From 9114b02d3460ca42ca4a41999b54477b17cf84e9 Mon Sep 17 00:00:00 2001 From: jessegrabowski Date: Wed, 8 Jul 2026 23:35:24 -0500 Subject: [PATCH 1/6] Ignore only generated paths under docs/source The blanket /docs/source/ rule also hid legitimate new source files; docs/.gitignore already lists the specific generated paths. --- .gitignore | 4 +++- docs/.gitignore | 1 + 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 7ea9f34..8ee762d 100644 --- a/.gitignore +++ b/.gitignore @@ -188,4 +188,6 @@ cython_debug/ # PyPI configuration file .pypirc .idea/ -/docs/source/ + +# Dataset cached at runtime by the introductory notebook +mcycle.csv diff --git a/docs/.gitignore b/docs/.gitignore index b4ccf7a..6b2e464 100644 --- a/docs/.gitignore +++ b/docs/.gitignore @@ -14,6 +14,7 @@ source/api/**/classmethods/ # Notebook gallery artifacts written by docs/sphinxext/generate_gallery.py source/examples/gallery.rst source/examples/examples/ +source/examples/introduction/ source/examples/introductory/ source/examples/advanced/ source/examples/case_study/ From b01295b1117dcdb7529751e09ba1c4b0a438332a Mon Sep 17 00:00:00 2001 From: jessegrabowski Date: Wed, 8 Jul 2026 23:35:39 -0500 Subject: [PATCH 2/6] Document fit/predict/FitResult and the Linear kernel in the API --- docs/source/api/kernels.rst | 1 + docs/source/api/optim.rst | 10 ++++++++++ 2 files changed, 11 insertions(+) diff --git a/docs/source/api/kernels.rst b/docs/source/api/kernels.rst index a76c23d..c37204c 100644 --- a/docs/source/api/kernels.rst +++ b/docs/source/api/kernels.rst @@ -28,6 +28,7 @@ Non-stationary .. autosummary:: :toctree: generated/ + Linear Gibbs RandomWalk WarpedInput diff --git a/docs/source/api/optim.rst b/docs/source/api/optim.rst index 4212168..595a733 100644 --- a/docs/source/api/optim.rst +++ b/docs/source/api/optim.rst @@ -3,6 +3,16 @@ Optimization .. currentmodule:: ptgp.optim +High-level API +-------------- + +.. autosummary:: + :toctree: generated/ + + fit + predict + FitResult + Training compilers ------------------ From 5a6ce1c855ae577ae35ffa922a524312d675f3ab Mon Sep 17 00:00:00 2001 From: jessegrabowski Date: Wed, 8 Jul 2026 23:36:01 -0500 Subject: [PATCH 3/6] Add Introduction gallery category with explicit section ordering --- docs/sphinxext/generate_gallery.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/docs/sphinxext/generate_gallery.py b/docs/sphinxext/generate_gallery.py index 4b1d181..7e4ebcb 100644 --- a/docs/sphinxext/generate_gallery.py +++ b/docs/sphinxext/generate_gallery.py @@ -43,12 +43,23 @@ # Pretty titles for known subfolders. Anything not listed is title-cased. CATEGORY_TITLES = { + "introduction": "Introduction", "examples": "Examples", "introductory": "Introductory", "advanced": "Advanced", "case_study": "Case Studies", } +# Order the sections appear in the gallery. Categories not listed here follow, +# sorted alphabetically. +CATEGORY_ORDER = ["introduction", "examples", "introductory", "advanced", "case_study"] + + +def _category_sort_key(category): + rank = CATEGORY_ORDER.index(category) if category in CATEGORY_ORDER else len(CATEGORY_ORDER) + return rank, category + + DEFAULT_IMG_LOC = None TITLE = """ @@ -214,7 +225,7 @@ def main(app): toctree_entries: list[str] = [] section_lines: list[str] = [] - for category in sorted(grouped): + for category in sorted(grouped, key=_category_sort_key): nb_paths = grouped[category] title = CATEGORY_TITLES.get(category, category.replace("_", " ").title()) section_lines.append( From 15e5e12246dec32bb6e603b81bb937b807156a2c Mon Sep 17 00:00:00 2001 From: jessegrabowski Date: Wed, 8 Jul 2026 23:37:33 -0500 Subject: [PATCH 4/6] Add kernel property tags reference page Gallery tag chips now link here via {ref} instead of dead markdown anchors pointing at a file that never existed. --- docs/source/kernels/gallery/expquad.md | 2 +- docs/source/kernels/gallery/linear.md | 2 +- docs/source/kernels/gallery/matern12.md | 2 +- docs/source/kernels/gallery/matern32.md | 2 +- docs/source/kernels/gallery/matern52.md | 2 +- docs/source/kernels/gallery/randomwalk.md | 2 +- docs/source/kernels/gallery_tags.md | 48 +++++++++++++++++++++++ 7 files changed, 54 insertions(+), 6 deletions(-) create mode 100644 docs/source/kernels/gallery_tags.md diff --git a/docs/source/kernels/gallery/expquad.md b/docs/source/kernels/gallery/expquad.md index f050064..1c1c419 100644 --- a/docs/source/kernels/gallery/expquad.md +++ b/docs/source/kernels/gallery/expquad.md @@ -11,7 +11,7 @@ kernelspec: # Exponentiated Quadratic Kernel -[Stationary](../gallery_tags.rst#stationary), [Isotropic](../gallery_tags.rst#isotropic), [Smooth](../gallery_tags.rst#smooth), [Universal](../gallery_tags.rst#universal) +{ref}`Stationary `, {ref}`Isotropic `, {ref}`Smooth `, {ref}`Universal ` The exponentiated quadratic kernel — also known as the **squared exponential**, **RBF**, or **Gaussian** kernel — measures covariance with the squared Euclidean distance between inputs, decaying as a Gaussian in distance. It is the default first choice in most GP applications. diff --git a/docs/source/kernels/gallery/linear.md b/docs/source/kernels/gallery/linear.md index cd51307..62838d8 100644 --- a/docs/source/kernels/gallery/linear.md +++ b/docs/source/kernels/gallery/linear.md @@ -11,7 +11,7 @@ kernelspec: # Linear Kernel -[Non-stationary](../gallery_tags.rst#non-stationary), [Isotropic](../gallery_tags.rst#isotropic) +{ref}`Non-stationary `, {ref}`Isotropic ` The linear (dot-product) kernel produces sample functions that are straight lines through the center point $c$. It is the GP equivalent of Bayesian linear regression: the posterior mean is a linear function of the inputs and the posterior variance reflects uncertainty in the slope. diff --git a/docs/source/kernels/gallery/matern12.md b/docs/source/kernels/gallery/matern12.md index 246f6ce..b9c6b87 100644 --- a/docs/source/kernels/gallery/matern12.md +++ b/docs/source/kernels/gallery/matern12.md @@ -11,7 +11,7 @@ kernelspec: # Matérn 1/2 (Exponential) Kernel -[Stationary](../gallery_tags.rst#stationary), [Isotropic](../gallery_tags.rst#isotropic), [Very Rough](../gallery_tags.rst#very-rough) +{ref}`Stationary `, {ref}`Isotropic `, {ref}`Very Rough ` The Matérn 1/2 kernel — also known as the **exponential kernel** — produces sample functions that are continuous but nowhere differentiable. The resulting GP is equivalent to an Ornstein–Uhlenbeck process: a mean-reverting random walk in continuous time. Sample paths look like noisy zig-zag trajectories rather than smooth curves. diff --git a/docs/source/kernels/gallery/matern32.md b/docs/source/kernels/gallery/matern32.md index fe31f92..85a0534 100644 --- a/docs/source/kernels/gallery/matern32.md +++ b/docs/source/kernels/gallery/matern32.md @@ -11,7 +11,7 @@ kernelspec: # Matérn 3/2 Kernel -[Stationary](../gallery_tags.rst#stationary), [Isotropic](../gallery_tags.rst#isotropic), [Rough](../gallery_tags.rst#rough) +{ref}`Stationary `, {ref}`Isotropic `, {ref}`Rough ` The Matérn 3/2 kernel produces sample functions that are once mean-square differentiable but no smoother — the function itself and its first derivative exist and are continuous, but the second derivative does not. This is the right kernel when the process you are modeling has a clearly defined velocity but acceleration is a meaningless concept (e.g. abrupt regime changes, piecewise-linear-ish behavior on small scales). diff --git a/docs/source/kernels/gallery/matern52.md b/docs/source/kernels/gallery/matern52.md index 23cdb3c..87bc3e7 100644 --- a/docs/source/kernels/gallery/matern52.md +++ b/docs/source/kernels/gallery/matern52.md @@ -11,7 +11,7 @@ kernelspec: # Matérn 5/2 Kernel -[Stationary](../gallery_tags.rst#stationary), [Isotropic](../gallery_tags.rst#isotropic), [Smooth](../gallery_tags.rst#smooth) +{ref}`Stationary `, {ref}`Isotropic `, {ref}`Smooth ` The Matérn 5/2 kernel is the smoothest member of the Matérn family that is *not* infinitely differentiable. Sample functions are twice mean-square differentiable — the function and its first derivative are well-defined and continuous, but the curvature can have jumps. This is enough smoothness for most real-world processes and avoids the overconfident extrapolation that the [exponentiated quadratic](expquad.md) tends to produce when the true function is not analytic. diff --git a/docs/source/kernels/gallery/randomwalk.md b/docs/source/kernels/gallery/randomwalk.md index a5979cf..e19ce04 100644 --- a/docs/source/kernels/gallery/randomwalk.md +++ b/docs/source/kernels/gallery/randomwalk.md @@ -11,7 +11,7 @@ kernelspec: # Random Walk Kernel -[Non-stationary](../gallery_tags.rst#non-stationary), [1-D](../gallery_tags.rst#one-d), [Rough](../gallery_tags.rst#rough) +{ref}`Non-stationary `, {ref}`1-D `, {ref}`Rough ` The random walk kernel — the covariance function of standard Brownian motion (the Wiener process) — is the textbook example of a non-stationary GP kernel. Its covariance depends not just on the distance between inputs but on their actual locations: $k(x, y) = \min(x, y)$ means that variance grows linearly with the input value, and two points are correlated only via their shared "history" from the origin. diff --git a/docs/source/kernels/gallery_tags.md b/docs/source/kernels/gallery_tags.md new file mode 100644 index 0000000..7040a86 --- /dev/null +++ b/docs/source/kernels/gallery_tags.md @@ -0,0 +1,48 @@ +--- +orphan: true +--- + +(gallery-tags)= +# Kernel property tags + +The covariance gallery labels each kernel with the properties below. Follow a tag from any kernel page to its definition here. + +(stationary)= +## Stationary + +The covariance $k(x, y)$ depends only on the separation $x - y$, so it is invariant to shifting both inputs by the same amount. The prior variance $k(x, x)$ is then constant across the whole input space. + +(non-stationary)= +## Non-stationary + +The covariance depends on where the inputs sit, not just on their separation. The prior variance and correlation length can vary across the input space. + +(isotropic)= +## Isotropic + +The covariance depends only on the distance $\lVert x - y \rVert$ between inputs, so it is invariant to rotation as well as translation. A single lengthscale controls correlation in every direction. + +(smooth)= +## Smooth + +Sample functions are several times mean-square differentiable, giving very regular draws. The exponentiated quadratic is infinitely differentiable; the Matérn 5/2 kernel is twice differentiable. + +(rough)= +## Rough + +Sample functions are at most once mean-square differentiable, giving visibly jagged draws. Examples are the Matérn 3/2 kernel and the random walk. + +(very-rough)= +## Very Rough + +Sample functions are continuous but nowhere differentiable, as with the exponential (Matérn 1/2) kernel. + +(one-d)= +## 1-D + +Defined for a single input dimension only. + +(universal)= +## Universal + +The kernel's reproducing-kernel Hilbert space is dense in the continuous functions, so it can approximate any continuous target arbitrarily well. The exponentiated quadratic is the canonical example. From 0a4c0d406f95acc5d290710af15a54ace3ce3a5c Mon Sep 17 00:00:00 2001 From: jessegrabowski Date: Wed, 8 Jul 2026 23:38:23 -0500 Subject: [PATCH 5/6] Replace sphinxcontrib-bibtex with per-page simplebib extension Citations render as numbered footnotes that link down to a reference list on the same page; anchors and numbering are page-local, so there is no central bibliography page and no cross-page label collisions. --- conda_envs/environment-docs.yaml | 2 +- docs/source/_static/custom.css | 34 +++++ docs/source/conf.py | 12 +- docs/source/dev/docs.rst | 27 ++-- docs/source/kernels/gallery/expquad.md | 1 - docs/source/kernels/gallery/linear.md | 1 - docs/source/kernels/gallery/matern12.md | 1 - docs/source/kernels/gallery/matern32.md | 1 - docs/source/kernels/gallery/matern52.md | 1 - docs/source/kernels/gallery/randomwalk.md | 1 - docs/sphinxext/simplebib.py | 170 ++++++++++++++++++++++ 11 files changed, 229 insertions(+), 22 deletions(-) create mode 100644 docs/source/_static/custom.css create mode 100644 docs/sphinxext/simplebib.py diff --git a/conda_envs/environment-docs.yaml b/conda_envs/environment-docs.yaml index 551bf8e..1d10cf2 100644 --- a/conda_envs/environment-docs.yaml +++ b/conda_envs/environment-docs.yaml @@ -28,7 +28,7 @@ dependencies: - sphinx-notfound-page - sphinx-autobuild - jupyter-sphinx - - sphinxcontrib-bibtex + - pybtex # .bib parsing for the simplebib extension (docs/sphinxext/simplebib.py) - pip - pip: - git+https://github.com/pymc-devs/pytensor@main diff --git a/docs/source/_static/custom.css b/docs/source/_static/custom.css new file mode 100644 index 0000000..fc4a93f --- /dev/null +++ b/docs/source/_static/custom.css @@ -0,0 +1,34 @@ +/* Per-page citations (simplebib): superscript [n] markers linking down to a + numbered reference list, with a subtle back-reference arrow on each entry. */ + +a.simplebib-cite { + vertical-align: super; + font-size: 0.7em; + line-height: 0; + padding: 0 0.1em; + font-weight: 500; + white-space: nowrap; + text-decoration: none; +} + +a.simplebib-cite:hover { + text-decoration: underline; +} + +a.simplebib-backref { + margin-left: 0.4em; + text-decoration: none; + opacity: 0.6; +} + +a.simplebib-backref:hover { + opacity: 1; +} + +.simplebib-references { + margin-top: 0.5em; +} + +.simplebib-references .simplebib-reference { + margin-bottom: 0.4em; +} diff --git a/docs/source/conf.py b/docs/source/conf.py index d150396..ca9f186 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -48,16 +48,15 @@ "sphinx_sitemap", "notfound.extension", "jupyter_sphinx", - "sphinxcontrib.bibtex", + "simplebib", "generate_gallery", "generate_kernel_gallery", ] -# Bibliographic citations: drop new entries into docs/source/references.bib -# and cite from prose with `{cite:t}` (textual) or `{cite:p}` (parenthetical). -bibtex_bibfiles = ["references.bib"] -bibtex_default_style = "unsrt" -bibtex_reference_style = "author_year" +# Bibliographic citations: drop new entries into docs/source/references.bib and +# cite from prose with `{cite:t}` / `{cite:p}`. Each page renders its own numbered +# reference list via `{bibliography}` (see docs/sphinxext/simplebib.py). +simplebib_bibfiles = ["references.bib"] # Use the document path as prefix for autosectionlabel anchors so the same # section title in two files doesn't collide. @@ -155,6 +154,7 @@ html_sidebars = {"**": ["sidebar-nav-bs.html", "searchbox.html"]} html_static_path = ["_static"] +html_css_files = ["custom.css"] # -- MyST / MyST-NB config --------------------------------------------------- myst_enable_extensions = [ diff --git a/docs/source/dev/docs.rst b/docs/source/dev/docs.rst index 199d348..9be0765 100644 --- a/docs/source/dev/docs.rst +++ b/docs/source/dev/docs.rst @@ -62,7 +62,7 @@ commit anything under ``source/kernels/img/``, ``source/_thumbnails/``, ``source/examples//``, ``source/api/**/generated/``, or ``source/kernels/gallery.rst`` / ``source/examples/gallery.rst``. -The two custom Sphinx extensions live at ``docs/sphinxext/``: +The custom Sphinx extensions live at ``docs/sphinxext/``: * ``generate_gallery.py`` — discovers notebooks under ``notebooks/``, copies them into ``docs/source/examples//``, extracts thumbnails, and @@ -71,6 +71,12 @@ The two custom Sphinx extensions live at ``docs/sphinxext/``: ``KERNEL_RECIPES``, emits ``kernels/gallery.rst`` with grid cards. Cards link to per-kernel pages when a matching ``kernels/gallery/.md`` exists on disk. +* ``simplebib.py`` — per-page citations. Provides the ``{cite:t}`` / + ``{cite:p}`` roles and the ``{bibliography}`` directive, parsing + ``references.bib`` with :mod:`pybtex`. Each page is self-contained: its + citations render as numbered ``[n]`` footnotes that link down to a numbered + reference list on the same page, so numbering and anchors never collide + across pages. Adding a new kernel to the covariance gallery --------------------------------------------- @@ -145,26 +151,29 @@ Adding a citation ----------------- Append the BibTeX entry to ``docs/source/references.bib``, then cite from -prose with either textual or parenthetical form: +prose with ``{cite:t}`` or ``{cite:p}`` (both render as a numbered footnote +marker): .. code-block:: markdown The kernel ridge regression view is discussed by {cite:t}`somekey-2024`. This approach goes back decades {cite:p}`smith-1985`. -Each page that uses citations should also include a per-page bibliography: +End each page that cites anything with a ``{bibliography}`` directive, which +renders the numbered reference list for that page: .. code-block:: markdown + ## References + ```{bibliography} - :filter: docname in docnames ``` -The ``:filter: docname in docnames`` clause restricts the rendered -bibliography to entries cited on the current page. Sphinx will emit -"duplicate citation" warnings when the same key is rendered from multiple -pages — these are tolerated; we prefer per-page bibliographies for -readability. +Citations are handled by the ``simplebib`` extension and are page-local: +``{bibliography}`` lists only the works cited on that page, numbered in +citation order, and the in-text markers link down to it. There is no central +bibliography page and no cross-page numbering, so the same key can be cited on +any number of pages without collisions. Plot helpers ------------ diff --git a/docs/source/kernels/gallery/expquad.md b/docs/source/kernels/gallery/expquad.md index 1c1c419..a8ac124 100644 --- a/docs/source/kernels/gallery/expquad.md +++ b/docs/source/kernels/gallery/expquad.md @@ -126,5 +126,4 @@ The three lengthscales show the kernel's effect on prior beliefs: The exponentiated quadratic is the canonical GP covariance and is discussed across all standard treatments; {cite:t}`rasmussen-williams-2006` Chapter 4 is the standard reference. ```{bibliography} -:filter: docname in docnames ``` diff --git a/docs/source/kernels/gallery/linear.md b/docs/source/kernels/gallery/linear.md index 62838d8..254901f 100644 --- a/docs/source/kernels/gallery/linear.md +++ b/docs/source/kernels/gallery/linear.md @@ -109,5 +109,4 @@ The three centers show the kernel's effect on prior beliefs: The linear kernel and its connection to Bayesian linear regression are discussed in {cite:t}`rasmussen-williams-2006` Chapter 2. ```{bibliography} -:filter: docname in docnames ``` diff --git a/docs/source/kernels/gallery/matern12.md b/docs/source/kernels/gallery/matern12.md index b9c6b87..30a538b 100644 --- a/docs/source/kernels/gallery/matern12.md +++ b/docs/source/kernels/gallery/matern12.md @@ -121,5 +121,4 @@ print(K.shape, K.diagonal()[:3]) The Matérn family and its smoothness-tuning role in GP modeling is discussed in {cite:t}`rasmussen-williams-2006` Chapter 4. ```{bibliography} -:filter: docname in docnames ``` diff --git a/docs/source/kernels/gallery/matern32.md b/docs/source/kernels/gallery/matern32.md index 85a0534..9082892 100644 --- a/docs/source/kernels/gallery/matern32.md +++ b/docs/source/kernels/gallery/matern32.md @@ -121,5 +121,4 @@ print(K.shape, K.diagonal()[:3]) The Matérn family and its smoothness-tuning role in GP modeling is discussed in {cite:t}`rasmussen-williams-2006` Chapter 4. ```{bibliography} -:filter: docname in docnames ``` diff --git a/docs/source/kernels/gallery/matern52.md b/docs/source/kernels/gallery/matern52.md index 87bc3e7..4ac9634 100644 --- a/docs/source/kernels/gallery/matern52.md +++ b/docs/source/kernels/gallery/matern52.md @@ -121,5 +121,4 @@ print(K.shape, K.diagonal()[:3]) The Matérn family and its smoothness-tuning role in GP modeling is discussed in {cite:t}`rasmussen-williams-2006` Chapter 4. ```{bibliography} -:filter: docname in docnames ``` diff --git a/docs/source/kernels/gallery/randomwalk.md b/docs/source/kernels/gallery/randomwalk.md index e19ce04..95c2f80 100644 --- a/docs/source/kernels/gallery/randomwalk.md +++ b/docs/source/kernels/gallery/randomwalk.md @@ -99,5 +99,4 @@ Prior samples fan out from the origin; the posterior pinches around each observa The random walk kernel and the broader connection between GPs and stochastic processes are covered in {cite:t}`rasmussen-williams-2006` Chapter 4. ```{bibliography} -:filter: docname in docnames ``` diff --git a/docs/sphinxext/simplebib.py b/docs/sphinxext/simplebib.py new file mode 100644 index 0000000..894c18a --- /dev/null +++ b/docs/sphinxext/simplebib.py @@ -0,0 +1,170 @@ +import re + +from docutils import nodes +from docutils.parsers.rst import Directive, directives +from pybtex.database import parse_file +from sphinx.domains import Domain +from sphinx.util import logging + +logger = logging.getLogger(__name__) + + +class cite_ref(nodes.Inline, nodes.Element): + """Placeholder for one in-text citation; resolved to a numbered link.""" + + +class bibliography_node(nodes.General, nodes.Element): + """Placeholder for a page's reference list; resolved to a numbered list.""" + + +def _cite_role(name, rawtext, text, lineno, inliner, options=None, content=None): + result = [] + for i, key in enumerate(k.strip() for k in text.split(",") if k.strip()): + if i: + result.append(nodes.Text(", ")) + node = cite_ref("", key=key) + result.append(node) + return result, [] + + +class CiteDomain(Domain): + name = "cite" + label = "simplebib" + roles = {"t": _cite_role, "p": _cite_role} + + def get_objects(self): + return [] + + def resolve_xref(self, *args): + return None + + +class BibliographyDirective(Directive): + has_content = False + # Accept and ignore sphinxcontrib-bibtex's options so existing pages parse. + option_spec = {"filter": directives.unchanged, "keyprefix": directives.unchanged} + + def run(self): + return [bibliography_node("")] + + +def _load_bib(app): + entries = {} + for name in app.config.simplebib_bibfiles: + path = app.env.relfn2path(name, app.config.master_doc)[1] + for key, entry in parse_file(path).entries.items(): + entries[key.lower()] = entry + return entries + + +def _persons(entry): + return entry.persons.get("author") or entry.persons.get("editor") or [] + + +def _clean(value): + return re.sub(r"\s+", " ", value.replace("{", "").replace("}", "")).strip() + + +def _format_person(person): + last = _clean(" ".join(person.prelast_names + person.last_names)) + given = [_clean(n) for n in person.first_names + person.middle_names] + initials = " ".join(f"{n[0]}." for n in given if n) + return f"{last}, {initials}".rstrip(", ").strip() + + +def _format_authors(persons): + names = [_format_person(p) for p in persons] + if len(names) <= 1: + return names[0] if names else "" + return ", ".join(names[:-1]) + ", & " + names[-1] + + +def _format_entry(entry): + fields = entry.fields + authors = _format_authors(_persons(entry)) + year = fields.get("year", "n.d.") + title = _clean(fields.get("title", "")) + + head = " ".join(part for part in [authors, f"({year})."] if part) + text = f"{head} {title}." if title else head + + venue = _clean( + fields.get("journal") + or fields.get("booktitle") + or fields.get("publisher") + or fields.get("school") + or "" + ) + if venue: + detail = venue + if fields.get("volume"): + detail += f", {fields['volume']}" + if fields.get("number"): + detail += f"({fields['number']})" + if fields.get("pages"): + detail += f", {_clean(fields['pages']).replace('--', '–')}" + text += f" {detail}." + return text + + +def _anchor(key): + return nodes.make_id(f"ref-{key}") + + +def _resolve_page(app, doctree, docname): + bib = app.env.simplebib_data + numbers = {} # key -> citation number (first-seen order) + backrefs = {} # key -> [ids of the in-text markers] + + for node in list(doctree.findall(cite_ref)): + key = node["key"] + if key not in bib: + logger.warning(f"unknown citation key {key!r}", location=node, type="simplebib") + node.replace_self(nodes.Text("[?]")) + continue + number = numbers.setdefault(key, len(numbers) + 1) + ref_id = nodes.make_id(f"cite-{key}-{len(backrefs.get(key, []))}") + backrefs.setdefault(key, []).append(ref_id) + + marker = nodes.reference("", f"[{number}]", internal=True, refid=_anchor(key)) + marker["ids"].append(ref_id) + marker["classes"].append("simplebib-cite") + node.replace_self(marker) + + for node in list(doctree.findall(bibliography_node)): + if not numbers: + node.replace_self([]) + continue + listing = nodes.container(classes=["simplebib-references"]) + for key in sorted(numbers, key=numbers.get): + para = nodes.paragraph(ids=[_anchor(key)], classes=["simplebib-reference"]) + para += nodes.Text(f"[{numbers[key]}] {_format_entry(bib[key])}") + for ref_id in backrefs[key]: + para += nodes.Text(" ") + back = nodes.reference("", "↩", internal=True, refid=ref_id) + back["classes"].append("simplebib-backref") + para += back + listing += para + node.replace_self(listing) + + +def _noop(self, node): + raise nodes.SkipNode + + +def _init_bib(app): + app.env.simplebib_data = _load_bib(app) + + +def setup(app): + app.add_config_value("simplebib_bibfiles", ["references.bib"], "env") + app.add_domain(CiteDomain) + app.add_directive("bibliography", BibliographyDirective) + # These nodes are always replaced at doctree-resolved; the skip visitors + # are a safety net for any builder if one ever survives. + skip = (_noop, None) + for node in (cite_ref, bibliography_node): + app.add_node(node, html=skip, latex=skip, text=skip, man=skip, texinfo=skip) + app.connect("builder-inited", _init_bib) + app.connect("doctree-resolved", _resolve_page) + return {"version": "0.1", "parallel_read_safe": True, "parallel_write_safe": True} From 33f97ce155fb1c7ea470d9e725f42ab732e6ba90 Mon Sep 17 00:00:00 2001 From: jessegrabowski Date: Wed, 8 Jul 2026 23:39:57 -0500 Subject: [PATCH 6/6] Add Introduction to PTGP notebook, replacing the VFF demo A lean quick-start: fit an exact GP to the motorcycle dataset with pg.fit/pg.predict, then a VFE sparse approximation. Replaces the benchmarking-oriented demo notebook. --- AGENTS.md | 6 +- README.md | 4 +- docs/source/references.bib | 22 + notebooks/demo.ipynb | 899 ---------------------- notebooks/gp-variational-stochastic.ipynb | 2 + notebooks/introduction/introduction.ipynb | 476 ++++++++++++ 6 files changed, 505 insertions(+), 904 deletions(-) delete mode 100644 notebooks/demo.ipynb create mode 100644 notebooks/introduction/introduction.ipynb diff --git a/AGENTS.md b/AGENTS.md index 2b26822..be6a6db 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -63,9 +63,9 @@ ruff format ptgp/ tests/ python scripts/joint_graph_analysis.py # op-count tables per model, with/without ptgp rewrites python scripts/inplace_audit.py # which linalg ops are inplace and why others aren't -# execute the demo notebook in place +# execute the introduction notebook in place python -m jupyter nbconvert --to notebook --execute --inplace \ - notebooks/demo.ipynb --ExecutePreprocessor.kernel_name=ptgp + notebooks/introduction/introduction.ipynb --ExecutePreprocessor.kernel_name=ptgp # install the bundled agent-skill docs into a Claude Code skill dir (optional) python scripts/install_claude_skills.py --project . # ./.claude/skills/ @@ -73,7 +73,7 @@ python scripts/install_claude_skills.py --user # ~/.claude/skills/ ``` The reference usage example for all three models is -[`notebooks/demo.ipynb`](notebooks/demo.ipynb). +[`notebooks/introduction/introduction.ipynb`](notebooks/introduction/introduction.ipynb). ## Where things live diff --git a/README.md b/README.md index 2140769..4994911 100644 --- a/README.md +++ b/README.md @@ -54,7 +54,7 @@ with pm.Model() as model: mean, var = pg.predict(svgp, np.linspace(-3, 3, 100)[:, None], fit) ``` -`pg.fit` picks a default objective from the gp type (`Unapproximated` → `marginal_log_likelihood`, `VFE` → `collapsed_elbo`, `SVGP` → `elbo`) and returns a `FitResult` that `pg.predict` consumes. For stochastic mini-batch training, staged VFE, or per-group learning rates, drop down to `pg.optim.compile_training_step` / `pg.optim.compile_scipy_objective` — see [`notebooks/demo.ipynb`](notebooks/demo.ipynb): +`pg.fit` picks a default objective from the gp type (`Unapproximated` → `marginal_log_likelihood`, `VFE` → `collapsed_elbo`, `SVGP` → `elbo`) and returns a `FitResult` that `pg.predict` consumes. For stochastic mini-batch training, staged VFE, or per-group learning rates, drop down to `pg.optim.compile_training_step` / `pg.optim.compile_scipy_objective` — see [`notebooks/introduction/introduction.ipynb`](notebooks/introduction/introduction.ipynb): ```python X_var = pt.matrix("X") @@ -73,7 +73,7 @@ predict_fn = pg.optim.compile_predict( mean, var = predict_fn(np.linspace(-3, 3, 100)[:, None]) ``` -Training uses MAP by default: the PyMC log-prior is added to the objective. Pass `include_prior=False` for pure ELBO. For exact GPs and VFE, use `compile_scipy_objective` with L-BFGS-B instead. See [`notebooks/demo.ipynb`](notebooks/demo.ipynb) for end-to-end examples covering all three models. +Training uses MAP by default: the PyMC log-prior is added to the objective. Pass `include_prior=False` for pure ELBO. For exact GPs and VFE, use `compile_scipy_objective` with L-BFGS-B instead. See [`notebooks/introduction/introduction.ipynb`](notebooks/introduction/introduction.ipynb) for end-to-end examples covering all three models. ## How it works diff --git a/docs/source/references.bib b/docs/source/references.bib index a05222f..eff5d14 100644 --- a/docs/source/references.bib +++ b/docs/source/references.bib @@ -6,3 +6,25 @@ @book{rasmussen-williams-2006 isbn = {026218253X}, url = {http://www.gaussianprocess.org/gpml/}, } + +@inproceedings{titsias-2009, + author = {Titsias, Michalis K.}, + title = {Variational Learning of Inducing Variables in Sparse Gaussian Processes}, + booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS)}, + series = {Proceedings of Machine Learning Research}, + volume = {5}, + pages = {567--574}, + year = {2009}, + url = {https://proceedings.mlr.press/v5/titsias09a.html}, +} + +@article{burt-2020, + author = {Burt, David R. and Rasmussen, Carl Edward and van der Wilk, Mark}, + title = {Convergence of Sparse Variational Inference in Gaussian Processes Regression}, + journal = {Journal of Machine Learning Research}, + volume = {21}, + number = {131}, + pages = {1--63}, + year = {2020}, + url = {https://jmlr.org/papers/v21/19-1015.html}, +} diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb deleted file mode 100644 index 99949ba..0000000 --- a/notebooks/demo.ipynb +++ /dev/null @@ -1,899 +0,0 @@ -{ - "cells": [ - { - "metadata": {}, - "cell_type": "markdown", - "source": "# PTGP VFF Demo\n", - "id": "fff54d16bc48672f" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "import numpy as np\n", - "import pymc as pm\n", - "import pytensor\n", - "import pytensor.tensor as pt\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import ptgp as pg" - ], - "id": "db0f87517989a0ed" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## Generate data, student-t noise", - "id": "4e68756f34dd225e" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "rng = np.random.default_rng(600)\n", - "N = 200\n", - "noise_std = 0.2\n", - "nu_true = 6\n", - "\n", - "# True GP hyperparameters\n", - "eta_true = 1.3\n", - "ls_true = 1.0\n", - "\n", - "X_train = np.sort(rng.uniform(0, 10, N))[:, None]\n", - "X_test = np.linspace(-1, 11, 200)[:, None]\n", - "\n", - "# Draw from a GP prior with Matern-5/2 kernel\n", - "K = eta_true**2 * pg.kernels.Matern52(input_dim=1, ls=ls_true)\n", - "K = K(X_train, X_train).eval() + 1e-6 * np.eye(N)\n", - "\n", - "f_train = rng.multivariate_normal(np.zeros(N), K)\n", - "y_train = f_train + noise_std * rng.standard_t(df=nu_true, size=N)\n", - "\n", - "plt.scatter(X_train, y_train, s=10, label=\"data\")\n", - "plt.plot(X_train, f_train, \"k--\", alpha=0.5, label=\"true f\")\n", - "plt.legend()\n", - "plt.title(\"Training data\")" - ], - "id": "9f709e82cae94b7d" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## Exact GP", - "id": "5279c6ca3c90a0cd" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "import time\n", - "import scipy.optimize\n", - "\n", - "# Data is 1-D (D=1); batch dim is None to allow any N.