diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index df5ff8d10b..655776e963 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -139,6 +139,7 @@ jobs: - | tests/dims/distributions/test_core.py + tests/dims/distributions/test_censored.py tests/dims/distributions/test_scalar.py tests/dims/distributions/test_vector.py tests/dims/test_model.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d1ef3a4227..2e69aa458a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -51,7 +51,7 @@ repos: - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.11.13 hooks: - - id: ruff + - id: ruff-check args: [--fix, --show-fixes] - id: ruff-format - repo: local diff --git a/Makefile b/Makefile index c92c5c1d4d..f43d1a6fd6 100644 --- a/Makefile +++ b/Makefile @@ -40,4 +40,4 @@ rtd: clean @echo "Build finished. The HTML pages are in $(BUILDDIR)." view: - python -m webbrowser $(BUILDDIR)/index.html + python -m webbrowser file://$(abspath $(BUILDDIR))/index.html diff --git a/conda-envs/environment-alternative-backends.yml b/conda-envs/environment-alternative-backends.yml index 7154296e0e..2246655242 100644 --- a/conda-envs/environment-alternative-backends.yml +++ b/conda-envs/environment-alternative-backends.yml @@ -22,7 +22,7 @@ dependencies: - numpyro>=0.8.0 - pandas>=0.24.0 - pip -- pytensor>=2.38.0,<2.39 +- pytensor>=2.38.2,<2.39 - python-graphviz - networkx - rich>=13.7.1 diff --git a/conda-envs/environment-dev.yml b/conda-envs/environment-dev.yml index ad1166c470..c96aa49c58 100644 --- a/conda-envs/environment-dev.yml +++ b/conda-envs/environment-dev.yml @@ -12,7 +12,7 @@ dependencies: - numpy>=1.25.0 - pandas>=0.24.0 - pip -- pytensor>=2.38.0,<2.39 +- pytensor>=2.38.2,<2.39 - python-graphviz - networkx - scipy>=1.4.1 diff --git a/conda-envs/environment-docs.yml b/conda-envs/environment-docs.yml index 19b8da5352..a1dd4a1151 100644 --- a/conda-envs/environment-docs.yml +++ b/conda-envs/environment-docs.yml @@ -11,7 +11,7 @@ dependencies: - numpy>=1.25.0 - pandas>=0.24.0 - pip -- pytensor>=2.38.0,<2.39 +- pytensor>=2.38.2,<2.39 - python-graphviz - rich>=13.7.1 - scipy>=1.4.1 diff --git a/conda-envs/environment-test.yml b/conda-envs/environment-test.yml index b63313bbdd..e17bc802b1 100644 --- a/conda-envs/environment-test.yml +++ b/conda-envs/environment-test.yml @@ -14,7 +14,7 @@ dependencies: - pandas>=0.24.0 - pip - polyagamma -- pytensor>=2.38.0,<2.39 +- pytensor>=2.38.2,<2.39 - python-graphviz - networkx - rich>=13.7.1 diff --git a/conda-envs/windows-environment-dev.yml b/conda-envs/windows-environment-dev.yml index 8411cfb603..d942055b70 100644 --- a/conda-envs/windows-environment-dev.yml +++ b/conda-envs/windows-environment-dev.yml @@ -12,7 +12,7 @@ dependencies: - numpy>=1.25.0 - pandas>=0.24.0 - pip -- pytensor>=2.38.0,<2.39 +- pytensor>=2.38.2,<2.39 - python-graphviz - networkx - rich>=13.7.1 diff --git a/conda-envs/windows-environment-test.yml b/conda-envs/windows-environment-test.yml index 1be6609a8d..e54eb38be6 100644 --- a/conda-envs/windows-environment-test.yml +++ b/conda-envs/windows-environment-test.yml @@ -15,7 +15,7 @@ dependencies: - pandas>=0.24.0 - pip - polyagamma -- pytensor>=2.38.0,<2.39 +- pytensor>=2.38.2,<2.39 - python-graphviz - networkx - rich>=13.7.1 diff --git a/docs/source/api/dims/distributions.rst b/docs/source/api/dims/distributions.rst index 229980b73a..cc34d68deb 100644 --- a/docs/source/api/dims/distributions.rst +++ b/docs/source/api/dims/distributions.rst @@ -39,3 +39,14 @@ Vector distributions Categorical MvNormal ZeroSumNormal + + +Higher-Order distributions +========================== + +.. currentmodule:: pymc.dims +.. autosummary:: + :toctree: generated/ + :template: distribution.rst + + Censored diff --git a/docs/source/api/dims/transforms.rst b/docs/source/api/dims/transforms.rst index c1fbae9b65..3094ad40a7 100644 --- a/docs/source/api/dims/transforms.rst +++ b/docs/source/api/dims/transforms.rst @@ -2,7 +2,7 @@ Distribution Transforms *********************** -.. currentmodule:: pymc.dims.transforms +.. currentmodule:: pymc.dims.distributions.transforms .. autosummary:: :toctree: generated/ diff --git a/docs/source/conf.py b/docs/source/conf.py index 5b1ec74400..9b660d24ca 100755 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -298,10 +298,10 @@ # intersphinx configuration to ease linking arviz docs intersphinx_mapping = { - "arviz": ("https://python.arviz.org/en/latest/", None), + "arviz": ("https://python.arviz.org/", "https://python.arviz.org/en/stable/objects.inv"), "pytensor": ("https://pytensor.readthedocs.io/en/latest/", None), "home": ("https://www.pymc.io", None), - "pmx": ("https://www.pymc.io/projects/experimental/en/latest", None), + "pmx": ("https://www.pymc.io/projects/extras/en/latest", None), "numpy": ("https://numpy.org/doc/stable/", None), "nb": ("https://www.pymc.io/projects/examples/en/latest/", None), "myst": ("https://myst-parser.readthedocs.io/en/latest", None), diff --git a/docs/source/contributing/developer_guide.md b/docs/source/contributing/developer_guide.md index b051b8fd2f..27382a4a9b 100644 --- a/docs/source/contributing/developer_guide.md +++ b/docs/source/contributing/developer_guide.md @@ -14,7 +14,7 @@ A more accessible, user facing deep introduction can be found in [Peadar Coyle's Probability distributions in PyMC are implemented as classes that inherit from {class}`~pymc.Continuous` or {class}`~pymc.Discrete`. Either of these inherit {class}`~pymc.Distribution` which defines the high level API. -For a detailed introduction on how a new distribution should be implemented check out the {ref}`guide on implementing distributions `. +For a detailed introduction on how a new distribution should be implemented check out the {ref}`guide on implementing distributions `. ## Reflection @@ -548,7 +548,7 @@ For example, see the [MH sampler](https://github.com/pymc-devs/pymc/blob/89f6fcf This is of course very different compared to the transition kernel in e.g. TFP, which is a tenor in tensor out function. Moreover, transition kernels in TFP do not flatten the tensors, see eg docstring of [tensorflow\_probability/python/mcmc/random\_walk\_metropolis.py](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/python/mcmc/random_walk_metropolis.py): -```python +```text new_state_fn: Python callable which takes a list of state parts and a seed; returns a same-type `list` of `Tensor`s, each being a perturbation of the input state parts. The perturbation distribution is assumed to be diff --git a/docs/source/contributing/jupyter_style.md b/docs/source/contributing/jupyter_style.md index e935fd00a7..a9c08f7636 100644 --- a/docs/source/contributing/jupyter_style.md +++ b/docs/source/contributing/jupyter_style.md @@ -70,10 +70,10 @@ This guide does not teach nor cover MyST extensively, only gives some opinionate ``` Example source: ``` - {ref}`how to use InferenceData ` + {ref}`how to use InferenceData ` ``` - Rendered example: {ref}`how to use InferenceData ` + Rendered example: {ref}`how to use InferenceData ` where `key` in the pattern (`arviz` in the example) is one of the keys defined in the `intersphinx_mapping` variable of `conf.py` such as `arviz`, `numpy`, `mpl`... diff --git a/docs/source/glossary.md b/docs/source/glossary.md index 2f9493f940..7a2ce662c4 100644 --- a/docs/source/glossary.md +++ b/docs/source/glossary.md @@ -53,7 +53,7 @@ Likelihood - For univariate, continuous scenarios, see the calibr8 paper: Bayesian calibration, process modeling and uncertainty quantification in biotechnology by Laura Marie Helleckes, Michael Osthege, Wolfgang Wiechert, Eric von Lieres, Marco Oldiges Posterior - The outcome of Bayesian inference is a posterior distribution, which describes the relative plausibilities of every possible combination of parameter values, given the observed data. We can think of the posterior as the updated {term}`priors` after the model has seen the data. + The outcome of Bayesian inference is a posterior distribution, which describes the relative plausibilities of every possible combination of parameter values, given the observed data. We can think of the posterior as the updated {term}`prior` after the model has seen the data. When the posterior is obtained using numerical methods we generally need to first diagnose the quality of the computed approximation. This is necessary as, for example, methods like {term}`MCMC` has only asymptotic guarantees. In a Bayesian setting predictions can be simulated by sampling from the posterior predictive distribution. When such predictions are used to check the internal consistency of the models by comparing it with the observed data used for inference, the process is known as the posterior predictive checks. @@ -76,6 +76,10 @@ GLM [PMF](https://en.wikipedia.org/wiki/Probability_mass_function) A function that gives the probability that a discrete random variable is exactly equal to some value. +[Maximum Likelihood Estimate](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation) +[MLE](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation) + A point-estimate of an unknown quantity obtained by finding the parameter values that maximize the {term}`likelihood` function. + [Maximum a Posteriori](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation) [MAP](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation) It is a point-estimate of an unknown quantity, that equals the mode of the posterior distribution. @@ -104,7 +108,7 @@ Hierarchical Ordinary Differential Equation Individual, group, or other level types calculations of {term}`Ordinary Differential Equation`'s. [Generalized Poisson Distribution](https://doi.org/10.2307/1267389) - A generalization of the {term}`Poisson distribution`, with two parameters X1, and X2, is obtained as a limiting form of the generalized negative binomial distribution. The variance of the distribution is greater than, equal to or smaller than the mean according as X2 is positive, zero or negative. For formula and more detail, visit the link in the title. + A generalization of the [Poisson distribution](https://en.wikipedia.org/wiki/Poisson_distribution), with two parameters X1, and X2, is obtained as a limiting form of the generalized negative binomial distribution. The variance of the distribution is greater than, equal to or smaller than the mean according as X2 is positive, zero or negative. For formula and more detail, visit the link in the title. [Bayes' theorem](https://en.wikipedia.org/wiki/Bayes%27_theorem) Describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately (by conditioning it on their age) than simply assuming that the individual is typical of the population as a whole. diff --git a/docs/source/learn/core_notebooks/dims_module.ipynb b/docs/source/learn/core_notebooks/dims_module.ipynb index 995674bd01..8af015a426 100644 --- a/docs/source/learn/core_notebooks/dims_module.ipynb +++ b/docs/source/learn/core_notebooks/dims_module.ipynb @@ -1,3324 +1,3324 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "17e37649edaa8d0d", - "metadata": {}, - "source": [ - "(dims_module)=\n", - "\n", - "# PyMC dims module" - ] - }, - { - "cell_type": "markdown", - "id": "79268b908558209c", - "metadata": {}, - "source": [ - "## A short history of dims in PyMC\n", - "\n", - "PyMC introduced the ability to specify model variable `dims` in version 3.9 in June 2020 (5 years as of the time of writing). In the release notes, it was mentioned only after [14 other new features](https://github.com/pymc-devs/pymc/blob/1d00f3eb81723523968f3610e81a0c42fd96326f/RELEASE-NOTES.md?plain=1#L236), but over time it became a foundation of the library.\n", - "\n", - "It allows users to more naturally specify the dimensions of model variables with string names, and provides a \"seamless\" conversion to arviz {doc}`InferenceData ` objects, which have become the standard for storing and investigating results from probabilistic programming languages.\n", - "\n", - "However, the behavior of dims is rather limited. It can only be used to specify the shape of new random variables and label existing dimensions (e.g., in {func}`~pymc.Deterministic`). Otherwise it has no effect on the computation, unlike operations done with {class}`~arviz.InferenceData` variables, which are based on {lib}`xarray` and where dims inform array selection, alignment, and broadcasting behavior.\n", - "\n", - "As a result, in PyMC models users have to write computations that follow NumPy semantics, which often requires transpositions, reshapes, new axis (`None`) and numerical axis arguments sprinkled everywhere. It can be hard to get these right and in the end it's often hard to make sense of the written model.\n", - "\n", - "### Expanding the role of dims\n", - "\n", - "Now we are introducing an experimental {mod}`pymc.dims` module that allows users to define data, distributions, and math operations that respect dim semantics, following {mode}`xarray` operations **without coordinates** as closely as possible.\n", - "\n", - ":::{warning}The `dims` module is experimental, not exhaustively tested and the API is being iteratively worked on, and is therefore subject to changes between any two PyMC releases. We welcome users to test it and provide feedback, but we don't yet endorse its use for production.:::\n", - "\n", - "\n", - "## A simple example\n", - "\n", - "We'll start with a model written in current PyMC style, using synthetic data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "20399d0df364361e", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "import pymc as pm\n", - "\n", - "seed = sum(map(ord, \"dims module\"))\n", - "rng = np.random.default_rng(seed)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "313be4498ce33680", - "metadata": {}, - "outputs": [], - "source": [ - "# Very realistic looking data!\n", - "observed_response_np = np.ones((5, 20), dtype=int)\n", - "coords = coords = {\n", - " \"participant\": range(5),\n", - " \"trial\": range(20),\n", - " \"item\": range(3),\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "12822088a0040760", - "metadata": {}, - "source": [ - "This model predicts participants' categorical responses across trials by combining participant-specific item preferences (constrained to sum to zero) with shared time-varying effects, then applying a softmax to model response probabilities.\n", - "\n", - "Notice the need to identify axes by number rather than dimension name, and the need to use `None` to create new axes in order to specify broadcast requirements." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4c97fc1f0b8eeec", - "metadata": {}, - "outputs": [], - "source": [ - "with pm.Model(coords=coords) as model:\n", - " observed_response = pm.Data(\n", - " \"observed_response\", observed_response_np, dims=(\"participant\", \"trial\")\n", - " )\n", - " # Use ZeroSumNormal to avoid identifiability issues\n", - " participant_preference = pm.ZeroSumNormal(\n", - " \"participant_preference\", n_zerosum_axes=1, dims=(\"participant\", \"item\")\n", - " )\n", - "\n", - " # Shared time effects across all participants\n", - " time_effects = pm.Normal(\"time_effects\", dims=(\"trial\", \"item\"))\n", - "\n", - " trial_preference = pm.Deterministic(\n", - " \"trial_preference\",\n", - " participant_preference[:, None, :] + time_effects[None, :, :],\n", - " dims=(\"participant\", \"trial\", \"item\"),\n", - " )\n", - "\n", - " response = pm.Categorical(\n", - " \"response\",\n", - " p=pm.math.softmax(trial_preference, axis=-1),\n", - " observed=observed_response,\n", - " dims=(\"participant\", \"trial\"),\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2efa25b6d8713c0", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:29.819511Z", - "start_time": "2025-06-30T15:53:25.547610Z" - } - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterparticipant (5) x trial (20)\n", - "\n", - "participant (5) x trial (20)\n", - "\n", - "\n", - "clusterparticipant (5) x item (3)\n", - "\n", - "participant (5) x item (3)\n", - "\n", - "\n", - "clustertrial (20) x item (3)\n", - "\n", - "trial (20) x item (3)\n", - "\n", - "\n", - "clusterparticipant (5) x trial (20) x item (3)\n", - "\n", - "participant (5) x trial (20) x item (3)\n", - "\n", - "\n", - "\n", - "observed_response\n", - "\n", - "observed_response\n", - "~\n", - "Data\n", - "\n", - "\n", - "\n", - "response\n", - "\n", - "response\n", - "~\n", - "Categorical\n", - "\n", - "\n", - "\n", - "response->observed_response\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "participant_preference\n", - "\n", - "participant_preference\n", - "~\n", - "ZeroSumNormal\n", - "\n", - "\n", - "\n", - "trial_preference\n", - "\n", - "trial_preference\n", - "~\n", - "Deterministic\n", - "\n", - "\n", - "\n", - "participant_preference->trial_preference\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "time_effects\n", - "\n", - "time_effects\n", - "~\n", - "Normal\n", - "\n", - "\n", - "\n", - "time_effects->trial_preference\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "trial_preference->response\n", - "\n", - "\n", - "\n", - "\n", - "\n" + "cells": [ + { + "cell_type": "markdown", + "id": "17e37649edaa8d0d", + "metadata": {}, + "source": [ + "(dims_module)=\n", + "\n", + "# PyMC dims module" + ] + }, + { + "cell_type": "markdown", + "id": "79268b908558209c", + "metadata": {}, + "source": [ + "## A short history of dims in PyMC\n", + "\n", + "PyMC introduced the ability to specify model variable `dims` in version 3.9 in June 2020 (5 years as of the time of writing). In the release notes, it was mentioned only after [14 other new features](https://github.com/pymc-devs/pymc/blob/1d00f3eb81723523968f3610e81a0c42fd96326f/RELEASE-NOTES.md?plain=1#L236), but over time it became a foundation of the library.\n", + "\n", + "It allows users to more naturally specify the dimensions of model variables with string names, and provides a \"seamless\" conversion to arviz {ref}`InferenceData ` objects, which have become the standard for storing and investigating results from probabilistic programming languages.\n", + "\n", + "However, the behavior of dims is rather limited. It can only be used to specify the shape of new random variables and label existing dimensions (e.g., in {func}`~pymc.Deterministic`). Otherwise it has no effect on the computation, unlike operations done with {class}`~arviz.InferenceData` variables, which are based on [xarray](https://docs.xarray.dev/) and where dims inform array selection, alignment, and broadcasting behavior.\n", + "\n", + "As a result, in PyMC models users have to write computations that follow NumPy semantics, which often requires transpositions, reshapes, new axis (`None`) and numerical axis arguments sprinkled everywhere. It can be hard to get these right and in the end it's often hard to make sense of the written model.\n", + "\n", + "### Expanding the role of dims\n", + "\n", + "Now we are introducing an experimental {mod}`pymc.dims` module that allows users to define data, distributions, and math operations that respect dim semantics, following {mod}`xarray` operations **without coordinates** as closely as possible.\n", + "\n", + ":::{warning}The `dims` module is experimental, not exhaustively tested and the API is being iteratively worked on, and is therefore subject to changes between any two PyMC releases. We welcome users to test it and provide feedback, but we don't yet endorse its use for production.:::\n", + "\n", + "\n", + "## A simple example\n", + "\n", + "We'll start with a model written in current PyMC style, using synthetic data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "20399d0df364361e", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pymc as pm\n", + "\n", + "seed = sum(map(ord, \"dims module\"))\n", + "rng = np.random.default_rng(seed)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "313be4498ce33680", + "metadata": {}, + "outputs": [], + "source": [ + "# Very realistic looking data!\n", + "observed_response_np = np.ones((5, 20), dtype=int)\n", + "coords = coords = {\n", + " \"participant\": range(5),\n", + " \"trial\": range(20),\n", + " \"item\": range(3),\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "12822088a0040760", + "metadata": {}, + "source": [ + "This model predicts participants' categorical responses across trials by combining participant-specific item preferences (constrained to sum to zero) with shared time-varying effects, then applying a softmax to model response probabilities.\n", + "\n", + "Notice the need to identify axes by number rather than dimension name, and the need to use `None` to create new axes in order to specify broadcast requirements." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4c97fc1f0b8eeec", + "metadata": {}, + "outputs": [], + "source": [ + "with pm.Model(coords=coords) as model:\n", + " observed_response = pm.Data(\n", + " \"observed_response\", observed_response_np, dims=(\"participant\", \"trial\")\n", + " )\n", + " # Use ZeroSumNormal to avoid identifiability issues\n", + " participant_preference = pm.ZeroSumNormal(\n", + " \"participant_preference\", n_zerosum_axes=1, dims=(\"participant\", \"item\")\n", + " )\n", + "\n", + " # Shared time effects across all participants\n", + " time_effects = pm.Normal(\"time_effects\", dims=(\"trial\", \"item\"))\n", + "\n", + " trial_preference = pm.Deterministic(\n", + " \"trial_preference\",\n", + " participant_preference[:, None, :] + time_effects[None, :, :],\n", + " dims=(\"participant\", \"trial\", \"item\"),\n", + " )\n", + "\n", + " response = pm.Categorical(\n", + " \"response\",\n", + " p=pm.math.softmax(trial_preference, axis=-1),\n", + " observed=observed_response,\n", + " dims=(\"participant\", \"trial\"),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2efa25b6d8713c0", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:29.819511Z", + "start_time": "2025-06-30T15:53:25.547610Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "clusterparticipant (5) x trial (20)\n", + "\n", + "participant (5) x trial (20)\n", + "\n", + "\n", + "clusterparticipant (5) x item (3)\n", + "\n", + "participant (5) x item (3)\n", + "\n", + "\n", + "clustertrial (20) x item (3)\n", + "\n", + "trial (20) x item (3)\n", + "\n", + "\n", + "clusterparticipant (5) x trial (20) x item (3)\n", + "\n", + "participant (5) x trial (20) x item (3)\n", + "\n", + "\n", + "\n", + "observed_response\n", + "\n", + "observed_response\n", + "~\n", + "Data\n", + "\n", + "\n", + "\n", + "response\n", + "\n", + "response\n", + "~\n", + "Categorical\n", + "\n", + "\n", + "\n", + "response->observed_response\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "participant_preference\n", + "\n", + "participant_preference\n", + "~\n", + "ZeroSumNormal\n", + "\n", + "\n", + "\n", + "trial_preference\n", + "\n", + "trial_preference\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "participant_preference->trial_preference\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "time_effects\n", + "\n", + "time_effects\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "time_effects->trial_preference\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "trial_preference->response\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "model.to_graphviz()" ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.to_graphviz()" - ] - }, - { - "cell_type": "markdown", - "id": "41bccc7c213e1be3", - "metadata": {}, - "source": [ - "And here's the equivalent model using the {mod}`pymc.dims` module." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3ff29a7a93236486", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ricardo/Documents/pymc/pymc/dims/__init__.py:66: UserWarning: The `pymc.dims` module is experimental and may contain critical bugs (p=0.676).\n", - "Please report any issues you encounter at https://github.com/pymc-devs/pymc/issues.\n", - "Disclaimer: This an experimental API and may change at any time.\n", - " __init__()\n" - ] - } - ], - "source": [ - "import pymc.dims as pmd" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c020e450cc165e46", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:29.997156Z", - "start_time": "2025-06-30T15:53:29.935025Z" - } - }, - "outputs": [], - "source": [ - "with pm.Model(coords=coords) as dmodel:\n", - " observed_response = pmd.Data(\n", - " \"observed_response\", observed_response_np, dims=(\"participant\", \"trial\")\n", - " )\n", - " participant_preference = pmd.ZeroSumNormal(\n", - " \"participant_preference\", core_dims=\"item\", dims=(\"participant\", \"item\")\n", - " )\n", - "\n", - " # Shared time effects across all participants\n", - " time_effects = pmd.Normal(\"time_effects\", dims=(\"item\", \"trial\"))\n", - "\n", - " trial_preference = pmd.Deterministic(\n", - " \"trial_preference\",\n", - " participant_preference + time_effects,\n", - " )\n", - "\n", - " response = pmd.Categorical(\n", - " \"response\",\n", - " p=pmd.math.softmax(trial_preference, dim=\"item\"),\n", - " core_dims=\"item\",\n", - " observed=observed_response,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "3985ee1b52708c0c", - "metadata": {}, - "source": [ - "Note we still use the same {class}`~pymc.Model` constructor, but everything else was now defined with an equivalent function or class defined in the {mod}`pymc.dims` module.\n", - "\n", - "There are some notable differences:\n", - "\n", - "1. `ZeroSumNormal` takes a `core_dims` argument instead of `n_zerosum_axes`. This tells PyMC which of the `dims` that define the distribution are constrained to be zero-summed. All distributions that take non-scalar parameters now require a `core_dims` argument. Previously, they were assumed to be right-aligned by the user (see more in {doc}`dimensionality`). Now you don't have to worry about the order of the dimensions in your model, just their meaning!\n", - "\n", - "2. The `trial_preference` computation aligns dimensions for broadcasting automatically. Note we use {func}`pymc.dims.Deterministic` and not {func}`pymc.Deterministic`, which automatically propagates the `dims` to the model object.\n", - "\n", - "3. The `softmax` operation specifies the `dim` argument, not the positional axis. Note: The parameter is called `dim` and not `core_dims` because we try to stay as close as possible to the Xarray API, which uses `dim` throughout. But we make an exception for distributions because they already have the `dims` argument.\n", - "\n", - "4. The `Categorical` observed variable, like `ZeroSumNormal`, requires a `core_dims` argument to specify which dimension corresponds to the probability vector. Previously, it was necessary to place this dimension explicitly on the rightmost axis -- not any more!\n", - "\n", - "5. Even though dims were not specified for either `trial_preference` or `response`, PyMC automatically infers them.\n", - "\n", - "The graphviz representation looks the same as before." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "994058c6fe2920b3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterparticipant (5) x trial (20)\n", - "\n", - "participant (5) x trial (20)\n", - "\n", - "\n", - "clusterparticipant (5) x item (3)\n", - "\n", - "participant (5) x item (3)\n", - "\n", - "\n", - "clusteritem (3) x trial (20)\n", - "\n", - "item (3) x trial (20)\n", - "\n", - "\n", - "clusterparticipant (5) x item (3) x trial (20)\n", - "\n", - "participant (5) x item (3) x trial (20)\n", - "\n", - "\n", - "\n", - "observed_response\n", - "\n", - "observed_response\n", - "~\n", - "Data\n", - "\n", - "\n", - "\n", - "response\n", - "\n", - "response\n", - "~\n", - "Categorical\n", - "\n", - "\n", - "\n", - "response->observed_response\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "participant_preference\n", - "\n", - "participant_preference\n", - "~\n", - "ZeroSumNormal\n", - "\n", - "\n", - "\n", - "trial_preference\n", - "\n", - "trial_preference\n", - "~\n", - "Deterministic\n", - "\n", - "\n", - "\n", - "participant_preference->trial_preference\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "time_effects\n", - "\n", - "time_effects\n", - "~\n", - "Normal\n", - "\n", - "\n", - "\n", - "time_effects->trial_preference\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "trial_preference->response\n", - "\n", - "\n", - "\n", - "\n", - "\n" + }, + { + "cell_type": "markdown", + "id": "41bccc7c213e1be3", + "metadata": {}, + "source": [ + "And here's the equivalent model using the {mod}`pymc.dims` module." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3ff29a7a93236486", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/ricardo/Documents/pymc/pymc/dims/__init__.py:66: UserWarning: The `pymc.dims` module is experimental and may contain critical bugs (p=0.676).\n", + "Please report any issues you encounter at https://github.com/pymc-devs/pymc/issues.\n", + "Disclaimer: This an experimental API and may change at any time.\n", + " __init__()\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "import pymc.dims as pmd" ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dmodel.to_graphviz()" - ] - }, - { - "cell_type": "markdown", - "id": "24f2cafa1500f08a", - "metadata": {}, - "source": [ - "We can also check that the models are equivalent by comparing the `logp` of each variable evaluated at the initial_point." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "11073d9b67a72f72", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:34.534342Z", - "start_time": "2025-06-30T15:53:30.457494Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'participant_preference': np.float64(-9.19), 'time_effects': np.float64(-55.14), 'response': np.float64(-109.86)}\n", - "{'participant_preference': np.float64(-9.19), 'time_effects': np.float64(-55.14), 'response': np.float64(-109.86)}\n" - ] - } - ], - "source": [ - "print(model.point_logps())\n", - "print(dmodel.point_logps())" - ] - }, - { - "cell_type": "markdown", - "id": "ee981114897c7ee0", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:31:08.349480Z", - "start_time": "2025-06-26T21:31:07.387385Z" - } - }, - "source": [ - "## A brief look under the hood" - ] - }, - { - "cell_type": "markdown", - "id": "e360b5f5e9e8ca1e", - "metadata": {}, - "source": [ - "The {mod}`pymc.dims` module functionality is built on top of the experimental {mod}`pytensor.xtensor` module in PyTensor, which is the {lib}`xarray` analogoue of the {mod}`pytensor.tensor` module you may be familiar with (see {doc}`pymc_and_pytensor`).\n", - "\n", - "Whereas regular distributions and math operations return {class}`~pytensor.tensor.TensorVariable` objects, the corresponding functions in the {mod}`pymc.dims` module returns {class}`~pytensor.xtensor.type.XTensorVariable` objects. These are very similar to {class}`~pytensor.tensor.TensorVariable`, but they have a `dims` attribute that determines their behavior." - ] - }, - { - "cell_type": "markdown", - "id": "5a1fa8ecb7309782", - "metadata": {}, - "source": [ - "As an example, we'll create a regular {class}`~pymc.Normal` random variable with 3 elements, and perform an outer addition on them using NumPy syntax." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9bece958e432c369", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:34.611874Z", - "start_time": "2025-06-30T15:53:34.598883Z" - } - }, - "outputs": [ + }, { - "data": { - "text/plain": [ - "TensorType(float64, shape=(3,))" + "cell_type": "code", + "execution_count": 6, + "id": "c020e450cc165e46", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:29.997156Z", + "start_time": "2025-06-30T15:53:29.935025Z" + } + }, + "outputs": [], + "source": [ + "with pm.Model(coords=coords) as dmodel:\n", + " observed_response = pmd.Data(\n", + " \"observed_response\", observed_response_np, dims=(\"participant\", \"trial\")\n", + " )\n", + " participant_preference = pmd.ZeroSumNormal(\n", + " \"participant_preference\", core_dims=\"item\", dims=(\"participant\", \"item\")\n", + " )\n", + "\n", + " # Shared time effects across all participants\n", + " time_effects = pmd.Normal(\"time_effects\", dims=(\"item\", \"trial\"))\n", + "\n", + " trial_preference = pmd.Deterministic(\n", + " \"trial_preference\",\n", + " participant_preference + time_effects,\n", + " )\n", + "\n", + " response = pmd.Categorical(\n", + " \"response\",\n", + " p=pmd.math.softmax(trial_preference, dim=\"item\"),\n", + " core_dims=\"item\",\n", + " observed=observed_response,\n", + " )" ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "regular_normal = pm.Normal.dist(mu=pm.math.as_tensor([0, 1, 2]), sigma=1, shape=(3,))\n", - "regular_normal.type" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5c0aa77e31170c54", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:34.700430Z", - "start_time": "2025-06-30T15:53:34.693544Z" - } - }, - "outputs": [ + }, { - "data": { - "text/plain": [ - "pytensor.tensor.variable.TensorVariable" + "cell_type": "markdown", + "id": "3985ee1b52708c0c", + "metadata": {}, + "source": [ + "Note we still use the same {class}`~pymc.Model` constructor, but everything else was now defined with an equivalent function or class defined in the {mod}`pymc.dims` module.\n", + "\n", + "There are some notable differences:\n", + "\n", + "1. `ZeroSumNormal` takes a `core_dims` argument instead of `n_zerosum_axes`. This tells PyMC which of the `dims` that define the distribution are constrained to be zero-summed. All distributions that take non-scalar parameters now require a `core_dims` argument. Previously, they were assumed to be right-aligned by the user (see more in {doc}`dimensionality`). Now you don't have to worry about the order of the dimensions in your model, just their meaning!\n", + "\n", + "2. The `trial_preference` computation aligns dimensions for broadcasting automatically. Note we use {func}`pymc.dims.Deterministic` and not {func}`pymc.Deterministic`, which automatically propagates the `dims` to the model object.\n", + "\n", + "3. The `softmax` operation specifies the `dim` argument, not the positional axis. Note: The parameter is called `dim` and not `core_dims` because we try to stay as close as possible to the Xarray API, which uses `dim` throughout. But we make an exception for distributions because they already have the `dims` argument.\n", + "\n", + "4. The `Categorical` observed variable, like `ZeroSumNormal`, requires a `core_dims` argument to specify which dimension corresponds to the probability vector. Previously, it was necessary to place this dimension explicitly on the rightmost axis -- not any more!\n", + "\n", + "5. Even though dims were not specified for either `trial_preference` or `response`, PyMC automatically infers them.\n", + "\n", + "The graphviz representation looks the same as before." ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(regular_normal)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d77168a8c6e89de9", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:34.805130Z", - "start_time": "2025-06-30T15:53:34.792870Z" - } - }, - "outputs": [ + }, { - "data": { - "text/plain": [ - "TensorType(float64, shape=(3, 3))" + "cell_type": "code", + "execution_count": 7, + "id": "994058c6fe2920b3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "clusterparticipant (5) x trial (20)\n", + "\n", + "participant (5) x trial (20)\n", + "\n", + "\n", + "clusterparticipant (5) x item (3)\n", + "\n", + "participant (5) x item (3)\n", + "\n", + "\n", + "clusteritem (3) x trial (20)\n", + "\n", + "item (3) x trial (20)\n", + "\n", + "\n", + "clusterparticipant (5) x item (3) x trial (20)\n", + "\n", + "participant (5) x item (3) x trial (20)\n", + "\n", + "\n", + "\n", + "observed_response\n", + "\n", + "observed_response\n", + "~\n", + "Data\n", + "\n", + "\n", + "\n", + "response\n", + "\n", + "response\n", + "~\n", + "Categorical\n", + "\n", + "\n", + "\n", + "response->observed_response\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "participant_preference\n", + "\n", + "participant_preference\n", + "~\n", + "ZeroSumNormal\n", + "\n", + "\n", + "\n", + "trial_preference\n", + "\n", + "trial_preference\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "participant_preference->trial_preference\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "time_effects\n", + "\n", + "time_effects\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "time_effects->trial_preference\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "trial_preference->response\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dmodel.to_graphviz()" ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "outer_addition = regular_normal[:, None] + regular_normal[None, :]\n", - "outer_addition.type" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ac95fe88c1877fbe", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:34.930130Z", - "start_time": "2025-06-30T15:53:34.887799Z" - }, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.61284312, 1.68384684, 1.72225487],\n", - " [1.68384684, 2.75485056, 2.79325859],\n", - " [1.72225487, 2.79325859, 2.83166662]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pm.draw(outer_addition, random_seed=rng)" - ] - }, - { - "cell_type": "markdown", - "id": "b7d78e2f3984adbc", - "metadata": {}, - "source": [ - "Here's the same operation with a dimmed `Normal` variable. It requires the use of `rename`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "4d9e8417789d4c18", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:35.055530Z", - "start_time": "2025-06-30T15:53:35.045241Z" - } - }, - "outputs": [ + }, { - "data": { - "text/plain": [ - "XTensorType(float64, shape=(3,), dims=('a',))" + "cell_type": "markdown", + "id": "24f2cafa1500f08a", + "metadata": {}, + "source": [ + "We can also check that the models are equivalent by comparing the `logp` of each variable evaluated at the initial_point." ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dims_normal = pmd.Normal.dist(mu=pmd.math.as_xtensor([0, 1, 2], dims=(\"a\",)), sigma=1)\n", - "dims_normal.type" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "1d0c30011c910370", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:35.264315Z", - "start_time": "2025-06-30T15:53:35.256071Z" - } - }, - "outputs": [ + }, { - "data": { - "text/plain": [ - "pytensor.xtensor.type.XTensorVariable" + "cell_type": "code", + "execution_count": 8, + "id": "11073d9b67a72f72", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:34.534342Z", + "start_time": "2025-06-30T15:53:30.457494Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'participant_preference': np.float64(-9.19), 'time_effects': np.float64(-55.14), 'response': np.float64(-109.86)}\n", + "{'participant_preference': np.float64(-9.19), 'time_effects': np.float64(-55.14), 'response': np.float64(-109.86)}\n" + ] + } + ], + "source": [ + "print(model.point_logps())\n", + "print(dmodel.point_logps())" ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(dims_normal)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "923e75f235dcf219", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:35.472551Z", - "start_time": "2025-06-30T15:53:35.465427Z" - } - }, - "outputs": [ + }, { - "data": { - "text/plain": [ - "XTensorType(float64, shape=(3, 3), dims=('a', 'b'))" + "cell_type": "markdown", + "id": "ee981114897c7ee0", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:31:08.349480Z", + "start_time": "2025-06-26T21:31:07.387385Z" + } + }, + "source": [ + "## A brief look under the hood" ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "outer_addition = dims_normal + dims_normal.rename({\"a\": \"b\"})\n", - "outer_addition.type" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "cd9e2a2634a0e898", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:35.758679Z", - "start_time": "2025-06-30T15:53:35.649256Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 3.76355516, 0.31059132, 6.5420105 ],\n", - " [ 0.31059132, -3.14237253, 3.08904666],\n", - " [ 6.5420105 , 3.08904666, 9.32046584]])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pm.draw(outer_addition, random_seed=rng)" - ] - }, - { - "cell_type": "markdown", - "id": "90d65f41-2279-4205-bc2e-c5446a176c61", - "metadata": {}, - "source": [ - ":::{tip} Note that there are no coordinates anywhere in the graph. {class}`~pytensor.xtensor.type.XTensorVariable`s behave like xarray DataArrays **without** coords. Dims determine the dimension meaning and alignment, but no extra work can be done to reason within a dim. We discuss this limitation in more detail at the end.:::" - ] - }, - { - "cell_type": "markdown", - "id": "e12c7cda782cac69", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:31:08.365035095Z", - "start_time": "2025-06-26T11:42:19.021166Z" - } - }, - "source": [ - "## Redundant (or implicit) dims" - ] - }, - { - "cell_type": "markdown", - "id": "4ac4877e24859dfe", - "metadata": {}, - "source": [ - "When defining deterministic operations or creating variables whose dimension are all implied by the parameters, there's no need to specify the `dims` argument, as PyMC will automatically know them.\n", - "\n", - "However, some users might want to specify `dims` anyway, to check that the dimensions of the variables are as expected, or to provide type hints for someone reading the model.\n", - "\n", - "PyMC allows specifying dimensions in these cases. To reduce confusion, the output will always be transposed to be aligned with the user-specified dims." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "cd6031d682b88e51", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:35.989010Z", - "start_time": "2025-06-30T15:53:35.965369Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "det_implicit_dims.dims=('a', 'b')\n", - "det_explicit_dims.dims=('a', 'b')\n", - "det_transposed_dims.dims=('b', 'a')\n" - ] - } - ], - "source": [ - "with pm.Model(coords={\"a\": range(2), \"b\": range(5)}) as example:\n", - " x = pmd.Normal(\"x\", dims=(\"a\", \"b\"))\n", - " det_implicit_dims = pmd.Deterministic(\"det1\", x + 1)\n", - " det_explicit_dims = pmd.Deterministic(\"det2\", x + 1, dims=(\"a\", \"b\"))\n", - " det_transposed_dims = pmd.Deterministic(\"y\", x + 1, dims=(\"b\", \"a\"))\n", - "\n", - "print(f\"{det_implicit_dims.dims=}\")\n", - "print(f\"{det_explicit_dims.dims=}\")\n", - "print(f\"{det_transposed_dims.dims=}\")" - ] - }, - { - "cell_type": "markdown", - "id": "db657b20b447c56a", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:31:08.365174897Z", - "start_time": "2025-06-26T11:42:19.389561Z" - } - }, - "source": [ - "This happens with `Deterministic`, `Potential` and every distribution in the `dims` module.\n", - "Any time you specify `dims`, you will get back a variable with dimensions in the same order." - ] - }, - { - "cell_type": "markdown", - "id": "e5f73575a51d316a", - "metadata": {}, - "source": [ - "Furthermore -- and unlike regular PyMC objects -- it is now valid to use ellipsis in the `dims` argument.\n", - "As in an Xarray `transpose`, it means all the other dimensions should stay in the same order." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "a1e3597de136004f", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:36.397445Z", - "start_time": "2025-06-30T15:53:36.371682Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "det_ellipsis1.dims=('a', 'b')\n", - "det_ellipsis2.dims=('b', 'a')\n", - "det_ellipsis3.dims=('b', 'a')\n" - ] - } - ], - "source": [ - "with pm.Model(coords={\"a\": range(2), \"b\": range(5)}) as example:\n", - " x = pmd.Normal(\"x\", dims=(\"a\", \"b\"))\n", - " det_ellipsis1 = pmd.Deterministic(\"det1\", x + 1, dims=(...,))\n", - " det_ellipsis2 = pmd.Deterministic(\"det2\", x + 1, dims=(..., \"a\"))\n", - " det_ellipsis3 = pmd.Deterministic(\"det3\", x + 1, dims=(\"b\", ...))\n", - "\n", - "print(f\"{det_ellipsis1.dims=}\")\n", - "print(f\"{det_ellipsis2.dims=}\")\n", - "print(f\"{det_ellipsis3.dims=}\")" - ] - }, - { - "cell_type": "markdown", - "id": "318c563fb2eb106b", - "metadata": {}, - "source": [ - "## What functionality is supported?" - ] - }, - { - "cell_type": "markdown", - "id": "88acbfae651fe294", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "The documentation is still a work in progress, and there is no complete list of distributions and operations that are supported just yet. \n", - "\n", - "#### Model constructors\n", - "\n", - "The following PyMC model constructors are available in the `dims` module.\n", - "\n", - " * {func}`~pymc.dims.Data`\n", - " * {func}`~pymc.dims.Deterministic`\n", - " * {func}`~pymc.dims.Potential`\n", - "\n", - "They all return {class}`~pytensor.xtensor.type.XTensorVariable` objects, and either infer `dims` from the input or require the user to specify them explicitly. If they can be inferred, it is possible to transpose and use ellipsis in the `dims` argument, as described above.\n", - "\n", - "#### Distributions\n", - "\n", - "We want to offer all the existing distributions and parametrizations under the {mod}`pymc.dims` module, with the following expected API differences:\n", - "\n", - " * All vector arguments (and observed values) must have known dims. An error is raised otherwise.\n", - "\n", - " * Distributions with non-scalar inputs will require a `core_dims` argument. The meaning of the `core_dims` argument will be denoted in the docstrings of each distribution. For example, for the MvNormal, the `core_dims` are the two dimensions of the covariance matrix, one (and only one) of which must also be present in the mean parameter. The shared `core_dim` is the one that persists in the output. Sometimes the order of `core_dims` will be important!\n", - "\n", - " * `dims` accept ellipsis, and variables are transposed to match the user-specified `dims` argument.\n", - "\n", - " * `shape` and `size` cannot be provided.\n", - "\n", - " * The {meth}`~pymc.distributions.core.DimDistribution.dist` method accepts a `dims_length` argument, of the form `{dim_name: dim_length}`.\n", - "\n", - " * Only transforms defined in {mod}`pymc.dims.transforms` can be used with distributions from the module.\n", - "\n", - "#### Operations on variables\n", - "\n", - "Calling a PyMC distribution from the {mod}`pymc.dims` module returns an {class}`~pytensor.xtensor.type.XTensorVariable`.\n", - "\n", - "The expectation is that every {class}`xarray.DataArray` method in Xarray should have an equivalent version for XTensorVariables. So if you can do `x.diff(dim=\"a\")` in Xarray, you should be able to do `x.diff(dim=\"a\")` with XTensorVariables as well.\n", - "\n", - "In addition, many numerical operations are available in the {mod}`pymc.dims.math` module, which provides a superset of `ufuncs` functions found in Xarray (like `exp`). It also includes submodules such as `linalg` that provide counterpart to libraries like {lib}`xarray_einstats` (such as `linalg.solve`).\n", - "\n", - "Finally, functions that are available at the module level in Xarray (like `concat`) are also available in the {mod}`pymc.dims` namespace.\n", - "\n", - "You can find the original documentation of these functions and methods in the {doc}`PyTensor Xtensor docs `\n", - "\n", - "To facilitate adoption of these functions and methods, we try to follow the same API used by Xarray and related packages. However, some methods or keyword arguments won't be supported explicitly (like {meth}`~pytensor.xtensor.XTensorVariable.sel`, more on that at the end), in which case an informative error or warning will be raised.\n", - "\n", - "If you find an API difference or some missing functionality, and no reason is provided, please [open an issue](https://github.com/pymc-devs/pymc/issues) to let us know (after checking nobody has done it already).\n", - "\n", - "In the meantime, the next section provides some hints on how to make use of pre-existing functionality in PyMC/PyTensor." - ] - }, - { - "cell_type": "markdown", - "id": "98a94f5939872e75", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:31:08.365484979Z", - "start_time": "2025-06-26T11:42:19.577389Z" - } - }, - "source": [ - "## Combining dims module with the old API" - ] - }, - { - "cell_type": "markdown", - "id": "3d305a2bb8313704", - "metadata": {}, - "source": [ - "Because the {mod}`pymc.dims` module is more recent, it does not offer all the functionality of the old API.\n", - "\n", - "You can always combine the two APIs by converting the variables explicitly. To obtain a regular non-dimmed variable from a dimmed variable, you can use {attr}`~pytensor.xtensor.type.XTensorVariable.values` (like in Xarray) or the more verbose {func}`pymc.dims.tensor_from_xtensor`.\n", - "\n", - "Otherwise, if you try to pass an {class}`~pytensor.xtensor.type.XTensorVariable` to a function or distribution that does not support it, you will usually see an error like this:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "df3d1f1ce895d02e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TypeError: To avoid subtle bugs, PyTensor forbids automatic conversion of XTensorVariable to TensorVariable.\n", - "You can convert explicitly using `x.values` or pass `allow_xtensor_conversion=True`.\n" - ] - } - ], - "source": [ - "mu = pmd.math.as_xtensor([0, 1, 2], dims=(\"a\",))\n", - "try:\n", - " pm.Normal.dist(mu=mu)\n", - "except TypeError as e:\n", - " print(f\"{e.__class__.__name__}: {e}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "335b094a6cdd0bd8", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:36.767188Z", - "start_time": "2025-06-30T15:53:36.745075Z" - } - }, - "outputs": [ + }, { - "data": { - "text/plain": [ - "TensorType(float64, shape=(None, None))" + "cell_type": "markdown", + "id": "e360b5f5e9e8ca1e", + "metadata": {}, + "source": [ + "The {mod}`pymc.dims` module functionality is built on top of the experimental {mod}`pytensor.xtensor` module in PyTensor, which is the [xarray](https://docs.xarray.dev/) analogoue of the {mod}`pytensor.tensor` module you may be familiar with (see {doc}`pymc_pytensor`).\n", + "\n", + "Whereas regular distributions and math operations return {class}`~pytensor.tensor.TensorVariable` objects, the corresponding functions in the {mod}`pymc.dims` module returns {class}`~pytensor.xtensor.type.XTensorVariable` objects. These are very similar to {class}`~pytensor.tensor.TensorVariable`, but they have a `dims` attribute that determines their behavior." ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pm.Normal.dist(mu=x.values).type" - ] - }, - { - "cell_type": "markdown", - "id": "eca88323e2529a57", - "metadata": {}, - "source": [ - "The order of the dimensions follows that specified in the {attr}`~pytensor.xtensor.type.XTensorVariable.dims` property. To be sure this matches the expectation you can use a {meth}`~pytensor.xtensor.type.XTensorVariable.transpose` operation to reorder the dimensions before converting to a regular variable." - ] - }, - { - "cell_type": "markdown", - "id": "9e256f247d3bd9e1", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:31:08.365732804Z", - "start_time": "2025-06-26T11:42:19.755931Z" - } - }, - "source": [ - "Conversely, if you try to pass a regular variable to a function or distribution that expects an XTensorVariable, you will see an error like this:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "380f7161ca2d22e4", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:36.879320Z", - "start_time": "2025-06-30T15:53:36.868282Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ValueError: Variable mu_x{[0 1 2]} must have dims associated with it.\n", - "To avoid subtle bugs, PyMC does not make any assumptions about the dims of parameters.\n", - "Use `as_xtensor` with the `dims` keyword argument to specify the dims explicitly.\n" - ] - } - ], - "source": [ - "mu = pm.math.as_tensor([0, 1, 2], name=\"mu_x\")\n", - "try:\n", - " x = pmd.Normal.dist(mu=mu)\n", - "except Exception as e:\n", - " print(f\"{e.__class__.__name__}: {e}\")" - ] - }, - { - "cell_type": "markdown", - "id": "e507c0a8e76447fd", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:31:08.365847399Z", - "start_time": "2025-06-26T11:42:19.826864Z" - } - }, - "source": [ - "Which you can avoid by explicitly converting the variable to a dimmed variable:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "b50363261fe81df6", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:37.226477Z", - "start_time": "2025-06-30T15:53:37.204756Z" - } - }, - "outputs": [ + }, { - "data": { - "text/plain": [ - "XTensorType(float64, shape=(3,), dims=('a',))" + "cell_type": "markdown", + "id": "5a1fa8ecb7309782", + "metadata": {}, + "source": [ + "As an example, we'll create a regular {class}`~pymc.Normal` random variable with 3 elements, and perform an outer addition on them using NumPy syntax." ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pmd.Normal.dist(mu=pmd.as_xtensor(mu, dims=(\"a\",))).type" - ] - }, - { - "cell_type": "markdown", - "id": "3f2202b55cbc8c4", - "metadata": {}, - "source": [ - "#### Applied example\n", - "\n", - "To put this to practice, let's write a model that uses the {class}`~pymc.LKJCholeskyCov` distribution, which at the time of writing is not yet available in the {mod}`pymc.dims` module." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "195f6f509e797f9a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "chol_xr.dims=('core1', 'core2')\n", - "mu.dims=('batch', 'core1')\n", - "y.dims=('batch', 'core1')\n" - ] - } - ], - "source": [ - "with pm.Model(coords={\"core1\": range(3), \"core2\": range(3), \"batch\": range(5)}) as mixed_api_model:\n", - " chol, _, _ = pm.LKJCholeskyCov(\n", - " \"chol\",\n", - " eta=1,\n", - " n=3,\n", - " sd_dist=pm.Exponential.dist(1),\n", - " )\n", - " chol_xr = pmd.as_xtensor(chol, dims=(\"core1\", \"core2\"))\n", - "\n", - " mu = pmd.Normal(\"mu\", dims=(\"batch\", \"core1\"))\n", - " y = pmd.MvNormal(\n", - " \"y\",\n", - " mu,\n", - " chol=chol_xr,\n", - " core_dims=(\"core1\", \"core2\"),\n", - " )\n", - "\n", - "print(f\"{chol_xr.dims=}\")\n", - "print(f\"{mu.dims=}\")\n", - "print(f\"{y.dims=}\")" - ] - }, - { - "cell_type": "markdown", - "id": "3f0dd0fea61f8862", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:31:08.366068103Z", - "start_time": "2025-06-26T11:42:19.946909Z" - } - }, - "source": [ - "Note that we had to pass a \"regular\" {class}`~pymc.Exponential` distribution to the {class}`~pymc.LKJCholeskyCov` constructor. In general, distribution \"factories\" which are parametrized by unnamed distributions created with {meth}`~pymc.distributions.distribution.Distribution.dist` won't work with variables created with the {mod}`pymc.dims` module." - ] - }, - { - "cell_type": "markdown", - "id": "6464ae49ff629660", - "metadata": {}, - "source": [ - "## Case study: a splines model begging for vectorization" - ] - }, - { - "cell_type": "markdown", - "id": "c7c622bbde1c675", - "metadata": {}, - "source": [ - "The model below was presented by a user in a bug report. While granting that the author might have written the model this way to provide a reproducible example, we can say that the model is written in a highly suboptimal form. Specifically it misses (or actively breaks) many opportunities for vectorization. Seasoned Python programmers will know that python loops are SLOW, and that tools like numpy provide a way to escape from this handicap.\n", - "\n", - "PyMC code is not exactly numpy, for starters it uses a lazy symbolic computation library (PyTensor) that generates compiled code on demand. But it very much likes to be given numpy-like code. To begin with these graphs are much smaller and therefore easier to reason about (in fact the original bug could only be triggered for graphs with more than 500 nodes). Secondly, numpy-like graphs naturally translate to vectorized CPU and GPU code, which you want at the end of the day." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "726f696e6bf17d44", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:39.748768Z", - "start_time": "2025-06-30T15:53:39.740157Z" - } - }, - "outputs": [], - "source": [ - "# Simulated data of some spline\n", - "N = 500\n", - "x_np = np.linspace(0, 10, N)\n", - "y_obs_np = np.piecewise(\n", - " x_np,\n", - " [x_np <= 3, (x_np > 3) & (x_np <= 7), x_np > 7],\n", - " [lambda x: 0.5 * x, lambda x: 1.5 + 0.2 * (x - 3), lambda x: 2.3 - 0.1 * (x - 7)],\n", - ")\n", - "y_obs_np += rng.normal(0, 0.2, size=N) # Add noise\n", - "\n", - "# Artificial groups\n", - "groups = [0, 1, 2]\n", - "group_idx_np = np.random.choice(groups, size=N)\n", - "\n", - "n_knots = 50\n", - "knots_np = np.linspace(0, 10, num=n_knots)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "2e8519125d17f0c8", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:40.367851Z", - "start_time": "2025-06-30T15:53:39.854962Z" - } - }, - "outputs": [], - "source": [ - "with pm.Model() as non_vectorized_splines_model:\n", - " sigma_beta0 = pm.HalfNormal(\"sigma_beta0\", sigma=10)\n", - " sigma = pm.HalfCauchy(\"sigma\", beta=1)\n", - "\n", - " # Create likelihood per group\n", - " for gr in groups:\n", - " idx = group_idx_np == gr\n", - "\n", - " beta0 = pm.HalfNormal(f\"beta0_{gr}\", sigma=sigma_beta0)\n", - " z = pm.Normal(f\"z_{gr}\", mu=0, sigma=2, shape=n_knots)\n", - "\n", - " delta_factors = pm.math.softmax(z)\n", - " slope_factors = 1 - pm.math.cumsum(delta_factors[:-1])\n", - " spline_slopes = pm.math.stack(\n", - " [beta0] + [beta0 * slope_factors[i] for i in range(n_knots - 1)]\n", - " )\n", - " beta = pm.Deterministic(\n", - " f\"beta_{gr}\",\n", - " pm.math.concatenate(([beta0], pm.math.diff(spline_slopes))),\n", - " )\n", - "\n", - " hinge_terms = [pm.math.maximum(0, x_np[idx] - knot) for knot in knots_np]\n", - " X = pm.math.stack([hinge_terms[i] for i in range(n_knots)], axis=1)\n", - "\n", - " mu = pm.math.dot(X, beta)\n", - "\n", - " pm.Normal(f\"y_{gr}\", mu=mu, sigma=sigma, observed=y_obs_np[idx])" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "c56dcb51a07a5b54", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:40.700094Z", - "start_time": "2025-06-30T15:53:40.463806Z" - } - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "cluster50\n", - "\n", - "50\n", - "\n", - "\n", - "cluster176\n", - "\n", - "176\n", - "\n", - "\n", - "cluster175\n", - "\n", - "175\n", - "\n", - "\n", - "cluster149\n", - "\n", - "149\n", - "\n", - "\n", - "\n", - "beta0_1\n", - "\n", - "beta0_1\n", - "~\n", - "Halfnormal\n", - "\n", - "\n", - "\n", - "beta_1\n", - "\n", - "beta_1\n", - "~\n", - "Deterministic\n", - "\n", - "\n", - "\n", - "beta0_1->beta_1\n", - "\n", - 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"sigma_beta0\n", - "\n", - "sigma_beta0\n", - "~\n", - "Halfnormal\n", - "\n", - "\n", - "\n", - "sigma_beta0->beta0_1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "sigma_beta0->beta0_0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "sigma_beta0->beta0_2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "beta_1->y_1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "beta_2->y_2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "beta_0->y_0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "z_2\n", - "\n", - "z_2\n", - "~\n", - "Normal\n", - "\n", - "\n", - "\n", - "z_2->beta_2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "z_1\n", - "\n", - "z_1\n", - "~\n", - "Normal\n", - "\n", - "\n", - "\n", - "z_1->beta_1\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "z_0\n", - "\n", - "z_0\n", - "~\n", - "Normal\n", - "\n", - "\n", - "\n", - "z_0->beta_0\n", - "\n", - "\n", - "\n", - "\n", - "\n" + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9bece958e432c369", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:34.611874Z", + "start_time": "2025-06-30T15:53:34.598883Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TensorType(float64, shape=(3,))" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "regular_normal = pm.Normal.dist(mu=pm.math.as_tensor([0, 1, 2]), sigma=1, shape=(3,))\n", + "regular_normal.type" ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "non_vectorized_splines_model.to_graphviz()" - ] - }, - { - "cell_type": "markdown", - "id": "6be0b8afd4990567", - "metadata": {}, - "source": [ - "This version of the model has several `for` loop and list comprehensions.\n", - "Every Python iteration extends the computational graph (basically unrolling the loop), which becomes infeasible for PyMC to handle.\n", - "\n", - "The use of 3 likelihood and sets of priors is otherwise fine and can make more sense in some cases.\n", - "\n", - "We can improve the performance of this model by replacing the loop with vectorized operations.\n", - "We'll demonstrate first with current PyMC features, and then with dims." - ] - }, - { - "cell_type": "markdown", - "id": "33d16a1bef908f3f", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:31:52.686637Z", - "start_time": "2025-06-26T21:31:52.509222Z" - } - }, - "source": [ - "### Old style vectorization" - ] - }, - { - "cell_type": "markdown", - "id": "d8add515e2e56179", - "metadata": {}, - "source": [ - "\n", - "First, we'll introduce coords and {func}`~pymc.Data` to explicitly define the coordinate system, making the model's dimensionality clear and facilitating posterior analysis with labeled dimensions." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "6ee0f6e24fa81b02", - "metadata": {}, - "outputs": [], - "source": [ - "coords = {\n", - " \"group\": range(3),\n", - " \"knots\": range(n_knots),\n", - " \"obs\": range(N),\n", - "}\n", - "with pm.Model(coords=coords) as vectorized_splines_model:\n", - " x = pm.Data(\"x\", x_np, dims=\"obs\")\n", - " y_obs = pm.Data(\"y_obs\", y_obs_np, dims=\"obs\")\n", - "\n", - " knots = pm.Data(\"knots\", knots_np, dims=\"knot\")\n", - "\n", - " sigma = pm.HalfCauchy(\"sigma\", beta=1)\n", - " sigma_beta0 = pm.HalfNormal(\"sigma_beta0\", sigma=10)\n", - " beta0 = pm.HalfNormal(\"beta_0\", sigma=sigma_beta0, dims=\"group\")\n", - " z = pm.Normal(\"z\", dims=(\"group\", \"knot\"))\n", - "\n", - " delta_factors = pm.math.softmax(z, axis=-1) # (groups, knot)\n", - " slope_factors = 1 - pm.math.cumsum(delta_factors[:, :-1], axis=-1) # (groups, knot-1)\n", - " spline_slopes = pm.math.concatenate(\n", - " [beta0[:, None], beta0[:, None] * slope_factors], axis=-1\n", - " ) # (groups, knot-1)\n", - " beta = pm.math.concatenate(\n", - " [beta0[:, None], pm.math.diff(spline_slopes, axis=-1)], axis=-1\n", - " ) # (groups, knot)\n", - "\n", - " beta = pm.Deterministic(\"beta\", beta, dims=(\"group\", \"knot\"))\n", - "\n", - " X = pm.math.maximum(0, x[:, None] - knots[None, :]) # (n, knot)\n", - " mu = (X * beta[group_idx_np]).sum(-1) # ((n, knots) * (n, knots)).sum(-1) = (n,)\n", - " y = pm.Normal(\"y\", mu=mu, sigma=sigma, observed=y_obs, dims=\"obs\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "bd94fa06a1190a05", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:41.063986Z", - "start_time": "2025-06-30T15:53:40.980450Z" - } - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterobs (500)\n", - "\n", - "obs (500)\n", - "\n", - "\n", - "clusterknot (50)\n", - "\n", - "knot (50)\n", - "\n", - "\n", - "clustergroup (3)\n", - "\n", - "group (3)\n", - "\n", - "\n", - "clustergroup (3) x knot (50)\n", - "\n", - "group (3) x knot (50)\n", - "\n", - "\n", - "\n", - "x\n", - "\n", - "x\n", - "~\n", - "Data\n", - "\n", - "\n", - "\n", - "y\n", - "\n", - "y\n", - "~\n", - "Normal\n", - "\n", - "\n", - "\n", - "x->y\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "y_obs\n", - "\n", - "y_obs\n", - "~\n", - "Data\n", - "\n", - "\n", - "\n", - "y->y_obs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "knots\n", - "\n", - "knots\n", - "~\n", - "Data\n", - "\n", - "\n", - "\n", - "knots->y\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "sigma\n", - "\n", - "sigma\n", - "~\n", - "Halfcauchy\n", - "\n", - "\n", - "\n", - "sigma->y\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "sigma_beta0\n", - "\n", - "sigma_beta0\n", - "~\n", - "Halfnormal\n", - "\n", - "\n", - "\n", - "beta_0\n", - "\n", - "beta_0\n", - "~\n", - "Halfnormal\n", - "\n", - "\n", - "\n", - "sigma_beta0->beta_0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "beta\n", - "\n", - "beta\n", - "~\n", - "Deterministic\n", - "\n", - "\n", - "\n", - "beta_0->beta\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "beta->y\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "z\n", - "\n", - "z\n", - "~\n", - "Normal\n", - "\n", - "\n", - "\n", - "z->beta\n", - "\n", - "\n", - "\n", - "\n", - "\n" + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5c0aa77e31170c54", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:34.700430Z", + "start_time": "2025-06-30T15:53:34.693544Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pytensor.tensor.variable.TensorVariable" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "type(regular_normal)" ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vectorized_splines_model.to_graphviz()" - ] - }, - { - "cell_type": "markdown", - "id": "a98bbe7f2e17783a", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:32:15.059906Z", - "start_time": "2025-06-26T21:32:15.024281Z" - } - }, - "source": [ - "Looking only at grahpviz does not reveal much difference. The real complexity is hidden in the underlying computational graph. Let us measure it." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "90f8f4ee3fd9f2a3", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:41.192185Z", - "start_time": "2025-06-30T15:53:41.162347Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Non-vectorized model has 806 nodes\n", - "Vectorized model has 38 nodes\n" - ] - } - ], - "source": [ - "from pytensor.graph import ancestors\n", - "\n", - "\n", - "def count_nodes(model):\n", - " return len({v for v in ancestors(model.basic_RVs) if v.owner})\n", - "\n", - "\n", - "print(f\"Non-vectorized model has {count_nodes(non_vectorized_splines_model)} nodes\")\n", - "print(f\"Vectorized model has {count_nodes(vectorized_splines_model)} nodes\")" - ] - }, - { - "cell_type": "markdown", - "id": "daa39293278cbb05", - "metadata": {}, - "source": [ - "This version of the model is much more succinct and performant.\n", - "\n", - "However, it's considerably hard to write, specially for beginners. It requires familiarity with Numpy unusual broadcasting syntax and mental gymnastics to keep track of the dimensions." - ] - }, - { - "cell_type": "markdown", - "id": "a532b2ec365bfecf", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:32:17.432572Z", - "start_time": "2025-06-26T21:32:17.415088Z" - } - }, - "source": [ - "### Vectorization with dims" - ] - }, - { - "cell_type": "markdown", - "id": "c24ce399f0eeae4c", - "metadata": {}, - "source": [ - "Here is a version of the model using {mod}`pymc.dims`, which we hope you'll agree is more intuitive" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "1bf2c6b99378b2db", - "metadata": {}, - "outputs": [], - "source": [ - "with pm.Model(coords=coords) as dims_splines_model:\n", - " x = pmd.Data(\"x\", x_np, dims=\"obs\")\n", - " y_obs = pmd.Data(\"y_obs\", y_obs_np, dims=\"obs\")\n", - " knots = pmd.Data(\"knots\", knots_np, dims=(\"knot\",))\n", - " group_idx = pmd.math.as_xtensor(group_idx_np, dims=(\"obs\",))\n", - "\n", - " sigma = pmd.HalfCauchy(\"sigma\", beta=1)\n", - " sigma_beta0 = pmd.HalfNormal(\"sigma_beta0\", sigma=10)\n", - " beta0 = pmd.HalfNormal(\"beta_0\", sigma=sigma_beta0, dims=(\"group\",))\n", - " z = pmd.Normal(\"z\", dims=(\"group\", \"knot\"))\n", - "\n", - " delta_factors = pmd.math.softmax(z, dim=\"knot\")\n", - " slope_factors = 1 - delta_factors.isel(knot=slice(None, -1)).cumsum(\"knot\")\n", - " spline_slopes = pmd.concat([beta0, beta0 * slope_factors], dim=\"knot\")\n", - " beta = pmd.Deterministic(\"beta\", pmd.concat([beta0, spline_slopes.diff(\"knot\")], dim=\"knot\"))\n", - "\n", - " X = pmd.math.maximum(0, x - knots)\n", - " mu = (X * beta.isel(group=group_idx)).sum(\"knot\")\n", - " y = pmd.Normal(\"y\", mu=mu, sigma=sigma, observed=y_obs)" - ] - }, - { - "cell_type": "markdown", - "id": "91593e5b3dbc885f", - "metadata": {}, - "source": [ - "Let us confirm it is identical to the previous version." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "5f55cf90d737dcf", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:42.362928Z", - "start_time": "2025-06-30T15:53:42.151483Z" - } - }, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterobs (500)\n", - "\n", - "obs (500)\n", - "\n", - "\n", - "clusterknot (50)\n", - "\n", - "knot (50)\n", - "\n", - "\n", - "clustergroup (3)\n", - "\n", - "group (3)\n", - "\n", - "\n", - "clustergroup (3) x knot (50)\n", - "\n", - "group (3) x knot (50)\n", - "\n", - "\n", - "\n", - "x\n", - "\n", - "x\n", - "~\n", - "Data\n", - "\n", - "\n", - "\n", - "y\n", - "\n", - "y\n", - "~\n", - "Normal\n", - "\n", - "\n", - "\n", - "x->y\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "y_obs\n", - "\n", - "y_obs\n", - "~\n", - "Data\n", - "\n", - "\n", - "\n", - "y->y_obs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "knots\n", - "\n", - "knots\n", - "~\n", - "Data\n", - "\n", - "\n", - "\n", - "knots->y\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "sigma\n", - "\n", - "sigma\n", - "~\n", - "Halfcauchy\n", - "\n", - "\n", - "\n", - "sigma->y\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "sigma_beta0\n", - "\n", - "sigma_beta0\n", - "~\n", - "Halfnormal\n", - "\n", - "\n", - "\n", - "beta_0\n", - "\n", - "beta_0\n", - "~\n", - "Halfnormal\n", - "\n", - "\n", - "\n", - "sigma_beta0->beta_0\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "beta\n", - "\n", - "beta\n", - "~\n", - "Deterministic\n", - "\n", - "\n", - "\n", - "beta_0->beta\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "beta->y\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "z\n", - "\n", - "z\n", - "~\n", - "Normal\n", - "\n", - "\n", - "\n", - "z->beta\n", - "\n", - "\n", - "\n", - "\n", - "\n" + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d77168a8c6e89de9", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:34.805130Z", + "start_time": "2025-06-30T15:53:34.792870Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TensorType(float64, shape=(3, 3))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - "" + "source": [ + "outer_addition = regular_normal[:, None] + regular_normal[None, :]\n", + "outer_addition.type" ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dims_splines_model.to_graphviz()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "3a9dd4669342ff9e", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:42.797760Z", - "start_time": "2025-06-30T15:53:42.794464Z" - } - }, - "outputs": [], - "source": [ - "# Comment out if you want to wait a long while for the results\n", - "# non_vectorized_splines_model.point_logps()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "86701f276baaa7c", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:44.309696Z", - "start_time": "2025-06-30T15:53:43.273978Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'sigma': np.float64(-1.14),\n", - " 'sigma_beta0': np.float64(-0.73),\n", - " 'beta_0': np.float64(-2.18),\n", - " 'z': np.float64(-137.84),\n", - " 'y': np.float64(-319962.47)}" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vectorized_splines_model.point_logps()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "f34c353131915cef", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:46.279261Z", - "start_time": "2025-06-30T15:53:45.344064Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'sigma': np.float64(-1.14),\n", - " 'sigma_beta0': np.float64(-0.73),\n", - " 'beta_0': np.float64(-2.18),\n", - " 'z': np.float64(-137.84),\n", - " 'y': np.float64(-319962.47)}" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dims_splines_model.point_logps()" - ] - }, - { - "cell_type": "markdown", - "id": "1bb865850774d0b2", - "metadata": {}, - "source": [ - "We have just two more nodes in the computation graph, with no impact on performance" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "ae2afa7841251f6c", - "metadata": { - "ExecuteTime": { - "end_time": "2025-07-08T17:06:40.561788Z", - "start_time": "2025-07-08T17:06:40.554136Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vectorized model has 38 nodes\n", - "Vectorized model with dims has 40 nodes\n" - ] - } - ], - "source": [ - "print(f\"Vectorized model has {count_nodes(vectorized_splines_model)} nodes\")\n", - "print(f\"Vectorized model with dims has {count_nodes(dims_splines_model)} nodes\")" - ] - }, - { - "cell_type": "markdown", - "id": "014c701b-1e23-4fc4-ae55-4279263b2ed9", - "metadata": {}, - "source": [ - "This wraps the presentation of the {mod}`pymc.dims` module. We invite you to try it out and report any problems you may find. If you wish to contribute missing functionality get involved in our [Github repository](https://github.com/pymc-devs/pymc)." - ] - }, - { - "cell_type": "markdown", - "id": "2059b00d-6d6a-41af-8e71-3709e6a33dcc", - "metadata": {}, - "source": [ - "## Questions and answers" - ] - }, - { - "cell_type": "markdown", - "id": "8d1a93d8-6fc5-42db-bae0-d4a3fd68abf7", - "metadata": {}, - "source": [ - "### How to get xarray out of XTensorVariables?" - ] - }, - { - "cell_type": "markdown", - "id": "0b8d9835-1bcd-4a20-a925-2ac88045113c", - "metadata": {}, - "source": [ - "Usually PyMC routines take care of converting outputs to Xarray for the user. \n", - "\n", - "If you want to evaluate an expression yourself you'll likely get a numpy array back. \n", - "To convert to a {class}`~xarray.DataArray` simply reuse the `dims` attribute from the PyMC variable." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "7ea9dc87-77e5-40f1-95ec-233984912443", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.DataArray (a: 3, b: 3)> Size: 72B\n",
-       "array([[-3.28099006, -1.57426294,  1.21146118],\n",
-       "       [-1.57426294,  0.13246418,  2.9181883 ],\n",
-       "       [ 1.21146118,  2.9181883 ,  5.70391243]])\n",
-       "Dimensions without coordinates: a, b
" + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ac95fe88c1877fbe", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:34.930130Z", + "start_time": "2025-06-30T15:53:34.887799Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.61284312, 1.68384684, 1.72225487],\n", + " [1.68384684, 2.75485056, 2.79325859],\n", + " [1.72225487, 2.79325859, 2.83166662]])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " Size: 72B\n", - "array([[-3.28099006, -1.57426294, 1.21146118],\n", - " [-1.57426294, 0.13246418, 2.9181883 ],\n", - " [ 1.21146118, 2.9181883 , 5.70391243]])\n", - "Dimensions without coordinates: a, b" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from xarray import DataArray\n", - "\n", - "x = pm.dims.Normal.dist(dim_lengths={\"a\": 3})\n", - "outer_x = x + x.rename({\"a\": \"b\"})\n", - "res_numpy = pm.draw(outer_addition)\n", - "res_xr = DataArray(res_numpy, dims=outer_addition.dims)\n", - "res_xr" - ] - }, - { - "cell_type": "markdown", - "id": "93ec3533ec122baa", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:32:29.344857Z", - "start_time": "2025-06-26T21:32:28.811467Z" - } - }, - "source": [ - "### Why aren't coordinate-based operations supported?" - ] - }, - { - "cell_type": "markdown", - "id": "b20324c9f4f65d4b", - "metadata": {}, - "source": [ - "The new xtensor variable and operations do not propagate information about coordinates, which means you cannot perform coordinate-related operations like `sel`, `loc`, `drop`.\n", - "\n", - "This limitation might be disappointing for someone used to Xarray, it is a necessary trade-off to allow PyMC to evaluate the model in a performant way.\n", - "PyMC uses PyTensor under the hood, which compiles functions to C (or numba or JAX) backends. None of these backends support dims or coordinates.\n", - "To work with these backends, PyTensor rewrites xtensor operations using equivalent tensor operations, which are pretty much abstract NumPy code. This is relatively easy to do, because it's mostly about aligning dimensions for broadcasting or indexing correctly.\n", - "\n", - "Rewriting coordinate-related operations into NumPy-like code is a different matter. Many such operations don't have straightforward equivalency, they are more like querying or joining a database than performing array operations.\n", - "\n", - "PyMC models will keep supporting the `coords` argument as a way to specify dimensions of model variables. But for modeling purposes, only the dimension names and their lengths play a role." - ] - }, - { - "cell_type": "markdown", - "id": "f963a53c229c04b9", - "metadata": {}, - "source": [ - "### One final note of caution on coordinates" - ] - }, - { - "cell_type": "markdown", - "id": "626c004fa7abdccf", - "metadata": {}, - "source": [ - "When you provide coords to a PyMC model, they are attached to any functions that returns Xarray or InferenceData objects.\n", - "\n", - "This creates a potential problem.\n", - "\n", - "Suppose we have multiple arrays with the same dims but different shapes.\n", - "This is legal in PyMC, as in Xarray, and some operations, like indexing or concatenating, can handle it.\n", - "\n", - "However, after sampling, PyMC tries to reattach the coordinates to any computed variables, and these might not have the right shape, or they might not be correctly aligned.\n", - "\n", - "When PyMC tries to convert the results of sampling to InferenceData, it will issue a warning and refuse to propagate the original coordinates.\n", - "\n", - "Here's an example where we have two variables with the `a` dim but different shapes, and only one matches the shape of the coordinates specified in the model." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "2c6f3a2b70a4d77d", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:46.850715Z", - "start_time": "2025-06-30T15:53:46.750208Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [x]\n", - "/home/ricardo/Documents/pymc/pymc/backends/arviz.py:70: UserWarning: Incompatible coordinate length of 3 for dimension 'a' of variable 'y'.\n", - "This usually happens when a sliced or concatenated variable is wrapped as a `pymc.dims.Deterministic`.The originate coordinates for this dim will not be included in the returned dataset for any of the variables. Instead they will default to `np.arange(var_length)` and the shorter variables will be right-padded with nan.\n", - "To make this warning into an error set `pymc.backends.arviz.RAISE_ON_INCOMPATIBLE_COORD_LENGTHS` to `True`\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "Coordinates:\n", - " * chain (chain) int64 8B 0\n", - " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n", - " * a (a) int64 24B 0 1 2" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with pm.Model(coords={\"a\": [-1, 0, 1]}) as m:\n", - " x = pmd.Normal(\"x\", dims=(\"a\",))\n", - " y = pmd.Deterministic(\"y\", x.isel(a=slice(1, None)))\n", - " assert y.dims == (\"a\",)\n", - "\n", - " idata = pm.sample_prior_predictive()\n", - "idata.prior[\"y\"].coords" - ] - }, - { - "cell_type": "markdown", - "id": "a20137348aa4f5bf", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:31:08.371358319Z", - "start_time": "2025-06-26T11:42:20.084741Z" - } - }, - "source": [ - "One way to work around this limitation is to rename the dimensions to avoid the conflict." - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "466f152cb249d1d2", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:47.572296Z", - "start_time": "2025-06-30T15:53:47.486879Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [x]\n" - ] - }, - { - "data": { - "text/plain": [ - "Coordinates:\n", - " * chain (chain) int64 8B 0\n", - " * draw (draw) int64 8B 0\n", - " * a* (a*) int64 16B -2 -1" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with pm.Model(coords={\"a\": [-3, -2, -1], \"a*\": [-2, -1]}) as m:\n", - " x = pmd.Normal(\"x\", dims=(\"a\",))\n", - " y = pmd.Deterministic(\"y\", x.isel(a=slice(1, None)).rename({\"a\": \"a*\"}))\n", - " assert y.dims == (\"a*\",)\n", - " # You can rename back to the original name if you need it for further operations\n", - " y = y.rename({\"a*\": \"a\"})\n", - "\n", - " idata = pm.sample_prior_predictive(draws=1)\n", - "idata.prior[\"y\"].coords" - ] - }, - { - "cell_type": "markdown", - "id": "9a051144b1259fc8", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-26T21:31:08.371499143Z", - "start_time": "2025-06-26T11:42:20.159762Z" - } - }, - "source": [ - "An alternative is to manually specify the coordinates after sampling.\n", - "\n", - "Note that when doing advanced indexing the name of the indexed dimension can be controlled by the name of the indexing xtensor" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "87726153a7d1fd2", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:48.186501Z", - "start_time": "2025-06-30T15:53:48.181799Z" - } - }, - "outputs": [ + "source": [ + "pm.draw(outer_addition, random_seed=rng)" + ] + }, { - "data": { - "text/plain": [ - "('a*',)" + "cell_type": "markdown", + "id": "b7d78e2f3984adbc", + "metadata": {}, + "source": [ + "Here's the same operation with a dimmed `Normal` variable. It requires the use of `rename`." ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x.isel(a=pmd.math.as_xtensor([0, 1, 2], dims=(\"a*\",))).dims" - ] - }, - { - "cell_type": "markdown", - "id": "38a8684f-4849-4f69-a7c7-7433182358c2", - "metadata": {}, - "source": [ - "However, silent bugs can still happen if the shapes are compatible but the coordinates are not correct.\n", - "For example, in this model the coordinates are reversed, but the shape matches, so the error is not detected." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "8443e99c-8c90-40e1-8189-943e374c1387", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:48.819667Z", - "start_time": "2025-06-30T15:53:48.747097Z" - } - }, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling: [x]\n" - ] + "cell_type": "code", + "execution_count": 13, + "id": "4d9e8417789d4c18", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:35.055530Z", + "start_time": "2025-06-30T15:53:35.045241Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "XTensorType(float64, shape=(3,), dims=('a',))" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dims_normal = pmd.Normal.dist(mu=pmd.math.as_xtensor([0, 1, 2], dims=(\"a\",)), sigma=1)\n", + "dims_normal.type" + ] }, { - "data": { - "text/plain": [ - "array([[[-1.53640397, 0. , 1.53640397]]])" + "cell_type": "code", + "execution_count": 14, + "id": "1d0c30011c910370", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:35.264315Z", + "start_time": "2025-06-30T15:53:35.256071Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pytensor.xtensor.type.XTensorVariable" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(dims_normal)" ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with pm.Model(coords={\"a\": [1, 2, 3]}):\n", - " x = pmd.Normal(\"x\", dims=(\"a\",))\n", - " pmd.Deterministic(\"x_reversed\", x[::-1])\n", - " idata = pm.sample_prior_predictive(draws=1)\n", - "(idata.prior[\"x\"] - idata.prior[\"x_reversed\"]).values" - ] - }, - { - "cell_type": "markdown", - "id": "1fd902d9-33fd-40d6-a66d-fc15f1064b5c", - "metadata": {}, - "source": [ - "In Xarray the results would be correct because it is aware of the coordinates, not just the shape." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "2d282c1a-8a30-476d-8d92-94ea9f9a6971", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T15:53:49.345188Z", - "start_time": "2025-06-30T15:53:49.337999Z" - } - }, - "outputs": [ + }, { - "data": { - "text/plain": [ - "array([[[0., 0., 0.]]])" + "cell_type": "code", + "execution_count": 15, + "id": "923e75f235dcf219", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:35.472551Z", + "start_time": "2025-06-30T15:53:35.465427Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "XTensorType(float64, shape=(3, 3), dims=('a', 'b'))" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "outer_addition = dims_normal + dims_normal.rename({\"a\": \"b\"})\n", + "outer_addition.type" ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(idata.prior[\"x\"] - idata.prior[\"x\"].isel(a=slice(None, None, -1))).values" - ] - }, - { - "cell_type": "markdown", - "id": "fa8552be-16b8-4dd0-9bdc-38df8d5609b1", - "metadata": {}, - "source": [ - "Is not a new problem with the {mod}`pymc.dims` module; it is a consequence of PyMC's inability to reason about coords symbolically.\n", - "But this kind of error is made more likely because functions from the {mod}`pymc.dims` module always propagate dimension names to the model object.\n", - "\n", - "We remind users that {func}`pymc.dims.Deterministic` variables are never required in a model; they are just a way to calculate and store the results of intermediate operations. If you use them, pay extra attention as to whether the model coordinates are appropriate for the variable stored in the {func}`pymc.dims.Deterministic` (and not just their length but ordering as well).\n", - "\n", - "Alternatively, you can use the regular {func}`pymc.Deterministic` without specifying `dims`, which will not propagate the coordinates to the model. Keep in mind that the respective dimensions will be considered unique after sampling, and operations between variables that had shared dims in the original model will broadcast orthogonally in the returned InferenceData variables.\n", - "\n", - "If you need variables to have the same dims but different coords, you can always fix them manually." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "f994c42a-8468-4e98-96e2-eec1086cc7d4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Dims determine the dimension meaning and alignment, but no extra work can be done to reason within a dim. We discuss this limitation in more detail at the end.:::" + ] + }, + { + "cell_type": "markdown", + "id": "e12c7cda782cac69", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:31:08.365035095Z", + "start_time": "2025-06-26T11:42:19.021166Z" + } + }, + "source": [ + "## Redundant (or implicit) dims" + ] + }, + { + "cell_type": "markdown", + "id": "4ac4877e24859dfe", + "metadata": {}, + "source": [ + "When defining deterministic operations or creating variables whose dimension are all implied by the parameters, there's no need to specify the `dims` argument, as PyMC will automatically know them.\n", + "\n", + "However, some users might want to specify `dims` anyway, to check that the dimensions of the variables are as expected, or to provide type hints for someone reading the model.\n", + "\n", + "PyMC allows specifying dimensions in these cases. To reduce confusion, the output will always be transposed to be aligned with the user-specified dims." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "cd6031d682b88e51", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:35.989010Z", + "start_time": "2025-06-30T15:53:35.965369Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "det_implicit_dims.dims=('a', 'b')\n", + "det_explicit_dims.dims=('a', 'b')\n", + "det_transposed_dims.dims=('b', 'a')\n" + ] + } + ], + "source": [ + "with pm.Model(coords={\"a\": range(2), \"b\": range(5)}) as example:\n", + " x = pmd.Normal(\"x\", dims=(\"a\", \"b\"))\n", + " det_implicit_dims = pmd.Deterministic(\"det1\", x + 1)\n", + " det_explicit_dims = pmd.Deterministic(\"det2\", x + 1, dims=(\"a\", \"b\"))\n", + " det_transposed_dims = pmd.Deterministic(\"y\", x + 1, dims=(\"b\", \"a\"))\n", + "\n", + "print(f\"{det_implicit_dims.dims=}\")\n", + "print(f\"{det_explicit_dims.dims=}\")\n", + "print(f\"{det_transposed_dims.dims=}\")" + ] + }, + { + "cell_type": "markdown", + "id": "db657b20b447c56a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:31:08.365174897Z", + "start_time": "2025-06-26T11:42:19.389561Z" + } + }, + "source": [ + "This happens with `Deterministic`, `Potential` and every distribution in the `dims` module.\n", + "Any time you specify `dims`, you will get back a variable with dimensions in the same order." + ] + }, + { + "cell_type": "markdown", + "id": "e5f73575a51d316a", + "metadata": {}, + "source": [ + "Furthermore -- and unlike regular PyMC objects -- it is now valid to use ellipsis in the `dims` argument.\n", + "As in an Xarray `transpose`, it means all the other dimensions should stay in the same order." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a1e3597de136004f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:36.397445Z", + "start_time": "2025-06-30T15:53:36.371682Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "det_ellipsis1.dims=('a', 'b')\n", + "det_ellipsis2.dims=('b', 'a')\n", + "det_ellipsis3.dims=('b', 'a')\n" + ] + } + ], + "source": [ + "with pm.Model(coords={\"a\": range(2), \"b\": range(5)}) as example:\n", + " x = pmd.Normal(\"x\", dims=(\"a\", \"b\"))\n", + " det_ellipsis1 = pmd.Deterministic(\"det1\", x + 1, dims=(...,))\n", + " det_ellipsis2 = pmd.Deterministic(\"det2\", x + 1, dims=(..., \"a\"))\n", + " det_ellipsis3 = pmd.Deterministic(\"det3\", x + 1, dims=(\"b\", ...))\n", + "\n", + "print(f\"{det_ellipsis1.dims=}\")\n", + "print(f\"{det_ellipsis2.dims=}\")\n", + "print(f\"{det_ellipsis3.dims=}\")" + ] + }, + { + "cell_type": "markdown", + "id": "318c563fb2eb106b", + "metadata": {}, + "source": [ + "## What functionality is supported?" + ] + }, + { + "cell_type": "markdown", + "id": "88acbfae651fe294", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "The documentation is still a work in progress, and there is no complete list of distributions and operations that are supported just yet. \n", + "\n", + "### Model constructors\n", + "\n", + "The following PyMC model constructors are available in the `dims` module.\n", + "\n", + " * {func}`~pymc.dims.Data`\n", + " * {func}`~pymc.dims.Deterministic`\n", + " * {func}`~pymc.dims.Potential`\n", + "\n", + "They all return {class}`~pytensor.xtensor.type.XTensorVariable` objects, and either infer `dims` from the input or require the user to specify them explicitly. If they can be inferred, it is possible to transpose and use ellipsis in the `dims` argument, as described above.\n", + "\n", + "### Distributions\n", + "\n", + "We want to offer all the existing distributions and parametrizations under the {mod}`pymc.dims` module, with the following expected API differences:\n", + "\n", + " * All vector arguments (and observed values) must have known dims. An error is raised otherwise.\n", + "\n", + " * Distributions with non-scalar inputs will require a `core_dims` argument. The meaning of the `core_dims` argument will be denoted in the docstrings of each distribution. For example, for the MvNormal, the `core_dims` are the two dimensions of the covariance matrix, one (and only one) of which must also be present in the mean parameter. The shared `core_dim` is the one that persists in the output. Sometimes the order of `core_dims` will be important!