From 0a65c1f12c23cce9442c25afac4d40c3923e7ab6 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Fri, 11 Mar 2022 16:34:51 +0000
Subject: [PATCH 01/21] Removed sd optional kwarg from continuous.py

---
 pymc/distributions/continuous.py | 64 +++++++-------------------------
 1 file changed, 13 insertions(+), 51 deletions(-)

diff --git a/pymc/distributions/continuous.py b/pymc/distributions/continuous.py
index 8b9a0f1529..965e641623 100644
--- a/pymc/distributions/continuous.py
+++ b/pymc/distributions/continuous.py
@@ -546,13 +546,10 @@ class Normal(Continuous):
     rv_op = normal
 
     @classmethod
-    def dist(cls, mu=0, sigma=None, tau=None, sd=None, no_assert=False, **kwargs):
-        if sd is not None:
-            sigma = sd
+    def dist(cls, mu=0, sigma=None, tau=None, no_assert=False, **kwargs):
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         sigma = at.as_tensor_variable(sigma)
 
-        # sd = sigma
         # tau = at.as_tensor_variable(tau)
         # mean = median = mode = mu = at.as_tensor_variable(floatX(mu))
         # variance = 1.0 / self.tau
@@ -706,14 +703,12 @@ def dist(
         mu: Optional[DIST_PARAMETER_TYPES] = None,
         sigma: Optional[DIST_PARAMETER_TYPES] = None,
         tau: Optional[DIST_PARAMETER_TYPES] = None,
-        sd: Optional[DIST_PARAMETER_TYPES] = None,
         lower: Optional[DIST_PARAMETER_TYPES] = None,
         upper: Optional[DIST_PARAMETER_TYPES] = None,
         transform: str = "auto",
         *args,
         **kwargs,
     ) -> RandomVariable:
-        sigma = sd if sd is not None else sigma
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         sigma = at.as_tensor_variable(sigma)
         tau = at.as_tensor_variable(tau)
@@ -863,10 +858,7 @@ class HalfNormal(PositiveContinuous):
     rv_op = halfnormal
 
     @classmethod
-    def dist(cls, sigma=None, tau=None, sd=None, *args, **kwargs):
-        if sd is not None:
-            sigma = sd
-
+    def dist(cls, sigma=None, tau=None, *args, **kwargs):
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
 
         assert_negative_support(tau, "tau", "HalfNormal")
@@ -1223,10 +1215,7 @@ class Beta(UnitContinuous):
     rv_op = aesara.tensor.random.beta
 
     @classmethod
-    def dist(cls, alpha=None, beta=None, mu=None, sigma=None, sd=None, *args, **kwargs):
-        if sd is not None:
-            sigma = sd
-
+    def dist(cls, alpha=None, beta=None, mu=None, sigma=None, *args, **kwargs):
         alpha, beta = cls.get_alpha_beta(alpha, beta, mu, sigma)
         alpha = at.as_tensor_variable(floatX(alpha))
         beta = at.as_tensor_variable(floatX(beta))
@@ -1781,10 +1770,7 @@ class LogNormal(PositiveContinuous):
     rv_op = lognormal
 
     @classmethod
-    def dist(cls, mu=0, sigma=None, tau=None, sd=None, *args, **kwargs):
-        if sd is not None:
-            sigma = sd
-
+    def dist(cls, mu=0, sigma=None, tau=None, *args, **kwargs):
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
 
         mu = at.as_tensor_variable(floatX(mu))
@@ -1909,9 +1895,7 @@ class StudentT(Continuous):
     rv_op = studentt
 
     @classmethod
-    def dist(cls, nu, mu=0, lam=None, sigma=None, sd=None, *args, **kwargs):
-        if sd is not None:
-            sigma = sd
+    def dist(cls, nu, mu=0, lam=None, sigma=None, *args, **kwargs):
         nu = at.as_tensor_variable(floatX(nu))
         lam, sigma = get_tau_sigma(tau=lam, sigma=sigma)
         sigma = at.as_tensor_variable(sigma)
@@ -2299,10 +2283,7 @@ class Gamma(PositiveContinuous):
     rv_op = gamma
 
     @classmethod
-    def dist(cls, alpha=None, beta=None, mu=None, sigma=None, sd=None, no_assert=False, **kwargs):
-        if sd is not None:
-            sigma = sd
-
+    def dist(cls, alpha=None, beta=None, mu=None, sigma=None, no_assert=False, **kwargs):
         alpha, beta = cls.get_alpha_beta(alpha, beta, mu, sigma)
         alpha = at.as_tensor_variable(floatX(alpha))
         beta = at.as_tensor_variable(floatX(beta))
@@ -2419,10 +2400,7 @@ class InverseGamma(PositiveContinuous):
     rv_op = invgamma
 
     @classmethod
-    def dist(cls, alpha=None, beta=None, mu=None, sigma=None, sd=None, *args, **kwargs):
-        if sd is not None:
-            sigma = sd
-
+    def dist(cls, alpha=None, beta=None, mu=None, sigma=None, *args, **kwargs):
         alpha, beta = cls._get_alpha_beta(alpha, beta, mu, sigma)
         alpha = at.as_tensor_variable(floatX(alpha))
         beta = at.as_tensor_variable(floatX(beta))
@@ -2742,11 +2720,7 @@ class HalfStudentT(PositiveContinuous):
     rv_op = halfstudentt
 
     @classmethod
-    def dist(cls, nu=1, sigma=None, lam=None, sd=None, *args, **kwargs):
-
-        if sd is not None:
-            sigma = sd
-
+    def dist(cls, nu=1, sigma=None, lam=None, *args, **kwargs):
         nu = at.as_tensor_variable(floatX(nu))
         lam, sigma = get_tau_sigma(lam, sigma)
         sigma = at.as_tensor_variable(sigma)
@@ -2878,11 +2852,7 @@ class ExGaussian(Continuous):
     rv_op = exgaussian
 
     @classmethod
-    def dist(cls, mu=0.0, sigma=None, nu=None, sd=None, *args, **kwargs):
-
-        if sd is not None:
-            sigma = sd
-
+    def dist(cls, mu=0.0, sigma=None, nu=None, *args, **kwargs):
         mu = at.as_tensor_variable(floatX(mu))
         sigma = at.as_tensor_variable(floatX(sigma))
         nu = at.as_tensor_variable(floatX(nu))
@@ -3110,10 +3080,7 @@ class SkewNormal(Continuous):
     rv_op = skewnormal
 
     @classmethod
-    def dist(cls, alpha=1, mu=0.0, sigma=None, tau=None, sd=None, *args, **kwargs):
-        if sd is not None:
-            sigma = sd
-
+    def dist(cls, alpha=1, mu=0.0, sigma=None, tau=None, *args, **kwargs):
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         alpha = at.as_tensor_variable(floatX(alpha))
         mu = at.as_tensor_variable(floatX(mu))
@@ -3437,10 +3404,7 @@ class Rice(PositiveContinuous):
     rv_op = rice
 
     @classmethod
-    def dist(cls, nu=None, sigma=None, b=None, sd=None, *args, **kwargs):
-        if sd is not None:
-            sigma = sd
-
+    def dist(cls, nu=None, sigma=None, b=None, *args, **kwargs):
         nu, b, sigma = cls.get_nu_b(nu, b, sigma)
         b = at.as_tensor_variable(floatX(b))
         sigma = at.as_tensor_variable(floatX(sigma))
@@ -3649,12 +3613,10 @@ class LogitNormal(UnitContinuous):
     rv_op = logit_normal
 
     @classmethod
-    def dist(cls, mu=0, sigma=None, tau=None, sd=None, **kwargs):
-        if sd is not None:
-            sigma = sd
+    def dist(cls, mu=0, sigma=None, tau=None, **kwargs):
         mu = at.as_tensor_variable(floatX(mu))
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
-        sigma = sd = at.as_tensor_variable(sigma)
+        sigma = at.as_tensor_variable(sigma)
         tau = at.as_tensor_variable(tau)
         assert_negative_support(sigma, "sigma", "LogitNormal")
         assert_negative_support(tau, "tau", "LogitNormal")

From 29c00160e2ecb3f256576c429565f4f39b591e32 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Fri, 11 Mar 2022 17:14:49 +0000
Subject: [PATCH 02/21] Removed sd optional kwarg from timeseries.py

---
 pymc/distributions/timeseries.py | 15 ++++-----------
 scripts/run_mypy.py              |  2 ++
 2 files changed, 6 insertions(+), 11 deletions(-)

diff --git a/pymc/distributions/timeseries.py b/pymc/distributions/timeseries.py
index 6691ad2e93..225a593880 100644
--- a/pymc/distributions/timeseries.py
+++ b/pymc/distributions/timeseries.py
@@ -108,15 +108,10 @@ class AR(distribution.Continuous):
         distribution for initial values (Defaults to Flat())
     """
 
-    def __init__(
-        self, rho, sigma=None, tau=None, constant=False, init=None, sd=None, *args, **kwargs
-    ):
+    def __init__(self, rho, sigma=None, tau=None, constant=False, init=None, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        if sd is not None:
-            sigma = sd
-
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
-        self.sigma = self.sd = at.as_tensor_variable(sigma)
+        self.sigma = at.as_tensor_variable(sigma)
         self.tau = at.as_tensor_variable(tau)
 
         self.mean = at.as_tensor_variable(0.0)
@@ -201,17 +196,15 @@ class GaussianRandomWalk(distribution.Continuous):
         distribution for initial value (Defaults to Flat())
     """
 
-    def __init__(self, tau=None, init=None, sigma=None, mu=0.0, sd=None, *args, **kwargs):
+    def __init__(self, tau=None, init=None, sigma=None, mu=0.0, *args, **kwargs):
         kwargs.setdefault("shape", 1)
         super().__init__(*args, **kwargs)
         if sum(self.shape) == 0:
             raise TypeError("GaussianRandomWalk must be supplied a non-zero shape argument!")
-        if sd is not None:
-            sigma = sd
         tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
         self.tau = at.as_tensor_variable(tau)
         sigma = at.as_tensor_variable(sigma)
-        self.sigma = self.sd = sigma
+        self.sigma = sigma
         self.mu = at.as_tensor_variable(mu)
         self.init = init or Flat.dist()
         self.mean = at.as_tensor_variable(0.0)
diff --git a/scripts/run_mypy.py b/scripts/run_mypy.py
index 7c8532c879..9a842efd4f 100644
--- a/scripts/run_mypy.py
+++ b/scripts/run_mypy.py
@@ -74,6 +74,8 @@
 pymc/variational/test_functions.py
 pymc/variational/updates.py
 pymc/vartypes.py
+pymc/sampling_jax.py
+pymc/step_methods/sgmcmc.py
 """
 
 

From 8acda640eba75e1c50cc807ea93c284626a32eef Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Fri, 11 Mar 2022 17:20:30 +0000
Subject: [PATCH 03/21] Removed sd optional kwarg from mixture.py

---
 pymc/distributions/mixture.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/pymc/distributions/mixture.py b/pymc/distributions/mixture.py
index 0e06fef5f8..a7d0ad8f76 100644
--- a/pymc/distributions/mixture.py
+++ b/pymc/distributions/mixture.py
@@ -477,17 +477,13 @@ class NormalMixture:
             y = pm.NormalMixture("y", w=weights, mu=μ, sigma=σ, observed=data)
     """
 
-    def __new__(cls, name, w, mu, sigma=None, tau=None, sd=None, comp_shape=(), **kwargs):
-        if sd is not None:
-            sigma = sd
+    def __new__(cls, name, w, mu, sigma=None, tau=None, comp_shape=(), **kwargs):
         _, sigma = get_tau_sigma(tau=tau, sigma=sigma)
 
         return Mixture(name, w, Normal.dist(mu, sigma=sigma, size=comp_shape), **kwargs)
 
     @classmethod
-    def dist(cls, w, mu, sigma=None, tau=None, sd=None, comp_shape=(), **kwargs):
-        if sd is not None:
-            sigma = sd
+    def dist(cls, w, mu, sigma=None, tau=None, comp_shape=(), **kwargs):
         _, sigma = get_tau_sigma(tau=tau, sigma=sigma)
 
         return Mixture.dist(w, Normal.dist(mu, sigma=sigma, size=comp_shape), **kwargs)

From 6b34983bbb0f1095b7c8bdd011ad93fc38e0a876 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Fri, 11 Mar 2022 17:44:13 +0000
Subject: [PATCH 04/21] removed pymc/sampling_jax.py and
 pymc/step_methods/sgmcmc.py from scripts/run_mypy.py list

---
 scripts/run_mypy.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/scripts/run_mypy.py b/scripts/run_mypy.py
index 9a842efd4f..7c8532c879 100644
--- a/scripts/run_mypy.py
+++ b/scripts/run_mypy.py
@@ -74,8 +74,6 @@
 pymc/variational/test_functions.py
 pymc/variational/updates.py
 pymc/vartypes.py
-pymc/sampling_jax.py
-pymc/step_methods/sgmcmc.py
 """
 
 

From 33e54637ef2cb3dcd76e90adfd56ce799928bcc1 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Mon, 14 Mar 2022 16:53:09 +0530
Subject: [PATCH 05/21] sd renamed to sigma

---
 pymc/tests/test_distributions_random.py |  6 +++---
 pymc/tests/test_idata_conversion.py     | 14 ++++++++------
 pymc/tests/test_initial_point.py        | 12 ++++++------
 pymc/tests/test_model.py                |  4 ++--
 pymc/tests/test_model_graph.py          |  2 +-
 pymc/tests/test_ode.py                  | 10 +++++-----
 pymc/tests/test_sampling.py             | 16 +++++++++-------
 pymc/tests/test_transforms.py           | 14 +++++++-------
 8 files changed, 41 insertions(+), 37 deletions(-)

diff --git a/pymc/tests/test_distributions_random.py b/pymc/tests/test_distributions_random.py
index f01be90541..bd9e6ad38d 100644
--- a/pymc/tests/test_distributions_random.py
+++ b/pymc/tests/test_distributions_random.py
@@ -837,7 +837,7 @@ class TestNormalTau(BaseTestDistributionRandom):
 
 class TestNormalSd(BaseTestDistributionRandom):
     pymc_dist = pm.Normal
-    pymc_dist_params = {"mu": 1.0, "sd": 5.0}
+    pymc_dist_params = {"mu": 1.0, "sigma": 5.0}
     expected_rv_op_params = {"mu": 1.0, "sigma": 5.0}
     checks_to_run = ["check_pymc_params_match_rv_op"]
 
@@ -871,7 +871,7 @@ class TestHalfNormalTau(BaseTestDistributionRandom):
 
 class TestHalfNormalSd(BaseTestDistributionRandom):
     pymc_dist = pm.Normal
-    pymc_dist_params = {"sd": 5.0}
+    pymc_dist_params = {"sigma": 5.0}
     expected_rv_op_params = {"mu": 0.0, "sigma": 5.0}
     checks_to_run = ["check_pymc_params_match_rv_op"]
 
@@ -1457,7 +1457,7 @@ class TestLognormalTau(BaseTestDistributionRandom):
 
 class TestLognormalSd(BaseTestDistributionRandom):
     pymc_dist = pm.Lognormal
-    pymc_dist_params = {"mu": 1.0, "sd": 5.0}
+    pymc_dist_params = {"mu": 1.0, "sigma": 5.0}
     expected_rv_op_params = {"mu": 1.0, "sigma": 5.0}
     checks_to_run = ["check_pymc_params_match_rv_op"]
 
diff --git a/pymc/tests/test_idata_conversion.py b/pymc/tests/test_idata_conversion.py
index 00f1575b86..97d6535ab3 100644
--- a/pymc/tests/test_idata_conversion.py
+++ b/pymc/tests/test_idata_conversion.py
@@ -52,14 +52,14 @@ def __init__(self, model, trace):
     @pytest.fixture(scope="class")
     def data(self, eight_schools_params, draws, chains):
         with pm.Model() as model:
-            mu = pm.Normal("mu", mu=0, sd=5)
+            mu = pm.Normal("mu", mu=0, sigma=5)
             tau = pm.HalfCauchy("tau", beta=5)
-            eta = pm.Normal("eta", mu=0, sd=1, size=eight_schools_params["J"])
+            eta = pm.Normal("eta", mu=0, sigma=1, size=eight_schools_params["J"])
             theta = pm.Deterministic("theta", mu + tau * eta)
             pm.Normal(
                 "obs",
                 mu=theta,
-                sd=eight_schools_params["sigma"],
+                sigma=eight_schools_params["sigma"],
                 observed=eight_schools_params["y"],
             )
             trace = pm.sample(draws, chains=chains, return_inferencedata=False)
@@ -249,15 +249,17 @@ def test_autodetect_coords_from_model(self, use_context):
 
         coords = {"date": df_data.index, "city": df_data.columns}
         with pm.Model(coords=coords) as model:
-            europe_mean = pm.Normal("europe_mean_temp", mu=15.0, sd=3.0)
-            city_offset = pm.Normal("city_offset", mu=0.0, sd=3.0, dims="city")
+            europe_mean = pm.Normal("europe_mean_temp", mu=15.0, sigma=3.0)
+            city_offset = pm.Normal("city_offset", mu=0.0, sigma=3.0, dims="city")
             city_temperature = pm.Deterministic(
                 "city_temperature", europe_mean + city_offset, dims="city"
             )
 
             data_dims = ("date", "city")
             data = pm.ConstantData("data", df_data, dims=data_dims)
-            _ = pm.Normal("likelihood", mu=city_temperature, sd=0.5, observed=data, dims=data_dims)
+            _ = pm.Normal(
+                "likelihood", mu=city_temperature, sigma=0.5, observed=data, dims=data_dims
+            )
 
             trace = pm.sample(
                 return_inferencedata=False,
diff --git a/pymc/tests/test_initial_point.py b/pymc/tests/test_initial_point.py
index c252594906..1a7560618e 100644
--- a/pymc/tests/test_initial_point.py
+++ b/pymc/tests/test_initial_point.py
@@ -95,12 +95,12 @@ def test_dependent_initvals(self):
     def test_nested_initvals(self):
         # See issue #5168
         with pm.Model() as pmodel:
-            one = pm.LogNormal("one", mu=np.log(1), sd=1e-5, initval="prior")
-            two = pm.Lognormal("two", mu=np.log(one * 2), sd=1e-5, initval="prior")
-            three = pm.LogNormal("three", mu=np.log(two * 2), sd=1e-5, initval="prior")
-            four = pm.LogNormal("four", mu=np.log(three * 2), sd=1e-5, initval="prior")
-            five = pm.LogNormal("five", mu=np.log(four * 2), sd=1e-5, initval="prior")
-            six = pm.LogNormal("six", mu=np.log(five * 2), sd=1e-5, initval="prior")
+            one = pm.LogNormal("one", mu=np.log(1), sigma=1e-5, initval="prior")
+            two = pm.Lognormal("two", mu=np.log(one * 2), sigma=1e-5, initval="prior")
+            three = pm.LogNormal("three", mu=np.log(two * 2), sigma=1e-5, initval="prior")
+            four = pm.LogNormal("four", mu=np.log(three * 2), sigma=1e-5, initval="prior")
+            five = pm.LogNormal("five", mu=np.log(four * 2), sigma=1e-5, initval="prior")
+            six = pm.LogNormal("six", mu=np.log(five * 2), sigma=1e-5, initval="prior")
 
         ip_vals = list(make_initial_point_fn(model=pmodel, return_transformed=True)(0).values())
         assert np.allclose(np.exp(ip_vals), [1, 2, 4, 8, 16, 32], rtol=1e-3)
diff --git a/pymc/tests/test_model.py b/pymc/tests/test_model.py
index 01d5f0c0f8..7f69566e99 100644
--- a/pymc/tests/test_model.py
+++ b/pymc/tests/test_model.py
@@ -58,7 +58,7 @@ def __init__(self, mean=0, sigma=1, name="", model=None):
         super().__init__(name, model)
         self.register_rv(Normal.dist(mu=mean, sigma=sigma), "v1")
         Normal("v2", mu=mean, sigma=sigma)
-        Normal("v3", mu=mean, sigma=Normal("sd", mu=10, sigma=1, initval=1.0))
+        Normal("v3", mu=mean, sigma=Normal("sigma", mu=10, sigma=1, initval=1.0))
         Deterministic("v3_sq", self.v3**2)
         Potential("p1", at.constant(1))
 
@@ -626,7 +626,7 @@ def test_set_initval():
 
     with pm.Model(rng_seeder=rng) as model:
         eta = pm.Uniform("eta", 1.0, 2.0, size=(1, 1))
-        mu = pm.Normal("mu", sd=eta, initval=[[100]])
+        mu = pm.Normal("mu", sigma=eta, initval=[[100]])
         alpha = pm.HalfNormal("alpha", initval=100)
         value = pm.NegativeBinomial("value", mu=mu, alpha=alpha)
 
diff --git a/pymc/tests/test_model_graph.py b/pymc/tests/test_model_graph.py
index 091a4ad316..14e3e0ebec 100644
--- a/pymc/tests/test_model_graph.py
+++ b/pymc/tests/test_model_graph.py
@@ -102,7 +102,7 @@ def model_with_dims():
     with pm.Model(coords={"city": ["Aachen", "Maastricht", "London", "Bergheim"]}) as pmodel:
         economics = pm.Uniform("economics", lower=-1, upper=1, shape=(1,))
 
-        population = pm.HalfNormal("population", sd=5, dims=("city"))
+        population = pm.HalfNormal("population", sigma=5, dims=("city"))
 
         time = pm.ConstantData("time", [2014, 2015, 2016], dims="year")
 
diff --git a/pymc/tests/test_ode.py b/pymc/tests/test_ode.py
index 647fff57e7..acb0280508 100644
--- a/pymc/tests/test_ode.py
+++ b/pymc/tests/test_ode.py
@@ -219,7 +219,7 @@ def system_1(y, t, p):
     manual_logp = norm.logpdf(x=np.ravel(yobs), loc=np.ravel(integrated_solution), scale=1).sum()
     with pm.Model() as model_1:
         forward = ode_model(theta=[alpha], y0=[y0])
-        y = pm.Normal("y", mu=forward, sd=1, observed=yobs)
+        y = pm.Normal("y", mu=forward, sigma=1, observed=yobs)
     pymc_logp = model_1.compile_logp()({})
 
     np.testing.assert_allclose(manual_logp, pymc_logp)
@@ -369,7 +369,7 @@ def system(y, t, p):
             y0 = pm.LogNormal("y0", 0, 1)
             sigma = pm.HalfCauchy("sigma", 1)
             forward = ode_model(theta=[alpha], y0=[y0])
-            y = pm.LogNormal("y", mu=pm.math.log(forward), sd=sigma, observed=yobs)
+            y = pm.LogNormal("y", mu=pm.math.log(forward), sigma=sigma, observed=yobs)
 
             with aesara.config.change_flags(mode=fast_unstable_sampling_mode):
                 idata = pm.sample(50, tune=0, chains=1)
@@ -400,7 +400,7 @@ def system(y, t, p):
             y0 = pm.LogNormal("y0", 0, 1)
             sigma = pm.HalfCauchy("sigma", 1)
             forward = ode_model(theta=[alpha, beta], y0=[y0])
-            y = pm.LogNormal("y", mu=pm.math.log(forward), sd=sigma, observed=yobs)
+            y = pm.LogNormal("y", mu=pm.math.log(forward), sigma=sigma, observed=yobs)
 
             with aesara.config.change_flags(mode=fast_unstable_sampling_mode):
                 idata = pm.sample(50, tune=0, chains=1)
@@ -442,7 +442,7 @@ def system(y, t, p):
             R = pm.LogNormal("R", 1, 5, initval=1)
             sigma = pm.HalfCauchy("sigma", 1, shape=2, initval=[0.5, 0.5])
             forward = ode_model(theta=[R], y0=[0.99, 0.01])
-            y = pm.LogNormal("y", mu=pm.math.log(forward), sd=sigma, observed=yobs)
+            y = pm.LogNormal("y", mu=pm.math.log(forward), sigma=sigma, observed=yobs)
 
             with aesara.config.change_flags(mode=fast_unstable_sampling_mode):
                 idata = pm.sample(50, tune=0, chains=1)
@@ -483,7 +483,7 @@ def system(y, t, p):
             gamma = pm.HalfCauchy("gamma", 1, initval=1)
             sigma = pm.HalfCauchy("sigma", 1, shape=2, initval=[1, 1])
             forward = ode_model(theta=[beta, gamma], y0=[0.99, 0.01])
-            y = pm.LogNormal("y", mu=pm.math.log(forward), sd=sigma, observed=yobs)
+            y = pm.LogNormal("y", mu=pm.math.log(forward), sigma=sigma, observed=yobs)
 
             with aesara.config.change_flags(mode=fast_unstable_sampling_mode):
                 idata = pm.sample(50, tune=0, chains=1)
diff --git a/pymc/tests/test_sampling.py b/pymc/tests/test_sampling.py
index c957df19a6..abf2f74e39 100644
--- a/pymc/tests/test_sampling.py
+++ b/pymc/tests/test_sampling.py
@@ -707,7 +707,7 @@ def test_model_shared_variable(self):
         x_shared = aesara.shared(x)
         y_shared = aesara.shared(y)
         with pm.Model(rng_seeder=rng) as model:
-            coeff = pm.Normal("x", mu=0, sd=1)
+            coeff = pm.Normal("x", mu=0, sigma=1)
             logistic = pm.Deterministic("p", pm.math.sigmoid(coeff * x_shared))
 
             obs = pm.Bernoulli("obs", p=logistic, observed=y_shared)
@@ -1106,12 +1106,14 @@ def test_density_dist(self):
         obs = np.random.normal(-1, 0.1, size=10)
         with pm.Model():
             mu = pm.Normal("mu", 0, 1)
-            sd = pm.HalfNormal("sd", 1e-6)
+            sigma = pm.HalfNormal("sigma", 1e-6)
             a = pm.DensityDist(
                 "a",
                 mu,
-                sd,
-                random=lambda mu, sd, rng=None, size=None: rng.normal(loc=mu, scale=sd, size=size),
+                sigma,
+                random=lambda mu, sigma, rng=None, size=None: rng.normal(
+                    loc=mu, scale=sigma, size=size
+                ),
                 observed=obs,
             )
             prior = pm.sample_prior_predictive(return_inferencedata=False)
@@ -1121,15 +1123,15 @@ def test_density_dist(self):
     def test_shape_edgecase(self):
         with pm.Model():
             mu = pm.Normal("mu", size=5)
-            sd = pm.Uniform("sd", lower=2, upper=3)
-            x = pm.Normal("x", mu=mu, sigma=sd, size=5)
+            sigma = pm.Uniform("sigma", lower=2, upper=3)
+            x = pm.Normal("x", mu=mu, sigma=sigma, size=5)
             prior = pm.sample_prior_predictive(10)
         assert prior.prior["mu"].shape == (1, 10, 5)
 
     def test_zeroinflatedpoisson(self):
         with pm.Model():
             theta = pm.Beta("theta", alpha=1, beta=1)
-            psi = pm.HalfNormal("psi", sd=1)
+            psi = pm.HalfNormal("psi", sigma=1)
             pm.ZeroInflatedPoisson("suppliers", psi=psi, theta=theta, size=20)
             gen_data = pm.sample_prior_predictive(samples=5000)
             assert gen_data.prior["theta"].shape == (1, 5000)
diff --git a/pymc/tests/test_transforms.py b/pymc/tests/test_transforms.py
index 8853a1cccd..328257d6eb 100644
--- a/pymc/tests/test_transforms.py
+++ b/pymc/tests/test_transforms.py
@@ -332,15 +332,15 @@ def check_vectortransform_elementwise_logp(self, model):
         close_to(a, b, np.abs(0.5 * (a + b) * tol))
 
     @pytest.mark.parametrize(
-        "sd,size",
+        "sigma,size",
         [
             (2.5, 2),
             (5.0, (2, 3)),
             (np.ones(3) * 10.0, (4, 3)),
         ],
     )
-    def test_half_normal(self, sd, size):
-        model = self.build_model(pm.HalfNormal, {"sd": sd}, size=size, transform=tr.log)
+    def test_half_normal(self, sigma, size):
+        model = self.build_model(pm.HalfNormal, {"sigma": sigma}, size=size, transform=tr.log)
         self.check_transform_elementwise_logp(model)
 
     @pytest.mark.parametrize("lam,size", [(2.5, 2), (5.0, (2, 3)), (np.ones(3), (4, 3))])
@@ -421,7 +421,7 @@ def test_dirichlet(self, a, size):
     def test_normal_ordered(self):
         model = self.build_model(
             pm.Normal,
-            {"mu": 0.0, "sd": 1.0},
+            {"mu": 0.0, "sigma": 1.0},
             size=3,
             initval=np.asarray([-1.0, 1.0, 4.0]),
             transform=tr.ordered,
@@ -429,17 +429,17 @@ def test_normal_ordered(self):
         self.check_vectortransform_elementwise_logp(model)
 
     @pytest.mark.parametrize(
-        "sd,size",
+        "sigma,size",
         [
             (2.5, (2,)),
             (np.ones(3), (4, 3)),
         ],
     )
-    def test_half_normal_ordered(self, sd, size):
+    def test_half_normal_ordered(self, sigma, size):
         initval = np.sort(np.abs(np.random.randn(*size)))
         model = self.build_model(
             pm.HalfNormal,
-            {"sd": sd},
+            {"sigma": sigma},
             size=size,
             initval=initval,
             transform=tr.Chain([tr.log, tr.ordered]),

