From cbc8ad433b0e4c3c4b473fcbdb8fecf5c62f7137 Mon Sep 17 00:00:00 2001 From: Patrick Sheehan Date: Wed, 15 Apr 2026 03:39:18 +0000 Subject: [PATCH 1/8] Add non-isotropic dust scattering, including: - Adding a new `IsotropicDust`, `HenyeyGreensteinDust`, and `GeneralDust` classes to handle different types of dust scattering. - Move functionality to update opactities for photons/the grid to the *Dust clases so they can each provide the relevant info. - Pass the camera orientation through the raytracing process to calculate the relevant scattering angles. --- pinballrt/camera.py | 3 + pinballrt/dust.py | 646 +++++++++++++++++++++++++++++++++++++++++-- pinballrt/grids.py | 139 +++------- pinballrt/model.py | 15 +- pinballrt/photons.py | 2 + pinballrt/sources.py | 4 +- pinballrt/utils.py | 18 ++ 7 files changed, 684 insertions(+), 143 deletions(-) diff --git a/pinballrt/camera.py b/pinballrt/camera.py index 37f16c6..4604e45 100644 --- a/pinballrt/camera.py +++ b/pinballrt/camera.py @@ -27,6 +27,9 @@ def set_orientation(self, incl, pa, dpc): self.i = np.array([self.r*np.sin(self.incl)*np.cos(phi), \ self.r*np.sin(self.incl)*np.sin(phi), \ self.r*np.cos(self.incl)]) + + with wp.ScopedDevice(self.grid.device): + self.i_wp = wp.vec3(self.i) self.ex = np.array([-np.sin(phi), np.cos(phi), 0.0]) self.ey = np.array([-np.cos(self.incl)*np.cos(phi), \ diff --git a/pinballrt/dust.py b/pinballrt/dust.py index d46ec30..b9359ad 100644 --- a/pinballrt/dust.py +++ b/pinballrt/dust.py @@ -19,6 +19,9 @@ import os import zuko +from .utils import GridStruct, random_direction +from .photons import PhotonList + from torch.distributions.multivariate_normal import MultivariateNormal wp.config.quiet = True @@ -237,6 +240,66 @@ def absorb(self, temperature): return direction, frequency + def update_photon_opacities(self, photon_list, iphotons, grid=None, inu=None): + nphotons = iphotons.size(0) + + if grid is not None and inu is not None: + wp.launch(kernel=self.set_photon_opacities_grid, + dim=(nphotons,), + inputs=[photon_list, grid, inu, iphotons]) + else: + wp.launch(kernel=self.set_photon_opacities, + dim=(nphotons,), + inputs=[photon_list, + self.ml_kabs(photon_list=photon_list, iphotons=iphotons), + self.ml_ksca(photon_list=photon_list, iphotons=iphotons), + iphotons]) + + @wp.kernel + def set_photon_opacities(photon_list: PhotonList, + kabs: wp.array(dtype=float), + ksca: wp.array(dtype=float), + iphotons: wp.array(dtype=int)): # pragma: no cover + i = wp.tid() + ip = iphotons[i] + + photon_list.kabs[ip] = kabs[i] + photon_list.ksca[ip] = ksca[i] + photon_list.albedo[ip] = ksca[i] / (kabs[i] + ksca[i]) + + @wp.kernel + def set_photon_opacities_grid(photon_list: PhotonList, + grid: GridStruct, + inu: int, + iphotons: wp.array(dtype=int)): # pragma: no cover + ip = iphotons[wp.tid()] + + ix, iy, iz = photon_list.indices[ip][0], photon_list.indices[ip][1], photon_list.indices[ip][2] + + photon_list.kabs[ip] = grid.kabs[inu, ix, iy, iz] + photon_list.ksca[ip] = grid.ksca[inu, ix, iy, iz] + photon_list.albedo[ip] = photon_list.ksca[ip] / (photon_list.kabs[ip] + photon_list.ksca[ip]) + + def set_grid_opacities(self, grid, frequency): + p = wp.to_torch(grid.p) + shape = p.shape + p = p.flatten() + amax = wp.to_torch(grid.amax).flatten() + + kabs = [self.ml_kabs(p=p, amax=amax, nu=torch.ones(np.prod(shape), + dtype=torch.float32, + device=wp.device_to_torch(wp.get_device())) * \ + f.to(u.GHz).value) for f in frequency] + + grid.kabs = wp.from_torch(torch.concatenate(kabs).reshape((len(frequency),) + shape)) + + ksca = [self.ml_ksca(p=p, amax=amax, nu=torch.ones(np.prod(shape), + dtype=torch.float32, + device=wp.device_to_torch(wp.get_device())) * \ + f.to(u.GHz).value) for f in frequency] + + grid.ksca = wp.from_torch(torch.concatenate(ksca).reshape((len(frequency),) + shape)) + def random_nu_manual(self, p, amax, temperature, ksi=None, batch_size=100000): import interpn @@ -350,17 +413,11 @@ def ml_step(self, photon_list, s, iphotons): return 10.**torch.clamp(test_x[:,0], self.log10_nu0_min, self.log10_nu0_max), 10.**test_x[:,1], 10.**test_x[:,2], test_x[:,3], test_x[:,4], torch.zeros(test_x.size(0)), test_x[:,5], test_x[:,6], torch.zeros(test_x.size(0)) - def initialize_model(self, model="random_nu", input_size=2, output_size=1, hidden_units=(48, 48, 48)): - if model == 'ml_step': - self.ml_step_model = NeuralSplineFlow(input_size, output_size, transforms=len(hidden_units), hidden_features=hidden_units[0]) - elif model == 'random_nu': - self.random_nu_model = MultiLayerPerceptron(input_size, output_size, hidden_units=hidden_units) - elif model == "kabs": - self.kabs_model = MultiLayerPerceptron(input_size, output_size, hidden_units=hidden_units) - elif model == "ksca": - self.ksca_model = MultiLayerPerceptron(input_size, output_size, hidden_units=hidden_units) - elif model == "pmo": - self.pmo_model = MultiLayerPerceptron(input_size, output_size, hidden_units=hidden_units) + def initialize_model(self, model="random_nu", model_type="MLP", input_size=2, output_size=1, hidden_units=(48, 48, 48)): + if model_type == 'flow': + setattr(self, f"{model}_model", NeuralSplineFlow(input_size, output_size, transforms=len(hidden_units), hidden_features=hidden_units[0])) + else: + setattr(self, f"{model}_model", MultiLayerPerceptron(input_size, output_size, hidden_units=hidden_units)) def learn(self, model="random_nu", nsamples=200000, test_split=0.1, valid_split=0.2, hidden_units=(48, 48, 48), tau_range=(3.0, 1e4), temperature_range=(0.1*u.K, 1e4*u.K), amax_range=(1*u.micron, 10.0*u.cm), p_range=(2.5, 4.5), @@ -401,9 +458,11 @@ def learn(self, model="random_nu", nsamples=200000, test_split=0.1, valid_split= # Set up the NN if model == "random_nu": - input_size, output_size = 4, 1 + self.input_size, self.output_size = 4, 1 + self.model_type = "MLP" elif model == "ml_step": - input_size, output_size = 7, 5 + self.input_size, self.output_size = 7, 5 + self.model_type = "flow" if nu_range is None: nu_range = (self.nu.value.min(), self.nu.value.max()) @@ -418,10 +477,11 @@ def learn(self, model="random_nu", nsamples=200000, test_split=0.1, valid_split= self.p_max = p_range[1] self.log10_tau_cell_nu0_min = np.log10(tau_range[0]) self.log10_tau_cell_nu0_max = np.log10(tau_range[1]) - elif model in ["kabs", "ksca","pmo"]: - input_size, output_size = 3, 1 + elif model in ["kabs", "ksca", "g","pmo"]: + self.input_size, self.output_size = 3, 1 + self.model_type = "MLP" - self.initialize_model(model=model, input_size=input_size, output_size=output_size, hidden_units=hidden_units) + self.initialize_model(model=model, model_type=self.model_type, input_size=self.input_size, output_size=self.output_size, hidden_units=hidden_units) # Wrap the model in lightning @@ -465,10 +525,12 @@ def test_model(self, plot=False): if self.current_model == "random_nu": self.plot_triangle_plots(model=self.current_model) - elif self.current_model in ["kabs", "ksca"]: + elif self.current_model in ["kabs", "ksca", "g"]: self.plot_opacity_model(model=self.current_model) elif self.current_model == "pmo": self.plot_pmo_model() + elif self.current_model == "scattering_phase_function": + self.plot_scattering_phase_function_model() else: #self.plot_ml_step() self.plot_triangle_plots(model=self.current_model) @@ -783,17 +845,21 @@ def plot_opacity_model(self, model='kabs'): """ import matplotlib.pyplot as plt - log10_nu = np.linspace(np.log10(self.nu.min().value), np.log10(self.nu.max().value), 10) + log10_nu = np.linspace(np.log10(self.nu.min().value), np.log10(self.nu.max().value), 100) log10_lam = np.log10((const.c / (10.**log10_nu * u.GHz)).to(u.cm).value) - log10_amax = np.repeat(np.random.uniform(0, 1, 1)*(np.log10(self.amax.max().value) - np.log10(self.amax.min().value)) + np.log10(self.amax.min().value), 10) - p = np.repeat(np.random.uniform(0, 1, 1)*(self.p.max() - self.p.min()) + self.p.min(), 10) + log10_amax = np.repeat(np.random.uniform(0, 1, 1)*(np.log10(self.amax.max().value) - np.log10(self.amax.min().value)) + np.log10(self.amax.min().value), 100) + p = np.repeat(np.random.uniform(0, 1, 1)*(self.p.max() - self.p.min()) + self.p.min(), 100) print(f"log10_amax: {log10_amax[0]}, p: {p[0]}") samples = np.vstack((p, log10_amax, log10_nu)).T - interpolated = np.log10(getattr(self, f"interpolate_{model}")(p, 10.**log10_amax, 10.**log10_nu)) - nned = getattr(self, f'{model}_y_scaler').inverse_transform(getattr(self, f'{model}_model')(getattr(self, f'{model}_x_scaler').transform(torch.tensor(samples, dtype=torch.float32)))).detach().numpy() + if model == "g": + interpolated = getattr(self, f"interpolate_{model}")(p, 10.**log10_amax, 10.**log10_nu) + nned = getattr(self, f'{model}_y_scaler').inverse_transform(getattr(self, f'{model}_model')(getattr(self, f'{model}_x_scaler').transform(torch.tensor(samples, dtype=torch.float32)))).detach().numpy() + else: + interpolated = np.log10(getattr(self, f"interpolate_{model}")(p, 10.**log10_amax, 10.**log10_nu)) + nned = getattr(self, f'{model}_y_scaler').inverse_transform(getattr(self, f'{model}_model')(getattr(self, f'{model}_x_scaler').transform(torch.tensor(samples, dtype=torch.float32)))).detach().numpy() plt.plot(log10_lam, interpolated) plt.plot(log10_lam, nned) @@ -869,7 +935,7 @@ def plot_triangle_plots(self, model="ml_step", nsamples=200000, batch_size=100, self.num_workers = num_workers if self.trainer is not None: - if model == "ml_step": + if model in ["ml_step", "random_direction"]: X_pred = self.trainer.predict(self.dustLM, datamodule=self) X_pred = torch.cat(X_pred) y_pred = torch.cat([batch[1] for batch in self.predict_dataloader()]) @@ -891,6 +957,9 @@ def plot_triangle_plots(self, model="ml_step", nsamples=200000, batch_size=100, targets = np.array(["log10_nu"]) y_true = torch.unsqueeze(y_true, 1) + elif model == "random_direction": + features = np.array(["theta"]) + targets = np.array(["p","log10_amax","log10_nu"]) if isinstance(plot_columns, str) and plot_columns == 'all': columns = np.concatenate((targets, features)) @@ -1000,6 +1069,510 @@ def copy(self, device="cpu"): return load(self.state_dict(), device=device) +class IsotropicDust(Dust): + def scatter(self, photon_list, iphotons): + nphotons = iphotons.size(0) + + wp.launch(kernel=random_direction, + dim=(nphotons,), + inputs=[photon_list.direction, iphotons, np.random.randint(0, 100000)]) + + def update_photon_scattering_phase_function(self, photon_list, direction, iphotons): + nphotons = iphotons.size(0) + + wp.launch(kernel=self.scattering_phase_function_wp, + dim=(nphotons,), + inputs=[photon_list, direction, iphotons]) + + @wp.kernel + def scattering_phase_function_wp(photon_list: PhotonList, + direction: wp.vec3, + iphotons: wp.array(dtype=int)): + i = wp.tid() + ip = iphotons[i] + + photon_list.scattering_phase_function[ip] = 1. + + +class HenyeyGreensteinDust(Dust): + def __init__(self, lam=None, kabs=None, ksca=None, g=None, amax=None, p=None, device="cpu"): + """ + Initialize the Henyey-Greenstein dust model. + + Parameters + ---------- + lam : numpy.ndarray + The wavelengths to use for the dust properties. + kabs : numpy.ndarray + The absorption opacities to use for the dust properties. + ksca : numpy.ndarray + The scattering opacities to use for the dust properties. + g : numpy.ndarray + The Henyey-Greenstein asymmetry parameter to use for the dust properties. + amax : float + The maximum grain size to use for the dust properties. + p : float + The power-law index for the grain size distribution. + device : str + The device to place the dust properties on ("cpu" or "cuda"). + """ + super().__init__(lam=lam, kabs=kabs, ksca=ksca, amax=amax, p=p, device=device) + + self.g = g + + with wp.ScopedDevice(device): + self.g_wp = wp.array(self.g, dtype=float) + + def to_device(self, device): + super().to_device(device) + + with wp.ScopedDevice(device): + self.g = wp.array(self.g, dtype=float) + + for model in ["g"]: + if hasattr(self, f"{model}_model"): + getattr(self, f"{model}_model").to(device) + if hasattr(self, f"{model}_x_scaler"): + getattr(self, f"{model}_x_scaler").to(device) + if hasattr(self, f"{model}_y_scaler"): + getattr(self, f"{model}_y_scaler").to(device) + + def scatter(self, photon_list, iphotons): + nphotons = iphotons.size(0) + + wp.launch(kernel=self.random_direction, + dim=(nphotons,), + inputs=[photon_list.direction, photon_list.g, iphotons, np.random.randint(0, 100000)]) + + @wp.kernel + def random_direction(direction: wp.array(dtype=wp.vec3), + g: wp.array(dtype=float), + iphotons: wp.array(dtype=int), + seed: int): # pragma: no cover + i = wp.tid() + ip = iphotons[i] + + rng = wp.rand_init(seed, i) + + cost = (1. + g[ip]**2. - ((1. - g[ip]**2.)/(1. - g[ip] + 2.*g[ip]*wp.randf(rng)))**2.) / (2. * g[ip]) + theta = wp.acos(cost) + phi = 2.*np.pi*wp.randf(rng) + + rpy_quat1 = wp.quat_rpy(phi, 0., 0.) + rpy_quat2 = wp.quat_rpy(0., theta, 0.) + direction_quat = wp.quat_between_vectors(wp.vec3(1., 0., 0.), direction[ip]) + total_quat = direction_quat * rpy_quat1 * rpy_quat2 + + direction[ip] = wp.quat_rotate(total_quat, wp.vec3(1., 0., 0.)) + + def update_photon_scattering_phase_function(self, photon_list, direction, iphotons): + nphotons = iphotons.size(0) + + wp.launch(kernel=self.scattering_phase_function_heneygreenstein_wp, + dim=(nphotons,), + inputs=[photon_list, direction, iphotons]) + + @wp.kernel + def scattering_phase_function_heneygreenstein_wp(photon_list: PhotonList, + direction: wp.vec3, + iphotons: wp.array(dtype=int)): + i = wp.tid() + ip = iphotons[i] + + mu = wp.dot(photon_list.direction[ip], direction) + + photon_list.scattering_phase_function[ip] = (1. - photon_list.g[ip]**2.) / (1. + photon_list.g[ip]**2. - 2. * photon_list.g[ip] * mu) + + def interpolate_g(self, p, amax, nu): + samples = np.vstack((p.flatten(), np.log10(amax).flatten(), np.log10(nu).flatten())).T + + interpolated = scipy.interpolate.interpn((self.p, np.log10(self.amax.value), np.log10(self.nu.value)), self.g, samples, method="cubic") + + return interpolated.reshape(p.shape) + + def ml_g(self, p=None, amax=None, nu=None, photon_list=None, iphotons=None): + if photon_list is not None: + p = wp.to_torch(photon_list.p) + amax = wp.to_torch(photon_list.amax) + if nu is None: + nu = wp.to_torch(photon_list.frequency) + + if iphotons is not None: + nu = nu[iphotons] + p = p[iphotons] + amax = amax[iphotons] + else: + if nu.size(0) != p.size(0): + p = p[iphotons] + amax = amax[iphotons] + + samples = torch.transpose(torch.vstack((p, torch.log10(amax), torch.log10(nu))), 0, 1) + + g = self.g_y_scaler.inverse_transform(self.g_model(self.g_x_scaler.transform(samples))).detach().flatten() + + return g + + def prepare_data_g(self): + sampler = scipy.stats.qmc.LatinHypercube(d=3) + samples = sampler.random(self.nsamples) + + samples[:,0] = samples[:,0] * (self.p.max() - self.p.min()) + self.p.min() + samples[:,1] = samples[:,1] * (np.log10(self.amax.max().value) - np.log10(self.amax.min().value)) + np.log10(self.amax.min().value) + samples[:,2] = samples[:,2] * (np.log10(self.nu.max().value) - np.log10(self.nu.min().value)) + np.log10(self.nu.min().value) + + g = scipy.interpolate.interpn((self.p, np.log10(self.amax.value), np.log10(self.nu.value)), self.g, samples, method="cubic") + + X = torch.tensor(samples, dtype=torch.float32) + y = torch.tensor(g, dtype=torch.float32) + + X_scaler = StandardScaler() + X_scaler.fit(X) + X = X_scaler.transform(X) + + y_scaler = StandardScaler() + y_scaler.fit(y) + y = y_scaler.transform(y) + + self.g_x_scaler = X_scaler + self.g_y_scaler = y_scaler + + self.dataset = TensorDataset(X, y) + + def update_photon_opacities(self, photon_list, iphotons, grid=None, inu=None): + nphotons = iphotons.size(0) + + if grid is not None and inu is not None: + wp.launch(kernel=self.set_photon_opacities_grid, + dim=(nphotons,), + inputs=[photon_list, grid, inu, iphotons]) + else: + wp.launch(kernel=self.set_photon_opacities, + dim=(nphotons,), + inputs=[photon_list, + self.ml_kabs(photon_list=photon_list, iphotons=iphotons), + self.ml_ksca(photon_list=photon_list, iphotons=iphotons), + self.ml_g(photon_list=photon_list, iphotons=iphotons), + iphotons]) + + @wp.kernel + def set_photon_opacities(photon_list: PhotonList, + kabs: wp.array(dtype=float), + ksca: wp.array(dtype=float), + g: wp.array(dtype=float), + iphotons: wp.array(dtype=int)): # pragma: no cover + i = wp.tid() + ip = iphotons[i] + + print("Hello from set_photon_opacities kernel, HG") + + photon_list.kabs[ip] = kabs[i] + photon_list.ksca[ip] = ksca[i] + photon_list.g[ip] = g[i] + photon_list.albedo[ip] = ksca[i] / (kabs[i] + ksca[i]) + + @wp.kernel + def set_photon_opacities_grid(photon_list: PhotonList, + grid: GridStruct, + inu: int, + iphotons: wp.array(dtype=int)): # pragma: no cover + ip = iphotons[wp.tid()] + + ix, iy, iz = photon_list.indices[ip][0], photon_list.indices[ip][1], photon_list.indices[ip][2] + + photon_list.kabs[ip] = grid.kabs[inu, ix, iy, iz] + photon_list.ksca[ip] = grid.ksca[inu, ix, iy, iz] + photon_list.g[ip] = grid.g[inu, ix, iy, iz] + photon_list.albedo[ip] = photon_list.ksca[ip] / (photon_list.kabs[ip] + photon_list.ksca[ip]) + + def set_grid_opacities(self, grid, frequency): + super().set_grid_opacities(grid, frequency) + + p = wp.to_torch(grid.p) + shape = p.shape + p = p.flatten() + amax = wp.to_torch(grid.amax).flatten() + + g = [self.ml_g(p=p, amax=amax, nu=torch.ones(np.prod(shape), + dtype=torch.float32, + device=wp.device_to_torch(wp.get_device())) * \ + f.to(u.GHz).value) for f in frequency] + + grid.g = wp.from_torch(torch.concatenate(g).reshape((len(frequency),) + shape)) + + def state_dict(self): + state_dict = super().state_dict() + + state_dict["dust_properties"]["g"] = self.g + + if hasattr(self, "g_model"): + state_dict["g_state_dict"] = self.g_model.state_dict() + state_dict["g_x_scaler"] = self.g_x_scaler.state_dict() + state_dict["g_y_scaler"] = self.g_y_scaler.state_dict() + + return state_dict + +class GeneralDust(Dust): + def __init__(self, lam=None, kabs=None, ksca=None, scattering_phase_function=None, theta=None, amax=None, + p=None, device="cpu"): + """ + Initialize the General dust model. + + Parameters + ---------- + lam : numpy.ndarray + The wavelengths to use for the dust properties. + kabs : numpy.ndarray + The absorption opacities to use for the dust properties. + ksca : numpy.ndarray + The scattering opacities to use for the dust properties. + scattering_phase_function : numpy.ndarray + The scattering phase function to use for the dust properties, as a function of theta. + theta : numpy.ndarray + The angles at which the scattering phase function is tabulated. + amax : float + The maximum grain size to use for the dust properties. + p : float + The power-law index for the grain size distribution. + device : str + The device to place the dust properties on ("cpu" or "cuda"). + """ + super().__init__(lam=lam, kabs=kabs, ksca=ksca, amax=amax, p=p, device=device) + + # Ensure that the scattering phase function is normalized for each combination of p, amax, and nu. + + scattering_phase_function = scipy.integrate.cumulative_trapezoid(scattering_phase_function * np.sin(theta), + theta, initial=0, axis=-1) / \ + np.expand_dims(scipy.integrate.trapezoid(scattering_phase_function * np.sin(theta), + theta, axis=-1), axis=-1) + + self.scattering_phase_function = scattering_phase_function + self.theta = theta + + with wp.ScopedDevice(device): + self.scattering_phase_function_wp = wp.array(self.scattering_phase_function, dtype=float) + self.theta_wp = wp.array(self.theta, dtype=float) + + def to_device(self, device): + super().to_device(device) + + with wp.ScopedDevice(device): + self.scattering_phase_function_wp = wp.array(self.scattering_phase_function, dtype=float) + self.theta_wp = wp.array(self.theta, dtype=float) + + def scatter(self, photon_list, iphotons): + nphotons = iphotons.size(0) + + test_y = torch.transpose(torch.vstack([wp.to_torch(photon_list.p)[iphotons], + wp.to_torch(photon_list.amax)[iphotons], + wp.to_torch(photon_list.frequency)[iphotons]]), 0, 1) + + test_x = self.random_direction_model.condition(self.random_direction_y_scaler.transform(test_y)).sample().detach() + theta = self.random_direction_x_scaler.inverse_transform(test_x).squeeze(1) + + wp.launch(kernel=self.random_direction, + dim=(nphotons,), + inputs=[photon_list.direction, + wp.from_torch(theta), + iphotons, + np.random.randint(0, 100000)]) + + + + def random_direction(direction: wp.array(dtype=wp.vec3), + theta: wp.array(dtype=float), + iphotons: wp.array(dtype=int), + seed: int): # pragma: no cover + i = wp.tid() + ip = iphotons[i] + + rng = wp.rand_init(seed, i) + + phi = 2.*np.pi*wp.randf(rng) + + rpy_quat1 = wp.quat_rpy(phi, 0., 0.) + rpy_quat2 = wp.quat_rpy(0., theta[i], 0.) + direction_quat = wp.quat_between_vectors(wp.vec3(1., 0., 0.), direction[ip]) + total_quat = direction_quat * rpy_quat1 * rpy_quat2 + + direction[ip] = wp.quat_rotate(total_quat, wp.vec3(1., 0., 0.)) + + def update_photon_scattering_phase_function(self, photon_list, direction, iphotons): + nphotons = iphotons.size(0) + + theta = torch.acos((wp.to_torch(photon_list.direction)[iphotons] * torch.tensor(wp.array(direction))).sum(axis=1)) + + scattering_phase_function = self.ml_scattering_phase_function(photon_list=photon_list, iphotons=iphotons, theta=theta) + + wp.launch(kernel=self.scattering_phase_function_general_dust_wp, + dim=(nphotons,), + inputs=[photon_list, + wp.from_torch(scattering_phase_function), + iphotons]) + + @wp.kernel + def scattering_phase_function_general_dust_wp(photon_list: PhotonList, + scattering_phase_function: wp.array(dtype=float), + iphotons: wp.array(dtype=int)): + + i = wp.tid() + ip = iphotons[i] + + photon_list.scattering_phase_function[ip] = scattering_phase_function[i] + + def interpolate_scattering_phase_function(self, p, amax, nu, theta): + import interpn + + dims = (self.p.size, self.amax.size, self.nu.size, self.theta.size) + starts = np.array([self.p.min(), np.log10(self.amax.min().value), np.log10(self.nu.min().value), self.theta.value.min()]) + steps = np.array([self.p[1] - self.p[0], (np.log10(self.amax[1].value) - np.log10(self.amax[0].value)), (np.log10(self.nu[1].value) - np.log10(self.nu[0].value)), self.theta[1].value - self.theta[0].value]) + + interpolator = interpn.MulticubicRegular.new(dims, starts, steps, self.scattering_phase_function) + + samples = np.vstack([p, np.log10(amax), np.log10(nu), theta]) + + return interpolator.eval(samples) + + def ml_scattering_phase_function(self, p=None, amax=None, nu=None, theta=None, photon_list=None, iphotons=None): + if photon_list is not None: + p = wp.to_torch(photon_list.p) + amax = wp.to_torch(photon_list.amax) + if nu is None: + nu = wp.to_torch(photon_list.frequency) + + if iphotons is not None: + nu = nu[iphotons] + p = p[iphotons] + amax = amax[iphotons] + else: + if nu.size(0) != p.size(0): + p = p[iphotons] + amax = amax[iphotons] + + samples = torch.transpose(torch.vstack((p, torch.log10(amax), torch.log10(nu), theta)), 0, 1) + + scattering_phase_function = 10.**self.scattering_phase_function_y_scaler.inverse_transform( + self.scattering_phase_function_model(self.scattering_phase_function_x_scaler.transform(samples))).\ + detach().flatten() + + return scattering_phase_function + + def learn(self, model="random_nu", **kwargs): + if model == "scattering_phase_function": + self.input_size, self.output_size = 4, 1 + self.model_type = "MLP" + elif model == "random_direction": + self.input_size, self.output_size = 1, 3 + self.model_type = "flow" + + super().learn(model=model, **kwargs) + + def prepare_data_scattering_phase_function(self): + sampler = scipy.stats.qmc.LatinHypercube(d=4) + samples = sampler.random(self.nsamples) + + samples[:,0] = samples[:,0] * (self.p.max() - self.p.min()) + self.p.min() + samples[:,1] = samples[:,1] * (np.log10(self.amax.max().value) - np.log10(self.amax.min().value)) + np.log10(self.amax.min().value) + samples[:,2] = samples[:,2] * (np.log10(self.nu.max().value) - np.log10(self.nu.min().value)) + np.log10(self.nu.min().value) + samples[:,3] = samples[:,3] * (self.theta.max().value - self.theta.min().value) + self.theta.min().value + + scattering_phase_function = self.interpolate_scattering_phase_function(samples[:,0], 10.**samples[:,1], 10.**samples[:,2], samples[:,3]) + + X = torch.tensor(samples, dtype=torch.float32) + y = torch.tensor(scattering_phase_function, dtype=torch.float32) + + X_scaler = StandardScaler() + X_scaler.fit(X) + X = X_scaler.transform(X) + + y_scaler = StandardScaler() + y_scaler.fit(y) + y = y_scaler.transform(y) + + self.scattering_phase_function_x_scaler = X_scaler + self.scattering_phase_function_y_scaler = y_scaler + + self.dataset = TensorDataset(X, y) + + def prepare_data_random_direction(self): + nsamples = 0 + + while nsamples < self.nsamples: + p = np.random.uniform(self.p.min(), self.p.max(), self.nsamples) + amax = 10.**np.random.uniform(np.log10(self.amax.min().value), np.log10(self.amax.max().value), self.nsamples) + nu = 10.**np.random.uniform(np.log10(self.nu.min().value), np.log10(self.nu.max().value), self.nsamples) + theta = np.random.uniform(0, np.pi, self.nsamples) + ksi = np.random.uniform(0, 1, self.nsamples) + + prob = self.interpolate_scattering_phase_function(p, amax, nu, theta) + + keep = ksi < prob + + if nsamples == 0: + samples = np.vstack((p[keep], np.log10(amax[keep]), np.log10(nu[keep]), theta[keep])).T + else: + samples = np.vstack((samples, np.vstack((p[keep], np.log10(amax[keep]), np.log10(nu[keep]), theta[keep])).T)) + + nsamples += np.sum(keep) + + self.nsamples = nsamples + + X = torch.tensor(samples[:,3:], dtype=torch.float32) + y = torch.tensor(samples[:,0:3], dtype=torch.float32) + + X_scaler = StandardScaler() + X_scaler.fit(X) + X = X_scaler.transform(X) + + y_scaler = StandardScaler() + y_scaler.fit(y) + y = y_scaler.transform(y) + + self.random_direction_x_scaler = X_scaler + self.random_direction_y_scaler = y_scaler + + self.dataset = TensorDataset(X, y) + + def plot_scattering_phase_function_model(self): + """ + Plot the learned scattering phase function model against the interpolated scattering phase function. + """ + import matplotlib.pyplot as plt + + log10_nu = np.repeat(np.random.uniform(0, 1, 1)*(np.log10(self.nu.max().value) - np.log10(self.nu.min().value)) + np.log10(self.nu.min().value), 100) + log10_lam = np.log10((const.c / (10.**log10_nu * u.GHz)).to(u.cm).value) + log10_amax = np.repeat(np.random.uniform(0, 1, 1)*(np.log10(self.amax.max().value) - np.log10(self.amax.min().value)) + np.log10(self.amax.min().value), 100) + p = np.repeat(np.random.uniform(0, 1, 1)*(self.p.max() - self.p.min()) + self.p.min(), 100) + theta = np.linspace(0, np.pi, 100) + + print(f"log10_amax: {log10_amax[0]}, p: {p[0]}, log10_nu: {log10_nu[0]}") + + samples = np.vstack((p, log10_amax, log10_nu, theta)).T + + interpolated = np.log10(getattr(self, f"interpolate_scattering_phase_function")(p, 10.**log10_amax, 10.**log10_nu, theta)) + nned = getattr(self, f'scattering_phase_function_y_scaler').inverse_transform(getattr(self, f'scattering_phase_function_model')(getattr(self, f'scattering_phase_function_x_scaler').transform(torch.tensor(samples, dtype=torch.float32)))).detach().numpy() + + plt.plot(theta, interpolated) + plt.plot(theta, nned) + plt.show() + + def state_dict(self): + state_dict = super().state_dict() + + state_dict["dust_properties"]["scattering_phase_function"] = self.scattering_phase_function + state_dict["dust_properties"]["theta"] = self.theta + + if hasattr(self, "scattering_phase_function_model"): + state_dict["scattering_phase_function_state_dict"] = self.scattering_phase_function_model.state_dict() + state_dict["scattering_phase_function_x_scaler"] = self.scattering_phase_function_x_scaler.state_dict() + state_dict["scattering_phase_function_y_scaler"] = self.scattering_phase_function_y_scaler.state_dict() + + if hasattr(self, "random_direction_model"): + state_dict["random_direction_state_dict"] = self.random_direction_model.state_dict() + state_dict["random_direction_x_scaler"] = self.random_direction_x_scaler.state_dict() + state_dict["random_direction_y_scaler"] = self.random_direction_y_scaler.state_dict() + + return state_dict + def load(filename, device="cpu"): """ Load a Dust object from a file or state_dict. @@ -1034,16 +1607,21 @@ def load(filename, device="cpu"): elif isinstance(filename, Dust): state_dict = filename.state_dict() - d = Dust(**state_dict["dust_properties"], device=device) + if "g" in state_dict["dust_properties"]: + d = HenyeyGreensteinDust(**state_dict["dust_properties"], device=device) + elif "scattering_phase_function" in state_dict["dust_properties"]: + d = GeneralDust(**state_dict["dust_properties"], device=device) + else: + d = IsotropicDust(**state_dict["dust_properties"], device=device) - for attr in ["kabs", "ksca", "pmo", "random_nu"]: + for attr in ["kabs", "ksca", "pmo", "random_nu", "g", "scattering_phase_function"]: if f"{attr}_state_dict" in state_dict: - if attr == "random_nu": + if attr in ["random_nu", "scattering_phase_function"]: input_size = 4 else: input_size = 3 hidden_units = [state_dict[f'{attr}_state_dict'][key].shape[0] for key in state_dict[f'{attr}_state_dict'] if 'bias' in key][0:-1] - d.initialize_model(model=attr, input_size=input_size, output_size=1, hidden_units=hidden_units) + d.initialize_model(model=attr, model_type="MLP", input_size=input_size, output_size=1, hidden_units=hidden_units) getattr(d, f'{attr}_model').load_state_dict(state_dict[f'{attr}_state_dict']) setattr(d, f'{attr}_x_scaler', StandardScaler()) @@ -1051,10 +1629,22 @@ def load(filename, device="cpu"): setattr(d, f'{attr}_y_scaler', StandardScaler()) getattr(d, f'{attr}_y_scaler').load_state_dict(state_dict[f"{attr}_y_scaler"]) + if "random_direction_state_dict" in state_dict: + hidden_units = (tuple([state_dict['random_direction_state_dict'][key].size(1) for key in state_dict['random_direction_state_dict'] if '0.hyper' in key and 'weight' in key and '0.weight' not in key]),) * len([state_dict['random_direction_state_dict'][key].size(0) for key in state_dict['random_direction_state_dict'] if '0.weight' in key]) + + d.initialize_model(model="random_direction", model_type="flow", input_size=1, output_size=3, hidden_units=hidden_units) + + d.random_direction_model.load_state_dict(state_dict['random_direction_state_dict']) + + d.random_direction_x_scaler = StandardScaler() + d.random_direction_x_scaler.load_state_dict(state_dict["random_direction_x_scaler"]) + d.random_direction_y_scaler = StandardScaler() + d.random_direction_y_scaler.load_state_dict(state_dict["random_direction_y_scaler"]) + if "ml_step_state_dict" in state_dict: hidden_units = (tuple([state_dict['ml_step_state_dict'][key].size(1) for key in state_dict['ml_step_state_dict'] if '0.hyper' in key and 'weight' in key and '0.weight' not in key]),) * len([state_dict['ml_step_state_dict'][key].size(0) for key in state_dict['ml_step_state_dict'] if '0.weight' in key]) - d.initialize_model(model="ml_step", input_size=7, output_size=5, hidden_units=hidden_units) + d.initialize_model(model="ml_step", model_type="flow", input_size=7, output_size=5, hidden_units=hidden_units) d.ml_step_model.load_state_dict(state_dict['ml_step_state_dict']) diff --git a/pinballrt/grids.py b/pinballrt/grids.py index 177f268..57a8bbf 100644 --- a/pinballrt/grids.py +++ b/pinballrt/grids.py @@ -10,7 +10,7 @@ import time from tqdm.auto import tqdm -from .utils import GridStruct, EPSILON, equal, equal_zero, planck_function +from .utils import GridStruct, EPSILON, equal, equal_zero, planck_function, random_direction class Grid: def __init__(self, _w1, _w2, _w3, device='cpu'): @@ -304,34 +304,12 @@ def photon_cell_properties(photon_list: PhotonList, @wp.kernel def update_frequency(photon_list: PhotonList, frequency: wp.array(dtype=float), - kabs: wp.array(dtype=float), - ksca: wp.array(dtype=float), iphotons: wp.array(dtype=int)): # pragma: no cover i = wp.tid() ip = iphotons[i] photon_list.frequency[ip] = frequency[i] - photon_list.kabs[ip] = kabs[i] - photon_list.ksca[ip] = ksca[i] - photon_list.albedo[ip] = ksca[i] / (kabs[i] + ksca[i]) - - @wp.kernel - def random_direction(direction: wp.array(dtype=wp.vec3), - iphotons: wp.array(dtype=int), - seed: int): # pragma: no cover - i = wp.tid() - ip = iphotons[i] - - rng = wp.rand_init(seed, i) - - cost = -1. + 2.*wp.randf(rng) - sint = wp.sqrt(1.-cost**2.) - phi = 2.*np.pi*wp.randf(rng) - - direction[ip][0] = sint*np.cos(phi) - direction[ip][1] = sint*np.sin(phi) - direction[ip][2] = cost @wp.kernel def random_tau(photon_list: PhotonList, @@ -355,13 +333,9 @@ def random_absorb(photon_list: PhotonList, photon_list.absorb[ip] = wp.randf(rng) > photon_list.albedo[ip] - def interact(self, photon_list: PhotonList, absorb, iabsorb, interact, iphotons, scattering=False, learning=False): + def interact(self, photon_list: PhotonList, absorb, iabsorb, interact, iphotons, iscatter, scattering=False, learning=False): nphotons = iphotons.size(0) - wp.launch(kernel=self.random_direction, - dim=(nphotons,), - inputs=[photon_list.direction, iphotons, np.random.randint(0, 100000)]) - t1 = time.time() nabsorb = iabsorb.size(0) #if not scattering and nabsorb > 0: @@ -372,6 +346,12 @@ def interact(self, photon_list: PhotonList, absorb, iabsorb, interact, iphotons, t2 = time.time() photon_temperature_time = t2 - t1 + wp.launch(kernel=random_direction, + dim=(nabsorb,), + inputs=[photon_list.direction, iabsorb, np.random.randint(0, 100000)]) + + self.dust.scatter(photon_list, iscatter) + t1 = time.time() if not scattering and nabsorb > 0: new_frequency = self.dust.random_nu(photon_list, subset=absorb) @@ -382,9 +362,9 @@ def interact(self, photon_list: PhotonList, absorb, iabsorb, interact, iphotons, if not scattering and nabsorb > 0: wp.launch(kernel=self.update_frequency, dim=(nabsorb,), - inputs=[photon_list, new_frequency, - self.dust.ml_kabs(photon_list=photon_list, nu=wp.to_torch(new_frequency), iphotons=iabsorb), - self.dust.ml_ksca(photon_list=photon_list, nu=wp.to_torch(new_frequency), iphotons=iabsorb), iabsorb]) + inputs=[photon_list, new_frequency, iabsorb]) + + self.dust.update_photon_opacities(photon_list=photon_list, iphotons=iabsorb) t2 = time.time() dust_interpolation_time = t2 - t1 @@ -512,7 +492,9 @@ def ml_step(self, photon_list, s, iphotons): wp.launch(kernel=self.update_frequency, dim=(nphotons,), - inputs=[photon_list, wp.from_torch(frequency), self.dust.ml_kabs(photon_list=photon_list, nu=wp.from_torch(frequency, iphotons=iphotons)), self.dust.ml_ksca(photon_list=photon_list, nu=wp.from_torch(frequency, iphotons=iphotons)), iphotons]) + inputs=[photon_list, wp.from_torch(frequency), iphotons]) + + self.dust.update_photon_opacities(photon_list=photon_list, iphotons=iphotons) wp.launch(kernel=self.ml_rotate_direction, dim=(nphotons,), @@ -524,18 +506,6 @@ def ml_step(self, photon_list, s, iphotons): return wp.from_torch(direction_yaw), wp.from_torch(direction_pitch), wp.from_torch(direction_roll) - @wp.kernel - def set_photon_opacities(photon_list: PhotonList, - kabs: wp.array(dtype=float), - ksca: wp.array(dtype=float), - iphotons: wp.array(dtype=int)): # pragma: no cover - i = wp.tid() - ip = iphotons[i] - - photon_list.kabs[ip] = kabs[i] - photon_list.ksca[ip] = ksca[i] - photon_list.albedo[ip] = ksca[i] / (kabs[i] + ksca[i]) - def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning=False, debug=False, timing={}, position=0): with wp.ScopedDevice(self.device): nphotons = photon_list.position.numpy().shape[0] @@ -567,6 +537,7 @@ def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning photon_list.kabs = wp.zeros(nphotons, dtype=float) photon_list.ksca = wp.zeros(nphotons, dtype=float) + photon_list.g = wp.zeros(nphotons, dtype=float) photon_list.albedo = wp.zeros(nphotons, dtype=float) photon_list.absorb = wp.zeros(nphotons, dtype=bool) @@ -583,12 +554,7 @@ def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning nphotons = iphotons.size(0) t1 = time.time() - wp.launch(kernel=self.set_photon_opacities, - dim=(nphotons,), - inputs=[photon_list, - wp.from_torch(self.dust.ml_kabs(photon_list=photon_list, iphotons=iphotons)), - wp.from_torch(self.dust.ml_ksca(photon_list=photon_list, iphotons=iphotons)), - iphotons]) + self.dust.update_photon_opacities(photon_list=photon_list, iphotons=iphotons) t2 = time.time() dust_interpolation_time += t2 - t1 @@ -707,7 +673,9 @@ def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning interaction_indices = iphotons_original[interaction] absorb = torch.logical_and(interaction, wp.to_torch(photon_list.absorb)) absorb_indices = iphotons_original[absorb] - tmp_photon_temp_time, tmp_dust_interpolation_time, tmp_photon_loc_time, tmp_absorb_random_nu_time = self.interact(photon_list, absorb, absorb_indices, interaction, interaction_indices, learning=learning) + scatter = torch.logical_and(interaction, wp.to_torch(photon_list.absorb) == False) + scatter_indices = iphotons_original[scatter] + tmp_photon_temp_time, tmp_dust_interpolation_time, tmp_photon_loc_time, tmp_absorb_random_nu_time = self.interact(photon_list, absorb, absorb_indices, interaction, interaction_indices, scatter_indices, learning=learning) t2 = time.time() absorb_time += t2 - t1 - tmp_dust_interpolation_time - tmp_photon_loc_time absorb_random_nu_time += tmp_absorb_random_nu_time @@ -730,12 +698,7 @@ def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning inputs=[photon_list, self.grid, iphotons]) t1 = time.time() - wp.launch(kernel=self.set_photon_opacities, - dim=(nphotons,), - inputs=[photon_list, - wp.from_torch(self.dust.ml_kabs(photon_list=photon_list, iphotons=iphotons)), - wp.from_torch(self.dust.ml_ksca(photon_list=photon_list, iphotons=iphotons)), - iphotons]) + self.dust.update_photon_opacities(photon_list=photon_list, iphotons=iphotons) t2 = time.time() dust_interpolation_time += t2 - t1 @@ -756,37 +719,9 @@ def propagate_photons(self, photon_list: PhotonList, use_ml_step=False, learning def set_grid_opacities(self, frequency): with wp.ScopedDevice(self.device): - p = wp.to_torch(self.grid.p).flatten() - amax = wp.to_torch(self.grid.amax).flatten() + self.dust.set_grid_opacities(self.grid, frequency) - kabs = [self.dust.ml_kabs(p=p, amax=amax, nu=torch.ones(np.prod(self.shape), - dtype=torch.float32, - device=wp.device_to_torch(wp.get_device())) * \ - f.to(u.GHz).value) for f in frequency] - - self.grid.kabs = wp.from_torch(torch.concatenate(kabs).reshape((len(frequency),) + self.shape)) - - ksca = [self.dust.ml_ksca(p=p, amax=amax, nu=torch.ones(np.prod(self.shape), - dtype=torch.float32, - device=wp.device_to_torch(wp.get_device())) * \ - f.to(u.GHz).value) for f in frequency] - - self.grid.ksca = wp.from_torch(torch.concatenate(ksca).reshape((len(frequency),) +self.shape)) - - @wp.kernel - def update_photon_opacities(photon_list: PhotonList, - grid: GridStruct, - inu: int, - iphotons: wp.array(dtype=int)): # pragma: no cover - ip = iphotons[wp.tid()] - - ix, iy, iz = photon_list.indices[ip][0], photon_list.indices[ip][1], photon_list.indices[ip][2] - - photon_list.kabs[ip] = grid.kabs[inu, ix, iy, iz] - photon_list.ksca[ip] = grid.ksca[inu, ix, iy, iz] - photon_list.albedo[ip] = photon_list.ksca[ip] / (photon_list.kabs[ip] + photon_list.ksca[ip]) - - def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, debug=False, timing={}, position=0): + def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, camera_direction: wp.vec3, debug=False, timing={}, position=0): with wp.ScopedDevice(self.device): nphotons = photon_list.position.numpy().shape[0] iphotons_original = torch.arange(nphotons, dtype=torch.int32, device=wp.device_to_torch(wp.get_device())) @@ -811,8 +746,10 @@ def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, debug= photon_list.kabs = wp.zeros(nphotons, dtype=float) photon_list.ksca = wp.zeros(nphotons, dtype=float) + photon_list.g = wp.zeros(nphotons, dtype=float) photon_list.albedo = wp.zeros(nphotons, dtype=float) photon_list.absorb = wp.zeros(nphotons, dtype=bool) + photon_list.scattering_phase_function = wp.zeros(nphotons, dtype=float) progress_bar = tqdm(total=nphotons, position=position, leave=True) @@ -827,9 +764,7 @@ def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, debug= nphotons = iphotons.size(0) t1 = time.time() - wp.launch(kernel=self.update_photon_opacities, - dim=(nphotons,), - inputs=[photon_list, self.grid, inu, iphotons]) + self.dust.update_photon_opacities(photon_list=photon_list, iphotons=iphotons, grid=self.grid, inu=inu) t2 = time.time() dust_interpolation_time += t2 - t1 @@ -858,6 +793,10 @@ def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, debug= s = torch.minimum(wp.to_torch(s1), wp.to_torch(s2)) + self.dust.update_photon_scattering_phase_function(photon_list=photon_list, + direction=camera_direction, + iphotons=iphotons) + t1 = time.time() wp.launch(kernel=self.deposit_scattering, dim=(nphotons,), @@ -899,7 +838,9 @@ def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, debug= interaction_indices = iphotons_original[interaction] absorb = torch.logical_and(interaction, wp.to_torch(photon_list.absorb)) absorb_indices = iphotons_original[absorb] - tmp_photon_temp_time, tmp_dust_interpolation_time, tmp_photon_loc_time, tmp_absorb_random_nu_time = self.interact(photon_list, absorb, absorb_indices, interaction, interaction_indices, scattering=True) + scatter = torch.logical_and(interaction, wp.to_torch(photon_list.absorb) == False) + scatter_indices = iphotons_original[scatter] + tmp_photon_temp_time, tmp_dust_interpolation_time, tmp_photon_loc_time, tmp_absorb_random_nu_time = self.interact(photon_list, absorb, absorb_indices, interaction, interaction_indices, scatter_indices, scattering=True) t2 = time.time() absorb_time += t2 - t1 - tmp_photon_loc_time #absorb_time += tmp_time @@ -918,9 +859,7 @@ def propagate_photons_scattering(self, photon_list: PhotonList, inu: int, debug= inputs=[photon_list, self.grid, iphotons]) t1 = time.time() - wp.launch(kernel=self.update_photon_opacities, - dim=(nphotons,), - inputs=[photon_list, self.grid, inu, iphotons]) + self.dust.update_photon_opacities(photon_list=photon_list, iphotons=iphotons, grid=self.grid, inu=inu) t2 = time.time() dust_interpolation_time += t2 - t1 @@ -1087,18 +1026,6 @@ def reduce_source_intensity(ray_list: PhotonList, ray_list.intensity[ir, inu] = ray_list.intensity[ir, inu] * wp.exp(-s[ir] * ray_list.kext[ir, inu] * grid.dust_density[ix, iy, iz]) - @wp.kernel - def set_ray_opacities(ray_list: PhotonList, - kext: wp.array2d(dtype=float), - albedo: wp.array2d(dtype=float), - irays: wp.array(dtype=int)): # pragma: no cover - - iray, inu = wp.tid() - ir = irays[iray] - - ray_list.kext[ir, inu] = kext[iray, inu] - ray_list.ray_albedo[ir, inu] = albedo[iray, inu] - @wp.kernel def set_ray_opacities_grid(ray_list: PhotonList, grid: GridStruct, diff --git a/pinballrt/model.py b/pinballrt/model.py index 4dd5df9..1e1873f 100644 --- a/pinballrt/model.py +++ b/pinballrt/model.py @@ -3,7 +3,7 @@ from .sources import DiffuseSource, EnergySource from .grids import Grid -from .dust import load, Dust +from .dust import load from .gas import Gas from .camera import Camera from schwimmbad import SerialPool, MultiPool @@ -234,7 +234,8 @@ def scattering_mc(self, nphotons, wavelengths, device="cpu", return_timing=False [nphotons]*njobs, [njobs]*njobs, [wavelength]*njobs, - [i]*njobs,)) + [i]*njobs, + [self.camera_list[device].i_wp]*njobs)) total_scattering = [r[0] for r in result] time.sleep(0.1) t2 = time.time() @@ -354,15 +355,15 @@ def make_image(self, npix=100, pixel_size=None, channels=None, rest_frequency=No # First, run a scattering simulation to get the scattering phase function + for dev in self.camera_list: + self.camera_list[dev].set_orientation(incl, pa, distance) + if include_dust: self.grid_list[device].set_grid_opacities(nu) self.scattering_mc(nphotons, lam, device=device, set_grid_opacities=False) # Now set up the image proper. - for dev in self.camera_list: - self.camera_list[dev].set_orientation(incl, pa, distance) - physical_pixel_size = (pixel_size*distance).to(self.grid.distance_unit, equivalencies=u.dimensionless_angles()).value image = xr.Dataset( @@ -448,11 +449,11 @@ def thermal_mc_task(args): return grid.grid.energy.numpy(), iter_timing def scattering_mc_task(args): - grid, position, s, nphotons, njobs, wavelength, i = args + grid, position, s, nphotons, njobs, wavelength, i, camera_direction = args seed(s.generate_state(1)[0]) iter_timing = {} photon_list = grid.emit(int(nphotons / njobs), wavelength, scattering=True, timing=iter_timing) - grid.propagate_photons_scattering(photon_list, i, timing=iter_timing, position=position) + grid.propagate_photons_scattering(photon_list, i, camera_direction, timing=iter_timing, position=position) return grid.scattering, iter_timing diff --git a/pinballrt/photons.py b/pinballrt/photons.py index 37afd55..9e0f186 100644 --- a/pinballrt/photons.py +++ b/pinballrt/photons.py @@ -17,6 +17,8 @@ class PhotonList: alpha: wp.array(dtype=float) kabs: wp.array(dtype=float) ksca: wp.array(dtype=float) + g: wp.array(dtype=float) + scattering_phase_function: wp.array(dtype=float) albedo: wp.array(dtype=float) kext: wp.array2d(dtype=float) ray_albedo: wp.array2d(dtype=float) diff --git a/pinballrt/sources.py b/pinballrt/sources.py index 7f3b44c..553ca81 100644 --- a/pinballrt/sources.py +++ b/pinballrt/sources.py @@ -19,7 +19,7 @@ from pinballrt.utils import EPSILON from .photons import PhotonList -from .utils import GridStruct +from .utils import GridStruct, random_direction from astropy.modeling import models import astropy.units as u import astropy.constants as const @@ -409,7 +409,7 @@ def emit(self, nphotons, distance_unit, wavelength="random", simulation="thermal photon_list.direction = wp.array(np.zeros((nphotons, 3)), dtype=wp.vec3) photon_list.direction_frame = wp.array(np.zeros((nphotons, 3)), dtype=wp.vec3) - wp.launch(kernel=self.grid.random_direction, + wp.launch(kernel=random_direction, dim=(nphotons,), inputs=[photon_list.direction, torch.arange(nphotons, dtype=torch.int32, device=wp.device_to_torch(wp.get_device())), np.random.randint(0, 100000)]) diff --git a/pinballrt/utils.py b/pinballrt/utils.py index 57890dd..c86a30a 100644 --- a/pinballrt/utils.py +++ b/pinballrt/utils.py @@ -69,6 +69,23 @@ def calculate_Qvalue(array1, array2, percentile=99.0, clip=None): return Q +@wp.kernel +def random_direction(direction: wp.array(dtype=wp.vec3), + iphotons: wp.array(dtype=int), + seed: int): # pragma: no cover + i = wp.tid() + ip = iphotons[i] + + rng = wp.rand_init(seed, i) + + cost = -1. + 2.