\n", - "X_var = pt.matrix(\"X\", shape=(None, 1))\n", - "y_var = pt.vector(\"y\", shape=(None,))\n", - "\n", - "with pm.Model() as gp_model:\n", - " ls = pm.HalfFlat(\"ls\")\n", - " eta = pm.Exponential(\"eta\", scale=2.0)\n", - " sigma = pm.HalfNormal(\"sigma\", sigma=1.0)\n", - "\n", - " kernel = eta**2 * pg.kernels.Matern52(input_dim=1, ls=ls)\n", - " gp = pg.gp.Unapproximated(kernel=kernel, sigma=sigma)\n", - "\n", - " fun, theta0, unpack_to_shared, shared_params, shared_extras = pg.optim.compile_scipy_objective(\n", - " pg.objectives.marginal_log_likelihood,\n", - " gp,\n", - " X_var,\n", - " y_var,\n", - " )\n", - "\n", - "t0 = time.time()\n", - "result = scipy.optimize.minimize(\n", - " fun,\n", - " theta0,\n", - " args=(X_train, y_train),\n", - " jac=True,\n", - " method=\"L-BFGS-B\",\n", - ")\n", - "elapsed = time.time() - t0\n", - "unpack_to_shared(result.x)\n", - "print(f\"converged in {result.nit} iterations, loss = {result.fun:.4f}, time = {elapsed:.2f}s\")\n", - "\n", - "params = pg.optim.get_trained_params(gp_model, shared_params)\n", - "print(f\"\\nRecovered: {params}\")\n", - "print(f\"True: eta={eta_true}, ls={ls_true}, noise_std={noise_std}\")" - ], - "id": "9ede0cd1ca9eb66f" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "X_new_var = pt.matrix(\"X_new\", shape=(None, 1))\n", - "predict_gp = pg.optim.compile_predict(\n", - " gp,\n", - " X_new_var,\n", - " gp_model,\n", - " shared_params,\n", - " X_train=X_train,\n", - " y_train=y_train,\n", - ")\n", - "predict_gp_y = pg.optim.compile_predict(\n", - " gp,\n", - " X_new_var,\n", - " gp_model,\n", - " shared_params,\n", - " X_train=X_train,\n", - " y_train=y_train,\n", - " incl_lik=True,\n", - ")\n", - "\n", - "mu_gp, var_gp = predict_gp(X_test)\n", - "_, var_gp_y = predict_gp_y(X_test)\n", - "sd_gp = np.sqrt(var_gp)\n", - "sd_gp_y = np.sqrt(var_gp_y)\n", - "\n", - "plt.figure(figsize=(10, 4))\n", - "plt.scatter(X_train, y_train, s=10, c=\"k\", zorder=3, label=\"data\")\n", - "plt.plot(X_test, mu_gp, \"C0\", label=\"GP mean\")\n", - "plt.fill_between(\n", - " X_test.ravel(),\n", - " mu_gp - 2 * sd_gp_y,\n", - " mu_gp + 2 * sd_gp_y,\n", - " alpha=0.1,\n", - " color=\"C0\",\n", - " label=\"y 2 sd\",\n", - ")\n", - "plt.fill_between(\n", - " X_test.ravel(),\n", - " mu_gp - 2 * sd_gp,\n", - " mu_gp + 2 * sd_gp,\n", - " alpha=0.3,\n", - " color=\"C0\",\n", - " label=\"f 2 sd\",\n", - ")\n", - "plt.plot(X_train, f_train, \"k--\", alpha=0.3, label=\"true f\")\n", - "plt.legend()\n", - "plt.title(\"Exact GP\")" - ], - "id": "32952e9ee751681c" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "### One-liner alternative: `pg.fit` and `pg.predict`\n", - "\n", - "The cells above are the step-by-step API: build symbolic placeholders, compile a scipy objective, run the optimizer, unpack into shared vars, then compile a predictor. When you don't need that much control, the convenience API in `ptgp.optim.api` does the same thing in two lines. It picks a default objective from the gp type (`Unapproximated` → `marginal_log_likelihood`, `VFE` → `collapsed_elbo`, `SVGP` → `elbo`) and bundles the trained shared state into a `FitResult` that `pg.predict` consumes." - ], - "id": "86dee7b581771c5e" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "with pm.Model() as gp_model_easy:\n", - " ls = pm.HalfFlat(\"ls\")\n", - " eta = pm.Exponential(\"eta\", scale=2.0)\n", - " sigma = pm.HalfNormal(\"sigma\", sigma=1.0)\n", - " gp_easy = pg.gp.Unapproximated(\n", - " kernel=eta**2 * pg.kernels.Matern52(input_dim=1, ls=ls), sigma=sigma\n", - " )\n", - " fit = pg.fit(gp_easy, X_train, y_train)\n", - "\n", - "mu_easy, var_easy = pg.predict(gp_easy, X_test, fit, X_train=X_train, y_train=y_train)\n", - "print(f\"Recovered: {fit.params}\")\n", - "print(f\"max |mu_easy - mu_gp| = {np.max(np.abs(mu_easy - mu_gp)):.2e}\")" - ], - "id": "27acaa10030a6fed" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## VFE (Sparse GP -- collapsed bound)", - "id": "4954e963df915b20" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "M = 20\n", - "Z_init = np.linspace(-0.5, 10.5, M)[:, None]\n", - "\n", - "# Inducing points: M is known (20), D=1.\n", - "Z_var = pt.matrix(\"Z\", shape=(M, 1))\n", - "\n", - "with pm.Model() as vfe_model:\n", - " ls = pm.HalfFlat(\"ls\")\n", - " eta = pm.Exponential(\"eta\", lam=1.0)\n", - " sigma = pm.HalfNormal(\"sigma\", sigma=1.0)\n", - "\n", - " vfe = pg.gp.VFE(\n", - " kernel=eta**2 * pg.kernels.Matern52(input_dim=1, ls=ls),\n", - " sigma=sigma,\n", - " inducing_variable=pg.inducing.Points(Z_var, Z_init=Z_init),\n", - " )\n", - "\n", - "fun_vfe, theta0_vfe, unpack_to_shared_vfe, shared_params_vfe, shared_extras_vfe = (\n", - " pg.optim.compile_scipy_objective(\n", - " pg.objectives.collapsed_elbo,\n", - " vfe,\n", - " X_var,\n", - " y_var,\n", - " model=vfe_model,\n", - " )\n", - ")\n", - "\n", - "t0 = time.time()\n", - "result_vfe = scipy.optimize.minimize(\n", - " fun_vfe,\n", - " theta0_vfe,\n", - " args=(X_train, y_train),\n", - " jac=True,\n", - " method=\"L-BFGS-B\",\n", - ")\n", - "elapsed = time.time() - t0\n", - "unpack_to_shared_vfe(result_vfe.x)\n", - "print(\n", - " f\"converged in {result_vfe.nit} iterations, loss = {result_vfe.fun:.4f}, time = {elapsed:.2f}s\"\n", - ")\n", - "\n", - "params_vfe = pg.optim.get_trained_params(vfe_model, shared_params_vfe)\n", - "print(f\"\\nRecovered: {params_vfe}\")\n", - "print(f\"True: eta={eta_true}, ls={ls_true}, noise_std={noise_std}\")" - ], - "id": "4a11ebce4ee3120c" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "predict_vfe = pg.optim.compile_predict(\n", - " vfe,\n", - " X_new_var,\n", - " vfe_model,\n", - " shared_params_vfe,\n", - " shared_extras=shared_extras_vfe,\n", - " X_train=X_train,\n", - " y_train=y_train,\n", - ")\n", - "predict_vfe_y = pg.optim.compile_predict(\n", - " vfe,\n", - " X_new_var,\n", - " vfe_model,\n", - " shared_params_vfe,\n", - " shared_extras=shared_extras_vfe,\n", - " X_train=X_train,\n", - " y_train=y_train,\n", - " incl_lik=True,\n", - ")\n", - "\n", - "mu_vfe, var_vfe = predict_vfe(X_test)\n", - "_, var_vfe_y = predict_vfe_y(X_test)\n", - "sd_vfe = np.sqrt(var_vfe)\n", - "sd_vfe_y = np.sqrt(var_vfe_y)\n", - "\n", - "Z_final = shared_extras_vfe[0].get_value()\n", - "\n", - "plt.figure(figsize=(10, 4))\n", - "plt.scatter(X_train, y_train, s=10, c=\"k\", zorder=3, label=\"data\")\n", - "plt.plot(X_test, mu_vfe, \"C0\", label=\"VFE mean\")\n", - "plt.fill_between(\n", - " X_test.ravel(),\n", - " mu_vfe - 2 * sd_vfe_y,\n", - " mu_vfe + 2 * sd_vfe_y,\n", - " alpha=0.1,\n", - " color=\"C0\",\n", - " label=\"y 2 sd\",\n", - ")\n", - "plt.fill_between(\n", - " X_test.ravel(),\n", - " mu_vfe - 2 * sd_vfe,\n", - " mu_vfe + 2 * sd_vfe,\n", - " alpha=0.3,\n", - " color=\"C0\",\n", - " label=\"f 2 sd\",\n", - ")\n", - "plt.scatter(Z_final, np.zeros(M) - 2, marker=\"^\", c=\"r\", s=40, label=\"inducing pts\")\n", - "plt.plot(X_train, f_train, \"k--\", alpha=0.3, label=\"true f\")\n", - "plt.legend(loc=\"upper right\")\n", - "plt.title(f\"VFE (M={M})\")" - ], - "id": "cb49e8eede8006fa" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "## SVGP (Stochastic Variational GP)\n", - "\n", - "Minibatch training with variational parameters." - ], - "id": "b6f690adb25232f6" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "M_svgp = 20\n", - "\n", - "Z_init_kernel = pg.kernels.Matern52(input_dim=1, ls=1.0)\n", - "Z_greedy = pg.inducing.greedy_variance_init(X_train, M_svgp, Z_init_kernel, rng=42)[0].Z\n", - "\n", - "vp = pg.gp.init_variational_params(M_svgp)\n", - "Z_var = pt.matrix(\"Z\", shape=(M_svgp, 1))\n", - "\n", - "with pm.Model() as svgp_model:\n", - " ls = pm.HalfFlat(\"ls\")\n", - " eta = pm.Exponential(\"eta\", lam=1.0)\n", - " sigma = pm.HalfNormal(\"sigma\", sigma=1.0)\n", - " nu = pm.Gamma(\"nu\", alpha=2, beta=0.1)\n", - "\n", - " kernel = eta**2 * pg.kernels.Matern52(input_dim=1, ls=ls)\n", - " lik = pg.likelihoods.StudentT(sigma=sigma, nu=nu)\n", - "\n", - " svgp = pg.gp.SVGP(\n", - " kernel=kernel,\n", - " likelihood=lik,\n", - " inducing_variable=pg.inducing.Points(Z_var, Z_init=Z_greedy),\n", - " variational_params=vp,\n", - " )" - ], - "id": "70a681d58a11169f" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "### Training strategy\n", - "\n", - "**Inducing point init.** `greedy_variance_init` picks a space-filling subset of `X_train` via pivoted Cholesky (Burt et al. 2020). It's a strong enough starting point that `Z` often doesn't need further optimization — but here we still fine-tune it in phase 2 to demonstrate the mechanics.\n", - "\n", - "**Two-phase training.** `svgp` is built once with `Z_var` as a symbolic placeholder. Phase 1 pins `Z_var` to `Z_greedy` via `frozen_vars` and trains only the hyperparameters and variational parameters. Phase 2 moves `Z_var` into `extra_vars`, making `Z` trainable, and recompiles using the state carried over from phase 1.\n", - "\n", - "**Learning rates.** Phase 1 runs a single exponential-decay schedule from `1e-2` down to `5e-3` over `n_phase1` steps. Phase 2 uses per-group rates:\n", - "\n", - "- `hyp` — fixed at 50% of phase 1's final LR (gentle fine-tuning once hyperparameters are near optimum).\n", - "- `var` — continues phase 1's decay seamlessly, starting at `5e-3`.\n", - "- `Z` — its own small steady rate (`5e-3`) for the newly unfrozen inducing points." - ], - "id": "150f772638717b6f" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "X_batch_var = pt.matrix(\"X_batch\", shape=(None, 1))\n", - "y_batch_var = pt.vector(\"y_batch\", shape=(None,))\n", - "batch_size = 32\n", - "n_phase1 = 1000\n", - "n_phase2 = 1000\n", - "\n", - "\n", - "def elbo_scaled(model, X, y):\n", - " return pg.objectives.elbo(model, X, y, n_data=N).elbo\n", - "\n", - "\n", - "# ----- Phase 1: Z frozen -----\n", - "phase1_base_lr = 1e-2\n", - "phase1_decay_rate = 0.5\n", - "phase1_final_lr = phase1_base_lr * phase1_decay_rate # LR at t=n_phase1\n", - "\n", - "train_step_1, shared_params_1, shared_extras_1 = pg.optim.compile_training_step(\n", - " elbo_scaled,\n", - " svgp,\n", - " X_batch_var,\n", - " y_batch_var,\n", - " model=svgp_model,\n", - " extra_vars=vp.extra_vars,\n", - " extra_init=vp.extra_init,\n", - " frozen_vars={Z_var: Z_greedy},\n", - " learning_rate=pg.optim.schedules.exponential_decay(\n", - " phase1_base_lr,\n", - " decay_rate=phase1_decay_rate,\n", - " decay_steps=n_phase1,\n", - " ),\n", - ")\n", - "\n", - "losses = []\n", - "t0 = time.time()\n", - "for step in range(n_phase1):\n", - " idx = rng.choice(N, size=batch_size, replace=False)\n", - " loss = train_step_1(X_train[idx], y_train[idx])\n", - " losses.append(float(loss))\n", - " if step % 250 == 0:\n", - " print(f\"[phase 1, Z frozen] Step {step}: loss = {loss:.2f}\")\n", - "elapsed_1 = time.time() - t0\n", - "\n", - "# ----- Phase 2: Z trainable -----\n", - "hyp_value_vars = [svgp_model.rvs_to_values[rv] for rv in (ls, eta, sigma, nu)]\n", - "phase2_hyp_lr = 0.5 * phase1_final_lr\n", - "\n", - "train_step_2, shared_params_svgp, shared_extras_svgp = pg.optim.compile_training_step(\n", - " elbo_scaled,\n", - " svgp,\n", - " X_batch_var,\n", - " y_batch_var,\n", - " model=svgp_model,\n", - " param_groups={\n", - " \"hyp\": hyp_value_vars,\n", - " \"var\": vp.extra_vars,\n", - " \"Z\": [Z_var],\n", - " },\n", - " learning_rate={\n", - " \"hyp\": phase2_hyp_lr,\n", - " \"var\": pg.optim.schedules.exponential_decay(\n", - " phase1_final_lr,\n", - " decay_rate=phase1_decay_rate,\n", - " decay_steps=n_phase1,\n", - " ),\n", - " \"Z\": 5e-3,\n", - " },\n", - ")\n", - "\n", - "# Carry phase-1 state into phase 2 (q_mu, q_sqrt_flat shared values).\n", - "for vv, sh1 in shared_params_1.items():\n", - " shared_params_svgp[vv].set_value(sh1.get_value())\n", - "for sh2, sh1 in zip(shared_extras_svgp[: len(vp.extra_vars)], shared_extras_1):\n", - " sh2.set_value(sh1.get_value())\n", - "\n", - "t0 = time.time()\n", - "for step in range(n_phase2):\n", - " idx = rng.choice(N, size=batch_size, replace=False)\n", - " loss = train_step_2(X_train[idx], y_train[idx])\n", - " losses.append(float(loss))\n", - " if step % 250 == 0:\n", - " print(f\"[phase 2, Z trainable] Step {step}: loss = {loss:.2f}\")\n", - "elapsed_2 = time.time() - t0\n", - "\n", - "params_svgp = pg.optim.get_trained_params(svgp_model, shared_params_svgp)\n", - "print(f\"\\nRecovered: {params_svgp}\")\n", - "print(f\"True: eta={eta_true}, ls={ls_true}, noise_std={noise_std}, nu={nu_true}\")\n", - "print(\n", - " f\"\\ntraining time: phase 1 = {elapsed_1:.2f}s, phase 2 = {elapsed_2:.2f}s, total = {elapsed_1 + elapsed_2:.2f}s\"\n", - ")" - ], - "id": "7a94d7e90de5b1e9" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "plt.figure(figsize=(10, 3))\n", - "plt.plot(losses)\n", - "plt.axvline(n_phase1, color=\"k\", linestyle=\":\", alpha=0.5, label=\"phase 2 begins (Z trainable)\")\n", - "plt.xlabel(\"step\")\n", - "plt.ylabel(\"neg ELBO\")\n", - "plt.legend()\n", - "plt.title(\"SVGP training loss\")" - ], - "id": "a1a27f7ec806beea" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "predict_svgp = pg.optim.compile_predict(\n", - " svgp,\n", - " X_new_var,\n", - " svgp_model,\n", - " shared_params_svgp,\n", - " shared_extras=shared_extras_svgp,\n", - ")\n", - "predict_svgp_y = pg.optim.compile_predict(\n", - " svgp,\n", - " X_new_var,\n", - " svgp_model,\n", - " shared_params_svgp,\n", - " shared_extras=shared_extras_svgp,\n", - " incl_lik=True,\n", - ")\n", - "\n", - "mu_svgp, var_svgp = predict_svgp(X_test)\n", - "mu_svgp_y, var_svgp_y = predict_svgp_y(X_test)\n", - "sd_svgp = np.sqrt(var_svgp)\n", - "sd_svgp_y = np.sqrt(var_svgp_y)\n", - "\n", - "plt.figure(figsize=(10, 4))\n", - "plt.scatter(X_train, y_train, s=10, c=\"k\", zorder=3, label=\"data\")\n", - "plt.plot(X_test, mu_svgp, \"C0\", label=\"SVGP mean\")\n", - "plt.fill_between(\n", - " X_test.ravel(),\n", - " mu_svgp_y - 2 * sd_svgp_y,\n", - " mu_svgp_y + 2 * sd_svgp_y,\n", - " alpha=0.1,\n", - " color=\"C0\",\n", - " label=\"y 2 sd\",\n", - ")\n", - "plt.fill_between(\n", - " X_test.ravel(),\n", - " mu_svgp - 2 * sd_svgp,\n", - " mu_svgp + 2 * sd_svgp,\n", - " alpha=0.3,\n", - " color=\"C0\",\n", - " label=\"f 2 sd\",\n", - ")\n", - "Z_opt = shared_extras_svgp[len(vp.extra_vars)].get_value()\n", - "plt.scatter(\n", - " Z_greedy.ravel(),\n", - " np.zeros(M_svgp) - 2.4,\n", - " marker=\"^\",\n", - " c=\"C1\",\n", - " s=35,\n", - " alpha=0.6,\n", - " label=\"Z init (greedy)\",\n", - ")\n", - "plt.scatter(\n", - " Z_opt.ravel(),\n", - " np.zeros(M_svgp) - 2.0,\n", - " marker=\"^\",\n", - " c=\"r\",\n", - " s=40,\n", - " label=\"Z final (optimized)\",\n", - ")\n", - "plt.plot(X_train, f_train, \"k--\", alpha=0.3, label=\"true f\")\n", - "plt.legend(loc=\"upper right\")\n", - "plt.title(f\"SVGP (M={M_svgp})\")" - ], - "id": "d03374b240a0a533" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "## VFF (Variational Fourier Features)\n", - "\n", - "Inter-domain inducing variables: a Fourier basis defined on `[a, b]` instead of pseudo-inputs `Z`. For Matérn52 VFF the interval must cover the prediction range because reference edge covariances are not implemented; keep a buffer around the plotted range to avoid boundary artifacts. The structured `Kuu = diag(d) + UUᵀ` is solved analytically (Woodbury), so each step is O(M) instead of O(M³).\n" - ], - "id": "f3c76e6e08586c59" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "vff_pad = 1.0\n", - "f_vff = pg.FourierFeatures1D(\n", - " a=float(min(X_train.min(), X_test.min()) - vff_pad),\n", - " b=float(max(X_train.max(), X_test.max()) + vff_pad),\n", - " num_frequencies=15,\n", - ")\n", - "M_vff = f_vff.num_inducing # 2K+1\n", - "vp_vff = pg.gp.init_variational_params(M_vff)\n", - "\n", - "with pm.Model() as vff_model:\n", - " ls = pm.HalfFlat(\"ls\")\n", - " eta = pm.Exponential(\"eta\", lam=1.0)\n", - " sigma = pm.HalfNormal(\"sigma\", sigma=1.0)\n", - " nu = pm.Gamma(\"nu\", alpha=2, beta=0.1)\n", - "\n", - " kernel_vff = eta**2 * pg.kernels.Matern52(input_dim=1, ls=ls)\n", - " lik_vff = pg.likelihoods.StudentT(sigma=sigma, nu=nu)\n", - "\n", - " svgp_vff = pg.gp.SVGP(\n", - " kernel=kernel_vff,\n", - " likelihood=lik_vff,\n", - " inducing_variable=f_vff,\n", - " variational_params=vp_vff,\n", - " )\n", - "\n", - "train_step_vff, shared_params_vff, shared_extras_vff = pg.optim.compile_training_step(\n", - " pg.objectives.elbo,\n", - " svgp_vff,\n", - " X_var,\n", - " y_var,\n", - " model=vff_model,\n", - " learning_rate=1e-2,\n", - ")\n", - "\n", - "t0 = time.time()\n", - "losses_vff = [float(train_step_vff(X_train, y_train)) for _ in range(800)]\n", - "elapsed_vff = time.time() - t0\n", - "\n", - "print(\n", - " f\"M = 2K+1 = {M_vff}, neg-ELBO: {losses_vff[0]:.1f} -> {losses_vff[-1]:.1f}, time = {elapsed_vff:.2f}s\"\n", - ")\n", - "print(f\"\\nRecovered: {pg.optim.get_trained_params(vff_model, shared_params_vff)}\")\n", - "print(f\"True: eta={eta_true}, ls={ls_true}, noise_std={noise_std}\")" - ], - "id": "9b72e99423193a39" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "predict_vff = pg.optim.compile_predict(\n", - " svgp_vff,\n", - " X_new_var,\n", - " vff_model,\n", - " shared_params_vff,\n", - " shared_extras=shared_extras_vff,\n", - ")\n", - "predict_vff_y = pg.optim.compile_predict(\n", - " svgp_vff,\n", - " X_new_var,\n", - " vff_model,\n", - " shared_params_vff,\n", - " shared_extras=shared_extras_vff,\n", - " incl_lik=True,\n", - ")\n", - "mu_vff, var_vff = predict_vff(X_test)\n", - "_, var_vff_y = predict_vff_y(X_test)\n", - "sd_vff = np.sqrt(var_vff)\n", - "sd_vff_y = np.sqrt(var_vff_y)\n", - "\n", - "plt.figure(figsize=(10, 4))\n", - "plt.axvspan(f_vff.a, f_vff.b, color=\"0.92\", label=\"VFF domain [a, b]\")\n", - "plt.scatter(X_train, y_train, s=10, c=\"k\", zorder=3, label=\"data\")\n", - "plt.plot(X_test, mu_vff, \"C0\", label=\"VFF mean\")\n", - "plt.fill_between(\n", - " X_test.ravel(),\n", - " mu_vff - 2 * sd_vff_y,\n", - " mu_vff + 2 * sd_vff_y,\n", - " alpha=0.1,\n", - " color=\"C0\",\n", - " label=\"y 2 sd\",\n", - ")\n", - "plt.fill_between(\n", - " X_test.ravel(),\n", - " mu_vff - 2 * sd_vff,\n", - " mu_vff + 2 * sd_vff,\n", - " alpha=0.3,\n", - " color=\"C0\",\n", - " label=\"f 2 sd\",\n", - ")\n", - "plt.plot(X_train, f_train, \"k--\", alpha=0.3, label=\"true f\")\n", - "plt.legend(loc=\"upper right\")\n", - "plt.title(f\"VFF-SVGP (M = 2K+1 = {M_vff})\")" - ], - "id": "48a96ee33289b1b9" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "## Comparison\n", - "id": "7577593dc6dadcaf" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "fig, axes = plt.subplots(4, 1, figsize=(10, 12), sharex=True, sharey=True)\n", - "\n", - "for ax, (mu, sd_f, sd_y, title) in zip(\n", - " axes,\n", - " [\n", - " (mu_gp, sd_gp, sd_gp_y, \"Exact GP\"),\n", - " (mu_vfe, sd_vfe, sd_vfe_y, f\"VFE (M={M})\"),\n", - " (mu_svgp, sd_svgp, sd_svgp_y, f\"SVGP (M={M_svgp})\"),\n", - " (mu_vff, sd_vff, sd_vff_y, f\"VFF (M={M_vff})\"),\n", - " ],\n", - "):\n", - " ax.scatter(X_train, y_train, s=8, c=\"k\", zorder=3)\n", - " ax.plot(X_test, mu, \"C0\")\n", - " ax.fill_between(X_test.ravel(), mu - 2 * sd_y, mu + 2 * sd_y, alpha=0.1, color=\"C0\")\n", - " ax.fill_between(X_test.ravel(), mu - 2 * sd_f, mu + 2 * sd_f, alpha=0.3, color=\"C0\")\n", - " ax.plot(X_train, f_train, \"k--\", alpha=0.3)\n", - " ax.set_title(title)\n", - " ax.set_xlim(-1, 11)\n", - "\n", - "fig.tight_layout()" - ], - "id": "4920ebc21de0f13e" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "fig, ax = plt.subplots(figsize=(10, 3.5))\n", - "ax.axhline(0, color=\"k\", lw=0.5)\n", - "ax.plot(X_test, mu_vfe - mu_gp, label=f\"VFE (M={M}) - GP\")\n", - "ax.plot(X_test, mu_svgp - mu_gp, label=f\"SVGP (M={M_svgp}) - GP\")\n", - "ax.plot(X_test, mu_vff - mu_gp, label=f\"VFF (M={M_vff}) - GP\")\n", - "ax.set_xlim(-1, 11)\n", - "ax.set_xlabel(\"x\")\n", - "ax.set_ylabel(r\"$\\mu_{\\mathrm{approx}} - \\mu_{\\mathrm{GP}}$\")\n", - "ax.set_title(\"Posterior mean: deviation from exact GP\")\n", - "ax.legend()" - ], - "id": "9e01067ad6ebe3e1" - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": [ - "## Wasserstein distance to the exact posterior\n", - "\n", - "Each approximation defines a multivariate-normal posterior over `f(X_test)`. We can quantify how close it is to the exact GP posterior with the [Wasserstein-2 distance between Gaussians](https://en.wikipedia.org/wiki/Wasserstein_metric#Normal_distributions):\n", - "\n", - "$$\n", - "W_2^2\\big(\\mathcal{N}(\\mu_a, \\Sigma_a),\\, \\mathcal{N}(\\mu_b, \\Sigma_b)\\big)\n", - "= \\|\\mu_a - \\mu_b\\|^2 + \\operatorname{tr}\\!\\Big(\\Sigma_a + \\Sigma_b - 2\\big(\\Sigma_a^{1/2} \\Sigma_b \\Sigma_a^{1/2}\\big)^{1/2}\\Big).\n", - "$$\n", - "\n", - "The mean term is the squared difference shown above; the Bures term penalises any difference in covariance structure (variances *and* off-diagonal correlations).\n", - "\n", - "Each model's `predict` only returns marginal variances, so we re-evaluate each posterior with full covariance using the trained shared parameters.\n" - ], - "id": "3f80860d933e8569" - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": [ - "from pytensor.graph.replace import graph_replace\n", - "from scipy.linalg import sqrtm\n", - "\n", - "\n", - "def _replace(outs, model, sp, extra_vars=None, shared_extras=None):\n", - " rep = model.replace_rvs_by_values(outs)\n", - " rmap = dict(sp)\n", - " if extra_vars is not None:\n", - " rmap.update(zip(extra_vars, shared_extras))\n", - " return [graph_replace(r, rmap, strict=False) for r in rep]\n", - "\n", - "\n", - "X_full = pt.matrix(\"X_full\", shape=(None, 1))\n", - "X_train_t = pt.as_tensor(X_train)\n", - "y_train_t = pt.as_tensor(y_train)\n", - "\n", - "# --- Exact GP: dense posterior on X_test ---\n", - "sigma2_gp = gp.likelihood.sigma**2\n", - "K_xx = gp.kernel(X_train_t) + sigma2_gp * pt.eye(N)\n", - "K_xs = gp.kernel(X_train_t, X_full)\n", - "K_ss = gp.kernel(X_full)\n", - "mu_gp_sym = K_xs.T @ pt.linalg.solve(K_xx, y_train_t)\n", - "cov_gp_sym = K_ss - K_xs.T @ pt.linalg.solve(K_xx, K_xs)\n", - "predict_gp_full = pytensor.function(\n", - " [X_full],\n", - " _replace([mu_gp_sym, cov_gp_sym], gp_model, shared_params),\n", - ")\n", - "\n", - "# --- VFE: collapsed q gives Sigma = Kuu + Kuf Kfu / sigma^2 ---\n", - "Z_v = vfe.inducing_variable.Z\n", - "sigma2_v = vfe.likelihood.sigma**2\n", - "Kuu_v = vfe.kernel(Z_v)\n", - "Kuf_v = vfe.kernel(Z_v, X_train_t)\n", - "Kus_v = vfe.kernel(Z_v, X_full)\n", - "Kss_v = vfe.kernel(X_full)\n", - "Sigma_v = Kuu_v + Kuf_v @ Kuf_v.T / sigma2_v\n", - "mu_vfe_sym = Kus_v.T @ pt.linalg.solve(Sigma_v, Kuf_v @ y_train_t / sigma2_v)\n", - "cov_vfe_sym = Kss_v - Kus_v.T @ (pt.linalg.inv(Kuu_v) - pt.linalg.inv(Sigma_v)) @ Kus_v\n", - "predict_vfe_full = pytensor.function(\n", - " [X_full],\n", - " _replace(\n", - " [mu_vfe_sym, cov_vfe_sym],\n", - " vfe_model,\n", - " shared_params_vfe,\n", - " extra_vars=[Z_v],\n", - " shared_extras=shared_extras_vfe,\n", - " ),\n", - ")\n", - "\n", - "# --- SVGP-Points (whitened): A = R^{-1} Kus, R R^T = Kuu ---\n", - "Z_s = svgp.inducing_variable.Z\n", - "Kuu_s = svgp.kernel(Z_s)\n", - "Kus_s = svgp.kernel(Z_s, X_full)\n", - "Kss_s = svgp.kernel(X_full)\n", - "L_s = pt.linalg.cholesky(Kuu_s)\n", - "A_s = pt.linalg.solve_triangular(L_s, Kus_s, lower=True)\n", - "B_s = A_s.T @ vp.q_sqrt\n", - "mu_svgp_sym = A_s.T @ vp.q_mu\n", - "cov_svgp_sym = Kss_s - A_s.T @ A_s + B_s @ B_s.T\n", - "predict_svgp_full = pytensor.function(\n", - " [X_full],\n", - " _replace(\n", - " [mu_svgp_sym, cov_svgp_sym],\n", - " svgp_model,\n", - " shared_params_svgp,\n", - " extra_vars=[*vp.extra_vars, Z_s],\n", - " shared_extras=shared_extras_svgp,\n", - " ),\n", - ")\n", - "\n", - "# --- VFF (whitened): structured Kuu_sqrt_solve handles R^{-1} without dense Cholesky ---\n", - "Kus_vff = f_vff.K_uf(svgp_vff.kernel, X_full)\n", - "Kss_vff = svgp_vff.kernel(X_full)\n", - "A_vff = f_vff.Kuu_sqrt_solve(svgp_vff.kernel, Kus_vff)\n", - "B_vff = A_vff.T @ vp_vff.q_sqrt\n", - "mu_vff_sym = A_vff.T @ vp_vff.q_mu\n", - "cov_vff_sym = Kss_vff - A_vff.T @ A_vff + B_vff @ B_vff.T\n", - "predict_vff_full = pytensor.function(\n", - " [X_full],\n", - " _replace(\n", - " [mu_vff_sym, cov_vff_sym],\n", - " vff_model,\n", - " shared_params_vff,\n", - " extra_vars=vp_vff.extra_vars,\n", - " shared_extras=shared_extras_vff,\n", - " ),\n", - ")\n", - "\n", - "mu_full_gp, Sigma_gp = predict_gp_full(X_test)\n", - "mu_full_vfe, Sigma_vfe = predict_vfe_full(X_test)\n", - "mu_full_svgp, Sigma_svgp = predict_svgp_full(X_test)\n", - "mu_full_vff, Sigma_vff = predict_vff_full(X_test)\n", - "\n", - "\n", - "def w2_gaussians(mu1, S1, mu2, S2, jitter=1e-6):\n", - " n = len(mu1)\n", - " S1j = S1 + jitter * np.eye(n)\n", - " S2j = S2 + jitter * np.eye(n)\n", - " S1_sqrt = sqrtm(S1j).real\n", - " inner = sqrtm(S1_sqrt @ S2j @ S1_sqrt).real\n", - " bures2 = max(0.0, float(np.trace(S1j) + np.trace(S2j) - 2 * np.trace(inner)))\n", - " return float(np.sqrt(np.sum((mu1 - mu2) ** 2) + bures2))\n", - "\n", - "\n", - "w2_vfe = w2_gaussians(mu_full_gp, Sigma_gp, mu_full_vfe, Sigma_vfe)\n", - "w2_svgp = w2_gaussians(mu_full_gp, Sigma_gp, mu_full_svgp, Sigma_svgp)\n", - "w2_vff = w2_gaussians(mu_full_gp, Sigma_gp, mu_full_vff, Sigma_vff)\n", - "\n", - "print(f\"W2(VFE || GP) = {w2_vfe:.4f}\")\n", - "print(f\"W2(SVGP || GP) = {w2_svgp:.4f}\")\n", - "print(f\"W2(VFF || GP) = {w2_vff:.4f}\")\n", - "\n", - "fig, ax = plt.subplots(figsize=(6, 3))\n", - "labels = [f\"VFE\\n(M={M})\", f\"SVGP\\n(M={M_svgp})\", f\"VFF\\n(M={M_vff})\"]\n", - "ax.bar(labels, [w2_vfe, w2_svgp, w2_vff], color=[\"C1\", \"C2\", \"C3\"])\n", - "ax.set_ylabel(r\"$W_2$ to exact GP posterior\")\n", - "ax.set_title(\"Wasserstein-2 distance from exact GP\")\n", - "fig.tight_layout()" - ], - "id": "ceca213c038a4c" - } - ], - "metadata": { - "kernelspec": { - "display_name": "ptgp", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.14.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/gp-variational-stochastic.ipynb b/notebooks/gp-variational-stochastic.ipynb index 929d1ee..3e843e6 100644 --- a/notebooks/gp-variational-stochastic.ipynb +++ b/notebooks/gp-variational-stochastic.ipynb @@ -5,6 +5,8 @@ "id": "0745825c-82ba-4f03-865f-212303bbec66", "metadata": {}, "source": [ + "(gp-variational-stochastic)=\n", + "\n", "# SSGP\n", "\n", "Port of GPJax's implementation: https://docs.jaxgaussianprocesses.com/_examples/uncollapsed_vi/\n", diff --git a/notebooks/introduction/introduction.ipynb b/notebooks/introduction/introduction.ipynb new file mode 100644 index 0000000..067c9e1 --- /dev/null +++ b/notebooks/introduction/introduction.ipynb @@ -0,0 +1,476 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2dc79244", + "metadata": {}, + "source": [ + "(introduction)=\n", + "# Introduction to PTGP\n", + "\n", + "PTGP builds Gaussian process models on [PyTensor](https://pytensor.readthedocs.io) and [PyMC](https://www.pymc.io): you define a kernel, put PyMC priors on its hyperparameters, and PTGP compiles the linear algebra into a function you fit and predict with in two calls.\n", + "\n", + "This guide fits an exact GP to a small regression dataset, shows what {func}`pg.fit ` and {func}`pg.predict ` do underneath, and swaps in a sparse VFE approximation. Minibatch training, non-Gaussian likelihoods, SVGP, and Variational Fourier Features are covered in the {ref}`companion notebook `. See the {doc}`kernel gallery ` for covariance functions and the {doc}`user guide ` for the full API.\n", + "\n", + "It assumes you know Gaussian processes and are comfortable with PyMC." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e0a505c5", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pymc as pm\n", + "import pytensor.tensor as pt\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "import ptgp as pg\n", + "\n", + "\n", + "plt.rcParams.update(\n", + " {\n", + " \"figure.figsize\": (14, 5),\n", + " \"figure.dpi\": 144,\n", + " \"figure.constrained_layout.use\": True,\n", + " \"axes.grid\": True,\n", + " \"grid.linewidth\": 0.5,\n", + " \"grid.linestyle\": \"--\",\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "165d8eca", + "metadata": {}, + "source": [ + "## The data\n", + "\n", + "The Silverman motorcycle dataset: 133 accelerometer readings from a crash-test dummy's head during a simulated impact, pairing time since impact (ms) with head acceleration (g). The response is smooth but sharply varying, and the noise grows after impact.\n", + "\n", + "We standardize both axes so the priors below are sensibly scaled, and de-standardize when plotting." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "a4156e7b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/MASS/mcycle.csv\"\n", + "csv_path = Path(\"mcycle.csv\")\n", + "if not csv_path.exists():\n", + " pd.read_csv(url).to_csv(csv_path, index=False) # cache locally; don't refetch each run\n", + "mcycle = pd.read_csv(csv_path)\n", + "\n", + "t = mcycle[\"times\"].to_numpy(float) # time since impact (ms)\n", + "a = mcycle[\"accel\"].to_numpy(float) # head acceleration (g)\n", + "\n", + "x_scaler = StandardScaler().fit(t[:, None])\n", + "y_scaler = StandardScaler().fit(a[:, None])\n", + "X = x_scaler.transform(t[:, None])\n", + "y = y_scaler.transform(a[:, None]).ravel()\n", + "N = X.shape[0]\n", + "\n", + "# Dense prediction grid in standardized space, plus its physical-unit times.\n", + "X_grid = np.linspace(X.min() - 0.2, X.max() + 0.2, 200)[:, None]\n", + "t_grid = x_scaler.inverse_transform(X_grid).ravel()\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(t, a, s=15, c=\"k\", label=\"observations\")\n", + "ax.set_xlabel(\"time since impact (ms)\")\n", + "ax.set_ylabel(\"head acceleration (g)\")\n", + "ax.set_title(f\"Silverman motorcycle dataset (N = {N})\")\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d478456d", + "metadata": {}, + "outputs": [], + "source": [ + "def to_g(y_std):\n", + " \"\"\"Map standardized acceleration values back to g (physical units).\"\"\"\n", + " return y_scaler.inverse_transform(np.reshape(y_std, (-1, 1))).ravel()\n", + "\n", + "\n", + "def plot_gp(\n", + " mean,\n", + " f_band,\n", + " y_band=None,\n", + " *,\n", + " ax=None,\n", + " color=\"C0\",\n", + " ls=\"-\",\n", + " label=\"posterior mean\",\n", + " inducing=None,\n", + " title=None,\n", + " band_labels=True,\n", + "):\n", + " \"\"\"Plot a GP posterior over the data: mean, function band, and optional noise band.