From 488bd9cfe06749dbad1b3944678369676a0e2e3a Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Fri, 1 Aug 2025 11:54:23 +0200
Subject: [PATCH 01/28] Add first version of deterministic ADVI

---
 .../inference/deterministic_advi/__init__.py  |   0
 .../inference/deterministic_advi/api.py       | 125 ++++++++++++++++++
 .../inference/deterministic_advi/core.py      | 123 +++++++++++++++++
 .../inference/deterministic_advi/jax.py       |  63 +++++++++
 .../deterministic_advi/optimization.py        |  90 +++++++++++++
 .../deterministic_advi/pymc_to_jax.py         |  81 ++++++++++++
 .../inference/deterministic_advi/utils.py     |  18 +++
 7 files changed, 500 insertions(+)
 create mode 100644 pymc_extras/inference/deterministic_advi/__init__.py
 create mode 100644 pymc_extras/inference/deterministic_advi/api.py
 create mode 100644 pymc_extras/inference/deterministic_advi/core.py
 create mode 100644 pymc_extras/inference/deterministic_advi/jax.py
 create mode 100644 pymc_extras/inference/deterministic_advi/optimization.py
 create mode 100644 pymc_extras/inference/deterministic_advi/pymc_to_jax.py
 create mode 100644 pymc_extras/inference/deterministic_advi/utils.py

diff --git a/pymc_extras/inference/deterministic_advi/__init__.py b/pymc_extras/inference/deterministic_advi/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/pymc_extras/inference/deterministic_advi/api.py b/pymc_extras/inference/deterministic_advi/api.py
new file mode 100644
index 000000000..d627c1882
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/api.py
@@ -0,0 +1,125 @@
+from typing import Callable, Dict
+
+import numpy as np
+import pymc
+import arviz as az
+from jax import vmap
+
+from pymc_extras.inference.deterministic_advi.jax import build_dadvi_funs
+from pymc_extras.inference.deterministic_advi.pymc_to_jax import (
+    get_jax_functions_from_pymc,
+    transform_dadvi_draws,
+)
+from pymc_extras.inference.deterministic_advi.core import (
+    find_dadvi_optimum,
+    get_dadvi_draws,
+    DADVIFuns,
+)
+from pymc_extras.inference.deterministic_advi.utils import opt_callback_fun
+
+
+class DADVIResult:
+    def __init__(
+        self,
+        fixed_draws: np.ndarray,
+        var_params: np.ndarray,
+        unflattening_fun: Callable[[np.ndarray], Dict[str, np.ndarray]],
+        dadvi_funs: DADVIFuns,
+        pymc_model: pymc.Model,  # TODO Check the type here
+    ):
+
+        self.fixed_draws = fixed_draws
+        self.var_params = var_params
+        self.unflattening_fun = unflattening_fun
+        self.dadvi_funs = dadvi_funs
+        self.n_params = self.fixed_draws.shape[1]
+        self.pymc_model = pymc_model
+
+    def get_posterior_means(self) -> Dict[str, np.ndarray]:
+        """
+        Returns a dictionary with posterior means for all parameters.
+        """
+
+        means = np.split(self.var_params, 2)[0]
+        return self.unflattening_fun(means)
+
+    def get_posterior_standard_deviations_mean_field(self) -> Dict[str, np.ndarray]:
+        """
+        Returns a dictionary with posterior standard deviations (not LRVB-corrected, but mean field).
+        """
+
+        log_sds = np.split(self.var_params, 2)[1]
+        sds = np.exp(log_sds)
+        return self.unflattening_fun(sds)
+
+    def get_posterior_draws_mean_field(
+        self,
+        n_draws: int = 1000,
+        seed: int = 2,
+        transform_draws: bool = True,
+    ) -> Dict[str, np.ndarray]:
+        """
+        Returns a dictionary with draws from the posterior.
+        """
+
+        np.random.seed(seed)
+        z = np.random.randn(n_draws, self.n_params)
+        dadvi_draws_flat = get_dadvi_draws(self.var_params, z)
+
+        if transform_draws:
+
+            dadvi_draws = transform_dadvi_draws(
+                self.pymc_model, dadvi_draws_flat, self.unflattening_fun
+            )
+
+        else:
+
+            dadvi_draws = vmap(self.unflattening_fun)(dadvi_draws_flat)
+
+        return dadvi_draws
+
+    def compute_function_on_mean_field_draws(
+        self,
+        function_to_run: Callable[[Dict], np.ndarray],
+        n_draws: int = 1000,
+        seed: int = 2,
+    ):
+        dadvi_dict = self.get_posterior_draws_mean_field(n_draws, seed)
+
+        return vmap(function_to_run)(dadvi_dict)
+
+
+def fit_pymc_dadvi_with_jax(pymc_model, num_fixed_draws=30, seed=2):
+    np.random.seed(seed)
+
+    jax_funs = get_jax_functions_from_pymc(pymc_model)
+    dadvi_funs = build_dadvi_funs(jax_funs["log_posterior_fun"])
+
+    opt_callback_fun.opt_sequence = []
+
+    init_means = np.zeros(jax_funs["n_params"])
+    init_log_vars = np.zeros(jax_funs["n_params"]) - 3
+    init_var_params = np.concatenate([init_means, init_log_vars])
+    zs = np.random.randn(num_fixed_draws, jax_funs["n_params"])
+    opt = find_dadvi_optimum(
+        init_params=init_var_params,
+        zs=zs,
+        dadvi_funs=dadvi_funs,
+        verbose=True,
+        callback_fun=opt_callback_fun,
+    )
+
+    dadvi_result = DADVIResult(
+        fixed_draws=zs,
+        var_params=opt["opt_result"].x,
+        unflattening_fun=jax_funs["unflatten_fun"],
+        dadvi_funs=dadvi_funs,
+        pymc_model=pymc_model,
+    )
+
+    # Get draws and turn into arviz format expected
+    draws = dadvi_result.get_posterior_draws_mean_field(transform_draws=True)
+
+    az_draws = az.convert_to_inference_data(draws)
+
+    return az_draws
diff --git a/pymc_extras/inference/deterministic_advi/core.py b/pymc_extras/inference/deterministic_advi/core.py
new file mode 100644
index 000000000..85e1eca45
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/core.py
@@ -0,0 +1,123 @@
+"""
+Core computations for DADVI.
+"""
+
+from typing import NamedTuple, Callable, Optional, Dict
+
+from scipy.sparse.linalg import LinearOperator
+
+import numpy as np
+from pymc_extras.inference.deterministic_advi.optimization import optimize_with_hvp
+
+
+class DADVIFuns(NamedTuple):
+    """
+    This NamedTuple holds the functions required to run DADVI.
+
+    Args:
+    kl_est_and_grad_fun: Function of eta [variational parameters] and zs [draws].
+        zs should have shape [M, D], where M is number of fixed draws and D is
+        problem dimension. Returns a tuple whose first argument is the estimate
+        of the KL divergence, and the second is its gradient w.r.t. eta.
+    kl_est_hvp_fun: Function of eta, zs, and b, a vector to compute the hvp
+        with. This should return a vector -- the result of the hvp with b.
+    """
+
+    kl_est_and_grad_fun: Callable[[np.ndarray, np.ndarray], np.ndarray]
+    kl_est_hvp_fun: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], np.ndarray]]
+
+
+def find_dadvi_optimum(
+    init_params: np.ndarray,
+    zs: np.ndarray,
+    dadvi_funs: DADVIFuns,
+    opt_method: str = "trust-ncg",
+    callback_fun: Optional[Callable] = None,
+    verbose: bool = False,
+) -> Dict:
+    """
+    Optimises the DADVI objective.
+
+    Args:
+    init_params: The initial variational parameters to use. This should be a
+        vector of length 2D, where D is the problem dimension. The first D
+        entries specify the variational means, while the last D specify the log
+        standard deviations.
+    zs: The fixed draws to use in the optimisation. They must be of shape
+        [M, D], where D is the problem dimension and M is the number of fixed
+        draws.
+    dadvi_funs: The objective to optimise. See the definition of DADVIFuns for
+        more information. The kl_est_and_grad_fun is required for optimisation;
+        the kl_est_hvp_fun is needed only for some optimisers.
+    opt_method: The optimisation method to use. This must be one of the methods
+        listed for scipy.optimize.minimize
+        [https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html].
+        Defaults to trust-ncg, which requires the hvp to be available. For
+        gradient-only optimisation, L-BFGS-B generally works well.
+    callback_fun: If provided, this callback function is passed to
+        scipy.optimize.minimize. See that function's documentation for more.
+    verbose: If True, prints the progress of the optimisation by showing the
+        value and gradient norm at each iteration of the optimizer.
+
+    Returns:
+    A dictionary with entries "opt_result", containing the results of running
+    scipy.optimize.minimize, and "evaluation_count", containing the number of
+    times the hvp and gradient functions were called.
+    """
+
+    val_and_grad_fun = lambda var_params: dadvi_funs.kl_est_and_grad_fun(var_params, zs)
+    hvp_fun = (
+        None
+        if dadvi_funs.kl_est_hvp_fun is None
+        else lambda var_params, b: dadvi_funs.kl_est_hvp_fun(var_params, zs, b)
+    )
+
+    opt_result, eval_count = optimize_with_hvp(
+        val_and_grad_fun,
+        hvp_fun,
+        init_params,
+        opt_method=opt_method,
+        callback_fun=callback_fun,
+        verbose=verbose,
+    )
+
+    to_return = {
+        "opt_result": opt_result,
+        "evaluation_count": eval_count,
+    }
+
+    # TODO: Here I originally had a Newton step check to assess
+    # convergence. Could add this back in.
+
+    return to_return
+
+
+def get_dadvi_draws(var_params: np.ndarray, zs: np.ndarray) -> np.ndarray:
+    """
+    Computes draws from the mean-field variational approximation given
+    variational parameters and a matrix of fixed draws.
+
+    Args:
+        var_params: A vector of shape 2D, the first D entries specifying the
+            means for the D model parameters, and the last D the log standard
+            deviations.
+        zs: A matrix of shape [N, D], containing the draws to use to sample the
+            variational approximation.
+
+    Returns:
+    A matrix of shape [N, D] containing N draws from the variational
+    approximation.
+    """
+
+    # TODO: Could use JAX here
+    means, log_sds = np.split(var_params, 2)
+    sds = np.exp(log_sds)
+
+    draws = means.reshape(1, -1) + zs * sds.reshape(1, -1)
+
+    return draws
+
+
+# TODO -- I think the functions above cover the basic functionality of
+# fixed-draw ADVI. But I have not yet included the LRVB portion of the
+# code, in the interest of keeping it simple. Can add later.
diff --git a/pymc_extras/inference/deterministic_advi/jax.py b/pymc_extras/inference/deterministic_advi/jax.py
new file mode 100644
index 000000000..1e9e63948
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/jax.py
@@ -0,0 +1,63 @@
+from typing import Callable, Dict, Tuple
+import numpy as np
+import jax.numpy as jnp
+from jax import jit, vmap, value_and_grad, jvp, grad
+from functools import partial
+
+from pymc_extras.inference.deterministic_advi.core import DADVIFuns
+from pymc_extras.inference.deterministic_advi.optimization import count_decorator
+
+
+@partial(jit, static_argnums=0)
+def hvp(f, primals, tangents):
+    # Taken (and slightly modified) from:
+    # https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html
+    return jvp(grad(f), (primals,), (tangents,))[1]
+
+
+@jit
+def _make_draws(z, mean, log_sd):
+
+    draw = z * jnp.exp(log_sd) + mean
+
+    return draw
+
+
+@jit
+def _calculate_entropy(log_sds):
+
+    return jnp.sum(log_sds)
+
+
+def build_dadvi_funs(log_posterior_fn: Callable[[jnp.ndarray], float]) -> DADVIFuns:
+    """
+    Builds the DADVIFuns from a log posterior density function written in JAX.
+    """
+
+    def single_log_posterior_fun(cur_z, var_params):
+        means, log_sds = jnp.split(var_params, 2)
+        cur_theta = _make_draws(cur_z, means, log_sds)
+        return log_posterior_fn(cur_theta)
+
+    def log_posterior_expectation(zs, var_params):
+        single_curried = partial(single_log_posterior_fun, var_params=var_params)
+        log_posts = vmap(single_curried)(zs)
+        return jnp.mean(log_posts)
+
+    def full_kl_est(var_params, zs):
+        _, log_sds = jnp.split(var_params, 2)
+        log_posterior = log_posterior_expectation(zs, var_params)
+        entropy = _calculate_entropy(log_sds)
+        return -log_posterior - entropy
+
+    @jit
+    def kl_est_hvp_fun(var_params, zs, b):
+        rel_kl_est = partial(full_kl_est, zs=zs)
+        rel_hvp = lambda x, y: hvp(rel_kl_est, x, y)
+        return rel_hvp(var_params, b)
+
+    kl_est_and_grad_fun = jit(value_and_grad(full_kl_est))
+
+    return DADVIFuns(
+        kl_est_and_grad_fun=kl_est_and_grad_fun, kl_est_hvp_fun=kl_est_hvp_fun
+    )
diff --git a/pymc_extras/inference/deterministic_advi/optimization.py b/pymc_extras/inference/deterministic_advi/optimization.py
new file mode 100644
index 000000000..8c243c9be
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/optimization.py
@@ -0,0 +1,90 @@
+import time
+import numpy as np
+from functools import wraps, partial
+from scipy.optimize import minimize
+
+
+def print_decorator(fun, verbose=True):
+    def result(x):
+
+        value, grad = fun(x)
+
+        if verbose:
+            print(f"'f': {value}, ||grad(f)||: {np.linalg.norm(grad)}", flush=True)
+
+        return value, grad
+
+    return result
+
+
+def count_decorator(function):
+    # If wrapped around a function, the number of calls of the function can be
+    # accessed by calling function.calls on the decorated result.
+    @wraps(function)
+    def new_fun(*args, **kwargs):
+        new_fun.calls += 1
+        return function(*args, **kwargs)
+
+    new_fun.calls = 0
+    return new_fun
+
+
+def time_decorator(function):
+    @wraps(function)
+    def new_fun(*args, **kwargs):
+        start_time = time.time()
+        result = function(*args, **kwargs)
+        end_time = time.time()
+        difference = end_time - start_time
+        new_fun.wall_time = difference
+        return result
+
+    new_fun.wall_time = None
+    return new_fun
+
+
+def optimize_with_hvp(
+    val_grad_fun,
+    hvp_fun,
+    start_params,
+    opt_method="trust-ncg",
+    verbose=False,
+    additional_decorator=None,
+    minimize_kwargs={},
+    callback_fun=None,
+):
+    # Note: "additional_decorator" will be called on the val_and_grad_fun and
+    # its purpose is to allow for additional side effects, particularly saving
+    # call results to files.
+
+    val_grad_fun = (
+        val_grad_fun
+        if additional_decorator is None
+        else additional_decorator(val_grad_fun)
+    )
+
+    decorated = count_decorator(partial(print_decorator, verbose=verbose)(val_grad_fun))
+    hvp_fun = count_decorator(hvp_fun)
+
+    if callback_fun is not None:
+        callback = lambda cur_theta: callback_fun(cur_theta, decorated, hvp_fun)
+    else:
+        callback = None
+
+    result = minimize(
+        decorated,
+        start_params,
+        method=opt_method,
+        hessp=hvp_fun,
+        jac=True,
+        callback=callback,
+        **minimize_kwargs,
+    )
+
+    n_hvp_calls = hvp_fun.calls
+    n_val_and_grad_calls = decorated.calls
+
+    return (
+        result,
+        {"n_hvp_calls": n_hvp_calls, "n_val_and_grad_calls": n_val_and_grad_calls},
+    )
diff --git a/pymc_extras/inference/deterministic_advi/pymc_to_jax.py b/pymc_extras/inference/deterministic_advi/pymc_to_jax.py
new file mode 100644
index 000000000..07438dc5e
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/pymc_to_jax.py
@@ -0,0 +1,81 @@
+from jax.flatten_util import ravel_pytree
+from pymc.sampling.jax import get_jaxified_logp, get_jaxified_graph
+import jax
+from pymc.util import get_default_varnames
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+
+
+def get_logp_fn_dict(logp_fn, var_names):
+    def logp_fn_dict(theta_dict):
+        as_list = [theta_dict[x] for x in var_names]
+
+        return logp_fn(as_list)
+
+    return logp_fn_dict
+
+
+def get_basic_init_from_pymc(pymc_model):
+    logp_fn_jax = get_jaxified_logp(pymc_model)
+
+    rv_names = [rv.name for rv in pymc_model.value_vars]
+    init_point = pymc_model.initial_point()
+    init_state = {rv_name: init_point[rv_name] for rv_name in rv_names}
+
+    return rv_names, init_state, logp_fn_jax
+
+
+def get_jax_functions_from_pymc(pymc_model):
+    """
+    Given a PyMC model, builds functions for computing posterior densities with JAX.
+    Args:
+        pymc_model: The PyMC model object.
+    Returns:
+    A dictionary containing three elements: "log_posterior_fun" is the log posterior
+    density, as a function of a flat parameter vector; "unflatten_fun" turns a flat
+    parameter vector back into a dictionary; and "n_params" is the number of parameters
+    in the model.
+    """
+
+    var_names, init_state, logp_fn_jax = get_basic_init_from_pymc(pymc_model)
+    logp_fn_dict = get_logp_fn_dict(logp_fn_jax, var_names)
+    flat_init, fun = ravel_pytree(init_state)
+
+    def flat_log_post_fun(flat_params):
+        param_dict = fun(flat_params)
+        return logp_fn_dict(param_dict)
+
+    return {
+        "log_posterior_fun": flat_log_post_fun,
+        "unflatten_fun": fun,
+        "n_params": flat_init.shape[0],
+    }
+
+
+def transform_dadvi_draws(
+    pymc_model,
+    flat_dadvi_draws,
+    unflatten_fun,
+    keep_untransformed=False,
+    add_chain_dim=False,
+):
+    # TODO: Maybe should take unflattened draws as input instead
+
+    non_flat = jax.vmap(unflatten_fun)(flat_dadvi_draws)
+    list_version = [non_flat[x.name] for x in pymc_model.value_vars]
+
+    var_names = pymc_model.unobserved_value_vars
+
+    vars_to_sample = list(
+        get_default_varnames(var_names, include_transformed=keep_untransformed)
+    )
+
+    jax_fn = get_jaxified_graph(inputs=pymc_model.value_vars, outputs=vars_to_sample)
+
+    list_res = jax.vmap(jax_fn)(*list_version)
+    samples = {v.name: r for v, r in zip(vars_to_sample, list_res)}
+
+    if add_chain_dim:
+        samples = {x: np.expand_dims(y, axis=0) for x, y in samples.items()}
+
+    return samples
diff --git a/pymc_extras/inference/deterministic_advi/utils.py b/pymc_extras/inference/deterministic_advi/utils.py
new file mode 100644
index 000000000..c2caddf12
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/utils.py
@@ -0,0 +1,18 @@
+from time import time
+
+
+def opt_callback_fun(theta, val_and_grad_fun, hvp_fun):
+    # Callback for jax_advi. It records only what will be needed
+    # to estimate the KL later on.
+
+    opt_callback_fun.opt_sequence.append(
+        {
+            "val_and_grad_calls": val_and_grad_fun.calls,
+            "hvp_calls": hvp_fun.calls,
+            "theta": theta,
+            "time": time(),
+        }
+    )
+
+
+opt_callback_fun.opt_sequence = list()

From f46f1cda52cecd458ee22b109c97abda7f20dca9 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Tue, 12 Aug 2025 18:13:10 +0200
Subject: [PATCH 02/28] Update API

---
 pymc_extras/inference/deterministic_advi/api.py | 15 ++++++++++-----
 pymc_extras/inference/fit.py                    |  2 ++
 2 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/pymc_extras/inference/deterministic_advi/api.py b/pymc_extras/inference/deterministic_advi/api.py
index d627c1882..4c7a4616f 100644
--- a/pymc_extras/inference/deterministic_advi/api.py
+++ b/pymc_extras/inference/deterministic_advi/api.py
@@ -69,7 +69,10 @@ def get_posterior_draws_mean_field(
         if transform_draws:
 
             dadvi_draws = transform_dadvi_draws(
-                self.pymc_model, dadvi_draws_flat, self.unflattening_fun
+                self.pymc_model,
+                dadvi_draws_flat,
+                self.unflattening_fun,
+                add_chain_dim=True,
             )
 
         else:
@@ -89,10 +92,13 @@ def compute_function_on_mean_field_draws(
         return vmap(function_to_run)(dadvi_dict)
 
 
-def fit_pymc_dadvi_with_jax(pymc_model, num_fixed_draws=30, seed=2):
+def fit_deterministic_advi(model=None, num_fixed_draws=30, seed=2):
+
+    model = pymc.modelcontext(model) if model is None else model
+
     np.random.seed(seed)
 
-    jax_funs = get_jax_functions_from_pymc(pymc_model)
+    jax_funs = get_jax_functions_from_pymc(model)
     dadvi_funs = build_dadvi_funs(jax_funs["log_posterior_fun"])
 
     opt_callback_fun.opt_sequence = []
@@ -114,12 +120,11 @@ def fit_pymc_dadvi_with_jax(pymc_model, num_fixed_draws=30, seed=2):
         var_params=opt["opt_result"].x,
         unflattening_fun=jax_funs["unflatten_fun"],
         dadvi_funs=dadvi_funs,
-        pymc_model=pymc_model,
+        pymc_model=model,
     )
 
     # Get draws and turn into arviz format expected
     draws = dadvi_result.get_posterior_draws_mean_field(transform_draws=True)
-
     az_draws = az.convert_to_inference_data(draws)
 
     return az_draws
diff --git a/pymc_extras/inference/fit.py b/pymc_extras/inference/fit.py
index ac51e76bb..aea31679a 100644
--- a/pymc_extras/inference/fit.py
+++ b/pymc_extras/inference/fit.py
@@ -40,3 +40,5 @@ def fit(method: str, **kwargs) -> az.InferenceData:
         from pymc_extras.inference import fit_laplace
 
         return fit_laplace(**kwargs)
+
+    # TODO Add determinstic ADVI

From 894f62bc56f8997e71b8d81e95b438645bdfe8c2 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Thu, 14 Aug 2025 11:07:43 +0200
Subject: [PATCH 03/28] Add a notebook example

---
 notebooks/deterministic_advi_example.ipynb | 1309 ++++++++++++++++++++
 1 file changed, 1309 insertions(+)
 create mode 100644 notebooks/deterministic_advi_example.ipynb