*wp.randf(rng) + sint = wp.sqrt(1.-cost**2.) + phi = 2.*np.pi*wp.randf(rng) + + direction[ip][0] = sint*np.cos(phi) + direction[ip][1] = sint*np.sin(phi) + direction[ip][2] = cost + @wp.struct class GridStruct: w1: wp.array(dtype=float) @@ -99,6 +116,7 @@ class GridStruct: kabs: wp.array4d(dtype=float) ksca: wp.array4d(dtype=float) + g: wp.array4d(dtype=float) velocity: wp.array4d(dtype=float) microturbulence: wp.array3d(dtype=float) From e7ae91108d3b248e42006394a82f5c59886f8d9a Mon Sep 17 00:00:00 2001 From: Patrick Sheehan Date: Wed, 15 Apr 2026 03:47:35 +0000 Subject: [PATCH 2/8] Update dust-demo notebook to get the relevant info for non-isotropic dust as well. --- examples/dust-demo.ipynb | 11874 ++----------------------------------- 1 file changed, 470 insertions(+), 11404 deletions(-) diff --git a/examples/dust-demo.ipynb b/examples/dust-demo.ipynb index 993a4e7..fb85b24 100644 --- a/examples/dust-demo.ipynb +++ b/examples/dust-demo.ipynb @@ -2,59 +2,156 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "461d2258", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/python/3.12.1/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ - "from pinballrt.dust import Dust, load\n", + "from pinballrt.dust import IsotropicDust, HenyeyGreensteinDust, GeneralDust, load\n", "from astropy.modeling import models\n", "import astropy.units as u\n", "import numpy as np\n", - "import pinballrt\n", "\n", - "from scipy.interpolate import interp1d\n", - "import matplotlib.pyplot as plt\n", - "import os" + "import subprocess\n", + "\n", + "import astropy.io.fits as fits" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "89f9421c", + "execution_count": 38, + "id": "b63d1d82", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Warp 1.8.0 initialized:\n", - " CUDA not enabled in this build\n", - " Devices:\n", - " \"cpu\" : \"i386\"\n", - " Kernel cache:\n", - " /Users/psheehan/Library/Caches/warp/1.8.0\n" + "../optool/OpacityTool -amax 1.0 -apow 2.5 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 3.1622776601683795 -apow 2.5 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 10.0 -apow 2.5 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 31.622776601683793 -apow 2.5 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 1.0 -apow 3.1666666666666665 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 3.1622776601683795 -apow 3.1666666666666665 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 10.0 -apow 3.1666666666666665 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 31.622776601683793 -apow 3.1666666666666665 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 1.0 -apow 3.833333333333333 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 3.1622776601683795 -apow 3.833333333333333 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 10.0 -apow 3.833333333333333 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 31.622776601683793 -apow 3.833333333333333 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 1.0 -apow 4.5 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 3.1622776601683795 -apow 4.5 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 10.0 -apow 4.5 -nlam 1000 -lmax 10000\n", + "../optool/OpacityTool -amax 31.622776601683793 -apow 4.5 -nlam 1000 -lmax 10000\n" ] } ], "source": [ - "data = np.load(os.path.join(os.path.dirname(pinballrt.dust.__file__), \"tests/data/diana_wice.npz\"))\n", + "a = np.logspace(0, 1.5, 4) * u.micron\n", + "p = np.linspace(2.5, 4.5, 4)\n", "\n", - "lam = np.logspace(np.log10(data[\"lam\"].min()), np.log10(data[\"lam\"].max()), 1000)*u.cm\n", - "kabs = 10.**interp1d(np.log10(data[\"lam\"]), np.log10(data[\"kabs\"]), kind=\"linear\", axis=-1)(np.log10(lam.value))*u.cm**2/u.g\n", - "ksca = 10.**interp1d(np.log10(data[\"lam\"]), np.log10(data[\"ksca\"]), kind=\"linear\", axis=-1)(np.log10(lam.value))*u.cm**2/u.g\n", + "kabs = []\n", + "ksca = []\n", + "g = []\n", + "scattering_phase = []\n", "\n", - "d = Dust(lam=lam, \n", - " kabs=kabs, \n", - " ksca=ksca, \n", - " amax=data[\"amax\"]*u.cm, \n", - " p=data[\"p\"], \n", - " interpolate=0)" + "for i in range(len(p)):\n", + " kabs_i = []\n", + " ksca_i = []\n", + " g_i = []\n", + " scattering_phase_i = []\n", + " for j in range(len(a)):\n", + " print(f\"../optool/OpacityTool -amax {a[j].to(u.micron).value} -apow {p[i]} -nlam 1000 -lmax 10000\")\n", + " subprocess.run(f\"../optool/OpacityTool -amax {a[j].to(u.micron).value} -apow {p[i]} -nlam 1000 -lmax 10000\".split(), capture_output=True)\n", + "\n", + " data = np.loadtxt(\"particle.dat\")\n", + "\n", + " lam = data[:,0] * u.micron\n", + " kabs_i.append(data[:,1] * u.cm**2 / u.g)\n", + " ksca_i.append(data[:,2] * u.cm**2 / u.g)\n", + " g_i.append(data[:,3])\n", + "\n", + " data = fits.open(\"particle.fits\")\n", + "\n", + " scattering_phase_i.append(data[1].data[:,0,:].T)\n", + "\n", + " kabs.append(kabs_i)\n", + " ksca.append(ksca_i)\n", + " g.append(g_i)\n", + " scattering_phase.append(scattering_phase_i)\n", + "\n", + "kabs = np.array(kabs) * u.cm**2 / u.g\n", + "ksca = np.array(ksca) * u.cm**2 / u.g\n", + "g = np.array(g)\n", + "scattering_phase = np.array(scattering_phase)\n", + "\n", + "theta = np.linspace(0, 180, scattering_phase.shape[-1]) * u.deg\n", + "\n", + "np.savez(\"dust_data.npz\", kabs=kabs, ksca=ksca, g=g, scattering_phase=scattering_phase, theta=theta, amax=a, p=p, lam=lam)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "96a02ab2", + "metadata": {}, + "outputs": [], + "source": [ + "data = np.load(\"dust_data.npz\")\n", + "\n", + "kabs = data[\"kabs\"] * u.cm**2 / u.g\n", + "ksca = data[\"ksca\"] * u.cm**2 / u.g\n", + "g = data[\"g\"]\n", + "scattering_phase = data[\"scattering_phase\"]\n", + "theta = data[\"theta\"] * u.deg\n", + "amax = data[\"amax\"] * u.micron\n", + "p = data[\"p\"]\n", + "lam = data[\"lam\"] * u.micron" ] }, { "cell_type": "code", "execution_count": null, + "id": "89f9421c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warp CUDA warning: Could not find or load the NVIDIA CUDA driver. GPU execution will not be available.\n" + ] + } + ], + "source": [ + "\"\"\"d = HenyeyGreensteinDust(lam=lam, \n", + " kabs=kabs, \n", + " ksca=ksca,\n", + " g=g,\n", + " amax=amax, \n", + " p=p)\"\"\"\n", + "\n", + "d = GeneralDust(lam=lam,\n", + " kabs=kabs,\n", + " ksca=ksca,\n", + " scattering_phase_function=scattering_phase,\n", + " theta=theta.to(u.radian),\n", + " amax=amax,\n", + " p=p)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "id": "adf95f57", "metadata": {}, "outputs": [ @@ -64,8 +161,24 @@ "text": [ "GPU available: False, used: False\n", "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n", - "/Users/psheehan/.pyenv/versions/anaconda3-2023.07-2/envs/pinball/lib/python3.13/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:751: Checkpoint directory /Users/psheehan/pinball-rt/examples/ksca_lightning_logs exists and is not empty.\n", + "HPU available: False, using: 0 HPUs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "*******************************\n", + "kabs\n", + "*******************************\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/python/3.12.1/lib/python3.12/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:76: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", + "/usr/local/python/3.12.1/lib/python3.12/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:751: Checkpoint directory /workspaces/pinball-rt/examples/kabs_lightning_logs exists and is not empty.\n", "\n", " | Name | Type | Params | Mode \n", "-------------------------------------------------------\n", @@ -80,11391 +193,361 @@ ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8f039d5c6b4d42729513059eca35205e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f064df582ab840268b785aac73947d65", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8580a94709e04822b980f3ff8ea86a91", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "edf80830992e463198d96be7a382435f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for model in [\"kabs\", \"ksca\", \"pmo\"]:\n", - " if model in [\"kabs\", \"ksca\"]:\n", - " d.learn(model=model, nsamples=10000000, hidden_units=(16,)*6, overwrite=True)\n", - " else:\n", - " d.learn(model=model, nsamples=1000000, hidden_units=(16,)*6, overwrite=True)\n", - "\n", - " d.fit(epochs=300, batch_size=10000)\n", - " d.test_model(plot=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1b4bb9e4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "log10_amax: -1.6217233441241388, p: 4.262695318455325, log10_temperature: -1.0\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "d.plot_pmo_model()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "30125b57", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "HPU available: False, using: 0 HPUs\n" - ] - } - ], - "source": [ - "d.learn(model=\"random_nu\", hidden_units=3*(48,), nsamples=10000000, overwrite=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "cb2e3007", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/psheehan/.pyenv/versions/anaconda3-2023.07-2/envs/pinball/lib/python3.13/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:751: Checkpoint directory /Users/psheehan/pinball-rt/examples/random_nu_lightning_logs exists and is not empty.\n", - "\n", - " | Name | Type | Params | Mode \n", - "-------------------------------------------------------\n", - "0 | model | MultiLayerPerceptron | 5.0 K | train\n", - "-------------------------------------------------------\n", - "5.0 K Trainable params\n", - "0 Non-trainable params\n", - "5.0 K Total params\n", - "0.020 Total estimated model params size (MB)\n", - "10 Modules in train mode\n", - "0 Modules in eval mode\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b5a297eaa66641489d035dbebf477354", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1fb4404b1119453ab39fd931a1c218b6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Validation: | | 0/? [00:00" ] }, "metadata": {}, @@ -11474,106 +557,73 @@ "name": "stderr", "output_type": "stream", "text": [ - "`Trainer.fit` stopped: `max_epochs=500` reached.\n" + "GPU available: False, used: False\n", + "TPU available: False, using: 0 TPU cores\n", + "HPU available: False, using: 0 HPUs\n", + "/usr/local/python/3.12.1/lib/python3.12/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:751: Checkpoint directory /workspaces/pinball-rt/examples/random_direction_lightning_logs exists and is not empty.\n", + "\n", + " | Name | Type | Params | Mode \n", + "---------------------------------------------------\n", + "0 | model | NeuralSplineFlow | 22.0 K | train\n", + "---------------------------------------------------\n", + "22.0 K Trainable params\n", + "0 Non-trainable params\n", + "22.0 K Total params\n", + "0.088 Total estimated model params size (MB)\n", + "46 Modules in train mode\n", + "0 Modules in eval mode\n" ] - } - ], - "source": [ - "d.fit(epochs=500, batch_size=10000)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "daaf9c09", - "metadata": {}, - "outputs": [ + }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/Users/psheehan/.pyenv/versions/anaconda3-2023.07-2/envs/pinball/lib/python3.13/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:433: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n" + "*******************************\n", + "random_direction\n", + "*******************************\n", + " \r" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7f4e9b33d5f5425694276f7fb6eac9be", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Testing: | | 0/? [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "d.test_model(plot=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "9da6a7d8", - "metadata": {}, - "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "p: 4.27963641391967, amax: 0.0005084393300744386, T: 0.14284917153748797\n" + "Epoch 4: 100%|██████████| 10/10 [00:01<00:00, 6.46it/s, v_num=61, train_loss=1.250, valid_loss=1.340]\n", + "Testing DataLoader 0: 100%|██████████| 2/2 [00:00<00:00, 57.24it/s]\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_loss 1.3696008920669556\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "Predicting DataLoader 0: 100%|██████████| 2/2 [00:00<00:00, 6.33it/s]\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -11581,18 +631,34 @@ } ], "source": [ - "d.plot_random_nu_model(100000)" + "#for model in [\"kabs\", \"ksca\", \"pmo\", \"g\", \"random_nu\"]:\n", + "for model in [\"kabs\", \"ksca\", \"pmo\", \"scattering_phase_function\", \"random_nu\", \"random_direction\"]:\n", + " print(\"*******************************\")\n", + " print(model)\n", + " print(\"*******************************\")\n", + "\n", + " if model in [\"random_direction\"]:\n", + " hidden_units = ((48, 48),)*6\n", + " elif model in [\"random_nu\"]:\n", + " hidden_units = (48,)*3\n", + " else:\n", + " hidden_units = (16,)*6\n", + " \n", + " d.learn(model=model, nsamples=1000, hidden_units=hidden_units, overwrite=True)\n", + "\n", + " d.fit(epochs=5, batch_size=100)\n", + " d.test_model(plot=True)" ] }, { "cell_type": "code", - "execution_count": null, - "id": "1527a8bf", + "execution_count": 5, + "id": "2c739068", "metadata": {}, "outputs": [], "source": [ - "# Bad:\n", - "#p: 4.480264827710928, amax: 0.00010237462737553694, T: 1687.739293126636 # Missing narrow peak at 10 microns, and too much power at long wavelengths" + "d.save(\"diana.general.dst\")\n", + "#d.save(\"diana.hg.dst\")" ] }, { @@ -11696,7 +762,7 @@ ], "metadata": { "kernelspec": { - "display_name": "pinball", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -11710,7 +776,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.12.1" } }, "nbformat": 4, From 9c8d4bc4725e7f29040b14ef9d1d0b0673262940 Mon Sep 17 00:00:00 2001 From: Patrick Sheehan Date: Tue, 12 May 2026 03:00:47 +0000 Subject: [PATCH 3/8] Add tests for learning all of the different types of Dust, with the various combinations of possible inputs. --- pinballrt/dust.py | 33 ++++-------- pinballrt/tests/test_dust_creation.py | 77 +++++++++++++++++---------- 2 files changed, 59 insertions(+), 51 deletions(-) diff --git a/pinballrt/dust.py b/pinballrt/dust.py index a049bc4..5197e47 100644 --- a/pinballrt/dust.py +++ b/pinballrt/dust.py @@ -868,7 +868,7 @@ def plot_opacity_model(self, model='kabs'): index = np.random.randint(0, self.samples.shape[0], 1)[0] - if model in ["kabs", "ksca"]: + if model in ["kabs", "ksca", "g"]: nx = self.nu.size elif model in ["pmo"]: nx = self.temperature.size @@ -1138,7 +1138,7 @@ def update_photon_scattering_phase_function(self, photon_list, direction, iphoto @wp.kernel def scattering_phase_function_wp(photon_list: PhotonList, direction: wp.vec3, - iphotons: wp.array(dtype=int)): + iphotons: wp.array(dtype=int)): # pragma: no cover i = wp.tid() ip = iphotons[i] @@ -1172,15 +1172,9 @@ def __init__(self, lam=None, kabs=None, ksca=None, g=None, amax=None, p=None, ab self.g = g - with wp.ScopedDevice(device): - self.g_wp = wp.array(self.g, dtype=float) - def to_device(self, device): super().to_device(device) - with wp.ScopedDevice(device): - self.g = wp.array(self.g, dtype=float) - for model in ["g"]: if hasattr(self, f"{model}_model"): getattr(self, f"{model}_model").to(device) @@ -1227,7 +1221,7 @@ def update_photon_scattering_phase_function(self, photon_list, direction, iphoto @wp.kernel def scattering_phase_function_heneygreenstein_wp(photon_list: PhotonList, direction: wp.vec3, - iphotons: wp.array(dtype=int)): + iphotons: wp.array(dtype=int)): # pragma: no cover i = wp.tid() ip = iphotons[i] @@ -1304,8 +1298,6 @@ def set_photon_opacities(photon_list: PhotonList, i = wp.tid() ip = iphotons[i] - print("Hello from set_photon_opacities kernel, HG") - photon_list.kabs[ip] = kabs[i] photon_list.ksca[ip] = ksca[i] photon_list.g[ip] = g[i] @@ -1392,17 +1384,9 @@ def __init__(self, lam=None, kabs=None, ksca=None, scattering_phase_function=Non self.scattering_phase_function = scattering_phase_function self.theta = theta - with wp.ScopedDevice(device): - self.scattering_phase_function_wp = wp.array(self.scattering_phase_function, dtype=float) - self.theta_wp = wp.array(self.theta, dtype=float) - def to_device(self, device): super().to_device(device) - with wp.ScopedDevice(device): - self.scattering_phase_function_wp = wp.array(self.scattering_phase_function, dtype=float) - self.theta_wp = wp.array(self.theta, dtype=float) - def scatter(self, photon_list, iphotons): nphotons = iphotons.size(0) @@ -1439,7 +1423,7 @@ def scatter(self, photon_list, iphotons): test_x = self.random_direction_x_scaler.transform(samples) - theta = self.random_direction_y_scaler.inverse_transform(self.random_direction_model(test_x).detach()) + theta = self.random_direction_y_scaler.inverse_transform(self.random_direction_model(test_x).detach()).flatten() wp.launch(kernel=self.random_direction, dim=(nphotons,), @@ -1448,6 +1432,7 @@ def scatter(self, photon_list, iphotons): iphotons, np.random.randint(0, 100000)]) + @wp.kernel def random_direction(direction: wp.array(dtype=wp.vec3), theta: wp.array(dtype=float), iphotons: wp.array(dtype=int), @@ -1482,12 +1467,12 @@ def update_photon_scattering_phase_function(self, photon_list, direction, iphoto @wp.kernel def scattering_phase_function_general_dust_wp(photon_list: PhotonList, scattering_phase_function: wp.array(dtype=float), - iphotons: wp.array(dtype=int)): + iphotons: wp.array(dtype=int)): # pragma: no cover i = wp.tid() ip = iphotons[i] - photon_list.scattering_phase_function[ip] = 2 * scattering_phase_function[i] + photon_list.scattering_phase_function[ip] = 2. * scattering_phase_function[i] def ml_scattering_phase_function(self, p=None, amax=None, nu=None, theta=None, photon_list=None, iphotons=None): if photon_list is not None: @@ -1534,10 +1519,10 @@ def ml_scattering_phase_function(self, p=None, amax=None, nu=None, theta=None, p def learn(self, model="random_nu", **kwargs): if model == "scattering_phase_function": - self.input_size, self.output_size = len(self.dims) + 2, 1 + self.input_size, self.output_size = self.ndims + 2, 1 self.model_type = "MLP" elif model == "random_direction": - self.input_size, self.output_size = len(self.dims) + 2, 1 + self.input_size, self.output_size = self.ndims + 2, 1 self.model_type = "MLP" super().learn(model=model, **kwargs) diff --git a/pinballrt/tests/test_dust_creation.py b/pinballrt/tests/test_dust_creation.py index bf6ab43..f5816e5 100644 --- a/pinballrt/tests/test_dust_creation.py +++ b/pinballrt/tests/test_dust_creation.py @@ -1,4 +1,4 @@ -from pinballrt.dust import Dust, load, suggest_opacity_sampling +from pinballrt.dust import IsotropicDust, HenyeyGreensteinDust, GeneralDust, load, suggest_opacity_sampling import numpy as np import astropy.units as u import pytest @@ -14,25 +14,31 @@ def test_Dust(): p, amax = np.meshgrid(data["p"], data["amax"], indexing="ij") - d = Dust(lam=data["lam"]*u.cm, - kabs=data["kabs"].reshape((-1, data["lam"].shape[0]))*u.cm**2/u.g, - ksca=data["ksca"].reshape((-1, data["lam"].shape[0]))*u.cm**2/u.g, - amax=amax.flatten()*u.cm, - p=p.flatten()) + d = IsotropicDust(lam=data["lam"]*u.cm, + kabs=data["kabs"].reshape((-1, data["lam"].shape[0]))*u.cm**2/u.g, + ksca=data["ksca"].reshape((-1, data["lam"].shape[0]))*u.cm**2/u.g, + amax=amax.flatten()*u.cm, + p=p.flatten()) assert d.kmean.value == 2206.6072 d.save("amax.dst") @pytest.mark.parametrize( - "dims", + "dust_type,dims", [ - pytest.param((), id="None"), - pytest.param(("amax","abundances"), id="amax,abundances"), - pytest.param(("p","amax","abundances"), id="p,amax,abundances"), + pytest.param(IsotropicDust, (), id="IsotropicDust/None"), + pytest.param(IsotropicDust, ("amax","abundances"), id="IsotropicDust/amax,abundances"), + pytest.param(IsotropicDust, ("p","amax","abundances"), id="IsotropicDust/p,amax,abundances"), + pytest.param(HenyeyGreensteinDust, (), id="HenyeyGreensteinDust/None"), + pytest.param(HenyeyGreensteinDust, ("p","abundances"), id="HenyeyGreensteinDust/p,abundances"), + pytest.param(HenyeyGreensteinDust, ("p","amax","abundances"), id="HenyeyGreensteinDust/p,amax,abundances"), + pytest.param(GeneralDust, (), id="GeneralDust/None"), + pytest.param(GeneralDust, ("p","abundances"), id="GeneralDust/p,abundances"), + pytest.param(GeneralDust, ("p","amax","abundances"), id="GeneralDust/p,amax,abundances"), ] ) -def test_learning(dims): +def test_learning(dust_type, dims): """ Test the learn_random_nu method of the Dust class. """ @@ -89,21 +95,38 @@ def test_learning(dims): kappa_abs = 1.0 * (wavelengths.to(u.micron).value/100.0)**power_law_index * u.cm**2 / u.g + silicate_feature + water_feature kappa_scat = 0.5 * (wavelengths.to(u.micron).value/100.0)**power_law_index * u.cm**2 / u.g + silicate_feature + water_feature - print("kappa_abs:", kappa_abs.min(), kappa_abs.max()) - print("kappa_scat:", kappa_scat.min(), kappa_scat.max()) - # Create the Dust object. - d = Dust(lam=wavelengths[0,:], - amax=amax[:,0] if "amax" in dims else None, - p=p[:,0] if "p" in dims else None, - abundances=(silicate_fraction[:,0], water_fraction[:,0]) if "abundances" in dims else (), - kabs=kappa_abs, - ksca=kappa_scat, - ntemperatures=10) + dust_kwargs = {"lam":wavelengths[0,:], + "amax":amax[:,0] if "amax" in dims else None, + "p":p[:,0] if "p" in dims else None, + "abundances":(silicate_fraction[:,0], water_fraction[:,0]) if "abundances" in dims else (), + "kabs":kappa_abs, + "ksca":kappa_scat, + "ntemperatures":10} + + if dust_type == HenyeyGreensteinDust: + g = np.tanh(p - np.log10(wavelengths.to(u.micron).value)) + dust_kwargs["g"] = g + elif dust_type == GeneralDust: + g = np.repeat(np.expand_dims(np.tanh(p - np.log10(wavelengths.to(u.micron).value)), axis=-1), 5, axis=-1) + theta = np.tile(np.expand_dims(np.linspace(0, 180., 5), axis=(0,1)), (10 if len(dims) > 0 else 1, 10, 1)) * u.deg + scattering_phase_function = (1 - g**2) / (4 * np.pi * (1 + g**2 - 2*g*np.cos(theta.to(u.rad).value))**(3/2)) + + dust_kwargs["scattering_phase_function"] = scattering_phase_function + dust_kwargs["theta"] = theta[0,0,:] + + d = dust_type(**dust_kwargs) # Test the learn_random_nu method. - for model in ["kabs","ksca","pmo","random_nu"]: + models = ["kabs","ksca","pmo","random_nu"] + if dust_type == HenyeyGreensteinDust: + models.append("g") + if dust_type == GeneralDust: + models.append("scattering_phase_function") + models.append("random_direction") + + for model in models: print('*****************************') print(f'{model}') print('*****************************') @@ -141,10 +164,10 @@ def test_dust_pickle(): p, amax = np.meshgrid(data["p"], data["amax"], indexing="ij") - d = Dust(lam=data["lam"]*u.cm, - kabs=data["kabs"].reshape((-1, data["lam"].shape[0]))*u.cm**2/u.g, - ksca=data["ksca"].reshape((-1, data["lam"].shape[0]))*u.cm**2/u.g, - amax=amax.flatten()*u.cm, - p=p.flatten()) + d = IsotropicDust(lam=data["lam"]*u.cm, + kabs=data["kabs"].reshape((-1, data["lam"].shape[0]))*u.cm**2/u.g, + ksca=data["ksca"].reshape((-1, data["lam"].shape[0]))*u.cm**2/u.g, + amax=amax.flatten()*u.cm, + p=p.flatten()) result = dill.loads(dill.dumps(d)) From 7c671282f2db816de8eb7fa36c11aa407f371e5e Mon Sep 17 00:00:00 2001 From: Patrick Sheehan Date: Wed, 27 May 2026 12:27:19 +0000 Subject: [PATCH 4/8] Update docs for non-isotropic dust. --- docs/dust.rst | 9 ++++ docs/dustcreation.rst | 109 ++++++++++++++++++++++++++++++------------ 2 files changed, 88 insertions(+), 30 deletions(-) diff --git a/docs/dust.rst b/docs/dust.rst index 7fac519..9d293d4 100644 --- a/docs/dust.rst +++ b/docs/dust.rst @@ -7,6 +7,15 @@ dust .. autofunction:: suggest_opacity_sampling +.. autoclass:: IsotropicDust + :show-inheritance: + +.. autoclass:: HenyeyGreensteinDust + :show-inheritance: + +.. autoclass:: GeneralDust + :show-inheritance: + .. autoclass:: Dust :show-inheritance: diff --git a/docs/dustcreation.rst b/docs/dustcreation.rst index e214cbb..18ee0c1 100644 --- a/docs/dustcreation.rst +++ b/docs/dustcreation.rst @@ -2,23 +2,28 @@ Creating a dust model ===================== Before running a radiative transfer simulation, you need to create a dust model that defines the optical properties of -the dust grains in your simulation. Pinball-rt provides a :class:`~pinballrt.dust.Dust` class that allows you to create and manipulate dust -models. To set up a dust model, the absorption and scattering opacities as a function of wavelength and grain size -distribution parameters (maximum dust grain size, size distribution power-law index, and sub-species relative abundances) are needed. At present, these -must be obtained from external sources and provided to pinball-rt. Here we'll use simple power-law prescription, but in -practice you would typically use opacities derived from laboratory measurements or Mie theory calculations. Note that -the opacities should include astropy units to ensure that there is no ambiguity. +the dust grains in your simulation. Pinball-rt provides a :class:`~pinballrt.dust.Dust` class that allows you to create +and manipulate dust models. In practice, there are three more specific dust models available: +:class:`~pinballrt.dust.IsotropicDust`, :class:`~pinballrt.dust.HenyeyGreensteinDust`, and +:class:`~pinballrt.dust.GeneralDust` that inherit from :class:`~pinballrt.dust.Dust` and enable more specific control +over dust scattering properties. To set up a dust model, the absorption and scattering opacities as a function of wavelength +and grain size distribution parameters (maximum dust grain size, size distribution power-law index, and sub-species +relative abundances) are needed. At present, these must be obtained from external sources and provided to pinball-rt. +Here we'll use simple power-law prescription, but in practice you would typically use opacities derived from laboratory +measurements or Mie theory calculations. Note that the opacities should include astropy units to ensure that there is no +ambiguity. To enable maximum flexibility, opacities do not need to be provided on a regular grid. Instead, the opacities should be provided at some number (nsamples) of points in the N-dimensional parameter space defined by relevant inputs for determining the opacity (maximum grain size, size distribution power-law index, sub-species abundances), and for each sample they -should be provided at a specified set of wavelengths. The Dust class will then use machine learning to learn the opacities -at any point in the parameter space during the simulation. A helper-function, :func:`~pinballrt.dust.suggest_opacity_sampling` is provided to -help provide efficient sampling of the parameter space, but the user is free to provide any set of samples they choose. +should be provided at a specified set of wavelengths. The :class:`~pinballrt.dust.Dust` class will then use machine learning +to learn the opacities at any point in the parameter space during the simulation. A helper-function, +:func:`~pinballrt.dust.suggest_opacity_sampling` is provided to help provide efficient sampling of the parameter space, +but the user is free to provide any set of samples they choose. .. code-block:: python - from pinballrt.dust import Dust, suggest_opacity_sampling + from pinballrt.dust import IsotropicDust, suggest_opacity_sampling import numpy as np import astropy.units as u @@ -42,27 +47,25 @@ help provide efficient sampling of the parameter space, but the user is free to kappa_abs = 1.0 * (wavelengths.to(u.micron)/100.0)**power_law_index * u.cm**2 / u.g kappa_scat = 0.5 * (wavelengths.to(u.micron)/100.0)**power_law_index * u.cm**2 / u.g - # Create the Dust object. - dust = Dust(lam=wavelengths[0,:], - amax=amax[:,0], - p=p[:,0], - kabs=kappa_abs, - ksca=kappa_scat) + # Create the IsotropicDust object. + dust = IsotropicDust(lam=wavelengths[0,:], + amax=amax[:,0], + p=p[:,0], + kabs=kappa_abs, + ksca=kappa_scat) -This creates a Dust object with the specified opacities, however a few additional steps are needed before the dust model can be used in a -radiative transfer simulation. The Dust object uses a machine learning model to produce opacity values during the simulation, as well as to +This creates an :class:`~pinballrt.dust.IsotropicDust` object with the specified opacities, however a few additional steps are needed before the dust model can be used in a +radiative transfer simulation. The :class:`~pinballrt.dust.IsotropicDust` object uses a machine learning model to produce opacity values during the simulation, as well as to randomnly sample photon frequencies emitted by dust grains during the simulation, but these models need to be trained first. To set up the training, we use the :meth:`~pinballrt.dust.Dust.learn` method. For example, to set up the model to learn the absorption opacity: .. code-block:: python # Set up the training parameters. - dust.learn( - model="kabs", - test_fraction=0.1, - val_fraction=0.1, - hidden_units=(48, 48, 48), - ) + dust.learn(model="kabs", + test_fraction=0.1, + val_fraction=0.1, + hidden_units=(48, 48, 48)) This example sets up a simple neural network model with three hidden layers of 48 units each to learn the dust absorption opacity. The training will use the kabs samples provided above, which had 100 samples across dust properties at 100 wavelengths for 10,000 total samples, with 10% of @@ -86,13 +89,13 @@ a simulation. In short: .. code-block:: python for model in ["ksca", "pmo", "random_nu"]: - if model in ["kabs", "ksca"]: - d.learn(model=model, hidden_units=(16,)*6, overwrite=True) - else: - d.learn(model=model, hidden_units=(48,)*3, overwrite=True) + if model in ["kabs", "ksca"]: + d.learn(model=model, hidden_units=(16,)*6, overwrite=True) + else: + d.learn(model=model, hidden_units=(48,)*3, overwrite=True) - d.fit(epochs=300, batch_size=1000) - d.test_model(plot=True) + d.fit(epochs=300, batch_size=1000) + d.test_model(plot=True) This will further create models to produce the scattering opacity, planck mean opacity, and random frequencies sampled from the dust emission spectrum. Having to train the dust model before every simulation would be inefficient, so once the model is trained it can be saved to a file using the :meth:`~pinballrt.dust.Dust.save` method, @@ -117,6 +120,52 @@ pinball-rt provides a pre-trained dust model that can be used directly without n # Load the pre-trained dust model. dust = load("yso.dst") +Creating a Henyey-Greenstein dust model follows the same process as above, but with the addition of needing to train a model to produce the scattering asymmetry parameter (g) as a function of wavelength and dust properties: + +.. code-block:: python + + from pinballrt.dust import HenyeyGreensteinDust + + g = np.tanh(p - np.log10(wavelengths.to(u.micron).value)) + + # Create the Henyey-Greenstein dust model. + dust = HenyeyGreensteinDust(lam=wavelengths[0,:], + amax=amax[:,0], + p=p[:,0], + kabs=kappa_abs, + ksca=kappa_scat, + g=g) + + d.learn(model="g", hidden_units=(16,)*6, overwrite=True) + + d.fit(epochs=300, batch_size=1000) + d.test_model(plot=True) + +Similarly, the most general dust model, :class:`~pinballrt.dust.GeneralDust`, follows the same process but with the addition of needing to train a model to produce the scattering phase function as a function of wavelength, scattering angle and dust properties, and additionally to randomly sample scattering angles during the simulation: + +.. code-block:: python + + from pinballrt.dust import GeneralDust + + g = np.repeat(np.expand_dims(np.tanh(p - np.log10(wavelengths.to(u.micron).value)), axis=-1), 5, axis=-1) + theta = np.tile(np.expand_dims(np.linspace(0, 180., 5), axis=(0,1)), (10 if len(dims) > 0 else 1, 10, 1)) * u.deg + scattering_phase_function = (1 - g**2) / (4 * np.pi * (1 + g**2 - 2*g*np.cos(theta.to(u.rad).value))**(3/2)) + + # Create the General dust model. + dust = GeneralDust(lam=wavelengths[0,:], + amax=amax[:,0], + p=p[:,0], + kabs=kappa_abs, + ksca=kappa_scat, + scattering_phase_function=scattering_phase_function + theta=theta[0,0,:]) + + for model in ["scattering_phase_function", "random_direction"]: + d.learn(model=model, hidden_units=(16,)*6, overwrite=True) + + d.fit(epochs=300, batch_size=1000) + d.test_model(plot=True) + Learning to step through high optical depth regions --------------------------------------------------- From 93f802a15681ec3d09bd48a6b4f8e174c82099da Mon Sep 17 00:00:00 2001 From: Patrick Sheehan Date: Fri, 29 May 2026 02:28:26 +0000 Subject: [PATCH 5/8] With GeneralDust sampling from the scattering phase function, this adds another place where we need to make sure arctanh(ksi) does not go infinite. --- pinballrt/dust.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pinballrt/dust.py b/pinballrt/dust.py index 069e4a3..5d5fcfb 100644 --- a/pinballrt/dust.py +++ b/pinballrt/dust.py @@ -1437,7 +1437,7 @@ def scatter(self, photon_list, iphotons): nphotons = iphotons.size(0) ksi = torch.rand(int(nphotons), device=wp.device_to_torch(wp.get_device()), dtype=torch.float32) - ksi = torch.arctanh(2*ksi - 1.) + ksi = torch.clamp(torch.arctanh(2*ksi - 1.), min=-8.6643, max=8.6643) if amax is not None: log10_amax = torch.log10(amax) From eddcff0c12efdc226bea103ef9b2c0fe648649e1 Mon Sep 17 00:00:00 2001 From: Patrick Sheehan Date: Fri, 12 Jun 2026 15:02:28 -0400 Subject: [PATCH 6/8] Enable fiducial_values for HenyeyGreenstein and GeneralDust; Enable turning on/off checkpointing, including setting the pickle protocol; add trained HenyeyGreenstein dust model. --- pinballrt/dust.py | 51 +++++++++++++++++++++++++----- pinballrt/tests/data/diana.hg.dst | Bin 0 -> 1767989 bytes 2 files changed, 43 insertions(+), 8 deletions(-) create mode 100644 pinballrt/tests/data/diana.hg.dst diff --git a/pinballrt/dust.py b/pinballrt/dust.py index 416ed6a..a6b0b30 100644 --- a/pinballrt/dust.py +++ b/pinballrt/dust.py @@ -23,6 +23,24 @@ from torch.distributions.multivariate_normal import MultivariateNormal +import torch +from pytorch_lightning.plugins.io import TorchCheckpointIO +from lightning_fabric.utilities.cloud_io import get_filesystem +from typing import Any, Callable, Optional + +class CustomCheckpointIO(TorchCheckpointIO): + def save_checkpoint(self, checkpoint: dict, path: str, storage_options: Optional[Any] = None) -> None: + if storage_options is not None: + raise TypeError( + "`Trainer.save_checkpoint(..., storage_options=...)` with `storage_options` arg" + f" is not supported for `{self.__class__.__name__}`. Please implement your custom `CheckpointIO`" + " to define how you'd like to use `storage_options`." + ) + # Override save_checkpoint to use a specific protocol + fs = get_filesystem(path) + fs.makedirs(os.path.dirname(path), exist_ok=True) + torch.save(checkpoint, path, pickle_protocol=4) + wp.config.quiet = True default_fiducial_values = {"amax": 1.0*u.mm, "p": 3.5} @@ -436,7 +454,7 @@ def initialize_model(self, model="random_nu", model_type="MLP", input_size=2, ou def learn(self, model="random_nu", nsamples=200000, test_split=0.1, valid_split=0.2, hidden_units=(48, 48, 48), tau_range=(3.0, 1e4), temperature_range=(0.1*u.K, 1e4*u.K), amax_range=(1*u.micron, 10.0*u.cm), p_range=(2.5, 4.5), - nu_range=None, overwrite=False): + nu_range=None, overwrite=False, checkpoint=True, pickle_protocol=2): """ Learn a model for either the random_nu function or the ml_step function. @@ -506,8 +524,21 @@ def learn(self, model="random_nu", nsamples=200000, test_split=0.1, valid_split= self.dustLM = DustLightningModule(getattr(self, model+"_model")) - self.trainer = pl.Trainer(max_epochs=0, callbacks=[pl.callbacks.ModelCheckpoint(save_last=True, - dirpath=f'{model}_lightning_logs')]) + if checkpoint: + callbacks = [pl.callbacks.ModelCheckpoint(save_last=True, + dirpath=f'{model}_lightning_logs')] + if pickle_protocol >= 4: + plugins = [CustomCheckpointIO()] + else: + plugins = None + else: + callbacks = None + plugins = None + + self.trainer = pl.Trainer(max_epochs=0, + enable_checkpointing=checkpoint, + callbacks=callbacks, + plugins=plugins) def fit(self, epochs=10, batch_size=100, num_workers=1, ckpt_path=None): ''' @@ -636,7 +667,10 @@ def prepare_data_opacity(self, model="kabs"): samples = np.concat((samples, np.expand_dims(np.repeat(np.expand_dims(np.log10(self.nu.to(u.GHz).value), axis=0), max(samples.shape[1], 1), axis=0), axis=0)), axis=0) samples = samples.reshape((samples.shape[0], -1)).T - targets = np.log10(getattr(self, model).flatten()) + if model == "g": + targets = getattr(self, model).flatten() + else: + targets = np.log10(getattr(self, model).flatten()) return samples, targets @@ -1189,7 +1223,8 @@ def scattering_phase_function_wp(photon_list: PhotonList, class HenyeyGreensteinDust(Dust): - def __init__(self, lam=None, kabs=None, ksca=None, g=None, amax=None, p=None, abundances=(), device="cpu", ntemperatures=1000): + def __init__(self, lam=None, kabs=None, ksca=None, g=None, amax=None, p=None, abundances=(), device="cpu", ntemperatures=1000, + fiducial_values={}): """ Initialize the Henyey-Greenstein dust model. @@ -1211,7 +1246,7 @@ def __init__(self, lam=None, kabs=None, ksca=None, g=None, amax=None, p=None, ab The device to place the dust properties on ("cpu" or "cuda"). """ super().__init__(lam=lam, kabs=kabs, ksca=ksca, amax=amax, p=p, abundances=abundances, device=device, - ntemperatures=ntemperatures) + ntemperatures=ntemperatures, fiducial_values=fiducial_values) self.g = g @@ -1392,7 +1427,7 @@ def state_dict(self): class GeneralDust(Dust): def __init__(self, lam=None, kabs=None, ksca=None, scattering_phase_function=None, theta=None, amax=None, - p=None, abundances=(), device="cpu", ntemperatures=1000): + p=None, abundances=(), device="cpu", ntemperatures=1000, fiducial_values={}): """ Initialize the General dust model. @@ -1416,7 +1451,7 @@ def __init__(self, lam=None, kabs=None, ksca=None, scattering_phase_function=Non The device to place the dust properties on ("cpu" or "cuda"). """ super().__init__(lam=lam, kabs=kabs, ksca=ksca, amax=amax, p=p, abundances=abundances, device=device, - ntemperatures=ntemperatures) + ntemperatures=ntemperatures, fiducial_values=fiducial_values) # Ensure that the scattering phase function is normalized for each combination of p, amax, and nu. diff --git a/pinballrt/tests/data/diana.hg.dst b/pinballrt/tests/data/diana.hg.dst new file mode 100644 index 0000000000000000000000000000000000000000..9d2de0866c59eb0fb1eff9be7ec94573a80c4bdc GIT binary patch literal 1767989 zcmZs@=TlYf(=`Z^K>?ABAW_K(k`&?EAQBXiEP_Z*5)avclB49DbCfJOR&`gs-)8=k z`8*$I*7m-C&-2#Iqg8r1oU`{8x>v7W-PewNtD>r;q^YU&|NC=a>Aq5KQC3-&dtre` zZdP@cdqr_cSjau4)c^fY~$SzeJ>SzVM@rBH5A+;L1%!EZ{k zN);+OWi_Q0U)(ct$}98SOKVE1i?S*!v%Y9&ROaQBmsM3)*5p)cD^zo`@Uk2qtSKw1 zu5wq@WR+DHRe#Zr`_Bu7+FPYIg?bJyl$)1RrJYwMztC1_q$uN7*$T}R^}n~QE-Ld> zXtgV};}tp`io5ZOdya3Fqg|ET6uSQmMLW0pO9j5DmvRSR$oE(1cXf6s?#C+(9N((^ zz1T~|gP53@H~$a*{Qc*xQnkV`yQ@$mW$+%pBLAUIZjq>sf5*`o-_R{LOUb%!bja6y z%S}?b0kv==ZH41C@6$TBzokG}IsdO!+ zJf|b>H{r{iutZ zp3wGxhF76Mza<)bMLi{0nM2x?c(k53AM*ik(1yfk3A{|JJk6W5E;R_BPkg~gv?kGl zxd+ekiWXjT6L-_%YhOoNm0YO980*>hj#mDi8x8%W<$rI%>sa|EDJC0J#o{eWPFO~6 z_L1=NKi{N&T95*@d7PH9c0Dxz&jgaGo#y^)2GKMtMf|g-HZ&t~S1sMpJWVOnv?Oop zyL-GtlM)~DP;@p8U=9OV%YLgSJJ#%`|;V;GY{GI^B;Y}N4cpE=Qr9gRw6@~7X@ zh$LUv+#~%P4gdR+XXy_ONjw~lSzs*&rBEy$uYY^_UoT-0Jv1Qw-L$WU+(!Kp4RO!U z2)g}AeNsB#yyA<}aO#z6?$DGbOD>Xcke zX_QB>h4a)QedP|P7Y6*B+9kfp<5OPAQo?IbnshcbdPHrK1>cX} zZZZN3Z%gH2+M{Oa?`Cmd9aaLqF-U_S`G#Bk@k4kepX(J-CpAiZ{N@9-`_s)&iH>RQ z3vT=6hs1qmaXe)a+#n^!KcNNcGWsrA8GnxEKYaXEDt6%$7~m`~e)%s4;US)=my#Rc z1}E7(RVVS>9WCm9#A~$@-RjcJGg{)y8Y#4>9OG#-uaWFvcjrGohg3`UbPnV1;G@c_ zfBhLI_q!6);S==?n&554KR$ccM6J(?={laqYTYC|5?DHttH~rm`IAo%A4!TWCESJ4}B*@G)k! zktK1bMj%Zmd1gt}nUjuhQd6cR+rkWn)=K*sQm*{2O35ebKW~2bPL~{iz`f6lwLVH2 z8h>|VD^*J2ZZB@710MSzX;UK~+%@OE6p44akEcpac;LMxnbz}eAkQSr8`BO0Zgt?p zB#EXRls|vIj~8QfcvnH&&lBPOzQE8)>i?V|N%()ng|28hUdsPxoV*eT2!h#cy3o8E zUa!CoT4|C_8e*jnQ6)jx>CJc2-?gmFi;-ykBQK`AyGQ?LTU4SXI-?;=3u@w(H&V7y zd9D=?Mq+oG>eGSaoe@$9Z%5H0jH){v_EJlWbRNUwVG?h^z=pWJkau27PPW|Q%yV~N z<6gXox0hplLM0z3HJ<&H|4IU`AJGjTU^VAMq{M1_SmrG+1jFH}>mQ?V!Vi;tobCAJ zD`q;ENGsgT6SO2GUZb((SG@EoNP4dGji-3=NucCWM89|}j;{modt17BK!14dg+zbk zg`*zsd5&dNr48E5gF8tEe3}B>9N;!T3H#qf1Fo4o!;oI7s!c z@%dXCb+wo7!UE2LUFuhCs@<8wzEOw8ZicRpt zgS)(Lo?poOqSQoqaWfWdPw?bj?