSOrSTqDVavHZZ5/h9ddfx8GDB5GTk4NWrVqhQYMGlREfERERERERUY1V6qS9UEREBCIiIioyFiIiIiIiIiK6RqmT9gkTJhQ7XJIk6PV61K9fH/fddx/8/PzKHRwRERG5hoyMDPj4+Dg7DCIiohqn1En7/v37sW/fPlitVjRs2BAAcPz4cahUKkRHR+PTTz/FCy+8gO3bt6Nx48YVHjARERFVrnfffRdRUVEYNGgQAGDgwIFYtWoVgoODsXbtWrRo0cLJERIREdUcpX4Q3X333YfY2FgkJCRg79692Lt3Ly5evIi77roLQ4YMQXx8PLp27Yrx48dXRrxERERUyebPn4/w8HAAwMaNG7Fx40asW7cOvXv3xksvveTk6IiIiGoWSQghSjNBWFgYNm7cWOQq+qFDh9CzZ0/Ex8dj37596NmzJy5dulShwVaErKwseHt7IzMzE15eXs4Oh4iIyOXqJoPBgOPHjyM8PBxjx45FQUEBFixYgOPHj6NDhw64fPmys0P8T662TYmIqGYrT71U6ivtmZmZSElJKTI8NTUVWVlZAAAfHx+YTKbSzpqIiIhcgK+vLy5cuAAAWL9+PWJjYwEAQghYrVZnhkZERFTjlPqe9vvuuw9PPPEEPvzwQ7Rr1w4AsHv3brz44ovo378/AGDXrl247bbbKjRQIiIiqhoPPPAAHn74YTRo0ABpaWno3bs3ANtzberXr+/k6IiIiGqWUiftCxYswPjx4zF48GBYLBbbTNRqDBs2DB999BEAIDo6Gp9//nnFRkpERERV4qOPPkJUVBQuXLiA9957Dx4eHgCAxMREPPfcc06OjoiIqGYp9T3thXJycnD69GkAQN26de0VuqvjPW5ERORqWDdVPG5TIiJyJVV6T3shDw8PNG/eHM2bN682CTsRERH9tyVLluCXX36x//3yyy/Dx8cHnTp1wrlz55wYGRERUc1T6u7xALBnzx6sWLEC58+fL/LAue+//75CAiMiIiLnmDFjBubNmwcA2LFjB+bOnYuPPvoIP//8M8aPH8+6noiIqAqV+kr7t99+i06dOuHIkSNYvXo1zGYzDh06hC1btsDb27syYiQiIqIqdOHCBfsD59asWYMHH3wQTz31FGbOnIk//vjDydERERHVLKVO2mfMmIGPPvoIP/30E7RaLT7++GMcPXoUAwcORERERGXESERERFXIw8MDaWlpAIANGzbgrrvuAgDo9Xrk5+c7MzQiIqIap9RJ+6lTp9C3b18AgFarRW5uLiRJwvjx47Fw4cIKD5CIiIiq1l133YURI0ZgxIgROH78OPr06QMAOHToEKKiopwbHBERUQ1T6qTd19cX2dnZAICwsDAcPHgQAJCRkYG8vLyKjY6IiIiq3Ny5cxETE4PU1FSsWrUK/v7+AIC9e/diyJAhTo6OiIioZin1g+i6du2KjRs3olmzZnjooYcwduxYbNmyBRs3bkSPHj0qI0YiIiKqQj4+PpgzZ06R4dOnT3dCNERERDVbqa+0z5kzB4MHDwYATJ48GRMmTEBycjIefPBBfPHFF6UOYO7cuYiKioJer0eHDh2wa9eum5bPyMjAqFGjEBISAp1Oh9tuuw1r164t9XKJiIjoxv744w88+uij6NSpE+Lj4wEAS5cuxfbt28s0P9b3REREZVOqpN1iseDnn3+GSqWyTSzLmDhxIn788Ud8+OGH8PX1LdXCly9fjgkTJmDq1KnYt28fWrRogV69eiElJaXY8iaTCXfddRfOnj2L7777DseOHcNnn32GsLCwUi2XiIiIbmzVqlXo1asXDAYD9u3bB6PRCADIzMzEjBkzSj0/1vdERERlJwkhRGkmcHNzw5EjRxAZGVnuhXfo0AHt2rWzd8FTFAXh4eF4/vnnMXHixCLl58+fj/fffx9Hjx6FRqMp0zKzsrLg7e2NzMxMeHl5lSt+IiKiiuBqdVOrVq0wfvx4DB06FJ6enjhw4ADq1q2L/fv3o3fv3khKSirV/FjfExFRTVeeeqnU3ePbt2+PuLi40k5WhMlkwt69exEbG3s1GFlGbGwsduzYUew0P/74I2JiYjBq1CgEBQWhadOmmDFjBqxW6w2XYzQakZWV5fAhIiKiGzt27Bi6du1aZLi3tzcyMjJKNS/W90REROVT6gfRPffcc5gwYQIuXLiANm3awN3d3WF88+bNSzSfS5cuwWq1IigoyGF4UFAQjh49Wuw0p0+fxpYtW/DII49g7dq1OHnyJJ577jmYzWZMnTq12GlmzpzJB+cQERGVQnBwME6ePFnk9W7bt29H3bp1SzUv1vdERETlU+qkvfAhdGPGjLEPkyQJQghIknTTs+DlpSgKAgMDsXDhQqhUKrRp0wbx8fF4//33b1iJT5o0CRMmTLD/nZWVhfDw8EqLkYiIqLobOXIkxo4diy+//BKSJCEhIQE7duzAiy++iNdff73Sl8/6noiI6KpSJ+1nzpypkAXXqlULKpUKycnJDsOTk5MRHBxc7DQhISHQaDT2B+EBQKNGjZCUlASTyQStVltkGp1OB51OVyExExER1QQTJ06Eoijo0aMH8vLy0LVrV+h0Orz44ot4/vnnSzUv1vdERETlU+p72iMjI2/6KSmtVos2bdpg8+bN9mGKomDz5s2IiYkpdprOnTvj5MmTUBTFPuz48eMICQkptgInIiKi0pMkCZMnT0Z6ejoOHjyInTt3IjU1FW+++Wap58X6noiIqHxKnbQDtve0du7cGaGhoTh37hwAYPbs2fjhhx9KNZ8JEybgs88+w5IlS3DkyBE8++yzyM3NxeOPPw4AGDp0KCZNmmQv/+yzzyI9PR1jx47F8ePH8csvv2DGjBkYNWpUWVaDiIiIipGZmYn09HRotVo0btwY7du3h4eHB9LT08v0gDfW90RERGVX6qR93rx5mDBhAvr06YOMjAz7Pew+Pj6YPXt2qeY1aNAgfPDBB5gyZQpatmyJuLg4rF+/3v6wmvPnzyMxMdFePjw8HL/++it2796N5s2bY8yYMRg7dmyxr4shIiKishk8eDC+/fbbIsNXrFhhf7ZNabC+JyIiKrtSv6e9cePGmDFjBvr37+/w7taDBw/ijjvuwKVLlyor1grB97YSEZGrcbW6yc/PD3/++ScaNWrkMPzo0aPo3Lkz0tLSnBRZybnaNiUiopqtSt/TfubMGbRq1arIcJ1Oh9zc3NLOjoiIiFyM0WiExWIpMtxsNiM/P98JEREREdVcpU7a69Spg7i4uCLD169fX+SMPBEREVU/7du3x8KFC4sMnz9/Ptq0aeOEiIiIiGquUr/ybcKECRg1ahQKCgoghMCuXbvwzTffYObMmfj8888rI0YiIiKqQm+99RZiY2Nx4MAB9OjRAwCwefNm7N69Gxs2bHBydERERDVLqZP2ESNGwGAw4LXXXkNeXh4efvhhhIaG4uOPPy7Tw2mIiIjItXTu3Bk7duzA+++/jxUrVsBgMKB58+b44osv0KBBA2eHR0REVKOU+kF018rLy0NOTg4CAwMrMqZKxQfTEBGRq6lOdZMQApIkOTuM/1SdtikREd36qvRBdG+99RbOnDkDAHBzc6tWCTsRERH9t+HDhxf7cNmzZ8+ia9euToiIiP6fvfsOj6Ja/wD+ndmebLJppJIQmoReBRJQpEgUBBERUBSQq1fsCqhgBUERLiIKIupF8QrSFPhxuVgQAQVRlA7SJQRIJSE92Tbn98eSJUsCpO+GfD/Ps0+yZ87MvjOzO+e8U4mo/qpw0r569Wo0a9YMcXFxWLhwocc/4o2IiIgqZv/+/WjXrh127tzpLPviiy/Qvn17BAUFuTEyIiKi+qfCSfv+/ftx4MAB3HbbbZgzZw7Cw8MxcOBAfPXVVygoKKiJGImIiKgW7dq1C0OHDsVtt92Gl19+GcOHD8dTTz2FOXPmYO3ate4Oj4iIqF6p0jXtALBjxw589dVXWL16NYqKipCTk1NdsdUIXuNGRESexlPbpjfeeAPTp0+HWq3Gtm3bEBsb6+6Qys1TlykREdVPtXpN+5W8vb1hMBig1WphtVqrOjkiIiJyM6vViokTJ2LWrFmYMmUKYmNjMXToUGzcuNHdoREREdU7FX7kGwCcPn0aX331Fb766iscO3YMvXr1wrRp0zBs2LDqjo+IiIhqWZcuXVBQUICtW7eie/fuEEJg9uzZGDp0KMaNG4eFCxe6O0QiIqJ6o8JJe/fu3fHHH3+gXbt2ePjhh3H//fcjIiKiJmIjIiIiN+jSpQs++OADeHt7AwAkScJLL72E/v3746GHHnJzdERERPVLhZP2vn374rPPPkOrVq1qIh4iIiJys8WLF5dZ3rFjR+zevbuWoyEiIqrfKnxN+1tvveVM2IUQqOJ97IiIiMhDrFq1ChaLxfn+3LlzUBTF+b6goADvv/++O0IjIiKqtyp1I7r//Oc/aNu2LQwGAwwGA9q1a4cvv/yyumMjIiKiWnT//fcjKyvL+b5Vq1ZISEhwvs/NzcWUKVNqPzAiIqJ6rMKnx8+dOxevvfYannrqKfTo0QMAsH37dowfPx4XLlzA888/X+1BEhERUc278uw5nk1HRETkfhVO2ufPn4+PPvoIo0ePdpYNHjwYrVu3xtSpU5m0ExEREREREVWTCp8en5ycjLi4uFLlcXFxSE5OrpagiIiIiIiIiKgSR9qbNWuGVatW4eWXX3YpX7lyJZo3b15tgREREVHt+/7772EymQAAiqJg8+bNOHToEAC4XO9OREREtaPCSfu0adMwYsQI/Pzzz85r2nfs2IHNmzdj1apV1R4gERER1Z4xY8a4vH/sscdc3kuSVJvhEBER1XsVTtrvvfde/P7773jvvfewbt06AEDLli2xa9cudOzYsbrjIyIiolpS8vFuRERE5BkqnLQDQOfOnbF06dLqjoWIiIiIiIiISij3jeiSkpIwadIk5OTklBqWnZ2NF154AampqdUaHBEREREREVF9Vu6kfe7cucjJyYGvr2+pYSaTCbm5uZg7d261BkdERERERERUn5U7af/uu+9cns1+pdGjR2PDhg3VEhQRERERERERVSBpP336NKKioq46vGHDhkhISKiOmIiIiIiIiIgIFUjaDQbDNZPyhIQEGAyG6oiJiIiI3CwrKwv//ve/MWXKFGRmZgIA9uzZg/Pnz7s5MiIiovql3El7t27d8OWXX151+H/+8x907dq1WoIiIiIi9zlw4ABuuukmzJo1C3PmzEFWVhYAYM2aNZgyZYp7gyMiIqpnyp20T5o0CZ9//jkmTZrkcpf41NRUTJw4EUuWLMGkSZNqJEgiIiKqPRMmTMDYsWNx4sQJ6PV6Z/mAAQPw888/uzEyIiKi+qfcz2nv3bs3PvzwQzz77LN477334OvrC0mSkJ2dDY1Gg/nz56NPnz41GSsRERHVgj/++AMff/xxqfKIiAikpKS4ISIiIqL6q9xJOwA89thjuOuuu7Bq1SqcPHkSQgjcdNNNGDZsGBo2bFhTMRIREVEt0ul0yMnJKVV+/PhxNGjQwA0RERER1V8VStoBx172559/viZiISIiIg8wePBgvPnmm1i1ahUAQJIkJCYm4qWXXsK9997r5uiIiIjql3Jf005ERET1w7vvvou8vDwEBwejsLAQvXr1QrNmzeDj44O33nrL3eERERHVKxU+0k5EREQ3NpPJhE2bNmH79u04cOAA8vLy0KlTJ/Tr18/doREREdU7TNqJiIioTD179kTPnj3dHQYREVG9xqSdiIiIXHzwwQdllkuSBL1ej2bNmuHWW2+FSqWq5ciIiIjqn0ol7VlZWfj6669x6tQpvPDCCwgICMCePXsQEhKCiIiI6o6RiIiIatF7772H9PR0FBQUwN/fHwBw8eJFeHl5wWg0Ii0tDU2aNMGWLVsQGRnp5miJiIhubBW+Ed2BAwdw0003YdasWZgzZw6ysrIAAGvWrMGUKVOqOz4iIiKqZW+//TZuvvlmnDhxAhkZGcjIyMDx48fRrVs3vP/++0hMTERoaCifJkNERFQLKpy0T5gwAWPHjsWJEyeg1+ud5QMGDMDPP/9crcERERFR7Xv11Vfx3nvvoWnTps6yZs2aYc6cOZgyZQoaNmyI2bNnY8eOHW6MkoiIqH6ocNL+xx9/4LHHHitVHhERgZSUlGoJioiIiNwnOTkZNputVLnNZnO29eHh4cjNza3t0IiIiOqdCiftOp0OOTk5pcqPHz+OBg0aVEtQRERE5D69e/fGY489hr179zrL9u7di8cffxx9+vQBABw8eBCNGzd2V4hERET1RoWT9sGDB+PNN9+E1WoF4LiTbGJiIl566SXce++91R4gERER1a7FixcjICAAnTt3hk6ng06nQ5cuXRAQEIDFixcDAIxGI9599103R0pERHTjk4QQoiIjZGdnY9iwYfjzzz+Rm5uL8PBwpKSkIDY2Fhs3boS3t3dNxVotcnJyYDKZkJ2dDV9fX3eHQ0RE5LFt09GjR3H8+HEAQIsWLdCiRQs3R1R+nrpMiYiofqpKu1ThR76ZTCZs2rQJ27dvx4EDB5CXl4dOnTqhX79+FZ0UERERebCYmBjExMS4OwwiIqJ6rVLPaQeAnj17omfPntUZCxEREXmIc+fOYf369UhMTITFYnEZNnfuXDdFRUREVP9UOGn/4IMPyiyXJAl6vR7NmjXDrbfeCpVKVeXgiIiIqPZt3rwZgwcPRpMmTXD06FG0adMGCQkJEEKgU6dO7g6PiIioXqlw0v7ee+8hPT0dBQUF8Pf3BwBcvHgRXl5eMBqNSEtLQ5MmTbBlyxZERkZWe8BERERUs6ZMmYJJkyZh2rRp8PHxwTfffIPg4GCMGjUKd9xxh7vDIyIiqlcqfPf4t99+GzfffDNOnDiBjIwMZGRk4Pjx4+jWrRvef/99JCYmIjQ0FM8//3xNxEtEREQ17MiRIxg9ejQAQK1Wo7CwEEajEW+++SZmzZrl5uiIiIjqlwon7a+++iree+89NG3a1FnWrFkzzJkzB1OmTEHDhg0xe/Zs7Nixo1oDJSIiotrh7e3tvI49LCwMp06dcg67cOGCu8IiIiKqlyp8enxycjJsNlupcpvNhpSUFABAeHg4cnNzqx4dERER1bru3btj+/btaNmyJQYMGICJEyfi4MGDWLNmDbp37+7u8IiIiOqVCh9p7927Nx577DHs3bvXWbZ37148/vjj6NOnDwDg4MGDaNy4cfVFSURERLVm7ty56NatGwBg2rRp6Nu3L1auXIno6GgsXrzYzdERERHVLxU+0r548WI89NBD6Ny5MzQaDQDHUfa+ffs6G3Kj0Yh33323eiMlIiKiGme323Hu3Dm0a9cOgONU+UWLFrk5KiIiovqrwkl7aGgoNm3ahKNHj+L48eMAgBYtWqBFixbOOr17966+CImIiKjWqFQq9O/fH0eOHIGfn5+7wyEiIqr3Kpy0F4uJiUFMTEx1xkJEREQeoE2bNvj77795qRsREZEHqFTSfu7cOaxfvx6JiYnOu8sWmzt3brUERkRERO4xY8YMTJo0CdOnT0fnzp3h7e3tMtzX19dNkREREdU/FU7aN2/ejMGDB6NJkyY4evQo2rRpg4SEBAgh0KlTp5qIkYiIiGrRgAEDAACDBw+GJEnOciEEJEmC3W53V2hERET1ToXvHj9lyhRMmjQJBw8ehF6vxzfffIOzZ8+iV69euO+++yoVxIcffojo6Gjo9Xp069YNu3btKtd4K1asgCRJGDJkSKU+l4iIiErbsmWL8/XTTz85X8XvK4NtPRERUeVU+Ej7kSNHsHz5csfIajUKCwthNBrx5ptv4u6778bjjz9eoemtXLkSEyZMwKJFi9CtWzfMmzcP8fHxOHbsGIKDg686XkJCAiZNmoRbbrmlorNARERE19CrV69qnR7beiIiosqr8JF2b29v53XsYWFhOHXqlHPYhQsXKhzA3Llz8eijj+Lhhx9Gq1atsGjRInh5eeGzzz676jh2ux2jRo3CtGnT0KRJkwp/JhEREV3bL7/8ggcffBBxcXE4f/48AODLL7/E9u3bKzwttvVERESVV+GkvXv37s4Ge8CAAZg4cSLeeustjBs3Dt27d6/QtCwWC3bv3o1+/fpdDkiW0a9fP+zcufOq47355psIDg7GP/7xj+t+htlsRk5OjsuLiIiIru6bb75BfHw8DAYD9uzZA7PZDADIzs7G22+/XaFp1UZbD7C9JyKiG1eFk/a5c+eiW7duAIBp06ahb9++WLlyJaKjo7F48eIKTevChQuw2+0ICQlxKQ8JCUFKSkqZ42zfvh2LFy/Gp59+Wq7PmDlzJkwmk/MVGRlZoRiJiIjqmxkzZmDRokX49NNPodFonOU9evTAnj17KjSt2mjrAbb3RER046pQ0m6323Hu3DlERUUBcJwqv2jRIhw4cADffPMNGjVqVCNBFsvNzcVDDz2ETz/9FEFBQeUaZ8qUKcjOzna+zp49W6MxEhER1XXHjh3DrbfeWqrcZDIhKyurRj+7Mm09wPaeiIhuXBW6EZ1KpUL//v1x5MgR+Pn5VfnDg4KCoFKpkJqa6lKempqK0NDQUvVPnTqFhIQEDBo0yFmmKAoAx03xjh07hqZNm7qMo9PpoNPpqhwrERFRfREaGoqTJ08iOjrapXz79u0Vvr68Ntp6gO09ERHduCp8enybNm3w999/V8uHa7VadO7cGZs3b3aWKYqCzZs3IzY2tlT9mJgYHDx4EPv27XO+Bg8ejN69e2Pfvn08FY6IiKgaPProo3j22Wfx+++/Q5IkJCUlYdmyZZg0aVKFnxLDtp6IiKhqKvzItxkzZmDSpEmYPn06OnfuDG9vb5fhvr6+FZrehAkTMGbMGHTp0gVdu3bFvHnzkJ+fj4cffhgAMHr0aERERGDmzJnQ6/Vo06aNy/jFR/yvLCciIqLKmTx5MhRFQd++fVFQUIBbb70VOp0OkyZNwtNPP13h6bGtJyIiqrwKJ+0DBgwAAAwePBiSJDnLhRCQJAl2u71C0xsxYgTS09Px+uuvIyUlBR06dMB3333nvGFNYmIiZLnCJwQQERFRJUmShFdeeQUvvPACTp48iby8PLRq1QpGo7FS02NbT0REVHmSEEJUZIRt27Zdc3ivXr2qFFBNy8nJgclkQnZ2doXPCiAiIqoJntY2LV26FEOHDoWXl5e7Q6k0T1umRERUv1WlXapw0l7XsREnIiJP42ltU4MGDVBYWIjBgwfjwQcfRHx8PFQqlbvDqhBPW6ZERFS/VaVdqtS5aL/88gsefPBBxMXF4fz58wCAL7/8Etu3b6/M5IiIiMiDJCcnY8WKFZAkCcOHD0dYWBiefPJJ/Prrr+4OjYiIqN6pcNL+zTffID4+HgaDAXv27IHZbAYAZGdn4+233672AImIiKh2qdVq3HXXXVi2bBnS0tLw3nvvISEhAb179y7zcWtERERUcyqctM+YMQOLFi3Cp59+Co1G4yzv0aMH9uzZU63BERERkXt5eXkhPj4ed955J5o3b46EhAR3h0RERFSvVDhpP3bsGG699dZS5SaTCVlZWdURExEREblZQUEBli1bhgEDBiAiIgLz5s3DPffcg8OHD7s7NCIionqlwo98Cw0NxcmTJxEdHe1Svn37djRp0qS64iIiIiI3GTlyJDZs2AAvLy8MHz4cr732GmJjY90dFhERUb1U4aT90UcfxbPPPovPPvsMkiQhKSkJO3fuxKRJk/Daa6/VRIxERERUi1QqFVatWlXmXeMPHTqENm3auCkyIiKi+qfCSfvkyZOhKAr69u2LgoIC3HrrrdDpdJg0aRKefvrpmoiRiIiIatGyZctc3ufm5mL58uX497//jd27d8Nut7spMiIiovqnwkm7JEl45ZVX8MILL+DkyZPIy8tDq1atYDQaayI+IiIicpOff/4ZixcvxjfffIPw8HAMHToUH374obvDIiIiqlcqnLQvXboUQ4cOhZeXF1q1alUTMREREZGbpKSkYMmSJVi8eDFycnIwfPhwmM1mrFu3ju0+ERGRG1T47vHPP/88goOD8cADD2Djxo08RY6IiOgGMWjQILRo0QIHDhzAvHnzkJSUhPnz57s7LCIionqtwkl7cnIyVqxYAUmSMHz4cISFheHJJ5/Er7/+WhPxERERUS359ttv8Y9//APTpk3DwIEDS92EjoiIiGpfhZN2tVqNu+66C8uWLUNaWhree+89JCQkoHfv3mjatGlNxEhERES1YPv27cjNzUXnzp3RrVs3LFiwABcuXHB3WERERPVahZP2kry8vBAfH48777wTzZs3R0JCQjWFRURERLWte/fu+PTTT5GcnIzHHnsMK1asQHh4OBRFwaZNm5Cbm+vuEImIiOqdSiXtBQUFWLZsGQYMGICIiAjMmzcP99xzDw4fPlzd8REREVEt8/b2xrhx47B9+3YcPHgQEydOxDvvvIPg4GAMHjzY3eERERHVKxVO2keOHIng4GA8//zzaNKkCbZu3YqTJ09i+vTpiImJqYkYiYiIyE1atGiB2bNn49y5c1i+fLm7wyEiIqp3KvzIN5VKhVWrViE+Pr7UDWoOHTqENm3aVFtwRERE5BlUKhWGDBmCIUOGuDsUIiKieqXCSfuyZctc3ufm5mL58uX497//jd27d/MRcERERERERETVpNI3ovv5558xZswYhIWFYc6cOejTpw9+++236oyNiIiIiIiIqF6r0JH2lJQULFmyBIsXL0ZOTg6GDx8Os9mMdevWoVWrVjUVIxEREREREVG9VO4j7YMGDUKLFi1w4MABzJs3D0lJSZg/f35NxkZERERERERUr5X7SPu3336LZ555Bo8//jiaN29ekzERERERERERESpwpH379u3Izc1F586d0a1bNyxYsAAXLlyoydiIiIiIiIiI6rVyJ+3du3fHp59+iuTkZDz22GNYsWIFwsPDoSgKNm3ahNzc3JqMk4iIiIiIiKjeqfDd4729vTFu3Dhs374dBw8exMSJE/HOO+8gODgYgwcProkYiYiIiIiIiOqlSj/yDQBatGiB2bNn49y5c1i+fHl1xUREREREREREqGLSXkylUmHIkCFYv359dUyOiIiIiIiIiFBNSTsRERERERERVT8m7UREREREREQeikk7ERERERERkYdi0k5ERERERETkoZi0ExEREREREXkoJu1EREREREREHopJOxEREREREZGHYtJORERERERE5KGYtBMRERERERF5KCbtRERERERERB6KSTsRERERERGRh2LSTkREREREROShmLQTEREREREReSgm7UREREREREQeikk7ERERERERkYdi0k5ERERERETkoZi0ExEREREREXkoJu1EREREREREHopJOxEREREREZGHYtJORERERERE5KGYtBMRERERERF5KCbtRERERERERB6KSTsRERERERGRh2LSTkREREREROShmLQTEREREREReSgm7UREREREREQeikk7ERERERERkYdi0k5ERERERETkoZi0ExEREREREXkoj0jaP/zwQ0RHR0Ov16Nbt27YtWvXVet++umnuOWWW+Dv7w9/f3/069fvmvWJiIjI/djWExERVY7bk/aVK1diwoQJeOONN7Bnzx60b98e8fHxSEtLK7P+1q1bcf/992PLli3YuXMnIiMj0b9/f5w/f76WIyciIqLyYFtPRERUeZIQQrgzgG7duuHmm2/GggULAACKoiAyMhJPP/00Jk+efN3x7XY7/P39sWDBAowePfq69XNycmAymZCdnQ1fX98qx09ERFRVN3rbVNttPXDjL1MiIqpbqtIuufVIu8Viwe7du9GvXz9nmSzL6NevH3bu3FmuaRQUFMBqtSIgIKDM4WazGTk5OS4vIiIiqh210dYDbO+JiOjG5dak/cKFC7Db7QgJCXEpDwkJQUpKSrmm8dJLLyE8PNylM1DSzJkzYTKZnK/IyMgqx01ERETlUxttPcD2noiIblxuv6a9Kt555x2sWLECa9euhV6vL7POlClTkJ2d7XydPXu2lqMkIiKiyipPWw+wvSciohuX2p0fHhQUBJVKhdTUVJfy1NRUhIaGXnPcOXPm4J133sGPP/6Idu3aXbWeTqeDTqerlniJiIioYmqjrQfY3hMR0Y3LrUfatVotOnfujM2bNzvLFEXB5s2bERsbe9XxZs+ejenTp+O7775Dly5daiNUIiIiqgS29URERFXj1iPtADBhwgSMGTMGXbp0QdeuXTFv3jzk5+fj4YcfBgCMHj0aERERmDlzJgBg1qxZeP311/HVV18hOjraeT2c0WiE0Wh023wQERFR2djWExERVZ7bk/YRI0YgPT0dr7/+OlJSUtChQwd89913zhvWJCYmQpYvnxDw0UcfwWKxYNiwYS7TeeONNzB16tTaDJ2IiIjK4UZo69Oy7fD3lqFRS275fCIiqr/c/pz22sbnthIRkadh21T9qnOZnki2YvbaXDQNUWPyvVw/RERUcXX2Oe1EREREnm7HETMA4FSqzc2REBFRfcSknYiIiOgatCVOiS9O4ImIiGoLk3YiIiKiayh5HfuSLflujISIiOojJu1ERERE16B1+217iYioPmPSTkRERHQNSr26ZS8REXkaJu1ERERE12Cxumbt9ezBO0RE5GZM2omIiIiuwXLFTePNVvfEQURE9ROTdiIiIqJrMF9xpD2vSHFTJEREVB8xaSciIiK6BrPNNWlPvmiHEALKFafJK4rgqfNERFTteD9UIiIiomu48pr2D/6XhxA/GUIArw03Qa+R8OtRM77clo8W4Ro8e5cRkiSVmk52gQJfg1TmMCIioqth0k5ERER0DcXXtIf4yUjNcpwaX/x3zykL4mJ0+G5vEWx24PBZKw4lWtEwUA2jQYJG5UjQtx0uwtJtBWgdqcEzA404n2mHQSshyFdVIzELIVBoEUi+aEdEoBp6TcV2FGTm2vHrMQv2nbYgNcuOYJMK4QEq+HrJUKuAIotAq0gNGgerYdBJOJ1qQ1a+gga+KuQWKlAEYLMDiek26LUSGphkmK2ABMBiEyiwCMiXQsopUGBTAKNeQlKmHYoAVLIElQzYFYEAowoBRhkBPjL8vWVk5StIvGBDoUXg7AU7zmfY4aWTYNBKsNgEvHQSfAwy1CoJgUYZkABFCAQaVQjzVyG3SEF2voKMPAW5BQJqFZCZpyAt2w5fgwyLTUCjlqDXSCi0CPh5yzBoJagvrSp/bxkGnQSzVSA1S0Fqlh1Wu+PRgDqNYzyjQYJaliBJQF6RQIifDKNehqIIBBhl+HrJKLQImG0CGtlR32oD1Cqg0CKQWyiQV6TAbBVQqxyfpdNIyCtUoFFLkABEBKngpZUgSxIKzAou5Cqw2R3L0VsvOeoXCVhtjmXtb3TEoNc6lpVeK0GnRrXuRFIUx7qVAGjVEuzCcXlJXpGCArOAze5Yp2arY179vGUoimOeAUCWHfH7GmRIkuO7olY55kUtAyq5euMlqiuYtBMREdENS7EoEEI4O/rCLiDsApIsQVJLLvUAQFJLkGTXutZLw+7qbMDizflQX3oGnE0Cjp63IiJAhZRMG9QCUCTHkfhiQzrrcDrFjv3nrIAk4fBZK17+MgvZuQoEgAf7GtGxsQb7E6xYuTUPeq2E9s20aBamQcuGGhh1EoRNADIgqy9f1ajYFEC5Il5FoLBQwe8nLPjugBmZeY64VYpAzxgtbBJQZHMkSB2jNYhtrkVWnoKEiwp89DKOJ1lx6rwVGlnC4SQrFFxaPkIgKc2G82k22OXLy2zrgSLIArBLgJAu11VfOjHBVqKuLATkS8tHqXBdq2tdCcClaRTXLSy6ynTLqOsSA+CyPrPyRam65zLKrls8XUkIqMqYrkpxJK9VrSsAl+VembplrSMBQJEdybteI0EnCxjUEooUATscZbIkINsBOwAbJBRZBeyKgAaAWgYklQQFjp0sOQV2FBaIq67P6vqeqC4l7xoAGhVgNMiwAbDaAatVgXyprl1yDNOoJciKgFErQVZLsArAoJWgkiVohAJZdsybgGMni90qACGgABAlYtMIAa0asEKCXZT8TglIKgm4tHOh0Cwg2xWoZMdCUqkuxQxADQFfbxX8TSqYrQI2u4D3pZ1BWr0ESBKEADQyYLMq0GlkKLIEg06CSgKEXYEsJEgaCbIsQaNyrAeVENDrJAhZhl1xPKbSbrHDbgfskgQhAXYFsNsF7FbHTjUhy7ALwGoTsBQpsNgErAKA7PgsWXJMV5YAlVYFuXiniSKgAiBrJKhVjjgkSUBlF446WhVsioDVBtitCiQhoMgSFDjmTSU71qeiCFglx04yq10AdgG9WoJaI0F1aceUxSZQVKCgwCJgxuV1AbtjHUGSoEgSZAmQ4JiGLDm2i7J86aUICEWgyAbYcOkRnsKxbYcAhMqx7oW4NFAREJIEITu+Uza7gNWsYEBnAwx6uVT7UJG2RFxxqVVFMGknIiKiG9b5jxPh83xrqLwcPeOcP7ORvf0ijG19ENA/6HK9RYkQVoHwRxpCbdIAAHL35SBrayZi7GqcMXojwEdG56YaNN2WBr2i4P/CTEi5aMe3ewvRLN+M2Mx8nDVosaWBj3O68n9T0N6m4FyICRk6R7fLN60IAzPykKzX4IstEr7Y4qg7ODkbflY7vs/yxRa9I4ZmVgt6pubCHqhDxKhwHD1nRW6hgsZ/ZkCdZcHJlv5IMWrRvYUW5jOFUG1OR45ajcwwkzOGfum5CDlnxbYgI8546QAA6ScLoVqWgxy1CuvC/Zx1+6TnoGGhFQUBRqhu8ka35loE2e0Q/01BoUrGz60CYVcAk5cMn10ZaJhnwe/+3kgKNkCjkmAw23BHQhZsKgnfxwSiSagaaVkKmpzMRnSuGacijEht6A2TlwS50I42u9IhqyUc7R2K3EKBRg1UMB3Mhv50PvJa+KAwxhcX8xRkZdnQ7rc02O0Cm9o2QFSIGl46Cc3O58Pn7zzYY3xQ0NZxqUJeoR3eq8/DrgDHbw0GtCpAAKpD2QhPzEdKiAHpLU3wN8oweclo8H/noVEBytAwCIMasgxY9ufAcDAb9sbeuNDWz3mEOOTbZAiLwP4OgZD9NAgxyQhPK4RubxZsDQ0o6B6IAouC/CKB0B9ToTLbceHWBjinOI7wh2cWIfxINlK8NTh8kx+0asfZAV0PZcBoVfBrtAmFflr46GU0zDOj8dFs5Jk0ON0xEEIAXjoJDX9Nhz7Pit1N/HBOq4ZdEWhksaJTQjaKfDTY3zoAeUUCZqtA3OksmPKtONLMhFNaLQrMAj45FvQ6l40sjQrrw/xQaHGclXF7Wg7Ciqz4JdCI096O70mg2YaBqdnIU8tYE+7v/J70Ts9FZKEFOwO8ccKoBwCYrDY8kJyNIlnGqoaX6/bIyEfTQjOOhBlxNtgLKhnwtSmI/SsDZgH8r0UQDFpHwtzqbA7CM4uw28cLB00GqFWA2iow/NxFAMB/ohzfP7sCtL+Yj5a5RTjga8A+Py8Ajp0qD5zLBAB81TAAuZduGtkhqwDtcgpxxEePA/7ezthGJ2YAAFZG+MOscuwUa5tdiI7ZBThh1GFngNFZ94GzmVALgTXhfsi7dNpFy5xC3JxVgL+9dNgedLnu8HMXnduIbI3jd988r8i5jVhRYhsxNOkijDYFq0psIxrnm3HLpW3EpmBfZ93ByVmObUSwL1IvbSMiCyzofSEXaTo1vgu5/LsfkJKNIIsNmxv44LxBCwAIK7Li9rQcZGrU2FBiGxGfmoMQs+s2ooHZijtTr76N2BFgxCmjo66/xYZBKdkoUMn4OuLyuu91IReNChzbiGM+ju+Jj9WOe5KzYJElrGgYUOJ7koem+Wbs9vPCYV8DAMDLpmBY0kUoAJZGBTrrds3MR0xeEfabDNhvcqx7raJg5LmLsAP4MjLAuYOo88UCtM4txGEfA3b7O+pKQuChs47vyYqG/rDIjnXfPrsA7bMLcdSox66Ay9+TBxMzkPqbCpHjo6D2cayj3D3ZyPr5IrxbGxF4RwNn3aRPzkIxKwgb1xAaf8c6yjuQi4s/ZcDesPI3MWXSTkRERHQN9ktHV3UaCbmFrkdKTqfZcTrNjuYAgk0qJNvLnsatrbS443YTfjliRsZ+O4wFsuNQZwkq2XFad3SwCqk5jjKzFVAUID3Hji/X5DjrDrhoR5BF4Mh5K84bJBxKtDo65ALQa4HhPbzQrbkWKhk4+lk+kGpD8zANborW43SaDTlnHM+tkyXHaf+K4pi/gEIZJknGA7d6Iaq7I1mwpJmRopfhZ1RhwuDLCURKYT7yj9kxPNYLkT1NkCQJ1otWJH+WC1knY/oDfs66Gd+ZkX/Yhhad9fC92ZGw2HJtSDqaCcjAyJ6XO8iZGfnIS5MRHamBqb2jo68U2XHuqCNRen24yXFkE8DFbWbknpXg46+Cf1NHYiLsapw1ODrh93b3gqx3jJctmZGdV4SbWusQ0PdygpX4kwwoQHhDrbNDnpOqRtZxGd7+KrRpo3fWPbdDBcWsYFQvb2eHPHevFRc1ErxMKgQ10zrrnv9Dhj1PIKalDtpgR3KTdwjITMlDk8Y63D30ctKUlJ0D20UbnrzTB7oIx+cVHJNx4XwuwsM1uOW2y8sn+ZQaVknBg728oW/kSG4K/y5AekYetKEaxN1xOSFMXZ4Pc5KClrFe8GrmmEbR2UKkrcpHkwANbh3hSNqLLAKWjYVQkhXc290Aqak3zFYBccEC3Q95aOCtwvODfKDXOo4aWzeZIRLtGNzVAKWpEXZFwNdih7IuD2pvFRY86g9FOL5fOT/YUHjUjrY9vODb2THPtmwrkpKzIGkkzBp9+XuS+YMVeQdtaBXnBVN3P8iSBGu+DUmLciGEwNyH/WBXHEc/C7bbYTloRYNWevTr5AONWoJaUWD9MhsA8PK9Psi1SbArAvI+O6TDZnRuokXbjt4osgooCuC//iKEAIZ01UPo1VAUAdMpO3yOFqFdIw0ad3QkeRBA0IYswCYwsLMeMGogSYDhlALfw0VoGa5Gg/YGmK2OyzOCf8iGbAFub6dHkbcadjtgPKvAp7AQwSYZjUNUzqP93hkydJLjEgrDpZ2LAZIETbbjLIgQPxlFFgFFwHFk2wbnJQ6Wchy5VUmOMxJkGdDbHX81asdlHrJ86WyFHAk6RUJUkAreAWrYBWDMEdBlSvDSOsodO0sEvLIlqCwSjHoJJi8JdgUwXDpLQZIcR+PVMqBRS9CqJcgy4KUDAoyOSx70hYrj6L3sKNOqAa1Ggm+BBFWRBC+dBF+DBAHAR5Ecl4PIQJsojfNYe5giw2CREBGggmiogRCAbFOgT3PUaBmhhh2OyzOCbDJ0RRJC/WV0aOyYhiwEvC7V7dBYC0XtiC0s0QLvIgmRQSqgqRZatQSNGjBlyChxYoxbSKKe3eY0JycHJpMJ2dnZ8PX1vf4IRERENYxtU/UrXqYX0y/CFGiq0unxE7/IQp4VmH6/CT8eKMKOg0WX6gJWxVHXpAdmjTJBgQRZ4+iMF5gVrPu1AM1DVejSQu9yGruwCeQWKdh31gaVLKFFhBqBBgkQjhgKrQL//bMQRxOtuHDpmmm7LKFZmBqRQSqkZ9ggBKA3SNj9t+Oie7UsMLybAbe21kGlvXytfFmn0ucV2KHYBLz1kktdYXOcSiqpJGdiXBwvJEDWyOWrC0DWVmNdISAu3RBQ0kil12dF6lZg3VeprlVxrs9K1S1ePuW8NKLCda9YnxWp6651X+XvydXWp6d9T662Pq+oK4SAoghYzI5TzNVaGSrZkSjLdkddWSNX6/eE24jK183Ny4VfoF+l2nom7URERG7Gtqn6VecyHb8oE3YFmPWQCWqVhP/+UYierXTILxJ477+5AIDn7vJB6yhNdYReJkUI5BQ4bopWloxcO3QaCUY9n+ZLROSJqtIu8fR4IiIioquw2QXsly5D1GkkeOtljOp1+VTlT58IuMqY1UuWJPh5X/38zECfmrkLPRERuR93xxIRERFdRclrRrUVfGwaERFRdWDSTkRERHQVxc9olyXHzZWIiIhqG5sfIiIioqswX7pZkVYN582KiIiIahOTdiIiIqKrMNsuP+6NiIjIHZi0ExEREV2FxXmknUk7ERG5B5N2IiIioqsovqadR9qJiMhd+Mg3IiIioqvQqIHoYBWCTXykGhERuQeTdiIiIqKraB6mwSvDTO4Og4iI6jGeHk9ERERERETkoZi0ExEREREREXkoJu1EREREREREHopJOxEREREREZGHYtJORERERERE5KGYtBMRERERERF5KCbtRERERERERB6KSTsRERERERGRh2LSTkREREREROShmLQTEREREREReSgm7UREREREREQeikk7ERERERERkYdi0k5ERERERETkoZi0ExEREREREXkoJu1EREREREREHopJOxEREREREZGHYtJORERERERE5KGYtBMRERERERF5KCbtRERERERERB6KSTsRERERERGRh2LSTkREREREROShmLQTEREREREReSgm7UREREREREQeikk7ERERERERkYdi0k5ERERERETkoZi0ExEREREREXkoJu1EREREREREHopJOxEREREREZGHYtJORERERERE5KGYtBMRERERERF5KCbtRERERERERB7KI5L2Dz/8ENHR0dDr9ejWrRt27dp1zfqrV69GTEwM9Ho92rZti40bN9ZSpERERFQZbOuJiIgqx+1J+8qVKzFhwgS88cYb2LNnD9q3b4/4+HikpaWVWf/XX3/F/fffj3/84x/Yu3cvhgwZgiFDhuDQoUO1HDkRERGVB9t6IiKiypOEEMKdAXTr1g0333wzFixYAABQFAWRkZF4+umnMXny5FL1R4wYgfz8fGzYsMFZ1r17d3To0AGLFi267ufl5OTAZDIhOzsbvr6+1TcjRERElXSjt0213dYDN/4yJSKiuqUq7ZJbj7RbLBbs3r0b/fr1c5bJsox+/fph586dZY6zc+dOl/oAEB8ff9X6ZrMZOTk5Li8iIiKqHbXR1gNs74mI6Mbl1qT9woULsNvtCAkJcSkPCQlBSkpKmeOkpKRUqP