diff --git a/notebooks/deterministic_advi_example.ipynb b/notebooks/deterministic_advi_example.ipynb
new file mode 100644
index 000000000..ef126bb5f
--- /dev/null
+++ b/notebooks/deterministic_advi_example.ipynb
@@ -0,0 +1,1309 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "083415c8-d5bc-4920-a45a-841db7886ac8",
+   "metadata": {},
+   "source": [
+    "### DADVI linear regression example\n",
+    "\n",
+    "This is a really simple example of deterministic ADVI (DADVI) in action.\n",
+    "\n",
+    "It's based on the notebook [here](https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/GLM_linear.html).\n",
+    "\n",
+    "We'll fit the model with both NUTS and DADVI and compare the result.\n",
+    "\n",
+    "Of course, this isn't the greatest use case for DADVI, since the fit with NUTS is super-fast anyway, so there's no reason to do variational inference. But we can check it to make sure they give similar results on a simple model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ea4459ce-54a4-4fd5-bafa-e5cd77f299c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymc as pm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b0f44abd-6fea-4f34-ab98-677b0858327a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on PyMC v5.25.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "import pymc as pm\n",
+    "\n",
+    "from pymc import HalfCauchy, Model, Normal, sample\n",
+    "\n",
+    "print(f\"Running on PyMC v{pm.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "df7fd6b1-4961-444e-91a2-20f74654c8a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RANDOM_SEED = 8927\n",
+    "rng = np.random.default_rng(RANDOM_SEED)\n",
+    "\n",
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "az.style.use(\"arviz-darkgrid\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d9da527a-baec-4a67-b6f5-d769282addbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size = 200\n",
+    "true_intercept = 1\n",
+    "true_slope = 2\n",
+    "\n",
+    "x = np.linspace(0, 1, size)\n",
+    "# y = a + b*x\n",
+    "true_regression_line = true_intercept + true_slope * x\n",
+    "# add noise\n",
+    "y = true_regression_line + rng.normal(scale=0.5, size=size)\n",
+    "\n",
+    "data = pd.DataFrame({\"x\": x, \"y\": y})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "886e6283-e6b9-41c0-b8fb-21c696e1de5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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sXD3TdlHS0W6ptNJnc3P5RfRQB0MDSj76fTV9/zQ13nShkk/f5ThwXJi7v7Z/6s+1bcWD2r7oX1TYZ2HNAseMWW8QPAYAAKiRdMZW19ri31df6zyAPL4OYNdaLnD9jB8tABAO3Oz1VnfPxFIVUjED2fT7hotsW7E//l4Nd31Hc649WQ0P/rNi/c87a2rFlTvkVA2dc50GL/qJssdeJDXO87jDEzFmvUPwGAAAoEZaU5ZWrhjLvHASQC63gAylK/yJHy0AEA7c7PVW6bXN+Axkk+8bLhnZpkS6V43dZ6up93wlH79NVn67o6aFnfbS9o//qQa/er+GF1+h/AEnSlbtQ42MWW8RPAYAAKHi98zPjnbnAWRWHg8OfrQAQHhws9c75b6nDetjxt83Zi721pNq+OnfaM41n9Ls+/5W8beedNTOlqXcgZ/U0BlXavDie5X96Ndk77SHx72tjDHrLYLHAAAgNIKS+ekkgEzgOFj40QIA4cLN3upUugla6XtaMF8EkGshO6zE5tvUeMNSNf3wLCUzfbKyg46aFpp21cjxKzS4/B4Nn32t8gefIsUSHnfYOcasd/yzlQEAAGagNPNTcrboSmnm57EL7ZoE8EpXGh/fZy5og6nSNi3FNgYA/5vquP7lC8aO1909BY7nH1jTVVDXWmnlisnnvkrnvdH/1rms+LiT8yjMWO8+p2SmT8nNt8navnX6BuPk9jtBuVSbcod8RorP8qiH7ig3Zi2roK9/bew5XIOZs2zb5lZOAPT399e7C56zLEstLS2SpIGBAbFrAv7FeIVfmV4M+uHisVzdv/ELyMykT4zV+phuv/LDfgf/YbwC/lV63L70Ektf/9ouuu76IV12+VjWZpSP5+lMcRbXqHLBYSf/7corLG3cJM6TbsiPKPGH+5RI9yrxysNGTe2GZmWPPlPZVJvsXQ7yqIPeKd2vvvmNJl18UaP+87vv6qprwr9vzZvn7mKFBI8DguAxAD9hvMLPnAbm/BTAK+3LqJn2ibFaP1PtX37a7+AvjFfA30qP33PnWtqyheP5eOXOcQvmy7WgMqWdnLG2vKLkxhuV2HSzYoPvGLXN792qbGqpcod9QUrO9qiHtRHlMUvwOKIIHgPwE8Yr/C6ImZ+LFhcmZBw3N0sb1s9seQrGan15mVWO8GG8Av7n1c3eMCl3jbV9u/1BOQtngePS1+pcJi3vZMmuigo5xZ//uZLpXsVf+KUsOT+H2Mkm5Y48XdlUmwp7HOlhJ2svqmOW4HFEETwG4CeMVwRBkDI/yTwOr6j+aIE5xisQDF7c7A2bqTKQRzOHTa7F0pnarEURVNb7byqx8UYlN96k2PuvG7XN73a4sq1LlTvydGnWHI96WH9RHLNuB4/D/W0BAIDIKrfi8qLF/lvUplx26ihWGg++jnZrwjaVitu43vsdAMBcd489IQglFWeUhOFcnc5U9xnKtSt3DbZxU/Fv05v4BI7LsAuKv/grzV6/Wk3fO0UNv/5Px4FjO96g7FFnanDpDRr68q3KtS4NdeA4zGO2lhL17gAAAIBXSldc9lvJACfZ0aw0HmyVfrSwTQEgOCrVTw36uXpNV+GDshJmn2GsrIQ9qaxE6TXY1dfa6um1fXctFiiD7yq5+VYlM+sU2/KSUdPCvA8r29qm7JFnSI0t3vTPZ8I8ZmuNzGMAABBqfs38rJR5Uy5jhwyJ4CGrHADCofR4fukllv7r57vom99o2vFYPY/rM8kaTmeK9Ygls88w/jvpWussA5nAcRVsW7FXH1XDhj/XnO+drIZf/JvjwLEdSyh72Bc0dO73NXjhncp+ZFlkA8ff/EaT/uvnu+jSS7i+rgbBYwAAEGp+nK7mZMomAeRgK7eNN6yPsU0BIGDKn7OLoZSLL2qsezBqTVdBq1abv293j61Vq2399lHb+NxU7juZqryEX2/i+97295T83Y/U+IPFaurrUPLJH8vKZx01LTR/SNs/8U0NfvV+bf/SZcrvf4JkRef7Lnez5+KLGiVJHe1ci1WD4DEAAAgtP2Z+mtT6I4AcTGSVA3CTm7VoYcbZzd76BaPcyhpeMF+OP4NpzWI/3sT3s9gbm9Vwz19pzjUnqeFnf6/4O39w1M62YsodfIqGzrpGgxfdo+zxK2TP2d3j3vpPpZs9o7gWM0fwGAAAhJIfMz/TGbMfXFL5C1wCAv5FVjkAN800q3RNV8GjnoVfEG72tqbM33eqrGEnn6GawLHfbuL7UnZQiY03qfFH56npR+cquelmWbkhR00Lc3bXyAmXanD5vRo+40rlP/wpKRb3uMP+FIQxG1QsmAcAAEJnusxPaeICLuMf91JrylLnMvuDRWmc1/ob3+fOZaw87lemP1qk+uyHAIKhNKtUcnaMKM0qPXahzXnDULU3e6WJx/UF870/Z5ucT6Y7T1V6rZkGjlkUeLLY208rkVmn5OO3yxp536ht7oCPK5tqU/6gT0vxpEc9DI4gjdkgsmzbJrQeAP39/fXugucsy1JLS4skaWBgQOyagH8xXuFnTn/cmP4IclM6U90PedN2jNXaSWeKWX6jnO5PpfvhlVdMXTcS4cZ4rY9aHY+r5VbADubWdBWmvNlbabyOboPOZdLyztpN9p5u25vsG+Uyhk0Wu3OzL6GTG1HimbuVzPQp/uqjRk3t2S3Kzj9b2QVLZM87wKMOBtdUY3a682u9xqyX5s2b5+rrETwOCILHAPyE8Qq/4kf2RIzV2qoUaKgkjD9aYI7xWntBGbNBuCkaVlPdJJhuvNbq5kIpJ9m+4x83eS2nbdlfy7P6X1Ry4zolN98qa8gsvpPfZ6GyqTblDv2clGjwqIfhUG7sOTm/1mvMeoXgcUQRPAbgJ4xX+BGZn5MxVmvP71mM8C/Ga20F7ZxBJqe/+Hm8zjRreLxFiwsT2jY3SxvWT33DhJv4JfJZxZ/7mZKZPiVe/C+jpvasnZQ96gzlUktU2O0wjzoYDX4er15xO3hMzWMAABAK1BOGH1S7/7DfAbVVXGjMrO74VAuN1YKbtWgRbqX7SrWB4+4ee0Lb0dfq7rHLvgY1Z8dY772mZOZGJTbdpNi2t4za5vc8WtnUUuWOWCQlmzzqIWCGzOOAIPMYgJ8wXuFnZH6OYawCwcF4rY+gTbF3M6sU1QvCeDXNGh6v2v0sKKVgPFHIK/7ir4q1jJ97QJZdcNzUTsxW7ojTlE0tVWGv+R52MpqCMF7dRuYxAABABWR+AgCcqpTRO8ovgWPJvaxShJtp1nBp2+nqJk+Vqb+8M6ZjF5rfjO9otwKbcWxte1uJzbcomVmn2NZXjdrmdz1YudT5yh55ujS72aMeAjNH8BgAAAAAEFlBKwnR0W6pp9eelFVa737BHyplDU9XnqXS/u7kRosUkZv4tq34K48okb5BiT/cJ6uQdd40nlTu0M8rm2pTYZ+FkhWgz43IIngMAAAAAIi0coGx0gCtHwLH0syyShFuM8kadnKjxGkAObSGBpR84nYl032K9T9v1LQwd39lU0uUPfosqWkXjzoIeIPgMQAAAAAg8oJQEmImWaUIt5lkDZtk2EcugGzbir2WVjLTp8RTP5GV3+68qRVX/uBTlG1tU37/j0lWQOs5I/IIHgMAAAAAIH+XhJhJVinCbSZZwwvmy7g0S7nXcrtmcd0XQB7ZpsSTP1Yy3af4W08YNS3stJeyC85Vbv65snfec+Z9AeqM2x4AAAAAAKhySQivpTNTv0el4OCC+cV/j7r6Wrsm/YU/mGYNl+4rGzdJncumb1vptTqXuRs4XtNV0KrV5vtxd4+tVattrekqVP3esbeeVMNP/0ZzrvmUZv/0bxwHjm1Zyh34SQ2dcaUGL75X2Y+tInCM0LBs2+asEgD9/f317oLnLMtSS0uLJGlgYEDsmoB/MV6BYGCsAsHBeK2/SiUhJG9LV6zpKqhrbfn3qBQcHP1vncukhgbLd4v7hZVfxms6UwyWjnK6zUv3qSuvKLapa6bvuNdz6zM57ld2WImn71Iy06v4a2mj/haadlVu/jnKLjhP9tx9jdpGVa2zyv0yXmtp3rx5rr4emccAAAAAEAKVMle9aBcm5QK0G9bHapLRm87Y6lpb/j2cBI4lqWstGchOhG2MtKYs17KGqw0Auxk4Hn090/243Dhx0i/r3ec164H/oznXnqzZd3/HKHCc2/d4DZ/2fzX41fs18olvEjh2qJ5Z5ageNY8BAAAAIODGMlfN6t2OZa7aWt4ZzdyimSw05oZisMysFu1UwbLWlCb11+1atEEV1jGyvDOmYxeaZ2R2tFu+3TdMxp1J2Q5JUn5EiT/cp0SmT4mXHzLql93QrOzRZyqbapO9y0FGbTH5RpnkbCyW3iirZn/HzPjvyAcAAAAAcKxS5molpT/I/Zpd6SWnC415ndFrUot2uj57WYs2qMI+RvySNewmJ+POJHBsbXlVs355mZq+d4pm3/kto8Bxfu9WDX/+n7RtxQMaOfk7BI6rVMuscriLzGMAAAAACLCpMlcrZXTxg9x8oTHJ2wzkcu+xcoWlK68YC/I57bOfs0rrgTESTJXGnaOxUMgr/vzPi7WMn/+FLDkP/tvJJuWOPF3Z1BIV9jhqhp8EozzNKodnCB4DAAAAQMDxg9xMOmP+HZT7jt0O0E4VQG5NmW83Ap0TMUaCqdx26+m1Ky5mab3/phKbblJy402Kvfea0fvldztc2dalyh3xJalhp5l/AEziZCwyBv2F4DEAAAAAhAA/yJ0rLjRmf1AD12yhMUkf1MD1JkBbTbAMzkw1RsbfBDAZI48+ltXBVDDwXOl2KzsW7ILiL/1ayXSf4s/eL8vOO359Oz5LucO/qGyqTYW9j5EsxpbXZpxVjpqybNv2Z9EeTNDf31/vLnjOsiy1tLRIkgYGBsSuCfgX4xUIBsYqUB/pjPliPpZl6dnn5mjhR5IzHq9T/fDmB/lk1WyrmbQzUbq9Rrm93fz8HXil3Hc7WovVyRixLEs33dKgyy4f1EUXWrrowmB+D0GzaHFhQuC4uVna0LdFyc23KpnpU2zgJaPXK8w7UNlUm7JHnSk1trjaVzhTOhabm6e4OTADUbwenjdvnquvR/A4IAgeA/ATxisQDIxVoPbWdBWMs1kl6Uc32LrqGltfW9moC84fmfF4rcUPcnivbLBsvXvr3le7v47uX53LpOWd7vWnlqYKzo+q9J2MjtdRV15BPWSvTdxeto7Z5TGdd+A6fWHfuxVX1vHr2LGEcod8VrnUUuX3O54sYx/w+kZZFK+H3Q4eU7YCAAAAAFyQzhTLIEhmC6qN/+H83auHNP/omFILZtYXR9O8YayWWbrdPRNLVUjF7djdY7uy/dzYX7vWSscuDGYGcukYGa/SGCkNdF16CYFjr41+5zsl3tNp+65X28HrdNCcPxi9RqH5Q8ouWKLc/LNlz9ndo56iGh3t1qTSPM3N7i5IipkJ5i1CAAAAAPCZ1pS1Y+q7VAxKdfdUznAqDUR98xtNrgWiOtotNTdPfIwf5NVb01XQqtXTb9NS3T22Vq22taarYNSmNHN8lJP9ygk39tfiYn7B3Z/KjZFKyo3XjnbCKl7q7rH1YN8m/c/W/6l7P/dpfSf1j44Dx7YVU+6gT2vozKs1eNE9yp5wCYFjH6p0owz+wFEOAAAAAFzS0e48IFcuEHXxRY2u9YUf5O4pzdJ1+h2WZummM9O3Kxeg3bA+ZhzodWIm+2sYMtjLjRGp/Pfg9XhFieygfnvdTfrkE+fphpOW6OwDblZjYshR08Kc3TVywqUaXH6vhs/8rvIHnSTF4h53GNWoxY0yzBxlKwAAAADARZVWkR9Vbuq724HjqWoem5QoQFExS7fyNi1VTZZupQCtk/2qGtXsr2EJHFeqCz7+e/B6vGJM7O1nlMisk52+XSfb70kGpVt/89bHtOXQNp247BQpnvSuk3CFk8VdOV/5A8FjAAAAAHBZpYBc+R/M7k0K5Qe5N0yCt9UEW520qUcAOQqB43JjRCp+D6W1WN0er5CUG1HimXuUzPQq/uqjRk3t2S1Kx87S/7zjPL207QDp19LK2ZY62j3qK1xRjxtlqJ5lR2GZwRDo7++vdxc8F8UVMIGgYrwCwVA6Vn+fLtRsoScARdNlN47+YHbr3DpdoC+MgcBa8+I7Nm3j1XZ0ur8Gmen2G8/t8Rp1Vv+LSm5cp+TmW2UNmcU88vssVDbVptyhn5MSDZO225VXBLsed5g5PX65dZyL4nidN88gZd8BMo8BAAAiYE1XQdd/39bKFWaZG6MX7p3LbC3vJNMKMFWaQeVlIK6ematR4naWbjpj3qZcHxbM14yDZbXcX+uhmjEyisUmXZLPKv7cA0pm+pR48VdGTe1Zc5Q98gzlUm0q7H7YhP82frt1Lpv5WIA3TI6RnK/8g8zjgCDzGICfMF6BYBgdq48+ltVXOsciAE4DAGTxAO5ZtLgwIRDX3CxtWD92Q2am51a/ZK5GiZtZumu6Cupaa74dxm7wydUbfNPtr0Fkss9PlX1M5nH1rPdeU3LjTUpsvEmxbW8atc3vcZSyrUuVO3yRNGtOxecyW8q/0hlbq1abn3dmej0axfFK5jEAAACMLPxIUpdeYumqa7xd6AlAed09E2umSsUgY3eP7UrA1k+Zq1HiZpbu8s6Yjl1oHvTqaLdc325e76/1YDJGKpWtGH38yxcE83uoObug+Au/VDLTp/hzD8iyC86bJmYrd/giZVuXqrDXAsftOIb5V2vKUucy2/hGGVnl9UfmcUCQeQzATxivQDCUjtUf/qhQ0xpzAGpX89hvmatREqYs3TDXPHYyRsqd/6TJJSwuvcTS17+2iySuhcuxtr2txOZblcz0Kbb1VaO2+V0PVi61VNkjF0uzmz3qIeqp2uzwattF8bcrmccAAACoipPacQSOAfdMNZ7GP+5WJqOfMlejJExZuib7a9A+mzT9GKl0/lswX9q4aezzX3WNrdmzh3TxRY3edzwobFvxVx5RItOrxDM/lVXIOm8aTyp36OeVTbWpsM9CyQre/gXnqj3fcJ6qH4LHAAAAEeL2Qk8Ayqs0nsqNQ8sq6Otfm9l78oO8tipl6QYtyGq6v45/PEiqCRyPtmtNFf8efd5llw9Kks4926POBsXwFiUfv03JdJ9i/c8bNS3M3U/Z1BJljz5batrFow4CmCmCxwAAABFTLhDQ02uHZmoyUG9ObsSUjkMyGYMlTFm61eyvQflsTpjcOC39Hi67fFDDw5YuOD/434MR21bs9YySmT4lntwgK7/deVMrrvzBn1Y2tVT5Az4mWcEs8QJECcFjAACACHJzoScAY2YaiJLIZPS7MGXpzmR/9ftnc6LaxSYtSzsWob3qGlvzj45IBv/INiWe/LGS6T7F33rCqGlhpz2VXXCecvPPlb3znh51EIAXCB4DAABEVEe7NSnjuLk52IEAoJ6qDURJEwPIhx4SU2qBd/1E9cKUpevG/nr1tXaga2a3pix1LrONF5vsaI9p9uwGXXb5oC660Ars53cq9taTSqZ7lXjiDlnZQcftbFnKH/gJZVNtyh90khQjBAUEESMXAAAgosK00BPgB9UHosYyGb+2slGtqZFIrAYfNGHL0p3J/ioVP1PnsuAGjkdVu9jkxRc16k+OSejgg7b5drymM+afa0e7I7cr8cxdxSzj135v1L7QuIty889RdsF5slv2M35/AP5i2X49ymGC/v7+enfBc5ZlqaWlRZI0MDDg2xMwAMYrEBSVxmqlhZ4kSlcAM1FNwMayLD373Bwt/EiSc6sPpTO2Vq02X1C09Fh75RX+y1KdUYDRZ5+lVoJwLbymq2B8Y0CS7vjBc8o91KfzDrlNs+2t0zcYJ7fv8cq1til38GelxCzTLgOeCMJ4ddu8efNcfT0yjwEAACImTAs9AX5UbUBt4UeSLvcEbglzlm61ffLjZ0FROlPcVyWH5/P8iOLP3q937unV+SMPSQdLchhfsxualT3qDGVTbbJ3PXhmHQfgSwSPAQAAIiRMCz0FCZl9QPBVW96go90KdF1gBE9rytLKFdOfz60tryq58UYlNt2s2ODb2tfgPfJ7pZRtXarcYV+Qko1udR2ADxE8BgAAiIgwLfQUJGNTh82+x9Ht1bnM1vLOmIc9xHQI/mMUWboIiinP50sLij//cyUzvYo//wtZTlOMJdnJJuWOPF3Z1BIV9jjK/U4D8CWCxwAAABHQ3VMI1UJPQWE8dfgD4wP9XWtVVbYj3EHwH0BQjT+f79bwlnI/u1n2mzeqsfC60evkdzusmGV8xOlSw05edBWAjxE8BgAACLlHH8vqqmvMFnoqF0Bm2rU5p1OHxyuXIc73Xh8E/wEEml3QshN/o1P7+3TA0H1KxPJSwWHT+CzlDv+isqk2FfY+RrI4hgFRRfAYAAAg5BZ+JKmLLrR0/fft0C30FAQmmdxOSougdgj+AwikoX4lN9+qZKZPsYGXdLAkOZwAUWg5QNnWpcoedYbUOM/LXgIICILHAAAAEbC8M6aFHymw0FOdOAkgEzj2J4L/AALBthX74++UzPQq8fTdsvIjjpvmlVDhsM8ql2pTfr8TyDIGMAHBYwAAgIhgoaf6qhSEJOjobwT/WTQQ8K3t7yn5xHol0n2Kv/OMUdM/Dn5IP379PF3wz+fInrO7Rx0EEHQEjwEAAIAaKReE7Om1tXXr2HPCFnQMiygH/1k0EONxI8EfYm9sVjLdq8STd8rKDTluV7At/eKNT+nGF5bqv948UQXFVbjdUke7h50FEGgEjwEAAIAaKg1CEjgOjigG/1k0EONxI6HOsoNKPPUTJdN9ir+x0ajptthu+tET5+jWl87Va0MfUnPz2Np5JmMbQPRw1AYAAABqrKPdUnPzxMeam/nhHgQd7ZZWrhjbTmEOHEujiwaOfaarr7XV3WNXaMGigWFVeiNhuv1gVOmNhHTG3vF61fYjamJvP6NZ9/+D5lx7smbf81dGgePc/h/T3S3/oZNu/6m++9RqvTb0Ia1cYWnD+pjx2AYQTQSPAQAAgBrr7pmYrSoVg5D8cA+GqAX/SwPmlYJMYS/hEWVu3khY01XQqtXmwcruHlurVtta01WY/slBlxtR4okfq7GvQ00/WKxZv++Wtf09R03t2XM1srBT2zp/outG1ugvfnCqcnZS0sQxaTK2AUQXZSsAAACAGioNpjQ3j2WvMnU4GCoF/8O67Vg0EJKz/WDUVPsDpVAqswZeUjKzTsnNt8ga6jdqm//QR5RNtSl32OelRIOjMWmyTQFEE8FjAAAAoEam+iE//nF+uPtblIP/UV40EGNmeiOhmMFsFqwMfSmUQk7xZ3+mZKZPiRd/ZdTUnjVH2SPPUC7VpsLuh+143GRMEkAGUAnBYwAAAKAGKv2Q54d7MBD8j+aigZhspjcS3MhgDgPrvdeV3HijEhtvUmzbm0Zt83scqWxqqXJHnCbNmjPhv6Uz5t9ZuW2yYL7CFaQHUBWCxwAAAIDHmDocfAT/x5R+XgLH0TTTGwmRLYViFxR/4VdKZvoUf+5nsmzn9ZvtxGzlDl+kbOtSFfacL1nlv4vWlKXOZcXyICbf2fht0rksmoHjdKa6cijVtgOCgOAxAAAA4CGmDgcfwf/JOtqtSYHCMC8aiPJmeiMhSqVQrMF3lNh0i5Ib1ym25RWjtoVdDla2damyRy6WZjdP30DS8s5YVXWhO9qtyGYcr+kqfBBwNzuWje6rnctsLe+MedhDoD4s27ZZSjMA+vvNCuUHkWVZamlpkSQNDAyIXRPwL8YrEAyM1fpLZ2ytWm0e/CgNmlx5RcjqewaIaQCr2oBX0MZr6eccFfQAH6qzaHFh0o2EDeudB9Eq1RKX/LdfOR6vtq3YK48omelV4pmfyipkHb+HHUsqd+jnlG1tU2GfY6fMMoY7OF+HV9DOr26YN2+eq6/HLREAAADAI8Wpw8W/TacOr1xRfG5Upw77QbV1Q0e3nVTMpExnwvVDtVygb9TV19rq7gnX53Wi2m0chn2ju2diBrpUDPya7Ael48bPgWNHhrco+dgP1LT2S2q6cZmST/3EceC4MHc/bf/ktzW44mfaftq/qbDvcQSOa6C4kOPEY/d0+3DoF3IEPkDZCgAAAMBDTB0OLuqGTsaigZNFeap7pYxh0/0g8KVQbFux1zNKZvqUeHKDrPx2502tuPIHnaxs6/nKH/AxyQrm/hB0LOQIlEfwGAAAAPBYtcHDMAUdg4rg/xgWDZwsnSneXJDMPvP477Jrrarax+rN7RsJlTKYfb0fjWxT4okfK5npVfzNJ4yaFnbaU9kF5yk3/xzZO+/lUQdhIrILOQIVEDwGAAAAgAoI/rNo4FSKU93NPnMYprq7fSPBzQzmWrFff1yFR9aq6fc3yRrZ5rydLOUP/ISyqTblDzpJihGW8ZsoLeQIOMFRCgAAAAAwJZNgSRQDyFGb6u72jYRAlULJDivxzF1KpvuUe+33kiSnPSo07qLc/LOVXbBEdst+nnUR7ii3D5eWVQni+AWqQfAYAAAAAFBWtYsGShODLmEr4VEqKlPd3b6REJRSKFb/80pm1im5+VZZw1uM2ub3PU7ZVJtyh5wqJWZ51EN4oXQfJHCMqCJ4DAAAAAAoi0UDnQv7VHe3byT4vhRKfkTxZ+9XMt2nxMu/MWpqNzQre9QZyqbaZO96sEcdRC0EfiFHwAUEjwEAAAAAU2LRQOfCPNXdzRsJfi6FYm15VcmNNyqx6WbFBt82apvfK6Vs61LlDvuClGz0pH+orcAu5Ai4yLJt257+aai3/v7+enfBc5ZlqaWlRZI0MDAgdk3AvxivQDAwVoHgYLyGS2lwdFRQA8fjpTPmNxLGt0tnbK1abZ6FXfqdXnmFiwsNFvKKv/BzJdN9ij//c1lyPv7sZJNyR3xJ2dQSFfY82p3+wBcqLeQohWM8R0EUz6/z5s1z9fXIPAYAAAAAwEVhnupebcB2tJ2fSqFY77+pxKabldx4o2LvvWbWeM8jFTt+md474LOyZ82ZcV/gL4FayBHwGMFjAAAAAABcxFT3yupaCsUuKP7Sb5TM9Cn+7P2yCjnnTeOzlDvsC8odc752PvJkWZYlDQxIEchkjJKgLOQI1ArBYwAAAAAAXFJpqjuBpjEzzWA2NtSv5ObblMz0KTbwolHTQssByrYuVfaoM6TGebIsqxg4Ruj4fiFHoA4IHgMAAAAA4AKmuvuMbSv2x98pmelV4um7ZeVHnDeNJZQ/+DPKti5Vfr8TJILFoefnhRyBeiJ4DAAAAADADDHV3Ue2v6fkE+uVSPcp/s4zRk0LO++tbGqJckefLXunPTzqIPwmnXEeOB5VblzPuKwK4EMEjwEAAAAAgZLOmNfLnUm76TDV3R9ib2xWMtOnxJN3ysoOOm5ny1L+oJOUTbUpf+AnpVjcw17Cj/y0kCPgNwSPAQAAAACBsaar8EGAxyzwOhrg7Vxma3lnzLX+MNW9zrJDSjy1Qcl0n+JvbDRqWpizm3Lzz1V2wbmym/fxqIMIirou5Aj4GMFjAAAAAEAgpDPFzEDJLPA6PsDbtVZVBYim6g9T3evDeucPSmbWKfn4bbK2v2fUNrf/R5VNLVX+4FOkeNL4vdMZWyd9yriZZ5nvcE/NF3IEAsC9260AAAAAAHioNWVp5YqxIM3V19rq7rErtCifGexWoKc41X3sdU2muo9+Dqa6G8iNKPHknWrs+7LmrD1ds373Q8eBY3v2XI0s7NS2zg0aPrdL+cM+X1XgeE1XQV/704Kuu37IqF13j61Vq22t6SoYvycA1BOZxwAAAACAwDAp/WBSUqJaTHX3njXwUjHLePMtsob6jdrmP/QnyqaWKnfY56VEw4z6MT7z/bLLizWVzz17+nZeZb4DQC0QPAYAAACACPLbonMmnASQaxE4HsVUdw8Ucoo/94CS6V4lXvyVUVN71hxlj1ysXKpNhd0Pd61Lxcz3sf3tsssHNTxs6YLzp96OXma+A0AtEDwGAAAAgIjx26Jz1agUQK5l4Bjust57XcmNNymx8UbFtr1p1Da/x5HFLOMjTpNmzfGkfx3tlixLuuqa4v511TW2bLt+me8A4DWCxwAAAAAQIX5bdG4mygWQe3ptbd069hwCdgFgFxR/4VdKZvoUf+5nsmzndYHtxGzlDl+kbOtSFfacL1neb+uO9phmz27YUbqi3pnvQDWCPPsEtUXwGAAAAAAipHTqvZMAsp+n3pcGkAkcB4c1+I4Sm25RcuM6xba8YtS2sMvByqbalD1qsTR7rkc9nNrFFzVKUtkAMoFj+F0YZp+gdggeAwAAAEDE+G3RuZnqaLcmZRw3N5sFRVAjtq3YK48omelT4pl7ZRWyzpvGksod+jllW9tU2OfYmmQZV3LxRY0aHh7aUcKCzHcEQZhmn6A2CB4DAAAAQAT5bdG5mejumRiwk4oZyN09ti/7G0nDW5R8/HYlM32KvfucUdPC3H2VXbBEuflny27a1aMOVqejPSbbLpD5jsAI2+wTeI/gMQAAAABEVBgWnSvtZ3PzWADPJKsOHrBtxV7fqGSmV4knN8jKb3fe1Iopf9CnlW1dqvwBH5cs/06RJ/MdQRO22SfwFsFjAAAAAIiwIC86N1VQY/zjBJDrYGSbEk/eqWSmV/E3nzBqWthpT2UXnKfc/HNk77yXRx10F5nvCKIwzT6BtwgeAwAAAEDEBXHRuUpBDZOsOrgn9tZTxSzjJ+6QNbLNqG3ugE8o29qm/EEnS7HghCq6ewpkviOwwjD7BN4LzhEZAAAAAOCZIE29dxLUIIBcI9lhJZ65W8l0r+Kv/d6oaaFxF+Xmn63sgiWyW/bzpn8euu76scXyJDLfEUxBnn2C2iB4DAAAAAAIzNR7k2w4AsjesfqfVzKzTsnNt8oa3mLUNr/Pscq2LlXukFOlxCyPeuit664f0mWXD+74N5nvCLIgzj5B7RA8BgAAAKB0xq5q5fRq28FfgrLoXDpjPo26XCBvwXyx31Yjn1X82fuVTPcq8fJvjJraDTsre9SZyqaWyN71EI86WBvdPQVddU35wPEoAsgImiDNPpnJNUtqgQcdCjn/LlcKAAAAoCbWdBW0arWt7h57+ieP091ja9VqW2u6Ch71DLVQLpN3w/qYVq4Y+2F+9bXm+4cXWlOWOpcV/zbJhutot3Z8ns5lBI5NWVtf1axf/YearjtFjT/+M6PAcX6vlIY/9w/atuJBjXz6f4QgcGxPKFVx6SWVM9/9OI6AcirNPvGTaq9Zrrt+SF/70wLXLFWwbNv2116Asvr7++vdBc9ZlqWWlhZJ0sDAgNg1Af9ivALBwFiFE+lMMQA8ymlArjTgeOUVFgG5GajXeJ2uBIRfF0wiU74GCnnFX/i5kuk+xZ//uSw53yftZJNyR3xJ2dQSFfY82sNO1lbp8fKb32jSuWdvn3a8+u14yfhBqUqzTyR/HftNr1ksy9JNtzRMKDNT7zHotXnz5rn6emQeAwAAABHWmjLPjCsXUAzzj7CwcrronB8zJ6vd39hPp2e9/6aSv7lKTWtOVeNtX1Pi+QcdB47zux6q4VP+WttWPKDtp/5tqALH0sTM929+o0kXX9ToqJ2fMt+ZaYJSQZt9Yn7NUphUn5xzgRlqHgMAAAARZ1Kb06+ZqDDDonOYwC4o/vJDxSzjZ++TVcg5bxqfpdxhX1C2dakKex8jWeHeL5Z3xnTcsdJJn3IWOB7V0W7VvdZ2OmOra23xb5NxPP540bVWOnYhGchhUelc4Ndj/0yuWS69xNIF57PvmiJ4DAAAAMDRjzECx+HAonPYYahfyc23KZnpU2zgRaOmhZYDlG1dquxRZ0iN7k6R9rugZr4XszbNgoHMNAkvp7NPpGAGkEs/n9MyM5iM4DEAAAAASZV/jBE4Do/i1PtiBqLponNScb+o99R7zIBtK/ba75VM36DE03fLyo84bxpLKH/wZ5RtbVN+vxMki0qYQcNME0jhmH1ics0yWmZmYGB77TsaAiyYFxAsmAfATxivQDAwVlGtoCycEyb1GK8smhUx299X4on1Smb6FH/7aaOmhZ33Vja1RLmjz5a90x4edTA4wnB+9XqxTI4T/hW2hXKnu2a59BJLX//aLpKCO15Nub1gHpnHAAAAACYozeYhcBxOQZ16DzOxNzYrmelT4sk7ZWUHp2/wAVuW8h/+VDHL+MBPSbG4h71ErZlkbZ5wvFmW6Wj7zmW2lneSne43YZt9Mv01C/vgTJF5HBBkHgPwE8YrEAyMVczUosWFCT/CmpulDev5EeYFxitclR1S4qmfKJnuVfyNjUZNC027KbfgHGUXnCe7eR+POhhsYRqv02Vtjgp6diomC9vsk6muWcI0Xp0i8xgAAACA57p77EkBhK1bi4+TeQz4k/XOH5TMrFPy8dtkbX/PqG1u/48qm1qq/MGnSPGkRz2E31TK2jzheOmhhzXhv7O4XniEafZJpWuWL1/gv/4GDcFjAAAAABNUykTzy0I5AD6QG1HiD/cWs4xf/a1RU7thrrLzz1J2wRLZu3zYow7C7zraLfX02pOyNv/vv8QmnA9YXA9+NN01i2UV9PWv1alzIUHwGAAAAMAOUwUAnAYQADeFbVq1m6yBl5XcuE6JTbcoNvSuUdv8h/5E2VSbcod+XkrO9qiHCAonM00qHf8JHKNenFyzXHWNrdmzh3TxRY316mbgETwGAAAAIKlyAMBJAAFw05quwgcLOrFY1w6FnOLPPaBkuk+JF39p1NSeNUfZIxcrl2pTYffDPeogTPjh5ojJTBMni+sROEatmFyzXHZ5cbHQc8+ucSdDguAxAAAAAEcBAALIqJV0xlbX2uLfJvvZ+P24a6107MJwZCBb772h5MYbldh0k2Lvv2HUNr/7kcq2LlXuiNOkWXM86iFM+eHmSDUzTcY/XlrqgsAxaqWaa5bLLh/U8LClC85nHzVF8BgAAACIOJPMMQLIqIXWlKWVK8z2s9At1mUXFH/xv4q1jJ97QJadd940MVu5wxcpm2pTYa8FkhXg7yGE/HBzZCYzTcotrkfgGLVies1iWcXSFVLx/22baxZTBI8BAACACEtnzKcclwssLJjvzxXYEVwmNyrCNHXeGnxHiU23KLlxnWJbXjFqW9jlYGVTbcoetViaPdejHmKm6n1zZKYzTcotrhfU8YZgqe6aJabZsxt2lK7gmsUcwWMAAAAgwlpTljqX2R9Mn3YecBsfWOhcxo8weCMyi3XZtmKv/lbJdK8Sz9wrq5B13jSWVO7QU4tZxvseR5ZxQNTr5shMZ5r87vfTL66HYPNDLe6pVHvNMrpY3mWXD3LNUgXLtm17+qeh3vr7++vdBc9ZlqWWlhZJ0sDAgNg1Af9ivALBwFiFCT//WIwCxmtlTmqzjn88MIa3Kvn47Upm+hR791mjpoW5+yq7YIly88+W3bSrRx1EOW6O1+n2YTf38XTG1qrV5q9V2odR4xfXm2nf4A9jtbjNtuVYLW7VZKFSk2uP8eP1wZ/3K7XAw475xLx581x9vboEj5999lkdfPDBtX7bQCN4DMBPGK9AMDBWgeBgvE6vNIAV2MCVbSv2+kYlM31KPLVBVm7YeVMrpvxBn1a2danyB3xcsrwP0sxUGG9MuT1ea3lzpNrg4Lf/e0EPPTz279DcwMEObt1cuPIKf9Wbj+L51e3gcV3KVpx22mk67rjjdP755+vUU09VMpmsRzcAAAAAAAER+MW6RrYp8eSdSmZ6FX/zCaOmhTl7KLfgPGUXnCt757086qD7xgKVZjVxx7IY7ZpkMdZbufIQpXWF3drHl3fGjBfa6+6xywaOp+r7+McRHPWuxQ3/qlvN49/+9rf67W9/q1122UXnnHOOlixZon333bde3QEAAAAA+FwQF+uKvfW0EpleJZ9YL2tkm1Hb3AGfULa1TfmDTpZiwVqyKJ0p1iWVzAKK44NRXWtlHOgMqlreHDENHM9kcT0ES1QXKkVldb2FZ9u23nnnHX3ve9/T5z73Oa1YsUI/+9nPIpFCDgAAAAAw090z9WJdvpLbrsTjt6uxt11NPzxDs9I3OA4c243zNHLcxdp20d0aPud7yh/y2cAFjqXRLMaxQNLV19rTbqeoZzF2tFtqbp74WD1vjpgurme6veFPTrYlgeNoqcsZ6KijjtLjjz8uqVh7RJIKhYJ+8Ytf6Be/+IX22msvLVmyROeee6523333enQRAAAAAOAjlWoe+yXT0ep/XsnMOiU33ypreItR2/w+xyrbulS5Q06VErM86mFtkcVoptLNkVp/F+mM+fYot70XzDfLdIY/VBq7jNXoqcuCeZK0efNm9fT0aMOGDRoaGtoRRB7tjmVZisfj+sxnPqOlS5fqYx/7WD266RssmAfATxivQDAwVoHgYLxWVssFxYzls4o/e7+SmV4lXvqNUVO7YWdljzpT2dQS2bse4lEH62+67eSL7WjAi/HqxwUhq11cb6xmtSJRszrM/Lhfmori+dXtBfPqFjwe9f777+u2225TX1+fnnnmmWKnLGtCEFmS9t9/f51//vk666yzNHfu3Lr1t14IHgPwE8YrEAyMVSA4GK9T82vg0dr6RyU3rlNi082KbXvbqG1+zwXKtrYpd/giKdnoUQ/9xdc3AAy5PV79/N2kM9XVnK62HfyndD8cFYSxKkXz/Bq64PF4jz76qG644Qbdc889GhkZKZuNPGvWLH3xi1/U0qVLdcwxx9Sxt7VF8BiAnzBegWBgrALBwXgtz2nwrGZBtkJe8Rd+oWSmT/Hnfy7LLjhuaicalTvyS8qm2lTY82j3+xYAYchilNwdr369OQKMt2hxYdJCpRvWByOrPIrnV7eDx76qur9w4UItXLhQAwMDuvnmm7Vu3Tq9+OKLksaykbdv367bb79dt99+uw477DC1t7fr9NNPV1NTU517DwAAAABwi+liXZKz2rrVsLa9pcSmm5XMrFPsvdeM2uZ3PbRYy/jI06WGnV3pT1CVbqcgBo7d5GQf93rfBqbjp1rcqA9fZR6X8+tf/1o33HCD7r//fuVyubLZyE1NTVq8eLHa2tp0xBFH1LO7niHzGICfMF6BYGCsAsHBeJ0onbG1arV5tmVpMO7KK6zqp87btuIv/0bJdJ/iz94nq5Bz3jQ+S7nDPq9saqkKH/oTySLAMl6Qsxgld8araUYxGciohzDMFoji+TXUZSsqefvtt7Vu3TrddNNN+uMf/zjpv48GlVtbW9Xe3q4vfvGLSiaTte6mZwgeA/ATxisQDIxVIDgYr5PVbbGuoX4lH79dyUyfYv0vGDUttOyvbGqpskefKTW6++M9LIJeP1Wa+Xj1xc0RYBp+rsVtIorn18gGj0fZtq0HHnhA//t//2+99tprEx6XxoLIu+yyiy644AItW7ZMc+bMqUtf3UTwGICfMF6BYGCsAsHBeC2vZot12bZir/1eyXSvEk/fJSs/4rxpLKH8wZ9RtrVN+f1OkKzgZNDWWhiyGCV3xmvdbo4ADoSpFncUz6+RDh5v2bJFt956q/r6+vTCCy9M+u+2be+ojSyN7SB/9Vd/pdNOO63GvXUXwWMAfsJ4BYKBsQoEB+O1Tra/r8QT64sL4L39tFHTws57K7vgPOXmnyN7pz086mB4hCWLUXJvvNbs5ghgwHcLlc5QFM+voV4wbyqPPfaYent7dffdd2tkZGRHkFgayzg+9NBDtdNOO+l3v/udpLEF9vr7+/Xf/tt/0zPPPKM/+7M/q9dHAAAAAAD4ROzNx5VM9ynx5I9lZQcdt7NlKf/hTxWzjA/8lBSLe9jL8KgUZIrygnDVBoAJHMMrflqoFP7h2+Dx+++/r9tvv119fX165plnJI1lFo8GhhOJhD772c/qggsu0HHHHSdJevbZZ9XT06NbbrlFQ0NDO557zTXX6OMf/7iOP/74en4sAAAAAEA9ZIeUeOonxSzj1zNGTQtNuym34Bxl558ne+4+HnUwnJwEowhCAfWXzphnEpcbuwvmc4MjbHwXPN60aZN6e3t15513anh4eFItY9u2tccee2jJkiVqa2vT7rvvPqH9wQcfrL/+67/W17/+df3Lv/yLbr311h1tu7u7CR4DAAAAgEuCMO3eeudZJTN9Sj5+m6zt7xm1ze330WKW8cGnSPFZHvUwvMhiBIKjNWWpc5ltXIt7/NjtXEbgOIx8ETweGhrSj3/8Y/X29urxxx+XNHEBPNu2Zdu2jjvuOF1wwQU69dRTFY9Xnh40b948/dM//ZMGBwd19913S5J+//vfe/o5AAAAACAqxhb8MgvwjS34ZXu34FduRIk/3FvMMn7lEaOmdsNcZeefpeyCJbJ3+bA3/YsAshiB4FneGdOxC81v7nW0W4zVEKtr8Pipp55SX1+f1q9fr23btk0IGI8GjZuamnTGGWeovb1dhxxyiPF7LF26dEfw+N1333W1/wAAAAAQRelMMTtNMssQHZ+J2rVWVQUpKrEGXlZy4zolNt2i2JDZ77/83sco29qm3KFfkJKzXetTVJHFCAQTtbhRqi7B49tuu029vb1Kp9OSymcZH3LIITr//PN15plnas6cOVW/1z77jNWjyufzM+s4AAAAAECtKUsrV5iVGChXwsCVYEMhp/hzDxQXwHvxl0ZN7WSTckctVjbVpsLuR8y8L5iALEYACL66BI//8i//ckegeHyWcTwe12c+8xldcMEFrtUmnq68BQAAAADAnEmNWpPat05Z772hxKablNx4o2Lvv2HUNr/7kcq2LlXuiNOkWdUnK4WZW/WsyWIEgGCre81j27a1++6771gAb4899nD19RsaGnTccce5+poAAAAAAGcBZFcDx3ZB8Rf/S8l0r+LPPSDLdj671I43KHfEomKW8V4pySI4ORVf17MGANRU3YLHowvgtbe369RTT1Ui4U1XdtttN/3whz/05LUBAAAAIOoqBZBdCxwPvqvk5luUzKxTbMvLRk0LuxykbKpN2aPOkGbPNX/viPFrPesocSvrGwDcUJfg8dKlS3XBBRfo0EMPrcfbAwAAAABcVC6A3NNra+vWsecYB45tW7FXf1usZfzMPbIKWedNY0nlDj21mGW873FkGRvwVT3rkDAJ6o7P+jap+0zWNwCv1CV4/Dd/8zf1eFsAAAAAgEdKA8hVB46Htyr5+O1KZvoUe/dZoz4UmvdRNtWm3PyzZTftatQWY+pdzzpMTEqAlMv6dhIMJusbgJfqXvMYAAAAABAOHe3WpIzj5mYHZQ9sW7E3NimZ7lXiqQ2ycsOO39O2Ysof9GllU23KH3iiZJF16Yaa17MOIdMSIKVZ39L0wWCyvjGKcifwCmdVAAAAAIArunsmBo6lYgZyd49dvsHINiUy66TrzlVTzxIlN9/iOHBcmLOHXvrw1zR48U81fMZ/Kv/hTxI4dllHu6WVK8aCSldfa+/YlgSOp1cMBpf//kxs3FT+cbYBRq3pKmjVavP9q7vH1qrVttZ0FTzqGcKAzGMAAAAAqKN0xtYxrcHPFisNZDU3j5WuKM26jL31tBKZXiWfWC9rZJvR++QOOFHZVJt+8PDJuuryuDoHpOWdrnwElOFJPesImUkJkFFkfaMSFrmE17gtCwBARKUz5pkvM2kHAJhsLFvMLOvLb9li5QJZG9bHJmRdXn/dsB5ac7saey9Q0w/P0Kz0DY4Dx3bjPI0cu1zbLrpbw+dcp7WPfFZXfS8uqRj04NzkrdIMZALHZiplcI8qN4bI+oYT1WS4U+4EJsg8BgAggkwWbxmPlbwBwD3js8WuusbW7NlDuviixmnb+S1brFIgq6Pd0tzcCxr5zTqdsf+tatmyRdri/LXz+xyrbOtS5Q45VUrMmvL9CHp4r+p61pBUOQN5umAwWd+YDotcwksEjwEAiBimtgGAP5QujnXZ5YOSpHPPnrqN3wKnUwYh8lnFn71fyUyvzn/7N9Ihzl/zvezOemHuYh18bpsKux3q7P3guUr1rNkGzlRTAqS0DYFjTIVFLuEVUoYAAIgYprZVz+tSH5QSAaKndDr7ZZcPTlnCwm8/+sv158tfek2zfnW5mq47RY0//jMlXvqN49d7M7lA/+t3/1un3nO/vtz7P/SDeyZGnP32+aOkXD3rUdUuAhdV1ZQA6Wi3JnznElnfKI9FLuEFgscAAESQk9p7o7jQLPJ6FWtWyQaiq6Pd0qWXjB1Xr7rGWT3Ueh6L05mx/sSU1z9+5ee6uOlralpzqmY9dLVi29529Dr5WKOy88/V4AU3qelP12nvL56j4XyTJIIefuGknjUBZDOmweBKWd9AqXLX+YsWFziGomqUrQAAIKKY2uac16U+KCUCL6Qz1e0P1bbDzHS0xzR7dsOO0hUm9VDroTVladWX39T239yiZUffqJ0HXpMGnLd/ZuuhemXPJTruojOkhp13PF7NtH54Z7p61pKzGquYyKQESLms79G2fOeYCuVO4CYyjwEAiDCmtjnjdakPSonAbWSyB9PFFzXqm99o2vFvX2aL2bbiL/1GDT/+pi5+77P6+pFXaOfCa86axpPKHnm6Btt+pDfPuk3HXdoxIXA8qppp/XCfk+sAk5lMKDIpAULWN2aCcidwC5nHAABEHFlezni9ijWrZMMtZLIH28UXNWp4eEhXXeOzbLGhfiUfv13JTJ9i/S8YNS207K9sqk3Zo8+SGudJklr3qdymo92adC4i6FE7JucZMpCdm+p7Hf/4+P9O1jdmgkUu4RaCxwAAgKltDnld6oNSInBDMZPdLKhAJru/dLTH9KMb8vUPnNq2Yq/9XslMnxJP/URWfsR5Uyuu/CGfUTbVpvz+H5Uss0mvBD3qZ3w9a6n6G6AL5ovjyDjVlAAp99xRBJBRCeVO4CbKVgAAAElMbXPK61IflBKBG+q5KGY6U9306WrbhVF3T6G+i2Ntf1+J9A1q/OFZauptV/Lx2x0Hjgs7763tH1+twa/er+HTL1f+gI9XFTh2Oq0f7mtNWepcVvzb9Abo6HGncxmB4/GqKQFS6blTtWF8QKLcCdxH5jEAAJBElpcJr0t9UErEP+qx6Jxb71mPTPY1XQV1rZVWrjC78TTaj85ltpZ3Rju/5brrx0pWSLXNFou9+biS6T4lnvyxrOyg43a2LOU//EllU0uV//CnpFi86j6YTOvnGOid5Z2xqkrXdLRbZByXMDnOLphv/vpkfWM8FrmEF6J9ZQYAACSR5VUNrxd0YsGo+qvHonNuv2ctM9lLay07/QyltZajnIF83fVDuuzysaBtTbLFskNKbL5VjT1tauo+R8mN6xwHjgtNu2rk+Es0uPxeDZ91jfIHf9qTwLFEhmU9VBt8JGg5xrQEyPis71FXX2tPe1wk6xsSi1zCO5Zt2+wlAdDf31/vLnjOsiy1tLRIkgYGBsSuCfgX4zVcnGR5jX8cEy1aXJhUl3TDevfuz8/k9Rmr1UtnisHYUU73/9Jxc+UVzmsHe/melWofmryXaV+me12OM2N+dIM9IeO49Ltw+7uy3nlWyUxfsSTF9q3TNxgnt99HlW1tU/7gU6T4rKr7MJ7Tz8c+Az8wOb+Ozchwvq+mM7Y2btIHMzLkeEbGTGa9INg4/04titfD8+bNc/X1KFsBAECEMbVtZrwu9UEpkfqpx6JzXr5nrRbFNDluROmH63RKv4tLL7F0wfkeLI6VG1HiD/cqmelT/JVHjPpoN8xV9ugzlU21yd7lw0Ztp2OyL3BuQtBUUwKkNWWpNSXj8hMEjqOJRS7hNYLHAABElNOpbRI/0svxehVrVsmuv3oEQr18z452a1LtbC8WxaxHreUgK/0uvvmNJp179vaymVHVHpOtLa8omVmnxKabFRt616h/+b2PUba1TblDvyAlZxu1dYKgB6KAEiDwUrHciW2c4T7+WEq5E1RCzWMAACLINMuL2mgTeb2KdZRXya623q1XdXKd7P9uB0K9es9KmezVqPSdV/oM//p/CwSOP1AaOP3mN5p08UWNFduU+27LbotCTvE/3KfZt6xQ05rPadYj33McOLaTTcqm2jT45Vs1dP4Nyh11pieBY2lijVfToAc1XgGgaHlnTFdeYX4+7Wi3dOUVVuQXq0Vl1DwOCGoeA/ATxmuw1aOWa5hMF7SbaSDRzdcP2litpi6kNPadmNSFNFWP2uBuvqfbNY+dbqvS921okLZvr/59w2j0u7z0Ektf/9oukpyN16n2e+u9N5TYdJOSG29U7P03jPqS3/0IZVuXKnfEl6RZc8w/zAxUW6uVGq+oh6CdX4Eoi+J4dbvmMcHjgCB4DMBPGK/B5+cgnZ95vaCT268fpLEahJsatVp0zu33dDvwbbqtSt9n1BmnS3/+7egdR8pJZ2wd0xozHq87Aqd2QfEXf61kplfxZ38my847fm873qDcEYuUTbWpsFdKsgjEAtMJ0vkViLoojleCxxFF8BiAnzBew4EsLzNer2LtxfODNlaDsFL4VIFQL997Ju/pVaa8abvPfL4wIeO4oUG6724Cx+NVNV4H31Vy8y1KZtYptuVlo/crzPuwsq1tyh51pjR7rnmHgQgL2vkViLIojle3g8dcsQEAEFEs3uJctQs6OapLWoPXDwqT+tr1WnCto91Sc/PEx7xYdM6N93S6KGY1tbRN2n37v08MHEvF0hVhrNldE7at2CuPqOHO/6Y53ztZDb/4v44Dx3YsqezhX9TQeWs1eOGdyn5kGYFjAABQEcFjAEBd+W1xLKAcrxd0YsGoMfVYoM6E24vOefWetVgU00m7b//3gh56eOzfDQ2q+HxUMLxVyd91q/EHp6tp3VeUfOpOWfmso6aF5n20/RPf0uBX79f20/5d+f2OpzwFAABwhLIVAUHZCgB+4tZ4pe4ugsbrUh9uv36Qz631WKDOtE9+rXlc6/rRU22T0sDxCcdL//dfYnXdhn421XiNvb5RyXSvEk9tkJUbdvx6thVT/qCTlU0tVf7AEyWL8yXgliCfX4GoieJ4peZxRBE8BuAnbozXICyOBQRd0M+t9QjWOu1LLYLZM3nPWt+cK+1TQ4MmlKoYDRxP99mibMJ4ffNVxZ+8U8lMn+JvbDZ6ncKc3ZVbcJ6yC86VvfPeHvQUQNDPr0CURHG8uh08Trj6agAAONSasrRyhXYED0b/33RxLALHgD94kZU9ejwYHfd+CxyX66OTY1kt3nN5Z0zHLjTfJh3tlhbMNy+BUtqnSoFjp58hiuzXn1Dhtz9Q0+9ulDXyvlHb3AEnKptqU/6gk6V40psOAgCAyCHzOCDIPAbgJ26OV6fZZ2SpAeZqdW71Ost10eLChMBxc7O0YX1tpuDX4xgV5OPiZz4/cXG8hgbpvrun3lZ+/Aw1l9uuxNN3F7OM//iYUVN7douy889WdsES2fMO8KiDAErx2xUIjiiOV7czjyl8BQCoq3otjsVCfYA70hlbXWuLf5ssgDZ+XHetnXps1WOBuvHv7fWic354T7f8679PDBxLxQzkSn0q9xmicpy1+l/QrAf/VXOuPVmz7/oLo8Bxfp+FGv7iv2rbigc18qk/J3AMAAA8Q/AYAFB3lQIgXgSO13QVtGq1eZClu6dYp3lNV2FG7w+ESbEEjVkA02kJmnI1j03eZybSGfNjz0wDofV4T7d099i6ff3Yvxsaxv6ebluN/wydy8xLZgRKPqv40/do9k0XaU7XFzXr0etlDQ84amrP2kkjx1ygwa+s11Bbt3JHfklKzPK2vwAAIPKoeQwA8IVy9S97em3Xa5yWZkmOf+9KSrMkq6klCoSVSf3amZZkGP+4l3VyW1OWOpfZxuU4xn8XpoHQerynG0q31RmLpT//VsxoW1VbazkorK1/VHLjjUpsukmxbW8btc3vOV/ZVJtyRyySkk0e9RAAAKA8ah4HBDWPAfiJl+O1NAgxys06mKbZzNTkRFDV+tw63Vhxq5ZvLcekFwsB+vE9q+WnbeU7hbziL/yyWMv4+Qdl2c5nrdiJRuWOOE3ZVJsKe833sJMAqsFvVyA4ojhe3a55TOYxAMBXOtqtSRnHzc3uZhZ6kSUJoPLYcnMROJMxPFPVBmNnEsStx3tWw2/byi+sbW8psekWJTeuU2zrH80a73G4Yscv03sHnip71k7edBAAAMAAwWMAgK9UWhyr1gFkAseAuZmUoDFdLK70fcY/Dm+xrUrYtuIvP6REpk+JP/xUViHnvGk8qdyhX1Cudal2PvoUWZYlDQxIEciMAgAA/kfwGADgG+UWxxoNOHkRbHAjSxLAZKVjy0nguNrF4sa/z9XX2qGum+sXbKtxhgaUfPw2JTN9ivW/YNS0MHd/ZVvblD36LKlxnizLKgaOAQAAfITgMQDAF+q1OFatFuoDosa0BE1QF4uLoshvK9tW7LXfK5npU+Kpn8jKjzhvasWVP/gUZVuXKr//RyUr5mFHAQAAZo7gMQCg7ipl+dZiunM1WZKoXpAWA0P1qilBs7wzpmMXmm/njnYrHFmsARLJbbX9fSWevEPJdJ/ibz9l1LSw017Kps5Tbv65snfaw6MOApVx/gUAVINb3QCAunK64NLKFWOPXX2tre4ed2tBdrRbam6e+JjbC/VBWtNV0KrV5tuvu8fWqtW21nQVPOoZ3FSuBM2o6cZvUBaLQ3S2VezNJ9Tw07/RnGtP0uz7/s5x4NiWpdyHP6WhM76rwYvvVfajXyNwjLrh/AsAqBbBYwBA3ZguuORlALlSlmQl6Ux1fai2XZClM8Vp7pLZ9hu/n3StjeZ3FyTlxvWG9THPbwABrsoOKbH5VjX2tKmp+2wlM32ysoOOmhaadtXI8ZdocPm9Gj7rGuUP/rQUY8In6ofzLwBgJggeAwDqotoFl0oDUG78kKk2S5IsHjOtKfMbAOUCkUHLWoyS6UrQEECejBtQ/mK986xm/ewfNefakzX77v+h+OsZx21z+52g4dP+XYNfvV8jn/gz2XP38bCngHOcfwEAM0HwGABQF8UFl4p/my64NPoDyI0Fl6rNkiSLpzomAUSTzHTUn19K0AQJN6B8Ij+ixFMbNHvdMs1Z+yXN+t0PZW3fOn07SXbDXI18ZJm2XXinhs/7vnKHf1GKz/K4w4A5zr8AgGoxfwoAUDf1XnBpJgv1FbN4zBbyI4unyMkiiPxwDRbTEjSSt4tgBkHpDSjJ2XdQegOqmmMoiqwtryiZWafE5lsUG3zHqG1+71ZlW5cqd+gXpORsj3oIuIvzLwCgGgSPAQB1Va8Fl5xmSUpT/8gyCYLxY2yiSt8d31WwVFuCRpq4/d24IRQk3ICqk0JO8eceVDLTp/gLv5Ql51nfdrJJuSNPVzbVpsIeR3rYScA7nH8BAKYIHgMAIsfNLEmyeKpX7rvr6Z24cCHflf8VS9AUs2hNS9BIxe3uRgmaIOIGVO1Y772hxKablNx4k2Lvv27UNr/7EcUs4yO+JM2a41EPgdrh/AsAMEHwGAAQKV5kSZLFU73S744frsFU7xI0QcYNKA/ZBcVf/LWSmV7Fn/2ZLDvvvGm8QbnDv1jMMt67VbL4vhEunH8BAE5Ztm1Hd5WSAOnv7693FzxnWZZaWlokSQMDA2LXBPwr6ON1TVfBOEtSGgvgdC4rBsum+u+jmpv5MebUosWFCd9Vc7O0YT3r+s5U0MdqlEwVICZwXIXBd5XcfKuSmXWKbXnJqGlh3oeVbW1T9sgzpMYWb/o3BcYr6sHN8286U10N9mrb1RPjFQiOKI7XefPmufp6ZB4DACLHqyxJsniq090zcaqsVPzuuntsvjNEBtPIZ8i29fzPf6sjtq1T4pm7ZeWzjpsWrITyh52qXGqp8vseR5YxIsPN8+/YjXmzBVDHbszbZW/MAwDqj+AxACCSvFqor6PdmhTwaW42+yEVJZWytZ0sIAaECTegqjC8Vckn1uv9B3u1oPCsUdNC8z56pHCevnPzWTqjZTct34/AFaLDzfNvOlOse2/adnwfutaqqhv7AADvcYUEAICLKmXxYKJy0/E3rI9p5YqxH45XX2vz3SFSOtotNTdPfIwbUJPFXt+khnv+SnOuPVkNP/sH7eowcGxbMeUOPkVDZ12j7zXepUt6vqp3t++mrrXFABgQBW6ff1tTlnHbcn0gcAwA/kTmMQAALiGL1rlKdVydLCAGhBVlXCrIDirx5J1KZvoUf2OzUdPCnN2VW3CesgvOlb3z3sVj0PcIXCF6vDr/mrSlljsABAvBYwAAXOBksSuCoEVOfjQSQEYUhf0GVLWLYj3zX0/r6KE+JZ9YL2vkfaO2uQM+rmyqTfmDPq305oRad2YRQkSX1+dfJ23dHn9RWqQPAOqFshUAAMzQdFk8lGEYY/Kjke8OURL2Mi5rugpatdqg/7ntSjxxh977fxfoT35zhmalexwHjvu3t+j7f+jU6fdt0HXbr1P+0M+puy+hVattfeNbBQLHiKRanX8rtXU7cGx8XPlAd4+tVattrekqVP3eABAllm3bwbwCjZj+/v56d8FzlmWppaVFkjQwMCB2TcC/GK9jnP4QItOtmOWzarX5d/Cv/17Q7evH/n3lFc6ml5NVxFgNiumOD0E/fpiMfav/BSUzNyq5+RZZwwNG75PfZ6GyqaX64WOf1ZXfm7Xj8ROOlx56ePLz/fY9Ml7hlWrPv6XHHqfn33Jtx8+kMOnDVOrxmcZjvALBEcXxOm/ePFdfj8xjAACqRBatmdaUpc5lxb+d/shb01UMHJ9wfPHfncvk6EceWUUICqfTyIN8/Jh2Ma18VvGn79Hsmy7SnK4vataj1zsOHG+3dtLIMRdo8Cu3a6itW7kjv6TzL5g94f2CEDgGvFTN+VeaeOxxev4t11ZyN3AssUgfANQSmccBQeYxAD9hvNY/4yXInGYEl37HZ5wu/fm3p7/vzXc8hrHqb6YZxUHPQC7t/39b/pqWHHSzEptuUmzbW0avtXngaN34QpvuevWLunD5nLLfw7f/eyFQgWPGK7xWj/rAixYXJgSOm5ulDevdy2Gr1wwwxisQHFEcr2QeAwDgA/XI4gkLp5+5NKvo9jtEVhFCI50xD2SUy0BOZ4LzA6ij3dKlXy3oE3v8XP9x/Cqd/+bnNOuhqxwHju1Eo7Lzz9Fg+436xRE36raXztFwvqlsxmF3j102cNzcHOxFB4HxTMf/6Pmw2namunvsCYFjqZiB7ObMCSczM4J+4w0A6i1R7w4AABBUyztjOnaheTZOR7ulBfOjGTg2ZbLqOz8OESTFG1C2utaa34CSimMhSDegrG1vKbHpFn11aJ1iH/2jUdv8rocol1qq7JGnS7ObJUkd7cX/Vu7YUHosGG80cMWxAUG3pqvwwfHD7IbI6PjoXGZread3uWSVah5XOpdXo9K1AtcGADBzlK0ICMpWAPATxitqLewLinmFsep/9ZhGXjO2rfjLDyuR6VXiDz+VVcg5bppXUoUjvqBsa5sKH/qIZJX/rNMtyjXV4348RjBe4ZTfS2dNdU72+lzt9SJ94zFegeCI4nilbAUAAIicStNSCRwjyKoN3Pg6cDw0oOSj31fT9xep8aYLlXz6LseB45e27adf7/xtDa18QNsX/YsK+yycMnAsVV6Ua9TKFZY2rI8FetFBYDw/LxZX6Zzs9eKfXi/Sh4mqLZsUpHJLAIrIPA4IMo8B+AnjFfVSy6yiMGCsomZsW7HX0kpmepV46i5Z+e2Om+YKcT3w+qd104ttemL7R3XnevPKeqWLco0K0iwFxitM1WuxOL/3x+tF+iTG61jZFLNtN1Y2RZ6WTQHGi+J4dTvzmJrHAAAgMErrGhI4BupsZJsST9yhZKZP8beeNGr6fmwvrX38XN324jl6a/seOx43rUlcblEuSTrh+Mk1VU3qqAN+52R/9lvg2Gm/Z9KPqRbpY5y7I50p1uuXzLbd+H2ka62qWjcEQH1wqwcAAB9jSuBkHe2WmpsnPtbc7G7wh+8dGFNuv469+YQafvo3mnPNpzT7vr91HDi2ZSl34Cf1k5b/1Em3363vPX2p3tq+x4QxbTKVvdLieA89rLKv4/XUeaCW/FDWKZ0xf59y/Z7pObTc7KTxr884d4efy6YA8AbBYwAAfGpNV0GrVpv/2OnuKS6ks6ar4FHP6qtSVpEb+N6BMRPGQ3ZYic23qbGnTU3dZyuZ6ZOVHXT0OoWmXTVy/AoNLr9H1w1fo+/84NPK28VJkNXWJJ4qcHzC8WN/T/U6XgSugHoptz8vWlyoWYmW1pSlzmXm7zO+353LZlbLvVxwklrn3jG5CefnckEAnKFsxQwMDAzo6aef1osvvrijbsrcuXP1oQ99SMccc4x23nnnencRABBQTAksr1LNYzemvfK9A2NGx8OBOz2nnX61Tsk3bleDXaY+RAUPv3W8mk9p076nfFaKz5p2MS3J2VT20tdZ+BHp0cfGXm/8f5/qdca/30wDV0C91bus0/LOWFXnvo52Swvmux84rua4AjN+KpsCwFssmGegUCjot7/9re6991795je/0dNPPz3lcy3L0sc+9jFdeOGFOumkk2b83iyYB6Aa6Ux1Aazp2jFea8P0gjvsF+hTfT63P3eYvnfGKqqWH1HiDz/VO/f2aZ+Rh42abh1p1vqXz9BNLy7RF9oP3jEe3FpMa6r/Xnrucvp+1Z4rZ9K2XDvGK9xQi8Xi/KRei/QxXsfU6voMqFYUx6vbC+YRPDbwuc99Ti+++KJxu9NOO01/93d/p5122qnq9yZ4DMCUl6sgM15rxy8rl9dbtcEkr97Pq/d1G2MVpqwtryi58UYlNt2s2OA7Rm1fT7bqyoeX6J5Xv6DthdkTxoNbN2X8dHPH7fMs4xUzNVUpF7+dm9xSz+MB43WiSjPDpPDugwiGKI5Xt4PHlK0w8O6770567MADD1QqldJuu+2mhoYGvf766/r1r3+t119/fcdz7rzzTr355ptas2aNGhoaatllABHF1PvwYEqgs8/n9rRUvndESiGn+PM/VzLdq/gLv5Ql5z+q7GSTckeertteXaJ/6Dpix+Pjx0O1i2lJE8dgY6Otq68de061rzPTKfIS51n4j9dlnfzGreOKG8cD1L9sCgBvETyuwj777KPzzjtPZ511lvbaa69J/z2fz2vdunX6p3/6J23fvl2S9Mgjj+g//uM/9Bd/8Re17i6ACCqugmwWSGMVZP+qFMgMewDT5PPVMoAc9u8d0WC994YSm25ScuNNir3/+vQNxnlqy2F6Y7+l+shXFqv7piZd3TX1eCgupmUbZ+mW1iQ+56yYBgbMs329qG3MeRZ+4iRTP2wBZLeOK4xB93S0W+rptSeVTQnLPgdEGWUrDCxevFjLli3TmWeeqXg8Pu3zH3zwQa1cuVKFQnHV9WQyqfvuu0977rmn8XtTtgJANbyaes94rY+oTQlMZ2ytWm0eoC39nq68YmYBmiB/74xVTGIXFH/p18Us42d/JsvOO28ab9DTyS/q7+9doo39KUmW0Xhwqz6wV/X8q+HmeZbximrUuqyT39TreMB4nSxqZVMQHFEcr5StqKNbbrlFiYTzr+ykk07SaaedpjvuuEOSlM1mdd9996m9vd2rLgLABEy9D5eoTQn0S1ZR1L53hNTgu0puvlXJzDrFtrxk1LQw78PKtrYpe+QZ2qexRSe22NpYxXiodiyWtnPrddzAeRb1VI+yTn7jp+NBlEWtbAoQNQSPDZgEjkeNDx5L0saNG93sEgBMi6n34RK1KYHLO2NV1QTtaLdcrWMYte8dIWHbiv3xMSXTvUo8c7esfNZ501hCuUM+q1xqqfL7HS9ZY/s642EizrOoh3qWdQLGi2LZFCBqCB57bP/995/w77fffrtOPQEQZeV+NJT+8OcHbTB090zcblIxs6O7xw7t9vNDVlHQvnc/TetHHWx/T8nH1yuR6VX8nT8YNS00f0jZVJtyR58le87uZZ8TtPFQC5xnUUssFge/qHQTg5sWQHjE6t2BsNu2bduEf1eTvQwAbuhot7RyxdjFGj9o/SWdmb72VrkpgaOuvtZWd0/463fVQ9C+9zVdBa1abd6v7p5ijek1XQWPeuYNJ2PHzXZ+Fnt9kxru+SvNueYkNfzs7x0Hjm0rptxBn9bQmVdr8KJ7lD1+RcXAcZDGQy1xnkWtFMs6Ff82Les0uo+yWBxmymnZlPHHxXqcJ7hOAGaO4LHHnnrqqQn/3muvverUEwAoXsCN/6EvRXuqsV84CfaVu0BvXzpxu0U9cOOFct/7hvWxuv8Qmko6U6wRLY32y1kgePzn7FobnB9MUQuUl5UdVGLjTWr80blq6jlPyU03y8oNOWpamLO7Rk64VIPL79Xwmd9V/qCTpNjUi0IHbTzUA+dZ1MryzpiuvML8pkRHu6Urr7C0vJNQAKpnWjalXucJrhMAd3DG8Nj69esn/PujH/1onXoCAJWnGqM+Jgf7Jm+Lchfoo88vFfXAjZumm4rpx4BZa2piv666xtZ111cOJJb7nEHIRnMydsoJaqC8VOztpzXr/r/XnGtO0ux7/1rxNzY7bps74OMaOv1yDV58n0ZOXC27+UPTtgnieKgHzrOoJT+UdUL0VFs2pfQ84fX5N+rXCYCbqKHgoYcfflgPP/zwjn/vvPPO+sQnPlHVa1lW+E/w4z9jFD4vUGvdPYWKqyBbltTR7uyeIuPVPce0Wrr0koKuumasHtz4bVG63S69pPh9jz6/3GOm2xOTlfveS7/PL19gybKm3nb1Utqvyy4flCSdd87kserkc/rVdGOnnHKf95jWYHxeSVJuu+LP3KNkulfxVx81amrPblF2/tnKpZbInnegJCnjsMZ1kMdDLc30PMu5FSgKQt3+KI/XY1otXXRhQdd/3za6bhh/nrjoQu/Pv5G8TkBZUR6vbrFs2+Y2igeGhoZ05pln6oUXXtjx2OrVq7Vq1ar6dQpAZF13/dCOAJIkffMbTbr4osYpH0ftldsWkhw9NrrN2J7uMP0e/fq9T9cvv/bblNPPEeTPa7/zvAqP/ECF3/VJg+8atbUOOEGx474i66jTZCVn73h89Pv42spGrbq0acr2YRkPXuM8C7jjyqsG9d2rh4zHitNjGtzz6GNZLfxIsmbtqhWF6wTAawSPPfKXf/mXuvXWW3f8+6CDDtJtt92mhoaGOvYKQBRFJYAUBqXbYrzpAsdTvcYPuppreoEedI8+ltVXOsfmnDsdD3793qMS0Arjcc7OZ2U/eY8Kj/xA9rMPmjVu2FmxY85T7Lgvy9rzyEn/2en+Grbx4JUw7n9APXDMgVc4TgMzQ/DYA2vXrtU//uM/7vj3rFmzdMMNN2j+/PlVv+bAwIALPfM3y7I0d+5cSdKWLVvErgnMXHdPYVJ5g3JTtZw+bxTj1Tul20KaulTFVNto9DUuupAFcaqxpst8Kqbkz+/dsizdePOsCT+Ixk+ll4JVqqKSqY5jpse3erO2vqbExhuV2HijYtveMmr7+MBReuuApTr2K6dJs+aUfY7p9xGm8eAFN8+znFsB82NUvY7xjNfgCct1AsxFcby2tLS4+noEj11211136Zvf/KYKhbFVOf/P//k/Ouuss2b0uv39/TPtmu9ZlrVjBx8YGIjEgAa8ZLIKsunzGa/eKt0WpcE+JwuT1LLuXxgFod6iE6Njdaqsdif7UpC4MXbqopBX/MVfFWsZP/+gLNv56u52YraeTJ6m/33XEj2+pZioMNXnND0vjArLeHCb2+dZzq1AkdOxVe0xzQ2M12AK7HUCZiSK43XevHmuvh7BYxf9+te/1ooVKzQyMrLjsW9/+9tasWLFjF+b4DEAE+mMrVWrzS+mSy+orrzCKvvDn/HqvdJtMYqLWpgYP1Y/9sl3JvxAam6WNqwPX2ZNkMaOte1tJTbfomRmnWJbXzVqm9/1YOVS5yt75OnS7OZpgyj1DLKEkRfnWc6twBi/H9MYr8EVpOsEuCOK49Xt4HH4fjHUSSaT0de+9rUJgePly5e7EjgGAFOtKUudy4p/m1wIdbRbWrmi+NzOZQp1xpjfdbRbam6e+Fhzs7ioRVWuu35oQuBYKmbadPeE7+LZ92PHthV/6SE1/PibavreKWr45WWOA8d2PKnsEV/S4JIfaugrdyj7JxdIs4sfdvzxWyquKj+6fesdZAkjzrOAtzimwSu+v04AfIjMYxc8/fTT+vKXvzyhLvF5552nv//7v3ftPcg8BlANr6YaM169R1YE3GBZlm66paFizeOw7VO+HTtDA0o+flsxy7j/eaOmhbn7K5taouzRZ0lNu1R8LlNya8vN8yznVmAyvx7TGK/B5dvrBHgmiuPV7czjhKuvFkEvvfSSLrroogmB4y984Qv6u7/7u/p1CgA+UG1GE5lQ9VXph9Lo41zcwoniIjBjgePRH0bj97Ew7VO+Gzu2rdhraSUzfUo89RNZ+e3Om1px5Q8+RdnWNuX3/5hkOZswOPr5Rj+vH4IsYcZ5FvAWxzS4yXfXCUBAULZiBt544w1deOGFeuutsZWwTzrpJP3bv/2bYjG+WgCAuXJTMTesj005dROYSnePXWb18OJ+VGk6cFD5auyMbFMi3avG7rPV1Hu+ko/f5jhwXNhpL23/2Nc1ePF9Gl58hfIHnOg4cDyKKbkAwoRjGtzgq+sEIGCIcFbp3Xff1YUXXqhXXx2rUXf88cfriiuuUDKZrGPPAABBVamGXxiDfeDFqQEAAJiySURBVPBO6b70zW80qaN94mVfmPYpv4yd2FtPquGnf6M513xKs+/7W8XfetJRO1uWcgd+UkNnXKnBi+9V9mOrZO+8Z9X96O6xI1PjOuzSmeq2WbXtAD/imIaZcuM6geMxoozgcRXef/99XXzxxXruued2PJZKpXTVVVdp9uzZdewZACConCz+EqZgH7xTLnB88UWNZZ8bhn2q7mMnO6zE5tvUeMNSNf3wLCUzfbKyg9O3k1Ro3EUjx31VgxfdreGzr1X+4FOk2MyqypWbkjsqiNs3ytZ0FbRqtfk26+6xtWq1rTVdBY96BtQOxzTMlBvXCRyPEXXUPDY0PDyslStXavPmzTseO+yww3Tddddpp512qmPPAABBZbJqeGntP+qzYbx0ZuK+dOkl1pSB41Hl9qkF84NRk7WeY8d693klM71Kbr5d1vYtRm1z+x6vXGubcod8VorPqur9y5nq+whrjeswS2dsda0t/m2yzcZv66610rELq1vQD/ADjmmYKTeuExbMF8djRB6ZxwZyuZy+8Y1v6JFHHtnx2IEHHqiuri7NnTu3jj0DJmJKDRAcpcE+J4u/lMuO8OP45VhUe60pS53Lin8X9yXni6yN7lOdy4IROK7L2MmPKPHUTzT7xgs15/uLNOuxHzgOHNsNzRr5yFe07cI7NbxkrXKHL6pJ4FgKR4Z51LSmzLdZuX0gCGMZKIdjGmbKreuE0bbjH+N4jKgheOyQbdv6y7/8Sz3wwAM7Httnn330/e9/X7vttlv9OgaUYEoNECyTg33OLiz9HuzjWFQ/yztjuvIK8xXoO9otXXmFpeWdwbg8rOXYsba8olm/vExN3ztFs+/8lhIvP+S4n/m9Uhr+/D9q24oHNHLyd2TvcpDjtk7VvXQHPGGyzUyy6wC/45gGN7h5ncDxGFFn2bbNEdaBV199VaeccsqExyzLUixm9gNrn3320b333mv8/v39/cZtgsayLLW0tEiSBgYGxK5pLp0pBl1GOT1RlZ7grryCO6OojPHqvnSmuqls1bbzEsci/4jCWPVs7BTyij//oJLpXsVf+KUsOf/u7GSTckeermxqiQp7HGXcNxOmP1KD9KM2TMdFJ6Yar9NtsyBtU2A6QTmmReH8GhZunks4HgdTFMfrvHnzXH29YKSW+EC5ncu2beXzeeP/AV5hiiMQXNWOOz+OV45FqCW3x471/ptK/ua7alpzqhpvX6XEC79wHDjO73a4hj/zv7RtxYPa/tm/8TxwHOayN/WaveDHcjuVMt4IVCBMwnxMQ/24eZ3A8RhRxYJ5QMiYLAjECQ6AVzgWIVDsguIv/VrJdJ/iz94vy3Z+s9+Oz1Lu8C8qm2pTYe9jJKt2+25xSm5xYTXTKblScVz6sexNvRaLW9NV+OC7NFt8a/R9O5fZnpV9KXdM7em1tXXr2HM4diLownpMQ7hwPEYUUbYiIChbAVNMqYGXGK9wimNRfTFWpzHUr+SmW5TMrFNsy0tGTQvzDlQ21absUWdKjS2edM+pMJZ3qPXUdT+U23EyXkvfz7S/QBAE4ZjG+RUcj4MjiuPV7bIVBI8DguAxqjHVDymCNZgpxitMcCyqH8ZqGbat2B8fUzLdp8Qzd8nKZ503jSWUO+SzyqWWKr/f8TXNMo4ip8cIt44l9a616nS8LlpcmJDh1twsbVhPNUKglji/QuJ4HBRRHK9uB48pWwGEGFNqAPgBxyL4wvb3lHx8vRKZXsXf+YNR00Lzh5RdsES5+WfLnrO7Rx1EKSflb9wM4Aah3E53z8RjpyRt3Vp8nGMoANQOx2NECcFjIORKfwgRrAFQD06ORUGYporgib2xWcn0DUo8uUFWbshxO9uKKf/hk5RNtSl/4CekWNzDXmIqlQK6XgRwax2wNlH6vs3NY8dSk9rQAICZqffxmGtm1Br59EAEdLRbam6e+FhzMz8wANRWpWPRmq6CVq0eW7Haqe6eYp3SNV0FF3uKwMsOKrHxJjX+6Dw1/ehcJTfd7DhwXJizm0ZOWKnB5fdq+MzvKn/QSQSO66zc6vaLFhc8C+CWe7/RY5NfAscrV1jasD42ZT8BAN6o9/GYa2bUA5nHQAQwpQaAH0x1LPrXfy/o9vXFf5tka4y/eO9aKx27kGyKqIu9/YwSmT4lH79d1sj7Rm1z+39M2dRS5Q/+tBRPetRDVKvWM6n8VG6nUsDapNQGAGBm6n08Tmdsda2V8etzzYyZIngMhFy9p9QAgFT5WHT7eumE46WHHi7+28mxqdzFOxfBEZUbUeKZe5TM9Cr+6qNGTe3ZLcoefbayqfNkzzvQm/7BNR3t1qQArpczqfxQ+stJpjMBZADwnh+Ox60pSytXmL0+18xwA2UrgBCr95QaAJCcHYseergYQB5V6dhUr2nj8Ber/0XN+vm/as73Ttbsn/y5UeA4/6GPaPgL/6xtKx7QyEl/TuA4ICrNpPJKPUt/mRzrKpXaAADMjJ+OxyavzzUz3ELmMRBS9Z5SAwCS2bFoNIBcKQOZi+CIy2cVf+5nSmb6lHjxv4ya2rPmKHvkGcql2lTY/TCPOgiv1GsmVb1Kf6Uz5se6ctd3C+aLDDMAmAE/Ho/9vLgrwonMYyCEnE6pIUMFgJeqORZVykDmIji6rPde06z/+n9quu6zarzjG0aB4/weR2n41L/TthUPauQzf03gOIDqNZOqXMDay/cbrzVlqXNZ8W+TY934Y2rnMgLHADBTfj0e+3FxV4SXZds20aIA6O/vr3cXPGdZllpaWiRJAwMDYtesjumJghMLqsF4xXRmeiwan4EsTcwydPJ6KAr0WC3kFX/xV0pm+hR/7gFZtvPVwe3EbOUOX6Rs61IV9lrgYSeDKZ2pbqGcatvNxHTHEq+uY6Z6XS+vm8qN1yBtKyBKAn1+RVX8ejyuNDNH4ppZiuZ4nTdvnquvR9kKIET8OKUGQPS4cSx66GHpjNOl2+8o/ncugqPD2va2EptvUTKzTrGtrxq1ze96sHKppcoeuVia3Tx9gwha01VQ11pp5QqzMg+jP047l9la3lmbyYv1WpzIT6W/qr0e4zoOANzl1+OxHxZ3RfhRtgIIEb9OqQEQLW4di/7827G6LVTlN+lMdRkS1barOdtW/OWH1XDnt9T0vVPU8MvLHAeO7XhS2SO+pMElP9TQV+5Q9k86CBxPIZ2x1bW2+LdJ2YXxwdSutbXZr+q1OBGlvwAAQVPPxV0RDWQeAyGzvDOmYxeaT43paLfIOAbgGjeORfVaqMpvZpop+rWVg1p1aZOHPZyBoQEln7hdyXSfYv3PGzUtzN1P2dQSZY8+W2raxaMOhktrytLKFWZZs+WCqV5fK9RrJpVpwLr0/cY/DgBArXDNDK8RPAZCyOspNX6t9wTAX2ZyLKpUvy1KQZrSTFHJ2ece//199+ohffSEpA4+yLNumrFtxV5LK5npU+Kpn8jKb3fe1Iorf/CnlU0tVf6Aj0kWk+hMmQQ967UuQnH2gv3BTROz2QuSPiivYXYMovQXACCIuGZGLXDFDcDImq6CVq02n57Z3WNr1Wpba7qcL3gEIJrKBaw2rI9Fcpp4MVPU7HOXfn/f/EaTFn4k6VkfHRvZpkSmT43d56ip93wlH7/NceC4sNOe2v6xr2vw4vs0vPj/KX/giQSOZ8BJ2YV6L6i7vDOmK68wf8+OdktXXmEZ12Wm9BcAIGi4ZkatkHkM+Iyfs3rdyIDrWquqprIDiAY/LVTlFzPJFL30EksXX9RYg15OLfbWk0qme5V44g5Z2UHH7WxZyh/4CWVTbcofdJIU47LVTZX2q3oHjkfVenEiSn8BAIKCa2bUElfhgI/4fQX0oNRKBBBMTheqkqJ3Mezkc5f//uqUnZsdVuLpu5TM9Cr+WtqoaaFxF+Xmn6PsgvNkt+znUQchld+venrtSK/UXuuANQAAprhmRq0RPAZ8IihZvUGolQggeFioanpByBS13n1eyUyfkptvk7V9i1Hb/L7HKZtqU+6QU6XELI96iFKl+1WUA8cAAPgd18yoB8u2bYqfBEB/f3+9u+A5y7LU0tIiSRoYGFAUd03TH//1DBZM995+CWTAG4xXuCmdKdZEH+X0eFF6nLnyimjMbKi0MIo08furyVjNjyjxh/uUyPQp8fJDRk3thmZljzpD2VSb7F0Pdr9vcGzR4sKE/ai5WdqwnrrStcS5FQgOxivqgWvm6kRxvM6bN8/V1yPzGPCRIGX1BiEDDkAwFBeqsj8o22O2UJWkD8r2RGfauF8yRa0tryq58UYlNt2s2ODbRm3ze6WUbV2q3GFfkJL1rcmM4jXF+P1IKu5X3T02528AAHyCa2bUC5nHAUHmcbQEKavXJAMO4cF4Dbd6Ldzp5wVD/chJpqjrY7WQV/z5nxdrGT//C1ly/np2skm5I76kbGubCnscNbN+wDWcx/2DcysQHIxX1BPXzGaiOF7dzjxmLhrgQx3tllauGDuoX32tre6e4gHOT4FjaXJf+cEJBNuaroJWrR475jjV3VOcRremq1D1e7NQlXOVMkW9YL3/ppK/+a6a1pyqxtu/psTzP3ccOM7vdpiGP/M/tW3Fg9p+6t8SOPaRctcUG9bHprwGAQAA9cc1M2qNshWATwVpBfSOdmtS35qbKcQPBE1QFu6MukqZoq4uhGIXFH/pN0pm+hT/w32y7LzzpvFZyh3+RWVTbSrsfYxksT/4TaWb0SywAwAAgFEEjwEf80tdy+lQKxEIh9aUpZUrzAJG5QJQBI69M1XAb/zjMw70DfUruflWJTN9ig28ZNS00HKAsq1LlT3qDKnR3elycI+TWUwEkAEAACARPAZ8z+9ZvTXLgANQE0FauDNqqskU/fIFDreHbSv2x8eUTPcp8cxdsvJZx/2yYwnlDvmscqk25fc7gSxjnzMZtwSQAQAAQM1jwOdqXdfSBLUSgXCqVHd9FIHj2nKaKTp5u01Tg3r7e0r+/kdq/MEZaurrUPLJOxwHjgvNH9L2E/9Mg1+9X9u/dJny+3+UwLHPpTPm47bcfpXOBP+8Xu1nCMNnBwAAMEHmMeBjfs7qpVYiEG6VxjGB49qaSaboVdfYmj17SBdf1DjhebE3NiuZ7lXiyTtl5YYc98WWpfxBJymbWqr8gZ+QYnHTj4M6ak1Z6lxWrG1uMm7H71edy4K/4M6arsIH34HZtcnoWOxcZmt5Jzk4AAAgGizbtrl9HgD9/f317oLnLMtSS0uLJGlgYEBR3zWd1LUc/7gf+lbt8xA8jNfoqHQTS2Jcey2dsbVqtflxtHS7/aCrWQfv95biT9xZXADvjU1G/SjM2U25+ecqu+Bc2c37GLWF/6Qz1S1qWW07P3FrTF15hfv13b08t0Z5mwNe4FoYCI4ojtd589xde4Rb5oAPTZfVW8+yEKYZcJSwAIKtdBwTOK6tYqZo8W/TTNHR7fb/LX9Zx7z2N2q65iTNvvevjQLHuf0/qqEvXa7Bi+/XyInfIHAcEtUGA8MQRCwuDGp2bRL0hUHXdBW0arX5NVh3TzHQvqZrmvI3AAAg1Mg8Dggyj6PDz1m9fs7WQW0xXqNn0eLCpIU7N6znHnStGGf/5UaUeOYebf91r+YOPGr0XvbsucoefbayqfNkz/uwYU+BYPDj9ZYX51au3QBvcC0MBEcUx6vbmcfUPAZ8xO8roFMrEYimSgt3knlcG06Pm1b/i0puXKfk5ltlDfVrtsF75D/0EWVTbcod9nkp0VBdR4GAcHIdFYbyW8VMa7PrxaBnWgMAAHcRPAZ8otoV0KWJPwgWzPc2OLu8M6ZjF5rXv+totzzvGwD3+XnhTlOhrflZyCn+7M+UzPQp8eKvjJras+Yoe+Ri5VJLVdj9MI86CPhTkBcGNTkumSQc+P1zAwCA2mO+KeATbtS1rFVWb5RrJQJRUi6IsGF9LJC1zMNY89N67zXN+q//p6bvfUaNd6w2Chzn9zhSw5/9W21b8aBGPvM/CRwjssqtz7BoccHXAdRqjmcd7ZZOOH7s3+WO3QSOAQBAOWQeAz5CVi8AvygXRFgwv/i3admcemfupjPFcjuSWbb0+O+ga62qOj67zi4o/sKvlMz0Kf7cz2TZzoPadmK2cocvUrZ1qQp7zpcszhmANPmY5ueFQWdyPHvo4YmPBSnTGgAA1A8L5gUEC+YB8BPGa7iVCyJs3z653rmTYMPoczqXFW+Q1YtpYMRvgRRr29tKbL5VyUyfYltfNWu8+6GKHb9M7x34OdkNO3vTQSAE6r0wqNNz60yPZyccrwmB5PHliJy8HgCuhYEgieJ4dXvBPMpWAEAF6Ux1J5Zq2wH1NlXG8fhMt9GpzuWme4+fBl2auVvPcTFdX8fzTeDYthV/+WE13PktNX3vFDX88t8dB47tWFLZw0/TUNsPlfjTnyv+0Yul2c0edxgIrkoLg/rNTI9n//dfJpYfInAMAAAqIXgMAFMIY41UoJKpFu5sTU0dqCgXxEhn7LIBi3qXfHAScPFF4Hh4i5KP/UBNa7+kxhuXKfnUT2QVso6aFubup+2f/LYGV/xM20/7NxX2PU4W5SlCgxua3ii3MOgov9Z1n+nxrKPdmvA5peLnJnAMAABKETwGgDJKawo6/eHop0xLwFSlhTsrBSpKF+7cuEn1D8BOodLnqGvg2LYVey2thrv/h+Zcc5IaHvgnxd59zllTK67cwZ/R0NnXafCiu5Q97mLZTbt63GHUGjc0vRHkhUFncjwLUqY1AACoL2oeBwQ1j4HaC3qNVC8xXsOt0gJ3lfbzdMb2deB4vHKZhnWZuj2yTYknf6xkuk/xt54walrYaU9lF5yn3PxzZO+8V9nnMFbDIZ0pBoBHOd0/S/fzK6+o/wwAP3ESYK3l8aza8Wp6PPPN8Q8IMM6vQHBEcby6XfM44eqrAUCIlK6+XmlV8ygFjhF+lYJLlcZFUALH0uTPUevASeytp5TM9CrxxB2yRrY5bmfLUv7ATyibalP+oJOkWHAv5SrdpPCiXZAVS8c4Ox+N8mPpGD9xct42uQ6oJ5Pj2VSfe/zjfv2cAACgPsg8Dggyj4H68Vtmkh8wXhGWzLVFiwsT+t3cLG1Y71FVr+ywEs/cVcwyfu33Rk0LjbsoN/9sZRcskd2yn+N2fh2ra7oK6lprvp+M7nedy6TlndGrvub0fBPF85IJv84smul4ne54xvUM4B6/nl8RLtxod0cUx6vbmcfRu+oGAEO+rZEK1FHpuAhi4LhWNT+td5/XrAf+j+Zce7Jm3/Udo8Bxft/jNLzo3zT41Z9p5JPfNgoc+xU15asXmEUffWyqhUErmWphUD+Z7njmNNM6CLWeASAKWOsAfkLmcUCQeQzUX1gyLd3AeMWommbuusjz8ZwfUfzZ+5VM9ynx8m+MmtoNOyt71JnKptpk73pw9X2Qf8eqXzM/g8JJ6YHxj2Miv2a+e1Xz+ITjpYceHvs34w2YOb+eXxEOrHXgriiOV7czjwkeBwTBY8AfSk/Io6L2w4rxCim448HLwJu15VUlN96oxKabFRt826htfq+Usqk25Q7/opRsNGo7ZX98PFYpwTAz3NCcGT9OBa5mvDo9npX+d9PXJQABTOTn8yvCgRvt7onieGXBPACoo452Sz299qRMS07MiJpKgSs/L7ZU6cK66sWxCnnFn/+5kplexZ//hSw5vyC1k03KHfElZVNLVNjzaJOPEnhOvm9+CE2t3os+Bl21gVA/BVBNjmdSMQPZ6X4xvn3nMn99bgCIAhZvh58QPAYAA5VqCnKCRlQ4yXTzYwDZac1PydmFuvX+m0psulnJjTcq9t5rRn3J73aYsq1LlTvidKlhJ6O2YVLp++aH0PS4oRld1RzPHnrY7Hqlo93SgvkEjgGgXrjRDr8geAwADgU10xJwkyeZuzVgcmFd8XPYBcVf+o2SmT7Fn71fViHnuA92fJZyh31B2dalKux9jGTV/3vxg3Lfd2lAdOWKYhCrGmFecZwbmtHk2vHMgbCOHQAICm60ww/8v6INAPhAuRPzhvUxViVHpDjNdPPbuEhnzC+sSz9H7/ff1Vt3rFFT1xfVePNyJZ65x3HguNBygLaf9BfatuIBbf/iP6vwoT8hcFyi9PsuDRxv326z4niJcjc0R/lh3MEbbhzPrr7WVjrD/jGdar8jvlsAbit3HF+0uEDgGDVD8BgApjFdpqXfAmWAF0wz3fw0LlpTljqXFf82ubDuOF/6mwt/p7//k7/UT79wij78zL8pNvCSo7Z2LKHcoZ/X0LldGuz8ibILL5Qa3V24Imw62q0JAVCpGBBdMF/qWlv8t8m+NH6f7VobroAONzSjq+rj2bjjMjWMp7emq8ANKwC+Mt2NdgLH8JJlR2GZwRDo7++vdxc8F8UVMOF/TgNmUZsyxHiNlnSm+GN4lNP9u3RcXHmF5WrAwrQcwejzp223/T0ln1ivRLpP8XeeMepTYee9lU0tUe7os2XvtIdRWy8EaayW7i+jRn8oseJ40XSfLcyfPexMxmu15VjCXMbFLX4958FfgnR+RbgsWlyYtNbBhvXkhVYSxfE6b567SSvsYQAwhSBnWgJu8mOmWzVZYa0pq2JWWOyNzWq4568155qT1HD/3zsOHNuylDvoZA2deZUGl9+r7AkrfRE4DpLpSjBIcnyMDXPwNKilY+C+ao+nBDOn15oyH0PlxibfNQC3VVrrAPASmccBQeYxUFtknVTGeJ1eGLPC/PKZXB2fRw4p8dRPlEz3Kf7GRqN+FObsptz8c5VdcK7s5n2M2tZKEMbqVAHRco9LlTOQox44nsnzUX9BGK9RwuwzVMJ4Ra1VWrxd4thTSRTHK5nHAFADfsy0RHCEtVaiXzLd3MgK+07nH3Tc2/+oOdeerNn3/JVR4Di3/0c19KX/0ODF92vkxG/4NnAcBKY15UefM/6x0W0f5gAOi6QBteckiz/Mxx0A/sFaB6g3Mo8DIiqZx88+N0cLP5I0vhvk50w9BJtfMi39Jop3b50ia712TLPCkrERfWbve7X6o336UPZRo/eyZ89V9uizlU2dJ3veh2fc91rx81idSVafpMhl36zpKqhrrflnG/3+OpdJyzvJG/EzP4/XKDOZHRG24w6mxnhFrbDWwcxFcby6nXlM8DggohA8vv77tq7/vq1vfqNJ55693fGA5kcRUHtRPAGbYHp57Ti5oP5x94s654Abdcb+t2qXBrPzaf5Df6Jsqk25Qz8vJWe71u9a8etYdWOMSBMDyE5fK8i4oRlufh2vYLo4JmO8ohYon+OOKI5Xt4PHCVdfDahSOlMMHEvSZZcPanjY0gXnm2Xqda2Vjl3IjyMA9Td6sTZ6fBr9fy723Dfld700r19+/2dKPd2rlZ/9L6PXtGfNUfbIxcql2lTY/XB3O4yqSzBIE7fzlVdYkwI4zc3lx1lY+KV0DBA1pccgAscAvGa6eLvk7LcHUA3SNOELrSlLl14ydmC76hpWNQYQbNRKrJ3x3/Ues1+XHvxPDf3zZ/SFgdX6+B7OA8f5PY7U8Gf/VttWPKiRz/xPAscecaum/MZNYsVxADXT0V68YTVe2G9YAagP1jqA35B5DN/oaI9p9uwGXXb5oCQy9QCY8eN07kpZABzHXGQXtOxjv9Ln+3u1//ADilvOFxy0E7OVO3yRsqk2FfZaIFlsg1pY3hmrarZQR7ulBfOLgeOpppCTbQPAC9099pQ3rDjeAHBT8Ua7bbzWwfjfHizeDjdR8zggolDzeLQOzXXXD+0IIEsUhAf8yG91o/y+kBS1Er1hDb6jxKZblNy4TrEtrxi1LexysLKpNv3OPl3zF7YYv3dQasj6bay6gcWrEFZhHK9hwXkcpRivqAU/JscEURTHq9s1jylbAd+5+KLGCSUsxk/15ochgFLpTPGuvFS+NMRUSmumezmtq3QaGT84Z8C2FXv5YTXc+W01XftpNfzy3x0Hju1YUtnDT9Pgkh9ocNkdujp9gVZ+u9m4zEF3j61Vq22t6XKe4Qx3VLoOcFIqBgBMlTvubFgf43gDwHOsdQC/oGwFfKmjPSbbLkyY6t3TaxNwATBJa8rSyhVmC0TUo2Z6R7s16ThGrUQDw1uUfPx2JTN9ir37nFHTV7btq1f2XKIFHWfLbtpV0uSbDpKzbcFCrfXj5AYyC8YA7iDbrWi6G1YSxxsAQPiReQzfIlMPgFMmGYf1msFQqVYipmDbir2WVsPd/0NzrjlJDQ/8k+PAsW3F9HzDZ3Tpr6/R6ff9RCt7luuHt+2y478XbzqYZY2xUGv9mK44TkYgUL01XQWtWm0+bsI2K8PpDSuONwCAsCN4DF9jVWPAmWpLLoRpBV4nP+DqGTgurZVYqZ+RN7JNicw6Nf7oHDXdsFTJzbfKym931PT92J7a/rGva/Di+7T7qv9U61mflP3B5U7pd+3mTQfGoHdYcRyonSCUgqoFblgBADCG4DF8jUw9YHrVZAilM3ZVGUJ+/zFY6QecXwLH1EqcWuytp9Rw399qzrUnafZP/5fibz7huO2v3jxRd7VcIa3+qbIfWyV7570kTf+j3o2bDmTpeau44njxb9MVx0e3LSuOA84wK4MbVgAAlLLsKCwzGAL9/f317oLnSlfA/OGPCpMy9ShdAUyUzhSDT6OcjIs1XYUdWUWjrrxi+h96oz8OO5dJF18U9/WKtX5ZFX26oCOLgErKDivxzN1KpnsVf+33Rk3f3T5Pt710tm558Vx9qeMA4zrXTraFk4xj0zFY7v2cjMFqhGl1aWqwIuz8NF6dnp8qPS/IY3b0Wsn0vDz+Wml5J3laYean8QqgsiiO13nz5rn6egSPAyJqweP//O67uuoa8x/xQBSZjIvSQNd0z5/qPb77/2I66VPFE5JfT8ClfR7ll8Cx6fPCxup/XsnMumJJiuEtRm23tByrf7pvie577VRlC7NcC9hWe9PBdBvWcptH8WIZCCq/jdeZ3AANQ/A1yMFveM9v4xXA1KI4Xt0OHidcfTXABdddP1Q2cCyxqjFQjsm42LjJ/PWDOh21o91ST+/E0je1qpluWitRishxLT+i+LP3K5nuU+Ll3xg1tRt2VvaoM5VNLVF810O0t1VQ1jAwMf67LlfGoHRbOM1WN9mGUb1ZACB4Kh3bpss4Hl83efxrVVJaN/nYhfUNwlb73kG4RgIAwASZxwERlczjm25p0GWXD+54LGpT5ICZMM0QGs90rAXh7m29Mo/9XsagHqytryq58UYlNt6s2ODbRm3ze6WUTbUpd/gXpWTjhP/m1fF+0eLCpJsOG9ZPnwHnxzIlQRirAIr8Ol6rmZXh5xkZgBv8Ol4BTBbF8UrmMUKru6egq66ZPnAsTZ0JsX27/cEUObOsvbEpcnbdp8gB1TLNEJrquaOC/EOu0g9drzN7i4t72cbTdafLig2cQl7xF36uZLpP8ed/LkvOL9LsZJNyR5ymbKpNhT2PnvJ5XmSFVVqo1cmCSZJ5lh4A+Fk1szIqHQ9Lb+A5PT6S6AEAQH2QeRwQYc88Ls3Uu/QSSxecb56pNx7ZfogqkwyhahcJ8/Pd22o/k9uiOgvCev9NJTbdrOTGGxV77zWjtvldD1W2dalyR54uNezsUQ+n5tZCi35ZsFHy91gFMJHfx2s1szJKj4cLPyI9+pj5udlPtZAByf/jFcCYKI5XFsyLqLAHjyXp+u/buv77tr75jSade/Z2xwN6/MVkQ4PFFDlAZiUbqgl0+fUE7MeyAZFgFxR/+SEl072KP3u/rELOedP4LOUO+4KyrUtV2PsYyarP9nD7pkO9F2wc5dexCmAyP4/XmRzTpmp7wvHSQw9P/1okesCP/DxeAUwUxfFK8DiiohA8tixLzz43Rws/kjQe0OMz9UwzGKZ7HhBUJhlCpj8K/XgCjtLY901W81C/kptvUzLTp9jAi0ZNCy37K5taquzRZ0qN7l7cmPLqpkO1tZPd5MexCqA8v45XN2ZTVJotWOk1wnDORjj5dbwCmCyK49Xt4DFzfuArCz+SrKrd+GBIR7u1o56rVKyx1t0z8eDAhSjCrlLd1nI62i01N098rLnZu7rAbjMZ006OEX62pqugVavN+9zdUywPtKarMLMO2LZirz6mhp/8d8259mQ1/PxfHAeO7VhCuUM/r6Fzr9dg50+UPbbT94Fjqbp9xnQMAoAflTtGblgfMz4mlh5Hxzvh/2/vzuOcqu89/r9PJmEZcBgQcAFRAbVsGRXFWq3Wpa3VuivgMHY6QlmkF7fea/tr9eKvve1te9WiFZSCU36dzmVRq3il2rreVr1SxU4URAuKKMqiMKwDJJPz+yNNyDaZczJZzklez8eDBzmZfDPfJOebTN7nez7fcen/3uDvdQAAnIHwGCUp0xd9/hBFqUs3Qyiqoy94bg66WgL2x3S694iWgDsea+OiyGU7oXf8PtG4SNk91gN75P17s3r+7gpVLpkk3ztPymg/aKlp+LCjdOCsm7RvynPaf+mv1D7kTMko/p8g+TrokM0YBACnyfQemc1BtXQHqqVI6QomegAA4FzF/+YG5Em6P2ovvizMH6IoadnMEHJ70FXjN9RQH7lsZ0zHv0c01MsV9RNr/NnNgE3eJ+w8Vs+W1er+5zvVa/656vH8j1Xx2XuW2pkyFDr+XLVdMVf7Jv9ZwTOmy+w90PLvzbd8HXTI1Sw9ACimfJyVke5Adbq2BMcAADgLNY9dolxqHuejDo1TFi0C8i2buq2SslokzIl1oxxTB7gA8l7fOdgm77sr5GtZoootb9nqW7iyv0JjrlZwzLUyqwbZaltoCxvDalxk//MgfqHWyQ2elOujnLBgoxPHKoD0nDJe7b5XWbl9Z3WTO7qev9fhVE4ZrwA6V47jlQXzyhThcdc4YdEiIJ+yDRPj2Qm6yvED2GnyEVQan6+TL7BUvjWPyziw21Z/QkO+qKB/otqHnS9VZFe/vhhyddDBqQs2MlYB93DCeG0JROrjR1l9j0p+b3vgPqPTxayzXUQPcAInjFcA1pTjeGXBPMAmN9dyhftkWze3K/V2u1K3NVMbty8sF1WM16QQclbbPXRQ3rVPqeeS69Vr0aXq9ubvLAfHZvc+Ojj229r77RXaf02j2k/8uquCYyn7ciXZBMdS6YwrAKUp16Wg7NRNjuemRXsBACh13mJ3AMinTKfIRa/nD1PkyqFT4O3tV4dOgTcTToG3Ipu6remMGZ3++uh9RX9H9P/rJ7lj3BTjNSmkdK9P82LT0im/RutH8gWWyLf6MRlt9s5uaT/6FAX9ExQ64euSr0f2D6AEZFs7WUp83caMdkfdbQClb3KDR6eNtX9WRl2tkfBeZrVu8pt/N/XaysT7ik704O90AACKz7nfiIEuYtEiFFJLwFTjoshlO/tV/H7auMj+bFe7M4SSgy6p88XirCwS5kTFek0KLfn1yRgch0OqWPesejz6HfV6+Gvq9vpCy8Gx2a2XDtZcp33XP662ic0Kjby87INjqbwWbARQPrp6VoadUj7JwXEUf6cDAOAM1Dx2CWoe2+PERYtQ+vKxwIxVduq2xi8SZme2Y/wiYVNuqHBF3ahiviaFlqm2u7F7s3xvPSLvW8vk2bvV1v22DxihYM1Ehb5widStVy67XFKcumBjOdZ4A9yqVMar1brJyZ+5Z4xT2iDZTZ/FKB+lMl6BclCO4zXXNY8pW4GSY/UUOSn1VHz+MEVX2Nmvch1S2gmfcnU6qhsU8zUppHS13XfvCuvFxpd1Ub8lqnj/BRlm2PL9mRXdFfrCJQr6Jyh85BjJcMfzUEy5qJ0MAKUgckaGGTtQbeczN3r92FOlN1ZFfsbf6QAAFBfhMUqK3UWLJAJk5JaV/coJIWU5BV1ueU2yldz3IYd/rvP7/kHXHLtMg3d8LNk4cSXcb5iC/gkKjrxM6tEnD70FgFROnbmP7GU6UN3ZInrRA9Xxt+PvdAAAiofwGCWDRYvgFJnCSjeHlG5Wqq/Job6bGnv46/r+BUs1/OCfZYSDlu/D9PgUOuGrkVnGg09nljFyjmAQmZT6wqblzG5wnNyOiR4AADgDNY9dgprH1sTXcs3uC4j4AoKcSf6CVFXVyWJmLuLWulGl9Jo0NZv6fWOrvjn4SV1z3BINO+x9W+3DfQYrOGa8QqOvkll5eJ56iWIr9ljlcxmZWK2Nmyz5vfyB+4ySONBQ7PGab7zeKCWlPl6BUlKO45Wax0AG5VTLFc6XPGPGrSFlKSmJ18Q09adFAR359yX601f/qJ7e/ZabhuVReNh5CvonqP24sySDUA750xKI1DyV7M0YjA+KGhcpq891uEON39D0qfZmlqabucr+4Q5WaiGnE//Z3VDP3+sAABQaM49dgpnHgHtdfFk4IaSsqpJWLHd3aOf28ZrpNXHsKfYH98q79intfnGxBoTesdV0a9tAPbbxGj324dW66ltHuSMkR04Ue6zaLQvj5jIyyJ7V173U949ij9dCceznLGBDuYxXoBSU43jN9cxjd6cXQBlpCWT3BpdtO+RGU7OZEFJKkdmuTc28LsWS6TVZ2BjWzFmm7denqTlyKu7CxnAOexrh2fauuj93l3rNP1c9nv13W8Fx6Niz1XbZ/frDcc/qwXdnauv+I/XgfJP3hRLi9M+GulpD06ceCnsenN/x+Cr1YBAds7KfsH+UjnJatBcAgFJA2QrABVhMxp0y1ddl0ZfisPKaxF8u2in2oQPyvve0fIElqvjkTVtNzZ59FRx1lYL+8TKrh0iSJg2XTMPklN8S45bPBiuLXhEMolQXNgUAAHA7wmPA4agZ6U4dfdGNv54AubCsvCbxilF709jxgXyBpfKt/oOM/TtttW0fdJqCNRMVGv5Vydst5efUdi8tbvtsIBiEFen2k+bFpjvr0wMAAJQIwmPA4VhMxn0yBSFWZuAh9+y8JvEyvT45C7zag6pY/7x8LYvl/ej/bDU1ux+m4MgrIrOMDx/e6e15Hygd2X02hIv62UAwCCtKYmFTAACAEkJ4DLiAncCRGVzF1dHzH7/Ii53Xk8Vhus7KmLAbIOdinBm7Nsn31jJ5335Unr2f2WrbfsQYBWsmKHTSxZKvp622KB123ksWPNymeQ8V/7OBYBBW1NUaKQcWqqo40AoAAFAMFEEFXILFZJyvo+c/3SJsVl/PfC3CVi7sjInk1yRe/OvTpXEWblfF+y+qxx+mq3LBV9XttYcsB8emt6eCY67VvkmPqG3SUoVGX01wDEvvJQsebtO9c/bFtov92VBXa6iqKvE6gkHEY7FZAAAA52DmMeAi1Ix0rpZAxzOOO6pLmu71jNakpWZ113X0mmTS2QzkbE+xN/ZslXf1Y/IFlsqz+1OLjyCi/fATIrWMR1wqdT/MVluUh8yfDWHNe8g5wbGUORgsdt9QfCw2CwAA4CzMPAZcJt0ss4svS61jyRerSHhYqHY1fkMN9ZHL8c9/pC5px7MC41/PhvrU4Dh6fwTH9nX0mnQm+TWJf/1sBcdmWBUbX1WPJ29W5YIL1P3lOZaDY7Oim4IjLtW+Cb9X27eeUOjkWkcHx4Uca0ivo8+G+FIVM6YV/7MhXTAYlW7WNMpLus+/Fcs9nc6uBwAAQP4Ypmny15cL7Nixo9hdyDvDMFRdXS1Jam1tFbtmZslfsKIIjiMWNobVuMj+8xF9XhvqpckN9o+vdVSjuLOZ4dF2bplB7qbxmm3d6Ph2F18WTqm9uWJ5B/tH2w75Vj8uX2CJPK0f2vqd4eohCvonKjjqCqlnX9t9LoZijTWk19Fnwy03Veqaqw4Udax29P7mlvc95Fdn+0E57Cdu+mwFyh3jFXCPchyvffvm9rsk4bFLEB4jHVuBVhlpCURqBUdZ/YKZ/MX0gftyO+O3lL4Yl9N4tXSgxjTl+fTv8rUslve9p2W0H7R8/6ZRofbhFyjon6D2IV+UDPeMYaeOtXKX/NnQp4+hV/63X1HHaim9/yH3rL7+pb6flNNnK+B2jFfAPcpxvOY6PHbPN1QACVhMpmOdlYpIpxClIjItbFXqX4jdqrNT7Jc07Zb3783q+bsrVLm4Vr53llsOjsOHHaUDX5qlfd95XvsvnaP2Y7/kquBYcu5YK2fpPht27jS14OG24nRI1t7frCz8h9LUlYVN2U8AAADyjwXzABdiMZnOZVpAKlkhg9t0/cp2ETbkV6ZT7F9YvEbjj1usb3zylHpstR7KmTLUfvw5CtZMUPtx50ieinx0vaCcOtbKUabPhnvn7NP+/YYmXVfY59tuMChZ25dQGnKxsGn8YrMAAADIPXdNcQLAYjI2WJmhVIwwK7lfBMfOk3a/uHa/vG8/pu9oopZ85RpdfdwjqvRaC47Dlf11cNw07Zv8Z+2/8kG1Dz2vJILjKKeOtXLS0WfDjGmHnuN5D3X82ZCPRQ+zDQaT9yUWVixduVrYlOAYAAAgf6h57BLUPIZEzchsOXWRJjfXrC7l8Zq8X/zghvW68phl8q15XMaB3bbuK3TMFyOzjIedL1V0y3VXHcepY63UZXp+DcPQI491171z9qX9uZTfRQ9ZUBFW5GJh01JQyp+tQKlhvALuUY7jlQXzyhThMVhMpmsync4tFf55srQIm4OV6niNvi4+z0FdcNSfNeuLS3V08HVb92F276Pg6CsVHDNeZr/j89RT53LaWCt1nb3nR8fqgofb0gbIhVj0kGAQsKZUP1uBUsR4BdyjHMdrrsNjah4DLkDNyK5Lfl6cFBxTs9oZWgKmnmzaqFkjlumKIX9Qv+7bpaCN9ttP1rIN43XJbRdpzCk989dRh3PSWCt1dj4bptzQU/v3t2neQ6mfDdOn2vvMsLvoYbYBMMExAAAAUHycBwg4HDUjc6eu1lBVVeJ1VVWFDWqpWe1A4ZAq1j2rM/4xVU9deJFuOGFhJDi2wPRVKlgzUcsOf0z1f/29BlxweVkHx1FOGGulLrvPhtT3mpaAaalmdRRntwAAAADlhZnHgMNFFpMxbdeMjJ/9x2IyEU3NZsIsSCkyK7Kp2SxI+JEpdGHGeOEZu7fI99Yyed9+RJ49W2y1bR8wQsGaiQp94RKpWy99Q9LRp3CKfVSxx1o5yPVng5X3IIJjAAAAoPxQ89glqHkMN9SMdHIfi12HtdRqVrt2vJphVXz4inwti1Xx/osyzHbrTSu6K/SFixX0T1D4SL9kOO91cYJij7Vy09n7Z0djtaN2LHoIFI9rP1uBMsR4BdyjHMcrC+aVKcJjON3CxrDtGXDSoaCioV6a3JCfSjrFDkPs/h43hDSuG6/7tsv39qPyvbVMnp0f2Woa7jdUQf8EBUdeLvXok6cOloZijzWkymascgAAKA7XfbYCZYzxCrhHOY5XFswD4Dgtgcip05K9cgvxAUXjIum0sbmfgVzsUhHZ1qxO7teY0e4vPVLwmemmKc+m1+VrWSLvP/4kI2x99TvT41PohAsV9E9UePDpzDK2oNhjzamcfEZGR1j0EAAAAEAUC+YB6LIav/XFlqLSBU2FDI6j7CwUlY1IXdKOf39H4vtVCjWrFzaGNXOW/ee2qdnUzFmmFjaGrTfav0u+Vb9T5aJLVbn0W/K9+5Tl4DhcNUgHzr5V+77zvA5cco/Cx4wjOLbACWPNiQq63+cYix4CAAAAkJh5DCBH7MwsLMQp7HZ+R75nRU5u8GQ1q7qu1iiZGcd5n5lumvJsfku+wBJ5310hI7Tfcv9Mw6P2oecp6J+g9uPOkgyOq9rhpLHmJE4+I8MKFj0EAAAAIBEeA8ghK8FQIYJjJ5aKyPZ+3B4cS9GZ6fYCQ8sz0w/ulXftU/IFlqhi6xpb/Qr3GqDQmGsVHHONzMOOstUWEU4ca06R1/0+zzLVPC7lwB8AAABAKsJjADmVKUAu1KJZkVIRpu0F/OL7XgqlIpwk1zPTPdvekzewWL53lss4uNdWX0LHnhWZZTz0K1KFz1ZbJGKsZea0MzKssLLoIQEyAAAAUD4MsxyWGSwBO3bsKHYX8q4cV8AsZZlmrkmZg5FcLTDlxoWq3CLb8dpZQJbx56ED8r73dGSW8Sdv2uqv2bOvgqOuUtA/Xmb1EFtt0TnGWmZd2u+7yM5YLWY/AfC3MOAmjFfAPcpxvPbt2zen98fMYwB5kTzjzmpwvLAx/M9ZjPZmtUVDjYZ6U5MbIjVr3VAqotxCt2xmphs7NsgXWCrf6sdk7N9p6/e1DxqroH+iQid8TfJ2y9GjQDI3jLVicsIZGZ2xuuihVB41qwEAAABEMPPYJZh57F7lFg4mu/iycEJwXFUlrViefkGyloCpmbPshyi/vCesJ5Yf2n7gPmt1Qov9HB8Kyu2FRYeCcsWC8kLr6njtbGb6jd8J6frTX5AvsFjejf9n677Nbr0VHHmFQv7xCvc/wVbbQij394Ry1pUzMrJlZazaDbCdEngDpaZU/xYGShHjFXCPchyvuZ55zJLyQB4tbAxr5ixTTc32g7WZs0wtbAznqWeF0dRsJgQjUiQo6ej5iCwwdSiAeHB+58/dTbcmBsdWF5gq9nPcEojUiZWsPc6o+NCmcVHkftyorjbxtY7uJ0f2/EQLr7tPU/ZcoJ7/c7Ot4Lj9iDHa/7WfaO+0l3Tw/B86Mjgu9/eEctfRfi8VL4DNdtHD5Pdqt74XAQAAAMiM8BjIk3IPB9PNsIvK9HykCyU6uu0v7wnrjVWHts8YZ+30aSc8x9kE5elm+7l5JmpdraGqKsmjdn154EuaM+5GPXXh1zV270Py7P3M0n2Y3p4Kjr5G+yY9orZJSxUafbXkq8xzz7NT7u8JiIju9/GqqopX+iGy6GHkst1FD6PvYaW86CEAAABQ7qh5DORJJBy0VxuyVMLBjk5pjr8+0/Nhpa5mU7OZMONYkl5bGbneLc+xnfqhpXia+KO/26rxRzyqq8Yt09GVn9pq2374cIVqJio44jKp+2F56mFulfN7Ag7JdEZGscb05AaPThtrvyxKXa2hMaMJjgEAAIBSRngM5FE5hoOZHoed58POAlNnjIsEx53dpxOfY6tBudP6nTXTVMVH/6dPnlys2rbn5RsRst60wqfQiRcp6J+o8NGnSIb7noNyfE/AIZlqHhd78TkWPQQKg7r3AADAbShbAeSZlTIMpRISWXkcdspSpLvtxZeFU37H3b/wuPo5zvScOLnftrTtkO+N36rytxer5yM3aNiBP8nnsRYch6uH6MA5/6q9U1/SgW/8QuFBp7oyOI4qp/cEHJLuNV2xvPP3LuRetqVfKBmDrqLuPQAAcCNmHgMFYGcWrVtDIjuPoyszkDtaYMrtz3G6/jcvNh2xoFbWTFOeT/8uX8tied97Wkb7QctNQ+EKvbD5fO0fNUHnfftMySitY51u319hT67OyEDXLWwMq3GRNH2qvec4+ho21Jua3FBa70cojOS691J26zRkU2IGAACgKwzTNJlG4QI7duwodhfyzjAMVVdXS5JaW1tVirtmplOWJfeGRC2ByIyYKKuPI/n5eOC+juu5XnxZOOG5qqqSVixP/QLv9uc4uf9RTut3xvF6YI+8a5+Ur2WJKj5719b9hnsfqdd1rX746FXadmCgJOc99lxy+/6Kzlk9GJCvgwbl8NlqVSE+q4BMOhvnyeP1d79PPduKzwTAGfh8BdyjHMdr3759c3p/TJ0ACij5dPVSCYlq/IYa6iOX7TyO+Oejob7j2pmZFpjKdJ/R20W54TmuqzVUVZV4XVWVO2YherauUfc//7t6zT9XPZ77fy0Hx6YMhY4/R22Xz9W+KX/WyKk36ur6I2I/L+VT+d2+vyIzu2dkUMIivyKLVtp7jlm0ErlkZ5w3NRMcAwAAZ2DmsUsw87i0WJ1F6zb5WAQm25mZbn2O3Tbz2Dy4T3tX/re8LUtUsTlg6z7ClYcrNPoaBcdcK7PPoJSfl9NsP7fur+iYU2a5ltNnq1XFng0OdLRvRcfrgofbdO+cfSk/B+AcfL4C7lGO45WZx4DL2ZlF6zbZBhxWg2OrC0y59TlOF5RHOW0WovH5erWvuEOhX56i7s/80FZwHDrmDLV9817t+87zOnj2zWmDY8n6zHS3c+v+iszyfUYGsseilSi2TPsgwTEAAHAaZh67BDOPSwP1Ta3r7It7Rz9363Ns9fEUtf+hg/Ku+7N8gSWq+Phvtpqa3fvokyOuUPX542X2G2qrbbYz2t3ArfsrrMvHGRl2lMNna7Zc8b6LksZnAOBefL4C7lGO4zXXM48Jj12C8Nj9+JJqXbanFJ8xTnptpVLaOf05zjYoLxSj9SP53loq79uPydO23Vbb9qNOVrBmgkInXCT5euSph+7EewIKodQ/W7uK8A7F1lG5qhnTDE26jn0PcCo+XwH3KMfxmuvw2JvTewMKqNizuezIFAZF/4/+PPp/uX5ZtbvAlHToOUsXHKe7nZOeYyuPtyj9D4dU8f5LkVnGG/4qQ9Y/YE1fpUIjLlWwZqLCA76Qvz66GO8JgDMkjzeCYxRaXa2h5sWJ5Yv69DFUV+spiy+3AADA+ah5DFda2BjWzFn2a8A2NUcWMFrYGM5Tz9L/TivhoN0V4EtRS8D+jMu6WkNnjEu87vJLU4M2Jz7HdoPyQvTf2L1FvlcfUOWCC9Vz+Xfl3fAXy8Fx+4AvaP+Fs7V32v/qwIWzCY47wHsC4Cx1tUZCjXkpMgOZ4BiFkK7u/c6dppqaC/e3KgAAQCbMPIbrtARMNS6KXLYzIy8+sGlcJJ02Nv8zkLsyi7YcZxtGFpiKvL5WZ3w1NZsJM47Hnir9623pj4s56TnONiiXEvs/ZnQOFtQyw6r48BX5Whar4v0XZZjt1tt6eyh40jcU9E9Q+Ei/ZJTP/poN3hMA58m0aCXjDfmUqWzKvIdMmSbv+QAAoPiYeQzXqfHbn5GXLrDJd3CcbTiY/NhaAuU123Byg0cP3GctOE5+ji+/VJpzT+a3Nac8x5GgPHLZzqnR8f1vqO9icLxvu3x/W6DKhy9Sz8e+I+/65ywHx+G+x8vzjf9X3n/7uw5e9DOFj6ohOO4E7wmA86QL76KY8Y98Sve36R+frNAtN1XGrmMfBAAATsDMY7iSnRl5xVp8KptZtFLiY+tyOOhSVh+z25/jyQ2erGbA19Ua2c84Nk15Nr0uX8sSedf9SUZ70HpTj0+hEy5UyD9R4WPGqTpahP9Aq/1+lCG3769AqbGyaCUz/pEPmf42nXJDT0nSvXP2SWIfBAAAxWeYrMTgCjt27Ch2F/IumxUwOwuGcxEcd3WBPTct7OdWPMcW7N8l35on5AsskWf7eltNw1WDFPRPUGjUlTJ79ZdUnivW5gr7KwqJsZpeIf5+ANLJtG/Fj9dfz92ueQ+xDwJOxecr4B7lOF77Rid65Qgzj+FqmWYgJ/9xno3ofTTUm5rckF2Vl2zDHkIi63iOO2Ca8mx5W76WxfK+u0JGaL/1poZH7UO/oqB/gtqPPUvyVNj61QSkHWN/BYrL6qKVEjXHkVv26t57ZJph9kEAAFB0hMdwvXRf8JoXpy5+E38bpy6wB+TEwb3yrn1KvsASVWxdY6tpuNcAhcZcq+CYa2QedlRWv35hY/ifpRnsfcnNxcEaAMiERStRLI5aKBcAAMAGwmOUhOQ/ruOD4+hiU3a+/BVjgT2gqzzb3pM3sES+d5bLOLjHVtvQsV9S0D9R7UO/IlX4su5DSyBS01fiYA0AZyG8QzFR9x4AALgV4TFKRl2tkTLjuKoqMbhy8gJ7QFZCB+R975nILONPVtlqavaoVnD0VQqOGS+z77E56U6N39D0qRysAeA8hHcotqIslAsAANBFhMcoGU3NqaUqdu2KXF9Xa1g6/ZTgGG5h7NggX2CpfKsfk7F/p6227YPGKuifqNAJX5O83XLeNzunejPmABQS4R2Kjbr3AADAbQiPURKSA6iqqkOlK+KDKzsL7BFiwXHag6pY/4J8gcXybnzVVlOzW28FR16ukH+Cwv1PyFMHD+FgDQCnIrwDAAAArCM8hut1FEDFX99ZgJxc7oIQC05i7PpEvreWyfv2I/Ls/cxW2/YjRivon6DQFy6WfJV56mF6HKwBAAAAAMDdCI/hapkCqEzBVWcL7JVbiNUSyG5xsmzbwYJwuyo2/CVSy/iD/5Vhhi03Nb09FfrCJQr6Jyh85Og8drJzHKwBAAAAAMC9CI/hWlZmLnYWIHe2wF45WNgY/ufiQfYee/T5b6g3NbnBk8celhdj7zZ5335MvreWyrPrE1tt2w8frlDNRAVHXCZ1PyxPPbSPgzUAAAAAALgT4TFcyc4p7x0FyJIyLrBXDloCkVXnpcyLmiWLf/4bFymrxYcQxzRV8dFr8gaWyLvuWRnhkPWmFT6FTrhIwZoJCh99qmQ483XgYA0AAAAAAO5DeAzXaQnYr5WaKUCWOl5gr9TV+A1Nn5p5UbNk6YJ7guMste2Qb80T8gWWyLNjg62m4T5DFKyZoOCoK6WeffPTvxxqajbL/mANAAAAAABuQ3gM16nxG2qoN/9ZasH6Ke/JAXJUZwvslbpMpT2SschZDpimPJ/+Xb7AEnnf/aOM9oPWmxoVah92voI1E9U+5IuS4Y5yIcn7TbkerAEAAAAAwG0Ij+FKkxs8OSmVYHWBvVJn5bETHHfRgT3yrn1SvpYlqvjsXVtNw72PVNB/rUKjr5HZe2CeOpgfHe035XqwBgAAAAAANyE8hmvZDY67usBeqcv02AmOs+fZuka+liXyrv0fGcF9ltuZMtR+3NkK1lyn9uO/LHnc93adab8p57EGAAAAAIBbuC+NALKQiwX2yiHUSvfYkxc5Izi2INgm73tPy9eyWBWbA7aahisPV2j01QqOuVZmn8F56mD+cbAGAAAAAAD3IzxGycvVAntjRtuf7exGyY+d4Ng64/P18gWWyLfmCRkHdnXeIE7omDMU8k9QaPgFUkW3PPWwMDhYAwAAAABAaSA8RsnLxQJ7DfXlERxH1dUaKTOOq6oI9NJqPyjvumflbVki78crbTU1u/dRcNQVCvrHy+w3NE8dLCwO1gAAAAAAUDoIj1EWsl1gr67WKMsQq6k5MTiWIjOQm5pNAuR/MnZ+LF9gqbxvPypP23ZbbduPqlHQP1GhEy+SfD3y1MPi4GANAAAAAAClg/AYZSPbMKrcQqzkkgNVVYdKV5R9SYFwSBXvvyRfYIkqNvxVhszO2/yT6atUaMSlCvonKDxwRB47WXwcrAEAAAAAoDQQHgOI6ahWbfz15RggG7u3yPv2I/K99Yg8ezbbats+4AsK1kxU6AvflLr1ylMPnSefB2taAvaD6a60AwAAAACgXBEeA5CUeZGzslzUzAyr4sNX5QssVsX6F2SY7dabVnRX6KRvRGYZH1UjGSX8PBXYwsbwP0ti2Nv/ovt3Q72pyQ2ePPYQAAAAAIDSQXgMIGNwHFU2AfK+7fKtfky+wFJ5dn5kq2m47/EK1kxQcMTlUs/q/PSvjLUEIrWUJXv7X/z+3bhIWZXUAAAAAACgHBEeA2XOSnAcVbIBsmnKs+kN+QJL5P3HMzLag9abenwKnXChQv6Jah98OrOM86jGb2j6VHv7X7r9m+AYAAAAAABrCI+BMtYSsB4cR6ULkF27yNn+XfK9s1zewGJVfL7eVtNw1dEK+icoNOoqmb3656mDSGbnAIadAyMAAAAAACAV4TFQxmr8hhrqzX/WkLUerMUHeA317guOPZvfkq9lsbzvrpAR2m+5nWl41D70Kwr6J6j92LMkT0Uee4mOWAmQCY4BAAAAAOg6wmOgzE1u8GRVA7au1nDXjOPgPnnXPiVfYIkqtqy21TTca4BCo69RcMw1MquOzlMHYUemAJngGAAAAACA3CA8BpB1AOyG4Njz2XvytiyR753lMg7usdU2dOyXIrOMh54nVfjy1ENkK12A3LzY1K5dh25DcAwAAAAAQPYIjwGUntABed97JjLL+JNVtpqaPaoVHH2VgmPGy+x7bJ46iFxJDpAJjgEAAAAAyB3CYwAlw9ixQb7AMvlWPyZjf6uttu2DxkYWwDvha5K3e346iLyoqzVSZhxXVaVfRA8AAAAAAFhHeAzA3dqDqlj/gnyBxfJufNVWU7NbbwVHXq6Qf7zC/U/MUwdL2xurgho21H67loD9OtsdaWpODI6lyAzkpmaTABkAAAAAgC4gPAbgSsauT+R7a5m8bz8iz97PbLVtP2KUgv6JCn3hYslXmacelr4H5u3T3AfbNGOaoUnXWQ9powvaNdSbmtzg6VIfkhfHq6o6VLoifhE9AMi1bA+C5fLgGQAAAJBvXfvWDgCFFG5XxfsvqcfjN6py4VfV7bUHLQfHpreHgqOv1r7aZWqb9IhCY64hOO6CloCpuQ+2SZLmPWSqqdnspEVEfNjbuChyP9lKDo6nTzW0YrlH06ceCmUenG+9bwBg1cLGsGbOsv/+0tRsauYsUwsbw3nqGQAAAJBbzDwG4HjG3m3yvv2YfG8tlWfXJ7bath8+XCH/RAVHXCr1qMpTD8tPjd/QLTdV6t45+yRZm+WbLuzNdvZduvuK/u7kRfSYgQwgl1oCphoXRS7beX9JPnh22lhmIAMAAMD5CI8BOJNpquKjlfIGFsu77lkZ4ZD1phU+hU64SMGaCQoffapk8OU8H6bc0FOSLAXImcJeu6zcFwEygHyp8RuaPtXe+0suD54BAAAAhUR4DMBZ2lrlW/O4fIEl8uzYYKtpuM8QBf3jFRx1pVTZLz/9Q4IpN/TU/v1tmvdQxyFKoYPjKAJkAPli5/0ll++BAAAAQKERHgMoPtOU59O/yxdYIu+7f5TRftB6U6NC7cPOV7BmgtqHnCkZlHIvtLpaj0wznDZEyWVo0hKwf1/pAp4xo8WMPwBdZiVAJjgGAACA2xEeAyieg3vlfedJ+QJLVLFtra2m4d5HKui/VqHR18jsPTBPHYRV6UKU5sWmdu06dJuuhiY1fkMN9ZFao3buK75vDfUExwByJ1OATHAMAACAUmCYpsky9C6wY8eOYnch7wzDUHV1tSSptbVV7Jqly7P1ncgs43eelBHcZ7mdKUPtx52tYM1EtR9/juTh+FexdDRek8OSqFyGJi2B7BaZyrYd4GZ8thZG8ntfVZVyevAM5YHxCrgH4xVwj3Icr3379s3p/ZG8ACiMYJu87z0tX8tiVWwO2GoarjxcodFXKzjmWpl9Buepg8iFulojZcZxVVVu6wxnGwATHAPIl+QZyATHAAAAKBWExwDyytj+vnyBJfKtflzGgV2dN4gTOuYMhfwTFBp+gVTRLU89RC41NScGx1IkRGlqNglPAJS0Qhw8AwAAAAqN8BhA7rUflHfds/K2LJH345W2mprdqxQcdYWC/gky+w1NexvKDzhTptO20y0kBQClhINnAAAAKEWEx0CRlGLdVmPnx/IFlsq7+jF59n1uq237UTUK+icqdOJFkq9Hh7eLBpQN9aYmN3i62mXkSEcLQ8VfT4AMoFRx8AwAAACliuQFKIKFjWHNnGWqqdleofamZlMzZ5la2BjOU8+yEA6pYv3z6vHYVFUu/Jq6/e03loPjfaGeWrZhvJYd/ojarlus0KgrLAXHktS4KBKko/iamsNpg2MpEpZMn3ooMHlwvv39HgCcLN3BsxXLPbz3AQAAoCQw8xgosJaAqcZFkct2ZiMlB6enjS3uDGRj9xZ5335EvrcekWfPZltt2/ufpJcPTtAPFl+ivaHeUkD63Jf5tN50X86dOgO7nCx4uE3zHkofHEclLyTFLDwApaKjsy4k3vsAAABQGgiPgQKr8RuaPtXel0nHBKdmWBUfvipfYLEq1r8gw2y33rSiu0InfUNB/wSFj6rRqYah6yutlTTI9OUcxbPg4TbdO2dfbDvT60KIAqDUWPls4r0PAAAAbkd4DBSBnS+TjghO922Xb/Uf5AsslWfnRltNw32PU9A/QcGRV0g9qxN+ZuV5cMTjR4qWgGk5OI5K93qPGS1mkANwHTufTQTIAAAAcDPCY6BIHB+cmqY8m96QL7BE3n88I6M9aL2px6vQ8AsV8k9U+zHjJKPjPmd6HgiOnavGb+jG6T0198E2zZhmaNJ11l6X+Ne7oZ7gGID7tATsfzZx8AwAAABuZZimyeodLrBjx45idyHvDMNQdXW1JKm1tVXlsmt2FJAWLTg9sFu+NcvlDSxWxefrbDUNVx2toH+CQqOulNlrgK22mVaqlwiOnSY6Xt9YFdSwoXttj9eWQHFrdgPlolw/W/NtYWNYjYvsfzZFP+sa6qXJDaxbjUSMV8A9GK+Ae5TjeO3bt29O74/w2CUIj0ubE4JTz+a35QsslnftChmhNsvtTMOj9uPPVdA/Qe3HnS15KrLuQ/LzEEVw7DzlPF4BN2Gs5k+2B8E4eIaOMF4B92C8Au5RjuM11+ExZSsAB0g+nbVgwXFwn7xrn5IvsEQVW1bbahruNUCh0dcoOOYamVVH56Q7dbWGmhebCY+/qoq6kAAA58k2ACY4BgAAgJsQHgMOUcjg1PPZe/K2LJHvneUyDu6x1TZ07Jcis4yHnidV+HLar6bmxMcvRYL0pmaTABkAAAAAAKDACI8Bh8h7cBo6IO8//hSZZbzpDVtNzR7VCo66SkH/tTL7Htf1vqSRqXQHK9MDAAAAAAAUHuEx4AD5DE6NHRvkCyyTb/VjMva32mrbPmhsZAG8E74mebtn9futsLJoIAEyAAAAAABAYREeA0WWl+C0PaiK91+Qr2WxvBtftdUfs1tvBUderpB/vML9T7TVNhsdPX4ptRY0ATIAAAAAAEDhEB4DRZTr4NTY/al8gWXyvv2IPHu32epL+8CRCtZMVOiki6VuvWy1zVamxx9FgAwAAAAAAFAchMdAkeQsOA23q2LDXyO1jD94SYYZttwH09tDoS9coqB/osJHjs72oWTFyuOPIkAGAAAAAAAoPMJjoAhyEZxef/nn8q5+TL7AUnl2bbL1+9sPH6aQf6KCIy6TelRl8xC6pCVg/fFHpXsexoyWavwEyAAAAAAAAPlAeAwUWNeC07BOO3yljn9jqXpueVYeM2T595oVPoVO+LqC/gkKDxorGcULXWv8hhrqTTUusvb4o+ID5IZ6gmMAAAAAAIB8IjwGCiyr4LStVQ0nPa4JVy5V3/YPIteZmZtEhfsMUdA/XsFRV0qV/bLveI5NbvDotLGm7QC4rtZgxjEAAAAAAEABEB4DRWApODVNeT5tkS+wWN53n5bRfkDdLd6/aVSofdh5Cvonqv3YMyXDk5N+51q2ATDBMQAAAAAAQP4RHgNF0mEAenCvvO88GVkAb9taW/cZ7n2EgmOuVWj0NTIPOyIHvQQAAAAAAEC5IjwGcqQlYL8EQ3w7z7a18rUslvedJ2UE91lub8pQ+3FnK1gzUe3HnyN5GNYAAAAAAADoOlImIAcWNob/WcNYlhd/k6T//n2bNj79Rw09bamOam+x9TvDPfspNPpqBcdcK7P6GLtdBgAAAAAAADIiPAa6qCUQWfxOkh6cH1nFrrMA2dj+vtYvXaIJrY+r6pRdUrv13xcaPE6hmgkKDb9QquiWbbcBAAAAAACAjAiPgS6q8RuaPvVQcNxhgNx+UN51z8nbsljej1fKL0kWs1+ze5WCo65QcMx4mYcPy13nAQAAAAAAgA4QHgM5EA2K0wXIxs6P5XtrmbxvPyrPvs9t3W/7kX4FayYqdOJFkq9nbjsNAAAAAAAAZEB4DORIfIBcYYT0zhP/q+3bl+qYg3+VIdPy/Zi+SoVGXKqgf7zCA0fmq7sAAAAAAABARoTHQA5df9k2jd2zTMdse0RH9twsHbTetr3/iZFZxl+4VOreO3+dBAAAAAAAACwgPAa6ygyrYuOr8rUsUcX653W62S5ZrDBhVnRT6KRvKOifoPBRJ0tG5oX2AAAAAAAAgEIhPAaytW+7fKv/IF9gqTw7N9pqGu57nIL+CQqOvELqWZ2X7gEAAAAAAABdQXgM2GGa8nyySr6WxfL+4xkZ7UHLTYNhr/7y2QU688aJaj/mDGYZAwAAAAAAwNEIjwErDuyWb81yeQOLVfH5OltNP9l3tB798Fo9vvFKfX5ggKb7jdjiegAAAAAAAIBTER4DGXg2vy1fYLG8a1fICLVZbheWR/+7+Rw9smGCXtl6lnpXVWjXgcjPHpxvShIBMgAAAAAAAByN8BhlryVgqsYfF+QG98m7doV8gcWq2LLa1n2Fe/XXm7pGP3z8am1uO1qSNH1qZKZxU7MZC44JkAEAAAAAAOB0hMcoawsbw2pcJE2fKn3ra/+QN7BUvjVPyDi4x9b9hIacqaB/on73t69o7m8ODatocCwdCooJkJ0r5UBCntsBAAAAAAA4GeExylZLwFTT7w7q4kF/0rjVS1S5eZWt9jsOVGv5R1dq9Leu1UlfPD4ys/g3Zuzn8cFxFAGyc8UfSLDzekRnlDfUm5rc4MljDwEAAAAAAAqL8BhlydjxoU5vXar/vfQP6mnusNX2zc9P0bIPJ+jZT76mG6b00ElfTCxJIaUPjqMIkJ2nJWCqcVHksp3XI/51b1wknTaWGcgAAAAAAKB0EB5nYe/evVqzZo0CgYACgYDeeustbdq0KfbzQYMG6fnnny9iD5FWe1AV778gX2CJvB++Yqup2a2XVnsv1+ynrtW63SdKOhQQtwSsB8dR6QLkMaNF8FgkNX5D06faC/TTHTDg9QMAAAAAAKWE8NiGxsZGPfbYY1q3bp3C4XCxuwOLjN2fyhdYJu/bj8izd5uttu0DRypYM1FNLd/QrxdUxq6PD4hr/IYa6s1/ljzoPDiOig+QG+oJjovNzoxwOzPNAQAAAAAA3Irw2Ia//e1veu+994rdDVgRblfFhy/LF1iiivdflGFaD/vbQj309KaLFT51gr5e548EhQsyB4WTGzxZlSyoqzWYcewgVgJkgmMAAAAAAFAuCI+7qLKyUqNGjdLq1au1b9++Ynen7Bl7P5N39WPyBZbKs2tT5w3itB8+TK8enKAfLLlUu0NVUos059Gwdu06dJtMQWG2ATDBsbNkCpAJjgurJZBdDels2wEAAAAAgESExzZ0795dfr9fY8aM0ejRozVmzBgNGzZMHo9H559/PuFxsZimKj7+m7wt/y3vuudkhIPWm3p8Cp3wNQVrJio8aKxONgxN6nUoILQaHKO0pAuQmxeb7A8FtLAx/M9SMPYWk4wG/A31piY3ePLYQwAAAAAASh/hsQ333ntvsbuAeG2t8r3zhHwtS+TZ8YGtpuE+xyjoH6/QqCtlVh6e8LO6WiMlKKyqshdgwf2SA2SC48JpCURqiEvWFi+Mip8Z3rhIWZWSAQAAAAAAhxAew11MU55PW+QLLJH33T/KaD9gvalRofZh5ynon6j2Y8+UjPSzEpuaE4NjKRIcNjWbBIZlhgMJxVHjNzR9qrXFC6PSlRQhOAYAAAAAoGsIj+EOB/fK+86TkQXwtq211TTc+wgFx1yr0OhrZB52RMbbJgdQVVWHZpzamQGJ0sCBhOKxsnhhFLWoD6FONAAAAAAglygICUfzbFur7s/OVq+HzlGP5+6yHBybMhQ67stqu+zX2jflWQXPnGk7OJ4+1dCK5R5Nn3ooUHlwvqmmZjNdc5SYdAcSoh6cb+qX94Szut+WAPuPVXW1Rqfjj+D4kIWNYc2cZf89qqnZ1MxZphY2ZrdPAwAAAABKFzOP4ThmsE3e1X+Qt2WxKj5tsdU23LOfQqOvVnDMtTKrj7HcLlMAZWcGJEpDR/tD/PVPLJc2bw7r7l9YPwbHYm72ZRp/BMeHUCcaAAAAAJAPhMdwDGPHh2p/5R6F/75U3dtabbVtH3y6gv4JCg3/quTtZqutlQCKALl8dHYg4dPNpp5YHvnZayul2/7NWoBMSJe9dOMvuRZ1OQfHEnWiAQAAAAD5QXjsEoZR2l/oK9Y+pe7P/D8Kh2wsgNe9SqFRVyjonyDz8GGSJLvPUlNzOCE8mTHNUF1t+iDw+kmGDCOseQ8dCmcMQx3eHu5jZX/4t9sqtGVLu/7vtcj2ayul790e1t2/qLB1vyfXuHu/iX9PKsT7U/L4iw+OM43bcmLnPcrOex/crdBjFUD2GK+AezBeAfdgvHadYZomBThz4Pzzz9emTZskSYMGDdLzzz9f5B65hxlsU+i/xkr7tlu6vTH4VHlO/5aM0ZfJ6FaZ9e99Y1VQ32o4lEDdclOlptzQs9N2Cx5u071z9sW2/7/GKo091Zd1P+AMya9rZ/vDtBt36a8vB2PbZ5/l00Nzq1JuZ/d+kdmXztmunTsPfWz16WPolf/tV8QeOU9n+xz7JAAAAADAKqYZofj2bOs8OO5WKc9p18s748/yTlshz6kTuxQcS9LYU326cXokMLETnky5oaduuSnyu2+c3pPguAS8sSpoO0x7aG6Vzj7r0Gv/15eDuuvHexJu05WQ7o1Vwc5vlMN2brDg4baE4FiSdu40teDhtiL1yJni36Mk6d45+2LPEcExAAAAAMAOZh7nSL5nHre2tub0/hwlHFLP35wvz56tqT/qf6KCNdcpNOJSqXvvvPz6lkB2tWezbQdnWtgY1sO/NW2fvn/bvx0qYSEdOv2/qflQ+YD46/PZl+jvvOHbRt4X5DMMQ3369JEk7dy5U/n+KEl+PquqKF3RGZ4zSIUfqwCyx3gF3IPxCrhHOY7X6urqnN4f4XGO5Ds83rFjR07vz2k8m1apxx//TZ5dmyRvdwVPvEhB/wSFjzpZoiYNCiTbAwK/vDusJ548tJ0c0tlZzK0lYGrmrMwLOKaTvPjZA/fld/EzwzBiH0itra15/QDuaBFDK4tdlrvk5yiK56p8FHKsAugaxivgHoxXwD3Kcbz27ds3p/fHlCM4QnjQqWqb/Cd5b10p74/W6eA3fq7w0acQHKOgsg1b//U2j6ZPPdQ22+A42of4+3pwvqmm5swfbulC1FKZFZ8pIK6rtf9clZu6WkNVSaW4q6pEcAzLWgLZjals2wEAAABwFsJjOIenQkbfITIqqCEM98llSGcnFC3l2bdWHhsBcmZNzWbCwQwpcnCD5whWLGwMa+Ys+2OqqTlyBsXCxnCeegYAAACgUAiPASAHch3SWQlFyz04jiJATi/5OYw/uMFzhM60BEw1LopctrO/xO93jYuYgQwAAAC4HeExAHRRvkK6TKFoKQfHLQH7jy3dc5Xr0MpNp++n2z9WLPcQssMySugAAAAAkAiPAaBL8h3SpQtFL74sbDtcddPsvxq/oYb6yGU7oXj8c9VQn30N63TcdPo+daKRK5TQAQAAAOAtdgcAwK06C+kkxX4e/T/bGsjx92F3Qb5oPxvqTU1ucMcxw8kNHp021rQdANfVGhozOrfBcfLp+9Hf05nk0/ezeTx2Wa0TLeVm30Tps7K/EBwDAAAApcsdKQLgIE47dd1p/SkXhV7Mbczo1OusLMjn5vqj2QatuQ5o3XL6PnWikS/lWkIHAAAAAOExYIvTTl13Wn/KRTFCurfeTr2uswX5qD+aO04/fd+pdaJROnJVQgcAAACAuxAeAxY5beV5p/WnXBQjpEsOI+N19NozGzD3rATIxXrenVgnGqUneQzYLaFTaJyZAwAAAHQd4TFgkdNOXXdaf8pFoUO6dK9Z/Osupb72BMf54+TT9yc3ePTAffZ/Z12toQfuM1xTDxvFVVdrqKoq8TorJXQKjTNzAAAAgNwwTNNkeoVFmzZt0le/+tW0P2tvb0/YrqioSHu73/72txo3bpzt371jxw7bbdzGMAxVV1dLklpbW+XUXdNqQFSoIMlp/SkXLYHsFj+z0y7Ta5ZuNnI01CzE6+yW8Zovyc9/VZXzZ2GiPOV6rHZ0JoST9vmWQCQAjrLat+TH9sB9HGBFYZX7ZyvgJoxXwD3Kcbz27ds3p/fnzem9lTjTNFNC4o50dLty2ElLndNWnndaf8pFvhdz6+w1S37dky+na4PcSX7+CY5RDjIdNEn32VMskTNzMn8uJuPMHAAAACA9zlEFsuC0U9ed1h90jdXXLPl1j8frnH9uOX0fyIV070srlntysiBoPjh9kUsAAADALZh5bMPgwYP17rvvFrsbcIh0M36bF5tFm4HotP4gO3YX5KurNVJeZ0kaMzpfPURUU3Pq875rV+R6xhlKSaZw1crZL8XCmTkAAABA1zHzGOgCp60877T+wD67C/KlCzAl6a2389A5xKQ7fT/KSbMvga6yEq7ameVbaJyZAwAAAHQN4TGKoiWQ3ZfKbNvlk9NOXXdaf2Df5AaPHrjPWnBcqACzlMZsV7nt9H0gW3bCVbcFyBdfFiY4BgAAACwgPEbBLWwMa+Ys+18qm5ojq6cvbAznqWfZyXTqOv1BtjpbqKmQAWapjdmu6Oz0faeGZ4BddkvoSOnHgFMOIHFmDgAAAJAdwmMUVEvAVOOiyGU7wUp8YNO4yDmzGZ126rrT+oP8KGSAWWpjtivcfvo+YIfdEjpR8WOgob7zA2GFxJk5AAAAgH2ExyioGr/9YCVdYOOEL6NOO3Xdaf1BfhQ6wCylMdsVpXL6PmCH1RI6yepqDT1wn6HJDc76M5MzcwAAAAD7nPV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",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 711,
+       "width": 711
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(7, 7))\n",
+    "ax = fig.add_subplot(111, xlabel=\"x\", ylabel=\"y\", title=\"Generated data and underlying model\")\n",
+    "ax.plot(x, y, \"x\", label=\"sampled data\")\n",
+    "ax.plot(x, true_regression_line, label=\"true regression line\", lw=2.0)\n",
+    "plt.legend(loc=0);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d8186653-8967-4658-a52f-6440237bccdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [sigma, Intercept, slope]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f0a4bf385eb49cc87f9502de8ca0d10",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 2 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with Model() as model:  # model specifications in PyMC are wrapped in a with-statement\n",
+    "    # Define priors\n",
+    "    sigma = HalfCauchy(\"sigma\", beta=10)\n",
+    "    intercept = Normal(\"Intercept\", 0, sigma=20)\n",
+    "    slope = Normal(\"slope\", 0, sigma=20)\n",
+    "\n",
+    "    # Define likelihood\n",
+    "    likelihood = Normal(\"y\", mu=intercept + slope * x, sigma=sigma, observed=y)\n",
+    "\n",
+    "    # Inference!\n",
+    "    # draw 3000 posterior samples using NUTS sampling\n",
+    "    idata = sample(3000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e43889e1-e5d9-4736-bf69-1f41bb2b271c",
+   "metadata": {},
+   "source": [
+    "We've done inference with NUTS. Now we can try DADVI."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b18297f4-6592-4afb-a1e3-364b81039e8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This single import should be all that's needed\n",
+    "from pymc_extras.inference.deterministic_advi.api import fit_deterministic_advi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dc6e3943-cd0d-4c66-a177-6e5f2a34c5a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'f': 713.4534726015399, ||grad(f)||: 967.1327633132877\n",
+      "'f': 479.931132929988, ||grad(f)||: 321.38918482649177\n",
+      "'f': 415.0696415398314, ||grad(f)||: 121.08420341297919\n",
+      "'f': 337.7224486499685, ||grad(f)||: 162.75528032449952\n",
+      "'f': 504.02428287446673, ||grad(f)||: 981.96731906951\n",
+      "'f': 264.4594240322506, ||grad(f)||: 133.6422132575744\n",
+      "'f': 238.3620698481016, ||grad(f)||: 295.47412016993616\n",
+      "'f': 202.3872063826712, ||grad(f)||: 78.51034098808502\n",
+      "'f': 191.15958110793596, ||grad(f)||: 37.53448393652803\n",
+      "'f': 184.84485574860963, ||grad(f)||: 107.08693948173128\n",
+      "'f': 178.74616664207306, ||grad(f)||: 35.669339324740314\n",
+      "'f': 173.99800346020643, ||grad(f)||: 8.829578264006848\n",
+      "'f': 173.90207541922908, ||grad(f)||: 1.0160571789804076\n",
+      "'f': 173.8315687218876, ||grad(f)||: 0.2777895329277002\n",
+      "'f': 173.82759779415377, ||grad(f)||: 0.057755540352186614\n",
+      "'f': 173.82750584115203, ||grad(f)||: 0.0016314353337682263\n",
+      "'f': 173.82750529818853, ||grad(f)||: 2.2617180758073807e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can fit DADVI now. It prints some progress of its convergence -- note we could change this\n",
+    "# to look different / remove it.\n",
+    "# Note that unlike regular ADVI, deterministic ADVI converges automatically. This is one of its key advantages.\n",
+    "with model:\n",
+    "    dadvi_res = fit_deterministic_advi()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c47cb209-907b-4873-a3bf-39bff5578502",
+   "metadata": {},
+   "outputs": [
+    {
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+       "\n",
+       ".xr-header > div,\n",
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+       "}\n",
+       "\n",
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+       "\n",
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+       "  content: \")\";\n",
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+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
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+       "\n",
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+       "\n",
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+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 32kB\n",
+       "Dimensions:    (chain: 1, draw: 1000)\n",
+       "Coordinates:\n",
+       "  * chain      (chain) int64 8B 0\n",
+       "  * draw       (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
+       "Data variables:\n",
+       "    Intercept  (chain, draw) float64 8kB 1.024 1.132 1.072 ... 1.06 0.9415\n",
+       "    slope      (chain, draw) float64 8kB 1.917 2.003 1.989 ... 2.093 2.046 2.052\n",
+       "    sigma      (chain, draw) float64 8kB 0.5302 0.4897 0.5021 ... 0.5441 0.4952\n",
+       "Attributes:\n",
+       "    created_at:     2025-08-14T09:02:12.068400+00:00\n",
+       "    arviz_version:  0.22.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0265cad7-da58-45ec-a83a-ef768ad7a7ed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0265cad7-da58-45ec-a83a-ef768ad7a7ed' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2eceeec3-7c45-4d7e-ba93-1d13eecd064f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2eceeec3-7c45-4d7e-ba93-1d13eecd064f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-fd493557-d432-44f4-801c-240b6c93dba7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd493557-d432-44f4-801c-240b6c93dba7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-841eb2c1-5f99-414e-b98d-040b80ecc102' class='xr-var-data-in' type='checkbox'><label for='data-841eb2c1-5f99-414e-b98d-040b80ecc102' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-4b14cfcc-4987-4c67-9e78-3106ad707c78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b14cfcc-4987-4c67-9e78-3106ad707c78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-087efd6a-3af0-44a3-ab49-49aa0ac30781' class='xr-var-data-in' type='checkbox'><label for='data-087efd6a-3af0-44a3-ab49-49aa0ac30781' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999], shape=(1000,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd704f8e-67ce-4aa2-b904-ba5ca4ef7866' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd704f8e-67ce-4aa2-b904-ba5ca4ef7866' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.024 1.132 1.072 ... 1.06 0.9415</div><input id='attrs-20dacd17-343d-4c90-b9ed-c9e84aa0e1a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20dacd17-343d-4c90-b9ed-c9e84aa0e1a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eca49719-a45c-40d2-83fe-866c058b407f' class='xr-var-data-in' type='checkbox'><label for='data-eca49719-a45c-40d2-83fe-866c058b407f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.02423358, 1.13214564, 1.072478  , 0.99841007, 1.04827592,\n",
+       "        1.01482212, 1.00686333, 1.0378902 , 1.0283221 , 0.98379442,\n",
+       "        1.03198199, 1.05201041, 1.07242427, 1.07454867, 0.94809642,\n",
+       "        1.02848707, 1.00260019, 1.02610458, 1.06883805, 1.07276731,\n",
+       "        1.02409935, 1.06319896, 1.01054579, 1.08466965, 1.02608234,\n",
+       "        1.12921922, 1.08256285, 1.04204586, 1.02490851, 1.10998724,\n",
+       "        1.06612953, 1.11989228, 1.06605638, 0.92315095, 0.9866524 ,\n",
+       "        1.18172176, 1.00836459, 1.01670677, 1.03160834, 0.99736694,\n",
+       "        0.99251318, 1.06833068, 1.08554496, 0.95350888, 0.99044707,\n",
+       "        1.09959851, 1.07221878, 1.03288865, 1.21815275, 1.02359065,\n",
+       "        1.07765238, 0.93752456, 1.10474129, 1.02262079, 1.02152724,\n",
+       "        1.08578299, 1.26163946, 0.94191896, 1.02697613, 1.02959188,\n",
+       "        1.11616078, 1.07308073, 0.96985303, 1.07873855, 0.98659023,\n",
+       "        1.14075991, 1.10028964, 1.02372132, 1.09171603, 1.08464893,\n",
+       "        0.94902426, 0.97527543, 1.01708894, 1.00200698, 1.05244998,\n",
+       "        0.97587417, 1.10305835, 1.09727215, 0.992403  , 1.14188873,\n",
+       "        0.97697696, 1.0301139 , 1.01365668, 1.02853078, 1.04910016,\n",
+       "        1.04916673, 1.06371857, 1.08100031, 1.02091666, 0.99710785,\n",
+       "        0.95942206, 1.01184389, 1.10094271, 1.02723867, 0.93995075,\n",
+       "        1.03878949, 0.98541613, 1.00822683, 1.01612199, 1.02049984,\n",
+       "...\n",
+       "        1.04343402, 1.08630445, 1.03480269, 0.92914828, 1.10605693,\n",
+       "        1.0164609 , 1.02400983, 1.20819081, 1.23374511, 1.09730258,\n",
+       "        0.97090626, 1.00648394, 1.0233964 , 1.12086542, 1.10114055,\n",
+       "        1.00309621, 1.06004915, 0.92752479, 1.10954258, 1.03301626,\n",
+       "        1.11615819, 0.98744769, 1.02026043, 1.1153196 , 0.99663706,\n",
+       "        1.16998398, 1.03583563, 1.11559131, 1.01989872, 1.06736821,\n",
+       "        0.96846236, 1.05644294, 1.04882904, 1.02808556, 0.96054129,\n",
+       "        1.00676139, 1.0603263 , 1.03447109, 1.10776001, 1.06860415,\n",
+       "        1.02088386, 0.9987875 , 1.04225094, 1.09002115, 1.02716016,\n",
+       "        1.04482285, 1.11519222, 1.06931526, 1.13311052, 0.98383476,\n",
+       "        1.06530887, 0.95936957, 1.02754557, 1.01991861, 0.94289119,\n",
+       "        1.10781833, 1.09270573, 1.05499586, 1.1113207 , 1.10407635,\n",
+       "        1.00620247, 1.05778944, 0.94591702, 1.04000685, 1.00446716,\n",
+       "        0.95204823, 1.03531636, 1.13389779, 1.03389275, 0.94733869,\n",
+       "        1.06619758, 1.10565022, 1.03408208, 0.94122729, 1.04245624,\n",
+       "        0.97263879, 1.00927691, 0.99928997, 1.11684691, 0.99609668,\n",
+       "        1.05751935, 1.13451328, 0.99478621, 1.08727973, 0.98795396,\n",
+       "        1.02913718, 1.17274048, 1.07302921, 1.07379724, 1.07174714,\n",
+       "        1.13900459, 1.0109294 , 0.94462211, 0.96500562, 1.01506402,\n",
+       "        1.02960575, 1.08311952, 0.95107408, 1.05994956, 0.94154183]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.917 2.003 1.989 ... 2.046 2.052</div><input id='attrs-30ea874e-2082-4faa-9087-a299494eb0e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30ea874e-2082-4faa-9087-a299494eb0e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be1ebdcc-ff33-4db3-a19a-3555280026f4' class='xr-var-data-in' type='checkbox'><label for='data-be1ebdcc-ff33-4db3-a19a-3555280026f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.91697266, 2.00334667, 1.98892008, 2.21246449, 2.09548297,\n",
+       "        2.13791721, 2.00092046, 1.99353576, 2.01696503, 2.04927131,\n",
+       "        1.89705003, 2.15023709, 2.05951416, 2.11096041, 2.1574465 ,\n",
+       "        2.06271442, 2.12626441, 2.05454434, 2.01716359, 1.93549484,\n",
+       "        2.05687313, 2.06259998, 2.09449797, 2.11572765, 1.98619852,\n",
+       "        2.0534112 , 2.04687089, 2.14280237, 2.15074432, 2.08292706,\n",
+       "        2.07313959, 2.12896403, 2.13781914, 2.08527514, 2.03922289,\n",
+       "        2.08891919, 2.03782409, 2.02008001, 2.01289906, 1.96121384,\n",
+       "        2.03792215, 2.14376328, 2.13352274, 2.0262721 , 2.04728544,\n",
+       "        2.11598492, 1.96484127, 1.92054547, 2.17912692, 2.10604964,\n",
+       "        2.04428335, 2.08777197, 2.17573851, 2.12211387, 2.06784255,\n",
+       "        2.09270497, 2.16172249, 2.11116528, 2.0859719 , 2.13621564,\n",
+       "        2.02920448, 2.10159269, 2.08347721, 2.11011689, 1.97801184,\n",
+       "        2.09642421, 1.98271113, 2.15969911, 2.10383467, 2.05227256,\n",
+       "        2.03844841, 2.07552461, 2.09639081, 2.20262033, 2.06354795,\n",
+       "        2.08422265, 2.08555662, 2.01532663, 1.8923331 , 2.05439962,\n",
+       "        2.05402556, 2.09614278, 2.09376097, 2.09544067, 1.90107797,\n",
+       "        2.05047342, 2.06520958, 2.00263974, 2.05900631, 1.96361798,\n",
+       "        1.98831187, 2.036075  , 2.10791378, 1.95734785, 2.05936628,\n",
+       "        1.95004469, 2.12812067, 1.98806304, 1.97977537, 2.03313343,\n",
+       "...\n",
+       "        2.05530695, 2.00217808, 2.11441924, 2.06249682, 2.05009886,\n",
+       "        1.98657164, 2.09668262, 2.03196947, 2.04352393, 2.04953399,\n",
+       "        2.00931414, 2.12690864, 2.1018454 , 2.1204206 , 2.09961675,\n",
+       "        2.07535717, 2.04098068, 2.1205835 , 2.14888293, 2.03443908,\n",
+       "        2.1732742 , 2.08582487, 2.02294894, 1.99318913, 2.03235507,\n",
+       "        2.10177151, 2.02774304, 2.07596736, 2.06460154, 2.1751096 ,\n",
+       "        2.10421417, 2.10006932, 2.17373393, 2.02043598, 2.11959153,\n",
+       "        1.92492828, 2.05951315, 2.0348166 , 2.03930556, 1.99986352,\n",
+       "        1.87102165, 2.0902266 , 2.08207058, 2.08337471, 2.12790317,\n",
+       "        1.9673689 , 1.91446766, 1.89179225, 2.10400844, 2.05124513,\n",
+       "        1.99217333, 2.10397891, 1.98707775, 1.93347469, 1.99235129,\n",
+       "        2.03588954, 2.07867537, 1.92756295, 2.13468686, 2.10931335,\n",
+       "        2.13600089, 2.08006471, 2.24951764, 2.06121473, 2.09622105,\n",
+       "        2.04410602, 2.03698884, 2.20533904, 2.03162615, 2.20700731,\n",
+       "        2.19161057, 2.04844913, 1.98522305, 2.07154886, 2.11393931,\n",
+       "        2.06769412, 1.96168876, 2.02024033, 1.98970737, 2.07018536,\n",
+       "        1.98646095, 2.0011341 , 2.22943759, 2.05596641, 1.92291756,\n",
+       "        1.99934292, 1.9652415 , 2.13451806, 2.04079166, 2.16918646,\n",
+       "        2.05669288, 2.14205927, 2.1223799 , 2.08985125, 2.12973614,\n",
+       "        2.03145339, 2.05918212, 2.09295493, 2.0457824 , 2.05163278]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5302 0.4897 ... 0.5441 0.4952</div><input id='attrs-068375f9-2108-4b50-8043-a25d86189c68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-068375f9-2108-4b50-8043-a25d86189c68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f526f8c8-565b-43a5-96b9-a0b62d6bd8b5' class='xr-var-data-in' type='checkbox'><label for='data-f526f8c8-565b-43a5-96b9-a0b62d6bd8b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.53015255, 0.48974337, 0.50214987, 0.54506267, 0.50507724,\n",
+       "        0.53105185, 0.5317347 , 0.5377762 , 0.52581713, 0.4981341 ,\n",
+       "        0.58849647, 0.54057837, 0.51142625, 0.52396477, 0.57521445,\n",
+       "        0.54655447, 0.53364766, 0.52248039, 0.5634492 , 0.53678421,\n",
+       "        0.52831535, 0.48425602, 0.49772001, 0.51590304, 0.53313106,\n",
+       "        0.47076414, 0.48440758, 0.55190266, 0.49896913, 0.52042571,\n",
+       "        0.5454315 , 0.49098492, 0.52627446, 0.56045215, 0.54212621,\n",
+       "        0.5779551 , 0.51754998, 0.5100735 , 0.51912387, 0.59712898,\n",
+       "        0.54210897, 0.55126015, 0.50794753, 0.52874732, 0.54606393,\n",
+       "        0.4967436 , 0.53459334, 0.49529004, 0.55559647, 0.54867529,\n",
+       "        0.5316045 , 0.54652462, 0.5249046 , 0.50191756, 0.50193375,\n",
+       "        0.53740496, 0.55181244, 0.54044538, 0.51068534, 0.51730578,\n",
+       "        0.53890784, 0.55202246, 0.52337899, 0.55534095, 0.51462914,\n",
+       "        0.53591017, 0.52359439, 0.52793408, 0.50578853, 0.5063827 ,\n",
+       "        0.53959348, 0.51484645, 0.53883154, 0.52399437, 0.51136522,\n",
+       "        0.5597166 , 0.55385763, 0.53618329, 0.52075431, 0.49152059,\n",
+       "        0.48458167, 0.52765521, 0.54078442, 0.5246824 , 0.53538894,\n",
+       "        0.54072892, 0.57977916, 0.51129132, 0.50734774, 0.52728755,\n",
+       "        0.51537562, 0.51687298, 0.5657848 , 0.56909803, 0.53805612,\n",
+       "        0.53230628, 0.56231002, 0.58054123, 0.5386571 , 0.52733389,\n",
+       "...\n",
+       "        0.51002185, 0.52671278, 0.55330783, 0.54956578, 0.48170754,\n",
+       "        0.52523513, 0.50683404, 0.53390153, 0.53196713, 0.5088871 ,\n",
+       "        0.54630742, 0.51664701, 0.5267468 , 0.47639725, 0.55933856,\n",
+       "        0.54263262, 0.5265029 , 0.53952616, 0.57484308, 0.55091962,\n",
+       "        0.52467183, 0.54330671, 0.51847638, 0.52217469, 0.55500906,\n",
+       "        0.59339147, 0.56652856, 0.55112975, 0.54379306, 0.53063737,\n",
+       "        0.50080311, 0.49916256, 0.4686819 , 0.51950147, 0.51119591,\n",
+       "        0.53359993, 0.52232431, 0.525242  , 0.56194112, 0.51220094,\n",
+       "        0.55890824, 0.54051912, 0.50781342, 0.55823819, 0.5425651 ,\n",
+       "        0.53192567, 0.53188257, 0.57019311, 0.52980983, 0.54080906,\n",
+       "        0.49986879, 0.58095159, 0.53010561, 0.53766203, 0.49737649,\n",
+       "        0.53047298, 0.54496468, 0.53772425, 0.55048247, 0.53958187,\n",
+       "        0.56532771, 0.52216727, 0.52190955, 0.5172137 , 0.49751614,\n",
+       "        0.53719759, 0.53703491, 0.53174331, 0.5282638 , 0.48961309,\n",
+       "        0.52261922, 0.56817977, 0.562544  , 0.54562333, 0.51747933,\n",
+       "        0.51146554, 0.52985844, 0.5494802 , 0.56405881, 0.53818859,\n",
+       "        0.54697677, 0.52404384, 0.57143866, 0.54777914, 0.52836731,\n",
+       "        0.58833479, 0.52167064, 0.53627105, 0.49238545, 0.52246454,\n",
+       "        0.51645733, 0.56248672, 0.5043927 , 0.53806378, 0.58094705,\n",
+       "        0.55577484, 0.55521349, 0.48293466, 0.54412583, 0.49516182]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9baa4722-a1a5-4154-a9ef-cb22a2c02aa7' class='xr-section-summary-in' type='checkbox'  ><label for='section-9baa4722-a1a5-4154-a9ef-cb22a2c02aa7' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6690fd0d-f55a-43d7-ba25-06671eca8ffe' class='xr-index-data-in' type='checkbox'/><label for='index-6690fd0d-f55a-43d7-ba25-06671eca8ffe' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f4e9800d-e926-4b89-8542-0bd10c9f566d' class='xr-index-data-in' type='checkbox'/><label for='index-f4e9800d-e926-4b89-8542-0bd10c9f566d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-acdd5a39-53ff-4e91-8a06-b7dee38c5660' class='xr-section-summary-in' type='checkbox'  checked><label for='section-acdd5a39-53ff-4e91-8a06-b7dee38c5660' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-08-14T09:02:12.068400+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "              </ul>\n",
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+       "  grid-column: 1 / -1;\n",
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+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
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+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
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+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
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+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
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+       "}\n",
+       "\n",
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+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
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+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
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+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
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+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
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+       "  margin: 0;\n",
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+       "  content: '(';\n",
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+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
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+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
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+       "\n",
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+       "\n",
+       ".xr-var-list,\n",
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+       ".xr-attrs dd {\n",
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+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       ".xr-wrap{width:700px!important;} </style>"
+      ],
+      "text/plain": [
+       "Inference data with groups:\n",
+       "\t> posterior"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# DADVI returns draws like MCMC\n",
+    "dadvi_res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2d911a25-5970-4c30-bd2e-21aa0fa74217",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/s_/l9wd4yls3mv1f4kkkz1xhhmm0000gn/T/ipykernel_40360/2054181913.py:18: UserWarning: The figure layout has changed to tight\n",
+      "  f.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 790,
+       "width": 790
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "f, ax = plt.subplots(3, 1)\n",
+    "\n",
+    "sns.kdeplot(idata.posterior.Intercept.values.reshape(-1), ax=ax[0], label='MCMC')\n",
+    "sns.kdeplot(dadvi_res.posterior.Intercept.values.reshape(-1), ax=ax[0], label='DADVI')\n",
+    "\n",
+    "sns.kdeplot(idata.posterior.slope.values.reshape(-1), ax=ax[1], label='MCMC')\n",
+    "sns.kdeplot(dadvi_res.posterior.slope.values.reshape(-1), ax=ax[1], label='DADVI')\n",
+    "\n",
+    "sns.kdeplot(idata.posterior.sigma.values.reshape(-1), ax=ax[2], label='MCMC')\n",
+    "sns.kdeplot(dadvi_res.posterior.sigma.values.reshape(-1), ax=ax[2], label='DADVI')\n",
+    "\n",
+    "for cur_ax in ax:\n",
+    "    cur_ax.legend()\n",
+    "\n",
+    "f.set_size_inches(8, 8)\n",
+    "f.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "853f8ee0-5730-47c8-ba6b-d2fed86677b4",
+   "metadata": {},
+   "source": [
+    "We can see that the posteriors don't align perfectly. DADVI does a pretty good job matching the means, but underestimates the variance somewhat.\n",
+    "\n",
+    "However, often DADVI is much faster than MCMC, and so this is a tradeoff that can make sense.\n",
+    "\n",
+    "Note that DADVI as explained in the [paper](https://jmlr.org/papers/volume25/23-1015/23-1015.pdf) has further tools to make variances more accurate, which we plan to add to PyMC later on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78df242e-a266-4dda-8ca4-43b85bc92327",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 637fc3bad3b851dae69920b46fc7e4bc728c74dd Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Thu, 14 Aug 2025 11:58:51 +0200
Subject: [PATCH 04/28] Add to API and add a docstring