From 01b0ce0e1a972145ea2d45d9efe1d9a6a29b38d6 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 04:53:04 +0000
Subject: [PATCH 06/21] renamed sd to sigma in benchmarks.py

---
 benchmarks/benchmarks/benchmarks.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/benchmarks/benchmarks/benchmarks.py b/benchmarks/benchmarks/benchmarks.py
index 55b13d110b..ea3c464c6d 100644
--- a/benchmarks/benchmarks/benchmarks.py
+++ b/benchmarks/benchmarks/benchmarks.py
@@ -32,17 +32,17 @@ def glm_hierarchical_model(random_seed=123):
 
     n_counties = len(data.county.unique())
     with pm.Model() as model:
-        mu_a = pm.Normal("mu_a", mu=0.0, sd=100**2)
+        mu_a = pm.Normal("mu_a", mu=0.0, sigma=100**2)
         sigma_a = pm.HalfCauchy("sigma_a", 5)
-        mu_b = pm.Normal("mu_b", mu=0.0, sd=100**2)
+        mu_b = pm.Normal("mu_b", mu=0.0, sigma=100**2)
         sigma_b = pm.HalfCauchy("sigma_b", 5)
-        a = pm.Normal("a", mu=0, sd=1, shape=n_counties)
-        b = pm.Normal("b", mu=0, sd=1, shape=n_counties)
+        a = pm.Normal("a", mu=0, sigma=1, shape=n_counties)
+        b = pm.Normal("b", mu=0, sigma=1, shape=n_counties)
         a = mu_a + sigma_a * a
         b = mu_b + sigma_b * b
         eps = pm.HalfCauchy("eps", 5)
         radon_est = a[county_idx] + b[county_idx] * data.floor.values
-        pm.Normal("radon_like", mu=radon_est, sd=eps, observed=data.log_radon)
+        pm.Normal("radon_like", mu=radon_est, sigma=eps, observed=data.log_radon)
     return model
 
 
@@ -58,7 +58,7 @@ def mixture_model(random_seed=1234):
 
     with pm.Model() as model:
         w = pm.Dirichlet("w", a=np.ones_like(w_true))
-        mu = pm.Normal("mu", mu=0.0, sd=10.0, shape=w_true.shape)
+        mu = pm.Normal("mu", mu=0.0, sigma=10.0, shape=w_true.shape)
         enforce_order = pm.Potential(
             "enforce_order",
             at.switch(mu[0] - mu[1] <= 0, 0.0, -np.inf)
@@ -88,7 +88,7 @@ class OverheadSuite:
     def setup(self, step):
         self.n_steps = 10000
         with pm.Model() as self.model:
-            pm.Normal("x", mu=0, sd=1)
+            pm.Normal("x", mu=0, sigma=1)
 
     def time_overhead_sample(self, step):
         with self.model:
@@ -133,8 +133,8 @@ def time_drug_evaluation(self):
         sigma_low = 1
         sigma_high = 10
         with pm.Model():
-            group1_mean = pm.Normal("group1_mean", y_mean, sd=y_std)
-            group2_mean = pm.Normal("group2_mean", y_mean, sd=y_std)
+            group1_mean = pm.Normal("group1_mean", y_mean, sigma=y_std)
+            group2_mean = pm.Normal("group2_mean", y_mean, sigma=y_std)
             group1_std = pm.Uniform("group1_std", lower=sigma_low, upper=sigma_high)
             group2_std = pm.Uniform("group2_std", lower=sigma_low, upper=sigma_high)
             lambda_1 = group1_std**-2
@@ -301,7 +301,7 @@ def freefall(y, t, p):
             # If we know one of the parameter values, we can simply pass the value.
             ode_solution = ode_model(y0=[0], theta=[gamma, 9.8])
             # The ode_solution has a shape of (n_times, n_states)
-            Y = pm.Normal("Y", mu=ode_solution, sd=sigma, observed=y)
+            Y = pm.Normal("Y", mu=ode_solution, sigma=sigma, observed=y)
 
             t0 = time.time()
             idata = pm.sample(500, tune=1000, chains=2, cores=2, random_seed=0)

From 72e4915f4ce805c7999fdb66e35eafb92f01c58f Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 06:47:26 +0000
Subject: [PATCH 07/21] though rename of sd to sigma is not required in
 test_mixture.py, just remaned it for consistency

---
 pymc/tests/test_mixture.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/pymc/tests/test_mixture.py b/pymc/tests/test_mixture.py
index 51754aa788..b987d02939 100644
--- a/pymc/tests/test_mixture.py
+++ b/pymc/tests/test_mixture.py
@@ -57,15 +57,15 @@
 from pymc.tests.test_distributions_random import pymc_random
 
 
-def generate_normal_mixture_data(w, mu, sd, size=1000):
+def generate_normal_mixture_data(w, mu, sigma, size=1000):
     component = np.random.choice(w.size, size=size, p=w)
-    mu, sd = np.broadcast_arrays(mu, sd)
+    mu, sigma = np.broadcast_arrays(mu, sigma)
     out_size = to_tuple(size) + mu.shape[:-1]
     mu_ = np.array([mu[..., comp] for comp in component.ravel()])
-    sd_ = np.array([sd[..., comp] for comp in component.ravel()])
+    sigma_ = np.array([sigma[..., comp] for comp in component.ravel()])
     mu_ = np.reshape(mu_, out_size)
-    sd_ = np.reshape(sd_, out_size)
-    x = np.random.normal(mu_, sd_, size=out_size)
+    sigma_ = np.reshape(sigma_, out_size)
+    x = np.random.normal(mu_, sigma_, size=out_size)
 
     return x
 
@@ -471,8 +471,8 @@ def test_list_poissons_sampling(self):
     def test_list_normals_sampling(self):
         norm_w = np.array([0.75, 0.25])
         norm_mu = np.array([0.0, 5.0])
-        norm_sd = np.ones_like(norm_mu)
-        norm_x = generate_normal_mixture_data(norm_w, norm_mu, norm_sd, size=1000)
+        norm_sigma = np.ones_like(norm_mu)
+        norm_x = generate_normal_mixture_data(norm_w, norm_mu, norm_sigma, size=1000)
 
         with Model() as model:
             w = Dirichlet("w", floatX(np.ones_like(norm_w)), shape=norm_w.size)
@@ -663,8 +663,8 @@ class TestNormalMixture(SeededTest):
     def test_normal_mixture_sampling(self):
         norm_w = np.array([0.75, 0.25])
         norm_mu = np.array([0.0, 5.0])
-        norm_sd = np.ones_like(norm_mu)
-        norm_x = generate_normal_mixture_data(norm_w, norm_mu, norm_sd, size=1000)
+        norm_sigma = np.ones_like(norm_mu)
+        norm_x = generate_normal_mixture_data(norm_w, norm_mu, norm_sigma, size=1000)
 
         with Model() as model:
             w = Dirichlet("w", floatX(np.ones_like(norm_w)), shape=norm_w.size)
@@ -694,7 +694,7 @@ def test_normal_mixture_nd(self, nd, ncomp):
         test_mus = np.random.randn(*comp_shape)
         test_taus = np.random.gamma(1, 1, size=comp_shape)
         observed = generate_normal_mixture_data(
-            w=np.ones(ncomp) / ncomp, mu=test_mus, sd=1 / np.sqrt(test_taus), size=10
+            w=np.ones(ncomp) / ncomp, mu=test_mus, sigma=1 / np.sqrt(test_taus), size=10
         )
 
         with Model() as model0:

From c9a71dc93823fb0ab4424d1225a3291cb47deb34 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 06:51:00 +0000
Subject: [PATCH 08/21] renamed sd to sigma in
 test_shape_handling.py::TestShapeDimsSize::test_observed_with_column_vector

---
 pymc/tests/test_shape_handling.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/pymc/tests/test_shape_handling.py b/pymc/tests/test_shape_handling.py
index 5cfb481f20..1fa7e3df30 100644
--- a/pymc/tests/test_shape_handling.py
+++ b/pymc/tests/test_shape_handling.py
@@ -389,12 +389,12 @@ def test_observed_with_column_vector(self):
             # But the second shape is upcasted from an int32 vector
             cast64 = at.cast(at.constant([3, 1], dtype="int32"), dtype="int64")
 
-            pm.Normal("size64", mu=0, sd=1, size=size64, observed=obs)
-            pm.Normal("shape64", mu=0, sd=1, shape=size64, observed=obs)
+            pm.Normal("size64", mu=0, sigma=1, size=size64, observed=obs)
+            pm.Normal("shape64", mu=0, sigma=1, shape=size64, observed=obs)
             model.logp()
 
-            pm.Normal("size_cast64", mu=0, sd=1, size=cast64, observed=obs)
-            pm.Normal("shape_cast64", mu=0, sd=1, shape=cast64, observed=obs)
+            pm.Normal("size_cast64", mu=0, sigma=1, size=cast64, observed=obs)
+            pm.Normal("shape_cast64", mu=0, sigma=1, shape=cast64, observed=obs)
             model.logp()
 
     def test_dist_api_works(self):

From 7bef2d6fb3ad59b04814c3ac694b0cd66d52a8c0 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 06:55:59 +0000
Subject: [PATCH 09/21] renamed sd to sigma in dimensionality.ipynb

---
 .../learn/examples/dimensionality.ipynb       | 189 +-----------------
 1 file changed, 4 insertions(+), 185 deletions(-)

diff --git a/docs/source/learn/examples/dimensionality.ipynb b/docs/source/learn/examples/dimensionality.ipynb
index 363d8e8a9b..333d3360ce 100644
--- a/docs/source/learn/examples/dimensionality.ipynb
+++ b/docs/source/learn/examples/dimensionality.ipynb
@@ -159,7 +159,7 @@
     }
    ],
    "source": [
-    "random_sample = pm.Normal.dist(mu=[1,10,100], sd=.0001).eval()\n",
+    "random_sample = pm.Normal.dist(mu=[1,10,100], sigma=.0001).eval()\n",
     "random_sample, random_sample.shape"
    ]
   },
@@ -236,68 +236,7 @@
    "outputs": [
     {
      "data": {
-      "image/svg+xml": [
-       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
-       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
-       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
-       "<!-- Generated by graphviz version 2.43.0 (0)\n",
-       " -->\n",
-       "<!-- Title: %3 Pages: 1 -->\n",
-       "<svg width=\"796pt\" height=\"138pt\"\n",
-       " viewBox=\"0.00 0.00 796.50 137.95\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 133.95)\">\n",
-       "<title>%3</title>\n",
-       "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-133.95 792.5,-133.95 792.5,4 -4,4\"/>\n",
-       "<g id=\"clust1\" class=\"cluster\">\n",
-       "<title>cluster3</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M120.5,-8C120.5,-8 296.5,-8 296.5,-8 302.5,-8 308.5,-14 308.5,-20 308.5,-20 308.5,-109.95 308.5,-109.95 308.5,-115.95 302.5,-121.95 296.5,-121.95 296.5,-121.95 120.5,-121.95 120.5,-121.95 114.5,-121.95 108.5,-115.95 108.5,-109.95 108.5,-109.95 108.5,-20 108.5,-20 108.5,-14 114.5,-8 120.5,-8\"/>\n",
-       "<text text-anchor=\"middle\" x=\"295.5\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">3</text>\n",
-       "</g>\n",
-       "<g id=\"clust2\" class=\"cluster\">\n",
-       "<title>cluster4</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M328.5,-8C328.5,-8 542.5,-8 542.5,-8 548.5,-8 554.5,-14 554.5,-20 554.5,-20 554.5,-109.95 554.5,-109.95 554.5,-115.95 548.5,-121.95 542.5,-121.95 542.5,-121.95 328.5,-121.95 328.5,-121.95 322.5,-121.95 316.5,-115.95 316.5,-109.95 316.5,-109.95 316.5,-20 316.5,-20 316.5,-14 322.5,-8 328.5,-8\"/>\n",
-       "<text text-anchor=\"middle\" x=\"541.5\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">4</text>\n",
-       "</g>\n",
-       "<g id=\"clust3\" class=\"cluster\">\n",
-       "<title>cluster5</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M574.5,-8C574.5,-8 768.5,-8 768.5,-8 774.5,-8 780.5,-14 780.5,-20 780.5,-20 780.5,-109.95 780.5,-109.95 780.5,-115.95 774.5,-121.95 768.5,-121.95 768.5,-121.95 574.5,-121.95 574.5,-121.95 568.5,-121.95 562.5,-115.95 562.5,-109.95 562.5,-109.95 562.5,-20 562.5,-20 562.5,-14 568.5,-8 574.5,-8\"/>\n",
-       "<text text-anchor=\"middle\" x=\"767.5\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">5</text>\n",
-       "</g>\n",
-       "<!-- scalar -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
-       "<title>scalar</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"49.5\" cy=\"-76.48\" rx=\"49.49\" ry=\"37.45\"/>\n",
-       "<text text-anchor=\"middle\" x=\"49.5\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">scalar</text>\n",
-       "<text text-anchor=\"middle\" x=\"49.5\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"49.5\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- vector (implied) -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>vector (implied)</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"208.5\" cy=\"-76.48\" rx=\"91.85\" ry=\"37.45\"/>\n",
-       "<text text-anchor=\"middle\" x=\"208.5\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">vector (implied)</text>\n",
-       "<text text-anchor=\"middle\" x=\"208.5\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"208.5\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- vector (from shape) -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>vector (from shape)</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"435.5\" cy=\"-76.48\" rx=\"111.03\" ry=\"37.45\"/>\n",
-       "<text text-anchor=\"middle\" x=\"435.5\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">vector (from shape)</text>\n",
-       "<text text-anchor=\"middle\" x=\"435.5\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"435.5\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "<!-- vector (from size) -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
-       "<title>vector (from size)</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"671.5\" cy=\"-76.48\" rx=\"101.23\" ry=\"37.45\"/>\n",
-       "<text text-anchor=\"middle\" x=\"671.5\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">vector (from size)</text>\n",
-       "<text text-anchor=\"middle\" x=\"671.5\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"671.5\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
-       "</g>\n",
-       "</g>\n",
-       "</svg>\n"
-      ],
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"796pt\" height=\"138pt\"\n viewBox=\"0.00 0.00 796.50 137.95\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 133.95)\">\n<title>%3</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-133.95 792.5,-133.95 792.5,4 -4,4\"/>\n<g id=\"clust1\" class=\"cluster\">\n<title>cluster3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M120.5,-8C120.5,-8 296.5,-8 296.5,-8 302.5,-8 308.5,-14 308.5,-20 308.5,-20 308.5,-109.95 308.5,-109.95 308.5,-115.95 302.5,-121.95 296.5,-121.95 296.5,-121.95 120.5,-121.95 120.5,-121.95 114.5,-121.95 108.5,-115.95 108.5,-109.95 108.5,-109.95 108.5,-20 108.5,-20 108.5,-14 114.5,-8 120.5,-8\"/>\n<text text-anchor=\"middle\" x=\"295.5\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">3</text>\n</g>\n<g id=\"clust2\" class=\"cluster\">\n<title>cluster4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M328.5,-8C328.5,-8 542.5,-8 542.5,-8 548.5,-8 554.5,-14 554.5,-20 554.5,-20 554.5,-109.95 554.5,-109.95 554.5,-115.95 548.5,-121.95 542.5,-121.95 542.5,-121.95 328.5,-121.95 328.5,-121.95 322.5,-121.95 316.5,-115.95 316.5,-109.95 316.5,-109.95 316.5,-20 316.5,-20 316.5,-14 322.5,-8 328.5,-8\"/>\n<text text-anchor=\"middle\" x=\"541.5\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">4</text>\n</g>\n<g id=\"clust3\" class=\"cluster\">\n<title>cluster5</title>\n<path fill=\"none\" stroke=\"black\" d=\"M574.5,-8C574.5,-8 768.5,-8 768.5,-8 774.5,-8 780.5,-14 780.5,-20 780.5,-20 780.5,-109.95 780.5,-109.95 780.5,-115.95 774.5,-121.95 768.5,-121.95 768.5,-121.95 574.5,-121.95 574.5,-121.95 568.5,-121.95 562.5,-115.95 562.5,-109.95 562.5,-109.95 562.5,-20 562.5,-20 562.5,-14 568.5,-8 574.5,-8\"/>\n<text text-anchor=\"middle\" x=\"767.5\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">5</text>\n</g>\n<!-- scalar -->\n<g id=\"node1\" class=\"node\">\n<title>scalar</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"49.5\" cy=\"-76.48\" rx=\"49.49\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"49.5\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">scalar</text>\n<text text-anchor=\"middle\" x=\"49.5\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"49.5\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n</g>\n<!-- vector (implied) -->\n<g id=\"node2\" class=\"node\">\n<title>vector (implied)</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"208.5\" cy=\"-76.48\" rx=\"91.85\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"208.5\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">vector (implied)</text>\n<text text-anchor=\"middle\" x=\"208.5\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"208.5\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n</g>\n<!-- vector (from shape) -->\n<g id=\"node3\" class=\"node\">\n<title>vector (from shape)</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"435.5\" cy=\"-76.48\" rx=\"111.03\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"435.5\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">vector (from shape)</text>\n<text text-anchor=\"middle\" x=\"435.5\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"435.5\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n</g>\n<!-- vector (from size) -->\n<g id=\"node4\" class=\"node\">\n<title>vector (from size)</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"671.5\" cy=\"-76.48\" rx=\"101.23\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"671.5\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">vector (from size)</text>\n<text text-anchor=\"middle\" x=\"671.5\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"671.5\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n</g>\n</g>\n</svg>\n",
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@@ -332,61 +271,7 @@
    "outputs": [
     {
      "data": {
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-       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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-       "<g id=\"clust1\" class=\"cluster\">\n",
-       "<title>clusterB (2)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M20,-121.95C20,-121.95 110,-121.95 110,-121.95 116,-121.95 122,-127.95 122,-133.95 122,-133.95 122,-223.91 122,-223.91 122,-229.91 116,-235.91 110,-235.91 110,-235.91 20,-235.91 20,-235.91 14,-235.91 8,-229.91 8,-223.91 8,-223.91 8,-133.95 8,-133.95 8,-127.95 14,-121.95 20,-121.95\"/>\n",
-       "<text text-anchor=\"middle\" x=\"96.5\" y=\"-129.75\" font-family=\"Times,serif\" font-size=\"14.00\">B (2)</text>\n",
-       "</g>\n",
-       "<g id=\"clust2\" class=\"cluster\">\n",
-       "<title>clusterDim_A (4)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M142,-8C142,-8 232,-8 232,-8 238,-8 244,-14 244,-20 244,-20 244,-223.91 244,-223.91 244,-229.91 238,-235.91 232,-235.91 232,-235.91 142,-235.91 142,-235.91 136,-235.91 130,-229.91 130,-223.91 130,-223.91 130,-20 130,-20 130,-14 136,-8 142,-8\"/>\n",
-       "<text text-anchor=\"middle\" x=\"201\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">Dim_A (4)</text>\n",
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-       "<!-- red -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
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-       "<text text-anchor=\"middle\" x=\"65\" y=\"-171.73\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- one -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>one</title>\n",
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-       "<!-- two -->\n",
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-       "<title>two</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"187\" cy=\"-76.48\" rx=\"49.49\" ry=\"37.45\"/>\n",
-       "<text text-anchor=\"middle\" x=\"187\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">two</text>\n",
-       "<text text-anchor=\"middle\" x=\"187\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"187\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- one&#45;&gt;two -->\n",
-       "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>one&#45;&gt;two</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M187,-152.77C187,-143.69 187,-133.83 187,-124.33\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"190.5,-124.22 187,-114.22 183.5,-124.22 190.5,-124.22\"/>\n",
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-      ],
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"260pt\" height=\"252pt\"\n viewBox=\"0.00 0.00 260.00 251.91\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 247.91)\">\n<title>%3</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-247.91 256,-247.91 256,4 -4,4\"/>\n<g id=\"clust1\" class=\"cluster\">\n<title>clusterB (2)</title>\n<path fill=\"none\" stroke=\"black\" d=\"M20,-121.95C20,-121.95 110,-121.95 110,-121.95 116,-121.95 122,-127.95 122,-133.95 122,-133.95 122,-223.91 122,-223.91 122,-229.91 116,-235.91 110,-235.91 110,-235.91 20,-235.91 20,-235.91 14,-235.91 8,-229.91 8,-223.91 8,-223.91 8,-133.95 8,-133.95 8,-127.95 14,-121.95 20,-121.95\"/>\n<text text-anchor=\"middle\" x=\"96.5\" y=\"-129.75\" font-family=\"Times,serif\" font-size=\"14.00\">B (2)</text>\n</g>\n<g id=\"clust2\" class=\"cluster\">\n<title>clusterDim_A (4)</title>\n<path fill=\"none\" stroke=\"black\" d=\"M142,-8C142,-8 232,-8 232,-8 238,-8 244,-14 244,-20 244,-20 244,-223.91 244,-223.91 244,-229.91 238,-235.91 232,-235.91 232,-235.91 142,-235.91 142,-235.91 136,-235.91 130,-229.91 130,-223.91 130,-223.91 130,-20 130,-20 130,-14 136,-8 142,-8\"/>\n<text text-anchor=\"middle\" x=\"201\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">Dim_A (4)</text>\n</g>\n<!-- red -->\n<g id=\"node1\" class=\"node\">\n<title>red</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"65\" cy=\"-190.43\" rx=\"49.49\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"65\" y=\"-201.73\" font-family=\"Times,serif\" font-size=\"14.00\">red</text>\n<text text-anchor=\"middle\" x=\"65\" y=\"-186.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"65\" y=\"-171.73\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n</g>\n<!-- one -->\n<g id=\"node2\" class=\"node\">\n<title>one</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"187\" cy=\"-190.43\" rx=\"49.49\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"187\" y=\"-201.73\" font-family=\"Times,serif\" font-size=\"14.00\">one</text>\n<text text-anchor=\"middle\" x=\"187\" y=\"-186.73\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"187\" y=\"-171.73\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n</g>\n<!-- two -->\n<g id=\"node3\" class=\"node\">\n<title>two</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"187\" cy=\"-76.48\" rx=\"49.49\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"187\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">two</text>\n<text text-anchor=\"middle\" x=\"187\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"187\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n</g>\n<!-- one&#45;&gt;two -->\n<g id=\"edge1\" class=\"edge\">\n<title>one&#45;&gt;two</title>\n<path fill=\"none\" stroke=\"black\" d=\"M187,-152.77C187,-143.69 187,-133.83 187,-124.33\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"190.5,-124.22 187,-114.22 183.5,-124.22 190.5,-124.22\"/>\n</g>\n</g>\n</svg>\n",
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@@ -509,73 +394,7 @@
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-       "<g id=\"clust1\" class=\"cluster\">\n",
-       "<title>cluster3</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M20,-8C20,-8 142,-8 142,-8 148,-8 154,-14 154,-20 154,-20 154,-109.95 154,-109.95 154,-115.95 148,-121.95 142,-121.95 142,-121.95 20,-121.95 20,-121.95 14,-121.95 8,-115.95 8,-109.95 8,-109.95 8,-20 8,-20 8,-14 14,-8 20,-8\"/>\n",
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-       "<g id=\"clust2\" class=\"cluster\">\n",
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-       "<path fill=\"none\" stroke=\"black\" d=\"M174,-8C174,-8 339,-8 339,-8 345,-8 351,-14 351,-20 351,-20 351,-109.95 351,-109.95 351,-115.95 345,-121.95 339,-121.95 339,-121.95 174,-121.95 174,-121.95 168,-121.95 162,-115.95 162,-109.95 162,-109.95 162,-20 162,-20 162,-14 168,-8 174,-8\"/>\n",
-       "<text text-anchor=\"middle\" x=\"256.5\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">repeats (3) x implied (2)</text>\n",
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-       "<g id=\"clust3\" class=\"cluster\">\n",
-       "<title>clusterrepeats (3) x None (2)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M371,-8C371,-8 520,-8 520,-8 526,-8 532,-14 532,-20 532,-20 532,-109.95 532,-109.95 532,-115.95 526,-121.95 520,-121.95 520,-121.95 371,-121.95 371,-121.95 365,-121.95 359,-115.95 359,-109.95 359,-109.95 359,-20 359,-20 359,-14 365,-8 371,-8\"/>\n",
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-       "<title>clusteryear (3) x None (2)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M552,-8C552,-8 686,-8 686,-8 692,-8 698,-14 698,-20 698,-20 698,-109.95 698,-109.95 698,-115.95 692,-121.95 686,-121.95 686,-121.95 552,-121.95 552,-121.95 546,-121.95 540,-115.95 540,-109.95 540,-109.95 540,-20 540,-20 540,-14 546,-8 552,-8\"/>\n",
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-       "<!-- with size -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>with size</title>\n",
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-       "<text text-anchor=\"middle\" x=\"256\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">with size</text>\n",
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-       "<title>with shape</title>\n",
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-       "<text text-anchor=\"middle\" x=\"445\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">with shape</text>\n",
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-       "<text text-anchor=\"middle\" x=\"619\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">with coords</text>\n",
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-       "</svg>\n"
-      ],
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 2.43.0 (0)\n -->\n<!-- Title: %3 Pages: 1 -->\n<svg width=\"714pt\" height=\"138pt\"\n viewBox=\"0.00 0.00 714.00 137.95\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 133.95)\">\n<title>%3</title>\n<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-133.95 710,-133.95 710,4 -4,4\"/>\n<g id=\"clust1\" class=\"cluster\">\n<title>cluster3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M20,-8C20,-8 142,-8 142,-8 148,-8 154,-14 154,-20 154,-20 154,-109.95 154,-109.95 154,-115.95 148,-121.95 142,-121.95 142,-121.95 20,-121.95 20,-121.95 14,-121.95 8,-115.95 8,-109.95 8,-109.95 8,-20 8,-20 8,-14 14,-8 20,-8\"/>\n<text text-anchor=\"middle\" x=\"141\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">3</text>\n</g>\n<g id=\"clust2\" class=\"cluster\">\n<title>clusterrepeats (3) x implied (2)</title>\n<path fill=\"none\" stroke=\"black\" d=\"M174,-8C174,-8 339,-8 339,-8 345,-8 351,-14 351,-20 351,-20 351,-109.95 351,-109.95 351,-115.95 345,-121.95 339,-121.95 339,-121.95 174,-121.95 174,-121.95 168,-121.95 162,-115.95 162,-109.95 162,-109.95 162,-20 162,-20 162,-14 168,-8 174,-8\"/>\n<text text-anchor=\"middle\" x=\"256.5\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">repeats (3) x implied (2)</text>\n</g>\n<g id=\"clust3\" class=\"cluster\">\n<title>clusterrepeats (3) x None (2)</title>\n<path fill=\"none\" stroke=\"black\" d=\"M371,-8C371,-8 520,-8 520,-8 526,-8 532,-14 532,-20 532,-20 532,-109.95 532,-109.95 532,-115.95 526,-121.95 520,-121.95 520,-121.95 371,-121.95 371,-121.95 365,-121.95 359,-115.95 359,-109.95 359,-109.95 359,-20 359,-20 359,-14 365,-8 371,-8\"/>\n<text text-anchor=\"middle\" x=\"445.5\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">repeats (3) x None (2)</text>\n</g>\n<g id=\"clust4\" class=\"cluster\">\n<title>clusteryear (3) x None (2)</title>\n<path fill=\"none\" stroke=\"black\" d=\"M552,-8C552,-8 686,-8 686,-8 692,-8 698,-14 698,-20 698,-20 698,-109.95 698,-109.95 698,-115.95 692,-121.95 686,-121.95 686,-121.95 552,-121.95 552,-121.95 546,-121.95 540,-115.95 540,-109.95 540,-109.95 540,-20 540,-20 540,-14 546,-8 552,-8\"/>\n<text text-anchor=\"middle\" x=\"622.5\" y=\"-15.8\" font-family=\"Times,serif\" font-size=\"14.00\">year (3) x None (2)</text>\n</g>\n<!-- implied -->\n<g id=\"node1\" class=\"node\">\n<title>implied</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"81\" cy=\"-76.48\" rx=\"65.11\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"81\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">implied</text>\n<text text-anchor=\"middle\" x=\"81\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"81\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">MvNormal</text>\n</g>\n<!-- with size -->\n<g id=\"node2\" class=\"node\">\n<title>with size</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"256\" cy=\"-76.48\" rx=\"65.11\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"256\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">with size</text>\n<text text-anchor=\"middle\" x=\"256\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"256\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">MvNormal</text>\n</g>\n<!-- with shape -->\n<g id=\"node3\" class=\"node\">\n<title>with shape</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"445\" cy=\"-76.48\" rx=\"67.35\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"445\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">with shape</text>\n<text text-anchor=\"middle\" x=\"445\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"445\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">MvNormal</text>\n</g>\n<!-- with coords -->\n<g id=\"node4\" class=\"node\">\n<title>with coords</title>\n<ellipse fill=\"none\" stroke=\"black\" cx=\"619\" cy=\"-76.48\" rx=\"70.92\" ry=\"37.45\"/>\n<text text-anchor=\"middle\" x=\"619\" y=\"-87.78\" font-family=\"Times,serif\" font-size=\"14.00\">with coords</text>\n<text text-anchor=\"middle\" x=\"619\" y=\"-72.78\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n<text text-anchor=\"middle\" x=\"619\" y=\"-57.78\" font-family=\"Times,serif\" font-size=\"14.00\">MvNormal</text>\n</g>\n</g>\n</svg>\n",
       "text/plain": [
        "<graphviz.graphs.Digraph at 0x7f57f1477e20>"
       ]