7MmTNhMpmcr8jIyOoJnoiIiK6rNtp6gO09ERHduNx+TXtNmzJlCrKzs52vs2fPujskIiIiqmZs74mI6EaldueHBwUFQaVSITU11aU8NTUVoaGhZY4TGhpaofo6nQ46na56AiYiIqIKqY22HmB7T0RENy63HmnXarXo3LkzNm/e7CxTFAWbN29GbGxsmePExsa61AeATZs2XbU+ERERuQ/beiIioqpx65F2AJgwYQLGjBmDLl26oGvXrpg3bx7y8/Px8MMPAwBGjx6NiIgIzJw5EwDw7LPPolevXnj33XcxcOBArFixAn/++Sc++eQTd84GERERXQXbeiIiospze9I+YsQIpKen4/XXX0dKSgo6dOiA7777znkDmsTERMjy5RMC4uLi8NVXX+HVV1/Fyy+/jObNm2PdunVo06aNu2aBiIiIroFtPRERUeW5/TnttY3PbSUiIk/Dtqn6cZkSEZEnqbPPaSciIiIiIiKiq2PSTkREREREROShmLQTEREREREReSgm7UREREREREQeikk7ERERERERkYdi0k5ERERERETkoZi0ExEREREREXkoJu1EREREREREHopJOxEREREREZGHYtJORERERERE5KGYtBMRERERERF5KCbtRERERERERB6KSTsRERERERGRh2LSTkREREREROShmLQTEREREREReSgm7UREREREREQeikk7ERERERERkYdi0k5ERERERETkoZi0ExEREREREXkoJu1EREREREREHopJOxEREREREZGHYtJORERERERE5KGYtBMRERERERF5KCbtRERERERERB6KSTsRERERERGRh2LSTkREREREROSh1O4OoLYJIQAAOTk5bo6EiIjIobhNKm6jqOrY3hMRkSepSltf75L23NxcAEBkZKSbIyEiInKVm5sLk8nk7jBuCGzviYjIE1WmrZdEPdutrygKkpKS4OPjA0mSqjStnJwcREZG4uzZs/D19a2mCGtXXZ8Hxu9ejN+9GL97VWf8Qgjk5uYiPDwcsswr16oD2/vLGL97MX73YvzuVdfjB6pvHqrS1te7I+2yLKNhw4bVOk1fX986+yUsVtfngfG7F+N3L8bvXtUVP4+wVy+296Uxfvdi/O7F+N2rrscPVM88VLat5+58IiIiIiIiIg/FpJ2IiIiIiIjIQzFprwKdToc33ngDOp3O3aFUWl2fB8bvXozfvRi/e9X1+Kn86vq6Zvzuxfjdi/G7V12PH/CMeah3N6IjIiIiIiIiqit4pJ2IiIiIiIjIQzFpJyIiIiIiIvJQTNqJiIiIiIiIPBSTdiIiIiIiIiIPxaS9Cj788ENER0dDr9ejW7du2LVrl7tDAgD8/PPPGDRoEMLDwyFJEtatW+cyXAiB119/HWFhYTAYDOjXrx9OnDjhUiczMxOjRo2Cr68v/Pz88I9//AN5eXm1Ev/MmTNx8803w8fHB8HBwRgyZAiOHTvmUqeoqAhPPvkkAgMDYTQace+99yI1NdWlTmJiIgYOHAgvLy8EBwfjhRdegM1mq/H4P/roI7Rr1w6+vr7w9fVFbGwsvv322zoR+5XeeecdSJKE5557zlnm6fFPnToVkiS5vGJiYupM/ABw/vx5PPjggwgMDITBYEDbtm3x559/Ood78m84Ojq61PKXJAlPPvkkAM9f/na7Ha+99hoaN24Mg8GApk2bYvr06Sh5z1ZPXv5U/djW1wy29e5va0qqa+0923oHtvWVUyfbekGVsmLFCqHVasVnn30mDh8+LB599FHh5+cnUlNT3R2a2Lhxo3jllVfEmjVrBACxdu1al+HvvPOOMJlMYt26dWL//v1i8ODBonHjxqKwsNBZ54477hDt27cXv/32m/jll19Es2bNxP33318r8cfHx4vPP/9cHDp0SOzbt08MGDBAREVFiby8PGed8ePHi8jISLF582bx559/iu7du4u4uDjncJvNJtq0aSP69esn9u7dKzZu3CiCgoLElClTajz+9evXi//973/i+PHj4tixY+Lll18WGo1GHDp0yONjL2nXrl0iOjpatGvXTjz77LPOck+P/4033hCtW7cWycnJzld6enqdiT8zM1M0atRIjB07Vvz+++/i77//Ft9//704efKks44n/4bT0tJclv2mTZsEALFlyxYhhOcv/7feeksEBgaKDRs2iNOnT4vVq1cLo9Eo3n//fWcdT17+VL3Y1tcctvWe0dYLUTfbe7b1DmzrK6cutvVM2iupa9eu4sknn3S+t9vtIjw8XMycOdONUZV2ZUOuKIoIDQ0V//rXv5xlWVlZQqfTieXLlwshhPjrr78EAPHHH38463z77bdCkiRx/vz5Wou9WFpamgAgtm3b5oxXo9GI1atXO+scOXJEABA7d+4UQjg6M7Isi5SUFGedjz76SPj6+gqz2Vy7MyCE8Pf3F//+97/rTOy5ubmiefPmYtOmTaJXr17ORrwuxP/GG2+I9u3blzmsLsT/0ksviZ49e151eF37DT/77LOiadOmQlGUOrH8Bw4cKMaNG+dSNnToUDFq1CghRN1b/lQ1bOtrD9t698ReV9t7tvWe9RtmW+9Qk8ufp8dXgsViwe7du9GvXz9nmSzL6NevH3bu3OnGyK7v9OnTSElJcYndZDKhW7duzth37twJPz8/dOnSxVmnX79+kGUZv//+e63HnJ2dDQAICAgAAOzevRtWq9VlHmJiYhAVFeUyD23btkVISIizTnx8PHJycnD48OFai91ut2PFihXIz89HbGxsnYn9ySefxMCBA13iBOrOsj9x4gTCw8PRpEkTjBo1ComJiXUm/vXr16NLly647777EBwcjI4dO+LTTz91Dq9Lv2GLxYKlS5di3LhxkCSpTiz/uLg4bN68GcePHwcA7N+/H9u3b8edd94JoG4tf6oatvW1i229e2Kvy+0923rP+A2zra+d5a+u9inWAxcuXIDdbnf5ogFASEgIjh496qaoyiclJQUAyoy9eFhKSgqCg4NdhqvVagQEBDjr1BZFUfDcc8+hR48eaNOmjTM+rVYLPz8/l7pXzkNZ81g8rKYdPHgQsbGxKCoqgtFoxNq1a9GqVSvs27fP42NfsWIF9uzZgz/++KPUsLqw7Lt164YlS5agRYsWSE5OxrRp03DLLbfg0KFDdSL+v//+Gx999BEmTJiAl19+GX/88QeeeeYZaLVajBkzpk79htetW4esrCyMHTvWGZenL//JkycjJycHMTExUKlUsNvteOuttzBq1CiXGOrC8qeqYVtfe9jW137sQN1u79nWe85vmG39ZTW5/Jm0k0d78skncejQIWzfvt3doVRIixYtsG/fPmRnZ+Prr7/GmDFjsG3bNneHdV1nz57Fs88+i02bNkGv17s7nEop3ksKAO3atUO3bt3QqFEjrFq1CgaDwY2RlY+iKOjSpQvefvttAEDHjh1x6NAhLFq0CGPGjHFzdBWzePFi3HnnnQgPD3d3KOW2atUqLFu2DF999RVat26Nffv24bnnnkN4eHidW/5EdQXb+tpX19t7tvWeg2197eDp8ZUQFBQElUpV6i6IqampCA0NdVNU5VMc37ViDw0NRVpamstwm82GzMzMWp2/p556Chs2bMCWLVvQsGFDZ3loaCgsFguysrJc6l85D2XNY/GwmqbVatGsWTN07twZM2fORPv27fH+++97fOy7d+9GWloaOnXqBLVaDbVajW3btuGDDz6AWq1GSEiIR8dfFj8/P9x00004efKkxy9/AAgLC0OrVq1cylq2bOk87a+u/IbPnDmDH3/8EY888oizrC4s/xdeeAGTJ0/GyJEj0bZtWzz00EN4/vnnMXPmTJcYPH35U9Wxra8dbOvdE/uN1t6zrXdgW18+dbGtZ9JeCVqtFp07d8bmzZudZYqiYPPmzYiNjXVjZNfXuHFjhIaGusSek5OD33//3Rl7bGwssrKysHv3bmedn376CYqioFu3bjUeoxACTz31FNauXYuffvoJjRs3dhneuXNnaDQal3k4duwYEhMTXebh4MGDLj+mTZs2wdfXt9RGsjYoigKz2ezxsfft2xcHDx7Evn37nK8uXbpg1KhRzv89Of6y5OXl4dSpUwgLC/P45Q8APXr0KPXYo+PHj6NRo0YA6sZvGAA+//xzBAcHY+DAgc6yurD8CwoKIMuuTaNKpYKiKADqzvKnqmNbX7PY1rs39hutvWdb78C2vnzqZFtf7be2qydWrFghdDqdWLJkifjrr7/EP//5T+Hn5+dyF0R3yc3NFXv37hV79+4VAMTcuXPF3r17xZkzZ4QQjkcY+Pn5if/7v/8TBw4cEHfffXeZjzDo2LGj+P3338X27dtF8+bNa+0xMI8//rgwmUxi69atLo+TKCgocNYZP368iIqKEj/99JP4888/RWxsrIiNjXUOL36URP/+/cW+ffvEd999Jxo0aFArj5KYPHmy2LZtmzh9+rQ4cOCAmDx5spAkSfzwww8eH3tZSt5NVgjPj3/ixIli69at4vTp02LHjh2iX79+IigoSKSlpdWJ+Hft2iXUarV46623xIkTJ8SyZcuEl5eXWLp0qbOOp/+G7Xa7iIqKEi+99FKpYZ6+/MeMGSMiIiKcj4FZs2aNCAoKEi+++KKzjqcvf6o+bOtrDtt6z2rrhahb7T3bege29ZVTF9t6Ju1VMH/+fBEVFSW0Wq3o2rWr+O2339wdkhBCiC1btggApV5jxowRQjgeY/Daa6+JkJAQodPpRN++fcWxY8dcppGRkSHuv/9+YTQaha+vr3j44YdFbm5urcRfVuwAxOeff+6sU1hYKJ544gnh7+8vvLy8xD333COSk5NdppOQkCDuvPNOYTAYRFBQkJg4caKwWq01Hv+4ceNEo0aNhFarFQ0aNBB9+/Z1NuKeHntZrmzEPT3+ESNGiLCwMKHVakVERIQYMWKEy3NPPT1+IYT473//K9q0aSN0Op2IiYkRn3zyictwT/8Nf//99wJAqZiE8Pzln5OTI5599lkRFRUl9Hq9aNKkiXjllVdcHkHj6cufqhfb+prBtt79bc2V6lJ7z7begW195dTFtl4SQojqP35PRERERERERFXFa9qJiIiIiIiIPBSTdiIiIiIiIiIPxaSdiIiIiIiIyEMxaSciIiIiIiLyUEzaiYiIiIiIiDwUk3YiIiIiIiIiD8WknYiIiIiIiMhDMWknIiIiIiIi8lBM2omIiIiIiIg8FJN2IgIApKen4/HHH0dUVBR0Oh1CQ0MRHx+PHTt2AAAkScK6devcGyQRERFVGtt6orpJ7e4AiMgz3HvvvbBYLPjiiy/QpEkTpKamYvPmzcjIyHB3aERERFQN2NYT1U2SEEK4Owgicq+srCz4+/tj69at6NWrV6nh0dHROHPmjPN9o0aNkJCQAAD4v//7P0ybNg1//fUXwsPDMWbMGLzyyitQqx37BCVJwsKFC7F+/Xps3boVYWFhmD17NoYNG1Yr80ZERERs64nqMp4eT0QwGo0wGo1Yt24dzGZzqeF//PEHAODzzz9HcnKy8/0vv/yC0aNH49lnn8Vff/2Fjz/+GEuWLMFbb73lMv5rr72Ge++9F/v378eoUaMwcuRIHDlypOZnjIiIiACwrSeqy3iknYgAAN988w0effRRFBYWolOnTujVqxdGjhyJdu3aAXDsRV+7di2GDBniHKdfv37o27cvpkyZ4ixbunQpXnzxRSQlJTnHGz9+PD766CNnne7du6NTp05YuHBh7cwcERERsa0nqqN4pJ2IADiuc0tKSsL69etxxx13YOvWrejUqROWLFly1XH279+PN99807n33mg04tFHH0VycjIKCgqc9WJjY13Gi42N5d53IiKiWsa2nqhu4o3oiMhJr9fj9ttvx+23347XXnsNjzzyCN544w2MHTu2zPp5eXmYNm0ahg4dWua0iIiIyLOwrSeqe3iknYiuqlWrVsjPzwcAaDQa2O12l+GdOnXCsWPH0KxZs1IvWb68efntt99cxvvtt9/QsmXLmp8BIiIiuia29USej0faiQgZGRm47777MG7cOLRr1w4+Pj74888/MXv2bNx9990AHHeV3bx5M3r06AGdTgd/f3+8/vrruOuuuxAVFYVhw4ZBlmXs378fhw4dwowZM5zTX716Nbp06YKePXti2bJl2LVrFxYvXuyu2SUiIqp32NYT1V28ER0RwWw2Y+rUqfjhhx9w6tQpWK1WREZG4r777sPLL78Mg8GA//73v5gwYQISEhIQERHhfAzM999/jzfffBN79+6FRqNBTEwMHnnkETz66KMAHDen+fDDD7Fu3Tr8/PPPCAsLw6xZszB8+HA3zjEREVH9wraeqO5i0k5ENaqsO9ESERHRjYNtPVHN4jXtRERERERERB6KSTsRERERERGRh+Lp8UREREREREQeikfaiYiIiIiIiDwUk3YiIiIiIiIiD8WknYiIiIiIiMhDMWknIiIiIiIi8lBM2omIiIiIiIg8FJN2IiIiIiIiIg/FpJ2IiIiIiIjIQzFpJyIiIiIiIvJQTNqJiIiIiIiIPBSTdiIiIiIiIiIPxaSdiIiIiIiIyEMxaSciIiIiIiLyUEzaiYiIiIiIiDwUk/ZasmTJEkiShISEhOvWjY6OxtixY6vtsxMSEiBJEpYsWVJt0/RU1b3s6Mb3xx9/IC4uDt7e3pAkCfv27XN3SERUT7GvUDvYV6CKYl/Bs4wdOxbR0dHXrVeR7Vpx3Tlz5lQ9wBpQr5P2P/74A0899RRat24Nb29vREVFYfjw4Th+/Hi5p2GxWPD++++jY8eO8PX1hZ+fH1q3bo1//vOfOHr0aA1GX/22bt0KSZJcXgEBAejevTuWLVvm7vBqTWpqKiZNmoSYmBh4eXnB29sbnTt3xowZM5CVleXu8KgaWa1W3HfffcjMzMR7772HL7/8Eo0aNbrueBs3boQkSQgPD4eiKLUQad2zdu1a3HnnnQgKCoJWq0V4eDiGDx+On376yd2hEVUI+wqu2FdwYF+h/mBfoXoNGDAA/v7+EEK4lO/duxeSJJW5bH/66SdIkoRPPvmkyp+/ceNGTJ06tcrTqW1qdwfgTrNmzcKOHTtw3333oV27dkhJScGCBQvQqVMn/Pbbb2jTps11p3Hvvffi22+/xf33349HH30UVqsVR48exYYNGxAXF4eYmBgAwEMPPYSRI0dCp9PV9GxV2TPPPIObb74ZAJCRkYGVK1fiwQcfRFZWFp588kk3R1ez/vjjDwwYMAB5eXl48MEH0blzZwDAn3/+iXfeeQc///wzfvjhBzdHSdXl1KlTOHPmDD799FM88sgj5R5v2bJliI6ORkJCAn766Sf069evBqOsW4QQGDduHJYsWYKOHTtiwoQJCA0NRXJyMtauXYu+fftix44diIuLc3eoROXCvkLZ2FdgX6G+YF+hevXs2RPffvstDh06hLZt2zrLd+zYAbVajcTERJw7dw4NGzZ0GVY8LgB8+umnld4RsnHjRnz44Yd1L3EX9diOHTuE2Wx2KTt+/LjQ6XRi1KhR1x1/165dAoB46623Sg2z2WziwoULlYqrUaNGYsyYMZUatyynT58WAMTnn39+zXpbtmwRAMTq1atdys1ms4iIiBBxcXHVFlNNqcqyu3jxooiIiBAhISHiyJEjpYanpKSI6dOnVzFC97JaraW+8/XZtm3byvzOX0teXp7w9vYWH3zwgejYsaMYO3ZsDUZYNkVRREFBQa1/bnn861//EgDEc889JxRFKTX8P//5j/j999/dENm18bdBV8O+giv2FdhXqG/YV6hexctz4cKFLuUjR44UgwcPFkajUSxfvtxlWP/+/UVgYGCZ/YprKWu79uSTT4qyUuDiuv/6178q9Bm1pV6fHh8XFwetVutS1rx5c7Ru3RpHjhy57vinTp0CAPTo0aPUMJVKhcDAQOf7sq5TE0JgxowZaNiwIby8vNC7d28cPny4zM/KysrCc889h8jISOh0OjRr1gyzZs0qtZcpKysLY8eOhclkgp+fH8aMGVPl07S0Wi38/f2hVruemGGz2TB9+nQ0bdoUOp0O0dHRePnll2E2m13qSZJU5t6sK68pK15GO3bswIQJE9CgQQN4e3vjnnvuQXp6usu4FVl2p06dcq6ra/n4449x/vx5zJ0713nUo6SQkBC8+uqrLmULFy5E69atodPpEB4ejieffLLU8r7tttvQpk0b/PXXX+jduze8vLwQERGB2bNnO+ukpqZCrVZj2rRppT732LFjkCQJCxYscJaV5/tQ8tqcefPmOdfTX3/9BcBximOXLl2g1+vRtGlTfPzxx5g6dSokSSoVw9KlS9G5c2cYDAYEBARg5MiROHv2bIXns1hRURGmTp2Km266CXq9HmFhYRg6dKjLelIUBfPmzUPr1q2h1+sREhKCxx57DBcvXiw1vbL89NNPuOWWW+Dt7Q0/Pz/cfffdLr/rsWPHolevXgCA++67D5Ik4bbbbrvudNeuXYvCwkLcd999GDlyJNasWYOioiLn8DZt2qB3796lxlMUBRERERg2bFiF5zE6Ohp33XUXvv/+e3Tp0gUGgwEff/wxAODzzz9Hnz59EBwcDJ1Oh1atWuGjjz4q8/OnTp2K8PBw52/mr7/+KvPazvJub65UWFiImTNnIiYmBnPmzCnzu/TQQw+ha9euzvd///037rvvPgQEBMDLywvdu3fH//73P+fw2v5tWCwWvP766+jcuTNMJhO8vb1xyy23YMuWLaU+PyMjAw899JDzdOcxY8Zg//79ZV4/d/ToUQwbNgwBAQHQ6/Xo0qUL1q9ff83lSZ6BfYXyYV/BgX0F9hUA9hWupWvXrtBqtc6j58V27NiBW2+9FV27dnUZpigKfvvtN8TFxTm/d2Vd016e7drYsWPx4YcfAoDLJT5X+uSTT5y/hZtvvhl//PHHNeepVrh3n4HnURRFREREiP79+1+37q+//ioAiEcffVRYrdZr1v38888FAHH69Gln2auvvioAiAEDBogFCxaIcePGifDwcBEUFOSyBzg/P1+0a9dOBAYGipdfflksWrRIjB49WkiSJJ599lmX2G+99VYhy7J44oknxPz580WfPn1Eu3btKrT3/LPPPhPp6ekiPT1dHDt2TLzxxhsCgFi8eLFL/TFjxggAYtiwYeLDDz8Uo0ePFgDEkCFDXOoBEG+88Uapz7tyT3fxMurYsaPo06ePmD9/vpg4caJQqVRi+PDhLuOWd9kVf06jRo2uOe9CCBEXFycMBkO59y4XL5d+/fqJ+fPni6eeekqoVCpx8803C4vF4qzXq1cvER4eLiIjI8Wzzz4rFi5cKPr06SMAiI0bNzrr9enTR7Rq1arU50ybNk2oVCqRkpIihCj/96F4j2GrVq1EkyZNxDvvvCPee+89cebMGbFnzx6h0+lEdHS0eOedd8Rbb70lwsPDRfv27UvtfZwxY4aQJEmMGDFCLFy4UEybNk0EBQWJ6OhocfHixQrPp81mE3379hUAxMiRI8WCBQvEzJkzRZ8+fcS6deuc9R555BGhVqvFo48+KhYtWiReeukl4e3tXWr5lmXTpk1CrVaLm266ScyePdsZs7+/v/M3+Ouvv4qXX35ZABDPPPOM+PLLL8UPP/xwzekKIcQdd9wh+vbtK4QQ4syZM0KSJLFq1Srn8DfffFPIsiySk5NdxitrT31557FRo0aiWbNmwt/fX0yePFksWrRIbNmyRQghxM033yzGjh0r3nvvPTF//nzRv39/AUAsWLDA5fNffPFFAUAMGjRILFiwQDz66KOiYcOGld7elOWHH34QAMSbb7553eUohOOIVEhIiPDx8RGvvPKKmDt3rmjfvr2QZVmsWbPGWa82fxvp6ekiLCxMTJgwQXz00Udi9uzZokWLFkKj0Yi9e/c6p2G320VsbKxQqVTiqaeeEgsWLBC333678zdUcnt76NAhYTKZRKtWrcSsWbPEggULxK233iokSXKZT6o72FdgX4F9BfYVroV9hWuLjY11+b0lJiYKAOLXX38Vr776qujYsaNz2L59+wQAMWvWLGfZmDFjXMYv73bt119/FbfffrsAIL788kvnS4jLv4WOHTuKZs2aiVmzZonZs2eLoKAg0bBhw+t+n2oak/YrfPnll2U2OmVRFEX06tVLABAhISHi/vvvFx9++KE4c+ZMqbpXNsRpaWlCq9WKgQMHupzqUbxhKPnDmD59uvD29hbHjx93mebkyZOFSqUSiYmJQggh1q1bJwCI2bNnO+vYbDZxyy23VKghvvIly3Kp0/qKf0CPPPKIS/mkSZMEAPHTTz85yyraEPfr189lmTz//PNCpVKJrKysCi+74s8pT0Ps7+8v2rdvf916JWPo37+/sNvtzvIFCxY4OzPFir8j//nPf5xlZrNZhIaGinvvvddZ9vHHHwsA4uDBgy6f1apVK9GnTx/n+/J+H4o3Pr6+viItLc2l7qBBg4SXl5c4f/68s+zEiRNCrVa7NMQJCQlCpVKVWv8HDx4UarXapby88/nZZ58JAGLu3LniSsXr85dffhEAxLJly1yGf/fdd2WWX6lDhw4iODhYZGRkOMv2798vZFkWo0ePdpZd7TTPq0lNTRVqtVp8+umnzrK4uDhx9913O98fO3ZMABDz5893GfeJJ54QRqPReapaReaxUaNGAoD47rvvSsVU1qlv8fHxokmTJs73KSkpQq1Wl+okT506tdLbm7K8//77AoBYu3btVeuU9NxzzwkA4pdffnGW5ebmisaNG4vo6Gjnb6s2fxs2m61UZ/zixYsiJCREjBs3zln2zTffCABi3rx5zjK73e7sfJbc3vbt21e0bdtWFBUVOcsURRFxcXGiefPm5VpW5FnYV2BfoTzYV2BfoRj7Cq5eeOEFAUCcO3dOCCHE8uXLhV6vF2azWWzcuFGoVCqRk5MjhLj8e9mxY4dz/CuT9ops1653enxgYKDIzMx0lv/f//2fACD++9//XnOeahqT9hKOHDkifH19RWxsrLDZbOUap6ioSMyYMUPExMS4NF7Dhw932bN4ZUP81VdflfnDSktLK/XDaNeunbjjjjuce7SLXz/++KMAIJYuXSqEEOKf//ynUKvVIjc312Waq1atqlBD/Prrr4tNmzaJTZs2iZUrV4pRo0aV6py+/fbbAoD466+/XKaRnJwsAIiJEyc6yyraEJfcEymEEGvWrBEAxP79+yu87CpCpVKJnj17lqtucQwl9woL4Wh4fH19XRqeXr16CaPRWOo6nMGDB7vsSUxPTxdqtVq8+uqrzrKDBw8KAOLjjz92lpX3+1C88Xn44YddPtdmswmDwSAeeOCBUvM1aNAglw3Z3LlzhSRJ4sSJE6U+r2XLlqJfv34Vns+BAweKoKCgax5xeuaZZ4TJZBJpaWmlPtdoNJbqAJaUlJQkAIgXX3yx1LD4+HgRFBTkfF/Rhvj9998XWq3WZWM+f/78UmUdOnRw+S7ZbDYRHBws7r///krNY6NGjUTjxo2vG19WVpZIT093/j6LO6/Lli0TAEodHcjIyKj09qYs06dPFwDEjz/+eN1YhRDipptuEl27di1VPnPmTJdOaW39Nq5kt9tFRkaGSE9PFwMHDhQdOnRwDnv00UeFRqMR+fn5LuMUJ/PF29uMjAwhSZKYPn16qbimTZvm0mmhuoF9BfYV2FdgX+Fa2Fe4dl9BiMuJcPG160899ZTo0aOHEMKxo1ySJGcc999/vzOhL3Zl0l6R7dr1kvYnnnjCpTwzM1MAEO+///4156mm1eu7x5eUkpKCgQMHwmQy4euvv4ZKpXIOy87ORmFhofO9VqtFQEAAAECn0+GVV17BK6+8guTkZGzbtg3vv/8+Vq1aBY1Gg6VLl5b5eWfOnAHguC6upAYNGsDf39+l7MSJEzhw4AAaNGhQ5rTS0tKc0wwLC4PRaHQZ3qJFi/IsAqe2bdu63OFy+PDhyM7OxuTJk/HAAw+gQYMGOHPmDGRZRrNmzVzGDQ0NhZ+fn3P+KiMqKsrlffHyKL52pyLLriJ8fX2Rm5tbrrrFMVy5bLVaLZo0aVJq/hs2bFjqmhl/f38cOHDA+T4oKAh9+/bFqlWrMH36dADAypUroVarMXToUGe98n4fijVu3LjU8MLCwlLrDkCpshMnTkAIUWpZF9NoNBWez1OnTqFFixalrnu88nOzs7MRHBxc5vAr57Gkq60bAGjZsiW+//575Ofnw9vb+6rTuJqlS5eia9euyMjIQEZGBgCgY8eOsFgsWL16Nf75z38CAEaMGIGXX34Z58+fR0REBLZu3Yq0tDSMGDGi0vN45XostmPHDrzxxhvYuXMnCgoKXIZlZ2fDZDI5l8mV6zcgIKDS25uy+Pr6AkCFfkfdunUrVd6yZUvn8DZt2tTab6PYF198gXfffRdHjx6F1Wots37x9tbLy8tl3CuX8cmTJyGEwGuvvYbXXnvtqnFFRESUOYw8C/sKl7GvcH3sKziwr8C+wpV69OjhvDfFyJEjsWPHDtx+++0AAD8/P7Rq1cpZtmPHDtx8882l7i1SUnVt14Drb1vchUk7HF/WO++8E1lZWfjll18QHh7uMvzZZ5/FF1984Xzfq1cvbN26tdR0wsLCMHLkSNx7771o3bo1Vq1ahSVLllxzg1MeiqLg9ttvx4svvljm8JtuuqlK0y+Pvn37YsOGDdi1axcGDhzoLC/r5g3lZbfbyywv2QkqSVzxPMfqFhMTg3379sFisVxzw1AZ5Z2nkSNH4uGHH8a+ffvQoUMHrFq1Cn379kVQUJCzTkW/DwaDodJxK4oCSZLw7bffljkPV24cq2vdKYqC4ODgqz7z92qNRE06ceKE80YkZXVMli1b5tIQT5kyBatXr8Zzzz2HVatWwWQy4Y477nDWr+g8lrUeT506hb59+yImJgZz585FZGQktFotNm7ciPfee69Sj0Opyvam+KZMBw8exJAhQyr82ddSW7+NpUuXYuzYsRgyZAheeOEFBAcHQ6VSYebMmeW6SdWVitfBpEmTEB8fX2adsjrF5HnYV7g+9hWqhn0F9hXqQ18BAAIDAxETE4Pt27cjLy8PBw4cwBtvvOEcHhcXh+3bt+PcuXNITEzEqFGjKhxjZblr23I99T5pLyoqwqBBg3D8+HH8+OOPaNWqVak6L774Ih588EHn++vtodVoNGjXrh1OnDiBCxcuIDQ0tFSdRo0aAXD8uJs0aeIsT09PL7Unp2nTpsjLy7vu8x0bNWqEzZs3Iy8vz2UDeezYsWuOVx42mw0AkJeX5/wsRVFw4sQJ51ExwHFn06ysLOf8AY7ldeXdGy0WC5KTkysVS0WWXUUMGjQIO3fuxDfffIP777+/XDEcO3bMJQaLxYLTp09X+lmcQ4YMwWOPPYaVK1cCAI4fP44pU6a41Cnv9+FqgoODodfrcfLkyVLDrixr2rQphBBo3LhxtXX4mjZtit9//x1Wq7XU3veSdX788Uf06NGjwh2JkuvmSkePHkVQUFCl9pwvW7YMGo0GX375ZakN+vbt2/HBBx8gMTERUVFRaNy4Mbp27YqVK1fiqaeewpo1azBkyBCXZy9XZR6L/fe//4XZbMb69etd9gxfeafz4mVy8uRJl73wGRkZld7elKVnz57w9/fH8uXL8fLLL1+14SsZ19XWU8m4gdr5bQDA119/jSZNmmDNmjUuiUbJzkRxbFu2bEFBQYHL0fYrf0PF2weNRsNn9NZh7CuUD/sKZcfAvkLFsa9Q9Xks5ml9hWI9e/bEZ599hh9++AF2ux1xcXHOYXFxcVi+fLlzx2fx89mvpiLbtarsRHSnev3IN7vdjhEjRmDnzp1YvXo1YmNjy6zXqlUr9OvXz/nq3LkzAEdDkJiYWKp+VlYWdu7cCX9//6vu5evXrx80Gg3mz5/vsudm3rx5peoOHz4cO3fuxPfff1/mZxU3kgMGDIDNZnN5hIPdbsf8+fOvvhDKacOGDQCA9u3bOz+rrHjnzp0LAC572Js2bYqff/7Zpd4nn3xy1b3n11ORZQeU/zEu48ePR1hYGCZOnIjjx4+XGp6WloYZM2Y4Y9Bqtfjggw9cYli8eDGys7Nd5r8i/Pz8EB8fj1WrVmHFihXQarWljliW9/twNSqVCv369cO6deuQlJTkLD958iS+/fZbl7pDhw6FSqXCtGnTSu1hFEI4T/uqiHvvvRcXLlxweSxNyWkCjnm02+3OU/9Kstls13w0UVhYGDp06IAvvvjCpd6hQ4fwww8/OL+7FbVs2TLccsstGDFiBIYNG+byeuGFFwAAy5cvd9YfMWIEfvvtN3z22We4cOGCy+luVZ3HYsUdgpLrJjs7G59//rlLvb59+0KtVpd6vEtZ66Aq3y8vLy+89NJLOHLkCF566aUy90ovXboUu3btAuDYjuzatQs7d+50Ds/Pz8cnn3yC6Ohol8SoNn4bQNnL9Pfff3eJEQDi4+NhtVrx6aefOssURXE+SqZYcHAwbrvtNnz88cdlJh9XPqKKPA/7CuXHvgL7CuwrsK9Q3va2Z8+esNvtmDNnDpo3b+6yHYyLi0NeXh4WLlwIWZZdEvqyVGS7VrwzpqqPuaxt9fpI+8SJE7F+/XoMGjQImZmZpa4pK7nHvCz79+/HAw88gDvvvBO33HILAgICcP78eXzxxRdISkrCvHnzrnqkqUGDBpg0aRJmzpyJu+66CwMGDMDevXvx7bffupzeBAAvvPAC1q9fj7vuugtjx45F586dkZ+fj4MHD+Lrr79GQkICgoKCMGjQIPTo0QOTJ09GQkICWrVqhTVr1iA7O7tCy+WXX35xPksyMzMT69evx7Zt2zBy5Ejn6a/t27fHmDFj8MknnyArKwu9evXCrl278MUXX2DIkCEuz5585JFHMH78eNx77724/fbbsX//fnz//fel5rO8KrLsAMdGCIDLc2/L4u/vj7Vr12LAgAHo0KEDHnzwQWena8+ePVi+fLmzs9agQQNMmTIF06ZNwx133IHBgwfj2LFjWLhwIW6++ebrfneuZcSIEXjwwQexcOFCxMfHw8/Pz2V4eb8P1zJ16lT88MMP6NGjBx5//HHY7XYsWLAAbdq0wb59+5z1mjZtihkzZmDKlClISEjAkCFD4OPjg9OnT2Pt2rX45z//iUmTJlVo/kaPHo3//Oc/mDBhAnbt2oVbbrkF+fn5+PHHH/HEE0/g7rvvRq9evfDYY49h5syZ2LdvH/r37w+NRoMTJ05g9erVeP/9912eYXqlf/3rX7jzzjsRGxuLf/zjHygsLMT8+fNhMpnKfA7w9fz+++84efIknnrqqTKHR0REoFOnTli2bBleeuklAI4GbdKkSZg0aRICAgJK7Y2u6jwCQP/+/aHVajFo0CA89thjyMvLw6efforg4GCXBDEkJATPPvss3n33XQwePBh33HEH9u/f7/zNlNzrXNXv1wsvvIDDhw/j3XffxZYtWzBs2DCEhoYiJSUF69atw65du/Drr78CACZPnozly5fjzjvvxDPPPIOAgAB88cUXOH36NL755hvIsut+5dr4bdx1111Ys2YN7rnnHgwcOBCnT5/GokWL0KpVK+fRQ8BxpKtr166YOHEiTp48iZiYGKxfvx6ZmZkAXPfkf/jhh+jZsyfatm2LRx99FE2aNEFqaip27tyJc+fOYf/+/deMidyLfYWysa/AvkIx9hUc2Feo2Per+Oj5zp07Sz0D/qabbkJQUBB27tyJtm3blvp+X6ki27Xi3+szzzyD+Ph4qFQqjBw58prT9wi1c787z1T82Imrva4nNTVVvPPOO6JXr14iLCxMqNVq4e/vL/r06SO+/vprl7plPXvVbreLadOmibCwMGEwGMRtt90mDh06VOpOqUI4HoM0ZcoU0axZM6HVakVQUJCIi4sTc+bMcXluYEZGhnjooYeEr6+vMJlM4qGHHhJ79+6t0B1hS760Wq2IiYkRb731VqnnE1qtVjFt2jTRuHFjodFoRGRkpJgyZYrLY42K5/Oll14SQUFBwsvLS8THx4uTJ09e9Y6wf/zxR5lxFT9rsqLLrryPcSmWlJQknn/+eXHTTTcJvV4vvLy8ROfOncVbb70lsrOzXeouWLBAxMTECI1GI0JCQsTjjz/ucidgIRzfs9atW5f6nCvvfFksJydHGAyGa959szzfh+K7YP7rX/8qcxqbN28WHTt2FFqtVjRt2lT8+9//FhMnThR6vb5U3W+++Ub07NlTeHt7C29vbxETEyOefPJJcezYsUrNZ0FBgXjllVec353Q0FAxbNgwcerUKZd6n3zyiejcubMwGAzCx8dHtG3bVrz44osiKSmpzHkq6ccffxQ9evQQBoNB+Pr6ikGDBpW6g3F57wj79NNPCwCl4iup+JEoxXcuFkKIHj16CKD0444qOo+NGjUSAwcOLHP89evXi3bt2gm9Xi+io6PFrFmznI/KKbm9sdls4rXXXhOhoaHCYDCIPn36iCNHjojAwEAxfvx4l2mWd3tzLV9//bXo37+/CAgIEGq1WoSFhYkRI0aIrVu3utQ7deqUGDZsmPDz8xN6vV507dpVbNiwocxp1sZvQ1EU8fbbb4tGjRoJnU4nOnbsKDZs2FDm9zg9PV088MADwsfHR5hMJjF27FixY8cOAUCsWLGi1HyOHj1ahIaGCo1GIyIiIsRdd91Vqq0gz8O+giv2FRzYV2Bf4UrsK1S8rxAeHi4AiE8++aTUsMGDBwsA4vHHHy81rKzvS3m3azabTTz99NOiQYMGQpIk53b8Wr8FXOXpFrVJuhQIERGGDBmCw4cP48SJE+4OhWpBVlYW/P39MWPGDLzyyivuDueGsG7dOtxzzz3Yvn07evTo4e5wiIiqHfsK9Qv7Cp6hXl/TTlSflXw0EeC47nLjxo247bbb3BMQ1agr1zdw+dpOrvPKuXKZFl8/5+vri06dOrkpKiKi6sO+Qv3CvoLnqtfXtBPVZ02aNMHYsWOdz4r96KOPoNVqr/r4DqrbVq5ciSVLlmDAgAEwGo3Yvn07li9fjv79+/OIcCU9/fTTKCwsRGxsLMxmM9asWYNff/0Vb7/9dpUen0RE5CnYV6hf2FfwXEzaieqpO+64A8uXL0dKSgp0Oh1iY2Px9ttvl/lcUar72rVrB7VajdmzZyMnJ8d5w5niuxxTxfXp0wfvvvsuNmzYgKKiIjRr1gzz58+/6k2IiIjqGvYV6hf2FTwXr2knIiIiIiIi8lC8pp2IiIiIiIjIQzFpJyIiIiIiIvJQ9e6adkVRkJSUBB8fH0iS5O5wiIiIIIRAbm4uwsPDIcvcn14d2N4TEZEnqUpbX++S9qSkJERGRro7DCIiolLOnj2Lhg0bujuMGwLbeyIi8kSVaevrXdLu4+MDwLGwfH193RwNERERkJOTg8jISGcbRVXH9p6IiDxJVdr6epe0F58i5+vry0aciIg8Ck/jrj5s74mIyBNVpq3nhXNEREREREREHopJOxEREREREZGHYtJORERERERE5KHq3TXtRET1haIosFgs7g6DAGg0GqhUKneHQUREdZjdbofVanV3GHQNWq22Rh7dyqSdiOgGZLFYcPr0aSiK4u5Q6BI/Pz+EhobyZnNERFQhQgikpKQgKyvL3aHQdciyjMaNG0Or1VbrdJm0ExHdYIQQSE5OhkqlQmRkZI3s8aXyE0KgoKAAaWlpAICwsDA3R0RERHVJccIeHBwMLy8v7vz1UIqiICkpCcnJyYiKiqrW9cSknYjoBmOz2VBQUIDw8HB4eXm5OxwCYDAYAABpaWkIDg7mqfJERFQudrvdmbAHBga6Oxy6jgYNGiApKQk2mw0ajabapsvDL0RENxi73Q4A1X5qFlVN8Q4UXo9IRETlVdxmcCd83VDc9yrui1UXJu1ERDconj7nWbg+iIiostiG1A01tZ6YtBMRERERERF5KCbtRER0Q1uyZAn8/PwqNM7YsWMxZMiQGomHiIiIHEelJUmqcBtdLCEhAZIkYd++fVets3XrVkiSdN0770dHR2PevHnXrFPVeKuCSTsREXmMn3/+GYMGDUJ4eDgkScK6devcHRIRERFVwtixY52JbsnXHXfc4azz+eef4/jx45WafmRkJJKTk9GmTZtyj1OZHfnFkpOTr5vY1xTePZ6IiDxGfn4+2rdvj3HjxmHo0KHuDoeIiIiq4I477sDnn3/uUqbT6Zz/+/n5ITg4uFLTVqlUCA0NrVJ8FREaGgqTyVRrn1cSj7QTEZHHuPPOOzFjxgzcc8895R5n7ty5aNu2Lby9vREZGYknnngCeXl5V60/depUdOjQAR9//DEiIyPh5eWF4cOHIzs7u1TdOXPmICwsDIGBgXjyySdd7vz+5ZdfokuXLvDx8UFoaCgeeOAB57PYiYiIyJGgh4aGurz8/f3LrJudnQ2VSoU///wTgOO55wEBAejevbuzztKlSxEZGQmg7NPjN27ciJtuugkGgwG9e/dGQkKCc9jWrVvx8MMPIzs723nUf+rUqc7hBQUFGDduHHx8fBAVFYVPPvmk+hZEFTFpJyKqJxSLAsWiQAjhLBN24SizibLrKpWvW1tkWcYHH3yAw4cP44svvsBPP/2EF1988ZrjnDx5EqtWrcJ///tffPfdd9i7dy+eeOIJlzpbtmzBqVOnsGXLFnzxxRdYsmQJlixZ4hxutVoxffp07N+/H+vWrUNCQgLGjh1bA3NIRERU2o3WrptMJnTo0AFbt24FABw8eBCSJGHv3r3OnfHbtm1Dr169yhz/7NmzGDp0KAYNGoR9+/bhkUceweTJk53D4+LiMG/ePPj6+iI5ORnJycmYNGmSc/i7776LLl26OPsEjz/+OI4dO1ZzM1wBTNqJiOqJc/PP4Nz8M1AKFWdZzp/ZODf/DC7+lOFS9/yiRJybfwb2XJuzLHdfDs7NP4OMH9Jd6ib9+yzOzT8DW+blo9D5h3NraC5Ke+6559C7d29ER0ejT58+mDFjBlatWnXNcYqKivCf//wHHTp0wK233or58+djxYoVSElJcdbx9/fHggULEBMTg7vuugsDBw7E5s2bncPHjRuHO++8E02aNEH37