---
 pymc_extras/inference/__init__.py             |  3 +-
 .../inference/deterministic_advi/api.py       | 33 +++++++++++++++++++
 pymc_extras/inference/fit.py                  |  5 ++-
 3 files changed, 39 insertions(+), 2 deletions(-)

diff --git a/pymc_extras/inference/__init__.py b/pymc_extras/inference/__init__.py
index a536f91e6..c7e518d69 100644
--- a/pymc_extras/inference/__init__.py
+++ b/pymc_extras/inference/__init__.py
@@ -16,5 +16,6 @@
 from pymc_extras.inference.laplace_approx.find_map import find_MAP
 from pymc_extras.inference.laplace_approx.laplace import fit_laplace
 from pymc_extras.inference.pathfinder.pathfinder import fit_pathfinder
+from pymc_extras.inference.deterministic_advi.api import fit_deterministic_advi
 
-__all__ = ["fit", "fit_pathfinder", "fit_laplace", "find_MAP"]
+__all__ = ["fit", "fit_pathfinder", "fit_laplace", "find_MAP", "fit_deterministic_advi"]
diff --git a/pymc_extras/inference/deterministic_advi/api.py b/pymc_extras/inference/deterministic_advi/api.py
index 4c7a4616f..2f23c1538 100644
--- a/pymc_extras/inference/deterministic_advi/api.py
+++ b/pymc_extras/inference/deterministic_advi/api.py
@@ -93,6 +93,39 @@ def compute_function_on_mean_field_draws(
 
 
 def fit_deterministic_advi(model=None, num_fixed_draws=30, seed=2):
+    """
+    Does inference using deterministic ADVI (automatic differentiation
+    variational inference).
+
+    For full details see the paper cited in the references:
+    https://www.jmlr.org/papers/v25/23-1015.html
+
+    Parameters
+    ----------
+    model : pm.Model
+        The PyMC model to be fit. If None, the current model context is used.
+
+    num_fixed_draws : int
+        The number of fixed draws to use for the optimisation. More
+        draws will result in more accurate estimates, but also
+        increase inference time. Usually, the default of 30 is a good
+        tradeoff.between speed and accuracy.
+
+    seed: int
+        The random seed to use for the fixed draws. Running the optimisation
+        twice with the same seed should arrive at the same result.
+
+    Returns
+    -------
+    :class:`~arviz.InferenceData`
+        The inference data containing the results of the DADVI algorithm.
+
+    References
+    ----------
+    Giordano, R., Ingram, M., & Broderick, T. (2024). Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box. Journal of Machine Learning Research, 25(18), 1–39.
+
+
+    """
 
     model = pymc.modelcontext(model) if model is None else model
 
diff --git a/pymc_extras/inference/fit.py b/pymc_extras/inference/fit.py
index aea31679a..68cbe7acb 100644
--- a/pymc_extras/inference/fit.py
+++ b/pymc_extras/inference/fit.py
@@ -41,4 +41,7 @@ def fit(method: str, **kwargs) -> az.InferenceData:
 
         return fit_laplace(**kwargs)
 
-    # TODO Add determinstic ADVI
+    if method == "deterministic_advi":
+        from pymc_extras.inference import fit_deterministic_advi
+
+        return fit_deterministic_advi(**kwargs)

From 3e397f7468f55a5b71cbd63c5312854681c8138d Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Thu, 14 Aug 2025 12:00:09 +0200
Subject: [PATCH 05/28] Change import in notebook

---
 notebooks/deterministic_advi_example.ipynb | 34 ++++++++++------------
 1 file changed, 16 insertions(+), 18 deletions(-)