From 23a0f0a335375ea4aa711c5908d8f4a89c715897 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 06:56:13 +0000
Subject: [PATCH 10/21] renamed sd to sigma in test_util.py

---
 pymc/tests/test_util.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pymc/tests/test_util.py b/pymc/tests/test_util.py
index c0500d3b97..345469a6f2 100644
--- a/pymc/tests/test_util.py
+++ b/pymc/tests/test_util.py
@@ -94,9 +94,9 @@ def test_hashing_of_rv_tuples():
     obs = np.random.normal(-1, 0.1, size=10)
     with pm.Model() as pmodel:
         mu = pm.Normal("mu", 0, 1)
-        sd = pm.Gamma("sd", 1, 2)
+        sigma = pm.Gamma("sigma", 1, 2)
         dd = pm.Normal("dd", observed=obs)
-        for freerv in [mu, sd, dd] + pmodel.free_RVs:
+        for freerv in [mu, sigma, dd] + pmodel.free_RVs:
             for structure in [
                 freerv,
                 {"alpha": freerv, "omega": None},

From 1e723bdada10d889be1d6708205ca7e44ad9c949 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 06:59:47 +0000
Subject: [PATCH 11/21] renamed sd to sigma in tests/models.py

---
 pymc/tests/models.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pymc/tests/models.py b/pymc/tests/models.py
index cac49ba3aa..463940d239 100644
--- a/pymc/tests/models.py
+++ b/pymc/tests/models.py
@@ -212,7 +212,7 @@ def beta_bernoulli(n=2):
 def simple_normal(bounded_prior=False):
     """Simple normal for testing MLE / MAP; probes issue #2482."""
     x0 = 10.0
-    sd = 1.0
+    sigma = 1.0
     a, b = (9, 12)  # bounds for uniform RV, need non-symmetric to reproduce issue
 
     with pm.Model(rng_seeder=2482) as model:
@@ -220,6 +220,6 @@ def simple_normal(bounded_prior=False):
             mu_i = pm.Uniform("mu_i", a, b)
         else:
             mu_i = pm.Flat("mu_i")
-        pm.Normal("X_obs", mu=mu_i, sigma=sd, observed=x0)
+        pm.Normal("X_obs", mu=mu_i, sigma=sigma, observed=x0)
 
     return model.compute_initial_point(), model, None

From 72186c4254b98931a0f4d722c9a094bdede31af7 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 07:01:15 +0000
Subject: [PATCH 12/21] renamed sd to sigma in distributions.py

---
 pymc/distributions/timeseries.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pymc/distributions/timeseries.py b/pymc/distributions/timeseries.py
index 225a593880..c059984958 100644
--- a/pymc/distributions/timeseries.py
+++ b/pymc/distributions/timeseries.py
@@ -393,8 +393,8 @@ def logp(self, x):
         xt = x[:-1]
         f, g = self.sde_fn(x[:-1], *self.sde_pars)
         mu = xt + self.dt * f
-        sd = at.sqrt(self.dt) * g
-        return at.sum(Normal.dist(mu=mu, sigma=sd).logp(x[1:]))
+        sigma = at.sqrt(self.dt) * g
+        return at.sum(Normal.dist(mu=mu, sigma=sigma).logp(x[1:]))
 
     def _distr_parameters_for_repr(self):
         return ["dt"]

From d827e70d31e3a0514e550abac0987f899e8fd9bf Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 07:02:38 +0000
Subject: [PATCH 13/21] renamed sd to sigma in data.py

---
 pymc/data.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pymc/data.py b/pymc/data.py
index d3624e88fc..3d74d659df 100644
--- a/pymc/data.py
+++ b/pymc/data.py
@@ -205,8 +205,8 @@ class Minibatch(TensorVariable):
 
     >>> with pm.Model() as model:
     ...     mu = pm.Flat('mu')
-    ...     sd = pm.HalfNormal('sd')
-    ...     lik = pm.Normal('lik', mu, sd, observed=x, total_size=(100, 100))
+    ...     sigma = pm.HalfNormal('sigma')
+    ...     lik = pm.Normal('lik', mu, sigma, observed=x, total_size=(100, 100))
 
 
     Then you can perform regular Variational Inference out of the box

From e14e799d5eceb16e5f1e987a0b6514ae318db114 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 07:05:08 +0000
Subject: [PATCH 14/21] renamed sd to sigma in
 examples/posterior_predictive.ipynb

---
 docs/source/learn/examples/posterior_predictive.ipynb | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/source/learn/examples/posterior_predictive.ipynb b/docs/source/learn/examples/posterior_predictive.ipynb
index 4faee5878a..4b9a820285 100644
--- a/docs/source/learn/examples/posterior_predictive.ipynb
+++ b/docs/source/learn/examples/posterior_predictive.ipynb
@@ -151,9 +151,9 @@
     "    b = pm.Normal(\"b\", 0.0, 10.0)\n",
     "\n",
     "    mu = a + b * predictor_scaled\n",
-    "    sd = pm.Exponential(\"sd\", 1.0)\n",
+    "    sigma = pm.Exponential(\"sigma\", 1.0)\n",
     "\n",
-    "    pm.Normal(\"obs\", mu=mu, sigma=sd, observed=outcome_scaled)\n",
+    "    pm.Normal(\"obs\", mu=mu, sigma=sigma, observed=outcome_scaled)\n",
     "    idata = pm.sample_prior_predictive(samples=50)"
    ]
   },
@@ -212,9 +212,9 @@
     "    b = pm.Normal(\"b\", 0.0, 1.0)\n",
     "\n",
     "    mu = a + b * predictor_scaled\n",
-    "    sd = pm.Exponential(\"sd\", 1.0)\n",
+    "    sigma = pm.Exponential(\"sigma\", 1.0)\n",
     "\n",
-    "    pm.Normal(\"obs\", mu=mu, sigma=sd, observed=outcome_scaled)\n",
+    "    pm.Normal(\"obs\", mu=mu, sigma=sigma, observed=outcome_scaled)\n",
     "    idata = pm.sample_prior_predictive(samples=50)"
    ]
   },
@@ -328,7 +328,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Everything ran smoothly, but it's often difficult to understand what the parameters' values mean when analyzing a trace plot or table summary -- even more so here, as the parameters live in the standardized space. A useful thing to understand your models is... you guessed it: posterior predictive checks! We'll use PyMC's dedicated function to sample data from the posterior. This function will randomly draw 4000 samples of parameters from the trace. Then, for each sample, it will draw 100 random numbers from a normal distribution specified by the values of `mu` and `sd` in that sample:"
+    "Everything ran smoothly, but it's often difficult to understand what the parameters' values mean when analyzing a trace plot or table summary -- even more so here, as the parameters live in the standardized space. A useful thing to understand your models is... you guessed it: posterior predictive checks! We'll use PyMC's dedicated function to sample data from the posterior. This function will randomly draw 4000 samples of parameters from the trace. Then, for each sample, it will draw 100 random numbers from a normal distribution specified by the values of `mu` and `sigma` in that sample:"
    ]
   },
   {

From e761f50c925a42b7440f0da3d067e9f9f8a3487c Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 07:06:11 +0000
Subject: [PATCH 15/21] renamed sd to sigma in developer_guide.rst

---
 docs/source/contributing/developer_guide.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/contributing/developer_guide.rst b/docs/source/contributing/developer_guide.rst
index 100bfbab0c..c1b7c90228 100644
--- a/docs/source/contributing/developer_guide.rst
+++ b/docs/source/contributing/developer_guide.rst
@@ -888,8 +888,8 @@ others. The challenge and some summary of the solution could be found in Luciano
 
     with pm.Model() as m:
         mu = pm.Normal('mu', 0., 1., shape=(5, 1))
-        sd = pm.HalfNormal('sd', 5., shape=(1, 10))
-        pm.Normal('x', mu=mu, sigma=sd, observed=np.random.randn(2, 5, 10))
+        sigma = pm.HalfNormal('sigma', 5., shape=(1, 10))
+        pm.Normal('x', mu=mu, sigma=sigma, observed=np.random.randn(2, 5, 10))
         trace = pm.sample_prior_predictive(100)
 
     trace['x'].shape # ==> should be (100, 2, 5, 10)

From 4c1a0014e80a5ba2ec3536e319db68413434ccaa Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 07:08:01 +0000
Subject: [PATCH 16/21] renamed sd to sigma in source/PyMC_and_Aesara.rst

---
 docs/source/PyMC_and_Aesara.rst | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/docs/source/PyMC_and_Aesara.rst b/docs/source/PyMC_and_Aesara.rst
index 66bb3e3e69..7bf1b5d63a 100644
--- a/docs/source/PyMC_and_Aesara.rst
+++ b/docs/source/PyMC_and_Aesara.rst
@@ -188,8 +188,8 @@ example::
 
     with pm.Model() as model:
         mu = pm.Normal('mu', 0, 1)
-        sd = pm.HalfNormal('sd', 1)
-        y = pm.Normal('y', mu=mu, sigma=sd, observed=data)
+        sigma = pm.HalfNormal('sigma', 1)
+        y = pm.Normal('y', mu=mu, sigma=sigma, observed=data)
 
 is roughly equivalent to this::
 
@@ -203,10 +203,10 @@ is roughly equivalent to this::
     model.add_free_variable(sd_log__)
     model.add_logp_term(corrected_logp_half_normal(sd_log__))
 
-    sd = at.exp(sd_log__)
-    model.add_deterministic_variable(sd)
+    sigma = at.exp(sd_log__)
+    model.add_deterministic_variable(sigma)
 
-    model.add_logp_term(pm.Normal.dist(mu, sd).logp(data))
+    model.add_logp_term(pm.Normal.dist(mu, sigma).logp(data))
 
 The return values of the variable constructors are subclasses
 of Aesara variables, so when we define a variable we can use any
@@ -217,5 +217,5 @@ Aesara operation on them::
         # beta is a at.dvector
         beta = pm.Normal('beta', 0, 1, shape=len(design_matrix))
         predict = at.dot(design_matrix, beta)
-        sd = pm.HalfCauchy('sd', beta=2.5)
-        pm.Normal('y', mu=predict, sigma=sd, observed=data)
+        sigma = pm.HalfCauchy('sigma', beta=2.5)
+        pm.Normal('y', mu=predict, sigma=sigma, observed=data)

From 853b0ff3abb664bb6635bfb9c1314f991d0d8110 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 07:38:36 +0000
Subject: [PATCH 17/21] renamed sd to sigma in examples/GLM_linear.ipynb