%@l^Al}7{ii`{EG+iW_0`n*wIOW~GZ2-QW%Jwno|G8Ex~rfU$QMy~793c@5Y4z`tP_Jjkm8 z5&4S8Q)N!bD?+C}h&G;D;1x}T@vLc7G%t%_UjL~=D_FoKj7|>VD_E#5UKD&(4s^Pv ziwF4OK`pkhiKlaUL4@;f6?;DZ!t)|TBONna^y4`}D-WOWg4Z3M#Wnw4!=5L3TJSbV z;Si62WL@%h#JMeiL z|Kd?$5{F^!*#VG7gsb|8OrA8xD`jlNVwg=N4`W>0Wnj3=;MhY#4V1In`5wZ`pm_TF z(>EP`yl9{;+DnMb;sJSOH^RBasfzoBO&HxMKlJ22!TS$1;4*t~;9mk%Z9XT?7WmgA zZ1^O>?HLTc8+-YOhs$uK748z|URJbfKy%zF=puu*f?zI-+#%k4=Ur94#J05y6*Zp2 z?ySQ^+r+yJXBxzgZE&mL7M=)!t+yE9hnfZ+g^iqsa*IeQ#_%p_v)E3n4weQ^3IyK4RMzf zp-vZkqQ*z~*s%~m{Z^}o2)FQ-C0;WycGtzr0vh)L$Ld2E66w6J00yspBY4YpwdvO^ z@Yn-k>i6j#A*3AO8Z))9;#Arf>imm(pV4?8?TJY0drKXL_OvUo>|4~zi>1hBwnZub zp%Fc6?x5e|2Rx%A07$+?TY{z#@c;97Qz(oIk?$GDD@>{trVR?S6g4Dh8FC(}F#rFg zm2wiQorK?LD=eIv6b~B|mX0Yp^2aOlDk}4;^2(~K6juM!mtrgP@{8*K`RXHl)w)4p z<5>7Ve)Ls&X+?QeQFY!wpSH!P?HUyJj(-!>jO?r`B*_lxipT%cZRD)DGB2w-FQcfk zGB39#rzpE5@9$i+6;J-BFGT+L$NssUW4gl0@oySis&M|h6s399d6f#6JY>19@d~#F zh5NrlDa$IwP(0EVPaO-L3tjS3?#e4sRajnGo$=4_Jku3kh0a<3EKx~$S;4>WKTB75 z7rJDnXvkm4FRv`ks#f?kD185UBO|vwCnH1Qmtu`y|GoPQYkAt%HCTD;@_g%(yt0Do zLhGV3>x4*!e@*tk7pf?)%d4zXJWp4=c&nWC&vI4eWR>J80>Xp#|JVP$aQuJurreir z2?gNqBIo61Pg-nC5eHcP%^R3ZMy9i!zt#W)uJ@7I4< z_}|xzm9J^@pD~xcu+Aze%Br%)l3V}xEdIG!TupXWyCOb21;UO}R%uqfBB4;V@ULo; zSO_U3DPEB*OCgOxZHo7W2C|$n^nct@inip|wDf1S00?XD0l;<8Ouj(K9MrIXZjJDT z2s%o?{p`6PfCWVWgq-I>fFX2lh*)~TtGPKqI6(gySk*$1JA$wqfcGnnSqmkf`$*5+ zT|_bW0o6KX(hAbE!&CZiX*~x42mU@~P0Q~s?n--4h*&H=RSPS0E z03qqG1b5vRKj=o`iZmi$m|-^S4IrG;PlN+?0oZJ`>Jk09*ZmcD#3G7(3*=!h>d4O% z?;8@-_=0Xz1OT}gRC9-}Q@sQY=kk0GZ9lTb-q7vyNHCx)ATzS2+h+mXnBXiFd>)VR zzLmx^!pYHwy3BYn8d0o*4+D&8&qGupemB}sj|9qe4+uL5*7eaE*{iD|EvNEvmG}gD z)SvDClBNWL*O6|fFM{WQh%p7*8hyuePqC2JJeJK{S%?H!m#{dj+bRMFLe2>9V2BH!)~_o-{m9= z>M7z8l{`a9#8mJmn8r#0ZahKrwcgdR2Jo^qnCg}s$f~Fl*(|MVrrO(s4`_zd`XkU| zOt7+qXSgxsyS%j{-@hQf-2@TL5ML~knH~*c^>TPp`6thQOhPE8Rvn&E=1Va9`KQ!h z9A#1=N1#pWd*FZwcLECqk)3g--8YF~oc7#C!(rTZN8%+rxXJ6BquX>+R~=65D^AP zMQXV4)K)y>)i_>=dq%%Nd-k|hIS#wJDIAg$KZj!8m|6dqZ+u%oJ^E5gIP%spo{z-` zYB7z)xUbpZ+iwzbxfQorhBbgET}CR3!;B?^N8o*zyyb~(_Q7+S)(Lr2DQM%(CouJC z=#;X@P%}vUI&QcwDrxcg!=mKJ;tfd5sx_T^fs{oS#=_?(-(x1+@9^#e=;c}l90?nB zVrZlsE@;w_dcC;oIoLV&VV5^7O{hy#qK$7LUPuNVarejq@a!HJO?h~`xeaNcV?6+m zI+`}8*}zzu6yAP6ZIk#EbeHyhzdTc?&UjH&P=-0J(i~Vw_WjtLN^W`}XSHXv?U7ao z;#;W8XC-`HL(L);mN=uumv*!u3V+fcN2@T<*%Ufet;)rUjLD|G2M@S;3}e^^KjSg= zeE(4FE6}zD$?C=zYou?3u-#_*;{deX5}wLAAy$wORt1vW-}%_WQ`^rlSDtxXTS{GU z-Z5@|$qOG!bR<7tUiH_o44%=qGgR=}*QRk2vv^y1hR0%Htp;Sag+d1E)Pk7RHlLOla&8?Zu0i;9cX~ z79bX~_&S-kuSD`r4z zZya7H+?C3!sl6JPJ2HWGMLT?pSO8U*dWBOJ{<~1im+=Nno;HJ3Aa4RqpfX=UQwKra z6d$QElIJVl>PsF9UJC^REY=I;cdao#c6sU$uv^uj$X-+;*)qXm3@iHsX}gdsrNGHH zv}A1%(6^`S57XL3Cc#@K?sUyf8Qub5-2tYd$a)qyR`hpVTpg5>e-YrLS} zI@B$)-e4DMkdPwgELb5MI)qd8!0?i&<`9$lYkK77=8&oY!o`Ct=J}M(u%0kf*Fiq>8F*Y9DhW;V_l@K*urUmY(L-R0PHtp z1~HMFLzCKhLjAj?uVsTA{wC>HLIv2yx||<17Vu3R9Xxm?l&C2|-P-X1g4=6Z%REGi zeJtWLAZ$_dJLG>zp?8dU$lI|D88#hx02vn5z6%o<`pxZMN-NA`Fi~IKXv>r4N9z1=tiSG^>T4^t;Skm>bx?L89J> zMf2hr%?}_$(?DF_j{O|thJo)2r^mUTCVUN&vdvQ}_aBx8i)bGYgr#ZP(U2ezZ|23v z2U4rBea34V$^z*$&Hoj&`mJ6FME_n8la_G4R)V}(9Sy^5!!RJ043;P(RFeF?6nxKwuVhL|*Yu24{T+`#uKCSWE#C#9nXW^^L2X zNDIe&zR|gh6!RK+C=Ka^!BJ-1#<;2By;l5604r0Kzq6jkw(#5(C_m+L9WmAJ#S) z4Kg(iRN6!&!6kaRUr9e-OTrk3;3qx#k0JzYqKA$ub95k-g@fJVn!NQRQ*f)hyEb(2 zb>!1Lor%FfK^KT5AK@YGu=^dq=iRP_WQSdkl2td^zp4R zNXo_z!jcA_(3Ki*Vyhzr=kWNHVgF5YRiE^(8;p`V`cu zu^0fcuI%%^2#q)L{@yy`^EY}l)qo^o$|lQMs}P~HJP!yp2di%th;)GE9X`%`DKrCV z+!Kn*e5~NEd)y3-!Jt4GdeLu7-Y^Y;xYun8veKUq@$?UERjcBzmr(kG!MqyzES|S3 z#0Sv!Q-t+*)gq=yjatFg0nFSMR)w3al&DLz96^TxHlGkpxzW@QYcTUX8=+*u@O!~? zylL|3ZaTo0_GSNH^r1c7ui&6`rcT|#W<2CBuj36S-d6h!`{ne|p0;ZcF0j7SN$;p9 znA={-e6-0p$KwUYrkbr@oSJAX#nr@pI? zZZl8hUONd(I?QbbAm}$318Re#;7(JF7!k=p+NI<~*ZY!5(I+h9q|@J+i5(xpxY|F17FWUCwi9T=EKJyZ zrY3Cq{+$nkI(fk7{#}3t7~GKe*C#;88?faKp84@g>n@b$;T#NL-2syuk=gnR&;`YH zFoZ^t0A;J!A7Kl)ClI3svDarbuI($Vs5M8|qePCGD{1LZz}LYo&*KnccHYHlOJMgM zcX=?&0CFT<0|_!|z0w#SZKuzRNpb#&v`}X|s4Ee{{}2{~5QPOg#;u@hj&q;8fhLcB z1f}F9%wR|^w+>^5z5eMB9N!|pJcyz}CH@75XOqaMaSmVUM3`H|^I)z#WL9RoLZ zF|W=6(4mArLI+>$Ai}-lKhZpJf&4&4-b;p z3F>ku2Gx(4J5J|CN}83(H%t-K+w9eHfnzTEa@0VAjT*v~On0?8hZG zo==Top&e3p%&TS-M~pv4kb1eXJ70`=HF)al9jSXIzIxo1ud zu;=|%PQOnYu%?r6+BW2&D}b({XI@yz_gb0YjA$PT5e2;RBpsf6Qz%T~!rLxznYHhr z70-D{A?Uw9TJb>tKKs_`+U-0IVXU?%VNIgD^*gJ%Xgs^aw4+zk5V()M-iZW0{tQ+&44RGr(uh20`U_3P z8%jB=fMT^G`pIW|0l%Df3cqDuf)|#o~lPcZ#!N^e zmF*M(%Sbei{={frKq~C_LC(VFe*SaU@z$AAA6%X>ztU$}95yFVil9TC=4ZLp~0MI8# zDsagw)c@>#3*OVjbAfTp!AzGy_-!OG;Qj|Zfg%|<3J*T}>hq8%D#Zi42JQ)ljUkh4 z0u~;9q;(TO*J}*(nl4Lu&{~Sn<8$;t(He3WzIEf4G}^t3L2AZ@e4?pXJ5*ZMFsDm9 zd>Coj4kVm=G>CL<-7f1h_10=j1zu&}ko-(nN=DgRZHjwilJ zr|#um(a5)ObLCh7Y0oPKv>)A{u%ewuh`MQfULE0LS^}3}rY_}g`hZ^)zvkh1yL6g} z%vSB^J8&b*hL1;|9swn2ZwXxk4Fens$MxlC(n9aSYd$;H;38v(f109um( zP^4eC)*{AMFQqX0jo^XD@T$2-SUad*TfCWBf{X78FK0U+p74f$@v=`24Vux|JygAa z)z_tZAr$gP9##xZKoP{lV+Pdz0Kx|(FA%LgSwrB{KwMZA zuC>kGK{P@j}eJxC|c8ig}qSEMucAA{iLtZQIev7J#l`o*^Bc$whCVr4BWsUEI9O}ol`o-X{7T$6YJ z03vLkbF-szZZ0^Uo)ev=!~^x^O5~{z_H#>5vZ_;m##<_`*mhctdt$75PjZE+_Nrus z!4jW4;O#c<2>B+8(u+)9fxke1?o79#i!!9v(?Tn!9;BfIAhaz?_&|@hesUXFbDxbio{3Atwhjb81S9Lvsm)AcBACxM z4_L{IHcH70g|MvCdyb*sBEHi>urKxb(Ey6?$@us$?#$$I zSj@It9gq9#fHH=J0YS!IIRoO;HD1tg%sX&(B|^OS!!D0Q#$874GhHL^qOJ5Sk8ev6 zuurj37rg$?3?ywGSAlYwnnk0vF@g`!b?^qZJ_0~}N(U);J5UDUVSO5?w5M5AT9(xU z^J0S_$LsNw6`usdTPMNHnhRPY$Uy6I!&Nsn`+sXkHW%e zt|$xS5>dY&aruTXoP=&I*!~6-mKo$}&q{eqi+|h7SxgK1N4crU)j=)>t=tI>)v$;2 zhtgm{(qpJ{)a@P-6a~E!;b9E~9KuDF#tU4$-rlhC4irBRI^Y&KCiq{axT9^xO`qtG zw+P`|Z61Phf&}&#ZChmp&`LZgMJCNBKKFsNzsZ;R)!JCeJ`tAx1Jmw(YAii-b-)a# zo?1&<{)k3Xd|mMwYk?J)33xM-`X#6_3yHe$*yAFhPuqsPs!LEjvv^Sj3N+|ZstCo9 zwz<8Id-IXR(Lk8=G_6|HJ_C(7G{O?>i(Ew3RpoFj&kQ)!3VPO3A44(04($c|@=X#x z0uz`tmv!AeM-loxpARDg45cvMNP9|WNl3tJOsk$@j@*Pg;a?$I->p$$lz=ww^}z| zuf#07Mf!83wVOPx4ZpJyX58unemR8j0;)vID%2J$)bzeW&g}_6)*Hx^)P-j|Hk)fUxXJzrQDoQ-GZf4@9Y7CxMOMQuQ4Dt36 zbv&dYTg!Qd(9MLYZqSuFY-%P1xcn!Xn!My7x5eiH`GK-S?WXcN6nnaQC)~^FmzD}F zX960<3J+l=R+SMdc_au4#j=t|m~e-SpIXu{bD1;_;)-nuR2%mnAzuvSd7k%B26UwI z-*1Xv8ybzkN5AwDqEp84qg9k*FO;(5{Mjxbt4t0XFFx5PRr= z*o%c&rpEilybJHz^Ur;m{y{~mdF;)7s;ZFQ45(HA9kuAlv}%hRVL%8+(-0xKJ>88) zo_IlVYE9%W?#$;aHK~G^zrZC|%0q>YJvV{W(uRd-;G>9Qbu)d*mR7u6X-GX38U+ty zLzl2beKMz><9S&AOi_Y?WS!xnsf!F96UUrjNu3Z}29W09-WMhnWeS+Z6rEL;!v&h; zgnlmI#S>a}4n|0({URXeeoTP~JS$sE;@;Lz4P!ZT;T^_R3m<$ zQokv(I~47BN=5FL@|Vip=|+iLWR+o(jw>qnj4UK9eo`_jf-=9G|cJT#iyWy_X`=Ey3ECCyv+=|iNEmEte2|H8iJ>tqdg6WzO z>Umq-mJFZ!LoLB*Y?2Z|Tp8c?NzM?ijD%H#R>)aY)7W^%1} zH2;hTeUVwdEzSD$!y5@clEyPS3)cT40wHm9xIYElA^qFnFY8X# z^O4%?YT*;Zml6tI&xB7DpGG*|&A}!kt7xV%7qB+C;9wzdNS|mQ20SA31`9wJsu{x7 zod>F6%}sS8!v$;775Wtv7N6MtLBC%h(b*UBS%6`h%h19gV=Ju8P{YAWIA!vlin*RQ zSRYI)1TJVHWjf?}RwN^MxCQXQ3+gjcm7=p>f8h~@8;S@&t)9Sz)F93WVIEz@jWEhE>O4}Pgxfc%}ZV1EfS1k-$0)7cMRehQH|U!>$mhukYf*=4$jkE)?&?~BiOD!CP)87aQ< zsT&k|8v24Hvc(S4!LSi-=!<2yOtY*)68%se#R?x4i93{$DIeBX@DSkb9G2D-%{TCi zW}0=uYRuTnK=8*eD>AL#UMM_yEiD4rI8Q=Oylp;$gnY6}sFWfYp`z%T~n4h;MYu z=PFrP5+xpb2aw(RifK$kqx}JYgMoH>Aa-0T(hQ{Up?sSRm~YWR1AZ{JbW#tk1y{~= zRYr@vWFvBFza><>eJ3J9WM4Gmi6j}2rnoDU+wUXOu-1Tde9XwA0QJG#m(FK7xuDP1 zSc}UV+7zwd7_KC)|RLK4I`Z*!M-DnoRvqKwfuZMc}gvpK@*3 zM{Ne~(;zra54S14SR$M)LEXO8zK`4nfmm*{x#hJ$bih0Zl;POO z%Bw6WJh|yF^wDDnbo~tqoo5REKP3Wk3n1!#L~Q)Yi+0FEAy$FNm&<91D#me!0E)Me zDmC9VGemW^z0f%_Gg_!iuYo}we2&nkRI6>DWDLyj;tO+IzWJUCx@84;-3=&%sQ};i zsL@Kl9;D9Q(TL})iURh%~j%+J6IX2CO~);?xxUq(188iMa(o%;bvX>%`~hUD_zvW@R0gb zYc8N~12Px38OI24-E&Yl=+hf~WzEfgHZqHyrWKwq{`5?aPMdU6O&w2xc07>*n8INg zYQ(lr!HF89kTf=8HGjeUe)EbPB%6eD6x4EF40jV5Iz=>j|2uN3r_opdbRE{oFo%xs zJPc(Abm&$@f49dP{xXtF_`MD^A82?7I_)$sa(f(~;)W9feaiv)uRo*D@gg2szyWPT zZv&HA1pgb-M+Rt%Chtp3<lWc zVGs3X!|UiULM;;~v^LR$GNtM964_0!@QR@3`YOFh*b?`e*5`Twl2n239xvz`tx)@r zxEhVh@c){yy3bw3cj>eQVS@W~BhA4O4`7}B)Z<_;y%l%jw4AA_EKhKUGs+YW==%lc zp&1RPbU{LVjj?QL45jzCY?$fXk-{rXZ24d$Tetq357xZQi1`> zLc&=CF0qSW+><$Cn;p#}KilJLH-8>*167s}{q)hT?1pGysQ0gR0lcg;B#myG$OwO$ z4E_XSpZnuEaxuh?p~`R+rGdI#eCVa34LAc!>(YhKKuYADC~?OZgIV%?ievzR?9kp9 z2eD+qZt;@_?I+U(788Y`jx@f&sRMZS2Vm+Pc56NY;ZmEf-$a^$Rs4noefT{S>;ugm z@W?{mRHJQkIp>_DC1qN9fRKJD>|-Az3Y-~zL!|(FIs{Z&55>w@(o)G|x-3Jaimu)c^(<)B-lLNlv^OLL=0i z=qGOOU*m^I&37R~Vh-4fy7?3NffA|u+Jp|)AkfE;| z=ICxiCy`ZH;Y;E926(&`@Da%ga*=Bbpw$fz{SXCl=upGTk3hUIfvo^*R4p)XOnKZM zLmA>4BX14u-2{M)BE0v=ywOvpcd#RdPlI>bT>`E7qup zs6<12TIET7DeK$4FEG5x5^U*bc`?45gAdTPmItogM2$b8Bj~!*bR};?mB{78FgQ*R zk84X=ysCExMh8I=-)k%evd)I;fS9g4gUZ*8P1U49WgCgFeuy*$An*{htJ4$wH65@% z^aJBv5wBq_E!hZI^A#Xazi98la~+-(dU6eV1p7QojpB1=HX_~ek6KX|nXZ#eJ7ofg zqp5?YvSlyQ>&82XWBDRRPZ38uw``_E&oh;BtbuY0Mp#7M{@_9W+{{AJi%0NL7Pu$1c1JW zEx@nZqOeFed<1%qav}1alL($DhLP|htsCGd>kE`>N06ufvVKcrnIN0JkCSPwTy`s* zo=8ojb8yCAA_&G~gAN=esUVkbvZ+52YN`t8$TBb4CGsljDVj9#stoT80NZd{1$YX1 z2U&B8HI{3YhP=REs&Q0>1|Os*%Ms!j8RZ~ddBfvqq>2}9pT!|fw4-s?%&byK(vQ9F zJ&gTaOWY9rD1KSaN!YOUbCgSaKtHC z(b6ji58P)?eD~m^bS%!CA9Vdm5X+q!B&!HD*V$;>=->e?L?00NfP0_Qik_rOn-;uW z9Ryg52W3MQna}Pc8K%8dA8z}}<93po3b!cH4*#(b)+sceTFtYusPUb@yUUls$~d3; zi%v|_OTt7o0?x2i^k?(~G~)Q2CorHNj?E($aOjRFbPGoIOWmDzbTNlvQO|Ssb>Mu+ z#RVTk1IWh`P(u!Z3CvP|4)6E?%6UT%!@A}!FMR&=Cmn^nL#vf3mghPVhezee)B{@j zhZ;Sfnd3;MKRjg<;bRVh65Oo6Mms|f?u96_1g<>~a6$cd04{jyNsIYL(r080+^Iqf zNVd-6EM0-1E8wM#rW#?9FC7K6SM(|1z=sTD(?%6ID)|n%uK|x6en*R35*pIXVEn%y zYJmd4ZP5sd_6?Jl;(mj& z0Xh50^}>DhrqfFK8`%9E&M@JoXST3hLd0u#tx}cVP`5p1x}qtIgk1&I4|%gePT_$} zpK3Y7)&1L@{lRExr0fxl~_r%_5&PxKL)_Fj40D>%z4r0-|%#WME z6>oX$yE4c)prTpQMgrO{b9u*48?0wj7Do|*V35s- zh;x#7pbSFIvnkqA-Q!|zvEVa1$*V{Seb8pEXmxn?Tbi^{Aq#WzKl(Hl7j;#4I1JEwsZCL zXAVtOiwD$hX+YBr_(UY;yQ3CMeZeweXd;v=9?&9+812xS&$CO6B@a4w=e;Ojlt((t zbYnkg;E2UIVDT12DKu7;ArP)AxW!IN_rT*Iw0SK@JctaH?NIKYqWw%7dzn}AN<_SH zN8lK@(3GBossQ3J+(r>h9d-59d60Oc9sv6Pcz|k*q6{DOnO3v@CR}P2x=GZ9xIXn2 zp=O9qE71Mt9*x|$3sJjS*19_0^Qj#T<$&tp*z^-V^8Nx5m!m36H+wy;r=JEZn|nyu!w}ycBsbswG=$E{6f448<#D zu)Y@AFq@O^AVvRU4zoX3&87VUUe=JTweZ{w-b_suX(_3ojK4mFi>Dx27m#%?ov>Hc zQRT=-rs*<7c;l0ow5AFNY!{)$e&AiLujmrp^Zk6C;fHkX7d~^Ai>z+H6p`XSd}vNJ zL!m6i;4ES*!u|qe1Zo2jxk2u58fphbq1I}{SFqRxkwqIuRgT%_f*T8s-h#mNFtOscR8hoiQ&E*~=7*Z9uc5AiSwyZjV73WstSM(u9Kr7*+| z@g1PKC<8goS)+aX5W?>1drgFTCtn@8YIlSb(!|d(;lX1N4O(?gDy4loRmibznKnFN zpVK(C+**u_oxG#<*P^nBr`}^PX8{2;TFwJ<|H(wG@S;Ip%Dva{*Y_gs^Kn?FrsQcu zzl$uAkoRo>Yx=#B8O$LlT;uN+kZv&@^a%qTdye(E{e~{AJy2t;qb1@T@{b{mq1%l& zb7aD>>x|KlAbK1@89w{-P2tFYqeWAni98Xv(@=<-fBH9CR1)j4a`a#t8af+g~j zR_^@FZSlhU4o`U)1|X&Ul^BXU;Yk$jpw)KV_omDRu>|XSiM*i&D#oa}6pfAF$l?}D zHyCjHYnc8Z4ZKFd{0(%zR^HFGl*&Qg4!G?*zBnk7U9ht^Fp`6pqQU`bG$ixCMYOIY z+KhxgK6*#;Oyg}6nDz-e@=*Wh^`IwsxQO^}z?X)2!UI^{fC=}9p&;Ig(2he-`k6qV zcIp(7p2)`5RYS4W`|1zxQad{PMZFeue{1Rr!>C}htuTvD+_O^-G)KILr%*tu0jtNk zfp{_;u220ar><26QJ33i zSMdO)J{tAsMSD4&8hZsUXaMi~22rjT&%l^Oq`?}lU~f8fwImRoF5vU98l$Ozw$%{P zZW|t>H!~RIjhf!n39V;H2G;#t?Ib+DhNf$)%Ak0K`YMA?Oj$_ZUMgo|j;GehxMpbn zHJv*FfZgsn(B(S_Uc=%ctrryItDtT$Dmr)jiX?MO9z?UYGj~RtO1bf#=JJ?O&R6?h zLwj;qg_ShXkPo7(6Rtsvl?5=zDJ{gL24_i*k9ZhC?Zbk^Z?pxgcbvuDux~*BYnURU%J2g#pvNt0 ztU%Cv{;+uY*+84Zm_}l$RTV3?3w6Uzi7&146AXM7$F>H0--}c-!&R_VSI#`}Mc-JeOx1@`W4qua7}}&>hARIy!%D+-x1n7Q z@vNM$ry{Ply8VF74Z(AJdAL3z91XXpyjsIJ$2AI#7&$}Nmpod|5f*QG9yG*1PDBS{ zLr!EINAJQKAHZ{10>*qA|kq4Bdx&jDcn>1@t2-$CsabgQQfkhBm zkTNE#R7l;(_J+UqT{vo*L&k%f9ATB>-pHXF=* zPgsS6ink^M`MME}&m$CR$`DcVaHBRbiWUts$;vjt{f-tW{5ihX*?__RhM)X?O3U^Y zjLHo9E_X|4p_XslkPAFBq0s`mDiGA-XaCfO4yr`{Luw5Dn2D)#?^~LEZop?bfK_|$ z%1)yrdl`Kp`O;ww4_ZoDR=}Q>N?HRB-{DIoEnY9dX+xCkKq;@I-pAqAILJZOP4WbhsKVjQ%=f(GixZxke4R$Kk*SDC-@X>dl*0QFm$tAQ^T7(BxC3|bXm!mC z$aJ;bnE!=vfC|*O&UFH@e;%N z&CObgAUb^Y78rtZ2`Cay|9jGj7PorF$a-Pdx3ust+yt?HKl?RU&)GAfP%DLNPXDX` zBg2b%?nbOwk?HI*ENWdnU8Vte>P-R6ZMYWmT||!?q{P{;$Z4TSLX9b|k)!$&f?jo>8Fv1N@=v9Wi9mmQS|v{40_ zc=0^?xahX{4KKN=%RaxQp_)wNq1eQgoWk!|q+&F~$&Z`4!sei00iI>gBX_x%b}hw= z0^}1Dh>1Nv#7__-{uQBv9YIZS@CQKU(nk8}27EXNLL7mM4_aW#i$K34NP+m)R zjZ_*y$u#7-vN})aHzLd$%XbOjJ2aKNVK;5wKLHT1K4dz>)M##HBZt5t>iSUb3(8Jg z4i&T%j~ZAr3P9!xD6ixr|Ldr|p2D<%H$6g=d*%mg!6PCL3KBQDGiEMIn5hk*n z8!yArF3+jcqB2wq-paSao=n;@?5N$L$_IRKO+|W@W@{W+V}SyP9<(M9zjJ*FtBHqe zJTRz%nrCr`P?h58DpvtEfOs|>&*SK5&S zh6Z8%n$$>(6}0OKBijwK4dU(snC-*bQXM25(1793;E>C-6hgm1B6XZU(Xk_3Z9}F4 zo8ZD%uHR*yWfbWCM}fgaMN zHXOR0kRJ$l1!9T*U5JI_rnK`B6tte#ABJjSmaQW2sT{B#{sgy}q`l;9YB9&9d4x`) z%V;hm6M^%v8diFVY~xIxT{nWm4vtiYi)>ivgu0C*0JWaG6I}~qi1_&o;fFP)9_H?Q%(bWg&G=rd6P@RP$15@e2YHdH2k$au@qZ49b>2zuQ1Sjh4fSa0LWIoExEA+N{E5urCu2b7>H#7SYn!o@}g?isZ8GE#AL{ov+IjwTW6m7JBO9 zDoQ}f5PFQL=M%d8c9W<( z*FF^Q${n*e;l4`JJ?|34@Cl+zSEpAd61EB%(6}$!#0F zpd*soAESa2#KYWIi~_pc$29Z!#TT5U5S7It1=O1YR=fsiTLAaF_!IzfUX9%EY0hb$r@$Fh;{h-~!M2p~E)E?{O=cMkBn%-~N;3RKo zBE3#2#OI}Gbey@!=Lsm5H_;j3QUgBXrxTG0KLyVzPl>XrG! z9K@QLG-OG1=5R?<@g;oT%U-u{);+a6f_PjK%1Lk1o!A?qGd-W zU@mSnijIaJZcTuX&YH+hzahto4UjH~*l1?&(Bo-+Fs~U@=oifj{UP3q&}Y`P96-&b zXk|Xu<2}n~)v}fX{kM!(JYwazJe|WU+zofg8=Aq=%z*6WCTir&3SfE7DFLmwZ(iu9GKxz8!*l& zV9g0Q;x>HY1+wyID&QrUz||8KZq4RC_%kNj3TDv+GxNp+i&|AJfKZEYqt|gI=^)0Z zj_3+Nhi`!&b^1fA_2tPDpvpYH3UBR1(+02TVvUf*RPxae9taa=IotyPTz2Lg;rb{C z{nR|IBVn(5OZ;qTG89#W{y>CEnL>1nvQRuVBNuSLffIo(mKJ%5xx(-nR)5A2gM?z; zSE$=i`5vpD1qzVOBXQzG1SsHe9d~{es%3oXUh@UK6js^Ga1}jdWuW-zir3~HobZve z?@eBH0)a;{FI)ai|2^cFz)J9~dsv};o_d5aBDn1sx#B3eDR`RKGu+~U4t*>od|~l< zsZzKN@CP&>(ev1JCtydQ4UWK{WO55%{tOs!<^jLNgqKY z`TT8sabW|ZPDNr=ATaj^ag`445!zPHu%?T#x$$zDv|j{Azo(Xm4%Wiy6{6613AI>A zwDFp6`By#?jb`*Zqb><3zJ5>Z!uS=GV!q9HL1Dd*dsLJl$PbI4L_MHH9}PT|)1W!? zyd=KHbL`e>HGvPD^wH{jtM3mKSq5O9BI0_|mT4wUq>G<4L31=(Zw8uxta-!zI~WF{ z?}eP*@mP@-qTst1yyM`j3%q*m&cC0*7om)xi}o;>rtyr%W_Sb!(TxWM{6JQ91Uin> zgq6_#M#bZrRD1qynlI;aqj=IlUcHTog?X$bH1J}PNI;r^)mx)RUpY^k{m9FALIOn{ zm}7c4!psCt$ef!5V`^i_HPGosZE{RnRFW85bV(x>&_ZS3@2uhV!UvzQEeD9sjSQfG zMejnhoeW7ThAI}y^SX$F9bk7Y!o(VZ0-P7V zm*6eSH2EH-*CV=iaK{RC^OaDo1J$cSzaRv|RqpbYA0vU36OT$T)5!8cO0)*N1*m#~ zTG6Rivb~yr4niHm`X0?X0yx@b!jE&hFrPWT3WjyRzxS30oFP`xLPCfwhO8z(AafOm z13vQPJqeGfeP!vQN{!+X_Gn!X!$6rIA?xK2z{K%4-V@YqmdM>6Ww1a{l?UFb#@13n zWjw;|1#N-XZ6eYn(}etJyO;uAdqx}H;2pe|X`g~;?t)4sbL(rM$)5~Z{4ClO=`f$R z)TH~o5aEWE=!HBJ&LetC-gQ9Z8l}LLTul&f@CA6vJ;dmFB%c8J6JWq;nqfB1C?Doz zD)Ba$?&du=G<_Ps7Pfp~{;I6#J{kzOF~94Fe1P7b+iLz*_5ywiAz+?co#{JDJJ{;yO@MnXfj5>28g{ZyM1<^B$qLDSJdX1pyPwzhe%!zN{`D!|@9R3xW4w;nv2K{4jNb}C#%iw9yN$`s z(X$cU&KSn@Ny3Y_2=73kio8W-DK(i}3>0Ms;Atk0 zY(c}#it%rX$sA>}5(*9`G?{sQo#$*mkfah*tup!2KVXp8J_I$(Ptf=#(ktWFUG#J@ zdVE?|N_AUs)=QJbnzQxR-S^FCWC3G3s)wHC^V#lPRka?t12mq*Ah>!(^YXP2jsyEK z7u`UPp$tJ*=EVK>PZ) z3nvw58w&=YT#R?i#9e>IYpm~t&F9a92u#ln{l&rH50o(OPoTj}z4$2ICqL?z1BPtw zw=+El_hj%LEvA2{!=DW+D~O%hEFTlrqIL|()}J|temw!zYV9OrS@Mtao^aHC9w<_V z)jD%~_XJMExY z=;8Uf*A(R(AI!(mK%rh!kHHe?&Gdt^LL(`W0km7aG}`6yT1#qwii{r7ev_X9{2qF{ z8hNs@eJ>TcvmlIi-B-9P1DfK0S4Z-ByR;FTR=o=asWj=f0wTvu&VKY0A5*KMx1zpt z+YXZ_pFDRDS^7h%6L2Y4ci2T`Q7H3X%HEy3NT5qcAC3BUc7@9AMka}G>4q&@Do%G} zg2h?Irz}aUJ!q-hN9AwFv@qm^fyRon|Eha^fuw$>rjENGiMb6k_Mc2c(>v)&Ps1rV zZFiB=Rh8*=N%u;v%~fhKhk0 zX5zmU%lrAP{C=XMZ3Gudx?qL_+8%=w>$%|vZ0GysJlzH$l5;Re~z^bj(gF;u-8oyw-{6Vorw3_VVKT)P^L0P@j{HO-}Q!rdYc)D zA`nk;t}>JhDjCFRjhhiCqi$p~5V0h5?z+x>bU52RRl51$ZmGtb_Y>=5ub^qlbocdJ zK!O0>Q>2#@@EZ5&Fp0}zUMj{T6XQMqYMZBe79)$nM6rFKU_N!hQ~$aKa59n9-}m=m zau#4WP`E+Nk~zkt)lm9x24}HQfEN6v+O7PhbfoHq_uv7Uag|AFIq_OKZ*{_$HXx&v zP<@w86J|GI&01;7W_S(**>xR!_l%4=A16^-urGMVyxo;f8+N zgQ5pRF$F}9)I53pmkytKhB-Pb&VXlYCn)(3(JA!3%y}Ln5m=%w{zsS=`r?qGYqDe{ zg?40H<1VmcmTy_+)%(Z+4ByqCt&@=N(Bcyq$69ZH#M@H;IU5=UaZ{Eji(A7KA^%#Q zuD^bru6E;EVD@F1jEzB1#@_0w$GkfKtf>W|gH`?jnK!IubJr11tRnp0hP0ka($(37 zLEyh`j1k4)>WOq5_A0>q{~^_ct^AQ4dTpoi2y^kxrZm}TDAd`3WXmp85Ui5rrJrG9nZRn=IuCXB`YIL&0ZVlruYUt?r;v}0gh0wPn0IB@-%!4hUv?s+5G+o=6 zl^GC|aH(w3X)mBcfiX8q@=uC%g#`5N1x;bpPggL;E1 zuic4Y$L9Ugg~xrmeTVX~Ii>EM%cpoGZ@y^cQu*Q)C%fd$GXQrSCwR)6)}5yDB5(9wVI(8h#xG2qJYg8p*e zj4Qh~576!s_E~z)?jhO8l}_q%Sx>o-Six6rE=YgN4pJv~V|P|z8!^Dd zqg>BW7A|R%QqURwcb{Im{1NOJg*9K4`P(Q=e)JXHa^*}o&Ve@5YiTZg&ASKsYc~F1 zEWmVcv(FDmzcpHP)+Idmks^w1F6FO(NP7?|!BEq{GMMhSpGx!A4Y7#c?|nutcSO=B zFAWxk4mu26tC5Ir-o;Z8Mjnx`e00ZNslJ!yYI=|^)wt;}KGzU||O zyDg;RE_mvJ4D7)*RJo~V@u%`5K3suXy!kp%PsEuq(G*a0PL{VnPvSMq!S4gffcbyw z<%jz+dYenFR$D+gt}^!wRKPF{-E~V>%QESt&rJBBG7)`fF?of5|DD=FfJNX(H=o{o zx0k9`RR{!GEr8-)RMFwmoQf9Jm<(07R3}_1v9~g^q~%q(blll(nA86nEKhy7pj5GW zrw^nSymd!U`GA$ju$*7if1j+b6}a=&6ZTLBn}MbyH{nc^?qFO^23O0cT|WjJ(2&U= z?HRm|;mi6P$xe7^H|`zz4o z5D{393NI1PFbqI4ALB|Whtz_QR+-mM{g8aUi8pK|n?pG2j=B4tMVP(z;gmET z+X5_~zo7eez2AuihOd$VPYNK2Ipp^h)B{>1$eZ^$uE(EHaQ@iL_RLCWxG6 zXJzm@8s4h1U-;uSm6obOw1QQIbRzhtyD~um?Tnk3Z2Bvsz<9Sv|!B&+tya z?m&=ZJ9_2w70#NB-^^9%v5eCzM0*=2oQZ(CHLD|bK9J7s2zoie=q`db7AZYFrrb;P zUtM z8+L3y_!cRfp~}sPcGCyWpwR_jRGl75d<+mE7>YGRf@k<K955>45?FY?U9kpUpNn4Z* zTqr=he{bFYUivNg>NP!15EU0^h9$E^u-El$Akga8#TmclCNI{VB>kD&%$2gfe=J->uahVfxEyk|P+PItYkOzI6PxvfV9= zfN(>;KrHk7eca5s10#@WG1we7doU3R2299)*&bB@_%)vSrz>q9ykANfaI41UJ-s63Iw8JH8Cvif*muV2>n(vDNBeI}S- zJ1%MAC?4P|#PHP}IF5M|d7ISJ>Te&2EW7!y3CPN95NJiLZojG5P4ad0qx6Rpj*?3# zdyI+g$s|5RnEvVEqrCXcBcRsb-E4n4wG}>sM8FB^p6n2JoZre*J&C0OC3bUx!!NhK zfuBP(SHnKMbKpElZ@B5fyf@y;bvKcBPx4z%=8x_w(UZGm`5}?UYDt*4e+Dk#33hx{ zhLgM?iu?h#UqOHqj~PkajZvpUO-9Xuz0}vEg1=2PLUMjeuX(#mH!{2##mm;*(=AuQ zh@OcAS*K+MM0%}fw?6kTZ!`WYeWAi)bgHyNEEid=!!Hisd{XiTdF>sW3 zsY5X!V<-WjCgMj9b4X>}5~1TyZ}=l+2&VVtvJ|tZv;C0Xlsm>mgiD6&83%eJa6A zR_WDS*%UXf9i<2`g4EuCUd?qD8Yi(tH0wyGO)R(8O*W$+qL6;U0Mlla7wFHvdL)vY zy@~y9k&Uh1L?gCR{??u@Ml&oTVtnAKn}C1~^fjEMP7k=Z+xpp~OzFQ3tMD}N*&#jg zR^5MfKPbuv<9~-SXOAtZ>{l00zm|dB+=*#T>-=T*-E(Xm;t})|>0h%nK9S(tjtTg= z%{WXqsP}`trd<7%zkYlzaToeyQ7p5MKqZs^nd9iJN@Oz*>wLK=S*p(CYZlcbX+QkP z9i~7r!h916?Racbo&WUOJAC4g#Bc}+$~!^e)L3jY^iUDe!(_Dn;SL?`k;kueL5+Z5 z8`x>5`#(r^h6;d2<7{Mvt7E2c5=rG;)OUgonkwCtMW*2|YV*edEHp(XeV6f;!y9~O zk_lC}xa{HoecNBS3_E{Cz8v z>AE!!AmIFt{b2FZkbo1}zPrdmI;@QpJ>Y%V50F@i%x3EGXx)6Cas} zxtPKpblSb4N3)z_PNz&#+E2JK zwW5)QDsMvbN9wG#d@Ycrv~y@WTpDh3%>9kpx$86~q=_VAMw8R@T=MBG732E!c=kgd zb=p7paRLK)@a%7hO6n4%`i+X)vQsKf>8h86T~98^vYt%kupj8@_}Ft^DB1^R5YjpQ zO=`K0s3T@J`tufJ4@}PbA!$kthGK%elQuFy(#Q4Wl^VvaSAkNEI^v?1~40 z)@QXZ+>N?gOy?q?>gshpk#&*d-;!UX$C6@Ux+(qE!xVSKqhd7k*XJ}LR&FfK28uCy%R#Hi2 zw4S+U=85e0`d!9gQIB^i#behUwc|lDt0f>VF$`q|lo8}30Y9XzKxVVgc^m)jW#pXh z@jIoi7R1XWQ?<*?Y~4C{J#K1i!ao{sZQi}Hzbg}lZ^3`P&3b9~pyTQA! z!_<4rFpSiC}g!l40_tp6w z#&g}}suvD(=X`2ku%0U+x{f!VN3YgS>OawVa3He{PqX$&mEte|m{k8TM7Mm)$FtMYfc4$P5}uGz|8snm zIVHJC56M5NwlhEM>k;=9`#smx-U#^1=u0H7;ag-l0pQdQ$K9!`?`9vM_?d+3q~kMP zVGlC}Wv~*aYon1%?DwBHvU24LKlz2}5=)0m01_nHK@EB);!)@mKG8?dy~vE%kH0oi zAOFz9QV#P+R&mO0AgeMkD+menay(L+LpjbR`Ku?X@z$H*miBXye1FIDEHJ~F;n%71^9ocr zqe7^p7<2P7vLj`|#hw7pY*n2)`1Um!7udQfsdN3$BkYMvO@DYf9|2N&g0&i5Hwux0 zJ-eMi{3x~wy>a^}9L=YD9v3d}H!^t56c)E#OVq~GCY9*MT!GV@TE9k(zp+CCsCQRd?X5!Mpuzk;{)uc?;UD}Ug z(9OAfx;8=2@m8a{C&k)i2bz=-DvjwfR$NHntbgjSfqLpDPy5|suZ-nWUbw3C(B0iq z|41(p(+#6Qw2@o@cB6T{vhkm;3_f7`MMkCQGC=Pi+}?(YI*}k>pf!hhh8%Zt0~n#Q7;o36Nb;X(goJhvMl@7MG0WaB{-iAwrbni>r zvfJ2x%k@pnjVR^6FY+B>$sk6&OLw|T`&LS+cpL zWOh6Lf%+DLmZ@z|pk{sXd1bNkB?a>`;KBDU``Gv}=79QNUE`5Xuo284Uk-j9FS0~M zSDpS5#!MJEunzeO0-QT_IZXw|olik4>6tbS$*gZNQrHFsBu&?(i(W6{>IIWco0hUd zJ?@4c{D<9H!FAN+2I88SW86X~&mFbPd7mB0B-GW1WB=}u^qgs%y791A&a*?rg0kWx zosaWPwr9ffop1PQya5ATUS(lwhtUz&odJ1AxdD9BkA0usALeq#xD}7R{9Q!`c2ZYG z2-81OPMEH{K3`X0jAl`|3X_POm)dOpzm8ZKn?cq^$e%nZSKM;-_VYFeFszK~l;tbf z-6cYP59y6`!MfJF8BQ&V*Yv|_)h}qhTI*bZt3=8()ms|e*kgH z=9SkEW&94=wcql?75%C+U0^5vcS6tHk@7SqwS9gh{pZte^BLFT*p3QM#~7sN;M!_p zpN=y)+&(MW*m3)?I&y`SkKXjc9t`NlTe`dWMW(uArH4vhOOa8#QH! z4kVd=Yf@Cr~t@da+~2%f8jNL%X9smx-+#MT4^nheh02cXt^kRbn& zq^qnzqyN0QbT09S+Z)|nywf0F9%RCmkIY&cSsZeY)Ng%)c0IeH8$8eFn?Y9FOB*D4 zpH7BzjOQTpM0MwD`aN@=D4Ewy&*FIP`!oYB>9XfhxHG8H ziY*MmV^{MrOrmVuD1j#&cZ05~*6V>`3^(Q{yr0XTExd6vJ#M{H{vi4UTHNoPl&9<7 zmH^hSJd9dyi|8!r(OttgUBD})GPJeBY|8sF?!E&gm_cd64VXn93G}qS6nhf zvkE<&B=fl@uvm+iu>*iDJdH3+AAXa_V}yUPz7k?96>LIRdX@Rjz?NZMdt4npa7Gwb z2u3kv!3^1w@4Qh+#{}@RTZT`+rNp{U|GUHzNN|7W>oD;Bw<|mj{q#T8*>o(g34NHg zMS%DX=k-SqvSu>2h*$eVEg%=M8vlq;A};TeVdG(Nr8kn{ADcsz`IirlMU1k z_3D2hw_aN4#@M^@>tF~KWve&$KR<0Efi9VjMki;3FQakEIK4(}(C8Co=|_EGq#fxi zW~91UJmyn~s-<=RK^bPskNT8H!=j%2gcRLmte12%Y{*1QtmC>Rn>N=Rsh$`}^!mgZ z&MLnW>0z2#V+<(ew|vhFe2LlNSmKX6L0V4$<}MnkNaqyn?KOUb{2g%`$+2||PKRzy zlo`kfR=NyWB6`y>;EZI{D~bDYTY$fLT~@3YqEEjgARD!gkUmCuP+GcqO#VGe;Uo8W z()Ir;lGBog9O;Xab`N~#)+bvyU#e!QLXh@!-9uElpqHPiJaZTmbN-)fB`0<&?B#9r zRh@G~gtv`y*Z&^Q(R)H%lc5-2Y-<}&>pUXY9VK%Dgh_{i?}zDaeZf>HPQ)ob1W>pp=V9xk+lRs771)ORXwzl`V za)0RZG4Bz@wNd6FROq3HhgGT{%+0F&I{kvZDnA{gQnQaz5ZQl-ZDW~Fl=o#mbAGU^#yF?;iR3$MN{B6LKe0a@5WJ*Nr#KFY=u4D!#H zZ{dO%Cf~ake6R8uV|E*OIJ1qAFnQ~7JU~ArNe>B}3Ek+b{~cBicz*d3ihC|ICzY^} zmUpQXvP1NoWoNr5W9x%m0c1ld<|BU56JgGJ@Zn{~C5*nnY_uR4?Vvy^P^I-AEr06r zL)~#CM2`W|atag3N_=CdF7%R29wNH>cHQ(f^S6?)Pn&c*D!-GZruJ9fF++u)(sWtR zyoVj-Av=lPF_R)3x56w-Fv zoHM9@CvtJ)q>r+BjZwm|LGaw1vfM^^4ZXs?H;huIEI$7vGq-r2g1gerG(t<`0BdbI z?8ZwbaAL((#@4rou(}ijXYSU&Pf(+TyeuBm3nXmRn=^Fvdfu}FqAg`4J^p%i!!9pG z7XhC~)hll_{+|sOGQ*|UK8}7vxQ1!@XDdy-2KZr2Wk@X(E4P-Mr-FF#xlGz8${dK% znNSo9VVAD5b@l5AJ(cQ*OD^3KVtrgg05kGO2Ge=+nK0$3t7GtYTxpql^6q9N4s50b zCI{@-p@+|d9E|O{)qXpgGY;b<^)C>ne^*REJRR@Nc)yq8YpVx*<5$}8YyKyB@wW6rAW47WHP^4A1vN;w ztnNIvmB~}iprtNdc|ecce?{dIYq_N_Htz_OtXTrlvM0l|y;FQ&R|dgFbqLcX8^FeE zZ`??vwq~WvVyYcD421d}rf}8HE?QkX?4p|rOx0mn?!==)F9c$ORvL!nKCp7RuF{IV zzz1^IsRXLHc}J^T2=nzU^wKE`z|EJ%8IWm2b%y=afI)hn-Y zLjE{*fVd|*-d?B&Sq7=UMWbO&`d41UdyO~$_Ea}M2U}FIBkPCY=#!r6Kh!nQu74PZ z-4LyRsV&b-Ovk(9yn_9qj=n=yc6zAuy6K3_#z-X@zB(B@4>BLeB_zu(X^NQ4mb_?rwrwhxlI^JGH*6{1s2 zIm=af@0cgg(4^;ZeBLrya+@7YsF4ibP5vk&K|s$my%}w?_mzAnhP^2-KhlNj&K3P< zk1l^jrsTHAZ=X(qrNFGRI27Vqo89G1(HVFk}TANuFSxNmN#`1&a!VbJ#$q z4G`1b~*FY1zSr)&okdT@S}7nF7j*;zNF66 z!3aL6JD%tkXZT8PxtRlH?PviAL^i!Rp>bWAmaqJQPqn%~D4$S9TEqGK)}T1-q|bT1 zTvQC~s8LygH<3(w!>LFfxV(#Bn2?FHWB`7KlJ-g_V~KGbphMROyzzyXJ@rhW_=~vz zMT?9bNBm5}aS71csC&W@%^9rdVxVq!e3Jrg%*3si`;SnVF!Z2EPi23!HJq;KW*QNs z21;e<$V1(^zZ6IYUjwG6Qn(k^ASy(rgY_B$P(>={SG399lg(Ub_4sMua8phgPZWyw zg1HT)Yme#56F7S+`2ppJK9D^`EL4vJ zR^sO#;L18y;#4TY%w!w%3{JB@iN7Ahlu*=L$v-ev1l0Qz>>!gj_eD^I5397mceJ@0 zN7dd3$o*of9x|;!UjPW;JL%`E%FFTMrIcJS#F-SRg{Cy=AjAB_o+%gnJX=}m1svZ4 zi$Mn>dZy>Ea=Vqc;m=dI=5+W!<>BgqC^zy%KcGo^(2OSon-gmyoc=fyje1VH{ z2sqbJjfJPBx$0K(6KT-ZbR{qqsX~@1lZ-PHi;PTO;-#yk#lo5o&XpRL5B$8-Xw_ zM*6pnu?s<--p_Up!2g%z&5jrC=mNPg!oBgBQP_|C#FMyv%t%SMT-@Hlz+w*m7(mT5_zhT>V-b%?8-QI;Nnb#uv4 z6K8CF58Tz6!>}`a!Nq$!Pd)~%>&hJf^yw2+Dfh;jQ`sP54yqNhS;s|67ePFnTq&R1~YVZoO*clu@O-8PI0}PDLAw>>l@0= zO8!KuP=LiTRq`JOp~)}}^V?7UWc9GEW_QHTk5Ubh#EK1vGn#=N5cZ9jff;lBQ!R1S zJA{5v4;+%UA~Ul+NS8*aJ28-6xWR$-0Ke)yj9_p6yhQxgYC8H6zwH;8w~Z;6nvZwl zz2)~g;L{*k*ZbMv#_fms!R42QmsQfm7pV6XBJdTP;tld|)t%{uNEMR#UKUERj=Iee zcxQ7Ym?YuLu-K0wQcE879{{9{ha4qkJH!*3$=)y0@l@s7dqiCHfVq^;lQLxuplZIR zk{sdiMm`4F7;s6tLDvTBt~cs2n}l1Cg4**c?vT_P#4okCXk1v4ZnpvfhGNfjyVPL{ zXKy@bjs>i_B|5w6gHhQb<=byll%xtyCPSt+1OD}BRz~dr1qmSd#c(OraMCo~U zIsR>s(i=A~$Xby83a+DBXefRkCK~9qj=lXSGf#nOWguLz!ML7CLbbs^zh%Rk<6e;O zk@U!X<)(Y2F6#>Slx1dQxcfm1<94nEKJR-V-%Rw|pOZU&LnBb#u7?*HbAl z;NLDG0N@o4g2167B`!}|+>MUD1n8fek zr5M;z?t06WI8;V2?~#$0XFZIgLB419%lb|OHUzDmu0==zt)=UOzP2mN<2;=D|=fs$l`QzYncO)p<_ zLZtitgJ_WoKMR1trWb|$L$;4(W!rIO`TX%|Dc^a9TUtdY);&4;FY0_~EY{;2T8%4O z%r#jaI)jLp2iZW$^{s?@sQgb4GjHhTB)o#GolZZlH@%sVYtq?6sDJqmLxwMIN6cH! z;b%DcLH_nfru>Sd*TM@-v}%)W!aI6%IC2d4HX^b{>9yoJr^3Lvdeg4}{M2!bxWC0# zcc1oSw-F>^keigB)LrnsRZcSW%7^fvm(_7=y5U_yaEZyTGsw~j3^zx3eL(-9PbcM$ z2PN`jh;38q^Y`l06>1HVrP_~VF18QOVwfx%STFt~N6;6><&}c9#!m|sN=TiFZzoDZ zS;0ba4rcH9LB05fNr#JiHeS9az5pUYtNq;KU@~(s3U{T)CNmO%A8-;Q!^9F~iH78& zNR*)cw32W3{B?z_W_X(M3)`BXzkTIKFJ2BhkgQ8b<2Ow?!j{?N%dBwsEvngt73OWR)Si z82-5<=v}Cj5s@zo+jNEQrhTDW7NyUPBQaAlQ3*17@S$@l^jb1xNngn=?2bj^)?7z! zxfb9{VvPFD5P(^tJ6Df)lfM*V44*(N*`-EpJ6e>d$M)$-7M8I-lyApke|@9aPoL|2 z6gB;=Jmck;$JHBAOqb4*i*lq_J|h8PGQHG7WrAU5KlhiJs95WaN0oIhCU>Zi}Kf z!)%JBAAi&OH9yZ>)HizriBb@cBz1{w1-V01x%CyoTu#l6Zt~U}S(Jn0Y<>LBYZZY#nGLE=^KyrRzpRilw^e^qbr58bVz55{>dQ)Y|6JCf;cTZs5_&dx&F&JQc z+gV;VY$xH#zn}y&S{;sc+r#h?cqx?Z7#(!FJd&+HQ`q6bV|zalezC^sRQ#SmXoJli z5z^r1%)ZtcS&TfdS6-<9EKASu1wTwpswZ%)m08MwT~y+=H`4MD6$fgRL9Z^qEs2p| zxV)Y3?&@yGtp@A)m}@q}3Pn<3ZF=g%W85MF!K_GZ;kw-*4FAoN7yg>=2nIM4LE}&i`31hV*caUfZF+om8Pa^<;rt7ye~4pF0T!hHG-ef8kUB&i3PVtE;7h83lc1(GA^9%T(XAtD#A5ju7g z1{9jV-M9Cx48QPT{Dt(oF=iW^@JGcQzKnQs0u+hwxN`~&vEU2M1M|qjQY^_gU-}2z z7(emuvROZ}XmcKGS_2YXHeLJ(67qZZ8?H$Irnck5)_C(=J5K0j89i$&!ywK)9_ugQ zVln}RaeBw0FC%CjkiUAuTYBF5nR>n`BLy+?$Dge^8?fs`dW=jJ%I3%kHI?dtt;R!( zL%TSQR+!(@dSoBAg9V)cN@#Ig|7`4k2e~VzG49ZVoWzg{e|9QoNAMv-zDb=wi)eLU z2v3iz>5%%5TnfiF3kkghZ5crnp*MYYOIb1Q9F)w^I=ob~{MCQMP0LxMzwZKLPGL%} z$!NMBi=lH%d7e)@c?Q>xKVD`dA0NJ&0$_NaBAYUAQLNIu^z^GzV9kVLzMvi6xXLRT zl*jO1hCk`f$2f6o6k$mJxr0B7+kWNFS^1KtWb>gWd(6;;0)iN;QULz^E6S2&oDHH6 zq%Vu57_YBM&$)0unJjfB5~bG)uc|Yb(e6n!VbhXz08~IWCT!<**aCPiYX`3D2elA$ zsbPV9I``G08*I0d^~r(H`jsc^7ooKOF|m|YiM`K_i{Nl37Dr((>tHx{NVlIS_xOVK6hKEWjv=1%V5Z$=Vkk$qeTOI|)=>CEblXs_KS?60ypeo^LZ z0*E4?rUAve@+=Ks-SH?`I%4o0*0)$Jkib=~o5A<;HgopxOt89^aSY+&?!O~aIP}l2 z!e5_JyYyPFoqRp(&YgV5ZZ8wmn2GWj>6GT=JEwz`^KB3QWr^`@Drf7>AiRA?6#v}1 z4_{Ly^JvmPx?oY32HpCGxi*z=N>k7cBFy0zboIluFzSKHSE~Suf%p0g?(m$9Q&qtL zok(hqDQ(j~kIPJf3bePvrgU1ysx8vw8Wm-We8!^g0}=Ouh7DBOGY?*dy;EMB=UhdA z^VdeIK;%shdg#CRCH(boS+;)oLH=A)cQcL>uMO!DnKWe?*1$JFFQ+OR9e(1IsdRqI zj<1R2P}mouW2HZzi4Vh4F)EaJqx7TCC*(6z`SgcfQFVogov@&7Zn8Sbk;`w_ON*9S-DSJHS^xS9&BZheYlv zSuJI71fhlxH?r{df>dzhbByrbEp^G}GmQnH%^hJbht#ET)OCFyLtU}hHyPAL0Cz`y zdFP6#X~aVP0*> z_dcEAjK5hu;}sYu=5hrO+XSAgQf?0VuNBiJ;R|(zKU36nTV5eG2~;G_kGt?&GA(;L%Lb$Rb}i6faXoq)i!IJ^y{Fk+YkMvvmX4O`Y#mmr=U zW-!wf$^gtANp4IKm@2v?ZC7vLxKNdrB0X~>TAe2vX*}tJc~D#So%M_>}3e{HZ>iIK9PAdV>Hn%=SN3DD4i0dY8BD z2x5Sr2NY}zIC0$FIE89#>gI6UOq{=T+uL7s0?^`3XhDanILi;C9Stu*4m4Yu@!}Vj zBx|N-6^HSXaWd`x;HI*)A-W;Ih{=X$Ci?-PmzurC zLo2M#bfn(obgOi?o=n6J{n%x=NSzx`_d|bo#kE=?{bs}bpvsHl>U%#G;=1--D%L|Z z9fNptJ^1qK^Vk!vd|GeX=;k!}LU4nr0WO%M9c!PP6BPna5@SH|U}2-M`CYz&K-O+@ zqbXLV-65p8L70#p8V>3R+MAi`Px|h7vdmtUxs*f3e-RO_&~TNfjmH{~LZ%nNvo6~5xLprH#ImtXuhV-z0bC&lz(xM# zq3H{zpkxj`@5i)2JD`Ryf`u{pV%8 zDRh<*X8v+f$8I26YuTp{7*WzvqJI_2bdcJ1p9EHzm?Pc;vCboVr1ilg$~5Rs=pn)M z75Q@4Kc2)pIhkr&byELuvXwC;^yxM!v(mGe31C9YUFisyMg}5)S0>PwaboB8d+Jrb zcd7h*!dGy3i+JpEXAE*1;91x2d2cFGsfq!beYlx>BnDA83N;~Y# zrtT_C=U0D{Oc;}z9ah|C1$5dvpHm@>d*M%AWObJCqI0@pr(p&?jUn>??J9gPZxQM9 z@=mfD^We#z$@g@FVj(6x3c=;Svz%gQi4ns~&U zp|&%bk9n_6AXis@lA${;5Za-z-g-XQq6AHn8Rr+WQ4IB;EUQZLlnV^69ldRvj&~q_NKl^v} z@UHjB-cIgfLdLh1629WQac`U8ei*}gX|gQZm%!)2zWhCL|61`YbY|%sUa=EHF+!GO zfU~LB727F0Ag^r2x%RUi6|UwtCg>P&x%YQGd4j6xsr}p?i z@jR?2@%>YKPI-EqJgiRdK8XpK&hrB=Ovv(M)T`I5f31!Wks%~$l{3E`dR%wDzISYv-bOOe!$zt_aB~t6b<~78MSYC>TeIR_M;`2xQGF;H(|i6{Px1Fus{P72&XZI zJ*>scI3seO2@koEVfD-dxhT`$`Oh|XIwVplX@ON5qAz$fjTz#@)%x5$i-D%qa#b0D*R>}c?dj{=y3Fw583tm|tM@Pd=Ybemq>!KP z7yJ0yZf_+&ANzUXjpj{t>%wTX*vMFc38QuMHa@wMwb<&u?(>%BLwfloX#Te}>wcOB ziI7@zysRxKwNe51!gO~CQzeYIi5kNbEq#=BZ^WiuzV*m}$X^06O+V8$H!#OM(5UnT zCuB1j>o@;N)GAdFkcYT|U-Un^8Hwp|e=TrqF3Q&d8EjxihnbFK5J{Z(V`un~ZHDeo z&JMgsHglh5wh6(=R}cZB-R`YFCm}H-%1f`^JZFz{9D==SgfW_C2{;mV<|xWBzzsu* zHrndGSZ-+Mo{F<~KdgVfH7)2rJ+N0gn76#wh)dQ(PJG6aiB)@UoFpKDSERqbUpoEX z+upsPJkIc_>u_K{;B1Rno-lT28}Htxe-@CzMm*PmrG7xEU*^6zI$Df*e&MgLh)>6aAnUCuuh?AkO8GSUItL z#5G@?q#Kjg6uq&}pg^Kf25uk3y*@geh#g=A4SBOaD9fm;9*8wl9%~d#RJ}TUP!l}> zQ2586m)Ij^Mkn9H$uTB9RJuFUiqV4l8+m|QTO^B)FeXa}yUSu4xTS~ZSkwP@oZNc` zvmgxQe&T-!ta3?@LlYNykxS7VY!~M>J1+W~3=8uUexv!{u!bP4O3dCj_=$f<)ShT? z;Dp6qfb8JABhGr(g9z6je8nc>SDpmr_vEk4?$K=wo+m&j6C1VztNf4pw)wq9nR*m< z{tu>kD7q9$CTr4w^RKr)a{)5L)B2@u*Q~0oQ>WvQ|9U<17I)`%^2&XJbV?P~k#{l{ zCDlbP1O)h)UkFLPV>T7j=G2JKcP!kM<+)?}-&Q?;p2*-^=>;UMktpF_zLd#^s}rV7 zk3Ze%i}xCnPBXd3gu)MJw>(oxFLoW5!Bb>nZV`EuCuAKCQ0#C^4=&?3{#cAdg5 zPbK1pF}5q@@t`orm`hKzwbxVDu18Qb;)^S3ekASr*!13PJ$K0*JY8i<+rl$1UF}Ih zc~hE)o;hGA%Zk;LJi-s8e898=lTz;WfQ`W%tPQ8iL#P?wt%K;$l8vGg)Y_=Ss*~fA-oEpSTBwk9Nl{ky3ONf|5R&@TCSc z+5`=a!cv~n(BMPrf$vR!xMnkv4<`x3fhBDyRS%A_t58O^-RAe+NUw2*H*8cFgMvR? zaJd8HriZsN(9H?LeO1pfUBg>1f}X!>o)##VJyy$2>c`p4&4OfI>1enDjTYaV(c<*~^tJIAEah-1XB&o3p1AsmOgu$k0I>Ox zZRky>RDQhg2_JxES=Y6v*B^9LYmS%_uPXx?l|X~hfVAJiVF1+J5!yj%^N}`_T4L@7rkH9YJ(R4%n7o1O>|~-0 zSRA3Nbk<)Sp)u1nRS~u1tl)(D&%kfCI0Wx8e9rKzWA-5?vt3~&fT$Z*G7k3>^x`ZF zXLPUHX{ResBEsER`EG*xHK~9!o2Oj8=ZyKwlxZT1ym7-JSMF?<#(~iQsV=x!s-6Uy zj$c|!43(DhOxk1R`wANFI|iOy#T#4hp(O z0857K4IM}~VPUuOZ%j7OohNX=4^fjopW_7q$~QtUT)S@h$Sg0KN<&2}uNAr!q={>f7DmJ=F%ivRL{=y%!vta5&X zL-{5B^svGPCxJdzUtJ|M-3z#pF1H6fsfR4s^ZXSDc~sr_I8a(HfLy>ZR0i&#d&Trb z>?+0l_3CkdILfW;=`PxKDL`#`C{+KPkJ@S!6PtHo?igVcOX_W2&wpeAKnTXtC0M>4 z!w4b`?eh5;=)EN$3z+-qSU!W~&Zv72^#7A*6qoUpVBaC#0-~8N;37VeAe-VZ>k0cm z9&kkVd-P~AvEm`w#6V4QQK-`|2(%C>jRDg8uzkLx`$w@aS@o5{+P>*&ZxCvdyRGT!Dr|rB0zM<>{&Fog`Hrr*$ zj?IAt_*j`r3y^!yUCdL=R{3X76@r;W?mOmk6~R&RJ5#q@#@dzcn*+cT?3xgbF-0G33i%et&c5LSIc_R!c9+A3yU6!@o zz^JwAhKvANbTNtIirnM!Gxjk_(|P>`gY!{O!189wzw0PVl?U)dK6}egM*6HP562i; z4~PonwDf2DEksCt1*DH~;p|VbA-cctJlqby^H1Ov2np#8IGKZAxo6}pqZ7IOuS0fzOHrD1D>9S{T`V+ z>Uz#pbNUGR>~bl)7@|UxuEECC0EbO~-AAmsvQIO(*6Y!21}Z?2OsU<7?AoQB_~>SO z5-Tsf*L`^eEll>)%Q+bo0MZLMhj^_#aHEE0S~K*1!Ksw&&v(ywj|5Bu;CCFSLv>e} zG+@E1af;I^K!9dDWS*GQ#gVFockglaOvQv< zIZGZCVr)3(lNmh?CeZCG?0ren8=v^`!6TYfO>IJybO${EI#rV)XIS19vNO`U!+je+ zTzOGBp2B>z7vHio_CDn-d*3k@00V=)`gQ2Y7DppB=_m)>co>zo)O8*K@f-uGlyWOh zfyVlMH*l4RSHsqiS7i(o^gcc!(l%MWdEk_x`|lB)>aj!V_=Y77upyo;%sS>kVTsZc z(JX{^I)rgc(u+QH`eX~Pr(QRP9|d{*;lnbJicT`N_e|*Y<$BzfNCcvhipNR-Tp3WH z&Gf6Xp3b=rzxk-FpHo9n6)I~MhF6J+&5%`%U79Vb#qXOz;i=>^YQJ%X1?*J1P3ror8@llX?um$+ z$|gOR#efF&;V8OJm$fCAb=}1A0MC3=WS2e!J)n?=Q8M3A(%H-i@HRwFA7-SJ>oZ5z z59bgN{6nvwo}`$l zk(i0Y0U|^&g-?G;4@Yx{P1zo^E`eC+^8`oe?@TZt())S==e`qr^@-a;aetXiCLmf` zwiwpDTMPYInE*K+pf&XQe_3@L>U$|LGJ6Oi{}5lchk^D)JNrMJcrIAXL z<_yh~G-xhOYn}Tadw=J<-t~RUL*4gvo#!5oee5X+;k|es#(+q!ZKvrJ!yyk+FGU;+ zeqKZ~SMdekWs$O)Z@W**GAFX8)nh?59hP4<1pjK_+~v$&_!jJapBi}bnq5GF6x$9T z^ya6SsR+o6KghJr#tb9l&ME2KA?@7EUn?fcC8);7FnLW5zN#zbi0Nd)F0_35cs}|h zqdr-(tQJUDHQacTptH)Dyg!HEWy?}DzduOeSDlO-tJNrWNh&k%%hq`}lXU*4zPOx| zb?A(%g(g+KgLt82zxwj@E{|8ExgfUv?IwQ%)xz#45E^@gR<_B=)tsm`h~_8z-yk%$u?GWJBD3bTI6 zYjD28-cw zcwr>jyhKAg09YGX2nYV$$TbeXg9zxem!@2cqZE{}+Tk?X{Fej$sOufUf4@dgCz;??pXkc;4151LST&I z^!hb=9R**Gl=%Kf_MhQS=P0e}bQY^v$eL+ra6(2NnxyiXj`_QvLvn1pTV2wCr|l~KY>~fcXaDp(bj!~K zX)k2-z+aoIr*I9rP=LA#wP3Bj`a_YaZ?aXIt?CKk|KX4}_?qvSRKP`N=y?}e3@kH( zQYmnE$D+0|-R8&sw^n3^G7IG2eH_AS8ZPOT?nhnUi+VoBHZcE&6w(OGr1Xxl#l5Oo zFKZkFj|^~5^S(r3#7XsPs1V_6MeR814oq8v8pPu_az;P(rQ#GnV#$~9{~wi4nkTbgchDJ*1!#I1u-D*<$T zJ;*k;0>HCFuIMz$dhm(KJXJ#Oxsp+?TTg{j$+{U$Hv)VgX+i_~DB8&NLt)5j=PLathdl48a39cH6&TBQMj-X%5V-J5QX9S z(dY3l)p(0jjy<8i-@&NW8n-f1@FkN-5jwO6nYnV|KWTVm#NLw5qxjCjl45~kqszH^C*Joq0lKs!m295*UgB@O$Ry?R z@xu;N()sJhjwk85#RX})r@G8!nHacFyCz|V9I`)#FYxlWgBw~{b*UfIAdShf*8?NA!CqR=`MgYyl{J^|4*qY8uOSskP zV{Rs|JysD=lMfARwz{jWC|4-2SB#K%{Ab)Ig1r-9}d#s3c0e(do@!yq%D`B7?w)0 zYA9~`uMzKlKS%YaXqKY!z0|~^-GfI=Y*&%*<~ZtV9Ta!?b0HN4f^mC|Ob^IB+aL&w z=W!NYr{oK3G&ES93d=ie4@!EqFnQOoH2~dZbj>-lRrdrSIK!aB~x5DvlhJ ze(5?$FWw~|*+>k4P_0zIZxOguBof`K-e@$s@t!5#>Y=@iUC1c~rfyhVv_qN4@9AE) zRIJahh)m<{TmEX*u^hWW7(T->Q*nf^{zWh9!jY#O?68|DLB>6Dj>+ebhM9v5Jyv7! znb3}^zfiF_5?lYBL&7zF`FN435X9D;NIgqgqJ5nt~KD%~Z|q=Xk%ObLz+Y zqcQJIgyj7gKN4?JS^U5_nu+CT6=0nRjn5id6$Qu@S`YG&)1TUstv7A*`BN5T=7l{F7(& zrNea0{#I+3r0x-%>Kt8-(rcTp*-n~FcDc&vDV{vrUR8M@MU`CIV5oVdA0y+@c4i^G zPH^@!5+nJKhLn7yhId=La!QS5c@tASX<2|Uc50pZgJt-oem~fM-H3Ulnr=Gr z22DWg&v5?32dK?9Y<9rIP+?X~Fr_EDF^TJi^s6Bw$DfyBaw(6!Z8+|yA`iRN3!;PV z+m_a|^a~KIE`|j#Oq6(j+B!#l4NDKF&3D2w%mSXLUUOgloJEuT%zlAC&6K*6GH0PR zu)8?$Xgq!du+|)eqv*MOL(_M(`$&HhH4Z~Tcw9#Q7b3Dh?-?Z420zLOWAaElSlQw> zzFvNS<6zXJB|*>(uwW$X!KX+!JVyi;p#PSPJbH=;+Vjqi>h&TXFX9*m;Z1RW|P! z=UuNtImxOI2WzmLd#x~?GD zyrKS?yT=K+qqURLP6bIC+Jr@HP#X?zY9$^6^PHA3b2o&Lps}gogbdx&YNHEt>XF+Y zR{E+s%YtOKOnr+7G}fl-t9c3_cyQb)Bu%dX$EDOdOZ5pq%x|z_>-=Z?)6f_Xo`A%D zS)GCBth|R!?#CiF=l}ohRK-KyRj1x;u%DBs8UKF>XI0I1<(OCt)BTr`?jC^T7Nba- zruRO3V0F&k6o4;i zp=`(=*^Q+UDUt!ml$f>sC#$>4U*QM3cs2G@0FW5RY$WAmtCpu0w~R-K*G#G{$5UYh z`-2x3DdTlCQ9G_0Xo|x#!OpRRJVn09=1wfOy+=IU@w)B`+8K(hk=R;u$7Hq^opO`k zUM81o#c1(}UP9{Ws>U!P(n4ypPnuFVad(m(9XYt5JYfww} z{URghr2o}tl;#=?!5Pi-eyCB#urR;ZJ#t?h8mrdSpOW%NPcTO8^HdebDd^xn$GD`* zLN!QH3cc;`_NcDBGsfYV(i^xN6dy4oji=^{X>9=4UTI|$5wFRdad@JIu%pSF~_Jd|O@ zFo3=d((WgJb(6;H3%l?=m73k(<_*5{f6cO4V0g*K4ZonP5U8oy#i}+^ZD2XKQMZ4m ztcB^Md_Sg;U|ss5wrlOKU6<`_iVON=F^LJky6vXKON;MT!gW9vX80^!Sg7^YC)lTY zZ2wPqT6Ry!MuvRJcy9P5$+#M^EajtJEj}^l6@9`hLl8>;J{N2NlfP_L*Q+DXj6_^} zRgs!@(Hc#0%mr-TlHWs&<|mxgKVS8`1UnZdBk9R;cBXw;rX{$DY5?FehM37ixP@Q7 zoC;-=)ewf`2u-_juU!4ud;N@RGxnR=u^1yL9-Zw*q~{=qd7XSL0aM^Fm(p`S;)BdK ze2ReQ8YF^bHKNQ$8SbB4?%vpW(RhC2(rcj~;iqxx5Rb}YP&-~6CSn+LPpz4q$<6+R zst=dtU}liD&;-Cf$wVFyRo{SyV zAs_ILRsm}neT{XqW+>BzSnNAqu`c=+D&2-%#$tB@4rcZRXa;&!SX4XXa{8We&{lo6 zS7YyS&W+M~fy-|{NE#Pk+<)MlS0N?RoBVk~{k;(mEXPXzlZgae!KU%?F<7%vW}WO! z7+X3bcRloDFUJ==(Ac}H;l^2$3u4J!CRN8^)48kBfj*a^=EtPzWwAv%V18A#_`N-- zMhuKZ-NzEHVelDGar!;FuLlqE4_{fRft)k=sz=&mY~YaC0X zXUbOf8Ydl6skh3GVC2o9TDt{0nUqoP!%*Ko`N<+j+Kc^Q2;cEg2=1oc78tK)o)$3u zvk?2yi_9VXZPemW@*d>@S4Zc>?{T4G}ifu|@*@Q~&s z#7vP1xkZ=bjqF_JRRz39>7!(J#~xRi=jW=a?0KFEMC&uIr@f6p`vx((!-PejuSu)f zCEO-MCZC>Da}oToNroH_R{;ECN-|%3t+%X<=Cu|jE!2|LE#0$xi``||JFId?L9BF8NV3a=}(IfNec9IVKCG{lC~FCRw#g)MIwn z@azndN>=shQIMHxJ^X=Uh2VxPP-0k{WzASu;+7q3H@veGa8hB!rl^%C6v92kP_7^h ztek*Cg(xA*%Dj!!j5*@#*jGWc2VsdnR#k6R&%u`(FH!T`7b4>@s_6XYZB{Kw9~1M5 zk- zlpQtHREa<$E?boWgX-?kg?;j<5t~oV9$eZZAan-bxentG4J92m@731rD<*oPr$X#y zTer7R-!-c!?;7_W>`@@h1O)JyRJ^hBBCf1sb(k7EapnqK$fjyaAKghoXlw=EN z(eeNaVxMa8g#F-L@$6G~Rm*Mp?r7Srx@<@kpnaqcdoIi1YaB_f8OB`JoOt!`1SDEM z>M1gkdWy7(pGllToUF$l!I|4h9Ygo_U(*(b-eT304eV$)o?2R~)o5AYZNmGko>!yA z|D7|Qgh+p&3?9HcvY}{)`K??IWS6uiRx)(`C!r_ul{O!kA*WwepC9aDA6q>Z}{rD4eG`)0#L_Rd9;&l zi4kpXhsst{=!yI2(b>n>7(&Oaee=E2a+$p? z1Nie^rp&xG4&I1n{i~Wlo5o-Bd5b|i_EB2zawrpbJ)TU#`vA7I#+h~kAkMXu|Bu-4 zU62?4bZRl(g#&$v!gpUoO{!pZC^y3^7461C93|!fyQ&ojlectcOQnNadScwAyF(U& z(Xl05*xS5-4~&Y1D&ShyEu}Y`i1pL;XKAM_4;kssNa1f@yp6yq?x>xri~>A-LF!Za z^#M4ZpX75#m{O^lm^OgGrR%tmQW&*?xHewonHtQAG0B0{s(NH_g)9sBFBL%0f*vt& z(iDZh|AE`+=eN4pdQmJxC2IMQ$y~Lt4eq~eJ=(NcmfY2M=Jscxwr#hun2Xw$F00-X zYMClmcfhjr?MvXR7vSSOrQ)&-mj@GdodEAN^JaF_YYt)ClHp*c7VZ-8FFrT!Mf>FQ z)Pon98Qk;&YxpB06{_qB@E*nO6H;c(>2}C0k#XVxMoVUUe$++b3|)b3mrV|_f}2cJ zjr+{Y&H(0%pO)#q*-mO;x^C`Q?Cbl>P?&Z*a|~%Wv3zfSIo~`OjDOyLOm$o)ni~hC zg;H*KR;~Manz*Z}oa;{#2?s_4)#pGQ?oa;wneX~~g>EX%AP>uza*sEvDayD6{#Yph z(zHPq$*+%kdCs?ITCFzZ(i@R`1YxF-IR)ctx7RR^A?x%HJ zJ`M05&pG~%EXe(Q`F@iCq>_rPA-^Nrhy)%I@0?5Na# zK=pBV_v&-AisyveZ8;KnHc& zXF!Hq!jYR%&;@yLrCC0My$0y^%7 z9D@4ck7{g^Z0Yf2>2?a#MyT{l&11E}un&SN2Ztkgu^MI}#^9#@Tw|D~k&t&2o%<3m zgRp(om|sM%i<V z^T3-2j-Or ziZR}ng}i?)6F@M=kaDYYq>ZzTq>^13SQtH5`nsC54cb+3&~TCND`Bd8=LcQbu$nD@ zu71>snQry#z87GxoA}6HYSMPSIdq78?&4FPohESZ`Be%Yp#iY+6*j(2Z%nFIqc$`S zuAIY9(jO@8dlfx9l!0L8+okGGJl_US&PCy&Tk@R zw~p3&uB1QYgTC>sc%7vp)x!mUJ!YiKY{2yl+xJ)uE9vy$-`a7qSBO4U!zFkz0BB>P zTLflEFH(nR<`r6HeBW`S@Q`om9o?K^+fe(q7FWSzhKcQ)Z zRk>1e&kP7jcLrc$NcG*}x&QGP?flp{vuLpu#cr}l*h=M2t;|1=-s@6lNza&!&^>NS z=a87s6G_-foH1v_U&wpSi`RfB_Q;=*GFdoBYPeoHpWVl`{P;+K-0@x)8;yGD==0|_ zBBI{{>1lYQp51#?#YapzpFZR70;E{vKklg^M)S)*mWZnfN40K^GOZjY#>4bZr-NsQ zbf`V+t!h3=jRmzbpYb!QJI)hhc!)X1YRr#Z!ecdija#F->VU~N6-UGrUP2hUguWhA zZRRoQMvnJ^G-*$FH_4ZWh@Y-OuhK@lowu33c@UPzaK}(?o#cvH_DuNaqE;hZU;~j>ZtztLh20rN<&&Y zcIiqv)J#->HX%d05n}n#BdwgxvPc`N>dm=jyiJoH_L!+D_CvD(KPh~i#f^V_L9A@lAgPlSV`qVxw&jUzHkBASpZ~B z@bAOY@euFu%85}gs?zk-c=*XO`~TtpZ}zAD033H%`<*fPs-IYdd8zX;u%y44F8q>5 z1X!%(ykSut!^Myq*!|k{xMajL(ffeZo^>N$caq=E`z-Pu$TrDzmTKlxzzK$bY2h^; z>%wo!_L~O|jIS8g;2hk27c0dI2{Z*gtf63<0rNWh6>`IiaLtgK-H~t$^8FKq;^T$b zfjK7ff+AY2&Z`bpOE3b9q`p2=^3^d5Hln=?(NlhXABh7!U^ zODbo@Ef4BQ*vNkDRhnd(W872|GEDZ!F37NxO(uVNa>-lx>jvTzqIqujYw4bSQWd7X z>t?L{HM3+yG<`)IGG!YU{0s$Ct5JB81+*6@RHwIdcD_^NR}9+|c)~`k!WLS4RW0mQ zpHF{ufJmvAx{JG=(u^cqHGIYAuEKLt0&IOehlWUeVYW6JGyC3xpD75F&w-anSm2gM zb_DD~g$IdIb6Gji%+z)RA>8&pY6lrKMP%G919|@s(S8{Jy_d;Hh_DQS5IX(bJftqr z;7R^55zF<(LDQaRJFy^tN`eTTRCg?TNnbNT9rv*z6K~Bz@D$l<2J!$)$P92wN7=9Q zir}VumkA@mjSQgO6Id*6;wu=lIz9KSVee>-deFJ8-4XBg(D_^kU(#_wZjF% zj-GHQ%cE+8)weZvR2!9jK$kW{XfjdXvzWs~pKVC)Ip&Exmx_Z}1h_@xM3n1UbYc#P z{`fqQ)a?WAkkGX5oc-&gJ4|*N^7rvUM-WwDnT*~%$B%+m+Ya3;a#X*L;~JuJGtZtw zrF_n?%t60Ztx@XFNPR$S6L_Z{TagNZ)I)`|p2S&y{He^pGrU3SHQBf)hRzVzkjRv; z#}S`SomBt9o%LK*e~CRC9n5icbowM1pUCpRkw0z$221&JQOxPwqsG~HFHypuZ`@%@ zTXOYEmwNJ=U1-L}aoEZ(Z5o=vZ*7P0MB_4T@K`gq80M1>RP}epd@{XPv1Z1&nkxP8 z{ZyT+N&U6^1iiQsO4zLGBB+^AYX>7q6MF(gZMnsNytd(FLrSMlwHnD6aCW5MLXwRu z6gXR|#&ZN0eCDccsA@i;X@j2*79_iv(hP5#!97(=%Qrq(ZLI7SaMv{-ei`#7^iP(ixaPhBR0O1DN7965^0G}b78nthtE#v zw_A@K%x<2fFu1lX>fl>#+CTFKUHj3peoT~}ztM7<3!X=Pc0VDEG!NBCt_I;}&>R}l zMd|XyjpP2PJ%UIHu2fm6mQZg3@3M<2ANON4pW`j-%+xYw(@>M~n1``<0EBKs`cu_H zVNy0Xmkvgm$|}%9SUfZLm1xm(BJ+g&E^u)S0*ZpIv6GIA52e10_$c5q4URiBT>EOd zFX3=D3ab`AVUrn!%@S4FNW%EfsH%GzMrrDu-NFVzW^Xba9bIAC6JWEB%!+({4hUEv zZXR*V#$f(-H(7uTn32S0MRyb5T>0prwxQ+5E^2t6>$1{uyccIPoAp-xV8Fw~9rf4i zzAj^fSEU+^?XrR#V?mh2NjcqJKN*imLJw8B{R;3nPXs$UZpotWG)54Vs4P*&OtnY@+n$MBYR)pP#@Q@l)X&#F=9V%v{~ZK*wi zjuLdiX#%p@#>0FoQk@`==I8L*tn}=xutfemTB7qx2j|I!MZBjdVhA0&dEt)9X0wnEK+OHr>>~ zE#y;Z&s8<)HqW*5pw8>lU0$R0DhQJk+whm~1sVKI6i0r%kfA$awzLUIp9{fIVWdNG znv7bRIIKQ@n$O?`TX8+IT=apA7&*ncWYQe7o)Q zpr0(M`eVEiwfHTC!38Mx@JUF-Ve51E0zNV&guMQ~AP;;9Nq0*?z*fbjLf@s3=e zjI;l)EZ7w3N32a!pJQW!tW24zO6H%aKUer^sj^TNK{OOX`GwJClE>xum>@JqIPn(# z|F=lvRM17$5TGdIr&g`bv%uY&j1W+0nz7?a?%o=KOuMOxz*k;)>K~sD^W`MR@M8P% zkzM<+HWXztf``{ymqnlR2z_D+;SZg>-Nq-y_LzSU)n9Xy`ss+fZ+2w3yFrSW72UCF zJX044)gD8NM=(hk7YB&OjZ`=mv-?(H%Kc^rF*sLtIt&b!HrTKHPUug^?B)$ z)7>C)CSER_GTz5S+|Yo}p)Bfnpf>#j5fasx_-gqV&CONF$|IuP$)ck&`rw9fDpdW5 ze<94PLI-Xi1(0;6CL;$nv5{wXV^sctW`9O-qjbF*S;y3*V?5yjT={dZ<@+OPV2eW& zvQ2pN=rJLV8J7CDS}GMiSW^$99ae+V^5!uaX$_Ia|FgMjn9K5h8jT_xO#9+O8C>!W zm6Gp~@>#80W}!&-$O5YkZn_aYVI!%bBJ`N>0Wh>X`W}#O5AAp6z!-n`JXP~ycwqT{ zIiLD|Gm|+sr|BiX^bf<SYkdSY@$p?>2ux%1P*8K#{p7#|6O2#28BKptZ zoI2>}mJGan3sG1qX{iON-770Q>`X|KAD(1Am*mPLwMjHNg#o6Xk?)e<`|qCeb~2sJ zvwS42>UV+hp`ZY64MR^@TkUU!g04mHG^NPDM@fk1d?Mw$_36_1MEY^+3t4*rTFW~l zY1*w1$WpAH?V%V2Is0z@iHM*g;+>CLNJE6wa6IQzbMim%2?k^Mahz?^jFBOMX=cg#` z-Dj-Sk6`R(E!Mb$uc!xvwy0Xn`&3ZQUXPm*R0<(^ziez%q{+z4rVWcsRug_97}G?- z>^-iam z`W%u4sW4&frJAoWF@SR235p#G4)PJID*dWVy@U-Sj!`woIq@0Q`oSnm-I=A~PrPwF zJ<3Zq3X9pj{Uy5O$p!Tp(6WRD8`vwfnIQ>hsU}N92%4c2A#M_F9iiE%XS_iiM9V5R zV9*jX)^f`o7lv_b!?0ANH;WGu>n`aHl=;_3Os|hhBWu4L&`3_NBflg=P5O|Ysd2r7 zhAq3BjyRi`6r;AY4a+d9&|ocAp!@iO9qt?p0eagCRUZ1>t@ zf5r@S`foAEK1y?t4pI8fyyo6lQOK*b%<;Z+@j5EohD4~n5*EcYslse0a6aYPMilMV zz+~<$*YMAFd?X#JGDSx{^LKIDQ+|6*w@sH+%Tri&I$=w8WbW?q3df;hAhnrA|FWyruoS-U7->|bCxm|+48`BO%0#q1pX z;@IuLv&PLk$WVv;^$%y!WW!rv(*stZ-Sarb<%?q*@f?{ zKMY7~k1$x`^&Lz(ZYe8#4}Ob0)(6#knhF@;q%6EayMTQGX`Db$A$PqXzap9KuGp|G zwaNZbx1)|22M*Xc6dZFjy}B)Hr?}BND^1VOu+xl81o2l1Z{DvuJSbw<9n2Z%_Kyic^8KSv;shrP^4DzvKqlOb|R4rsX&ZZR*9X&u%}379SB2K39z zXE1a0c@14YUpmH3HX(;z?rP3DiW_J&K6<}JB5S0d(ub+Hz_rD)E0`QTX>&%6KFo&m zoG_ac<(h?=QygxTQNE8j-d4wf5*(;3%EpCwu!)+cfm+}5HQHj zSj-fRtX|=%+IJm}Fs_D6#~oQa&l9{rIWLxPkyQx0)Aemzw9sZH}Wl$3BWc zyVa8I!qgLSM&$?8@8h=tr0tYn3Yn+1D{AAqk@??!8NHT($Zd<-n?T!cY1~p3$H6o@_X3bLp^b-;V{%tod?p6Gybq{e3>nHHI=otrEZY zpb0q93KIF-7vDwc)QU{d?wQ20SCA;U>H8~bE3!+WdU7a!S8POqk(VJ~3!d_Ib4J0A zbLKI!o@4AjzE37Rue>FhAgcj*Jd&p6M!{|Q5~7*|blkM^M$JH~*uok{+3aH%>>W-y znT|oV@|rnj#ywY}XEmvJnCV^%P3|w;{=d1Xk;%I<9e`@Td*iaNNT$f`w9*RYu3C?1 zQo8Lza-8~R8Eza2dYgwNOhGTsB>I6($2>i?^&ESa^We!FWU%Osi~12tx(+cIOd>#Q zQmqM8R}eY{vCvO2-Ie$6!aBX-)nnB9E%hh!Qw+lcfsV2}N{-K|1&mNz+uO|JBlP`b=BmC2M4m%Gjvn>}m_=PwcjqjH&hKapB zV&uC#mCmQZz}dbqEXFi0YXwisB;;V7Mo>Q=8UeK8^9$AL^G?%+qI6aJ>U=g1X%$pt zio?*V{&&}(nHX$@c2uKNZ4fdB$gch|Q_c2c){D#4D1*6fgQZ)N@^(v&$5GBmeYapP zH7~~@F5c4R41a?WEpW%cBfQFZES6RJ9?9@gOcy(XLf@OK)smZ!jWdO^cE(5578`N0 zt-jr`yRCyIvQdREk>qi;qT_@be92Ob4G^a?i9m{u)csDTmo|E~(mbNcN0W(qaYi*f zML9=6e~i1xRIRzhLJ9mtwG3RvmsLiCoCokz4P;$LKVhNfq{|16FdGykz~PmHP8q7L4#jqJ(C2s!BB*CrvW*Bouw!kDvio}BWE}2F|+GMJ}(<;`!pwl2a&B* zMgKQ}Eg^YE*$x@=|1ShN!%B6l-i$ar6WeI_OKqm=xy<#bT5smjuye~OwzcraP!6M{ z`O95|y!sC>RcR3O_>L^9&YR5W}bArM_% z%rW9;RQ=Pd&waghSUCJ(=e-vo6t()?oyuPf?*NnS29eQ@oL9K_*J`XN&-bpo5s~qV zMFf~=huq0G!{BA?)M}ZmJ8Tn8Drg8q0tNfw^>^ba|w8b%1pjb`7P0k-Pe=18^ zSi~b2f~7J=J{!SqK-E>oc*1tX{1;>nRR)BXh7xpl@n*EUNxFS~lI;P6J(RMwBUYbZ z%O?4+xe)dzpcMG{rxbD4v44P_i2MDkoacFs>ltAvJ?Vs`$K3F!-Du` zFP~)CnouQ0Qnn$^u?eempBvsGF+9fD zamWB5A(fRWWL+9j7$&d~qED%*2!dS_3rw%!j%WtviGS^&zBHF=3YZ_JTI~Z&jdGpm zSpp8hMH?hQwEm)6#)$)N*I$9~=E!XPhpf2B*O%6ZjsrquI}?>B$NPpP3oQdIQB6?L zkQ#8PZW&VbCyW<<94ix@eF;EIYypiM$imcNQ^9-crtYWX#qH(KW0{D5^qx5MUEph9 zSFOH%Cyob7b;%w5VSQ@C-jT9*gY;5kq|90yxP-7o)W7mR6u+>O=C`@EndZ_~RHwJ} z>?Sj5Ts$NrrBwd{`lwq0+)+#N$*`gc3|8XDMZl)a#YUzckl%dC9O661c*<(RI#s(% zt?frwTNRk8!ds2(mToifXt(+*iy3Oukym-h%u4zJGLD$uJ&;Axl?+zmieyxpi})AH z-HkIvNZYSR96BEZ>4X`f_)o^JGQ^GYzzj7^{Tmb2hfcc_(?OYiKtT{X0Hrv*M@`4R{g2w{=_hAS9XLQtS+b8KWGhdG z4@GD-l*!>+zj&MI>PLcxu|}f^j(lalS5~BTfbq;-I^G5md`;lR81Xf%I`XlMzi3}_ zL;fwd1Gc!#cghPSA2A@`doxaDrWcgvgTzvS37-1dFkNq!XA^Ws=mI<{xI##TS(k2B)sbMqXma; zO;pjl28HGyRZlqd# zU_7$9h(0t~oiRM`V1YIfugdc*8pd)&Mxg6kPyx9yCuKeo8ok7N!}~lm$7aa z`Nr9IyE5SL)pyJNdoLB?Bf>DRKRuiwr{DsX5Y(w>x1>f#d$p`1QCsn3v-o5LiIGct zE|dG7x^1E{(~>oFe?nx`+XiZq7&nu3#{5vD@jOH81fsYNvSuZam+F7~`m_;PO7oo5 zT4?{*jx2ZHO;wF#XBp)m$ivdlscq_ljAp0>&IQzKoON{OUPtV>8Q1cRlX$ufdsF^x zYHrcE?LDR>Y7xPREeE`)3?oEDf7*#WNPLn&Tf}@a#viMeEPpbzg>2%w6+tt97z>wg z%rn`344P|_Z}vA?T{o>w`w4b}@Jzn%IOVo`DTY5e@pM(0&#OQ&*nQ$j8Mkd>ZuB{6 zd#|u=RJkD33t?F2DdO*5gm^VjHi+E{N{^|nv{$h#_;O(3g)8W1QMxVk{qy{&Ej?Sb z00f|}CVX%Z&q6Us4nACU-~Mm{EDE+C!~%4CMPUCIKyB;=p+A8)t2eAjpsTMJ)n8xZ zX`EWVk$`xk%#5W*V{%in9)G+zFB+g>%(Wy zFDNs@ELE4lim$8fD@Sc!QU66>Ll|-~5l57hD}PC3mCKMVRWJ8(hSuh)9w_99G5O)gc?+d z3vSi7w-v6I_Z)c3A5-O<)F(@821f6Nz5Nb`MUwsLcVNb$qZ+wtgwQeU<%U?`z!~E4 zSiaofp)Y||92+mgc#a7p_!!So6X2(a_o2&WRd-cRkuOj`&qUaIC-_l12Nc=_rQy-R z-xZwC{DmDwr2d$j_Yx`s?aU3ZP@Ox}M!xYj!0{GFq*INP9#-G6etpLuyaFZy`NyT< z6UhjwacUv_dQYm6Y$ldatNs7IXo_N#wLswKfNO9h^(E_;=qON-fy{O?!W5Ld*m*0n zY!qn(OkPRibNDP3@+F~6v|W|9D2A>hR5Ywc^8$GCZ*WcsL@W3ZLKw2+gq?m5AUO#r3(jb}XGyc%+* zeib@_yrHEl7=HjFeb`FACfUOWqDC`rA%`Ql#`nQMxvt}AlXj99c-hJ55=6>T`m8^& zG8g0V*wH0|>uuDrKrIp@J^!Kgn`HVhn`{J>zKaACD?InCYB{2p2IpE~T&MD$q$4G% z1!pd(zE6fsx!gTLIDCPkE~I}fhS&Xrj2^p6bcp%-kpLRmt90wqG)z4L$?#>V0DeOs z4@sMY^oK|%C9GA%8)Xa!z5=2EYN)zhzN5hfc5iUxN#~(VR7{D%TM~R5$)vsMavLo>!C4QZOfUu7>{K3zlh1lM5^C;!!<=<^@U{yjSNP7JgBog2lEw{F zov~MpEB;JfylQrZ5^knR$!L1%5#ISGrMK5^!2JB+csm>(s%@+$yT&V3a|&3+bT!^y zQTxh7Cc?G5k9oK*S)hHR4NEq26~DvSnA^AXWc~rEO)x^Af28#99ej|_BTKdME?hf* zfg`D)wb~2gmJN=U9nSg<0%MvNCcYbUgqBLe8&{=&}a7*=iO3bkO zdjx;sT2vyFbU0A)vWhIt6qHzaaukz3ZWKhmFwP%3Ur1tRIt1In`1ah=tRe!Lzw(Pk zU8ms_z3&9U5?zArP_56oz&`nMOZ!yPOK9%*|1wiGaap4#J|5wQ8c3ef7IcGL!<^I^ zNx8^b<=&6jv_O0Hbr)~hRl-#cW);ga61`!RyI?T;S)T%$IPPV}I(F>xB+$Q7wO;}< zRAaiyTFGeC9irzd$Z5SlZR$gA6v02dsGdeQffpz|Cv%*}<|lXvwfZ^-mo=a&a|~Y> z#B^It6=_}uwmUwH+cEa|vrB27J(s4_`_6&Ar6I!zyBW<`i+w=LSPp;b!wuEA12b9R zK-CL)4Tn7{>uKuy5j7o69shuzzl?j5L>T9Js!wIzvNoBL)Z%@f6&l50AyE|VnHU#R zJS}SFy&67PsH2df%e2wI!Szfq9AL6Q4LQi303zt^Al!Cv0t!huUb8$wQVc%9!9B$AWev^OVyB1 zQGli%XFSz_dkB>4Ag9@J&7<(6aWMm-?pLfq#5IS9t7!(((o3vuUOmXHA^F7z0+z7S z*EYb&YN)E^y^tyOs%xhS+~1HTV|NIp=N{a90kCg_edcQh`ADWA+MdQz#h20bPumHI z=Yn;j?~}WRQ|zt;`F0QA7#Cz4;`PeLPHV{Z_{0*Ed+S4p8(PvcMedDs)dLsjBi}ljP)$q$m?4hpsyvA!b z1p^#F7h59FhbLx%fJfZtzL|0ERK%||gjKTWDBjRE1ZYL24Pqp-lumvVjYUgaYy zJ$RRy7;deK_~eUx|9JE?d2~?X+%c<{$frzFh^xLPNSB!@O?B#(@Kd{>qxs;9T^sGu4IUz-`Es1M|5|sfu}x9k(%>ZAL;7mT+*NZzjl3W_i6WimNSHU4C@Pm6LeNscov?ZorcX@>ZPn&sPT?I+~=*g zPe={zzeEw)yJgr+=FS@q1!~SYLp~oj^77Qs6%fT_8Y$&(YTN=SwiQUcircIv>Yv|v z*~nr|##`BX25?Y6!rflQfGRdY`7Ze7`tmqxi)P&JrU%KmbQ{ml-U;Ak-U17;&Kq|M zZB8&O>kkjQh)o=SYNSHd^~dn}RT@eRV$0V9ZJZ&S-tCd)nArSmV#;dV0Vi0KYZzFO ze;j*<`hA^e#jr0v-J^X`Yp}GZT1DT0qpUy2-4F3Wl}5m2?Mhz1!n05Wrq;Y3o_xcM zy1#pNd8UHTrNWMmFhySzY}5)SpRIOz!VE5|;*pL&dfGkrN7}IcU4j!L_>z(}l(R>z zGX2y;^-xK2U?)2mW*_eHIB1 z(oV>MNv8#1;wOub;vPJb_Hda!YaA*d_927}E!Os|@uam=GCg&?<9+;i2jFrL1l(=-%Fn>vfqHQ3UOf9BvMB@$n35|p7Uhp| z1ur)`MTN^4)mL=E4?TFHzC9*3QyYcg-0?&=P_#_K9r2mlcrmEQQB~^@wa1f*EHs^~ z4UEYMCqyCGE0BF2=;0np-@gyH#KOVLs@~!wToTbXHs_mdq$`MUNKfVhoctlUs=s7J zdKbbn{AGp^@dWJDpFK%3aF~p9w~HDLhfBwDQ+(ug?(iKj`bhXAE^2^fGHN^HfPD5i zXo@}XSZxdIXZ{%X?mfmWlcjGmf}Ahy-lw|H8gH(tk)1I@saR;lK2@D1l^)s{4B2yb zO&5f4ZC@~`4}OSVZ1q^)&p|) zJ~4_us&UV|Pp`mUL_RYV zA9ALI76v*#|3S6oAXFIc`Sb!vm`!3Myxo{;O2v+^gG$iEUe*5I&sTMqQ4YoIhx2!N z1R(AhhNwz6QWC-IxiESGDZ@mxwVwdh6W&Ceynw3oCHQ@CCZs6~;VN3>>jfRyRS^Yj z7e(i4(DfNUi%HvPP2*8QK6HmsGMquPs|jiDv_RzkeJnMO<;iv)>wiBQR~_B`@H#_I zCX(Cl?6BMwgjjpbIXjA$PZ5)R^|U#Y#((y+uet?O%Fuqag}* zVYxR9w;j^D^G23%CSB^cMfyv+Cs21t4)xU=QE4&GoaZ+icE`DyZij~R+buyS;O?&N zOqGQsg5?yJ63fu8nt^s|H!A;sP1KHibSwB+2*!a!vDeB^ zb7Si7UfGbz7xDODtE9WKmZf^2j;gHWH|t<*5y#l)nEqu<-)f#A-Bd>BxuId*TP~=qazT$Qa+x^6j;mSrLAyOm(O5I`~V{Zhr_qmt@++&~Pn%HMdp$N!V^JVRJ{1 zdc+bNFUw3Z_N+b{q$=xfQtQJ0H80cK^F&L7y8gb6-BLgGhLFW$hS{zx<8YdMriD|6 zg^ky%ML_3=?g?>fmayTbs=E}Ei#Z00jw3INN#0cc($lsb#+a5wsiVFF!$_BnOmP$Z zu`}8a<@j;KfEM{#sB6X88>~8*dRe9hqpkF?n+dga`SKWb@ZQvIh8=%oNO zc~O&c4p+{mVmjUn6DRM>S^zS!eCc(Zm5m1h=qP(9GN2=<|8bOB4iOb^qoJ}#T3yvz zI49mCzvH=xMH#j*Jxy0ob9%|=640mwB=p^*Fv)vG3<99IqnGUpXSXqdn> zasL2?dejG8RWrAwHv+)dq_)jv@=YX$neSt3hO6p*3P=?