\n", + "\n", + " * `dims` accept ellipsis, and variables are transposed to match the user-specified `dims` argument.\n", + "\n", + " * `shape` and `size` cannot be provided.\n", + "\n", + " * The {meth}`~pymc.distributions.core.DimDistribution.dist` method accepts a `dims_length` argument, of the form `{dim_name: dim_length}`.\n", + "\n", + " * Only transforms defined in {mod}`pymc.dims.transforms` can be used with distributions from the module.\n", + "\n", + "### Operations on variables\n", + "\n", + "Calling a PyMC distribution from the {mod}`pymc.dims` module returns an {class}`~pytensor.xtensor.type.XTensorVariable`.\n", + "\n", + "The expectation is that every {class}`xarray.DataArray` method in Xarray should have an equivalent version for XTensorVariables. So if you can do `x.diff(dim=\"a\")` in Xarray, you should be able to do `x.diff(dim=\"a\")` with XTensorVariables as well.\n", + "\n", + "In addition, many numerical operations are available in the {mod}`pymc.dims.math` module, which provides a superset of `ufuncs` functions found in Xarray (like `exp`). It also includes submodules such as `linalg` that provide counterpart to libraries like [xarray-einstats](https://xarray-einstats.readthedocs.io/) (such as `linalg.solve`).\n", + "\n", + "Finally, functions that are available at the module level in Xarray (like `concat`) are also available in the {mod}`pymc.dims` namespace.\n", + "\n", + "You can find the original documentation of these functions and methods in the {ref}`PyTensor Xtensor docs `\n", + "\n", + "To facilitate adoption of these functions and methods, we try to follow the same API used by Xarray and related packages. However, some methods or keyword arguments won't be supported explicitly (like {meth}`~pytensor.xtensor.XTensorVariable.sel`, more on that at the end), in which case an informative error or warning will be raised.\n", + "\n", + "If you find an API difference or some missing functionality, and no reason is provided, please [open an issue](https://github.com/pymc-devs/pymc/issues) to let us know (after checking nobody has done it already).\n", + "\n", + "In the meantime, the next section provides some hints on how to make use of pre-existing functionality in PyMC/PyTensor." + ] + }, + { + "cell_type": "markdown", + "id": "98a94f5939872e75", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:31:08.365484979Z", + "start_time": "2025-06-26T11:42:19.577389Z" + } + }, + "source": [ + "## Combining dims module with the old API" + ] + }, + { + "cell_type": "markdown", + "id": "3d305a2bb8313704", + "metadata": {}, + "source": [ + "Because the {mod}`pymc.dims` module is more recent, it does not offer all the functionality of the old API.\n", + "\n", + "You can always combine the two APIs by converting the variables explicitly. To obtain a regular non-dimmed variable from a dimmed variable, you can use {attr}`~pytensor.xtensor.type.XTensorVariable.values` (like in Xarray) or the more verbose {func}`pymc.dims.tensor_from_xtensor`.\n", + "\n", + "Otherwise, if you try to pass an {class}`~pytensor.xtensor.type.XTensorVariable` to a function or distribution that does not support it, you will usually see an error like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "df3d1f1ce895d02e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TypeError: To avoid subtle bugs, PyTensor forbids automatic conversion of XTensorVariable to TensorVariable.\n", + "You can convert explicitly using `x.values` or pass `allow_xtensor_conversion=True`.\n" + ] + } + ], + "source": [ + "mu = pmd.math.as_xtensor([0, 1, 2], dims=(\"a\",))\n", + "try:\n", + " pm.Normal.dist(mu=mu)\n", + "except TypeError as e:\n", + " print(f\"{e.__class__.__name__}: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "335b094a6cdd0bd8", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:36.767188Z", + "start_time": "2025-06-30T15:53:36.745075Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TensorType(float64, shape=(None, None))" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pm.Normal.dist(mu=x.values).type" + ] + }, + { + "cell_type": "markdown", + "id": "eca88323e2529a57", + "metadata": {}, + "source": [ + "The order of the dimensions follows that specified in the {attr}`~pytensor.xtensor.type.XTensorVariable.dims` property. To be sure this matches the expectation you can use a {meth}`~pytensor.xtensor.type.XTensorVariable.transpose` operation to reorder the dimensions before converting to a regular variable." + ] + }, + { + "cell_type": "markdown", + "id": "9e256f247d3bd9e1", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:31:08.365732804Z", + "start_time": "2025-06-26T11:42:19.755931Z" + } + }, + "source": [ + "Conversely, if you try to pass a regular variable to a function or distribution that expects an XTensorVariable, you will see an error like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "380f7161ca2d22e4", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:36.879320Z", + "start_time": "2025-06-30T15:53:36.868282Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ValueError: Variable mu_x{[0 1 2]} must have dims associated with it.\n", + "To avoid subtle bugs, PyMC does not make any assumptions about the dims of parameters.\n", + "Use `as_xtensor` with the `dims` keyword argument to specify the dims explicitly.\n" + ] + } + ], + "source": [ + "mu = pm.math.as_tensor([0, 1, 2], name=\"mu_x\")\n", + "try:\n", + " x = pmd.Normal.dist(mu=mu)\n", + "except Exception as e:\n", + " print(f\"{e.__class__.__name__}: {e}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e507c0a8e76447fd", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:31:08.365847399Z", + "start_time": "2025-06-26T11:42:19.826864Z" + } + }, + "source": [ + "Which you can avoid by explicitly converting the variable to a dimmed variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b50363261fe81df6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:37.226477Z", + "start_time": "2025-06-30T15:53:37.204756Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "XTensorType(float64, shape=(3,), dims=('a',))" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmd.Normal.dist(mu=pmd.as_xtensor(mu, dims=(\"a\",))).type" + ] + }, + { + "cell_type": "markdown", + "id": "3f2202b55cbc8c4", + "metadata": {}, + "source": [ + "### Applied example\n", + "\n", + "To put this to practice, let's write a model that uses the {class}`~pymc.LKJCholeskyCov` distribution, which at the time of writing is not yet available in the {mod}`pymc.dims` module." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "195f6f509e797f9a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "chol_xr.dims=('core1', 'core2')\n", + "mu.dims=('batch', 'core1')\n", + "y.dims=('batch', 'core1')\n" + ] + } + ], + "source": [ + "with pm.Model(coords={\"core1\": range(3), \"core2\": range(3), \"batch\": range(5)}) as mixed_api_model:\n", + " chol, _, _ = pm.LKJCholeskyCov(\n", + " \"chol\",\n", + " eta=1,\n", + " n=3,\n", + " sd_dist=pm.Exponential.dist(1),\n", + " )\n", + " chol_xr = pmd.as_xtensor(chol, dims=(\"core1\", \"core2\"))\n", + "\n", + " mu = pmd.Normal(\"mu\", dims=(\"batch\", \"core1\"))\n", + " y = pmd.MvNormal(\n", + " \"y\",\n", + " mu,\n", + " chol=chol_xr,\n", + " core_dims=(\"core1\", \"core2\"),\n", + " )\n", + "\n", + "print(f\"{chol_xr.dims=}\")\n", + "print(f\"{mu.dims=}\")\n", + "print(f\"{y.dims=}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3f0dd0fea61f8862", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:31:08.366068103Z", + "start_time": "2025-06-26T11:42:19.946909Z" + } + }, + "source": [ + "Note that we had to pass a \"regular\" {class}`~pymc.Exponential` distribution to the {class}`~pymc.LKJCholeskyCov` constructor. In general, distribution \"factories\" which are parametrized by unnamed distributions created with {meth}`~pymc.distributions.distribution.Distribution.dist` won't work with variables created with the {mod}`pymc.dims` module." + ] + }, + { + "cell_type": "markdown", + "id": "6464ae49ff629660", + "metadata": {}, + "source": [ + "## Case study: a splines model begging for vectorization" + ] + }, + { + "cell_type": "markdown", + "id": "c7c622bbde1c675", + "metadata": {}, + "source": [ + "The model below was presented by a user in a bug report. While granting that the author might have written the model this way to provide a reproducible example, we can say that the model is written in a highly suboptimal form. Specifically it misses (or actively breaks) many opportunities for vectorization. Seasoned Python programmers will know that python loops are SLOW, and that tools like numpy provide a way to escape from this handicap.\n", + "\n", + "PyMC code is not exactly numpy, for starters it uses a lazy symbolic computation library (PyTensor) that generates compiled code on demand. But it very much likes to be given numpy-like code. To begin with these graphs are much smaller and therefore easier to reason about (in fact the original bug could only be triggered for graphs with more than 500 nodes). Secondly, numpy-like graphs naturally translate to vectorized CPU and GPU code, which you want at the end of the day." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "726f696e6bf17d44", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:39.748768Z", + "start_time": "2025-06-30T15:53:39.740157Z" + } + }, + "outputs": [], + "source": [ + "# Simulated data of some spline\n", + "N = 500\n", + "x_np = np.linspace(0, 10, N)\n", + "y_obs_np = np.piecewise(\n", + " x_np,\n", + " [x_np <= 3, (x_np > 3) & (x_np <= 7), x_np > 7],\n", + " [lambda x: 0.5 * x, lambda x: 1.5 + 0.2 * (x - 3), lambda x: 2.3 - 0.1 * (x - 7)],\n", + ")\n", + "y_obs_np += rng.normal(0, 0.2, size=N) # Add noise\n", + "\n", + "# Artificial groups\n", + "groups = [0, 1, 2]\n", + "group_idx_np = np.random.choice(groups, size=N)\n", + "\n", + "n_knots = 50\n", + "knots_np = np.linspace(0, 10, num=n_knots)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "2e8519125d17f0c8", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:40.367851Z", + "start_time": "2025-06-30T15:53:39.854962Z" + } + }, + "outputs": [], + "source": [ + "with pm.Model() as non_vectorized_splines_model:\n", + " sigma_beta0 = pm.HalfNormal(\"sigma_beta0\", sigma=10)\n", + " sigma = pm.HalfCauchy(\"sigma\", beta=1)\n", + "\n", + " # Create likelihood per group\n", + " for gr in groups:\n", + " idx = group_idx_np == gr\n", + "\n", + " beta0 = pm.HalfNormal(f\"beta0_{gr}\", sigma=sigma_beta0)\n", + " z = pm.Normal(f\"z_{gr}\", mu=0, sigma=2, shape=n_knots)\n", + "\n", + " delta_factors = pm.math.softmax(z)\n", + " slope_factors = 1 - pm.math.cumsum(delta_factors[:-1])\n", + " spline_slopes = pm.math.stack(\n", + " [beta0] + [beta0 * slope_factors[i] for i in range(n_knots - 1)]\n", + " )\n", + " beta = pm.Deterministic(\n", + " f\"beta_{gr}\",\n", + " pm.math.concatenate(([beta0], pm.math.diff(spline_slopes))),\n", + " )\n", + "\n", + " hinge_terms = [pm.math.maximum(0, x_np[idx] - knot) for knot in knots_np]\n", + " X = pm.math.stack([hinge_terms[i] for i in range(n_knots)], axis=1)\n", + "\n", + " mu = pm.math.dot(X, beta)\n", + "\n", + " pm.Normal(f\"y_{gr}\", mu=mu, sigma=sigma, observed=y_obs_np[idx])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c56dcb51a07a5b54", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:40.700094Z", + "start_time": "2025-06-30T15:53:40.463806Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "cluster50\n", + "\n", + "50\n", + "\n", + "\n", + "cluster176\n", + "\n", + "176\n", + "\n", + "\n", + "cluster175\n", + "\n", + "175\n", + "\n", + "\n", + "cluster149\n", + "\n", + "149\n", + "\n", + "\n", + "\n", + "beta0_1\n", + "\n", + "beta0_1\n", + "~\n", + "Halfnormal\n", + "\n", + "\n", + "\n", + "beta_1\n", + "\n", + "beta_1\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + 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"\n", + "\n", + "\n", + "\n", + "\n", + "sigma_beta0\n", + "\n", + "sigma_beta0\n", + "~\n", + "Halfnormal\n", + "\n", + "\n", + "\n", + "sigma_beta0->beta0_1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_beta0->beta0_0\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_beta0->beta0_2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "beta_1->y_1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "beta_2->y_2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "beta_0->y_0\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "z_2\n", + "\n", + "z_2\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "z_2->beta_2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "z_1\n", + "\n", + "z_1\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "z_1->beta_1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "z_0\n", + "\n", + "z_0\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "z_0->beta_0\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "non_vectorized_splines_model.to_graphviz()" + ] + }, + { + "cell_type": "markdown", + "id": "6be0b8afd4990567", + "metadata": {}, + "source": [ + "This version of the model has several `for` loop and list comprehensions.\n", + "Every Python iteration extends the computational graph (basically unrolling the loop), which becomes infeasible for PyMC to handle.\n", + "\n", + "The use of 3 likelihood and sets of priors is otherwise fine and can make more sense in some cases.\n", + "\n", + "We can improve the performance of this model by replacing the loop with vectorized operations.\n", + "We'll demonstrate first with current PyMC features, and then with dims." + ] + }, + { + "cell_type": "markdown", + "id": "33d16a1bef908f3f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:31:52.686637Z", + "start_time": "2025-06-26T21:31:52.509222Z" + } + }, + "source": [ + "### Old style vectorization" + ] + }, + { + "cell_type": "markdown", + "id": "d8add515e2e56179", + "metadata": {}, + "source": [ + "\n", + "First, we'll introduce coords and {func}`~pymc.Data` to explicitly define the coordinate system, making the model's dimensionality clear and facilitating posterior analysis with labeled dimensions." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "6ee0f6e24fa81b02", + "metadata": {}, + "outputs": [], + "source": [ + "coords = {\n", + " \"group\": range(3),\n", + " \"knots\": range(n_knots),\n", + " \"obs\": range(N),\n", + "}\n", + "with pm.Model(coords=coords) as vectorized_splines_model:\n", + " x = pm.Data(\"x\", x_np, dims=\"obs\")\n", + " y_obs = pm.Data(\"y_obs\", y_obs_np, dims=\"obs\")\n", + "\n", + " knots = pm.Data(\"knots\", knots_np, dims=\"knot\")\n", + "\n", + " sigma = pm.HalfCauchy(\"sigma\", beta=1)\n", + " sigma_beta0 = pm.HalfNormal(\"sigma_beta0\", sigma=10)\n", + " beta0 = pm.HalfNormal(\"beta_0\", sigma=sigma_beta0, dims=\"group\")\n", + " z = pm.Normal(\"z\", dims=(\"group\", \"knot\"))\n", + "\n", + " delta_factors = pm.math.softmax(z, axis=-1) # (groups, knot)\n", + " slope_factors = 1 - pm.math.cumsum(delta_factors[:, :-1], axis=-1) # (groups, knot-1)\n", + " spline_slopes = pm.math.concatenate(\n", + " [beta0[:, None], beta0[:, None] * slope_factors], axis=-1\n", + " ) # (groups, knot-1)\n", + " beta = pm.math.concatenate(\n", + " [beta0[:, None], pm.math.diff(spline_slopes, axis=-1)], axis=-1\n", + " ) # (groups, knot)\n", + "\n", + " beta = pm.Deterministic(\"beta\", beta, dims=(\"group\", \"knot\"))\n", + "\n", + " X = pm.math.maximum(0, x[:, None] - knots[None, :]) # (n, knot)\n", + " mu = (X * beta[group_idx_np]).sum(-1) # ((n, knots) * (n, knots)).sum(-1) = (n,)\n", + " y = pm.Normal(\"y\", mu=mu, sigma=sigma, observed=y_obs, dims=\"obs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "bd94fa06a1190a05", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:41.063986Z", + "start_time": "2025-06-30T15:53:40.980450Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "clusterobs (500)\n", + "\n", + "obs (500)\n", + "\n", + "\n", + "clusterknot (50)\n", + "\n", + "knot (50)\n", + "\n", + "\n", + "clustergroup (3)\n", + "\n", + "group (3)\n", + "\n", + "\n", + "clustergroup (3) x knot (50)\n", + "\n", + "group (3) x knot (50)\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "~\n", + "Data\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "x->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y_obs\n", + "\n", + "y_obs\n", + "~\n", + "Data\n", + "\n", + "\n", + "\n", + "y->y_obs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "knots\n", + "\n", + "knots\n", + "~\n", + "Data\n", + "\n", + "\n", + "\n", + "knots->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma\n", + "\n", + "sigma\n", + "~\n", + "Halfcauchy\n", + "\n", + "\n", + "\n", + "sigma->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_beta0\n", + "\n", + "sigma_beta0\n", + "~\n", + "Halfnormal\n", + "\n", + "\n", + "\n", + "beta_0\n", + "\n", + "beta_0\n", + "~\n", + "Halfnormal\n", + "\n", + "\n", + "\n", + "sigma_beta0->beta_0\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "beta\n", + "\n", + "beta\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "beta_0->beta\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "beta->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "z\n", + "\n", + "z\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "z->beta\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vectorized_splines_model.to_graphviz()" + ] + }, + { + "cell_type": "markdown", + "id": "a98bbe7f2e17783a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:32:15.059906Z", + "start_time": "2025-06-26T21:32:15.024281Z" + } + }, + "source": [ + "Looking only at grahpviz does not reveal much difference. The real complexity is hidden in the underlying computational graph. Let us measure it." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "90f8f4ee3fd9f2a3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:41.192185Z", + "start_time": "2025-06-30T15:53:41.162347Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Non-vectorized model has 806 nodes\n", + "Vectorized model has 38 nodes\n" + ] + } + ], + "source": [ + "from pytensor.graph import ancestors\n", + "\n", + "\n", + "def count_nodes(model):\n", + " return len({v for v in ancestors(model.basic_RVs) if v.owner})\n", + "\n", + "\n", + "print(f\"Non-vectorized model has {count_nodes(non_vectorized_splines_model)} nodes\")\n", + "print(f\"Vectorized model has {count_nodes(vectorized_splines_model)} nodes\")" + ] + }, + { + "cell_type": "markdown", + "id": "daa39293278cbb05", + "metadata": {}, + "source": [ + "This version of the model is much more succinct and performant.\n", + "\n", + "However, it's considerably hard to write, specially for beginners. It requires familiarity with Numpy unusual broadcasting syntax and mental gymnastics to keep track of the dimensions." + ] + }, + { + "cell_type": "markdown", + "id": "a532b2ec365bfecf", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:32:17.432572Z", + "start_time": "2025-06-26T21:32:17.415088Z" + } + }, + "source": [ + "### Vectorization with dims" + ] + }, + { + "cell_type": "markdown", + "id": "c24ce399f0eeae4c", + "metadata": {}, + "source": [ + "Here is a version of the model using {mod}`pymc.dims`, which we hope you'll agree is more intuitive" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1bf2c6b99378b2db", + "metadata": {}, + "outputs": [], + "source": [ + "with pm.Model(coords=coords) as dims_splines_model:\n", + " x = pmd.Data(\"x\", x_np, dims=\"obs\")\n", + " y_obs = pmd.Data(\"y_obs\", y_obs_np, dims=\"obs\")\n", + " knots = pmd.Data(\"knots\", knots_np, dims=(\"knot\",))\n", + " group_idx = pmd.math.as_xtensor(group_idx_np, dims=(\"obs\",))\n", + "\n", + " sigma = pmd.HalfCauchy(\"sigma\", beta=1)\n", + " sigma_beta0 = pmd.HalfNormal(\"sigma_beta0\", sigma=10)\n", + " beta0 = pmd.HalfNormal(\"beta_0\", sigma=sigma_beta0, dims=(\"group\",))\n", + " z = pmd.Normal(\"z\", dims=(\"group\", \"knot\"))\n", + "\n", + " delta_factors = pmd.math.softmax(z, dim=\"knot\")\n", + " slope_factors = 1 - delta_factors.isel(knot=slice(None, -1)).cumsum(\"knot\")\n", + " spline_slopes = pmd.concat([beta0, beta0 * slope_factors], dim=\"knot\")\n", + " beta = pmd.Deterministic(\"beta\", pmd.concat([beta0, spline_slopes.diff(\"knot\")], dim=\"knot\"))\n", + "\n", + " X = pmd.math.maximum(0, x - knots)\n", + " mu = (X * beta.isel(group=group_idx)).sum(\"knot\")\n", + " y = pmd.Normal(\"y\", mu=mu, sigma=sigma, observed=y_obs)" + ] + }, + { + "cell_type": "markdown", + "id": "91593e5b3dbc885f", + "metadata": {}, + "source": [ + "Let us confirm it is identical to the previous version." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "5f55cf90d737dcf", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:42.362928Z", + "start_time": "2025-06-30T15:53:42.151483Z" + } + }, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "clusterobs (500)\n", + "\n", + "obs (500)\n", + "\n", + "\n", + "clusterknot (50)\n", + "\n", + "knot (50)\n", + "\n", + "\n", + "clustergroup (3)\n", + "\n", + "group (3)\n", + "\n", + "\n", + "clustergroup (3) x knot (50)\n", + "\n", + "group (3) x knot (50)\n", + "\n", + "\n", + "\n", + "x\n", + "\n", + "x\n", + "~\n", + "Data\n", + "\n", + "\n", + "\n", + "y\n", + "\n", + "y\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "x->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "y_obs\n", + "\n", + "y_obs\n", + "~\n", + "Data\n", + "\n", + "\n", + "\n", + "y->y_obs\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "knots\n", + "\n", + "knots\n", + "~\n", + "Data\n", + "\n", + "\n", + "\n", + "knots->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma\n", + "\n", + "sigma\n", + "~\n", + "Halfcauchy\n", + "\n", + "\n", + "\n", + "sigma->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "sigma_beta0\n", + "\n", + "sigma_beta0\n", + "~\n", + "Halfnormal\n", + "\n", + "\n", + "\n", + "beta_0\n", + "\n", + "beta_0\n", + "~\n", + "Halfnormal\n", + "\n", + "\n", + "\n", + "sigma_beta0->beta_0\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "beta\n", + "\n", + "beta\n", + "~\n", + "Deterministic\n", + "\n", + "\n", + "\n", + "beta_0->beta\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "beta->y\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "z\n", + "\n", + "z\n", + "~\n", + "Normal\n", + "\n", + "\n", + "\n", + "z->beta\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dims_splines_model.to_graphviz()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "3a9dd4669342ff9e", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:42.797760Z", + "start_time": "2025-06-30T15:53:42.794464Z" + } + }, + "outputs": [], + "source": [ + "# Comment out if you want to wait a long while for the results\n", + "# non_vectorized_splines_model.point_logps()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "86701f276baaa7c", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:44.309696Z", + "start_time": "2025-06-30T15:53:43.273978Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sigma': np.float64(-1.14),\n", + " 'sigma_beta0': np.float64(-0.73),\n", + " 'beta_0': np.float64(-2.18),\n", + " 'z': np.float64(-137.84),\n", + " 'y': np.float64(-319962.47)}" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vectorized_splines_model.point_logps()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "f34c353131915cef", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:46.279261Z", + "start_time": "2025-06-30T15:53:45.344064Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sigma': np.float64(-1.14),\n", + " 'sigma_beta0': np.float64(-0.73),\n", + " 'beta_0': np.float64(-2.18),\n", + " 'z': np.float64(-137.84),\n", + " 'y': np.float64(-319962.47)}" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dims_splines_model.point_logps()" + ] + }, + { + "cell_type": "markdown", + "id": "1bb865850774d0b2", + "metadata": {}, + "source": [ + "We have just two more nodes in the computation graph, with no impact on performance" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "ae2afa7841251f6c", + "metadata": { + "ExecuteTime": { + "end_time": "2025-07-08T17:06:40.561788Z", + "start_time": "2025-07-08T17:06:40.554136Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vectorized model has 38 nodes\n", + "Vectorized model with dims has 40 nodes\n" + ] + } + ], + "source": [ + "print(f\"Vectorized model has {count_nodes(vectorized_splines_model)} nodes\")\n", + "print(f\"Vectorized model with dims has {count_nodes(dims_splines_model)} nodes\")" + ] + }, + { + "cell_type": "markdown", + "id": "014c701b-1e23-4fc4-ae55-4279263b2ed9", + "metadata": {}, + "source": [ + "This wraps the presentation of the {mod}`pymc.dims` module. We invite you to try it out and report any problems you may find. If you wish to contribute missing functionality get involved in our [Github repository](https://github.com/pymc-devs/pymc)." + ] + }, + { + "cell_type": "markdown", + "id": "2059b00d-6d6a-41af-8e71-3709e6a33dcc", + "metadata": {}, + "source": [ + "## Questions and answers" + ] + }, + { + "cell_type": "markdown", + "id": "8d1a93d8-6fc5-42db-bae0-d4a3fd68abf7", + "metadata": {}, + "source": [ + "### How to get xarray out of XTensorVariables?" + ] + }, + { + "cell_type": "markdown", + "id": "0b8d9835-1bcd-4a20-a925-2ac88045113c", + "metadata": {}, + "source": [ + "Usually PyMC routines take care of converting outputs to Xarray for the user. \n", + "\n", + "If you want to evaluate an expression yourself you'll likely get a numpy array back. \n", + "To convert to a {class}`~xarray.DataArray` simply reuse the `dims` attribute from the PyMC variable." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7ea9dc87-77e5-40f1-95ec-233984912443", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (a: 3, b: 3)> Size: 72B\n",
+              "array([[-3.28099006, -1.57426294,  1.21146118],\n",
+              "       [-1.57426294,  0.13246418,  2.9181883 ],\n",
+              "       [ 1.21146118,  2.9181883 ,  5.70391243]])\n",
+              "Dimensions without coordinates: a, b
" + ], + "text/plain": [ + " Size: 72B\n", + "array([[-3.28099006, -1.57426294, 1.21146118],\n", + " [-1.57426294, 0.13246418, 2.9181883 ],\n", + " [ 1.21146118, 2.9181883 , 5.70391243]])\n", + "Dimensions without coordinates: a, b" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from xarray import DataArray\n", + "\n", + "x = pm.dims.Normal.dist(dim_lengths={\"a\": 3})\n", + "outer_x = x + x.rename({\"a\": \"b\"})\n", + "res_numpy = pm.draw(outer_addition)\n", + "res_xr = DataArray(res_numpy, dims=outer_addition.dims)\n", + "res_xr" + ] + }, + { + "cell_type": "markdown", + "id": "93ec3533ec122baa", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:32:29.344857Z", + "start_time": "2025-06-26T21:32:28.811467Z" + } + }, + "source": [ + "### Why aren't coordinate-based operations supported?" + ] + }, + { + "cell_type": "markdown", + "id": "b20324c9f4f65d4b", + "metadata": {}, + "source": [ + "The new xtensor variable and operations do not propagate information about coordinates, which means you cannot perform coordinate-related operations like `sel`, `loc`, `drop`.\n", + "\n", + "This limitation might be disappointing for someone used to Xarray, it is a necessary trade-off to allow PyMC to evaluate the model in a performant way.\n", + "PyMC uses PyTensor under the hood, which compiles functions to C (or numba or JAX) backends. None of these backends support dims or coordinates.\n", + "To work with these backends, PyTensor rewrites xtensor operations using equivalent tensor operations, which are pretty much abstract NumPy code. This is relatively easy to do, because it's mostly about aligning dimensions for broadcasting or indexing correctly.\n", + "\n", + "Rewriting coordinate-related operations into NumPy-like code is a different matter. Many such operations don't have straightforward equivalency, they are more like querying or joining a database than performing array operations.\n", + "\n", + "PyMC models will keep supporting the `coords` argument as a way to specify dimensions of model variables. But for modeling purposes, only the dimension names and their lengths play a role." + ] + }, + { + "cell_type": "markdown", + "id": "f963a53c229c04b9", + "metadata": {}, + "source": [ + "### One final note of caution on coordinates" + ] + }, + { + "cell_type": "markdown", + "id": "626c004fa7abdccf", + "metadata": {}, + "source": [ + "When you provide coords to a PyMC model, they are attached to any functions that returns Xarray or InferenceData objects.\n", + "\n", + "This creates a potential problem.\n", + "\n", + "Suppose we have multiple arrays with the same dims but different shapes.\n", + "This is legal in PyMC, as in Xarray, and some operations, like indexing or concatenating, can handle it.\n", + "\n", + "However, after sampling, PyMC tries to reattach the coordinates to any computed variables, and these might not have the right shape, or they might not be correctly aligned.\n", + "\n", + "When PyMC tries to convert the results of sampling to InferenceData, it will issue a warning and refuse to propagate the original coordinates.\n", + "\n", + "Here's an example where we have two variables with the `a` dim but different shapes, and only one matches the shape of the coordinates specified in the model." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "2c6f3a2b70a4d77d", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:46.850715Z", + "start_time": "2025-06-30T15:53:46.750208Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling: [x]\n", + "/home/ricardo/Documents/pymc/pymc/backends/arviz.py:70: UserWarning: Incompatible coordinate length of 3 for dimension 'a' of variable 'y'.\n", + "This usually happens when a sliced or concatenated variable is wrapped as a `pymc.dims.Deterministic`.The originate coordinates for this dim will not be included in the returned dataset for any of the variables. Instead they will default to `np.arange(var_length)` and the shorter variables will be right-padded with nan.\n", + "To make this warning into an error set `pymc.backends.arviz.RAISE_ON_INCOMPATIBLE_COORD_LENGTHS` to `True`\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "Coordinates:\n", + " * chain (chain) int64 8B 0\n", + " * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499\n", + " * a (a) int64 24B 0 1 2" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with pm.Model(coords={\"a\": [-1, 0, 1]}) as m:\n", + " x = pmd.Normal(\"x\", dims=(\"a\",))\n", + " y = pmd.Deterministic(\"y\", x.isel(a=slice(1, None)))\n", + " assert y.dims == (\"a\",)\n", + "\n", + " idata = pm.sample_prior_predictive()\n", + "idata.prior[\"y\"].coords" + ] + }, + { + "cell_type": "markdown", + "id": "a20137348aa4f5bf", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:31:08.371358319Z", + "start_time": "2025-06-26T11:42:20.084741Z" + } + }, + "source": [ + "One way to work around this limitation is to rename the dimensions to avoid the conflict." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "466f152cb249d1d2", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:47.572296Z", + "start_time": "2025-06-30T15:53:47.486879Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling: [x]\n" + ] + }, + { + "data": { + "text/plain": [ + "Coordinates:\n", + " * chain (chain) int64 8B 0\n", + " * draw (draw) int64 8B 0\n", + " * a* (a*) int64 16B -2 -1" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with pm.Model(coords={\"a\": [-3, -2, -1], \"a*\": [-2, -1]}) as m:\n", + " x = pmd.Normal(\"x\", dims=(\"a\",))\n", + " y = pmd.Deterministic(\"y\", x.isel(a=slice(1, None)).rename({\"a\": \"a*\"}))\n", + " assert y.dims == (\"a*\",)\n", + " # You can rename back to the original name if you need it for further operations\n", + " y = y.rename({\"a*\": \"a\"})\n", + "\n", + " idata = pm.sample_prior_predictive(draws=1)\n", + "idata.prior[\"y\"].coords" + ] + }, + { + "cell_type": "markdown", + "id": "9a051144b1259fc8", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-26T21:31:08.371499143Z", + "start_time": "2025-06-26T11:42:20.159762Z" + } + }, + "source": [ + "An alternative is to manually specify the coordinates after sampling.\n", + "\n", + "Note that when doing advanced indexing the name of the indexed dimension can be controlled by the name of the indexing xtensor" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "87726153a7d1fd2", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:48.186501Z", + "start_time": "2025-06-30T15:53:48.181799Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('a*',)" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.isel(a=pmd.math.as_xtensor([0, 1, 2], dims=(\"a*\",))).dims" + ] + }, + { + "cell_type": "markdown", + "id": "38a8684f-4849-4f69-a7c7-7433182358c2", + "metadata": {}, + "source": [ + "However, silent bugs can still happen if the shapes are compatible but the coordinates are not correct.\n", + "For example, in this model the coordinates are reversed, but the shape matches, so the error is not detected." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "8443e99c-8c90-40e1-8189-943e374c1387", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:48.819667Z", + "start_time": "2025-06-30T15:53:48.747097Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling: [x]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[[-1.53640397, 0. , 1.53640397]]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with pm.Model(coords={\"a\": [1, 2, 3]}):\n", + " x = pmd.Normal(\"x\", dims=(\"a\",))\n", + " pmd.Deterministic(\"x_reversed\", x[::-1])\n", + " idata = pm.sample_prior_predictive(draws=1)\n", + "(idata.prior[\"x\"] - idata.prior[\"x_reversed\"]).values" + ] + }, + { + "cell_type": "markdown", + "id": "1fd902d9-33fd-40d6-a66d-fc15f1064b5c", + "metadata": {}, + "source": [ + "In Xarray the results would be correct because it is aware of the coordinates, not just the shape." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "2d282c1a-8a30-476d-8d92-94ea9f9a6971", + "metadata": { + "ExecuteTime": { + "end_time": "2025-06-30T15:53:49.345188Z", + "start_time": "2025-06-30T15:53:49.337999Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[0., 0., 0.]]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(idata.prior[\"x\"] - idata.prior[\"x\"].isel(a=slice(None, None, -1))).values" + ] + }, + { + "cell_type": "markdown", + "id": "fa8552be-16b8-4dd0-9bdc-38df8d5609b1", + "metadata": {}, + "source": [ + "Is not a new problem with the {mod}`pymc.dims` module; it is a consequence of PyMC's inability to reason about coords symbolically.\n", + "But this kind of error is made more likely because functions from the {mod}`pymc.dims` module always propagate dimension names to the model object.\n", + "\n", + "We remind users that {func}`pymc.dims.Deterministic` variables are never required in a model; they are just a way to calculate and store the results of intermediate operations. If you use them, pay extra attention as to whether the model coordinates are appropriate for the variable stored in the {func}`pymc.dims.Deterministic` (and not just their length but ordering as well).\n", + "\n", + "Alternatively, you can use the regular {func}`pymc.Deterministic` without specifying `dims`, which will not propagate the coordinates to the model. Keep in mind that the respective dimensions will be considered unique after sampling, and operations between variables that had shared dims in the original model will broadcast orthogonally in the returned InferenceData variables.\n", + "\n", + "If you need variables to have the same dims but different coords, you can always fix them manually." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "f994c42a-8468-4e98-96e2-eec1086cc7d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (chain: 1, draw: 1, a: 3)> Size: 24B\n",
+              "array([[[0., 0., 0.]]])\n",
+              "Coordinates:\n",
+              "  * chain    (chain) int64 8B 0\n",
+              "  * draw     (draw) int64 8B 0\n",
+              "  * a        (a) int64 24B 1 2 3
" + ], + "text/plain": [ + " Size: 24B\n", + "array([[[0., 0., 0.]]])\n", + "Coordinates:\n", + " * chain (chain) int64 8B 0\n", + " * draw (draw) int64 8B 0\n", + " * a (a) int64 24B 1 2 3" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idata.prior[\"x_reversed\"] = idata.prior[\"x_reversed\"].assign_coords({\"a\": [3, 2, 1]})\n", + "idata.prior[\"x\"] - idata.prior[\"x_reversed\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1a5481d-085e-4c89-b0fa-3b4c6317c36b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pymc", + "language": "python", + "name": "pymc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/docs/source/learn/core_notebooks/model_comparison.ipynb b/docs/source/learn/core_notebooks/model_comparison.ipynb index f2ce1b01a0..80be38a300 100644 --- a/docs/source/learn/core_notebooks/model_comparison.ipynb +++ b/docs/source/learn/core_notebooks/model_comparison.ipynb @@ -1,718 +1,718 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running on PyMC v5.15.1+68.gc0b060b98.dirty\n" - ] - } - ], - "source": [ - "import arviz as az\n", - "import numpy as np\n", - "\n", - "import pymc as pm\n", - "\n", - "print(f\"Running on PyMC v{pm.__version__}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "az.style.use(\"arviz-darkgrid\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(model_comparison)=\n", - "# Model comparison\n", - "\n", - "To demonstrate the use of model comparison criteria in PyMC, we implement the **8 schools** example from Section 5.5 of Gelman et al (2003), which attempts to infer the effects of coaching on SAT scores of students from 8 schools. Below, we fit a **pooled model**, which assumes a single fixed effect across all schools, and a **hierarchical model** that allows for a random effect that partially pools the data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data include the observed treatment effects (`y`) and associated standard deviations (`sigma`) in the 8 schools." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "y = np.array([28, 8, -3, 7, -1, 1, 18, 12])\n", - "sigma = np.array([15, 10, 16, 11, 9, 11, 10, 18])\n", - "J = len(y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pooled model" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on PyMC v5.15.1+68.gc0b060b98.dirty\n" + ] + } + ], + "source": [ + "import arviz as az\n", + "import numpy as np\n", + "\n", + "import pymc as pm\n", + "\n", + "print(f\"Running on PyMC v{pm.__version__}\")" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [mu]\n" - ] + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "az.style.use(\"arviz-darkgrid\")" + ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e4b2a561cda3414582abfc04566354b3", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(model_comparison)=\n", + "# Model comparison\n", + "\n", + "To demonstrate the use of model comparison criteria in PyMC, we implement the **8 schools** example from Section 5.5 of Gelman et al (2003), which attempts to infer the effects of coaching on SAT scores of students from 8 schools. Below, we fit a **pooled model**, which assumes a single fixed effect across all schools, and a **hierarchical model** that allows for a random effect that partially pools the data." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "
\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The data include the observed treatment effects (`y`) and associated standard deviations (`sigma`) in the 8 schools."
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "
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-       "
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-      ],
-      "text/plain": [
-       "\n"
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "y = np.array([28, 8, -3, 7, -1, 1, 18, 12])\n",
+        "sigma = np.array([15, 10, 16, 11, 9, 11, 10, 18])\n",
+        "J = len(y)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 6 seconds.\n"
-     ]
-    }
-   ],
-   "source": [
-    "with pm.Model() as pooled:\n",
-    "    # Latent pooled effect size\n",
-    "    mu = pm.Normal(\"mu\", 0, sigma=1e6)\n",
-    "\n",
-    "    obs = pm.Normal(\"obs\", mu, sigma=sigma, observed=y)\n",
-    "\n",
-    "    trace_p = pm.sample(2000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Pooled model"
+      ]
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Auto-assigning NUTS sampler...\n",
+            "Initializing NUTS using jitter+adapt_diag...\n",
+            "Multiprocess sampling (4 chains in 4 jobs)\n",
+            "NUTS: [mu]\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e4b2a561cda3414582abfc04566354b3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 6 seconds.\n"
+          ]
+        }
+      ],
+      "source": [
+        "with pm.Model() as pooled:\n",
+        "    # Latent pooled effect size\n",
+        "    mu = pm.Normal(\"mu\", 0, sigma=1e6)\n",
+        "\n",
+        "    obs = pm.Normal(\"obs\", mu, sigma=sigma, observed=y)\n",
+        "\n",
+        "    trace_p = pm.sample(2000)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_trace(trace_p);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Hierarchical model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [eta, mu, tau]\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "az.plot_trace(trace_p);"
+      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cc8f825adf0245648b219def7e283c9b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Hierarchical model"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/html": [
-       "
\n"
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Auto-assigning NUTS sampler...\n",
+            "Initializing NUTS using jitter+adapt_diag...\n",
+            "Multiprocess sampling (4 chains in 4 jobs)\n",
+            "NUTS: [eta, mu, tau]\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "cc8f825adf0245648b219def7e283c9b",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 24 seconds.\n"
+          ]
+        }
       ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "with pm.Model() as hierarchical:\n",
+        "    eta = pm.Normal(\"eta\", 0, 1, shape=J)\n",
+        "    # Hierarchical mean and SD\n",
+        "    mu = pm.Normal(\"mu\", 0, sigma=10)\n",
+        "    tau = pm.HalfNormal(\"tau\", 10)\n",
+        "\n",
+        "    # Non-centered parameterization of random effect\n",
+        "    theta = pm.Deterministic(\"theta\", mu + tau * eta)\n",
+        "\n",
+        "    obs = pm.Normal(\"obs\", theta, sigma=sigma, observed=y)\n",
+        "\n",
+        "    trace_h = pm.sample(2000, target_accept=0.9)"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "
\n",
-       "
\n"
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "\n"
+      "source": [
+        "az.plot_trace(trace_h, var_names=\"mu\");"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 24 seconds.\n"
-     ]
-    }
-   ],
-   "source": [
-    "with pm.Model() as hierarchical:\n",
-    "    eta = pm.Normal(\"eta\", 0, 1, shape=J)\n",
-    "    # Hierarchical mean and SD\n",
-    "    mu = pm.Normal(\"mu\", 0, sigma=10)\n",
-    "    tau = pm.HalfNormal(\"tau\", 10)\n",
-    "\n",
-    "    # Non-centered parameterization of random effect\n",
-    "    theta = pm.Deterministic(\"theta\", mu + tau * eta)\n",
-    "\n",
-    "    obs = pm.Normal(\"obs\", theta, sigma=sigma, observed=y)\n",
-    "\n",
-    "    trace_h = pm.sample(2000, target_accept=0.9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "az.plot_forest(trace_h, var_names=\"theta\");"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_trace(trace_h, var_names=\"mu\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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lfvBIKaeUUcbLz+WMzjiz0nx+KnutzQlrzc/rpyHMi318HcbqU1lZqTvuuEPBYFALFizQCSecIEkaM2aMhg0bpttvv10FBQU69thjDY8UqeK26xf8XM74wnOxtzup3LQxdpS9hjltXpyw1vy8fhrCvNgn7jA2a9YszZ49W5I0e/bsyH9L0quvvhr5b8uyNGfOHD311FP67LPPdNxxx+myyy7T2LFjlZYW/a7okiVLNHfuXG3YsEH79u1Tdna2hg8frlGjRkXeOgxfjyZJJSUlKikpiXx8+NqsdevWaf78+Vq5cqW++OILHTx4UNnZ2br44os1evRotWrVKu5J+ec//6mdO3dq3LhxkSAmSSeccIKuuuoqzZo1S0uWLNGwYcPi3ieA1Kjvbjw3lZvaUfYa5qZ5Afwi7jCWn5+v4cOHa8GCBcrPz1d+fn7kuQ4dOkT++5577tHKlSt17rnnqqCgQK+++qpmzZqlgwcPavz48XX2OWPGDD388MM6/vjjNXToULVv317vvvuu/vSnP2n16tW6//77JUmFhYXauXOnXn31VQ0ZMkR5eXlR4/v73/+upUuXasCAAfrBD36gffv2aeXKlZoxY4bWrl2b0HVuK1eulCQVFBREPVdQUKBZs2Zp5cqVhDEASUHZK+BvcYexgQMHSlIkjNV3rdX69ev1/PPPq3PnzpKksWPH6vzzz9fcuXP161//Wsccc4wk6a233tLDDz8cCTfp6emSas6s3X777frb3/6mV155Reeff36dMFZYWBi5hqu2X/3qV5o8eXKdC/Ety9Itt9yiZ599Vu+995769+8f19e6ZcsWSVJ2dnbUc+FtW7dujWtfABCv3JyAcnNMjwKA3RK6mzIeY8eOjQQxSerYsaOGDBmiPXv2aPPmzZHtTz75pCRp2rRpkSAmSYFAQDfeeKMCgYBeeumluD/vSSedFHVHZCAQ0JVXXilJMe+CrM/u3bslSe3bt496Lnyd2K5du+LeHwDUZ/MWS8vesrR5izduRACQuKRfwN+jR4+obccff7ykugFm9erVSk9P17PPPhtzP23atNEnn3wS9+c9cOCA5s2bp5deekmffPKJqqqqZFlHfrh9/fXXce8LgHu4tYHfztb92kzOC237QGxJD2Ox7jBs2bLm0xw+fDiyrbKyUocOHapzI8DRqqqq4v68v/nNb7R06VLl5OSoqKhInTp1UsuWLbVz507NmTNHBw4cSPhr2LVrl4LBYJ3nGjprBn9yQnO3nxvE3drAb2/rfm3m5sUJbfux+Hn9NMQL8+KEu3XjYazaIhx4VqxY0ex9rVmzRkuXLlVBQYEefvjhOm9Xrlq1SnPmzElofzk5OVq3bp22bt0aFcbC14rFup4M/uSMnisaxN3Gj637zlgrsbB+YnP/vLil2T+ha8bCIaf2Ga6m6t27tyoqKiIXyzcmXIsR63P/97//lSSdc845UdeNvfvuuwmPbcCAAZKkZcuWRT0X3lb7blIAaApa9wFICZ4Zy8jIkCR9+eWXzf7EV199tcrKyvSHP/xBDzzwQNQZqO3bt2vnzp3q2rWrJCkzM7Pez33SSSdJkt577z1dffXVke3/+c9/9PDDDyc8tgsvvFDTp0/Xk08+qcsvvzzSNfbll1/qySefVDAYVGFhYcL7hTfRIG6Wc8+2NM5vrftOWCux+Hn9NIR5sU9CYey73/2uOnfurJdeeknHHHOMjj/+eAUCgToBKF4/+MEPNHbsWD344IM677zzdNZZZ+mkk05SRUWFtm7dqvfee0+//e1vI2Gsb9++atOmjZ544glVVlaqY8eOkmru3uzdu7d69+6tRYsWafv27erTp4+++OILvfbaazr77LP1yiuvJDS2jIwMTZo0STfffLOGDx+uoqIiSTV/DqmiokKlpaW07yPCCdck+Lkp260N/Ha27tdmcl6csFZi8fP6aQjzYp+EwliLFi00e/ZsTZ8+XS+++KL27NkjSbrkkkua9MlvuOEGDRgwQHPmzNHbb7+tXbt2KTMzU126dFFxcbEuvvjiyGszMzN1//33a9asWXrmmWe0b98+STVhrEWLFnrooYc0ffp0vfnmm1q7dq2ys7N188036wc/+EHCYUySLr30UgWDQT300EORP07es2dPXX/99TrzzDOb9PUCSD63NvDb2bpfm9PnBfCjgFW7/8FHQqGQ8vPzNXfu3KTu9+qrr9bKlSsjf6YplvLy+E77BoPBuF/rF8xJbMxLNLfMid2t+26ZFzsxJ7ExL9Fqz8nRl1c1h6//UPjKlSsVCoUk1dyR2bp16ybtZ//+/erdmytvASSO1n0Avg1jxcXFdR4ffRdmIlq0aBG1PwBIBH+XEvAv34ax+v62ZlO0bNkyqfsDEJ/62vcl5zfwh9ndxG/3vNC6DzTOt2EMSCbTLdVeaMpuivrb9yWnN/CH2d/Eb++8OLV1vza/rp/GmJoXp951m0qEMSAJzHddub8p26+83sRvfm3Eg/UTm5l5cUtrfjIl1MAPAEg+mvgBf+PMGJAEppvF/dqU7Y6zLo3zchO/6bURD7+un8YwL/YhjAFJYPoaB782ZdfXvi85v4E/zO4mfrvnxfTaiIdf109jmBf7EMYAuFZDd+q5pWne7iZ+t8wL4CeEMQAwKDMzoNLpAXrGAB8jjAGAA9DED/gXd1MCAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGBQS9MDAAC32rzF0rZtUlaWlJsTMD0cAC5FGAPgCOUVVlL3Z1nVqqhM7j7DKndamj5DWrX6yLa+fSzdOEHK6ODsUBael2Cms8cJ+AlhDIjD3r2p+aWeLK1bW44fY2MuHpbs8ZcneX9HpKVJ7dpJU28PqE8vafVa6Z4ZlkaOkqqrnf59qJmXxYsMD8NBnLp+2rYlMPsFYQyIw9ALnfeDuq4dpgfgK9XV0k0TAhp8Ts0vy8HnSJYlTZ7i9OPkCOcf03Zy5vpZ9jphzC+4gB8AmqBPr7qP+/Y2Mw4A7seZMSAOixc5+1+omZlBVVSk7m05O7jtTM3qtTVnxMJWrTE2lCZx+jFtJy+sH7gbYQyIg9Ov3UhPD2j/fmePsTEvPJfc/WVmZKqisiK5O/3/TZpsaea9liyr5ozYqjVS6X2W+vaRpk1x9vchPC9OP6bt5IX1A3cjjAFwhGTf3RcMpikQSM0v2DumSFPusOpcIzbgdGnyrQFlOvwuxVTOC4CmIYwBQIIyMwMqnR6gZwxAUhDGAKCJcnMCys0xPQoAbkcYA4AEcUYMQDIRxgAYkezG/aOlooHfzc37YbXnhRZ+wBl8Hcbmz5+vkpKSyOOioiKVlpY2aV9lZWUaM2ZM5HF+fr7mzp3b7DGieZzYqp0KTm0Qb0jyG/ePlvyqAnc374cdmRda+Gska/1whyqaytdhLGzIkCHKy8tTt27dIts+/fRTLVy4UOvXr9f69ev19ddfKysrS6+99lrMfWRnZ6u4uFiSNHv2bFvGjca5rbuq6ZzZIO41Xmjer80/66MxyVk/NOajqQhjkgoLCzVixIg62959913Nnj1bLVq0UNeuXfXNN980uI/s7GyNGzdOEmEM8DKa9wEkG2GsHgMGDNDTTz+tU089VW3atFGvXr0a/yA4jl9axt3YIO7WszJub96vzS/rozFuXD/wFsJYPU4++WSdfPLJpoeBZvLLNRxubBBPduP+0VLRwO/m5v2w2vPil/XRGDeuH3gLYQyAEam+ky8VTfNubt4Po4EfcB7CGADEieZ9AKmQZnoAAAAAfsaZMQDGpLL4ldLXaJS8As5EGAMa4JYiVTeWvkqpLn6l9PVoixe591hJJafMCTdU+BdhDGiAe+oXKH21g9tLX2uOZ46VaM6YE0pj/YtrxgAgAZS+Akg2zowBDXBLKaZbSyvdc+bxCDeXvi5eFHDtsZJKzAlMI4zVY8eOHfrTn/4UeXzo0CGVl5dr4sSJkW0333yzOnbsaGJ4sIlbruFwa2llKotfKX2N1rZtwLXHSioxJzCNMFaPqqoqLViwoMFtxcXFhDGgGVJ5dx+lrwDcgjBWjy5duujDDz80PQwADkLpK4BU4AJ+SSUlJQqFQho/fnyT91FWVqZQKKRQKJTEkQFwotycgAoGBQhiAJLC12fG8vLyVFxcHHncrVu3Ju8rOzu7zr6ysrKaNTYA7sLZMgBN5fswlpeXl5R9ZWdna9y4cUnZF+BXyWzkT0UDfyxua+WvPS808gPO4OswBiTCCQ3d9XFKg3hzJbeR356qAve18h+Zl8WLDA7DQZywftxy5zZSgzAGxMnZnVjOaBD3Ize38jv7mLaT+fVD+76/cQE/ADQTrfwAmoMzY0CcnNzG75UGcbeeqXFrK7+Tj2k7eWX9wL0IY0CcnHxNh1caxJPZyJ+KBv5Y3NbKX3tenHxM28kr6wfuRRgD4BjJvLsvFQ38sbitld+ueQEQP8IYADQDrfwAmoswBgBJkJsTUG6O6VEAcCPupgQAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwKCWpgcAAE6weYulbdukrCwpNydgejgAfIQwBqBJyiss00NokGVVq6Ky8TFW7rQ0fYa0avWRbX37WLpxgpTRwX2hLJjpvjEDfkcYg2Ps3dv4L87Wra24Xuc3Jubl4mFO/z6Ux/WqtDSpXTtp6u0B9eklrV4r3TPD0shRUnW107/GaIsXNfw8ayiaU+akbVuCtF8RxuAYQy+M54fhjpSPw52Yl6aqrpZumhDQ4HNqfhEOPkeyLGnyFPO/nJui8XXEsRLNGXOy7HXCmF9xAT8A3+vTq+7jvr3NjAOAP3FmDI6xeFHj/yrMzAyqoiK+t5/8xMS8xHcm0x1Wr605Ixa2ao2xoTRbY+uINRSNOYFphDE4RjzXS6SnB7R/P6fyj2ZiXl54ztZPl7DMjExVVFY0+rpJky3NvNeSZdWcEVu1Riq9z1LfPtK0Ke471hpbR6yhaMwJTCOMAWgSp9+1FwymKRBofIx3TJGm3GHVuUZswOnS5FsDynT41wjAGwhjAHwtMzOg0ukBesYAGEMYAwDVBLDcHNOjAOBHhDEAvsXZMABOQBgD0CCnN+3Xp6EGfq+17h/N6dfzAaiLMIaUS2aztVOasp0mlfPi/Kb9+tRfVeC11v2jNdTCzxqK5oQ5oX3f33wdxubPn6+SkpLI46KiIpWWljZpX2VlZRozZkzkcX5+vubOndvsMXpBcvuonNGU7TzMSyK81rp/tIbXHMdKNPNzQvu+v/k6jIUNGTJEeXl56tatmyTJsiyVlZXptdde0/vvv6/PP/9chw4dUnZ2toqKijR69Gi1bt26zj6ys7NVXFwsSZo9e7btXwOAxNC6D8ApCGOSCgsLNWLEiMjjAwcO6Je//KWOOeYY5efnq6CgQAcOHNCyZctUWlqqJUuWaO7cuWrbtm3kY7KzszVu3DhJhLGjxdOsHy+asmNL5bx4qWm/Ni+17h+toTXHGorGnMA0wlgMaWlp+u1vf6uf/exnysjIiGw/ePCgxo0bp6VLl2revHn6xS9+YXCU7pHMayFoyo4tlfPi9Kb9+jTUwO+11v2jNbTmWEPRmBOYRhiLoVWrVrr++utjbr/uuuu0dOlSvfPOO4Qx+IJb78xrqIGf1n0ATkIYS1DLljVT1qJFC8MjAdBUtO4DcBLCWIKeffZZSdKgQYMMjwRAc9G6D8AJCGMJeOONN/T000+ra9eu+tGPfmR6OACSgLNjAEwjjMVpzZo1Gj9+vNq3b6/77rtPxxxzjOkhAbZzUxt/Qw38Ei38AJyDMBaHtWvX6uc//7nS0tL06KOPRvrI/M5EY7UTmrKdyK55cVcbf8NVBX5t4WcNRXPSnNDE70+EsUasXbtW1157raqrq/X444+rd2+aIcPM9E+Zb8p2JuYlUf5t4edYieacOaGJ358IYw0IB7HDhw/rscceU58+fUwPCUAS0cIPwAkIY/VYt26drr32Wh06dEiPPvqoTjvtNNNDcpxkNuvHi6bs2OyaF6+18fuxhZ81FI05gWmEsRgqKip07bXXaufOnTrrrLO0fPlyLV++vM5r2rdvr1GjRpkZoEOYuLaBpuzY7JoXN7XxN9TAL/m3hZ81FI05gWmEsRh2796tyspKSdKbb76pN998M+o1WVlZvg9j8B833aHXUAO/RAs/AOcgjMXQpUsXffjhh6aHASCFaOEH4BRppgfgBCUlJQqFQho/fnyT91FWVqZQKKRQKJTEkQFIJYIYACfw9ZmxvLw8FRcXRx43pz8sOzu7zr6ysrKaNTbASdxU9hrWUOmrVwtf3fQ2MoAjApZlue+nrMuVl8d3104wGIz7tWFOKS5MFe56ii3V8+K1uyjDha83Tahb+LpnT03/mFvFc4czayiaXXPitkLXpvwO8rracxIMBpO2X1+fGfMir/3SjOacckZnYV4S4dXC1/jWP8dKNHvmhEJX1IdrxgD4EoWvAJyCM2MeY6KI1U68xRIbb1MmzouFr7xN2TTMCUwjjHmM265JSBTljLGlel7cVPYa1lDpq1cLX+NZ/6yhaMwJTCOMAWiUG+/Sa6j0lcJXAE5CGAPgOxS+AnASwhgA38rNCSg3x/QoAPgdd1MCAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMCglqYHAABus3mLpW3bpKwsKTcnYHo4AFyOMAbAFuUVlq2fz7KqVVGZ3M9ZudPS9BnSqtVHtvXtY+nGCVJGB3eEstrzEsx0x5gBryOMwdf27rU3IKRK69aW47+Wi4fZPb7ypO8xLU1q106aentAfXpJq9dK98ywNHKUVF3t7Pk/4si8LF5kcBgO4ob1U1vbtoRoryGMwdeGXuieH8AN22F6AL5QXS3dNCGgwefU/DIcfI5kWdLkKe48jrxz/DeXu9bPstcJY17DBfwAkIA+veo+7tvbzDgAeAdnxuBrixd541+YmZlBVVQk/225ZPLKWZjVa2vOiIWtWmNsKM3mleO/udywfuBthDH4mleuvUhPD2j/fmd/LS88Z+/ny8zIVEVlRVL3OWmypZn3WrKsmjNiq9ZIpfdZ6ttHmjbF2fMfVntevHL8N5cb1g+8jTAGwBZ237kXDKYpEEju57xjijTlDqvONWIDTpcm3xpQpkvuTEzFvABoHsIYAMQpMzOg0ukBesYAJBVhDAAaECt45eYElJtjdlwAvIMwBsAWbit99ULB69EoeQWciTAGWzW3WNFt5Yx2ccO8uK301RsFr3UtXuSOY8Vuds0JN0ygPr4OY/Pnz1dJSUnkcVFRkUpLS5u0r7KyMo0ZMybyOD8/X3Pnzm32GL2m+fUG7ipntA/zkmxeK3iVwuuPYyWaPXNCWSvq4+swFjZkyBDl5eWpW7dukW1vvPGGnnvuOW3cuFHffPONDh48qBNPPFH9+vXTmDFjlJubW2cf2dnZKi4uliTNnj3b1vEDSA0KXgHYgTAmqbCwUCNGjKizraysTKtXr1bv3r3VuXNntWzZUp988omee+45vfDCC3r44Yd1xhlnRF6fnZ2tcePGSSKMNaS5JZOUM8bmhnlxY+mrlwpepZr154ZjxW7MCUwjjNXj5ptv1qRJk6K2v/322xo1apSmT5+uZ5991sDI3K2510xQzhibG+bFbaWvXih4PVrbtgFXHCt2Y05gGmGsHq1bt465/YwzzlBGRoY+/fRTm0cEuJvbSl+9UPAKwB0IYwn64IMPVFlZqf79+5seCoAUouAVgF0IY41YtmyZPvjgAx04cEBbt27V0qVLFQwG69yFCcC7KHgFkGqEsUa89dZbevzxxyOPs7OzNXPmTPXs2dPgqADYibNjAFKJMNaI3//+9/r973+vPXv2aNOmTXrggQf005/+VHfddZcuvvhi08MDXMHu9n2p+Q38kjdb+C2rWvydcMBZCGNxateunXr37q0HHnhAl112mW677TYNGjRIHTt2ND00JMCrzeNOb1W3v31fam4Dv+TNFn6pvNkVM17jhPVDO7+/EcYS1LJlSw0cOFD/93//p7Vr1+rss882PSQkwI1dV/GhVT0VvNjCL3l5HTSV+fVDO7+/pZkegBt9/fXXkqRWrVoZHgmAVKOFH0CqcWasHmvXrlWvXr2itr/55ptasmSJOnTooL59+9o/MDSLV9+ecXqDuJvPxHithV/y7jpoKqevH3gfYawel19+ubp3767u3bvrhBNO0N69e/Xhhx/q3XffVatWrXTXXXcpPT3d9DCRIK9el+H0BnG72/el5jfwS95s4c/MyFQgUGl6GI7i9PUD7yOM1eN3v/udVqxYoXfeeUc7duxQWlqaTjzxRF1xxRW65ppr1LVrV9NDBFzD7vZ9qfkN/JI3W/iDwTSVcxIIcBTCWD2uu+46XXfddaaHAcAgWvgB2IEL+CWVlJQoFApp/PjxTd5HWVmZQqGQQqFQEkcGwAlycwIqGBQgiAFICV+fGcvLy1NxcXHkcbdu3Zq8r+zs7Dr7ysrKatbYADgLZ8cApIrvw1heXl5S9pWdna1x48YlZV+AF9ndwp+MBn7Jey384XkxcR0fgNh8HcbgTqabsp3ICQ3ijbG/hT85V6l7r4W/Zl4WLzI8DAdJ9vrx6l3bSB3CGFzHzZ1VqWO+QdyraOH3g+SuH9r0kSgu4AeARtDCDyCVODMG16E9PJobGsTdfCaGFn5vc8P6gbcRxuA6XI8RzQ0N4na38CejgV/yXgt/eF5YR0e4Yf3A2whjAGxh9917yWjgl7zXwp+seQGQPIQxAGgALfwAUo0wBgBxyM0JKDfH9CgAeBF3UwIAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYFBL0wMAgFTavMXStm1SVpaUmxMwPRwAiEIYAxCX8grL9BASUl5xSJMmV2vV6iPb+vaxdOMEKaOD+0JZMNN9YwYQH8IYHGXv3oZ/4bdubTX6Gj+yY14uHuaueU9Lq1S7dtLU2wPq00tavVa6Z4alkaOk6mp3fS2StHhRcvbDGop29Jy0bUvwhb0IY3CUoRc29ktihy3jcB/m5WjV1dJNEwIafE7NL9bB50iWJU2e4s4g0vjaiBfHSrS6c7LsdcIY7MUF/AA8q0+vuo/79jYzDgBoCGfG4CiLFzX8L9LMzKAqKsptGo172DEvyTszY5/Va2vOiIWtWmNsKM3W2NqIF2soGnMC0whjcJTGrtVITw9o/37eQjiaHfPywnMp3X3STZnWQjPvPSTLqjkjtmqNVHqfpb59pGlT3HcMJes6JtZQNOYEphHGAMTFbXfz3TujvcbfWF7nGrEBp0uTbw0o02VfCwBvI4wB8KRgME2l09PoGQPgeIQxAJ6WmxNQbo7pUQBA/QhjADyLs2IA3IAwBiAmtzXu11a509JvJ1Tq3feOfA1uat932/V5AJrH12Fs/vz5KikpiTwuKipSaWlpk/ZVVlamMWPGRB7n5+dr7ty5zR6j1yXaBE57eGypmBe3Ne7XlpYmtWt3yLXt+8lq24+FNRStoTmhjR928HUYCxsyZIjy8vLUrVu3el9TWVmpiy66SF9//bUKCgr02GOP1Xk+OztbxcXFkqTZs2endLxeknh3Fe3hsTEvtbm9fT+1nW4cK9HqnxPa+GEHwpikwsJCjRgxosHXTJ06Vbt37673+ezsbI0bN04SYQxwAtr3AbgFYSwOr7zyil588UXddtttmjp1qunheEqireI0ZceWinlxY+N+bW5u309W234srKFozAlMI4w1YseOHbr99tt16aWX6uyzzzY9HM9J9HoMmrJjS8W8uK1xv7ZJky3NvNdybft+Kq9TYg1FY05gGmGsEZMnT1aLFi10yy23aNeuXaaHA9jGzXf03TFFuvPuFpo85VBkG+37AJyKMNaAhQsX6l//+pceeOABZWRkEMYAl8jMDOjRhzL0/gc76BkD4HhppgfgVF999ZXuvPNOXXTRRSosLDQ9HAAJ+njTIYIYAFfgzFg9br31VrVs2VK33HKL6aEAKefmgtejVe60NH2GtGp1ZWSbmwpfa3PzW8UA4kcYi2HBggUqKyvTfffdp44dO5oeju80VEhJYWVszZ0XNxe8Hq2m8FWuLXytLRXlr6yhaEfPCUWvsBthLIYNGzZIkm644YaYzy9btkyhUEinnnqqFi5caOfQfKHhSgUKK2NjXsLcXvhaW2rqRThWotWdE4peYTfCWAynnXaaqqqqorZXVVXp5Zdf1gknnKCCggKdeOKJBkYHoDEUvgJwE8JYDEVFRSoqKora/tlnn+nll1/W//zP/+jOO+80MDJ/aKjwknLG2Jo7L24veD2amwtfa0tF+StrKBpzAtMIY3Cchq7XoJwxtubOi5sLXo/m9sLX2lJx7RJrKBpzAtMIYwA8ddfeHVOkKXdYda4Ro/AVgJMRxhLQpUsXffjhh6aHAaABmZkBlU4P6Nsd7bVx4056xgA4HqWvkkpKShQKhTR+/Pgm76OsrEyhUEihUCiJIwPQVP/TtaUKBgUIYgAcz9dnxvLy8lRcXBx53K1btybvKzs7u86+srKymjU2AMmxeYtFEz8AR/N9GMvLy0vKvrKzszVu3Lik7AswyStt/OUVhzRpcrVWrT6yzW1N/F66lg9A/XwdxpBaqWj5pj08tmTOi1fa+NPSKl3fxE8Dvz2CQdMjgN8RxpAytIfbiXk5mhea+FlD9li/uvHXAKnEBfwAPIsmfgBuwJkxpAzt4fZJ5rx4qY3f7U38rCHAHwhjSBnaw+2TzHnxShv/lGktNPPeQ65u4mcNAf5AGANQh1fu4Lt3RnuNv7GcJn4AjkcYA+BJwWCaSqen0TMGwPEIYwA8LTcnoNwc06MAgPpxNyUAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAa1ND0AAHCCzVssbdsmZWVJuTkB08MB4COEMQBNVl5hmR5CvSyrWhWVjY+vcqel6TOkVauPbOvbx9KNE6SMDu4IZcFMd4wTQGyEMbjW3r3ODQJ2a93aMjIfFw9z8vegPK5XpaVJ7dpJU28PqE8vafVa6Z4ZlkaOkqqrnfz1HbF4UfyvNXWsOFkq56RtW4IyGkcYg2sNvZBfKEfsMD0A16qulm6aENDgc2p+aQ4+R7IsafIU9xxfia0FjpVoqZuTZa8TxtA4LuAH4Ht9etV93Le3mXEA8CfOjMG1Fi/iX5xhmZlBVVTE97ZcMnnl7OTqtTVnxMJWrTE2lCZJZC2YOlacjDmBaYQxuBbXYhyRnh7Q/v32z8cLz9n+KeOWmZGpisqKRl83abKlmfdasqyaM2Kr1kil91nq20eaNsUdx1gia8HUseJkzAlMI4wBaDIn38UXDKYpEGh8fHdMkabcYdW5RmzA6dLkWwPKdPDXB8A7CGMAfC0zM6DS6QF6xgAYQxgDANUEsNwc06MA4EeEMQC+xdkwAE5AGAOQECe37tfWUAO/F1r36+Pk6/gAxEYYg+2a03RNe3hsds6Ls1v3a6u/qsALrfv1aayNnzUUzcSccDc4avN1GJs/f75KSkoij4uKilRaWtqkfW3atElFRUWRx1lZWXrttdeaPUYval43Fe3hsTEvifBC6359Gl9fHCvR7J8TmvlRm6/DWNiQIUOUl5enbt26RbYdHdSONmfOHA0cODDyOBgMqri4WJL0xBNPpG6wAJKC1n0ATkEYk1RYWKgRI0bEfC4c1I6WlZVV53HHjh01btw4SdKCBQuSP0gPaU5zPk3Zsdk5L7TuO1tj64s1FI05gWmEsUY0FNTQNM25VoKm7NjsnBcnt+7X1lADvxda9+vT2PpiDUVjTmAaYQxAQtxyt15DDfy07gNwEsJYIzZs2KCKigodOnRIXbp00RlnnKFgMGh6WACagdZ9AE5CGGvE3Llz6zxu06aNfv3rX+uXv/yloREBSBZa9wE4AWGsHl26dNGkSZNUUFCgE044QZWVlXr77bc1c+ZMzZgxQ23bttXVV19tepgAmoEzYwCcgDBWj/z8fOXn50cet2nTRsOGDVOPHj102WWXafbs2frpT3+qli2ZQvibUxv5/djA75br+QDURZJIULdu3dS/f38tX75cmzZtUigUMj0kT6qvDZv28NhMzotzG/n918DfWPu+xBqKJdlzQrs+EkUYa4LwBfx79+41PBLvqr/Livbw2JiXRHi1gT++DjiOlWjJnRPa9ZGoNNMDcJvDhw9r3bp1kqSTTjrJ8GgANBUN/ACcgjNj9Vi3bp169uxZZ9vhw4c1ffp0bd26VQMHDlTnzp0Njc776msRpyk7NpPz4tZGfi828Mfz1y1YQ9GYE5hGGKvHZZddplAopFAopOOPP16VlZVauXKltmzZohNOOEF33nmn6SF6Wn3XXNCUHZvJeXFqI78fG/jjuVaJNRSNOYFphLF6XHvttVq1apWWL1+uyspKtWrVSqeccoquv/56jR49WhkZGaaHCDiCU+/go4EfgFsQxurx+9//3vQQAKQIDfwAnIQL+CWVlJQoFApp/PjxTd5HuOYiFApp27ZtSRwdgFTJzQkoK0vatq2mABYATPD1mbG8vDwVFxdHHnfr1q3J+woGg3X21b59+2aNDXALN5a+St4sfnXqW8YAGhawLMuZP0k9rLw8vrt2gsFg3K91u3gLF7nrKTbupkxcuPj1pgl1i1/37KnpIXMj7qZMXNu2AV/9rE0E8xKt9pyEO0eTwddnxuAc8f9Cp7AyNuYlUV4sfqX0NXEUtMIJuGYMgG9R/ArACTgzBkeI5+0VibdY6sPblE3jteJX3qYE3IkwBkeI9w/rUs4YG6Wv0RoqfZW8WfxK6SvgToQxAM3i1Dv4Gip9lSh+BeAchDEAvkTxKwCnIIwB8LXcnIByc0yPAoCfcTclAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMaml6AADgd5u3WNq2TcrKknJzAqaHA8BmhDEArldeYUVts6xqVVRGb3eSyp2Wps+QVq0+sq1vH0s3TpAyOqQmlJmYl2AmARNoCGEMMGDv3uT+Mmzd2kr6Pt3k4mGxvvZy28eRqLQ0qV07aertAfXpJa1eK90zw9LIUVJ1daq+n/bPy+JFtn/KhPh9/RytbVvCs90IY4ABQy9M9g/+HUneH+xQXS3dNCGgwefU/PIbfI5kWdLkKd4KBsk/3pON9VPbstcJY3bjAn4AMKhPr7qP+/Y2Mw4A5nBmDDBg8aLk/sszMzOoigrnvy2XKs4/81K/1WtrzoiFrVpjbCgpk+zjPdn8vn5gHmEMMCDZ12Skpwe0f7+zf+Gl0gvPRW/LzMhURWWF3UNJyKTJlmbea8myas6IrVojld5nqW8fadqU1Hw/TcyL069B8vv6gXmEMQCuF+tuvWAwTYGAs3/B3jFFmnKHVecasQGnS5NvDSgzRXcgumFeAL8hjAGAIZmZAZVOD9AzBvgcF/ADgGG5OQEVDKoJYcvesrR5i3uvgQOQOM6MAfCEo4tf3VD6GmZn+asT5oUSWKAuwhiQQnYVSVJaGav41T13x9lb/mp+XpxWAsv6ia2xeXH6jRlu4uswNn/+fJWUlEQeFxUVqbS0tEn7Kisr05gxYyKP8/PzNXfu3GaPEe5mX+UCpZVu5pfy1zDnVZGwfmJreF4oh00eX4exsCFDhigvL0/dunWLeu7bb7/VQw89pNdff11ffPGF0tPTlZOTo0svvVQ/+9nPIq/Lzs5WcXGxJGn27Nm2jR2AN1D+CvgXYUxSYWGhRowYEbV948aNuvbaa7Vz506dffbZOv/881VVVaVNmzZp6dKlUWFs3LhxkghjOMKusktKK514tiUxfih/DXNaCSzrJzbmxT6EsXrs3r1bY8eOlSQ9++yzOvXUU+s8f+jQIRPDgsvYdU0FpZXRxa9uKH0Ns7P81Qnz4rRrjVg/sTEv9iGM1eOpp57S559/rjvvvDMqiElSy5ZMHeAkR9+h56ZyUzvLX900L4BfkCjq8fLLLysQCOj888/XJ598orfeekv79u3Td7/7XZ111lk65phjTA8RgEdQ/gr4G2EshgMHDuijjz5Sx44dNXfuXM2aNUvV1dWR508++WQ98MADCoVCBkcJwGtycwLKzTE9CgB2o4E/hsrKSh0+fFgVFRV68MEHddNNN2n58uUqKyvT2LFj9dlnn+n666/X/v37TQ8VgEds3mLRvg/4FGfGYgifBTt8+LCuvPJKXXvttZHnbrjhBm3evFmLFi3SP//5T1166aWmhgngKLVb+J3QNB8PO9v3JWfMCw38QF2EsRjat28f+e/BgwdHPT948GAtWrRI69atI4whiokmbxrEa9Rt4XfHLfn2tu9LTpgXGvjdIZF5cdodsm5DGIshPT1dxx9/vL766it16NAh6vnwNt6mRCxm+q5oEHcrv7XvS07shGP9xBb/vNDG3zxcM1aP73//+5Kkjz/+OOq58LasrCxbxwTAm2jfB/yNM2P1+MlPfqKFCxfqkUce0bnnnhs5G7Z9+3bNmTNHaWlpOu+88wyPEk5kol2cpuwazjvjEh8/te9LNPC7BfNiH8JYPfr166fRo0frr3/9qy655BKde+65OnTokF599VV9++23+t3vfqfc3FzTw4QDmbh2gqbsGrVb+J3QNB8PO9v3JWfMi9OuL2L9xMa82Icw1oCJEyeqe/fumjdvnhYsWKBAIKC8vDxNmTJFQ4cONT08AEepfZeeW5rm7Wzfl9wzL4CfEMYaMWLEiJh/RBwAkoH2fQBcwC+ppKREoVBI48ePb/I+ysrKFAqFaOUH0CS5OQEVDAoQxAAf8vWZsby8PBUXF0ced+vWrcn7ys7OrrMv7rQEkCjOjgH+5PswlpeXl5R9ZWdna9y4cUnZF4CmC7fwO6FpPl52tvCbmBca94GG+TqMAXawo9mbBvEjjrTwu+eWfHtb+O2fF6c17h+N9VOX0+529QPCGJBi9nRf0SDuZl5v4Xd+/xvrpzba9O3HBfwA4AC08AP+xZkxIMXsaBunKfsI55+Fic3LLfxOa9w/GusHphHGgBSz4/oLmrKPCLfwO6FpPl52tvCbmBenX4PE+oFphDEAnhK+c89NTfN2tvC7aV4AvyCMAYBhtPAD/kYYAwCHyM0JKDfH9CgA2I27KQEAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYBBhDAAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMKil6QEAgFds3mJp2zYpK0vKzQmYHg4AlyCMATCuvMJK+j4tq1oVlcnfbyyVOy1NnyGtWn1kW98+lm6cIGV0cFYoa+q8BDOd9XUAXkIYg2Pt3Rv9C6N1ayvmdr9z+7xcPCwVYy9PwT5jS0uT2rWTpt4eUJ9e0uq10j0zLI0cJVVXO+370rR5WbwoycNwELevn6O1bUtwdhvCGBxr6IWxfjjusH0c7sC8mFRdLd00IaDB59T8Ehx8jmRZ0uQp3vkFH3s9eoW31s+y1wljbsMF/ACQBH161X3ct7eZcQBwH86MwbEWL4r+111mZlAVFfa9/eQWbp8XL5x1Wb225oxY2Ko1xoaSErHWo1e4ff3A/QhjcKxY1z2kpwe0f793fyk0ldvn5YXnkr/PzIxMVVRWJH/HMUyabGnmvZYsq+aM2Ko1Uul9lvr2kaZNcdb3panz4uXrkNy+fuB+hDEAxqXiTr1gME2BgD2/YO+YIk25w6pzjdiA06XJtwaU6bC7EO2cFwDxIYwBQDNlZgZUOj1AzxiAJiGMAUCS5OYElJtjehQA3IYwBgAJ4OwXgGQjjAGwTSqa9uuT7AZ+N7XsN6SxeaFpH7Cfr8PY/PnzVVJSEnlcVFSk0tLSJu2rrKxMY8aMiTzOz8/X3Llzmz1GJ3FCQ7XXmrKTxS3zkpqm/fokt6rAXS37DWl4XrzctF8fN6wfL9/NCp+HsbAhQ4YoLy9P3bp1i2wbPHiwtm3b1uDHzZs3T6effrokKTs7W8XFxZKk2bNnp26wBjmjC8pbTdnJw7ykmh9a9iWnrHO7OX/90KrvbYQxSYWFhRoxYkSdbSNHjtSuXbuiXlteXq558+YpIyNDvXodqdzOzs7WuHHjJHk3jAF+R8s+gFQgjNVj1KhRMbc//vjjkqRLLrlErVu3tnFE5jmhgZum7NjcMi9uP+vi9ZZ9yRnr3G5uWT/wLsJYgv7xj39Iki6//HLDI7GfE65ZoCk7NrfMSyqa9uuT7AZ+N7XsN6SxeXHCOrebW9YPvIswloD3339fmzZtUs+ePXXqqaeaHg7gOnbeqZfspnk3tew3hAZ+wHkIYwkInxX70Y9+ZHgkAOxGyz6AVEkzPQC32LNnjxYtWqS2bdvqoosuMj0cAIbk5gRUMKgmhC17y9LmLe6+Dg6AeZwZi9PLL7+sqqoqDR8+XMcee6zp4QCuZVfxa7JLX8PcXv6ayLxQAAvYgzAWp2effVaSPy/cT6VEixbdUM5ogpvmxb7i19TcHef+8tf458UvBbBuWj+J8uMNGW5EGIvDxx9/rA8++EDf/e53IyWvSI7Eqw6cX85oBvNiF7+Uv0ruryKJn3fXD2Wx7sA1Y3Hwc50FgGiUvwJIJs6MNeLgwYNauHChWrVqpWHDhpkejuckWjBJOWNsbpoXL5xt8UP5q+SfAlg3rR94E2GsEa+99pp27Nih8847T506dTI9HM9J9HoGyhljc9O82FX8muzS1zC3l78mMi9+ud7ITesH3kQYawRvUQLJZdcdeqkqN3V7+Sulr4DzEMYa8cgjj5geAgAHofwVQLJxAb+kkpIShUIhjR8/vsn7KCsrUygUUigUSuLIADhVuPyVIAaguXx9ZiwvL0/FxcWRx926dWvyvrKzs+vsKysrq1ljA+AunCkD0FS+D2N5eXlJ2Vd2drbGjRuXlH0BfpHKNv5UNfAfzW2N/DTwA87j6zAGZ2qoCdvLTdnN4dZ5SW0bvz1VBe5r5KeB/2huXT9H88vdr15EGIPjNNxD5d2m7OZhXkzxciO/Fzrh4uON9UPbvntxAT8ANBON/ACagzNjcJyGWr9pyo7NrfPilTMvXm3kp4EfsAdhDI7T0HUPNGXH5tZ5SWUbf6oa+I/mtkZ+GvijuXX9wDsIYwCMSeXdenY1zbutkZ8GfsB5CGMA0Aw08gNoLsIYACRBbk5AuTmmRwHAjbibEgAAwCDCGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAMIowBAAAYRBgDAAAwiDAGAABgEGEMAADAIMIYAACAQYQxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMChgWZZlehAAAAB+xZkxAAAAgwhjAAAABhHGAAAADCKMAQAAGEQYAwAAMIgwBgAAYFBL0wPAERs3btSiRYu0fv16rV+/XuXl5crPz9fcuXMb/Ljnn39ec+bM0ccff6xWrVqpX79++s1vfqMePXrYNHIzZs2apdmzZ9f7/KuvvqouXbrYOCJ7rVmzRrNmzdIHH3ygQ4cOqXv37ho1apSKiopMD82IwYMHa9u2bTGfi2cdud3ChQv13nvvad26dfroo4908OBB/fGPf9SIESNivn737t2aNWuW/vWvf2n79u3q3Lmzzj//fBUXF6tdu3Y2jz41EpkTv/w8+eqrr7Ro0SKVlZXpk08+0TfffKOMjAz169dPv/jFL9SnT5+oj/H6sZLonKTiWCGMOciSJUv00EMPqVWrVsrNzVV5eXmjH/PnP/9Z9957r7KysvSTn/xEe/bs0UsvvaSf/OQn+t///V/179/fhpGbNXz4cGVlZUVt79Chg4HR2OPf//63fvGLX+iYY47RD3/4Q7Vr107/+te/NH78eH355Ze69tprTQ/RiPbt2+uaa66J2h7r+PCa++67T9u2bVMwGFTnzp3rDaaSVFVVpauuukobN25UQUGBfvjDH2rjxo16/PHH9c4772jevHlq3bq1jaNPjUTmJMzrP0/mzp2rRx55RKeccooGDRqkjh07auvWrVqyZImWLFmiGTNm1PkHnR+OlUTnJCypx4oFx/joo4+sdevWWQcOHLC+/vprq3v37tZVV11V7+s3b95sfe9737POO+88a+fOnZHtGzZssHr27GldeOGF1uHDh+0YuhH333+/1b17d+vf//636aHY6uDBg1ZhYaHVs2dPa8OGDZHtO3futM477zyrR48e1meffWZwhGace+651rnnnmt6GMa89dZbke/7Qw89ZHXv3t169tlnY772vvvus7p3727dc889dbbfc889Vvfu3a2//OUvKR+vHRKZE7/8PHnllVesFStWRG1/5513rB49elgDBgyw9u/fH9nuh2Ml0TlJxbHCNWMO0q1bN/Xo0UOtWrWK6/Xz58/XoUOHdP3116t9+/aR7Xl5ebrooou0adMmvffee6kaLgz597//rU8//VQXXXSR8vLyItvbt2+vX/3qVzp48KAWLFhgcIQw4cwzz4zrDKBlWXrmmWeUnp6usWPH1nlu7NixSk9P1zPPPJOqYdoq3jnxk/POO0/5+flR208//XQNHDhQlZWV+vDDDyX551hJZE5ShbcpXWzlypWSpEGDBkU9V1BQoPnz52vlypUaMGCA3UOz1TvvvKPVq1crLS1NOTk5OuOMMzxxHUN9wt/3goKCqOfC29555x1bx+QUBw4c0Pz58/X111/r2GOPVa9evWJeA+NnW7Zs0ddff62CggKlp6fXeS49PV39+vXTsmXL9MUXX+jEE080NEpz/PbzpLaWLVvW+X+Oleg5qS2ZxwphzMW2bNmi9PR0HXfccVHPZWdnS5K2bt1q97BsN2vWrDqPO3TooFtuuUXDhg0zM6AU27Jli6Qj3+PajjvuOKWnp/vi+x7L9u3bVVJSUmdbr169NHPmTJ1yyimGRuUs4WMjJycn5vM5OTlatmyZtmzZ4tlfsA3x28+TsM8//1zLly/Xcccdp+7du0viWIk1J7Ul81ghjLnY7t271bFjx5jPHXvssZKkXbt22TkkW5166qm66667lJ+fr86dO2v79u16/fXXdf/992vixIlq3769hgwZYnqYSbd7925JqvPWdG3HHnusp7/v9RkxYoT69++v7t27Kz09XVu2bNFf//pXLVy4UKNGjdLzzz8fWRd+Fj426puL8PbwceYXfv15IkkHDx7UzTffrAMHDujGG29UixYtJPn7WKlvTqTUHCuEsSS7++67deDAgbhfP3LkyHr/1eEHzZmvoUOH1nmuS5cuuuqqq9S1a1eNHj1a9957r2d/eCJacXFxncd5eXn605/+JKmm4uCZZ57R6NGjTQwNLuDXnyfV1dWaOHGi3nnnHf34xz/2/BnAeDQ2J6k4VghjSfb000+rqqoq7teff/75TQ5jDZ0BaezsiVOkYr7OOOMMnXLKKfroo4+0e/duz50Naeys5+7du5WRkWHnkBztiiuu0MKFC/X+++8TxnTkZ0J9ZzPC2722bprKyz9Pqqur9Yc//EEvvviiLrnkEk2ZMqXO8348Vhqbk4Y051ghjCXZBx98YNvnysnJ0QcffKDt27dHXTcWfq8/1nVFTpKq+QoGg9q6dav27t3rqR8U0pHrN7Zu3aqePXvWeW779u2qqqpS7969DYzMmYLBoCQlFPq9LPwzIXzt4dHC2/18xv5oXvx5Ul1drZKSEj333HO66KKLdPfddystrW7Bgt+OlXjmpDFNPVaotnCx8F2Sb731VtRzy5Ytk6SYt+t6XVVVlf7zn/8oPT098ovYS8Lf9/D3uLbwNq/fQZuINWvWSPJH8Ws8cnJy1LlzZ73//vtRAbWqqkrvv/++unTp4skLspvCiz9PaoeOoqIi/elPf6pzTVSYn46VeOekIc05VghjLjZixAi1bNlSf/7zn+u8ZbVx40a9+OKL6tq1q2cb+Hfv3q3NmzdHbd+3b58mTZqkPXv26IILLoh5O7LbnXHGGTr55JP14osvauPGjZHtu3bt0l/+8he1atXKd9d9bNq0SXv37o25ffr06ZKkiy++2O5hOVIgENCPfvQjVVVV6cEHH6zz3IMPPqiqqir9+Mc/NjQ6M/z08yT8Ntxzzz2nCy64QPfcc0+9ocMvx0oic5KqYyVgWZbVpNEj6TZt2qRHHnlEUs03dtGiRfrOd76js846K/Kau+++u87H1P5zSOedd17kzyEdPHjQ038O6bPPPlNhYaF69eqlrl276jvf+Y6+/fZbLV++XF9++aW6d++uOXPmeOZfsker788hbdu2Tb///e999+eQZs2apb/+9a8aMGCATjrpJLVt21ZbtmxRWVmZDh48qOuuu06/+93vTA8zpZ555plIyfNHH32k9evXq1+/fpG3mvr3768f/ehHkmr+Bf/Tn/5U//d//6eCggJ973vf04YNG7Rs2TL16tVLTz75pNq0aWPsa0mWeOfETz9Pwn9XMT09XSNHjowZGgoLCyOF0n44VhKZk1QdK4QxB1mxYoVGjhzZ4GtitQA///zzeuKJJ+r8ofAbbrjB038ofPfu3Zo5c6bWrFmjbdu2aefOnWrdurW6du2q888/X1dddZXrf0A0Zs2aNbr//vvr/KHw0aNH+/IPha9cuVJPPfWUNm7cqG+++Ub79u1TMBhU79699bOf/SxmQa7XTJw4scG/vDB8+PA6/5jbtWtX5I8/f/PNNzruuON0wQUX6Ne//rVnrouKd0789POksTmRFPXH1L1+rCQyJ6k6VghjAAAABnHNGAAAgEGEMQAAAIMIYwAAAAYRxgAAAAwijAEAABhEGAMAADCIMAYAAGAQYQwAAMAgwhgAAIBBhDEAAACDCGMAAAAGEcYAAAAM+v8AaLpPjmFHbikAAAAASUVORK5CYII=",
-      "text/plain": [
-       "
"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Leave-one-out Cross-validation (LOO)\n",
+        "\n",
+        "LOO cross-validation is an estimate of the out-of-sample predictive fit. In cross-validation, the data are repeatedly partitioned into training and holdout sets, iteratively fitting the model with the former and evaluating the fit with the holdout data. Vehtari et al. (2016) introduced an efficient computation of LOO from MCMC samples (without the need for re-fitting the data). This approximation is based on importance sampling. The importance weights are stabilized using a method known as Pareto-smoothed importance sampling (PSIS).\n",
+        "\n",
+        "## Widely-applicable Information Criterion (WAIC)\n",
+        "\n",
+        "WAIC (Watanabe 2010) is a fully Bayesian criterion for estimating out-of-sample expectation, using the computed log pointwise posterior predictive density (LPPD) and correcting for the effective number of parameters to adjust for overfitting.\n",
+        "\n",
+        "By default ArviZ uses LOO, but WAIC is also available.\n",
+        "\n",
+        "## Model log-likelihood\n",
+        "\n",
+        "In order to compute LOO and WAIC, ArviZ needs access to the model elemwise loglikelihood for every posterior sample. We can add it via {func}`~pymc.compute_log_likelihood`. Alternatively we can pass `idata_kwargs={\"log_likelihood\": True}` to {func}`~pymc.sample` to have it computed automatically at the end of sampling."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_forest(trace_h, var_names=\"theta\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Leave-one-out Cross-validation (LOO)\n",
-    "\n",
-    "LOO cross-validation is an estimate of the out-of-sample predictive fit. In cross-validation, the data are repeatedly partitioned into training and holdout sets, iteratively fitting the model with the former and evaluating the fit with the holdout data. Vehtari et al. (2016) introduced an efficient computation of LOO from MCMC samples (without the need for re-fitting the data). This approximation is based on importance sampling. The importance weights are stabilized using a method known as Pareto-smoothed importance sampling (PSIS).\n",
-    "\n",
-    "### Widely-applicable Information Criterion (WAIC)\n",
-    "\n",
-    "WAIC (Watanabe 2010) is a fully Bayesian criterion for estimating out-of-sample expectation, using the computed log pointwise posterior predictive density (LPPD) and correcting for the effective number of parameters to adjust for overfitting.\n",
-    "\n",
-    "By default ArviZ uses LOO, but WAIC is also available.\n",
-    "\n",
-    "### Model log-likelihood\n",
-    "\n",
-    "In order to compute LOO and WAIC, ArviZ needs access to the model elemwise loglikelihood for every posterior sample. We can add it via {func}`~pymc.compute_log_likelihood`. Alternatively we can pass `idata_kwargs={\"log_likelihood\": True}` to {func}`~pymc.sample` to have it computed automatically at the end of sampling."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb4ee7e2455140be83266344a5a74d3a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "bb4ee7e2455140be83266344a5a74d3a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "with pooled:\n",
+        "    pm.compute_log_likelihood(trace_p)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/html": [
-       "
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+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Computed from 8000 posterior samples and 8 observations log-likelihood matrix.\n",
+              "\n",
+              "         Estimate       SE\n",
+              "elpd_loo   -30.58     1.11\n",
+              "p_loo        0.69        -\n",
+              "------\n",
+              "\n",
+              "Pareto k diagnostic values:\n",
+              "                         Count   Pct.\n",
+              "(-Inf, 0.5]   (good)        8  100.0%\n",
+              " (0.5, 0.7]   (ok)          0    0.0%\n",
+              "   (0.7, 1]   (bad)         0    0.0%\n",
+              "   (1, Inf)   (very bad)    0    0.0%"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "pooled_loo = az.loo(trace_p)\n",
+        "\n",
+        "pooled_loo"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "
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-       "
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+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "aad84975afc6417e9e4bd7b21fe6d9c0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "\n"
+      "source": [
+        "with hierarchical:\n",
+        "    pm.compute_log_likelihood(trace_h)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with pooled:\n",
-    "    pm.compute_log_likelihood(trace_p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Computed from 8000 posterior samples and 8 observations log-likelihood matrix.\n",
-       "\n",
-       "         Estimate       SE\n",
-       "elpd_loo   -30.58     1.11\n",
-       "p_loo        0.69        -\n",
-       "------\n",
-       "\n",
-       "Pareto k diagnostic values:\n",
-       "                         Count   Pct.\n",
-       "(-Inf, 0.5]   (good)        8  100.0%\n",
-       " (0.5, 0.7]   (ok)          0    0.0%\n",
-       "   (0.7, 1]   (bad)         0    0.0%\n",
-       "   (1, Inf)   (very bad)    0    0.0%"
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Computed from 8000 posterior samples and 8 observations log-likelihood matrix.\n",
+              "\n",
+              "         Estimate       SE\n",
+              "elpd_loo   -30.82     1.08\n",
+              "p_loo        1.17        -\n",
+              "------\n",
+              "\n",
+              "Pareto k diagnostic values:\n",
+              "                         Count   Pct.\n",
+              "(-Inf, 0.5]   (good)        4   50.0%\n",
+              " (0.5, 0.7]   (ok)          4   50.0%\n",
+              "   (0.7, 1]   (bad)         0    0.0%\n",
+              "   (1, Inf)   (very bad)    0    0.0%"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "hierarchical_loo = az.loo(trace_h)\n",
+        "\n",
+        "hierarchical_loo"
       ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pooled_loo = az.loo(trace_p)\n",
-    "\n",
-    "pooled_loo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aad84975afc6417e9e4bd7b21fe6d9c0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "ArviZ includes two convenience functions to help compare LOO for different models. The first of these functions is `compare`, which computes LOO (or WAIC) from a set of traces and models and returns a DataFrame."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/html": [
-       "
\n"
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "
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rankelpd_loop_looelpd_diffweightsedsewarningscale
pooled0-30.5781160.6866450.0000001.01.1058910.000000Falselog
hierarchical1-30.8200051.1670100.2418890.01.0809540.231679Falselog
\n",
+              "
"
+            ],
+            "text/plain": [
+              "              rank   elpd_loo     p_loo  elpd_diff  weight        se  \\\n",
+              "pooled           0 -30.578116  0.686645   0.000000     1.0  1.105891   \n",
+              "hierarchical     1 -30.820005  1.167010   0.241889     0.0  1.080954   \n",
+              "\n",
+              "                   dse  warning scale  \n",
+              "pooled        0.000000    False   log  \n",
+              "hierarchical  0.231679    False   log  "
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "df_comp_loo = az.compare({\"hierarchical\": trace_h, \"pooled\": trace_p})\n",
+        "df_comp_loo"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "
\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\n"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We have many columns, so let's check out their meaning one by one:\n",
+        "\n",
+        "\n",
+        "0. The index is the names of the models taken from the keys of the dictionary passed to `compare(.)`.\n",
+        "\n",
+        "1. **rank**, the ranking of the models starting from 0 (best model) to the number of models.\n",
+        "\n",
+        "2. **loo**, the values of LOO (or WAIC). The DataFrame is always sorted from best LOO/WAIC to worst. \n",
+        "\n",
+        "3. **p_loo**, the value of the penalization term. We can roughly think of this value as the estimated effective number of parameters (but do not take that too seriously).\n",
+        "\n",
+        "4. **d_loo**, the relative difference between the value of LOO/WAIC for the top-ranked model and the value of LOO/WAIC for each model. For this reason we will always get a value of 0 for the first model.\n",
+        "\n",
+        "5. **weight**, the weights assigned to each model. These weights can be loosely interpreted as the probability of each model being true (among the compared models) given the data.\n",
+        "\n",
+        "6. **se**, the standard error for the LOO/WAIC computations. The standard error can be useful to assess the uncertainty of the LOO/WAIC estimates. By default these errors are computed using stacking.\n",
+        "\n",
+        "7. **dse**, the standard errors of the difference between two values of LOO/WAIC. The same way that we can compute the standard error for each value of LOO/WAIC, we can compute the standard error of the differences between two values of LOO/WAIC. Notice that both quantities are not necessarily the same, the reason is that the uncertainty about LOO/WAIC is correlated between models. This quantity is always 0 for the top-ranked model.\n",
+        "\n",
+        "8. **warning**, If `True` the computation of LOO/WAIC may not be reliable.\n",
+        "\n",
+        "9. **loo_scale**, the scale of the reported values. The default is the log scale as previously mentioned. Other options are deviance -- this is the log-score multiplied by -2 (this reverts the order: a lower LOO/WAIC will be better) -- and negative-log -- this is the log-score multiplied by -1 (as with the deviance scale, a lower value is better)."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with hierarchical:\n",
-    "    pm.compute_log_likelihood(trace_h)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Computed from 8000 posterior samples and 8 observations log-likelihood matrix.\n",
-       "\n",
-       "         Estimate       SE\n",
-       "elpd_loo   -30.82     1.08\n",
-       "p_loo        1.17        -\n",
-       "------\n",
-       "\n",
-       "Pareto k diagnostic values:\n",
-       "                         Count   Pct.\n",
-       "(-Inf, 0.5]   (good)        4   50.0%\n",
-       " (0.5, 0.7]   (ok)          4   50.0%\n",
-       "   (0.7, 1]   (bad)         0    0.0%\n",
-       "   (1, Inf)   (very bad)    0    0.0%"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The second convenience function takes the output of `compare` and produces a summary plot in the style of the one used in the book [Statistical Rethinking](http://xcelab.net/rm/statistical-rethinking/) by Richard McElreath (check also [this port](https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3) of the examples in the book to PyMC)."
       ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierarchical_loo = az.loo(trace_h)\n",
-    "\n",
-    "hierarchical_loo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "ArviZ includes two convenience functions to help compare LOO for different models. The first of these functions is `compare`, which computes LOO (or WAIC) from a set of traces and models and returns a DataFrame."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
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rankelpd_loop_looelpd_diffweightsedsewarningscale
pooled0-30.5781160.6866450.0000001.01.1058910.000000Falselog
hierarchical1-30.8200051.1670100.2418890.01.0809540.231679Falselog
\n",
-       "
"
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "              rank   elpd_loo     p_loo  elpd_diff  weight        se  \\\n",
-       "pooled           0 -30.578116  0.686645   0.000000     1.0  1.105891   \n",
-       "hierarchical     1 -30.820005  1.167010   0.241889     0.0  1.080954   \n",
-       "\n",
-       "                   dse  warning scale  \n",
-       "pooled        0.000000    False   log  \n",
-       "hierarchical  0.231679    False   log  "
+      "source": [
+        "az.plot_compare(df_comp_loo, insample_dev=False);"
       ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_comp_loo = az.compare({\"hierarchical\": trace_h, \"pooled\": trace_p})\n",
-    "df_comp_loo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have many columns, so let's check out their meaning one by one:\n",
-    "\n",
-    "\n",
-    "0. The index is the names of the models taken from the keys of the dictionary passed to `compare(.)`.\n",
-    "\n",
-    "1. **rank**, the ranking of the models starting from 0 (best model) to the number of models.\n",
-    "\n",
-    "2. **loo**, the values of LOO (or WAIC). The DataFrame is always sorted from best LOO/WAIC to worst. \n",
-    "\n",
-    "3. **p_loo**, the value of the penalization term. We can roughly think of this value as the estimated effective number of parameters (but do not take that too seriously).\n",
-    "\n",
-    "4. **d_loo**, the relative difference between the value of LOO/WAIC for the top-ranked model and the value of LOO/WAIC for each model. For this reason we will always get a value of 0 for the first model.\n",
-    "\n",
-    "5. **weight**, the weights assigned to each model. These weights can be loosely interpreted as the probability of each model being true (among the compared models) given the data.\n",
-    "\n",
-    "6. **se**, the standard error for the LOO/WAIC computations. The standard error can be useful to assess the uncertainty of the LOO/WAIC estimates. By default these errors are computed using stacking.\n",
-    "\n",
-    "7. **dse**, the standard errors of the difference between two values of LOO/WAIC. The same way that we can compute the standard error for each value of LOO/WAIC, we can compute the standard error of the differences between two values of LOO/WAIC. Notice that both quantities are not necessarily the same, the reason is that the uncertainty about LOO/WAIC is correlated between models. This quantity is always 0 for the top-ranked model.\n",
-    "\n",
-    "8. **warning**, If `True` the computation of LOO/WAIC may not be reliable.\n",
-    "\n",
-    "9. **loo_scale**, the scale of the reported values. The default is the log scale as previously mentioned. Other options are deviance -- this is the log-score multiplied by -2 (this reverts the order: a lower LOO/WAIC will be better) -- and negative-log -- this is the log-score multiplied by -1 (as with the deviance scale, a lower value is better)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The second convenience function takes the output of `compare` and produces a summary plot in the style of the one used in the book [Statistical Rethinking](http://xcelab.net/rm/statistical-rethinking/) by Richard McElreath (check also [this port](https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3) of the examples in the book to PyMC)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The empty circle represents the values of LOO and the black error bars associated with them are the values of the standard deviation of LOO. \n",
+        "\n",
+        "The value of the highest LOO, i.e the best estimated model, is also indicated with a vertical dashed grey line to ease comparison with other LOO values.\n",
+        "\n",
+        "For all models except the top-ranked one we also get a triangle indicating the value of the difference of WAIC between that model and the top model and a grey error bar indicating the standard error of the differences between the top-ranked WAIC and WAIC for each model."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_compare(df_comp_loo, insample_dev=False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The empty circle represents the values of LOO and the black error bars associated with them are the values of the standard deviation of LOO. \n",
-    "\n",
-    "The value of the highest LOO, i.e the best estimated model, is also indicated with a vertical dashed grey line to ease comparison with other LOO values.\n",
-    "\n",
-    "For all models except the top-ranked one we also get a triangle indicating the value of the difference of WAIC between that model and the top model and a grey error bar indicating the standard error of the differences between the top-ranked WAIC and WAIC for each model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Interpretation\n",
-    "\n",
-    "Though we might expect the hierarchical model to outperform a complete pooling model, there is little to choose between the models in this case, given that both models gives very similar values of the information criteria. This is more clearly appreciated when we take into account the uncertainty (in terms of standard errors) of LOO and WAIC.\n",
-    "\n",
-    "## Reference\n",
-    "\n",
-    "[Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing, 24(6), 997–1016.](https://doi.org/10.1007/s11222-013-9416-2)\n",
-    "\n",
-    "[Vehtari, A, Gelman, A, Gabry, J. (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing](http://link.springer.com/article/10.1007/s11222-016-9696-4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Last updated: Tue Jun 25 2024\n",
-      "\n",
-      "Python implementation: CPython\n",
-      "Python version       : 3.11.8\n",
-      "IPython version      : 8.22.2\n",
-      "\n",
-      "xarray  : 2024.2.0\n",
-      "pytensor: 2.20.0+3.g66439d283.dirty\n",
-      "\n",
-      "matplotlib: 3.8.3\n",
-      "numpy     : 1.26.4\n",
-      "pymc      : 5.15.0+1.g58927d608\n",
-      "arviz     : 0.17.1\n",
-      "\n",
-      "Watermark: 2.4.3\n",
-      "\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Interpretation\n",
+        "\n",
+        "Though we might expect the hierarchical model to outperform a complete pooling model, there is little to choose between the models in this case, given that both models gives very similar values of the information criteria. This is more clearly appreciated when we take into account the uncertainty (in terms of standard errors) of LOO and WAIC.\n",
+        "\n",
+        "## Reference\n",
+        "\n",
+        "[Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing, 24(6), 997–1016.](https://doi.org/10.1007/s11222-013-9416-2)\n",
+        "\n",
+        "[Vehtari, A, Gelman, A, Gabry, J. (2016). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing](http://link.springer.com/article/10.1007/s11222-016-9696-4)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Last updated: Tue Jun 25 2024\n",
+            "\n",
+            "Python implementation: CPython\n",
+            "Python version       : 3.11.8\n",
+            "IPython version      : 8.22.2\n",
+            "\n",
+            "xarray  : 2024.2.0\n",
+            "pytensor: 2.20.0+3.g66439d283.dirty\n",
+            "\n",
+            "matplotlib: 3.8.3\n",
+            "numpy     : 1.26.4\n",
+            "pymc      : 5.15.0+1.g58927d608\n",
+            "arviz     : 0.17.1\n",
+            "\n",
+            "Watermark: 2.4.3\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "%load_ext watermark\n",
+        "\n",
+        "%watermark -n -u -v -iv -w -p xarray,pytensor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "hide_input": false,
+    "interpreter": {
+      "hash": "baf205d70af30bf8b721a304f5a44beb31bf8af014f6b7340f1a7ae004926653"
+    },
+    "kernelspec": {
+      "display_name": "pymc",
+      "language": "python",
+      "name": "pymc"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.8"
+    },
+    "toc": {
+      "base_numbering": 1,
+      "nav_menu": {},
+      "number_sections": true,
+      "sideBar": true,
+      "skip_h1_title": false,
+      "title_cell": "Table of Contents",
+      "title_sidebar": "Contents",
+      "toc_cell": false,
+      "toc_position": {},
+      "toc_section_display": true,
+      "toc_window_display": false
     }
-   ],
-   "source": [
-    "%load_ext watermark\n",
-    "\n",
-    "%watermark -n -u -v -iv -w -p xarray,pytensor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "hide_input": false,
-  "interpreter": {
-   "hash": "baf205d70af30bf8b721a304f5a44beb31bf8af014f6b7340f1a7ae004926653"
-  },
-  "kernelspec": {
-   "display_name": "pymc",
-   "language": "python",
-   "name": "pymc"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
   },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/source/learn/core_notebooks/posterior_predictive.ipynb b/docs/source/learn/core_notebooks/posterior_predictive.ipynb
index 23f632e72e..679c7c16ed 100644
--- a/docs/source/learn/core_notebooks/posterior_predictive.ipynb
+++ b/docs/source/learn/core_notebooks/posterior_predictive.ipynb
@@ -1,4386 +1,4386 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "(posterior_predictive)=\n",
-    "# Prior and Posterior Predictive Checks\n",
-    "\n",
-    "Posterior predictive checks (PPCs) are a great way to validate a model. The idea is to generate data from the model using parameters from draws from the posterior. \n",
-    "\n",
-    "Elaborating slightly, one can say that PPCs analyze the degree to which data generated from the model deviate from data generated from the true distribution. So, often you will want to know if, for example, your posterior distribution is approximating your underlying distribution. The visualization aspect of this model evaluation method is also great for a 'sense check' or explaining your model to others and getting criticism.\n",
-    "\n",
-    "*Prior* predictive checks are also a crucial part of the Bayesian modeling workflow. Basically, they have two main benefits:\n",
-    "\n",
-    "- They allow you to check whether you are indeed incorporating scientific knowledge into your model -- in short, they help you check how credible your assumptions before seeing the data are.\n",
-    "- They can help sampling considerably, especially for generalized linear models, where the outcome space and the parameter space diverge because of the link function.\n",
-    "\n",
-    "Here, we will implement a general routine to draw samples from the observed nodes of a model. The models are basic but they will be a steppingstone for creating your own routines. If you want to see how to do prior and posterior predictive checks in a more complex, multidimensional model, you can check [this notebook](https://github.com/aloctavodia/BAP/blob/master/extras/multinomial_ppcs.ipynb). Now, let's sample!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on PyMC v5.15.1+68.gc0b060b98.dirty\n"
-     ]
-    }
-   ],
-   "source": [
-    "import arviz as az\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "\n",
-    "from scipy.special import expit as logistic\n",
-    "\n",
-    "import pymc as pm\n",
-    "\n",
-    "print(f\"Running on PyMC v{pm.__version__}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "az.style.use(\"arviz-darkgrid\")\n",
-    "\n",
-    "RANDOM_SEED = 58\n",
-    "rng = np.random.default_rng(RANDOM_SEED)\n",
-    "\n",
-    "\n",
-    "def standardize(series):\n",
-    "    \"\"\"Standardize a pandas series\"\"\"\n",
-    "    return (series - series.mean()) / series.std()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lets generate a very simple linear regression model. On purpose, I'll simulate data that don't come from a standard Normal (you'll see why later):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'1.59, 5.69, 4.97, 17.54'"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "(posterior_predictive)=\n",
+        "# Prior and Posterior Predictive Checks\n",
+        "\n",
+        "Posterior predictive checks (PPCs) are a great way to validate a model. The idea is to generate data from the model using parameters from draws from the posterior. \n",
+        "\n",
+        "Elaborating slightly, one can say that PPCs analyze the degree to which data generated from the model deviate from data generated from the true distribution. So, often you will want to know if, for example, your posterior distribution is approximating your underlying distribution. The visualization aspect of this model evaluation method is also great for a 'sense check' or explaining your model to others and getting criticism.\n",
+        "\n",
+        "*Prior* predictive checks are also a crucial part of the Bayesian modeling workflow. Basically, they have two main benefits:\n",
+        "\n",
+        "- They allow you to check whether you are indeed incorporating scientific knowledge into your model -- in short, they help you check how credible your assumptions before seeing the data are.\n",
+        "- They can help sampling considerably, especially for generalized linear models, where the outcome space and the parameter space diverge because of the link function.\n",
+        "\n",
+        "Here, we will implement a general routine to draw samples from the observed nodes of a model. The models are basic but they will be a steppingstone for creating your own routines. If you want to see how to do prior and posterior predictive checks in a more complex, multidimensional model, you can check [this notebook](https://github.com/aloctavodia/BAP/blob/master/extras/multinomial_ppcs.ipynb). Now, let's sample!"
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "N = 100\n",
-    "\n",
-    "true_a, true_b, predictor = 0.5, 3.0, rng.normal(loc=2, scale=6, size=N)\n",
-    "true_mu = true_a + true_b * predictor\n",
-    "true_sd = 2.0\n",
-    "\n",
-    "outcome = rng.normal(loc=true_mu, scale=true_sd, size=N)\n",
-    "\n",
-    "f\"{predictor.mean():.2f}, {predictor.std():.2f}, {outcome.mean():.2f}, {outcome.std():.2f}\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, variation in our predictor and outcome are quite high -- which is often the case with real data. And sometimes, the sampler won't like this -- and you don't want to make the sampler angry when you're a Bayesian... So, let's do what you'll often have to do with real data: standardize! This way, our predictor and outcome will have a mean of 0 and std of 1, and the sampler will be much, much happier:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "'0.00, 1.00, -0.00, 1.00'"
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Running on PyMC v5.15.1+68.gc0b060b98.dirty\n"
+          ]
+        }
+      ],
+      "source": [
+        "import arviz as az\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import xarray as xr\n",
+        "\n",
+        "from scipy.special import expit as logistic\n",
+        "\n",
+        "import pymc as pm\n",
+        "\n",
+        "print(f\"Running on PyMC v{pm.__version__}\")"
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "predictor_scaled = standardize(predictor)\n",
-    "outcome_scaled = standardize(outcome)\n",
-    "\n",
-    "f\"{predictor_scaled.mean():.2f}, {predictor_scaled.std():.2f}, {outcome_scaled.mean():.2f}, {outcome_scaled.std():.2f}\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now, let's write the model with conventional flat priors and sample prior predictive samples:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling: [a, b, obs, sigma]\n"
-     ]
-    }
-   ],
-   "source": [
-    "with pm.Model() as model_1:\n",
-    "    a = pm.Normal(\"a\", 0.0, 10.0)\n",
-    "    b = pm.Normal(\"b\", 0.0, 10.0)\n",
-    "\n",
-    "    mu = a + b * predictor_scaled\n",
-    "    sigma = pm.Exponential(\"sigma\", 1.0)\n",
-    "\n",
-    "    pm.Normal(\"obs\", mu=mu, sigma=sigma, observed=outcome_scaled)\n",
-    "    idata = pm.sample_prior_predictive(draws=50, random_seed=rng)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What do these priors mean? It's always hard to tell on paper -- the best is to plot their implication on the outcome scale, like that:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "az.style.use(\"arviz-darkgrid\")\n",
+        "\n",
+        "RANDOM_SEED = 58\n",
+        "rng = np.random.default_rng(RANDOM_SEED)\n",
+        "\n",
+        "\n",
+        "def standardize(series):\n",
+        "    \"\"\"Standardize a pandas series\"\"\"\n",
+        "    return (series - series.mean()) / series.std()"
+      ]
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Lets generate a very simple linear regression model. On purpose, I'll simulate data that don't come from a standard Normal (you'll see why later):"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_, ax = plt.subplots()\n",
-    "\n",
-    "x = xr.DataArray(np.linspace(-2, 2, 50), dims=[\"plot_dim\"])\n",
-    "prior = idata.prior\n",
-    "y = prior[\"a\"] + prior[\"b\"] * x\n",
-    "\n",
-    "ax.plot(x, y.stack(sample=(\"chain\", \"draw\")), c=\"k\", alpha=0.4)\n",
-    "\n",
-    "ax.set_xlabel(\"Predictor (stdz)\")\n",
-    "ax.set_ylabel(\"Mean Outcome (stdz)\")\n",
-    "ax.set_title(\"Prior predictive checks -- Flat priors\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These priors allow for absurdly strong relationships between the outcome and predictor. Of course, the choice of prior always depends on your model and data, but look at the scale of the y axis: the outcome can go from -40 to +40 standard deviations (remember, the data are standardized). I hope you will agree this is way too permissive -- we can do better! Let's use [weakly informative priors](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations) and see what they yield. In a real case study, this is the part where you incorporate scientific knowledge into your model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling: [a, b, obs, sigma]\n"
-     ]
-    }
-   ],
-   "source": [
-    "with pm.Model() as model_1:\n",
-    "    a = pm.Normal(\"a\", 0.0, 0.5)\n",
-    "    b = pm.Normal(\"b\", 0.0, 1.0)\n",
-    "\n",
-    "    mu = a + b * predictor_scaled\n",
-    "    sigma = pm.Exponential(\"sigma\", 1.0)\n",
-    "\n",
-    "    pm.Normal(\"obs\", mu=mu, sigma=sigma, observed=outcome_scaled)\n",
-    "    idata = pm.sample_prior_predictive(draws=50, random_seed=rng)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'1.59, 5.69, 4.97, 17.54'"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "N = 100\n",
+        "\n",
+        "true_a, true_b, predictor = 0.5, 3.0, rng.normal(loc=2, scale=6, size=N)\n",
+        "true_mu = true_a + true_b * predictor\n",
+        "true_sd = 2.0\n",
+        "\n",
+        "outcome = rng.normal(loc=true_mu, scale=true_sd, size=N)\n",
+        "\n",
+        "f\"{predictor.mean():.2f}, {predictor.std():.2f}, {outcome.mean():.2f}, {outcome.std():.2f}\""
+      ]
+    },
     {
-     "data": {
-      "image/png": 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dHXQ6HTn+m9/8BjMzM7j55pvxoQ99KCYilJeXh+9///sQCAR46qmnVtxeXwpr+89//vO48sorlz2u1WrjRn44HA7uv//+mOhqdnY27rrrLgBYtiX9wgsvYGBgAHv37sXnPve5mJ0ekUiEb37zmygoKMCf//xnzM7Obnh8LC+99BI+9rGPIRAI4Gc/+xluuummmMc9Hg98Ph+qqqqgVquXvb6ioiKjtJPxdNvsv5ubmwEAarUahYWFMc9xOp3o7e0FgJhre//+/WAYBt/4xjfQ1NQUc65oacPvfve7mMeam5tRVVW1bHxqtRr/9V//BWCxzOBqnOm7WY33v//9kMvleP3115fJiI4fP46enh6UlZWt61630j1yvdfwk08+CQC48847Y64xHo+HL33pSygpKVnzGNfLhRdeGDfpfvfu3bj55psRDAZx6NChuK9d6R6+VoaHh/HJT34SXq8XX/3qV9f8Hd1yyy3Yu3dvzLHrrrsOarUac3NzOHXqVMy5Dh06BC6Xi/vuuy9m7Dk5ObjvvvvOuJu8GsmeD1fj6NGj6O7uhkgkwre+9S2IxWLyWH5+Pr75zW8CWEyuXKn059e//vWYezbL1NQUAMQtCSoUCpfdG7Yy1Nne4tx7771EZ7Vnzx6yLVpdXY1f/OIXMT8Mlssvv3xN5xgbG8PAwAAA4Nprr132OIfDIdvpK+mRL7vssjWdcynnn39+3B/r3r17IZfL4fF40NPTs+zx2trauJPz6dOnMTExgerqatTV1cU9J+tERGu3WY3qFVdcEXf7LZ59VqOnpwfT09PIy8vDe9/73jW9dj1cccUVEIvFeOedd5bdGFkHfOm2HaufjbdwA4CSkhIoFApMT0/D4XCccQxOpxNGoxFcLhc33HDDmsZfXl4eV1rBOqbj4+NrGrtEIkFdXR1CoRBOnjy54fEBwGOPPYYvfvGLkMvlePrpp+NOJPn5+aioqIDZbMYDDzyQsL43XazmbEdPiE1NTRgeHiaOKPt7USgUKC0tBbCokz5x4gSEQuEy52Xp+To7O5c9Nj8/j4MHD+LBBx/E1772Ndxzzz2455578MorrwDAivkjQGLfzWoIhUJ88IMfRCQSwTPPPBPz2FNPPQUAuOmmm9YkkWNZ6R65nmt4bGwMg4ODABC3RnpWVtaG78lrxeVy4cUXX8T3v/99/O///i/53thrZKXvbaV7+FqYnZ3F7bffDqfTidtvvx0333zzmt9jpWs13r3n6NGjYBgG27Zti7to1ul0G6pnnez5cDXY7+eCCy5AYWHhssfr6upgMBgQiUTi5nUUFBRgz549cd97586dAID77rsPb731VtwFwtkC1WxvcRoaGqBQKAAsrlrz8/NRX1+PCy64AHx+/K+3oqJiTedgbyJyuXzFFXF1dXXMc5eyVEe7VlZ7fUVFBWZmZuKuqlf6rOxENDAwcMabXnR9YfYcK40nNzcXMpkMc3Nzq74nC+uUqFSqDUU6EkUikeDyyy/H888/jxdffJFE4qempvDGG28gOzt72eTM2iqRCWp6evqMpSbZBLqioqI1V71ZKcLPXpdLE9zYsX/5y1/Gl7/85VXfm/2eNzK+zs5OdHR0IDs7G0899RT5XcTj+9//Pu666y488cQTeOKJJyCXy7Fz506cf/75uPrqqxOuJHPo0KG4UcEbbrgBe/bswdGjR3HgwIFlj+/btw/79u1L6Bys8+twODA+Po6SkhJ0dHQgLy8vZhekqakJBw8eREdHB6699tq4eu2hoSEwDAOfz4cdO3asel42MZjl1Vdfxb333ks0n/Fg81WWspbvZjU+9KEP4Ve/+hUOHDiAO++8E9nZ2Ziensbf/vY3ZGVl4T/+4z/W9b4r3VPWcw2z96m8vDxIJJK4z13rPLAR/vCHP+D++++H1+td8Tnz8/Nxj290nIFAAJ/97Gdht9tx1VVX4X/+53/W9T4rlWxl7z3RjiI7D6429oqKinUnQSd7PlwN9rOsds7q6mqYTKa48/9q5/zEJz6BY8eO4Z133sFtt90GgUAAvV6PpqYmXHnllcQZPxugzvYWZ2md7USI3nraLOIla2wGK31WVu5QVFSEd7/73au+x9lWq/r666/H888/jxdeeIE423/+858RCoVw2WWXIScnJ+b5kUgEwGLkLd5OSTRyuTwlY2ZZa1ITO/aVojLRJKP+uVarBZ/Px6lTp/Ctb30L+/fvX/Ea3LNnD1599VW8/vrrOHLkCLq6uvD222/jzTffxMMPP4yf/vSnOO+88854TqPRiBdeeGHZ8ebmZuzZswcDAwNxH6+oqEjY2S4pKYFSqYTD4UB7ezv27NmDoaEhXHrppTGLRHY3qL29fUVnm/3ticXiNUVXx8fH8fnPfx4+nw+33XYb3v/+96OyshJisRhcLhdvv/02PvGJT6z4+rV8N6tRWlqK9773vTh48CD++te/4tprr8Uf//hHBAIBfOADH1h3uc2V7pEbuYZXW8Cvd3HPjidRTp06ha9//evg8Xj44he/iIsvvhhlZWUQiUTgcDh49tln8fWvf31FCdpG5ivm/0/sP3LkCJqbm3H//fev+3Ov53WpsP9GSMfcv9o5RSIRnnjiCZw4cQJvvfUWurq60NXVhVOnTuGJJ57Ahz70Ify///f/NnG0qYM625Qzwmr7ZmZm4PF44ka32ehLqnSAS1ufR8NGh9dybnZLWy6X44EHHkj4dew5VhqP2+1OOKoN/HtydDgcYBhmU27Ae/bsgUKhgMPhwLFjx9DY2LiihARYjCY7HA7cfvvtZ4xEJgIbnXY6nZibm0tpTfeysjL09vbihhtuSFg+tZHx5eTk4Gc/+xk+9alP4c0338Ttt9+Oxx57bMXoolAoxOWXX07GNj09jYceegjPPvssvvKVr+C111474znvvPNO3HnnnSs+ft111615QR6PlpYW4myzDtdSTaVWq0VeXh46OjowMTFBZEWsEw78+7fH4XDw3e9+N+EF1Kuvvgqfz4f3vve9+NKXvrTs8f7+/lVfv9bvZjU+/OEP4+DBg3jqqadw9dVX4/e//z2ARV1vslnPNczep6anp+H1euMuklcqXcnK41aKNI+MjCQ0Bpa//e1vYBgGt9xyC26//fZljyciPVsvP/jBD/Dyyy+jpqYGP/3pT+NW50oFrD59tfKgiZYOjUey58PVYN+HnePjsdH5f+fOnSSKHQqFcOjQIdx99914+umncdlll8XNZdpqUM025YyUlpaSLdd4iXUMw5DIWaqaVvzrX/8iyRTRvPHGG5iZmSG6xUTZsWMH8vLyYLPZYLVaE34d61z87W9/W1aHGsCqNVfjUVdXh7y8PExPT6+YILQUdjJcb21rAMT5euGFF3Dq1ClYLBaUlZXFjaSy5QUPHjy47vNFU1RURDR+qa6jypaiXMvYNzo+qVSKX/7yl3j3u9+Njo4OfOxjH4tJXFuN/Px84kiOjIwk/LrNgJ3wOjo6liVHsnA4HKLbZm2nVqtjkuNKSkqg1+sxPz+Pt956K+Hzs7aItwPBMAz+8pe/nPE9NvLdRMOWTTt58iQeeughjIyMYMeOHSnZ9l7PNVxWVka2719++eVljwcCAaJxXwrrMK1Ut36lGugrsdr35vf7VxzHRnnqqafwq1/9CsXFxfjFL36xbMculezZswccDgc9PT1xteg2m21DdfSTPR+uBvsbf+uttzA5Obns8dOnT5Mcl2QkNPL5fFx++eVkxzlT+g1sFOpsUxLi4x//OADg0Ucfjbn4GYbBo48+CqPRiJycnHXrFc+Ez+fDfffdF1M8f3x8nESlb7rppjVJVQQCAe644w4wDIM77rgDR48eXfaccDiMw4cP4/jx4+TY5ZdfjpKSEoyMjODBBx+M2VK1WCz42c9+tqbPxefziZTja1/7Go4cObLsOSdOnIjR37GRwf7+/rgOfyJce+214HK5JDoHLFZDiBdlvO2225CTk4Nf//rXePzxx+M2/hgcHMSf/vSnhM/P1k/+8Y9/jL///e/LHrfZbKs2qUmU//iP/0BFRQX+9re/4Qc/+EFcPa/T6VxWRWej4xOJRPjZz36GSy+9FN3d3fjwhz8cM1ENDw/jj3/8Y9zxvPrqqwAW9f8brcCQTNhJd2BgAK+++irkcnncfAd2wv31r38NIP4C/HOf+xyAxQRv9vNGwzAMuru78fbbb5NjbKLZ3//+d0xMTJDj4XAYP/nJT1ZtQhXNmb6bRGGbS/385z8HkJqoNrD+a5gd38MPPxzj8EUiETz44IPLmg+xtLa2EllOdMIbwzB48skn4/4eVoP93l588cWYsfv9ftx3332rRmnXy6FDh/Dtb38bUqkUv/jFL5JWzSlRKisrSV3t++67L+Zzz83N4b777ku4clM8kj0frsaePXuwa9cu+Hw+fP3rX49poDQ9PU2apF155ZVrtvNTTz1FqhVF43Q6SXWXZMj7MgEqI6EkxE033YSuri786U9/wvXXX4+mpibS1Kavrw9CoRA//OEPU9Ye/JprrsHrr7+Offv2obGxkRTx93q92L17Nyn9thZuueUWjIyM4Fe/+hVuvvlm1NTUoLq6GkKhEE6nEyaTCW63G/fddx8plch+zk9+8pN4/PHHcejQIezYsQMzMzPo6OjA3r170dPTs6Ytwo985CPo6+vD73//e9xyyy3Ytm0bVCoVPB4Pent7MTg4iCeffJI42eXl5airq8OpU6fw/ve/H3V1dcjOzkZeXh6++MUvJnTOkpISnH/++Xjrrbfw/PPPg8PhrNg8oLS0FI8++ijuvPNOfO9738Mvf/lL1NTUoKioCB6PB3a7HQMDA9i1axc+8IEPJHT+9773vfj85z+Phx56CHfddRfUajUMBgNpGmOz2XD//fdvuPydWCzG//3f/+FTn/oUfvnLX+IPf/gD9Ho9SkpK4PP54HA4YLfbUVBQELNQTMb4srKy8NBDD+Hee+/Fn/70J9x888349a9/jbKyMrjdbvzv//4vvvGNb8BgMJDko/7+fpw+fRocDmdZacl0U1hYCK1WC5vNhpmZGezbty+u7Il1ytkkxnjO9sUXX4yvfvWr+N73vofPfOYzUCgUUKlUkEqlcLlcMJlMmJqawu23304iXHv37sX27dvR09ODyy67DM3NzRCJRDhx4gQmJiZw++234xe/+EVCn2W17yZRrrzySvzgBz/A5OQk8vPz45aITAbrvYY//OEP41//+hfefPNNXH311TFNbSYmJvChD30ITz/99LKqSmVlZbjlllvw5JNP4qMf/SgaGxshl8thMpkwOjqKT37yk2SBkQjXXXcdnnzySZw+fRqXXHIJ9uzZAx6Ph6NHj8Ln8+HWW28lZQqTBdssqby8nCz6lpKXl4e77747qeeN5r777oPZbEZbWxsuueQSNDc3g2EYHDlyBHK5HBdffDFeffXVNTUCY0nFfLgaP/rRj/CRj3wE//znP8l3GAqF0N7eDo/Hg+3bt6/amXgl/vCHP+Cb3/wmKisrUVNTQ37/7LXR2tq6rNPnVoU625SE4HA4+P73v4/3vOc9ePbZZ9HT04OFhQUUFhbiuuuuw+233x63XnCyqKysxIEDB/DQQw+hra0Ns7OzKC8vx1VXXYXbb7993YkfX/7yl7Fv3z48/fTT6OzsxFtvvQWBQICioiI0NzfjoosuwqWXXhrzmubmZvzhD3/A/v370dHRgX/84x+oqqrCXXfdhY9//OPLnn8mOBwOvvGNb+CSSy7B73//e3R3d8NqtUImk6GyshLXXHPNsgji/v378aMf/Qjt7e04ePAgQqEQKioqEna2gUV9NruN39TUtGpJqKamJrz88sv43e9+hzfeeAMnT55EIBBAQUEBysrKcPXVV6/5c3/6059Ga2srfvvb3+LIkSP4xz/+AYlEgtLSUtx2221J0+nV1NTgz3/+M37/+9/j0KFDMJvNOH78OORyOUpLS/Hxj388btnFZIyPx+Phe9/7HsRiMZ555hncfPPNeOKJJ1BVVYWvfOUrOHLkCKxWK9maLy4uxjXXXIMPf/jDSdsGTiYtLS2kTOFSCQmLTqdDbm4uZmdnweFwVpSW3XrrrWhtbcXvfvc70gGSy+WisLAQtbW1y357fD4fv/3tb/Hzn/8cf//733H48GFIpVLs3r0bDz/8MObn5xN2toGVvxu2utOZyMrKQnNzM/7617/ixhtvTKkeeD3XMI/Hw6OPPorf/OY3eOGFF9De3g6JRII9e/bgpz/9Kf7xj38AiJ8A/pWvfAXl5eX44x//iK6uLkgkEuzevRsPPfQQPB7PmpztnJwcHDhwAPv37ycJwHK5HOeffz7uuOMOHDt2bGPGiQO742ixWGCxWOI+p6KiIqXOdklJCQ4cOIBHHnmEdCQuLCzE+973Ptx111347//+bwDrS8BP1Xy4ElVVVXj++edJgOn1118Hl8uFSqXCFVdcgVtvvXVd5/z85z+P119/Hd3d3eju7sbc3BwKCgqwc+dOXH/99Xjf+963YlW1rQaH2cheBoWSYvbv349HHnkEd9xxx6pJYBQKhbKZuN1uXHjhhfD7/fjnP/+56VKFjXLrrbeivb0d+/fvX/NCmbIx3G439u3bh7m5OfzrX/9KeEeYzodbF6rZplAoFApljfzf//0fvF4vrrjiiox1tI1G47Ici0AggP3796O9vR0FBQVrbuxDSZwTJ04sOzY9PY27774bs7OzuOiii1ImvaRkFmdHfJ5CoVAolBTT2dmJ5557DkNDQ2hra4NIJCLJnpnId7/7XRiNRhgMBhQVFcHtdsNsNsPpdCI7OxsPPPBA2nognAvceOONKC0thUajgVwux/j4OE6fPg2v14vy8vJ16ZwpWxPqbFMoFAqFkgAOhwMHDhyAUChEfX09vvjFL264lXgqufHGG/GXv/wFZrMZJ06cAMMwKC4uxvXXX4+Pf/zj0Gq16R7iWc1nPvMZtLW1kWR7gUCAqqoq7N27Fx/96EfPuoZplJWhmm0KhUKhUCgUCiVFUM02hUKhUCgUCoWSIqizTaFQKBQKhUKhpAjqbFMoFAqFQqFQKCmCJkjGweVypeW8bCMIyupQOyUGtVNiUDslBrVT4lBbJQa1U2JQOyVGuuyUSKIrjWxnEFwu/ToSgdopMaidEoPaKTGonRKH2ioxqJ0Sg9opMTLZTpk7MgqFQqFQKBQKZYtDnW0KhUKhUCgUCiVFUGebQqFQKBQKhUJJEdTZplAoFAqFQqFQUgR1tikUCoVCoVAolBRBnW0KhUKhUCgUCiVFUGebQqFQKBQKhUJJEdTZplAoFAqFQqFQUgR1tikUCoVCoVAolBRBnW0KhUKhUCgUCiVFUGebQqFQKBQKhUJJEdTZplAoFAqFQqFQUgR1tikUCoVCoVAolBRBnW0KhUKhUCgUCiVF8NM9AAqFQqFQKBQKZb14PB5kZWWlexgrQp1tCoVCoVAoFMqWIxgM4vjx4zh9+jTy8/Px/ve/P91Digt1tikUCoVCoVAoWwqHw4GOjg54vV4AgEqlSvOIVoY62xQKhUKhUCiULYHb7UZ7ezuGh4cBADKZDC0tLdixYwdcLleaRxcf6mxTKBQKhUKhUDKaUCiEkydP4uTJk4hEIuDxeNixYwfq6urA52e2O5vZo6NQKBQKhUKhnNMMDQ2hvb0dc3NzAICKigq0tLQgJycnzSNLDOpsUygUCoVCoVAyDo/Hg46ODgwMDAAAJBIJmpqaoFQq0zuwNUKdbQqFQqFQKBRKxhCJRHD69GkcP34coVAIHA4H27ZtQ319PQQCQbqHt2aos02hUCgUCoVCyQjGxsbQ1taGmZkZAEBJSQlaW1uRl5eX3oFtAOpsUygUCoVCoVDSysLCAo4ePQq73Q4AEAqF2LNnD7RabZpHtnGos02hUCgUCoVCSQsMw8BsNqOzsxOBQAAAYDAYsHv3bmRnZ6d5dMmBOtsUCoVCoVAolE1ncnIShw8fxtTUFACgoKAAra2tKCoqSvPIkgt1tikUCoVCoVAom4bf70dXVxdMJhMAICsrC7t374bBYACHw0nz6JIPdbYpFAqFQqFQKJuCzWbD0aNH4fP5AAAajQaNjY0Qi8VpHlnqoM42hUKhUCgUCiWluFwutLW1YXx8HACQm5uL1tZWlJWVpXlkqYc62xQKhUKhUCiUlBAMBtHd3Y3Tp08jEomAz+dj165d2L59O7hcbrqHtylQZ5tCoVAoFAqFknT6+/vR0dGB+fl5AEB1dTWam5shlUrTPLLNhTrbFAqFQqFQKJSk4Xa70d7ejuHhYQCATCZDS0sLKisr0zyy9ECdbQqFQqFQKBTKhgmHwzh58iROnjyJcDgMLpeLHTt2YMeOHeDzz12X89z95BQKhUKhUCiUpDA8PIy2tjbMzc0BAMrLy9HS0oLc3Nw0jyz9UGebQqFQKBQKhbIu5ufnceTIETgcDgCAWCxGU1MTVCpVegeWQVBnm0KhUCgUCoWyJiKRCIxGI44fP45gMAgOh4Pa2lrU19cjKysr3cPLKKizTaFQKBQKhUJJmPHxcbS1tcHlcgEAiouL0draivz8/DSPLDOhzjaFQqFQKBQK5Yz4fD4cPXoUNpsNACAUCtHY2AitVntWtllPFudGNfH/n5///OfQ6/XQ6/U4fvx4uodDoVAoFAqFkvEwDAOz2YwXXniBONo6nQ7XXHMNampqqKN9Bs6ZyLbFYsH+/fshFovh9XrTPRwKhUKhUCiUjGdqagptbW1wOp0AgPz8fLS2tqK4uDjNI9s6nBPOdjAYxD333IPa2looFAr8+c9/TveQKBQKhUKhUDKWQCCAzs5OmM1mMAwDgUCA3bt3w2AwnDNt1pPFOeFsP/bYY7BarXjhhRfwy1/+Mt3DoVAoFAqFQslYent7ceTIESwsLAAA1Go19uzZA7FYnOaRbU3Oeme7p6cHjz32GO666y5otdp0D4dCoVAoFAolI5mdnUVbWxtGR0cBALm5uWhpaUF5eXmaR7a1Oaud7UAggLvvvhsGgwG33XZbuodDoVAoFAqFknGEQiF0d3ejp6cHkUgEPB4Pu3btwvbt28Hj8dI9vC3PWe1s/+QnP4HD4cDzzz+/poslNzc3bXqkvLy8tJx3q0HtlBjUTolB7ZQY1E6JQ22VGNROiZFKOzkcDrzzzjvweDwQiURQKBR417veBZlMlrJzpopMvZ7OWme7q6sLjz/+OO644w7odLo1vXZ2djZFo1qdvLw8UiCesjLUTolB7ZQY1E6JQe2UONRWiUHtlBipstPc3Bza29sxNDQEAJBKpWhpaUFVVRVCodCW+W78fj/6+vpQWloKuVy+6edPxME/K53tUCiEe+65B3q9Hp/85CfTPRwKhUKhUCiUjCAcDqOnpwfd3d0Ih8PgcrnYvn07du3aBT5/67iFLpcLJpMJdrsdoVAIubm5uPbaa9M9rLhsHauuAa/XC4fDAQCoq6uL+5wPfvCDAICf/vSn2Ldv32YNjUKhUCgUCiUtjIyMoK2tDW63GwBQVlaG1tZW5ObmpnlkiRGJRDA4OAiTyUSSOIHF6PJFF12UvoGdgbPS2c7KysINN9wQ97GjR4/C4XDg4osvRn5+PioqKjZ5dBQKhUKhUCibh9frxZEjR9DX1wcAEIlEaGpqglqtTvPIEsPn88FiscBsNmN+fh4AwOFwoFAoYDAYUFpamtGypLPS2RYKhfjOd74T97F77rkHDocDn/rUp1BfX7+5A6NQKBQKhULZJCKRCEwmE7q6uhAMBsHhcGAwGLB7925kZWWle3hnZHJyEiaTCX19fQiHwwAWfTydTgedTgepVJrmESbGWelsUygUCoVCoZzLTExMoK2tDdPT0wCAoqIitLa2oqCgIM0jW51IJAKHwwGj0UhaxANAYWEhDAYDVCrVlitHSJ1tCoVCoVAolLMEn8+Hzs5OWCwWAEB2djYaGxtRU1MDDoeT5tGtjNfrhdlshsViIZ0ruVwulEolamtrUVRUlOYRrp9zztl+4IEH8MADD6R7GBQKhUKhUChJg2EYWK1WdHZ2wufzAQBqamrQ2NgIoVCY5tGtzPj4OEwmE/r7+xGJRAAAYrEYer0eOp0OIpEozSPcOOecs02hUCgUCoVyNjE9PY3Dhw8T2UVeXh5aW1tRUlKS5pHFJxQKoa+vD0ajkchcAKCkpAS1tbWorq5OW3PBVECdbQqFQqFQKJQtSCAQQFdXF0wmExiGgUAgwO7du2EwGDLSWfV4PDCZTLBarfD7/QAAHo8HjUYDg8GA/Pz8NI8wNVBnm0KhUCgUCmWL0dfXhyNHjsDr9QIAlEolmpqaIJFI0jyy5YyMjMBkMmFwcBAMwwAAZDIZ9Ho9tFptRstckgF1tikUCoVCoVC2CLOzs2hvb8fIyAiARae1tbU14/qGBINB2O12GI1GzM7OkuPl5eWora1FZWVlRidsJhPqbFMoFAqFQqFkOKFQCCdOnMCpU6cQiUTA4/Gwc+dO1NXVZVQpPLfbDaPRCLvdjkAgAADg8/nQarWora3dMt0qkwl1tikUCoVCoVAymKGhIbS3t2Nubg4AUFFRgZaWFuTk5KR5ZIswDIPh4WEYjUYMDw+T4zk5OTAYDNBqtVuiiU6qoM42hUKhUCgUSgbi8Xhw5MgR9PT0AACkUimam5tRXV2d5pEtEggEYLVaYTKZyEIAACorK1FbW4vy8vJzRiqyGtTZzhCmpqYyahuIQqFQKBRKeohEIujp6UF3dzeys7PB5XKxbds27Nq1CwKBIN3Dg8vlgslkgt1uRygUAgBkZWWhpqYGer0+YyLumQJ1tjMAj8eDv/zlL5BKpSgvL8euXbsyMpuYQqFQKBRKahkdHUVbWxtJKlQqlairq4NcLk/ruCKRCAYHB2EymTA6OkqO5+XlwWAwQKPRgM+nbmU8qFUyALFYDJVKhYmJCVgsFtjtdtTW1mLHjh3Izs5O9/AoFAqFQqGkGK/Xi6NHj6K3txcAIBKJsGfPHuzZswculytt4/L5fLBarTCbzfB4PAAADoeD6upq1NbWorS0NG1jY5mZmcloRz9zR3YOweVyceGFF8Lv9+PVV1/F+Pg4Tp06BYvFgrq6Omzbti2jLyIKhUKhUCjrg2EYmEwmdHV1keodBoMBu3fvTmvAbWpqCkajEX19fQiHwwAAoVAInU4HnU4HqVSatrEBi6UF+/r6YDabMTU1hYKCArz//e9P65hWgnpwGURpaSmuuOIKDA0N4dixY3C5XOjs7ITJZMLOnTuh0+kysiMUhUKhUCiUteN0OtHW1oapqSkAQGFhIVpbW1FYWJiW8UQiETgcDhiNRtL6HQAKCgpQW1sLlUqV9vyy6elpogIIBoMAFoOWer0+reNaDepsZyCVlZWoqKhAX18furq6MDc3h7a2NvT09KChoQFKpZJm91IoFAqFskXx+/3o7OyE2WwGsJhc2NDQAL1en5b53ev1wmw2w2KxYGFhAcCiA6tUKlFbW4uioqJNH1M0oVAIvb29sFqtMYuAnJwc6PV6aDQalJWVpVVusxrU2c5QOBwO1Go1lEolzGYzTpw4gbm5Obzxxhs4efIkGhoaUFlZme5hUigUCoVCSRCGYWCz2XDs2DH4fD4AgFarRWNjI0Qi0aaPZ3x8HCaTCf39/YhEIgAW88j0ej1qamogFos3fUzRuFwumM1m9Pb2EokNl8tFdXU19Ho9ysrK0jq+RKHOdobD5XJRW1sLrVaL06dP49SpU5iensahQ4dQWlqKxsbGtK84KRQKhUKhrI7L5UJbWxvGx8cBAHK5HK2trZueYBgKhdDX1weTyUTkKwBQUlICg8EAhUKRVslqKBSCw+GA2WyOiWLLZDISxU7HwmQjUGd7iyAQCLBr1y7o9XqcPHkSJpMJY2NjePnll1FdXY2Ghoa0lwWiUCgUCoUSSzAYxPHjx3H69GkwDAM+n4/6+nps27ZtU51aj8cDs9kMq9VKouo8Hg9qtRoGgwEFBQWbNpZ4zMzMwGw2x7R5Z6PYOp0OZWVlW1ZCS53tLYZQKERTUxNqa2vR3d0Nm82GgYEBDA4OQqPRoL6+Pu0ZwhQKhUKhUACHw4GOjg54vV4AgEKhQFNT06bO0yMjIzCZTBgcHATDMAAWO1GybdSFQuGmjWUpoVAI/f39sFgsJOIPLEaxdTodNBpN2qUsyYA621sUqVSK888/H3V1dTh27BgGBgZgs9nQ29sLg8GAnTt3pvUHRKFQKBTKuYrb7UZ7ezuGh4cBLDqPLS0tm5ZrFQwGYbfbYTKZMDMzQ46Xl5fDYDCgqqoqrVHi2dlZEsX2+/0AFnPVqqqqoNfrz7o279TZ3uLk5ubi4osvhtPpRGdnJ0ZHR3H69GlYrVZSozsTWrtSKBQKhXK2EwqFcPLkSZw8eRKRSARcLhc7d+5EXV3dpvTLcLvdMBqNMVIMPp8PrVYLg8GQVrlpOBwmUeyxsTFyXCqVoqamJiMSMlMFdbbPEoqKinDZZZdheHgYnZ2dmJqaQldXF0wmE3bs2AGDwUBrdFMoFAqFkiKGhobQ3t6Oubk5AEBFRQVaWlqQk5OT0vMyDIPh4WEYjUYSSQcWy+KxUpGsrKyUjmE1ZmdnSV1sVivORrF1Oh0qKirOqih2PKizfZZRUVGB8vJyOBwOdHZ2Ym5uDh0dHTAajaivr4darT7rL2oKhUKhUDYLj8eDjo4ODAwMAAAkEgmampqgVCpTet5AIACbzQaj0UgcfGCxV4fBYEirExsOhzEwMACLxYLR0VFyXCKRQKfTQavVQiKRpGVs6YA622chHA4HKpUKCoUCFouF1Oh+6623cOrUKTQ2NtIa3RQKhUKhbIBIJILTp0/j+PHjCIVC4HA42LZtG+rr61Mq33S5XDCZTLDb7QiFQgAWm+KwUpFUR9JXw+12w2KxwGazxUSxKyoqUrYAcLlc6O3tRXl5ecbW3abO9lkMl8slW0hGoxEnT56Ey+XCoUOHUFJSgoaGBpSUlKR7mBQKhUKhbCnGxsbQ1tZGkg9LSkrQ2tqKvLy8lJwvEolgcHAQJpMpJlKcl5cHg8EAjUazKZrwlcbGRrFHRkbIcbFYTLTYya6+4vf70dvbC5vNRmqFDw4O4pprrknqeZIFdbbPAfh8Pnbs2AGdToeTJ0/CaDRifHwcBw8eRFVVFRoaGlJ2g6BQKBQK5WxhYWEBR48ehd1uB7BYjnfPnj3QaDQpkWz4fD4cP34cR44cgcfjAbAYKa6urobBYEhrJNftdsNqtcJms5EW78CinFWv16OysjKpuWKsNp0tecx2vORyuaiqqsL555+ftHMlG+psn0NkZ2djz549pEa31WrF4OBgTI1umUyW7mFSKBQKhZJRMAwDs9mMzs5OUuVDr9ejoaEB2dnZST/f1NQUjEYj+vr6IBQKMT8/D6FQiJqaGuj1+rT102Aj7BaLJSYZUywWQ6vVoqamJul+BOvU9/b2Yn5+nhzPz8+HVquFWq2GUChEXl4eXC5XUs+dLKiznSGEw2GySks1EokE73rXu7B9+3Z0dXXB4XDAbrejr68Per0eO3fu3HKtUCkUCoVCSQWTk5M4fPgwkSsUFBSgtbUVRUVFST1PJBKBw+GAyWTCxMQEOV5YWIjdu3dDpVKBx+Ml9ZyJMjc3R6LYbIMeYDGKrdPpUFVVldQodjAYRF9fH+x2e0yzm+zsbKjVami12rR3vFwL1NnOAHw+H1544QVIpVIUFRVBq9WisLAw5efNzc3FRRddhMnJSXR2dmJkZARGoxFWqxXbt2/H9u3b01ouiEKhUCiUdOH3+0kJXWAxCXH37t0wGAxJlYx4vV5YLBZYLBbiyHK5XCiVStTW1kKn06UlYhuJRDA0NASz2RwTxRaJRCSKncxkTIZhMDY2BrvdDofDQZI/2QRLrVaLqqqqtC04NgJ1tjMALpcLoVAIn88Hk8kEk8mE3Nxcsj2S6vI4hYWFuPTSSzEyMoLOzk5MTk6iu7sbZrMZO3fuhF6v35IXN4VCoVAo68Fms+Ho0aOkooZGo0FjY2NSm65MTEzAaDSiv7+f7GyLxWLo9fq0NnjxeDywWq2wWq0xUezy8nLodDpUV1cnNYo9NzdHkh2jSxiyftDZ0LKdwzAMk+5BZBrpWEEyDAOv10tar7MrOmDxAtdoNFAoFJuSbczW6Ha73QAWuzvV19enLAFkrWSyLiuToHZKDGqnxKB2Shxqq8TIRDu5XC60t7eTDoe5ublobW1NWiJiKBRCX18fTCYTkaUAi9VMDAYDFArFMkd2M+zEMExMFJt1DYVCIbRaLXQ6XVKj2KFQCP39/bDZbDHVVbKysqBSqaDVatcs00nX9ZRIgQka2c4QOBwOKisrIZFIEAgE0N/fD7vdjrGxMYyMjGBkZASHDx+GUqmERqNBaWlpyhxfpVKJ6upq2Gw2HD9+HB6PB2+//TZOnTqFhoYGVFdXp+S8FAqFQqGkg2AwiO7ubpw+fRqRSAR8Ph+7du3C9u3bkxLF9Xg8MJvNsFqtJFrO4/GgVqthMBjSpj+en5+HzWaD1Wol1U4AoKysjESxk7mzPTExAZvNBofDQRJNgcWgolarRXV1ddpKGKaSs+8TnQVkZWWR2pRLt1dsNhtsNhukUilJEkhFAXsulwudTge1Wg2TyYSTJ09iZmYGr776KoqLi9HQ0IDS0tKkn5dCoVAolM2kv78fHR0dpNJFdXU1mpubk1LxY3R0FEajEYODgyRaLJVKiVREKBRu+BxrhS2hZzabMTQ0FBPF1mg00Ov1SfUrvF4v7HY7bDYbZmdnyXGZTEZkIumqrrJZUBlJHNK1rXWmLZDx8XH09vair68vZkVYVFQEjUYDlUqVkhJEwGJbWLZGNytxqaioQGNjI/Lz81NyzpXIxK3HTITaKTGonRKD2ilxqK0SI912crvdaG9vJ8l/MpkMLS0tG+6wHAwGYbfbYTKZSNMbYDF6azAYUFVVtaad6WTZyev1Ei12dBS7tLQUOp0OCoUiaVHscDiMwcFB2Gy2GFkKn89P2Q49lZFQkkJJSQlKSkrQ1NSEwcFB2O12DA8Pw+l0wul0oqOjA1VVVdBqtaioqEhqAkNWVhYaGxtRW1uLEydOkBqbw8PDUKvVqK+vT2uLWAqFQqFQEiEcDuPUqVM4ceIEwuEwuFwuduzYgR07dmxIwuB2u2EymWCz2UhAjM/nkzbqcrk8SZ8gcRiGwcjICMxmc0x0PTs7m0Sxc3Nzk3a+qakp2Gw29Pb2wu/3k+MlJSXQaDRQKpXnZJUz6mxvQfh8PlQqFVQqFbxeL/r6+mCz2eByudDf34/+/n4IhUKSZJBMLZhYLEZrayu2bduGrq4u9PX1obe3Fw6HAzqdDjt37tzyWcMUCoVCOTsZHh5GW1sbqXpRXl6OlpaWdTucrCTDaDTGlMfLycmBwWCAVqtNi3PJSjcsFktMhY+SkhISxU6WNtrn8xG56/T0NDkukUigVquTXiJwK7IhGQl7gblcLrjdbuTk5CAvLw+1tbWoqKhI5jg3lUyVkZyJ6elp2Gw29PX1xbROzcvLg0ajgVqtTrojPDU1hc7OTnKT4fP52LZtG+rq6lJ2g0n31uNWgdopMaidEoPaKXGorRJjM+00Pz+PI0eOwOFwAFgMHDU1NUGlUq3r/QKBAGw2G4xGY4wzW1lZCYPBgIqKiqRJJBK1E8MwGB0dhcViiWlnnpWVRaLYyYquRyIRDA8Pw2q1YmhoKKZ1enV1Ndlh38wKZpksI1mzs20ymfDss8/ijTfeiCnXspSysjJcdNFFuPHGG1FbW7uWU6Sdrepss0QiEYyMjMBms8X84DgcDikjmOyM39HRUXR2dsLpdAJY3KJia3QnO7OYTmSJQe2UGNROiUHtlDjUVomxGXaKRCIwGo04fvw4gsEgOBwOamtrUV9fv66A0MzMDJGKsPlLWVlZRCqSigjumey0sLAAm822LIpdXFwMnU4HpVKZtHl4ZmaGyESia3AXFhZCq9WmNHfsTJwVznZ7ezsefPBBnDhxAgzDgM/nQ6/XQ61WIzc3F1KpFB6PB7Ozs2TrIhQKgcPhYNeuXfjCF76A5ubmDX+ozWCrO9vR+P1+0o49uv1rVlYWSVIoKSlJ2vkGBgZw7NgxknEskUiwa9cuaLXapGnI6USWGNROiUHtlBjUTolDbZUYqbbT+Pg42trayDmKi4vR2tq65qR+tpOi0WiMCTLK5XLU1tZCo9GktFzdSnYaHR2F2WxeFsVWq9XQ6/UJOYGJEAgE0NvbC7vdTgJqwL+rl2i12qSdayNseWf7U5/6FN58803IZDJceumluOqqq9DQ0LDqqjAQCODYsWP4y1/+gkOHDmFubg4XXnghHnvssbV9ijRwNjnb0bjdbtjtdtjt9phMZJlMRmQmyViVRyIR2O12dHd3k/Pk5uaioaEBCoViw+9PJ7LEoHZKDGqnxKB2Shxqq8RIlZ18Ph+OHTsGq9UKYHGntbGxETU1NWuSNfh8PlitVpjNZjKXcTgcVFdXw2AwJK3RzZmItpPP54PdbofZbCaN54DFqmQ6nQ4qlSopjj+bWGm329Hf349wOAxgUSZSWVkJrVaLysrKpBZi2Chb3tl+97vfjdtuuw3/+Z//ua7tAb/fj2eeeQa//OUv8fbbb6/59ZvN2epsszAMg/HxcdjtdjgcDgSDQfJYMjOGw+EwqdHNFvEvLCxEY2Pjhm5SdCJLDGqnxKB2Sgxqp8ShtkqMZNuJYRhYLBZ0dnaSShg6nQ4NDQ1rqmc9NTUFk8mE3t5e4mQKhULU1NRAr9dvek3ovLw8GI1GWCwWOBwOEsUWCATQaDTQ6XRJK8G7UlAuLy8PWq0WarUaIpEoKedKNlve2fb7/UnR4CTrfVLN2e5sRxMKhTAwMEDKCLLweDxUV1dDo9GgvLx8Q6vXQCCAnp4e9PT0xNTobmhoWFelFDqRJQa1U2JQOyUGtVPiUFslRjLtNDU1hba2NiJzyM/PR2trK4qLixN6fSQSgcPhgMlkipFcFhQUoLa2FiqVKqmdFBOBrfLBltmNHpNer4dKpYJAINjweYLBIBwOB2w2G8bHx8lxVpKi1WpRWFi44fOkksnJSRQXF5OFyGaSkgTJc4FzydmOZn5+npQRjC7ELxaLSRnBjeiyFhYW0N3dDYvFQn4QSqUSDQ0Na5KvpNtOWwVqp8SgdkoMaqfEobZKjGTYKRAIoLOzE2azGQzDQCAQYPfu3TAYDAkFibxeLywWCywWC0n443K5UCqVMBgMCTvryWR8fBxms5nINyQSCfx+P9FiJ6uc79jYGGmdzgbCgMVgGNs6fbMXGGvB7XYTLfnc3Bzy8/Nx9dVXb/o4UuZsHzlyBFwuF42Njas+r6+vD5OTk2hqalrrKdLKuepsRzM5OQm73Y6+vj4iAQEWowVsxvF6t5LcbjeOHz+O3t5eAIsaOJ1Oh127diVUmjCT7JTJUDslBrVTYlA7JQ61VWJs1E69vb04cuQIKXWrUqnQ1NSU0DwyMTEBk8kUI8sQi8XQ6XTQ6XSb3i/C7/eT4hLRwa78/Hw0NTWhoKAgKeV0PR4PaZ0eXblEJpOhpqYGGo0GEolkw+dJFSsVfRAIBGhtbYVGo9n0MaXM2TYYDOBwONi7dy9++MMfrnhR3nvvvXjxxRdhNBrXeoq0Qp3tf8NmYdvtdgwODsaUEWRXv1VVVeta/U5PT6OzsxNDQ0MAFmt019bWoq6ublW5USbaKROhdkoMaqfEoHZKHGqrxFivnWZnZ9HW1kYqg+Tk5KC1tRXl5eWrvi4cDqOvrw9GoxFTU1PkeElJCQwGAxQKxaYn/E1MTMBisaCvr4/ow/l8PtRqNXQ6HQoLCzd8Pa0kFxUIBFCpVEmvSpZs2JreNpttmR8SXc64qKgoYzXb605Z5XK5ePXVV/HBD34Qjz322JZuYkNZGbZAfXV1NXw+H1lROp1ODA0NYWhoCFlZWeQHu5Ytt/z8fOzbtw/j4+M4duwYJiYmcPLkSZjNZuzYsQO1tbUpLadEoVAolK1DKBRCd3c3enp6EIlEwOPxsGvXLmzfvn3VgI/H44HZbIbVaiU7tTweD2q1GgaDIaldlhMhEAiQKHa0c5iXl0dKKicjiu10OmG329Hb20vaxwOLfVDYQgiZPMdOTk4SmUv0DjubrKlSqbZMx+p1W/mqq65CTk4Ofvvb3+LGG2/ET37yky0nF6GsDaFQCIPBAIPBgNnZWVLYfn5+HmazGWazGTk5OdBoNNBoNAlnbJeUlODKK6/E4OAgOjs74XK5cOzYMRiNRuzatQs1NTUZVV6IQqFQKJvLwMAAOjo6SIWMyspKtLS0QCaTrfia0dFRGI1GDA4Ogt3El0ql0Ov1qKmpWVOFkmTgdDpJFJvVSPP5fCiVSuj1ehQVFW34HF6vF319fbBarTFyFKlUSubmTG6d7vF4iA6b7dcB/Dt3TKPRJK3yymayocj2V7/6Veh0OnzjG9/Axz72MXzta1/DBz/4wWSOj5Kh5ObmorGxEQ0NDRgdHSW1ON1uN7q6utDV1UVWzwqFIqGM6aqqKlRWVqK3txddXV3weDw4fPgwenp6SI3uzWz9SqFQKJT0Mjc3h46ODgwODgJYdBqbm5tRXV0d9/nBYBB2ux0mkynG2SwrK0Ntbe2m14ZmG8JYLBZMT0+T43K5HDqdDlqtdsNRbFbuybZOZxcWPB4PCoUCWq0WZWVlGTt/BgIB9Pf3o7e3N6ZpEJ/PJ1XRysrKtnTQbcP7BzfeeCNUKhXuvPNO3HfffbBYLPjqV7+6pY1CSRxWM1VeXo7W1lb09/fDbrdjdHSU/LW1tUGhUECtVqO8vHzVHzyHwyHbW2azGSdOnIDb7cbrr7+OgoICNDQ0ZESnKgqFQqGkjnA4jJ6eHpw4cQKhUAhcLhfbt2/Hrl274kof3G43aaPOSib4fD5poy6Xyzd1/JOTk7BYLOjt7SVRbB6PB6VSCZ1OlxSN9PT0NKxW67JCBsXFxdBoNFCpVEmRo6SCSCSCkZER9Pb2xjTNARYXRmq1GgqFImPHv1aSItbZs2cPDhw4gM985jN4+umn0dvbi4ceeigZb03ZQggEAmi1Wmi1WrIVZLPZYorkSyQSUrczNzd3xffi8XjYtm0bampqSI3uqakp/OMf/4DD4YBOp0vKlhuFQqFQMouRkRG0t7cTGUFZWRlaW1uXzRkMw2B4eBgmk4kk2gOLlTVqa2uTEjVeC8FgkESxoxMwc3NzodfrodFoNtxrhK29bbfbY84hFotJ6/TV5tZ0Mz09TXTkbBUZYNFGrA57s5sGbQZJU8ZXVFTg2WefxZe+9CUcOnQIN954I3WGzmGkUil27tyJnTt3kiSNvr4+zM/P4+TJkzh58iQKCwvJ6nsl7ZxAIEB9fT0MBgNOnDgBk8mEkZERWK1WKBQKNDQ0ZPSNhUKhUCiJ4fV6cfToUVIWViQSoampCWq1OuZ5gUAANpsNJpMppmV5ZWUlDAYDKioqNlUyMTU1RaLYbEdmVsKh1+s3HMVmZSI2mw0DAwOkGgdbwECr1W64+Vwq8Xq9ZIEQnRAqFAqJDjvTm+ZslKSmoYpEIjzyyCN46KGH8NhjjxGNFeXcpqioCEVFRWhqaiI3jOHhYUxOTmJychJHjhxBZWUltFrtino6oVCI5uZm1NbWEk13f38/BgYGUFNTg127dmV0bVAKhUKhxCcSicBkMqGrqwvBYBAcDgcGgwG7d++OiUzPzMwQqQgrzcjKyiJSkc1M/AsGg+jr64PFYsHk5CQ5npOTQ6LYG03AZAsRjI2Nkc6YwGIHSY1GA7VavelJnokSCoWIDntkZIToyNkFgkajQUVFRcYuEJLNupzta6+9dtWGNp/73Oeg0+nw4IMPgjaopLCwK32FQoGFhQX09fWRrbCBgQEMDAxAKBRCqVSu2B5WJpPhoosuQnV1Nbq6ujAwMACLxQK73Q6DwYAdO3Zk7M2HQqFQKLE4nU4cPnyYJA8WFRWhtbWVlONjGAaDg4MwGo0xyXNyuRwGgwEajSYpLcsTZXp6msw5bBSby+VCoVBAp9OhrKxsQ+8fCARI63S2aYtEIiFR4JqamoytxsEwDEZHR9Hb27usK2VJSQnUajWUSuWGpTRbEdquPQ60qc3m4nK5iIaLbZcL/FvDpVarY6LW0XaamJjAsWPHMD4+DmAxylFXV4dt27ZldP3QzeBcvZ7WCrVTYlA7JQ611Znx+XywWq04duwYACA7OxsNDQ3Q6XTgcDjkcbPZTMr9cTgcVFVVoba2dsNO7VoIhUIkih0dYZbJZNDr9dBqtRsK8jAMQ1qn9/f3EyeVbR63Z88e5OTkZGwUeGZmhszh8/Pz5LhMJiMR+M3YdUjX7y5lHSTPdqiznR4ikUhMGcHo7GS2S5RCoYjbJWpoaAidnZ0kOiISibBr1y7odLqMvUGlmnP9ekoUaqfEoHZKHGqrlWEYBlarFZ2dneDxeJifn0dNTQ0aGxshFAoxNTUFk8mE3t5eMgcIhULU1NRAr9dvavKcy+WC2WyOaQrDyiDYKPZGtOFzc3Ow2Wyw2+1kQQEsBppqamqgVqshFosz8npaujvNkpWVBbVaDbVavaYmd8lgyzvbL7744oYGcs0112zo9ZsNdbbTD7uV1tvbi7GxMXJcIBCgrq4OxcXFKC0tjbnRMQyDvr4+dHV1YW5uDsDiynr37t1QqVQZW2M0VdDrKTGonRKD2ilxqK3iMz09jba2NiKPqKqqQl1dHYqKitDf3w+j0UgeAxa1ybW1tZva6TAUCsHhcMBiscSMRSaTkbrYIpFoQ+/f398Pm80WI4thnVSNRrOsuESmXE+hUCgm7ypah11ZWQmNRoPKyspVu3mmki3vbBsMhnU5KgzDgMPhwGg0rvm16YQ625mF2+1GX18fbDYb5ubmIJFIMD8/v2JHrEgkAovFgu7ublJaKD8/Hw0NDaisrEzXx9h06PWUGNROiUHtlDjUVrEEAgEcP34cRqMRDMOQKlP19fU4evQozGYzkRByuVwolUoYDIZNjYzOzMzAbDbDbrfHRLGrqqqg0+nO2CPiTIyPj5OqXKzWG1is5KbRaFBdXb3igiKd1xPDMJiYmIDdbofD4Yhp+55IRbHNZMs72/v37192kQ0MDODPf/4zRCIRzj//fFRUVABYrI/59ttvY2FhAVdffTWqq6txxx13rPMjpAfqbGcu4+PjmJiYwMmTJ2N+9MXFxVCr1VCpVCT5IhQK4fTp0zh16hR5bmlpKRoaGjZ9eysd0OspMaidEoPaKXGorf6Nw+FAR0cHcaaVSiWUSiUGBgbgdDrJLqRYLIZOp4NOp4NYLN6UsYXDYRLFZvN+gMUodk1NDbRa7YbGMj8/j97eXlit1pgShTKZDFqtFhqNJiFZTDquJ7Y/Rm9vL/mOgMWyvmwEPtPK7m55Z3spDocDN954I/bu3YuvfOUryzozzc7O4rvf/S5ee+01PPvss1CpVGs9RVpJx5fFMAzkcjkp4k9Zmby8PDidTgwODsJuty/bzlpad9Tn8+HkyZMwmUxEA1hdXY2GhoZN7yq2mdAJPzGonRKD2ilxqK0WnbW2tjaMjIwAWKyoUV5ejunpaaLxlUgkkEgkMBgMUCqVm5ZfMzs7S6LYfr8fwL+TL3U63YbqdIfDYQwODsJqtcaUvOPz+aTSVmlp6Zrec7OuJ7/fT3TY0YmgAoEASqUSarV6mXwzEwgGg5icnER5eXlaKuClzNm+66670NPTg1deeWVFbU4oFMJll12Gbdu2Yf/+/Ws9RVrZ7Jukz+fDiy++iOzsbIhEIhQWFqK4uBiFhYWbtsLfSiy98Xi9XiIziT4uEolIwfyCggJ4PB6cOHECVquVSJw0Gg3q6+vPyo5VdMJPDGqnxKB2Spxz2VahUIg0LotEIgiFQpBKpYhEImSHkcfjQaVS4bzzzts0fW84HEZ/fz8sFktMHpBUKiVR7I30apicnITNZkNfXx9x4IHFkndarRZKpXLdJQpTeT2xDXPsdjsGBwdJwxwOh0MKE6wmcUkH8/PzcDqdZKd7enoaDMMgPz8fV1999aaPJxFne13Wa29vx7vf/e5VfyR8Ph/19fV4++2313OKcwoul4usrCyEQiGMjY0tuxEUFhaiqKgIhYWFKCgoyKiLPhMQi8XYvn07tm/fjunpaXLDW1hYwOnTp3H69Gnk5eVBq9Wivr4e27dvR2dnJ0lS6e3thcFgwM6dOzNCd0ahUChbkaGhIbS3t2Nubg6zs7PE0fb5fAAW5zO9Xo+amhoIhcJNWZS43W4SxWbHweFwUFlZCb1ev6Eo9sLCAnp7e5cFeiQSCWmdvpmNdtZCdGfn6MVBfn4+0WFnQrCPYRjMzMwQxzpaehSNTCbDjh070jDCxFiX1+b3+2OydFfC6XTG6Gop8cnKysK1114LDocDq9WKyclJTExMYGZmBh6PBx6PBw6HA8CiY56Xl0e6MhYVFWXsjzkd5Ofno7m5GXv27MHIyAhsNhsGBwfhcrlw5MgRHD16FOXl5aTj2IkTJzA6OorTp0/DarWSGt2b2SSBQqFQtjIejwcdHR3o6+vD5OQkXC4XiouLSWOasrIy1NbWrtghONlEIhEMDAzAbDbHVPyQSCQkir3e3czo1ulDQ0MkEszj8WIkjJkmtQAWSw2ybdOjNeRisRgqlQparTahKG0qCYVCxAdi/5b6kRwOB/n5+SguLkZJSQmKioogkUgyekdpXc62Xq/HsWPH8M477+Bd73pX3OccPnwYR48exc6dOzc0wHMFp9MJsViMkpISaDQacLlcokOanJyE0+mE0+nEwsICpqamSC1SYLEGaXT0u6ioKKbF7bkIW4qosrISfr8fDocDdrsdExMTGB4exvDwMLKysqBUKlFaWoqBgQFMTU2hq6sLRqMRO3fuhF6vT1sJIwqFQsl0IpEIenp60NbWhuHhYTidThQXF6OmpgbZ2dkkqLFZuTFutxtWqxU2m41UogKAyspK6HS6DTn7LpeL7IRGv3dRURGJBGdiZ8SVyujy+XzSNj2diwOfz4eJiQkSuZ6amiILmOixFhcXo7i4GEVFRSguLiYBsWAwiKmpKTgcDlRXV0Mmk6XjY5yRdWm2//nPf+Kzn/0sBAIBrrrqKlx55ZUoLy8HsFiN5ODBg/jLX/6CUCiERx55BJdccknSB74a4+PjOHjwIN5880309vZicnISubm5aGhowG233YZdu3at+vrNXhnNz8/jj3/8Iylpx+VyIZPJkJOTg5ycHOTm5pJ/RyIRsuqbnJzE1NRUTPMXltzc3Jjot1wuP2uau2xk9bpahrVYLIbL5SJlmWQyGerr66FWqzMySnEmMnmVn0lQOyUGtVPinAu2Yud6i8WCmZkZ5OTkkEYmtbW10Gq1Zwz6JMNOkUgEg4ODsFgsGB4eJsfFYjFqampQU1Oz7ii23+8nMpHoxi0ikQhqtRo1NTWbspBYq50ikQhGRkZgt9sxMDCwrEGcWq2GQqFIyw6u2+0mjvXExETcohBisZg41yUlJcjLywOXy0UkEoHL5SK+j9PpxMzMDEmKlMvlaenrktIOks888wweeOAB+P3+ZY4IwzDIysrC3XffjZtvvnk9b78hfvjDH+IXv/gFqqur0dzcjPz8fPT39+PQoUNgGAY/+tGPcOWVV674+s2+SUYiEXR0dGB+fh4jIyNxnWcWgUBAHO+cnBzIZDJEIhEsLCxgdnZ2RT0Tn89HYWHhWZF8mYwbNMMwpO6pw+EgDjbDMAiHw/B6vRCLxeDz+cjLy0NjY+OWq9F9Lkz4yYDaKTGonRLnbLbVzMwMXnrpJRw5cgQ+nw8CgQAKhQL19fWora1dkwZ6I3aam5uDxWJZFsWuqKiAXq9fdxSbYRgMDw/DZrNhYGCARFnZ3dKamhpUVFRsavAqUTtNTU0RHXa0TXJzc6HVaqFSqTa1GEAkEsHU1FRM5JrVzUcjl8tRUlJCHGw2Ou12u8nu/mrBRYlEgsLCQuzevTstFcZS3q59ZGQEBw4cwLFjx4iGu6ioCHv27MF1112XNufklVdegVwuR3Nzc8zxo0eP4qMf/SjEYjHefvvtFVfd6ayzPT09jfn5ebjdbrjdbszOzpJ/ezyeVcvaiEQi5OTkQCgUIhwOIxgMYmFhAV6vF6FQaNnzt2ryZbInslAohIGBAdjtdlKqKRKJYGJiAgsLC5DL5cjNzUVZWRkaGhpQUlKStHOnkrN5wk8m1E6JQe2UOGejrVwuF/7+97/jX//6F9HQVlZW4uKLL8aOHTvWlTu0nojt0NAQzGZzTBRbJBJBq9VCp9OtW0bASlB6e3sxPz8fM0a2dXq6EuhXs9P8/DypxjUzM0OOC4VCosNmtfOpJhAIxGitnU7nMueYx+ORoB+rt87OziYSWafTSZzr6MRNlqysLBI4ZP/YwOFZV2d7K/OJT3wCb7/9Ng4cOLBi5mqmNrWJRCKYm5tb5oS73W7SMCAeDMOAx+OBYRgEg0H4/X6EQiEIhcIYjdlWSb5M5Q9q6Y0rFAphZGQEk5OTyM/PR1FREfR6PRobG9OeSHImzsYJPxVQOyUGtVPinC22YhgGg4ODaG9vx+HDh4kTWlxcjPe9731obGzckBQhUTt5PB5YrVZYrdaYua6iogI1NTWorq5eV6Q5GAySutLRTW2ys7OhUqlQU1OzaY7qaiy1UzAYRH9/P3p7e0kdc2DRka2qqorpM5FKPB4PcazHx8fjfpdCoZBErNmkWTbiHR219ng8y17L5XJRUFBAnOqioiLIZLIVd04y2dleVxjzkUceQW1t7Rm12K+++ipOnz6dUR0k2cjtVojgLoXL5SI3Nxe5ubmoqqqKeSwYDMaNhrvdbgQCAbIVxufzwefzEQ6H4fF4SDJCMBgEl8vF2NgYHA4HhEIhBAIBsrOzY6QnZ3vypUQiQV1dHerq6jA1NQWbzQapVIrS0lIMDQ3hxIkTsNls6OjoQHNzM1paWjI2IYNCoVDWg8/ng81mI83A2MS64uJiXHrppXj3u9+d8jyWSCSC4eFhmM1mDA0NkeNCoZBosdcTDGIYBmNjY0RCyO76cjgcVFRUQKvVoqqqKuOS4xmGwcjICHp7e9Hf3x+zW80WVlAqlSmbnxmGwfT0NMbHx+F0OjExMRGzA8CSk5MTo7eWyWREZ221WnH48OEYnXU0crk8JmKdn59/1uSardvZvvbaaxNytp977rmMcbZHRkbwzjvvoKioCDqdLt3DSSoCgQAFBQVxV+GsnjvaAXe73RAIBDHtVv1+P+bm5jA1NYW5uTn4/X5kZ2dDKBRCJBKR/xYXF6OsrIw44WdT8mU0rD2bmppI0X+r1QqHw4H+/n709/fjn//8J1paWrBv376zsjEOhUI5d5ienobRaCSVK/r7+8EwDMrLy3HeeefhggsugEgkSukYPB4PbDYbrFZrjDNXVlYGvV6/7ii2x+OB3W6HzWaLyWvKyclBTU0NNBpNRuYxuVwuWCwWdHd3x9iDbfmuVqtTEvAJBoOkChobvWZzm1jYyHN05JqtouZ0Okli6Wo6a/avoKDgrA7kpTS8Gw6HM8YJCwaD+PKXv4xAIIAvfvGLq65ac3Nz0zbuVEgT8vLySLWYaBiGgcfjwezsLGZmZjA7O0v+5ubmEIlE4PF4MDc3h7m5OaJhNplMyMrKgkgkgkgkgkwmQ0VFBZRKJVQqFcrKylJ+09psCUdBQQF27doFn88Hu92OY8eO4ciRI3C5XPjnP/+Jt956C+eddx4uv/zyZbsO6STTpS6ZArVTYlA7Jc5WsVUkEkFfXx96enowNjaG+fl52Gw2hMNh6PV66HQ6XHjhhSgrK0vJ+fPy8ohc5fTp0xgcHCRRz4KCAuh0OtTW1sYEhhIlFArB4XAs03jL5XJoNBro9fqMzMHxer2w2+2wWCwxVVDYhjM6nQ7FxcVJPyfbVG9sbAxTU1Mx0eesrCxIpVKUlJSgpKQEpaWlkMlkmJ6ehtPpxMjICLq7u5fprIVCIbKysmLK9hUVFaXMR8jU3926NNsGgwHXXnst7r///lWfd+ONN2JoaAiHDx9e9wCTQSQSwZe+9CW89NJL+I//+A9861vfWvX5marZ3kzC4XBcfTibwMA64R6PZ9mqVSgUQi6Xo6ysDBUVFaiuroZCoUBubm5Sth4zxU6zs7Noa2vDG2+8QW6IAoEAer0e559/PnQ6XVqj3Zlip0yH2ikxqJ0SZyvYyuv1wmq1wmw2w+v1IhwOY3h4GH6/H6WlpcjLy0N9fT22bduWsuBTVlYWjhw5AqvVGqPZLS0thU6ng0KhWJecY2JiAjabDQ6HI6YhSllZGbRaLRQKRcZJSUOhEAYHB2Gz2UiiPrAYPa6trUVxcTEqKyuTIm9hGAazs7MxJfjiVTGTSqUkYp2fn49wOByjtY6ns+bxeMjPz48pvrCazjqZZLJmO2Fn+9577yX/fuGFF6BQKNDQ0BD3ueFwGH19fTh16hT27duH/fv3Jzjk5BOJRPCVr3wFL7zwAq6++mp873vfO+ONIx1fVk9PT0xN7UwmEAgQ53tmZgajo6MYHh7G6OgoZmZm4iZrcjgcon0uKytDVVUVysrKyGdeS5Z3pk1kDMPg6NGjOHToECkVlZ2djaqqKuzYsYPc3De7pmmm2SlToXZKDGqnxMlkWzmdThiNRjgcDpLL4/V64fV6kZOTg6ysLCgUCjQ1NaUkWMCW1rNYLJieniYOG9sER6fTrSuKzUaDbTZbTO1mmUwGjUYDjUaTcfk10SVo+/v7YxYGbLMcpVKJsrKyDV1P4XCY9OdgNdfxKn2wXRmLioogEAjg9XpJgG12dvaMOuuioiJSEzsdnBXOtsFg+PeLOJxVS9Cx6PV6PPLII2nbVo9EIrj33nvx4osv4qqrrsL3v//9hFaFm/1lzc3N4bnnniNNbQoKCqBQKEg0eCvB/jgHBgYwODiIkZERjI6OYm5ubtk1w+fzIZVKIZPJkJeXh8rKSuTn58fUEc/JyVnmpGbqRBaJRGAymfDqq6+iv78fbrcbIpEI1dXVKC4uhkKhgEajQVlZ2Vm9yt9qUDslBrVT4mSardgAmMlkwuTkJDkulUqxsLBAEuRlMhlaWlpSUrbX6/XCZrPBYrEQB1sikUAqlUKv168rih0Oh0k0eHh4mMwxfD4fCoUCWq0WpaWlGdeUbHZ2ljRXi44Oy2QyqNVqaDSamKDbWq8nn88Hp9NJIteTk5NxuzKysg6RSAQOh4OZmRnSoTqezloqlaKgoABFRUWkSkg6GuOsxFnhbHd0dABYXIl95CMfwQUXXIDbb7897nMFAgGKi4tRUVGxhuEml2hH+8orr8QPf/jDhH/I6fiyent7MTo6CpvNFuOUyuVyKBQKKJXKjNUiJYLb7cbAwAD6+/sxNDREtIE+ny9mhS0SiYgDLpVKyf9Hd9GsqqoCwzCQyWQZkxMQTSgUgtlsRkdHB2lhzN78c3JyIJFIoFarodVqU7qYyrQJP1OhdkoMaqfEyRRbeTweWCwWWCwW0kyEx+NBoVCAYRjSXZDL5WLHjh3YsWNHUuUVbAUNi8WCwcFB4vBlZWVBo9GgpaVlXe/LVorq7e2NmT+Ki4uh1WpTWpVjvfh8PvT19aG3txdOp5Mcz8rKglKphFqtRklJSdyFwZmuJ7fbHVOCb6WujEVFRcjNzQWPx0MoFML09PSq9axZGQibwJhJCaShUGhZtL6yshLvec97Nn0sKauzfe+996KxsRE33HDDugaWaqKlI5dffjl+9KMfrekGkk7N9ujoKHFKR0dHY1ajMpkMSqUSCoUChYWFaRljsmDrbLKr78HBQUxOTmJhYQELCwvw+Xzw+XwIh8OQSqUxDnheXh7m5+fB4XBi2tpHy3AkEkm6PyICgQBOnTqF06dPw+Vywel0IhKJoKysjIyvsLAQGo0GKpUq6Q0TMmXCz3SonRKD2ilx0m2r0dFRmEwmDAwMkOANG0EWi8U4fvw40ehWVFSgpaUlqfLFhYUFEsWO1gIXFxdDp9NBqVSS7ryJ2snn85HW6dPT0+Q4G7xYbynAVBIOh0klq6GhITKfs2UGNRoNqqqqzuifRNspuisj+xfdLZIlNzcXBQUFEAgE4HA4pGlMojrrTLNlItF6lUqFCy+8cNPHds42tdm/fz8eeeQRiMVi3HrrrXEv5H379qG2tjbu6zMlQTIQCGBwcBD9/f0YHh6O2daRSqUk8bC4uDjjtsnWg8/nIyWD2C5SXq8XPp8vxgnPzs5GKBQiUW+JRLJs14LP5y+To7COeHQjn83A6/XixIkTsFgsCIVCcLlc4PP5EIlEJPrCtgLWarXrbjO8lHRP+FsFaqfEoHZKnHTYKhQKwW63w2QyxZy7rKwMBoMBeXl5OHbsGPr7+wEsOqlNTU1QKpVJOT/DMBgdHYXFYolpc56VlQW1Wg29Xr/MKUmkkRsbGY92VrlcLqqrq6HVatfUHn6zmJiYQG9vL/r6+mKixgUFBSS4kmgJxUAggEAgAKvVSroyLu0IzZbgEwqFpIEdW2UsHnK5nEhB0q2zXgm2AhobuY73WcRicUxNb41Gs+JnTiUpa2ozOTmJvr4+qFSqmAjrwMAAfvzjH8NqtaKsrAyf/exnUV9fv55TbAi2xI/X68Vjjz0W9zkVFRUrOtubTSAQwN/+9jdkZWWR1X5eXh7kcjmUSiU0Gg2CwSCGhoaIDMPj8eD06dM4ffo0xGIxcbxLSkoy7keTKEKhEJWVlUQvyN4wWOfb6XRiZmYGYrEY8/PzCAQCWFhYwPT0NPh8PgQCAXg8Hvn809PTMRGQ6PPEc8RlMllKMtTFYjFaW1uxbds2HD9+HL29vQBAunjy+Xx4PB4MDAxgYGAAQqEQSqUSWq12y+9gUCiU1OJ2u2E2m2G1WkmCHZ/Ph0ajgcFgQG5uLoxGI9566y2EQiFwOBxs27YN9fX1SdHbLiwswG63w2w2x0Sx2X4WKpVqzffV2dlZ0jo9OuG+oKCA1Jbe7KDJmXC73aT7cLQdJBIJVCoVNBpNQk5ZvK6MbD4XC1uGj41aB4NBTE9Pr6izjpaCZJrOGlic610uV0x1lHgNc3Jzc1FSUkIc7KXR90xbdEWzrsj2d7/7Xfz2t7/FX//6V6hUKgCLF8jll18eU5tRKBTixRdfTNrKebPY7IjEwsICfvSjH4HD4UAgEEAmk5FVL4fDQU5ODnG+8/LyIJVKMTc3h4GBAQwNDcVkMAuFQuJ4l5WVbVnHeyWCwSBCoRBsNhtxwJduoUVff2wnTA6HA6/XG/cHHI1UKl3miOfk5EAqlSbNltPT0zh27BhZFPL5fFRWVkIoFGJgYCBmcsnNzSWTy1qlMTQSmRjUTolB7ZQ4qbYVq4U2Go0x3RVlMhlqa2uh1WqRlZWF8fFx0rEPWOw02NrampT8HzaK3d/fvyyKrdPpkJ+ff8b3iLZTIBBAb28v7HZ7jKZZKBQSmUim5S0FAgE4HI5l7d7ZHJ0zJcQvdTLZsrpLyc/PJ0mswGIgcWmDGQCk43Om6qxZwuEwaZbD6q2j/RggtmEO62CfSWqZyQmS6wrjdXR0QKvVEkcbAJ5//nlMTk7iqquuwh133IHXX38dDzzwAB5//HF885vfXM9pzhkikQhKS0sBLO4ajI6OIhgMgsfjkaYxLpcrxtnj8XjIzc1FeXk5QqEQPB4PXC4XfD4fSYjJysoijnd5eXnGtZ9dD2zybfQWnMfjiZGesFouv98fs4WXm5tLtNFsVIRt6uN2u+H3++HxeODxeDAyMhJzXjZTP15EfK03s/z8fLz3ve/F2NgYOjs7MTExAYfDgezsbOzYsQM5OTlwOBwYGBjA7Owsjh07hmPHjqG8vBwajSYja8RSKJTUEwgEYLPZYDKZ4Ha7yXF2p5aVVCwsLODtt9+GzWYDsOiw7tmzBxqNZkPRP7apl9lsjjl/YWEhdDod1Gr1mu5NbBlAtvQdG5lNhawuWbDSFpvNhsHBwZhoMnuPrq6ujhs9DoVCMR0Z4zmZ4XCY7HJzOByEw2H4/f5lgSIej0e6HGeqzprF7/fD6XRibGxsRb01O7ezf0VFRQlfSwzDYHJyMqOuk6WsK7J93nnnob6+Hj/72c/IsU984hNob2/Hm2++SVa011xzDfx+Pw4ePJi8EW8C6VgZsRIJu92OyclJoskKBAJEt8zn88Hj8cDhcCAWi5dlW0ciESwsLMDv98Pr9YLH40EsFkMkEkEikaCyshIKhQKVlZVb2lk70+o1HA6TrlasBjxewX4+n08iAMXFxZBKpQiFQjEt7dlumks1ckvfJ7paSrQjnkhG/ODgII4dO0aiTxKJBLt27UJ1dTUGBwdht9sxNjYWcz5WXrRaWSsaiUwMaqfEoHZKnGTbanZ2FkajEXa7nUQ02YoetbW1xMliGAZmsxmdnZ3EidPr9WhoaNiQ7GJsbAwWiyWmNrdAICBR7IKCgjW9n9vtJve16IiwXC5HTU0N1Gp1ytvCrxW2AkpfXx+p7AIsjpkNPi7dffR6vTFJfdPT0zFOZiQSQSAQQHZ2NrhcLhiGQTgcXhYYk0gkEAgEMdVBMlFnzcJKYcbHxxPWW6/180QiEYyPj6O/vx/9/f1YWFhAfn4+rr766iR+ksRIWWR7fn4+5ocQDofR1dWF7du3x2wdqdVqvPbaa+s5xTkHq2+rqalBJBLB9PR0TLZxtLSAYRj4/X4EAoFlK2CJRAKJRIK8vDzMzc1hamoK09PTYBgGIpEIYrGY1PLctm0b1Gp1xpVI2ig8Hg9FRUUoKioix9jkS3ZVPTk5iUAgQFrTsrD6tqKiItTW1qKgoAA8Hg/z8/MxTjjriHs8HoRCIVKbdClCoTCuI56Tk0NuqFVVVaisrITdbsfx48fh8XjwzjvvoKenBw0NDbj88suX6QFtNhtsNhukUikpI5ipUQ0KhbJ22BbmJpMpZqdNLpfDYDBAo9HERE+npqZw+PBhUke7oKAAra2tMffBteD3+0kUO7qUHNtCXa1Wr0n7GwwG0d/fD5vNRu65EomESE8yMUfF4/GQ+260DUQiEdFhRy80orsyjo+PxwR5GIYhFbZYu0UiEeJLsXFPHo8Xo7MuLCyEVquNKy/JBBiGwczMTMznXo/eOhEikQhGR0fR39+PgYGBmEVPVlYWtm/fvqHPkkrW5WwXFxeTJC8AOHbsGLxeL5qbm2OeFwqFzjpHbjPgcrnkR7Zt2zYAsUkTExMTcLlc5MfJRl15PB5ycnJI0h37OtYpnJ6extTUFMbGxmC1WvH3v/+dJCWyyTTFxcWQy+VbOvIdj9WSL1knfGZmhshIHA4HgMXvIrokUnl5eUyDp0gkQtraL3XE2R0Jn88XE71hYeuHR8tTLrnkEoyOjuLEiROYnZ3Fa6+9hsLCQjQ0NGDXrl3YtWsX6TjmcDjg8Xhw4sQJnDhxgnQcU6lUGZc8RKFQEsPn8xGpCOtgcTgcVFVVoba2FmVlZTHPDwQC6OzshMlkArDodOzevRsGg2FdkpHx8XESxWYlEnw+n0Sx1+oQj42Nkdbp0TuEFRUVaGxsJHWfMwV2UWC32zE6OkqO83g8VFdXQ6PRoLy8nEgXTp06RXTH0c5fIBCAx+MBh8MhFUKEQuGyiH20zpqtELL0OZmU0Li0G+XExERS9NarnW90dBQOhwODg4Mx0lA2R626uhrl5eUoKCjI2N23dclIvvjFL+Lll1/G3XffjfPOOw/f+MY30NXVhaeeeiqmhftVV10FDoeDv/zlL0kddKrJlNJ/qxEMBolOmXXA4yVM5OXlobCwkDjg7I2kr68PQ0NDy5IL2dqc1dXVKCkpIUmZcrkcubm5GbFtlart7GAwSKLeKyVfAos/8Oh6pEVFRXEXlcFgcJkDzv576c0pGi6XC5FIhJmZGUxMTEAgEEAoFEKlUuFd73oXiaSEQiHSPW1kZIQsvtiyWA0NDUlN7DxbofKIxKB2Spz12Gp6ehpGoxG9vb3Eyc3OzkZNTQ30en3cVuN2ux1Hjx4l9ym1Wo09e/asOY+EjWJbLJaYLf/8/HwSxV5L4Mzj8ZDW6dHRXZlMhpqaGmg0GrIDmwnXFJtwarfbMTAwELMoKC0thVqtRllZWUzkenJyknxPoVAI8/PzWFhYIAsHtthB9EKC1VlHR60TifCm006s3jo6iTOZeut4hEIhjIyMwOFwLCsCIRKJoFAoUF1djdLS0pj5LZMTJNflbFutVtxwww3EAAzDoKWlBb/5zW/Ic4aGhrBv3z7ccMMN+Pa3v73WU6SVreBsLyV6K4d1wOPplNkuUmwE2+PxwGw2w2KxELkK67Tn5OQgPz8f+fn5RFO2tDKKXC6HTCbb1JI7m/mDWpp8uVIb29zcXCJdKSoqglwuX9XJXVhYiOuIz83Nxbx/IBDA8PAwxsfHwTAMia7s2rULZWVlJCLO5/MxPDwMm81GbCORSBAOh6FSqaDVatesqzxXyJQJP9OhdkqcRG0ViUTQ398Pk8kUs/uVn5+P2traFcvmzczMoK2tjcgxcnNz0drauizqfSYmJiZIFJt1MPl8PlQqFXQ63ZokKKFQCAMDA7Db7aTSErDoiLGlTEtKSmJek+5ryuVyER12tFQzJycHpaWlkEgk8Hq9MbrjSCQCr9cLj8eDYDBIItcymQwSiYTMhRwOJ6ae9UZ01ps95y0tPbgUkUhEotbr0VvHIxgMYnh4mDjY0QsesVgMhUJByhtnWp5SSpva9PT04Mknn4TL5cL27dvxiU98AlKplDz++9//Hr///e/xuc99DhdddNF6TpE2tqKzHQ82OYP94UxNTS1bkfJ4PJIgKBKJ4PV6MTAwgMHBQXi9XiwsLJBky9zcXOTn5y/bDmITBNn64Oy/U9XFMZ036Gg9fSLJl6zzXVhYmFC0iWEYeDyemORMthWv0WiMKYlVUlKCiooKIhkRiUTIyckBh8PBzMwMaQIkFArB4XCQl5dHEnkysRxUukj3hL9VoHZKnDPZyuv1wmq1wmw2EyePy+VCoVDAYDAsc0pZQqEQuru70dPTg0gkAj6fj127dmH79u0JOztsiT2z2Rwzxry8POh0Omg0mjVFsZ1OJ+x2O3p7e2MikKWlpaR1+kpRznRcU16vF319fbDb7aQPA8MwCAaDRIbJFiZgddasvDASiYDL5UIikZDnskTn+7BVQpIl/0iVnZbqrScmJuJqw3NycohjvV69dTwCgQCGhobgcDhWbNynVCpRVFSUUEDvrHS2z2bOFmd7KWwiX3TZoWiNGUtubi6pzOHxeEiyg9/vx8LCAgQCAaRSKbKzsxEMBuNGeoFF7WC0BIV1xjfaljzTJv2lnS+dTmdcSU/0zbioqAj5+flr2moLh8MYHBxEW1sbent74fP54Pf7iVRo6XuJRCKMjIzA6XTC6/UiOzsbQqGQRAnq6uqg1+szSg+YDjLtespUqJ0SZyVbOZ1OGI3GmKoeIpEIOp2OtFJfiYGBAXR0dBBnqLq6Gs3NzTFBrtVwOp2kUUx0ng8bxS4uLk7487EOq9VqjZGdSKVSaDQaaDSajJJHREfdR0ZGEAqFMDc3B4/Hg+zsbGRlZUEmk5E5j533OBwOKb/L7iACiemsk0my7JSI3prD4SzTWyfzs/n9fgwODsLhcGBkZCQmACiTyaBQKKBUKteVLEud7S3G2epsx2N2dpY43uPj4zEZ1yxcLhfhcJi0jZVIJCSKwjp6OTk5iEQicLlcmJmZwezsLFa6tEQiEeRyeYwURS6XJxxNyfRJf6XOl0vtsTT5sqioKOGIwfj4ODo7O8nWM5fLhVKpRGlpKbxeL9xuN8LhMEZGRkgjoKmpqWWReIFAgKqqKhKBiq6Wkmmlt1JFpl9PmQK1U+JE2yocDqOvrw8mk4lUCgFAKh4plcpVo9JutxsdHR2keY1MJkNzczOqqqrOOI5gMEi02NHddOVyOYliJ5pMHYlEMDQ0BKvViqGhoZjqGQqFAlqtdtUGLvFI5TXFMAzGxsZiovisVI/L5ZLADxu59vl8yM7OJo41O8+xu7/RWuvNrvy0XjsFAoEYxzpaZ87C5/OX6a2THYDx+XwYGBhAf38/RkdHYxzs3Nxc4mAn0ghpNba8sz09Pb1hIyTzfVLNueRsL4WN0rLab6fTGfPjDAaDmJmZQSAQQCgUgkwmg1QqRVZWFnJycsiPRi6Xw+12E+eb/W88yQWLVCpd5oTn5uYui9hmgp3WSrKTL1mGhoZw7NgxYg+xWIydO3eS2rcul4s43+zf6OgoqQ4QvbMRfW6hUEi+U7ZmeLQjfjZFw7fi9ZQOqJ0SJy8vD4ODg6TBGPs7YyPJBoPhjJG7cDiMU6dO4cSJEwiHw+Byuairq8POnTvPuCM2NTUFs9m8LIqtVCqh0+lWlKnEw+VykYh49P2iqKiIyNLWW3UsFdfUzMwMuru70d3dTcrvseMWiUTIyspCMBgEwzCQyWTkTywWL9NZJ5J/sxkkaqe16q2Li4uRn5+fks/HSlL7+/sxNjYWE2zKy8uL8RWSxZZ3tnfv3o1bbrkFH/vYx9blLE9NTeFXv/oVnnnmGXR1da359ZvNuexsLyUSiZCIKOuAsxrDUCiE6elpTE9Pw+fzQSKRkKhAcXExSWiI1luFQiHifLtcLszOzhKHMB4cDgcymSwmKVOpVBLt3FZmbm4uxvmOp6kHFlf+xcXFxBFeevNnGAZ9fX3o6uoiixmZTIa9e/ciLy9vxUgTG3E7deoUrFYrie4sLCwgKyuLTDjxJnaxWBzXEZfJZFvue8nE310mQu2UGGNjYxgaGkJPTw9xMCQSCQwGA2pqahKS0Q0PD6O9vZ10aSwvL0dLSwtyc3NXfE0wGERfXx/MZnNMzf/c3Fzo9Xqo1eqEJXw+n4+0To9+L7FYTFqnrzaWREnGNRUKhTA8PIyuri709PRgeHiYNHcLhUIQCAQQCATIz88n85NMJiPSkegOjCvd79JNPDutVW/NOtipjMrPz8+TGtjR/SuAxfrsrE+QjGsnHlve2b733nvxpz/9CTweDxdccAGuuuoqNDU1rZqpPDExgY6ODrz00kt4++23EYlEcM011+C73/3u2j5FGqDO9urMzc3FSE9cLhfC4TBcLhemp6fhcrmIkyyTyVBYWIgdO3ZArVav2PHQ7/fHRMDZf0fX1GSRSCRYWFggOnDWCWdrVm9mZZRkstbOl0uTLyORCMxmM06cOIGFhQVIJBIIhUI0NDSgoqJi1XMvrSQQiUTg8/kQCATIboNAIIiJEsWDw+GQ+uFLm/hEZ+pnElvld5duqJ1WJhQKwW63w2QyweVyQSKRYH5+HmVlZTAYDKiqqkpoETo/P48jR46QOv9isRhNTU1QqVQrvmZ6ehoWiyWmuyQrK9PpdCgtLU3oM0S3IR8YGCALfy6Xi6qqKtTU1KC8vDypi+n1XFMLCwtwOp0YGRmB0WhET08PxsfHsbCwgEAgAL/fD5FIhNLSUpSXl5McJIlEEuNYsyVxtwJ5eXmkGhbbldHpdC6bH6P11uxfqpPhPR4P6eI4MTER81hRUREp07cZ0pst72wDgNlsxkMPPYQ33niDrNaLi4uhUqnIRDo/P4/Z2Vn09fWRqglcLhd79+7Ff//3f6OmpmYDH2fzSMeXFYlEiPRiqxEIBEjVE6fTibGxMTidTuJ4szIU9man1+uxc+dOaLXaM964vV4vcb5ZB5yVssSDz+cvc8Dz8vK2bPWNpZ0vE0m+lMvlGB8fx8DAALFTWVkZGhoaEirlNT8/T7qmRdtZLBZDpVKhqqoKPB4vbiOfM7W1l8lkcR3xdE561IlMDGqn5bjdbpjNZlitVpJoxufzUV9fj4qKioQmYWDx/m8ymdDV1UXKydXW1qK+vj6uRCMUCqG3txdWqzWmQlFOTg70ej00Gk3Cv6nZ2VnYbDbY7faYHcb8/Hxotdo1RcTXSiLXFFvb2ul0Ynh4GBaLBUNDQ6RULdvePC8vD2VlZaiurl5Wy7qwsDBunfJMhtVbs5+zr68vrt66qKiIRK5TobeOh9vtJg52dB4CsFgli3WwE03eTRZnhbPNMjY2hgMHDuC1116DyWSKW4mCz+ejtrYWF154IW644YaEV9aZwmZ/WT6fDy+88AIEAgG5abB/6ahjvVEYhoHL5cLExARGR0dhtVoxMDAAl8u1rHamRqPBtm3bsG3bNhQXFycUNZHL5RgcHCQSlOikzJUqo2RnZ8ckY7LO+FbrtLiW5MvS0lKMjIxgamqKRLmrq6tJ17ZEmJqaInVooyPa7ESsUqliEim9Xm9M8x62fOHc3FxciQxLdnZ2XG14dAWAVEGdyMSgdlqEbYBiNBpJ0iKwKN0yGAzQarUoLS1N2Fbj4+Noa2sjzy8uLkZra2tcyabL5SJabNa5Z5tY6fX6hOtsBwIBOBwO2Gy2mGgk2zyrpqZmU/Krll5TkUiEBBXYpiZTU1Ok8/Hk5CTpTM0mM1ZXV2Pbtm1QKBQk4JApDdjWwvz8fEwyY3RCKxvMFAqFKC4uRmlpaUr11vGYnZ2Fw+FAf39/zNg4HA5KS0tRXV0NhUKx6YGtYDBIrpHq6uq0LKpSXo3E6/XCZrNhenoac3NzkMlkyM/PR01NzZauZLDZE0owGMRLL71EulAthc/nE+dwq0ZrvV4vxsbGYDQaYTab0dfXF1NyiM341mq1MBgMKC8vR1FRUVxneKVJn22dvlSK4na7V6yMIhaLY2waLZnYKqyUfMneoP1+P4aGhsj2tkwmw44dO3DBBRckPKGylQjsdjsGBweJ48zhcFBRUQGtVksi3iu9nq0fvvQvns4wGram7VJHPFndMakTmRjnup0CgQBpox69A1lRUYHa2lpUVFSQoEgitvL5fDh27BisViuAxQVnY2MjampqYoIroVAIDocDZrM5Jootk8lIFDuR+ZZhGJIcHd0lkf0N19TUJCx3SRZisRgWiwVjY2Po7++Hw+HA7OwsPB4PKc03Pz9PWp0LhULS1XL79u0k0TQTddarwQZNovXW8SSDMpkMJSUlROufKq3zSrhcLvK9RO9ycjgclJeXk1bpm+XvsRXP2PlucnIyJtgkl8txzTXXbMpYoqGl/9ZJuiaUrKws9PX1xSQQzszMrBitFQqFMQ4i64xvBUcxGAzCZDLh1KlTpCQWe/PncrmQy+XIz8+HUqlERUUF6XrJOsVr+Y7C4TBmZ2eXJWauVhlFJpMts2tOTs6KzmSm4fF4iHPAav08Hg8GBgZIVILL5UKj0RBNd6KZ9z6fDw6HA3a7PWbyz8rKgkqlgkajWVPNXrbmbXREnP1bTR/O5XIhlUrjOuJraah0rjuRiXKu2ml2dhZGozFGEy0QCKDValFbWxtXi7qarRiGgdVqxbFjx4jmVqfToaGhIUauMTMzA7PZDLvdviyKrdPpEi6zNzc3R2Qi0Qvb3Nxc1NTUQK1Wb1rghs33YfND2ByV+fl5hMNh0lwmFAohFApBLBajoKAAOTk50Gg0qK+vR01NzZZzrtmIfXTkOp7eOj8/P6ZSCPu9bOZvb2pqijjY0YtKLpeLsrIyKJVKVFVVbYr0z+12xzjWK3VwlkqlKCgowO7du5Na3SRRqLO9TtIxoZw8eRJ8Ph+BQAAikYis4rOzs0mhfdZRdLlcmJubWzFay5bQi5ajZPK2GsMwpEPiqVOnSLmmhYUFcDgc5ObmoqCgAPn5+ZBKpVCr1aR80Uayx1ntd3QkfGZmZtXKKGy7+uhFTqZW4Ii+QUd3vrTZbDh27BjJFufxeCgvL0dZWRlp1pBo50tW79nb2xuzK8NOjhqNZkO6PZ/Pt6IjfiZ9+EplC5fulpyrTuRaOZfsxDAMBgcHYTKZMDIyQo7n5uaitrYWGo1m1aDGSraamppCW1sbWaTm5+ejtbWVLE5DoRD6+/thsVhi2rfLZDJSFzsRx5iNhtvtdoyOjpLj7IJYq9WuqRX7emCjkGxtblYSMjc3RxYtWVlZ4HK5EAgECIVC4PF4ZNdKKpWSLpRbretttN6azWVaSW8dncy40jWV6t+e0+kkGuzoIBQ7N7AO9npLPCbCwsICqXzGOtfxCiSwlbKkUimxl9frhcvlQnV1NXbv3p2yMa4EdbbXyWZPKB6PBwcOHCDb/vHg8XgxTjh7c2Kzr30+H7xeL4LBIAQCwTLnj8vlxjiKmVy9g11ZW61WjIyMEM3v/Pw8pFIpKisrSR1oLpcbk31dVFS04Zuyz+cjTni0Lnxppy0WtpV9dGKmXC7f9OSQpZzpBm232/Hmm29iaGgIc3Nz8Pv9KCsrQ0lJScw1sbTzZUFBwbIIP7tF3dvbC4fDEeMIl5WVQaPRQKFQJG3XhWGYZfpw9m+1hSiwuCMU7YhXVVWRmrtbLWK2mZwLzrbf7ydt1Fmng8PhoKqqisjbEmGprQKBALq6umAymcAwDAQCAXbv3g2DwQAul4vZ2VkSxWYdDPa8er0e5eXlCd2nx8fHYbfb0dfXF5NIXV5eDq1Wi+rq6pRd48FgEGNjY2TxzSZoR+9OcTgcIgsrLi5GXl4e2WkTiUTgcDgkEVuj0WyJvhzAorPHVgmZmJiAy+Vadg9i9dZs1LqgoCDhIE2yf3tsgIst0xe948Hn81FRUQGlUonKysqU7JSzTdZY6SO7CFsK2/hNJBKBz+eDw+HA7/evWC64tLQUl19+edLHeyaos71O0jGh9Pf3IxgMYmJiAgsLC/D5fKTm8WoRvKWEQiHidLNdH/1+PzgcDqk3Gv3H3gCWylEyRXPPasbYpIz5+XkEg0E4nU4iI1ia6CiTyYjjXVJSArlcnpQFxfz8fIwWnP33St+PQCBY1iVzM22byA2aYRg4HA50dnbC7XZjYWEBDMOgpKQEAoHgjJ0v2frf0VvpwWAQ/f39y6JqfD4fCoUCarU6YedhPUTrw5c64/EWs9GLXFaWsvQvWfrwrczZ7GxPT0/DZDLBbreTCGR2djZqamqg1+vXnHQVbave3l4cPXqUOAcqlQpNTU3Izs4mUezomsRSqRQ1NTWoqalJKHAwPz9PKpNEb/vLZDJotdoN7y6tdl673U6i1iMjI0RfzcLj8SCTyVBaWgqlUonq6moEg0Gyy8b+9ng8Hqqrq6HRaJJeXjAVsHpr1rleTW/NOtgb0Vsn47fHdtRkHexoZ1UgEKCyshIKhQKVlZVJXZBFIhHMzMzE5BXFm1eAxWtfKBSSYE44HF51js3JyUF+fj750+v1KwYsUwl1ttdJptXZDoVCxAH3+/3k39HHvF4veSzeV8o+5vV6Y/4YhgGHwwGfz49xwqVSaUwr8ZKSEpSUlEAqlaYt+seWG5qcnER/fz+Af8sM2PHz+fxlWjJ224l1wJNZHolhGMzNzS1zwmdnZ1esvsFq7ZdGw5O9RbeWG3QkEoHFYsGJEyfITTgvLw87d+6EUChcd+dLj8eD3t5e2Gy2GEdAIpFArVZDq9VuatJPMBjE3NxcjCMeiUQwPDy84s4FsLjAYBtiLJWmbKXt7Y1wtjnbkUgEAwMDMBqNMZKN/Px81NbWQqVSrftel5eXB4fDgba2NrLgzMnJQWtrKyQSCamLzUZ9ORwOKisrodfrYxItVyIcDmNwcJDs/rH3fD6fD6VSCa1Wu2yHaiMwDEPK7vX19ZH78FJpRHZ2NmleotFooFKpkJ+fj+npaRLtjnacampqSAO0VEoUNgLb2G1iYoKUtV2aS7Ka3joZrPe3F4lEMDo6ioGBAQwMDMTcu7OyslBVVQWFQoHy8vKkzetut5tErVmddTxnmfU12POygcJ4sCV98/PzY/67dB4/q0r/nQtkmrO9FhiGgd/vj+uUs/9mH1tYWIDb7V7mgK+WlCYUCklHx7y8PBQWFiI/Px9isRhCoTBG6iISiZCdnZ30CGZeXh65eSwtpB8Oh8Hn8yEWiyEQCODxeJb90NkbY7ReLtmRn0gkArfbvSzZdbU66hKJZFm7erlcvqEJf63XUygUgtFoxMmTJ4nzWVJSgsbGRqIr9Xg8xPFmb6iJdL5kO9z19fXFaPEKCwvJxJyOetusnXw+X0zJwuiI+EpJysDipBEdBY92xDPVgVgPZ4uzvbCwAIvFArPZTCZ3LpcLhUIBg8Gwplbm8WD10u+88w4ikQh4PB62b9+OnJycZbs9EomERLETSeqdnJwkpTijf0MlJSXQarVQKpVJCSTMz8/DarUSScrQ0FDcRbZMJkNlZSXUajU0Gk1M6bXp6WnY7Xb09vbGvDY3NxcajQZqtRpVVVUZd01F941g9dZL55Clems2uJAq1ho4GRkZIRHs6OskOzublOgrKyvbcMI/2wciWg6y1H+IRCKIRCJE9snhcBAKhc5YISw6Yp2Tk5OQH0Gd7S3GVna210o4HI5xxH0+H+bn58mPh23H7nK5lm0RsnA4HIhEIojF4pg/1tFmne/oP9YpZ/+bnZ0NkUiU0ESx1E7z8/PE+R4bG4sZI+u4CoVC+P3+FVvaSiSSmJtnquqXhkIhogOP1oWvVv5uabt6tjLKmca3kevJ7/fj5MmTMBqNxNGsqqpCQ0PDshvLWjtfFhQUIBQKkRJO0Z3qKisrodVqUVlZuWlbyWeyE8MwpGFXvLKFq91CRSJRXEdcJpNtmco2LFvd2XY6nTAajXA4HOSaE4lE0Ol00Ol0a6pgsxKDg4Nob28n10x+fj7kcjlGRkZiotgVFRUwGAwJRbEXFhbI7lC0/SUSCTQaDbRa7Ya684XDYYyOjpJo+8DAAJxO57LrmsfjobS0lEjBdDodioqKYn6nbPOVpWNl63drNBoUFhaS45lwTXm93mX1rZOpt04GZ7JTOBzG8PAw+vv7MTg4GLNLJxQKSZOZsrKydY87WmfN/i29z4dCIfh8PvD5fHJ/Yxgm7kKELX7AOtSsg70RmSV1trcY55KzvRZ8Pl9MXVDWufL5fAgGg8v+GIaBSCSCRCIhzrhEIlk1UsvKQJY65dF/ZWVl8Pl8EAqFy24cCwsLxPEeHR2Nibjm5ORAoVCguLgY4XCYdCWbmppadnNlIxfR8pNUNsAJBAJx29WvtMvA5XKRm5u7LAoenfCajOtpfn4e3d3dsFqtxEZarRa7du1aVceaaOfLrKwsBINBeDweRCIRiMVi8Hg8ZGdnk6oJ0ZNzKtiIncLhMDweT1xHfKUtURZWlsL+l3XEpVJpxiUtA5l/f4pHOBxGX18fKS/KUlRUhNraWigUiqQseubm5tDR0UFq0PN4PPB4vGVNvNgo9pl20ti69jabDUNDQ+Q+xmqbtVrtuvIe2PrObEMbh8OB4eHhuNdqTk4OqquriSxFo9Gs2M2yv78fvb29MZIWtkyhRqNBRUVFXCcvHddUdH1rtvLVUti8H1Y+udn1rZcSz06hUAjDw8NwOBwYGhqKub+KxWISwS4tLV3zdRKJREjzNNaxXpr06ff7MT8/Dy6XSyLWbALs0vMJBIJl0eqN7NquxFnvbLOOQlZWVlpqHCabzf6yAoEA/vrXv4LL5YJhmGUR4ug/oVCYcROxx+OJkUuw0Vq2VupSJ5x1prKysohmi8fjrdphMJrohLbs7OwVnXIej0e0dpOTkzE3e7bzmFKphFwuJw4hu5CIp9+Vy+UxZZo2Ek1KlIWFhbhOeDzHFfh3AyS5XA6FQgEOh4O8vLwNR+1mZ2fR1dUFh8MBYHEi1ev12LlzZ8INNWZnZ2Oc76W/M6/Xi8nJSSwsLCArKwtSqZQkGbFto5MRfVxKqm7QgUBgxbKFZ9KHL03QZB3xdCYubyVne35+HmazGRaLhSxYuVwu1Go1aYSSDCKRCHp6etDd3U3kVQKBAEqlkpy3oqICer0+od0al8tFqnlESy/Yhl8qlWpNi352TKycg60QsvT+wefzSdRao9EQLfVKrFZ9qKSkBGq1Gkql8oxjTfU1Fa23Zh3seAGM/Px84lgXFRWl5D6zEVg7BYNBDA0NkUVStN0lEgkUCgUJKK3FT5ibm4spuxets45EIlhYWMD8/DwikQi5hrOzsyGVSpctVtmiBdER682qenbWOtvPPvssnnnmGVgsFjAMg2uuuQb3338/AOCVV17Bn//8Z3zpS1+CQqFY7ynSwmZ/WX6/Hy+88AJ4PN4ZM2m5XC7EYnFc2Ub0X7ob27Ca5Wi5BFsffCXYyHe03pvP55PShqzUhXWi13LpsvINj8cDj8dDarsKBALIZDIolUqoVCpUVVUhOzsbgUCA3KQnJibiaq2FQiFJHmUTAzdLGsAucJYmZkZri6MXJVlZWXHb1a9VIz05OYnOzk5Se1ggEGD79u3Ytm3bmjWL0Z0vWSecTfBloyrT09Ok6oxMJoNGo8Hu3buh1WqTFhVJxw2azZeI54ivtujMysqK64jLZLKU68O3grM9NjYGk8mE/v5+cn+QSCTQ6/XQ6XRJzQkYHR3FO++8A4fDQRIsVSoVRCIRioqKUFZWhpqamjNWMvH7/UR6ER19F4lEUKvVqKmpSSiI5ff7MTk5idHRUdjtdpLEODc3F3NNscm+rNZap9Ohuro6IdvMzMwQxz16rmJ/m2q1ek1BiGRfU2xFr9X01jwej+xYsvfuTM6tCAQCcLvd6O7uxsjISMw9XiaTEQe7sLAwIYeW3XWMdq7ZBQjbxXp+fh6BQIBErCUSCaRSaYyd2N1VtgcGK3FMR94Ny1nnbIfDYdx111149dVXSfaz1WrFtddeS5ztgYEBXHrppfjv//5vfOYzn1n76NPIZn9ZoVAI//jHPxAIBOD1eskFDvw7uSASiSAYDCa8OszKyiISjmjnPFrSIRKJNr3EUigUWpY06HK54ibfAP/+QUfXB1epVDH1xdmkT7YiS7T+nH0sOpLIlhNitejRNy+BQEBW5IWFhRCJRMRO7LYZ20I42mFno/PRNb+Li4s3NRIZXRnF5XIhFAphcHAQs7OzKy5MRCJRTGUU9t9nmnxGRkZw7NgxTE1NAVhceOzcuRN6vX5DC465ubmYyifshOl0OmMWPGx5tLq6OtTW1iIvL2/d13ImOZEMwywrW8hWT1ltoQosLlbjOeLxok/rIZPsFE0oFEJvby+MRmPM+MrKymAwGJLegtzr9eKtt95Ce3s7Sc5WKpUoLCxERUUFdDoddu7cidnZ2RXfg2EYjIyMkNbp7D2IzVuoqalZUXrBfubp6WlMTk5icHAQfX19GB0djStfkkgkyMvLg0KhINHx4uLihK+JhYUF9PX1wW63k987sP6OsdFs9JpKRG+dnZ0dIwnZbL31evD5fBgcHER/fz9GRkYgEonI4iYnJwdKpRIKhQIFBQWrvk/0dRKdT8MWUpifnyeFEdjywGxDoei5KysrCwUFBZDL5cS5zs3Nzbi8k7PO2f7Nb36D+++/HxdeeCG+853voLCwEAaDIcbZBoDLLrsM+fn5eOaZZ9Z6irSSjijXgQMHIBQKV41sczgcogWM1kgxDEMc8lAolLAcY6XERtYRZx31zVj1+3y+ZQ54vO1OYHHyCAQCyzo5rraqDofDMZVY2Ei5x+PByMgIBgYGMDIygoWFBaI3Z8sNFRQULOvAyTpFrGPEJspFl09kJR2lpaUoKytDRUUFSkpKIBaLN6V8InvjCYfDy3YZZmZmVnXeoruQsv+Nd3ONrtHNvq6+vh4ajSYp24bRyZdsmTaHwxGzFZydnY3S0lIYDAYoFIqEOl9Gk6lO5FLC4fCysoXs30qLVWDxdx6vfnhubm5cfeVKZJqd3G43zGYzrFYrWUzz+XxoNBoYDIaEJsC1EAqF8MYbb+DVV18lEejS0lLodDoYDAbodDoSxV7JVm63m7ROj77X5+XloaamhkTGo4ne6ZmcnITD4cDAwEBcSZJQKIRUKkVxcTGpEFJRUZFwNQcWtrSgzWbD8PBwjA67oqKCJDFv1Nla6zW1Fr11dH3rTJNexiM63yha+w4AlZWVKCgogFKpXPG6Zhgmpp715OQkpqenSelg1rFm5SBs8E0mk8XcB9iE/OiIdbobtCXKWedsX3PNNZicnMQrr7xCJrR4zvZ//dd/oaenB2+88cZaT5FW0vFlGY1G8kNgGAYMwyAcDpNV52qT6VLYKOtSh5z9Y6PkiX71bCm96Kh4vL9URAtYzWu0HjwUCq3oKLJlg5aW0EvEuY2uSWq32+HxeGKSPVkdmkwmQzAYjKlzDvy75jfrgMdLOmKbPMjlcpKAydp1pdKJ8RJBE+FMNx62Xf1SOcpq7erjVUaRSCTo7e3F8ePHyWvlcjkaGxtRVVW15nGfCZ/PB6PRiO7ublgslhj5jEwmQ2FhIQoLCyGXy8/Y+RLIPCdyPbBbzfEc8ZX0/QBIe+x4ZQuXLlwzwU5sRNhoNGJoaIgcl8lkMBgM0Gq1SU9k9ng86OjowCuvvEI+v1QqxXnnnYc9e/agurp62e8z2lbBYJAkJEbX82aTgGtqamIilB6PJ0Ze5XA4MD09TTrpste6QCAg3RgrKyuh0WhQVVW17i66bFdBu90Oh8MR48SnqjznatfUWvTW0ZVCMk1vvRper5eU6FtaSSs/Px8KhQJKpRJKpXKZnaKvE/aP9ReiHetwOBzjVLO9Mng8HolUR9euzmRJzZk465ztXbt24fzzz8ejjz5KjsVztv/nf/4Hr7zyCk6ePLnWU6SVTGnXzuqz2cRIPp+/LLrKlu6bn59fU7dJLpcLPp9PWqACIP9lGAahUAjhcHhN3Suj5SkryVeScaPOzc2Fw+FYJkdZLVIbXRuc/ZPJZCs6sat122Lb2SoUClRVVYHH4y0rn8g63mNjY6TqCauNW/qTY2+EbCWKeM5CdHnEpc74UkedvVmu98bj9/vjJmVG12uNhpX65OTkYGZmBsPDw+Dz+cjOzkZJSQkaGhpQWlq65nEkQigUwsDAALq7u0njHI/HA5/Ph7y8PBQXF5PI1kqdLzPBiUwlXq83riO+VMu7lOzs7BhHvLq6GgzDICcnZ9MbWwUCAdhsNpjN5hh5RkVFBWpraxMqobcWGIbB0NAQTp06hcOHDxMnWSQS4T3veQ8uueSSVStUyOVyGI1G4riy91EOh4Py8nLU1NSgqqoKoVAoxlkaHh4mW/3swh0AyVtgd5zUajWqq6tJpaSNfB9ut5vosKPvoVKplETHU1WNY+mixOl0Esf6THpr9m+rOYcej4f0iIhefAGLixpWgx2tfReLxbBarTFyELYc79KItVQqJU41O58IhULiTLMR60TKx241zjpnu7GxEfX19fjVr35FjsVztj/0oQ+ht7cXbW1taz1FWtnsL4thGJw+fRoLCwsYHx+P6e54JlgpCOvcsitWNqLNyktYR3CtUXKBQECi5OxftGyFYRgEAoGEpSs8Hi9GqrJSxHy1yWOlHxQbqV0qR1mpfB6Px4vRKrPR8KVbZgzDwOl0krbx0TWxeTweysvLieO9WlQtEonA5XJhZGQEQ0NDGB0dJVKZpdVaoqPaYrF4TY4El8uFSCRCYWEhQqHQMqd8aQQ90a1gr9dLKs1E23fphBgKhTAyMoLx8XFyLpVKhdbWVqhUqpR1XIyu8Ts5OUlkPoFAANnZ2UQ2EQ1b/ze6A+ZWm7zXS3Rb+2gHfKW679HBADaiujQinuy29rOzs8RpZSP0AoEAWq0WtbW1Sa8IxDZzsVgsRK4RDAaRk5ODlpYWXHHFFatGTj0eD2lcE92CPScnByqVCnl5eaTqDpuTEK3J9/l84HA4EIvFxFkqLi6GUqkkmuO8vLwNLyzYpMze3t6YpmBsJRW1Wr2uknFrgZX1WSwWjI+Pn1Fvzda3zjSdcCK43W7iYDudzpjH2A6abEOgpX0LxsfH4Xa74XQ6Y6LWbOUyNmItk8lIXf/oEnts07lzgbPO2b7pppvgcDhw6NAh4pgsdbanpqawb98+1NfX44knnljrKdJKJtTZZsvtLN0SWrpNlKiTG+3MikQi4pCzRCIRkqDJvvdqXfOi4fF4MdIV1iEH/h19D4fDq5Y7W0p2dvaKOvLy8nIEAoGEyyAuLCwsi9LGcxJZ2ModSxMHWUd6amoKDocD/f39MUl7XC4XZWVl5MaZSBR/fn4+JpqzdMJh/83WY5bJZBCLxTHNiKIj6tGSgaU7JSuRlZUVN1K+tPlQdnb2so6grHZ9aVWUmZkZ+Hw+DA8Pxzgd7HZ0eXn5MqlPMrf/p6enSac9dnHp8/mIbILtLhqJRJbZiZX4sM53MpybrQYr1YqOiLOd6VbrMMtKjeI54okuGtmosslkwvDwMDmem5uL2tpaaDSapFZbYluRm81mDA0NYX5+npTeKyoqgl6vx8UXX7xiZ0l2d8Vms5EqPWKxGDMzM2TxHg6HMTX1/7H358GN5dd1OH6wEiAIgCAIEuC+dTd7ZrpnpmeVvJZdssqSI9uJo7gSybJsefnDjh2vKluJy658LceOE5edOF6lsmwrsRbbqiSKNVYlsjwjaWbUs/T0TLO7ue8gCZAANwDE8vuDv3P7vg8fQJDNrXvmVr0iuGF57/M+n/M599xzUxZgvb6+jmKxKDprHp2dnejo6Djy7rb07h4bGxM/cOAO284OkMeVtchmsxa9dTab3XPvNTU1CbC+l/TWdpHNZmWd0IWlwK49IteJYrEo/R5mZ2cxNzeH9fV1WYtzuZx0X9TjxHQCqdbC/M0U9x3Y/uQnP4lf+7Vfw7vf/W589KMfhdfrtYDtUqmEf/Nv/g3+/u//Hr/xG7+B7/7u7z7UBzitOAtgu56oVCoCyO1A+UFBswa4gUAAHo9HGHK+3s7OjgX011p4zfB6vVLgqbXkfG76cu8nXeEEfTc2iHTuMPXgtazXtB6cALxcLkvnLn3tHA4H4vG4NBaol1ngxEsAvry8XNXzm7ZV2vObHbxyuRwaGhqwuLhoAeWm1KXezZr+XNVAuf6Z1+sVwDY3N4evf/3rAnwdDgfa29vR2dlpYZHvRm9fLQgQR0dHMTMzI/cCrw/13WScqnW+1OD7IMWX91Nwfsrn8xY2XEtTat27bre7qn94Q0MD8vk8RkdHMTIyItfB4XCgu7sbw8PD6OjoONLPs7W1hdu3b+P27dvY2NiQosDNzU20tbUhHo/jypUruHjxoi1bv7y8LBs6Sj42NjbE0cjhcIiEZ2NjA+vr63C73XsAU0dHh0UacdSZFfpsm4XFLS0tosM+6vFcLpeRTqctemu7jGp3dzcCgcCRbyxOK9bW1gRg260H1NNns1nMzMxgenoamUxG1mveP9oVpKmpCX19fQgEAodqYf5mivsObJdKJXzwgx/ECy+8gI6ODnzjN34jPvWpT+GBBx7AlStX8KUvfQmzs7P4hm/4BvzJn/zJPTcgTqOpzR/+4R+iXC5LgVcikUBHRwfa29vvmmnQmm47QK5v8v3C4/FIipMt2XUHKQJygnEeB2HJCcoJyDl+SqUSyuUy/H7/HqagVhzEBlE7d2g5Sq126tT9ut1uaetNKyUGU4W9vb0HWlDoREDgXa0Cn96+XLSi0ShaW1v3Hcvaw9wE4vqxLgQ9SLjdbgHgOzs7mJiYQDKZRKlUQqVSkfdaLpct9QM6WFCqNzqhUOjA6eR8Po/JyUmMjY1ZUueRSASxWAyDg4MIhUKWRkjVOl82NTXVVXx5P0U9C9nm5qYtEGcWodr/pNNpAapMhT/00EN49NFHj7RRGgssb968iZmZGdnsr6+vY3NzE+FwGH6/H319fXjiiSf2SEa2trYwMjKCl19+GQsLC/J/zJjQoz+fz2N9fd2inaXeWuuNj8uGbn19HePj4xgbG7Nk3xobG6Uz61G6tVBvrT2u7fTWrJfQ7PW9Xi+RTqcxNTWFyclJSz1BpVIRMiKXy2FxcRGpVGqPTFQz1sFgEB0dHejo6LAw1h0dHff8eTqO2NnZkbUpn8+jr6+vZkH4ccWxNrXJ5/P4jd/4DXz605+2van+2T/7Z/jlX/7lY21xfVxx0oN6ZWUF/+7f/Tu4XK49LCaLz8jAtbe3Ix6PIx6Po6Wl5cjOr5aQ2MlVaHJfT2j3Eq3BJhghi61Z+YOw5IFAAMViUQpGzaJRSleoga4nyNhqIK6Bucfjkbbi9bRT39nZQaFQwPb2NgqFgpwHr9dbtQim3tje3rYsbCsrK3uAjNPpRF9fH/x+vzDgd+v5TWmTaZ9ognL+rNoGK5PJYHp6WjYwbrcbiUQCoVBI7Ct3dnaQz+f32Cny0FIfDcLr7VTGorCxsTFUKhVJZZvNOerpfAnAUnxJAH4SHUZPMu6GNTL14WtraxgdHcWtW7csGtbGxkbJOHC+sLMtPKg+fGtrC2NjY7h165ZlsxoMBoVsYLOXp59+Gp2dnQDuZJquX7+O119/HRMTE1LkXCwWpRNuuVy2sJFtbW2oVCqWDFR7e/uxjolCoYDJyUmMj49bpFtut1vaph+mxbtdbG9vW1hruyZjXq/XAqztmn7dq8XJKysrArDpW725uSlWq8ViEYVCwZak4AasubkZ3d3d6O7u3reF+b16nuoN1n4RNJME2u97c92LRqP4ru/6rhMneE+kXXs6ncbzzz+Pubk5lMtlxONxPPXUU1X1bfdCnMagJuNH1i2dTotHZrVgN7mWlhZLR6zW1laZ9I9Sv0XGWoPxra0tbGxsHBgwO51OAbT82tDQYJmMtW5dg39t8F8r2Bbe6XRaNOraApHa54PYIGqW3Ol0WsChHdDM5/NyPbe2tiwa9I6ODly8eLHuLnF2QT2oZpVyudwePSRbn3OsNDc3H+ukpO0R7UD5zMwM3njjDWQyGRSLRXi9XrEu4/vimDPHQalUkqZCbrdbQHhDQwOi0Sii0aiAXt4TdsCMzR2uXr2KyclJy+asvb0dg4OD6Ovrs6T2q3W+NEMXXd4PxZdHseBvb2/j1q1buHXrlozNSqUi2Tyv12thxGtlUwiO7aQpjY2N0lL81q1bmJ6eloXZ6/Wir68PxWIRk5OTKJfLcLlcuHTpErq6upBOp5FKpTAxMYFbt25hZWXFkgHi3EWnG2b5PB6PMLfnz5+XWojjDMqkxsbGLM1xgN2mPoODg+jt7b3rdcBOb20GNxlch+qZX+4VEKmL5CcnJ0Xql0qlxHrU6XTusWmlHj8Wi6G7u1v6ABy0hfm9cp6AO3NqNYCsNyH69+YaTOKFxJmWmeqjXC5LBvzy5cv4/u///hP/zCcCtu/HOCua7XK5jJWVFXF2oJQglUrV9M9lKtbv90uxF1OWOl11VJ3ldJRKpT3MuAnOq/k4m8E2sVp/HQgEEI1GLek6s5CUoK7eYFt401oRgABygul6grcU/5csR6FQgMPhwObmpsXWC9gFAJQyXLhwAf39/Whubj709SFQGR0dxdLSku2Y5mtygbxbC7HDRLlcxtjYGF555RWsrq5iZ2cHfr8f58+fR0tLS1Wgrjd42mWnmlSBaf7m5ma0tLSIzCYcDqO9vR35fB5utxupVApzc3MWmYnL5bIwg3agnZ0vCb5TqZTtewmHw8LyxWIxNDc33zP2W3ez4C8vL2NkZAQTExNyXnidz58/X9XhI5fL7Wlnn8lkpLjQLgqFgtRfAJBUfldXFx555BH4/X5cvXpVHGt8Ph8SiYSQBcvLy5ifn0c6nUYul5MsWjweR3d3N1pbW8Xakps5k7k9bnCUSqUwNjZmKQAGdscXu0QeVv9M1ySuOdX01pFIRD77YfXWZxlEVioVJJNJXL9+HVevXsXc3JzICvP5vIUo4sY/GAwikUigu7sbfX19R5aBPq3zRCczEyBXA8w8zOeoBpTNQ8NRbU+sSRWXy2X5Xs+f8Xgc73znO+9PZvt+jLMCtmtFPp9HKpXC4uIiFhYWBIhzIsjn83sWI7KyWi7B1LfWFerjOIBAuVyu6q6if1ZtaJqMrQnGA4HAHtcM3ZpWg/N6CwS5uGrHFVorkimnNKbac5pMPfXd2WxWnDIot2lsbERbWxuGhoaka5j2B69nMtHjqVAoiHdtNQ9bh8MhDSIIvk+qYKlUKmFkZATXrl2TyToWi+HKlStIJBJ7/p7nmhP99vY2tre3kUqlpHNaPUWvbrcbLS0te4ptK5WKaPd3dnZkYg8Gg+jv78eFCxfQ1tYmenRzk2Lad9UqvtTSk7NcfHnQBb9UKmFychI3btyQjovA7nW9ePEient7D72ZZNpeg/DJyUncvHkT8/Pzcr2pE25tbUUul8OtW7dkI9TQ0IC+vj60tbUhm81icXER6XQa5XJZWErWzjQ3NyMUCtXdmfA4wNHm5qZYW66trcnPaV85NDS0b/tuu6BURstCzLnB6XRasqexWOxIJIxnCWxXKhWsrKzgxRdfxKuvvoqbN29KVgO4Q/7woJa6p6cHfX196O7uPrYW5kdxnpjFrQWUze+1dFRL/EgiacbZ/BlDZyCrAWZ+tbufKNWiGxa/93g8IhmlFfHly5eli+tJxrGD7RdeeAEvvPBCVccEYHeA/vqv//phX+JU4l4A29ViZ2cHmUxG2raSnUilUntuJKYcXS6X+DnrgkHTkspkxQ/q/3yQsAOmBMtOpxNLS0sHAsv8fJwo+Rl5gzP1RTCuQf9BCgP5nKaWnJ+JFouaeS8Wi5Y0LRk1nfamzRNT5LprWnt7OxKJxB52cL/ubGSvmBK1KwJlCpQMXiQSOVYmtlAo4PXXX8frr78uE3ZnZyeuXLlyKCAB7C4y+l6gBGRtbU20uvTj5kLi9XpljAC7QMcs8gsEAgLkAoGAMKimjzm/5/NkMhmkUql7rviy3vlpc3MTN2/exK1bt2ScO51ODAwMYHh4GK2trUf2nnK5HMbGxnDz5k1hsrn4+nw+FItFLCwsYGxsDPPz8zKm/H6/FDRvbm7C4XDIhpqg+vz58xgYGEBfXx/6+/sP1JnwqEDkzs6OdLSlrSCwO2d3d3djaGioaralWuRyOQHWXBsOo7c+ijgtsF0sFsXCdXx8HCMjI+LPr+9xbsK7urpw/vx59Pb2SkOhk6zJMM8TXadqSTX075ihASCg+CAHZRomQDbBMn9mFrs7HA4LYLYDz3Sx8ng8lqwwiRRTTri9vS11Wcwcd3R04P3vf//9w2yvra3hJ37iJ3D16tV9ta4OhwM3btw46EucatzLYLtaENCtra0JGCfrxqI3Ow2VBt/8qh1I2NyCpvoalN9tQV614HmqVCrI5XJV5SqUGhzW+pCgnB6nwB0d8t2y5LojqLZBpJtLMpmUFr4ssiwUCiiVShYnFd39k9Z5BIADAwMIBAIC0KvZIDI2NjYshZd2TSZog0fm+7g6uG1vb0s7dp7X/v5+PProo0e2yBWLRfGPZkdSOs+YjYaKxaIw6HSg4LXzeDziaFLvZoQdQcvlsmzyKGPQGnQedp0vTzr2m58WFxcxMjKCqakpGTeBQAAXLlzAuXPnjnQ+WFxclMYztNzL5XKWLBewK6e6ffu2zBderxdut1vkSGTiKpUKYrEYurq6JKOjwSX14bqdPQ+7TMTdzOXUmo+NjWFqasrCElarI6gV2WxW7ulkMmmR4DECgYDFIeS46zkYJwG2mfGamZnB1NQUZmZmsLCwIASD2a8iHA6jv78fDz74IK5cuYKhoaFjzTZpptnuyOVy8Hg8WFlZkb9l74qDAGYSCcyg2jHLdoyzzto5HA5bwKzBsgmi+ZVWxSZgtpMDauklSRD9PTcT1GsDu/fNwMAAfumXfun+Adu/+Iu/iM997nM4d+4c3vve96K7u7vmYHzyyScP+hKnGvcj2K4W5XJ5DwhfW1tDNpu1gHBz18w0q5al0D6P4Xa7LSDcBOOHTUMe9DwRkNcq7qzXtcS0PjS7XrLw0k6nflCWvLGxES6XC5lMRnTAnIRYhMnXJPDT4fV6USgUhKVtbGxEKBQS1pQbomo2iCwE5EJdLYPFtuim5/dRRDabxSuvvILx8XEAu6Dn/PnzuHz58pEtgOZ4ouaXjjP0YtfZiJ2dHWGn19fXBcQ1NjZKIVQkEpH7RmvOa0WpVBLwSG/mUqlkAd68r5jViMfj6OjoQDgc3lOgdZRhd98Vi0WMj4/jxo0blt/F43FcvHgR3d3dR/Z+8vk8rl27JrZ7PEd03CH7ys3w3NwcVlZW5P5gdz6C7lAohKGhIVy5cgUPPvigbL5M20Jeg2rh8XgsnuFsbV8ulw+0EV1dXZW26bquJRgMYmhoCAMDA/umyHXGivetXY1Mc3Mz4vH4qftbH+WaR+lXOp3G/Pw8pqamMDs7K9p8TZQwm9HY2IhwOIxEIoHLly/jySefRH9//6HGrHbUqCbPMGUc+XxeNvP8alcQ6PF4pE5B96I4CGDm74DdedQEw9XAsv65ndTjqEA066K4Fnu93j0N1Mh4M3tFq2AePT09+PEf//H7B2w/+eSTaGxsxP/+3//7QKm1eyVOA2xvbm6iubn5VDwi7YJWXRqAE5CzkEFb9XAScTgcckMTuLFjpRler3cPI64fV2Nhj2NTYnbPtGPL6wXKdtaHZMo5gZRKJQsrbrpt2LHk3BilUilxqnE6nQL0aCnl8XiwtbWFYrGItbU1lMtlYTV02DUDotbdzo/c7/ejWCyKpCKdTtf0/Ka28yhkEOl0Gi+99BJmZ2flHF+8eBGXLl26a2a93vG0vb29p0smNeEsjNSbkUAggIGBAVy4cMGi+yWzajq12FkqMl2swffm5qZtRtHv96OpqUmKP9mm2ZS0aJnLQTa8+jytr69L6p33hdvtxsDAAC5evHgkHs65XA4rKyu4desWrl27hrGxMTm/TqdTmH7WOXCcr66uSjOn9fV1OJ1OkUAFg0F0dXXh0qVLGBgYqIttZ5dUs1CT/uF214J1JfQMNxnxYDAoG4OJiQmMjY1Zegc0NDSgv78fg4ODiMViVd+b1lvzMNeQ49JbH0Ucdi7ndWbjnOnpaczOziKTyYgkDLhTH8T5j+5dra2t6OzsxIULF9Db24v29vY9HZWpWa5X37y5uVnTNcPUO+uiQDtds5ZkhEIh7OzsWH7O+iE7JrkaWOb39bjTMHNcL4gmcCZgtgPU3IDycLvdstax/ombDfZe0H9Pxynav/L+InZ46KGHji2jXiuODWw/9thj+KZv+ib8zu/8zmHe15mPkwbbW1tb+OxnPys2UdRqniW9JoOFSSYTnslkLECDGmgNwvWu2Ol0im6yWlTTjHd3d6NYLJ74eSkWi/t6kR/G+tAE5DpDQCbG1K9vbGwI6E6lUpYF1ul0IhKJoKurC263W/T5ZMQ3NzctwJuMAQtgqG/Xhx2g5YRPQKhlEOaESjadTNphLdEWFxfx0ksviVtIQ0MDLl26hOHh4UM7qdzt5o3e62Qmb926tSf9HwqF5H72+/1S7BoOh8Un3O6c2BWCbm5uiiSAUrD19XVbj35zE2teR2aoTJ25HUBPJBIYGRnByMgIZmZm5DmCwSCGh4cxNDR0aBBHDS319AsLC2KHqp0w/H4/EokEWltb4XA4sLOzIxvYjY0N0d3y58PDw2hvb0djYyMGBgYwNDR0pJrxUqlkaWvPo1QqWfzDzf+hXGl7e1s6rzY2NmJwcBAPPfQQurq6bOc3rbemv7W5MafLEJnr49JbH0XU2yiJwDqVSmF6etqS2dDjg+CTrKfH40Fzc7NI6Lxer2w4Ghsb92Ru9TxrB5DNgkD9PUMzy/qwY6H176rJL3w+H2KxmHQF1r8/rKUjQTTXlnpAdC1ZB+tcCIj1YxoJkI3muSK41muF/n/KK/UcZhJz5px533WQ/IEf+AGUy2X8xV/8xaHe2FmPk75YxWIR/+///T/Zlesgg6MB+FnNJrD4i+Cbj+0AKJkD7ujphQ3cAX/VgqwR5Rwn6aSyX5jWh9X05PUEGWbNkmuATmCztbWFubk5jI+PY2JiQjY+TqcTm5ubwua0tLQIICWTr/XntL8zO4Jyo0R2mwsEZS46mBGhtWE2m5XiGH1EIhF0dHSgq6sL3d3de5il/WJmZgZXr14VV4bGxkY8/PDDOHfu3IGv+3FlSt544w289tprmJqakuteKBRE3226WTQ2Nkqret0xs54FlY2O5ufnsbCwgIWFBUsBEQ8WQzc0NMDn8yEQCOx7vorFIlZWVrC6uor19XWRs8TjcQwNDaG7u1vGhsma213TcrksBdw8qKleX1+XItZKpSLAhd7pplQN2AU3c3NzmJ6eRi6Xg8/nQ09PD+LxODo7OzE0NISenp4TBZyRSES6vXIunJqawu3btzE9PW3ZGLEIORqNSqqf/uFut9sif7Cz4NPtzuPx+InprY8i9L1XLpeRyWSkJ8Hq6irm5uaQSqVkPtnY2BCgqyUEHMfcnDocDiEBnE6nZBO8Xu++2mYdtcCynWyDY78ao6y/1+B6P6Kg3jlKb8gJou0ANTcKtTTRhUIBxWJxD4A2v3c6nRYQzWvJDQr/ll/N+7ehocFCCJhfDzKf33dg+6tf/Sp+5Ed+BL//+7+Pb/7mbz7UmzvLcdIXq1Kp4IUXXkBDQ4NICFjUYQdU6VZAnexxtfw9qsjlchYGnCC8FujU6SKmywBIMZO2vrIL2jRVA+KBQODUFiSmNmuB8WpSAbswWWimWLe2trC6uiqLULFYlNbyoVDIsjHg4q8nZf7OHIMEW2RFOTHayU04ltfX1+WwAwwej8eS6mZHSVPqohelSqUiHt3cpIZCIVy5cgW9vb1npmHExsYGxsfHMTo6ikwmI+e3XC5LcXEtTTC7zelOmftZjNXT+ZIpW0q5mFlxOBzI5/NYWVnB2NgYZmZmpEirVCoJa7pfutbhcMDn80mWixs8OgpxHDkcDpkXmAVjupmSGH3dQ6EQ2tvb0dTUhJs3b+KFF14QOVMsFsNDDz2EBx54AAMDA6euRWan0vHxcYvkyuPxoL29HS0tLZa6mcXFRfERt+ulQCvDRCIhlnPxeByhUOhIG5gddxQKBSwvL2Nzc1N6AbAfgJ4ryBw7HA4hZLxer9T8MFtHAKm1vNoGFtirb7aTbPCxdueqxTibRYLH1acgEAhgfn7ewj7vB6JNAK010YVCwQKCudbagWo69WirW20DCOxmU6rNR8zikpk2wfRRSpruO7ANAH/3d3+HX/3VX8W3fMu34O1vfzva29urAr4nnnjiMC9xanHSF2tjYwOf+cxnLP7RbrdbmBwO8vX1dWGAdHACJlg5y169OgqFggWE86udBR2joaEBXV1dUjSmJwHNfuznQKInADtbw9PQfemoVCp7wLedbGW/DABdRVhcoxcHdpg7f/48wuEwdnZ2bAs719fXkUqlkEqlLL+vZffJ5kNk6ILBoIxlbga0J7b5Ocjq8/85KbPYUxd0NjQ0SPdVpiWj0SiuXLkibbdrxUlO0MvLy9KMRNcAkO0OhULY2tqSosxqG1KHwyFNesx29dXm4UKhIOe8VudLjq18Pi/3Bc9nU1OTMIZ2OvNsNivXlPpyuzoUdnSl7IKbQZfLhXA4LOeCjWPIUsfjcayvr+ONN97A888/L3Z/TU1N+NZv/VY8+eSTp969OJfLYXV1FS+99JJFSsLOlQMDA2hvbxepiam31r7x+XxeQA8Z72qgmsV+pltKrTFxFFGr1XYul0Mmk7FInlKpFDKZjIBDOsmQGdW9DNhzwO/3C5jV3Xr5twTX0WgULS0tkhVg9obWnPsxzPz+pLIg7JBLAF2NlW5oaEA2m7Vln01QzU2DHSNtMswkRzimNJCm9JBa61rh8/mqMtP1ZM+OKu5LsP3Xf/3X+M//+T9bmhVUi7es/2pHsVjEpz/9aXE+KBQKsoPXEwHTzHSg2NnZkSY2ZgSDQZGdtLW1Hbs/8lGG9grXunAyQ2ZTG2CXtQiHw2hubhZXBpfLJRpzs8Bsv8njuJxUjjpyuZxtQyBW2+sGFcyWpNPpPeePrdx7enrQ0tJiW9zp9/sthZ3ap5tgPJ1OVy0kpWctn0sD8q2tLYsLRC6Xk00lFxMWCmmfdz2mS6WSyCh4/8TjcVy+fBkdHR2Wgk9tg3gaE3SpVMLs7CxGR0cxNzcn49HpdKKrqwtDQ0Po6uqybEh1cWatc2zKUJqbm9HU1GTL9LPz5dzcHF577TXcvHnTAsAjkQgSiYT4CxP8Njc3o1wuC3jnGOA9ajbACAQCcLvdInmanp5GOp0WNpLXlvewvs70QdcNmTKZjDiMPProo3jyySdFw6mlLDqtf5xRKpUwNzeH0dFRzM7Owu/3yz3Y2dkphY7pdLouvbX2tyZbqvXh2jWFzlHVgrpXOyBuZvj2a7W9vb2NjY0NmVM551CmxJ/puYj3r+m4wXvU5XLJGtfU1CSba44BFupxA0gwTEvM3t5e9PX1IRqN7gHTp7Xmsb6nWlGh9ok22Wi7AkMA4ixVTc7BgxklnRWktpnN13gtuCZWI04YvNdM7XS1WpDTivsObP/1X/81fvmXfxmVSgUPPPAAurq6auqIP/rRjx70JU41Tvpi5XI5fOYzn0FDQ4PIB1hJT5C4tbUlN5behbPlNIFHPp+39VE1i9Risdihi9ROK+gVXqlUMD09bQHhtToEkgHkEQqF4HQ6xfrPBOK1mHUGU5l2rHgtJ5WTDE489HHWoHxlZQWTk5OYnp5GKpWyZAIIgqPR6J4xQutDs7hTWyFub29jcXHR0u6ZjXo0A2M+JwE4xzJZdfqM60YJZLN0atLj8chiNTc3h2QyKYC9paUFPT09lowFX7etrQ3lcnnPe9A2iMcZ1RwpfD4f+vr6bIv6uNkhA04gbmpO9Wc1AXhzczPy+Txu3LiB8fFxFItFAclkyTc3N7G+vi7nkc2kNjc3hU00F9xwOIzW1lZ4vV6pzZicnMTY2JilaYjD4RBnkMHBQYTDYTQ0NAizm8lkMD4+jvHxcekGyu6QTU1N6Onpwfnz5+va+JLFq+bKwscsVqyX2VxaWpJaCb0B6u3tlXuCzK7dvNzY2Gjxt45EIoeStxUKhT2WhWyelMvl9tjLadcIFqIBEGcMAnyzSJDzhGY9KRGiO5LJjuqMGjfuwWBQ7kktiyQDzV4Di4uLWF1dtZyTWCyG3t7eE28sw/OhWehqIJpscDVJh/6ZLhKsxkQ3Nzcjl8uJA4dp1cr6Gb6mlu5xfdsP6vn9/qqA+jgb2B1FULbY2dlZ1/p91HFsYPtd73oX5ubm8Ed/9Ed46qmnDvXmznKchmb7ueeeEyAE3Ckw4K6X6SYNDk0tLTW0kUhEtGrsxsQiNQ0cgsGgxR+5ubn5nmC/zd1rNa9wNiyxCxbNEHRoRo3OBhqE6+9rsUiM/bpvnkSast5d/sbGBsbGxjA6OoqZmRnLguD1eg/8npn61UWdfr9f0s1kqQjC7VKjLPCkbMTn88lCov2ndbDwsrm5GS0tLfD7/cJ0c5qLRqN7ijHtMiU67GwQ7SwTjyKqeS2Hw2HxWq5GbNCiTrPgBOT6PqAXMxsm8fO0t7fj0UcfxeXLl1EqlcRHfGFhAVNTU8jn89IIhOeem07t406PdpfLJUwuz28gEEA8HscjjzyCxx9/HC0tLZbPUC6XMTs7i9u3b2N2dlaIh6mpKbGw0x1FKWGh5EJbKvJn+7F2dkFwaDLkHIeLi4uYn5+XomKe/2AwKIyqnRtJc3OzgOu2tra6W0uz1bbJNHNDqplm3XBLd+DTh2ZM7UJ3/CP7rJtvactRrlUE8gSDpnuEdkeJRqPShItzVDabxdTUFKampvZkzNvb2wVgH4cOX2fsarl0sNmbHYg2WWnt0FGNjaY8iHOm3uTruaajo0OK2LkeaTBNfXutcLlcVeuXmpqaJGPOubKex/y+3r+3e1zreXmYXZ3p/KVrjiqVCvr7+/F93/d994/P9sMPP4wnnngCf/Inf3KoN3bW46TB9ubmJj796U/XXPT1ZKfbi5PBIANox2xRw8UJlM9lsuScELX2+yyy3/WCyP28wu3C4XAgGAzuAeHhcNjC9pDxM4H4xsZGXX7cJ+GkcpiU2tbWFqanp8VeS08P3JyRtbQr7qxnIwLckZQQvJBB0x3SPB6PRQ9qVscXCgVLEafdeefCRu9dahS1LCIUCknGhGlWApmDdAXVrLj5mItovRuWcrls6SKoNxYdHR0YHBxEb29vXQVZrGdYWFjAtWvXcOPGDck0ABB5AcE65Q/mpqKjowNNTU0CkFOpFObm5jA5OYmlpSUZK5yXtra2EAqFZIPz+OOP44knnrDVVK+uruL27dsYHx+X90UJ2Pb2NiKRCLxeLy5duoRLly4dqBCNQIq6cu1jbvczu2tOa0I2MdI+zJQu0CLT4/GI/KG1tRUdHR3ivMNF2fRu5tyRzWZlTHNO55xiumYQYJhh1/WVXQMpvyDA08XnLGbmfa0BOd2JzE6EvH8oU6F/OC0tufltamqyBVbsMD0/P7+H+Y9Go4jH40gkEmhoaDg0kON1ZjGlPvd6A8OxorvG2n1lAaY+r9r7mo95zkyPaDvwzblOvy/Op7lcTgglfY3N60yGXEtPTZ36WQx259W6f/2Ypgj7hcvlQm9vL37hF37h/gHb3/Ed34ELFy7g937v9w71xs56nIbmJ5lMolAoYHFxcU+BWj3txpmuIwAhGNzc3LQY5zMVz4kBuKOvpNWWLhSJRqNIJBLo7u5GT08PEonEqbPfd6vLIqjQzih2XuFmsF0zAThBuDmJFQqFmsz4fgxENScV0zFiv7jb85TL5aTFMdP3jGAwiN7eXvT29lqabpRKJdtCTvNn+wXTtfT/JltkdgzT597pdMqCqm3C+L/0661UKqIP7e/vx4ULFyxZIt3Uh64rwJ0Fju+J8hz9v3qxN38GwMKW6oYz3HiwEEz/bz6fx8zMDCYnJ4UtrVQqcLvd6OzsRG9vr/hO2zFD6XRashbMkBWLRTQ2NordngabdATRaWoCM/4vzyEt/lj0SqtPvo+Ghga0tLQgEolYfI/D4bCQC3TgMM8RGzIBu8Dr4sWLIgPajyEzz8FBHhNk5nI5LC8viySKWS1uKHVHPro08GcNDQ1VreXsllxtt8nH/F7f67ogkF/1PWHX0ERb1On222a9h94oc4PCDCoBKN+DBunaI5pSBN1VmBkBZn82NzfFM5tyFEY4HEY0GpXNVbUw2WWCMjsHDl4frpG1vupNiAmg+VV/7v2KEM2Oi/y8Gujrr9U2916vV7J9+nyaX496bdbjz3ysf1btbzh/mQW0ulmQlpXp1zXfg547dfdqfu/xeHDhwgV0d3cf6TmoJ44NbP/X//pf8Wd/9md45pln0NzcfJj3dqbjNFqmP/fccwAgtlj6pudu25xQ9CDW9kj6KwBhTth6eHt72+KjTAChd+h8Xyao83g8ov0mY0Pmgimp47I/YtiByHrTU/sttltbW3ssCrPZrKQP9evxq9/vRzgcthT1hMNhYSvM1+b14KbIPLi5qgbYTCBk14CGKW3N2Nb63Pudr52dHSwtLWFhYQErKyvS9hrYlcxQcxoOh2teE2DvgqMXG6Zq9SRsvg+zEQUXSb048nt2NCWIYjqe7ddLpRJ8Pp9soPgc9TIpekHVC52+r3i/1suSm77kmhEjS722tmYBKV6vV7IOfr8f5XIZKysrosnnuaKLiVkwye6TLP7d3t4WQLS2tiZATG/8CeCYJdKbdMokXC6XbDZNK0hm8Twej4yhRCKB7e1tub/p4BGNRus6d3cTBMNra2uYnp7G3NycpWaAfvPUJZPA4KGvOVlgbsz4c44rAggTZJtMqdvt3gMyNLjSbhIcK/r5K5WKsKQ6C8WxwzGg1xdtvUgQzb8zGXy6C2k7SzvQxC6ofF0+NzMfHR0daGtrE1DJQ9d5cJ7gmgfsLfgzNeb8qjs08r1r1w4+1u/N3DCY3zNDrD8nANsaGR7cmNuBSYYd0dLZ2YlSqSSOTnavWwv0HvZxreDY0p9PF83WS6wAsLjG2B31asbvuwLJUqmEn/7pn8bU1BR++Zd/GU8++eSZFs8fNE6jg+Tv//7vw+FwWAanmSqq9Zg7fV35zUlHp8U0U0gmnGkafXDSNCvVTVaFC5Ce/MnSkVnh4sCD2nGCEw389WerBtYaGxvrKvg4ytALitby1WKp2QhGt603mUszeB3t3AB47Pe52UWNwNdMK97tZogd8AjETADGBjrU7x4mNHNlFmLp7/XYt2vIoM8JQTTHNhdFbYMViUTE+lF7z5rPXy9wpraVizuZUF2QVqlUZAOix1OtBdnhcAiLz82zHjsETbxH6a9OoMB6hVAohMbGRktX2Gw2a/l8BILRaFQ25SxW1LaNXq9XWGx+VmZk3G63FApqhtjlconUYG1tDclkEsAu4Dh37hweeeQR2UDUCyw4v5HFJxCg7lN3O+X74MYinU5ja2vLAoo5j9l5LvMa6+tL60lmSAgWOK44pvSmmvU5ulOoCYjNx2bw/83W2VpfTUDJgmNNtAQCAcvcxAJ8fdSylK1UKuIqlMlkMD09jbGxMUxOTso8QR9syqwikYiYAnBd4ZrB7/W9YbLWvD5mG29TogFAGnHZ1V1wbja9/O1C142QxNLf75eJ1nUwdgWJdnPmaYBIXSdmEkJ687xf8LyTjLM7Drom8X1pzfrm5ib6+/tx/vz5w37kQ8exge1v//ZvBwDMz88D2D2ZsVjMdiJwOBz44he/eNCXONU4DZ/tP//zPwcAmXjIHOq0tWZH9Ndqj6v9Xreb5VdTw2aX7jTBOJ9Xsyh8bDJyegIhCLDrzMXJkhOtXVqVbXZNVqjaV30Ad/TvesNgpmf5t/ul0ajv1UVJLGTRf6fvDY/HYzvxaEBhB654DTQrrIvB+DPNQpHN1M9DfbHJiHMBNNmxao8dDgdKpZK0115cXJQFENgF+fRHbmtr25MWr+f5zXNn/k4z4uaxubmJVCplAXe022LQTi2ZTMp7DwQC6OjokMYp9H3u7u5GV1cXYrEYPB6PRVdJmQx/xqPeYCEoJRv6M/J+0xIdgkgu9lNTU1hcXLToOl0uF6LRKC5cuIChoSG0t7dLMxr+Pxcpc7Fji2gW87Fx1vPPP4/x8XEBgixabGtrQ0NDgxRmZjIZ5PN5pFIpLC0tWRq6+Hw+9Pf3Syv1VCqFl156CSsrK9jY2JDW6pS57OzsWNxEaG9m50HPa2DXDZCh2V5uLvT9ynqClpYWaQ3f1tYm2Ssyj9qC0pRtHAU4Ygre7j5fW1uzWG6urq7a1vvw/HEs6cJWznecp1taWuRaxuNx2eToTYbdGq9bf8/OzmJiYgKTk5PiC82ND+cZp9Mp2Y2dnR1pb6/XJF4HPVfp885zz7/T9RFmcSGPeh2iKpU7drF2xYj71aU4HI6qQJoFtAeNowbbbKxWLbtKm8V6QrPPfKzXt8PUfZkbGt3oiNJYu4hGo/iu7/quEyd/jw1sDw8PH+jvR0ZGDvoSpxqnkYZ49dVXLUb95oJrBz408KgHwACwAHEN6PVjrVdkO+V0Oi2TJxlxshQcQgQEAITF4MFJXQNwne7X4Bq4o4fj4qofRyIRWwawGgNUiyGsFiYANx+bjRfMn+tiI5Nd05sAvTFoaGiweCRHIhFEo9EDMcTsCrm+vg63243Z2dkTc1Ipl8uYn5+XAkutZfZ6veju7kZvby86OjqOXWqkQ9cxUCakvY43NzeRTCaRTqdFnsEFn+NUj0G21+7o6EAikUBnZycSicSexcVuDJjfUwNdK8xOnzoblc1mRRID3Onuqe83fh6yhXbjX7f87ujoQCwWE5CVSqUwOzsr2R1gV7N//vx5DA0NWewUK5UKFhcXcfv2bdy8eRPr6+uyIdGeyrwmMzMzSKVScr4J8DSwrFZHobM1ZDV1aCkG57idnR2RhuTzeZl/Ghoa0Nvbi0uXLuHRRx9FX1/fXRWHHxU4smthrotbdWhHELLa/Dszy+nxeCxyBbs6EG3vp5lkTbqQTeemT2dJHY47cjePxyPjXK8DwWBQig61rprEAbMGJmvNbCotSunRTv/w/Zq6sa7GDsxtbGzsy9jyvZuWr4dpMV5PHHQ85fP5PSy0ngNJ6u0XXq/XFkBrYH2Yz0rzAru6JtaQVAtupDkWNGH3+OOP19XI7KjjWJva3M9x0mB7e3sbv/ALv4BisSg7ebZoJnOiO2hVazmrF1guInataA+762NFPptYEKCYqX1dmEamVXeo4qTLSZPAU7fa1ZXzZrW3z+dDLpezMC4aDHFS5gSubRR1KtP8+UncClycNAtKUFHt9XUzGD2pcxEjwNcaZZfLhebmZksNAKUGlB/o6nzN+pqZBPOxnabQdFIpl8tIJpOYmprC9PS0heV1u93o6upCX18fOjs7T92TPBKJYGlpCclkEjMzM3jhhRdw48YNcYagzlNrSAHsAeEtLS1obW1FJBJBe3u7LPp2XuQ6i1EoFCwbssXFRSnKW1pa2qPPJhtI2zk2vKLulbIXguTV1VUBD5VKBT6fD/F4HPF43NKZU8fW1pY0LOIGmraivb29IpPgPbS5uSk2i3Sy0IWWun6hUCiIbIMLJ8Gf3cLNsa3nBS1R4+NgMIhEIoGWlhYBW9rhg+TBzs6OpPITiQQefvhhPPzww5Z6g6MYUwddRwqFgnRWJag2ZVoMbs55rxFg252/xsZGqbMhMKUUTB+s6aEsghkCXRvB66WBG+dzzj2cq0Kh0J6CNs75gUAA4XBYNt6sIzBlHXS2sWvkU0uuwU0dx4veJHCjVSs4z+nmSvrrSTt76PHE+83uILDebwMP3PmMdgevxd18Tt0rxPxKGSjxgHZ74WOdAddkHDX1djgmEongPe95z/3DbN/vcdJge35+Hj/6oz8qk5kOAlWCcJ0m09poXSCzn72YHUA3AbkJ3PnYBPi622M6ncby8vIeJooTASduMhblctmir2V7bk7K1NWa1cy0gbLzMNW7XbtJRDt6mBOJHQDX32vXFjvwbv59te/twD/bFpss9Pb2dlUQTiCj9YZc1DiR1vKPtgu9uNppxrnAVZPtuFwuS/qW45Sfj44vOk1MqUlnZ6cUHnGBZPX/ftmFuwk7YLSxsYFXX30Vt2/fFjaaOud0Oo25uTmLxpmZHnZD9Hq9UvxFX2HNAHMDBdzpkMnNly7i4mfz+/0CUtfW1qRwmuCZ3tlaRqJfi04thUJBQJrH40EsFkNrayuampqkGdDc3BxWV1dF/83MFMGPdnjg+9aZJn62pqYmkVVw0SyXyzImWVdw4cIFAe/MvGnnDrKaegGlXp8MGTck+r1oizwWbVJ7/MADD0hXx+OI/cD2xsaGsNVkrLXURgdlZ9ws0zvbrBfhvUltOOUtZoaFa4xd0xWzeyEZarfbLQy22USMAJvzEO/RanOzHtd6jrJrLmTXfIiESjqdtmxM2QqejjjVgrU0vD/Za4IF3lwjThqwAZDNgAmgXS4XFhcX97jG1Aqz6NCULpp1EAeNYrFosazk/E5/f45Rs37Mzj7Srvi3Fo4hHjL/94EHHqjZYPG44i2wfcg4DRnJSy+9hMXFRczMzMhgpb8q02x2WmxOhNqFgSk2glYuthqQ8/FRTigET7pVLScMLblwOp3CLFBOwKI+DWQdDoe0YCZAIMBkZ0TTTki7LtQC41zAOLFWK944jQmXwcWQaWTdKTCTyewZF3xcqVTQ2NgoQEK7lgCouTmotiHg4qrdQOyabOw3nRBQaasrrdePRqNSYFkv463Z/P3kPXZgnUVaGuRrq7s33ngDi4uL8vMLFy5geHhYGOZsNov5+XnMzc1hZWUFmUxGNpe6PoL3HAvWqkk6XC6XsGiRSASVSkWuN5lhaslZpc/XodREW9IVCgXLYphOp4W51HUclUrFUsTJFDJ15LSzY0aEdRPMqPh8PpFBmcWEHo9H5Gg8j4ODgxgcHLQs/mYBm9Pp3NOkZ3p6GisrK8LCctOh9c28N8hOUpOcSCRw7tw5AVj7Ff0dNgi2ySCbMpBq0hg6GtFBh7KXdDq9p0CQ0iAtq6D0Zb/OhbqotZp9HZ1kCGC5tjAr0Nvbi/7+fku3TM7npttQNU9zl8tl6ZqqQ2/mTJs8svh67tD1PpwLSbwAdzYjBGh2YC4YDAqbraUppnvPYUNL2qoddhIWkzghmWSuWzqDdrdSPc47vG85dunUtb6+voeVNrMNpkmC/qrJLm0dabrt2IHxap/tvnMjYYyMjOCTn/wkvv71r0sVOZsX/Mt/+S8PrO0+K3EaFwvYO1C4c8xms5J25YKztrYmns16YjUHu9456omJj7XjgslqczIjSLGzVdITd7VgMYZOI5nMAxlVne4zbdU4wdBTmBOKnfMKGSCd+tQTAzcqepHRukBOBgQPmhHXk9xpSSAqlf29wu2YbdoSmk179vscukjPjs3nudaFRVoDqW0N9caARWosqAPuNGFiYWJbW5t0OKPc6Cg5gnoyAOvr65ienhbm2OVyiWZbL9oEBwTgyWRSitfMe7NSqcjn1A4uHo8H6+vrIm0h2NjZ2dljzamZdM0G6nudmwr9usViEel0WooXyWDSQYQMMNuwE8DQ9lFLutjdr6WlRdK7+pzMzc1hZGQEm5ub2NnZQSgUQnd3d10pas2+MtPDOYFjjhuQTCZj+T3fJ+9lSi/05tPr9VrahTMLsV920C5oKUlgPDU1VbWLrdPpFJmFLrLmPK+ddzhuNLurM4saUOtaAztnDj7WTlDm+djc3BSApZ1MGhsb0dPTg97eXrS3tx+JLtnv90vHSG01SaZfr23c6OnzqZ2w7MCcLtbmedPacE0e8PnsgByvlz4IxJmxYtbG1Ebr+a/aJksHi7S1NrqjowOlUkl+ftiOteYmiDUfxBSci7k5389ZhXOAef65rjC7osGyCah9Pt+RadzvS7D9Z3/2Z/it3/qtqguf2+3Gz//8z+MDH/jAYZ7+VOOsgO39QqdxeGQyGZmw6GtqAnI+pt2S7jSlbwJq3oC9kgU7Q3mtCa/le8o0qN4xM+2kJ0J2xCT45nttbGy0+LWSDaJmGMAeNsc8qL01mVwNKLVeWQNy7taZpmORTiQSkQmYTTvuNlV3mNjc3EQmk0GlUsH09LRMorVSq2RKdcfM5ubmQ0/qdqGr/O0O7a5gOnkEg0HpKBeNRmUhMm0Vtfa/XslPIBAQXfN+sqDl5WWMj49LKt3lciGRSCAYDMqCyoVKj2VuNjnuKM/Q2SqCR51J0DpFXTRtyneAO04amrknKKM/smantVUnmzNxXuB55FEoFASIlctlGSs63Wtq/fP5PKamprC+vg6HY9d+8NKlS6LT599pe8/19XWLLMDO0cXj8cimpFAoyLUgiNQZEmbRSCDwOuhur2anz0AggPb2dgsAD4VClnG8vr4uwNp0A2GGgBvfSqUiWUbeA7Ra5DjRY0U7hnCzROCos5OaKNFsNKOWO4eWnRWLRczOzmJychJzc3MWKWMgEEBPTw/6+vrQ1tZ24LmM8jGztTgfc0xVC7fbvUcv7ff7ZdNZKpUsdSfaOpE/qxfisMaIblxaC891zXTj4rniuDfXRbtNW0NDg4WFtvOUNsFntd4S+vNq5p/f674R2Wx2TwfN/cA0M1Z6g6G7g/J6aEB9txlzXYB7kAMAurq6bKV0xx3HBrafe+45/PAP/zD8fj++//u/H9/93d+Nzs5OOBwOzM7O4nOf+xz+x//4H8jlcvjYxz6Gt73tbYf6AKcVpwG2b9y4IWlrU0NdSz9dK+zAOHfZnPTsQLh+XC6X4XQ69wXjOkx9eTVdMcERY2NjQ4ovqb/TWthcLodMJiO2X1xgyaYRvJhFhG63ew/Y1xNptYPFiyYzzucxbx1T0sP3RusnAli7YhtTD29aIuqf18sCmBM0LcN0QdTa2lpNizq/37+nbT1lAkcdukI9mUzi9u3bmJycRDKZtGiF2TI8Go3ucYw4jJNKtU0uGUeCY6aAFxYW8Nprr+HatWtYWVmRyn7KLfRmjYBXZ2m4OHBTRCkKZTjaQpPFZCyaJvutJVBc3DRTpwuWmXHgazBcLpekzLWuen19HcvLy1hbW5O/c7lc0q2yt7dXMiGaedYyLrL6zHxx7JgFt9pTnrIWnWkjgOFCT0/wZDKJTCYjGyqXyyUFqsFg0LIZ4UaG7KXOxLhcLss8QADi9XoFNHGzw5bjrDngPKDnDM7Pra2tFrtGMrUsOiSw5vzKehXOE7yudiCa86cJnM3NZ615olAoSFfS+fl5C+gKBoMCsJlFrBV2nsc6o1UrqK+1s8ijHOJuQsuLTEkLQSnf/9ramviw67FMZw8tw9GZVNO5SrvksCNma2urFCU3NzeLRNHO2UXLZvjeqVG367mg9fb6PtSuRTwXAPZsyO0ayuhrwWwAgJpf7cDvYUHzYaOjowPveMc77p8CyQ996EP42te+hk984hO4cuWK7d+8/PLLeN/73oe3v/3t+OM//uODvsSpxmn4bH/mM585VEFbrSJGu4JG/VgXIJp6Om0TxxvaBOSaBSM4cLvdloY2+20IuBvWIJyPycJx8iYA4ILLtB3ZIablWSCki3AoR2BFPgD5PFoSo9OLPPQmxSxcoXaN7h66CKQaa6ArrHm+NKtGdqPahkYXtVbbiBF8bG9v7zseCAjW19dFkpLJZLCxsVH1ummbQs2IH0dxysbGBqanpzE+Po6ZmRkLg+PxeGRTVS31r6VElBVw/JfLZTQ2NsrGUzvE8HXo7cqFWDt78O859uhPHYlEZPxrRwQCMI4V3mdcrNlJsbm5WYCsLmxjipmLIZklbRlJ2VOxWMTIyAhGRkYEaOdyOWlww06RTHnTaYhsoJZ2ac22z+dDa2urFJTpcZXNZjExMSEbh+bmZvT19cHlconMgjUH1KHz2mgdMYEumXPeb9wYUlZGCQ51zrwuZB81EDaD8xo3VdqeUd/LvI+1lt/j8QhADoVCAp64IeNYIrDRxAmdprS+nYy89rXWc6IuHNSa5HqACx/ncjmpLUgmk/K5KpUKAoEAEokEOjo6EA6HLf/LMajnPTvrShMs8TEBnWZ9+VlisZjMM/z7aoCu2mfUvzf/j783a0zMehP9GewyYZzPdWaJmzwTMJtyRbOuRo8lc83mnK9rSih55LnWa4x27bArIOdraFMF0zv9qG0KTyq0FIpfL1y4gMcee+zE38uxge2nnnoKDzzwAD7+8Y/X/LsPfvCDeOONN/D8888f9CVONU6D2R4dHUWpVBIXABMEmprj4w6ySpoNJsDmIlwsFi03OAe9ttwDIIBRV6FTk11vEQcZLqZPU6mUsBIsPqO9GBcBDm2m6siCuly7zT40AL9bBoWaUjI7ZCu1VIZSDjONp6UE/MrPrAtbTUDOz1TNBumgmzftfMPrSbaPLBBZIXNy16wO/cH5NRqNIhgM3jXbUCgUsLa2hvHxcUxMTGB+fl7GJkEhAY22DOP75oJJwMvQzh56AeffaKaVwInAlpu3XC6HpaUlOQfxeBx9fX1wu92SSeDiu7q6isXFRayvrwt71N3djSeeeAIXL160aLJTqRSWl5extLSE2dlZkVbwoH+1ZgPz+bx0auSYiUQiePDBB/Hwww9L0WWpVMLc3BxGR0cxPT0tILNYLEqa2OfzYXt7W7JNCwsLkmGoVCry3IFAAIuLi1hbW4PDsdvUg8WP2glDAwOCCDpABINBeDweGWtLS0uYn5/H0tKSaNa5YeKGWjuY2H3lmOPmgQfvP+3AoceAlpdpYMvn0s/N0AV72p+dcxYBjh5HWu5Ty27T7mfVHIH075jd5OaZYJGyQN6n9D7XB8+R6XpiF1pap5ldSgyrxWEIJjN4PTUbrR/bZSHtQmcjza/cEHHzobMa3Jjzq35985yaMjFTmmKyz5z/WTuhrW71Y7351i5Q1Hfr62538L3Uc3ANN/9fk271HAd5Tbv/tYv7TrN9+fJlvOMd78Bv//Zv1/y7n/3Zn8Xf//3f49q1awd9iSOJa9eu4fd+7/fw8ssvo1gs4vz58/jBH/xBvOtd76r5f2dds82Fww6E3+3jgwQZg2o2cXaTtAZmgLWdtfYR5yLEz6r/LxgMCuDjhsC0zNOOD/yMZOEDgYCwSlwI2KiEAJx6z6OMcrls6TBIGYF2KqAnsN05tAMTPE/aao8FnNRYEpRUu/b76fZqfQ6zhb2ZsuRXbcVIdkUvxFoDqQGQncaZoJXMsJZgMLSUSRfiaTCl7c0oS9Lgh4+psWQhXTQaFdaRLF2lsttg5o033sD4+LhkhZhVcblcotnmeyOL3dnZKWxiLXaSFm7MpjDtTTZVu9QQ+FHrbropECToeoRgMCgOHXabYJ/PJ/aeyWQSqVRK3HKy2awshJRGVbv/ed7ICmuffACSYSFrB9xpHc7undzY6k2H3gxyvJjFcJrIMM+vlv3oDRawmx0xLTB5aPBNoMvzx00yZUAEP3qzSj2wDhOMmD+r9TdaPkjZkC6m1cWDPB+1XpuAzyxyM+9jzaba1Q7Ysa7sBWDec/ocalDL/gS81iRb9jtHlOqYHtJ6vaFEiHOOyYTvZwRgFxzvNBjg8zGzw3tZFy7yWun5jNfL3FjrIk1df2VaJdr97Cjrcc5K3Hdg+53vfCd2dnbwzDPPVGUmi8UivuM7vgMejwdf+MIXDvoSdx1f+9rX8KEPfQherxfvfve7EQgE8Mwzz2Bubg6/+Iu/iB/6oR+q+r9nHWwfVxw1iNep2XrBuBlkG/QEzQ5zwB17KLNYRYM2MgyceAnUCEqoi6W+lxMTgXcikUA8HhcwUCuFy9/rr9XSm9UOfe50S2kW3lHSYtp4mbcyteqcpLW2085zVadQTScX82cEq7yOXBA18NHXWhcc2unzdCqQh11oAMDHlGjw3FKLy4WUQEcXUO7s7Mii3tDQIJsxLlhciAleqI9kaEaLOlzKqBwOhzDpzLY0NjZKwxm3e9dbnC3ODxtkvicnJ7G8vGzpusdxzLFOGQE3RNzscvPKRdt03OE5I1tNGYfD4cDk5CRefPFFJJNJsU4MhULiXMHzbnZ4ZBaCz1MqlSybB80mszArEAhYUvaaQQYgBdVa5sCvJmvI19Vpe2bNeG01mOa9ZPo+t7S0CPvvcDiwvr6O+fl5LC4uyv1qjl0AokHn/7PgjJIgsz5GF0NWcwRaX1/H+Pg4pqenxcGG9yezglp/aydt4OvYuUvcrY1ctfB4PBYbV1P7r9eIWoy+LhzV2TnOAbyPdab2oFkDbkw5tnW2gudVZ4A5N7DJW63gfRgIBOB2u4VB11aW8/PzezaN+tDyKxNY27HBHBf7gXL+rJpM7ywEz1dbW5vUmpxkHBvY/g//4T/g4x//ON7znvfgIx/5iKVSG9jVWP5//9//h7/927/FBz/4QfzCL/zCQV/irqJYLOI7v/M7sbi4iE996lO4ePEigF3G5Pu+7/swNzeHL3zhC1Xbep4G4M1ms3C5XPLa++nTaunazO/r+X/9f7WAYDXWrdbBScgEcAQs1ElqZoqLhU7lcrfvdDoFQGjpg9bG8iCA1JXmmp1imIurZqj0REvtG4G5XVrPZGjsJBdHERrgEoBriQEZ//0YGa1314uWmXHQr6mdXEzXDn2ttCSDjCLHAMePXVqR59vOf9WONeNjYBcsaekOxxGfVy9I7GRHMKVT3pQ9uVwuYVK54SEjrqUpOn1Lucjy8rKAS7/fj/Pnz+OJJ55AS0uLxeOdTFs9KdzNzU1MTExgbGxMNp0OhwOJRAJDQ0MIBoNi6zc6OoqpqSlxWyF7x+59LS0tcg/ZLcjURbNAMZVKIZPJ4NVXX8Xi4iKAXdatvb1dMh5s6ENWfXBwED09PfLeaXF3+/ZtjI6OYnZ21lKMTEkCN4DmJo9FlQRNHEfcmOuD2mduLpubmxGLxdDS0gK/3y+uICwWpkWjLposFot7uqZWk51xQxuLxZBOp5FMJrG4uCiF3+ycp+dDh8MhdSuRSAQtLS2WbopaVqTnNersl5eXZf0gsOS5px0jcKd42K4jIrt3mvcxz+9+jj52zj3MuGi9tymBcrvdsgHUGwB9fsrl8h6ZjTnHHwLGyPjWhehmUboG7hoAc/zpuVDfo3bgXWcfdeE2pR92zD9/Fo1Gsbq6atlEcs7Xhbdadsqxu7Ozs8cGlPNdPfabDGY3agF0XWtRS+5RLZhB0jKcahIhnYXgfNzd3Y1v+7ZvO9Rr300cG9heW1sT0NrY2Ihv+qZvEuA6Pz+Pf/zHf8TGxga6u7vxmc985kjb4NYTzz77LH74h38Y//Sf/lN89KMftfzub/7mb/DhD38YP/mTP4mf+ImfsP3/kwbbW1tb+PSnPy2Ff2/FbnDCNkGyw+Gw2GrZ6TW1Jo5uELowkOw3CwM5cVVj6cmgagBOxpgTGYtNNKNiN3mbrJUueKzmSGI+h5nupgZWT7YNDQ1YXl62dPXSBZAs/NMSDXNDpr/nOdBAXFsgkiEnaOXf2HUwYwGoTs9rbW+trIfWJ/p8Pvl/Fvitr6/LIq1lDrSAI3Po9/sRiUTQ1dUl44GLFIE1z5E5TZKJ4kFt8szMDBYXFy0uNtS+k91yOBzipqIBPc+j6cwQDofR1NSEtbU1TE1NiTackqjz589jeHgYLS0tAHY37qOjoxgbG5MMCLsT0hveZKnK5bKF0d/Z2RENNjcYbrfb0pjG5/NhaGgIDz/8MDo7OxGPx1GpVKSYdXZ2VnTg5XJZGOqdnR2srKxI3QflDWTSmQ3gNdRpfZ1q12wh73MyuNrZgyCHTbO0vlbb5hHUajkNbf+4qSVIJHDkmDWbkVTTIm9vb2NtbU2kMtqeVUteOL+QUNAbImZazHuE45m2hdTds26CvsdkZu8GkJTL5T0g2iwip/uLJjxMJw+32y0uLpoo4ZytPd41EWTq6TU7X83FSc+zBMCaDNDX1TzqkZEwU2Lq1o+iEHE/bbuWxRGMkrgC9nb85XXhGmb6sWt2ncXF5uZBb3zMjKQpyePf6mvM98D3rwtV6w2dYS0UCrh06RK+8zu/82An9wiiHrB9qNxQc3Mz/vIv/xK/8iu/gi996Uv4u7/7uz1/863f+q341V/91RMH2gDwwgsvAAC+8Ru/cc/v+LMXX3zxRN9TrfD5fOjt7UW5XJaUz37FAfv9Ta3/s6viNb8exesd1/+2tLRgbW1NXDRMhxBO/PW0tfV6vRamwenc9eWlAwqLGmnfRfCxvb1tmby4CFKGwLQvJy292GirPTP9yQXVnBw58enMgE5XcrHW2Qa32y0LHhl7zY4RlPBcm5X3mknU6XRzAXO5XNjZ2ZGOYsViUfS4bNaiU+Zc8KulN4E7XuHcGLDIkC2atT5fX0syi/F4HF1dXejp6UFnZ6dMhvPz85iensb09LQsRDs7O0ilUpIu57khQ0kgSqcPgkaeo6WlJYyNjUmXyfb2drjdbrS3tyMWi8mYWlxcxMTEhOiRFxcXRW9ZKpVs/WGLxaKAfp5/j8eD5uZmxONxtLe349q1a3jjjTfE85mSDkpkOjo6MDg4KF05HQ6HFPLy/GoNLGVfHL8Ox65r0cLCgsXmMBqNYmNjA88995y8X+2wwA0MGX5mW4BdD+jm5mZ0dXUhHo8jHA7L2CPo4SaKGzYNjCnL0e4rbrfb0hwmmUxKYWm1Bh0sPqNDSFtbm1gH5vN5rK6uolAoyPuNx+NV5xIt/XK73VhYWNgD4HhvcN6hBp+NXOj9rYvoeG8yq8fNOj3+KUni82pQx/tkamrK8l6ZadGWgfrQTK52HyGTSrJDz0UmkCYrrt8zr5920Kk2T1MyaEpaTN2xdtaoFcViUd6/aU/IBnHAnW6GJoDS7LTOYmmZhS6srZUNYN1Jtb8zf+73+/f8v925srNkZQZZa91JYNXiWjl/ECwDd8CxHfOsM0EcY3bFlHaHSR5pxxpz08T1kOOL62dTU5PU7pw0s11P3HW79pmZGVy9ehVLS0sAgLa2Njz22GPo7u4+kjd4mPjX//pf4wtf+AI++9nP4qGHHtrz+0cffRThcBhf+tKXbP//zarZvlei3vOkJ1c9qdoBtWpBtouLCCcWNqjY3t6WidvhcAiDptk5XWgF3PGl1fZieuLSQJePCZK17EcDZ7IKmkloaGhAuVy2yFd0ipKLim6OYceoc4FkgZ5ZkMbJm+8ZqK6v1K4FbKZCzSpBORewcrksaX4yqlwwNKvICZegifpehvYKD4VCKJV2/WwnJiZw69YtATkMl8uF7u5uDA8P48EHH5SmNebYGh8fx8jICNLptPy8vb0dFy9eFNmEDjLtExMTuHr1qkg7GhoaMDg4iObmZtH6kh1eWloSxofyAALLUqkkm5LNzU3Rrno8HkQiEcTjccRiMVm0+B64mdRd45iK1tdSb6Q4jl0ul7iPaOZLAytuwnQqm5vOYrFoaSlNyYlmtTXLxiJPjg8ytHqTWCgUsLKyYjn0hpbvo1QqWRx8OKbr7ezHsaHfv1mYzCYxbrcbMzMzFu9pzjnmJlkDFW6at7e3LdIWzeLyOmtJAmU7DQ0NezIjjY2NcDgccq+azZX0JsF07iFw0WNZZ7j0xtnOLUPfh3ZFlrFYDIVCwfLzelxM7KJSsTbNMs99rX4CDNo5mjIPfR5PI+zWvHokP6z/sTu40dM1QTx/bDBXTRqqswR6g831Uo8dhik/5YaMf2tuMmqx3Hot024/ly9fxk/91E/dPzKSsx4/9EM/hOeeew7PPPMMent79/z+m77pm7C1tYWrV6/a/j8ByklFpVLBc889Jw1b7oYVBlD3bvIw/3PQvz2t919PsGrfZDr4eHt728IkE3Czza12DtF6WN1QwNyVE4zojoBMbWuADdyRcXCxswPM+vk0ODY7K+pDa+pqMVMHqb5noSobQOjNDRdynhOeUw3ItTaXEzJBBSdSgopEIoFEIiHMdXt7OxoaGuT1dAtiNhPhgsJugwAsgIXONNwwcdFwu93o6urCwMAAenp6kM/n8cYbb2BkZESyBm63G0NDQ3jwwQfFw72e83Xr1i1cvXpV0sOUAVC2wUgkEhgYGEA0GsXW1haSySTeeOMN3L59G+l0WsARgWlTU5NsKJhO5gJKKzcumBx//J5jqlKpSDaB14wyF7NYWbu+8Pxr7TM1t2SgKAnRBcoez25XyN7eXgwODgpr3dLSIkVjvH/YoIeMMJl8U+fKDpAsdG5ubq46r3NjbrbX1j9jJsSUtBC4ELiaTK7e1Hq9XkQiEZGnECSHQiHJfoyPj0tDLx7c6BSLRaRSKSwtLck9pZlFDXiowabcTs8X+jpr2ZjOamlQbSdpIyBm5oGfRfvv6030UXjwc6PIe9l8vN+cxXHGc66/soD0LIZ2MtEOKdXcU7S++TBBWQ3taXURPLOmpqadY4VWnjpLz405rxM3uHrDbm46eZiA3azXIQlVLpcRi8Xwm7/5m2eS2T7UyKJeS1eYm0G2zq716FmPTCZzoq+3ubmJF1988Ug8R98MUc95MoG4nTzCdNswZRrVijJ0uoyA2NR4EzTq98OvZHfJ4mgpS3Nzs1jLaXZPTx56cSRbysnWLDokYNKvr3+vU7JkqFiwo9kpuniY58/uZ3pzwp9xPmBKfW1tTYrtKGMgwNeHudi7XC5cv37dVt9OkMzJV0/WOvUI7C66wWAQlUoFPp8P6+vrMoGnUikBqw6HA2NjY/i///f/Ym1tDeVyWbyJW1paMDw8jKGhIWGOD5KZam9vxzve8Q784z/+I770pS8hmUwC2LWhPHfuHB577DEMDQ3B4/FgfX0dL774Im7evIm5uTm55qVSCa2trdJxkCyltpPUGnYe2sHB7/eL7pzzdSaTgd/vR3d3N5qamtDX14dgMCgdRzXAp8Z9aWlJgDXbqXPRpTyECzjHNTeoBLs3btzArVu30NLSYpGXaE2tHT9kdg4NBALI5/NYWlraUwOhmTjzMcePLsJkBokbEF2UZeelzCJcZgc0Kw1AwCH/hnKecrlskVn09/dLm3Rq19npcGlpSeRQlKtsb2+LHaNmDQGr5arOhmmdcTAYtGxQeO20ltcsFKRlpp6bOPbMYFZAH/F4XLIdJAJIZmhWmo/3A4+sY2C2RBeC0hnHLiqViki1jiu48am2ruiiP55ffmUx72GDpEU1H/FaX82gBz5lWswmra6uWhpCmcFsq954BgIByZQRT+rxpokYjulqmdV8Po+BgYH7y43kYx/7GH7rt34Lf/qnf4q3v/3ttn/zla98BT/8wz+MD3/4w/jABz5w0Je4q7gXZSQrKysCjsx0i+ksoh/v97fV/u6g/2NWfNf7P0f9/kul3VbK7PingZ15aPBsuprYVdRXc9QArE0G+JhBdkgX21TT3unPwe8JvnXxEvXOTJnX0jfr0Fput3u3o59mCvTjgwTZumqOJfrnJhNhyne4QJvnjQBMO4voFLdubEIgoTc+POdatqIBBZ0pNHOtte38f62TN1upk80Nh8PSfjmRSAjjaxZi2RVqEaDOzs5icnJSPht1zbqDIHW92k2EGwQCZPr26lbTACwMkMfjsWQ2WLTJ8cBNFSUllBl1dXWhs7MTTU1N6OzsFOZ/a2tLZDR0w6D3PQEOn7OtrQ3d3d3o7e3FwMAA2tra0NjYiHQ6jZWVFaRSKczPzwvQXltbE+YOgMXpQC/QdNvgBoFjv54lTUsozK/1sIFawmE6lbjdu11hmRXQ91wul8Pi4qJkXcju8R4gW0zAr+cgjk2OMa1hBe4QXHrTTeCi5yaCGOBODYYZzPrQsYf6bgJmrfdmxsGUCOgiUl5LDdRpS0jrP557LV0znWXITEciEQHU2lXlJMg9SjOqOWbU+nrY4DxQD0C2+1m956RSqYjdLOcCZg+44dnc3JQ11VzndGEmC6O51jkcjj31PnxfWmKk5ynOVfVGf38/vuVbvuVQ5/hu4tgKJP/+7/8eiUSiKtAGgLe//e2Ix+N45plnThxs9/X1AQCmpqb2gG0uaJcvXz7R97RfsHr8zaLZ5oRrZ+NjpsH074HdAhY+ZmgNs5aUaHBLsKSbSmhwV+1npo5Ug0xOaCYIdbvdsgvnopPNZqUTXyqVElChNdtkVbXDCdtzU7OqmSeyg3Ybl0AggPX1dduNlq7itks96sfaXo0AzWyxrH9vylHsLK24OHLBrNVunVEq7XZYJWNLMGNqYE0WTzPvBP66et7j8QiQMVk7Pa4o/SkWi3Idr1+/LhkCfiYWqemMCg9dgKg3clxkHA4H5ufnkc1mhVXV4ENLgPSGUG9Q+F6pY2ea3G4ccyFeWFjA+Pg4isWiAK3m5masr6/j61//urDo6XRa6g10aFDmcDhEXuHz+bCwsIClpSVcvXoVDodDAKouvqQcKxKJwOPxIJ1OWwqKeW2j0Si8Xq+4pSwtLYnOVrevd7vde+zmtM0ox7S54ePhcNyxFNV6VJ47bsi0rEOPM5fLJQWhLDIkeNEWmPxbM3NF+Rivp3ZB0mPB5/MhFApJRqytrQ3t7e2yCQHuSC9WVlawubkpz1upVAT08h7gOWfGgfc672stUdPkgAZEGlzp+ZcspN40shBVF9aRcNGfW7s4cRyS6c9kMpaCRd2avJosRMveqq1B1b4eRF5nFybLvB9Q9nq9iMfjMufyOumMrJ5j8vm8FASbGVfea1oupe8NykbqCfN9NjY2IhqNWtZEBsea7qDM88kMEHDH456NjpxOp6V5TzgcFolSOBy2yCfd7t3+BafBbNcThwLbdiDWLs6dO4c33njjMC9xV/HEE0/gD//wD/Hss8/i3e9+t+V3zz77rPzNW3H3UQ9YtpvA9ISlmWjNSOvv9VcCWTM1rLXLpj2eBtBMjZrNG/Sh/+YgXqQHCS7UMzMzmJ2dxfz8PJLJpIVVyOVySKfTkjrW75WFY+3t7UgkEuJIQIatu7u7rqKget6nXZe+1dVVSSWmUimk02lLqpBBTaf2KCZ7p5tDmM4DTO1Sm5tOp6UQjVajHo9HOguyU2KhUNijuaXFIzc4Ztqf7E06nZbsCdkVZhiYyudiRr0wwdTGxgbm5+ctwIBpay4yuhhIj0UAFq0/QTAXIqb+ea/ozQLPuVmop9kjgnMuZmQVd3Z2MDc3Jxprh8OBWCwGt9stFoyFQkFqGAqFgrD72qqQxY1mVoPWixw3ZLsItDTr7vP5EIlE0NnZiYsXL6KxsVFsAtm0p1AoYG5uToC62+22gA9+1ZIMfegNnWnPps8Zx73WjnITtr6+LuBfM3ymCwedjFhwqje8eoyYlmoa3Ovrx3PK80o2eG1tDdPT0zXvYYJ0fjadXdMEg5aYcC7l63Pc0InELOrkcwLYw1wy+Dl4PVgrEQwGkUgkLH7z/Hu+7vr6OlKplNRK8HfVMpRcY7SWn+9HkyjabUO/R7P2SP9Mb3pMpxUNAHk9qX/XsiIts+PmXmdiNYD2+XzIZrOW7J35voA72Q1zvdVzSz1BWZ7eRJuN0Die7DJ31dZfbb3I2NnZkQyiPjjOGJxD6O/vdDqFsCEQNwvZz1IcCmyvr6/X9aGCweCJ658B4G1vexu6u7vxv/7X/8IP/MAPWJra/MEf/AE8Hg++53u+58TfV7UoFov4/Oc/L8zbcYadFIGaKLvDbuesvzdTtqaFkSnPqDYZanlGre8JqDmxkz3Ui6jWautDuyQQQNZzfk4qWEhEhxP6YxPUbW5uYmVlRfTNBBuaVdeglXpXXemvq/2Zlq1XnmIWkNqlRZkO1C4nTIkXCgUsLi7ayljIpOmmRvpvOBYo4SDDEQqFsLy8LB7UOkVuvi/aD3JBIsuztLQk3seaKabjx9zcHKampvbIkQhCKAshO6dlMgxzs0e2mYC6UCjItdFadcpw8vm8LPKhUAixWMziAgHcufcY5vf657lcDktLS8Igl0olYSppTcjrwM9I9p4ZFu3VrLX5ZBy5QeM4IeA3JT9kqvm/qVTK0jmStn/Uh3KDQ+BJFo3jjL/j82kQyfHp9XplU8Z6BF2DYY5Ns77DrFvg5oHzI+c4Zj04drVriFmLwOtr1i3YgUhdr6EdYfg9rwX/rlqY0rdqEhx+Pj7W0hay8Hoe0UXdevPA+5NOULRwNTNv+r0Bd+xo9TiykyXqNWY/OZF+37p4VMsczK867KRw9bzuYcLj8VikbqYsUb+uNg8w3aB00avdZoFztwbEnAfIGFfbjNTapJjva78NDb+SjTe99rlpN1/j3Llz+MVf/MX7p0AyFovh5s2b+/7dzZs3667OP8pwu9349//+3+NDH/oQ/tW/+le27dq7urpO/H1VC7pieDweSaUdJvSisJ+e2TyqPZed5tgEzrzh9QSrwbJ2ldC/06BoP52rnqxDodCeKuV6z/PdnN+TCofDIWCju7sb5XJZ7AKpmSPTSNaWjgjr6+sysRKEaAcSs6CZshrTBpDjiMCpmk0ibdB4mCnlakHGU9vPkXUlQCTI0ylzpoUJ+O1Cjxc+D2AFCfRNX11dFYbM6XSKa4JOM1PTrYtE9XNxkW5sbLQUAhaLRUml833w/JMZN6ULtG7jeWxoaEBbWxv8fr9cA16jzs5OtLe324IA3pt0ItHuMJTi8HOwQFKziOVyWdhyh8MhOmm+lmZxTYcEDTb4mamN10wtXyuXyyGTyWBlZcUW8PI5CAq4CeIcwOyE1+tFMBhEW1sbXC6XMHl6I8TrY54vLTvS4Mq8xmTzeGgZzPb2tvwdfZej0ajIOg6qPz2K0OOY8wS9yHlolxp9mMXedteEOnpeF7sNFckcSoI0cHe5XLIpAe5Yxh0EuGoQaUoBNZjja+r7w5TWcUwyzNevtr5Vq+WxA776e/2+eJ7sSCqOZf2/1WR3Ws9vd1T7P+IQ7YR0VoOSP9O5xJRgnaU4FNh++umn8Td/8zf4/Oc/j3e96122f/P5z38eo6Oj+O7v/u67eoOHjaeffhqf/OQn8bu/+7v4/Oc/j2KxiPPnz+Pnfu7nqr7n0wqfz4f3vve98Pl8UghlV+leKz3Eg0E2AbBqvGodujiRN7N5k+r0kFnMxonJzvLKTpumd9P1LEJ8/ubm5jOhyzoOBqPe5y8UCtLEhC2b0+m0pRixXN71VNZpTab++TOm6gnGUqmULIjAnQpyemLHYjEkEgl0dnais7OzattqM3K5nMgByM6TSaUkhOCE3fvYYZEMJTcU1GnqlCPfP+8DakPN88n/1b9nqpR6a+3QwN9pll7rWSnpWF9fFy05gbPL5RJ9r9aXMitAkKGvj763eK8QmIZCISlgKpfLSCaTKBQKeOCBBzAwMCDXWzdKcbvdkgnI5/OYmZlBuVyWGpGHH34Yzc3NsulhOJ1OxONx9Pf3I5FIyKbs9u3b4j6QSqVEG6oZWAB7Us3FYnGPrIcMtQmKtbaT8xLnOTLRnIsItoFd0JXJZCR7QEkVLf/IlHFh5vypgZqWTVBLz3EajUbR2tqK5uZm5PN5GXu8ziwe7OzsxIMPPlh31ugowo6N571iNvvS3R0Zes2hjaFmbLUMiNdIs/3MzvAc6o0xwTfXIP2aWgpi6uT51dwsa4JnP+bePCd8DcpV7IgdvcnSNRu6hse8V3WGghkg7UGun5Pnw66AtFr4/X5sbW3Je9eFhCRT+Jl0sapdLY9+H/r7an9v/m21vzd/bz4/54b9/n+/5671t48++uiJb2jrjUO5kYyNjeF7v/d7US6X8d73vhfvfe970dPTAwCYnp7Gpz71KXzqU5+Cw+HAZz/7WZw/f/7I3/hxxkkXKZbLZfzDP/wD8vk80ul0zaplU9ZhJ/HQl5TAudZOV+vLdJBpM3WNOt1k/p7g4DjjzVRIepDY2NgQQMsCzLm5OYu3KXf/TFfv7OxIIx6OAy5gZAq1y4IZHo/HwmzzKJfL0nAlmUzadkdkF0AWdQWDwT0NNvRX3YXPDC72ZittsvMEk5Q+ORy7hXpsaW2nZdSpVhbh6SIdv9+PTCaD+fl5KTLzeDyIxWLo7+9Ha2srRkdH8eKLL+L111/H7OysZCQoLyCTTWcNWp9xw6QLx3gfOxwOYcbJBLrdbunQyfdLsB6JRFAoFJDNZgV8Dg0NoampCTMzM5b5prW1FQMDA+jr68P29rZsjlZWVsRhQgeZcd11sFgsyvmm7luDXC214ByjHW00+OG41hkQbjY4FwJ3GFHOg3oO0uDHLKgi666BAEGknjcdDodsEJjtYLYlHA6jt7cX3d3diEajcLvdIgczwVw9j+udP/U40RIvbgLMTYydztqO/SWo1BITAkQNtrVLkPZv1+wrN+t6/SDA5kaTjix6HeL7qFQqAjBZk0CAqecju89oklIcb6a0wi5MrT+vi85U8Xzoc2Ey01pOpF+Tm1dNPtFnn8WusVhMpHJ9fX3Y2dmxnYPfijtxWtjgWJva/J//83/w4Q9/2BYYVioVNDQ04KMf/eiZY5HriZO+WFtbW/izP/szlEoli6UdJy69g6Mko9qu2g40A3ccD6oBZLvvTRu3sxJvge36wu/34+rVqxgfH8fk5CSmp6eRTCYtFeFcCGin1draiq6uLgwPD1v8m7UcQVexA3fs/XSDCYIgnQ2howq10z6fTyrPtbZXBwEVAQIXUJ0q5H1hOm0UCgXR+fIecrvdYkPX1NQEl8uFUChkAZEEXLpoSUc2m0UymUQqlZLndbnudFZ0OBxYXFxEMplEPp8X8NLQ0IBEIoHh4WGEw2HxwzYbULAQye/3S/HQ6uqqFKGyQE9b1vF8eL1eOb8tLS3w+/0iV+H79Pl8qFQqwsiGQiHE43GRiRBU8jlZEEo5E+ckLReoZbmnAUU9bbe1ZEgDaKbTNzY2kEwmkUwmRQrEOgbKWripKZfLAozZSTQSiYhenr8jENeOQrlcDslkErOzs9JEhoyw2+2W4lAWA2tQGI1G4XA46rKiM2UDWrttdnbke9C+9KbkQKfSmSHRmwzNzFYLu0xLLVkCZU+UBK2trYmjjgbrLMjkuYvFYiLF4H2u5yc9fkwA3NLSgvb2dvG9p0tFtc/FDSezS7oBk7Yl1TJJykq0btgsjuW9ZLf26uvC4HWpR97B6xcKheB0OmWjwTmC442bVK2RNwsUqz0+i2v8YeO+BNvArivJxz/+cXz1q1+VCtF4PI63v/3t+MAHPiAWfPdanPTF2tzcxJ//+Z8LQNDaRrtgmruWk4b5vanVvZfjLbC9N0qlkvgW82CqUgfBB1kjduPTIJrgVbeC7unpQU9PD9ra2hAOh7G5uYm5uTmMjo5ienra0hLZBCVcLE09oy5GBO64BmgNqGaX9WO7e4P/t7m5iWQyie3tbfmfeDyOy5cv48KFC3vcZfYbT4VCAZlMBjdu3MDIyIg0DuHCzFoLMvi68YrT6ZQNDP2qtVazoaEB29vbSKfTWF5etizkhUJBbK98Pp+FpdPa4kqlgnQ6jaWlJQEpzFBoZpmLMFk2pp9NeQU3NnoTQ3ZYFxrz9bVzS2Njo7D/BEC6RToBNS3ctB+z2bnRlDpUC+rK+d75v2RNl5eXsbi4iEKhIOegqalJNhh6POzs7Ig8K5vNCrNK54zOzk7ZlPB9rq6uSlEpzyN/T5adDDLHfqVSsUg2TM00gel+YRaSc93Q4M+8r0wnJgI43WxGt7S3K67m/2m5iBmVym4TIGZHlpeX5T4j++xyuZDNZi0+6jq7xs24meHSmSg9LzCTQ3vURCKBeDyOlpaWPXNGpVLZo1/PZrPifMQNg5ZqcoyZ18Z0kdGAlpIqzmmmIwllcvrzcTPFeYRj2S50fY6dA4+dE48Ok7Dbr3bKLHo1AX49m7njivsWbN+vcRoykq9+9auyu7eTa+jv3+yppDc72K5UKhbP7pWVFaTT6T3axUAgIGCPB9PcOra2tkT3nUwmMTExsceCkMwzWRoCLEpQAoEAwuGwsC2snuciphvSaBbXLGgyF0Tt4mC2fyaDS9342NgYbt26JQVwTqcT/f39GB4eRiwWq3o+a42nZDKJW7duYXJy0lKgSk2w2+3G8vKyWNO53bvFca2trTh37hx6e3tlUediSqZae7CThaUMZnNzU66n1+sVu8PBwUGcO3dO3KB2dnYEsGUyGdy6dQuvvfYaZmdnhY2mA4TOCLCQTTtpmFpxburp4BEKhUSXTFswyjO004bu5Km1taVSyfL56ll6+Nm58dP2hjzoAqIPtrVPJpMyrrkZJdNPIE2tfrlclqwBNzZkYSlbMN1AKMnSxbMEggSFdm4iZLCrOV7w3PEaaICrfa41g6kBt1nceZzspR2rWg2sARBGmVIX6uz1RoE1IpxPGhoaZIPL4uaVlRWsr69bJGYaBBPMUoalNc7cSDBzZ5IBOjgGTNs7XnfWcOj3wXu6XnhF6Zdmq7lBKxZ3O2yyRwg39GTkq40vu/GmWXQ7cH6UdQamfLWa6UE9oN7cBFR7j/cd2P7bv/1b9PT04MqVKzX/7pVXXsHk5OSZstmrJ04LyL3ZQWS98WY7T7T804VpdiyHz+ezFBieO3fO4rxAMFdND02pSCqVsmi/2Z1QFwUy1a0XCWqam5ubhdHcj1UhKCD404wPQX2tzSUX3o2NDdmMhkIhPPTQQ7h8+XJdk6A5nvL5PEZHR3H16lXMzc2JNMbj8YizRCaTwdLSEorFooDMaDSKixcvYmhoCK2trahUKlI4qdunE2hqpozFgPysLOZMpVLSYU8DPd24g5KDdDqN6elp6eZIhxVd5EpWTbsBcVEksNZaUqfTKde3tbVVZCgkBpha15sou8YfBNxmepvghQ1p+D1/r+30zHoVzSTvF8zirK2tIZVKYXZ2FgsLC9jY2NjDIIbDYbS2tiIcDkvmwfTU1tIAZmlMZxltT8bzzfuGn49sPwF0OBxGW1sbOjs70d3djc7OTtHyHyb43kyHkaN4fBQRCASQSqUsbdl5f5gAnFI3brR9Pp9Ihvi/mpXWJAHDri6J45sF4PF4HG1tbdKlNBqNWhj/ekPPt9xYm/UnJktf6zzponKOF8p3CD5Z6FrPa9gBcp3JIfg2gbAp79L3JjeXxx3VAPvFixfR3d197K9vxrGB7eHhYXzv934vPvrRj9b8u4985CP47Gc/ixs3bhz0JU413gLbZzvu5/NUKBQswHplZcXWD9ztdos7Apu5UG/Lw+12i5yCYNqMUqmETCYjQJBsk7bjYpqSWRVdLGYHrsh0R6NRkZ90dHQgHo9bmND9WtCXSiXRguuFOJPJYGJiAhMTE5Zzw8YYkUhEFlW3220p3CSg4/dspLK0tIQbN27g6tWruHnzJjKZjIBS2reRHd7e3ha7PLfbjc7OTvT19UlfgXQ6LYyqBmoa8BDcckFjBssuisUi0uk0FhYWsLi4KIWTZMuZQSgUCsJakwXldaGeuKOjA729vcJAh0Ih0XDrxj7U3W5vb1u0wOz4psEQAZEucNMODvpamBpVu+tvp7/Xml2CL74vDQC1M4F2raDem5kJZhJyuZyMedYaUXJBCQ9BN9llLbuh1SHBCgE5ZSS8FlqbzufQ9nbcYJr6dm6+GhsbRZbDr83NzaeW5STYulvQrtvaa1estbU10eNns1kpPtT6fRbXapmLeT64seO1Ma3i9FjRRZ20NdTglvMF5xEeLDrWIPgg7LBZ2G13OByOuntwcGOtD11/xfvBDvjXCweZwbPr2sksPIuP9fxXzdihmtnDQTd4LS0t+Cf/5J+cuA792Nq11xscwG/FW/FW7A3qrMkgLy8v73Ht4KKgJ346vuRyOUxMTOCNN96wnST9fj9WV1ctjgS6NS/lHcCdpitut1tadZOx1TpjPZG73W4BfalUCouLi8ImEaRNTExgenpagEtPTw8GBwfR1dWFWCwGv99f9fyQzQqFQgB2vbVv3ryJubk5hEIhXL58GTs7O9JBkwBHFzsVi0WsJjxQPAAAwrhJREFUra3tsYpkoR0tBOfm5lAqlQQIhkIhDAwMYHh4WGwWtc8t07culwuzs7N45plnLGCaoZlhpsartaY3F0MNJMvlMkKhEDwejzCz1DmzQJLv+/z58+jr65PswtbWFqanp7G2toZcLofXX39dFkXNSOuUO0MzV2TjtXxCM7WmDIVMudbT6gJvXWjJRZkAiEBA/17bvPF5qnWB5euWSiWxxyRYYefVaDSKjo4OJBIJ2Zjevn1bahC4saNkSBep8d7o6OjYs5kLBoPo6uoSRxPtGmIeOvi5qeHf2tqSTUC12hyCb32w+PM4gxZ6h6kFok6fvSXm5+ctsoj19XXZlCcSCSlWpqsSrw3vP12YyMwEHYPomW8H1LjZYkaP0hZ2U+XGk+CexZ120gvtuc05lABUy6BY2KhtRrXNYWtr6x4pBQCEw2HMz89bWHKdkeT3vJ/z+XxNQorzEucmNsnS/t9kq01mnhvuWr0OGNo6Ux/c7B8kW2Bmaex6igwMDNT1XKcRx8psv//978fNmzfxwgsvHPoNnka8xWyf7bgXz1OlUpHGHSsrK1hYWBBnENOiS0/onAw1ODNtuHTDAx0+n08YT+1bbEZDQwOam5vR1taGRCIhLiRmutJ8H3axvr6OlZUVLC4uYnx8HFNTU6L71npK2gq2t7ejr68Pg4ODe1hpxvz8PEZGRjAzMyObimAwiAsXLuDcuXNVGWHNjCeTSczMzGBubk6OlZUVATwsaguHw2hpaYHD4ZAungR7PKdcOM0iIO2Bq7tEEpRQ18vFiteeCxgzBCxs5SLK3/M6k/0j8NWa7kAgIAsR2e2GhgaUy2Wsrq5ieXlZ/s/p3G137Pf79zSq0ow1meWmpiY5By6XS/TiBCssjDXZZb4WgbhuUKMlRvy5llzoDQg/MwFoNc9jnj+64vA1XS4Xmpub0draKgVzuhBUjxtuglm0ynPT1NQkDhg8t6FQSCws29raJOO03xxVLpctwFs3rNKNpDQLqaUItIIzNxuNjY1SHEhf8ObmZrl+xxm0/OR71SCaj7WsIxAIVGVs7fT5PBobG5HP522LL3U4nbuNqmKxmFhj+v1+S6bJBHDaKlQ7qzCjZPbA4KaKG8h69c8maNfyLZ3F8Xg84pO/n7MIYG04pdcHzjF2a0W1sGPJtb0v70uziFXXnOwXZh2IJic0yVRPFue+0Gz/l//yXyyPL168iG//9m+3/VsyWl/4whfw9re/HX/6p39a51s+G/EW2D7bcZbPEx0GlpeXMT8/j2QyiYWFBWnkosECsFfm0NDQIAyiBtGmk4GdSwd1vtQYk8k2J/XW1lYkEgnRhLa1tR2bWw1TwktLS5iamsLExATm5+eRzWb3yFrI/Pb09KC3txdOp1OKghgdHR24ePEiurq65JyYdmEsjqP93tLSkizyTE2z+I0Fck1NTSiVSrJ4a99gXUxEEMhzybQyW4tThsCFgaCaC5053ZLR5d/wNbmAEmDmcjkBrGSwyA61t7eLQwyBDSUlfL9kcyORCIrFIlZWVgBA7PAefvhh9PX1iXORtlnUTZIWFxctIEqPUw1c7Dyeq9nT0RZRW5qRIebGRRdp0uOcmwGHw4F0Oo2JiQlMTU2J9Io+61obXm2z2NDQIGwoz1M0GkUgEMDGxgYWFxdFEsR7iwWKZnbG6XSir68Pfr8f7e3t+2ZwqoXeNGgplT7s2E0NcjT739TUJJ72iUQCiUQC0Wi06mbVLjSYMt1jtB3ofsENYjweR6VSqVr4epDQEjzdPMsMn8+H1tZWxGIxqW8xHYqqPT/lLcyWsQZD2xtqS05uHPlZdD2KvhfMx5wnOO+woZUdOD/oebJ7PV3cS5bYTjfPx/p7rmGcR3kttZac7jumbGW/Zj467IC/KWXp6Og4lYZ3Rwq2h4eHJe2nvZ9rRTQaxR/90R/hwQcfrOclzky8BbbPdpzGedK6OrJ5fEzf5ZWVFWQyGSm60qEbxTDNTZYAuAOiyf7tx44wjavZRTbR4P9FIhGUy2XEYjFh3WKxWF0Ly3HGzs6OsPtjY2OYmprC8vKySFDIKFHbzK58Q0NDiMVicDgcls9MH2it69Z6WG46NAPFQj9a0NGhggsE7eF4MOVLva4Gc1qTqIGOXfMKOyaKzDGBoAY0TF1TslMoFASARqNR9PX1yfXk4kn5BPXjm5ubwrrRUz0UCmFrawvz8/Oy4AUCAfT09KC5udn2ulVjIVmAaY5Vnn8u4loGoNl6vndTw2leDwIPFtVSb837TVsRhsNhRKNRafKjr7dpw6c3R7WCwDeTyQCA3Gf01W5qapIxYZ4rZnF4LzY3N9+1xFJvhHRjm+XlZSwtLYlTB+Vidmu2Lgjl+QqHw2hqarLUZvCo146Q2R/NRGtHGY7Z457L19fXJUPB4nI7xrW5uVnAN5vJ1Ht9OGfRApJgvJpVH/XOHI+66JDSMI5pgmGfzyfZQbOoUbt2aLbblNhwM1xvcONgbpr1z8x1jht8O5DOWh69seKGle+T97VZk8INiFmLYf7soYceOpXeLkcKtv/mb/4GwO7k9Eu/9Et47LHH8H3f9322f+vxeNDW1oZHHnnk1Bf2w8RbYPtsx1GeJ4JVOxCtH1N+wTQpJ0St8dOaVzYRIqCm5lPrSatN5qbOjak0vja9YO26Mvr9fmGvzp8/b2FVjjI0C1LNJYJpTJ3+1sWanEy3t7eleQhZZa1D1lZP2slBaxpN2zMCqlwuJ7Uj/EoJCN+Dx+NBqVRCQ0MD4vE4uru70draKq4bepEhw8zX1Z+dKWW+T100x9di8akJKMnUamtFLlwNDQ2i3+W46u3tFSmECUjNcZXP5y0NcczxwjQwNxSxWEzsEvVGIRaLSSpbs82HKdLjhoWyo6WlJczNzUk2gpplbRnJAk2dAdAEEItWqYvXUig645DJj0ajko3QUha+hqmJ1cVjzNZQusCfOxwOYcbb2tqksK9QKOzJLgUCAbS3t1s2wUdd7Ki14qurq5idncX09DQWFhawtLQk2SU9FnUwI8AsQzgclgwJHUHsJB77FT7rOOk1T0uEyIDb6Y6pnyYD3traKhaj9QbdUTQLzq6vduH1ei3Fr5QpsZbE1GzTx90uTFDqdrst3ulagsU5FYDMc1qCoiU3BNn6Z5SPaVDO32vPfo4vfR/p7xnmZsGc03WWwGToe3p68Nu//dvHXrNgxrG5kbz//e/HN3/zN+NHfuRHDvXGznq8BbbPdtRznjT7qNOsJoiuZrvE9D2BLe3vtBRAp8FpHcXCQrs0KDXYJog2vydQ097XS0tLtinRcDhsWbRZTFipVBAMBsWerhYg1uysLjqx+x/dSU1rA+2+7seklEolccDQiwadF9hhkr7U9EDW1nXUNEYiEWGknE6nNKMAII4ioVBIih0JTl0uFzo6OsSzu1onSx0ul8uSNqWbB8fK0tIS5ufnxdKM44bnRAMRarjpyaut+oLBoFjIUbLS1dWF7u5uWThN+Y/JpJtfy+WygNu1tTVZ0Pj+c7mcFJb19vbiypUrCIfDAO5+fmIGQrcW1xtWDfZYvLa5uYmlpSUsLCxgdXUVuVzO0vpaM6g7OztyTfX4YHbDBCZaHsSNCs+7dv1obW2VImE2zyHoWVtbs8hXON51h0S/3y+flwdtELUkoLW1FR0dHejs7JQC4nolHro5i5Z36GI6c6nnRph++gTf2obS1MPrOSwYDKK1tRXt7e1obW0Vpx/eF/W897Ow5m1vbwvwJgjX6wIBa2Njo8Xjn+y3ZprtDv07znk85zzvlGlpgMzHHo8H0WgUACxOTi6XSzKuvG48tK68XjZbuwDZdeyslWk1P6v5VUtrdMdcs2GQuUkAIPe7dsfS2UF9/54/fx4/8zM/cybdSN5qamMTb4HtsxtkIOfm5vZ4RGsQXQ0wUSOrNaVkPLmIcnGi7pWLDW9gU2IQDocFYFcD0HYFHhrYkt3VjTgILLWOj0V3oVBIUr0aHOvHtYqPGHYAudZjk/3S9m7mV93JTvsJU7ZBsEemb3h4GA888AC8Xi/W1tYEFOpCsc3NTczPz0vKlvpZsuTFYlEmY+qZo9GoLEwMgpu2tja0trbuOU+8ZgSzZFC4uC0tLWFpaUl8yNlQQztmcKywgI0MIQBpTqH1mW63W5hBjgemiUOhELq6uvY0MmFxKzd6LITT1obVIpfLYXp6GlNTU1hYWJBFcWZmBhsbG4hEImhtbcWjjz6Ky5cvo7u7u+b8VCqVbME0v+63idHgmbKTdDotbhwEfmzP7fP5hD3MZDJ7mDfNsvF/eY9w7OrCMV1LYYZO0xNQ8nxT3+12u7GysoKJiQlp1lIo7HYEZqGizowRZHETaYbXu9u1khtB3u8a4HFDXM8yfhB5x8bGBhYWFsRucnFxURxdtPyHQcJB62ipr9cg3GzaEgqFkEqlbL3KqwHYeoBttefTNo3VnqNYLFrcSaiNN8PhcFhcRlhjcJgol+17IFDi5fXu7SBpR94wQ6aDY0STJ5qF5u/0JlYDXP29y+WyFMybGuqmpiZ5D7qmwtwA2/0OgAByngO9SeVjLa8xme2uri588IMffAts3yvxFtg++WAq2/T+1NIDpquqgUhTY1YqlfYUaZBRohMBizbMKnkG2/+2tLRYdI3atgnAHqBrJ7HQgFgXMpL9NMPtdgtA0+C6Wpjet263W1pOa2cIPUHpVtL7gWZtbaUnWrogMO1p+gqz6HNmZgYjIyNYWFiQ98xGEn6/H6lUSph4fh6OAS0ZobyDTVympqbE1k63MieYYgaC17KjowPd3d3CFhG0malROj6YRY52hY6Ud/BaUTNM8NvQ0CBd77a3ty02i319fTh//jw6Ojqws7ODV155BTMzMyiXy3C73RgeHkZzc7Ntcdx+YRbgmgfBAUH21NQU5ufnsbGxgenpaaTTaZGQfPu3fzsuXLggtl+mVrgeH2AuyDozQPCXzWblPejCKa/Xi+7ubvT19aGjo8O2yJHFwXSP0F/t7mvelyaLxw0V789MJmO57vuxhNr7mbaMLOxituDSpUsYGBiQ95xOpzE3N4fp6WnMzs6KFSjT+dy80bNbu49QRkPtuJ4vOA51kyl+9mpAcz+Qq88LLRV5js1iWeBO9kBr7imRcrvdkjUxLQ1pE3lWQherav26CUa5QdIbMa43tYBmtd9xcwZA7kv6wtsBYqfTiVAoJBu0lpYWRKPRfe0gmQk2ZSpmQWM9wU2IBuF6c8fjsNdXr9mmA8rg4CD6+/sP9bx3E8cGtqu5kNi+gMOBL37xiwd9iVONt8D20YXdjt3uca2qZC6APLxer4AqDR45YVGH5nA4BNxqnTW1bnbgkowMwS29R+82yGQSXGsmS6fEOUkydW2CZK0D1l8JCvVnaWpqqqvamwU7Gihr8Myv/P1B6jByuRxu3bqFmzdvYnNzEzs7O9jY2BDnCbJNvEbcULERBUE2dbTsqshiIW4CyLTQaYOSCC07MRs7UCvd3NwsQNnhcFjAFa8TAZl2dmhvb0dHRwd6enrEtlCnYQFgZmYGY2NjmJubE4DudDrR1dUlfuNcVN944w288sorUsj5wAMP4OGHH67qFKOtDfX4vhswTo1oMpnE/Pw8RkdHMTU1JZs2h8OBvr4+nDt3zraITG8Q+Zy8l6iVZ5TLZczPz2NqagrT09OWe6KhoUFcaeihfthg50im7ZlVMdlknb7m+GTTH84r9MBmZoXsJ4sRyY673W7Z9PPasH6AgF1ne7Q/OIvmyPBxftQaV14L3VzE7/fXdBXiRsC0XzTbdh8UBNEfXIMfPc/aycx04yDKprh54NzNTYppB8evnOfNwwSwh/mdLvQzf8c4ieJLhl2XWztnFLsMCbA754TDYXHa0Vmwet8LCTFtS2oCcnb/rCf2A+P1WM2acV9Y/+kYHh4+0N+PjIwc9CVONd4C2/sHNZV2emiTlTY1axowag2wBtA6pUfHAD0J0+LKrrgCgIBQMi1msQVZIg00atlNmd3vdNMB8zE1w3px53vVX8k8cUEHYLGw2y/tXi24cLW2tqJYLO4Bz2Y3u+PoQreysoKRkRHcuHHD0jqZk2g+n5fKfV5LTvocWwQluniHjA4jEAjA4/HI74A7QLqxsVEawVDTalpO8W95Xfx+PyKRCNrb29HV1YX29nb4/X7JbLBgKhQK2S5SlUoFyWQS4+PjmJyctFzDWCyGwcFBsdhjLC4u4mtf+5pYVrW3t+Ppp5+uawKvFXZgPJvNIpVKYWVlRQAnx1s1H3bKftLpNPL5vMh++vr68OSTT+LcuXMYGBhAOBwW/Xo1hrRQKAjAnpubs/hyezwedHR0iPQHwJ7/189pSgW0hMBOK6t/RxKA50UTAtXqOABYOgvS2US7KPAcs6mIrm8geDadQciIaqkMwSjBsW6ExM/NeVE/D6VFPFwul8yFmgk12VD9c84Vuo28lg1Q6mSCXZOpZb2F3pCwWyQJAqfTKSBRkwV6I2K3uTclHOYRCAROXEqgiy8JwI+q+LJebMA6Ag3EV1dXqxZSut1uS2dSAvKDFoMyzI2XnnP1z+w2JXbBvgZ2Bc9608W478B2tahUKlhYWMBzzz2H3/3d38V73vMe/PzP//xRPf2JxZsNbFPHTObBrtECFyW9szX1Uhogc3Ex9WD6NQHsAcHVgouPx+NBKBRCuVyWSVjrNLmQmayEz+eTlBoP3qjaLqkaoLabuKkVXl1dxfz8PObn50XbyMWDKdVKpWKp7K+3y5tOHWuwbMo19M/4vMc1nrhJ0gUuTO29/vrruHbtGubn54XF5/VnMSAXeBY38rrr7oNk6DgetTyErhJut1sK5nid2ZwnGo0KgKFulGAvl8shk8lgZmZGrPGSyaSwQrpyn50kBwYGMDg4iKGhIUl968hmsxgbG8P4+LhlgW1qasLg4CAGBweleJWxvb2Nr3/96xgbGwOwu7A8/vjjGBoakr/R4LBesEk7Si3x0Gy3/nvzGpoFVfr+8Pv9Ms65YLJrJRdpNnTRzgcE66lUao8bA4u/Wlpaqm5gTjqYUtce19qViOCVG0Rt8ch7MRwOSzaFzYUIEDweDzKZjMwXWqJE4O33++X6Ane6i+p51O12y3zLg9eJILmhoUHqEtjMh+47Whtrui7tF9zMmjUqGgxVY9q3trYECBaLReluqseF1tJT+qe1vSwQrPX+dLGmli4RpJ1EsPhyaWlJPMDtNnMspiX4Zp0J427mci2xYp8BAvJqoJeSOy2HaW5uPpAne633Q6LOTrJyEJtJvleOu0uXLiEej9/1ezxonKpm+8aNG/gX/+Jf4CMf+Qje+973HsdLHFucRbBtaoJ1Sq6au4T5d2aqj/6rZF80O2gXXPi1hMEEjLVAtF26TjNFevdKdkJrS8vlMrLZLMrlMmZnZ6UVuZneY0FSNBqVyWu/7mmcALgAacZPf09wvby8LAV6dlINNiKgfpKTO4G/qWk2JRv6d4cFILXGU+X/32TABFt2PzO/chIslXa9nNktcn5+XphBLtpMi3OjxDHBlDVBCNsXk4nTOnaOoWAwKKl8AhOPx4OWlhYBtPQ7BnYXOt21k/pXrWGnXIEbt+XlZczNzWFmZgaLi4vCIOrqeN0x0Ov1yuvwb1wuF9rb2xGPxxEOh23ZWcpLKDtgkyFKSnRBlxkESnpM6vG6H2tE5tDU/2ofagCWzQkbdFAbT5mJtiSkjR5t+Mj4stOhtr2Lx+PCYtul6k2WVGe27NL75s/3+x0BNQtrufhrEGCe+1KpZGHAqennOOJB3W6ptNtxUoMW08daj4XZ2VnLHNvS0iJjTDOVmUxGLBFNuzU9JpgFMgkEvqe2tjbE43G0tLRI51MWgnMsc1Nhyv/qDX2P20lCOjo6ZMO8vr5uAYOrq6siXzLnLj2mtCaadR773QOsOyEAN0H5UYBKu6hUKmIbycOuCYvT6ZSNaywWw7lz5+oGn/VGuVyWc67Hl905Z9ApiptrjuvjaIrGe9NOrkLiwNy4RCIRvOc973nzFUi+733vQyaTwf/8n//zuF7iWOKkwXalUsGLL76IYrEoaTbThq3WZdKV96YBvf5ZrefQkg0yVrpwjoPXtBLThXN6QTMnM4Jnkw2pNqkVi0Wk02lLRzAyhrpA0uFwWFL8uuq/Gli2A9TVtM1kBbTeWrM/3FTQeo4tz+mWYALqo56U7FhmLsRerxepVGqPFdRBJCq0TWOKfHV1VXyQyQpTCmDasXGMmKyXbvCiweXq6ipSqRQ2NjYEtOo28XR2IIvY0dGBeDwOn89nkewwja+BNbMdZuqZjRaqFdyyaDOVSkm3OJ4PnkMWR8ZiMfT09Iikwi5zsbGxgfHxcXm9QCCAgYEBy2ZQn3NzjDJjYqb+ze9NlpEbWO0EoTMM9WhZI5EI1tfX5ec7Ozu4fv06XnvtNWHvMpmMbAb4PrlAd3d3IxaLWXSidvNEvW4qte4JMxNnHvUwuBwvZptw7ebh9XpRLBbF9lE3ZJqfn68657I4UIPwhoYGLC8vY2JiAqlUSv7W5/Ohr68PQ0NDaG1tBQALCCEDz2JLyraoF+f39P02MxckBnQHTc5dHCe0myMZwPGmN66Uhuji4v0iEAhga2urKkPe0NAgLPfm5qYAwmrFuA7HbrdUvk8CcRYaMsOzX3BTqMekfnyU87jZ+XJ5eXnPehQIBFAqlSwOSvV2vjxokEQxQXit82a680QiEYRCobuqtagndB+Hra0t9PX1HVs35Fpx6mD7J3/yJ/HlL38Zr7766nG9xLHESYPtzc1NfPrTn96z6JuFgWQvTHYZgC3boxkqSj4Ijk3ZhgbR1bw0GVrPp22HmDrkUe/ukrt9FpmwWYRmJziR+3w+SRPpxc4E1PVM9HbhdDrlebhoaF02G3kkEgl0dnYiHo8L+3TY4MZAN3nRAItWhuYGQXfWMoFluVyGz+ezaNtN/1YCUL4HMztCWzVthaglO1y0maGg/aEeB9UaXJBdrVQqAhi4saOzArXWehyw2I5ty1l4ZgYBp7Yds2uZTcacDiHVmNCNjQ0kk0lMT08jlUqJDpWbGg1gHQ6HtMbu7u5Gb28vYrEYZmZmMDExIc85ODiI9vZ2WwceXhs7QA1AijRNMMDPexyLnF2mJJfLYXR0FF/+8pdx/fp12fzxvPK9cbPE9+n3++va8JkFnJR+8ZxwnGr2q94W0FraYYJojuG7ySpxnGh3FD6uxryykM3lcom/PKVk/N3Q0BAGBgbEXcQu9IZDg/K1tTUsLCxgbm5Oijw1uUMmmyx4Q8NuG/tAIGAhWXRBpz5HlLGQWDClg/o1yGjXA3753Nw8soCa85HpVW/3v1qXzCxaqVSSLBo3LHb9DMww631MQH63959ZfJnL5Wz130dRfFlv0M7SlKJUO18Ox263Wl2UGYlEpBD9OOJNo9nWsba2hne/+91wOBx49tlnj+Mlji1O42J9+ctflslPd0izY52AXXCkWWwAtmwDdYP7dS0E7vh2arBUrenK3cTGxgbm5+fFxzWZTEpxlu5URcaQ74sgaz//aJ3ytzPl11/z+bywPuywZwJTehnzoP7XLMTSmyLTvN/MMpjp37sJM1XMzUEwGJTiUL3h0tIi3e2Luj7TWcHUB5NZZuFgS0sLgsGgZZzy92TD9HhqaGjA+vo65ufnsba2JkCSLBvTxA6HQ1LhwWAQDQ0N8p5MABoMBhGLxST12tLSIguqncWWvpeqTdDr6+sYHx/H2NiYpVtnY2OjaLhDoZDIAMbHxwWM63NMoEUJUTwex/DwsOhn7cLpdFoAtAmqT6MzL8/T9vY2pqenMT09bWFvWfSaz+elu2BbWxvC4bCw3vrzUYrT3NwsoJkZEzK0dtImHRxrpvRKO/sQpJss9XGybrUW/XK5bPEHNxlxHZVKRaRQW1tbluLFgYEBPPTQQxgcHDxwkTML2dbX10U2NT8/j2QyKVZ+zHSSpPH7/ZaMpm5axXXKBOS6O6cZDQ0NiEajlkZFJiHAOape8oQ1B5xDOM9q6RqzasAuaNZAkPN7oVCw+Dvro5rjhw5tcWl3HHQNDYVCGBsbO5HOlwcNSis5jsmGV9tMs7ZGn3M6o9xt3Hdg+8UXX6z6u62tLUxMTOC///f/junpaXz/938/fuVXfuWgL3GqcdIXa2NjA5/5zGcQCASwsbGxB0QTVAB32AHACiRrgWgyAnaNVvT3dhN2Nb9VDTSpZSRDpy2r2I6Z7aIzmQy2t7ctTiIET6ZbBjvb8W8JmpqamiyNKrSURfu6moWNZFEJru0abVQqFUkh6k5ddrp4Alced7tvJUgmi85NgZ09l17UvF6vheEhS+50OsVBgvrQnZ0dOQecGPV54DnkoeUeTOs3Nzejv78fQ0NDArTJqJIltFtMstksbt26hdHRUQv7yFSvXti2trakqNVkQnw+nywmuiDvsKEn6EKhgMnJSYyNjSGZTMrfuN1u9Pb2YnBwEIlEAgAsaWnddGF5eVmA6OjoKFZXV8WakvIaYDczQ+nJ0NCQWAGy3fhZKBZk0OXgtddew+LiomWsRyIR9PX1oa+vD+FwGJlMBi+99BKmpqYA7I6p4eFhdHR0YHp6Grdu3cLS0pKMV2asaLtJaRnrCijZYUZHy+r0/aDvCc5l1WQq/JldtuMo4jCLvi5ks/MKL5VKkvnTmz/6dw8PD2NgYOCudbQbGxvSBXV2dtZSxKnrNmjdx/vf4XDsIRO03zbXMTrPsAhvPyDNolGCdu1aZNYRVbtn9PrEWiUWXtp1SqQjERsokRF3Op3iw27e+zzq+TwHdVKxG0/7db5ksNMn2e+WlpZjl3cAdyw3Cb5JJlYjlzwezx4AHolEDnSP3ndge3h4eN+FoFKp4IknnsB/+2//7Uh2LCcZJ32xyuUy/uIv/gIrKyvY3Ny0gC7u9s3CLoJP3ZDB9E7loa3N9rPE0pZ8XNiqteLWPwPuLI66eKvajcXCGT3B8P1qyYZ+zAmins6I1I1Sw0twrXXpTOWaRWI87zqNX0sjqx+TvdSezPr5zQJIAnqfz2cp9LH7PNodwezOp3XZXBTL5TKWlpbk7ylDoRRJA2umqiORCOLxuEz4XOhYSPjwww9jYGDgQN6sBFjz8/Pyc4Intuom4CdD3draKvfBQYtdDxrhcBivv/46RkdHMTMzI5vZYrEoadpwOCypXJ7zalKAUqmE2dlZLC0tyTjo7e1FIBCQhZFadx10P+ns7ERfX5/ITE7LpYONbaamppBMJi33XWtrK3p7e9Hb24tQKCSFaVqyMDs7i9deew0LCwui6+/o6EAikUChUEA6nZauhDqi0Si6urrQ29sr19tO3sF7wmQetXxivzguMH7Ui77pFc6N3Pz8vGXjSstP6npZkKm7jB60+G9nZ0fG7dLSEpaXl2V+0a4hWtNNp5RqwJNrh9vtxvr6umzo9fynN1T1MsHMBpvPYa573IiYEi5m9Bi6sJdEQywWkwLoeDyO1tZWy/3J+pZqgHy/QkfTSSUYDKKjowPlcrnmmNTFl6yhqKf4srW1dY9T0nFFpVKxED266LfafMp1SRdlsqeBGfcd2P7whz9cdfL3eDyIxWJ44okn8PTTTx/0qc9EnPTFSqfT+MhHPiK6RgBVd9z65/UyF2Q092vFrV1MqoUGnJpNJduhNwoEyk1NTRbbvVgsJkx6LScCABaNOt9fY2Mj0um05f3Seo6FbJRCmOPU5XJZXEKqeWtr1phAWH+1Y5c1o3aYYDMIE0yzYtwsgtTfM1vA683rQTsvsj5Mp/KadHd34/z587h48SLC4TCmp6fFJQPYXWyGhobk9/VGNpvF7du3MTo6KiCMntos3tJFtywu7ejoEMaaIPduZUvVYmlpCa+//jqmp6cFEOZyOZHgsGitWuhFkQtjNpvFzZs3pSFPT08PnnzyyT0bhGw2i7m5OZGf0CpRb07JhEciESm87OrqOtbUcDabFYC9vLwsPy8Wi4jH49IECMCeosNqS8na2hqmp6eFSAgEAhgeHsb58+dlkWd3zfX1dcs9G41G0dfXJ6D+IEG5hgnC9ff7hQnGTUayGvA5qUV/e3sbo6OjeP3113H79m1ks1lxBCKj2draapmXGhsbbUF4vRsLgrqlpSU57CQNHCvsI0DnHj1mAOwrCSQop6+3ZsmBO0C6XqkJSSG9lvHnLE6mXEQ3Jqq2LlIPrte39vZ28avmwUwt53k7QF7Ng1pvdDkmNRiv5qRST/ElcCdbeNzFl9WCTmO6KJPreLUIBAJ7ijIHBgYscrWTilMvkLxX46TBdrFYxOc+9zlhCwjaqum1qWXTMgamGQmedUMXspiaheVzmT8zGVpaQnHSIxOwsbEh1mt6IvT7/XtS/A6Hoy5LOe2kUYsV58RTqysjP1swGJT30dbWhkgksiflbPf4OJhEFi1pGQt3+alUSoq7tO6bX8nY6BQ7JTy6aJEbEMpyWLgYDofR3d0tTVXa2toQDAZRqVQwNzeHGzduYG5uTt5rKBTC8PAwhoaG6p50yWK/8soruH37tuXzafcJYPfadHV14eLFixgeHhb2+qgb7BDcc0FbXl7G6OgoJicn5T6nTpyLjAbGzL6YC1swGLTIZbLZLJ5//nk5h8FgEE899RS6urrqep87OztYWVnB1NQURkdHpVW6ufiyMKutrQ39/f1yLaPR6KFlA+l0GiMjI7h9+zaSyaRlA02gwAWtFjCi84ipjaZmemVlBdevXxcJRFNTEx555BEMDg7K+Njc3BQ9uJ1chWz63Tb8AY4XjHd1daFYLB6bTMUuisUiZmZmZPzoBk4EYrXmNvqDa4tC3ea9VmxtbWFlZQXJZFKYVRM0ulwuWRva29vl6+zs7J5CTp2dqKemhd74ej3SaxrXQ8pJ6mmqovXiJJe0HIXFlKaUkcFCbxbihsNhYZHtbBCZ5eRz66NSqWBxcbGuMamdVEwyoKlpt6twvZ0vCb6Pu/iyWrDmRYPwTCZTtai2p6cH3/Zt33ai7xF4C2wfOk4jDcGK3mQyaWtVx8dm97F6gwUuZgdBO1kDUz3UB7I4x9Qs03WBiyor5u2A4t0ENx905iiXy1heXkalUtnjIRuLxdDR0YHOzk50dHScWPMCwFp0RICXzWaFWaD20iye1IyJlp8wFWvq5FlFr1O4TqdTACAnWLLRXNi0rrlQKGB0dBQ3btywsAcEwB0dHXVNrARHL7/8Mt544w2k02lZ2ExdbVNTEzo7O3H58mU88sgjB2LKq8XOzs6eLIBO23JDwq6JmvVwOByIxWJ48MEHEY1GJeOhF6f9wH+pVMJrr72G1157TXTZly5dwqVLl+5645DNZrG4uIiJiQmMj49jbm5uj1sC2W/KTwYGBiQ7EA6H4XA49jSOoCZ3YmICs7Oze5igcDgsha96o8WujhpEm93d9hsz5XIZt2/fxquvvip+zZFIBFeuXEF3d7flb7e3tzEzM4OpqSksLCzscaUh4833ddRxN2CchMBhmfG7DdZOsWaA4fV6ZWNLhppF0dXC6/XusSnUXuF2USqVhE0lALdjVDlHs1iWY1YHu7/q869BeT3uIXQn4sbP6/VaOmASTHON4SZlP2cbelVT3kMwTuJHuz4RkJv3DoG2nW0ngXoikZDahkqlskeqQla8Hg9000nF7/dLQ7itrS0xCjBDF18ShJ/kZlJHoVCwyFB49PT04G1ve9uJv59jA9uVSgU3btzAjRs35EaORCK4ePEiLl68eKYKew4TJw22t7e38ZnPfAY+n6+unStwR+ZgguZqgJopLLuuf9ThscsV09lm8d/dLBS6O6D51e5nTCux/a3efXMh83g8aGtrEwlCLBY7do9NNlzhBEcgTfkKJ1rtQqJDd5ozHRQ8Ho/FZYZdMl0ul0yubKKjAQ4Ly9rb29HW1ob29nZhI8yxvLq6ipGREYyNjQlj5PV6ce7cOVy4cKFmqp4pyVQqhWQyiVu3bmFqakp0gZSvuFwuAWxNTU1obW3FAw88gKGhoQMzkibgMR0Cavmj08VhY2PDsonp6OiQuYo6wMPc83Nzc/ja174mC1NnZyeeeuqpY9M/FgoFLC8vY3FxEaOjo5iampK265QOEPBTwkXrrVAoJEWo6XTaAlDoVU+ddCQS2WOJ19jYaDueDhvFYhE3btzAa6+9JvdIe3s7rly5gvb29j1/n8/nBXjPz89bNqjBYBA9PT1isXhS608tMF6pVLC0tLTvc7Dw+DjBeDqdxujoKMbHxy33SyQSwdDQEPr7++H1evd4he/X4MTOK5yuEtUKpLX0ZG1tbU/9TUNDg8znBHT7bVrZoEiDcFO7Xw+TTUZYg2GunQTkBNN2h2bgdSMgbnYJxrVlL2uy+Nn52qFQyGJzap4nsuZ8j9qXXBeN8vNzzqzXSYWWusw2k8E3m6ydVvFltbhvNNvlchkf+9jH8IlPfMKi59MRi8XwgQ98AD/4gz94qif9buKkL1apVMIXv/hF6WxWCzST8dRd5PaTZ2iJhqkXqwbu9e6XiwBtBAlc6gXPZBFqRSaTEQYkmUxaqu0ZZEDOnTsnRRNHredloRfZCm5EaEeWzWYt59duEtdFkZTikCkMhUIWuQ9ZFH0b6kI86hB1Op5WiFyQ4vE4IpFI1Qr2crmMmZkZjIyMYGFhwfL74eFhW+uwUqkknTKZ4aCt29LSEpLJpEUjyfGSSCTEVq2rqwvnzp1DZ2dnzeuktep2BUX7TVF6rFYqFWmGVKlUZHEIhUIYHBzEwMAAgsGg7XmqNzY3N/Hiiy9icnISwO64fPLJJ9HX11f3c9QTLAC0O8zNBzXPvEZs6sNxRnvIpqYm0ZYODQ3h0UcfxYMPPljXBuE4FrJ8Po/r16/jxo0bAkC6urrw2GOPVV3ACoUCZmdnMTU1hbm5OQvICQQC6O7uRl9fH9rb20+N+KHPdjVWvF6ZSi0wftC24+VyWQorp6enZe5yOBzo6OjA0NAQuru7LXMBSY/DeIVrAE7ttp4HOIfevn1biB5TMuJ0OtHS0iKSk7a2tgNnK5lxtJOo8HE9INSuyZEupvV4PNLzQbPjGpBns1mRDOqDmUsCcX6lcwvvT239SOMA1m9pEwFeV225ShMCbRBQKpUsLHk13bsugNaWsZo0ounBaRVfAvcJ2M5ms/jRH/1RvPrqq6hUKgiHwxgeHpYUXiqVwsjICDKZDBwOBx5++GH88R//8Z6F7V6I07hY7LymLbGqtdGut21rPp/fM8FTv62lF4FAQG4QSg6CweCeYsD9mt3UG0zra5bDjqGk/RJZa46lu72h6KGqW+Zqb1/qpqtNPGz2wImmubnZUgBKCQK16tvb2xawrm85PYlRi89UIxulABA5CAF2PROY3+/HCy+8gJs3b4rGzeFwoKenBxcvXpTmQJVKReQuBNc6k1CpVKRzZC6Xk2ZCpVJJugNSohKJRHDu3Dn09/cLK0eGxXRS4c/202QScGi9tNYgFotFTExMYGxsDOl0Wv6voaEB/f39GBwcRCwWq/r89Y6ncrmMGzdu4OWXXxYZ1cWLF/HII48cuJiI111LPMz0eL3NWWibx8V0eXkZU1NTmJiY2GOxRw16S0sLwuEwgsEgOjs70dPTI+xUJBKxJUqOcyHb2trCq6++ilu3bsn9MTg4iEceeaTmGlIsFjE3N4epqSnMzMxY7lm/34+enh709PQgkUgcW6GtXdRzro5CM67BuN1RjRnP5/NicakZeK/Xa3HC2e+91+MVzuCmV4Pwvr4+aaJTLpeRTqct64KdLII1C7oQ8W7XJXa+pBzDDpjXA5X0vajBuHbSASDrAptYMYPLTCltcjmevV6vZIC4hlOfrvXi2k7XtJA1wbmuo6FchRstyl90XwazSJTzup7LS6XSHgcuzjHd3d1S4H1cxZf3Bdh+//vfjxdffBFDQ0P4uZ/7OXzzN3/znsmrUqngy1/+Mn7rt34Lo6OjePLJJ/GJT3zicO/+FOOkL9bW1hY+9alP1WVpx9C2f2TvuHvmBFEqlSyuINRt64rj1tbWugpg7iby+bww1mQwzA2Dy+WSxZ6Av5oTxH43FFt/azBNmQd9pWsxGTy3TOtFIhFLS3im2ZliByCSl3Q6jdXV1T2pev3euBhxEmOmgJMfrZkoCWlrazuQj3QqlcKNGzewtLQkGQKfz4fz58/j/PnzcDqdMqnzsGtA4HA4hGXnhMwFoKGhAS0tLZLtSCQSaG9vh8fj2aObrkdTWa26nt0GzcW0WCyKc4purOJ0OqUQtKurqy6AVc8EnUwm8bWvfU3+rq2tDU8//TRaWlps/54OBnYgmgC7ntS21vxq2ZDOdLhcLiSTSUxNTYnrB4MNJAKBACqVihQemjIc3fGRemjamsViMZGjHPfcmM1m8dJLL0nWwOl04sKFC7h8+fK+kopSqYSFhQVMTk5iZmbGco83NDQI451IJI4963oU5+qowXg1mUo2m8XY2BjGxsYshWfBYFCyQfWyk9or3AThdnMM1zy6/+jmYeFwWDJpPFZXV/eAXjqgaTnhUYM5bo7Ne1l/X09HVJfLZWl4YwfMdefn1dVVKYwcGxtDKpXC2tqakEG6J0elUpFMqma+WUel/5bGCTQ50IDcbP5GAM6sunZv0XbBfD+aTDE3KSzyb2lpkS7MXV1d6OjokMZldxP3PNj+/Oc/j5/5mZ/BE088gT/+4z/ed+HP5/P40Ic+hK9//ev47d/+bbzrXe+q/12fgTgNn+1nn31WrP/spBn6q8vlsuiFqbM2gyk4DaztClCOOtbX12VyTCaTtl6fPp8PsVgM8XhcnEvqXQBDoRDGx8dF4kEdMQsmstnsvmwpwSMndjLTTH1xYTIn7Z2dnT2gmk1L7IKOFSwazOfzeyQb1J7ziMViBy6uK5fLmJycxMjIiDBVZCpo2Uapgd0iTaeAlpYWyTyk02kL672zsyObi3w+j6ampn3t8Rher3cPiCawq7ebHyvyx8fHMTk5aWEx29raMDAwgL6+vgM3uKk1QW9vb+Pq1asYHR0FsDtuH3nkESQSiT2+0vqox4aMzS20Ptpkw6qd23K5jMXFRbHp0xsaj8cjwLKjo2PPWMrlcuLDOzc3h8nJSdmEbmxsyL3j9/tFNxqNRnHx4kX4/X65R+6mkdB+kUql8NJLL4m7i9vtxoMPPogHH3ywLiBVLpexsLAg50dnCbxer+jTOzs7j9wBBziZRb8WGOfj/cJkxnO5nBQTs0EYsKunp5PRYYGs6RW+trYm82m14ByjnVF2dnbET3ppacn2XmNWlAD8JDLsdhtsE5zXE2SZ9TzQ2dmJYrEoc6XZKIY2eboDs+5QbEpNKEk1Ox+z6F5/zy7E2nLYDqDrpkPEGGS/19bWpN7IrvkQs7m8bh0dHYhGo3vIl/1qsu55sP3jP/7jePbZZ/HFL35RUs/7xcLCAt7xjnfgG7/xG/EHf/AHdf3PWYnTuFiA/UApl8tYW1uzAGu73T0AsRbicRLFCtx9k7WulvpjAR+Z62pOFNrRg7IGXYDIXf1+ExedUgik9aaDNkb7uSdwkiDwrFalDeyCgZaWFvj9ftFkU6NnhtZbMw162BT31tYWbt26hVu3bllY00AggHg8bvt+HQ6HNGfgBsPlcuH69eu4fv26TNzJZBJbW1syObPxjR17ZLYXN0H13TAWmUxG/KhN5k23TT9s6PtO++C+9tpreOmll7CxsdtwJxqNIh6P12VFBlhTymSzzJTyQa47ASQZbBNA9vT0HIq55RzDzSvdPzRgqFQqUi/Aa5pIJIT9Pi7rxoWFBVy9ehUrKysAds/p5cuXceHChbo/Y6VSQTKZxOTkpFjiMdxutwDvrq6uIyuwPq1FXwfBuJ1efD9mvFQqSdOhXC4nmb7Gxkb09/fj4sWLGBgYuOsGU5FIBPPz83s6ZmYymZrOGtor3Ol0itSymrMKC3w570aj0ROVFQG716OWbryWzaGdz7aeT5qamqTTMZuFEYybz0kbXwAW/Tdrq3RHaLL11GnXOjQotwPkNADgtdLngvp0ymLY9drMyuif6YO/P8oi7oPEkYHtb/zGb0R/fz/+/M///EBv4P3vfz8mJibw7LPPHuj/TjtOE2xPT09bgHUqlbK9ARsbGy3A+qRM6NlNTKf27IpatAwiFotJCpjar42NDSlko0aYYFpbJ9kFK6WDwaAw09ScU3d+EN/hcrmMTCYj74HAutp74A6cLZHpUMLOgGYcRm+9XySTSbz00ku4ceOG+I7u7OzIa7CyfnNzU5hJpuk8Ho9MyNPT0xgdHcXCwoK0s6dlWTAYFMaitbUV3d3d6OzstJV72LUYvpvI5XJieaeLsbWmtK2t7cCvacc+uVwuSdWycl8D+0AgsAdYcMGrdRwF8NTSiOnpaUuq2ufziQvHUWuS2Qqa2anp6WmRg2lpUGNjo2ywQqEQEomEMN+tra1H5s07NTWFl156SWwK7Ty664lKpSIbiqmpKcvmzeVyoaOjA729veju7r6rDeJZANv7RS0wrpnxfD4vxIeZQWlvbxfJkQmA6ingrHWe2AHYBOG1GHsWpbP+hfZ95lrAOU1nE48zU1NvmDaHfOxwOLC4uFiXJA+AZZPPzKp2F6klY2MdTnNzM4LBoGivqWs37RH5mNnb/Q6+rh0IpwyWFsdsYMSO2ZQb0UXKLLp94IEH8OSTT979hThgHBnYvnTpEt75znfiP/7H/3igN/CzP/uzeOaZZ/Daa68d6P9OO05DRvKVr3wFq6urSKVSe36v/S3JRh63zppBa0Ay15QW6GAHQBYx+nw+5PN5qbxmAeLq6qroRPcr9GRlswmmY7EYzp8/L1r0gwbbRGspCB07zCADrJ1EaCnH82Hq9O5Wb10ttra2kEwmce3aNbz22msW+7NgMIh4PC5tvWkB1dLSIhsXXdxCC7lkMiksAws3mQHw+/3o6urC8PCwWOQdR7pdB9ucj42NYXZ21uKW0NnZiaGhIXR1dVV9H3buHabO0i7lzE1JqVTC9PQ0kskkPB4PAoEAHnjgAVy8eFEABKUex7kwF4tFzM/Pi/bYLPrr7e1FT08P4vH4ibFz5XIZlUoFN2/exMrKChYWFizab6aw2TGWGzGmhbVE67BzV7lcxujoKF599VXZ1DY3N+PKlSvo6ek51HOmUilMTk5iamrKkoVyOp1IJBJyro9SmnSvhAbjLBxk19Pp6WkL6CWrGI1GLaRPrQJOyiMOep4oIzE14dWyjpRKsF6mUChYpBTcrIXDYUvhJefTsxAcT5zjTHmKvkb11IKwQJKbEi0/ISFjzi0sbmW3xkgkgmAwKH/HpkEs+qRvtwnO2UG4lnyF6xLNCsh8A3eKN+lfzgxiKBTCww8/jB/5kR858et2ZGD7G77hG3DhwgV87GMfO9Ab+KEf+iGMjIzgK1/5yoH+77TjpCfJzc1NfOYzn0FjYyO2t7f3SB5O6qbfrw0vbwgCEUoLcrmcgFbdhKdawQj9OhsaGmSHys/MBTkcDldlRepdyGiJpsF1NVbE6/XK++Dh9/uRTqcFWC8vL++ZyFico11T7haUspsgNypc4NjwiNZQwWAQiUQCoVBIfMw1KNCpR2qvs9msMCWcAMPhMLq6uqRRxdDQEAYHB0/MSWhpaQnj4+OYmJiwZBOi0SgGBwfR398Pn89nse+yK1aql/VhERHZn46ODkxMTODmzZtitTU0NITHH3/8xJoi7ezsiH56dnZ2j50dGezTtrPT953uGriysoK5uTlhHrX7kVn8qu91bqIPkpUrFou4efMmrl27JuMlFovhscceq1vmaBerq6vCeOvP6XA4EI/H5RrUMybuB7BdK9gt9vr165iYmJBN/c7OjgAfXfRdLbi2VQPjB7n/2G3QBOF2XuGUKrJZG2VSlFXQYk8X7Nfj+X1cUe940g1vDuNyRAnJ1taWpfjRNGRoaGgQFxSu4WTDaSBQLcrl8h5LRAJzPt7c3BQpEcE3f8au0ebGolwuo6OjA5/4xCfuXbD9gz/4g3j55ZfxD//wD2hubq7rxdfW1vAt3/ItuHLlCj7+8Y/X9T9nJU5jkkyn07JLPCl/cnb5Wlpawvz8PGZnZy27ToJmDhFqqejxncvl9oBP7eRBdprFh1pLTo3VYSYvc+IplUpYW1vbU7hYDewHg0ELsOYOfWNjw1LYaTcO/H6/pXnM3Xp9a+eUVCqF2dlZsUJMpVKYn5/H6uqq6OGamprEqs2u2NXn8wmw6ezsFEC0sLAgaVlKf6hdbGhoQG9vL4aGhhCPx09kospmsxgfH8fY2JjY/7F4lCDM6XRaFol63TtMfbRZeKjH3NraGl5//XXcvn0bwC679fTTTyORSBzbZ2fQL3pychJzc3OWTE9TU5O0Jj/JRi21oh4XIG4QdaMszY5ubW1Z2G/NgBPQ0H5wv/uqUCjg+vXreOONN2Rz0tnZiStXrtx1V8lsNoupqSlMTk7uyTa2tbXJtammWb7fwbaOWpKveDyOtrY2NDQ02BZxNjY21tSO2zHj2lHFzqnIjFKphGw2uweEa69wFvJpIEfArTs5dnZ2WiSBJ7UZP8rxVCwWq2rGq821lJAQENN9xe12W+yBue4Hg0G0tbWhpaVFAHhzc/OBpVnat9w8NjY2sLi4iIWFBSwsLCCTyaC/vx//9t/+23sXbP/VX/0VfuVXfgXvfOc78Tu/8zt1fZCf+qmfwjPPPINf+7Vfwz//5/+8vnd8RuIsFUgeRbDoK51OY3p6GjMzM1hYWMDS0pIlvcMOdLT7IfDXi57H49mzw6XMg4Bat7w+6nQ7Af7ExITFG9tuGDudTgHV+iu7bK2trVkKO+1Y71AoJAvGUeitU6mU5Rokk0lsbm5K2oyT/tbWlmXj0tbWJm24Tb9pu2rthYUFzM/P4/XXX8fGxoa4wjQ3N6OtrQ2BQABtbW0YGhq6K4eBeoLp6NXVVdy+fRu3b98W8M8UYTgclhbj1YISGdPBQwPresdbsVjEq6++itdffx1+vx/5fB4PP/wwHnzwwWOVZrAT4uTkJObn5y2LWjAYlBbkra2tx/YeDhuHmZ82Nzf3gG9tDcbiU639DgaDaGxslDllv+YYW1tbuHbtGm7duiXns7+/H48++uiR1Eesr6+Lq4nZFbK1tVWAt36tNxPY1pHJZKRbpQbRoVAIQ0NDltoHdsidmZmp6qiyHzyhi4VZP1IPGNet1s3OmQSkujcAyRuy32S+tfPPUXh+28VJjifd+0GDcTubQ81I839I0Nmx4SzMj8fj4kR2FBLFSqWCfD6PeDxu63523HFkYLtUKuF7vud7MDo6ire//e34pV/6JQwODtr+7djYGH79138dX/nKVzA0NIS//du/vec6Sd5rYNuu6GtrawvLy8uYn5/H4uIiUqmUMIg7OzvylV32mFrXBRG8SQKBgLgNUGqgQd5xgJNKpSKtpfWxtbVl60dOjbI+WKkOWFn8anprh8MhemtOBAdtl8yK8/X1dWGq5+fn5TXtpA6U1TC1pltkDw0N4aGHHhIf0lrvJ5fLYWxsDDdv3hTJzPT0NBwOh+hm6Z07NDRUE9jWG9zI2TVnYZOIhYUFLC8v79HGNzc3y7ii/2qtgkMW+9xtTE9P44UXXpDNFXXZxyWbyeVyAtYWFhYs5yAcDqO3txd9fX1VPbvPShzFgk9bSQ3A19bWZNwQiJPRpPUggZMG36b9YDabxSuvvILx8XEAu5vt8+fP4/Lly0fGQG5uboobTDKZtIDBSCQim6X+/v43JdhmVCoVLCwsYGxsDFNTUxZZVCKRwODgIHp7e9HW1lb1PHEurWVveBxgnGuP2b5+cXFRnJrIfutgJ1s2cOnr68PAwMCR1Fedtc1brT4C7DnBNYCHneEAyTuCcK69dDo66Pp7z1v/AcD8/Dze9773YX5+Hg6HA8PDw3jooYdkgUin07h+/TpGRkZQqVTQ2dmJv/zLv7wrDd1pxVkC26ZdkN1BVlQX/LETIvW9wJ2JRzcPoENFNBpFJBLZA6SPwmh+vygWixbPagLrajZIiUQCXq9X5Cl2GrFCoWAB1naNdNxut8Xfuq2tra4dNos8NDhg8wHN3NlNLiy6pAzF5/NJipObHHZnHRoaqotxXlxcxK1btzAxMSE2jNlsFl1dXQiFQggGg+jp6cHQ0BA6OjoOBFgLhYKtPlr/zE7esbGxIdIYtgvnOGNXRxbL2ck7jiOy2SxeeOEFzM7OAtiVajz11FO4fPnykd/zW1tbArAXFxergrJ6ZXlnIY5rIeNYIfjmQq1B1tbWloX9pp1kMBi0gO9oNIpMJoOrV69aPLovXryIS5cuHWkGZ3t7G9PT05ienrY0VgJ25SzRaBS9vb13LWm512NnZwdTU1MYGxvDwsKC/NztduOhhx5CW1sbEonEgRnh4wLjtTTjm5t3vMKXl5cxMzODubk5sYW1K/pvaWlBR0cHuru7xdu93v4EjLMGtveLcrm8Z93Q9WArKytyX1frSUA7WZ3hotWotjzU69l9AbaB3UnxN3/zN/G5z31OgITuJgTs6rS+53u+Bz//8z9/T7ZqB04HbK+srCCfz2N+fn4PqAEgmlYWIG5tbQmoZoqLmmoeXq9XOr/F43F0dHSIXlqD6UAgcGKuBltbW3tAtV0BC7A7EZoykJaWFls2pF69tXYJYQdEM3Qb2mw2a3FcYNqbBTZai8qgHzXBJTtldXd3o7u7G06nE6Ojo7hx44alAJXuH52dnfsuPLlcDuPj47h586aA/JWVFbHjam1txcDAAOLxuBQYmmG3kTMnyHq6ogGQZgu0UGRVu9frRXNzM86fP4+hoaFTYW9LpRKuX7+Oa9euoVQqwel04qGHHsLly5fhdruPbIIm6zk1NYVkMmn5XTQaFYB9FNKG04iTWsiKxeIe9tt0XWDxpZaecAxS/w1AOmpys3fp0iUMDw8f+cbOlAf5/X5hPrnZ7evrQ2tr65nQ359WbGxsYHx8HKOjo8hms5KlpL3mUWXcgKMD4/Voxre2trC2tiabL8oE7RxSqGmOxWKyLkQiESHB7Njcew1s1xMselxZWcH8/LylSV06na5ayMm6HGrpmY0Oh8O4dOnSqWxujxxsM9bW1vC1r30NIyMjoo9pbm7G8PAwnn766XuKrbGL03Aj+au/+it4PB6kUikLqNZaau2VWSwWLcCak0J7e7vcwJ2dnWhubkZTU9OJeHDrKJfLdbcwByCFlATVlDzYgeHm5mZMTk4KyKymt6YlHgE2QQ79pDWA1jpSs5kCq6MJrjc2NuB2u0V+Qz1aS0sLOjs70dnZaWuDtba2hpGREYyOjgpr7/V6MTQ0hOHh4bpAWDKZxM2bNzE+Pi6s/dbWlviLt7a2or+/H0NDQ+js7MTs7GzVjEitphE62MzCbKTADptLS0uYnJy0sFYulws9PT0YHBw8MJt+lDE3N4fnn39ebN0SiQSefvppy4J+NwtZLT1vLBZDX18furu771mAreM0F3z62Gv2e2dnx7IR3traErabZILX6xU5GrA7J7S3t+OJJ57A0NDQsYzLQqGA9fV1XLt2DXNzc2fSWeYsBK+ndpUBdnXw2oXouOIkZCrb29uYmJjA1NQUZmZmxM9fkxi6IVgoFJLaJ7asD4fD6O/vr5v4uB+CNVUsftTWw8RG+Xzecn28Xi8GBgbw0z/90/dugeSbLU56Qcnn8/hP/+k/iYba6/WKQTx1vLrylwCvpaVFUlN9fX0n0ordLg7SwtzhcCAUClm01ZFIpKamkjrPpaUlLC4uCjtuPq/2tw6HwyiVSnvANK3vqjlbkNWmBIcSCJ5z/dVsKmT3GcrlMmZnZ3Hjxg0LGI1EIhgeHsbg4OC+LFs+n8fY2Bhu3boloG5lZQVutxvhcBjBYFBSbY2NjaKR8/l8NSv9gd3Fwq67IReMxsbGPQ0hyuUy5ufnMT4+junp6T16zIGBAfT29p74Bk/H1tYWXnzxRUxMTADYbdTwxBNPoL+/f8/fHhRE0qliampKOhsy2tvbxZv5brvrnbU4S+xasViURisE4GxgosETW1w3NTWhUChgZWUFhUIBXq8XbW1teOqpp3Dp0iW0trYeWfdI4M65KhaLmJ2dxdTUFGZnZ8+EZ/pZikgkgpWVFczOzmJ0dBRzc3MyNzudTnR1dYm//ml1fDxKMO7z+WQzxixsNpuVIkNuOvx+v9QrBINBtLa2YmdnxyIBJRBvamp602zaSqWStKdfXV2Vc8gmdN3d3Xj/+99/4u/rLbB9yDgNsP2Xf/mXYsxPCzQN7iiB0F0ZT8p2SMfGxsFbmGtQHYlE9gWX++mtA4EAtre3JXXs9/vhdrstWur9WACn0wm/3y+m/tzYFItF+Hw+y3tkoxrTGaHWBJfL5XD79m3cvHlTWHeHw4Genh4MDw/XtJbjJD81NYU33nhDgPrKygpyuZyY+OuGIXbANhAIoFKp2IJo6hEPUoCSTqcxNjaGiYkJCyseDocxODh4JO2b7zbK5TJGRkbw8ssvy0ZpeHgYjz76aFXwXw+IzGQy0vxEb/QO48F8r8ZZAtt2kc1msby8LACcG37ahJlyMM6xzc3N4iii+xs0NzcfGuDZnatSqYT5+XkpsNRzVENDg6Ub6L1mKnDYMM8TmeCxsTGL5aLP50NfXx+GhobOjFPPUYFxp9OJnZ0dKTqkrLJUKmFnZwe5XA4ulwsej0fYby39JOmiQTgz2m+WDVyhUMDa2ho6OzurasCPM94C24eM05CRfPrTnxZWEtit0tWFe0fRLOUgcdgW5hpcB4PBunbcm5ubAq613tr0+vb7/fB6vQiHw9ja2tp3IvH7/cIosIBSs1/VLANpQ0dwHYlE6l78UqkUbty4gYmJCdkg+Hw+nDt3DhcuXJDUYrXGLJlMBtPT0+LgwdbY1PmFw2FxSmlpabEF0PxZV1eXtLc+bGxtbWFiYgKjo6OW++IsLn5LS0v46le/Ku8zFovhbW9727468WogcnV1VQC2tpNyOBzo6OhAT08Penp6Dlwxf6/GWQfbZrA5FJnv5eVl6VybzWYtHTrdbjdaW1tx7tw58TV3u92WzrWtra11bybr8SRfWFgQ4K31qV6vF11dXWInd1qNVE4iap2n1dVVjI2NYXx8fM/mnjaCJ9VJ+TChwbgJymuBce35TclJIBAQ2QSZfxJxWjKlw+l02oJws8X5/RT3TYHkmyVOo137V7/6VfHuZLOUk0oN3U0LcwLserV1ut353NwcpqenBcQTCOfzeTgcDulEZlresajG7XZbCj21VRhfhwUXqVTK1t2ksbHxrrrZAbvXb3JyEiMjI1hYWJBNgt/vRyKRQDgcFoC/tbVlK69ZX1+Xc5LJZOTzRSIRtLW14fz58zh//jwGBwcRDocRCAT2TXsfduIpFouYnp7G2NiYxWXB6XSiu7tb9OBnZcLO5XJ46aWXcOvWLQC7LOFjjz2Gc+fO1XUP6fNUq313R0cHent70d3dfaxa0rMa9xrYtotMJmMB30tLS5idncXc3JzMPT6fTwquqP8m4LWzH7RzlTjIuSqXy0gmkwK8NbB0u93o6upCb28vurq6jlTqchainvPEjQltBPX82dnZKbK1e21TUi8YJ8BeWlqSgn1T8w3sznss2mRzM7u1jFJOOxB+r51DM94C2/dYnCXrv6OOg7YwN51Ampub62Z5yY6zOnt2dhYLCwsWTTRDA2tOErqNrwbVXV1dKJVKAnhyuRxWVlYsh10ls8fjsaSJo9HogZgR2hmRhU6lUhgZGcHt27fFEaZUKiEajSIej9d042lsbERDQwOy2Szm5+elcr1UKqGpqQkdHR0YHBzExYsXDy3POMh4qlQqWFxclAVNp+La2towODiIvr6+Y7eBPEhUKhXcvn0bV69elYzL+fPnceXKlQOB4Z2dHbzyyiuYnp62SKJcLpcA7J6enlPVoJ+FuB/AthmFQgHLy8uYmprCCy+8gFu3bqFUKqFSqSASiSAUCiGfz6NcLsPpdKKxsRGhUMgiFwqFQhbwzXb0hzlXlUpF3s/U1JRlbr4fx+NBx1ShUMDk5CTGxsYsjj8ejwe9vb0YHBw8sS64xx0ajLvdbil211aZuuOlDoJ03bvA5/OhUqlIXwe6RekIBoMWAM7H98om7y2wfY/F/QC2j6qF+X5BCzxqIVdXVzE/Py+NXNbX1/ekylh9zUUqkUiIxsz0OzXZ02KxiHK5jNHRUSmQstOMO51OWfQIruvRWVcz6ifIBnY3LGwUxM/m8XjE+UQXG7LIUDdmyeVyuHnzJq5evYqFhQWsra1JgWdnZycefvhhDA0Nob29fd/zXyvq1SKPjo5iYmLCsrCz+c3AwMCZdNJIp9P46le/Ku2hI5EI3va2t6GtrW3f/61UKuKgMjMzI+40wC6T2NnZib6+vvuSSbybuB/BthnpdBr/+I//iJGREXE4CYfDksnh3LC9vS0ddjlnkRV0Op3o7e1FQ0MDYrGYdH09DABcWVkR4G1mWhKJhADvezXTcjdjKpvNYnx8HGNjY5Y1oKmpCYODgxgcHDyTc9dholYPDkoiZ2ZmhLihS5UZBN10MCuXy8Kcs7GYPgjGm5qapCBTg/CzRL4Ab4Htey7uNbCdy+X2eFcfpIV5S0tLVZakWCxawDQPneriz1hVbb5eU1MT4vG4xZKQVdS1wAztfzRjnU6n0djYuGcnT+DOFG9LS4uFgd/Z2bHVSOvDTt7BoCNKMplEsViUiai9vR0PPPAAzp07J4yX3bnc2dnB+Pg4vv71r+PWrVtYXl5GqVSSwtdLly7h4sWL6O3tPTKAV2080aN7fHzc4qbh9XrR39+PgYGBuwb6xxWFQgEvv/yyNM/yeDx49NFHMTw8XFPWUitV39zcLI1mOjs77/lU6nHFmwFsM5LJJF566SW53/P5vGykaTsIwGI/qFtUm5s+r9drYb9bW1sPrPWvVUMQj8cFeN9LRbpHNaaSySTGxsYwOTlpIZTOalbuoHHQ81SpVJBKpcT3m413aJnHg7VAbF7ndDrFAW1rawu5XE5AuLa55UHwbYLw06pjeQts32NxVsF2rRbmduHz+faAat3CHLDujjWI5mPti83W3ATWdE4xb8RoNCo6w97e3rqbFGxsbAhbnUqlsLKyYquz5kJFcN3S0oJSqbRvl816giw0vzqdTiSTSdEue71euFwuDAwMYHh4eF8D/VQqhddeew0vvPACFhYWsLW1JSx2f38/HnvssWNjYPR4KpVKmJmZwdjY2B57rc7OTgwODqK7u/tMuyBMTEzgxRdflPHe39+Pxx9/vKoUiFpP+mCbRWhsqfzggw9aWMO3wj7eTGCbMTs7i6tXr8rnbmxsxKVLl9DW1maxHtTjJ5fLoVQqYXV1VeZadr/U91dTU5Ol+DIajda90ctkMsJ4a9cO4N6ynzzqMVUsFjEzM4PR0VFLvYnL5ZJ6k9P0/T9sHMV52tzclPFKly+CbtYraCLJ7XZL/dP29rYctCksFAqyuTQxQCgUEnMHDcaPu6D1LbB9j8VZANsHbWFu512tHTjsGrhQ61XNc5pAnI118vk8nE6nxWva5/NZ/K3b2trqYlby+byw1QTXdg1vPB6PMMZ+v1+KQNgcgBKPeoax1+sVIG36ShNcczFcWFjAjRs3RGYA7C6OFy5cwLlz52qmbclif/WrX5XGT5VKBT6fD4lEAo8//jguXryIjo6OY9UWRiIRjIyMYHx8HBMTExbGhx0mBwYGznwKOpPJ4Pnnn8f8/DyA3bH+1FNPobOzc8/flkoli8uD3mRpezW94L4ZQeRh4s16niqVCiYmJvDyyy+LXCEYDOLKlSvo6+uDw+GQuhEWX25tbYkTkG5dDdwpaDMLvx0Oh3S/ZIauubl53zkim81K90rKqhitra1CepxFScVxjqlqTkp+v18af51GR9vDxHGcJ+1Vn0wmxa2HUS6XsbOzA6/Xi6amJgHSwB0LYAJvDcL1cxCMEzMEAgHEYjG0tbUhHo+jvb1dJKRHsRbel2A7lUrhk5/8JF588UUsLy9X1QI7HA588YtfPMxLnFqcxsVKpVLY2NjA5OTkvi3MTSeQYDAoRvla5sGv+/lOsvukz+eTlCkbo3g8Hgsb43a75WahJeF+hTrFYhHpdNoCrrlosXlPPp+XFt/0zaacwtxg0I1Eh9Pp3OMnbYLq/d7nzs4OxsbGLJ1Rgd1GLRcvXkR3d3fNCSGdTuPq1at4/vnnsbi4KD7PLS0teOCBB/D4449jYGDg2AubqGVMJpOWRjpNTU2yyBxVS+TjjGKxiGvXruH69esol8twuVy4fPkyHnroIcuYLBaL4l88MzNjmYt8Pp+AjWqNQ96sIPKg8WY/T+VyGTdv3sS1a9eEGIhGo7hy5cqejV84HMb4+Lil6Y7WFReLRcm4cUx6PB40NTVZxrbH47HYD0aj0ZpsNf35p6ensbi4aPkdpVK9vb1npsvzSY2pdDqN0dFRjI+PWzJckUgEQ0ND6O/vP9Pym5M4T5VKBdlsVoA3G+6Y4fP5pBaBIFx72WezWWHONRi3I9MIxgOBAFpaWgRbtLe3o6OjA7FY7EDZ1vsObI+NjeF973tfVV2wGSMjIwd9iVON0/LZNkFkY2OjNILx+Xzi0GE2aKin7TaL9XQBotvtFs01m0CY15M2WGSto9FozRScttxbXl7G3NwcksmkpJ0IrDVLrtvcVntuv98voDmRSKBcLlsKD/1+/6F3xtlsVtqoE6i53W5po15rYSoWi7h58yaeffZZ3Lx5U64fQd7TTz+NBx544NgXt3w+L1X6bBseCARQKBTuySr9mZkZPP/888IGdnV14amnnpKi3Vqd+RobG4XBruczv9lBZL3x1nnajZ2dHbzxxhu4fv26jLtEIoErV64gFosBsD9X29vbFstBs1lXPp/HxsYGnE4nnE4nyuWy2LjpqNeudGtrSzS7CwsLlrk9HA5L5+HTZHdPekyx++3o6Cimp6clq0vv/KGhIXR3d5+5uo3TuvdyuZyF+TbHLHCn6ZvuC+L3+y0ZnY2NDYvtJok3gvBqOJI1Zsz0aEacGW89v993YPvHfuzH8A//8A/4ju/4DvzYj/0Y+vr6zrS5/EHjNHy2v/KVr2BnZ0caLLCrFNnpalIPBlM9pu80v7pcLvG3ZmdGOxePYDAowJptz6tFLpfD0tIS5ubmBFQnk0lsbW0hn89bGEayNvrghObxeGo2Z9HyDuBobqhKpYK5uTncuHEDc3Nzls9/8eJFDA0N1WSg0+k0nnvuObz44otYWlqS4qhoNIpHH31UZA7HqQ1kK/ixsTHMzMzsWTiuXLmCcDh85haOWrG+vo4XXngBMzMzAHbZ+CeffBI9PT3Y2dnB7OwsJicnMTc3Z8l4NDU1CcBua2s70KbiLRBZX7x1nqyRy+Xw2muvYWRkRABIT08PHnvsMfT19dXlH51Opy2+39oRqFKpYGtrC8ViUeYRdoQ12T424iIIj0Qilrknl8thZmbm/9fenYe3VV174/9KlmVbnudBsi1btuR5kOMhIYFmKFBSUkILbaGEllI6QWmhQ+iv5b10glsKLaUvFNp7S4G2hAAtEMLYkEAIHmI5nkfZkjzPtmzLssbfH37PrhTbiWxLluysz/PkaZFsaXvr6Ghpn7XXglarxcDAgNPnSWhoKLv6w31Z2CjePKaWW6AAFj9LpVIpZDKZz2wU95X3ntVqZccsF4Avt2IdGhrqFHwvlw7FBePcajr3jyuEMDExccG4h1uAc1wRr6io8MpVG48F2yUlJYiLi8Px48c3zUrZanijXfs///lP+Pn5LUmP4HBpElzTlvMD6vN3WttsNoyPjzu1PV+u9nRUVBTi4+NZC3juSxN3mdPx3+TkJIaGhtgGC71ev2z6EFeBhCvvx62IOwbRq0nvON96TjwmkwldXV1obW11+rIhkUiQlZUFsVi84jFtsVjQ1NSEDz/8EB0dHexvDwwMREZGBnbu3Ins7GyP50CPjo6ynfcXuiTqKydoV9hsNjQ3N6O+vp4FF7m5ucjOzsbg4CA0Gg0GBgacVlVCQ0ORkpICqVS6riBhM82TN9E8LW92dhb19fXo6upiX7qLi4vXVB/fYDA4pZ6Mj487HfNWq5V1z+Xz+bDb7ewKoSMu3dBxAyaXs20ymZy+tDo+fkhICNs4vNovrWvhK8eUXq+HWq2GWq1etgSqTCZzqRSup/jKPC1Hr9ezL4uOHaAdcdV4uNTT2NjYi1bdslqtGBkZweDgIIs7uAZ1XKWU88XFxeFnP/vZhselHgu2lUolPvGJT+DRRx9d08B83UYf1CaTCcePH2cr2uene4SGhiI4OPiCBxDXnIELhEdHR5fkOvv5+bETb3h4OEQiEUwmk1Ndaa6eNNdSnNtEOTMzs2KjmOjoaBawJyUlISEhgQXT60nvWMlaTjxTU1MsVYSbF6FQyFJFLrR5aHR0FKdOncLZs2ednjchIQGlpaWoqKjweMvy2dlZVlPWsQW7SCRCWloaZDLZksvBvnyCdjQ4OIjKykr2d3H1xsfHxzE4OOi0uhEWFsbyTi9WCcZVm2WevI3m6cKmpqagUqmg0+kQHBwMo9GIrKws5Ofnr/kLOLeS6BiAn78gwzXT8vPzA4/Hg81mg7+//5KragEBAU7Bd0xMDGuWcqF0rJSUlBX3O6yXrx1TF2ruFR8fz8oIbnRDIV+bpwtxjEW4/z0/FuH2Mjnu/1rNF1PHxT8uEB8dHUVWVhY+85nPuPtPuiiPBduHDh2CyWTCCy+8sKaB+TpfqEZyMQaDwWnVemJiguU9mc1mmEwm2O121lQlMDAQfn5+LIg+/2V33ODABd58Ph9CoRBCoZDtRI6MjIRYLIZYLIZEIkFsbOyGpym4Ok9cmkVra6vTRsGIiAjWmXGlb9cWiwVnz57F6dOnoVarWcAXEBCA/Px87Nq1C5mZmR4tlWcymaDVaqFWq502OwkEAqSkpEAmkyExMXHFD0FfP0EbDAacPXsW3d3dMJlMMBgMiIqKYt3POBERESzAduWktlq+Pk++gubJNaOjo+jo6EBnZyeAxS/1ubm5yMnJcUsNfa5EquPq9/mX2xcWFtjijd1uh9VqXfa5Q0NDnVJPjEYj+vr6lt1onJycvKSSz3r58jFlsVig0+lYuVSOn58fUlNTkZ6efsEroe7ky/N0MTabDZOTk07B93Jdqx0rlcTFxSEqKmrVx9mWy9muqanBl7/8ZTz++OPYs2fPmgbny3wx2Obyrbm6wePj42yzIfe/XPF5rqXwSoXlucCGC8oXFhaWtHDlSuydvxHHF8rEXewNZTQa0dnZifb2dvam5vF4SElJQVZWFhITE1f83b6+Ppw6dQoqlcrphCCRSLBjxw5UVFR4dH8Ct4FHrVZDp9M5XeJNTEyETCZzufGNr56gbTYb2traUF1djaGhIdaoyLHOd1RUFNvA5enKKb46T76G5sl1kZGRaGpqgkqlYnWwg4KCUFBQAIVC4dZVYqvVyjacOZYedMR1CuQ6B1qtVthstiWBouOGNC6ndnx83KmcG1ejngu817PYslmOqbm5OXR3d6Orq2vZK4sZGRkeWQjgbJZ5chVX85vL+3bsxszhKp85pp9crDHRlgy2T5w4gWeffRaf/vSnsWPHjgteZiotLV3tU3iVtw7qiIgI9Pf3Y2ZmBv39/ejr60N/fz+GhoYwOzvLis5zuKA6LCwMoaGhTpe2goKCIBKJEBAQwDZecqva3GVGxxOtQCBYUmLKmzlqF7LSG2p8fJzVleaC1MDAQGRmZkKhUKx4mWphYQEff/wxzpw5A61Wy24XiURQKpW4/PLLkZqa6pk/xmHsarUaPT09ThtOwsPDWR72avM/ffEErdFocPz4cXR3d2NmZgYhISFIT09HcHCw12oC++I8+SKaJ9dxc2W326HRaKBSqZxqdBcXFyMtLc1jq6Kzs7NOl/KX22zGfQ5wX3AtFsuyG9K4AJ1LORQIBOyzRiAQQCKRsO6rq12534zH1NjYGLq6upbsmYmOjoZMJkNaWprbOyhuxnlaDYvF4lSpZ2RkZNn9YBEREU4bL8//nNhywXZWVhZ4PB77JnKxE0Zra+tqn8KrNvrFslgseOWVV6DT6ViVkPNPeo653FFRUUhISEB4eLhTxY6goCB2QpycnMTY2JjTt3AO1zyBy9uLjY1d0lnSlzm+oWw2G7RaLVpbW512lEdHRyMrKwtpaWkrrryo1WqcOnUK9fX1TifNjIwMXHbZZSgtLXVb6/TlzM3NoaenB2q12umYCwwMZHnY68kF95UTtF6vR1dXF06ePIn29nYAi5diU1JSkJ+fD6lUipSUFK99ufOVefJ1NE+uO3+ubDYbOjo60NDQwFadIyMjUVJSAolE4vHxWCwWjI+POwXgy1WR8PPzY/neXFOT80u9cft4TCYT69EQEhICoVAIsViM1NRUJCcnu5TXvJmPKS5NsaurC319fR7tyruZ52ktuBLCXOB9fpdWDleamFv5lsvly8Y8nuaxYPvw4cOr+kb+4IMPrvYpvGqjD+qxsTHcf//9bPWAK+PHbTiUSCQQi8WsrmRAQAArQO9Ys3KlUjlcXh73bzVtgX1RZGQk+vv70dHRgY6ODvbhxefzIZVKkZWVhbi4uGV/d3Z2Fh999BE+/vhjpzzusLAwlJaW4vLLL0dCQoLHxm42m6HT6dDd3b0kD9Dd7YS9eYLW6/XQarXo6elBW1sbdDod22ykUCiwe/duyOVynygZeql9kK0VzZPrVpori8WC1tZWNDY2spW7+Ph4KJXKDS8zp9frnVJPHPf9cHg8HgIDAyEQCMDj8WCxWJzqIs/OzmJiYgLj4+OsYlZoaCir4pGWloaUlJQV0w+3yjFlNBrR09PDGhlxAgIC2MIJVUxaH67UsGOt7vO/CKakpHgltZnata+RN+psf/jhh/D393datXb8QmMwGJxam4+NjS17mYVrn+6449wX8qzdhavr3djYyL5YiEQiyOVyyOXyZbuAWa1WtLa24sMPP0RzczML+vh8PrKysrBz504UFRV5bLOj3W7H4OAg2+HumArkyR3uG32CnpqagkajgVarxeTkJGuXrNfrER4eDqlUimuuuQZSqXTDxuQK+iBzDc2T6y42VwsLC2hsbERraysLGJKTk6FUKj2a+3sh3KV8xwB8uQpUgYGBCAwMZJ9Pjh2Hx8fHMT4+jvn5eafgOy0tjZXydDxHb8Vjanp6mnWrdKwcExYWhoyMjDWVhNyK87ReVquVXa3h6nNze6s2GgXba+TtDZImk4kF1FxwvVz9bcc8a27FeiNzXTeK1WpFT08PWltbMT4+zjptxsXFITs7G6mpqcuuBI+OjrLGM2NjY+z2mJgYlJeX4/LLL/doAfypqSl0dXWhp6fH6fULDQ1lediefL024gQ9MTHBAmzu8p3VakV/fz8MBgMiIyMRFxeHsrIyZGdn+2SqEn2QuYbmyXWuztXc3Bzq6+vR2dnJVoszMjJQWFjoE3tmuNbbXPrJcl2jBQIBQkNDWcodVx95aGhoyWeXQCBAcnIyFAoF8vLyUFhY6FIH5M3oQossW2Wzu6/ZcjnbW503XiytVoupqSl0d3evmGcdERHhtGIdERHhk8GLu8zOzqK9vR2dnZ1shcXPzw+FhYUQi8XL1lk2Go2s8YxarWar2EKhEDk5Obj88ss9GvTNz8+zPGyuCgH3/Onp6UhPT18xxcXdPHXiGRsbYwG2Y2MgPp8PgUCA8fFxBAUFQSAQQCqVorS01CfSRVZCH2SuoXly3Wrnanp6GnV1ddBoNAAW30sKhQIFBQVu32y3HmazeUnLecdKJRyuz4Kfnx8MBgP6+/uh0WiW5N3GxMQgLi4OCoUCKSkpiI2NRVRUlEdLqnqD2WxmZVwd0xcFAgFSU1NZGdeV0nPpveeaLRtsDwwM4P3334dGo1m2djOwGCT+6le/WutTrJrZbMaJEydw4sQJNDQ0sPrEGRkZOHjwID7/+c9f9I280S/W7OwsXnrpJbZiCyyufjqmg2z2POvVGBwcZHm+3DEVEhIChUKBzMxMJCYmLtl81Nvbi48//hjnzp1j9/H5fCQkJKCiogLbt2/32CqyxWJBb28vq8fKjZnP50MikUAmk0EikWz4B4i7Tjx2ux2jo6PQarXQarVOJRH9/PwgkUgQHR2N/v5+DA8PA1g8fsvLyzdk89d60QeZa2ieXLfWuRobG4NKpcLAwACAxaZhXI3ujW6k4gpu79D5q9/n467C2mw2jI2NYWBgAENDQ7DZbCwd0rH1tlgsXtL9cqt0q+YalHV1dTl9+QgODkZ6ejoyMjKWlDul955rtmSw/Yc//AFPPvmk04a886uTcK1rN7IaiVqtxjXXXAORSITt27cjLS0NMzMzeP/99zEyMoLdu3fjySefvOAb1xsvVlNTE4KCghAQEIDo6GifWs3YCBaLBWq1Gm1tbU7zn5iYiOzsbEgkErYazb2hpqam0NDQgMrKSqcNeCEhIcjNzcWuXbsgk8k8soptt9sxMjLC2qY75s/HxsYiPT0daWlpXs2XX8+Jx263Y3h4mAXYjpd6Hct9xcfHo62tjeXQ+/n5IT8/H/n5+ZtmdYo+yFxD8+S69c7VwMAAamtr2dWxwMBAVqPb199XXAdBx3/L7S8KCAiAv78/dDodJiYmYDabMTs7C4vFgqCgIERHRyM6OhoikYi1+3ZcgNoKn5Gjo6Os5KvjFYKYmBi2wTQwMJDeey7acsH28ePHcc8990AsFuMb3/gG3nrrLZw5cwZ//vOf0dvbi+PHj6O6uhpf+cpXsHv3bpSVla3pD1iL4eFhvPfeezh48KDTRgyDwYBbbrkFTU1N+N3vfodPfepTKz6Gt3O2LyV6vZ61UedOyAKBgLVRPz+nmjuRv/vuu2htbWWrKP7+/khKSsKOHTugVCo9toqt1+uhVqtZnWgOVy9aJpN5vAmLq1Z7PNlsNgwNDUGn00Gr1TqVBvP390dycjKkUilrZNHX14eqqio2D2KxGOXl5Ztu38Cl+L5bC5on17lrrrga3dwKaEhICIqKiiCTyTbNSq9jGTdu9ZtLleSu5prNZszMzMBsNrOru3a7HfPz87BYLKzRjuPGQsema5v96q/VamVXR/v6+pZcHS0pKUFoaOiWTht1hy0XbH/pS19CfX093nrrLYjFYtx3333417/+5bSC/cwzz+Dhhx/GX//6V2zbtm21T+ERx44dw7333oubb74Z999//4o/R8G2Z9ntdvT396OtrQ19fX3s9tDQUGRnZyMjI8PpkqndbsfAwACamppQW1uLiYkJzMzMsHrhubm52LFjB6RSqUdORkajERqNBmq12qmsk7+/P6RSKdLT05GQkOBzH36uHE9cx0qdTgedTudUfUAoFCIlJQVSqRSJiYlsRW12dhY1NTWsAVBwcDDKyso83vjHUy6V99160Ty5zp1zZbPZ0NXVhXPnzrErTBEREVAqlUhJSXHLc2y0hYUFjI6OYmFhgZ1XuSuTFosFk5OTmJiYwMLCAkQiESs9KBAIWKfj8zeQLtc/IiIiwufOyxez3L6f4OBgWK1WSKVSZGRkrKv/wlbmy8H2mr4Gtre3o7i4GGKx2Ol2Lm0EAL785S/jpZdewpNPPon/+Z//WcvTuB33rdfXL8NtVSaTCV1dXWhra3PKVROLxcjOzoZYLHY6Mer1enR2duLs2bPo6elhb6LIyEjI5XKUl5ejoKDAIyupXEWN8xsW8Hg8JCUlQSaTISUlZVOupFitVgwMDECr1UKn0zld4g0MDERKSgpSU1ORmJjo9OXFZrOhpaUF586dg8ViAY/HQ05ODoqKijza/IeQSxmfz4dcLkd6ejra2trQ0NCAqakpnDhxAnFxcVAqlR7tDeAJAQEBkEgkiIyMhEwmg91ux+TkJNt0OTIygtjYWFitVkxNTWF8fByTk5Pg8XgIDQ2FSCTC/Pw84uPjWSA6Pz+PiYkJTExMoKOjA8DiZ75jj4mYmJhVl93baEFBQcjJyUFOTg4mJyfR1dXFrgq0tbWhra2NdRbmuu8S37emSMFkMjkVaOf61XP1dDkKhQKnT59e5xDd5+WXXwYA7Ny508sjubRMTU2xVBGu/JFQKIRMJkN2drZTsGw2m6HRaNDU1ITm5mYMDw+zTmUJCQnIzc3F3r17ERER4ZEvTSvl0EVFRSEjIwNSqXTZWt6+zmKxYGBgABqNBr29vWwVCVg8uXNt0uPj45e9OjA0NITKykqWthMfH4+Kigqv1QQm5FIjEAiQl5eHzMxMNDU1sa653BXmkpISREVFeXuYa8Lj8RAVFeU0fqPR6FT1hCslOD4+juHhYVitVqjVapbfzbWMDw0NxcTEBMbGxmA2mzE0NMQKJQCLfRnOD8B9cfMpsLiwVFpaivDwcDQ3N0OtVkOn02F6ehq1tbWora1liz+pqambcvHnUrGmVyY2NtaprBnX+aqzs9MpZYR7Q/iCI0eO4IMPPkBFRQWuuOKKC/6sN1uXb5XgxW63Q6vVoqmpie2sDwgIYAFzZmam02ooV4GktrYWfX197PiKiopCcnIytm/fjry8PI/UxZ6ZmUFnZyc6OztZLqFAIEBYWBgyMzORmZm5KT/EzGYzJicn0d3dDZ1O5/RFJzIyEmlpaUhLS7tgCsz8/DyqqqrYSlF0dDQqKiogl8s37O/YCFvlfedpNE+u8+RcJSQkYMeOHVCpVGzvyr///W9kZGRg27Ztm2rfxIXmKTExkf1/m82GiYkJDA8PY3BwEO3t7ejp6cH4+DjrLAgsXp3LzMxETk4O68EwOTnJelbY7XYWtLe3twNYTMtxbPsdHR3tc/nR3MZzk8mE7u5udHR0YGhoCNPT01CpVGhsbERaWhrkcvkFywhudb56jlpTzva3vvUtNDc349SpUwCA2tpa3HzzzSgvL8eTTz4JkUjENlEWFRXhhRdeWPXAHnrooWV3MK/k0KFDK3ame//993HXXXchLi4OL7zwwkXrHFPO9toZjUZ0dnaivb2dlYjj8XhITk5Gdna208lzdnYWarUazc3N6O7uZqvYgYGBiIuLQ05ODgoKCiCVSp1Wsd0xTyaTieVhc+XqgP/UPU1PT0dSUtKmO2GZTCb09fVBo9FgamrKKV0nJCSErWDHxsZe8G+z2+1ob2+HSqVi78OsrCwUFxezK1lbxVZ4320EmifXbeRc6fV61NXVoaenB8B/0k4KCgp8/irceudpfn4ew8PDLL2iu7vb6Yokn89HVFQUpFIpcnJyEB0dDX9/f5YzPjY25rTRnePn54eoqKgl5Qe9ZaV50uv16O7uhlqtXrJhXyaTQSaTbaovXuvlyznbawq2jx49ip/+9Kf4y1/+gu3btwMAbr31VlRVVUEgECA4OJh9yD/11FO4/PLLV/sUKC4uXlVnqWeffRbl5eVLbj916hTuvPNOREVF4fnnn0dycvJFH4uC7dUbHx9nJzvuaga3wqBQKFienMVigU6nQ2dnJ1pbWzE8PIzJyUnw+XzExMQgKSkJxcXFUCgUK65ir3WeuM2AXV1d6O3tdbrqkpSUhPT0dJc7evmShYUF9Pb2QqPRYGBggOWXBwcHg8/nQyqVIjU11eVNNWNjY6isrGRdN6Ojo7F9+/YtuylnM7/vNhLNk+u8MVfj4+NQqVTo7+8HsLhwkJOTg7y8PJ9Ok3DnPFmtVnR0dKChoQEdHR1sEybwnw2UUVFRSExMRGJiIuLi4thGy8nJSYyNja3YqCcgIMBp82VMTMyGlXZ1ZZ6Gh4eXLUUbFxfHStFutYWS8225YJtbPYuKimIB0ezsLH7961/j3//+N6anp5GWloY77rgD11577aoH7i4nT57EXXfdhcjISDz33HMuV0ugYNs1NpsNWq2W5Q5yoqOjkZWVhbS0NJZDNjo6iq6uLrS3t6O/vx8jIyMwGo0IDw9HbGwssrKykJ2dDalUetG8s9XO0/j4OGub7lhtIyIiAjKZbFNuMjEajaxE3+DgoFO9+/DwcEilUhQWFq7qUujCwgLq6urQ1tYGYDHdRKlUQqFQbLoV/tXYbO87b6F5cp0352pwcBAqlYpVTgoICGA1un0tp9fT8zQ6Oor29nY0NzdjaGgIMzMz7IpreHg4oqOjERkZCaFQyBrqxMXFQSgUsjKzY2NjmJiYWDYlNjQ0lAXfXF1wT8zxauaJa7LW1dWFgYEBpzKCKSkpyMjIQFJSks+lybjDlgu2NwMu0A4PD8fzzz+/YorJcijYvjCDwYCOjg50dHSwqw98Ph+pqanIzs5maToGg4Hllul0OoyMjGBiYgJCoRBxcXFITExEbm4uFArFqvKsXJmnubk5dnnNsaNZUFAQK5+0XLt3X2YwGFiAPTQ05NSxNTIykq1gc1+AV3M8dXV14ezZs+zLiEwmw7Zt27ZE44iL2SzvO2+jeXKdL8yVTqdDbW2tU03rwsJCZGRk+EygtZHzNDExAa1Wi+7ubgwODmJmZoYF34GBgWyDJncVICgoiAXf0dHR4PF4mJqawtjYGEZHR9m8OuI2ejqugIeHh697sWKt82QwGNDT04Ouri6n3w8KCkJaWhoyMjI25X6klVCwvcG41JHw8HA8++yzSE9PX9XvU7C9vJGREbS1tUGj0bCVVJFIBLlcDrlcDpFIxFqnd3Z2QqPRYHh4GCMjIzCZTKz7l0wmg0KhcFr5Xo2V5slsNrOTKbcpE1jMv0tJSYFMJtt03+hnZ2dZgO2YWw6A7cBPTU1dNi/PleNpamoKlZWVbLd+eHg4KioqnHLrtzpff9/5Cpon1/nKXNlsNqjVatTX1zut6CqVSp+oi++teZqenoZGo4FWq2X1vGdnZ6HX6yEQCCAUChEREeGUdsHlf3MBeHh4OIxGIwu+x8fHl0199ff3dwq+o6OjV30l1R3zND4+zhqyOV7hjYyMREZGBtLS0nw+x/9itmywbbFYcPLkSTQ2NmJychIFBQX43Oc+BwAsFzcjI2NDL12p1Wpcd911MJlM2L9/P9LS0pb8jFgsxvXXX7/iY1Cw/R9WqxU9PT1obW11qkATFxeHrKws1khmYmICnZ2d6OnpwfDwMIaHhzExMYGQkBDExcUhPj4eCoUCcrl83d+kHeeJa3jT3d0NrVbLKm4Ai1VyZDIZpFKpz+YsLmdmZgY6nQ4ajcapiQ6wWAlIKpUiOTn5ohtfLnQ8mc1m1NfXo6WlBTabDQKBAEVFRcjJydlUX0bcwRffd76I5sl1vjZXVquV1ejm8pFjY2OhVCq9+sXaF+ZJr9ezBQ3ufGuz2TA3N8ea6Pj5+WG5UEkkErHgOzY2FgEBAU6532NjY06fSZzg4GCn1vMxMTEX3Cvk7iZJ/f39rIzg+T0kMjIykJyc7HMpR67YksH22bNn8cMf/hCDg4Osmc11112HBx98EADw9ttv47vf/S4ee+wxXHnllWt5ijWpqqrCoUOHLvgzZWVleO6551a8n4LtxRXV9vZ2dHZ2sm/Bfn5+SEtLQ3Z2NqKjo2E0GtHd3Y2uri4MDw9jdHQUw8PDsNlsiI2NRWxsLJKTk1lDBne9eSMjI9nz9vT0OK0mhIWFsTzs8zuM+TK9Xg+tVguNRuP0pQZY/NKQmpqKlJSUVTVkWOl40mq1qK6uZm2RU1JSUFZW5vPNHjzFl953vozmyXW+OlcmkwnNzc1obm5mQaBYLIZSqfRKWp2vzdOFriSKRCKEhIRAKBSyBjrnh098Pp8F0Vz6icViYbnfo6OjmJqaWjZwj4iIYCvfsbGxiIyMZAsfnpqnhYUF1q3ScWFHKBRCKpVCJpOx0s6bwZYLtru6unDDDTfAbDbji1/8IkpKSvDd734XBw8eZMG2yWRCeXk59uzZg0ceeWT1o/eiSznY5upd63Q6dkIICQmBQqFAZmYmhEIhBgYG0NnZid7eXkxNTbFV7MjISMTFxSEmJgYymQxyudytJ3Au/2x4eBg6nY7dHhAQgLS0NMhkMqdmS77u/EuZHB6Ph4SEBNbJca2X9s4/nvR6Paqqqli1gtDQUJSXl0MikazvD9nkfOF9txnQPLnO1+dqfn4e9fX16OjoYCubUqkUSqVyQ0vF+fI8cXtkdDodW1TkREREQCKRICQkBGazmXV4dEzP4ISEhCAmJoatfoeHh7OmO9w/LsXHkZ+fH6KjoxETE8MyBDz52uj1eqjVaqjVaqfxhIaGsgUsXy8j6MvB9pqWGp944gksLCzg6aefXrEbo1AoRG5uLlpbW9fyFGQDWSwWqNVqtLW1OR2oiYmJyMrKQnJyMmZmZtDU1ITu7m7o9XrWRIDP5yM2NhZpaWmIj49nq9juKp/HlQpUq9VsZzVX0k4ikSAjIwMSiWTTpD5MTk6yANtx4yZ3CS8lJQUpKSlu3ZhotVrR2NiIxsZGWK1W8Pl81iBhM14qJISsT1BQECoqKpCTk4Nz586hu7ubnZfkcjkKCws3ff7ueolEImRlZSErK2tJ9aepqSl2/g4NDWXVn4RCIUZHR1nny8nJSczOzmJ2dhYajQbAYhDN5W+np6ejrKwMwGJONZf7PTo6CpPJxD5ntVot5ubmEBgY6JT7zaWuuENYWBiKi4tRVFSEoaEhqNVqaLVazMzM4Ny5czh37tymTc30BWta2b7sssuQnJzs1KwmKyvLaWUbAO69916cOnUKZ8+edc9oN8ilsrKt1+tZG3WuLqdAIEBGRgYUCgWCg4PZTubR0VHo9XoMDw9Dr9cjKioK8fHxCAsLQ3p6OuRyudvqMNvtdgwNDbEPAMfW4lyeYWRk5IbVOF2v8fFx9kHm2GSGz+cjKSkJqampSE5OdvvfExkZiaamJlRWVrKGB0lJSaioqPD5FYqN5Mura76E5sl1m22uJiYmoFKp0NfXB2DxcyA7Oxt5eXkerc282eYJWLxq39vbC61Wi/7+fqeSgCEhIeyKZFxcHCwWC0sf4Va/l6vhHRoaylIv4+LiEBERgdnZWfa7XLDvWOL1/N/lgvCoqCinJnDrYbFYoNVq2WIXx1eLDmy5lW29Xo+EhISL/pzBYFh2cwDxHrvdjv7+frS1tbETK7D4hs3OzoZMJsP4+DgaGxuh1WpZp62RkREEBAQgLi6Olc3jVrHd9Q13enqa7ZY+/zJWeno664a1GU7Qo6Oj0Gg00Ol0Tp29/Pz8IBaLWYDtqdWBubk51NbWorGxEcDiKk1ZWdmqSmASQi4NUVFR2LdvH4aHh1FbW4uRkRE0Njaivb0d+fn5yM7Opqtg/49QKGTdGc1mM/r7+6HRaNDX14fZ2Vm0tLSgpaUFIpGIBd55eXksIOWuDDuufnNlCLu7uwEsftnhgmexWAy5XI7Z2VlMTEyw/O+xsTHo9folv8tVTXGsgBIWFram8oMCgYD9rVw53a6uLkxPT6Onpwc9PT0QiUSsjKCvtkr3BWta2f7EJz6BqKgovPLKK+y25Va2r7rqKvB4PLz11lvuGe0G2Yor2yaTCV1dXWhra3NaXRWLxcjOzkZoaCirSz07O4uZmRkMDw9jfn4eMTExiI6OZm8quVzuttxoo9GInp4edHd3r7hBIy4uzulE4YvBtt1ux8jICDQaDXp7e52+LAgEAojFYkilUkgkEo92qLTZbGhtbUVdXR0CAgJgMBiQnZ2NoqIiuuy3Al88nnwRzZPrNvtc9fb2ora2lqVKiEQiFBYWIjMz062rmJt9nhxZLBYMDAxAq9Wit7fXqYtjYGAgSxE8fyXYZDItWf12/F3gP92AubxvbvXbbDazwJsLwpfLGxcKhUvKD64nTWhsbAxdXV3QaDROzxcdHQ2ZTIa0tDSv9GjYcivbFRUVePXVV1FZWYmKioplf+bdd9+FVqvFzTffvJanIG4yNTXFUkW4qwzcN/PMzExMTEygqakJQ0ND7JLX+Pg4RCIREhISEBISgsjISMjlcshkMrcEbFarFX19fVCr1ejr63MqPSQWi1ketq+vpHDpLlqtFjqdzqkqir+/PyQSCaRSKcRi8Yb8LcPDw6isrGQnm5SUFOTl5W2ppgWEEM9LTk6GRCKBWq3GuXPnMDs7i48//hjNzc2sRvdW7iq7FgKBgAXUVqsVg4ODLPA2Go2sEZxQKERycjKkUimSkpIgFAqRlJSEpKQkAIufK+evfpvNZraCrVar2fM5pp4oFAoEBgZiZmaG5X1zn+cmkwkDAwNOqSDcxk3uX3R0tMsLQdzvlJWVoa+vD11dXejr68P4+DjGx8dx9uxZiMViyGQyJCcnuy2tZTNb08q2Wq3GwYMH4e/vj3vvvRef/OQnsWvXLhw8eBCHDx/Gu+++i1//+tcwm8147bXXkJyc7Imxe8xmX9m22+3o7e1FW1ub05srPDwc2dnZCAkJYWXmuDcxV7KP66Ll7+/PVrG5jpDrNTIygu7ubvT09Djlra3227A3V0NsNhsGBwfZZhnHb/Xnn0Q36gRjNBpx9uxZdHV1AVhcRSkpKUFpaanTJkyyvK20uuZJNE+u20pzZbVa0d7ejoaGBna+i46OhlKphFgsXtdjb6V5WonNZsPQ0BD7zJifn2f3cYsyqampF1xgCg4ORnt7u1MJwfNXv4HFz/jzc7/tdjumpqac0k+Wm3Mej8fKDzp2v3T1SgZ3lVqtVmNsbIzdvpHVwnx5ZXvNdbbfe+89/OAHP1j2kgWwOMGPPPII9u7du5aH96rNGmwbjUaWKsKlMfB4PCQnJyM1NRVzc3Po6uqCXq+H1Wplmx65wvwBAQGIiIhgq9ju2Bij1+vZJkvH3OXg4GCW58W1F3fVRr+hHFcpdDqd0xeFgIAAlpeXmJi4od/g7XY7Ojo6oFKp2JjkcjmUSiUCAwMviQ8yd6B5cg3Nk+u24lyZzWZWo5vbtJ6YmIiSkpI1b47fivN0IY7phjqdjvU6ABZXqpOSkli6oeNV5PPniQugudXvldrHC4VCp+A5Li4OQqHQKf2E++c4Fscxcave3OO40o9hamqK7b9yfNywsDBkZGQgPT3dI30dtmSwDQD9/f145plncObMGfT398NmsyEhIQE7duzAbbfdhpSUlLU+tFdttmB7YmICra2t6O7uZjujAwICkJGRAZFIxC4f2e12tsPZbrcjOjoaYWFh8PPzg1QqhVwud0sBe5PJxPKwHRsDCAQCSKVSpKenIzExcc2XITfiDXWx/DuuyUxiYqJXdmKPj4+jsrKS5blHRUVh+/btTisHl9oH2VrRPLmG5sl1W3mujEYjGhoa0NbWxlIAU1NToVQqER4evqrH2srzdDF2ux1jY2PQarWsxB7HsVJVSkoKEhISLjpPXOt4xwB8uQIVERERrB8GV1GMx+Nhbm7OKf1kbGzMqRIYJygoiAXvXDrJSumldrsdg4ODrIyg43gSExMhk8mQmprqtn1MWzbY3qo2Q7Bts9mg1WrR1tbmFNBGR0cjPj4eZrMZWq0WJpMJVqsVY2NjWFhYQGBgIKKjo+Hn54fw8HAoFAqkp6evu+ycYwvY3t5ep3JIYrEY6enpSE1NdUvusqfeUBaLBX19fSzAdjwxOO4sT0hI8Fq+oslkgkqlQnt7O+x2O4RCIYqKipCVlbUk6L+UP8hWg+bJNTRPrrsU5mpmZgb19fUsfY3H4yEzMxOFhYUIDg526TEuhXly1cTEBCsR67hKzePxIJfLERUVtaoeDDabzWn1e2RkxCmg53Cr31wAHhsbC6FQCLvdjunpaacNmJOTk8uWH+TSV7gV8KioqCWfR1xMolarMTg4yG4XCARITU2FTCZb1yIcQMH2puPLwbbBYEBnZyfa29vZhjw+n4+EhAQEBgZicnKSPcbc3ByrPMLVpebz+WwV25XyjRczNjYGtVqNnp4ep5SiyMhIloft6onXVe58Q5lMJhZg9/f3OwXY59dM9faGoO7ubtTU1LCcv/T0dGzbtm3FXeX0QeYamifX0Dy57lKaq8nJSdTV1bGuvn5+fsjKykJ+fv5FF3EupXlajampKbavanJyEsHBwZibmwOPx0N8fDz7XFrtZ+v8/LzT6vfY2Niyq99cN2gu/5u7YmGxWFj3S+73lwvgubb1jivgjr0dZmdnWRlBx+powcHBSE9PR0ZGxqqvknDjpmB7E/HFYHt0dBStra3QaDTsm2VgYCDCw8PZ5SibzQar1cq+fQqFQpYPHRYWBoVCAZlMtu5V7NnZWZaH7fgNPCgoiOVhe7ICxnrfUCaTiW1WGRgYcFqFDw0NRWpqKlJTU32m9fv09DQqKyvZakB4eDgqKiqQmJh4wd+jDzLX0Dy5hubJdZfiXI2MjKC2tpZdaRUKhcjLy0NOTs6KVzQvxXlaLb1ej8nJSTQ2NjptPAQWm7xJpVKkpKQgNDR01Y9ts9kwOTnplHqyXPAcEBDAVr+54JlL/eDSVxwD8OUa9wQEBDiVH4yJiUFgYCBGR0dZfrdjumZMTAxbsHM1Ztn0wfZ6NjnyeDy89957a/59b/CVYNtqtaKnpwdtbW1Ob7KgoCBWQ5k7OOfm5mAymWCz2RAREQE/Pz/w+XykpqZCLpdfNDC7mJUuAfn5+SE1NRXp6ekb1klqLW8oo9GI3t5eaDQaDA4OOl0KCwsLg1QqRWpqKqKjo9093DWzWCyor69Hc3MzbDYbBAIBCgsLkZOT49JGTPogcw3Nk2tonlx3Kc9VX18fVCoVJiYmACx+XhUWFkIul1Oq2xpx8zQ7O8sWihzTR4HFFFJuoWgtq8Icg8GwZPXbcUEKWIzrzl/9dly51uv1TsH3xMTEkscAFhe3uAA8MjISBoMBGo0G/f394EJTPp8PiUTCSgJfKMbY9MF2VlYWeDwe1rIIzuPx0Nrauurf8yZvB9uzs7OsJieXmsHl5wJgmxasVivm5ubYfVwuV2hoKFvFXk9hea7MnVqthk6nW7K5gcvD3uhmKa6+oebn59mJaXBw0On4jYiIYAG2L3a90ul0qK6uZlVlkpOTUVZWtqrVC/ogcw3Nk2tonlx3qc+V3W5HT08P6urq2EppaGgoiouLkZaWxlLyLvV5ctVy82QwGNjn29DQ0JLPt9TUVEil0nV/vtlsNrZxkvvn2LSNExgYuGT1m7uiYbPZWPoJF4QvVz2FC+LDwsJgNBoxOTkJo9HIjpfAwEBWRnC5CjhbJtjOycnBgQMHsHfv3lWlIqy1LJC3eOvNPz8/j8rKSuh0OvbGWVhYgFAohM1mY6uZRqMRdrsdVqsVwcHB4PF44PP5SElJYavY68kvnpycRFdXF3p6epwatYSHh0Mmk3msbI+rLvSGMhgMbHf38PCw0wkoKiqKBdjr+ebvSTMzM6iqqkJfXx+Axbzx8vLyNdWqpw8y19A8uYbmyXU0V4tsNhs6OjpQX1/P9ppERUVBqVRCIpHQPLnoYvNkNBqdFpbOv3LLrXi7Kxabm5tjgffIyAjGx8eXbJzk8XiIiopyCsAdF4u4zpmO/xzjDY7ZbIbRaITBYIBAIEBISAgrU5yRkeG0L2zTB9vHjx/H66+/jg8//BBWqxUikQhXXnklDhw4gIqKCq9vHHO3jX6xLBYL3nnnHczNzWFubg5GoxFWqxV8Ph8BAQHg8/mwWq3sMgyfz2ffGENDQ1ld7PW0XzUYDCwP2/HvDwwMZG3TfSWH+fw31IUurcXExLATjeNlLl9jtVrR3NyM+vp69trn5eWhoKBgzRVc6IPMNTRPrqF5ch3NlTOLxYKWlhY0NTWx1MeEhATs2bNnw6+MbkarOZ5MJhNLmTx/T1JISIjTniR3xW5Wq5WtfnPpJ8sFziKRCDExMSz4jo6Odvp8m52dXVJ+kLuizlVH4fLKg4ODERISgpCQEMjlcigUChQWFi6bc+5pbt8gOTU1hTfeeAOvv/46zp07Bx6Ph9jYWOzfvx8HDhxAdnb2ugbsKzb6JDk/P48XX3wRc3NzbCWb+6ZmtVrh5+cHs9nMNiRwjWoUCgWSkpLW/IaxWCzQ6XRQq9WsDjewGMwnJydDJpNdNEfKGyIjI1mDGa1Wy2pNc+Li4lh90rVsGtloAwMDqKysZLuyExMTUVFRse7Vd/rAdw3Nk2tonlxHc7U8o9GIxsZGtLW1sSuzXDfK1TY3u5Ss9Xgym82s2lZfX9+GlrOdnZ11Cr4nJiaWrH47Vi3hAnDHq+Yrdb+0WCwsKOc+N4OCgpCfn4+vfvWrbv07XOHRaiS9vb147bXXcOzYMfT09IDH40Emk+HAgQO49tpr170hz5s2+iS5sLCAo0ePws/PDwsLC7Db7fD394fVaoXNZmNvgpCQEGRmZiIzM3PNq9h2ux1DQ0OsyLxj0fq4uDjIZDJIpVK3dI90N71eD41Gg/HxcWi1Wqf7EhIS1lwOyVsMBgNqamrQ09MDYPHkt23bNqSnp7vl8ekD3zU0T66heXIdzdWFzc7OoqGhAQMDA5idnWXxQ1FRkVdTFH2VO44nrlGbRqNBX1/fkkZtjp2QPbHA5hggcwG4Y+t6TnBwMNt0ya1+OxYE4B6Hy/3u7e2FVqvF2NgYoqOj8dOf/tTtY7+YDSv919DQgNdffx1vvvkmxsfHERUVhY8++mi9D+s13gi2jx07Bj8/PxiNRlZVBFhcxZZIJFAoFBCLxWv+9jk9Pc3ysB03N4SGhrI8bF9Ms5iammKF/rnXJTg4GAaDAYmJiUhJSUFKSsq6Umg2ms1mQ1tbG+rq6mA2m8Hj8ZCVlYXi4mK3XlKlD3zX0Dy5hubJdTRXruHxeDhx4gRbPOHz+cjKykJBQcG6S9RuJe4+nqxWKwYHB9kVYsdSfQEBAUhOTkZqaiqSkpJcqny1VjMzM0tWv88PSfl8PisXyOV/n/95bzAYMDExsa4YaT1cCbbX384Pix0Ck5OTERcXx+o9E9cJBAJERkZifHycVR8JDg5mq9hrXak1Go3o7u5Gd3e3U+lAoVCItLQ0pKenu6U9u7tNTEywgv6OO5b5fD4SExNRUFCA8PDwTXkyHhkZQWVlJSuLFRsbi4qKCp8qOUgIIRshIiICu3fvxujoKFQqFQYHB9HS0oLOzk5Wo9tdrbzJf/j5+UEikUAikWD79u0YGhpigff8/Dy6urrQ1dUFf39/FniLxWK3dIB2FBoaitDQUHY112KxsBVrLgA3Go0YGRnByMgI+72QkBCn4DsqKsrnN9yueWV7fn4e77zzDl5//XVUVlbCarUiNDQUV199NQ4cOIBt27a5e6wbZqNfLIPBgJdeegkikQgRERHIyspa8zc0q9WK3t5eqNVq9Pf3sy8+fD4fYrEYMpkMycnJHv22uhZjY2MswHbc4MDn85GUlASpVIrk5GQEBAT49BtqJUajESqVCh0dHQAWVw9KSkqQmZnpsW/im3GevIHmyTU0T66juXLN+fPU398PlUqF8fFxAIvpDQUFBVAoFD73mbWRNup4stvtGB4eZoH33Nwcu08gEEAsFiM1NRUSiWTDNrbq9XqMjIyw2t+Tk5NLVr/9/PwQExMDpVLplQVEt6eR2Gw2fPjhh3jttddw4sQJGI1G+Pv74xOf+AQOHDiAyy+/fEvsLPbGSVKv1yMmJsYpj2o1hoeH0d3djZ6eniVdmNLT05Genu5TK8F2ux2jo6OsTJ9jaoufnx/EYjGkUumyb+rN9EFmt9vR2dmJ2tpadqkuMzMTJSUlHn89NtM8eRPNk2tonlxHc+Wa5ebJbrdDq9WitrbWqUZ3UVER0tPTt1z1M1d443jiOlNrNBrodLqLLoJtFLPZvGT1m/tsjYyMxGc+85kNGwvHbcF2fX09Xn/9dRw/fhwTExPg8XjYtm0bDhw4gKuvvnpTVHxYDW83tXGVXq9Hd3c31Gq10xshJCSEtU33pXrSF/vWLJFIIJVKIRaLL3jpcLN8kE1MTODjjz9m1VIiIyOxfft2xMXFbcjzb5Z58jaaJ9fQPLmO5so1F5onm82Gzs5O1NfXszJykZGRKCkpgUQi2chhep0vHE9cYQKtVrtseie3f2o9jfTWiisJuNGBP8ftTW3kcjmuvfZaXHvttT6Z6+suvhxsLywsQKPRQK1WO+Uw+fv7QyqVIj093SNlfNbKZrNhaGiIlelz3H3M5YNJpVIkJSW5nA/mCyeeCzGZTKirq0NbWxurLFNcXIysrKwNLaPo6/PkK2ieXEPz5DqaK9e4Mk8WiwWtra1obGxkV23j4+O9ljLgDb52PE1NTbG0T8dx8Xg8xMfHe60y2KZvasMF22vNmWpqalrT73mLrwXbNpsNfX19UKvV6O3tdapUkpSUBJlMhpSUFLdvXlgrm82GgYEB6HQ66HQ6tukTWNycmZKSAqlUisTExDUdU7524nHU09ODmpoathKTlpaGbdu2eaUcoS/Pky+heXINzZPraK5cs5p5WlhYQFNTE1paWlijFolEgpKSknW3JPd1vnw86fV6tuLtWIgB2PieF74cbLscndntdqeC6MTzRkdHoVarodFonALWqKgoyGQypKWl+UzJO6vVioGBAZYistE1PL1tenoaVVVVGBgYALDYIre8vBxisdjLIyOEkM2P21SelZWFhoYGdHR0oK+vD319faxG91ZLad0MwsLCkJ+fj/z8fMzMzLCr2FwFkZGREdTU1CA6OhpSqdTnuzl7ilvqbG813lzZ7u3tZXnYjnlRIpEIaWlpkMlkiIqK8sr4zudYJL+3t9epQU5QUBBrCxsfH+/WANuXvuVbLBY0NDSgqakJNpsNfn5+KCgoQF5entd3z/vSPPkymifX0Dy5jubKNeuZJ71eD5VKBY1GA2Axd1ihUKCgoMArecOetBmPp7m5OXZ1e2hoyKmCSGRkJIsP3HlVwpdXtinYXoY3XiyunWpnZye7TSAQICUlBTKZzGdWhM1mM/r7+1kXKserHcHBwSxFJC4ubsuXtOvt7UV1dTXbnCqRSFBWVuYz39p9ZZ58Hc2Ta2ieXEdz5Rp3zNPY2Bjq6urQ398PYPFzMzc3F7m5uVuiOhqw+Y+n+fl51ulxcHDQqRdLWFgYUlNTIZVK191vgoLtTWajX6zZ2Vm89NJLCA4OxtzcHBITEyGTyZCamuoTBf1NJhP6+vqg0WjQ39/P8uWAxcon3DfU2NjYDdmY6e0Tz8zMDKqrq9Hb2wtgcQ7KysqQkpLitTEtx9vztFnQPLmG5sl1NFeucec8DQwMQKVSsbzhrVSjeysdTwsLCyzwduwFAiyWeORSTtcST1Cwvcl4o55lY2MjwsPDER0djZCQkA19/uVwbwiNRoOBgYElbwgu9yomJmbDx+atN5TNZkNzczPq6+thsVjA5/ORm5uLwsJCn9mc6mgrnaA9iebJNTRPrqO5co0n5kmr1UKlUrE0zJCQEBQVFUEmk/lMla7V2qrHE7eQxwXe518p56qVxcfHu/TaUbC9yfhaNZKNYjQa2eaG8y/1hIeHswDb2znj3pinwcFBVFZWshN4QkICKioqEBERsaHjWA1vH0+bBc2Ta2ieXEdz5RpPzZPNZkNXVxfq6+tZP4eIiAgolUqfuwLpikvheLJYLOjv74dWq112DxhXx/tCKbW+HGz73nIc2VAGg4EF2MttYuACbF8OKj3JYDDg7Nmz6O7uBrD4pt+2bRtkMpmXR0YIIWQ5fD4fcrkc6enpaG9vR0NDA6ampnDixAnExsaipKQECQkJ3h4mcSAQCFhKqmN1s97eXszPz6O9vR3t7e0ICAhgK95rLR/sDRRsX4Lm5uZYXczh4WGn+y718jwcu92OtrY21NXVsTKGWVlZUCqVW2bTDSGEbGXcZsnMzExWo3t0dBRvvfUWxGIxSkpKvH6llizl5+eH5ORkJCcnw2azYXBwkC0KGo1GdHV1oaurC0KhEBKJBKmpqT5fZpfSSJaxFdNIZmdnWYDt2HkSAGJjYyGVSpGcnLwpAmxPXyoaHR1FZWUlxsfHAQAxMTGoqKjwSn76elwKlx7dgebJNTRPrqO5cs1Gz5PBYGA1urk0yfT0dBQVFfn0Zx8dT4vsdjuGhoZYPw+ueRyw+MWqrKwMcrl8w8dFaSSXuAt1doqPj2ednXxhQ6YvWFhYgEqlQnt7O4DFbpdKpRIKhWLTbqwhhBCySCQSoaKiAjk5OTh37hy6u7vR3d0NjUYDuVyOgoICn2kUR5bi8XhITExEYmIiysvLMTo6ymKc2dlZtLe3eyXYdgUF21vM9PQ0NBoNtFotJiYm2O08Hg8JCQmsrA6dUP7Dbrejq6sLtbW1rFNnRkYGSkpKtlxzBEIIudSFhYXh8ssvR15eHmpra9Hf34+2tjZ0dXUhJycHeXl5lC7o43g8HuLi4hAXF4fS0lJMTk4iLi7OaWOlL6FgewuYnJxkAfbU1BS7ncfjISkpie3ipcBxqcnJSVRWVrLc9YiICFRUVNDmGUII2eKioqLwyU9+EkNDQ6itrcXo6CgaGhrQ3t7OanT7YllXslRkZCRCQkJ8Nt2GjqJNanx8nAXYer2e3c7n85GUlITU1FQkJycjMDDQi6P0XWazGefOnUNLSwvsdjsEAgGKi4uRnZ3tE506CSGEbIyEhATs378fOp0OKpUKU1NTqKmpQUtLCwoLC5GRkUGfC2RdKNjeJOx2O8bGxqDRaKDT6ViLcGBx565YLGYBNl3+ujCNRoPq6mq2uSI1NRWlpaWUu04IIZewlJQUJCcnQ61W49y5c5idncWZM2fQ3NwMpVKJ1NRUbw+RbFIUbPswu92OkZERFmBzxfmBxZ23YrEYUqkUEonEJ9q6+zq9Xo+qqir09/cDWOyEWV5eDolE4uWREUII8QU8Hg8ZGRlIS0tjNbqnp6fx/vvvIyYmBkqlEklJSd4eJtlkKNj2MTabDcPDw8uWtvH390dycjKrKUm5ZK6xWCxobGxEY2MjbDYb/Pz8kJ+fj/z8/E1TEJ8QQsjG8fPzQ05ODjIyMtDc3Izm5maMjY3hnXfegVgshlKpRHR0tLeHSTYJitZ8xOjoKJqamtDU1MQqYgCL5ee4CiJJSUkUHK5SX18fqqqqWNqNWCxGeXm5T9dUJYQQ4huEQiGKi4uRlZXFNk/29/ejv78fUqkUSqWSPk/IRVGw7QPm5uZw/PhxiEQiGI1GBAQEOAXYtDFj9WZnZ1FTUwOtVgsACA4ORmlpKaRSqXcHRgghZNMJCgpCeXk5q9GtVqtZkQKuRndwcLC3h0l8FAXbPiAoKAhyuRyhoaGIiopCQkICBdhrZLPZ0NLSgnPnzsFisYDH4yEnJwdFRUWU104IIWRdQkNDsWvXLlaju6+vD+3t7ejq6kJ2djby8/MREBDg7WESH0PBtg/g8/nYvn07tWRdp6GhIVRWVrJa4/Hx8aioqHCplSohhBDiqsjISOzbtw/Dw8NQqVQYHh5GU1MTOjo6kJ+fj+zsbNpXRRg6EsimNz8/j9raWnR1dQEAAgMDsW3bNmRkZHh5ZIQQQray+Ph4fOpTn0JfXx9qa2sxOTmJ2tpatLa2oqCgAHK5nK5UEwq2yeZlt9vR3t4OlUoFk8kEAMjKykJxcTFdxiOEELJhJBIJxGIxuru7ce7cOczMzKCyspLV6JZKpeDxeN4eJvESCrbJpjQ2NobKykqMjY0BAKKjo1FRUYHY2Fgvj4wQQsiliMfjQSaTQSqVshrdMzMzOHXqFJqamqBUKiEWi709TOIFFGyTTWVhYQGnT59GTU0NAOeyTLRqQAghxNu4Gt2ZmZloaWlBU1MTxsfH8e677yIxMRFKpZIWhi4xFGyTTaOrqwtnz55ltcZlMhlKSkogEom8PDJCCCHEmb+/PwoLC6FQKNDQ0IC2tjYMDg7ijTfeQEpKCkpKShAeHu7tYZINQME28XlTU1OorKzE0NAQgMXGNDt37kRiYqKXR0YIIYRcWGBgIMrKypxqdOt0OvT29iIjIwOFhYUICQnx9jCJB1GwTXyW2WxGfX09WlpaYLPZIBAIUFhYiJ07d2J6etrbwyOEEEJcFhISgp07dyIvLw8qlQo6nQ6dnZ3o7u5GVlYW8vPzERgY6O1hEg+gYJv4JK1Wi+rqaszNzQEAUlJSUFZWhpCQECqjRAghZNOKiIjAnj17MDIyApVKhaGhITQ3N6OzsxO5ubnIycmhJmxbDAXbxKfo9XpUVVWhv78fwGK3rvLyckgkEi+PjBBCCHGfuLg4XH311ejr64NKpcLExATq6urQ1taGgoICKBQKWlzaIijYJj7BarWiqakJDQ0NsFqt4PP5yM/PR35+PnXhIoQQsmVxNbp7enpQV1eHmZkZVFVVoaWlBcXFxVAqld4eIlknimKI1/X396OyshIzMzMAgKSkJJSXl9MubUIIIZcEHo+H9PR0SKVSdHR0oL6+HjMzM/jggw/Q09MDhUJBV3g3MQq2idfMzc2hpqYGGo0GACASiVBaWoq0tDTvDowQQgjxAj6fj6ysLGRkZLAa3RMTE3jvvfcQHx+PkpISxMXFeXuYZJUo2CYbzmazobW1FefOnYPZbAaPx0N2djaKioogFAq9PTxCCCHEqwQCAQoKCiCXy1nBgOHhYRw/fhzJyclQKpWIjIz09jCJiyjYJhtqeHgYlZWVmJycBLC4QaSiogJRUVFeHhkhhBDiWwIDA1FRUQGJRIL6+np0dnait7eX1eguKiqiGt2bAAXbZEMYjUbU1tais7MTwOIJpKSkBBkZGdRmnRBCCLmA4OBg7NixA7m5uVCpVNBqtejq6kJ3dzcUCgUKCwupRrcPo2CbeJTdbkdHRwdUKhUWFhYAAHK5HEqlkk4MhBBCyCqEh4dj9+7dGB0dhUqlwuDgIFpbW9HV1YXc3Fzk5uZSjW4fRME28Zjx8XFUVlZidHQUABAVFYWKigra3EEIIYSsQ2xsLK666ir09/dDpVJhfHwc586dc6rR7efn5+1hkv+Hgm3idiaTCSqVCu3t7bDb7fD390dxcTGysrKoQD8hhBDiJmKxGElJSdBqtVCpVNDr9aiurkZLSwuKioogk8koVdMHXBLBdm9vLw4cOACDwYDPf/7z+NnPfubtIW1Z3d3dqKmpwfz8PAAgPT0d27Ztg0gk8vLICCGEkK2Hx+NBKpUiJSUFnZ2dqK+vx+zsLE6fPo3m5mYolUokJyd7e5iXtC0fbNtsNhw+fNjbw9jypqenUVlZicHBQQCLeWXl5eVISkry8sgIIYSQrY/P50OhUEAmk6G1tRWNjY2YnJzEv//9b8TFxaGkpATx8fHeHuYlactf03/mmWdw7tw53H333d4eypZksVhQW1uLV199FYODgxAIBCgpKcGBAwco0CaEEEI2mEAgQH5+Pj772c8iPz8fAoEAIyMjePPNN/Hee++x0rtk42zplW21Wo3f/e53uOOOO5Cdne3t4Ww5Op0O1dXVmJ2dBQBIJBKUl5cjNDTUyyMjhBBCLm0BAQEoKSlBdnY26uvr0dHRgb6+PvT19SE9PR1FRUUICwvz9jAvCVs22LZarTh8+DBSU1PxzW9+E3V1dd4e0pYxMzODqqoq9PX1AQBCQkJQXl5OOWGEEEKIjxGJRNi+fTtyc3NRV1eHnp4edHd3Q6PRQC6Xo7CwEEFBQd4e5pa2ZYPtp556Ci0tLThy5MiqW4CHh4d7rWqGL7dftVqtaGhogEqlgtVqRWhoKAoLC1FcXAyBYGMPJV+eJ19C8+QamifX0Dy5jubKNTRPrnHHPEVGRiI1NRVjY2Oorq5GX18fent7MTg4iIKCAhQUFKw6XvI1vno8bclgu62tDU888QS++tWvIi8vb9W/Pz097YFRXVxkZKTP5lINDAygsrISer0eAJCYmIiKigqEh4djZmZmQ8fiy/PkS2ieXEPz5BqaJ9fRXLmG5sk17p4nPz8/bN++HYODg1CpVBgdHcWHH36Impoa5OfnIysra1PW6PbW8eRKgO+zwfZDDz0Ek8nk8s8fOnQIUqkUJpMJP/rRj5CSkoI777zTgyO8NBgMBtTU1KCnpwfA4uWobdu2IT093csjI4QQQshaJSYmYv/+/axG9/T0NGpqatDa2orCwkLIZDLqjeEmPhtsHzlyBAaDweWfv+qqqyCVSvH000+jo6MDL7zwwqa/HOJNNpsNbW1tqKurg9lsBo/HQ1ZWFoqLi2leCSGEkC0iNTUVycnJ6OrqYjW6P/roIzQ1NaGkpAQpKSneHuKm57PB9lo3NLa0tMBms+HGG29c9v4jR47gyJEj2Lt3L5544on1DHHLGhkZQWVlJSYmJgAstoWtqKhAdHS0l0dGCCGEEHfj8/mQy+VIT09He3s7GhoaMD09jRMnTiA2NhZKpRKJiYneHuam5bPB9lpddtlly+bPjI6O4tSpU0hPT4dSqUROTo4XRufbjEYjVCoVOjo6APynbFBmZia1eyWEEEK2OIFAgNzcXGRmZqKpqQktLS0YHR3F22+/DbFYDKVSSQtva7Dlgu2bb7552durqqpw6tQplJaWUrv289jtdnR2dkKlUsFoNAIAMjMzUVJSgsDAQC+PjhBCCCEbSSgUQqlUIisrCw0NDejo6EB/fz/6+/uRlpaG4uJiqtG9Clsu2CarMzExgcrKSoyMjABY3FVbUVFBLV0JIYSQS5xIJEJFRQVycnJw7tw5dHd3o6enB1qtFnK5HAUFBRCJRN4eps+jYPsSZTKZUFdXh7a2Ntjtdvj7+6O4uBhZWVm0+5gQQgghTFhYGC6//HLk5eWhtrYW/f39aGtrQ1dXF7Kzs5Gfn0/FEy7gkgm2y8vL0d7e7u1h+ISenh7U1NSwai9SqRSlpaUIDg728sgIIYQQ4quioqLwyU9+EkNDQ1CpVBgZGUFjYyM6OjpYje6NbnK3GdCMXEKmp6dRVVWFgYEBAIvfVMvLyyEWi708MkIIIYRsFgkJCbjmmmvQ29uL2tpaTE1N4ezZs6xGd0ZGBl0ld0DB9iXAYrGgoaEBTU1NsNls8PPzQ0FBAfLy8jZllyhCCCGEeF9ycjIkEgnUajXOnTuH2dlZnDlzBk1NTVAqlZBKpd4eok+gYHuL6+vrQ1VVFWupLpFIUFZWRruICSGEELJuPB4PGRkZSEtLYzW69Xo9Tp48iZiYGCiVSiQlJXl7mF5FwfYWNTs7i+rqauh0OgBASEgIysrKqBMUIYQQQtzOz88POTk5yMjIQEtLC5qbmzE2NoZ33nkHSUlJUCqViImJ8fYwvYKC7S3GZrOhubkZ9fX1sFgs4PP5yM3NRWFhIW1aIIQQQohHCYVCFBUVQaFQoKGhAe3t7RgYGMDAwACkUimKi4sRHh7u7WFuKIq+tpDBwUFUVlZienoawOIGhoqKCkRERHh3YIQQQgi5pAQFBaG8vJzV6Far1dBoNNBqtcjMzERhYeElUwWNgu0twGAw4OzZs+ju7gaweIBv27YNMpnMyyMjhBBCyKUsNDQUu3btQl5eHlQqFXp7e9HR0QG1Ws1qdAcEBHh7mB5FwfYmZrfb0dbWhrq6OphMJgBAVlYWlEolFZcnhBBCiM+IjIzE3r17MTw8DJVKheHhYTQ1NbEa3dnZ2Vs23XVr/lWXgNHRUVRWVmJ8fBwAEBMTg4qKikt28wEhhBBCfF98fDw+9alPoa+vD7W1tZicnERtbS1aW1tRUFAAuVy+5Wp0U7C9ySwsLEClUrFumEKhEEqlEgqFAjwez8ujI4QQQgi5OIlEArFYjJ6eHtTV1WFmZgaVlZVobm5mNbq3SlxDwfYmYbfboVarcfbsWRiNRgBARkYGSkpKEBQU5OXREUIIIYSsDo/HQ3p6OqRSKavRPTMzg1OnTrHGOFuhyzUF25vA5OQkKisrMTw8DACIiIhARUUFEhISvDwyQgghhJD14fP5yM7OdqrRPT4+jnfffReJiYlQKpWIjY319jDXjIJtH2Y2m3Hu3Dm0tLTAbrdDIBCguLgY2dnZWy6fiRBCCCGXNn9/fxQWFkKhUKCxsRFtbW0YHBzEG2+8gZSUFJSUlGzKGt0UbPsojUaDmpoazM3NAQBSU1NRWlqKkJAQL4+MEEIIIcRzAgMDUVpaiuzsbNTX16Orqws6nQ69vb3IyMhAYWHhpoqHKNj2MXq9HlVVVejv7wewWJ+yvLwcEonEyyMjhBBCCNk4ISEhuOyyy5CXl4fa2lrodDp0dnaiu7sbWVlZyM/PR2BgoLeHeVEUbPsIi8WCs2fP4qOPPoLNZgOfz0dBQQHy8vK2bN1JQgghhJCLCQ8Px549ezA6OgqVSoXBwUE0Nzejo6MDeXl5yMnJ8fYQL4iiOB9gNpvxxhtvwGw2w2azQSwWo7y8HGFhYd4eGiGEEEKIT4iNjcVVV12F/v5+qFQqjI+Po66uDm1tbdi3bx+io6O9PcRlUbDtA6xWK+bm5hAZGYnS0lJIpVJvD4kQQgghxCeJxWIkJSVBo9FApVJhZmYGH3zwAQ4ePOjtoS2Lgm0fEBgYiBtuuAGxsbGYnp729nAIIYQQQnwaj8dDWloaUlNTodFofLo0INWP8xFCoZDK+RFCCCGErAKfz0d6ejpSUlK8PZQVUXRHCCGEEEKIh1CwTQghhBBCiIdQsE0IIYQQQoiHULBNCCGEEEKIh1CwTQghhBBCiIdQsE0IIYQQQoiHULBNCCGEEEKIh1CwTQghhBBCiIdQsE0IIYQQQoiHULBNCCGEEEKIh1CwTQghhBBCiIdQsE0IIYQQQoiHULBNCCGEEEKIh1CwTQghhBBCiIdQsE0IIYQQQoiHULBNCCGEEEKIh/Dsdrvd24MghBBCCCFkK6KVbUIIIYQQQjyEgm1CCCGEEEI8hIJtQgghhBBCPISCbUIIIYQQQjyEgm1CCCGEEEI8RODtAVxKzGYzTpw4gRMnTqChoQFDQ0MAgIyMDBw8eBCf//zn4efnt6rHbGhowOOPP466ujpYLBbI5XJ8+ctfxjXXXOOJP2HDtLa24s0330RzczOam5sxOTmJsrIyPPfcc6t+rD179qC/v3/Z+9b6mL7EnXMFbN1jCgBmZ2fx+OOP45133sHo6Cji4uJw1VVX4c4770RwcLDLj6NQKFa87+DBg3jooYfcMVyPcsfrbDKZ8PTTT+O1117D4OAgwsPDsXv3bnz3u99FdHS0B0e/cdY7T6+88gruu+++Fe9/9tlnUV5e7q7hesWrr76K2tpaNDU1oaOjA2azGQ8++CCuv/76VT2OzWbD3/72N7z44ovQarUQiUTYsWMHvve97yE5OdlDo9847pinqqoqHDp0aMX71zLvvmZ4eBhvvvkmPvjgA3R3d2NsbAzh4eFQKpW4/fbbUVhY6PJj+coxRcH2BtLpdPjOd74DkUiE7du3Y8+ePZiZmcH777+PBx54AB988AGefPJJ8Hg8lx6vsrISt99+O4RCIfbv34/g4GC88847+N73voehoSHcdtttHv6LPOe9997DU089BX9/f6SlpWFycnJdjxcaGopbb711ye1isXhdj+sL3DlXW/mYMhgM+NKXvoTW1lbs3LkT+/fvR2trK/73f/8XNTU1+Nvf/oaAgACXH08sFuPgwYNLbs/OznbnsD3CHa+zzWbDN7/5TZw+fRpFRUW48sorodVqcfToUXz88cd48cUXERUVtQF/jee48/2wd+/eZY+NrXAOeuyxx9Df34/IyEjExcWtuLhxMffffz+OHj2KzMxM3HLLLRgZGcGbb76Jjz76CEeOHIFUKnXvwDeYu+YJWFwoKisrW3L7Zjj/XMxzzz2HP/3pT0hJScFll12GqKgoaLVavPfee3jvvffwyCOPuPxl12eOKTvZMENDQ/bnn3/ePjc353T73Nyc/frrr7fL5XL78ePHXXoss9ls37dvnz0vL8/e0tLCbtfr9fYrr7zSnpuba+/r63Pr+DdSR0eHvampyW4ymewjIyN2uVxu/9KXvrSmx9q9e7d99+7dbh6h73DXXG31Y+qxxx6zy+Vy+8MPP+x0+8MPP2yXy+X2P/7xjy4/1nqOR29z1+v80ksv2eVyuf2ee+6x22w2dvvf//53u1wut//0pz/1yPg3irvm6eWXX7bL5XL7yy+/7MnhetVHH33E5uKpp55a09/78ccf2+Vyuf3mm2+2LywssNtPnjxpl8vl9ttuu82tY/YGd8xTZWWlXS6X23//+997Yog+4e2337ZXVVUtub2mpsaem5trLy0tdTpGVuJLxxTlbG+g+Ph43HzzzRCJRE63i0QifOUrXwEA1NTUuPRYlZWV0Ol0+PSnP+30TTY0NBTf+MY3YDab8c9//tN9g99gmZmZyM3Nhb+/v7eH4vPcNVdb+Ziy2+04evQoRCIRvvWtbznd961vfQsikQhHjx710ug2lrteZ26+7rnnHqercV/4wheQnJyM119/HUaj0f1/wAbZyu8Hd9uxY8e6V+i54+nuu++GUChkt19xxRUoKyvD6dOnMTAwsK7n8DZ3zNOl4Morr1x21X7btm0oLy/H9PQ02tvbL/o4vnRMURqJjxAIFl8KV3O2q6urAQA7d+5cch93m6uB+6XAZDLhlVdewcjICEJCQpCfn7+qvK9LwVY+pjQaDUZGRrBz585lv+wqlUqcPn0ag4ODSExMdOkx9Xo9jhw5gsnJSZZPeKFcbl/hjtd5YWEB9fX1SEtLWxI88Hg87NixA0eOHEFTUxO2bdvmppFvLHe/H1paWjA1NQWLxQKJRILt27cjMjLSPYPdAqqqqth78Xy7du1CdXU1qqurcd1112384HyQRqPBM888g4WFBcTHx2P79u2Ij4/39rA8jouVuP+9EF86pijY9hEvv/wygOVP7MvRaDQAgNTU1CX3xcbGQiQSQavVum18m93o6OiSTUr5+fl49NFHkZKS4qVR+ZatfExx414pP08qleL06dPQaDQuB9ttbW24//77nW7btWsX/vu//9unNwe643XW6XSw2WwXnE/uuTZrsO3u98P5G5YDAwPx7W9/G3fccce6xrkVGAwGjI6OQi6XL7vgxL0Gm/X84wnHjh3DsWPH2H8LBAJ86Utfwg9/+MNVF1rYLAYGBnDmzBnExsZCLpdf8Gd97ZiiYNsHHDlyBB988AEqKipwxRVXuPQ7s7OzABYvaS4nJCQEMzMzbhvjZnb99dejpKQEcrkcIpEIGo0Gf/nLX/Dqq6/iy1/+Ml577TWEhIR4e5het5WPKW7cK73O3O3cHFzMbbfdhiuvvBJSqRT+/v7o7OzEE088gQ8++ABf//rXceTIEZ/9wHPH6+zu+fRF7no/SCQS/PSnP8XOnTuRkJCA6elpfPzxx3j00UfxyCOPICgoCLfccotbx77ZuHo8bdbzjztFRUXh3nvvxe7duyEWizE/P4+6ujo88sgjeOaZZ8Dj8XD48GFvD9PtzGYzfvjDH8JkMuH73//+Rc+vvnZMUbC9Bg899BBMJpPLP3/o0KEVV4Def/99/PznP4dYLMbDDz/sphH6BnfO03rceeedTv+dnZ2NX//61wAWSzEdPXqU5cx7i6/Mla/zlXn60Y9+5PTfxcXFeOqpp3Drrbeiuroa//73v3HllVe6/XnJ5nN+1YjAwEBcd911yM3NxWc/+1n84Q9/wBe/+EWXLosTkpmZiczMTPbfIpEI+/btQ2FhIQ4cOIDnnnsOX/va13z66tpq2Ww2HD58GDU1Nbjxxhs3ZSoRvbvX4MiRIzAYDC7//FVXXbXsB/6pU6fwne98B9HR0fjrX/+KuLg4lx/zYt/KZmdnER4e7vLjeYK75slTPv/5z+PVV1+FSqXyerDtC3O1lY8pbnVypZVW7vb1XOHg8/m44YYbUF1dDZVK5bPBtjte542YT2/z9PshMzMTJSUlOHPmDNRq9abI9/cUV4+nla4ykMXUpr179+Lo0aOor6/Hnj17vD0kt7DZbPjxj3+MY8eO4cCBA3jggQdc+j1fO6Yo2F6Durq6dT/GyZMncddddyEyMhLPPvvsqourc4GWVqtFXl6e032jo6MwGAwoKChY9zjXwx3z5Enc5qTVBG+e4gtztZWPKS4/j8vDPR93+3q/wPjSMbUSd7zOycnJ4PP5Hp9Pb9qI9wN3vMzPz6/rcTY7kUiE2NhY9PX1wWq1LkkR4PJql8ufJ/+x1Y4nm82G++67D//617/w6U9/Gg899BD4fNeK6PnaMUWl/7yAC7TDw8Px7LPPrunFLi0tBQCcPn16yX3cbdzPkOU1NDQA2BpNJdxhKx9TUqkUcXFxUKlUSwJhg8EAlUoFiUTi8ubIldTX1wNYzNP1Ve54nQMDA1FQUICenp4ljTnsdjvOnDkDkUi0JEjdTDz9frBarWhqagIAJCUlrflxtoqysjL2Xjzfhx9+CGDznn82Cnf+2QqfaY6B9jXXXINf//rXq94H40vHFAXbG+zUqVNOgfbFVn7MZjPUajV0Op3T7du3b0dycjKOHTuG1tZWdvvMzAz++Mc/wt/ff1PmNa3VSvOkVquX/ZavVqvxm9/8BgBw7bXXbsgYfcWleEzxeDzccMMNMBgMeOKJJ5zue+KJJ2AwGHDjjTc63T4/Pw+1Wr2kDmt7ezvMZvOS51CpVPjzn/8Mf39/XH311e7/I9xkta/zyMgI1Gr1knQKbr4effRR2O12dvsLL7yA3t5eXHvttQgMDPTsH+NB7ponLqB2ZLVa8Zvf/AZarRbl5eWrSiHc7CYmJqBWqzExMeF0O3c8PfbYY077Mk6dOoXq6mrs3LlzSwSRrlppnpY7ngDgr3/9K6qqqiCVSpGfn78RQ/QYLnXkX//6F66++mo8/PDDFwy0N8MxxbM7niWJR6nValx33XUwmUzYv38/0tLSlvyMWCzG9ddfz/67r68Pe/fuhVgsxokTJ5x+dqVWwv39/fjRj360qVtrq9Vq/OlPfwIAGI1GvPnmm4iJicGuXbvYzzz00EPs/680T48//jj+8pe/oLS0FElJSQgKCoJGo8EHH3wAs9mMr3/967jnnns27g/zAHfNFbC1jymDwYAvfvGLaGtrw86dO5GTk4OWlhacPn0a+fn5eP75552Cw6qqKhw6dAhlZWVOZdsOHz6MkydPoqSkBImJiRAIBOjs7MRHH30EHo+H+++/H1/84he98Se6bDWv8+HDh/HPf/4TDz74oNO5yWaz4Wtf+xpr115aWgqdTod33nkHYrEYR48e3bLt2lczTwqFgv2Lj4/H9PQ0qqurodFokJCQgOeff37VaYS+5ujRo6itrQUAdHR0oLm5GUqlkl21LSkpwQ033ABg8Zz8hz/8AXfeeSfuuusup8f5yU9+wlprX3HFFRgdHcXx48cRHByMF154YdnPzM3EHfO0Z88eCAQC5OXlIT4+HvPz86ivr0dLSwvCwsLwP//zP15P91sv7m8XiUQ4dOjQspuH9+3bx5pNbYZjinK2N9DY2Bj7dvXGG28s+zNlZWVOJ+oLqaiowN///nf8/ve/x/Hjx2GxWCCXy/H9738f11xzjdvG7Q1jY2NLurOdf5tjALmS8vJyqNVqtLa24uzZszAajYiMjMTll1+Om266yeW65r7MXXMFbO1jSiQS4fnnn8fjjz+Od955B1VVVYiNjcVtt92Gb3/72y6vwu7duxd6vR5tbW04c+YMzGYzYmJisH//ftx6662b4oPOHa8zn8/Hk08+iaeffhqvvvoqnnnmGUREROBzn/scvvvd7276QBtwzzzddtttOHfuHM6cOYPp6Wn4+/sjJSUF3/zmN/GVr3zF65uO3aG2tnbJOUilUjldvueCyAv52c9+BrlcjhdffBHPPvssRCIRPvnJT+J73/veluiH4I55+sIXvoDTp0+jpqYGU1NT4PP5SEpKwq233orbbrsNCQkJHhn7RuJS0wwGA/74xz8u+zNisdips+tKfOWYopVtQgghhBBCPIRytgkhhBBCCPEQCrYJIYQQQgjxEAq2CSGEEEII8RAKtgkhhBBCCPEQCrYJIYQQQgjxEAq2CSGEEEII8RAKtgkhhBBCCPEQCrYJIYQQQgjxEAq2CSGEEEII8RAKtgkhlxyFQuH0LysrC9u2bcNNN92Eo0ePwtuNdV955RUoFAo8/vjjTrcfPnwYCoUCVVVVXhqZZ913330oKirC+Pi4V57/8ccfh0KhwCuvvLKm3zcajdi5cye+9rWvuXlkhJDNjIJtQsgl6+DBgzh48CCuvfZaZGRkQKVS4Sc/+Qnuvfdebw/NY1YK5L2tvb0d//rXv3DTTTchOjp6TY9RVVUFhUKBw4cPu3l0rgkMDMTtt9+ODz74AB9//LFXxkAI8T0Cbw+AEEK85aGHHnL6748++gh33HEH3njjDVx77bXYvXu3l0a2vHvuuQdf+9rXkJSU5O2huN3vfvc7+Pn54atf/aq3h7IuX/jCF/B//+//xaOPPoqjR496eziEEB9AK9uEEPL/XHbZZThw4AAA4L333vPyaJaKi4uDTCZDUFCQt4fiVoODgzh58iR27ty55lVtXxEYGIgrr7wSDQ0NaGlp8fZwCCE+gIJtQghxkJOTAwAYGhpitykUCuzZswcmkwl/+MMfcPXVVyMvLw/f+ta32M/Mz8/jqaeewnXXXYfi4mIUFxfjxhtvxD//+c8Vn6u2thZf/vKXUVxcjG3btuGrX/0q6uvrV/z5C+VsGwwGPP3007j++uuhVCpRVFSEq6++Gg888AB6enoAALfccgvuu+8+AMAf/vAHp7z18/OUT506ha985SsoLS1Ffn4+rrrqKvzmN7+BXq9f8tyOuc4NDQ34+te/jvLycigUCrS2tq7493Befvll2Gw27N+/f9n7Ozo68P3vfx979+5Ffn4+Kioq8JnPfAa//OUvMTIywubm0KFDAIB//vOfTn/b+Skz//73v/H5z38ehYWFKC8vx1133cXm6HzcnF/o3/mvx6c//WkAwJEjRy76txNCtj5KIyGEEAdzc3MAAH9/f6fbbTYbvv3tb+Ps2bMoLS2FQqFAREQEAGB8fBxf+cpX0N7ejtjYWJSWlsJut6Ourg6HDx9GU1MTfvrTnzo93vvvv48777wTFosFBQUFSE5ORltbG26++WZcf/31qxrzyMgIbrvtNnR2diI8PBxlZWUQCoXo6+vDCy+8gNTUVKSlpWHXrl2wWCxQqVTIyspCdnY2e4yUlBT2/5966ik8+uijEAgEKC0tRWRkJFQqFf70pz/h3Xffxd/+9jfExMQsGUdNTQ3uv/9+SKVSXHbZZRgZGQGPx7vo+N9//30AQFlZ2ZL7mpqacNNNN2FhYQEKhQJ79+6F0WhEb28vnn32Wezbtw9xcXEoKSnB6OgoTp8+jZSUFJSUlLDHcPw7//GPf+C//uu/wOPxsG3bNsTGxqK+vh433HDDsmlDjo/jyGq14o033oDVaoWfn5/TfUqlEv7+/jh16tRF/3ZCyNZHwTYhhPw/drsdJ0+eBLC4mu1ocHAQQqEQb731FuLj453uu++++9De3o5Dhw7hBz/4AYRCIQBgbGwMX//61/H888/jiiuuwOWXXw4AmJ2dxY9//GNYLBb86le/wmc/+1n2/I888gj+9Kc/rWrcP/zhD9HZ2YlPfepT+OUvf4ng4GB2X19fH2ZnZwEAd9xxB2JiYqBSqbBv3z7cddddSx6roaEBv/vd7yASifDMM8+gsLAQAGAymfCDH/wAb731Fn72s5/h97///ZLffeWVV/D9739/VdU45ubm0Nrairi4uCXzCgDPPfccFhYW8KMf/Qi33Xab031qtRqhoaEAgBtuuAEpKSk4ffo0SkpKluTjA0B/fz8efPBB+Pv748knn8SuXbsAAGazGffddx9ee+21Jb9zww034IYbblhy+y9+8QtYrVbs3r0bSqXS6b6AgADI5XI0Nzejt7cXycnJLs8HIWTroTQSQsglz2q1QqPR4Mc//jHq6uogFApZAOzonnvuWRIQtra24tSpU8jPz8d9993HAm0AiImJwc9//nMAiyuqnLfffhsTExMoLS11eh4ej4e7774bCQkJLo+9oaEBH3/8MaKjo/GLX/zCKdAGAIlEgqysLJcf729/+xtsNhtuueUWFmgDgFAoxP3334/AwEC8++67GBwcXPK7crkct99+u8vPBSwGzFarFWlpacvePzExAQDYsWPHkvtkMhni4uJcfq6XX34ZCwsL2L9/Pwu0gcWrGP/f//f/uZwLf/ToUTz33HPIyMjAb37zG/D5Sz9K09PTAQBtbW0uj48QsjVRsE0IuWRxObc5OTm46qqr8MorryA4OBiPPvqoU1oFsBgI79mzZ8ljnD59GgCwb9++ZYOunJwciEQiNDY2stvOnj0LALjmmmuW/Ly/vz+uuuoql/+GM2fOAAD279+PkJAQl39vJdzYrr322iX3RUdH47LLLoPNZoNKpVpy/+7du11KG3HE1dQODw9f9v7c3FwAwAMPPICqqipYLJZVPb6jC817ZGQkLrvsMpce44EHHkBERAT++Mc/rjjnXIoR92WBEHLpojQSQsgl6+DBgwAWA+mQkBDI5XJceeWVywZ+0dHRTqvWnP7+fgDAb3/7W/z2t79d8blMJhP7/9ymPrFYvOzPrnT7crgV5vO/HKyVq2MbHh5ecl9iYuKqn29mZgYAlqzIc26//XbU1taiuroahw4dgkgkQnFxMa644gpcf/31LI3EFeud9/7+ftx1112w2+147LHHLpgewv09y20oJYRcWijYJoRcspbL611JQEDAsrfbbDYAixvp3BXw+rILrVyvNEcXwgXL3MbU84WEhODZZ59FbW0t3n//fVRXV6OyshIfffQRnn76afztb3+DVCpd9fOulsFgwDe/+U1MTEzg//yf/4OKiooL/jyXJx8WFubxsRFCfBsF24QQsg5cfvW+ffuWbOBbCZdnzK2Kn29gYMDl5+dWk3U6ncu/cyFxcXHo6+vDwMAAMjIyltzPjXm5zYxrwdXVnpqaWvFnuMoh27ZtA7CYevKrX/0Kx44dw29/+1s89thjLj1XbGwsenp60N/fv+zfttK82+12/PCHP0R7ezu++MUv4qabbrroc01PTwMAoqKiXBobIWTropxtQghZBy7P991333X5d7hycm+++eaS+ywWC9555x2XH4vbOPjGG2+suDrsiCtpuFLuMxfQHjt2bMl9ExMTOH36NHg83pIKHGuVkZEBgUCwYp3r5URHR+POO+8EAHR2drLbXf3b3nrrrSX3TU1N4aOPPlr29x577DG8++67KC8vx09+8hOXxtjd3Q0Aq9qcSgjZmijYJoSQdSgsLMRll10GlUqFBx54gKUPOGpra8MHH3zA/vvqq69GREQEqqurnZre2O12PP7446ta2S4oKEB5eTnGx8dx//33w2AwON3f19eH9vZ29t/cqvpKwe3NN98MPp+P5557zmlTp8lkws9//nMYjUZceeWVa8rPXo5IJEJ2djZGR0eXzQP/xz/+gd7e3iW3czWsHcdxsb/t+uuvh1AoxOuvv842lgKLpf8efPDBJXMHAMePH8eTTz6J5ORkPPbYYxAILn5BeGFhAR0dHUhMTKSyf4QQSiMhhJD1evjhh3H77bfj73//O44dO4asrCzExcVhdnYW7e3tGBwcxKFDh1id7ZCQEPzyl7/Ed77zHRw+fBj/+Mc/WFMbrVaLG2+8ES+++OKqnv/WW2/FsWPHcPr0aSiVSgiFQvT29qK1tRU/+tGPWN3woqIiREdH4+2338Ytt9wCiUQCPp+Pz372s1AqlSgoKMDdd9+N3/72t/jCF76AsrIy1tRmcHAQUqkU999/v1vn7xOf+AQaGxtRVVWFAwcOON33wgsv4L/+67+QkZEBmUwGPz8/dHd3o62tDQEBAU5dPCUSCRQKBZqamvC5z30OmZmZ4PP52LNnD/bu3Yvk5GQcPnwYP/vZz/DVr36VNbU5d+4c9Ho9rr32Wrz++utOz//oo48CWAzk//u//3vZ8X/ta1+DTCZj/61SqWA2m3HFFVe4a4oIIZsYBduEELJO0dHReOGFF/Diiy/ijTfeQGtrK+rq6hATE4Pk5GTccsstS1qR79u3D88++yx+//vfo7GxEWq1Gvn5+fjFL36Bnp6eVQXb8fHxeOmll/DXv/4Vb7/9Ns6cOQM+n4+EhATcdNNNTp0RAwIC8NRTT+G3v/0tGhoaUFNTA7vdjpKSEpYa8o1vfANZWVl45pln0NjYCKPRiKSkJNx+++244447VizTt1af/exn8cQTT+DYsWNLgu27774b7733HqsnbjabER8fjxtuuAG33XYbq2fNefzxx/HrX/8aZ8+eRXNzM2w2GxISErB3714Aiyv38fHxePrpp9HQ0ICAgABs27YN9957L44fP75kbNwG2NraWtTW1i47/oMHDzoF21zAfuONN659UgghWwbPbrfbvT0IQgghl7Zvf/vbOHnyJE6ePInY2FhvD2fNjEYjdu3aBalUiqNHj3p7OIQQH0A524QQQrzu7rvvhs1mw//+7/96eyjr8sILL0Cv1+Oee+7x9lAIIT6Cgm1CCCFeJ5fLcd111+Ef//gH6yq52RiNRvz5z3/G5Zdfju3bt3t7OIQQH0FpJIQQQgghhHgIrWwTQgghhBDiIRRsE0IIIYQQ4iEUbBNCCCGEEOIhFGwTQgghhBDiIRRsE0IIIYQQ4iEUbBNCCCGEEOIhFGwTQgghhBDiIRRsE0IIIYQQ4iEUbBNCCCGEEOIh/z99ilzhUuBbQAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "
"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As you can see, variation in our predictor and outcome are quite high -- which is often the case with real data. And sometimes, the sampler won't like this -- and you don't want to make the sampler angry when you're a Bayesian... So, let's do what you'll often have to do with real data: standardize! This way, our predictor and outcome will have a mean of 0 and std of 1, and the sampler will be much, much happier:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_, ax = plt.subplots()\n",
-    "\n",
-    "x = xr.DataArray(np.linspace(-2, 2, 50), dims=[\"plot_dim\"])\n",
-    "prior = idata.prior\n",
-    "y = prior[\"a\"] + prior[\"b\"] * x\n",
-    "\n",
-    "ax.plot(x, y.stack(sample=(\"chain\", \"draw\")), c=\"k\", alpha=0.4)\n",
-    "\n",
-    "ax.set_xlabel(\"Predictor (stdz)\")\n",
-    "ax.set_ylabel(\"Mean Outcome (stdz)\")\n",
-    "ax.set_title(\"Prior predictive checks -- Weakly regularizing priors\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Well that's way better! There are still very strong relationships, but at least now the outcome stays in the realm of possibilities. Now, it's time to party -- if by \"party\" you mean \"run the model\", of course."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [a, b, sigma]\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'0.00, 1.00, -0.00, 1.00'"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "predictor_scaled = standardize(predictor)\n",
+        "outcome_scaled = standardize(outcome)\n",
+        "\n",
+        "f\"{predictor_scaled.mean():.2f}, {predictor_scaled.std():.2f}, {outcome_scaled.mean():.2f}, {outcome_scaled.std():.2f}\""
+      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "900e72a405e44fb19b36750b2d22d75d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And now, let's write the model with conventional flat priors and sample prior predictive samples:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/html": [
-       "
\n"
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sampling: [a, b, obs, sigma]\n"
+          ]
+        }
       ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "with pm.Model() as model_1:\n",
+        "    a = pm.Normal(\"a\", 0.0, 10.0)\n",
+        "    b = pm.Normal(\"b\", 0.0, 10.0)\n",
+        "\n",
+        "    mu = a + b * predictor_scaled\n",
+        "    sigma = pm.Exponential(\"sigma\", 1.0)\n",
+        "\n",
+        "    pm.Normal(\"obs\", mu=mu, sigma=sigma, observed=outcome_scaled)\n",
+        "    idata = pm.sample_prior_predictive(draws=50, random_seed=rng)"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "
\n",
-       "
\n"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "What do these priors mean? It's always hard to tell on paper -- the best is to plot their implication on the outcome scale, like that:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "\n"
+      "source": [
+        "_, ax = plt.subplots()\n",
+        "\n",
+        "x = xr.DataArray(np.linspace(-2, 2, 50), dims=[\"plot_dim\"])\n",
+        "prior = idata.prior\n",
+        "y = prior[\"a\"] + prior[\"b\"] * x\n",
+        "\n",
+        "ax.plot(x, y.stack(sample=(\"chain\", \"draw\")), c=\"k\", alpha=0.4)\n",
+        "\n",
+        "ax.set_xlabel(\"Predictor (stdz)\")\n",
+        "ax.set_ylabel(\"Mean Outcome (stdz)\")\n",
+        "ax.set_title(\"Prior predictive checks -- Flat priors\");"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 14 seconds.\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "These priors allow for absurdly strong relationships between the outcome and predictor. Of course, the choice of prior always depends on your model and data, but look at the scale of the y axis: the outcome can go from -40 to +40 standard deviations (remember, the data are standardized). I hope you will agree this is way too permissive -- we can do better! Let's use [weakly informative priors](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations) and see what they yield. In a real case study, this is the part where you incorporate scientific knowledge into your model:"
+      ]
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sampling: [a, b, obs, sigma]\n"
+          ]
+        }
+      ],
+      "source": [
+        "with pm.Model() as model_1:\n",
+        "    a = pm.Normal(\"a\", 0.0, 0.5)\n",
+        "    b = pm.Normal(\"b\", 0.0, 1.0)\n",
+        "\n",
+        "    mu = a + b * predictor_scaled\n",
+        "    sigma = pm.Exponential(\"sigma\", 1.0)\n",
+        "\n",
+        "    pm.Normal(\"obs\", mu=mu, sigma=sigma, observed=outcome_scaled)\n",
+        "    idata = pm.sample_prior_predictive(draws=50, random_seed=rng)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with model_1:\n",
-    "    idata.extend(pm.sample(1000, tune=2000, random_seed=rng))\n",
-    "\n",
-    "az.plot_trace(idata);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Everything ran smoothly, but it's often difficult to understand what the parameters' values mean when analyzing a trace plot or table summary -- even more so here, as the parameters live in the standardized space. A useful thing to understand your models is... you guessed it: posterior predictive checks! We'll use PyMC's dedicated function to sample data from the posterior. This function will randomly draw 4000 samples of parameters from the trace. Then, for each sample, it will draw 100 random numbers from a normal distribution specified by the values of `mu` and `sigma` in that sample:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling: [obs]\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "_, ax = plt.subplots()\n",
+        "\n",
+        "x = xr.DataArray(np.linspace(-2, 2, 50), dims=[\"plot_dim\"])\n",
+        "prior = idata.prior\n",
+        "y = prior[\"a\"] + prior[\"b\"] * x\n",
+        "\n",
+        "ax.plot(x, y.stack(sample=(\"chain\", \"draw\")), c=\"k\", alpha=0.4)\n",
+        "\n",
+        "ax.set_xlabel(\"Predictor (stdz)\")\n",
+        "ax.set_ylabel(\"Mean Outcome (stdz)\")\n",
+        "ax.set_title(\"Prior predictive checks -- Weakly regularizing priors\");"
+      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "45d14f63f2c44fd18abf5e5e3cb1e487",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Well that's way better! There are still very strong relationships, but at least now the outcome stays in the realm of possibilities. Now, it's time to party -- if by \"party\" you mean \"run the model\", of course."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/html": [
-       "
\n"
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Auto-assigning NUTS sampler...\n",
+            "Initializing NUTS using jitter+adapt_diag...\n",
+            "Multiprocess sampling (4 chains in 4 jobs)\n",
+            "NUTS: [a, b, sigma]\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "900e72a405e44fb19b36750b2d22d75d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 14 seconds.\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "with model_1:\n",
+        "    idata.extend(pm.sample(1000, tune=2000, random_seed=rng))\n",
+        "\n",
+        "az.plot_trace(idata);"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "
\n",
-       "
\n"
-      ],
-      "text/plain": [
-       "\n"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Everything ran smoothly, but it's often difficult to understand what the parameters' values mean when analyzing a trace plot or table summary -- even more so here, as the parameters live in the standardized space. A useful thing to understand your models is... you guessed it: posterior predictive checks! We'll use PyMC's dedicated function to sample data from the posterior. This function will randomly draw 4000 samples of parameters from the trace. Then, for each sample, it will draw 100 random numbers from a normal distribution specified by the values of `mu` and `sigma` in that sample:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with model_1:\n",
-    "    pm.sample_posterior_predictive(idata, extend_inferencedata=True, random_seed=rng)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, the posterior_predictive group in `idata` contains 4000 generated data sets (containing 100 samples each), each using a different parameter setting from the posterior:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "
\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "
<xarray.Dataset> Size: 3MB\n",
-       "Dimensions:    (chain: 4, draw: 1000, obs_dim_2: 100)\n",
-       "Coordinates:\n",
-       "  * chain      (chain) int64 32B 0 1 2 3\n",
-       "  * draw       (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
-       "  * obs_dim_2  (obs_dim_2) int64 800B 0 1 2 3 4 5 6 7 ... 93 94 95 96 97 98 99\n",
-       "Data variables:\n",
-       "    obs        (chain, draw, obs_dim_2) float64 3MB -0.5997 0.312 ... 0.4695\n",
-       "Attributes:\n",
-       "    created_at:                 2024-06-25T12:59:45.204631\n",
-       "    arviz_version:              0.17.1\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.15.0+1.g58927d608
"
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sampling: [obs]\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "45d14f63f2c44fd18abf5e5e3cb1e487",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n"
+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "
\n"
+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       " Size: 3MB\n",
-       "Dimensions:    (chain: 4, draw: 1000, obs_dim_2: 100)\n",
-       "Coordinates:\n",
-       "  * chain      (chain) int64 32B 0 1 2 3\n",
-       "  * draw       (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
-       "  * obs_dim_2  (obs_dim_2) int64 800B 0 1 2 3 4 5 6 7 ... 93 94 95 96 97 98 99\n",
-       "Data variables:\n",
-       "    obs        (chain, draw, obs_dim_2) float64 3MB -0.5997 0.312 ... 0.4695\n",
-       "Attributes:\n",
-       "    created_at:                 2024-06-25T12:59:45.204631\n",
-       "    arviz_version:              0.17.1\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.15.0+1.g58927d608"
+      "source": [
+        "with model_1:\n",
+        "    pm.sample_posterior_predictive(idata, extend_inferencedata=True, random_seed=rng)"
       ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idata.posterior_predictive"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One common way to visualize is to look if the model can reproduce the patterns observed in the real data. {doc}`ArviZ ` has a really neat function to do that out of the box:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, the posterior_predictive group in `idata` contains 4000 generated data sets (containing 100 samples each), each using a different parameter setting from the posterior:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_ppc(idata, num_pp_samples=100);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It looks like the model is pretty good at retrodicting the data. In addition to this generic function, it's always nice to make a plot tailored to your use-case. Here, it would be interesting to plot the predicted relationship between the predictor and the outcome. This is quite easy, now that we already sampled posterior predictive samples -- we just have to push the parameters through the model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "post = idata.posterior\n",
-    "mu_pp = post[\"a\"] + post[\"b\"] * xr.DataArray(predictor_scaled, dims=[\"obs_id\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "
\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "
<xarray.Dataset> Size: 3MB\n",
+              "Dimensions:    (chain: 4, draw: 1000, obs_dim_2: 100)\n",
+              "Coordinates:\n",
+              "  * chain      (chain) int64 32B 0 1 2 3\n",
+              "  * draw       (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
+              "  * obs_dim_2  (obs_dim_2) int64 800B 0 1 2 3 4 5 6 7 ... 93 94 95 96 97 98 99\n",
+              "Data variables:\n",
+              "    obs        (chain, draw, obs_dim_2) float64 3MB -0.5997 0.312 ... 0.4695\n",
+              "Attributes:\n",
+              "    created_at:                 2024-06-25T12:59:45.204631\n",
+              "    arviz_version:              0.17.1\n",
+              "    inference_library:          pymc\n",
+              "    inference_library_version:  5.15.0+1.g58927d608
"
+            ],
+            "text/plain": [
+              " Size: 3MB\n",
+              "Dimensions:    (chain: 4, draw: 1000, obs_dim_2: 100)\n",
+              "Coordinates:\n",
+              "  * chain      (chain) int64 32B 0 1 2 3\n",
+              "  * draw       (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
+              "  * obs_dim_2  (obs_dim_2) int64 800B 0 1 2 3 4 5 6 7 ... 93 94 95 96 97 98 99\n",
+              "Data variables:\n",
+              "    obs        (chain, draw, obs_dim_2) float64 3MB -0.5997 0.312 ... 0.4695\n",
+              "Attributes:\n",
+              "    created_at:                 2024-06-25T12:59:45.204631\n",
+              "    arviz_version:              0.17.1\n",
+              "    inference_library:          pymc\n",
+              "    inference_library_version:  5.15.0+1.g58927d608"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "idata.posterior_predictive"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_, ax = plt.subplots()\n",
-    "\n",
-    "ax.plot(\n",
-    "    predictor_scaled, mu_pp.mean((\"chain\", \"draw\")), label=\"Mean outcome\", color=\"C1\", alpha=0.6\n",
-    ")\n",
-    "ax.scatter(predictor_scaled, idata.observed_data[\"obs\"])\n",
-    "az.plot_hdi(predictor_scaled, idata.posterior_predictive[\"obs\"])\n",
-    "\n",
-    "ax.set_xlabel(\"Predictor (stdz)\")\n",
-    "ax.set_ylabel(\"Outcome (stdz)\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have a lot of data, so the uncertainty around the mean of the outcome is pretty narrow; but the uncertainty surrounding the outcome in general seems quite in line with the observed data.\n",
-    "\n",
-    "## Comparison between PPC and other model evaluation methods. \n",
-    "\n",
-    "An excellent introduction to this was given in the [Edward](http://edwardlib.org) documentation:\n",
-    "\n",
-    "> PPCs are an excellent tool for revising models, simplifying or expanding the current model as one examines how well it fits the data. They are inspired by prior checks and classical hypothesis testing, under the philosophy that models should be criticized under the frequentist perspective of large sample assessment.\n",
-    "\n",
-    "> PPCs can also be applied to tasks such as hypothesis testing, model comparison, model selection, and model averaging. It’s important to note that while they can be applied as a form of Bayesian hypothesis testing, hypothesis testing is generally not recommended: binary decision making from a single test is not as common a use case as one might believe. We recommend performing many PPCs to get a holistic understanding of the model fit."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Prediction\n",
-    "\n",
-    "The same pattern can be used for prediction. Here, we are building a logistic regression model:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([1, 1, 1, 0, 1, 0, 0, 1, 1, 0])"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "One common way to visualize is to look if the model can reproduce the patterns observed in the real data. {doc}`ArviZ ` has a really neat function to do that out of the box:"
       ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "N = 400\n",
-    "true_intercept = 0.2\n",
-    "true_slope = 1.0\n",
-    "predictors = rng.normal(size=N)\n",
-    "true_p = logistic(true_intercept + true_slope * predictors)\n",
-    "\n",
-    "outcomes = rng.binomial(1, true_p)\n",
-    "outcomes[:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ricardo/Documents/Projects/pymc/pymc/data.py:304: FutureWarning: MutableData is deprecated. All Data variables are now mutable. Use Data instead.\n",
-      "  warnings.warn(\n",
-      "Auto-assigning NUTS sampler...\n",
-      "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (4 chains in 4 jobs)\n",
-      "NUTS: [betas]\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "az.plot_ppc(idata, num_pp_samples=100);"
+      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4c70a9d38b1b4bd89e67763fe8d2fd39",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "It looks like the model is pretty good at retrodicting the data. In addition to this generic function, it's always nice to make a plot tailored to your use-case. Here, it would be interesting to plot the predicted relationship between the predictor and the outcome. This is quite easy, now that we already sampled posterior predictive samples -- we just have to push the parameters through the model:"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/html": [
-       "
\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "post = idata.posterior\n",
+        "mu_pp = post[\"a\"] + post[\"b\"] * xr.DataArray(predictor_scaled, dims=[\"obs_id\"])"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "
\n",
-       "
\n"
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "\n"
+      "source": [
+        "_, ax = plt.subplots()\n",
+        "\n",
+        "ax.plot(\n",
+        "    predictor_scaled, mu_pp.mean((\"chain\", \"draw\")), label=\"Mean outcome\", color=\"C1\", alpha=0.6\n",
+        ")\n",
+        "ax.scatter(predictor_scaled, idata.observed_data[\"obs\"])\n",
+        "az.plot_hdi(predictor_scaled, idata.posterior_predictive[\"obs\"])\n",
+        "\n",
+        "ax.set_xlabel(\"Predictor (stdz)\")\n",
+        "ax.set_ylabel(\"Outcome (stdz)\");"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 6 seconds.\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We have a lot of data, so the uncertainty around the mean of the outcome is pretty narrow; but the uncertainty surrounding the outcome in general seems quite in line with the observed data.\n",
+        "\n",
+        "## Comparison between PPC and other model evaluation methods. \n",
+        "\n",
+        "An excellent introduction to this was given in the [Edward](http://edwardlib.org) documentation:\n",
+        "\n",
+        "> PPCs are an excellent tool for revising models, simplifying or expanding the current model as one examines how well it fits the data. They are inspired by prior checks and classical hypothesis testing, under the philosophy that models should be criticized under the frequentist perspective of large sample assessment.\n",
+        "\n",
+        "> PPCs can also be applied to tasks such as hypothesis testing, model comparison, model selection, and model averaging. It’s important to note that while they can be applied as a form of Bayesian hypothesis testing, hypothesis testing is generally not recommended: binary decision making from a single test is not as common a use case as one might believe. We recommend performing many PPCs to get a holistic understanding of the model fit."
+      ]
     },
     {
-     "data": {
-      "text/html": [
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
betas[0]0.230.110.030.440.00.03211.493013.301.0
betas[1]1.030.130.781.290.00.03673.852720.491.0
\n",
-       "
"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prediction\n",
+        "\n",
+        "The same pattern can be used for prediction. Here, we are building a logistic regression model:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([1, 1, 1, 0, 1, 0, 0, 1, 1, 0])"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "          mean    sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
-       "betas[0]  0.23  0.11    0.03     0.44        0.0      0.0   3211.49   3013.30   \n",
-       "betas[1]  1.03  0.13    0.78     1.29        0.0      0.0   3673.85   2720.49   \n",
-       "\n",
-       "          r_hat  \n",
-       "betas[0]    1.0  \n",
-       "betas[1]    1.0  "
+      "source": [
+        "N = 400\n",
+        "true_intercept = 0.2\n",
+        "true_slope = 1.0\n",
+        "predictors = rng.normal(size=N)\n",
+        "true_p = logistic(true_intercept + true_slope * predictors)\n",
+        "\n",
+        "outcomes = rng.binomial(1, true_p)\n",
+        "outcomes[:10]"
       ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "with pm.Model() as model_2:\n",
-    "    betas = pm.Normal(\"betas\", mu=0.0, sigma=np.array([0.5, 1.0]), shape=2)\n",
-    "\n",
-    "    # set predictors as shared variable to change them for PPCs:\n",
-    "    pred = pm.MutableData(\"pred\", predictors, dims=\"obs_id\")\n",
-    "    p = pm.Deterministic(\"p\", pm.math.invlogit(betas[0] + betas[1] * pred), dims=\"obs_id\")\n",
-    "\n",
-    "    outcome = pm.Bernoulli(\"outcome\", p=p, observed=outcomes, dims=\"obs_id\")\n",
-    "\n",
-    "    idata_2 = pm.sample(1000, tune=2000, return_inferencedata=True, random_seed=rng)\n",
-    "az.summary(idata_2, var_names=[\"betas\"], round_to=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, let's simulate out-of-sample data to see how the model predicts them. We'll give the new predictors to the model and it'll then tell us what it thinks the outcomes are, based on what it learned in the training round. We'll then compare the model's predictions to the true out-of-sample outcomes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Sampling: []\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/home/ricardo/Documents/Projects/pymc/pymc/data.py:304: FutureWarning: MutableData is deprecated. All Data variables are now mutable. Use Data instead.\n",
+            "  warnings.warn(\n",
+            "Auto-assigning NUTS sampler...\n",
+            "Initializing NUTS using jitter+adapt_diag...\n",
+            "Multiprocess sampling (4 chains in 4 jobs)\n",
+            "NUTS: [betas]\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4c70a9d38b1b4bd89e67763fe8d2fd39",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 6 seconds.\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "  \n",
+              "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
betas[0]0.230.110.030.440.00.03211.493013.301.0
betas[1]1.030.130.781.290.00.03673.852720.491.0
\n",
+              "
"
+            ],
+            "text/plain": [
+              "          mean    sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
+              "betas[0]  0.23  0.11    0.03     0.44        0.0      0.0   3211.49   3013.30   \n",
+              "betas[1]  1.03  0.13    0.78     1.29        0.0      0.0   3673.85   2720.49   \n",
+              "\n",
+              "          r_hat  \n",
+              "betas[0]    1.0  \n",
+              "betas[1]    1.0  "
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "with pm.Model() as model_2:\n",
+        "    betas = pm.Normal(\"betas\", mu=0.0, sigma=np.array([0.5, 1.0]), shape=2)\n",
+        "\n",
+        "    # set predictors as shared variable to change them for PPCs:\n",
+        "    pred = pm.MutableData(\"pred\", predictors, dims=\"obs_id\")\n",
+        "    p = pm.Deterministic(\"p\", pm.math.invlogit(betas[0] + betas[1] * pred), dims=\"obs_id\")\n",
+        "\n",
+        "    outcome = pm.Bernoulli(\"outcome\", p=p, observed=outcomes, dims=\"obs_id\")\n",
+        "\n",
+        "    idata_2 = pm.sample(1000, tune=2000, return_inferencedata=True, random_seed=rng)\n",
+        "az.summary(idata_2, var_names=[\"betas\"], round_to=2)"
+      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Output()"
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now, let's simulate out-of-sample data to see how the model predicts them. We'll give the new predictors to the model and it'll then tell us what it thinks the outcomes are, based on what it learned in the training round. We'll then compare the model's predictions to the true out-of-sample outcomes."
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/html": [
-       "
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+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sampling: []\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "260f5cf3f0314c24bccdefb2d8ad3871",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Output()"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+            ],
+            "text/plain": []
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "
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+              "
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+            ],
+            "text/plain": [
+              "\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "source": [
+        "predictors_out_of_sample = rng.normal(size=50)\n",
+        "outcomes_out_of_sample = rng.binomial(\n",
+        "    1, logistic(true_intercept + true_slope * predictors_out_of_sample)\n",
+        ")\n",
+        "\n",
+        "with model_2:\n",
+        "    # update values of predictors:\n",
+        "    pm.set_data({\"pred\": predictors_out_of_sample})\n",
+        "    # use the updated values and predict outcomes and probabilities:\n",
+        "    idata_2 = pm.sample_posterior_predictive(\n",
+        "        idata_2,\n",
+        "        var_names=[\"p\"],\n",
+        "        return_inferencedata=True,\n",
+        "        predictions=True,\n",
+        "        extend_inferencedata=True,\n",
+        "        random_seed=rng,\n",
+        "    )"
+      ]
     },
     {
-     "data": {
-      "text/html": [
-       "
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-       "
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+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "            
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+              "              
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+              "                
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+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "
      <xarray.Dataset> Size: 13MB\n",
+              "Dimensions:      (chain: 4, draw: 1000, betas_dim_0: 2, obs_id: 400)\n",
+              "Coordinates:\n",
+              "  * chain        (chain) int64 32B 0 1 2 3\n",
+              "  * draw         (draw) int64 8kB 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
+              "  * betas_dim_0  (betas_dim_0) int64 16B 0 1\n",
+              "  * obs_id       (obs_id) int64 3kB 0 1 2 3 4 5 6 ... 394 395 396 397 398 399\n",
+              "Data variables:\n",
+              "    betas        (chain, draw, betas_dim_0) float64 64kB 0.3311 0.9692 ... 1.113\n",
+              "    p            (chain, draw, obs_id) float64 13MB 0.5169 0.7004 ... 0.8773\n",
+              "Attributes:\n",
+              "    created_at:                 2024-06-25T12:59:58.670730\n",
+              "    arviz_version:              0.17.1\n",
+              "    inference_library:          pymc\n",
+              "    inference_library_version:  5.15.0+1.g58927d608\n",
+              "    sampling_time:              6.474128246307373\n",
+              "    tuning_steps:               2000