---
 docs/source/learn/examples/GLM_linear.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/learn/examples/GLM_linear.ipynb b/docs/source/learn/examples/GLM_linear.ipynb
index 1765e4eef2..baa506fb94 100644
--- a/docs/source/learn/examples/GLM_linear.ipynb
+++ b/docs/source/learn/examples/GLM_linear.ipynb
@@ -135,7 +135,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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//dd/1aOPPrr9tkgkotWrV6u9vd324w8ODlY1bj8xDEOtra2SpKGhIbGLAt7EXAX8g/kK+ANzFfCPyeYrrRnKs9M+oZqfx+Rqvk+aOUVffVjx5GpFn/+ljFzG+qbRBmUOP1Pp9sXK7X2MZEz/NQ/je2tbW5uj90flcRVM09S9996re++9V01NTTryyCN16KGHatasWWpqatLWrVv11ltvacOGDXr11VcnbGsYhr773e9WFRwDAAAAAADvqjYADnJwLNnrp0tw7Kya7ZMjg4pvvEPx1IAiQy/b2jTXur/SicVKzz5XanI2+MT0ER5P08jIiP74xz/qj3/845Q/u+eee+o73/mOTj311BqMDAAAAAAAwBusBMgExz5jmoq88UfFU6sVe+ZeGdkx65tGYsoecrrS7R3K7neCZNDT2qsIj22aOXOm/vmf/1m//vWv9eijj+rtt9+ecpujjz5a5513ns4//3zttNNONRglAAAAAACAt1QKkAmOfWTbR4ptWqd4akDR956xtWlu572VTixSZvb5Mnfaw6UBwkn0PJ6md955R88//7xee+01bdmyRaOjo2pubtZOO+2kT3ziE5o9e7ZaWlocezx6HgPwCuYq4B/MV8AfmKuAfzBfp6c0KG5pkbZsGf93gmNviry9UfHUgGJP3S0jPWx5O1OGsgd9Pl9lfODnpUjUxVFOFMa5Ss9jj9ljjz20xx6cKQEAAPawoA4AAAir0gpkgmMPS48o9vR6xZMDir69wdamuebdlJl7gdJzF8ps2delAcJtNBQBAACosZ7enFasNNXXb6/yoa/f1IqVpnp6cy6NDAAAoDa6Og2VXqjd0lJ+ET3UnvH+c2q4/x8084aTNeNnf2MrOM7s92mNfOV7Gv76fRo76b/VLDhOpqqrKq52u7AgPAYAAKihZMpU76r8n6+7wXqAXHx5Z+8qPuR6GV9cACA4OKa7p6/fnFBxLOUrkO2eXIeDMmOKPXW3mgYu1sxVZ6vhjz+Use1DS5uajbM0Nu8Sbb1kvUYX9ip7+JlStMHlAY+jOMM9hMcAAAA11J4wtHzZeEWNlQC53AIytK7wJr64AEBwcEx3T7mexwV2Tq7DGcbQK2p48N8088ZTNGP9nyv6+h8sb5vd55MaPfOftHXZAxo7+X/I3OUgF0daHsUZ7iI8BgAAgeP1KqGuTusBMiuP+wdfXAAgODimu6fcZ5v16yK2T65jmnIZRZ/7hWbceplm3vwlNfyhR8bIoKVNzXiz0u2LNXzx7RpZ3K/M0edI8RkuD3hyFGe4i/AYAAAEil+qhKwEyATH/sIXFwAIDo7p1asUmFf6bDN3jgiQa8D48C01/Pb/qfnG09W07s8Ue/khy9tmdz9Ko1/4jrZe/qC2nf63yu1+pIsjtYfiDPcQHgMAgMDwW5VQpQ+5fKj1J764AEBwcEy3r9JJ/ErPUeEk/rZtJgGyG8ycoi/9RjPu/JaabzpdDb+/RpGt71jbNNqo9OzzNfzVAY103apMYpHUMNPlAVen/JydWBjCXLXPME2TWegjg4PWLiHwM8Mw1NraKkkaGhoSuyjgTcxVeJXdD4Re+ABZru9f8QIy0x0T87X2ptqvvLDfwXuYq4A3lTtmX3xRZPt8/X//+YGuvZ5jejKVD4ALSsPhSsFx8b9dc7WhDU+I90kHGMPvK/bEbYpvWKPI5tdsbZvb5WClEx1KH32ONGOWSyN0R+k+dcW3m3XZpU2hmattbW2O3l/M0XsDAACos8IHwMIHxsL/y30w9EqAVzpmJ4Nj1Eel/dAr+x0AwJpyx3TDyOlb35RuunkkFGGUFflWHzu+982dM3kQPFmrj/aEJtzHdTeYmjtHoWwDYptpKvLao4qnBhR79ucycmnrm0biyhx2htKJDuU+8SnJ8OfzXTpnv3fVsG7+wYg2b2auVoPKY5+h8hiAVzBX4XV+rPycvyA3IThuaZHWr5t+lzHma/24XVWOYGGuAt5WekyfNcsgjCqj3GesbdvyrcWsViOX3lf3EmlpN51XKxrdrPiT6xRPDSjywfO2Ns3N+oTScxcpM+d8mc27ujTA2ivdxwqCPledrjwmPPYZwmMAXsFchR9M9qXEi8Gxmx9uma/1FdYvLrCPuQp4H8d0a8p91iquHLbzWSyZMqk4noxpKvLWhnyV8dPrZWRGrW9qRJQ9+FSlEx3KHniSZAQznHerOMPLCI9DjvAYgFcwV+EXfqj8pOdx8IXxiwvsY64C/sAx3Ro/ncT3nbGtij11t+Kp1Yq+s8nWprmZeygzd6HScy+UufNeLg3QG8J6soeexwAAADZ4vZ+wlS9Wlfo2w/v6+s0J+52U3w/7+k1eUwDwmSAf06ut8J1su3K9ovtXm577LOYnkXefVjy1WrFNd8kY22pr28wBJ+WrjA85VYoEPw6s1GaGz9b2cGoMAAAEXlenoZaWibe1tNT/A2OlypuuTkPLl42P77obTPX1U4XoN+Wqygt4TQHAX8qFUQX1PKYnU9U9bvF2Pb05rVhp/3fo6ze1YqWpnt5c2X8v/TxDcFyFzDbFnrxTTas71fzDcxVPrrYcHJtNbRo7bqm2XnqvRi+4SdnDzghlcHzFt5v12wd30Tcu98ac9RvCYwAAEHiVqoTqxcolmwTI/lbuNV6/LsJrCgA+VHpM/8blhn774C664tvN22+rxzHdidA3mcovZifZ+x2Kn5PeVZOH2F49ie91xuCLavjVP2vmDSdrxj1/pegbf7S8bXbf4zQ6/9+09esPaOzzfy6zdX8XR+ot5ebqZZc2SZK6OvkcVg3CYwAAEGherPy00+uPANmfqCoH4CQnKktRvfLH9HycctmlTXWrZnQq9JVk+32p3HMyWcsLL57E96xsWtFn7tWMW7o1s3e+Gh77gYzRzZY2NRt31tgxXRr+2jqNdPxQmSPPkmINLg/YWyrN1QI+h9lHeAwAAALLi5WfyZT9RWLKfcglEPAuqsoBOMmtdgKwxtoxvT6fLdoT9t9LJgt97bwv2TkJ7sWT+F5kbHldDQ/9h5pvOk1NP/lvir36e8vbZvecq9Evfldblz2gsdP+l3K7HebiSL2L4gz3BL/RCQAACKWpKj+liQu4FN/upvaEoe4l+UohO73+isfcvURVLWgD99n94iLVZz8E4A+llaWStWNEaWXpcfOqWwgt7PxwTLfzuFP9PlbuazrBMYsCl8hlFX3p14onVyv64oMyZD28NGNNyhz1FaUTHcrtOdvFQfpDtcUZ0sT9fe4cPmOXQ3gMAAACx2rlp1Sf4G5pd6SqL/JdnQYfaj2MLy6APyVT1QWr1W5nR76y1N57lZ12Apicn47pToa+le7LieDY6niDzPjoHcU23qZ4ao0iH75pa9vsrocp096h9FELpMadXRqh/1Cc4S7DNE3qsn1kcHCw3kNwnWEYam1tlSQNDQ2JXRTwJuYqvMrOF5tqft6PmK+109Obs/3FRRrfD7uX5E8uIJyYq7Xnlzlr9b0qDO9ptVRp/6g0X+t1TLdS6Vt8u537amnRhL7F1QbH1fxcIJg5RV99WPHkgKLP3ycjl7G+aTSuzOFnKp1YrNw+n5SMgD5HDih3Us/Ke2stTgbWUltbm6P3R3jsM4THALyCuQovSqby/R0LrH4JKf3ycs3VwarSYr7WlperGOFtzNXa8tt7xlRBW6iCuBqa7Ng81Xyt1zF9OqHvVPdl5T44iV9iZFDxjXconhpQZOhlW5vmWvdXOrFY6dnnSk3OhoFhEsb3VsLjkCM8BuAVzFV4lV+qyGqJ+Qr4A3O19vwWdDlZWYrp8fJ8rSb0ncz8BbkJ4XNLi7R+XfnPSX47IeMa01TkzT8pnlyt2DP3yMiOWd/UiCp76OlKJzqU3f/TkhGsz6T14OW56hbC45AjPAbgFcxVeBmVnxMxXwF/YK7Wh98usXeyshTV8/p8tRP6TqaaEDrUJ/G3faTYpnWKpwYUfe8ZW5vmdt5b6bkLlZlzgcyd9nBpgOHk9bnqBqfDYxbMAwAAgVNtABzE4BgAUJmTi43VQul4CY5Rqq/fnLBfSPn9pK/frLplRfFJikoL3IVxUeDIO0/mq4yfultGetjydqYMZQ/6nNKJxcoe9HkpEnVxlED1CI8BAAAAAKFWKUD2UnBc0NVpqH+1uUNlab3HhfqrNvStdB/l2qNUuq9QnMRPjyj29E/zVcZvpWxtmmveTZk5Fyg9d6HMWfu6NEDAOYTHAAAAAIDQKxcglwa0XgiOJWcqSxE80w19K91H8TZW7yuIjPefVzw1oPiTd8jY9qGtbTP7fVrp9g5lDzlNija4NELAeYTHAAAAAADIHy0hnKgsRfA4EfpaqbIPZYCcGVPsuZ/nq4xfe9TWpmbjLKVnn6t0okPmLge5NEDAXYTHAAAAAAB8zMstIZyoLEXwOBH62mnPUosA2QuLHxtDryq+YY1iT9ymyMgHtrbN7n2M0u0dyhx2phSf4ch4gHrx6RKWAAAAAAA4r1JLCLclU5M/RqVwb+6c/N8LrrvBrMl4UX92Q99y+0kyZb+vd7n7qrT/2tHTm9OKlfb34b5+UytWmurpzVX/4LmMos/9QjNu/bpm3vxFNTx6k+Xg2Iw3K92+WMMX366Rr/5YmaPPJThGIBAeAwAAAACg8i0hCtwOZCsFZpUCwkJgtm2bSYAcMk6FvpLUvcT6fZS7r+4lzix4l0yZ6l01Pjar+3DxHOldVflETDnGh28r/rtr1HzTF9S07s8Ue/k3lrfN7n6kRr/wd9p6+YPadvrfKrf7kbYeO2yqPcng1MkJ2Ed4DAAAAAABwZfy6pULaNevi9QkkK0UmE0VHBcHZlQgTy1Ic6Q9YTgW+i7tjuiaq+339e7qNHTN1fntndCeKF8dXUm5OWIpyDZzir70G82481tqvul0Nf7u/yny0duWxmlGG5WefZ6Gv7paI123KZPokBpmWto2zOpaVY6q0fMYAAAAAAKgpzen3lXS8mX2eo8WgpfuJaZjAZDfOLHY2HTkA7MdH2PuHFkKjgv/1p4w1J7QDuOdO8eZqlC/C+IcWdod0XHz7Pf57eo0dtgvqt1HnN637Mw5O207thv+QPEnblV8wy2KbH7V1thyuxysdKJD6aPPkWbMsrVt2JWeJJOszcPSk2TV7O+YHm8d9QAAAAAAttXrUu8gsLrYmNsVveUeY8MT5dsJTDVmN9oJ+F2Q54hXQl8nWZlztoJj01TktUfVePefa+YNp6jxN//XcnBsRuJKH/FlDS9cpeElP1H62K8RHFehplXlcBSVxwAAAADgc5NVrlaq6uJLuf3FxiR3K5DLPcbyZYauuXo86LM65nKVpWHGHPGfSnPO8twd3aL4k3cqnhpQ5IPnbT1+rmVfpRMdysw+T+bM3ar8LVDM9apyuILwGAAAAAACgC/l9lS72JjkbkuIyQLk9oT9142gcyLmiP+Ue836V5vasmX8Z3Z4bUxTkbc2KJ4aUOzp9TIyo5YfzzQiyh58itKJxcoeeJJkcMG+06zMQ+aftxAeAwAAAEBA8KXcuvxiY+bHPXDtLTYm6eMeuO4EtFUFZrBksjlSfBLAzhx57PG0DjnYzRGj9DWbdB6MbVXsqbsVTw0o+s6Tth4jN3N3ZeYuVHruhTJ33tuRcWNyjlSVo2YM0zS917AHkxocHKz3EFxnGIZaW1slSUNDQ2IXBbyJuQr4B/MVqL1kyv6CPoZh6PkXZmresfFpz9XJvnzzpXxH1bxW09nOjtLXq8Dp183Lz4Fbyj23hX6sVuaIYRhae1ujvnfVsC69xNCll/jzefCT+QtyE4LjlhZp/bqIIu8+o1hqteKb1skY22rrPjMHnKR0okPZg0+RonFnB4wplc7DlpYKJweqFMbPwW1tbY7eH+GxzxAeA/AK5irgH8xXoLZ6enO2q1kl6Uc/NnXt9aa+ubxJF311bNpztRZfyuG+yQIzp1S7vxb2r+4l0tJuf17aP1k4X1DpOSnM14JrrqYfsptKX6uGyDZ9YZ+f6c9OWKO904/bui9zRqvSc85Xeu4imW0HOD1U2OT2SbIwfg52OjymbQUAAAAAOCSZyrdBkOwtqFb85fk/rxvRnNkRJeZObyyWL/WGZbWu0O3rn9iqQsq/jn39piOvnxP7a+8q6bh5/qxALp0jxSrNkdKw6xuXExy7qfj53n/my+o8fI3O3PN2tTZsltLW7ye77zylE4uVOeyLUqzBpdHCrq5OY4e2PC0tzi5Giunx5+lBAAAAAPCg9oSx/dJ3KR9K9fVXrnIqDaKu+HazY0FUV6ehlpaJt/GlvDo9vTmtWDn161mqr9/UipWmenpztrcrrRwvsLJfWeHE/ppfzM+/+1O5OVJJufna1Um04pa+flM33TimL+x9r647canWnT5fi/f7QT44tsBs2Eljx3Rp+GvrNNLRp8xRXyE49phKJ8ngDVQeAwAAAICDrCxaV1AuiLrs0iYNDW1zZCxuV66GRa0rdK30rLYzjkqms78GoYK93ByRrC026fR8xUR3/PB1RX5zi356xq3afcZ7trbN7jlH6fbFyhzxZSne7NIIMV2V2is5dYzD9BEeAwAAAIDDrARy5S59v+zSJsfGwJdy5+QrdK0FrAXVVuhWCmjtBL12VLO/BiU4rtQXvPh5cHu+4mO5rKIv/Vpv/3RAXx19UNEjrFfsj2Sa9NLOZ+mA8zqU22uOi4OEE2p5kgzTQ3gMAAAAAC6oFMiV/9Ls3KXvfCl3Xi0qdK1sV48AOQzBcbk5IuWfh9J+rE7PV0jG1ncVe+JWxVNrFPnwTR0oSRZ3sQ9ih+r6xzt092tn66PMzlo+y1BXp4uDxbTV4yQZqkd4DAAAECLJlFnVIlzVLvYEhF25L8Hlgyjn5hdfyt3jZoWune1qGSC7vb/Wg505IrHYpGtMU9FXH1YsNaDYc7+QkctY3zQaV+awM5VuX6yGfT6pth9LHxXtt3PniM8tHlXPk2SoDuExAABASFxz7bD+87qc7S++hQ/53UtMLe2m0gqwq/RLcL2C48nGw5dye9yo0E2m7G9XbhxOBGa13F/roZo5UsBikw4ZGVT8yTsVTw0oMviSrU1zs/ZXur1D6dnnSU1t228vVBrnP68QHHuVF06SwT7DNE2WL/SRwcHBeg/BdYZhqLW1VZI0NDQkdlHAm5irgH8YhqHnX5ipr3WPJwDVVsJdc7W1np0AdjR/QW5CENfSIq1fN35CZrrvrXaDyyC2Iqilqfrl2n0+e3pz6l1lf7vxE3xy9ATfVPurH9nZ50t/tnQbPgvbZJqKvPknxZOrFXvmHhnZMeubGlFlDzlN6fbFyu7/acmYfD/kSinvSqZMrVg5/asy7H4WDeNcbWtrm/qHbKDyGAAAIATmHRvXFd9u1veuGpbk7mJPAHbU1z/x0n8pHzT29ZuOBLZeqlwNC6crdJd2R3TcPPvBV1en4fjr5vb+Wg925shkwbE0/npffJE/n4ea2/aRYk/dpXhyQNH3nra1aW6nvZROLFRmzoUyd9rD0jYcv7yrPWGoe4lp+yRZ8bGWqvL6oPLYZ6g8BuAVzFXAP4rn6//7zw907fVTf3mmIhFwjtUK1em+t3qtcjUsglah63RFtZdYmSPl3v+kHVtYfONyQ9/65i6S+CxcTuSdJxVPDij21E9kpIctb2fKUPagzymdWKzsQZ+TItQ8Bk211eHVbhfG761UHgMAAKBqXZ0RmWbOlcWeAOxosvlUfLtTlYxeqlwNi6BV6NrZX/34+001Ryq9/82dI214Yvz3v/Z6UzNmjOiyS5vcH7hfpEcUe+YexZOrFX0rZWvTXPOuysy5UOm5C2XO2telAcILqn2v4T2qfgiPAQAAQsaNxZ4A7KjSfCo3Dw0jp299c3qPyZfy2qlUoevHgNXu/lp8u59UExwXtmtP5P9c+LlCK6gLz3dpsD5hvP+84qkBxZ+8U8a2LVNvUCSz3wnKJDqUOfR0Kdrg0ggBTAfhMQAAQAiVCwL6V5uBuTQZqDcrJ2JK5yGVjP4RtArdavZXP/1+U7Fz4rT0efjeVcMaHTV00Vf9/zzYkhlT7LmfK54aUPS1R21tajbOUnr2uUonFsnc5WCXBgjAKYTHAAAAIeX0Yk8A8qYbRElUMnpZ0Cp0p7O/+uH3m0q1i00ahravIXDt9abmzA5HBb+x+TXFU2sUe+JWRUY+sLVtdu92pdsXK3PYmVJ8hksjBOA0wmMAAIAQ6+o0dqg4bmnxdxAA1FO1QZQ0MUA+7NCIEnPdGyeqE7QKXSf21+tuMH3dM7s9Yah7iWl7scmuzohmzGjU964a1qWXGL79/S3JZRR94Vf5KuOXfiND1hccM+PNyhx1ttKJDuX2OMrFQQJwC+ExAABAiAVtsSeg3qoPosYrGb+5vEntibFQrAjvJ0Gs0J3O/irlf6/uJf4NjguqXWzyskub9MljYjrk4K2enK/JlP3fqXg748O3FXtireIbblHko7dt3Ud29yPzVcZHfkVqmGl7DAC8wzC9eITDpAYHB+s9BNcZhqHW1lZJ0tDQkCffhAEwVwE/mWy+VlrsSaJ1BTAd1YQ2hmHo+Rdmat6xcd5bPSaZMrVipf3FREuPs9dc7c0K1emGjGHk9c/CPb052ycFJKnvR1kl7/itrvz8Gh2SuV+GmbW8rRltVOaILyvdvli5vRKSEc59A97i9bnqhra2Nkfvj8pjAACAEAraYk+A11QbqM07Nu7wSOCEoFfoVjsur/4+YZdM5fdVycZ7+fAHeqLvNp319hotP/FVKW398XJtBynd3qH0UedITa3VDRqAZxEeAwAAhEzQFnvyCyr7AH+rtrVBV6fh657A8J/2hKHlyyy8l5umIq8/pnhqQJGn7tWnlZYsdpgwI3FlDvuCMonFyn7iU1QZAwFGeAwAABAiff25QC325Bfjlw/bex4LQX/3ElNLuyMujhBTIfyHRIUu/KPie/noFsU3rVMstVrR95+3db+5ln2VTixSZvb5Mmfu5uygAXgS4TEAAEBI3HTziK69PliLPflBVZcPa2KFeO8qVVXxCGcQ/gPwo9L38l+vSenkzWt0ZHq9jMyo5fsxjYiyB5+idKJD2QNOkiJRV8YLwJsIjwEAAELgscfT+t5Vw9v/bqVnZ7kAmUuv7bN8+XCRcq1FeN7rg/AfgJ91XTisI4fXa/dXBjS7daM0Yn3b3MzdlZm7UOm5F8rceW/3BgnA0wiPAQAAQmDesXF9c3mT/vO6kUAu9uR1diq5K/WkRu0R/gPwo8i7zyiWGlB80zqdMvaR1Gp928wBn8lXGR98qhRlEU8g7AiPAQAAQmLFN5o1Z/Y2Jeba247FnpxhJUAmOPYmwn8AvpDZptgz9yqeGlD0jcdtbWrOaFV6zvlKz10ks+0AlwYIwI8IjwEAAEKkPWHINM2pf7DMdpi+SiEkoaO3Ef4D8Cpj8CXFU7covvE2GaNDtrZNDh2rI776VWUOO0OKNbozQAC+RngMAAAA1FC5ELJ/taktW8Z/htDRm8Ie/idT1fVtrnY7eBf7ggdk04o+f7/iqdWKvfI7W5t+mN5JP3l1gda+vEjPf3iYlh9pqOsoXhcA5REeAwAAADVWGkISHPtHWMP/nt6celdJy5dZWzCwoBCqdy8xtbQ74uIIUSvsC/VlbHlD8Q23KPbEWkW2vmdr242Ds3XLyx265/Uvq2Fms7Z8mL/dzmKgAMKH8BgAAACog65OY4fQsaWFL+9+ELbwP5ky1bsq/2c7IVNxNXbvKum4eVSd+p2T+4JUXUukUFYv57KKvvSbfC/jF38lw8xZ3tSMNemp+Fn6P/cs1JOb50gaP04Vvy4EyAAmw+k+AAAAoA76+icGx1I+hOzrt9+TGrXX1WmopWXibUEN/9sThpYvG/+9rrvBnHI/LdfGI3SBXwA5tS/84TFTK1ZOvW25+1qx0lRPr/Xw1M+Mre8q/vD1ar75i2q6Y7liL9xvOTjO7nqotp36N+rd5X59deA7OwTHUv54Zff1BBA+VB4DAAAANVYaprS0jFevUv3lD5XC/yC+dlYWDCwIQ//nMJvuvjB3jrRipabcttJ9BbqS3TQVffVhxVIDij33Cxm5jPVNo3FlDjtT6fYO5fY5Vn0/lq67qfJctPN6AggnwmMAAACghiYL1rh82D/CGv5bCZkIjsNhuvvC8mX2wspQVLKPDCn+5B2KpwYUGXzJ1qa5WfsrnVik9OzzpOZdJNmbiwTIACohPAYAAABqpNKXeb68+0PYw/9K+ynBcbhMZ1+gkv1jpqnIm39SPDWg2NM/lZEds76pEVX2kNOUbu9Qdv8TJWO8K2kyZf85K/eazJ1TXV9qAMFCeAwAAADUgJUAhADZ2wj/88r9rqWLPwYq4MOkprMvhLqSfWyrYpvuyi+A9+5TtjbN7bSX0omFysy5UOZOe5T9mfaEoe4l+cUN7Txnxa9J95JwBsfVLsgYyoUcERqExwAAAIDLuHzY/wj/Jyr9XQmOw2s6+0LYKtkj72zKVxlvuktGetjydqYMZQ/8rNLti5U96PNSZOooZ2l3pKq+0F2dRmgrjnt6cx8H7vaO24V9tXuJqaXdkak3AHzGME2TpTR9ZHBwsN5DcJ1hGGptbZUkDQ0NiV0U8CbmKuAfzNf6SqZMrVhpPwApDU6uuTqAPT59wm6IVW3o5ce5On9BbkJY2NIirV9HeBJG09kXKvURl7wZHFuer+kRxZ65R/HkakXfStl6jFzzrsrMuUDpuQtlzvrENEeMSnivDi4/vrdOV1tbm6P3x7s6AAAA4KL85cP5P9u9fHj5svzPhvXyYS+otndo4bWT8tWUyVTwvqz29U9sTyDlA7++/uD9rlOp9vUNyn4x3X2hdM54PTi2wvjgBTU88I+aecMpmnHv/7QVHGf2O0GjZ/1fDX/9lxr77BUExzXQntjxuD3V/huKhRwB0bYCAAAAcB2XD/sXvUPLq1QpGuR2HeWE/VJ3p/aFrk5jh37JLS0+24+yY4o99wvFkgOKvfaIrU3NxhalZ5+rdKJD5i4HuzRAVMJCjkB5hMcAAABADVQbHgYtdPQjwv+JJgtNim8PS4CcTOVPLEj2fufi56p3larav7zAyX2hUvWy1/cjY/NriicHFNt4myLD79vaNrt3u9KJxcocfqYUn+HSCGFVqBdyBCZBeAwAAAAAUyD8z6sUmoRpwcCC/KXu9n7noFzq7uS+4MtK9lxGuU33KvfoD9T07AMyZL0FiRlvVuaos5VOdCi3x1EuDhLVCNtCjsBUCI8BAAAAAFOyEpqEMUAO46XuTu4LfqtkNz58W7En1iq+Ya2yH72Vv83ittndjlC6fbEyR50tNcx0b5CYtnL7b2lbFb/OX8AuwmMAAAAAQEV2Qk8C5GBf6u7kvuCbSnYzp+jLv1M8tVrR5++XYWatbxptVOaIL+erjPdulwz/veZhVboPEhwjrAiPAQAAAACTSqbsh57lgr8g9n8uFoZL3Z3cFzY8Iceql10z/IHiG29XPLVGkc2v2No013aQ0u0dSh91jtTU6s744LpALOQITBPhMQAAAABgUu0JQ91L8gvD2Qk9i4O/7iXBDo4Lgn6pu1P7gpXguNy2xf937Tk0TUVef0zx1IBiz94rI5u2vmkkpsyhX1AmsVjZ/Y6nyjgA/LyQI+AUwzRN613dUXeDg4P1HoLrDMNQa2urJGloaEjsooA3MVcB/2C+Av7g9bmaTJlVBcDVbudnpZXGBX4OjotNZ1+QpBUr7Vdhlz6n11zt8EKD2z5U/Ml1iqVWK/r+c7Y2zbXso3SiQ5nZ58mcubtzY0JdVVrIUQrOfA46r7+3uqGtrc3R+6PyGAAAAAAwpWqDurAFx1LwL3Wf7r7gpUr2yFtPKJ5ardhT62VkRixvZxoRRQ7/giLHf01bd/ukTCPiyHjgDX5byBFwE+ExAAAAAAAO4lL3ypZ2R3TcPPvVy12dhjO9s9PDij11t+KpAUXf3mhr09zM3ZWZc6EyiYWatf/R+RuHhqQQVDOGhW8WcgRqhPAYAAAAAACHVLrUnaBpXD0q2SPvPaNYckDxTetkjH1ka9vMAZ9ROtGh7MGnStG4DPoZB5KVxS0JkBE2hMcAAAAAADiAS909KLNNsWd/lq8yfv0xW5uaM1qVnn2+0omFMtsOdGd88AwrwXEBATLChPAYAAAAAIBp4lJ3bzEGX1I8dYviG2+TMTpka9vsvvPyC+Ad9kUp1ujOAOEpyZT14Lig3Lx2pK0K4DGExwAAAAAA30mm7PfMnc52lXCpu0dk04q+cL/iydWKvfI7W5uaDTspffQ5yiQWKbfb4S4NEF7VnjA8tZAj4CWExwAAAAAAX+npzX0c8tgLXwshb/cSU0u7I46MhUvd68/48E3FU7co9sRaRba+a2vb7B5HK92+WJkj5ksNM10aIfyg7gs5Ah5FeAwAAAAA8I1kKl8dKNkLX4tD3t5VqiokKjcWLnWvk1xW0Zd+k+9l/OKvZJg5y5uasRnKHHmW0onFyu01p6qHT6ZMnfz56rbjtfaueizkCHidM6daAQAAAACogfaEoeXLxoOa624w1ddvVtiifHWwE2FP/lL38fu0c6l74XfgUnd7jK3vKf7IDWq++UtqumO5Yi/cbzk4zu56iLad+jfauuxX2vbF71YdHPf05vTNP8vppptHbG3X129qxUpTPb3Wg24AqDcqjwEAAAAAvmKn/YOdthLV4FL3GjBNRV99RLHUasWe+4WMXMb6ptG4Mod9SelEh3L7zpOM6VebFyrfv3fVsCTpwvOn3s6NyncAqAXCYwAAAAAIIS8tOFcNKwGy28FxAZe6u2RkSPEn71A8tUaRwRdtbZqbtb/SiUVKzz5Pat7FsSHlK9/H97fvXTWs0VFDF3118tfSrcp3AKgFwmMAAAAACBkvLTg3HZUC5FoFx3CYaSryZlLx1IBiT/9URnab9U2NqLKHnKp0YrGyB5woGe7so12dhgxDuvb6/P517fWmTLM+le8A4DbCYwAAAAAIES8tOOeEcgFy/2pTW7aM/wyBnQ+MbVVs0135BfDefcrWprmd9lJ67oXKzLlQ5s57ujTAibo6I5oxo3F764p6Vr4D1fD71SeoHcJjAAAAAAiR0svurQTIXr/svjRAJjj2j8i7TymeXK3YprtkpIctb2fKUPbAzyrdvljZgz4vRWofb1x2aZMklQ2QCY7hZUG5+gS1QXgMAAAAACHjpQXnnNLVaexQcdzSYi8YQY2kRxV75h7FU6sVfTNpa9Nc0y7KzLlA6bkLZbbu59IArbvs0iaNjo5sb2FB5Tu8LmhXn8B9hMcAAAAAEEJeWnDOCX39EwM7KV+B3NdvenbMYWN88ILiqQHFN94pY9tmW9tmPnG8Mu0dyhz6BSna4NIIq9PVGZFp5qh8hy8E8eoTuIvwGAAAAABCKigLzpWOtaVlPMCzU1kHF2THFHvuPsWSqxV77RFbm5qNLUrPPlfpRIfMXQ52aYDOoPIdfhLEq0/gHsJjAAAAAAgxvy84N1mwUXw7AXLtGZtfU3zDLYo9casiw+/b2ja7V0Lp9sXKHH6mFG9yaYTOovIdfhO0q0/gHsJjAAAAAAg5vy44VynYsFNZB4fksoq++CvFk6sVfek3MmROvc3HzHizMkedrXRikXJ7HO3iIJ3X15+j8h2+FJSrT+AuwmMAAAAAgO8uu7cSbBAg14bx0TuKPbFW8Q1rFfnwTVvbZnc7Il9lfORXpMadXBqhe266eXyxPInKd/iP368+gfsIjwEAAAAAvrrs3k5FHAGyS8ycoq/8TvHkgKLP/1KGmbW+abRBmSO+rHSiQ7m9j5EMf74WN908ou9dNbz971S+w6/8evUJaoPwGAAAAIAkKZkyq1o9vdrt4B1+WnAumbJ/KXW5IG/uHLHfVmNkUPEnblM8tUaRza/Y2jTXdqDSiQ6ljz5Xamp1ZXi10tef07XXlw+OCwiQ4Sd+u/oEtROp9wAAAAAA1F9Pb04rVprq67feo1TKh44rVprq6c25NDK4rVwV7/p1ES1fNh4YXHeD/X3DLe0JQ91L8n+2UxHX1Wls/526lxAc22Kairz+mBrX/6Vm3nCyGn/9b5aDYzMSU/rwMzVyYa+GL1mv9LxLAhAcmxNaVXzj8sqV716dS0CxSlefeFEyVd24qt0uzKg8BgAAAEIumTLVuyr/ZzuVccWhY+8q6bh5VCD7jV8XnFvaHalqf+vqNKg4tmPbh4o/uU6x1GpF33/O1qa5ln2UnrtImTnny5y5u0sDrL3Syvcrvt2sC8/fJtOcPJCi8h1e56erT6T8Ce/eVdLyZfbGlb9iwFT3ElNLu6mntYpnCgAAAAi59oT9yrhyoSNBiL9YXXDOq1WT1e5v7KdTi7y9UY0/+xvNvP5kNd7/XcvBsWlElDn4VI2ce52GL/2Z0idcHqjgWJpY+X7Ft5t12aVNlrbzUuU7FZso5rerT0pPeFsdV/Hilr2r2J/toPIYAAAAgK0qUzuLlcGbWHAOO0gPK/bUesVTA4q+/YStTXMzd1NmzoVKz10os2UflwboHUu7I/rUcdLJn7cWHBd4ofK9+orN/DGDis1g8ePVJ/kT3vbGVW5xS04kWkd4DAAAAECStS+KBMf+x4JzKBZ57xnFUmsUf/JOGWMf2do2s/+JSicWK3vIqVI07tIIvcmPle+0KEIxq1efSN4LkO2d8J56cUtURngMAAAAYLtKX8gIjoMhf9m9+XH1ob0F5yR9XH1IcOxrmTHFnr03X2X8+mO2NjVntCo9+3ylEwtlth3ozvjgimoqNmlRFExBuPqkmhPe37jc0EVfZf+1yzArdXWH5wwODtZ7CK4zDEOtra2SpKGhoYoLDwCoH+Yq4B/MV1Sj0uI5EsGxG2o9V5Op6qoHq90O9WcMvqz4hjWKb7xdxoi975bZfY5VOtGhzOFfkmKNLo3QP/z83mo1OJzuCUOOFd6UTJlasdL+61q6P1xztTdOJEy2n5bebmVxy6Boa2tz9P6oPAYAAACwg9KKHoLj4PHjZfeoQjat6Av3K54aUOzl39ra1GyYqfRR5yiT6FBu98NdGiBqrZqKzROOp0dyUATt6pNy+3P/anPC55bC4pZDQ9vqMUTfo/LYZ6g8BuAVzFXAP5ivmI75C3ITvoC1tEjr1xEEuIG5CicZH76p+Ia1im1Yq8jWd2xtm93jaKXbFytzxHypYaZLI/S3IMxXqxWbpf9u9369UqGKiYJ29clk++03Ljf0rW/uIsm/c9UuKo8BAAAA1ERf/8TKHSlfgdzXb1J5DHhRLqvoyw/lexm/8IAMM2d5UzM2Q5kj5ivdvli5vea6OEh4hZWKzROOlx5+RBN+jh7JwRC0q0+6Oo0d9t+WFqmrkxPe00V4DAAAAGAHlXoee2WxHAB5xtb3FNt4m+KpNYpsed3WttldD1EmsVjpoxZIM1pcGiG8ykqLouL3g0rHfxZVRT1NfsI7p299sz5jCgrCYwAAAAATWLmUmQAZtRK0S6sdY5qKvvaoYqnVij37Cxm5tPVNI3FlDvtivsp433mSEeDnCVOavGLT2P7vkr0eyQTHqKVKJ7yvvd7UjBkjuuzSpjqNzv+o3QYAAACwXaUAoKvT0PJl42HAdTeY6usPfu9A1E9Pb04rVtrfz/r6Ta1Yaaqn13rbBt8YGVL88VVq/sFZarplieJP/9RycJybtZ+2fe5KDS+7X9vO+jflPnEcwXEdJVPVHT+r3W4ylVoUFVQ6/hMco57K7X/r10Um7K/fu2pYN908Uo/hBQKVxwAAAAAkWQsArFSgAU5Ipkz1rsr/2c5+Vrwf966SjpsXgApk01TkzaTiqQHFnv6pjOw265saUWUPOVXpxGJlDzhRMqgh84Ke3px6V0nLl9k7fhb27+4lppZ2T/+1tNOiyEqPZIJj1NJUJ7yl8f31e1cNa3TU0EVfZf+0i/AYAAAAgK3KMQJk1EJ7wtDyZfb2s8At1jW2VbGnfqJ4ckDRdzfZ2jS3055Kz12ozJwLZe68p0sDRDW8cmKkmhZFVnokA7Vg9YS3YeRbV0j5/5smn1fsIjwGAAAAQi6Zsn/JcbkAee4c767CDn+yc6IiSJfOR959SvHkasU23SUjPWx5O1OGsgd+VulEh7IHnyxF+MrvRV44MWKnYrNcgFypRzLgNnsnvCOaMaNR37sqfyzlhLd9vJMAAAAAIdeeMNS9xPz4EmrrgVtxwNC9hOAY7gjNYl3pUcWeuUfx1GpF30za2jTXtIsycy5Qeu5Cma37uTRAOKmeJ0am26KoUo9k38077MDri5RWc8K7sFhecYDMCW/rCI8BAAAAaGl3pKpLoLs6Db6AwXVTBVl+Do6ND15UPDWg+MY7ZGzbbGvb7Cc+pXSiQ5lDz5BiDS6NEG6px4mR6bYo+uOfTD38yPjPVOqRDP/xSi/uSqo94X3ZpU0aHR3RtddzwtsuwzTNuiyP/MILL+jggw+ux0P72uDgYL2H4DrDMNTa2ipJGhoaUp12UQBTYK4C/sF8BfyBuTq1Sot7ST4KjrNjij13n2KpAcVefdjWpmZji9JHn6N0okPmroe4NEDneL2KsVpOzlcrvYeLb69WMmVqxUr791c6DrfHifpwav+45ura9Jq3eowonat/SuY8fWxxQltbm6P3V7dlVufPn6+LL75Y69at09jYWL2GAQAAAADwia5OQ8uXjX/p91twbGx+XQ2/+Q8133i6Ztz9320Fx9m9Ehr90j9o67IHNHbq//RFcNzTm9OKlab6+u0Fq339+RCrpzfn0si8pXS/vu4GU/MX5BwPZPMVm/bvr6vT0AnHT7yttEdy6fjtvuaov3wvbnuvYz0XKa32cYIeHLuhbpXHRx55pAwj/4K1tLTo7LPP1sKFC3XEEUfUYzi+QeUxAK9grgL+wXwF/IG5at38BbkdFutav65utVGV5bKKvvhgvpfxi7+WIeuvqxlvVubIryjd3qHcHke7OEjn+a2K0S435utUFb5OsVvVbbWymArkYAja6x3G99bAVB4XmKapzZs360c/+pHOPfdcLVq0SGvXrtXIyEi9hwYAAAAA8JhKi3V5ifHRO4r//j/V3HOGmu78pmIvPmg5OM7udrhGT///tHXZr7TtjO/4LjiW/FfF6AVdnYZaWibe1tLifA9hN4JjiQrkoLDyOvolOIYz6rZg3lFHHaVNmzZJ0vYKZNM0lUqltGHDBv3jP/6jzjrrLC1cuFBz586t1zABAAAAAB5RqeexJxbrMnOKvvJ7xVMDij53nwwza33TaIMyh5+pdPti5fY+RjL8H8RYWRCugDCq8omRejwXyZT916Tca86iqv4T5EVKYV/d2lZI0saNGzUwMKD169fro48+yg/IMLaXkBdC5SOOOEKLFi3SggULtNNOO9VruJ5A2woAXsFcBfyD+Qr4A3O1slotKlaVkUHFN96ueGpAkaFXbG2aaz1A6fbFSh99jtTk7KXGXjHVa+SJ19Amp+erVxeD7OnNqXeV/ccv/D7dS6Sl3XW/6B1V8up+aUcY31udbltR1/C4YGRkROvXr9ctt9yiP/3pT5ImViMX/j5jxgydeeaZWrhwoY499th6DbeuCI8BeAVzFfAP5ivgD8zVyXkyfDRNRd54XPHkgGLP3iMjm7a+aSSmzKFfUCbRoex+JwSiyngqng7/q+DkfPX6c2O3R/J0t4O31KoXt1vC+N4ayPC42HPPPac1a9Zo3bp1GhoaklS+Gvnggw/WokWLdM4552zfCcKA8BiAVzBXAf9gvgL+wFwtz3OLN237UPFN6xRLDij6/rO2Ns3tvLfSiUXKzLlA5szdnR+bxwWhirHAqfnqyRMjQAlfLVJaIozvrYEPjwvGxsb0s5/9TGvXrtXDDz8s0zTLViPH43GdccYZWrhwoT796U/Xc8g1QXgMwCuYq4B/MF8Bf2Cu7shucOZm0BZ5e6PiydWKPXW3jIz1Bd5NGcoefLLSiQ5lD/ycFIk6Mh6/8nsVY4ET89VzJ0aAMvw+Z8P43hqa8LjYq6++qjVr1uj222/Xe++9J6l8NfJ+++2nhQsX6vzzz9euu+5at/G6ifAYgFcwVwH/YL4C/sBcnSiZMrVipf3ArDTouOZqo/pL59PDij21Pr8A3ttP2No0N3M3ZeZcqPTcC2W27Fvd4weUn6sYC6Y7X710YgSYTBCuFgjje2sow+OCbDar+++/X2vWrNFDDz2kbDY7IUSW8jtFNBrVaaedpo6ODp100kl1HLHzCI8BeAVzFfAP5ivgD8zVHdVrsa7Ie88qllqj+KY7ZWz70Na2mf0/rXTiq8oecqoUjdt+7KDzexVjwXTmqydOjABT8HovbqvC+N4a6vC42Ntvv60bb7xRfX192yuPJe1Qjbz//vvrkksu0YUXXqh43P9v3ITHALyCuQr4B/MV8Afmank1W6wrM6bYsz9TPLVa0dcfs/VY5oxZSs8+X+nEQpltB9kcaXgEoYqxYLrztV4nRgArgtSLO4zvrYTHkn7/+9/rlltu0c9//nOl0xNXtJ2sN/I+++yjv/3bv9XnP//5mo/XSYTHALyCuQr4B/MV8Afman0YQ68onlqj+MbbZIzY+76V3edYpRMdyhz+JSnW6NIIgyEoVYwFTszXmp0YAWwIWi/uML63Oh0exxy9Nxe9//77uvXWW7V27Vq9+uqrknYMiqPRqE488US98MILeuONNySN90Z+/fXXdfnll+vP//zPtXTp0rr9HgAAAACAOstlFH3+fsVTA4q9/JCtTc2GmUoftUCZxGLldj/cpQEGS6WQqfD/wr8X/u/FEMpp1QbABMdwi51AOMxzN2w8Hx4/+OCDuuWWW3T//fcrm83u0JbCNE3tscceWrhwoRYuXKi99tpLkvTQQw9p9erVuu+++7aHzKZp6t///d917LHH6pOf/GTdficAAAAAQO0ZH76p+Ia1im1Yq8jWd2xtm93jKKUTi5U58iypYaZLIwweK2EUIRRQf8mU/UricnN37hxOcASNJ8Pjt956S2vXrtWtt96qt956S9J4lXHxAnmf/exntXjxYp166qmKRqMT7uOkk07SSSedpBdffFHf+c539Pvf/377tj/84Q8JjwEAAADAAZ6/9N7MKfrSQ4qnBhR94X4ZZs76prEZyhwxX+n2xcrtOUcyCETsoIoR8I/2hKHuJabtXtzFc7d7CcFxEHmm53E2m9X999+vNWvW6KGHHlIul5tQZVz486677qoLLrhAixYt0ic+8QlL953L5XTBBRdo06ZNkqS99tpLDzzwgCu/h9voeQzAK5irgH8wXwF/8ONc9fKiX8bW9xTbeLviG9Yosvk1W9vmdjlE6USH0kcvkGbMcmV8QZdMmVqx0n4/1NLA+ZqrDU+GUX6cr4AVnj8haFMY52rgeh6/+uqruuWWW3Tbbbfp/fffl7RjlbFpmjrhhBO0ePFinXHGGYrF7A07Eolo0aJF+s53viNJeu+99xz/PQAAAAAgTJKpfIWaZK9KtDgc7F0lHTfPwcDBNBV97VHFUqsVe/YXMnLpqbcpbBqJK3PYF5Vu71Bu3+OoMp4mqhgBf6IXN0rVLTy+++67dcstt+iRRx7ZHhBLE3sZt7a26rzzzlNHR4cOPPDAaT1ecZVyNpud1n0BAAAAQNi1JwwtX2avzUC5NgaOBA6jmxV/8k7FUwOKfPCCrU1zs/ZTOrFImdnnyWzedfpjwXZLuyNVnRzo6jTomwoAHlG38PjKK6/cXllcWmV87LHHavHixTrzzDPV0NDgyOPZrVYGAAAAAFRmp0+tnf63lpimIm+lFE8NKPbUehnZbdY3NSLKHnyq0u2LlT3gM5LhTusMv3Pi8nWqGAHA3zyRqJqmqZ133lnnnHOOFi9erEMPPdTxx2hpadGnPvUpx+8XAAAAAMLMSoDsaHA8tlWxp36ieHJA0Xc32do0t9OeSs9dqMycC2TuvFd1jx8S4/2s7S1aN97P2nStnzUAoHbqGh6bpqlEIqGOjg6dddZZmjFjhmuPNXv2bP3whz907f4BAAAAIKwqBchOBceRd59WPLVasU13yRjbamvbzAGfzVcZH3yyFPFEDZWnebKfdYgEbcEyAP5Wt3fNjo4OLV68WEcddVS9hgAAAAAAcEi5ALl/taktW8Z/xnZwnB5V7Nl78lXGb/7J1nhyTbsoM+d8pecuktm6n61tw85T/awDwE6oW1zxbafvMxXfANxSt/D4O9/5Tr0eGgAAAADggtIAudrg2PjgRcVTA4pvvEPGts22xpD9xKeUTnQoc+gZUsyZNXTCqK79rAPETvuPchXfVsJgKr4BuInTUQAAAAAAx3R1GmppmXhbS4uFtgfZMUWfuUczbunWzB/MV8PjqywHx2bjzhr75MXauuQnGln0X8oceRbBsQO6Og0tXzb+ul13g6m+fnPCzxAcT640DC597krlK74nPne9q/L3MxkqvlFQaT9xYzuEB+ExAAAAAMAxff0TW1VI+QrkyYIzY/PravjNf6jh2tPU9JMrFHv195YfK7tXQs8e/V1tXfYrjZ36P2Xuesh0ho4yKgXIBMeVlYbBVgLkcjY8Uf52nn8U9PTmtGKl/f2rr9/UipWmenpzLo0MQcBKAQAAAABQZ8mUqWPa/b9AVmmY1dIy3rpiQtuDXFbRlx7M9zJ+8UEZsh54mPFmZY78itKJRfqv+47Wdf9kqvtNaWm3o78KirjSzzokptP+o6DcNgTHKGCBS7iNymMAAEKOS9wAoL7GK8bsVX55rWKsXJi1fl1kQuXl2lXv6MkbrlVzzxlquuObir34K8vBcXa3wzV62v/W1mUPaNsZ38kHx0XBB+9L7iqtQCY4tq7a9h9UfMOKaircaXcCO6g8BgAgxOws4lKMFb0BwBnFFWPXXm9qxowRXXZp05Tbea1irFKY1fVVU/tu+51mbBzQKXvdr/hHGcv3a0YblDn8TKXbFyu39zGSYUz6eAQf7uvqNHaoOLbUzxoVK5CnCoOp+MZUWOASbiI8BgAgpLjEDQDqL18xNn4c/t5Vw5KkC8+ffBuvBaeTBhEjg4pvvEPx1IDOHnpZ2sf6fb780QF6fe8OJbrOlZrarD0eXFepnzWvwdSqaf9Rug3BMSZjJUDm+IlqUCoEAEBIcYlb9dxs9UEbESB8Si9p/95Vw5O2sPDaF/8dxvN16Wsn/1GNP/1LzbzhFDU++C+KDL1s6b7MSEzPN35Ry37bo3N/+RN980dL1Hd7a+XHI/iomXL9rAuqXQgujKpp/9HVaUx4viUqvlEeC1zCDYTHAACEmJUefAV84MxzczVrVsoGwqur09A3Lh8/pl57vbWeqPU8DidT4+PZKfahvn/Rj/X1zLlqHrhI8U13yciOWbqf0Rl7a9tJ39bwZfdpzxVX6djzT5T58VdVgg9vsNLPmgDZOrthcKWKb6BUuc/38xfkOH6iarStAAAg5LjEzTo3W33QRgRuSKaq2x+q3Q7T09UZ0YwZjdtbV9jpiVoP7QlDf3nRRjVuGNCCg+5W/MMRy9uaMvTgW5/X0KEdOv2yz0uR6PZ/q+bSfrinYj9rG31WMc5O+49yFd+FbXm+MRnancBJVB4DAAAucbPIzVYftBGB06hk96fLLm3SFd9u3v53T1aMpUcUe+JWNf1okTo/XKgLDlyruGktOM4176axEy7X8GU/V6zrWp2+7NQJwXFBNZf2w3lWPgPYuYoJ9tp/UPGN6aDdCZxC5TEAAJBEpZdVbq5mzUrZcAqV7P522aVNGh0d0bXXe6tizHj/OcVTaxR/8g4Z2z60tW1m/08rnVis7CGnSdG4JKk9UXmbrk5jh/chgo/asfM+QwWyNZM9p8W3F/87Fd+YDha4hFMIjwEAwHZc4maNm60+aCMCJ+Qr2e0FC1Sye0tXZ0Q/+nG2/sFpZkyx536ueHK1oq//wdam5oxZSs8+X+nEQpltB9l+aIKP+inuZy1Ze58p9/41d444jnysmvYf5X62gAAZldDuBE6ibQUAAJiAS9yscbPVB21E4IR6LoiZTFV3CXW12wVRX3+urgtkGUOvqOHBf9PMG0/RjPV/bis4zu7zSY2e+U/a+vUHNHbyX1YdHFu9tB/Oa08Y6l6S/7Od40Hxcad7CcFxQTXtPyr97GTbMDcg0e4EzqPyGAAATECll3VutvqgjYh31GvROScetx6V7D29OfWukpYvs3fSqTCO7iWmlnaHu8blppvHW1ZINawYy2UUfeEBxZOrFXv5IVubmg0zlT5qgTKJDuV2P2Jaw7BzaT/HQPcs7Y5U1bqmq9Og4riInWPs3Dn275+KbxRjgUu4IdyfygAAwARUetnn5qJOLBhVf/VadM7Jx61lJXtpr2Wr4y/ttRzmCuSbbh7R964a3v73WlSMGR++pYbf/j8133i6mtb9ma3gOLvHURr9wne0ddmvNHb6/+dacCxRZVkP1QaQBJd5dtt/FFd8F1x3gznlMZGKb0gscAn3GKZpspf4yODgYL2H4DrDMNTa2ipJGhoaErso4E3M1eCxUulVfDsmmr8gt0Nv0vXrnDlPP937Zr5WJ5nKB7EFVvf90jlzzdX2ege79biV+h/aeRy745jqfjnGjPvRj80JFcelz4Wjz5WZU/SlhxRPDSj6wv0yTOsnOszYDGWOmK90okO5veZKhjOvl9Xfj30GXmD1vXX8agzr+2kyZWrDE/r4agxZvhpjule8wL94751cGD8Ht7W1OXp/tK0AAABc4jZNbrb6oI1I/dRr0Tm3HrdWC2LaOWaE6cvrVEqfi29cbuiirzq/QJYx/L5iT9ym+IY1imx+zdYYc7sconSiQ+mjF0gzZtnadip29gXel+An1bT/aE8Yak/IdvsJguNwYoFLuI3wGACAkLN6iZvEF/Vy3FzNmpWy669eQahbj9vVaezQO9uNBTHr0WvZz0qfiyu+3awLz99WtjqqquOxaSry2qOKpwYUe/bnMnJpy2MzI3FlDjsjX2X8iU85VmVcjOADQUf7D7gp3+7EtF3hXnwcpd0JKqHnMQAAIWY3dKJH2kRurmYd5pWyq+1361afXCv7vhtBqBuPW6mSvRqVnvNK4//Xf88RHH+sNDi94tvNuuzSporblHtuy74Wo5sVf/y/1LzqK2q+ZYniT6+3HBznZn1C2z773zW87H5tO+vfldvveFeCY2lin1e7wQd9XgEgX+F+zdX230u7Og1dc7UR+oVqURk9j32GnscAvIK56n/16ucaFFOFdtMJE52+bz/N12p6Q0rjz4md3pB21asvuFOP63TPY6uvVenjNjZK27ZV/7hBVHguv3G5oW99cxdJ1uZq2f3eNBV5a4PiqdWKPbVeRnZbxfsoZhoRZQ8+Ven2xcoe8BnJqG2YUG2/Vvq8oh789N4KhFkY56rTPY8Jj32G8BiAVzBXg8HLQZ2Xubmokxv37Zf56ocTGrVadM7px3U6+Lb7WpU+TsE5Z0t/cWX4jiHlJFOmjmmP2J6r24PTsa2KPXW34qnVir6zydZj52buoczchUrPvVDmzntVM3wgdPzy3gqEXRjnKuFxyBEeA/AK5mpwUOllj5urWbt1336ar35YLXyyINTtx672cd2qkre73elfyk2oOG5slO67l+C4WDVzNfLu0/kq4013yRjbauvxMgd8Vun2DmUPPkWKsBwOYIef3luBMAvjXHU6PObTGgAAIcciLtZVu6iTld6kbt63n9jprV2vBde6Og21tEy8zY1F55x4XKsLYlbTS9vOdlf+5cTgWMq3rghiz+6aSI8q9uSdalrdqeYfnqt4crXl4NhsatPYpy7T1kvv1egFNyp76BcIjgEAwKQIjwEAnuC1BbKActxc1IkFo8bVa4E6q5xedM6tx63FgphWtrvyL3N6+JHxvzc2quLPY3LG4Itq+NU/a+aNp2jGPX+l6Bt/tLxtdt/jNDr/37T16w9o7HNXymzd38WRAgCAoKBthc/QtgKAVzg5V+m7C79xs9WHG/ft1/fWei1QZ2dMXu15XOv+0ZO9JqXB8QnHS//+L5G6voZeVnauZtOKPv9LxZOrFXv197buz2zcWemjz1U6sUjmroe6MGIgvPz63gqETRjnKj2PQ47wGIBXODVX/bBAFuB3fn5vrVdYa2UstQqzq33cWp+YKx1PY6MmtKooBMdT/V5hVjxXN7+yUbHUGsWeuFWRre/Zup/sXgmlEx3KHPFlKd7kwkgB+Pm9FQiTMM5Vp8NjmlsBAOqqPWFo+TJtDxAK/7fbw5PgGPAGpyunC8eCwpz3WnBcboxWjmO1eNyl3REdN8/+69HVaWjuHPstUErHUyk4tjL+UMpllXv6Z8o98l9qeuY+GbL+BdeMNSlz1FeUTnQot+dsFwcJAADChGt8AQB1V88Fsui1DDinpzenFSvt97Dt689fgdDTmyv77/VaoK7AzUXn3H7cWi+I2dVpTOhpLOUrkEuD4+Kfd/p58yPjo3cU//21arrpDGX7vibzmV9YDo6zux6m0dP+t7Ze/ittO+PvCY4BAICjCI8BAJ5QjwWy3Aq6gDBKpkz1rsr/2U4AWDyve1eVPzFTrwXqSscnubPonJced7r+9f/mJlQcS/kK5ErjKTf+UJygM3OKvvI7zbjrv6n5ptPV+NurFfnwDWubRhuUPupsDXf8SCNfu1OZYzqlxp1dHjAAAAgj2lYAADyj0iXMblQcFwddxY9fSWnQVc0l4UAQudWCplLPY7fbHCRT9o875Y5jdltA1Otxp6uv39Sd68b/XtzzeKrXqnj83UtqO+6aGxlUfOMdiqcGFBl62damudb9lU4sVnr2uVKTs/0MAQAAyqHyGADgKeUq0OYvyDm+qFI+6LJXqUevZaAyp1vQlPuZ9esiNauybU8Y6l4y+fgmU/w8VBOE1utxp6P0tTpngXTfvfZeq65OQ9dcbdhapM83TFORN/6oxp/+pWbecIoaH/wXy8GxGYkpc9iXNHLhzRru/qnSx3UTHAMAgJoxzDAsMxggg4OD9R6C68K4EibgR27P1dIgosDpBbKsVjQ7XfkM1FKt31unmi/VBsd278MpTi8C6PXHtctLr5XnbPtIsU3rFE8NKPreM7Y2ze28t9JzFyoz5wKZO+3h0gABVIvvrYA/hHGutrU5e5KZthUAAE/q6jTUv3pin1M3Fsiq1CqjINTBB1CF6bagsbpQ3GSP4bRaLzpX78e1w2uvlVdE3nlS8eRqxZ66W0Z62PJ2pgxFDj9dkeO/pq27z5NpBLAKGwAA+ArhMQDAkyotkFXLAJngGKhOuXlVekKo2uC40mMU3w538VqVSI8o9vRP81XGb6VsbZpr3k2ZuRcok1ikWfvPzt84NCSFoDoKAAB4G+ExAMBz6rFAVrVBF4DJlc6rqeaTXxeKCyNeq3HG+88pnlqj+JN3yNj2oa1tM/t9Wun2DmUPOU2KNsgw/P1cAACA4OE6KACAp9RzgazSxb4IjoHp6+o01NIy8bbJWtD4caG4sAr9a5UZU+ypu9U0cLFmrjpbDX/8oeXg2GycpbF5l2jrJes1urBX2cPPlKINLg8YAACgOiyY5zMsmAfAK9yYq15ZdGn+gtwOvZbXr+N8q5P8shBYUNTzvbWaxS/ZP/wjbK+VMfSq4hvWKPbEbYqMfGBr2+w+n1Q60aHMYV+S4jPK3z+fg+GysM1ZNzFfAX8I41x1esE8vgkDADzB6qJLblcgV+q1DGf09Oa0YqX9166v39SKlaZ6enMujQxOK9eCpqDS/PXDQnHIC8Vrlcso+twvNOPWr2vmzV9Uw6M3WQ6OzXiz0u2LNXzx7RpZ3K/M0edMGhwDbuP9FwBQDXoeAwDqziuLLlXba5kqHuuSKVO9q/J/tvPaFb82vauk4+aF77nzm8nmdfHtgVw0DYFhfPi24htuUeyJtYp89LatbbO7H6V0+2JljjxLapjp0ggB63j/BQBUi8pjAEBdVbvoUmkFcjI1vcrganstU8VjT3vCfvV4udeGL67eVumEUC2uIPCbao9f0z3uoQwzp+hLv9GMO7+l5ptOV8Pvr7EcHJvRRqVnn6fhr67WSNetyiQWERzDM3j/BQBUi8pjAEBd5RddylfD2F10Scp/+ZnuoktTBV2Fxyn+f1enQRVPlexUj9eqzzWcY7UFjeTOFQR+09Ob+/j4Z+/3LzzP3UtMLe2mHmTahj9Q/IlbFd9wiyKbX7W1aW6Xg5VOdCh99DnSjFkuDRCYPt5/AQDVIDwGANTd0u5IVQFqV6ehuXPcC46LH0cq/2Vr+TJ7ARhVPHlWvsDyxdV/vNKCxi84AVVnpqnI639QPDmg2LM/k5FLW980ElfmsDOUTnQo94lPSQbPP/yB918AgF2ExwAAT6jHoktOBl1U8dhX6bnjufKfalvQSBP3gemeEPKT/GXknICqudEtij95p+KpAUU+eN7WprmWfZVOdCgz53yZzbu6NEDAXbz/AgDsIDwGAISSk0EXVTzVK/fc9a82ty9UKPFc+YUXWtD4ESegasQ0FXlrg+KpAcWeXi8jM2p9UyOi7MGnKp3oUPbAkySDNiHwP95/AQBWER4DAELJ6aCLKp7qlT53fHH1r3q2oPEzTkC5aGyrYk/drXhqQNF3nrS1aW7mHsrMXaj03Atl7ryXSwME6sfp999kqroWOtVuBwCoDcM0TZZp9pHBwcF6D8F1hmGotbVVkjQ0NCR2UcCbgjJXnf6iUxrwtLQQhlo1f0FuwnPV0iKtX0eFnxOCMl+DbrKAmODYvsi7zyiWWq34pnUyxrba2jZzwEn5KuNDTpUita21Ya6iHpx4/x1f/NPe8Wl88U/5bvFP5ivgD2Gcq21tbY7eH5XHAIBQc7rXMlW01enrn3iprJR/7vr6TZ4zhAaXkU9TZpveuO8eHTI4oOgbf7S1aTreJrP9fKUTi2S27u/SAAHvceL9l8U/ASDY/HVqDwAAH+jqNNTSMvG2lhZrX6TCqFy1dsF1N5jq6w9+dQBQ0NVpaPmy8WMFwfHUjMGX1PCrf1H0+yfr8I1/ZSs4zu57nH4x61/1mdt/qWuf/+8ExwgVp95/84t/jh+brGzL4p8A4B+ExwAAOKxSFQ8mKvflcf26iO0voUCQcALKgmxa0Wfu1Yy13ZrZ+2U1PNarJnOzpU3Nxp01dkyXhr+2Tjdm/0t//sP5Suca1LsqX0EJhIHT77+lJ74qbUsrHgDwF9pWAADgoEo9j+1cyhkGlb48WllADAiqILdxmW6feWPLG4pvWKPYE7cqsvU9W/eR3XOu0u0dyhzxZSneTOUjQsut918W/wSAYKLyGAAAh1BFa52VL492qpiAoAhyG5ee3pxWrLT/O/zoRxmt/u4Deuf7y9Xcc4YaHr7ecnCcNpqUnnOhhi9aq5GL1uix3PmTBscEWAgDt99/K23r1ryr9ooBrjQAAGuoPAYAwAFU0Vpn58sjzx3CZLK5UXy7X+dANQtqGVvf1cYfrdXZ79yiyz/9ppS2/njPbjlMa19apLtfO1tde7aoa8/x53HesaYee3z8ZwmOERa1ev+t5eKfPb059a6Sli+zd1wsPBfdS0wt7aamDgAqITwGAGCarFbxSISgyZT9qqOuTkNvvmXqznX5v193g6m5c2Tp8vJqL5EHai3oJ6DyC2pZ+B1MU9FXH1YsNaDIM7/Q8cpIzdYew4zGlTn8TKUTi3XfA8do4AFtf6w//snUw4/k/05wjDCq9v1Xmjhvrb7/lm7rRnBczUkpaeLxtneVdNw8PisAQCWcYgMAYBrsVvGEvQ1De8JQ95L8n61+eezpzenOddIJx+f/3r3E2hfXvn5TK1aa6unNTWfIgOvC0sal4u8wMqj4Yz9Q8w/mq2ltt+LP3KOoMpbud3N0f237/F9o67JfaduX/0W5fY9V10UTWwYVguNiBMcIk2ref6WJ89bq+2/xtm4u/pk/KWXvuEivcwCwj8pjAACqVOsqnqBY2h2xXOVTXFX08CPSOWfL0uWlVBXBL8LWxmXi72Dqd7f8UacOrdHh6XtkZMcs309OUd33xmla+1KHHnnvBF2+e1Rdx+0YuBdXHBcjOEYY2Xn/LdbVaVT1WaUWi3/aOS7S6xwAqkPlMQAAVapHFU9QWP2dS6uK7rxLVBUhMKo9AVVaaee3RZ+6Ltiqq7sGNHDy+Vr1uS4dMbrOcnCc23lvbfvMSo0s+6WeP/YqPfzeiTIVKVtx2NdfPjh2svIRqDe787/wfljtdlbVcvFPK1dmEBwDQPWoPAYAYBpqXcUTRlQVIajyJ6DMjxd7sncCStLHiz355zgSeedJxZMDij31E30+PSzNsradKUPZgz6ndGKxsgd9XopEJUldnfl/L3dsKD0WFHO68hGoF68uFlePxT8rfVbgswEATI9hmqa/ShVCbnBwsN5DcJ1hGGptbZUkDQ0NiV0U8CbmKmptqi9/fDmcHPPV26pd2NEXC0KmRxR7+qeKpwYUfStla9Nc867KzLlQ6bkLZc7ad9KfK1fhWHqpfLnbvXiMYK7CqmQq39e/wOr+XDpfrrna2atz6v1ePdXxwMnHY74C/hDGudrW1ubo/VF5DAAAfIGqIgRVtcGNl4Nj4/3nFU8NKP7knTK2lUlyK3hs8AQdffFiZQ85TYo2TPnzpceGcsFxLSofgVrKt3Wy1wPd7bZOVhf/LB6v2xXIXj9h5HeBPvkJYDt6HgMAAN8o19dw/oIcwTHgBZkxxZ66W01rvqaZq76ihj/+0HJwvHmsRT98/ms6576faOmvb9aqP3zJUnBc0NVpTOipWqz4mGClNyrgF3b251pX/E61+Keb87Dc8YBe587r6c1pxUr7r11ff75qvqc359LIADiNymMAAOArVBUB3mIMvar4hjWKPXGbIiMf2No2+cExuuWlRfr5G19S404ztGVr/na71Yh9/WbZiuMTjt/xPtyufARqycr+7HZwXO3in6XjdmotiHLHA3qdOyuZyvfrl+wdQ4v3xd5VqmrdEAC1R+UxAAAeZ3dF9Olu5we1qiriuQfydtincxlFn7tPM25bpuabv6SGR2+yHByb8WZtbOrQogdu1ZLf/Eg/ee0cdV/WpPXrIlVVI1ZaHO/hR1T2PqhARpBU2p9r0dYpv/in/fsvHrdTi3+W63lcwDx3Tr5tir1jqNttUwC4h/AYAAAP45LA8ipVFTmF5x7IK54LxodvK/67a9R80xfUtO5bir30axmyNkeyux+p0S/8nW5u+5UuGvj/9MyWIyVNr63EZMHxCceP/3my+yj3WJz4gV/Vu63T0u6Irrna/v13dRq65mpDS7unH02UCyerPSmFqXmpbQoAd9G2wgFDQ0N65pln9PLLL29fuXHWrFnaZ599dMwxx2jnnXeu9xABAD7EJYHlVVpJ3anLz3nugbxkytQPVuV04u6/02F/XKOmt+5XRFnL249mG3Xv61/WgRd06JDPtqvvx9J1NzmzoFbpsWDesdJjj9tbHK/4sZyqfATqpd5tneq5+GelcJJWNe7xQtsUAO4jPK5CLpfTH/7wB/385z/X73//ez3zzDOT/qxhGDrxxBN1ySWX6OSTT67hKAFgHCsh+5MXV1Kvt8m+gFgJiezguQckDX+g47bdpgfOW6NZ2Vdtbfrihwdp7cuLtO7Vc3RRd6tO/5xhK0CYKpCY7L6K37esBkZdnca0eq3yHgsv6eo01L964tU5QV8szsqxhQDZPZWeW4JjIBgM0zS5ZsOmL37xi3r55Zdtb3fWWWfp7//+77XTTjtV/diDg4NVb+sXhmGotbVVkrZXcgOoXk9vTr2r7H9YK3zY616ispcSMldrx+oH76B/QJ/q93Pj9w/Kc898hWWmqcjrf1A8OaDYcz+TkU1b3zQS1/MNX9A/3bdIf3j/U5KMCaHuipX250jp3LrmakMbnpCt+ebm/HT6PZa5iumarJWL196XnGJ3fjt5PGC+TlTpyjApuPsgvC+Mc7Wtrc3R+6PyuAoffLDjYiAHHnigEomEdtttNzU2Nuqtt97S7373O7311lvbf+buu+/Wu+++q5tuukmNjY21HDKAkOLS+2DgksD6VRXx3CM0RrcovmmdYqnVir7/vK1Ncy37Kp3o0Oqnz9X3enbdfnvxXMgvqGXaDlpL20oU/lzuMazcR+H/06k0LuA9Fl5Ti7ZOXpJM2X//det4gPq3TQHgHsLjadh33321cOFCnXfeedprr712+PdsNqs1a9boH//xH7Vt2zZJ0iOPPKL/+I//0P/4H/+j1sMFEEJceh8cYb4k0MlL3asR5uceAWeairz9hOLJ1Yo9vV5GZtTyplkzogffOlnDRy/WyZd8Vn0/NnRdT+W5sLQ7UlVQWtpWwokQ2on3Nd5j4SW1auvkJU6dlGIOOieMbVOAMKBtRRUWLFigJUuW6Nxzz1U0Gp3y53/1q19p+fLlyuXyq67H43Hdd9992nPPPW0/Nm0rAFTDjUvvmav1EbZLAp281H26Xw79/NwzXzHB2FbFnrpb8dSAou88aWvT3Mzd9bgu1P+6/QK9Pbq3pPrMBS/1GXbyPZa5imrUo62Tl9TreMB83VHY2qbAH8I4V2lb4QG33XabYjHrT93JJ5+ss846S3fddZckKZ1O67777lNnZ6dbQwSACbj0PjjCdkmgl6qKwvbcI3gi7z6jWGpA8U3rZIx9ZGvbzAEnKZ3oUPbgU3RkNK7zdjbrOheqndNuVBjyHot6YrE4bx0PwixsbVOAMCE8roKd4LigODyWpA0bNjg5JACYEpfeB0fYLgl06lJ3J4TtuUcAZLYp9sy9+SrjNx63tak5o1XpOecrPXeRzLYDJvwbc2Ei3mNRD/Vu6wQUhLFtChAmhMc1sv/++0/4+3vvvVenkQAIs3JfHEq//POl1vv6+ie+ZlK+sqOv3wzsa+eVqiI/PfdeuqwftWcMvqR46hbFN94mY3TI1rbZfecpnViszGFflGINZX/GT3OhVniPRS2xWBy8otJJDE5aAMEQqfcAwmLr1q0T/l5N9TIAOKGr09DyZeMf2PhS6x3J1NT9t8pdElhw3Q2m+vqD38OrXvz03Pf05rRipf0x9fXne0z39OZcGpk7rMwdJ7fzrGxa0Wd+phlrL9XM3i+r4bGbLQfHZsNOGjvmIg1/bZ1GOvqUOeorFYNjv8yFWuM9FrWSb+uU/7Pdtk6FfZTF4jBdVtumFB8Xa/0+wWcEYPoIj2vk6aefnvD3vfbaq04jAYD8h7jiL/tSuC839gIrYV+5D+idiye+ZmEPbtxS7rlfvy5S1y9Dk0mm8j2iJXtjKv4de1f550tT2ILycowtb6jhoavUfNNpavrJtxV75XeWt83uOUejZ/wfbb38Vxo77W+U2+2wij/vp7lQL7zHolaWdkd0zdX2T0p0dRq65mpDS7uJA1A9u21T6vE+wWcEwBm8W9TIunXrJvz905/+dJ1GAgCVLzdG7VkJ+8p9QC/8fKmwBzdOm+pyTK+FZu2JcmOq/OWn3O/oh2q0sAXlE+Syir7wK82445tq7jlDDQ9fp8hWa23RzFiT0nMu1HDnLRq56BZl5l4oxZun3M5vc6FeeI9FLXmlrRPCpdq2KaXvE26+/4b6MwLgMHon1MAjjzyiRx55ZPvfd955Z332s5+t6r4MI/hv8sW/Yxh+X6DW+vpzFVdCNgypq3Pqc4vMVecc027oG5fndO314/3gil+H0tfsG5fnn+/Cz5e7zc5ricmVe+5Ln9OLLzJkGJO/fvVQOqZrrzc1Y8aILru0aYf5auV39Kqp5k455X7fY9r98ftKkrH1XcWeuFWx1BpFtrxha9vcrocp3b5YmaMXSI07S5JSFntc+3Uu1Np032N5bwX807M/zPP1mHZDl16S080/MG19bih+n7j0Enfff8P4GQHlhXmuOsUwTZPTKC4aHh7Weeedp5deemn7bStXrtSKFSvqNygAoXXTzSP63lXD2/9+xbebddmlTZPejtoq9zpIsnRb4fXitXSO3efSi8/9VGPy4pirYfX38Ovva5qmzBcfUu6RVTI3/VTKZaxvHG2QMftsRY7/moz9j5/wpanwfHxzeZNWfGPyyuMgzIVa4D0WmL5rrh3Wf143YnueWD2ewVmPPZ7WvGPjNduuGkH/jADUAuGxy/7yL/9Sd9555/a/H3zwwbrjjjvU2NhYx1EBCKOwhEh+V/o6FJsqOJ7sPv6rt6VmH9CD4rHH0/pa9/h151bngxef+7AEWkE8xpnDg8r9aY1yj/yX9P7z9jbe5UBFPvU1RT7ZIWPmrjv8s9V9NUhzwU1B3P+AWuN4A7dwjAamh/DYRb29vfqnf/qn7X9vaGjQj3/8Y82ZM6fq+xwaGnJgZN5mGIZmzZolSdq8ebPYRYHp6+vP7dDioNwlW1Z/TmKuuqn0dZAmb1Ux2etTuI9LL2FBnGr19Nq/HFPy5nPf12/q2uvH+x4XX0ov+atVRSWTHcPsHNvqzjQVefNPiiVXK/b0T2VkxyxvmslF9cBbp2p0zmKdeslnJKPy8aFgqucjSHPBDU6+x/LeirCze3yq5/Gd+eovgfiMgKqEca62trY6en+Exy5Zv369rrzySuVy41/U/umf/knnnXfetO53cHBwukPzPMMwtu/oQ0NDoZjYgJvsrIRs5+eZ3w+smgAAngxJREFUq+4qfR1Kwz4rC5PUuvdfEPml7+JUDMPQ2tsay1a1W9mX/MSJuVMX2z5S7Km7FE8OKPre07Y2ze20l/6ghfpft56vd7ftIWny39Pue0JBUOaC05x+j+W9FbA+r6o9njmF+eo/vv2MgGkJ41xta2tz9P4Ij13w29/+VsuWLVM6nd5+25VXXqlly5ZN+74JjwHYkUyZWrHS/ofq0g9W11xt7PDln7nqvtLXoYAPtrCrMF8/8/kPtHnzxC9N69cFr7rGT3Mn8s4mxVMDim26S0a6fMuackwZyh70OaUTi5U96HNSJDZlkFLvoCVo3HiP5b0VyPPD8Yz56k9++owAZ4RxrjodHgfv20KdJZNJrVixYkJwvHTpUkeCYwCwqz1hqHtJ/s92PhB1dRpaviz/s91LFOiqMS/r6jTU0jLxtpYW8cEWVbnp5pEJwbGUr7bp6w/eB2jPz530iGIbb1dTf4ea+85XPDVgOTjONe+qseMv1/DSn2v0vOuVPeRUKRKTNPHYLeVXli+8vl4IWoKG91jAPRzP4BbPf0YAPIjKYwc988wzuvjiiyf0JV64cKG++93vOvYYVB4DqIYblxszV91HZQSc8qMfmxP6+QX9Mk2vzh3j/ecVTw0o/uSdMrZtmXqDIpn9TlAm0aHMoadL0YaKP8tlubXl5Hss763ARF4+njFf/cmrnxHgnjDOVacrj2OO3luIvfLKK7r00ksnBMdf/vKX9fd///f1GxQAfKzaqiaqoeqn0pelwu18wIUVpfvSNy43dNFXjQm3B2mf8tzcyY4p9twvFEsOKPbaI7Y2NRtnKT37XKUTi2TucrDl7Qq/X+H39UrQElS8xwLu4XgGJ3nuMwLgE7StcMDbb7+tSy65RO++++72204++WT967/+qyIRnmIAgD3lLsdcvy4y6eWbwGRK96Urvt28fQXxSpcE+5WX5o6x+TU1/Pr/qvnG0zTj7ittBcfZvds1+qV/1NZlD2jslL+yFRwXcFkugKDgeAYneOkzAuA3JJvT9MEHH+iSSy7R66+/vv22448/Xt///vcVj8frODIAgB9V6uMXxLAP7ikXHF92adOEnwnSPuWJuZPLKPr8LzXjtmVq7vmiGh69UZHh9y1tasablU50aLjrNo18dbUys8+V4jOqHkpfvzmhQk8Kbo/roEumqnvNqt0O8BqOZ5guJz4jcCxGmBEeT8NHH32kyy67TC+88ML229rb23XdddepsbGxjiMDAPiRlQVgghT2wT3lWlWUBscFQdin6j13jA/fVvx316j5pjPUdOcKxV76tQxZu+/s7kdq9At/p62XP6htX/g75fY4atrjKXdZboEfX98w6+nNacVK+69ZX7+pFStN9fTmXBoZUBsczzBdTnxG4FiMsKPncZVGR0e1fPlybdy4cfttRx55pG688UbNnDmzjiMDAPiRnZXDS/v/0aMNxZKpcvtS5XqBcvvU3Dn+6Mlat7lj5hR9+XeKp1Yr+vz9Msys9U2jjcoc8WWlEx3K7d0uGc49z5M9H0HtcR1kyZSp3lX5P9t5zYpf695V0nHzqlvQD6g3jmeYLic+I8ydI47FCD0qj6uQyWT07W9/W48++uj22w466CDdfPPNmjVrVh1HBkzEpTWAP5QP+yp/uCxXIeHFuctxqPbaE4a6l+T/bGcxoeJ9qnuJP4Ljusyd4Q8Uf7RHzTd/WU23XabYc7+wHBzn2g7StlP+SluXPaBtZ/6jcvscU5PgWApGhXnYtCfsv2bl9gE/zGWgFMczTJdTnxEK2xbfxrEYYUN4bJNpmvqrv/orPfDAA9tv+8QnPqFVq1Zp1113rd/AgBJcWgP4R1DDPo5D9bO0O6Jrrra/Cn1Xp6Frrja0tNsfHxFrNndMU5HX/qDG9X+hmTeeosZf/5sim1+x9FhmJK70EV/WyMJVGr7kbqWPXSI1tVra1o56t+5wS9hPQNl5zexU2AFeFtTjGWrLyc8IHIsRdoZpmhxhbXj99dd12mmnTbgtEonIsFk1su++++rnP/+57ccfHBy0vY3fGIah1tZWSdLQ0JDYRe1LpvLBS4HVN6zSN7prruYMKSbHXHVeMlXd5WzVbucmjkPeEvT56trcGd2i+KZ1iqVWK/r+87buO9eyj9KJDmVmny9z5m62x2aH3S+qfvli29ObU+8q++Mr/H7dS+SbEyEFk83VqV4zv7ymwFT8dDwL+ntrUDj5GYFjsT+Fca62tbU5en/++jTlAeV2slwup2w2a/s/wC1c5gj4U7VzzotzleMQasnpuRN56wk1/uxvNPOGU9R4///PcnBsGhFlDjlNI+ddr+FLf6b08ctcD46D2vamtN+v1YrC0h6T1fxeXqx2rlT1RliBoAjq8Qz15eRnBI7FCCsWzAMCys6iQLzRAXADxyH4SnpYsafuVjw1oOjbG6f++SK5mbsrM3eh0nMvlLnz3i4NsLz8Zbmm7Qrd4vnpxbY3+RNQ9hY3dOIE1Hi1s70FuMarnU3Xqp3LHVP7V5vasmX8Zzh2ws+CejxDsHAsRhjRtsJnaFsBu7i0Bm5hrsIqjkP1x3ydXOS9ZxRLDii+aZ2MsY9sbZs54DNKJzqUPfhUKRp3aYTWBKntTTGrxwcnjiNeaLdjZa6WPp7d8QJe55fjGe+t4cax2D/COFedbltBeOwzhMeoxmRfqAhsMB3MVdjBcai+mK8lMtsUe/ZniidXK/rG47Y2NWe0Kj3nfKXnLpLZdoBLA0SxWp6Aqne/Vatzdf6C3IQqt5YWaf06OhICtcR7KzgW+0MY56rT4TFtK4AQ4NIaAPXGcQheYAy+pHjqFsU33iZjdMjWttl95+UXwDvsi1Ks0Z0BoqxKLXCcDm/90G6nr3/isVOStmzJ384xFABqg2MxwoTwGAiJ0i9DBDYAas3Kccgvl6rCR7JpRV+4X/HkasVe+Z2tTc2GnZQ++hxlEouU2+1wlwYIK2p5AspKgFzP4Lj4cVtaxo+lVvpCAwCmr97HYj4vo9aopwdCpKvTUEvLxNtaWviSAaB2Kh2