\"\"\"\n", + " if ax is None:\n", + " _, ax = plt.subplots()\n", + " ax.scatter(t, a, s=15, c=\"k\", zorder=3, label=\"observations\")\n", + " ax.set_xlabel(\"time since impact (ms)\")\n", + " ax.set_ylabel(\"head acceleration (g)\")\n", + " ax.plot(t_grid, mean, color=color, ls=ls, lw=2, label=label)\n", + " if y_band is not None:\n", + " ax.fill_between(\n", + " t_grid,\n", + " *y_band,\n", + " color=color,\n", + " alpha=0.12,\n", + " label=\"observation ±2 sd\" if band_labels else None,\n", + " )\n", + " ax.fill_between(\n", + " t_grid, *f_band, color=color, alpha=0.25, label=\"function ±2 sd\" if band_labels else None\n", + " )\n", + " if inducing is not None:\n", + " ax.scatter(\n", + " inducing,\n", + " np.full(len(inducing), a.min() - 5),\n", + " marker=\"^\",\n", + " c=\"r\",\n", + " s=45,\n", + " zorder=4,\n", + " label=f\"inducing inputs (M = {len(inducing)})\",\n", + " )\n", + " if title:\n", + " ax.set_title(title)\n", + " ax.legend(loc=\"lower right\")\n", + " return ax" + ] + }, + { + "cell_type": "markdown", + "id": "1e576b55", + "metadata": {}, + "source": [ + "## Exact GP\n", + "\n", + "The model is a zero mean, a {class}`Matérn 5/2 ` kernel scaled by an amplitude $\\eta^2$, and Gaussian observation noise $\\sigma$. The hyperparameters are PyMC random variables, so priors go on them the usual way.\n", + "\n", + "{class}`pg.gp.Unapproximated ` is the exact GP. {func}`pg.fit ` optimizes the hyperparameters to their MAP values (marginal likelihood plus PyMC log-prior, L-BFGS-B) and returns a {class}`FitResult ` that {func}`pg.predict ` consumes." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "476a0e15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ls = 0.475\n", + " eta = 0.909\n", + " sigma = 0.471\n" + ] + } + ], + "source": [ + "with pm.Model() as exact_model:\n", + " ls = pm.InverseGamma(\"ls\", alpha=3.0, beta=1.0)\n", + " eta = pm.Exponential(\"eta\", scale=1.0)\n", + " sigma = pm.HalfNormal(\"sigma\", sigma=1.0)\n", + "\n", + " kernel = eta**2 * pg.kernels.Matern52(input_dim=1, ls=ls)\n", + " gp = pg.gp.Unapproximated(kernel=kernel, sigma=sigma)\n", + "\n", + " fit = pg.fit(gp, X, y)\n", + "\n", + "for name, value in fit.params.items():\n", + " print(f\"{name:>6} = {value:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "139b5728", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mu, var = pg.predict(gp, X_grid, fit, X_train=X, y_train=y)\n", + "_, var_y = pg.predict(gp, X_grid, fit, X_train=X, y_train=y, incl_lik=True)\n", + "\n", + "sd, sd_y = np.sqrt(var), np.sqrt(var_y)\n", + "mean = to_g(mu)\n", + "f_band = to_g(mu - 2 * sd), to_g(mu + 2 * sd) # latent function f\n", + "y_band = to_g(mu - 2 * sd_y), to_g(mu + 2 * sd_y) # predictive, for a new observation y\n", + "\n", + "plot_gp(mean, f_band, y_band, title=\"Exact GP posterior\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4d09ebf1", + "metadata": {}, + "source": [ + "The inner band is the posterior over the latent function $f$; the outer band adds observation noise and predicts a new $y$. The function band is tight where data is dense and widens past the edges.\n", + "\n", + "The Gaussian likelihood assumes constant noise, but this data is heteroscedastic (quiet before impact, noisy after), so the noise band runs too wide early and too narrow late. Input-dependent noise needs a non-Gaussian likelihood or a kernel like {class}`Gibbs `; see the companion notebook." + ] + }, + { + "cell_type": "markdown", + "id": "e76f8a51", + "metadata": {}, + "source": [ + "## Under the hood\n", + "\n", + "{func}`pg.fit ` and {func}`pg.predict ` wrap a lower-level API. For a custom optimizer, frozen variables, or staged training, call the pieces directly: {func}`~ptgp.optim.compile_scipy_objective` builds the loss and gradient, you run any optimizer, and {func}`~ptgp.optim.get_trained_params` reads the result back. This reproduces the fit above." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "2cb37ac4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'ls': 0.475, 'eta': 0.909, 'sigma': 0.471}\n" + ] + } + ], + "source": [ + "import scipy.optimize\n", + "\n", + "X_var = pt.matrix(\"X\", shape=(None, 1))\n", + "y_var = pt.vector(\"y\", shape=(None,))\n", + "\n", + "with pm.Model() as manual_model:\n", + " ls = pm.InverseGamma(\"ls\", alpha=3.0, beta=1.0)\n", + " eta = pm.Exponential(\"eta\", scale=1.0)\n", + " sigma = pm.HalfNormal(\"sigma\", sigma=1.0)\n", + " gp_manual = pg.gp.Unapproximated(\n", + " kernel=eta**2 * pg.kernels.Matern52(input_dim=1, ls=ls), sigma=sigma\n", + " )\n", + " fun, theta0, unpack, shared_params, _ = pg.optim.compile_scipy_objective(\n", + " pg.objectives.marginal_log_likelihood, gp_manual, X_var, y_var\n", + " )\n", + "\n", + "result = scipy.optimize.minimize(fun, theta0, args=(X, y), jac=True, method=\"L-BFGS-B\")\n", + "unpack(result.x)\n", + "\n", + "params = pg.optim.get_trained_params(manual_model, shared_params)\n", + "print({name: round(float(v), 3) for name, v in params.items()})" + ] + }, + { + "cell_type": "markdown", + "id": "1e602a3b", + "metadata": {}, + "source": [ + "## Sparse GP (VFE)\n", + "\n", + "Exact inference factorizes an $N \\times N$ matrix: $O(N^3)$ time, $O(N^2)$ memory. Sparse GPs summarize the data with $M \\ll N$ inducing points; the VFE bound {cite:p}`titsias-2009` fits them at $O(N M^2)$ while staying close to exact inference.\n", + "\n", + "{class}`pg.gp.VFE ` takes an `inducing_variable`. {func}`~ptgp.inducing.greedy_variance_init` places the inducing inputs by pivoted Cholesky {cite:p}`burt-2020`, and {func}`pg.fit ` refines them along with the hyperparameters. Everything else matches the exact GP." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "6bed98f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ls = 0.520\n", + " eta = 0.931\n", + " sigma = 0.474\n" + ] + } + ], + "source": [ + "M = 15\n", + "Z_init = pg.inducing.greedy_variance_init(X, M, pg.kernels.Matern52(input_dim=1, ls=0.5), rng=0)[\n", + " 0\n", + "].Z\n", + "Z_var = pt.matrix(\"Z\", shape=(M, 1))\n", + "\n", + "with pm.Model() as vfe_model:\n", + " ls = pm.InverseGamma(\"ls\", alpha=3.0, beta=1.0)\n", + " eta = pm.Exponential(\"eta\", scale=1.0)\n", + " sigma = pm.HalfNormal(\"sigma\", sigma=1.0)\n", + "\n", + " kernel = eta**2 * pg.kernels.Matern52(input_dim=1, ls=ls)\n", + " vfe = pg.gp.VFE(\n", + " kernel=kernel,\n", + " sigma=sigma,\n", + " inducing_variable=pg.inducing.Points(Z_var, Z_init=Z_init),\n", + " )\n", + " fit_vfe = pg.fit(vfe, X, y)\n", + "\n", + "for name, value in fit_vfe.params.items():\n", + " print(f\"{name:>6} = {value:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f7ee94a3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mu_v, var_v = pg.predict(vfe, X_grid, fit_vfe, X_train=X, y_train=y)\n", + "_, var_v_y = pg.predict(vfe, X_grid, fit_vfe, X_train=X, y_train=y, incl_lik=True)\n", + "\n", + "sd_v, sd_v_y = np.sqrt(var_v), np.sqrt(var_v_y)\n", + "mean_v = to_g(mu_v)\n", + "f_band_v = to_g(mu_v - 2 * sd_v), to_g(mu_v + 2 * sd_v)\n", + "y_band_v = to_g(mu_v - 2 * sd_v_y), to_g(mu_v + 2 * sd_v_y)\n", + "\n", + "Z_final = fit_vfe.shared_extras[0].get_value() # inducing inputs after optimization\n", + "z_ms = x_scaler.inverse_transform(Z_final).ravel()\n", + "\n", + "plot_gp(\n", + " mean_v,\n", + " f_band_v,\n", + " y_band_v,\n", + " color=\"C1\",\n", + " inducing=z_ms,\n", + " title=f\"VFE sparse GP posterior (M = {M})\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "582f4503", + "metadata": {}, + "source": [ + "## Accuracy\n", + "\n", + "With 15 inducing points against all 133 observations, the sparse posterior mean sits almost on top of the exact one." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "e202fc57", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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gnAsAfQ+AxTTWfTHrftQ+FwpnB7TMMSiH26dkcJ09jNjxaA5XxoXQIIAPzNqaNWtoLQALjr4HQDnQ9wCVz5Q1n8v1FrLv6RqKq2fPY4p179QKq0++YI2SgXWSs3JLdpsg/eWXPdPOmjcBdTPmvSmbfzxlyM1rzOV6k3ndTrVF/BpNZNU9nlE2XKfxXFzXf+o/Z3we5dUrWyX2G3OB4x4A9D0AFgNzAW3HQEzD/V0qDOy0s+5rFVMm2Kq8NzInfyPiJ4BfKfikgFm6/fbbaSsAC46+B0A50PcAlc9UkjNlzWeyatUqbdiwQYup7+kaGFPvzgcUO7BVywo9cte2KxNaXtHB+8nl9E0p+xc/79n27fGOIX7+uWfZWfwzaW9tti8UOBZOh0MNIa/qg167hOfP792prr6RWZVXR2WqxH5jLnDcA4C+B0A5ZfMF7eiLauu+fg3sekj+gce0ztGtsDuvVO3aOQveG7sGc3P2WphfBPABAAAAAFhi3G63PSb5TG688cbjDiLPtf4d9ynR/YRarWE5G9Yo75u7H6uWGnNBgCnBP5P3XnftcX/GIa9bbbUBHegfqsry6tWkUvsNAACASjUUS2vzvhF1du5RfM9f1J7p1CpHvwqBZqXDK4+7ZD4qFwF8AAAAAACWoI0bN9pjkh+aUWseL7axyvd3d9u3yZ5tanQl5Whcp4I7WO7NqojS/Dd97AOHZeKbx2a+WT4XPC6nTlzRUpXl1atNJfUbAAAAlSqXL2hXfzHrvn/3Q/KarHsdUI0rp2TtOi5khrh0A5il1tZW2grAgqPvAVAO9D3A0mGCbVdddZXuvvtuOzPaBFdN+evFlEHbvW+3Bju2yJuPK+JzyVF/giwH+QazZYL0l1/2TD2weYsGhkbsMe9N2fy5/ozNa5oLA3r7B6ZdZ9WqlUuuvHo1qoR+Yy5x3AOAvgfAQhpLZLXLjHXf16Vs/061O4ZVp3Flg21K++rm9W8HPY55fX3MHYdlWdYcvh4AAAAAAMBTsyz17HlM/fu2KdG3W4GaJnkibZKDH5UWq1//7o+6/sOfnnb5LV/6mDa+6Top2LCg2wUAAAAsdvmCpX3DCe0fHFX0wHb5Ez1a7hySy+VRJtQuy+kp27bF9j8uf8sanXTBlQqGGcZsMeCSdmCW7r33XtoKwIKj7wFQDvQ9AOZdPqfeHQ9osHOLkn075atbpgFHC8H7ii3Z36Sv3vA2Xf2M5VL3I9JoV9m2EThaHPcAKAf6HqC6RFNZbTkwpj1792p8931qSe7RGkefFGhQumbVggXvu8fyC/J3cPwooQ/MUl9fH20FYMHR9wAoB/oeAPMqn1P/rgc0uG+74kPdcjeslS9Uo8RgjoavwJL9NbW1WnPiyWqq9asrMa5lue3yFjJSNik1nig5yR3B4sZxDwD6HgDzxRRB3z+S1N7BMcW6d8gTPaB1zkF53C6lgifIcnkXtPETWYqyVwoC+AAAAAAAYGHkcxrpfNgO3keHeuRuOlGBQJDWrzBut0sXnX/uxONMrqCBWFppT1gZebSib7eC+ayUTUgtp0vuhf1hEgAAACi3RCan3f1xDQ70KtnzhJoKw2p0jCrnb1LK10D1McyIAD4AAAAAAJh/hbziXZvVv3ebYsO9cjSsVZDg/ZLgdTvVHvFrMJbWvrhH6UCzlvfuVX0uKYcJ5LecJvnC5d5MAAAAYEGy7nvHU+rsj2q8Z6eco/u0xjkkn8tSOrRGlsvHp4Cn5LDMngSgqFCQ4v2Sv07y+GkVAAAAAJgLhbzSBx5V166tGh3sVjK8Wg21BHSXGvMT01gyp2g6o5aQW+2FPrXU1cjVsEZqPV0KNpR7EwEAAIB5k87l7az7/sEBJQ48rvr8oFqsEeUDDcr6mxZt1n1s/+Pyt6zRSRdcqWA4Uu7NgSQGIgMmSw5LfVulAw9K8aEpizo7O2krAAuOvgdAOdD3AJhThbxyPY+pu+MJxYa7FQuuVH1N6LDVxlIFGr7CORwO1QU9agr51R/Pq9NqVddoUum+7VLPI9J4d7k3ETgMxz0AyoG+B1h6TDWqR/YN2+c96b33a2V+n1qcUWVqVykbaF4UwXvOuSoHAXxgknQ2o329Axrrelzq2SwNd5gUAnvZI488QlsBWHD0PQDKgb4HwJwp5GX1HQzeD3VryLtCjZEaO9B7qIEYAfylIuB1qa3Wr1i6oL25Bu2LuRTr3la8YH5o98R5NrAYcNwDgL4HwPHI5Qva2RfVE3t7NLzzfvmGt2mduuXzBZSqPUEFd2DRNDDnXJXDXe4NABaTaCqnkXhGyXRUqfRWNWVScqWjUvOp5d40AAAAAKi8Icr6H1df53bFh/arx9mmlkiNnIsg8wTzz+Nyqi3i13Aso850WGm51d61TQ35rBy5VPE82+niowAAAEDFGktktas/qtHeDmUHdqvNMaRaZ1rp0EoVPMFybx4qGAF84BA5y1JvyqVxf5MyXbvUko7Ll03STgAAAABwlMH7oa4dGh/sUpfa1FhXawd1UT3MxRpNNT6NJ7Pan/Iq629WZt9ONWczcufSUusZkttX7s0EAAAAjkqhYGnfcEL7+4cUO/CE/Mk+rXQMyeENKxlcITk478HxIYAPHIHX7dJo1quks02ZvgE1J2O6cPVKaeyAFFlOmwFYMBdeeCGtDWDB0fcAOC6mPPrAExrv3qHh3k7tLbSqrqZGfvfM2dbtNfzItVTVBjzyup3qi6WV9rQr271PLZmk/IWc1HKa5Ksp9yaiinHcA4C+B8DRiKVz2tUf00jvXiX7dqjVGladM65saJny3sV9XMs5V+UggA8cgcvpKJb6i2e0J9ui9PiYIomdKgQKcqbHpcaTJBdfHwDzLxKJ0MwAFhx9D4DjMrRbif7dGuzZq31Wm0KhkEK+pz5/8rkprb+U+T0utUf86h9Pq9PRquzAoJozadXmMsUgfqix3JuIKsVxDwD6HgCzYVmWDowmta9/RLED2+SK9WitBuU2Y90H10nOxR8z4pyrcnB5OzBTqb+wTzVBj/ZlI3ogvlw9nduU6X1C6nlEyiRoOwDz7s4776SVASw4+h4Ax2y8W5nB3RrYv0tdVrP9Y1Yk4JnVUztH8jT8Eud2Ou2L5S2XR3tyzTowltbQvsdkmXPssf3l3jxUKY57AND3AHgqqWxeW7vHtaujQ6O77lNdbI/WOPukcIvS4RUVEbw3OOeqHJWxRwFlVOPzyOtyqWu0oM5CizLd+9WcjCucTZIlAAAAAAAlyRFZA9s1sG+7+goNyrsCagl5l2z75ApSX0LqiUvdCWkkZS4El1yO4u3k+6XbkEdaES5OAXd1Xyw/nnJqb7Je2WRUmY7H1JzLyJ2JFyveOck3AQAAwOLQN55SZ9+oot3bZI3t1xoNyev1Kh1aJ8s5u4uVgaNVpaeLwNHxuc2PBwWlHV7tzrcpPTysltRjqs+n5Wg8UapbJTko9wgAAACgfHK5nO6++2719PSovb1d69evl9u9QKf95gLn/ic0vH+XhvIBDVshLQt75VgC50nRjPTEiHQgJvWUAvZxqT8pFaxjf91mv7SyRloZLk4mqG8eR7zVcXpZ6/fI63aqO+pQ0nIr27FNzemUAvbF8qdL7qV78QcAAAAWv3Qurz0DcQ309yhx4AlFcgNqdkSVD7Yo7a8v9+ZhiSOAD8xSrc+hZjtLIKe96QZlUjFlOraqOZuWy2QJNJ0sufhKAZhbq1evpkkBLDj6HqDy3Hrrrbr++uvV1dU1MW/lypW66aabtHHjxvn94/mc1Pe44n17NJjIqjvXqOYan1xHmUVtzrkWi/0x6b4+6f4+aeuwCdRbqldUyx2D6rPqNaDDf7ALKqWHfH+jjNzKyKOsuTWB6YOPM3Jp3Appt7WsOGWWaffAMj00UCfJMSWwf36rdEGLdE6T5F/Cp5l+t0vtEb8Gok6lLLcy+zvUlEqoLp8pVrzz1ZR7E1EFOO4BQN8D4FBDsbT29I1qvHun8iN7tVxDCnpcSodOkOWq3AtNF9M5F2bmsCzrOK4XB5aWwd592rv5LuVTMYWa10y7XjKT12A8pUZ3Rm0aVEv7KvkaVxd/YPAGF3SbAQAAAFQ3E7y/5pprpl1+yy23zF8Q3/yk0LdVmf7t6u4+oN2FNvk9XtUFPRVXDt8E6v/SV5x64gWd6ujSpc5Hdalzi57m3KmQI22v+6HsG/Xd/JWHvUZEMT3i/+uj/ttRK6AHCifrTdl/OGyZxymd3ShdYAL6rVLbEj3dND9NjSSySqVTWukYVF1NQM0rTpar9TQp3FzuzQMAAECVyOUL6hyKq7evT/EDjyuU7lerc0yFYLNyvoYlWyortv9x+VvW6KQLrlQwHCn35oAMfGD2ukZyWllfTH0IeF1qcwWKWQLONmUP7FdLOqFwLlUM4gcbaFoAc2LTpk3asGEDrQlgQdH3AJVVNt9k3s/ELL/66qvlcrnmfgNGOmSN7tNA7351O9rkcLgUOcbB3Sefcxm5XF4PbN6igaERNTfW6/xzz5Lb7ZrTaw8eG5J+tVd6sF8K5kbsYP37XFt0iW+Lmh3jR3zecsfQxP1Gv9QelNpD0lpvTjpw9NtR40gqePDigENlC9K+gVE9NFCrrzzm1KpwMZB/6TLpJJO4v0SYoRYaQl7F3U51xprVHh1RdvcWtWTT8rWeJNWtXrI/lqL8OO4BQN8DwBhNZLS7b1zjPTuVGexQm4YU9krp0BpZLv+SaKRDz7mwePEpAbOUzk997HE51RbxF0upFNqUHRpUc/Ix1efScjSZHxhW0rYAjtvY2BitCGDB0fcAlcOMeT+5bP6RmOUmQHX55ZfP7R+P9knDezTSs1v9jmaNZ5xaFjn2ce8nn3P9+nd/1Ge/9HX19g9MzGtradb73nmtnvecS45rs/OWdG+PdNtuqTC2X9e47tY/OR/Vqf6Z27HkuY0DWnlqMRt+cnl7T/LYAvhGIrBMa/1SV6wYtJ/s295/VYtjRH8snKk/JM7WH3afpR/trtdJEekFa6TLli2dMvshn1ted0h9UbcS6VFldz+mpnRKtZmE1HyK5JyHi1BQ9TjuAVAO9D3A4pEvWHbWfU9fnxLdj8ufHNBa54isQKNS/qYldSHpoXEuLF5L5BQPKA+nw6HmGr/Gkll1JBuVScWV6diq5lxarkxMajqZHxgAAAAAzJuenp45XW/WUmPSwHYl+narJx9RX9qt5vDRj3t/JCZ4f/2HP33YfBPML87/wDEF8c2PVb/rkn68RwonuvRB9/d0mW/LrJ+fd/mV9TcqEmnQmtrDl5tljz/nm3IUcsXJyslZyD75uJCVOz0iX7xbvkS3feuNd8uVT+m01cv076uL27hlULq/X7q/T8onx3S6c6/9+i9z/dmejM2Ftbottl7feuSZ+u/Hw7piRTGYvyKsimculm+3L5av1960W/l925VKJtWcTcrBsHUAAACYQya2s7tvTNGeXUoNdKhVg6pxF5QJr1bBHaCtUTYE8IFZcs3wO1Qk4JHX5dSBuEMpt1fZzu1qyaTlyyal1jMkt492BnBMfD76DwALj74HqBzt7e1zut6sZFNS/xPKDnWoJ+lSbzaoGp9bfo/ruM+5TNl8k3k/E7P8ivXPnPWQANGM9MtO6ecd0mimOG+lw6tLnI/N+Ly8K6B4w+mKNZ6teMOZygTbZs6+cThkuXz2NOP2TH5gWXZQ33J67Ic+l3R+a3F625lSvnOLtOvw1zjXuceePuT+rv6v8HTdtvcyvaPjbJ3Z5NILV0sXtknu47+WouwXy497XOqIubSyb7/SyZhaUnH5Wk+RalrLvYlYQjjuAUDfA1Rn1n3XcEIHenuV6H5C3kS/TnCO2MMjF7PuK/hg+hjjXFhcHJZlRn0DYAz27tPezXcpn4op1LzmqBslmy9oIJqW35nVCqtfzU3NCretlVpOl/xHSNEAAAAAgOOQy+W0du3aGcvor1q1Snv27Jl1wHtGhYLU+4isgZ3qHhpWZ65F6byllhrfMZfOn+zeBzbrLe/+4FOu999f+IQuOv/cGdcZz0g/2CH9ep+UOkKpyK96btKVrgcnHltyKBlZNxGwT0ROlJzlzXto2HeHWnbfKlcu+ZTr9lt1+nH+Ev0of5mGvCv0srXSC9dUfnn9VC6vgfGkmgsDavAW1LD8REXaT5Qa181ZxTvzPTLDUZhKFeZil/Xr18vtrvCGAwAAwBFFU1nt6h1TtHeXkv0darEGVWtn3S+v2qz72P7H5W9Zo5MuuFLBcKTcmwNzUTOtAMzOUDw/q1J/bRG/cg6v9hRa1T04rJF9j0vdm6VYP00N4Kht27aNVgOw4Oh7gMphgow33XTTjOvceOONcxO8N0b3SmPdGhkZVK9aFM3k1RQ+9nHvDz3nGhgamdW6M61n0hR+2yV96q5undz1gyMG790O6ZGG59v3E5GTtf/M67Rtw3+p4xkf08C6VyhRf2rZg/fG8Krna9v6r2rPBTeof+3V9kUF5kKDI2lxjOpt7l/o/3zv1zesD2r39kd07e+k2/dImQoe69LvdmlZfVDjvnZ1pUPq73xcPbseVv7Aw1I6dtyvf+utt9oXwVxxxRV63eteZ9+ax2Y+qgPHPQDoe4DqUChY2jeU0GN7ujS88z4Veh/XGqtboVBYqcjaqgjezybOhcWBAD4wSyNJa9al/lpq/PL7fNqTb1F3NK/+zi0qdD8qDXcUf00CgFnavn07bQVgwdH3AJVl48aNuuWWW7Ry5cop881jM98snxPJUWm4U4nBveq2GjWYzM3ZuPelc67mxvpZrTvdel1R6aN/Tsn72P/oNsc/6Dr3z3S2Y/fE8oBbevk66euXSy+44HTtuuhT6njGDRprv1gFzyIdQN7pUrLuZPvCAnOBwc5LvqC+dRuVDkxfRt6U1/cqq9G09NWt0lt+J/2i01SNU0VyOZz2ebanplGdhTYN9R9Q146Hlei8XxrvOebXNUH6a6655rAKFuaxmU8Qvzpw3AOAvgdY+sZTWT3aNazOnY8qtvs+1cX3aIUnqkLdamUDLUu2ZP6xxrlQfuW/nBxYouqCXjsjvytRp0w6qlzHVjXnMvJkE1KzyeaYowwYAAAAAFXPBOmvuuqqKWXAN2zYMHeZ9/mcNLhDudF96s0E1ZdxKew9/nHvD3X+uWepraVZvf0D067T3tqsC847a8q8dF66ZYelRMd9+qL7e2p3D08se5P7Dn3ceZ1eulb6q9VSyDMxqqDSNatVabKBZg2uvUqDJ7xMwdHtquv+vWr77pUrn5pYZ3ehXb8pPG3i8VBK+vIW6bZd0qtPki5fKbkr8DfKWr9HfndEPVGvasf7ldu1WXWJuBqXr5Oj8STJ5T6qsvnXX3/9jOuY5VdfffXcfY8AAABQlrHue3p7lOjZJne8Xysdw3KGGpQONFdN4B6VhwA+MI9CPrcdxO+PSml5ldu7Xc2ZpAK5lNRyhuTx0/4AAAAA5qyc/uWXXz4/rTm8Wxrv1tB4Qn2FNllWQRGTzj7H3G6X3vfOa3X9hz897Trvve7aKQHVhwekXzxyQH+b+5Yu9mw9bP0Xu+7VSZe8Vo7AEhvL0eGwS/2bqefU16u2/wHVdd+t0PDjerDxRfL2O+0LGyYbSEp7HrtH9+xco0tOadezV5jsdlUUr9uptrqgRhPLtS8+rNy+bUrEx9W2clze1tMkf+2sXsdc7HJo5v2hzPJNmzbN3/cKAAAA82YsmdXuvlHFu3coOdip5vyQanxSNrRG2Sool4/KRgAfmKUVda5j/nGhPeJXf9SpDrUp171fTZmkanMZqeU0KVDHZwBgWuvXr6d1ACw4+h4AU8T6pdEuxYYOqNfRprFUVm2RwJyMe3+kc67nPecSSR/QZ7/09SmZ+CYz3wT3i8ul4ZT0va1JPa3/x/qu6w55XIfXh095GzRw6mvlmGVQt1JZLr/G2i+xJ0+iT2f6G/SNbDHj/pedUqZQXK9OUX3G81X5CxndufV8/fvul+vKs1fp9AZVFDN0XUPIq6SnWfuifjUP9CmXGFdjdFSR9hOlutVPmY1vKlXMxmzXQ+XiuAcAfQ+wtOTyBe0bTqivp0vxnp328fEa55hU06S0v8m+ELZaHWucCwuPAD6wAMyYkK21Pg3HHdqTbVNmcFC51GOqz6flaD5Nqpl+7EIAAAAAKJtsShrcqfzIXvUUIhpIWoocHC5sPpkg/eWXPVMPbN6igaERe8x7Uza/lHn/QJ/06MP36qPO76rNPXLY8/NyaXj1C+xS8wV3dVU+ywaL55d1PuktZ0hXr5Nu2SX9717pdY7/U9CRtpc/33W/rsw8oJ/+5WJ9p+nletGZrWqosKYKeF1qq6/VYMyrRHRQ+Y4tio0Nq2lZn3zNJ0nh5mmfa4aZmI3ZrgcAAIDyG01ktKd3RPGe7UoO7VNLflBhn1uZ8FpZLl+5Nw+YNQZ3AGZp/+jh2RxHmyHQFPYp5PepM9+k7rjUt2er8j1bpOEOybL4LAAcsbQnACw0+h4ANnOOMrhDGj+ggURBg7mwnXVf43MvyDmXKad/0fnn6sXPe7Z9a4L3eUv6wbaMvA99TTe5vqg2x+HB+7H6M7XnWZ9W/8mvrrrg/ZGYoPzbzpS+sT6jv/b+esoyp8PS1a4/6uPD79Xeu7+lO7aPKXswW7+yLpgPylXfrs5Cqwb6+7R/20Ma3nWfrN4tUjY5bdb1ypUrZ3ztVatWacOGDfO05VgsOO4BQN8DVL5MrqBd/VE9sXOHhrf/WY7+x7VavQrUNipdu4bg/RzFubBwyMAHFlhtwCOP26n9MYcyiivX8Zhachl5zY8KzadITkqYAAAAAFgExvbbwfvY2JB61aaxZGZeSufPenPS0ncf7NM7o1/QGe69hy1PeBs1dOprNd7yjKouizmdxqBLo2e+QdaeX6g23jllmdeR12sddyq+927d2vVX8p3xIp3dHlQlqfF5FPTUaTgW0GhqRNm92xQbHVTzsiEFWtZKtSsk55N5LG63WzfddJOuueaaaV/zxhtvnKj6AAAAgMWpfzylrt4BxXt2KD3SrRZrUMGAT9ngOlkub7k3DzgmBPCBMgh4XGqrDag/6lDaciu/d5taMkkFcimp9QzJTSkXAAAAAGWUjkrDe5Qb3qfuQoMGE/kFKZ0/nR2j0ufuT+nH1kfU7Byfsixnl8t/oQbXvcweCx7TcLo03vYsjbc+U+HBhxTZdovqUl1TVgk50nqj9VONbPmNfrHrpVp+7nPVWuOtqGz85tqAEn6P9kbDahgZUib+sCKjw2poH5Cr+UTJHyn3ZgIAAGAOJDN57ekf01jvHiV69yiUHVC7O6V8qF0ZH8d8qGwE8IFZqg/MbQaH+eGrPRLQQNSpjkKbcge61JRJKZLPFIP4vho+GwA65ZRTaAUAC46+B6hyhbw0sMPOwB/I+jSY9cnpyM9b6fyZzrlMFf879kpf2SrlCn59wfUKfdLzjYnl47429Z/3bqVrVs3rti0pDodizU9XrOk8jXf/SbU7blNdbmDKKvWOmF6Xulnd99yhP7W8RqecdaFcrsqpahD0uuVvqNFowq/O+KjauzsUHx9U0/igwk0rpbqVyrkCuv7662d8HbP86quvJgt/ieO4BwB9D1BZCgVLB0aT6u3tVrx7uwrRXrVrWN5QvTKBlVQ5XsA4F+aPw7IYeBsoGezdp72b71I+FVOoec2CNIz5Cg4nMsqkM1rpGlRTTVANy0+Uo/V0KdTEhwMAAABgYQ3tlvoeV2y4W7vybeodT9ul8xc6+z6Vk/5zi/Tb/ZPnWvq85z91letPGmi6UINnvVUFd2WVel9sHIWs/J2/U9Oen6jWmlrdoOQ7rqtV9/RXaF0FJjKlcwUNRZMK5UbU7BpXMNKsxpbl+tNj+3TFxrc85fN/85vf6PLLL1+QbQVmI5fL6e6771ZPT4/a29u1fv16e0gIAACqwXgqq47eYcW6dygxuE/1+UFFfFIutIzzguMQ2/+4/C1rdNIFVyoYrsCD/iWoPLXvgArUMZybl9c140c2hnwKBf3qyDepJ5ZVX8djync/Io3um5e/CaBy3HHHHeXeBABViL4HqGKJYWmkU7nRAzpgNWkwll2w0vmTz7m649J7/3Ro8N5w6M/LrtW+0/5a/ee+ix/p5oDl9Ci59nk6sOHzemL5KxRXYMrylOXRVxOX6d1/kL79hJTJq6L43E611wWlcKv2FJarezSl/Tsf1ROP/GVWzzdBUixtlXTcc+utt2rt2rW64oor9LrXvc6+NY/NfACVpZL6HmAxyOYL2t0f1ePbHtfQtj8p37tVK60e1dTWK1O7jvOCMse5MPe4PBOYpXxhfpuq1u+xfxTbH3Mqm44qv2eLmrMZebNJqfEkycn1NkA1SqfT5d4EAFWIvgeo4tL5Jvt+bL/68jUaSrvkdObtc5WFPOfaMWLpkb9sUnfmYklPjr/ud0l/d4502XK/otqwINtUTQrugHT61dq77gqlttyus4bvlMeR13/lX6z9Vou9zi27pD/3SO86RzqjURXDXDgfCXgU9tVoNOnXcDIlp++wq0OOyGQ4Y2mrlOMeE6S/5pprDpvf1dVlz7/lllu0cePGsmwbgKXb9wDlZioY90fT2t/To2TvTqVGutVsDSoQDCkXXCfL9eT5Asof58LcISIILCIBj0tttQENWLXqyterp/MJJbufkPq2SLlMuTcPAAAAwFJmKoBFexVNZjRQqNF4MqOG0ML+IPbIQEHx+76tG5xf00fc35mYvyIsff5SE7xf0M2pSpavVr7zX6dHL/i0/uC9TF/OvXjK8v1x6f1/Lg5vUBg9oEricjrtCnit9TU65bxnqblp5qsQVq1apQ0buFgEi6Ns/vXXXz/jOmZ5Pl9hJTIAAJhBNJXVY/v6tefxBzWy8165B7dplWtEvrrlytasIniPJY0APjBLPtfCNJXJwm+L+JVwhNVptaq7q0NjXY9LPY9ImfjCbASARSMSYcwhAPQ9ABaAOdcY3afc2AH1WPUajGVUF1qY0vkl20fyCj34Zb3Wdaf9+P+5f6ernH/QJe3F4P2qmgXbFJiKB/XL1bD+bXrfBT41+g9vkuF9W3XW/e+T//7/kCs9VlFtZp931wX13ndeO+N6N37kvXLlEgu2XSiPSjjnMmPem0z7mZjlmzZtOu4LBX7729/qe9/7nn1rHgOo3r4HKJdMrqBdfeN6/InH7HL5mQNbtCy7Xw2RWmXrT1TeW8uHs8jjXDh+lNAHZmll/cJ9XVwOp1pqfBpOONSRblduoF/ZTEqNubQcradLwYYF2xYA5UXGDwD6HgDzzrKkoV3SeI8Gsn4NZEzp/IJqfAtTOt/4XWdG5+/+op7jenjK/I8EfqgD5z1TcvHzRblc1Cad1Sh943Hpjn3FeS7lJyokrBv9k+J/2Kz+k/+fEivXS47KyRV54RWXye106jNf+m/19Q9OzG9vrtcn3n6VXvq0VqnrL1KgXgq3SuEWybVw3wssjEo45+rp6ZnT9aYr0W+y+CdfKLBy5UrddNNNlOYHqrTvAcpRLr93PKWe7v2K9+5UZrRXTYVhBUJh5YInKku5/IqKc+H48EkBs9QfzaulxrWgY/SZ0n7jLqc6Ei3KjY8om9mi5lxG7pZTpAi1I4FqsHnzZp177rnl3gwAVYa+B6gysT5pvFeJ2Ij6rHa7hH5bJLBgf/5XOxO6bM+NutC1bcr8qKtOg8/4B4L3i0DII/3tOdL65dK/PypdkfqNTnU+GeQLWXGdsP1rGtj/e42dfa3S4RWqFM97ziW6/LJn6oHNW7S/b1CBcK3OPPN0NfosdfSPKTLQo9pwjYKRZilQJ4WapFBL8b6TFKaloBKOe9rb2+d0vSMF76+55prD5ptgvpl/yy23EMQHqrDvARbSWDKrfT0DivfuUHxwv2qyA2rxOVQIr1LWE+LDqNA4F45d5VwWDZTZeNoqy9+t9XvUVBNUV6FRvSm3evc8pkz3Y9LgrmKmDIAlbe/eveXeBABViL4HqCL5rDTcocLYAfUW6jSczCsSWJjS+eZ05sdbx/SCPR/Xhc6pwftRT4t6LvqI0uGV874dmL2zm6QvrZea29fo8cLqw5Y3x7drzT3/qIYdt8iRz1RM07rdLl10/rl6xQuv0AvXP0NtkaBSllcd6Yh2FJZrd9ynru4Diu57VIXuzdL+v0j77pH6HpeivVKuct4rKvO4Z/369XY2/ExWrVp1TBm9pky+ybyfiVmez+eP+rUBVHbfAyyEZCavbQcGtX3rwxra/idlux/TskKPInVNytWdqALB+yUR58LRIwMfqAABj0tttQH1R51KZz3KdWxVczatUC4pNZ9GRgoAAACAYzPSaQcgR1MFDeYCyudzqgnP/08FeUu6ZfOA3tz/Ka119k5ZNuRbqcELP6Ccr37etwPHNm7mc847RduHP6HfPXSH3ly4TUFHemK5W3m17/2panrv0eAZb1a88ayKa2af2ylfjU/5gkfRVE4HUl4NuJsVcVqqG06ozrlPYXdevnCd5K2R/LXFjPxAgxRslLzBcr8FLDFut9suZX+kLPmSG2+8US7X0WfU3X333VPK5h+JWb5p0yZdfvnlR/36AAAcSTZf0P6hmIa69yg50KHceL8aNS5fTZ1ywZUqOAlforrxDQAqhMmAaY/4NRB1aG/OrXzXLjWmk6ozV/q3nMYPBAAAAACOTmpMGu1SZrxPvWrRcCKr5rDPHs5rPmUL0g8e2K+/G/202p3DU5YNhE7W4AXvVcETntdtwPE7ucGl1c9+oT6z5Rm6tPdbutz18JTl4XSfwg99SsNtl6j/lNcq762tuGZ3OZ2qC3pVG/AokclpOJnTkIIK+2oVchRUO55UraNPNc798vpDxUC+r1byRw4G9OuLj138/Ibjt3HjRruU/VyPU9/T0zOn6wEAMJNCwVLPWFL93Z1K9ncoNdqr+vywQqGQcqG1yrn8NCBghtm2LGpwAyWDvfu0d/NdyqdiCjWvmdIwubwlt2t+f8iaDfOVHU1mlUoltdIxoIa6iJpWnCSHCeIHG8q9eQDmWDKZVCCwcGPQAgB9D1AlzE8B3Q9J/U9of8xSZyqkfEFqCvvm9c+av/GDe/fofbFPq94Rm7Kst/ZsDZ//bln8aFdxHh2wtHnz/fp761tqdYwetjztrlH/aW/QeOszpXm+QGS+pXJ5xVM5JTJ5+zeCkM+tkMehGkdKdc6kwo6kPF6f5A1LvrDkqZECJqhfVwzsm4C+kxEtF5NKO+cyJe9N1rwJqJsx703Z/GPJvC/57W9/qyuuuOIp1/vNb35DBj5QxX0PMBcGY2kd6N6vZN9uJYZ7FM4OqNbvlRVuo1T+ApkuzhXb/7j8LWt00gVXKhiOLNTmYAZcAgzMUjq3OAL4JhumPuhVzOVQR7xV2dEhZdKPqjWblrvlZCmyotybCGAOjY2NcUIHYMHR9wBVYGy/FO1TLBbTYKFdsXRayyLz+yNywZTNf3D/EYP33Y0X6cBpb1PQ5Z3XbcD8OLvZoRM3PEP/8tiZelrvD/U612/kdDw5vqYvF9XKLV/S4MgO9Z32xor+GPxul/xhlxosS6lsQfFMTmPJnDwul0K+OoU8DarJ5FSTSymU6JVf+yRP4MmAvrktZeiboL43VPEXNVS6SjvuMeX057KU/fr16+0s/pnK6K9atcq+UABA9fY9wPEYS2Z1oLdPsd6dig8dkD81oHaPJUfjMuW9BIurMc6Fp8Ylv8As9UQLi6qtwj6PmmuD2m81qzfpVvfuLUoe2CIN7DB1aMq9eQDmyH333UdbAlhw9D3AEpdNSSN7lR87oB6rQYPxrCIBr10ufD4T/m/eEtN1I4cH7w+0Xa6R896p7jg/UVSyoEf66/OCKjztTXqDPqonCqsOW+eLg+drMKklwVxcH/C67KoVy+uCqvF7lMrktX80pT3jlnbEg9qWadb2wnL15EIaT6SUG+6SerdIPZul/Q9I++4tTn2PS2MHpHSs+GXBgqr24x5zQYApwT+TG2+88biy/AEcrtr7HlSHaCqrbZ3d2rnlXg1u+5PSB7aoOduj+rpGFRpPInhfBostzoXpkYEPVPiV/+11AQ1EXUpl3crteUKNqZQi2YTUcrrkJnsFAAAAwCGG90ixHg1nXBrOeWVZOdX45vfnge/vkH6wN6QG13P1fs8PJ+Z3rXixxk991cEMZH5MWgqe0SqduuFE/eujn9Bp/b/Su923yefI6fu5Z+s7o2fqR3dL7zxLumy5lgynw6GQ121PBctSMpNXMpu3s83Mrh3wuBTwhOX31CroslSbySiYi8of65cdEjVZ+PZ0MEvflNkvZel7gmToY95t3LhRt9xyi66//vopmfgmM98E981yAABmK5bOqbuvX+O9u5Uc7lEh1qd6R1LeSJPy/jXKO7koDHgqBPCBCud2OtVa69NwPKLOtEe57g5lUnE15dJytJ4u+WrKvYkAAAAAFov4kDTerXR0UH1Wq0biGbXUBuxs4vly+55iAF9y6D/zL9WIwvqk+xvqbnvOpOA9lpJar/S+893atP8lesWWp+sdjlv0qdxr7GXxrPSvD0n39UlvP0sKe7Sk2MF8n9uejEyuoFQ2r2gqp8FYXh6X087cD3gi8rnrFXIVFMykFMxE5S/0F0uamqC9CehPlNyvmxTQp9wy5ocJ0l911VW6++671dPTo/b2drtsPpn3AIDZSmRyOtA/qPFuE7g/oHzUBO4T8tU2KR9YpbyTkCQwW3xbgFlqDjsX9Q8EpmzfuNupjrhT2aEBZVJJtWZTcpsgfri53JsI4Bidc845tB2ABUffAyxRhbw0vFsaP6C+fETDKSnoc8vnnr9znd90SV/dOnXez52X68VnrFRk2YlTgveL+ZwLx2bDCumMxuX63MPvUXRo6rJNB6StQ9I/nzGoy/u+qd5TXqdMsG3JNbXX7bSn2oDHzs5PZwtK5fIaimWUyxfk9Tjld3vkc/vk89QpYOUVyqcVTI0pYPXJ43bKYYL4kzP0Jwf03b5yv8WKx3HP1HL6l19+eRk/DaB60PdgKTHVh7oHBjTWvVsJO3DfrzrF5Ys0qhBYpRyB+0WDc67K4bAsBtcCSgZ792nv5ruUT8UUal5TkQ1jfggYGE+qxRpWkz+v5pUnK9B6klS/hswWAAAAoJqN7JV6HtX4cI925VrVH8toeV3AviB4PtzTI/3Lg1Jh0pDefpf0yWdKp9bPy5/EImX2AVOJ4VvbpNyUkRIsfcvzGW1wPaKC06v+E6/R0KrnS47quJijFNBP5/JK54q3psqezw7qO+U1pfcdOQWVUlBJ+Qsp+bweuf01T2bom3L7JpAfqC8G9SlJCwAAFrBUfm9/v6J9HXbGfXa8XxHF5KtplBVskUXgvmLE9j8uf8sanXTBlQqGI+XeHJjEXVoBmJ1dg7mKaCq/26X2uqCGXM06kPKrZ89Wje3bIvU9JuWz5d48AEfp9ttvp80ALDj6HmAJymWk0S7lx3vUW6jXcDyr+qBn3oL3m/st/fHhR6cE702i/wcvmD54XynnXDh6Tod01Trp3y6V1kwa5e3lzj/YwXt7nUJGbTu+pxPuv0G+2P6qaGbz/TPl9OuCXrXW+rWyPqjGsE8ep1PxTF69Yyl1jGS1M+bVtmS9nsgv0/ZUnfaMZtXb36vRfVuU3PeQ8vsflLruk/bdI/Vusb/rSsfK/fYqBsc9AOh7gKMzFs9o+55ObX/4D+rd+geNdzyk4NgeNYd98raepkJ4GcH7RYpzrspBCX1gCTJX7LdF/BqON2hvyqN8104lEzG1pBNytp4m+WvLvYkAAAAAFtLoXinWp6GMU0NZjxzOvMK++Rl8fPuwpfRD39e3PL/UFx0v0+dyG+WUQ+9/mnQeo3tVtTW10hculb6zXfrJbuky16OHrRMc26W19/6TBtZdrcHVL5KqKHPL4XDI5zaTU7Uqfj9zBZOZX1AmV9BYKq+BrCWXyy+fOyivy6lAMq1gPKaABuV3ZOUN1MgXisgXqpVjIju/oZih76qetgQAAHPLFPMeiqU00NOlxOBeJUcHVIj1K+LKyBtplBVYRdAemEMcuQNL+Er+prBP426n9sQ9Wj7Qr0wyptZMUr7WU6Ta9nJvIgAAAICFkInb496nxvvVV2jWWDKj1prAvPypfVFp7/0/13tcv7Qfv8v9UzUoqsHT36SL2ykCCMnjkq49XbqgRfrEw9fpD9mz9CH3dxVxJCaax2nl1LrrFtX2/UXdZ/y1UjWVOcTdXF2g7/Y6FfI++eN5Nm8pk8/b5fdjObey+bC8rlr5XAUFoykFxwflt/bJ63bJG4zIF47IH66TN9QgBU0wv0HyBsv91gAAQAUoFCwNjMU00NOp1NB+JUf75UwMqtZjydPQrIK/QYUqGf4IWEgE8IFZCnrmp7TkfKv1e+Rz16g76lIiNqT8jkfUkEwo0h6VGk+UnPzjCixmra2t5d4EAFWIvgdYYkY6pWifBrJ+DaedCvtc8pp69nMsmpHuu++PusH5gynzr/Ler/2tL1NOjUvynAvH5uwm6UsbHPrKlvW64sDZ+qTnG7rS9eCUdQLRTq2970MaWPMSDa59mSzn/FSNqLQsfa/bTOa7XJxXsExAv6BMtqBozqWhQkCFQr18haxCmaSCI/sU0C45/WF5QxH5Q3Xy19QrUNsspwnq+yJV/dsAxz0A6HuAwyUzefUPDmmsf6/SIz1KjvXLmx5WxOeRp6lNBV9EhXkajgvzh3OuyuGwzKW7AGyDvfu0d/NdyqdiCjUvrSv8zQn9YCwtV2pU7a4R1bSsVsvydcWS+p75yb4BAAAAUGbJUenAA4p1b9eu/DL1xnNaXhewK3bNpVxB+s6fd+tfEh+Tz5GdmJ9yBHTgGR9SqnZpnV9hbv2hW/rSo5Y25O/VRz3fUqMjetg6qdBydZ/xN0pGTlyUzZ/L5fXA5i0aGBpRc2O9zj/3LLndrrJtT94yAX1L6Vy+WII/m5G/kFDISihgJeX2+uTy18pjB/Qj8kdaFKpvkSfUSKl9AACqONt+OJbUUP8BxQf3Kz0+qExsUIHsmAKBkFw1LbK84XJvJuZBbP/j8res0UkXXKlgOEIbLwJk4AOz1D2W17JI+U6+j5f5ga6lxq9xT6M6Y14t69mnTCKq1nRCvvbTi2X0ACw69957ry666KJybwaAKkPfAywR5nr9kQ4VxnvVXwhrOFVQJOCZ8+C98YNHR/XexOenBO+z8qj7ae+ddfC+0s+5cOwuXSadVu/Q5zc/U88dPEM3eL6tl7jumbKOP35AJ/zlIxpa/QL1r3uFLNfB9PNF4Ne/+6M++6Wvq7d/YGJeW0uz3vfOa/W851xSlm1yOZwKeKWAt/idsiyfXWo/nc9rKJNXIR2XYzSq4Mig/E5LDn+tXIFIMaBf26RgXatCdc3yB5Z+qX2OewDQ96DapbJ5+yLE0f69So30KB0dkuLDCjkzqg03yNF0kiyXX2QDVz7OuSoHAXxglhLZpfHPU7Gkfp26ox41jPYrm35ETYm4IstPlupWV3XZPGAx6uvrK/cmAKhC9D3AEhEflKL9Gh8f01BhmfKFvGp8c/8zwK87c7q67wtqdw5Pmd99+luVbDit6s65cGyaAtLHL5J+0VGr65/4W/0if5E+4fmmWhyjE+s4ZKlp7y+VCq/S2LJLF03w/voPf/qw+SaYX5z/gbIF8acrvV/j80g1fhWsBqWzBcXSSRVS41LsgHz9HXL6auQMFAP67nCjApFmBetbVFsTmbggYCnhuAcAfQ+qUS5f0EgspZHB7mK2/diAnW3vz46r1u+Tp6nZLpMvh5PA/RLCOVflIIAPVCGf26m2urAGo24lEkMqdD6mRDKmlmWjcrWcInmX/hX2AAAAwJJWKEgjncqN92hAEY0k82oIeu0g3lx6bEhq3/YtXeDaMWX+geUvUGx5+YOWC82MUjj5MgT7UfH/dkEEw+V0zEsVhKXA6ZBeslY6p1m68aELdMX4afqQ+3va6P79xDoP6Azt8V6iM7Q4yuabzPuZmOVXrH+mXK7FF/g2+6EJyAdMKdyasApWu11qv5AcVy4xqsLwfskd0Jg/IlcwIncwIk9tiwKRFtVE6u0EgaUY0AcAYKnK2yXyUxob6ld8uEfp8QFl48MqJIYVdqQVDjfI1XiiCm6/CuXeWKDKEcAHqpRdUr82oHFvq/ZG/Wrv7lAqNqrm6KjCy0+VatvLvYkAAAAAjlW0W4r1azSW1GC+TW5nYc4DbX0JaccDv9GHXL+bMr+/9iyNnvpqLTl2BN6SVcgrXygok8srlysol88rmzf38yqYDCWnR3K6NBGit+84VIrZ5/OWnE6H3E6n3E7J7XLaQX2PeexyEOCXtLpG+tyl0s3bw3r/rrfp54Vn6l88X1eDonpP5i3af49DV62TXmeuPy9j/NiMeT+5bP6RmOX3P7xFF51/rirhdwK/1yd5m6VIs72vF1IxFVKjsoZ3KjnkUdQTltMf0UAoIleoSd7aFoVqG1Qb9NpDdPg9BPQBAFhsQfuReEojQwN20D471q90YkSFxKiChYRq/H55GptU8NXZ2fYE7oHFgQA+MEsnNi3Nr4u5Yt7vblBP1K/wWL9yic2qiUfVvHxtMRvf5Sn3JgJV7aUvfWm5NwFAFaLvASpcPiuN7lNm9ID6VaexVFatNYE5/ROJnPSTe7fpc45vT5k/7mnR8NP+1g5gV/Q5l1WQM5+SM5eUlY4rm4orl83IVPnPF4pZ9CajOmCC7y6X3C6XPAGXnMrLWchIDlcxkO/yyHJ5VbDvm1uvcnIrW7CUK1jK5wv2/Wy+oGQ2r1y+OM/jdsnvccrvdsnncVZlxr7HKb3xNOmCFunzm8/R8xL/qrOde9RltdrLf7xbur9P+vvzpJPrJGcuJYeVU94TXrBtNGPFzuV6i43D6bIz781kLmDx5+KyUuPKJw8okzig2EBQUW+NRgMR9YYa5Qo3ylfTpEjIr9qAu/h7QwUE9DnuAUDfg6U4pv1YIq3xkUHFR/qUHe9TOjaqfGLEDtrX+rzy1NXL8q0sHquWe4OxYBbVORdmxCcFzNJYqqCIf2mOD2/GwGurC2ksuVJdsUG17t+mpMnGj5ls/NOlYEO5NxGoWp2dnVqzZk25NwNAlaHvASrcWJedfT+UtDSU8ynoKR7zz5WCJX3rwSHdkPuCPI78xPy0w6f+p//9MQdQy3nO5Shk7WC9mVy5hPKZpBJ5l2KWTxmnT97gCnl8AXlcbgXcLnk9bvm8bvncbvkm3XcrL+VSUi4j5Q+dklJ+TJaVV84VUtYVUMYVVFYeZUwQP2cC+XllsgWl8gWlMnmNpTLKRJ8M6Jvh0ExAtJoC+mc0Sl9aL33ziYB+0Tm1cH5XTLr+j9I1J0rXW/+juv771XPamxRtuWBBtq25sX5O11vUHA4VzHfbZOCH2xXMJ1WTicpKDioT61M8FlKsL6ioJ6QRU3431ChPuEmhUFC1AY8dzDcZ+nPZF80VjnsA0Peg0mVyBY0lsxofH1difEDp6JBy8RHlEubCuzEF8zHVej1yR+ol/wqC9lVsKce5lhoC+MAsDcSWdsdmxsKsC3qU8baqdzykmqFeZRPjqo2Oq3nlyXI1rpWcS/f9A4vVI488QgAfAH0PgNnLpqSxA0qMdGvA0aR4JqdlkbnNvv/htoz+ZvRzanaOT5nfe+bbla5ZVTnnXJYlVzYqT3pYjlxKSfk1VvAqWqiR5WmRt6ZWvlBE4Zp6hU2J8NoaBX0eBTwuOwN/pte1qyDkklIuXQzom8/Fvk3KkYnLY08xBVPdkpWXPEHJH5a8IeWdPiVzBcXTeSUyOSXSuYmA/ngqq8Fo2g7oh3zFyeVY+udpfrf09rOki9qkf9ssDaSmXlCye9c2Nfv+z3686pHPa6z1QvWc8kblfZF53a7zzz1LbS3NM5bRb29t1gXnnaUlxQTz3UF7UrBVrnxKDZmomjLjKuQGlBjvU2w8qJj8GvfVqc8E82ua5AvWKOw3wXy3asxtwFz4Uv4Mfc65AND3oBIz7OPpnKKJlGKjg0pHB+2Avfk9P58clysbU1ApBb0+eWprZfnbZLn8MgNCobot9TjXUkIAH8AU5mr4lvoajSd86o72KbtvqxKxMbWsHFK4/TTJX0uLAQAAAIvV6F5ZsV4NZLwayrhU6zfB5rn7geb3B6Tv7fKoxnWhznR0yuko/gzYs/plirU9QxWhkJc7MypPalhZOdWviEZMWXZ/RN5QnQLhiPw19aoPB9QQ8tqZw2bM+lkz2fFub3E6kmxSSo09OaVjUiYuZWJSYlguK6ewr1Zhf0SqCZucfiUz+SkBfVNu31ycMZbIyOtxKeR1Keh1L/nM/POapf/YIH1tq/R/XcV5PmX0r56vTlkv0nefQsNb1XvK6zXWdnHxM5kHbrdL73vntbr+w5+edp33XnetPczCUmYCAtmAXwo0y1HIKZCNKpyJypkbUjo7pPhYj6JjAY0qqGF/nVzBennDDQr4fQr53BMZ+osloA8AwGJhWVbxuM8cB6aziidiSsfGlE2MqZAaVy4xpnwyKpmAvZVUncuSy18rR22TCt4aWU435fGBCkUAH8BhzI8+dSGf0r4V6hsbVW3/fvugIDw8pOYV6+RtWmuP5QgAAABgEUlHpbFuRYf7NaRWu5RmU9g3Zy/fOS59YbO559CX8y/RE9ZK/bvnP5RuOFXDJ71Ci50jn7Gz7d2ZMSUdAQ2oWaOOGrnrlincsEy1oaAagl7VhzwK+9x2lbJ54QkUp5q2aQL60eJtrE/K7bN/hA37TCWAsOTwKW9ZiqZydpnUaDJrB/VjmbyG4xkFPW4FfS67SsC8bX+ZhTzSu8+VntUuffERKZZ26ueFZ+kdjtunDOngzsa04rH/VKT3HnWf9mbl/I3zsj3Pe84lkj6gz37p61My8U1mvgnuF5dXDxMoyPnq7UlWwc4AjGRjasj2ypJLyUy/Ehm/YiN+DSukAX9EnlC9PMGIQn6vgiag73cr7POoxu9W0Lt092UAAEqyptpSNq9UtqB07uBtKqF0fFzZ5KhyiagKqagK6bjymYRd/SagtGqVldsXlLPOVJBqU8HlV4F/N4ElwWGZS3gA2AZ792nv5ruUT8UUap465nQ8XVDIV32lRQqWpbFEWvlorxoUk6+uXXWtK9W08hQ5a9vnLZMBQFFvb6/a2g7+uAsAC4S+B6hQPY8q3/OY9o6ktTtVY483bbJb50IqJ/3dH6T9sSfnuR3Svz+tR2taIsVS2sdpvs65nNm4Hbg3Y9vHnDUaLNQo6orI17BK4fpmtdQG1F4XsIP2i4LJxo8PSolBKVkK6o8Vy/CbimimLLzPBPOdyhUsjSezdjA/ljZTsZxqLm8pbAdB3fK4lu557HhG+vIW6ffd0mmOvXYm/tnOjsPWy7sD6jvpNRpZ/ux5O4fN5fJ6YPMWDQyN2GPem7L5Sz3z/qhYlpz5lB3Qd+Xi9rAVJshgLqZJKGCX248qZA974ArUyhesVcDnsStLmP3YBPPNrdmv53qf5rgHQDnQ91QHE37L5s1UODg9ed8O0mezyiQTyqVjKmSSyqcTKmQTsjIJ5TNJ+9ZTSNvB+oAycrmccvpCcnqC9vFNwR2wjwmB4z3niu1/XP6WNTrpgisVDM/vMFSYHQL4wCwD+OYHELereoPV5qBiLBqVJ9GriNchX+NKNbavVv2KUyVfTbk3D1iyksmkAoG5HbcWAOh7gCUoOSLtf1DDXU9op7VM0bTUHvHP2cubzPtSyfKSvztHuvLYh7w/zFyfc5lS3t5Erxy5pKKuOg3kQ0r4mhVoXKHa2nq1Rfz25Pcs4iCrCeYnhqT4QDGYnx4vBvRzqWIgP1gveYoXT2TMOdtEMD+nWKp4a0qSmxLl/iVcmvzPPdJ/bpHG03m91fVLvcf9I/kc2cPWizWcoe7T3qJssLUs24lJ7Oz8uB3MNxfZOK2ccq6gUs6AkpZXCcunuPxKO4N2QN8drJXPH1bAV8zKD5VuvcWqE8dTen/RnHMV8gennGSV7ueL962CfRGESqMXl+5PnidHMYhz2OQ4OLkkp7s4maqKJGQAZbVo+h7MKgifL1h2FaRCQcoVCvZjcyFl6TaXLzz5OG/mFZTN5ZXNpFXIZVTIpVXIpmVN3KZkZVMqZBJSLiNHISOfsvIenDzK2RfLOn2mclOomFnvDtqVboD5OOcigL/4EMAHZhnA3zWY04lN/ANpxtyJjQ4qlO5XqLbO/vGrecWJCreeKLloH2Cu3X777XrpS19KwwJYUPQ9QAXqeUSZ7i3qHMlrTyqo5hrfnAVs79ov3fjw1HlXrJTec67m1Fyec7nSY/Im+xR1hNVr1SsVXKZg0wrV14TtoH1rrb/yMtPND7wmK99k55sLNpKjxVsThAvUS/66iaHOzHnbcDytkXhW0XRO0VRWDjnsMcZNRrMZNm2piWakbzwh3blPWuvotrPxL3DuOGy9gtOjgbVXaWj1i/gRfBFxFLJ2QN+ZS8iZS9qPLZdPWadPSfntgH7C8iru8NuBDLc3ILc/JI8/KJ8vIL8J5B/M1g94XfbkdzvlnsX3fM6Pe0xQ3QTh81mpkJXyuYO3kx7nM5OWTQ7YF0zNiIOB+0LxsbktjWBsHSmAP7khSwF756SAvrk1C00Af/LkPnwyfYg9eYuTPe/g/aP4zadwMNBlAlmF0m1BE/PsYJgJhJmmKjy5XnHSxGPz9uzmPLjMftcH1ykuK77/Q8vLTm4Wp339guPgbem+6RGLQ0g6nZLb6ZTL6ZDb6Sjeug7eHpzvdTnldRfvA3OFc675ZfqHUkC9FHzP50tB+CeXTfRRk9Z/sr8ygfon+6R8vniRVSGXlZXPqmD69Xzx/uSpOC8jmWB9PiuPVZDbkZNHeblUvHWbW8vcz8nh8crh8qng8slyeVVweu1/AwnWYz5Md85FAH/xIdoG4KiYsRT9TS2Kpes0MNKj2thWJceGVNN3QM2rT5W/fhlXcQMAAAALyQRxYwMaHRnWQGG5Am7HnAXvD8Skvi136a9dMX0t/0JZcmpFWHr7mVqUTNDPG++xM5261Kyop0XB9lPVEqnRsrqAmsM+OSs1AOMNSt5VUt0qKR2TYn0HM/MPBvOHdtiBTRPMD/hqtLzOXMhR0Ggiq5F4xg7ij6dyGklkVWPGFw+45FpCJVdrvMWqEBuWS198ZJmuSXxYr3f9n97v/oFCjvTEes5CVq27blGk50/ad977lQ00l3W7UWQ5Pcr56iQz2TMKdiDflUuqJp9QJD8ohyzlXQGls26lM26lxz1KyqXxgktj7oBc3oA8vqB96/b65PF45PV65PX45PN55fd6FTC3HpOxP4vgfikQX8qKP+z+wWkiMJ+ZFKyfvI4Jxpv1JgfqD3k9O0hvUi1dxaB76daezH2zQeY/jkPuOw6JWucO3h7M2J+cuW9fEHAwo9/cOpwqOFyyHE7l5ZTlcNmP7XlyK+80y9z2soLTffC+WzmnRwVz33xmcinv8CrvMKGoJ+cVzHwTiD8YhLcD9eb+wSDak0H5UkD+YDB/yjJrYtNN61iTAvj2Wzt4f+LtT/nsnrxbCtrbLWZuJz22L3Nwyu4Lzb8NJiHRBOnNVHxcvPXYQf1iEL8UzJ983+xPpf3KvDaAuZMrlZ0vFCYy25/Mcj8k892+La5XOCRr3u5XSn2QHaE/GIA3F4yV+ux8Tpbpw02A3u4rS338wZ7NKvVwdi8nj6Mgt2V6yfyU+S5TZabYQ8rpctr/xhUntyynz+5Pzf2Cy6us08tv6QCOiAA+gKNmTkZq/D6F2lYrGospMXxAmdiwoiaQX9+i+mVrFW5cIfsyZgAAAADza3SfkqM9GrJqFE0X7AzzuZDJS//zwH79p+vbCjgyusT5mD6Qf5s+8PR6+Rfhrwnu9IjciX4NWzUacLTK07xWLS0rtaohpNZa39IKqvjCxan+hIMXcJhg/qCUGi2W3B8/IPnr5Q3Wq6XGr6aw1y6tPxTPKJbKaSyV1YGRjMI+jyIBt1xL6NztnCbpP9ZLN+9w6ru7n6ffFp6mT7m/pktdj01ZL1NwKOerL9t24imY4LIpGWwuSinNKmTtoL4nn5G3kFHEkZAjn5HDkVc271Y66VYm7lZabmULTiUL5mVccrldcru9crnMrdsO7Ltcbjvb2kzGvs2/PfhY8jid9q3TYS5ZKgZkzK3TBGnsyHEpO/7QYP6kx3YQ3n3kTHe3f1I5+4PZ8HaQ/sk+yoSqS1nmJuhklDLXJ88vmDUPZrEXg9wm2F0KfpeC4gfLP08KitvPzT1Zrr8wUb7fTKmJgJX9XieqBFh2iMoE9u0QlcMOU6lgB/VNwL4Y/LfnmfdjMvYdB7P6S9n9B9vAYU9OuR3O4n3z3p1uOZ1OOcyFFU6XnGaZfb1CMehevHahmElvZjjtELxVnH8w0l9swWID2c8pmNY4eC2DaZmDD+z/TWrPfHFVO9PWNEHecihTKpVtOewpXTBB/uKFHx5XMaBvAvummos9Hbx4zq4AYZJfPE/eN1PFXjwGzAPTZ5mhf+wpV5wOHR++dN8E6EvBeTtzvhSInzIVZOUydvDdkUsf7Iuzclv2pUV2trsJvrutrHwmqG4H2O0eze7FzAViZrnTYYL09iVNdlDeXs/K2//OmH8H7L7fYQLy5qImc5GTS9bBi53k8B68f/DiJ6fpG838pXOMBWBhLcJTbmBxqvVxoH0oU2osUlOjbPBkJUYHleztUGr4gEYHDyhY36b6tjWqb10lx8EyjgCO3urVq2k2AAuOvgeo0Ox7a7nCfteclYb/9taM3p/+dwWcGfvxZa4t+pXvBh0I3zQvPycc6zmXCeB5Ez1KZzLqslpUqGlXTfvJam+s16qGoJ0duWSZwHuosTjlMlK8vxjMN/tFYlga7pDcPjkDDaoP1Ko+6FUsnbMD+WOJjB3UPzCaXHKBfHOBybWnS5cuM9n4zXrd+D/q6vwf9EHP99TgiNnrXDt2rZY97tZrTpFCnLJWBJO9mPeawsOHLijYYwcXA/tZ1ZqgvlXMnrQKeeVyeeWTOTvDMm+CQnIoZV7EYdkZ1kFPuwZ3PmFnX5sYq519be7b1xEUgzMmcGuC7OaxCTib757TDlCXytGH7AC0CdzIZR6b8PLBcHIpQlzaXDuYbIfo7Uz5gpWZKBX/ZIZ5MbBsh51L803s6GDAvpS1fjBBvXg9weRl5qkmqFV8iSfL0U8E94uB/VIZefM+nA538bHTUbyewLyNgxnr9q1lTWSUmoCWKf1sB77MrZWW0wS/lLMDXib4ZVrQslyyCi77VuZ+KeBVqi5gwmYHy/zbt3YYrVR54Cj+TZjIxJ88rEDx1rzilJT8g3eL80uzSpUNSksmVzgo3prtzllO5XIuZbNO+yIRE9LLWE47uD9quZV1eOX2+OTymSoQfvncLvk8LjtT3wTzzRAPYb9bIZ9bIa/pc/mtsZot5XMuE3RP5wpKZfNKHQzQT0z5/MHbYqZ8MUhfzKzP2RfSFOxS99mDgXlz8ZQZG97U9/CYWytnjxFvgvHmscvKym2GYbED9Qez4U0Q3pGXwzo4mcC+fWsemwuSzL8pxf7IdHb2faepRGL6dc/BDPlilZJipZLSukfZNwGLEHGuyuGwJtcaAqrcYO8+7d18l/KpmELNa8q9ORUnm8srFR9RIdovr8shT6RVvtoWRVpXq2nZGnm8c5MJBAAAAOCgnkeU6HpEHWN5daVDdpn4uSiL/uceybP5W3qj+84p8/ef/jaNLb9s0TS/OzUsZ7xf/Vatop4mBdpOUX3LMq1pCqnWX8VR2XRUivZKsf6DwfwhKZeys/IVrLczgJPZvAaiaY0eDOSbwP5SC+QbJhhw2y7pBzulcGFc/+j+HzsE+U+5t9jL63zFYP+zl/ObfFU4mEFvQrTF8somw9pSwQSPDpZ8f3K842KItxTwNssME+h3mCD/wYjvpLhv8aZ0O5EPfrAU/MQ2PBlOLo3gbmfZH5xfDNwfvIDg4Pj1zolM9FIJ+CeD76Wgu/2cScvt7SsF3w8+Lgbjp5aUn9d2ngieFasWmMCZw741j4tl/e1Wtm8Lh8yb2koze3I4gSeD75OWTXw4pVecNG9iTumDsa+UeHKefde8B5N5qycDfiYL9+AQByb4VzAXmMitjGUmE9h3KS2PUpZbacsjpzcglz8sjz8sbyBsZ+WHDgbza0xA3+dW0OtaWtVisKSZIH3CBOizeaWzxWB9KWhvbrOHZNebDPqsuX9wXHlzAYupYuFVTj5HVl5l7MC8x8oUx4Y3t8rIWcjZF2uai4TsQLx5bGXlyJtbM8/c5icy3yey4+3sd9eUx0/OXzrHOcBcie1/XP6WNTrpgisVDEdo2EWAAD4wywB+10hOK+spWjEb5sQzFRtVLtovlylXVNtiB/JrWlaqvm21wuFaTkiAWdq0aZM2bNhAewFYUPQ9QIUwgdn9D+pAx1btyi+T2+1RXfD4g9b9Cem23z+o/3CZTPsnDbU8S71nXzdvUc6jOueyLHni3Uok4up3tsjTsFLh9pO0orlObbV+zjdKTOnr+IAU7ZGSw8Ws/OSonZWvQIPkr1UyZx0WyK/xe1TrX1qB/J649F+PSff3mxicyVWe+t7OaJDecZZ0/uBP7ADj4JoXy3J5y7a9WDhH0/cUs96LpdVL8eVifvfUDO+JR9Yhw9YfDOyXetGJYP9EUH6eA+s4PvbFB4VJFyccvG+yes0QD6XgYiEzEVA0Y1yb2gUmFJmSV8mCWwnLo7R8cvhCcvvDcgdMUL9GgWBINX63IgHTB3sI6C9xlXLOZQLw5qK/ZCZv3yYyuWJmfbagdDb/ZBn8/JPBehOgNxWQTFUor9OSXxn5DgbovVbGnlyFtBz5tJz5dDEwnzdZ8ge/R5Pu21VEpowhbwLx5r7ryXl25jz9JnA8xz0E8BcfopHALKUPq9GG6Ziru4M19VJNvTKJqDLjfUqP9ig50q3h/bvkralXsK5V4YY21UXq7HHDABzZ2NhYRTSNKehjrrBOp7NKp5PKZNLKZDIHf+AyP5EWyxAW75uCio5iZonLJa/XZ5cWNCd3PrfzyVuXGfOQEzCgHCql7wGq3ug+xYYOaMSqVTIvLa9xz0m28tceHNGXnP81ZX7c26z+0988rz+Ozvqcq5CXa3yfhpI5JUMrVbP8DDW3rbDHul/S5fKPhSnxXdtenFLjxfL60YMl9k1AP9qtQKBBqyKmxL5bP7/7fnV29ysYrtWJp5yiupBftSYjfwlkqrWHpBsulO7rNYF8p/qSU5dvHZZu+kOP7vT+xB4rt67n9+o55fWKNT+9XJuMRfh7j11uXg67Sj6qUCnrXu6nrgtgWU9mCxey8hTS8uXjqlfaDkzmHV6lM16l0h4lRz1KFjyKuXwaDtTJE6qTNxiRPxxRbdBnB/NNUN+U4cfSsdjOucy48iZAH8/klEgfvM2Y7Pony+Cb+/ZvP7mCvX7x9xuXvI68ah0Z+b1mjHkznElaTjPlUsUAvZ1BbwLzmScD9AVzP1OsZmEH4r12oL7g9Mpyhw4+NkOWEMIC5hJxrspB7wdgXv1/9t4DTrKqzPv/3lC3cnXO0z05wsCQJAoICq4JA7C6q66KG9VVF/XP6q76GlkVs77uu7KuaVUw7qqLsCQBRUQZGBgYmNQznXNXDjf8P+fc7p7ume6enp7u6XS+cOZW3bp16/atqlP3nN/z/B4rEpfNKWQpJnsodj9DtsciFSmnP1JOR7SCYFkN8aoGyssriFiqW1IoFjN2IUc2nSKbTVLMZykVChRLeexCAccu4JRKOE4R1y7h2PaI86CGK6wGhQWlNiLcjwj50rYsEEazorIRjGKI28EIAdOU9QKFjWB52JIZhaGAmrBQKBQKhUKSG8RL9zA0NEiP1yQn9n1h6eT47jMub8t8lUrDrxMuEL/Z3We+HTcQWfCTLyZ+vcEDdJcsjIoNVLScwbqmBsrmwHlg2RNK+K1iLWR6/Kz8rG+vf/sPvs+NX/oxh7sHxjavr63mb/7yLzj/gudRFrJk3ea5+IwtNOfXw44auH2vb61fksXCBR7/bHxLivcCK9fL6p23kKo+i67Nb6AYqV/Iw1YoFEsNTZMuHqNOHhPiRDwX3cljOgUSToEyJy3vi6zlXCZIJh0i7VmkjTBDkQRmpJxAJEEoXkFZNEpFRDjuWNKCXKGYDaJMiBDoMwXR/Kx6Idb7mfWjzbfDF1M4QqQP6S4RrUilUSJoiIz6AobIoC/lZSa9EOWFIC+FeWfcUrhRjArzYim+F2ZkTLBHV/M8CoVCMRlKKVMoZohKEj85jGCEsChL4Lm4hTR2Zginfy+pXoN0uIyhSDld0TKseC2hWDnBcJxgJErYMmVdsMWWpS+iTP3aeCMV67wjde38jOMRO71Ru7zRunNH16TTNNnUoEsxFcFgcGFOjmNj51NkMylymRT5bIpCxhft7WIOp5CTgr0Q6j0xke6ISqKurAdpGgF000QzAyPGpJ6o2ePXcBz5YoilnGpwbRxHo5gPUhIZCKLqmeZXPivoIbCi6OEyAokaoqGwnDiuiPhivshCGK31qFAolknfo1AoZs5gK6m+Dga0BCUbaoInP7z/Yw9UHfw5FweemrC+Z/115Mo3zvu7c9xL/lKGYt9BhowKovVrSKw5k3UN1dKeVXGiWfmNfssPc/t3/4Pr/+nrx2zW1dPHhz9+C5/68Ps49/znkRwqyWuwqGUueZekoAGv3wxXrPJt9R/tgToG2a4fOGbbeN9jRPt30b/mZfSuvQbPUL+Ryw3VhShOOZqOa0Zkm7DaKRK0s4TtLLX2IJ7dTSZlkU0FyXpBsmaYZKSMrlglVrSKWHkVFbGgHCOrYPelx6kacwnHRCHOC7E+JQV7m0zeltn2IrN+vGhvaS4R3SaqFanSS4SsIkFPCPF5tNJIJr0U532xfsz+Xsz3CoHeGMmiN8K4VkKK9EK0V/b2CsXiQV33LB00T/TgCoVC0td1iNad9+Lk00SF2KyYX0T3U8pgZ4dwc0PkHQ3bjMkaYEYghC5aKIYRjMmaYFY4RjCaIBgMYeqaFO4MIYDrmrwvRPBRMVxMJ40K6XLpHSuyi5p1QoS3XWH7xJGl4+C6Nq7j4Ijbti3vi9ue58j1Qqz0XLGdH4kqxMjRfYt//MXIi8q6kUKtN9F0YQkurKEMNCFniqVopoUZsGTdUnH8YhLSNDQCuo4hloZG0DAIBnxbcSVaKub8u1hMk0sPkU0Okk0PUcikKBay2IUsjhDsizncksgI8NDMMLoVlN9TwwhgmAEp1muziZwesRUUg0Ex8PNbXq5zdIuSHiRLiCRRhklgh6sJxGuIhS2iQb82oJisqIxayjJXoVAoFCuH7ADu4UdpO7CbZ51G4mGLWPDkMtCHC/DV+/fyDf4PAe1InuBw2Tbaznu/75qzgHi5QbL9bRSjjUTr1lK/YQcNFfEFPablgG3brFu3jsOHD0+5zar6av73x98gXXQZzBTlpWNF1JKB1ssB8fc83AX/7ynI5zK82/whbzTuxNCOnS4rhqplNn6q5lwlRigUivlHlIyxs+hOTi6Fu2XOM0l5YXKEMcJxrFglgWgF4bJqysrKZWZ+IrT0A60Us0dY3afyJdIFWzaRYZ8TGfZCpBdZ9oWidFcM6UViWomwXiKsFQl7BYxRm/sRYX5CRv1RIr2/DOIZfn16hUKxtEm37SZUu4aN511FJFa20IejUBn4CsXM6c84VEWXxwTFokEMJqwYphWDsiYZZezlU7ilFG6mRwrnBQJkEdZKISkY6lI4DKHpJp5uogtRXBSfG1lqmlgGRgR84YXoSaF9TGQX68TSdeV6hCDv2iBEebGduO2OrhNCvFjnyueJpdheLHVhdyayiEcmdaREPzY2EpnFIsvezzAeX/9bWoaP2JD690XOMriagSYyYQxLCqHoQTAtdPG3iHUBC92KyL/dNEMELQNrRNA/UjPckJNooYCqG76ceOaZZ9iyZcvc7tQu4OWTvmCfGiKXGiSfTVPKp7ELGex8BqdUpIjIog9hWGH0aA2WFZGBJnM6ETBiK+iINomloG4XKCulqLD7cAIxMsU+Uv2HGdLK6DYr5ERFTIj4YYvaRJDG8rDKPFAoFmvfo1Ao5o6hQyT7Oxjy4giPm+gclKH69115PuZ9hYB+5Be5YMToOuPvTpl4P9WYqzjcQX64H6N6LZX1G2nZuJ1YSE0UzwX333//tOK9oK2rj8N/vFdm4XcHIgznHQYyBUxdl4GUYiyylBGXthc2wNm18LP9UT619y+4rXg5/yfwHzxP3zNhWyvfR8vjnyNdeTpdm15PId6yYMetmDvUfM/cY9sOj+7cRW//IDVVFZy7YzumqebUThjdwLHiOMQpifsxF0tk55cyaKUhSrlu0llhtx8iY8UYjiTojFUSSlQTq6ilKh7xS+wo57plO+YSyUMikz6Vt6VoL5Yiwz5XsCnmMxSEi2IhQ8AtENVL1AihXmTWaw6GM7ndvS/SB/1MejOMq5fJ+2IeVqFQLH3Udc/SQfW6CsUMGcx5VEXV6Zo3hIgXiEIgKiVuOQXkOoRkDaW8zPz1ikN4mW6ctCcFcSGCO+gjt42xdbYvp4884uPL5X6TLyemOjUPY0SI1zWxFMK8f1tkyiNEdbnUZZY8plgKcVwEChh4YiJTCpnjxUz/tqjzPeHPk4EDrr+U9uG2vO/fFsEBJWlZ7i9MHAykIbnmL23PoKSZFLwAKS0gxX1ZNzwQ8Z0KrAiGEFeDISxRlypgELZGBf2R+wFjyU+urUT27NlzcgM6EXhSTI0I9sNkkoPkMsMURgR7EcHvFDLkXZ2SHkYPRjCjTQRCURKmvnB1TsdZCtqhCjTXxigOEy/2k3C7qbcSFLw4qUyM/kwZzxlV9KTjdA7nqEuEpJAfmQMxQ6FYqZx036NQKOaP7ABOqodkcpBup4myaOCkg+se6oCLem9jjdk9YX336X+NHapkocZcwu0q33cIp5Qn3LCZspZtNK/euOjKay1lOjs7Z7RdV9ag3LBJaO0MhcL0BqIMFA26kzlZ9kzUYzak89jSttW/fiNc1QLfe3Y1r239IK/QHuL9gf+kVhuasG1s4EnWP/yPDDZdTu/667CD5Qt23IqTR833zC2/uudBPv3lW+nq6R1bV19bw3vffgNXX3HJHL/aCkOMkwMx2UThDzFOTpQylJfSuPkO8kMdpAdDDJhRkuEEffFKgvFq4hV1lJeXyaArVcJxaY+5REnPdNEeE+zT2ZxMyCjm0lKwL+WzUMwS0UpU6LYU64OaQ4ASWmlUrLdl1rwrRXprxO5eiPQWnmYqhxmFYpmjrnuWDmpmW6FQLF50A1ePQCACIX+VmJo0XUdGhIo8ds11J9yWS3Ff+CCOZQppI4L6SBPBAqNLKcKPivE6nmaM294vYX9KGfnbDNfG9Gx5US3Eft9iPCcjYjXXoeQGsAsmxYJFCVM24VaQw2JIC/p1w60oRjCKHooSsEIyUz84IuRHLNFMeVsI/UrYX0aU8lBI4RVGMuyFYJ9Njwn2IrveLhXJEMQxwpjBcsyKRkJWkPgingwXkd52qEo2zSlgFoeJZDsIazqVwTIyWj/dQ9XsTdXQl4rQOZSnVgr5IeIqQ0+hUCgUy4mhQwz3tjHgJmSQafQkA9aEdf5Du/bwDeNXE9b3Nb2QVO05LBQl2ybXsx/TNImv2kbthh3U1DYu2PEsVxoaGma23dYLoGEjerqPytwAZZl+Bh2X3kiUfkenYyhHImwRD5kLFwA6R5QH4W+3w8vXavzH05dwRdfZvNP8MW827sDUxJjTR4wqK9vvJdH1Ww6f9T6yFSrwTaEQ4v2NH7z5mBMhxHx//U1KxJ/jcbITLJONWBOWk6emlMHLJSmme8ikLAa1KOloOf2RMqx4NdHKGsrLq6mIhWT5RsXiRpQxTOdtkuk0mXSSrCh3KMT6nO+gKAR7YXsf1mzKDYcQJYIitckVJW9MXETmvC/We4G4n12vatIrFArFkkAJ+AqFYumhG3j41muesTz/NhH1OiXSWrwoL8YjMnJW1KTKyhpVwoPA0YIUSxbFUoBi1hf1i55Fv7iEN8LooRhmKI4RimEFTEKmPpaxL0R9Ie7L2wFDZTctZkSQii3E+rSsX+/mU+SzYjCXJp9NURCDuVxK1jXLu4YsReGYEYxgI1YiRCxgLtnBurBuK4VrZdNLWczCIOX5fUTDSer1YbqyNezNVNCXLtCVzFETC9FUHqYsoqx2FQqFQrHEyQ5gp7pJJofpcRupjJ38b9tXd0HMHiYdCJEg579MsIbeTX/GQlEoCfF+H9FQkEjDZpq3nkdY1WGcFy677DKam5untdFvaWnh8j+5BkTpsmIWUp0Y6W6qc0MkUr1UDHUwYEXoKUbpLIhsfGtZOCGtisE/nQdP9ke4dffruW34cj5sfpOLjacmbNdjR/nC4XW8MgQ14QU7XIViUdjmi8z76RCPv/CyCzFEf6KYczwjhG2EIFSF4TpU2GnK80mczEGyKZ3hngiZjgQD0XKCsUoi5TWUVdZTkYjIsoyKBcbzsIs5KdSnU0kymRT5dJJSIUOxkKNUEHb4eUQqj7DDD+MQMDxZbtOvRS+y6WMUZVa9dcpKICkUCoViftA8UShFoVBI+roO0brzXpx8mmjNmglnJW97hMylnUmgWAG4NrpTOKrlpduAIwZyWoA8QZmtLy7584RkJrawKdeDcZmxb4XCY9n6QtiPBoWYb44I/H4Gv6qfduoYGhqiPB6DUhZKOSnWUxR16pPksxlyQrAfsUoTYn3JdsgRJONZslaZeE9DVkAOxpeqYD8TdDuHleuWLhalSB15q5IOvYFuJyozwWpiQRrKw6ypiirHCYVipn1PubIDVigWHR076dv/Rw4lPYa0clk25mR4oANu/oN/u55+PhG4lSuMnRw8+/1kqk7nVCPGXI5Toth7gLJYiJgQ77edjxkSVsGK+eL222/n+uuvn/Lx2267jeuuu27iSteBTB+kOiDbT2awh8H+bobtAN1ODAIxKqLBZXPd5Xr+9+WbT3ucXvgj7zf/k/W6X37gXcW/46fuJaLiGle3wHUblJC/1FDzPXPDw4/u5K3v+qfjbvf1z3+MC87dMUevqpgRolyknUMrJilmhykWS2QRCR4JArEEVlSI+bUkKmupKK+Qc0GKecYuMtTfRczSyKRTZNPDMru+kB0n1hdzuMUCllbEMkyMYAjTCqGJuvTC7l7WpVeJCgqFYm6ue9JtuwnVrmHjeVcRUcHTi4KlHxKtUCgUiiPoJq5ogejEaC1pwV+Q1uNRO0/cGZIZ/NI6ywxRdC0KuSD5nC/s57wgA8JmKyAytuOY4ShWICAHcULcF3UuR634hbAvrfiVsD83GfVCpB8V68VyOAn9DsVCllw+RyGXpZATAn6Gggs5LyDfL9FcswIrGiQU0Clf5oL90YhghXx8DUYxhZXtwsz3syacpCFUTYfbyIE+m2TeZjhbZF1NjKpYcKEPWaFQKBSKEyM7QDHZTXLYz76vTUzj2DQDBgvw1SeO3O+iik+E30vLGc9RqNi0IO9OJl9EHz5IZTxMWdNmGjY/T7pHKeYXIc4Lkf7GG2+ckIkvMvNvueWWY8V7gW5AvM5v+STRsi4i1d1E+rqI9HeSzvXTl49iRqspj4aWvK2+rsFlTXBRg8Y9h8/hDc+eyQtLd3OFvpOfuRfJbWwXfnEQfnUIrmqG69bmaaaHQrxloQ9foTgl9PYPzul2ijlE03BFeUoxxxOtl26O0WKKUnYIu6+LTG+ATDDBcLSc7mgZobIa4jI7v4ZoyEJb4n34giIC3sTcTjEjmyhtmBNW+NkM/ck0hi2y6nPYI4J9yQHNCmNaYSKRBGZlGIygnO8TiCKiCoVCoVgZKAFfoZghbUMOG6rVV0axNBERuY6Iyg3EjrLi9zP0A06BoDNAuZ3H03RZG0tE8xbtIAU7QD7ji8QpgvQSQrfC6MEIZjCMZYVktv54YV+0kKX7y8DKEpKnFOdd2xfo7SI4hSO3xdIZXZbALsho60I+RzGf48GhWnYE9pH3TIqeKZcZL0TeTWBavlgvMptqAjqGskfDseLkAjFpqx9KHyJQGGBteJDaYB0HcrU8ly+RLTo0VYRZXRVVn02FYgruv/9+rrnmGnV+FIrFxNAhhnraGSRBMGDKa6+TuTT5yhOQLB1ZJ3b37rM0ColTL94LY8CBdIHBgsmWeIjKlq3UbDgXgkq8P1UIkf5Vr3qV7P87OztpaGjg8ssvn5nNdSghm1a5lsqKLhK1HfR2d2INdpFL7WMoFcFK1BKNRJe8CBQQWfar4Ypmk3sOX83/99zVeOO+R6NC/i9bYXXbnbwg8H0OVVxIYeu1FKMNC3XYiuOg5nvmhpqqijndTjF/CKt1L1yFIZrnEiqmsbPDOEP7Rbwg6e4EyXCc3kgCK1ZJtKxaZucnEuUYK31+53hJGaLUTMkX68XtYj5FTrgnimQMmV0vBPusdE88YG6hvDCEbkUwQzVYZRFilrXkg94UCsXiRl33LB2UGqlQKBQrFSHUC9stMzxptr5u5wk6OcIj2fqeZuKaQRzdolgKUCiaFJImBSwyXoABLD9KOBjBkNHCQRkxbJm+iD/aRjP2pU2/qWMuxcGfiKAWYrsQ5WVzwC35QrxYLx8bvS8eH1k3+hy5vohTKlIsFigV89h2iYJrUHBNcpgUpFjvvzdPO03y/RJCfdDQiQcMqkxdDeqmQtOwQ5XYVhmBfB+h4f0Y4WG2RFJ0BtZwoM8lXbAZzpVYVy3sXU8ug1GhUCgUinknN0Qx2UMqNUiv20R93JK1hh/duUtmMgox5Nwd2zFnWL/2/g74bdfEdX++CdYkOOW4nkdvMk803wHGaurWbKN83TlKvF8ATNPkyiuvnP0OjACUN2OWraKheoDy/sN0dx3GHOwlN3iAdDJEuLwOM7wAH7R5E/LhnsPwg+egO3fk8ThZ/tr8b3m7ZfC3OL/5Hc+WXwqnvRo3Ur1wB65QzCPid6i+toaunt4pt2moq+G8s7ar92Exoel4wQSGaOWrsOwsTi6FnemhMHiIrBkmFYrTH01gRcqIJCqJS7v9aoKhie6PKwYxtyME+nGZ9b6TYl6WN8zlMuSyQrzPyPme0XmerGNR0ELogUqsSBCyUNm4Ts3tKBQKhWJSlICvUCgUikmz9Z0J2foemiuy9UUrEnJyROT9om/FplvyOaVigHzRpIQQog2Kns6wZzJkBDACQYxACDMghP0QhhmQk4RWwJL2/FYwIJdBK+hnlYnMcmMOBH4RBe25vsgult7octw6uRQivGj2kXXy9shy9P7Y7fGPi/0JYd4+sk6K9Q6uU8K2bWwPSjKL3iDv6TKbvigEe2IUvThFV9z3KNriWDQp1suaoUVoKI9g6ksw0GGh0Q1KkTrsYAXBTAdGaS+rYnnKIqvZn6sgmU9JIb+pPMKaqsjSDCZRKBQKxcpguI0BkX3vxglbFvfc/xs+/eVbJ4gkQjR579tv4OorLpl2VwN5+O9d3URJkMEPFtxUDq9ZzynHdl16hvNU2F1URU268oyI9/FTfzCKuUNkDkarCEerWFO/iYHuVno7DpIe7CHbf1gG8YbKa/GCFVI4Wo5C/lvNX1KuZca2M3DZOnQfxYceZGfZlQROf6UUwhSK5YQIIhO/Qzd+8OYpt3nP226YmbOHYuH670AUQ7REPUExp5FPUcwlsXsPUHBdsqE4Q+E4gXCCUDRBOF5BLFFBNFFOIFwGy2nuQszxiGz60RKHMrt+ZGnncUp5WeKwkBdifVbWlRauiXkvQNazyLkVMjkjaAUImqIMpU65cSQZoz9rK/FeoVAoFFOiBHyFYoZUhJV9kWIFo2l4RghHtKMfc20p5Iss/YBblFn7Iotf82w0IWbrOiXXoJQ3KOWEqG9QQqfk6GQ9sWsdwzTlIF42IezLpYmu+QK+KTL1dV3anYvH5H1xWzxX9zA0xpqoTylFe0aEe9mEhb0rDnZE0Bci+ejjo/fHCfkTRP9jRX5PBC3IaTgdBx0XDRuTkqdhy7/PoOQGKInbnib/XsfVKLkuJdul5HgUHRfX9eTfNBqoEAtqWBFrgq276zlKvJ8De8B8fDWBXA/h4f0yEGVbtJF2o5kDvVkyBUdm42+oiVEWCZzsyykUy4LNmzcv9CEoFIpRCmkKQ11khvvp9xp54uHf8b4Pf+qY8yPEfF80uWlKEV9c4nz1cZvPaZ8jHsxyU+kv+R3befcOONVxbEXbpTuZo97rozIaoG7tNgr5MiXeLzesCJXNWymrX09n+wEGOg+SGegh199HWaCbQKxaOieN1vZd6kL+lc1wbzu0P7eR3aXVbNNbJ2xnYfO84V+RffA+fhO/GmPLS6itWPqOBEsdNd8zd/i/PzfNOshMscjQDfRIOaFIubwrBOtSLkkpO0RxsI2cbpIMRekPxgiEo4TCccLxMqJS0K/ECMXBDPmBAYva+l6UORQi/Wg7ItKLrHpRBtGz85TyOfL5DMViiZwXIC8z6y3yXoScmyDn6nIqSiSkhEyD8oCBZWhTlo9RfY9CoVgIVN+zdNA8UWxOoVBI+roO0brzXpx8mmjNGnVWFIqTZUQc1117RNAvSVFfCvuei+Y5uK6D49i4jjuytHE8/CY0dyHK6zoaGoahyTrvuq7JdUKsF3PNuub5S11uLiegRUSzWKdpngwSENk9YtCkyY38JgIExJPEs11hGyczgPylN27pi/Q6NhqO5wv2juvKY5TaPh6O48p6l2K97Yr7nswqE9uYuuYHHOiaFOv9QARN3l/qdUCXGkYxiZXtohSuphhpZCi2jkNp//1oLAuzoS5GQ9nEshIKhUKhUCwovXvo2vMwbQM5kmYVf/7mt01rTyxEkl/d/vVJMxxFhnBp14/5h8APx9b9MfYCgue/RU7SnyoKtktPMkej1kdVWKdu7WmYjWdASGUkL3eyhSKHDh8i3dNKsr+TUHGIajOHFq6gFKqSgZfLZRi0s9el59nfcU3mh6zXOyfdLusFuStwBYNrX8KZzVVYiygx+WTKdCgUR39+hG3+csu8X6rfkbk6blckKJTy2Lk0TiGDV8xgaTamFcEIxwhYMYLhKMFQiGAkRigSIxyOoQcjvqgfEEvr1HTIsqShEOkLvkA/KtjLZR5sv8yhf7sgAxWK+RylUpG8FqDoBsgRkJn1QrTPCzdF26PkuHKuRwj2fplIY0JCxkpnqX5HFjvqvKpzNV+k23YTql3DxvOuIhJT47LFwNIOcVYoTiEHBmzWVqqvjEJxQghxWjNxZ5hRY4y0wGjmvBxreb5YLgR+d1QU9wVysXRFcr2IBB8Zk4mXFAK/phm+wC8mo8Vh+LEAI4Hf4vEj90dD2eQryttCmfdvyuaJbHlHCvUia168lh8woPmBAroILGBEpBeDtlHB3g8kOBmRXvU9c4tjJcgbIYLpNoxSFs3OEUqso9Mt40B/RtbhFRmBLZURFVyhWNHccccdvPjFL17ow1AoFMKadbCDzGAvA14DB/bsmVa8F4jHf//YLi44d8eE9f15uPupQ9xm/mTC+rWxIh2nULzP2w69qTyNxhA1IZ261dvQG7ZL8V71PcufSNBiy4YN9NU309bRQaqnlf0DnVTmk1QVDuAG45RC1UteyBeX/2fV6lB7Ic8ln8eDux/gRckf0aj1T9guohW4xv4fis/eyX/tuZRn6l/OOevqaVngKhK/uufBFZVBrcZcc48Q6Y7+HVpOLNXvyFwet0iqCAYjsgnEWLogMtMLGZxcBm+4kxBFLMOT5RQNM4hhhQgEQwRDYYKhCMFwBMsKEggE0PQAGAHfkUVcl+ijt0fnk7xxboujszXjyiYKAV4ki8hyhqWREoelkfWizKEQ6Ufu20Vcp4BdLOKUCiPOiQEKBMi5JgXPIk9UOkgWhYOi7WKLDBNN88vACDv8kO+mOGqHPxuWc9+zVL8jix11XtW5mguWc9+z3FDvkkIxQ2QmsEKhODWMZMjLm+LHygBzhs7mYtAo20hm/Oi4Ttz2/xf/TRzvjan7Mpt/dNXYDUT+vy/W+1n+0g/gFGXOq75nniz1E2uwst1Ekr6lfnO8mUC0UYr4wkFBDNDX18Tk+65QrEQKhcJCH4JCoRAk2xnoaWfIDRMKhRkcHJrReRGZTkfztccdPqL9K5Z2pCBSIVBG9+Y3nlrxPpmnIZChLlCiZvVp6I3bIexb86q+Z+VQHQtSvn417VW1dPT2M9R9kMFkJ3V2ikTqAE4gRilUs+SFfEFzwqD5gss5mLuYPzx1DxcN/IQqLTlhG/G9vFa7l2T3w5x/6Cusqwzx4ha4uBGCxqkXByarYT6TMh1LFTXmUqyE78h8H7cQssNBSzYSFWPB8dmSjV0U9eILeNk8QS1NULcJYhMwNHQjgC7LKpqYoonbgYC8HzD9+2L+xZ8h8uRMjXBhFGK68EyUI3YxB+SUcBwHzxHivFgKh8cSnkzG0LE1U5Y7LHoBirIEokVBi2J7JrZw0R8pdSiy6sXtgAGW6UmRPhoRx+I7Qs4ly7XvWarfkcWOOq/qXM0Vy7XvWY4oAV+hUCgUywqZES/T8Bf6SBSLGk2nGG3AKAwTSrdiOHmaomkC8Y0cHsjK6HoxeN9cF5duCgqFQqFQnHLsIoWBNjJDPQx4NdSEA9J+dCYcvd3DXXBG/y84I3BgwvrurW/GsU5Nqm++5Gfe14WKNOhpalZvRavdCuGZ/U2K5Ye4xlpdFaUmHmR/vIy+gdUc7j5AtNhDvZcmtMyE/LJwgLJzr6ateBlPPX0P23p/QbU3MdjmNudycoR4agDZvvokXNwAlzfBGdVI16/5tuUVGZPTIR5/4WUXLjs7dIViOX9HFuK4xbxMKGDIRiQo10lRX4y1bZeMLSz4S7h2Ca9kY+oeJi4BrYSpFTE1cd+R/Z6Mq9c0KdfLUolyiC7KJIrVovyhhuvpOJqBI1wgvSCuruN4hhTvXU0bK3NYkkvfXdEQr2k4ftlDXSNqmVimX/5QlTtcWd+RxY46r+pcKVYmSsBXKGbIqY56VygUCtX3zD9OsIy8GSSUbkNzCtR5Hmb5JlqHC3JQLyLvtzTEZS07hWIlUVam6p0pFCeCbdvcf//9dHZ20tDQwGWXXSYzxk6KVAf9vR0kbVPWkBU1VUXtUGE/Op2NfkNdjaw1PErehp/t6uUH5o8nbDdUdwGpuudxKsiVHPpSBeoiHk1ePzWrNqNVbYR43YTtVN+zMolYJqc1JuiNBzkYi9E3OMyevlbqAgMyU325CfmmFaLqzJfQ476Irv2/pv7Qf1Pt9FDwTP7NfumEbXM2/O9huO+wTWUQLmoyeUETrC8bLQ02t4haxbMt07GUUfM9iuX+HVksxy1FfdOQzeeIsC9KJo6WTSy5kBOZ8CPlE33XfL+E4piz4kjZw1H8zHzhnug7KvqlFUWZRg9NE2K9H1AQC+ry9mIQ6Zdj37NYPmvLDXVe1bmaS5Zj37NcUQK+QjFDmivU10WhUJx6VN8z/3hGiFx8DaH0IUKpg1ThoZdvoXW4RMlJSyF/a0NcTi4rFCuFyy+/fKEPQaFYMtx+++3ceOONHD58eGxdc3Mzt9xyC9ddd93sduo65PsPkx3sYoAKaiKBsZrConboZLako7znbTdMyGj6z2fhHc63CBvFsXVFM0bXlr/gVJArOvSlC9RGNZq8Hmqa1qNVroWK1cdsq/qelYsQUWoTIcojFoeiFu2xGJ0Dw/Sm2lkVqSDuDEkh37bKKYWq/frISxxPD6BtuJLudZeT7nyYzt4+arOVdPcfu+0r9N9wE9/nO60v5KP7ryQUK5NC/mVN0BCdu2OarPzGyWy3VFBjLsVy/44s9uOWwruhSdt6IbmfCELYF0L+ydSiXyiWY9+z2D9rSxV1XtW5mkuWY9+zXFGesArFDOlJHakVqVAoFKcK1fecInSTfGw1up2TIn5F8mnWletkiy4HetPsahtmOFc6VUejUCw4O3fuXOhDUCiWjHh//fXXTxDvBeK+WC8enxWpTgZ6O0kVNaxwAlOkk40gaobe8pGbZCb+eMR9sX58TdGDSUgd/AMvMv4wYdveTX+GY82/00a2aNOXKVAbM2nWeqipbUQrbwGRfT8Jqu9RCOviDbVxdqyqYH1TDeHGLTxtbuGAuY5MbC2a5xBO7ccsDMqax8sC3SDTdDGJHddw80XwtcvhVeug3E9MlemmbzbvoFYb4h8CP+Sh4Dt4d+H/8tiz+3nrPfCuX8Ntz0F7+uQPZbZlOpY6asylWO7fkaV63DMNAFuK4v1y7XuW82dtIVHnVZ2ruWQ59j3LFRVqoVDMkGTBo/bUlIdUKBQK1fcsBLpBPtZCKH2YUPIgeC5rK7ZyOAX7+9LSzm9TfZzq2NhsqkKxbGltbWXHDmVpqFAczzZfZN5Ph3j81a9+9YnV+HRdP/t+QGTfJ6geyb4fjxDpr7z0QmmnKTJyxKSesM0f/zrCZvbWJwp80fzmhOemEpsYarx03t/cTNFmQIr3IZq1bmrKq6BiDdRuQ/rbLtK+Z17KIShOmLJIgDNCZbRHLeKhAF3DMZ5IV7EmnKDCHcTKdUsRvxiuxw1EltUZbo7DW0+DN2+Fx/ug7eCzbB8+OPZ4ULN5jfGAbL93N/Hd1Av5wfB5fPOZIGvicHEDXNQAq+MnbrM/mzIdywE136NY7t+RpXrcy53l2Peoz5o6rwuN+gyuzL5nuaIy8BUKhUKhUCjGrowM8vEWNLdEONVKfGA3LTGXkGmyvy/DM11JBjNHLIgVCoVCsXIRIu/RmfdHIx6/7777TmzHmR76ezpIF0oEIuUTsu/HI+z0Re3Ql1/9Ark8Okjg7sNwReqnrNL6xta56HRvewto+rzb5gvxvi4eptkcoCYe8i3z604DY/GK4cIxYd26dbzwhS/kDW94g1yK+7N2UlCcFKJ+cXNlhLNaytlcn6ChtoaDgQ3s0deTim+gFKwimGkjmG5Dc5bf9Zmhw9m18IZV3ThGaNJtztOf5fPWV3kk+Hd8zLyVRHof333W4233w1/fC998Gp4bmrlZwWiZjuk4ukyHQrGSWKrfkaV63Iqlh/qsqfO60KjPoGI5Macj52eeeYZDhw7R19dHOBymtraW7du3k0gk5vJlFAqFQqFQKOYPTacQa5aTwaFUq1zVWLmVTs3kUH8WQ9M5rSlBInRsRqRCoVAoVg4iQ3sut5N4Hvn+VrIDnQxQRtUk2fczIVmEe3a380Pj5xPWD7RcTSHewnySt0dq3idCNFkpagIuVKz3M+8Dk4uQi6kcwtGMlkO47bbbuO666xbk2FY6oYDBtsYE1XGLWNCgOxngiVScVaFK6qwEVr6PUPIAdqiSUqhq3gNUTjXDjZeSqj2X8o77qTx0J8Fc9zHbJLQcrzfvlu0Zt5nbncv4SeYSbtub4La9UBWC82rheXVwZjWEppkN9Mtw3MSnv3zrhGxdkb0rBMDxZToUipXIUv2OLNXjViw91GdNndeFRn0GFcsFzfNOrmjYPffcw6233sr//u//SuH+aHRd56yzzuLaa6/lLW95C9XV1SfzcgrFvNLXdYjWnffi5NNEa9ZMeMx2PExjadZUUigUSxfV9ywgnitFfNDIJ1aTFXb6WRPbdVlbHeX0pjIi1uLNIlQoToZcLicDchUKxdTcfffdMkP7eIix8pVXXjmzU5npo/2pB+lre47h6DqqYrMTvL+w0+Mvuj7BxcZTY+vyVgUHLv40rjl/duNF26U7lZPlZprCRRq0IajcAA1nQLRq0fY9wjZfZNpP56jQ3NzMgQMHVHbiAiM+Y639GTqGc3QM5aBUYJ3ZS7zYi5XtQncKFCKNy85WfwzPJda3k6pDdxAbeHLaTYuewXtKf8t/uRdNWB/U4cwaX8wXTYj7k2HbzrRlOpYTasylmNXnZol+R5bqcS9Hlnvfoz5r6rwuNOozeGJ9T7ptN6HaNWw87yoisbJ5f38U8yjg//jHP+YDH/gAzz77LGIXTU1NnHfeedTX11NZWSkH3v39/TIrf+fOnRSLRYLBIG984xv5yEc+Ql1d3WxeVqFYMAE/U3CJipGuQqFQnEJU37MIRPxMh1wKET9TvpXWjImuwdqaGKc1JmRWmEKx3Ojq6pLX9QqF4uRE35aWFvbv3z/jifFc66Mcfvr3tGcMKmsbprTPn47dA/CBh0q83/wubzDuwtD8If/h7X9Psv4C5ouS49KVzFMZCdAQ8Vil9aBVrvMz78uaFnXfMy/BGIp5RZQ0EuWNepJ5ulN5asw8q9xOmZ0uhHzbKqMUrll22fjjsdLtVHTcR3nnA5jF5KTbPL/wOQ5708+/rS/zhfxza2FjOSxjLWlK1JhLoVCovkehUKz06x4l4C8+ZpU2dumll/Lggw+ydetWPvnJT/La175WTkxMhRDv7733Xr75zW/yne98h+9///t8+9vf5hWveMXJHLtCcUrpTLlsUAK+QqE4xai+ZxHY6UebsDIdhJIHZTZ+c+VpHEzCof4Mhq5JET8gipQqFMuI3/3ud1xzzTULfRgKxaLGNE1uueWWSW3XR/nMZz4z86y23CAD3Z3kMkmM2PpZife2C19+AooE+LD9Jn7oXMqngv/OqsooybrzmS8cz6UnVaAsHKAmatJEB1piFVSsnbF4v5B9z7yUQ1DMKxVRix3hAIcGssSCJl1JkyfstayNJqgIRAlmOgkn91OINs6r68RCUow10b3pz+ne8KfE+x6jvP1+4v070TxXPn44chqryuvo7YW8M/G5V+h/pECAh91t7Bs22DcM33sWYgE4uwbOqfWXlYu36sWcosZcCoVC9T0KhWKloK57lrmAn0ql+OlPfzpjAd6yLK6++mrZenp6+MQnPsGePXtm89IKhUKhUCgUpxZNoxhtJJhpJ5Q+jKcHaCnfxoGhEocGMhiaJuuyCjFfoVAoFCsLURNd1Ea/8cYbJ2TiC7t1Ie6fSM30XP8h0gMdDHplVESCszqen+2H1tSR+09669i14yPo5Vn5ezYfuJ5H93CBiGVQG7No0XvQQxVQvgpEBv4SoKGhYU63U5waxLWXKGtUHbPY35uhO5ln33A1FYEYqxNRgvkeWQ5p2Wfj6yap2vNkMwuDlHc8QHnH/WjrLuOfGqDowBP98Eg3PNIFvXm4yfwem/R2+r04v3LO43/c5/GIu4V0yeLXHcgmWJ/wxXzRtlSAuUxPoUKhUCgUCoVCsSwE/Mcee2zWL1hbW8vnP//5WT9foVAoFAqF4pSjaTKDK5RqlRPBcd1kdfkWDg7mOaxn5QTylvo4uhLxFQqFYsUhRPpXvepV3H///TJDW4i8l19++YnVky2kGOg6TD49iBFbN6vs+54sfPfZieuuXAWn1+g4xJgv8b43VcAK6NQmgrRYSQzPgLJmqNkKs/g7FoLLLrtMBl0crxyCeF8Vi494KMD2pjIqoxbRoEHnsMnj+bWsiyaoNMPSUj+UPCADMl0zzHLGDlbQt/YV9K15uagFJddZhm+PL9rfng593W1s2tUuH6vSUvyZeQ9/xj1kvSC/dbdxr7uD+9wzafNq2ZdEttv2QtiA7dWwoxrOqoHm2LzFBSkUCoVCoVAoFCueWQn4CsVKpCa2NCafFArF8kL1PYvMTj/WLCeAg+l24rpFS8UGDg5kZa3QgKGxoTaGpmYyFcuAM888c6EPQaFYcnb6J1MbPdfXSnqgi2EvRll0dgLjvz4JhXE22cIK+4ZtzBue59GfLkgBrz4eYk2kSCCXhuqNULsVTGvJ9D1zXg5BccoRQZTNlREp4idCGbqSOfYP6/TLbPwY4Vw3wfRh7GA5pVDN8lee5d+nTbp6W+bhSZ8S0QpcaTwmm2Cf28B9I2K+yM7POZafxd/tb18V8sV80c6s8e/PB7bt8OjOXfT2D1JTVcG5O7ZjmnP7XVRjLoVCsRCovkehUKi+RzEdSsBXKGZIWUgJ+AqF4tSj+p7Fhaeb5OMtMhPfNQLEzRAtlat8K31DxzR0aeWqUCx11qxZs9CHoFCsHOwC/V2HKaT60eJrZpV9LwS1zp4uoG5MtHvzViibnRP/jBjIFnE8j4ZEmJaEhpXu9i3zqzdBqGzJ9T1zWQ5BsXBEgyanNyWojFnEggGZjf9Ebi1ro3GqAlGsTCeh1EEKsVWyLNJKJFN5OoP5fhI9v8ews1Nut17vlO0G/kdm5//B3ci/OS/l164faNOfh7vb/CZoicEZ1bC9ym9z0f/86p4H+fSXb6Wrp3dsXX1tDe99+w1cfcUlzBVqzKVQKBYC1fcoFArV9yjmXcD/yEc+ctxtdF0nkUiwdetWaU9nWSceja9QLCR7+2w2VKuYF4VCofqelY5nBClEm6SVvqeZuEaIpvIq2gdzGJqGZeo0lS9ve1bF8udnP/sZ11xzzUIfhkKxIigMHCY12M2wEyQRjZzw80su3PZkkp9aH+RZbxX/XHozenkzV7UwbwxlSxRKLg1lIpDNIpw6CPFGKF8NiYYl2/fMSTkExYIj3JDEtVhlxCIeMulKBjg4ZDBgxlmdCBPOHbHUdwLzU15iMZOt2CJb59YbiPbvItH7KLG+xwkUBqZ8jsjOf77xJP/pTO00cijtt58f9O+vjh8R82cj6Avx/sYP3nzMeiHm++tvmjMRX833KBSKhUD1PQqFQvU9iumYEzXywx/+8AS7WGGlN8rR68X9qqoqvvjFL/La1752Ll5eoVAoFAqF4pTiBqKUwnWEUodlVr5XGcRJRDk0kCVgasSCJmXhlZnVpVAoFIrjY9u2LxK3txPId7OxwkWLrZpV9v3PD8CfF2+nwkxzvvYMv7Dez/6Ka7C1a+flrcgUbNLFEo1lIZorwsSy7X7GffkqqNrASi+HoFg8hC2D0xoT0lY/apl0iWz87DrWh4JUmmFZEmnFWOpPgriGTdecJRueJ0sMxPofJ963k8jQs2jeuJocI2zasIXeIdjVP7Fkx0v0h3mn+WOe9Naw213DU2KZWs3PU9EJgv5plbBVtAqoj0x92oVtvsi8nw7x+Asvu1AF2CgUCoVCoVAoliVzIuDfe++9fOELX+CXv/wlb3zjG7nkkkuoq6uju7ubBx54gG9/+9u89KUv5c/+7M/44x//yJe+9CXe8IY30NTUxPOf//y5OASFQqFQKBSKU4qY8NXcopzsFBOg5ZWnkw8HaBvIETINtq8qIzjH9TkVCoVCsfS5/fbbj7Fpr60q5z3v+Cte8sJLT2hfwwX4/bOH+UfjnrF1Ac2hPGLRx9xTsF0GMkXqEiEay8OUOSJj14OyZqjZKqz35uFVFYrZI5JIxGdViPj7eoWIH2D/oMmQHqE5YRHJdBBKt1KICkv9Fey4p2kU4i2y9a95ObqdJdr/JPH+x8ey8wuReq7aXMZVI84fzwzAzj54rBcuSD/NZr2NzbTxGuPBsd0edmukmP+Uu5qnMmt4It3MHa1VuOiUW76Yv6XCF/Q3lENw5NJZ1Lwfb5s/GeLx3z+2iwvO3THfZ0ehUCgUCoVCoTjlzMnopLW1lbvuuovf//73bN++fcJjQtB/+9vfzsUXXyyt6D7+8Y/LzPtzzjmHz3zmM0rAVywZIoGVF5GvUCgWHtX3LG5ExlYw0z6SiR+gtmIbrcPQMZQjFPCzvsa7ESkUSwURjKtQKOZHvL/++uuPWd/TP8T7PvwpDF0/IUvo7+7xuFH7DoZ2xAUvF6qjf/VLmGsc16UnlacyGqC2LEiVkYfsEFRvhNotEAid9GuovkcxX4jrsm0NCaqiQZmN3zkc4MlUiPWhABV2L6HUAQqRBtwVaKk/Ga4ZIVX3PNlEdr6V7cIsDo89HtBhu6h3Xw1v2AJrfrMHMsfup1nvpZleXmz8fmxdwQvQ6tVy0KvnQF89X+m6hiQxTA3WlcGmchh8dnBGx9nbP7PtluWYS7icuiV0J49u59DdIppTlEHGdqjqmM01J0/VoTvBc9E8FzxHuizYjkvRcaXrge262J4uW2mk2Z5Gj17Hg+EXSNcFEbwhfnGE+arrwYbSMxg45AmSx5LLgh6lZEQImDqWDgEDf6mDZUBQh2gA4hbERpbxAETMFWmGoVjBLMm+R6FQLHlU37PCBPzPfe5zchLiaPF+lDPPPFPWkvvsZz/L61//ermdyMh/6KGH5uLlFYpTQmOZyqJUKBSnHtX3LIFspWgjodQhgiMiflNiC/sH8nQO5WTd1dVV0YU+SoXihLngggvUWVMo5sE2X2Tez5Ul9MEk5A/v5FJr14T1vZv/fM4ziV3PoydVJB40qYmHaIgAg+1Qsca3zQ9XzMnrqL5HMZ+IoMr6shDlkQCxHpPucIC9A2Fq3SCNoTDhTAd2sIJSqFqpiBNPHMVog2yTodt5Itm2Gb8PQa3EJq2dTbTL+5+3XyOXtgfPDvkt1z+zPqWmqmLpjLk8F90pjAjueXS3QDFUI0tzHU0w1Upl2z3oTs5/jhDoR4V6J48hl4VJyxwcWn0tT9S/msE8DBVgcKQ5uSJfHPz+rA79t842flJ6waSP/X/WN9iiH3GUGcXxNAaJM+TFGCDOoBdnwIvLdWJ5yKvgV+65FLDGnqNrvpAvWsKCqjBUh6D6qGVFCAyleyqWAWq+R6FQqL5HMR1zMqrfs2cPL3nJ9BH+jY2N3HbbbWP3N27cyC9+8Yu5eHmF4pTQMeyoCyuFQnHKUX3PEkDTycdWEU4dlLVU40aIVRVrOTyYxQroxIImVbHgQh+lQnFCPPzww0pIUyjmGFHzfrxt/slYQovMx288afMv5ncmrE+VbyNVcw5zjbDNN3WojgdZVRZEH9oH8QYoXw1lq+bsdVTfozgVjLokVcUsIpZJ11CIp4cOsSZsUl7sIpjOyQBNVrKl/gngmiGeuez/EUodlNfDYhlKtUqXKpnpPQ1dXgU5jnXvCLVspyYRpDdZmPK5q8oMXjz0bbwHfoIWiGAGo5ihiHQP6Fl/7aTvnyh9ZeYHZNAtjCrAHv0Zl6qoKAFyxM1EExnuno3m+i1dfSauGT5mn9G+x0n0PjoizguxPe+L7WNNrCtIwf5oWnfcSHqSPjuQ76ey7S5mw8/3FfnUnmPXJ9CZ5FTPCGfsXB1LmMnfI+EMU02Sai055XM35/9jwn2R0T9chLPsx1id7+HgcD2/9xro8Kpl2YXxQn9lEGrD0BTz26qovxQBZiLjX6FYCqj5HoVCofoexXTMyWgkHo/z29/+dtptfvOb3xCLHbEiy2Qy8nkKxVIhWzoykFMoFArV9ygmoJvkY81ywtIxQpRZCXLRctoHcgQNQ04Oh4VfpEKxROju7l7oQ1Aolh2dnZ1zZgn9+x7YPnQX6wNH9umh0b3lDXOeOZzMlSjaLk3lYVoqIwSyHWBFoazJt8+fQ1TfoziV2fgNZWHKwxb7QiZdkQ3s7YlRp5s00kc4eYB8vBnPOPnSECsBNxAhW7lNtlGEnXsw3TZB1LeynZil9Ng2xXA9l1fD04PQnT2yP003eM+LGvn/fnRgytf87IssmrxOyOO31JHH/mHoOirCUBmCqpEmMrd3tN5Fbcf/HrOvtTP4G5+76DMUJxHwwzJb/m5mw0CqwB4NhorIjPnRbPnGTJj/M6s9Co2+OOn68QL4iTLdcyPa1EEW09HnJSZk34/nlcZDvNL4zYSyCwe8evZ7DX5zG9hfaGRfvpHdg8IS5gjiSGsjsEoI+1FoicP6Mlgd9+37FYrFhJprVihmiAgI1Cb/LRKBcyLgTm4m/9XkeEgE63maqs2i+p6lzZwI+CL7/lvf+hbvf//7+cAHPkA0Gp0g1H/sYx/j17/+NW984xvH1j/55JOsWbNmLl5eoVAoFAqFYsHxjCDFSD2hdBuuEaSm+gyyRZ2O4ZwU709vKsMQ6SIKhUKhWJE0NExuP32iltCi/vBtT6b4nvnjCesHmy6nEF/NXJIrOiQLReoTYZoqwoTtJJRyUL0JaraArtQQxdJGXKOJbPzKqEWrZdIzECfd/wzNpkUi1Uox0oBjJRb6MJcknmGRL1sn23j0Uppgtgsr2wVGiPfW+usH8rBnCPYPw75heGPIY60W5sY78xxOHkmoaE5o3HJViOtOE1n0xyLqtz/QNXnf9C+mzZ/Ocib0iV6b4dSRGCltJGN8YzpI3ex2yQ+eyfP9Y13wOU0LwQwMvIqeQYYwGUJkPb8GvXA1mAwhln/NfhkOOraoWu8Z2Ojouo5pGJiGTsDQsTQXS/dbQPPkMhmo5S0JCBp+HXs5pNF8sXyofTNFJ4XpFgiI5hUIOhksNzftsafMSs6IQaoE6aK/zI+ci0at/5iyC1u0w2zhWBebQ24Nu7017HZXs9tbzUPuaXRlQ3Rl4dFx24ljbo7BugSsK/NFfXE7PnkMgUKhUChmieu6ZGydjO337+nSkXZG3y8I2UMYTh7TKWC6eQJuHsvLE3QLBL08AUqY2ARw5FK0rBfmYvfrOJ4o0eL/FusjffvH9W/wCu2BKY9H7K1EAFsTzcTRAjiaia0H+d/aG8gk1hIbKd8ifhPE7YpSN7HkXuxADMeK4wQS2FZczvspFEtOwP/kJz/Jfffdx7/8y7/wta99jTPOOIO6ujoZPf/EE08wNDRES0sLn/jEJ8YyD/bu3cvf/M3fzMXLKxQKhUKhUCwKHKsMp5QllGnHM8M0VpzGgYGCFPGjQYMNtcp9SKFQKFYql112Gc3NzdPa6DfU1XDeWdun3c8vDsL1xR9TbmbG1pX0MD3rr5/T4y05Ln2ZvF/zvixMmenAYBdUrvUFfJGFr1Ask2z8xvIwFZHRbPyzaO14mirNoD7TJW3QS6GaOXe3WKm4gRi5sg2yjUdky19Y7zeB3XE956/u538u7uOR3QcZHBhkdSTPy1YXCBpTOyQWmVzYF1haadbHLcqWPDnJy15rhHj+1C85gYwXJEuQrBciS4iMN7nDQ7tXzYdLb5TCfNoLT1hKwd4LyuVkf6sQHlaFoSJ4pJXLZmJZfzYmToyKFTO1m5/ql6nY/K5Jc/41t4RRTGOUUrKZYlkUt9OYxWEiwUo+eZT1QcnxhfzTf9cv3sgZ0aL30kIvLzZ+L++fl//KpGUZRLBFa8pv97YfWV8Tgk0VsFW0SlifUBb8CoVCMR7Hscmlhymkh7Hzw3i5YSgkMYtJrFKSiDNMzEkS90TL8Bv3NG4ovXfSk/hr607Zb58oQsRPTvETXgp4MM1vWUAGA9jg5cZXypH8eJ/LE5P8tl9v7OZTgX879rU0i5yRoBSI4VoJtFACL1SOHarADlZSClaMBH+quT/FIhLw6+vreeSRR7jpppv4/ve/L7PtRwmHw7zpTW/i5ptvpra2dizzoL193NWSQrEE2FCt6t8pFArV9yiOTzFSRyh5UFqExgJRVlWso7U/QzhgEA8FqEsoK1bF4ueaa65Z6ENQKJYdpmlyyy23cP31Uwvt73nbDRjG1DNQwwV4+Nl2fmJMrI08sO4anGDZnB2r47n0pPJS0KyNB6mJGjCwH+J1UN7iL+cB1fesPGzb5v7775eJHmKuSAS6iO/KQmbjV0QtDoXOZKgrTmZApznXQ9ApUIg0KteJU8hw46Vjt5933pH1u22P7lSB/mSGwXSWVCZDLpulUMjKzO+pLOQFe9xmfs12mdE9HlGCZOy259/2RrL2hEhexCDNsfb5gsfcDfxz6U1SlJeZ8ASlMJ8TS5kZ76/PYeHN0MZ+iDj/4bx47H7YPCLGrw4eK85XhKDc8u8vlvrvwrpYChqh6V1lxiOOvdKAVMsLKWQ7CWY6sbIdE8ouTEc/CXopn/SxvzX+i3P1PfL9+qO3kSfcdaSJ0JuH3k54aKQijXAY2FgGWyp9UX9LhR9colDMB2quWbHQ5EouPXmdvpzvhNOfh4EClDLDvDf9L1R6A1R4KXRthqWFNUhoR4KMj0b8Hs4GKcDPA1P9tseZ3EUm4BUJ2H0gmthk+Nht7qx8Pa2NL6EuArVhP2BOOgbYeaL9T1AK18jmmNEFCw5Vfc/SYc5GRTU1Ndx6660yA3/Pnj0MDw+TSCTYsmULgcAMQ1EVikXMcN6lLDT7mmEKhUKh+p4VgqZTiDXJWp+OESZuJahLVHB4IItl6ESDJrGgCgpTLG4OHjyoyl0pFPPAa171Sj7/8X/kk5/9Kt39R2Z86mtreO/bb+DqKy6Z9vn/+Sy8m+9iau7Yunyohv6WI0LPyeJ5Hn2pIhHLoDoWlJnJWrIdAmFIrIKqiVmzc4nqe1YWt99+OzfeeOMEVwrhUiECXa677roFy8Zvktn4AfYFN9EfLeNQ+5NU5bqosA9QjLdIa3jFwmGZGs0VIdmgamy958FwEdrS8N6cL0KI1jeyFO3f8q/ga84r5vR49nlN7HOaTug5huZnv8csf1k+lSgvblsQWmFDh761E98jkbkvgqOD2U6szOiyQy41URd5hGDVan50hkZ7BjoycDgF+5N+u7T0BBcau7nSeExu63oaz3qrpKD/qLuZ33lbafNqZJma3YN+G6U+AturYEc1nFGtBH3F3KHmmhXziee6FDODZId7KaX7IdNPIN9HpNhPudNHjdvHN52r+Iz9p8c81yLC/wsd9O+coMZcztQCvnCfmQ2aDIFzcScJhMt5QYa9yMh2PmJbixKWNkmdmnGkvMkF/Ch5Zst/dVXw845x+zKhKQYXBTv48PDnx9aL+UIh5Bdlq5WZ+8VoA4VIPXawQs4tzheq71k6zPkloBDrTz/99Lne7ZKkra2ND37wg9xxxx309/fLaPJXvvKVfOhDH6KiYuYRqIrFQW9aCfgKhWIF9j2ei+4UpHWoiBaVSyePbZWTCjWQGqlXKJdFyBRt4ukDpG2NrGuR0uOktbisMyUm1UTMrmzekWyShAVloy145L6Y0BKTW0sRURerGKmXVvquEaSy+gxylknbYI6QZbC9qUzWmFQoFiuPP/64EvAVinmgr+sQl52zhbO//E88212gt39Q1rwXtvnTZd4LhO1v6tDjXGHtnLC+d9OfzamgOJQrydmvmliI5soIRn4IilmoEXXvN89rBrLqe1aWeD+ZG4UQ88X62267bcFEfEHEMjm9KUFHTGTjh0m1P0lmuI360j4ob8YJxBbs2BSTI5LYfKv4qc+QOyLyiyzDbAmytt9ahxyiIWNsXW6kjY5bRscwo/tgZL14zZDh14Y/ulliqUN0XE3dxIhgL8ZAqiLDzBFWxDnRyjdNfM+dIsFMG6FUq2yF6CoZ7CDq24t25I132HLvPjii9cts0i3aYbboh3kd98p1bV41v3O38Dt3q2ytnnCb0ejKIttdI7FGzTE4s9pvQtgX769CsSTnexRLGvE7lCxCfzpLoO9ptEwPwVwP8WIP1XY39V7vMY4zE9CgUeuf9CHhPjPgxajUZuaAMp6olpO/c6PlWsa3p4tX0s25uEYIzwzimSE0M4hmhtADogXRTQvdMNH0kWaYmIbOdzQQ02ii7r34UXY8/zfZ4wae8G6Qt2UTDk8uFBwo2K50e7Ltklw6dhHHtnGdEk6xwPlanGEb0qNzmyX/9nNuEz93LqCclDwHlVqSSpLHDQgQdHkTdb+MDc8OwQa9R0RGjGE4OYz0IULpQ8fsw9UtOacoxHwh6qerziRbsYW5QvU9S4cVFsN56ti3bx8XXXQRPT090gZQOBGIMgNf+MIXpKD/0EMPUVV1JFJYoVhK2LbDozt3jU04nrtjO6a5MD5ti+lYFIqlhsheiA48KWsQmoVhf1kcxigm0e0cmlPAdAuTPvf/Otdwc+Fa8od24WQGMaIVhFq2U6lneSz0oWO2T3oRBrw4g8TpF0svLi0ORX3HvV4Tz7qr6Gei9a8YxorJkFUxfwJmXQLWlUFL3Lc2XGxM1h8ZZlaK+J4Zpq7yNA4OFOgcypMImWyoVTWxFAqFYiXhOi4DnQcoDvcQrajnguaKE5qg+/pTcK3u1/gdJV22hWTt8+bsGLNFWwbjNZaFWFUZxvLEbFYnVKz1M++DSrRUnDxiAlVk3k+HePzVr371cQNbTk02fgP7IlFS7VHaug9Q1d9KuKwWJ1yzYMemmB1i0n80y308e0MeG6rVWV1qczIieC2fWCfbdAiR3zhqXGu7HvcfdOhMuzTEdC5bY7BK72OV8SCvMR4cE2Eeck/nxtLfTEhBPZz2288P+mvFWFWI+efVwtZKMBfhWFWxPFFzossf1/VIpYbJDPZwyC7jmVIdnRlk68j6wWYbtQHuCt5y7JNnkBDTpPVN+Vi3Vzkm4Pd7CQa1clJ6GVkzQcFMULLK8KwEhMsww2VYkThWOE4kGOSHU/4MHCmPc2oQHbJQzSePtHrBFM9yvPNJF89nsAAHC37g30DeI5vLYedSUEihF5NYpWEqvSHqtQHqtCHqtAE6vMkvKFZpvTM/arcohf1Rcb+vGKSY2CKDA8ejuTah5AHpAuqavguBYnkxKwH/5S9/OR/96EfZsWPHCT+3UCjw1a9+lVAoxN/+7d+yXPm7v/s7Kd5/8Ytf5B3veMfY+n/4h3/gc5/7HB/4wAdkuQGFYqnxq3se5NNfvpWunt4TtvxczseiUCwKPBezMCgtsQK5fiyxzPfTt+Zl0pbpaIzBvTQ+841ZvdQju1tpv+utOKkj3z8jXoN5xetgksuDhJaVbQ3dU+5zj7uKq4ufGrsvomZFhszwADw1cGQ7U/NFfCHmr0/4EyabKxZ2omTq/ujNXHPOKmn5GA1EWVWxngN9GWIhg8pokMqoStdQKBSKlUJ/Txu5oV48z8WKnFi9+kd74I+98Edu4D73TN5v/ier9R66t7x+ztI4S45Lf6ZAbTxEQ1mYqIiW6z8MsZG694mGOXkdhULUvB9vmz8Z4vH77ruPK6+8csFPmMzGb66gPXYOXeE4qc7nyPcfIh7LoZWtmleLU4ViMbNU5mQKsWb2XnAzkeG9hIf38ssH/sA//lcHh5NHajo3JzRuuSrEdacdKQNbrw2yRhPj16l/Z8Ue9g777Uf7IGLCWTVwXh2cU6Ps9hXzx1L5/imOj8gkH06myA524iY7CWQ6iRe6qC510uD1ENH8AKQv2q/kdnsS9yJv9gGFtQzJMiE1YagK+X1W1Ug76L2D/kiQWKycQMCXEaeWwpcXwg1UOISKtmZsrfgtiIy0urEg66EidGdhbxYeysGOLDRkoTsHPVkojri/dHpV3OucSbPWK8X80HTuCEfx5QP13LHfnwtdP24udJvRwbrf+0lUxVAVhVgL+XgL+ZGlsOVX16lLG80TBe5OkI0bN7J//36uuuoq3vSmN8kMcyHIT8fTTz/Nt771Lb75zW/S19fHv//7v/P617+e5Yg4N+vXr5e2oyITX9ePDOZSqZS00henXQj80Wh0QY9VcaylZevOe3HyaaI1R7pnQabgEhX+Zyv84vDGD9485eO3fOSmU3aRuJiORaGYT47ue0R0ZSDXg5Xtxsp1T1iK9bp3rJ3T49v+P3YGzpQ1AEWmgKgH2J6G00pP8G1r6u/RVNz+VInrf5ib8vHbrg1PmPiYKQ85p/HnpQ9M+tgr9N9QIMBv3W0kOfa3U1hxPa8OLqr3J0xOZZ3I4/VHn/3wjVxzbiP52GqylVvpcstJ5ktsqotzZnO5stJXLEq6urqor69f6MNQKJYNYvy357EHGNr/B6xoBcHEzCfahAXk2+6DtnHlJM+qLPG5TU+RqdkxZ8fXlSwQtQyaKsI0V4q69x3g2lCzBRp2gDH/P66q71kZfOc73+ENb3jDcbf79re/vejmjTIFm4OHD5Fte5JC70HCuk2wZi3aHJaxUJx61HzPypmTOd5xHz2W7Vr9Cu6ueC2P9yHbc0NHnPj/NfBZDngNPOiezu/dzRQmkbU2lMG5tb6gv7F86ZaIUyyuvmepfv9WMkKB689Dz3CGQO9TmJkOYvkuqkqdrPI6qZiBVf1PnYt4V+ntkz72u+DfyQzwoxnyonRptQya1WQC1ZRCVRCpJhCvIpKoJhZPYEhPesV8vu/tI/OwbemR2ykPJzdMk9ZLs9Yjg8XW6p2s1bpYp3XKBKjxvKTwCXZ7E7Uqwcv13/Al68tTvr6rB2QQmy/or/bF/fhqUk5o0r4n3babUO0aNp53FZHYiQWcK+aHWY3Ad+/eLa3gP/GJT3DnnXdiWRZnn3025557rhSnRX33fD4v674/88wzPPzww7S3t8tJASH6f+Yzn+H0009nuXLPPffIpfhbx4v3gng8zsUXXyzPmzgviyGaXDEzgiLlc4XbMonIzukQj7/wsgvn3eZwMR2LQnGq+p6KtrupPvAzmVGvjVVgnBnff7yP701SpqlPm9nFWMYLkiVExguRdIO8485np93+XXfZnH/mJsJakYibIuyk0GdwzMnwKrbE/BpeIvM+My4Y9T3mD2jRe3E8jce99TzgnsEDzuns9DZgY5IuwT1tfhM1J0W2w0UN/kSJEPcXsj/61Fe/xVXf/SzhTDuuEaS6ajupgkbXcJ6ycIaNdcpKX7H4KCtTgzWFYi4Z6O8lN9QtS9RYsfUn9Nw7D00U78WVwZtPD5ApmxvxfrTuvRi6VsYsGsvDaPkkFNJQvdGve38KxHuB6ntWBmLeaC63O5VEgybb1q+lI5FgYH+EbM8BSh17sKpWE4wkFvrwFLNkpc/3rJQ5mZkc97vv0bnq4m3Ek3ulhXG+cis7qmHHSNydGKM+2Q+Hu3q5uvdRue5v+G/yXoCH3W3SJUe0g57ff41m53//OSi34Px6uKAeuc+jrZAVK4/Z9D1L9fu3UsgUXQYH+2nLmjxXqBgTbsVS1GTfpA1wZ/DzE580w4/Baq3nmHUhA+oi8Kh2AeVGgXyoDidaixmvJVxeSzwak2Zd4gpFXaWcesS5rw77TZRaGfcIJbeczkw57emNtKZgZwq5bEt7lHkp1mqdrNW75PKAN3lyxQa9fdrX190S4eR+2caTjzTQt/aVDDc+fy7+TMU8MqtReCAQ4D3veY+0if/ud7/LrbfeKuu7//a3vx2rEzY+sb+mpoa//Mu/lNufccYZLHf27Nkjl5s2bZrSwUAI+M8+++y0Av4555wz5WN/+MMf5uBIFSfCwUGHDdWnMKVzkSFqmo23ZZoM8fi3fvBTqqsq57X22UyP5feP7eKCc+duYlOxMuprnaq/US+lsbI9WLmucctu2k//W0rh2kn7HmGJPxsatf5J13d4VXzPfgF9lNHnjWskyBAhFAwTD1tUR3RqRyy1ep/bSXfyn6Z9vY7hEnfFrzvy/fNcjFIGo5TCLCZHlimM4jDBbCfBdBvBTDtnrl/FLasmZhz25aG3p5uW5/zvvKF5nK3t5Wx9L+80f0zKC8uJkv92LuRX7rky60EMin7T5TcxHj6jGi5pgMua5j4zf6b90cNPtXLp1jpC2Q48M0xj2WkcGMgRHzKljX5V7KhCnArFAiOuVYXLlkKhOHnE2Liv/QD54R5CiRo0febXFXkb/vOouLkXtfiWiXNZ9z5dsGkqD7GqIozpFkFk31esHql7f+oCzVTfszK47LLLaG5untZGv6Wlhcsvv5zFiJjzaqqtpiJ+MR3PRUl376fQf5BCro5oZQ2GstRfcuO2lT7fsxzmZGbyeZjJcbcPZPih9jIufMHphJL7ZbbieKIBX4S/2n4Sxu1KWCFfbjwum+CgWzcm5ovxap6gtFn+1SG/hUXQeS1cWA/nznPQuWLxMpu+ZzF+/1YaJRe6UzapwW6c4Q45n1WWb6fObud0OqXl/RfsV/F9+7pjntvq1eF6Gro288QckUzTpdeRDK3itauhIQqNUaT1fUVwtJrWG+f2j1TMO6JambDEF+3CcTGrJVejPZ3gYCpBa3Izv05BPAm5SYxQ016YvW4ja7QuTG3UH+b4hLKdc1aGTTG/nNTVaSQSkcK8aMlkUgr4hw4dkpn34XCY2tpaKdifdtpprCSGh4enzR4YXT80dKytyUwRzgajgQKjA+DRWnKjbN68mS1btnDHHXdQKBTGXlsMgnfu3Elra+vYtsItQBz37373u7F1Z555piwD8LOf/WxsXV1dHRdccIF0D+juPlLHWEzwHjx4kMcf9y9UBeeff758PTEJM8rq1avZsWOHrGU3ep6CwSAvfvGLF9HfVA5aORvEe5l36U27E6yNRHSkuMAaJRHUqI0bHB60pWgkMHRYW2nSn3EYzB35QV5V7g8e2oaOPL8irFEVNTgwYOOMvJTIHm2uMOlJOSQLR56/psKgYHt0po4cU01Mpyyks7fPHlsXCWg0lhl0DDtkS0eeLy4Kj/6bGuL6jP6m3a0zEw5v+eqRetrV1dW8+c1vlq4Tc/k3PXlwZscitqteY6+o92k+/6Zf3v0An/3qv8syKOPra73hL94k3+Ol+Dcd/T7t+sNv+Mq/fmPCYKimppqb3vFWtp9z4Yn9TZ5LuDRAjddHrdZPbqCLUL6baKGHaKEbyxmXSjcOLdnJc+lKkiXoymn05DTu6oRofw3/wvEZ8GK0e9V0yFZFm1fNo+7mSbdNEeWz5lupCXlSnN9WrXNGwMXCldkBwkWrIS6iwr2x96l96MS+f/77pNGWDAOi1VIR1aiqHXmfRkKAg7pLS7k26fu0zntiyteJazleZPxBNmEN9hPnEn7gvIBnPH+ixfZGagb3wq27PS5rcLl2g0ZVeG4+eyfWH/nuQ+v1LkzHoqJvAHE6H3wGTtt+BhvWrV2Bv7nqb1qs75NAvU/q+6T6iLn5Pm3aspWDbZ3k9VUg3GX67BlfG/26S2ewcMTVLaB7XForHp/rayOLymiQVM7hf1vFNUoddOU50yqxpoxT1pcLxr+W+n1anr+5Tz75JK997Wv59Kc/zVS8733vk5mDS+Fv2rbpLJ7aHSFZ1KFffNHcZTsmXCx/00MPPcQ3vvGNCWPT0fmHl73wkhP+mwSjf4N6n47/Pp3onMx8f/am+zyIuYrRz95Mj3vPIXHc4tY6jGHx2eOY79Pz+6ceo8pj1rt5k34nb+JOmZ3/O3erFPPvdXfI7PycAw92+k0IeZsSnhRwrmjWGUirPmIl9XtiPyfyN3X0DDDTz/EF57Ko+/LF/D7Vx3QyjsYTHXm8TKdMxqgsdFJrt9PktvMCrQtLO8rqcpweKmzQJ0MknYg5O1EHfTyin2jX6uk16hm0GjDKGvCi9aQDNRjBOJquyb/pDaGR3ysXBtKQL6zs92m5/k2rohp20WFVDVxc4/9N4aDBw+0OB2R5VI32rMbXcy/j35yXEcBmvdbBVq2VrfohtmiH2Kq3UqMlmYp3PLGWWAesirg0hF1WRcAIbWKVW6S3t4+dd9+3ZMcaOxfh+Gm2aN74VHnFnPBXf/VX/Nu//Ztsb33rW495/P3vfz+f/OQnZbvpppvUWV9E9HUdonXnvTj5NNGaiXVFRKe7kiOyH350J2991/RZt6eq9tJMj+Xrn/+YijadI1ZCfa0T/htHfz4niVisav0Ftc99f9J69MfjS+ab+L/5q8geuc6TNNHLQ6F3ytudXqWM2m116/ylV8dBr45DXh0pIsfsU2QJNMf8JiI7V8WgKerbbJn64v/+hYb3U971INH+XYQy09tDjbLTXSeFfJGZnz7qnAiLsT9ZDa9c59tYnQwnej40J08o2Uq+bB3pytPZn7GkFeumujib65WVvmLxIAYxKgNfoZgbnt29k/7nHiGkOVhVEzP5pkOUlPnM3Qd4KQ/wZfuVDJDgug3wpq3Mad37sKWzqiJCi6h7n+6BYhZqt0DjWWCc2pRA1fesLG6//XZuvPHGCZn4IjP/lltu4brrjs1aW8zkB9rp3Pc4uZ79DGdLFKKNVMQjmEeVVVQszrHpSp/vWcpzMifyeZjL4zaKSaIDTxEbeFKOU0/ELe+9pb/idmdyhxExu3B6FTy/0S8JJ7JrFcuX2fQ9i+n7txzIlsbVJx+1ux+xvBdC/V3W+04oW36Up9zVvLT4yQnroiY0xeAd2g+oNvOUog2YiQYilQ0EYpWgHHwUJ0jOhgNJ2DcMzw3Bs0NC3D/yeA1DUsgXgv42vZXTtFYp9Oew2F64FZcj16mWDuvCGbbVWlxx3hm8ZMdadJHZpVhQ1NXpPDCaYT8aZXE0wq1g/HaKpYGIdFrJCPsxkW19PJumk629NBPbs5kcS0NdDeedtf2E971cOdG/ffz2leUJPv3lry9Ifa2TOe4TeY9nUkPsli98mdfX7yVU6MPK9xLI9XDgeR+lEBvn+T6CY0YnFe9t1+P+gw6daZeGmM5lawzMoy6GIvluKd57rkP+0C6czCBGtIKOltN4UeFTHPZqpP3eZFSF4MwRob457i8bwg77du+ib2CQmlAF564+uc/9bL9/J4MQu7vK1snbZn6A6MCTxPqfkJMlwpJ/Mnbo+2Wr1wb43FG2ZXkHfrIf/vsAvGAVvGa9f55m81nr6u2nsryMgaHJf/OPPh+eEaIUrsbKtBM2IzSWn87+gQKdIZOqmEX1CVrp27YtI1A7OztlnVgRlWqa6vJOcfKIyGGFQnHyJLM5sv1tkOsj0DB5ibWpuO05eKf2fS41dnGdcT/f8F7GhWv/RISizWnde/Hb01gWRhPCfXYAqjZC9aZTLt4LVN+zshAi/ate9aoJ1zIia2Yp1uwNVTaxJhxl8GCMQNtesqlD9BTricXixEOmtN1XnDzzVft5pc/3LIUx4Vx8HubyuB0rQbL+QtlEcL+w0I71PU6sfyeRwWemDebfY26FSR42sbEx2NWvsasfvrYLtlfD8xt8Mb9MifnLjtn0PYvl+7eUEOUZu7LQnvIYFLpMsp1wtp3KQjur3HaecM/m3x1xjT2Rw9Qym8zXJFHZR7ym2RfsRQKNSKQps0ZzgP705P6eE5j7XMlz4SuBsAnbKv02SqYEe4dhz6AQ9Mt5aqicX+fPHPvdiZCnReuZIN4Lii48k4nyzAG4v2cvLz1r7Sn+axSToWZ45wFh3yAQNe4n47nnnpPLTZtObAJHsbAI65WVjPhxf+/bb5g2svlkay+JyGkxuBp/ESouSv/+727g+c+/hKKDtL6xXYPXv+UGPnPz1MfyktfdwANdBuJKS5jQPPbbB/nJd25lqP/Iviuqanj1G27gvIsukVbhxkiTt/Ujt0cfm2wbEZ0matZYhr9cjIFpU51X8X5Olpkw2fbHYz7qa83FcU+3/SjB1CGefPCu4/69Hf1pdj3wS65cN+6nM91Dp7aKvhz0jGu1yRo+dtTzb3+qxI135jmcPHL535AwueGFm1izdbvMoBeZ9HvcZjLPPMjgPbfipI4ckxGvIX3FDSS2rmJV9EhG/aoRsV6siwQmyUaYxTk52b7gPW+7Yd4mXu1QJcONl8omyhSEUq2Udz5AWeeDmKVxYaYjnHnupVzbB788yDGuBsIt867D8LO7HiRz/61kh+bnO3L0+bCDlZjFFFa2i0SwjNpECx2DOSKWQSIUwJqhLcJyylpTLD6E7ZdCoTh5ejpayQ33EgpF0cyZ2770ZGGgdReXBnaNlYz5e+12DqbXkQmdedLHlSs6pAslmsrDNFWECYiaicOHoWwVVK6BcDkLgep7Vh4i8PDKK69kOaCFy6nceD6xWJTew/uI9B2iK1uks1BGZcwipCbMT5r5qv280ud7ltqYcLafh3k7bk2Tgf2i9a95Kbqd97Pz+3dKUX98dn4hUs/HL6qTWZIPd8Fvu6BtZBj7d8Z/8Rrj19Jq/z53B791t/F4X5DH++CrT8IZI5n5F9YrMX+5MJu+Z7F8/xYbwiRzsOB/n0T2/GAyhZlqI55to85uY5N2mOdpbVRo4+aNxByuAUPEJhXwiwQ45NWyVjtipT2eAa2cnkATqVATdqwJq6KRcEUTXqgMQ9N4yzz8nScy9znbeVLF0ka4sJ5Z7bdRxLy1+N15ZhCeHgjx3HCL1EymYsequAo+XSQoAX8eeMELXiCXov6B67ro4yzTUqmUrM0UDodlPQXF0kHUTRG1TlYy/o/7TScs7gqefuoJasJF3FIRzymCXQCniOYU0J0iv3liP/9y2yPHPE+8zvs/fDPV19xEdIt/cfFe8/v8VeAPtLymkU/e1UNn8ogq15gw+acXVfEn8R/hPPkTGb38y90pvvGjIwLXKIP9vdz6+Zv52YGbuHpbGa8z7pbRZ+I5DgZFzyCLRYGA3zyxHH/fv/2Ut5o2r1bu0xwR9YWgH9YcyvQ8jhFE1w0sUxt7bFT0H70fHLktluPb6GMT1o3bTuxnumSOqezkxHn110+0Fzye/dx0iGjOueLEj/vX3PjBT025feXhs7jwtTdiB4+djI73PUZp9//M6LhE5vx4vv3HXr45SeR8E7UyOU7Uoz/s1fK9p1z+5cfH1sgTn92P/Xg31de8YuzzLcT7vp8d+7cLMV+sf9/ZN/GSF1wy5+dwLvqCUz4Q0HTyibV0JdbSvfF1xHsepaL9PmIDvtiRrjyNmpo63lwD12+A/2mFn+73B3U6rvy+T3W+5+I7MuX5EBM80UZCqQM46ZiMgk7qIbqSefb3pdlSn5iReH/99dcfs16I+WL9bbfdpkR8xUkhanaJLEiFQjF7soUS6Z5DuJk+rJqmE3ru9/a43Gh8f8K6VPk2MlVnnPRbYrsufZkCNfEQ9WVhYpYJQ4fBikOiAcoXzoFD9T2KJY8ZxGo6i6ZQnGQ8htW+lyGnRGeynEjQoiJioats/Fkz0zHniY5N1XzP0hwTzubzcCqO2zVDpGrPkU2oilamg/iImJ+Pr5GJH1sq/CbK4hxO+UL+i1t3strr4S/0u/gL7pJzUA+7W8cE/Z199ezs0/jKLthRDZcIm/16iFsnfciKBWK2fc9i+P4tFM5INr2wCZdN1gOHZDrNX/FjKdRfqbdTqw0dedJxYhk2au1TZje3m81E8RgINpGPNOIlmghVNEGiETcQlduNL9o4n7WqT2Sebz7nBBVLD1E+VDTh5iLY329T1E2eHoCnB31hvz9/ZPsdq44/L6k4NaxsNXKeWL9+PVdddZUU8L/yla/wjne8Y+yxD33oQ2QyGf76r/+aaNTv5BVLA5H5veLwPHQ7i1kcRiumKOXSXLHa5uIPvYo/PLWP/sFBBoaSfP5/9h53V3+S/zlXdtwx6WPCVvztdxybOTsekY0c2XQhmm5Qpw2xUW9n4+nwt9vCEyzJL19jYOg5kVc0tu/P3XX8fbdsfS0vNY4NIJgJ/1R6M99xXjTyen4Tmb4NWhv/E/xHaQHgeBp5LNlyBOVAbOy+5y9FK4zcf8Tdwn+5F0/6ei/QH6OISV4GEFg4uoVrWGi6iS6a4Teh69/12ent5D76hVsxN1xIKGBg4vCJL06//XQIEfKEcR10VwRyFNGdvLztFrPHtez/3Oc+x1ujdxF0Mni5JJ+/pXPa7T/4g518+sIeDlvlZGzfTki8R6Kdla5kc2Rm1gm1R23XqE1e566DKk7Pf13WXhdW+O13vXXa/Yrs7+uvvpDGCHzu1unfg89+5Vauvnx6S8j5spYcj7jQv/LSCydYcQmLtoWK8vb0wJiFYSDXS3nH/eQSvu3+aATqtRvgFWvh7jY489kvkym5vPbeP8z4PM3kvFaUl/Get72Fupqqac+HZ1iUQjUEMx24ZoSmiu3sGyzSOZSnMmpRGw9Na5svMu+nQzz+6le/esVF3SvmjqlKQSkUipnT1dUus+8jYtRtzXwS5FAKrI5HOMM6MGF976bXTh+5OcO6933pIvGgSVU0SHXMgtwQlHJQswmqNyN99RcI1fcolgXiO1SziUQwTjQSJdqxl0iqhy63jvYhh4pIgFjw1JeoWA7MdMx5omPTFTnfMwcs9Jhwtp+HU3rcmkYx1kS/aKtfOukmwlFvTTDJ5oP7JqwPaiUuM56Q7UN8m4Nu3YiYfyYP927jj71BvvKEL+Y/vwkuqFNi/lLjZPqehf7+zTd528+kHxXoe1J5zFQHnXmdXc6aY7YPYvHG4J2YwlXqBFmnd3JBrUNj3Bizu2+MQkVQJOm/i96R2vSjv9wn/gonz4nM8wk3gvmeE1QsbVzvSCDZq0YcLHpz8Ni+Nva5dVy4dmHc2BTHogT8eeKrX/0qF110EX//93/P3XffzdatW/nd737HvffeK63zP/7xj8/XSysUJ4wQnIfTWZr2fhe9MIxVGiZsDxNzhrEoTfqc82vEKMgXyL/3kEV3sjjl/lvKNCmsT4UQ4Mfbik+GyD4W9cDDa3bgekcmLkX98AmW5rPc9+HWwzDLqhYiC38yQhw5J4bmEaUgG6R8m6Zp0G1vUgFfw+Ub1qenfqI70kpw936b/xrKTvs6oqTAx3+2iy9ueITOg3vp7zsxZ4VRgmU1fDO5ne/eP1JmQIcbSt/jTPcpAjiyllsAWwYJmJ64XcTyhBnVUZ7m+MfdJXxjp6FtsMDvdz4t33ux/fHeY/H4N+96nN0tx77JOb2SzTOcC/+Bczn/VTxb1qEXmfUDxCfdzkOX4r2kfdcEK/zJyA31cnZxl/DnOu57MBNLyPmylpzMum0uyybMFaVwDb3rr530MeFq8aqKg6znYe4+bFNIZmd8nmZyXgeHhqV4P5PzYgcrMEopArlu4laCuvhaOoZyRIMGZeEAwSlsVkWd2PG2+ZMhHhdZjMvFklahUCiWGvmSQ7L7IE66B6u89oSE9+/stvmY+YMJ64Zqn0eubMNJH1dSzIbiybr3wj5fc0qQ6oSKNVC1AazxOUQKheKkSDRgBGNUW2Hi/a1EejroC9XSk9PIFBwZtBkQAyfFjFG1nxcfCzkmPJnPw2Iby4ZSh/A0A807do5klDV6N2/S7+RN3EleZudvk2L+vX07+HxvPaYGZ9X4Nvvn10NMxQktexbb53g2iASb1pQfwCqE+q5USQr1VcU2NumH2aS18TKtjWatF13z+IX+PN7mvOuY/YgEJ1GWcr02fYJPEYt+q5FspAk33oRZ3oQdb+SfRcLOpJfri+N3+kTm+UZvz2Tbpf75UcwNYqhaG4GLK5JcWVvJxvqYOrWLBCXgz2MW/qOPPsoHP/hB7rjjDn75y1/S0NAgBX2RhV9ZWTlfL62YJ5bMuNpzZca8yEAN5PsI5Psx8wO42QH03AC7o+fzy9DL6M7itxwM5MHCZE/o3hN+OSGg3/SiWt79o7Ypt/nMi0IY0xSHP9qWfCqixUEaohB1tRl7Es103xEnxazRLQIelI56KREtPVtElv5kBKcIqDiZv93JDHK2/hy/y07M8joR4pffwKHMRLExEuhmk7H/hPc10+Me3W6m2xuZyWtWHXTr+a/kFuCx4+7jZ+ltxNwLj+xT8yNyK0NQE4a6CNSGR9rI7XvuGeQf59jm8Xjbzpe15HKhdt9tJ/TZufOZQc47Zx7Oq8jGEFb6yQM4Zic1oUqSRpSu4TzxUIatDZNna3Z2Tj8gPdHtFIrJCAYn/x1SKBQzo6u3j9xQD1ERnRee+dhv9wCs7b+XtYEj1y2i5Evvhj896VNftF2S+aK0zZd178X4ZuAwRKqgbJVvn7/AqL5HsewIxqHxLIKBMI2hKPGeg0T1KL1embzmi4VMGbipbPUXtvbzkpnvUSzbWuCZqtPZc/n/IzL4FPG+x4n17cTKT+74JwhpJS43HpftT9xH+NPiB7E9+H2P34SYf3YtPL/BF/OFI51i8bGS+h4xZyrq0x9M+oL94WEbJ9VNTfEwm/Q2tmptvFI7zGqt28+in6I0xKYp7O4Fe2lmPf48iEghGgo2kYs2oZU348ZXkY+tkgkfohzjKFOnoy0u5mOeb6XOCSpWVt+z1FEC/jzS3NzMN77xjfl8CcUpZG3l4vm6iLrxoVQrVq5HCvXiot7M9aJn+wgV+jCmidjtSTXx/Uk0YBGpmPTCJDTfev5EeN1pOh+3b5JW9OMzjQOJGt599Wp2nFZilx7BNkK4elDavbtGEITtu2kx3NID/OK4r/OByyq44Fww89ey134JngiNlHEB/q+ON+4CTPP8VHQ3tBt+8oXj7vuis8/k8MaLwHPkczWxdG00t4TuFOVS3pZLYfkubvvr/3ZNFW8q9+1nhJtBUTQHYgMe9jNhuZ3unZgvVl08wMUh305rfAvZxRkHL4iSAjPBiFbITPiZbj+eVQmd/AveN1a/fTziYnk2zPQ4Rreb6faHItsmXd9JFbeHXzMjAf/aMyq48ByoCEFVCBIWsn7dqbZ5PN6282UtuRzQ7RxmYfCEPjv3DVYw/BA8Pzz351VY/5fCtQSz7bJ+WlPF6ewdLMoJ3dp4kKrYsSKqCAicCTPdTqGYjBe/+MXqxCgUs6TkuAx1HaSU7CURr5owSTgdwrrw+7vzfM388YT1Q00voBg9uT7dldb5BVl/W/y+xIImpHv8dIuyJqjeyGJA9T2KZYkRgLrT0YJxEmaIyMBB4rk+uhPVDBZc2odyUsQXpS20kyyTsRKYj9rPi2m+R7Fya4G7Zoh0zTmyiYsCK9NBvH8nsb7HiQw+PeW80v3OmcesE2L+I90emZ5WvvhEC2fV6DIzX9jsR5SYv2hYjn2PuJ7tyflC/cHUEcFeiPfOyHymgcOu4F8S0QpTCvVTsVbrpCZYoj4WoDmG3+L+siV7FYfsiyjEmiiG60Ff/ME7M2Uh5hYVy5fl2PcsV9Q7pVDMkP6MQ1X0FP3we67Mnvd0Czs4sebIcAFSPZ1c/cyHZ7XrevqnfKzPKztGwBf12EV2wAAJklqMrB6nYMSwAzHcQBwvGEMPJvj0macRffOFHHhmF9nkIE01FZx/tl97SUQzimnLqa7JLjndof57j8zY9swOVWIzs0ymMy+sp772P4+777MuvIzkSUZlCyFX2HOLJl31G09jT+OtE2u9CzHfGan5Pu6+HyQwss4psi2+hvdPUm7GKGmknjxrwn5kc4vyNYTdmgg80D2by9Z4NCe0ae3lYxU1XHXhdqJDDpetMY67fXMCvv6KMD0ZT4qfz1sdYXtp8gFxaQY/MaIcQpYgOSzynr8sb7EIJg5RSE4dCRpNlPG1hvfzL4UEA/URzPiN2Kmpo9PD5TWc9rzLiAeRdWjFgDUqlqZvKxe7YDt/f08Nvb3Tf07+7mrxmWbebP3EQGe2FoCzfc2VhmuG2X/+x0l0P8IFwdtoTjw37WfeSNQQatnOM4PwrLedRGUNyYG5Pa+inxdW+lamk7gVoya2ns7hHIlwgPKIdYyDyWWXXSaDBKez0W9paeHyyy8/oeNQKMbzzDPPsGWLcCdRKBQnSlf/MPnBDqJuGi3SONPYSx7tgQuT/0NNYHhsna1Z9K579Um/CclcCcPQqIha1CVCUMpCtg+qNkL1Jl9gXASovkexbBHCvChVEUxgBkLUDR2mLNNNV7CWwUKIwWyRVN6mIhIgYqmpulNd+/mUzvco5pxlWQtcuLXFmugXbfVL0e080YGniI0I+uOz84OrzqSpz68XPp4t2mF+EXw//V6c3wycxgN927nd205DTbVvs6/E/AVnqfc96RIcGPaF+tZhl1yyl3C2jTVuOxv1Nhq9cr5l/9kxz3MwaPeq2ThNNv3RiCSudLCWUmwV3z49i2OVHbNNNjx54s5yYCHmFhXLl6Xe96wk5mxUUCqV+NnPfsYjjzzC4OAgjnNsVKCIJL711hExS6FYYgzmPKqic58JamU7CWY6ZBu9bWW7ZKb3Y/XX8YvIq2QNIBGpeDgFyRLEqeXq0Oxes16bXBQtD8K3Aq8jYXkQLsMMlxGKlFEWDVEd0agMQpUBVdPu3WDbJTsWle3ZorFU0w1cPSykZE4sF38iTiDGobPeO7ONPY93mb/mxg99espN/s+7b+Dqcw1SQ28n4+S56a+f4G2f/v6U27/3La9i9QWn0expuGjsx+D/VfhRtKMOBOK2aKHcK7mj9EJKmimz8UueIUV92bQAthbE1U05MB2VJ0eXb7jhQb7+uanfs7f93d9y6aWnETSQ7YHIW/nA/5l6+4/9ww1c/bzp3mODm96xOD6Dx9v23X/zZlmnanRiQlzEi9c4mdccxbadCZMek+17WaDpJOsvIFl7Hh94y9f5m8//95Sbnn/FVbSPRG27moH1/BvgZ3P/OSlGGggl90sRv9aqYJg4Pak8bYNZVldFyefzfOlLX2L//v2sW7eOT37yk7z+9a+fcn+f+cxnFvWElW3b3H///dLmXzgFiKAE01ST1YuJPXv2KAFfsSRYbP2J43oMdLVSSPYSj8bxjJmlFYnrqJ8+neTb5s8nrB9c8yfYoZPLjinYLqlCicayMI3lYQzhVDV0GBJNULEaIounvJvqexTLHvF9azwLzBChQDtrRJ3faDldwRjDOZuBbGlEyLewTOVveqpqP8/HfI/i1LIcaoEfLzs/VXuObGKuJ5hpl0J+ePg5Xrh9NVeK8oAp+HU7PNgJHRm4RPdrYVdpKV5uPCybYN9gA7/t38Z3dm0jX7GVM1aV87x6P8FBcWpZSn2PSCjbOwz7h1yGB/swUm1UF9vYqLfzEq2NDVqHn1EvpiFGpiL2uQ3czLECvuBZr4mNTC7gZ61qSvFVlOLN5KNNFGLNFKKNeMLNdYUy13OLS6W8iGJ+WEp9z0pH8zwRk3NydHR08KIXvUhGy0+3OyHgTybsKxSLhb6uQ7TuvBcnnyZas2bCY3v7bDZUm7PMpu8fJ9R3YmX9ZaAwMO1Tb7cv5b3230z62B+Df0Wllp70sWEvQptXI6MZRevwqkgalZRClXjhKox4DfUj9bpHa3YHFsFv9q/ueXDebM/mc9+LnRP92xfLuVqI414sn8Gptr3q8ou5876H5uWcLJb3fSG483/v45YvfpX2gezYOuFGcctVIa47LcC37Bdxs/06sviRU5lnHiR1360Uhuf2XBnFJFaul2z5Bvrip3Mw6bCxLsb3v/Rx/u9XvjzhGkoMtITN8BNPPDEhE19k5t9yyy1cd911LFZuv/12brzxxiV33CsNEZh7zTXXLPRhKBRLrj/pHExzaOd9aL27ideuxQ1EZvS8e9oguutbvMW8Y2xd0Yix7/mfn/E+prLOF2VZysImLZVR6stCMNzmP1izCRrOAn3xiISq71GsGFwXBg/AYCsMH8Z1XQatOnqyHkPZEsO5IuGAKTPyjUX0HV2uzHq+R6FYhIip+f1J2Pz4zWwuPHHc7YXI+oi3jd74VuJNWzl9VYUS81dw3yM+P/152DfsNyHanzf8P6xxWtkohfp2okKonwG2p7Ot8A2K0qYUqkOwJgGr43B94YecMXyvzKgvxldRiK0aEeqbpHOiYnLUPJ9iPvuedNtuQrVr2HjeVURix7pcKJaogP+6172OH/zgB3L5l3/5l3LSZKqsh9WrV5/syykUS0rADw3vZ/0j/zSr4/mdu4U/LX5w0sc+G/gqEQoc8mqlSN/mVZMM1ECkmvJYhMYoNEWhIQr10aUTSXt0BvBc2p7N574XOyf6ty+Wc7UQx71YPoOj2z55sI/T11QzOJTkvR/+1JT7vuUjN00qHs/kNcUAYLrI3Kn2vZwQ5+mJB3+JvetntFhJLl9jTLCuF0FRby6+l+e8VfK+5zrQsYuXVA9y7tq5+5wE0214ukm2fCP79LV847Mf4ef/+fUpt3/nO9/Jy1/+8rHMU2Gbv5j7NSG2XX/99VM+fttttykRf5GgRDTFYmcx9ieu67Frz7MMPfsbqrRhNGFPPwNKDnz4nh5u827E0o4Ea3Vt+nNpm3syCFtu23FZVRlhbXUUo5CEVBfUbIams8FaXKkXqu9RrDiyA9D3HCTbIULraDcAAQAASURBVNOLHa2nz47Qm84znPOz8eOhAImQqYT8FSaiKRQnS9OurxDvewzDPhKoPhP2ew38a+LvWdW0mnPrIHGCNcoVS6fvEapQd9ajq7efvuEUv86tlaL9kKiFOo5fWe9jsz4SAHqC/HvLzUSrW6RoHx//WRKOUJoKUDtVc4sLPb+rWFwoAX+FCfhVVVVs376d++67b26OSqFYhAJ+3vYImZq8uhH16YPpw4TSbXIpBJehpssZaLlabjuQh9YUHEhC11CWrwy89YSPZcCL8Xt3C39d+ocJ6y0dmmLQLFocVkWRYr1ooq63QqFYXoi+x8Tlxde/ddr6VSID/Fe3f31WAQvzte+liOYUqN17G1WH7kAbV7U4H6zmeu9mnkhOzIIUw823bINXrvNLm540rk04uZ9CrIne0HpecNH5uCJYYArEe5LNZrEsa0nYXAv7//GZskcjgkAPHDiwIj5ri52hoSHKy8sX+jAUiiXVn4jSJwcffwCnYxflVXW4oZl9h/5rP2x+9qu82nhwbF3OqubAJZ+ZsQX/ZORth95knqaKMOtrYn5Ab/9zUNYC9duhrInFhup7FCsSpwT9e/3SFsOHpb1+IVJPd9qWQTjJXIl0QQn588nYfI9CsdxwHTm+jA3sItr/pLTc173p3XFdT2NH4V9JEkPEtJ9WCefXwfn10GIO4hphaemvWFp9j+uUGO7rJtnfiZPsJJzrpLrUzjraSWg59rv1XFH87KTP/XLgC7zM+N1xXyNvxMhHV+HEmyjKjPpVZMvW4xnq86JQLIW+R2XgLz7mJMRL1GU9//zz52JXCsWiwigOj4n0ZvIQ0ay43Y7h5I7Z9qnDB7i507cXGpzgJBThn4MVk9aeF1ZCrV4d+71G9nkN7PMa2e82yGjXgpmQ0YlXxUfE+hHBXtjdj0sKVSgUKwARLTudwC4Qj//+sV0nXPdvPve9FBE11bo3v4FU7Xk0PvWvBHPdcn3XaX/Jh8sjfPFxuG9cmTYX+PpuP2Dr7WeAdbI6kW5SjNQTzHTxs9t+Na14LxC2+l/4whd473vfy2JH1KieTmwTiMdFQOiVV4oqjgqFQrF0+hMRF9/T3UVuqIdqy8UNzsxyMG/DD57z+DO3iZQeJq7544z+jdeelHgvrPP70wWqYkFq42EilulbdYuggkSj3xQKxeLACEDtVghXgiiZkWwnOLSPlrJmauJxelOFMSG/fSinMvIVCsXM0Q1y5Rtl6133anQ7R2ToWSIDuzH6nqYssx9DjmqPsNtbLcV7gevBrn6/iXHv18Lf4yrvIZLhFpzK9RTia8gn1pCPtZzUdYti7jDz/ZjpDrIDndjJToLZTiqKndS6fRjaUXmc4+aXV2vdBClS4Nj38Tl3FYwT8HN6lExkFV6iCVvY30d9sd62yuYos0GhUCgUsk+fi9Nw+umn09raqs6oYsmj5fpZdfBHhNOHiTzVh1lKzvi5gXQbjx5lMTTKY+4GarUhKc5LkX5ErD/k1eFopsyiFzWARHuZWMahJqyueRQKBbQNOdLqaibMdLvZPGc2+17KZCu2sO/Cm6nb+wM01yZTtZ0g8J6zYG0C/uNpxuXnw91t0JaGfzoPKk8yuNyxErjFYToO7Z/R9vv3z2y7hUbY/M/ldor5F0ivueYadZoVi5LF2J8MZktk+g6h5/sxyqtxZjh5+fODwqZU46tcw/ecF/DOwM94TdkzDDecXOkaIfYFTZ2KqEWN8CvNDYJTgIo1UL1p0Q40VN+jWNHE6yCUgN4YJDtkNn44VEZLeR018aAS8ud5zKUs9BUrAVFbPF19pmxsgm47R3jwWYpdu7H6n2ZVaT8Pu1unfP5m5zl03aM81wrtR7QAD03WLpdifnw1OSHsx1fjBvxAAMXc9j2akx/Las/ZfkLBflGzPgn/0Ps51nuTzBEc59JPiPvrtQ52e2sI6rC2DNaPtLPNs2grRnyxXgr15Yv2WlKhUBwfdd2zwgR8kfX1xje+kd27d7Nt27a52KVCsTDoFnVd98/qqZu0NjRcPGmoPJG/Lb1bLiuCvvAjhPpr4v5SZNafdMamQqFY1og6VXO53ana93LIxu/a/Ea/MNwIYox67QakQ8qn/giOfSRCfc8QvOsB+OfzYONJOo+LLPzVNTOb7BA20kuBhoaGOd1OoVCsXBZjf9LZN0B+sIsqPYcTXD2j52Rt+NHeI/cHSbB79Rs4b7NzUjVB8yWHbNGhqTxEU3kY3bUh1QkVa6F6AwSUjalCsWgJhKHhTIhUgiWy8Tuh71nCiUZaKssmFfKjQZNEKEDAULWEFQrFiQv6mZozoeZMSsCzpRwNQyX+tB9+1wUHU0e2rSTJWt13qDsaUX4ulGmTjc4jJYFKwUoK0QZyiXX0bHydentOoLSfleslkOvFyvX4y3wveqZHrs9qYd5R9iUp2ndkJiYXvCRQzxbjxIL8s4TpCTTxptU2sQa/XOvEn5R1DLM05h0UCoViOTEnAn5tbS0vf/nLueiii3jnO9/JOeecM2XNzEsvvXQuXlKhmFNEPbk7nuzi0edS/AOV1DIw7fZJL8IebxXPus084zXL5bNe05h4L+rUrx4R6EcFe5FVXybSNxUKheIEOXfHdlmHfjqr+4a6Gs47a/ui2veyYZLI8vPq4EvnJ9n8hw/xzdKV/JvzUjlt0Z+H9/8WPnYBbD6JmAdPD/C6617Fp7/5C1zhWzgForazuPZaClx22WWyJvV0ttctLS1cfvnlp/S4FArF0mOx9SfJfIlMbytk+wjEKrH1mQ2zf34AkmK2fISwCa9e79vdnox1fl+6QHXMoi4RJhwwYOAQRKr9mvfx+lnvW6FQnMJrz/IWCFdAaC+kuqWtPtn+ESE/MibkD2WLpPI2XcM5rIAhhXz5vVcoFIpZ4AXCrKkRDd64BToz8Eg3PNwF9uAg+9wG1uszdzgKFAZko5SHjZNvU3noV3i6SSlUTTFUTSlctWJqptcNP0Z1qoNAfgBTnKt8vzxfZnF6R1iLLI925Mlx7Hna7zbCFD8DHV41PWY9mVADXryBSEUDkapVOKFK+dtz2lz9YQqFQqFYPAK+mBjRNE3W/fvoRz8qb09Xq1WhWGwUSg7vuf1xefuqQAtXGL6An/cCPOc18azXzB53lVw+4zbTReWY91DIgPUV8Pwy2DBiLSSy6lXwu0KhmAsqwhqmafDet9/AjR+8ecrt3vO2G6SYe6LM576XM8JW/4L9XyBKNx8I/Cdb9MO8v3SDzMYXGZX//DB87ELYdBKZ+Ga8lr94xeV846f3TrnN3//932NZS6PWoGma3HLLLVx//fVTbvOZz3xGfdYWCZs3b17oQ1Aolkx/0tGfJDfQTjlpnLBQ4I9PtgQ/2jdx3SvXgXC7PxkGMkVClkFlJOhb52f7wbP9mvdVU8ycLyJU36NQjCMYh4YdEOvyrfWFkD+wH8KVhKM1Y0L+QLbIcKZEqlBiMFNEFL6Kh02ilomuLI5nPOZSKBTH0hCFa9b5LVVczR09t7CrM43Xv5cN7kFO0w9ymnaQ1XrPtKfvV0ONfPE+2FIBWythawU0Rf14pZp9P8S0MxO2twMxKeiXQlWUQpU4gQSOFccOJLCtOE4gjh0swxE11xcSz0W3sxh2zl+WMrIcqxDgjaJYDktHv85tb5306Zv676Jy+MlZvfQarZunvWNdn572WmQp1zatgXSoASfmC/VV1XVUxYIERIzYuO2VUqNQrDzUdc/SQfOE6n6SfPjDH55WtB/Phz70oZN9OYViXrjok3fTMZznYn0XMXJSrG/16nDHWeJHzSP1f0bF+kYh1quxnkKhOAX86p4H+fSXb52QLS+y54UAf/UVlyzafS9HGp6+lcq2uyes+6O7gb8u/gO9I8PhaAA+fsHJ2ekL67xbPvsF/uO/H8R13bH1QpQS4v1nP/tZlhq33347N95444TMWZFJK8S46667bkGPTaFQLC0WQ3+SLdo8+dRTpA88wqpwkVJizYye9709Ht991htz8BLjjH9/IcQCnJR1fm+6QHNFhPW1UULYMLAPKtf7ltyxmtnvXKFQLCxOCQYPwtBhvyRGKQvxBgj54pXtetJWXwTxpPM2w/kSxZJLLGQSD5mYurLXVygUc4ftwrND8Fgv/LEXOoeybKHVF/T1g2zTDrJB6yCg+fLwp0p/yledaybsIxGAM8tzfDt1w6yOIRdfy/4LPj7pYw27/w3DzuLqQVkazzUsPFmeSBtx2BOm/0du+83FCcQYaHnxpPusf+abhJP7Jwr2Tv64x+nqFk9c/g16cpq0u2/PwKGU3/4y86+8Wj/xUq4i4exvSu/mPneHPHIRDLGubKQl/KUo46pQKBQnQrptN6HaNWw87yoisQUOkFLMnYCvUCwH/vJbj3LX7u4JF5JSqC/3l0HP5txGEcG+oIepUChWGAcGbNZWHjHMsW2HR3fuord/UNalF9b2c5VhOJ/7Xm5E+5+g+YkvykmB8XR4lby1+B52e76AExsR8cVvyWwR9e6yqQE+9h9383T7IM1r1vGZWz7L6atrWarYts39999PZ2enrFEt3JzUZ21xcccdd/DiF08+eaVQLCYWuj/Z252i86lfEx18xrcgDcSO+5x0Cf7t7l28V/s2n7Ov5Vfuufz5Zp3XbeKkrPM7h/NURAKsqY5SE7X8TF0h7tVuhdotLAVU36NQHIf8MPTvg3QXDHeCaflCvhkc6wuSeZuBdJFkoUQqVyJTsAkGDKJBk0jAmHECzkoecykUihMjU4In+nwxf2efX5c9gE2L1s16rYO9XhP7vcZjnrdRa+Ou4PtmdbpTVWdw6OybJn1s06/fRqAg/EhOjEK0kb0XfWbSx9b8/iNEh55hNpye/3fSk9jdv9u8nXeaPzlmve3pdHpVHPZqOOzVjixr6NRqIVpDZVkZa8t1KdaL0q2iDJNCoVCc7HWPEvAXH6p7VyhGePmZjbTEXKoy+1hjDdPS1Dyh7PHePpR4r1AoTjnOkaTrMcv7C87dMS+vNZ/7Xm5kqs5g//M+QsvOWwhmj9QAbNQG+J71Md5Q/Eee8NZLkeYDD8MnLvSDwWbDz3/7NJ/50v+js3dI3t/9h9/y2G/u49Of/jRv/PPXsVTtr6+88sqFPgzFNBQKBXV+FEuChexPCrbDYG87heFe6oIGJTM6o+f9dJ/H27Tb2ay38TXr89L+1Kt8Mx6zV/CTOZuAoVERtagS4n22z88qk9b5M7P1XwyovkehOA4iKKfxLEh2QKgckp2+04YVly4buhmiPByQLVO0ZUb+cLYkbydzJXk/FvTt9S1TZeVPNeZSKBQnhnCfu7DBb4LurBD0TZ4ebOKZwSaZbT4ZaS/MF+1X0qT10aT100Qf9drAWOb+dOzNJbj3ENSE/BJEEdMXssVSd2Y3ltGE24kMhoK8DRkbWSJPlD6qsE1mdqV3LBXaMGnvWAH/EXcLX7f/hE6vki6vSi6FcN9LGcGAKQV6MY8glleVwaoYqK5boVCcLOq6ZwUL+A8++CCPPfYYQ0NDlJWVcfbZZ3PJJcp6V7H4ecWZjVxUZ9O682mcvDNBvFcoFAqF4miK0Ub2P+//0LzrS8T6d42tL9OyfMf6JG8o3sTj3gZfxP+tL+ILK7sTLW1w4wc/dcz6ns52/uL1f0YoYExbA1qhUCgU80f3cIF8/2ESXhIvXD1iwzo9qaJwwd7JWcbesXVbtVb2B3RyszyOkuOSypdoKA9RXxZCtwuQ6YWqDVC9EYyT8OVXKBSLD9HXlDXJLEyirb6IL77zwnXDikG0FgIhKdKLVp/wGMoVGc6VSBdsMnmb7lQeQ9Okxb4Q9HU1AaJQKOaQugi8qMVvAjEm3jMIzwzC0wOwZ8gXxjup4rP2xPGsjkstgzRq/azS+qjRhqjQUlSSpEpLydtVJPl1soYvPj756z8XLMyq3KkoP3Lt/0DOPvaxWwMBGicxeUp6YVJESHkRUoQZ8BL0ewn6SNDnldHvlTHoxSd9vYfc7fxB205LHJpjcGYcXh6H1XGoDc/o0lKhUCgUy5g5E/D/+Mc/8vrXv549e/bI+8KZf9SWa/PmzXzrW9/i3HPPnauXUyhOOUHlIq1QKBYA1fcsbtxAjNYd76P+2e9QdfhXY+sTWpZvW5/kjcV/ZKe3gdSoiH+Rb3E305IGn/7yrdNu8w833shrXvMaZT+vmHNEIK5CoZga23Hp7esmP9xDjVHCDs7sO/PjvX72/XgGq84mV7Zh1qdbZNSWhQNUx4LEAgYMHIR4PZS3QKRySb2Nqu9RKE4AYZ8vgnTKmmG4zc/KF0L+4H4IRCEmhPywdOeoiQVlyxZthnIlKeZnRsT8tmxRBoVGLZOwZaxIMV+NuRSK+UWUljun1m8Cx4PDKdg3DK2pI603JyrR63RRJTPS/+jNxp3I429K7yJMkbBWIEKBEEV0WfXelTXj9dGl5i/FevFo0ouSmyL5/7P2dXzNfvmIWB8mSVTa4nvMzM2kKgQNEWiIQnMcWmIQcG3OqFflWhUKxalFXfcsHTRPKO0nyd69e6U4n0wmZbb9FVdcIesPijqE9957Lw888IAciD/yyCNs3Lhxbo5coZgH+roO0brzXpx8mmiNX79YoVAoFIrj4nnU7rudmgM/nbBaDOz/ovj/jU08JCz45IWwZgYi/sOP7uSt7/qn425355138aIXvVC9SQqFQnEK6RjKsf+p36N376I6EaEUHpmRnobhAvznPb/ny+bnJqzfd8EnyMdnN/ZIF0rSPr+5MsyG2jhmtgdKOajZAk1ng66ikBWKFYNw3xgv5Gf6pIDvC/mRCZu6nkcybzOULZHKF8kWHTJFh0LJWfFi/lJABPo+unMXvf2D1FRVcO6O7bIc2ko/FsXSR1jVH0pDa/KIqC/s+PvyUFrkpS6EfX9l0HcfECK9EOvro9AYhbowhFQhY4VCschJt+0mVLuGjeddRSSmkjoWA3Py0/HRj36UdDrND37wA6677roJj334wx/mhz/8Ia997Wv52Mc+xje/+c25eEmF4pTTk3KojatBiEKhUH2PYhI0jZ7118mo/doDPxlbHddyfNP6lzERP1mE9/8Wbr4IaZM3HWISbCY8vf8QL1JvimKO2blzJzt27FDnVaGYBBED39U/RH6wiyY9ix1smtF5+vFel7frP5ywbrDmebMW74UAN5gtURsPUpcIYTp5yPZD1SY/K3cJiveq71EoTgIzCFXroWzVOCG/D4YOgS6KQ1dAqFz2DSLLvjwckK3ohEjmhJBvy6AgIeanizb9mYIU8yOWaMvbZn8pzfeIElvCpaurp3dsXX1tDe99+w1cfcUlK/ZYFMuDSAC2VPhtPCL9UIylRYa+EPP7ctA7suzPQ6Y0Uqve9u3v50LsDxkQDfjCfMSEqAlxy8+krxxtotsNQUXI32Y59z0KhWL5oPqeFSbg/+///i+vfOUrjxHvR7n22mu55ppr5HYKxVIlWfCoPY7YolAoFKrvWcFoGr0brpPL2v0/niDif8u6WYr4f/A2M1yEDz8Cn7sEyoJT705ksMwEI1oh6x8HjJlZ9ykUM6G1tVUJ+ArFFPSli+T62wgUhzCjCYr68WvMDxXAPvQ7tpiHx9aJoK/+DdfO+jwPZopELYOKiEVFOAD9rRBvgIoWCJcvyfdP9T0KxVwK+c2QFEJ+J+SHITcAqS4Ixn0x34rJ61bL0GUJDtGKjksq7zt7CDE/N5KZP5AuEgwYhEWzjGV33blU5nuEYH7jB28+Zr0Q0P31N50y4XwxHYti+SPih8TYWbSZFB0SAn5unKA/WtNe2uVrE5ej603dF+EjI6K9cQpilpZK36NQKJYXqu9ZYQJ+X18fW7ZsmXYb8fjPf/7zuXg5hUKhUCgUikVL73ohxggR/0dHVhoB0oTH7gobwE/+AT56AQSmmP8U9pMig2V8RsvRVNfV03zaubQN5lhbHZ3Tv0OhUCgUk9MxkCY/0EadlsQOzSx7/kfPiez7H00U4GsvpBBbNavTnLcdsiWbpvIwDeUh3y7bCIwI+KoUmEKhEDN+FlSug/LVvjtHutvPyBdifroLHNsX8kXAjxmSp0yI+VXRoGwlx/PFfJGZny+RKzmyJVMlEYEkxfyQ5Yv6yzk7f7EgrOpFtvt0iMdfeNmFGIaxYo5FoZgMMcYOWH4JO4VCoVAolipzEjJbU1PD7t27p93mmWeeobq6ei5eTqFQKBQKhWJR07v+NfSs87Mq7UCMzvM+wJrmlgnb7OqH/7vLtwOcDFE7UthPTsd73vYWhrIFOoayMkNKoVAoFPPLULZIdqANLTdAMBjCNY8EZ03FQB6Mww+xQe8YW+fK7PtXz9rCX2TDVkYs6hJhQp7wje2DRNOStc5XKBTziOgTYrVQvx1aLoSms6H+DKhcK71AGDgA/Xv97PxCGlz/mjJgaFRGLdZURdjSkGBjXZz1NVHWVEZl2Q6RhZ/O27QNZOlK5qUNf8F2ZR+lmHtEnfnpAnsF4vH/n737gHPsLO/F/zv9qEszkqaXne221173zrphDLZjDNjkJph7CXD/BHMp13DjmwRTkxBihwAJCVxIIO1ybZqpxrGxDQ5gwOCG27bZ6UW969T/5301M7uzuzOjmdX05wvnc6QjjXSkWb8jnd95nveXv3l2UY/Lfl+W4/DfXcmodV7IV9mJGyayZZP/3UuXDD6lQiJfRiJbwI9++ou69uVnTz7Dp3shhBBCCCGrVIF/1VVX4d///d/x1a9+lc91f7yvf/3ruP/++/H7v//7jXg6QlZFb4QOhBFCaOwh9Zvc+jo4koZC8xkwAt145x5gpFgL7qf9cADoDQK/w46fnkSt7eSdJ5lbMoo/fuuNuPbS09Bvj2Myr+NIqohdrUH6FZGGuPbaa+mdJOQkRtJl3j6/WcjB9rTW9R59Y7+F94hHp1Zh0q2Xw/C1L+k9ZvNUS5KAiE9FlE3Omj4EBFqB8PptnT+Nxh5Clpmi18YKtvBK/InaUs0BRgEojgNmBVA8tRb7qg9QvJBFAWGPwhcXLiqmg0KVVeZbKFYtVKaq8wsFC5bt8Hb7uiJBU0Re1b/WK/TXw/GeyWR60ferhfO1hU25ZTvu0cV24DgWXNuEItiQ4UBma9eGCAfS1FrkazbpS20NQUJ1/GBd+zJ+4GnkWyS4ogRRUiBIMgRRhigrfJFVL0RFhyhurCkZCNlIYw8hZOOhsWeTBfh33XXXTED/d3/3d7jyyivR1taGsbExPProo3j88ccRCATwp3/6p414OkJWRdVyIa/EBEiEEEJjz4aR7L1+5jKbU++PzwPe9xNgrHT0Pv/nOaDTB5wTnzvEv/oVF/OqG3ZALtYcwfln74HiGlAKg2gL+PBMLoxxn4q2oAchNmkfIacom83C41m4spiQzYSFVZnkKOxCAj5FQFVZeNLSRBnQh36CXnl8ZpsNCcmtNy9pH1gAky0baGWt80MeiKzyXhA3TOt8GnsIWUF6qLawNvsszK9kptYszC/WAv3cCGBXa2G+4gVkDYKkwSOr8CgaYn6Nh8MsxC8YFkpTYT4L+Nk6XbBgOg5UWYIuizzY1+S1F+ivh+M97DtAPTSPDxOZPBzbgGOxcN6BChuyaENn6+nFdSBIEgRNgaQoPGCXZK0WtLOuDZIMUZQgCLXLEg/fa5d3Ztj3ja8uuC89u/Yi0r0bjmXAtmw4tgnHrp004JRLqGTH+D4KigciW1S2eCFrOn8+Qja69TD2EEI2Hhp71o+GfBratm0bHnroIbz5zW/Gf/7nf/JFEISZtlk7d+7EV77yFWzfvr0RT0fIqhjNO9im0VnBhBAae8jSsTn47roAuONxwLQseFFFDj584kngry8HOv31PQ77iMXaNttKAJ7KJLpUHyayARzxFrHHE+Kfwwg5FU888QRuuukmehMJOcZopoxqchBNyMPWm4A6xlpWff8B6ZuztqXbXwHT27Kk9zZVMhDwKIj6VPglC8gmgOZttWUDhB009hCyClgo622qLYxtTgX5WaCcmarOLwJmqXbdqgKOAYgKD/RlWUdI1hDyaIBfg+EIKFtAkQX6ho2yYaHKAn3L4e3Y2YlIkihCk0Sosgh1DVTp13u8h839fuxJteft3cOnvVpWLGh3HZxzxla0xpoxNnlMO6/jtEXDuHqrBsUagiirkFQWzquQFQ0KXyuQZQ0Sq35X2O0qFEWHIKuANL0ogCifuPDttdd647ZL0PWhv8bg4OCc+9Ld3Y03veN/QYIN06zCMAyY1QpMi62rMCpFVEs5mOUCLKMC1yjCrpZglZIwzQpsSYWg+iHrQUieAD8JhL7jkFO1Kv8Nz4OONRNCaOwh82nYN/zzzz8fL7zwAn7605/i17/+NT9zPhQK4eyzz8all17aqKchhBBCCFnXegLAn55VQvczn4YuVPEm449RtFR85BfAX18GBNTZ9//hjx4/SQv9GD7wrrfiVVdcDE/uIGJqGuMF1kpfw2ShinhAX/kXRgghGxirJE2mEjDzEwhKVRjawpWQqQoQGn4UnXJiZpslyEj1La36noVhtu2iKaSihY3z2cOAvwUIdR0N3ggh5FSxoNYXrS184KrWwnxWkW+Wjy5sO18qtdusJOBYUB0bqusixMJeUYalSajILNR3UWbdS2wHhuXCYOuyi1LBRYZVocoSFBbqSyJktoisg5VQO1dqZhp1d9Za4IVDx2ybKiQSprfNOf/69PbpkwZiUIujs07MYk3jZwgCfvDYL/GXn/9/GJ1MzWxuizXhj97xX3DdvgvmfDtn7aPrTG2rrQGH38T317XhOqxK3YZts/He4WvLBUxHgCuIeNdt1+NP//qf53yuD7//Heg47QIoqg5V80BVNYiKVvudStrRgH46rF9i63pZlnHPPffg1ltvnfM+d999N6/sBxR+kgBr4HAytmmgVMyiWsyhUsxPhfp5mJUi7EoedmUSZm4QRUGHoAch6yGomgZVYv826KRlUr95v1fzqesIIYSQtaXhp+hfcsklfCGEEEIIISdSypN4ff9fQZeG+PVPKl/Ae8zbMVIUeCX+Ry8EJPHoQYY77vrECY/BDjrw7R+9E6+5bA+08hi6NQ8OZyIY9Kpo9mmQRDqgRQghjTKWraCSGELIycP1RWpt6xfwjYPAEbsbjwun4zLpt3xbuuNKmJ6pUGwRHNdFumggGtB4eK9UU7WgibXOZ+2vCSFkucissp7N9XTMfE+OA1jTYX4JMEq166x637EA26qtHQuya8Fv2/C7NsACapcF+A6qJmu3X6vOZ5dN24RpObAMGxWHhdYuz7vZZ1oW7EuSyNcs3JdYeIvpcVjg46HLrosnD9+PuTLny3TY65wJ/GcH/w88+gu89+N/f8LPsDD/vR/7O/yNa+PVrzh/njdxah9Fib91bDZ5i72FbG56vq6F9JYrQJIkSIoM2SNBYZXysgyvLPOK+Vt3XYamzl34+F99BiOjYzOP3tXZgXv+8i9wy61vrAX0KxBs33LLLbj33ntxxx13zKrE7+rq4uE+u70erENAIBzjywzHRqWYQyE7gXImgXI+BauYhV3OwslNouwoyIpeiJ4gVN0HrypBXuLJCGRzWPB7Ne6kEJ8QQsiaI7jTfe4JIUiMDeDIU4/ArhTgi82eQzJbcRDS6QsBIWRl0diz8XQ+8xmExn8+a9unrZvxKat2kOuGXuAP99Ta+11369tmVQgcj1UM/PC+L8JbGoIt+/AyeqC07sQZHSF0RuYocyGkDv39/ejtXf/zaRPSCJbt4NeHxpB+8XFscQdghbfCZZWL88hWgbc8DFTt2vVzhJfxl03fhnPOW2Hp9c1jfKxMyYRp2+hq8qIvIkNIHgSi24C2swFfMzYKGnsI2SAceyrIZ4G+ORXoT4X77DAkr0Cfqph3XTiOg7JloVy1eMt91m6fhfwV04FpuzAcdp3N2WpPXXchixJvxc+r9SWJzyPN1tJ0yC8Itbb8c1XVT2HRfyGXhT8YOkmA78KyLNx0/fUYHzsamB+vpbUF3/7OtyEILJh3YbuoVdA7LizXhWljZr/ZCWC1kxBYxwEW0rNFri2KAk1VoWtsrcGjqdBVGboiQjumzTfbp8ceewyjo6Noa2vDFVdcwR9vNazIvlhVVHKTKGUmUc4lUClkYJUysEtZVGwgCz9sNQyPrsGrynxqBrL2rVQ7+8V8r17p/47oeM/mnDqBkNU219hTGHoeerwX28+/Fl5/aFX2jTSgAv+jH/0ob1N0++23o6mpiV+vB/uZD37wg0t5SkJWHYX3hBAae0gjjOz+A+j5I9BKozPb3iN/EwNOC77uvALf7a+12W9KPDvvQQaG3f7L3zyLi8/eDT3Xj3avHy+nEhjyqLyNPh28IktF4T0hx4y1uQpKySH4nCxEPbBgeM9869DR8J7pV3egcOH7oS7hWKHlOMhXDLSGPWgJahByg7VqWNY6fwOF9wyNPYRsELx9vlSr3q/n7gB8U8uxTNtB2bRRMWy+Lk2tq6YNy2bBOAv4ayF/yXZqFe0s9K/WwnMWz7MKfrawMJ9fnlqLM9m+AMEfQd6Zffxy2m9+9eN5w3tmfGwc9z7+Es44/7Kp9v8CZKX2POy6IgnwyBJv+866CGiKNBPKa7IIfeq6LksQ6+iixaryr776aqwFK7Ivsga9qZMv/OSQcgZGYRLF9ASKmQSCuQSs8iCKJR3pkh+G6INXk3mYz95ffiIH2bTt7FlwW+/36ovO24uVRMealx9NnUAIjT2bLsD/8Ic/zD/MvvGNb+QBPrteDwrwyXp2IGFhW7Ths04QQgiNPZuMo/hx5Oz/hb5ffAiymZvZ/hfK/8GI2YyfOafjH54DXlNN1/V47CxyV9Jg6U0ImZOI2h4k8k0YTJewNeZfxldCNrL7778fN91002rvBiGrznFcjGeKqKaGERcKMLXOBX8mbwDf6Z+97XVbsaTwnskUTQR0Bc0+FX47V6tc3aCt82nsIYQci4XdbAnqbC71o1gzUcOeasVvzV7zxa5V6rMQ33YcsCy/dplVx7OK/9q6Vmjvwun/Fdzuc48W4E/V4LPYNzExXtcvxWcXeBesWpt/AerUvisyC+qnF4lOsD1V7MQQXzNUtsR2IFJOwcmNopgeRzE7iWguiaqRQd7wI2v4YUCCR5Hh02Tosjjr5AyyOdrZs+/LjbxfI9Gx5uVFUycQQmPPerekNPKRRx7h6+7u7lnXCSGEELJG8KNP7gnXa4cr3NockbylJLUXXA2mtwUDe/8nep/8M4isnSgAVbDxD8qn8DrjIzjoduA/kvW1WGYt4Phj6s3w5A6hVc7ixeQRjPq3oy2k88oTQgghS5MoVFFMjUAzM9BUBRVl4elJWHhfto5eD6rAq3uW9vwVy0bZstEV8KLFJwKZcaBpKxDdXpvnmBBCNiEWwtaq1yUE5rkfb2Hv1KrxpwP96eusgn/ar48AZ3dHah39p04QYGtWuV0+vb6TpS4/azvO621qwKsjdWPz3vuiEH1RBGI7ECiMw82PoZJP8nb7ldwYCo6KnBNCpqjz6Qx8ai3MZydUkNVpZc4q7+fDbr9m38UNa2c//X25Ufcj68Nq/FsjhJBGW9IR3X379s17nRBCCCEN5DoQHJMHvWxdu25BcG0I7LLL5pRkl2vLTOnIsdUF/DKb4bG2TYAzNedk7TaXB/lTa35dAgSJr11RhivIcMXp6+w2to2tqYJhqcrhHRg+/Q/R9exnZraFhBL+SfkkXmt8DMmWPfBFYiim527319YSw/ln75n6HYswvK3wFccQV3Qk0y0Y8GvY1Rpc8j4SQshmxgKckUwZleQQ2oQ8TD264ByaJRP43iFr1lftm/sAXV7a86eLJiJeBVG/BrUwAnijQKiDBxaEkMXNzc2OXbF232TzqLXPXziY+TWAnubjG/jXdN14He7s6sLg4OCcP88KnK688spT2ldyith0DeFuCKEueCoZePLjQHES1XwSxfQYCpU0cmIIWdeHRKHCv/v6VImH+axTAlkZq9HOnn1WY+3553veWd+ryYawlqdOIISQejXkm8s///M/Y+/evTjzzDPnvM9zzz2HX//613jzm9/ciKckZMV5FQqpCCHLhwXzgl2dCelF2+DrgNAEb2YErqDAkRS4ojIToruCevQyavNM1kL1qQMQwtHA/mQBPgv/4bDJHqcu80DfgevacNl2Nr8gO2HAYutq7bprQeDbLcC14QgyHL5P8sxlm4f7Chwe/Nf2hxf7z3rBR3dLZFfYWhBmrvM1O5cAtfkqN6pc60UYL0+g5cBXZ7Z1i5P4ono3/ovxp/C84q0o3n9ie8Fp77/9rbPOFrcVPxxZRxxppFOHMB4KoD3sOaHtKCELaWlpoTeJbHqZkolcegJCOQ2vZOP+x5/BX/3tP847X+v3+x18VfgTPCVvxefsm5CSW3BD79LeyqLBTgRwEfaqiMolgF0Pstb5Wzfs74bGHtIo9913H+64445ZoWtXVxfuuece3HLLLfRGk7rHHnbSB/t3c+utt855n7vvvpsqONcK9t3RE6kt9lZo+TFokVE0lRIoZ8ZRKowgowSRRQBF08VYrgxZlODXJV6dv5G/e64Fq9HOnp1oyT6rnaxt/1zfq1cKHWtePmt56gRCVhuNPZsswP9v/+2/4cMf/vC8AT6by+6uu+6iAJ+sW+0haqdDCDl1tXC+Wgvrj1lY5bsjqXBFFY6o1oJYSUGrqKIknQZH1OBIbFF5ZTwPzVlAPlMdX1scSLAhwD52nsepxXFZy0g25yNqcz+yxXbgOhZvql9bO5AFB6LkQBYtSLAgORZEtnZtyC5b166zkw3YZWn6pAO23a5CcYq1y2y769RCfVGZCvfZWp3Zxiv+2f5Nt6pk5xHwUwrYZbaPx1bPiGDFEZLALgsQ+bbaokgCpHU6HUCi90aopTFERh6d2XaOeAAflP8F78PORT+e4W2BnjuEFsmLyeQEBv0aTm8PNXivyUZ30UUXrfYuELLqRrJlVJMDiAo5fPeJA7jj45+bd77Wfa+4DKVDP8cucZAvb5B+jKf8r4BHfvvRM9fqxP5Gp0sm4gENcb8EqTAEhHtq897LKjYqGntIo8L7k4WtLMxn2++9914K8cmixh520gf7d0MnhawzbKqZcBcQ7ABKCXj8Q/CUkmgqJlDOjyIv+5DyBFG0JRQqFtJFg08/5tdl6Md01yGNs1rt7GsnWt7JW6bPdyLmSqNjzcuHpk4ghMaejWDFeofZts3nqCJkvRrJ2vTBihBSP8eeCucrU8uxQb0Gl4XxsgeWFqpdlnTYsoeva9drS3LkCELdp/NAns3byOZstKbmbLTNo9dr8zgasF1nJuCetUxtY/NEshCcVRawAJytJeG46/xnprZNLcLMfY5uZ1X8/HWxKn3bmLrM1qybQAUC6yJgVQF+nVXwG4Bl8PvCKfDbWfcAR1BhiyosFuwLGmxpqorfZSciTJ2AML22Hf5eOHweSwcVk22rvQ8sHFFkEYo4tZbYIkBmO72WCQJGdv8BlMok/Knfzmz+XfEh/OEjDy96zjbWpYG1eW42UkimDyERiSIT9vAKTkLq9fOf/5yCNLKpFaoW0ukU7MIkPE4Bn/jCvQuOx4XOC/EH+NbMNnZCXI/fwcQSvgdnyyY8ioiwR0XYSgJ6qFZ9H2jFRkZjD2lE23wWss6H3f66172OKqbJosYeFuLffPPNs6ZluOKKK+jf0XrAvg/647WlnIaQG4HXPwFvOYloYQJFWUNKCyFneVCs2kgWDP5jAU2GT2Pfn9f498l1ZDXb2bOQ/upXXDxrKiT2PKs5/zkda14+NHUCITT2bAQrFuC//PLLiEQae/YcISupZE6VgRJCyLFcd6qqfjqkr0C0KnxeehbGOzIL6nUe1E+H87bs5eE9D/AlD6qCDhMSDOtoOG8atcu+QhYvjBcgi+BBtMwqzcVaSK3KAjyiPLNdnqlGF49b136G3V77+VqozUN9XrleC+bZNpF1s2/kCXe2BVgVgAX5Vrm2NstT2ypTgf7Umt+nWAv42c+xeQwlFbaswRY1mIIKCwoP8Nl7YzpOLby3XVRtB6Zl85McDHbZdlAyLJgWq+UHFEmCrojQFQmazF7nGjupUJQxeOZ70ffEn0Irj/NNj/XbKORKS5qzzdKaoFcziAtZTE4cwaCfAnyyOOPjtX+HhGxWo5kyKskBNCOHJ16ewNhEYsHx+NkffR13bR2a2eZAQGbrTYt+bvY3jFUCtoV1tHhMCOUiENsFNG/DRp9nnMYecqrYv8X55ipn2O2PPvoorr76anrDyaLGHja20b+bdW66vX5kC5AbhpQfQ7CURLA4CVOSkdYjSNtefiIfW7IZA5oiIaAp/PskFaet73b27PnX0nzndKx54/5bI2Qto7FnEwT4f/AHfzDr+re+9S309/eftPJ+YGAAP/nJT3D99dcv9ekIIYSQ1ec6PJw/vrK+VlWv88VSQ3A88dp1xVsL6EUdFXhRFjSYrlQLmVlAX66F9JJo8JBdlWpBvCqL8KgyD+ALY8Du1gDfps5Uldcqy6cvT//c9PY1dVBBkgHJD2j+E29z3alAv1QL9fkydXkm9K9CsiuQjCxUts1hwb4OyJ5awO9hax0QJR7cs5MgqpaNiuXAMNllFubbMGwXFcNGpmTwYESVJeiyCF2V+Pu3FgJ9R/Fh6Mz3YMsv7uJTEIwWWFeBJc7ZJgi8lX5TcQSpnI5kth3JsI5mv9b4HSeEkA2mYtqYzGRhZccQFooYy7G56Bd2Xv7oVChMMn4JDF/bop+f/a0K6DKavTJ85WEg0A6EuwHVi7WG5hknaw07kaSR9yOEbFDsb2p0e216mtwwkB+FUkwiXpxATBZQ1JuRsrzIVSwUDYv/bWanhvs1BX5dWrfTt60Fa7WdPdl46N8aIWTTBvhf/vKXZy6zoOCpp57iy8mw2y+88EJ86lOfWurTEUIIISvHdSDwVvAsqDdmgnpWaV9rba9PVdUHeWW4Jeo8pC+DVdNrKAkelF0NpuXCMhweEPNwnQfwgE+REJaUmdCeBfOstT0P6aeuTy+PvAxc1NfM29tvOCw0V/TactLK/alQ3yjWFhbuG6Wj1fvstkqmFvSLMlTVC1Xxws8Oxnj12uPzSkaXh/qsgqJUtfkBGB7ymzbSBYNX8rP336NIq94isRLsxdjO29D+4j8hHGOhz+Elz+3mKH5A9iJuZTA+vh+DwQCafOraOsGDEELWoPFcBZXEEEJuDoLmRzQWq+vnzgumZr5i16rvX7vo565MnYQWD+iIS3lA0Gtt80NdWGtonnGyFrEuEI28HyFkg5NVoGlL7e9sfpSH+UIxCX9xAn7HhhmKIeP6kC6bKFRs5CsmhtMGvIoMv0eGLlP17kZpZ082Jvq3RghZzwTXZeVvi3fkyBG+Zj/e19eH9773vXjPe95zwv3YH17WOt/n85363hKyzBJjAzjy1COwKwX4Yr30fhOy6YL6WmU9C+pdNh+7pMIR2ZzsKqqotXCvCDoqLKAXNB7Yl6FBkFkYPxW6zwT1tWp4dl2batvOQmK+VkRoksTXvPp7I4bzy8U2AaNQC/J5qD8d7k9V7/PtJcAxAMU7tXhqa0nhD8G6HpQMG8VqrZqibFiomKzlvs0vs9+XX5N5oL8qYbfrIjzyGJLRC3HlG9+JVGL++QEfuPeLcx7oEGwDWu4wDjmtUHrOw2l93TwUIoQQcnKW7eDJw5NIvfg4+pwjcELdMKDiulvfNv98rSEFg+/W+TQ1zGT0Ikyc/e5Fvc3su/VotoKQR0ZvWEHcHAKatwOd5wJ6aM21zWfHAeZrVd7V1YXDhw/TwXiy5v5tdnd349ChQ/RvkxByIscGCuNAdggoJYDCJJ/izfVGURCDSJctZMsGClUW5ltgf/X9usy/P66Frm6EEELIUhWGnoce78X286+F17+2vn9uVkuuwO/p6Zm5/KEPfQhXXnnlrG2EbDTZioOQTi2yCFm3c9Q7Bg8zpyvp+dq1ZoJ6S1RREX0wxAgMyFNBvcYr6auuxivwRFbhPVUpr0siglOBPQ/kp8L56TnW+XW+/dTmyWPT0/T20glFM1gIPz1v4TG/Yx7qV/PHLIWpdvwloJQCWAjCflYNQNYCCGpeBHV9pkKfBfm5sskXdjlbNpEsGvxADFvYCRkrRhCQ6bgCLJL/k3ef2pxtrqTC1pvQUkphZGw/hsJRRH0anTRCFkRjD9msJvJVlFPD8DtZyKqGiuzhX5oXmkPz06+UZ8J7Jrtt8dX37O8P+8gQ8ihoticAfwsQ7lpz4f1yzjNOYw85VWyO8nvuuQe33nrrnPe5++67KbwnNPaQkxMlINgO+FuB4mQtyC8mIBQnETAnEfA2wwhEkC47yLAgv2LxqnzWYt+nyQjqtZP7CakHHWsmhKwGGns2QYB/LBbgE7LRTRYowCdk7VfSG1NhvVkL61loz6rpBYm3vrehoCIoMKDDkJtQdRVUBZVX0ZvQIao+CJoPkuqBoig8nPfJtSp6FsxPh/PHXz72gH2jPf300xTgL4SlHVqgthxbqc+D/FxtXWFrVrmfBwpjgF0FFB+g+qFoAYQ9GsIehYf5rKIiw1skWihUTYzlypBFic916FNXtrKCtXuznDtx16e+hGr2aOWnHIzhzv9R3/yApt4MXzUDrZJAYWIA42EP2kKeZd5zst7R2EM2I14BnymhmhxCF7IwtbYF59CMNEfxv6/w45bTWfv8monm81ENdC/quR3XRbpk8i4prWoZEuuTx1rnR7ZgM80zTmMPaYRbbrkF9957L+64445ZJ5qwrhAs3Ge3E0JjD5kXm1Yt0AL440ApCWQHeZDPFjW9Hy2eJsSam5A3daRKBj8RnH1/ZN8dVYl9d5ThXa2ObmTdoGPNhBAae8iyB/jHsm0biUQC1Wp1zlZlhBBCyFKq6HlIz9csoJ+qqmeXXbtWSS8qMCGjChbSB2CIEqqCgipUHtbbkgeiyhYvD+lFzQNVltE81f5+OpjXlanLDaqiJ6uAVdt7m2oL4zhANQuU00A5A1SyU4F+odYakTU/1PxQ9Aiifi+ifo1XQrIgP1syebv9QtVCpmQipCv8gMxKBfnXX3MZdp93Mf7w356FkB/DxYFxPNH5JvwyIOEWF5AW2g1BhOltRUt+FEOThzDa3MoDouU88YQQQtajRMFAMTUGxUjzv/0VxT/vHJrNTRH8NuPiA/irWffLb7950c/NOr+wqVsiuoCgmQCatwJNWwGp4V/ZG4LmGSdrHQvpb775Zt4tgp1Iwv7NXnHFFVR5TwhZHPadzxetLey7ZIYF+ZM8yBeT+xHSwwiFY6iEdKSKBjJFk58EzgJ9dp1V5LMTwSWBqvIJIYQQsjgNOxrw7LPP4s4778QjjzwyZ3jPwg82HxkhhBBywt8Ix+JV8zPt7qfDepsF9BZcQYYjKXAEBRZkGPDAEAMwBKnW8t6thfSurEFUpkJ6TYPE2t4rKvySwOc2Z6H8dEA/3fpen1qTDV5BcWzrfataC/J5oJ+uBfmsSp9VVjCeCHyeMHwhD1qDOnKsLWKRtUY0kS4byGVMhL0Kr8hfiZM7+sISPn5VEFcOfBk94gTeZezAd9MX43uHgd/pW/jnbTUATU3DW06gOHYQo5EgOiPeZd9vQghZT0azZVSSA2gVcrA8zbWD9seRZQkXnbeXX3582MWbUh8BjjkmPxk5F5XA4qa+sR0HhYqJtrAHLUIKgqcJCLQB/hjWqn379vFq5oXmGWeBKSGr2U5/MVM4EELIvKa/T/LvjUNAfhwoTQLJA9DVANp9UbQEAry1PgvvCxUbuaqJ4bQBryLD75H58QhCCCGEkBUL8F988UVccskl/PIrX/lKfOc738FZZ52FlpYW/PrXv+YV+VdeeSVV35N1rS1AZ8sS0tC56Ker5/llkx8kd0QVrqjA4pX0ei2gFyUYjgSDVdKzlvfQICgaJB7S65AUL5+jlrWnC/FW97ND+WMr6tfjXHQXXnjhau/CxiRrtZaIbHHdWpv9qUqKmWA/sR9QPJA8EUS0ICIeFbmKhYl8hVdKpousVaKFiFeFR13eAzGh0cfx5uEvQBRrJ0L+hfJFPGf04isvtuGiViBeRxZveFsRrR7AYHIAYxMdaAl2r8v/JsjKoLGHbDas20ouPQmhlIRPMlFR5593nv3peHn/8/j/xJdnbc8tofqenRzGOrs0ywa8MIBAH9C8DZtxnnEaewghq4HGHrIoehDQTwMiPUB2GMiP1r5HZo5AklQ0e6NoivlRNBwe5LMp2liYnyxUIUBA0CPDu8JTs5G1iY41kyVhX0RcGwKbTtS1a9f5cCLA5ePK9DLVRYRvl056cjLZnGjs2WQB/sc+9jGYpolf/vKX2LNnD0RR5K3K7rrrLhSLRbz73e/G97//fXz5y19uxNMRsio0mf7IEVKPmVCeV9PXgnrRrk7NRc+q6Kdb3SswRA2GwKrpWTjP1mw+egU2NN7qXlLZvPQ6ZLXW6j4gC7zd/XQlvTbT6v5o+/uN1hY8FJo/QCANwL7E8IMwQaCprxbeFyZqgT5rtc8C/dwIoAUR9EYQiPl42DKhyciWDD7noVwREPaqvOXycqj6OmZdDwhl/K3yWdxsfBR//5yMu85f+LuYK6kQfVEEcglUxvdjtDmG7qhvWfaXrH809pDNZiRbRjkxgGYhB1tv4tOPzOepBHBz5VvAMfn0RHgvKqE62qIcw7QdlEwLHSENcYwBoS6gqRdQdGzGecZp7CGErAYae8iSqD4gtgMIdwM5FuSPAaUUUByHkB+F3xeFPxyGMd1ev8SCfAu5qoV0sXbynl+T6aTqTYyONZM5pw9l3UgdA7ANOLYFl00dytaOzRfbFWFDgs3C+doP8//zuF6Yeix+S207u6fIpuaSFIiSAkFWa2tJgcsWkS0qhfybBI09myzAf/TRR3HDDTfw8H6ay878AeDz+fD5z38eZ555Jj74wQ9SiE/Wrf60jW3RtTkHJSGrVk1vV6aC+ipfs6CefVK0RRWWwOahZ4sPhhiGISq1+eh5q3tWRa/zRdK8EBUdiqJCk0QEZDYfvcSDUN7mfiacr61VSYS4wUL6+Tz44IO46aabVns3Ng/2TcfbVFvsbUApUQvzS8lamJ8dhiDKiPhbEIr7+YGYyUKVV+Szynx2MgkL8htd2V4JbsHYztvQ/uI/zWw7Q+zHu+Rv4VPjb8BPR4FL2xd+HFOPIlJKYzA1gsnRI2gN74K6TCcdkPWNxh6ymZQMC6lUCnZ+AkGhjKo2+6Spk7nvZQcXOmdipziEmJDl2wrbX7vo52YH84O6ipiYg+oJAP5WILjw82/UecZp7CGErAYae8gpYSfdNW8Fwj1AYax28jf7/siq8gvjUD1NaPU1Ix4IIFsxkS6YvLU+mz5nLFuGqkgIago/5rES07ORtYOONW9SrlM7nmpVp0L6CmAZsM0qLFdElU0b6iowXBk2C9lFDwRBgsim4JBkSKIIgRVosEBeViAKIlxWjQ+X/6+W20+F92xxHJi2CZc9h2PxtVsx4dolwLEhwwKbrFQV7KliKi9fQ/bAkbQFT2wm6w+NPetHQ9JI1iJ/+/btRx9UllEqlWZdZy30v/nNbzbi6QghhKwU9gFwqoJ+OqAXphZbkGEKCipQp4J6L29zbwi1gN6RWUDvqbW6ZyG96oUiKwjKAg/hWUh/Yrv72poCRbImsC9DgdbaYlaOHowpJvmadRyK+uKIxANIFAwktSrSZQNjuTKavBp8WmNP+kp3XgNf+nmExp+Y2fZO6X48aJ+Hf3iuF2fFAL+ywIOwL3aBVoSTI6hMHMRwvB1bWsInvatlWbMCGTbfMftMN9f2xTwGIYQsp8WOPaPZCqqpQTS5Wbh6CBDnH6deTANPp0Q8jRvxFftVuEV6DG9vPYByeMei9rNqOahYNuJeAc1CEfDvrLXOP4WD96sx7tI844SQ9Wp6zGSFSX6/nz6rklP//hjqrJ2Ix8L73FDtuyO7nNwPUQsg4mlGJObjJw+mSwayJQv5qskvs+AtoCnw6RIkCswI2WBhPSuAKgNmGbZZQcVV+fHUWjdSHaIchKBrkNlUoaoXguqBV/XyzqRs2lBZUiEpMmRZgSyrUNg0oaIAmRU5HfPVYaqmtnZ5ZpsLy3ZgVCswzQqsagUWO1nAqMAxK3x/HKOKcrUIu1qCUynBzaehYRw6TN4ZlQf7mg+u4ufdHQkhK6Mh3+SbmppQKBRmrkejUQwMDMy6j6qqyGZrlQmEEHKq2IcPx3VhO4DtOLUzCO1aOyGHtRPin1hEuCI7S1ConS3IDobyOX/YWc1sqwB2sySwtcDnH2MfelgL9k03F5ljQ5yqomfhvMvO/jSqcB0DpqugLCgwBZUH9lXXx9veQ/Lw+cFFhX2w9EHgIb0HHlVHgLW5l2eH8qzd/fQ2tt5MVfRkg1RVRHqBYGdtjsPsUK2qojAOSZhAiy+GSNyPsZzMw/xEoYKyaaPJpzZuPBEEjO5+K7zpl6AYmdpuCTbuVv4Bv1P9OL7ygozbz1z4YWw1BL+eQiE7gtTwfrQ1ncO7WxzrvvvuO2lL5De84Q342te+Vler5LkeY6ltlQkhpB6LHXsMy8FkOgcjM4KIWISpL9wC/2sHjl6uQsUz4VeicM4rl1R9z7q2xDEJJdgChDtr07ksEY27hBCy9DHzb/7mb+izKmkM9v3Pz86ujs10cWPfG/lUbdlBfkzK64nAG4ygJejh4X16qr1+vmIikzbgVWT4PTKfOpAQsn6wKUUlqwTRKgFmEbZpouLKPKwvuyosMcI7kcq6H6Lmh6b74dU8UHQfNN0Hj6bCo9amDdXV2jHUxvGcsMVxXBi2w08sLlUqKBeyKBdyMEo5WOUcKtUiLKMMp1IEiml43RFoigzJE+QnJdmKjyr0CVlGgjvd6/4UXHbZZYhEIvjOd77Dr99444342c9+hueffx7xeBzFYpG31/d6vXjuuecasd+ELIvE2ACOPPUI7EoBvljvrNsm8jbiAfrgvBpYUM/OErQqRdhGkZ8dyAJnNn+PBBey4NYCMlGGIMkQZ1p2OrVW725tzavJ2TbHhSOIfJl+FIs3DBJhCxJsV4IlSHweIEFUeAvQWrAv1sL9qdCfXV9Xgb/rwLUqcE0DLgvprVqLJt6myXVg8bnoa9X07GxKQdYBVkXP2iaxsz+VWhU9r6ZXfNA0ZSaMZxXz05drQf3Gm4t+tTz11FPYu3fvau8GOZ5jTwX5w7WKiuJ4bZzxxZGyPRjNVZEoVPmXoKhf4/9tNIp/8kn0PHXPrG2ftm7Gp6xb8FeXAqc1LfwYgl1BdfwAyqGtaD39cvR1tMw6mHnrrbcuer/YPMjT4dhCj3HsfcnaRGMPWY+WMvYMpkrY/9Jz0CeeRlx3UPV3zvscA3ngDx+dve1jFwLnxBe3r2XDRqpkoNdvYqunBKnlNKDzPD4v5VJslHGXxh5CyErYKGMmWUfMcu37I5uerZwCSinAKABaiE/f5ioe5Cs2D/JzZRbm28hXLT6ftV+X4dfk9XHciSwKHWte51x3qrq+BNEswTWKqNouytBQcjWYIqtcr4X1kh6A5A1A8wb54vfUujZ6p8L6tVbkZDsuioaFYrmKUjGHSiGHaj4Js5jm4b5TzkG1S/CKBj/5QNKDsFVWna+v9q6TUxh7CkPPQ4/3Yvv518LrD9F7uVEC/I9+9KP45Cc/ifHxcT7n/fe+9z0e4rOWfZdccgmefPJJHDlyhFc9vPe9723MnhOywgE+WTmmZcGqFmBXSrCrRcAsQZElyCw41nyQVQ8P6UXWPogFxdJ0CyEFoqJAkdXaGc+uzef54WsWXk9dZms2N5BjWXBsY2ptzlx22bxAfJna7go82LdcmQf7LOw32dqR+Np2Rbg88Jengv1aJf90VT/bxr51sW1sPjOR1f6LrD+AwHdz1me0Yy6zDgHHn8jAz0Ng0xdNzWnEL/Pt7PVZrAcgYBuAw0J6E7BNPlc928bvIyqArAGSBoGt2XslaxBZWM/eV8XDq+n52aCaH7LuharIR0N6qdbeniroCZn+D9OptdZnFflTcxyy/3orvjYM5R2kClUe0IR0FQFdXvKchpZl41dPPYvJZBqx5ghuUh5HdOKnR293RdxkfAx53xZ8Zh+g1HG+gJAf4Z0CPB2nI5GzkJyc4Cdevu1tb5tVuVovVuF6+PBh3gGlr69v3seYvu9S50gmhJCTtUFe7NjDDkw9eXgCqRf/E712P9xQFxzZO++b+9e/AR4eOnp9awj49OWL63rPxknWtj+ki9gujSLSthVoOwsItq3YayeEkM2Kxkyy6t8fWSc3Fuaz9voszGcL6xbpbQL0MAxXQLpoIlM+WpXPurt5VRkBnRVS0HzUhKxeYF+GZBYhmCU4RhEVR0IJGsquDlv2QPb4IXvDkL0hKN4wdG9gJqxnJ+KwLqVLPS602ljnskypilwmhWI2ASOfhFFMwyrl+AlJfpShs+p8XwSOFqZW++sQBfgbtIX+29/+duzcuRPlcpkH+Ndffz1vPfXhD38YX//613nl/R/90R/h3e9+dyOejpBVMZi20BWheXuXi2UaMAq1M/kkx4CkeqCoPnjCUci6j3/g0f1heAMRePxBPi0Hq47nVUpsntLFHTWtVc9OBdss5K4t05dZkF/l8wHZfDH4/tmWCYdVq/PFnLpemmrbP9W6n1X026yqX4A1Vd3vCAIcFvKzA8Uu6wEgsj4AtW0C21bbLYGfT1UL5oWpmYp4xwB+sfZTEluz+ZNcG6Jg88syu8zuJEoAm4eIvSdsUT0QpCAEVk3PugnIKm9xL6o+iBqbQ4mdBcpOiPBC0Tx8Xnplan56NocSWRvYfIxXXHHFau8GmQtryRFsB/yttfA+M8APxui5fvR5muFTglAVCYl8lR90ifpV3r1jMX74o8fxV3/7JYxNTM5s+2C8GfdcpeK/7DT4dVlwaq30C3+Grx+Q8bt1TMPs+uL4+YP34q//9SMYn0ye8u+YBUfs3+v05Xrue/XVV5/y85LlQWMPWW/YHMaLHXsm2dicGoXHykDRNFQWCO8nSsCjw+yD2dHPnbcsYcr6kmHzn4kKWYSCYSDQAgRasZKvfa2isYcQstw20phJ1iH2XXC6vb5RmgryJ2sV+SzIz49BVf1o8YQR8/uRrzr8hPB82eQV+ZOFCi8Goar8jYGONa+jCnujAKdaRNllgb3OF0FpguQPQvaE4PWFofojCPgD/EQbVsDBquvXa1h/MqygKx70IB7sgOu2I1exkMkXkc9MosIC/WIKhTybKiSNECag614I3ggsJVg7Zk3WDBp71o+GpJGs0v6Nb3zjrG0srL/99tuRSCR4NddGGqzI5lS1V3sPNh7HcWCU8rAKCbhGAYo3hECsG4rHD80fgscfgTcQhj8Q4W3bG4aNRxKbw13mc7ifDIvX1Jmwn1W2V6cCfmPqMgv6p7dZU6E+W9hlE/ZU9T677jo2f6286t+urVkXAMet3eayenoe0vMSfbj8oPDRhd8kqhDYGdmSBEFkHwBZSC9CZK9BkCGybYoKSdUhKjqk6UVja60W1rMAn4L5dSebza72LpC6g/w2wBcD0ocBLQgxO4Q2Jwd/qBW67EGyYGAkW0GzT+XVE/WG93fc9YkTto9OJPF7XwXkN3hwy+m1dsu7xUG8S/4m/nb/Lbi8HejwL/DYj/4cf/Spf2/o73d0dHRZ7ktWHo09ZL2pd0yZvh+vgs+UUEkOoFPIwdQWDtAf3p/GA8qf45/s6/B1+3JEfSouWWTRPOucxCrqoh4gJpUgBnuBpq2LPwvgFF77WkZjDyFkuW2kMZOsc6oXaN4KRLYApQQP73mQX83yKn0xN4yQFkDIH0Y1GEC6ZCJdNlCq2ihUTAyVDHgVGT5dhi6v36rezYyONa/FwL4K0WIV9kU4lQKqrojidGCvNvGwXvaFEfRFoPsj8Pt8M4G9rmyekJqNNyGPgpAnDMTDqJh9SBerSCcmUUwPo5CZQKqYgp5KIiiOQPEG4Wph2Ir/lL73kMagsWeTBfishf6WLVtw2223zdrO2vO1tBydU5UQQo6ttrcKST5OqIFmqC1bEGhuQ6S1C/5QlIfUawIP+6cq2ufB9lZirdB4Vb85tbZqa1bt70638nenLjvHbHemPrzUAnzeX3/68rHbWKcBtkx3HTj+Mn0AImT1sZNqotsBXxTQAvwgTCA/hD49BF0OIlkyecWnZbsIepQF2+azyvv5vO9hF6/b7fKpO5jbpfvxoH0ePvvMFvzFxXMPC/U89lJP6lyO+xJCSKPGlOn7sYPghdQo5EoauiKgwg4mzSNbBbaMfh/bpWH8ufglvFf+On4TuxWSsLguOYWqxTsdRZ1J+ONtQLgT0IOLeoy5XlOj7kcIIRsZjZlkbVblx2sLKxhhFfmFCaCcASpZPmWbZlto1YOIR0LI2zrSZdZa30ChaiNdNODChU9V4NclyIvs+EbIZibYxlRLfFZhX0DVAoqCB2WBBfZdkPUgJB7YN8ETjCDg8/NjOSywV6hIagY7eaEt7EVbuAfFagcmsyVkEqOopEeQzE/CLmYQLI7AJzkQfU2w9Wa47Fg2IWReDfmv5OMf/zjNbU82PPqbfOoqpSzMXILPi8Oq7f3xXujBKEIt3WiKd0LWTl4Nv26wL0kim1deW+09IRuIptG/p3XJEwE6zgHS/YDqh5ofRo87DH8gBlnUMZ6rwnZdRLy818dJsTnvj22bfzLD6QoeGg7iVV2VmVb6LFR6e/L9eGgQeGX30h97sbq7u/l0D6yqlc21PF9r0un7krWLxh6y3uzbt29RY89Ipoxq8ghvY295mhc8EfI/DhTwfvGhmetxIYPTgiZyx5wYxcbWyWQaseYIztu7B7IsnVB9ny2b6NAMNOkiBNY2P9KLlX7taxmNPYSQ5baRxkyyAbHjSaHO2sJa7LMwny2VDFDOQsyPIOTYCGl+GGE/MpYH6YqNIqvKr5oYzRpQJYnPte1RJYhU6LGm0bHmlSc45lRgzyrs8zBsB1nobPZ2QO2AzFri+8IIsMA+UGuJT4H94vg0Gb54EE6UdQ7pxWQ6i3xiBNXcOHK5BNRsGpF8AoovzL+HuZK+TL9tMhcaezZZgN/R0YFcbvrQBSEb05YmOitsqcxqBeXUECS7Ai0Un6q2b0GktQeBcJwqxwmZx3XXXUfvz3rFOomwloisGj8RhJAfQzQ3DFX1QgwGMJ43kSxU0eRjU2ScGByxEKgeLwQuw6tQC5X+2XolPmH9F375S88D57cA4ZOcA1LvYy/G3XffzbuqMPfccw9uvfXWuu5L1iYae8h6I8ty3WNPvmIimxqDW0wiINmoqKF5H7tkAc1DP4RPrM5sK0gh5Dv3zUx3wrqaHHtiVGs8hg+866141VWXzWzLlU14ZKDZTcIf214L7xfo8tTo177W0dhDCFluG2nMJJugxb7aA0R6gGp+KsxP1C5X81ArGcTNEmKKjpLiRcajI2tqPMgvGBaSxSqfuo2FadRif22iY80rE9iLZmmmwt4yLeSmWuJDbYPiC0Dyscr6CPRgMwX2DSSKApr9Gpr9cVTbmjGZ34HJxCQqiQGMp0YgFVJoKh6C5vXB1qNwFF8jn57Mg8ae9aMhPXVuvvlmPPTQQyiXy414OELWpGTRXu1dWHdsx0IhMYjy+EvQvX4Euvegffcl2HH+NejefQECkRYK7wlZwIsvvkjv0Xqnh4D2s4HW04HYTgRVoE9KoC2gwHJcTBaqvCrzeKyCsx6BLedgsvd38OTpf4I/c95S+yIKIG8CX/ztyX+m3sc+FqtUet/73sfXx2+/9957ccstt8xsY5fZtnruS9YmGnvIelTv2DOaraCaGECTO119P//X4h8dLuNNwgOztqV7XwNXUnl4f8ddnzihqwm7zraz2xnbdfiJAzExh3A4DPhbgEDjWtpvlHGXxh5CyErYKGMm2UTY9GxNfUDXBUD3RUDHuUD7WUD8NAi+OG9L3YFx7JBGsMOTx1a/jc6gAlUWkSkaGMqUkS4ZMCxntV8JOQYda16mCvtqFnJhGGLyJdgTLyOTTmC44GIUcaRDO+G2nQX/tosQO/0KdJ51JXadeQH2nr4be3pb0Rv18SILao/fWJosoTPixd5t3dh6xvloPu0VULvOQcLbh6GSAiM9CDV7CJKRrU0/S5YVjT3rh+CyPqenKJ/P46qrrkJTUxM/i/WMM85ozN4RssISYwM48tQjsCsF+GKz21keSFjYFqUq/HqwYaWSS8LKjUDRA9CaOhBp24KWnl2QVWqLQ8hi3H///bjpppvoTdsoWLXExAtA5ghKuRSOODGMF104LhALaLNaHLJ2zNfd+rZ5W923tcTwwL1fnKkQ+toB4J9emH2fey4Ddh2X19fz2OwA5he/+EVMTEzwuUJZG1H2PJZl4bHHHsPo6Ois7SezmPuStYXGHrKezTf2VEwbT+0/gtz+n6JPHEc1smPeAN+0gUd+9D28D/82s60seNF/xWdhQF1wLGWV+D+874vIVmyIdhVb5XG0btkDdJ4H6MEGv/L1P+7S2EMIWUnTY+Z3v/td3HDDDetuzCQEtgmUWXv9FFBOA5Ucn7YSRhFGuYCCCWRtBUVX5+u8KUIShJkW+xRSri461nzqBLsKyWIV9iXYUxX2RV5hrwGKD7I3AMUbhuQNwxNohj8Y5i3x2UL//lc3O0iXTIykcihMDPDOvXYhgWbkEFBc2N44LNYljaYBWdGxpzD0PPR4L7affy28/vm71JGV0ZA08qyzzoJhGPj1r3/NL+u6jng8fkI7WHb94MGDjXhKQsgaVS0XUU0NQhZd+OJ98Ec70LrldD5vECGEbHqscqLtLN4y2SsNoi89DNkbxUhFwniuinhQhTQVJLG5k1n7ZVbBOZf33/7WWQcZX9sHPDIE9OeP3ucLzwF3XwaIx3wsq+ex2UmZ11577Unbjl599dV1/SoXc19CCGmU+caesWyFt20MO1k4voWr7x8bMPF77veAY8bQRNd1cGQPfvWrp+YN7/nzTUziZ08+g55tO9AnJRGOddTmtV2G8J6hcZcQQhY/ZhYKBfrMStYnNhWPP1ZbWI1eNVcL9Ks5qJUcmowimswSysUsCvkUio6LoqsiV1ExWZAhKF74NAVeTYIsNqRRLyHLx3Uh2hWIxwT2hgVkBQ1llwX2ccihWmDvp8B+TWM5Iet00OSLItscwnC6D7nEELKJASTyCUSzkwioCVjeOGx1eb43EbJpAnzHcaAoCrq7u2dtP764vwHF/oSQNco0DZTTIxCrOXjC7fA2tSHeswvheBedLUcIIceSNaD1TEDSoEkKepJHIOpBjFY9GM+yEF+bOXhSmzv5zrrmVuYPLQL//Qzgj392dNtLGeDRYeCqztm/hrkeuyUaxl13vg+3vOEN9HsjhGwolu1gMpmEkRlFm1SGqR03MB7HdoHKwR+jRcjMbKsKGopbXsUvTybTdT3vwOgk9u7sRlgH9Eg7EJnd6YsQQggh5JSxQjo2fRtbGB7o53mY76lk4ank0FwtoJDPoVTIoVzKolodQ7EqI2crcBUvdN0HzeuFLFEHUrIGOBYkqwzRLvPA3qoUUXUlPm0gC+wFrQOKPwDJG0KAB/YR+AJUYb/ehLwKQt4wClE/RtLdSI4PIT15GOn8BGLGGDxaEqa3BY7iXe1dJWTFNeSvcX9/fyMehpA1rTNMbdTmUi5kYKYGoPoj0GNnoLlzK+Kd2yHKyor+jgjZiPbt27fau0CWgygB8d08zFdFBd3JQ5AcEyMI8urQlqA+086NBe1Xv+Ji/OqpZ3lYxOavP//sPXO29zwrClzcCvxszMVN4n/iCulpfPyFd+KSVgH6cZ/8jn/spkgQp7UoiLRuQSk5CG909smZZPOgsYdsROP5KirJIwjYGYj+CCDO/3X4ZyM2ftf+DnBMQdpE21Ww1QC/zMbjeviDATS7SYRbd9bCe1k9tReygdHYQwihsYeQRgb6wdrCuv+w6mWjiGA1h2A1z6cPLeTTvAMFW1jLfaM4gkqqDMg6VN3LF0n1wJE0uCId42s0OtZ8DNeF4LB2+GVeYe8aJVimgSJUlFwNVcEDUY1C9gQg+8IIecLQg80IBPwI6AoCukwt8dc5NrXHjtYg8uHtONLchtzEAMYnDkOpJBE3B6FpHhjeOFyJpuc9VTT2rB90Oh0h5JS6bxRToxArKfjivQi1bkHbltOhemsHNQkhhCxwQKV5K6B4oUgyOhOHIReSGPY0YSxXQesxIT5reX/ReXvrfjvf3TuE96S+jIvE5/n1h41z8LWDF+NNO0+87/GPnc8kYGSGkRjcj25WKVpn9cXx8y6zEIa1JSWEkMVYrrHEcVyMJzOopkYRFQowtW3z3p8VrSVe/jl6xImj+wYJpa3Xz1w/b+8e3hFlvjb6LfEorjqjCwG/DDXUCgTaTvm1EEIIIYQs6fun5q8trPs+q3x1bISMAuxKHrlsGrlMGsV8FpVyEZVqCflcAYqbhk80ocviTJjvSPrUWl3whMi5WJY96yR19rmKfTclm4PgWDyor7XEL/PAvuIIKLg6Sq4KU2yG5PFB9gSheEPQvSF4AxEEfD4EPTIPe+Wp4yVkY2EnZJzRGUEy7MWRpnYUJ45gIHEE/koKMaMfgh6AqcfgsvGHkA1uWY6qptO1s/e6urqW4+EJWRVDGRvbohRETGNnQZYm+6GIgLd9F+K9pyHauZ3a5RPSYCzEuOmmm+h93ciCbbwSXxZktMn9ENLjcLUYJvNVtIZ0iOxAyyKdMfhvCEyF98yfKv+KVx/Yi2u7PIgv0HXMG2xCbmQSWmIYpcnD8LZuX/D57rvvPtxxxx0YHByc2cY+B95zzz245ZZbFr3/ZPXR2ENWw3KOJcmigUriCFQjBckXgrnAAZ+nJh283vj2rOr70fgrYOlNM9fZQWY2nckdd31izsd563/7PUTlAiIte4CmLfRZeQE09hBCVgONPWRTd4bTQ5D0ECLhTkR62PE+E5lsBrkcW7KolvLIlHKolkuQqgZ8kgmvWIBHSENyDbgQeZDmiNpMqO8uULH/wx89Xvc0cRvZpjnW7Do8pK+1w6/U2uHbNspQeSv8kuuBLUcg+/yQvWFo3iCfw94XCCHgUWsV9poMUVz8sRGyfjX7NUS8UYyF/Rhu6kBhoh+Hk0cQrmTRXD0IxxODqTfT96sl2DRjzwbQsN8SC+w/9KEP4d/+7d8wOTkJQRB49QTzxBNP4CMf+Qg+/vGP45xzzmnUUxJCVkm5mIGZHIAejMIf70X79rPhC0fp90EIIUvlbQLa90KSVbSK/XAmRnBEasVEvop4QFt0iD+28zb4fvYcRNfm11uFNN4pfh1ffvFN+F8LfBSTRBFisB1GdhCJoQPobu4EFM+8gdutt956wnYWwLHt9957L4X4hJAFLfdYMpbKopIeQouQh+XZuuD9j7z0FN4kHj2RwIGAyvYbT7hf7SDznSc9CP2Ot70ZN1y0Fb6wD2qkA/DU13KfEEIIIWS1yIqCaDTGF9bBKFcxkSoayOTzKBXyyBZzmCwV4BhFBCQTPtGCT7Igw+ThrGTmINlV3s6oFujXFhbqO6KKBx57Anfc9ZcnPC/7HFU7KfLOTRXib8ywvsLnrWdryS7DsQxUoPF2+EVXRQUxiKoPoh6A4gnC4w1C94fg97F2+DKCugKvKvF8iWxu7KSN9rAHsYCGoXAQY4lOFMYPIpsdQmspDZ+RheFrgyMvUKlCyGYO8LPZLC677DL89re/xd69exGNRvHCCy/M3L5nzx785Cc/wf/9v/+XAnxC1nvL/PQohHIKvlgvQm1b0Ll9LySV5p4hhJBTxloZtu2FJMpoAWCPTaAfcaQKBqIBbVEPZfjakey5HrH+b89se4v0AL428go839uN044WkJ6U3x9ApqBDSw2jNH4A3s49J70fO1mTVcvOh93+ute9DpJE7RAJIViVsSRbNlFKDEAspaB4/TCl+cfUg1ng2tLs6vvhpotgeFtPen92kPnqV1w8qw3s3jNPRyqbR0SYRKRlFxDZsuj9JoQQQghZ7fAs7FX54kZ9KFSjSBdNpEoG8mUThaqJdKmESrkAHVUEZZOH+l6hCsmpQrCrEG0Dol2FYLLQv4S7P/P5eZ+TnRR5zb6L6fvjegnreQv8o4G9YBswBHWqup7NX9+MqqBC0gO1dvieEIK+ELxePwIeBf6pwF5X6HgBmRubXnJL1MenmjwSDiEx0Y7RkZfgMSbRlh0E9CAMT7zWVYSQDaQhAf6f/dmf8fD+y1/+Mt785jfzavuPfvSjM7d7vV4+d+HDDz/ciKcjZFVEPJv7rD/TNFCe7IcqAp72XYj17kascwe1qSFkme3ceZJJy8nGJatAyxlQXQctlgk7kcBhO4pMqXbgZDEm+16L0NhPoVYStYcWHHxU+TI++NwHcc/lAubrPseq8KVgO6q5Q0iM9KM72s1bK56s3eixra5Pht3+6KOP4uqrr17U/pPVRWMPWUnLPZaMpvMoJ4fQhBxsT8+C9//2AQM3u2E4LhsrXb7N2HFi9f2xWDv9i87bO3N9Ml9BTEjD29wGrakbUKkqpB409hBCVgONPYQsjFVD81bmuoLuZi8qpo1MyUSm7EemFEGxavFlomKhajsIKC6CHhMB0eDhPmuh/ouf/xSjicy8z8Mq8X/9xH/iovPPqVXuz9OKf71bV8eaHbsW1k8F9qyyHo4FR1BQFjQe1pddL6+yd2UfJE+tup4tPm8Afl3lQT2rsGehPQtkCVksjyphV2sQCb+Gw/4IcmOHcSBxCK3VDILmQZieFtjaiceuyDoeeza5hgT43/jGN/CqV72Kh/dz6enpwS9/+ctGPB0hq6LZt3nP4CoX87CSh3nLfF+sBx07WMv82GrvFiGbwq5du1Z7F8iqhPinw+NYaLFegp1Job/aBFkS4NfqP3jhSjrGdr4Z3U//9cy2C8UXcUb+J3h06BW4qmv+nw94daTzfui8Cv8gvD0n9t4fHR2ta1/qvR9ZO2jsIStpOceSsmEjNz4ApzAJ3aPDXKC94lgJ+NGoiofc96JXGMV/l76HS5uyqAR6635Ow3Jgl3MIakBTSxcQ7l70fm9WNPYQQmjsIWR9YBXTrSG26DOt9muBvlmrzjcs5KoKRssaBATg1WS8XHi6rsdOJJKQK4ljWvHrU634p9fahqi0XavHmlkVfS2sZx0UaidfCK4FW9JhCBoK0FCGHyVWaS9okLRaWC97gwh4AvDysL4W1LMTPryKRPPXk4aK+jWEPM044tcxGo5jfPhFZMoJtJcmoRkZGN42uNLiimA2k7U69pBlCvCHhobw+te/ft77+P1+3mqfkPXqcMrClqaG/CezrpTyKdiZQXhivYi0buHhPbXMJ2TlPPDAA7juuuvoLd9s2JzzLWfAb7MQ/0VYhSwGSiFeGe9ZRGu5fOxc5KNnI5D4zcy2P1b+HW948Rxc0uaHLi9Uhd+CSu4QkuOD8EZ7AF/zrPu0tbXVtR/13o+sHTT2kJW0nGPJSLoAIzWIiJuF7VngzCUA3zoEOLWie/S7bfgH/W3Yc757bDf9BWVKVcSQ5uOm2twLSBu3cqzRaOwhhNDYQ8j6brXPsOp8NoURW3Jlk1fmF6o2PKHZ3yfn4t9yNorNeyBZpZm27LVAuQzZyPDLriDNBPruVKjPg31h/VR2r/qxZt4Cv3q0Db7Dwnr23or8vbRENm+9FxUphJKroOh64CheyJ4AZD0I1RtAXJV5ocF0ZT1bazKFg2T5sS4O2+IBNPs0HPIGkZkYxMHJg4hZKTTlDsPWm2HqzdQ9eC2OPaRuDfktBQIBTExMzHufw4cPIxqNNuLpCFkVtrP53vhiehxucRK+lm2I9exC65Y99EePkBVWrVbpPd+sND/QegZCrgNr4HnY1QKGC0BLwANVrvOghCBgdOeb4U09B8kx+aaokMMfWPfhvgNvwW0LNHgIejQk8iF4EkMoTxyAp7dp1t8BNkVSV1fXvK2vu7u7ccUVV9T5oslaQWMPWUnLNZawSvjMxBCquUnENBWW4p/3/jkDeHBg9rbXbasdlF7Mc6Kcgs+roSneAQTaF7XPmx2NPYQQGnsI2RjV+WxpCepwXRcloxbo9/zOq/CFj3dgfHR4zp+Nt3Xi9CvfgJJciy0EuwrJYvOrszC/PBXql2eCZ8FitxchV5MQbQOuIM+E+Ws92F+xY80zQX2Vv5/8smNAcEw4kgpH8sAUFFQlH8qigjIL66HDFDyQPX4e2KseP5pUmXdSCGi1sN6vyfCpMlXXk1UV8ak4Sw9jwKdiOBjF5Oh+ZAuj6KgkoFsFVL3tVI1/nM2Yc23qAP/888/Hd7/7XeTzeR7mn6zV4fe//33ccMMNjXg6QsgyYx+ui8lhCNUMfK3b0LrtTEQ7ttP7TgghK80TBlpOQ5Njwe7/LSxHxHheQFtIhyzWdwDC9LYg2XsT4oe+NrPtTdJDeP2hfZjo7kPcO38VvuKPolw4hNTEMDqi40CgdeZ2WZZxzz334NZbb53zMe6++25IEp2BTwiZ23KNJePZMqrJAQTtDNzwwtX73+sHqvbR6806cEXH4n5zmVIFUSELX3Qn1OhWlv4v7gEIIYQQQjYQQRDgY0GvJqM97MFn/uav8cY3vnHO+996+504kCjxznNeFhir7GSAECQtfPROrjvV2v3YUL/Eg/7pan22nh3sSzzEc0QW6KtwRWVqra7JcH/RXAeCw167WVs75tH3wrVrr1XSYYsaqkoIFVdBFayqXkXJZa3xdYi6H4onAE3TEVKPvv8BTZkJ7OsuJiBkBcmSiL6YH81+Vo3vRSrRgkOjL6DVTiGS74fhaYGtheh3QjZngP+e97wHr371q/Ga17wGX/jCF2bd9sILL+Dtb387KpUK3v3udzfi6QhZFdomyR4cx0Fx8ggkpwpf+2507NiLUJzm7SRktYRC9AFz0/NFIcR3I+o4MPufhwkJEzmBzzUoHlMNP59E7w0IjvwEemWcXxcFFx+S/gmfev4j+F/nzf8FPORVMVmMwJcYQmXyMHRfbNZ8g7fccgvuvfde3HHHHbOqZ1k1LQvk2O1k/aGxh6y0Ro8lbC7W5MQQKrkJRFQBtnLiiebHYsH9tw/P3nZTH6As4hhl1XIglibhCYTR3NIF+GOL2mdCYw8hZHXQ5x5CVg47YZOF+sd/5mvv7MQf3fVnuOxVN6JYsVA2bZQMCxN5k3/GkkWBV/WzYF/jaw2Srs9+cB7sV2bCfF6pPx3sTwfaPOSuQjCLtZDbOTbcV3iwz6r4XZEt09elZelIWvexZtflc9ALztQyc/loYM9CenYygsP2m78WFY7sRRUqD+srYEG9hhJUmKIOWfNB1rzwaArC7P2cCut5VT2rsNdk6IrIf1eErBchj4IzO8MY8KoY9AYxNvQCCtUJtJcnIZt5VL1ts45nbVabJefaCASXldo2wEc/+lF8+MMf5oO6oigwTRORSATpdJpX8/7lX/4lPvCBDzTiqQhZNomxARx56hHYlQJ8sd5N907btoXi+CEosgRfy1Z07T4f/kh8tXeLEEIIkz4Ca/wFjPa/gAEnBkP0IB447oDFPPyJp9Hzm7+cte1/m2/FORddjdOa5v/ZZKGCQOkIWlq70L77IiB84jzSlmXhscce452X2DzVrNU1Vd4TQharUWMJq77vf+YncMZ/i6amGCwtMu/9Hz5YQPf+L+MfrVfjGXcrvDLwlWsA7yKmr5/I5hGrDCK65XS07r4M0IOL3m9CCCGEkM3+ma9q2chXLBQqFl8XDQsV0+ZL2bB5uM9CfUmohfps0RQRmixClU4SOk9X7LNwf3q+92PWR8N9E3AsiK4BwT42KGfhuHRMmC/yeeKPvQx2EsD05RnT+3HM/gjTgTzrYe3U1u7J1vbRsJ6F8+zx2T7wkwumTjKQal0EbEGB6cq1sH4qsGdz1rNW+ILqhaJ5oakSPHLtfWJhPTsRotYZoRbas+sU1pONJFU0cGAij+TYAJA8iE4xBZ9bRNXXAUfxrfburUmFoeehx3ux/fxr4fVTQdmGCvCZRx55BJ/5zGfw85//HMlkkp/BetFFF+F973sfrrrqqkY9DSGrEuBP5G3EAxv39CTLNFCaOABV9yPQuhXdp10A3X9MeypCyKp46qmnsHfvXnr3SU3iAIzxlzBy5GUcsuLw+HwI6vWnS51PfQqhyV/OXB9zI/jvnk/jzy+T5y0osBwHk4kketU0OneeB33LRYC0iFSLrDs09pD1jH3Ffe7AYSRf+ini1hjc6M55W6PaLvCzh7+J/8+9j1//qX0anmp9Pfbt3V33c1YsG6WJw2gNB9G750Iorac15LVsNjT2EEJo7CGEnOyzXcmwUayyML+2LlQtVFmgPxXss0DfsByYtgtFqoX5PNCXRR7ws2BfFE/ypfeYcH+6Qr9W2X7Mmi08UDdrAbtjQwAL2tk2BwLsqe21UH7qgU94KmEqhnH5l28RI1YYbWqhdhIARLhs6iX+mZVdluEIMq8WZmG9LbGQXoEBCQYL610JFVflIT0L7EVVh8YCevaa2ckM7D3glfUSn6eeVdezwL42JcHGPb5NyLHY2HBgooDxZBqlkecRd5OIOZOw1DBMT2xjTJ+xBHPlXBTgb9AW+tOuvPJKvhCyEeWqLuLzd95ct4xqGZWJA9ADzQi0bUf37vOheuhMNELWgiNHjlCAT45q3grVNtBUrcAYGcDhYht0Wap7HrqxXW+GL/kMZKeKR+yz8CHrv2GgKuOXE8AFLXP/nCyKUH1B5Eo5pMaH0N40CDT10W9mA6Oxh6xnmZKJymQ/5NIkxKY47AUOzDwxbOD1zg9niqMukZ5Hq+9MlFF/gF/I59AsGwjGO6E00/i4VDT2EEJWA409hKxtrDK8Vi0uzwr1WXjPgvxStRbk8zDfqIX5rIqfBfos7GeVuIZdC9ZZuF9bhGMuy1DkICRVOPk0dXx+ebPWep8H9bWq+FqFPquWn7ruWPyEgKkfOm59bIBfC+rTwwkEottYHT4sV4TpslMBamvDqQX1FUfiYb1ts3m+2T6zhR0DEHhIH5w6OYEF9XyZmmZAZ3PYKxKfG5yQzYqdrHJ6e5C31u/XvJgYPYhCSUOnlYSe60fV3wFX0rDZbOSca6NpaIBPCFl/jHIBlcmD8EQ6EGrfysN7Sdl8f7gIIWRdYAcTYrsQtCool/JoSycxVpDRFtJPfqDhOJbejImdt+Hf+gP4u/R5M638vvwCcG4ckOZ5CFbpP1kKI5geRTUxAC3QDij1t/AnhJCVMj4+gkp6DGHZgq3P3zqfHUct738MUSE3s60seFDtvWZR1fdKaRxqtA1N7X00NhJCCCGErECo7+WV5TJwTBBlO7Vgn7Xa5y33TZtX77NqfdOuVegfXde2G1PXbV5BL0ASawv7ji2ztTi1FlT+vDON8dl3cJbFC7Oa5PPI3nFcvmYnGjgu4Lgu/9zJFtbhzrZd+JHAb0pR/vPs+WRBgCSx52ZV9AKvpvdPnWygTlXU1zoL1NbHBvYn7S5ACOH/nXY1efkxrf3aTkwkmnBg4iV0iGkE8/0wvG2wVZr6jKxNFOATssnD++rkIXii3Wjq2I7OHedClGlYIISQNY211YvtQtQooVp6CoVqDqmiiKi/vpOv0p1XYXcQEB6rHVhwHRsvPvss7h5NY9/2CM7buweyfGIrLd6G0ONHrpJHemIIrZEjQGznMrxAQghZOlaFVZw4DJQmoEQWrr5/NmHjtcZ3+cHXaaOt18BRvHU/ZymbQlgTEYp1QGnqoV8fIYQQQsgqYUE4m9OdLcdjwf10q/3pCv1jr9dC/FqQzwr2bRa+8+u1hYXwtWC+FsYz/Dv1VFDPL7AwXxD4R0t2AsB0OK+wbdPXBRmSJCI/DuxsDfCTAxRZhCKKU1X2R6cA4GE9a4kvizRHPSGnIORVcGZnGAc1GSOeAPqHX0CLqyJeGoNolWF64rWzcQhZQ5aU1Ini0v5gsJ+xLGspT0nIquuNSBsyvNejXWjq3IWunWdDEDfWayRkI7j22mtXexfIWqR6IUW3I1YuwDr8PA5VVRSV2px29dgSBPZ1AN97+HGkf/Ql2PlJ/AvAl9Z4DB9411vxqqsuO+Hngh4FE9UwwplRGKlBqMEOQPMvwwskq43GHrJejY+PopoZQ1A0YOtNC95/7KUn0C1Ozlw3IcPcfl3dz1cxLSjlcaitW9DcuR2Q6GTYU0FjDyFkNdDYQ8jmMN0yH/Oc+86CelYhb9ls7cKyWZhfu8zWtZx+en20wp5vdaeq8afCelYUz9fiMZenqvpZUG93XIOAz8sDfkLI8mPTT+5qDfBjW4fVMzE2fAiVkoROKwG9cARVXydcceN/n9toOddGtqR/ja94xSvojC+y6VQtl3+42giMynR4342mrl3o2sHCe5oTiZC1KJvNwuPxrPZukLUo0AJPbAsixTw6x0bQX5Shyr7aAYk6dI0/jsT9nzhh+9jEJO64i22/84QQn1cCaB6kq354J4bRGuoHWs9o2EsiaweNPWQ9Ym1S82OHYOcnIAdjfG7R+RzOunhV8Tuzqu8Ho5fD0uZvu3+sci6BoMeDcKwdcrjjVHaf0NhDCFkl9LmHEDKt1j6ftahf/vdkLJNEOOCjN5+QFcROsOkIe+BTJbwsb8PYRBAHky+hW0xBzx9G1duxqG5s69FGyrk2uiX9KXr00UcbvyeErHGjeQfbNHFjhPcT0+H9TgrvCVnjnnjiCdx0002rvRtkrWrehkg1j0oxi5Z8BomCgtagtuCJlpZl44v/50vz3uev/vZLuGbfxZCk2WfmhjwKxqtBhLMjMDIjUEMdgKf+sIusDzT2kPVoYnIClcw4fCgD3i0L3v+lF5/BDeKRmesOBNg7bqj7+aqmBak0CbVtK5o6tlHLxQagsYcQshpo7CGE0NhDyOYS9qq8pb4qSxjRvDgw9gK6FB2h4hBMvRmW3oyNaqPkXJsB/ZYI2USOhvddU+H9OVR5Twgh6xmb+iS2E7HOrWhWqtDsPDJlc8Ef+9VTz/JK+/mw23/5m2dP2M6q8HVNR9oNIT0xBKT7Wd/AU3oZhBByqlh708zoQZi5caiBeG18nMdECbgk851Z244Ez4Pha6v7Ocu5SXi9PoRi7VCCLUved0IIIYQQQgghK0tXJJzRHkRfawTe7r04LHRiUu2EbGShFYYA16FfCdlYAX6xWMRvfvMb/OQnP2n0QxNCTjm8Pwgt2o0Im/OewntCCNkYtADk2HY0dWxHG5Iolku8jfR8JpPpuh56rvuxKvwEAijk0jAyY0AxsaRdJ4SQRplMJFBNj8JjFyD4Fq6W+PVLB3Gx+PysbfbOG+t+PoNX309ADbWjuXMHVd8TQgghhBBCyDojSyJ2tgSwoyWI5u5dGNC2YVDshAMReu4wBNtY7V0km1jDAvyhoSG8/vWvRyQSwXnnnYcrr7xy5rbHH38cp512GrXeJ+tazL9+G1YYlTwMHt73oKlzF7p3UuU9IevFWWedtdq7QNaDYAf88R4+B3OnkMBkvgJ7njOFY831tbyf636sCt+jqEgJEWQnB4HMAFXhbzA09pD1xHVdpEYOwsxOQA1EAXH+meIKJnDGxOzq+0HPaaiGt9X9nJXsBHRvEMFYO9RgbMn7TmajsYcQshpo7CGE0NhDyObFpqHsbvbi9PYQOju6kQydjsNoR1UJQ8/3QzRL2EjWc8612TTkNzU6OooLL7wQ999/P2644QZcfPHF/CDKNHbbxMQE/t//+3+NeDpCVkVIX58Dm1kpwJg4BDXawyvvu3eeTW3zCVlHent7V3sXyHrA5ryP7kBz+xaEvRqakUGyMPdZwuft3YPW+PyBU0s8hvPP3jPn7SGvgqTjQ65QgpEbBwoTp/QSyNpCYw9ZTxKpFCrpEchmFpJ/4TD9iQNjeKXwy1nbjB31V9+bpgmUJqGG2hDt2r6kfSYnR2MPIWQ10NhDCKGxhxAS9WvY0xlCb1sMRuwMHHBaUdBboRUHIVczG+YNWq8512bUkN/URz7yER7QP/TQQ/jGN76BV77ylbNuVxQFl19+Of7zP/+zEU9HyKo4kLDW3TtvVYqoTBycCe97KLwnZN1hJ8cRUhdJgRDbiVjnNsSkIoRqEfmqedK7yrKED7zrrfM+3NWvuR6SNPcc0rUqfKVWhT9BVfgbDY09ZD1JjxyEkR2H4m/mY+F8TAdoG/o+ROHoCedjSjcqsTMXVX2v+UIIxDqgsYp/0jA09hBCVgONPYQQGnsIIYxfk7GnI4QtLWGo7WfggNuOjN4FpZyAUhrfEN0n12POtVk1JMD//ve/j9/5nd/BFVdcMed9uru7MTIy0oinI4TUwTZKKE8chNLUjXAHhfeEELIpeCJQ49sQae9Du5hAtlCC7Zy8lf6rrroM93z0zhMq8buCAu59gwfv63oOw/n5v5jwKnxLR75UgZEfB/JjDX05hBCykGw2g3JqmKXqUILxBe//+JCDPc5Ls7YVtl5f9xz2pmkApQS0UCtiXTvoF0QIIYQQQgghG4gqizitLYi+eADBjl04IPQgoXdBtMvQikPAPFNWEtJI808OWKfx8XFs3z5/60BWhV8sFhvxdISQBThmBeXxg5DDHQh1bKfKe0II2UzCPQhVsijk0oim00gVNcQC+pwh/tWvuBi/eupZZPqfwd7kd3FFrwRJZEHWy/j0c79Ex8UXzFuFr6kykk4EwYkhRANxwN8CiNSOixCyMpIjB1HNjkP1hSFI6rz3ZcUSXz8s4q+Nv8B14i/w3+XvokfOoNpxcd3Pxyv9fREEYp3QA00NeAWEEEIIIYQQQtYSURSwPe7nx71ksRcHx3SYkBC3J6Dn+lENdMEV5+/+RsiaCPCbmpowODg4731efvlltLa2NuLpCFkVXqW+qpzV5toGyuMHIATiCLZvRe+ucyFKFKQQsl61tLSs9i6Q9YZVkUZ3IFpIw8g/hWy1gLKmwKNKc7bTv+i8vcB5e+H72Tikwm9mbntt7t/xTOps9DXN/aUk7FExnrPQXMoilJuAEhwFQh3L8tLIyqGxh6wH5WIBhcQQnGIKnvadC97/qQRwOMcuifi+cxF+YFyIr1yQQbNY39di2zLglNL8uaj6fnnQ2EMIWQ009hBCaOwhhBxPEARsifqgySJYncvApAqzoqBNnISe70fV1wlH9qy7N2695FykQS30L730Unz729/G2NjJ26bu378fDzzwAK688kp6z8m61R6aex7gtcKxTZTGD8L1NCHYtg29uy+ALDfkPB1CyCq56KKL6L0ni6d4oMX6EGrtRpuQQrJQhlPHPF3Gnt+DfczHwx5xAsnnHlywtZimyEiKYeQmh4DsEPuDRL+1dY7GHrIeTA4fgJGZgOQJQZRP3mnkWN88OPv6JW0CmiORup+vmhmH7AsjGOuCJ1D/z5H60dhDCFkNNPYQQmjsIYTMpT3swe62ELpbmjHm24EjQjsqegu0/AAkg58hvq6sh5yLNDDA/8AHPoBKpYJ9+/bhBz/4AUqlEt/OWuaz6zfeeCNEUcQdd9zRiKcjZFWMZNd2GOE6NioTB+EofgTat6H3tAugqtTGhZD17uc///lq7wJZr4KdiLR0w+fzIeRkkC2bC/6I6e/A/uarZ217XeWbeGksP+/PhTwqkpbOn8PKTwD50VPefbK6aOwha51RKSE/MQirMAk1tHCnt/4c8OTk7G0399X/fI5VhVVKQQu1Ito1//RxZOlo7CGErAYaewghNPYQQuYTC2g4oz2MLfEIcsEdOIR2lPzdUEtjkCupdfXmrfWcixzVkNLcCy+8EF/4whfwjne8AzfccMPM9mAwWHsSWcY//uM/4vTTT2/E0xGyKkrmwpWLq8Z1UJ08BEvUeeV9z+4LoOsLVyERQta+8fHx1d4Fsl6JIoTmbYi2pWAeehYHyz74VJlXzM/HPf31KP74cfhQ5teDQgnCC98GWn9/zp9h7cTY4yaFCMKTw2jyR4FAGyCu7bN6LcvCY489htHRUbS1tfGTUalzTQ2NPWStSw4fRDU7DugByOrCn3u/dWj29d0RYPciprBnzyX5muCPdcMXCC9hj0k9aOwh6xl9rli/aOwhhNDYQwhZSMir4IyOEGRJxIC0FS9nNWzzi/AXhyA4Jkzv+pgGdU3nXGSWhk2M/Za3vAXPPfcc3v3ud+OCCy7A1q1bcc455+Cd73wnnnnmGfz+78990JcQcgpcF9XEYRiOCH/bNnSddgGvtiSEEELgCcMb64W/uR1tSCFZNOAu0Erf0YI42PHaWdteYz6I/SPJeX8u5FGQsjRkKnatCj83vKZ/Affddx/6+vpwzTXX4LbbbuNrdp1tJ4SsbbZRQXb8SK36Prhw9X2q7CIy8hiCKM5su3lr/c/nWBVYxTS0cCti3VR9Twg5EX2uIIQQQgjZ+HyajNPbg+iL+aE1d+MFpxs5XzckqwS1MMyzGkIapaGTY2/fvh2f+tSnGvmQhJD5uC7M1AAqlg1f2y507roAwUCt8wUhhBDCNW1Bc+skKrkkMtUM8tVmBPX5p1iRd74KyZEH0Oym+fURtwk/OZjG9vbmOX9GlyUoooiUGEY4MYymQAwItANSQz9uNuwg+6233nrC9sHBQb793nvvxS233LIq+0YIWVh69CCquQlYkg8+3bvg/Z996SV8Uvk8Pij/M75qX4nvKq/GRa1zj2fHMzJjEHxR+KPd8PvpszYhZDb6XEEIIYQQsnnoisRDfEkUMCh24IWkgp0+GcHSILTCIKr+TkBoWO002cQEd6EyLEI2kcTYAI489QjsSgG+WC/WOjMziGKhCF/7TnTsvhCxaGy1d4kQQshalB9D7vCTGDvyIg677WiN+CGL83+ZKL/0MDqPfAOftl6H++x9sCDjzy8GzorO/TPshLJkoYpt8iR62tshde4FIr1rrr0tq7RnYf1curq6cPjwYUjS2p4CgJDNyDUr2P/kj5A98jSEpq3weOfvPFWxgOyP7sFVwpMz2w54z0b10g/U93xWGYWRl6F3nI6+c65C0B845ddACNk46HMFIYQQQsjmZDsuXh7PYyBZRCKVxE4cQagyAtGuoOrvhiuuvYKW+RSGnoce78X286+F1x9a7d0hjWqhz842vuqqqzAyMnLS24eHh3H11VfjG9/4Br3pZN3KVhysJU5+HKVCDt7W7WjZfh6F94RsUP39/au9C2QjCLQiGOuEP9iMKDJIFY0Ff8SzfR/eG/hr/F/7ah7eM//+0vzdwFgVvsSq8IUIcslhIDME2CbWEjbn/XzhPcNuf/TRR7GZ0dhD1qrs+GFUM2Ooil7onoWr7588ODwrvGfsHa+p+/mMzDhcXwy+aDeF9yuAxh6y3tDnio2Bxh5CCI09hJDFYhX4O1sC6I36EY9G8QL6kPL0wFYC0PP9EOyFj72thrWWc5FlDvC/+MUvIpPJoL29/aS3d3R0IJfL8fsRsl5NFtbOwOYUkyhkJqDFtqK5by/aWhee+5MQsj49/fTTq70LZKNo3oZoew+apAoco4SSYc1/f1HGG3ZqszY9lwKeSc7/YyGPgpSpIGOIsAuTQHb+sHyljY6ONvR+GxWNPWRNsqpIjfTDzE9ACbZCEIR57267QHzg+7O2DSlbYEVPq+vpXKsCo5SFJxRHvHP7Ke06qQ+NPWS9oc8VGwONPYQQGnsIIUshigJ2tPixJepDW6wJL2ELknonTL2Zh/iiVV5zb+xayrnICgT4zz77LM4777x578Nuf+aZZxrxdIRsbtUsSslhyNE+RHr2oKuza7X3iBBCyHqg+qA29yLU2o12Iclb3TsLzKS0uwk457jZWf5tgSp8jyJBFAQkEUY+OQJkhwFr7Zx13NbW1tD7EUJWTmHiMCrZcRShw+ubv3U+8/RgFq92H5/9GH3XAwsE/9PM3ARcTzM8zV0IBf1L3m9CyMZFnysIIYQQQjY3dmL51pgffVEfOmMh7Bd6Mal2wvC1QSsMQDILq72LZDMH+KlUCvF4fN77NDc3I5FINOLpCNm8zCJKE0fgRnoQ7NiN3i3bF6w8IoQQQmaEuxGJdyHg8SDsZpEpLdze/vd3zr7+2xTw9OT8wX/YoyBtskWGXZhYU1X4+/bt43Pcz6e7uxtXXHHFiu0TIaQOVhXp0SMwsuOQAq38RKGFyAd/CE04Os4lxBjszgvre7ttA0YhDT3cgljnVvoVEUJOij5XEEIIIYQQltH0xfzYFvOjOxrEQaEb40onqv5OqMVhSEaO3iSyOgF+NBrF/v37570Puz0cDjfi6QhZFW2BhvznsnRWBZXJwzACHQh27EDfjtN5ixZCyMZ24YV1Bg2E1EOUIDRvQ3N7L6JiDuVKCYZ18tZZlmXj5796Cvt/8Qi6Mk/BdWx4UME7pW9h57OfhOvMHeJ7VImvWRV+ITkK5EYAs7ImfkeyLOOee+6Z9z533303JKn2GjYrGnvIWlNNHkEhNYqCo8JfR/X9S5MVvNr8j1nbxrtezcfBerA2/Y4egd7UjkgouOT9JotDYw9Zb+hzxcZAYw8hhMYeQkgjdDd7sb0lgN6YH0fETozKXaj4e6CWxiBX02viTV71nIvUTUYDXHrppfj2t7+NF198Ebt27Trh9hdeeAH3338/brzxxkY8HSGrQpNXLywXHAvVyUMoazGE27Ziy469UCQaaAnZDEKh0GrvAtlovE3wRHsQzCTRkkgiWdLREtRn3eWHP3ocf/W3X8LYxOTMtmDQj7uu1fDW003AAR499CRi2+aeQinsVZAuASlBRaAwATE7BES3YS245ZZbcO+99+KOO+7A4ODR7gCsMp+F++z2zY7GHrKmWFWkRvt5qO76uyCJC38Ozr30KMJCceZ6Hj6IfVew4Wthtgkzn4Qa34lox1bqeLWCaOwh6xF9rlj/aOwhhNDYQwhplI6wB5IggKVJR1LtsAUZHQERev4I4Dqw9OZNm3ORxWlIAvj+978flmXhsssuw2c+8xm8/PLLKBaLfP3pT38al19+OWzb5vcjZL3qT9ur88SODStxCEUpgGDrVnTvOhcerSHn3hBC1oEHH3xwtXeBbERNW9DU2o2g4gJGHmXDnhXe33HXJ2aF90wuV8DbvpbEfb+ttaPu7b8XrjN3FOZVa3+rUggjnxwD8qM8hFtLB9sPHTqEhx56CP/yL//C14cPH6bwfgqNPWQtMdODyCdHkbMV+H0Lz0U/WrBxdfEHs7YdaXklHFmv7/kKCZhKEHqkHc2RpiXvN1k8GnvIekWfK9Y3GnsIITT2EEIaqTWkY1dbEFuavRgX4xhSulEJ9kKpZqCUJzZnzkUWrSEp4Pnnn4/Pfe5zuP322/G+972PL8diLUj//u//nlpSEbJYrgMnfQQZW0GgfSs6dp6LoLe+A4+EEELInGQNcnMvQvEJtA4dwXDRB13xwLYdXnk/nzserOB1u2X0ikP4z/2PI7zzFXPeN+RRkC6bSEE5WoXfvHVNtb29+uqrV3s3CCHzsQykx/ph5iZgeTugygufg37k+V/gGvHoSUgGZEg7XlVf9b1jw8xPQmXTjXRspSmrCCF1o88VhBBCCCFkWjygo1aHD/Qno3BkCV1BVonfD8GxYXhbAYGq4cncGlbG+/a3v51X4LMg/4knnkAmk+Fz3l900UX4wz/8Q+zevbtRT0XI5uC6QG4I2aoNX+tOtOw4D82hhSuOCCGEkLoE2hGOdSKfGoNeyiJfVfD8c789ofL+eIM5F4/227i6T8bWwa8jue1iQFJOel+vKiFbNpEWwmhOjSHoHwFCnfwEAkIIqYeTGUQuMYqMrSJQR/V9ruriwvR3Z/Wa2x++HNDrm5LGKSZQlXwIR1oRi8bol0QIIYQQQgghZEliAQ2CEATL8fsTgCvL6GLt9AtHoJZGYHjbKcQnc2poH24W0n/2s59t5EMSsmYEtZU9G0oojCGdL0Fr2Y6mvnPQHo2s6PMTQtaGnp6e1d4FslGJIsSmLQi3TKLS/yIOlwKYSKTq+tHRQq2OtdWdxMCLP4Lv9Fed9H6CIPAq/FTFRMqVeRW+sMaq8MnJ0dhD1gTLQHa8H2Z2HFW1FWFFWvBHXnjpefyBeHjWNmnXa1BXk0DXgZGbgBrpQ7htKySRqiFWGo09hJDVQGMPIYTGHkLIcon6NexqDfLLRxKAo/Shh1Xi5/qhFYdQ9XUAQkNmO1+TORdZupX7V0HIOhcPLHzAsFHE8iRy2RTk6BaEtuxFb3t8xZ6bELK27N27d7V3gWxk/hhC0Q74/CGEnQw8gdoXioW0+Y9+hNw+8k0IZmXO+7IqfMcF0ojwan/kRngoR9Y2GnvIWuBmB5FJjCJry/D7Awve37CBvrEfzNr2kvdc2IGO+p6vmERZ0KGHWtDS0rrk/SZLR2MPIWQ10NhDCKGxhxCy3CH+7tYgeqJeZFwf+uWtqAT7+G1aYZCfTL4Rcy6yBgL8++67D1dddRVGRkZOevvw8DCfX/Qb3/hGI56OkFUxmLZW5HmEag7F1DjsUA8CXXuwtauLVzASQjanRx99dLV3gWx0TVsQae1CVMxjx7YtaI1H5717LNaMC3q8M9cjyKH64gOwLBs//9VT+M4PH+Frdv1oFb6MpCEibcpwi5NAdnDZXxY5NTT2kFVnGShMDMDIjqMgN/OTgRbyyDDwgepb8Tnrd5Bza+OUveP6+p6PV9+PQwm0INy2BYpE57qvBhp7CCE09hBCNgv63EPI5tLs13BaW4iH+DlHx2GlD+XgFriiAj0/ADh19Y1bNzkXOXUNOSrxxS9+kc95397eftLbOzo6kMvl+P0IWa+qKzB+ilYJ1eQASr4uBLtOw9a+PmrdScgml81mV3sXyEanh+Br7oIvEkerVMA73vZf5737nf/j7XjQ85pZ2557+D5cd+sf4G3v/VP874/dw9fX3fo2/PBHj/PbfaoMx3WREiLIJ0epCn8doLGHrLrcENKJEeQsGT5fYMETWlmnj28eBCYRwSet38XF1c/iX8PvhBvdWdfTueU0Sq4CT7gFLa0n/15Llh+NPYSQ1UBjDyGExh5CyEpo8qkzIX7e1nBI3oZKsBeOrEEvrEyIvxI5F1lDAf6zzz6L8847b977sNufeeaZRjwdIRuSYBuwkoeRU1sRbNuOLdt2Q5OpnQkhhJAVEOlFU0sXwlIZl1xwJj7x4f+F1nhs1l3Y9Xs+eideddVl0E+/nrf8Yu77rYk3fy2LsYnkrPuPTUzijrs+wUP8WhW+gtSxVfi5IfrVEkJOzjJQSgygmhlHRorAr8sLvlNPTgCDhaPXy4IHHadfxtqALPwuuy7M7DjkQByB1i3QlIWfjxBCCCGEEEIIOZUQv2jLOCRtRSXQC1v2Qs8fARyqkCc1DTkykUqlEI/PP0d3c3MzEolEI56OkFWxrF00HQtu6jASQgSBtj507zgTfo0OHBJCAE3T6G0gy0/1QmvqRiA6jpaJCZx3wQV44IpL8aunnsVkMo1YcwTnn70HklQ7saw36sP39BvxxvL/xR0PVuZ96L/62y/hmn0X8yr8bNlESgijKTWGgG8YCHYCskq/4TWIxh6y3CzLwmOPPYbR0VG0tbVh3759kOWpz7+5IWQmR3n1vccfgFhHCP/NQ7OvX94OxI/O9jG/agYlW4Av3ILWtq4lvBrSKDT2EEJWA409hBAaewghKykyFeILENCfLPIQvy8gQMsP8BC/GuiBKy5PPkSzxa0fDYkko9Eo9u/fP+992O3hcLgRT0fIqtjStEyBuutAzB5BwtLhb9mK9m1no8lPgR0hpOa6666jt4KsjHA3IvEuBBVANPIwHBcXnbcXN77qSr6eDu+n+U67Ft8+rGEw5877sKwS/5e/eZZX4Qc9CtKGjLQhwS0mgNzwMr8oslQ09pDldN9996Gvrw/XXHMNbrvtNr5m19l2Vn1fTQ2inBnlJ/wE6qi+P5gFnj7uXPGb++rfHzM7AdEfhz/eCw+dRLuqaOwhhNDYQwjZLOhzDyGbGwvxd7cF0dvsQ9EWcUjpQyXYA0sNQc/3Q3DM9ZVzkbUZ4F966aX49re/jRdffPGkt7/wwgu4//77cfnllzfi6QhZFcniMkwO4rqQcoNIVAA93oemvr1ob/I3/nkIIevWXH9bCWk4SYHc1I1gvBMtQhrpogHXnTuc3x7V8UD1rLoemlXxMz5Vhmk7PJQrpkeB7DBgL88XEnJqaOwhy4WF9LfeeisGBwdnbWfX2fb7/uULyEyOIG+pUD1+yOLCX1kfeZlN4XF0vNrTDGyv89xxoZpD0bCgh+Noae9e/AsiDUVjDyFkNdDYQwihsYcQspohPmunXzBFHJb7UA12w9TC0HP9fNrldZFzkbUb4L///e/nLRAvu+wyfOYzn8HLL7+MYrHI15/+9Kd5cG/bNr8fIetVujx/heFSyKUxpItViJFeBHr3Yks80vDnIISsby+99NJq7wLZTIIdCMc64feo8No55Kvzz7vVyuaXrgNrwc+IU1X4KVNGqirBLSWA7FBDdp00Fo09ZDmw74x33HHHvPe5408+hvzkCCYRRFBXFnzMRNHCB9IfwvfUP8ZN4uOQYeF1W+vfJyM7DsEfhy/Wi4CHumCtNhp7CCE09hBCNgv63EMImQ7xd7XWQvy8KeAIC/EDPTA9zbVK/AaH+MuRc5E1HOCff/75+NznPodcLof3ve992L17N4LBIF//z//5P/n2v//7v8eFF17YiKcjZEOQywnkcxmYwW4Ee87Cjo44RHHh+T0JIYSQZSOKEJt6EY73IC5kkC1V4cxThf+6fecgEpq/c0xbSwznn71n5rpfk2FYU1X4KarCJ2QzYXPeH195f7zB0Qk88kw/JM0HVV746+rQCz9Hu5DC6eIRfFr9HB7S78R50TorCowCytUq9BCrvu/FqZ6c8PDDD+Nf//Vf+ZpdJ4QQQgghhBBCFtLs13iI3xv1ImuAh/gVHuLHoBeOQLAr9CZuQg0J8Jm3v/3tePrpp/HOd74T5557LrZu3crXt99+O9/+tre9rVFPRci6Jxk5lDMTyHs6Eeg6A9t7OiFLDfvPkRBCCFk6XwzBWAe8/iDCbhbZ8twt7hVFwu+9/V3zPtz7b38rJEmauc6q8ENTVfjpqgCwKvzcMP3GCNkERkdH67rfoZSFUB3V9yXDxbmp787algmdBvGYMWc+Vn4CjjcKb7QbIb+OU5kWoK+vD9dccw1uu+02vmbX2XZCCCGEEEIIIWQhUb+GnS1B9DZ7kTFcDChbaiG+HoOeH4BoUYi/2ciNfDBWcf/Zz362kQ9JyJrRGa7vQOBCRLMEKz2EpNqGYOdu9G3pg6405rEJIRvPvn37VnsXyGYjCEBkC5rbJlE58CwOlPwI6PKc81C/46bL8OPhO/H8d78EOz85s701HsMH3vVWvOqqE9vssyp8dmJASokgkh6D3zfM2/dDWjiwIyuDxh6yHNra2uq6XyQar+vz8f6XnsXvCQMz1x1XgLL7NfXtjFVBtZSH3taDaHsPloqF9LfeeusJ21mnAbb93nvvxS233LLkx99saOwhhNDYQwjZLOhzDyHkeLGABhcB3g3zSLIEwbMFXUEBLgRohQFU/d1w5KWffN7InIusswCfEDI/Nl+JkB3AuBhFoH07evp28ipEQgghZE3xhOFp6oQvPIpYKo1sWUezT5sz73/36y/DxzovRmXgWdjFNGRfBF968x70hE7+pYBX4eusCt9Gswv4p6vwI6fWwpoQsvYPUnZ1dc3bRj8ea8bl55+14GNZDrB1bHb1/fO+8yEEWuvaF3bCkaFF0NTUjubg/FOBzLkPloU77rhj3vuw21/3utfN6kRCCCGEEEIIIYScTDygA1OzWfIQ37sFnUGBH4Bj7fQrPMT30Ju3CTS8Z7dt2xgfH8fAwMBJF0LWq6FMnXNpzsWxIGf7MeoE4W3ZitbeM2qDMSGELDBfMCGrItKLSEsXwmIFlXIJluPMedcLW4AtIQme3r3wn34l9N69+PpBEYIzd/t9vy6jajlIChEUU6NAbgSwac7otYLGHrIcZFnGPffcM+993v7WNyPgURd8rBcP9eNCPDdrm7Pjhvp2xLFQLaSgh+JobtsCgZ2JtMT/TuY7GYFhtz/66KNLevzNiMYeQgiNPYSQzYI+9xBC5hIP6tjZGkR3sxfJkokhpRfVQDeq3jZohUGIVnn1ci6y/gL8Z599Ftdffz0CgQDa29uxZcuWExY2DyAhm5LrQM0fwbihQY32oqnnTHQ1e1d7rwghhJC5qV7oTV3wRlrQjAyypbnDeFEA3rj96PWLxd/iHZMfRfC5f57nZ2pV+GlTRoZN41VMAPkR+o0QssGxdvKsrTyrxD9WeyyMD9/5P3D9Na9YMFB3XaD5yPdmbduv7IIU21bXPjjFJKpyAJ5wK6JNTViq0dHRht6PEEIIIYQQQghhWliI3xJET7MXiaKJEbUXlUAPDN90iF+iN2qDa0gL/RdffBGXXHIJv/zKV74S3/nOd3DWWWehpaUFv/71r5FIJHDllVeiu7u7EU9HyPriulAKw0hUBDjhHoS7z8K21tCSK30IIYSQFRPqQlN8CMXMOFLlIkyPAkU6+fmfl7YDZ76Yxv82/w4XS8/zbfb4AZTKvwPTE5uzCj+bNpFSwoikR+H1xYBgByBSq2lCNnqIf/PNN/Oqo9GXn0IYWWzpbMGgEIdfW/gr6sHRBK63fwYc83G60Hc91DpPrK3mJ6GFexFq3QKJnYG0RG1tbQ29HyGEEEIIIYQQMq01pMOd6qffnyhCCGxB61SuxEL8qq8LjkKFohtVQyrwP/axj8E0Tfz0pz/F/fffz7exAzIPPPAADh8+jLe85S14/vnn8dGPfrQRT0fIqoh4lnZwTylPIF8qo+ztRKD7LOzoaD6lA4WEkM1l586dq70LZDNTdGjN3fBH2hBlVfjluavwJQF45fYA2oXE0W2wEdj/zXmr8IMeBSlTQbrsTFXhU6XqWkBjD1mJdvpX77sMb7ruApy3LYqU1ISApvBxYSHS/h9AFo5O6zEstkPtOruu53VKGVRdGXowhpaW1lN6Dfv27Tuhk8Dx2EnsV1xxxSk9z2ZCYw8hhMYeQshmQZ97CCH1aAt5sDXmR0+zD+P5CsZ5JX4vDF8HtOIgRLO0IjkXWacBPpvT74YbbsCePXtmtrmsryEAn8+Hz3/+84hEIvjgBz/YiKcjZFU0+xZfDShXUqgW0kirHfCz8L67FZpMVYWEkPrt2rWL3i6yukJdiLR0IKiYMCoFmPbR0Ox4r+iU8WXxdbO2xcd/DKU0PufPBHQZZctBSgijnB4FssOAQ/NxrTYae8iKyA6hkh1HxlJQsCUEPQtX34+ki7ii+sjsbZ3XA0J9X22N/ATkYJxX38/VUWQxJyHcc889897n7rvvhiTR5/960dhDCFkNNPYQQmjsIYSsZZ0RLw/xu5t8GM1XMKH1oBLsRdXXORXiF5c15yLrOMBnLfK3b98+60BGqVSadZ210H/wwQcb8XSErIrDKWtR95eMHOz8OEalVvi6TsfW7o66WoISQsixWDcbQlaVrEJr6oavqb1WhV+auwpfFgH/tktxyDla1SrBQejAAlX4OqvCV5EqW1NV+GMNfxlkcWjsIcvOqgK5EeQTI/wEHn+d1ff5Fx6GX6jMXE8hBO/WS+t6SreaR9W04AnFEW/rRKOmA7j33ntPqMRn19l2djupH409hJDVQGMPIYTGHkLIWtfV5EVfzIfuiBcj2SomtR5Ugz2o+juhFYYgmoVlybnIOg/wm5qaUCgc/ccRjUYxMDAw6z6qqiKbzTbi6QhZFfMUHJ5AtMoQc8MYRhz+9l3o6t6CZr+2nLtHCNmgqtXqau8CIUCoE5FYB4KyDbOSm7cK/+puCf94XBV+bPwnUIujC1fhI4xSegTIsSr8RfzhJQ1HYw9ZdtkhGNlxpC0VOVPi48BCMmUTlxVmn9i2P/4qfqJRPYzcBER/DP5YN3RVQaOwkP7QoUN46KGH8C//8i98zaaSo/B+8WjsIYSsBhp7CCE09hBC1gPWRn9LzI+uJg9GshUk9R7eTr8aYCH+cF0h/mJyLrIBAvytW7eiv79/5vq5556L//iP/8DExAS/XiwWcf/992PLli1YT9hrEgRhzuV3f/d35/zZr3zlK7jgggvg9/sRCoX4vIff/e53V3T/yeoQbANKbgBDThP0+FbEu3fwFieEEELIuiUp0JpZFX4HYsgiUzLmvKsiAb6+S7Df6ZjZJsJF+MA36qrCTxertSr8wtxt9wkhG6T6PsWq7yPwKHJd7exHf/s44kJm5noJGvy7rqnrKV2rgmq5AD0UQ7y9F43Gus5dffXVeNOb3sTX1DafEEIIIYQQQkij9TZ70Rv1oSPswWC6jJTejUpgy6JCfLI+NKSf97XXXotPfvKTPKhnc96/4x3vwPe+9z2cffbZuOSSS/Dkk0/iyJEjC84PuFadddZZeO1rX3vC9jPOOOOk93//+9/PX2tnZyfe/va3wzAMfPWrX8WNN96Iz372s3jXu961AntNGk2rZ2oQx4KaH8CoHYDY1INI9+noi/rpl0EIWTJ2Ehgha0KwA00tHSilx5Ao52B4VKisZ/5JXNsj4gsHXo+/wmdmtjVP/BSZ4utg+NpO+jOs+na4YiKthBFJDcPriwGBVqCOltqk8WjsIcsqMwgrP460qSFjCmgJLFwNX7GA+xMdiGMPXiE9y7c9F7oKPq2+z9pmbhKCrxm+5k74vZ5TfglkedDYQwhZDTT2EEJo7CGErBesuLgv6oPjuHBdF4OZMsRIN8JTt2v5IRj+DtiKf+k5F1kTBJf9hk/R6OgofvzjH/NKA9Y+n/nMZz6DD3/4w8hkMvB6vXj3u9+Nj3/84xDFhhT9r1gFPusa8F//63/Fl7/85bp+5qc//SkuvfRS3pXgl7/8JSKRyMxjsc4E7CSHF198Eb29ja/6IKcuMTaAI089ArtSgC+2yN+R60DPD2CyKqLo70Fk2/k4o7N5znCDEEIIWXcygxh76QlMjA4iqXcjHtDnvOvX9jv43UN3Yqc4NLMt0Xo5xvf84Zw/ky2bME0L2+QxdHT3Ae3nAoGWhr8MQsgqMivA4C+QPvI0Dlhx5C0ZLcG5x5Jp3+sHPlfL7bFbOIJ3KN9D76W3QPbHFvxZxzZRGn4eWttu9Oy9EpHw9KENQgghhBBCCCFk/WHR7svjBfQnChjLVdEd8SBcGYCeO7xgiH8yhaHnocd7sf38a+H1U0HZWtCQZLGtrQ1vfOMbZ8J7hgX2k5OTPNzP5/P48z//83UV3i/VP/zDP/D1n/zJn8yE9wwL7G+//XY+r9Y//dM/reIekqWayNtz3+i60IojyBkOcnoH/N17sbMtQuE9IeSUPfXUU/QukrUj2I5gUxxPv3AADz7wA/zkid/Ask7+9/E1W0R83rkJDx+y8K/PGHwdHHkcamlszodnVfhl20UaIZRSo0B2kP+N3Ugsy8LDDz+Mf/3Xf+Vrdn0torGHLJvsEOziJDKWhrQhIeRZuPredoFvHTp6/QW3Bz/pfmdd4T1jFRJwtCA8kTYK79c4GnsIITT2EEI2C/rcQwg51Ur87XE/eqI+tAQ1DKTLyHl6UAmydvpdUIvDkE7STn/enIusKcuaqLN5/1paWvg/pPVsZGQEn//85/lJCGz9zDPPzHnfH/3oR3x93XXXnXDbq1/96ln3IetLrjp3gKCUJ1CuVjEut8HffSZ2dETh0xoyQwUhZJNjU9AQslbc9/VvYNdV/wVv/uPP4ROf/hL+8I4P4rpb34Yf/ujxE+77kx8/ji/9/f/BNf9Swm3frPD1tk/n8JP7/n7OxxcFASFdQcLyIJMvAsVEbdkg7rvvPvT19eGaa67BbbfdxtfsOtu+1tDYQ5at+j43gkJyDCkhDPYtUVcW7t/3izFgpHj0uiwAN9bZLMt1bJi5BLRQHE3t1AVtraOxhxBCYw8hZLOgzz2EkFMligJ2xAPobvIhHtDQnyoh5+2thfj+qRDfyNedc5G1hRLGOvzHf/wHX451xRVX4Ctf+Qq6u7tntrH2+MPDw/D7/bwrwfG2b9/O1y+//HJdvxzWcn8uTz75ZF2PQZafXEnBKWcxjDb4u87Elo4WRHwqvfWEEEI2FBYy33rrrSdsH5uYxB13fQLAnXjVVZfxbSzQr22bbTDn4q3/50n8Tct3cc11N5z0efy6jGzFREoOI5QagY9V2Pqi7NRibMT3b3BwkG+/9957ccstt6zKvhGyYrKDcIsTSNsq0oZYV/U9881jqu+ZfR1AtM5p7M1SGrasIRBuQXMzTclBCCGEEEIIIWRjhfg7WwNwXBeOCwykiuhp7pm5XSsMwkA7bDWwqvtJFo8C/Hl4vV588IMfxGtf+1peHcWw6vsPf/jDeOSRR3D11VfzVjc+n4/fls1m+ToUOvn8ENPbM5kMTtWLL76Il156aeb6vn37+Pqxxx6b2bZz507s2rULDzzwAG/dP70P7OQDtt/HnuV37bXX8v1/4oknZradddZZvPX//fffP7ONdVS46KKL8POf/xzj4+Mz22+66Sb09/fj6aefntl24YUX8ud78MEHZ7b19PRg7969ePTRR2feL03TeMeCtfOawoAQxjb2O604mCw4M/ctVh1osoD+9NE2I2wGTUOIw5JDSB5+gS9r7zVtxN8TvSb6PW2Of3vTNtJr2oi/p43+mkqlEt75zndiPp/82y+i9/Tz+eW/+MwX57/v330ZXWe/kndrYrZF5eP+5qoYNQT4c+N46FdDAIbW9e/Jtu0F3793vetd/P1gy1p4Tcxa+Le3Ef972rSvKTmJJ558nvWuAhCHIlrwqhIOJI5OI+FVBLSHJIxkbZTMWlVAf87Fb1Ozg/5zmywUq+IJn8uDmoB4QMJg2kJ1arPo+tEelFG0NXznO9+m39Ma/7fHHPtc9N/T2vw90bi3icfyDfqamOnXsFFe00b8PdFrot/TRvy3xx5no72mjfh7otdEv6f18m+vY9e5sCsm0i/8FGm+RUWT1ouOUj+ez/fBdmvFMew7c1dE5u30Zyry9R3odAxMTibw1MOPrpnXtG8DjBFLJbjuBptY9Djsl7CYdjS///u/z+cknQ+bq/Syyy7jv/C/+Zu/wXve856ZVvsdHR18GRqqHWg+lmmaUFWV/9IqlcoSXg1ZbomxARx56hHYlQJ8sdktNi3bhSwdrf4TrTLU/ACOODEgtgutPTuxuy2w7qeMIISsLeVyGR5PnWWGhCwTNlc7a/e+kC/+zcf5+m3v/dO67nvReXtPehs7a3g4U0aXVkKP3+XT06DjHGz09++hhx7iJ4iuBTT2kIZL7AdGn8HAZBoHjAg8ioSgvnAF/uCP/xm5YhlfsK/HAbcTZ0eBj19c31Oa5RzKyUGEevdix7lXQZbp/PW1jsYeQgiNPYSQzYI+9xBCGs20HTw/ksORZBG5ioWeZi/8hQFoucPQCwMwvG2oSoFZOde0wtDz0OO92H7+tfD6T16kTFbWhj+CsXXrVui6Xvf929vbF7wPO/Dztre9jQf4P/7xj2cC/OkK++mzK463UIU+Wduq1tEAX7ANqIVBjLjNsEI9iHdux44WP4X3hJCGY387KMAnq210dLSu+00ma+f31mMiMfd9RUFASFcwaXoRzo3AX0wApRTgbcJGfv/qvd9KoLGHNJRZBnIjKGbGkEIrDMtBzK8t+GPJTBZXVB6GLpu4VX4MD9lnI93939k3qrqe1siOQw3GEYr3UHi/TtDYQwihsYcQslnQ5x5CSKMpkojdbUGwqu3+RAEDydKsdvosxC8oEuSpruJkbdvwAT6reFoOsVhsZt77aayVPqu+Hx4e5gdg29raZv3M/v37+XrHjh3Lsk9keY3mHWzTRMCx+EA36YRQ9LSjuWs3drWys5ZE+hUQQhqOnSzGWvUQspqO/0wzl1hzpO7HnBDmv69fZ231TaTFEEKpUfj9A+s2wK/3/av3fiuBxh7SUJlBoDiJtKUja4CfoMNO1FlI6YUHoQvmzPUzpEEkWus70GAZZdhmCd7ANkTbjx6wIGsbjT2EEBp7CCGbBX3uIYQsB1VmIX6Ad7c8kihiIFVCT1MPXP4dXMBwRsZOJQdbDdIvYI2jxHGJ2DwITF9f36ztV111FV+zuReO94Mf/GDWfcg65DrQC0PIOh4k1RYEus/AjrYwdKU2hy8hhBCy3rCpgdgJj2wKIbZm14/H5pvq6uqa93HisSjOP3sPztu7B63x2omOc5GCMTyt7cF8EznNVOE7PmSzGYBV4Zfrr/BfS+p5/7q7u/lcXYSst/Ghrur7/CjK2QkkhTDKls1P0FlIsVzBRfn/mLXtpfirIUj1nYNezU1A9sUQiHdB0+rvyEYIIYQQQgghhKxnmizhtLYgupt9PNAfTJdQ9nejEtrCb1dLY5CM3GrvJlkABfgLnAVnGMYJ23/0ox/hU5/6FL/8pje9adZt73jHO/j6z/7sz5BOHz3I3N/fj7/7u7+Dpml4y1vestDvhaxRWnEE/z979wHfVnnuD/ynvbflbcdxJiMQ9iaBhNFCKbQkhRZaWmhvL6W3I6Xjtn+gm5aEDqDtLVBogfaSdFxKyx4Je5OQkJ3Y8ZJt2ZYla+tI5/95j7ETObEi2/LU73tvKuvV0TnHR/LDkZ7zPG8so4ZPXarMxzuvwgWH6fBzdxIREU1F69atUy5GFPOzX3311cqtuC/Gh04ftGbNmpzr+uLVlyEtq6DVanDjDdfmXNZ1zrXY26fB2/7c+yeSfIk0EIAd4Z62/ireaSif47d69WpoNLwgkKZffDis3qb+6vu0CcGkClZDftX3/q0b4FSFB++HZDNcR5yT1ybT6RQykQCM9hJ4K+tGtr9ERERERERE05woOhVJ/DqPGVq1Ci2BmJLEF+LWWibxZ2oL/T/96U+j3uCnP/1pTBff+ta38P777yvVUNXV1crYe++9pyTwhR/+8Ic4/fTTs54j7n/961/H7bffjmOOOQaXX365chHAww8/jJ6eHtxxxx2oq+OXSNNRhb4PaSmJZrkCxtpjUVfqQamN1TxENL6OPfZYHmIaFyIJt3LlyoPGm5ublfG1a9dixYoVg+PiZzG2atUqZZkBNdVV+H9f+hROnV+CnmgCJXYTLjj3TADfxm133ov2zv1ZerPTC/OSa2FZKB4H1u4CTvIkIWv0Oavwu1IWuHp9sHo6gVgvYHJiuhn2+NXUKMn9A4/1VMDYU9xGGh+GlYwCfe1IhPzolksRTUiodJkO+7SUlMax3Y+J7n6DNjrPg0ef37l3sq8HapMdFnc5zFZHXs+hqYGxh4gYe4ioWPC8h4jGm0mvwZGVdsiyjIauKFoDMXgq5iCu75+qTkwVnYSMtJ6fm6cilSxeuRFSq9VQHVA1IVZx4P1DGVgmnU5jurj33nvxj3/8A1u2bEFXVxdSqRTKyspw2mmn4YYbbsBZZ5017HP/+Mc/4s4778TWrVuV43X88cfjxhtvxMUXXzyhvwONTFd7E/ZtfB7peBgW7/4LLbTxbqjjAezNVEBddSxqKysxv8x62Pc9ERHRVCTaYItK2gMTyUOJxHJDQ8NBVeHiuRs2bIDP51PmbBcXOkot76Jp25toiargKqmCTtPf5EmS0nhr42b4uwPwelxwzl6EVa/0r8+LXnxe+298xrABjaffCsl46PntxZxdrb0x1Gj7MMulg3XWYqB8EaarQx0/Vt7TTIkPB/HvANrfQ3sgjN0JB2RZBbfl0BfsHGjf+6/iw213DN5PylpsPv3XMFoPf/FOJpNBuHUrLKV1qDtmCewl5Yd9DhEREREREdFMFU5IeL81iMbuCDQqNSqdRmWqaGNwT38S31yGYGcrjKV1mHfS+bwQfjpX4N93330Hjf3973/Ho48+qszxKb6ILC8vR3t7O55//nm88MILuOSSS3DZZZdhOrn22muVf6Pxmc98RvlH058mGVQS+FukehjKZ6O8tBxzS5m8J6KJ8cgjj+CjH/0oDzcVlEgg50rOCeLx9evXY9myZQe1gx86pimZDbOnDZ7IdvRGS+Cx9VfYinb6p564OGvZ473AyT3/xFe1f4NRlQLSQEnjo2hf+JmcVfj+pAXO3jZYPB1QxWcBRjumo0Mdv6mIsad4jSU+HFx970OyrwtdcinCcQkVzsNX38sZGXN8/8oae8t8Fhx5JO/7NxtUYo/o+GH3lOX1HJo6GHtoPC+aE99Xif8OEzH2ENFUwPMeIpooVoP2g0p8oGPLS/CpTkCFo7/ruCCS+Gq5vyqfpo5RfXIZmph+7LHH8MQTTyj/0fnIRz6S9djNN9+sjItWiwPzwxNNF+pUGPpIO1pU/ZU73vIaLCi3QaNm5T0REU1f4ovsQi4Hiwee0kpEuluRjnRDslRCq+6vwh/qE/OATa9b+pP3H3C2PIeu2ZdAMrgO+RyrUYtgPIWA2gZ7dzts1iag/Oj89o2IJic+9DYB4U4EMiYEk2qY9Kph48KB2hq34ny5YfB+RlZBtfAi5CsZ8sNsL4WzrBZgtyyiop4KZLpMW0NERERENN5sRh2OqLSjYwuQkNJoD8VR4agZ/Nwsh3fxRZhiDv8NSh5+/OMfK9X1Q5P3A0Tl4KWXXqrMGU80XailOAyRNnRoStFrqlHGjqiwQa8tyJ8NERHRpBFVaIVcTjCKKnx3JVxyL0LR5LDLHeUGttjORpu8v2W+Rk6hpDG74vagKnyTDv60Db2BLsjhDiDRl/e+EdEEx4dkRKm+T4W70Z1xoC+eUv6G8zE0FryjPx72ksq8npuIRaHNxGF0eOEurc3rOUQ0M5P3oohkaDcRcV+Mi8eJiIiIiIrNwOfyOo8F8WQaHaE4EtZqxJ1z0WeugaxS80L4KaQgmchNmzZh7ty5OZcRj7/33nuF2BzRuFNnUjCEmxDQeODXlsNdswDuEi/MerbbI6KJVVbG9r9UeGecccZh564Wj5911ln5r1Spwq+CyWKDFPYjLWcOuZi4sPfj83X4jZQ9NYSz+RloE73Drt5m0CIFNXphQajb11/dS+OGsad4iRbToko1l9raWmXatGH1NgMRP3ozZgRTKhh0Gug0h//o6fc14fj0pqyxcP3Fee97os8Pna0EztJqqHX6vJ9HUwdjDxWibb6ovM9FPJ5Op3mwibGHiCYVz3uIaLJizxEVdtSWmBFJSPD39Sfxe23zkbDWANrDT31H0yiBr9frlSR+LuJxnS6/qguiyaaVoghrnGiFF46aozGvzI6zzjh9sneLiIrQqaeeOtm7QDPQyy+/fNgvrsXjL7744ojWayqth8kjqvCDOavwTygVc1ovhW9IFb5n37+HfY7qgyr8TsmGUKATmVA7kAiPaP8of4w9xUvMDy1aTOeyevXq4S8C+qD6Xor0oEt2IBgT1ff5JdN1u7JjwFb1fHhrF+T13GRKgjreC4PdC0/FrLyeQ1MPYw+NlZjzfmjl/VDi8fXr1/NgE2MPEU0qnvcQ0WTFHpdFjyPK7ZjlMSMYl9AVTiBmLEXMVscK/JmWwF+2bBkee+wx3HnnnZBlOesxcf+OO+7A448/juXLlxdic0TjLqJ1Yp9cAkvNMajz2lBmN+K1117jkSeiCcfYQ1N6juuhzG64S6tgtNiQCvuRGXJeeGAV/mXzdfidlD39kqv5aWiSwWFXb9FrkVFp0Zux9lfhB3N/QU+jx9hT3MT80GvXrj2oEl/cF+M5548W3TEifgRlM0IpQK9Rw5DHFFSh3m6cHH8la6y1+qK89zke6oLeYofDUw6tyZ7382hqYeyhKXuOQzMaYw8RMfYQUbGd93isBiwQSXy3Cb3RJBLSoTtp0uQpSD/wW2+9Fc8//zy+8pWv4Je//CXOPPNMpQ1DR0cHXnrpJTQ0NMDtdivLEU1lYo6PtNaMHr0d+spFqPbYUes2K4+J9zMR0URj7KEpO8f1MKylc2Bqb4UzsgN9sVI4zIZDLndaBfDVbeegI/0IylT9rfM1mSRK9j2GjnlX5q7Cj9jhCLTBFvJB46wF9JYR7yflxthDIkl/2WWXKdWsItEl4oFom59z+g3RFSPUjrRSfV+OYEyCx5Jf9f07DR2YDTvKEVDuN6EC5fNOyOu5kugoEu2CobwO7orZfPGmMcYemsrnODRzMfYQEWMPERXjeY/XZoAs2yHKb/Z1Ryd1v2icEvhz5sxRrtq4/vrr8cwzz2Dv3r1Zj5933nm46667UF9fX4jNEY0b2eRGxD4XKpcdlR4X5pZalWQBERHRTJzjOleL2cPOcX2YKvxowIdgyI+MqQrqQ/y3VKMCLpmvx+82fwQ36x4YHHc1PYWuWRchrT90Ba3FoEUwpkUgY4bV3wa3vRkoXTjy/SSivNrpi25r+ZK69mLDc89id1sXJGsl5i5YCKMuR8L/A5EUcGf7kfil9Ct8VPMyvqD5FxrKP4RqdX4N42LhIIx6LayuMhgdpXnvLxHNPON6jkNERERENMOU2o1If9BBU3x7p1L+l2ZMAl+YO3cunnrqKbS2tuLdd99FMBiEw+HAcccdh6qqqkJthmhcadQaqJ1VqDDosKDcBo2awYqIiGbuHNcrV64c3RzXh2ErnwNjRyvs4R0Ix7ywD1OFf04V8KXty3C9/E94Vf2t8zWZBDz7HkPnvCuGXb/LrEdn2AFHwAdHsO2DKvz+jjlENDnW/flPWPWNG9Hs6xwcKystwTdvuA4XnHtmzuc+0QTEJPGTFn9NL8GzmrPwhyMPPQXHUGKqDinsh83thbu8lvP1ERW58T7HISIiIiKaaSocJiUXFkumYdLzPHmqUMlDJ60nKmKZjIyOvriSGMinWoiIiGg6W7duHVatWpVVpSaq1sQX3znnuM5DcM+baN3xFjrjanhKD12FL/yrAUhu+zf+n+6hwbG0xohdZ/4Kab1t2PX7gnF4M35UeawomXsS4F0wpv0lorHFklzJsjU/+PawSfxUBrj2WaA7vn/sqgXAlfPz23Y4HIEutBfOusWoP34ZoNGNeP+JaOYZz3McIiIiIiKi8cYEPlGeGhsbUVdXx+NFRBOKsYfGmyRJI5vjOk9ypBsNm15ET+sOSO55sBkPXYWfSAPXP5vAo/gKvKrQ4Lh/9qXonDt8QjCeSqOnL4x5ah+qFxwP7axTAZ1pzPtN/Rh7aCQxpH72bDS3tAy7THmpF0+uu+eQseW5ZmDNxv33DWrg/vMAu/7w2xbXove074Pbqkf1kafDWXsUX7hpjrGHpsM5Ds08jD1ExNhDRMWC5z1F2EJfePPNN/Hkk08qbfQTicRBj4u5xO+9995CbpJowmzatIkJfCKacIw9NNXmuM6XyuKBq7QK8UAb/EE/rIYq5VxwKIMG+FC9Ab/feTG+pXkIGxrT8IUzKG38JyqrL4DK6Djk+kWnHI3WgN60GZbONngdzYA3z5JdOizGHsqXSI7lSt4L7Z1+vPnuZpx64uKscdEL7u97s5c9rza/5L0QTaRgSodgtB8BR2kNX7QZgLGHpsM5Ds08jD1ExNhDRMWC5z1FlsAXlQ/XXHMNHnzwQeVn8eXsgZ35B+4zgU9ERERUPJyVc9Dd0QJzy3ZEE6WwGA+dlfvwLOCKf5rx22eiaA2lPxiNo/zx/8KNN3x+2NbbTrMenUE7HIEOOHtboXPWsAqfaIL5moZk4Ifh7w4cNNbW8D7OjLTChyWIwwA1gEvr8992vK8bbosdTm8VVEb7SHabiIiIiIiIiGjKEt+RjNmdd96JBx54AFdffTXeeustJVn/1a9+Fa+88gp+8pOfwGaz4YorrsDevfl9uUNERERE05/K7Faq8E1WB+JB/7DLvfjiS9j799sPSN73a+/sxqqbbsWTz710yOcZtGro9Eb0po0I+H1AMHcVMBEVXoU1v+W8HtdBY1UNf8MPdffjFcOX8TXtX3FeWQgVlvzWF5fS0Cd6oLd54SqfNcK9JiIiIiIiIiKa4Qn8P/7xj1iwYAHuv/9+HH/88cqY0+nEqaeeim9/+9t4/vnn8be//Q3PPfdcITZHNClOOeUUHnkiYuwhGiFX5VwYHOUwJnsQS6YOelyS0rjtztxTLInH0+ns5P4Ap0kPv2xDuKcdiUALkIrzNSoAnvdQXuJBLFk0CzVlByfnD1RR5sVJxy3KGutu3Y2jMtuVn92qML6i/TtWVPXkfeCjfb2w6HVwlJRBY/XyBZshGHuIiLGHiIoFz3uIiLGHxj2Bv2PHDpx77rlZY5IkDf583HHH4eKLL8ZvfvObQmyOaFI4HIeeg5eIiLGHaHhqixsObyWMFhsSh6jCf2vjZmV+7Hzmzz4UvVYNg8GC3owBgU5W4RcKz3soL4F90Ma78aOvfjrnYt/40rXQaDRZY6bd/8q6/676aFRU1eW12VQ6A1W0G3qHF66yWWLONr5gMwRjDxEx9hBRseB5DxEx9tC4J/BFy/wD/4NjsVjQ05NdPTFv3jxs395fYUE0HT311FOTvQtEVIQYe2gmcFfOhd5RDk28C4nU/os8h5sX+1ByLecw69Al2xEJtCPR0wykYmPe52LH2EOHFesFIp1AIoQzli7HTf/9dZR5S7IWKS/1Ys0Pvo0Lzj0za1x0zDgh8WbWWEftxXkf9L5oFHZNEhanF3pnJV+sGYSxh4gYe4ioWPC8h4gYeygXLQqgqqoKra2tg/fr6+vx9ttvZy2za9cuJbFPRERERMVFaytRqvDjvT7Egp0wlFTmnBf7UErd9mEf02nUMBot6I2HYOpsRaWrGfDOL8i+E9EwevcB4U6ENU6EIjJOO+1UXHre2Xhn0xblghvxty3a5g+tvBdS2x6DWiUP3t+FWaiak91ifzgZWUYm3AW9zQ1PeTWg1fMlIiIiIiIiIqIZpSAJ/JNPPjkrYf+hD30It912G374wx/iYx/7GNavX49HHnlEaaNPRERERMVBTKm0YcMG+Hw+eOwmVFm8UPn3ICWVQqftPw09cfEipUo3Vxv9aqcWF7qbEMAJOavw2+MO2APtiHY3weyoBvTmcfm9iIpeLACE/UCiD51yNYKxOBxGHfQ6LU49cXHOw5OIhHBiZANwQNf7neUXoU6dXxv8vlgSdrkPJueRMLmri/6lICIiIiIiIqKZpyAt9D/+8Y8jnU6joaFBuf/Nb34Ts2bNws0334xjjjkGX/7yl+F0OnHrrbcWYnNEk0K8p4mIGHuI8rNu3TqlK9Py5ctx9dVX48MfvRwfuuoreOGd3YiH9ifrtVoNbrzh2pzrun25DqXNj0MtxYddRqtWw2K2IJA2o6ujFeht4ks1BjzvoZzE31e4AxGtC6FkBnEpA6sxv2vD+7Y+BaMqNXjfJ3tQufDUvKvv4+EeGM02uEvKAeP+adxoZmDsISLGHiIqFjzvISLGHspFJYsJ7MdBMBjE3XffjT179qCurg6f/vSnUVFRMR6bIiIiIqIpRCTvV65cOezjP/qvK/CRy6+ARr0/4ffkcy/htjvvzarEr7arcfv5Bqw4Sqfc75h7BbpmX5IzudfWE8JsuQ3lc4+Bfd4ZgJ5TOBEVVLQHaH0b6GlAg6oaTT0x6LUa2E39f6e5SKkEZq3/MpwID4495roKs078cF6bDidSUPXsgbe8BrOPPRuwlY/pVyEiIiIiIiIiKqoEPtFMI6aCWLp06WTvBhEVGcYemo5t80XlfXNz87DLlJU48Y97fg57SeWQ56bx1sbNyvzZT3a5cIlzB76mf2T/4zobdp71K8ga47DrDsVTQKgNFU4TZh15MlRlRxXoNysujD00rLaNQMcWRDJ67IqY0B6Ko8ppglp1+Bb4/veewtKO+wfvB2Uztp95B6xmU14HvKO7BxVyJ8rnnQDXwrMAtYYv1AzD2ENEjD1EVCx43kNEjD007i30iYqB6CpBRMTYQ5SbmPM+V/Je6OjqxVtvvoZMJp01Ltrpi/mzP3LBOfjKRYtxf+Yi9Mn7E3vaVB/czc/kXLfVoEVY60I81IVAR7MyRzeNHM97aNjq+4gfSEbRmbEiKOajN+rySt7L6TQWdPw7a+w16/K8k/fxVBr6ZC/0thI4SmuZvJ+hGHuIiLGHiIoFz3uIiLGHJiSBn8lkcMcdd+DUU0+Fw+GAVru/Jeq7776L66+/Hjt37izU5oiIiIhoCvL5fHkt19OXRDLUNezj85zAHK8V96cvyBov2fcvqNKJYZ8nEokOixldkgVBfyvSPY0j2HsiyinQCPR1IKJzIhhPI5ZKw2bc/7kvl97dr6IC+6fISMhamI7I/vvOpS8ag1Mdg81dBrWDU7MRERERERER0cxVkAR+MpnEeeedh69+9avKnPc2mw0HduafPXs2/vCHP+Chhx4qxOaIJoXBYOCRJyLGHqLDqKjIL7FWXlOHVF8n5CFV+AdaORe4V/oQwvL+lvnaZAjulmdzrtti0CJh8CAW6kF3ezMQZxedkeJ5Dx0k0tVffZ+Kwp+2IRhNwWbIr/oecgY1Lf/MGnrJsAQelyuvA51KZ4BYD4xWF1ylVYAuv6p9mn4Ye4iIsYeIigXPe4iIsYfGPYF/22234fnnn8fNN9+Mjo4OXHfddVmPO51OnH322XjyyScLsTmiSXHhhRfyyBMRYw/RYSxZsgQ1NTU5l6mtrcVZS84FtAYkw8NX4R/tASpdNvwpfX7WuKdRVOEnc27DaTOhS7Yg5G9FqptV+CPF8x7KIi7O7t2nVN9H9W70xiXEUhLsJl3WYpKUxmtvbcSjTz6v3Ir7gt/XhMr0/u4caVmF1PyL8z7IoVgSLlUYZlcpdM4qvjgzGGMPETH2EFGx4HkPETH20Lgn8EVl/RlnnIGbbroJarUaqkNUYYgq/KampkJsjmhSbN++nUeeiBh7iA5DTKO0Zs2anMusXr0a3pp50DsrIIU6lercQxGnlJ+YD9wtfRgReX8nHF2yF67W3FX4Rq0GGVMJYuEAutqbgFgvX7sR4HkPHVR9Ly62SSfgT1sQjKVgHVJ9/+RzL+HCldfhuq9+D9/54RrlVtwX4/e312Fp4nbcJ12AmKzHS9pTUVNRltdBTssZpKK9MBkMcJeUAab8qvZpemLsISLGHiIqFjzvISLGHhr3BH5DQwNOPfXUnMu43W709PQUYnNEk2LHjh088kTE2EOUhxUrVmDt2rUHVeKL+2JcPO7xVsLkLENaY4AU7h52XSd4Aa/TjgeGVOGXNDx6+Cp8iwndsg19Xa2I+xv42o0Az3vooOr7cLtSfR+ISYgmRPW9dnARkaRfddOtaO/cP8e9IO6L8cefewmt8OL70mdwRuLXaJ/7ybwPcDiehkMOK/HC6K7uv7KHZizGHiJi7CGiYsHzHiJi7KFxT+CbTCb09uauahLV96KVPhERERHNfCJJv3fvXjzzzDN44IEHlFtx0acYF9QaNZyVc6FzlCMZ6shZhX/FvP4q/OhBVfjP59wHnUYNtcWLWDgEf3szEOXFpEQjFu4EIn4gnYJfsiAUS8Fm1EGj6v8oKdrk33bnvTlX0f3svZAz/e30zVY7jqzx5LXpjCwjHAnDppXg9JQC1vIxvYCSJOHZZ5/Fgw8+qNyK+0REREREREREMzKBv3jxYjz11FNIJg9dBRUMBvHkk0/i5JNPLsTmiIiIiGiatNNftmwZrrrqKuVWo9FkPe4t7a/CT6r0SOdIrp9cBjjsDjyYXp41XtL4KFSZVM59cFoMCMCOSHcrwp17xvgbERWZTAbobQLCHYjoPQjEUogMqb5/a+Pmgyrvh0r3+RFv2qz8/LF6QJ1nEX00KcGWCcFoL4GtpBrQ7N/uSK1btw719fVYvnw5rr76auVW3BfjREREREREREQzLoH/+c9/Hs3NzfjUpz6FUCiU9ZiozL/mmmsQCATwxS9+sRCbI5oUS5Ys4ZEnIsYeogLSajVwVMyFzl6OVLA9ZxX+lfOB30sXIy7rBsd1iR44W9fn3IZGrYbO5kUsEkKXrxWIDN+un/bjeQ8pwh391feZNDpTRgSj2dX3gr87kNfBSkcCcBqAc6rzP7ahaBIuVQR2dxlgqxj1iyKS9CtXrlQ+sx5I3BfjTOJPHYw9RMTYQ0TFguc9RMTYQ+OewL/yyivx2c9+Fn/729/g9Xrx29/+Vhk/8cQTUVFRgUceeQTXX389PvzhDxdic0REREQ0Q5SWVcHoLEMso4McGz4ReGo5YLE58NABVfibdMch5phz2G3YzQb0ql2I9bSit21X/5zeRHT46vtgMxBuR1jnQTAmIZaS4DDtv4hG8HpceR1JjcWFj9QB+uxGHMOKp9LQp4IwWGxweMoBg3VUr5hok79q1aqcy4jH0+n+Fv9ERERERERERDMigS/ce++9+MMf/oAjjzwSfr8fsizjnXfewdy5c5XH7rjjjkJtimhSbNiwgUeeiBh7iApMr9PAXl4PrUNU4XcMW4Wv/qAK/3fSxXg8fRI+kvgRLgvfiAZN/WG3oVaplBbckUgYXR2tyPR18nU8DJ73EPp8QNgPyIA/aUAwmoTdqFf+ng504uJFKC/15jxgFrsDztlH4cN1+R/XYDwFt7oPVlc51I6qMb2Xh1beDyUeX78+dzcPmhiMPUQ0GRh7iIixh4iKBc97ijCBL4hW+e+++y7C4TBaWlrQ19eHzZs3K9X5RERERESHUlZeDYOjDJG0Bqr48FX4p1eIIlwX/jP1NWyW65GRgbW78zumNqMeYX0JkoEWdLfsVFqCE9EwxN9HsEWpvu8T1fdxCTEpA5tRe8ipMG684dqch/K+85O4sexd2PX5HfFUOoNMrA8WnRaukjLA7Bn1S+Xz+Qq6HBERERERERHRtErgDzCZTKisrITFYhmP1RMRERHRDGIyaGEtnw2VrQyp0PBV+BoVcMW87LFnm4GO6OG3oVKpYLGLNuApBDqbkezJXZFLVNRCbUC4E7JKhc6kHoFoEg6j7qDq+wEXnHsm1vzg2wdV4tfYVVh7uQmnHVmB2QuPz3/zMVF9H4bZ5YXWUQmoR/+xVUzpVsjliIiIiIiIiIimZQKfaCZasGDBZO8CERUhxh4qpip8k6sMYUkLdaJ32OXOrgQqD7hGNC0D6/KswjcbdEiZy5AItMHfsguQkgXY85mJsaeIpSUg2Kwk8EPaEgRjEhJSBtZDVN8PTeI/sfYe3Lv6e7jnMjueudqMhq9YseIoHZ4xX4xymza/zcsZxOIx2NVxOD3lgK18TL/OkiVLUFNTk3OZ2tpaLF26dEzbocJg7CGiycDYQ0SMPURULHjeU4QJfDFvwsUXX4zS0lLodDpoNJqD/mm1+X1pQzQVLVy4cLJ3gYiKEGMPFQu7SQ+TdzbSZi+kXFX4amDlkCr8p5sAfwxQpQ+fkLfbnQhKGgT9rYh07inU7s84jD1FLNQKRLogazTwJ3XojSXhMA1ffT+0nf5pjnZcewywrF4LjVqFLtkO9xFL8t58OJ6GE2GY7CUwOssBrWFMv474DLpmzZqcy6xevVr5vEqTj7GHiBh7iKhY8LyHiBh7aNwT+P/+97+xfPlyPPbYYzCbzTj11FNx9tlnH/TvrLPOKsTmiCbFE088wSNPRIw9ROOotKwKJmcZQikNNDmq8M+pAsrM++/PQissb92JOa99G8hIObeh06ihtlUg0duBzpY9QCJcyF9hxuB5T5FKp/oT+OFOBDVuZcqJlKi+N+R3Iba4iKaqNfuc+d/6CzHHk18SPiPLCMUScKnCcHjKAFth2tqvWLECa9euPagSX9wX4+JxmhoYe4iIsYeIigXPe4iIsYdyKUhJ/C233KJU3YtE/vnnn1+IVRJNOYlEYrJ3gYiKEGMPFRO31YA2bx3CPe1I93UCBiegOvh6U60a+MRc4NfvyVij+y0uU78MdVxWHnO2v4zeytzVvjarFYGwBfoeH3padsA954Rx+52mK8aeIhVsUZL3skaHzoQWgUgUTnN+1feCtnE97HJo8H6fbIJ2/nl5bz6alGCRIzCaLLC6ygCTE4UikvSXXXaZ0jnO5/Mpc96LtvmsvJ9aGHuIiLGHiIoFz3uIiLGHxj2Bv2XLFlxxxRVM3hMRERHRqKlUKpSWVyPSWYZQRxfcySAkg+uQy55bA/xllwqJlB5qVX/yXijZ+3/oLT8TUA/fDlskI0Vr7mj3LnS1N8FRVgeN1cNXjoqblASCon2+HwF9OUJ9KWRkwKLP8yNjRkLJvn9lDf1TvRzHVFjy3oVQTEIV+mD3zALslSg00U5/2bJlBV8vEREREREREdGUa6FvtVrhdrsLsSqiKcvhcEz2LhBREWLsoWLjtRlhLKlDTO+BLKrw5f3J+QPp1MCKucBv0h9FSt6frDfEOuD0vXjY7ZiNRsT1JUj0tMC/b/uw2ylWjD1FKNisJO8zGgP8cQ16Iyk4TTrlwpp8GFtfhTvdNXg/IesQrr8Q6vyejngqDXU6BosOsLu8gMU72t+EpjHGHiJi7CGiYsHzHiJi7KFxT+CLKoZXX321EKsimrJEi00iIsYeovGlVqvgKa2C0VGGUEoFbbJ32GXPrwFiBi/Wpc/OGvfu/YdSDZyLSEqaXV6EIxEE/K2IB1oL9jvMBDzvKTKp+AfV950IaFwIxVIQl7SY9cN3ssgiZ2Df80jW0KM4CyfPOnQHjUMJxVPwqPtgcZVC7ajI2UWDZi7GHiJi7CGiYsHzHiJi7KFxT+D/7Gc/w549e/CjH/0IMquXaIbauHHjZO8CERUhxh4qRuVOE/SeOoS0biDsH74KXwNcPge4S7oUyQOq8PVxP1xtLxx2O0adDrK1XKnC79y3A0jnTvoXE8aeItPbpCTv0zorukT1fTQFpzn/6nuT7w14U22D9yVZjaaqjyDf/H8qnUEykYAdcTg85YCt8O3zaXpg7CEixh4iKhY87yEixh7KJc8JDbN97nOfO2jsqKOOws0334w//OEPWLx4MZxO50HLiC+A7r333tFskmjS7du3T3lvExEx9hCNL51GDU9ZFWL+MvQFAjDFuvHa1hb4uwPwelw4cfEiaLX9mcELZgFrd3vxcPocXK19ZnAdJQ3/h97KsyCrdTm3ZXV40NfWBV13K0Ide2GvnM+Xl+c9xSUZAUKtQLQbPYZqBKPJ/g4V+jw/KsoZ2Hb9I2voX/IZOH1e2Yiq793qCMwON3T2MkBnHOlvQTMEP3MREWMPERULnvcQEWMPFTyBf//99w/7WGNjo/LvUJjAJyIiIqJ8VDhN8Hvr8Mhjj+Le+/8MX9f+VvrlpV7ceMO1uODcM2HQACvnAne9/1Gs1KyHQdVfRa+Pd8HZuh6BmvNybkejVkPjqEK8pwkdzXtg89ZApTPxRaLiEdgHhDuR1jvQFQN6YxLcFn3eTzd1b4U32Tx4PyOrsL38EszLcxUZWUYknkKFug8Oz5GAvWI0vwURERERERERUXEn8BsaGgq/J0REREREHzDqNHjt1dfxo9W/OeiYtHf6seqmWwF8W0niXzgL+NseD/4sLcNntU8OLudteAS9lUsga3JnEi1WG0JhI3SBVnQ174C3nh13qEjEQ0BfOxDvRZe+FqFYAhq1CiYxP0WeHo8fhTeS38KXtf/AieqdeCxzCk5fUJX388NxCQ51FGaTGSa7FzC5RvnLEBERERERERHNDCqZk9YT5SUWi8FkYkUeEU0sxh4qVpIkYfbserS07K/sHUpU4j+57h5oNBo81gis2xzAC4avwqhKDS7jW/AZ9NRecNjtxeJRZPy7Ya8+AvXHLYXecvB0UNPhmG3YsAE+nw8VFRVYsmQJtNpRXa/L2FMsfO8BHe8jCTV2RaxoDkThtRlg/GCKisORZeDLLwANIeUeTlNvxbxyFz5xwuHnsJekNN58dzN2NrdjoVvGxy44B875pwH2wz+XZi6e9xARYw8RFQue9xARYw/los75KBENCgaDPBpENOEYe6hYiUR0ruT9QCW+SAAK59UCKpMLD6aXZy1T0vAIVOnkYbdnMpohGVyI9bShY9+2/szkNLJu3TrU19dj+fLluPrqq5VbcV+MjwZjTxGI9iit85EIwZ+2oTeWhFGrzjt5L7zdOZC8F1R4LXMUzlxw+AT8k8+9hAtXXofPf+17uO32O3Ht9+7CMR+9Husef3H0vw/NCIw9RMTYQ0TFguc9RMTYQ7kwgU+Up9dff53HiogmHGMPFStRRZ4Pf3dAudWpgSvmA7+TLkFM3t8yX5fshbvlmbzWZXZXIBHtRbC9GX1duS8emEpEkn7lypVobs7eZ3FfjI8mic/YM8OJC1QCjUC4HXG9G93RNPpiKTjNeU5c/4F1u7Pvn1EBVFkPn7wXU2CIC3AO1NzWiZVXXDHqi05oZmDsISLGHiIqFjzvISLGHsqFCXwiIiIimnJEC/h8eD3758teVg3oLQ78MX1+1jIljf+EKh0/7Lp0ot28vQqJniZ0NGxDJnn450yFtvmrVq3KuYx4PJ1OT9g+0TQQ6QIifiAZhU+yoDeWgtWog06T/8fDrT3Alp7ssRVzD982/7Y77825DN+vRERERERERFTsmMAnIiIioilHzN9eU1OTc5mKMi9OOm7R4H2Re7xyPvB76WJEZIMy1iqXYHv1JyCr8psL3mL3ICFrEO1uQWfTNkyHqQaGVt4PJR5fv379hO0TTYPq+959QF87wjoPgrE0okkJdlN+fyOCPtqOp3f2d78YcLwXmOvM/by3Nm4+qPJ+KL5fiYiIiIiIiKjYMYFPlKdjjz2Wx4qIJhxjDxUrrVaLNWvW5FzmG1+6FhpN9nzdS6oAi9WO26UV+G7qc1iauB0/7z0HUOeXnFSrVDB4ahELdqKnrQGx3k7MhKkG8l1uAGPPDNbXDkQ6IadTaE+Z0BNNwmHUQ6PK/6Oh4/0/4Y7QV3GL9n5UoksZu/ww1fcHTnlR6PcrzRyMPUTE2ENExYLnPUTE2EO5MIFPlKe6ujoeKyKacIw9VMxWrFiBtWvXHlSJX+F1Ys0PvoULzj3zoOdoVMCnFgD3pj+Mh9LLkYIWL7YBjaH8t2s0GJExexHv3gdfwxYgk572Uw3ku9wAxp4ZKpMBgs1KEr9X40IwJiElZWA15l99bww1oLR3IwyqFK7RPoX1hq/hXHsbjvGMbMqLQr5faeZg7CEixh4iKhY87yEixh7KhQl8ojw98sgjPFZENOEYe6jYiST+3r178eRTT+OWNb/Bj3/yQzx59y246Iyjh33OGRXAbPv++zKAh3aMbLsWVyniSQmRrhZ0t+zEdJ5qoLa2FkuXLh3Rehl7Zqi+NiDcibQMdCaNCESScJn1SueJfNl2/V/W/ffkOThtXgXyWcWJixehvLSk4O9XmjkYe4iIsYeIigXPe4iIsYdyYQKfiIiIiKZ8O/3zz1uOz13zGZy8/FL0aDzQxboAOXPI5dUfVOEf6JV2YE8w/21q1Bro3DWI97Shq2kHUtFeTNepBlavXn3QVANUhNIS0NsE9HWgRyWq71MQWXeLIf/qe0O4GaU9b2aN/a/2Upxakd8FAFqtBjd84Zqcy/D9SkRERERERETFjgl8IiIiIpoWyuxGWF3lCGvsSEAHbXL4jPypZcA8R/bYgzsAVToOtRTPa3tmsw0pvQOxnla0730fkEUt//SZakDcF+PicSKEWoCIHymo4U/o0RNNwWXRjejAOHYPqb7PzEb1vGOVi2byIcsyTjzlZKz5zudRXZbdc5/vVyIiIiIiIiKifvmXWxAVubKyssneBSIqQow9RPvptWqUOozo9dSh29+Hypgfkt4BqA6+JlW0875qAXDzG/33jUhgUdczqH/hUfTVnIvOuSvzOrRmdyVibdvQ29kER0kl7OWzp+RLIpL0l112GTZs2ACfz6fMIS7akI+28p6xZ4aREkBvi9I+36/yojeWgkGrhlGb//tDH2mDx/9a1tgf1Zfi6ur82+9Hk2loVRmsOGshvvzpv+CFxgR8Xb1jfr/SzMHYQ0SMPURULHjeQ0SMPZSLShZlEERERERE00A8lcbbjd3o2fU65qMJMDggGV2HXFac5X7jZUDXuxf36lejVNXfBj+tMWDXGb9E2jCkRH8Y4WAA2kgrbDWLMPe4pVAbzAX9nYjGnX8n0L4Z8VgEu5IutPbGUG43QafJvyFb6ebfwdv+wuD9bZka/H3uT3HpnPzX4QvGUYJe1Dm1cNYfD5QvGvGvQkREREREREQ007GFPlGeXnstu+KIiGgiMPYQZTPqNCi1m6Bx16IbLujiXYCcOeRhElX4Vy8A9siVUGH/Mpp0At6G7FbguZjtTiRUJsS6W9DZ+H5RvCSMPTNIIgwEW4FoFzrhQm80BYteN6LkvS7aAU/7S1lj9+JSXDAr/3XEpTTSmTQcqj7YPOWAvWpEvwYVB8YeImLsIaJiwfMeImLsoVyYwCfKU0dHB48VEU04xh6ig1U6TbC6ytENG1JqHbSJ4LCH6dgSYI7HiLukS7PGXS3PQhfz53V41SoVjJ5qxEJd6G5vRLSnbca/LIw9M0igAQh3IKK2oScORJISHOaRzaTm2fMPqA+4CGZPpgLGWafANILV9MVScGvisJvN0FhKANOhO2dQcWPsISLGHiIqFjzvISLGHsqFCXwiIiIimlYsBi1KbCbAWYuAyg1d3J+zCv+qBcCf08vQIpcMjqtlCd49f8t7mwa9AbK1AonuZrTvfR+ylCzI70I0rqI9QF875HgvfGkbAtEk7EY9NKr8PwbqIz642l/MGvtt5jJcVJ//OlLpjFKB75KDsJVUAPaK/j9OIiIiIiIiIiI6CBP4RERERDSlSZKEZ599Fg8++KByK+5XOU2wu8vhz9iUOe1zVeEf7QGOLdXhl9LHs8advhdhCLfkvR9WlxcxSUakuxXdzdvG9DsRjTtZBnoalAR+r9qFYEJGUsrAZhxh9f3ev0MNefD+7kwl0jWnw67Pfx3hhASHJgWbQQW91QNYy0a0D0RERERERERExUQly+KbHSIiIiKiqWfdunVYtWoVmpubB8dqamqwZs0aLDztfOxtbERFZCtKpHbEHHOBYSqLG0PAVzZk8Lj+W5inbh0cD5WehOZjv5b3/kSjUWR6dsNePh+zF50Ko5vzeNMUFWoDfBshBVqxK1OO5kACTrMOFn3+CXx9uBVzX/0mVAck8L8ifRmXn3Ma3Mb81pGRZbT2RjFL04O6ihKYak8A3LNH8QsRERERERERERUHVuAT5amxsZHHiogmHGMPFXvyfuXKlVnJe0HcF+NvPv9v2Nxl6JJtyBymCr/ODpxTo8ZqaUXWuL3zTZiCu/PeJ7PZjLSpDPGuBrTt3QI5GcFMxNgzzaUlILAPCLXDDycC0TS0GtWIkveCZ9+/s5L32zM1yFSdknfyXgjHJZjVGTjUcZjspYC9ckT7QMWFsYeIGHuIqFjwvIeIGHsoFybwifK0adMmHisimnCMPVSsRJt8UXmfyy3f/TbsJi1i5ir0atzQxf2AnBl2+asWAM/jJGzM1GeNl+5+eET7ZnV7Ec7oEPU3oqvhPSAz/DanK8aeaS7YDEQ6lSkfOlNGhGIpuM0j6Hn/gYetn8bPUlcgIFuV+79Ofxwfn5f/R0jR7K0vkYJb3Qe7ywtYSwGtYcT7QcWDsYeIGHuIqFjwvIeIGHsoFybwiYiIiGjK2bBhw0GV90OJxxs3vwm7uxz+jKjCN0Kb6B12+RITcEm9Cj+Xrsgat/a8D0v35rz3TaNSw+SpRTjUi67WBsQ686/gJxp3qTgQbIHc14422YVAJKnMe6/TjOyjX1oGHtxjxG/Tl+CsxC/xvdRnkak8EV5T/uuIptJQQ4ZDDsPiLmP1PRERERERERFRHpjAJyIiIqIpx+fz5bVctLcLbpsBYVMlQlpRhd+Vswp/xVxgs+ZovJQ+6uAqfHl/q/DDMRn0SNuqEe9uhq9hGzLh7ryfSzSuAo1AuAPBtAGBpAYJKQO7STfi1bzUBrR+MENEGGb8OXMeVswf2cfHUEyCRx2Gze6AyuIFjPYR7wcRERERERERUbFhAp8oT6eccgqPFRFNOMYeKlYVFRV5LVdVVYkyuxFWVxm68qjCt+qAT8wDbpM+kTVuDu2FrfPNEe2j3e5ASONEzL8PftFKX0pgpmDsmabiISDUhlS4Cz7JgZ5ICm6LHmqVakSrycjAw7uyx86tBsrN+a9DXDiQzqThRB9sngrAXjWifaDixNhDRIw9RFQseN5DRIw9lAsT+ER5cjgcPFZENOEYe2imzm//7LPP4sEHH1Ruxf2hlixZgpqampzrqa2txdKlS1FuN8JtMaBHX46wznPYKvyL6wCfcS6eSJ+UNe5t+L8RVeGLpKjVXY5AXEKgvQGR1vdH9PypjLFnmgo0KNX3XZIZPUlAp1HBrNeOeDWv+oB9fdkfGlfOHdk6QrEk3JoE7GYDNJYSwOwZ8X5Q8WHsISLGHiIqFjzvISLGHsqFCXyiPD311FM8VkQ04Rh7aKZZt24d6uvrsXz5clx99dXKrbgvxg+k1WqxZs2anOtavXo1NBoNtBo1yh1G2Fxl8OdRha/TAFcvBFZLK5CWVZBkNR6SlmFD/TeAEVYqG3VaqBw1iAT9aG/eg3SgCTMBY880FOkC+joQDfeiI2NHX0yCy6wf8Wos/k1YtzP7opqzq4Aqa/7rkDIZxFOi+j4Im7scsFeM+G+LihNjDxEx9hBRseB5DxEx9lAuTOATERER0YQQSfqVK1eiubk5a1zcF+NDk/grVqzA2rVrD6rEF/fFuHh8QIXDBI/ViG5dKSL6w1fhL60CMrZq/D/pc1ievA3fla7Fb/e4R1VAb7eYENKVIdG1D537tgHx4MhXQjQWmQwQaEQm5FOS94FYGjajDjrNyD7umYK7UbfxZ7gn8Q18XP0CNEhDpN3FtBMj0ReXYNNKcOhk6G1ewFo+shUQERERERERERUxJvCJiIiIaNyJNvmrVq3KuYx4PJ1OZ42JJP3evXvxzDPP4IEHHlBuGxoaspL3gl6rRqndAIuzHF0Z+2Gr8NUq4LNHAH9OL0OjXKGMbeoC3vaP/HcTrfSdTg/8khGh9r0It2wF0gdPC0A0bvragHAngpEYuiULUukM7KaRt8737vmrcjtL3Yk1+t9hte53OL0CqLXlv46MLCOcSMGt6oPNXQbYygHNyPeFiIiIiIiIiKhY8ZsUojzNmjWLx4qIJhxjD80UGzZsOKjyfijx+Pr167Fs2bKD2ukPHTuUSqcJvmAMTb2lKFWHYIm1QjI4AdWhr1k9vhRYXAJs7No/dv824DgvoBlht29xAYHWXolQqAHalj0wWuzQVhyF6YqxZxqRkkBgH1K9rejMONETS8FtNigXlhxycSmNtzZuhr87AK/HhRMXL4JWq4Gpdwds3e9lLbs2vRSfHGH1fTghwaSW4VBFYXLMB+yVY/ntqMgw9hARYw8RFQue9xARYw/lwgQ+UZ4WL17MY0VEE46xh2YKn89X0OUOxajToMxuQrezDJ3dnZil6YE2EYBk9Az7HFGF/5UX999vCAHrW4Bl2V378+Iw69GZrIQp0IKOFhuqzE7AUYXpiLFnGgk0AH3t6I4DXZIBOk0GJr3mkIs++dxLuO3Oe9Heub/VRHmpFzfecC2+4NiQtezrmYWQS45EvSP/XZFlGX3xFCo0fbA5Rev8MkBnGv3vRkWHsYeIGHuIqFjwvIeIGHsoF7bQJ8qTqAgkIppojD00U1RUVBR0ueFUOU1wW/ToVJciZvBAF+8G5Mywy891AkuH5Ngf2C7D5HsDWvHcEVCpVHDbreiEG5H2PehteR+I9mA6YuyZJmIBINiCaG87OuBWkufi/T9c8n7VTbdmJe8FcV+MP/7ixqzx21MrcMWCkbWiiKbSUEOGA32wivb5rL6nEWLsIaLJwNhDRIw9RFQseN4zfTCBT5SnYDDIY0VEE46xh2aKJUuWoKYmd1l7bW0tli5dOqbtiMrjMrsRJmcpumQ7MhqTUoWfy9ULAe0HZ8XHqnbjrswPUL/llyjdvW7E29dp1DDZPehImdHdtBOJti1AIozphrFnGshkgO69SAfb0JG2KhX4dpMeWrX6kG3zReV9LqueiiOdkZWfX04fhbT3CMxzjmyXQjEJHnUUDpsDKksJYBrhCqjoMfYQ0WRg7CEixh4iKhY875k+mMAnIiIionEn5rFfs2ZNzmVWr14NjebQrb9HotJpgsdigE/lzasKv9wMXDQLOEO9GY8YbsLJ6h3KuNP3IoyhxhFv327UIW32IpCQ0dG0A5mOrf3zlBMVUqgVCHcgEOpDZ9qGdFqGzXjoGdLEnPdDK++Hag7JWN+YVn7+hfRxXDFvZLsTl9JIZ9JwqkKwussB+/ScPoKIiIiIiIiIaLIxgU+UJ4PBwGNFRBOOsYdmkhUrVmDt2rUHVeKL+2JcPF4IFoMWXrsBJkcpuvOswr9yPrBVcyR2ZvYnHVWQUbbzQTGx94j3wWM1oEdbilBfBJ3NO4HOrf0V09MEY88Ul4oDgUbEuprQLrsRiEkosRqgVh265b2/O/f7f4AvnMH69LFIeRbiCPfIdikUS8GtTcJh1ENjLQHMJSNbARFjDxFNEp73EBFjDxEVC573TB8qWR7FN5JERERERKMkSRI2bNgAn8+nzHkv2uYXovL+QGIu8HeaAmhtbcExqr0wR5oRc8wFVMNfv/pYI/D++xtxv/7nWeP7Fn8DYe/xI96HpJRBZ28Ys9XtKKushaPmKMC7ABgmyUqUt46tSLdvQbO/F3slDwwaNZxm/bCLv/bWRlz31e8ddrVPXWXB7dU/x2dOnYVFI8i/p9IZtIdimKvuRG1tDfQ1JwCO6vxXQEREREREREREg1iBT5Sn7du381gR0YRj7KGZ2k5/2bJluOqqq5TbQifvBZtRB6/VCL29FN3Irwr/glnAPsuxeCG9KGu8bOefgYw04n3Qa9Vw2ExolkvR074Pcf9eoLcJ0wFjzxQW6QZCbQh0taMTbqV1vsOky/mUExcvQnmpN+cytQ4VemvPhtY9suS90BeX4NCkYDeqoLd5AVvFyFZA9AHGHiKaDIw9RMTYQ0TFguc90wcT+ER52rGjfz5cIqKJxNhDNHpVLhO8Nj3a4EXCWAJdvBuQh29jr1EBX1ykwk+lTyIj76+SN0bb4Gp9flT7YDPooNWb0IYSdLXsQrprFxDuxFTH2DNFZdJAz15Eu5rQmbGjJ55RWuerDtPVQavV4MYbrs25zE/Ps+BXmZX49MIR7pIsI5xIwY0g7O5ywF4JqAt/UQ4VB8YeImLsIaJiwfMeImLsoVyYwCciIiKiGUlUJXssBuisJeiBAxnt4avwj/YA3opZWJdekjXu3fM3qKXoqPbDbdUjAjM6MzZ0N+8EOrcD8eCo1kVFLtiMdMgHf18E7ZIVNoNe6fSQjwvOPRNrfvDtgyrxa+wqrL3chNiCi1Du9eJI98ir762aNOwaCUZHKWCrHNkKiIiIiIiIiIgoizb7LhERERHRzFHtMqErnEBr2Au3MQhTuBmSwQWo1JCkNN7auBn+7gC8HpfSZlxUKn/uSOD/ta/AR+RXYVYllPXoUiGUNPwTnfOuGPE+qFUqeK0GdIRsMKrSMLTvgkOjAyqPA3TGgv2ukiRhw4YN8Pl8qKiowJIlS5TpCmiGSEaBwD70tDfCDy/SGXGRysheX5HEX3b2aXh//V8hbforqqwqLK3TIKoy4+zER3HLgpFX34fiKdSqQ7B5yvpb52v1I1sJERERERERERFlUcmyLGcPEdGh9Pb2wul08uAQ0YRi7CEau80tQWzzhVAR3Y6K+B6ktWb8+5VtuO3Oe9He6R9cTlQmizbjIsn5vzsBz56/4qvavw8+nlbpsOeMNUiZRjhB+AdEm/FgNIV6bRdqvA4YyhYAFccAIpk/RuvWrcOqVavQ3Nw8OFZTU4M1a9ZgxYoVI14fY88U1L4Zkeb30NQdxr6UG2V2Y97V90OlZWD1+lZ8MrEWF2rexOrUCrxbehn++8SRv6djsTjmanyonn88UHMyoDONap+IBMYeIpoMjD1ExNhDRMWC5z3TB1voExEREdGMVuk0osSqR6vsRcJYgqefeRarbro1K3kviPti/MnnXsJlc4B/aC9Gh7z/4j2NnELp7odHvR9Wgw4GnQat8KCzuwfpngYlKYt0aszJ+5UrV2Yl7wVxX4yLx2maC/uRDrahu9uP9rQTdpNu1Ml74cVW4IVwFb6Y+ho+lrgF96U/hE+NsPpeCMUkeFQh2NylgLWMyXsiIiIiIiIiogJgAp8oT6IlLRHRRGPsIRo7t0UPj9UAmN3wp634yb2P5lxeVOZrkcZVRxuxWlqZ9Ziz/WUYg3vGtC/JtBo+dTm6/B1AoBHo2AKkpVG3zReV97mIx9Pp9IjWy9gzhYj3Rs9edPsa4JedSKu0sBtHPzWCaL3/0M7999+R5+PkKiNm2Ua2nlgqDVVGgkMVhs1VDjiqR71PRAMYe4hoMjD2EBFjDxEVC573TB9M4BMRERHRjKZSqVDpNKHEZsAzG/fB5w/kXF5U4r/57macVg7sdp6NbZnarMdLdz4EjHIWKrVKBa/NgJ5YBm3qCgS624EekcTfPKokvvjgNbTyfijx+Pr160e1vzQFBBoR7mpBIJqCP2VWukmI9/RoPdMCtEX231ergE+Oqvo+BY+mD1aHB2pbGWCwjnqfiIiIiIiIiIhoPybwiYiIiGjGE0lPj0WPnlAsr+X93QGIHOnnF6nxU+lTWY+1x3VQp/Nbz6HoNGq4LQZ0RNJoRjkiAVGJv6+/Ej8zskp5n89X0OVoiomKqRYa0d3ZjLaMGw6zXnn/jFYqDfzlgOp74bwaoNIysvUkpQySUgoOOQy7p4LV90REREREREREBTT63otERWbBglGUJhERMfYQTQmiYrnKZUJdbX5tvr0el3Ir2oo7Zy3C8y3HolLVjZ9In8JrqWPx+xRQMoYzaYtBi1RaRmdYgs5WhtnBDhj7dxQoOxpQa/JaT0VFRUGXG8DznikgnQK6dsLfugddGRvSaiNso22dL8so2/UX/Dt9MvyxuYPDWjVwxbyRry4UT8GjjsJms0NrKwVMztHtF9EQjD1ENBkYe4iIsYeIigXPe6YPlSyPsv8nEREREdE0Ik5732rowodOX4zujrZhl6so8+KJtfdAo+lPovclgW8+H0Jr0oI0+sfOrgS+dcLY96mrLwEZMiptGtSrO6F1lAGu2UDZUXkl8SVJQn19fc42+rW1tdi7d+/g70PTRMdW9Ox7Dx2dXdiX8aLcaR519b21axNmvfsz5WeRxF8tfQINcgUumQ38x9EjW1c6k0FbIIa5mlbUzl4A/ayTAUvJqPaLiIiIiIiIiIgOxhb6RHl64okneKyIaMIx9hAVtgq/zmvH9d+6Jedy3/jStVnJbpseuGShfTB5L7zQBrzTOfZ9clv1kDKAPyqjRVWOTEi0028EOrcCmcxhn6/VarFmzZqcy6xevXrEyXvGnknW14Fo1z4EOlvRIrvhthpH3zpfzijV9wMu0ryBm7R/gkENrNhfjJ+3UFyCUx2B1WyG3l4GmD2j2y+iQ2DsIaLJwNhDRIw9RFQseN4zfTCBT5SnRCLBY0VEE46xh6iwSqx6fOzyFfjKT36D0lJv1mPlpV6s+cG3ccG5Zx70vPNrgTn27LE7NwNxaWz7o1apUGrTI5yQ0BnNwKcuB8LtQE/+SfwVK1Zg7dq1qKmpyRoX98W4eHykGHsmUSoOyb8T/pY98GVcMBpMypQLo+XwvQRjuClrTFTgf6QecCvzNuQvI8sIJ1JwqUKwlVQCjqr+aR+ICoSxh4gmA2MPETH2EFGx4HnP9DGGmTuJiIiIiKZfFX61y4RLLv0YTj75JMQ2PYpg2244qo/ASccfM2ylukYFfOkYYNVLwMD8Ux1R4M87gevmx5DRmka9Txq1GqU2IzpCMWg1RuhslSjta9ufGC094rDt9EWS/rLLLsOGDRvg8/mUOe+XLl3KtvnTjSxD7tqBjpa9CMSBuM6BMrNu1KtTS3GU7X44a+yR9Olo0NThe3NGvj5xoYkFMVj1WlicZYCldNT7RkREREREREREh8YEPlGeHA4HjxURTTjGHqLC81j08FgN8NtLMf+EM1FxVAXSWjOkw7SZX+CCMmf4Iw3998vRjXOaHkJ1dzOaz/gpZPXoT631WrWyT519cajVJhjsVXCEWgE5DWRSQOlRgFZ/2Hb6y5YtQyEw9kySUCu62hoR6u1Ch6oS5Ta90qVhtEoa/wldIjB4PyFrsVpagcvmAfbcb6eDyLKMUCyFWoRg81QB9kpAzYZuVFiMPUQ0GRh7iIixh4iKBc97pg+VLL6JISIiIiIqIt3hBDa1BNHZ3oZFqt0whZsRc8wFVLkTklEJuP554LLUo/iK9u+wqPqn2Gmb+0kEZl885v0SCVJR5VzpNGG2UwNzpAUwOQBnHVB2FKA3j3kbNEUlIwjveQPtezZin+SAw1ECkz73RSW56GKdmPvKjVCLC0A+8BvpEvxWdQX+sAwYaWF/JCEhEglhjq4HNQtPhKrmFEDD68GJiIiIiIiIiAqNJRNEedq4cSOPFRFNOMYeovHhtuhRYtVDZXGjBw5kNCZoD6hUHo5ZC1x/DFCv8g0m7wXPnr9DGz/88w/HbtIp1fiiEr85lEHSUQckwkD3bsC3CYgHMREYeyZYJoNk+zb4W3bDn9TBaHGNKXkvlO38c1byvlN24i7po7h87siT90IwlkIpgrB7KqAS1fdM3tM4YOwhosnA2ENEjD1EVCx43jN9MIFPlKd9+/bxWBHRhGPsIRofKpUK1S6zMvd8q+xFwlQCXbwbkDOHfe7JZcALJVcgKO+vhjfIcTi2PVSwiwsyMuAPx9EUTEESSfyMBHTtAnzvAZEujDfGnokl9+6Dr3kvQn1BxIxlcJhGP++9YO55H47ON7LGfpa6AkajCRfXjXx9sVQamnQMNp0Ee0klYK8a0/4RDYexh4gmA2MPETH2EFGx4HnP9MEEPhERERFNaZIk4dlnn8WDDz6o3Ir7havCN0Bt8aBHzr8KX7hykR2/lj+RNVbe9QrM3VvHvF9iznOvTY9oMgN/KIHm3gTSjlpAo/8gib8ZCLWNeTs0RcSD6Ni3DVF/E/xqL0psRuUCk1HLpFGx44GsoY2Zevw9cyauWQgYtaOb2qFE1QuzuwJqRyWg1Y9+/4iIiIiIiIiIKCcm8ImIiIhoylq3bh3q6+uxfPlyXH311cqtuC/GC6HaZUKp3YBWeJEw5l+F7zYC1gXL8H5mVta4/f37+6vlx0ijUqPMbkAonkJHXxxNgRgyom250QH07AY6tgI9ewFZHvO2aBKlJQSb30ewvQGdaRNcLhc06rF9RHO1Pg9juClr7AepT2OOQ42l1SNfX0LKIJ2MwqaR4FSq70exEiIiIiIiIiIiyptKlvmtH1E+YrEYTCYTDxYRTSjGHipmIkm/cuXKYR9fu3YtVqxYMebtbGkNYrsvhNLwdlTG9yCtM0Myeg77PHEW/ceXduK2+C1Z4w1zrka0/kMohFQ6g/ZQHG6zDuUOE2rcJqhivUCfD3DOAlx1QMl8YIxJ36EYeyaALCPeuhlNOzch0NWBjKsedrNhTKtUp8KY9/LXoU2FB8f+kT4DX0t9CbeeBiwqGfk6/X1xuBJtqCzzwjv/ZMBdP6Z9JMqFsYeIJgNjDxEx9hBRseB5z/TBCnyiPAWDQR4rIppwjD1UrESb/FWrVuVcRjyeTqcLUoXvtRnQNsIqfNHlfPlx8/G39NlZ42V7/wptoheFoNOoUWYzoieaQmcojtZADLLJCYiW+r37+lvqt28CUnEUEmPP+Ev696ClcTvC3T7ErdVjTt4LpXv/npW8j8oG/Cx1BU4rH13yXlxAkkpEYNVKcHhZfU/jj7GHiCYDYw8RMfYQUbHgec/0wQQ+UZ5ef/11HisimnCMPVSsNmzYgObm5pzLiMfXr18/5m05zXp4rAZobR50wYGMxgRtIpDXc6utwM5ZVyIkmwfHzHIM+s1/RqHotWqU2ozoCifQEUqgLRgHDFbANRsItwP+nUDbO0C0p2DbZOwZX1JvK1r2bEG0owFBQwXcdmtB1hsqPRk+/f5pHX4jXQK/yoPPHjHK9cVTKJF7YXaVQ++qAbT6guwn0XAYe4hoMjD2EBFjDxEVC573TB9M4BMRERHRlOPz+Qq63OHUuMzwWkUVfgniI6jCFz60wIH7NJdnjc0OvARt93YUikGrhtdmhL8vgY5gXGmrD50J8MwF0kmgSyTxNwKBxv7e/jRlZaIBtOzahEjHHvRoPPC4nVCLdg4F4DMvxHnRH+PbqeuwMVOPu9MX4aI6oGoU1wekMxnEo2HYNBJcpZWAo7og+0hERERERERERLkxgU9EREREU05FRUVBlzsch1mHEpsBOpsX3bCPqApfpwaqFp+H7Zma7HW+