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      <xarray.Dataset> Size: 2MB\n",
+              "Dimensions:  (chain: 4, draw: 1000, obs_id: 50)\n",
+              "Coordinates:\n",
+              "  * chain    (chain) int64 32B 0 1 2 3\n",
+              "  * draw     (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
+              "  * obs_id   (obs_id) int64 400B 0 1 2 3 4 5 6 7 8 ... 42 43 44 45 46 47 48 49\n",
+              "Data variables:\n",
+              "    p        (chain, draw, obs_id) float64 2MB 0.5904 0.2295 ... 0.3397 0.5857\n",
+              "Attributes:\n",
+              "    created_at:                 2024-06-25T12:59:59.047195\n",
+              "    arviz_version:              0.17.1\n",
+              "    inference_library:          pymc\n",
+              "    inference_library_version:  5.15.0+1.g58927d608

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+              "
      <xarray.Dataset> Size: 496kB\n",
+              "Dimensions:                (chain: 4, draw: 1000)\n",
+              "Coordinates:\n",
+              "  * chain                  (chain) int64 32B 0 1 2 3\n",
+              "  * draw                   (draw) int64 8kB 0 1 2 3 4 5 ... 995 996 997 998 999\n",
+              "Data variables: (12/17)\n",
+              "    acceptance_rate        (chain, draw) float64 32kB 0.8535 0.6245 ... 0.9594\n",
+              "    energy                 (chain, draw) float64 32kB 239.0 238.5 ... 236.7\n",
+              "    step_size_bar          (chain, draw) float64 32kB 1.181 1.181 ... 1.194\n",
+              "    perf_counter_start     (chain, draw) float64 32kB 1.238e+04 ... 1.238e+04\n",
+              "    smallest_eigval        (chain, draw) float64 32kB nan nan nan ... nan nan\n",
+              "    reached_max_treedepth  (chain, draw) bool 4kB False False ... False False\n",
+              "    ...                     ...\n",
+              "    diverging              (chain, draw) bool 4kB False False ... False False\n",
+              "    energy_error           (chain, draw) float64 32kB -0.5477 ... 0.004425\n",
+              "    tree_depth             (chain, draw) int64 32kB 2 2 2 2 2 2 ... 2 2 2 2 2 2\n",
+              "    process_time_diff      (chain, draw) float64 32kB 0.001024 ... 0.0008914\n",
+              "    lp                     (chain, draw) float64 32kB -236.9 -237.8 ... -236.5\n",
+              "    perf_counter_diff      (chain, draw) float64 32kB 0.001023 ... 0.0008892\n",
+              "Attributes:\n",
+              "    created_at:                 2024-06-25T12:59:58.698238\n",
+              "    arviz_version:              0.17.1\n",
+              "    inference_library:          pymc\n",
+              "    inference_library_version:  5.15.0+1.g58927d608\n",
+              "    sampling_time:              6.474128246307373\n",
+              "    tuning_steps:               2000