t3xwQcf4Ntvv73mUX4iIqLqUhfa9Q0bNsBoNLq83n777avWv+2225xJ+9atW3H77bejZcuW2L59u7Psakn7Rx99hKZNm+Ldd99FixYtMGrUKJed6VqtFiaTCZIkOY/6G41G5/ABAwbgiSeeQLNmzfDSSy8hKCgIW7ZsqdR8Vzcm7UREVCe8/fbbLo1+YmIiAODHH39E3759ERERAR8fHzz00EPIyMhAQUHBVacVFRWFiIgI5/vY2FgoiuKyR71169ZQqVTO92FhYS6nv+/evRuDBg1CVFQUfHx8nJ2I4riIiIjqu969e2Pfvn0ur/Hjx1+1fq9evbB9+3bY7XZs27YNt912mzORT0pKwsmTJ3HbbbeVOe6RI0fQrVs3l7LY2Nhyx9quXTvn/8WJvadc9sYb0RER1RMNn24EAJA0krPMt4sJPh19IcmSS92I8VGOuurL5T4dfGFs61OqbvgjkaXqerf2qd7gAYwfPx7Dhw+//Lnh4UhISMBdd92Fxx9/HG+99RYCAgKwfft2/OMf/4DFYoGXl1elP0+j0bi8lyQJiuI4mpGfn4/4+HjEx8dj2bJlaNCgARITExEfHw+LxVLpzyQiIiqvutCue3t7o1mzZuWuf+uttyI3Nxd79uzBzz//jLfffhuhoaF455130L59e4SHh6N58+aViuV6rtXuuxuTdiKiekLWlj65SlJJkFRSjdStbgEBAQgICHAp2717NxRFwbvvvgtZdsRxvVPjAcfR8KSkJISHhwMAfvvtN8iyjBYtWpQrlqNHjyIjIwPvvPOO84Y4xTfOISIiqg11vV0vi5+fH9q1a4cFCxZAo9EgJiYGwcHBGDFiBDZs2HDVU+MBoGXLlli/fr1L2W+//ebyXqvVwm6310jsNYmnxxMRkcfIy8tznj4HAKdPn8a+ffuuesp5s2bNYLVaMX/+fPz999/48ssvsWjRout+jl6vx5gxY7B//3788ssveOaZZzB8+PByPzomKioKWq3W+bnr16/H9OnTyz2fRERE9YHZbEZKSorL68KFC9cc57bbbsOyZcucCXpAQABatmyJlStXXjNpHz9+PE6cOIEXXngBx44dw1dffeVyA1kAiI6ORl5eHjZv3owLFy5c81I6T8KknYiIPMaff/6Jjh07omPHjgCACRMmoGPHjnj99dfLrN++fXvMnTsXs2bNQps2bbBs2TLMnDnzup/TrFkzDB06FAMGDED//v3Rrl07LFy4sNxxNmjQAEuWLMHq1avRqlUrvPPOO5gzZ065xyciIqoPvvvuO4SFhbm8evbsec1xevXqBbvd7nLt+m233Vaq7EpRUVH45ptvsG7dOrRv3x6LFi0qddO7uLg4jB8/HiNGjECDBg0we/bsqsxerZFEyWcE1AM5OTkwmUzIzs6Gr6+vu8MhIqp2RUVFOH36NBo3bgy9Xu/ucDzO1KlTsW7dOpfnutaGa60Xtk3Vj8uUiG4EN3KbLkkS1q5diyFDhrg7lHJbsmQJnnvuOWRlZZU5vKbael7TTkRERERERLXu/vvvR2BgIM6dO+fuUK7LaDTCZrO5ZecJk3YiIiIiIiKqVSdOnAAAl8ererLiM/TcES+TdiIiqlemTp2KqVOnujsMIiKieq0ij4LzBO6MlzeiIyIiIiIiIvJQTNqJiG5Q9ew+ox6P64OIiCqLbUjdUFPrya1J+88//4xBgwYhPDwckiRh3bp11x1n69at6NSpE3Q6HZo1a1bq2XtERPVd8bVWFovFzZFQScXPgtVoNG6OpHaxrSciqrziNqOuPE+8vivue1X3de9uvaY9Pz8f7du3x7hx4zB06NDr1j99+jQGDhyI8ePHY9myZdi8eTMeeeQRhIWFIT4+vhYiJiLyfGq1Gl5eXkhPT4dGo4Es86QqdxJCoKCgAGlpafDz86szN9ypLmzriYgqT6VSwc/PD2lpaQAALy8vSJLk5qioLIqiID09HV5eXlCrqzfN9pjntJfnOX0vvfQS/ve//+HQoUPOspEjRyIrKwvfffddmeOYzWaYzWbn+5ycHERGRlbLc1t3n7Lg6HlrlaZh1EuI72iAXsMfHxFVH4vFgtOnT0NRFHeHQpf4+fkhNDS0zM5WfXmmeE219UDNtvdERO4khEBKSspVnw1OnkOWZTRu3BharbbUsHrznPadO3eiX79+LmXx8fF47rnnrjrOzJkzMW3atBqJ51SKDVsPma9f8TqCfFTo0VJXDRERETlotVo0b96cp8h7CI1GU++OsFdWZdp6oGbbeyIid5IkCWFhYQgODobVWrUDhlSztFptjZzhWKeS9pSUFISEhLiUhYSEICcnB4WFhTAYDKXGmTJlCiZMmOB8X7znvTq0ilRDp9FXevz9CVacvWBHbhGPhBFR9ZNlGXp95bdRRO5QmbYeqNn2nojIE6hUKu4ArqfqVNJeGTqdDjpdzRzFbhOlRZuo0qc+lFd+UT7OXrCjyOIRVygQERHVWTXZ3hMREblTnbo7UWhoKFJTU13KUlNT4evre9U9756s+Dr2IiuTdiIiIuDGa+uJiIiqqk4l7bGxsdi8ebNL2aZNmxAbG+umiKpGp72UtPNIOxEREYAbr60nIiKqKrcm7Xl5edi3bx/27dsHwPGYl3379iExMRGA4/q00aNHO+uPHz8ef//9N1588UUcPXoUCxcuxKpVq/D888+7I/wqKz7Sbub9JIiI6AZV39t6IiKiqnJr0v7nn3+iY8eO6NixIwBgwoQJ6NixI15//XUAQHJysrNRB4DGjRvjf//7HzZt2oT27dvj3Xffxb///e86+9xWnh5PREQ3uvre1hMREVWVxzynvbZ40rNwd5+yYNH3eWgWpsZL9/AZskRE9ZUntU03Ci5TIiLyJFVpl+rUNe03Gp3G8fdksg2nU23uDYaIiIiIiIg8DpN2Nyo+PR4A/rM1342REBERERERkSdi0u5G4QEq5/9p2XY3RkJERERERESeSO3uAOozL52Mt0aZ8Mqy7Gqfdk6Bgn0JFlzIUXBHRz28dNw/UxMsNoHsfAVZBQqy8hVk5wvkFCrIKVBQZBXo01aPm8I1zvpCCJhtjvWTU6Agt9BRP7dQoMAsENtCi8gg15+lEAKFFoG8IoHcQuXS38v/+3vL6NtOB0m6fOaGIgSKLo2TXySQb1ac/+cVKcg3l/i/yPHZAsDwOC+E+KlQaBGICFBBr5VARERERETuw6Tdzbx1jqTIYgNsdgG1qvJJUn6Rgt1/W/DnSQuOnreh+BaD3joJ8R0N1RFunSWEI9FNz1FwIccOH4OMVpGaq9ZXhEBOgUBGriMZdyTkl5PzrHyB7AIFBeZr38dx9ykr2jXSILdQQc6lRNtyjdsXbNpfhC5Ntcg3Ky6JuV259vxt3FOIYJMK+UWO+gVmAaUSt5hc+F2e8//oYBWmDPWFLNfvxF1RHDtNiqwChWaBQqtjh0iBxfHXbBVo2VCDyCA1iu/rWXIHChERERFRVTBpd7OSRzKLrAJGlQS7InAy2YboYDV0mmt3/oUQ+DvVjm2Hi/DHSQtsZZxln5Z9nYzvBmG1C6Rl25GereDCpeQ8PUfBhVzH/1cmy0/eaYROIyEzV0FGrh0ZeQoychVk5irIzFOumygX06oBk5cMP28Zvl4yTF4SdGoJ3+4tAgAcOGMtcxwfgwxfLwm+BhlqlYTdpywAgD8v/b2STg0YDTKMegk+l/4eOGNFgbn4yHvpvQFaNWDUy/DSSTDqJXjrZXg7/7/8XghgyZZ8FFoEvLQSiqwCCWl2zN+YB1kCzFaB+I56NAvTIK9IQUGR4yh+nvny//mXjuBf+b+3TsKwOC/Y7QL5ZoHGIWo0anB501N89kHBpXF8DI5leTU2u2OnRIHl0l+zcunvFS+Lo7zIKtAiXIMOjbUosjgScMdLcby/lIyXTMpLvjeX6x6RhTBoJZitAjqNhLgYLbQqx3IsTviLSvy12IDuLbQY0tVQ6QTfrjh2GFyevmN9NwxUcafBNdjsAhabYx04/pb433pl+aX31sv/m4vLrQIWOxBglPHo7UZ3zxYRERHdwJi0u5lKlqBVO460F5oFCsx2LNiYi+SLCnq31eGBW7zLHE8RAntOWfHtnkIkXricqUcEqNC1uRZdmmlxItmGJT/l40y6rVxH8a02gawCBUE+ssd2+ouPmKdk2R2vi8qlv3ZcyFVwrQcYSgD8jTLsikB2gcCH3+ZdvTIAWQL8vGX4G2X4eUvw85Jh8nYklMVJup+3BINWKnN5tYvW4Nh5G3wMEny9ZMdfgwwfL9nlJoTFjidZcfisFUa9DB+9BKNBgo9edvw1yNCqS49jswvsPGaGAGDUyZcScQnGS8m4poxxrqZDYw0EAFmS8MO+Qqz+tRCHEi/vcDiWdO3ldTUFZoGPvnMdNypIBau9+NT90mcSdGmqhUYNlyQ836xUIIl2dTrVju8u7USpLI3KsZPNcOlV/P+x8zbnjgAAKLQIbD5gvu70Nu4uwp5TFtgVOBPvAKOMDo21gIBLwm++lOyXfH+1MzaMegkmLxlFVgGb3bGTJLqB2jEN2+WzAxzTgXNnQnGZt05Cq0gNNCrHzpwWDdUI8pEddS/VM5eoXzydkmUQQPNwNQKMKlhsjjJL8Xi2y3XNtuJpOcoCfWQ0D1fDbodzmMXmGG6xXf5ciw0QAmgWpoa/t+wYVioRF7BYSyfm5d0ZV16hfrz0iIiIiGpWvU3aFYsCIYQz2RJ2AWEXkGQJUolER7E4eniSWoIkV2NdqwIIR12DVoLFJvDzoSL8fKgIZisAWUJypr1UXUiOI7EbdhUiLdMOAUCjkdClmRa3tdEjOkAChOOzLuY5OpNn0mz4cH0Onh7kA1l9uYOp2BQUFik4cNaGfWesOJxohdkq8M++XujcVAdZc7musAkIRUBSSZAuJf9CERA2AUgof10AsrZ8da12gZRcgXMZdpzNsOF8ug3JGXbkmgWU4iRZCKgvJepCAgw6GcEmGQ2MMoKMEgJNKjTwUyPIV4a/UYJaAf5OtWLu//IgyRICjDKCvGUEGiX4G1UI9FMhwEdGoI8MXy2gkqRKr/umwWo0CVSVue4Viyg13WZBKjQPVpf9PSmRFziXpQyo1TJuaaV3rk8oV3z/StS9ct1fWRcCgE1AkQT6ttNDJTuOHBtUwLrfClBoB4QkQaMCvHWASec4gm/wkp07Cbw1jssxjF4yvL1kJGXa8eO+QuhkCV5aCUdSHd/pxAt2yEJAFoAiAZAkqGTAbnesz30nzLCVOC3/yroAYNAARo0EL50EvUGGQSfBWyfBSwN4ayQYDDK89DJ2nbQgIdUKL7UEvUaCVifBoJOh10rwVgN6NaDXyTAY5MvJuCQcf71kGHSOctWl9XTl+jQX2pGcaYfWIMOuAFsOmaHYFBg0gF4jQ2+QodNcmq4soFdL+DvDjjW/FyElS4EkBFSX5i0tW8EP+xw7F1SKgATAVmKei+sKwLFXCYBaBrzVgEoGMgsF8oqAvCK7s+7BPAv2nb6886V4unbJsT5L/o4EgGw7sPOYxVl35+Gr17WXsY6K6x5LspW7bvF0c3LsOH3eetV1f+Xvfm+uvdx1cWmei5elCgI6lWP7qdY4doppVQIGlQStWoJaC2hLlOtkQKORodPJ0KoBjVqCFgJGneTYhlWxfSAiIiK6mnqbtJ//OBE+z7eGystxB/ecP7ORvf0ijG19ENA/6HK9RYkQVoHwRxpCbXJcA527LwdZWzPh1dIbQQOCnXWT/n0WSqGCsDER0ARpAQD5h3ORuSkDhmZeaHB3iLNuypLzsOXYEDIqHAathOwCgWO/5mBYRh6S9RpsCvZ1HLECkLosCdYMK5T+DbDyhIKTKTZEFlgwKiMXujAdWo5pCKPekZClLEuCJcWMBveEICLckcyFmW3o9HsmUnLzED46Ala7wKEzVmSvTYHqghnbAo0446UDADSw2KBekYSUKC3Cx0U6472wPhWFpwsREB8EYxsfAID1ggUpXyZBZVQh4rEoZ92Mb9NRcDwf/n0C4dPRFwBgy7Yh+bNzkHUyGj7VyFk388cLyD+cB58e/siM9sbpVDvOnTUjZmc6LIrAl5GBzrpdM/PRNa8I+00GnGtoRJi/CuHeQKtf06BRSWjwRCP4XTpL4OK2TOTuyoZPFxP82wUAcHSQz85PgBbA+49HQm1wnEac/etFZO/MgrGDDwK6XV73ie+dBhQg/J+RUPs4fiq5e7KR9fNFeLc2IvCOBpfX/SdnoZgVhI1rCI2/43uSdyAXF3/KgNdN3ggadPl7kvzZOdjz7Ah9KBzaYMdyzz+Sh8zvL8DQ2IAGQ0Mvf0++PA/bRRtCRoZBF+FYn4UnCnBhQxp0DfUIGRHmrJu6PBnWNAuCh4VC38hxD4OihEKkr02FNlSH0FHhzrrpq1NgTjIj6O5geDVznM1hPl+EtFUp0ARqEDa2Ifq2c3xe2tfJaJJfBH3vQPi384FWLcGcYkbqsiSofdUIH3r5e5L+f6koPFmAgNsDYWzqi+ZhGsSFyEj+4jxkgwy/cZE4kGCFTiPB+GcmVAkF8O7pj4CuftCqgaxUKxI/OQuhkpAyIAxeOgleWhnGvReh/jsf+q5+8I/1c1xWUqTg/EeJAICoiY2dMVzckoHcXTnw7WaCX/sAdG+hg2JRcG7+GQBAw6cbOXccZW3PRM7v2fDp5Av/rpe/a4nvngYABD4eBZXBUTf796wytxFpn56FbBUIfqQh1AEajL5NjZzd2Ze3EXGX1/25hWegFCroNyYCbaN9kZWvQJ+QD+m3i1AaGrAnyhcWm+OIfrMdadCY7cjpHQxNiBYGrQTd2QJI2zOhidQjcGgo9BoJapWE5CXnYM2wQnVHMFINGsfOieRCmDelI1Ovwd+dAqHXADqNhKhdF6DLsaKgRxBUUQZH3XQzpB/TIAdqcbRjIPKKBHRqwGtrOgxZFuxp6IvMQD10GglBRVZ0PHYRFoMaf8c1cOyQ0EgI25MJQ3oRim72xxG9DsmZdvhZbOh4OBN2nQp/3xIMrUaCTiMhdN9FGFIKYenkB6mlEXqNhMILFmBdCuwqCad6h0KnAbRqCQ0OZsHrXAEs7U2Q2/hAp5GgMdtRuPw8LHbgVL8waNWAViMh8K9seCfkw9raF+hgglYtQWNXgBXnIUmA/2ONoNNJ0Gok5G3PRO6fOY5tRC+/y9uIeQmO78mTUZD1jvbBZRvRtfQ2wl4N2whtLz2IiIiIylJvk3ZPUvK6dl8vGSGNNNiUD+QXOZJ2RQAX8xV8/WM+UnQaaNVAXAstGh5TwdBA7UzYr2TUy/jXGD/M/TgdAJBboGD1jgLsOGpGvlkgPkdBiAACvGW06aSHLAG/7bDCaq/EHcxKMFsdpzIfOlKEg6eBOzrp0cp0eR6FcNwQ7lSKDeYzFhgu2rFuWz4O7nEcifKyKWhqd9zN3FsnoWGQCpGBKtx0zgqfM1a06uGNwJ5+jmVTZMe5PY759/Mu/2n9KrnsU9qpbBIc19OXdYp+RRj1MuJiHDsqLhyTUaB2XFdffO8GH4MEf28ZkkZChxI3T8z8W4U8tQQfLxnel77vdf0hiQ0D1WgYCORlFSFTJcHgLWNEz8uXwyQdVsGWI9C8qRa60Es7d/ItyFA7jtqX9bsP9JUREenYYVhgtuCCXkZQuBq39L483ZSTalgsdjSJ0sDQxFG3SLEhTSVBo5VwZ6fLyz31tBZmlYKOfY3wanF5505qcg7U/irE3np5uumJKhTmyogK16BTG0e5Jc2MlLPZUBlV6FriUp8LSbkoyJLhH6qBT7QjBqsXkGyUIetkdO7h5aybkZaH/Asy/ELU8L1U15YrIUkvAzLQMe5y3czMfOSlyDAFq2G6VFcpsuPcpe+tv1F2ntHj+FYTEREReT5JiGtdBXzjycnJgclkwsX0izAFmjzi9PhVOwuw9aAZI+IMuDVGh5RsO95YnQsvnYQX7/HBZ9/nISnDDpsE3Nxch2GxBvh7y+U+7fnVZReRcVFxOUXVz1tCt8Za3NxMg8gQNWSVjHMZNkxbkQ2jRsKcsSaotI6jTAVmBQdOWSBJAl1v0pc6jV0RAmcuKjh4xopDiVacTbO5nPraJESFcX28cfysBSeSbDiSakdW/qVT5UuczurtJaNJiBqNG6gQFSAjIkANf7/LN9Uq81R6ISAunZEgaaTS67Midd2w7itVtwKnvFeq7hWXO1SkbnVdGlGuuiXWZ0XqVmjde9r35Grr0wO+J+64hKZcdevINiK3IBcmkwnZ2dnw9fUFVV1xe89lSkREnqAq7VK9PdIua12PypbsuF1Z70rVUrdEB3Z4nBeGdPVyHm00XjogVWAWePvrHFhsgNFLxpje3o6bVBVPu4xnaJfscBeLCFQjNdsKCUCbKA16t9GhTZSm1KO8QvxUkGUJeTYgswg4f96CncfMOJBghe3SzZtCAjWIDnbc1OrgGSv2nrbgcKIV+SUffSZJCA9SoXGwGr8cMePvVDteXZ7j8llqGWgUrEbjEBWaBKvROESNwOvcAE9SS5CuODomSVKZy6Gs5V6RukDtrPtK1ZXLno+y1n1t1y1zHdVU3autzyp+T4AaXPdV/Z5cbR15wPekVtd9dXxPPHgbQURERFRSvU3aPTS5+bgAAFDVSURBVIkkSdCVeGS4t/5yJ85iA1o1VGNcPyNMXpW7S/F9cV6IaWhF60gNgk2qq9bTqCQEm2SkZCmYuiK7zLtT/3SwCFYbcOCMxWW4QSuhdaQGbRpp0CZKA5OXDCEETiRbkZKlQCUDTULUaBGhRosIDZqEqKt8qjUREREREdGNjkm7B1Jduqt5Zp6Cvm11uK+HF1RVuLtwkK8KvdtcPVkvKTJIjZQsR0Ju8pLQrbkO3Vto8ecpCzbuLnLeVdoxXRmdm2jRvrEjCb8yRkmSMPFuX6Tn2BEVdP1nzhMREREREZErJu0e6pmBRuQUCrRsqLl+5Wo0pJsBDUwymoep0bKhxpmIKwrw/d4i+HnL6NLU8Rz4Rg1U172Zm9+l55oTERERERFRxTFp91ARgWpEuOFzg00q3NPNq1R5o2A1Fv7TH5IE3nWdiIiIiIioljBpp3K78sZ1REREREREVLN43jIRERERERGRh2LSTkREREREROShmLQTEREREREReSgm7UREREREREQeikk7ERERERERkYdi0k5ERERERETkoZi0ExEREREREXkoJu1EREREREREHopJOxEREREREZGHYtJORERERERE5KGYtBMRERERERF5KCbtRERERERERB6KSTsRERERERGRh2LSTkREREREROShmLQTEREREREReSgm7UREREREREQeikk7ERERERERkYdi0k5ERERERETkoZi0ExEREREREXkoJu1EREREREREHopJOxEREREREZGHYtJORERERERE5KGYtBMRERERERF5KCbtRERERERERB6KSTsRERERERGRh2LSTkREREREROShmLQTEREREREReSgm7UREREREREQeikk7EdH/t3ff4VHVefvH7zMzmRRSCGBIIXQFEVFpIeDKolEsj4hdlxXE8iiLLkvEn6Aiiwq4FkRFxVWUfWygK7Kuq7hsBDUuitJRejECKQRIJ23m+/tjdDACyqTNCXm/rmuuC858z5n7nMnkk8+cBgAAANgUTTsAAAAAADZF0w4AAAAAgE3RtAMAAAAAYFM07QAAAAAA2BRNOwAAAAAANkXTDgAAAACATdG0AwAAAABgUzTtAAAAAADYFE07AAAAAAA2RdMOAAAAAIBNBb1pf/bZZ9WxY0eFhYUpJSVFK1as+MXxs2bNUrdu3RQeHq7k5GSNHz9e5eXljZQWAADUBvUeAIDaCWrTvmDBAqWnp2vKlClatWqVzjjjDA0dOlR5eXlHHf/GG29o4sSJmjJlijZu3Ki5c+dqwYIFuvfeexs5OQAAOF7UewAAas8yxphgvXhKSor69eun2bNnS5K8Xq+Sk5N15513auLEiUeMv+OOO7Rx40ZlZGT4p91111368ssvlZmZedTXqKioUEVFhf//RUVFSk5OVmFhoaKjo+t5jQAACFxRUZFiYmJO2NpEvQcANHd1qfVB29NeWVmplStXKi0t7XAYh0NpaWlavnz5UecZOHCgVq5c6T+kbseOHfrggw908cUXH/N1ZsyYoZiYGP8jOTm5flcEAAAcE/UeAIC6cQXrhfPz8+XxeNS2bdsa09u2batNmzYddZ7f/e53ys/P19lnny1jjKqrq3X77bf/4uFykyZNUnp6uv//P37zDgAAGh71HgCAugn6hegCsWzZMk2fPl3PPfecVq1apYULF+pf//qXHnrooWPOExoaqujo6BoPAABgX9R7AAAOC9qe9jZt2sjpdCo3N7fG9NzcXMXHxx91nsmTJ+uGG27QLbfcIkk6/fTTVVpaqv/93//VfffdJ4ejSX0HAQDACY96DwBA3QSt6rndbvXp06fGRWa8Xq8yMjKUmpp61HnKysqOKNROp1OSFMTr6QEAgGOg3gMAUDdB29MuSenp6Ro1apT69u2r/v37a9asWSotLdXo0aMlSSNHjlRSUpJmzJghSbr00ks1c+ZMnXXWWUpJSdG2bds0efJkXXrppf5iDgAA7IV6DwBA7QW1ab/22mu1b98+PfDAA8rJydGZZ56pxYsX+y9Wk5WVVeOb9vvvv1+WZen+++/Xnj17dNJJJ+nSSy/VtGnTgrUKAADgV1DvAQCovaDepz0YTvR74QIAmh5qU/1jmwIA7KRJ3qcdAAAAAAD8Mpp2AAAAAABsiqYdAAAAAACbomkHAAAAAMCmaNoBAAAAALApmnYAAAAAAGyKph0AAAAAAJuiaQcAAAAAwKZo2gEAAAAAsCmadgAAAAAAbIqmHQAAAAAAm6JpBwAAAADApmjaAQAAAACwKZp2AAAAAABsiqYdAAAAAACbomkHAAAAAMCmaNoBAAAAALApmnYAAAAAAGyKph0AAAAAAJuiaQcAAAAAwKZo2gEAAAAAsCmadgAAAAAAbIqmHQAAAAAAm6JpBwAAAADApmjaAQAAAACwKZp2AAAAAABsiqYdAAAAAACbomkHAAAAAMCmaNoBAAAAALApmnYAAAAAAGyKph0AAAAAAJuqVdO+fft23X///br++uuVl5cnSfrwww/1zTff1Gs4AAAAAACas4Cb9k8++USnn366vvzySy1cuFAlJSWSpLVr12rKlCn1HhAAAAAAgOYq4KZ94sSJevjhh7VkyRK53W7/9HPPPVdffPFFvYYDAAAAAKA5C7hpX79+vS6//PIjpsfFxSk/P79eQgEAAAAAgFo07S1btlR2dvYR01evXq2kpKR6CQUAAAAAAGrRtF933XW65557lJOTI8uy5PV69fnnn2vChAkaOXJkQ2QEAAAAAKBZCrhpnz59urp3767k5GSVlJSoR48eOuecczRw4EDdf//9DZERAAAAAIBmyRXoDG63Wy+++KImT56sDRs2qKSkRGeddZZOPvnkhsgHAAAAAECzFXDT/qP27durffv29ZkFAAAAAAD8RMBNe3p6+lGnW5alsLAwde3aVZdddplatWpV53AAAAAAADRnATftq1ev1qpVq+TxeNStWzdJ0pYtW+R0OtW9e3c999xzuuuuu5SZmakePXrUe2AAAAAAAJqLgC9Ed9lllyktLU179+7VypUrtXLlSu3evVvnn3++rr/+eu3Zs0fnnHOOxo8f3xB5AQAAAABoNixjjAlkhqSkJC1ZsuSIvejffPONLrjgAu3Zs0erVq3SBRdcoPz8/HoNWx+KiooUExOjwsJCRUdHBzsOAADUpgbANgUA2Eld6lLAe9oLCwuVl5d3xPR9+/apqKhIktSyZUtVVlYGumgAAAAAAPATtTo8/qabbtK7776r3bt3a/fu3Xr33Xd18803a/jw4ZKkFStW6JRTTqnvrAAAoBHk5ubqhhtuUGJiolwul5xOZ40HAABoPAFfiO6FF17Q+PHjdd1116m6utq3EJdLo0aN0pNPPilJ6t69u1566aX6TQoAABrFjTfeqKysLE2ePFkJCQmyLCvYkQAAaLYCPqf9RyUlJdqxY4ckqXPnzoqMjKzXYA2Fc9wAAHZjt9oUFRWlzz77TGeeeWawo9Sa3bYpAKB5q0tdCnhP+48iIyPVq1ev2s4OAABsKjk5WbX8Th8AANSzWjXtX3/9td566y1lZWUdccG5hQsX1kswAAAQHLNmzdLEiRP1wgsvqGPHjsGOAwBAsxZw0z5//nyNHDlSQ4cO1b///W9dcMEF2rJli3Jzc3X55Zc3REYAANDAYmNja5y7Xlpaqi5duigiIkIhISE1xh44cKCx4wEA0GwF3LRPnz5dTz75pMaOHauoqCg99dRT6tSpk2677TYlJCQ0REYAANDAZs2aFewIAADgKAJu2rdv365LLrlEkuR2u1VaWirLsjR+/Hide+65mjp1ar2HBAAADWvUqFHBjgAAAI4i4Pu0x8bGqri4WJKUlJSkDRs2SJIKCgpUVlZWv+kAAECjczqdysvLO2L6/v37uU87AACNLOA97eecc46WLFmi008/XVdffbXGjRunjz/+WEuWLNF5553XEBkBAEAjOtaV4ysqKuR2uxs5DQAAzVvAe9pnz56t6667TpJ03333KT09Xbm5ubryyis1d+7cgAM8++yz6tixo8LCwpSSkqIVK1b84viCggKNHTtWCQkJCg0N1SmnnKIPPvgg4NcFAAA1Pf3003r66adlWZZeeukl//+ffvpp//VsunfvXqtlU+8BAKidgPa0V1dX6/3339fQoUMlSQ6HQxMnTqz1iy9YsEDp6emaM2eOUlJSNGvWLA0dOlSbN29WXFzcEeMrKyt1/vnnKy4uTn//+9+VlJSk7777Ti1btqx1BgAA4PPkk09K8u1pnzNnTo1D4d1utzp27Kg5c+YEvFzqPQAAtWeZYx0DdwwRERHauHGjOnToUOcXT0lJUb9+/TR79mxJktfrVXJysu68886jfhkwZ84cPfbYY9q0adMRt585XkVFRYqJiVFhYaGio6PrlB8AgPpgt9o0ZMgQLVy4ULGxsfWyPOo9AKC5q0tdCvjw+P79+2vNmjWBznaEyspKrVy5UmlpaYfDOBxKS0vT8uXLjzrPe++9p9TUVI0dO1Zt27ZVz549NX36dHk8nmO+TkVFhYqKimo8AADAsS1durTeGnbqPQAAdRPwhej+8Ic/KD09Xd9//7369OmjFi1a1Hi+V69ex7Wc/Px8eTwetW3btsb0tm3batOmTUedZ8eOHfr44481YsQIffDBB9q2bZv+8Ic/qKqqSlOmTDnqPDNmzOA2dAAA/Ir09PTjHjtz5szjHku9BwCgbgJu2n+8CN0f//hH/zTLsmSMkWVZv/gteF15vV7FxcXpr3/9q5xOp/r06aM9e/boscceO2YRnzRpUo0/RIqKipScnNxgGQEAaIpWr15d4/+rVq1SdXW1unXrJknasmWLv/Y2NOo9AACHBdy079y5s15euE2bNnI6ncrNza0xPTc3V/Hx8UedJyEhQSEhITUujHPqqacqJydHlZWVR70NTWhoqEJDQ+slMwAAJ6qlS5f6/z1z5kxFRUXpb3/7m/8w+YMHD2r06NH6zW9+E9ByqfcAANRNwOe0d+jQ4Rcfx8vtdqtPnz7KyMjwT/N6vcrIyFBqaupR5xk0aJC2bdsmr9frn7ZlyxYlJCRw31gAAOrJE088oRkzZtQ4rz02NlYPP/ywnnjiiYCWRb0HAKBuAm7aJenVV1/VoEGDlJiYqO+++06SNGvWLP3jH/8IaDnp6el68cUX9be//U0bN27UmDFjVFpaqtGjR0uSRo4cqUmTJvnHjxkzRgcOHNC4ceO0ZcsW/etf/9L06dM1duzY2qwGAAA4iqKiIu3bt++I6fv27VNxcXHAy6PeAwBQewEfHv/888/rgQce0J/+9CdNmzbNfw57y5YtNWvWLF122WXHvaxrr71W+/bt0wMPPKCcnBydeeaZWrx4sf9iNVlZWXI4Dn+vkJycrI8++kjjx49Xr169lJSUpHHjxumee+4JdDUAAMAxXH755Ro9erSeeOIJ9e/fX5L05Zdf6u6779YVV1wR8PKo9wAA1F7A92nv0aOHpk+fruHDhysqKkpr165V586dtWHDBv32t79Vfn5+Q2WtF9y3FQBgN3arTWVlZZowYYJefvllVVVVSZJcLpduvvlmPfbYY0fcOcaO7LZNAQDNW13qUq0uRHfWWWcdMT00NFSlpaWBLg4AANhMRESEnnvuOT322GPavn27JKlLly5NolkHAOBEE3DT3qlTJ61Zs+aIi84tXrxYp556ar0FAwAAwdWiRQv16tUr2DEAAGjWAm7a09PTNXbsWJWXl8sYoxUrVujNN9/UjBkz9NJLLzVERgAA0MCuuOIKzZs3T9HR0b963vrChQsbKRUAAAi4ab/lllsUHh6u+++/X2VlZfrd736nxMREPfXUU7ruuusaIiMAAGhgMTExsizL/28AAGAPAV+I7qfKyspUUlKiuLi4+szUoLgwDQDAbqhN9Y9tCgCwk7rUpYDv0/7www9r586dknwXqmlKDTsAADi2wYMH68EHH9Rnn33mv2o8AAAIroCb9rfffltdu3bVwIED9dxzz9n+Fm8AAOD4dOrUSa+88ooGDx6sli1bKi0tTdOmTdPy5cvl8XiCHQ8AgGYp4KZ97dq1WrdunX7729/q8ccfV2Jioi655BK98cYbKisra4iMAACgEcybN087d+7Ujh079MwzzygpKUl//etfNWjQIMXGxuqiiy7SY489FuyYAAA0K3U6p12SPv/8c73xxht6++23VV5erqKiovrK1iA4xw0AYDd2r007duzQyy+/rGeeeUYlJSVNYq+73bcpAKB5qUtdCvjq8T/XokULhYeHy+12q7i4uK6LAwAANvDdd99p2bJl/kdeXp4GDBigwYMHBzsaAADNSq2a9p07d+qNN97QG2+8oc2bN2vw4MGaOnWqrrrqqvrOBwAAGsn//d//+Zv0/Px8DRw4UIMHD9att96qfv36KSQkJNgRAQBodgJu2gcMGKCvvvpKvXr10ujRo3X99dcrKSmpIbIBAIBGdOONN6p9+/aaOHGibr75Zpp0AABsIOAL0Z133nlav369Vq9erQkTJtCwAwBwgnjuuec0YMAATZ06VXFxcbr00kv1xBNP6Ouvv1YdL4EDAABqqU4XovtxVsuy6i1QQ+PCNAAAu7Fjbfr222/1ySef+A+Xr6io0KBBgzRkyBBNmDAh2PF+lR23KQCg+apLXQp4T7vkO+ft9NNPV3h4uMLDw9WrVy+9+uqrtVkUAACwoR49emjMmDFasGCBVq9erTvuuEOZmZm65557gh0NAIBmJeBz2mfOnKnJkyfrjjvu0KBBgyRJmZmZuv3225Wfn6/x48fXe0gAANB48vLytHTpUv9e9i1btigkJEQDBgzQkCFDgh0PAIBmJeCm/ZlnntHzzz+vkSNH+qcNGzZMp512mv785z/TtAMA0ET94Q9/0LJly7R582a5XC71799fV111lYYMGaKBAwcqLCws2BEBAGh2Am7as7OzNXDgwCOmDxw4UNnZ2fUSCgAANL7Vq1dr+PDhGjJkiAYNGqSIiIhgRwIAoNkLuGnv2rWr3nrrLd177701pi9YsEAnn3xyvQUDAACNa/ny5cGOAAAAfibgpn3q1Km69tpr9emnn/rPaf/888+VkZGht956q94DAgAAAADQXAV89fgrr7xSX375pdq0aaNFixZp0aJFatOmjVasWKHLL7+8ITICAAAAANAsBbynXZL69Omj1157rb6zAAAAAACAnzjuPe179+7VhAkTVFRUdMRzhYWFuvvuu5Wbm1uv4QAAAAAAaM6Ou2mfOXOmioqKFB0dfcRzMTExKi4u1syZM+s1HAAACI6CggK99NJLmjRpkg4cOCBJWrVqlfbs2RPkZAAANC/H3bQvXry4xr3Zf27kyJF6//336yUUAAAInnXr1umUU07RX/7yFz3++OMqKCiQJC1cuFCTJk0KbjgAAJqZ427ad+7cqfbt2x/z+Xbt2mnXrl31kQkAAARRenq6brzxRm3dulVhYWH+6RdffLE+/fTTICYDAKD5Oe6mPTw8/Beb8l27dik8PLw+MgEAgCD66quvdNtttx0xPSkpSTk5OUFIBABA83XcTXtKSopeffXVYz7/f//3f+rfv3+9hAIAAMETGhp61AvPbtmyRSeddFIQEgEA0Hwdd9M+YcIEvfLKK5owYUKNq8Tn5ubqrrvu0rx58zRhwoQGCQkAABrPsGHD9OCDD6qqqkqSZFmWsrKydM899+jKK68McjoAAJqX427ahwwZomeffVazZ89WYmKiYmNj1apVKyUmJurZZ5/VM888o3PPPbchswIAgEbwxBNPqKSkRHFxcTp06JAGDx6srl27KioqStOmTQt2PAAAmhXLGGMCmWHPnj166623tG3bNhljdMopp+iqq65Su3btGipjvSoqKlJMTIwKCwuPevs6AAAam11rU2ZmptatW6eSkhL17t1baWlpwY503Oy6TQEAzVNd6lLATXtTRxEHANgNtan+sU0BAHZSl7rkaqBMAACgiXr66aePOt2yLIWFhalr164655xz5HQ6GzkZAADND007AACo4cknn9S+fftUVlam2NhYSdLBgwcVERGhyMhI5eXlqXPnzlq6dKmSk5ODnBYAgBPbcV+IDgAANA/Tp09Xv379tHXrVu3fv1/79+/Xli1blJKSoqeeekpZWVmKj4/X+PHjgx0VAIATHnvaAQBADffff7/eeecddenSxT+ta9euevzxx3XllVdqx44devTRR7n9GwAAjaBWe9oLCgr00ksvadKkSTpw4IAkadWqVdqzZ0+9hgMAAI0vOztb1dXVR0yvrq5WTk6OJCkxMVHFxcWNHQ0AgGYn4KZ93bp1OuWUU/SXv/xFjz/+uAoKCiRJCxcu1KRJk+o7HwAAaGRDhgzRbbfdptWrV/unrV69WmPGjNG5554rSVq/fr06deoUrIgAADQbATft6enpuvHGG7V161aFhYX5p1988cX69NNP6zUcAABofHPnzlWrVq3Up08fhYaGKjQ0VH379lWrVq00d+5cSVJkZKSeeOKJICcFAODEF/A57V999ZVeeOGFI6YnJSX5D5kDAABNV3x8vJYsWaJNmzZpy5YtkqRu3bqpW7du/jFDhgwJVjwAAJqVgJv20NBQFRUVHTF9y5YtOumkk+olFAAACL7u3bure/fuwY4BAECzFnDTPmzYMD344IN66623JEmWZSkrK0v33HMPV5EFAOAEsXv3br333nvKyspSZWVljedmzpwZpFQAADQ/ATftTzzxhK666irFxcXp0KFDGjx4sHJycpSamqpp06Y1REYAANCIMjIyNGzYMHXu3FmbNm1Sz549tWvXLhlj1Lt372DHAwCgWQm4aY+JidGSJUuUmZmpdevWqaSkRL1791ZaWlpD5AMAAI1s0qRJmjBhgqZOnaqoqCi98847iouL04gRI3ThhRcGO16jq6gyqqgyio6o1Z1yAQCok4Cb9h+dffbZOvvss+szCwAAsIGNGzfqzTfflCS5XC4dOnRIkZGRevDBB3XZZZdpzJgxQU7YeFZtr9TzH5Woa7xL91wRHew4AIBmKOCm/emnnz7qdMuyFBYWpq5du+qcc86R0+msczgAAND4WrRo4T+PPSEhQdu3b9dpp50mScrPzw9mtEYXFWFJkgrLvEFOAgBorgJu2p988knt27dPZWVlio2NlSQdPHhQERERioyMVF5enjp37qylS5cqOTm53gMDAICGNWDAAGVmZurUU0/VxRdfrLvuukvr16/XwoULNWDAgGDHa1QxPxwSX1jmlTFGlmUFOREAoLkJ+OSs6dOnq1+/ftq6dav279+v/fv3a8uWLUpJSdFTTz2lrKwsxcfHa/z48Q2RFwAANLCZM2cqJSVFkjR16lSdd955WrBggTp27Ki5c+cGOV3j+rFpr6yWyquCHAYA0CwFvKf9/vvv1zvvvKMuXbr4p3Xt2lWPP/64rrzySu3YsUOPPvoot38DAKAJ8ng82r17t3r16iXJd6j8nDlzgpwqeEJDLIWF+Br2wjKvwt2c/gcAaFwB72nPzs5WdXX1EdOrq6uVk5MjSUpMTFRxcXHd0wEAgEbldDp1wQUX6ODBg8GOYhsxLX44RL6U89oBAI0v4KZ9yJAhuu2227R69Wr/tNWrV2vMmDE699xzJUnr169Xp06d6i8lAABoND179tSOHTuCHcM2IkJ957EfqjRBTgIAaI4Cbtrnzp2rVq1aqU+fPgoNDVVoaKj69u2rVq1a+c9zi4yM1BNPPFHvYQEAQMN7+OGHNWHCBL3//vvKzs5WUVFRjUdz43b6mvaq6iOb9q+2Vujuvx3UvI9LZEzwmvqScq8KOBIAAE5IAZ/THh8fryVLlmjTpk3asmWLJKlbt27q1q2bf8yQIUPqLyEAAGhUF198sSRp2LBhNa6W/uPV0z0eT7CiBcxbWfOq78ZjZDxGlsOS5bJqjJMky2XJctQc6/5hF0elRyqvMlq8olSp3UL19c4qLfqqXJL0340V6tPBpR7t3f7lHqowem95mTxeo9+eGaZ2rUN8y/UamWojOSTLaanaK1V7pFDLSMaXodorfbWtUl6PUajDaO9Br/aVGrWOdqiy2mj77iqVHDIqqzY6VCUZI4U4jcZfHKnOCSFyuA7vl/FWeyWvJKdUWilVVhtt21ul3XnVKik3Mk5LB0u8imnhUOc2TqWe4pbb7ZD1w5cV/ryW5Ag5vFxTbWS8RpbTOnKsJIe7HscaI1PlG2uFWEe+n4GMDeC9r9PYKq///azV2J/8nBzt/azz2J+9n4GMDdZ7X+efk2O9n/Xwc2IcktdILqflH2uckpElr5E8VUaeaq88xlK15fvMWpakKt/YSsuXwWlJXq9RqEOq9hpVyyGPMTJG8lR5ZbyScVryGkser1F1lVF1lVeVHkuVxrfcH7NVV3tVbSw5XJacDsllSU5j5HRacoQ45HJK0eEOtXAZOSzf7yNjWb7X8hp5q4yMfK9njOT1SlVVXt+6WJY8kjxeyePxZfB4JI/D4ZvX+N4jyxgZh+89+mGryfIYOR2WIlo45fzhLfVWGVV7vKr2Wr5le428XqOoEKlFqEP6yc+fPMa3sR2W9MN7L2OkH79YdVk/bNzAxxqv742xXJZcTsnpsOTyepXQyqkQt6NOvyPMUb74PV4BN+0/6t69u7p3717rFwYAAPa0dOnSYEeoN3teyFLU+NPkjPBdQK7o60IVZh5U5OlRanVBm8Pj5mTJVBkl3tJOrhhfc128pkgFyw6ouztE650Rqqo2ev/rQ4r5V662/NOrpQkxUojvT6mupRUqePGAFka59e/YKP9yr9h7UJHVXs35JkZjf99aJ8U4tPXzQrmWH9CByBC9ERapsgrfH3LDsguU5PDq664t9VWx7w/U5LJKDckvVnWoS1+2jfEv9+KcQrWprFbGSVEqDXdLklqXViv72SxlxYRo329OUusopzxeI/fH++TeX6H/xkVpi9s39qSKKl2UW6Qwl1OLElv6lxueWaQVh6rkTW2l3hfEKircoar8SuW8ulfOSKcib2gnyXdV/ex/5ql8W6la/ra1WvXzZasurFb2y7vlCHWo3R0d/Ms98J98lX5TopbnxCq6n+/1PKUe7f3r95JDaj/+8GmVBz/Zr5I1xYpJbamYgb7bC5sKr7JmZ8lrjKJuaa+wMIciQi0VZR5Q8ddFiuobo9jBrXwL8Eq7n/nOt03+N1nF1ZYOlnpV/lWBQr4pUnmnFlJKK0WFWwpzW6qalyWXJbW7rb1cUb73s3hVoQo+PagWp0Wq9YUnyes1Kqs0ynshS6bCK+9l8bKiQ9Qq0iHXthIVfHxAEae0UPRFJ6mk3Kis3Kuy1/fIlHkUMjxexeEulZYbxWSXybH8gMrjwpTTu5XKKozKq4w6/3ef3IeqtbpbrApahKhFqKXEokp12FigougQfd+7tSzL13d0+DJf4aXV2tyjpXLC3XI5paj9Feq5rUDeWLe292nt+zLGSKduOKDI4irtPSNW+TGhKqswiq+oUvtVB3Qowqn1Z7RRyxaWyquk9qv3K7qwUrtOiVF+m3BVeYxCCyt1xrcHVRbi0CfdWsvtkizLUs9tBWpdWKmvT2qhHdFhCnNbauf0qv/GA6oOcWhNv5PkcvoaxU5bCtVqX7n2d4tWcacWsiSFV3mUuCxPHstS9kUJCg2xVO2RYtcdVHhWmUpOjVZRlyhVe428ZR61y8iVkbRxSLw8P/Q9cZuL1Hp3qfYktdDOxEi1CLMUahn1zMyTZUlbf9tWh7yWyquMTtperMQ9pcpqE64t7SLltCyFuKTffJUnI2l5rzaqcjtkjJS8t1Sdsku1JzZM37SLksfr6/ku+HafnF5pSedYlbic8hqjLgcOqde+Uu1sEapPWkVKkkKc0hVZBxXq8eofCTEq/OF3xMkl5Uo9UKrvw91aetKRvyP+1TZG+0N9YzuVVug3+0uUHRaiJXHR/rHDsgvUssqjj+KilRsWUuN3RF6oS4uP8jti2UlR2vPD74iE8iqdn1ek3BCX3k84PHZobpHaVlTpkzaR+i4itMbviKKf/Y44d1+R2h2q0uetIrU90jc2trJal+YUqszp0N+TYv1jB+cXq0NZpb6MbaHNUWGSpKgqjy7PLlClw9L/tWvlHztof4m6lFZoZcsIfRMdLkmKqPbqqr0HVSbptfat/WP7HyhV95JyrY0J19qYCEmS2+vVdbt912J5NbmVzA+NeJ+DZTqt+JC+iQrXyljfWMsY3fD9AUnS/HaxqnT4ft+eUVimMwoPaVNkmFa0auF/vd9n7ZentVPtbz/274gf7f3r9/JWeJVwUzuFxPreo5J1xTr48X552tX+aKhaNe27d+/We++9p6ysLFVWVtZ4bubMmbUOAwAAgm/w4MHBjmArjh/++KusNtqeU63eP3muTbRDk66I1ovP+S7GW3XktXol+fakPzC/UFHhltrkl+s3Bz3KPuRQWVzNPS+lFUZZ+zxSWM0zGCPcllK7uVVV7TvHvqvlkuugV5enhKu4dZgiQi19/VmRlCcVlxn9Z12Ff96hRV619UoV1Uby/e2u2BYORYU7FBnp1Lmnh6p1lEOrd1ZJ+3zPL99cqTezCxQb6VCPFkbdCzw6eNCrt+cVSJJiIiydmVWuDmUeLfy4VEXbpJMTXCrJq1DfQo8UYrTk4xKdFO1U9kGPkrdWKL7Eq7XfVOi7fcXyGqnsYJV+s79aRpaemHtQxkid2jrV7fsKxRV6tOqbcm3NK9LBEq+qD3l0ab5v4776RqH/D/JBpYd0WrFHX60v16rdBQp3Wyoq/WGskf7yt4Kf/EFeoTNKvNq0s0orCkv82+f3+z1ySJq7oECeMJc8XqlDXpnOOFCtPevK9UX2QZUc8u1xvC63Wm6v0buLS1Uc4vsiqFd5ufrs9+i7tYf08feHL+B4VV61Ijxe/fOjEh10+/7k7lJSoUFFXu2uqtLHlYf8Y1uWeBVdbbQjt1r7friGwv6ySrU65FWe16tl3xx+P/+n0KtWVV598321ssN8Y5MOVatTuVH+fk+N9z62wKO4Cq++2lqp7339itqWV6lVuVcFHktfbz/8d/z5RV65Kow2763WzkLf9NYVHnWvMjpkjHbvP3yETftDRjEeo0OVRiXlvkdVVbV6VRqVVxut2nH4/ojOfI9cZV6t2Vmpjft92yyy2qMrir2qtiz9ffnh7ZB6oEonl3i1enOl1ueUSZJCPV5dW+ZrdjLWH163fgerFVZhlJXv0fpq3+u5vEYdDv0wdl2Fqn/Y03lmgUetq4wOlHi1M/fwevT7Ya/81pxqVfywyze00KukKqOCMq+y8g+Prar27ck/WOpVyY9H01T69ih7ftKLVXkO7+0+FpfT9wWM8R3E4HvdH+5U4TW+xl/y7QAOd/u+/LAsKcRpyemRoiMc8kQ65HJIbVwOuQssxUQ4dFpyiH74cVdssUMRlqVTk0IUH+uWx2sUecCrsAJL0eGWuia4VFVtVFjmleOH5Tst/fDFjBTmteSwfFmjwn3/tiwpotCSq9JS6yiHqtr49pTHlBuF7bfkcFs6vUOInA7J6ZDiq52K8FjqEu9SZLxbkpG7zFLEAUshDktd413y/rCxYiscCqu0lNjKKWeSbz3CqjwK3WfJY6R2rQ/fuSOm3FJIuaWWLRz+6S6PpZAc39Zs19rp/x3RstJSyCFLLVtY/rGWMQrJ9j2f2Mqp6h/e+5bVDoWUWoqJsJTUyuk70sEjOZ2Wf2d8sFgmwBOwMjIyNGzYMHXu3FmbNm1Sz549tWvXLhlj1Lt3b3388ccNlbVeFBUVKSYmRoWFhYqOjv71GQAAaGB2rE2fffaZXnjhBe3YsUNvv/22kpKS9Oqrr6pTp046++yzgx3vV/24TQ/uO6iY1jF1OvT19c/K9OnmSv22Z6iWbaiQy+v706naki7tF65h/SM06x+F2vx9tbyW5HT59hpeNTBcg7u59eQ/i7V9n8d/CKZljJy+I+HV/9RQ9evqVmm50d8/K1VkmKUzu7rVs4NbHeNc2p1frYRoh0JckiPk8B+txzqUed/Ban27u1pZB7wqKfeqyiN1iHWoS7xT4eEOJbUJkcshOS0jeXTEYc+7squ0I7tKy7dValf+D52IMXL9kNfjOLzNHMbIYSSPJf8fyD+OleRvmH461mtJ3jqMdTkkr1Oq9FjHt1xLCg91KCbCUqsISzFhliq9UlGFVHzIq/Iq32GyJYeMqi3536OjLldSuMMoxGnJHWbJyNKBEt9uWOdPxjodvi9Wot2+L3yKK72KbuGUyyFVVhpFui2Fh1n+owXC3ZbCHEZul6XwcIcsy1JZhVFJmVfG45Xb7dAhj6/J83gkl4yqqo0iWzgUGe5rLJyS9h2oVnGF1KKFQ5FhlhwOS5XlXpVXelVtSRGhTlmWVFzmUbTbksPhO5S5tMKryFCHwpy+n02PZcnpshTi9B2RHGL5mkin26nKat+XFy5jFB4ihYY55HRZKj7ky1td6fuZq3b4PgNer1FlhdGhCo88smQcvsOsi8u8MtVGLUKlaodDFVW+w6UrK3ynCbhcvsP8Xf4MRi6HJafb4W9KXUZyO41cbodcLkv5xV65LN8h5FUeSQ5LYaEOhbkthTmlcJev+ZLTUpXHdySCw+OVw2H53zuHJTm8vs+H5bDkDLHksHzbylHt9TW2boecDt80yxg5vJLbbSk0zCHLksorjRwe45vHZcnl8r0XDq+RJd961jgt4RinRnirvLKcVs3THWxwGsUvnu5gk9Mo7HoKTXFJsVq2blmrWh/wnvZJkyZpwoQJmjp1qqKiovTOO+8oLi5OI0aM0IUXXhjo4iRJzz77rB577DHl5OTojDPO0DPPPKP+/fv/6nzz58/X9ddfr8suu0yLFi2q1WsDAICa3nnnHd1www0aMWKEVq1apYoK396twsJCTZ8+XR988EHAywxWrXe4HTXOy//pH24/H/dzP451uX3jP/1hb2e1w9fQhDstnXOa75DPKwa20H/WlatXB7f6dnXXWM6EK2O06MtD+mhNuaLCLU26MkbhbkshTkuhIYezDOgWekSGjm1Djr5erqPkdViKax2iuNZHn+dno6Wj3HK+Y0KIOiaEaMhZ4TpQ4tWm3dXac8CjxFZOJbd2qlWUQwWlXh0s8apTW5fC3JbyC71a912lCsq8Soh1yumwVFjqVVmFUdEhr1pH+d6DikqjyHBLYSG+vVatoxyKaeGQ0yE55DuMeWt2lf/IBqdTigy1FB3hUItQS62jnQr/4b0oLffK45X2FXm0e79HYW5LoS5LldVGbaKdiok4vH1/uo2PpaDUq8JSr6o8vusfWJbkdvqOfAh3W4pp4cvg+tnPTnmV0Xd51XK7LEWGW4oMcygsRDV+5tC8RIYd65mj/0z8tMn1j3RYcoYe+QE91ljLfZTfacf4HVHXsZbLkvWzdbHFWOsYY4/yOz+QsdIv14dAxv60uQ9UwHvao6KitGbNGnXp0kWxsbHKzMzUaaedprVr1+qyyy7Trl27AgqwYMECjRw5UnPmzFFKSopmzZqlt99+W5s3b1ZcXNwx59u1a5fOPvtsde7cWa1atTruQm7HvRkAgObNbrXprLPO0vjx4zVy5EhFRUVp7dq16ty5s1avXq2LLrpIOTk5AS2vsWu9VL/b9O/Ly/TR6nL//y86K0znnxnmOwc38vhuxFNRZbRia4XO6OhWdETAN+8BADRxdalLAVeNFi1a+M9jT0hI0Pbt2/3P5efnB7o4zZw5U7feeqtGjx6tHj16aM6cOYqIiNDLL798zHk8Ho9GjBihqVOnqnPnzgG/JgAAOLbNmzfrnHPOOWJ6TEyMCgoKAl5eU6/1IT/b4dWrY4iiwh3H3bBLvvNVf9MjjIYdABCwgCvHgAEDlJmZKcl3S5i77rpL06ZN00033aQBAwYEtKzKykqtXLlSaWlphwM5HEpLS9Py5cuPOd+DDz6ouLg43Xzzzb/6GhUVFc3+/rIAAAQiPj5e27ZtO2J6ZmZmwA10Y9R6qWHrvftnhzS2ijrKceUAADSQgM9pnzlzpkpKfFfcnDp1qkpKSrRgwQKdfPLJAV85Pj8/Xx6PR23btq0xvW3bttq0adNR58nMzNTcuXO1Zs2a43qNGTNmaOrUqQHlAgCgObv11ls1btw4vfzyy7IsS3v37tXy5cs1YcIETZ48OaBlNUatlxq23v/8RMLocM5XBgA0noCado/Ho927d6tXr16SfIfKz5kzp0GCHU1xcbFuuOEGvfjii2rTps2vzyDfhfPS09P9/y8qKlJycnJDRQQAoMmbOHGivF6vzjvvPJWVlemcc85RaGioJkyYoDvvvLNBX7s2tV5q2HpfXlWza//5xcgAAGhIATXtTqdTF1xwgTZu3KiWLVvW+cXbtGkjp9Op3NzcGtNzc3MVHx9/xPjt27dr165duvTSS/3TvF7fZfVdLpc2b96sLl261JgnNDRUoaFHXo0VAAAcnWVZuu+++3T33Xdr27ZtKikpUY8ePRQZGRnwshqj1ksNW+9/3rQDANCYAj6nvWfPntqxY0e9vLjb7VafPn2UkZHhn+b1epWRkaHU1NQjxnfv3l3r16/XmjVr/I9hw4ZpyJAhWrNmDXvQAQCoB6+99prKysrkdrvVo0cP9e/fv1YNu3Ri1Pqwn5zT3uEkzmcHADSugM9pf/jhhzVhwgQ99NBD6tOnj1q0aFHj+UAvX5+enq5Ro0apb9++6t+/v2bNmqXS0lKNHj1akjRy5EglJSVpxowZCgsLU8+ePWvM/+Me/59PBwAAtTN+/HjdfvvtGjZsmH7/+99r6NChcjpr36w29Vo/9Kww7dpXrTbRTl2VGh6UDACA5ivgpv3iiy+WJA0bNkyWdfibZ2OMLMuSx+MJaHnXXnut9u3bpwceeEA5OTk688wztXjxYv8Fa7KysuRwcHsUAAAaS3Z2thYvXqw333xT11xzjSIiInT11VdrxIgRGjhwYMDLa+q1vkWYQ+nD6navdwAAassy5ufXRP1ln3zyyS8+P3jw4DoFamh1uak9AAANwc61qaysTO+++67eeOMN/ec//1G7du20ffv2YMf6VXbepgCA5qcudSngPe12b8oBAED9iYiI0NChQ3Xw4EF999132rhxY7AjAQDQrNTqWLTPPvtMv//97zVw4EDt2bNHkvTqq68qMzOzXsMBAIDgKCsr0+uvv66LL75YSUlJmjVrli6//HJ98803wY4GAECzEnDT/s4772jo0KEKDw/XqlWrVFFRIUkqLCzU9OnT6z0gAABoXNddd53i4uI0fvx4de7cWcuWLdO2bdv00EMPqXv37sGOBwBAsxJw0/7www9rzpw5evHFFxUSEuKfPmjQIK1atapewwEAgMbndDr11ltvKTs7W7Nnz65xa7YNGzYEMRkAAM1PwOe0b968Weecc84R02NiYlRQUFAfmQAAQBC9/vrrNf5fXFysN998Uy+99JJWrlwZ8J1iAABA7QW8pz0+Pl7btm07YnpmZqY6d+5cL6EAAEDwffrppxo1apQSEhL0+OOP69xzz9UXX3wR7FgAADQrAe9pv/XWWzVu3Di9/PLLsixLe/fu1fLlyzVhwgRNnjy5ITICAIBGkpOTo3nz5mnu3LkqKirSNddco4qKCi1atEg9evQIdjwAAJqdgJv2iRMnyuv16rzzzlNZWZnOOecchYaGasKECbrzzjsbIiMAAGgEl156qT799FNdcsklmjVrli688EI5nU7NmTMn2NEAAGi2Am7aLcvSfffdp7vvvlvbtm1TSUmJevToocjIyIbIBwAAGsmHH36oP/7xjxozZoxOPvnkYMcBAACqxTntr732msrKyuR2u9WjRw/179+fhh0AgBNAZmamiouL1adPH6WkpGj27NnKz88PdiwAAJq1gJv28ePHKy4uTr/73e/0wQcfcAVZAABOEAMGDNCLL76o7Oxs3XbbbZo/f74SExPl9Xq1ZMkSFRcXBzsiAADNTsBNe3Z2tubPny/LsnTNNdcoISFBY8eO1X//+9+GyAcAABpZixYtdNNNNykzM1Pr16/XXXfdpUceeURxcXEaNmxYsOMBANCsBNy0u1wu/c///I9ef/115eXl6cknn9SuXbs0ZMgQdenSpSEyAgCAIOnWrZseffRR7d69W2+++Waw4wAA0OwEfCG6n4qIiNDQoUN18OBBfffdd9q4cWN95QIAADbidDo1fPhwDR8+PNhRAABoVgLe0y5JZWVlev3113XxxRcrKSlJs2bN0uWXX65vvvmmvvMBAAAAANBsBbyn/brrrtP777+viIgIXXPNNZo8ebJSU1MbIhsAAAAAAM1awE270+nUW2+9paFDh8rpdNZ4bsOGDerZs2e9hQMAAAAAoDkLuGl//fXXa/y/uLhYb775pl566SWtXLmSW8ABAAAAAFBPanVOuyR9+umnGjVqlBISEvT444/r3HPP1RdffFGf2QAAAAAAaNYC2tOek5OjefPmae7cuSoqKtI111yjiooKLVq0SD169GiojAAAAAAANEvHvaf90ksvVbdu3bRu3TrNmjVLe/fu1TPPPNOQ2QAAAAAAaNaOe0/7hx9+qD/+8Y8aM2aMTj755IbMBAAAAAAAFMCe9szMTBUXF6tPnz5KSUnR7NmzlZ+f35DZAAAAAABo1o67aR8wYIBefPFFZWdn67bbbtP8+fOVmJgor9erJUuWqLi4uCFzAgAAAADQ7AR89fgWLVropptuUmZmptavX6+77rpLjzzyiOLi4jRs2LCGyAgAAAAAQLNU61u+SVK3bt306KOPavfu3XrzzTfrKxMAAAAAAFAdm/YfOZ1ODR8+XO+99159LA4AAAAAAKiemnYAAAAAAFD/aNoBAAAAALApmnYAAAAAAGyKph0AAAAAAJuiaQcAAAAAwKZo2gEAAAAAsCmadgAAAAAAbIqmHQAAAAAAm6JpBwAAAADApmjaAQAAAACwKZp2AAAAAABsiqYdAAAAAACbomkHAAAAAMCmaNoBAAAAALApmnYAAAAAAGyKph0AAAAAAJuiaQcAAAAAwKZo2gEAAAAAsCmadgAAAAAAbIqmHQAAAAAAm6JpBwAAAADApmjaAQAAAACwKZp2AAAAAABsiqYdAAAAAACbomkHAAAAAMCmaNoBAAAAALApmnYAAAAAAGyKph0AAAAAAJuiaQcAAAAAwKZs0bQ/++yz6tixo8LCwpSSkqIVK1Ycc+yLL76o3/zmN4qNjVVsbKzS0tJ+cTwAAAg+aj0AALUT9KZ9wYIFSk9P15QpU7Rq1SqdccYZGjp0qPLy8o46ftmyZbr++uu1dOlSLV++XMnJybrgggu0Z8+eRk4OAACOB7UeAIDas4wxJpgBUlJS1K9fP82ePVuS5PV6lZycrDvvvFMTJ0781fk9Ho9iY2M1e/ZsjRw58lfHFxUVKSYmRoWFhYqOjq5zfgAA6upEr02NXeulE3+bAgCalrrUpaDuaa+srNTKlSuVlpbmn+ZwOJSWlqbly5cf1zLKyspUVVWlVq1aHfX5iooKFRUV1XgAAIDG0Ri1XqLeAwBOXEFt2vPz8+XxeNS2bdsa09u2baucnJzjWsY999yjxMTEGn8M/NSMGTMUExPjfyQnJ9c5NwAAOD6NUesl6j0A4MQV9HPa6+KRRx7R/Pnz9e677yosLOyoYyZNmqTCwkL/4/vvv2/klAAAoLaOp9ZL1HsAwInLFcwXb9OmjZxOp3Jzc2tMz83NVXx8/C/O+/jjj+uRRx7Rf/7zH/Xq1euY40JDQxUaGloveQEAQGAao9ZL1HsAwIkrqHva3W63+vTpo4yMDP80r9erjIwMpaamHnO+Rx99VA899JAWL16svn37NkZUAABQC9R6AADqJqh72iUpPT1do0aNUt++fdW/f3/NmjVLpaWlGj16tCRp5MiRSkpK0owZMyRJf/nLX/TAAw/ojTfeUMeOHf3nw0VGRioyMjJo6wEAAI6OWg8AQO0FvWm/9tprtW/fPj3wwAPKycnRmWeeqcWLF/svWJOVlSWH4/ABAc8//7wqKyt11VVX1VjOlClT9Oc//7kxowMAgONArQcAoPaCfp/2xsZ9WwEAdkNtqn9sUwCAnTTZ+7QDAAAAAIBjo2kHAAAAAMCmaNoBAAAAALApmnYAAAAAAGyKph0AAAAAAJuiaQcAAAAAwKZo2gEAAAAAsCmadgAAAAAAbIqmHQAAAAAAm6JpBwAAAADApmjaAQAAAACwKZp2AAAAAABsiqYdAAAAAACbomkHAAAAAMCmaNoBAAAAALApmnYAAAAAAGyKph0AAAAAAJuiaQcAAAAAwKZo2gEAAAAAsCmadgAAAAAAbIqmHQAAAAAAm6JpBwAAAADApmjaAQAAAACwKZp2AAAAAABsiqYdAAAAAACbomkHAAAAAMCmaNoBAAAAALApmnYAAAAAAGyKph0AAAAAAJuiaQcAAAAAwKZo2gEAAAAAsCmadgAAAAAAbIqmHQAAAAAAm6JpBwAAAADApmjaAQAAAACwKZp2AAAAAABsiqYdAAAAAACbomkHAAAAAMCmaNoBAAAAALApmnYAAAAAAGyKph0AAAAAAJuiaQcAAAAAwKZo2gEAAAAAsCmadgAAAAAAbIqmHQAAAAAAm6JpBwAAAADApmjaAQAAAACwKZp2AAAAAABsiqYdAAAAAACbomkHAAAAAMCmaNoBAAAAALApmnYAAAAAAGyKph0AAAAAAJuiaQcAAAAAwKZo2gEAAAAAsCmadgAAAAAAbMoWTfuzzz6rjh07KiwsTCkpKVqxYsUvjn/77bfVvXt3hYWF6fTTT9cHH3zQSEkBAEBtUOsBAKidoDftCxYsUHp6uqZMmaJVq1bpjDPO0NChQ5WXl3fU8f/97391/fXX6+abb9bq1as1fPhwDR8+XBs2bGjk5AAA4HhQ6wEAqD3LGGOCGSAlJUX9+vXT7NmzJUler1fJycm68847NXHixCPGX3vttSotLdX777/vnzZgwACdeeaZmjNnzq++XlFRkWJiYlRYWKjo6Oj6WxEAAGrpRK9NjV3rpRN/mwIAmpa61KWg7mmvrKzUypUrlZaW5p/mcDiUlpam5cuXH3We5cuX1xgvSUOHDj3m+IqKChUVFdV4AACAxtEYtV6i3gMATlxBbdrz8/Pl8XjUtm3bGtPbtm2rnJyco86Tk5MT0PgZM2YoJibG/0hOTq6f8AAA4Fc1Rq2XqPcAgBNX0M9pb2iTJk1SYWGh//H9998HOxIAAKhn1HsAwInKFcwXb9OmjZxOp3Jzc2tMz83NVXx8/FHniY+PD2h8aGioQkND6ycwAAAISGPUeol6DwA4cQV1T7vb7VafPn2UkZHhn+b1epWRkaHU1NSjzpOamlpjvCQtWbLkmOMBAEDwUOsBAKiboO5pl6T09HSNGjVKffv2Vf/+/TVr1iyVlpZq9OjRkqSRI0cqKSlJM2bMkCSNGzdOgwcP1hNPPKFLLrlE8+fP19dff62//vWvwVwNAABwDNR6AABqL+hN+7XXXqt9+/bpgQceUE5Ojs4880wtXrzYfwGarKwsORyHDwgYOHCg3njjDd1///269957dfLJJ2vRokXq2bNnsFYBAAD8Amo9AAC1F/T7tDc27tsKALAbalP9Y5sCAOykyd6nHQAAAAAAHBtNOwAAAAAANkXTDgAAAACATdG0AwAAAABgUzTtAAAAAADYFE07AAAAAAA2RdMOAAAAAIBN0bQDAAAAAGBTNO0AAAAAANgUTTsAAAAAADZF0w4AAAAAgE3RtAMAAAAAYFM07QAAAAAA2BRNOwAAAAAANkXTDgAAAACATdG0AwAAAABgUzTtAAAAAADYFE07AAAAAAA2RdMOAAAAAIBN0bQDAAAAAGBTNO0AAAAAANgUTTsAAAAAADZF0w4AAAAAgE3RtAMAAAAAYFM07QAAAAAA2BRNOwAAAAAANuUKdoDGZoyRJBUVFQU5CQAAPj/WpB9rFOqOeg8AsJO61Ppm17QXFxdLkpKTk4OcBACAmoqLixUTExPsGCcE6j0AwI5qU+st08y+1vd6vdq7d6+ioqJkWVadllVUVKTk5GR9//33io6OrqeEjauprwP5g4v8wUX+4KrP/MYYFRcXKzExUQ4HZ67VB+r9YeQPLvIHF/mDq6nnl+pvHepS65vdnnaHw6F27drV6zKjo6Ob7A/hj5r6OpA/uMgfXOQPrvrKzx72+kW9PxL5g4v8wUX+4Grq+aX6WYfa1nq+zgcAAAAAwKZo2gEAAAAAsCma9joIDQ3VlClTFBoaGuwotdbU14H8wUX+4CJ/cDX1/Dh+Tf29Jn9wkT+4yB9cTT2/ZI91aHYXogMAAAAAoKlgTzsAAAAAADZF0w4AAAAAgE3RtAMAAAAAYFM07QAAAAAA2BRNex08++yz6tixo8LCwpSSkqIVK1YEO5Ik6dNPP9Wll16qxMREWZalRYsW1XjeGKMHHnhACQkJCg8PV1pamrZu3VpjzIEDBzRixAhFR0erZcuWuvnmm1VSUtIo+WfMmKF+/fopKipKcXFxGj58uDZv3lxjTHl5ucaOHavWrVsrMjJSV155pXJzc2uMycrK0iWXXKKIiAjFxcXp7rvvVnV1dYPnf/7559WrVy9FR0crOjpaqamp+vDDD5tE9p975JFHZFmW/vSnP/mn2T3/n//8Z1mWVePRvXv3JpNfkvbs2aPf//73at26tcLDw3X66afr66+/9j9v589wx44dj9j+lmVp7Nixkuy//T0ejyZPnqxOnTopPDxcXbp00UMPPaSfXrPVztsf9Y9a3zCo9cGvNT/V1Oo9td6HWl87TbLWG9TK/PnzjdvtNi+//LL55ptvzK233mpatmxpcnNzgx3NfPDBB+a+++4zCxcuNJLMu+++W+P5Rx55xMTExJhFixaZtWvXmmHDhplOnTqZQ4cO+cdceOGF5owzzjBffPGF+eyzz0zXrl3N9ddf3yj5hw4dal555RWzYcMGs2bNGnPxxReb9u3bm5KSEv+Y22+/3SQnJ5uMjAzz9ddfmwEDBpiBAwf6n6+urjY9e/Y0aWlpZvXq1eaDDz4wbdq0MZMmTWrw/O+9957517/+ZbZs2WI2b95s7r33XhMSEmI2bNhg++w/tWLFCtOxY0fTq1cvM27cOP90u+efMmWKOe2000x2drb/sW/fviaT/8CBA6ZDhw7mxhtvNF9++aXZsWOH+eijj8y2bdv8Y+z8Gc7Ly6ux7ZcsWWIkmaVLlxpj7L/9p02bZlq3bm3ef/99s3PnTvP222+byMhI89RTT/nH2Hn7o35R6xsOtd4etd6YplnvqfU+1PraaYq1nqa9lvr372/Gjh3r/7/H4zGJiYlmxowZQUx1pJ8Xcq/Xa+Lj481jjz3mn1ZQUGBCQ0PNm2++aYwx5ttvvzWSzFdffeUf8+GHHxrLssyePXsaLfuP8vLyjCTzySef+POGhISYt99+2z9m48aNRpJZvny5Mcb3x4zD4TA5OTn+Mc8//7yJjo42FRUVjbsCxpjY2Fjz0ksvNZnsxcXF5uSTTzZLliwxgwcP9hfxppB/ypQp5owzzjjqc00h/z333GPOPvvsYz7f1D7D48aNM126dDFer7dJbP9LLrnE3HTTTTWmXXHFFWbEiBHGmKa3/VE31PrGQ60PTvamWu+p9fb6DFPrfRpy+3N4fC1UVlZq5cqVSktL809zOBxKS0vT8uXLg5js1+3cuVM5OTk1ssfExCglJcWfffny5WrZsqX69u3rH5OWliaHw6Evv/yy0TMXFhZKklq1aiVJWrlypaqqqmqsQ/fu3dW+ffsa63D66aerbdu2/jFDhw5VUVGRvvnmm0bL7vF4NH/+fJWWlio1NbXJZB87dqwuueSSGjmlprPtt27dqsTERHXu3FkjRoxQVlZWk8n/3nvvqW/fvrr66qsVFxens846Sy+++KL/+ab0Ga6srNRrr72mm266SZZlNYntP3DgQGVkZGjLli2SpLVr1yozM1MXXXSRpKa1/VE31PrGRa0PTvamXO+p9fb4DFPrG2f7u+p9ic1Afn6+PB5PjR80SWrbtq02bdoUpFTHJycnR5KOmv3H53JychQXF1fjeZfLpVatWvnHNBav16s//elPGjRokHr27OnP53a71bJlyxpjf74OR1vHH59raOvXr1dqaqrKy8sVGRmpd999Vz169NCaNWtsn33+/PlatWqVvvrqqyOeawrbPiUlRfPmzVO3bt2UnZ2tqVOn6je/+Y02bNjQJPLv2LFDzz//vNLT03Xvvffqq6++0h//+Ee53W6NGjWqSX2GFy1apIKCAt14443+XHbf/hMnTlRRUZG6d+8up9Mpj8ejadOmacSIETUyNIXtj7qh1jcean3jZ5eadr2n1tvnM0ytP6whtz9NO2xt7Nix2rBhgzIzM4MdJSDdunXTmjVrVFhYqL///e8aNWqUPvnkk2DH+lXff/+9xo0bpyVLligsLCzYcWrlx29JJalXr15KSUlRhw4d9NZbbyk8PDyIyY6P1+tV3759NX36dEnSWWedpQ0bNmjOnDkaNWpUkNMFZu7cubrooouUmJgY7CjH7a233tLrr7+uN954Q6eddprWrFmjP/3pT0pMTGxy2x9oKqj1ja+p13tqvX1Q6xsHh8fXQps2beR0Oo+4CmJubq7i4+ODlOr4/Jjvl7LHx8crLy+vxvPV1dU6cOBAo67fHXfcoffff19Lly5Vu3bt/NPj4+NVWVmpgoKCGuN/vg5HW8cfn2tobrdbXbt2VZ8+fTRjxgydccYZeuqpp2yffeXKlcrLy1Pv3r3lcrnkcrn0ySef6Omnn5bL5VLbtm1tnf9oWrZsqVNOOUXbtm2z/faXpISEBPXo0aPGtFNPPdV/2F9T+Qx/9913+s9//qNbbrnFP60pbP+7775bEydO1HXXXafTTz9dN9xwg8aPH68ZM2bUyGD37Y+6o9Y3Dmp9cLKfaPWeWu9DrT8+TbHW07TXgtvtVp8+fZSRkeGf5vV6lZGRodTU1CAm+3WdOnVSfHx8jexFRUX68ssv/dlTU1NVUFCglStX+sd8/PHH8nq9SklJafCMxhjdcccdevfdd/Xxxx+rU6dONZ7v06ePQkJCaqzD5s2blZWVVWMd1q9fX+PDtGTJEkVHRx/xS7IxeL1eVVRU2D77eeedp/Xr12vNmjX+R9++fTVixAj/v+2c/2hKSkq0fft2JSQk2H77S9KgQYOOuO3Rli1b1KFDB0lN4zMsSa+88ori4uJ0ySWX+Kc1he1fVlYmh6NmaXQ6nfJ6vZKazvZH3VHrGxa1PrjZT7R6T633odYfnyZZ6+v90nbNxPz5801oaKiZN2+e+fbbb83//u//mpYtW9a4CmKwFBcXm9WrV5vVq1cbSWbmzJlm9erV5rvvvjPG+G5h0LJlS/OPf/zDrFu3zlx22WVHvYXBWWedZb788kuTmZlpTj755Ea7DcyYMWNMTEyMWbZsWY3bSZSVlfnH3H777aZ9+/bm448/Nl9//bVJTU01qamp/ud/vJXEBRdcYNasWWMWL15sTjrppEa5lcTEiRPNJ598Ynbu3GnWrVtnJk6caCzLMv/+979tn/1ofno1WWPsn/+uu+4yy5YtMzt37jSff/65SUtLM23atDF5eXlNIv+KFSuMy+Uy06ZNM1u3bjWvv/66iYiIMK+99pp/jN0/wx6Px7Rv397cc889Rzxn9+0/atQok5SU5L8NzMKFC02bNm3M//t//88/xu7bH/WHWt9wqPX2qvXGNK16T633odbXTlOs9TTtdfDMM8+Y9u3bG7fbbfr372+++OKLYEcyxhizdOlSI+mIx6hRo4wxvtsYTJ482bRt29aEhoaa8847z2zevLnGMvbv32+uv/56ExkZaaKjo83o0aNNcXFxo+Q/WnZJ5pVXXvGPOXTokPnDH/5gYmNjTUREhLn88stNdnZ2jeXs2rXLXHTRRSY8PNy0adPG3HXXXaaqqqrB8990002mQ4cOxu12m5NOOsmcd955/iJu9+xH8/Mibvf81157rUlISDBut9skJSWZa6+9tsZ9T+2e3xhj/vnPf5qePXua0NBQ0717d/PXv/61xvN2/wx/9NFHRtIRmYyx//YvKioy48aNM+3btzdhYWGmc+fO5r777qtxCxq7b3/UL2p9w6DWB7/W/FxTqvfUeh9qfe00xVpvGWNM/e+/BwAAAAAAdcU57QAAAAAA2BRNOwAAAAAANkXTDgAAAACATdG0AwAAAABgUzTtAAAAAADYFE07AAAAAAA2RdMOAAAAAIBN0bQDAAAAAGBTNO0AAAAAANgUTTsASdK+ffs0ZswYtW/fXqGhoYqPj9fQoUP1+eefS5Isy9KiRYuCGxIAANQatR5omlzBDgDAHq688kpVVlbqb3/7mzp37qzc3FxlZGRo//79wY4GAADqAbUeaJosY4wJdggAwVVQUKDY2FgtW7ZMgwcPPuL5jh076rvvvvP/v0OHDtq1a5ck6R//+IemTp2qb7/9VomJiRo1apTuu+8+uVy+7wQty9Jzzz2n9957T8uWLVNCQoIeffRRXXXVVY2ybgAAgFoPNGUcHg9AkZGRioyM1KJFi1RRUXHE81999ZUk6ZVXXlF2drb//5999plGjhypcePG6dtvv9ULL7ygefPmadq0aTXmnzx5sq688kqtXbtWI0aM0HXXXaeNGzc2/IoBAABJ1HqgKWNPOwBJ0jvvvKNbb71Vhw4dUu/evTV48GBdd9116tWrlyTft+jvvvuuhg8f7p8nLS1N5513niZNmuSf9tprr+n//b//p7179/rnu/322/X888/7xwwYMEC9e/fWc8891zgrBwAAqPVAE8WedgCSfOe57d27V++9954uvPBCLVu2TL1799a8efOOOc/atWv14IMP+r+9j4yM1K233qrs7GyVlZX5x6WmptaYLzU1lW/fAQBoZNR6oGniQnQA/MLCwnT++efr/PPP1+TJk3XLLbdoypQpuvHGG486vqSkRFOnTtUVV1xx1GUBAAB7odYDTQ972gEcU48ePVRaWipJCgkJkcfjqfF87969tXnzZnXt2vWIh8Nx+NfLF198UWO+L774QqeeemrDrwAAAPhF1HrA/tjTDkD79+/X1VdfrZtuukm9evVSVFSUvv76az366KO67LLLJPmuKpuRkaFBgwYpNDRUsbGxeuCBB/Q///M/at++va666io5HA6tXbtWGzZs0MMPP+xf/ttvv62+ffvq7LPP1uuvv64VK1Zo7ty5wVpdAACaHWo90HRxIToAqqio0J///Gf9+9//1vbt21VVVaXk5GRdffXVuvfeexUeHq5//vOfSk9P165du5SUlOS/DcxHH32kBx98UKtXr1ZISIi6d++uW265Rbfeeqsk38Vpnn32WS1atEiffvqpEhIS9Je//EXXXHNNENcYAIDmhVoPNF007QAa1NGuRAsAAE4c1HqgYXFOOwAAAAAANkXTDgAAAACATXF4PAAAAAAANsWedgAAAAAAbIqmHQAAAAAAm6JpBwAAAADApmjaAQAAAACwKZp2AAAAAABsiqYdAAAAAACbomkHAAAAAMCmaNoBAAAAALCp/w/PwcHTckHteAAAAABJRU5ErkJggg==", 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", 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" ] @@ -279,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": { "metadata": {} }, @@ -290,25 +317,25 @@ "text": [ "Desired Coverage: 0.95\n", "\n", - "Lower Bound: E[coverage]: 0.9499999874808238\n", - "Lower Bound: MC coverage: 0.95875\n", - "Lower Bound: E[shortage]: 0.2083156101099218\n", - "Lower Bound: MC shortage: 0.20895113826412742\n", + "Lower Bound: E[coverage]: 0.9500004972564056\n", + "Lower Bound: MC coverage: 0.95125\n", + "Lower Bound: E[shortage]: 0.2083162391762252\n", + "Lower Bound: MC shortage: 0.20359211021787377\n", "\n", "CP Lower Bound: E[coverage]: 0.9866365389898434\n", - "CP Lower Bound: MC coverage: 0.99\n", + "CP Lower Bound: MC coverage: 0.98375\n", "CP Lower Bound: E[shortage]: 0.2377199754795652\n", - "CP Lower Bound: MC shortage: 0.23750591278276076\n", + "CP Lower Bound: MC shortage: 0.23251518249780176\n", "\n", - "Upper Bound: E[coverage]: 0.9499999874808238\n", - "Upper Bound: MC coverage: 0.9399999999999998\n", - "Upper Bound: E[excess]: 0.14134920784807223\n", - "Upper Bound: MC excess: 0.14230161204926164\n", + "Upper Bound: E[coverage]: 0.9500004972564056\n", + "Upper Bound: MC coverage: 0.94875\n", + "Upper Bound: E[excess]: 0.14134949412240885\n", + "Upper Bound: MC excess: 0.1434229892899832\n", "\n", "2-Sided Bound: E[coverage]: 0.95\n", - "2-Sided Bound: MC coverage: 0.95875\n", + "2-Sided Bound: MC coverage: 0.95375\n", "2-Sided Bound: E[width]: 0.4179930286465216\n", - "2-Sided Bound: MC width: 0.4193758095668225\n" + "2-Sided Bound: MC width: 0.4161829550848068\n" ] } ], @@ -348,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": { "metadata": {} }, @@ -393,7 +420,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.3" }, "orig_nbformat": 4 },