HenpzWrFyfNVqq/r6831Ke3pzDo4UfmdseUMND12t5ptOV9Nd37YVHGf3nK3RM/6Ptl7+K42d9jcExx5Rusq9myfCSx/ruhvGj01eCY6XLzO0fl1k0nECAJxX72Mxn5dRD1QeAyHCpTUA6m2y49C//t+c7lyX/7udio3iD/C9q6Tj5lFREWq5rKIv/Ubx1ICiL/5Khmn9C5IZa1LmyPlKJxYrt9ccFweJ6ejqNHaoOHbrRLiX2u1UCqzttNoAAFSv3sfiZMpU7yrZvn8+L2O6CI+BkKj3pTUAUOk4dOc66YTjpYcfyf/dynGp3Ad4PgiHk7H1PcU23qZ4ao0iW163tW1210OVSSxW+qizpRktU2+Auqr1iXAvtP2yUulMgAwA7vLCsbg9YWj5Mnv3z+dlOIG2FUAI1PvSGgCwchx6+JF8gFxQ6bhUr8vG4SGmqegrD6vxJ1eo+cZT1fib71kOjs1oXOkjz9ZwR59GvrZO6U9eRHDsA+VOQBW4+Tmmnm2/7BzrKrXaAABUz0vHYjv3z+dlOIXwGAi4qS6t4UsGALfZOQ5ZCZD5IBxyI0OKP/YDNf/gLDWtvUTxZ+6RkctY2jQ3a39t+9yfa+vXH9C2+f+i3L7zJIN9xw/qeSK8UrWzm5Ip+8e6cp/tkik+2wFAtbx4LLbyPZ7Py3ASbSuAAPPCpTUAwq2a41AhQC7XwoIPwiFlmoq8mVQ8tVqxp++Rkd1mfVMjquwhpynd3qHs/idKBrUTflPPHpP1bPvVnjDUvSTfn9LOsa74OeleIi5PBoBpyB+LTc8diyu9//F5GU4zTNPkVLSPDA4O1nsIrjMMQ62trZKkoaEhsYtWx+4bBm8wsIu5iqlM9zhUHCBLE0MbK/eHcb6dr2NbFdt0V34BvHefsrVpbqe9lE4sVGbOhTJ32sOlAfpXMlXdYjnVblctq8cRNz7HTHafbn5mKjdX/fJaAWHj2/dWVMWrx+JKJzklPi9L4ZyrbW1tjt4fpRdAAHnx0hoA4eLEcejhR6Rzzh7/97B/EK72mOzHY3nknU1q/MXfaeb1n9eM+75jOTg2ZShz4Oc0cs41Gr7s50p/+psEx2X09Oa0YqX9Fg99/aZWrDTV05tzaWQ7Pl69ekx6qe1XtaEDwTEAOMerx+LS96Swf16GOwiPgQAqXOYo2b+0pvDGw2WOAKbDqePQX1wZqdtCVV7il7BvWtKjim28Q039HWruO1/x1ICM9LClTXPNu2rs+GUaXvozjZ5/g7KHnCZF6M5WTjKVv/RWshd6FoepvavcPylRzxPhVtvtsG4EAMAL6rmwK8KBT9VAQC3tjui4efYvkenqNDR3jrXg2KuX7gDwBieOQ5UWqgrLB+LSsE+y9mWgOAC7+QemTjk5rXnHxl0bZ7WMD15QPDWg+MY7ZGzbMvUGRTL7naBMokOZQ0+Xog0ujTBY2hOGli+z1yO4XJjq9vt4vXpM2q12LjxW8f/DcmwCAHgDn5fhNnoe+ww9j+EVPb0521/opPEvZd1L8sES/Iu5CrfRw23cdPtHf+NyQ9/65i6SPDJfs2OKPXefYsnVir32yNQ/X8RsbFF69rlKJzpk7nKwSwMMvnr2Erajlieqk6l8pX6B1d+19Dm65urqw3XeWwH/YL7CC/i8PLUwzlV6HgOoO79c8grAv8oFVuvXRUJ7mbidS+TLh33e+MhnbH5NDb/5nppvPE0z7v7vtoLj7N7tGv3SP2rrsl9p7JS/JjieJiv7VL2DY6m2PSZp+wUA8BM+L6NWaFsBeJSXW0L45ZJXAP401UJVUjgvE7fyu3sh7NtBLqPoiw8qnlyt6Eu/kSHrX2DMeLMyR52tdKJDuT2OcnGQ4VRpn/LkvlQDtWj7BQDAdPF5GbVEeAx40HhLCHsH+PGWEKbrLSHsvCGF9QsoAPusLlQlhfMDsZ/CPuOjdxTbcIviG9Yq8tFbtrbN7naE0u2LlTnqbKlhpksjhFR+n+pfbYb6ktdaVjsDAGAXn5dRa4THgMc4sTBS7ypVVTVjl2+r4AB4EgtVWePpsM/MKfrK7xRPDij6/C9lmFnrm0YblTniy/kq473bJSP4r6VXlO5TntiXAADADvi8jHogPAY8xm8tIfxUBQfAu5Ip+8eLcsefsFw27rmwb/gDxTfernhqjSKbX7G1aa7tQKUTHUoffa7U1OrK8DC1rk5jh5MQLS18wQQAwCv4vIx6ITwGPMhvLSE8XQUHwBfyC1WZH7fssbdQlaSPW/aE64Nw3cM+01TkjccVT65W7Nl7ZWTT1jeNxJQ59AvKJBYru9/xVBl7QF//xH1Jyp+U6Os3ef8GAMAD+LyMeiE8BjzKby0hPFcFB2Daar1wJwtV2VO3sG/bh4o/uU6x1GpF33/O1qa5ln2UTnQoM/s8mTN3d2mAsKv080RLy/j7OJe4AgDgHXxeRj24u6IWgGnp6jS0fNn4wf26G0z19ee/xHkpOC7o6jTU0jLxNi55BfyppzenFSvHjzlW9fWbWrHSVE9vrqrHZaEqa8qFfQXF7xVOiry9UY0/+xvNvP5kNd7/XcvBsWlElDn4VI2ce52GL/2Z0scvIzj2kHKfJ9avi0z6+QMAANQXn5dRa1QeAx7np5YQXPIKBIOfFu4Mo8lOHhbf7li1aHpYsafWK54aUPTtJ2xtmpu5uzJzLlR67oUyW/aZ3jjgikonollkBwAAABLhMeALfmgJwSWvQHD4beHOMKlV2Bd57xnFUmsUf/JOGWMf2do2c8BnlE50KHvwqVI0bvuxURtWrmAiQAYAAADhMeATdV8YqYKaVsEBqAm/LdwZBtMJ+y6+yMLrkRlT7Nl781XGrz9ma2zmjFalZ5+vdGKhzLYDbW2L2rMzZwmQAQAAwo3wGPAJr7aE4JJXILj8tnBnkE037DOMnL71zfL3bQy+rPiGNYpvvF3GyKCtcWX3nZdfAO+wL0qxRlvboj6SKftzttw+FYRFd2q9KCgAAIAfsWAe4AP1WBjJCqtVcCy6A/iX3xbuDKJqw77i1+3a60099nh6/AeyaUWf/Zlm3LpUM3vPVMMfbrYcHJsNMzXW3qnhi+/USEefMkedTXDsI+0JQ91L8n+2M2eL96nuJf4Pjuu1KGg9JVPVff6qdjsAABAMhmmafBrwkcFBexVBfmQYhlpbWyVJQ0NDCvsuaqUlRPHt9R6XUz8P72OuhkulvuYSc9ptPb059a6y/zwXXrdLLzF05RW7yNz8uoZ/06PYhlsU2fqurTFk9zha6fbFyhwxX2qYafdXgMeEueo2mcoHwAVW51XpcfCaq53v7e7We+t0jyHdS6Sl3dQdAcX4LAz4Qxjnaltbm6P3R3jsM4TH4TJV4FqvQNbLX7pQO8zV8CmdwwUEx7VRddiXzGjerN+p6clbZT79c8m0XjFpxmYoc+RZSicWK7fXHNuPDXiVV0+Cu/Heyuc2wB18Fgb8IYxz1enwmJ7HgEd5eRX0/CWvpu0KluLxBuGSVyBsvLxwZxjYPWYaW99TbONtOjG1RpEtr8vOx+Tsrocok1is9FELpBktU28A+EyYFgVtTxhavsze58VyvzOf2wAACCfCY8CD/LAK+tLuiI6bZ78KrqvTCMQiO0AYeXXhThQxTUVfe1Sx5I8Ve+4+Gbn01NsUNo3GlTnsS0onOpTbd55k8Joi2MK0KGiYwnIAAOAswmPAY/y0Cnq1909wDPhPpZ7HtTpp5YTA9nkdGVJ8052KJwcUGXzR1qa5WfsrnVik9OzzpOZdXBog4E2VQlWvh6h2j0thCssBAIBzWPUA8BhWQQfgNaVhwjkLpPXrItuPOVI+hOjrr9wYIZmqb3+xnt6cVqycepyl+vrz/UJ7eq33Cq4J01TkjT+p8Z6/1swbTlHjA/9kOTg2jagyh35BI+ffpOFLf6r0p5YSHCO0ij9DSfnj2fwFOU+HqNUez0oVH7sJjgEAQDlUHgMeREsIAF5RGibMO1a6c520915mVZdBdy8xtbS79ueuk6l8n3bJXqV08e/fu0pVHZsdN7ZVsad+onhyQNF3N9nbtmVvjc25UOnZF8jceU93xgf4UOnxrLhFj9dCVCeOZ8Wuu8HcoZ+9135nAABQP1QeAx5FSwhvqbZist6VlsB0lKs4fuzx/J8L1WrlKvZKK+FKA9h6zIv8glH2KqW9tmBU5N2n1PiLv9PM6z+vGb/4O8vBsSlDmYM+r2jnKsX++6NKn7iC4Bgoo6vTUEvJ+pBeXBTUqeNZ8X0QHAMAgMlQeQwAU+jpzal3lbR8mb0vkPWutASmY7LLl/fey5y00thqz9B6BbC+XDAqParYM/conlqt6JtJW5vmmnZRZs4FSs9dKLXtr6bWVnfGiJoKbN9uD/DToqBOHc9KK469GJYDAID6IjwGgAoCdak7YFGlhTunCiyKb3/zTVN33qWy91MvflkwyvjgRcVTA4pvvEPGts22ts184nhl2juUOfQLUrQhf39uDBI1x8lM9/hxUdDpHs/8FJYDAID6ITwGgAryl4Zaq+wp8FKlJVCN/MKd5sch1Y7BqZUAed6x8lxwXGC3Urpm486OKfbcfYqlBhR79WFbm5qNLUoffY7SiQ6Zux7i0gBRT5zMdM9k8774dj8GyFMFx34LywEAQH0YpmnSkNNHBgcH6z0E1xmGodaPL60dGhoSuyi8wGqg5IWKxVphrgbfVJe5T7a//+u/5zwbHBerFJ5ItRu3sfl1xTesUeyJWxUZft/Wttm9Ekq3L1bm8DOleNPkj+Hh+UobBuvsvseE6T2pWlM9R7V+Dqudq3aOZ1bC8tJtAOzIy++tAMaFca62tbU5en9UHgOABX651B1w0lTBXLl5Udo/08vzoHT8NR13Lqvoiw/mexm/+GsZsv4h1ow3K3PU2UonFim3x9HujbEGaMNgjy/7dnuYlefIznNeT1aPZ5V+Z7/8rgAAoLYIjwHAIs9e6g7UUV0DWAd0dRo1XTDK+OgdxZ5Yq/iGtYp8+KatbbO7HZ6vMj7ybKlxJ1fGV0u0YagOJzOdYec58kuoOtXxLEhhOQCEAVdnwSvCU6oBAA7o6jS0fNn4G/F1N5iavyDHl3SEWlenoZaWibe5GcA6qdKCUY4xc4q+/JBmrFup5htPU+Nvv285ODajDUoffY6GF/dr5OI7lGn/aiCCY6nQU37i8XSq552e8nnl3osKzx3B8dQqLQo6mXLPeTLlrcteKx3P7IblducmAMBZPb05rVhp//jb129qxUpTPb05l0aGMKLyGABs8nulJeC0SoGFl+eD6wtGjQwqvvF2xVMDigy9YmvTXNuBSic6lD76XKmptfoxeBxtGKrn97Yx9TTVoqCTKX7Ou5dM3dqnlqwczwqshuXF2153g6m5c7z1OwNAUHF1FryG8BgAqlDrS90Br3I9gHWJlQWjqhq/aSryxuOKJwcUe/YeGdm09U0jMWUO/YIyiQ5l9ztBMrz3vLmBNgzV42Rm9ZZ2R6r6Ut3VaXguRLW6AF7xv1nh5bAcAIIsf3WWvRZCXJ0FNxEeA0AV/FppCTjJtQDWZa4sGLXtQ8U3rVMsOaDo+8/aGk+uZR+l5y5SZs75MmfubmvboKCnfPU4mVm9ar9Ue+nLuJ3jWTW8GJYDQBhwdRa8hPAYAGzya6Ul4CRXAtgacHrBqMjbGxVPrlbsqbtlZEYsj8M0IsoedLLSiQ5lD/ysFIna/l2ChjYM1eFkZnjVagE8gmMAqA+uzoJXEB4DgA1+rbQEnFSrwMJpdheMkiYZf3pYsafWK54aUPTtJ2yNITdzN2XmXKj03IUyW/ap5tcItKnaMMydU939BnXVcU5mhpdjxzMAgKdxdRa8IFLvAQCAX0xVacnK5AgDu4GFV+ZFMmX/w3Xp+H/+42e15ZbvauYNp2jGz/+3reA4s/+JGvnKVRq+7JcaO+nbBMcVdHUaammZeFtLi7Rtm8mq40XKzcX16yKemXNwjxPHs+tuMJVMsW9UUu3zw/MKwGnljuHzF+QIjlEzhMcAYIHVSku+tCPI/BxYtCcMdS/J/9nWglGL0vq3i+9Wz0lf062nnqN9Xv2RjG0fWtrWnDFLY/O6tbX7pxq98GZlD/+iFI1X+yuExmRtGIpXHbd6bC1ddTwooQ4nM8Ot6uNZ0b7BAniV9fTmOFkFwFNK399p64VaMkzT5NOkjwwODtZ7CK4zDEOtra2SpKGhIbGLot7sXg4UlsuHmKvh1NObU+8q+/t1YV50L5GWdjt37tpuO4LCz0+1nTH4suIb1ii+8XYZI/bee7P7HKt0okOZw78kxRptbesWv8zXSm0YSoX1WGz19wrq7x90duZqte1YgtrGxSnJVD4ALrA6d0rn3DVXGzzPAeeX91YEy/wFuR0WyV2/jrrQSsI4V9va2hy9P3oeA0AF1VZaShP7UrFSOYJiaXdEx82zHzx0dRqOz4PxINt67872hFEUZJsTg+xcRtHn71c8NaDYyw/ZGovZMFPpo85RJtGh3O6H29oWeVZ6yhcL46rj9LlFsWqPp3weqaw9YWj5Mntzp9zc5HkG4DQWyUW9EB4DQAX5S0NN25WWxV/auTQ0vIJaFeaFwCKZMie0MZCshWKlbQyOm2fqmIPeUnzDWsU2rFVk6zu2xpHd4yilE4uVOfIsqWGmvV8C203VhkGS5QA5qMExJzOB2rFz8iWoxxwA3sIiuagnwmMAmIKXKi3hH9VUxUqavCoWE0y3MsxQTt9d8lud8OIaRe+7X4ZpvT+lGZuhzBHzlW5frNyecySDOT4dVnvKS1MHyEEOcTiZCdSWlQA5yMccAN5h5eosAmS4iZ7HPhOWnsfPvzBT846N2+5H4/VqPSBIwtg7yir6JdaO3f6vuzS+p3P2u13dc29RS/Z1W4+V3fUQZRKLlT5qgTSjZdpjryWvztfp9pQvVtofOaghTlCvaECeV+dqmFltqRPUYw4mx3xFLUx1rOFYNLUwzlWnex4THvtMGMLjm39g6uYfmLri28268Pxtlie2W4sxASgvjG/CdrDQYu1M+aH6Rzn94bZHdOGBa3T63j9XPJKxfN9mJK7MYV/MVxnvO8+3VcZenK9OnWQph/kDv/LiXMXUi3lyzAkn5ivcxiK5zgjjXGXBPARaMpUPjiXpe1cNa3TU0EVfra6HJdU1AOqJfom1M+lzff4WJX94p774xoCWn/SCrfvMzdpP6cQiZWafJ7N5V2cHDEnOtWG49XbtsOo48weAk0rfZwiOAbiNRXLhJYTH8JT2hKFvXC5de33+YHft9aZMk9WNAVjnpUu66ZdYO+PPdU5zWjdo70cGFH/9pzopuk3a2dp9mEZU2UNOVTqxWNkDTpQMrmJx23R7ym94QtqyZWL1CKuOA3BDV6eh/tUmJ6sAuI5FcuE1fCuC53R1RnTFt5u3//26G0z19Ze/rIDQBUCxnt6cVqyc/Jgxmb7+/OXzPb3WF02zqqvT0PJl48el4mMaxzAHjW3VJXPW6BfnX6i+z39V5+x/hxqj2yxtmttpT2078Vsavuw+jS74vrIHnkRwXEPVfqnZ8IR2uIy8oNJnBwCoRl//xOBYGj9ZBQBOyl+dlf+z3auzCt87WCQXTqLnsc+EoedxoR/NTTeP6HtXDW+/ncbwgLd4rXeU1xepo1+iOyLvPq14arVim+6SMbbV8namDGUP/KzSiQ5lDz5ZySeinqlYd4PX5ut0sYAVgipoczUoeA9HOcxXuM1LV1T6WRjnqtM9jymrgWdddmmTvnE51XoArGlPTF7hO5latr0prUDmS+c0pEcVe/IONf34q2r+4bmKJ1dbDo5zTbto7FOXafjSezV6/g3KHnq6elZFPFexjslV+gxQqdIfAKpR7pizfl2EYw0A11X7vYTgGE6j5zE8raszItPMTejbU9prjNAFQIHXF6mjX+L0GB+8qHhqQPGNd8jYttnWtn9471MaPKRDJy45Q4o1bL89mcov2CbZW1yEhVrrw8q8ZdEYYPqodsub6mSVxLEGABB8VB7D86jWA2CHlcrDel29QL/EKmTHFH3mHs24pVszfzBfDY+vshwcm40tSjVfrPN+uU6X/fYH+osffll9a+ITfsbrFesYZ3fVcaoCgep4cf2AerB6sopjDQAg6AiP4QtdncaEhXAkqvWAcpKp6r6wVLudV3lxkbpy/RLLjQ95xubX1fCb/1Dzjaer6SdXKPbq7y1v+3Z8rka/9A/auuwBHbz8f+pLnYdu/7dyz7WdL/9T7T/MQXdUu+p46evK8wxUVno1htX3ptKrMfw+1zhZBQDAOMJj+ALVesDUqqkUSqbMqiqF/PClsNyXufkLcp4IjumXOIlcVtEX7teM2y9Xc88ZanjkekWG37O06XCmSWtfWqi1u67VzD9bo8zs86R4kyRrX+ydqFinWs89rDoO1IbTV2P48YQaJ6sAAJiInsfwvEqrG9NbDMirpm9rT29u+zaS9b6thTl56SU5XXnFtIbtutJ+hPVoe0O/xKkZH72j2BO3Kr7hFkU+fNPWts9sOVy3vNSh9a99RRdfunPFyjCp8nNd6WesVBzTO9ldS7sjVT0/XZ2G5s4hOAascmr9gMLnjOXL7L2vFe6ze4mppd21r3XKn6wyPx67vZNVkj4eO8ccAEBwGKZpckrURwYHB+s9BNcZ///27jw+qvre//j7TDIEAoaAgCguIK4sSRW01dpqXVq1bnULxvhLI4ooLdTqvdb2Wu1yq7Wl/lxBBCJtjIBLFSsWfyraW/WKimaQRQuKKAqiENZAJjPn98d0htkyOWcyyzkzr+fjwYOcyXxnvpk53zkz7/mez9cwVFlZKUm674HNmvZg4hvSfJ12DjiZnXHR4gvNdIxmZRzF38efGys05livWltb5eTDydnnBRMWqVu4IPsfSK0+J0X5mmYGVbLuf+X1zVPJmpdkBDusNy3poU0Dz9SNj9XIt6VakmH5MYt/rO+/J7FecaovLaXMPY+5fN6jj61OH69AMXPSWO3qNSrV7+PfZ2TyNTpXWDQQXXHSeAXQuWIcq/369cvo7THzGI41c3Zb0uBYYrYekIydcbHsPfu3H/+B7tprDI051puihTOkKnuTzdcMu/USpSJ5TWvbIu/yp+T1zZOn9WNbTYOVh8hfPU7+EeervFc/jdkTlC8LM8PSnbGeqdl6AOAE3TkbI1T+wt5xzWmLkaZ73wTHAIBCw8xjlymWmcePP1mmu+7eFbksnVlezBZAsbI7UyianbF2xeUex3+Dm+4M0u4qhBlXGWWa8nz2jry+uSr9YJGMQLv1pp5SdRx2ujqqahQ46OuSEft4ZPO1Pt0Z692ZrZctxTjjAnAjJ47V7hxLOQMHhcyJ4xVAomIcq8w8RsFrag5q2oNdB8dS5zMi9uwxXVljDcgEuzOFOrtumFs/0HXW7+jLszXDl3qJ/7Znu7wrF6i0ZZ5KvvqXrabBffaXv+pSdYy6SGbvgZ1eL1szw7ozY707s/UAwGm6s35AZ6+H0XXI7Zbdcv2xEQAAl2HmscsU+szj+Nl6115j6PLL7M/Wi1b0M/5QtOzMFLIStMa3cfI3uE6Z+VmsZ0B4Ni6X1zdPpaueleHf1XWDfzNlKHDoyfJX1Sgw9FuSpySLvexcpmas52vmezJOHq8A9nLyWO3O+gHJ3qvHf4EdvqyrWcwN9WKSBxzByeMVwF7FOFYzPfOY8NhlCj08lqTZD5ua/bCp66eU6+IL91ge2NFvKMvKDMcuWgTkUmdfrCTbx+0GXU49CHOKbJ74d6n0/efkbZmnko3LbDUN9h6gjlEXyz/6YpkVQ7LUQWvS+SLFzu3ZbZ8pTh2vAGI5daxm4rUs1WSPrm6LSR5wIqeOVwCxinGsEh4XuWIIjw3D0JoPe2vMsV7bAzt6th4BEhBiZ6aQnQ+HTjwI2x3Pbh//TpjZ7PnyXyr1zZd35dMy9my31bbj4G/IXzVOgeGnSiX5X3wxWzPWuzNbL1OcOF4BJHLiWM3kWRTphNBuP1ajcDlxvAJIVIxjNdPhMef7wJHGHJteiBAdhtTVGpHT4aTQKXFNzbEvErwZRaFLVbc1mbpaQxUVsZdVVGS+JnA2tPjsj+dkrxMtPne8mZjVGNSkyYmva11pag6VB5rVGEz/zjvaVbrqWfWaV6fyP5+nHu82WQ6OzZ591T6mQTsbFmr3xY0KHPE9VwTHkrXjSrLbtTMGAcBJkr02Llzgsf1aGJbsfYbd+3fDexIAAAoJC+ahoLFoEYpZqplCnS0U151FwvKtmBapa/GF/k7J3qJ/0ftE4xxp7Bh7M5CN1nXy+ubLu/xJGW32zoQJHHCM/FXj1HHE96TSMltts83O8SDVcaWr27UyBgHAKVK9Ntp5LYy/zfj3GZ215706AADOQNkKlymWshWZPqXASYsWAbmQTt3WQql57IRSDrmQsxIdwQ6VrFkcWgDv41dt9dHs0Vv+o89TR1WNggOPtNU2V+IXas3UIquZrp3cXU4drwBiOWWsZqP8W1fvM6LbExzDDZwyXgGkVoxjNdNlK5h5jKIQPzuC4BiFLJ2ZQvE/J/vw5paZkukGwG4KjqXuzYC18rpnbN8g77LHVbrsMXl2fmGrb4FBR4dmGR/1falHb1ttcy0bM9azMVsPAHIlG2djWP1CbfoMU81zTd6rAwDgIMw8dhlmHnePExYtArIp3ZlC0awuEua2b3ALdUZyRhd5M4MqWfuqvL55KvlwsQzTel1ks7SnOo48W/7qcQruN0oynPuYJZOp/cOpi7W6bbwCxSrfYzUbZ2PYPU5FIziGk+V7vAKwphjHKgvmAWli0SLkUrqLrnVnsTa7M4WiF7tJ1SadRcKcJq+Ly2VZqufH6j5h7PpK3iUPqXz299TrrxNUuuZFy8FxsP9w7Tnl59o54WXt+d5/Kzh4tOuCYykzM9a7MwbdOK4AFJ7Q2Rihn+2ejRF+TYs+GyOdxUjD3LJgLwAAhY6yFSgKLFqEXJrVGPz3KfD29qvwftpQb2p8g73v9lp8mZnFOHpU8suTnZZqGEH96Drbd5Fz+VpcLpeSPT9dnvZrmvJ8+maolvG//p+MoN/y/ZkerzoO/6781TUKDhnryrA409IZg8met9Gj3FdCBUBhGd/gSeuYV1drxLyGdfcMC7cs2AsAQKFj5jEKXrI3rgsXeJjxhayIDyqt7lfxQaXdGch2ZwrFB12SEuq2xoufGTTtQVNvL7UeOOZLdZX9GZ7JXjecHujFPz+dBse7t8q79M8qn3OOyh+rl/f9hZaD42Dfg7TnWzdo14TF2vP9Pyp44HEEx/+W6dl6AJBP3T0bw84XaqnKVvAeHQCA/KPmsctQ89iejNYCBSyyu19lcj+0U7d17wxpw9Zsx3B/r/yhoRuu7y/JHbWjnFqLNtOS1nZ/2pBnwzJ5fXNVumqhjMAey7dnGh4FDv2O/NXjFDjkRMnge+dUnFpbuxhrvQFuVEhjNfp9htXgOPxlWnyY7LZjMYpDIY1XoJAV41jNdM1jylagYFmtsSax6j0yy85+lemg0k741N3TUr9W7a4Q0crz4vbgOL62e6+SnTqj37Nqu3e+BnastHVbwT77yT/6EnWMukjmPoMz3NPClYnayUAxcuoXL0hfV+8zUh1zR4+Slr3He3QAAJyA8BgFye6iRRJvTpFZbgkqiy3oSvW8OOH56I7o/h9e8b5qD5+nMwb9TX28O6UO67fTcchJ8lfXKHDoKZKHtwnIHMJBdCYfawUgN9IJjsPtqqtCP/MeHQCA/OJTIQoOixbBKQo5qHSztBaXc7imZlONM9v0/QOf16VD56m6/7u22gd79VfHqAvlH32pzMqDstNJFDXCQXSmGBY1RSwmeQAA4C6Exyg4oUWLzC5rrMWLfnPKokXIlEIMKgtB/PPi5ufjmT9/pPI35mnRd59SZY+tttoGhoyVv3qcOg47QyrtkaUeotgRDiKV0KKm9sJBNy5qihAmeQAA4D4smOcyLJhnHafHwkk6W0ncbUFltEJYeCDp4nILXDC7MdCukjUv6avn5+rA9jdsNd3u30dr+56vQy+pkbnvYVnqIJwm3+M1nwuJwh2KZVHTruR7rOaClYX0ktl7JoI4EwGOUAzjFSgExThWWTAPsKjYarnC2epqjYQZxxUVnHaZT/GLy0mhGchNzabqag1HfgFlbFsv77LHVLrsCXl2fakDbbTd6B2tB5bUaNFnZ2p3oJcmVhiqq81KN4EE+VxIFO7glrUC0H3dXbCX9+oAAOQWX9kCLtPiS38mNvInVVCJ3IsPICoq9v5u+gxTU34a1KTJpu3np6nZ1KTJpmY1BjPVVSkYUMmHi9XzrxNVPvMM9XjjQXl2fWmpqektl3/0pdp1+ePq/eP52v+sC7U70EtS6O/kdaEwuOW4UFdraOKEvaHP9BmJY4xwsLil2kfYNwoLkzwAAHAPwmPARWY1OijQgmVdBZUEyLmVLIBYuMATE1i8vTT0v53nJ74+a3eDOWPHF/L+7zSVzzpDvZ66TqUfvSJD1m4zsO/h2n3qLdo54WXtOeNXCu43UlJsMENt98LgtuMC4SC6kmwfOfu8IPsGAABAnlC2AnAJFhxyp87CkOjLWTk8d1KFU/GnTIfldPEmM6iST96Qt2WeSta8KCPYYb1pSQ91HHGm/NXjFNz/a5LR+cr1nPZbGNI/LgTzelxgIVF0pZAWNQUAAHA7wmPAJViN3H3sBJUEyNlnZVZjOgFyRmZLtm2Rd/lT8vrmydP6sa2mwcpD5K8eJ/+I86Ve1hZG4HWgMKRzXJg5u03THsz/cYFwEF1hrQAAAABnIDwGXIQFh9yjs8c/ejE1O89nNhdhKwZ2xoOdALlb48w05fn8XXlbHlXpB4tkBNqttZNkekoVGH6a/NU1Chz0dcmgClWxsvM6MnN2m+66e1dkO9/HBcJBpNLVoqYAAADIDcJjwGVYjdz5Onv8ZzUG1ThHmjhBtmYgh2+vod7U+AZCQrtafPbHg5UAOe1xtmeHSlcukNc3TyVffmD1z5AkBffZX/6qS9Ux8kKZfQbZaovCZe11JKhpDzonOJYIB9G5ZGsFhPcVztQBAADILcJjwIVSBQUEx/nVWVCZqjZpsuczXJOWmtXdV11lqKHe/Hdwb308RD8vY46NXUQvnfqsno3L5fXNU+mqZ2X4d6W8bjRThgLDvh2aZTz025KnxHJbFA87x4VrrzF0+WX5D44JB5EMawUAAAA4i2GaZveWg0dObdmyJd9dyDrDMFRZWSlJam1tFbto51J9+JYIjqX0yz10p0zE3hnGhq0Z4XtnGEvjGzyO/yLAbWO1u/tC/PMRlvJ58bep9P3n5G2Zq5KNy2zdb7B8gDpGXyT/6EtkVgyx3e9cy8dYQ6KujgvXTynXxRfuyet4tRIORl+O4mH1ONnZ7wuB246tQDFjvALuUIxjtV8/a2vhWEV47DKEx4iXVqBVJDoLcbsSH+Kmo7NArKsPvp0FlU58PotxrJ59XjChPuvCBYn7iPHVanl98+Vd8ZSMPdtt3UfHwd+Qv2qcAsNPlUq83e1yTuRzrCFRZ8eF66eU66ore+V1vBIOojNWn/tC30eK8dgKuBXjFXCHYhyrmQ6P+aQGuFxdraGKitjLWHBICWUimpqtHSDiy0S0+NI7sHQ2k7Ku1tDECXt/F983twTHxShVfVZJUke7Slc9q17zrlDvOeeqxzt/sRwcmz37qn3MD7WzYaF2X9yowBHfc01wnO+xhkSdHReuurJXfjr0b1Ze27p6jURhsruoKfsIAABA7hAeAy7XZaBVpKqr7H+4TPbhNRun06f64Etw7EzJSgGEPdO0Tv96cKrKH/qOei68USXr37J8u4EDjtHuM+/QzqtfVvvJN8nsNyyT3c4JJ4+1YtXZcWHm7Lb8dEiEg+hcuouaxu8jfAEFAACQHSyYB7gYCw6llmoBqXi5Dm2T9S2dRdiQfUn3jXEB/fPhxdrng3n65qBXpZ3Wb8/s0Vv+o89TR1WNggOPzEKPc8/JY63YpDou3HV3aKHGiy9M3jZbdavTDQel5AuJorBkYlHThnr2DQAAgGwhPAZcitXIrbESauUrzIrvG8Gx88TvGzeM/0I1Qx9X6czHdeaOjdIg67cVGHi0/NXj1HHU96UevbPQ2/xy8lgrFlaOC3fdvUu7dxu6/LLYx31v3Wp7x4y9davNTutWEw6iK+MbPBo7xv6XF3W1Bl8qAAAAZBkL5rkMC+ZBYsGhdFgJVaIvzyWri7A5TaGP1fC+YSiobwx8Tf95ynwNbX9ZhhmwfBtmaU91HHm2/FU1Cg4eLRmFPw6dPNYKWVeP7yOPmpr2YPLft/hMTZps/7mJv8/770ldfiRbM5uBQlLox1agkDBeAXcoxrGa6QXzCI9dhvAYrEaevlSnc0v5eYzi+5TPvthVyGO1qdnUvIe/1PkH/1UXH/KYDuz9qa32m0sOVe9vjZN/xHlSz75Z6qVzOXGsFTIrr/eGYejxJ8sipSvir2f3mMExBsiOQj62AoWG8Qq4QzGO1UyHx5StAFzE7oJDkrUapMXCaWUiqFntQKapj/7xlg5561E9/93/J6+nw3LTgEr1/Kdn6PGPa/T2V2N1/3c8qu5ZnM+f08ZaIbNzXLjqyl6S9tY+jn6doW41AAAAgGScf140AEmsRp4pdbWGKipiL6uoyH1Imyx8WbjAk/B8NTUX9/OVM7u3ybv0Lyqfc65Gv/1/dNaBz1kOjoN9D9Sek36q3RNf1sdjp+rtr45TQ33q0/eLgVPGWiFL57hw1ZW9dO01yY8LyY4Z8a9BBMcAAABAcSE8BlwitOBQ6Ge7Cw6Fw4BcLTiUbkCdi2C7qdmMmQUphWZF5jKkTRW+WAlvkCGmKc/nPpUt+oV6zzhZZS//Tp7Na6w1NTzqGH6a2i58SLuuXCT/8VfLLN9XdbWG7r/H6HThsGLihLFW6NI/Lng6PS6keg0iOAYAAACKDzWPXYaax3D6gkOzGoNqnGM/VAiHEg31ylrw5oQ6rIVUs9q1Y7V9p0pXPSuvb65Kvlhpq2mw9yB1jL5E/tEXy9xncJY66H5OGGvFxMrre7LxmqodzyGQH649tgJFiPEKuEMxjlVqHgNFLt0AOFczjhvnhH62U7M3OqRonCONHZP5oLuzMDb68mzXGaZmdX55Nn2gUt9ceVcukNG+01bbjkNOkr+6RoFDT5E8HDpTccJYKzbZOC5QtxoAAACARNkKABlUXWW/5EKyoClXwbGUuzIR1KzeK6dlTTr2qHTF0+o1t1blfzlfPVoetRwcm736qf24q7TzykXafdFDChx2OsFxF5ww1pzKyeV8OkPdagAAAACExwAyyk5AlIvSDFbuIxehlptqVmfTrMagJk22//g2NZuaNNnUrMagpesbWz5Sj1fuVO8ZJ6vn33+mks/esXxfgSFjtfvsP2rn1S+r/Vs3yKw82FZfi5VTxpoT5Wq/zzTqVgMAAABgChWAjLNScsEpwbGdPnfX+AZPWiU56moNjR7l/uA462VNAn6VrHlJXt9cla77X1t9M8v2kX/EBfJXXSpz38NstYXzxpqTOLmcj9X7l2JrHhf6cwYAAABgL2YeA8iKVDMMcxEcO7VMhJNrVmdbtsqaGNs+U49X/6/KZ56qXn/7ia3gOLDfaO3+7m+1c8Irav/OzwmO0+DUseYUTi3nY/f+Fy7wFOWscQAAAKDYMfMYQNYkm2HYPNfMycJLoTIRoRl/dstEhPtaCGUinMbOrNOUXzIEAypZ+z/y+uap5KN/yDCtn9ZvlvZSx9HnyF9Vo+B+I9P9U/BvjLWuZWy/z4Gu6lZLxTNrHAAAAIBkmKbJtBEX2bJlS767kHWGYaiyslKS1NraKnZR94sPI8K6CkVafOmdph3dLhO3geS6M1a7Csg6+72xc5NK33tCXt98ebZ/bqu/gX0Pl796nDqOPlcq28dWW3SNsda1dPf7TLAyXq3ef74DbqCQ8T4YcA/GK+AOxThW+/Xrl9Hbo2wFgKyrqzVUURF7WUVF6tlq6Sww1eIzExaYshJKJTtdPtdhVrqn7Lv1VH9bZU2ulupP+l/1fOYnKn/oVJW9erfl4Ngs6SH/0edqV80javs/T6vja7WOCI4L8fku5pIsVuW7nE8qdutWU8ICAAAAKA6Ex0COFGJYZFVTc2ypCim08FJnYUP8AlNWQolw2By9wJSVxy4+bM6HdIJyyRl9745kAdTZ5wUjz2Ffb6seuHyOJrR/X70ev1Kl/1okI9hh6baDlQdrz7f/UzsnvKw9Z92p4JBjJcMZIWWxPt8I6Wq/l3IfHFO3GgAAAEBnCI+BHCjmsCh+Nlv0DOTOgmG7C0xFh81hVhaYiu6b1bA509IJyiVn9D0T4gOobdtMVfd7R7855ma9eNZ3dOL2O+XZstbSbZmeUnUc/j21XTxbuxqek39sg9Qrs6frdFexP98ISdzv9/4uHyUgQnWr7d9/9N9R6HWrAQAAgGLFgnlAlsWHRZK1xYXiw6KxY9xXF7Sz06CjL+/sMbGzMNOy9zLTt3w8vqGg3N4CVE7pe6bU1Rp66rHt+nbfZ3Tx0Pk6ouIDW+2D++wv/+hL1DHqIpl9BmWpl5nB842wulojYQHRrsr5ZNP4Bk9ax5m6WkOjRxEcAwAAAIWK8BjIsmINi1LVz7QaDFu5XmeL8aV6nPNdWzSenaDcaX3vLs8XK7T2ibl68oS/qby0zXI7U4YCw74tf3WNAkO/LXlKstjLzCrm5xt7pSrnk6/nmLrVAAAAAOIRHgM5UGxhkZW/IRMBcrL76eo2nfr4phOUO6XvtvnbVPr+c/L65qlkg08jJMtHo2D5AHWMvkj+UZfI7Dskm73MqqJ6vpEgWTmfcJBs5wwVpK/Fl97ZPOm2A6Kx/wEAADchPAZypFjCIjt/Q3cC5PjTvePvx2rY7KTH125Q7qS+W2F8tUZe3zx5VzwtY8+2rhtE6TjoG6FZxsNPlUp6ZKmHuVXozzeS6045H2TGrMagGudIEyfYe4zDz1FDvanxDSwbgvSw/wEAALcxTNNk1R0X2bJlS767kHWGYaiyslKS1NraqkLbRa0EB9GXu0mLL7TAX5jVvyH+b7//nuRlOjorUZHsflLN7LPTt3xwS98tjdWOdpWu/n+hWcafvmnr9re2V+jpT36gJ9ZeorMuP9QRf3M2uOX5Rvd19TqfzeNAoR9brcr2cQpIxcr+l2yssv8BzsSxFXCHYhyr/fplduF4vrYGcix6dXopNMPs7POCrg+OpVDdy4b60M92/obox6ShvvP6mXW1hioqYi/rbIGp+MfZTWGcm/seZrR+oh7/M1XlD31HPRfeaCs4Duz/Ne0+83bNP+hl/Wn5f+rjncM0fUbow3MhKoTnG12zWs4n/vhQqPt9voTWIbD3GBfCOgRwBvY/AADgRsw8dhlmHhcOO7No3SZbtfzSeczOPi8YE8ZVVEgLF7jjezOn9z1hrAb8KvnwldAs47X/lCHrY9f0lqtjxHnyV9UoOPCoyOWFMCPfKqc/30if3f04G/t9sRxbrbL6GBfTaxByJ9V+FT1W73tgs6Y9yP4HOBXHVsAdinGsMvMYKBB2ZtG6TbozYuwEx9GPXWczd5qaY+siS6FZnW6YyeemvpvbPpf3tftUPvN09VrwI5Wu/R/LwXFg4NHaffqvtPOaf2jPabfGBMdS8pmYLT7nPQbd5abnG/a0+OyHj8Wy3+eTlVneBMfIFiv738zZbQTHAADAEQiPgTwhLLIu2Qf4hQs8KT94pRM2O4Ur+m4GVbL2f9TR3KCOqWPV4/X75Nmx0VLT3YEyrep1gXZdNldtdU+oo+pSqUfvTq9vtayJW7ni+Ubasl3OB+lLFeARHCPbUu1/M2e36a67d0V+x/4HAADyibIVLkPZisLAAlnWpbPAlCTXLkro+AUVd22Wd/mT8vrmy7P1E1tNg/0Plb+qRu+Y52nUmErbd51uORQnc/zzjYzJVjkfq4rh2JoujsnIJ/Y/wL04tgLuUIxjNdNlKwiPXYbw2P0Ii6xLtyZlNCths1Me53SC8pz03TTlWf+WvC3zVLr6eRkBv/WmHq86Dj8jVMv4wOMkwxmPtRM49vlGQSr0Y2t3FfI6BHC+zva/a68xdPll7H+AU3FsBdyhGMdqpsPj0ozeGpAH+Z7NZUeqMCj8f/j34f+L9UOrneAs/rFL1capj7OVvzfnfd+9Td6VC1Tqm6uSr9bYahqsGCJ/VY06Rl0os3zf7PTPxRz5fANFrK7WUPNcM2HRSsYbciHZ/te3r6G6Wk9RfMAFAADORs1juNqsxqAmTbZfE7Sp2dSkyaZmNQaz1LPk92klLOpqAZVikM4CU8mMHpX8cqc9znaD8qz23TTl2bBMZYt+od4zTlbZ4v+2HBybhkcdw09T2w9maNf45+U//mqC4yQc9XwDkMQ6BMivZPvf1q2mmppz9z4VAACgM8w8hmu1+Ew1zgn9bGdGXnRw0zhHGjsm+zOQuzOLthhnG4YWmAo9v1aC4/iwWep6galkj/PoUblflCqdoDwrfW/fqdJVz8rrm6eSL1bYa7vPYLWPukj+URfJ3Gf/9PtQBBzzfAOISFVzthiPwcitVPvftAdNmSb7HwAAyC9mHsO1qqvsz8hLFuJmO4BJNyyK/9tafMU1+2l8g0f332NtxnEobA79PHGCofvvMTS+oeuXt+jHuauwOVvi+271A2Km+u7Z9IF6vPgb9Z5xinq+cKut4LjjkG+q5LLZKr3hLflP/DHBsQX5fr4BxEr2vmDhAg8z/pETyfa/554p0fVTyiOXsf8BAIB8Y8E8l2HBvETpLqpmtxRCd2okz2oMWp5FGy3c54Z6WQpDi52b6l9nqg9ptevYo9IPFoVmGX+21FZTs1c/+UdeKH/VpVK/Q4pu4YFMcfO+CncqxoVCusKilcinzvav8FidObtNd929K+H3AJyDYyvgDsU4VjO9YB7hscsQHidn9wPg14+Xpt5pPYzNRIhLWIR8M7asldc3X97lT8rYvdVW28CQMfJXjVPH4d+VSnuEbq+Lsco+DzhHMb5pTiVXXzwDyaTar6LH6n0PbNa0B9n/AKfi2Aq4QzGO1UyHx9Q8RkFIVSc4/g26JL2xJPTGPZc1ktMNwwjR0C0Bv0rWLJbXN1el61631dQs20f+o89XR9WlCg443FbbvbPt7dVq3PtFjclsewBZwToEyCd7+59Hphlk/wMAAHlFeIyCkewDXvPc2NWrv358KDiOvl6qN+D5qJEMZIKx7TN5lz2m0vcel2fnl7baBvYbLX91jTqOPEvylnfdII6bFrMEUFxYtBL5xP4HAADciGldKCjxC81FB8cTJxiaeqf1RXA4VRWuEwyo5MOX1fOpa1U+6wz1eGO65eDYLO0l/6iLtevyx9V2+Xx1jLooreBYcs9ilgCKD4tWIp/Y/wAAgBtR89hlqHlszdnnBWOC44oKaeGCvd+VsEgOComxc5NK33tS3mXz5dn2ma22gX0PU0f1OPmPPk8q28fe/XYxVqkpCjhHMdZ6S4Wa7MinVPtRqrHK/gc4C8dWwB2KcaxS8xjoQlNzbKkKKTQDObrGsZ0ayQRZcCTTVMknb6jUN0+lq1+QEeyw3rTEq44jzpS/apyCBxwjGdnZv63UCmW8AcgH1iFAPrH/AQAANyE8RkGJD6IqKvaWrogPrqzUSCbIguO0tcq74il5ffPk2bLWVtNg5cHyV9XIP/IHUq/MfhPZGb6oAQAAAADAvQiPUTA6C6KiL+8qQC724JjTeB3KNOX5/F15ffNU+v5zMgLt1psaJQocdpr8VTUKHPwNych9qXu+qAEAAAAAwJ0Ij1EQUs1g7OrU+bpaIyHIqqhQ0QVZsxqDapwjTZxg728PP/YN9abGN7AGZ0bt2aHSVc/I2zJPJV++b6tpcJ/95R99iTpGXSSzz6AsddA6vqgBAAAAAMB9CI/helZOfe/q1PmuaiQXuhafqcY5oZ+T1aXtTPRj3zhHGjuGGciZ4PliZWiW8cpnZPh3WW5nylBg2LfkrxqnwLBvS56SLPbSPr6oAQAAAADAXQiP4Wp2aqYmC5DfedfUG0v2XidVjeRCVl1laOKE1AubxUv22BMcd4O/TaUf/F3elrkq2eCz1TRYvq86Rl0s/+hLZPYdkqUOdh9f1AAAAAAA4C6Ex3CtFp/9xbbiA+To4NhKjeRC1lV5j2gsdJY5xldr5PXNk3fF0zL2bOu6QZSOg74uf/U4BYafKpX0yFIPM8POYpYAAAAAAMAZCI/hWtVVhhrqzX/X6bUeXtbVGgkzju3USC5kVv52guMMCLSrdPULKm2Zp9JPl3R9/ShmWV/5R14gf9WlMvsfmqUOZlY6i1kCAAAAAID8IzyGq41v8Nius9vU3HlwHEaA3Hl9aILj9BlbP5XXN1+ly5+UZ9dXttoG9v+a/NU16jj8TMnbM0s9zLzuLGYJAAAAAADyi/AYrmc3OO5OjeToywtZsr89fqEzgmOLgh0q+fAVeX3zVLL2nzJkdt3m30xvuTqOPlf+6nEKDjwqi53Mju4uZgkAAAAAAPKL8BhFIxM1kqfPMDV6lL3A2q3i/3aCY3uM7RtV+t7j8i57XJ4dG2y1DQw8Sv7qceo46hypR+8s9TC7+KIGAAAAAAD3IzxG0ehOjWQpFGg11BdHcBxWV2skzDiuqCDU65QZVMnHr8vrm6uSNYtlmAHrTUvK1HHU2fJX1Sg4uEoy3PsY80UNAAAAAACFgfAYRSWdGslSKNgqxiCrqTk2OJZCM5Cbmk0C5Gi7Nsu7/El5ffPl2fqJrabBfsPkr66Rf8QFUs++2elfjvFFDQAAAAAAhYHwGEUn3UCq2IKs+LIDFRV7S1dQVkCSacqz/m15ffNU+q9FMgJ+6009XnUcfro6qsYpcOBxrp5l3Bm+qAEAAAAAwP0IjwEk6KxebfTlRRsg794m78oFKvXNVclXa2w1DVYMkb+qRh0jfyCz94AsddA5+KIGAAAAAAB3IzwGECPVQmfFvLCZZ8N78vrmqnTVQhkdbZbbmYZHgUNPkb9qnAJDvykZniz2sri0+OzPbO5OOwAAAAAAig3hMYCIVMFxWFEFyP5dKl31rLy+eSrZuNxW02DvgeoYfYn8oy+Wuc/+Wepg8ZrVGPx3TWV7+154H2+oNzW+gSAfAAAAAIBUCI8BSLIWHIcVeoDs+fIDlbbMk3flAhntO2y17TjkxNAs40NPkUq82elgkWvxhRbjk+zte9H7eOMcpVWTGQAAAACAYkJ4DEAtPuvBcViyANnVC5117FHpv56Xt2WuSj5baqup2bNS/lEXyj/6Upn9DslSBxFWXWVo4gR7X14k+3LEtfsqAAAAAAA5QngMQNVVhhrqzX+XAeg6OA6LDpAb6t0ZHBtb1srre0ze5U/K2N1qq21gyBj5q8ap4/DvSqU9stNBJGVn9rudWfUAAAAAAGAvwmMAkqTxDZ60TuOvqzXcN+M44FfJmsWhBfDWvW6rqdmjj/wjzldHVY2CAw7PUgdhhZUAmeAYAAAAAID0ER4DiEg3AHZLcGxs+0zeZY+r9L3H5dm5yVbbwH6j5K+qUcdRZ0ve8iz1EHalCpAJjgEAAAAA6B7CYwCFLRhQydp/yuubp5KPXpFhBi03NUt7qeOo78tfVaPg4FFZ7CS6I1mA3DzX1LZte69DcAwAAAAAgH2ExwAKkrFzk0rfe1LeZfPl2faZrbaBfQ9TR/U4+Y8+TyrbJ0s9RCbFB8gExwAAAAAAdB/hMYDCYZoq+WSJSn1zVbr6BRnBDutNS7zqOPxM+atrFDzgWMkgbHSbulojYcZxRUXyRfQAAAAAAEDXCI8BuF9bq7wrnpLXN0+eLWttNQ32PVj+6hr5R/5A6tUvO/0rcG8v9Wv4ofbbtfjsL9CYSlNzbHAshWYgNzWbBMgAsiLd17FMv/4BAAAA2eLJdwcAIC2mKc9n76rs7z9T7xmnqOyV31sOjk2jRB2HnaG2i2Zq15XPyT/2SoLjNN0/bZf+T8M2NTVbryUthQLdSZNNzWq01y7V7UUvjldRsfd302eYamo2k7QCgPTNagxq0mT7ry+Zfv0DAAAAsomZxwDcpX2nSlc+E1oAb9MqW02DfQbLX3WJOkZdLLPPoCx1sHi0+Ew9ML1NkjTtQVOmaa1ERHTQ2zhHGjumezPw4oPjcI3j6MvD/zMDGUAmtPhMNc4J/Wzn9SXTr38AAABAthEeA3AFzxcr5fXNU+nKZ2T4d1luZ8pQYOhJ8ldfpsCwb0keXvYypbrK0PVTynXX3aHnw0qAkizozUZwHN0PAmQAmVZdZWjiBHuvL5l+/QMAAABygRQFgHP5d6v0g7/L2/KoSjb4bDUNlu+rjlEXyT/6Epl9D8xSB3HVlb0kyVKAnCroTYeV2yNABpAtdl5fMv36BwAAAOQK4TEAxzE2fyivb568y5+SsWdb1w2idBz0dXVU1ajjsNOkkh5Z6iGiXXVlL+3e3aZpD3YeoOQjOA4jQAaQLVZeXwiOAQAA4GaExwCcIdCu0tUvqLRlnko/XWKrqVnWV/6RF8hfdanM/odmqYNIpa7WI9MMJg1QMh2ctPjs316ygGf0KHHKOIBuSxUgExwDAADA7QiPAeSVsfVTeZc9ptL3npBn11e22gb2r5a/apw6jjhT8vbMUg9hVbIApXmuqW1Rk8czEZxUVxlqqA8tVmXn9qL711BPcAwgc3L1+gcAAADkGuExgNwLdqjko3/I2zJXJWv/KUNm123+zfSWq+Poc+WvqlFw0NFZ7CTSER+gZCs4Gd/g0dgxpu0AuK7WYMYxgKzI1esfAAAAkEuExwByxti+UaXvPS7vssfl2bHBVtvAwKPkrx6njqPOkXr0zlIPkQl1tUbCjLuKiszXGE43ACY4BpAtuXr9AwAAAHKF8BhAdplBlax7PTTLeM1iGWbAetOSMnUceVZolvH+1ZLBh283aGqODU6k0Ay8pmaTAAVAQeP1DwAAAIWG8BhAduzaLO/yv8rrmy/P1nW2mgb7DZO/ukb+o8+XelV2er0Wn/2yBciu+MWhKir2nrodvYgUABQaXv8AAABQiDz57gBQ7Fp81uv9ZqJdVpmmPOvfVtnC/1Dvh05R2f/80XJwHFCp/EecqbaLH9auHz4r/7H1KYPjpmZTkyabmtUYzFDn0V3xwcnECYYWLvBo4oS9Ycn0Gaaamh247wJAN/D6BwAAgEJFeAzk0azGoCZNtv9h0nHB6Z7t8r7ziHr9+TyVz6uTd9XfZAT8lpp+tusA3bPiJzpj0Yuate1PChz89S7LU0R/SG+c49Agvcg0NQcTgpPwDLu6WoMABUDBShYc8/oHAACAQkHZCiBPWnymGueEfrZzOmt8cDp2TP5KN3g2vCevb65KVy2U0dFmuZ1peBQ49BT5q2r0zGsnavYLoe+xrDwOyT6kU7oiv2bObtO0B5MHJ2Hh7fBzxyncAApBquA4jNc/AAAAuBnhMZAn1VWGJk6w92HSEcGpf5dKVy2U1zdXJRuX22oa7D1QHaMuln/0xTIrDpAkXT5MMg3T0uNg5UM6cmvm7DbddfeuyHaq54QABUAhsXNM4vUPAAAAbkV4DOSRnQ+T+Q5OPV9+oFLffHlXPC2jfYetth2HnCh/VY0Ch35HKvEm/N7K45Dvvx+JWnym5eA4LNlzPXqUmD0OwFVafPaPSbz+AQAAwI0Ij4E8c3Rw2rFHpf96Xl7fPJWsf9tWU7NnpfyjLpR/9KUy+x3S5fVTPQ4Ex85UXWXouom99MD0Nl17jaHLL7P2nEQ/1w31BCcA3Ke6ylBDfaj8lJ1jEq9/AAAAcBvDNE1W7XCRLVu25LsLWWcYhiorKyVJra2tKpZdtLOANB/BqbHlY3l98+Vd/qSM3a222gaGjJG/qkYdh39XKi2zfd/xf29FhbRt297fExw7R3isvr3Ur+GH7rQ9Vlt8+avXDRSbYj22Zlu6r2O8/qEzjFXAPRivgDsU41jt169fRm+P8NhlCI8LW16D04BfJR8ultc3T6Ufv2arqdmjj/wjzldH1aUKDjii212JfxzCCI6dpZjHKuA2jFfAHRirgHswXgF3KMaxmunwmLIVgIPEl27IRXBsbP9cXt9jKn3vcXl2brLVNrDfSPmrxqnjqLMlb3nG+lRXa6h5rhnz91dUsLAQAAAAAABALhEeAw6Tk+A0GFDJx6+Gahl/+LIMM2i5qVnaUx1HfV/+qnEKDh6VuT5FaWqO/fulUJDe1GwSIAMAAAAAAOQI4THgMNkMTo2dX6p0+ZPy+ubLs229rbaBfYero+oy+Y8+V+pZ0a1+pJKqdEeyxQQBAAAAAACQHYTHgINkJTg1TZV8+qZKWx5V6eoXZQT91puWeNVx+JnyV9coeMCxkpHd0NbKooEEyAAAAAAAALlBeAw4RMaD07ZWeVc+LW/LPHm2fGSrL8G+B8tfdan8I38glfe31TZdnf39UmItaAJkAAAAAACA7CM8BhwgY8GpacrzeYu8vnkqff85GYE9lvtgGiUKDD9V/uoaBQ4+QTI86f45tqX6+8MIkAEAAAAAAHKL8BjIs4wEp+07VbrymdACeJtW2br/YJ/B8lddoo5RF8vsMyjdPyNtVv7+MAJkAAAAAACA3CE8BvKou8Hpvv5VumDIPJWufEaGf5fl+zVlKDD0JPmrxykw7NuSJz8vBS0+639/WLLHYfQoqbqKABkAAAAAACCTCI+BPEk3OC0xd2vd35/TxYfMV/VXLdJX1u8zWL6vOkZdJP/oS2T2PTDdrmdMdZWhhnpTjXOs/f1h0QFyQz3BMQAAAAAAQDYQHgN5Yjc4NTZ/JK9vrq5qfVrGMVtt3VfHgcero7pGHYedLpX06E63M258g0djx5i2A+C6WoMZxwAAAAAAAFlEeAzkUZfBaaBdpatfVKlvnko/ecPWbZtlFfKPvED+qhqZ/Q/NQG+zJ90AmOAYAAAAAAAgewiPgTxLFoAaW9fLu2y+St97Qp5dNupSSArsXy1/1Th1HHGm5O2ZqW4CAAAAAACgyBAeAxnW4rNfgkGSWlo6dGyf/5HXN1clH/2PDJldN/o301uujqPPlb/qUgUHjbB93wAAAAAAAEA8wmMgg2Y1Bv9dw1iWF38zdnyh5c2P69AvHlOv8g227i8w4Ej5q8ep46hzpLI+6XQZAAAAAAAASIrwGMiQFl9o8TtJmj4jNGu40wDZDKpk3evytsyTZ/VLOl4Bqdza/ZglZeo48iz5q2oU3L9aMqj7CwAAAAAAgMwjPAYypLrK0MQJe4PjpAFy2xZ5l/9VXt88eVrX2br9YL+h8lfVyD/iAqlXZYZ6DQAAAAAAACRHeAxkUDgojgmQTVP/55R35G2Zp9J//V1GwG/59kxPqToOO10dVeMUOOh4ZhkDAAAAAAAgZwiPgQwLB8hNs7fp+wcu0On/mq/yjatt3Uaw4gD5q2rUMfIHMnsPzEY3AQAAAAAAgJQIj4EM82xcrvED5mr895+V12yz3M40PAoMO1n+qhoFhp4keUqy2EsAAAAAAAAgNcJjIBP8u1S6aqG8vnkq2fierabB3gPVMepi+UdfLLPigCx1EAAAAAAAALCH8BjoBs+X/1Kpb568K56W0b7DVttPe5ygfb87ToHh35FKvFnqIQAAAAAAAJAewmPAro52lf7reXl9c1Wy/m1bTbfsqdSCT36gRV9dohnzhimQpS4CAAAAAAAA3UV4DFhkbPlY3mXz5V3+VxltW2y1XfrVsXp8bY1e+PwMtQfLJElNzWZkcT0AAAAAAADAaQiPgVQCfpV8+LK8vnkq/fhVW03NHr21vPR83fbsJVq9/QhJUkWF1L4t9PvpM0xJIkAGAAAAAACAIxEeA//W4jNVXRUKco3tn8u77HGVLntcnp1f2LqdwKAR8lePU1PLWbpvZnnk8okTDNXVGmpqNiPBMQGy80TvB7loBwAAAAAA4FSEx4CkWY1BPTwnqN/Wv6rv9Z+vkg9flmEGLbf3q6c06vvyV41TcPCoUEA804z8PhwcS3uDYgJk55nVGFTjHGniBHvPR/gLgYZ6U+MbPFnsIQAAAAAAQO4QHqPorXhzk/Tqk/rbaY9pyJb1ko1yxmu2DddjH9fo2U/O1R2n91X14NiZxVJscBxGgOw8LT5TjXNCP9t5PqKf78Y50tgxzEAGAAAAAACFgfC4G3bu3KkVK1bI5/PJ5/Np2bJlWr9+feT3Q4YM0UsvvZTHHqJTpqmST99UqW+ujvvXCzp+hN960xKvVnu/q9+9UKN3Nh8rydDECYaqq6wFx2EEyM5SXWVo4gR7z0ey55vgGAAAAAAAFArC4zQ0NjbqySef1OrVqxUMWi9tAAfYvVXeFU/J2zJPni0f2Woa7Huw/FWX6tH3L9D/ndkvcnk4IG7xWQ+Ow5IFyKNHiQAyT+wE+na+KAAAAAAAAHAjwuM0vPnmm/rggw/y3Q1YZZrybPDJ65un0lULZQT2WG4aVImCh31H/qpxChxygpoeNTqtZVxdZaih3vx3zVzrQWJ0YNlQT3Ccb1YCZIJjAAAAAABQDAiPM6S8vFwjR47U8uXLtWvXrnx3B5LUvlOlq/4mb8s8lWxaaavpxrb99MTHF+updRfpwsGDVTfUWkmK8Q2etGre1tUazDh2kFQBMsFx7rX40qsjnW47AAAAAAAQQnichrKyMlVVVWn06NEaPXq0Ro0apeHDh8vj8ejUU08lPM4zz6ZVoVnGK5+R0b7TcjtThgJDT5K/qkZPvfFtzfh/JZJCwWHzXFPbtu29bqrAMN2wipDLWZIFyHb2A2TGrMbgv2fz26sHHg75G+pNjW/wZLGHAAAAAAAULsLjNNx111357gLi+Xer9F9/D80y/vxdW02DvfqrY9RF8o++RGblQZKkyw+TTGPvDFMCw+IUHyCzH+RWiy9UBkayt6Bk9OzwxjlK62wAAAAAAABAeAyXMzZ/JK9vnrzLn5KxZ6utth0HHq+O6hp1HHa6VNIj4fd1tUbCTNOKCnuzH+F+7Af5U11laOIEawsYhiUrK0JwDAAAAABAegiP4T6BdpWseUnelnkq/eR/bTU1yyrkH3G+/FU1MvcdnvK6Tc2xgaEUmnna1GwSHBYR9oP8srKAYRj1qAEAAAAAyCzCY7iGsXW9vMseU+l7T8iz60tbbQODq+SvHqeOI86UvL26vH58CFVRsbdkgZ3T5+FuVvaDdBc6ZDE366wEyATHsVhkEAAAAACQCawiBGcLBlSyZrF6/vUalc86Qz2WPGg5ODa95fJX1WhX3RNqq52njpE/SCs4njjB0MIFHk2csDdQmT7DVFOzmaw5CoTV/WDSZPv7QlNzqN2sxmDG+lvo6mqNTscgwXGsWY1B9ksAAAAAQEYw8xiOZG7fqODbzeq15M/ybP/cVtvAgCNCs4yPOlcq62OrbaoQys7p83A3O/tB9M8s5pZdycZgfD3qYg+OWWQQAAAAAJBJhMdwlvad6rH4v9Wx8hkp2GF5arxZ0kMdR54lf1WNgvt/TTLshx5WZi8SIBe+dPaD6J9ZzC274h97guNYLDIIAAAAAMgkwmOXMdIIRd2k7G8/Uenaf1q+frDf0NAs4xHnS736SZLSeYSamoMx4cm11xiqq00eXV9xuSHDCGrag3vDGcNQp9eHe3RnP5BS7wt2btstol+PcvnadMXlhprnBmKC44oK6YrL3f14Zoqd16hC3C+RXL7GKwB7GKuAezBeAXdgrHYf4bHLVFZW5rsLWWPu2KQOK8Gxp1TG0WfJc3y9Sod9U2XdHPxvL/Vr2oN7U6jrp5TrqitT10b+0XVSz55tuuvuXZKkaQ+aOuEbvTXmWG+3+oL8ycR+IIX2hZ49y2LazpzdpmkP7r2Oldt2m759++bsvmbObtO2bbtiLtu2TXr8ybKCe1zTlew1qhj3SySXy/EKIH2MVcA9GK+AOzBW08P0IjhHV/WJKw+U5/SbVXrjUpWOe0ieQ0/KyLdGY4716rqJocDETnhy1ZW9dP2UcknSdRN7ERy7XCb2g7C77t6lmbPbJIUCuuhw2W5A9/ZSv+XrZqKd08U/nn377n0NiH7ckbhvZnK/BAAAAAAUB8M0TXvLsSOlU089VevXr5ckDRkyRC+99FJGb7+1tTWjt+c03lfuVI+3Zke2TRkKHHqyOqovU2DoSZKnJGv33eJLb4GodNvBmbqzHyx7z4wpY1FREVuT125JgFmNQc1+2LTdrqk5VLLgyh8aGt+Qve8IDcOIfHO7detWZftwEv67wsKPS2eXIyT+8enufgl3yvV4BZAexirgHoxXwB2KcaxmumoBZStcptB38vaTrldw/yqVb3xH6neIdhx8soL7HLD3Cln8+6tGp/f4ptsOztSd/aBqtCHT7Hwxt8svMyzfdovP1OyHQ9ed9qAp0wxaWgwuevGz2Q+bGnNsMCdfbpimmdVxkGxRt/DjGfp/7+Nu5/EqBvGPT3f2SxSGbI9XAJnBWAXcg/EKuANjNT1MNYKzeEoVOOJMlZz7e5WcdJ3MiiH57hFgS12toYqK2MsqKmQ7yKyuMjRxwt4202eYampOfZBLFrAWwqz4ZH9X/ONZV2v/8SommdovUZxafOmNpXTbAQAAAHAOwmMAyKCmZjNmZqcUmumZTpBpJxC1ErC6kZ2/iwC5c5ncL1FcZjUGNWmy/bHU1Gxq0mRTsxqDWeoZAAAAgFwgPAaADIkPOqNneqYbZFoJRAs1OG7x2f+7kj1exT77MRv7JYpDi89U45zQz3b2leh9rnEOM5ABAAAANyM8BoAMSBbgLlzgychM2GSB6B/+FOz0flMFrG4KcaqrDDXUh362E4hHP14N9cpo6Q63nb6fzf0ShY/yOQAAAABYMA8AuilVgBv+P/z78P92ZwbH387TC6S/Lwpqz5691+kqYA33s6He1PgGd3x3OL7Bo7FjTNvhU12todGjMhscz2oMqnGONHGCvecvX497LvZLFD47+0qhngUBAAAAFDN3pAeAAzlpBqKT+lJscrmYW12tofPP27udTnAsue808nQD4EzPOHbT6fssMohMKubyOQAAAECxIzwG0uCkBYSc1Jdik4/F3P7jpx6VlcVeVlaWesYop5F3n5tO32eRQWRDqn2F4BgAAAAoXITHgE1OmoHopL4Um3wt5tbUbMbMOJZCM5Bv+M/kXwIQ6mSOnaA1X487iwwim5LtK2efF3T0awxn5gAAAADdQ3gM2OSkGYhO6kuxycdibvHPXfQM5DeWJAbIBMeZ5/TT9524yCAKS/wY2LZt7++c9hrDmTkAAABA9xmmaTK1wqb169frjDPOSPq7QCAQs11SUpL0eg8//LCOP/542/e9ZcsW223cxjAMVVZWSpJaW1vl1F3UakCUiyDJSX0pNi0++4u5pdOus+fuhv8M6o0le6/39eOlqXd6cvJcu2WsZkNnj69Txliu9ku4R6bH69nnBWO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zhtrEbbuHGjfve732nRokVdniGwbt06XX311TrmmGP029/+VocddljG+gEgufjxTs60FzOPYUn8NyjRp5Nb0d7eHrPd3VN/Mt0fu98oAU7mtPFq1VtvvaX/+I//iHkz/aMf/SjhAzBQKJw4Vjdt2qQ777wzsn3TTTepX79+3b5dwO2cNl47az9x4sSUx80TTjhBP/3pT2MumzlzZrf6AjiJ08Zq2KpVq3T++efr73//u0zTlGEYOvfcc9XY2KjXX39d7733nl5//XXNmjVL55xzTmSRrnfeeUcXX3xxzFl5ALIjfryTM+1FeAxLysvLY7btfoMS/w2O3dox2e5P/O0Bbua08WrFqlWrdO2118bc92WXXcbiIChoThyrv/nNb7Rt2zZJ0vHHH68f/OAH3b5NoBA4bbwme+9aXl6uH/7wh122veyyy2LO6Hn33Xe1ZcuWbvUHcAqnjVVJ2rp1q66++urIOPN6vZo2bZr++Mc/6sQTT1T//v3l9XrVv39/nXTSSZo6daoeeOABeb1eSaFSHD/60Y+0adOmbvcFQOfixzs5016Ex7Akfqe3W7slulZUaWlpt7+B6W4h8u4WQgeczGnjtSvr1q3TVVddFQmsJOnss8/WL3/5y6zeL5BvThurL7zwghYtWiQp9MH2V7/6VbduDygkThuvPXv2TKiDetxxx6lPnz5dtu3Ro4dOOumkyLZpmnr33Xe71R/AKZw2ViVp+vTp+uKLLyLbP/nJT/Sd73wnZZtTTz1VU6ZMiWy3trbGlHYDkHmZfP1IdntuRngMS/bbb7+Y7Q0bNlhua5qmNm7c2Olt5bo/kmL6U1paqn333bfbfQKcwmnjNZWNGzfqhz/8YcxMim9961u688475fFwiEJhc9pY/f3vfx/5ecKECTr00EO7fZtAoXDaeJWUsODW4YcfbrntEUccEbMd3T/AzZw2Vk3T1FNPPRXZLi8vV11dnaW2V1xxRUz49MwzzygYDHa7TwCS23fffWMWlv38889ttY8/lna1MKabsGAeLIn/APnZZ59Zbvvll1/G1IoZNmxYt/szZMgQlZWVRU4LsNMf0zRj3kQcdNBBkVOCgELgtPHamc2bN6uhoUHr16+PXDZ27Fjdd999jEkUBaeN1ejT1qdPn67p06fbar9kyRKNGDEisn3cccdpzpw53e4X4AROG6+SNHz48JhjaN++fS23jb/u1q1bM9InIN+cNlbXrVunzZs3R7arqqrUs2dPS2179uyp0aNH64033pAkbdu2TR9//HFW358DxaxHjx468MADtXbtWkmhL5/CNcqtiH696dmzp4YMGZKNbuYF07pgyX777ad99tknsr1y5UrLbVesWBGzPXz48G73x+PxaOjQoZHtjz/+2PIpBWvWrImpXZOJ/gBO4rTxmsyOHTt01VVXac2aNZHLRo4cqQcffNDyG2rA7Zw8VgOBgKV/qdoxOwqFxInj9bDDDovZjl+oJ5VCXtQHxc1pY/Wrr76K2R4wYICt9gMHDozZpj45kF3RX0Dt3r1bH374oaV2O3bs0Lp16yLbw4YNsxw6uwHhMSwbM2ZM5Ocvv/xSn3zyiaV2S5cujdk+7rjjMtKfsWPHRn4OBAJqaWmx1O6dd97JSn8AJ3HaeI22e/duXXPNNVq+fHnkssMPP1yzZs2yVKsRKCROHqsAYjltvB5//PEx23ZKT8Sfyt+vX7+M9AlwAieN1fgvZuIX1OpKW1tbzHYh1VAFnCg6Z5IS86PO+Hy+mIkV8bfjdoTHsOzUU0+N2X7uuecstQsvviOFDp7f/OY3s9Kfv//975baxV/vtNNOy0h/ACdx2ngN8/v9mjx5st56663IZYcccohmz57NB1cUJSeN1bfeekvvv/++5X8vvvhiTPvjjz8+5vd/+ctfut0nwEmcNF4l6cQTT4wJkuKDr1TiPwxHl5wB3M5JYzV+bZ3os+6siL9+//79u90nAJ0jZ0qO8BiWnXbaaTF1SB977LGYmlDJvP766/roo48i2yeffHLGvi09/vjjY8KmZ599Vtu2bUvZ5uOPP9Zrr70W2R45cqQOOuigjPQHcBKnjVdJCgaDuummm/TKK69ELtt///3V2NioQYMGZex+ADdx4lgFkJzTxmtZWZlOP/30yPbq1av17rvvdtluzZo1evvttyPbgwYNsrXYHuB0ThqrgwcPjnmf++GHH2rVqlWW2i5btixSe1UKrfvDe2Ygu4YNG6Yjjzwysv3qq6/q448/Ttlm69atWrhwYWR7wIABBXdWIOExLBswYIAuueSSyPa6des0Y8aMTq+/Z88e/fa3v41sG4aha6+9ttPrf/rppzryyCMj/+K/8YnXo0cPXXXVVZHt7du364477uj0+sFgULfeemtMDcbrrrsu5X0AbuW08SpJt912m5599tmYPj788MMFtZAAYJcTxyqA5Jw4Xq+77rqYleF//etfpzwtvqOjQ7fddptM04xcdsUVV3R5P4CbOG2sfuc734nZvu2227qsUb5nzx796le/irmMYziQnujxGh0MdyZ6/AeDwYTjZrw77rhD27dvj2xfffXVMcfmQkB4DFsmTpyo3r17R7bvvfdePfzwwwmL4mzevFlXXXWVVq9eHbns7LPPzvgpcXV1ddp///0j20888YR+//vfJxyMd+zYoRtuuEGvv/565LJjjjkmZrYGUGicNF7/9Kc/ad68eZHtyspKNTY2xix8CRQrJ41VAKk5bbwOGzZMNTU1ke3ly5fr6quvTqhpLIVqv06aNElLliyJXDZkyBDV1tZmtE+AEzhprE6YMCFmJvQ777yj8ePHxyyuFe3DDz9UfX29li1bFrmsrKwsZuIUgOw588wzNXr06Mj2a6+9phtvvFE7d+6MuV57e7tuv/12Pfnkk5HLhgwZossuuyxnfc0Vw0wVnwNJvPzyy7r22mtjDrxDhw7VN77xDVVWVurjjz/W4sWLtXv37sjvDzvsMM2bNy/lYliffvppTF2YIUOG6KWXXuqyP8uXL9fll18es5jAoEGDdPLJJ2vffffVhg0b9NJLL8WUtBg4cKAef/xxDR482PLfDbiRU8Zr/De8hmHI47H//WX8KthAoXDKWLUj/raPP/546hyjKDhtvLa3t+uHP/xhTCmKsrIynXjiiTrssMNkGIY++ugjvfrqq9q1a1fkOr169dIjjzyikSNHWv7bATdx0lidN2+efvnLX8ZcVlJSojFjxmjEiBHq06ePtm/fruXLl2vp0qUJIffvf/97XXDBBVb+bMB11q9frzPOOCPp76IXoZNC4yaZhx9+OGEh2bD4z6Lvv/++pT5dcskl+uqrryKX9e3bV6eeeqr2228/ffnll3rllVe0adOmyO/Ly8v16KOP6qijjury9t2msOZRIydOOeUU3X777brtttsige3atWtj6jFFO/roo3XfffelPAB3x8iRI3XvvffqxhtvVGtrqyTpiy++0GOPPZb0+gceeKDuvfdegmMUBaeN1zDTNBPeCADFzKljFUAip43XHj166IEHHtBPf/pTvfrqq5JCp7wvXrxYixcvTtpm4MCBmjZtGsExCpqTxmr4DIHf/e53kbA6EAhoyZIlMWcDxCsvL9ctt9xCcIyCZuezYWfXy/S82CFDhmjGjBmaPHmy1q9fLylU2/ivf/1r0uv369dPU6dOLcjgWKJsBdJ0wQUX6Mknn9Tpp58ecwpOtIEDB2rSpEmaP3++DjzwwKz251vf+paeeeYZXXDBBerVq1fS6/Tt21dXXHGFnn76aU7xRVFx2ngFkBxjFXAPp43XyspKzZo1S7/61a902GGHpbzeNddco2effTbmlFygUDlprNbU1GjBggW67LLLYkpqJNOnTx9dfvnlWrBggS688MKs9QlA50aNGqWnn35aV1xxhfr27Zv0Or169dIFF1ygZ555Rt/85jdz3MPcoWwFum3Lli1aunSpNmzYoJ07d2rAgAE66KCDdOyxx3Z6SkE27dy5U2+99ZY+//xzbd26Vf3799eQIUM0duxY9ejRI+f9AZzEaeMVQHKMVcA9nDheV61apTVr1mjjxo0KBALq16+fDj/8cI0ePTqtslFAIXDSWA0EAnr//ff1wQcfqLW1Vbt27VJ5ebkqKyt15JFH6ogjjuB4DzhIe3u73nrrLa1fv16bN29W3759tf/++2vs2LFdfhlUCAiPAQAAAAAAAAAJ+NoZAAAAAAAAAJCA8BgAAAAAAAAAkIDwGAAAAAAAAACQgPAYAAAAAAAAAJCA8BgAAAAAAAAAkIDwGAAAAAAAAACQgPAYAAAAAAAAAJCA8BgAAAAAAAAAkIDwGAAAAAAAAACQgPAYAAAAAAAAAJCA8BgAAAAAAAAAkIDwGAAAAAAAAACQgPAYAAAAAAAAAJCA8BgAAAAAAAAAkIDwGAAAAAAAAACQgPAYAAAAAAAAAJCA8BgAAAAAAAAAkIDwGAAAAAAAAACQgPAYAAAAAAAAAJCA8BgAAAAAAAAAkIDwGAAAAAAAAACQgPAYAAAAAAAAAJCA8BgAAAAAAAAAkIDwGAAAAAAAAACQgPAYAAAAAAAAAJCA8BgAAAAAAAAAkIDwGAAAAAAAAACQgPAYAAAAcJgf//jHOvLIIyP/xo8fL9M0LbXdvn27TjvttJj2Dz74YJZ7DAAAgEJEeAwAAAA4zG9/+1sNGTIksv3Pf/5TDz30kKW2v/jFL/Tpp59Gtk888URNmDAh430EAABA4SM8BgAAABymb9++mjp1qkpLSyOX3X333XrnnXdStnv00Ue1aNGiyPaAAQP0hz/8QYZhZK2vAAAAKFyExwAAAIADHXPMMZoyZUpku6OjQzfccIO2bduW9Prvv/++br/99si2YRi68847NWDAgKz3FQAAAIWJ8BgAAABwqKuvvlonnXRSZHv9+vX6xS9+kXC9trY2XX/99dqzZ09M229+85s56ScAAAAKE+ExAAAA4FDh2cMDBw6MXPb888/rkUceibneb37zG61ZsyayHT9rGQAAAEgH4TEAAADgYPvuu6/+8Ic/yOPZ+9b9jjvu0KpVqyRJzzzzjJ544onI7yoqKhLqJQMAAADpIDwGAAAAHO6EE07QNddcE9lub2/XlClTtHLlSt16660x1/3v//5vDRkyJNddBAAAQAEyTNM0890JAAAAAKkFAgFdccUVevvttyOXeb1e+f3+yPZll12m2267LQ+9AwAAQCEiPAYAAABcYsOGDTr//PPV2tqa8LsjjzxSjz32mMrKynLfMQAAABQkylYAAAAALjF48GDdfvvtCZeXl5frrrvuIjgGAABARhEeAwAAAC7S1taWcNkBBxyggw46KA+9AQAAQCEjPAYAAABcYt26dfrlL3+ZcPnq1at155135qFHAAAAKGSExwAAAIAL+P1+XX/99dqxY0fS3//lL3/Riy++mONeAQAAoJARHgMAAAAuMHXqVL333nuR7aOOOkq33XZbzHV+/vOfa8OGDTnuGQAAAAoV4TEAAADgcK+88ooefvjhyHZ4gbzLLrtM55xzTuTy1tZW3XjjjQoEAnnoJQAAAAoN4TEAAADgYF988YV+9rOfyTTNyGW33HKLDj30UEnSr371Kx1yyCGR37355pt64IEHct5PAAAAFB7CYwAAAMChgsGgbrzxRm3evDly2bnnnqsLL7wwst2nTx/96U9/ktfrjVw2bdo0vfnmmzntKwAAAAoP4TEAAADgUA888IDeeOONyPYhhxySUOdYkkaNGqUbb7wxsh0IBHTDDTdoy5YtuegmAAAAChThMQAAAOBAb731Vkz5Ca/Xqz/96U/q06dP0uvX19fr5JNPjmxv3LhRN998c9b7CQAAgMJFeAwAAAA4TGtrq2644YaYhe9uvPFGjRo1qtM2hmHojjvu0KBBgyKXLV68WH/+85+z2lcAAAAULsJjAAAAwGFuvvlmbdiwIbJ9yimn6Ic//GGX7fr3768//vGP8nj2vs3/wx/+oBUrVmSjmwAAAChwhMcAAACAg/z5z3/WSy+9FNkeNGiQbr/9dsvtv/71r2vixImR7fb2dl1//fXauXNnRvsJAACAwmeYpmnmuxMAAAAAAAAAAGdh5jEAAAAAAAAAIAHhMQAAAAAAAAAgAeExAAAAAAAAACAB4TEAAAAAAAAAIAHhMQAAAAAAAAAgAeExAAAAAAAAACAB4TEAAAAAAAAAIAHhMQAAAAAAAAAgAeExAAAAAAAAACAB4TEAAAAAAAAAIAHhMQAAAAAAAAAgAeExAAAAAAAAACAB4TEAAAAAAAAAIAHhMQAAAAAAAAAgAeExAAAAAAAAACAB4TEAAAAAAAAAIAHhMQAAAAAAAAAgAeExAAAAAAAAACAB4TEAAAAAAAAAIAHhMQAAAAAAAAAgAeExAAAAAAAAACAB4TEAAAAAAAAAIAHhMQAAAAAAAAAgAeExAAAAAAAAACAB4TEAAAAAAAAAIAHhMQAAAAAAAAAgAeExAAAAAAAAACAB4TEAAAAAAAAAIMH/B2xZEbuxETG9AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]