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+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "
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+              "Dimensions:  (obs_id: 400)\n",
+              "Coordinates:\n",
+              "  * obs_id   (obs_id) int64 3kB 0 1 2 3 4 5 6 7 ... 393 394 395 396 397 398 399\n",
+              "Data variables:\n",
+              "    outcome  (obs_id) int64 3kB 1 1 1 0 1 0 0 1 1 0 0 ... 0 1 1 1 0 1 1 0 1 0 1\n",
+              "Attributes:\n",
+              "    created_at:                 2024-06-25T12:59:58.707843\n",
+              "    arviz_version:              0.17.1\n",
+              "    inference_library:          pymc\n",
+              "    inference_library_version:  5.15.0+1.g58927d608

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+              "                      
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+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "
      <xarray.Dataset> Size: 6kB\n",
+              "Dimensions:  (obs_id: 400)\n",
+              "Coordinates:\n",
+              "  * obs_id   (obs_id) int64 3kB 0 1 2 3 4 5 6 7 ... 393 394 395 396 397 398 399\n",
+              "Data variables:\n",
+              "    pred     (obs_id) float64 3kB -0.2718 0.5346 -1.073 ... -0.9459 -1.438 1.557\n",
+              "Attributes:\n",
+              "    created_at:                 2024-06-25T12:59:58.709527\n",
+              "    arviz_version:              0.17.1\n",
+              "    inference_library:          pymc\n",
+              "    inference_library_version:  5.15.0+1.g58927d608