dy9UmRQKxajToMRqQGdfHL7eODr7EoBaCzhnAXob0L0b6NwKdGwBpGTBtksFlIqhbec7iLTvRo9khNNdBo2qcB/J/rwTCEtq/G/6XFya/CG0Oj2unD+6dYXiEtxyACZXOYzuWkCjK9h+EhERERERERHR8JjAJ8rTsccey2NFRBOOsYeK1ZIlS1BTk50QH6q2thZLly4t2DarRRW+Ta9U4ceMnhFV4R/h0eDxkmuyxsqlVsjvP4JCMuk1cJsN6AzH0NYbQ3ckAYjqbWsp4KwBgi39LfV9G4F4cNTbYewZB2kJ7TvfQdC3G4GYBFtJDXSawn0ca+oDHtt34IgKV84D7KPoep+RZcSifXBo03CWVrH6niYMYw8RTQbGHiJi7CGiYsHznumDCXyiPNXV1fFYEdGEY+yhYqXVarFmzZqcy6xevRoajaZg23SYdMpc80Z7KboyI6vCF8445gg8giVZY6H23Ygm87sIIF8WgxYOk15po98ciKEn8kG1vd4KuOcCiT6gczvQ9m5/Qn8UGHsKTJbR1bAJ3W27EAz1wlhSD4OucO9d4b5tIvG+/365Gbh4lKev4bgEZzoAg7MclhJW39PEYewhosnA2ENEjD1EVCx43jN9MIFPlKdHHilsBR0REWMPUW4rVqzA2rVrD6rEF/fFuHi80GrdogrfgHaVF1H9yKrwLTogcfSn4JcdCMkmfCv1eVwZ/xZ+v7Xwp9w2gw4Oow4doTiaAtH+dvqCaHPurge0RqBrF9DxPtCxFUiPrJU/z3sKq7d1BzqadiLc7YPWMxsmQ2Ha0WsTvTD2NWKjH3ijI/uxzx0BjOYaAVmWEY6EYBfV995KVt/ThGLsIaLJwNhDRIw9RFQseN4zfWgneweIiIiIiIYjkvSXXXYZNmzYAJ/Pp8x5L9rmF7Lyfmh1e7nDhJ5oGTr9HTArVfi9kIzuvJ5/VIUVD/m+gr+0lqID/c95uhk4pQw4raKw+2o36SCmT+8IxZRrDNIZGeUOA1Sipb69AoibgcA+IBkBEiHAMxewlBR2J+iw+vxNaNu7FTF/I2THLFhNloIdtfIdf4K98w28oboIRnwMcRiU8aPcwOnDvN8kKY23Nm6GvzsAr8eFExcvgla7/+8pkpRglwLQl5TBVjqr/6IQIiIiIiIiIiKaMEzgExEREdGUb6e/bNmyCdtetcsEf18C+wJlKFH1wR5vhWRwQsmW5+HsYxfib2L6+fD+sV+/Byx0A67+/GpBK/HVUKGjL44MZGTkDCqdJqigAoyO/kr8UCvQuQ1IhPurqT1zmJSdILFQD1p3bUS0YzdS5nLY7Y6CrdvqfweOjteUn6+UH8UZ+tfwydR30SKX4tojAXEdx1BPPvcSbrvzXrR3+gfHyku9uPGGa3HBuWcq9yPhPlRoU3B6K6AS7xciIiIiIiIiIppQbKFPlKeysjIeKyKacIw9RBPPqNOg0mmE3VOBjrQNGY1RqcLPl0EDfOM4QHNAAjWUBH69SZkKfVy6BpRYDfCH4vD1xtHcE0N6YENaA+CaDRidQM8eoHMr0PIWEOnKuU7GnsIk75u2vY5w+x6kdDbYXF4UilqKo2L7fVljvbDCJ3uwtApY4Dp08n7VTbdmJe8FcV+Mi8ejSQmmRDf0zgo4ymbzQg+acIw9RDQZGHuIiLGHiIoFz3umD5UsJjkkIiIiIqJBUjqDd5oCaGxuxtzEDjiTbYg55uZdhS88vAv40/bssRuP6sPSetu4HOmElEFnXxxusw5emxG1bjM06gOuIkgngWALlH77jhrAUQW45wBa/bjsTzGLBDrRtO0NRNp3IyWrYS2dDbW6cNdOl+94AJ6mxwfvS7IaH03+CDtVdfj9OUCp+eC2+ReuvO6g5H3WOku9ePB/VqMCnSidezxKjjiLCXwiIiIiIiIioknACnyiPL32Wn+LUiKiicTYQzQ5tBo1ql1mON1laM+MvApfuHwOcMQHldAWxHCL9n5ctftr6O7pGZd9NmjVKLcb0RNNoT0UR2N3BKn0AdfqavT91fgmV381fsdWoPVtIHxwUpexZ/T6ulvRtPU1RNp2IAVdwZP3xuBeuJueyBq7N/0hvC/X4bL6g5P3gpjzPlfyXhCPb3nnFegc5XCW1zF5T5OCsYeIGHuIqFjwvIeIGHsoFybwifLU0dHBY0VEE46xh2jyiGR4id2ImKkCAbUTunhXf/V6njRqYNVxwDLte3jS8C1co30KDlUU+nf/gPSBifUC0mn6k/h9cQntQZHEDyOZPmCfxcToZg/gmQskw4B/O+Db1J/MT8UGF2PsGZ1gxz40b30TEd8OpLQWWEtnFTR5j0waldvuhgr73z8tcgl+KX0cpSbgE/MO/TR/dyCv1UdDQWXqCK2rplB7TDQijD1ENBkYe4iIsYeIigXPe6YPJvCJiIiIiA51oqxWocZlhqukAh1pO9KjqMKvsABXVbShWrV/zvlTM+9g23vj19lHSeI7DIgm0/AF42jwRxBJStkLiWp8d/0B1fhbgJa3gJ69QHrIspSXQOsutGx/C+G2HZD0TthKqqEWF0wUkGibb+rblzX2vdTnEIMR/7kIMGoP/Tyv54NWEIdRUV0Hd+VsVt8TEREREREREU0iJvCJiIiIiIZRYtUrVfhpezW6MfIqfMF7zPnYrpmfNbbc/0fs84fG7bhrVCKJb4SUltEajGGvP4yeSPLgBZVq/HlARgI6twHtm4GWN/sfk8enS8CMI8vobtqG1l0blTnvM5ZSWN0VUBU4ea+LdaJ0z1+zxh5Jn471mcU4vRw4uWz45564eJEyx30uFSUunLd8GXSu2kLtMhERERERERERjYJKlvnNHBERERHRcHqjSWxqCiDU8AbmoxnQ2yAZ3SM6YPFAKxa9+R3oVfur259UnYGSpV8atmq6EMSpfiCaQiwlwWs1otSuR7nDBM2hksupKBDy9V+gYK8ArGWAe3Z/lT4Nd4Dhb3gP7Y3bEfU3ALYKWB0l4/FCovbdn8HW/d7gUK9swfLEakQ0DvzuHKDElHsVTz73ElbddOuwj//i5q/gP//r6zC4mcAnIiIiIiIiIppMrMAnylNjYyOPFRFNOMYeosnnNOvhtZugcs5CD1yjqsI3uqrwXvnHssYukF/Gq2+/hfEkqsDdFj2cJj06QjG0BGJo7IogmT7E/uvMgGcOYPGi0dcFdGwFWt/uv03FxnU/p6VMBu2730V7w/uIduyFylEzPsl7APb2V7OS98JPpE+iCw5ctfDwyXvhgnPPxJoffPugSvyyEjd+/o1rsPLyj8HgrC70rhONCM97iGgyMPYQEWMPERULnvdMH+NY70M0s2zatAl1dXWTvRtEVGQYe4imhlqPGd195egKNMGtCkCb6B1xFb7p6IvR3P06aqT9c5h/Mvh7/HXvHJxRP75V7haDFjqNGv5wAgkpo/yrcZlhO1T5v8mJTcEM6jwS0L0HiPYAET9grwIcVYAuj2zxDJeJhdC2dzMCbQ2IdjdB45kNk8U+LtvSpMKo2PGnrLHXMkdgbXop6u3AR0ZweiqS+MvOPg1vbdwMf3cALqcdR5dr4KqaD3f1fEDN67tpcvG8h4gYe4ioWPC8h4gYeygXJvCJiIiIiA4gSRI2bNgAn8+HiooKLFmyBFaDFhUuEyKeWfB3hVAZ74BkcAKqESQ81VqEFn8B0ls3QYu0MuRWhXHUrt9ij/vbmOMc3+SpXqtGhcOIrnACbb0xpNIZVDhM8Nr0UOEQLfWtpf3t88MdgH87EOkCQq2ArRxwVAN6C4pOJoO4vwGtjdsQ8TchEu6DzjMXJvP4HYuynX+GNhUavJ+Qtfjv1LXKa3bDMYBmhG8brVaDU09crPzc190KvZyEraQKRkdZoXediIiIiIiIiIhGgSUWREREREQfWLduHerr67F8+XJcffXVyq24L8arXWY4PBXoVduRgF6pwh8x12zsqbk8a+gM9RY0vvUYwqnxfxnUKhVKbUaY9Vr4gjE0B6Jo6olBysiHfoJG15+sd9cD6QTg3wb4NgFNb/S31o/vTyzPePEQeve8jn1b30Bf8/sIJjIwli8c1+S9ECo9CTH9/tb8d0qXYq9ciYvqgAVjaNyQSiWVzgoGZwW8tQvFfAuF2WEiIiIiIiIiIhqTokrgp1Ip/OpXv8JnP/tZLF68GHq9XpkX9J577jnsc//4xz/i5JNPhtVqhcPhwNKlS/Gvf/1r2OVjsRhuvvlmLFiwAEajEaWlpVi5ciW2bdtW4N+KJsopp5zCg01EE46xh2jiiCS9OF9rbm7OGhf3xfij//d3VDhNMHjr0SE7oIt3AfIh5pI/DGnBR9BqPiJr7IuZh/GPtxogD5NHLzSHSYcSqxH+vgRaAzHs8YcRjO2/guCUOmv2E7RGwFEDeOb1/85dOwDfe0DLm/23sQBmrEwGma69aNv6Mtr3bERvZzP6jJVwltVBrxv/hmZ9Jcfh09qf427pw9iWqcHv0pfAZQA+vXBs6032+qCzuGHzVsNoG9l0EETjhec9RDQZGHuIiLGHiIoFz3umD5UsT9TXhJOvt7cXLld/mUpZWZmSwBdfyN5999247rrrhn3eN77xDaxZswbV1dW4/PLLkUwm8b//+7/o6enBHXfcgRtuuCFr+UQigWXLluHll1/GiSeeiHPPPVfZjvhSWGzzueee4x/JNCQuyjCZOOcrETH2EM3Utvmi0n5o8v5ANTU12LV7D95rDaFr5+uoTjZAb7ZBMnpGvD1tvBvVL30bFjkyOLYnU4GHZ/8YH51vxESRMhn4+5IAZJRYDXBb9Ch3GJHOACZdjmt90ykg2g3EegCDDbB4AUsJYKsAzCWAZobM1BUPIt6+A76WvYh2taBLMkPvLIfdZJiwXXi2Gbh9Y//PeqSQhA7fOh44u2r060yn4oj6dsBSdSTmHn8uTFZ7wfaXaCz4mYuIJgNjDxEx9hBRseB5z/RRVBX4ZrMZjz32GNra2tDe3o7Pfe5zh33OK6+8oiTv58yZg/feew+/+MUvcNddd+Htt9+G2+1WkvuNjY1Zz7n99tuV5L1I9r/++uv42c9+hj//+c/461//img0qmw3kxl5tRZNrqeeeoovAREx9hDNUGLO+1zJe0E8/tKLL6DWY4GlbDbaMw5oY6Oswjd60HnU57PG5qh9mLXnIWzuwoTRqtUotxtgMWjRLlrq90SxuyOMp7b1QkYEKLkPAAB29klEQVSO63xFa31bOVCyANAYgJ69QPsWoOUtoOlVwL+jvyp/ul4rLCWB7j0I7n4NLdvfQaCjBe3qUti9NROavA8lgXu27r8vkvfHe4GzKse23lSvD2qrF9bSWUze05TCz1xExNhDRMWC5z1ExNhDuRRVAl9Uv3/oQx9CRUVF3s/53e9+p9x+97vfHazeF+rq6vClL31Jqba/7777BsdFQ4OB5/z85z+HWr3/EH/0ox/FWWedha1btypfEhMRERHR1ODz+fJertRmgKukEiprCYKSDtrE6NrHRytORlvZOYP3t2dq8Mf0+bj1HaAnjgkjppSyG3XK9ADJdAYtvTFlvLEririUzv1ktQawlgLehYDR0V+V37kVaNsINL/R32I/0Agko5gWYr1A53ak972K9p1vorNhC3xRGSHzLJS63dBrJ/bj0x+29ifxB4imCNcvGtt09XIyjGSsDwZHOUpr5hdkP4mIiIiIiIiIqHCKKoE/GqLdvXDhhRce9Ji4GODAZYQ9e/agqakJ8+fPx+zZs/N6znBOOOGEYf8RERERUeHke4GnWE4kvOtKzDCXzka3ygVV1D+qKnyh96irETRU4D7pAnw0+UPslGvQmwB+9jaUNvYTSVTjl9qMcJp1yv2mQBS7OvrQ2ZdA5nCV9Co1YHIB7nrAM7c/sd/bBLRvBlrfAZpeB3ybgFAbkOq/QGDKSEv9+9X6NuSWNxHc+xZadr6Dzq4uNKZLoXVUwGs3Qz2WrHm+5AzKdj4EfcSH19qBp4c0hbhiHlBhGXv1vcpWBlv5bFgsY1wZEREREREREREV3AyZnHJ8RCIRtLa2wmq1HvJL3Xnz5im3O3fuHBzbsWOHcisS+IdyqOeMxvbt2we3JSxZskS5PbCyf8GCBVi4cCGeeOIJpVOA4HA4sHTpUmzcuBH79u0bXPb8889HMBhUWv4POPbYY5VOA4888sjgWFlZGU499VS89tpr6OjoyOouIKYS2LRp0+DYKaecomzvwHZAs2bNwuLFi7F+/Xple4LBYFAukJgOv5OYemGm/U4z8XXi78TXaSa998T6hJn0OzFG8HWaKu+9dDqNhoYG5W8iFAqhpKQEXV3D96+vrKxU/vYO/HvSaR0IJHrQ15tAJN2f+BbmlmgRjGfgD+/PwlfY1DBoVWgM7K9qtxt0UJ3+Y7y5RY9E8/5ra7f0AL/bnMEFVfufX+3UKLctvfuf7zKp4LFo0NAjDSb8DRqgxqVFZ18aocT+xHudS4OEJMPXt3+dXqsaDqMau7ukwTGzTgWbAYgk1OiNZbC3OyLOjHHOfDu6IxLea91fTX9KnRUOk1ZpuT/4OrkNWFxdhvVtRgTj/ftqUAdwYVULtkfs2NGz/5guOWkRYLBhw0uvDPs6DY17e/fuxZYtWxAIBHDeeefhmGOOUaa3GlXcO+MoNO7bh00t4f3LajMIpysQkU2ACoiGgXgqjVKbBs0BCYkPDr9GDcx2a9EdSSMQkwvyOtV3PoWS1n/D2fwMHkhdDUB0aOi/cKDaCix2Stjdtf91qnRo0BZMI5qS83/vqWcDaUATSqAeYCznf5+m1HmEWPbAbfE8gud7PIfleflEfNYQBt5r/PzEz4T8nMvP7hN5biTWw+9Y+L0Rv9/jd5YTfW4k1sXvlusm7LPGaKlk0fO9SN1yyy34/ve/j7vvvhvXXXfdQY+3tbWhqqpK+dfS0nLQ46lUSmnLL/4NvPHFXPef+tSnlH8PPvjgQc95+umnlf/4i39PPvnkOP1mRERERJTLunXrsGrVqqx57z0eD7q7u4d9ztq1a7FixYrB+0kpg007GxDa/Soq5XZkPAv6K9FHIS0DN70GbBxy/cD3TgROy3/2p4ITLfR7wkloNCo4TXrYTVqUWA1K0l71QWI5L5k0kAgBiT4gGQZUWsBgBfTinwUw2gCjs7+KX4xp9Xm/bjU1NVizZk3Wa3Pw9jNAKgIkwkBS3Ib62+XHupHs60Z3XIVu2YKupAHimgO3WQ+zXqN0W5gohnAz6l//HtSZ1ODYL1Ifx6/SH4dGBaw5E5jnHMMGZBlS5w5EDF5455+CefMWFGS/iYiIiIiIiIioyFvoiyvcxBdp+f676qqrxn2fRvLF3sD1EhP5ZSAVhrhyhohoojH2EBWeSAKvXLkyKwksDCTvRSL/QCJBPDR5L4j50KsqK2HxVKAnpYMmPnzy/3BEgvabxwMeY/b4mo1AYwgTTlSaC0atBuUOI0xaDTr74mjqiWKPP4zdnWEEosnDt9YfIFrqi+S8sxbwHgE4awCNDoh2A/7tgO89oO0doPk1YN/LwL5XlJb26NgK9OxVWtyve/C+Q75u4r4YX/e/fwGkBJCKA/EQEPIBXbv6W/gr63wVaHmzf73t70Hy74Q/GMPOlAd70l60RPXQaXWocppgMWgn9HxdlUmhevNdWcn7kGzGunT/1fOfnD/G5L14CRIBhFMyjK4ylFXXjXWXicYFz3uIaDIw9hARYw8RFQue90wf066F/pw5c2A0DvlmMwfR6nS0BtoWD7Q7GGpgfGC5fJ4j2rMOfQ5ND8O9pkREjD1E04ckSUoFdy4mk0nplNTZ2alMoyTaimk0/W3Rh6pwGNFdNhc9vZ1IhZqhNrj7k9Wj4DAA3zkB+NYr/RX5C1VNWKVai5++/kV8/ywr3PmfAo/ZQJt4Qcz9bjfpYDVqEUlK6A4nEIgkEYylYDPqUGLVw2nWQ5NvwlsspzP1/7N4lXnfkYr2V8f3dQLphFItDo2+vxJfo4cka7Dqm9/KudpVX/8qPnZcCTSit31GAqQ4kIwBUqw/qS8uGNCZIWkMCMKFTlmNYDKN3kgSJj1Q6TRBo56c65tLd6+FMdyUNfa91GfRhhIc6QJW9M/CNXpyBqneDsjWcphL58BuMoxxhUTjg5+5iGgyMPYQEWMPERULnvdMH9Mugf/ss89O2LYsFovSPr+1tRU+n0/5AvdAu3btOmi+ezGfRK457g/1HCIiIiKaGGIesKEV3EOJqZNEwj6fTk6iSru2shLJrnL0NnXCE+uGbCkd9f4d4QauO1JGYvuT+I72LzCoUtCl78KPXr8RPzlDDeMknr2LRL7NoINV35/ID0RS6I2kEIwm4TDp4bbq4TDpoBcJ9JEQ0w4orfSt2S33RSI/nVKq6je8+g6aff6cq2n2dWL9049h2clH9q9TXCCgNQEmJ2StAeEk0BtLIhRKKfsfjCWgUatQZjcp3RQmi6V7C0r2/Ttr7P/Sp+OfmTNg0gKrju/v0DAWmqgfgYweJnclKipqx7YyIiIiIiIiIiIaV9Ouhf5EO/fcc5XbJ5544qDHHn/88axlBjoE1NbWKgn8hoaGvJ5D04PBwEolImLsIZruxEWZhVxOcJh1cFTOhdZehniwoz/5PAaf0T2DW3R/UpL3wlLNJlwafRi3vdNfmT8RcuXgxUULVoNO6T5gN+sQjElKa/29/jB2tIew1x+BP5xAQsqMfgdEFwOdGTA6AGspfNH8Prb4UlbAuxAomQ84apAwuNER12KnP4ZdnX1o6IqguTeKcCINp1mHMrtxUpP3mlQYVe//LmusRS7BTanPKj//59FAuXls21ClE0j2dSFtK4epfD4cFv3YVkg0jviZi4gmA2MPETH2EFGx4HnP9MEE/mF88YtfVG5//OMfIxAIDI43NjbirrvuUt7sn/1s/xdsA19oDjznm9/8JjKZ/V9cPvLII3jxxRdx5JFHYsmS/vksafq48MILJ3sXiKgIMfYQFdbQjkpjXW5AdUUlzJ4qRFVmZMK5K8UPp7dqKSL2uVlj/6l9FF7/K7hvKybEbPfhS/3Fea9Fr1Vaz7vMekSTabQEYmjoDmN3Zxjb20PY7Q+jsy+BWGpsFzVUeD15L5dKywhEk0qyvv+CgjD2dUfRFU5Aq1ajwmFSEvdm/SQ3I5NlVGy9B7pEz+BQRlZhVfI/EYIFZ1YA51aPfTPaiA/dKgdMJbWoKi8b+wqJxhHPe4hoMjD2EBFjDxEVC573TB8qWRYTTBaPW2+9Fdu3b1d+3rhxIzZt2oTTTz8d8+b1Tyx55pln4rrrrst6jpgn9fbbb0d1dTUuv/xyJJNJPPzww+ju7sYdd9yBG264IWv5RCKhVNi/8sorOPHEE7Fs2TI0NTVh3bp10Ov1eO6553DKKadM4G9NhSDeNwsXLuTBJKIJxdhDVFiSJKG+vj5nG/2amhrcc8896OzsVBL54sJLrfbwyd4Wnw/t77+ITPcemCuOhEoz+gSxNh7A7Ne/C32yd3AsJutxefIWnH10HS6uw7jqjqThsWhG/LyMLCvJ+lgyjWhSgk6thtmghdmggVmvgcWghUGjhkGrgUGnhk6jgvi/vF63JVcobfKHU1Veiqf+eo/SpUBsP5xMI5FKK4l6q1ELo3bkv894crY+j6qtd2eN/Ua6BD+XroDHCNy1BLCNsVhekwwhHmhD0DoX3iPPxJHV+V0IQTRZeN5DRIw9RFQseN5DRIw9lEvRVeCLVvh//OMflX8ieS+IRPvA2EsvvXTQc9asWYP7778f5eXl+P3vf48//elPOOqoo/Doo48elLwXRFX+M888g5tuugm9vb34xS9+gaeffhqXXnop3nzzTSbvp6kdO3ZM9i4QURFi7CEqLJGIF+d2uUSjUVxwwQW4+uqrsXz5ciXhLy7EPJzKsnJYPJVI66xI9g2faM6HZHSh5divIaPafxGASZXE/+hvx7rNIbzZgXEViI3uGl/1B1X5JVYDalxmOC16SJkM2oNx7OuKYm9nGLs6w9jREcJWXwjbfH1Klb5owd8RSiAQSyIYS6E3llJ+FpX0PdEkgok0bl71hZzbvu5zV6G5N46WnpjSIt+i16DaZVb2Zaol7029u1Cx7b6ssc2ZOvxCulz5+euLx568h5yBNtIOv8oDQ+ls1HodY1wh0fjjeQ8RTQbGHiJi7CGiYsHznuljkvtGTrz169eP6nmf+cxnlH/5MplM+P73v6/8IyIiIqKpY8WKFVi7dq3SZenASnyPx6N0WBL/DiSWWblypfIc8dzhqNUqlM9aiGRvB2LtO6C1lUCjGX0WNuacB98Rn0PV1t8PjlWrunCn/lf4/NvfwY/P0GLOFM7Jihb7Jp1G+ec2y0ikM0hKGaTSGcSSMpLpDDIZGTqNGnqNClpNf0X+APmAW9Ez7MjFJ+CH3/sG7vif+9Hp7xpczustwZf/4xpccM6Zynz2WrVK2fZUJbor1Gz6BdSylNVd4aupLyEFLS6rBxZ7x74dXbwLvZIWOkc5XGV1sBqK7qMfEREREREREdG0xG9xiIiIiKjoiET8ZZddhg0bNsDn86G0tFSZRmlo8v5AIuH/sY99DBrN8NXcLncJAt4qSKF2xHo6YPXWjGk/e6uWwtjXCE/zU4Njp6q3YVXmQXz/jWtw+5lAiQlTnkioiyr4oZXwouW+mLM+lU5DSsuIJtNKwl6k35XW+v3//8GtCkvPPh1nnXEKtmzZqnS6Kivx4OTjF+V8TaYSVSaFmvd+Ad0BUyMI30p9AXvkKtTZgE8XYMYmVToJVbQH3eoK2Mvmo8ZjHvtKiYiIiIiIiIhoQqhkWdSzENHhiC+JnU4nDxQRTSjGHqKJ8eyzzyrt8g9HTJO0bNmynMvEwwHsfXcDwq1bofEuhNFoGNvOZSTUvXMrLIGtWcM3pr6Aty1L8bMzAHOBL8uNSzKM2qlbxT5tZSSU73wInuYnB4d+J30Et0pXQqcGfnkWUGcf+2YMfU3wJ7WIuRaidsFizC21jX2lRBOA5z1ENBkYe4iIsYeIigXPe6YP9WTvABERERHRZBNV+IVazmh1oaSiFiaHF5FAm1JlPiZqLZqP+S8kjSVZwz/S/gGOvl34yZtAMj22TdAEUWvhW/AZ3G35AhKyFuvTx+Ln0ieUh645ojDJe00yhLQUR6/aDWNpPapdrL4nIiIiIiIiIppOmMAnypNosUtENNEYe4gmRkVFRUGX89bMh8VdARsiCPaFx7h3QFpvR/PiryOj1g+OGVQSfqf/BVq7AvjxW0CqgEn8ll5eETBeHt4F/Lh7KVYmb8J/pb6EDNQ4qRS4ZHYBVi5noI92wA8PtCWzUe52wKibHtMLEAk87yGiycDYQ0SMPURULHjeM30wgU9ERERERW/JkiWoqck9X31tbS2WLl2a17FSGawor6mH2eGFHO5AXBp7Qjxuq0PrUf+RNVam6sXHNS/irU7gp28DqcyYN0Pj6FUf8MCO/p83yXMRghW1VuCbxwPqAsxYoIt3I64yoFfjhrWkFlUuE4qRJEnKtBgPPvigcivuExERERERERFNF0zgExEREVHR02q1WLNmTc7jsHr1amg0+Vczm731cJVWw6NNojfYN/ZW+gBC5afBX/eRrPnTf5vuv/96B3DbO0CaSfyp44DXvCEErH43+2GbDrjpZMCsG/umVOkktPEedMpu6Erno8JpgkFbfNX369atQ319PZYvX46rr75auRX3xTgRERERERER0XTABD5RnhYsWMBjRUQTjrGHaOKsWLECa9euPagSX9wX4+LxEdEZUVJZD5NopS91IRhLFWQ/O+d+An0li9FYdwUeNF4pUreDj73s608SjzWJ7zIVoBy82Mkyqt7/Ldz7HkdvXMYP3gDiBzRi0KiA75wIVFgKszl9tB0RrRNBvRcOp6coq+9Fkn7lypVobm7OGhf3xTiT+FMfz3uIiLGHiIoFz3uIiLGHclHJcgFKgYiIiIiIZgjRblvMCebz+ZQ570Xb/JFU3mevLInI3lfh2/E2GjIelDjd0GsLcA1tJg2oNeiOA99+BWiLZD98bjXw1cX9SWKaHJ7GR1G+6y/Kz89ozsaXIp9DAvrBx7+0CPhwXWG2pUn2QR/rxF5VNaSqE3BUtRc1bjOK7e9WVNoPTd4PvRinoaFh9H/PREREREREREQTgAl8ojw98cQTuPDCC3m8iGhCMfYQzQA9DejY+SY6O9vRrqlEhcMIlapwmXV/DPjWK0BHNHv8vBrgv44d3dzqDT0SZru1mO4kKY23Nm6GvzsAr8eFExcvgnYC2srb219F9eY7ocL+a6X/lj4Lq1L/qfx8UR1w/aICbUzOwBTcg6C+DHt19aiZNRfH17qg1RRXszUx171ol384zzzzDJYtWzYh+0Qjx/MeIpoMjD1ExNhD1C+TyaCnpwd9fX1IJBJg/e/MEwwG4XA4Jns3piWVSgWDwQCbzQa32w21eny/d5n+38oRTRDxHywioonG2EM0AzhqUFLWhnhvOwKpKPriWthNBZj0/ANeE/DT0/qT+CKZv0KzHjWqTtzevBKi2F9Ueo/0eoGxtuCfCp587iXcdue9aO/0D46Vl3px4w3X4oJzzxy37do630b1lt9kJe+Dshm/li5Tfj7GA3zhqMJtTx/tQFpnQXvGAYunBlUuc9El7wXRMaOQy9Hk4HkPETH2EFGx4HkPTcXkvehoFo0OqQ6gGcVqtU72LkxbsiwjHo8r/yKRiNLlbzyT+EzgExERERGNJ40WGlctPGWdiLc1Y2/MCJNeA10Bk6xl5v4k/nuv/Btfx0PKWJ9sxt37Llba6H/x6JEn8ad78n7VTbceNC6S+f3j3x6XJL6lezOq3/sVVPL+ye4lWY0vp76MfXI5Ksz9894XYhYFQZ2KQpPqQ7epDiH9bMy1G1FuN6IYiekuCrkcERERERFRMRGV9yJ5r9VqUV5eDovFMu4VxjTxent74XQ6eehHeZGLSNy3t7crfyvib6akpATjhX99RHliWxEimgyMPUQzhL0SVk8lXCYtSrRx9ESSBd/Eos5/Dibvhe/q/oxPaJ7HvxqBX20CpBFU1Rs007ttvqi8z0U8nk7vT7IXgjmwHbUb10AtS4NjGVmFr6f+Ey9kjoVJC9x0MmDXF2iDcgaGaBsSpnK0yKVwu1yocpqhGc2cCTPAkiVLlKvfc6mtrcXSpUsnbJ9o5HjeQ0STgbGHiBh7iKC0zRdE8l60CGfyfmbSaKbxFz6TTPxNiL8N8Tdy4N/MuG1vXNdONIPwyz4iYuwholFTawBnDdzls+BFAJl0Bn2JVEEPaNh9FNKa7Orrn2rvwYfVr+HpZuD7bwDR/bnlnGpc07dRl5jz/sC2+YciHn/z3c0F26YpuBu1794GdSb7wozvSNfhn5kzIFLq3zoeqLUVbJPQxfzIaIzoVnuQtFWhxGZAqc2AYiWqRNasWZNzmdWrV/PLiimOn7mIiLGHiIoFz3toqk7rICrvaeYSCWgam4G/kfGeCoUJfKI8bdy4kceKiCYcYw/RDGIth9ZWCrfdgjJ9HL3RJFIFnGw+7piDpsWrkFHrBsfUKhm/1N2FC9Rv4B0/8O2XgZ744dfV2VfY6vSJ5O8OFHS5wzH0NaH2nZ9Bk45ljX8/dTUeTp+j/PzZI4CTylAwaikKbTKIqLkM+9QVKHOYMcsj2hsWZ/X9gBUrVmDt2rUHVeKL+2JcPE5TG897iIixh4iKBc97aCrO7y2w8n5mE63faWxUH8xROfA3M16YwCfK0759+3isiGjCMfYQzSBi7jjXLNhLquFV98Ju0KI7kizoCX/UfRRaFv0XZNX+03y9Ko3f6n6FqzRPY08IWPUS0HSYLl+hxPh+CBlPXo+roMvloo+0ou6dn0ArRbLGf55aifvSH1J+/vgc4GNzUDhyBvqID0lTGXwohdnqQqnNCLelUL35pzeRpN+7dy+eeeYZPPDAA8ptQ0MDk/fTBM97iIixh4iKBc97iGgyJJOFn9KxWBP4440JfCIiIiKiiWLxAlYvSpx2lGnFVc8yQvE8+9rnqa/0BLQe9cWsMVGJ/yPdffiadh06YzJufBnY0o0Z6cTFi1Be6s25TEWZFycdt2hM29HFOlH39k+hTYayxu+UPorfpC9Vfl4xt7/6vpCf7XTxLshqPSKGErSry+C1G1BXYi7cBmZIO/1ly5bhqquuUm45xx8RERERERERTSdM4BMRERERTRSRyXXWQWuvQIWuDyUWHULxJBJS4VrpC8GKM9F65BeyKvGFr2j/gVu1dyOWSuO7rwEvtmHG0Wo1uPGGa3Mu840vXTumpK4qncSst38KXaIna/w+6QKsllYqP6+cC3xmYWGT92opDm0igKSlHI0Q896bUeU0w6zXFm4jREREREREREQ0qVTyeDfpJ5ohYrEYTCbTZO8GERUZxh6iGcq3CejYCn8kjYaEFb2xFCocJqgL3IbL6n8XNe/9CupMdou0p9PH48upLyMOA647Eri0PjvRLKVlaDXTez71J597CbfdeS/aO/2DY6IyXyT3Lzj3zDGv3+F7CZVbfgc1+i+++It0Dr4jXSc+YinJ+08XOHkPWYYx1ADJ6EaneQ72oQLzy6w4rtYFnYbXZdPMwPMeImLsIaJiwfMemmq2bdum3B5xxBGTvSvTSl1dnXLb2NiI6SCTyUAtpnicJNPteE3m3wtLNYjyFAwGmcAnognH2EM0Q7lmA5FueGI7ETLZEEtp0BNJosRqKOhmwt7j0HjCd1G78TZoU+HB8fM07+Ah1U9wbfIbuGerDe1R4LqjAN0Hn+ES0vRP4Isk/bKzT8NbGzfD3x1Q5rwXbfML1U79deOZ2JDWY7X6DjyWOQXflUTVvwqfmAdcvaDAyfvB1vkaxE2l2JfxosxpQI3bzOQ9zSg87yEixh4iKhY87yGiyZBOp8c1gb906VJs2LABrB0fO5ZqEOXp9ddf57EiognH2EM0s0iShGeffRYP/vWfeHZTEzJGF6p1QSVxn5DSiCSlgm8z5pyHhpNuRtJYkjV+gnoXTlH3XzH8r0bgmy8Dvkj/Y76+wrb0n8x2+qeeuBgfueAc5bZQyfutPcD3XgP+mToZlydvwTdSX0QGalwxTsl7VVq0zu9B0lKJdm01dDodvDYjyu3Gw7/XHnxQuRX3iaY6nvcQEWMPERULnvcQ0WSIRD744meSiO8nxD86PFbgExERERFNgHXr1mHVqlVobm4eHKupKMWaL1+KZRdehIRkQGdfAgatGtoCXw2dtFSh4aTvY9a7P4Mx3KSM3Za+Ek9mTh5cZmcv8OUXgBuOAaoL2whgepMzUKfjyGjNyt0n9wG/2QJIH1zj8J48R7m9cj7wqfmFT96L1vmGiA8pUymipkq0JYyoLzGizmOGapiNHfK9VlODNWvWYMWKFQXeQSIiIiIiIiKiw5szp/87FDo8VuATEREREY0zkVBduXJlVkJVaPZ1YuV//x7PPvkEPBY9bAYtusPJcWk1JhldaDjxJkRcR6Kr9kM44rSL4R1SwB2TgNveAf68W638XOzUUhw1m36B2ndXI5lK4ZcbgV+/tz95P+CT84GrxqHyXtDFuyGrVEiay9Akl8Jl1qHMYYTTrB/Ze625WRkXjxMRERERERFNlunQMW7t2rU4++yz4XA4lKmVFy1ahJ/+9KdIJBLDTotxww03oKqqCkajEUceeSR+/etfH/L7nX/+859YtmwZKioqYDAYUFlZiSVLluA3v/nNQcv29PTgO9/5jjLXutgPsT/iuU899dRBy95///3Khf7i9oknnlDa2YvlxVhra6vSlVBsZzgXXnihsuyWLVuy1vnxj38c9fX1yvbtdjvOOOMM5bU7kJjTXjxXtM8XxM8D/8R+DKirq1P+DSWO66233opjjjkGZrNZ2c5ZZ52lvA5DDWzrmmuuUX6+4oorUFJSohz3E088Ef/6178Oek4ymVRej+OPPx4ul0vZhtiPj370o3jmmWcwFalkTkRAlBcRCA4VWIiIxhNjD9H0Jz6Iig86QxOqB6opc2PnE3ejMWZASyAGi14Lu0k3LvujyqQgqzSASo2+JPCrTcCr7QcvV20BvnkCMMeBoqSNd6N242qY+vYp959Sn4UvRL+ozHN/oE8t6E/gjwe1FIMh3IS4vR7d9iOxJ6zDvDIrjq91w6TXjO69VlODhoaGgk0nQFRIPO8hosnA2ENEjD1EwLZt/VPsiUTxeJoOHeP++7//W0nWi6Tw5ZdfDqvViscffxzvv/++kgB/+umnlantBJEzEslhkbjv7e3FJZdcotz/29/+Bp/Ph+uvvx533XXX4Lp///vf4z/+4z9QXl6Oj3zkI8o2Ojs78d577ynJ/jfffHNw2X379inJb3GuIpLZJ5xwgtICXySo29vb8T//8z/4/Oc/n5Vs/+xnP4uLLrpISeB/6EMfUi4kEM9/+OGHccEFFyiJf7EtcUHCgcS+itdh8eLFeOuttwbHRdJerEMsLy446O7uxmOPPaZcEPC9730PP/zhD5XlxO/+y1/+UtkHsd8333zz4DrEMRLJ9oGfBbFPA8TxOv/885Xk/8KFC3HxxRcjGo3ir3/9q3JsxAUMP/nJTwaXF8+dPXu2cmzEayK+BznttNOUix3E75lKpZSk/DnnnDP4nE9+8pP4y1/+gqOPPhrnnnuu8nu1tbXhpZdewsc+9jGsXr16yv29MIFPRERERDSOxNXky5cvP+xyz9zxVZx8zkXY0xNDezCGMpsJeu34N8wSF4P/uxG4ZyugySRwl+7XuFO6FO/K8yA2f+0RwEdmj091+VRlDO1F7cY10CUCWeM/TF2Fe9MfVn42aID/OhZYWjVOOyFnYArtRdJUiphzPrYlvXCadTiywo66EsvY3mvPPKNcsU9EREREREQ0UQnJgY5xwxHV1pOdxH/11Vdx+umnK8nsN954Q0m0D1wwf9lllynJ8x//+MdKkn8gIS0S1qIqXXwmFxX1gkgmn3TSSdi7d6+SmBbV/IJIwosKd3EBQ2lpada2u7q6lIT+AJGgfuGFF/DnP/9ZqTIfIJLl4rEdO3YoyeyysrKsBL6oThdJdlFRfyCRwBaJbHEBxdCE9W233YZvfvObSpX6l7/85cHxPXv2HNT2XiTcxcUBYt/E9sXFCwfus/h9h6sdP1QCX1wsIY6nWKfoTqDV9s/+LpL3J598snJ8X375ZeV1OTCBL9xyyy1ZFws8+eSTyu8t1iWOwUB3BFF1L6rvX3/99YMKGsRFCR6PB1Pt74Ut9Iny9Mgjj/BYEdGEY+whmv7EVcx5Ldcbhy0TRJnNCLdZD384gcw4tNIfSiTmL54N3H4mcKvpQSzTvIt1+u/jS5r/QyaTwf+8D/zgTSB46C5xM4ssw9nyPGa/+cODkvfvZ2bh3+lTlJ+rLMAvzhrH5D0AfdSHtNaMpKUcndoK5cOv16pHtcs09vdanssRTTSe9xDRZGDsISLGHqLxJxLgInGci3g8nU5P6svxhz/8QbkV1eUDyXtBJJVFlwC1Wo177rnnoOeJJPRA8l5wu934f//v/yk/33fffVnLinUNVPAf6MDk/aZNm5REuGhff2DyXnA6nfj+97+PeDyuVPoPJdrCD03eC5deeqnSmv6hhx466Dj/8Y9/VPbpyiuvPOyc9Xq9Hl/60pcGp0IoxDEXFx3cfvvtg8l7QVzgMHAMD3XMZ82apbxOBxJdBmpra5WLLwaIdYvvVMTrI16/oUaavJ8o+48EEREREREVnGgxltdytfVAtAulbiciSQNiqTQCkSQ81v0fAMfTsYk3USv3f/DSqjK4UbcWZ2k242vJ6/FGhweff76/VfxFdYBuBl4GrI+2o2LrPbAGth702NPp4/GV1A2IwojTy4GvLQbM4zPDgUKTDEIjxRG11yNin4OOniQqXSbUeizQatRjf6/luRwRERERERFRIYhkdK7p3gTx+Pr16ye1Y9w777yj3Io260PNnz8f1dXVyrR0ogpeJNIFkXQeqA4/0MDc7+++++7g2Kc+9SnlQoWjjjoKn/jEJ5SW/KJ63+v1HtQJYKB6XFSZD+X3+7MqwQ8kqtYPRbSNF10ERLJeVKp/+MP9HQbffvttpRW9eOzAiwiEpqYm/OxnP1MS9eLnWCyW9bhopT8WfX192L17t1LFL9rnD3XuB6/DgcdwgGj3f6jpAUX3hIHjJ4iLFsR0BY8++qjyHHFRhJiS4JRTToHZbMZUxQQ+EREREdE4Eh/GxIeHXB9UxdXBS5dfCHRtgybSgSpnJRKpDFp7YwgnUrAaxjFb/AFTqOGgsVPV2/C44dv4VurzeDJ1Mu5+H3isEbjuKOCk0hnSVj+ThqfpcZTu+SvUmeRBD98tfRg/lT6pNC/73BHAx+aM7++tSiehj3Ygbq1B3FEPf1wDk16FUptB+VeQ99oHXyIQERERERERTYTp0jFOJMxzXfguxkUiWyw3kMAXSe9DJZIHKvgH1il8/etfV5b/zW9+o7SrF/PGiwpx8XletLE/8cQTB9u6C08//bTybzjhcHjY7R6KqLAXCXzxbyCBL34WPvOZz2QtK9r/i4sBAoGAkvAW89Q7HA7ldxVt7MXzEonEuB9vQVwwMdTA8R9KXFAhOkoe6OGHH1YuRBDTEQy03Dcajbj88suV6QQGpiGYSmZg7QzR+JiKf8BENPMx9hBNfwNt1nIRHxY03rmAtQxIhGFCEpVOo5KwDURSSEjZHzzGQ+fcldi3+BuQdLascacqgv/R/xJ36n6NWap2tEaA778B3PQ60NSHac3Q14T6N29G+a4/H5S8j8oG5cKFH0tXwa5X48enAR+fO84XLYiWbpE2pIxuJC2V6NOVoCeaRJndgFkei/KhviDvtUN8sUA0FfC8h4gYe4ioWPC8h4rNdOkYJxLUQnt7e84LDAaWG5i7/lCt/wfWceCywqc//Wm89tprSpL+3//+N6699lplPnnR/l3M+37gc371q18p7d+H+ze0Pb+Q67sDUe0/b948ZQohkRRPpVL4y1/+olxUMJDQHyBa2ot9vPfee5XOCOKCgx/+8IdKRwCxr5N1vEdDdB8Q+71z507lAowHH3wQZ555pnIrkvhTERP4RHk69dRTeayIaMIx9hDNDCtWrMDatWuV6ugDiftiXDwOnQlwVAPWUqDPB5dZj1K7AR6rHp19caTl8U/ih73HY89ptyLsPvqgxy7WvIZn9DfiB9r74EUv3vEDX9oA/HYzEDq4cH1KU2VSKN29FnNe/y5Mob0HPf5CehHOT/4MD6fPwREu4FdnA8dkd5EbF7p4F2SVSknex+yz0dYbQ5nNiGqXGQ6TrnDvNaIpiuc9RMTYQ0TFguc9VGwGOsblMhU6xh133HHKrUhYDyVavbe0tGD27NlZ1d9iLvhXXnnloOUH1jGwzqHEOkTS/O6778Y111yDnp4evPjii1kxYuB+oVitVqXSXlTOi6p0cQGBuADhk5/8JHQ63UG/ryBazh9qSoRDGSgYONQFDYdis9kwZ84cpRX/rl27Dnr8+eefV26PP/54FIp4H4qpDMQ0AuJihpdeemmw48FUwgQ+UZ7EFVFERBONsYdo5hCJU9F+7JlnnsEDDzyg3Ip507ISqs5Z/VX4GQmIB1FuN6LEaoDVoEVXX1K5unq8SQYXXp1zI9rnXQlZlV2prVOl8Wnt09hg+Bq+pl0HsxzFvxqBzz8HPLIXiEmYFpxtL8Db8H9QydkfKIOyGd9I/Qc+nfo2WuVSfHQ28NPTgRLT+O+TOhWFNhH4IHk/B10xGRq1CqV2I2rd5sK/14imIJ73EBFjDxEVC573ULGZLh3jPve5zym3P/rRjwbnmR9ISH/jG99QWrOLivmhvvOd72S1kxfJeLEO4bOf/ezg+BNPPKEk/IcaqLwfmJNdtNIXbev//ve/4w9/+MMh93Xz5s2Dz8uXaLkvOgCo1Wr86U9/Uv4J4gKCoerq6g55MYNIfN9zzz2HXL/H41FuRZX7SI65+L7rxhtvzEr8d3V1KRX/A8uMlngdX3/99YPGI5EI+vr6lPemXq/HVKOd7B0gmi46OjomexeIqAgx9hDNLOJDwbJly4ZfQKMFXLOAeAAItkFtsKHKZUJSyqA1GENvLKVU5o+3qKRCd91HEHEdhcptd8PUty/rcbMqga9o/4GrNM/gh6mr8X+pM/H794EHdgDnVgMfmgXMtmNKEtdAPIKlWIrncAQaBsf/nT4Zt6SugR9OLPIA1x0JzD30dGqFl0nDEGlF0lKBuG0WohorugNRzPaaMcdrgVajLvx7jWgK4nkPETH2EFGx4HkPFaOBjnGrVq1Cc3NzVkW0SO5PhYvOTz/9dHzzm9/Ez3/+cxx99NFKe3WLxYLHH38cW7ZsUdqui0Tz0Lb/Inkvlr/kkkuUtvR//etflfbv119/Pc4+++zBZa+44gpl7nWxHpEgF4lrUWX/5ptv4oQTTsDy5csHlxXztZ977rnKBQOiff0pp5yiVO2LLgDvvfeesj+vvvoqSktL8/79xMUD4nifc845ePbZZ5XvDhYtWnTILgFi30WLfvG6iCr8qqoqZZviIoSVK1f+//buBDzK6uz/+C+TZLLvCwkEwg5BBdwVFRSstdi6grt1w4W3Llj0FZWquJW20Netf/VVq77uu1XrjoJi3VBAa4GKyr6FQPZ9Mv/rPmliNsIAWSbJ98M115DneWbmPCeTcz0z97nvI8vgb8q+h3j++ed18sknu+oCVro+Oztb55xzzg7bZBMjrH+trP+oUaPc40pLS93z2AQF+31Yf+0uy+63igY5OTkuk9/Ov7CwUK+//ror3X/FFVe4SgDBhgA+AAAAEEziMqXCjVJJnlSaJ29MmrKSo1Tp82lDQbm8oR7FRHTMZXx5wkD9cPDtit/0qXp9/6y8ZT/NPjcpIUWqaVDUyzLw/76q9jY8SZqYLR3eW4ropAn0Ib5K+UNrJzxsLZM+2iDNWyf9WBiqV0Mu1qvemdquOP2u6jy9XXOQesdIM0dIh/Rq57Xum4go3SifN1aV0Rkqj+mjDdvKlRbnVZ/EaCV2wIQNAAAAAAA6ggWDTzrpJFeC3QLcFvy2svmdnXnf0B/+8AcX0L733ntdhroF5K3Mu2XU2+SDptna9rNVvrv++uv1zDPPuMzxgQMHasaMGbr88ssbHTt79myXwf7VV1/pjTfecMF8C3Dba06dOrVRGfusrCx9+eWXuueee/Tiiy/qySefdBnqGRkZGjFihHtuC77vDsu4twC+BfStpH5LRo4c6UrYz5w507XVjrUAu1UFsIkELQXwp0yZotWrV7t+sEkQ9hhbPqG1AL7137vvvqs///nPbtKCnW9YWJh7rTvvvFNnnHGG9oRNlJg1a5arJGDnY7+f5ORkDRs2zP0+bFJFMArxd0QdTqAbsNk/J5xwQmc3A0APw9gD9FCl26T1X0nbvpdShkih4dpWUqnVeaXaXFimXvFR8oa132pYK7dWa3Bq40kCITXVSlo3T2k/vKywqkK3baO3vyaU3KZS347bEhsuHd23Nis/K1YdIqw8Tylr3lbi+vf1VJ8b9cLWfvomT2r6wecYzxf6tCZHNeGxOmuo9Iv+UngHLzIWVpHv2luWOFglKaO0tcKjgrJKDcuI08isRIXvRvY90FVx3QOAsQdAT8F1D4LNsmXL3L1lKaP7ys/Pd8F3BP/fCwF8AAAAIBht/pe05V9SVZmUVLvu2Pr8Uq3fXltKPzMhSp6OTBP/D091mVJW/10pq9/Qj3tfrtdXhujdFdu1vCJJ1Zn7KMRTO2v+kfA/aIM/Va/6xuhz/zD55dHAeGlEspSTLI1IktKi2i7TPbSyUNH5KxS76TMlbv5UHtW47c9Xj9U11Ze2/JgQ6VcDpNOHSHGdkOge4qtQZNEqlcdmqzRlL5WEp+jHrcUakBqjfbISlRxD9j0AAAAAoP0RwAeC6++FEvpAgFatWuVKbQBAR2LsAXqwlEG1mfi5K6TyAikyQRkJUSqvqlFFdY1yiyuUHhuhkHYI4heU1yghsuXM75qwKOUOmqSnfkjUHy69S5u2bK3fl5SSpkG/uFAxWYN0VOhSt+2ssHna5E/S675D9HHx3vqmKFNvrkqTT6FKiZRykmqD+nYbEC8FXFigdJuqNi9XxLZlSilarvSq9S0ednzoP/TH6tPd2vZ1rMcOyZDOz5H6dFBVgGb8NYooXqeqqHRVxvZRVVSaNuaVKCU2Qr0Towjeo0fiugcAYw+AnoLrHgCdoaKiQhEREXR+F0AAHwjQ0qVLCeAD6HCMPUAPFhYhpQyUKouk/LWSN1ahnlD1TY5Wpa9GG/LLVFBW1S5rpOcW7ziAb95+f6Gmz7qz2fbtebla9MRs3X3pUVKvn7ZnhGzXlLA3NUVvup8r/GFa5c/QD75MfZ/bW99v7q1H/JnarFSVhsXLG+pRZKgUESZ3b7e9tFLDa1Yqo3KVhlYtV5a2BHQuJYrUEM865dYkanCCdGQfaWwfuckDnSmiZIObDFEZ01tl8QO1vbRSNTV+ZcRHKDslpnMbB3QSrnsAMPYA6Cm47gHQGcrKygjgdxEE8AEAAIBgFZcpFW+RyvKl4k1SfB8X3O6bFK2q6hptKChXeJhHMd6Ou6yvrvbpT/c+3Ooxf3zmY/3X5REK9bRcHSAipFrDQtZpmNY12ze6/AFtrYprtn182CKdGfZqwO1cWdNbD/km6lPv4Tp0sFe/7i31bf60ncLWvA+pqVR5wiCVJg5VlT9EW4rKlZ0co/6psfIGXIYAAAAAAAAA3Q0BfAAAACBYWXn8lMG1JfStlH5kkuSNVmxEmDITo1RdI20pLFdofIgiw2rXnm9vi5Z8o01bcls9Zl1+pZ4NO0MT0zcrLvdLhfoqAn7+aFUoX80j7aX+1lPmff4Q/cufrc9rcrQodJQieu+tsVkenZ1Y243BwlNVovDyPJXHD1BZwhCXhb9pW6mSo73KTIxUWhyl7AAAAAAAAHoyAvhAgA4++GD6CkCHY+wBoIhYKbGvVF4oFa6vDeiHhCgtNsKV0q/x1yi3qEIZ8ZEKD22bzO3MuB0/T27e9oCeY011qtbvM1khvnLF5S5WzLZvXdn4iNINCqss3OHjokIqJH/z7WVqHNiu9IdqWcggfRc+XJtihqsicahS46OVHSsdFC3tIPm/U4XUVLnzr4jpo/K4bFVHJrtlEOz32D81WgNSKZ2Pno3rHgCMPQB6Cq57AHSGmBi+d+gqCOADAUpISKCvAHQ4xh4ATmK2VJInlW2XSrdKMWluc++ESPl8fvlq/NpcWK7MhEiFevY8iB8RtuPod1pKUkDPUXecPzRShRmHulsdT1WxIko2umC21+7/E9gPrSzU3P182hYllfuk8ura+wqflFyQraUFR6siPFFVKcMUlT7YrduWI7lb0PPXKKJ4naoikty69xWxfVXtq3G/t77J0eqfEqOIDqqiAAQrrnsAMPYA6Cm47gHQGUJD+d6hqyCADwTonXfe0QknnEB/AehQjD0AHE+olDJIqiiQtq6UIhOkUK9CFKI+SVEugF/tgvgVykiIlGcPa8av2u7T4NSWPyocMHofZaSntVpGP7NXmg7cd58d7q8Jj1VZ4hB3aypcUq+WHtRrhKQRXfYDjLd0s/whYS54X5Y42BUZWJdfpqTocFc9IT2+9SUCgJ6A6x4AjD0AegquewB0hsLCQiUmJtL5XUDb1NgEAAAA0L6ik6X4rNrs+4L19ZtDQ0JcBnd6XIQiwj2unH6Nv4Ua9G0kLCxU11x2YavHXP2bC5nV3bDPKvIVWl2q8tgslSYOld8Tri1FFa7Mf+/EKA1Oj2233xcAAAAAAAC6FgL4AAAAQFeRPFCKz5RqqqWy/PrNYZ4QZafEqFdcpCz5fltxZbs24+fjD9fcW2a4TPyG7GfbbvtRy1NdrvDSzSq3de8TBrnqA4XlVe5m1ROGZsTJG8bHMgAAAAAAANTqqhUogQ6XnZ1NrwNg7AHQucK8tUH88iKpYLUUEVdbXt9KtId61C8l2pXT31BQpu2llUqK9u7Wy8RH7LwEvwXpJ4w9VIuWfKPcvO1uzXsrm896ag3UVCuieK0qozNUEddPVdHpqqyu0aaCMvVNitbA1FjFR9qiAQAMn7kAdAbGHgCMPQB6Cq93974nQscL8fvbsb4mAAAAgLZll++bvpa2LJf8NVJCVqPdxRXV+nFrsTYWVCguMowAcWfx+13w3h/qVVnCIJUk76UahejHrSVuYsWQXrEa2iuu05oHAAAAAECdZcuWufucnBw6BQiCvxdqNQIBmj9/Pn0FoMMx9gBoxmrkpwyR4jKkyuLaWwOxEWHKSo5WenyECsoqVVJZvcuduHb7rj8GjXlLN1kUXxUxfdy69wrxaGNBuSLDPeqdGKmBqTF0GdAE1z0AOgNjDwDGHgA9RVFRUWc3AQEigA8EqKCggL4C0OEYewC0yBstJfWX4vtI+WulGl+j3UlRXvVJjFKvuChtK65UWWXj/TtTsWuHo4mw8jx5qktVHttPpUnD5A+NcEsalFf51Ccx2mXeh4XyUQxoiuseAJ2BsQcAYw+AnsLn4wufriKssxsAAAAAdIbq6motWLBAGzduVGZmpsaNG6ewsLCu0+4NG5TpLdW4wXEKK1gnJWU3Oi4tNkJVvhrVyK/cogqlyKto7+6dX3W1r9Fa9weM3kdhYaHqqtrzfEIrCxVenqfy+AEqSxwinzfeTaDYUlihAanRGpQeo5iIsC79HgQAAAAAAMGnf//+7n7VqlW79fiqqio9/fTTeumll/Tll19q69atCgkJUa9evTR69Ggdd9xxOuOMMxQT07iqoB3TkMfjUVJSkkaOHKkLL7xQZ5111h6cVc/Et0NAgCIiIugrAB2OsQdoH88//7ymT5+utWvX1m/r27ev5s6dq8mTJ3etdmema+7lJ2vy8bFSdEqj4zMTIhXi/klbisrllxQTQBC/YXL42+8v1J/ufVibtuTWb8tIT9M1l12on48/XF1Ne56Pp7pM3tKNLvO+LH6gqqNS5avxa932UmUmRKivLW0QF9ml34NAe+K6B0BnYOwBwNgDoKdoGmhvaPny5TrllFP0r3/9S4mJiRo/frwGDBjgEg3Wr1+vDz/8UK+88oquu+465eb+9J1KQzfddFP9RIAVK1a44z/44AM3GeDPf/5zu51XdxTi9/vtezwAAACgR7DA6amnnrrD/c8991xQBlB32u7bL9LkSZOl8NoAcUObCsq1oaBcW4rKlBwdUZ8BHkiwe/qNs3e4f+4tM7pUEL89zyfEV6nI4tWqjOqlsoRBKk8YJPuotXZ7mbyhIRqcHqe9esfL4wnpsu9BAAAAAED3tGzZMnefk5PT2U1BJ2TgW2XAAw44QBs2bNDll1+uO+64Q7Gxsc2Oe/fdd3XNNddoyZIlLU4MaBpynjdvnn72s5+5///www/17evqlnXA3wsLLwIBstlHANDRGHuAtmUlyy3ruTW2P9jWBAuo3fe8LN+2VZK/ptm+jIRI9U6MVK+4KG0rrVBJRXWrz5VX4nNl5i1TvTW2P9j6akfa9XxqfIooXquqiCRVxGa58vlma3GlfDU16pMUpSG9Yl3wvqu+B4GOwHUPgM7A2AOAsQdAU5999pkmTZqkjIwMeb1eVzHvkksucQHuhk488UQXvL7nnnuaPcfvfvc7t2/KlCn12yorK3Xvvfdq4sSJys7OdpWAkpOTdfTRR+vNN9/c4S9i3bp1uuKKKzRkyBBFRka6xxx00EG69dZb3f758+e711q9erW72f/rbuedd17985SVlbX4/Ndff707tzPPPFN33313i8F7Y8H4RYsWBfyGmTBhgoYPH+4C+1988UXAjwMBfCBgVu4DADoaYw/Qtmy98YYly1ti++2DT5dr96atmr/kB6loY4v7M+IjXSA5I742iF9cUbXD59pe5ndrxDcsM98S2//F4m/UFbTb+fj9iihZr5rwKFXG9nXr3ivEo/zSSm0vrVSWBe/T4xQZHtql34NAR+C6B0BnYOwBwNgDoKFHHnlEhx12mAuoH3XUUZo2bZrLTn/ooYfc/Zo1a+qP/etf/6p+/fq5rPTFixc3yjy3LPYRI0a4gHidbdu26corr1RRUZELhv/2t7/V8ccf7x5rQX17jaYsYD5q1Cg3SaB3797u8bamfFxcnG6++WZ3jGW2W/n6hIQEd7P/191skkGdioqKZs9fWlrq1r1vWAK/NVZSf1fUZeW3Vr4fze1aLwMAAABdmJUEa8vjgq7dpR6pokgqL5AiE5rtT4+LqP//psLaWdexEeEtPldu3vaAXjPQ4zpbe52Pt3STfRxVRUxflSYOk98TrsKyKuUWl6t/SowrnZ8U4+3y70EAAAAAQM/Uf8bf1ZWsmn3cbj/23//+t8u0t4C4TcDv06dP/b7333/fBd0tgP7yyy+7bZYJb8HvcePG6bTTTtNXX33lAuJnn322y663JfKio6PrnyMpKcllyGdlZTV63YKCAjdp4L//+79dcD4qKqo+Y9+W2LPA/5NPPuky5BuqSxCw9low/9FHH3U/1wX2A2ETBCywb20aOnSo2tJ7773nJkta8P7AAw9s0+fu7gjgAwAAoMfIzMxs0+OCrt2DR0oJiVL+aik8WgoNbzGIXzfpeVNhuWwedFwLQfy0lKSAXjPQ4zpbe5xPWHmePL5SlcUNVGnSMNWERaqovEobC8uUnRyjgWmx6p1Y+6G7q78HAQAAAADo7u677z5VVVXprrvuahS8N+PHj3fZ8q+99prLoLcMeDNmzBhXyv66665zwf/c3Fxt2rRJDz74oPbaa69Gz2FB/abBe2NZ8xdccIFbUs9KzY8dO9Ztt9ey9eztdZsG742V9t9T1lbT9Hzr2KQAa0NDltU/evToZsfWTRywPrTA/SuvvOIy8K+66iq3ZAACRwAfCJDNoAKAjsbYA7T935R9uGmthLmVPjvyyCO7ZrsnniJtXS5VFksFa6WkAVajrNmxabH/ycQPkTYVlFsVeMVH/hTEz0oMVf/R+ygjPa3VsvOZvdJ04L77qCs4oI3PJ7SyUOHleW69eyub7/PGq7iiWhvyy9UvOVoD02LUN/mnWfZd/T0IdASuewB0BsYeAIw9AOp88skn7t6y71tas33Lli3y+XwuU3///fev337ttde6pfCeeuop9/MZZ5yhKVOmtNix3377rf70pz/pww8/dNX3ysvLG+1fv359/f8//fRTd/+LX/yiTX5JLa1tv7MS9xbAt/5oyDL+Wwrgz5o1q/65EhMTdcQRR+jCCy90FQmwawjgAwAAoMewdbrmzp2rU089dYfHzJkzR6GhteuVd7l22zpkqUNrA/i5K6SSXCk2vcXjLYjv+c9nsy2FFar2+ZUUHV7/gS0sLFTXXHahpt84e4evefVvLgy6vtqRtjyf0KpieUs3qjw2W2XxA1UdlarSymqt316qrORo9U+NUXZKTLd6DwIAAAAA0N3l5eW5ewuwt6a4uLjRz/ZdykknnaS3337b/Txt2rQWH2cBecvkr66u1oQJE1xmfXx8vDwej5YsWaK//e1vjdapz8/PbzU7vi3UVQBsOHGgIZuYUGfmzJm6/fbbd/hcdZMBsOcI4AMBshlGJ5xwAv0FoEMx9gBtz9YOszXIrCxZwyxoy4q2wKrt79LtDvNKacOkylIpb6Xkjam9tSAlJkIeT4hCQ6Tc4kptKapQWlyE1uX7NDg1TD8ff7ikGfrTvQ83yly3THYLhtfu7zra4nw8FrwvXq+KuL6qiO+vytg+Kq/yad32UvVJilL/lGgNSG25v7v6exBob1z3AOgMjD0AGHuA9ltTvquxUvZ1a9JbYD1Q3333na6++mq3xr091rLvP//8c0VGRjY67rbbblNZWZk++OCDZpX3fv/737sAfkOWxd5acH1X2cSDuuesc8ABB7jS/vb9hJ3HkCFD2uS1sGcI4AMAAKDHsQCpzYy2L2ytXJnNNrYPTsGe9Rxwu6OSpOQBUnV5bSn9lMGSp+VL/6Qor7xpHoWHliq3qEIbC6x020/l9C2oPWHsoVq05Bvl5m13a8Rbmflg76sd2ZPz8VSVKqLEgvdZKo/v724V1T6t2VaqjPhI9UuO0aC02B2WnesO70EAAAAAALqrQw45RF9++aU++ugjHXdcYBMXLGP+tNNOU0lJid566y1XGt+y1C0L//7772907MqVK5WcnNzisnlNy9TXtce8+eabuvTSS3faFvtOobKyUrsiOjralfy3Uvm33HKLHn/88V16PNoHAXwAAAD0SFbK3MqVddt2J2ZL5QWqLivUgrff0MYyr1vjfdzBo9xzNBTjDdPAtFiFhXqUV1yhsiKprMqnqPDQ+vLzhxzQfG2zrmp3zsdTXaaIkrWqiMlSeVx/lccNUGV1jVbnlSo9PkL9UmI0JD2w4H1Xfw8CAAAAANAdXXbZZfrf//1fXXXVVS4TfejQoY32W3D8s88+c2u717HM+8WLF+vaa6/VMccc4z7nW9n5Bx54wJXLb7iEnq0dv2LFCn399dcaOXJk/faHH364vvx+Q7/61a/cY1599VU9/fTTLtDekGXmNyyvn5KS4p7bsvyjoqICPm+bcPDOO+/oiSeeUGpqqqsUEBPTvLqgVRdAxyCADwRo2LBh9BWADsfYA2C3eTx6/sNlmj79Kq3dsLl+c9/MdM294TeaPPGoRod7Qz2u9Lvdl1SWa2txuRKivIqP/Ckbv6fyVJcrsniNKmJ6qyKun8rjB6qqxq/VeSVuyYF+ydEa2ivOLUcAYPdx3QOgMzD2AGDsAVBn+PDh+utf/6oLLrhAe+21l4499lgXxK+qqtKaNWtcZn5aWpqWL1/ujn/llVd077336uCDD3ZB77oseAu2jx49WhdddJErUT9w4EC3z7LyLVB/+OGHu8C+lexftGiRFi5cqEmTJumFF15o9Mvwer16/vnn3cSAM888000KsKz88vJyLVu2TPPmzVN1dXX98TZ54IsvvnDtHjt2rCuNP2rUKDcRwNjPLendu7d7rpNPPll33nmnHnvsMTf5wNrt8Xi0efNmffzxx67Efnp6uusntK8Qv9/vb+fXAAAAANDB7ANew1neTT1376xmQXzjl19bCiu1sbBMuYUVigjzKDnGu0uZ5d1JiK9ckUVrVBmdofK4bJUlDlWpW/O+TCkxXmWnxCgnM85VLwAAAAAAoCuyYLDJycnp7KYEhW+++UZz5851a9Vv2rTJZaNbkPuwww5z5fItuG0BfQvS19TUuAz8AQMGNHoOW8/+xBNP1IEHHugC9BaMN6+//roL9n/77bcu2H/QQQdp5syZ+uGHH3T++efrkUce0Xnnndfouey1Zs+e7UrpW9Z9XFycBg8erOOPP1433HBD/XFWxt8qArz22muu3T6fT+eee64rjx8IqzBgkw9efPFFt5RAXl6e+z7IgvZ2rvZ6dv6xsbGNHlf3nVFPCTkv64C/FwL4QIBs7RKbtQQAHYmxB8DusNnXNkt67dq1OzzGMvF//PDZFtdcf+tf23XIgFit316mzUXlss9faXFehYb0rCB1iK9SkcWrVRWZpvL4bJUmDlN+WbW2FFUoMyFSvROjNCwjTuFtHLy335+tfbdx40ZlZmZq3LhxzZY9ALojrnsAMPYA6Cm47kGwIYDfM1gJfMv6R/D/vfSsb+CAPVBRUUH/AehwjD0AdocFf1sL3pu1G7do/qdLWh57qv1KjArXgNRo9UmIcln4G/LLVVL5U1m2HhG8L1qlqshUlcf1U0nCUG0srNDWkkr1T4nWkF5xGpEZ3+bBe6ucYJMvjj76aJ1zzjnu3n627UB3x3UPAMYeAD0F1z0AOkNPyZDvDgjgAwAAAN2MZW4HdFxuXqv7o71hGpAWoz5JUW6t9/zSKuUWlcvnr1H3z7xfo6qo2uB9YdwQrd5eripfjQalWsn8eA1IjWnzNe/rlj1oOvnCfrbtBPEBAAAAAAC6PwL4QIAoKwKgMzD2ANgdVnY9oOOS41oee6J+KqvvDfW4jPMBKTHKSoxSWGhIt87G91SX15bNj0hSRWxfbYserB+2lSvGG6pBabHaJytB6fGRbf66VjZ/+vTprR5j+239OqC74roHAGMPgJ6C6x4AnaGlZRQRnEL81EsAAAAAuhULBlvZ9dbK6PfLTNMPL9yi0PShUmh4QM9b6avR+vwybSupVF5xpbyhIUqO9So0pHvMC/ZUFSuiZL0qozNVEZuljd7+2lTsU2ZChPokWdn8WEWEtc+H3Xnz5rly+Tvz3nvvacKECe3SBgAAAABAz9QRa3oD3cWyDvh76R7ftAE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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "max |mean_VFE - mean_exact| = 1.50 g (M = 15 inducing points vs all N = 133 observations)\n" + ] + } + ], + "source": [ + "ax = plot_gp(\n", + " mean, f_band, label=\"exact GP\", band_labels=False, title=\"Exact GP vs VFE sparse approximation\"\n", + ")\n", + "plot_gp(mean_v, f_band_v, ax=ax, color=\"C1\", ls=\"--\", label=f\"VFE (M = {M})\", band_labels=False)\n", + "plt.show()\n", + "\n", + "max_dev = np.max(np.abs(mean_v - mean))\n", + "print(\n", + " f\"max |mean_VFE - mean_exact| = {max_dev:.2f} g \"\n", + " f\"(M = {M} inducing points vs all N = {N} observations)\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "949e4399", + "metadata": {}, + "source": [ + "## Next steps\n", + "\n", + "- {doc}`Kernel gallery `: covariance functions and what they encode.\n", + "- {doc}`User guide `: models, likelihoods, inducing points, and training.\n", + "- {ref}`Stochastic variational GPs `: SVGP, minibatching, non-Gaussian likelihoods, and Variational Fourier Features.\n", + "\n", + "## References\n", + "\n", + "```{bibliography}\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}