diff --git a/notebooks/deterministic_advi_example.ipynb b/notebooks/deterministic_advi_example.ipynb
index ef126bb5f..24166f3e1 100644
--- a/notebooks/deterministic_advi_example.ipynb
+++ b/notebooks/deterministic_advi_example.ipynb
@@ -136,7 +136,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8f0a4bf385eb49cc87f9502de8ca0d10",
+       "model_id": "1902eaec771740abb86ced51c17708b6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -174,7 +174,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 2 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 1 seconds.\n"
      ]
     }
    ],
@@ -209,7 +209,7 @@
    "outputs": [],
    "source": [
     "# This single import should be all that's needed\n",
-    "from pymc_extras.inference.deterministic_advi.api import fit_deterministic_advi"
+    "from pymc_extras.inference import fit_deterministic_advi"
    ]
   },
   {
@@ -267,8 +267,8 @@
        "              <ul class=\"xr-sections group-sections\">\n",
        "              \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_posterior57adc936-c440-48cb-820b-1d615c54fec9\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_posterior57adc936-c440-48cb-820b-1d615c54fec9\" class = \"xr-section-summary\">posterior</label>\n",
+       "                  <input id=\"idata_posterior0e0ae069-eb60-4641-8470-b3317d72493f\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_posterior0e0ae069-eb60-4641-8470-b3317d72493f\" class = \"xr-section-summary\">posterior</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -725,8 +725,8 @@
        "    slope      (chain, draw) float64 8kB 1.917 2.003 1.989 ... 2.093 2.046 2.052\n",
        "    sigma      (chain, draw) float64 8kB 0.5302 0.4897 0.5021 ... 0.5441 0.4952\n",
        "Attributes:\n",
-       "    created_at:     2025-08-14T09:02:12.068400+00:00\n",
-       "    arviz_version:  0.22.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0265cad7-da58-45ec-a83a-ef768ad7a7ed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0265cad7-da58-45ec-a83a-ef768ad7a7ed' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2eceeec3-7c45-4d7e-ba93-1d13eecd064f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2eceeec3-7c45-4d7e-ba93-1d13eecd064f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-fd493557-d432-44f4-801c-240b6c93dba7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd493557-d432-44f4-801c-240b6c93dba7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-841eb2c1-5f99-414e-b98d-040b80ecc102' class='xr-var-data-in' type='checkbox'><label for='data-841eb2c1-5f99-414e-b98d-040b80ecc102' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-4b14cfcc-4987-4c67-9e78-3106ad707c78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b14cfcc-4987-4c67-9e78-3106ad707c78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-087efd6a-3af0-44a3-ab49-49aa0ac30781' class='xr-var-data-in' type='checkbox'><label for='data-087efd6a-3af0-44a3-ab49-49aa0ac30781' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999], shape=(1000,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd704f8e-67ce-4aa2-b904-ba5ca4ef7866' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd704f8e-67ce-4aa2-b904-ba5ca4ef7866' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.024 1.132 1.072 ... 1.06 0.9415</div><input id='attrs-20dacd17-343d-4c90-b9ed-c9e84aa0e1a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20dacd17-343d-4c90-b9ed-c9e84aa0e1a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eca49719-a45c-40d2-83fe-866c058b407f' class='xr-var-data-in' type='checkbox'><label for='data-eca49719-a45c-40d2-83fe-866c058b407f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.02423358, 1.13214564, 1.072478  , 0.99841007, 1.04827592,\n",
+       "    created_at:     2025-08-14T09:59:22.382706+00:00\n",
+       "    arviz_version:  0.22.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-82bad586-956e-4b0e-9d31-6db45a728882' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-82bad586-956e-4b0e-9d31-6db45a728882' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cc873bdf-86c1-4455-b034-5e91e967ba6e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc873bdf-86c1-4455-b034-5e91e967ba6e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-2e2ac284-da5f-43a5-811a-08a3bebb5bf4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e2ac284-da5f-43a5-811a-08a3bebb5bf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ba5dd1a-fade-495d-9ad2-be87a917956b' class='xr-var-data-in' type='checkbox'><label for='data-0ba5dd1a-fade-495d-9ad2-be87a917956b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-d628e1d0-d82a-4831-9f08-fe3175a0e53b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d628e1d0-d82a-4831-9f08-fe3175a0e53b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f26e5a0-bd0a-4859-bfbf-b7b066ef242e' class='xr-var-data-in' type='checkbox'><label for='data-9f26e5a0-bd0a-4859-bfbf-b7b066ef242e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999], shape=(1000,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-030dd6d7-4e23-44a3-bfc5-994bf3096062' class='xr-section-summary-in' type='checkbox'  checked><label for='section-030dd6d7-4e23-44a3-bfc5-994bf3096062' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.024 1.132 1.072 ... 1.06 0.9415</div><input id='attrs-cfce0f01-6259-4bfb-8d33-daaa3e652aa1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfce0f01-6259-4bfb-8d33-daaa3e652aa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53bc4f49-f687-4fcf-8ddf-ccc4b75f8206' class='xr-var-data-in' type='checkbox'><label for='data-53bc4f49-f687-4fcf-8ddf-ccc4b75f8206' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.02423358, 1.13214564, 1.072478  , 0.99841007, 1.04827592,\n",
        "        1.01482212, 1.00686333, 1.0378902 , 1.0283221 , 0.98379442,\n",
        "        1.03198199, 1.05201041, 1.07242427, 1.07454867, 0.94809642,\n",
        "        1.02848707, 1.00260019, 1.02610458, 1.06883805, 1.07276731,\n",
@@ -766,7 +766,7 @@
        "        1.05751935, 1.13451328, 0.99478621, 1.08727973, 0.98795396,\n",
        "        1.02913718, 1.17274048, 1.07302921, 1.07379724, 1.07174714,\n",
        "        1.13900459, 1.0109294 , 0.94462211, 0.96500562, 1.01506402,\n",
-       "        1.02960575, 1.08311952, 0.95107408, 1.05994956, 0.94154183]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.917 2.003 1.989 ... 2.046 2.052</div><input id='attrs-30ea874e-2082-4faa-9087-a299494eb0e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30ea874e-2082-4faa-9087-a299494eb0e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be1ebdcc-ff33-4db3-a19a-3555280026f4' class='xr-var-data-in' type='checkbox'><label for='data-be1ebdcc-ff33-4db3-a19a-3555280026f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.91697266, 2.00334667, 1.98892008, 2.21246449, 2.09548297,\n",
+       "        1.02960575, 1.08311952, 0.95107408, 1.05994956, 0.94154183]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.917 2.003 1.989 ... 2.046 2.052</div><input id='attrs-39b252a8-a016-4ad6-bf12-625ac6634920' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39b252a8-a016-4ad6-bf12-625ac6634920' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb5b1645-2f72-498a-95c8-027c85f095be' class='xr-var-data-in' type='checkbox'><label for='data-fb5b1645-2f72-498a-95c8-027c85f095be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.91697266, 2.00334667, 1.98892008, 2.21246449, 2.09548297,\n",
        "        2.13791721, 2.00092046, 1.99353576, 2.01696503, 2.04927131,\n",
        "        1.89705003, 2.15023709, 2.05951416, 2.11096041, 2.1574465 ,\n",
        "        2.06271442, 2.12626441, 2.05454434, 2.01716359, 1.93549484,\n",
@@ -806,7 +806,7 @@
        "        1.98646095, 2.0011341 , 2.22943759, 2.05596641, 1.92291756,\n",
        "        1.99934292, 1.9652415 , 2.13451806, 2.04079166, 2.16918646,\n",
        "        2.05669288, 2.14205927, 2.1223799 , 2.08985125, 2.12973614,\n",
-       "        2.03145339, 2.05918212, 2.09295493, 2.0457824 , 2.05163278]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5302 0.4897 ... 0.5441 0.4952</div><input id='attrs-068375f9-2108-4b50-8043-a25d86189c68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-068375f9-2108-4b50-8043-a25d86189c68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f526f8c8-565b-43a5-96b9-a0b62d6bd8b5' class='xr-var-data-in' type='checkbox'><label for='data-f526f8c8-565b-43a5-96b9-a0b62d6bd8b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.53015255, 0.48974337, 0.50214987, 0.54506267, 0.50507724,\n",
+       "        2.03145339, 2.05918212, 2.09295493, 2.0457824 , 2.05163278]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5302 0.4897 ... 0.5441 0.4952</div><input id='attrs-ff3920cb-48a1-4d98-aa96-10c2c6aeffaa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff3920cb-48a1-4d98-aa96-10c2c6aeffaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbb377af-71a1-4524-8bfe-41a2256b151d' class='xr-var-data-in' type='checkbox'><label for='data-dbb377af-71a1-4524-8bfe-41a2256b151d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.53015255, 0.48974337, 0.50214987, 0.54506267, 0.50507724,\n",
        "        0.53105185, 0.5317347 , 0.5377762 , 0.52581713, 0.4981341 ,\n",
        "        0.58849647, 0.54057837, 0.51142625, 0.52396477, 0.57521445,\n",
        "        0.54655447, 0.53364766, 0.52248039, 0.5634492 , 0.53678421,\n",
@@ -846,10 +846,10 @@
        "        0.54697677, 0.52404384, 0.57143866, 0.54777914, 0.52836731,\n",
        "        0.58833479, 0.52167064, 0.53627105, 0.49238545, 0.52246454,\n",
        "        0.51645733, 0.56248672, 0.5043927 , 0.53806378, 0.58094705,\n",
-       "        0.55577484, 0.55521349, 0.48293466, 0.54412583, 0.49516182]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9baa4722-a1a5-4154-a9ef-cb22a2c02aa7' class='xr-section-summary-in' type='checkbox'  ><label for='section-9baa4722-a1a5-4154-a9ef-cb22a2c02aa7' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6690fd0d-f55a-43d7-ba25-06671eca8ffe' class='xr-index-data-in' type='checkbox'/><label for='index-6690fd0d-f55a-43d7-ba25-06671eca8ffe' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f4e9800d-e926-4b89-8542-0bd10c9f566d' class='xr-index-data-in' type='checkbox'/><label for='index-f4e9800d-e926-4b89-8542-0bd10c9f566d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "        0.55577484, 0.55521349, 0.48293466, 0.54412583, 0.49516182]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c9f139b-6642-487d-a3a8-d721f59bcdd6' class='xr-section-summary-in' type='checkbox'  ><label for='section-4c9f139b-6642-487d-a3a8-d721f59bcdd6' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c759756a-d462-4969-81cc-d49014499794' class='xr-index-data-in' type='checkbox'/><label for='index-c759756a-d462-4969-81cc-d49014499794' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-337053fa-dc27-4edc-80e3-8a931c6afb32' class='xr-index-data-in' type='checkbox'/><label for='index-337053fa-dc27-4edc-80e3-8a931c6afb32' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-acdd5a39-53ff-4e91-8a06-b7dee38c5660' class='xr-section-summary-in' type='checkbox'  checked><label for='section-acdd5a39-53ff-4e91-8a06-b7dee38c5660' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-08-14T09:02:12.068400+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd></dl></div></li></ul></div></div><br></div>\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a981a3f3-78e9-48d7-aa42-fcbe7efaa939' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a981a3f3-78e9-48d7-aa42-fcbe7efaa939' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-08-14T09:59:22.382706+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
@@ -1215,7 +1215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "2d911a25-5970-4c30-bd2e-21aa0fa74217",
    "metadata": {},
    "outputs": [
@@ -1223,13 +1223,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/s_/l9wd4yls3mv1f4kkkz1xhhmm0000gn/T/ipykernel_40360/2054181913.py:18: UserWarning: The figure layout has changed to tight\n",
+      "/var/folders/s_/l9wd4yls3mv1f4kkkz1xhhmm0000gn/T/ipykernel_44223/2054181913.py:18: UserWarning: The figure layout has changed to tight\n",
       "  f.tight_layout()\n"
      ]
     },
     {
      "data": {
-      "image/png": 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SckOCojxfSfruu+8c47POOksej6dI14h2BBYAAAAASkTM7/8n154tjlpOz3ukuKQwdVSOJVRVVp+nZLU/yDFBn+LG3CX5MsPYGAAAiHR5P4T/448/lJ6eftBjJ06cqKysLElS1apVdfzxxxf6cf766y/HuHfv3kXstGTk3YPicMtC5b2/V69eqlKlSpEeq7w854qMwAIAAABAsbm2LJR3zieOmr9hN/lbFu1badEkWP84+Y691lFz7VylmF+fCVNHAAAgGnTs2FF169aVJPn9/kPOOsj7AX7v3r3ldrsL/Thz5sxxjNu0aVO0RktI3759Zf6Z7bts2TKtXLnygMdlZmY6ZpMUdTmobdu2acOGDaFxSkqK6tSpU+R+ox2BBQAAAIDiCQYUO/5hmTybRlt3jLJPeZCloA4jp9stChzlfPMeM/czuVdNClNHAAAgGvTp0yd0+2DLQm3atMkxY6AoyyNlZWVp3bp1oXF8fLwaNmx4BJ0WX+3atR2bXh9slsWkSZOUkZEhKTdsOPnkk4v0OMuXL3eMW7ZsWbRGIUliAS0AAAAAxeKd97ncWxY4ajnHXy+bEp43pRWKO0ZZfZ9RwscXyPj3LwUV+9ODyrhqFMtpAQDCIhCw2rW7cMcaIwWDuV9aSN9lZa0txc7Kv6TKkttd/r+w0a9fP7377ruy1mrRokVavXq1Gjdu7DhmzJgxof+ezZs3V/PmzQt9/fzLTFWtWlUuV/i+O3/mmWeGwpdx48bppptuKjBbJG+QccYZZxR574n8z7lGjRpH2G10I7AAAAAAcOSydyvmj5cdpWBKY/m6XHuQE5CfrdpY2T3vUdz4h0I1V8Z2xfzxknJOeSCMnQEAotHEyVYv/J9Vamphz7CSCn1wxEtJke74r3RKz/IdWtStW1ft27fX3LlzJeWGEzfddJPjmLwf4OfdB6Iw8m62LUmJiYlH2GnJ6NWrl5599lllZWVp+/btmjFjhrp27Rq6f8uWLZo9e3ZoXNTNtqXy95wrKpaEAgAAAHDEYma8I5Pl/DZZ9qkPSZ6YMHVUMfnbXSR/4x6OmnfuZ3JtWRimjgAA0Wr4M0UJK5Bfamru32FFkPdD+bFjx4ZmykjSvHnztH79ekmS2+0u8ubRe/fudYzj4+OL0WnxJSYmqkeP/b9r5V8WasyYMaHn37RpU7Vq1arIj1HennNFRWABAAAA4IiYPVvlnf2Bo+ZvdpoCDY4PU0cVmDHKPuUBWXfs/pINKnbCI1KevUEAAABKymmnnabY2NzfPbZu3apZs2aF7sv7gf7xxx+vatWqFena+WcXZGZmHuTIspM3oPn1118dAUPefTyKOptkn/L4nCsiAgsAAAAARyTmz1dl/FmhsTVuZZ80KIwdVWy2Sj3lHH+9o+bePE+e+V+HqSMAQDS6a4hRSkq4u6i4UlJy/w4rgsTERMfG0vs+tM/OztaECRNC9SNZHikpybkPV/7ZB+Fw7LHHhvaVyMrK0qRJkyRJCxYs0Nq1ayUd2WySfcrjc66I2MMCAAAAQJGZnavkWfCNo+Zve4Fs1cYHOQOF4etyjbyLvpcrbW2oFvvb8/I3P12K59MjAEDpO6WnUY/uKsKm20ZVkqpIktJ3pbPpdgXZdHufvn376ueff5YkTZo0SUOGDNFvv/2m3btzXwCVKlVS9+7di3zdKlWqOMY7d+5UMBgM68bbLpdLvXv31scffyxJGjVqlPr3769Ro0aFjskbahRV/ue8ffv2I282ihFYAAAAACiy2N9ekLGB0Nh64pRzws1h7ChCeGKUfepQxX+zf9Nyk5Wu2CnPK/uMR8PYGAAgmrjdRinJhTvWGKPk5NwPoV0uoyjPKyqcfcs97dixQ5mZmZo0aVIowJCkU089NbRsVFHExcWpfv36WrdunaTc5ZHWrl2rxo3D++WWM888MxRYzJkzR3///bdjNsmRLgclSc2bN3eMlyxZcsTXimYsCQUAAACgSFwb/5JnxXhHzXfMv2Ur1QxTR5El0PBE+Vr0cdS8C76Wa/P8MHUEAAAiVf4lkD7//HNNnz49ND6S5aD26dixo2O8cOHCI75WSWncuHFoQ21rrR5++GHt2rVLUsGNuYuqRo0aqlu3bmicmpqqjRs3Fq/hKERgAQAAAKDwrFXslOecpbhk5XS5JkwNRaacHvfIehMctZgpz4uvrQIAgJKWN5RYtmyZAoHcWbT16tVThw4djvi6nTp1cox/+umnI75WSco7i2LRokWh26eccori4uKKde3y+pwrEgILAAAAAIXmXjdV7g2zHLWcrjdIsZXD1FFkspWPUk7XGx01z7qpcv/9R5g6AgAAkapZs2Zq0aJFgXpxlkeSpF69ejk2op4xY4bWr19frGuWhN69e8vjKbhTQnFmk+xz9tlnO8Y//PBDKABC4RBYAAAAACg077Q3HONgUh352l8Wpm4im6/jvxSsVMtRy51lEQxTRwAAIFLlDyeMMcUOLOLj43XeeeeFxtZaPfvss8W6piTl5OQU6/zk5GSdcMIJjlrt2rULLGF1JNq3b6927dqFxhs2bNAnn3xS7OsW9zlXJGy6DQAAAKBQXBv/kmfdNEct57jrJE9MmDqKcN7cjczjfh4aKrm3LpJn2Tj5WxbvAwQAAIC8+vXrJ2NMaFypUiXVqVOn2Ne95JJLNHLkSKWmpkqSpk6dqk8//VSXX375EV3v119/1YwZMzR48OBi9XXdddfpmGOOCY2bNGnieP7Fcf311+vWW2+V/Wcpz7ffflsdO3ZU+/bti3wta63efvtt1a1bt0RmgFQEBBYAAAAACiUm/+yKxJryH33eQY5GSfC3OVfBWe/JtXNVqBbz+4vyNztNcnvD2BkAAIgkVapU0aWXXlri161ataoeeugh3XHHHaEP8F966SVlZWXp6quvLnRIsHfvXn3wwQf66KOPij3zQ5KaN2+u5s2bF/s6B9KlSxddeeWV+uCDDyTlzo7473//q8cff1zdunUr9HU2btyo559/Xr/99pseeOCBUum1PGJJKAAAAACH5dq6SJ7Vvzhqvi5XM7uitLk8yj7xv85S2t/yLPw2TA0BAAAUTdeuXXXdddc5am+++ab+/e9/648//pDf7z/ouVu3btVnn32mCy+8UB9++GEo9CjvBg4cqK5du4bGmZmZGjRokO69914tXbr0kOcuX75cL774oi699FL99ttvpd1qucMMCwAAAACHFTPtTcfYxqfI1/7iMHUTXQLNTlegVnu5N88L1WL+fFX+1mdL3vgwdgYAAFA4V199tVJSUvTMM8+ENqFetmyZBg0apISEBHXq1Ek1a9ZUcnKysrOztX37dq1Zs0bLli0rcC23213W7ReZx+PRs88+q6efflo//PBDqD5p0iRNmjRJNWrUUPv27VW1alUlJSVp165d2rFjhxYtWqTNmzcXuF5FeM4lhcACAAAAwCGZHSvlXv6To5bT+UrJmxCmjqKMMco5aZDiv74qVHLt3SbvXx/Ld9zA8PUFAABQBOeee64aN26sl156SQsXLgzVMzIy9Pvvvx/2/NjYWF166aW68sorS7PNEuPxeHT//ferQ4cOeuutt7Rly5bQfdu2bdOECRMOe40aNWpo4MCB6t27d2m2Wq4QWAAAAAA4pJgZb8lo//R7G1NJvg5HtlEijkygwfHyNzxJnrX7lwXwznpfvk7/YpYFAACoMDp06KB33nlHv/76q0aNGqVZs2Zpz549Bz3e5XKpRYsW6tu3r8444wylpKSUYbclo3///jr99NP1/fffa9KkSZo/f/4hl8GKjY1V586d1a9fP5188smKjY0tw27Dz9iKsvAXSlVqamq4W6jQjDFKTk6WJKWlpVWY9fSAQ+F1jUjE6xqRqLRf1yZ9vRLe7SNjA6FaznHXK+ek20v0cXB4ri0LlfDJhY5ads/75Os8IEwdlR7+vUak4rWNSMTrGsXh9/u1ZMkSbd26Vampqdq1a5fi4uJUuXJl1alTR61atVJCQtnP6i3N13VGRoYWLVqknTt3KjU1VRkZGUpMTFRSUpIaNWqkZs2ayeOpOPMMSjpEqjjPHAAAAECZ887+0BFWWE987nJQKHPBo9rI36i7PGumhGreme/K1+ESyc3m5wAAoOLxeDxq27ZtuNsoUwkJCerSpUu42yi3XOFuAAAAAEA5lb1H3oXfOkq+dhdKCVXD1BByjr/eMXbt2SzPou/D1A0AAABQsggsAAAAAByQd+E3Mjl7Q2NrXPIxuyKsgnWPUaCu8xt5MdPfloIHXwcZAAAAqCgILAAAAAAUFAzI+9fHjlKg6amyVeqFqSHsk3P8DY6xK/1veZaNC1M3AAAAQMkhsAAAAABQgHvVJLnS1ztq7F1RPgQadlPgqDaOmnfaG5INhqkjAAAAoGQQWAAAAAAowDv7I8c4UPNoBeseE6Zu4GCMco5z7mXh3rFc7pWTwtQQAAAAUDIILAAAAAA4uLYulmf9dEfN1/lKyZgwdYT8As1OVaBaU0ctZvqbkrVh6ggAAAAoPgILAAAAAA7ev5yzK4KJ1eVv0TdM3eCAjEu+Y69zlNyb58m18a8wNQQAAAAUH4EFAAAAgBCzd7s8S3501HwdLpM8MWHqCAfjb9VPwaQ6jlrM7A/C1A0AAABQfAQWAAAAAEI8876QCfhCY+uOkb/9JWHsCAfl8sjXaYCj5F4xXibfZukAAABARUFgAQAAACBXwCfvvC8cJX+r/rIJ1cLUEA7H1/YC2ZjE0NjYYIElvQAAAICKgsACAAAAgCTJvWqyXHu3OWq+zgMOcjTKhdjK8rW9wFHyLvhGyt4TpoYAAACAI0dgAQAAAECS5J33pWMcqN1RwRqtwtQNCsvXaYCs2f/WzuTslXfB12HsCAAAADgyBBYAAAAAZNLWybP2N0fNx94VFYKtUk+Bpqc6at6/PpaCgTB1BAAAABwZAgsAAAAA8s7/yjG2sUnyt+gTpm5QVDnH/Nsxdu3aIPeKCWHqBgAAADgyBBYAAABAtAvkyLPwW0fJd/Q5kjcuTA2hqIJ1OitwVFtHLWb2B2HqBgAAADgyBBYAAABAlPOsmCBXxg5HjeWgKhhj5OvsnGXh3jhbrs0LwtQQAAAAUHQEFgAAAECU88z7wjEO1O0iW61pmLrBkfK36K1gpaMcNe/cT8PUDQAAAFB0BBYAAABAFDM7V8uzbpqjxuyKCsrtla/DZY6SZ8loKTM1TA0BAAAAReMJdwMomk2bNmn+/PnauHGjMjIyFBsbq+rVq6tx48Zq1aqVYmJiwt0iAAAAKhDv/C8dYxufIn/zM8LUDYrL3/YCxfz5qkzQJ0kygWx5F34nX5erw9wZAAAAcHgEFhVAMBjUyJEj9eGHH2rhwoUHPc7r9apTp0667rrr1L179zLsEAAAABWSP1vehSMcJd/R50kevgRTUdnE6vK36C3vkh9DNe/cz+U75t+SYYI9AAAAyjd+Yy3n1q9fr8suu0x33333IcMKSfL5fJo+fbqmTZt2yOMAAAAASfKsnCiTle6o+dpfFKZuUFLyLwvlSv9b7jW/h6kbAAAAoPCYYVGOLV++XFdffbW2bdsWqhlj1L59e7Vq1UrVqlVTdnZ2aJmodevWhbFbAAAAVDSefLMr/PWOk01pFJ5mUGKCdTopUKOV3NuWhGreuZ8q0JhZ2AAAACjfCCzKqZ07d+raa691hBWnnXaa7r33XtWrV++A5yxbtkzffvutKleuXFZtAgAAoIIyu7fIvdb5rXt/2/PC1A1KlDHydbhM7vEPhUruVb/IpG+QrVI3jI0BAAAAh0ZgUU49/vjj2rx5c2h8ww036I477jjkOS1atNA999xT2q0BAAAgAngWfy9jg6GxjUlks+0I4m/dX/bXZ2Ry9kiSjKy8875QTvdBYe4MAAAAODj2sCiH/vjjD/344/5N8k499dTDhhUAAABAoVkr74JvHCV/i76SNyFMDaHEeRPka+OcMeNd8LXkzw5TQwAAAMDhEViUQ2+++Wbottfr1b333hvGbgAAABBpXBtny5X2t6Pma3t+mLpBafF1uNQxNpmp8iwbG6ZuAAAAgMNjSahyZt26dZo6dWpo3KNHD9WvXz+MHQEAACDSeBd86xgHUxorWLtjeJpBqbFVm8jfoKs8f+9/f+Gd/5X8R58Txq4AAEBZ2bhxo84//9BfSvF6vYqJiVGVKlVUrVo11a9fX82bN1enTp3UsmVLGWNKrJ8BAwZo+fLloXG1atU0cuRIud3uIl9r1qxZuvnmmw95jNfrVWxsrJKTk1W9enU1aNBALVq0UOfOndWkSZNCPc4333yj++67LzTu37+/HnjggSL3m991112nefPmhcZvv/222rZtGxr/+OOPeuyxx0Lja665RgMHDiz241YEBBblzOjRo2WtDY3PPPPMMHYDAACAiOPLKPAte1+b86QSfDOK8sPX/jJHYOHeMEtm5yrZqoV7kw4AACKbz+eTz+fT3r17tXHjRs2fPz90X82aNdWvXz9ddNFFqlatWrEeZ/ny5Y6wQpJ27NihadOmqVu3bsW69sHse2579uzR+vXrNWfOnNB9DRo00FlnnaXzzz9fiYmJB71Gnz599OijjyozM1OSNHHiRN15552Ki4s74r42bNjgCCsaNmzoCCuiHUtClTN5f3AkqUuXLuFpBAAAABHJs+wnGV9GaGyNi2/cR7BA054KJjg/YPDO/zpM3QAAgHBzu92OP4eaQbF161a9//77uvDCC/XJJ584vmRdVKNGjSpSvaiMMUV6bn///bdeffVVXXDBBRo9evRBj0tMTFTPnj1D44yMDE2ePLlYveZ/vH79+hXrepGGGRblzIIFC0K3jzrqKNWsWVOStHbtWn377bf67bfftGnTJmVmZiolJUWNGzfWiSeeqHPOOafYSScAAAAiX/7NtgONTpKtVDNM3aDUuWPkP/pcxcx8J1TyLvpOOSfeLnliwtcXAAAoc506ddL//vc/R81aq71792rXrl3atGmT5s+fr5kzZ2rmzJmhYzIzM/Xyyy9rzpw5evLJJ+XxFO0jZb/fr3Hjxh3wvilTpmjXrl1KSkoq+hPKo2/fvnrwwQcdtWAwqD179mjPnj1au3atFixYoKlTp2rhwoWhY9LS0vTII49o4cKFGjJkyAGv3b9/f40ZMyY0HjNmjPr06XPEvY4du3+2s8vlUt++fY/4WpGIGRblyO7du7V169bQuH79+vL7/XrllVfUr18/vf7661qwYIF27NihjIwMbdiwQb/99puefvppnXbaaXr11VeLlXQCAAAgspm0v+XeMNNR87Vhs+1I52t3gWNsMlPlWTkhTN0AAIDyxBijSpUqqU6dOjrmmGN01VVX6ZVXXtGnn36qHj16OI6dMmWKhg8fXuTH+OOPP5Samhoa5w08cnJyNH78+CN/AofgcrmUlJSkOnXq6IQTTtDAgQP1zjvv6K233lKnTp0cx37zzTd67733DnidY445RrVq1QqNZ8yYoW3bth1RT3PmzNGGDRtC4y5duoS+sI5czLAoR9LS0hzj6tWr67777tP3339/2HMzMjL00ksvacmSJXrhhReKnHSW5AY60Sjv3x9/l4gUvK4RiXhdIxIV5XXtXTzSMbZxVRRsego/D5GuahMF6h0r9/oZoZJ3wdcKtCq/yw/w7zUiFa9tRCJe1+VX/v8exphC/zdq2rSphg8frk8++UQvv/xy6EvSI0eOVOvWrQ+7mXde+ZdAuv766/Xqq6867r/gggvyn3ZIxXlu7du316uvvqqXX35Zn332Waj+5ptvqlWrVurWrZvjWvtmQewLNILBoMaNG6cBAwYUqWdJjpkaUu7+xQfquzjPr6IjsChHdu/e7Rj//vvvoVpcXJyuuuoq9evXTw0bNpTP59PSpUv15ZdfauTIkaF/NH766Sc9//zzuuuuu4r02MnJySXyHCBVqVIl3C0AJY7XNSIRr2tEokO9rq218i91rhHs7nCBkqvzja5oEOx6lQJf7w8s3Gv/UBWbLpPSMIxdFQ7/XiNS8dpGJOJ1Xb7s2bPHMfZ4PEX+DPDmm2/Wnj179O6774Zq7777rq644grFxsYe9vzU1FT9/vvvoXGNGjV08803a/z48Vq6dKmk3CXyd+7cqSZNmhS6r0qVKjnGMTExRX5uDz/8sFJTU0NLNFlr9dZbbxXYU6JKlSq67LLLHDMwfvrpJ916661Ferzs7GxNnDgxNE5MTNQ555yj+Pj4AscmJCQ4xnFxcVHz+S1LQpUjGRkZjvG+sCI5OVmff/657rjjDrVs2VJxcXGqXLmyunTpouHDh2v48OFyufb/p3z33Xe1ZMmSMu0dAAAA5ZtdN1PaucZRMx0vDE8zKHPm6H5SnPNDpOCszw5yNAAAwH6DBw92hAnbtm3T119/XahzR40aJZ/PFxqfddZZcrvdOueccxzHfffddyXSa1E9/vjjjiBg0aJFB9xUu2HDhurcuXNovGzZMsdeGIUxfvx4xxfW+/bte8CwItoxw6IciYk58KZ3jzzyiFq3bn3Q884++2wtWLBAH3zwgaTcNPDdd98t0ppy+ZejQtEYY0LfIkhPT2cvEUQEXteIRLyuEYkK+7qOmf6pvHnGwZRG2pvYWOL3wKgR0/osef/6ODT2z/pUuztfK7nK39tC/r1GpOK1jUjE67r82rVrl2Ps9/uP+DPACy+80PFZ4zfffKMzzzzzsOd99dVXjvEpp5yitLQ09ejRQ88995wCgYAkacSIEbrqqqscX8o+lPyzR3Jyco74uZ111ln66KOPHD136tSpwOu6d+/emj17dui4L774QoMGDSr04+T/uzjttNMO2nP+L7ZnZWWV289vS3rmR/n7zTSKJSYmFqg1bdpUvXv3Puy51113nT799NNQYjl58mQFg8FC/5DzfyYlx1rL3yciDq9rRCJe14hEB31dB3LkWepcO9jX+mzZ3JPKpDeEn6/thY7AwrVnq1yrflWgaa8wdnV4/HuNSMVrG5GI13X5kv+/RXH++/Tp08cRMCxdulSZmZmKi4s76DmrVq1yrALTrFkzNWvWTNZaVatWTV26dNG0adMkSVu3btX06dN1/PHHF6qfknxu/fv3dwQWc+fOdVxr37VPO+00Pf/888rOzpa0f1mowuwlvGPHDk2fPj00rlu3rjp06HDQnkvy+VU0LAlVjhwosOjVq3BvHqpXr6727duHxunp6VqxYkWJ9QYAAICKy736V5msdEfN3/qsMHWDcAnWaKlArfaOmnf+Vwc5GgAAYL+EhAQ1bdo0NPb7/Zo/f/4hzxk1yrl/Wt++fQ85zr85d1lp2LChkpKSQuNt27Zp/fr1BY5LTExUjx49QuPU1FRNnTq1UI8xduzYUNgjSf369YuaTbSLisCiHKlWrZq8Xq+j1rx580Kf36JFC8d4y5YtJdIXAAAAKjbvopGOcaDuMbJV6oWpG4STr51z3xL36l9kdvO+AQAAHN7RRx/tGK9du/agxwYCAY0bNy40drvdBVaR6dmzp2Nz6V9++UV79+4toW6LJv9y/Ad7bvk35B4zZkyhrp/3OGNMgbAG+xFYlCNer1cNGjRw1PatlVYY+Y9NT08/yJEAAACIGlnpcq+e7Cj5Wp8dnl4Qdv6W/WS9+z8YMDYoz6IRYewIAABUFNWqVXOM8++Rkde0adO0ffv20PjYY49V9erVHcfExcU5VpfJysrShAkTSqjboinsczvuuONUo0aN0HjKlCmOjbQPZNmyZY6VcDp27Kg6deoUo9vIxh4W5UyzZs20cuXK0DgnJ6fQ5+Y/NjY2tsT6AgAAQMXkWTZOJuALja3bK3+Lw++RhggVkyh/qzMdS0F5F3wj33HXSYbvswEA/hEMSFmF/CKsMbJef+7tjF3sjxVXRXK5w91FqahcubJjfKjAIv9yUH369Dngcf369XMcO3r0aJ19dtl/uaawz83lcqlPnz6hPS9ycnI0fvx4nXfeeQe9dv6lrgqzWXk0I7AoZ4477jjHdKmiLOu0efNmxzglJaXE+gIAAEDF5F2cbzmoJr1y30gjavnaXugILFzp6+X++08FGp4Yxq4AAOWFe9lYxU58TK6MHYU+55+4QgV3Z40+wYRqyj7lAQVaHPgD+oosPj7eMc7IyDjgcbt379Zvv/0WGickJKhnz54HPLZz586qVatW6HPNuXPnasOGDapbt27JNF1IhX1uUm7gkHeT7tGjRx80sAgEAvrpp59C47i4OJ1yyinF7Day8RWacua0005zbLgye/bsQp1nrdWcOXNCY7fbrVatWpV0ewAAAKhATPp6uTfMctR8bLYd9YK12ilQvaWj5pn/dZi6AQCUN3E/P1iksAJOrowdivv5wXC3USryf4ifmHjgiOrnn39WdnZ2aNyzZ0/FxcUd8FhjjGNvC2ttWDbfLuxzk6RGjRo59vOYP3++1q1bd8Bjp06dqp07d4bGvXr1cuzbgYIILMqZWrVqqXPnzqHxxIkTHS/qg/ntt9+0cePG0Lh9+/aqVKlSqfQIAACAisGz+AfH2MZVUaDxyWHqBuWGMfK3u8hR8qyYIGUc/n0HAACIXnv27HGMk5KSDnhc/sAh/0bV+eXfgHrMmDGyZby0WGGf2z75l3UaO3bsAY8r6t8FCCzKpVtvvTV0Ozs7W48++ughj9+7d68ef/xxR23AgAGl0hsAAAAqCGvlXfKjo+Rv2Vdyx4SpIZQnvtb9Zd3797wzQZ+8i74PY0cAgPIi6/RHFEyodvgDcUDBhGrKOv2RcLdRKvJuoi0V3PdBktauXasFCxaExjVr1nR8OftA8s9Y2Lhxo/76669idls0hXlueZ1++umKidn/e/WBQpY9e/Y4lsaqVauWunTpUgLdRjb2sCiHTjjhBPXs2VOTJ0+WlJvExcTE6IEHHijww/L333/rzjvv1OrVq0O1du3aFUgmAQAAEF1c25bItXOVo+ZrxXJQ+EdcFflbnCFvnlk43vlfyXfMVVKeJWoBANEn0KKPMpqdXuhNt40xqlIl99vo6em7yvyb8eVOBG+6vXjxYse4cePGBY7JP6OgT58+crkO/535Pn36aNGiRY7rHC7oKElLlixxjA/03PJKSkpS9+7dNWHCBEm5IcucOXPUqVOn0DHjx493LI3Vp08fx1YAODACi3Jq+PDhuuSSS0JBxHfffacJEyaoe/fuatCggXw+n5YtW6apU6fK5/OFzqtatapeeumlQv1DAAAAgMjlWTLKMQ4m1VGwTqeDHI1o5Gt3kSOwcKWulmvjbAXrHhPGrgAA5YLLLSVULdyxxsgkJufe9nmkaA8sIlRGRoZWrlwZGnu9XrVp08ZxTDAY1JgxYxy1jz/+WJ988kmRH2/ixIkaPHhwgc2wS8OaNWu0a9eu0LhmzZqqU6fOYc/r169fKLCQckOWvIEFy0EdGT7VLqeqVKmit99+2/GDv3v3bo0ePVqvv/663nnnHU2ZMsURVjRp0kRffPFFoX6gAAAAEMFsUJ6lzjdI/pZn8s15OATrdlEwpZGj5p3/VXiaAQAA5dqYMWMUCARC41atWhXYSHvGjBnaunWroxYMBhUIBAr1J6+MjIzQ6jOl7ccfncuoduzYsVDnde3aVdWq7V8+beLEicrKypIkrV+/XvPmzQvd165dOzVo0KD4zUYBAotyrF69evriiy80aNAg1a1b96DH1axZU0OGDNGIESN44QMAAECujX/JtXuTo+ZvdeZBjkbUMka+thc6Sp6lY6WsXQc5AQAARCO/36+vvnJ+qeFAswXyzygorlGjRh3+oGLau3evRo4c6agVdiaE2+1W7969HdeaMmWKJBWYaZJ/k24cHEtClXNer1fXX3+9rrvuOs2fP1+rV6/Wtm3bZIxR1apV1bp1a7Vq1SrcbQIAAKAcyb/ZdqBaUwWrtwhTNyjP/G3OVczvL8oE/ZIkE8iWZ+ko+TtcFubOAABAefH6669rzZo1oXGNGjUKfAC/d+9e/fLLL6FxfHy8Ro8eXeQlnS655BKtXbtWkjR79mxt3rxZtWrVOvLmD+PJJ590LAfVqlUrde3atdDn9+vXT59++mloPHr0aJ122mkaO3ZsqBYbG6tTTz21ZBqOAgQWFYQxRu3bt1f79u3D3QoAAADKs4BPnmXjHCV/q/4sB4UDsgnVFGh6ijzLfwrVvAu+JbAAAACSpC+++KLAHhTXXnutYmJiHLUJEyaElkOSpF69eh3R/hN9+vTRG2+8IWn/nhhXX331EXR+aIFAQK+99prGjx8fqhljdOONNxbpOs2aNVPLli21dOlSSdL06dM1YcIEbdiwIXTMySefrMqVK5dM41GAJaEAAACACOL++0+ZzFRHzd+SDf5wcL425zvG7i0L5Nq2NEzdAACA8mD16tW677779MILL8jm2Uj9vPPO0znnnFPg+PzLNx3pBtN9+/aVyfNFm5JeZkqSFi5cqNtuu61AEHPjjTfq+OOPL/L18j7XQCCgp59++qD34/CYYQEAAABEEM8S55vFQK32ssnsc4aDCzQ6ScFKR8m1Z0uo5lnwjXJ63RfGrgAAQFmw1iojI0O7d+/Wxo0btWDBAs2YMUMzZ850BBWS1LNnTw0ePLjANdavX6+5c+eGxrVq1dIxxxxzRP3UqlVLnTt31qxZsyRJ69at07x5845o1ZlgMKi9e/dq9+7dWrdunebPn69p06Zp/vz5BY696KKLdOWVVx5Rz71799bLL78svz93ic3du3eH7qtRo4aOO+64I7putCKwAAAAACKFL1OeFeMdJTbbxmG53PIffa5ipr8RKnkXj1RO9zslT8whTgQAABXJX3/9pRNPPNFRCwaDBYKJ/BISEjRw4EBdeumljtkP++SfBdGnT58DHldYffv2DQUW+65/uMBizJgxGjfOuSxqYZ5bSkqKbrvtNvXt2/eI+01OTtaJJ57o2MNjn969e8vtdh/xtaMRS0IBAAAAEcK96hcZX0ZobI1L/hZ9wtgRKgpfm/McY5OVLveqiWHqBgAAlJZAIOD4c6gP9I866ihdddVV+vrrr3XZZZcdMISw1mrMmDGOWnGXQDrllFMUFxcXGo8fP17Z2dmHPMdaW6Tn1qBBA91yyy36+uuvixVW7JN/E/LD1XFwzLAAAAAAIoRnyY+OcaD+cbKVaoapG1QkNqWhAvWOlXv9jFDNO/8bBQi8AACIaG63W7GxsUpKSlL16tXVoEEDNWvWTJ07d1bLli0PO1Ni9uzZ2rRpU2jctm1bNWhQvOVIExIS1LNnT40dO1aStGfPHv366686/fTTi3Qdj8ej2NhYJScnh55by5Ytdcwxx6hx48bF6jG/bt26KSUlRamp+/eSa926dYk/TjQw9nDzYhAV8v4woeiMMUpOTpYkpaWlHXa6GVAR8LpGJOJ1jUi073Vts3bJ91QbmYAvdF/W6Y/K3+7CMHaHisSz6HvFjb0nNLYyyrh2vGxSnTLvhX+vEal4bSMS8bpGJOJ1XXgpKSklej2WhAIAAAAigF0yzhFWWJdX/uZF+xYaopu/+RmyMZVCYyMrz6LvwtcQAAAAog6BBQAAABABggtGOsaBRidKcVXC1A0qJG98gU3avQu+lWwwTA0BAAAg2hBYAAAAABWczUyTXTHZUWOzbRwJX9sLHGPXrg1yr5sepm4AAAAQbQgsAAAAgArOLh4r5V0Oyh0jf9NTw9gRKqrgUW0VqNbcUfMs+CZM3QAAACDaEFgAAAAAFVzB5aC6S7GVDnI0cAjGyN/OOcvCs/wnKSs9TA0BAAAgmhBYAAAAABVZZprsyl8dJZaDQnH4Wp8t6/KGxiaQI8+SUWHsCAAAANGCwAIAAACowDwrxktBf2hs3bHyN+0Vxo5Q4cWnKND0FEfJu/DbMDUDAACAaOIJdwMAAAAAjpx76RjHOND4ZCkmMUzdVDzBoNXOndLGTdKmTdKmzdLmzVaBgBQXL8XHSXFxRnFxUkK8lJAgNWwoNWkseb0m3O2XGl/b8+VZPi40dm9ZKNe2JQrWaBXGrgAAABDpCCwAAACAiiozVe6/pzpK/pYsB3UoGRlW02dIU363WrxY2rxZyvEd7ixboOLxSE2aWLVsLrVsYdSihdS0iRQbGxkhRqDhiQpWOkquPVtCNc+Cb5XT674wdgUAAIBIR2ABAAAAVFCe5T/L2EBobD1x8jfuEcaOyqedO61+/0Oa8pvVzFmFCSgOz++Xli3L/fPDqNxAIyZG6tbVqlcvo25dpfj4ChxeuNzytzlXMdPeCJW8i0cqp/udkicmjI0BAAAgkhFYAAAAABWUZ1m+5aCa9GQ5qH/4/VYTJ0kjvrdasFCyBSdJlLicHGnyr9LkX63i4qRuJ1id2suo6/EVc+aFr80FjsDCZKXLs3KC/C37hrErAAAARDICCwAAAKACMhk75F433VFjOSjJ57Ma97P08SdW6zcU/jyvV6pVS6pdS6pTW4qNk7IypawsKTPrn//NlHbslDZuPPz1srKkiZOkiZOs4uOlU3tZXX6pUYMGFSe4sMn15a9/vDzrpoVqngXfEFgAAACg1BBYAAAAABWQe/nPMja4vxCToEAULweVnW3142jp08+ttmw59LFut9Spo9T9RKPmzXNDimrVJJercGHC7t1Wy1dIS5dJS5dZLVsmrVt/8FkcmZnSj6OlUWOsTu5u9a/LjVq3qhjBhb/N+Y7Awr32D5ldG2WT6oSxKwAAAEQqAgsAAACgAvIsG+sYm5ZnSN74sln7qBzx+axGfCd98pnVjp0HPy4+Xup6vNT9JKMTjpcqVz7ywKByZaPOnaTOnSQp9zrp6Va/TpEmTraaPVsKBAueZ630y6/SL79aHdM5N7jocoxkTPkNL/zNz5Cd9JhM9m5JkpGVZ+EI+U64OcydAQAAIBIRWAAAAAAVjNm7Te71Mxw1V9uzw9RN+MydZ/XM81Zr1hz8mFYtpX9dbnRC19LdR6JKFaOz+ktn9TdKTbP65dfc5aDmzJWCBwgvZs2WZs22atVSun6gdGyXchpaeOPkb9lP3nlf7C8tHCFf1xsl4wpjYwAAAIhEBBYAAABABeNZ/lO+5aASZZr3kvZmh6+pMpSebvXaG1ajRh/8mHZtpauuNDru2LKfwZCSbHTu2dK5Zxtt32H1zQirESOkPXsLHrtkqXTHnblLRd16k1Ht2uUvuPC1vdARWLh2bZB73TQFGpwQxq4AAAAQifhKDAAAAFDBeJbmWw6qVR8Zb3yYuik71lqNHmN1+YCDhxVdjpFeftHotZeNjj/OhH25perVjK6/1qVvvjS68XqjalUPfNyvU6Qr/m31zntBZWWVr2W9gke1UaB6C0fNM/+bMHUDAACASEZgAQAAAFQgZs9WuTbMctSiYTmotWutbr3d6omnrdJ3Fby/RQvpf68YvficS506hj+oyC8x0eiKy4y+/MxoyGCjugfYszonR3rvg9zgYtJkK1te9iMxRv62FzhKnhU/S1npYWoIAAAAkYrAAgAAAKhAPMvGyWj/B9k2ppJMsx5h7Kh0BYNWX39rdfXA3P0g8ktIkP57q9Fb/zNq17Z8hRQHEhtrdM5ZRp9+ZHT3nUbJVQoes2WLNPRhq9sHW23aVD5CC1/rs2Rd3tDYBHLkXfJjGDsCAABAJCKwAAAAACoQzzLnclD+ZqfKeOPC1E3p2r7d6s67rV58ySonp+D9PU+WPvnA6KILjNzu8h9W5OV2G53V3+jTj40uvEByH+Cd2azZ0r+vsfpxdDmYbRGfIn+zUx0lz4Jvw9QMAAAAIhWBBQAAAFBBmN2b5d4421ELtOwbpm5K16TJVlf+x2r6jIL31a4lDX/S6LFHXKpRo2IFFfklVTa6/VaX3n3bqFPHgvdnZEhPDbe6536rHTvCG1r425zvGLu3LpJr6+IwdQMAAIBIRGABAAAAVBD5Z1fY2MoKNOwWpm5Kx549Vo89GdTQh612HWCvigsvkD5636jbCRU7qMivaROjl14weuRho5o1C97/+x/SlVdbTf4lfKFFoGE3BSvVctSYZQEAAICSRGABAAAAVBAFl4M6TXLHhKmbkjdvvtVV11iNHVfwvurVpReezZ2NEBcXWWHFPsYYndLT6KP3jM7sV/D+9F3SAw9ZPfJYULt2hyG4cLnlb3Ouo+Rd/IPkzy77XgAAABCRCCwAAACACsDs2iD3Jueu0/4WkbEcVCBg9eHHVrf+12rzloL3n9JL+vBdo2O7RGZQkV9iotG9d7n01BNGKSkF7/9pvHTNQKslS8o+tPDlWxbKZKfLs3JCmfcBAACAyERgAQAAAFQAnmU/OcY2tooCDbqGqZuSs32H1aAhVm++bRUIOu9LTJSG3mc07EGjpKToCCvyOqmb0YfvGfU4ueB9mzZLN95qNeL7st2Q2ybXl7++83XnWfBNmT0+AAAAIltUBBbBYPDwBwEAAADlmGfpGMfY3/w0ye0NUzclY+q03CWgZs0ueF/HDtIH7xj1PsPImOgLK/ZJSTZ6bJjR0PuMKiU67/P5pOdesBr2mFVGRtmFFv62+TbfXvunzK4NZfb4AAAAiFxREVj07NlTr7zyirZsOcD8cgAAAKCcM+nr5d4y31Hzt6y4y0H5/VavvRHUnXdbpaU573O5pGuuNvq/541q1YreoCIvY3KDmw/eM2rfruD94ydIA2+wWrW6bEILf7PTZWMr7+9PVp6F35XJYwMAACCyRUVgsXXrVr366qs69dRTdcstt+i3334Ld0sAAABAoeXfbNvGJStQ//gwdVM8GzdZ3XSr1aefFbyvRnXppReMrv63kdtNWJHfUTWNXnrB6LJLCt639m/puhutxv5UBqGFN07+Vmc6Swu/lSwz2wEAAFA8URFY7OP3+zVhwgQNHDhQp59+ut5++23t3Lkz3G0BAAAAh1RwOagzJJcnTN0cuYmTra6+1mrR4oL3ndhNev8do44dCCoOxeMxuvlGl554tOASUVlZ0mNPWL30SlB+f+kGF762FzrGrl0b5f57aqk+JgAAACJfVAQWXq9X1trQ2rfWWq1bt07PPfecevbsqTvvvFMzZ84Mc5cAAABAQSZ1rdxbFzlq/pZ9wtTNkcnKshr+XFAPPmy1d6/zPq9X+u+tRk89blSlCmFFYZ3c3eidt4xaNC9435dfS3fda7V7d+mFFsGaRytQvaWjxubbAAAAKK6oCCymTJmiIUOGqEGDBrI295d2Y4ystcrJydGoUaM0YMAAnXXWWfrkk0+0Z8+eMHcMAAAA5PIsH+cYBxOqKVDv2DB1U3SrVlsNvMFq5A8F76tXT3r9VaOLLojujbWPVN06Rv97xeicswreN32GdP1NVuvWl1JoYYz8bS9wlDwrxkuZaaXzeAAAAIgKURFYJCcn65prrtG4ceP03nvv6YwzzpDb7ZYkx6yL5cuX67HHHlP37t314IMPauHCheFsGwAAAJBnqXP/ikDz0yvEclDWWo38MTesWL2m4P29T5fefdOoZQuCiuKIjTUaMtil++428nqd9/29LndfixkzSye08LXuL+ve/6AmkCPvklGl8lgAAACIDlERWOR1wgkn6KWXXtLkyZN12223qXbt2gVmXWRmZuqrr77ShRdeqIsuukjffvutsrOzw9w5AAAAoo1JXS33NueGD/4WfcPUTeHt2WP10CNWw5+1yv9rdHycdP+9RkPvdykhgbCipPTrm7shd0qKs757t3TnXVbfjCiF0CI+Rf6mpzpKnoUsCwUAAIAjF3WBxT7Vq1fXTTfdpAkTJuh///ufevToEZptkXfWxfz583X//fere/fueuKJJ7Ry5cpwtg0AAIAokn92RTCxugJ1jwlTN4WzaLHV1QOtJk4qeF+zptI7bxr17U1QURratTV663WjZk2d9UBQeuH/rJ5/MahAoGSDi/zLQrm3LpYr354rAAAAQGFFbWCxjzFGvXr10htvvKEJEybo+uuvV/Xq1QvMuti1a5c++ugj9e/fXwMGDNCYMWPk9/vD3D0AAAAimWeZM7DwN+8tudxh6ubQgkGrTz+3uvEWq02bCt5/4fnSG68ZNWhAWFGaah1l9NrLRid3L3jft99JDw6zys4uudAi0OAEBSvXdtQ8C74tsesDAAAgukR9YJFX7dq1dccdd2jy5Ml68cUXdcIJJzjut9bKWquZM2dq0KBB6tmzp1544QVt2LAhTB0DAAAgUpkdK+XevsxR87foE6ZuDi011WrIPVavvW4VCDjvq1xZevIxo9tvcyk2lrCiLCQkGD02zOiqKwve98uv0uC7rPbsKaHQwuWWv815jpJ38Y+SnyV1AQAAUHQEFgfgdrvVp08fvffeexo3bpyuvvpqJScnyxgTmnFhrdX27dv15ptv6owzztAtt9yimTNnhrt1AAAARIj8syuCiTUVrNs5TN0c3KzZVlddYzVtesH72reT3nvbqPtJBBVlzeUyuvY/Lj08tOBm3HPmSrf812r7jpIJLXz5AguTnS7PivElcm0AAABEFwKLw9izZ4/27Nnj2HR7X3Cxb6+LQCCgCRMmaMCAAbr22mv1999/h6tdAAAARAjPsjGOsb9Fb8mUn1/fg0Gr9z+0un2w1Y6dzvuMkf49QHrpBaNaRxFWhNNppxo9N9woMdFZX7FSuvFmq7/XFT+0sFXqyd+gq6PmWcDm2wAAACi68vOOpxzJysrS119/rYsuukgXXHCBvv76a2VlZYXut9bK7XYrLi5O1lrHJt2//fabzjnnHE2ePDlM3QMAAKCic21fJveOlY6av2XfMHVTUHq61V33Wr39rpXN93l3tWrSi88ZDbzGJY+HsKI86NzJ6JUXjapVddY3bZZuusVq8ZLihxb+Nvk23/57qkw6S+cCAACgaAgs8lixYoUee+wxde/eXUOHDtWCBQtCm2/vWwaqRo0auuWWWzRp0iRNmTJFQ4cOVbNmzULBhTFGmZmZuv3225lpAQAAgCPiWeqcXRGsXFvB2h3C1I3TosVW/7nOauq0gvd1PV56/22jYzoTVJQ3zZsb/e9Vo3p1nfW0dOm2261m/1W80MLf7DTZ2KTQ2MjKu3BEsa4JAACA6BP1gUVOTo5Gjhypyy+/XGeddZY++eQT7d69OxRUSLlhxXHHHacXX3xRkyZN0i233KIaNWqoUqVKuuKKK/TDDz/ojTfeUNOmTUPnZWdn6/333w/TswIAAECFZW2BwKI8LAdlrdW331nddKvVli3O+1wu6YbrjIY/aZSSQlhRXtWpbfS/V4xatnDWM7OkIfcUM7TwxsnX6kxHybPwWykYOMgJAAAAQEGecDcQLmvWrNEXX3yhESNGKD09XZIcsySstUpISNA555yjK664Qk2bNj3k9Xr06KGuXbvqsssu06JFi2St1e+//14WTwUAAAARxLVtsVxpax01f4vwLgeVmWk1/Dmrnw+wj3LVFGnYQ0adOhJUVAQpKUYvvyjd/6DVjJn769nZuaHFM0/lLiF1JPxtL1DM3M9CY9fuTXKvm6pAwxOL2TUAAACiRVTNsAgEAho7dqyuuuoq9e3bV++//77S0tIKzKZo1qyZHnzwQf3666968MEHDxtW7BMbG6vrr78+NN68eXOJPwcAAABEtgLLQVWpp2CtdmHqRtq0yeqGWw4cVnTsIL37NmFFRZOQkDsbpmcPZ31faHGkMy2CNY9WoEYrR43NtwEAAFAUUTHDYsOGDfryyy/1zTffaMeOHZIKzqZwu9069dRTdcUVV+i444474sdq1qxZ6HZOTk6xewcAAEAUOeByUH0kE55A4K85VkMfskpLL3jf5ZdK111r2Fi7gvJ6jR4eKg0zVpMm769nZ0t33Zs706LIQZQx8re9QO5Jj4dKnhXjlZ2ZJsUnl0TbAAAAiHBREVicfvrpoU2zJcn884Zv3ybal1xyiS6++GLVrFmz2I8VFxfneAwAAACgsFyb58u1a4Oj5m8ZnuWgRnxv9eJLVoF8WxBUSpTuu8fo5O78vlvReTxGDz2Q+75o8i/761lZ+5eHKmpo4Wt1pmJ+HS4T8EmSTMAn75If5ev0r5JsHQAAABEqKpaECgaDkpxBxbHHHqsXXnghtIl2SYQVeeVdZgoAAAAoDM+yfMtBJTdUsEbrMu3B57N65rmgnnuhYFjRqKH09huEFZHE4zF6eKhRz5Od9X2hxZy5RXxfE58if7PTnI+x4NtidgkAAIBoERUzLCQ5NtG+/PLL1bx581J5nLp162rJkiWlcm0AAABEMBuUZ+lYR8nfsm+ZLgeVmmb1wINWc+cVvO/EbtKD9xslJhJWRBqPx+jhB6WHH7Ga/Ov+elaWNORuq5f/T2rVsvD/3f1tLpA3z9Jm7m2L5dqyUMGj2pRk2wAAAIhAURFYNG3aVJdffrnOOeccVapUKdztAAAAAAW4Ns6Ra89mR60sl4Nas9bqzrusNm8peN+Af0kD/2PkchFWRKqDhRaZ/8y0eP0VqW7dwv33DzQ8QcHKteXavWn/9Rd8oxwCCwAAABxGVCwJNWrUKF1xxRWEFQAAACi3PMucsyuCVZsqWK10ZgXnN3ee1Q03FwwrYmOlh4caXX+ti7AiCuSGFkY98i0PlZoqDb7LKjW1kMtDGZf8bc53lLyLf5B8mSXUKQAAACJVVAQWAAAAQLkWDBQILHxltBzUxMlWtw+22rPHWa9ZQ3rtZaPTTiWoiCb79rQ4prOzvn5D7kyLjIzChRa+tufJav9rx+TsKfAaBwAAAPKLisDiyiuv1JVXXql///vf2rFjxxFfZ/v27Y5rAQAAACXBtWGWXHu3OWqlvRyUtVaff2n14MNWPp/zvqNb526u3bIFYUU08nqNnnjUqHkzZ33JUmnow1Z+/+FDC5tUV4FGJzmvO+/LkmwTAAAAESgq9rCYPn26zD/fTsvOzj7i62RnZ2v69OmSFLoeAAAAUFzeZWMc40D1lrJVm5Ta4wUCVq+8ZvXVNwXvO+nE3GWg4uL4fTeaJSYaPfO0dOPNVpvybK0ybbr09DNW991z+PdEvvYXy7NmSmjs3jRHrm3LFKzRorTaBgAAQAUXFTMspNxvkAEAAADlTtAv97KfHKXSnF2RnW310LADhxXnniM9/ghhBXJVr2b03HCjKknO+phx0ptvH/79VaBxDwUTazhqnvlflWSLAAAAiDBRE1gAAAAA5ZF73XS5Mnc6aqUVWGRkWA2+y2ryrwXvu+E6o8G3G7ndhBXYr0EDo6efNIqNddY/+kT67vvDhBZur/xt82++PZLNtwEAAHBQBBZFEAgEQrfdbncYOwEAAECk8CzNtxzUUW1kkxuU+OPs2p27ufacufke3yM9eL/Rvy43LHuKA2rbxmjYQ0aufO8eX3jJavZfhw4tfG0vdG6+nb1LnuXjSqNNAAAARAACiyJITU0N3U5ISAhjJwAAAIgIAZ88K352lPwtSn52RWqa1X/vsFq02FlPTJSeG250xukEFTi0k7oZDRnkfJ0EAtLQh6w2bjp4aGGr1FOg4YmOmncey0IBAADgwAgsimDGjBmScjeXq1mzZpi7AQAAQEXn/vtPmax0R83fsk+JPsb27Va33m61fIWznpwsvfKi0TGdCStQOGf1NxpwhbOWvku6936rjIyDhxa+9hc5xu6Ns+Xavrw0WgQAAEAFF3WBRVGnuft8Pm3cuFFfffWVXn/99VC9RYsWJd0aAAAAokyB5aBqtZdNqlti19+82erm26zWrHHWq1eXXvk/o+bNCStQNAOvMep2grO2cpX0+FNWweCBQ4tAk14KJlR31Nh8GwAAAAfiCXcDJaV169aHPcZaq1NOOeWIH8Pa/b+AF+c6AAAAgPw58qyc4CyV4Gbb69db3TbIautWZ73WUdL/PW9Uty5hBYrO5TJ66AHp+pus1qzdX//lV+n9D6X/XHWAk/7ZfDtm+puhknfRSOWcNEjyxpV6zwAAAKg4ImaGhbX2oH8Ke9zh/uybndGkSRP17t07HE8TAAAAEcK99neZ7N2Omr9FySwH9fc6q5v/WzCsqFdXeuUlwgoUT2Ki0VOPG1Wu7Ky/+77V5F8OPMvC1/ZCx9hkp8uz/KfSahEAAAAVVMQEFlLRl3sqKmut2rZtq9dff11er7dUHwsAAACRrcByUHWPka1cq9jX3bTJ6vZBVjt2OOuNG+WGFbWOIqxA8dWrZ/TIQ0bufO8oH3vSasXKgqGFTa4vf8Nujpp3/pel2SIAAAAqoIhZEurYY4896H15N8tu3769YmJiCnVNY4xiYmKUlJSkJk2aqGvXrurSpUuJ9AsAAIAo5ssquBxUi+IvB7Vtu9V/B1tt3east2ghPT/cKDmZsAIl59guRjffJL30yv6AIisrdxPut9+QqlRxvt587S6WZ+0fobF7wyyZHStlqzUts54BAABQvkVMYPHRRx8d9L5WrVqFZl+88MILqlOnTlm1BQAAABTgXvOrjC8jNLbGJX+LM4p1zZ07g7p9UFAbNzrrLVtILz5nVLkyYQVK3kUX5G66PWr0/tqmzdKjT1gNfzJ3z4t9Ak17KZhQTa6M/dN/vPO/Uk7Pe8qyZQAAAJRjEbUk1KHk38sCAAAACBfvktGOcaDesbKJNY74eum7ghp4wy7HJsiS1KSx9PwzhBUoPcYYDb7dqF1bZ33qNOnDj/Md7I6Rv815jpJ30XeSP7tUewQAAEDFETEzLA7llltuCd1OSkoKYycAAACIetm75V41yVHytzzy5aD2ZlgNuXu3liwNOOr160svPGsKLMsDlLSYGKNHh0n/udZqZ+r++jvvWbU5OnfpqH18bS9UzIy3Q2OTlS7P8p/lb92/LFsGAABAORV1gQUAAAAQTp4V42UCOaGxdXnlb977iK6VnW11971Wc+cFHfVaR+UuA1WtGmEFykb1akbDHpJuH2QV+OflaK308CNW774tHVUz97VoUxrK36CrPH9PDZ3rnf8lgQUAAAAkRdGSUAAAAEB54Fn8g2McaNxdik8u8nX8fquhD1v9NcdZr15d+r/nTegDYqCsdOpodN1A5+sufZc09CErn2//Er3+dhc7jnGvnyGzc3WZ9AgAAIDyjcACAAAAKCNmz1a5101z1Pytzizyday1+r9XrP7401lPrpI7s6JuXcIKhMfll0rdT3TWFi2WXv1fnsCi2akKxld1HOOd/1VZtAcAAIByjsACAAAAKCOeZWNl7P7lm6w3Qf4mvYp8nS++kkZ856wlVTZ64TmXGjUkrED4GGN03z1Gdes4619/K42f8E9o4Y6Rv825jvu9C0dI/hwBAAAgulX4PSxeeeWVArX8e1Yc6JiSwN4YAAAAKArP4h8dY3+z0yRvfJGu8csU6/i2uiTFxEivvFRZTZvslbX2IGcCZaNyZaPHhknX32yVkyeDePoZq6ZNpcaNjHztLlTMzHdD95msNHlW/HxEM44AAAAQOSIisDDG+S2yAwUW+Y8pCQQWAAAAKCyTukbuLfMdNX+rom00vGix1SOPWeXPJJ54tJKO6exVWloxmwRKSPPmRoPvkJ58ev+LNTNLGvqw1Vv/k+JTGstf/3h58iyR5pn/FYEFAABAlIuoJaHK6ttkfGsNAAAAReVZMsoxDsZXVaDhCYU+f+Mmq7vvs8rOdtavH2jUt09sSbQIlKgz+xr17+esrVkjPfeilbVW/nYXOe7zrJsmk8rm2wAAANEsIgILa+1hQ4R9x5TEHwAAAKBIrJV3Sb7loFr2lVyFm/C8a7fVXfdYpaY66/37SQOuYM8KlF93/NeoeTNnbew4afRYyd/sdNm4ZMd93vlfl11zAAAAKHcq/JJQH374YYkcAwAAAJQW19ZFcqWucdQKu/SNz2f1wINWa9Y668d2ke4cZEpl6VOgpMTGGj36sPSf66wyMvbXn3/RqnVLr1q1OU8xs94L1b0LRyin238lL7OGAAAAolGFDyyOO+64EjkGAAAAKC2exT84xsEq9RSs3bFQ5778qtXsv5y1Jo2lRx828ngIK1D+1atndM9d0oMP75+tnp2du5/Fu09dqGp5AguTmSrPygkKtOp3oEsBAAAgwkXEklAAAABAuRX0y7PUuX+Fv9WZUiFmRoz7yerb75y1alWl4U8ZVapEWIGK45SeRuef66yt/Vt6+t1G8tc71lH3zP+y7BoDAABAuUJgAQAAAJQi99o/5Nq73VHztep/2PNWrLQa/pxz/7SYGOnpJ41qHUVYgYrnlpuMWrRw1n4aL03z59t8+++pMqn51kADAABAVCCwqKAee+wxtWzZ0vHnnnvuCXdbAAAAyMez6HvHOHBUW9lqzQ5ydK7du3P3rcjOdtaHDDZq1ZKwAhVTTIzRow8ZJSY663d/cJr83iqOmofNtwEAAKISgUUFNGfOHH3yySfhbgMAAACHk71bnhUTHCX/0ece8pRg0OqJp6zWb3DWzz1b6tubsAIVW926Rvfe5Xwd78mO1Y/rz3HUvAu/lfXnlGVrAAAAKAcq/KbbpSEYDOrbb7/V+PHjtX79esXExKhJkyY677zzdOKJJ4a1N5/Pp6FDhyoYDIa1DwAAAByeZ9lYmcD+aRLW5ZHvMJsJf/KZNOV3Z611a+m2WwgrEBl69jC68AKrr7/ZX/tg4YU696gPQ2OTsUN2yViZtmeHoUMAAACES1QEFjNmzNBXX30lSXK73Ro2bJhiYmIOeGxaWpquv/56zZs3T5Jkbe66wYsXL9aoUaN0zjnn6IknnpDLFZ7JKW+++aaWLVsmSapRo4a2bdsWlj4AAABweN78y0E17iHFpxz0+JmzrN56x7lvRZUk6dGHjWJiCCwQOW6+wWjhQqvFS3LHq/c01awdx+iYarNCxwRnfiwXgQUAAEBUiYoloT7//HONHDlSP/zwg3bt2nXQsEKS7rnnHs2dOzcUVBhjZEzum0Nrrb7//ns99thjZdJ3fqtWrdLrr78uSYqPj9egQYPC0gcAAAAOz6Stk3vDLEfNd/Q5Bzla2rLV6uFHrPJOpDVGevhBNtlG5PF6jR55yKhSpf21b9Y6N9+2K3+V3bmmbBsDAABAWEVFYPHnn3+Gbp955pmHPG7y5MmhkMJa6/izr/bZZ5+FZmCUFWuthg4dqpyc3HVcb7rpJtWtW7dMewAAAEDheRaPdIxtXJXcGRYH4PdbPfiwVVq6s37tf4yO7UJYgchUu7bR/ffsf32P33iG0nOSHMcEZ35c1m0BAAAgjCI+sFi3bp127twpKXe2xEknnXTQYz/+OPeXYWutXC6XhgwZomnTpmnmzJl64IEH5Ha7Q7Mt3n333dJvPo/PP/9cM2fOlCS1aNFCV199dZk+PgAAAIrA2gLLQflanil5DjzT94OPrBYuctZO7CYNuKK0GgTKh+4nGV1yce7tnGCsfljnnIUUnP25FGDzbQAAgGgR8YHFmjVrJOWGFbVr11ZSUtIBj9u7d6+mTJkSml1x2WWX6ZprrlGVKlVUqVIl/etf/9Jtt90Wmm3xyy+/KDs7+4DXKmlbtmzRc889F3oew4YNk9frLZPHBgAAQNG5Ns6WK32do+Y/yHJQCxZaffiRs1anjnT/vUYuF7MrEPluvM6ozdG5t/MvC6W92+VeMbHsmwIAAEBYRHxgsWnTptDtxo0bH/S42bNnKycnJ7R3xZVXXlngmCuuuCIUFGRlZWnp0qUl3O2BPfroo9q9e7ck6eKLL1bnzp3L5HEBAABwZPLPrgimNFawVrsCx2VkWD36uFUgz74Vbpf08FCjpMqEFYgOHo/RsIeMKlfev/l2Xt55X4apMwAAAJS1iA8s9uzZE7pduXLlgx43a1buhojGGDVr1kwNGjQocExiYqJat24dGq9ataoEOz2wn376ST///LMkqVq1aho8eHCpPyYAAACKwZclz7KxzlKbc3N30M7npVesNmx01q6+yujo1oQViC61jjJ64N7c1/03ay523Of++w+Z1LXhaAsAAABlLOIDi7zLNsXEHHjNYEmaM2dO6HbXrl0Pelzt2rVDt9PS0orV2+Hs3r1bjz76aGh8zz33qEqVKqX6mAAAACgez/KfZLJ3h8ZWRv7WZxU47pcpVj+OdtbatZX+dXlpdwiUTyd2M7r8Umn8ptOVluN83+OZ/1WYugIAAEBZivjAIi4uLnQ772yLvAKBgObOnRsad+nS5aDXi42NDd3OysoqgQ4Pbvjw4dq6daskqVu3bjr77LNL9fEAAABQfN4FXzvGgQZdZSvXdtS2b7ca/ox11OLjpQfuM/J4mF2B6HXdtUYtjz7Q5tsj2HwbAAAgCnjC3UBpy7sM1Lp16w54zJw5c5SZmRkad+zY8aDX27t3b+j2oWZsFNeMGTP01VdfhR7noYceKrXHknKXwsKRy/v3x98lIgWva0QiXtcobWbnarnXz3DU/O0vdrzegkGrJ562St/lPPeO/xrVq1v07xPxukYk8XqNHnnI6qHbLtSAph+G6vHBnVrxywTVPrVfGLsDio9/sxGJeF0jEvG6Dp+IDyyaNGkiSbLWasWKFdqxY4eqVavmOGbcuHGh27Vr19ZRRx110Ovt3LkzdPtQe2IUR05OjoYOHRraAPyGG25Qo0aNSuWx9klOTi7V60cTlu1CJOJ1jUjE6xqlITB9lIJ5CwlVVemY82U8+2fpfvJppqbPyHCcd/ppMbr80krFfjPE6xqRIDlZuumBjpr5SRd1qT4zVN/9y5eqfdqlSq4S8QsFIErwbzYiEa9rRCJe12Ur4n/Ta9WqlWJiYmSMkbVWr7/+uuP+7du3a8SIETLGyBijE0444ZDXW758eeh23bp1S6XnV199VatXr5YkNW7cWAMHDiyVxwEAAEDJsQGfgn994ai5Ol7kCCtWrPTruRedYUWNGkYPD03km1tAHt1PitHOxlc4ap2qTNULDy0MfbELAAAAkSfiZ1gkJCSoR48e+vnnnyVJH3/8sfbu3avevXtrx44deuONN7R7d+6miMYY9e/f/6DXWrVqlWMfjMaNG5d4v0uXLtU777wTGg8bNqxUl57ap7Q3EI90xphQ2pqens6bKEQEXteIRLyuUZrcKyYobs82R21P8/6y//yeFQhY3Xt/UNnZzvPuvdtI2qUj/XWM1zUikTFGZ9x2rnY/MkyV3Wmhet3Nn+n1N+/UZZdE/HfvEKH4NxuRiNc1IhGv68Ir6ZV7Ij6wkKSbbrpJEyZMkLVW1lqNGDFCI0aMkJS7VNS+b7O1bt36kDMsJk6cGLpdrVo11a5d+6DHHolgMKihQ4fK5/NJks477zwdf/zxJfoYB8MPXcnZ9zoDIgmva0QiXtcoaZ75XznGgdodFazWTPrndfbl11aLFjvPuegC6bguJfe7GK9rRBJvXLy8x1wizXkjVDunwXfq++atatsmVm3bMCsJFRv/ZiMS8bpGJOJ1Xbai4msprVu31h133OEIJ/a90PYtFRUbG6vHH3/8kNcZO3aspNyErXPnziXe50cffaS5c+dKyk2m7rrrrhJ/DAAAAJQ8s3uL3Kt/ddR87S4M3V6/3uqtd5xvcurXl264jg9cgUOpdPK/HOOqsTvVo+ZEPTjMKj2dDw4AAAAiTVQEFpI0cOBADR8+XDVq1HAkYtZatWjRQh988IFat2590PPnz5+vBQsWhMYnn3xyifaXlZWlF198MTS+6667VLVq1RJ9DAAAAJQOz6IRMnb/dtvWmyB/iz65t63V08/aAktB3TPEKDaWwAI4FFOjuQL1jnXULmj4pbZulR570ioYJLQAAACIJFGxJNQ+Z599ts466yzNnz9fGzdulCQ1adJELVq0OOy5W7du1ZVXXhkan3LKKSXaW05OjjIy9m/AOHToUA0dOvSQ5+SfivTdd99p5MiRofG5556rJ554okT7BAAAQD42KO+Cbxwlf6szpZhESdLIH6W/5jhPOf9cqUN7wgqgMHztL5Z7/YzQ+Pga09Qgca3+nNpQn34u/evyMDYHAACAEhVVgYWUu5xT+/bt1b59+yKdd+qpp+rUU08tpa4KCgQCRT7HWus4LxgMHuJoAAAAlAT331PlSl/vqPna5i4HtXWr1av/c37J5KijWAoKKIpA896ycY/LZKWFauc3/EovLrpTb71t1a4tASAAAECkiJoloQAAAIDS4J37mWMcqN5CwVrtZK3Vs89b5ZlEK0m6a7BRQgIfrgKF5omRr815jtLZ9b+T15WjQFB66BGr1DSWhgIAAIgEUTfDorxKSkrS0qVLi3TOtGnTHMtUnXfeeXrqqadKujUAAAAchNm9Se6VEx01f7uLJWP083irP6Y6j+/bWzr+OMIKoKh87S5SzKz3QuOqsanqVWuCftrYV9u3S48+bvXs05LLxc8XAABARcYMCwAAAOAIeed9UWCzbd/R5yg1zer/XnJ+47tqinTrzXyYChwJW7Wx/PWOc9QubPhl6Pb0GdJHn5R1VwAAAChpBBYAAADAkfDnyDP/a2fp6LOl2Ep68SWr9F3OwwffYZSURGABHCl/+4sd4+NqTFeDxDWh8TvvWc3+i6WhAAAAKrKoXBIqIyNDc+fO1aJFi7Rjxw7t2bNHPp+vSNcwxuiJJ54opQ4BAABQ3nmW/yRXxg5Hzdfhcv051WqCc5Uo9TxZ6nEyYQVQHP5mp8vGJTs2376w0dd6fuGdkqRgUBr2qNV7b0tVq/LzBgAAUBFFVWCxbds2/d///Z9GjRqlrKysI76OtZbAAgAAIMp5537qGAfqHau9ic303AvOb3hXrizd8V8+PAWK7Z/Nt/PuZXFRs+/08uLb5AvGSJJ27JQeedzqueGS283PHQAAQEUTNUtCTZs2Tf3799c333yjzMxMWctUYQAAABwZ19bFcm/8y1HL6Xi53vvAavMW57E332hUrRofnAIlwdf+Isc43qbquhPHO2ozZ0kfflyWXQEAAKCkREVgsWrVKt10001KT08PzY6QcmdKHMkfAAAARDfvHOfsimBiTS3RKfriS+dxHTtIZ/Ytw8aACGdTCm6+PaD1V6pe3Xncu+9bzZrNezcAAICKJiqWhHr22We1d+/eUFDhcrl02mmnqVevXmrSpImqVKkij6fi/VUcf/zxWrp0abjbAAAAiC5Z6fIs+dFRyml7kZ55waNAcH/N65WGDDKh30EBlAx/+4vlWT89NI7bPF1PD16r6+5vGPoZtHb/fhbMcAIAAKg4Kt6n9EW0c+dOTZo0ScYYWWtVrVo1vfHGG2rbtm24WwMAAEAF5F04Qsa/fz806/Jo5MaLtGix87h/XS41bMgHpUBJ8zc7XTY+RSYzNVRrl/OVrr1miN54a/+sip2p0rDHrF54lv0sAAAAKoqIXxJq5syZoaWcjDEaPnw4YQUAAACOTNAv75xPHKWM+qfqxfdqOGr160v/upwPSIFS4YmR7+jzHCXvwhG64qIcndDVeejsv6T3PmBpKAAAgIoi4gOLLVtydz00xqh+/fo68cQTw9wRAAAAKir3ivFypa931N5ZeLkyMpzHDRlkFBtLYAGUFl/7Cx1jk5WmmFXjdf89RjWd+aE++EiaPoPQAgAAoCKI+MAiMzMzdLtVq1Zh7AQAAAAVmrWKmfmeo5Se2FbvTjzGUevXR+rcibACKE02pbH89Y931DzzvlRystGwh4zc7jzHWumRx622bye0AAAAKO8iPrCoUWP/12u8Xm8YOwEAAEBF5to4W+7N8xy1V+dcJWl/OFElSbrpBsIKoCz421/sGHvWT5fZuVrt2hpdP9D5c5iWJj30iJXfT2gBAABQnkV8YFG/fv3Q7R07doSxEwAAAFRkBWZXqI6+WXK6o3bLTUbJyQQWQFnwNz1NNj7FUfPO/0qSdNkl0ondnMfPnSe98z6BBQAAQHkW8YFF586dVb16dVlrNW/ePPn9/nC3BAAAgArGpK6Re+VER+2thQMUsJ7QuHMnqU/vsu4MiGKeGPnaFNx8W/5sGWN0/z1GtY5ynvLRx9KfUwktAAAAyquIDyxcLpcuvDB3Q7bMzEx999134W0IAAAAFU7M7A9ktP9Dzr2Byvp2zQWhsdcr3TnIyBhmVwBlydfuIsfYZKXJs/xnSVJSUu5+Fh6P85xhj1lt2EBoAQAAUB5FfGAhSTfddJNatGgha62ee+45rV+/PtwtAQAAoKLITJVnwQhH6ctVFykjkBgaX/kvowb1CSuAsmZTGslfv6uj5p3/Zeh2m6ONbrre+bO5Z490/4NWWVmEFgAAAOVNVAQWMTExevnll1WvXj2lpqbq8ssv159//hnutgAAAFABeOd+JhPIDo39QY8+W31FaNygvnTFZeHoDIBUcPNt9/oZMjtXhcYXXSid0st5zoqV0jPPW1lLaAEAAFCeeA5/SMU3Y8YMSdKdd96pYcOGaevWrfrPf/6jDh06qEePHmrWrJmSkpLkchUtvzn22GNLo10AAACUF75Meed86iiN2dBPW7NqhcZDBhvFxDC7AggXf7NTFYyvKlfmzlDNO/8r5fS4W5JkjNE9Q6RVq6zWrN1/3rifpKNbSxecl/+KAAAACJeoCCwGDBjgWE/YGCNrrebOnau5c+ce0TWNMVq0aFFJtQgAAIByyDv/K7kydjhqH638d+j2mf2kTh0JK4CwcsfI3+Y8xcx8J1TyLhyhnBNvlzyxkqSEBKMnHpWuvcEqI2P/qS+9YtWiudSuLT/HAAAA5UFULAm1T97pvvsCDGvtEf8BAABABPNnyzvjHUfpty0nadmuVpKk5CoqsDY+gPAouPl2emjz7X0aNDC6/17nz2wgIA192GrHDt7fAQAAlAdRE1jsCxgIHQAAAFAYnoXfyrV3q6P21rIbQrdvudmoShUCC6A8sCkN5W+Qb/PteV8UOK5Hd6N/Xe6sbd8uPfSIld/P+0MAAIBwi4oloW655ZZwtwAAAICKJJCjmOlvOUrTth2vuamdJEnHdJZ6nx6OxgAcjL/dxfL8PTU0dm+YKbNzlWzVJo7jBl5jtGSp1cxZ+2tz5kqvvW512y2EkAAAAOFEYAEAAADk41k0Uq7dmxy1fbMrYrzSnYOMY480AOHnb3aqggnVHPvOeOd9qZye9ziOc7uNHh4qXXO91ZYt++tffi01aWLVvx8/2wAAAOESNUtCAQAAAIUS9Ctm+puO0uwdnTVzx7GSpCsHGNWvxweaQLnzz+bbeXkXfSf5swscmpxs9NgwI6/XWX/2eau581gaCgAAIFwILAAAAIA8PEtGyZW+zlHLnV1h1KihdPml4ekLwOH52l7oGOduvv3TAY9t3cpoyCBn+Oj3S/c/aLVpE6EFAABAOBBYAAAAAPsEA4qZ9oajND+1nf7c1k3GSHcPMYqJYXYFUF7lbr59gqN2oM239+nX1+jSi521tDTpnvutMjIILQAAAMoagQUAAADwD8/S0XKlrnbU3vxndsVFF0jt2hJWAOWdr70zgXBvmCWzY+VBj7/xeqOuxztrK1dJjz5uFQwSWgAAAJSlqNh0+2B8Pp/mzZundevWKS0tTXv37pW1lk26AQAAolEgRzF/vOwoLU5rrSlbeqhOHWngNYQVQEUQaHpKwc2353+pnJ73HvD4fZtw33Cz1Zq1++tTfpfeetfq+mv52QcAACgrURlYzJw5U++++65+//135eTkFLj/QIHFr7/+qjFjxkiSkpOTdffdd5d6nwAAACg7ngXfFNi74n9Lb5ZkdM8Qo/h4PrQEKoR/Nt+OmfF2qORd+L1yThokeWIPeEqlSkZPPyENvNFq16799Y8+lho3tDrjdH7+AQAAykJULQmVkZGhwYMHa8CAAZo0aZKys7NlrXX8OZjmzZtr5MiR+u677/T+++9ryZIlZdg5AAAASpUvUzFT/+cozd3ZUb9u6alzz5E6d+LDSqAi8bW7yDE22enyLBt3yHPq1jV6bJiR2+2sPzXcav4CloYCAAAoC1ETWOzZs0eXXnqpRo8efcBgwphDvwmtXbu2Tj755NC5o0aNKpU+AQAAUPa8cz6Ra+82R+2lxbfrqKOMbrqesAKoaGxyg4Kbb8//8rDnde5kdMd/nT/zOT7p7vus1q4ltAAAAChtURNY3HbbbVq2bFlo7PV6dc455+iJJ57QU089dcjZFfucfvrpodu///57qfQJAACAMpa1SzHT33KU/tjaTbN2HKu77zRKSCCwACoiX/tLHOPczbdXHPa8c882uuA8Z23XLmnwXVbbdxBaAAAAlKaoCCzGjh2rP/74IzSLomPHjvrpp5/09NNP6/zzz9exxx5bqOt0795dkmSt1dKlS7V3795S6xkAAABlI2bWezLZuxy1lxffrjP7SccdS1gBVFS5m29Xd9S88w4/y0KSbr3Z6ISuztrmLdKQu6327iW0AAAAKC1REVi88cYbodvNmzfXe++9p1q1ahX5OjVq1FC1atUkScFgUCtXriyxHgEAAFD2zN7t8sz6wFH7eePp2uZto1tuJKwAKjS3V/42zqkS3kUjJV/WYU/1eIweeciodStnffkK6b6hVj4foQUAAEBpiPjAYuvWrVq8eHFoPHToUMXHxx/x9Zo0aRK6vXbt2mL1BgAAgPDy/PGaXP7M0DhgXXptya26c5BR5coEFkBFd8DNt5cfevPtfeLjjYY/aVSvrrM+a7b05NNWwSChBQAAQEmL+MBizpw5knI31a5du3ahl386mCpVqoRup6WlFetaAAAACB/XtmXyzv/CUfth3Tlqc3JTndSNsAKIBDa5vvwNT3TUvPO+OMjRBaWkGD033Cg52Vn/abz0+lsEFgAAACUt4gOLbdu2hW63bNmy2NdLSEgI3c7IyCj29QAAABAG1irnxyflUjBUygrE6vv0m3TbLYQVQCTxtb/YMXZv/EuubcsKfX7dukbPPmUUH+esf/qZ9OXXhBYAAAAlKeIDiz179oRuV6pUqdjXyxtSxMbGFvt6AAAAKHs5CyYoOXWqo/bBymt08911lZBAYAFEkkCTXgom1nDUPEWYZSFJrVoZPTLMyJ3vHfRLr1j9OJrQAgAAoKREfGCRlJQUur179+5iX2/r1q2h23mXhwIAAEDFYH3ZCo4b7qhtzqwld/dr1KoVYQUQcdxe+dte4Ch5F4+UfEWbMX/C8UZ3DSn4b8TTz1iNn0BoAQAAUBIiPrCoWrVq6PaKFSuKda2cnBzHBt61atUq1vUAAABQ9lZ//qGqu9Y5at9nDNLFl8WHqSMApc3X7iJZs//tr8nZI8+S0UW+zpl9ja671hlaWCs9+rjVlN8ILQAAAIor4gOLo48+WpJkrdWGDRu0cuXKI77WuHHj5PP5JElut1sdOnQokR4BAABQNjYu36qmm1531OalddLpt50pl4vZFUCkskl1FGjU3VEryubbeQ24QvrX5c5aICg9OMxq2nRCCwAAgOKI+MCifv36atiwYWj85ptvHtF1cnJy9PrruW9ujTFq166dYwNuAAAAlG8+n9XGj19Uose5DMyuE+5VzaMi/tdiIOr5OlziGLu3LJBry8IiX8cYo+sHGl3oXGVKPp9031CrOXMJLQAAAI5UVLwzO//88yXlzrIYOXKkRowYUaTzg8GgHnjgAcfsjCuuuKJEewQAAEDp+url+epZxfl74Cx7njr2bRemjgCUpUCjkxWsXNtRO9JZFsYY/fcWo7POdNazs6Uh91gtXERoAQAAcCSiIrC48sorVa1aNRljZK3V/fffr+eff16ZmZmHPXfFihX6z3/+ox9++EHGGBlj1KBBA5155pmHPRcAAADlw6jRQR2380lHLSOQoIZX3R6ehgCUPZdbvnYXOkqeJaOk7D1HdDljjO4cZHT6ac56ZqY0+C6rZcsJLQAAAIoqKgKL+Ph4PfXUU3K5XDLGKBgM6q233tJJJ52kQYMG6dNPP3UcP3r0aL366qu6/PLLdfbZZ2vatGmy1spaq5iYGD3//PMyhjWOAQAAKoIlS6zmfz5KHavOcdRT296guGo1w9MUgLDwt71Q1rhDY+PLkGfJD0d8Pbfb6P57jE52bo+hPXuk2wdbLV9BaAEAAFAUxlobNb9Bffnllxo2bJiCwaCk3CWi9gUPef8a8oYR+46x1srj8eipp55S//79y7bxMpCamhruFio0Y4ySk5MlSWlpaYqiHytEMF7XiES8rqNPaprVTTdk6K12/VUrfnOoviemvnTDj5InJozdlQxe14hEpfm6jht5qzwrxofGgeotlTlghFSML6Xl5Fjd+4DVtOnOepUk6f9eMGrWlC+8IRf/ZiMS8bpGJOJ1XXgpKSkler2omGGxz8UXX6y3335b1apVc4QVkkLLPe0LJ/LWrbVKSUnR22+/HZFhBQAAQCTy+60efsTqzCrvOsIKSXL3uSsiwgoARedrn2/z7e1L5do0t1jXjIkxeuJRo2M6O+vpu6TbB1mtWsWHHAAAAIURVYGFJJ1wwgn66aefNGTIENWuXTu01FPeP5JCt5OTk3XzzTfr559/VteuXcPcPQAAAArr9TetNizeqKuaveOo++p3VaDpqWHqCkC4BRp2U7BKPUftSDffzis21ujpJ4w6dXTW09Kl2wZZrVpNaAEAAHA4nnA3EA4JCQm65pprdM0112j16tWaNWuWNm/erLS0NOXk5CglJUXVq1dXp06d1KZNG/arAAAAqGDGT7D6/Evpyc7PK86dHapb45Kv173FWvoFQAVnXPK1u1ixvz0fKnmWjlF2z3ukuCrFunRcnNHwJ6Uh91jNyTNpIy1N+u8gq5dekBo34t8fAACAg4nKwCKvxo0bq3HjxuFuAwAAACVk5Sqrp56x6lh1lvrWG+O4z9f+EgWrtwhTZwDKC3+b8xTzx8syQZ8kyQSy5V30vXydryz2tePj94cWc+ftr6emSv+9w+rlF6WGDQktAAAADiTqloQCAABA5Nq+w+que62ys4K6q+1TjvtsbBXldLs1TJ0BKE9sYnX5mzmXhvPO+0IqoQ01ExKMnnnKqF1bZ31nqnTbHVZ//83yUAAAAAdCYAEAAICIkJGRG1Zs2SKdXf87HZ28yHF/zgk3S/EpYeoOQHnjb3+pY+zauUquDTNL7PoJCUbPDTdq28ZZ37FTuvUOq3XrCS0AAADyi5oloYLBoJYtW6YlS5Zo06ZN2rlzp7Kzc9czjouLU9WqVVWnTh21atVKzZs3Z98KAACACsTvt3roEatly6REzx7ddvSLjvuDVZvK1+HSA58MICoF6h+nYEojuVLXhGreuV8ou96xJfYYuaGFdMedVosW76/v2CHddnvu8lD16vHeEwAAYJ+IDyymT5+uL774QlOmTNHu3bsLdU5SUpJ69uypiy66SF26dCnlDgEAAFAc1lq9+JLVn1Nzx9c2f0PVYnc4jsnueY/k9oahOwDlljHytb9Esb88HSp5Vvyk7IydUkLVEnuYxESj55+R7hhitThPaLFte+7yUC+/KNWtS2gBAAAgRfCSUCtWrNCAAQP073//W6NHj9auXbtkrS3Un/T0dI0cOVIDBgzQVVddpdWrV4f76QAAAOAgPv1c+m5k7u36iWv1r6YfOu73N+mpQKOTwtAZgPLOd/Q5su6Y0NgEfPIu/LbEH6dSJaPnhxu1aumsb92WuzzUxk0sDwUAACBFaGAxevRoXXTRRZo5c2YohDDGFPizz4Hu23fe1KlTdf7552vcuHFhfEYAAAA4kAkTrf73xv4P+gYd/ay8Ln9obF1eZfe4KxytAagI4lPkb9HbUfLO+1KywRJ/qMqVjZ5/1qhFC2d969bc5aE2EVoAAABEXmAxZswY3XnnncrMzHQEFfsCCEmqWrWqGjVqpI4dO6p9+/Zq2LChUlJSHLMs8oYamZmZGjx4sH7++edwPjUAAADkMXee1WNP7v+Ar2uNP9Sr9kTHMb5OA2RTGpd1awAqEF/+zbfT18m9ekqpPFZSZaMXnzVq3sxZ37wld3mozVsILQAAQHSLqD0sVq1apfvuu0/BYDAUNlhrVblyZZ1//vnq1q2bOnbsqCpVqhzw/NTUVM2dO1e///67RowYoT179oSu4/f7dffdd6t58+Zq1KhRWT0lAAAAHMCq1Vb3PmDl8+WO3cavO9s87TgmGF9VOV1vCEN3ACqSYJ1OCtRoJfe2JaGad+6nCjTpUSqPl5Rk9OJz0n8HWa1Yub++aXPu5tyvvSSlpLCnBQAAiE4RNcPi0UcfVWZmZmhGhTFGt9xyiyZPnqx7771XPXr0OGhYIUkpKSnq2bOn7r//fk2ePFk33XSTY+mojIwMPfroo2XxVAAAAHAQGzZa3XGn1a5d+2sXNvxSzZJWOI7LOel2KbZy2TYHoOIxRr4OlzlK7tVTZNL+LrWHrFLF6MXnjJo2cdbXrZMGDbHavZuZFgAAIDpFTGAxa9Ys/fnnn6GwIjExUe+8845uueUWJSYmFvl6lSpV0m233aa3335b8fHxoeDijz/+0OzZs0u6fQAAABTCtm1Wtw+y2rFjfy3Jm6Zb277iOC5Qo7X8bc4v4+4AVFT+1v1l8wScRlbeuZ+X6mMmJxu9+LxR/gn8y1dId91rlZVFaAEAAKJPxAQWn376qSSFZlY88sgjOuGEE4p93W7duumRRx4JXVeSPvvss2JfFwAAAEWTmpY7s2LTZmf9gW6vqZIr3VHL7nWv5HKXYXcAKjRvgnz5Qk7vgm8kX2apPmxKcu6eFrVrO+vzF0j3DbXy+QgtAABAdImIwCInJ0eTJk0KbZR9xhln6Mwzzyyx6/fv319nnHFGaEPuiRMnKicnp8SuDwAAgEPbs8dq8F1Wa9Y6673arNDpyc5vQfta9FGw3rFl2B2ASODr4Nx822TvkmfJqFJ/3OrVc5eHqlbNWZ8+Qxr2mFUgQGgBAACiR0QEFnPmzFFGRoaszf1F7uqrry7xx/jPf/4Tup2RkaE5c+aU+GMAAACgoKwsq7vvs1q2zFlvUN/qye5Py9hAqGbdsco5+c4y7hBAJLApjeRv1N1R8875VLKlHxjUrWP0wrNGSUnO+uRfpOHP2dB7XQAAgEgXMYGFJBlj1LRpU3Xs2LHEH6Njx45q1qxZgccEAABA6fH5rO5/0GruPGf9qKOkN+74RXEb/3Ae3+U/skl1y7BDAJHE1zHf5tvbFsu1aU6ZPHaTxkbPPm0UH++sjxotvfI/AgsAABAdIiKwWL58eeh2586dS+1x8l4772MCAACg5AUCVo88bjVturNeNUV6cbhPR80Z7qgHKx2lnOOuLcMOAUSaQKOTFaxSz1Hzzvm0zB7/6NZGTz9hFON11r/4UvryK0ILAAAQ+SIisFi3bl3odvv27UvtcTp06HDAxwQAAEDJstZq+HNWkyY765UqSc8/a9RkxxdypTk3tMjpPljyJpRdkwAij8stX3vnXhaeZeNk9mwtsxY6dzJ6ZJiRO9+79Zdfs5r8C6EFAACIbBERWGzfvj10u27d0lsCoE6dOqHb27ZtK7XHAQAAiGbWWr3ymtWo0c56fJz07NNGzeqkK+bP1xz3BWq1l79V/zLsEkCk8rU9X9YdGxqboE/euZ+XaQ8ndTO67x7jqFkrPfKY1fwFhBYAACByRURgkZaWFrpduXLlUnucpH92QLPWKj09vdQeBwAAIJq994H0xVfOWoxXeuoJo7ZtjGKmvS6T7fxdLLvnPZJxfrgHAEckPkX+1mc5St55n0v+7DJto/cZRtcPdP67luOT7rnP6u91hBYAACAyRURgkZOTE7q9L1QoDXnDkOzssv1lFQAAIBp8+ZXVu+87P4hzu6RhDxkd09nIpK4psJ68r0UfBet0Kss2AUQ4X+cBjrHJTJVn8Q9l3se/LpfOcWYnSt8l3Xm3VWoqoQUAAIg8ERdYeDyeUnsct9sduu33+0vtcQAAAKLRj6OtXnrV+QGcMdL99xp1Pyn3W8axU56TCfpC91u3N3fvCgAoQcHqLeRvcIKj5p39Ye66TGXIGKM7/mvUrauzvnGjdNd9VpmZhBYAACCyRERgAQAAgIpt4mSr4c8W/OBt8O1GZ5yeG1a41k2XZ8V4x/2+TlfKVqlXJj0CiC6+zv92jN07lsu9bmqZ9+HxGD38oFHLFs764sXSsMesAgFCCwAAEDkILAAAABBWU6dZPfKYVTDorN9wndG55/yzfrsNKvbX4Y77bXyKco6/voy6BBBtAo27K5jSyFHzzv4wLL0kJBgNf9Kodi1n/bffpVdeI7AAAACRg8ACAAAAYTN3ntX9D1rlX23zX5dL/7p8/2aznqVj5N6y0HFM9gm3SLGVBQClwriU08m5l4Vn1WSZ1DVhaadaNaNnnjaqnO+fva++kX4cRWgBAAAiQ+lt+FDGjMl9Qztnzhxt2LChVB5j27ZtpXJdAACAaLRkqdWQe6yys531886Vrh+4P6xQIEcxv/+f45hg1Sbyt7+49JsEENX8R58j+/v/yWTvCtW8f32snFMeCEs/jRoaPfW4dPtgK9/+7Xz07AtW9etLHdqbg58MAABQAURMYCFJ1loNHly6my4aY2TLeKM1AACASLN6jdXgIVYZGc5679OlO24zoS+jSJJ33ldypa9zHJd90iDJFVG/ygIoj2IS5Wt3oWJmvhsqeReOUE6326S4pLC01KG90V13So8/uf99qd8v3T/U6q3Xpdq1CS0AAEDFFVFLQu0LE0rzDwAAAIpn4yarO+60St/lrHc/Ubr3biOXK8+HbTl75Z36muO4QJ1OCjQ9pQw6BQDJ1/EKWeMOjY0vQ955X4SxI6lvb6PLL3XW0tKle+63ysjgfSsAAKi4IiqwkHJDi9L8AwAAgCO3fbvV7YOttm931o/pLD38oJHH4/x9K2bme3Jl7nTUsrsPlvi9DEAZsUl15G9+uqPm/etDyZ99kDPKxvUDjbp1ddZWrpIefdwqGCS0AAAAFVNEzKOvU6dOuFsAAADAYaSn586s2LjRWW9ztPTkY0axsc4QwuzdLu+s9xw1f5NeCtY9prRbBQAHX5f/yLtsbGjs2rtdnkXfh3UvHbfb6KGh0vU3W61Zs78+5Xfp7XetrruWYBcAAFQ8ERFYTJw4MdwtAAAA4BAyM3M32F69xllv2lR65mmjhISCH6x5p70u49u/yYU1LuWcdHvpNgoABxCs1U7++sfLs25aqBYz8135214gudyHOLN0JSYaPf24NPBGq115ltn78GOpcWOr008ltAAAABVLxC0JBQAAgPLF77ca+rDVosXOer260gvPGCVVLviBmkn7u8Aa8f6jz1GweovSbBUADsp37LWOsSttrdwrJ4Spm/3q1jV6bJiRO19u8uTTVsuWszQUAACoWAgsAAAAUGqstXr6Gaup05z1mjWkF58zqlr1wN/+jfnzVZmgf/913DHKOeGW0mwVAA4p0PBEBWq0dtRiZrwj2fCHAp07Gd1xm/Pf05wc6f6hVunp4e8PAACgsAgsAAAAUGpef9NqzDhnLSlJev5Zo1q1DhxWmB0r5Vn8g6Pm63iFbBL7luH/2bvv8KiqrY/jvzMtlYSEUKRJF1AQBLEiKhZEr4pe2/UqIAjYRVTUK4q9Y0HFdrFdfe0FRWyIBRtgA0F6R3oCIXXafv8YGDgpkD4l38/z5En2OmVWYM9k5qyz9wYiyLLkO3SYLeTcMFfOtbMjlJDdmWdYGnSmPbZ+g3TH3UaBAEULAAAQGyhYAAAAoFa89Y7Ra/9njyUkSA/eZ6nN/uXPq+758UlZ2n1xzbiT5e1zaW2lCQAV5u90soLpLW0x9+wXIpRNaVdfYanbQfbYrNnS5JcoWAAAgNhAwQIAAAA17svpRk88ab9A5nRId95u6aADyy9WODYvlHvxp7aY75CLpaSMWskTACrF4ZKv1xBbyLXyOzk2L4xMPiW43ZbuGm+pUaY9/vKr0szvKVoAAIDoR8ECAAAANWrOL0Z331f6wtiNN1g66sjyixWS5Plhoq1tEtLk7TW0RvMDgOrwHXiWTIkiqnvW8xHKprSsLEt3ji+9CPdd9xqtWUvRAgAARDcKFgAAAKgxS5Ya3TLOyO+3x0cMt3TqKXsvVjjWz5Vr2Ve2mLf3UCkxrabTBICqcyfJ2+NCW8i1aJqsrcsilFBpB3e3dMVl9tfc/PzQItyFhRQtAABA9KJgAQAAgBqxaZPRjTcZFRTY42cPki66sOxj9lRqdEVShnw9L6rBDAGgZvh6/lvGkxpuWzLy/DQpghmVds7Z0okn2GPLV0j3P2RkDEULAAAQnShYAAAAoNoKCoxuvNlo8xZ7/LhjpauvtGRZ+xhdsXaOXKtm2mLeQ4dLnpSaTRQAakJiunw9/20LuRZ9Iit7eYQSKs2yLN04xlL7dvb49K+kt9+JTE4AAAD7QsECAAAA1eL3G40bb7S0xGwoB3eXxt1iyence7FCkjw/PmlrB1Oy5Dv4gppMEwBqlPeQwTJ7FFWjcZRFUpKle+60lFqi9vvUJKPf/2CUBQAAiD6uSCeAvdu2bZsWL16sVatWadu2bTLGKD09Xc2bN1ePHj3UoEGDSKcIAADqMWOMHnvC6OdZ9nirVtJ9d1vyePZdrHCsnS3Xmp9tMV+fUZI7qSZTBYCaldRQvp7/lufnZ8Mh18Kp8h5+mUxmu70cWLdatrQ07j/S2Ft2FygCQem28UaTnw8t0g0AABAtKFhEmWAwqDlz5uiLL77QTz/9pMWLF5e7r2VZOuKIIzRkyBD169evDrMEAAAI+b83pQ+m2GMN06WH77eUllaxi2CeH5+2tYOpTeXr9s+aShEAao33kMFy//Y/Wd58SbtGWTyj4oEPRjgzu6OOtDTkYqOXXtkdy86Rbr3daOJjkttN0QIAAEQHpoSKMgMGDNBFF12kV155Za/FCil0R+MPP/ygESNG6LrrrlNeXl4dZQkAACB99bXR08/YpxTxuKX777XUokXFLn451v0i15qfbDHfoZdKroQayxMAak1Shnw9Sq5lMVVW9ooIJVS+oYMtHdbHHvtzvjTxKaaGAgAA0YOCRZTJzs4uFWvTpo1OP/10XXLJJbrssss0aNAgNWvWzLbP1KlTNWrUKBUXF9dVqgAAoB77c77R3feUvsh1638sHXRgxe/U9fxUYnRFShNGVwCIKd5eg2XcyeG2ZYJRt5aFJDmdlm6/1dJ++9nj730gffo5RQsAABAdmBIqSrVo0ULnnHNOmcUJSQoEAnrrrbd03333hYsUs2fP1mOPPaaxY8fWdboAAKAeWb/e6Kb/GHl99vjloywdf2zFixWOv3+Ta9UPtpjv0OGMrgAQW5IyQmtZzHouHHIt/Fi+Q4cp2PiACCZWWlqapXvulEZdYeT17o4/+LBR+7ZSx45MDQUAACKLERZRpnnz5rr33nv1xRdf6LLLLiuzWCFJTqdTF1xwgSZOnCiHY/d/46uvvqqNGzfWVboAAKCeyc83uvFmo23b7PEz/iFdcF7lzlVq7YqULPm6nVO9BAEgAry9hthHWcjIM3NCBDMqX6eOlm4YYy9MeL3SLbcZ5eYy0gIAAEQWBYso89577+nss8+W0+ms0P79+vXTqaeeGm77fD5Nnz69ttIDAAD1WCBgNP4uoxUr7fHD+kijr7FkWZUYXbH+D7lWzbTFfIcOl9yJNZApANSxpAx5e19iC7lWfCvnmlkRSmjvTjnZ0pln2GPr10t33mMUDFK0AAAAkUPBIsq4XJWfpWvPgoUkzZs3r6bSAQAACHtqktGP9vWx1aaNdMdtllyuyk0j4vnxKVs7mJwlX7dzq5khAESOr9cQBZOzbDHPtw9LJjoLANdcaenArvbYTz9LL74cnfkCAID6gYJFHGjdurWtvWXLlghlAgAA4tWHHxm99Y491jBdevA+S6mplStWODb8KdfK72wxX+9LJHdSddMEgMjxpMh7xOW2kHPjPDmXfBahhPbO7bZ09x2WMjLs8ZdekX78iaIFAACIDAoWcSA/P9/WrsooDQAAgPLM+cVowmP2i1dut3Tv3Zaa71f5BVo9s561tYNJmfIdXMkFMAAgCvkP+qeCGW1ssYSZj0oBX2QS2ofGjS3debsl5x5XBowJTQ3193qKFgAAoO5RsIgDixYtsrXLW6gbAACgslavNrr1dqNAwB4fe4Ol7t0qX6xwbFki19IvbTFfryHSHovVAkDMcrpVfNRoW8ixbbVc896OUEL71rOHpVEj7a/nO3ZIt95mVFxM0QIAANQtChZxYMqUKbb24YcfHqFMAABAPMnNNbrxFqO8PHv8ogulASdVvlghSe5Zz9vaJiFNvoMvqGqKABB1Ah1PVKBZd1vM89PTkje/nCMi7/xzpWP72WOLl0iPPGZkonQNDgAAEJ8oWMS4WbNmadasWeF2gwYNdPTRR0cwIwAAEA/8/tDIirVr7fF+x0iXDqtascLatkauRVNtMV+PC6WE1KqmCQDRx7JUfMz1tpCjYKs8P02KUEL7ZlmWbhlraX/78oj6ZJr00dSyjwEAAKgNLHYQwwoLCzVu3DhbbOjQoUpJSan0uSyrahceELLnvx//logX9GvEI/p1xRhjNOEx6dff7PEDOknjbnHI6azav51nzguyTHD347iT5et1Mf8X1US/RjyK9X5tWvWRv92xci3/Ohxz//qK/N3OlslsF7nE9iIlxdK9dxkNHxVUYeHu+KOPG3XqYKlLl9j7f4hGsd63gbLQrxGP6NeRYxnGd8asm266Se+//3643a5dO33wwQdKSEiIYFYAACDWvfxqoR58uMAWa9zY0puvpatpU2eVzmly18s/4TAp4A3HHEeNknPA+OqkCgBRy2xZJv+Tx9oW3LY6HCfnxa9H9YWPzz4v1nU32OcC3G8/h97+v3RlZDBJAwAAqF2824hRL7/8sq1Y4fF49NBDD1GsAAAA1fLNt1499Ii9WJGYKD35eFqVixWSFJw5yVaskNMjx1GXVfl8ABDtrKz2chw50hYzS2fILPwsQhlVzMknJWjwRYm22Pr1Qd1wU578fu53BAAAtYsRFjHo008/1ejRoxUM7p5S4f7779egQYOqfM5t27bVQGb1l2VZSk9PlyRt376dhekQF+jXiEf0671btsxo5BX2qUAk6e47HDru2GrcDVyQreTnj5flLwqHfAefL+8J46t+ToTRrxGP4qZfe/OV9OIpcuRtCoeCaS1UOGSq5E7cy4GR5fcbXT06qD/m2uMXXmDp8lHc91gdcdO3gT3QrxGP6NcV17Bhwxo9H2tYxJgff/xRN9xwg61YMWbMmGoVKyTxpKtBxhj+PRF36NeIR/Rru+xsoxtuNqWKFcMvsXRsv+q9V/D88rKtWGEsp7y9h/HvXwvo14hHMd2v3cny9r1BidNuCIccuevkmv2CfEdcEcHE9s7plO683dIlI4y2bt0df+3/jDp1Cqr/cdE7pVUsiem+DZSDfo14RL+uW9waEUPmzp2ryy+/XF7v7ukUhg0bphEjRkQwKwAAEOuKi41uGWe0caM9ftIJ0uCLqnnyoly5f3/NFvJ3OU0mvWU1TwwAscHf+VQFWvSyxTyznpe1fV2EMqqYRo0s3X2HJVeJ2xzve8Bo2XIu2gAAgNpBwSJGLF68WJdeeqkKCnbPKX3OOefoxhtvjGBWAAAg1hljdP9DRn/Ot8cPOlAae4NV7YVh3X+8Lsu7e/FWI0vePtxsAaAesSwVH3+rjLX747cVKFbC9DukKL9bs9tBlq692v53oKhIuvlWo9zc6M4dAADEJgoWMWD16tW65JJLbOtMDBgwQHfeeWfkkgIAAHHhvy8affGlPda0qXTvXZYSEqo55YevQJ5fXraFAh1PkslsV73zAkCMCTbuLF/3820x18rv5Fr4cYQyqrgz/iH941R77O+/pfF3GQUCFC0AAEDNomAR5TZu3KghQ4Zo8+bN4Vi/fv308MMPy+Hgvw8AAFTdtE+NXnrFHktKkh68z1JmZvXnJ3fPfUtW0TZbzHsYoysA1E/eo65WMCXLFkuYca9UkB2hjCrGsiyNvsZS1y72+KzZ0guTKVgAAICaxRXvKJadna0hQ4Zo3brdc5v26dNHTzzxhNxudwQzAwAAse7X34weeNh+ocnhkO64zVL7djWwmKrfK/ecF+2htsco2KRr9c8NALEoMV3Fx99mC1lF20JFiyjn8Vi6505LmRn2+KuvSTO+pmgBAABqDgWLKJWXl6fhw4dr+fLl4Vj37t01adIkJSYmRjAzAAAQ61auCi2y7ffb49debenII2qgWCHJteB9OfI32WLew0bVyLkBIFYFOp4of8eTbTH3oqlyLp8RoYwqrnFjS3fdYcnptMfvvZ9FuAEAQM2hYBGFioqKNGrUKM2fv3v1y06dOumFF15QampqBDMDAACxLifH6IabjPLy7PHzzpHOOrNmihUK+uWZ/YIt5G/ZR8HmPWvm/AAQw4qP/49MQrotlvDlHVJxXjlHRI+Du1u6+kr734rCIummW4xytlG0AAAA1UfBIsr4/X5dc801mj17djjWpk0bvfjii0pPT9/LkQAAAHtXXGw09haj9evt8WP6SpePqqFihSTXwqlybF9ri/kOG1lj5weAWGZSGqv42LG2mCNvoxK+fShCGVXOWWdKAwfYY+s3SLfeZuTzUbQAAADVQ8EiihhjdNNNN+nrr78Ox1q0aKGXXnpJWVlZ5R8IAACwD8Gg0V33GC34yx7v0lm67T+WnM4aKliYoDyznrOFAs26K9D6iJo5PwDEAX/XM+Xf/0hbzD3vrZiYGsqyLI0ZbenAEksS/TFXeuRRI2MoWgAAgKpzRToB7Pb333/ro48+KhXr379/pc7TokULffHFFzWZGgAAiGHGGE18yujrb+3xZk2l+++xlJhYc6MrnEu+kCN7uS3mPWykZNXcYwBAzLMsFZ9wp5yvnC7LVxAOJ3x2qwov/lAmJbpvWEtIsHTvXdKlo4w2bd4d//gTqV076dx/Ri43AAAQ2xhhEUXKuhPFGKNAIFDpLwAAgF3+97r09rv2WGqK9OD9lho1qsFCgjHy/PyMLRTIOkCBdsfW3GMAQJww6S1U3K/E1FCF2Ur4/FYpBkYpNGpk6f57LSUm2uNPPm3086zozx8AAEQnChYAAABxbOo0o2eft184cjqlu++01K5tzY56cC7/Ws7NC20x32EjJYu3nABQFn+3c+Rvbx9R71rxjVxz34hQRpXTqaOl/9xk/1sSDEq332G0ahVFCwAAUHlMCRVFWrZsqUWLFkU6DQAAECdm/mD04EOlLxj95yZLvXvV8BRNZYyuCGa2k7/jSTX7OAAQTyxLRSfeqeQNf8iRvyUcTvjmQQVaHSaT2S6CyVXMccdaGjZU+u+Lu//e5OVLY28xem6SlJbGlIAAAKDiuN0NAAAgDs2dZ3TbeKNA0B6/+gpLJ51Y8xePnKt+kHPDXFvM22eE5HDW+GMBQFxJzlTxSffaQpa/SImf3CD5vRFKqnKGXCwdf5w9tnaddOvtRj4fIy0AAEDFUbAAAACIM8tXGN14s5G3xHWuCy+Qzj2ndu509fw8ydYOpreSv/OptfJYABBvAm37ytvjQlvMuWmBPN88EKGMKseyLN0y1lKnTvb4r79J9z9kylyvEQAAoCwULAAAAOLIho1GY24wysuzxweeIo0aUTvFCsfa2XKu+8UW8/a5VHIw+ygAVJS37/UKNGpvi3n+eF2uRZ9EKKPKSUy0dP/dlho1ssc/+1ya/BIFCwAAUDEULAAAAOLE1q1Go6832rzFHj/yCOnGMZYsq7ZGV5RYuyK1mfxdz6iVxwKAuOVOVPHACTKuRFs44fNxsrJXRCipymnSxNID91pKtP8KevFl6eNPKFoAAIB9o2ABAAAQB7ZtM7p2jNGaNfZ4t4OkO2+35HLV0uiK9X/IteoHW8x36HDJ6amVxwOAeBZs3EnF/W+zxSxfgRI/ukbyFUYoq8rpfIClO26z5ChxteGhR4xmz6FoAQAA9o6CBQAAQIzL3REaWbFipT3epo123ulaO8UKqYzRFSlZ8h10dq09HgDEO/+Bg+Q78CxbzLl1iRK+uitCGVXeUUdaGn2N/W9PICD95zajpcsoWgAAgPJRsAAAAIhh+flGY240WrLUHm/RXHr0IUtpabVXrHBsWiDX8q9tMV+vSyR3YtkHAAAqpPj4cQpkHWCLuee/L9fcNyOUUeUNOsPSv863xwoKpBvGGm3aRNECAACUjYIFAABAjCooMLp+rNFff9njTZtKj0+w1Lhx7RUrJMnz87O2tklsKF/3c2v1MQGgXnAnqugfj8p4UmzhhK/ukWPdLxFKqvJGjbB0/HH22OYt0g03G+XlUbQAAAClUbAAAACIQcXFRjf9x2jen/Z4Vpb0xARLzZrVbrHCsWWJXEs+t8W8vYZIJS6uAQCqxmS0VdFJd9tiVtCnxI+ukbVjfYSyqhyHw9J/brLUvZs9vmyZNPYWo+JiihYAAMCOggUAAECM8XqNbhln9Otv9nhmRqhY0aJF7RYrJMk963lb2yQ0kK/Hv2r9cQGgPgl0GiBv72G2mKNgqxKnXCX5iiKUVeUkJFi6725LrVrZ43/MlcaNN/L7KVoAAIDdKFgAAADEkF0jK36eZY+np0mPPWKpdevaL1ZYOSvlWjTVFvP1+LeU0KDWHxsA6hvv0aPlb9PXFnNunK+EL2+TTGxc7E9Pt/Tw/ZYyM+zxH36U7r3fKBiMjd8DAADUPgoWAAAAMaKgwOiGm4xmzbbHU1OlRx+x1K5d7RcrJMkz63lZJhhuG3eyvIdcVCePDQD1jsOpooEPKdiwtS3s/usjuX95MUJJVV6LFpYmPGwpNdUe//xL6fGJRiZGii8AAKB2UbAAAACIAXl5RmNuLD0NVHKyNOEhS5061k2xwtq+Tq6/pthivoMvkJIyyjkCAFBtiekqPOMpGXeyLez59mE5S6wnFM06tLf00P2WEhLs8Xfflya/RMECAABQsAAAAIh6ublGo68vvcB2ampoGqiuXeqmWCFJnp8nyQr6w23jTJCv15A6e3wAqK9Mow4qGviQLWbJKHHaWDnWz41QVpXX7SBL995lyeWyx198WXrrbYoWAADUdxQsAAAAoljONqNrrjP6a6E9np4mTXy0bosVVs4queZ/YIv5up8rk5JVZzkAQH0WaH+8io++zhaz/EVK/PByWdvXRSiryjusj6Vx/7FklfgT9sRTRp9Mo2gBAEB9RsECAAAgSm3danTVtUZLltrjmRnSxMctdayjaaB28fw8SZYJhNvGlShfn0vrNAcAqO98hw6X76B/2mKOgq1K/GCkVJQboawqr/9xlq6/rvTfsfseNJr2GUULAADqKwoWAAAAUejv9UZXXG20cqU93jhLevIJS+3a1m2xwspeLtdfH9livoP/JZPSuE7zAIB6z7JU3P82+VsfYQs7ty5T4sfXSAFfhBKrvDP+YWnkpfa/Z8ZI995P0QIAgPqKggUAAECUWbbc6LIrjdaWmN2jWdNQsaJ1q7otVkiS58enZZlguG3cyfIeOqzO8wAASHK6VfSPxxVo1N4Wdq3+SQnT7whd9Y8R//5X6GtP4aLFp7HzewAAgJpBwQIAACCK/DE3NLJi61Z7vGWLULGiRfO6L1Y4tiyWa9Entpiv57+l5Mw6zwUAsFNCAxWd+ayCyfZ1hNx/viv37BcilFTlWVZolMW/LrDHjZHufYCiBQAA9Q0FCwAAgCjx/Q9Go683ysuzx9u3k5583FKzpnVfrJAkz49PydLuC0bGkypvr6ERyQUAsJtJb6GiM5+WcSXa4gkzJ5QqNEczy7J02QiKFgAAgIIFAABAVJj2mdEttxp5vfZ4926hYkVWVmSKFY5Nf8m15HNbzHfIYCmpYUTyAQDYBZt1U9EpD8rI/nci4dOb5fj7twhlVXkULQAAgETBAgAAIOL+702je+4zCgTt8aOOlB592FKDBpEpVkiS5/vHbG2TkCbvIRdHJhkAQJkCHU+Ut9+NtpgV8Crpwytk5ayMTFJVsK+ixXsfULQAACDeUbAAAACIkGDQ6Mmng3pqUukLMKecLN1zp6WEhMgVKxxrZsm14ltbzNtriJSYFpmEAADl8h0yWL6Dz7fFrMIcJb13qaz8zRHKqvL2VrSY8JjRiy8bmRhaVBwAAFQOBQsAAIAIKC42uu0OozfeKr3tgvOkW26y5HJFrlghY5Tw3SO2UDC5kXyMrgCA6GRZKj7uP/K36WsLO7avVeL7I6XivHIOjD7lFS0k6b8vGj32hFEwSNECAIB4RMECAACgjm3fbnTtGKOvvym97fJRlq64zCHLimCxQpJz6Rdybphri3kPv1zypEQoIwDAPjlcKjrtUQWadLWFnZv+UuJHV0kBbzkHRp9dRYtLhpT+e/ju+9Kd9xj5fBQtAACINxQsAAAA6tC6dUYjrzCa96c97nRK/7nZ0r/Oj2yhQpIU9Cth5qP2UMPW8nc7J0IJAQAqzJOiokHPKpjeyhZ2rf5JCZ/eIplgOQdGH8sKFSyuu9ZSyTr+l9OlsbcYFRZStAAAIJ5QsAAAAKgj8xeEihVr19rjKSnSIw9aOuXkKChWSHL9+Z4cJRZp9R51reR0RyQfAEDlmJQsFZ71nIJJmba4e9FUeb59KEJZVd1ZZ1oaP86Sy2WPz5otXTvGKDeXogUAAPGCggUAAEAd+G6m0dWjjbZts8ebNJaenmipd6/oKFbIVyjPj0/aQoGmB8nf6eQIJQQAqAqT0UZFg56VcSfb4p5fXpJ7zosRyqrq+h9v6cH7LCUm2uPzF0gjrzBas5aiBQAA8YCCBQAAQC0yxuj1N4xuGWdUXGzf1qG99OzTltq3i5JihST3b6/Kkb/ZFvP2vU6yeNsIALEm2OwgFf3jcRmHfWhCwrcPyvXXRxHKqur6HGrp8QmW0tLs8TVrpBGXGf36G0ULAABiHZ88AQAAaonPZ/TAQ0ZPP2NkSlxD6XOo9NQTlho3jp5ihZW/RZ5Zz9li/v2PUqD1ERHKCABQXYE2R6v4pLtLxRM+u0XOld9HIKPqObCrpaeesNSksT2+Y4c0+nqjDz+iaAEAQCyjYAEAAFALcnONrrvB6ONPSm87daD04H2WUlKip1ghSZ7vH5PlzbfFvH2vi1A2AICa4u96hor7Xm+LWUG/Ej+6Wo4Nf0Yoq6pr28bSpKcsdexgjwcC0kOPGD3xZFCBAIULAABiEQULAACAGrZ6jdGIy41++730tpGXWrrpBksuV3QVKxwb58v153u2mK/rmQo26RqhjAAANcnX+xJ5D7nYFrN8BUp8f6SsnBURyqrqmjYJjbToe3TpbW+9I429xSg/n6IFAACxhoIFAABADfr1N6ORlxutXWuPJyRI99xp6aILLVlWdBUrZIwSvr5PlnZf2DHuZHmPHh3BpAAANcqy5O03Vr4DTrGFHYXZSnpnmKwdGyKUWNUlJ1u6505L//5X6W0//RxajHv1GooWAADEEgoWAAAANeSDKUajrzfascMez8oKrVfR75goK1Ts5Fr8qZzrfrHFvH1GyKQ2iVBGAIBaYTlUfPL98rc+3BZ27FivxPeGS4U5EUqs6hwOS6NGOPSfmy253fZtK1dKw0caff0NRQsAAGIFBQsAAIBq8vmMHp4Q1MMTjAIB+7ZOHaXnJ1nqfEB0FivkK5Ln24dsoWBaC/l6DYlMPgCA2uXyqOj0JxVoepAt7Ny6TEnvj5JKrGUUK0452dJjj1hqmG6PFxRIt95uNPHpoPx+ChcAAEQ7ChYAAADVkLMtNKrigymlt/U9OjSyonHjKC1WSHL/8qIcO9bbYsXH3CC5EiKUEQCg1nlSVDjoWQUz2trCzg1zlfjR1ZLfG6HEqufg7paee8ZS+3alt735lnT1aKMtWyhaAAAQzShYAAAAVNGSJUbDRxr9/kfpbf/+V2jNiqSk6C1WWLl/yzPreVss0KK3Ah1PilBGAIA6k5ypwrNfUDC1mS3sWvWDEj4dKwUD5RwY3ZrvZ+mZpyydXMafsrnzpKGXGv36G0ULAACiFQULAACAKpg+w2jUlUYbN9rjHo90+7jQfNoOR/QWKyQpYcY9svyF4baRpeLjbpGibVFwAECtMGnNVfjP/8okNrTF3Ys/VcJXd0kmNi/sJyVZuvVmS9dfV3pdi5wc6doxRi+9YhQIxObvBwBAPKNgAQAAUAmBgNGzLwR1+x1GxcX2bU0aS09PtHRi/+i/4O9c+qVcy76yxfzdzlGwSZcIZQQAiAST2U6FZz0n4062xd1z35Tnh8cjlFX1WZalM0+3NGmipWZN7duCQemFyUbXXGe0cRNFCwAAogkFCwAAgAravt3ohpuMXv1f6W3dDpJeeDaKF9fekzdfCV/dYwsFkxup+OjREUoIABBJwWbdVHTGkzJO+3AEz8/Pyv3LS5FJqoZ07mxp8vOWjji89Lbf/5CGDDP6+huKFgAARAsKFgAAABWwcJHRsBFGs2aX3vaP06QnHrWUmRkDxQpJnh8mypG3wRbz9hsrJTWMTEIAgIgLtD5CRQMflrHslwkSvnlArj/fi1BWNSMtzdID91q6dJglR4mrIDt2SLfebvTAw0EVFlK4AAAg0ihYAAAA7MPHU40uv9JoQ4n1KpxOafQ1lm4cY8ntjo1ihWPTArl/e9UW87c+Qv7Op0UoIwBAtAh0PEnFJ4wvFU/4Ypxciz6p+4RqkMNhafBFliY+VnqKKEn66GNp2AijRYspWgAAEEkULAAAAMpRXBy64/L+h4y8Pvu2RpmhURVnD7Jkxcoi1cGAEr4YL8sEwyHj9Ki4/20stA0AkBRaz6j46OtsMcsElTBtrJwl1j6KRQd3t/TiC5b6H1d62+o10sjLjV59zcjvp3ABAEAkULAAAAAow4YNRldcbfTRx6W3de8m/fd5Swd3j62L/O7f/yfnxnm2mLfPCJmMNpFJCAAQlXyHDpf30EttMSvoV+LH18q58vsIZVVzGjSwNP42S/+5yVJSon2b3y89+7zRldcYrVlL0QIAgLpGwQIAAKCEb78zGjLcaOGi0tvO/WdoZEVWo9gqVljZK+T57lFbLJjRVr4SF6QAAJBlyXv0aHl7XmQPB3xKnHKlHGvLWNApxliWpVMGWJr8gqXOB5Te/ud8aehwo/c+MDKGwgUAAHWFggUAAMBOXq/R4xODumWcUV6efVtiojR+nKWrr3TI5YqtYoWCfiV+epOsQLEtXHTCeMnliUxOAIDoZlnyHnuTfAedbQ/7i5T0/ig51v8RocRqVquWliY9aemiC1VqQe6iImnCY0bX3WC0aRNFCwAA6gIFCwAAAEnr1hlddqXR2++W3taypfTc05ZO6B9jhYqd3HMmy7lhri3m7XmRgq36RCgjAEBMsBwqPuEO+TqfZg/7CpT03gg5Nv0VocRqltttaeSlDj31hKWWLUpvnz1Hunio0aefM9oCAIDaRsECAADUe199bXTJCKNFi0tv63+c9N9nLbVrF5vFCsfmRfL88KQtFsxoI+/RoyOUEQAgpjicKj75Xvnb97eFreJcJb07TNbWZRFKrOZ1Oyi0IPegM0tvy8uX7r7XaOzNRps3U7QAAKC2ULAAAAD1VlGR0cMTgrptvFF+vn2bxyPdMCa0KGdKSmwWKxTwKuHTm2QFfeGQsRwqOvk+yZ0UwcQAADHF6VbRqRPkb9PXFrYKc5T0zlBZ21ZHKLGal5Rkacy1Dk14yFLjrNLbf/hJumiI0dRpjLYAAKA2ULAAAAD10l9/GQ291OiDKaW3tW4lPTfJ0hn/sGRZMVqskOT58Sk5Ny+0xXyHDleweY/IJAQAiF0uj4r+8bgCLQ+1hR35m5X09hBZ29dFKLHa0edQSy+/aOnkE0tvy8uX7nvAaMyNRhtZ2wIAgBpFwQIAANQrfr/R088UaOQVQa1ZU3r7ySdJLzxrqUP72C1USJJz5Uy5Zz1viwWyOsl7+BURyggAEPPcSSo8c5IC+x1sCzt2rFfS2xfHXdEirYGlcf9x6N67LGVmlN4+a3ZotMWHHzHaAgCAmkLBAgAA1Btr1xpdPDRXT00qVCBg35aYKN081tKtN1tKTo7tYoW1Y6MSp90oS7svnhiHS8UD7pdcnghmBgCIeZ4UFQ56ToEmXWxhR+7fSnrrIlnb10YosdpzTF9L/3vZ0sknld5WUCA99IjRVdcarV5D0QIAgOqiYAEAAOKeMUZTPjYaMjyoP+b6S23v0lma/LylU0+J7SmgJElBvxI/uV5WYY4t7O07RsESF5cAAKiSxDQVnv1fBbIOsIUdO9Yr6a2L47JokZZmadwtDj1wr6WsMta2+P0PacglRi+/auTzUbgAAKCqKFgAAIC4tn59aI7pBx82Kiy0b3M6pKGDpUlPWmrdKsYLFTt5fnxSznVzbDF/++PlO2RwhDICAMSlpAwVnvNi+UWLOFqIe09HHWnp1RctDTyl9DavT3r+v0bDRhjNX0DRAgCAqqBgAQAA4lIgYPT2u0YXDzWaNbv09pYtpKeftDRsqEMuV3wUK5wrZ8r983O2WDCtuYpOukeK9ZEjAIDos7eixZv/lrV1aYQSq10NGli6ZaxDjzxoqVnT0tuXr5BGXWH02BNBFRRQuAAAoDIoWAAAgLizYqXR5VcZPT7RqLCo9PZz/5mgl/7r0IFd4+civrVtjRI/uaHUuhVFp06QkhpGLjEAQHwrr2iRv1nJb10sx6a/IpRY7Tusj6VXX7J03rmSo8TVFWOkd96TLrzYaMbXLMoNAEBFUbAAAABxw+czevFlo0suNZq/oPT2rCzpqSca6PZxqUpKip9ihYp3KPHDy2QVbbOFvX3HKLjfwZHJCQBQf+wqWjS2r5VkFeYo6e0hcqz/I0KJ1b6kJEtXXe7Qc09b6tC+9PbNW6Rx40PTU65ZS9ECAIB9oWABAADiwq+/hQoV/33RyOcrvf30f0ivvezQsf08dZ9cbQr6lTh1jJxbl9nC/vb9WbcCAFB3kjJUeM5LCpQolFvFuUp65xI5V/8cocTqRufOll541tKoEZY8ZbzVmDVbunio0QuTgyoqonABAEB5KFgAAICYtmmT0e13BHX1aKMVK0tvb9lCeuJRSzeOcSg1NY5GVezk+fYhuVZ+Z4sFGnVU0SkPsG4FAKBuJaap8Oz/yt+yjy1s+QqU+P6lci7+PEKJ1Q2Xy9K//2XplcmWDu1dervPJ734stEZZ23TN9966z5BAABiAAULAAAQk7xeo1dfM/rXxUbTZ5Te7nBI/7pAenmypUN6xueFe9fct+T59RVbLJiUqaIzJ0melAhlBQCo1zwpKhr0rPxt+trCVsCnxI+vleuPNyKUWN1p2dLShIcs3TneUlZW6e1r1wV1+VU7dP3YgFavZrQFAAB7omABAABizk8/G118idGzzxsVlbGodscO0nOTLF0+0qGEhPgsVjiXf6OEr+6yxYzTraLTJ8qkt4hQVgAASHInquj0J+XvcKItbMkocfod8vzwZGhV6jhmWZaOP9bS66+EFuV2lnH15cefpIuGGj3xZFC5O+L73wMAgIqiYAEAAGLGsuVG148N6vqxRmvXlt7eoIE0ZnRoDunOB8RnoUKSHGtnK/Gja2QF/bZ48Ql3KtjikAhlBQDAHlweFZ32qHzdzi21yfPTU0r4crwUKGPRqTiTnBxalHvyC5a6HVR6eyAgvfWOdMGFRu9/aOT3U7gAANRvFCwAAEDU27jJ6N77gxoyzOinMtbstKzQotr/96qlQWdYcjrjuFixcb6SPrhMVqDYFvf2Hib/gWdGJikAAMricKr4hPHyHn55qU3ueW8p8YPLpOIdEUis7rVvZ+mpJyzdcpOlRo1Kv0/Znis98qjRJZcazZ5D0QIAUH9RsAAAAFFrxw6jZ54L6oJ/G33yadmzR3TtIj0/KbSodsOG8VuokCRr61IlvTtcljffFvd1OV3evtdFKCsAAPbCsuQ98ioVHT9ORva/065V3yvpjQtkbV8XoeTqlsNh6dRTHJr2UYaGX5Iot7v0PstXSKOvN7p2TFALF1G4AADUPxQsAABA1CkuNnrjLaPzLjT63+uS11t6n8wM6aYbLD3zlKXOneO7UCFJ1rY1oWJF0TZb3N++v4pPvkeyeFsHAIhe/h7/UtFpj8o4Pba4c+syJf3feXL8/XtkEouAlBRLo69J0WuvONTvmLL3mfOLNHyk0e13BrVuHYULAED9wSdbAAAQNYqLjd5+1+jcC4yefNooN7f0PklJ0rChlt54zdJpp1pyOOK/WOHYskRJb14oR95GW9zf+ggVnTpBcrgilBkAABUX6HSyCs95WcHkRra4o2Crkt4eLNef70Yos8ho0dzSPXc69MSjltq3L3uf6V9J/7rY6NHHg8rJoXABAIh/FCwAAEDEFRcbvfteaETF4xONtmaX3sfplAadKb35mqWhgy0lJ8d/oUKSHBv+VNJbF8uRv9kWD+x3sIpOnyi5POUcCQBA9Ak276HCC95UoFEHW9wKeJX4+a2hxbj9ZQytjGOH9LQ0+TlLN91gKSur9PZAQHr3fencC0JTZW7bRuECABC/KFgAAICI8XqN3v/Q6PwLjR59wmjLlrL3O/YY6dWXLI251qHMzPpRqJAkx5pZSnpnSKlpoAKNu6hw0LOSJyUyiQEAUA0mvYUKz39d/v2PKrXNPfdNJb11kawdGyKQWeQ4naGRo2/8z9KoEZZSy/gTX1gk/e916ZwLjJ59Pqjt2ylcAADij2VMWctXor7JycmJdAoxzbIsNWzYUJK0bds28bRCPKBfozbl5xt9+JH05ttGW7eWv1+fQ6VLhlg66MCaKVLEUr92LpuhxI9HywoU2+KB5j1VeOYzUmJahDJDtImlfg1UFP26ngj65fn2IXl+faX0puRGKj7lAQXKKGrEsor27dxco/+9bvTOu5LXV/a5kpKkf54tXXCupbS0+nNDB6IPr9mIR/TrisvIyKjR81GwgCQKFtXFixjiEf0atSE72+jt94zef1/Kyy9/v969QutUdDuoZj98x0S/NkbuOf+V57sJsmTPz7//kaFpoNzJEUoO0Sgm+jVQSfTr+sW1cKoSPr9Vlr+o1DZv70vkPeoayRkfUyBWtm9v3GT03xeNPv1MCgbL3ic5WTrrTOmcsy01akThAnWP12zEI/p1xVGwQK2gYFE9vIghHtGvUZPW/W30xltGUz+RvHuZlvqQnqFCxcHda+fDdtT3a1+REr64Te6FH5Xa5O9wgooGPsKaFSgl6vs1UAX06/rHsXmREqdcJcf2NaW2BZoepKKBD8tk7B+BzGpWVfv26jVGL79q9MWX5RcuPG5pwADpgvMstWpJ4QJ1h9dsxCP6dcVRsECtoGBRPbyIIR7Rr1Fdxhj99rv09jtGM3+Q9taFeh0iDb7I0iE9a/fDdTT3a2vHRiVOuUrOjfNKbfN1PVPFJ90lOVwRyAzRLpr7NVBV9Ot6qmi7EqeNlWvFN6U2GXeyio+9Wf6Dzpas2L0YX92+vXq10UuvGH35VfmFC8uS+h0jXXiBpS6dY/ffCrGD12zEI/p1xVGwQK2gYFE9vIghHtGvUVXFxaG7/95+z2jZsvL3syzpmL6hD9Ndu9TNh+lo7dfOFd8q4bP/yFFgX3XcyJL36NHyHTo8pi/OoHZFa78GqoN+XY8ZI/dvr8rz3cOyAqUXb/Dvf6SKT7xTJq1FBJKrvprq26tWGb30qtGX0/d+U8ghPUNTRR15RGhhb6A28JqNeES/rjgKFqgVFCyqhxcxxCP6NSprwwajDz82mjJF2p5b/n4ul3TySdK/zrO0//51+8E56vq1r0ie7x6R5/f/ldpk3MkqGviwAu2Pi0BiiCVR16+BGkC/hmPTX0qcOkaOnBWlthl3sor7jpH/4PMlyxGB7Kqupvv26tVGr79h9Onnkt9f/n7NmkqDzrR02kApPZ3CBWoWr9mIR/TriqNggVpBwaJ6eBFDPKJfoyJ8PqPvf5CmfGw0e87e7/BLTpb+cZp03j8tNWkSmQ/K0dSvHZsXK+GT6+XcuqTUtmB6KxWd8ZSCWR0jkBliTTT1a6Cm0K8hSfIVKGHGfXL/+U6ZmwPND1Hx8bcq2KRLHSdWdbXVt7dsMXrrHaMPpkgFBeXv5/FIJ54gnT3IUqeOFC5QM3jNRjyiX1ccBQvUCgoW1cOLGOIR/Rp7s3qN0cdTjaZ9Ju3rT0jLFtI/z7Y0cICUnBzZD8ZR0a99hfLMelbuOZPLnuqibT8VDbhPSqrZN32IX1HRr4EaRr/GnpzLv1HCl+PlyNtQapuxHPJ3P1fFR14dE387a7tv5+WFihZvv2O0NXvv+3bpIp06wFL/46UGDSheoOp4zUY8ol9XHAUL1AoKFtXDixjiEf0aJW3bZvT1N9IX043+mLvv/Q/tHZoz+fDDJIcjOj4ER7pfO5fNUMKMe+TIXVdqm3EmqPjYsfJ3P5/1KlApke7XQG2gX6OU4h1K+PYhuee9XeZmk5iu4qOukf+gf0pOdx0nV3F11beLi42mfyW9877R4sV739fjlvr2lQYOsNS7F2tdoPJ4zUY8ol9XHAUL1AoKFtXDixjiEf0aklRQYDTz+1CRYtZsKRDY+/4pKdJJJ0pnnWmpbZvo+7AbqX5tZa9QwncPy7XsqzK3Bxp3UdHAB2UadaiTfBBfeL1GPKJfozzOVT8o4cvb5di+tsztwYb7y3vkVfIfcEpUrm9R133bGKP5C6R33zea8fXe17mQpMZZobXG+h9nqUOHUL7AvvCajXhEv644ChaoFRQsqocXMcQj+nX9lZ9v9NMs6dtvjWb+IBUX7/uYbgdJp59m6bhjpcTE6P1gW9f92tq2Wp6fnpbrr49kmWCp7cbhkq/XEHmPuEpyeWo1F8QvXq8Rj+jX2Ctfkdy/TJZn1vOy/EVl7hLIOkDeo65RoN2xUTVyMZJ9e+tWo4+mSh9MMdqyZd/7t2gu9esnHdfPUucDKF6gfLxmIx7RryuOggVqBQWL6uFFDPGIfl2/bNwUWjx75vdGv/6277vvJCk9TRowQPrHqZba7B8bH2Drql9b29bIM/t5uea/LytY9j9moEVvFfe/jYW1UW28XiMe0a9REVbuOiV885BcSz4rd59A487y9R4m/wEDJIerDrMrWzT0bb/f6OdZ0rRPQzenVOR9X7OmoeJF36MsHXSg5HLFxns/1I1o6NdATaNfVxwFC9QKChbVw4sY4hH9Or75/aHpAWbPMfrhJ+1zbuNdPB7p6KOkE/tbOqyP5PHE1ofVWu3Xxsi5+ge5f3tNzuVfy1LZ5w4mN5L3mBvk73J6VN3xidjF6zXiEf0aleFc/ZM8302Qc+O8cvcJpjWX75Ah8h10luRJqcPs7KKtb2/fbvTldGnqp/te62KXlBSpdy/psD6h94NNm/B+pr6Ltn4N1AT6dcVRsECtoGBRPbyIIR7Rr+OLMUYrV0mz50hzfjH67XepsLBixzod0qGHSif0t3TM0VJycux+KK2Nfm3lb5Zr0TS5574lR/aycvcz7mT5el4kb+9LpMS0aj8usAuv14hH9GtUmjFyLpsuz/ePy7l1afm7uZPl7/IP+bqfq2CTrnWYYEg09+2ly4w+/8JoxjfS+vUVP65tG6lPH6lnD0sHd5MaNIjd94qommju10BV0a8rjoJFPbRt2zb9+uuv2rBhg/Ly8tSkSRO1bNlSPXv2lNPprJHHoGBRPbyIIR7Rr2NbIGC0fIU0d540d57RH3NVobmKd3G5pEN6Sn2PtnRsPymjYXx88Kyxfl28Q66lX8q18GM5V/9U5voUuxhngnw9/iXvocOl5MyqPR6wF7xeIx7Rr1FlwYBcC6fK89PTcmxbtdddA027ydftbPk7niQl1ezFlvLEQt82xmjxEunrb0ILda9dV/FjLUvq0F7qcbDU42BLB3eXGsbJ+0iULxb6NVBZ9OuKo2BRj6xYsUKPPPKIvv76a/l8vlLbGzdurPPOO08jR46Ux1O9hTopWFQPL2KIR/Tr2JKXZ7RosfTn/FCB4s/5Un5+5c6Rmiodebh09NGWDjtUSkmJvw+XVe7XxsixdamcK76Vc+V3cq77VVaw9N9m2yGeVPkOOlu+3pfIpDapZuZA+Xi9RjyiX6PaggE5l30lz+wX5Nwwd6+7GodLgdZHyN95oPzt+0sJDWotrVjr28YYLVsuzfja6IcfpSXlD14pV+tWUteuUtculrp2ltq3l9zu+HufWZ/FWr8GKoJ+XXEULOqJDz/8UOPHj1dBQcE+9+3atasmTpyoli1bVvnxKFhUDy9iiEf06+hVWGi0ZKm0cKG0cJHRX4ukNWuqdq4O7UNzEB95hKXu3eJ/AcUK9+tgQI6tS+VY94ucf/8m59o5cuRtqNBjBDPby9vzwtAaFRGcIxv1B6/XiEf0a9QYY+RY94s8v7wYWmNqL6MiJck43Aq07KVA237yt+snk9G2RtOJ9b69ZavR7NnST7OMZs+RcnMrfw6PW+rUSeraRerSxVLXLlLz/UL/NohNsd6vgbLQryuOgkU98M0332jUqFEKBne/kWrTpo0OO+wwNWzYUKtXr9aMGTNUVFQU3t6hQwe9+eabSk1NrdJjUrCoHl7EEI/o15FnjNHGjdLS5dKyZdKy5UbLlklr1krBvX/WLleTJtKhvaXevSz1PkTKyKhfHwzL7NcBrxzZK+TY/JccmxbKsXmhnBvny/LmVfi8xp0sf4f+8nc9U4HWR7CYNuoUr9eIR/Rr1AZrx3q5/nxP7nnvVPxGhPTWCrQ+TIEWvRVo2VsmrXn1coijvh0IGP21UJo1W/rtd6P5CySvt2rnathQ6tp5dwGj8wFSejrvp2JFPPVrYBf6dcVRsIhzmzZt0oABA5S/cx4Py7I0duxYDR48WA6HI7xfdna2rrnmGs2aNSscO/XUUzVhwoQqPS4Fi+rhRQzxiH5ddwIBow0bpJWrpFWrpdWrjVatllaskPIqOa1TSS2aS927Sd27heYQbtWqnt69FvDJ2rFBjh1/K9WXLbNlmbzr/5Ije7msbWtkmUClT2kcbgXaHhOaPqLdcZI7qRYSB/aN12vEI/o1alUwIOfKb+VaMEWu5V/L8hft+5hdh6Y1V6BZdwWbdFWw6YEKNOlSqfUv4rlve72hAsZvv0u//xGaorSo4v+0pTRpInXqKHXsIHXqaKlTJ6lJ43r6XjbKxXO/Rv1Fv644ChZx7o477tDrr78ebl999dW64ooryty3uLhYgwYN0rJlyySFnkjvvfeeunbtWunHpWBRPbyIIR7Rr2uW32+0cZO0bp207m9p3Tqz87u0dq3k3ftyCBXicoWmeOp2UKhA0a2blNWoHnygM0by5snK2yhH3qZQYSJ3nazcdXJsD3238jbucwqIigg22C9UpGjTV4HWhzPlE6ICr9eIR/Rr1BlvvlzLZsi16BM5V87c5xpVZQmmNlUwo62CmW1lMtoqmLG/TIP9FGywn5RgnwWhPvVtn89o6TJpwQJpwV9G8/8Kve+tjvQ0qWPHnYWMjpY6dZBatpScznrwnjeK1ad+jfqDfl1xFCzi2JYtW3TssceGF9hu3bq1PvnkE7nd7nKP+fHHHzVkyJBw+6STTtLEiRMr/dgULKqHFzHEI/p15RQUGG3YKG3aJG3cJG3caHZ+D7U3bZIClb+Jv1xOh9SmbWi4fJcDLHU+QGrXTvJ44uzDWsArK3+zrLxNuwsSeRt3tjfJkbcxtN237zWfqiKY3lKB5j0VaH5IaBqIzPZM94Sow+s14hH9GhFRnCfn6h/kWv6NnCu/lSN/S7VPaRIaKJjaTCa5kUxSQykpUwmZoUJGoU8yrgTJlSjjTpRcSaG2O0nGmSCF33JYu79blj1Wqr3nfpb9GMuScbgkp0dyuiWHu87f1+TmhkZhLPgrVMT46y9p2/bqnTMpUerQIbSYd7u2ltq1ldq1ldLSeM9WV3jNRjyiX1dcTRcsXDV6NlTL9OnTw8UKSTr33HP3WqyQpCOOOELt2rXT8uXLJYXWvygoKFBycnKt5goA9UlBgdGWLdKWrdKWLdKmzdLGTSZcjNi4Ucqr+HIHlZaaGho50b6d1L69pQ7tQx/CEhNj+EOYCUqF20IFhz2LEfklChKF2XWWUjCtuYKNuyjQpIuCjTsr2KybTGqTOnt8AAAQYQmpCnQ8SYGOJ0kmKMemv+RcO1vOtXPkXDdHVlHlr6xbxTvkLN4hbV0Sju0a85lQQ2lXh9lVuHB6Qj+7EmTcKVJCqownRcaTKnlSZTypMgkpoZ8T0mSSGsokZcokZ8okZUju5AoVP9LSLB3WRzqsjyRZMsbo7/U7CxgLQsWMxYsrN/q4sEia92foS9p9QTEry6hdW6ltG6l1a0utW0mtW0mZmUwrBQDRjIJFFPnqq69s7QEDBlTouJNPPlmTJk2SFJom6vvvv9eJJ55Y4/kBQLwpKjLaunV3ISL03ZRoS4WFdZNPWpq0f+vQV+vWltrsH7pTLKbm6jVG8ubLyt+0czTEriLEphIFic1VmnKh2ul5UmRl7C8rc38VN2ipYEY7BTPbKpjRVkpMq/N8AABAlLIcCjY9UMGmB8rXa0iogLF1mRx//ybn5r/k2LhAjs0LZQWquMp0lLACPingk3wFqs67TeP07CxgZOz83kgmJUsmpXHoKzlLwZQsmZQsKSEtXNywLEstmofWXTuxfyjm9xutXi0tWiItWWK0eIm0dGnl13bbsiX0NWu2tGchIyVFat3KqHVrqWULS/vtJ+3XTGq+n9SokeRwxMj7bgCIUxQsosgvv/wS/jkrK0utWrWq0HE9e/a0tWfPnk3BAkC9FQwa7dghZedIOTu/snPshYit2aHvtTkqojypKVLzFlLLFtr54cxSq1ZS69ZSRsMo/3DkKwxPz+TYWYQIj4TY1a7F6ZkqwiSmK5jaVCatuYJpLWXSWyiY1lwmrYWC6S1lJaar4c7hqnkM6wUAABVlORTM6qhgVkf5d8UCPjlyVsjKXiFHzgo5spfLkb1Sjty1sgrr17TLVsArK2+DlLdhn/sap1smeVcxI6tUQcORnKX2jRqrXessnXJyaBzKrpEYS5ZIi5cYLVkqLV4ibd1a+Vzz86W/Foa+9ixkSJLHLTVrZtSsmdS4sdQ4S8rKspSVFfq5cZaUns6aGQBQmyhYRImNGzdqx44d4XaXLl0qfGzJRbZ3LcINAPHA7w8VIHbskHJ3fs/JkXK2Sdk5Rtty7MWJbdukQPXXVq6yjAypaROpSZPQ96ZNrZ3fQwWKtLQoHC1hWydi8x7Fh93fHXmbZRXnRixF40yQSW0qk9o4VJBIbbKz3UTBlJ0/pzSW3Il7P1G0/dsDAIDY5XQrmNVJyuqkUkuV+Ypk5W2QY8eG0OjSwhxZhdtkFWbL48+XfAXyF+ZJ/kJZviLJH/qyfIWh7yaCb2hrmRXwydqxXtqxfp/7moQ0mZQsBZOz1C4lS21TsnRi9yyZI0LFju3FDbT87wZavKaBFq9O1aIVCVq9WvJVcSCv1yetXhP62iML2z4Oh5TWwCg9PVS8aNhw5/d0KTnZUnJyaBRHSrJCP+/8npwiJSdJSUlR+HkAAKIIBYsosWsNil2aN29e4WOzsrLkdrvD61+sWLGiRnMDgKowxsjrlQoKQ1Mq7fm1K5afHypA7MjbXZQItXd+zw3NSRstUlOlrCwpq5HUrGmoGLG7MBGauikhIUo+fAQDoQ/GBdmyCreGvhfs/J6/2T5SIoJ3ABpZMimNdhYfQkWHsgoSSkyn2AAAAGKHO1Emo40CGW1sYcuylLxzEddyR3saI5lA6HsosMc3s3sfldxeYlvJ75KsoF8KeKWALzSdVcAXunll19RQgWJZ3nzJmyerOG/3zzvb8ubJKsqVVZgd+grU7hSfVnGurOJcObKXl7k9SVIzSUdKUrJkurtleqfJ50xVQbCB8rzJyi1MVG5BgnLyElXk96gokChv0KPiQKKKAgnyBhNsseKAR96gR76gR8XBBHkDHvmCbnmDnp1fCfLmebQu161Vq92SbTKtfY/edTik5CQTKmIkS4mJoa+EhJ0/J5SMWUpMkBL22JaQECp87HnMru0JCUxrBSC2UbCIEhs3brS1mzVrVuFjLctS06ZNtXbt2jLPBQBl2VVQKPZK3mKpqCj0c1GRVFwseb2S01Ws4iIpJyeowp3xoiITKjoUlC5AlPyK5EiHykhKlLIahwoRuwoSWVmWrd2oUQQXufYXhxZ5LN4R+tBWlCureIdUnCuraHvZRYnCHFkV+MBUm0xCuoKpjUPFh5QmoeH+qU0V3KMYYVKyJAdvRwAAAMIsS7Jq5/2RKefnqp1s59plhdmh959FObIKcna+H90qK3+LrIItcuRvCf1cXPlFyyvLCvhkFW5VgrYqQVKGJLklpe/8qmFBY+0sZiSEihkBd7jQURRIVFEgUcWBRBUGElUUSFJxYFc8SUW7fi5KUnFBgor8SSoKJCo7mLBze+LOokro+OKdRRVVYLURj8coKbF0kaNkcSMxadd2q+ztiWXH3O5Q4cXh2NldubEIQA3iCkGUKCiwz/ednJxcqeNTUlLCP/v9fhUXFyshIaFGcqsPjDHami35d01Gava4OUX2G1vKjEvKzgkNAs7dbhQ0xn5M+HFU5fiej1Ve3P47lf1zVY+JFnv7dwhvMmVsr0J8n//ve4nvmZMxkgnu/edgUOH/+2Bw97l2/bzrKxgMTZHk80sBf2iosz8g+X2h/uvzh74H9vjZ59tdjCgulor3+Hnv/89GUgQWeahBqalSZkZomHZ5hYisTL9SAht3/mcEd//HyISG4u9qbw9K27XHfrv+o0LbrWBQCvpCXwFf6G6z4K471XyhBaYDe27z77yDrSC0NoS/MLTYoa9Q8hbsbBfKKt4RdYs5GneyfSqmPYoSwT2KE/ucngkAAACxy7KkhFSZhFSZhq33vb+/WFbBzuLFzmKGlb9FjvzNtraVvznq3v+Wx2EZJTi9SnDWTb5BY5UoeOwqbOz+2Rbfo/hRlJ+ootxE+YJuBYxTXuNUoXEoaJwKlPM9KIcCxqlA0KnAznbQOBU0Vmik9M7vlsMKDYa2rJ3FC4c2efeT5XCEChvWzuLGrp+d9phzZ/HD6Qxtc+76eY/4ri9bzLFz/51x5x77ulwlf7Z2t51l7+d0ld4WKsjsHuy9Z3uv2xT63Xb9rJ1xE5SCZvf3YHD35/6yvpe3fdex4fMEdp83UOL4sh4rENi1jwmfz5R4vMAeH3t3FafK++50hgpX+9ovvP8e//fWzv/TcN8oo5/s7Wv34+/uE7sea+c/ffj/Z89A+P+tnO0Oh5SXF5BlSbm5e45qs58jM0Nyuyna1SQKFlGiZMGissWGkvvn5+dX6hz1uRq+fr3RmBuNVq2uzlmMpG01kxCAciUkSA0aSBkNpczM0CLVGZmhdkaGlJlhKSNjd3tfbxocK79X4qs3yCrMrov0o55xevYY+dBk57RMu4oQjcNteVL3ea5o/auy59+7+vy3D/GFfo14RL9GvKrXfdudKKW3lElvuffRHcaERhbnb5FVsHl3gaNku2BraPSxv7iufoOIc1hGya5CJbsKI53KPm0qaqwbZj+qP3J6RjqVnaL0rsy4Em//xhW71tcgVbr2amnAyY5az6i+oGARJYqL7X9g3W53pY73eDx7Pd++NNw5h2Z99OAjeVq1uv68wQEiKTRPqxX6SrKUkmKpQQNLaWkOpaVZSt/5tau9+8uh9DRLHk/NfqjzffeQVB+KFcmZUkojWSlZUkqWrNQsqUEzWQ2aSmm7vu8nJabXqw/O6em1MC8AEGH0a8Qj+jXiFX17bzIkVWDUhiTjL5aKcqXiHTKFoWlUVZQrFeXKePOlXYua7/xu/MWhn32Fkr84vC0c9xeF1vnwe6VAceh70L/vRGDTJHGzRh/4sIbMfC3SqQC1akee9PAEo/PPaxjpVOIGBYsoUXI0xK4FtCvK67UPP2Q6qIq7965U3XvXvu8WBhB/3Fd9HekUAAAAAFSD5UqQUhtLqY2jdpRvfXWopPmRTgJAzGGsSpQouWZFZUdIlNx/zzUtAAAAAAAAAACIdhQsokTJgkV+fn6ljt9zf5fLxQgLAAAAAAAAAEBMoWARJZo2bWprb9y4scLHGmNs+5c8FwAAAAAAAAAA0Y6CRZRo3769rb1u3boKH7tlyxbbmhft2rWrsbwAAAAAAAAAAKgLFCyiRJMmTdSgQYNw+6+//qrwsQsWLLC1KVgAAAAAAAAAAGINBYso0qtXr/DPW7du1erVqyt03K+//mprH3rooTWaFwAAAAAAAAAAtY2CRRTp37+/rf3pp59W6LjPPvss/HNCQoKOOuqoGs0LAAAAAAAAAIDaRsEiihx//PFyu93h9ttvv21bm6IsP/74o1asWBFu9+vXT8nJybWWIwAAAAAAAAAAtcEV6QSwW1ZWls4991y99tprkqTVq1frueee0xVXXFHm/sXFxbr77rvDbcuydNlll9VJrrFu27Zt+vXXX7Vhwwbl5eWpSZMmatmypXr27Cmn0xnp9IAqiaZ+HQwGtXTpUv3111/KyclRYWGhUlJS1KhRI3Xt2lVt2rSRZVl1mhNiVzT1bUlauHChFi5cqOzsbBlj1LRpU7Vt21YHHnhgnecC1JT8/HzNmTNHGzdu1LZt25SZmakWLVqoV69e8ng8kU4PAADEobVr12rVqlVav369tm/fruLiYiUlJSk9PV0dOnRQ586deR8C1EMULKLMyJEj9cEHHyg/P1+SNHHiRCUnJ2vw4MFyOHYPiMnOztY111yjpUuXhmMDBw5U165d6zznWLJixQo98sgj+vrrr8scvdK4cWOdd955GjlyZK38Ubzpppv0/vvvV/s8gwYN0v33318DGSEeRLpf72nHjh164YUX9O6772rz5s3l7teiRQudf/75Gjx4sBISEmo1J8SuaOrbRUVF+t///qeXXnqp3L7dunVrXXDBBRo8eDDFb5QpPz9fCxYs0Ny5czV37lzNmzdP69atC29v0aKFvvrqqzrNadOmTZowYYI+++wzFRQUlNqenp6u008/Xddee61SU1PrNDcAwG55eXlasmSJli9frm3btsnn8yktLU3NmjVTjx49lJmZGekUEeWi4X2I1+vVpEmT9Ouvv+rPP/9UXl7eXvdPSkrSKaecomHDhqlDhw61mhuA6GEZY0ykk4Dd119/rcsuu0zBYDAca9OmjQ4//HA1bNhQq1at0owZM1RUVBTe3qFDB7355pt8kNyLDz/8UOPHjy/zw3hJXbt21cSJE9WyZcsazaGmChYXXnihbrvtthrICLEuGvr1Lr/99puuueYabdy4scLHtG3bVk899ZTat29fKzkhdkVT3165cqVGjRplm4Jxb3r27KnHH39cTZs2rZV8EHtefPFFvffee1q6dKnt/V1JdV2wmDlzpq6//nrl5OTsc99WrVpp4sSJ6tKlSx1khlgSDRfA9uT1erVgwQLNmzcvnNOqVau058feRYsW1Vk+iE3R0q/nzZunzz//XD/88IMWLFiw178h3bt31+DBg3Xqqacykhk20fQ+JDs7W0cccUSlj3O73brqqqs0cuTIWsgKsSxaXq8r4sUXXyx183GfPn306quvRiij6MUIiyh07LHH6r777tP48eNVWFgoKXSxZOXKlWXu36VLFz355JMUK/bim2++0U033VSqCHTYYYepYcOGWr16ta0ItGDBAo0cObLGi0AOh6NKd90GAgFb+6STTqqplBDDoqVfS6EpcoYPH267Q8ayLPXq1UsHHnigGjRooO3bt+vPP//Ub7/9Ft5nxYoVGjx4sN566y01b968RnNC7Iqmvr127Vqdf/75tgu6brdbhx9+uDp16iSXy6VVq1bpu+++C4+O/O233zRixAi99tpr/G2GJGn27NlavHhxpNOwmT9/vq688srwe01JatKkiY455hhlZWVp/fr1mjFjhnJzcyVJa9as0aWXXqp33nlHzZo1i1TaiCIVvQBWV6ZMmaKXXnpJixcv3uc6gEB5oqlfDx06VD/88EOF9587d67GjBmjd955Rw8++KCaNGlSi9khlkTj+5Bd0tPTdcABB6ht27ZKT0+Xx+NRbm6ulixZojlz5oRfz30+nyZMmKD8/Hxdd911Ec4a0SCaXq8rYu3atXriiScinUbMoGARpc4880x1795djzzyiL755ptyp8I499xzNWrUKOb024tNmzZp9OjR4Rcwy7I0duzYcqfZmjVrliRp6dKluu222zRhwoQay+Xee+/VvffeW6ljFixYoEGDBoXbLVq00GGHHVZjOSE2RVO/NsZo3LhxtmJFp06d9Mgjj6hTp06l9l+wYIGuu+668N3qmzdv1j333KOnnnqqxnJC7Iqmvh0IBHT11VfbihU9e/bUQw89pFatWtn23bZtm8aPH69p06ZJChXxajofxJfk5GQdeOCBmj9/foVGEtWkoqIiXXHFFbZixSWXXKLRo0fb3lPm5eVp3Lhx+uSTTySFXq+vvfZavfHGG3WaL6JTtF0AmzdvnubPnx/pNBDjoqlfZ2dnl4o1b95cPXr0UJMmTZScnKwtW7Zo1qxZtpsbf/zxRw0ZMkSvvfaaMjIy6jBjxJJIvQ+xLEsHHnigBgwYoGOOOUYHHHBAuSOCsrOz9dBDD+m9994Lx5577jkdc8wx6t27d12ljCgVTa/XFXH77bfX+Xv+WEbBIoq1a9dOTz31lHJycsKLjebn5ysrK0utWrXSIYccwhzZFTBp0qTwXa+SdNVVV2no0KGl9svMzNQLL7ygQYMGadmyZZKkTz75RMOHD4/o2iB7/nGWQsUshvgimvr1rqGXu6Snp2vy5Mlq3Lhxmft37dpVL7/8sk499VTt2LFDkjR9+nRt3LiRKXQQVX37k08+sV38at++vSZPnqzk5ORS+zZs2FATJkxQUVGRZsyYIUmaOnWqhgwZou7du9dIPohdCQkJ6t69u7p166aDDjpI3bp1U/v27eVwOHT88cfX+YeX//3vf1q/fn24ffbZZ2vs2LGl9ktNTdUjjzyinJwc/fjjj5JCI4i+/PJLnXDCCXWWL2JHJAtxZUlISFDnzp21du1abd26NdLpIEZFul9nZWVp0KBBOvvss9W2bdtS240x+uyzz3Tbbbdp+/btkqRly5bp9ttv525eSIqu9yEZGRmlrnGUJzMzU/fdd58sy9K7774rKdTfX3rpJQoWKFOkX6/L88EHH2jmzJmSQjef7229T4RQsIgBGRkZ6t+/f6TTiElbtmzR22+/HW63bt1aI0aMKHf/hIQEjRs3TkOGDJEU+mM4adIkTZw4sbZTLZPP59PUqVPDbcuybKMtUD9FW7/edRFrl3PPPbfcYsUuTZs21TnnnKPJkyeHc/r55591+umn10hOiE3R1rdfe+01W/vWW28ts1ixi8Ph0Pjx4/Xdd9/J7/dLkp566ik9++yzNZIPYtejjz4a6RTCfD6fXnjhhXC7QYMGZRYrdnE4HLrjjjs0YMCA8Minp59+moIFouoCmCS5XC517txZ3bp1C3/tmrrvoosuomCBCommfp2ZmambbrpJF1544V5nVLAsSwMGDFCrVq104YUXhkfPffbZZ5o7dy43TiCq3odUxfXXX68PPvggPFX2rgu/qN+i6fV6b7Kzs8PrVliWpZtvvplpzSqAggXi2vTp023TaZ177rlyu917PeaII45Qu3bttHz5ckmhudQLCgr2epGqtnzzzTe2ocC9e/cuNQ0J6p9o69clF9nu0aNHhY475JBDwgULKTQVEOq3aOrbeXl5tpFDLVq00JFHHrnP45o1a6a+ffuGR1l8//33ys3NVVpaWrXyAWrKzz//bJvm7LTTTlN6evpej9l///115JFHhi8QzJ8/X2vWrOE9ST0XbRfA9lZ4Ayoqmvr1888/L5er4pdsDjzwQA0ePFjPPPNMOPbZZ59RsEDMy8zMVLt27bRkyRJJUmFhoXJycpjyrJ6Lptfrvbn33nvD773PO+88HXzwwRHOKDY49r0LELu++uorW3vAgAEVOu7kk08O/1xcXKzvv/++RvOqqPfff9/WZnQFpOjr1yUXuEpMTKzQcUlJSbY2U50hmvr2woULw3dxSRUvxEmhYtwuPp8vXLwAokHJ59mez5+9Kfl8nD59eo3lBAAorTLFil1OPfVUW3vPmy+AWJaammprG2MilAlQcd99950++ugjSaGpoMaMGRPhjGIHBQvEtV9++SX88661PyqiZ8+etvbs2bNrNK+KyMnJ0TfffBNuJyUlVfiiAuJbtPXrli1b2tp7zou+N+vWrbO1999//xrJB7Ermvr2nnegS6rU+ipNmjSp8XyAmjJnzpzwz06ns8J33u5ZiJPo1wAQjUq+n2YqNMSLv//+O/xzamqqGjZsGLlkgAooKCjQ7bffHm7ffPPNjLqvBAoWiFsbN24ML+grSV26dKnwsSUXbN21oGtd+vjjj21To5x00kml7ipA/RON/bpv37629rRp0yp03CeffBL+OTk5WYcddliN5IPYFG19u6ioyNbe29zRJSUkJNjaS5curXY+QE0IBoNauXJluL3//vsrJSWlQse2a9fONoIuEu+NAAB7l5+fb2tXZZQGEG1mzZplm4a4X79+cji4nIno9thjj4Vv0jz66KNLjYDD3vEMR9zaNZ/5Ls2bN6/wsVlZWbZ501esWFFjeVUU00GhLNHYrzt37qxjjz023P7+++/1f//3f3s95uWXX9ZPP/0Ubg8dOlQNGjSokXwQm6Ktb5e8+yU3N7fCx5bct+TvBkTKunXrVFxcHG5X5nlmWZaaNWsWbq9Zs8Z2YwUAIPIWLVpka+/5ug3EovXr1+s///lPuO12u3XZZZdFMCNg3+bOnav//e9/kkI3s+050gIVQ8ECcavkQsCVebNmWZZt+o+S56ptixcv1vz588PtFi1a6PDDD6/THBCdorVf33XXXbbpe8aPH6/rrrtOP//8s/Ly8mSM0Y4dO/Tjjz/qqquu0r333hve97jjjuNNJ6Kub5ec1qkyoyR2LQi4y/bt21VQUFDtnIDqqs7zTLJPjeb3+5lqBACizJQpU2xtPkMiFuXn52vevHl6/PHH9Y9//EOrV6+WFBoxdP/996tjx44RzhAon9/v17hx48LrIV522WVq3bp1hLOKPYwPRNwqeXEoOTm5UsfvOUWC3+9XcXFxqWk+akvJ0RVnnHEGCxJDUvT26yZNmujNN9/U+PHj9fnnn0uSpk6dqqlTp5Z7TGpqqoYPH64RI0bI6XRWOwfEtmjr2506dVJqaqry8vIkSb///rtycnKUkZGx1+OMMbb1h3YpKCio9O8E1LSSU4VU53lW1vkAAJGzcuXK8OKuUmidohNPPDGCGQH75vV61aNHj3DbGKNgMFhqv0MOOUQ333xzhdfeAiLlhRde0MKFCyVJ7du317BhwyKcUWxihAXiVsmLX5W9cFVy/7r6UB4IBGxvNCWmg8Ju0dyvGzVqpIkTJ+q5557b5127rVu31mOPPabLLruMYgUkRV/fdjqdOuaYY8Jtr9eryZMn7/O4jz76KHwX2J4YYYFoUNPPM/o1AESHYDCoW2+91TZV35lnnmkbAQ1EI2OMAoFA+KusYsVpp52me++9l2IFot7KlSv19NNPSwrNAnDHHXdUai1E7EbBAnFrzzmaJdnmN6+Iki8qJc9XW2bOnKnNmzeH271792b4GMKiuV9v3LhR1157rUaOHKkNGzbsdd/Vq1dr+PDhOv/881mQGJKis29fcskltvbkyZP1xRdflLv//Pnzdffdd5e5reQi3kAkROPzDABQfU899ZRmz54dbmdmZur666+PYEZAzfn44481cOBAjR07Vjt27Ih0OkCZjDEaN25c+P3xWWedpUMPPTTCWcUupoRC3Cp5F2BlF4b0er17PV9tYbFt7E209uuFCxdqyJAhysnJkRS6m+C0007TWWedpc6dO6tBgwbasWOHFixYoPfff19Tp06VMUa//fab/vnPf+r555/nj3k9F419u1u3brrgggvCi8j7/X5dffXV+uc//6lzzjlHnTp1ktPp1OrVq/XRRx/ppZdeUmFhoaTQ1Dl7jvJgOihEg2h8ngEAqufLL7/UU089FW5blqV77rlHmZmZEcwKqJiEhATbYvF+v1/bt2/X4sWLNX36dL377rsqKChQMBjUBx98oIULF+qVV15Renp6BLMGSnvnnXc0a9YsSVJGRoZuuOGGCGcU2yhYIG6VvDhU2bsAS+5fct7m2pCbm6uvvvoq3E5KStKAAQN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",
       "text/plain": [
        "<Figure size 800x800 with 3 Axes>"
       ]
@@ -1269,11 +1269,9 @@
    "id": "853f8ee0-5730-47c8-ba6b-d2fed86677b4",
    "metadata": {},
    "source": [
-    "We can see that the posteriors don't align perfectly. DADVI does a pretty good job matching the means, but underestimates the variance somewhat.\n",
+    "We can see that the posteriors don't align perfectly. DADVI does a pretty good job matching the means, but underestimates the variance somewhat. However, often DADVI is much faster than MCMC, and so this is a tradeoff that can make sense.\n",
     "\n",
-    "However, often DADVI is much faster than MCMC, and so this is a tradeoff that can make sense.\n",
-    "\n",
-    "Note that DADVI as explained in the [paper](https://jmlr.org/papers/volume25/23-1015/23-1015.pdf) has further tools to make variances more accurate, which we plan to add to PyMC later on."
+    "Note that DADVI as explained in the [paper](https://jmlr.org/papers/volume25/23-1015/23-1015.pdf) has further tools to make variances more accurate, which we plan to add to PyMC later on. For now, its key advantage is that it converges automatically and reliably, removing one headache from ADVI."
    ]
   },
   {