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+              "                      
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+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "\n",
+              "
      <xarray.Dataset> Size: 800B\n",
+              "Dimensions:  (obs_id: 50)\n",
+              "Coordinates:\n",
+              "  * obs_id   (obs_id) int64 400B 0 1 2 3 4 5 6 7 8 ... 42 43 44 45 46 47 48 49\n",
+              "Data variables:\n",
+              "    pred     (obs_id) float64 400B 0.03558 -1.591 -0.7009 ... -0.8064 0.1015\n",
+              "Attributes:\n",
+              "    created_at:                 2024-06-25T12:59:59.049869\n",
+              "    arviz_version:              0.17.1\n",
+              "    inference_library:          pymc\n",
+              "    inference_library_version:  5.15.0+1.g58927d608

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+              "                      
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+              "                  
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+              "            
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+              "            \n",
+              "              
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+              "            
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+              "            "
+            ],
+            "text/plain": [
+              "Inference data with groups:\n",
+              "\t> posterior\n",
+              "\t> predictions\n",
+              "\t> sample_stats\n",
+              "\t> observed_data\n",
+              "\t> constant_data\n",
+              "\t> predictions_constant_data"
+            ]
+          },
+          "execution_count": null,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
       ],
-      "text/plain": [
-       "\n"
+      "source": [
+        "idata_2"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "predictors_out_of_sample = rng.normal(size=50)\n",
-    "outcomes_out_of_sample = rng.binomial(\n",
-    "    1, logistic(true_intercept + true_slope * predictors_out_of_sample)\n",
-    ")\n",
-    "\n",
-    "with model_2:\n",
-    "    # update values of predictors:\n",
-    "    pm.set_data({\"pred\": predictors_out_of_sample})\n",
-    "    # use the updated values and predict outcomes and probabilities:\n",
-    "    idata_2 = pm.sample_posterior_predictive(\n",
-    "        idata_2,\n",
-    "        var_names=[\"p\"],\n",
-    "        return_inferencedata=True,\n",
-    "        predictions=True,\n",
-    "        extend_inferencedata=True,\n",
-    "        random_seed=rng,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "\n",
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-       "
      <xarray.Dataset> Size: 13MB\n",
-       "Dimensions:      (chain: 4, draw: 1000, betas_dim_0: 2, obs_id: 400)\n",
-       "Coordinates:\n",
-       "  * chain        (chain) int64 32B 0 1 2 3\n",
-       "  * draw         (draw) int64 8kB 0 1 2 3 4 5 6 ... 993 994 995 996 997 998 999\n",
-       "  * betas_dim_0  (betas_dim_0) int64 16B 0 1\n",
-       "  * obs_id       (obs_id) int64 3kB 0 1 2 3 4 5 6 ... 394 395 396 397 398 399\n",
-       "Data variables:\n",
-       "    betas        (chain, draw, betas_dim_0) float64 64kB 0.3311 0.9692 ... 1.113\n",
-       "    p            (chain, draw, obs_id) float64 13MB 0.5169 0.7004 ... 0.8773\n",
-       "Attributes:\n",
-       "    created_at:                 2024-06-25T12:59:58.670730\n",
-       "    arviz_version:              0.17.1\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.15.0+1.g58927d608\n",
-       "    sampling_time:              6.474128246307373\n",
-       "    tuning_steps:               2000

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-       "
      <xarray.Dataset> Size: 2MB\n",
-       "Dimensions:  (chain: 4, draw: 1000, obs_id: 50)\n",
-       "Coordinates:\n",
-       "  * chain    (chain) int64 32B 0 1 2 3\n",
-       "  * draw     (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
-       "  * obs_id   (obs_id) int64 400B 0 1 2 3 4 5 6 7 8 ... 42 43 44 45 46 47 48 49\n",
-       "Data variables:\n",
-       "    p        (chain, draw, obs_id) float64 2MB 0.5904 0.2295 ... 0.3397 0.5857\n",
-       "Attributes:\n",
-       "    created_at:                 2024-06-25T12:59:59.047195\n",
-       "    arviz_version:              0.17.1\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.15.0+1.g58927d608

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      <xarray.Dataset> Size: 496kB\n",
-       "Dimensions:                (chain: 4, draw: 1000)\n",
-       "Coordinates:\n",
-       "  * chain                  (chain) int64 32B 0 1 2 3\n",
-       "  * draw                   (draw) int64 8kB 0 1 2 3 4 5 ... 995 996 997 998 999\n",
-       "Data variables: (12/17)\n",
-       "    acceptance_rate        (chain, draw) float64 32kB 0.8535 0.6245 ... 0.9594\n",
-       "    energy                 (chain, draw) float64 32kB 239.0 238.5 ... 236.7\n",
-       "    step_size_bar          (chain, draw) float64 32kB 1.181 1.181 ... 1.194\n",
-       "    perf_counter_start     (chain, draw) float64 32kB 1.238e+04 ... 1.238e+04\n",
-       "    smallest_eigval        (chain, draw) float64 32kB nan nan nan ... nan nan\n",
-       "    reached_max_treedepth  (chain, draw) bool 4kB False False ... False False\n",
-       "    ...                     ...\n",
-       "    diverging              (chain, draw) bool 4kB False False ... False False\n",
-       "    energy_error           (chain, draw) float64 32kB -0.5477 ... 0.004425\n",
-       "    tree_depth             (chain, draw) int64 32kB 2 2 2 2 2 2 ... 2 2 2 2 2 2\n",
-       "    process_time_diff      (chain, draw) float64 32kB 0.001024 ... 0.0008914\n",
-       "    lp                     (chain, draw) float64 32kB -236.9 -237.8 ... -236.5\n",
-       "    perf_counter_diff      (chain, draw) float64 32kB 0.001023 ... 0.0008892\n",
-       "Attributes:\n",
-       "    created_at:                 2024-06-25T12:59:58.698238\n",
-       "    arviz_version:              0.17.1\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.15.0+1.g58927d608\n",
-       "    sampling_time:              6.474128246307373\n",
-       "    tuning_steps:               2000

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-       "
      <xarray.Dataset> Size: 6kB\n",
-       "Dimensions:  (obs_id: 400)\n",
-       "Coordinates:\n",
-       "  * obs_id   (obs_id) int64 3kB 0 1 2 3 4 5 6 7 ... 393 394 395 396 397 398 399\n",
-       "Data variables:\n",
-       "    outcome  (obs_id) int64 3kB 1 1 1 0 1 0 0 1 1 0 0 ... 0 1 1 1 0 1 1 0 1 0 1\n",
-       "Attributes:\n",
-       "    created_at:                 2024-06-25T12:59:58.707843\n",
-       "    arviz_version:              0.17.1\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.15.0+1.g58927d608

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      <xarray.Dataset> Size: 6kB\n",
-       "Dimensions:  (obs_id: 400)\n",
-       "Coordinates:\n",
-       "  * obs_id   (obs_id) int64 3kB 0 1 2 3 4 5 6 7 ... 393 394 395 396 397 398 399\n",
-       "Data variables:\n",
-       "    pred     (obs_id) float64 3kB -0.2718 0.5346 -1.073 ... -0.9459 -1.438 1.557\n",
-       "Attributes:\n",
-       "    created_at:                 2024-06-25T12:59:58.709527\n",
-       "    arviz_version:              0.17.1\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.15.0+1.g58927d608

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-       "\n",
-       "
      <xarray.Dataset> Size: 800B\n",
-       "Dimensions:  (obs_id: 50)\n",
-       "Coordinates:\n",
-       "  * obs_id   (obs_id) int64 400B 0 1 2 3 4 5 6 7 8 ... 42 43 44 45 46 47 48 49\n",
-       "Data variables:\n",
-       "    pred     (obs_id) float64 400B 0.03558 -1.591 -0.7009 ... -0.8064 0.1015\n",
-       "Attributes:\n",
-       "    created_at:                 2024-06-25T12:59:59.049869\n",
-       "    arviz_version:              0.17.1\n",
-       "    inference_library:          pymc\n",
-       "    inference_library_version:  5.15.0+1.g58927d608

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+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Mean predicted values plus error bars to give a sense of uncertainty in prediction\n",
+        "Note that since we are dealing with the full posterior, we are also getting uncertainty in our predictions for free."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "
"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
       ],
-      "text/plain": [
-       "Inference data with groups:\n",
-       "\t> posterior\n",
-       "\t> predictions\n",
-       "\t> sample_stats\n",
-       "\t> observed_data\n",
-       "\t> constant_data\n",
-       "\t> predictions_constant_data"
+      "source": [
+        "_, ax = plt.subplots(figsize=(12, 6))\n",
+        "\n",
+        "preds_out_of_sample = idata_2.predictions_constant_data.sortby(\"pred\")[\"pred\"]\n",
+        "model_preds = idata_2.predictions.sortby(preds_out_of_sample)\n",
+        "\n",
+        "# uncertainty about the estimates:\n",
+        "ax.vlines(\n",
+        "    preds_out_of_sample,\n",
+        "    *az.hdi(model_preds)[\"p\"].transpose(\"hdi\", ...),\n",
+        "    alpha=0.8,\n",
+        ")\n",
+        "# expected probability of success:\n",
+        "ax.plot(\n",
+        "    preds_out_of_sample,\n",
+        "    model_preds[\"p\"].mean((\"chain\", \"draw\")),\n",
+        "    \"o\",\n",
+        "    ms=5,\n",
+        "    color=\"C1\",\n",
+        "    alpha=0.8,\n",
+        "    label=\"Expected prob.\",\n",
+        ")\n",
+        "\n",
+        "# actual outcomes:\n",
+        "ax.scatter(\n",
+        "    x=predictors_out_of_sample,\n",
+        "    y=outcomes_out_of_sample,\n",
+        "    marker=\"x\",\n",
+        "    color=\"k\",\n",
+        "    alpha=0.8,\n",
+        "    label=\"Observed outcomes\",\n",
+        ")\n",
+        "# true probabilities:\n",
+        "x = np.linspace(predictors_out_of_sample.min() - 0.1, predictors_out_of_sample.max() + 0.1)\n",
+        "ax.plot(\n",
+        "    x,\n",
+        "    logistic(true_intercept + true_slope * x),\n",
+        "    lw=2,\n",
+        "    ls=\"--\",\n",
+        "    color=\"#565C6C\",\n",
+        "    alpha=0.8,\n",
+        "    label=\"True prob.\",\n",
+        ")\n",
+        "\n",
+        "ax.set_xlabel(\"Predictor\")\n",
+        "ax.set_ylabel(\"Prob. of success\")\n",
+        "ax.set_title(\"Out-of-sample Predictions\")\n",
+        "ax.legend(fontsize=10, frameon=True, framealpha=0.5);"
       ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "idata_2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Mean predicted values plus error bars to give a sense of uncertainty in prediction\n",
-    "Note that since we are dealing with the full posterior, we are also getting uncertainty in our predictions for free."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "
"
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Last updated: Tue Jun 25 2024\n",
+            "\n",
+            "Python implementation: CPython\n",
+            "Python version       : 3.11.8\n",
+            "IPython version      : 8.22.2\n",
+            "\n",
+            "pytensor: 2.20.0+3.g66439d283.dirty\n",
+            "\n",
+            "pymc      : 5.15.0+1.g58927d608\n",
+            "numpy     : 1.26.4\n",
+            "arviz     : 0.17.1\n",
+            "matplotlib: 3.8.3\n",
+            "xarray    : 2024.2.0\n",
+            "\n",
+            "Watermark: 2.4.3\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "%load_ext watermark\n",
+        "%watermark -n -u -v -iv -w -p pytensor"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "_, ax = plt.subplots(figsize=(12, 6))\n",
-    "\n",
-    "preds_out_of_sample = idata_2.predictions_constant_data.sortby(\"pred\")[\"pred\"]\n",
-    "model_preds = idata_2.predictions.sortby(preds_out_of_sample)\n",
-    "\n",
-    "# uncertainty about the estimates:\n",
-    "ax.vlines(\n",
-    "    preds_out_of_sample,\n",
-    "    *az.hdi(model_preds)[\"p\"].transpose(\"hdi\", ...),\n",
-    "    alpha=0.8,\n",
-    ")\n",
-    "# expected probability of success:\n",
-    "ax.plot(\n",
-    "    preds_out_of_sample,\n",
-    "    model_preds[\"p\"].mean((\"chain\", \"draw\")),\n",
-    "    \"o\",\n",
-    "    ms=5,\n",
-    "    color=\"C1\",\n",
-    "    alpha=0.8,\n",
-    "    label=\"Expected prob.\",\n",
-    ")\n",
-    "\n",
-    "# actual outcomes:\n",
-    "ax.scatter(\n",
-    "    x=predictors_out_of_sample,\n",
-    "    y=outcomes_out_of_sample,\n",
-    "    marker=\"x\",\n",
-    "    color=\"k\",\n",
-    "    alpha=0.8,\n",
-    "    label=\"Observed outcomes\",\n",
-    ")\n",
-    "# true probabilities:\n",
-    "x = np.linspace(predictors_out_of_sample.min() - 0.1, predictors_out_of_sample.max() + 0.1)\n",
-    "ax.plot(\n",
-    "    x,\n",
-    "    logistic(true_intercept + true_slope * x),\n",
-    "    lw=2,\n",
-    "    ls=\"--\",\n",
-    "    color=\"#565C6C\",\n",
-    "    alpha=0.8,\n",
-    "    label=\"True prob.\",\n",
-    ")\n",
-    "\n",
-    "ax.set_xlabel(\"Predictor\")\n",
-    "ax.set_ylabel(\"Prob. of success\")\n",
-    "ax.set_title(\"Out-of-sample Predictions\")\n",
-    "ax.legend(fontsize=10, frameon=True, framealpha=0.5);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Last updated: Tue Jun 25 2024\n",
-      "\n",
-      "Python implementation: CPython\n",
-      "Python version       : 3.11.8\n",
-      "IPython version      : 8.22.2\n",
-      "\n",
-      "pytensor: 2.20.0+3.g66439d283.dirty\n",
-      "\n",
-      "pymc      : 5.15.0+1.g58927d608\n",
-      "numpy     : 1.26.4\n",
-      "arviz     : 0.17.1\n",
-      "matplotlib: 3.8.3\n",
-      "xarray    : 2024.2.0\n",
-      "\n",
-      "Watermark: 2.4.3\n",
-      "\n"
-     ]
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "anaconda-cloud": {},
+    "hide_input": false,
+    "kernelspec": {
+      "display_name": "pymc",
+      "language": "python",
+      "name": "pymc"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.8"
+    },
+    "toc": {
+      "base_numbering": 1,
+      "nav_menu": {},
+      "number_sections": true,
+      "sideBar": true,
+      "skip_h1_title": false,
+      "title_cell": "Table of Contents",
+      "title_sidebar": "Contents",
+      "toc_cell": false,
+      "toc_position": {},
+      "toc_section_display": true,
+      "toc_window_display": false
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "93c75725ab6c99bb5d4e456f6806f02a4d03e25646320eea2cba27ee709ef180"
+      }
     }
-   ],
-   "source": [
-    "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p pytensor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "hide_input": false,
-  "kernelspec": {
-   "display_name": "pymc",
-   "language": "python",
-   "name": "pymc"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
   },
-  "vscode": {
-   "interpreter": {
-    "hash": "93c75725ab6c99bb5d4e456f6806f02a4d03e25646320eea2cba27ee709ef180"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 4
 }
diff --git a/docs/source/learn/core_notebooks/pymc_overview.ipynb b/docs/source/learn/core_notebooks/pymc_overview.ipynb
index 05ddf4b022..415ff1733c 100644
--- a/docs/source/learn/core_notebooks/pymc_overview.ipynb
+++ b/docs/source/learn/core_notebooks/pymc_overview.ipynb
@@ -1088,7 +1088,24 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pymc",
+   "language": "python",
+   "name": "pymc"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }
diff --git a/docs/source/learn/usage_overview.rst b/docs/source/learn/usage_overview.rst
index 89b6bbb6c9..5534f128a3 100644
--- a/docs/source/learn/usage_overview.rst
+++ b/docs/source/learn/usage_overview.rst
@@ -1,3 +1,5 @@
+:orphan:
+
 TODO: incorporate the useful bits of this page into the learning section
 
 **************
@@ -61,8 +63,7 @@ This example will generate 1000 posterior samples on each of two cores using the
 
 The sample is returned as arrays inside a ``MultiTrace`` object, which is then passed to the plotting function. The resulting graph shows a forest plot of the random variables in the model, along with a convergence diagnostic (R-hat) that indicates our model has converged.
 