#Ivs3N(*$6fZ-Q5->PNtx zU%r=#i&ugHgr1?6`^u%s@ZJw*+*S+MaRLP6#4nd}b;$%|DeK~%7e0i03g$^09TJES zq}7Rw>sD--2ii5Cin!oOPK0MlhqdU}zLWcU2C|Bkp;TO;toixL_9td*(c&cICuHz4 zvV#7~(CuhY{1V+Im_<_P_}&VPfu~yx)#I9-Fiz|S#ITjbUE0C=u0g8xF4s~?arZYT znNBFc?bjTA%6w#1AIE&kKi6KUe`!vphDeYeb6^okXY2Tgo)QIDYXU<03<91qZ zrY8C_b&jb*nfL@9BT+&OV*(5BqgG$KkUwN%nlIq$FXxEan!SP){;;9Zc$j_XG|J`^ zCDpM1Hc|bICeVRX98)W&(w&az=pNUb@cRFYL`laL!H&A&MWoJ^JMAQn9X(=!`B^Dp(|4r53I1I?f3P6wbsw%x61asRF%4;o0Mlp7} z$y~mx?ieH=Hg6Np+9Gw%reuo6RkvjX6=3se7k509=3?W)d8&?gKRO7}8d0RC;_oMD zzIy0c61KJCESLONh61qW@*ir@#CuNFm8iEn0ePI#Wd0AD__ENztfW87Jdc8 z`i*F!l1yPn5o+kH7mUy+ z)t1SjjlMK|-jor3-~{V5ewmWLx#xC0r*cBt(_Cbe2c03=+HDU+TfcLf_t3C!Q~HB* z2-Dis)F}tP^{0`_yc2%8KMVPQqm$vsyl$)3qA^H-#%nzA(iwi^*GV4ks}Rc@3HSD& zIriib8Ri8GT<*r150otSsU9;Kb$Lo2;m`YA^(~tpyw1Z6+PWkXWJCC?u6t@G`L>Sw zNOlyEh4_8dcy4(?&AX?P=}}PC;l=N02!PF{r&rZH3WinAe8yEVC=yGHT+O*L$01Lf zI`)~Qc!9PlI&S}vTmIOY(Ay&@YVW1fKpLhyy2(W9DF6kvDTB#4)qx#}D0Je3_i8x@ z;eH{1WcKk%HRNsbbK%3*FXvD{du_Kjh?516Sq#$S;oH<~xpCIcTBcJHK@a(iU*j>C zxUe;H&&33bVJ`^^um(}Iiy1J0q#5~Qs3|jI z&DjIzG(zm(S>T|?)Ha!hrt|NEq$d?h)jZ5shN-E%jOqKKIz#k&>^0+7fSNBx!-j#7 z3>FEDW{++N2AbN>6rC`{SkDmQPrs7^`Ei_yNbJKZI(c}n=}~UfQ#ZEOaW#x3mVb_W zG7S$uQ29EtUPrLSG8iE7AS0!%O`%NF@pyKOO3@uQ9OaiM>fd3Le7gu~kf(VJL?{i@ z{nI)hONKqEzM*qCn}J6BMNgS<%zCL4BEK%!WvS6S=y%R;*;dVwL^joa%(vu>Cqe=C z2lZPh!rx9KBmm366fAaB-NxJhp=tC|CP1bRjd4zqT6jB*dPTYPKF~n0S24&JyY|!k z9> zQzeQLvXV`98fNy)%8cVUuYc_Ox}Wdy{O~+a@%h~Mb)Dz?e4o3^Nfl-0pCjSc8wMq? zJoJ&+&%iC@qzK4ue|f%$qP#BCX$S|ntv(3Dran_V$r#q3ul$H$f`;{;x&xT4NFXZL zn$1^|r)!K>FsBkd2UoY3vjQM^kvTuJjbC zpq-{1Q-{R1?LA<8kgltA)RA#gCr?C3kGE9>pGLCyg4BiwLGbj{V|Ss5=NBql4JLg@ z{)vhZ23!opC!E)FcE$^`@ltv7>2BCACgn42IXj?xn>dzU8{#5RH}%xj-dP=fYKE?W zDyoKb`ALP;SIT_IE2vq!vcl3acu*=6h>u?dKdWxv#__uXcSMi_Tx1;7m#rY? zfR(@69OMU?b>J3A$XoB{XTRZ}oY6h{kFLzqBiSlV55O$=x|ORcoq8E6l+4{cKG$gS z@xJfDH9d2VyRehUH?>~Tuu78`4j_XJ97~6fm|+_T%UCkW-aV$=|9GR96Rw5iL4#y4 z*<{{#j;au{v43>uK9ZH(15rv%B7X8sSKTd%gn#F3jn1EQcC71l4p_JJ0` zJQDLiq#^c+Dz-d#?|`g6P%?UOt2G?^>k+e%p`7^u(qflmRs~ThWNy#e;4hYNH&%n< za}i{{eB~m$X}Nj@?z{u3I9Nf4HBJ%+S-BX1H+Ut^UT?7&t&Z;gm(kLcr-4 zp(feTOfr5`L}mSFaB3CYp*Gz_**W>Xl|nQaD(s~CU9!|g%TFi6$LwrsCSLwS?Xb8D za?uP#1mOBL*UM7LASn_vvT;T?J3f#qv%G9IuP7}C?phY4Y3rZmstFM^>h5d0Ot(Jv zG9O`{2m}d4b3dZ2C+i=~Ak&{yXu!%%jVE%hNFYFUst?O&J8X4e;+982QWL`3)NY>= zTfIz|3!#Ra7{$^c$O%sAXYaf$wCNv7SK2y(RH-rnyj1^;Kr(8#njZMP{^u^Wp;z%; zkFhR~$Bx5OX}-XHb|Jgn1UO63vW5)#eDkq-c>Uf@A6ZFInI7Q&l1x7_g@uV}#lYRX zF#nb!hNx55@h=GN;vMgfg!}yN&cA>8YleOGB56Muq_zU?Ud!1^L%VdA00lL1jBd$3VjQ;M+-F}{ zqv4khMM}T3t}}xb-}C=__(uIB&n$Fwwco9~Qt)S~M4)v4BkU?D&f$QI6K8xU15w`4 z6`oPb#@iP;tc|!y&C+N}DtcSX$;M{@JJVP6Ush6Y{vcnT#U!hw5LC4p6t0HZv7KyL z%;A2f583aHPSxYd{4sHvvHd}$LvLSJ3AdT9x|)!n4(R!_$F5nEt+|ku%znj8dm}aU z31SG!T%((H-W;$^VccJpZ*I`}LkIXl5Z>R4lWEj{JoJDgC{rQ7 zot)f4a0f?orH8DJ{Mlycp=?&AQ31onkain;%uvMpzGf)z4~NZAsH<>ty=3Wbf1H&Q zMr2$#oRxR@Ne-)wbd_#Bfg{5Nv*csa<0JTe{dkc@QmJdwm9E>P*PbHjYjE5by7huA zx&rbOE69Y zz?AbJhM>$LHgHs%TEj89Syh!ML0p%`}(91tmXze?=-_%BA zH3!tH!OU-@VdbRnCeg-j$459mAhk=C6;RM49fW*w(`d8S)$XC@2zs;n{)@Dqjx$-|AlL1qe_sNW_v&A{ z$ZfL)6+3Gtsa(F!zdOvhTpyX~7^%O{*a@mHWiSR>Hb7is!$FqsL7zv}uKg|)TkeM* zjK0AOS<+=}5oYZ2=y|=F;Kes)N#FVDONp6aIkr=8(=yqdf;yS%W%qUc1ewkZa;*fmb$^;}_B*E@Y^J&eJ~OeG={yrxAugYF z=SfO)IXTEYlcYF_GQMcwHQZ**j0d;q=1sbj-nwR*U#J>;woNx;OiKOY3S_;|WG3TC z#gBY6uJ^F94}5Gtc8Iz|^=i}p54!pJS-!uIm$1V_0=Y>4zjrvQnf}7+hqa=JtLH3? zw_Ipj8evuxDood(y^*C`aI@Q`;mPLsGqAhm;9Dn-pO1$2*JB8Jh2}Ik%=dlr_hazs z6M)5I53ak3m#5z0TX8C#oeaz79@#FjW4m5FPgU*`GCD1Fmhg|)(N|@KpWw`n`Ld9i ztdAWPUE{KTK&G$e)15q}hpmygHh)#LM>=+}Cki6bTPSrGZ=TOWh+Xx%Bas_6c$?9@ z+QePJG28W&S4I*KF$lBkQ!Xgopq>2tfaZgyb9yNswf(o5TlXitFz;wC9@R{DC-T6i zJEl}I`>xbK&oUm((DPd(X=22Buac#mc<3R2ZhEr7x;8OQH=Z}aXWtvl{69nw8a830 zS5n^v783O+lkPB3+jMo9x~NM7p`s&hhKJO!Stp2+5aTYmG_B`vI-XH`-SyhJ8<&7= znFK64xTZVK2F&SakTqXj?rH?H<#vORBMBJ?K#d(QZZ@t)RG<|Es?t^eR2eMzcp!bA zppx#t|KzY#bMBw9Xust5aYiBFo7-dHTXg6Cc=S0j<;GF@7NJ;4Hhw5knn<41V>;3w zf_TKS*Jy|=un9Q{ZI};re0Y^FkZK3NVwufA84v2pGq`?vk zP6kh(3npBcpvxcYmUE2JRQWz9w+p#`Ua9|9vYvH?wV2_QT7O=@jG|BFvgw*0rU%5z z&~4@<QPz9$*alx> z<7(6mci{aqssZ&;B_6saBFn)?rLdNVe2argRcPATS1e=4Z>hLYO#s=RbxmaG{~-En z++U+9j&-_qM7`g9wHm`_+($ld2`{g=eOV-4w>PV>Cg*6|1UG3 zIC@HY{Uc98(4S%QnId9E>xT(yz0$M)#B)5gBf8u|MjrD%U!kNwlOkWknr9V?{(#-_ zs}R_M6Ea)E#ZmAm^a467ntenJ0}e?9~yY75AV`7xGBZy*R6Xus95)Ax=s)CG3^Q@T$JE2cY$bulXpqnLPfAGS{jS>85P#tkq< zZq6j#aWYKKMW7cxgRLaCS$1D{860}ke_?^HmoOi)sobk{X>&Lv17_#VaR}zb=qYQ+ zKs#aN(gRYr8~%558&L6;U#TGOix`rPz*>}BnlReRPfoz?+uzzzH(-T%>omy;&oZ_3 z$@Wlppd0|7(|=-4Ke=TRGfu0Lh)91nzLhTZz~d<Do{XsN*1YTcj5NKRmdShu zU8{hS&$|3cy8fD@!YyF+O)|cXQn_#Uq!T6~O)=`u4gKpj-sr+5j1)lwTjzIVi*&i& zBbkyhziU*+$hZyck-Q#Z`WpF@AvI3CHnGEAa!}qrdr(_1oZ9-U(ZP}6b(U1`bW`li;=NPMX73i5gLk1b--|tl`ODzXMpWw-n8B=!*>YJdd||2$4*O!5 zdU!~FeG;9zc zUrOC>NH?5~2qR-%RB^y79+Xj&jt_}66G%<3Pb7V-+1tv_v0-y1te-G;4Fyh~QB@a*D7^O)+M>}Dz4I`ok zrExFgnq}dmI+zaVmxF+QY4UVM6nj@^|>23)#+gjMSqi`(X4 z;Zi`XGSf!peN2NyCq6K`SDMUl-em9&sR@6<2#JatVY(sKV5J+LX3ld&Wlp&;Plu`F zTfEdYn&nx8gUT+d?RRmXr86ag-lys-IoI_-2o~>*$rTx#YpUQ7Vo&O7-@DC5w4S)b z3D3R&RXJ7`@96nsJB%ICQv3X!H>$q}zS+T^;IM;+m3%$BT~|IirDW>9t_YW5f9X}^ z&1C)YGl*25wCswPstnyh#JC{>uxE21?52f>9X-Ml%3~l!ke+aT24I=-)9`#=ru3po zJ4ZhV!;Pgr!_GK=T{q{MnHIvQ)%PzG7aF(0hpaeDprz`PZhxds-GB;AGS3h;IYEkE zX3n1B%p*~i(O0jy;qR1O3?e#%BzV(t^nc2@ZVa`yF)W$Kq|0q}r>zkgX`2uwLv))} zVQp*fWjW!q?Q@IJ700h#r{RNQ+&i{sT#Xdx> zsiYJiABeI@Go8+{y?T5*5X`h?M6aK8TPQNy2E{S`(_a4OGeEjIBJcec;I@%-`kn}= zX2B};j3+DpWXbL>WI6EJn?imO#py(+XQj>`MI<6o4>Z-eFzjfW7y{G$;@=Td1ok>y z_Q%|#s1-$v0DM-YDic0~`9c?GbnmBBOsQTZMS3NG2!KVz14{Ey7%2RZ^T(&GXEKe? zkH=1*u1LLu;Y|cjdaY%Y@WMAelZAF;lAcl`I*D5T!5p?>spY4&w0g^zJ6F^rpp`1O zJGlt3G?)Zo1jVRCSsy(lK(a{72!2y;(&>O=smfL~c3lnebT(bi-@b60sShVTctDob z?mor~SF3Bzp!+h^^_w@4dsk*CB)UvePzLPGd6Bj3nK9$#9WWR0X4D|2aa;E=%m>G; z!z{mJlY$JJJ^g4RRXyBTyZ))$!jX&F!sG6`k*bt_fUyrV_x+kuj)$BuM77dQpn;`L zy49tSHB3f<{5XyI>;cNT!@BaN5f<10xV2*lm z$&Lg6p(oOnuO5qwAP}+SGk51rSVPW zW<;UIN!smTf_>1TO8qzO{3dN~9E$Q{tcrYd3R54gbf3i|y<}lreyoy%N)WyqR=V|r z?oTKBxF2P&l4g7UHNq%&=`PsmiOKhu1q(fj27SX{@6?S}zYqUkabgU)YU$mFfUPLNr^D>!i)T#1|l|`|s~Khr#GI83c25 zuqR`0@47K5tn(>`lQI7|PPMt%|0NhWjWrGE;8fHu=lhBSC;Nbal4BcWhrncvwBKf* zE>f>sKJ3SYc6;OA-+Fn33F%k)@dKa0p?U(mgx&ibUmlacB%!_|iA(5PweJmeV^0Pj z+D!Cd&9p@{(CBUuEr(b!A10rT7$@UP+WH%#@)hy3jZgVl%3966E zU)~vfTCom$>~XBF3uCSv?h!@37$2s$oYw5OyG3-fOL@KraDHQH>OfX$cY67RiQ^A& zfTwr7zNAN!)vX<9!RMQR-A@FyRn`u;f8<7F`8g}^Lfol#xl|yN@-^Y0aqXe*@IyU% z`FlNFWNx1Pamq{CWrC-46mfK9?rXW#+We1~^fdK*b$XCA(hac7BPsL(WNzxCDsj-Y zuZwy8er0Tb8?Sp=kfojn%i=NJatGrjeZgC)67IpNIc9Ix8GX%=0j2wcBl)v$Rxspl zT}Sj_DoOtfQ>pu9#s{rjH+7C0`?R7DxEvgC3zAd5D{LEdF!zNX$SI`%0m_L`H=KCA z{gHCW*3ALZeku`1mX#xns=FU)Dqp{|UbqqJ#w@ed11ArIwG7cYe}b)HGE71qiYHA6 zdEf2~A$ZSleJ0S*I1I_5@AWUT&M>)&8=;~c>^x|O}hn5i8o|Mb)FTiD; zA3DevN3=lZtgQ6>;izPMLMe;Iz*gcZs^~Mez7KFt%b?x7*lTQ4J5lHzPcRd$Id1GN zyRTHUxh1+I>jv(EUJV&&`?>?I{?-XwD1U&k8OESsKX!UQdzY zauP(ZaBJwuOEA&A6gB9T-mA{RB=r{2nabh-YqQg)77EC26#W-4w!49*F1?B+1qeG} z2Dc5aRFnCCEw^IXhrs4x5`ph^o2LVdI4~u8%9m3emTy7-Id*SR`zbvitn5BK#h{(w z!t;Wd(S0HslbnyA%ADB+OBf%w+yEJ!D&hf3)oC|9a@z-eA4I!GJmWuQJtD^Z+ezAl zRrfP0Ge4q4hLT6xp zy74TP(am;g`g=0rSzRtoe!`q0paP4warW@;9HfimOgq$`RgvkuhLn!5K37;Ji9SnW2=B0GvZM_!kT@_y zXk`5*XFFiN-@V5Wh=<3aAwX9vCZdH<9zWG73-H2PnT_H~Kl2};=NI~fvN^CxPWYlO z-Gl|8*%>nJOo)teT#t~- zuQHgA0S*q*H6IYRE)~iB*B`eE11|Yh9N~brd4z z|9FQPp>DdxRoufgkU^#SOYNdr0F~<2waD@iUQ)TRXs`RN3PkA}qq*4j3}wsTtVJV3 zQ)X1A{7?{6P^BzaZOXbJBMjR3K|*HC!ipDbw~AZ8%J9T@szBmwYY^xvKG8Rp<=dMJ%M9!bqnzN$t62t!QEue*=;Fh;h$^2MPjEUDCIUzIV6$k)Fb0&uLpq^^^yhf zt1@Q)PTg?Reea?}F)vOsYkTyJ{!HAa4JKR-`NGJws3?rx5)%>J@2FdsPMJJ~q9oqR zQkZGBSB8Ue=DUkGkx;TQ>_6s)0JLkcUaS~D&=)B^+YCo&Acwx-E2>qN6Lryo)ss@M-am^EqX`Z^N z_LmgsANEq~MYv3usMuwafMHe4r?rA7t6Wi^%8PsaRO;MZk;6E>Uh+CC5BK13k<4Xb zY%yl_F|1|4_h{T_SP_r=5#cLM{7C(kt>j|1oJR|}pkBmxZO>*{&~a|9l#xci)q8hP zhw1*Z>`5WmBxK99fJ?V2A|KHcm!;tXk!A$u?b9;({s0+3BKX)>?)?JfcBr+LCju(^KwB|8~3GQ9?4a zEKz~SpXK~o!SBDM30>H^2?GP}v;s}#IsfsAj zb5Gv)()`wzEPq{|fEL4p=^W@bfDY>m7H)j}M)fo&X&2Lj9G-@+&5T zozSgV{o=E!EL*A1KIxHnM_KKDaO)n9B|7&^HxzNlOj9?99ZfOI8H<9o_@;*~bmf~H zdyJC-fqKQ-{}#zxbFlUV{TSRZ0>_+Y-$I$*W2Cbft_tXB^-yOR6D1WH)~4O-%LXrI z?=fo13`sGZAPz}b1fr!U25D_RjZ?}LcFGZI5#VYhLVw#0K`@=YE|WEt%9_3I)J*)_ zWdQZ8sbiWxM@h+{Cw6)U2dRCB!A3*2VFc_-f{pfp9+eTYPOIlcCZ74q+i)V0odr&) z<{zoT(OcewbsHy5fkmm=jmhJi_PV=_NOjI%(?)6OQY4ueBMT}JDvAguUu{=1CG-3A&xfI{isa1VkGN4cia0O@*7Uq0m~s6DBOH%f+X zZ$dxE5TAKfXk{1mfEkcX$Q{om2`7IcJFvZ#8+6LT$)RNpdM@(Bo;*GOADQC#tL5AA z%?8VzW!V zSM0clvjJl-&)c*W)<9K99q`92TR3GW7WVEF={z%2e&wD@TloD?6SL%rYtJqzpdy zJ%;I8Vt(B(khqc)t)V()Aed=Ub&kY3Ven}8Ml{JLdfhg{9_sHGcsGzmSHFFdXKOsS zPCbZKb>VhOi#T28U6|$6bNB(23v7-_RA+VX9S{94F9#2YKt-2>9!!Hky&)>UA^mSi z-_1Xfs_VC9q(CvvylroCk(+Vq#OXIi3a9Zpf>~c%jyNdB54+N{>RO$J4 z8e!@|jipD#V-|66f(wJ8F6G=WXxb@#?`8FpGl7f3J#W$TuhM#y5a&+)oBC-xF1A!= zUx(;&hX*udb;b@Aj8dwv=( zlz~MLJE#d{`YuF&tlJm`A58T;Y~gM9aGvDD-dTHV}| z&TKDIhvRcDlTiHofnjamW5L9qA*Q&QtakbFVM3hPi4{I~DGy}tBxi=ymR`6H(_OVR z93Sh!y;N}EplP=1%muul&<|0IWPSnsXfgv^aT1B>PLyUtg}sx8gU9ta{6~L15-00% z#b=dI{B88Ohf^I@j80wj$Cn_~%3Myu*=}^mEgX z?sq=s6X;{wbInrg5 z#el#*TWNQ`l%O7^$m&D3y@YZ6`fp<5PP|$S`SAl3Cg?w#8GZAUamCc71kh5w;VYH9 zb!WZ`u#)n(APf7wl-Zy??%i!1h5iRq&eLcByGCy$Vd64 zV%2`X(O;Av1p5E(O@@B7sO+=y^Y~d9!Gf9lrAcY)J^s8@Mct557O3kUH>2Q;GnSR6 zcP>B02P0K=e#aK$(M9iI-SX)_JQ#b$A<;1tidXW5atlEf};n2)9UVAzmz9Yk~`tOyUMCd9Ix$&EH&wv5WC-=+GSQN(GtI}~U8JDB$zS`vE z@b@ati3iMzazt@q`xCSTKbEDm<_5+eXL#xf{quGn z0e%L9gXDU}?Fj^8#7#->i5ob)i^%d%v}g-9b&>P<1HMaHqHpZ0ZDugwucq<%8mIOJ z8#8ev7;g#IKCH4>`W$fDia+c*juf<^W(!AIaQJrzh}NQTW~iCnEM~25!y^^tiVZ7G zze@abkw~q%g9jf4+NI0FATVF4DT4-Vw$Uw{H^r*VSU`74{_VkochJ>XG;X%e6vJ;< z4>%k%4TXA?W{XI!7J`CRpE6+0Yu@-P4KVW>dTqA28jtiyn#@{13?cFh(fBO?PP{}i zbaR#i0LGwyGarFZeVXY2FzR1h%>_uL%-3kVZ#gC1S7gm!x%lxVEVO+<*t+{Jvck?N zC>p7-PH(9_E(PF-Q~fda9VVxIb5~SBa|7(>(wZ>b>*0;+l4T_?j##jshBLW zyI5ClHI=8=+4kUdgaBJM>2=|X?sFnuc6TcvUhAcq?Fu|#va8;0m6dl2C7MUKf6GFQ zl&cb7X)n;#Qg(v`1MFZ_#*3Kb!#1K-lx9u$eBys}Czh@*@1lHjQ{LG!eo*&cA%x@> z#2|*t`Q%cWHnANb8T2=y?yok}e}mh+9x*1aJ0I3idl}%nmY~06p7o}Ald-G5QeVK6 zeOFgXEYYQ3M?b+qkg$zQGPv(QfB7>jMo8k|^*Gta>8i8-*A*hrirV~pqrbTuNYc-M z3%OSE%{0SQ<>~2zvf!@#0`_G+N%B^QE=#waJNHRiqAnckH&&QK`T|T^%agUu+aa2j zy0tLWQMUr0KRId}T%^tehBFnTLHu0<9Fjk$eul_N@BzcGh{G8n_j?&|dyjcxp7ft9 zqzg->l!3#T_7RnJQHEsVjU`s?lpcD9=>FPykM9R;`)->p`E_*HRh*D@*|=$EaMd-4 zK_fyrheje5RUQ{EcsU#I&8ofC2M+qHa){yZhp#vUU;%!-k~MQA0pY+dK#xLrVj6QM zWgXNM*TVOD&-k$@v02yebT2en)301<6!~-oSZEA%1BkoAuxKlyN8mu5RhF z;eA(?JiWdv>})Yp{430|0cPYLA%kVZ$cwG)CjS2yg4tJOJ=6?L&3roj_!X?@c7ZJA zc=FxK<(%gxl9nTNSx2K97lSw}DA*e&X>2(jD8FAWT(1^hVt?u%0AkmX|d+xigPV@3(0Vt-T5#xds;!sKeIE-^%^VikmX z0h2SuqTt79Ig^$0k+9N8szO=K7Dq)fA%c&9+6YY0tY5;}o`xT{F?e4Orx5UA39^hw z@keVUUjEr!y?f|2+a5YUMO|RtJvM#hx^$ZPV>s4!C!}=}ho1yyI|1{$!}KRcXcq~H>fvpM z%~qMp)oaWQP-b$wqOe%kc(Gft(jIq%S(1TRmoUh459sW+?aj^2l_fcQ15}-jKIVOa;&1CVgRb>9;F$DsxRGVI-lLU z^zX}8F2Fre9UC7#o*JZz9hg#ZoPI?0!V_vd?I&H_hLio3jDsb-VjfPxH*dmJr7)6K zJ@3F*Od;}(JYX(B&_PHOjYdN06Ye{B%alugSiYT=j^sdsY?End>5@BAcJA0^x(uHI z(0J^F;h@%%H)!~jaLr(d{(c(kgsG}emWI3jW=5dFX$w)`e_OcV`?~Y)MKj(1OMUW< z4C8E?;d+fskuo0-q2v(}&fowoxDN-Pq(c+&Nq+*}$1U`B>yl*&@e=x!e$E4oP zNVvyu{&U`nXiMb*intKNF4N)a_(a^D_#t+jeLEw%^8`9oU~@>;T<#IqZM}v@|9qoI zgMkmG-NovO0332QAzu#9w|_+8awA1@km4S|GO-Ysxt8d~yD zXcZZh&M-35cVdGb)7+`=YOp`M2@Ltg`2`&U(HMHSz{|Ki;DV;~S8uABXFPLKLc)QE z3r`O48Ji$HWR;ukwg5V1md5*ivT%`g?o_w%bw78?w?dSnTU};EQJkXj0E@U9`e$4aR-zdbuPa&CeR&ho zJ{$+^x9gF+SifqiOv1Nqa3I^iX!}??sJb=K9&=F#GgvfhB~^e$jyLbxfM={1Zk-PD zWIPt}Q$4lqjFd<71O9sBqV9N3iV@$9&;E`JW=0AIl^aF2q0)B2bf4H9!&5Y;+$IIC zsm5XjoYeW{5*kjG?h;KiEwvo@zF!cSigyCQr&xcP4&8BXJI1S79rE2O8|DC6UWw>{G@m0QkKcdzhz}dw&T(X; z00^r^dEi-R%vfo@7jO8-H6Wl5P(zUv$Xc=EA?B^&20&Krq31l@ zorzCh-p24sI~QgvtUFUSauuHIwqI+e?%x+>dT>N88v$oes>)1IKv0U3E9J)1}-Q@4WH zv=EKl8f9{tdEIj8qLeZkr{N5k)|H9Gb*ar@!GipA*-Wx-9RuBwphqGRZ>c$a>B^C< zR5mbtqeU8?rbL<5TTg8C+G9pWtm$9Dy7Luh)UFPu`+ba-VbWep6g)Jd>ys(!HZ!FUtfw(=Y%~9cFplTNsgw`TA&OmK-4x*&y`aDAA6IlK zwHz~q4dRopL0|2+^IK(Za0y&PN&#M8kjX394kP#7DLwKG>UQTo?51`<_)tojGG4s2 z2I$!+Wx<=!1uFHj%FX-fMH?4A_t->!GK9K6ir&&bTs{j+Jwxel6$T*%sQ+WTjR?h) zgON_|FM`$8SA6U;-AgwK_sX~J_hl|0L!6<1*vP^rStq{Ajz~1D&o~^!JIMU2gNzAH z$J@ky|3!6XW8>7L1UD4$H=v& zYq}n&sO@K6^{V{7$9$A3SM5GDoyz4IRb-!-4-D~9WTqKAOe8g$f^Re$v zpsiK8$|{jf64JXpQt7^ur(h68-Zjd2OVj@h-7{b;^s3ifr_w<5k{G4^ZX3tFJQ}z? zb6k2}9x!B)k_Oil-R7w5QuS=8&B2{U*jYDSN~9tdhpu)TEaTfgGo&?m4!9Zp=t8zO z9s5>kqyMH8?VjI8>w`P-Wb(P|hh^c`d0UL_VO_&O2p68IO)0xP)Aho_SCO~4dD9kJ+^6;)5f%O~U;H=j?0HZ6Dk;nM`zia}3XXP|=7F zcvU>VuL&bneMCs{LH$Xb%$z8c8SZlK5r}K>v!kBUyw}Lk6()L0$38RuSR(3NpM#k`b}vkIHk zEA6qeY<17n%=$OPwg=!Pq>u=x#Y+7y%FVvS>t?eXx9LCcsF=U`E{j=a5^GtvC-@cU z85-=SAR(pFAF^jRRVSv~f@ZOUm~A#4C9h=RArkD9f%vgu=FoE$O*bGrQL$dF%Ec=; z+QEt%76L&hr935p-$F6I@7Ci~b->4WNoOeJbU^03)K=(p7gx5NWo_v8TPIHILA@49 zr054DcBp%BHzLmmJxfi13~EM@qMJ>^^l6+gjKe>Fr*{+$jh+ZSmZs<7y{*!A(G$P6 z{HSq{zShG4(7KP>!@5^om zO*05VkMv`42{D-k{2k0ZAvDwlv7MLB9QksK=Am-=W#e}*SOvN0^60Y^hMQRUB^|r9 z7F$3#hL>DBuD=#|s8l`fCKGutO(%J7mkd0@IVXv4CJKLU-KqcW)Z;4o_*FenA`4H= zVAN_JWOj49O2ZPj>y6zqdqtHX8R^cu!(j`);lcSX3_{%c1XDsxtw8x*|0wIhQg0A` z`@W6m?d4Gk6i(~DTr2CMhsrZN)16iBFulh>>@*-Ynr`03Sh3UlmdTkHuv;>i_C_LZ=H;x={leHsEt1Zge_t)j&(36fl4YsJJzqzot2u0LAN?!vj(AQrh$V!-n?7}TQs7;^Xch3# z#FvU-gzvkPbh;bI6A05RUF1N1LIPexrdq>f?vhyz@eQe};V!=c9fMJMu5nE5F|A{z zSr@T{tP&(xPr4F_WvZs3hmm_!5&XJm`mJnJo0Iz8KE%ayX%7ov9%3>U`;Gp4L)MH_ zDuVWr;v;$_K)wE8%~y<&3x(f!o({J7;0Lk@+fiXslRP`o$C~u_X;S{-6ss88Fj#a< z>UNVzbc7Qs86}46%$*h$Rw(N)YPMyZHBu-;U(xN4 zr~S4Wj5no0o?l$Y7Jr4g_3o1TggoWUq*v~?XE&#i;bn6Wvbcb4JSSt(@ZH&a#VYIB zOOSq5rXVZLiQ&9llLwgi<;fN)3)W4CjqAs^;S~G;MBstlmUK9BT}^SM#&AZT`*HT) z)%)U)CPYSHO2Z?BvE8}{mcr^)X_9ge>KvZ%-bJY;gta(~5=|C2!JUmGlvy<#fI2N? z)yqg4^c*fmgOV5e_?MuAUiFv8XFiaIN78$N z2nbC|M6}og8S+&BIKrkOCURTRp6&vMvLf)nO}+Yz*5O83_BbTN?#2_Tb~%}CA7iqV zY9C5}6{BLnpYjN4xSoTBuA>4y#*t`4UI7yQ`)TGKbxP__1~5mDKP+G&JIfZCYX{PN z;LI2OWR;SU20y%;{&%c;=FzaTHF_X5?ycfQm#}L;Ub%S_*m`hga~^u|*(3-}@OY-# zRJt0RY1>(@c>1ubVP^jS1f|M~!AnKFK7-@%^#M>=5$wv1!4&LqV=Eb7TNFYk`ZCtL zX`4D^q-663OS<_P*?#na|nsRzz2T7_A!KxvtjkllQgw*4_e|wmI zko{KZo?Zyn8!~e0T#$O<@QC6oW`G(&Ph>tm0DOWsRO-4EpK#Q&T4z6`V?NnK*hixHk4YnQ}{NJ9N7T-IBih zjTq-`PCN9PyE>Sp7q*iPSRxH7KM8Ham{`EpLw8WkLH+NnZn>$RZj~l_v{MO0>E@z` zUeD8xnk6q3u6>2cG(IMZ^2chc9eckG>3@oHuG~sWQ;xo8tqGcv)0GJ~4?R@;l5qPN zN3vj6lGFlt2pFTNz9q=?C*^$frYH7xL=WMZpj-VAky46f7w{B#s1-i7S$#Mu1CiV- z?P-*t(0nO_`$<76NG%=yd@uzGgyL$f6SuwCa*KT8ufUKfz7ZJf-F`H0o0W-SJtd|& zJq8qDhbO?NhEw{_0kguh;hB>zW5|ut?MI#hpJbapVBs6H_sF+ryO=#ZYX+eh7#6{G zOufAVsmG~qJ9(aEi}$ZlIQWgg{*a!CEKpf5P~TRdifEYi4PC>mEc<6r%zL2GnzbjI zDYZUD44-6}fO?tsHi)WYe{h<|8RR4CUf}!UtJEEl2^$YZh;{WCu-mHF&T!8%@xWBz zYOoF!ZX~d=Mr7!qaf;hu7`{1Tco;nYw|B2kvFUX!!vkvbXpak5c~=ZdqTv@r2a6&ox+lJoMr3CZ-$%g*r@(`V=Qi-v; zZ9hV|gvNF=YeBDGWossFvje$J`^b(2VPT(R+dC5mT%h zWWteW<3Rx9L^OUdCr~{(77dApbD(py;*`asr8KH2OLY_rG zz`PaX@=Mt}>y?8RzRz%OVcRSogc*FxQ3s#tB|DdGM0^q1pND8tN2f=qbSX#ohfYA< z`zR1xWF>YK!bN1E8bedT@xXs+ynf-MyM2!HUouH+_qkkeYB3-SL!JdV_GT=sJ89Pz z3q9_NEjpMft2Q!=Oe5YfcGfuM93%_tWW~i>*xO~&#|mljJpS$j||%8WHK!rPAUdm0P3DIB6NG(+0vck$IZT-lV^u zvB$RULe`caNW~}k)gC&;ouu?GF_)VAGG;@q1JNu5uSa^1=!(5W4C(N1mhlsMBSXbF zIbPHMuB55>O$T-nC1DNt1x~yFD7I=&W9Gl!J4`;9IsODBdyR+AzFQ2AeFXDK7(~wp z@e&tuAf6-&_2&TeoAg9uHr5<5UcT~BuYlf|Zp~)ob(y9=BC5zM#qaPeGm}(KulS^1_+aC6m1qbs z-exBY8B8SlY6;w#r?d|1B0eIk??AQY1ZEU-8GO)47Q6&ka_sf45Wa4X8B;XE=$Q{0 zW(>4jX0ad@!GtV0G00g<+C>TnjYmNqhjgi?-<`bz!w8Dmz}ovv9lsm8(?L(MF-As-}Ps^{NkS$Go?k7E#labpN_QavddIR!U8(%$dFAC z3YZhtkr4%R4ySN{%2$9>_kN(8_k)m3iae@^vjsuB$YLAh0guD5sH^>316lVRiUJ#9hw6caR`xP{sab{fltEA`5 zDB|nW??s!$6LtEcA|K&KjY4X1JBi#NoRto_=O{G?w;3LSExv(MzZrE|*IiC<_-$Cy z;gFZ{$CZuSAy>MXU8qX*h~BDOj{~apw)bPtBj~@7@G}@TQi(B{1grdWr17}a-pzO~J%RUy{G`9KqwU{WH###Tr^cR%s{Gyv*Q9Hln9|5It(rlbf3p5s+BYS* z8IC-71=TFUJPid;w_NVT#H)RFoKr9Hz%n#=eA5-g^ZR?a-a8q79R*t-O5$f>EoS($ z`iRW`Hy3B}c|IKFEBLY+&fA(J0vn)GQkf`iJM_$JmBBzcaBP`bjOwu?y3wOso}1v! zs;MVWv$6Sos)Y6ywi4@E7iG^FYBsgvVtqB)T{%fSs&7qdHI^Yg_oegP9)m<3CUu|m zTNu(0dUIIcyFYFc~~D@$sieO^H($dD@{&`Y(tbVx?67B_`FM+si2r+**Nxi1Y6BL^bn1zzU}3XgM7vg30dPe7--Lc&Sg8Q zLS*cuLWfB?#IQ*CFhe&OopY(&e%CdI9e*kDr$BTV&%7p^Cvh&p6p}AiGbg{kuXUbPGRF>Yosd-B=Gt zdb2<6co%5;PgM@rFYVQXLaobp7-CO>^n8>qnrq7s^9S`M2>NEi=7oA8f?!YZ>+~(s z6#~gL8wtO$i`3J!i(EP6xr52<3crLBXy2QUyqM^wXU@5%S{N6fT$1HC@)@tC16=wY zT<8!*45bDFPK(KN=m49YzS((EQ6D8^RKj3PA~2xYd9Oj+ZsMo%qwxNIDWg%wbSJx{ zbHDs^xX7x`cqIPoQ)g18Kw;TlnK>1Y4`dv4y=t)d5nMt)z@0#uyW^Qi$3u@c47Ykx&Iq^gL!fNzET3zsH9^`@SV zh~>0t5{yTtdgSNT9HI`d1I}TSgb>}lb^Z<6#q0Za$``?+0 zht|H+J;^}zpsp=|Gxh1UaJU~;#(>LT{(R_K2AJ)5OV^(Ra@!dZ&a^Hh{~y;yMtAMn zX`pwdFhbcyO05g``d@#=Fq=#YKguv3#TQ+6mX@k$8CQny3#rxR4`ICZ_+^vw^X&t2 zPwi>G((v+=v$12p?g~Z~CXiS?c3r;s%bKHkT{FH=^mb`JZq4@B3h8Bu+8?gyd+@RI zF0sVdruda!SQhwTx-FwVA=d+};H#{3JFeHl_?31XN4=7pvb7jolj_&(nC2NK&7^dr zZUrYNBcjc+JabUu&v>fq;1`QmuiCr>Z^l#zIyWcdh!lceER)fi;TEL*u=x|Z!sVP} zrgA&Xjep6&(vPatQ=tyhrJF(-B*eIC3PTwKq-WnAkijfpb%cp&o>Eo9vA$q>2(E4z z`N>w3x%`mcJqEQ5^rmmk#FKyh>AmL>vk*BGAY8|>bqwKNFvhky1PYal^c0;0K?Z=V z93*i=k9%zbFLYOqG=I94fY#C28T^K)Z<1N60awxxp|M040PfW7#|5@19nqfgJ0T4H zH-d%1JvU*6OV3!R^BE_nS{8!#7^%!!JaAJ8TJHFo z!(0l+1-Y=xk}%B^(7MaVT{cePH)DubE(Bt1rny9@KW)<-fQKXZ_EUcYZy6VH_%ZJl zpMA#tj9HdEpKq0_TaRutZs{_j#FM%@Hj7^yu^)_`z;wP&|SMu@!{;hqZ#c0 z0tQWMOyrwJ12Kuc`2aV|88Ys&uxjnP1In^?1Qs0Sfryn^5y__%x=B|ZN^~_`6U_gz z;&Fbdi08Y|jUkk3m~Q!%Ehxg*O{tX5&+qljB*H^$9}mt}9Np`4Oc7E<{x3LsZ!&I4 zw>_3(nJ?;ttn@$G!{;zf7lf9PK(M@Ds$vw&5Tep>Z91us8P+`qJb>QX^Dwj;aJAZ7 zk7X&RS11^DrZPxYh76MKzn}+PW7H+)E1th4pPzsgoZIv%M~o6EM_&WIW4a@aFmi>o zy*s-l$&v|nUp(O{n|a<4{q;(up3VRp8A_vPk|J~X@-KSn0`OhyirJ;?Mq1-OQa_`l z#vw0GFQlGS&O4LCWHA;LdlAOduLRH>8dh@g+)Z=JGS-s)bg;|?O892b<`v?k#Fsjh z9@&1?4!a{$Mb6A$?XY!X+hv8~NpO-0L1x(;skq+jZFoGrRre266 z0!0x8n6YvIw&V9$29CL+tt0GL{w=jRh{*skS}O`sq;IeOa!E0+=r_;Qh}*r)AZu%E?f})KS=i$cJ5K0wIFhsXHeXt;o;r9PeX^6_kfy(h&aMa~ z{VOJgqpaq6Xz0Dq0}q3l)66mFRUF|mzoUVDDuPTB(O2xt-Uh-!Z)OAxceWL_qdvxM70CjdouLcbu*UIb>|ZqUbEC@Jq&{ar(V8F+^FfjUMSLK z#3ZX<^Q$Z@F?%%_oxvgYJ!VB7&z*zFj7j;9Aae&>;+8vl`WA56sq3=v6&g|J7na9i zp)Bacm;9xFzyb5J(#T>qlIPMho1zqvB^pXzrieKr+kw=j%;9_?VS|Eym?- z0Mi^VJEc`g6WohjbM>zhVB(s-E}1>O?g#^)CbI2*RZP3O>|fK4`Px8RDtJ-I<|BO3Wa{x?bJB z7hGI-H+6@kW~%h0Yv|jryY@~N*zjeAhd?(y_}h|-rxK%0sa3IlZb zHh0Iw9q0;^V~H2&-;A-zcD}5lcLE7)UpxQKwD|L9X`cr2`qUO2C<-_FKcPZgJa#8s zkNG63qV2lpZ6L2&&1eF1nCw^Z7CJ=0y~RuSW?xM(-HV!p1J_W&UbU6D@f!NjC%+0Q z8ZddlQ>u*!$^mngG*F+2#79e%;Uf%)HmezQXZX}2v+hL6d;44 znV-$aT_!Hj_u|xDMR!mefVzCy#OIF9?{iOFM=KaOdE?Y=K)RB{cGb(ddhDDl#`_jO zBOPe?FPth}wH*USZ1E<-<=_Pfu4Uw*<-H#6r{$N0QIIPG+a6(_AWm~7dL-l)60@N0 z>eah((tX5Lg+*LE|15|;h!z>#ykEXQK=10G`pQZmkTlFSH0WRNLEyg#n0&h{3%jNA z8qpKC$6;owt?#+|^dV*UUQlzCQKB#fHuzb?w0NVoU5>gZ+#4xw(3Js!Z^@^4^X=oh zNxB{$LA??4KQfS!uA5Zx`3LeX+VUug2u_DVI?}vZ1~??dsvm0HvxB4L-k3QA9R1EA z@L?hSCHYdDz*8PS7L@2>!5u+@y3;?r7PCvB5as-{$38wnp^Xe<-Smi?BC%MlE6?pg z>}#Gfoova@W2d=g?;4u+Mb_R}5SyEk4J5cRMo(YBIVxlAg?x??JQ8yqHX)6YEiWl# z_#xey**i(?$~aFr_WBjl#r4f^58-MS%tk)0yNpEiUDiclf9`UgV+3trqyX{)Gky8A zst4+DR=7KA@zcdPmPA>Zhb0`P<+L6>CO?jzrTO!ROfg<88XOyvy3?Fp85%-V?6#n3&-FN|CXZWD5-X3X(?nK1EBKnHS_x^yXnYxcMeAx{1)GK`bk|y%c zJ?Qn@@{7@5uVK=9>`2fK8>TPOYrh7xw%?UH6tV}o?7!}%YvWB?SWiTwfBQXlz(cDV zsK4Xu|B*W*kq@!eqqt{3;6)31v3Nh9bnSXoCR$BTvze=vf9hpYS7zZ%vWa@` z;uYjMop)PD3ForYZk1n{D(uPR|i$*bm&cHqYWj#kjITEy|mTDE9&J-pXzU# zBA%PaczoEjOc5=wWCW8nKsy_X;6|qHM>PoSs@<*65oL9htzBKxY8?Y%oI$K*{?LxQcAr#4oTXlrGHFaxc<8cv%T>?0 zE9d`lbSBJr2J@7X30#ZWR@v1WFFT#`yacX zbJtz#-fr=}&vVWmzI*y^cC-H{e~Lm8srSx@4;(nIYy2% zx&^5^82lfR3s!+eBv^{!RHOI?D?4o3=B!Wa0)C)B8_+J2#{ILCU#w9pBR(2X01I2sIrTgJ97W2{2T~)? zLGs1Pz`F8XlYBh)z0z94<^%a&-V7B#54ae!FF$WM~Lz{A!8 zV9+>W&)&QEAYu%t12KCeC28ae9$i!o;qvzh5D$R7@Dy4Y0!P|O!qCh?bd=S_AkJRk z1XCP%FWq99R4i?_S3`|!k1jaL_Yg$;zI=Z~pIoAgAxpmYx9G~x(x7(1{q9NZAgKz< ztg-|$mM%xracNTRw#1ILLVlt*CG9qLxvzvO=#pwz1E+)lj33{oONE^iDE4@PG}t7t z4MCS`ws+BJ-QtTsT9kh0SE}+9#^|=H^pxNBD4d!qK6d9UM>A{0C-dE92h(2g$kvgn zCGiLh0vBZbB8p0mH5|cV;ELG}Zv)IVduU(iB~augRb!rh<~fH@pynSzZ*h>snA-R% zz0t>MiUuS&?tF3(sM*cm@}KD)!Tfh4Wn)}8!1ssrX)o7?ZU1-HGBQqMsvlA2=3ang zAa%nj>cd&bOdujgwe@n1Uq?(y1NVO>(cmYcRdb(J;`<3@dOn*=lz=^#*+j!Ab-s-T{oU&s&$x}z(Rce z9uM^oEcnC8q}HS{Ps>G}E*1{*+vG-o2;bnUCv zTgK573?A4W1*>3tUTP|eIl>%%`V=II+kPmkVjyG#T`NVFkEqd$MabzAhXS_jJgBPA z(4PYmf-L2Yl^T2vu@gIYlGW5J50h$1K2($>i?*ihD_Dwo`D07A1+@o8Qb3d*Pm@dL zGcIn78XXR?(}7des2#^T_$JB*xA@B!*VE%F6<6NxCjA?Rn6OKOAF2g$*_gcr-SqD@ z$&c5$ZYX=*aRA&T`}ocVl1q4txHqXTr(i`&Y9`cyxBBghwV>YlAc^Rn14jia_p2XP z6g0k3_UrIKE0{T^j2NARlckIqN*u7@8FNIcVled5dmGsF%U{!PFtw$I4y8UgPfa)V zqzr<*d+GUMWnUCg1hp>SRW`1o4eN*1pL|5>ru3?f0)+I3ksS~Xm>pB=FAP6R8DdWA z%pQ~4eoAFAf|G7F4p>s!!5>bLaJoiFH=%|haTzeOpQ~jHcd7qOqUtpdST2>10ndkV z=Cz?{TlteOtbG$%B!;7+B1eFNAN8ZXGol5s(*2h_q5QF$+z0?Iv&d{GO?U%{-r577r!j_QZs?I)nCp2X5^3#^R9(lwZO>F= z7T2d63^2+%fLdVpL1+QZcZ&`{&??y|Fr+8gtXQT?o@)=tK6t zZRy{s%IIwwV5`?H^~;VZR#gXhC+H&cLfjY1p%Z%$sSjjgj^yn2(2DluM9zEt(N)@v z)RZ8+dRlBT4VzPY^t6%j9(ROci-qQwrnWcHOq-YpP+{s_a(fwI1jflml8-Cy#|OW@ zvPUiHS*!EcZPa=&VVAL+A$`1m|HVtFgOh{13@2V-z;auL{R>m#P*|b~-SG5>VixK> zM{lKBiMx?yjm=qN`aZ=Vr^bbMzt6j_iJqeP)k>t38K*^A-|M;0U5CtFZrHQIHvmAI zQpZ$(sQQ;_bCZhGd0B-W-Vb|_5GY;8f*HIdzJ$9%Hv?^5Fz$juCoI(;h#!e^Whmr* zuBJ7WdK$`JVLlm_li#Rhne+1(M*eQ(iYOdtj7%Jq^>=FZEn@%4oxiPyZ=KW>>tUvH z+1`B4coPOv8s5z%jC7a+s^Qu^3% zzo~lScatLqF$Vni5aNz36njppQqa7Xiva5ywXJ#+HD8~sx#?97NDk(2?0`}yKB)qXr4 zmpM&7R78s_(`ty7ZR?yHh0VLhg-q#T?`a`5*@8#SdcI!>kFh7 zrym#Tw0S2!szO1o9FC_Pa)?qh$BIBbKMt#2Uqot#L=;@}b?!Vx8Dg)>Ub61|j4Mus z*-<{WS^ZAq?+-9PAWOoEa6?U~Wxn8mD%Xss-(wJS90U4;3!FNyx?OM&%Os=T;sMH3 zZOR)Rr!sgZ^ABHQR|ti!y=Ux^ca6qPx1MvJjj0ADCphK>ahR)p!l=|D~uAAI~_h102Gj^iBC=!5As8JbCLu zi6aTJhOgMkQG_0u8-zt-T$(QmHkKgu7?VDT%7cls{ddUJM@8av1@_Mat|@zwY9-1(7vVDoOPeK_tJH&ZN9Tx;hI2R~al%MqrI=Px)N z^K`Wq1vhB5Ar)tL6-r-$8{;tZ*7qY$1`;yj+Zp|q@|XN&gQ_`f{UN}xx*?tPqSZT? zUi)|ka>KoBo>P#E*8y+m{8`A_dsi*l^JJ6Yh#xo%5{l&N+CYk3Sm$pxMS%u_Eh`q7 zg;}7Nsl*BuhqPx8tBPA4WJ}FC-_s|!Kq#~6h@_U1Yhql*@t7TudD9yCoBDWCL+@2o zrg5i7gvQUEYQ{>9M*|~GYLmg%jk;vJipyoWnX15{(k?LNtapZ|cq< zs?lmfTK%JC!MKMPP6~q$BW-#?N!Csdlc#TkK{#$N!ApIcf*H+8T8XIcT2Zjzz6DZRyQGa$lxOwGJHW8B+L>sPf`AbUgb4Y ztk8OMZ<~A7at?!%v^UUX{Dih{yzGdsV`MlGxR9E`)X?IrT7ptpEj6#k@}xG$Uq>qI zH_oU=nMck$a3nns)aJRDM#+PNZ={soJzf;Z$!0k^9K1l^5ioa?;gcG}c#@b9o~}sH z&G0iV>bJvLew@`Z$<|+Zchqk|ZAWN^hQFQ=@NdhsZ`R>p!!7;B#|sG>dSR`H?Yl%e2YW!`i(jy;lUp4A>gmy@pxH38Xfe>wMex&;C zd8z$*>-l@Cd=E%)0bYnL_4i!jNxWjz2lh{eA)ebL^9cgGWe`MKt_NdeGmglt+#VdW zE+e59&kti%GCkklQ*-~ z;2Gzal&b<94kb&|TjPCHaxuPP1L(!bG=Hk&>#KH|qLABvYWg|hK!c&aU+3g{I3)6f zxW9v_|3HufU+gNs&Zyt<7+eC0X`;f(WL19R9MV!{&imE8Bpi_LeZ>5*OJN@&4S9xN zUn84DN73J)==7Hl4X?Np@)OD(C&RTUe0NSf;Q{cd^$Fj(pt@-Rhj{LcG+n!4B;N{< zN_cKKsWg%+YcAv}=7@y86dgsunS{`ZmR>Tz(t*Bhu}$d&^TAvTgDo@~K;9))PBEZg zVvtpaEO`P_X;8zkp#hsWRYV-q8?Cl95+O|!FI5RIplQ`7T$J ztTp)t5E@btvjpL&(N|Joj~lGf;rL3rSMCm*Bdos}lEzHg%s*uMOpwu%rRLtCwJ+=GToC(r8qDJt@2rl(;gQ?{QHI4{Eee`;%|+6;3C!t@*m z1yrcodjQ2mz{1}XLYo=^vii#f&R_!Dp~@u`6uQVgd{e&wmO|9HlbSm9fZFaqIKwv7 zYMznwUe>~076*pCTVe6PEalLUTRm!3i*;WcM9Or`d>^X+hgaXH`Z{{loqrrg%LT0OXa>7X7Ry>%NfKRB8IQprVX$=>;$Do z28zf8K0~C}7zihH_(;!fx4hhjKS<^(dLw@lRaubXezy1#2Ds)qly9kTrE({R z4gWHJ8ADPpe;<+)Yg0ed%z#L=TQvGgZ^@xBy>EgK{wnQU7*?q32b9oskzr1<~P7B3w9(sCJg!Ji@&Ru%@hK%ysLas zEx6-1Zd5fDJxWsTH}wxq5iFJNM4`92a1^jUnXznXqlc-&Ggnp;i*k+IEETL}lK7%w zq&n@Cq2nM$Kk2!Qa5m*(9D`&e#0%Ruqf5kHsxv|jM)1EAi1`e9UM3rOII=9N^0;D= zWu0;BqB&9uO1x?y1TB1 zVvXmI>`EZ(&};Sh1AY#T1yof+{(vg70^G2AQeuwv7z@FY@Nuolc1O>Vkv%P&ArI70 zq}JKF)Bc2cZBbZo2u}&BYWP;rRi(^0$<&QVAaC{mZ5dq|2Mpu&HA>uou0}B7*K0Ah zvbX~qr}_)w*aZm{8vmVsg6aBAN&`Kf$Rrnc*@p&ZWd7D0e-A?98L-XdO8FRt_My>!SBvYSf-$)QcmVH((E z7);hKy>3)iYgDI)B}sjX2AMadDZn^oxLlR4SbW|1MPiBNV={a_OHe{D%?lf=c+(*wrBtQ5oR`CjaB7*2&}XUh#wMjS(}9ELGgkZ4Yia+=G2P}VYs zgw*&No+pi-z{6oun*vFIhoZohVZbRD|t6?ZimlY{E);>G8+J*n40D%>ve_p`u2fOvG@4^mT)u&2P} z0pL@aCb{@&ZgR-I`T33&N2@j+$hy<4sJd)Tvf_6MI%*lyzgiCB8OW0|Xw8jhH22uQ zQ*&mfKn@hTWm)_WTwkoH)?cJ2$cxx@;+^z41n4xy${`HPD&IB}VF3D^55=N}ob0~? zyTJuLX86u;W)CrNGgnkKl8xJn0@>M@v2s^4B_U*mC9IlT?+LkiIDoh23f_X8-p4Zkp< zB)*}>EKMhz+|`yBn@9q^q+j)3NIPP}N6`5~o;$(DbowzN&`<7})hE}XdcxiGnC~`0 z+N4&3Wcl6~BkP3p1$nVK)PzD!1_S(N&!MJ_ct=R*$83VTDO-1XruSID!vMe=Ql)%k z6K@b~?e)b83rZ=e6i4<~CY_&MBdPkFaElcjRSM=k=U1{8n?sssS|)>o2?fbPMaI2Q zGZ!9W`D9V2-T&}rKzRzX9bZ03c}5fr>pga10OfI=D4UldHT>G|eS#7EKrLFxw%I#C z=fKH`V1x4Fl2@(_?xyIB6S{d$joenhb97WwhoyyzOf9*Y$Q%+rjeR{IU>v#Xn{{^| zfprpFIRAV)&oczSAT^rk98Fh=mDKIZ+)d z9Q6ndo=new0Kv4XNq5r|FJPuBb*5HgPwe;Aut=rY@?+N0@l?7UOqm$Gickxz3E+L}6b^7YB}>3c z8(c44Bf#EHqIK1sCxbBjt>)vEUhGmE{zqKUxHpLMLY^!A4r{M+P%SD~NWW`%#NjAa zwcm6iUk2S66GzGKaAMmydYWUw1V!q53hfU2o*+F3G$1EIy|i$HU#(ZpstM0Wt~3?dUd70mu%1I7_8veN>#?iJu5QdoW1l*WJ!fepkXa{? zT&}7tAsSdcrXTt~NVYpsw#cx}qfwg)r#@3|!F}p*q5j~-^Typ0S82?>T|nz(MKDxq zxAEfA@Y#Va$>`)FRx*m~8&ypV7jULXeQBPoA-RKedDrEehwlaMd~wV!zLjJ$=tg3t zNu8|6GAdnpi2GQ6F13c|0UX!N(UZ7{M?rk**0~HEU<10}qXrHLyG?x(*-Df23PJ6^ zvWKq8NHBn!xM_ih8xAW5!Kmq6smgnzOB3Hu?mGm)Sj8f4zL$SK(jI{i#Y0d!-pOtK z1i+ns*hr26I1={{lBNBj${wdaCvNL8yyIS_ zOZRCS6Tj!HA27e`eF!dU)B6kj+=Og*@;{J-nfthh?Rcyg*^$bJ06w}vpiqmjIfK@f z{I>|;qO?INr=Oak(X6;P#m0UFljUT4b*y*Hy(y zo$g9e)i=0n?Q)0Ymjxu8Xkjnrb|5EXCtq928?vA4uT*8Z8JLn;HALmqj|7CvMLZ;( zNAW~Hv$-*{l#Z+b%vNL~BGE3w!*B{X>-jGHsOi<`SKL%Rp#c7D9ecKRUX`0^#ThR+ zhf3CHRDn534C74ah+3x%3*7tSWi@T>ZTJKdJ#3@`ZW?rQP?k{ZLX+W}iS+-9{|v$0 z@ME)F3MjQ8Q!j#rIhKV#G0Z-EE@sJMqVej*Eg4ZTzEvAXpJkiLM3l~&c1!nu892xl zPSa(TMP4-~7J ztJ3`#xX%#49cQ%>nN30L8&x6Z3XJv9#5je z$}g4#!{nI=l-RbInE}Z8$p>=E8|qK7EL)qNQyB;=kdbf#D|mj@QalVd(nXP( zpa?0s?&DG$!BNVl?G*&Q>zsvYFCkq!w+`VeS(YbiJvj&yr+4*~A$zpE@xOD9xTvNQ zCsh-QLIT?0a4Uk{hI>;j7XUcjQnwQ;MjhK7;vwnLTO%f8{a(bKq>g#&D5zkRx-c(N ze;>^mxptNmDiROd@~3ylO@T$KEe{d>RS5s&6IKnNwA{~uV=?Jc_e`6ENgffjgbr>R zu1EKBJq_|Nm!!f#VaRj^Ig>Oahv{u3zhO-e3fM&+P#d+1BZ=NLHlC8U6f#<5zF^gxw+B8T zU!{D?(s4fiJDl=Hk~8SH9}hFN*OaXnOh5)@isYy=bOF9ghq+|uF!ufY1|6$9L&xa; z;=UbwiEO3I#!==D-PY$xk+e$wnL8vIwqKs^PyPxLq?JdUa#DW? z5b&3OtRJaya@!))QB)=DuVXnk?IyDCZ(L3(z=2 zj#h52>+fsFW69r@-@7SYShX<&BipXVvytFGfiz~9<-{L*q*Ds*HnQ>&l4bdWmFZNI z5AGeIn{h4w;%T9?hcaD<&zm&jtTp;jyCMKDzZfG|e~f3&fn0g59dK9;eUk3ow6O9_ zYyvAhc|g8p8(&gwV4;vTS7VJpD}hgvc6@YFHGhPJeW-jmxr^ZPoS}c>XV) zi?i^xKauy&Ab`M5y)jdZ)Ky;D%8x_R@*gVX@aUsV+`5Zi#o-0n0%?QT^=chk`*MLC z3>{5aO;n|_@Y=l{ri1b`PBre1BHKV+MIhGuNnfXNCqr%Sj(bKTTGc+4nrFnKzlmS3 zgfVj#&9-AstnTg6zyRI^+BBi%^qLHMaooJ?C;_BQ&=NGKy6o7?s2Dae^o+$HE@|Q% z@aPL#1E(30Zz&%t-wUr|2+L(aQ6czo10>riQzHX* z1jKTui+7r+4{p4O3$un=DdDSI9@eoGqSXl<6X6CK+a@gxoS_6$@`6QW%ppW7m_!oZ3Lws}yEMxpPG2TZO44{!xFdtCtk zcU+AflC`ungg^Rkg-c7Gg{F&V8oG$iQz8nR4~i(%=6EzRDs`lkXddR8@l- zxdKR6J$e_~P~d&W+8A7Xsb->MJ4Wg>&DIZ#wu0;D}XQCAGxFgXsEM1_X0x%^0x%CE`Xi;mJ7t&W0r!&2}WwAg5i z!9RRPGy6S{>o{sk_w_P4_sqeRd)3#1U_{UL;p7IN%CxI$2ZPPr2f@@B58Tk1A3H96 z%{ytBJ%jQ_wsRi@QW`*eXl; zoofzV1#~i-8Hx0a@m}WvW4awx1xm3IYfv@aB-rW{NDHmcXW1#JXLQ)# z_L76C&b|v23}W1vi~c9guH=Q2q{c4$rHRI;W-_$jypQQuoa1EwUA~t7ocJafi`NN# zI0g3IdLe*TE%=sN~ccRwHuJnL{@wFQt}lyA%? zs+o(?oUd2z(f`1el+Fz{sj_;AhI4-8+-ovJFnjV{oc5@dxtg_#z+s*Eh%A!%Jf{uD za@H$YBBk!Du|NMh7G^=3v&fp%skw7*5bo=M0Dp_+>sMtwmb>&M^N&4b0ca%NAT9Vk zP2;_t%ovo*{6}m@mz60I-?W(^y;ngT-dy%CX?Bwz9ws&Hrv_f0$qqG+M_7IF_doyt z8o-@6j+S*o3<*TWNzD$9Q<|TiBqKDu$F!ei#K482Jx`7MKHeEJxsMFpXH|V))~!i$ zZep^gRonA4E_fYWM;9j!t^LCV4mO@6UPa)^aF!iQ2nHXq-_B43eiEBwF`M%a!Bl&M7d_jaGj;+SeVs~WBV z>E2%2d(G)M{~m?0`?qHYR9ZT7!xJt&NXsyu<|k94>Wy+V-G<&#P+$)q+GW-`#R>Iq zmsYxN;;~?~V(11EFJq@&tOLnLed0++Q`4l!%yiQm-T3+Xv`*S}U?tYr!bLfGjOYai z82Ooa)4C~@mrey4rrXqvf-t-znCc!9{*xBBiMTLu^Z3T! zeBwC9D%aZoF>>5PsQwEXTHTLVXy74i+pV`Aj{$b3C{S3_;m;1HGb~4uM)?z}7I>W@>0s@%l#*m` zyi6$5f9NBP6K!hfIp5NcUu`kM?($U&#E4BO6n9#OQg>o|>Z$Y$$+lE{3J*Z14l0($ z4Zb9Te{jS~m0g8Xk>vgxDSIFr=E+Rb(9oqA6}BZPQ0=TG>`{MXFEKUR407wwQ%gi& z^QzST;3*6To9<2|yb8Yd6tp61hhN{pQY|22udk-w_Pnjb_kKO9@#Eo9)5AnOY`4okB`jKRax7aGekLN9>+vB(`D0Q^Qz?iGNHq4zHZY)QAIrH{_{upz#3-l6JVWF| zC)tjrEw1r#q1h$w33hh0WQ*ZgX*WFf**=tBnK%XL&jb~0k;n3)k-Jrm-UL*vaiAkR z51r%+-WWNTPe`d6y-R?=_^Ay0sFs&IP4H4qo}#|dN+fcGc()m0SPUa{tVffVuiF!% zsDXWlkMfx1#C=v-Urs}e$B%m*RlhEtDNHf={_R`Oj=cRqdPFJ?-jgK~lR*FfEceil z)k1;P8gKs37h#;@qMfl-b8#OCiE8e|6kC&&Gm2g~r^nF1X@p;piPNgx8OFjU-yM|Q0`xc<6z*=^I;lHK>Ie{!okcSwevsYOMkEaXCWC&6!PCA{h^`68Nv4K*Bzd;KLFXYnDJ_qrlj zbV3M~2NMdVooQr!v;U;#i}gwCP$N2>)Gh-&%lf4>cW;aOmgER7Dtx6I3(Mj&|KIc^ ze;BV>!k_%c0&Qi-7h!kZ_wP_$@BGy_Izp-AY|41Ael^N#scEUXne`u- zsT884mY0D%eTy`6%6PvjZ%1#!<&?Y#4qYd{NsUtfRm<184ipRWc8^#2f!e$bq}sdL z-rz%uRBtH8?KG`4O!Po?&UQI~ic~)(z;98lIkaiTJ#ozsa?|j6TrtAbqvjH+6I{Zh zwCs@a!(76an(}z{2A&2qFa~GQj^%H=tQCAQ>|~6Yqsh^J7eY{qJNQl%o{g=jL2w%A zScT#JeAo>KC&TICIp;eOV7>=xTN!w=3O&F=^WJ{`4CkV+cQVsv8lme%&G_f{ zCWGEu3^GlB{C6n_KpZ`iD#L-OlkM~LI2U3r6D&73A@9vZpx&!Cke}VB7!H{?-A1B& zj^Xq&cSB7AM<1z_c2zAk>Ez=VIIdu<dF&Ue)~PZ@be|Ld3QHeQfWC4 z(xa>+O>uN^cL(jc{E059Xo$L0?fuYCM4hVoMS#E5lG9sH%pwrpX7?C_4SR~>2q)K! zxGa+y5aJ#Yg7iC`f1eLJHJ1(Ux6MAo)AUev?fh(`I(EZqzJN?<^E`^|ZWi;zcx*_o zh#}OhpP!F(c^ZW;iv#GCxk|otfuopV=N@lyOeV}Au;3eTkrg+Qz7=9=?%~DXj9=#hF)?Fz zkEWfaDV%6ib<4VwOuIpNK9P?SukeuH0ewK17F=nE8l_P13tirPsWwm0c01%Eb@Wcl z$Qdr#Ht56~ne)L6+{%8FVNUXkyIpm8b$B0X*r^kNEd9moPa0Ri@cb+=tWM>L{un%dby@rT=>(kqO2jcb-b`nhR zRMq~3;`BL~sb!?jya46(3pSUkGe|bptK}=9eocx_*bWB8`{Tarw1ThRp!W-Q+dP&; z3>QSHUwhq5XON&$ly60boJ?uYQkeEIP_-7BuzC0Zi}Alxzk#i%mDI}g86p0~h9`jiDVVqqosyrQA+n*bDptDjf1^55 z(J>ii1peRQ!_w%_7cA3D35n#{FHpqrU?0!f$pk&EsxDsre3BJR*{+MHur|qf^R%T*fL`u&N zjt}!ZZEG(TYW1A17D@FZnR*feUOKK?KCsO((g?S@67S73ky%|=rLUv`TR>h&LyFNC zdH1E|6ze9cOeb=<$4=w!K^f0T2Z(SNW#8OVhjkDgX*=sUh@6z2TUPJ z52Iz)LUcn}=S6A0BRz*mQhZFh|5CB(84Fw|)%MwHmda^+N}%2G(?v$;6&zKy8E~c1 zxXxsy3y+qHpYnJT{8GMg>J|PtsJ6>8ph3~#E3!3eFb*xPuq08g`V?CUI0N58{!odG z0X*72hO)9i2BOgnionl`6UoMxqWE-j%DfAq!(UAJs{IwAf0L@Ajcob)pEa^~%kNtp z9d$hrG2zLw4v0L@!WUP&Pi@)2B>M(+oPN2Nh?QpRle`FU~!!0#*)4(#4%`mwY-a@b=)OSnV)1n_ifvb;9#a_(< z7GFH0Hr~s4918aT$o@a}H$oY9oEKTkXZx*jK7uq*lb4WmB4<2o!+_1pdpMa2S$eEF zW61WZu{R-B?_)9Vr`}z=0w{v|39?3?)F|p>9dCI>O%z2z!^|bpR{C-Rv3RjEbW3%9 z){5|*1A|q1Q;}^nWHAVTxImT7-E`tW6waWmglth@alr+SlOBFTo@~`cAyNH*d+&sA zj@vT^p6HsSBR~d|yfu>dz_&nEcB(*&fBRlW{^0mGu?_T+s$c~s2B_W#eV{kzHV_hj zvj`tVMdmdlJxH;&z4TeQZrtgB*NO!QIfnXX%QdWclu&%t-uoxTvh&wK)DbLYlJB`E zj98g{LM93;g)JSvS)|5}xk|N>zRM~pQ+{4EERWI&z84I&VSK#zjvu2niNL2GOk4Ru zH^Au`nt^BU?B!3C;ge~0PL+ju8Apx~eEp`UTqh07RM(>m=;n7K#eS(f&p_2_MChg? zIlCt^c>>E0qHC02*&JXGPw^ThF10{Au{*<|QWl_BTFe;xL%1RMckk~qB}Q69PKYU9 zrgxLF4~`8gWUa`XnEwWu%am`nrr_&zjH~i+EZQB?3Y{gYcee)Sopd@x%E&FP7|qgd zxu0|F-(y1Z@Q0d$ugcFjlrD{`Z$I944p+Rc=5AxOE3mn&>g3v4z)jo#=X4D0UqEg* zM^Xu1`9ZwfqZY5?jBm@Z!wEo4je*r1-VK3ew+3T|23AwyUsKh57*$`x_JTD!Sy%Yl zNU_M>BfqY=VZ;VdDH^9#!($^pfk*$5k76J(t9TPkU;W2`cE;RW-{iO z;4YgqFR#ZJAcb`#{J-y(B^tns0cwq?6C`HR6^%`Q#eFib<0FetfL!N8i=Ld2QE+tk zNi~9x`GF6}P*vJ8k8)ltiDIl({v4I@LK15;%q{ts04bWTrSsRTM2$a;M;w?v`GpE{ zBAsYm9|(O-O*Kx4r&q@w{FIGC*pe>^%ZDdKwF?Gt%9A950cN!UzCq8H?hiyNv=XHJ7G_Af z+d#FA;;DuQ7s zBLM)5kE9WPBA#mluyS1nrN#f1r%9-m6v)_~OCPAc{>C1+?({}U&Smj@tP?K%Y$P#u z3lom&PvQ-(zj{V0X{wN#jZ`=~^Ugn0jS~g7MF`-44*tqyEg{8D&A!!$h*6|qHEK<^ z{=SD=ApmtjcLQB{)HPLMN77;Gm2H>|`vx2LcFUaEEtz*T1szMlpwy^Yj%M`?ds835 zO=!fuzxd8cM*MLS-=y^IsZ5%F2$1U3kH<2eLmzLSpFRJ=*x-P1EZNG^hh=x*{h5H=G;T%EBJj*c;}TZro^+Q{xWsfOY&Gy}MmV`i)n|3V zOTOVd$AA&=W~mtv0_K+|Q3R<6Lbj>_S2i{p&;k7Ahni*W zF84f{iLAMIrX6tB&m zg&D8iKHb**J3k2-FOkYyh?eF&M&1Uo!^Exiowhb#*;HV> zW;i?a#3%6XRsU;fEOdEg#=B$OQU8Je@5ZCYrtr`Q)t-j+Bn+!j181Z+%kVRye!aJDcOR@BTVTf7 znA;XQh-D?nUzbB_fv)OJY{CkMR*`6DqR)f>*YZ~VMr4!E!wS>4JH)u*D^*A1?_;

VF4J@%L_Artyg~Y2{7tM+XvEJ)~-O;p+S1aDYv+kYlZW zI=h;l$zrsn{0!FN>YV!hT$asM+kSd}YsgPxMt7O$rLmHAZ@g_YBw~L*vBsQ$n9lab z8+>2ATO^oFv5V%H^y78;nBfm*nrC*H+i=I;Q7s?`Fal^f@Phbsl=&7o5-l z2~U>6rl@VU2`1`X{nX9l8Au!`tzEXiUd)!&VB>RqDj!lWKf!<9U^%=p#a;wsw@jbE z?4|nL7!R7)=tAcIb2Obx4g(?oeOrXr9QRbMhxl2luv=acpKemAB?aUI^ytgLG4g*Z zZ8_2kncr*2%pLP|e8b$11Pt=+Y^eg%L%>y8Ii!~4N#A+qIXj$D*~ zq6Y9nCSt%2E74>!7?pV@OF>ZtjKAEhRdsAO&Fqt>5O`^RNhS9;xhzL%^J9XHq+;d~ z<`QrsYU!k^umaY!0DyYG?np-5*%hr89vfDWMjG!5NRIR&Nv#CIH5YEazAGD1oDqg* z3QInc;%JhAL)gEni#p`x@d+Gsy>JqD1~Y;kD+N?Nw>k6v%L5WAi~4V?azAKwcx0S7 zl8XY5#L_1GSGHap3$O(ZqP%4}jGVwQWJtw-yH(R$RBIWw{WhMYHdsp=_B13`RmL5? zXFT$|a2R9SOHOD!@I0J^*ASS*_eW*>sSVM;Or|gcTzt*nxO_kjT4ASKys$*eMU3aF zj;BU|UFMbNKr7>T&U2}f;Y0XVZgj5b1uwXu+9}D7-yqZ&B~43)Lnt8;OGdtC%wXEp zIsLN-m_T)0wuht@VErx6*9JVZblLD%y*I#L^&hz2zerIZp7r}PKLF5J#vUYhTaln# zmsM#9(=b41(inXfCSY%2Vf)DdEuZlU-S1-r?ND3rVAoFH+YT_7ykjd*~LAJS? zB11Kg(`-=R%zgHn@;Ox+JDl%d&}k7_3_;;ak2BKoT^jP+aF>!O zvjWu|rEK1E&Deh{#zxjlT(yhC1w-R#W`b!#md>cZ&q)%{sm$Ulw6v5Yl>Cmu7yrPE zfS{kem&!L!!*eMv=<>sG)HTZ5F8gGgnYcnN6?Lc|G%_Fdfo{2)LEKospx#bgfQ`L3 zK5F`{;p>lb%|67*ts755cR)eO;WeYL-G?z0Yp1>FLxu{D0z=DD-dUz(;{i@#28dP; zN|kvBRg)xZFAR(48H@|N(X{sRgerZ(8Q1dj=C{cJwk7a`#1X@)G)r%GtBf%C^>3Ka z7B#j*H_NdT$R#z_*7RD2_ee|5$s;Cg)S{zxysCYU7}Gy_Iy@|2i&?c=IQCwK9+I;= zj1`;9c6Byoxc*nHX8*rnZS=)!*?=^RT!bUhvcuJd*1Kj^8?h&J1AK>hq(GD;S#Tc$Xy3UX`k`%uK$2m0J;rz|_oc zsgs}YuoUYkW3{S!q)&bA+}nI2m{IHlfA>Sx#Hj7Fl)5^CZXu2v4@g7I9OiVugs?Ol z`;V({v8tkwJ39F(jim0>X&sx!N_hnLRdb6|yTHbJaBF!M>zfKo)a>OH5L`~Rf|(s!Uc_#Z!_!jEB0TrNpp=u5TYbPewVdcf6|DOgH(0`-vd+X{R{~r^_scH10lq z%Ls!2XNMY(1LIzCR7=ihg#JJyJWh3lkj~bk)fQVIn+GWr%!5qzY-B_F6(it?J=T2# zNo0v+La~&^2J(%k&Lh3G2O@|Ui%wwAX{v2}VjMbni}73RK+=XBu+ zSIFyh+}lZQ_;2D-nW4AC@XnN_jJQJNlUE-FQT3XxV(Rj5BB4XzxCT$d9Why!ju7=d zoBGP^4*BtsgX%Rv&(&F;>f1q5JiwwD`I+;dyGgaWzfui{g)Ui&2j`>CdI~FzjUCU` zv}!+pQ^;J8ur_r6S&pX{jLSR^tu5-?aq#njQwg!DBokcU(lJBzgHV58B$`m0F7v!T z&1$mrNNLsiXFR_yWjEBO&khsEv7L_r`Gq~ARdhsZ4;B+Q^MNg=*rp+WVnBdiU(;0! z%>txP%hCT6&l11^7x;}H_Ebu#gXyf344&N?9IsEAaufxFB7pRLwc_%^9uGAP*CuI@ zyN{6KW(wD*SfAWTEYf1Ud*V;T_lE{-BiT*{l0{ECml+<&@P_(*Mopa4@m0H8%swI$ zPV|udiLe0_l3w6?VKx8xK%hC*IgM}Zh_F`Mp`_i`K}oArtsVm%%B0U%_1|D)qcppM z1^#7_@gl|AT{S1yJsKJHMYj`*P);+Qy(U@YX zzlSB+ul^jcI|$C%G~B<$$DG9agO)c)!(w&+l4Qm-?NapKlWQ7t*5!}3pq)&3wlSl? zU+adeCF1o`=iAqU)EIqR?PNQ;Ip_NS$Qej`=4m^gN^_M><{oBaox79iKoN27wpw~Y zO%Sk1Cw)lzV+0%huhokPM!sr)Q6>!u*veJgtA_NHoKn9c60z#r_f$iwEGL2~KB|RF z4j!_Kc+7sX8S z)Aac@Hk4cqOIzsZ)iXLjKyRJ8X<;TrmJ?(&L^=$qzbZ9uw^;>8MA-UDO07;KG-~v{ z+fyL!oQ&~jso7)ZdN9L?y>?Ko7x-dZW!f%7wtQGE%H@@C{uQLxChr>;Ze-v!)^LS` zl*78nVh#NVh*;JN0nZ-lv-u>`_zJnhC0>Vp3dYIkhece=whm|-ywqCuYv}t@m(s^4~u!;TUge5J6lVO%0V^_OGsxK9$}qZ+dGA_{Ic zWGIygpoKB_R^vl>$w_44mrlQQeYO4cLe<9@5G(juFsoIHB#&)ZdFVU@nI^sHLb9pxAxQt5C-L!?2O#n1Ne{mRTQf)s0wB z!mUQ~*5n-p_1$`R9H7E-3zTy}ZQUbrJrMHN7w7zq)LmM-qgr(FFv8;tSX#(ZTa1$R|6|Q z&O3LImR{rxX)@*W=J!hDdywK>woC`#Rp0q*PqpTgi)?k;u!o{oR>OT?e4tYI@M897 z)qfs*;_D^VQL;iHLBnhRbJ=Ee(g3=*qR31G)MptFc9ImSPsoNo5`*86j(wd#(bS-f z-eq^N>ZNaOP&#aq{DJ1*j2kvq_<^%mbiIJW%z3=ZJT{e??&Eqv&9`7~Pt#`pjaL(1 zyc~FY4NaKUJ_|0=YWzWy%tt12o7dg0J+lKIC%dZo>lmI=9b*g~RgDJ=;bSH<_mCs| z3+TeyhToh7Qv z*>HFq_JC1b#D_BD^M^Vq*k>dMBQsrmbvLM)m-?1KuO0!}fN|yesq}k_2~OSYRI0|k z0s}BSz*ZXItB!E1%|>CW4B?zgF;J2ArZ{12xoUkRorO@b zG+L^Pu#mS1B7_dy)SSm7f>iAIHV5O*j&(J>cRy>oQ9Ryv<)dQIf+V7GjpZT?#AE=a z?b2*z(-$fxk``vFf&`Vc-^O6JGYT$a=hEmo?13N;?@@u*ZH&2}%q7pcQ42fr6ifLd zIMcWsu4pSARQ;qY`{EADO!!rhLaJInBfmq>kUY-~mIc|i3(6u?-lu&9^1z49tBo_4 zu{~-Y67a5st6u}9lfHYXGu0I96XWR#l#boIPI0qa^!jiQJu(b|l;IbCjkIk?mW|0h z<*HPFkfH0wu7`zZ>~HXo>gTD$T=4iaOn44b+PCli)mIuvw3Ij=y7PhFCYj!il*@3O zaqV%WbmzXn6LZ1aDBLoJvr7Q+jfvonveQ>lOtl5$gZg_%{lTYoJ_31kP>B4f3mBx? z(Z-hh_`$Cn%fEN{JT@Q`E~^Fl=#hiL9K)3$VS?md4NQ(iu5_bBg6>Wd3~hO-x&Kh= z0ZRGg)yM$yI~82k`KwVcZo6nOrKiPKJa+MLBgUCs`@k1nXcPxnp_-pY@~p7s7&{(2 zybV!RRHBA=^Ar7`F3K;!gUiLI5Tr5cmCfLjt@vm)<7|o#Lf@*S)j7rp&WL|Sa&p0o zzKjlXCOtj?Njz;?kWZW?hp}!1k}KPi$uOXPDWpg)cWW zZ2}7PqTGErD>ZgK#Jl+X0TbG^AhTyax}zXAB^lN}+|-B>uR5arY+mLO#o{*gtfvs6D0pgcVBGI4&TYD@hT3WL|L!*EImyO_)| zYqVTCTtS-A#W7rN(?RrEZ)ci`3X0^NDGv-tS-ARp>L}XAP%1txSiAoYsXm}8Ze{!* zC&y5+Ksw1u8TQ1((kw~ErT^=(d6FFpDIaB0E z{5R}Io+pKW=mJwV037dlJ;!s zd=dR9sXKHyvL7Lwk{F92%mSPK$` zGFEXtu3DuqR=qNGj^@iv^`~g3`ssqEktEE`tOjfo5;Xc80pyrFO!hY9$F$eAJd_(w!_;g8Mq?f(vSSVc; zAJx1cam;{=sw9ZK`TyN7ze_AHY82gR`}LmsXRa%sW5C*a8mso;l7k*Qza#lY`J3q? zAE#nz;jZdhR~BEFWrT#&Bf>+aZ~j;^e2Z*5h=a|Ytm1) z`5WnAp~p~_x+3=JRwmVuhUQ2<&>Ib$FC5*9G4k~8;~TCD)p6$8z}^)q+3^H;gf8WPXyMWHx{0LZwUgL7!)dH zbJI=j)N%GOFsN1ed4vf6fee3q^x6Kpk@gY2VH%Xr?t4^h<*eFD3=H91h*V#dCG6Jk z!l=lTn3Os4`!Wn|HgVK6g&^E~FK{C!+`@E2MrjU=^WaYRq05t*Ft~C|NCJsv8S*-# zt<1l7PG`X-n>>kS5T7v~bzN$Wk|Q|FVV)nBXn3Si6F1d#9_(EjiwW(--Hh)xxvD-d z)`DhWZ`j_1TU*cFucML`(@sve@`8S6%`*B+$_^YbQE^Gz6_*4Q**&5UB=T0 z9;L|)oQ`o1-t__=VgTRoS84i$PU7t6R7VF1Ozd}FQDXE}^%s?n6jwc*^hY>!+ z19bsxq1~M%0#voW@1?ZxuFl6|q1I%iNPcrh;b{j5wCD7*#;ak~nvVt3G-dsa2-+23 z_q<>3mwFZpTbSf`Q4$6ch`Wc%qx!p34Tnbs8$SDY2LmwXPZ)b%a|AseHfe^}CF$HD z9cRdsxTtw&nY^z$xJKW{ymWmM--Av29mMH2N4(-O9`Ud9Y+zU9Qq*@ng7+OGvKh)$ z>0i!%O8&_VF4~E#owVL~PDrizY}kBbiqF%jMK|z2ez}v>d$Afz_W%j~K4{vlJN8+g zAY-nkuTNwp03qJOm=+`MzZ~Sx$6%wt4RAfjj20kP)juLJ39AIRFXOMj*b=JzMYyVN zkaoYMR;e!aBD3GKrTH1=6fTH8;Eb^s=t&IPLWfy1i4SmWH)*H7Giv%4tu!{457M!* zo$e+^4Bo3QDu_=>n5l7K_x$zaST8G8oyJT|danNX2ASfghix!|&`P?X=k4k{u3$l} zCJ<9h0`WHuc0FEK{jzR{%%0$zCuEpQBUC{Xqit|;e+)v80RUjH>UVJ`zH*OyVjm$t z3>NSdT&KN|6x7c*7^!M|s`b=yo_dm|X>dTQdYr><6ESZg>W;V3s}krVrGP|9DBg(vu5MECE7KHMsCoqIfnCYS3Nh( zt{IMwj9L?Z^S87X^0}%Z_$wENpV;EdBkWBDlqbfHr^N2ptt(q59VRS$l8WFb=g%iaxq2`Sf{b)7dc1Ve0_<r_)T;ytxf(Xms9f3L(L zP!b`qJeUBiuE96e6c~pdh9jkHnj%-NdC;j3K(Li235ahh^j6& z_Qnw2j)kaS*L1Cw$D`wggIXoC*n@RoqZT{?OcYG0`n#N5)q8-$jw7%Mo_5nBP-8>K zWsW|-ZYEl3*g6b^_x^B%0PP@ObUr0W)xKoIl_|v<@Y@F?r8oi>s@}nYa_g5{}Fqhxo6`VTb?YY`GBwH@GN#%zs^G$;45(3R|My4@`g(R zV5FrJ^rbl^FrxUk{MfNh>fbQbh=#nr!<@u0MQ`1y(!K&6Lu5bSN~9@AE1ZcwbJ4}w z0|&9}ghJnfv7m2KSmyTaqSH~W_ce)y#BYC3uY{`&%Hbz?3Tbk%6`s?Mog1|+%sL^e zoiN3pwQe`7|33X+441CPnS|WEfn3rr6lpaUtN|liEekL|)U~c!<#rTz&s8wKH5p)7 z1q(X{nbr}Oj0((Bq_gNL_;V@^Ej4VW9a}$Vcw2PI!j&64W8=`60fmqHE^H;JM<-o zeTcuMEI;50-^ZzztzEC5>1^ivo=W{CUz?ZhM2@zG@&%0O334=f2>3VZ}VP$~VyCCBxXoiuNkzF<-?eQd1j%Es(J zLP^VCpm{a9Myp>u+zH0}2>*^`OZ6LBiiack9}f`XJ9&We%)Qjy4$0)25(!+`{Re~F_1m=cRe3wZ#WZPGSv(5XoO0JAM18LRYrwJR}Txs$*C2z~(A zO;fgUQnO0YD8rUMKDZ6;oMa2*((Y$q7>Q?$re=j zkR8m8OEyHBwNi0M2WdZn6VqvT>suTAEVtgC&C!&f08n8K7@D!|(n=}<2KCo@N7^}2 z(68i)vgGp{0t}DE&uI% zwO7dh0pW2uBQ8fD5@7tNvxgqweLkM%{r}!tFLQ5Lh3(F)+bl<1dW>%$1YEa-9&x)AO2o)%+LHHfrZj@* zodjof0-Y8qu36B(zHhZ@ zm*g9Rb4>x|zE5*fK7%UNe` z5==kPhAge!P4QMIh-e^-+%Z_Cz>z?wAQ+F%(^`z<#%y??efpOz5Oav zmi(j&u8|Rm7o>6b1?<9d;n|haaENE5Ee`IXx-i{1?vdG`m+)hM;6_=MS85(_f6HR*_ymw*nZ0T>mRWZ^*^eX8#!8+3K55VmV4yP8YES`DkFggQn zBptXawsD4U#Ac(~cu)CK!c`KE)~(XIQ2l|{$ZQ0cS}GsUU~Qv$rPv=~{N(XRbR zH}my`D(w2y<}hjUCTavqeuj)2CfpwZ6ps}F=mx2iDkL#lDJ{u-SqtKM7We+i!Xr|b z$05j^^Eqkqq|S0lKkYuP^=WAJgI7E@5H-czEyMy$Yj+;_=BYNEL)hvM7~^>zL-ySt z#`HbFRLT$PKN-Am&!iN~?2}7=JTh3v`qpzY@Rk^L@xF`y102Nd=fQB4(?~C&ErqzK z<3Aiw#U(4Iokfl<{`Y+xRyf^C@GN^{7{5Q%e$po;9#@prL#~KsijGmZG9K>^ z5m`m!gJoMF&~sMaDFXU+65q!Wr!+Ej7OLSZlT3QjcGv||A1)I)92JZ$NC(Qg(JpfD zV_C2#KL5$B_TPWbuev0h*2Qe;%`onJ0OCssK&utPFlx}70R>m=ZP$IYwiqb%o6zu| zEWvL7a?sfmW`p#5X^&NEOWhca>wW?Xqf20Y&|}8g78w3eyzmOE)*4+yBn!_`gqB-ZYOPvjmK+ku0jh3?9118H6%6-leM zlC-JgLuG<%>VO4`e6gXR06r6vr3PF!3{(qQ}eOvI5lc?bcLJ+LW{KnS^=r!V(F{GsHuFl)p zZpBH5>vNCe){Qah^RQODLlN`tTfch4p7)p8NIq@MC(Rvmj#C@gh7~#;hI5RLC+Zo0 z3xdf#CpAuc(cE$4e+`ZJp*^c`!1$*rZ7S4;aLnbsH?arP{CKQ&T=E8Kj^(A#JbFUQ z=^y|_KI_2MJ6drEg}JAdC;cA*HF%+6`#^$onml7k2g9&+MMhhoazUp;f-yET-ne%D z7x#-Tqra4}^@h%|li;5IGnq2;(kWV+_fkIxe$mB!3cF<7!fwg8;~@DtvtrdjN2`|? z6g!b{KH?653hL;S4Ch%27vw~*>TiT?9ucGv5k1_&a4Pw=HN=WiXMi|e>sIn^SKTe* zs^l`fQ@T{*u3TP~Z0tBFq+GKgwbT6kAzDEY~bU5eXC1cH9Z^4Mxtc)^6wug2)eR?O;Q)~HYxSdn> zHax@U-1{Up4+jcNm%2*iC&uP$bMeLo|JOV3nq9kf zYC|wOgi)vHXG;2a+H*Ot1gGwc-Rh!9gP}Ntw_UsBTblyb2TK1EE;{AO)K~=Z<>4iv?daOHikS^F>7Pv z`ZcLk$Tg-My#yM?kI2Ww01SO?h}p2?n9fV*3%)qvI&D`7G{fg82|^(HuH`W zdpC7qKURL7wuHS+l&M?h#^quee~gKlHS4x+<9CJtRK0{^sdi_s;w9l*hW8!-^0XIl zBL}X&K^jVZ$$2+I8by$%>h{B-*9LQ=&8&2^-P+*AqN>Zpk?cB(pC{$m;$fLBN(EFT!>u2XABT)HCsLW9lY3&!TNln}nAz9*$IyM^ zQzD%dX8hk49n$Lk33sd;q(_(5;w+RJP+ ztAc7)CszZ?d?M&Kqq5fuEbGG#VP{C4V1NMB%R2m&VrU1OvwX*FwdyD@06vp*w}YwFGw&72^+xk&oVG;`S^J`h!*r&mTp%6Sanr%zFxMx1gWI{Tx#j3o$*VM<&}>>|9J2Z zoBd@f%gc5rz}kZTBNNM}&4(ZDJ?Vt|U9k)8F4eO2Sl?0RrX2Ic1C5)jd$pT(3H^AI z3^p<7Q(#N)Y3d}c*`#GR68|655+jhEqem?R*6nuyMr8UOf8irP_si!1?aZ)@Uz`5- z{MtHVt!j46M-qy5CYZvURVjW*_{NA%!C!XN#7)yO!OVg$G z7{=ZQs`9_k zMt>aI399o!lNzi@r1cuj@g3h&(s{(^!p867LHwVg`CptyY(Z52LrOVzo*reFW-x!Q+n~yCZI{Gp- z?9ot@o&2X;zMbMof$nS8%lE7mwzqq)Uehticyc|ZH?$}FAxg#kPlAFD!iNt+LZOxh zE$L((ko+=F3vzv=f+|D$XqIG%Trq7U?9QRY!ilT|0eij7uLsS6s$b*5DP##ca|nuE z|7G4}1P^NP*KlT&LUrJrmUBqKFMuy3^8M1y)g%l8zf&H}2+MY^edHK3c}$X!Kum|F z#ZX|lYEROZ4uF98WW#be9&Mb`V#^kEfKFQw;4falLamQ`aZ`Svv6zt-osWBya}%@N zlDG%+Q%>rwPWo&zx)lwp!dkvagtG0Gx^OE{WXf0&RRCkBO*}I;Z8~vb&psdgkmDpJ@`W|PSmxZ@krcQ7GDrU4;3dtWJxwGc!b3D4HxV|jt_skrL_ z#`(6cBBnWG_k@}8ki-VYggWaAD6kI}viwa5{@)$yaycsZ$NAE<1KXu@J~@^Z@wsdQ zZYJt7NYN^{2FP$!qhKYbj`C94WT%6-0E(;tkKcxjbx3zOa{puP(=8=D7k+vYG-3sg zlQPIRKc|vXhLX}KF8hR^_%7el{V`4vCE%?Q7a-D=@V6{Q)CHT7jy-YOtCRL{5kWcW zd)B|Di^HNoXID}d4?Ozq^U~TeO%e_p+)PuOEL{P49NfO)G?6b&&?aCd2QsJiHUP_j z!zo_0lj|qVgKUna@#Yk^kgy$yKkV-UEWH~jBSk4mq!AaL{OzzJ7J8Ga)#NOPoTc90 z7Ipyl#roO_NL|F~J0*ahL-vA|d;Ji_QtU~u6+Ty<$k2bm_|x5_asc3%DMFjFJ=n6N zfmo!2b}`)Aq}O??mu}r1X?%LgT+B)fl;8Q@HcEYTi9WJOtu!bFyV3Hs_94cak~+Es zv^5qr$;imk?!D_^5GS_ct;?Q=WBu=M_q{89*Y-oqhVtkYS6KH$rk)X9{#td<63xEd zC!esEDUlfv!@JfdU?8bY`0!2{gaYpUOWuC&|LQV+KRm_gVgeUSuG(8+#kHCaf0S#% z#H7pEd6vTxJzBG4n4EoTH&I;r222!nx;%3)1(iP!+|h|-7uySMy?#9;H^+oUzR;!s zOzRS7vIPHnD z0^&0Ia&%g2KtDsqDTm;k5$3yzu|Lry%C}9uKndxUJH9V7ndDL1acOemzKQX$>GQXr zpbo#Da!K+loN^Kt*qRN;y)7`MUm=b@Y3mUe`5eJ-{?rZ!>D#L;B*A95`zNvFy3Ir# zVX4b5xM(2cK#q*MzLupESmejr7slxzcCwsECO{iE6yYE+G?la(X3#oQgT?v|AS{!A zt87x5U+kusw+Z9tmKVF(HIqiW{&&vnt~%0hKf~xbbR0t2iZ{OQ)!z6x+%SU~dH3IU z_Csi(>)-EPSdoYM`+g1F(C_g8+ZH92FIL#?;qP@cK`8>zo^$y99~V|)YDNk(;J9#J z4KkmdgO8<9QF`xL;F70qJQS4o=81*8e<#b*9;tPg3S$?>4u-AlFQJL9TR!J**7AJ7 z3K!G=QtG@MZ1Ockmruy-y;R6csW<`t9ZJV@DV9SS-bujtFwom3{{?y?Bs1@BLC1Hc05D)kv2sZ<@{F8_ju`w1^b$Lw@ z_C+f$qX+1U{JJBw;UknivE1|Ly%vMaaIXoOO)bQI-9(9)qRD8mK-7b+xblVG9*iMC z#@c=MU1n3@^+;**PlMB4%fx-aUN#gtzaawR>_*_kKIn%{*(UnM{bRSl5(Z#Cpk0M1 z$pBGYMG;@zkM!VU-ml z!9IIle`q$>G3(Qr!UGZt>)L}dCR=fw6p`F#Q3O0BF87wec8qEkyLr1Xa3C3d}% zWp7BGC7)RS9 z?FzyKQZi_TnZMrU2S}}>b`pGMb4u*clI8&B;Y_hFg&(5Y!h}ak4a?rLsheXrDOSjVtLoVC<9q%+>0o!1jvS&C9;Zpa=BI{` zPdVg~a*nZ?wk>Ggvh;bEU~Kx%YvXh6IRy}Tp7=%vVxDrwo!5yFMzTrBU^c$INs_rW z_lQD&X1tTE|K$RZrz!?UaC!Oy4JI@419%L+ZTe4YuKegb&ULVUKmFLUCiBnM~ znZSA>MaxN&ePercCMr9bLX`&fF^`lws1~v|U4@kN)8l#Q^n$QWE`3p0)*nT)0Dw`H7h1z#fjLF3(NBlV1By5I+U4(;GnQ zr(=~9<{v83#J@c7;=+Yk%Z+|P_&@RdcM0@#IG0=LzkQ1rh%Z5Rzsb*V^LA6FEF@+S zm`wBYzoio|XKgcaIVQh%LU;{lnCG`p*%2MR3!IGEa~Q8TN6Omh!m&5bx@boz%G=If zNN`+*H}(iQfJ=QZ^#zu_ST52QFKH$N+6^SDI+e@eFBx#`+`|peiDCu`4S}gtA=yiL zk#UsK4Uj@zYj{)q^*Eoi0HiMCZTHZEO=oQ?B}}~ahjh{~$Ilwq`_3!mLy0GzclGTJ zu{vp^G^Rm+`Q1ukxB0~b`tiIh0Qa_2>=^z!6!QvNnDI?rO-NO99SHd~k;*1qUb{?a z$NfzQ%<;mM$KIR+D5U%Wf84SQI67y6{@>ZUus3>(Ek5RkD^)!Ik0Cxo?eLk41UXxT zKGa@mIN@juvFdmCu~nUtn1KXth4Z4vsuL%7Nc&EN8>gM9*`go+)s9rYmfoXb*Z2r} zH8B$m_&T3tq=F92ALusODswg;-_rU>zuskw1bbB-iA%p(3?NI|XxkD7fX!1=zetwm ztNiq5zP!^(X@7VtlHj)D63+G!{ACGKEQxGqRvjae)~I!BF&0(0D72AAc%)ItE zChT>Pdc@%e5K8_#9(dMl+@_rvh8X}@6+8wlZK0T|dJkKyht~Q^YXR3&XyLWhc=F+g zkhI#CdomdGfIl1E$c%abT#p|`#FNsUA@f;?7ge^WT?=&H)mA8NBwDW_7!4-nLI@3Y z;BiXD?fnt7F_~t7MkjC2Xz`bOsMq)?b@nl9Jn{@ieLgr11un&!bw|OoL~9!{Nb3>l zi@GMmkP57hF585m{C*DOER19?J*5MvyVw6gWtUdIya)0^z&KDs2hd)$OPG9qxmBvo zvnR?vOYM#>Fvo7?GC*+Y8q%`?Ti1^P9zjd9=wTZsCRzeY%j6qeMdcGIJjiFouj zKKmVi))#vl<}GxG?b_i8wsB@g@c&1^mq|>YcnJT6al}0T{lxF!C!tR@E=Bn+mw(gY*(9=FG zePBeM*e{ z+F`r9j!&pwbM%0@<-z{E0{N3P%DrvpMiR+jjO_5a;Nlqu-w79O% zN@dt!@;t<=9KK?;nfx5s6ck{DPY>h$8Gesn{(kmXO>_O^rzp(Nrqw>GKY z2;_3Hs?bBI$ubClVHdCiV<5sVPSU~>E!O1FVMNVQW@7Bly}n_xq}I>czu5G67ArcRz9d1MS}ropJ>T-iJolQHeFQ9oSNe8Lgx`a3(aw zgJ8}iYM<2JlhQk!oR;r*iD9wV9TT+Xsr(}MY9U&1Euqr>v?G+%uxb^a7t>K^ZXU=I z8ht16kRGlpQ;V(J zy;{o#xqH$Db=q2hho>(g2yeW&SN>XDDj1q%?gY)(j;GfHm{RPyw^6!5Z8t+4Gk3~t zvU%bwE!PwL;d~ruZ@wnw8T<-49r>D|@3oCgXsKDZGjDGaRCvu5P-Yn?Fv6WTXeHk6 z11u7bej#%&vFxaGoMi!90O^63y-gDL-Hp7~;e0U?Roao`c3>lS1v06G_b7Y28(@Q+ z4tfS5`TfYgHlALKj#P)*fjlxCGG!tLoGcx_GJQo$-(_PyhNXVhZLcC;OUpJ(Gq9?Z z(Hmd2BcF|&+6D#EOdbcnR8A-d>R*y6or~4holroxwb>geM`7wt8;@2UpCFx^h-EJU z+m}`|K?*Kyg1rs2RN%t7zZq){;~${mCudt)5eM3rl?aBq6}Z+*8_%Pb7~_D*o!WWI zl(0fKK;|CcK`Bj;IaW%ip0=;p8;lwFRovVvLNNz08J=-0(38n1|EWP$p$fuCdnj)$Yds#xSQN#K7!RK z^Scgiuwf?oDh7J~wyfV(or+5bRgLKPbe#48KTp|OQ`p52ZI2+I-0LKzVGxbgc{q8H z&xfSj8OcUIXA5m6xvM?t23p$4F;1S$H(vTBO8difGzJu!a~+ULo)v~Akzsu=cDh4^ zUBs}?qO23UFn=rK>2}buwV2fRD`KRfgohtPk*ayGvNuwd2$e+#&ngSw0I==WSX+77 z_%TO`&xDMn%HnpY1N_(@`B9XdavS!W@$bCm!fGb+YOkY5z8i581P5N_0*8ZtcGO2H0Fm@7m0YO!pjZHnza64j|roT#o#`f&>1H-|e)V zf13y2Sgw^f(Z}uC&o1B4{v@Lx_G>FMu%tbm66DVd5)m#@*h_~aV@7k1oxsfoU#&F{d0HT%t@6UDju>nbVCYE&IE;TbwYs56;( zR|oQB+#i1V3zxMl{chWAmv6pIpzb%-IOWQ6SV=OKH7u9P@?i;9uGv+?v4AlAFSs0*QPYWbLc3WmZ!J?|(xiSs zfpI6?d;@7xn|VXsFq+zR7sD;n!9K}4npaGmA<o}2EaP0pe3T~#Jy&<~1`V==^K&D^EsQ94d z0aK2ti@CHDwVK{YKschTKkI;rYT(VQWaeU&K@hYi@A(RB0LF9(q$kY!`fgsVJtNC6 zy)4Jny$%s>MB=aF%|np(7Dz0}5M8#HDQhs`-yrTys{P*<2-`)Z;>|-({T6`%`<=N3 zgk)(0x~zTgFeD5Gu)eB0M!r(gz4ndCeC&@nQ*wuF9!CZ~>*t$VnrLohWy{xW89GaS z+Xv}BPR-{gC@J}J0V=OmM%<0mhOxB(s5KB`4l;NMU3JAF%Ji*bsquYir6xyCKik1& ze$B$hFqBd2)@a=Uv;pi&CRc{tg9wejGM(~(4B|n0gJs4w+=@wzIp%^%9a!