From d954ec7a8879daf559997b8dcbbc9135f4e22c53 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Thu, 14 Aug 2025 15:09:15 +0200
Subject: [PATCH 06/28] Add jax to dependencies

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index c7bc7e325..963258f87 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,6 +40,7 @@ dependencies = [
   "better-optimize>=0.1.5",
   "pydantic>=2.0.0",
   "preliz>=0.20.0",
+  "jax>=0.7.0"
 ]
 
 [project.optional-dependencies]

From aad9f21eba04cfeeb70fbad67169ce4f26d105ec Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Sat, 16 Aug 2025 16:42:25 +0200
Subject: [PATCH 07/28] Add pytensor version

---
 .../inference/deterministic_advi/pytensor.py  | 91 +++++++++++++++++++
 1 file changed, 91 insertions(+)
 create mode 100644 pymc_extras/inference/deterministic_advi/pytensor.py

diff --git a/pymc_extras/inference/deterministic_advi/pytensor.py b/pymc_extras/inference/deterministic_advi/pytensor.py
new file mode 100644
index 000000000..8f79e15b8
--- /dev/null
+++ b/pymc_extras/inference/deterministic_advi/pytensor.py
@@ -0,0 +1,91 @@
+import numpy as np
+from scipy.optimize import minimize
+
+import pytensor
+import pytensor.tensor as pt
+from pymc import join_nonshared_inputs, DictToArrayBijection
+from pymc_extras.inference.laplace_approx.scipy_interface import (
+    _compile_functions_for_scipy_optimize,
+)
+from pymc_extras.inference.laplace_approx.laplace import unstack_laplace_draws
+
+
+def create_dadvi_graph(
+    pymc_model, n_params: int, n_fixed_draws: int = 30, random_seed: int = 2
+):
+
+    state = np.random.RandomState(random_seed)
+
+    inputs = pymc_model.continuous_value_vars + pymc_model.discrete_value_vars
+    initial_point_dict = pymc_model.initial_point()
+    logp = pymc_model.logp()
+
+    # Graph in terms of a flat input
+    [logp], flat_input = join_nonshared_inputs(
+        point=initial_point_dict, outputs=[logp], inputs=inputs
+    )
+
+    draws = state.randn(n_fixed_draws, n_params)
+    var_params = pt.vector(name="eta", shape=(2 * n_params,))
+
+    means = var_params[:n_params]
+    log_sds = var_params[n_params:]
+
+    draw = pt.vector(name="draw", shape=(n_params,))
+    sample = means + pt.exp(log_sds) * draw
+
+    # Graph in terms of a single sample
+    logp_draw = pytensor.clone_replace(logp, replace={flat_input: sample})
+    draw_matrix = pt.constant(draws)
+
+    # Vectorise
+    logp_vectorized_draws = pytensor.graph.vectorize_graph(
+        logp_draw, replace={draw: draw_matrix}
+    )
+
+    mean_log_density = pt.mean(logp_vectorized_draws)
+    entropy = pt.sum(log_sds)
+
+    objective = -mean_log_density - entropy
+
+    return var_params, objective, n_params
+
+
+def fit_deterministic_advi(
+    pymc_model, n_fixed_draws: int = 30, random_seed: int = 2, n_draws: int = 1000
+):
+
+    initial_point_dict = pymc_model.initial_point()
+    n_params = DictToArrayBijection.map(initial_point_dict).data.shape[0]
+
+    var_params, objective, n_params = create_dadvi_graph(
+        pymc_model,
+        n_fixed_draws=n_fixed_draws,
+        random_seed=random_seed,
+        n_params=n_params,
+    )
+
+    f_fused, f_hessp = _compile_functions_for_scipy_optimize(
+        objective,
+        [var_params],
+        compute_grad=True,
+        compute_hessp=True,
+        compute_hess=False,
+    )
+
+    result = minimize(
+        f_fused, np.zeros(2 * n_params), method="trust-ncg", jac=True, hessp=f_hessp
+    )
+
+    opt_var_params = result.x
+    opt_means, opt_log_sds = np.split(opt_var_params, 2)
+
+    # Make the draws:
+    draws_raw = np.random.randn(n_draws, n_params)
+    draws = opt_means + draws_raw * np.exp(opt_log_sds)
+
+    draws_arviz = unstack_laplace_draws(draws, pymc_model, chains=1, draws=n_draws)
+
+    # TODO Constrain
+
+    return draws_arviz

From ef3d86b5bf712ea1c7e04183a98783b9828a7297 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Sat, 16 Aug 2025 17:05:03 +0200
Subject: [PATCH 08/28] Fix handling of pymc model

---
 pymc_extras/inference/deterministic_advi/pytensor.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/pymc_extras/inference/deterministic_advi/pytensor.py b/pymc_extras/inference/deterministic_advi/pytensor.py
index 8f79e15b8..2986e0612 100644
--- a/pymc_extras/inference/deterministic_advi/pytensor.py
+++ b/pymc_extras/inference/deterministic_advi/pytensor.py
@@ -1,3 +1,4 @@
+import pymc
 import numpy as np
 from scipy.optimize import minimize
 
@@ -52,14 +53,16 @@ def create_dadvi_graph(
 
 
 def fit_deterministic_advi(
-    pymc_model, n_fixed_draws: int = 30, random_seed: int = 2, n_draws: int = 1000
+    model=None, n_fixed_draws: int = 30, random_seed: int = 2, n_draws: int = 1000
 ):
 
-    initial_point_dict = pymc_model.initial_point()
+    model = pymc.modelcontext(model) if model is None else model
+
+    initial_point_dict = model.initial_point()
     n_params = DictToArrayBijection.map(initial_point_dict).data.shape[0]
 
     var_params, objective, n_params = create_dadvi_graph(
-        pymc_model,
+        model,
         n_fixed_draws=n_fixed_draws,
         random_seed=random_seed,
         n_params=n_params,
@@ -84,7 +87,7 @@ def fit_deterministic_advi(
     draws_raw = np.random.randn(n_draws, n_params)
     draws = opt_means + draws_raw * np.exp(opt_log_sds)
 
-    draws_arviz = unstack_laplace_draws(draws, pymc_model, chains=1, draws=n_draws)
+    draws_arviz = unstack_laplace_draws(draws, model, chains=1, draws=n_draws)
 
     # TODO Constrain
 

From 6bf92ef8a70b8bfc97fea280b2db620ee4c310c6 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Sat, 16 Aug 2025 17:08:50 +0200
Subject: [PATCH 09/28] Add (probably suboptimal) handling of the two backends

---
 pymc_extras/inference/__init__.py | 14 ++++++++++++--
 pymc_extras/inference/fit.py      |  5 +++++
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/pymc_extras/inference/__init__.py b/pymc_extras/inference/__init__.py
index c7e518d69..ad17211ca 100644
--- a/pymc_extras/inference/__init__.py
+++ b/pymc_extras/inference/__init__.py
@@ -16,6 +16,16 @@
 from pymc_extras.inference.laplace_approx.find_map import find_MAP
 from pymc_extras.inference.laplace_approx.laplace import fit_laplace
 from pymc_extras.inference.pathfinder.pathfinder import fit_pathfinder
-from pymc_extras.inference.deterministic_advi.api import fit_deterministic_advi
+from pymc_extras.inference.deterministic_advi.api import (
+    fit_deterministic_advi as fit_deterministic_advi_jax,
+)
+from pymc_extras.inference.deterministic_advi.pytensor import fit_deterministic_advi
 
-__all__ = ["fit", "fit_pathfinder", "fit_laplace", "find_MAP", "fit_deterministic_advi"]
+__all__ = [
+    "fit",
+    "fit_pathfinder",
+    "fit_laplace",
+    "find_MAP",
+    "fit_deterministic_advi",
+    "fit_deterministic_advi_jax",
+]
diff --git a/pymc_extras/inference/fit.py b/pymc_extras/inference/fit.py
index 68cbe7acb..390d2bc0d 100644
--- a/pymc_extras/inference/fit.py
+++ b/pymc_extras/inference/fit.py
@@ -45,3 +45,8 @@ def fit(method: str, **kwargs) -> az.InferenceData:
         from pymc_extras.inference import fit_deterministic_advi
 
         return fit_deterministic_advi(**kwargs)
+
+    if method == "deterministic_advi_jax":
+        from pymc_extras.inference import fit_deterministic_advi_jax
+
+        return fit_deterministic_advi_jax(**kwargs)

From 32aff4642a1104c4c993840394f6b0f75b2b535e Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Mon, 18 Aug 2025 18:30:17 +0200
Subject: [PATCH 10/28] Add transformation

---
 .../inference/deterministic_advi/pytensor.py  | 57 +++++++++++++++++--
 1 file changed, 52 insertions(+), 5 deletions(-)

diff --git a/pymc_extras/inference/deterministic_advi/pytensor.py b/pymc_extras/inference/deterministic_advi/pytensor.py
index 2986e0612..98794ac35 100644
--- a/pymc_extras/inference/deterministic_advi/pytensor.py
+++ b/pymc_extras/inference/deterministic_advi/pytensor.py
@@ -1,10 +1,14 @@
+from collections import defaultdict
+
 import pymc
+import arviz as az
 import numpy as np
 from scipy.optimize import minimize
-
 import pytensor
 import pytensor.tensor as pt
 from pymc import join_nonshared_inputs, DictToArrayBijection
+from pymc.util import get_default_varnames
+
 from pymc_extras.inference.laplace_approx.scipy_interface import (
     _compile_functions_for_scipy_optimize,
 )
@@ -52,8 +56,50 @@ def create_dadvi_graph(
     return var_params, objective, n_params
 
 
+def transform_draws(unstacked_draws, model, n_draws, keep_untransformed=False):
+
+    filtered_var_names = model.unobserved_value_vars
+
+    vars_to_sample = list(
+        get_default_varnames(filtered_var_names, include_transformed=keep_untransformed)
+    )
+
+    fn = pytensor.function(model.value_vars, vars_to_sample)
+
+    d = {name: data.values for name, data in unstacked_draws.data_vars.items()}
+
+    transformed_draws = defaultdict(list)
+    vars_to_sample_names = [x.name for x in vars_to_sample]
+    raw_var_names = [x.name for x in model.value_vars]
+
+    for i in range(n_draws):
+
+        cur_draw = {x: y[0, i] for x, y in d.items()}
+        to_pass_in = [
+            cur_draw[cur_variable_name] for cur_variable_name in raw_var_names
+        ]
+        transformed = fn(*to_pass_in)
+
+        for cur_name, cur_value in zip(vars_to_sample_names, transformed):
+            transformed_draws[cur_name].append(cur_value)
+
+    final_dict = {
+        # Add a draw dimension
+        x: np.expand_dims(np.stack(y), axis=0)
+        for x, y in transformed_draws.items()
+    }
+
+    transformed_result = az.from_dict(posterior=final_dict)
+
+    return transformed_result
+
+
 def fit_deterministic_advi(
-    model=None, n_fixed_draws: int = 30, random_seed: int = 2, n_draws: int = 1000
+    model=None,
+    n_fixed_draws: int = 30,
+    random_seed: int = 2,
+    n_draws: int = 1000,
+    keep_untransformed=False,
 ):
 
     model = pymc.modelcontext(model) if model is None else model
@@ -86,9 +132,10 @@ def fit_deterministic_advi(
     # Make the draws:
     draws_raw = np.random.randn(n_draws, n_params)
     draws = opt_means + draws_raw * np.exp(opt_log_sds)
-
     draws_arviz = unstack_laplace_draws(draws, model, chains=1, draws=n_draws)
 
-    # TODO Constrain
+    transformed_draws = transform_draws(
+        draws_arviz, model, n_draws=n_draws, keep_untransformed=keep_untransformed
+    )
 
-    return draws_arviz
+    return transformed_draws

From 138f8c217050805af4d39acd98e9d3b91afc2fb8 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Tue, 19 Aug 2025 18:07:51 +0200
Subject: [PATCH 11/28] Follow Ricardo's advice to simplify the transformation
 step

---
 .../inference/deterministic_advi/pytensor.py  | 41 +++++++------------
 1 file changed, 15 insertions(+), 26 deletions(-)

diff --git a/pymc_extras/inference/deterministic_advi/pytensor.py b/pymc_extras/inference/deterministic_advi/pytensor.py
index 98794ac35..3a062b6e9 100644
--- a/pymc_extras/inference/deterministic_advi/pytensor.py
+++ b/pymc_extras/inference/deterministic_advi/pytensor.py
@@ -6,9 +6,14 @@
 from scipy.optimize import minimize
 import pytensor
 import pytensor.tensor as pt
+
 from pymc import join_nonshared_inputs, DictToArrayBijection
 from pymc.util import get_default_varnames
-
+from pymc.backends.arviz import (
+    apply_function_over_dataset,
+    PointFunc,
+    coords_and_dims_for_inferencedata,
+)
 from pymc_extras.inference.laplace_approx.scipy_interface import (
     _compile_functions_for_scipy_optimize,
 )
@@ -59,37 +64,21 @@ def create_dadvi_graph(
 def transform_draws(unstacked_draws, model, n_draws, keep_untransformed=False):
 
     filtered_var_names = model.unobserved_value_vars
-
     vars_to_sample = list(
         get_default_varnames(filtered_var_names, include_transformed=keep_untransformed)
     )
-
     fn = pytensor.function(model.value_vars, vars_to_sample)
+    point_func = PointFunc(fn)
 
-    d = {name: data.values for name, data in unstacked_draws.data_vars.items()}
-
-    transformed_draws = defaultdict(list)
-    vars_to_sample_names = [x.name for x in vars_to_sample]
-    raw_var_names = [x.name for x in model.value_vars]
+    coords, dims = coords_and_dims_for_inferencedata(pymc_model)
 
-    for i in range(n_draws):
-
-        cur_draw = {x: y[0, i] for x, y in d.items()}
-        to_pass_in = [
-            cur_draw[cur_variable_name] for cur_variable_name in raw_var_names
-        ]
-        transformed = fn(*to_pass_in)
-
-        for cur_name, cur_value in zip(vars_to_sample_names, transformed):
-            transformed_draws[cur_name].append(cur_value)
-
-    final_dict = {
-        # Add a draw dimension
-        x: np.expand_dims(np.stack(y), axis=0)
-        for x, y in transformed_draws.items()
-    }
-
-    transformed_result = az.from_dict(posterior=final_dict)
+    transformed_result = apply_function_over_dataset(
+        point_func,
+        unstacked_draws,
+        output_var_names=[x.name for x in vars_to_sample],
+        coords=coords,
+        dims=dims,
+    )
 
     return transformed_result
 

From 7073a7d13568b593c8c16661b0a09c8d20bb9756 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Tue, 19 Aug 2025 18:09:48 +0200
Subject: [PATCH 12/28] Fix naming bug

---
 pymc_extras/inference/deterministic_advi/pytensor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pymc_extras/inference/deterministic_advi/pytensor.py b/pymc_extras/inference/deterministic_advi/pytensor.py
index 3a062b6e9..71d265d43 100644
--- a/pymc_extras/inference/deterministic_advi/pytensor.py
+++ b/pymc_extras/inference/deterministic_advi/pytensor.py
@@ -70,7 +70,7 @@ def transform_draws(unstacked_draws, model, n_draws, keep_untransformed=False):
     fn = pytensor.function(model.value_vars, vars_to_sample)
     point_func = PointFunc(fn)
 
-    coords, dims = coords_and_dims_for_inferencedata(pymc_model)
+    coords, dims = coords_and_dims_for_inferencedata(model)
 
     transformed_result = apply_function_over_dataset(
         point_func,

From 609aef7687e83c90c1abc31446db8b2bcb368053 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Tue, 19 Aug 2025 19:33:41 +0200
Subject: [PATCH 13/28] Document and clean up

---
 pymc_extras/inference/__init__.py             |   4 -
 .../inference/deterministic_advi/api.py       | 163 --------------
 .../inference/deterministic_advi/core.py      | 123 -----------
 .../inference/deterministic_advi/jax.py       |  63 ------
 .../deterministic_advi/optimization.py        |  90 --------
 .../deterministic_advi/pymc_to_jax.py         |  81 -------
 .../inference/deterministic_advi/pytensor.py  | 208 +++++++++++++-----
 .../inference/deterministic_advi/utils.py     |  18 --
 pymc_extras/inference/fit.py                  |   5 -
 9 files changed, 153 insertions(+), 602 deletions(-)
 delete mode 100644 pymc_extras/inference/deterministic_advi/api.py
 delete mode 100644 pymc_extras/inference/deterministic_advi/core.py
 delete mode 100644 pymc_extras/inference/deterministic_advi/jax.py
 delete mode 100644 pymc_extras/inference/deterministic_advi/optimization.py
 delete mode 100644 pymc_extras/inference/deterministic_advi/pymc_to_jax.py
 delete mode 100644 pymc_extras/inference/deterministic_advi/utils.py

diff --git a/pymc_extras/inference/__init__.py b/pymc_extras/inference/__init__.py
index ad17211ca..a9f6d76bf 100644
--- a/pymc_extras/inference/__init__.py
+++ b/pymc_extras/inference/__init__.py
@@ -16,9 +16,6 @@
 from pymc_extras.inference.laplace_approx.find_map import find_MAP
 from pymc_extras.inference.laplace_approx.laplace import fit_laplace
 from pymc_extras.inference.pathfinder.pathfinder import fit_pathfinder
-from pymc_extras.inference.deterministic_advi.api import (
-    fit_deterministic_advi as fit_deterministic_advi_jax,
-)
 from pymc_extras.inference.deterministic_advi.pytensor import fit_deterministic_advi
 
 __all__ = [
@@ -27,5 +24,4 @@
     "fit_laplace",
     "find_MAP",
     "fit_deterministic_advi",
-    "fit_deterministic_advi_jax",
 ]
diff --git a/pymc_extras/inference/deterministic_advi/api.py b/pymc_extras/inference/deterministic_advi/api.py
deleted file mode 100644
index 2f23c1538..000000000
--- a/pymc_extras/inference/deterministic_advi/api.py
+++ /dev/null
@@ -1,163 +0,0 @@
-from typing import Callable, Dict
-
-import numpy as np
-import pymc
-import arviz as az
-from jax import vmap
-
-from pymc_extras.inference.deterministic_advi.jax import build_dadvi_funs
-from pymc_extras.inference.deterministic_advi.pymc_to_jax import (
-    get_jax_functions_from_pymc,
-    transform_dadvi_draws,
-)
-from pymc_extras.inference.deterministic_advi.core import (
-    find_dadvi_optimum,
-    get_dadvi_draws,
-    DADVIFuns,
-)
-from pymc_extras.inference.deterministic_advi.utils import opt_callback_fun
-
-
-class DADVIResult:
-    def __init__(
-        self,
-        fixed_draws: np.ndarray,
-        var_params: np.ndarray,
-        unflattening_fun: Callable[[np.ndarray], Dict[str, np.ndarray]],
-        dadvi_funs: DADVIFuns,
-        pymc_model: pymc.Model,  # TODO Check the type here
-    ):
-
-        self.fixed_draws = fixed_draws
-        self.var_params = var_params
-        self.unflattening_fun = unflattening_fun
-        self.dadvi_funs = dadvi_funs
-        self.n_params = self.fixed_draws.shape[1]
-        self.pymc_model = pymc_model
-
-    def get_posterior_means(self) -> Dict[str, np.ndarray]:
-        """
-        Returns a dictionary with posterior means for all parameters.
-        """
-
-        means = np.split(self.var_params, 2)[0]
-        return self.unflattening_fun(means)
-
-    def get_posterior_standard_deviations_mean_field(self) -> Dict[str, np.ndarray]:
-        """
-        Returns a dictionary with posterior standard deviations (not LRVB-corrected, but mean field).
-        """
-
-        log_sds = np.split(self.var_params, 2)[1]
-        sds = np.exp(log_sds)
-        return self.unflattening_fun(sds)
-
-    def get_posterior_draws_mean_field(
-        self,
-        n_draws: int = 1000,
-        seed: int = 2,
-        transform_draws: bool = True,
-    ) -> Dict[str, np.ndarray]:
-        """
-        Returns a dictionary with draws from the posterior.
-        """
-
-        np.random.seed(seed)
-        z = np.random.randn(n_draws, self.n_params)
-        dadvi_draws_flat = get_dadvi_draws(self.var_params, z)
-
-        if transform_draws:
-
-            dadvi_draws = transform_dadvi_draws(
-                self.pymc_model,
-                dadvi_draws_flat,
-                self.unflattening_fun,
-                add_chain_dim=True,
-            )
-
-        else:
-
-            dadvi_draws = vmap(self.unflattening_fun)(dadvi_draws_flat)
-
-        return dadvi_draws
-
-    def compute_function_on_mean_field_draws(
-        self,
-        function_to_run: Callable[[Dict], np.ndarray],
-        n_draws: int = 1000,
-        seed: int = 2,
-    ):
-        dadvi_dict = self.get_posterior_draws_mean_field(n_draws, seed)
-
-        return vmap(function_to_run)(dadvi_dict)
-
-
-def fit_deterministic_advi(model=None, num_fixed_draws=30, seed=2):
-    """
-    Does inference using deterministic ADVI (automatic differentiation
-    variational inference).
-
-    For full details see the paper cited in the references:
-    https://www.jmlr.org/papers/v25/23-1015.html
-
-    Parameters
-    ----------
-    model : pm.Model
-        The PyMC model to be fit. If None, the current model context is used.
-
-    num_fixed_draws : int
-        The number of fixed draws to use for the optimisation. More
-        draws will result in more accurate estimates, but also
-        increase inference time. Usually, the default of 30 is a good
-        tradeoff.between speed and accuracy.
-
-    seed: int
-        The random seed to use for the fixed draws. Running the optimisation
-        twice with the same seed should arrive at the same result.
-
-    Returns
-    -------
-    :class:`~arviz.InferenceData`
-        The inference data containing the results of the DADVI algorithm.
-
-    References
-    ----------
-    Giordano, R., Ingram, M., & Broderick, T. (2024). Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box. Journal of Machine Learning Research, 25(18), 1–39.
-
-
-    """
-
-    model = pymc.modelcontext(model) if model is None else model
-
-    np.random.seed(seed)
-
-    jax_funs = get_jax_functions_from_pymc(model)
-    dadvi_funs = build_dadvi_funs(jax_funs["log_posterior_fun"])
-
-    opt_callback_fun.opt_sequence = []
-
-    init_means = np.zeros(jax_funs["n_params"])
-    init_log_vars = np.zeros(jax_funs["n_params"]) - 3
-    init_var_params = np.concatenate([init_means, init_log_vars])
-    zs = np.random.randn(num_fixed_draws, jax_funs["n_params"])
-    opt = find_dadvi_optimum(
-        init_params=init_var_params,
-        zs=zs,
-        dadvi_funs=dadvi_funs,
-        verbose=True,
-        callback_fun=opt_callback_fun,
-    )
-
-    dadvi_result = DADVIResult(
-        fixed_draws=zs,
-        var_params=opt["opt_result"].x,
-        unflattening_fun=jax_funs["unflatten_fun"],
-        dadvi_funs=dadvi_funs,
-        pymc_model=model,
-    )
-
-    # Get draws and turn into arviz format expected
-    draws = dadvi_result.get_posterior_draws_mean_field(transform_draws=True)
-    az_draws = az.convert_to_inference_data(draws)
-
-    return az_draws
diff --git a/pymc_extras/inference/deterministic_advi/core.py b/pymc_extras/inference/deterministic_advi/core.py
deleted file mode 100644
index 85e1eca45..000000000
--- a/pymc_extras/inference/deterministic_advi/core.py
+++ /dev/null
@@ -1,123 +0,0 @@
-"""
-Core computations for DADVI.
-"""
-
-from typing import NamedTuple, Callable, Optional, Dict
-
-from scipy.sparse.linalg import LinearOperator
-
-import numpy as np
-from pymc_extras.inference.deterministic_advi.optimization import optimize_with_hvp
-
-
-class DADVIFuns(NamedTuple):
-    """
-    This NamedTuple holds the functions required to run DADVI.
-
-    Args:
-    kl_est_and_grad_fun: Function of eta [variational parameters] and zs [draws].
-        zs should have shape [M, D], where M is number of fixed draws and D is
-        problem dimension. Returns a tuple whose first argument is the estimate
-        of the KL divergence, and the second is its gradient w.r.t. eta.
-    kl_est_hvp_fun: Function of eta, zs, and b, a vector to compute the hvp
-        with. This should return a vector -- the result of the hvp with b.
-    """
-
-    kl_est_and_grad_fun: Callable[[np.ndarray, np.ndarray], np.ndarray]
-    kl_est_hvp_fun: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], np.ndarray]]
-
-
-def find_dadvi_optimum(
-    init_params: np.ndarray,
-    zs: np.ndarray,
-    dadvi_funs: DADVIFuns,
-    opt_method: str = "trust-ncg",
-    callback_fun: Optional[Callable] = None,
-    verbose: bool = False,
-) -> Dict:
-    """
-    Optimises the DADVI objective.
-
-    Args:
-    init_params: The initial variational parameters to use. This should be a
-        vector of length 2D, where D is the problem dimension. The first D
-        entries specify the variational means, while the last D specify the log
-        standard deviations.
-    zs: The fixed draws to use in the optimisation. They must be of shape
-        [M, D], where D is the problem dimension and M is the number of fixed
-        draws.
-    dadvi_funs: The objective to optimise. See the definition of DADVIFuns for
-        more information. The kl_est_and_grad_fun is required for optimisation;
-        the kl_est_hvp_fun is needed only for some optimisers.
-    opt_method: The optimisation method to use. This must be one of the methods
-        listed for scipy.optimize.minimize
-        [https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html].
-        Defaults to trust-ncg, which requires the hvp to be available. For
-        gradient-only optimisation, L-BFGS-B generally works well.
-    callback_fun: If provided, this callback function is passed to
-        scipy.optimize.minimize. See that function's documentation for more.
-    verbose: If True, prints the progress of the optimisation by showing the
-        value and gradient norm at each iteration of the optimizer.
-
-    Returns:
-    A dictionary with entries "opt_result", containing the results of running
-    scipy.optimize.minimize, and "evaluation_count", containing the number of
-    times the hvp and gradient functions were called.
-    """
-
-    val_and_grad_fun = lambda var_params: dadvi_funs.kl_est_and_grad_fun(var_params, zs)
-    hvp_fun = (
-        None
-        if dadvi_funs.kl_est_hvp_fun is None
-        else lambda var_params, b: dadvi_funs.kl_est_hvp_fun(var_params, zs, b)
-    )
-
-    opt_result, eval_count = optimize_with_hvp(
-        val_and_grad_fun,
-        hvp_fun,
-        init_params,
-        opt_method=opt_method,
-        callback_fun=callback_fun,
-        verbose=verbose,
-    )
-
-    to_return = {
-        "opt_result": opt_result,
-        "evaluation_count": eval_count,
-    }
-
-    # TODO: Here I originally had a Newton step check to assess
-    # convergence. Could add this back in.
-
-    return to_return
-
-
-def get_dadvi_draws(var_params: np.ndarray, zs: np.ndarray) -> np.ndarray:
-    """
-    Computes draws from the mean-field variational approximation given
-    variational parameters and a matrix of fixed draws.
-
-    Args:
-        var_params: A vector of shape 2D, the first D entries specifying the
-            means for the D model parameters, and the last D the log standard
-            deviations.
-        zs: A matrix of shape [N, D], containing the draws to use to sample the
-            variational approximation.
-
-    Returns:
-    A matrix of shape [N, D] containing N draws from the variational
-    approximation.
-    """
-
-    # TODO: Could use JAX here
-    means, log_sds = np.split(var_params, 2)
-    sds = np.exp(log_sds)
-
-    draws = means.reshape(1, -1) + zs * sds.reshape(1, -1)
-
-    return draws
-
-
-# TODO -- I think the functions above cover the basic functionality of
-# fixed-draw ADVI. But I have not yet included the LRVB portion of the
-# code, in the interest of keeping it simple. Can add later.
diff --git a/pymc_extras/inference/deterministic_advi/jax.py b/pymc_extras/inference/deterministic_advi/jax.py
deleted file mode 100644
index 1e9e63948..000000000
--- a/pymc_extras/inference/deterministic_advi/jax.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from typing import Callable, Dict, Tuple
-import numpy as np
-import jax.numpy as jnp
-from jax import jit, vmap, value_and_grad, jvp, grad
-from functools import partial
-
-from pymc_extras.inference.deterministic_advi.core import DADVIFuns
-from pymc_extras.inference.deterministic_advi.optimization import count_decorator
-
-
-@partial(jit, static_argnums=0)
-def hvp(f, primals, tangents):
-    # Taken (and slightly modified) from:
-    # https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html
-    return jvp(grad(f), (primals,), (tangents,))[1]
-
-
-@jit
-def _make_draws(z, mean, log_sd):
-
-    draw = z * jnp.exp(log_sd) + mean
-
-    return draw
-
-
-@jit
-def _calculate_entropy(log_sds):
-
-    return jnp.sum(log_sds)
-
-
-def build_dadvi_funs(log_posterior_fn: Callable[[jnp.ndarray], float]) -> DADVIFuns:
-    """
-    Builds the DADVIFuns from a log posterior density function written in JAX.
-    """
-
-    def single_log_posterior_fun(cur_z, var_params):
-        means, log_sds = jnp.split(var_params, 2)
-        cur_theta = _make_draws(cur_z, means, log_sds)
-        return log_posterior_fn(cur_theta)
-
-    def log_posterior_expectation(zs, var_params):
-        single_curried = partial(single_log_posterior_fun, var_params=var_params)
-        log_posts = vmap(single_curried)(zs)
-        return jnp.mean(log_posts)
-
-    def full_kl_est(var_params, zs):
-        _, log_sds = jnp.split(var_params, 2)
-        log_posterior = log_posterior_expectation(zs, var_params)
-        entropy = _calculate_entropy(log_sds)
-        return -log_posterior - entropy
-
-    @jit
-    def kl_est_hvp_fun(var_params, zs, b):
-        rel_kl_est = partial(full_kl_est, zs=zs)
-        rel_hvp = lambda x, y: hvp(rel_kl_est, x, y)
-        return rel_hvp(var_params, b)
-
-    kl_est_and_grad_fun = jit(value_and_grad(full_kl_est))
-
-    return DADVIFuns(
-        kl_est_and_grad_fun=kl_est_and_grad_fun, kl_est_hvp_fun=kl_est_hvp_fun
-    )
diff --git a/pymc_extras/inference/deterministic_advi/optimization.py b/pymc_extras/inference/deterministic_advi/optimization.py
deleted file mode 100644
index 8c243c9be..000000000
--- a/pymc_extras/inference/deterministic_advi/optimization.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import time
-import numpy as np
-from functools import wraps, partial
-from scipy.optimize import minimize
-
-
-def print_decorator(fun, verbose=True):
-    def result(x):
-
-        value, grad = fun(x)
-
-        if verbose:
-            print(f"'f': {value}, ||grad(f)||: {np.linalg.norm(grad)}", flush=True)
-
-        return value, grad
-
-    return result
-
-
-def count_decorator(function):
-    # If wrapped around a function, the number of calls of the function can be
-    # accessed by calling function.calls on the decorated result.
-    @wraps(function)
-    def new_fun(*args, **kwargs):
-        new_fun.calls += 1
-        return function(*args, **kwargs)
-
-    new_fun.calls = 0
-    return new_fun
-
-
-def time_decorator(function):
-    @wraps(function)
-    def new_fun(*args, **kwargs):
-        start_time = time.time()
-        result = function(*args, **kwargs)
-        end_time = time.time()
-        difference = end_time - start_time
-        new_fun.wall_time = difference
-        return result
-
-    new_fun.wall_time = None
-    return new_fun
-
-
-def optimize_with_hvp(
-    val_grad_fun,
-    hvp_fun,
-    start_params,
-    opt_method="trust-ncg",
-    verbose=False,
-    additional_decorator=None,
-    minimize_kwargs={},
-    callback_fun=None,
-):
-    # Note: "additional_decorator" will be called on the val_and_grad_fun and
-    # its purpose is to allow for additional side effects, particularly saving
-    # call results to files.
-
-    val_grad_fun = (
-        val_grad_fun
-        if additional_decorator is None
-        else additional_decorator(val_grad_fun)
-    )
-
-    decorated = count_decorator(partial(print_decorator, verbose=verbose)(val_grad_fun))
-    hvp_fun = count_decorator(hvp_fun)
-
-    if callback_fun is not None:
-        callback = lambda cur_theta: callback_fun(cur_theta, decorated, hvp_fun)
-    else:
-        callback = None
-
-    result = minimize(
-        decorated,
-        start_params,
-        method=opt_method,
-        hessp=hvp_fun,
-        jac=True,
-        callback=callback,
-        **minimize_kwargs,
-    )
-
-    n_hvp_calls = hvp_fun.calls
-    n_val_and_grad_calls = decorated.calls
-
-    return (
-        result,
-        {"n_hvp_calls": n_hvp_calls, "n_val_and_grad_calls": n_val_and_grad_calls},
-    )
diff --git a/pymc_extras/inference/deterministic_advi/pymc_to_jax.py b/pymc_extras/inference/deterministic_advi/pymc_to_jax.py
deleted file mode 100644
index 07438dc5e..000000000
--- a/pymc_extras/inference/deterministic_advi/pymc_to_jax.py
+++ /dev/null
@@ -1,81 +0,0 @@
-from jax.flatten_util import ravel_pytree
-from pymc.sampling.jax import get_jaxified_logp, get_jaxified_graph
-import jax
-from pymc.util import get_default_varnames
-import numpy as np
-from sklearn.preprocessing import LabelEncoder
-
-
-def get_logp_fn_dict(logp_fn, var_names):
-    def logp_fn_dict(theta_dict):
-        as_list = [theta_dict[x] for x in var_names]
-
-        return logp_fn(as_list)
-
-    return logp_fn_dict
-
-
-def get_basic_init_from_pymc(pymc_model):
-    logp_fn_jax = get_jaxified_logp(pymc_model)
-
-    rv_names = [rv.name for rv in pymc_model.value_vars]
-    init_point = pymc_model.initial_point()
-    init_state = {rv_name: init_point[rv_name] for rv_name in rv_names}
-
-    return rv_names, init_state, logp_fn_jax
-
-
-def get_jax_functions_from_pymc(pymc_model):
-    """
-    Given a PyMC model, builds functions for computing posterior densities with JAX.
-    Args:
-        pymc_model: The PyMC model object.
-    Returns:
-    A dictionary containing three elements: "log_posterior_fun" is the log posterior
-    density, as a function of a flat parameter vector; "unflatten_fun" turns a flat
-    parameter vector back into a dictionary; and "n_params" is the number of parameters
-    in the model.
-    """
-
-    var_names, init_state, logp_fn_jax = get_basic_init_from_pymc(pymc_model)
-    logp_fn_dict = get_logp_fn_dict(logp_fn_jax, var_names)
-    flat_init, fun = ravel_pytree(init_state)
-
-    def flat_log_post_fun(flat_params):
-        param_dict = fun(flat_params)
-        return logp_fn_dict(param_dict)
-
-    return {
-        "log_posterior_fun": flat_log_post_fun,
-        "unflatten_fun": fun,
-        "n_params": flat_init.shape[0],
-    }
-
-
-def transform_dadvi_draws(
-    pymc_model,
-    flat_dadvi_draws,
-    unflatten_fun,
-    keep_untransformed=False,
-    add_chain_dim=False,
-):
-    # TODO: Maybe should take unflattened draws as input instead
-
-    non_flat = jax.vmap(unflatten_fun)(flat_dadvi_draws)
-    list_version = [non_flat[x.name] for x in pymc_model.value_vars]
-
-    var_names = pymc_model.unobserved_value_vars
-
-    vars_to_sample = list(
-        get_default_varnames(var_names, include_transformed=keep_untransformed)
-    )
-
-    jax_fn = get_jaxified_graph(inputs=pymc_model.value_vars, outputs=vars_to_sample)
-
-    list_res = jax.vmap(jax_fn)(*list_version)
-    samples = {v.name: r for v, r in zip(vars_to_sample, list_res)}
-
-    if add_chain_dim:
-        samples = {x: np.expand_dims(y, axis=0) for x, y in samples.items()}
-
-    return samples
diff --git a/pymc_extras/inference/deterministic_advi/pytensor.py b/pymc_extras/inference/deterministic_advi/pytensor.py
index 71d265d43..c8d5d6a2d 100644
--- a/pymc_extras/inference/deterministic_advi/pytensor.py
+++ b/pymc_extras/inference/deterministic_advi/pytensor.py
@@ -1,11 +1,15 @@
 from collections import defaultdict
+from typing import Tuple, Optional
 
 import pymc
+from pymc import Model
 import arviz as az
 import numpy as np
 from scipy.optimize import minimize
 import pytensor
 import pytensor.tensor as pt
+from pytensor.tensor.variable import TensorVariable
+import xarray
 
 from pymc import join_nonshared_inputs, DictToArrayBijection
 from pymc.util import get_default_varnames
@@ -20,22 +24,137 @@
 from pymc_extras.inference.laplace_approx.laplace import unstack_laplace_draws
 
 
-def create_dadvi_graph(
-    pymc_model, n_params: int, n_fixed_draws: int = 30, random_seed: int = 2
+def fit_deterministic_advi(
+    model: Optional[Model] = None,
+    n_fixed_draws: int = 30,
+    random_seed: int = 2,
+    n_draws: int = 1000,
+    keep_untransformed: bool = False,
 ):
+    """
+    Does inference using deterministic ADVI (automatic differentiation
+    variational inference).
+
+    For full details see the paper cited in the references:
+    https://www.jmlr.org/papers/v25/23-1015.html
+
+    Parameters
+    ----------
+    model : pm.Model
+        The PyMC model to be fit. If None, the current model context is used.
+
+    n_fixed_draws : int
+        The number of fixed draws to use for the optimisation. More
+        draws will result in more accurate estimates, but also
+        increase inference time. Usually, the default of 30 is a good
+        tradeoff.between speed and accuracy.
+
+    random_seed: int
+        The random seed to use for the fixed draws. Running the optimisation
+        twice with the same seed should arrive at the same result.
+
+    n_draws: int
+        The number of draws to return from the variational approximation.
+
+    keep_untransformed: bool
+        Whether or not to keep the unconstrained variables (such as
+        logs of positive-constrained parameters) in the output.
+
+    Returns
+    -------
+    :class:`~arviz.InferenceData`
+        The inference data containing the results of the DADVI algorithm.
+
+    References
+    ----------
+    Giordano, R., Ingram, M., & Broderick, T. (2024). Black Box
+    Variational Inference with a Deterministic Objective: Faster, More
+    Accurate, and Even More Black Box. Journal of Machine Learning
+    Research, 25(18), 1–39.
+    """
+
+    model = pymc.modelcontext(model) if model is None else model
+
+    initial_point_dict = model.initial_point()
+    n_params = DictToArrayBijection.map(initial_point_dict).data.shape[0]
+
+    var_params, objective = create_dadvi_graph(
+        model,
+        n_fixed_draws=n_fixed_draws,
+        random_seed=random_seed,
+        n_params=n_params,
+    )
+
+    f_fused, f_hessp = _compile_functions_for_scipy_optimize(
+        objective,
+        [var_params],
+        compute_grad=True,
+        compute_hessp=True,
+        compute_hess=False,
+    )
+
+    result = minimize(
+        f_fused, np.zeros(2 * n_params), method="trust-ncg", jac=True, hessp=f_hessp
+    )
+
+    opt_var_params = result.x
+    opt_means, opt_log_sds = np.split(opt_var_params, 2)
+
+    # Make the draws:
+    draws_raw = np.random.randn(n_draws, n_params)
+    draws = opt_means + draws_raw * np.exp(opt_log_sds)
+    draws_arviz = unstack_laplace_draws(draws, model, chains=1, draws=n_draws)
+
+    transformed_draws = transform_draws(
+        draws_arviz, model, keep_untransformed=keep_untransformed
+    )
 
+    return transformed_draws
+
+
+def create_dadvi_graph(
+    model: Model,
+    n_params: int,
+    n_fixed_draws: int = 30,
+    random_seed: int = 2,
+) -> Tuple[TensorVariable, TensorVariable]:
+    """
+    Sets up the DADVI graph in pytensor and returns it.
+
+    Parameters
+    ----------
+    model : pm.Model
+        The PyMC model to be fit.
+
+    n_params: int
+        The total number of parameters in the model.
+
+    n_fixed_draws : int
+        The number of fixed draws to use.
+
+    random_seed: int
+        The random seed to use for the fixed draws.
+
+    Returns
+    -------
+    Tuple[TensorVariable, TensorVariable]
+        A tuple whose first element contains the variational parameters,
+        and whose second contains the DADVI objective.
+    """
+
+    # Make the fixed draws
     state = np.random.RandomState(random_seed)
+    draws = state.randn(n_fixed_draws, n_params)
 
-    inputs = pymc_model.continuous_value_vars + pymc_model.discrete_value_vars
-    initial_point_dict = pymc_model.initial_point()
-    logp = pymc_model.logp()
+    inputs = model.continuous_value_vars + model.discrete_value_vars
+    initial_point_dict = model.initial_point()
+    logp = model.logp()
 
     # Graph in terms of a flat input
     [logp], flat_input = join_nonshared_inputs(
         point=initial_point_dict, outputs=[logp], inputs=inputs
     )
 
-    draws = state.randn(n_fixed_draws, n_params)
     var_params = pt.vector(name="eta", shape=(2 * n_params,))
 
     means = var_params[:n_params]
@@ -58,10 +177,36 @@ def create_dadvi_graph(
 
     objective = -mean_log_density - entropy
 
-    return var_params, objective, n_params
+    return var_params, objective
+
+
+def transform_draws(
+    unstacked_draws: xarray.Dataset,
+    model: Model,
+    keep_untransformed: bool = False,
+):
+    """
+    Transforms the unconstrained draws back into the constrained space.
+
+    Parameters
+    ----------
+    unstacked_draws : xarray.Dataset
+        The draws to constrain back into the original space.
+
+    model : Model
+        The PyMC model the variables were derived from.
 
+    n_draws: int
+        The number of draws to return from the variational approximation.
 
-def transform_draws(unstacked_draws, model, n_draws, keep_untransformed=False):
+    keep_untransformed: bool
+        Whether or not to keep the unconstrained variables in the output.
+
+    Returns
+    -------
+    :class:`~arviz.InferenceData`
+        Draws from the original constrained parameters.
+    """
 
     filtered_var_names = model.unobserved_value_vars
     vars_to_sample = list(
@@ -81,50 +226,3 @@ def transform_draws(unstacked_draws, model, n_draws, keep_untransformed=False):
     )
 
     return transformed_result
-
-
-def fit_deterministic_advi(
-    model=None,
-    n_fixed_draws: int = 30,
-    random_seed: int = 2,
-    n_draws: int = 1000,
-    keep_untransformed=False,
-):
-
-    model = pymc.modelcontext(model) if model is None else model
-
-    initial_point_dict = model.initial_point()
-    n_params = DictToArrayBijection.map(initial_point_dict).data.shape[0]
-
-    var_params, objective, n_params = create_dadvi_graph(
-        model,
-        n_fixed_draws=n_fixed_draws,
-        random_seed=random_seed,
-        n_params=n_params,
-    )
-
-    f_fused, f_hessp = _compile_functions_for_scipy_optimize(
-        objective,
-        [var_params],
-        compute_grad=True,
-        compute_hessp=True,
-        compute_hess=False,
-    )
-
-    result = minimize(
-        f_fused, np.zeros(2 * n_params), method="trust-ncg", jac=True, hessp=f_hessp
-    )
-
-    opt_var_params = result.x
-    opt_means, opt_log_sds = np.split(opt_var_params, 2)
-
-    # Make the draws:
-    draws_raw = np.random.randn(n_draws, n_params)
-    draws = opt_means + draws_raw * np.exp(opt_log_sds)
-    draws_arviz = unstack_laplace_draws(draws, model, chains=1, draws=n_draws)
-
-    transformed_draws = transform_draws(
-        draws_arviz, model, n_draws=n_draws, keep_untransformed=keep_untransformed
-    )
-
-    return transformed_draws
diff --git a/pymc_extras/inference/deterministic_advi/utils.py b/pymc_extras/inference/deterministic_advi/utils.py
deleted file mode 100644
index c2caddf12..000000000
--- a/pymc_extras/inference/deterministic_advi/utils.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from time import time
-
-
-def opt_callback_fun(theta, val_and_grad_fun, hvp_fun):
-    # Callback for jax_advi. It records only what will be needed
-    # to estimate the KL later on.
-
-    opt_callback_fun.opt_sequence.append(
-        {
-            "val_and_grad_calls": val_and_grad_fun.calls,
-            "hvp_calls": hvp_fun.calls,
-            "theta": theta,
-            "time": time(),
-        }
-    )
-
-
-opt_callback_fun.opt_sequence = list()
diff --git a/pymc_extras/inference/fit.py b/pymc_extras/inference/fit.py
index 390d2bc0d..68cbe7acb 100644
--- a/pymc_extras/inference/fit.py
+++ b/pymc_extras/inference/fit.py
@@ -45,8 +45,3 @@ def fit(method: str, **kwargs) -> az.InferenceData:
         from pymc_extras.inference import fit_deterministic_advi
 
         return fit_deterministic_advi(**kwargs)
-
-    if method == "deterministic_advi_jax":
-        from pymc_extras.inference import fit_deterministic_advi_jax
-
-        return fit_deterministic_advi_jax(**kwargs)

From f17a090959578c4d5bf7befd3638b010bbbea893 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Tue, 19 Aug 2025 19:50:46 +0200
Subject: [PATCH 14/28] Fix example

---
 notebooks/deterministic_advi_example.ipynb | 698 ++++++---------------
 1 file changed, 183 insertions(+), 515 deletions(-)

diff --git a/notebooks/deterministic_advi_example.ipynb b/notebooks/deterministic_advi_example.ipynb
index 24166f3e1..ec1f40a9f 100644
--- a/notebooks/deterministic_advi_example.ipynb
+++ b/notebooks/deterministic_advi_example.ipynb
@@ -136,7 +136,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1902eaec771740abb86ced51c17708b6",
+       "model_id": "6ee428103b2349c88e63b57d15f04646",
        "version_major": 2,
        "version_minor": 0
       },
@@ -219,27 +219,41 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "'f': 713.4534726015399, ||grad(f)||: 967.1327633132877\n",
-      "'f': 479.931132929988, ||grad(f)||: 321.38918482649177\n",
-      "'f': 415.0696415398314, ||grad(f)||: 121.08420341297919\n",
-      "'f': 337.7224486499685, ||grad(f)||: 162.75528032449952\n",
-      "'f': 504.02428287446673, ||grad(f)||: 981.96731906951\n",
-      "'f': 264.4594240322506, ||grad(f)||: 133.6422132575744\n",
-      "'f': 238.3620698481016, ||grad(f)||: 295.47412016993616\n",
-      "'f': 202.3872063826712, ||grad(f)||: 78.51034098808502\n",
-      "'f': 191.15958110793596, ||grad(f)||: 37.53448393652803\n",
-      "'f': 184.84485574860963, ||grad(f)||: 107.08693948173128\n",
-      "'f': 178.74616664207306, ||grad(f)||: 35.669339324740314\n",
-      "'f': 173.99800346020643, ||grad(f)||: 8.829578264006848\n",
-      "'f': 173.90207541922908, ||grad(f)||: 1.0160571789804076\n",
-      "'f': 173.8315687218876, ||grad(f)||: 0.2777895329277002\n",
-      "'f': 173.82759779415377, ||grad(f)||: 0.057755540352186614\n",
-      "'f': 173.82750584115203, ||grad(f)||: 0.0016314353337682263\n",
-      "'f': 173.82750529818853, ||grad(f)||: 2.2617180758073807e-06\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15730952a26c4ff0bcb302b0aecc4451",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -259,20 +273,7 @@
     {
      "data": {
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-       "\n",
-       "            <div>\n",
-       "              <div class='xr-header'>\n",
-       "                <div class=\"xr-obj-type\">arviz.InferenceData</div>\n",
-       "              </div>\n",
-       "              <ul class=\"xr-sections group-sections\">\n",
-       "              \n",
-       "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_posterior0e0ae069-eb60-4641-8470-b3317d72493f\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
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-       "                  <div class=\"xr-section-inline-details\"></div>\n",
-       "                  <div class=\"xr-section-details\">\n",
-       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
-       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
        "<defs>\n",
        "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
        "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
@@ -721,486 +722,153 @@
        "  * chain      (chain) int64 8B 0\n",
        "  * draw       (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
        "Data variables:\n",
-       "    Intercept  (chain, draw) float64 8kB 1.024 1.132 1.072 ... 1.06 0.9415\n",
-       "    slope      (chain, draw) float64 8kB 1.917 2.003 1.989 ... 2.093 2.046 2.052\n",
-       "    sigma      (chain, draw) float64 8kB 0.5302 0.4897 0.5021 ... 0.5441 0.4952\n",
+       "    Intercept  (chain, draw) float64 8kB 1.033 1.054 1.07 ... 1.035 1.038 1.069\n",
+       "    slope      (chain, draw) float64 8kB 2.009 2.046 2.063 ... 1.981 2.08 2.038\n",
+       "    sigma      (chain, draw) float64 8kB 0.475 0.4933 0.5824 ... 0.5156 0.5347\n",
        "Attributes:\n",
-       "    created_at:     2025-08-14T09:59:22.382706+00:00\n",
-       "    arviz_version:  0.22.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-82bad586-956e-4b0e-9d31-6db45a728882' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-82bad586-956e-4b0e-9d31-6db45a728882' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cc873bdf-86c1-4455-b034-5e91e967ba6e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc873bdf-86c1-4455-b034-5e91e967ba6e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-2e2ac284-da5f-43a5-811a-08a3bebb5bf4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e2ac284-da5f-43a5-811a-08a3bebb5bf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ba5dd1a-fade-495d-9ad2-be87a917956b' class='xr-var-data-in' type='checkbox'><label for='data-0ba5dd1a-fade-495d-9ad2-be87a917956b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-d628e1d0-d82a-4831-9f08-fe3175a0e53b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d628e1d0-d82a-4831-9f08-fe3175a0e53b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f26e5a0-bd0a-4859-bfbf-b7b066ef242e' class='xr-var-data-in' type='checkbox'><label for='data-9f26e5a0-bd0a-4859-bfbf-b7b066ef242e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999], shape=(1000,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-030dd6d7-4e23-44a3-bfc5-994bf3096062' class='xr-section-summary-in' type='checkbox'  checked><label for='section-030dd6d7-4e23-44a3-bfc5-994bf3096062' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.024 1.132 1.072 ... 1.06 0.9415</div><input id='attrs-cfce0f01-6259-4bfb-8d33-daaa3e652aa1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfce0f01-6259-4bfb-8d33-daaa3e652aa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53bc4f49-f687-4fcf-8ddf-ccc4b75f8206' class='xr-var-data-in' type='checkbox'><label for='data-53bc4f49-f687-4fcf-8ddf-ccc4b75f8206' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.02423358, 1.13214564, 1.072478  , 0.99841007, 1.04827592,\n",
-       "        1.01482212, 1.00686333, 1.0378902 , 1.0283221 , 0.98379442,\n",
-       "        1.03198199, 1.05201041, 1.07242427, 1.07454867, 0.94809642,\n",
-       "        1.02848707, 1.00260019, 1.02610458, 1.06883805, 1.07276731,\n",
-       "        1.02409935, 1.06319896, 1.01054579, 1.08466965, 1.02608234,\n",
-       "        1.12921922, 1.08256285, 1.04204586, 1.02490851, 1.10998724,\n",
-       "        1.06612953, 1.11989228, 1.06605638, 0.92315095, 0.9866524 ,\n",
-       "        1.18172176, 1.00836459, 1.01670677, 1.03160834, 0.99736694,\n",
-       "        0.99251318, 1.06833068, 1.08554496, 0.95350888, 0.99044707,\n",
-       "        1.09959851, 1.07221878, 1.03288865, 1.21815275, 1.02359065,\n",
-       "        1.07765238, 0.93752456, 1.10474129, 1.02262079, 1.02152724,\n",
-       "        1.08578299, 1.26163946, 0.94191896, 1.02697613, 1.02959188,\n",
-       "        1.11616078, 1.07308073, 0.96985303, 1.07873855, 0.98659023,\n",
-       "        1.14075991, 1.10028964, 1.02372132, 1.09171603, 1.08464893,\n",
-       "        0.94902426, 0.97527543, 1.01708894, 1.00200698, 1.05244998,\n",
-       "        0.97587417, 1.10305835, 1.09727215, 0.992403  , 1.14188873,\n",
-       "        0.97697696, 1.0301139 , 1.01365668, 1.02853078, 1.04910016,\n",
-       "        1.04916673, 1.06371857, 1.08100031, 1.02091666, 0.99710785,\n",
-       "        0.95942206, 1.01184389, 1.10094271, 1.02723867, 0.93995075,\n",
-       "        1.03878949, 0.98541613, 1.00822683, 1.01612199, 1.02049984,\n",
+       "    created_at:                 2025-08-19T17:50:03.733248+00:00\n",
+       "    arviz_version:              0.22.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.25.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6df1892b-ad7d-4181-82ee-6c86d6c27c80' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6df1892b-ad7d-4181-82ee-6c86d6c27c80' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f2df7f53-5659-4b1d-86fe-18dc5f84696d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f2df7f53-5659-4b1d-86fe-18dc5f84696d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-6a98b700-c7be-4622-964b-42a51feb15c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a98b700-c7be-4622-964b-42a51feb15c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f42733e0-c766-4833-9335-0b088fbd83fc' class='xr-var-data-in' type='checkbox'><label for='data-f42733e0-c766-4833-9335-0b088fbd83fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-6ae49c83-8b33-4c06-acee-51fde5e2fcba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ae49c83-8b33-4c06-acee-51fde5e2fcba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75f650ea-447c-4b0b-af94-b1574717898e' class='xr-var-data-in' type='checkbox'><label for='data-75f650ea-447c-4b0b-af94-b1574717898e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999], shape=(1000,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-694ec04b-0219-4055-ad80-c8e92fdb515d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-694ec04b-0219-4055-ad80-c8e92fdb515d' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.033 1.054 1.07 ... 1.038 1.069</div><input id='attrs-e0ed3696-5838-4486-b8f0-59d52060087d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0ed3696-5838-4486-b8f0-59d52060087d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a762b03-0093-4cda-8843-ac169e93067f' class='xr-var-data-in' type='checkbox'><label for='data-1a762b03-0093-4cda-8843-ac169e93067f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.03322021, 1.05371756, 1.06952535, 1.07168684, 1.10113775,\n",
+       "        0.99596338, 1.08933887, 1.02621925, 1.0846885 , 1.14191563,\n",
+       "        1.08141276, 1.02008058, 1.09879434, 0.98483523, 1.00136917,\n",
+       "        1.1421273 , 1.05671746, 1.00377814, 1.08699367, 1.03806556,\n",
+       "        1.08564975, 1.01925913, 1.08511609, 1.00545549, 1.12737332,\n",
+       "        1.05292982, 1.07612063, 1.11245937, 1.104032  , 1.02770992,\n",
+       "        1.12400587, 1.04376826, 1.0968951 , 1.08172164, 1.01715204,\n",
+       "        1.03459048, 1.09135703, 1.03417927, 1.07435218, 1.01259613,\n",
+       "        1.06599677, 1.04201867, 1.04630453, 1.03073456, 1.07489753,\n",
+       "        1.04607738, 1.06794489, 1.08054239, 1.04374861, 1.01499328,\n",
+       "        1.0370354 , 1.00320067, 1.01481523, 1.11833827, 1.02761816,\n",
+       "        1.07596896, 1.07424398, 1.11580837, 1.08791928, 1.06636238,\n",
+       "        0.9846451 , 1.05327208, 1.04472899, 1.05468312, 1.05978126,\n",
+       "        1.0555246 , 1.0502117 , 1.07206996, 1.09628804, 1.05996606,\n",
+       "        1.03362806, 1.00826373, 1.05455842, 1.0295575 , 1.08455588,\n",
+       "        1.04074644, 1.10437406, 1.06103914, 1.10619371, 0.99507688,\n",
+       "        1.0453811 , 1.01683386, 1.05807921, 1.04674656, 0.98074214,\n",
+       "        1.03583398, 1.06310479, 1.05814009, 1.09866709, 1.03418801,\n",
+       "        1.04885841, 1.06767458, 1.06239383, 1.0617037 , 1.05373531,\n",
+       "        1.02727192, 1.0820085 , 1.01152272, 1.01371037, 0.9933967 ,\n",
        "...\n",
-       "        1.04343402, 1.08630445, 1.03480269, 0.92914828, 1.10605693,\n",
-       "        1.0164609 , 1.02400983, 1.20819081, 1.23374511, 1.09730258,\n",
-       "        0.97090626, 1.00648394, 1.0233964 , 1.12086542, 1.10114055,\n",
-       "        1.00309621, 1.06004915, 0.92752479, 1.10954258, 1.03301626,\n",
-       "        1.11615819, 0.98744769, 1.02026043, 1.1153196 , 0.99663706,\n",
-       "        1.16998398, 1.03583563, 1.11559131, 1.01989872, 1.06736821,\n",
-       "        0.96846236, 1.05644294, 1.04882904, 1.02808556, 0.96054129,\n",
-       "        1.00676139, 1.0603263 , 1.03447109, 1.10776001, 1.06860415,\n",
-       "        1.02088386, 0.9987875 , 1.04225094, 1.09002115, 1.02716016,\n",
-       "        1.04482285, 1.11519222, 1.06931526, 1.13311052, 0.98383476,\n",
-       "        1.06530887, 0.95936957, 1.02754557, 1.01991861, 0.94289119,\n",
-       "        1.10781833, 1.09270573, 1.05499586, 1.1113207 , 1.10407635,\n",
-       "        1.00620247, 1.05778944, 0.94591702, 1.04000685, 1.00446716,\n",
-       "        0.95204823, 1.03531636, 1.13389779, 1.03389275, 0.94733869,\n",
-       "        1.06619758, 1.10565022, 1.03408208, 0.94122729, 1.04245624,\n",
-       "        0.97263879, 1.00927691, 0.99928997, 1.11684691, 0.99609668,\n",
-       "        1.05751935, 1.13451328, 0.99478621, 1.08727973, 0.98795396,\n",
-       "        1.02913718, 1.17274048, 1.07302921, 1.07379724, 1.07174714,\n",
-       "        1.13900459, 1.0109294 , 0.94462211, 0.96500562, 1.01506402,\n",
-       "        1.02960575, 1.08311952, 0.95107408, 1.05994956, 0.94154183]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.917 2.003 1.989 ... 2.046 2.052</div><input id='attrs-39b252a8-a016-4ad6-bf12-625ac6634920' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39b252a8-a016-4ad6-bf12-625ac6634920' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb5b1645-2f72-498a-95c8-027c85f095be' class='xr-var-data-in' type='checkbox'><label for='data-fb5b1645-2f72-498a-95c8-027c85f095be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.91697266, 2.00334667, 1.98892008, 2.21246449, 2.09548297,\n",
-       "        2.13791721, 2.00092046, 1.99353576, 2.01696503, 2.04927131,\n",
-       "        1.89705003, 2.15023709, 2.05951416, 2.11096041, 2.1574465 ,\n",
-       "        2.06271442, 2.12626441, 2.05454434, 2.01716359, 1.93549484,\n",
-       "        2.05687313, 2.06259998, 2.09449797, 2.11572765, 1.98619852,\n",
-       "        2.0534112 , 2.04687089, 2.14280237, 2.15074432, 2.08292706,\n",
-       "        2.07313959, 2.12896403, 2.13781914, 2.08527514, 2.03922289,\n",
-       "        2.08891919, 2.03782409, 2.02008001, 2.01289906, 1.96121384,\n",
-       "        2.03792215, 2.14376328, 2.13352274, 2.0262721 , 2.04728544,\n",
-       "        2.11598492, 1.96484127, 1.92054547, 2.17912692, 2.10604964,\n",
-       "        2.04428335, 2.08777197, 2.17573851, 2.12211387, 2.06784255,\n",
-       "        2.09270497, 2.16172249, 2.11116528, 2.0859719 , 2.13621564,\n",
-       "        2.02920448, 2.10159269, 2.08347721, 2.11011689, 1.97801184,\n",
-       "        2.09642421, 1.98271113, 2.15969911, 2.10383467, 2.05227256,\n",
-       "        2.03844841, 2.07552461, 2.09639081, 2.20262033, 2.06354795,\n",
-       "        2.08422265, 2.08555662, 2.01532663, 1.8923331 , 2.05439962,\n",
-       "        2.05402556, 2.09614278, 2.09376097, 2.09544067, 1.90107797,\n",
-       "        2.05047342, 2.06520958, 2.00263974, 2.05900631, 1.96361798,\n",
-       "        1.98831187, 2.036075  , 2.10791378, 1.95734785, 2.05936628,\n",
-       "        1.95004469, 2.12812067, 1.98806304, 1.97977537, 2.03313343,\n",
+       "        1.06899859, 1.04257078, 1.08758315, 1.0987634 , 0.99383811,\n",
+       "        1.08673273, 1.02245787, 1.05652938, 1.06603507, 1.08749668,\n",
+       "        1.02363128, 1.04894393, 1.01882759, 1.03708812, 1.01580446,\n",
+       "        1.07114355, 1.08418248, 1.13563688, 1.08484639, 1.06252274,\n",
+       "        1.08223777, 1.03623537, 1.06549358, 0.99033368, 1.07196064,\n",
+       "        1.08538163, 1.01806955, 1.0362579 , 1.06562302, 1.02332603,\n",
+       "        1.02094121, 1.04413596, 0.95132295, 1.0718015 , 1.10910077,\n",
+       "        1.04195555, 1.08417389, 0.97467751, 1.04224927, 1.02095729,\n",
+       "        1.0415575 , 1.04976663, 1.03291724, 1.03327229, 1.05916072,\n",
+       "        1.02508845, 1.04494963, 1.00066162, 1.08340542, 1.11585892,\n",
+       "        1.04821342, 1.09386951, 1.03479999, 1.08524746, 1.04126213,\n",
+       "        1.0642749 , 1.09273814, 1.12211764, 1.11692555, 1.04239027,\n",
+       "        1.01800202, 1.06177611, 1.08184505, 1.14352262, 0.97175291,\n",
+       "        1.0645999 , 1.09066149, 1.0497653 , 1.08522007, 1.02638569,\n",
+       "        1.03272143, 1.01656997, 1.06315342, 1.02622978, 1.02708829,\n",
+       "        0.98472192, 1.10973262, 1.07970371, 1.1030834 , 1.0587159 ,\n",
+       "        1.03575917, 1.10571843, 1.02405268, 1.03457422, 1.12308228,\n",
+       "        1.07374779, 0.99441592, 1.08583898, 1.0864686 , 1.08231916,\n",
+       "        1.08296171, 1.02915659, 1.11645043, 1.06223446, 1.04184045,\n",
+       "        1.07102462, 1.07674975, 1.03475673, 1.03843024, 1.06868367]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.009 2.046 2.063 ... 2.08 2.038</div><input id='attrs-d77ffcba-7450-488b-8917-ca0516864459' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d77ffcba-7450-488b-8917-ca0516864459' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e469481-9237-4153-9fee-3da120f03f18' class='xr-var-data-in' type='checkbox'><label for='data-0e469481-9237-4153-9fee-3da120f03f18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.00929359, 2.04613298, 2.06269054, 2.05351671, 2.06124664,\n",
+       "        2.04251201, 2.11883439, 2.06534653, 2.00031365, 2.18710465,\n",
+       "        2.14440695, 2.09273058, 2.06072532, 2.00981934, 2.04962304,\n",
+       "        2.03529346, 2.08513048, 2.0670887 , 2.07888598, 2.07444069,\n",
+       "        1.95591053, 2.03596239, 2.04283974, 2.10428414, 1.98366053,\n",
+       "        1.99671086, 2.06069345, 2.11209758, 2.08031408, 2.02250548,\n",
+       "        2.05274904, 2.01389165, 2.09423844, 2.11584371, 2.19223506,\n",
+       "        2.016225  , 2.09662889, 2.05654088, 2.00080844, 1.98013185,\n",
+       "        2.10451414, 1.97743491, 2.02807483, 2.10089774, 2.02994571,\n",
+       "        2.22200127, 2.03371281, 1.99069563, 2.14942961, 2.09087251,\n",
+       "        2.05848951, 1.99466908, 2.09443697, 2.09792379, 2.01194483,\n",
+       "        2.08231435, 2.04192766, 2.11337002, 2.06926661, 2.01692751,\n",
+       "        1.98299414, 2.00677196, 2.09197433, 2.07567755, 2.14402701,\n",
+       "        2.0756996 , 2.08853288, 2.11436305, 2.06658919, 1.99441002,\n",
+       "        1.94795739, 2.02978596, 2.06315447, 1.96665993, 2.01629823,\n",
+       "        2.11141896, 2.11899955, 2.04626373, 2.05672302, 2.04236203,\n",
+       "        2.14889964, 2.04176451, 2.00787205, 2.04478148, 2.12298998,\n",
+       "        2.09858755, 2.02406437, 2.04969567, 2.18241483, 1.98960708,\n",
+       "        2.10474415, 2.09735798, 2.02153651, 2.07867391, 1.93014706,\n",
+       "        2.03193646, 2.02694996, 2.11557073, 1.98436801, 1.98442671,\n",
        "...\n",
-       "        2.05530695, 2.00217808, 2.11441924, 2.06249682, 2.05009886,\n",
-       "        1.98657164, 2.09668262, 2.03196947, 2.04352393, 2.04953399,\n",
-       "        2.00931414, 2.12690864, 2.1018454 , 2.1204206 , 2.09961675,\n",
-       "        2.07535717, 2.04098068, 2.1205835 , 2.14888293, 2.03443908,\n",
-       "        2.1732742 , 2.08582487, 2.02294894, 1.99318913, 2.03235507,\n",
-       "        2.10177151, 2.02774304, 2.07596736, 2.06460154, 2.1751096 ,\n",
-       "        2.10421417, 2.10006932, 2.17373393, 2.02043598, 2.11959153,\n",
-       "        1.92492828, 2.05951315, 2.0348166 , 2.03930556, 1.99986352,\n",
-       "        1.87102165, 2.0902266 , 2.08207058, 2.08337471, 2.12790317,\n",
-       "        1.9673689 , 1.91446766, 1.89179225, 2.10400844, 2.05124513,\n",
-       "        1.99217333, 2.10397891, 1.98707775, 1.93347469, 1.99235129,\n",
-       "        2.03588954, 2.07867537, 1.92756295, 2.13468686, 2.10931335,\n",
-       "        2.13600089, 2.08006471, 2.24951764, 2.06121473, 2.09622105,\n",
-       "        2.04410602, 2.03698884, 2.20533904, 2.03162615, 2.20700731,\n",
-       "        2.19161057, 2.04844913, 1.98522305, 2.07154886, 2.11393931,\n",
-       "        2.06769412, 1.96168876, 2.02024033, 1.98970737, 2.07018536,\n",
-       "        1.98646095, 2.0011341 , 2.22943759, 2.05596641, 1.92291756,\n",
-       "        1.99934292, 1.9652415 , 2.13451806, 2.04079166, 2.16918646,\n",
-       "        2.05669288, 2.14205927, 2.1223799 , 2.08985125, 2.12973614,\n",
-       "        2.03145339, 2.05918212, 2.09295493, 2.0457824 , 2.05163278]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5302 0.4897 ... 0.5441 0.4952</div><input id='attrs-ff3920cb-48a1-4d98-aa96-10c2c6aeffaa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff3920cb-48a1-4d98-aa96-10c2c6aeffaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbb377af-71a1-4524-8bfe-41a2256b151d' class='xr-var-data-in' type='checkbox'><label for='data-dbb377af-71a1-4524-8bfe-41a2256b151d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.53015255, 0.48974337, 0.50214987, 0.54506267, 0.50507724,\n",
-       "        0.53105185, 0.5317347 , 0.5377762 , 0.52581713, 0.4981341 ,\n",
-       "        0.58849647, 0.54057837, 0.51142625, 0.52396477, 0.57521445,\n",
-       "        0.54655447, 0.53364766, 0.52248039, 0.5634492 , 0.53678421,\n",
-       "        0.52831535, 0.48425602, 0.49772001, 0.51590304, 0.53313106,\n",
-       "        0.47076414, 0.48440758, 0.55190266, 0.49896913, 0.52042571,\n",
-       "        0.5454315 , 0.49098492, 0.52627446, 0.56045215, 0.54212621,\n",
-       "        0.5779551 , 0.51754998, 0.5100735 , 0.51912387, 0.59712898,\n",
-       "        0.54210897, 0.55126015, 0.50794753, 0.52874732, 0.54606393,\n",
-       "        0.4967436 , 0.53459334, 0.49529004, 0.55559647, 0.54867529,\n",
-       "        0.5316045 , 0.54652462, 0.5249046 , 0.50191756, 0.50193375,\n",
-       "        0.53740496, 0.55181244, 0.54044538, 0.51068534, 0.51730578,\n",
-       "        0.53890784, 0.55202246, 0.52337899, 0.55534095, 0.51462914,\n",
-       "        0.53591017, 0.52359439, 0.52793408, 0.50578853, 0.5063827 ,\n",
-       "        0.53959348, 0.51484645, 0.53883154, 0.52399437, 0.51136522,\n",
-       "        0.5597166 , 0.55385763, 0.53618329, 0.52075431, 0.49152059,\n",
-       "        0.48458167, 0.52765521, 0.54078442, 0.5246824 , 0.53538894,\n",
-       "        0.54072892, 0.57977916, 0.51129132, 0.50734774, 0.52728755,\n",
-       "        0.51537562, 0.51687298, 0.5657848 , 0.56909803, 0.53805612,\n",
-       "        0.53230628, 0.56231002, 0.58054123, 0.5386571 , 0.52733389,\n",
+       "        2.16366324, 2.06333972, 2.03461789, 1.99466549, 2.09804749,\n",
+       "        2.0433769 , 2.11547635, 1.9601239 , 2.04000006, 1.95112803,\n",
+       "        2.08672939, 2.10277313, 2.10095218, 2.05878697, 2.01812262,\n",
+       "        2.09372884, 2.03944724, 2.18670034, 2.12210964, 2.04451422,\n",
+       "        2.06763441, 2.08033455, 2.04751905, 2.05118917, 2.09271084,\n",
+       "        2.00939006, 2.01110284, 1.93207635, 2.04025252, 2.1015311 ,\n",
+       "        2.11630368, 1.99480337, 2.1763338 , 2.15909798, 2.06173198,\n",
+       "        2.08419049, 1.99875715, 1.99260581, 1.92716922, 2.01799548,\n",
+       "        1.9813644 , 2.05710098, 2.04826101, 2.12746496, 2.02476532,\n",
+       "        2.1198507 , 2.05308121, 2.08118358, 2.12051447, 2.14229714,\n",
+       "        1.98819464, 2.00681759, 2.05704726, 2.06976601, 1.949699  ,\n",
+       "        2.0772595 , 2.07101476, 2.03414956, 2.02115201, 2.05119083,\n",
+       "        2.03539997, 2.10326917, 1.99024013, 2.01666225, 1.9528098 ,\n",
+       "        2.05274253, 2.07347499, 2.08924441, 2.01396725, 1.9728478 ,\n",
+       "        2.0199168 , 2.04944924, 2.01315438, 2.12937937, 1.98690715,\n",
+       "        1.97354247, 2.17909435, 2.01715712, 2.1046337 , 2.17286504,\n",
+       "        2.04530992, 2.00698557, 2.03221988, 2.0077802 , 2.07942115,\n",
+       "        2.12130684, 2.00226179, 1.99851678, 2.16660079, 2.09095054,\n",
+       "        1.85413116, 2.12100762, 2.17219544, 2.07908154, 1.97044574,\n",
+       "        2.05723013, 2.00969075, 1.98145972, 2.07979321, 2.03796762]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.475 0.4933 ... 0.5156 0.5347</div><input id='attrs-ee5307ea-4362-4c73-bf5c-1ebedf592aa2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee5307ea-4362-4c73-bf5c-1ebedf592aa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb7a9532-68b9-4085-8916-b37a43040084' class='xr-var-data-in' type='checkbox'><label for='data-cb7a9532-68b9-4085-8916-b37a43040084' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.47503679, 0.4932745 , 0.58243867, 0.43098883, 0.5092622 ,\n",
+       "        0.6026603 , 0.51874833, 0.51616572, 0.5847336 , 0.52212259,\n",
+       "        0.54206577, 0.53455176, 0.49366069, 0.54688721, 0.52348032,\n",
+       "        0.55420135, 0.51892887, 0.55550875, 0.42373603, 0.50807845,\n",
+       "        0.52244441, 0.55280442, 0.49893306, 0.60826132, 0.45691609,\n",
+       "        0.5260236 , 0.48707692, 0.54682035, 0.52577162, 0.53837948,\n",
+       "        0.52147195, 0.54084623, 0.51215806, 0.51878635, 0.47528613,\n",
+       "        0.5355799 , 0.54462882, 0.57700036, 0.55909726, 0.57182524,\n",
+       "        0.60242584, 0.50182916, 0.48555848, 0.5376337 , 0.54148354,\n",
+       "        0.52600748, 0.53511096, 0.49970305, 0.50040006, 0.56514149,\n",
+       "        0.50739752, 0.48742087, 0.55105439, 0.49947908, 0.5249142 ,\n",
+       "        0.4790983 , 0.48788332, 0.50662933, 0.44893267, 0.52123849,\n",
+       "        0.59628876, 0.52515537, 0.54606397, 0.4715505 , 0.52469273,\n",
+       "        0.47549403, 0.55734858, 0.52251945, 0.50659575, 0.52152499,\n",
+       "        0.47411978, 0.55515501, 0.50870011, 0.49874491, 0.54898505,\n",
+       "        0.4881043 , 0.55020955, 0.50980945, 0.52116609, 0.54679363,\n",
+       "        0.49601086, 0.53307073, 0.49289802, 0.55470811, 0.53136678,\n",
+       "        0.44993329, 0.54151465, 0.54108359, 0.50567858, 0.49680954,\n",
+       "        0.51624026, 0.55586116, 0.49787488, 0.58215495, 0.50713456,\n",
+       "        0.55819803, 0.44617425, 0.48033557, 0.49389815, 0.48877765,\n",
        "...\n",
-       "        0.51002185, 0.52671278, 0.55330783, 0.54956578, 0.48170754,\n",
-       "        0.52523513, 0.50683404, 0.53390153, 0.53196713, 0.5088871 ,\n",
-       "        0.54630742, 0.51664701, 0.5267468 , 0.47639725, 0.55933856,\n",
-       "        0.54263262, 0.5265029 , 0.53952616, 0.57484308, 0.55091962,\n",
-       "        0.52467183, 0.54330671, 0.51847638, 0.52217469, 0.55500906,\n",
-       "        0.59339147, 0.56652856, 0.55112975, 0.54379306, 0.53063737,\n",
-       "        0.50080311, 0.49916256, 0.4686819 , 0.51950147, 0.51119591,\n",
-       "        0.53359993, 0.52232431, 0.525242  , 0.56194112, 0.51220094,\n",
-       "        0.55890824, 0.54051912, 0.50781342, 0.55823819, 0.5425651 ,\n",
-       "        0.53192567, 0.53188257, 0.57019311, 0.52980983, 0.54080906,\n",
-       "        0.49986879, 0.58095159, 0.53010561, 0.53766203, 0.49737649,\n",
-       "        0.53047298, 0.54496468, 0.53772425, 0.55048247, 0.53958187,\n",
-       "        0.56532771, 0.52216727, 0.52190955, 0.5172137 , 0.49751614,\n",
-       "        0.53719759, 0.53703491, 0.53174331, 0.5282638 , 0.48961309,\n",
-       "        0.52261922, 0.56817977, 0.562544  , 0.54562333, 0.51747933,\n",
-       "        0.51146554, 0.52985844, 0.5494802 , 0.56405881, 0.53818859,\n",
-       "        0.54697677, 0.52404384, 0.57143866, 0.54777914, 0.52836731,\n",
-       "        0.58833479, 0.52167064, 0.53627105, 0.49238545, 0.52246454,\n",
-       "        0.51645733, 0.56248672, 0.5043927 , 0.53806378, 0.58094705,\n",
-       "        0.55577484, 0.55521349, 0.48293466, 0.54412583, 0.49516182]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c9f139b-6642-487d-a3a8-d721f59bcdd6' class='xr-section-summary-in' type='checkbox'  ><label for='section-4c9f139b-6642-487d-a3a8-d721f59bcdd6' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c759756a-d462-4969-81cc-d49014499794' class='xr-index-data-in' type='checkbox'/><label for='index-c759756a-d462-4969-81cc-d49014499794' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-337053fa-dc27-4edc-80e3-8a931c6afb32' class='xr-index-data-in' type='checkbox'/><label for='index-337053fa-dc27-4edc-80e3-8a931c6afb32' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "        0.51116898, 0.54979073, 0.4940003 , 0.50454278, 0.53792363,\n",
+       "        0.44837046, 0.5429691 , 0.57230746, 0.49581106, 0.53178167,\n",
+       "        0.56763811, 0.52561527, 0.51928586, 0.59468213, 0.53756113,\n",
+       "        0.49613791, 0.52049194, 0.52327519, 0.47653091, 0.52433866,\n",
+       "        0.59347927, 0.52014275, 0.49106378, 0.48659228, 0.54741349,\n",
+       "        0.57075592, 0.45107388, 0.53436195, 0.5324162 , 0.49076083,\n",
+       "        0.54236723, 0.46593185, 0.54327047, 0.5397138 , 0.53886672,\n",
+       "        0.53677111, 0.53949954, 0.53020623, 0.51105563, 0.4765418 ,\n",
+       "        0.50467066, 0.4431123 , 0.49631762, 0.53532801, 0.46056312,\n",
+       "        0.60505812, 0.56612181, 0.52686561, 0.56007317, 0.52408584,\n",
+       "        0.51691378, 0.47073902, 0.52766416, 0.60988229, 0.50667505,\n",
+       "        0.57054829, 0.54906455, 0.5193653 , 0.46989256, 0.45080155,\n",
+       "        0.57944439, 0.53250615, 0.49281844, 0.54773989, 0.50528816,\n",
+       "        0.52144446, 0.47748306, 0.59299112, 0.49101675, 0.54955619,\n",
+       "        0.54990187, 0.58604861, 0.51901846, 0.55754382, 0.56902047,\n",
+       "        0.56276662, 0.6054205 , 0.55363626, 0.5249528 , 0.4980976 ,\n",
+       "        0.49589476, 0.51132459, 0.53115181, 0.53104795, 0.51579533,\n",
+       "        0.55112247, 0.53068207, 0.55084148, 0.50925099, 0.56981815,\n",
+       "        0.55675414, 0.50205612, 0.47738315, 0.5990464 , 0.51243009,\n",
+       "        0.50482366, 0.53225564, 0.52565307, 0.51562967, 0.53474016]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7172c10e-4b3a-423b-89a7-9046c027ba0f' class='xr-section-summary-in' type='checkbox'  ><label for='section-7172c10e-4b3a-423b-89a7-9046c027ba0f' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8efcefd6-b990-4256-94ed-7cf10603debf' class='xr-index-data-in' type='checkbox'/><label for='index-8efcefd6-b990-4256-94ed-7cf10603debf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d3dce473-84a3-4aa4-a8ab-befcacf02aed' class='xr-index-data-in' type='checkbox'/><label for='index-d3dce473-84a3-4aa4-a8ab-befcacf02aed' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a981a3f3-78e9-48d7-aa42-fcbe7efaa939' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a981a3f3-78e9-48d7-aa42-fcbe7efaa939' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-08-14T09:59:22.382706+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd></dl></div></li></ul></div></div><br></div>\n",
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+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-96870f76-3479-4976-a285-84b5ca4638d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-96870f76-3479-4976-a285-84b5ca4638d4' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-08-19T17:50:03.733248+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.25.1</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "Inference data with groups:\n",
-       "\t> posterior"
+       "<xarray.Dataset> Size: 32kB\n",
+       "Dimensions:    (chain: 1, draw: 1000)\n",
+       "Coordinates:\n",
+       "  * chain      (chain) int64 8B 0\n",
+       "  * draw       (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
+       "Data variables:\n",
+       "    Intercept  (chain, draw) float64 8kB 1.033 1.054 1.07 ... 1.035 1.038 1.069\n",
+       "    slope      (chain, draw) float64 8kB 2.009 2.046 2.063 ... 1.981 2.08 2.038\n",
+       "    sigma      (chain, draw) float64 8kB 0.475 0.4933 0.5824 ... 0.5156 0.5347\n",
+       "Attributes:\n",
+       "    created_at:                 2025-08-19T17:50:03.733248+00:00\n",
+       "    arviz_version:              0.22.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.25.1"
       ]
      },
      "execution_count": 9,
@@ -1215,7 +883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "2d911a25-5970-4c30-bd2e-21aa0fa74217",
    "metadata": {},
    "outputs": [
@@ -1223,13 +891,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/s_/l9wd4yls3mv1f4kkkz1xhhmm0000gn/T/ipykernel_44223/2054181913.py:18: UserWarning: The figure layout has changed to tight\n",
+      "/var/folders/s_/l9wd4yls3mv1f4kkkz1xhhmm0000gn/T/ipykernel_26101/1628930406.py:18: UserWarning: The figure layout has changed to tight\n",
       "  f.tight_layout()\n"
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     {
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",
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",
       "text/plain": [
        "<Figure size 800x800 with 3 Axes>"
       ]
@@ -1237,7 +905,7 @@
      "metadata": {
       "image/png": {
        "height": 790,
-       "width": 790
+       "width": 808
       }
      },
      "output_type": "display_data"
@@ -1249,13 +917,13 @@
     "f, ax = plt.subplots(3, 1)\n",
     "\n",
     "sns.kdeplot(idata.posterior.Intercept.values.reshape(-1), ax=ax[0], label='MCMC')\n",
-    "sns.kdeplot(dadvi_res.posterior.Intercept.values.reshape(-1), ax=ax[0], label='DADVI')\n",
+    "sns.kdeplot(dadvi_res.Intercept.values.reshape(-1), ax=ax[0], label='DADVI')\n",
     "\n",
     "sns.kdeplot(idata.posterior.slope.values.reshape(-1), ax=ax[1], label='MCMC')\n",
-    "sns.kdeplot(dadvi_res.posterior.slope.values.reshape(-1), ax=ax[1], label='DADVI')\n",
+    "sns.kdeplot(dadvi_res.slope.values.reshape(-1), ax=ax[1], label='DADVI')\n",
     "\n",
     "sns.kdeplot(idata.posterior.sigma.values.reshape(-1), ax=ax[2], label='MCMC')\n",
-    "sns.kdeplot(dadvi_res.posterior.sigma.values.reshape(-1), ax=ax[2], label='DADVI')\n",
+    "sns.kdeplot(dadvi_res.sigma.values.reshape(-1), ax=ax[2], label='DADVI')\n",
     "\n",
     "for cur_ax in ax:\n",
     "    cur_ax.legend()\n",

From 9ab2e1ea882c0d9e1000498b6d225a21df38081d Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@gmail.com>
Date: Wed, 20 Aug 2025 12:41:03 +0200
Subject: [PATCH 15/28] Update
 pymc_extras/inference/deterministic_advi/dadvi.py

Co-authored-by: Ricardo Vieira <28983449+ricardoV94@users.noreply.github.com>
---
 pymc_extras/inference/deterministic_advi/dadvi.py | 11 +++--------
 1 file changed, 3 insertions(+), 8 deletions(-)

diff --git a/pymc_extras/inference/deterministic_advi/dadvi.py b/pymc_extras/inference/deterministic_advi/dadvi.py
index c8d5d6a2d..2db7f8aab 100644
--- a/pymc_extras/inference/deterministic_advi/dadvi.py
+++ b/pymc_extras/inference/deterministic_advi/dadvi.py
@@ -160,16 +160,11 @@ def create_dadvi_graph(
     means = var_params[:n_params]
     log_sds = var_params[n_params:]
 
-    draw = pt.vector(name="draw", shape=(n_params,))
-    sample = means + pt.exp(log_sds) * draw
-
-    # Graph in terms of a single sample
-    logp_draw = pytensor.clone_replace(logp, replace={flat_input: sample})
     draw_matrix = pt.constant(draws)
-
-    # Vectorise
+    samples = means + pt.exp(log_sds) * draw_matrix
+    
     logp_vectorized_draws = pytensor.graph.vectorize_graph(
-        logp_draw, replace={draw: draw_matrix}
+        logp, replace={flat_input: samples}
     )
 
     mean_log_density = pt.mean(logp_vectorized_draws)

From a8a53f353f88901b33bc3d692976b1fbafcbee49 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Wed, 20 Aug 2025 12:55:06 +0200
Subject: [PATCH 16/28] Respond to comments

---
 .../inference/deterministic_advi/dadvi.py      | 18 ++++++++++--------
 pyproject.toml                                 |  1 -
 2 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/pymc_extras/inference/deterministic_advi/dadvi.py b/pymc_extras/inference/deterministic_advi/dadvi.py
index 2db7f8aab..cff742cff 100644
--- a/pymc_extras/inference/deterministic_advi/dadvi.py
+++ b/pymc_extras/inference/deterministic_advi/dadvi.py
@@ -12,7 +12,7 @@
 import xarray
 
 from pymc import join_nonshared_inputs, DictToArrayBijection
-from pymc.util import get_default_varnames
+from pymc.util import get_default_varnames, RandomSeed
 from pymc.backends.arviz import (
     apply_function_over_dataset,
     PointFunc,
@@ -27,10 +27,10 @@
 def fit_deterministic_advi(
     model: Optional[Model] = None,
     n_fixed_draws: int = 30,
-    random_seed: int = 2,
+    random_seed: RandomSeed = None,
     n_draws: int = 1000,
     keep_untransformed: bool = False,
-):
+) -> az.InferenceData:
     """
     Does inference using deterministic ADVI (automatic differentiation
     variational inference).
@@ -101,7 +101,9 @@ def fit_deterministic_advi(
     opt_means, opt_log_sds = np.split(opt_var_params, 2)
 
     # Make the draws:
-    draws_raw = np.random.randn(n_draws, n_params)
+    generator = np.random.default_rng(seed=random_seed)
+    draws_raw = generator.standard_normal(size=(n_draws, n_params))
+
     draws = opt_means + draws_raw * np.exp(opt_log_sds)
     draws_arviz = unstack_laplace_draws(draws, model, chains=1, draws=n_draws)
 
@@ -116,7 +118,7 @@ def create_dadvi_graph(
     model: Model,
     n_params: int,
     n_fixed_draws: int = 30,
-    random_seed: int = 2,
+    random_seed: RandomSeed = None,
 ) -> Tuple[TensorVariable, TensorVariable]:
     """
     Sets up the DADVI graph in pytensor and returns it.
@@ -143,8 +145,8 @@ def create_dadvi_graph(
     """
 
     # Make the fixed draws
-    state = np.random.RandomState(random_seed)
-    draws = state.randn(n_fixed_draws, n_params)
+    generator = np.random.default_rng(seed=random_seed)
+    draws = generator.standard_normal(size=(n_fixed_draws, n_params))
 
     inputs = model.continuous_value_vars + model.discrete_value_vars
     initial_point_dict = model.initial_point()
@@ -162,7 +164,7 @@ def create_dadvi_graph(
 
     draw_matrix = pt.constant(draws)
     samples = means + pt.exp(log_sds) * draw_matrix
-    
+
     logp_vectorized_draws = pytensor.graph.vectorize_graph(
         logp, replace={flat_input: samples}
     )
diff --git a/pyproject.toml b/pyproject.toml
index 963258f87..c7bc7e325 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,7 +40,6 @@ dependencies = [
   "better-optimize>=0.1.5",
   "pydantic>=2.0.0",
   "preliz>=0.20.0",
-  "jax>=0.7.0"
 ]
 
 [project.optional-dependencies]

From bdee4468ef5cef32a6c66868be9c4e4759371d21 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Wed, 20 Aug 2025 13:35:43 +0200
Subject: [PATCH 17/28] Fix with pre commit checks

---
 notebooks/deterministic_advi_example.ipynb    | 14 ++++----
 pymc_extras/inference/__init__.py             |  2 +-
 .../inference/deterministic_advi/dadvi.py     | 35 +++++++------------
 3 files changed, 21 insertions(+), 30 deletions(-)

diff --git a/notebooks/deterministic_advi_example.ipynb b/notebooks/deterministic_advi_example.ipynb
index ec1f40a9f..0629f0098 100644
--- a/notebooks/deterministic_advi_example.ipynb
+++ b/notebooks/deterministic_advi_example.ipynb
@@ -65,7 +65,7 @@
     "rng = np.random.default_rng(RANDOM_SEED)\n",
     "\n",
     "%config InlineBackend.figure_format = 'retina'\n",
-    "az.style.use(\"arviz-darkgrid\")\n"
+    "az.style.use(\"arviz-darkgrid\")"
    ]
   },
   {
@@ -916,14 +916,14 @@
     "\n",
     "f, ax = plt.subplots(3, 1)\n",
     "\n",
-    "sns.kdeplot(idata.posterior.Intercept.values.reshape(-1), ax=ax[0], label='MCMC')\n",
-    "sns.kdeplot(dadvi_res.Intercept.values.reshape(-1), ax=ax[0], label='DADVI')\n",
+    "sns.kdeplot(idata.posterior.Intercept.values.reshape(-1), ax=ax[0], label=\"MCMC\")\n",
+    "sns.kdeplot(dadvi_res.Intercept.values.reshape(-1), ax=ax[0], label=\"DADVI\")\n",
     "\n",
-    "sns.kdeplot(idata.posterior.slope.values.reshape(-1), ax=ax[1], label='MCMC')\n",
-    "sns.kdeplot(dadvi_res.slope.values.reshape(-1), ax=ax[1], label='DADVI')\n",
+    "sns.kdeplot(idata.posterior.slope.values.reshape(-1), ax=ax[1], label=\"MCMC\")\n",
+    "sns.kdeplot(dadvi_res.slope.values.reshape(-1), ax=ax[1], label=\"DADVI\")\n",
     "\n",
-    "sns.kdeplot(idata.posterior.sigma.values.reshape(-1), ax=ax[2], label='MCMC')\n",
-    "sns.kdeplot(dadvi_res.sigma.values.reshape(-1), ax=ax[2], label='DADVI')\n",
+    "sns.kdeplot(idata.posterior.sigma.values.reshape(-1), ax=ax[2], label=\"MCMC\")\n",
+    "sns.kdeplot(dadvi_res.sigma.values.reshape(-1), ax=ax[2], label=\"DADVI\")\n",
     "\n",
     "for cur_ax in ax:\n",
     "    cur_ax.legend()\n",
diff --git a/pymc_extras/inference/__init__.py b/pymc_extras/inference/__init__.py
index 39b0a90e7..18cf7468e 100644
--- a/pymc_extras/inference/__init__.py
+++ b/pymc_extras/inference/__init__.py
@@ -12,11 +12,11 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
+from pymc_extras.inference.deterministic_advi.dadvi import fit_deterministic_advi
 from pymc_extras.inference.fit import fit
 from pymc_extras.inference.laplace_approx.find_map import find_MAP
 from pymc_extras.inference.laplace_approx.laplace import fit_laplace
 from pymc_extras.inference.pathfinder.pathfinder import fit_pathfinder
-from pymc_extras.inference.deterministic_advi.dadvi import fit_deterministic_advi
 
 __all__ = [
     "find_MAP",
diff --git a/pymc_extras/inference/deterministic_advi/dadvi.py b/pymc_extras/inference/deterministic_advi/dadvi.py
index cff742cff..24f686140 100644
--- a/pymc_extras/inference/deterministic_advi/dadvi.py
+++ b/pymc_extras/inference/deterministic_advi/dadvi.py
@@ -1,31 +1,28 @@
-from collections import defaultdict
-from typing import Tuple, Optional
-
-import pymc
-from pymc import Model
 import arviz as az
 import numpy as np
-from scipy.optimize import minimize
+import pymc
 import pytensor
 import pytensor.tensor as pt
-from pytensor.tensor.variable import TensorVariable
 import xarray
 
-from pymc import join_nonshared_inputs, DictToArrayBijection
-from pymc.util import get_default_varnames, RandomSeed
+from pymc import DictToArrayBijection, Model, join_nonshared_inputs
 from pymc.backends.arviz import (
-    apply_function_over_dataset,
     PointFunc,
+    apply_function_over_dataset,
     coords_and_dims_for_inferencedata,
 )
+from pymc.util import RandomSeed, get_default_varnames
+from pytensor.tensor.variable import TensorVariable
+from scipy.optimize import minimize
+
+from pymc_extras.inference.laplace_approx.laplace import unstack_laplace_draws
 from pymc_extras.inference.laplace_approx.scipy_interface import (
     _compile_functions_for_scipy_optimize,
 )
-from pymc_extras.inference.laplace_approx.laplace import unstack_laplace_draws
 
 
 def fit_deterministic_advi(
-    model: Optional[Model] = None,
+    model: Model | None = None,
     n_fixed_draws: int = 30,
     random_seed: RandomSeed = None,
     n_draws: int = 1000,
@@ -93,9 +90,7 @@ def fit_deterministic_advi(
         compute_hess=False,
     )
 
-    result = minimize(
-        f_fused, np.zeros(2 * n_params), method="trust-ncg", jac=True, hessp=f_hessp
-    )
+    result = minimize(f_fused, np.zeros(2 * n_params), method="trust-ncg", jac=True, hessp=f_hessp)
 
     opt_var_params = result.x
     opt_means, opt_log_sds = np.split(opt_var_params, 2)
@@ -107,9 +102,7 @@ def fit_deterministic_advi(
     draws = opt_means + draws_raw * np.exp(opt_log_sds)
     draws_arviz = unstack_laplace_draws(draws, model, chains=1, draws=n_draws)
 
-    transformed_draws = transform_draws(
-        draws_arviz, model, keep_untransformed=keep_untransformed
-    )
+    transformed_draws = transform_draws(draws_arviz, model, keep_untransformed=keep_untransformed)
 
     return transformed_draws
 
@@ -119,7 +112,7 @@ def create_dadvi_graph(
     n_params: int,
     n_fixed_draws: int = 30,
     random_seed: RandomSeed = None,
-) -> Tuple[TensorVariable, TensorVariable]:
+) -> tuple[TensorVariable, TensorVariable]:
     """
     Sets up the DADVI graph in pytensor and returns it.
 
@@ -165,9 +158,7 @@ def create_dadvi_graph(
     draw_matrix = pt.constant(draws)
     samples = means + pt.exp(log_sds) * draw_matrix
 
-    logp_vectorized_draws = pytensor.graph.vectorize_graph(
-        logp, replace={flat_input: samples}
-    )
+    logp_vectorized_draws = pytensor.graph.vectorize_graph(logp, replace={flat_input: samples})
 
     mean_log_density = pt.mean(logp_vectorized_draws)
     entropy = pt.sum(log_sds)

From 3fcafb6b64fabe89d8a89a4ccd7bd00bf24c8740 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@gmail.com>
Date: Thu, 28 Aug 2025 09:48:40 +0200
Subject: [PATCH 18/28] Update
 pymc_extras/inference/deterministic_advi/dadvi.py

Co-authored-by: Jesse Grabowski <48652735+jessegrabowski@users.noreply.github.com>
---
 pymc_extras/inference/deterministic_advi/dadvi.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/pymc_extras/inference/deterministic_advi/dadvi.py b/pymc_extras/inference/deterministic_advi/dadvi.py
index 24f686140..b96855449 100644
--- a/pymc_extras/inference/deterministic_advi/dadvi.py
+++ b/pymc_extras/inference/deterministic_advi/dadvi.py
@@ -152,8 +152,7 @@ def create_dadvi_graph(
 
     var_params = pt.vector(name="eta", shape=(2 * n_params,))
 
-    means = var_params[:n_params]
-    log_sds = var_params[n_params:]
+    means , log_sds= pt.split(var_params, 2)
 
     draw_matrix = pt.constant(draws)
     samples = means + pt.exp(log_sds) * draw_matrix

From ad46b07c8831ce82dd63cb4a56870d7eee096054 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Thu, 28 Aug 2025 11:09:03 +0200
Subject: [PATCH 19/28] Implement suggestions

---
 pymc_extras/inference/__init__.py             |  4 +--
 .../{deterministic_advi => dadvi}/__init__.py |  0
 .../{deterministic_advi => dadvi}/dadvi.py    | 32 ++++++++++++++++---
 pymc_extras/inference/fit.py                  |  4 +--
 4 files changed, 31 insertions(+), 9 deletions(-)
 rename pymc_extras/inference/{deterministic_advi => dadvi}/__init__.py (100%)
 rename pymc_extras/inference/{deterministic_advi => dadvi}/dadvi.py (86%)

diff --git a/pymc_extras/inference/__init__.py b/pymc_extras/inference/__init__.py
index 18cf7468e..f8d7808c7 100644
--- a/pymc_extras/inference/__init__.py
+++ b/pymc_extras/inference/__init__.py
@@ -12,7 +12,7 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-from pymc_extras.inference.deterministic_advi.dadvi import fit_deterministic_advi
+from pymc_extras.inference.dadvi.dadvi import fit_dadvi
 from pymc_extras.inference.fit import fit
 from pymc_extras.inference.laplace_approx.find_map import find_MAP
 from pymc_extras.inference.laplace_approx.laplace import fit_laplace
@@ -23,5 +23,5 @@
     "fit",
     "fit_laplace",
     "fit_pathfinder",
-    "fit_deterministic_advi",
+    "fit_dadvi",
 ]
diff --git a/pymc_extras/inference/deterministic_advi/__init__.py b/pymc_extras/inference/dadvi/__init__.py
similarity index 100%
rename from pymc_extras/inference/deterministic_advi/__init__.py
rename to pymc_extras/inference/dadvi/__init__.py
diff --git a/pymc_extras/inference/deterministic_advi/dadvi.py b/pymc_extras/inference/dadvi/dadvi.py
similarity index 86%
rename from pymc_extras/inference/deterministic_advi/dadvi.py
rename to pymc_extras/inference/dadvi/dadvi.py
index b96855449..590f2d7f1 100644
--- a/pymc_extras/inference/deterministic_advi/dadvi.py
+++ b/pymc_extras/inference/dadvi/dadvi.py
@@ -5,6 +5,7 @@
 import pytensor.tensor as pt
 import xarray
 
+from better_optimize.constants import minimize_method
 from pymc import DictToArrayBijection, Model, join_nonshared_inputs
 from pymc.backends.arviz import (
     PointFunc,
@@ -21,16 +22,18 @@
 )
 
 
-def fit_deterministic_advi(
+def fit_dadvi(
     model: Model | None = None,
     n_fixed_draws: int = 30,
     random_seed: RandomSeed = None,
     n_draws: int = 1000,
     keep_untransformed: bool = False,
+    method: minimize_method = "trust-ncg",
+    **minimize_kwargs,
 ) -> az.InferenceData:
     """
     Does inference using deterministic ADVI (automatic differentiation
-    variational inference).
+    variational inference), DADVI for short.
 
     For full details see the paper cited in the references:
     https://www.jmlr.org/papers/v25/23-1015.html
@@ -57,6 +60,19 @@ def fit_deterministic_advi(
         Whether or not to keep the unconstrained variables (such as
         logs of positive-constrained parameters) in the output.
 
+    method: str
+        Which optimization method to use. The function calls
+        ``scipy.optimize.minimize``, so any of the methods there can
+        be used. The default is trust-ncg, which uses second-order
+        information and is generally very reliable. Other methods such
+        as L-BFGS-B might be faster but potentially more brittle and
+        may not converge exactly to the optimum.
+
+    minimize_kwargs:
+        Additional keyword arguments to pass to the
+        ``scipy.optimize.minimize`` function. See the documentation of
+        that function for details.
+
     Returns
     -------
     :class:`~arviz.InferenceData`
@@ -90,7 +106,14 @@ def fit_deterministic_advi(
         compute_hess=False,
     )
 
-    result = minimize(f_fused, np.zeros(2 * n_params), method="trust-ncg", jac=True, hessp=f_hessp)
+    result = minimize(
+        f_fused,
+        np.zeros(2 * n_params),
+        method=method,
+        jac=True,
+        hessp=f_hessp,
+        **minimize_kwargs,
+    )
 
     opt_var_params = result.x
     opt_means, opt_log_sds = np.split(opt_var_params, 2)
@@ -151,8 +174,7 @@ def create_dadvi_graph(
     )
 
     var_params = pt.vector(name="eta", shape=(2 * n_params,))
-
-    means , log_sds= pt.split(var_params, 2)
+    means, log_sds = var_params[:n_params], var_params[n_params:]
 
     draw_matrix = pt.constant(draws)
     samples = means + pt.exp(log_sds) * draw_matrix
diff --git a/pymc_extras/inference/fit.py b/pymc_extras/inference/fit.py
index 68cbe7acb..7aae11774 100644
--- a/pymc_extras/inference/fit.py
+++ b/pymc_extras/inference/fit.py
@@ -42,6 +42,6 @@ def fit(method: str, **kwargs) -> az.InferenceData:
         return fit_laplace(**kwargs)
 
     if method == "deterministic_advi":
-        from pymc_extras.inference import fit_deterministic_advi
+        from pymc_extras.inference import fit_dadvi
 
-        return fit_deterministic_advi(**kwargs)
+        return fit_dadvi(**kwargs)

From 6cd0184eaec62304f89726efffe6991693010685 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Thu, 28 Aug 2025 11:10:14 +0200
Subject: [PATCH 20/28] Rename parameter because it's duplicated otherwise

---
 pymc_extras/inference/dadvi/dadvi.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pymc_extras/inference/dadvi/dadvi.py b/pymc_extras/inference/dadvi/dadvi.py
index 590f2d7f1..50bd68488 100644
--- a/pymc_extras/inference/dadvi/dadvi.py
+++ b/pymc_extras/inference/dadvi/dadvi.py
@@ -28,7 +28,7 @@ def fit_dadvi(
     random_seed: RandomSeed = None,
     n_draws: int = 1000,
     keep_untransformed: bool = False,
-    method: minimize_method = "trust-ncg",
+    opt_method: minimize_method = "trust-ncg",
     **minimize_kwargs,
 ) -> az.InferenceData:
     """
@@ -60,7 +60,7 @@ def fit_dadvi(
         Whether or not to keep the unconstrained variables (such as
         logs of positive-constrained parameters) in the output.
 
-    method: str
+    opt_method: str
         Which optimization method to use. The function calls
         ``scipy.optimize.minimize``, so any of the methods there can
         be used. The default is trust-ncg, which uses second-order
@@ -109,7 +109,7 @@ def fit_dadvi(
     result = minimize(
         f_fused,
         np.zeros(2 * n_params),
-        method=method,
+        method=opt_method,
         jac=True,
         hessp=f_hessp,
         **minimize_kwargs,

From d6481057f80f5f7822862a2ffae284f3a262d757 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Thu, 28 Aug 2025 11:10:38 +0200
Subject: [PATCH 21/28] Rename to be consistent in use of dadvi

---
 pymc_extras/inference/fit.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pymc_extras/inference/fit.py b/pymc_extras/inference/fit.py
index 7aae11774..4be59b2b3 100644
--- a/pymc_extras/inference/fit.py
+++ b/pymc_extras/inference/fit.py
@@ -41,7 +41,7 @@ def fit(method: str, **kwargs) -> az.InferenceData:
 
         return fit_laplace(**kwargs)
 
-    if method == "deterministic_advi":
+    if method == "dadvi":
         from pymc_extras.inference import fit_dadvi
 
         return fit_dadvi(**kwargs)

From 9d18f807ed7aba7984fc33d6bd7eb4c653cd8002 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Thu, 28 Aug 2025 15:20:28 +0200
Subject: [PATCH 22/28] Rename to `optimizer_method` and drop jac=True

---
 pymc_extras/inference/dadvi/dadvi.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/pymc_extras/inference/dadvi/dadvi.py b/pymc_extras/inference/dadvi/dadvi.py
index 50bd68488..0eef6f22c 100644
--- a/pymc_extras/inference/dadvi/dadvi.py
+++ b/pymc_extras/inference/dadvi/dadvi.py
@@ -28,7 +28,7 @@ def fit_dadvi(
     random_seed: RandomSeed = None,
     n_draws: int = 1000,
     keep_untransformed: bool = False,
-    opt_method: minimize_method = "trust-ncg",
+    optimizer_method: minimize_method = "trust-ncg",
     **minimize_kwargs,
 ) -> az.InferenceData:
     """
@@ -60,7 +60,7 @@ def fit_dadvi(
         Whether or not to keep the unconstrained variables (such as
         logs of positive-constrained parameters) in the output.
 
-    opt_method: str
+    optimizer_method: str
         Which optimization method to use. The function calls
         ``scipy.optimize.minimize``, so any of the methods there can
         be used. The default is trust-ncg, which uses second-order
@@ -109,8 +109,7 @@ def fit_dadvi(
     result = minimize(
         f_fused,
         np.zeros(2 * n_params),
-        method=opt_method,
-        jac=True,
+        method=optimizer_method,
         hessp=f_hessp,
         **minimize_kwargs,
     )

From 9f86d4f581c6c52e9731e6750f223478202414fb Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Thu, 28 Aug 2025 15:21:19 +0200
Subject: [PATCH 23/28] Add jac=True back in since trust-ncg complained

---
 pymc_extras/inference/dadvi/dadvi.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pymc_extras/inference/dadvi/dadvi.py b/pymc_extras/inference/dadvi/dadvi.py
index 0eef6f22c..f832da66f 100644
--- a/pymc_extras/inference/dadvi/dadvi.py
+++ b/pymc_extras/inference/dadvi/dadvi.py
@@ -109,6 +109,7 @@ def fit_dadvi(
     result = minimize(
         f_fused,
         np.zeros(2 * n_params),
+        jac=True,
         method=optimizer_method,
         hessp=f_hessp,
         **minimize_kwargs,

From 3b090cad90e52902d624cf1d85a43714ea9d4c52 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Thu, 28 Aug 2025 18:13:11 +0200
Subject: [PATCH 24/28] Make hessp and jac optional

---
 pymc_extras/inference/dadvi/dadvi.py | 18 ++++++++++++++++--
 1 file changed, 16 insertions(+), 2 deletions(-)

diff --git a/pymc_extras/inference/dadvi/dadvi.py b/pymc_extras/inference/dadvi/dadvi.py
index f832da66f..2f52c81e7 100644
--- a/pymc_extras/inference/dadvi/dadvi.py
+++ b/pymc_extras/inference/dadvi/dadvi.py
@@ -29,6 +29,8 @@ def fit_dadvi(
     n_draws: int = 1000,
     keep_untransformed: bool = False,
     optimizer_method: minimize_method = "trust-ncg",
+    use_jacobian: bool = True,
+    use_hessp: bool = True,
     **minimize_kwargs,
 ) -> az.InferenceData:
     """
@@ -73,6 +75,12 @@ def fit_dadvi(
         ``scipy.optimize.minimize`` function. See the documentation of
         that function for details.
 
+    use_jacobian:
+        If True, pass the Jacobian function to `scipy.optimize.minimize`.
+
+    use_hessp:
+        If True, pass the hessian vector product to `scipy.optimize.minimize`.
+
     Returns
     -------
     :class:`~arviz.InferenceData`
@@ -106,12 +114,18 @@ def fit_dadvi(
         compute_hess=False,
     )
 
+    derivative_kwargs = {}
+
+    if use_jacobian:
+        derivative_kwargs["jac"] = True
+    if use_hessp:
+        derivative_kwargs["hessp"] = f_hessp
+
     result = minimize(
         f_fused,
         np.zeros(2 * n_params),
-        jac=True,
         method=optimizer_method,
-        hessp=f_hessp,
+        **derivative_kwargs,
         **minimize_kwargs,
     )
 

From 93cd8318de596ad91f94da22bec031c752e1d52d Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Thu, 28 Aug 2025 18:15:15 +0200
Subject: [PATCH 25/28] Harmonize naming with existing code

---
 pymc_extras/inference/dadvi/dadvi.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/pymc_extras/inference/dadvi/dadvi.py b/pymc_extras/inference/dadvi/dadvi.py
index 2f52c81e7..80eee66ca 100644
--- a/pymc_extras/inference/dadvi/dadvi.py
+++ b/pymc_extras/inference/dadvi/dadvi.py
@@ -29,7 +29,7 @@ def fit_dadvi(
     n_draws: int = 1000,
     keep_untransformed: bool = False,
     optimizer_method: minimize_method = "trust-ncg",
-    use_jacobian: bool = True,
+    use_grad: bool = True,
     use_hessp: bool = True,
     **minimize_kwargs,
 ) -> az.InferenceData:
@@ -75,8 +75,9 @@ def fit_dadvi(
         ``scipy.optimize.minimize`` function. See the documentation of
         that function for details.
 
-    use_jacobian:
-        If True, pass the Jacobian function to `scipy.optimize.minimize`.
+    use_grad:
+        If True, pass the gradient function to
+        `scipy.optimize.minimize` (where it is referred to as `jac`).
 
     use_hessp:
         If True, pass the hessian vector product to `scipy.optimize.minimize`.
@@ -116,7 +117,7 @@ def fit_dadvi(
 
     derivative_kwargs = {}
 
-    if use_jacobian:
+    if use_grad:
         derivative_kwargs["jac"] = True
     if use_hessp:
         derivative_kwargs["hessp"] = f_hessp

From 7b8487294d9ae98f5c40793c7947030b79188898 Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Fri, 29 Aug 2025 11:17:38 +0200
Subject: [PATCH 26/28] Fix example

---
 notebooks/deterministic_advi_example.ipynb | 278 ++++++++++-----------
 1 file changed, 139 insertions(+), 139 deletions(-)

diff --git a/notebooks/deterministic_advi_example.ipynb b/notebooks/deterministic_advi_example.ipynb
index 0629f0098..0cf1d177c 100644
--- a/notebooks/deterministic_advi_example.ipynb
+++ b/notebooks/deterministic_advi_example.ipynb
@@ -136,7 +136,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6ee428103b2349c88e63b57d15f04646",
+       "model_id": "ca1af33594e84a2db519d729405704f2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -174,7 +174,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 1 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 3_000 draw iterations (4_000 + 12_000 draws total) took 2 seconds.\n"
      ]
     }
    ],
@@ -203,25 +203,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "b18297f4-6592-4afb-a1e3-364b81039e8d",
    "metadata": {},
    "outputs": [],
    "source": [
     "# This single import should be all that's needed\n",
-    "from pymc_extras.inference import fit_deterministic_advi"
+    "from pymc_extras.inference import fit_dadvi"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "dc6e3943-cd0d-4c66-a177-6e5f2a34c5a1",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "15730952a26c4ff0bcb302b0aecc4451",
+       "model_id": "498e10628ec243e7b595a5d05ad15a8e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -261,12 +261,12 @@
     "# to look different / remove it.\n",
     "# Note that unlike regular ADVI, deterministic ADVI converges automatically. This is one of its key advantages.\n",
     "with model:\n",
-    "    dadvi_res = fit_deterministic_advi()"
+    "    dadvi_res = fit_dadvi()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "c47cb209-907b-4873-a3bf-39bff5578502",
    "metadata": {},
    "outputs": [
@@ -722,137 +722,137 @@
        "  * chain      (chain) int64 8B 0\n",
        "  * draw       (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
        "Data variables:\n",
-       "    Intercept  (chain, draw) float64 8kB 1.033 1.054 1.07 ... 1.035 1.038 1.069\n",
-       "    slope      (chain, draw) float64 8kB 2.009 2.046 2.063 ... 1.981 2.08 2.038\n",
-       "    sigma      (chain, draw) float64 8kB 0.475 0.4933 0.5824 ... 0.5156 0.5347\n",
+       "    Intercept  (chain, draw) float64 8kB 1.035 1.124 1.073 ... 1.072 1.056 1.044\n",
+       "    slope      (chain, draw) float64 8kB 2.135 2.02 2.016 ... 1.916 2.137 1.978\n",
+       "    sigma      (chain, draw) float64 8kB 0.5326 0.5372 0.4899 ... 0.5494 0.5225\n",
        "Attributes:\n",
-       "    created_at:                 2025-08-19T17:50:03.733248+00:00\n",
+       "    created_at:                 2025-08-29T09:17:23.403627+00:00\n",
        "    arviz_version:              0.22.0\n",
        "    inference_library:          pymc\n",
-       "    inference_library_version:  5.25.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6df1892b-ad7d-4181-82ee-6c86d6c27c80' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6df1892b-ad7d-4181-82ee-6c86d6c27c80' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f2df7f53-5659-4b1d-86fe-18dc5f84696d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f2df7f53-5659-4b1d-86fe-18dc5f84696d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-6a98b700-c7be-4622-964b-42a51feb15c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a98b700-c7be-4622-964b-42a51feb15c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f42733e0-c766-4833-9335-0b088fbd83fc' class='xr-var-data-in' type='checkbox'><label for='data-f42733e0-c766-4833-9335-0b088fbd83fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-6ae49c83-8b33-4c06-acee-51fde5e2fcba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ae49c83-8b33-4c06-acee-51fde5e2fcba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75f650ea-447c-4b0b-af94-b1574717898e' class='xr-var-data-in' type='checkbox'><label for='data-75f650ea-447c-4b0b-af94-b1574717898e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999], shape=(1000,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-694ec04b-0219-4055-ad80-c8e92fdb515d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-694ec04b-0219-4055-ad80-c8e92fdb515d' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.033 1.054 1.07 ... 1.038 1.069</div><input id='attrs-e0ed3696-5838-4486-b8f0-59d52060087d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0ed3696-5838-4486-b8f0-59d52060087d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a762b03-0093-4cda-8843-ac169e93067f' class='xr-var-data-in' type='checkbox'><label for='data-1a762b03-0093-4cda-8843-ac169e93067f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.03322021, 1.05371756, 1.06952535, 1.07168684, 1.10113775,\n",
-       "        0.99596338, 1.08933887, 1.02621925, 1.0846885 , 1.14191563,\n",
-       "        1.08141276, 1.02008058, 1.09879434, 0.98483523, 1.00136917,\n",
-       "        1.1421273 , 1.05671746, 1.00377814, 1.08699367, 1.03806556,\n",
-       "        1.08564975, 1.01925913, 1.08511609, 1.00545549, 1.12737332,\n",
-       "        1.05292982, 1.07612063, 1.11245937, 1.104032  , 1.02770992,\n",
-       "        1.12400587, 1.04376826, 1.0968951 , 1.08172164, 1.01715204,\n",
-       "        1.03459048, 1.09135703, 1.03417927, 1.07435218, 1.01259613,\n",
-       "        1.06599677, 1.04201867, 1.04630453, 1.03073456, 1.07489753,\n",
-       "        1.04607738, 1.06794489, 1.08054239, 1.04374861, 1.01499328,\n",
-       "        1.0370354 , 1.00320067, 1.01481523, 1.11833827, 1.02761816,\n",
-       "        1.07596896, 1.07424398, 1.11580837, 1.08791928, 1.06636238,\n",
-       "        0.9846451 , 1.05327208, 1.04472899, 1.05468312, 1.05978126,\n",
-       "        1.0555246 , 1.0502117 , 1.07206996, 1.09628804, 1.05996606,\n",
-       "        1.03362806, 1.00826373, 1.05455842, 1.0295575 , 1.08455588,\n",
-       "        1.04074644, 1.10437406, 1.06103914, 1.10619371, 0.99507688,\n",
-       "        1.0453811 , 1.01683386, 1.05807921, 1.04674656, 0.98074214,\n",
-       "        1.03583398, 1.06310479, 1.05814009, 1.09866709, 1.03418801,\n",
-       "        1.04885841, 1.06767458, 1.06239383, 1.0617037 , 1.05373531,\n",
-       "        1.02727192, 1.0820085 , 1.01152272, 1.01371037, 0.9933967 ,\n",
+       "    inference_library_version:  5.25.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-631fc9a1-c5a4-44c4-8b35-c38eb8d513f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-631fc9a1-c5a4-44c4-8b35-c38eb8d513f5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 1</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1439726c-811b-4211-ae7b-b13ee1f5bccd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1439726c-811b-4211-ae7b-b13ee1f5bccd' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-2e822d03-4a54-4895-9108-40b5c6e132e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e822d03-4a54-4895-9108-40b5c6e132e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb083b4e-5642-48bd-a027-0387fba70b55' class='xr-var-data-in' type='checkbox'><label for='data-bb083b4e-5642-48bd-a027-0387fba70b55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-0772daa3-eca1-45cf-8f82-9303fd9d7d41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0772daa3-eca1-45cf-8f82-9303fd9d7d41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1107e2c-c52d-498a-acb4-78883c5c4858' class='xr-var-data-in' type='checkbox'><label for='data-b1107e2c-c52d-498a-acb4-78883c5c4858' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999], shape=(1000,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f726f00-99bc-4945-98ee-e0645ef9f11d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f726f00-99bc-4945-98ee-e0645ef9f11d' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.035 1.124 1.073 ... 1.056 1.044</div><input id='attrs-6050140c-0e27-4f85-aea9-ed9f39313c7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6050140c-0e27-4f85-aea9-ed9f39313c7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0fefd73-bdb7-4843-ad62-3af67a5642bc' class='xr-var-data-in' type='checkbox'><label for='data-a0fefd73-bdb7-4843-ad62-3af67a5642bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.03450719, 1.12404185, 1.07284057, 1.0355449 , 1.05269621,\n",
+       "        1.06486973, 1.03567461, 1.01001655, 1.0779964 , 1.07547399,\n",
+       "        1.00593378, 1.06321878, 1.04291872, 1.03859156, 1.08286933,\n",
+       "        1.07476485, 1.10689774, 1.05456632, 1.0262287 , 1.12423367,\n",
+       "        1.03339705, 1.03681747, 1.12365433, 1.03800955, 1.04847361,\n",
+       "        1.13263132, 1.06365727, 1.07778219, 1.07465272, 1.05553961,\n",
+       "        1.04925698, 1.12508203, 1.04941021, 1.07083481, 1.01335218,\n",
+       "        1.03190349, 1.07838952, 1.04467789, 0.99704356, 1.12914412,\n",
+       "        0.98616493, 1.05983754, 1.12465586, 1.03059777, 1.11454044,\n",
+       "        0.93745207, 1.04271849, 1.04304819, 1.05811418, 1.05921293,\n",
+       "        1.01376329, 1.09078683, 1.06059874, 1.00874282, 1.06422887,\n",
+       "        1.03552499, 1.09279141, 1.04641005, 1.05898508, 1.06925862,\n",
+       "        1.02026127, 1.0885863 , 1.1083716 , 1.09426568, 1.10026869,\n",
+       "        1.02119611, 1.06798453, 1.05042329, 1.06575947, 1.04552637,\n",
+       "        1.05586102, 1.06004754, 1.07245808, 1.03031088, 1.11274971,\n",
+       "        1.0402724 , 1.07501044, 1.0756596 , 1.0889485 , 1.05687303,\n",
+       "        1.05226843, 1.06614733, 1.13440352, 1.03897371, 1.13661279,\n",
+       "        0.97957715, 1.04040987, 1.0036449 , 1.05483566, 1.05877806,\n",
+       "        1.06934829, 1.04022991, 1.0661489 , 1.00662534, 1.12294633,\n",
+       "        1.07419408, 1.02992842, 1.00412583, 1.04125683, 0.98389852,\n",
        "...\n",
-       "        1.06899859, 1.04257078, 1.08758315, 1.0987634 , 0.99383811,\n",
-       "        1.08673273, 1.02245787, 1.05652938, 1.06603507, 1.08749668,\n",
-       "        1.02363128, 1.04894393, 1.01882759, 1.03708812, 1.01580446,\n",
-       "        1.07114355, 1.08418248, 1.13563688, 1.08484639, 1.06252274,\n",
-       "        1.08223777, 1.03623537, 1.06549358, 0.99033368, 1.07196064,\n",
-       "        1.08538163, 1.01806955, 1.0362579 , 1.06562302, 1.02332603,\n",
-       "        1.02094121, 1.04413596, 0.95132295, 1.0718015 , 1.10910077,\n",
-       "        1.04195555, 1.08417389, 0.97467751, 1.04224927, 1.02095729,\n",
-       "        1.0415575 , 1.04976663, 1.03291724, 1.03327229, 1.05916072,\n",
-       "        1.02508845, 1.04494963, 1.00066162, 1.08340542, 1.11585892,\n",
-       "        1.04821342, 1.09386951, 1.03479999, 1.08524746, 1.04126213,\n",
-       "        1.0642749 , 1.09273814, 1.12211764, 1.11692555, 1.04239027,\n",
-       "        1.01800202, 1.06177611, 1.08184505, 1.14352262, 0.97175291,\n",
-       "        1.0645999 , 1.09066149, 1.0497653 , 1.08522007, 1.02638569,\n",
-       "        1.03272143, 1.01656997, 1.06315342, 1.02622978, 1.02708829,\n",
-       "        0.98472192, 1.10973262, 1.07970371, 1.1030834 , 1.0587159 ,\n",
-       "        1.03575917, 1.10571843, 1.02405268, 1.03457422, 1.12308228,\n",
-       "        1.07374779, 0.99441592, 1.08583898, 1.0864686 , 1.08231916,\n",
-       "        1.08296171, 1.02915659, 1.11645043, 1.06223446, 1.04184045,\n",
-       "        1.07102462, 1.07674975, 1.03475673, 1.03843024, 1.06868367]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.009 2.046 2.063 ... 2.08 2.038</div><input id='attrs-d77ffcba-7450-488b-8917-ca0516864459' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d77ffcba-7450-488b-8917-ca0516864459' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e469481-9237-4153-9fee-3da120f03f18' class='xr-var-data-in' type='checkbox'><label for='data-0e469481-9237-4153-9fee-3da120f03f18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.00929359, 2.04613298, 2.06269054, 2.05351671, 2.06124664,\n",
-       "        2.04251201, 2.11883439, 2.06534653, 2.00031365, 2.18710465,\n",
-       "        2.14440695, 2.09273058, 2.06072532, 2.00981934, 2.04962304,\n",
-       "        2.03529346, 2.08513048, 2.0670887 , 2.07888598, 2.07444069,\n",
-       "        1.95591053, 2.03596239, 2.04283974, 2.10428414, 1.98366053,\n",
-       "        1.99671086, 2.06069345, 2.11209758, 2.08031408, 2.02250548,\n",
-       "        2.05274904, 2.01389165, 2.09423844, 2.11584371, 2.19223506,\n",
-       "        2.016225  , 2.09662889, 2.05654088, 2.00080844, 1.98013185,\n",
-       "        2.10451414, 1.97743491, 2.02807483, 2.10089774, 2.02994571,\n",
-       "        2.22200127, 2.03371281, 1.99069563, 2.14942961, 2.09087251,\n",
-       "        2.05848951, 1.99466908, 2.09443697, 2.09792379, 2.01194483,\n",
-       "        2.08231435, 2.04192766, 2.11337002, 2.06926661, 2.01692751,\n",
-       "        1.98299414, 2.00677196, 2.09197433, 2.07567755, 2.14402701,\n",
-       "        2.0756996 , 2.08853288, 2.11436305, 2.06658919, 1.99441002,\n",
-       "        1.94795739, 2.02978596, 2.06315447, 1.96665993, 2.01629823,\n",
-       "        2.11141896, 2.11899955, 2.04626373, 2.05672302, 2.04236203,\n",
-       "        2.14889964, 2.04176451, 2.00787205, 2.04478148, 2.12298998,\n",
-       "        2.09858755, 2.02406437, 2.04969567, 2.18241483, 1.98960708,\n",
-       "        2.10474415, 2.09735798, 2.02153651, 2.07867391, 1.93014706,\n",
-       "        2.03193646, 2.02694996, 2.11557073, 1.98436801, 1.98442671,\n",
+       "        1.06026285, 1.05856002, 1.12645802, 1.10663868, 0.99962505,\n",
+       "        1.01264119, 0.99539589, 1.10675695, 1.07942247, 1.059351  ,\n",
+       "        1.0431628 , 1.00263697, 1.0100349 , 1.06836279, 1.06204536,\n",
+       "        0.98267112, 1.04619062, 1.03399779, 1.07933802, 1.02541312,\n",
+       "        1.05430098, 1.01414103, 1.0584926 , 1.09169297, 1.04353866,\n",
+       "        1.075776  , 0.99387987, 1.0520299 , 1.03210475, 1.08752915,\n",
+       "        1.03131456, 1.0650897 , 1.04549413, 1.03393103, 1.0197556 ,\n",
+       "        1.04313008, 1.02277172, 1.0320253 , 1.09684357, 1.05479382,\n",
+       "        1.02216497, 1.08027538, 1.07594051, 1.03228856, 1.0645997 ,\n",
+       "        1.04087824, 1.07186868, 1.0359763 , 1.02019948, 1.06027197,\n",
+       "        1.0588793 , 1.07008607, 1.05743038, 1.01390011, 1.05462943,\n",
+       "        1.13608908, 1.04652145, 1.0786749 , 1.01870285, 1.04566494,\n",
+       "        1.03842399, 1.06607466, 1.06282215, 1.04895395, 1.084762  ,\n",
+       "        1.03285704, 1.04833865, 1.0297054 , 1.0447197 , 0.96034714,\n",
+       "        1.10255616, 1.04554731, 1.00736384, 0.94247991, 0.97579409,\n",
+       "        1.03592156, 1.12877216, 0.98829111, 1.07300577, 1.05256632,\n",
+       "        1.06001004, 1.02358679, 1.11122867, 1.03175528, 1.10593716,\n",
+       "        1.05215054, 1.06375037, 1.06743093, 1.0762863 , 1.07555632,\n",
+       "        1.07424439, 1.03239951, 1.03692767, 1.01004056, 1.0211714 ,\n",
+       "        1.09189748, 1.03729933, 1.07150997, 1.05575355, 1.04397902]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.135 2.02 2.016 ... 2.137 1.978</div><input id='attrs-2cc15363-9038-41fe-be0f-73fc94682bb5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cc15363-9038-41fe-be0f-73fc94682bb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e979fdd7-ad4f-426c-b8a9-776d29573457' class='xr-var-data-in' type='checkbox'><label for='data-e979fdd7-ad4f-426c-b8a9-776d29573457' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.13526056, 2.01976166, 2.01643312, 2.1100348 , 2.07716026,\n",
+       "        2.06160349, 2.06613744, 2.03665422, 2.09809492, 2.09254394,\n",
+       "        2.02756415, 1.96559854, 2.09336456, 2.09941884, 2.07120116,\n",
+       "        2.13126182, 2.02666477, 2.06201123, 2.05417933, 2.12346161,\n",
+       "        2.09077862, 2.0074942 , 2.12080933, 2.00242344, 2.06793194,\n",
+       "        2.06001931, 2.0466106 , 2.08614369, 1.97368227, 1.98172394,\n",
+       "        2.14971156, 2.10285983, 2.1481018 , 2.01721005, 2.13593258,\n",
+       "        2.04115995, 1.96189652, 2.04707556, 2.0781611 , 2.07677721,\n",
+       "        2.07627751, 1.97791313, 2.11992672, 2.12936483, 2.12374748,\n",
+       "        2.0466026 , 2.01615419, 2.05596055, 1.9777476 , 2.13762518,\n",
+       "        2.03398458, 2.04633336, 2.14269094, 2.10244966, 2.10752382,\n",
+       "        2.0693766 , 2.22017251, 2.13948308, 2.14241035, 2.02394195,\n",
+       "        2.10883631, 2.0551413 , 2.1365009 , 1.99529271, 2.14329751,\n",
+       "        2.07287906, 2.10762982, 2.15293469, 2.12383469, 2.0634459 ,\n",
+       "        1.95373318, 2.10923418, 2.07188133, 1.99761423, 1.96427444,\n",
+       "        2.04238656, 1.97135546, 2.07041228, 1.88843565, 2.11176987,\n",
+       "        1.99602479, 2.0804802 , 2.0478783 , 2.09637487, 2.07691669,\n",
+       "        2.15639889, 2.10513962, 2.09618181, 2.0727942 , 2.11354571,\n",
+       "        2.02491604, 2.07390872, 2.09198968, 2.02821292, 2.1144547 ,\n",
+       "        2.1213888 , 2.06974038, 2.13031491, 2.07850523, 2.07986512,\n",
        "...\n",
-       "        2.16366324, 2.06333972, 2.03461789, 1.99466549, 2.09804749,\n",
-       "        2.0433769 , 2.11547635, 1.9601239 , 2.04000006, 1.95112803,\n",
-       "        2.08672939, 2.10277313, 2.10095218, 2.05878697, 2.01812262,\n",
-       "        2.09372884, 2.03944724, 2.18670034, 2.12210964, 2.04451422,\n",
-       "        2.06763441, 2.08033455, 2.04751905, 2.05118917, 2.09271084,\n",
-       "        2.00939006, 2.01110284, 1.93207635, 2.04025252, 2.1015311 ,\n",
-       "        2.11630368, 1.99480337, 2.1763338 , 2.15909798, 2.06173198,\n",
-       "        2.08419049, 1.99875715, 1.99260581, 1.92716922, 2.01799548,\n",
-       "        1.9813644 , 2.05710098, 2.04826101, 2.12746496, 2.02476532,\n",
-       "        2.1198507 , 2.05308121, 2.08118358, 2.12051447, 2.14229714,\n",
-       "        1.98819464, 2.00681759, 2.05704726, 2.06976601, 1.949699  ,\n",
-       "        2.0772595 , 2.07101476, 2.03414956, 2.02115201, 2.05119083,\n",
-       "        2.03539997, 2.10326917, 1.99024013, 2.01666225, 1.9528098 ,\n",
-       "        2.05274253, 2.07347499, 2.08924441, 2.01396725, 1.9728478 ,\n",
-       "        2.0199168 , 2.04944924, 2.01315438, 2.12937937, 1.98690715,\n",
-       "        1.97354247, 2.17909435, 2.01715712, 2.1046337 , 2.17286504,\n",
-       "        2.04530992, 2.00698557, 2.03221988, 2.0077802 , 2.07942115,\n",
-       "        2.12130684, 2.00226179, 1.99851678, 2.16660079, 2.09095054,\n",
-       "        1.85413116, 2.12100762, 2.17219544, 2.07908154, 1.97044574,\n",
-       "        2.05723013, 2.00969075, 1.98145972, 2.07979321, 2.03796762]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.475 0.4933 ... 0.5156 0.5347</div><input id='attrs-ee5307ea-4362-4c73-bf5c-1ebedf592aa2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee5307ea-4362-4c73-bf5c-1ebedf592aa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb7a9532-68b9-4085-8916-b37a43040084' class='xr-var-data-in' type='checkbox'><label for='data-cb7a9532-68b9-4085-8916-b37a43040084' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.47503679, 0.4932745 , 0.58243867, 0.43098883, 0.5092622 ,\n",
-       "        0.6026603 , 0.51874833, 0.51616572, 0.5847336 , 0.52212259,\n",
-       "        0.54206577, 0.53455176, 0.49366069, 0.54688721, 0.52348032,\n",
-       "        0.55420135, 0.51892887, 0.55550875, 0.42373603, 0.50807845,\n",
-       "        0.52244441, 0.55280442, 0.49893306, 0.60826132, 0.45691609,\n",
-       "        0.5260236 , 0.48707692, 0.54682035, 0.52577162, 0.53837948,\n",
-       "        0.52147195, 0.54084623, 0.51215806, 0.51878635, 0.47528613,\n",
-       "        0.5355799 , 0.54462882, 0.57700036, 0.55909726, 0.57182524,\n",
-       "        0.60242584, 0.50182916, 0.48555848, 0.5376337 , 0.54148354,\n",
-       "        0.52600748, 0.53511096, 0.49970305, 0.50040006, 0.56514149,\n",
-       "        0.50739752, 0.48742087, 0.55105439, 0.49947908, 0.5249142 ,\n",
-       "        0.4790983 , 0.48788332, 0.50662933, 0.44893267, 0.52123849,\n",
-       "        0.59628876, 0.52515537, 0.54606397, 0.4715505 , 0.52469273,\n",
-       "        0.47549403, 0.55734858, 0.52251945, 0.50659575, 0.52152499,\n",
-       "        0.47411978, 0.55515501, 0.50870011, 0.49874491, 0.54898505,\n",
-       "        0.4881043 , 0.55020955, 0.50980945, 0.52116609, 0.54679363,\n",
-       "        0.49601086, 0.53307073, 0.49289802, 0.55470811, 0.53136678,\n",
-       "        0.44993329, 0.54151465, 0.54108359, 0.50567858, 0.49680954,\n",
-       "        0.51624026, 0.55586116, 0.49787488, 0.58215495, 0.50713456,\n",
-       "        0.55819803, 0.44617425, 0.48033557, 0.49389815, 0.48877765,\n",
+       "        1.95377299, 2.07299154, 2.08773249, 2.0950276 , 2.0840157 ,\n",
+       "        1.94326163, 2.1146822 , 2.03226881, 2.0327619 , 2.15917823,\n",
+       "        2.1647793 , 2.0489871 , 2.01168585, 2.12423138, 2.07436678,\n",
+       "        2.05096594, 2.01863714, 1.99049235, 2.03861127, 2.05794492,\n",
+       "        2.05342403, 2.06229559, 2.02606089, 2.02588043, 1.97129773,\n",
+       "        1.9862503 , 2.05746853, 2.03718671, 2.05751782, 2.08399276,\n",
+       "        2.01749703, 2.0273369 , 2.08165982, 2.04587386, 2.06884539,\n",
+       "        2.11138419, 2.08389299, 2.0665134 , 2.0662659 , 2.0529737 ,\n",
+       "        2.06338812, 2.11736249, 2.02716203, 2.05943285, 2.10110448,\n",
+       "        2.04586508, 2.08758385, 2.11562952, 2.0112505 , 2.09746652,\n",
+       "        2.00545621, 2.07737075, 2.06264581, 2.13263472, 2.15148326,\n",
+       "        2.0327248 , 2.14187075, 1.95844653, 2.11834145, 2.0789889 ,\n",
+       "        2.12580091, 1.99683959, 2.07587761, 2.00148807, 2.06914397,\n",
+       "        2.0828802 , 2.05046188, 2.18535514, 2.10103676, 2.04384018,\n",
+       "        2.20641808, 2.03272998, 2.08264412, 2.05596795, 2.13429528,\n",
+       "        2.15763574, 1.99226268, 2.0615368 , 2.01405204, 2.11076401,\n",
+       "        1.97203144, 2.09147639, 2.11059479, 2.04237988, 2.00835348,\n",
+       "        2.00578911, 1.95496487, 2.01592049, 2.11643542, 1.99534615,\n",
+       "        2.09458572, 2.03907215, 2.11650492, 2.0667664 , 2.20412043,\n",
+       "        2.04051161, 2.12280236, 1.91616671, 2.13718065, 1.97779649]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5326 0.5372 ... 0.5494 0.5225</div><input id='attrs-5ffecad8-2931-424c-a922-1556cc6db33c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ffecad8-2931-424c-a922-1556cc6db33c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-944be671-3e0e-4c35-8d6b-781bf6dd1d6c' class='xr-var-data-in' type='checkbox'><label for='data-944be671-3e0e-4c35-8d6b-781bf6dd1d6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.53264018, 0.53717447, 0.48992772, 0.52784995, 0.57495731,\n",
+       "        0.52021126, 0.48452951, 0.50852162, 0.53890904, 0.55340005,\n",
+       "        0.52866686, 0.51638479, 0.51678022, 0.53452048, 0.52253251,\n",
+       "        0.50468992, 0.54709953, 0.49655893, 0.51929181, 0.54087612,\n",
+       "        0.55549906, 0.4960475 , 0.50259419, 0.54051639, 0.51313389,\n",
+       "        0.48945666, 0.51928298, 0.50201345, 0.5711675 , 0.51704249,\n",
+       "        0.55083564, 0.56031261, 0.52319737, 0.5397157 , 0.52000357,\n",
+       "        0.53081549, 0.51794995, 0.50646896, 0.54164087, 0.50659058,\n",
+       "        0.54561014, 0.5044941 , 0.50907795, 0.51322754, 0.5389222 ,\n",
+       "        0.48278161, 0.51162806, 0.53620641, 0.54856009, 0.52705693,\n",
+       "        0.49550617, 0.5152358 , 0.51043223, 0.53360809, 0.53592136,\n",
+       "        0.5111027 , 0.53302632, 0.54810645, 0.52316052, 0.56580837,\n",
+       "        0.52733979, 0.51659383, 0.49696807, 0.5283922 , 0.50891086,\n",
+       "        0.51962843, 0.51180083, 0.48832967, 0.50527832, 0.50534059,\n",
+       "        0.54619304, 0.56943168, 0.49405664, 0.49086313, 0.50816539,\n",
+       "        0.5092055 , 0.53231752, 0.50111379, 0.54712904, 0.59172865,\n",
+       "        0.50459637, 0.53398471, 0.52215424, 0.54201509, 0.48829487,\n",
+       "        0.51921649, 0.49641592, 0.50928266, 0.5468533 , 0.50367815,\n",
+       "        0.55179881, 0.52593306, 0.51342638, 0.52577341, 0.52186543,\n",
+       "        0.56506259, 0.54184535, 0.53798149, 0.53190068, 0.52866095,\n",
        "...\n",
-       "        0.51116898, 0.54979073, 0.4940003 , 0.50454278, 0.53792363,\n",
-       "        0.44837046, 0.5429691 , 0.57230746, 0.49581106, 0.53178167,\n",
-       "        0.56763811, 0.52561527, 0.51928586, 0.59468213, 0.53756113,\n",
-       "        0.49613791, 0.52049194, 0.52327519, 0.47653091, 0.52433866,\n",
-       "        0.59347927, 0.52014275, 0.49106378, 0.48659228, 0.54741349,\n",
-       "        0.57075592, 0.45107388, 0.53436195, 0.5324162 , 0.49076083,\n",
-       "        0.54236723, 0.46593185, 0.54327047, 0.5397138 , 0.53886672,\n",
-       "        0.53677111, 0.53949954, 0.53020623, 0.51105563, 0.4765418 ,\n",
-       "        0.50467066, 0.4431123 , 0.49631762, 0.53532801, 0.46056312,\n",
-       "        0.60505812, 0.56612181, 0.52686561, 0.56007317, 0.52408584,\n",
-       "        0.51691378, 0.47073902, 0.52766416, 0.60988229, 0.50667505,\n",
-       "        0.57054829, 0.54906455, 0.5193653 , 0.46989256, 0.45080155,\n",
-       "        0.57944439, 0.53250615, 0.49281844, 0.54773989, 0.50528816,\n",
-       "        0.52144446, 0.47748306, 0.59299112, 0.49101675, 0.54955619,\n",
-       "        0.54990187, 0.58604861, 0.51901846, 0.55754382, 0.56902047,\n",
-       "        0.56276662, 0.6054205 , 0.55363626, 0.5249528 , 0.4980976 ,\n",
-       "        0.49589476, 0.51132459, 0.53115181, 0.53104795, 0.51579533,\n",
-       "        0.55112247, 0.53068207, 0.55084148, 0.50925099, 0.56981815,\n",
-       "        0.55675414, 0.50205612, 0.47738315, 0.5990464 , 0.51243009,\n",
-       "        0.50482366, 0.53225564, 0.52565307, 0.51562967, 0.53474016]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7172c10e-4b3a-423b-89a7-9046c027ba0f' class='xr-section-summary-in' type='checkbox'  ><label for='section-7172c10e-4b3a-423b-89a7-9046c027ba0f' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8efcefd6-b990-4256-94ed-7cf10603debf' class='xr-index-data-in' type='checkbox'/><label for='index-8efcefd6-b990-4256-94ed-7cf10603debf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d3dce473-84a3-4aa4-a8ab-befcacf02aed' class='xr-index-data-in' type='checkbox'/><label for='index-d3dce473-84a3-4aa4-a8ab-befcacf02aed' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "        0.51840877, 0.53424211, 0.53230351, 0.51840357, 0.53568988,\n",
+       "        0.51861031, 0.53998692, 0.50997164, 0.50886542, 0.55812584,\n",
+       "        0.50072572, 0.5091212 , 0.48889386, 0.53539297, 0.51115525,\n",
+       "        0.54935576, 0.54626145, 0.52735292, 0.56348777, 0.51979597,\n",
+       "        0.50125276, 0.51590024, 0.51041533, 0.52272456, 0.54969642,\n",
+       "        0.50673748, 0.53936026, 0.52537917, 0.53152831, 0.53690814,\n",
+       "        0.51956512, 0.52349571, 0.55828896, 0.56220242, 0.51103659,\n",
+       "        0.52519377, 0.51913478, 0.48173689, 0.56643878, 0.51146205,\n",
+       "        0.52543889, 0.51829738, 0.53251095, 0.51040147, 0.55282101,\n",
+       "        0.48898372, 0.5455761 , 0.50603169, 0.52392182, 0.48820922,\n",
+       "        0.52766853, 0.50774293, 0.55526201, 0.52760508, 0.50884203,\n",
+       "        0.4889245 , 0.49749765, 0.4768087 , 0.49304827, 0.5094859 ,\n",
+       "        0.50554459, 0.50485576, 0.50819975, 0.48156455, 0.52926563,\n",
+       "        0.52540975, 0.51211321, 0.51647577, 0.51201661, 0.51002989,\n",
+       "        0.55418525, 0.52488632, 0.52798765, 0.50050277, 0.51918048,\n",
+       "        0.53881021, 0.53565841, 0.51765   , 0.52228744, 0.53795119,\n",
+       "        0.53609387, 0.53075469, 0.5208383 , 0.50786692, 0.54616517,\n",
+       "        0.51306281, 0.51950628, 0.52768231, 0.51758285, 0.56975647,\n",
+       "        0.56948705, 0.54578575, 0.55377635, 0.53860837, 0.53324766,\n",
+       "        0.51397341, 0.5115702 , 0.51110905, 0.54938327, 0.52246268]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d86ab683-8998-4613-b464-3b0dab04fc85' class='xr-section-summary-in' type='checkbox'  ><label for='section-d86ab683-8998-4613-b464-3b0dab04fc85' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3973cc8b-8bc8-4ec2-a1ec-2aee80be60f0' class='xr-index-data-in' type='checkbox'/><label for='index-3973cc8b-8bc8-4ec2-a1ec-2aee80be60f0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ea22dfa5-b42d-4bd9-9211-1fe65dfa4bcb' class='xr-index-data-in' type='checkbox'/><label for='index-ea22dfa5-b42d-4bd9-9211-1fe65dfa4bcb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-96870f76-3479-4976-a285-84b5ca4638d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-96870f76-3479-4976-a285-84b5ca4638d4' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-08-19T17:50:03.733248+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.25.1</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-173af013-b1fd-49f9-8400-bc11447bc2e2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-173af013-b1fd-49f9-8400-bc11447bc2e2' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-08-29T09:17:23.403627+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.25.1</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 32kB\n",
@@ -861,17 +861,17 @@
        "  * chain      (chain) int64 8B 0\n",
        "  * draw       (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
        "Data variables:\n",
-       "    Intercept  (chain, draw) float64 8kB 1.033 1.054 1.07 ... 1.035 1.038 1.069\n",
-       "    slope      (chain, draw) float64 8kB 2.009 2.046 2.063 ... 1.981 2.08 2.038\n",
-       "    sigma      (chain, draw) float64 8kB 0.475 0.4933 0.5824 ... 0.5156 0.5347\n",
+       "    Intercept  (chain, draw) float64 8kB 1.035 1.124 1.073 ... 1.072 1.056 1.044\n",
+       "    slope      (chain, draw) float64 8kB 2.135 2.02 2.016 ... 1.916 2.137 1.978\n",
+       "    sigma      (chain, draw) float64 8kB 0.5326 0.5372 0.4899 ... 0.5494 0.5225\n",
        "Attributes:\n",
-       "    created_at:                 2025-08-19T17:50:03.733248+00:00\n",
+       "    created_at:                 2025-08-29T09:17:23.403627+00:00\n",
        "    arviz_version:              0.22.0\n",
        "    inference_library:          pymc\n",
        "    inference_library_version:  5.25.1"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -891,13 +891,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/s_/l9wd4yls3mv1f4kkkz1xhhmm0000gn/T/ipykernel_26101/1628930406.py:18: UserWarning: The figure layout has changed to tight\n",
+      "/var/folders/s_/l9wd4yls3mv1f4kkkz1xhhmm0000gn/T/ipykernel_84113/2317749157.py:18: UserWarning: The figure layout has changed to tight\n",
       "  f.tight_layout()\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x800 with 3 Axes>"
       ]
@@ -905,7 +905,7 @@
      "metadata": {
       "image/png": {
        "height": 790,
-       "width": 808
+       "width": 790
       }
      },
      "output_type": "display_data"

From 7cd407eec0262a0a81ca823b155499f1ad7cff1e Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Fri, 29 Aug 2025 18:11:50 +0200
Subject: [PATCH 27/28] Switch to `better_optimize`

---
 pymc_extras/inference/dadvi/dadvi.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/pymc_extras/inference/dadvi/dadvi.py b/pymc_extras/inference/dadvi/dadvi.py
index 80eee66ca..c0dc082f6 100644
--- a/pymc_extras/inference/dadvi/dadvi.py
+++ b/pymc_extras/inference/dadvi/dadvi.py
@@ -5,6 +5,7 @@
 import pytensor.tensor as pt
 import xarray
 
+from better_optimize import minimize
 from better_optimize.constants import minimize_method
 from pymc import DictToArrayBijection, Model, join_nonshared_inputs
 from pymc.backends.arviz import (
@@ -14,7 +15,6 @@
 )
 from pymc.util import RandomSeed, get_default_varnames
 from pytensor.tensor.variable import TensorVariable
-from scipy.optimize import minimize
 
 from pymc_extras.inference.laplace_approx.laplace import unstack_laplace_draws
 from pymc_extras.inference.laplace_approx.scipy_interface import (
@@ -31,6 +31,7 @@ def fit_dadvi(
     optimizer_method: minimize_method = "trust-ncg",
     use_grad: bool = True,
     use_hessp: bool = True,
+    use_hess: bool = False,
     **minimize_kwargs,
 ) -> az.InferenceData:
     """
@@ -82,6 +83,11 @@ def fit_dadvi(
     use_hessp:
         If True, pass the hessian vector product to `scipy.optimize.minimize`.
 
+    use_hess:
+        If True, pass the hessian to `scipy.optimize.minimize`. Note that
+        this is generally not recommended since its computation can be slow
+        and memory-intensive if there are many parameters.
+
     Returns
     -------
     :class:`~arviz.InferenceData`
@@ -110,9 +116,9 @@ def fit_dadvi(
     f_fused, f_hessp = _compile_functions_for_scipy_optimize(
         objective,
         [var_params],
-        compute_grad=True,
-        compute_hessp=True,
-        compute_hess=False,
+        compute_grad=use_grad,
+        compute_hessp=use_hessp,
+        compute_hess=use_hess,
     )
 
     derivative_kwargs = {}
@@ -121,6 +127,8 @@ def fit_dadvi(
         derivative_kwargs["jac"] = True
     if use_hessp:
         derivative_kwargs["hessp"] = f_hessp
+    if use_hess:
+        derivative_kwargs["hess"] = True
 
     result = minimize(
         f_fused,

From cb070aa4d08dc803fbb3b707cabbda939df9661f Mon Sep 17 00:00:00 2001
From: Martin Ingram <martin.ingram@konux.de>
Date: Fri, 29 Aug 2025 18:38:55 +0200
Subject: [PATCH 28/28] Replace with pt.split

---
 pymc_extras/inference/dadvi/dadvi.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pymc_extras/inference/dadvi/dadvi.py b/pymc_extras/inference/dadvi/dadvi.py
index c0dc082f6..eb4ef98c5 100644
--- a/pymc_extras/inference/dadvi/dadvi.py
+++ b/pymc_extras/inference/dadvi/dadvi.py
@@ -197,7 +197,8 @@ def create_dadvi_graph(
     )
 
     var_params = pt.vector(name="eta", shape=(2 * n_params,))
-    means, log_sds = var_params[:n_params], var_params[n_params:]
+
+    means, log_sds = pt.split(var_params, axis=0, splits_size=[n_params, n_params], n_splits=2)
 
     draw_matrix = pt.constant(draws)
     samples = means + pt.exp(log_sds) * draw_matrix