From 8e2be8dd24b4456ad5faff1409fb518433363b67 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 07:39:23 +0000
Subject: [PATCH 18/21] renamed sd to sigma in pymc/model.py

---
 pymc/model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pymc/model.py b/pymc/model.py
index b529b3f3cd..52b02fffbe 100644
--- a/pymc/model.py
+++ b/pymc/model.py
@@ -482,7 +482,7 @@ def __init__(self, mean=0, sigma=1, name=''):
                 Normal('v2', mu=mean, sigma=sd)
 
                 # something more complex is allowed, too
-                half_cauchy = HalfCauchy('sd', beta=10, initval=1.)
+                half_cauchy = HalfCauchy('sigma', beta=10, initval=1.)
                 Normal('v3', mu=mean, sigma=half_cauchy)
 
                 # Deterministic variables can be used in usual way

From 8ba28b57114e13ed729cb1c007cf87e05795f2c7 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <purnachandramansingh135@gmail.com>
Date: Tue, 15 Mar 2022 07:56:51 +0000
Subject: [PATCH 19/21] fixed examples/GLM_linear.ipynb

---
 docs/source/learn/examples/GLM_linear.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/learn/examples/GLM_linear.ipynb b/docs/source/learn/examples/GLM_linear.ipynb
index baa506fb94..1765e4eef2 100644
--- a/docs/source/learn/examples/GLM_linear.ipynb
+++ b/docs/source/learn/examples/GLM_linear.ipynb
@@ -135,7 +135,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 700x700 with 1 Axes>"
       ]

From e45e65bd42fce533029855d157be138abbdd591e Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <42216008+purna135@users.noreply.github.com>
Date: Thu, 17 Mar 2022 19:27:28 +0000
Subject: [PATCH 20/21] added a note "sd has been removed" in Release note

---
 RELEASE-NOTES.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/RELEASE-NOTES.md b/RELEASE-NOTES.md
index c1e84d9b4e..e5c219d512 100644
--- a/RELEASE-NOTES.md
+++ b/RELEASE-NOTES.md
@@ -96,6 +96,7 @@ All of the above apply to:
 This includes API changes we did not warn about since at least `3.11.0` (2021-01).
 
 - Setting initial values through `pm.Distribution(testval=...)` is now `pm.Distribution(initval=...)`.
+- In favour of `sigma`, the deprecated `sd` has been removed.
 
 
 ### New features

From 7d01461c5af4401baac7f7007daac41d8b8f12a3 Mon Sep 17 00:00:00 2001
From: Purna Chandra Mansingh <42216008+purna135@users.noreply.github.com>
Date: Fri, 18 Mar 2022 07:52:19 +0000
Subject: [PATCH 21/21] =?UTF-8?q?=E2=9C=94=20Updated=20deprecation=20note?=
 =?UTF-8?q?=20and=20added=20link=20to=20PR.?=
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---
 RELEASE-NOTES.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/RELEASE-NOTES.md b/RELEASE-NOTES.md
index e5c219d512..b126fe99cf 100644
--- a/RELEASE-NOTES.md
+++ b/RELEASE-NOTES.md
@@ -96,7 +96,7 @@ All of the above apply to:
 This includes API changes we did not warn about since at least `3.11.0` (2021-01).
 
 - Setting initial values through `pm.Distribution(testval=...)` is now `pm.Distribution(initval=...)`.
-- In favour of `sigma`, the deprecated `sd` has been removed.
+- Alternative `sd` keyword argument has been removed from all distributions. `sigma` should be used instead (see [#5583](https://github.com/pymc-devs/pymc/pull/5583)).
 
 
 ### New features