<)L`V3? zhhbWvxF>k%W*@$P3hnQPlwgDnAmrmMk`9CX8gpnaljmnS#)r$YxcZUNkq{Yvwh8lB zY9jMC$R9gdc!ub5W8E_1vt6hZ887tJAGmFXL7FEZK_F4LRKCID=}1tNd|w6DbSaUt zP^t67BI{47h}=%L92+51@3iL)Hmf(6^Z~h|2AO>11`=Ek(yZaa$6lHZyB*xzWz4}Q zgW<7HkMp+1?nVOksl&LNv$5;K$z|2RoCMtiF}>d?*5I_kRZ%jSi$pg;esILr!1*a1 zhK8=zdHsS{uG9{aQDrH3+`p`f8Z_~10O;Sd^@Dx|8DDUG{RD>dGnvj|)TwC?m#|Fy zAwN%Z(S2IB-@1f8h!nuayq3eIEXrWY+Se&2-b&B@g6I5%CYH;504lQ#8S#~j_Byb2 z+B##T-cNq|z1Vz>>;Tilxr&b|+GjBlGaSahaQ__ZyUKTHC-WvtD5#&gbvjrUSJ(hg zQx4Ly7FbYf95+Ef_;D#*JPA!a>Ea0}wzCn|rWD3aLGe5bQ6^o5C+I2x`0Pq#g6~E*i=dceFo*Qs zJe`4kTev{T`zspC5PkZSnnm34G0M2P-s5-M-ZE_^?LJtIam^a+ul|z(S z7IcSSlJ}uH;<;_REdqR&BNLBrnY-tpf$j_AwAq>2unKt46G-ZOo|JG+#%l1`LpOUK z{ny1uRAn%c-0KYxvYUrNSgN?G>S)*_r5-?hxOjJqJ@j8tq$ctI+s9Z8&ni(8M@OPsVAP%0%zVH#(p4;P>X}Wlfga zgQ0Z7)ICSj8~gzXwA?i^J0g9RO}@s-!f9S_=#@D|+j2@&AR>4`YxYNALi#O4+b*oH zqTU8!p!w~kwj2q4$`xil&y3@bKZAM-IHs0^T9M^xR-TrAX3@&r3+4*^lbRj;K)s9Y z7}lfA4ggF!HksF7eg(Q5ZKbJ78B-TwZQ5L9CoW`yb{w^Lw7J}0arJN_p%7}^{$%^D z=rrRQ9uxaK6XN%W&gZYDjA#`L0P0y_@SHKT;|J@frZge0NNkfIJuDu z+AMNp4C`Q8_gDC920LK2@C>`&n#>LNq-D2@qb-$>_$k9tn+Rq5j~<0)Bla^9YqNCX zb>1;XPFS0K2l4e(I+^H_Kz0^Q!mxjT%Dqk;*AF~HgVj$st^YDO8>y3JEoD$9QDC)6 z-WclXL18vTcPsX+nP}uEAO(0AkP~z;-^B0I!BDH>3eNjy95p4N@{jqXcmiqv)WwUi za5^5Y^?6tM)K+un1^90uAbSo^2?)xoClS=z)p=`QfR`x#x3mi%2-xj4BlwjE#K2njUUFEYxdhsJ1oT? z(bD6DCT-HV#+H-rYxQAGLiiF9OJndW??U3QE!UFGX?L3)FU%^ez0$(pvyuCO)^FiK z+kk@$vTS=0z+;jmOvj1+V#x#nNFBP`9v1*TxfBh+|BQM_ISTlP1*Y48v&0SfvRn6` zP*i0?GO-FrHoP-C?&mQ26ra6pWyQ3XP7}Y)aAAWtZtDWG-K?AVfqSSc$|KEj4eK{9 zA2;S+;hE&4?crIS2qT8xR(yalHdl8 zw(1!W+%-Q65BS5*rYw+M#C|T`!a97Exol9+hXY$}46b$t(YAB^DBDlc@GbFLaT~3D zbCzvLkTw`#*wF8HLHhNep|kpDt$ee8dIa;Cyg3I{R?W#3Y(ik0uA%wUVAGHkU5GyD zWZSSt{yo-)i(m%gz$zT{_ay1|#=Kn7AJ5ndjgD+2+N(duv@U{Moy%5M2L8;ze$W)Z zAM`jFq%}y(2W#eQ=qVOO)l2h5U}`CN7`}TNxK)dBpC`x7B4aS@!1YLr&#THQTosey zWsbcJUrXRI#*-^V*ZZ?)qb*=6pSM`UF^7wR+7!$j!0iz!>UURJxWQdlL-CTHJFY+A zIRQ~6{Lo_2W?zf9>4j{>oz>r96E`NZWY24qZQ~2EgrNPPn~fXzqDv2GdIss*()^xp zl+kb#n})(v;)E7g+hq1A0m!cqC^<|AV`{>ri+(6FDIc{uk+5cz*_a52^x@5FP=_CG zw#3307l8xs^$R#iyYC?^bKzi;%lXoG)Y`+`X55R;>3?a=G$%TfmXoJs#O@f&BihiQ z0jXfPw2{WO7XedM7XT=Pdlma)k+&aAbmQ5&0KwC|0%o-7Q5Xk17AEXS#Vtcj7J+%K z1lw1gICmE7j2Ht!o>|KLD-j~8DfFziKLdoKz*X729QhW0DAxkYKiwHPORA2bWY}-V zD<~m&(MBGlR=YhB7Q}y1y6^K=pUI9)%Fke(PlW@QzN_q_(*7tC8*SR`dCo&g-kQxf zEG;a4?9HWgu}lh~`ayKTesZivrPXU{(a7I~xd}+aAo;7$r?|br6x?19y_vmfG8}1r ztGV%dt5uFsjS=v^jTZlM1TvJuTvIO;Xveo;S7r)zVqZG1WA(QUILc6v;~6Z>_q7b} z6IQdjUfxf7UjK3}G(axz+oAKYq#w3HxFK`jps}YIw0cM;SD)s2|N88{@X*Vadr!Xy zfOh&Jt%eC8zR36~+&p%Pr(SyKFXPSxn>u}PrL;=@IrGTbHKEvUyNzuMD=z`x5McRQ zES(!yfegPucD3jn3MEs));TO|nXfhk3IiE&#jn2C zDf_Mg9KOh>@M!r76^K%OxyU)TDKD|YUlv_-F~UN9EZyU2N6>(7Vw=ZXiuJ!jXlUC_ z%Q6r}TzPDz(%PfnA|G=uHW`VKMt28)_W1h5gwgPuM?uM%tAW}A=`mwYWpc2S=T2C( zTH{9Z=J{a{Q=}bfR}lB5SO^U}AB#{6GO`@PV)7HP8AAA7YLQ-2x-`(+Jt z?KA7K+Jn+{8@<8A{s5--;x`}KOge_;3-xmV(0}L!xEpc=7ZsS+}u*7@8meA3cv#u&~`La8N9B z;DI|3g_c_uQk>*O+Vi!Q$=5sJ%w7j`5Xkmt^UXNj|=RrFFhwUT`Ag zr%NAgzX|}Gd?)iP@q)d6oFE@>?!r<2CdDuglSBOxaa0{QHG&$kkR3NHzdbW~tS)Ik zL?MB-31>{Jwm~gzx=uZAAk7`P!^2X4Hs$3@oId1vaD%jiQeWOqOa~@(+#thmkjKx2DJv*bvCpIZAF{o6 ztG)b_Au1_05 zXtPc0(+_KB9u^8~`16ETz9o`|>tgQQP~_G??PkQC-QLF>#Q%wLYbNSgmit3o-A(KH zO0Nd+v-t;$O2BjV7s-s|Xt_^8yuEG3tH;b9)8TtuMA2?O^7D%`#`l1X>p;8?r|@M` zdQ!UXM6R_3yv8e(W^Lj_G1@q$q>!hNOw#ssj>QkM%^AvX@!wRUf+{*)uTzL8LkUpl zrP>wX?G2`uiPc!j{s^gmK>=Wi4%oZrA{Ta1*s0loTs~I2V&vNjS#Tq7GORN^YnArA z+GZoyen4g-e8D0gufJuKidK4AFoNykMMN#VZ_KLmTH_bZTMsanuT1|7gKzt^$@b7C z{v=~0E&lWuzeNRr*51X)%ZDgrwF0CxlFMOewLKy~gi!={4j#Xjrj?NpBVUlkdYMj> z$q2LwDXr!q@h4?0Iv>>La4lu7CM}PBIGZk+IO%4BvoUJ@Z~QmfnC}3k))uPoPnp%v zw`bfwKw9IIkXUGhIKpG%DqCX{(A^~qCv~nwnwsJsZsQk6WN9@cagzC#F8LSWe%Q%I z{d0g!KhFXf6r7=_Uu!m7VFG?fX;qy*fQ4UwKF^EUH-Gu8%xAa^CN}ZYJZ~4qXCcK4 zX_P;nUb;lefFPUmX-s!0;K#tmtnZB4uRns08*_)+h|FYr+jAYca04{^YqM>Kbl71B zSo;Ppa?qx8!DnKk=JIiaeKGhe55%g8FNJ2x{;y%H?bq%;1Y>g zn%?Rk+x4Ru?A1jfV75{%UvIk}x=H@Pio4U;gqBKGk&G7bvuEfloya^)kQke)H3`oi z7%59#i%fLt$SPYJA79~v%W8mq9*ItVa*LDgM|z|^?+#=`gC%TZK4DCWTN!9p?z&F9 zwa#2NulzAr-?TOAqIrH1|5_KmE8m3OldnZuvE?fGP&*1Q!N^dIM&Vc+7^8vFkOreQ zvvR#I#{+7l`^d?)aocnRukpr$7U!gm>i@x&kMTx0IDmW~!_%@(NFVi54dhvW9JRI} zj6&c8t>p7QCG6u=7;}4M-*Zxr@YH_~ID}KQ)JvO8_A_{MJn;Z!KsR$et?#MlLq=GJ zE)yO`=*pzt&t2+VY%fpUaRSB9;KB6EN0j$?5oa4Pn?t!2hyEBxz5=0{()-l6r#SW|&{q+p^^^+b#`)@Lw`bf$%6`K3U zn|CQx$6ZFIwnecS7xO@j^IvYh_9Md;gdP*n@(bEzJBQ!mLxx=+zOynDO-JR27ux*S z&8*smdm1hw;8-NxIv$dW)n7{<#ov)voFtuQvI012s6^}Wlun>noz2ByZV%H%YCyOO zp0!mzS~>n66dSIdUInR=pQn;ZKZUcCJW{JeP|&64` z-1UMxnzMv-G9i+eoRKMT%FjbGlz#TAwpx+h=lkr0dt>pjb&=NC(gtb9;x%cxwnb^5 z3=z4O!(2m)(0O8uphe>BwQ7!N%;&}vKo{?REC5CUU zuo#ff*tkBl@efFZ)#L#8D!AcRtUA9jOw03Ud7r^9nYr}Kp)x_P;?tc62o>Cx6`1|^ zM1Ka!+@%Lr%@@!8DH5*__{Hq>-+!!)K4$A$5+@8_cCax?j_8BI>w8Wx=d0C?S8vjf z&d_FP06E7gZHP{?V zPZ9c7lLCOWxUQ|d6;`McgF60-(660O(z*4AU&p+}k%m3Sf4;|!j8h0hq4mvlzMCZZ z(yPo%T2^4yNwYrCZS~<(NOH*IgIhPOw<7#<84QTMr3CKrATHY6_~MHD(2SUqR#dx7 zzg{$6oAGD!kF0it43D`U(j>a zrX4o7ht$GxwM=Q2x_8AZZIBuKE2%)rvN1+?Wb~R)aZVLIuIqaU7JWF6hrF;~XST~v z4-{1Y9Fb9`n{rJHK$q=W=Agspfr<2BNtb_kXrVc+<^FtK|HB+0@xO%g;G!>J(;0-} z!%8h%m0&zq?BUeQphIU&QV?e~aO?`gc!#rP;k#`s+{J=J0;s$H36aYji23Y6H%qX4 z*XF@X{fXrg*&W{gEaKX67R~%x@5o(DUN@IslES{CQ@F_zcKW`<0t^7_h;u&Lo%Oc& zwCydxEe}S-VC-4X>U^A*o1M0GV5ufXPGQXEO=8|WJHXsta6~@?BtD~3)kM6ku{P=Z(uthD#uZY{ zDRWz&M!h?1p4sH{MOvIJms}xzq?V^p!6wLs1?Xa=4@~l5hfeMFjDf!kxWAsm9l1&X zfAPe@t;k#>-ak@4mE4FzK7qj>LAk(mYc=qKIdLQ{YaMR7%$g$Ri5B->B(xknLZsD5 z;SzQ=trg>>|VzX*2{;8XxY*ZGr9dNPaAVo#}7Sylt5@Z0s^JwixAw`7t1~V zoJu!7^rBU-+nG*u#a-0Dib=MJ2cvBVb!m@3lq-;2Tlg}oz@QfcJrs|zfh6SOn~4XvDWKAMo4|( zQ7`$k)dsJkRp&PX^T>*QJD-18nl`!ETw=KBKd;UJz6r4(0hQ}W1({nq8OA<$@nLn4;nwB12V&S_e)H=>5N4Vm)3(BUqKOmVcJhParW?m$C#SwOcf5bQ)KM#W5Ss9fNN*^*NT1V9{5tXl%rb77c?Rs zi%4IOd%}8WTbNc8M^&sL6!>d)oLfUKq2d7=gjA17h5b8TrNVeR=|J#4_L$p(@-b@D z@t2A3`?dRqRQf=7_Cs*&mnDC#xG8hSE%2>7ANd(`yuv*=R(`wPfak`h`8z!|UKaxK z2Bm8P&6Yz2G=3`er@0q{LnBk*`HVK)KbU1mqTe%thFRiU{T z&X?pwQNAFP8}V{W$=Ha0tT3zy!Pm8F`3Bpet5KlHuVE+5rQ2t;oGL*t+vTfvn3SH} z`w;rLWF)F**Z;%iRWex|81$4S2aON8{slbgAZ=sckLsU2Iuw9(bBAHp17M93-g}0nAX4h2$hTEOZM#|}^=N zF4kHkMWdjt>~*%DWewr+=@5+2*8((UkidN={z9O%zAzqk`sIFT_I_i#5=dec5#vPC z0(j>>+(HAPTu&ipGTutk^xTMGr^afc_}u6nvTW~ZX=5E0;Ya38hfpdp;ez{Q`LfL< zkW+fI672=sFgUlKpJ`HR=9rRDvj%I?9|+eB%ITKBuVwC0FwhpYt^}`j3C+a1c=xCb z1%i&?DXO(Tn>T7idWd1h51tC#63dm-v%$DK+xL{Th~>YKvtV_F~t7ET1OHwhARkxWy9IJ?`TkpjLe)gSV`A46 zv##VQHk_V7Sl~s_^7|s4Jh9I9Ch7r8d>*`pC3&w60dM(LjN|OxFn;6@lDZ7sO3~ph zAy;sJNa@7OGuL!9+k_@Y`qJHqfNzzm{G&+kBEnH3t?_4Rmm%8re8oc{pi%~9^8WET zmhY_EP9Xn5-k*iqVKuN!CbL~wCPJh60V0Y2cmk|r3DC6tSt?BQNTEsDaOLJ{&;jrL zg=-qohJ+Km?`jrxNWFt+lG&N+d?)UL<)tU&TlRq@OKr_6ZgVK`nSAyo4g3?((=XMR zN{AX$({7+4pVLeXy2+$jyR2)#t>+X8%RpefH97#KRIy50(MYS}=qF>8Joj3n*+aba zj~i~n13-GrIZ2C)RG%?NF6mNY(GAPbS|)B<2VZYS0IvA~tScjS>nP+5i<@S7zol$g zyiF{*$+(Uevp<~4>ny*<)k&4VbX>a?m0}Vp)nrKR7EBt3Whgch++tat@lDL?j?zhl z1&UO^-;dyuhfNr-22=x^R&1c@9pBcIDt|X7P#->~r7XjEW8wd4srSnB!GJ(2fM?eb zVX*v>T)T{BSsNF6wZlLrFg`cdk z=OPvn)UGhId0pTGZF+4{g-jESey;rgYVZo{%C+X`ni%scJAiQelPusXM&hESZha<(JWz)U zu_N#(Bn+tA)t?0<7Z$10c_2NWCm<&%!AWyj3mzQipk;!r|4)Xp-NE`!$&{^7mrt3C zmruFI9lvCDIBZ{uVVH-J+jvaM*$SrvXHfu09b&lWhiGGWPAdvgZWjOH(5{1>r-Ul! z9IDb7af5u19pSza-4X4$74^V^aE1O`qcA-bi!@x8$)gPG5IBfA)=cl>tG+;+Z4>t5 zx$jWwsLalbnP;`B02s19iy*<8=F@HtAKUEjp6YB%;)7eyZjd$@O;)D+%cuP&AKo!? zjk1ieZo4g#gg^1R;B5|8;b;D{%O|{(YAhmVs?E z?^5EPzPQ9l25J=b^N|%6&|5Y%pO z(+;j}K$aby9c=+xnU5psW_7OR3z24rSnb(r(@!$x^!i>F1lg?qx6hs0EZC!olu%jD z!zm7%eT-*&^N7M@3s*BhYX(s9iy+fo{+yS#B9`=!eCB}QB4qZAB?Gf5i15IJb7#2p zW5f#FS9gSYKye>GG8ck*wUoVII(Kfi-1kzf7rWXk0rYfO%d#b2LAP_<>;-ao4o{ zmDv&CAT=qk?Q9IoBIqE@hIgZOsFK*N-i|-{MzK`AOl&(L^T9?3HaG%7Yl|Ii+LQ;t zY`>3mzyVmL!Y#LBZ|$*d+2gXqQGXR1A1@&1;O%hWUNa?^C|s#bZM0$9n52^qa;#>1 z&C-4B4>I-=Zy4jZTdX3IVQZ(>O`CSFxypgXKE=QOe4B}ue$u&(%Ake@Yna*OU}@qI18=Na?Hca_$k z4~5IlTt$VEOs1ebbX#Q7C{Jk=7=Uv4cBx2&drs_HOj!EYRa#sF%5%@4`WRNln;HtZV5_ zD_DX#8SnCN?1KnGuXhS)>WYqp-4?1q0?f4o#q3+ssoe-)A)Qq`{_K6QT?*4+u?b!H zZTa>9%Qz6Z6^^e8ie(@jbu8o7_e2mu^?Jc?P=(L3aTzLf^D*mOwPKg{?AUH|k?9j@ z_WI8go#oq?(OZHp>b}yi79#|8kyf}jdfyw5_8;GKJWDIs5&8~jjWr&KyDz=L_>~@v zH{H9OVWkzSeA`ldiHYu5_WP`V5~^EtG74L|MwWsPuM`kxJI1sshU5D$$wc0DOVFiU z7h0>wxff;uho>Sl3~5u+E%ez7eA*>XOhcEI`uL|cMJJ`hAsy%yo+P6y`Sx!-%|@9E zI<8+mZSwQ7bl9*c^w%)N&g!-3C#d6o8P4=WN(&;f13;5ugG82wUS(RmkMstiB-ZRe zRsuz!P~~^L7@77{Kb(~2ZHM4m>)ZeX|8%0nL|u3mvG2OCSsx-Tcf#E$F)I(^s5-RG zoq~vQ9Ydntpd}0z*ngQ3R_??e(z`-O9C-?BTamOrynX)k2`1yT-aqDKbr=qrRBKa5 zTVfjVfsDzw{KGq>ilX(ucR2oU(YavAPNZz?Fb8Z^5vA$41GZxz3|ZTP7qglLsxNMV zgm(aYbzVEUcBS(Sz`SR!H<_rQVQyf~T&1_nJYIJ608lIV~ zb&=WaeE9MH~;E^Qm@ikK)P( zq>1CAQTiT5Nz86BvC(d9@I;peX)|1~+B1PNA0cyHBK>sivNsv;10OPpukKxy5zi|oHdeopsbd+8*|FW8P79*7?^x-#tFW>x97n7lNLkqK%#BVq zypuLCP(((3u+!NdCwcJOu;R8APE-YXTO4bp=Y~WG(0&G$Fp8YM(?`$AYZ1cfu!B~( zVM}zL(sfpT+#;@=MsWv;9tO7C3Zym?5Na72Kv#t}?&7)H?p`oq2e-)JN|+8C<(KE4 zF$$JDhcf%JM@Q{5h*V!iJ7Mk1&|52x2I-Q0B=HV-ae7K2=w{R$Dtd&QplNxfgKY)B z+{r()9M5lxq@0d2p^1m4h#aGG+5B;^W+=fnoUtC`SP3tu>yYLZUN(RlR}Ufo_gLI z>ip;W4RR`0=mnaez{lc)nw+wu_VBNn*s3ltG^N^98Zo5J`nS|Gcj=8IoRY~boV__w z!bULmW)%P|v;yZ&QZvP!Z3>;MSrZuOXQeypWQj>}DeG-O^1c(=w}VGz3s%nKGyDcw zQU+9OL#k9jg=78roTYgA6%Wa@OeFmQtJ`xo+%mz;*V>Sim&(67A5FAc60kHB;eeMr z9CSR-$fS2RbrfeXAL#M2gVrA0gr`hpSLg{H^1&ez2DK4KSL5iXIhyoqZvz0yC?ZHEq!S+SGclIL4O^j@~sPZ(1!)7p(`XsKgYxuak$MlEJm#RetVtm~;9}Bb+Mclfw&l#JEZM)6FP(u>lgY~_@>C&^$`3+$be+J!@{wTAC zuN@Mq%safPnfP|@olLq)t0j*Naf9Y*fkiPX2|4WL*-ymvd&^c3)&k}oX1gdV71v%H z#2>E$Zheg^+Gbm)lUL=VHSiFQj^4kW+#orVVv0*??JdEf^y)Vg_gcm`pXb?#gcuF8 z;7muRuQ?Q^gW7oZ)lH}{yec{Ks8Zbp1$HS{g{9v`|T_$Tb*OT?crqlHE^AKrIhIcpV3bv z0^hEy@;t!obv!2~AGKbk43>(K>w&;*Vq;RE$p9^>2{Eu-)&9qUGMz@$J!br8y9_;2 zv8U1NiAMznPqrmR$mo`A;NI1?FzF36=HN}yO}Z|XIo}YS5<}${bp0{;i=mxTR#*6O z^2?7fayagan3SD*($uGwnN8xv(otlej^vS-a2r20G0W^TXK6AH2JBUSKR!(U?KEVAZKA)q)>a!M z|8rs6fx>8KT1wdB24-FYoKxTQM~2@@Szi2g<+Yd1+upizo9QYD@)uu2xgfj*AxGsc z3`3{-;>{w!;D^_Y7^%TdFJzHTHn>?M^WB7UmJ*v`536y=j2>gmu{xeBb9RW*lm$W_ zgqYFyO=wJL%P&;Qnx5WAl+}3?5B2MrUc;m9_M}a1VXsML!r1D8YqIi8od%Ya>!OcR z;laUN0M*x=iUBkvP5kpcAhj?*f5PB-2?ncFrr9ej z7n2jhd53ga*aaOp!|8s#Vh_eFe+7Zer7;`~7i{!>HbdL%nlvZQpklA-7#peH$lx19 z8W^=oV8O#KKZn?P&yQ7{(UhR6@85PKN5lJaflZj*BA%-&TKXZK8VR*(WaJEFh_viM zy?e2L#)gXk^q)1P?%PZdEPDK+W~~jJ9$poeq4VZJz2>Y^GTEdS8HBWC-O@w1cfcYW z8nP%A!B6>+VTD}ugTGt0SzI8hI%tjqob-hk2n~*wRz3Ap;r0m)WQ?5-gcG>98{X*! z23=4k)gMWnY#|0CDq5C~>dgo8H(E(!!eQNQsV83opVvKZN`2HZQ~C>70-7sc9yDEb zbG)JlbSKVh<)8tBV&gdCD<|K9od=}W9jIfY|GuOfD~BquBGW!#d#SIo592hObmdj7 z^mf9C+9QSdhECeA>w*x0++39k5Md}_Y_hrOcmhPB9=@e3$P2(EEIfPZbe+zVCVO7J z`qm?=MVdXNdlzcmuXa2;6mr(#m+=0=ijtdoB z_TW^HVLTSOzcI2Uu6Ldx5?i{=o5@J!ekZQ@-=OOa(V9I+*|TC~;`$AI_Xx*+QZEL) zx0Js)sA*a84wbdi*|_#H~UmW1ch65mP(!Ze<2gn&ZO87(}U64|}5B zN3I;zxHK5UUuL@5k(;T$q>efrB-C0zfbX@I;-ted_dz1P0TsD-pU zoWVueajF$}3bKsqcQ?XvncrzDkU!VKnAJ!HLDt;KY-xL}`>;;7IpBdZ+&~e2`0QIL zi3Y;dLs&3=Lk6Qi62mU4o5p~$iOb;#J^xUankG$0!hs`ncMSoTs6tdPqO$$=oaHkf zJu~*;wlj8|bwy*`VlS0JAmDMmb^p;GPO_hrR`ET+NwHoDhodb&FTV?PB|#-i4-@o! z8dBfPz9H5FVGKJ0@)a0lGb-ubL!zd=UNW4DsJC!H29VMt(I`K31+me`2iErzz;ESJ zmr8KH!WA2RbFFz&nRv|JbbHrsdeh}+1YYBvo?-+}PP%&dj6BT1Yp%FW#U@G5wKzSy z$Ec4kb;8O|MNtZ1M5r_-) zaWgq5nS2jy!`wLFw<@f3Nn|L=Cwd_YZ&})za6n7f^pq_(Riq`Liu4^o15s* z%oB9G?d^WFuGd<#t0P5N;t3JM*;&zOgFWoD_Eh<&IfjxYK4R zt#+5OutlkmF?!L5$rpNAjOyoc)TPKC(*<$w6s$*MQ*KBx#~&cIj2V9F%;*1WESEjwZN6ls4mfvmppntf0+mWbb`4ZPTsmEU7pXzk? z`J1wO>V%2R{=L zN(>jeZH+%)!k7-Ea%hAi6?%2Qf7mINv6EgH-4tK~{x%)qG>l8=R*w$k8W9MSwLlS8 zyMchl2K!+JTeI}g_OtPM{lU^sHQqAsFt1n7OL5)2IR zaKTInc;)=UxZW9Z@2?*@?l9$_kh$zbK)o^4RHou+uS9zg&N3@MhEI7rlSHFfO>gD~ z?lJ{jBb+SrHyQkLAd{VW_JuTc;CQmF4>_@Rcpi*E`@t+dV5rG@z;%Ymq1Thd7Zj`? zNwl;Fi9ULjciQchfhEY)e(=-zEGx+&7Sg;_IHLa>cFrJ4#Nu1$dJ3a2|=B}0JGAW zTjgff@uRg*xDq-jAu%?j6hel=K9Z*g z05s2aBLo~y8X|YozCeca!W8d=dO8Cm0ZmbyqPx!Bx8qoT>`all>--F-dKk0N zBdsCW8up#0=#?N{X~Z|6x2Y?j?pfnbDl&1B|3ECglu<2zCO1WeH-muc3f-YOdkit} z=O#Mw*!O4AeBJaMm{3TJ8KyiA@vi^m;+yqmupq<@krq%`0J9;obUu(Qe15{F9lfbVHV z5cMO%QV-{FkQuU&psQmroohte#yn)E2hDEuefy;hTrb3v!wwH|K|f^pF|T9z zlSSSCIQ;_H&u9OqY+aEcqbgI^#BA3~$Mu4`Yr^v|fx*s*zzpy?uNN)3IY@=Sh)UtNc;9kd;NG66|JwL|? zw{Nt-zzw)d+n1|SMOot10XJe6 zb91KuNX;=EFeaFBp1R(7uPM}9Dx#$$<))JPU>U(X4qq{(iqVDakKMRvaDYv^FYUP= zIZnXS7qNrG9lrGzYwM}E9B%4{0N8~Pz3y_wgG10Y5riH6dipuO5;L^wMn%^wIz;~N z!A-psgUzlz113b(Uy*6?KEfgy&i*&j(gVqX#&yaXdd75qJ@Vj9-XT*I3rKzW)0qRN z-MTiLMyw!%cWunXj?E?T7udu`JK`61)D}J=U~tzP0a7V{=x*uOD|=<}7~5%>N!CkH zK0w(x}Urcewk=WWHFvH$a+eyBS z?|XhRl*^Us*f(H?d9X^Wfo3QcW*8oAJmAB_w-cp?KO;Nca}NXV`r23dBr+gdcYb~W zS6i$bZB7Bc#(^dkx_H;&7!X3}b|hy^+V&X--{*y*Tx3oUu?`b0kKMYq*YqyVAQBD= z^ZB?YnMen)G@`--;J^Xa$LSuMJXqDAxbczHI}W7HWZi8KX@b%u0Fg6lkbIup^)DLC zTAyG`37xu}tleJPn-18>kK>8AFfNOp7XF8l2#%qKH|;-3lgn;fWp?zSxBdm8ICxfO3kjL4aSu|Bmzm#*4pi(n>4tsy0i!Uf$zM?_ zK<-{zuYh60(Wqv$bM&PwE6!r#iq#Pmb{g8W2JP?03+-pD-;lk#{_%;c_#Jrf+tcSK zpz`#rZBPn7QVe^*aI)V-*X2)@&*x;=mWs>NSky~(p6)#g{8UfEF-VnI_TuBpns=={ zZttB&z=qYEmYT!-auY6wWnyLcWP?y|2#dKaqc#TRx5btaZ%t-!Pz8L$WOi^t17t#b z@pH{?0KK&+(%pZ9KYU>EBG&zN_F3Y=DL%f5!T-mJ@Sq596>uihd*K%=xElH#Vx-yu z2gb})?qOCh$0JO{Ka(0)*{_!gd}eZP9>8l(5WPt8UH+!|=|yWctIly%jn`R4i#C1- zHdT_L>oQ)q5AZUjiDwtkaOsbgdUL?YGysnYZ|o^Olf=kt)T-tQ82Ae#$t?m-@Faw3KqPzbGZ| zZct3e*;E%|oO|y@DcNuc35J)NwPxWjWhuJ*b~GSXj|mmC1QmUDpA_Zs<^{KPONjEi z;f1AUFEqf2VwF@~Z4q5S9dnOv@_BHQC{?=Ofg9#NSW|?Cs^HIa4$Ch>?qAfoe2dl{ zFO^>a4T<38UOG>ZGJnje~eYbk&w#;(dyB*y=nYK8NX?4q#srKPaM{j8#yLrXQdCP z&}~eTn2lg-0lBFyg4DM7%Xf{qbc%Z<6C8LAPo99CT}qH84}!*_w=$e|`YEpIzLV~W zJZoGT6y*b8>grj(-Ab38(A6Hg0*6I5)RAv!zDKyY?V|J-?n(I{dOkj!isVf4tA%OIfz3tNA2ECH)_kz%%{@PO?ilt zEo2C_hXy|W^<+Ph+^iyd$lGzSxM;)yBN@C)D| zWW(zn-U@rQtY{9Hk4rrNor2g3EJtrNhqVgwnFNyb%48(JhNonn#(Wg_FsIpdf)xGB zG8CfojT^eUvNH}%eel3r)@rjk^LiO!6!EBrJ2#mfllx0RYqz4%Lidi%%b@yaGOn1Ss{c_`#iuE!L zS9OZbPqidOATXIi$2%|J8vfE2G$Bjx%@8iAID=ng4qDR1L<^HjF|tzSH{tvl{$r)P zV`a_K#AD4HDsVdx1V&z(bqr@^!5!-huILSZt_OELRc~K_6}#Q>7oe`D#|8Vd0u-W@ zquZ!kiHO9ENpI{;-Qz&{1B27iuywuRa9)w_&y6kAlrepHosDhk@iXFv%1?TlY=<6o zQx*pZFnvM7Y^KrEPjTBTvK0+p{eAC&40%0=IpN^h9WKA^T&N>&(o-)Ibklv6{@!8t z(JOfS$Cr~Y<|G=Jx>uSoe8sLr5d8+UsOB0QAHeYPm2(6le-48}>&`1X&%4>XGeL}f z@iO!tTw0}Z3=_8ZBGZYiF|kG2dOd`HYy;48>8phD+Z@50R|?KOVo$(#Mq)KYaORD= zABT_Ws~}ZV9UH4$bdxPX@xo<{fc$dRBfC-@B0$Z$JwRHMoFVz1$2t;I*LWkAM<~%? zs{uG_6aPvJM+b;w%kT+5xP-=hq<8YT!YOp7AUunAc(MR6KcGP-4iFD4N$FvH$iyd7 zYe?#lCqhNlUapeTqq^PaDJMx2sdVq;hD-GD9rf*uwUj;erQqV~30)VLcS6993$hfy z*Ylz$j7Q>o>0+n?NUsK?J!yErNPdfozeM-OfxaGjp-ioN^s+N`W4a^O8=?t$72-)L3iQ%H-PE2dy7r-T zCjgD5dAm8fzigGQKJPx7ohHR@7(mvFU?tijWkj9R{Vz_s=yf+!j9v(nnLBAFCOI`1 zNcV^RD4^7QCaUNz!0-NLAosepe=4iWVG!s5JRW~?2hsl)1$v3E2@&4e5XAjNR66b% zaA6(Po1l$(?6sJjP8wsJP^1-EybLU@a25E1g>yEt@gm%Sc-59J>8R-jVCetwY!>8) zQ1ewyTvaW%f~j~$<=voeJ#J-rO$D5EqQoYhp%#A%^s22@v@0KC1Ra*)X8TMb>P4vH zthbC$8-F6LDcV32{Jk-fZAM1+VSvVQu+?VFm^Fm5E>5@P5q^~tQAt(QLs z$lg>jNevnOgI(fe%+Z++U2l#oNs&QuPo9gBKUn?{y__d27f`%(XRJ?6lFSm((D55` z=DjQcnA|OJyVJC*rtu~`FQaKc`C<`d-OPs@sz&AGef5S*9%2KbSk9l6o<6R7{nTl5 zzJJauWt&MRGZ6WoafG>;GU)kd=6XbT9%uYpy>2}biNH^S?L>s?_u|q3Dsm*4kmP3I#cME>EgPlVVnk%mDza*sm#^?j8aNE zw`K0CbUZ@ZVsz(Scm40E31k1u88D2mz{sDaZC96ofe|Y`7b@L_VSwBcwLjiAMf#5s zUJ?$_%wjSsCYscTqu9K5c3Tq*H%oi=p>O)fds@&MEyLd@--KJA@Jf-5JUlkkNL|jM z<3O_>C9Bt=!3pc@m^mBso!GGDR`u za)wP(1kRvX8s0tD{a=h|tehhm%KUW5-=J-)3AyH7v95sI5nJAN*$WRt>HidWU9A7b z`8b#^@?2}@WGf0DitO7FPM_}?R?M!Zh&ow@AE3V&wkqB9l@F|gA%6Kl589>E_OMD@ zg$%nKA`O?Ri!;olP)}^;{Mqx{e1RM5%F9@qFq($j8zB*Q=tk^;6vGPrDKNmu6&ZkN zKC%a1A;a&PfjDRy(mfuG|A49CKO-E|-;@I;i?0j>%>Y9yzXQ5dHX@|^Bnt(pK-RUE zZ-P)O)Ns!JAn$jU9%%cS6QA%NA*%P`n2YQC-*-FLBDM50>HVz zw~1f!j9?r62N1ANG20KbQ^U&ny>HQ|FlTR?*>)DA0n^v22#zHoZGS;l&_;+bZ z0Z(P21vzLXb48Gp;5Yj3v3vS2X{S0Kt%eY$;)Lk}fNkFUtd)n#i4=+~o_|Zih8hWo$t1pxR7YXCBbwrh#fkT@lKy}MenauWP{B0#K|Xl zMA$D?cp`c-@qP!^tyC(nVZb|xn2LaKvstq4j4MO`>#1^@#%XY*#?WsDoMei7s51heM_fNsAlXJslH zA3Y|NDIjrun#rtq>=K{Ldfk=%9cT?xa|-OX zD1TIz#L>>**h~H2eZ~0vmR;`YSoH#39>pB7lo#s3J(*wD77hBwm)Et{a) z0loSzM|XIBCs263msp}m%8xV5!Q4%{Xyctk55)5OwJexi1wxPO-b8dU#@&>TykkMJ z>D%Jv4?~IVP1*dwn5+E`AH5Y8kKXB?~X8w8w$h zTz{^szkQ2i#cwq}WCEMgAuV8;Td4es8FU7l>Jk{R z_A+0py`U<`(W>I`ETW2Y?8gX@;Q%8rsr-!XslBD!&Z#f7?l`b|#=wi^KDW@<#Uq@j z47lh`=g)R<8@-Pi4I&M;)}|Lwr1o~Q=w^(WD>Lc{H@p$kAaE9Yf^olH7#<`Iz((nf zF1m$;Ea_I4Go&(q$QV-dQ@5Q|5A6sVen|C~Qz|GN@G$G}9m>jcHyqO-I@|jKdar#S z>)TW^?zH?kBGlj!4B&qrVz7xj(v>WvFt0Z#$y+7wVcwEnL^Mm(lS~V=uLkeitiGj0 zql?`#w9gc0NzY-No~JhH2e5}VX*}f!F~44Zcg_k89_8xj4Jb8(s~J?mce9gZF#&XZ zkM%SB%2>1uy0XvETm3uO(L^EDZW+9`4`K>yGw7qojN#!Mv_voQ29vig^3PrnQw#a} zZyXA^M^9LST_BEp)t#qF9IRTo$|AF5k1t+f;lZ1Ex0ju-IUr1p{0 z@()o&^rKPR^z;cV+bScAN$8;KrLhJv)+ZH&)Z{t>3_mfHWUO3X`%tH$P$>KKLYOplq z^7eg!7?p00{}*#X8gz@=hD~nTo}aYGbdbS0Gd2fs8hr&40KuT-To(jpKkTUYJT^@rTXYie_B9T`FT|re>bAYQ7dAC#n*2SnMmxV1%Q+r zcqpmcO&7dg&=UrU*C36jF6dG!s$m}=qRy@9*>L2unY`Wn2TNOKau@KE(^${-2P8i! z*VCQb&KS|sj5No@NHYcZM!ZdMb=T=rxC(bj-A)5}AvGC{fsdjoeH+dV96EQ^0>E01 zmwyO(iiQn5W*p{VMFe>BOv6+yaD+KNO&;1KzwRV_1aSiRM z(v$aDStiS<33lZToCu>gm_haSfa&3PoGF7CW#SuJ*9P2a2y)B^f*#PDRDa31G5+5D zS_sy#%<$U8dndjTe*&h+An>I|zvet(9Ejh?M=hNLe%33QI1`_I66ir-plcJJ#ba|f z)$xPbx^`P3f%Z6kEsjbkvb zqC>sC|CX<7M#I>(S18q2fpaWHC-T8kRvEwg4tOS2)`@y>oBs#J49lk&$J)$nwU=2= zh}1Y(?Kk1kTKKWcM~U(*r1N;f9s{pXu(odDR;s`?@x2cEBP zbC-Y2_?$LWW=7AYY3UuLDHn>goL-WJ9+Tudc*OuT{E? zhpDg_d%R2!ToSxe*DI+b<05UoD$Ea<-{?a8)2i;epOHWLC-$1ywkp5BFVIHVdw z&WnwzQe&dV_!s5td-kIg+rJ=|ilGBjh7NJJw67xkGu-ql@lKw!;22WgB3to{#z;T( z2E5dvCzExVg}P`2$Kn%Fx zGgFr&s$Kc<`>8O%#dLvGm~<|`qaY;a_Z&K8wQs-4Ba)3W7NIr}y-;s4QjFTHhc@Un&T0go&8j2YqbAqSy7pb#{+*_a zw_?8VxSjty#Dmz-O16d*^DFyTp`1lc0JNk2@tvW%hrdwnJ>upd>oft-jMETz4foE{ zHRX2e6nayvhn!9KpfsxVLI7rNjc@d38^&!uyiUFG)e+OdUYe#ax`xAOhQ}HFAB^;+OKG$bjJ6*L$33ze1j%BU4YM9z-B-D3B<1y# z9=wH#PjdQh7x9fAsgIF7WRp~EGu`0Ke)CpEw~&b~0?}^0aWWExWTMg)6tx(*IgIoL z2PEwBBGzHhO^=>^xHnwI6_`nxQOC&l4ZYN3mXt+O@NIOn{kfF<7%{l`^(&{lk()-h z)L+b558j_Wem4!fB^enY2r$N;}ft}9Hc0$H65F2k?2N%2$Nhi{tZ7e^6t z;sCy(Lb1{r-Utu3d6=Uq&w70XI>J|PKHbA!G}C9)xaGwszGX>SxZncjFJ+&BX?jgk z^@FtUG(CZ3`NtRz5y(>^w_n8s{j`Fax z(ncEZGhS%z!^s`TxfCF;DW0Ri4vhw1;Mzxzm;gS1Q)TIcCr;Oyxthj`kfMF2N6)Np zAVG5x>WYOw?xOD)l&<`KdDxPWv)#=UWuAPWm!Aen@inc0rv|!fV4_UA`#na!;yuMO zWPDE(aF0>)u#%b_um89>E2o=j%umKZtf-tz`tOHmtl&BwakHSJxs&1e%2_)pmHu<* zsOZyUad(dcl&WZz{S%xlvuEHuA2<&O=^@zIB zpJ~#-x;;?LvZ!&JK_HHn90%afc%aGmrNQoJu~X0+<%+tU?DP0R-R%e ze*~{LrBDo}xLAyBtEI_%Z=w*=>XJ){6%I5z7i}BFFKnqjf!PUYn9=Cu;o+Plb9Y~< zoUB*U^n{KXlV;`D2nJ=whx?bGwm4yCo!DYKof1fa5ct$;mPorH_hd5u-BYJN>5@at zh2L*_C&kVvMu#UNjJ7OY#AW=G8RPv{ShTG>a}0;ryaRzRdZ+7bsCfA;Lsr->=6K}k zzvqwgTho9{1asL9i5WwQfh5bHak0!*{q3jH?#f0~dXI z-}&fXH>MK9fasqeP7qsAKuFUEnzZ06lK>UZMB2M<<8{}75lmzy z$zcb|r~~?8yqZG-4fJH7$ko)ZY@v7x9U)CY{O&yBGXb@A)yfb(AFh)do}*4mx^8h{ zKMtcxxEz+HK~EY)&(A1kDxl2!UB3HBg*gKnIYqtkoXru2d0xW@kM5UMg1O^2BcWH# z4XibIN&a2d6Tw*dJ<__v7WCBPWP&DWlJQ%9z8`SysSu_4QgQ<~z*Y)J$c7(&D(~Ri z80oh_UJ4^tK$sEQmJt|Cgr%I}doV+48`LY^V%V}K;7X2O^}{}+cmMRrAw3=MN*@q`45L`3 zcK>U2Ww*4PL>Q*?)aB>j0+0o)5prv(+oMx4pG7JwhHTzUR6JiA=fS9C#&3WV_vv2O z2!Fg=t6_o{I7iC=zFbno`7GAts>e?d;L8uY_rbDynui+3PRu04SeSMnm%o9>3$0Dm z>P2#{lV)J(2k)7!nWPu7pWOvGj~V{k{aTqJSVP$SEfAMF|LA}FHsH^0#f!NN8l$2d zGA7hr+HG_{Op4z8yiLzo9t5+!m9;&0jbB!VVI9}&#xmt~1em-3ZEV76bT%SyBK~~+ zFTz8JfEv%@43MrND^rlP??CYSr22>nbH5${D493kZGs^5y^ptP^QXvVOIGiqE=)Ec z|Gxu>wyu|+5(HfN9+0CK!r`BC*oKjD2;RC;eFpIUOFB(F5p)6_E_DyRt-Pak^Nu$= z4A)luIx#{wIqorqFyr%EEMGTHk9fT=7Oes-ikQ>?8WgnuGKt(S{7w^QXZmFH!Z%Bo56UnJ0uX>EG{}a<3|HH|SA}BBe!Og=aE{E)>fw zF%xMvbE_B(E-k`OT=k!;v03WC2MUi+UM`iR%f!9Z$(RR5_*EXLU=xVO))Xmw8+hyAMKl{1kdg528WkkgDqifjsgcP6h;9{zj@8vXf_T;`f)f%S0kyI-2o27g8BnLBtDeC2(`w%InH+d=>tE0iv9BoBZwNn;T zq~ip(^wZ-!-C`9(aM{e+{J6!@6mK`hIY>nSKNi7oJz@vk%iufAH|#;*1?hKnd&{3Q zVOFoippY^IOIhHS;m4_H`7%MmOStZF86 zTaU+Z(Z<5&A9?2k$yPjk0qZdz%|{-Q_V01bZwo|TGIg_$RG#^uwxu76jh515CS2;= z1q^O!D&APHP$Qn@V~IFz3-Zs9O`O|0rgssHXbi>K!0YIuSpKOrT2@K(K_nfCl^;)W zWtaCzONjn=gQ}=%RJ6oP#vEKtDgWzfFOH%W^zA?>ih?xfeo~)V05cxED^*>!%Y+RL zyrYgDm$vJMZ|9Ikc5#Xe*Mm{92^{`EKt$PAX&6;k_N zuZ6uR#4i(;{nUd*&?{IeYfHC-NqXdesqReT_WP3X#m^$}q*bOAN7KXmDdcuTQ97#~ z1v{NN&QcZhE`&Mw$6RQ5roIyAG^V_a885%kKJuuDL&Sr?uXuoEFC+Kg>p~Hm;X`mZ zd}s5^U|Q;>w(tvY*|K$L9P+<;uylT83+YH@j88G_^7s| z3kvjI2ih1qn@R{RodtU9!_$OQz^>DtrkAl8TNfESE{(B423NXvl6M+2*Msg#x}E(C z+{^)T=g_y3+l7}v1X8)%k69ESj;M1iqmjWIaRkUvi|w+Ok>&YXoipjS)2{eco_2~2be;!M8krNKyO9Xp2hyD^ z0Er&PF|F+2!a5A}S%vYh+s>6>1UGL$-yb`Hm^N-hNycQ}g6AI8m1xyTkORkto-mQo zhfgk3d_W0L!l=nOygDNZjUw5W#AGo2CB0?(CKFh;T0O-_P1_@6MQlpu-^hsj+nItW zG1qIET;Wf}x{w-QS@=qN0CIF#m)-j+^B=Kd&u-&IDjpklbc(;}!vWUuJU@`qi6mJI zHV0cA(ZiReXurF``!K?p{nZf&WD}4q;i4=7X-L|l1s+y1|K;vGhA{p4MA@^f=aOf} zi2uj{Qg^>mCoMl@>z^O-&W_imJ4`P}qunjwxgPyHNtmwyaZ*K2%hS&htKW*A)nR3; zjKdwKd=$x?z9?isny$NF#qn;yXwim!nV+Ox?=`n?Fji82K}z;B73ALmy~1qu2Mn$# z%XW6u0|-lSBFFjrr0ywHv7rp~cF70RNO}$G-&y}UFEcN( zpt|T9j$_@9fNjnNSy_9=pS7z;uOmK6SBS52`t;WI{_S9cglbR66>b>D&Um7JUBQyd zC@rn+}HTl%B;P0S=#yVhE_9r074Nfv2(w|Tq`TO)z=cL@wy?uw*n4XnC@8VF#yeo^dKPh>ApdF@EqU5#D3Xy zMHh`OPThCssZ892f;I$~>93wC39nc*T=xB%{JM|L!8+m6UU+hZD|D~@;CV+MM;owh z$m3ZihsN**rGH)s^Kq0O&`if;8F(3Z+03K|?<2H}*WC>)wM2PXs0_s-v#w?8v9;DZj)Iq{{FC>@(u;yK{29)W`E?q1LjDH5_!s7&emO z#k~Ffj)gASfhGab$4w^>5ac!(n)%1^^%Wap5);SOBpauFxKoVA0${Q#1<%)bTz$2Z z{(U!O3$jXget=wbf#akb^n}YkEYnbix)wnPt~9x$ z$#?k4DZFD3CU*|PY1oU&XVO6gHg9WVZAykR9FxizJ!nh<26E}4aHM#gEwswcX#Q_9 zk0c~20gTi$D^}*ukX~=sUEp5^{bS{xT$P!Fr~oLkRHi<{rAfN_(Z%cgt?;^M$l8^hkecW3c@Cx`8|3>hQbhwX1YGs5 znFmZBGIC3k9~^#4TM9RV{qFxxMA(yL)Z5j(mH}FY=dWHudcYqrAiCW9p4UTtg2DTESuao+YD3dg#ehj;#3Eq@Yf3iG3E?C>H9c-tF{dXl$6Ft9`odm4yrXxTJi;bYtNe`9ZBKm)(`5 ze`Y>_VqcAm1Y6WcsvSFbYT{Sj~sH#Y<41iHI!{6Ibj6 zF>HO=&pRFg1|F22T@N|^XhtMmdSHZ#)G$Z|8R@fRnx!!qu10U&rv9r}%FNL8$+w)W zhcsm$^-MK@%v?0{bdR06t#(}t&{G$vvsXM#5z%nJu0IN}67 z?@7bE{y^ZTPkrW0m+<5(Ctaj5{Sk5ldA=!acclu7fr74+1tIc_z*%Y|s zJX>{3_!C(Ie$aU^pof^UOqvh{|idiWy~!?5!1BcpBkJnmwyyOIxEXkj2&w*D5O4I_$*4l>2#3lt{3;7+|(=4 zO8@?J-1{4SKI$?EB_I7A2j?Y-W z)*}i{YQa8|v^$AE%#(GSw8JuJVM34A&&i)dk>`Qcji)HgAd8Bdk{X1yJJI8R9lOKP z%;*-gZ_2%JF9$J@%V$`n$ZV>veS^hniHs$@+~9TC{Pz*`gd*uKnqi7vVF!C1A{ADq z8`++_OI*CcuDZ3`*|PkxkXLxe`_IJz*nTTZyyIUN9DLVTy#FoC>)?LAi`~&=6|eH_ z#a9)tzQz=S&tL27|XlD4LHvKCEC|}BV%rXb; z-wpbjuNzz%^*o$Q<>6hf>eG4Vkkg5`fv;~Pt}FD-GSKU|hxz^Zi^@7o>UN&DyNIuN zn+R>ta~qXAg!!I|xWK6xbuBUk?D=~DmriL>`&0dQKgvd(DRB4<3Pg(lSHw<`b(uQz z$jop@G%&ki?#FhU0`mZnqf+Dq0#8^Ez-~&-KHsG_~)ehjh1?rt`Z zo6FQ*APafvMuvNHt^>M)>aZ;@knh^lJD*gx|BdVcU65{JPr=8*8+2~LJ@XFxQHPz#_d!V??Wjs?Sv>#_)X zf2(BUn_i4GP8rCTuN!P#h>xNr>*ApYsxJ#Gm7Q#5|6_w*z~1sg+yKKUDe z+N^1ou3+9%R;~flCs4VyU_Eym|KUPO&>`kE!l>6_;3lM3uegv;DVKHc zM|$R~a6T=%oNC|F?RO0v)Mw5?buf|{&zA8)T3!GiiS4%@gc5V7_cVvHJ>@ zCTphamKY(T!XJ;*@lKtTw*6Kakunz~OHa?KPba?W>GQf0lvs)n{=G}rg5`_U$#AbT zQAg8IbjIcC{tMCvt6dj%?z=4%{wM*`<*TURbQ+hQe>e0)1Re0?L!i|5Ve^+kK=S9s zPF-?ThVGErW-p1Zb{Zm?;KR=U?)uK-4X*51uAgR?r_!G1kk*n1t|se+g70azFd3F zp&*R?FzbKJrQ!`bxT%XWUSTVjRO$!akPXUcLGvbMS6fH8XR-AZOwtvcZ7Da@RjBPyFXh_5&l1B4IKU4i!D+0m>7SPlBBXFP7H z`%hD3AyG(DFLn?ErrH2udLSCP{`sal;fu(#;5pMUx z96o0hGj&j}WV}ZoYfa<@OO0HFvE-&vkx4y|!>@XY@u$tjO`5NMy{T-R;%~Sh3{=&C z18IAVzDL|4)n6$hOTd1#k?~m8RqxTK>1&3vHm8Khw3j{OXSxVgOG7COsH9}h%stv5 z{o80k!9L9a_mp8Ii(z`^+znc<$xE67+@+tN+TjoJHR*5>H;)RhYdZG(2tSt9eyU*g z-`u$O>cwYNsTI{CVOmZH@Z~Ev^C`30&ZTzfk^^9GGfdY=wsc%dhraS6JY0-pdaHVU zmK$gQlg}E!c`H~`jik49;}KJu*Y+%<_2VC5ZD_8{$JiS22;Du~VE{WWb4MQ?lj(=i z4XBoJA58kvWqLFc9;1_H(Hu-9Ae-P9l-%qRd_vh>Pt!AWL+Ck`drLQY&^v>{#*Xel zovJAk0=QvpMy#E63BP(IkU_mt?m^~DojI?!E=mh>RAPIDgS7Q=TtCWX-?<|Ro8bo;oOI_AatidV*~6=`~D4HP%nR$ z2@A8srthy9gMP`+)%1$8xqNIC`$f?-vbTJm{&ij5urEL_2}sOMuC6Ed&@dlFbO!$G z#4|QtfvjD|eXQzVk<>ns%o;lS#3X%3O!r=#JoFg$?FTE6`|Po#74C;Aa4+>CXE;n5 z^(DN+U?w%^M)sM0loQC(h7G=H2c~_)L-(9}N@;1`PMP*dC(t;V7A8NSJT%eZ05ZiC zdZ2W*g-OqBv(^6!Wfrgpb}MBfug5KZDoVCaU9;W8H$8AXh{Imi8#WXN;%SHC4(Sa* zYJya5CsGjxw5w$EZ+=p;q5+xrJgj?%Dehc-@0L;cfeL^;^eO>eYTrN4jj; zJTbIsNJ>L8q~W2FF`G>hIuC!|;PG?~(nARXz_B%6Ml<+Zqai9lU`P6}u3aD1OPb(u z?2O#Q+t-`&zi@`-FCggwJbykpD*>I z4u-9D0iBN!54(_J{iMq`&dKCP#i|=wm6y;(TcOf! zPQkj`LB@l(_ri{Iwa^JLXkdh9bt3ICr_z=3|A2fDJFxdBf$;#M+H)6}kjUaZ ziXG^}lvQMV=o*l`h~t6*RCej!%wZ>T!w{8z#zFo%A%mxs(_?G^42AVk1;!h|Mzq}C zhLWF=?l5V7j*)|>S=XcISt5Xs_@`d(;oeI4hHewlKtkfn0fr+DWJSqW0PC@eUNDa05B zv~EaBEDHJm5&q@ymxPR542~Ymc=CjDze5yl>mQ#F>B_L3CcU&T_|(TVfL-e~dUssP6s<=yOw{eB8 zOq=ZvlBx?k1dq^?#u#}$_U@c|6{II(4&z)<-x^EGToVJX7?^JG(?|U?I80f1C4+ES zBF@`Be#&38VIG=XP5STq+eZ3##6`I|>N?)BME)>+&S>RXq!?6cT}^rWb-Sg>jbefx zJ9Y)6#7LX^W}jiNOV5GQGPsnvPcStbB>7h9rQVyxd_35p1wDF)Y?cw-{*!4xlH6~Q z!`$1j#v2R^u26dArj2I0QzovHaf?R|T+ThOzS?j@9oV%IRBIsnwO)HCHDoy&DEB!U zsk>390qjP@foFy!|2DE^c&jYkuddtZPA4h-B(26Z_gd|7w+962Ke5{qd2v=q7^1kS zx1vwvknPYtJ5KNyJ=hH!8F!XGhx}^*?2a77Xx#i3>emhM*oZq#&<5__D^uyG(YS|D z6RU-Q?|&9db|}(K7hD5fAvj-3r={zCx)!Rx?U$A?nFt5eSaFP-Xj>-^wn$H`Xh4G}Purwse+H?#8ElZExMr0X1_FNaA)iW@B-32+k zT166t3`HRDi6CNnc&L3`)d~RMuWolHdYCvM)jmO(=;LhNktH7v6CQsW3)O}!)?YUQ z)b;&vkiEjxMUF9>N;^WH7HI`(Qx$#f^NmkdMqJcJ__oD7#)nYnpPGh`n0CbMM__~-Fq_oU)9`e83MS-J~#t>nON zOUsq-ENh3x{jA$k1prc`0;mrp)N4Ed{W~n1WbqyyyQ_SdVRBA3;tfbU5WXK=^7k`) z0l)`aJGt38EQ0iZvEHWZ_Zm`Fd@klA5?8btx*?ZZam=Clx&wp3*@abF9e$s;*>NBFjS-th<`fe%VmdQCS z1BzW>1aR4G%%F8Dd5k&-2Bq4`1JD-&K5QGD0+-oBoNB#~C5Ja6NP%Fuy{d|ewR^XG9S z%ngy{KfU?^tWc5@!A+MtvXXu2rj+_+ z8ZwrsC!?9SYM}W^M4~~n43IJo6_Kk)ye?pL*HoM_H>Ne|K4HHlW{VDVr28k{yY}8Y zzN+>u~|4`5mM-6!hpf!m=82jOI~R@L0(c00h)MVL|}gHxrh zUJJxVv|WgUz|w7p5Z8zpwL2GWEwN#mvf6#;ZIp~ZCTyg?iWp`D6FkRjg~(5E$4_R_ z%BU(l7^_R7rR9Oj+HdoXvP;7j&SN4izrod`6iWvyV?A8%=tV#xdoZja01V(o;i> zSLV*pzc-GiNNEL=$y)YvfPV+@f{`#XfYi?Mr*t6TP5bTv%bjoqb$UF^g1sC;`gg%e zJr_?}nE|9@;a^kehFj1JH}0N8Rqx2ZBw~rxz~|tP;9d6T01x#>!20L#F+1N(_41h> zG&f`W0Nz2@=-$g*;jnSxYZ)N8U-yn;&i;MM&tX#OS?#54oDjaMI&=>Mr>e z?ZnM*s>e57^vowakm{i4Bi74HhrvvqIZ71RC^h$0q5k<^Z@oY$Qcy!&|owqo$f zKkQ9;B*|z-c~Ize{wOCVe|K6_f(7vSDl?8|1yUoG*c?XZ>2@IgO&Fis^3(Zb;&aLq zYwx)LwKktA`lD zH-um?>T8stHshpzU>*zxjy}o9GDMYGsPgsE|J+HH)Z=dn+FMigmZP2{F*^h9ydr~P zcuSe$@YgY#4TlL~rR1xPc_Ha((_M)BD$f)cVb5e*{^gH9Jc4u)&o=VIbn%^q+JJF- zMk%=qLW?C~Byff>K@VV^2PrVSOBaVhg%&*5C8w?!ws!*pxQ|Pi*8jk1M&_i7S5^nJAOH5Rz8ob7u6!bzKyGj!0u{XDR|P zN_ta;S~6Uaoj|-Ub^-YUNRNo@8c4N22`Wx@QxWz6P{JUyHK8V%LeGHwTYeQBVW z4ylvX`ybxE^h*NU*Wt-+B9;O``dW-$tkVVHrVEmh`P|js-r^zQu zDz+1v&h0Y!+gb0Dc5iA{G)cj9>J8-wDV{|nx*Jhb zEH2C7tH1#My-S(tlH^zq-F^qPAfavJ@EHB%mdBmnq(8MV1zmi>S{<&EKOFZasG2e| zxB&N|v*_BKt8NET_45X;Z~_LHLpF^e?;rPY3xj)Yc;ae~bx<7&5UOxMJ_9OL}#kU|Dz0`KzFt8nQJiN;b586VaK znt{wlsB2-kbjc?onkBsj?l2^6(Lwj7@z1jO;A$)==DA8glpK(L600C}=>*clh*!6# zO7_h<9+r;H$=2O)q{wYAoD!C7A2Z+ILyqdQk5bA?FJ1jar98iC3YUSy>XSKz&`EZlH3CMH; zzrx8fMB2+QHARLA0!vc&SH?a=8Qa74B z4w?|$<#64%KQv4sxPz}xzLiZc+FRG3=>AwUA9eLSC7Fg%);|%WD@Lb@0aTW_|5TbM z^uz-UnoEq#Zqq*wzE_k2IoaOzKSn=fIo0|xj_~01C#CCvtsX8=$ilP<1zu)Xh4RO1 zw!KcL8H;J$HRdo`KXwu65ikuszX>%thP}{TU{G0%Gawn|8DLYces6(HtMeFxVNf7x z7`?3Q0bt9i0ea)Gy6ykT4aixfnPmFz(|(zWCq?B8Mf2J~C$Ojlh^#%_fuWh<8Bl;z zZ|@+iV7^+tKr^`BI;LK|;fEJujvFb@)%2rBW&Q~Eg{PR!PI>(53N=!7*pgwrn3)ga ztdWIeX{Uw&BN*z+7xd_^>o5rH6-gsZZp8f8UDw6ePfLf9vD808Q)K2o?LtOutV?h1 z3I%rPI;OShO8$#Is=2|UYfgRH_*QDwqu8kJ|BwolpYlUD-Sm0~@3>d~GP3yP9+QWj zj@kjb|1CK17G$p!L{jSDqQ2)r{N9yhc6ILu5u#?k%ri=ag7su;y2D<4( zi8%I2;yc=JWW~&M#D^0YQ`=9eV_eTKoGEil0UovK4!LI5XO)i`+}S&dPE8>^APcYc zpCfiAw@8^x3-7Qi@9s-U!!%yJ1}WChNks!+4eTu9cdgm*X`TYK&s8zp>L1lT@A*_ zOqMm=X$q!7ty^49FfI-AER*HT5~}@+R9{De=ndH=rHAp!FKFA*HR~0e{&ofx@d>b- zx5h z)%GqeW{bD{d1U3xEU-Jh3>i(sM80znF?9+h9y5m(TNU#J5B%sl{eByZ-|`Z6#;+h5 z3H38(BdoLd0PVMU(Y7J$t~UcwnrtiV5r>M~FVbmp^y9EBP)340I0Al%FeQ&@lgsi= zdaRLelUibn+4;g2LgopIdGApq$RX5<-&K;@h#yyLFnkNRafK+U8b;gPan#>8q0__& z#y&F<;(g5j%kD5O{Q@0V(~oxE&Keg$*C=*mM=YrnyDaKdbC5wIjDBHvLV~pTBf7EG z5~kAo)ckAS4l7FgCr5ui}I>>*>Y?Fh?)!$1^dNNTt>2lZbZRZwfEgpAx#__ z9=YtxGDKfrIzRr=C1*4rO)U|Um3G%7pmN2wmsTD}yS4=dSPqn5*On=bwXw$tZ_T~) zlRV^y&?facIXbdTs$(qI=47Ued&YhIPk#VjD#T9kkGyB@*4?i1&*m0_-dF_H%8@K- z-E-mEI$jdZ*zoWgx98UbFf>LN&g;(&(um4p@)Zl7|KPjU#I3#;$BgH#WM>f9xGx>s z>!f9rP`j0-LZVX zE&7%dDLB!V>Mq?KYm2fa(MKHjK&8#uF%;E>GJHe>Pg+{th+UCo+w@dsGPErPd!pxM z2vu6r;KgK~Dw$8gtmgY;g`u0(1U;A5L~GsVH6Vu_dF6Zd#RPLzp04Lhx({G9 z=4tNxY@IpI5f3esG9C3k4vCPd)TGa0TD*%gwjxMN3t&URt?j6P${krU8MZecH8)6> zqF$n*zB-0!lM6`)yY`1_pLAw(ry?%s4__TW{har^DSx)?LJ zDBlxh^fN3a9|q8VcV80lha!tkT}42uZpRrK!stKYs{V*QW))P{e72prsFNSG`Yhxo zfnLnTo~}TO_FV@VYciz*(}T2FnRvs0|HA{4!AV*-?$&qCOLkvxpUfXo(Im?!c zXSXa|A9uDI^G-w(mf8Xu%_V->Z#zpVYnT^jiubcW>4M8{;Izp0B+EVP=u><;0b0k< z154|}6J_k?GHwUC%FE4ae=c30-(yP=b!+aIGm$#K+qUNH1z>25i(YJdpaag=zz3>E z5%MpO_aUOpROiY%TtN$tB*zi=bd!d?EA;Pco4wWhqT?+po`o#J%LZxOD->1kW*+K; zH$vc$c~#S-=`cqWD|4t$25fKh#m@C?XyhQ3O5)WGR(R!{`pAk<++TY}}015P20m zYG1)oLJPl1v%3?JIE@DnHwBGO=kMC~ET>Xz2WJCUtG@;T>*Tdf@Ee?&pI6e`3YA%M zf7^?Vk8R+~Y=DmVYOAqwqlY#-J9^x}>XydvBw(cRsO`=bRNvnVE8bXtRU|_h(spo@ z?bR8wcBI`M^rBt0w)_W`jpSiX?0=0zu7Y)q*;bxmBG1MgtPA$PigaH@Z1KG<$Vwb9D}IC;^D-Uy*z<^X{NE@}s~eh0ml7`M-&d_`LiWz(_OXpSD=K?qnxvRj@5K zj<>82@r%XMq-AINbE!Q}TzkML{KKOM^uVio859(fWxGb0geG?Jdu<_ywc@UHQ37g< z@rtD4EA$0OOvwOPy5uP&jy1p2s*SE}$0*UNTy8fF9(G4t*8RA&TJ6SZM1~qCfJMvyIg0khFig~p-_+Pdsa>-vneZNb`S2O z+lXXHJU3<9#*WFet;zD4Mlx*pecD58i^Nz%0prUQHfwC-;UDyi4@! z)j8PxUOFUcp@}AYkZ9wARfdLdt?hA){r*&lj*RU_OYLD(f7x!)@kd8*>HvT@zwL>i z)Vcc@lS-ThdH8dW6z_>u%rn08!sdEP7ET;G#A{{&QN0`CLG5_&vi5#rCI>CxZJ~G& zU<4$P;VrSowNitW44EUfK`BV6yc@(lBBc8|gz8wrC-{jH@hlX_Km@dpkpP62V(+jq zFo|Iu4w8Rlu#|0(dd|JY0n6s0E?ke@eJL4>Z77+^L%e>y&21xc@Iz-A4MRJU_CO{C zKq`MRhp+zXw)@)qgdVtc+VcjC{cU*|%b#k&ZL4kHFoBlNbN*bn32C<-OOxU?SCi19 zTpz|Pu9fasR!$KGA1KqQ&%59}fSp!Qn_zE}vO$<$C>kA8*w0mAG&hibd+O?I$WV~7 z!w_mMe0f2q&*dDZxb+A6u<<>vi#+%`Q7&F`+&;;+G6K0B)yB2xW2g5HkEHlYI(aQi zemZ<8jgGw^*<`D$8z}&m$j`-`)WQqrA&6RQ51vvS3Aytk59}0&IeQ)CC(5xXOZyOW ze&LR9i2<$4w%FdgKP4eR3Q$7OgVI}>2M<}KbK*MvoutiJCV92iY`cSt#6lNL#X`hy z;sTdT=3anOWK~*kYZ*Wk9N@!rU`Z4pL>@fykQ8P&9lPZ(L?8&Bwr{w&Au+;=#D5QZ zt~Dv!Y%#mRB}(*f`x|fE>kh)N3M{|ow3TC$2_vuBjq!;#8I~4`h0u9ZPF5Xi54#PQ|4LFc zs(i}t8L!BNvKpn9U=}SPTR2XuSM1;kHMX-SDP`P+_~HcZId&W0O4k`LbMcL}^`B!b z)m;4!?$10R7W$uE-IcTQge>|)_~@CJ%Ta?B71{9Ua`-4%ua#anvrDw*n!kCf6Z_fK z*85*>D}pwrAl7wQ*S_ z2e?J2{fw_S^2n>UXa#S8&3B#`K`PC<`oQiFlZ&5CgJ11{_Pv`J9JXr3`LD zc$kRNaO9LbA`T`q(lqpy6x&Wf^O>o0=p2&+svHBg@;+z18dg!a`o+EVWQNx5trvAu1S^lv$m_T+5Q1aha#K)-i)9%%CDd(dav^O(^JMJ&cI^({!YzBU-^qpPJcJDAR^b*I(4yzI z_n~A0pbx8(EklX1J~*D^SWfDCw&oo&QD<{G01bpn-+_{D(84n^^cZ8M%XT{zjF^+& ztnIeY)w=LPsy1;fJwPyaZ$DI4L{e~+{+n!Bbm zI7?`0I4d(q@{kKp8@ux`*0y;Y$Tp7+*@yX85p$M%Rw%unZQ;q!&ePl1&Lqo_4>NgW zdZoFm-P^B7`3{7GP;@uF4zv=j25$^c(cwr4&%`-*#`8JF*)F~TqW-?oubXXt$=fM1 zT+Y1&X%{)?-)m8L+{Wt2SnI;P*=D(!I<+QYE!gwbVk$%O@4EJ%4lsA_At>J&p6SM{ zj~;&gT{}{b*&OaZ3Eo7nFf2N4Nz%qprYu=sGWg7r8@v!!R>>eYbp~2GO>sbqbhx7G zCkbJ^@ss(JM|9wfW!JQWQ*+#q;Uk=P@`tkkBAku=L1!>Qc=)ir!9f0is;rAg7}=X76&HaT=QN=v~kk`+X1UyxLJPNLM(%;llS@Rs42qpErK?!wijI$-j>;VTxJfn#*90a{?z zD(rP`f=V}*p1fp zIOAa>H>LC>TYVz(zW+Nu(oNiqoSH}3E70a(W2ZD{Y>ByPJ9WnQIVb*KP;^MU-pA_5 zt7QhKKzYCwE^?j~zQKi!HXpLt^v}5uI*1nwlDqB7yVrQC7g0=B*ADJtGHMelj~Be* z29|zFs!fNTDXrAG9UPl);2v%Kbo4s+`SSj=$X17?M*C4b&8K(mCNh2FDOYJWoX1Eh z;94y|m8gFn%A5mo(jl|gJXQoj`;Vezrfzf6K+CWA!@)Ybm4~cdyVvDj7@m}7skjK& z{ITJTO@B&fCQ+$cB=a2oCa5_}O|d1sj=T7bt?9L0zyESo=Fwxe%x#P|(S^&5n3Jg} zX$oJxiatjbQgSu%GD@x_86lw))rT9lJ*yAj2M-3X5!up3pr9Q2o{6_kGEZk#thvS4 zzquf@cXZ0ZatIgt>*67P*?=ef=L0PYT*h6pSWE$mU1v|(wD!S;%fLko;Pw3kFj~IP zI~!R<4bpYUS&RJ4YVC3GV|M+ZEh!jYEET)#%zY52LmLtLP}_}|WDuTe;V>}9nSQb0 zGm^g?^q&M2^%h1m;6WsqK9UU{ob*rqapu2HObt*>X7zlq zopAAclCF%F^MoSRvP+Z=;2gUzJvWvelyA7ypTlZqI4-(@tz&zBgC~_MIV*gc<)mmO zM{{Q^PslU4LY@6J{`j)n+OyJd-#YjaA052}XwBh^9DXk4h#>?A4^j7Kos(*JjFAb; zLs-D&YmuS%!L|d*n7TvSvw=+?XZ;N)0w9F8?B|&L)K_eR!=`Kp-6~wqpN3`K;Lhbj zr+!%WU|(n;>TKqQWt}&WT|r%pj$E~2iWRf`mh)U3?3wZ7So=lNlfvKAYGXHFJN^DD zp@{sHoYd>?=Dhb?3U-nqw6Xfj`p6e zs}*aR_NGbeFFwBwTV)zUcc6}V82xvD^`)SXTv-{8LgHuk3E0%Q=uK182v{gq$uXS7TTZ=%*>{Og2yk|UYuF$ zC;j)O-r}|$?`{NXM=)PaITw=h_h2vs=kPe8J_eIKm|E1z7?S;o+id$|{HW<50$^j8 zx*kX#8gc9?mfLTgI`mA(cG!-k+wGN-^B1vZP#KMAmDU_P8c*RONcUf(WTO1>JsNjS zI}#v`93RkR(dkc5SiCz+Jq!Uwbb^x{}z?7k^R;H=!KcZCMNqcaz~$$vF6r zzJqOX@gSlAww2gYBBkrnY^(Pj!dp|R`TMPyZ6~Mk&Hjs%In0vgy|rS)J6pgrPtAMs zfc?LHU2?y070SWhF;{Jlz>h~hrY#XI*@-RKE)-225@r_fU{MtN*WbJcm5e6QvI>{ z`sQ_Ly$R`Gvw{W%2VjszT5cz0Nva#x(rUbkJNMe6$n}=d(6DfVAM;?>q;$quq<8>}tMn73xt#x^-2fTx2etRL zMMzuN^>5gg&k^PvsSA<ojix?`EqHv7X7SNh7A;EkFOXRD0=fpN+;r3!PxeUu7P;%VnCOP2vUnSR4aICz1R=Vet6~ zjE4#8zVWo;rP?4(d$f+t#bQT^)TRL~ec7oZTx$1&t)ecy$yrzpUw{r`Sog!IaR!|f{b~l=*h&8eUZ2OLtU&{>o11y6q z8dpBroV^~63*a}uWsU_z!T*zt(oycbh560;JxwdI<6HKz1+Px}OXusuHlK^qu+NQk zV3IL8>dH=?{;=D!)oDh0Rf`e`RZKA@kCYZln1HICGLmvK=#dT0-;Z3j>4N)OV~uzG zdCBYZ7F&9jKe2;VsOpdsaD07$1-DBmIOr5%A`;flFFG8(C>sKxj_go?|-B?wn zHd>eF7dy}nvi=34Mx4%p`W6ewFuJ>U3zw3NMO>b zEJ_%L4q+nn2_>kujKD{icCEMk1OHxshnP=>++PE!ucO7{N4aTfWadRi-^T68#>= zwVt~zlaSmAX$aYL59c4O$u~ykVxx?W-~PIXsC2V&R!Jp!k{4!Z%><;D3mSiu!m$B!UKB2H+{FLbm zu9rd3jcUzE_Zu!piB$B(;X!(b4U|HDivD@aaA@1w8}N;?lxLT0uOD4LzwZ%Y36?b| z4N`MZNAKA-U%I$byPe>!T4%@BEumH0Z;_KTroHi6=e*w*8|4eM{MC+FO9n}5TmJ-g z%jX+hVwpIky#WS}*QsTp&$N$BxvDj5c#GC|Ct%ko*l)TJyq)2Ap&E-jZVI zaI?9N8QBqS%L%mLON;MXC;FG&DLLp;V?ZjU`#KFb!M21C%U-+! z7fw@g5rkmn8e0w;xK5k(PX`>hx1+M2;ojgcuVEMYgmGkt7E%1XvJRMR{Arf*WN0G z#cfG8o917Qxy;T>`SP9mD;!qO9s0&qU`|A&G}*RWmfWrDFm(JqVc-EN_>ymI`=X@p z1I+(>mhJKZuH!U8xlrrcLyMc--^VlYw30hY_tRq=EeA{)MpBcM9f25b{v7`>-h~(I zL+B`o)Fn2x#Fn&u4Km(Ns1n@i&-~AqD~&5m*o{Mny)++sQ6S@=b=qGil{+{Qz;?$_8m&x1*sY*jRc%5_KcK2pE=3_{$2PQFh^y#qn9Bv*fcJ^A9j#T%&#;`TI7S zwEb_=>u^BI9q9L^Ex0plR(+vO>31!Cm&FAv2CmrZHZB;wF(C9vS^4@soah8Zn#`_y zymjlrbW-I=^@~iC()DB}EXcscTSw6oIuew&!uJW_F~{FqqiTKy<8RZ#w~}uUT8~-# z1y@Ao7u+%In&{`!8O1dUUWVXG?RmB)KiR^k!X5GNXz7N{^;JgR-Fk3k6J_qRr;(Rm zSGi^BaVf6F>36QD-_1w8fR~3XZI*AJvq+J`r+}*%+$2U znUCM3KYpANm5}}o`m$LDUTnf?E9Ral5!v};=jK*9JFi4uBf22pwyQ^H_k@{ zB|Xe|9F+AkH8bedlb{!mUp~!Dv3~XFX-0Z#R#HZB`m?0CW&Gvq^yeuKk6vb_y?*cZ zGWp?~HxJ*trN6jnSCpQX;$D*e@_PEKtrwSF&OKg~{yN34EX6tQe}3BAT|Nj&=|BX^ zyv(z(XgW~ooI8!S)@q&SiW?*+<%ULEy=**;os=gA0Lc>`=DZeYn~dDFvX{{S2V`haX$tg={}-=U`qYqPdoGTwTnnms)U5 z|Hc3^bPk2olcI{4P&tFGy8rl8f2@kakVLUB0)ud!4%f zO=n4S!5qi`RGmf5A-4#7q%)Bv7P!*8s9}@-J$lfEufVbx*LD(%efUfg_{WUoR0kGA zih+(Cd&)mA!PbYRdH+)`V-=qmi=q>~kBQ%+-^d1%V)|FsxZHoZoIHE^_U>7V4lH*w zcKeA-PulUjetSx;scLjEXM;G5Z;rt%hjI$`qX*hB%;hf8*~nM7DSk0eFlnY7AZEVi znU-zVVjr#JvDSpIpE`Ui(9;~>!<0GscE`uWtsbtv){J@qA_27K^IHzJ3 z-uNu;AosG1=k>qZ3R~=^mc@E@Nf>*HS?^{ZyQt-rn9^M6Q!geDwBnDumx-n&AH5x4 z`5G?|X?8eB|9N9g`up?cm0JB{Y9W_3o3$^Lqx~v1_amg$)dz9zSiO(BP6ZS&d020WXwU-!w-D-;29CMlULnfqtP_>*x|s`^Bn|= zKE?BXy)<*s#WlO3j4-8Anf7;aG&<&tEmX5bzWZRTjcbiBtx$OK$R=E>A5QW@RJcXC zU`X)Uc|qMc(uUX$?OwZ6rp|C+^R)jBV>M04IPElP z34e2q51kC?z;RZ7hI^XqbNc(E4}N7MrOb^1-TU#;8<}#AWWh4*NV;=f1~gtrJ+KFU zU0;KBMU^E%W^*ax8$}3xYC-0ubK6+J9e>16P9)-kIk#w?bJpKGxo?j+_g1Xzr3LHHgSR$wdQqGd0F!tY?yv>rQSeQMbUI zDa=2{gS`KOyS|x@LVK0qw7ZzQN zo#w-zCSs-A$wat%!WzCx2iJo<7WtJLAw7u4cdW3E*t1gaW7gf~4`mZv0UtJR=6A67b{qnT%r{npSOOgE5r%1WM}klI`OeNDE-6wkv0hY;-+9?z%* zc9=2P{M&?Qn9N5z!npov>l-ah#OFSA0+pQ?hQ9}u7ep(sSedadsyA}duVpdrW*vd< z`NNSi=50QOJFib6k+=5bMju0I>xt~B^N!{N zhQyE*@0Pzl0L-}b=@2Ei2+X|vL9tOTC)G|S%SuOT;Eoo#n@c=m>B=#gXO9-wWx7IO zYxw-!KJG*DtpnVnHmzO`Om{hWnrGYDpJEQkn($4v|GlUBbG5r!fz8w>1Ed}DwlKx~ zj3=j@&3(sx#BS)`%Btt_tTo{OD??Tml3#Kz^qz4u>$M>15Pv$v%gnijYttp|^D--< zPDIP^htQ#dQ;!~k-x%V)@asS5&J^|=CRrqmqgll|M-H%#QjVn`%;i3JQ&Fn@Bu#9z z|8!q|fk~4>MHmjHjvefKjZWX!o+GPJ9Dxqm?&;2k>X_}>U|bh+Nu0=i=QEx4Kpejx zI=Ls>$2?El1-r@D3522^2TC4lEmxP|M?IP!a$}1Q^N6yvOS+i7m+R5XTsWG=ThyYH zSFdr>`i-E{@GBNTwpa>6golSl+jOF&0Y)pxR zIduU&xnyG5x#2Vpbk0IeXR|@)Z=x4|=^u8XIQwNPX>lAOyq}4}u3E?e{lPKPi=%-7 zbUtiOop$ZNn#pI1F6rno((%2`bFF{s`Ea#^x%FO}c1x>=uZeV)%F9fY^dYCDT=KCs zq>qZRm)dsV0*LX2af`Ww*7; z3sI(e18kjp>I54t1PJoEqSFA#FR-HJHjq{USd`k_@4Id180uGRjTHzP=MfcWb>_5? zv&ki&JiHC8{F-HbOAUV8ILm>#5s9tFY#NQRe?k35+$#Tki7|FK@5PDU;c94(8YmONw=4?(H10P2S~*>7?rAUfQL}z?G4EvL zLM(}(Jfn}ooC}=PKQ^Wu=SG#s<`>+v$;bp)-poPXioO#yV@j@mJyKL*ak!qxkq#}5mYvDnf z?|srT1)5?{+?NA^Z`E2C9EDq5&7s2} z&Y;vEN46SMbtE`jI^0b3fgo2MusDE|*0~3Pr>$PZmbwB@glQe>1uZ?LU)SiMuW@>N z1ZJ~v+tcs`QbFV03=EwuPd3meQbkNiI>F8BuRC%L^v0tw(8IPpT&D=j|2Pgfnuie0x z7|yNOve_o>g52~+8s}E{)=w(a1VAEsPDB_(q7fNuJbL*(Tk+e~y!1$Z!e)>2V2}{A zB=2ezQZ%C7D4Rb%39j6s9+p896kj)E4>Vfk1FcfahpG|U@97vZRW9ZpwDj*ME&1Y2 zSEiYGbn{XWHv`1@8j`5Z+~CRVn72=L(92xN;FjSZPGFlxc52s4`LoHwmtrb7w28x! z*)=?&`mp@=GW$W3pR{n(U+fr(ue8Ga9bhdsjBA~rW9J61{2nM(JGXfo!qF9n!KMip zv-gs;vbi&By-4qB)A8e$!R2aB%FiGcIx9c85_9s4DhcjSr;o`vcOxi>CIMW(gZsdP z-`YYbH&J?|o*6(c#+k1hx|jLI-8|6pm_wJgBAk?FOIL_5Gdb{+ZC(4C6R&5HONWDS zy>{umqqzVJ8Cs^@Zl31Fm6JMaM?BZ8dVcT2rci7>Y4*P^Btf~F3@>Ru6`tZ~Ry+g$ zrXwynm~3ZIwKQ7$tjpQ&@^alXU$ZfC-&q;_$j*1dAR6}K0Q$;qH3jTVCyCD6oQ#Bg z)Jt9}pTL=njDBBh=@TW>SnF;@zd;K6y@387w-rjNvwq||49F|>mes;%VDO+(nTuTwBHZ6(pa4?|q@GWf(u}C}g0*i&=cVrB@ znoI;_P;)aM@Yao=(j52Fl+m~*op#Lehh5IUmewy}(q!Ec9omY+jl=`CiUbnQuF5Ug z(`;r7zscg^2S8=e7T%+3Cx>Cri+#UHI<858RFo&uVa)+9dnnsq7B{kM6PskhH~pLk zM%;)rUQfLZQkU+NujjAp5^(h6lb`ekxHwqot1}OESjMfJ^k}zs1?X1J>F+7|_*6olgcPilUm2%iq(&cClESK56 zoJW_hIrd_$_BkB`o`1XeKejyGh-r5wUJtE2Mz>E{x(A;8{&XvD16z0|~Q=Z22t=sYRH~e}K-TaHLtjm1q zpVa#RHW9&U7sHRUIW=os&9;!>)cth%!2)1Q4d}wMyk76cljDa#(7Akbs8fiqabSQ5 z{PCYV8vpkm8OV_undgivPEp|o-)=;Fi&Fv8!Gn+8sVbg1d0PhGv8aW2(sn|IJe&yzQy#w4(MpWJ=0&A@e*F@bmukdUyB&jUf2U-YTbF{y%J=t>6y3DtdDfWELTe?%g z@>!s@=e8!E!4j}6r?Oo#x9OJrbvEJ4xE~Aq@S%Os>5Ve#VRdOf;1A~hJPn8*WZ0I& z&F6U7R@q;|_OnE37t({%EX~)LkcSp6w<6w|IJ*tr@3I$}pX}0VaxHrQ^f_Z{)_R{@0O$>uFGh5rJpt)s2tx#~*Xo?j5TCcN~&6tPR^%10$1w zPXQjF2imrSy`MZ8wrsnuSUp~3{75c0}MV2D93@*^uus*r?5%L^sdhG3Zh_`=q0UHE(;Bkc zo<3%gmB&yI-|hd)&um*A@_hd;9#*``x?PZxK6m2?Ojhhz$Lu?k61Dq{&b>t>@a%h~ zJd<)B3pv|`+!wpE7y^=rEq%S}EI1}=^YC??D`(k+-+zGQVpnr9OFOqF-U9=3rIyeJ ziR!!DxcY-HB;RX`H}?A^Mko7LiZ~6+SO7ut^CnY;xal!@A`Tq3jtFA1vGl6juo^Ak6*==+n z)#bb}%vy#vIbU=&i5&E-)O zl5;cF%T~_Im?WS0Pnw! zZvSinnWaD(;UjI3jXG~XojP*{0kJe}(kg-P3XPUfym zZ{u605`3|fm+*W31K1!U+VN3qPioUmUvput6y`{t8^m1-w@TF!Y30ahAj&WX^2i-c zybFjw<{*_g8>CIf-b>L3XXE--hvUGVJSXGh=XeL-3*SnpajQ(e+TvsabTM)h?sg)A zK*^uavCBLhSq*Kb&T5^94{_ZR+<^24aYHhhYO{M))(Paj{l1QIDz!50M#BDt$H!A~ z99vJuPU*X>eU{t zbT^q~I-kk|CE`O#}vb7K8rO4hBTyg*Nr>@9*ti77BF zjjwm-c$(KK`YUnwV<&w6O;&|wuP*ZYlGW07En5nl&4(Zdr+3$T#><(>4rCSeYHn;wH zhs&kN!<@2`8jgzpjd(3cLGV*7Z%;zt3(~(6ufR8N5(^qv^M*ysIK_fD zpb}knET5&7u-mGIH~l!h{%u;yNShT7815AeHG6U2$2`{R&1-e~CJ*^XAZ0Z2C`fAY ziMnl4Le~Lg_vfo;4$`duoXrs#ee@oEKIv{ySQyr5Y#jnPYEJ;9*m6%C%_$6&QAGKt zqVe`?wF^}o(gY}Vk&$}LnXt4hX1$hyZBhm#4Fa5-R&QjF+LDDTrN&RFsd@Qv{(zR= zWQ%4~Q?h03F@Iip*8>P-yrc!GrFYMn1Mb|=zcO%1O7Yj3@C2>MSgmbfWiLnv5P#AI zuJfY)y{2<#4}vsBH;~7FJ0;J-ELWITV}O3=g3#~4a$m$*-c~>ERjz}(_VG2{A0MKV zsjTuO1~X{CSz1F_F-2jwAvUy4;w*}#{3X)=-GK#`g(;csQK??WhSAa>GcWDAi~oSO zN<@izOb@g3^l>Ssp2xvF@kBaCcgk0q7vbS4!pl+T;%u4we`EmQE3;GxB~99J`T_7z zf0ynHoOYA1d5|MZ56@-#@Cx4pBtKgE*|Axv{dm#&8rA_-E8ul;(tqCcK`jjmx_`mP z_{4?DU@R&1#JBw-{ntq`XG}A64zc#F6QZ_|@mg!~r8T>-`ku{~=tjkvEPk(Wuk)axM}uUwsMiyzh|?o^7s+$kCz5? zujWdPBaM%pOj6)Jiqv(=%fx`%oZpY@Fv+d=pC`TJ`@4gjQ=_6e82lrRH)T2r(JkVH zCh<0RONRs9s228w2P(&SPG+BT`^vS2H*4P@o$sQrb1Y2>l7~2c&*2@)H}6f=1qvR_ z>fm_&7Vg9)Z%UW)aQz+1Q9+KT?#TEyPZPnM>Qb4BbTrA%_#H;BzTgGQU>gNk>dj*5 zIObFGe?4IqO3UI_{+;0M2|hN-(|)2LVf%nYt;{8pUa$Wwnl+|_JJ+vpH>o#SX7vO4 z;fo6RErWZcJc6b6(Uy?A+N4ukKCqJ^ai=XLBepM1LHU z66wiwu~^O;n%?@8PRXKN6n`mXysYs zYgXUS;_g>2=LN_e(e4!>1mQ>8w1=>IqV(8TG1zNAA%%M|X4N=n)!nF7P+g0kUwEObIUBEY{}_>BfueYXV87|GerY zk0R3)vxT8VRxF9WF_vp_4C`8kleH55T=3KlcW*nlvXfGNUWm*A8M_K)z-tDi*6O^L zow*D$G3$%Y5FYdp<_!;I@z-DPpfyn-eYflmbNRpeT7w|^c43b_^w<7X5D0#WLooUN zJ`fKJ#r8@IgW0JW`EfM@%3Q*$)~{eGAp*&k8J8_Gzf8Y5n_a8u!zfKLh(XEeD277| z;=AwSN1A!8K$n05BX+nw3if5wGuy1w^%)E5?BE5+LmbE^nj zhDw!;9MZ;QXTyA@i-%Zk5hND3+LH{}xw{EIm8JhKX+8(pD@AEoYx=|5BzA%H`%^mY zXig>wA(2JgBk)MnN}~m^((7VU4njRgxP@g7Cf+mX34?AOOv;&i(xx4E_;Axb9e#hA zF?S%C=4NnByP-`v+Uz2A{>yyKRgVXEF<&|@-}gxBvXg1}gGtBUPPFnns8VK0nN~nu ze&AI8lr0s0+UsjV*tN2K_FpkNWaS)GK zSQLHQN|7Ew-C=shMsdrQq|V2{QMLYs=D%?h_n~1ssCgc z&&!o1{_5wRr2R6woa@=AOWGBbunf8F|A0_i-YUyq)S+E>WfF6NR0IZqaEDtsr55gC z*~JX+O&)A+$+--!XSo#l&^oW0vmfL$IQ`aTZi;i%5m1v9@$noyQZ6Q3zFk3?E+rtS zoxyNg>2u`+WKrwv&8vb(-6xR$n%erF&G^0J&eomB9nD7EO+7DgD&vD4IJD)Xj=7kJ zIe}aC+h*ipj6lA3^$!Vc6_L%aZ{P<1gu3NjW)k$32YFY9zw^woA^u;>>WNyG1;UIWduGwM9 zP0#@@!B4NZ=k?opav3&5m9Xz$xtI%1@^6EaqnFvcP0HUtL3dvH^zLe`Vsrb_MS8K$mu}$YucM*qwzda+kU}J;8>SH`GY-ziO2W zDi?zrwn&| zczGFDRx+pQll-PLRJAtn`yxbqfIndDn~QPTnZC;p#_EqpR|@V50gyuw%*mU z{W5iW-A&T4GFFl)s+}(Qmbgn}FuOJ$TA+g|mtbgQZE4jPzOq_6LfMGUz5ZycS^a^q zU>0n{(6|Q;BsXC>HW`H~&L-}OE~Jv=#vV=XlAb8wX9yKhzk0Cg1PVCfUC)qC#-NG$&_|(vP2k) zhBDT3ELHNtcF4&b+8KODYo0m5bgkZ|1OuZjMHs7-p4$5f$!jD+`guhPYyR~t=RYH| z)ycdtSDSc_=NmCvB&xFuv#8Iyg5UO1j+6O?sHXSS{8dQRi6`;`n~p|zpk7AeW_ zHv4oQC_-P%5!R1*gou8%f+Oa(jN)bc$uA{>nt5;k^2_;7MSjIh*av287WeF-o>mZ&w-G0A!QgQZn;z) z46>%A0%biq8>SU9>T0~7p_&@ESU66%)JLA&VM_dNh3dkxU9OB^*U5cc9Xz|1_~3fI zN-&7`*h!xd1ac31-s$Xv{$6}(@q$*qbl?*e{7`y?tsMBLNjowv0ay{p4j3=$= z(}X~f zmBUrRs9@ty0A428Jugx&+GHv4(`V#zA>?u3GYc!EDN_op7D$EY4>Bh72bF~=qY0i< zLZ!b&`yzI6L3;U0j8Ln6@EiR{!$-3^3`poF1PUtBl|ox@z3?(uIPL|RfV(@Im)BnK z#YMQ;we=20W$u|TE~H(~`b2$Jh=>C<@gS2ElPhmq!xu`0WQ?s+cKz^;=bYT1UHTUr zy=Fasz5u{Ca(X|WxDu57s=W_Z2{{j2dBwW7I~6+l=0n__y;QGrw3|4IIldW(&pUc& zIA4e$i$_Xg$g15!_ERd|K?8(bQ?hLiacrIM*)lxXTLa_7%GCIa3!z>MuUZ8%v0J}H zC3(9N)T_U?<#duSSs_c>j^T%k=FN`3&+F?gTGbvqy>UF_e` zi5)(G&6(!~NG-oz~`IANvW_Gkw=zu7uVMloMJhh`gg@h1PL zh`s5!>gj55Tu84wY?;Vt^l~pae`BmGL5wa>@NV)H5Z}uJ3uM-1zpGgv?<+G2r#;PD zyx8L_UTKvx-I3@kflGW^zO%)BIT%>;TAk=io=lgtzn0llzGETfkyJ!{w(g=f+;e6r zJxB!g=p}d=dec~Rdy0NN9H6z3Or3f1QmXtN%np0Mopw5eIFO_wb?MI9;%Z{HZszEw zV>y@H6-hdo$~bIu_&~@}`5sJl%IRnrS)u$Ux7|la1MW&AKsM%)P0voN_pS`yJd!Nm zbO;fc+Lfq30<2#2NH&Q#?J@eDXH7())duNf2kI4npxo$~KOd&wSs+|6Ps)+r6dk|4 zWxEgbppqCv9SIChCd?gQL**4KbFTF?o3OzBD&Po4H`xho3G2O$!-Yf{d@n<+;wQM< zEH!VW#LL7pE$6M&e|Ey@v&e}1F0(xlSL#AO z+)muY{>}tli9}H>a*j<$wBAk@E&r?KP+{)oJ}GoI=VkUC3m%htzOfmTJDMzJ=YeWm9Lm;{S@+F{G<*i-Iqzz#i zg_e_1icG;{s8aD*Z?Q^#@l{)WUb!UkQwvA;L%!j5$ES=ws`oyt)=^LnzvH0&RivdU zC|ugoUip}tr$ezx{@NjUb!l1hHD0MISc6V)eF?$A#ePZhpGz4zvj_wXJjnH;kfcfC zHnw%Ylo3_G=79~jh|v5r9U0f&1gmwvC~NZ)9?ul=#5cdc6LN>0cQ_x9Y7GKJx zA4O3Q=rAYNL}#@es8tOgnb*#J9DS)$I&v2nE0&UT*S(lWF}#iPmQNmRf|XgX)7h@( zQP_*5_-rSW9+b2P(1JPRwlCGGfFKgT9%()7z>Ymh{%hrz7ksD4T_-XE*{w!6=_I$8c=v}C7E_aZsX>U~i zJz3yIyY=^F?rNPbv7>#?Xi~xiZb&0q>4TWh%7UMRl68TmBTFUtfaVfy27!y9l>`+XP0UrV zvwR=Xfm=*~*RhXuk~fbmpX8eR+!ABIOetj9t6e_i4vnpO{*sX-XvA#kd+u`43xg7i zQ-9r+HaA)}Hhf59@5$zW#LU!>+IPUpwXM+%d-j}A#xNV9C|G`eIiQUc=g7cot6v!*n20{Hlu}zg|)A9%ICVT5C5b@s;7gCj^ z?D2WYL(%4=2nX;B^}XV}2A8eUMmmL|g-YY@T?Hv70VZH@I9f)I@wOG(`4|)*C3bgg zpYwHC8V2Rr`=R%iTgLZ;%V(ta^<6e2H#^KB(n{OjPY1t?0AU-Fv?x_y0xm!F#z?P9 z)Q

IAM~W!rn*=wJe}cht@|rM7o;8S($jxNTOsNRG0Fz2V$sB|qErZcD*y0;SGFk1V!MmfKHzyJ(w=sgs|!`TyZ^Y3XJjC!lTsNWWm@~6pRzl_3C73_0)#ym zOLXda%yQ(q2JKLw<9Dp2rsMCDZ?ahE|3z&Ky^|j0L7d|6a=yHh1jP;Bes{?kM0>o_ z`Wv9$pf8pg&695Kux=q?x%Wrk?sOx+X3WYTpzha=m@y4B6l>Fh==p4Iz2Lvx)3_hp z!P)+?;xJr{UM&s)o4x`06;_6N`+Fs2nu%fSEw@Rgi@Clso7t~aALtm4r3H?+=MX~n zOZryucT+k?d9rk0Jc3D!o&SFvorgD<{Ts)Ty|tGLm83#xd+IJ)Qb|)$X(-wxrN}19 z-ee2el5FQ(*FW~WKAq>B-%otM_x%~y9`DQP7Ooeu5;c{vOWf2C&f)t7Z^9^D*u>*@ z$DFJeCu1C>%*NRYySYI}@0du`&a($-_;Pf#PGscpmc}>K$s-i1xy9;3uGTtG0K>Hu zt&MeiRSP_}bFV!zx>xe-jQQlhnqiV5+Udfe$Dc7D*gaT>M$`T;l2=vqEJ=aO0v$dTeIF34{ zbuLu@EK3_e(0Pa;@Qg##Vb*0Y>)lzNTJb~|=w+iU4nfo+!=BczU}-#xmUN>!>^tZ7 z^K&S6Sfp0k2oq^cHfwGNnG>a!B$$;CrZ8p^9&5|Q7OK|Xx2BD3lA~@R4o6v#=7ydM zzv}B{U0%<)LCuRt-*<=IAzT&!QMW$1!@>ywIU%Hqk*)+&Gu^BYXF=+T(pJRrQo2?i zMwVfpYv&;VoK|nLH^O0q)8(sBWcjM_qsRW0b~Il{2y{F%q4CWLZjtAVt9OKG7PZvU z{KN%|ZE`mvDc``}J!1}iknJ;1>!Gc*KZmTz+1Md?i(VX6Vy1)_<8}l{7u%YECCbjx z^UKdIACIomu5)alBEs!;_;p$(Q?>EJ6VrQp56+m^8rR3f6rd7(JZ2g}u>(@-s4Ycpb1>6Vf_~eN zq3|1FO=9$oc0%^mCT_dnq?Iy!gE)Y{4%~R1%r;9QRh)OO;;+56^0YK2%G6c1@^Ab@ z`SlP?sVqXHu!~6)QKhWo0jPRpl>V_re$ZC0({wbPmt3teZj`;X{qi%%%eVMI-u^Z6 zm3k=%xaE_K9J%lxoY4YX4r)yjEpBWSm^+!cl;dv2M`-hZ_mViTBAwhR)f+dWUdR>H zK6yczDE2Hh2BE-?5Ul-tQTvEA4ZB%kr{8S%Qx5AY{%yS<8fy;18KkDZQl^Cv7DO_> z>l(nQUe*zENi^k|qI&)Q;h;e2xu_~OZp}rZI|Uw#w0`2v>+bGWG3M`VRU}%ikDIvuuu`S zpA$N)-GhX82h>kypA%nCcms~q4eHRmJfv@*F@Sj~BPgFqvzUsCYVFqPyQn|f0k&)B zQ^ZAuaR834I&@q*LXDo`7wx>L#m$MrJL+9!=<_-xd(*wm__u`VtgTyb5LoCu$>Rzl zF|%@S?}v!bopRA8(t{@ern>!@mQ_O+ym}rPXv!?wIm&PF5x7Y#3iOtr32I5?+O3-6}C&8qD&LMzkq znVzK!D{+MLF(;X!ap3?XnK?);c!My>JMzyCn81Ca2Nfv-s<7|9btgG=n7n}{=R2~5 zewl=9eq;zebnCg)1*r&6i&fD zHqQ344DP(?jY7-ae9w{TLl$zP#FQeH^4h|U58;=h5AL*^wF@(=IxWcL^JdA@K`N_Q za~{|vSRuHq`X|~r8rb{=5Ow7l>3DrW=?RzI2hx(LUGPWjasN*Ydq+qDF^1lmk%mMM zSYdwm#n;dIBt<6}0l65pqg--61Yh(Y#?&YWZ{|!;nfHRa3|6ZBM1!*-eP#%0QNT?z zO@-*s(h(*?Tyd~GKk|<;e;soH6{13_wqdK#J+o_D3fKQF+}?VzYWH4bpix-k_`2$%^)5)KvbzI;QSR&yC)=iGVEB-p(2bTX9BA>1NSY>ATH8XQ9=fuxu-l zYZ>KAmK2A#Ee!vTjlxpR(tDlA&NiuubfiGq+wyt|zW_9^CHAI=Bmm(6_$MKHnR=}A z_Wq7m#Emp*Nt0f8>*ibOdwE;QmYlw!l{tu6Q<_WTIB~yUwd#g3t2QG2ho1aH<(6D1!Rnub=C{}rV>qwdtm}F+OrFD3_Ik?0UhV^ zVLRhQ6`+$RD0U#oA#GvTo`V75g_reX^D1iUP@_ygdYE(mE;Q5u#sl8Jq_hJn|w`>WpT=)#}B_sl3AZw7q>2f`RI6J>1V@huo-= z!8h0Xjj{5>-o^S5CT*9o01UA2B0EWkoPn=NKDpoiWdNKrb@-jkoTe@G9Y93d&R`$ExM_+c5&RsLT zev=2Fj4~;sTPXUo-P(5?$aO^bviueTolo`wVv2yIl0#}i=##1SQc7>J^nTe8ecjE( z<*6Lf@y#6mIKwQ;6P_cZ{koLHH3IU<5ulFVj0keLNiehb+%A_VGn=5lN*RznSp zp6|?SzI#);3^ovXTfnj%E$7Wrc}%OoP_Wi9bOtP|>tqyYT}+mNhdiu?Xdbrb@Ke*3 z*6S;ktB9lGl~k;W%`*L9GJp)752ogZ6ixP_S zn~&RLUyv%+lKbgMrm9drlut0B^yW&f;BC}MDFNIGlXM_n-=POLDbk0gfW;?{QLx2{ z_4;E=in4lX58S!!F$m1qj4$QeTWNTKRQN+JGWLD(Cr6x)8njFWa3_diI&pFyqpB8= z7qy|%^7IhlxXcvZG`CB-P%y>5W?4S;WuP{1uBRH`bS~I17vc{DPEf^W4Tt$hb4`;i zO>IX{Jrq-rQbn8_oL?REFu)lMd(oB+&eltJgNxLXKy^LSy?t-)pfzL2Iy;W*LSZR6G zW10UD1|2lz!2&OYT32K4(!xUKFqDV7%-upAkB;b9%QwjaOZJ5!(V z8UHmuyktV8b$;Kjctqla4{Y0%EXAY(cyu@AGn*MQ;9*63Tz9oY*~?N?LbxnnfEB*s z7SN=j)0_!7?_iI^v<;}7GU{}@nGE7Hx zw?2Nkz5%6J&Qlho?)IhiWO}sZAb+0uPZtd-9{aEs@~eba3NB${Oladi3iX^ZgBzsC zi%;}yz3yNW?TUT&HP$Od794R5I;7x2)-E2^h`lkknR<9GqF@#8F2}(ls-&e^h>Ncw z$2o=;{_D{$3|NyeXx_;`@p)2QEWaITGH_Z-&Ef*cR$-hinKN;GqjaKcv5)l>3vqGH z8s?**7pYx|{|I)nbtu9y;aH%vbu$p0!OY9Z&tjGe2oTuLmdysqJ0U|9-+5n2j!*yB zVfK>>&oq0S_YfpmD{TbyY*;}rKjTh+F&+Mk?3HH9J0ykOxDd=+0y&p)%SC9im7KnBw*K+5-dutA7trmHO~g;Li ztBqe)_fh*U6YM}26enrLQ`~DS>NP5}&tAt_ct4w)<%9<>afhwPFZd;{#W}|uTSFg^ z1Drjg%_r9GWpQL#-|-ZxO*pu^m9$&vkAK0g0_i)Y9ST~OlNH2WeBHa#5bW(Ie$dI> z0hYA1MTcUK=_q!{t1r@VCJF^ieIXM#wD+2ib>O^qxn^sLqqXb)QPxuX24UK^Qs$F% zISZC)0;NeBQa5;EOfG!!6$VffJz7sGLzNx6lhDs}&PMRLje9i(DsPm*!OysW$bYUh zgxvTdg^AL51esN!9igf4(+~$zwD#qy%~H($COaR8Q;2ABbps!X7ylwXy4^=RqnRAZ z&WBGSKnG=D1*g7nCUdL5slET0i8ES&#sH8^s5Pd%C@Ee`m{?x+?idh#gL#7z^k(6!Ixg=v&YA1?}Nc0Cs z2W#6FY6YZ^);>h=u-{{?y6H?Pw_6LgX?+k2tGH^geFxvN-V%tYi1)Sa1`jZrODKJ( zIN(@9oy36~NPyEZz)9ZJNh;*c)Txm}GE1Rb+&ZZ|Ep&|0-tliJoiwF6P@-NWUvJBw zbX3V(;GJ01GRv5fJd@U+4d%(+fk>97Fu~lA$wNm_{_Phz?kNs*1cv*0&do}FC3WXu zhf;tNo+DVtHooVJRUn)5u|8+muPyf4D@=|*PFAuBRWCDwCZUhiRD!Mth`Afoe}se)cg)h_LN=WXSjvH64_giJ%!|FGFb?WLRb z$dz+$SD+rL2mqW(rXx=~kv%F-lDhZytgP_!NzGf$*0ARHVMw)I4UI zoqmuQc#BnHU3@^Cs)YYcx8Jzm-7pHc9r4S1E)cnFrO+zIL%N<4zo7d+NQ$ZZrS00B z${^*1N(Gv`<&wi9C@HmEt^2q6Rl@_Jpe~%0L-~Jee5_N?Xc}bF7SZ+{r%s&ugzY62 zuWfgp^|I`4;ONFVLp=i#IH`TxE@Ij|O4i(`F%H)LOnk=Kx32cq-VNGsrlWG}Id_ks zL~-rlyDCnhGaHrk4STa*d)8ec%E_&_zr_3RCrXJ8eND@pjQB?q7T*k&-_H~@R4S&b zv@Y7&00Mb4m_FvAA73lrj9VGmM9C1$4wEix^+A|mG@PHlI>Lz2mwb*qyJK(d`LyjG zwl~Y3vh~A)8KFOs+_X;EG80)#u_J3?o5~aJIsth%Xr8qBlGrq9&r+Sf>h>1N2r$lG zr1md^Vc|p{lumvT;N4hjS(xLiix5%|hinq8Q`lQMN!oMbBJ=Pp&Am%#^xZzwZeF*| z9;`u@+wu86If!n!pW2RR@xEf@3s{w>aJKqP#re52fjS#Qc|}45VCi^8M%GuvLixEe zU^;3TV9|X}>g~Z8=}p(_b8&29dVALAs|KCI8K_khV8aauD{`Oo-8iR}Ci3$QPK+kq zCoL<2ID_t!2Abi|ndlQ>LtyG;-8vtAiI`dRUai?|Dif&(Y%Q?JVy3Zm&=FjZ&x^Rh8`o0HHP!0_j_DmG2uF1}ylkUot@l8^A)XQ3T z_XIk$$N1sjv36#$e8{pf8R?nSZ+!K^gW6Oe@Sxd2qa}fCkd{XrA7)xPz;8BMX!5aI zvE!CD9`xqLbCK`8pt`S6Ek;Z(r$4^S+gcL}IHZ^20cKs_jg&0A?nI9pgBf^Tu6A(8 z%onIn!bCL)m`(Ei#m1dz#eA-C06MCqY#JSub|Hm;i76f&)m-$wmL_=8?bnR!J}w0T zd|}Bkk`UHtJd8Ui?Ys25uaG>t_f8hGqD@2b+5?fk=w_2I8Qy6l-S5runM&q@Cx;jd zM{cQ&C_p#ZIiA*$ux&bf-<5+I)}`Cj(`khX12k%B@IE0;81nQXL8rg}aeY5BNFbxJ zy$K{%@%0g^w-5%!G#LJ;UDrG9!z~ zh8}pYoo?E*+JODc)BLGp(I5nd?!KurPw1tD8Ygx<{DSz-T@(9Q@~MuKsoh(5o(<+e zo%e^qeA8t9i_+!CH7z>Js+pttIxWy731m18ccorOULaJb$kYDbfhodTh}PpqxeMjb zyEop(CM$MjFM~4jAW?p-rNJTo8r)QQ*)+RR(nuYi0F-+Xe_)C4R%;o&xd0n5$un4p zXX>IyBpwc((je!c`oKOQmILZCg?JQ|no68wM4MZPa1(hYO(Dgbkzqbi;g3^h%Fvkf z@DyM!r4wP>Xt2=conP48zs`=N7>_(*T|K{v&g z!4aTB0fgQ?LR%L{S4E&I`ZQr8rtS3x3Dqt7)lG|n!*2Ol&#r%vz7@vIezj$j`}OA> z>>RCn+OS=S{A7P7+|ochgyoLAr@t>!JjhPQWI93nrP0|+2*05v8QS84e^P;ISM-VC z-NScyEPm&>kkYF%`4K1sSL<*N6OCfG@%Vh?_t-7lw=hwQls`pX&Bh>mNJM<#FsppI zytbFbi@^J(ONLWDu=f}kny5CN+>bffkmF;$etau4Ou9WNc4&+7V2evIS)T2ypfD+e zm&#)y`w1?FG(ygvP{xo-fv2@~eZ*s(BD;6~8Li?tf*d>`{E0W{6^DxQW866}$x zJ*)~0q<8jM(34N~+dW;N+vzYH2d>T(Oow4j;#FA*X##J+V<6%? zb?g+$+4J_~Z|a=xA2ld}(a$3T_WWzIXFS~1_ra6}S+a0EHY@g&@w69_iRBD%aI_qB z$RYEbEmTngDfHW{ms)gq74gVcmv9O+wZiV-fU$=T$p7DOcR1U+TW%4|Cq$7A+hZ4G zY0qc23eD0l5hl57oj7%A+;Px<8-MQCnd~8Z^HPOy8IwG_`{52yQ5|MPDgwsC+Is%#HUO`EtPLMwOGe33{w=we5NjP&K2qy( zk%fN4@FM1@<#!%}-uvDjYcxNBvtqOk5`zAZ^%(@XU>zA=sY|hv8_n}hW*?7!gk9se zlCj5^;2Kkof=R0dK`v)d+waph1cOm+V9{FC-=mm%NBL|uuR&2gqJ6F;Iws}PUbk5k8t^2TksdoAI|i< ztQcURh?sy6;9M+^PQbIejLplP795kG!G~$Q<$5#U;qkFeT*Rz%H*#c#pXm6*=Oj?_ z(%pLKixatk(+ipizg|R}On;DKDr%p+mgO~3e7x?cc3odB{gg=FL|T=dHU&X|^-B3B zB-O8f=i9UHQK*0^8^Qeytk>$z2w=utO3D3OeiSqxIveB#?9>;cLL=yya^_*8Uel2d zeUFqjdx(>n9t;}%*Bm4U+$onEO0Z}EXj4Co~F&p-`qKp#wL->n-5pR=wSX>~+tjN{r8$L`v% z^U=HU&$Vx-$GW53L4*Eqy6eA#Pu8Q%7%n8ayr3vK6qBXc2XBUcVdtINkp-VNe%$7( zU$p~nFJXw>1s%s3Aqn{#Z*b<3lH0ntM;mqS`6lZB*Kk{0pe;dLR_B+voshZd8f?C) zC7i|_H%w70g1|a(8gzfEMGd>^ymg2QRbHXb&0 zO89q2>1m-$n8PVXlyn~T!BP9Bj2U%Z&Fzt(f*V?eMg%%bsSiQkGHRCt?qbdqntMje z;DnM?8Htf1n&MdR^n>%>JG_BRpD_a(tF(eRg$!LyI3+(ktP39rk7z*#$cK^(F@2Si z9eitE@-Ksj{eWKe6|r|D{}en`8T2{y)fWdU(ZODawEBfSo9neRvKXLr&+0%NOhqwV zgbxAkCW=q?YN27!Pbl-HtPBPm=#LkhB6Y?Yoz}cpa;0H2LaOE;>(J>hWJ&$xCo{2| z!dxl!X*mk5j0Cx&*nfuMM+t6Jsngz)07#8+qwhKcyv_-7FJV6%NtL<`j|I7BzJz+Gxqcv-2N%?;@&5ur8OrbD}> zKAb}>mi{DftZ!4VGe$uoVUDlY=9nJT&+;a{E?mg>+I;amJi#JCmC^=2$Z*jig zc>tl`AV(d1?qj84yTeESF#PbWwCXu4$IpNARQqhvP~9ZN@2~O+)>2O^P5%V#*4|58 zJJSQOL~3+sE2R2e>N)ma&6Pr|tj+iMMV=e2JWpiF7C}!j|N5|NG8lH;jdZ1fqUz(f zeTXb7gMP3ga3LKm|0u6>>Do>Gn+}})%zaTFdj0TroqMtd)UR|Z;ea)8p^drufK+dV z10vo-ko8jJ%YLvWbRk}cq8(}cf)d*T9049Vo8~A>tBkoSQ+5yDYmFoIe}EKYfl-%D zU6J#NJJ4B;tM%d1Lx!Wj;7KisA;2!jAv+LQ2 zL_zf5wTXup%ltW*ao*d@@?XiJlwaLFDsL9NO z%Y7Ar0q&?L6}1CWv6@q*$^~&U<+R)PABJSU6du@V$Fr^d-dlKXzBIa6FYJSm5w$lB zzRA?}@MpJS&8Zx}*XYCsSL^n!C!z9-qehq4Lz*;E3-LyTLX~!2^*cw))`x9m9hSAv zfe3lOPS|YNh8Au|2$7kM7L>rw&3Fdi?r-72bJ9cxMd2_x?ZjV>kYHE6As@A_-U$4hi`Re$t_YA zBcrL5!BJ%Ln|USJCj%^W#^$~LTp0`nVsKQZJ~{;=BG&?LKq~|ADK&_^AObEE^}{$2=>F5&p|9SA=qcM zfoM_u1@a>i+0vI3Y`W=&5iunn1+QMdZIaxx2`e0}74&8k*==%U2?$ju&Bgd*k^x}n z8xm3L#0z$6n>(s}meq%xh?JTLV=Uj7!R$|XNFX-LfYk1MaO^%_VkM$%0Lp9kwZ{eQ zlGc6CWR?xhU~{cSCyeu%X*WouOcuzEZMtxm(a9+oYMeA6J~`5CWR}}$?f-BRtOot5 zKR=&|=VGYl!p=N!o&yBrUMBQA0nD$Vi#=~o(*rFKhzH-X9)t^mMZt=|p-*AiSj@-_ql`f88J{d?kOzyVk8wOfCz zkV!mPOA1dDng)CGp9#{gy<5aw6C;zfV^0=RvfVJWzeus2mT;dj8p6Ki{23EV)|FC{;Fx2=nnxQ{y?+^c`te37S? zzsHi!1vt$Xv#mttuEUrW8Jmv#%TE()qEt>h??s?*HW_q3+!thfHJK8?@lC^iyVk7Z zI@;X;Jp1Y<^(XeZQKR|o&BgeG#NQ%V%?~IZrG{7;Nqu=Ql@po4qnNyd%S^)Yw2av5 z^rv+Y$#lXFT@2jpW8Jwxk=|p{n*#COBJ*jwHl9K4!yCe-oivC1es}6~EGFHJ^%))y z2vlov^7*Z>K}3X*S4msX)4DF&pQki~tC*AA8!{egx@eD{HZA-^H@NIx?R$mwzNkxV z81VTl=Erb)0Bp_&6CZ=|Cn>5xqLk@h2x9y#*Vv8eLPigRF)20*U*RlMK!7?lcv`2k zwbV)i~qS$}}IDzpDw83EIPO39a!6OhfYjC;vQr~@L1u=V$G*d0mL{WHJ7C2p~(?E(CSBMq~>>=b-D9B z8A#uZus+R^{fLla7t8G=v8pNNaRNy9`eiiA$n;_6f>C%cOK_dwsB}$wU_t&v>ibh6Y&vtz_DizUG`10csTEcy`J{2R$xGal=g9n}7 zDS4ek5hsu{b;e$ni4Sicy8sj`h1=!VYvv6ioe^CPQWG4?IZPc%e=g0EYk;?wIJD6N zuXt8JS^?&4AWBJ)A{=#JeL_sz>A;#4LceDIDebuL+2CA?t99b4l!Ry*<@fiL$uZi_ z6;yFsjas6x)CUapr`sKM1LtRpECgFk3QpvIZ>}?r7=0l4r_?a664774OTPq6$rkYoF zr0G<+b~Eh6dhhk|93{pG%2Gm{Rel6sNL5_Ks_ePtL1!2q)Y!j_6<{q8`n(bI2|7oe z;AXO+lUf(2#l{vQGx$l%_OZ&2AY^1?yZ}3%n(TqqpYMup2koD_i9gtDewu3^;A``b znWuCfqX1MzqMp*hXFIuUsy47Orm}#`r2a1cTBxi1*#+I^YWt2aXBh})UiUj%M^Ug< z6mD=kOl*e&{}4Snzv}(RvtU7ln z9{GLW4lHPE<%4TnS@#_dl*|_N(#_R_YQX>hO_4g^Jw|=d{x2#ogeQ zjBJ3T+9ZDkJt0!=XQ%CUT>Oa6ZPntOgShz36n21#b>GSQl&D{yY!Vu3FtBSqSoKs4 zCNk)_dyxu8(Y6qUD1;STX;0Ns+g0*)ry7J3Q!NHm|CWC-7p5G%Opql)nP3W%GH=n^ zLmX`0ll1p%Xx&PO02rBx2K#Qlc_7h-Az$uR>W2s^zkAcu%48W8s3FQIGo|C;3iu8& za~<*6dXwv?6!Zml`tTQMWR`j&WYJP-sAa zrE-Q)$m82e5+Uax2T-Y1X%zEp+{UT{Ag%G_1P)?MhzjV$t%UQ8tF*UVUZNL!`h3{JLQB3l-X4;4%qCxub8^FrtFR$kQID7e~;9!h&~Ca zbgl_F>ga?tEd?a`nGD=9X@YXT&yaBgyr+c+ZsST#gma`nkxxxNfcYcX2VC#(ktRO} z>(GXj3~%|#DFL{z^DC{pWWr@&br|Z08IS(=Rz78!o3-GN&53>iUk`we=kFBEVk7>h|s(wxCZ zD64K>tCJxFgSqEIOrHjdVf;g!X|3NvN^5%8WPiwbd^{_&@*L-eh^d(gAQ)+Lydg0F8 z4$;v_Z_V?dsgW@x6yb9nK~JBb_b~| zAZ(!U=55;az!_kp20H6MD2QZlBQeywLv+~{sIyZL-vMkkK_i-d^UU^u_E7-Kp8RKwHdE>_ROFoPL%6YwI<|zn&I=m!?#86go*5@7;(9vez0NH_h`(%qNXV+X!1+BnLj+b@4g!xaC18CBnWPfCyE z+~SxhU)<_x>^(OE7X_SBLy$6@(#Cq7{NR<3b@(+MRs?URSq-4V@`q8>@nCVTi{T}# zp7SZ?4!`R6R0FI18eV3s7Oh35xTfJsK!hnt1TH{2G4&S|`5P}KFJ$VY!Fc!4>f?6w zfnrmZuB9C6`F2hkw`+23s-bQCXd7?RiTM4=<%c0pyI0NaL`(|?#^$It!%*;?} zCIdU!0RwPiT%ufW;r?VCq(v&L1RXAz1s#8-zI5#=+;SYw29y4>ECF;^01|?3%*!g zO%H*ugX_nUCI)l$tC`I9i^MOMJ8iLtF^-FOCp_Yt%${f>MAAX}i68R7a3MXx)Y~V( zmqL}4nt>-ol;Kx)l3BQn9^>+dd|j{#Yh&#EWr-@2i??q|>lW-VAVe=dH_2CZIBNwedb;wl4OWUO|NOReXwSh?` zzhY5z6fUn!(5Bd}2)4d2I=jJ<{$ecAo>*^Lpx0x&)Vz=uX5P72*OR3^=$Qc9^3+oc znM^Bls7lHVNWh^-h6l-iS`7?@G?=ky1k4* zvgi~WD|x-2B)t|KVz%uXE?_~s)8(6^xr|LIcA!I;I@C5MrKM}*BTvhAmF9nxAIBZ& zImFMWiY9j@8O#i5dC>ig2XUVs>9`tb!yZOo=r5X83@+QF6FRdRhlHN(m?L(t zWhVgC7|S&>#g|R9=0ZCE_#UlYklSA!2{>}s94@L8H}n_6%wOdzOQ&+s05D^{z+b9~fLbRj~c z@SP0fVMzULD6Mn#PR?-ljn4-<9!xxBY6ZffR;rOg<=4U14cZJ zAZ`aw|K64@m|s8gq`&K^JqOR2BVzStM8<@x_1=Cz2QcQz%lg-7#lsJV_V&Mq zpuk3@A{(<+n^R@!nvy`4mTPtp(4-Vo8>J>w*KzZJBSDKrYxVk<=%@HwPS;Ig4Rwc- zCpAZudlFM8IOKI3j2N8{yFl_*XFxxJcBXaWuy)d3BHh6t-zmsFdj(IopYR0ZJ=SMy z2f?NWa4fN27Kl0xf^6>Bh)IaiIq7%guBO?S4!a7?LfeBT~xtOG|(O;Mek<$cuburB6Y7 z{|Scv_d%!~?4z#zl)$6Tn%vhI#G*z<-MmRW{UwYYX1oFTB=BtlMrWz~Om&5EN705~cf2&2F^6-RAYI}?UW?Vf z%*~W?BGMuc?MRis4hY3V4{luC>tv-x1!!l&A%@*+H)5tYO^LF@C>TWiaYzkS%iD4RO9Dp(L zL6Frz@RF3^b*iY3d%(LeIvdmkg))Un@dy`*xhAOdnj08ZHd115ET|J^WwfweQ z>n^V{c{`omjB(QaD$*1tv?2c}hc}bnOuqv=!E`Yd%B@)s4q(`SC$#4KI#Fw$8MvyR zGkl`VjEH=9;|Q_g)>VA+=c(HQJVh5hb+WwrdB6eV1r`t-8>b|mzZWwAW25{%%Tjv} zZ`mOAG^M$FdIAXpKp3os;el+7~6bBZs#;F*ljODr8W*0t?^| zbuxHlE0eFYBs4O7*{U6;6n(_Qy2Hpb=9->`R13Do9en77qOJr!8D5%WnkJElls8&S zpoN80?vd$ZALJLZp`75R$um(Q!2?HbLKA&5`^W`#G_-e9x-dZD_KyF6Ws^b9H2)cB zGTLt7QP_YfJg$`dd(^?KfZGT5eP-vgP|w_D&5JOU4R3h68I*4Qso(EYpA_+LIAI1& zbl#NqAgo&uRn;b0xFFMsFj1}ot-)hnEmYwgng_263n<3)^7uY~>gBv-WC3*+r zv=d}?-&;N`KgOyn2ChctEm*gV3ni=kZ$mV_lzL!7eAriWa zub+g&oyb%E(&<|dsEFrmS6{-5>!g=rtMAT(hd z+-fSoi7YOHz#bwzGvOl}Y>SXqX2;3s4gS;um!&))4nbl$!O`LrR%~Ezd=2=W-5DSB zCzMvUl_2*J5||(jCU~BfY&pIGl`=|8Ybs_JjY3}g9tL_b+B!KzrVl>fVtn(qln1-- zd0COMsebEB7STzTS4g>6HqR~&iufocIZj|WEhZ6;o%mP4NTFHSCu!79?2=O0vg<5c z??#T&wC571-|s?!(;xO|pYwKm0DS(MYLQW)Ya>R#Ul#=P7I%_Q!!mzEdY|)*+`S(e zRbyffaDQdik=;tOwl;m%`BhSwinST@6n#=6t!$;qtO1QROk$0KF&N zunELh$Pj>18A&&zE#K>6I49X?FgI44$Ld#C=|ifwy}JHrT?C*0BTMhMow~)Zrq3hJ z5%pS2VqfP@(m7AKFN)rH8%#En>npssE@dmujW-HnAtZA*gOZ<8C=U<|VPR~Ax~Fy@ zV8bz`A7OB<7CTt$NL6d`e`0`{2k!_Kbtz?X})^;vC4L#*@#GKJQ8B=&6;GU z$bhH{GFCVtKt^k5_gXs~7M2_*c?hfC?L=ouoMpm5iv|j_fs-5E^uU0mN25v?A4usI zhBC=GZI}`F!A+v`aX$df&enAwnS0N$Pp*LuVXZ=9$u2lTs3AtDKj1m^n{OaA(Q*h54sak@nR08#`GiokEPHVUYc9 z0f2@&gnM^grGvNuO`Nuf!j>FcZG_l?SrsD_tvph zp_E&%IfIu{s@;dc8VX{Y12VdmIIqlHc`o&7Y=#zJY{Rd(44CVy_OI2BO!CtqsOyro z_X0dD$K!rjHH^I>=M%m@!)Apm_H@DeE2RH0S0IcEEk2{ap~V>_+)KgV28~6UJn>4SS^IY5ylK+df0(Y+Iofb4}MCcc14GH5o?>)!POT{stx(wt3Oc+CPTo* z)ezhnlrldaw;aiB6Gb?{&w2m++0MOtAYCJDrxY=aQVIe{OWU!T27D;hZ;&Qa#4y8- zN2DZP2ef}DU+&bBEECx~!NkY&GOx{V|3ex~K9)&okGXGey>{mEmt=GcyBkix&HhP9 z3(zoh=5KAijpaze6eiyC$8^F=>9077wwuBkA2kg11awWV8yyA2S-laBYqOmIXd`%} zU0)DSaC9&~7whN+EyndV4dM0D^2AetzBHk=R>TSBtZB^{K_Ps-#x2d#Oo#hXdV(R6 z*8wTTXryrfI&5OCg$_bXv2@t#x91<;aEJrQn?)ITvz`OTt$TmKQ)@Y?MO0Svvz1PU z1Dw;HwF%>P<>xc8(P56(@$>|CIcMgvQ;?L%A>|ze20v&!$pp%d_q|X;%~AwJAZ=P7 zu@!P@;Ko|vv^%Bkbtbp9kPP821mze&n;BGb#~xOzzdSs7ZLSO=F;0F-_!x!d^-G&& z=4l$m6o;{`$Fq_5{f+=6<7rG$aAa?VFuAP&v?_?Xn`|n5Rk%-6yt@G3umTKV(|n=} zp(nwTGFh=WpeS_e_D(N~ESGf%cphI*D>dT`#_lLU2A11{tvsUi321_;L{OO6rLCN# ze7{ZL;O}#izAkhJF<%8mX-iG_brAi~Cj{3pzIhuR;jvu6q%+eBhD+?WW$})5!3nn8M`r zIm17u6|WnUqtoS>hn0G4oenrWhI0Sj^z&np#M9rgkY*P2=ajSM5x7d{r8qA_<%Gfg0FCgjsDtGbiBodGS%xCU`!5MWiNI9PFB2@%Gg`h zKsf|9XVZ~DKN;l{b#aexCjkeeGu!TY?vf_vIU{QRe()oE%;`0p)}ct5;Lf|H)Z}@a znYaR1O2rpPr`Pt_2-me!hQm3-k@(%*D&4{!R^VgUc^)}L(1S@^I{KW;>9nUd;*U;$ z@ppW!DPh1)^C;Ecxf;##^}5@J6pZ;sAh(_dFp43;~MX?t||E%ru;HawB< zZ%nqCo8Y($rJqOP0b+jseEcs;KL{27@;S{cB4V01;l5^S3BbOH=Ht_+4}5g89$#^f zN0)$%0nWo=SmRQQpHE6Hzr?Vekj-3qWQ;k zEa)H?-sJI&4bA-njViDYJb_R}Lf|%;(8O$-3l~yw0Wl9ee~hc;OYRf@w4IERv|q?E zsaZ@z0COQ6a-SbT3Bb>Nn36#;X%v#ZDM`lB-8DwH=nvuuOrXQW>R8J^A#llcyeQNA zw1}B6ysk}i&ztHOP-D_pW(o!mbUbo@;|J|=B?UOBx!G3}sqn-Csy(gMM>y}gG=>oB z++#M~80~aSNA~ek|7}d72FbH=N4!>R1%}pgFk-nJ4bmn6Y%<9!S^hk*)3#`SJ>^Ah z%$ohIvlJBYJKCa)MEFkWm`$XqS>1~{&zyJ3b+&9SawkNrmFb9C-0RF%3S&L3&0)um zhFtKnsP#hCXg7@m(MQp`;}I9w&0-{06iuW$V%X>NLnLmw6LMEU?NX%j;6GdB-g-Zr93tCEqD8!{vRYolQczg zEN%J+lXP)+5X!$T3npfK25w);p?|LF=ppU2wUcjrela^1(|$G_Ayg%m7-MBJatnFp zy9zzjq;!<&=!0F5|97L?PIx2Lsy^VLx;bU?(H_zdOrs&LH}yqLET->SMEn7|DBwUQ zJL&urB^`PK_R9q`=o*{-9eqC3CI|7be0A#w#S#QM=CA`H>?k+u2o;c6_zQ>W3}DcM z4jm-B_6)(Em%^w}X4X5wKy^Aq&EGrDh}h;DvZ8SeFZx938@#%dnRG8$seHbn%Wrd} z*p^^saPLj|n6^$lmxYKLcUT8JW2K1_JHp=K(ysA(=Q# zP+uDo?Xp%WsOgF-mdR5%-b60rpX%H``tJ;wem6cLttrM8%k#kPN!CsWa@~2yr0?dx z`F7PUx-+p*wK_wqGuJy?KIe{NDdztJ zO)7|de8$}qcKl&uh(mwffbXU#Ld5KzzUP3+lkB8DXH6T*EhT*}qdV8Zuw|Dq#zt`X z(JcBab0v8(W?W+Wb;n-`0N#-pe1m@~PJ9Wxo6Ai4kdM32Yi)bUm*PB75+w~^7|p_D z8i<<^hyIG*!6mloSk6AF+rV~)-(bTuBPOP)hdl5m9s(!l$p|FJ2XF53_ZjWXl6p)q zQ~8<~9gEliif-fMfPIe=Ny^LIA*}RKa<4MCO6!k)G{aHpdp-q4&zi<~`F=9hWUo(h z0nPF94Q6X4)@b=9ynmCvnq`l@MsYPqQkwA|;?Hx(Q0p;h61^Yw+g0s$C%|N8NbEnu zJPC-aXNSLHW54xFy^K^JFjisi4KF)1gG?f^NChEIbsz?bhRiXsL6cdcN!%|T*S9E| zsvso%I4_<}N|b^d8=b7PfjjofO`9OYDYP?EI5#J4hTVV>cccNo`m%IUZNv-Op|?6W?J(uei>mtOQg_-`=TcXrI{eV zeZK(pV_n6Gcay8;zt)8vQpC+f5kNO1cUx$8uZ0b^indy!)t zZET@Ba3*;>OozKZn}$7aFvu4Uz4zxb=;Dwz2gKR(3vKaYpvS!XT^Gr914HN!ildR9 zBgmpW>OcKvtDtg!IUD~}=FcKW8DF5)(zN0oPbj;pU$t=!3gaDT*Cq=WbdfurH&#N7mU>(pY9%1f`m`-V0DakSX zq%4R+Y$W85z*~h2Mm{W#u%ES^x5w=Be6l3qgH$kkL%YiU!6H64cy3I46fSXjLXgd6JQN2 zTBfW%UlyoAMuZX_!l-Cw1_UaG7RSdMWGU7gg&1*6b03}CXn3M80@tCQn%zTIf*s!P zBntV{=@o|7&DcB~8^Azth|fl!C1T&1&L9z1(04d>mydOvwz|AySQXlSI(}{Z2`;ba z3Zn`g4dhas%b7K=P9c1NR=7*A=5bD~i4b`&B-jr=zU6t?{Q|~qqv^)|&K=ZlR4Nzk z6SVMU&}ZWd21G>T=Mp;EjxG9~v;LEeAXz3rF#!X2GD3qQ#8E_aDUxf9Z&00Ai5K_B zmD(I_2>F7VZRgB$Z)t1Lfeo%uEdX5$fXkcvbUdEXZZ3y6vfQu^Q) zSp3t-QUiyvG zM`oZW8eT=aTcH=WX}`0ZgZ1)D_DSam$fo!?>lt$*k<4HZb8$77`ep<|lRFc*GoV@Qo@8sC|*1^xZ|D*AiuMkX?&D2WdM zd7O^vP^aZkbe}E1UfN6#b}(o{998Z!RzY!K$TesOv{@2I9Y^lS5K4y}>Qs!~Itt;> z23PaUM1F>ckCM9m{TAS3r-w$g;vV!Z(LW#5yh<3xjmngC@4j8 z0!)ylMDKA8scz85d(r?64`%W&X0ZctgMBZ#aNWW^(;ZT~pGSgZ7UPZ-{k9hJ211Xu z=LgHz7+=jrHi6j{^b`ic!2LpU_3d(o+y{7yJCM*dF!vjQnP+ zJt^cZbRvga`hA^(Z;Vp1*}jT2o_qZT02YqotNch%;;347{P;Z?q$qM5W?Y2~eNx~e zHU9$?HTg!GT$vHVi0u7OLQUgjMHpxQl>00VmeEhAPjWXuvScALP^h`ohV*khq1+%d z#p!REh{CibIC%Jjv%eZlgWfx5cWC=Hdg)fL&$Od2@i}%-7fT&8l?cqW*$MU4?BStJ zXEs8L6*SX6AmGde_ZxE)1BI?b=_QhcPXJwF6aSZr)~+G2^(FuusTZgdZP{rwk&a(E z7klJAVBVjeVz&$$*d0uXycGe`E8y*KWghWauLOQsa)+VtHkXO-U@Y~@)C1Jf+Ot}< z!IYoFgjM)Kog{18eavhr4AmB0xDAhdOTFvutl$FOlwY_i^bI+)7G~4Gpb(U>frTRI zeX4`EqzTd)vj<-EPY~3%bXvzBYyWLtU2s{-?yQeS;8f}$!D+0GHkMwFIW)Oe3Z6Jx z5BA-0-9HWGk=a%{kdc& z@~-xOq(kxl`rj+0J(+=F22YhdKo6}>7ljf;uD19hre^*LyaA%EQQC6}WxFKxhd-P$ z<9=7mxaWIeunkjps$XREVONSp3wwr&o%$B#1~FnF;a zlVwgoxcgtPVzqN?9R_(@PL#mXm&!R>Nu{^Io-DHS$%z>Cjo>hK2T3d3$(Xw-|9W5JKT#MqX z+e=aX2Mpy=8M=%Hnpw4TJ@_pwr|F3_JImLtaPENg(T5ir$CwRiihm_@5gdLsAQLvn zk09cH?7Gh|w2jc=U?P@F0lR8INEJ#6A3_$#v*PaTL)VZEe7Zl&Pg{>g@q}MbWq?Ty zjArsgVb+m%##!9{{a=w7am@(}IRU&OpAvnTx8eQ{-WwyCQvO!%~K zEA=(@AP)OHa>hcLNTZ)isFL9OZTq;jVx`mVO7H`lwD;UzA!4}%MO25l>3j?Rj%u&kKSVAq@rpWFpW7?%M`9(Hn z4re1}HY*PYcw2kZjk^tZRs}c28NEz}; zIzePjmlKtkOt(-4BLn}XXz6RMb*DYJ{g^CX)hUjsLzhk>UksitE@v>DHARaf%A&#E zM@!f7jIu46@L|PzKHQlCElA8lWa?>25m3x36@?jXvO&z6?Xz(bXp2YLF> zH7-nwl3_<`JG%Ju*jcHg?gWVrinq+uyzY#dKk#y;GWv0o6t8@FfGj%p`qa7*=_E<^ z&tYvv;*Q<%M4~p*w3IK4z79CZxjdmkzwh(_gI05;?vplqTa@{0=^enh0Th=0_$vH6vfL`r?y#sRw z?ql3UPG>_%!mWd_pF&Av_PJM_ z+ByQYuB{G-k(osXkZSji9Sk)*l}rM9_z=G=4tsR}$q9s~>)f90FNF+=uM}VBYn7R1 zqm80KO)STud~0?c)8+qGTPGTkie-F@)-XEyQo~7&GAqOm1yZeyD-qDY+IWKxD17y%sYkhdF+W7K6Aq+w8j=y%b za7mDOE3oXsD)x$bWcDz-^Mc)ESm?;o1+$ZM0(r<_ppHZMV}+|S%^S3F5=k^@{q`(@ z1vkp?r4HuqM=(d3%y;ojJmYA?dCX!R`Glm6L{h1D6vA-A$zg`g0p26Ma{mh^p)SMV z#*xS)(woXAbspZqra_;CTW+tz&wTD+fEvBDL#w4k+F^e<_U68nTr+d|8sVhVyjy&$ zIFh8|pJ<)taYtZ8{2-0DxG^UY|I_*=`8=hr@$Hk5UHCht@hJl-S;H|cyB)~?|KRjs z<#G=0guN_>+kZ>s*=VEL`}ytrP@%KsjZ|Mp`Siw*Vr94NQeuG zr*StM!yu+}m&Hw-6pAI=z97z1;R>$}{?TYQEPTXw80ZKb_~|d2zs|CW>JD0I-i*3- zb$_)+D&;o@q)8gj1#)JQ_xtF;FQ3Q=qkGG*=s%JOyxRocQE{qNtm7NFaFG67@(9EQnL^d}?BFtB|L*I66T=A+3k zbSa#^2d%rv3r^sjU*s_h@LziwZf7d=KkmWP`oYfPly&8#G~{sq68n?5*%w*M3Q7x7 zbN8|XS)4!6OJ^$iI`{NkT`SDE`a57m~m-EWTq8 z;6S#;#ulHI<;^;c9V|d4ww>07`y5EtaF*Ap2#p2b4cxqI@Ooq`*C z*MWy?e4z%JK`63%*BK*kS_9vLOx-_hAm079=$R&i2a<1ZZlUwdn5x@}S{S?iBuh1r z8cMySClN#(ZrL~UpsN#C`f-BW`1ki4=H0kat#rm<2<~v|`@YENQ84ioP#>l#@w;z` zha>(GgPvqy3;lWb&{-Qic4u?NdhR=lbpCv&9iRAn3tnW0v_>l+04=|c{FY1p4I3k9 z?GLYS_h9{7>}j%ka)Nx&Co*q;Ui`=J3h|?>q}B_-ue2PAp?}rDE|8O1mc4|oDR5z* z1Id^CJk1UJwowb6Kjq;{um8%?U7l8MG*#0jl6ueNe+UG+b;ODl>vJ@7)Za?!R;=B% zKwh^yYjx&*JI`6mx$Xs<15E~VP^+ZMFk#I3??u5v9&E}ErO~-Z5q9n0y@`U-YT>Te zEytDb8ZA063t79Nf2EV$=Y$7(1zhjebR6+NAt)x2#j{BVT@1g^JyLhfL2w5j1a@IN z%5N^>u9UPzsijp|+w=C%&>RxX!1cRu2j68bcrxrlpxJqPRc;nQ&uU`=Fawv8g z8wo32igcJ7*Mh9@(gOj~h)+v)oF#0;p(tN_+l>vW*dYV|g&EGmGgym1K7rJynD2v~ z9C9}$LG0S%1sFF#O0oy}jEegV&hX#`R-{`yA%{sql|Zyf%tB1Ijr<+M?DSVOgOcon zh#T;=V8Xy@|4m7sIyTy?VT1hgw1zdyI|IjQ7r(cbEbWYvm>RvsSAxEJei7 zd8cKVvCJ5uxtFGnLt$!lW1vT}m3rQVNVIN~-|CR;)}7?OAMEC>=Cow<_6-KdCSy|g z@U(VrcelhPq+HYG^SIlGqxIh#X(sb5jj^Gdg&k8+7QvvyQz-OmcV1T zbWRr%Ij9Sd!+7T(A&4Cas_4v(u?`k5PZ`~2rG>0-vy5hh9&mLBG6KYL?&<1e6w?=1 z_nwWyS?4hKak%oXj6| zxSVQZiM6_-MeuG@MtDsRL1-?3nhiRR$?99pRL#2U&^uyMqBqW6nYNwFvRbuMO_|xzFh^k*I~FzU+YjHkKL&ao-eeLYwbbnjay-SkS1N&e%Ez(*s8F>s4IEW z{gn^bOHWkbcGK+SEuGI{2EylYMa@prQSg{YC?ZhQWr+3I=COD)%BNicE=$GXhZt;T z89GA#Sgqq0sXW6@E#H01gmF)PDxiDwhSof@w>VnE^|LpDn>FbCjrTrAyVOV?n&1g5 z4j`#_?*WdOnPye68(X9_$M7=xR*>>k}kx)NA=`OSFYxf6}jmcb;4BmD59&WpBm7{Y) zhpeRdvl3AyN8+nChnopwHpxIECSR@c6+yJb%GW#x9P)_nzFnEb)Ua8}&jcNN##?-Q z0Q10R27%h$foPfIKWt!})NZchn~&OC_9yC;QPLE+U3KY%HXVbqUFs?NWh%Y941w|0 z-*<0ca|P35z+bg9DW#3z)xlOOyjK;xH>-?!s%<;d`J+=zP8q zvEia42*pm9u&+QXEob{MF|wG5;Jt_R+*y);CS6ZpEUlc)R&sg2NJC2Ar?>7L zH2_wRZfKFU=0Abf>8wqAWF zl0uDz&MV!kd8f3VLW_3Y+#SNO5J3Ic39u#w-r^^(r2o;AUF$3kLG-+b2`pV9yka5I zU!JQ`8J)YNbb|tK$DGaV4c=nrZ(0+286)M+n=c*B_R|s=*OW1{n>=Y%00NF1M1#sc zU2wA;)=0wbV4Z(O7aj9dv_EXuYRkp=6^0$Xq>DH*4Ma}o zI>T}GJ9PhLODvja;wA8cN7j6|2rIlC@bn8FQUm(87Arjw$`f?&{6e%w8!Asbweudn z!I;*VC;M;J0s~2Xdyyx{-#3`pUX*o{j671p6zTN;!h<#8fTMd0^}EbDnF`KcgCm|i zV$S>Qozb2v|d(*a?<;>PAZWSq0_;;i(dEJ%#tD30kUje437bJG4qQrTu`(>Ra2;sik? zql9!K-BY01cVO7i@~tcBd+16`JSBBLGS3{8gW+hWA8uS}MylSMPIYEPojAK)Cbwyw zc|ooAMz6G*xU&UX%xn0e6}Fayu#Y4#{tAFRD;Yev>nJp?TY~gkGOVWP?Ir0tDcx6@ zz=ym;!N|fl8H5#jzam?Qa2#OAv3ASwxj`_XGe^C6|8^7k^0(P%7cadq#{oBZNX8;a zjcI%AOKCbHkYhPX<7)+dP8syMY?lg$ftx)8NiJX^&&wr$Ei9sdyg@Ee^-^rKe|0h`S>?fMAnSQy859E-&j@CCc> z=>XjtctgXvJG4jUx8izkj+G{AN$h*i?d#GTr6maRH$-Pze}_}^;}~%&+!Mgx-ajlI zXJqEEksKsz#G8BJ2|ToUY2294?9eQfzP`3(AD}rclg?XDv?sLsgpmU^I;1X3>j~J?`EpRPM(OPHh zQ0rrUU5gQ~MrGgz7^FxS4#VFkK^W*6q%0q+we-=+fXmyY=+*~!%bS~8kme%rAf-h{ zA(Vrr(W^HcE)>?EJdd?5-Y3O>n(7i6>#ns31({^|Cqsu~lTBZ%^lg>CtTch#1o8T< ztuh&}Wo+ftaaq`;{X4lHL#Us-oG0Jz{Q91f^Cm(j%>QeXfzH5z)Q-&=wufmCxR7}W zsj4x}@G|`f&;A*hI`9)QgfuNmtKK%;)p@L21&G=!>5uW$D?rJc&1iWNk}QQRR^8Uk zhdn9j;p;`CJH|~|2-5l~8x2^M4QH;PE1_Q%ddy0yKR?<))$}F|#K|58GcF7MIGU55 zPuF7-mN1(&F=UTr2HqBH?KKdBTy&-vj>_0c&HXRD`fhQ|H%yeDMG_aCWj)zF#-6V6D@<@Qto!_(l8~{dv- zl-8Rj%UiEa4*Lvq1RRy5kp+AWhV75$@20Kn0C$Ge0<_PE|4g6SANlr3+Ou3NG(UU~ zAQ&j6LC&W@Xvi8AQ7WRw|dgD`2^wRFn1ca^G83<#`%Y;|b^Mp$f^Jh@9 z)$*o!y@(@5U8olPAbZiiUCuKl6QCESs`*H4OH{*E}XGfV1gyOLDgQkH8dgN-{E_|w?Y+41?I~^JVXEGmN^?jU3;t)H6u2=GftRSsI(AXJ&Vdee_DlI- zxI?wd+wX|y4oG>t?5{srZI@YidQ5+K8sf3_n_&yICs!N5*>1dW#`MdErs&|~=l^RS z)8_L?UnOuP&5LEX8)y%0Hf^OTsD+ql77&73fW3->2GhXxb?i^X(Z%>_P zc8}BsZ8YcMD2zTA=)be*=<7{R&a3I9x+mMN$oDlAB-60a_4+HEE7LqhUg|eR1G*J= zVdhs5re%nmcD+k`9BU?(y}u;&d$nT^4J=r+YRSi%lPtgMO%&X0ZwoAc;ysG~U%0#F zlCL|ZG{Z50RNuJEt1g-TgtCx_41r*%w+o3Y63lQ9?Mr>BOLujM(S9K)hHqaD%RIEV zM^V`zp*RDzQ74=?_%P2+i|*q?{-V%8F<(t!~bb$qJJhSZ+vWJT- zgPHFl5smqPhx*OKVCl5`hz{*X8v>^D##63{z&y?~wTyE)8bR@e_O(c2Nsg)^G_JKG zM1JLB_}r_U@F9k@pC?#8E^S%6E^&|V8Pb%tO*;~+GQqN zZa=TTqfV1@%h8_?W$C(MYB8>-7H4@HpJs!=I^lvQ9Al~5LX&j@8aD)I7Q|Rvpj9l2 zurE>vP98{dAS$6|oUL7yR;A*2k};A7t+G$6yl#cim0%QQxJT-TfZM(rPGui z{h6@-BZ(Fh!+LI&#vqVc`|jM)4w-<9G$-ISYKNR*l;-bW8|6m@QOjTBAAKX}!R(F8 zRQ7JEcedPfJRvI{Kvwnlec#;&fRk)tyZUqiq6?Jq`fNmb+*Ey7!F=Cm6eEz}I>YlX?mlz$BN(yyB&th3#@A7rpi!&xyoIZO(z`29mn4C;4Os z0`U`fZz)~+y%?-36A}Aa8zPf{cg}7)!46>x@D*3M+;20VtzJhW%mAS;)_62SyTZ`= zZIbtZWaW~Yd*MnxaoMZ%Fhon^RotZ?sWz6!+vBwO9947-&n%(skFS7U9`KJ2f{%ah z4wkrx5Gi`|4na#%kRFF&Wt6%s>0tng!L}yZVXIrn@2$@1{lYRZ9>z@M&gMOV=aVns z);NQYO-xGm7QNgO;pR-I?3EOuN+Cb|Ii7$E@2` zxAc=Tb0(pWk1(5ldlMkI2gk!B4Ep?(Hh)e`q`wU3p%dx?oqW&P8%ba5$_gT;QR`&( zru#GcJKa3|&YLb?t&aL2i!$_qB7?WDG>5IW89@&##ko;o?yzgXCDThl{T(j{J>(thYH@IMy^^A|qHxem ztePJpi~`F(3=lkP15!P6cB9tpv*OTB?S}Q?U9K&iN;D_P7$D7DW3t|l{3?Pusgx)p&he;&Ki04Zke(22kYC~gOhO;NuCS%R+C%ju1Zu{gr zVg-I~h$0qT4tWGE>^?hf)IKIcp5wr@@UF{3|5xzGx0^5NPdCelm_xj2%WELc-YA%1 zoxXur1@p9{Ojw(dL%Uw)#G3*uyG_r_)u-4HVqEPz{rZlTup&1%O81eQLAbLOX*4g9 z*Rg}phdAdiaRK)6z(L8gVrU5xvM8me&=dGOb_^J?56nS;`$DrXzdDLOTCmN+iUH+i zz)%ddy@&u9t)xs2lCGTLI#xcc;tCT_wz^Bmu}$r z{{TW;h4b~Q>^&bO-w$N!OQgOAn@QucGzdHcfl}tdZQF93u?oGm$a9%=S>4Uq%-hUC z3Gnibl=5Ru_OnPo!*;4As3VhHXx26)rB#L_@k{GX=b@uch7mJ%nKI`wKE({J=gzL! zg};0B_Xh2ELF`*)2opdZG3seB!B)B9%5xHCRF3P?Lw00H{_eYkEh3lq$nL|nQ2EWP z^;pHhwhyJ6^QDJcvm40#$CEmH>traV=pSDh*1CNWfDI^UQaBP4RF^lNB)h7hzjGq+ zNNe!e{&JxKDTr{oWSEicv7~_3tWAXtf2UHbve1%D-9}2lt)^-HO&g}~pj%tMcyPLY zxgQRQHq>;>IL>VD05e3zr-9XIN}rM!?}7&P1&9w5KHQ-$__q2fXu|OX!C9-7c0j=(oyU zSMG3NTMvU5+K{mhSbg*9%jcqzjvnbtht&0>(aMoDVBj>^f;Vl&nO@M98+@YXh!z~h zeFWY@^p&ODtB1K-f19d!AqdjTaJT6?~7{BGqDnZFOB3l*D zjAmQQTIZ-6#Deh7J@~8@*{~^clv~0tC%t6?^~+;1>#P8zSW=nudp0hHlHJGT{|*FD zyVq&ZkP4qA4^*W8j((v5o~IbGPp5WMAImN*%kp~|&ayadlR6cD@9lA8jJ5CSU1G#v zrkA(>Bx}U99+<5hweRX}I$m&}^5KNeQ@U1n>Rif!00$?K^56=6R_A+BA?BcrfuSa> z>U90`On%&@j_@V=tis))9+_P4M3|-!tHrIPKv0XF z8%PWEi4mu{^9nV8;a1~t6lv=^x>eWHt+jmqtN_|)Wl3ruVj+ukf*SIMu8K#jhhvk*-7e_tR{GrLFgr6?^Iv8bc;e$kX%kgVkou6Q8^DJ-SX zXW<@~2w+m^yd9)I#$@GluVQuDq73W06p{eR-w^}c9_7Y?yhGyM^f(rrOAk{B6R;Eo z(s@pLZsBI&4#LzU^Uemc1ZqTDeS*LbZlf4C_HKh=fCzYa?QPsy2gJWPJv2Jk-Lgi< z(~q$bgl2obe3h9Kh7v$KQ?!sqoIo4PMw87e~Kbt1k+{`vLr-QI2?p%xp0f9#n0#$@RJ-DEHji{=yhT=EnM! z;hO&a%sx+^*Pj=3$T(CuD>!s@ITzTN5_X`FX%O<=+2VBv=+J&)tTxL3C*!xY&9KhP zOsEVWzwF3errBORYjm%zC3=f?qRiS1Vo&Bn^hbm=!hpd2rsUgM(!X?(VP;o$?{p48 zK~@`19KstjzI5F^&CgCW-MrfJH3=7aSX%gSiL-Xl24G4OhrW_r`t=|a2%zl+hxE%f zrk(8tf>#`8?9FjZL4l6aeB{Wz&Yda`%+10XLa&q{ZB*gHsGyD%(??8tG*mEHV`v{Iu#4uif7M^3b{*ifuB!t$W(lyY)$j?lv zx@S2NBo{ghEz*)nihO4qFa@lDa% z4cc-t#@^rwCUipbUmijq!0f;N6D{PowB^y~yN`Is(KTLYY*syw%Cm1= zPkXzQ4S>yr@jA!(SPta2H?@$JuMB;-`{61Rgz2~c2Rwb1YPR-W`X-L2aT$v;rJOhG zVZe$EXTbT+6C3EmA6B55M71d}pt={00AAx>KdoeO$97u~yM36D zZb!pRp?#ST(#Yi0@d#s78ApzJmFE*AG$;@e8BhD9fODwmaR|ZZBP6;&&LsfvrQ~+2_;_Jx3X1 zJ6QN;=Xc>)Nr->-dvGAw16tZVlcrnUwNMFVu@bk9l6F#o=dmA%_{&o;Ohl%$!kqX>mgevp)8^+OZRD? z6Y<=X3}(HtUuUs%V|M`Oz6Pl%)wa*H`x+nhXOOIp?6b4n$0N>*Il~Mulf!9-7oKK% zFl7nGHgbka+%2i$&KKa(aE8yYm}2;kl-ioIg;R#hSQwCN<@EN2BSc2 z;{aZz)6L%2%@Tj(f?w(yxK4tvp;*RAAKdfUdgH8NflkHS_!si{7AkD$ZGXKVE4WFLF+jPw3$eflDT_B&AgW?+J=L!^mEfq0r z+xApxMcz%35G*jB$KV+=&5g)qNQdqpH`M4j(3S2|=4FZ+&_xpWSD?kRFIb>W2lGF5^>pDWs%rGK3mPiFv+L(c06c=x1|vtSG$Z!q|uewVuYGJ@Xg+Oy>@ zQE3rRjHS^IGHnB)?qKN-+Y)9KvXiBcq}lg8WM;RXOQo;21vyx5AG1R6i-pAf1Dftp z27PzA)*>goIs5!7d(I3&|IDRB7g)t+lqvJn+CuFthmNj)0s{r5&9@tN9fR(Njc()X zU0O>@?*6&GsFCl06LB)D><$N;=24D9y|llVC72t|?$civsFk2gyR|#cRTq3fjGnuh zvEMNF#wYEAt3xZX_~q9aHgiVi90pL3+hKc*Pu^Nib46O#hL5SN&j7E7(CYFAd&@C9{qBz{#FD*dqZ^3t zer(|?zg?J|y6HAY!PHzjwGQGGE14w}Z1QFhJo>GiXM864h|W(utp*-%>?jwXia(yP z*);7PN(%Jz%&@T}9Oodkq6nb_xyaCO*wEoB0e#=HkwqKUIiw;_+U|OBApam^Ac^kp zK=IJY8AO|b3}=6GwWOblvK0^>HV`e45%h`?SlJ@EEDdar0e9m($#8_0-*{uZ2B|t7 zl<}HX0VCW({AUPG9&8W(kYT38>lpkBKKd;XYdE+8DISYS#t!@h(v*lY88uNU2nMI8 z(!!IqW#TyY(u6_6XKjJRxwVv>=QvZbo^Ya8xQ35Dj=przb{t%|3 zDobpc*)V%hYQ8ENtxt!f3VrhXq(PVrKu;cU7rZN}?qmkRvsBe)_6gy@d+trbNyeU` zuHXdrLN$!!Q2RF}BQsc{iWmT(c+g}0{?6kf-~5HG=-3LmKO*we_MLV~B`IP`c?W3i z{>r-0bMaCB-7;0t&H5Y8)f1O+HBq^hNrw#G%AG@=_tA!sI&H(?H0W>??JJg3>96pF zS6iF3+jy0YD0ra%d$v&9Pa>;-Z_`sPey6~UT@$Px9}t2mt=x*h4|AedjP~@zg8kX! zvw7}?VYf2XO z_qkP*^_MeS(w$i6WvpE1CZ!tm9uI*Q1xzp_;=XuN^5~atIwduqk-SHlZ1d%5mV#8H zh<1ziKzFHp2IX5gj^cO6lMpnf$*`yXyT^K$u!+#EBx^M#bl|Eh+R!%^3rj0Q=Y^t6u7|;oLIM$=Eii1opNNPwB;Z~IGgFVx5 z6|dhS3$fTP!VK+=xWk{i0ea%O4WGiXYT?=AOhqk-&1&Y>BS#NtyT8e-*Nf)inzj!}KV+ruFCuVxtwUhKE^z};sh#v@%Y;p8eV5(EuvSr{}0R`<32 zbjle<9^)9zTE8|YaMx#9@XCO>cqdiHVINXyO|F2zXLJJUV#7VU?bB;5J(|Qq3|)`2 zvOncuIUS^xoRL7*sdMw*O;U0l!T5H1uTICLp`8swe!EtEQnKQxoiUR)$Mqr}*|13) zvv?sg6;E_I?VVQLaU#*QoGZ)c!te|KGc>KVs+u%ibKGw2qbv4gtdG=MDe*Yk1J;WM zHnEE%GDE;q&(;l+RSg;w(v;|DK$GnxbN~ek>?ucnMbAi zxa8dg8>zHgpTXGlE4ikVM_+0cb+{=+tWV}1=I#mMf}N80CS2Ncv9+Yr5cet~3*L*I z0ha@iZ^pQk9prOu8{BNn=OH=?-FV2xvM*Oh_iG0uEza_3Zt|3QQ)mW{Lp(5T?%ZbK z!V&!ut;OiNFw+~D$Xxxg(B<>|fk*LOZ)8y0rEJkHcgu1%nf*U<6st-Zy2AEH$6DiGg&eSsI9 z$6cG~_1B+CZX_CwaY;&I*9{tmwP=)OYwi4x0?UPHdnp0+3Yv9?PVJCV9)Ll7E(h2M zzJ_rCJoH>D6zB7e{5W|Qs&XpY?@;^bm(}iyHf_d#m1zURDKc)%y)}lp3-M_C`bb9h zQ4Y3yKtsWG8$8yqkUIz#G#pNFk#45n5EnAgM58&T5sXp4E%#8@WiFN-`*j}Wv;l%M z`@Nnr{FVow#4WAI!-Gx`r5PE#MRj!JuyVvPCu#|Kg36KK`( z2&oBWUBLyM2RF;J&j}4N((w5m-QZDtc|EN?k577^H!km~VEO0A|2Q07_qF*Qn*bbw=zzgQ&t>tfvnBDe^E#7+pdTxrGNP48p_faA#3DFn^?ZV@eW2Yl~cU z%lKC=*Xkb~!rIUVs0+4HI_Z#w2$1!R@H(#KVBhHYXU||m9Zlk^6p^mU0K8(!Sp$v< zs5N_1|D0Br>|Fo|vr_lS-wk-T+G4rp_>~7-a^IxAPfb7g8Su4uQ!u0>`VCicWDl!X(9Bk?|)ptSIVOUiR^f2awQP9Zxp~#xPr?z(see`g|r;8 z^dw9B9v<4l`r~A}y<~9xVw;m!i9je1tkoJLMmBIVPvwzY5hpy~C*vuV39FX}CV=5nc*FCDPNs zwB>Tbb))~_h+aPE#1|F`jqAS>uNUE{R=8xpd9O=Iz$n(3;xN)Z1Q!fw8e7LrtAH+R z9Y`{xFi~T(GUQ9XhpgU1aGjXi&McVP-8h-h_UkYw1Vn}8Y4tU&KxS&?*CVa;cju-= zSeqs9sL2Wyrfh>IOPqDnIT^O3zzSSAY`gz4QsFSQ2$853nTG+?bwiS%-Yh0*JgY& zr{=eGUOJO*hx<_iKUSqWh$lfp{!VW*;rDq5T(p+c+36j=){=V9ART|bM?I84zna>mH8NRpjQ}b=!I6( zrjtfQ>CKeErzCuIB=(AQ#!HbSlqapAkue`sny$J|yjj;#Y}Uv?koe zSl{E$1d?nd+FxegTFcaPEk%G%K1#f#)5dQ7JdYagFzwsll0Dw&+(w{($8O*v)}Q+y zFmq%l#P2leV}D!l5^?eox8D7nuFfl3W@~wtacVPmY1GbgBKV}ohDWSnL#XCo-lwfD zmMwH|Y1u(tw1-Jdj{XrRBQNN@*q$;%w|Y7D=xfDH!vXb++u$U+KC*j%Q{a zitrol9$H{!_{`2+l?kD~G@AE*TpTbcmkPfkU&Nd`{D$GPYyvT3owDpX9V^bcA8&)M4zi?lH%suH9AcqPWs3T(B$i^3GY(~~)Oumd z+hZgjfy~$67n}m2`uVyMF&mLGBBxyZ-&Te>e&%Wo9FM`x_l1h8b*|(+lUce6C~LMH zVk;Y5dD(fBsgmVr!#;UpTZ{wh1Ss6wc zAz~k(Uz%lwq{>SvMJmT0>7Ok5{sM@>6PY`M5?rQ#_+pZ@ACVtCdxz_xwD1ea+&}4i zzlV|m168DR>o#Y&1aZtDbS{^Pcid|Uh?KM*k72*+bSmf8mm~w-YJU#*jkcN81?XrV zb=XWO)ZsNZbv$h?V&5dCx8+-ovBP_`i|g|?T1)i&^s!Vujdq|-htigG#Dy`wJ?Y_L zzCe9N%8y&3FRzyR2d>y*y{aI9kiw{Vt=B%Xpecc9r#kI1IlOWye!vpce?~J*CwOXk zww+;nTRbMc+d|OHljMkg+|@-_pm)b)W!vSwykU79*{otI2GfVuDv@vZ2ukQ3fDIYZ zY<#g1be%u|gMZZ`R7RlKJ*V}be0j7PcB_}yn6hG=h?c#&nY$h@rcK)p?&XSbauSxT zFbqPvo;q1xxk~ScgYn#vIY}Sn*5YG zr_9A;jr*8)Zoy411n?sr$5<(l=!fAaV}|yfr+0VUzhg2Y#aijLO$LzYZ!&S~?$!U0 zFh&kV04J0lQgf7DFn8nO2$7*5nbIaJK{AehIK~_aH-5XQeZWqubikIP9(CBc9VVG` zB7iy#Id z=gh7EY`fH+*SeInLw1(bBrVyY%eq7o{I=EyZKZq=9vFRsljC6dn!R0Sk2-RhvP0O= zT2I4k@`}`EJ-h%hY+*ZF-DuKr06Ng-KcPr-qOKP}xm=g;nW5RJ(UELKq0LMqxh6g7 z7qdWdSXg^A=%CzINo<~AKZ!lw?P0;H*E*%^@PB4lBYIET-O`Zfj?M0u*iIA}Yt_5& zjjz&e_%((mV8R7x;T*${evsls!%J{oHukeUm8!ju_j1B39C1dvHQy1jfg6sIMcV(IpBr5*Z|@@% zx_E$PZ6b@(qjmnqyOKJ)s3$L||8OnMd$W;bCW4nQwb>k+MqhB3(wX9bX#^!_jRqOM zh|6NS%gRr)mL>Y*DXU$jc^kQAg#yK=?ocj+Co$(tPClRo@3kie$BOcc;?OEhLKZLu^@%enA8fHV>r%n=|T&Zv$rt$UOM(Z=&nM5x!-irPfJ$}4_)iUStfn88`f$73+u12oGtJPTL04?zpy`m>OrEdhjpr&XVSqy6dTDrRXUR>`*=>aD zH7s{K4RmIlC)BZ=g?>JRKS$?6INLnJ164Tj^2piaIO`+C=k7t_T4s&rHhgfk>}%y7 zByoxaCkdx37kKlcr(_pvbP;Fnku;tFeVzh-{36acC7n#54XjCxRP1y{uomKiH|3;h_h~*{5@~NO z0~cY&$$0B3zn#q#oLXZ0NxXh~lHH|bEwKG!C*q;uXL!qIBIB>fjawuiMA(`yo_j(J z2_Bk(lrm#GrH|po$_O}Z+(b6Q;u8EtdeT7baJKReOF@D&8hz&dDFkN%76fg!Lamhr z7?x6u>&v6iL&9Wz#zAJD8-7{(dvpx?pY5m^8uWOoGJF|=KK+rZW3DbzVsA-1jJ!@0 z7cn#%x44Y@2=V~R;0SXFWLSpwfMwO0NJf;)Bq+;^Sy@tizzmF=*sbHYImjd`#&tgP z30zVL(?|LQ;lBfVu@j%XIyutTvV&p+8RCCZOar#g|K}38-^H>i8_!NVcA*HMh!-g1 zpUQxP<@naO+wGO4S5tGCM#R;-_98#hbFyQtu+7u=|zFrmPgDx)m}gnq

(F;&ZO3XcRVI$}5b&HK^I`S6r`0LFh9R~2?X$9*j5BqTvZ z#EWKd2Pb}6e`X>vz!s$2mcb+^?r_B>KCt}7X$v~6BPa~sFomHcNT*+DmkrRUFFF#6 z4r)AWMr7pca14aNe82CB$42?>rO#}PoY$KCFEWA&sqF5chJGmj^&p+X6-!Y%r|iCep_@IrY!;(=EOOaZ)# zH1s#bA7mi&7cyTxjYJE<+x+glGzCG!;^D7zABCD)7>aep3G-?g+!ncJf&P7D;{ey} z^bH&}{Z7AL#IE}vP0yANZ3VnAb>%iECz-eA3XpMYC!5|v1PpbXw4V#~{Um(pTD&I? zcZ0T?U*3`hD_(!b&2sn4aTZ|3Q1b>SJKtV&n?DbP1d&V_HN&W+IKb9Tim!6IdM`=C zjVsy)XA`Hknj}!u2Ufl43eHRIenVP-4gZCTD|6>mlt<{?tvKo86NQ&aER=rv>Soz= zS0`@zYdd2hv=3*(L0d@+fCtD!79k&_d?>#T*n6`^=}}jRIU1dKZ|G*BHBDtg)Ey35 zkN&s`hIjYA2SAB_@U-O^hG}S$9~qX<8niwm3uvR|u&*%Vhy&8hBv;_AXAsL_Qy&?@ zWI^;MGlnLnMQW}|+YL->A_C#v71< zFBE4{j-6P9el58McIFVm3w`Wt?rujSb{T%5f2n}N2h0N%esMQkY{|AV`Eaw25i%23 zZ}VN_$`W-k+~?p|t`8SDu5#=6zr3z<-3(%dreo$$aBS zw25SplVF1Tx6j(@W{KV@C1G3zyLU=kLP8H{Gmq49BKp%7)3oQh{jUFihA_+b{nsch zOs5#a?N&%hjs53lx#cZ+(A;;LmmqWcGc5|BF?w<6qO|z#qwzWR1i)OWr=m}~s%p(H zt-$E^?3aaSO?Z-mfy;P*(nABkxT974JL)8_L5 zpJ_MN1$+8-Q#0HxmxC!u{4nf=M7C4(c$BZsM(wkIiO*zugb}I6l$=Lu_; zV+2GG1u}+d;Y2V*2SY!;6=7|YzDSOOQDWc1@JB|RED>7wDQzn>D7@+~wE0w`X+y{Z zRk~4T4dU^kc1tHX+>=y`wGM6GW-Uw3z(;>t-QM_;RGMcPS#QdFq;3)!ET7IAev5Oq zdc2OuU0~7v;H`DnFy)yg&ob^*5ZFS zLF(T^-?3TRqhI~^$&AXHCT3wCtgi{kN-{1oa1By!?Xq0FWEjKGA$WfS5hoN6DV93z z^TJe{fo26dyyF$;KF`hK;6pVgD&bN zh`cG?On6Zp(&xin%MP8VU{_{Z&)8rJB?(sz{A(eB(Dbh?WE*QLZJttg((r28-~tcO zcL+&_mVAPFh-7l95uXjHdB{P{0Ahdbj$}mAEptH>Zxza^H`f6YoyOo@I1^RC;-%=E z)BQMv{U3L(lVBra^LpDIj%H?{4j-vQ06bU)y1e_{j(h@oZ%Vsg~)UrhQ1sZWRd{4 zd<&QPc8WUV&nN}N`#_uQ=o8pXdezfTT9G7h6G3Uj^__;9PrmJdb($r0u=sF|UmtEV zWD3dnj;;TOEiuf#-vfP!7e);i<|+O2`9h)+UCNlBjr5#955WbhuHa701{X`9pPv+6 zU9BvP%TPE28(omlrei@eNT)Rm)q4V^O$2g=ZKny2T_2KiKQcuuX`xIIXbvH;y*nij zGym-ct2FsF>1tr8J?PZG5WT}m!lAF-AM?>u@_A2;0@|QahQe&{C4XCFI@T1$5r=Z^ z^WG9jgYYdjaqwOuUn{*pBk*N#Kb{Ht=WRwRv|M}AbU2*=?XLbs>}jlC9mrL+4munw zDWj3hL>AStW>EMgZQ8Em0o&FC+;7y9*Ez7Xn~ctq{eJBZN8YNoe!7i6N4&Z_QcJg- zGuv9Hy9TZ0*Ur%S<51Lfd<6&{evDC?`{(4W)VHLU6gITqvmYxN^(Z~*Nv6zi^k zAQPsQ=@hsRr6pl+@P@CT4S4RAjEHUPWRxfDv5SH6UjDJ`1^3Tqu%tBe1Hr~mD))bA zI#q$v{v&vv;A`L@lU{NU0Iy4#j|PaT3DO16r01zp3fm z`2NE8gMF+?zx7C2Bo<_fy~FW5aX!urceuv<45zKWT)!zX17MQE%nVZUek_?bJaMAi ziMM|2j^QXMw9%(Ip~G#|-z>Vo$jhKs>YwUgcfYpr0rYvlV*;)4=bJ1Bmt$P`@Cey%vR8Ta|uw%zRZJQ}g=ikJLx zvmCgkB@P5obKHL#(oHRe3(l}kE^3Xh=4lm8z54scI=r1J7mMpBZ%#;6JWGqagt;Sc zj|3AO0`QT_$4WB}k38lg<8RGW;VpQJ{ad$gr#AdrCo@<8=6zF0yM9*SI5+KOVf?VoD>8|`yV~XOtUp& z9hQwuO~u;N@!9q&jwfG0Q4Y#X)O%9SZkC&9yXwr74pO6F|KRx< zEI}^@;jhkIyu{D{kku?HKV=77M02(jT{BbPkhAfNsIXEmQjL-)g~0wyaZbh_-omKQ zYNd@OGnv3x7-r5Rb@Fyp+bTRkXCAob7pf=#sr8eRb^b)10J5>Qh1#tkI=)Dr7Jbob zGnHqapMUX(;@ztIGfV0*mA26>*@R)J&KuV68maHh0>yR{Iwr1_gYyRaaC#8g>46@b=lV;A9 zb(Z9N_SQpW%GKAZB$|c-k6;QGEh+K2cIDH)gKQ;-jZ*27SAKT&urXzm7xrBxRC}>gdD{C%N-<~ziO~Tlx@put zMLelFALA|apO~8d0A>(MtJohFM8lQIq&T(`;ys)Pfu|Cje54beTzuHFJ>gm3kcG zophFdd2jzCNpx~@=N;O7lu9%#=4Y?^JW%>-v^T{w)$X&Q#-QVpZ!OZw^A*<;6Wp!v zjPNoQS7b5Lv0a{T`L@oShnvmvFUhNFg zNieNCd*p;xJfJiOnW&CN=%fw0ZVzkOb4vd?TVn1$xhYMjZ7dIx~%950A(VF#^yOd$L?VrmomQcUrSy~m$xGv2LG-E^<_D&REv>`@DX$aL{ z*B{CFAx|cNJ=D277$5k(*PkWDGYXl+e2`W7FdvFop7P}Z@6Mf(e@MRPOV8?XUW~e_DX%K zyCp?OpJ(4OMOl!L+Vh_lxe$E%vc-MCmcU|4qbEUThpA421E!yzSG}50oD|;Ge2sVFOr~3Xb@Q1wHvo zS`MS3Q^R6x84nBg(jDK@?6XNa;9q{m8~M*3<|vp)$t8^Y68 z(g8@dqh&lW(?Dx(1Bh`Bfc}Q}*Kq@PSTGs86K5f4(6JC^40&IFF%o*b-qau5xfROcrtHn)=kKVs2 zba|W%(b0XU46Bc}=3pEsm09SN!2c!YG@dK3{Zq^0_R>EP7knuz!T>`SHSb8!`lGb3 zoTjl~$^gYO=FFLGC^tHmoYXLV6l4vZaDwVK9#V3?r{iv;X9^8xzVR|USopHDZk7Ps z8wZ1}xD8}&(tGH>2TB2ybT%4|jA#JqfN2@YA^5`n6&<<2{?8$|5M$`~TOq>ik}$Mh z-kHk+n{)<-ycmCl#!(XBuj#=+kFp~4p6fz@{M%~=tNh_3-FqH0*exgeyjFSxT9Kj8 z4>E6a1SSIsPwFzithadGj!fFJ>NAos*E~lpq4nvl7C(ETGZtG)iYtLGSUOTQ&L2~f zHSYn3d?Xk@aT;<3*oMC1=qzIkU8zz}qnp9@sHr0U!hNcfVw5;XaTK4TL6>|fBkuH1 z4h+CSuy~;Y7f*TVEUM@mB0i5V(Jtkvb-Jz&YlScTcgB&;-~2&Fp`E*cZXvbs%?zjH znA3(XbvLvR6f`8JK^`X53esAC+$7UO`32!>w--pojiE$Y3~ARhGbAYGZ1{nY_sHva zd!0XaCW-N`EaX2{YZ~y{**&39PMgT!oqT^U9UIR&T3(&@K8qS?q>Wh3J`7I zaWqlJ?x2$TBcyiI>4)U~^;-s>VObm=PJ7awcrWOi2pi&%w1rIL60e*lu-talI4h72 z(4uT+M#jdFeEcgF0SquDY*~DEJlPbwX0#zeMoqb3ar4cD@Mt#o-#((^-^j%@hrdLP zf3RrAyeCG}cKKY%Lb4}uQ@lgqHsc1r0bE?a)3*&lW7^%{s5L{ z+D`waY&1Ib35!~+^=tLJBN^E0r@-UQPz?k{=&=oago_uo6_wfp)un#hOcv<}E^#HV zIHS{B^xxJPB%|O9q?FnSYQpZ|(onKqpwp)ttY6rRBP zT}(~RwLYS)A0QD&8vbb6MmKxJ{73q6EN3NOY$I}S)`4i5L3`%=)7?t?3vmcQn6>?#w9?z{!c)v}p@#oR z?$7|>@M|hEzB1<|7fL?W>h=L za;-HssC?HRTfhIYh_3aHyqv|>cN_|`<#6B#@bV0W-yFXdDgHyG(X`9ul^GX2c!ZJl zs}woRas|_RCx%__+8+5Xg|0oVciffk`h|zBjM3hE90FRj9d}0{*R|wRh$l{5jPRq9 z``_JnM;V*WPW0VKRLC8Hv|$4L_R8#8BSQKuOM0_E;$MNLc@c>gqd?4qeIvyICn4XP zc;P4AiQ(*99X0V%2v-fIJTwUakG4CzP^cQe_C~&02$ZMo_`C68>gcuC1^!VZ%kihQ9q;9) zmh9YZtAqSJFQW~Hq|TdU#IH{-jB=U={U-m` zvKa`(*lUg~ncp>ErP)V|AM?$E9Kz6HLN#8bikU{Id}RRxAf+it#(+TygLVIqy}{)3 zv_s4B=$OOgW?6GFfgLHly$&z5#+s7dFhhHBeO)|XU{S_6l{I$`9G14%Hl%6Gu>@_; zqwtg3sJVLkngPjol33)qh*c7&?}^APg8>-vZaET*dsrb|go|H~A0l9Cumyykv6~nY zWFaIQhzUtbADKjwdpF5!+;xS1{Mttfe-9*oon<$rYyUlLlR>O&Hii2Bh4V zNgsA&9s#Miy?E~DKIiPS$7^5v#qKP^ zGbox@lK1;^5c5xVlU*)HX)NaXA}D>U&R&5Em46kY|LDhf!S;^m_vG+!U!E|$&)y4F zK#ZZEZpTo>359OeKa$LV6eA^F{?e23%{ul)QYq_iD|*o6GxaCl&$Sr@6XwM38SRytC+4lZ>>x`?6llE zb{ZQpddG*2VLtDHc#^X8$n7_a%gWO}XX|Em?m6E}Y)VCVHf92_9cqiUHAg)nWAX;ip)62rn*#=Af+0oX(J`2|;j7BmMY!t)LYp6NL=h>_RZJ2&CwS z^qe#-64H)iWDrWVcf2UpjZh3cs330Me3YNkzuw*CO-5I2ax(Nlcw(i9Ts6$5UX~}$ zvKjn^W<85b>~liC1O z2^h~O=9mHj3BhRLZe32%Htz#w<{8d-c_PPln@2zjB%|?%1f~U@J$pKFl@J((8HyR( zYMpW-S;?^Bq5BumYNdqMHd(x{R4cSI0nUvWp`|BXe_pt32kza#hU@!+1yU{Z6r4%afKDR6MgTs zn_A_a$koA_ObL;ZGuhD!UIzs?dt6&DBqH5&0j_CW@kTDk9F0>lb!4w`^zZ5PmE1KR zRxaS0A$E@Z@#=l+^vf~RTHJ{c{Mf3tM;=stm{^Bw8XRJ#)Q4~$HF)}X`SO$X;Di)% za|d@{P9$Jtb>L5MdhnnPGj#r5p8N%^3Qg^1Em()W?lR+%$##;sF|5J(gi;Z#e=bO= zVTc>re)g=7LGR#w|IC2J8zv2hTsOuSd606ZJ-k^PY3oD@|GwubpJKR&rYG{}?(JUI zNhcEhpcA<=h4(IAlMe32vsl6j7XyHJ`RE40Ypfz!FD?(G9;#fNR5Ao0}Mu=4q{auM0c0>7^N(+NL$yvQdDMM(stxm8}kD*@e;> zY+8E^7`6F|=_g|j3vWUNBIvmO&O*-mbTMR8!U|Y79zOl=hIX;fBgdah3uJ^=Vyb*+ ztA1^utyib8JWae`hrtt%g(K8|Zy}E}ky0U35%FwyrOp#W4X#o!R9B?;r1J+P!9uR) z{4(elUvBwbr%{E+;42zNH=9i6nNFoG9Q7w&BLTz?S_eoF8ejz2zT0K_%1JwtVyTJ1 zs3W)5si9WF9I%BWH-MQ%9sT$Wv2EAFaF-1a-K?0W zFQk0ea(*-_1Dkf>jf1gIi^Vam);ab9zVp@ZbO>&5t;)(gNGw1lMB5xrTyQ64bVX`t zVVgH6=;8McXJ=4BYGOAV)1)U~TX}#@7S24EpKG}lCAUq#&Mz4<RAFcd*HKt(b%c%T$8Rs^c_8G%i?t^DqZF`lia!VotD4o&mzjlG6uVPx=!&IV~0U%bZqrA z0j!tQJ-!7qqZJ*EpFA%EW_S+lo}LcR%*fu?$U-Svvnh)o|FWXDS=hvICn60(xpY(a z)f$=~q?HO9ni!fPVg+E*7|3`?2BbAl!RCuCS{89%ro-Wm za0GOdXUK22O8q6w$;3{;qhfou#pNofvnXe&S)PQsxU}=2wvj!kdobG zq^7_f3t@CSwP}JnE)XN_&YU&;HETZjOI3<6APN&|ND3?0u5vQ6+D@q1_biKp?a%t4 z?fkADas9nDcCGw9&R?^K8(IDQHvW2nP5H{1{CmJJ%C+DM=YV^GNAr!lNC8rYwo)z) z3nN)oo$O74VNn+yV%M>P0`2~I2Q@v)zq?6x*vzCABE##P*~rdq&~K2L zvJ%`(%l%5?2?n?`es8sw+e_yv1MhvXV{ZbYX^`T-WF}9$^OG3lY08&j4rISvv}(;M zT|~c3y~S|;c&xL?ez%l5Z)R>Iv5ib-W(w3C&J^fckBTk%A%3HFMe;6RPk8gsNqjCy zOA~mL5t$6gL24jqKZoh~%5s0lL1C}6r%vFavzRJwP!qdEtCNeB<^MDUaYo9kR!LRb z4ke%d_H59@XGuKkuk;@B7MmHR$%U(@X&Oz~j@6p9RfMst^I{{gJUN;b7i z8ul)81$tD6@+#UV>REAqgcxc8h?RTi)|X{I$!7Ga`gFi4oDZ1=~Q(AYC83#I_C?x?}Zf;mU39fMtdcuiV zU^?Ob{s`WU8rBed3HuUPtmrN2wU>_&=60^w?@grLU_z%imr5re9^v2pzOS_yw}dMA zdSA+4^lPI^AKnVf+hUJ?t-b_V%Xu-4_jD?4*-FUzhi@5<%jOyu%BUGPSs|UNBoEPj z4z7t(&!$l-lR@qExuIjP*rG+&ytq5=02}&~^Dm%??t}{J+`Uu>|Z{A|ua^4px9wSOsI{6K zE3Dw-K^d10ds$wIGPI5ooHrGJ2wCwsfnZv9Umdv^6=RSorKhCgrF>d$?cIZghjYcz z+PqHs6Co(a$da~*^^G_7AP^_;BcQlbp|^5zxHvA@BegpFgeS?8b29akOUH~ZI^Du# zD$T5%ZdvqAxeri7v=`tY?OjIE_d$v_>f948fjDX78peJDsM@{d!FCbmeM@~)T}U0RTiR8stci~f!D#OfdY7Xj7Wz8y(xwEz;L1BgNi zX+2y}gUTwC?q!B|D_J0q8{VPc9SINKU~HsmHO5&-6RBaH1^;_pN)CpDk>2!5YqDM{ zi9e3IB%6j0LmCyE#WKDlD8Th}FmR1lA3Vg)vw0^$}q{s9YGf-8T*z*EG zD|aX2Fq0(ANQ6IxqJLa0*Zo0K5tl__-SMuJ?^cLji3z^xzpV^Aqq4t+VA2c@0p7;X z+vRVt)Iof+kLD{KIC0wSbSz)B&W}{s3h}yK1H-~H@FHMH>(+m#*oSCSU zg8Ryxovm8G98=XV4X##LPPn$6Rs5j6kM6OXpQYH@3Jp%vNxL;XZ2(I|D?9zhZq|wH z53e2w7%FWKc%aSi3=4O-%i-K*PrR)}olVu@GdJ96UNIPLqBsXb3<`Ud!;*?4}a#=t`QxYEN@H1I>hqpdN(rE zo%k5j_ak9B00X7OSK4W&gjC)#Y{;!hd5e$Kd>}}q+YEg?qZ2#1JY!5O$>!R%X`z{a zE{&Noz8Mf9|D4r^{f=*4q2}Dn6rNqnsD9ALKPw}v;Y_`V-fOl3*@+(k9;Ba$FamXg zF)t{A1q>5VVi=inOcg}xT~g2o%pEbElna4J?wx~nN=Qi-!NAGyA&b{hXT}Kg+=QFB zlf441L(JPa&(Lk$>xNgHTPewHy2P~|`KgtOt8PqR{Kf?U?GOYZFFAAl@iZ7`K7x?! z+dCI0EBu($osErjw65HHzeneoF_!}qC1f&`BC}8gmCL#3WEZIJ;85=~nREM@0P_BaZo<4Qe_ck0}Qb2i1n?5i_6sZ_R zY5X;Rzzj||E!NKKtfEtYU*ET0TF^S8^3y&c zmt$#0dsS=qPOaM{-*9HSj-HO&AfFg$A+-m2zfl?Vu#R44x>kI!HyMQXR7gP-@c0A$ zy+f4JN#zn%fgM4WNUIneM``HMH$8h`Ls|jx^(kNL+^`^0_9nu# zVaV?*#-xR+X$JbGzY?-@E^))I?oU5SACxmUsv*ZOl7N(f5W!Mq5OF0^k%8i%tBott zL`Mt%70rs zF<{~(sOmRj#~MB)B$7({#4G(q6&K&mF@JmFfPMCf40ddR6_n}RM_Kf+vI7!uvkL{* zAAldz(4w6YATtUw2^_Rkr}FRO7sO19cdi?-#HgX6lR|eZ^w#T>=MM1Oew>ABZHjpc z^VZWF==?9x9Z1Ovcwj!+2Lp`jpO=&W@Ar6|Soh->vd(l>tPD>xADka#~#+zE<@z%G2-8^tS@1a1wq+s^O?X->}#q-_w*RexUEPMEF%%(t1RVLM@X zmhY}2|MnfLL-)!i{o+OyW6{+BCVJ5XOY=@RrlxyRdD+8z$!F_fe?~pRc(qygC)yQz z0Y7G5i_lbBzsm`%F(UR+`j7Lm@7Rj3TXOXi-}xmx3x)6%{$t|TJHX%AktULiY{M{{ zF?rv^vh(piLbC+LWCEQ@DFw`cIL&I^Td98i(Omua`P}JlDdV2jKlBn?QXDJ$ErVek zr4L{`H{d%`aL~tkAIcdGq@8lHRz(^Qic_5@(tsi;isAE!#6+mhXK-@>0lI`Sh{gSw zJ7XUwnSPTZ-$77m`$e{zLnrE>btBgd z-@WFTfPvORCo0bU()&?AZ#8b&{6|U%J_tcoy!}qJFCih4+`K%N!C-JDJ+u#IDzzCI zQx=+@v}}dtXZlkLE_-sH>7gS!tw~@>UQz;dK#54ng->Y()81lNke-+zN@#d!Ai=M= zq;+BX^C(S(0SIy3`6XtqvQ>Z(Q%Hthz)xZv%FKS^gnfaqA*AEJG_JjfS7j60pJjHV zb5M9ZPIg%8lccxocs19yftxSH2(DXEzW*kd(IG@l!<{G{QLM!1~)2)w%N~7>!X+-Wh$=U zR=s`9Hh<&FH&ak?Ge1qT59RUn9%!L!p1;gI^R`@G!%nv+$&lm@YQtf&wceKHBdspz z9_D;71MR&`U@YM`TK(bF3OFVSJdVnAA|g8IM5laj5-0Raz;SC!HZN`n=;1ZczxxAj zrkp}Ej!5grl_4foush?LHXK^X1*nvw*JQ#>iyEr?tk> z3UIl_$rhMl?ot{`;S`7tD2)@j$4}|ZF-PnDX`)rF&%n!K@MHWAZKO|*&}TtrUl2dn zudtq<+_T4x%D{e1Dg4w7BtbITw#1|Rs%T=xi(GW0(Q_%Jte0ONEd9r376WC9t2X!_ zMxbiYlH)Pax%_nYIk}*DnnkIG?&m5NN>hR>LpiHF;xAm`PC{>YQo5-{bLHArKli(2 zaB<6n(YW8QNiUl?a6o1rtck-=w4yEy+cld7sxEmGgyC*@f)&z!SWEP;qZN2F13wAQ zA-1R}P1@7eNHez)E)V&F6nuTkF2Y0zw^__4Ud`r;6Wx>OVciP!2+7f3eAzeLg0L&M zQMz-<`eozYcTE1mi>t(Wr}doZXF2ZFD6Y}~j~YHiI8cJ|K~6AA6RWmsaTtd9mvkj= zk%kRSM&osgv+VIctVi+MvWM%?i1@T*AKh*D)*X-BCVleb)otTE(+Zk_vNXy3;rJsm z9kiZ4;nmujEE7*reP#xBRR+xi#7H`_pGm>m>S#{~h@10N8QwYXMuWN_Q~hDJaV zPA&#ddV(pkGHCY!OfdKYka6UFm>6 zBt65^I+&>?&oI`6Q~rtCaTXvn%k5%My4crRnRf)W*P{g?QW9jQr7%qbp{w^{$v8u8 zV%rqLfmNYu{v7mwh++Af9nZiBu6#O!({MCDiDCWf@^!gpC(IoGy97&g)qd`E}cmPsW?^Jksb#r(IQou~$O ze{(ey9_(OgBBjz=#`|5m7v;BHdeB$xh)?779Vy(Fx-A~~UHZkz+UK}dzv0kUn@IsQ zw7rzZYlvzMD)+AZW;y4zoOJ1R9e1L4$%ajG)o}N$6smF$6Y81Lr&Z6d5VL3AR|I59zN-m#B{E14bvS@SY*(tHkv{KLhJN0nZBSDAb^5yMS9w~P~Ujm6#q6(W`&ZT+^;hS zoUjpP=6c~PPM_Ypd$m`YcUbgs{T%A5bFNR%+HZX+O*;?hx zf%I~)2x&BVkbb^lXo>aEgfVT%c&RbI#*unsvQ<7L(WTse4T(H@*ohPh&u3b+okeP2 z>;d~7oLYSdKV{;2J#X9Tzh#BcIK^QXQw_QpO1?secI>mqljn3k>D2jaew0SMj=#nZ z)C&9JtYcXh9$s=b1ZC3qOzUx9OxhS0s&Izziw$ZwkiD5tZG z0z=$Ua*uYet&W6qYywO(Kb$Ps1qv8JjYvhw-~9X=yV>7%9k;V5%rAiiVEY-q`o}ux zdrya_Zxs-x@@3gUuY`@PW=Jd7or_Si`X8Pg3BSF^!%Fvbz-~^O@$521;@cIKV)WbC zDbQG<*dPI_pP+w_3&c>ADw;PUGSh3>bP`uEIZV_x?n(e8XJOEyZxF?l6Pd2o;8; zaQD?i-08CWaqLp7G$7lB|6wx#W!xs~&l}8K+l;w-?i6@Qy0)8wTl*SbV(PV*>A9+r zALuN4@$}ar$Mnrn+|w_HZ}&@<=~sw-qn$6)ayYdKx-2WR9v}u2=-f_RUbxuJFoAh+ zGgtK=*UPl5n~^2|K*HtSjJmR2irMI9Z4Nto#6TH0B}>~SytEM>H^cN7bkl@AbU0Fb zH}<(9y?4?{Uv$vaIeeHy_HG$wT;4U#=c}s#dGoQJtlEQ`uM_&lQ_nI-8&g`%of+P_ zOgfSb)c6wdS)HLfpCqXk`6dzS}cb5Ndf()|fPS%ABGIyIzAyLfb|FM;m zsSW20>yqMvJmfPEt2?#b{MWq2}P$gPg|XIohN&n<-BSAUsu9 z>>!3?jis;nLW`Ei3S*rbla1em(t~g8`B`$ z0!k8U$*AcAodZ<^3Y%o1`Hbp&Vws2d+Dv;XI_$e+52Vn}OW1 zt|?Fb_SA*L`y`F)b;8~-wx4%DCEdHEfIt4cIr=FU@TZq`V&y&Lx5E6M(4htMz!Ryn ztdsG#TK`4>xe@L?K9esnq$2CTq-neMtY$S0_fPm}^DD#>4BiU1ncgPjftwz`-sRk_ zp;HLu7aBqx2@-bE)iYwItY_Y0IaQ#+xz;7{m8Kq5LM|;!c|%@4-XPW5P&$2YqrukS zB$f(a=3=(_k%yM%VGK(=9|gzAuyJmP9vIn&`vLCz_Ay$8zPYpDIY(TYL7aaNkf}f1 zQ)Swu^nbB^KQRc*%B2H!#h+zJcxwG56t@WF>hhiXW`Fz7ZM_mMuYcf-`AWQSPJ!a_Hza4MzCqNMC$TiQ) zCY8qn{>9@pbl*A68MlNYlhf>E8}eS6eLO_!$X;DX*x~LR4}!0tB z$I#86P9)K(S|ooQttYz>oq9Y>cR01CIADI<&%rV#9*=u;vx_Iin)(FVfAh`U)0uU_ zoF_uBs>@@FlhT41v{Zna^Gyx*bRy+OFv1a|RKEscPUd&-$A-$NlU@n<-dnJIOHbGum@E z9-TiMrJeR^N@{+mPV7$8(kt%P+S>^2pY$^rwh7Mu57!*3Xd^d&vp4T%cE>WQzP9P$ z()7ZHO;K!o(M@w4=;a@v)=^P@ph$c+8kFZG_V9B?uo7r$uoL;_n0#@ZZlS&BB_bx|AwezuseRq;8dEYszo zU1B=}IRs_+)&8U|pNWkf??C@#wXeW*g?iW$vMn z5`CJA+enXZHfDd;hvC!G?sV2H-L(<95`xF$W~|H6%=pL4d|iD6Qy3qUbTPYM%^!6kG;DXXj#k)(Gh1SGoQ}{PNXQUw z2FV_Vmg%SG01QxdL;JK0SNRT`LC}2{h0+|(OOJ77TOn`&O3XV9Y<1S^1PC;xaqr4I zVq^RN#^r_tT>^m#0)gP(lU(#=mi=urH_+8FD~DBug+FV13^ z@KC^~=O@FY!lb?(1pC#y_P`GiB;7Z-0d3CK8VV|%DMZIZc#(zlYl6snO#|3!{&sMu zJ942z2i9XoICMi{IJ`Q71}dsg-$JzYy=gYJ6?p1-+~#{ER=GZ9*Yvvu|&F-+~byMU@zVrffx z3*gkJ@j|}JxEm;I{Ynr%6Zv*Mx4;JLP&l%}QP?gezn~~X{rGkyaozsa3_I8z6SA%%ShZq`BDUzSun6J#s@H^W}fUO?I8ltIuCNNgDwm~Uk5psUICx{3I&z*05)$5n!$$fZ zDOzB=|9a6k#;O4I=-kdAp?AoH-YW&RU9grfj$^$&fEXeYLjdI%#p6skJN<|cGq?kF z*Nko@63Z~-z#n1`yH1B08r^f3Lnzrj@Pt%uE||@nUE<>CxF1DNVm@RnP_%HY-wpGb zPDNis+x0WaTZ>-X$w;P*8k8J^4dgT9KEMU}ugA>QEj0z%7+Mmnb!TZ-$DR$#gsZd~ z*niW@sOuNmmkF)jE1&NOe0D6Pe3>@x)d{l3X>bPXcaq-B=6RaSv_VHaw76&VVwyJo zNXGKs*%s)8i!ixEMqgiavDPPga`D;%&>>U%X-_?CrYoI2lO@$pa#mRGD}*7_6&7yI zglFnz`3=jo!4M7zE&9-<6Jg{%;ttG4K|z&$Tw3DbkF-(_MTf&m=W3W%ULnVyX+1Q4 z#MJYE|E}^vHu?6F|E#~sPf0kU6q?e^OiB}L`-PKW;kH0Opa#F1qb!3wiK6JEP58>jC(PhbgjqQ4P_yr7H z5u9Xu&LP2dj27l-Kvu7ShLZLBnv>^4V6or`s~9Y-pv80K=Yeah1Nby!ld;J^Fp@=v z#}o$Mweg@@$p#~`l{#`!=ecGRZu&h$I)FW}=6OG@Owu+k^R#^3X~z|=BpDscAd8Q_ zoLTv@4;!$4gU(AF&Ar&|>FYk0TUahw+~T1DsrIuZ3zO`zl1GYq~i$j9bxwkuUDO!8Tkswk*F|L zN@!qpMsN1*dk7z)g1uTTQ@1e$HtFWg7q%Qh33f(5(Ar~yYZ0$M9R8DkF=pKAua(Dl zL~t2?Zok9jDicDW;C+k_?ax9I<_|oOYAcJOsSvvo`P!5+jm%!~!!Z3xX81(v<#)QU z8xuXY$tzLoo}!zow!gDSIl<*khaxQN)EJ3b47Jyyep%y9s_LXCFeg%4hiSOWG|+$O zW28E5J4wS}8NjLjc1eK&`8^GS*hcV)qnsShO1p4$S_Y%zdG6hBWbCti-Hhp{amLA7 zPEqiYqp5Cg!iKj?+tv)Q)H_kRO;QnSGItYN2;(@s3YPp@ew6S9b`LKrFM%yBOV(rgXfZIAr=HU|VqZ;_c$Fvb_m36! zumhbhE}9OPDz;_r9=71G0vm*|DZ}qM^d!q6LjP;BzF(ptGQp%4v6=s5kX}_?*c$68 z#G~7RpTwQE$40fs$WX9O-9WCG=tgX^0ND%fLn*hBF-pG;##g(}YvJ+ZZ<+CT`|g(g zLbd1|we00mY9d1*RUf@_#YO>-D+36G%kqh{ZAjU*&p`(8BAOkMw$W8YH#A;%Np+Ho zbYMUVpE+!}4Y4)>tqV+uoXlsE_2-efdgn7p43Rl8$*;>yMaJ=x_NXJec!Uv$6wdLE zp)b0n6UVj8VQHjHjr6XehfSyxViM<%MeFxG-n`873>SI~_ZP8>P$(Ok85D;H*C*c` z!o&EB3QQw!ZQDX#_r&Fm2>uWPW3qh1(((QQTT!dU4)>%Yo8wuO#XUE4B%i-13x*tr zAe#I7lA&_B>hKk$_zs*v4MW63WtK954x>f zo1g&V(L5f%1Lh3u%G@oqZI4Wok+s%^i-{X*02`%-YyCF+VE&*@O89?=n86&xYNMA9 zYr!+)!ZLg0>T~Ac5NaXEgEDYNrrE_GXf0aF0}UgEG@iHa6F+-cNq)z?>2c!F3bq(; zpigU-ytrzwYg^#uwVtk7&*(DwrqM7Frb7l3erK$OkLM^F4U21<&*k z+%Nk3?xoXGm&sFyW%yLk_4jO3yHGRfy#x9=Wfga({Jt**4ZP1Ww8Pj^c5Xgj>m7F7 zfsZJ88wPFon2WU{nSjgW6KHx#ls1yB?OanzNt?)lVmsXVlgY-h4@Z?rH|&De4SjAK@3C$HOp?0?!Au@f(LrSv|P#e1f9 zgHOhz{^$hzc~#5S#yMe0Cvu=mhE7T)x%MxYrwlF}PivQ-3}#B@De6PocJj$-A%mxb3E0~iSx8=P z1ubK%hqN!_X?#)3d8L2+tLd@)*@y&<1q_ZKNLvY!5WUb|u4O>_~QbMV6HqWgX6aknDwuH3JmE#9cu zUi<7{yn<~=``=z5v?N~E8uBKOK}I1?e>e-@2))L%eTz~ zbKZrDhZ_zchaY1b8T@BNc$_RqwSIkqWLDw4jf;&}F*Uzo!tIVHAAFtIK8oxWIKqnu zpsaf+-B)mE8G}Xgx#cyLvk$BQAe8TYsZ8ZDQ`gaxL?Et5guwABOgAu-g^Uu#5S;r>N--HS{+<1biCq|++s3Lp~-Z%9VEBxZJM9A;RUt4*GZDzgdvda`My)) zQC4Kk*H7tAS#Cuq$v6K15cepKE|kO~B2~}aIDAQ6*qF&$X`ooM>pl=Jsd?++lN8Ux z+C%)0NwpjHY)q&2q9dmsvHgWH{OuIx4Vs^4N8j#-<7pP;*fiKWNJ_T}*MoXrQa&BspavgdO8#l-GQ*X&yMj^CL7SR7XXW;+NAGd?%nH-R_qmO*%u9>d*r5;Y~3Wo z2H9R4cI_BShJHtU(uw1_K3352n+G`&h}Ydn)-^~6^s119$$T=_bL6@!4&9*4K9T`& zF+hEGIH4+yweQC*Ek7R}K_{>@BobT7$yFz#Z#rA?vH7~NXE}So4buKY`scB$ zgm3XiM$!#WuNx*7zTzM2I3TcixEzdDI0eK(dbS@!`WhH1;BfVc^A%J{ zuT)$)bQ?T`ZIO}q7h9J$ zZ1K3(Ts5#KuF*f4K6EAz3-KETQh(&kVGk?bT6TDyz;`KSK(SulHDps!n2kBd++~gk zagN(^`ToFo^kI$=_!_V;3$KJHL>L7Jdb?+r%$!rQntcB+EnAs=iyKq7LECUQNhz?# z8W+C|T1?7}RxZ=3Ls1D_-37lVVb2bk8Gdy{YF}jB!HW#~&+G9-n_v`@ws5RJqr!3F zAk}*b{kA8s-J?Hl=Iu7{O4n>@x4X4emS&2o{@kC(fCiqr4`<)z3kc=Ob8U`|H-QjT8?QwRkMKcO;pToxGHt4wg=ZM$%Dr(yOqh@sDcdGAkbt zUDKK!@7P07ZpUQ)%zqx%>42D941jjFTt8$ST`PdePjf@RUOphO{ORc9E!>*|so^s- z+I0jC@rDT9>mq5m=|_kqzg@6LM@*QVC%Zkw^ml`neL)N~hn!6_Dcj@51vNC65KnOJ zYwu=Yk#Qe^VZc~I{Po4~b62HaiP7&|+FCp5dTsoB7wgtG_M~~E5H%mfE$n=yJ?~)_ zqz`@%UIL{?zVb6SVDTwb#Kvj+DfK5vV1ISABD0ahatbU<-8SUZaa(A=5}PdM?hd64 zh9ax8Keu6*mz8_Q|K&0LY({VIzNAenU95Evo__>pgDAb^%0zP= zbv*xmj0vrwSUPw21Xh)ojUo5Y0@VlK(J2E7Xu^u6?1WMN^Q7_UK=EdYPFUyv*N>P*|W2 zAb(Bci^jUS71{aS{PKMO=%>0}l+tB`fE zQ&>BMedq=y1|14nqo3Cq3Iu%mzVmNi((|!Wf1X(Nfm*QTb?vN_k?bY|zYeO0BYX=@ zZTCNFFEhHX0?fI+j+^u=hldu@3nd*dAeGy7fGuD@Ga)P7jGIZgWYQcrIqk10+!z2e zoOF{8Y>@5%J~@VQouD1eOzrw06-nn&yd54^$Z?qr(oe^ilcFAak0zTt>tgM_C+*U; zg^(og_ARc%k9bG03Uyi&mt^=F?6mtH%ll(IyCf}qg`@ara>r8#m5wlE!y zSJ5MX$>%%_#Tx}6WqhCi!jS*w!zQOpMD^w48%G_3NQlyv3;`Zvn)h=I?VsID8aJx@8s2XUE*P{uMnI4K2Zwi)JsncuVe z_IhDJ2&!uURrGtW)RXPduGP{{a>$3ypU%@<$Dd0zjshhZzrFeu0G`9_eY+MOa(%{2IvMW)7r_L>(NYJiH1wq~u!kk`D*MQ-3~36M3@%lN;PGK13eA z=~oO>%WGy$95)w#`3aOO)#kZZr&c4gpCT`W=s%c`_c`0M=~Uv;j7>VV`4~bDe*)JI z!MCAwfvibOB%aG8LPScQle1~3uiYUQJNoK2`j4!PndjQ=c@x2g7%N~WcDEigMS5Qs z9x?2(H`f? zVN|#bn~@TIb{yN3e8cCLpBK?JNl*u2W;;FkP6#~taqScNx$Uw*G@^A!r0Vo50D4mL z@z_}+7!&c4V6{ivk(+0#f{x$Sp~N@lqF};WD4ChbmL5m0OA+pSmz$MA#7*{wX}~-M zzx-+v-CHEIbT|#=P$4}Sz1FRvTb3E3;^f1J|Jz3oiJ9QIPyTvfgr}WukxCW_%hH_> zx#_@1{DI!h(rCq{BIo}?((lBtWmYfNbSg&|LXd3u|G>gAtA&^9$iFQO2V&q4l>+H{ zLrg)qEiF-TQu!Y@O*>>TU8rJ|k{4YjJ;}K_Ue=M9TJa$)ObF^PmRu=S`#nj|SFNEUc9hL3-J^dp&Z)rUcl8!UXaq{?n;*KHd zJIRBmSm#j0GlL4bhHugyyA^G94rMVM@Y=9=%7& ze=l&79`I&!u-6!FrQ=uO#Fpk5aBMo@m;Z4k6Gsop*PHA)JxMxzLOKGLAs({mr*mn@ z5FI0TNp-Zz&s!dRcejqceYI(c=@>`?k17GN%nVJA^bAM&-GlhQocu2AJ6Xuz(p+tXY(fO)#G>CHWz zJ3?rBi@GeCjA18hhiTHSzA0ZEtu<+e6{f{-@L!8Yp|Y@>TgCwDGL5pg0LLYP+|Qi@*Q^dcE$Npic*5dFLMa zb=t@BcZF6l-sJe>4r=MneG0JhCYSDK#tLF0me|G!uRO-9qDD(1CD1k;!}C`3YkZo*5VVG z=cO4nTo;Njx#x|krVClRFFyz}XHCwbEYSWr`_k=u&JEjN#-(bPFCRY24@0mhw!|AJ zwCNgS@N(R_cRfsAg+1?eY!dxp&*6#$GS=~==Nwv1AyXAofVBW{k?B?npjFCbxs zb^5ecp5L~S4gbxG%S~S{&Ic-EK^kxCf?*c4d4KQ`8}-Wv>k}~TpefAi_5?jhS|YyS z6ZE6YF#TCh+>3r~mu95_oS(PC=L7E^+N#NS!?hEJJ2jZKcj)v}lNh?T+4C`EArv8_ z@u6%RgV=jKF-%ZR97)i-FE@}>Dm=nkK;0+u+-Zr&udT$tAT4n6rF*DLNA^^e-B2&XI2PpA&Y6LEB$P=fm zbq1dO=G=C*Z8MzbnDVA~czX4lK;UTS6)nOhb8J(wiC#CKuw(Ql-PFIA>^xw48P{L9 z3B_Caee>g!IvZ$mr|one_@Wy6_@*LrVFoVIg-rB7Sj>5Tq2$XjApPy)ZQAH?3VLI7 z*6jw|d1rU<`+_C1%f9tHK5z?(VPzb@6w6ah^?sC4rS22)PK7XX`4z)g` z&2JTC+d-*#PBN_A^qsv;OwXh_naDy*@J?$TZ=t_yaPmn3WGR&qExMQI!nu{3(at?A zX@*Jy7O_Oer6FkTQlg>H6gB(9K(pUBp@#9Smzee%WNIrK%MK|iV0ndX&tN!==SEqE z%kSsB;n>0d^lzHfcv%1WK9!Pu9d`xFT(m(tpP6zW53)g|ns27RpQk3hn~UG&iN5}} z{*3mfQpC`aM`-2dC%6HBq#sJ)w>pJ#Z}F4bwKRrs$qKio`ANf)jzU>=UU{xxutDQG z8GkZ-IbTuYtQ|YfClhDse8Mg#r8>JNU;hO9iAiVAtIc8E#tf1-F|Q&~#n1_zc>xhC z_6~vb+8*bOK8Hc#p!c17xrY6zD}j*=$_Or`WRzYZq;o-jA?fv6;judhVAF>PEeVJh zD%(yMWtmm*)iyz~xbpBHOIk3#r z`Y-q`XFEna-NAzxp+Eaf8`=&jcRq*e1N@-EM>ev+edAf2l=A0jMUQB5;~Ka>yM&+eT3F%kDlaMEkOzEump<0H)iehXG1- zXS%sFhaeF?2&wRnlj_^vW~?SvjMc9q0L{r*Izf%l9Rc8B_t88z(^uS2Q1qGIMLtiF zpJbh-KAhR!4urawW7X(M{9 zM8bwVABzb8#DbJ+JHJ^(Vd>x3Zrp$o!v8-J+**=q3d)?nsNLnkpj8=+)`1(!tQeg* z9GpUOhf>??u*dp0^rV*+nym%Wcmkpy>xvKVRyXF6Bkx%M`tYiYCS%>coEW7=T5$>b zwcw8F2!~q+FLx%=?l8HdYwxf#Kc&=Eb*A>Z!Tbh8gmpPT16^!$wc>dwhVLK!;vT$X zqkOw!#xv{qM=4YAqXvDuekmD%!$BtPmHgIEak-}dM*`7o2QSn2pnt-H$|D&yxh zGU9+Q)i0Dpa*tiP3VDMkO~OMvwHtm_j_8kreuwqQe{P#R981aEwh&M>82257Akf|kC0MeK zrIfKdIL5p4)rQzL<+xk_xujgVWHK%Y?B~0rOovR|HA&h|hCdie4gG=7U_$1RXI#Tp zL;whzw=}~Qn|Bz(txdGCD4c8^I$<2uG>X*-h8FPUwb=Qu!CJqG4-Ugg6Ps*?ALtLX z_5jmmbe6xV$WSY~cR$Hw8Jx@88 z4=~Y`b073sZt^~trHsZ(?f`W;JiW2q6t!`9xyMrJ6pxrx`C}$q_%*Qn%R9{3JQGG( z>gRKaToaeN)^%w>LJ=zH^TFjLoo9(aneZ6NJGO|8Yg33ZB{T)BpcPm6^54MHc!MTi z)&+^@*-LC9uqE0_Wgv;8X_i0utnujuAFDDIS}m6NIgb4H zF4O1i=+9MN2!YFf&Ep8@2Z_$#u2~_ierg)CYnMGiFMqn=Mz-Q}s1ulAAFnN2tSbD$H2y=$>Y1;~RxTP~3*4nhE{+kr| z`}!0uk>BmCD@ z#Oh3-WhOVqxmiwEw`TJ}5;+mLVC84D?Hn7rxJ%}ISq3N=8NU%I^U((XzzuDGg2~4TY^#s$a5(A5;O(TqLdY z(6kxu@Hp>Ct%Ew(n;q__;+;LMr@jbo`IBAfkE*r0$gY+NK(K4(>C%n_(GZEm7r))~ zT7jb}lqRrgJpk?SGKl?c$Iq@pUPGnUi7WvJw;KK=@|`_m=6pis@PROgk3*EGNPzvK`44RT4z&D+cvzQp)iM;FcS3N zNg3V?#AjntIG<`J!z@E*522c=mq%-Dp3^x~ZPWgAS#*45lBq;JwtERl*uO~5rkKpz z1!fr%SGL_E8~u~lop_?X?`|ChMz<4*E8nIihJo*QkdBG%6f`psY5#LEdvIVKEbZES zgo`?|KH7CFuf%5lCI!KlOra&}&=SK+fgHX*b221L?q+Ha7v-}rSARTN=6$S(J`avS z1xtrFe{I_w#mr#-HdcPF!tXczreC4rj5s>MQZl-L1Qrq)i>34g^zxZy741VWLW@sGz3qTXH>Nx@Lblmy8 zBPN2@dhAVwJ&Wi;d@EPxP{nrCU;pK~Wt;PUB>y%LLAM)M^|Ll|LmW_$#k@*O=Dj6% zVGfl6M+PrR0KZokUhKSY@Xk^A5m1~G9eZotJ004f6r}@V5<~A~5`0OYbga@YxF4`9 z6JeXvmCSj}Ym9f3v>;R{ih`^5038jldYPH(VhEJJV2UQ=UQ?Dm$ou{}a`j?R4*!68 zN@m}X$pqsq<;m2yNVWy&_-@zrGO(m@Ym!a!xy|7!6J@UZO*_Y34D@vCBXc;5}!lV?wu+C^(_;D!JQGKH-jUMZc ze5E7UUMNE2&Ju1gS#p8Yj%%O{`R?Rp0Z2RFShqxH-@F3CnFvbrG8-a4))V?cXkcbv z8WzCCkI;v_0QaABf&==Ef*IJQEv~pdUDs259{_1I9URczQKbE4CStW|xw{s5Fn>&K zA!pOLx72RNPxbAO(IS<5 z)6kn0WQnypJo1+J7E|sjJ0W9lAqXa*PI)EY;ve&`c5TX%;*Dns>*C$enx&^*EdO11 zqOSQNmMzC=Q`N5zfyjo1KD}+4(=R=~$qthRHP`y)N&SSOrA=em?KLvxAiYRZH>Ozs zorgjI_CgsxuO#${Y={)yWITxb)UiEHKEF0@${`)Rr2hg59#*1;X3Stx`no8P>R%N3 zZyDqDJ0Fu4x=(Kt5?Azk-P@&B877`+&k2uoM>8>!h>{9iwsHDPiHth{8j|@W{2~xX zD2Ax%$vjCesi%hQ76klriv!B;>wTu>AnGOtSo9_;>eGLee$sEpZG3wIJOTdCY#z=8 zWpqaRopvKw0TYGi*$x|5-=n3Nr1P>iK3mF!H6}{!cGf#Wfsl9})AnNroAF)Sx4v9) zfjbLS0lx{NOjuO3FfU#I#si%@xBR&dFVhJo)sjBmt*sC7>*wzve?PXHwoaK!-7C|l zh~;H+UxJyFMvDLCd7X?r6@ItOXE$@|o~*^#0d zkp(nBOO~>g$E461)WN8>oW>~d6-D^b1^CU)(&1#SNurW+(0Es3(|?cmBMCDGwDAYQ zp?q7xhL9)Jg4?q2n)B%N#jlK1vi!74qK%qQKs9CYDCSR*VQr;RkPWQ7EM4}`XdpWs zC*Shv=O-`lT?+&34DLv}bYYbn+=SR5D)kw7W21gNujJLMGy3x$@Z)F5_y9VSc`K>! z@r~+SDFSKBOFNxD^cu!YsmvoWMg@i^)XVpDr1{TF_OV6UoT;7#cyV8->mbbkr%R1v z-`fM3B*T!XY|O;4exWqD3RTvMtdZ#s{+xz8>!T+~@4G$7^3( zxD`Io9J5GAwhjU|6Nvw%sF;t*pSWd3?^mzLObO8hc1bkjL-b>+-c*Z+Td$duQp@BA=YteNzB1BV6p_2^CS=H z{LhX9b-(4UV~}OSZ_WLz&M{*x5r{TaWy_geRE{5L+e<#t#Ea8DykJOv+$Y*X#%o`b zCn1mhpZ*|v=tj>FcLQX7g>GO@x4$^CNOi8BV zr{fE!YV(InTeZ{y(^2Tjseajq7^5Z3q?%HAQ0R)8bH~YTcIRx45bAxoziZk4;bVq+ z9YT>n7xvzhi$2Obdbli=2yUx<1Iy+P*Q8c`#onV8lGGi!xhDy!AGx^{1rITK+wXD?$zH>QV6kW!tQOa z?%LvjvTG%#)C51P%s@DJmO9ZIyx}%f6vHy)D~nsy)~k4hqplog|D#t)@3h|-ZJFMA z{>U*`D_}d9ja0Tb(mIB&pe$Y{Kls45JDBZO&bii+Y-*kSJ1g}Ltf?MUV*D$GqD9P% z_0F3+4YRx7yM55so53!D+P@jr5$R=3wKrB112-jdmnvC9J%?+UNN#X{r0=meXEyML zJ2LdtPzJHfeMDHre!Ey{&4Uk+3Bnnp-NzGNrU^;&j9~8ZEHPy__ZuYX1O3gIGFZ>$ zOI#pnN&w2zb^sH-U}9MGz*tn0kD7+o0%=*vYdXOUec(b)Qx()=1b4=npYvn@74;aNGEmHJGub{_3SOt|H`rC1RoZz@n*EuwEiJj)`VP81 zQYt^Bc(+dPbGJUEpsjezcRpuA%DJyJC-HOXQP;=^%ghZ^o1U*>S}dcoq$gw9ndWO2 zZb4P^>^0(0j@LGJLm-88u#zLu9Kf9gWB@-c=cE>cZJ zhas(B;F?F5+CQMYb?YUC@pWg@6B&m~{!`n$FS9{JJVc6lQbf#UNTd3UOM4LI2*VJ! zw;(MD-+~@9uEF#xnCJ{Wzt*gzu1+4_mEW1kwv{S`61dLY?GC3sNc{$TkeojwR3mn6 z=S8!JRw2ZB_v3g`W3sCiu73_8DE~;$-qU|8wfl@hYGCqj-)<9S8wIWH=~0QqojGw#ANRB zoD@Y}Bx#SJF-{IlJ`hjDx6nFNfNRp@pa;E7gp ziK_YR0fQ3S`2i`@<^%3j|M|CiZPTypOEWL3*1jW~*sqpgN-8MKCGdxYcHz=%bR|K_B0;oxk_MIXoz6Cr&*N|k9!CLeJ0Iw##6 zyxgwQoa44$CbE$vo<=( zN0QgikFyxW4v%3p#*))s+XUO8z7X1)6*7GX(lAdQ+!hY+h?SF}&E!=~kLZ-PWgVCA z*ExU!(u{NKoI?z=Vi!*xh1{7df=Hj1$%h+^KNxZ{0JTnDPv*kjg6Nb5$Q4i@{nB*C zbXKBEYTyOm7R+H4d&nR-HW}B&QtBFik1*-GQz6 zig3H`G&J=+2r5hHxrt^wL%R|K`=tE38K`hnN>;67_8&r+fA>EbhYg!kDTiXr@tsf? zKf$*A(v~A;TPfuobViHhsSE0mzk|U z7+RJM8<8n`zLAk@A>31_nDp-_{YNfj2O6SW8ILk6fBamnYdcS3g}Qp$KNDWUGIR^lJNip_ z%FD{ITf?XA43&Zz^Rb|1@I~{LV=_Y`Vw3Yr=TQJfNg-Hd})!-~F}ya-mjS8F$B z-L|)aK4eMJ38m)t^rm)XO7#s#fW##qUDrzkTQaSqKH8oO7ZDAi98ZxH;e(%zMmNj~ zC064*@f8y~9BFv==^xzgZ~q1<8kVLL$g=)3G7pe@9Pxng&PD${3<)qpwzv`i?4%%) zA)qLh;-@dpnK|kqQTViT*!(6oZThWLe?(7GLe}58EA0`9F&5P+Mdz^#m8Kh-#>^62 z?Zy`lRz%8LIPYY})k->p8$y_91{&iYO_0(rJfj4;`vUdreb~#fE`NRT^uHW8s_g)s z>Y0*HtYc~8LrnX^c0aDnw|nctO%!8KWpQy`wB(TxgeISFELbNbP!dWqn~`~X*;X)QTVMu(OkInZFIB1b_odvTI}Dx zgK+BL15Np9U1%yt-s>KA`h^WDp~vCq8~bm*NE z&iWvhAzw==dVNPSz*uQJt>0IX#Kz+X0;P8geV{g8{zuZ_B<6_n;vX9yZAquz%iIyB z67Tbfp~pU*H3Q|2(Qli#qZ&Amlzq6b=)bbGt)?bNzus+GI`t;vGjBmx+`q5wur;Ef zyU^ydu4fHlP{n$OOK=}rWDTX&H)px``=*UY4pHWZ_xI#)hCxE0DvCawL28x|w;K7r z>2#!-awJPHGty)PER`F<&#WtM3%RBJum02N{q}|{9()@rYvHyd^B-DH`ygtIRBy2^ z3m2}@Mno<^`_ATomyFo%Wpj6zIRpk%o#1YQLy)g&mpnLBINJ{C&u#W3+K?A&DMtl1 z;XUl*wA>sSV-Qrg^v^9s zyZ1FhI(^8Qq+*d6Wzf7+d*WqbEnojd=WfP7kUwlRW$oiD$ zekjEUcA%+$uQ_dR?~hg}dE^m}Kg~M#f9Cq+?RB>H?Rur{v1>elrp<)= zVjBS{oAlyB8bx%Li_O>i=POJF?=-qx2& zUa>a>az1Wa7Ql0~`iC0ecRFyl!6ZH2gj<_aRsS-cWqP)XVQ1rFi$$B96 ztHHaaLyHd?)Y7M`ZaK1Jc_-;#i55El^v{ii^(zBmrqMC_fNH#5t*9rGca4~`mg6c$ zDK`Z@dSH-6qcNC>Vv@&F9Yc+KNvH@PmgzHxpO^@E{|6w8d|Drv*sHM-##2Ml5ETZ# zGWlG7Ucw5N`{Hqu;@7FjP?_OILixPq23N0{EvKLx@rO1I;>IqWs2C%ap zbdihZ8TR6Q=?+SNNb)=wXZ+i{PYCavj&d zgPmwQ!A7tu%=Oe&HoL=4^WNGSM8{zKiNhw%;tbVLlWX+RYY0)}2TrtL9pvJ-xIgW= zPZacD)BANP##QRioBRt7#?&hL z3z>k-P?`mXi`~5*9D{Sy^_E2y;s?~hb$6Z4e*GOUU!!R-*5OdNXHE#D{97cSM8-<_ z!GvY!wJ!9VK%RGuJ%#$7p7GU;u4UbmDdYHLZ2M=gN>MHmG zt@Tgz`=R7Z{87P*=Qp+Fy)C5uWBMaFDZ$kcw7&D;H$ANLpVTxIjz}AXUC<7cYeNjM z+^nY&sPAew%QyEv-TRQF6Qpu!Q2HpVW?Pf5E7;;n;}_@NiXNDHp#$+qAa0u{fqcC; zu~Y6xtdp-s z<#JVDkYN~bZEvJ_=s%_#9E{4kdfCh_yN-vlbTgU8acR9LKaYlew6$^`XlF$3Y3RG- zv<4zhCEI@cRT`2x{08Su=C8@Z#bie-0`c2(%&?Z1-EjSg*7)maCBKKkA}p1~_O07x zn<1wDUMjttrPxj3+g`@3Z7{?K-rEZn|dD3}?^FFMOlkRc1eY<)@56(#ZA~ z$H~koW?&g2)*+*>n=@F?K)jaf^tz91MB#dvYIjD{V8+^MCcI!=A8ClV@|KHq{#=(-nNu zfXz$!f-5md2=@hk>{L!?ED*sTL5NI-rT~|&P0Q9lb*I&;^F7zQ++fb@!*Yxqn4IJY zb*I-1jp1Av1WmfVILVlHH|h3?bugUoR>YIo=Makf9JFQ23|HCmZ7bj7W-3KCy7ckY zop)Ts3Bovf(`m=$Zqd_t#o6CH6=Rww)8VkwuCY9Vk@dC%v36XEJhC2zh#=gs+x6F0 zKCl=O%%<=^$i*T(#ZLZo%Sy?)#meR%cz#a8&;m1Y0Unhg|D32wI&Rac+mIVE3^RA@ zGczsw+X+J0AsvDy8kN}o{-VGU|{#{l~y0+Y76csV>}L{{}Qq3XUK^BkOP2 zY%p-R=Q0ww`N%SfX0IQL+ShVose|nL0%jR_6iOr?8(e9tj35h*mdRC{jfM3h+tPh1 za1GUt*HZ8Xk9*jpj_V!0f|l`O-(}{0j5J*)(Ix|6s-^tI8f%U1o&)IP4xajzk6wCx za1}qcYV(^YCRvf~<`c+2kb?Y2B8R3|ZN_l}{Arq5`lbB+VNP;}=&m8vb~zVRt;0?) z!GJO#j&QX{hA+NDP8zk9v2X-9U+t`v(|93sF@_8o$FD0Y?KKvH_BZ+K7l*H|<=o=G z$rs4h%hw&!opi(W7(vN7ff9lOozy*MXDo<-1B%YD80oyWhHvspibW-rJuzOMBhd`@ zKma*=xS`%E!?u9c*v189^s&K$^gfm@o2+OD%Xy1@*V&t|3?Q>XC-=(Ge)0xDVg0FG zN(D{rQgJ+jnTj&DKL&L|7SsE%msHUe%~y5i5~;NxHnYqdh{?ZPHg<`dKriZUPTj=g zSo@kd+w}pCw6(R};4%*wHce?%iY~b8wc2bD&5T0Ui8rR0%jC%YN*p7 z5I+f|eD>-bR_a&cTn zTg^e9!>`r~!$XfJo;t?x0<4?;?)_4?p5Vgert5h~S0WgfEvEl~S)se^+-a{}W1h@_ z;2yF8W*X(RjQP9~!vBD)Hjqxlxf7`6Z`p=MMb%8xv~`rxqN|DXYvTg1++m4zS$oK^ zj9=ht4f5z3<_|u>aBg3Ta-h^H{oy(E1P-GGy3TxBAxo&6yLgo?+a&)Wn@c{OZ2HoN zH5gQi5jt}r#LRiuQ3ldR=}#a2uF$x-H{~C+bz8VqrEm}kGou49{ug?84@v z2dR(Y);C{Q1F(W`Uh=*DbWfg{$)&~9q9MYz*aLVbdPRCn(-u!JmxcP0T z4KA-NCu$8KfXK+5L;>GIk;D$2iP5rzPi#=*6R2AuL*pJ75RLpL5Nk5Nn z=)N(T_SgFLLK5w?joZVgGRMJuKYi&MN~)h;H?-#xxA%*VLL9M&F;JX7gD$;7aqy%V zXD#KV6eZw=(H!B$6|d9kutbs*TA7O-{Oy90Y|(!=vsXf~&4eyiP_DIxdv6=|tsq=W zllYApANFr}gC~}^9f(!-PE=c}H>=8#;FUs->oz+iqU^?(LZj;j-;p3LJ*wTkk>_gcb&TL`@$-5th z6)d*1J|7gg^e!KAutIIJ*Py6L=xFJ2{=Oz$4Hwd^JW^R? zi<{D_c*iaN=CLDOmFdHH+7lcXIjVastHncGt{H71UFq+1=^!aoIt#S-l0ra$;%N$( za&UZn(=S4x@A4gXyGxlq=A-#Z|2*O#4E&otBcgL4?(Bu?A#AHQJ<2vZ4Y!X>(GUS3 zKmXFNF9T%yBNGNmo!xW%KX%1rMIAa1)g(PTX{8Ew-v@oBR zobz{vx(mT)v%L$nxHy#G&IN^1(88x!@DckDs6Vw%7{a<^!6J4J5QdUlF?`~G+O;W zAaJ>#w9R_KC(flfTUWN);0~7TMkkC&(|=w8{#-Z`d>L1LmdRCNAS7XkY}2P|BaGp| z!I~3>Xkn@TN?|rWkr_o>1!tI!oG~vBkxt~JB8_%pEAf*Z$JF7+Bs?u~kdjM}n9RhV zaJ(0cO_kp8-OPo<^@Uv+MR}AooXjS?)yjuS^lhH!K>&uS>LrEg#NC}z3m_R?JL|7q z&-hI;Q+TjT+KGiamQK~@E(s;9B774zZ%th~@irS=sQs(a1^5`dAJIFq8q`YZA?fvh!QW=k<-rBe~-d$YHc#QxF4L+P1MgYB2?|TJrK2kEFW~yu{iFv zY1pc@{gk0~?@<5?ac5RA(2y+4;f;Hxg@Oc$Is1Duo@wTK_kr%msVl*`)BKCs*~~$- z0tkoiCwRLc=Xn16azNT~A*4{dj!KuSbvX27#_Kc=B!8n8gztjb#L}59n~=V#v$odh zbRhGWwzA01W42LRy%lF+M*r=|)I!5%k>Y}7E6D<>a7PDXFxpBUakbV)8>^+|$YvGZ z4P-|IyI}ieC=@55jRfZ}Jc=oEyf{=naCjWG4S1{*xVzXSllq;8QQp=HyoSmh(vD+1 zh$LnVlYSDI-mDeDcxFGH96=X$7+Fb$Nd8Fl3DBgmoK2PXI3wEs0ubB^?SViRm!<;D zqjo%S4hW&?kr%gWUZ!*y(PnwA*{5t*lL)BRico-{+^!}j?_vJF95?iw#ld)^<7PUL z4z9x(NP~~@gw2>kO1w#{tzD3^*SVVs>5pvO=ubJwepMaDZ|RoN6lAEv)06#fHia@1 zA{NMWIx)UB(ouC%#&6@RN}J>%CXcwHdGb4sTbas{fP=T?59^d%6Tv4O-{hlynovr9 zVZt#AUu1?#&nNWvz(tdTkS37J{mEJy>P$;l<0n}>D$C^$vRrWFYY|XMEZGbGNw+m8^U;Rtk?DIS0Q0 z7rRr7?F@M|T4-_Ci}!{au{HQ*GJ1<_-u5+GLIct=Eg)kkVBlbZe}QmvD)fL3(Fn55 zTU$Tk?ai{k)$3nvWHKcCH&0+c|CL!5u`^np=&2rfp zl7VfZhvSU2qmyxp)+@8NLb8;8nDgs#xIuJ~^I$MMm!w<1p2Ev)xJ8XS@$|Nbmw4k} zd{}Zxq-uH1b?-g=zm?JnQwK4-3?_Xb!ySBug)_Os4csiLBkl1w(ynxJ}dw{Pxs{`<%W`MRDllI8?i7&994-5H)nzG%l0G5KB6`#Kl1 z%K#jIkjQC|(#jmv@X+>jF4PD#4`;OO;Ro&8h>(oz)6RF8(JsJSN!;Qf1p zger;O`Xf?$&xI>pzT&(s=?Bx-(*Jbzn%!pGGqvYQCTbDnW1{r!fhtz%};>m*Evi4IU5-~iR?^!XR|)^P_q7w?#o z{#5FYTk>TiFakFKi(M(kyO)YEopU}Wi)goQZP?|Dcmrk9pBoe8=Nlbo_6}FRVykt+ zkqXGP{MvL&$ja@Pzh_f->X@VD=ZaOU_>`IBG3-bWRAqeRN}4JTy3$a+GR?yZbiJz;5BKpIpc><{;Ff`X z(EA>kDw*ZDsv#jK;(CtM@>^EBXsgV$RV*fz%ylWr52!J z0SIDwo21koPrmaw*6jNm1GwIRZ@91`(#zF)?2WPeY1}QGD-N$DK)Y7(5rxQI3B6+m zu&k3>mu7>PUkim`)INo|o{GR39p8K_$W#nkk}({=;Lek=2& z(I0Q5BLc}Vc(0MYerqsuT`YDqbbU~&POww0`24Hn%PPE{+ThbvOUZu9-;|`R^IB(m zD1pbVd9sZ7sFVV`&usRhopnkZ*F|8~jMr2UE=4cSOC#quqhou+@il%Dj9z(m^KCqr zvcf3^Zb=t1mY$(9d-pVA*u71=Z*7hRL!*tB{?P$D9`lD}aN+q=*VqkWhEgf>gxJ@J z#2f=Iu_~zgq!lR6hTI(J#oo%d)Yk@n-;4D8Wm8Aw4<#dNS5hQB*X=+qWxkNVA9x$% zwp%h>mxtjsUviJ;oD29?ur1eE?~>VmgN1D7Nqw-4$t=z5fwK8!|XU)QgwxGiYwdgX+J6B78t-3VpTu zh89P{>L!q?p5a^!aO~e@FjZbU$Y~Y4rHs63KbCJoTbMLZWN2LX2ffjX4@#?V#z$#P z;ZI3TCdJ`*mA;8XL}xaq5^xrnrojz6q;Y!ya%P3!N`!Sdlg}KFBvxHq=SuNuvyN|F z=MQh}xzl*6mD2A33~N@pcj*xHTo)3;-W<+$x13hFUv_?nR>&_I3=^ zQbJ;&xwiV9ogRKddgb@d4G^@FA|NG;XRv(xsgGl-InoI>m<~go-yv<`fHK$HaZ&3Z zvxqh_XG>b<5)HQfA)|HNUb63`LD%t@7@8#gp5eR06oo=hp68d#nS#ifcAU7yftfj1 z)6Qu1v2CYWXWb6@5$Ry^C=2VH;ioX562AYNRKLrRVs{iqjR($6n^f3au2(ljg}sI4 z1DUIn762}2tF}xTy(i zriG#G5;$*GJooS^IC*VG87$fDIOzv!)BPb4lR!2DWaI{p_>c^^d03t|?616e!ysD| zWMfd84dJeBf}J?GbjkNyh9O!P*zLYu_mVlQe;GS&-Gp&mJN7Uh2l$|i8T@%s z`nHMzxOc_!up;>Z=N;5s`7bl%r@?gx>Efwqm{xf}mpS(d?Xg3twcs07N$*=V&7oCN z9ZF#DMw#~?p8XC z6-uY>GL2n6>ZU3&D~F)c4Umj1!c0vhfLB#}*nq-flQ*N@zmbQ-g`HjZ3_5gs;OwRN znt#`8yV-hg%?sAQ-c)zAJ0^@#T+Y_Y(3c4@%m&rrv%9%G#b;Sr?{zmQ!cD!0k>fhO zUi)7=TfQ5F0SsqCIG%ov5*(7(Nxl-th3ZV*UDLGUO3Nm`O{W9Vej=)??1d>xa}x8% z^_$dV#z@-e95Ohn3>tN!7)`*w%#1klT94DRRXxod`Smkk|G zz&kaQBxP_<@~W*e;${WY@pH`QAZonci{CAlpJ(`aP+F6bh;d3+nM5eGLDA63#ry*2 zD2_lW8J))~R&gxl^z3P?hqR?;8H$UbXz9URg37&$3ao^6nIM}mvf{{FM5U1AH#T=h z3Qj(7v5q_sk(MAcTlxL{Pp1Oo`7-+7^xFd%aa|ZCQz*l}GLmI$U9i8;_V)?MP3Nr_ zxijsu$cc22n~=f~1sg`@b$c2iBAj8**rb{J%ud7WF@@+p z;ByLqxim(x==cgmuS93h~^&Us?3Bi3Z!{Z1AoHRSGu>BK|T(;7Bg8u8({3z5}|8@Sf0~$LNMu#gWJmE{R zX8vISH_5RYt5!dwB8ZClP=KL<5 zO1|UBBB59?=UsA@dF!~Q6`0x^T-7Shyo5k=6K@-dU+}S{YRYa z4cop8ex_@nfD~d9hvebD*O=LlGXDnWXu(}^7x%$R`%Kz|9uq&reBMDSDfa8^({ve>?mioum_fo>K8Cul58-$!DNlTqgn_g+5@owgAP;-@r(5(}rH1S+& zPOz1wFW*Yfg;-R=B&-sMPFFY=_N)F&LAr)4ex7-BMxd|(0)wh+55-ChQNKXYme}_t zv}}tbLn+5$tIk}GiP}Jlg(Dmky@>0XQ5>^Xal_Btx|bt!mk7c6RDkCDsJN_r>Gm)+ zoX>8T*|Q2q0${Df#00<5ru{DbI|KX}LiuVVce$9b=EqAd!{yXp5AL(XdRLU*i1xqZ z&zBf+_AD1|ruAfCV(}7LS*5h-!v<00>Ths%;v!~9$RcEbU{c5w!D9#mu|W`^LLl-*r*)fT`{M^743mPH<a=pp5oD3w@*=|h ziF;O}{ruU&1NL~99foq}k?(OW^kczNza`$HK@MQzUL9Dq{{?5;dmK~Iqb;Yu@;Qms%S|LBTBW9)WD`j)pUu=xzBm38y zp*X!U{9PONnxjs2M^M!7Ms0zp|BY9F?BasLOKs=45TL!Q*R@NqR^3yJK<&SP1v6*?+xL zq%SI?5P@fAT@5>VLOZh`YW-ctBOZvoeg~jawtihLC2OV64Ob>l{_TaI0c}mjTYm0! zTL&{$g65xsH))NP`+gI_M?t!Wb;>L7#(sLk(Jw8Bc2j@o1R3lcweIoqII+PK31o7N zkN)&fj#iTfpmou1SLh0DG(-`+rTS(Fss~C3s_-ZalKn}1bfvew=j;8v@zIf;x_E}e zu87g1a}cb%k$V{;HTC6|P~mzly?fq#&@w0M;-RE;{D{9i=pSmQTnknT{coXCpX{^w zl$~`^+ppl4{@snVG#r3k%H4w`ASt@9XF2_R2JRv!ea{eg&?Zn&hTF4lOfG_ZT;yTh z+=JgT`N5<@q)>IcetzYDpZ=Q4R*{xKE@P?Nis!dm;9i%)(M z3s{}Cf<2zd;10^na}I~O0UM?Bp&0s>+Cwt@VC6aLeNXNo0l0E*R7a*hO7lxYShwn( zN$2N54$7jap9I93(9hdA08$ib*if`JNvBuqAmL$fs8%t}h7L0>OE=@N!HZ!I>HPE{ zPYk`f8oI7x;LZj2{+6w>v?6~kO-4VEZYALOJ=CUna9;&5SYPQlnpcD zN)`!(@-2N|_n0-qLDZOvFg=$yxlEn(C=e6xh}6m`66ri){y*H+dUIaf?y0rXdgg*u za-Rkf?Q+cv*ZrSO`0;1Bjo?@=>|={yw?Iv`Eu_PI_cxUDhCjdR=4!o@n8 z!Or)tU$dFZPiqQ}U`@{BI5rxU7?7aDaJHfLnnDh5@!iWBCbvjgrcUif(|7wiefCUs zvkt%6&IPP7o{(#ZEMChpbBswn=OyEM)}?cXvqarrbaDl=0&vEQFnE718TwWK5YNcm ztGs_~hogVEVP3uKuWe7BB)eHY7clmq9B*?5jo>_`XU8(f+ZXKoc*=iF!IS(aBqeoW zhl%4K-A_a7Q-zRjT-;Owi2^?02d>ymnl>B!`$Treu0$6W)+BvC@_VHM>9t)&zl_rf zXY2h2h$YZ@Q>Z)IN3}Q+RRcGjG1H`a`XX=ehvo8T>q(uZmW%Y1@Az~q2D>I-6CdK3 zGy%MqRK1WE@aIx+TdJY7 z5U{~IlPsm4-a#mbg&c*YKnH-Fg6Bz<9)D9doXUN^C5(a5*U&j2Meg2aE&Vd#f8eU* zql-x2qR)RzwFgh=)ZPO!q+=jjbnwi6^mWxq)ILU*}h~Sxxc*yL}FOu4`Nf9u0;Xm; z0YB5h&vs?vc#x&)4UNY>69li*!rRMJb39-xuSV|`Na0J^=+|I%1|FNq+n3G(UI;dH z)VZ&Xznx+xZ@+&?D`C72a{WB^N+;AWymin~yK$hk$GH5LJx{m^P}iGb4!P_~A-k9l z%^wA*8V`5tQ}#!xNZvvkb_Mz6JWn$b@i{B{Dz)fQE-hIDaIneR#V(b=KhJhfuH)aw zA4!2{NG``dpB|;9EVmcUUtQP&3MzDSuHGb@%Wa`dBydq@qKO5(A;IOJ{;M_#_4 z*0B)82F6BnlrOY>Qxe*PSY-I%EoRzk$4hMvN#DQ`cSZ|rNY6|qRwu4V+hM|--=T!43#FgAPRRUkdUSf?q@AyECUvxf;egER(tgB!dNpCj z1oB_vF9o(%`mTGDe@sB0k!x5y?5mS&X;DpI9Ku32&_PGI#S9U+eXu5lMY}Q{~%jeZd_d{JebrK;U`6}OXUz+~c z%sB0f<`l@Zfr!QX>XiDA^ruJx?vegF#_`qapA~8TF?QDKIHgT_&mJ z_FOvj8x1X{l>k?yG5a3IlZ2FEN$X8F!3-mcb@tW+n$ZQd!mt7@yzDK^S$CqBt%MIeHFrE8{ zDV*BA(#6m_LZim)dg#$JB8rc?I7KSq+6vzyE#ro0)^dP1K~edVTU`My*!Zqk?28&0#xB17pl zo_kgMz-RK?y&-sV759uHcZ_`=+j18f==tCxdHyE-k@O6eOFCpH;)fw=8^jaSE4-<)Dp@U~# zsuq&&gzH~RufN`d8n3yq)fFQ#z+#}#EhL6t&9V(r05Q9F4p!y^O5+_`y;lx-I3^)C;GyXfRyG})jFoE|dsj`ROT+@HVIb_b~gEqOwo zZ5&wq4%!wT$;3N^tZ0M$I(Iz<@QHjm210;*JFc77Z`+6eu>zlh3X-%MAui)fzc3;} zz)nQq(tSg1)|*N8arRQm+z8W*6CJ_zZDH*{SOs%nrO21T1AJWnS&UT|t*q?-{95mb zKq=;a|GIcfIxo@gV8Q+*^C`9rxuRqiUKVfTN9fPAHiv(p278LqO4=d=Ew(;H(Wnwtp6W-4VwI-o=J>~Op zi{KtVR`Tme2`&D}L&;Mu@OUNk153Gd-u{3SUc$Vv4EMlm#`g>5R(Hs+SDO$L@J#NW zX!b$2x8XnVD`eUAr2V^QUaBqX(Z$W1P03m}fMPySqd*Y!Xhj zNm{;vKZOGI{bxTNC0EeHKX+mh0RTe6WKWa)Vlpvyt--93vu}LtDt|745OS6#sFmqP zW2pUFv|vBppYd@XJ**G-9O=hY_kz5M3}V`?vnO6vz~)BPRSyIUJ#WN_6jM* zqv8=_cz%G6AnyH2z4X>O$1J+MaRnAV5p7&^4q-r+ypy#&nprP9SpJaH4eqI#gfV?w zi|AOt7K=$7=IsNBR*TNA)xI;U;7BBRXeHfp#>oo1hsWEAaVkb#bZom6^H>%^d1oRO z6-4ZObRBx-w|1Q|MlweJ#Sl=m-7``vT^VufPzQII;;oBZFhmD$ka4~rFH(BG<%)GK zwmvelXaCxmhbbOZK8msk^5>qp-~bls2zs#%GYv`9RsD(`uH&hT&QcOBMFv}2jq1{l z|CIcj*6z5u5}%&sK#}k)BlA25HpeVVd+(3@k*New9lV8Rwk}J-$8>}Yyjw(2AXltB z5VsG?=DUiGBL+y3*U+GBWSe1*` z`!65B1YerC^BH7@xN`+*S)`wEx_;qhkh0fW{;Y`9)`8HMytg>g2Q%7r$JyFJ!|V$e ztY#?bRkA0Htc%@raTk(CJ0izh0mS_C7PIy8`>M{e6|_s@v>K~7`Yg$gG6%}2h=vCf zLJ$*rOBb-t+W|RF52Vkfs4vyv_=Omy8E{c}?=+-CD)*GS0)L>dMS(0>;hAt#gjW$99;7dy9do3`W3*ST7c zvJP{v8R+Fi_e|+M?TUE9@tLO5l1M4kYKkKpWGN%q-uUO*p9%2R(6dOHlP+c0EZ5zC z9kW>}BWVOA#I`>#b5H--lijVh*N%Pn%{BIV?{f((CmBhSs(m_}okX4Vb2?l`*ZHj4 zZClxV24bM;O_$NX%z#kiz0mdO`%;Qgi}R#I=O65Lbf<mkg9$#ZvcJ{S5?v^9-6>FpdWI8?ATAvl6WUZDhF)8VQ6Q(tNTE4E4*{crRf+bzJ zzr|GPXRkW5_?p8ARvm4B1QLzX#$a;xy7~MLAzO5lp@`CUKGxR4H2t{@;}b)a^hdUR zvb$lok$?8mjSUz@o(L$_5X3Qo~Flr z(w|36d9j@QRRP&r-~9t4)}Y zrSn|BS?zh}Zf(oOd=$Si%uU+3Q){=|FlWj~D(@g2`OCA7x}l@OOjRz^j!jBowp8B} zup9eVkpF9v>BHpCK1+*lwixEZLe^R-ToVlOZ z_Q$=Hs?VR;^lrQ&qg(4us_@c!ZF)n9fmvC}!vAC;MnLco(8VMA{k9Y{Y9x6@w3ekX zC=f5>n^N@42i%IVv>|5ow&Z!2uHEsu%-nkqExe_pcQKMMP2Ao=h%ky8Ed0p*t}vo+ zZ#f1ZkiU^z9m$S#$4e9WIa_NdUiv(eJ`+uRGs$FM?X%Xh;xX6%l3y>wlYXHei!K~B zUSOCkZZ$=!JSY=Fb>O`IVRncX#5%&XCco{;Z~RNy{Yt*Wn5bnN2$cI=ncxHUp;VkO zgOjm4dEuUi4Z<%`f1J)rl5S4#pZ0N%5el{Vo4-7u+W7i7BgJR}Hzet&{Fd@GCoA)a^gcMt z6Q0X(yn?y437;M$lTCQHP=+WJa*ft<-#&b|=g|ANC@FC`9ER5VLsK;e+H_2v=>(7(=< z;MbAdo73?-K$fsD?Hg{9fJ7ztOF^hs_+7^Cw6n3-u0j<@bl!Pb${rl$%ZCYg=eOqE zHy#0=!XV#YbdqBlk+0ew%}P62N;fba09G=LE&gK?gayprVH8c``RDOlc;BxTNKh#X zZ;Z##_?>nz;xs?aoC?stuj3Gwr4T7jgWrO`FHF-uH)EE}%$g}_PqC3n4lqCS?x}Tm zp}AZGrJIf`$~hBS3|!bkLvaWvU9|jj+y`z7N*Rn^>{4dqYy$ zerP-eDq`3!S;9}9anoPUXm9+<)YwDsqRl77y^;1$W|ZNh7YK2Kc1F@xk`u$Erj4%F zsor z9kh+>_zR`ek2H-Tc=<+oyy7dH4obe)@5o?=c&+;%8u( zxofSsOAR5L@dGv|Kh`PQPVVj3KWLhryt(4Q{RmykL|Tg3-=Xb%ce!a&?ZnaO*{U!w zt>vfRIt+aW#A+X7_eni)|8QR0#08!qF`p@w!69pDx7QJBS2DujB?3$~qcKDl;ccy-rsCFgf;iDFQwJ(2YY3Uvq~5AD1yUk=>1 zvuw7V#|>x#wd2lx$yV&oaJ9h#j7|TZg7s|rBQ-Oeqq$+6}nmkVoMz}EaGC9F| zDR4y#N{tMAA|94klMOMvd%p>_>%1JTGxTJk zP%0ex%L%tI9e)Q|;vKxSvmiPsqt~td{90vo27ELMovAe6QQ+)U<@R6mk$H(5=uuXDII6QQvA-F(w`soc&#n*gPa1$_YvOXleh zN}6oVd%7jA$?ss|D(ukP?BjuuDY{x`)@fT93N6ph+7OYNd{#*Fgwx?9CDKlI!4MI` z0xdYSuGZ-zPYE(1cXqKNy^j-l&X6fj(x3jxgdYVo7)KI!wnad@L9`(C0Wz6tCSq2| zaL5Tml>1obnXs1~>1v$_J@lHM8vM?$@V9@Lfz?R1DeDb9M|oZO!F|w+?)5}!>1B|S#3XIls^k--?peIFq+3W%o)`Nyby`0H)u|jF%~7xmqrUk8kMQV{lJ+@Ga|n#B}7>oQ7b{ z_ZC9ZZZhf{R_GkcfV$n@k6~s??esdX$4o9V*s>a*p&Dn>j3Ah#F`T*TQfg;~tx*(86KmE9uOmf6nRV@TzYIl%ZXXf^mL>=5qm;x*~9H!v<4T6tYZeU%)_e|*wX z;x_5Ufc`>+N>)kn20p4lDkk))1cBy5#}pi zJBa7PnB{IR@eKd@+er_lsGEz_w%m9jlc?J~r+6*gY{ml76mtN`J&b}6Ll4O4M(N?h z7tyta@-OHJ144C**s)Q*k=hDF`WHPu!bW5H9l6Al|8C?mF79TXO|HZ#vm+o@>W;ExqIb$4I@_e>!2jj|Mbim5=^ii4LCLg-x8fW5QCQ{H}8- zbD*5swDrhAM6cP5{vz?KwXX~ud?UM)cTfi6SZfJ+SUdu@l~SF~c%~~Dpab0YDrtHW zm9zqG4_EEICx3Usk-!v%yXX*lb5Ho*f93^v=5oSbV2V1yd+Rxn6*Ty=KV@dj@jt|g zmz_w)7PcVnFb!Z&Y7Vj=Bd}JOE77KlC$x$fJ6#8To*dN*KBXz{=~ir z`*3H+b;*fw1TrQhc3;ViuW@%0b(j{v5agLjen)ZV;CFDsj5VD%bzPHXHUm$VF6rMu zXix%TPOlXrgbe9Gc~4!HG1dl`Z(BOf3NSYL6228RKDCaSVAr+y9wOJs7tM1V`K#EC zc{Ax{ly1dGkr1Y8OX%7S0!&luqw$U^bm%>ou=GUmD;sCpHlF)AW2({bW-`EfA})S>pTu{NDf4viv~!RU{!IUo&o9L+Z=f1#xW6}Xb9a5~pF!2E*3 zOhhr3?Rdn7{UEvX*C_Eqi&zJH3%o%Wz$#$~2FS|U^j z(t~ZoKZps2dkO0rba1UQLFB6M6{NR08_TzqD_CLY!|*L>j+fDd!wC0yjvG|~T2lGI z@uP>{dzsiI=OCt_MHgS#;XF2ABCAnyK$0?0M}6A)bT1v?^bqM{(s~y-#^-f5F!jD8 z5gz>OQspI-mD%e>raYr!zHlw+WwS}yWHM_46p>T{e11tU7u5thqKyGnsrpnR9fF zbrtC^WXZpb7X>GH>;^`0nB-0u_hKE$hxly#`Ra>i0v}g?N%L~aH-T5X^}>OBzq(9^OE1r-!^Wk1RG829>mM;G6f?|-h}?~haV$S5UBY3D7a zRHE?=ZSA2XA)>vdlB~=~NU~RUcJ{d4&iTha_sjQtd;ij#m*?wwUDvtJe4NMWkv?zz zp6Wo-BM22!Ce>R|LPPcuv|qo4qSZs|K5+@V4X>l&uCG6;VAgKO<1_)5j%Ty`H)CkS zZa`3QPV#)-141!<r~NfDZN zI?X6goY5*zgw;SEf8KFFhSU`yhg)ynJYcdfs{_sSsRn6rr&)~oayxL+D$tJr9g(%B zZ7+=rGN?5;U|^WQw7{XqjWAy9*T4`hS_@$TT_?rb`|+wFsnCYKVO~-g#7348D^`D8 z33nd(q-2)!0|-ZEmMhN8KN#9PNX`vjCH-x0ZNK~_3eYEiGrav6ig3|nM6OH65ncx7 zBu}yp#T&8bFodo`^%>+%D_b#+NR;ZFAE8781Uh@KOJ!O%BWShLcGY7k4(A;gzqnot zfqT^OYv%$ZbhWd>XpZJtqcTLcYi6VLQ8_Xb+OzLySF$a{_{#T=tvV@P>~SY9XtQ+g zmnM_O)263NferWP28z3ui`4znOnK=1a8nCRnV@O!y%cQ(U4YT5*=6Rgj3f8ok7lkR zn@OEjJ64jvUcyCeAw6J%$RVku6;sF8;MHSqv5pBzm*Pl9qkV=@D`hCu`8AAOmWod< zJ06=kw^i3%+<+P4okLD@;jkM{aH0IYgr5f&qldLkf0?m6Ee5}I4Sd&0F7R)3*0ilG zT=-}TL!UNFX%2g6_=g5eDZCoBi=tVBC({9ps1NFRsuBv`8B$Ao>kRFsk={0ZPrb`F zvurN56m8;S&%gfSU>)@4U`7*_{d`Wxtp81^TE)fxCJRS&fn-BCW8>cXYYA38atA8t z`!yEa$c8Yl8^a4yK}_)SF9Wqp5inEd-nVVtb_69TO=;3vdOnTAzKxR@JN8+@@#@G29j2GI zgloqrZ+zNm9rfTPtEKJ*lOv?UU|?!kT<<-A9~(ahJ(YqR$iz1pyD38{VroQ_`-QfgmP?4 z&oXfyE$KJcJ=kcbn-4tW22tm=qw}##Mm#p@1T-yZ49t6zf`Za6-y2#G{LVz*6pESG z1@WKdacQ?yXYJx>#*m{Q=k`hkd(F~lY`sdc1_8RHJ;AX;Up41di(Ga~-#T}^_b#j+ zWMy!!BTMDCLl|3$TQVyD_HqqspQ6gflAnEK*bwqIf{z%D}5SEA@K=R;S>EPx8=q zKlx+K-Lb$|jtK|3N;xET$B3yZn*J8;ZVemA?3A=$%{)`|xAl z`Hwv%hFJjg?i}5Etx8Gy^nCg1qXizK5pjXF|CNxjb7TOab>CHIvvGGpCM2W@ji!Hj zSk)HJvX!%$;n07@rxHFz+grY#Xvy5|TOF;P=RUkaKew~mkxSb6+MQW`mR80fOCv6q zoKYwZdtot4foS|ao_9rpQAOF$uZQm^v^=Ebsm3iHb_e^)B1_W?U zX#lsW!-iR76b43FW}$(~z73Pk9M0{Jhjknz;I;L)900?0JeQ?SrXM3mYg`U`3N+K2 zze2i>u{_iTJ(b8uJIQWl&%cThj7S5>xKD^A5&)wv`8LF(a?N?+)vy&`kFWN9h`nr* z$&-9DWXB6}&&zpyV)oIv6;e zO1mNb(8E37AE^@;H<<$?48ce0{yk+`lOF+(ODp(}S#q4)dC-gq)Ru?Kc%lGZ*WltJW~hlGrxiWIx@6jm-CMkZD=I3c%;3bL@Wgv@odrR8zYVx#T z9hPHFxIluj;OA%^x+vv-=Z&>IBV!pEn7Mps0_CDF^5cCb+mf%HQny8F4AGVrhv*O2 zz(jx_&rYYfns|dl1rzIk+k@YZxn-R>0AY~Os6d{AiWU3LR0@P}@QZOs6E5jMCPjVT zx%a|}h>NChMEc;n4jg(3q}u#@L_6+a7!euywd#rfd1ibbslDtWEk{oBb&cqf?&E&U z)RF>%rYf0TNo6hlY~(eHqG;?9Qsw)6Pp_i6>Zi{6Xx(L`eI!d8f;{Qcj#o1rZW{OY z4?_EMGpeaWhHZe;;6g6B+=-_58Pzm?EA0axmf{`q(@q&Qp6#{nzSr`I^hTt5ME|Zf z3OeeM7T%UzlP719J+w02H}s{KF@@Dzv;a(F4xjCDxvq4-=%8&ly^k_z$3%pR*e95E zSfz9}RI85@x=~=3vJ-BacZ&u$5SjEXhul%H+E-p19Z$H&%?yLoJ?Ma~17Cr|6Y$c9 zLtT99PnrKbukbxv>M~Ky>{9>L>+nEQV70QmWiICi7#i(DCMWT!N8j>QEx3KS7)T>J zGD3#qh-ts=z#;&6db*mP6exRBq&&_?>zP&J7xVs=#Ulzy<@LPBC+%Lo4-91N(7a%K z5=nC#5(%teGDNSWC+(wBXq~!|8iiu4HFUML+UzSs?>N+sC&5Q~&LC^_xrJWzm!Hd|WR zuxcVj`nzxhWKJu1a*%K{(X1WZw*l?D>TKQKC*N*jZVcZZgYbUu<^)^aKsL>y(rvqPX*^btST6Rf?snG0@KOf0qmx7;$ZtRbYOV+3iHq@+4nFBR=;3O?VK(f7!8Md|Ee{jiFWdpae@@#Ru`?7p zNk?q4l?o|l3i)k5f{X8@LOV+IGs6+-`HDO&fFHLjY(pz47E_?2pcV;1ma9 z*q`L}8`vL`XuKO}l2dzRDjNZAIS`q~X3*Z6g$0FY#BCX-JjDEAlV&VEcsj;0c7 zd}myhmOq0=(1c_RgOQFL_bV>ezHO12N93QQbuUu?B}o1uzIP1Yk+P}tYqV>L4nyrP zzqpn0E>W3XI_8%gBd!pEM|#VG6o10IZTRv+^Bicq_ljH8Bb&fR(pQmt}_Sr#IWy0(sE2H z5$RtDWj~86dyz!z&SpN@dT@(&BNlyT0%X}2GigS0kqP`v4tJh)pVq}NsD$jSfRkLD z(zQ`geg%-`(r)8?uXYWL{eqtgNcEfTf9G8=KQmbu`No=b{#i=@n3O+WexDir>J0yq zAthoJ(@gwzq%+^?&67{O2DQJnkF@biq9Ij4!;)hzbQDOT-Ovs5wA>o92;8x+e6|`* z1{mk$Q9K9DH3LW(Ormw1;Ph3JERuT;c!@HnRi{@V!Lw*yFmw@8`pjpIA5N5$jNe6@ zl-OHgbiAHMKQqQWZhr{+VKKwbf|Iri@hwcZhhV8C_7?YY7Ab79$Gcn%ScY_f>|GYW z3Orva+$7c6G^N2+?ah`$e-ecns@=R6D0wvdT@Jqeq4v?BiHLT|C{7dUOsp~NU$7ja z?(=R)UlhRvcJqz=I>jQ(6#7s+0`3A$iIg{ZyjDivH6v@Jh4-s4eSRdLWoYZg&^TR! zT3s0)wpM#KbE8_YL&E}q%Um_ByU+bw_@w{fqQA|~C8(*gS^b z_*sY!VG)K_xLdx*KO>+uGU#mGc`w7s=S_8%(KuOJj8f`1%!mzmY7R zJS|t#4PGM)uv@OwswW9=h{X6ySE8{xA+Ktuy-_HoL22Xqeb=vBK9OP|Kzp@~QF@CP z5X15lXa$DG$qcdU4yo8fNcNkZtH=%{%h{LeiZeHLk)Q;(4t27y5p%w@&Zw3x0)~~9 z_p-_rFQqd~LAT;N&mtT7gvEpeH%=f9O-Ro;yYdT_QDo+IH*tmq542c&9FxsVijnQa zuQZFZkIlyW!x^U&5Ww1;R==Nzss72H3HWg%d-$U|fOL-FtuPiJa=3D=J zuHB|*cG65~oxt}bZFpHrZe@n9Mum*-)3N{=ddWQ>l*a9F5*bCP)i$h^CKf`I_{$y? z_D2<9T^Om7t}DBA(H*{fnt}I_Ai*b>sXQwy#x!T4R7zbOP(!ds>D?4 z3Afmse99!@+Q{ff8T{-G5N#E*Idz=SXw24)0{6jK&0D(;;M)Tl;8vKK@8q5qd^RCW z1L(s6?WY)n1J~XYM^*Rsmi1aj-owui~-fBTb`5Vdg$LNJ%8v!-9o~)d%*=ZkLNVL&zt2Bt|W2=MdU<%y-1s$gz6xQ zwM^Qhj010KrcMk-*o3pe9_ORi(Qx>(0Gwi_GG^swxHl8iq-3X&SA?ey37^~swwRa5 zwI}V_E>ovjdl6#Ph^RGPW=j@7eSSh}23Pp4_CAVwwmh-p2aacn7Kc8C3GtRxQ0Rl=hccq$&VH4wY!tpLi^XxqSF8!(Dzz^PvbBvD1 z9!N*>4kDOb!<}{M#BJu1OCEd7WNdh7be;$V?EamV_5kY4`}o1VA=jy-A$sej<9 zvF>iq&f&agnH=*Rh4+I%uavO^kDg#z2f%;n*!>m$&$NWZC_x&TfNNr!*1E{qUfN)0 zr~@S64jDoXFv>cSNYqT94Tz#0IG00)Y#QG^R6Ap~Unf&T(!Fafp4I68HQcbVXfzje z4yCl)o4GUdthrkzAMan8YG=v(J#=LA4Lm%`Kprv}@Knid$#gusRlX(Ls31Ntxp&`N z!|T-Hd}i|+H|s?J)}(}JicjW0(K#C;4EhzAR1qG;m|j%P1Q($4n2AlBLebehJNXraqp6qoc+1|2=!qBrmS5Bajefp_|0Yu;1rQubu?Kw=z-xatW1^&`c z_#|J~AK0^uzu`SYlxCASN6uy^#k6@3(x}1o(eOt%f ztfMEx3FijZKuxNDBT~@LWQwKvq{GL@y5vROv`+KnPXN`sHAaGN<%IC`ic@acyQxMd z*|DnpASEh&3n6VJi(TX!TX8^0HRE`gE@eP}n_@2Wo&BDV0go7d#%(fr8^yf{ACyVZ zjq1Oo_d=hF6tBCzUc3Ex11dG~sMyi_Y}Yp(T!s;vF73T2z5X-d(!>Fe1ykZNOv^tU zs51Ueu|WqzKFrzFEAS&J$mG0xGjKzzFJ|)dTwZEn<*767*2`B^i)kJL^vh@4WA?hE zQl0z(S`-?<=dGt_?ncT_cKU}uf>FKoHdV85sR?^-2Fd$M+iT>$VjIAD8M}Lh8AGc` zf-Gv0ul-rRvFj3in_K;O@dnqh3)|(JyS4S|v8$SU2xU9H7Tko;}oDSSyjRak9=a{~Xk znkP-XBDRM*#Mg_+%>Wvi733HCB!N4^dCHhJ{wH*j5=lw(I$1hnYv#~pl1QMzR2xWs zrs(J)nMz_9m{zU`|DuHkpWj0OSa15YXf-@01DAGlk7-wU{0cV)*wkY#S?PFb#r|M1W@T z>#})m`0aFn9Xstz9f8gU0)AU|dNE#`*K3QBFM^NU6d3<8T5}1EP|Q*=654mt5sTYt znln#BB+9T(95{>kx*FG{Nc;C7jt$ayK3PVkm^J?0f9qVf^iV%L6TAoaf!g-otvMKZ zcm$lRPdBBD5`aliHo@KIENzCxPKvMIP3MtQZjjOa!5G!ZQkc8-Jc!{XFBP_t=Xa2! z2>_4I)|tavb_BK#2g@(aJDWPo@&HkLuHIi#(-_OnG|7Ex0JYe6lAxO-jxZ|60jK33 zjshprAcYC+ezlV$(1OC0C-OU-ZJj-!ZCsgOgbsGAy`!a_==O|#CRshe{3wbJI+yOg zIzeQ1CpqT2QIiLGcQWw)TFfPH|A2V?2?xECp^ed;Nw?%82y|VunpPMu*~ta1OF0#& zBm{7gKT(UXG1c%_>Bx77=MyLE=0%J@!w9t>S%@p`lL zN8cyF48W!QeZeGjwd9%1W~aMgRH|^<$sUE0T3>$=cVJ3-%*0~24;)!3?L(f{hgZ_Q z`|u<7trb)Ki@P*O!Jp5p-!C&;JgIIuKXjOm=P^7(b``slq5WzyGZOAuH z7z*|ObjCO0(kE{9>^07>DIM~O#P!_GU-AoQYy}i>Z=?eh3h{+>ZF^yOx2sX>6fU$J zPx}^zi}ceDT8fTdeCHCFW2jkkm3d9YhJG{*2WsG&{MCN0CcczU3~nBtn3k;4imf;h ztYL}kH~UaWc?q4oma|owVdx6h{tUhdN_tl~izc*cXEv}tZ+5(d$>J2pWpuZ+`bz&X zo-m}754BBxByoo2{y|dk=p3r245#%6C%|}NjtlzBTq;@9tK6)xEhw3K=m6oJ*OFse z7UReE&5-LDk*_h=Ijc_eXc0Y^CNAO@j8Fl%eFk<>8QggPrS_7Jm%J>0l0jU*Zzjd$ zyq@75PYxp^3)26Y{P5Ettn3fufpc#LkRpAI`$ajFV4_B~@Gk16X#?LmBdwv_w%)_3 zXw1TooO6-nUedNT$`~k{NK9xyOQ^vt)CchArLgIsX&9TubTy-Dzkk9|ENQo){gZi{ z7qWOHoCy%pU54j0Sx`^~spThg7kYEdi#*z{g=Be&i_7$6)z!KIm{E$z-D zWgt_Le&rHFQLrubwtjQ5(lZeurvHF~MO)5au-|WnUO(n@kSFWnZcrqws!RSQUtzTa zT-33396n%&48{p*@9ki|W%xQ)u}^V0`3%S#u3Fw6NR@auBq>a44#P}*NXH)OFX%!^ z1B~hfgs8?XwmB)2PZGDV)yhqS}3dYo@VIdte;8l zWTRW_0<}02@$BLYM&g-3Xl{}8tUz0I8s;{3Ln=eH`J^YHgle=9WY^)y5i>D#IPt}M zcD$S19pr^bJNQC*&(>GB*^pA&=fPb=ge()AG28U{pLT^29<}-s<4kHJ z(DwYLSfS{Sf+#y=9LO%#`OohdDt1yTQF@I>i4$A-U-Andr0Sluztb*v7Nx)9@Yonp z;ddRM{VNU@G!I;WHiW$3yv{=Xzcy&Q)PPdg4hLB}BqPb}^Sm~lfn!VH4$`&+#E$;F z9d20SG4uu#KuulxV>rAU+PlmmmV1X-u~ADtayOZU=KSTf^gx+{4*4nHKAQ}}idgCK z4&xpX=NBJxSw-5}dh=fUPS~yo9d%H90|M8&Tj^dlth?xesTfZ1>W$cabbA{gO zEuw+ZHG;$G7jYLRsGC$P^<&IfBfo{&VJ!_h>+m)gPt#BIWcz`=T8aZKLouA&u(v0& zGQMA1vw27Ksl7}^vjto^Og|(=6cs4IB z)^5O8qd*dggnm1ZPt;3-D&iXJDi1U5akVcmB9LUirOI^E{LzIZju56UDss%^=I4zp zVDK%n0tP#F6(?TqsLfpGJ`}~$D(!*BhHINIH5qi#n|SyNH$7jz=kT|EQXR>_#Q#79 zGmO$h=KwLDBNO&1OiS78wi8afWMYqlb^h!Qnciwlr<>$G4%OUMtE{94nWPH-(Qj($ z2>kZOiU%MHd(dD5+G_>@Ny}ck1hf%gYs4w!vM1bR#O#rvg~K+>3~)WkM<&1->(s@MBW3W_4>oj{ifSW7AeAR)Z9I2=?S!I zv2hkQIatwgq};SjzFeHhzjx@I41e-rBd4E4ANX?9&U*AH z?M|>3aS01TUZQ}=kU)9u?8=SpHE4mgXSoHhblzk?f5ZZ;xzA3vANjH+=m>E4bZA+R z#iN;qLLyIJW-14E`0P$VsSHlHoQ{Iq6HxtBjX}}9lc}``QlSF(pMjCXfGro9n1_+? zLZ5*m(Z#(;WIY1Xep0$#Yp=nJ7fHd|*YYjE(YmWmspR#X*pVXlaNB#!XsrCuh>K5N z;7;36ZTa|!m#e~I1=4rckpw}Ybi``;X%o?`UPFVR0dwx~*7`eFWyxS?8Z(f-+)NL0 zPlE(owCrv}QX3+*#1#$CThSK#dO0%y`%=Iogl!*fq*`~oucbnhcf(N|K3w!gJeyEyb1!v>TCb^L)T8}N(Z*W7 zKV;+6)u@7EA7Lu?&;>J=4eev6jXQbd@9V2cI4!9$%#%9s#YO*NMo_KIM|1G|raY~E z4|5Km)?PjfYV@|$zH&DI#9t}?07F0Ps*H(~o?sLAl^@tcZhg5+umyjr{)%+%!r$`r zPJMUE31metef24t=V;x?e9C?5hgD|x#gn}F7Po<-_JP@wfyLQO4JC zgnvZ~*UKC&Nxs~TiPJDb$hQ>h&M%^*l14pYh2>0U0 zUfd6YtGSGj4+u*29+Svu_)H8f6|3A=Qi@zH)u(iBtHNQcwc$tP7qA+9sD(8ip2wxc z19blofB)6VIJnJ-I$f~&bRLoa3|-jA<_8+TI)2c5_hrntM8m+(CYW*M+Kr zU-3YUbY@=KC#YUzVnkrjajxNwSj88+BfRt*R>HQ-L9Q~kWyWH)- zZcc6R$Dv`QTmWuQ*DEcsL*BZ0HSEeAN{IxPH74C&s?efqegL-Rr*E3s9`?0|K(ry4 z|H<7DuFcN!b(MANuC`|)cI0plos;1en02=gL^E91SFGdEQ!L$2?c__Ql%WiF9zR9V zQCA>YL~J(4ru9mBM|5mPy!cs^Ire-54a=V z5nAT(?Ob!%dL2PJk7{L#j$kykVYL(lAy5#|rb)$wkF%lclCeML z1t0}=`kZmpRQ?;Vkw5%&lEa!}#qC)BqOE2G8=&$pgh}K;wLhA|spD?0-o)`p6tZ#A zfO~LO3p$(QY5`Z(r9156FXSiJXPx3qyyx!>B(Zr@ivC`9JWJ25OWKOW&v+qrO;~Be zTh+JkvrgO(KwR6RWH$Mx0`k0_@i=kEPEN7S@dOFnRm(Q=I|-F>Ej(aPmU)`a+mp*f zC#C@2;7te8*o&WBA%*@Sn7w8inFrTHfJ^ya^NC%iEi;#^C0T~A$_T>JDT6*tr8N}R zH_>%}wJt}d9Ib}|+Id2%pbpi_YaZDtrYc6~LA3N7$Gx+Q#zma;1|hX;b>?~YsndgN zIDaM^o=d)T@lvofZd5qhQa8lt*DGFNly@R!KeaiCZRxgokrIEA7>iNHC(g?VI)u1w z&lWZobu*v9m7jMrl`b}~?A1#tKkf*AcH#l85`jE@{(%mMIh_Z8%+2Y2BE4&vD$lm} zn@euuz4}xcOy9SYR?bgaxEV<|n_iYBt(uoo3IZ;08}*e+*)}5I)tiHj&%Di->ia?teSI{Z zL9UjO4)->A!0P>3oU-9`EIYYhdfY%LQv&lxmK%}6zN0R;+?eWYIj!btxd+5#bBb1L zk4|y>p=5P+%HbDZGg$PvFJ=hW-`R=_(;wlI$Lv2h=6GY>=>g(P`tNJ`jt`FvCf1DP zo;l;iJ5HX@ytHQ?ph!SIrkQlEG)&ee)D1fajq8b6K*q!|D0kG8yol1r+n2P8RBhYa?V zB2RwZAwyZa$oLo*ny)f>=IIu#g^@S**;)5}Y&~$R-Ry4HYQAX5M(VAgXii~Kt8ec| zNzg`h(8(_B?s95m{slkjv|rEfA!Zprqm$P`JIY_5BLTwEIqvxAa_$szv}8waZw`ln zYKbcWBe=qMT7|%dAlYN)g;?nie8t8HB>x-u&kMvNTiMpl& zL2TVQ^zdWk8!ifsmb=gj-KWq%-{QIMc^QU9g3RM?wzuA(lN^JQAv#ERK$E}?9kX!* zaWB)40j%*Y%~)w1Srl?bnkkNf4Zr5ClP73;&3=a+DuAKvG#d{xk(>h)dDiLv+pa6R-KK`X`DxAXy?%Tn?M-f2lS4Np4Jq{bvyOQW}viu%cpQn1y=TRNWTVg2_|x_yGk6=!&^lp5f@^c%ki z&lHExBt_Bs2S~0k979KrZ;^%5VkV##azjg`D2}gH6OLl92+?T|fQb|+TZzD6ZMQKo ziV*VODngsTfke2Io@n}h?R#&JApbOq!v?o&gcxLiTq^HAEBTKO^2CwNr|z;4;3uAV zXG_o}rz5icJQm2Ok3uRCwp|(@x@s>EER>S#pJZ$$BGW-!Gf4Vr4qPh2IQu2`{t8da z@iW%0dAq?w9hY44HzhiH205v_OGU3GNxm_|z)^c}k!fV|)S(qw8*Xr*<;Ru!FZ5O1 z9D2nF_9h-=@*4U3_g}=NMMz3Ny};w0fR5(B9Q_(-qcy86hHh1}x%`+vD{#}Edzs4} zUG$N-KXE?Jm8stljH?I7A|Dt|M)6EicWyfoJkPIu9 ziVeIIEU&NdM~imx9+QtC=ME9F0MeSmS5}9b0KOxZnnp!}4IlU>Uny95?%5*u$v`i->kJLsEF z9x=X-j-?YW^u<8D4%Cv3t=PtWoJf|Q`*!hebnN}$29+i+bKf`kz1XDVApB)k>h7GT zA%G`zUBiijlzapCs>MHBCPNu+VjVc46Zh~Pi00#*V}e*S$ec?!3*9t-JjBYCa!H1w zP$x9p1v`3TTc_1D^wi<#@`xYuor^xo4i6*d^+NBVbUzpRd5q3Cjr&sygr>xxB5e>` zI93lnf>Iu3{X+^5F(C}>r%BKEry{MnQs#Gu8!>GMJ&CksY6GVhEbR}aFC@yr3c2X- zr@0^LFm}M%?m#?E9%N`m5_wl}4pbQka4NM0Kh@cKYP$|mF4)apm+QaX_GEI#pikwi zX0`JXfMl~wY-@|7x4U)n(To4cEF)Pnj$z@f!?1a^>-XSn`JIYdsN9e0`*-#pKds$= z7dUbbe%&G_bK*E(h?3lVk}vxcCJSbMdc7{(AddgxX`LXztH2F!UQUPRv)(xQPQu0h z6f&15`8F|;`@l$-ftbL-NchjZAI+@Zn4Ar#=KB{!p3=Z@{iz*KX1!+v*Zxv@xsFRYa|lLszv zeX4Cdu)+%>sv()Won-P-evqR|wf7p*gZD!pP$}mQBQw`C@c{u>%j6-Pdl(3%-)#JL zl3yc)-c-dtYr~uLltf3QvuO_kJaNzyUw+}#?!#10r~`q0Ovy2fe*Afw&LuK$&0D*2 zGHx=Mw1@K8-$qRzE+0w_|mIakoztnJ)~9d@b9MO?>P^4d+Nwx zzT4m$3XtRGbW{!AF*dLqQ`EaIopUb>^Fa)n(ouFT+RfE8s*7TamVZpVs0G*E05TNl zlK(0LL%~quM}*4IQ%>NUi`3uL-~VwgRWBos@%_W>IXNX$vaX777nkXfnI4;t%2duN56)u0`sqqLXMCo|5tj%W`p}q_n z=*5VEW8_83&XP#kqJ89}&Vb)#KS4iUL8Sh9GmQ!(E#~oJ~4*?G@|B#dzifOFId+~e+20#k)xr->?HNO zq<>%xz{Tt1LNCg1Ea00^ZI`NxzE0Nqb>}eS(^A9x%p@O|D(&&0miSyJ&jtwj1+6)G zi7Qwq5FSK5@Pz5pB%ZMQ29e*wQ+lC#^|pJzFOZ!oTJwy$Gu1w*s=^r#hdhJZ)2=T$ zPdROao~SvCEwA*TOu>!M(Ka50{r9xCKSGfVBqXqfZ69~-@HTx`9t4py0P2K!HXDeh z&?CeL4lvFx5uH^_;RZU^p;?jstxpm5Dn8t4cRUH;>(k9j3pj=3J=ZbQ|G1H+VTfV! zzs?`rjUUdXs|wbtllE5Z6>VJoLYUL-f))6lvpb0Q?UuIaXZs%@y}$1Taz{XdxLJp` z$&c(9t>&@}1W@GPqhlDR{LnxgxGB2Zt*Dn$c2gQ3v$6&*=E8by-oijKVvLbDvhV;^ zPC<6P?PSVADmK#?j(fn!AWuA*Tu_02wYidA$&AE%?_ zx==f&S$J3m_xelSEg~|`Ve? zl%~A2PcSux*l5LzbmkzU5r0YL-B5ntf+eIQR68k@>%;}{toS%wt5uOv+Wg#XEu611 zzlrON20x73ResRW@!b;A1M#oTfFoi7TBlFLGw3V%Taxe#=h>m+O<5ac8UuOEm227k zei^UUoIsm=z-b(S>T4<6sCDEsboE6dp~+KM2q-B6HeI9sn0QwP&!-sY=70}6Q|5^2 zQArD)*%&u_YmIvV+Mx_*-^wo~K=3YJH6 z;^qxei8W)%3*nJ)-NL7v-M6pgj{kLoyt>rozSi%uvqBGIQAVB^a#@*;`SQd8X5Sc` z&czJrJ4m(ftv0{&fuGCT>GO!A{iUQsbm9T|azJ5!kyR|<`icfr@2y|F~P%4kVfpnC}3_S(%(-6tB zg0IJ}c>>-Kd5b*u>ChqWGetn@U5Cnn!ocCw{S2sPS7A}}fp~Obu_ozlc`Zv%-z%Z^ z_07^9%rB7cSnWFUatp~3E=JyeJg&_2cC1Ij{%n(6{l&qx6Bo3feig;~hO{+=4k|!w-IepL28e~p!ZSnY9p{y};q)L+I-^(mE zxc5aMrl~@k$&=$~PQjw2HCXD(Jy(2~7|a(!?y^m$qt79)O}m*>!^l-0{o{c)!)AUL z{ZfY}83fcGgb<)8u3mZf0uOlJ1k*#0;sMTXynDu7Cz+M`aU}#xE+k{Kg`zu|>NzfU z9VlU>u|*qD6F=TOct?g5b6_j>@-D}toSzp7)w--2ZJVjZ0_L{d_Fk~%mT&23>xck4^4Q9cACt35s z5Y#Tl_y!s}`}I3y87!p%qb#!i>QK4j7Zvo&uC@PcZ!TFF^lG(MMf`xJ^orYpzygLl}n3I;VL# z(%@m;f}EF`Q3^Nqhiq(9rJsz0V`7x9j?(}Dc#HU>^uuKZ`bGuBHw2PwiU?PZI~cV| zleARtSi_EhMyHvn((l+>XELC)5#p?OFKhhajnXa!yR?jjfbu>SMV`#V3XGC6r{fP1 zg<9g7dY5?EP~Gt)04y;EkWfThuHu72pNDtrqP6~!3X<>KG452w9O40UKo2dW4)+Qo3?F2mUxI&@=PdWe)JWkK|MHRv620m;Oj0X@xBi z;Gf3wmOM1PYLR#3&xNxT8SxOSj84z?=K@!IXc@9N_a6w57#~u}W;}}KqEtyfA2MZ* z%WhE*QiCB)+JW7nUrahMxvlzrrA)2|4HDzdsr5QbJJP#|lu!RBKS^e|Ydel(9=%0 z_XgB^5n2&04*<;mWX=nmPzm5ObZD*AyoQsC3e`^+{vTw>W7v1K@qwE5i{iI-Bzmes zOh44u=g{0JedY-p9neCpy4*34#hx-I3F5MZt#|gLn=k`NLX(c2-Do@~WVt!`xYWPE zhP5E$KXmx$D|dhsOKEYRJj@U8?cntalva(mcix@5ZSRWT(#Ek5xbtE^6bM(2eVEuSIue!0jMuxgVQmQfiP~GPXDY zH5;~XghbRHMf*3#7mG7{frpRBnHJ7%+J8?Px`8a!i$;lOk&rCu2 zX0z)5c`0&LKMhN}#_rEtQ4H3#m7CgTvfe&pX`$!W6IE#&l!q*6$ zAgb0GKa!3^hjoJcUAzo2(W0xSytRaG=hD{N@r;T$uG;m`-dfN2){_`5Qr8K#$?vrw zoKoRkE>|6QxtJ{`$_>zeu^&-7e-m*+>h!BWJ2!EjvqmAIg3c4A0h+uP2q*z3XlfnH zu6DChj@*tRHAmic|2FV|UfS(oIDsWIu6xR;-eKe`Q#Smc2QH68yf!Bn*dj;mX=Ea?5lL{>qSC zLxZ8EXD-U{OJ~B7ew^a@4_a`H*()ca_ehg314FP?IW8N^xv;@-RgaC;~UXTZSEGna~`_% zPZSLUuIIzAnGvKXZOM(G*Ef+!fII#G*7k1(XV|Ibpl-GEKYNO+-_{f9n8wsQFlhia ze83_Gv?THE`+MliPG7#FQpafqh{Y(-+#6xYVre~u-4~-h0`k_MaG6*MJOTKDzG%|# zMa$IaJcUoxY?iOaiQ)EU-!zT&T5=-+B&TVuJhV?MBeD-LybabPDM&fSiRvK=% zqi5bg#u(1`{!HNYXRw%b4v;G#9AfrB7dy$Uv_0&-7x%l`i~aAW6UL29!lVrD)FF82 zh-B)e=&sXp?Wa21+jW;Mj~Llte`p;C^)^xp5z;sm4@N5ht44yBfq_Ymp zkXfab+i9-##Lq2`iGd3A+Ffrigr=K=`u$FS97iQ~Bi)eJN`aLbhzzKlDxU&kY%xC6|p#ZP1j^egJS z07qcqo=gZ+q0Z@CIB<1=4dkG9J<$B@I2VrRfz*eYmbUWk4)4#=7)dio@qgO9Ce77) zykFp?M2{Qwqdgnqp4>D8=YB}d2_V~U09XD>;Tip#!;9ByO`4Om8UrSMu^ZrgjpkJu z17`#ECZ$ZKen4LU+l@VXvcOC00K&Rrhs(csYpyR>wTQ^4Tp6EA$nzMyAMv4SDUTA67nN$Kw#tRrzUu<<#QdAPp=Ql?X@q=>8f zYlT*(guGxg>mgseghaNnTUB@WYfA(PM!&Oe*yS2Co=ztsnNiM^ zfm)QKNT4_3L;ea=oQFs^Bx7+02>ZTK7S#H;=|M5rj#@{q`K%(Yu-eO`KtTlTdaJy(@AtIOW-dvV{X7Cb>YAtZN88)|U zLhD6hdBZI^0$B=mv>>*q!^EBKw(EG>^r?g1+ylCe(T^1w%m8_c{1>J_mf>K~n~ogf zJVO638~<>eaBTZ01;Q0y@6QXY?k4@+KVbQJ)*wJ4g!KCS!miYOZ_NcT^ESRaC$RltDNGNx?ngZ!05y_(Q zwoJ1y3;w-NC^&&WF$k{@Ju}K1`d95t(tm75&l>GL#l;xl;?0o^ zDAdM-rpri*uOK}nbZ|J=zFezgXM|oLpI2I9n_{9PmNTWz4{c0Nr4W(*hIYW$1_<_C zew=)xwPf|q>&R8Duwy+ldvxlhG})n%f5swyV{|aN;w=|neQXOw23?^~PU`e&=!8)e zO#%%(aat;kZ34|D;}5rC^te{J7Myb0;B1}5570VWF-(iyH)Sq% zr9lu4Q$FP(Nb);U;5KX}_qZW?5x`eS2K)Ms~;CSmCBMCma)aLQ9Vw;R6lOQ+-Uc*!$z z!V4SrJlEl^#;|aX<+SHY6V3f;Hy&K{;w%asFqLKS*1KA3pbh=cQ%4;wm(`F6zLY}# zyzFzhR7ERb+?Tjb`hAm(B18E>=#IQZnYJg_0SSUDi4U@JZ%HOE1?_gUl3iHaoE?zE z%s5n3B7R(@WLG>$A!F;&t&{O2V` z57XBN;E6IBcw^6J&rPYm6q&1`Fir|o>Tl44L?zwM@;E`b)rXElCmq}37^EY(FF0S@ zKW;L7U3dQTRTBw9s`6GajpKx2)QCQQu-!}rXqH<(o`vWX1%Zvn>;C>BkwItfz}9}I z#ynnTx2`uCsBr}@&2#IMh(^C+j2cVGw7#!aYby zS*N663$I&&;rn@yR01cG@BGOY&VCM7`*s{ji?>5TG1qeo!cHi$kGnLqJN2N4wIa@p zE5?l!QwLoE0jX{jdMj|-h%Qf1>CHIir@fSya8loFK9Pc+n<7p0?}{wF#NN#BB*$2@ zPm0ZuwE{<-db~`L(Q$@H%#lEGwpItrza1~6ox47Ya;x*!#xsuAIu4{)O0t(N(a2^l z;o;lZ8N6$44A8G;9-1N6nYi^Z#|*k;7O=gvA*&9z;8LaentJ%wQ*M}h_8Z1N2W(fz z3j7>tzodid%DQaHAhjPOv)9Ick_j+)=C%j?dp1M5c4r*p9C}|GFE`N^3y2#Ku`w3Z zo|_au(kUO@2`xQ#*Y>JOtZnwON5$u&P|DfZ@9w&k$UQG!8;%cOtwZn*T-g0Nl%coQ z)|^AycW^hm)w24M)*Vw&0`xKuTgWu>4J$OS3#f zFByo@gp>M^WB-fGHncp-d*ljSQErm!V6C)$_#w*l?)d4{M>s?GIanVbYHJ`XYXy(3 z`L@z}8(Ls%$2aRX~I{G7xIb(EZam-zF-Wo>d@Tl}<>cn2?`B|08XagK99 z7acEZ9oY;vz5f(`NtGJa(xy%Hw8VqdOtE|W!E3qs1vWcKyzLCMz|P$94x&5Y6zt(t z+{44(cF=rcboSX~@Uz@djLpo=`y6~bUU|hvzi1bJUvL_2GG*%7uNVg=lQA(!>VWG> zWVJF3x_2pb$Ajr`VeRA;q%s{dGE7fIq)cv+%18{y$Y&k@sP!Z(aT;b1YU37}vcu0N zsC%{EP{rNny>dOp%#kp(bJc13Z#&!C{yHsE3$}XD!`_JG&peYKL}|6B$ek=kIvO)w zbn9#^ZFBf!LHL3F#sssOxksH}+ZYj->gS$1^T`l{Y>MpKGS(#gQ2aW9RUCc zf?pb?)ok(12UzB$<2K+twl^-*9tMk;rjUs-I7FQFP9c^&ts zUg+{=CH1(=)z{)-RhPxI-3JeNFc5C`Mn;OGJs<7fp#3Wi{@g|Kk1#C2-%zjJy`!{gf{7siO3T%uU2~ znlp0zjqJ)8hgJ%5X8--?QhgtWI>X`D%VZWqg^Z0IkeUPA~v%}8-Bltm3P1}$CdGMLGL2Kw%S3Nj)xBx3;q*>LN|Z{C9u z{~HUZI$(mU(*BXVT`l?lg`g!6#r}=X@@Hq7`SZr7S_S^l!MeQxWmll354pP~I&?We zDiR4aM(^)JQcPJ&s{>;=qhAK`6#F7I8QD}uUb16TWM`%iFxba(-GcuIYJV$d zIi!u?Ul|j{Kujqz4k(?M*t^`9+Tula0WG&mNBm`E+cQrqbu)QB#%n>0buQtKvxjlV zXf2cOcOf<=EuHlVy)$RXJ7xMp;94-ye8WV-ij^{j&zgPclm7m)!2~uD7t*fY_ayI| z$H~-c$4l}8pQHy<@EUfR6;c)*0lR_qF3olOX6&8xQ#yV5Lf}MU3t_SeJ@6l&)2=TZ zTlXa{R0Gq_y@C{g8|pb%a?fg+E4w<74a2jH``80e=zlNM4-6w7gY9VP#vL$SlvSq@ z7sqho-9o%>D=pusuy8(|ZT5A{iqLDh{sngvek-23~?E?>4%6h1n%$ zdJ<_cWJO!=N*NT_wD?(f*8{n{q#gE_K?6dJw}F;i8v?&+RMXTBR7O(>4*P(Uv^iM2 z?x#RsX6%^$!1v$2OPg3*%Z+1?HyM{+iZ5wXaN0RGd5n;7BHm}Y6kFnnX`SHWcG9S= z!=V5(1?V}e9YB>%$yhe8Jo4`3eNtzjB>1)-|1A)t@Xt~dLjDGUf03IraD(BrdcgU9~4xZ{P%P9mB?q2QCvzj*(;-)$t^$ zC-WW+es@1K)sA+z&T9QpyuR4m8~Fgn*=Bt*wAQ-dVp-WIo**BNmcJtg zj5Vy^&9>$T_-WN))B}g!g%{Dh{RwO9!WviX3t65SJs9>>yQvPbYvWk2O5UwtJ@20X z9#i!F^>)U6^L6>TB$^X3LX(U`y|_p&i^Mbn#@g%%)>Nuou$L zc})K2$@VP-hrC*ATK?YcBvX?2c{^Kxl*vHcTcLCi5%Ze7Z~HwvHs(5+`L=ZmTtg>U zM)KG$(^ZNiSc>qr;vp~5 zB5mmD@xb&|e58;C7D-hAx~R;?=cb`ZbakUM2+T~@TC`Ko}I_hU|(Hky=c$(ncfu@^yj2d`NRF*cGswoSig zO8GHZbn&bDV%R5|cccwGZ{b}t%5r;Va`nkUa$3|OETe_{8@cfi+EbT_)#LWLGJAWIjL~ug;kp#r#XC3w(5lth5X@R0 zyES&i3w~Z*nK+_<$wJVLDsmR-GSe^dOMtI0_ z;|reAi8purkdGgq+~h}&&LL4n()A`ty2-ytU!aV9*kd@PUS0i|`usTzN;nqNGO*$T zA8h8-M^9Rx;iC6@78JD$8g54eK5jH}d~L6`Y{Z0ul!cOW7tT0Y$z!M|nhb(=VSpce zdTP;76V66M*G)~6)#pLzd0z6QhXGM%DbL}!(~h}jv$Wnnc+A6ka{GdmXh)hIL>?tN ze}f1*!ZVUHA9NO)obRHx+zjSh|By9=P^Uso`#rEX(n{nkM}r*ppfP zngE_oe>s7us=Af9Wt|6js$crU_nd(Y9@&3}ifplIuec_|@$Tof0&!g28JHZqJe;-f zuKtMnOyx2nLWa`N>87qwyGB~y=G;S2=&Qg+4QXi>y9-E?cb$_7CgeO`PmZL3ky$R* znj`C_Y;U3)d=$mlD1`{cow8Slwr_nE=mJe0jv*r03rN^ALA$O{To|TnN{b&dgHrm) zF>{ZX;%=CA?GN+SHmQE#4AP_V?0;q9zqj0PrhV znX-i|Ll-uV%}js+EK~=gbxY`dv@q1U=7b-BrnQ2+ozgfjGexoc>B$|R1olqP%If4qB{dh&c zCc7Y39VxCyjh2&1rokZke@Fy6_@JWuopzWtz2BS)5w^XBn^{XJbjwgubF{Pi=f3-1 zx?a)5u#Gq?_+Q+Qs&y&SFLdw8sO|F*4+at;=FFUA9;b)(@{yLVBa`7~ojL!X{EIfs z;!<`1QK{D+gKOQ1bXEJMo1yO;Zt~!9=Kna*-qE*PNax+);1^x@rLz0I^gVD?#x)?6 zd94-KvO`vYIpGA9;BuA3Q@Na zuAuHcUtg&G`V#M>rWn_#9iY!&bcUX|ReJ@g*j1gFK; zjbfljj$N0(ceRRYqV@!Y2ODh04XyYRcj_?hv5shZG@k%|2gve2lFovw%C2kM2HjxS z4Hg&}CpoEKCq76-7W%QRyxLNok}@KtKc>W6t06{)hKiKI0qTJAAnJzV^D- znrr4c&lz*(NVe%_FTiLLu$s&tsRm##A>4Bm3<@om7M8@5#_1){ER>D|p$Kg53FlQW z4Ck%<_!4{!-+>Tgug)diM*x1Hb0!hhglscxXXhbgj8UslKg(h=L}ZLnyO3a683rb?2`h{r@k5g#cii_g| z6|XMGpE{39Ul7A@R(UQ8cLJgn89#Ds8wz>u0VC)?GKwji7gFCR@2i&c+ z4Jep8u67yI=`Y7&7v|y+pL*bvTJ!20TUQ^;Cc)rqhL)oE8s%^FE1_M^f(cuDPOj#! zGav0Tj_zfRlN@H*`KQ;pO^I3yvk+7^)-4&|`FO<_wtgYp@u~J4GMIDt>dz|^3|*|1 z57AtOsJd!z%Ea@ZZ=$-u86e9ON$G1~C(OnMG{eOT9#-e#Wj9g-o zefPBzZP69Ymh|G=6|dmhBE>bE(`D#opqF*(@&%kKn2!O}XwHrO7lhi{E8e~57^9<* zv67=SgRGZUHmdt^viuB`F$Xj-bVOcQ8KPkaoe5-w={Hwv+v%;>uSc;pqa=Z-i!aHcWdYAXgV_xWLE+zF)&#U@X%DF&B z$4UMXj99q=+hs6-+3xfMN9(#RQv08P%?`)YE{wfQp$m1;bQjNMT4uTOU$yzD7msh_ z!L47`X{SHrCd38RSm(K2%A8xLbY~yDKIqt`Wt~jIa7W?~*9ktpH*nJ-IU^?CKX;2R zZE5i1+LcRlvWvAQeH$`ZxY~g@s1tkM{nYSdT%|_Ts=Wb@P`X~n6*4_MqwIh2R;|q> zbfw-JPW|q=lt6B{#y4r#Ju_jN3sYFYi=EYhWuD|Q zD6rYTc()1rjEnXkT*volz~;eBl~~C^z}xRj_7Z+`{y*;6t0&c{WnH+#BUFEdtM;d)}tnQ?w>?Imx% zw1fp|$MdL3LI(`RODS}=0@~ova_!lwi)W3Q++sX988Jh`exH-+WMj-w!Xw)1b0Jg# zb!gk0j|4Y)hrEG-FFoT2xm`6ErQB27o;jg)i_x~7QVks7QM$COTz7$B>>$2x;c}Eg z(7Irqx);dKw{gAUJZd;-4`WLY?n}Rp`WXj$;Q1SA^|802?r2#|W~7^S$>)QAyg&^R zOTk6|9#f_o-Ag}n{*md%m!;${Az&hTV%;WAql+{fsS^0gr1?HiZD+;RFjr(5eas#- zOHN4YMK3!m^X%FOTP*bASd9Im=Yz+aNH)+#^oGmQyE@V&WL@7}#3l_3z$U583`P4^m~Qa-Pde@wm`&&L zc3leNtWgBl$#(U{-NT){#g?H5j@{>NCU5LKB5erti_>;ezs#hNb^bYOtQqc3GiiL{ z(G4k7(-S)SimLKdnm$SRI>FsCKL#K8T;wNhk=IYlf zI}8QC-a{gw(1+(Sg;MHXGaG~#>v@Ew>%r=Cg;9m|NsqG$5d0aHxenYnBcvQr@HF9T zgQN9kq0s??>J`zBYm%$@S&r0HYd=-Zf{;16{{5z&4dW3}x1x z8S!=r(^94V8OC2bOM(~os?VMCE8KrkmH=8gSbP1wxM~G%U}D&XJOLG)ga&k#KYOse zxZ%5`0>7SdmfFL44XxqQ6xs*yT1{(tb2H4k096bf#2&2isIaSv&PoP&qf(bzCSyUK z(oBxl4^Z16&u@}Bu3rO45N*C~kZ7M`KVa@k!7*fP)Csp0p-xuFW2m1F`*Ahr-FU@c z*A(7+$+^4fMyheOct(#F1b>hzCu?oUHScAn{P+!7o?xG5l}15nyeU2CPkb2md?Idr zIEWKioSuD_P8!w?4}xKttk@-t&c63xy;cP4ABYMWLM>GpWQsPs`W|$RXN0X(y;^lV zi10?H6UZo=&YY;nq>(U6kSxX@VOyAMXt7kx`F8DvRm98s=)_hwyq9SQC{o4_lMMKF zgHISk(s~D6DGK>JpD$uD#Rl4k1KLfEWAyerQ@<B#JPrV^m7V5SvsKKU%4Z$eXhv|q)Scq{LgY+iyvCQ(%m{0 zh#vS8ca%H6bRZ=0ftVOB|J^l)o(f1H&gHk(T1Xna1$R+}un4-kS}|WJSG5Et@T?ro zp+!pu&cSbWIP}$*Y$UQp8=gd7I`55>)5u7-7Z*8zIjJT(_)QE`O=A#9#XTRwoGfRb zHC$Vzwk=EdzDRwbkT_cBx)mw?7Hk~0WG2&`_T$aJ+OGmRe;6tM>wG#PLb_a`E-2we z$BDxS?JU=b3oN$PG-;ea9)F13S}}g)gJ&T`!pvIK9BH`527e=-f0}@1EtPqgDBqlP z(3Yc{%xT#XqmFrJn3WK?=umbDV%a2PpLXk_!HrFD1Ya*P2+IW?reg*!;M~9Q4)c7dCsi=iu2$&!6G-OhQwUr9w!dO*DR%ud`n~Mzd&KQW^hX4) z@ES_8-1R)peU}cy0fCj2+LunOtvo@RV`cCma#Vow;ubb4ad^s0E*aw?pgE`*qD7o5cM#aPF*Jo%1p%yd_#U~y8 z65vSr8ln-dNi$4RM!aaA#Yq8-m4s5XY* zh5OzX>@RuH2Zp#Y865F|yBsJ;2Dn}NEOi`PKC^1KaZ62Rr)wvTBq2#? zWFV%10_rbm2PavG!_!J4qiU-isf0$;n%M}A67kZnr#EFDnDa5tIp_9KZ<7lUIR73oeP{&%!NIPre!b!O*9*DY`+K8 zX~Wle^%+z=5TiL=$XJFL5I27I=g)cWq_Vq?xJu<0GHLf^YK8vX1e$1-laz)1tM9<0 z`VNEfO=j`=a!<-aCXIdEtqFd8V($et8)eMH9kZ0c@_Z@%01JX0VQPi6qTa^0a2qB*W1%Xs&~&9~ zk@sbpyLuJ+^b7o&)O`XdjAO8*lQ+|Oi7BryVi*ka_}&ygWGM-uVw3UOPCild-#oA< zm}10q#M>hz&{j*s$Mc#GFWV7}d*Q3wbkS);h)GH?;7@*>{NlV@3K^L5P!>;k%7Qam zr-2+!DT6+onOIqmO97PMz$;B8po>?f|D+vW$qQzl&+jSP#N)=i55%UPHU|Ar$&cW02zU#}y%CkdV=qpsK!Rsa6zzl(XD7PlP` zhvxsM-TZ7ps8*$GHFZ(+u9ocHXc|6ofJYz2=-di+qy<$teNl#wdyw|PK}f!1;8!1B z5j4#s7SL#&;zD(OJbw!MVe%Kl&N|NV-V3nz^f2M%chU*{_w+j;Qx}=HNyQNoM)A&U zawSs=dC3M3wDs?(+ce-mkcCg75%hgw*M=^C0ML`RQu5o{3ZxVap502}iqk%rm%kM}A24y-~-shlek2y8;z+G1_!L7OrH8d`Nc9=ogzq1)g4`40y#U~tx#T2TmREvsfs!Jjtv%tSw z@N|D~L~<(JH$J1A9Yqgxo#8;wjO$X8#rOf_V94HbJgno6r+4zlCSAPA@6n=-Oo9?X z%QTU=C;1&k2!qs38Cz>ey=RjSl3PVxkNVl0-z;9v`KKe} zB14$g%zHE(#D(;>T!_#0Yw%N}$OCoK#JTHPap+2E*WxrLfSICpp0?P`zRS;Sjh0Zihc=EGOf^W;kI1P%MwSn4n$a)Ju@ATQl!sX6*Li=YM_;BLK3 zIp!sur>JQtWFJE$R}rIbjM-~x{>006X3-%PqdiVkk6YuU@1tSAdaPsaQt2c`oa1lw z04KV^2#!ybj!K_D;8>QKC&T;fX*q#{`Jy%i8JdGvxXHuPz%Vv0%)p*7x~NPV@`Db9 zY2DF(%?#C)L=Ih$8g3lvXfUB$%+BqTVQsl+r-Sw|w70#{8n}g6+&m{j4$N13NVrJl z(~$ieVaaqo>ktHHFk>e^-1kT9bzdNswIG%}w03IV%V!9sgLT%EhEQ#|>&`hdYy*BU zGgbS5)WbS(!k{IOK5`*KD9{p)ViAkCXv1wML#NEQmI*py5WH{FDSx+ykxjZ4B){LR zr>KcT^#^5`gROJ09_@u6f>g;UuhYg2YSNK2ueq}Yhgtb-s2T7mBd-I3eFe%92-UZs z6_xoY0$`K*fN+<6l7_W3_hH6s&gjB5Y()p|)Fh1z!JL0BxFyDviOKae@Y;X&dzslV zXp)H!iPkP}%az+!uI=fja)-1`O7Ba;gGm~5xPT*4d?$i#!lciP?1~!yNeNP5Y-$et>BW-`aNHs_?B#M==)jgPE z(yU!le#2=!Z?yCpL8;>hNVV;cMNQBS=ga56pOumK$O^`LKm;^7|+%YECEYtVbW7NKUq4I?&0hmn1Jn zCb_x2=SbfSq9ZB7u1``XBaw9F(LD#b)ZP9vZEpo_!yXM{&BpolKCRex|35%SCnKMJ zJ}AuBdrG7twE+MCXxEzHjB&B=Kfm0AI!NI96Ypc}`I#^oSSDjBhM2VK8JWSu2uV46+^KL4l0cI7BZ<7{ z+#BGvI{rRMx)Qzd6Z^Gf&s!TnsSgy?J}H?Rt%yhIe|;;XT)9#IEaHlZXDFb_2zrtK z`@#j8LjRX;ls*?qmuqD3QA`rwTF?qijx0QNr+^o`Q+uAG7m~G7%cYmR8QQZ$i#LB# z*kMksclJGF`fLIMTmtq;quJF{#N&lLdEV7}{ZT7cYTpA+nvRN+4dLFd6y@u8=pQ!& zbKa%>k98_&FR+u8Wh4kX=!bDxA>-MO4DQpC=V)M@Evg0A$q0-|-7?d-bxptR^5Zg7 zWmY=M*%u0(reW?b)^|1=b!Y&mRU2MXe(S#GVZFS)`?Ge3nQg;rRYh;crF15`!IC2P zMv#krwSU9$T@=dHDc_&b zzp66_)~wR<%^YkoLf-X2E8-l;>do+jxIRLMc;rrIpK4_cVMoz($-So+WY^v;+JDxpL*L>VS-qM!0ywl+frW@5NAJBUa| zH80lWEauq#zpG7UGh9cHphBqkvci6!E99(Z{mkUs3bNSDCEG&R_^GG2uyLkvOmX-X zU!^$e=6^L4iw0wki}bzMqN}$!A7Aqv?E$QEh1AvUKof$lHfKU=xa}CSy|noYJcADQ z)|V`Av=q7YfBLROa&#oj6MH@+{fFa}=0yHJuT^JOD8(yFd(OK`D=dm^!BF63(hG%3 zM>4<#ExB;)A3^LCt>%4Ey($Iv8LO`^|8`yt)mOxOKqE4W-|sou1Y{GfR5J zV*~c`??ujcNq-q|zWg>|sYS#FIO6u_jE|)-w16y^U1U4DK2zP@&mgu^YQbFet^+g$ z@L~i#=iEBo4mqzsatWHO_@fi^)m+!c z71HO+@sAxl#eV%YMT$!g?X-#;+~;9IDc>lg$%gWa=x`D!`2m5raYx$TcUP1?B5n+w zhlivA1=Qet=)=QvW*pAYyJb5~XY+B%+r1IZZfMjS5~S$xu1|(ZX!g-fpMCs#rS@jr zz`x72BjKfgq!aLfHhNnx|M^RFo;za-ey4?Qf9~#RZM%%bLra&2{u@BgjFV%i<8fH| zVsd#~UekB%oCZ@npzzDPq&$`jJkPOqZ^t4xIcgt2vgmPR+hv;*Eyy(|wdk}I^WIG| z>UrwTO_O%~^?ntu)EGbd?YjK>2XZpL*vo+BGLphH5?+>DBsp5fZ0k_mu|n`^_|96r zmdn-sfXZyL{CO+&2^g0?o%`sBMr`@#%lsh-pm`vI18 zQ2myLS05FcEHVHEtmv1ci#K_M5qz4QV0z!FW|qU^gk$)Z9c|U2bPWF=p;oMAE8ZQJ z0-~t|t&D%|D?iLScTyl}aOZl>tY-b@Fo^T?Pmtu2KH^eVrN|?mgC9UV63H~}G(L`21$l0gschOP_q|Qjf){8e zh9%fDcIgwM-yy>XTy-fNPFmwBNXug_LcSK_aWOucFgEI~{ta0J5|ggv37vU*=D35k zRXQ%OHlpodMX!%OrjsiX41nN_;7!s}Hg<%dxip&zl`Lk`VZC&_=%N#VxIr1+FCF_h z)df__(tBJa>Wogom$>JXX2Y{c`GAq%bkH5z;AULYuMgQ$>Yz_`C~dnLh45`n%(_HR z{P~f)p3?n}TQs+mHWMtMnaP!di!qz@irgq*+^k6HJ8U?oeK)OJ5tGT(W6y0ym+!@5 z^k~(NkOxA@D$_@N{N!5#XgDd{{!$0RshN}f+x=eqO{e}JS$v^625%jT`6k)*=O|}V z9};ku=n9jRdzFHfn__9z@jmPh}& zR7XP>#dggw(HzGocO8H3J*IiPvUP;%T!;N-6joTiSf(R*7bgtRNat1oNRiSPX!Sv- zbU?eqOe?%h4>{<9SUx*J&|K;!ur5G@$IWew3Pb=ARmhle4WRCJw}Rhaz#x1-M9JTs z4C?$*Ey76{LXY;~9zx9sYBmu+r{vi&Z_D|FG@Zb!`(*?koHNNvH+Xe-uYZTy>oSt> zsm#dv0mjHrC$9u>F9Ex+qa4i$9aCQ+9)L~#ljN9W*Y-<&ubeUzX;E4paD1zQs`^WB_?Z(=d5uY2 zh2em!n&V{I?+afUYgoz>{iGxeGxb)!*O5tjN`|(>d%@B7Na?i$oO4Se?D_jl1hrehD-g@=obA`8;=aeFz7oi7(;N z+#o5wOh5%V(}g5e(!Oq!7jxnE$`7CYT%)0AY!(u#xG=}D*-bWS+~RIMyd&+;v>RYl z%>ATwPDjuw{RgCKRiJ_bOOxu01h0c&%{S=WX6JynDAUfPNL|Ht#l#`4v!oG!UqiTc z<~|mqKh7k`h@R@u((M8>3jc++i$qKr^-m`&{DzY<5#*IUI;UM~?gq;brU1OSCY85v zHM#A(IoClHPbcRFmBjZ5I%DCKV}2j+)cDZFS`iS3U+|MFiO)c@f{wp6dIJ0T14A$p z@Y(G7GO5Z!z+vutO}B;i`fFbJmem+rP1MHyRB2bxQ*L87yf-KQx2N4MuJX!bo@ zRQyWez<7&=>iQ4Fo455vt3KhGPHo}Cy%c_BI@M_QH#+6)sZ*wE>4O*A^H^uDat4D( zZ$(R=vp3oLd@`^jCqUDj4d3TZTJAUXV9C4a@=SWTeeLl(FCWknoIg5Tq`(QPg8gKk z%$&e-Mr#@WxKkFkfH`o4@uSDy-^T!KwDLJ2(jYR>$F(fm!9Tms`(a$joL>MksX%3L zt_HMC`wj`1JN&>l`TAlF?Kbe7)hA%UtAS=ojhWZkvvVUK|BBG|L?y|zoweo!*S<-M z;S46hCQDbP0zze|s_gE#o}g16?aJK){kZg;-<;mXHUBCT7lE_NltJgWtMQ+U>7D?) z%pfJ}?#oM8*S*91cIwaT@1^}XsUwm$Z>4=3fBdzdfaxg}FArAyEl|rTrXs&HyRY5( z$ifPAG;r5*99={kRm|;hG@#-j8CflpG5>KMLyrP>Uf#snE=j*5uR%?mL5#1obN`Ez zPE^AiHyPUdQGSYPjj^PGKzTtwEn|T?7W;-HpSy5p?-4u>DaZskH7#RoO_k1G+xA@1 zuxPMZTN@iTMyb8RAg$t|(R!MwU zE&9TVgX1&SZY>knF+p3U^0-ZCzYK`b;Y$Ekadp@$m|eV0_;+A$2#!*>0^yJ$1}*PD z=fWHHakqOvt=BRqPe&7ig2Whuq^X|m5cD%~C ze@Qx=dC0U@rD11H(yj#;Z6;uyLFH_be$xiGXa+g+#<_@O6>Jk6=+xl=T8~$8$qKb9 zmSoR_>EwO3;eOg)V7O?aW*va5%}kA3gL>}Vj7nj=;6p9*dHK{#Lqd1fJvLB}Zf*^H zewD{u6h2g9f)TBI|d4i|_PZ2_sc=F{s~llx+z z5;_Yt2a+;YBzmK3;=PwymtWEU7Zb;e=zrz<nuLkjvMHkq}%Iohw@By_4@Mj zYiTY*Gd07)j#^mW(QI^_<9); zf=I2=E?-=OHfj2&68rYZ^ooC_K7tV$e~DF~Fs#KMu1C2{UA$VImIZyn%QBhXiX*z)quXJ5vrAbmNcLv|cOC&r*L_yD&oYrgAJZJ4mjr z-^MTGrtNqq-=ObfFx%&2%S{u7&Yd}XS1SMWuy@Vvl}hu*)hzA1=99?MoRzPV!|aG~IyUas z26|aMQ-TogA__S1_n6(7pp$l1>Kgg}_{=|;M^_T+e()wEOVF5o7t@yuG~dUGD9lLL z?gtoKh@=tOD7iMT_q#_-*d;Q_CP5x3PSg{Re8}P3nc5u#zox;y~GkP`eJ>v=( zEP)`x62Wx7f7P{yi@0BNXxl+nJi6h`j#Z}-w(lSW4_$kY0)}k@dTQx{3JT^tbgmO_neXO{n&7JJ*6=knsa)uauHCXIH7?ANOV&ba}js&OFvrXAe`J2(PJGYQSRQ*7yeHHs^_8f zz9s4^BN>QDO2ukj+6!`lm=ur12<#*7P5Z?g8F<(=yzDG)j}Go3ovhW@Q%U3+uR;fc z50dndN+3h5M4IqX?W~P^5y5%MWuz=2?UdsM2PId`l*A}!Jb`xpI!}n#xJ~DmgHWRR z)>mg%vO#$itsiNQX$T9ocgv12WVRET+dp2|4{_Z#DLxosCRNJNbzI3D8L&tkoZ`(I z)3uCGj`2*29;oMbHmFwn9um5r;3ejgmK{seO6pLTb7tU zE&{S76V4$r98E*Jwmo(h$j0N@ifV3Jsr+7ki|cGk8-GsjUB_jrl3Y^7#nLH*O!a(* z4_MDc}wYfCdTOZbso+_dvJys*3k`J zEbrB{Zri)%W)$Tst{p*xv7bC0I4RkO^5Buw;ztnIM)SIE9p$Q z!+1N()kQ1QUK%F44O_L_Uw^%1UfA)q1Y*5L1?&u`^vkc5+F9Asdxrbia3A81cjw3( zG=;fX4-caHYiX-TwC3+aKJ93#h3BqLO~DrO zL*p`we_-3}a!BWIv96f|{vT6Kb^VZzy*qFJo?0S3Hec=wgcXJeqWy+W0GZvtTfSX{ z6BF&y@#I6c_7RXraBVw7-*`H}iMt6GesZ^&oW@kXO3icdq_3|7?N!W&IHhw>Nm~8H zBg6|qq{p?KK5QLfRfXKKVjT89Ew;Dzg>#Fi-A<#fOAs$^s>5b-SnDNc#Q|5C43Ed` z3sc%v_|&YHuRn6s%kn<$O+CJo)Ud;9eh=Zd<2rQgU^5{;dcuBytYCNuuJTWgc;HF2 zRwXJXVmGioOhV;$`jZToBI7;4!fG=Ea+W=MW@nu{Dz)bU5#qP!UY5>1QhfsvDvwDB zUvDxtI+D88`9eNZ`bu_}&V+}7g%u5dLdd9Ip=k!*` zbkaPR|YtS1(9n2JI&F>Fg*%mRJ)wJjh_Ir&?er0-9D`8Ri^;!(^q(0DF% zZ?vxpx~B`g43uL2*jba>C@@XQD7!u$JFjINRSq>H4gv?x=K!s9&?x{*#}wC7!CtN- zAt9Rg(E+@Akv1LI$(MHAL|DMJBjhdDs!zI(n(hGfkeRbp4?LL+0Z1b}n410MyInrq zvzPXVFwlKmNvJX4YG<8Kag0J@|9V@SqjB?J3#6vkzO2IH^cpx0`Zh=EH)vi0l^RMb zdEPP`K@HLauYpk`m+UN_Ua}_kz*{z!7O(r#`guG0yo=zvSXx5N%-`n($;Hte9u^0Y z2ka`Mb73>k0fJ+ED<*xM;*ECOv^U`|a6d)EGe0xpM) zD$MsT7p4m+s7yW+&_ZXmfmA97J1Wymzi_rvcAt583P1oJ&U6rFlk@}%_5);xXQ%C9L}*68 z0kXz8Ozv^CA{i3!$F`F-P`Q=wSj0iRM}rcLlMTB zAV`WfIdu2|zsdCSlZL*vIxsxJZ~(FOIQ3oW<`iZ6)PBidrhOn}V5Eq0lSP)eMT9=8ukrmt!`N-OpsH$1hekv2}|tB0d$a#*oqt>G6wNp7RyYJw1! z>cIZ3+P^Xz*I-PllVh{I*&D_mVKA^1PwlAR_DUNS;0?x#O&mCk$}l@=9O==los_mVxDz4~pU6lR&|VDH<#PuDW*`!&v3RpdvZoHcpX4^w(KeZBcZT6BVWuF==YD^M}?0{Z~B!?!ax*qWwim)X?vt?W&)40@u30Oj+HKJ9-&8hhrw zE8*q06dBn?`NK^4n9+tnZT7GN4si1)cS0rbKxm>Y)M; zbs4p2&$+WwMqavcAYIyeG+E|9^VSo@#U+x9Nddvdl(uuLAX#!_;>~yaMx5g7-81_%8je}e8AS5WiGqYa)H5J?PB!7%~8XxJo_(TwkTeyg)} zI!5w7ERz-#bDzv!h($FLqfnK>*Xc(&cX(JAq5^boBPq^U&B>&M{8j0e$0B}vg9}i2 zkAAexd+un#I+R8(t7@0R8{x#C-(DWDPOo%?YbO^S)Jb0`YPCwt4D9s4j#? z@~;t@&0=MBN4%|!1GozP(sF>+j`Fu6Rxx4V7R~MD$CM#?OU*-l{D+>mt3+4{-ve(s)`M!j#4dl;`jT-}eam6B!5#GF=ZncIefY&i16z zxW(zN0VZGyI->*WW>Usw`I~hd1In|d^*{29%=b6CpXkq0w*$Q}%xiFst$ zMSpMdI0bIcQOeH;r1PyBwBP6@DB1y6?tgXb#sNFnc`C@p%*X3j4z@NbG6j)C{L)iG z?eteN-x<)K%TEy=5+lvJZA57oD{T|XD5|#A+O`4l&7)M3MzRGQ9vBvmdlA?)9he>j zf~I>rpW&X8Bg1JVsW^qn`*s(xW{d%0Je5}LlNJYe>sZPgnc1lAY-xowe+fT&m8%9r zzK)&^hi4YspGn%ZDtrrXNq>j~5#PiSC+o>O;^aIj_2(1Qgty63dBvUSKNRg_(+n&c z1vs%HER9@Qim{K?C>K0jchiKwA%SPrZ1k01s~;f_ZENW135s`sHR_j6+~RJ`aMYb# zpoS2?lT2A!Bl&^sZ#5^)k7p=FIN->fFTP-g&%YU-zqaF$7sw>tQXd6`FFl?X$J&6Y zsx--P>RkUxqo2rWT!{#F#el+sPWb*o0JekK1!U#8tV4`43_CQ!9q`%mk4q+|f; zAIjhzDd#+1`{s*Zwy`h6_;Isba|h6Z)lEM(kG|0wsb%W>{1jN)A6s`&?x-LW({)SdNSw##rS=!j_RGe`0myoN4z1 z8zj>ySx$IB#cMAk9+ZW(anFB8_MZY1?+nr!%np3fh)RX z#FTU|ZFR81y-sNB0i#m}j=2%4U|i9fIrwd}_=R;4jaaT>{Hs!aFp+u%#_Si8)D=o! z8esypdBgTjkY|#^LqVZ9*C@Ro%p=DU-gxtfPMni`UaC6=)pN{1iOxU5{MJ((%YF}rcz$JJD}-KB74 zsPv?;_e3C154~Rv5d)F`Z)r@%UUOs>pfQjVlwpFlza!~TPW3Tpx3nbh%S7Rq9SD$4 zDmvVu`slSNo?L;tpKyOdejcUSiU$L>l!ARvlmmXtf^tAe{5i}|UzGU}e$@mLe_Ukz zg4ZrCI3!>83ESl_*Xt+H2AkH|o7u&Hi4eMyW%kzkXK$z)LfQc5o-QP#B>>;@D+KBa zsXYD>6!iC2Cve+xUa*p-IBB^cL(d5-AY(flxSS`C66+pXMm1Q9gnkG!3*5uXIKuAM zJ&{>_vc8RRZaVjbl}*^E+F`zmxXu`HI7DFHu(QrGDO_t;Jo835jY#e(cS9M2sCH1* zUDO%SWsaE~NnRic9;{pC!S%LuRMrT=Q}-=fmN%nWyQSl!t0_Ef&erlcGOZQd)I5|q zxFKor;f+Rg$PqGsVKSzCKe8v}Viqf_{&09BZ%?Nbpnx!$p^gO;`7SNoeO`;btgSX_ zz9|LxTl_$YeA6lae-M;V_Gjqezq>w=yp2*HbsnaFJktJEIzdpzan0KvzdQ>bYhxUH z-KoWppFWm}kZm@aw$lk+YW4vwjZcSW_m}~JWG~|~Oz=Te!~R@UYyB5ST1I?Sx`aI7 z+S!3>n+r48^t4+6=XLxIvXmp0*)kmeFL=R?tUHAsFGLqS!@NL=`J59RgBSD6ia<_k z@+?TyyZW7f!ol{7>!w6@%g7MWFo7**LKLcLFw4VCe$|pt@7=+MV<}lX&1Pdl8rhFp zR3fCcg{Detrri{OlH9xey9@B`7lo>tf5QW_({;^5N6Yc9!ovimhl_R2UYe5wt|10v zgiKH&w2)80|D@SNE+ll_V=S0_vj+)Tw+r-#kh%6pm$lr!vxAfT9`88ts&SN2;+qQxq4i$Oq9U@7?gA*{Cc*+RvB^rG01EfS>Y< zq)&wOXWZh4u<^AHu3CWhC8?$DZXxL79y1Y&TMB?B@l8S!);!-F*q~eh79n` z5fZo+k%&32uk?g#^G38oqle|V9@&`N|BsZTojQ;fyv{VUDt~01_A|wFJeqF#d2_oL z?^Cl1ya1kDxf`Z*I5H*liiZ`vE&9;*EOw`}Y2-@4-IuL?&ZaS%@Bx0@-6##EBGiE(d0!^FHGXN=MY&~Z^ z_Za->YKBgo#F};6lgjLnhU+nh=-_b1i-G^b=)p)b7A439K_X;aNfFy+=n&rK;5)6m zD@E>xi>nrSpoogWWp|3-;u*HYlS`A1E6?D8;fT@IcV*<-IlfnLD+!VNo#fOWZs@U$RG4M zSye+(t-UK`b>@%D^%#u$iqTSx)=W0DPJZ7yr z`;r8jVQgYfU;u-45cZtwk9K~FA0CT z4|)eJGE4x4?$jx9_q2DJ1IsT&o@!9SQz>8ckB4>p6Kbyn+9Hffqx466ag*7a3UqmG z2C+P!o?VA?GD!6hXA+Cbc>9VqJJCVRk#c=2Z3eX0%~c{pOfyG_#l4;#Pqj^~y-p})Jf?c;!RwE#?8swWn`^$AHiQ^t0&Vxzr5 zJs8iYglQX6ohlu0GDzdH8*W$l(GeQdTUL^FT9$g5=Qb!>P{#LRh6ik>fu3wfD^fWC z-Wx)`j^u4{09F4Ne8d_259tU$DP_fm%rEB}ngIusqc65o2YR=Gt zJ0=(-1J~=?|%<#~6=`D)Hkd zQfB;5`LX56?r`j82Ws~#BLz$y5>-8?MV{KZmqVVBrU$(4z;6Ei{b`yRze4ia?@_P} zy`*he`%i{hIWR zX7i*vj)6;B%DOsYAkF>HP!%!u={Og)+VBN_%8-%N>o!;Gnxp1NYHd2_I*NPIdN@si z=&XH=;`|O2mR^XP238{7hOE9sCS(ZB^E<-{8u}^ozt)jlxOyAYf~J}Bu(sXNiCwp) z8PTAcg?@qgHO|{gii1ROgH)3P_1B7nE1d{S-a2JmBwJzaf)bH|7Y0En70z72@0o^J z0nZ)WAByBFJ!}bhrllwW{7kO;A1$^V9&zi3!xA0cqU?-EIz?lhj)pog$P0E~9lNIm z99lWrw;9BREZ_jN9Mtc30uOsyFA{a&vn-Okbi7YbQxG#tX=EDtk}PfiAPBQkcOsKA zhKuF0)$8;Y0% zK}CwLs@0-{oJrN&*S^|DL-l{wr-)}#ft-CkjtjoXKEe`nO(2e-%5oQ>Dbd5ZS>ampQQ^qI_vqFd(AvmB_kq`}rO1QlcG9sSwQ(>U zNUp)6EU>}(E_8S2K6xbrW;iob3Tp3ge7JDFn7&E0>)$W1wP@251b*J&m!$~aOX-I^ zk$-=+OY|br<5U@@{`x_zwzHolE4ESNnZ_e{=c-?0rI>C>w`)or`g|PDx^|z{+LI=m z{w$JiK4M#F2X(slwG8x${1$V;PuUa_B>Q8FBz4h@77hv{3B?XJx z08CNCttV$`f$PyzsxJrYeTmyilW@K`eayj}psRhLYD>4bFmSdPzyFKG>^m zd$l2+&0H{ZH6XnrZ;v^Ggd!P+aei7lBN8|0Z;Wcy8x&c+{tQMDT)#}Vu*n^S;>ROM zQ=w_?-IA^~jEs>nL%LLy@eboafPw)T%y1wMqtoh)jAuGZ&kF8T^&NVEJxm!qOY<|9 zzr2XIc-2RTo@%kDbv#x(98c2Meh}qSmV6E83NG=#&tL~UYh+*x$-*@6f3n>4j2P#a zaYX1UwFl{DbqO&ZP$_kp8+yu#O@74 zPX0(HUR_@q$O=lI(}E1-*SbL2%8_W7ny==rp&*SSATJQg+oFoDqdOWoj^8n-{JBLH zIG{SPRSlGGfS;(^g18%)`1B^t52tm66CR-00n_XqX#2Pu-!MGQX!IHu(n-EBC*uYg z2JFUn&~Es^HI7VR8j!X#2k*e~iK7GWniTu7nBtAxz?PH7Ez|0c&qLCUFOaca+Y@E< z1Z(Mr>nc`?J{Wba)jkJa+mPyZoTWzpE5Qzy*C(g{JV-wAkP0c!(Dq%fmRAVCh;rc7 z2<=>~G~hK2u46T0%K_$U9dKkk#X5U5@X$d-qcAFchxP|LS%)!H-K(|wGar~<=_TE1 z37*iEP2FMNYX5018vb_&=)?+2P0ZqwQFwwdX%*Uj>*-+w+fCV{rN?x}mtFXa`k|4> zT?^1!J-jX9=88!6aQL#8V7KFVH!4vw$7~QO;_d2P|0C^fvm>ymKB|=uHJ@cn$~dDH zj_l+hI+pEqmp%XoE{rt6Cu^d@VO_;^m!c(mOw*Y2g9|czWwRaN`Yz7LaF_k!@|3ZT zksxU%{M?l!UujA=oK;Qw$JfyhTu^An#hY5S&Wq+pc!FvnZ)K4+(UI9#l&~W0;4b^5 zcSa!#b7iGrgY>#tC*YVw;D^t6S)cA;!x>-wjKvav*9&;LHN5d$y0?vDC;bcu(emB+ z4Tbm6Rz=8@;RdK8F3S#h_8&en5Ro-0QcmadKVL4-Ll%Ri9_$ORP?}(6thUF?UDyxX zr5HMxzN5RAOaDHOvm7o>)5SvyF@suth!h+#JEj#zQilK41F99;6NnrQ$T@1S{@JZ1 zPz7z&?_aK?+DR=M7%gxS8UDCFXT8Ti{#$-Q}zmzZ@8F`SNNCg*# zj-NX#6Bmr}Xh-EwILag?R_YS}I$ASZikmIbC^xa0C$1Ox~keittj=0%eC1<2O54Ne}qBfkSjz zW2ht_9PV~u=E$8Z(PY>a-0S<8E zY*}nvW@>zF>eFZGX_-lONiQxhD@=Qvwh+&~}A=%6ir%dP#%T;s1c=}3%UwTYdjrLvL)3xTE-AJb;rJ}w;X%89~XY;lin z-adpE+7Ag~(z1^_bVG}6_6c#%4?~b`)AX? z;h*Nok2MzsT4EcYG2JE5UFe8STavh;J!iNw-)(k5{GBOrzqH(TNLx2>Z>yuV!xo3k z&!z+rbJ>pXm9FF4jiB1@V97>V@f7PSvVD@mcZk~;z2xu3Q1q~4n9E*ta4#WQ zBf~i#efD(BHs3GS=}X&Ipi8@Lnd`G*Zt2llycmM33LOq(BjJ4Pl+Gx}{|KD;d_7K5 zCpY=43om@0D>NOlj^L)-cABzt&oSwJsf9KzO`|K7qbucMWMh6y`$-mTJ{JC#Pfxye zD3xII>rwIF&$Adqbk`pu?J3am$@s}LusAxKpX~bhCh{~5l(u_Z)Hm9i zrR}y4$VBQ!#IxUKW*E0cd+`2Eplh$t?bw2;>@vBOzdpM3#EE^8vfaB#<}5uhZ#I;C zmgQm$q|3GoKcwv5QEiI1-2+1@tsD1qDcD8WOXTO1ciM?;#M5({iqB!w%B#5aIVbn& zHzab5O)m{!s{=MqY~~b#T>XGmp3$DO#C$Vc-)S524VTBQt88AWjo`0vg?zujvI+VZ zSzrx%=(jyLNypE>CU4xHfFy6+b8r_@@P~X5xBr*6z6t!qar)o!IP=83_YrQ_-xQAk zehUw=?onLUAW_aA!Y8&i% zghkYl6p9zyi9HGMNFe)z?2-1T#OgTNS6gN{i<#1vYe=3H>|$M5VWY8aGAiw_vk@0? zTekQ$Ix7Xzy2l*ZoQ_00=schSu7{rtJl=Png>OJ~X7}u;0m>#d{#x`lID&VCo#Otv z+t`{x+YRbn9NKl=17*`BD}fQ9Nw<1lq1wi(YUV9k#vFu!hbuE*0-fHtmg6 zy6xtz%*eVNX=aKGXK^`0|D3^1uSC3MCQJsjAbvUbG%^*pq&|}+*4pgiQyiu4GMZ!K zb|NC^7%s|`Z9B1YsaGN=G>1}vtZyG#p$??LSIhQ*mIJ73%hbu3lo%fbmm%X^+sSn} z20v_kkTw}@Kj$F4-+YRjy%uKcxGe~@0w-EZEYyM`)`pAQeK;=myMMV9oTtfND^YST zp8*cJ8m-pNjjwr~MB;YHMt-_;y%c7`Eozg#2a@-O%V@q^hdLg7PvA4a8!QjSfOToz zD|U8Mh)&S2&DwN zX|Jy^Vh&O3v!&8WzXzC!QP0z74n^QbRq#?tTQHNQm?n_o>}IVty&&<&t9ztgATfZb zvw<^OnsP;|pYmt68daaKd1x*MJ~>Hc>@_F0AGc=VvknJxZyCMXL~Qu~B%O&@k6qWt zlgLztLXjpC3K^o3&aFulDiNZRBtwcQ3Z+@|tRl_xtkS5~+UFm8KF71}^*(Rv_q(oh z_VC@q*?S)0Xa+A#_JeAG|(OJ4yojdylF`_aTLP>8D zvQnM|fvS|IF~H(B;lN+BY}3;@dfS&eEx;Ie=HcB~^T_f?pP*rqu284odG`r&sfG^S z_c*}Pb=RfOIL4j#Hon+zBxCa0L+SNBV18WUruUdQ)L}@u@x1kb)0oVD^(gy^qaHoY zPg|kP6!6bo8|;uV$Nw=l<`(G?Wr>Bue>bnw%~0aTigUUyFQ$FZcvj>A3bxA*R=5U zO;e?+cJccN3+#2<|Mxq3VU zd}9#)*mcawG8{tgejvA_UUo*C|Dp`T1-dE@k0Uikx;7R~(O_9tVdt0apB_PhuZ+`xJ!ZUCrIh>g;2Pzw$5ZIo zdTtEaQjQ*Uuuc)?uyUQNls!FzfWoe^sPg#=$ z9dL0532{Pya`sb!5UxUDy?o3$Qhy83)i@vz31Ith3#$z*)o^e@F7`F{VN%8H%a(=g zb5Q0S7j&2zToag(fI9@k<{GbI<#$Anaf3Jj#S403wn|O7mkcRuZ$yGR3>U+*b~OF# zjVl*mX0BJT9=;EHl;ILY+R}sQWmwi_CY5xaK#<_e-&=VnOx4zydX(u$F*+-5R2k@8 zDYtAp@y<|aT)jMl)(T>a}s&SO0Tt&u=BHAi(lblMi<%pjQsvz9R{ zBH-aq57-+%b4}bOjAoVjgDIXI#V=h49xK$5^E{9q2`3(}3Y9=~fS<{spoz`_!1&HTf&tUWAcVnpv2g7yVHrByn6XVIF=& zp>|U$>fW}SI1Gz87Vg(&@+_b*4%=iV;C4nfCZSPT>IyHZya;2e71^zytZ?BatQc0O zb!ptWT*8NX6|9FMGPsCY*S`Wxf0~bf-FW_y3-WR%_iSJ=&K0z5&^Z?#`)?2W?$cX%XD(kX;59%B_aCn_DFjZ=A zw69B=6ZCJ!aj+0V`m1|!kN)t5t?uz;fvRAKdbayMHnz(Wo2KVo8FIH19+h)xN%>w~ z{)G1rsxaO>`V<=f0bFgZvyOSZLYCYiuC3V8UOD+G}qrZVu(5pW0$9tBLZA`_#ooCpcv`3d=RVD zhIn};A7qNFVH6S7O#~8Z+unO;C%`M zi98Of&?DLR^*ZF~QdR(Q|BYmbIf|L6VCcvJJp3`0%iib+9~GeJ=7l7u^5Et&xxfc^V4Q^BZyf07N``jE(d; z6)1-ICCCI<&H~JAK3|%a*24U2~Nm(W~M} zU3UB&AK8(u2O_|TN~6j%x+`Ye-Y4uiNa~IsJP+}P)%!H>EwQ`!Xr#LxfX;*+B*GI1 zmYJ&<@;0Q5VkU69N)J63pztTbalf9wFP->x=y@Ujk@;SQ0sKl?_l&;74%(nuYXGCg z!41O2+N1pPYB;M%KMkA5=pSP@TXt=rNR9}QXt6miZ;vN2_@r{jY0E%ZX|hxqn<*v$ zF8iVPGUf(cncd-~Kv%=PSJVm7Wf#JhVD`AH@|BRtZ6_r~iGqQ^z` zSdXU1$G%{{iJ?*xrN7x3g_!jYsJp4-#K-Y z95gA5jt}9SXB%+*d)Qn@KvACZ_;~!NsZ^U**L-~m73%kZPwBd!ioY<9=W!mI^s*a* z*?12r`4XEh9gzq!?ki(Qa?F5P%H?7EN8NS;>%bUQ6D8L$5{y(07e_00`R6VY-DF|l zV?qz)MDiMA2fg>oSONFq8PMptcloq6>_VYPH3x7nJJ3sdV_te6C?9*+$T*U>aBmo^ zh*2gfI8Uws?o6odF{inE9UC*Q9MY(ftK6M|v%qK4C+JrAb zU`3}o{}lK(QUkA1TqJ!H>+FDjY;Xv$X6nee+1%fahw0nF5;dqqOHe@uGyWl0!X`Zy zLA5N~Pt`MNZ*q+-?KLJ$uN-zZ)L(cOq6a>)CFsyA-?!7$29Z$TB1&?PN{;HXuyHoTZRy$}{E#YVRKWXSSr@G(gE zN(K+|*kL_qv}WX$vmW7u1$%V=0}f%GFUID(0v3vbbmw~;82gB+QCEVJShJTYi>`hL zQ4k#(MX_KrZm7r}DZZr_{L=Y|$u`ZLi9-R2M4IO*RHb8l3r!rgvWNd)BwY@C%+1oyMasfaHtA-DAkc=K75yKUT6S(`&t2;B zlWa>AYYLHbwDrH?4#G97H{t2wSjkeOT|fQYulUnyhcie!%riecJ5=Du*?>%Q)hJoc zA0KUxTM#n;Qr`D6F}uH@Or>%GpN4VeemL<}S@(%QjMvo$Lw4=4f2Bt*nn%w@=!N_H z&O@OHv~CfoSqeaO6+TKQ<9qHREqd6+8O!v`5$3o_OU^?(X9GZudX?;8!V_dN1XZj+ z63UQ=KJ^yc*u{Qe05VO%%@(Kk@S|8|@u8AUF_}WXtt>t`&jF_fm!{6@p;7>zC@ z(!$v+)ZNqp)FGmw(t8pm@6at~B~|vi$j2{QP`B~N7j@}|3q~=FFW+nX^o+4P9j>%b z=-+XMQ}gMh&PuEyVxKyO*phTehCo9qDMb1ys)LEB&v&%3(GjKO`ZscPLzQ3mcIj!B|LyDz(SRgCp@+) zKw%rewTAEbm*F6Y$*t2^$ok8OI{y1W5_6)M_yn#+KP8hf6OIPyk}!6*fS?$3Hkq`E?s}q`=-al_XiGoi4rJjoV6%)RTi)%m11;}XLHqH+r+s$n z4#m7vUFfGt#~@Wc=8}jZD%Z`SfnH}L+~tY)_f_d-FWay*D|m==!X2%<-y0UyM7<<>mUMuuPNT1PjAyK^qUw$`>}Y;k!FNoDk^lsqOv?lu1?iqPXW*V*uhx`!`K zmV7Ia)TdnKtAmF(I0MF4!SH_E(FvZ4uUhT;1{yD3FhVlwfc9u86P=y<~(-5266D>#YCuixVAth2A^eY!C# z2tSx8c2$>z>K>^`LQzl#+F-9TLIlEILG8$JfV(CU@Gv5n2&+FChQ76R`_G<2Tp!OZaef(;XZB=n#1K0{^BR7hgIK z38>%6%uKuAcbo4O(H15-^6x)&qttK@nJ6|u%tiJ{-&{`_o<@qp6GF5|l651v(u4LV2p5 zeE=UUaiM(DNe{Lt3lx9tf_(xH{G5bNn=r4EmG0d2LPnT;VfZtrt1ULtB@SrsNJY$W)9@DG*2Of>T)x&Q=8frVqd;a#qCs}`m7(9)EM{^s=b&sjz zy7n|z1q{GHXg8|IgrUjES{ZRY&Ytr-3<(fYdL08*4_=HLqI^9T4=p;lpo3g(4w_e+ zA845Li`a~di=^u|-?6%ks@CMbH{d?_PB(`FBkL$-BGzX?|FlO#`qcB-fE_9BhUd9d_aZ8a=5Vg5fQDl3aOA&dcZ5tnM1@W;P+kl$iT^lPf11T-FUqJOwM)_8<{C>oMTK-qF&r==9SmpxksXaIGHXR(KB5)=L)H&V4HIcX>HE2+@>m7$YSj=P1NU9~* zqkI$53BDO~^qGMcDv9HCPx=PmxZ*u^Wl@3QgdLZ(S3~Yp$zRtMcUy0P)*}OI5Av~q)0!}%T z{SXb%_&Te#PI;ggGaL=8*CKbMqfpdsvaD1Y&)2^ZbTqoc*-MwvXZ%&YvA%aom)(FC z7j?t&;A_{|Sg15Gi2Lgqqlt~Vu6p^wJF~GrQg$%%07Sqcs)@AQxrOE7L{{p*dg`g} zMze?2gI!QB_>(NCm~dV7RR4HpI73%=!Xp_B$I7H)2Uc_ojhnKjq;vCqhdr>J_5Ieh zm!t%dv7Cv1!2?$1s=+O)0VM*jaMv@78bvKN>>n7_mv)6|6AI#XdJ2H8m zM#eL%DK6hRadFd7& z<2uqxdT%yE+Tx#pLj&rTpSKyyT*E@&Ke&Jt_vv{tl9dsJ?T6 zxQLS}^8vwbo3U!o`gn;6@38PbV9|J2-YL0nurI7K8`)^R6aTQAM)E*Zk z7UMf@@WutSVt9;#$P@b?;_$QTOSf*hV_E+~uWew@gdTqtkBG#%>LJi?^t50&Yc9QG z{EZi@CIwoN#XU%S+2ro9Y*(i0n!L@|W4GRIj76B9=_$@v8+jbN`UyEXDA?s|Pyt%s zl8QSQS+KOpcPZY9toER@bkV+&ndg|gX61VJ#5IP|8q3Sj1F?05`O0AK2EI@PY2YB)fJ<9!kXQo9VLj=;CH)awJ-8D~ zU=q>X{|pqej0!0=wM=EoDI?QT|D^|6d0f+TGP03BgLOZ2A9o|uO@gd#PQv8Yr?3mw z)P-elx3YiC?*Z~3b~nKK5?HaBXlG6s3f^G!p!C9KrbjWARYh#hyd3@6_>qGFJc08| z)(CNWIrt+~9OVicb#SA(!Di^Sfq;gO5odjfz1Uc}*GKoKuB>32K&Ep&L*1p!bJFu%;R4Wc(-S(D;^Tpe>Vl`cx1&n)+x<1te5mP*O(I6{5ui_B+mhI+)%Z_q{E z56d+k#SBFlK?o-SFeE^~7WCBXTPV$YyHjByhY?Vh2aeXh_xT}yGsce5(+;TRZTWMW zqs3-Cr86jkDNd|1YC#sq;v7d8Ot;3I;kqyD;Xr13aT=IcApQ2aB=2{6QBpx&6hz0Y z72SWXtC)U{dC`CEgZF0`0BG|-fw=Y)I6!O|G5>(a)1z$s6ejOn#nMy|&hWQ7%!D}y z`2O0w1b_dL?r`8UluK|c27)`0fyHP-q(4Kukz?piojO?%c|Q!#bV7xCOIM!miG&pX z!Y$WQhnsJtZ9dkS@BQ-O=VzeA3d@~@WgRNg9Po}Y!3X+pq#zH3A4G%*oCtX-a)9gY zK-*HZw0L4TdlY`zA!)L8GSsfu%X_6H`Vs=9|D^bP1F>TUJO5nMU#VJ0#;4R1$4wTb zwE97o4gP8J^%dYqI(I!HRvKxP;4!;|>Bd}5$sefrx}-ZGOi_w_C66NW_@6dROi})q z!ma3)Fy3gVEWgv=591%K;6BLMM?ADnXoSg{lbLW7eG2KF#00OAWzu6$5H#+bk}h1G zBCxDfxu&Oo<_qXGKGWNjP*A8G66Jyql`4lg}aA zd3X^+?79SeUQt<_UYx##5{{{}pM-J~YUJ@Gt97N*14ME{J>^qtm#%;{6Y8Xjpokwg z3sjxa?T*x;$O_xyURBm#bOcWf9i0ZEOEKEqIf(0l?VL$`Eko&i{}CHs!4mbN*92E% zZqF_Web=Pi5^ZB01C77`{R}F&3WkiM6U46gmDs;E9=S?Vc+Sg);SN~3JgZN1$(HeM znAUdWOfW)PE#`dgnJ$TDe)`hXVwlm?czLY1ljL$Mc`|3ajwYk z0POd=*-ho)&${6OwhX>LV@0*P#s$gNocScUAQMwOi+N*(%D4{BYib>G2=%a_zQEE?Vf1Mia#Y6SdKB@T+c%L{#3U6) zy(*F^m6_%_Xo%GXsh7CR_~qv3<|PU|)LF zJ8a?hXBsM@gZuS5>0r)&70^#j(w@`zVXH36WThjh*(e$eg*k-|Hd!&^gTcFC*)*PC z4GO^#b}y4Abkjjz5-a6C0BHR#W7*1%KVZO*FKZXdk^($fs(B&%zX-YNk!w;})8S__ zjsJ%^sI+i;7PG^w93L*t6^3zA$zgD%bOnGeg{Z_yJ;3b{ocE5ZeZcoT10c4Uw8g?H zu)_4~^_cbbzhUB@)Nclo4@ukhCm-0ypvuZO$MGZ{zyQY8`R>qbG{&OCy4HsaDOp@M zr?`Dc99ciUK^CXfhf)PS79*(oj{;de_Y8wf9=iIlE3Rw4*-hC!#Wfyf_@X(D^I)K4!GIezf5-a04b(Z&cV1(TE%+^dj_=O)&r^>ew2w!U4 zkzrjJtE;6Yo&_A-Ex#T*MDzL(R|JzQTv7-RFLUkWY3WOVCLZp(CEgn{He#qNKGRJW z`W8Y#H2HM~5g%7s_J^sM!a6ppEs&{g*B4D*fjCA+ob((GpXm9f?G!8I16f1rMDlH2 zaX6c;g<+6Kk+q*2Sz&IX{A7U)1TtHl{0mVRG7K}4fEMhdAjfRt#)AjEP@^Ci&ZX^#T+=|xlx8tY*Gf_2z~XBn@@9a&-*Z2txwX3kZ89+ z=Y(#md%Hae)+_@~9^Zo906aRufN>aDW*kT|Qde^K-Sad_=pQ+s^`Gtd19&)%SHY3; zqv$VS&s&qi;a@+3rbn*e{9p>lkQSHoy6t$LafFluLRuOmWh4#N$Ga(fdvP;(x9z>m zJ4RnYZA|leY3d6%n3jvdLhnwyAZAu1f196s0d%~zZnqx++bAA(uR6T>4FmpM@Hn<@ z*5!M2g|$(Z#vG|DFh>S#yHM)mW%0Hy!ZoPrHqQ|RG+~vcJ3RskDuf-=gwXt+s$8cWwri5-=IEQW(XK}$+PHcwc-UY#anOH+GWkuKByRGXJ4ULDTLRWh^BFk`v zLO*KNn6xrPoh0e$ZYhg~=|FOgN8;yH!7`QSFuemPoI}|BrCgR$3iz&x?)1XbebUEg zk*jK5e8e&Yc>cxDQt6@_HzN;I%Iyih-`E9fquY!nIHvzHqZLx0pdBhPKgg_MC_Ub< z09hMR9T>FZKkvQr#bWDGZ~+&ghU9R=6{O*xVVZs)6{@F0Py;FQ&c7bS?g@gbh>kFQ zCV603RGV=k%Y(5q3XSRJ?#V!m$rR_GwbUCW?I2jV2BNLz;))w4>i$D0LifFkZ00%6 znuT7XAj`JXN|zkA#g7f&>x*uc-4pUN%hH!`X!)Wf2=hSs2=`G^qWmH1Bc`bvmLs^8(&zhJ$P^p@uDk%aBLx5q9pU02 zS#XTU$H*YWDbr&+@5x`zZ~JU@OQH|f%AcgnMco&L7+LBrOKbUa(4>ieO7ng4r`(%M z3(V0N0J7*%M+n^_duOBrV>Sr-iA zSJ?dAB0saENP>WXya>5h+Nm4?)14A?&XHQ2nr>d*;Ye-7UU-~i61 z-SBKo8M1Uq(;kc-XC*$Q^seHol%*k*I3KlkJNIsraz2Ev*J0F|7~%m2L6H_;#GoqN zxRpg0Wn9nQK=;R#9ajHOprXl*msCri8@&4$)gi#UW%)R799C)34vzmYp+!A+(bK{D z5C36$q&pb=P#9uYd^{;PJiZbu+*^h6bdXPs`j1SyXpJ zr7x37)6%T&T|#n9&Vn{vwa4igw>|*Mtn?3;ucinL-~un=q-s(Ov#<`xowarEa%B=7X+Ev;LK_1)q+%cPG9` z!w&9%Ml$t^F)?D!gnHB+XNF+Mw$&t@Y?YeCG94Zu-uQ%7+l8+9GBQ_bhO z^8WAJY|xBYzdyeqE*+%k2clY}X)gj&&VzK-{%rm|x*bq-yRXO3>z)Vl8w+~tq+YoT z?>g1-H{M`xb)K<8oHy&{n@Cf!LC7lTHlgd@+<>(-a0am)(mK5O#CGtt{-8FYrTok} zDGhN1G$ZN|q%M5JK`I!;{ni;s;{?JD%e$XoGbYL9jq&x^6T0z)9&|&iqV1x*W5a;O zK*&4|BAP=3G@%LDEqx6PEdIk|$kcd88kw!5HlEUTj)Bk2`ewIX)zj{Tn!W1vL)w7T zAq5<7CFLl;Jh7BRYRf_W`<4{n=OZ%U>2@j!I$c^WHH?dxQODo^lk zN4RAh3s@ZtHT3Gqk9x+Ar{XoC(+vc}Z6vK!{COOz((AspTH&HXC(J!sz!y-ju%_!>R^S!Nl0!nyQr9$N@AOAW^Vzzoe8fI|;D z)jdszy#6bbhYk3(BKWJ)_YSZwQWn|UFGC8Gn2tSRE7g$*BSB)#jU)I*8+fB5jGnxW zTKy36-LkfCztr8~<=L7>!j=RoMsUQ)h>ktit)G$DyErvPw*z4S+gOoBk|Z&&F^`Ed zTYQ>WDlwAJ z&4X_acYlGK^q=Jw92@jm z!nr=jQ(y?e(>hcw#&emL`zTF=j+B#MqKGXk)jVy zESub^ZVi^|3}6Ss*GLEDURj6Dnmdv(>`dePj>6jMzJ1(W1%|Y8Dzy6U%6==p>H&)X zmdXDR-%3JFQ|2`bn0@8VmLw-z*cz?T?BMgKk7;ojx-pfOYe9Kc9JXppKswIUH*~g_ z8q88%?*9RRs#1?#^OKP?CdhKc*rVI^k84|inaB4uZR;y6X;@vg$HTAKohFOw0=Mzl z+4%6rT?lGt9_0e2;43s!H-gH=NW?3>8l;=vz;a{N^-rHCo9y}V?Pp-37Qz#*Q4Vxc zms#uei;$Qk&cvs@BM(uoL?$$B%Qy=+9x~qvyH|b@Vw3zv+gGGoFX6r;64YfSzY9X* zK)Sx`y1_df?rhR?_Il=>?lNShrW4=rXAT;J%C!rv)>#KhGoa+%(j0anfpw6FEz_0f z(Egu@SgVSYiquG%&Jv9OFfXjubGyK~c6B{F7oK!G8dxB*XYC6a zVXifvYEht+7%Au|xaF<6h?B79^A&8_h$ketfV%hiZrE+q=6Aw5SA&b2l2lju5cM~KG%ikfsYmAY^YuD?JsvrPY?(Ftct`R$7|3%%F>A@^#qd_AW z^uD`#nzC|*6)n>>DF{sCdy+ZvdgNK^CS&u52oqMWZbg@Wk^m!s9?`#CdEzdlX&qYr z*l85ASAN>_zuU}X!S7UEpLdUK5N&@qC$2S@V3QO&FlCA)DNyqnzp9H6u_*vT0VK#g zB>h)*+in@+7ZVF{qTJrN84L$c416#}jnud{SFS@Wk5>*-axW9B%lQh|DM%BLXyVgMaJ4_> zI`VRU?`G!D;CLX-3`NyHKS)Ug55!k0m;QXbX53F#P`p`I}zgBS%?T3g*34H4ypn_pC1yT*_ruzx$+0S$W{4H+;H;V%zW$DRY zCP+55AZN&FeL;}!2s4}L$xhR&hd%MeP`#W^;@wPxFH&0z0QNb?U;;}8`~G|RfE(o% zQZ;@0sCwEJvrk|Te!58Map(`?Exw|B z@)O8ls|POVZ}~AsB37g{(kmQctRmh?Bpa*Ow(`orJ$n7-F^^c@~1ixa% zs#e$vVDT%|?tFqRbhyTdbUk>KfPIVtuNA%KA>CAD6Vlt=y%CcMiPJofeY(qHDD<$g zqPOy2UX(uP!w>#7T4T@u^U8C_^M)S7H(|DE3|NFwR{`s8XjTfs{!_GS`IY=y=2MjG zTX%#8oagbj+qMSpPBRwhtJHZo=;Cb%CHUBiDl`JzC6LZOTX#jpW3mWKTdwH8FBpWb zJJp%j(vycN>sA(0XyF!lLhuxq!svn1H71RSI-64l`Y|vIP3jRQq$u44fO8b?a^kux zz>%lO@HVRhLhX=lRXK5=6@+CRz}t+Xqh^*#rLIaQ8Xj0a1T<3kC(Gg@FKTU_Z`QB}E0q zbuCb>OP$GNpj1|<5%@t~Ier~5tzkz6x;rJ==b7OXj*0v@9)@eVPN46)Rr=%gAB3BP zQVJHQ0#vMpUR5^(L3lFND($7NjX<6&Sf1r^Du8v30cPp^zn(&WLiJmK)H)3$6L zd^Lsp$Ya7FepEUMQYp4G%l^hC8%QudsiEBzEqG4O)@J`T`SI5-!DwjIe4Kb~K6$^9 zsCne5JbNFu>9;Bfg&(83mO?2~tTyq?eFZv}(C3rfxQ!1{$TwbWVL%>^>6lEYT$j+Z zvJ~~tw0d)SaBL2Y40xIn^ce)gb>TL5Y1ntjndhAWO_y01rPh2VZL9v3CZzTa;xMI8 z8Xmep3h959qE}PBdEf>#qAuhDGrLX9TJiziFUP$nmB8eZvqA6wMLe8GBBTQ-5vixy z=d6B;|HhHQtD2_M+s9CB6U_I1#G-ad2d@adm!ru~Q#nKT<_a>zQ7r;WN~`7pQNiEW zK1OJZpIUqG6As=v#a%x}VBMknu@`q$X6hAKj3~}+#JrS{&LuRrvmo!)1SyAPMV$?2 zIUU!cSt_)ge(bmxgNeX)J`1)sb_2OISM)7__UTwY(qoo>;NFUuwvARofB`cJVT9~S zGyg1>nbS~DHy(r+3)c@A&dh(vM5I?_0xmC;TX;Xoh*;rzK&_?^!ZaQwPL5}(={H&2xhhY%@r&KCw1=B_N+N73pCMsJH#L0%KN{dK3Qz$7C=phY53r>)$yfev5LI zAbz2GNJwA|dP3iec$7pAcagq&^+Xyk=(1Sg{u{EZd(Tr_=4Pzh{1e}2kZjyYJu#Pe zqP=`aOsw_dV?FCb1-p82FaNXdG*dZlNwp<$#KIGhI3Mr+8*wgiT(7ctBNeyreSpYc z@^tGqGql#U>&;~%4X z^@b|lG&6L21b{^x_{a!aKDU|QOSP^~<&rf3zD&iCqc6T7U7#q>`Q-v{%Jez}s;43v z%d#n81N)Y57O(7O8kQJG@SbiXwQp_00VeCn_?vT#z$8Q2Cd0WdLFNtJA^&aG^>Jpe zAi*VCs&>gNspT3v$KD~%=DjmmpA|hyID-$at5BQRPjp)AW&+ zOMEK!A-VQA0+%rgifYlEUzn+XXNk>BhGIh)C*ogxV7s-)onZ>H?42|HP;yO^;Lz}X!|klQRFQ}Q)?C|nv0FS7$sK50!h zLf_|yfg8Pv5cA?WX-g)rv>0)2UEgkFh!4#c)A z&h!Ya-X!tyOE2voE9wSRl=C)eeGQ(mSyZXkLiz#cG8pAd;?;QPQ!39+rt_0-@nopf zEvxQN^`N0M8ELp#9_q|{-rk1ge~!W!yo^fhLVqrtEi_Jd!zJXfHEIj5_`o;*SVdZy z?X?pc&mRaB1MD<)y+^{@FT%^Sdhn$#zQcDQ-|u6TIHa6%1yQzZ9MJMtf@{+MzC>3#1$TOjR74Xu{0;k+rtg64nMhZ^eEBfm{PCrC&;3Q!OJO5IVN!k$DW#9nKH&HJ3aFV zVr8AfY7Bn<55M6aRO#@{#H(+kr?@Zfhbm3dtel~O2SIkQc24k2vBoacLYiECn(_rJsJdu?l3X< zdvv6j_n1~V)Prg<3*%srRH1{Vjc=Ub{x)TG76e!`K@_j&nnirVU-)2^Fm9*S)yVj> zyuyFak(B-Rh+SqZtgP3?I}E?O*z|X4-VOkPpcBrFp!A2!>Up3h*$dHE`08!lwb$F* z-*^ObuKw8l@c_7;{Z77r;>)I40?-}3`A|>WL&ir?onGm!S5O$e=qca{{&5O4=|gb>-i2@|`N^l$d;(FLt6y=9o~XY;rC1&x;wduEH;H8hsA?Gfv@T zYXohJXw0}uWe7p8na6{45ATznT>KT&pK|DqjNgLs^G4<8HoM0WK$wPT+=w;^USo9; z>K?i5_@5iC=q4&^)W2z!;|xvVkkEE6f3wV3HPE9bHOl1?pT6#AawtDO^2FSuGLz+p zxu{Ss_!sNC+1Jf*tAuwCo^y|Yyj+MSJ$Ixe*My%F89sX6^MV1q`aQgCSPwlk94)_# zA3n>awGe}L;93IVK8maAjt4({Wi*R3@dSo+vhog5^;~|AaiK~0DK`Jf9cDGXd1Xk{ zkExp7wj0Q5AwDV5U*BY-G~Md*zMC@TzT1chMVUX)#YxUzjQQZ4c9S06W1LY27rZ;F zyZp?lVLKxNiqoBVExCrK(=K2tZfsQ+Lu;R77?8UWi!G;RF&-c3qICK>kWHxIMbBh9 z2>qLR2_1o&pSqb;0G*c5hF>q=%fX8qMyxQUOZ?b>;Wl!EoGx9GN=rS0+NMO9bD4Je zHxyzn_z*Y4-2~2(oMiU%X4H@nb5i#p`yEzMP{NPBjN9X28AdcCD&we2+Xg6Y?|(E1 zHCcnw`qy2VO1gfR$J=D{>`qy^$=3Ge9FEdIe9bo}jUf6S3Nuo)?(FsqfT6b%_o3pp zrC!)>K2m}dGEeCh(lbmQ(h|cOWi_8nm|h7;C}1?CL7E2emge$zd}vZ#Ns|fNBDNpgGGLq#FZZ~e~pZpsUkCRP^#=U7=K0voJ zjF!Han=wvJZ;eo4-`+cINiZ7IhL_#8X_Nj1(UbH-9``z5Lg`4zEk57AxxdVPGl>{7KidFXHTpQEPu;VNl(5bCVf{ufo(qebZ3YaAC=YfTlu5x1gQW;z}3)H;az`JC$m`aw*-0 z+}YiCOsQw2zN~Six?t+%=y1Wx>>-))^Zs*2FOw<%nqxwfK1BR*fv&dN^qEgzyacP( zvKdK+dY(ntI%Sd)T`E+oE+kxkVWhXn<-&2@L60C6{og=l;_XPGnZV;d9VP@i_*=tH z&Uv4}a?TH#CS~nOtS}C}&UxAeVa7RyL-L&I1opa!G(2`mxu9!fa5hoXJ{b$P0-%N& zFZAcXbH_dyP;NgaOI*(iuvL&Qq*~K)6BZULQF9rpG$L;$Mo%Z5@s$fxfE5CxDU_w> z);mz0bIWdg)NgwdT~P;Dsk1a#X52_U@E)^-i}8c^oq@dTQBQ!B0kzLb7Hy>B2pr;r zeSy~*b3UZb^4Y~*Xjqro09P4B;39oP>qgb)hqsS?^56#_tm1mSZnniGu$Fa(F6xHc z|9)-(n+J$FddwE^E^BnhJ|W+@of*gS{R}6H5&IHIP1nO zM)kM_uYn1(_^;C{f7gchAlWY?r{qbqnJFSO2zwY_*02l(Bc4Ml=Zv1e9;i(v-j0Ox zdLHzEB)Tmj9;p8M2&q;(GT>whQ?-;cdncIaf`gMd%^aw`!TlM4ZXrjn0Td%=c#Q6S zAqz=4Pk1eq;=&KhPpCl)0saKQ_V*yhU{u8&LpDnuVf^crtES5z<4I*J-w8&re?4{L z;n0Yl^#nb3@*I2*(4~2BVLic^#%W-8P6n^N;2(P3MK3#hSQ2wRKJK1srw8~nGB~aq zUh1|2q%T1hWW*QEZ-OdPzBMR(%LDkeZNC$yqzg}fh!co)sPYX3>y$ZlefM5+8D;iH zj_agxAIlp>ekaxTlTt>S>NK|c(Jj+GK8H*q(RCLe<@iP!BRinS{uk|81n*vZ?9GRU;{D9OVko zugc*6x4H3kb51NhyLKXH^yUj>`t3|~LJtl|+@#K=LdFKRK+P(Mqnr1gwMB)I&mrlN zmM1TW|K>@M#QKE>?1ror+=?zzm;(wRLhf*jLTcJ4B1YU7jw`B=!DSjU3=M9@lv5ci;$fBMx z=4kP)Oh!3GnNa%A-auw>uH!~H=y3}I$sd{Kt!->+dXMoEAj=2V$3-}L-NTqW>XtJ! z1B2Gk3m!Xh^SP#`6PLC3VUX@RjKyA2p@HvSTs$a$sMl>$7fGM6*OM2_A<$U1dB%4l zen%^M){Vbad`+)!xdr~EAv2v)8Kf%_Gjn0aVvrvu3(Z>7)rXySAf!amW+W8}ldT6K zl;uH|HoP4Mvy~rGZk3I{Lt!lb>JJwrxn(QNBNy_Yp1KG&(T6fj906z}^RpmP+&jD; zW^fy~qey!1-iXX$rR0NmaN8Bm9cH{os=~j-hVtVjY&VyKM#HaPiUNYWSETwm{7e^z$p0 z@SEwWmTypeWh9c509q!9U!u=G)5}|LBDi~`Ib?hE5o4Ara1oy8BZb3s$8pwjPZ~A@ zim4E^HqZz%i9mq{P z3z0eE!LUgrFp{xM8M@aQNx_9Il%j{4SwK97!{CJr-ZGkGK!?*^Ujnr`MwV5$28tJ6DPmk?Ke8tdQTR@u~<~#s2AY!H=v?pD}s_7ko*FNVhKBW)-qix4qJc1 z5%3O@b$h)GJ)Mv(27HD30Bkm3^D>3KETi&p9cI8x z7DpYzFkW1Z zfUZAg!89K1L7US~2=hH;HWn4eH^RAc(#FE-zH|1s9=REv{k|Wjc7Hg-GZSO3Q}Y?i zqR8ZQ*yD6mM;XK^mz7wT)B*$ARnGBvWM6Hd{EOQK+_AdXi05EN3W<>#Tiv?!{yi%K z;~f034n-_|IkPeO*q&x!zd3Uc&Q>!!y2N%EN3vPEjrpKq>(C>N)L$cc zRfh=x`aE|Uqe1c3>7dOh3PA$Z7m0@>SSt{)9vep%^$I-W(gQkkS)e(UyZ$=TYNspw zv(qcd(iTPq8ucZU(s4vL+ZdZtg5VD&O6^`W|FjgP@#Z^)(+Fbf$OV)>+8)1D`%(aLLWmRKr9$MJY(QT1ky2&$nDE-YBtqr$#e zy*iGF;^j=8(rpd^Q$IVD`XeY!btG~J2g-Nkxp}d3iW1srBi$K6EimcLKR()w&RiiV zd+LZ9|CF8&JTd_8tvDGWVsY5DVy^tQ++*^jMEC3Q5nMfwsg2>g4B(Fi|G%UWWKT(Ygus@XZ69fZqQg6_;|L zyNe;8c&Y9Q+=<3lM42~@ItOJu{!G}KpxZtMQEom6Z%%LU@eKs>pb2A|uE&A;P3k0; zv`c=32qR=LbBw;(@(}2mqBytRjHJ#(4z}8XNz#5C{J6pG)yMpg@XK9UUr2g?N5H{{ zc5L75?ME9R^`{U^7l4tc=|<{~0}+8mUTKeAdSwefbESee@ z=KL}be^d5eXt5>S!2^>%2C*WruU%5KA9&5llUfq86^Jy$lWoz#TAKuegM?MQZh%p} z#;<7fJVk_B_u5E8l?)x`Ds(%HQOb+wm!ywds?o>Cd|<@}!K6l{?Zz9a-jIQ6gr48e zWiq9*s-j?^X+)isc9sB}N5hrquF}1gQ-)efwqLr3`-3 zD2Q%k4vb26cMGwOGXmA;b_$52FA}N&5>=K6EYYx00Qv~(511jI6KdaC9IMs`XV_K} zBF~lW@SO3hBz3;V%7Wh+J|m?Ebbo-OAMdp#QEEk$SOt-F!)BTOat@MB|G3IGw+!%Tf;@^j_@7DeHhddCFD3b(xbl?~{yB>; z=p5%T%I+}eJ&8^&uqmlIX!XGTD!Vd#9KwK>xv*e8xsg3F{P-?9>k-Q^D{}&jOU3j7 zS#_0}eMX$$IXv1+7{zzT^xUNbx-*lHrUOn! z;bcxhP|lB_K-X4l8FS#D_#qRV$sB_NGZ2zFFv{O6#VnC|qLbSnA4RrF6&ygK*It|V z(;dL(YHq$~L%0hKx;Pp$h2w!^shiJlduM}~VFRV@o)1U&IF%;m;I=eiw-Wc}VLA=| z3|HY*ZKyEQc|XaDvdh=wFZFMPnJ#P}UAQl0H$R2r`-94Vpz^;`iA9v4!S#mxp$oem zo7=0YbYMc9R6Fc4!8#JSBUI`h05_)i6=#d^4s9Jt=d^LBw4O7i@7)yX*U%E|UW-n+8hJZt3!IW{!xEt>4PlbKzK_hpW! z>iG@AFy37(>C-_X-6i>R!w6+V&Iw)q{DQGrzBnSa@pyO)K(hAok?8^hDihLnVJ9xW znJ%s?ogIiYhW}$d5qd5Tl1`(moP$(u()0J6xJFU&@~;^6ZVia=f=^$*xH6^~3nZl{ zKSR|zG__yFJ+lclE!GX{ksipprMqvCqdKG)Hqh1?%+e1Y=Y_=t?*Csx9lWFgiK`fh z_2_rrk2&O1VYV=#3h$SOF~S~V{NF6UX(x|Ob|c->q<^L(FIKwLk+YdZq$rCiV|X$2 z;B-)1@I6b9Vb7Kj$k+?gXLS`R#wgO%Ak$}XbearSx4EB}rH5<_2L254&=ZLmtX7r5 z?JoMeKQQ&jV->9XPxps< zBl7Pf|D*olhvDf@!&O-)GsUivf>oT44t%&Cqer7*SUV{uk!8T9F*JD#YW@xKF|84v zl*WTd=P;?iHt8ACN`OXj$_-3j?Mq&wJFm*XwM_(n_!{H7_?Xa_o6C9>$0w~1OakoUgd@C4dftx z0xX%77Aoj)c-TQjv~D@AYo(X(U6C=L(=oV6<9L|WEO}M`y$E@w>9Sti&7)bB%CMEW zci~cx)}7VAQbF|&CuGP99qm4m1=`ITfS;xDW7`KFl=bEKji*>yBK-kCtSvJJlK~ye z&B&eVE3gcgpoHYJo&6hJa`|dz49@#pf%jZab4Y)`=W3|ATcLlE9Y_iQ9v zd)>UI!XG=(_SzNYk%9Xj>SKYJ(nnG_Y?#aG>QuySJ#z4FG$Y*2pv1a$}cq3mu;aZ={KZoY` zTmpMh3l2R0J4{7MJO9|FYxhdAyA9iozMq(&7d9I;7~jh$>muU}N6btIhTI*DfFsd+ zbO&dDo~BG-@RXENZX0IYg=s%11n0gBvwhLsa*u1zKnmo1ki1|uA~$QeGN>UVQ(-XI zzzCy7q*k48dKej;&&I4LcU+1Dr1Q?4wA!E<< z^a(aa+VJX!4B;@JNUG-|W7&aMny8STxT7iZ=cCb-Y2>P#9nao2l2nJ8Y&p0GeC1En zT5<6)u^@$57oMGr6|T;0#c}O&)>Bt=4A;soC*tdyEEXU#6@1)JfAxS|Z)~ADmGsB| ziLGvq_uq8TpjU(R&^#M~MHyH7TwdL0Kn4~d+%=5@N^ej*UoWS~{B@X4oKmsp43z!@ zj+20^+>zmHjcsXG!I!!0?Y)Juoi*<>GojdbV>!t4X3Jnfn$dw;h%$cZu`_}nA>~n0 z<|WG*2$?)dKtzo)=Ii|B*bVqw{U$Nn>lp9A_p6awy|Ps=PG> zDok2z!Ki*vS30+oOijYv6CrYc%cZ;IpiNy#sjhyoGlf8ku|B%$GWMGLo50rXdh!lS zpMo$_`uX}kO)>;N&Akt@Vbieh3ClBuex>Y|t+X7m$dd9f=&?yUAL+##`0Lo5DthgA zeK8G`+wF9{kX>cx_&J|K>UBQ-_w_5 zAuS5gM+9bGkhlB6X#qOmrN{JYI<7UrEBT3>cVG5yWLZam<8o&U<4>jQby?pEMl)td zy>;-D!bqVa0(D6Av%9=cmK1dOAzkP3IJqH6>DBwVe}y|8*@3&4gD^mXoudn-6lmHL zvIByAC`{IM%ciJIwSjIMPMk9hff;oF1~psALk_lcj!}Vc#QjOb{KeuM2LHi5VG z&jj|C;I@T`MZWWHY1@ams69E z8Xgd0j;W_{vYxydwshmAO&!|{i${>_?UCHdD@Wk-1>YZY*2Y!J#HyW zkvv8^Up`DOz>u-R8LLN9ACZon=mysv`F8+rJU%)(MsMU;8^~C&1w($Dvi*63bVIH@ zxjY+b_=przO9(hDn|QP5rK~%OXVxjcpgT6`*?s#Q z`9g-r(aqB5Y21q<)Trx*ED>C=1v;>fSWDR*e4Y@^tQ>9+6rWeWKL_znp?IY4v)0V*@OT^}I%vRBtvQt&4)>_hy-lH0&HC*|$oph^T`cO|g35 z<})ZarUDYKMN$XBC(N6*NM-XJ(OmmVb4Wr`jMaNvI=orPCc-4cf>!3Gr&L8JslQL(^2N{ApR zC}jYOsE7%OexP(ocS?76iNsp7|FQdQ@A}qV7vX)+*?ZzQv1ew}KO7BEwq27|5+iS{ zRGSSOV#`bSWOB!01fFM78GuGsWx{Jl*tnJ7Pna>f9 zN1U5QEP_|qM{qbvr~UCZqEjS3xyR7@$-1CbD{1yz41^ZON0 zC>76TlWJ{Dkv^Z6(OtZ;4^ICve0{8Qzu>aqf=*batMYY(n^Id65(`6EdmndIQ1@6Y$v|mkG${4BvJUK z^uS>V`Bkl*<*S>5CUD8N8a;r+!NHQT{{a*yr-AQ_$HPcF#uI&q&ZCBJR)9?01Um4Y zxJJuLni!>Gc>AkD3e5K)i>?nJUV~_*a5I%M09F3kgb}Ha?+~OC=J)}FS``Zlkt5gtH4LXPpUQ2?p$&70HcuE*N}RAue)|ACO;&_ zu&hBXBqUV0X?HJ*5F;Z}^;1@NqbbLMicZe!^a*98GY=%+eMIBanvGAG8ajox_>q05 zn)5O&;VwFC9EX=wiy{d)bU$`W=bL}tl)L&xD%~5I%7W&&pmU5WCALeYKDresL${ve z#1un!4JB?Qb{6oq$awy9nwVr_r9*Uf(5u}iJgfdY+DI|`Ib8nO;1smcO+MgtNi~GS z!pA9`K_q1$AuNUgsJoDvD$PyG_x5u>ng_-Ib4^A-Pz??Lb*5+SGvN73A(x)m)V+}2(aH|`ajAw-)Z#n5xnU3xMgWEc~edkMz| zSapmE{u-k$K4>!bC6Mjd5Ct5Dc|hv7SGP1sZOJyjZjgpRf+1Hm?TqcOm2$qC ztqBnEy-#$pu+?yGpRc)1((=%j0@wn;&1~% zkK&hH$Bn)=V*8E;?MI%whZOC5SF@`l7TQ(wf1^^MvZnM7)}q!`6C@ zHVW7QOzKHfgHm-<=M@-ayGcf@XGT{2nJSIYljQjt|hzGE|1m`S%;-+k<-ZywJV9(s%m-p2Z)#$595)cIaKMuJ zIh%Lc?EER)N$E%tFxxKurb7O>k>X9Rnr5CXDI2_8S=*z!>=9PO9bdzfy>R@Fr*0aL zdqZr6-~qY65%+eUq5Frw-nKmm9ZNJB|9pBJs7hQB|6SOyb_g>M2#iwK&~ur)7=aX}A|1s|)QBPx?8r_TW;~rIAL(<}_O6|=E-4fNnP5WnB{>k8@ z;>*wI$&fxOC4Xzd49nCEFUE@cDJr6ysDh?1`^Iay1pLI~LYSv~ z8I*tyKKH09+hzq*#PU`67X1(5QwfI)#d&-lubB7k{^EkvEI?9yc(JWU4|-mNWBmJX znA_pqZ2W>*L{>761-xE7h)X==agz`Dxrfpn$3q8=EZZnp_eYA^)sBajrrybHl^1N| zwN6uZ$iMPjT+|GE(&+4=7QKJG(^i~}Y*IoZqF;ddlxnYtIBQvkj`vVlin3(LMsKMr z5ye`=qE)l@qnELB8`4G104`5rBxa$_S*LD_Nf=a~KN_MV#=3Kn?qPUZt-7tp-V|(K zVwtRPQmxavbcVO>!fNT;iZ@u)u;F%ImVR>mHDvxO+b_e-*vq(aChztSRpQ83!_?pZ z92~gX2HU42a4;cT5cv5ufIJ z#KYO68gZn}<|SW#<##hl)t}Yc&2SYfssBvGM`$o|{j}$`i*W6{=+d0BYtqI)EX+tl z*eJRp5h%+~s`~}M26>ogi@#syb`$!gepTJx(d>#OV0ohRe>aIC4Y0G>4DNGZ8fh&o zG&TQ$XxA)T^ibS=>XV9=PBQ_znLMXH%q5JkL1C#|_{|C$!RBIOlTu($*Hd$4RwzGi zqkH;(_4z0nPYOa(j=y)JW(CA0$erihNL5qZkA?Y3a)4Vi?_AT2@BbnbI=@(~w^8X$ zkFpOE(r!FH&RCd}7cuq`(sWE#?q1Sl(UYHu{nSuC5}}3NomF!MCN4qAp5P4bz~WQa z*;`l4UdY-`ndy?0+1q_(YyKFYa*%DnRU0Q3)A< zTR-C@Fsn?`UW;|L8003HbiV{vg(Ik{X7JUAA8_e1!Rb zWYq)E{$g@8^j&p1^4_)6@%hY=F+Q1jY_6sI>ulW5O6j*!1 zFx~SFCX=!pd`{AC(l9?7CmpHJwas8S68c@uCMz%q(wcYdoYcp#W*2Me@xSW9bHGDp z_;9#t&l)}e0wrqt6X7f}2wNDihvYY+RgW1=|Hg@BL)m%y7L(n4c;t7>Z{QQfU|`V+ zY2VMLI?ObA;06Y? z5NNeL_X$>EC`d7&PqOwJ1!Xx68sZKfb7PsZm3}Tu7SnW0`P+hNnk;*joqs<uqB5%?gbhKVWX_tADh4~gEh5o+zaK5HpbGbV2Iia|aznLBKyx=14;T{|IoQ>-_> zfE1s9^iEcf>bEbbu@q~>X+wXeI5(A*{I)~@eWmcBPEfMdPApV29In?0KZB8(P~BH# z`5=7zDr@F!wj3v>oj)uj_^Y9Oz20sgbG}hQpBe`Hnu#ik{l?qydm402Xcgb!deTT$ zB!h<;>tvjCdb&e8h&fS2Eh)4Nf8i4;G9_hTP%qXV1u64}F>^8)gmqv(Xn|AOd)AZ7&?Dr`g?l>B)y}Rn>RKc?p>fojn=beUC&B zcEC9MWIv5$9Ag=~pW=&faLReOPL31{gVuFKFmCKIO_ULs->MD6c7B{H*ag7P8dvv0 z@1+Bt`oM1#v8rNM!x(n>n?+igZnN{O&HONBOhLi~3uNKtV~?}>y!O9i&!zFGH)0*+ z0?Ue@KIc8BEYQ0mJlB4x=ZamaX93)~#h*-M@HUvYr2DX9zF)%k9ShUQx0r;o7yH@p z5l1$+xijYg_AiV(z9Q6u561q88L}{=L$_%zEn0TTILuYQ#({+Q9N0KvxOiWOzPgrs zUuBgrTK=AedA+jDE+RqZm5Z{TtEpY&q^9&WX^6OXX8<(hvj?>(V6+?KD)WO=@b+GQ zeaZaqBu%KlPy{~(E1+waFiH!EQ#sY*D5b|MOJICZE#s4$`P<$ggy@ATuchu#ET3xR zBi`S+HJG@Io*)A^v|r{OWhw;SRx@ST+H)KQg( z)!02*!ry8Ad`JC0^Zy-P&W!@yM~s{2)YKzYY{sjY^t$}j`^vT*+Ss+h8qiSxzh5?kB$-(pnFj5H6z0Hr<8i_dW zp;X{~Y$!mTQ zp4GygeO9XIwZ84M9ItvAvp)*oy4e>*tLIwe2K=l|<1kZL&%kAd{=lGpQjqx$<5`2_ zqPFdJf5cg)6JnTQhT%CHF_z%+n$#RKmB0U{NpK0Sn!Ex*4@==~)x#{LUnEU^ZsXKs z5>KmTb5CLi_@YljVIu8}dqD3g&i1-R_d#`2>ta|w#xt2gsd{zLK+R5|S`1g)kcP7^ zqK<_ylo#8&{#3QPLeycnke+kptRs&C!RIe~IeTB12~u#w0?ZVt-)s{spn+PtUEiQn z()Hu#-AJ}yM$i7l0UsgH269LNgE}DhUsau<-TE5A6*cduC3yR^9bWRn34X6egAa16 z0~k!ww)MOfri9XJf}@*p38$EpSIc`VWa`O3^P#=22|shpeizccy1u8LLSalODp=R5LV?3zG2^{Yw z${6y#rKTBYqJg>eRs?72SROEYBIkR!cZPo1uOF<2v+``c{Vqge1HMfYcN5Vs@v5=i z)JVyik#dXK3g__QhA8JN+=ES}kOd8pt_T?l=8X;icqHT2r=#!c^x3quypyVLnQZ^t zUAEETj}~is@qazIPyM)wTrmMBYFj*m+;;4H0*7T(q$3FpS^^EG%3GdAlY1-l}xcWX(z1)eT!(}P>DSh@V|4hQCTev@*!Vx@f z-+_hA=O{iSek^ynAU(0#UE^-wt*c2%$al!`4>>W8fK1X}H7@-(Uh+Ff^Yr+mWLyBM z5!RZe%HC_6Hg!-9pGYGkYQTfP-^ilsl!f0@Af>_dG3J~2n~Q`))aWkl5{<=@$!I3I z1e8$2adVcOV9?K@DY7pWSc1g`{dWY#8C({u869yIOdd047iRS$-i zOT{6U;hm2`kO`FJ}Y~eWc-}hn&E!>e=J3ADHnWWkw>bzlf68B@00V%(ak-4Eg8cwOMym&>C zy&od;R3?vIR0|hS0O&L?m9)<@352Asn%K1~@)$qbVAF>qu7ScOu=@May_R8UZvsJY z69%!Jn9s=i5hg2Fg1Dya1Ze)%^p#>ThHwK|i9J9&%GA~I?_fd8d1TRI$1|bOs6=n7 z5e;uq(?7_9f-}y!I`?@&%VK$wDtdSNJ(SGIAl#+}BVCKZ3KG{WWnSnh+aLo60&c5c zDSRE>u0X6t-;$@PW}TeQe7uTA4j6G!c{gMRK^!%XyQ_{X`_F21T+yRB|3sL;xy1dk z7{|Z7XMl#pYSoFDCSwiXsj-tWGM%b(-0OgBFPOJ%rFRkd-46iUXlwG zA|+s3latAw(I8Q@2$_PQpeJYTgPJ&CpY z1|&4+#_mDTW!zrr6R&Zg7j2!nhY0jAdQ1OlK=qzX4BDsZi1q)VYFuyd8RDfvwR(y?*2FN{1@Q`p2`qry9o(vfj~qC4Tn-+%DAVtCBp|_ z%ff>&E#0kBm<8DP9@5DBTm2z5 zuRp8GZ-^>G;EG5Pso?}n?F^m}j@xgTyXKE6s^zgb$QSWK??U5;(rS4UcSl!)ATjYN z_`Y4>Y5*k%()%qK*XaH|Y|NUfHbWeN+X+@!6{xRJp(21h#F7k!X&EZzjE{XWHj7fh zJh$&C{~sR27`Q$R%KBp@Aq_2Oa4%mQ6daI+pzpcTcv=G}nR&2e+v0{^Yo!OC)pV96 zPh|#Li7E}rgGQ!tm?%cd5|mS|t8dUMK9mn{-5_UyR$B-~xl!W>awa4hs?f#VJ7k1O zjYl92p>{)3ffx}1UTP^4VZLw*^YigKEKWK4gtI(M;jEEt#~t2@j7jC>zR2?o zOVbG$nR;{QjIJmFIFL1t9;00JugPdwf>aE*z{ooISSEksi{E4=GXU$T=hCS)K!>Q7 zdO}F^gAv7ArG?o%xfu<73P9qdW}p~;9=td`>`?P z^JU@w**NV~)TxrE0Oi~P6o&NwdmtwY`tCzTnu2{2A>1LHu>#o!X}92}%wN`8X|0=4 zqSO-S2;kjAeZGo+GyNtk!zCh{G&2W2e-lOcY$=fPYom#UM^(;vKd zXqutO2*uxUK-8c#NH-D8h9Vx%)Za^Z~v>fwd3F$6BU{L*+l|kd#C{{mk~HTwlk!-)_r2>+9-}Bkwsg{|@g!;b3dchc zNr+VSGfb z-bdA^*hH0fL)t#zPR|4ZPm{*QGd#LdUk-+_bVj^TYnP9+w&1rWK(B5W!kAaf8i^A{ z1uyhajhPOr;WSPKN7bs~2FYVGAlh55^JUdnSbvLgu)ioGii> ze3lAFMar!Qy9lxa7u;^ort;HFy7(md+LGF{$*#hE7?*Jrj%Cq^c?(W4t7 zDO0Nsy!la z1B0bn53s!vX*-}{Y{IqS zt9b3XRCJpi4TXJ=`6ZX6%GwD{w@w+tD^VvBI{2=^{;K{Dr%CGFiOKjfi#cS2Z ztROHVTB=!nXE3&gQo^<~QXhClwhTbJDnn|G7X*D+#I>(C)mpsXd)p}~JuN-oku21q z6fvtegHiQH=vngrJ71=2C1{lXYN$`C zX9IY6bK->ve*ab|r06!2RXB^Xav{kwO)zlwXxw|&GYWlhH%EyAI?y})2+V4g+6;ZA z)*rF#ySNXVG&Mn#VI%w{pi8xp=rb|dey&#D-o`EJGr}^AtLJqEsL$aaE>5@^%&qZ% zqyFs4!>={Pem6)(h|VKgUx=dzgwgX0i$?+x8CGIAZ!JjA>i!dG~sfvriZ`k>v zJYP?@s~85v=Og6!ZL68xeBA%-gB(^z2&&rh)g~QPMv^R<>3E%okCJnu-dwuRQch>Q zb5r$nW1>fFZSa^(ClmN?@dq$zN_t=V0lY0nHr*;}h{P5E5sw)5n;m|vcR7}l1bP0t z0Tw*52tn%z!+BreUsiaT8=&M8TtAD9^%Hw0>U-Vhj96DidPjq^rRqb8dZ2wB6`YhG zOTE2f_@a@0!RCM~9?pnPVy@5#)B3;u4oH{T-na5BOp}K{$_YtBUPnY_@JDjRH+(ghGx9x_jnHNXS{QdYIF1Z+}+&c z`BF#nw$&$@nespZllY-UXYNgu`$|5;-mQuG4A7<^_$yLcEWeD518SK*LmTZR)ya7L z=cj3`>TMc{w`zMJW!$iG;-h~oOf{(nGjbzk@jF`Wd(=b?#9kmm#1Cv{r-SBDzmWR{ zrr1U!9`+Jnc^1}{!J*p2k#jLsKLDWo zWgt>8H*IUoZa?TPMHeCOl8#(vu#^*YSs;b=fe7eiN*=6zbdvUAOiu|keZyjhU#PPG zw0k&kIRp2(P8C9_&k=4}VYYg$Q~O?Ue>58^Fl56<=4pt%$EO3BxLwaQJTwx~>c9L< zsy?%u{3$ko*53cP=Rq&LD2j0K+DO)yq^tUYTi$$dP=-Z!Cb(-Tb#O#<<*{Chkt6IIN zs@jbLr9z27dv7?=fPcmo8u#;_Dj3lKZkwuJV&FwsIfWJQxr=*M5thsIrSA*Uu$8R6 zzei`(3Iv*q=J!dj^XOK-0~ga(&27e@U*q{tL#52p0mqchmCC|ysdM2UxO0ShFN@oZpo75k|rBIWyao|Hqr1xwK31yy3chX8a1 z$pT*pZ~5BX4m@M&m6^*(!K@$u2)^@AXxDtTb2IlLQY{eP>e2HVNx5eG^LXkx#HHoM z7rekh%8nT)HM9=X?K51ZCwRG3JlE3 zeTPyv0e?tp4ZRUx?;Ygxu8R6Q*gEn8soKNimTsu!6fH>1W{P)`{xtbZz@jF10V#{G zA!Driok>JYx)*wQ%4jk|UyFA!X?Qd1CS5gr)#5TP#SY#KXco+B=WYzapZjos+^`7@ zRxK&`Sq5S;Dt$+EQ%foG1Qmx=RoXp`V%skn*DcGt*@BuldiP1V^gs=lJ*4Pb9^umU z8mEGg%0^_nn?;~+{RvWZ{^G)6rq9OCblKkV>nXOM#n9>G~V_|_0^MAy%| zAvoa70-F7*o_zGmgNOo5hsgQIIu@X9@TTQX|zu+If zPZ^UaNE2+K?zhx@(lM4f#fE91QZ3%$xw6hF5p!3noe;blWas!*^OwR!NXE}gQWYQ- zH)Mqprb5_Q9*TVQLrzAas?Han!h*r^j?CrkB7>=OYjkc5N_7&nHBsZcZa^;LaE(RH zxiX*=RnV;tBv?ZPtKf2%@$sV*tX<*xBvYFn;AKn*wWpf&6L+E6>@l<+dso6k21CLS z0y6i^?A4H4nEp@`F~cYcW1Pu$;(cE(L1eq%H9-%~U%Hr@Yi(=4q3+b`NzRgAf5Gdv)=Usx%v1Rlg4#u2`FMW zn)Ky4nY#IMR(sRG5XXYZXr56`&Q~?i*l+<@9x!g+$8GI}O7yGiG|Gh3fBP1zm&}Qj z{IYf5%|Sipb~x8Y^mvVNoUCq)`K<$dIaF;Wp%N}E2$5@zl72^InRGxOHwook+=+I* za7>aBGlUcewCX<~rB#)PfDRvN})xwp+mi)Ba8*dPeK5;lF^g~`bG_#ud?h) zE@F$P+WeGK#&x3kv&L%={f@V(a7D)RuH!rF$LLAPQ&M#Z#0H$^E&;@ad!QPVQAx0@ ze+MR3f%W)%&uIEQ0Ct*D}WER1jl30FFbV2CFEPu5yZ4=J5Ea+mIjxhP?)ih_4FHp ziClMHv5)0$Grr2;o*Pn`#^>o5@{svVmcZ{9rS<41HJJum&xWbNfL*+mptS)gQFBqT z=#U<>$(7YIcTYnAb1wPfdMvZO=?|K_j3uF%o3CVZQ3KT43}B%lR_E_I0X0mGd150S zJ7d|ZYNw9Ei3LwX*!^n2hA?r$GAHq=7j3aS3OJwF@BuHsIet9 zd)T<)yK^4_#JFa8%ms}15NeEjIp)nCC+{GZ+{kXpXee2t`rml_pQ53Ge~t2Dpoz%_ z6evxjixe?^_d^%THOMbg&la9ekfqVJ`h}F)zK~+WHhF(iA{a4^(lH__MOR5OCh)sg zj;hD-GSU8+Gz6qyhVYKk7_8<_9%qGnk}jz#Tgve$MJ3nfqi5QY?P|D)ZY|~?LEs$x zRGsaUD9HbkPa1^cx&TFWTkRsq-ik#uEMz=IwGiLQAHp#}q+;(4E2)M;;o)Z@V~}yU zF#FQ{O}uOwpRUrEN%BLBvc7!_GBrE?XC;R3d7`I`Fc0UbRLII3!L>q0Vt3Pnt6RpLtxmNNu; zvpFE)w01aZp75Q;_wZ|y*_XY3G7@l(Wu1DUIvqd0mHvOx1|O}KzOyj=I3(>n3~$-} z03l@ga~nw3Zk(~sWSZj>2(OlXr7_{~A=aZ>pIEybfK+I5JLaAcOX^=BpumGp&yB13 zU;^TV{d%rWm0uVq{CloR%UzuIVdI>h>Ro`JIj&J^;=EdV{_Q++b1(&GlR@Ur5S{Y0 z&I=T_vakpLMUz)v05;LD(r6}qT=~FiXqV5kf0)F+KU`y>>)clOZ#8PdSyjGBWz(Q) zEd*N3X6zKf?@@l9Au#FH^rUFd^!arRpVs$%80fWEFs;*Yk;*f1g!71Wj2ZzL&*$?l z>tmrZbxgE%0;Q-pc?>vI>$Rzpy)yR{uVn-{T#@S7&zMz+zfIBGV=e(6#S_SqDJ$2X zdO_t@o&h-P!`d_snla!(b-serZwSPu^eMj{kAx3Zo3DO9I?HZ?%w#k~svjchzygW2 z2&uWiBFI!g@VUT!Y{dmnvU#u6-qP=?fJzNGYE_sEE_L}P%ZH`T62m~s2soZ*ya%Au z7T_WiPG8aN|KNWUa>$>63vJS=Z9_jdSk-=dt(bdq3uL&V>a(s%_ikQy$m)x#{Yk6> zDSq*;22~TolP8QMHO#CKxXL6Z4m}>(BD0nI5TF+vHA;t`b3)kNA#FiiI!T|Ev2QQU z5*fIpGaQkv<{dvJ>j=k1%Dx*xL0w4@t(L+v;o%etl<1~9qc!gYvo#dXC)zTkS%XCX zlZR^Uzfc~-{UML{KS5z&D$RB$Ri`^p+eg_=oi)fbgxVGGu2H%v&jYfR6fE{)TWVdO zfT2=m!C;}6mayAFV;!GAph;S)D&B9GAefb#Pf@| z6uN@z2TSfyn=x4A0RqDgjC`LAMroh0h|_ky!RQ23xX1-_@Cy{wjR80)YpU7P57OY9 z{5dN?(CPu+?aB5SelBB&pyUI@WWh#p*A3yq-`h9!$jyf^pa! zrgD?8mgYQErpdSgd@aGIP42EX&bSfEw&G3bx%xZZzunkt!rH9;ViTqC9h3wV2kz_B znLWtPHAS6h@+T)qhYU2FCQxJ=&wJdRF`|IJtBG2HDgvz4QYyg0hqw)cG)oRdo?uAE z)=}*}`!T!fBDb%8ks2 zt3(Ho*QJxtgX?EiX~1pv2P#qlpq3wI9pHibq_*3Vl16+_GCm*Gtc495VN$%5+e66- ztf3=`j6&S(av%|r?O}+Uw8tQU@M4LGSQVWLMdRXaT~DNP7b@ZHkl$_@vP1Z+0rXBj zN!+QmbtX3zheTgv)MrGxpSrsnw`khP$Uy+K(_GTiA#l5&p=u=DN3EY+{CLF}PW zulVB^U9iGjs#gcSpyvi}l;wRc8TaoX2Eg9PB{h=6(P5+UV?P+Md@e}VXf;|;tq#DL z&S|RUufaLdUfRAenxQ+ElnWgcG)+z-O@ra_;OA5MD=6#ce zD8;b6T3BT&ocuz$HV6zw>US_9DjJa4GQ5Jay`-Y!IkuJK*H2RjdKmmRo=T?|N+-2C}91;Tu$y324bu zk?%!tyv?~BJ;Shl{2=SMQsrc%Mt4GzMCp&s{CI=)g?tW?_Pj)B9;>?d=zh^rxb(o3 z%FSCeP5o;fA7E$$3?{i<9M4Y^w{E>KqC;6hH;SQYhc*rXxztBPcxOPr!eAV-ZPyThy;DgWa%P0@aHM6**-N+Eu+l7(}LV?b{0zZ z5#0j}jj#RQVHrmCAJ*OI0!RObAch|zT?d-fywy-Ls+0e~U{ z96T=E`N&v3So0BsU6Zk!6$MB+w;vYpkumV;7w)+L^9UoUxE7fUx4DG*K99FZuj2_< z^jfuhKU9;klo^s7T*df5g$%96vv06rgDy6i(lLB5p7O?PY;T7VeH$Gdk@^!*1^YIK z1#CsJDVaC+-bIYorJ8{YMvUP{H9Qq9MvgCsi=W_I~~GtGDKu1917 zbn0^(CB+O+#j3I^84g-d`(@Bl&F6tRs9rgj@QNPXGlp9I;56>l&wZ=`by|FGcZ8QA z4|D&i4l~uIC%|8&;+!vXs}=-zkvb%ED=i;t`g~J!pSh}}!ALnTb0=lhhhJLRMq}81 z!$aU}6Vu&^f7<6LOE|n8vgGWU&4=_QeYFwK!TSp1CSNi?q!tfi>l}`FgW*X#9alJ+q60Z0=B7Jtz#em1|M3;sY+!G@-!U;G8U11m zJDQ*y;gP+U|*_j-M4CjAfcG0`xDp)RqXeFH6-aRMHpJSqx&( z24vM!7^{+0`8`%H?f##o?gZxPxAAVzN$EPPDCY%@DX|@(iwwDP*u@&VzxaTq{!+U$ zn^XjX0}4(_!M>P$9EY#BpMm15=V4^FJ&htjnjRkUDb?gLIfgCm<&DRng;n}^fKCkp zv$~5IYp(0$yzB$XFq!xd{c^pfFMQAa0$L{-~XmuYW0!vqg93-P0*sQtz z>}upQS__^J-j(7L`FGhMkzw1XL(-Ye&tO)uIU8=kc^x%!Zo|X&Yrbe3!SgQ!rV?PU zgUXIKZUi7T+DRZ=2)C*~sdDY=t>F}{@c_<3p@p92JRb{4w81=_A*rCdr%Ss%0}&>& zV2c*u;ZNAV1i)x*L}urukE5o3)lHEQ&Q!~zR9r-Cyg?rPa@{~3!zdo?S(u?8Xk{UfBT}Pqh zD$aG5syq#!mN^;WL-+Ny7F9sQB!{U3!9Vc&TlEG;zMGSQACwJ>l4hw|4zfT2faum; zxE8&-V%##&@|83NX@V7=O1RC^hVVWiCKaRSWEnaX;Rn&W7X3Mg$AroJ0VzM_1NH$? z?P}xxMd9)XLox1*`@GH{(d=m=>+l*gFpAtf#MK$S&JDKOFxDIeNhZ^XS;{d2UvW|U z_;U$l#NBsPTOWBZt1f)#C5882!_VES>tF<(zWg@lKi_Lo>jx{Z>RFTZcaF0%4Dy1@ ziJL5?%?RF2VpSiJv_tQq4-qzbT6ZhdF!Lq>d4vI&(pApnQ25>e#|iTfeF%05&KB9k z>b=t}Wl(2n;S(y}%xCy&PdrJh%@7!4u6Nz98Zuxt8!F0qD$OtS*9YDLI+eKCeBD=V zK8{K{hAK(Jclf@BAM>x`edekx9sP$L7`HZ6?+q}MjgnQ-Y%1GNSwk&n8Z1Z# z&qZPfNR$cJ4RfgzIL!?0=P7Su_{q@{E)?6~M*{bocL1(>UAg+ph)bdNC56YdQC3@^ z+A}!!)jHoO1Ic6=@Usy5A`66SR`uS}n3$(Ja&7>(n1L}+r$O2&(vUvmc!XMaVEsz~ zv2#Yk51G5Djoq@81|3#wx<@KLp34l;?iP(6L%y84snJK8>XoWNRi$F}bz57~1vknt zcH&dNRYQ+V2-XUaAJut5)-FlM50;=dzQN(roJaTv4tnqMLuAl-5E=UaxpIli2wWHOb*K<6{O0Dd_9XJ|k*X~WoJy|BNi>k47 zW`C5T;I;bZfSW--1rvM~8K+)dnhAC)R2vUEy-O{pd4NMLM#iznSZFE-aN&thny_f| zI8^NRmP|eRj6rPSjHL5~%y_AFjiwmu;Z)RuMp)z1`$)X`KhbJDZ;WpF8KKzCF) zhG{N@ZJ%N!hs8~qk#!@`oZS6=olg7Do^|Ld9@uM0bpkTBD#Pg-PB$+dO9lH1Sfr=? zx~ZmPK#V?vkXjmSH4FhCVd~HMv|GSHJ=F*$nNsD%nX+*_fX`IQ`hM+zX+NI1H7>N3 z;&Behnt5mdi_E}y*Mhdc*)k-oR^uc=O2KEKr9@4bNL8>-5Z;PmfDBnM92Q)QS8Qtd z0rp@L?{8bxzCMk(@a=}s%ls9p|7J47kgPITZ3kaIniqH^fh`Pi(oJ+Dl@C=X42a?4 zGFj)e`=7kqsh00j9gHsSP(46VGTKJ^2+i${*0A^ z+>BkU-$G(l{V7}AHYUh#5Q<#3c9+E+Fz#D2!qxvo%2*|#E#XRu>UP(pp3IQy4ArZX z9Sl}Ve|9Jx*^MBCorsYc62;J}{)sAiVge9_*`c2;3_#{w*7kwwz4{DiL=EHA>bnw3 zwK~+GNUN&w1e%D3uB19!$?7irz+^5%=AA7!Ax$h#>5NbXC~haI#Tt9)y8RA1ILtyPy=Swf4 zG^hH_KnS(D{MSzq_{m$QKjLK}OdO4Kwf2v1pm@J={UW^=!qI`uv!Ps7OHs!gc9e1U zt!j!yYN`$QC`YvrBDx~LDj3zP0YUzPoig}}6;HpwCqlGlJ|#-U84cTEuMfMt3WV)( zM}ogy*L_B*cOK#}^jJf5T+gCcKTJ~iG2Zf+8+f?-p3JOj@Yj}3_q3#`q5oKDfg_d{ z{t;$O+M`@UM)-Z2-Wd8yuB5{vloam`4S@@?a3&uiUdKb*wZ}rpITIQaV{x}kK)kG2 zpbr<`;g|p2fwGhs-{QG!5At_GYuCUZ9@)NH;mar?0|!V;pl~y??4}k0bG_cBgV?I} zcR)n7VSP7*90GS(P>)y{j})w;8#MA$DHa#EPe!uQVkb4J3*$PIWy!$R(R1F)(u1JT zX(E+Ttxj*^+Y+=}|+bFl&?l)dHY9U2en9 zRd1OEfynZENb!60$c$*I6Zd>n&Ew{zs^+X??mv(ppsG!6TvXjIvT59PKZLnl(an%* zJx0bpNy8mYsSyy=>fvF?9hE1u=4rZ$O$t9Vi{8!^THvNJ+wlk7q+k#WB3x(xM^4({X^_YQ>l03_bA}DTFz7eB1)leAhSuN zg&wcE=hfXL7(ui{qFGJnC&TX3oMCb=tkR=wo~t@%(kyUyhCEpn=a%y_K$#UMuAL6N zsf;nIhLQ9|R=h%ZAdYH@TTnLg0r@yivqm{u0e3wi_YYxr-G3?gJE|Ess@dEWPoR?; z&YqhH0zHEm5r#F85XB3}o58ajx?x6EXpXf&7mkFJ?GT801p81ZUE)6>4A@~Wf1{2- zUfe+LugSG#-G9)vB-sEGx?)gH(i%e;ggS=<5Te3R({?iNqH6Z?Gg#Zq8S*z4G^-O$ z6X)-n@ssM#lRu8hL|fW(Dp&n)RWGtbo`R_nswgnSVA~P5_DF3OYppaMzLFtNR>nmg z92LIWpZr!ghXtgVLa@dA{J4`dMyp+V!sJg?kO#+_p72|1nZ8XxyYxZUHG&o<9Uv^O zK{9ewU060jbBp#Imb1TJr#Fw7qJsu)n{%fTJKUpbJm?M83q=SEaDb0|1WEKM*~PfH zP|}oyUvY_X!9hBv(Ag;_9Pat2-1Ww}ug|j|9Mx;xNXhvi_1Y_c&J3c|z<7W^#Y^ga z)R=A!zK#=6K$1s~o`R#riYmSzzK1|(wSEraoEKU$KNz9m+)@4!;!=TXC3PP%SZSg0 zIziP%v4w5Tay>kMY;fFv#8sRp#9-e^^~P}OUuVk9yXW^3Z^rK=8qos zLPxcmm0J1_l_oUjzO!2hp}wV1ZN{nI3u@CvQ)oR9_O{8ycfNg`GfWz#q=>~hKf4zC ziub_tk;_grfL&&zxsE=7#OPN|#UVwl@yc!dm`$=adXT{{c&IO>KXF5q9e530yGSH9 z;YBroRkOU9&vfiTzqWS+Z>sl!lslYZMNni2Na{a?c^d;Kb55$Q?2o`5ME+~T_vzRG zcM#IP7c%GfRL_2t9#!-dkn7O-8aRo1eM;v4q4X?wIDL%6cbqmJB+BNVymN4aF||>q z>UWTah7N|3d^jK-Y#Oi#L(%o>3cy*rS`E*FuceMAwE&u&DJoxa#M1+UcsyV#ug76J~x0wF2Zv`rtTs{#Xhq9Rx5Ok>bQ)LI|J(AA+A`w zzbkV)Sqr16WxzsJP_aSR&W8Hq_&y2e1OxmB+qX!y;R5^kY^VCdbaMpT>-u`=ts4C( z14h_m9B=9V2%>P@_4Y86GobUCX!0UtgiAHeiK{HYV;cHsVAvI@tAbdi>W%;S<0bge zVOXD$Du=z^$kXeSc-xz5<=%oQMe8hJd#Vr%^w20siZ!e zOwq9If0k0pZesK*fb}8bBx#7?C!I->8h2bB+BSQ%CJa%hmae8_rH%rVne@lV&Xmky z_P5DuR|1kv2(&C+kaqLC*;*v(xeXlh|2~PjJl0<3Eg65J1)Wn(q+%gxmzDsIQDlr< zn=Xb_tJbO}Lpa4|TW?iOxN3O!6j}%2(W;8)p8Qs|N9|uw` zY4uvrlJWO=W?DGzGIp{x{F&2rpa%VW`vz$2Cu~N}*WbwY;dFZ~=d=0ea4K35dTv>G zuS)&SviCOX0@*a@N^73l4K9)e_wal_e#|_pHqotd-h|C+h)W8Tt)EyX$2S?DAIJdP zGgV#tcRV+dbu_L(Z6j0*dOE=)>b{@JLW*7$N`XHA4v*D6zXg1&5Y>p#dbwqzD%2FI z1*8ga=Ri`m{JYff8=}l{_jy}hs|w*JdB|v{M;fQY)wH?yS(e~K8@sf<_s0VyM(R{6 z&(fLP|4)?EygfJk!cx`5;RN=8<<}cgv8w0->6kH$NT_#?P$sGi4Q2QdPsjXXB%7Jw zdMZ_IMd}Crl4Wx>V#&)%E&kqriZr9 zfTpWB`jh)WdN!~?q(E#|hxGuF8Bsxd*B;d68g`s67J66o{yU}P@{S$4XVX{O4kEMd zJJ{RVM=5IPet_17?VWkLGUnf*A%)K4mortb_6f(+8SVX+nnzkL2fg;cfRY4sC0Gi5 z0g7(vB8KXE#Xxozv2EP5#5tF?clxUvyBLruL?$SZkDs0DhJ$*Tlq@*O@V8GCcA(-= zj9e=gas@7jL^Hv6Kgk?KmhhqZVSpQ=e2&0>(pg4s^WCHmGU}X(E74^)rBj?%MxmZHCqL29|}5uCt7&NEbR;we#Z zzi~$9?n-UEw31E)z^J6%7Y!JZWntapn3S;#a1=%5CbXO#Qmrov71BU=mrN z2FfwIuh+7?5R~U?kz-qC87~

%Pg1>7DtQqCkEfn_6tfXt9eyNdH)3L70%*G1_&orF+P6%X~# zxmfPXL;46+HuQZppuhr8{7+Us2Y5b~1*Uy$1w4fNqk3~vk?V#)Go;oCiG&Aj1kSnv z>a;2g|A9lkz;lv;W)5nkp0qxtlm+j7DHdjbTqxUp8UUlZ*^q6ijiTBCN$xcK4$10& zSFN?Oj_$m67@jQ=$mQa~oI!PegV=jGU6HNGH1zoLLDhH99|9UO#pNY{MDbnB^uveJ zb(8o}4^saWiBC23<_YK=d^Gw9k$sg${eJR-xEE?lF#tvD8eP2E_i!T-q)Ki!`69Ds zf})fq`QayQTkzD&g!YZtXvU8WGMlVNsskAYgFr&1nnwVa9uGypO(B4qKI2I2&$FnQ zk;+`Rx9G&B`=9aYNQM{|pH$CPKyHRDL`il01D98P)>Vd&Sl`z5k1~^Y#_mEKWP&t0 z@72JJ4vaMGTCi@M|x2d4lq^QP5i<&P!Citbye$kUtyo7nR za~Yo=_B5SiBwSG!1>Os(T#AiqFWe!WFyybji)949nDIF5V$euCtmeO>e`_!%|OtYejzErh&0Q};a%(KY8DuWm-*f)@J zd8eu{l@cR-w_0#}iazssT|`iuf13D+b&hc8#yA!STt7U)XR0S4Lw~a!i2m!vx_@iG zt67abmFaIBercJiw~jb!q2+qXCq??-*#MZQxT@1zC#&If8>gn&cBXG(j8ql#{>{-1 zTzg7cWaFIiUpzlDX*6HeX9`nkI3*ro4eP2gR0~r}I$jLlA7qb+#0ZY|*gHm87|~b! zA&aUr-4K1w~(01qUx8RjTSaVq0_ns@B-b5LvdMDFN~% z!TS||PQUm)rg&AV*Kfw*_|pllvx z&)<>PdOw^GeUEvtx3n@+0AZ;&yb#vqonMHDxDVZp3U<|D%?i}7=Q|iNi0e5C#atdtH-av9;{t|i0X)tZ7edh<2?EaP-Y z8xD5oWx$r4!!*;G%VNV*foy!!wR3J<#Q}&78GEumNO1~g8Cp?9@ljD*u5V+f?+|bK z5_2^GSmsM2C#tIs8wLWT@sRE1oj6mrh805L8uKtUxcFHJ(v_A?_NOI?g?@zd_>WBt0!4 ziMQzCnLYoCfQJ*L`PgYs){wK)n^0sKeBjuZiZW@m+6&1y4D}~Q*00FmSAO1m(LuJJ z;PN7@Wm28@82FiB^9XZMjtn?|(DGCgqE-{6?Sp2^@O9a^d4LNH@YXuMJW|z1_ypZ> zsx3;|-I;T@BPGc03>3l851?mZ(iE+qF}QON`)5ujx^n=gcVqD#^jkZu+(MMTUz4Bb zsjy~S;r3Atp~lb49#Zk;DjP?iTa%qkgi>0PnH~;!Y~4I{ zhEjq`H#=^sm^MN(5rekG|8R#BIGLx6O8RwMI54(`p|rkB3O#qAprnGHmVh<7FvQ3t15^OhGE5MhEaGSLnd!z^Egq6J!z{3?S_)r-?!%|OwbMJ2EgRO>(#!KPf}P29)oAw- zb`Iz1$|BhO%zu}fJ|(&;^$LRwY>#vKxDiKj2=NwC7La2VVU_kwSF^}(EBxV-Fkk4$ zvd8#Rdw8aP&&KCqc*f;e{JeEqAKgw9zUhOOaXVEW$tr)-YPNmyDCj1*Vl&6Q32xdE z*$Vv2-XoUo8Cv3gKUTx}YJ9hL92OJwnD)PDRSh=}tVcP^bP_tH`s_%LmE(=-mDLnQB|$2XPp8)B5so?e{nZF~7pMUXqcO^*HRBX1#(8QI zmYq0wgu)bE|KY*(|J^LFjGGPi{7}DmGM$N1EX~i~e9(tI44@l(N8a+dto!(0P-q!9xq_m11hGj|Y1TwI>#8SEq}rwq>h+u5!YH8CXK%|u7!Dey z0_>?eX#;zQ42I4*oD0K`(Frbs*QuSd_8vixyiF3fY@Z$@hBgu}(>=}h@XwlY_iHe- z3)sH`4E9bn_;R0yCu2vX>OOQE`-}fjW2x#bUV-c_lCHP# zsYaHya6B@^cg8I%9e;Kk**Jv1w{a68sM9!EXPwO_CV3wc+W7PJwImiWLlyD{m^yn7 z3Ajb>0D-CX=4IbyMvo)u@dsU`FHrryXCEd|aRAT!_fHb`x+*;&@Y9h%3}c_+X5}Ax z_$DWVe7_&_{fm+f&GveLu2MQqgDX?Uaiq}ni=)Qcb_&Dz=u!~Y3rD!=vl=}_LA7x+ zg=n|SDHYIGBUkp)4yOu{s(lyOO7WF^Ku8VVes}NwEy0IhGnGj|%?bUtm;Sv^z@2f; zJIXu{<1bC(HllzP{#?u}-Fgg%3;wpL_KTpPEAqbT|14bl(&N_p0BRVoYt*=u!@