-.. image:: ./images/forestplot.png
-   :width: 1000px
+*(forestplot image removed — original file no longer available)*
 
 See also
 ========
diff --git a/pymc/backends/arviz.py b/pymc/backends/arviz.py
index 63f8370523..c9fee648af 100644
--- a/pymc/backends/arviz.py
+++ b/pymc/backends/arviz.py
@@ -333,7 +333,7 @@ def sample_stats_to_xarray(self):
         data_warmup = {}
         for stat in self.trace.stat_names:
             name = rename_key.get(stat, stat)
-            if name == "tune":
+            if name in {"tune", "in_warmup"}:
                 continue
             if self.warmup_trace:
                 data_warmup[name] = np.array(
@@ -504,12 +504,11 @@ def to_inference_data(
     save_warmup: bool | None = None,
     include_transformed: bool = False,
 ) -> InferenceData:
-    """Convert pymc data into an InferenceData object.
+    """Convert pymc data into an ArviZ InferenceData object.
 
-    All three of them are optional arguments, but at least one of ``trace``,
-    ``prior`` and ``posterior_predictive`` must be present.
-    For a usage example read the
-    :ref:`Creating InferenceData section on from_pymc `
+    All three of ``trace``, ``prior`` and ``posterior_predictive``
+    are optional arguments, but at least one of them must be present.
+    For a usage example, read the :ref:`InferenceData schema specification `.
 
     Parameters
     ----------
diff --git a/pymc/backends/base.py b/pymc/backends/base.py
index 993acc0df4..528552650f 100644
--- a/pymc/backends/base.py
+++ b/pymc/backends/base.py
@@ -113,7 +113,13 @@ def point(self, idx: int) -> dict[str, np.ndarray]:
         """
         raise NotImplementedError()
 
-    def record(self, draw: Mapping[str, np.ndarray], stats: Sequence[Mapping[str, Any]]):
+    def record(
+        self,
+        draw: Mapping[str, np.ndarray],
+        stats: Sequence[Mapping[str, Any]],
+        *,
+        in_warmup: bool,
+    ):
         """Record results of a sampling iteration.
 
         Parameters
@@ -122,6 +128,9 @@ def record(self, draw: Mapping[str, np.ndarray], stats: Sequence[Mapping[str, An
             Values mapped to variable names
         stats: list of dicts
             The diagnostic values for each sampler
+        in_warmup: bool
+            Whether this draw belongs to the warmup phase. This is a driver-owned
+            concept and is intended for storage/backends to persist warmup information.
         """
         raise NotImplementedError()
 
diff --git a/pymc/backends/mcbackend.py b/pymc/backends/mcbackend.py
index d02a6dbebb..891afa51f9 100644
--- a/pymc/backends/mcbackend.py
+++ b/pymc/backends/mcbackend.py
@@ -34,7 +34,6 @@
     BlockedStep,
     CompoundStep,
     StatsBijection,
-    check_step_emits_tune,
     flat_statname,
     flatten_steps,
 )
@@ -106,16 +105,26 @@ def __init__(
             {sname: stats_dtypes[fname] for fname, sname, is_obj in sstats}
             for sstats in stats_bijection._stat_groups
         ]
+        if "in_warmup" in stats_dtypes and self.sampler_vars:
+            # Expose driver-owned warmup marker via the sampler-stats API.
+            self.sampler_vars[0].setdefault("in_warmup", stats_dtypes["in_warmup"])
 
         self._chain = chain
         self._point_fn = point_fn
         self._statsbj = stats_bijection
         super().__init__()
 
-    def record(self, draw: Mapping[str, np.ndarray], stats: Sequence[Mapping[str, Any]]):
+    def record(
+        self,
+        draw: Mapping[str, np.ndarray],
+        stats: Sequence[Mapping[str, Any]],
+        *,
+        in_warmup: bool,
+    ):
         values = self._point_fn(draw)
         value_dict = dict(zip(self.varnames, values))
         stats_dict = self._statsbj.map(stats)
+        stats_dict["in_warmup"] = bool(in_warmup)
         # Apply pickling to objects stats
         for fname in self._statsbj.object_stats.keys():
             val_bytes = pickle.dumps(stats_dict[fname])
@@ -148,6 +157,9 @@ def get_sampler_stats(
         self, stat_name: str, sampler_idx: int | None = None, burn=0, thin=1
     ) -> np.ndarray:
         slc = slice(burn, None, thin)
+        if stat_name in {"in_warmup", "tune"}:
+            # Backwards-friendly alias for users that might try "tune".
+            return self._get_stats("in_warmup", slc)
         # When there's just one sampler, default to remove the sampler dimension
         if sampler_idx is None and self._statsbj.n_samplers == 1:
             sampler_idx = 0
@@ -210,8 +222,6 @@ def make_runmeta_and_point_fn(
 ) -> tuple[mcb.RunMeta, PointFunc]:
     variables, point_fn = get_variables_and_point_fn(model, initial_point)
 
-    check_step_emits_tune(step)
-
     # In PyMC the sampler stats are grouped by the sampler.
     sample_stats = []
     steps = flatten_steps(step)
@@ -235,6 +245,16 @@ def make_runmeta_and_point_fn(
             )
             sample_stats.append(svar)
 
+    # driver owned warmup marker. stored once per draw.
+    sample_stats.append(
+        mcb.Variable(
+            name="in_warmup",
+            dtype=np.dtype(bool).name,
+            shape=[],
+            undefined_ndim=False,
+        )
+    )
+
     coordinates = [
         mcb.Coordinate(dname, mcb.npproto.utils.ndarray_from_numpy(np.array(cvals)))
         for dname, cvals in model.coords.items()
diff --git a/pymc/backends/ndarray.py b/pymc/backends/ndarray.py
index a08fc8f47e..5d8d1be62b 100644
--- a/pymc/backends/ndarray.py
+++ b/pymc/backends/ndarray.py
@@ -97,7 +97,7 @@ def setup(self, draws, chain, sampler_vars=None) -> None:
                     new = np.zeros(draws, dtype=dtype)
                     data[varname] = np.concatenate([old, new])
 
-    def record(self, point, sampler_stats=None) -> None:
+    def record(self, point, sampler_stats=None, *, in_warmup: bool) -> None:
         """Record results of a sampling iteration.
 
         Parameters
@@ -238,5 +238,5 @@ def point_fun(point):
 
         chain.fn = point_fun
         for point in point_list:
-            chain.record(point)
+            chain.record(point, in_warmup=False)
     return MultiTrace([chain])
diff --git a/pymc/backends/zarr.py b/pymc/backends/zarr.py
index 9b7664c504..3e827d5fa2 100644
--- a/pymc/backends/zarr.py
+++ b/pymc/backends/zarr.py
@@ -159,7 +159,11 @@ def buffer(self, group, var_name, value):
         buffer[var_name].append(value)
 
     def record(
-        self, draw: Mapping[str, np.ndarray], stats: Sequence[Mapping[str, Any]]
+        self,
+        draw: Mapping[str, np.ndarray],
+        stats: Sequence[Mapping[str, Any]],
+        *,
+        in_warmup: bool,
     ) -> bool | None:
         """Record the step method's returned draw and stats.
 
@@ -185,6 +189,7 @@ def record(
                 self.buffer(group="posterior", var_name=var_name, value=var_value)
         for var_name, var_value in self.stats_bijection.map(stats).items():
             self.buffer(group="sample_stats", var_name=var_name, value=var_value)
+        self.buffer(group="sample_stats", var_name="in_warmup", value=bool(in_warmup))
         self._buffered_draws += 1
         if self._buffered_draws == self.draws_until_flush:
             self.flush()
@@ -272,18 +277,18 @@ class ZarrTrace:
     """Object that stores and enables access to MCMC draws stored in a :class:`zarr.hierarchy.Group` objects.
 
     This class creats a zarr hierarchy to represent the sampling information which is
-    intended to mimic :class:`arviz.InferenceData`. The hierarchy looks like this:
-
-    | root
-    | |--> constant_data
-    | |--> observed_data
-    | |--> posterior
-    | |--> unconstrained_posterior
-    | |--> sample_stats
-    | |--> warmup_posterior
-    | |--> warmup_unconstrained_posterior
-    | |--> warmup_sample_stats
-    | |--> _sampling_state
+    intended to mimic :class:`arviz.InferenceData`. The hierarchy looks like this::
+
+        root
+        |--> constant_data
+        |--> observed_data
+        |--> posterior
+        |--> unconstrained_posterior
+        |--> sample_stats
+        |--> warmup_posterior
+        |--> warmup_unconstrained_posterior
+        |--> warmup_sample_stats
+        |--> _sampling_state
 
     The root group is created when the ``ZarrTrace`` object is initialized. The rest of
     the groups are created once :meth:`~ZarrChain.init_trace` is called with a few exceptions:
@@ -525,6 +530,7 @@ def init_trace(
         stats_dtypes_shapes = get_stats_dtypes_shapes_from_steps(
             [step] if isinstance(step, BlockedStep) else step.methods
         )
+        stats_dtypes_shapes = {"in_warmup": (bool, [])} | stats_dtypes_shapes
         self.init_group_with_empty(
             group=self.root.create_group(name="sample_stats", overwrite=True),
             var_dtype_and_shape=stats_dtypes_shapes,
@@ -555,7 +561,7 @@ def split_warmup_groups(self):
 
         This method takes the entries in the arrays in the posterior, sample_stats
         and unconstrained_posterior that happened in the tuning phase and moves them
-        into the warmup_ groups. If the ``warmup_posterior`` group already exists, then
+        into the ``warmup_`` groups. If the ``warmup_posterior`` group already exists, then
         nothing is done.
 
         See Also
@@ -683,6 +689,7 @@ def init_group_with_empty(
                 for i, shape_i in enumerate(shape):
                     dim = f"{name}_dim_{i}"
                     dims.append(dim)
+                    assert shape_i is not None, f"{dim} shape is None"
                     group_coords[dim] = np.arange(shape_i, dtype="int")
             dims = ("chain", "draw", *dims)
             attrs = extra_var_attrs[name] if extra_var_attrs is not None else {}
diff --git a/pymc/blocking.py b/pymc/blocking.py
index 9f9e27cebf..b13822eb1b 100644
--- a/pymc/blocking.py
+++ b/pymc/blocking.py
@@ -58,7 +58,7 @@ def __call__(self, x: RaveledVars) -> T:
 
 
 class DictToArrayBijection:
-    """Map between a `dict`s of variables to an array space.
+    """Map between `dict` s of variables to an array space.
 
     Said array space consists of all the vars raveled and then concatenated.
 
diff --git a/pymc/data.py b/pymc/data.py
index cfade37910..bab24e8300 100644
--- a/pymc/data.py
+++ b/pymc/data.py
@@ -255,7 +255,7 @@ def Data(
         random variables). Use this when ``value`` is a pandas Series or DataFrame. The
         ``dims`` will then be the name of the Series / DataFrame's columns. See ArviZ
         documentation for more information about dimensions and coordinates:
-        :ref:`arviz:quickstart`.
+        `ArviZ getting started `_.
         If this parameter is not specified, the random variables will not have dimension
         names.
     coords : dict, optional
diff --git a/pymc/dims/distributions/__init__.py b/pymc/dims/distributions/__init__.py
index da85bc1463..6c49789089 100644
--- a/pymc/dims/distributions/__init__.py
+++ b/pymc/dims/distributions/__init__.py
@@ -11,5 +11,6 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
+from pymc.dims.distributions.censored import Censored
 from pymc.dims.distributions.scalar import *
 from pymc.dims.distributions.vector import *
diff --git a/pymc/dims/distributions/censored.py b/pymc/dims/distributions/censored.py
new file mode 100644
index 0000000000..29d9a56136
--- /dev/null
+++ b/pymc/dims/distributions/censored.py
@@ -0,0 +1,51 @@
+#   Copyright 2026 - present The PyMC Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+import numpy as np
+
+from pymc.dims.distributions.core import DimDistribution, copy_docstring, expand_dist_dims
+from pymc.distributions.censored import Censored as RegularCensored
+
+
+@copy_docstring(RegularCensored)
+class Censored(DimDistribution):
+    @classmethod
+    def dist(cls, dist, *, lower=None, upper=None, dim_lengths, **kwargs):
+        if lower is None:
+            lower = -np.inf
+        if upper is None:
+            upper = np.inf
+        return super().dist([dist, lower, upper], dim_lengths=dim_lengths, **kwargs)
+
+    @classmethod
+    def xrv_op(cls, dist, lower, upper, core_dims=None, extra_dims=None, rng=None):
+        if extra_dims is None:
+            extra_dims = {}
+
+        dist = cls._as_xtensor(dist)
+        lower = cls._as_xtensor(lower)
+        upper = cls._as_xtensor(upper)
+
+        # Any dimensions in extra_dims, or only present in lower, upper,
+        # must propagate back to the dist as `extra_dims`
+        bounds_sizes = lower.sizes | upper.sizes
+        dist_dims_set = set(dist.dims)
+        extra_dist_dims = extra_dims | {
+            dim: size for dim, size in bounds_sizes.items() if dim not in dist_dims_set
+        }
+        if extra_dist_dims:
+            dist = expand_dist_dims(dist, extra_dist_dims)
+
+        # Probability is inferred from the clip operation
+        # TODO: Make this a SymbolicRandomVariable that can itself be resized
+        return dist.clip(lower, upper)
diff --git a/pymc/dims/distributions/core.py b/pymc/dims/distributions/core.py
index aee8e4cf7f..918b28a794 100644
--- a/pymc/dims/distributions/core.py
+++ b/pymc/dims/distributions/core.py
@@ -13,7 +13,7 @@
 #   limitations under the License.
 from collections.abc import Callable, Sequence
 from itertools import chain
-from typing import cast
+from typing import Any, cast
 
 import numpy as np
 
@@ -25,7 +25,9 @@
 from pytensor.tensor.random.op import RandomVariable
 from pytensor.xtensor import as_xtensor
 from pytensor.xtensor.basic import XTensorFromTensor, xtensor_from_tensor
+from pytensor.xtensor.shape import Transpose
 from pytensor.xtensor.type import XTensorVariable
+from pytensor.xtensor.vectorization import XRV
 
 from pymc import SymbolicRandomVariable, modelcontext
 from pymc.dims.distributions.transforms import DimTransform, log_odds_transform, log_transform
@@ -345,3 +347,25 @@ class UnitDimDistribution(DimDistribution):
     """Base class for unit-valued distributions."""
 
     default_transform = log_odds_transform
+
+
+def expand_dist_dims(dist: XTensorVariable, extra_dims: dict[str, Any]) -> XTensorVariable:
+    if overlap := (set(extra_dims) & set(dist.dims)):
+        raise ValueError(f"extra_dims already present in distribution: {sorted(overlap)}")
+
+    op = None if dist.owner is None else dist.owner.op
+    match op:
+        case XRV():
+            # Recreate dist with new extra dims
+            dist_props = dist.owner.op._props_dict()
+            dist_props["extra_dims"] = (*(extra_dims.keys()), *dist_props["extra_dims"])
+            new_dist_op = type(dist.owner.op)(**dist_props)
+            _old_rng, *params_and_dim_lengths = dist.owner.inputs
+            new_rng = None  # We don't propagate the old RNG, because we don't want the new and old dists to be correlated
+            return new_dist_op(new_rng, *extra_dims.values(), *params_and_dim_lengths)
+        case Transpose():
+            return expand_dist_dims(dist.owner.inputs[0], extra_dims=extra_dims).transpose(
+                ..., *dist.dims
+            )
+        case _:
+            raise NotImplementedError(f"expand_dist_dims not implemented for {dist} with op {op}")
diff --git a/pymc/dims/distributions/transforms.py b/pymc/dims/distributions/transforms.py
index 14dbb9444f..e898ab0508 100644
--- a/pymc/dims/distributions/transforms.py
+++ b/pymc/dims/distributions/transforms.py
@@ -203,6 +203,4 @@ def backward(self, value, *rv_inputs):
         return value
 
     def log_jac_det(self, value, *rv_inputs):
-        # Use following once broadcast_like is implemented
-        # as_xtensor(0).broadcast_like(value, exclude=self.dims)`
-        return value.sum(self.dims) * 0
+        return as_xtensor(0.0).broadcast_like(value, exclude=self.dims)
diff --git a/pymc/dims/distributions/vector.py b/pymc/dims/distributions/vector.py
index 7107712e67..0995f891e8 100644
--- a/pymc/dims/distributions/vector.py
+++ b/pymc/dims/distributions/vector.py
@@ -229,7 +229,8 @@ def dist(cls, sigma=1.0, *, core_dims=None, dim_lengths, **kwargs):
             raise ValueError("ZeroSumNormal requires atleast 1 core_dims")
 
         support_dims = as_xtensor(
-            as_tensor([dim_lengths[core_dim] for core_dim in core_dims]), dims=("_",)
+            as_tensor([dim_lengths[core_dim] for core_dim in core_dims]),
+            dims=("__support_shape__",),
         )
         sigma = cls._as_xtensor(sigma)
 
@@ -238,16 +239,25 @@ def dist(cls, sigma=1.0, *, core_dims=None, dim_lengths, **kwargs):
         )
 
     @classmethod
-    def xrv_op(self, sigma, support_dims, core_dims, extra_dims=None, rng=None):
-        sigma = as_xtensor(sigma)
-        support_dims = as_xtensor(support_dims, dims=("_",))
-        support_shape = support_dims.values
-        core_rv = ZeroSumNormalRV.rv_op(sigma=sigma.values, support_shape=support_shape).owner.op
+    def xrv_op(cls, sigma, support_shape, core_dims, extra_dims=None, rng=None):
+        # ZeroSumNormal expects dummy dimensions on sigma for the support_shape
+        sigma = cls._as_xtensor(sigma).expand_dims(core_dims)
+        support_shape = as_xtensor(support_shape, dims=("__support_shape__",))
+        core_rv = ZeroSumNormalRV.rv_op(
+            sigma=sigma.values, support_shape=support_shape.values
+        ).owner.op
+        core_dims_map = tuple(range(1, len(core_dims) + 1))
         xop = pxr.as_xrv(
             core_rv,
-            core_inps_dims_map=[(), (0,)],
-            core_out_dims_map=tuple(range(1, len(core_dims) + 1)),
+            core_inps_dims_map=[core_dims_map, (0,)],
+            core_out_dims_map=core_dims_map,
         )
         # Dummy "_" core dim to absorb the support_shape vector
         # If ZeroSumNormal expected a scalar per support dim, this wouldn't be needed
-        return xop(sigma, support_dims, core_dims=("_", *core_dims), extra_dims=extra_dims, rng=rng)
+        return xop(
+            sigma,
+            support_shape,
+            core_dims=("__support_shape__", *core_dims),
+            extra_dims=extra_dims,
+            rng=rng,
+        )
diff --git a/pymc/distributions/distribution.py b/pymc/distributions/distribution.py
index b434a6db25..76af9df884 100644
--- a/pymc/distributions/distribution.py
+++ b/pymc/distributions/distribution.py
@@ -219,7 +219,7 @@ class SymbolicRandomVariable(MeasurableOp, RNGConsumerOp, OpFromGraph):
 
     This is a subclasse of `OpFromGraph` which is used to encapsulate the symbolic
     random graph of complex distributions which are built on top of pure
-    `RandomVariable`s.
+    `RandomVariable` s.
 
     These graphs may vary structurally based on the inputs (e.g., their dimensionality),
     and usually require that random inputs have specific shapes for correct outputs
@@ -305,7 +305,7 @@ def get_input_output_type_idxs(
         tuple[tuple[int, ...], int | None, tuple[int, ...]],
         tuple[tuple[int, ...], tuple[int, ...]],
     ]:
-        """Parse extended_signature and return indexes for *[rng], [size] and parameters as well as outputs."""
+        """Parse extended_signature and return indexes for ``*[rng]``, ``[size]`` and parameters as well as outputs."""
         if extended_signature is None:
             raise ValueError("extended_signature must be provided")
 
@@ -392,7 +392,7 @@ def make_node(self, *inputs):
             )
             if size_arg_idx is not None and len(rng_arg_idxs) == 1:
                 new_size_type = normalize_size_param(inputs[size_arg_idx]).type
-                if not self.input_types[size_arg_idx].in_same_class(new_size_type):
+                if not self.input_types[size_arg_idx].is_super(new_size_type):
                     params = [inputs[idx] for idx in param_idxs]
                     size = inputs[size_arg_idx]
                     rng = inputs[rng_arg_idxs[0]]
diff --git a/pymc/distributions/multivariate.py b/pymc/distributions/multivariate.py
index 192fff6e30..802a5ab9a2 100644
--- a/pymc/distributions/multivariate.py
+++ b/pymc/distributions/multivariate.py
@@ -952,6 +952,8 @@ class WishartRV(RandomVariable):
     @classmethod
     def rng_fn(cls, rng, nu, V, size):
         scipy_size = size if size else 1  # Default size for Scipy's wishart.rvs is 1
+        # Scipy doesn't accept batch nu or V
+        nu = _squeeze_to_ndim(nu, 0)
         V = _squeeze_to_ndim(V, 2)
         result = stats.wishart.rvs(int(nu), V, size=scipy_size, random_state=rng)
         if size == (1,):
@@ -2659,9 +2661,9 @@ class ZeroSumNormalRV(SymbolicRandomVariable):
 
     @classmethod
     def rv_op(cls, sigma, support_shape, *, size=None, rng=None):
+        support_shape = _squeeze_to_ndim(pt.as_tensor(support_shape), ndim=1)
         n_zerosum_axes = pt.get_vector_length(support_shape)
         sigma = pt.as_tensor(sigma)
-        support_shape = pt.as_tensor(support_shape, ndim=1)
         rng = normalize_rng_param(rng)
         size = normalize_size_param(size)
 
@@ -2677,8 +2679,10 @@ def rv_op(cls, sigma, support_shape, *, size=None, rng=None):
         for axis in range(n_zerosum_axes):
             zerosum_rv -= zerosum_rv.mean(axis=-axis - 1, keepdims=True)
 
+        # sigma has core_shape = (1, 1, ...) (as many as there are zerosum axes)
+        ones = ",".join("1" for _ in range(n_zerosum_axes))
         support_str = ",".join([f"d{i}" for i in range(n_zerosum_axes)])
-        extended_signature = f"[rng],[size],(),(s)->[rng],({support_str})"
+        extended_signature = f"[rng],[size],({ones}),(s)->[rng],({support_str})"
         return cls(
             inputs=[rng, size, sigma, support_shape],
             outputs=[next_rng, zerosum_rv],
@@ -2719,7 +2723,7 @@ class ZeroSumNormal(Distribution):
 
     Warnings
     --------
-    Currently, ``sigma``cannot have length > 1 across the zero-sum axes to ensure the zero-sum constraint.
+    Currently, ``sigma`` cannot have length > 1 across the zero-sum axes to ensure the zero-sum constraint.
 
     ``n_zerosum_axes`` has to be > 0. If you want the behavior of ``n_zerosum_axes = 0``,
     just use ``pm.Normal``.
@@ -2774,7 +2778,7 @@ def __new__(cls, *args, n_zerosum_axes=None, support_shape=None, dims=None, **kw
     def dist(cls, sigma=1.0, n_zerosum_axes=None, support_shape=None, **kwargs):
         n_zerosum_axes = cls.check_zerosum_axes(n_zerosum_axes)
 
-        sigma = pt.as_tensor(sigma)
+        sigma = pt.atleast_Nd(pt.as_tensor(sigma), n=n_zerosum_axes)
         if not all(sigma.type.broadcastable[-n_zerosum_axes:]):
             raise ValueError("sigma must have length one across the zero-sum axes")
 
diff --git a/pymc/distributions/shape_utils.py b/pymc/distributions/shape_utils.py
index 62e45d95e9..17d533a9f2 100644
--- a/pymc/distributions/shape_utils.py
+++ b/pymc/distributions/shape_utils.py
@@ -251,7 +251,7 @@ def change_dist_size(
         The new size of the distribution.
     expand: bool, optional
         If True, `new_size` is prepended to the existing distribution `size`, so that
-        the final size is equal to (*new_size, *dist.size). Defaults to false.
+        the final size is equal to ``(*new_size, *dist.size)``. Defaults to false.
 
     Returns
     -------
diff --git a/pymc/distributions/timeseries.py b/pymc/distributions/timeseries.py
index c2ed54a16d..44f28eef1e 100644
--- a/pymc/distributions/timeseries.py
+++ b/pymc/distributions/timeseries.py
@@ -505,7 +505,7 @@ class AR(Distribution):
         Whether the first element of rho should be used as a constant term in the AR
         process.
     init_dist : unnamed_distribution, optional
-        Scalar or vector distribution for initial values. Distributions should have shape (*shape[:-1], ar_order).
+        Scalar or vector distribution for initial values. Distributions should have shape ``(*shape[:-1], ar_order)``.
         If not, it will be automatically resized. Defaults to pm.Normal.dist(0, 100, shape=...).
 
         .. warning:: init_dist will be cloned, rendering it independent of the one passed as input.
@@ -912,7 +912,7 @@ class EulerMaruyama(Distribution):
     sde_pars : tuple
         parameters of the SDE, passed as ``*args`` to ``sde_fn``
     init_dist : unnamed_distribution, optional
-        Scalar distribution for initial values. Distributions should have shape (*shape[:-1]).
+        Scalar distribution for initial values. Distributions should have shape ``(*shape[:-1])``.
         If not, it will be automatically resized. Defaults to pm.Normal.dist(0, 100, shape=...).
 
         .. warning:: init_dist will be cloned, rendering it independent of the one passed as input.
diff --git a/pymc/distributions/transforms.py b/pymc/distributions/transforms.py
index 5e3f934953..c56cba8a55 100644
--- a/pymc/distributions/transforms.py
+++ b/pymc/distributions/transforms.py
@@ -309,7 +309,7 @@ def backward(self, value, *rv_inputs):
         return value
 
     def log_jac_det(self, value, *rv_inputs):
-        return pt.constant(0.0)
+        return value.sum(self.zerosum_axes).zeros_like()
 
 
 log_exp_m1 = LogExpM1()
diff --git a/pymc/gp/cov.py b/pymc/gp/cov.py
index 624009bab3..533ee96d91 100644
--- a/pymc/gp/cov.py
+++ b/pymc/gp/cov.py
@@ -622,6 +622,7 @@ def power_spectral_density(self, omega: TensorLike) -> TensorVariable:
            S(\boldsymbol\omega) = \frac{2 (2\pi\alpha)^{D/2} \prod_{i=1}^D \ell_i}{\Gamma(\alpha)}
                                   \left(\frac{z}{2}\right)^{\nu}
                                   K_{\nu}(z)
+
         where :math:`z = \sqrt{2\alpha} \sqrt{\sum \ell_i^2 \omega_i^2}` and :math:`\nu = \alpha - D/2`.
 
         Derivation
diff --git a/pymc/logprob/transform_value.py b/pymc/logprob/transform_value.py
index 2e1b96d343..8e28076406 100644
--- a/pymc/logprob/transform_value.py
+++ b/pymc/logprob/transform_value.py
@@ -16,6 +16,7 @@
 from collections.abc import Sequence
 
 import numpy as np
+import pytensor.tensor as pt
 
 from pytensor.graph import Apply, Op
 from pytensor.graph.features import AlreadyThere, Feature
@@ -113,10 +114,18 @@ def transformed_value_logprob(op, values, *rv_outs, use_jacobian=True, **kwargs)
             )
         # Check there is no broadcasting between logp and jacobian
         if logp.type.broadcastable != log_jac_det.type.broadcastable:
-            raise ValueError(
-                f"The logp of {rv_op} and log_jac_det of {transform} are not allowed to broadcast together. "
-                "There is a bug in the implementation of either one."
-            )
+            lb, jb = logp.type.broadcastable, log_jac_det.type.broadcastable
+            broadcastable_axes = [
+                i for i, (ai, bi) in enumerate(zip(lb, jb, strict=True)) if ai or bi
+            ]
+            try:
+                logp = pt.specify_broadcastable(logp, *broadcastable_axes)
+                log_jac_det = pt.specify_broadcastable(log_jac_det, *broadcastable_axes)
+            except ValueError as err:
+                raise ValueError(
+                    f"The logp of {rv_op} and log_jac_det of {transform} are not allowed to broadcast together. "
+                    "There is a bug in the implementation of either one."
+                ) from err
 
         if use_jacobian:
             if value.name:
diff --git a/pymc/model/core.py b/pymc/model/core.py
index 38e9f2711f..3552308bee 100644
--- a/pymc/model/core.py
+++ b/pymc/model/core.py
@@ -1826,9 +1826,10 @@ def debug(
 
         The method will evaluate the `fn` for each variable at a time.
         When an evaluation fails or produces a non-finite value we print:
-         1. The graph of the parameters
-         2. The value of the parameters (if those can be evaluated)
-         3. The output of `fn` (if it can be evaluated)
+
+        1. The graph of the parameters
+        2. The value of the parameters (if those can be evaluated)
+        3. The output of `fn` (if it can be evaluated)
 
         This function should help to quickly narrow down invalid parametrizations.
 
diff --git a/pymc/printing.py b/pymc/printing.py
index 7969799cc6..d253c588ce 100644
--- a/pymc/printing.py
+++ b/pymc/printing.py
@@ -21,11 +21,11 @@
 from pytensor.graph.basic import Constant, Variable
 from pytensor.graph.traversal import walk
 from pytensor.tensor.elemwise import DimShuffle
-from pytensor.tensor.random.basic import RandomVariable
 from pytensor.tensor.random.type import RandomType
 from pytensor.tensor.type_other import NoneTypeT
 from pytensor.tensor.variable import TensorVariable
 
+from pymc.logprob.abstract import MeasurableOp
 from pymc.model import Model
 
 __all__ = [
@@ -41,21 +41,24 @@ def str_for_dist(dist: Variable, formatting: str = "plain", include_params: bool
     This can be either LaTeX or plain, optionally with distribution parameter
     values included.
     """
+    dist_op = dist.owner.op
+
     if include_params:
-        if isinstance(dist.owner.op, RandomVariable) or getattr(
-            dist.owner.op, "extended_signature", None
-        ):
-            dist_args = [
-                _str_for_input_var(x, formatting=formatting)
-                for x in dist.owner.op.dist_params(dist.owner)
-            ]
-        else:
+        try:
+            dist_args = dist.owner.op.dist_params(dist.owner)
+        except Exception:
+            # Can happen with SymbolicRandomVariable without extended_signature
             dist_args = [
-                _str_for_input_var(x, formatting=formatting)
-                for x in dist.owner.inputs
-                if not isinstance(x.type, RandomType | NoneTypeT)
+                x for x in dist.owner.inputs if not isinstance(x.type, RandomType | NoneTypeT)
             ]
 
+        dist_args_str = [_str_for_input_var(a, formatting=formatting) for a in dist_args]
+
+    if (print_name := getattr(dist_op, "_print_name", None)) is not None:
+        dist_name = print_name[formatting == "latex"]
+    else:
+        dist_name = "Unknown"
+
     print_name = dist.name
 
     if "latex" in formatting:
@@ -63,30 +66,22 @@ def str_for_dist(dist: Variable, formatting: str = "plain", include_params: bool
             print_name = r"\text{" + _latex_escape(print_name.strip("$")) + "}"
             print_name = _format_underscore(print_name)
 
-        op_name = (
-            dist.owner.op._print_name[1]
-            if hasattr(dist.owner.op, "_print_name")
-            else r"\\operatorname{Unknown}"
-        )
         if include_params:
-            params = ",~".join([d.strip("$") for d in dist_args])
+            params = ",~".join([d.strip("$") for d in dist_args_str])
             if print_name:
-                return rf"${print_name} \sim {op_name}({params})$"
+                return rf"${print_name} \sim {dist_name}({params})$"
             else:
-                return rf"${op_name}({params})$"
+                return rf"${dist_name}({params})$"
 
         else:
             if print_name:
-                return rf"${print_name} \sim {op_name}$"
+                return rf"${print_name} \sim {dist_name}$"
             else:
-                return rf"${op_name}$"
+                return rf"${dist_name}$"
 
     else:  # plain
-        dist_name = (
-            dist.owner.op._print_name[0] if hasattr(dist.owner.op, "_print_name") else "Unknown"
-        )
         if include_params:
-            params = ", ".join(dist_args)
+            params = ", ".join(dist_args_str)
             if print_name:
                 return rf"{print_name} ~ {dist_name}({params})"
             else:
@@ -169,10 +164,9 @@ def str_for_potential_or_deterministic(
 
 
 def _str_for_input_var(var: Variable, formatting: str) -> str:
-    # Avoid circular import
-    from pymc.distributions.distribution import SymbolicRandomVariable
-
     def _is_potential_or_deterministic(var: Variable) -> bool:
+        # FIXME: This is an (insufficient) hack. For model_repr we know which nodes are named variables
+        # and we should propagate that information instead of guessing based on whether something was monkey-patched
         if not hasattr(var, "str_repr"):
             return False
         try:
@@ -183,9 +177,7 @@ def _is_potential_or_deterministic(var: Variable) -> bool:
 
     if isinstance(var, Constant | SharedVariable):
         return _str_for_constant(var, formatting)
-    elif isinstance(
-        var.owner.op, RandomVariable | SymbolicRandomVariable
-    ) or _is_potential_or_deterministic(var):
+    elif isinstance(var.owner.op, MeasurableOp) or _is_potential_or_deterministic(var):
         # show the names for RandomVariables, Deterministics, and Potentials, rather
         # than the full expression
         return _str_for_input_rv(var, formatting)
@@ -227,25 +219,23 @@ def _str_for_constant(var: Constant | SharedVariable, formatting: str) -> str:
 
 def _str_for_expression(var: Variable, formatting: str) -> str:
     # Avoid circular import
-    from pymc.distributions.distribution import SymbolicRandomVariable
 
     # construct a string like f(a1, ..., aN) listing all random variables a as arguments
     def _expand(x):
-        if x.owner and (not isinstance(x.owner.op, RandomVariable | SymbolicRandomVariable)):
+        if x.owner and not isinstance(x.owner.op, MeasurableOp):
             return reversed(x.owner.inputs)
 
     parents = []
     names = []
     for x in walk(nodes=var.owner.inputs, expand=_expand):
         assert isinstance(x, Variable)
-        if x.owner and isinstance(x.owner.op, RandomVariable | SymbolicRandomVariable):
+        if x.owner and isinstance(x.owner.op, MeasurableOp):
             parents.append(x)
             xname = x.name
             if xname is None:
                 # If the variable is unnamed, we show the op's name as we do
                 # with constants
-                opname = x.owner.op.name
-                if opname is not None:
+                if (opname := getattr(x.owner.op, "name", None)) is not None:
                     xname = rf"<{opname}>"
             assert xname is not None
             names.append(xname)
diff --git a/pymc/progress_bar/rich_progress.py b/pymc/progress_bar/rich_progress.py
index 6c980221ee..09607ebc1f 100644
--- a/pymc/progress_bar/rich_progress.py
+++ b/pymc/progress_bar/rich_progress.py
@@ -13,6 +13,7 @@
 #   limitations under the License.
 
 from collections.abc import Iterable
+from sys import stderr
 from typing import Any, Self
 
 from rich.box import SIMPLE_HEAD
@@ -212,7 +213,7 @@ def _create_progress_bar(
                 finished_style=Style.parse("rgb(31,119,180)"),
             ),
             *columns,
-            console=Console(theme=theme),
+            console=Console(file=stderr, theme=theme),
             include_headers=True,
         )
 
@@ -323,5 +324,5 @@ def RichSimpleProgress(theme: Theme | None):
         TimeRemainingColumn(),
         TextColumn("/"),
         TimeElapsedColumn(),
-        console=Console(theme=default_progress_theme if theme is None else theme),
+        console=Console(file=stderr, theme=default_progress_theme if theme is None else theme),
     )
diff --git a/pymc/sampling/mcmc.py b/pymc/sampling/mcmc.py
index b063fe8846..205d1c5550 100644
--- a/pymc/sampling/mcmc.py
+++ b/pymc/sampling/mcmc.py
@@ -571,18 +571,19 @@ def sample(
         A ``TypeError`` will be raised if a legacy :py:class:`~numpy.random.RandomState` object is passed.
         We no longer support ``RandomState`` objects because their seeding mechanism does not allow
         easy spawning of new independent random streams that are needed by the step methods.
-    progressbar: bool or ProgressType, optional
-            How and whether to display the progress bar. If False, no progress bar is displayed. Otherwise, you can ask
-            for one of the following:
-            - "combined": A single progress bar that displays the total progress across all chains. Only timing
-                information is shown.
-            - "split": A separate progress bar for each chain. Only timing information is shown.
-            - "combined+stats" or "stats+combined": A single progress bar displaying the total progress across all
-                chains. Aggregate sample statistics are also displayed.
-            - "split+stats" or "stats+split": A separate progress bar for each chain. Sample statistics for each chain
-                are also displayed.
-
-            If True, the default is "split+stats" is used.
+    progressbar : bool or ProgressType, optional
+        How and whether to display the progress bar. If False, no progress bar is displayed. Otherwise, you can ask
+        for one of the following:
+
+        - ``"combined"``: A single progress bar that displays the total progress across all chains. Only timing
+          information is shown.
+        - ``"split"``: A separate progress bar for each chain. Only timing information is shown.
+        - ``"combined+stats"`` or ``"stats+combined"``: A single progress bar displaying the total progress across all
+          chains. Aggregate sample statistics are also displayed.
+        - ``"split+stats"`` or ``"stats+split"``: A separate progress bar for each chain. Sample statistics for each chain
+          are also displayed.
+
+        If True, the default ``"split+stats"`` is used.
     quiet : bool, default False
         If True, suppress all logging output and progress bars during sampling.
         This is useful when sampling in loops or when no output is desired.
@@ -1124,18 +1125,10 @@ def _sample_return(
     else:
         traces, length = _choose_chains(traces, 0)
     mtrace = MultiTrace(traces)[:length]
-    # count the number of tune/draw iterations that happened
-    # ideally via the "tune" statistic, but not all samplers record it!
-    if "tune" in mtrace.stat_names:
-        # Get the tune stat directly from chain 0, sampler 0
-        stat = mtrace._straces[0].get_sampler_stats("tune", sampler_idx=0)
-        stat = tuple(stat)
-        n_tune = stat.count(True)
-        n_draws = stat.count(False)
-    else:
-        # these may be wrong when KeyboardInterrupt happened, but they're better than nothing
-        n_tune = min(tune, len(mtrace))
-        n_draws = max(0, len(mtrace) - n_tune)
+    # Count the number of tune/draw iterations that happened.
+    # The warmup/draw boundary is owned by the sampling driver.
+    n_tune = min(tune, len(mtrace))
+    n_draws = max(0, len(mtrace) - n_tune)
 
     if discard_tuned_samples:
         mtrace = mtrace[n_tune:]
@@ -1304,7 +1297,7 @@ def _sample(
     try:
         for it, stats in enumerate(sampling_gen):
             progress_manager.update(
-                chain_idx=chain, is_last=False, draw=it, stats=stats, tuning=it > tune
+                chain_idx=chain, is_last=False, draw=it, stats=stats, tuning=it < tune
             )
 
         if not progress_manager.combined_progress or chain == progress_manager.chains - 1:
@@ -1375,7 +1368,7 @@ def _iter_sample(
                 step.stop_tuning()
 
             point, stats = step.step(point)
-            trace.record(point, stats)
+            trace.record(point, stats, in_warmup=i < tune)
             log_warning_stats(stats)
 
             if callback is not None:
@@ -1488,7 +1481,7 @@ def _mp_sample(
                     strace = traces[draw.chain]
                     if not zarr_recording:
                         # Zarr recording happens in each process
-                        strace.record(draw.point, draw.stats)
+                        strace.record(draw.point, draw.stats, in_warmup=draw.tuning)
                     log_warning_stats(draw.stats)
 
                     if callback is not None:
diff --git a/pymc/sampling/parallel.py b/pymc/sampling/parallel.py
index cf3fd223e5..78df40581b 100644
--- a/pymc/sampling/parallel.py
+++ b/pymc/sampling/parallel.py
@@ -249,7 +249,7 @@ def _start_loop(self):
                 raise KeyboardInterrupt()
             elif msg[0] == "write_next":
                 if zarr_recording:
-                    self._zarr_chain.record(point, stats)
+                    self._zarr_chain.record(point, stats, in_warmup=tuning)
                 self._write_point(point)
                 is_last = draw + 1 == self._draws + self._tune
                 self._msg_pipe.send(("writing_done", is_last, draw, tuning, stats))
diff --git a/pymc/sampling/population.py b/pymc/sampling/population.py
index 5bd1771704..7d1d9902f9 100644
--- a/pymc/sampling/population.py
+++ b/pymc/sampling/population.py
@@ -458,7 +458,7 @@ def _iter_population(
                 # apply the update to the points and record to the traces
                 for c, strace in enumerate(traces):
                     points[c], stats = updates[c]
-                    flushed = strace.record(points[c], stats)
+                    flushed = strace.record(points[c], stats, in_warmup=i < tune)
                     log_warning_stats(stats)
                     if flushed and isinstance(strace, ZarrChain):
                         sampling_state = popstep.request_sampling_state(c)
diff --git a/pymc/smc/sampling.py b/pymc/smc/sampling.py
index deec9a4576..3189d13edc 100644
--- a/pymc/smc/sampling.py
+++ b/pymc/smc/sampling.py
@@ -414,7 +414,7 @@ def _build_trace_from_kernel_state(
                 var_samples = np.round(var_samples).astype(var.dtype)
             value.append(var_samples.reshape(shape))
             size += new_size
-        strace.record(point=dict(zip(varnames, value)))
+        strace.record(point=dict(zip(varnames, value)), in_warmup=False)
 
     return strace
 
diff --git a/pymc/step_methods/compound.py b/pymc/step_methods/compound.py
index a9cae903f0..389bd3b30a 100644
--- a/pymc/step_methods/compound.py
+++ b/pymc/step_methods/compound.py
@@ -92,6 +92,10 @@ def infer_warn_stats_info(
                 sds[sname] = (dtype, None)
     elif sds:
         stats_dtypes.append({sname: dtype for sname, (dtype, _) in sds.items()})
+
+    # Even when a step method does not emit any stats, downstream components still assume one stats "slot" per step method. represent that with a single empty dict.
+    if not stats_dtypes:
+        stats_dtypes.append({})
     return stats_dtypes, sds
 
 
@@ -351,16 +355,6 @@ def flatten_steps(step: BlockedStep | CompoundStep) -> list[BlockedStep]:
     return steps
 
 
-def check_step_emits_tune(step: CompoundStep | BlockedStep):
-    if isinstance(step, BlockedStep) and "tune" not in step.stats_dtypes_shapes:
-        raise TypeError(f"{type(step)} does not emit the required 'tune' stat.")
-    elif isinstance(step, CompoundStep):
-        for sstep in step.methods:
-            if "tune" not in sstep.stats_dtypes_shapes:
-                raise TypeError(f"{type(sstep)} does not emit the required 'tune' stat.")
-    return
-
-
 class StatsBijection:
     """Map between a `list` of stats to `dict` of stats."""
 
diff --git a/pymc/step_methods/hmc/base_hmc.py b/pymc/step_methods/hmc/base_hmc.py
index 297b095e23..c3e6d75e5c 100644
--- a/pymc/step_methods/hmc/base_hmc.py
+++ b/pymc/step_methods/hmc/base_hmc.py
@@ -273,7 +273,6 @@ def astep(self, q0: RaveledVars) -> tuple[RaveledVars, StatsType]:
         self.iter_count += 1
 
         stats: dict[str, Any] = {
-            "tune": self.tune,
             "diverging": diverging,
             "divergences": self.divergences,
             "perf_counter_diff": perf_end - perf_start,
diff --git a/pymc/step_methods/hmc/hmc.py b/pymc/step_methods/hmc/hmc.py
index 1697341bc8..57fd5219b1 100644
--- a/pymc/step_methods/hmc/hmc.py
+++ b/pymc/step_methods/hmc/hmc.py
@@ -53,7 +53,6 @@ class HamiltonianMC(BaseHMC):
     stats_dtypes_shapes = {
         "step_size": (np.float64, []),
         "n_steps": (np.int64, []),
-        "tune": (bool, []),
         "step_size_bar": (np.float64, []),
         "accept": (np.float64, []),
         "diverging": (bool, []),
diff --git a/pymc/step_methods/hmc/nuts.py b/pymc/step_methods/hmc/nuts.py
index c927d57e31..f674e852ee 100644
--- a/pymc/step_methods/hmc/nuts.py
+++ b/pymc/step_methods/hmc/nuts.py
@@ -110,7 +110,6 @@ class NUTS(BaseHMC):
     stats_dtypes_shapes = {
         "depth": (np.int64, []),
         "step_size": (np.float64, []),
-        "tune": (bool, []),
         "mean_tree_accept": (np.float64, []),
         "step_size_bar": (np.float64, []),
         "tree_size": (np.float64, []),
diff --git a/pymc/step_methods/metropolis.py b/pymc/step_methods/metropolis.py
index c042bc1f3d..6bae4d92c6 100644
--- a/pymc/step_methods/metropolis.py
+++ b/pymc/step_methods/metropolis.py
@@ -146,7 +146,6 @@ class Metropolis(ArrayStepShared):
     stats_dtypes_shapes = {
         "accept": (np.float64, []),
         "accepted": (np.float64, []),
-        "tune": (bool, []),
         "scaling": (np.float64, []),
     }
 
@@ -316,7 +315,6 @@ def astep(self, q0: RaveledVars) -> tuple[RaveledVars, StatsType]:
         self.steps_until_tune -= 1
 
         stats = {
-            "tune": self.tune,
             "scaling": np.mean(self.scaling),
             "accept": np.mean(np.exp(self.accept_rate_iter)),
             "accepted": np.mean(self.accepted_iter),
@@ -331,7 +329,6 @@ def competence(var, has_grad):
     @staticmethod
     def _progressbar_config(n_chains=1):
         columns = [
-            TextColumn("{task.fields[tune]}", table_column=Column("Tuning", ratio=1)),
             TextColumn("{task.fields[scaling]:0.2f}", table_column=Column("Scaling", ratio=1)),
             TextColumn(
                 "{task.fields[accept_rate]:0.2f}", table_column=Column("Accept Rate", ratio=1)
@@ -339,7 +336,6 @@ def _progressbar_config(n_chains=1):
         ]
 
         stats = {
-            "tune": [True] * n_chains,
             "scaling": [0] * n_chains,
             "accept_rate": [0.0] * n_chains,
         }
@@ -351,7 +347,7 @@ def _make_progressbar_update_functions():
         def update_stats(step_stats):
             return {
                 "accept_rate" if key == "accept" else key: step_stats[key]
-                for key in ("tune", "accept", "scaling")
+                for key in ("accept", "scaling")
             }
 
         return (update_stats,)
@@ -448,7 +444,6 @@ class BinaryMetropolis(ArrayStep):
 
     stats_dtypes_shapes = {
         "accept": (np.float64, []),
-        "tune": (bool, []),
         "p_jump": (np.float64, []),
     }
 
@@ -505,7 +500,6 @@ def astep(self, apoint: RaveledVars, *args) -> tuple[RaveledVars, StatsType]:
         self.accepted += accepted
 
         stats = {
-            "tune": self.tune,
             "accept": np.exp(accept),
             "p_jump": p_jump,
         }
@@ -537,7 +531,6 @@ def competence(var):
 
 @dataclass_state
 class BinaryGibbsMetropolisState(StepMethodState):
-    tune: bool
     transit_p: int
     shuffle_dims: bool
     order: list
@@ -574,9 +567,7 @@ class BinaryGibbsMetropolis(ArrayStep):
 
     name = "binary_gibbs_metropolis"
 
-    stats_dtypes_shapes = {
-        "tune": (bool, []),
-    }
+    stats_dtypes_shapes = {}
 
     _state_class = BinaryGibbsMetropolisState
 
@@ -594,9 +585,6 @@ def __init__(
     ):
         model = pm.modelcontext(model)
 
-        # Doesn't actually tune, but it's required to emit a sampler stat
-        # that indicates whether a draw was done in a tuning phase.
-        self.tune = True
         # transition probabilities
         self.transit_p = transit_p
 
@@ -649,10 +637,7 @@ def astep(self, apoint: RaveledVars, *args) -> tuple[RaveledVars, StatsType]:
                 if accepted:
                     logp_curr = logp_prop
 
-        stats = {
-            "tune": self.tune,
-        }
-        return q, [stats]
+        return q, [{}]
 
     @staticmethod
     def competence(var):
@@ -695,9 +680,7 @@ class CategoricalGibbsMetropolis(ArrayStep):
 
     name = "categorical_gibbs_metropolis"
 
-    stats_dtypes_shapes = {
-        "tune": (bool, []),
-    }
+    stats_dtypes_shapes = {}
 
     _state_class = CategoricalGibbsMetropolisState
 
@@ -793,7 +776,7 @@ def astep_unif(self, apoint: RaveledVars, *args) -> tuple[RaveledVars, StatsType
                 logp_curr = logp_prop
 
         # This step doesn't have any tunable parameters
-        return q, [{"tune": False}]
+        return q, [{}]
 
     def astep_prop(self, apoint: RaveledVars, *args) -> tuple[RaveledVars, StatsType]:
         logp = args[0]
@@ -811,7 +794,7 @@ def astep_prop(self, apoint: RaveledVars, *args) -> tuple[RaveledVars, StatsType
             logp_curr = self.metropolis_proportional(q, logp, logp_curr, dim, k)
 
         # This step doesn't have any tunable parameters
-        return q, [{"tune": False}]
+        return q, [{}]
 
     def astep(self, apoint: RaveledVars, *args) -> tuple[RaveledVars, StatsType]:
         raise NotImplementedError()
@@ -919,7 +902,6 @@ class DEMetropolis(PopulationArrayStepShared):
     stats_dtypes_shapes = {
         "accept": (np.float64, []),
         "accepted": (bool, []),
-        "tune": (bool, []),
         "scaling": (np.float64, []),
         "lambda": (np.float64, []),
     }
@@ -1011,8 +993,7 @@ def astep(self, q0: RaveledVars) -> tuple[RaveledVars, StatsType]:
         self.steps_until_tune -= 1
 
         stats = {
-            "tune": self.tune,
-            "scaling": self.scaling,
+            "scaling": np.mean(self.scaling),
             "lambda": self.lamb,
             "accept": np.exp(accept),
             "accepted": accepted,
@@ -1090,7 +1071,6 @@ class DEMetropolisZ(ArrayStepShared):
     stats_dtypes_shapes = {
         "accept": (np.float64, []),
         "accepted": (bool, []),
-        "tune": (bool, []),
         "scaling": (np.float64, []),
         "lambda": (np.float64, []),
     }
@@ -1213,7 +1193,6 @@ def astep(self, q0: RaveledVars) -> tuple[RaveledVars, StatsType]:
         self.steps_until_tune -= 1
 
         stats = {
-            "tune": self.tune,
             "scaling": np.mean(self.scaling),
             "lambda": self.lamb,
             "accept": np.exp(accept),
diff --git a/pymc/step_methods/slicer.py b/pymc/step_methods/slicer.py
index 180ac1c882..5ea92fc916 100644
--- a/pymc/step_methods/slicer.py
+++ b/pymc/step_methods/slicer.py
@@ -72,7 +72,6 @@ class Slice(ArrayStepShared):
     name = "slice"
     default_blocked = False
     stats_dtypes_shapes = {
-        "tune": (bool, []),
         "nstep_out": (int, []),
         "nstep_in": (int, []),
     }
@@ -184,7 +183,6 @@ def astep(self, apoint: RaveledVars) -> tuple[RaveledVars, StatsType]:
             self.n_tunes += 1
 
         stats = {
-            "tune": self.tune,
             "nstep_out": nstep_out,
             "nstep_in": nstep_in,
         }
@@ -202,18 +200,17 @@ def competence(var, has_grad):
     @staticmethod
     def _progressbar_config(n_chains=1):
         columns = [
-            TextColumn("{task.fields[tune]}", table_column=Column("Tuning", ratio=1)),
             TextColumn("{task.fields[nstep_out]}", table_column=Column("Steps out", ratio=1)),
             TextColumn("{task.fields[nstep_in]}", table_column=Column("Steps in", ratio=1)),
         ]
 
-        stats = {"tune": [True] * n_chains, "nstep_out": [0] * n_chains, "nstep_in": [0] * n_chains}
+        stats = {"nstep_out": [0] * n_chains, "nstep_in": [0] * n_chains}
 
         return columns, stats
 
     @staticmethod
     def _make_progressbar_update_functions():
         def update_stats(step_stats):
-            return {key: step_stats[key] for key in {"tune", "nstep_out", "nstep_in"}}
+            return {key: step_stats[key] for key in {"nstep_out", "nstep_in"}}
 
         return (update_stats,)
diff --git a/pymc/variational/opvi.py b/pymc/variational/opvi.py
index 509e285f90..aecf712caf 100644
--- a/pymc/variational/opvi.py
+++ b/pymc/variational/opvi.py
@@ -1598,7 +1598,7 @@ def sample(
         try:
             trace.setup(draws=draws, chain=0)
             for point in points:
-                trace.record(point)
+                trace.record(point, in_warmup=False)
         finally:
             trace.close()
 
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 3ca21cf6c4..e55991b2eb 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -16,7 +16,7 @@ pandas>=0.24.0
 polyagamma
 pre-commit>=2.8.0
 pymc-sphinx-theme>=0.16.0
-pytensor>=2.38.0,<2.39
+pytensor>=2.38.2,<2.39
 pytest-cov>=2.5
 pytest>=3.0
 rich>=13.7.1
diff --git a/requirements.txt b/requirements.txt
index 8e23e80665..ebbf0b097b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,7 +3,7 @@ cachetools>=4.2.1,<7
 cloudpickle
 numpy>=1.25.0
 pandas>=0.24.0
-pytensor>=2.38.0,<2.39
+pytensor>=2.38.2,<2.39
 rich>=13.7.1
 scipy>=1.4.1
 threadpoolctl>=3.1.0,<4.0.0
diff --git a/tests/backends/fixtures.py b/tests/backends/fixtures.py
index a4f28a1262..a6a4699fe1 100644
--- a/tests/backends/fixtures.py
+++ b/tests/backends/fixtures.py
@@ -195,11 +195,11 @@ def setup_class(cls):
                 stats2 = [
                     {key: val[idx] for key, val in stats.items()} for stats in cls.expected_stats[1]
                 ]
-                strace0.record(point=point0, sampler_stats=stats1)
-                strace1.record(point=point1, sampler_stats=stats2)
+                strace0.record(point=point0, sampler_stats=stats1, in_warmup=False)
+                strace1.record(point=point1, sampler_stats=stats2, in_warmup=False)
             else:
-                strace0.record(point=point0)
-                strace1.record(point=point1)
+                strace0.record(point=point0, in_warmup=False)
+                strace1.record(point=point1, in_warmup=False)
         strace0.close()
         strace1.close()
         cls.mtrace = base.MultiTrace([strace0, strace1])
@@ -244,9 +244,9 @@ def record_point(self, val):
         }
         if self.sampler_vars is not None:
             stats = [{key: dtype(val) for key, dtype in vars.items()} for vars in self.sampler_vars]
-            self.strace.record(point=point, sampler_stats=stats)
+            self.strace.record(point=point, sampler_stats=stats, in_warmup=False)
         else:
-            self.strace.record(point=point)
+            self.strace.record(point=point, in_warmup=False)
 
     def test_standard_close(self):
         for idx in range(self.draws):
@@ -270,7 +270,7 @@ def test_standard_close(self):
     def test_missing_stats(self):
         if self.sampler_vars is not None:
             with pytest.raises(ValueError):
-                self.strace.record(point=self.test_point)
+                self.strace.record(point=self.test_point, in_warmup=False)
 
     def test_clean_interrupt(self):
         self.record_point(0)
diff --git a/tests/backends/test_arviz.py b/tests/backends/test_arviz.py
index 5512981edf..27316b219a 100644
--- a/tests/backends/test_arviz.py
+++ b/tests/backends/test_arviz.py
@@ -749,9 +749,9 @@ def test_save_warmup(self, save_warmup, chains, tune, draws):
         post_prefix = "" if draws > 0 else "~"
         test_dict = {
             f"{post_prefix}posterior": ["u1", "n1"],
-            f"{post_prefix}sample_stats": ["~tune", "accept"],
+            f"{post_prefix}sample_stats": ["~in_warmup", "accept"],
             f"{warmup_prefix}warmup_posterior": ["u1", "n1"],
-            f"{warmup_prefix}warmup_sample_stats": ["~tune"],
+            f"{warmup_prefix}warmup_sample_stats": ["~in_warmup"],
             "~warmup_log_likelihood": [],
             "~log_likelihood": [],
         }
@@ -786,9 +786,9 @@ def test_save_warmup_issue_1208_after_3_9(self):
             idata = to_inference_data(trace, save_warmup=True)
             test_dict = {
                 "posterior": ["u1", "n1"],
-                "sample_stats": ["~tune", "accept"],
+                "sample_stats": ["~in_warmup", "accept"],
                 "warmup_posterior": ["u1", "n1"],
-                "warmup_sample_stats": ["~tune", "accept"],
+                "warmup_sample_stats": ["~in_warmup", "accept"],
             }
             fails = check_multiple_attrs(test_dict, idata)
             assert not fails
@@ -800,7 +800,7 @@ def test_save_warmup_issue_1208_after_3_9(self):
                 idata = to_inference_data(trace[-30:], save_warmup=True)
             test_dict = {
                 "posterior": ["u1", "n1"],
-                "sample_stats": ["~tune", "accept"],
+                "sample_stats": ["~in_warmup", "accept"],
                 "~warmup_posterior": [],
                 "~warmup_sample_stats": [],
             }
diff --git a/tests/backends/test_base.py b/tests/backends/test_base.py
index 0f450119a7..bbfbedab32 100644
--- a/tests/backends/test_base.py
+++ b/tests/backends/test_base.py
@@ -43,7 +43,7 @@ def test_init_trace_continuation_unsupported(self):
             B = pm.Uniform("B")
             strace = pm.backends.ndarray.NDArray(vars=[A, B])
             strace.setup(10, 0)
-            strace.record({"A": 2, "B_interval__": 0.1})
+            strace.record({"A": 2, "B_interval__": 0.1}, in_warmup=False)
             assert len(strace) == 1
             with pytest.raises(ValueError, match="Continuation of traces"):
                 _init_trace(
diff --git a/tests/backends/test_mcbackend.py b/tests/backends/test_mcbackend.py
index e72731af6b..89fa2ccba0 100644
--- a/tests/backends/test_mcbackend.py
+++ b/tests/backends/test_mcbackend.py
@@ -119,7 +119,8 @@ def test_make_runmeta_and_point_fn(simple_model):
     assert not vars["vector"].is_deterministic
     assert not vars["vector_interval__"].is_deterministic
     assert vars["matrix"].is_deterministic
-    assert len(rmeta.sample_stats) == len(step.stats_dtypes[0])
+    assert "in_warmup" in {s.name for s in rmeta.sample_stats}
+    assert len(rmeta.sample_stats) == len(step.stats_dtypes[0]) + 1
 
     with simple_model:
         step = pm.NUTS()
@@ -201,7 +202,7 @@ def test_get_sampler_stats(self):
         for i in range(N):
             draw = {"a": rng.normal(), "b_interval__": rng.normal()}
             stats = [{"tune": (i <= 5), "s1": i, "accepted": bool(rng.randint(0, 2))}]
-            cra.record(draw, stats)
+            cra.record(draw, stats, in_warmup=i <= 5)
 
         # Check final state of the chain
         assert len(cra) == N
@@ -254,7 +255,7 @@ def test_get_sampler_stats_compound(self, caplog):
                 {"tune": tune, "s1": i, "accepted": bool(rng.randint(0, 2))},
                 {"tune": tune, "s2": i, "accepted": bool(rng.randint(0, 2))},
             ]
-            cra.record(draw, stats)
+            cra.record(draw, stats, in_warmup=tune)
 
         # The 'accepted' stat was emitted by both samplers
         assert cra.get_sampler_stats("accepted", sampler_idx=None).shape == (N, 2)
@@ -293,13 +294,20 @@ def test_return_multitrace(self, simple_model, discard_warmup):
                 return_inferencedata=False,
             )
         assert isinstance(mtrace, pm.backends.base.MultiTrace)
-        tune = mtrace._straces[0].get_sampler_stats("tune")
-        assert isinstance(tune, np.ndarray)
+        in_warmup = mtrace.get_sampler_stats("in_warmup", combine=False, squeeze=False)
+        assert len(in_warmup) == 3
+        assert all(s.dtype == np.dtype(bool) for s in in_warmup)
+
+        # Warmup is tracked by the sampling driver and persisted via `in_warmup`.
         if discard_warmup:
-            assert tune.shape == (7, 3)
+            assert len(mtrace) == 7
+            assert all(len(s) == 7 for s in in_warmup)
+            assert all(not np.any(s) for s in in_warmup)
         else:
-            assert tune.shape == (12, 3)
-        pass
+            assert len(mtrace) == 12
+            assert all(len(s) == 12 for s in in_warmup)
+            assert all(np.all(s[:5]) for s in in_warmup)
+            assert all(not np.any(s[5:]) for s in in_warmup)
 
     @pytest.mark.parametrize("cores", [1, 3])
     def test_return_inferencedata(self, simple_model, cores):
diff --git a/tests/backends/test_zarr.py b/tests/backends/test_zarr.py
index af9c9e0a06..b90834f367 100644
--- a/tests/backends/test_zarr.py
+++ b/tests/backends/test_zarr.py
@@ -132,7 +132,7 @@ def test_record(model, model_step, include_transformed, draws_per_chunk):
         else:
             manually_collected_draws.append(point)
             manually_collected_stats.append(stats)
-        trace.straces[0].record(point, stats)
+        trace.straces[0].record(point, stats, in_warmup=tuning)
     trace.straces[0].record_sampling_state(model_step)
     assert {group_name for group_name, _ in trace.root.groups()} == expected_groups
 
diff --git a/tests/dims/distributions/test_censored.py b/tests/dims/distributions/test_censored.py
new file mode 100644
index 0000000000..eb96f7a4da
--- /dev/null
+++ b/tests/dims/distributions/test_censored.py
@@ -0,0 +1,88 @@
+#   Copyright 2026 - present The PyMC Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+import numpy as np
+import pytest
+
+from pytensor.xtensor import as_xtensor
+from pytensor.xtensor.shape import Transpose
+from pytensor.xtensor.vectorization import XRV
+
+import pymc.distributions as regular_distributions
+
+from pymc.dims import Censored, Normal
+from pymc.model.core import Model
+from tests.dims.utils import assert_equivalent_logp_graph, assert_equivalent_random_graph
+
+
+@pytest.mark.parametrize("lower", [None, -1])
+@pytest.mark.parametrize("upper", [None, 1])
+def test_censored_basic(lower, upper):
+    coords = {"space": range(3), "time": range(4)}
+
+    with Model(coords=coords) as model:
+        dist = Normal.dist(np.pi, np.e)
+        Censored("y", dist, lower=lower, upper=upper, dims=("space", "time"))
+
+    with Model(coords=coords) as reference_model:
+        dist = regular_distributions.Normal.dist(np.pi, np.e)
+        regular_distributions.Censored(
+            "y", dist=dist, lower=lower, upper=upper, dims=("space", "time")
+        )
+
+    assert_equivalent_random_graph(model, reference_model)
+    assert_equivalent_logp_graph(model, reference_model)
+
+
+def test_censored_dims():
+    """Test that both censored (and the underlying dist) have all the implied and explicit dims."""
+    coords = {
+        "a": range(3),
+        "b": range(4),
+        "c": range(5),
+        "d": range(6),
+    }
+    with Model(coords=coords) as model:
+        dist = Normal.dist(
+            mu=as_xtensor([0, 1, 2], dims=("a",)),
+            sigma=as_xtensor([1, 2, 3], dims=("b",)),
+            dim_lengths={"c": model.dim_lengths["c"]},
+        )
+        assert set(dist.dims) == {"c", "a", "b"}
+
+        c0 = Censored("c0", dist)
+        assert c0.dims == ("c", "a", "b")
+        c0_dist = c0.owner.inputs[0]
+        assert isinstance(c0_dist.owner.op, XRV)
+        assert c0_dist.dims == ("c", "a", "b")
+
+        c1 = Censored("c1", dist, dims=("a", "b", "c"))
+        assert c1.dims == ("a", "b", "c")
+        assert isinstance(c1.owner.op, Transpose)
+        c1_dist = c1.owner.inputs[0].owner.inputs[0]
+        assert isinstance(c1_dist.owner.op, XRV)
+        assert c1_dist.dims == ("c", "a", "b")
+
+        c2 = Censored("c2", dist, dims=(..., "d"))
+        assert c2.dims == ("c", "a", "b", "d")
+        assert isinstance(c1.owner.op, Transpose)
+        c2_dist = c2.owner.inputs[0].owner.inputs[0]
+        assert isinstance(c2_dist.owner.op, XRV)
+        assert c2_dist.dims == ("d", "c", "a", "b")
+
+        lower = as_xtensor(np.zeros((6, 5)), dims=("d", "c"))
+        c3 = Censored("c3", dist, lower=lower)
+        assert c3.dims == ("d", "c", "a", "b")
+        c3_dist = c3.owner.inputs[0]
+        assert isinstance(c3_dist.owner.op, XRV)
+        assert c3_dist.dims == ("d", "c", "a", "b")
diff --git a/tests/dims/distributions/test_vector.py b/tests/dims/distributions/test_vector.py
index 8cfdadb372..a71ca2306d 100644
--- a/tests/dims/distributions/test_vector.py
+++ b/tests/dims/distributions/test_vector.py
@@ -99,3 +99,23 @@ def test_zerosumnormal():
     # Logp is correct, but we have join(..., -1) and join(..., 1), that don't get canonicalized to the same
     # Should work once https://github.com/pymc-devs/pytensor/issues/1505 is fixed
     # assert_equivalent_logp_graph(model, reference_model)
+
+
+def test_zerosumnormal_batch_sigma():
+    coords = {"a": range(3), "b": range(5)}
+    sigma = np.array([1, 2, 3.0])
+    with Model(coords=coords) as model:
+        ZeroSumNormal(
+            "x",
+            sigma=as_xtensor(sigma, dims=("a",)),
+            core_dims=("b",),
+        )
+
+    with Model(coords=coords) as ref_model:
+        regular_distributions.ZeroSumNormal("x", sigma=sigma[:, None], dims=("a", "b"))
+
+    ip = model.initial_point()
+    np.testing.assert_allclose(
+        model.compile_logp(sum=False)(ip),
+        ref_model.compile_logp(sum=False)(ip),
+    )
diff --git a/tests/distributions/test_multivariate.py b/tests/distributions/test_multivariate.py
index 67ac8644d0..27d145aecb 100644
--- a/tests/distributions/test_multivariate.py
+++ b/tests/distributions/test_multivariate.py
@@ -1763,6 +1763,13 @@ def test_batched_sigma(self):
                 sigma=batch_test_sigma[None, :, None], n_zerosum_axes=2, support_shape=(3, 2)
             )
 
+    def test_batched_transformed_logp_shape(self):
+        with pm.Model() as m:
+            x = pm.ZeroSumNormal("x", sigma=np.ones(3)[:, None], support_shape=(2,))
+            assert x.type.shape == (3, 2)
+        assert m.logp(sum=False)[0].type.shape == (3,)
+        assert m.logp(sum=False, jacobian=False)[0].type.shape == (3,)
+
 
 class TestMvStudentTCov(BaseTestDistributionRandom):
     def mvstudentt_rng_fn(self, size, nu, mu, scale, rng):
diff --git a/tests/distributions/test_transform.py b/tests/distributions/test_transform.py
index 45cef9d86e..370a57f6eb 100644
--- a/tests/distributions/test_transform.py
+++ b/tests/distributions/test_transform.py
@@ -657,12 +657,12 @@ def log_jac_det(self, value, *inputs):
 
     buggy_transform = BuggyTransform()
 
-    with pm.Model() as m:
-        pm.Uniform("x", shape=(4, 3), default_transform=buggy_transform)
+    import numpy as np
 
     for jacobian_val in (True, False):
-        with pytest.raises(
-            ValueError,
-            match="are not allowed to broadcast together. There is a bug in the implementation of either one",
-        ):
-            m.logp(jacobian=jacobian_val)
+        with pm.Model() as m:
+            pm.Uniform("x", shape=(4, 3), default_transform=buggy_transform)
+
+        logp_fn = m.compile_logp(jacobian=jacobian_val)
+        with pytest.raises(AssertionError, match="SpecifyShape"):
+            logp_fn({"x_buggy__": np.zeros((4, 3))})
diff --git a/tests/logprob/test_transform_value.py b/tests/logprob/test_transform_value.py
index 61e2a1356e..d60b92c0b1 100644
--- a/tests/logprob/test_transform_value.py
+++ b/tests/logprob/test_transform_value.py
@@ -578,6 +578,20 @@ def scan_step(prev_innov, prev_rng):
     np.testing.assert_allclose(logp_fn(**test_point), ref_logp_fn(**test_point))
 
 
+def test_halfstudent_t_with_frozen_dims():
+    """Regression test: log_jac_det had mismatched broadcastable dims vs logp when
+    dims were frozen to a single-element coordinate, causing a ValueError.
+    """
+    from pymc.model.transform.optimization import freeze_dims_and_data
+
+    with pm.Model(coords={"x_dim": ["only_one"]}) as model:
+        pm.HalfStudentT("x", nu=7, sigma=1, dims="x_dim")
+
+    fmodel = freeze_dims_and_data(model)
+    [x_logp] = fmodel.logp(sum=False)
+    assert x_logp.type.shape == (1,)
+
+
 def test_weakref_leak():
     """Check that the rewrite does not have a growing memory footprint.
 
diff --git a/tests/sampling/test_mcmc.py b/tests/sampling/test_mcmc.py
index 8a3a99dbe8..c781204a71 100644
--- a/tests/sampling/test_mcmc.py
+++ b/tests/sampling/test_mcmc.py
@@ -414,6 +414,33 @@ def test_sample_return_lengths(self):
         assert idata.posterior.sizes["draw"] == 100
         assert idata.posterior.sizes["chain"] == 3
 
+    def test_categorical_gibbs_respects_driver_tune_boundary(self):
+        with pm.Model():
+            pm.Categorical("x", p=np.array([0.2, 0.3, 0.5]))
+            sample_kwargs = {
+                "tune": 5,
+                "draws": 7,
+                "chains": 1,
+                "cores": 1,
+                "return_inferencedata": False,
+                "compute_convergence_checks": False,
+                "progressbar": False,
+                "random_seed": 123,
+            }
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", ".*number of samples.*", UserWarning)
+                mtrace = pm.sample(discard_tuned_samples=True, **sample_kwargs)
+            assert len(mtrace) == 7
+            assert mtrace.report.n_tune == 5
+            assert mtrace.report.n_draws == 7
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", ".*number of samples.*", UserWarning)
+                with pytest.warns(UserWarning, match="will be included"):
+                    mtrace_warmup = pm.sample(discard_tuned_samples=False, **sample_kwargs)
+            assert len(mtrace_warmup) == 12
+            assert mtrace_warmup.report.n_tune == 5
+            assert mtrace_warmup.report.n_draws == 7
+
     @pytest.mark.parametrize("cores", [1, 2])
     def test_logs_sampler_warnings(self, caplog, cores):
         """Asserts that "warning" sampler stats are logged during sampling."""
diff --git a/tests/step_methods/hmc/test_nuts.py b/tests/step_methods/hmc/test_nuts.py
index 8d497f3011..c453652079 100644
--- a/tests/step_methods/hmc/test_nuts.py
+++ b/tests/step_methods/hmc/test_nuts.py
@@ -157,7 +157,6 @@ def test_sampler_stats(self):
             "step_size",
             "step_size_bar",
             "tree_size",
-            "tune",
             "perf_counter_diff",
             "perf_counter_start",
             "process_time_diff",
diff --git a/tests/step_methods/test_compound.py b/tests/step_methods/test_compound.py
index 6c8957f9b3..a7a08bd500 100644
--- a/tests/step_methods/test_compound.py
+++ b/tests/step_methods/test_compound.py
@@ -36,11 +36,11 @@
 from tests.models import simple_2model_continuous
 
 
-def test_all_stepmethods_emit_tune_stat():
+def test_stepmethods_do_not_require_tune_stat():
     step_types = pm.step_methods.STEP_METHODS
     assert len(step_types) > 5
     for cls in step_types:
-        assert "tune" in cls.stats_dtypes_shapes
+        assert "tune" not in cls.stats_dtypes_shapes
 
 
 class TestCompoundStep:
diff --git a/tests/test_printing.py b/tests/test_printing.py
index 7efba2a81e..bf228eb6dd 100644
--- a/tests/test_printing.py
+++ b/tests/test_printing.py
@@ -319,6 +319,57 @@ def random(rng, mu, size):
     assert str_repr == "\n".join(["x ~ CustomDistNormal", "y ~ CustomRandomNormal"])
 
 
+class TestDimsDist:
+    def setup_class(self):
+        from pymc.dims.distributions import Normal as DimsNormal
+        from pymc.dims.distributions import ZeroSumNormal as DimsZeroSumNormal
+
+        with Model(coords={"group": range(3), "obs": range(5)}) as self.model:
+            mu = DimsNormal("mu", 0, 10, dims=("group",))
+            sigma = DimsNormal("sigma", 0, 1)
+            zsn = DimsZeroSumNormal("zsn", sigma=1, core_dims="group")
+            DimsNormal("y", mu + zsn, sigma, dims=("obs", "group"))
+
+        self.expected = {
+            ("plain", True): [
+                r"mu ~ Normal(0, 10)",
+                r"sigma ~ Normal(0, 1)",
+                r"zsn ~ ZeroSumNormal(f(), f())",
+                r"y ~ Normal(f(mu, zsn), sigma)",
+            ],
+            ("plain", False): [
+                r"mu ~ Normal",
+                r"sigma ~ Normal",
+                r"zsn ~ ZeroSumNormal",
+                r"y ~ Normal",
+            ],
+            ("latex", True): [
+                r"\text{mu} &\sim & \operatorname{Normal}(0,~10)",
+                r"\text{sigma} &\sim & \operatorname{Normal}(0,~1)",
+                r"\text{zsn} &\sim & \operatorname{ZeroSumNormal}(f(),~f())",
+                r"\text{y} &\sim & \operatorname{Normal}(f(\text{mu},~\text{zsn}),~\text{sigma})",
+            ],
+            ("latex", False): [
+                r"\text{mu} &\sim & \operatorname{Normal}",
+                r"\text{sigma} &\sim & \operatorname{Normal}",
+                r"\text{zsn} &\sim & \operatorname{ZeroSumNormal}",
+                r"\text{y} &\sim & \operatorname{Normal}",
+            ],
+        }
+
+    def test_str_repr(self):
+        for formatting, include_params in [("plain", True), ("plain", False)]:
+            model_text = self.model.str_repr(formatting=formatting, include_params=include_params)
+            for text in self.expected[(formatting, include_params)]:
+                assert text in model_text
+
+    def test_latex_repr(self):
+        for formatting, include_params in [("latex", True), ("latex", False)]:
+            model_text = self.model.str_repr(formatting=formatting, include_params=include_params)
+            for text in self.expected[(formatting, include_params)]:
+                assert text in model_text
+
+
 class TestLatexRepr:
     @staticmethod
     def simple_model() -> Model: