Mercurial > repos > goeckslab > ludwig_visualize

changeset 7:197c8cc761aa draft default tip

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planemo upload for repository https://github.com/goeckslab/Galaxy-Ludwig.git commit e2ab4c0f9ce8b7a0a48f749ef5dd9899d6c2b1f8
author goeckslab
date Sat, 22 Nov 2025 01:15:52 +0000
parents b6953aa083ed
children
files ludwig_experiment.py ludwig_macros.xml test-data/breast_config.yml test-data/breast_sample.csv test-data/ludwig_experiment_report_test.html test-data/ludwig_experiment_report_test_breast.html utils.py
diffstat 7 files changed, 961 insertions(+), 22 deletions(-) [+]
line wrap: on
line diff
--- a/ludwig_experiment.py	Sat Sep 06 01:52:48 2025 +0000
+++ b/ludwig_experiment.py	Sat Nov 22 01:15:52 2025 +0000
@@ -1,10 +1,15 @@
+import base64
+import html
 import json
 import logging
 import os
 import pickle
+import re
 import sys
+from io import BytesIO
 
 import pandas as pd
+from ludwig.api import LudwigModel
 from ludwig.experiment import cli
 from ludwig.globals import (
     DESCRIPTION_FILE_NAME,
@@ -21,6 +26,11 @@
     get_html_template
 )
 
+try:  # pragma: no cover - optional dependency in runtime containers
+    import matplotlib.pyplot as plt
+except ImportError:  # pragma: no cover
+    plt = None
+
 
 logging.basicConfig(level=logging.DEBUG)
 
@@ -158,44 +168,435 @@
         LOG.error(f"Error converting Parquet to CSV: {e}")
 
 
-def generate_html_report(title, ludwig_output_directory_name):
-    # ludwig_output_directory = os.path.join(
-    #     output_directory, ludwig_output_directory_name)
+def _resolve_dataset_path(dataset_path):
+    if not dataset_path:
+        return None
+
+    candidates = [dataset_path]
+
+    if not os.path.isabs(dataset_path):
+        candidates.extend([
+            os.path.join(output_directory, dataset_path),
+            os.path.join(os.getcwd(), dataset_path),
+        ])
+
+    for candidate in candidates:
+        if candidate and os.path.exists(candidate):
+            return os.path.abspath(candidate)
+
+    return None
+
+
+def _load_dataset_dataframe(dataset_path):
+    if not dataset_path:
+        return None
+
+    _, ext = os.path.splitext(dataset_path.lower())
+
+    try:
+        if ext in {".csv", ".tsv"}:
+            sep = "\t" if ext == ".tsv" else ","
+            return pd.read_csv(dataset_path, sep=sep)
+        if ext == ".parquet":
+            return pd.read_parquet(dataset_path)
+        if ext == ".json":
+            return pd.read_json(dataset_path)
+        if ext == ".h5":
+            return pd.read_hdf(dataset_path)
+    except Exception as exc:
+        LOG.warning(f"Unable to load dataset '{dataset_path}': {exc}")
+
+    LOG.warning("Unsupported dataset format for feature importance computation")
+    return None
+
+
+def sanitize_feature_name(name):
+    """Mirror Ludwig's get_sanitized_feature_name implementation."""
+    return re.sub(r"[(){}.:\"\"\'\'\[\]]", "_", str(name))
+
+
+def _sanitize_dataframe_columns(dataframe):
+    """Rename dataframe columns to Ludwig-sanitized names for explainability."""
+    column_map = {col: sanitize_feature_name(col) for col in dataframe.columns}
+
+    sanitized_df = dataframe.rename(columns=column_map)
+    if len(set(column_map.values())) != len(column_map.values()):
+        LOG.warning(
+            "Column name collision after sanitization; feature importance may be unreliable"
+        )
+
+    return sanitized_df
+
+
+def _feature_importance_plot(label_df, label_name, top_n=10, max_abs_importance=None):
+    """
+    Return base64-encoded bar plot for a label's top-N feature importances.
+
+    max_abs_importance lets us pin the x-axis across labels so readers can
+    compare magnitudes.
+    """
+    if plt is None or label_df.empty:
+        return ""
+
+    top_features = label_df.nlargest(top_n, "abs_importance")
+    if top_features.empty:
+        return ""
+
+    fig, ax = plt.subplots(figsize=(6, 3 + 0.2 * len(top_features)))
+    ax.barh(top_features["feature"], top_features["abs_importance"], color="#3f8fd2")
+    ax.set_xlabel("|importance|")
+    if max_abs_importance and max_abs_importance > 0:
+        ax.set_xlim(0, max_abs_importance * 1.05)
+    ax.invert_yaxis()
+    fig.tight_layout()
+
+    buf = BytesIO()
+    fig.savefig(buf, format="png", dpi=150)
+    plt.close(fig)
+    encoded = base64.b64encode(buf.getvalue()).decode("utf-8")
+    return encoded
+
+
+def render_feature_importance_table(df: pd.DataFrame) -> str:
+    """Render a sortable HTML table for feature importance values."""
+    if df.empty:
+        return ""
+
+    columns = list(df.columns)
+    headers = "".join(
+        f"<th class='sortable'>{html.escape(str(col).replace('_', ' '))}</th>"
+        for col in columns
+    )
+
+    body_rows = []
+    for _, row in df.iterrows():
+        cells = []
+        for col in columns:
+            val = row[col]
+            if isinstance(val, float):
+                val_str = f"{val:.6f}"
+            else:
+                val_str = str(val)
+            cells.append(f"<td>{html.escape(val_str)}</td>")
+        body_rows.append("<tr>" + "".join(cells) + "</tr>")
+
+    return (
+        "<div class='scroll-rows-30'>"
+        "<table class='feature-importance-table sortable-table'>"
+        f"<thead><tr>{headers}</tr></thead>"
+        f"<tbody>{''.join(body_rows)}</tbody>"
+        "</table>"
+        "</div>"
+    )
+
+
+def compute_feature_importance(ludwig_output_directory_name,
+                               sample_size=200,
+                               random_seed=42):
+    ludwig_output_directory = os.path.join(
+        output_directory, ludwig_output_directory_name)
+    model_dir = os.path.join(ludwig_output_directory, "model")
+
+    output_csv_path = os.path.join(
+        ludwig_output_directory, "feature_importance.csv")
+
+    if not os.path.exists(model_dir):
+        LOG.info("Model directory not found; skipping feature importance computation")
+        return
 
-    # test_statistics_html = ""
-    # # Read test statistics JSON and convert to HTML table
-    # try:
-    #     test_statistics_path = os.path.join(
-    #         ludwig_output_directory, TEST_STATISTICS_FILE_NAME)
-    #     with open(test_statistics_path, "r") as f:
-    #         test_statistics = json.load(f)
-    #     test_statistics_html = "<h2>Test Statistics</h2>"
-    #     test_statistics_html += json_to_html_table(
-    #         test_statistics)
-    # except Exception as e:
-    #     LOG.info(f"Error reading test statistics: {e}")
+    try:
+        ludwig_model = LudwigModel.load(model_dir)
+    except Exception as exc:
+        LOG.warning(f"Unable to load Ludwig model for explanations: {exc}")
+        return
+
+    training_metadata = getattr(ludwig_model, "training_set_metadata", {})
+
+    output_feature_name, dataset_path = get_output_feature_name(
+        ludwig_output_directory)
+
+    if not output_feature_name or not dataset_path:
+        LOG.warning("Output feature or dataset path missing; skipping feature importance")
+        if hasattr(ludwig_model, "close"):
+            ludwig_model.close()
+        return
+
+    dataset_full_path = _resolve_dataset_path(dataset_path)
+    if not dataset_full_path:
+        LOG.warning(f"Unable to resolve dataset path '{dataset_path}' for explanations")
+        if hasattr(ludwig_model, "close"):
+            ludwig_model.close()
+        return
+
+    dataframe = _load_dataset_dataframe(dataset_full_path)
+    if dataframe is None or dataframe.empty:
+        LOG.warning("Dataset unavailable or empty; skipping feature importance")
+        if hasattr(ludwig_model, "close"):
+            ludwig_model.close()
+        return
+
+    dataframe = _sanitize_dataframe_columns(dataframe)
+
+    data_subset = dataframe if len(dataframe) <= sample_size else dataframe.head(sample_size)
+    sample_df = dataframe.sample(
+        n=min(sample_size, len(dataframe)),
+        random_state=random_seed,
+        replace=False,
+    ) if len(dataframe) > sample_size else dataframe
+
+    try:
+        from ludwig.explain.captum import IntegratedGradientsExplainer
+    except ImportError as exc:
+        LOG.warning(f"Integrated Gradients explainer unavailable: {exc}")
+        if hasattr(ludwig_model, "close"):
+            ludwig_model.close()
+        return
+
+    sanitized_output_feature = sanitize_feature_name(output_feature_name)
+
+    try:
+        explainer = IntegratedGradientsExplainer(
+            ludwig_model,
+            data_subset,
+            sample_df,
+            sanitized_output_feature,
+        )
+        explanations = explainer.explain()
+    except Exception as exc:
+        LOG.warning(f"Unable to compute feature importance: {exc}")
+        if hasattr(ludwig_model, "close"):
+            ludwig_model.close()
+        return
+
+    if hasattr(ludwig_model, "close"):
+        try:
+            ludwig_model.close()
+        except Exception:
+            pass
 
-    # Convert visualizations to HTML
+    label_names = []
+    target_metadata = {}
+    if isinstance(training_metadata, dict):
+        target_metadata = training_metadata.get(sanitized_output_feature, {})
+
+    if isinstance(target_metadata, dict):
+        if "idx2str" in target_metadata:
+            idx2str = target_metadata["idx2str"]
+            if isinstance(idx2str, dict):
+                def _idx_key(item):
+                    idx_key = item[0]
+                    try:
+                        return (0, int(idx_key))
+                    except (TypeError, ValueError):
+                        return (1, str(idx_key))
+
+                label_names = [value for key, value in sorted(
+                    idx2str.items(), key=_idx_key)]
+            else:
+                label_names = idx2str
+        elif "str2idx" in target_metadata and isinstance(
+                target_metadata["str2idx"], dict):
+            # invert mapping
+            label_names = [label for label, _ in sorted(
+                target_metadata["str2idx"].items(),
+                key=lambda item: item[1])]
+
+    rows = []
+    global_explanation = explanations.global_explanation
+    for label_index, label_explanation in enumerate(
+            global_explanation.label_explanations):
+        if label_names and label_index < len(label_names):
+            label_value = str(label_names[label_index])
+        elif len(global_explanation.label_explanations) == 1:
+            label_value = output_feature_name
+        else:
+            label_value = str(label_index)
+
+        for feature in label_explanation.feature_attributions:
+            rows.append({
+                "label": label_value,
+                "feature": feature.feature_name,
+                "importance": feature.attribution,
+                "abs_importance": abs(feature.attribution),
+            })
+
+    if not rows:
+        LOG.warning("No feature importance rows produced")
+        return
+
+    importance_df = pd.DataFrame(rows)
+    importance_df.sort_values([
+        "label",
+        "abs_importance"
+    ], ascending=[True, False], inplace=True)
+
+    importance_df.to_csv(output_csv_path, index=False)
+
+    LOG.info(f"Feature importance saved to {output_csv_path}")
+
+
+def generate_html_report(title, ludwig_output_directory_name):
     plots_html = ""
-    if len(os.listdir(viz_output_directory)) > 0:
+    plot_files = []
+    if os.path.isdir(viz_output_directory):
+        plot_files = sorted(os.listdir(viz_output_directory))
+    if plot_files:
         plots_html = "<h2>Visualizations</h2>"
-    for plot_file in sorted(os.listdir(viz_output_directory)):
+    for plot_file in plot_files:
         plot_path = os.path.join(viz_output_directory, plot_file)
         if os.path.isfile(plot_path) and plot_file.endswith((".png", ".jpg")):
             encoded_image = encode_image_to_base64(plot_path)
+            plot_title = os.path.splitext(plot_file)[0].replace("_", " ")
             plots_html += (
                 f'<div class="plot">'
-                f'<h3>{os.path.splitext(plot_file)[0]}</h3>'
+                f'<h3>{plot_title}</h3>'
                 '<img src="data:image/png;base64,'
                 f'{encoded_image}" alt="{plot_file}">'
                 f'</div>'
             )
 
+    feature_importance_html = ""
+    importance_path = os.path.join(
+        output_directory,
+        ludwig_output_directory_name,
+        "feature_importance.csv",
+    )
+    if os.path.exists(importance_path):
+        try:
+            importance_df = pd.read_csv(importance_path)
+            if not importance_df.empty:
+                sorted_df = (
+                    importance_df
+                    .sort_values(["label", "abs_importance"], ascending=[True, False])
+                )
+                top_rows = (
+                    sorted_df
+                    .groupby("label", as_index=False)
+                    .head(5)
+                )
+                max_abs_importance = pd.to_numeric(
+                    importance_df.get("abs_importance", pd.Series(dtype=float)),
+                    errors="coerce",
+                ).max()
+                if pd.isna(max_abs_importance):
+                    max_abs_importance = None
+
+                plot_sections = []
+                for label in sorted(importance_df["label"].unique()):
+                    encoded_plot = _feature_importance_plot(
+                        importance_df[importance_df["label"] == label],
+                        label,
+                        max_abs_importance=max_abs_importance,
+                    )
+                    if encoded_plot:
+                        plot_sections.append(
+                            f'<div class="plot feature-importance-plot">'
+                            f'<h3>Top features for {label}</h3>'
+                            f'<img src="data:image/png;base64,{encoded_plot}" '
+                            f'alt="Feature importance plot for {label}">'
+                            f'</div>'
+                        )
+                explanation_text = (
+                    "<p>Feature importance scores come from Ludwig's Integrated Gradients explainer. "
+                    "It interpolates between each example and a neutral baseline sample, summing "
+                    "the change in the model output along that path. Higher |importance| values "
+                    "indicate stronger influence. Plots share a common x-axis to make magnitudes "
+                    "comparable across labels, and the table columns can be sorted for quick scans.</p>"
+                )
+                feature_importance_html = (
+                    "<h2>Feature Importance</h2>"
+                    + explanation_text
+                    + render_feature_importance_table(top_rows)
+                    + "".join(plot_sections)
+                )
+        except Exception as exc:
+            LOG.info(f"Unable to embed feature importance table: {exc}")
+
     # Generate the full HTML content
+    feature_section = feature_importance_html or "<p>No feature importance artifacts were generated.</p>"
+    viz_section = plots_html or "<p>No visualizations were generated.</p>"
+    tabs_style = """
+    <style>
+        .tabs {
+            display: flex;
+            border-bottom: 2px solid #ccc;
+            margin-top: 20px;
+            margin-bottom: 1rem;
+        }
+        .tablink {
+            padding: 9px 18px;
+            cursor: pointer;
+            border: 1px solid #ccc;
+            border-bottom: none;
+            background: #f9f9f9;
+            margin-right: 5px;
+            border-top-left-radius: 8px;
+            border-top-right-radius: 8px;
+            font-size: 0.95rem;
+            font-weight: 500;
+            font-family: Arial, sans-serif;
+            color: #4A4A4A;
+        }
+        .tablink.active {
+            background: #ffffff;
+            font-weight: bold;
+        }
+        .tabcontent {
+            border: 1px solid #ccc;
+            border-top: none;
+            padding: 20px;
+            display: none;
+        }
+        .tabcontent.active {
+            display: block;
+        }
+    </style>
+    """
+    tabs_script = """
+    <script>
+        function openTab(evt, tabId) {
+            var i, tabcontent, tablinks;
+            tabcontent = document.getElementsByClassName("tabcontent");
+            for (i = 0; i < tabcontent.length; i++) {
+                tabcontent[i].style.display = "none";
+                tabcontent[i].classList.remove("active");
+            }
+            tablinks = document.getElementsByClassName("tablink");
+            for (i = 0; i < tablinks.length; i++) {
+                tablinks[i].classList.remove("active");
+            }
+            var current = document.getElementById(tabId);
+            if (current) {
+                current.style.display = "block";
+                current.classList.add("active");
+            }
+            if (evt && evt.currentTarget) {
+                evt.currentTarget.classList.add("active");
+            }
+        }
+        document.addEventListener("DOMContentLoaded", function() {
+            openTab({currentTarget: document.querySelector(".tablink")}, "viz-tab");
+        });
+    </script>
+    """
+    tabs_html = f"""
+    <div class="tabs">
+        <button class="tablink active" onclick="openTab(event, 'viz-tab')">Visualizations</button>
+        <button class="tablink" onclick="openTab(event, 'feature-tab')">Feature Importance</button>
+    </div>
+    <div id="viz-tab" class="tabcontent active">
+        {viz_section}
+    </div>
+    <div id="feature-tab" class="tabcontent">
+        {feature_section}
+    </div>
+    """
     html_content = f"""
     {get_html_template()}
         <h1>{title}</h1>
-        {plots_html}
+        {tabs_style}
+        {tabs_html}
+        {tabs_script}
     {get_html_closing()}
     """
 
@@ -217,4 +618,5 @@
 
     make_visualizations(ludwig_output_directory_name)
     convert_parquet_to_csv(ludwig_output_directory_name)
+    compute_feature_importance(ludwig_output_directory_name)
     generate_html_report("Ludwig Experiment", ludwig_output_directory_name)
--- a/ludwig_macros.xml	Sat Sep 06 01:52:48 2025 +0000
+++ b/ludwig_macros.xml	Sat Nov 22 01:15:52 2025 +0000
@@ -1,7 +1,7 @@
 <macros>
     <token name="@LUDWIG_VERSION@">0.10.1</token>
 
-    <token name="@SUFFIX@">2</token>
+    <token name="@SUFFIX@">3</token>
 
     <token name="@VERSION@">@LUDWIG_VERSION@+@SUFFIX@</token>
 
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/breast_config.yml	Sat Nov 22 01:15:52 2025 +0000
@@ -0,0 +1,12 @@
+input_features:
+  - name: RPL23AP24
+    type: number
+  - name: RP11-206L10.9
+    type: number
+  - name: RP11-465B22.8
+    type: number
+output_features:
+  - name: AGE_CAT
+    type: category
+trainer:
+  epochs: 2
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/breast_sample.csv	Sat Nov 22 01:15:52 2025 +0000
@@ -0,0 +1,9 @@
+RPL23AP24,RP11-206L10.9,RP11-465B22.8,B3GALT6,UBE2J2,RP4-758J18.2,RP4-758J18.13,SSU72,RP11-345P4.9,RP1-140A9.1,TMEM52,RP4-740C4.5,PEX10,TNFRSF14,LRRC47,DFFB,LINC01134,RP1-37J18.1,GPR153,KLHL21,AGE_CAT
+0.1211,0.4986,3.354,19.86,24.1,14.84,3.367,38.35,6.023,0.7205,1.089,1.356,15.64,108.6,24.97,5.79,0.1377,0.0,15.68,24.43,post
+0.0,1.291,25.7,24.05,27.97,19.14,6.678,52.93,17.52,0.6363,0.412,3.319,14.88,99.48,35.01,5.38,0.8004,0.0,7.475,29.76,pre
+0.0,0.5758,6.357,19.08,26.45,17.41,5.742,54.13,12.31,0.7358,1.308,2.053,12.47,100.9,29.83,6.159,0.2316,0.0,12.34,36.57,pre
+0.1467,0.6449,0.4005,11.81,21.93,8.1,1.557,43.66,5.914,0.5921,1.111,1.372,10.96,96.68,23.17,4.133,0.09032,0.0,11.1,20.92,post
+0.1191,0.3823,0.1858,12.3,23.48,13.18,2.318,56.68,7.94,0.7591,9.574,0.6858,16.57,74.72,30.11,3.118,0.2482,0.0,38.67,26.86,post
+0.0,0.4321,2.784,18.11,30.75,11.82,2.282,56.51,9.529,0.7135,1.213,3.559,17.05,122.8,38.61,8.208,0.3281,0.0,19.0,36.32,pre
+0.09253,0.4649,2.418,14.98,17.76,11.07,1.637,31.54,7.266,1.219,1.212,2.812,12.59,135.5,25.63,5.315,0.2016,0.0,8.482,15.74,pre
+0.09606,0.4759,0.07494,11.48,14.75,8.049,1.554,32.96,7.075,0.6939,1.234,1.029,11.62,74.73,21.45,3.678,0.0182,0.0,14.31,10.59,post
--- a/test-data/ludwig_experiment_report_test.html	Sat Sep 06 01:52:48 2025 +0000
+++ b/test-data/ludwig_experiment_report_test.html	Sat Nov 22 01:15:52 2025 +0000
@@ -55,6 +55,39 @@
               background-color: #4CAF50;
               color: white;
           }
+          /* feature importance layout tweaks */
+          table.feature-importance-table {
+              table-layout: auto;
+          }
+          table.feature-importance-table th,
+          table.feature-importance-table td {
+              white-space: nowrap;
+              word-break: normal;
+          }
+          /* sortable tables */
+          .sortable-table th.sortable {
+              cursor: pointer;
+              position: relative;
+              user-select: none;
+          }
+          .sortable-table th.sortable::after {
+              content: '⇅';
+              position: absolute;
+              right: 12px;
+              top: 50%;
+              transform: translateY(-50%);
+              font-size: 0.8em;
+              color: #eaf5ea;
+              text-shadow: 0 0 1px rgba(0,0,0,0.15);
+          }
+          .sortable-table th.sortable.sorted-none::after { content: '⇅'; color: #eaf5ea; }
+          .sortable-table th.sortable.sorted-asc::after  { content: '↑';  color: #ffffff; }
+          .sortable-table th.sortable.sorted-desc::after { content: '↓';  color: #ffffff; }
+          .scroll-rows-30 {
+              max-height: 900px;
+              overflow-y: auto;
+              overflow-x: auto;
+          }
           .plot {
               text-align: center;
               margin: 20px 0;
@@ -64,12 +97,150 @@
               height: auto;
           }
         </style>
+        <script>
+          (function() {
+            if (window.__sortableInit) return;
+            window.__sortableInit = true;
+
+            function initSortableTables() {
+              document.querySelectorAll('table.sortable-table tbody').forEach(tbody => {
+                Array.from(tbody.rows).forEach((row, i) => { row.dataset.originalOrder = i; });
+              });
+
+              const getText = td => (td?.innerText || '').trim();
+              const cmp = (idx, asc) => (a, b) => {
+                const v1 = getText(a.children[idx]);
+                const v2 = getText(b.children[idx]);
+                const n1 = parseFloat(v1), n2 = parseFloat(v2);
+                if (!isNaN(n1) && !isNaN(n2)) return asc ? n1 - n2 : n2 - n1;
+                return asc ? v1.localeCompare(v2) : v2.localeCompare(v1);
+              };
+
+              document.querySelectorAll('table.sortable-table th.sortable').forEach(th => {
+                th.classList.remove('sorted-asc','sorted-desc');
+                th.classList.add('sorted-none');
+
+                th.addEventListener('click', () => {
+                  const table = th.closest('table');
+                  const headerRow = th.parentNode;
+                  const allTh = headerRow.querySelectorAll('th.sortable');
+                  const tbody = table.querySelector('tbody');
+
+                  const isAsc  = th.classList.contains('sorted-asc');
+                  const isDesc = th.classList.contains('sorted-desc');
+
+                  allTh.forEach(x => x.classList.remove('sorted-asc','sorted-desc','sorted-none'));
+
+                  let next;
+                  if (!isAsc && !isDesc) {
+                    next = 'asc';
+                  } else if (isAsc) {
+                    next = 'desc';
+                  } else {
+                    next = 'none';
+                  }
+                  th.classList.add('sorted-' + next);
+
+                  const rows = Array.from(tbody.rows);
+                  if (next === 'none') {
+                    rows.sort((a, b) => (a.dataset.originalOrder - b.dataset.originalOrder));
+                  } else {
+                    const idx = Array.from(headerRow.children).indexOf(th);
+                    rows.sort(cmp(idx, next === 'asc'));
+                  }
+                  rows.forEach(r => tbody.appendChild(r));
+                });
+              });
+            }
+
+            if (document.readyState === 'loading') {
+              document.addEventListener('DOMContentLoaded', initSortableTables);
+            } else {
+              initSortableTables();
+            }
+          })();
+        </script>
     </head>
     <body>
     <div class="container">
     
         <h1>Ludwig Experiment</h1>
-        <h2>Visualizations</h2><div class="plot"><h3>learning_curves_temperature_loss</h3><img src="data:image/png;base64,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" alt="learning_curves_temperature_loss.png"></div>
+        
+    <style>
+        .tabs {
+            display: flex;
+            border-bottom: 2px solid #ccc;
+            margin-top: 20px;
+            margin-bottom: 1rem;
+        }
+        .tablink {
+            padding: 9px 18px;
+            cursor: pointer;
+            border: 1px solid #ccc;
+            border-bottom: none;
+            background: #f9f9f9;
+            margin-right: 5px;
+            border-top-left-radius: 8px;
+            border-top-right-radius: 8px;
+            font-size: 0.95rem;
+            font-weight: 500;
+            font-family: Arial, sans-serif;
+            color: #4A4A4A;
+        }
+        .tablink.active {
+            background: #ffffff;
+            font-weight: bold;
+        }
+        .tabcontent {
+            border: 1px solid #ccc;
+            border-top: none;
+            padding: 20px;
+            display: none;
+        }
+        .tabcontent.active {
+            display: block;
+        }
+    </style>
+    
+        
+    <div class="tabs">
+        <button class="tablink active" onclick="openTab(event, 'viz-tab')">Visualizations</button>
+        <button class="tablink" onclick="openTab(event, 'feature-tab')">Feature Importance</button>
+    </div>
+    <div id="viz-tab" class="tabcontent active">
+        <h2>Visualizations</h2><div class="plot"><h3>learning curves temperature loss</h3><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACR6ElEQVR4nOzdd1hUR9sG8HsLXXpXQRQQpNhAEXts2INdk6gxxsQYK4lJTLF8JjHGhokmxiSapq9dktixN6yogIodUaSqdCm7e74/Vsq6YEAXdoH7d11zJcycnZ2zA/LwnDNzRIIgCCAiIiKiOkOs7QEQERERUfViAEhERERUxzAAJCIiIqpjGAASERER1TEMAImIiIjqGAaARERERHUMA0AiIiKiOoYBIBEREVEdwwCQiIiIqI5hAEhERERUxzAAJCIiIqpjGAASERER1TEMAImIiIjqGAaARERERHUMA0AiqjIuLi548803tT0MIiJ6BgNAIh3322+/QSQS4dy5c9oeSo2Tl5eHZcuWISAgAObm5jA0NETTpk0xefJkXL9+XdvDqxK5ubmYO3cuDh8+rO2h6JwrV65g7ty5iIuL0/ZQiLROqu0BEFHtde3aNYjF2vk7My0tDb1798b58+fRv39/vPbaa6hXrx6uXbuGDRs2YPXq1SgoKNDK2KpSbm4u5s2bBwDo2rWrdgejY65cuYJ58+aha9eucHFx0fZwiLSKASARVYhMJoNCoYC+vn6FX2NgYFCFI3q+N998ExcuXMCWLVswZMgQlbb58+fjs88+08j7vMjnQpqRk5MDExMTbQ9DZ8ZBVBm8BExUSyQkJOCtt96Cvb09DAwM4O3tjTVr1qgcU1BQgNmzZ8PPzw/m5uYwMTFBp06dcOjQIZXj4uLiIBKJsHjxYoSGhsLV1RUGBgbFl9BEIhFu3ryJN998ExYWFjA3N8e4ceOQm5ur0s+z9wAWXc4+ceIEQkJCYGtrCxMTEwwaNAipqakqr1UoFJg7dy7q168PY2NjvPLKK7hy5UqF7is8ffo0du7cifHjx6sFf4AyMF28eHHx1127di0zW/bmm2+qZIrK+1wuXLgAqVRanHkr7dq1axCJRFixYkVxXXp6OqZPnw4nJycYGBjAzc0NCxcuhEKhUHnthg0b4OfnB1NTU5iZmcHX1xfLly8v97zj4uJga2sLAJg3bx5EIhFEIhHmzp1bfExsbCyGDh0KKysrGBoawt/fH//8849KP0XzdPz4cUydOhW2trawsLDAu+++i4KCAqSnp2PMmDGwtLSEpaUlPvroIwiCUObntGzZMjRq1AhGRkbo0qULYmJi1MZdmTEdOXIEkyZNgp2dHRo2bAgAuHv3LiZNmgQPDw8YGRnB2toaw4YNU7nU+9tvv2HYsGEAgFdeeaX4sym6VP7s51SkvO/hssYBALt370anTp1gYmICU1NT9OvXD5cvXy53zoi0hRlAologOTkZ7dq1g0gkwuTJk2Fra4vdu3dj/PjxyMzMxPTp0wEAmZmZ+OWXXzBq1ChMmDABWVlZ+PXXXxEUFIQzZ86gZcuWKv2uXbsWeXl5eOedd2BgYAArK6vituHDh6Nx48ZYsGABIiMj8csvv8DOzg4LFy78z/FOmTIFlpaWmDNnDuLi4hAaGorJkydj48aNxcfMmjUL3377LQYMGICgoCBcunQJQUFByMvL+8/+i4KH0aNHV+DTq7xnPxdHR0d06dIFmzZtwpw5c1SO3bhxIyQSSXHwkZubiy5duiAhIQHvvvsunJ2dcfLkScyaNQuJiYkIDQ0FAISHh2PUqFHo3r178Wd69epVnDhxAtOmTStzXLa2tvjxxx/x3nvvYdCgQRg8eDAAoHnz5gCAy5cvo0OHDmjQoAE++eQTmJiYYNOmTQgODsbWrVsxaNAglf6mTJkCBwcHzJs3D6dOncLq1athYWGBkydPwtnZGV9//TV27dqFRYsWwcfHB2PGjFF5/R9//IGsrCy8//77yMvLw/Lly9GtWzdER0fD3t7+hcY0adIk2NraYvbs2cjJyQEAnD17FidPnsTIkSPRsGFDxMXF4ccff0TXrl1x5coVGBsbo3Pnzpg6dSq+++47fPrpp2jWrBkAFP+3ssoax59//omxY8ciKCgICxcuRG5uLn788Ud07NgRFy5c4GVn0i0CEem0tWvXCgCEs2fPlnvM+PHjBUdHRyEtLU2lfuTIkYK5ubmQm5srCIIgyGQyIT8/X+WYx48fC/b29sJbb71VXHfnzh0BgGBmZiakpKSoHD9nzhwBgMrxgiAIgwYNEqytrVXqGjVqJIwdO1btXHr06CEoFIri+hkzZggSiURIT08XBEEQkpKSBKlUKgQHB6v0N3fuXAGASp9lGTRokABAePz48XOPK9KlSxehS5cuavVjx44VGjVqVPz18z6Xn376SQAgREdHq9R7eXkJ3bp1K/56/vz5gomJiXD9+nWV4z755BNBIpEI8fHxgiAIwrRp0wQzMzNBJpNV6ByKpKamCgCEOXPmqLV1795d8PX1FfLy8orrFAqF0L59e8Hd3b24rmiegoKCVOYpMDBQEIlEwsSJE4vrZDKZ0LBhQ5XPr+hzMjIyEu7fv19cf/r0aQGAMGPGjBceU8eOHdU+k6Lv79IiIiIEAMIff/xRXLd582YBgHDo0CG148v7zMr7Hn52HFlZWYKFhYUwYcIEldcnJSUJ5ubmavVE2sZLwEQ1nCAI2Lp1KwYMGABBEJCWllZcgoKCkJGRgcjISACARCIpvldNoVDg0aNHkMlk8Pf3Lz6mtCFDhhRfUnzWxIkTVb7u1KkTHj58iMzMzP8c8zvvvAORSKTyWrlcjrt37wIADhw4AJlMhkmTJqm8bsqUKf/ZN4DiMZiamlbo+Moq63MZPHgwpFKpShYzJiYGV65cwYgRI4rrNm/ejE6dOsHS0lJlrnr06AG5XI6jR48CACwsLJCTk4Pw8HCNjPnRo0c4ePAghg8fjqysrOL3ffjwIYKCgnDjxg0kJCSovGb8+PEq8xQQEABBEDB+/PjiOolEAn9/f9y+fVvtPYODg9GgQYPir9u2bYuAgADs2rXrhcc0YcIESCQSlTojI6Pi/y8sLMTDhw/h5uYGCwuLMr+vNeHZcYSHhyM9PR2jRo1SmVeJRIKAgAC12yyItI2XgIlquNTUVKSnp2P16tVYvXp1mcekpKQU///vv/+OJUuWIDY2FoWFhcX1jRs3VntdWXVFnJ2dVb62tLQEADx+/BhmZmbPHfPzXgugOBB0c3NTOc7Kyqr42Ocpev+srCxYWFj85/GVVdbnYmNjg+7du2PTpk2YP38+AOXlX6lUWnwpFgBu3LiBqKiocgPrormaNGkSNm3ahD59+qBBgwbo1asXhg8fjt69e7/QmG/evAlBEPDFF1/giy++KPe9Swdsz86Tubk5AMDJyUmtvmjuSnN3d1era9q0KTZt2vTCYyrrs3/y5AkWLFiAtWvXIiEhQeV+xIyMjDL7fVnPjuPGjRsAgG7dupV5/H/9TBBVNwaARDVc0cKBN954A2PHji3zmKJ7wP766y+8+eabCA4OxsyZM2FnZweJRIIFCxbg1q1baq8rnVl51rNZmCKlf/lWxWsrwtPTEwAQHR2NTp06/efxIpGozPeWy+VlHl/e5zJy5EiMGzcOFy9eRMuWLbFp0yZ0794dNjY2xccoFAr07NkTH330UZl9NG3aFABgZ2eHixcvYu/evdi9ezd2796NtWvXYsyYMfj999//85yeVfR98uGHHyIoKKjMY54NuMubp7LqX2TuXmRMZX32U6ZMwdq1azF9+nQEBgbC3NwcIpEII0eOVFtYU1kV/R4oep8///wTDg4OasdLpfx1S7qF35FENZytrS1MTU0hl8vRo0eP5x67ZcsWNGnSBNu2bVO5tPfswgVta9SoEQBlhqh0puXhw4dlZpqeNWDAACxYsAB//fVXhQJAS0vLMi9hFmUiKyo4OBjvvvtu8WXg69evY9asWSrHuLq6Ijs7+z/nCgD09fUxYMAADBgwAAqFApMmTcJPP/2EL774Qi0wKlJ6Xktr0qQJAEBPT69C760JRVmx0q5fv168GEJTY9qyZQvGjh2LJUuWFNfl5eUhPT1d5bjyPhtA+T3w7PEFBQVITEys0BhcXV0BKAP36vp8iV4G7wEkquEkEgmGDBmCrVu3lrnFRuntVYoyN6WzNadPn0ZERETVD7QSunfvDqlUih9//FGlvvRWKs8TGBiI3r1745dffkFYWJhae0FBAT788MPir11dXREbG6vyWV26dAknTpyo1LgtLCwQFBSETZs2YcOGDdDX10dwcLDKMcOHD0dERAT27t2r9vr09HTIZDIAymC3NLFYXJzJzc/PL3cMxsbGxX2VZmdnh65du+Knn34qM6h5dhseTQgLC1O5h+/MmTM4ffo0+vTpo9ExSSQStQzk999/r5a9K9qr79nPBlB+DxTdf1lk9erV5WYAnxUUFAQzMzN8/fXXKrdWFKmKz5foZTADSFRDrFmzBnv27FGrnzZtGr755hscOnQIAQEBmDBhAry8vPDo0SNERkZi//79ePToEQCgf//+2LZtGwYNGoR+/frhzp07WLVqFby8vJCdnV3dp1Que3t7TJs2DUuWLMHAgQPRu3dvXLp0Cbt374aNjc1zMzlF/vjjD/Tq1QuDBw/GgAED0L17d5iYmODGjRvYsGEDEhMTi/cCfOutt7B06VIEBQVh/PjxSElJwapVq+Dt7V2hRS2ljRgxAm+88QZ++OEHBAUFqd2DOHPmTPzzzz/o378/3nzzTfj5+SEnJwfR0dHYsmUL4uLiYGNjg7fffhuPHj1Ct27d0LBhQ9y9exfff/89WrZs+dytS4yMjODl5YWNGzeiadOmsLKygo+PD3x8fLBy5Up07NgRvr6+mDBhApo0aYLk5GRERETg/v37uHTpUqXO9b+4ubmhY8eOeO+995Cfn4/Q0FBYW1urXP7WxJj69++PP//8E+bm5vDy8kJERAT2798Pa2trleNatmwJiUSChQsXIiMjAwYGBujWrRvs7Ozw9ttvY+LEiRgyZAh69uyJS5cuYe/evSqX75/HzMwMP/74I0aPHo3WrVtj5MiRsLW1RXx8PHbu3IkOHTpU+A8YomqhncXHRFRRRdtOlFfu3bsnCIIgJCcnC++//77g5OQk6OnpCQ4ODkL37t2F1atXF/elUCiEr7/+WmjUqJFgYGAgtGrVStixY0e5250sWrRIbTxF28CkpqaWOc47d+4U15W3hcazW9ocOnRIbXsOmUwmfPHFF4KDg4NgZGQkdOvWTbh69apgbW2tsg3J8+Tm5gqLFy8W2rRpI9SrV0/Q19cX3N3dhSlTpgg3b95UOfavv/4SmjRpIujr6wstW7YU9u7dW6nPpUhmZqZgZGQkABD++uuvMo/JysoSZs2aJbi5uQn6+vqCjY2N0L59e2Hx4sVCQUGBIAiCsGXLFqFXr16CnZ2doK+vLzg7OwvvvvuukJiY+J/nffLkScHPz0/Q19dX297k1q1bwpgxYwQHBwdBT09PaNCggdC/f39hy5YtxceUN0/lzf3YsWMFExOTMj+nJUuWCE5OToKBgYHQqVMn4dKlS2rjfZkxCYJyK6Nx48YJNjY2Qr169YSgoCAhNjZW7ftPEATh559/Fpo0aSJIJBKV7zm5XC58/PHHgo2NjWBsbCwEBQUJN2/erPD3cJFDhw4JQUFBgrm5uWBoaCi4uroKb775pnDu3LkyjyfSFpEgaOiuayKiKpaeng5LS0t8+eWXGnuUG2leXFwcGjdujEWLFqlcaici3cF7AIlIJz158kStrugpGWU9to2IiCqO9wASkU7auHEjfvvtN/Tt2xf16tXD8ePH8b///Q+9evVChw4dtD08IqIajQEgEemk5s2bQyqV4ttvv0VmZmbxwpAvv/xS20MjIqrxeA8gERERUR3DewCJiIiI6hgGgERERER1DO8B1CCFQoEHDx7A1NS0QhvVEhEREb0IQRCQlZWF+vXrQyyufD6PAaAGPXjwAE5OTtoeBhEREdUR9+7dQ8OGDSv9OgaAGmRqagpAORlmZmYa718mk+HcuXPw9/eHVMqp0ybOhW7gPOgOzoVu4DzohuqYh8zMTDg5ORXHHpXF7w4NKrrsa2ZmVmUBoImJCczMzPiDrWWcC93AedAdnAvdwHnQDdU5Dy96yxkXgRARERHVMQwAiYiIiOoYBoBEREREdQxvECAiIqol5HI5RCIR8vLyeA+gFslkspeeBz09PUgkEg2PrAS/O4iIiGo4QRCQlJSEx48fw8jICPHx8dyPVosEQYCxsfFLz4OFhQUcHByqZC4ZABIREdVwSUlJSE9Ph52dHUQiEYyNjRkAapEgCMjNzX3heSh6fUpKCgDA0dFR00NkAEhERFSTyeXy4uDPysoKOTk5MDQ0ZACoRYIgQC6Xv9Q8GBkZAQBSUlJgZ2en8cvBXARCRERUgxUWFgIAjI2NtTwS0rSiOS2aY01iAEhERFQLMONX+1TlnDIAJCIiIqpjGAASERFRjdetWzf89ttvFT7+9OnT8PDwQGZmZtUNSodxEQgRERFpxejRo+Hp6YnPPvvspfvasmVL8cKJimjVqhWOHz8OU1PTl37vmogZwBrkSYEcd9JlkCsEbQ+FiIioygmCAJlMVqFjraysKhUA6uvrw9bWts7eO8kAsIa4kZyFwIWH8fmxbHRZfBQHY5O1PSQiItJhcoWAh9n51Voqk6D45JNPcObMGfzxxx/w8PCAh4cHtm3bBg8PDxw5cgSDBw+Gr68vzp8/j/j4eLz33nto3749WrVqhSFDhuDkyZMq/T17CdjDwwObN2/G+++/jxYtWqBXr144cOBAcfuzl4C3bdsGf39/HDt2DH369EGrVq0wfvz44r34AOUTPr788kv4+/sjICAAixYtwscff4xJkya94CxpDy8B1xBrTsQhO1/5V1BSZh7e+u0cBrdqgNkDvGBhrK/l0RERkS7ZGZ2Iuf9cRlp2QbW+r009fcwb6IN+zf974+LPPvsMcXFxcHd3x9SpUwEAN2/eBAAsWbIEH3/8MZycnGBmZoakpCR06dIFM2bMgL6+PsLCwjBx4kTs2bMH9evXL/c9VqxYgZkzZ+Kjjz7Cn3/+iQ8//BCHDh2ChYVFmcfn5eVhzZo1+PbbbyEWizFz5kwsXLgQS5YsAQD8/PPP+Pfff7FgwQI0adIEf/zxB/bv34+AgIBKflLaxwxgDWFvZqBWt+1CAnouO4p9l5O0MCIiItJVs7ZFV3vwBwBp2QX4ZFtUhY41NTWFnp4eDA0NYWtrC1tbW4jFyrBk6tSp6NChA5ydnWFhYQFPT0+MHDkSTZs2hYuLC6ZPnw5nZ2ccPHjwue8xaNAg9O/fH40aNUJISAhyc3MRFVX++AoLCzFv3jz4+vrC29sbr7/+Ok6dOlXc/tdff+Gdd95Bz5494erqitmzZ8PMzKxC56trmAGsId7r6oqL8Y9x+HqaSn1qVj7e+fM8Braoj7kDvWFlwmwgERHVbL6+vipf5+TkYMWKFTh8+DBSU1Mhl8uRl5eHBw8ePLcfDw+P4v83NjZGvXr18OjRo3KPNzIygrOzc/HXdnZ2ePjwIQAgKysLaWlpaN68eXG7RCKBt7c3FApFpc5PFzADWEMYSCX4eXRrTGhhBFND9bj9n0sP0GvZEeyKTtTC6IiISJcsGOwLm3rVnxCwqaePbwY3/+8D/8OzizkWLlyI8PBwhISEYN26dQgLC0PTpk3/8wkZenp6Kl+LRKLnBmtSqervV5FIBEGonQsvmQGsQUQiEbo6G2BsUFvM/ucqDsamqLSnZRdg0rpI9PN1xLxXvWFTT/2yMRER1X79fB3Rx8cR6bnVexnYwlgfEnHFV9Xq6elVKHt24cIFDBo0CD179gSgzAgmJCS88DhfhKmpKWxsbBAdHY02bdoAUD6H+cqVK/D09KzWsWgCA8AayMHMEL+O9ce2yATM+/cyMvNUl8jvjE5ExO2HmDfQG/2bO9bZJe5ERHWZRCyCtY4nAho0aIBLly7h/v37MDY2LjcYbNSoEcLDw9GtWzeIRCKEhoZq5bLrG2+8gZ9++gnOzs5o0qQJ/vrrL2RkZNTI37O8BFxDiUQiDPFriP0hXdCjmb1a+6OcAkz53wVM/Os8UrLytDBCIiKi53vrrbcgkUjQr18/BAYGIjGx7NuYPvnkE5iZmWHkyJGYOHEiOnXqBG9v72oeLTBhwgT0798fH3/8MUaOHAljY2N07NgRBga6HWiXRSTU1ovbWpCZmQlzc3NkZGRUyaogmUyG06dPIyAgQOU+BUEQ8M+lB5jzz2Wk56rfD2FupIe5A70Q3LJBjfwrRReVNxdUvTgPuoNzoT15eXm4c+cOGjduDAMDA+Tk5MDExIT/3lcDhUKBPn36oE+fPpg+fXpxvSAIGpmH0nNraGio0vayMQczgLWASCTCqy0bIHxGF/TxcVBrz3hSiBkbL+Ht388hOZPZQCIioheRkJCATZs24c6dO7h27Rrmzp2LhIQEDBgwQNtDqzQGgLWIrakBfnzDDytfa13mdjAHYlPQc+kRbD53r9auaiIiIqoqYrEY27Ztw9ChQzFq1Chcv34da9euhaurq7aHVmnM09dC/Zo7ol0TK8z55zJ2RKneT5GZJ8PMLVHYGZ2Irwf5or5FxZ+bSEREVJc5Ojpiw4YN2h6GRjADWEtZ1zPAitdaY9UbrcvcC+rwtVQELTuK/52JZzaQiIiojmEAWMv19nFE+IwuCG6p/qzErHwZZm2Lxpg1Z3D/ca4WRkdERETawACwDrA00UfoyFb4ZYw/7EzVl6ofu5GGoGVH8eepu1AomA0kIiKq7RgA1iE9vOwRPqMLhvo1VGvLKZDji7AYvP7LacQ/ZDaQiIioNmMAWMeYG+th8bAWWDuuDRzMDNXaI24/RFDoUfx24g6zgURERLUUA8A66hUPO+wL6YyRbZzU2p4UyjH33ysYufoU7qTlaGF0REREVJUYANZhZoZ6+GZIc/w5vi0alLEdzJm4R+iz/Ch+OXYbcmYDiYhIx3Tr1g2//fZb8dceHh7Yv39/ucffv38fHh4euHr16ku9r6b60SYGgIRO7rbYO6MzXg9wVmvLK1Tgy51XMWzVSdxKzdbC6IiIiCrm+PHj6Ny5s0b7/OSTTzBp0iSVOkdHRxw/fhzu7u4afa/qxACQAAD1DKT4apAv1r8dACcr9WxgZHw6+iw/hp+O3GI2kIiIdJKtrS309dX3vtU0iUQCW1vbGv3cawaApKK9mw32TOuMsYGN1NoKZAos2B2LwT+exI3kLC2MjoiIKkwhB3LSqrco5BUe3saNG9GxY0coFAqV+vfeew+zZs1CfHw83nvvPbRv3x6tWrXCkCFDcPLkyef2+ewl4KioKAQHB8PX1xeDBw9Wu2Qrl8vx6aefolu3bmjevDmCgoLw+++/F7d///332L59Ow4cOAAPDw94eHjg9OnTZV4CPnPmDIYOHQofHx906tQJ3333HWQyWXH76NGj8eWXX+Lbb79F27Zt0aFDB3z//fcV/rw0reaGrlRlTAykmPeqD/r6OuKjrVG4+8y2MJfupaPfd8cxrYc73u3cBFIJ/44gItIpl8OA3TOBnNTqfV8TW6DvIsB70H8e2rt3b8yfPx+nT59GYGAgACA9PR3Hjh3Dzz//jNzcXHTp0gUzZsyAvr4+wsLCMHHiROzZswf166s/3OBZOTk5ePfdd9G+fXssWrQI9+/fx1dffaVyjEKhgIODA5YvXw4LCwtcuHABs2fPhq2tLfr27Yu33noLt27dQnZ2NhYsWAAAMDc3R0pKiko/ycnJeOeddzBo0CAsXLgQt2/fxueff4569eph6tSpxcdt374d48aNw6ZNm3Dx4kV88sknaN26NTp06PCf56Np/M1N5QpoYo090zpjfMfGEIlU2wrkCizaew3BP5zA1cRM7QyQiIjK9u/U6g/+AOV7/jOtQoeam5ujc+fO+Pfff4vr9u7dC0tLSwQEBMDT0xMjR45E06ZN4eLigunTp8PZ2RkHDx6sUP87duyAQqHA119/DXd3d7zyyisYP368yjF6enqYOnUqfH194eTkhIEDB2Lw4MHYs2cPAMDExASGhobQ19eHra1tuZeY169fDwcHB8yePRuurq7o0aMH3n33Xaxdu1Ylw+nh4YHJkyfDxcUFwcHB8PHxQURERIXOR9MYANJzGelL8EV/L2yZGIgmNiZq7TEJmRi44jiW77+BQrmijB6IiIjKNmDAAOzbtw8FBQUAgH///Rf9+vWDWCxGTk4OFi5ciD59+sDf3x+tWrXCrVu38ODBgwr1fevWLXh4eMDAoOQJWK1atVI7bt26dRg8eDDatWuHVq1aYdOmTRV+j9Lv1apVK4hKZUtatmyJ3NxcJCUlFdd5eHiovM7W1hYPHz6s1HtpCgNAqhC/RlbYNa0T3u3cBOJnsoGFcgHL9l/HwBUnEJOQoZ0BEhFRiQHfKS/HVjcTW2Dg8gof3q1bNwiCgMOHDyMxMRHnzp3DgAEDAAALFy5EeHg4QkJCsG7dOoSFhaFp06YoLCzU2HB37tyJhQsXYsiQIVizZg3CwsIwePBgjb5Hac8uGhGJRBAE7Sys5D2AVGGGehLM6tsMvX0c8NGWKNxIUd0W5mpiJoJXnsCkrq6Y3M0d+lL+fUFEpBXewYDXQODJ4+p9XyNLQCyp8OEGBgbo1asX/v33X9y9exeNGzeGt7c3AODChQsYNGgQevbsCUB5T19CQkKF+3Z1dcXff/+N/Pz84izgxYsXVY6JjIxEq1at8PrrrxfXxcfHqxyjp6entlClrPfau3cvBEEozgJevHgRJiYmcHBwqPCYqxN/Q1OltXK2xI6pHfH+K66QPJMOlCkEfHfwJgZ8fxxR99O1M0AiIlIGYiY21VsqEfwVGTBgAA4fPoytW7cWZ/8AoFGjRggPD8fVq1cRGxuLDz744D8DsdL69+8PkUiEzz//HDdv3sSRI0ewZs0alWMaNWqEmJgYHDt2DHfu3EFoaCiio6NVjmnQoAGuXbuG27dv49GjR2VmB1977TUkJSVh/vz5uHXrFg4cOICffvoJb775JsRi3Qy1dHNUpPMMpBLMDPJE2KQO8LA3VWu/lpyFQT+cxLd7YpFXWPFtAYiIqG5p164dzM3NcefOHZUA8JNPPoGZmRlGjhyJiRMnolOnTsXZwYowMTHBqlWrcP36dQQHB2PZsmX48MMPVY4ZOXIkevXqhRkzZmD48OFIT0/Ha6+9pnLM8OHD0bhxYwwZMgSBgYGIjIxUey97e3usXr0aUVFRePXVVzF37ly8+uqreO+99yr5aVQfkaCti8+1UGZmJszNzZGRkQEzMzON9y+TyXD69GkEBATo1OaTBTIFVhy6iR8O3YSsjE2i3ezqYdHQ5mjlbKmF0VUNXZ2LuobzoDs4F9qTl5eHO3fuoHHjxjAwMEBOTg5MTExUFiRQ9RIEQSPzUHpuDQ0NVdpeNubQyQzgypUr4eLiAkNDQwQEBODMmTPPPX7z5s3w9PSEoaEhfH19sWvXrnKPnThxIkQiEUJDQ1Xqr1+/jldffRU2NjYwMzNDx44dcejQIU2cTq2nLxUjpGdT/D25A5o5qn8T3kzJxpAfT+LrXVeZDSQiItIBOhcAbty4ESEhIZgzZw4iIyPRokULBAUFqW26WOTkyZMYNWoUxo8fjwsXLiA4OBjBwcGIiYlRO3b79u04depUmRtI9u/fHzKZDAcPHsT58+fRokUL9O/fX2X5Nj2fd31z/DO5A0J6NoWeRPUvHoUArD56G32XH8O5uEdaGiEREREBOhgALl26FBMmTMC4cePg5eWFVatWwdjYWO3GzSLLly9H7969MXPmTDRr1gzz589H69atsWLFCpXjEhISMGXKFKxbtw56enoqbWlpabhx4wY++eQTNG/eHO7u7vjmm2+Qm5tbZiBJ5dOTiDG1uzv+ndIRvg3M1dpvp+Vg2E8R+L9/r+BJAbOBRERE2qBTN2oUFBTg/PnzmDVrVnGdWCxGjx49yt0pOyIiAiEhISp1QUFBCAsLK/5aoVBg9OjRmDlzZpk3kFpbW8PDwwN//PEHWrduDQMDA/z000+ws7ODn59fuePNz89Hfn5+8deZmconYshkMpXn/2mKXC6HIAiQy3U/cHKzMcbmd9ri5+Nx+O7gTRTKS+4NFARgzYk72H81Cd8M8kHbxlZaHOmLqUlzUZtxHnQH50J7ZDIZBEFQK6Q9mpqHoj7KiiteNs7QqQAwLS0Ncrkc9vb2KvX29vaIjY0t8zVJSUllHl/60u3ChQshlUpVnsdXmkgkwv79+xEcHAxTU1OIxWLY2dlhz549sLQsf+HCggULMG/ePLX6c+fOwcRE/akZL0uhUCArKwtnzpzR2WXlz2ptCHzZqR5WX8zFrXTVXwzxj57gtV/PopeLPkY0M4KhtObcsFwT56I24jzoDs6F9ohEIhgbGyM3NxcymQwKhQI5OTlcBKJFgiBoZB7y8/NRUFCAqKgotWAyJyfnpcaoUwFgVTh//jyWL1+OyMjIcidBEAS8//77sLOzw7Fjx2BkZIRffvkFAwYMwNmzZ+Ho6Fjm62bNmqWSfczMzISTkxP8/f2rZBWwXC7H2bNn0aZNG0gkld9rSVsCALz6igJrT97FsgM3USBT3cdpX1wBrmZI8HWwNwJdrbUzyEqqqXNR23AedAfnQnvy8/Nx9+5dGBoawsjIiKuAdYCmVgGLRCLo6+vD3d1d5ZF2QMlVxxelUwGgjY0NJBIJkpOTVeqTk5PL3UnbwcHhuccfO3YMKSkpcHZ2Lm6Xy+X44IMPEBoairi4OBw8eBA7duzA48ePiwO3H374AeHh4fj999/xySeflPneBgYGahMCKB/1UlXbIIhEIkgkkhq3zYJUCrz3ijt6+Tjioy1ROH9XdXf6e4+fYPTac3gtwBmz+njC1FCvnJ50R02di9qG86A7OBfaIRaLIZFIkJiYCFtbW8hkMkilUgaAWiQIAgoKCl54Hopen5qaColEAiMjI7XM+sv+nOnUT6m+vj78/Pxw4MABBAcHA1BeVjhw4AAmT55c5msCAwNx4MABTJ8+vbguPDwcgYGBAIDRo0ejR48eKq8JCgrC6NGjMW7cOABAbm4uAKh9uGKxuFK7jtN/c7Wth03vBuK3k3FYtDcWeYWqn+/60/E4ci0VCwb7onNTLTzHkoiohhGLxWjcuDESExORkJBQ/OgzBoDaUxTA6evrv9Q8GBsbw9nZuUpuq9CpABAAQkJCMHbsWPj7+6Nt27YIDQ1FTk5OcbA2ZswYNGjQAAsWLAAATJs2DV26dMGSJUvQr18/bNiwAefOncPq1asBKBd4WFurXlbU09ODg4MDPDw8ACiDSEtLS4wdOxazZ8+GkZERfv75Z9y5cwf9+vWrxrOvGyRiEcZ3bIzunnb4aEsUzjyzLUxC+hOMWXMGI/yd8Fn/ZjCrAdlAIiJt0tfXh7OzM/Lz8xEZGQl3d3dmYrVIJpMhKioKbm5uLzwPRdn0qgrkde67Y8SIEUhNTcXs2bORlJSEli1bYs+ePcULPeLj41Ui4fbt22P9+vX4/PPP8emnn8Ld3R1hYWHw8fGp8Hva2Nhgz549+Oyzz9CtWzcUFhbC29sbf//9N1q0aKHxcyQlFxsTbHinHf48dRff7I7Fk2c2id547h6OXFdmA1/xtNPSKImIagaRSASpVApBEGBoaMgAUIuKVmbr8jzo5KgmT55c7iXfw4cPq9UNGzYMw4YNq3D/cXFxanX+/v7Yu3dvhfsgzRCLRRjb3gWveNjh461RiLj9UKU9KTMP4347iyGtG2J2fy+YGzMbSERE9LK4Vp90grO1Mda9HYAvg31goq++gnBr5H30XHYE4VeSy3g1ERERVQYDQNIZYrEIb7RrhL0zOqOTu41ae0pWPib8cQ7TN1zA45wCLYyQiIiodmAASDqnoaUx/nirLb4Z7AtTA/W7FMIuPkDPZUexJyZRC6MjIiKq+RgAkk4SiUQY2dYZe2d0RlcP9e1g0rLzMfGvSLy/PhIPs/PL6IGIiIjKwwCQdFp9CyOsfbMNFg9rATND9WzgzqhE9Fx2FDuiHvDZl0RERBXEAJB0nkgkwlC/hggP6YIezdS3g3mUU4DJ6y/gvb8ikZrFbCAREdF/YQBINYa9mSF+HuOP0BEtYVHGdjB7Lieh57Ij+PtiArOBREREz8EAkGoUkUiE4FYNsG9GZwR526u1p+cWYtqGi5jwx3mkZOZpYYRERES6jwEg1Uh2poZY9YYfvh/VClYm+mrt+68mo8fSI9hy/j6zgURERM9gAEg1lkgkwoAW9RE+ozP6NXdUa8/Mk+HDzZfw1m9nkZjxRAsjJCIi0k0MAKnGs65ngJWvtcaPr7eGTT31bOCha6notfQoNp6NZzaQiIgIDACpFunj64h9M7rg1Zb11dqy8mX4eGs0xqw5g4R0ZgOJiKhuYwBItYqViT6Wj2yF1aP9YGtqoNZ+7EYagpYdxbrTd5kNJCKiOosBINVKvbwdED6jMwa3bqDWlp0vw2fbY/D6L6dx71GuFkZHRESkXQwAqdayMNbH0uEtseZNfziYGaq1n7z1EEGhR/FHRBwUCmYDiYio7mAASLVeN0977J3RGcP9G6q15RbIMfvvyxj18yncfZijhdERERFVPwaAVCeYG+nh26Et8PtbbVHfXD0bePrOIwSFHsWvx+9AzmwgERHVcgwAqU7p0tQWe2d0xmsBzmpteYUKzN9xBcN/isCt1GwtjI6IiKh6MACkOsfUUA9fD/LFurcD0NDSSK39/N3H6Lv8GFYfvcVsIBER1UoMAKnO6uBmg73TO2NMYCO1tnyZAl/visWQH0/iZkqWFkZHRERUdRgAUp1mYiDF/73qg/9NaAdnK2O19ov30tH3u+P44fBNyOQKLYyQiIhI8xgAEgEIdLXGnumdMK6DC0Qi1bYCmQLf7rmGwT+exLUkZgOJiKjmYwBI9JSxvhRzBnhj07uBaGxjotYedT8D/b8/hu8O3EAhs4FERFSDMQAkekYbFyvsntYJ73RuAvEz2cBCuYCl4dcxZNUp3M2Qa2eAREREL4kBIFEZDPUk+LRvM2x5rz1cbdWzgVcSs/DFsSwsP3ATBTJmA4mIqGZhAEj0HK2dLbFzaie819VVLRsoF4DvD93CwBXHEX0/QzsDJCIiegEMAIn+g6GeBB/39sT2SR3Q1L6eWntsUhaCfziBRXtjkS/jZWEiItJ9DACJKqiFkwX+ndIRU7q5QfJMOlCuELDy0C30/+44Lt5L184AiYiIKogBIFElGEgl+KCXB7ZNbAdnM/Ufnxsp2Rj8wwks2H0VeYXMBhIRkW5iAEj0Arzrm2F+J1NM7eYK6TPZQIUA/HTkNvp+dwzn7z7W0giJiIjKxwCQ6AVJxSJM7eaGfyZ3hHd9M7X226k5GLrqJL7ccQVPCpgNJCIi3cEAkOgledU3Q9j7HfBhr6bQl6j+SAkC8MvxO+iz/CjO3HmkpRESERGpYgBIpAF6EjEmd3PHjqkd0aKhuVp73MNcjFgdgbn/XEZugUwLIyQiIirBAJBIg5ram2Lre+3xSR9P6EvVs4G/nYxD79BjiLj1UEsjJCIiYgBIpHFSiRgTu7hi19ROaOVsodYe/ygXo34+hc/DopGdz2wgERFVPwaARFXEza4etkxsj8/7NYOBVP1H7a9T8QhadhTHb6RpYXRERFSXMQAkqkISsQhvd2qCPdM7o42LpVp7QvoTvPHraczaFoXMvEItjJCIiOoiBoBE1aCxjQk2vhOIOQO8YKQnUWv/35l7CFp2FIevpWhhdEREVNcwACSqJmKxCOM6NMae6Z3QromVWntiRh7eXHsWMzdfQsYTZgOJiKjqMAAkqmaNrE2w/u12mP+qN4z11bOBm8/fR69lR3DgarIWRkdERHUBA0AiLRCLRRgd6IK90zujo5uNWntyZj7G/34OMzZeRHpugRZGSEREtRkDQCItcrIyxp/j22LBYF/UM5CqtW+/kIAeS49i7+UkLYyOiIhqKwaARFomEokwqq0z9s7ojM5NbdXa07Lz8e6f5zHlfxfwKIfZQCIienk6GQCuXLkSLi4uMDQ0REBAAM6cOfPc4zdv3gxPT08YGhrC19cXu3btKvfYiRMnQiQSITQ0VK1t586dCAgIgJGRESwtLREcHPySZ0JUcQ0sjPD7uDb4dmhzmBqqZwP/vfQAPZcewc6oRC2MjoiIahOdCwA3btyIkJAQzJkzB5GRkWjRogWCgoKQklL29hgnT57EqFGjMH78eFy4cAHBwcEIDg5GTEyM2rHbt2/HqVOnUL9+fbW2rVu3YvTo0Rg3bhwuXbqEEydO4LXXXtP4+RE9j0gkwnB/J4TP6IJunnZq7Q9zCvD++khMWnceadn5WhghERHVBjoXAC5duhQTJkzAuHHj4OXlhVWrVsHY2Bhr1qwp8/jly5ejd+/emDlzJpo1a4b58+ejdevWWLFihcpxCQkJmDJlCtatWwc9PT2VNplMhmnTpmHRokWYOHEimjZtCi8vLwwfPrzKzpPoeRzMDfHrWH8sG9EC5kZ6au27opPQc+kR/H0xAYIgaGGERERUk6lfZ9KigoICnD9/HrNmzSquE4vF6NGjByIiIsp8TUREBEJCQlTqgoKCEBYWVvy1QqHA6NGjMXPmTHh7e6v1ERkZiYSEBIjFYrRq1QpJSUlo2bIlFi1aBB8fn3LHm5+fj/z8kixMZmYmAGVAKZNp/hmvcrkcgiBALpdrvG+qnOqaiwG+DghwscScf64g/KpqFvxxbiGmbbiIHZce4P8GesHW1KBKx6KL+DOhOzgXuoHzoBuqYx5eNs7QqQAwLS0Ncrkc9vb2KvX29vaIjY0t8zVJSUllHp+UVLJqcuHChZBKpZg6dWqZfdy+fRsAMHfuXCxduhQuLi5YsmQJunbtiuvXr8PKSn3TXgBYsGAB5s2bp1Z/7tw5mJiYlH+iL0ihUCArKwtnzpyBWKxzyds6pbrnYqyrAA8jY/wW/QTZhaoZv/CrKTh5MxWjvY3QsaEeRCJRlY9HV/BnQndwLnQD50E3VMc85OTkvNTrdSoArArnz5/H8uXLERkZWe4vRoVCAQD47LPPMGTIEADA2rVr0bBhQ2zevBnvvvtuma+bNWuWSvYxMzMTTk5O8Pf3h5mZmYbPRPkXxdmzZ9GmTRtIJOobCFP10cZctAPwRq98zP33KvZcVt0kOqdQwKqLubj2xBbzX/WCg5lhtYxJ2/gzoTs4F7qB86AbqmMeiq46viidCgBtbGwgkUiQnKz6yy05ORkODg5lvsbBweG5xx87dgwpKSlwdnYubpfL5fjggw8QGhqKuLg4ODo6AgC8vLyKjzEwMECTJk0QHx9f7ngNDAxgYKB+2U0qlUIqrZqPViQSQSKRVFn/VHHamAsHCylWjfbHruhEfBEWg4fPbAtz6Foq+nx3Al/098Iwv4Z1IhvInwndwbnQDZwH3VDV8/Cy/epUflhfXx9+fn44cOBAcZ1CocCBAwcQGBhY5msCAwNVjgeA8PDw4uNHjx6NqKgoXLx4sbjUr18fM2fOxN69ewEAfn5+MDAwwLVr14r7KCwsRFxcHBo1aqTp0yR6aX19HREe0gUDWqivaM/Kk+GjLVEYu/YsHqQ/0cLoiIhI1+ncnwchISEYO3Ys/P390bZtW4SGhiInJwfjxo0DAIwZMwYNGjTAggULAADTpk1Dly5dsGTJEvTr1w8bNmzAuXPnsHr1agCAtbU1rK2tVd5DT08PDg4O8PDwAACYmZlh4sSJmDNnDpycnNCoUSMsWrQIADBs2LDqOnWiSrEy0cf3o1qhf3NHfLY9Rm1bmKPXU9Fr2VF81q8ZRrZxqhPZQCIiqhidCwBHjBiB1NRUzJ49u3g17p49e4oXesTHx6vcUNm+fXusX78en3/+OT799FO4u7sjLCzsuat3y7Jo0SJIpVKMHj0aT548QUBAAA4ePAhLS0uNnh+RpgV5OyCgsRXm/XsF2y8kqLRl58swa1s0dkYlYsFgXzhZGWtplEREpEtEAjcR05jMzEyYm5sjIyOjShaByGQynD59GgEBAby3Q8t0dS4OXE3Gp9ujkZypvkm0ib4En/TxxOsBjSAW145soK7OQ13EudANnAfdUB3z8LIxh07dA0hEL6d7M3vsm9EFw/waqrXlFMjxxd+X8dovp3D34cttH0BERDUbA0CiWsbcSA+LhrXAb+PawNFcfTuYU7cfoXfoMaw9cQcKBS8AEBHVRQwAiWqprh522DujM0a1dVJre1Iox7x/r2DE6gjcSWM2kIiormEASFSLmRnqYcHg5vhzfFs0sDBSaz8b9xi9Q4/il2O3IWc2kIiozmAASFQHdHK3xd4ZnfFGO2e1tnyZAl/uvIqhq07iZkq2FkZHRETVjQEgUR1Rz0CKL4N9sX5CAJys1LOBF+LT0fe7Y/jx8C3I5AotjJCIiKoLA0CiOqa9qw32Tu+MN9u7qLUVyBRYuCcWQ348ievJWdU/OCIiqhYMAInqIGN9KeYO9MamdwPhYq2+OfSl+xno/91xrDh4A4XMBhIR1ToMAInqsLaNrbB7Wme83bExnn1SXIFcgcX7rmPQDydwNTFTOwMkIqIqwQCQqI4z0pfg8/5e2DIxEE1sTdTaYxIyMeD74wjdfx0FMmYDiYhqAwaARAQA8GtkhV1TO+HdLk3w7JPiZAoBoftvYOCK44hJyNDOAImISGMYABJRMUM9CWb1aYZtkzrA3a6eWntsUhZeXXkCS/ZdQ75MroUREhGRJjAAJCI1LZ0ssGNqR7z/iiskz6QD5QoB3x+8iQHfH8ele+naGSAREb0UBoBEVCYDqQQzgzwRNqkDPB1M1dqvJ2dj0A8n8M3uWOQVMhtIRFSTMAAkoufybWiOfyZ3xLTu7pA+kw1UCMCqI7fQ77tjiIx/rKUREhFRZTEAJKL/pC8VY0bPpvh7cgd4OZqptd9KzcHQH0/iq51XmA0kIqoBGAASUYV51zfH35M74IOeTaEnUc8G/nzsDvouP4ZzcY+0NEIiIqoIBoBEVCl6EjGmdHfHv1M6wreBuVr77bQcDPspAvP+vYzcApkWRkhERP+FASARvRBPBzNsn9QeH/X2gL5E9Z8SQQDWnohD79BjOHX7oZZGSERE5WEASEQvTCoRY1JXN+ya1hEtnSzU2uMf5WLk6lOY/XcMcvKZDSQi0hUMAInopbnZmWLre+3xaV9PGEjV/1n5I+IugkKP4sTNNC2MjoiInsUAkIg0QiIW4Z3Ortg9rRP8G1mqtd9//ASv/3Ias7ZFIyuvUAsjJCKiIgwAiUijmtjWw8Z3AzG7vxcM9dT/ifnfmXgELTuKI9dTtTA6IiICGAASURWQiEV4q2Nj7JnWGW0bW6m1P8jIw9g1Z/DRlkvIeMJsIBFRdWMASERVxsXGBBsmtMP/veoNY32JWvumc/cRtOwoDsYma2F0RER1FwNAIqpSYrEIYwJdsHd6Z7R3tVZrT8rMw1u/nUPIpovIyGU2kIioOjAAJKJq4WRljHVvB+CrQT4wKSMbuC0yAT2WHcG+y0laGB0RUd3CAJCIqo1IJMLrAY2wL6QLOrnbqLWnZuXjnT/PY+r/LuBRToEWRkhEVDcwACSiatfAwgh/vNUW3w5pDlMDqVr7P5ceoNeyI9gdnaiF0RER1X4MAIlIK0QiEYa3ccK+kM54xcNWrT0tuwDvrYvE++sikZadr4UREhHVXgwAiUirHM2NsObNNlgyrAXMDNWzgTujE9Fr2VH8e+kBBEHQwgiJiGofBoBEpHUikQhD/BoiPKQLejSzV2t/lFOAKf+7gIl/nUdKVp4WRkhEVLswACQinWFvZoifx/hh+ciWsDDWU2vfezkZvZYdRdiFBGYDiYheAgNAItIpIpEIr7ZsgPAZXdDb20GtPT23ENM3XsSEP84hOZPZQCKiF8EAkIh0kq2pAX58ozVWvNYKVib6au37r6agz3cncPReAbOBRESVxACQiHSWSCRC/+b1ET6jM/o1d1Rrz8yT4aeLuXj7z0gkZjzRwgiJiGomBoBEpPOs6xlg5WutseqN1rCpp54NPHI9Db2WHsWGM/HMBhIRVQADQCKqMXr7OCJ8RhcEt6yv1paVL8Mn26IxZs0Z3H+cq4XRERHVHAwAiahGsTTRR+jIVvh5jD/sTA3U2o/dSEPQsqP469RdKBTMBhIRlYUBIBHVSD297LF7agd0aqi+XUxOgRyfh8Xg9V9OI/4hs4FERM9iAEhENZa5kR4mtjLBL6Nbw8HMUK094vZDBIUexW8n7jAbSERUCgNAIqrxunrYYl9IZ4xs46TW9qRQjrn/XsHIn08hLi1HC6MjItI9DACJqFYwM9TDN0Oa44+32qKBhZFa+5k7j9B7+VH8cuw25MwGElEdp7MB4MqVK+Hi4gJDQ0MEBATgzJkzzz1+8+bN8PT0hKGhIXx9fbFr165yj504cSJEIhFCQ0PLbM/Pz0fLli0hEolw8eLFlzgLIqpunZvaYs/0Tng9wFmtLa9QgS93XsXwnyJwKzVbC6MjItINOhkAbty4ESEhIZgzZw4iIyPRokULBAUFISUlpczjT548iVGjRmH8+PG4cOECgoODERwcjJiYGLVjt2/fjlOnTqF+ffVtJIp89NFHz20nIt1maqiHrwb5Yv3bAWhoqZ4NPH/3MfouP4afjtxiNpCI6iSdDACXLl2KCRMmYNy4cfDy8sKqVatgbGyMNWvWlHn88uXL0bt3b8ycORPNmjXD/Pnz0bp1a6xYsULluISEBEyZMgXr1q2Dnp76ykEA2L17N/bt24fFixdr/LyIqHq1d7PB3umdMTawkVpbvkyBBbtjMfjHk7iRnKWF0RERaY/OBYAFBQU4f/48evToUVwnFovRo0cPRERElPmaiIgIleMBICgoSOV4hUKB0aNHY+bMmfD29i6zn+TkZEyYMAF//vknjI2NNXA2RKRtJgZSzHvVBxvfaYdG1uo/15fupaPfd8ex8tBNyOQKLYyQiKj6SbU9gGelpaVBLpfD3t5epd7e3h6xsbFlviYpKanM45OSkoq/XrhwIaRSKaZOnVpmH4Ig4M0338TEiRPh7++PuLi4/xxrfn4+8vPzi7/OzMwEAMhkMshksv98fWXJ5XIIggC5XK7xvqlyOBe6oTLz4Odsjh3vt8eS/Tfwe8RdlH5iXIFcgUV7r2F3dCIWDvaBh4NpFY66duLPhG7gPOiG6piHl40zdC4ArArnz5/H8uXLERkZCZFIVOYx33//PbKysjBr1qwK97tgwQLMmzdPrf7cuXMwMTF54fGWR6FQICsrC2fOnIFYrHPJ2zqFc6EbXmQeeloDzu3r4eeLuUjMUc34xTzIxMCVJxHc1BAD3QwgFZf97wWp48+EbuA86IbqmIecnJfb1krnAkAbGxtIJBIkJyer1CcnJ8PBwaHM1zg4ODz3+GPHjiElJQXOziWrAuVyOT744AOEhoYiLi4OBw8eREREBAwMVB8t5e/vj9dffx2///672vvOmjULISEhxV9nZmbCyckJ/v7+MDMzq9yJV4BcLsfZs2fRpk0bSCQSjfdPFce50A0vOg8BAIb3kCP0wE2sORGH0utA5AKw9VoermToYeEQH3g5av5nuTbiz4Ru4DzohuqYh6Krji9K5wJAfX19+Pn54cCBAwgODgagjKQPHDiAyZMnl/mawMBAHDhwANOnTy+uCw8PR2BgIABg9OjRZd4jOHr0aIwbNw4A8N133+HLL78sbn/w4AGCgoKwceNGBAQElPm+BgYGagEjAEilUkilVfPRikQiSCSSKuufKo5zoRtedB7qSaX4vL83+jWvj5lbonAzRXVbmKtJWRj84ylM6uqKyd3coS9lNuW/8GdCN3AedENVz8PL9quT3x0hISEYO3Ys/P390bZtW4SGhiInJ6c4WBszZgwaNGiABQsWAACmTZuGLl26YMmSJejXrx82bNiAc+fOYfXq1QAAa2trWFtbq7yHnp4eHBwc4OHhAQAq2UEAqFevHgDA1dUVDRs2rNLzJSLtaeVsiR1TOuK7Azfw01HVTaJlCgHfHbyJfVeSsWhoC/g2NNfiSImINEcn/6QdMWIEFi9ejNmzZ6Nly5a4ePEi9uzZU7zQIz4+HomJicXHt2/fHuvXr8fq1avRokULbNmyBWFhYfDx8dHWKRBRDWKoJ8FHvT2xfVJ7eNirLwCJTcpC8A8n8O2eWOTLeHM9EdV8OpkBBIDJkyeXe8n38OHDanXDhg3DsGHDKtz/f63ydXFxgSBwg1iiuqR5Qwv8M6UDVh68iR8O34KsVDZQrhDww+FbT7OBzdHK2VKLIyUiejk6mQEkItIWA6kEIb08EPZ+BzQrYwHIzZRsDPnxJBbsuoq8QmYDiahmYgBIRFQGnwbm+GdyB8zo0RR6EtXtYBQC8NPR2+i7/BjO332kpRESEb04BoBEROXQk4gxrYc7/p3SET4N1LOBt9NyMHRVBObvuIInBcwGElHNwQCQiOg/eDqYYfukDpgZ5AF9ieo/m4IA/Hr8DvosP4rTtx9qaYRERJXDAJCIqAL0JGK8/4obdkztiBZOFmrtcQ9zMWL1Kcz5OwY5+Zp/FCQRkSYxACQiqoSm9qbYOjEQs/p4lrk59O8Rd9F7+VGcvJmmhdEREVUMA0AiokqSSsR4t4srdk3thNbOFmrt9x49wWu/nMZn26ORzWwgEekgBoBERC/Iza4eNk9sj8/7NYOhnvo/p+tOxyNo2VEcu5GqhdEREZWPASAR0UuQiEV4u1MT7J7WGW1drNTaE9KfYPSvZ/DJ1ihk5hVqYYREROoYABIRaUBjGxNseKcd5g7wgpGeRK19w9l7CFp2FIeupWhhdEREqhgAEhFpiFgswpsdGmPv9M4IbGKt1p6YkYdxa8/ig02XkJHLbCARaQ8DQCIiDXO2Nsa6twPwZbAPTPTVs4FbI++j57Ij2H8lWQujIyJiAEhEVCXEYhHeaNcIe2d0Rkc3G7X2lKx8vP3HOUzfcAGPcwq0MEIiqssYABIRVaGGlsb4c3xbfDPYF/UMpGrtYRcfoOeyo9gTk6SF0RFRXcUAkIioiolEIoxs64x9MzqjS1Nbtfa07HxM/Os8Jq+PxMPsfC2MkIjqGgaARETVpL6FEX4b1waLhjaHqaF6NnBHVCJ6LTuKnVGJWhgdEdUlDACJiKqRSCTCMH8n7A/pgu6edmrtD3MK8P76SLz313mkZjEbSERVgwEgEZEW2JsZ4pex/lg2ogXMjfTU2nfHJKHXsiP4+2ICBEHQwgiJqDbTWAD4+++/Y+fOncVff/TRR7CwsED79u1x9+5dTb0NEVGtIRKJMKhVQ4SHdEaQt71a++PcQkzbcBET/jiPlMw8LYyQiGorjQWAX3/9NYyMjAAAERERWLlyJb799lvY2NhgxowZmnobIqJax87UEKve8MP3o1rBykRfrX3/1WT0WHoEW8/fZzaQiDRCYwHgvXv34ObmBgAICwvDkCFD8M4772DBggU4duyYpt6GiKhWEolEGNCiPvbN6Ix+vo5q7Zl5Mnyw+RLe+u0skjKYDSSil6OxALBevXp4+PAhAGDfvn3o2bMnAMDQ0BBPnjzR1NsQEdVqNvUMsPL11vjh9dawLiMbeOhaKnouO4JNZ+8xG0hEL0xjAWDPnj3x9ttv4+2338b169fRt29fAMDly5fh4uKiqbchIqoT+vo6IjykCwa2qK/WlpUnw0dbozB27VkkpPMPbCKqPI0FgCtXrkRgYCBSU1OxdetWWFsrH4R+/vx5jBo1SlNvQ0RUZ1iZ6OO7Ua2werQfbE0N1NqPXk9F0LKjWHf6LrOBRFQp6juRviALCwusWLFCrX7evHmaegsiojqpl7cD2ja2wv/tuIJtkQkqbdn5Mny2PQa7ohPxzeDmcLIy1tIoiagm0VgGcM+ePTh+/Hjx1ytXrkTLli3x2muv4fHjx5p6GyKiOsnCWB9Lh7fEmjf9YW+mng08cfMhgkKP4o+IOCgUzAYS0fNpLACcOXMmMjMzAQDR0dH44IMP0LdvX9y5cwchISGaehsiojqtm6c99s3oguH+DdXacgvkmP33ZYz6+RTuPszRwuiIqKbQWAB4584deHl5AQC2bt2K/v374+uvv8bKlSuxe/duTb0NEVGdZ26kh2+HtsBv49qgvrmhWvvpO48QFHoUa47fYTaQiMqksQBQX18fubm5AID9+/ejV69eAAArK6vizCAREWlOVw877J3RGaPaOqu15RUq8H87rmD4TxG4nZqthdERkS7TWADYsWNHhISEYP78+Thz5gz69esHALh+/ToaNlS/VEFERC/P1FAPCwb7Yt3bAWhgYaTWfu7uY/RZfgw/H70NObOBRPSUxgLAFStWQCqVYsuWLfjxxx/RoEEDAMDu3bvRu3dvTb0NERGVoYObDfbO6IzR7RqpteXLFPhq11UMXXUSN1OytDA6ItI1GtsGxtnZGTt27FCrX7ZsmabegoiInqOegRTzg33Q19cRH2+NQvyjXJX2C/Hp6PvdcUzv4Y53OjWBVKKxHAAR1TAaCwABQC6XIywsDFevXgUAeHt7Y+DAgZBIJJp8GyIieo5AV2vsmd4Ji/Zew28n41B6j+gCmQLf7rmGPTFJWDS0BTwcTLU3UCLSGo39+Xfz5k00a9YMY8aMwbZt27Bt2za88cYb8Pb2xq1btzT1NkREVAHG+lLMGeCNTe8GorGNiVp71P0M9P/+GL4/cAOFcoUWRkhE2qSxAHDq1KlwdXXFvXv3EBkZicjISMTHx6Nx48aYOnWqpt6GiIgqoY2LFXZN7YQJnRpDJFJtK5QLWBJ+HcErT+DKA+7WQFSXaCwAPHLkCL799ltYWVkV11lbW+Obb77BkSNHNPU2RERUSUb6EnzWzwtbJraHq616NvDyg0wMXHEcy8Kvo0DGbCBRXaCxANDAwABZWeqry7Kzs6Gvr6+ptyEiohfk18gSO6d2wsQurhA/kw2UKQQsP3ADA1ccR0xChnYGSETVRmMBYP/+/fHOO+/g9OnTEAQBgiDg1KlTmDhxIgYOHKiptyEiopdgqCfBJ308sX1SBzS1r6fWHpuUhVdXnsDivdeQL5NrYYREVB00FgB+9913cHV1RWBgIAwNDWFoaIj27dvDzc0NoaGhmnobIiLSgBZOFvh3SkdM6eYGyTPpQLlCwIpDNzHg++O4dC9dOwMkoiqlsW1gLCws8Pfff+PmzZvF28A0a9YMbm5umnoLIiLSIAOpBB/08kCQtwM+3HwJsUmqt/FcT87GoB9O4J3Orpjewx2GetzSi6i2eKkAMCQk5Lnthw4dKv7/pUuXvsxbERFRFfFpYI5/JnfED4dvYsXBm5CVemScQgBWHbmF8CtJ+HZoC/g1stTiSIlIU14qALxw4UKFjhM9u/cAERHpFH2pGNN7NEUvLwfM3HIJl5/ZFuZWag6GrjqJ8R0a44NeHjDSZzaQqCZ7qQCwdIaPiIhqPq/6Zgh7vwN+OnILyw/cQKG8JBsoCMAvx+9g/9VkfDu0Bdo2tnpOT0Sky/ggSCIiUqEnEWNyN3fsmNIJzRuaq7XHPczFiNURmPvPZeQWyLQwQiJ6WTobAK5cuRIuLi4wNDREQEAAzpw589zjN2/eDE9PTxgaGsLX1xe7du0q99iJEydCJBKprE6Oi4vD+PHj0bhxYxgZGcHV1RVz5sxBQUGBpk6JiKhG8XAwxbb32uPj3p7Ql6r+uhAE4LeTcegdegwRtx5qaYRE9KJ0MgDcuHEjQkJCMGfOHERGRqJFixYICgpCSkpKmcefPHkSo0aNwvjx43HhwgUEBwcjODgYMTExasdu374dp06dQv369VXqY2NjoVAo8NNPP+Hy5ctYtmwZVq1ahU8//bRKzpGIqCaQSsR4r6srdk3tiFbOFmrt8Y9yMernU5j9zxU8kQnqHRCRTtLJAHDp0qWYMGECxo0bBy8vL6xatQrGxsZYs2ZNmccvX74cvXv3xsyZM9GsWTPMnz8frVu3xooVK1SOS0hIwJQpU7Bu3Tro6emptPXu3Rtr165Fr1690KRJEwwcOBAffvghtm3bVmXnSURUU7jZmWLLxPb4vF8zGEjVf3WsP3MPnxzOxImbzAYS1QQa2wdQUwoKCnD+/HnMmjWruE4sFqNHjx6IiIgo8zURERFqW9IEBQUhLCys+GuFQoHRo0dj5syZ8Pb2rtBYMjIyVJ5t/Kz8/Hzk5+cXf52ZqVw1J5PJIJNp/r4YuVwOQRAgl3N3fm3jXOgGzkP1ezPQGV3crTFrewzO3U1XaUt7ImDsb+cwwr8hPuntAVNDnfsVU+vxZ0I3VMc8vGycoXM/nWlpaZDL5bC3t1ept7e3R2xsbJmvSUpKKvP4pKSk4q8XLlwIqVSKqVOnVmgcN2/exPfff4/FixeXe8yCBQswb948tfpz587BxET9gesvS6FQICsrC2fOnIFYrJPJ2zqDc6EbOA/aM81XwL56Rth49QkKFKptG8/dx/6YBLzdwhjN7fTK7oCqBH8mdEN1zENOTs5LvV7nAsCqcP78eSxfvhyRkZEV2pMwISEBvXv3xrBhwzBhwoRyj5s1a5ZK5jEzMxNOTk7w9/eHmZmZRsZemlwux9mzZ9GmTRtIJNyDS5s4F7qB86Bdge2ANx/mYtb2GJyJe6zS9jBPwMLTORjaugE+7eMBMyMGgtWBPxO6oTrmoeiq44vSuQDQxsYGEokEycnJKvXJyclwcHAo8zUODg7PPf7YsWNISUmBs7NzcbtcLscHH3yA0NBQxMXFFdc/ePAAr7zyCtq3b4/Vq1c/d6wGBgYwMDBQq5dKpZBKq+ajFYlEkEgkVdY/VRznQjdwHrTL1d4MG94JxB8Rd7Bg11XkP3PFa0tkAo7dTMPXg3zRvZl92Z2QRvFnQjdU9Ty8bL86lx/W19eHn58fDhw4UFynUChw4MABBAYGlvmawMBAleMBIDw8vPj40aNHIyoqChcvXiwu9evXx8yZM7F3797i1yQkJKBr167w8/PD2rVrmT4nIqoAsViENwKcsbCrKdo3Ub9vOjkzH+N/P4eQjReRnsuttYh0gU7+eRASEoKxY8fC398fbdu2RWhoKHJycjBu3DgAwJgxY9CgQQMsWLAAADBt2jR06dIFS5YsQb9+/bBhwwacO3euOINnbW0Na2trlffQ09ODg4MDPDw8AJQEf40aNcLixYuRmppafGx5mUciIiphayzB7+P8sTkyEV/vuorsfNWb1LddUGYDvwz2QZA3/10l0iadDABHjBiB1NRUzJ49G0lJSWjZsiX27NlTvNAjPj5eJTvXvn17rF+/Hp9//jk+/fRTuLu7IywsDD4+PhV+z/DwcNy8eRM3b95Ew4YNVdoEgXtbERFVhEgkwmsBzujiYYtZ26Jx9HqqSntqVj7e/fM8Braoj7kDvWFloq+lkRLVbSKB0Y3GZGZmwtzcHBkZGVWyCEQmk+H06dMICAjgvR1axrnQDZwH3VHWXAiCgM3n7mP+zivIylPfssKmnj7+71Uf9PV1rO7h1lr8mdAN1TEPLxtz8CY3IiKqEiKRCMPbOCF8Rhd087RTa0/LLsCkdZGYtO480rLzy+iBiKoKA0AiIqpSDuaG+HWsP5YObwHzMraD2RWdhJ5Lj+CfSw94yw1RNWEASEREVU4kEmFw64YIn9EZPb3Ut4N5nFuIqf+7gHf/PI+UrDwtjJCobmEASERE1cbOzBCrR/th+ciWsDRWzwbuu5KMnkuPYlvkfWYDiaoQA0AiIqpWIpEIr7ZsgH0zuqCPj/p2MBlPChGy6RLe/v0ckjKYDSSqCgwAiYhIK2xNDfDjG35Y+VprWJexHcyB2BT0XHYEm87dYzaQSMMYABIRkVb1a+6IfTM6Y0CL+mptWXkyfLQlCm+uPYsH6U+0MDqi2okBIBERaZ11PQN8P6oVVr3hB5t66s9YP3I9Fb2WHcX/zsQzG0ikAQwAiYhIZ/T2cUD4jM4Y1KqBWlt2vgyztkVj9K9ncO9RrhZGR1R7MAAkIiKdYmmij2UjWuLXsf6wN1PPBh6/mYbeoUfx56m7UCiYDSR6EQwAiYhIJ3VvZo9907tgqF9DtbacAjm+CIvBa7+cQvxDZgOJKosBIBER6SxzYz0sHtYCa8e1gaO5oVr7qduPEBR6FL+duMNsIFElMAAkIiKd94qHHfbO6IyRbZzU2p4UyjH33ysYufoU7qTlaGF0RDUPA0AiIqoRzAz18M2Q5vhzfFs0sDBSaz8T9wi9Q4/il2O3IWc2kOi5GAASEVGN0sndFntndMYb7ZzV2vJlCny58yqGrTqJmynZWhgdUc3AAJCIiGqcegZSfBnsi/UTAuBkpZ4NjIxPR9/vjmHVkVuQyRVaGCGRbmMASERENVZ7VxvsmdYZb7Z3UWsrkCnwze5YDPnxJK4nZ1X/4Ih0GANAIiKq0UwMpJg70Bsb32mHRtbGau2X7meg/3fHsfLQTRQyG0gEgAEgERHVEgFNrLFnWmeM79gYIpFqW4FcgUV7r2HQDydwNTFTOwMk0iEMAImIqNYw0pfgi/5e2DIxEE1sTdTaYxIyMXDFcYTuv44CGbOBVHcxACQiolrHr5EVdk3thHe7NIH4mWxgoVxA6P4beHXlCcQkZGhngERaxgCQiIhqJUM9CWb1aYat77WHu109tfariZkIXnkCS/ZdQ75MroUREmkPA0AiIqrVWjlbYsfUjnj/FVdInkkHyhQCvj94EwO+P46o++naGSCRFjAAJCKiWs9AKsHMIE+ETeoATwdTtfbrydkY9MNJLNwTi7xCZgOp9mMASEREdYZvQ3P8M7kjpnZ3h/SZbKBcIeDHw7fQ//vjiIx/rKURElUPBoBERFSn6EvFCOnZFH9P7gAvRzO19psp2Rj640l8vesqs4FUazEAJCKiOsm7vjn+ntwBIT2bQk+img1UCMDqo7fRd/kxnIt7pKURElUdBoBERFRn6UnEmNrdHf9O6QjfBuZq7bfTcjDspwjM+/cycgtkWhghUdVgAEhERHWep4MZtk9qj496e0BfovqrURCAtSfi0Gf5MZy6/VBLIyTSLAaAREREAKQSMSZ1dcPOqR3R0slCrf3uw1yMXH0Ks/+OQU4+s4FUszEAJCIiKsXd3hRb32uPT/t6Ql+q/mvyj4i7CAo9ihM307QwOiLNYABIRET0DIlYhHc6u2L3tE7wa2Sp1n7/8RO8/stpfLo9Gll5hVoYIdHLYQBIRERUDlfbetj0biC+6O8FQz31X5nrT8cjaNlRHL2eqoXREb04BoBERETPIRGLML5jY+yZ1hltG1uptT/IyMOYNWfw8ZYoZDIbSDUEA0AiIqIKcLExwYYJ7TBvoDeM9SVq7RvP3UOvpUdxKDZFC6MjqhwGgERERBUkFoswtr0L9k7vjMAm1mrtSZl5GPfbWYRsuoiMXGYDSXcxACQiIqokJytjrHs7AF8G+8CkjGzgtsgE9Fh2BOFXkrUwOqL/xgCQiIjoBYjFIrzRrhH2zuiMTu42au2pWfmY8Mc5TNtwAY9zCrQwQqLyMQAkIiJ6CQ0tjfHHW22xcIgvTA2kau1/X3yAnsuOYE9MohZGR1Q2BoBEREQvSSQSYUQbZ+wL6YyuHrZq7WnZBZj4VyTeXx+Jh9n5WhghkSoGgERERBriaG6EtW+2weJhLWBmqJ4N3BmViJ7LjuLfSw8gCIIWRkikxACQiIhIg0QiEYb6NUR4SBf0aGav1v4opwBT/ncB7/0VidQsZgNJOxgAEhERVQF7M0P8PMYPy0e2hIWxnlr7nstJ6LnsCMIuJDAbSNVOZwPAlStXwsXFBYaGhggICMCZM2eee/zmzZvh6ekJQ0ND+Pr6YteuXeUeO3HiRIhEIoSGhqrUP3r0CK+//jrMzMxgYWGB8ePHIzs7WxOnQ0REdZBIJMKrLRtg34zO6O3toNaenluI6RsvYsIf55GSmaeFEVJdpZMB4MaNGxESEoI5c+YgMjISLVq0QFBQEFJSyt5d/eTJkxg1ahTGjx+PCxcuIDg4GMHBwYiJiVE7dvv27Th16hTq16+v1vb666/j8uXLCA8Px44dO3D06FG88847Gj8/IiKqW+xMDfHjG62x4rVWsDLRV2vffzUZPZYewZbz95kNpGqhkwHg0qVLMWHCBIwbNw5eXl5YtWoVjI2NsWbNmjKPX758OXr37o2ZM2eiWbNmmD9/Plq3bo0VK1aoHJeQkIApU6Zg3bp10NNTTcdfvXoVe/bswS+//IKAgAB07NgR33//PTZs2IAHDx5U2bkSEVHdIBKJ0L95fYTP6Ix+zR3V2jPzZPhw8yWM++0sEjOeaGGEVJeoL1HSsoKCApw/fx6zZs0qrhOLxejRowciIiLKfE1ERARCQkJU6oKCghAWFlb8tUKhwOjRozFz5kx4e3uX2YeFhQX8/f2L63r06AGxWIzTp09j0KBBaq/Jz89Hfn7JDbyZmZkAAJlMBplMVrETrgS5XA5BECCXyzXeN1UO50I3cB50B+ei4swNJVg+vDn6ettj9j9X8PCZTaIPX0tFz6VH8WkfDwzzawCRSFThvjkPuqE65uFl4wydCwDT0tIgl8thb6+6csre3h6xsbFlviYpKanM45OSkoq/XrhwIaRSKaZOnVpuH3Z2dip1UqkUVlZWKv2UtmDBAsybN0+t/ty5czAxMSnzNS9DoVAgKysLZ86cgVisk8nbOoNzoRs4D7qDc1F5FgC+6miIP2IEnExQfW5wdr4Mn4Zdxv9OXMPbzY1hY1yxz5TzoBuqYx5ycnJe6vU6FwBWhfPnz2P58uWIjIys1F9S/2XWrFkqmcfMzEw4OTnB398fZmZmGnufInK5HGfPnkWbNm0gkag/e5KqD+dCN3AedAfn4sX16ATsv5qCL/6+jNRs1WxgdKoMnx7Lwce9PTDSvyHE4uf/DuM86IbqmIeiq44vSucCQBsbG0gkEiQnqz5AOzk5GQ4O6iuoAMDBweG5xx87dgwpKSlwdnYubpfL5fjggw8QGhqKuLg4ODg4qC0ykclkePToUbnva2BgAAMDA7V6qVQKqbRqPlqRSASJRFJl/VPFcS50A+dBd3AuXlxv3/oIdLXF/+24gq2R91XacgrkmP3PFeyOSca3Q5vDycr4uX1xHnRDVc/Dy/arc/lhfX19+Pn54cCBA8V1CoUCBw4cQGBgYJmvCQwMVDkeAMLDw4uPHz16NKKionDx4sXiUr9+fcycORN79+4t7iM9PR3nz58v7uPgwYNQKBQICAjQ9GkSERGpMDfWw5LhLbD2zTZwMDNUa4+4/RBBoUfx+8k4KBRcKUwvRyf/PAgJCcHYsWPh7++Ptm3bIjQ0FDk5ORg3bhwAYMyYMWjQoAEWLFgAAJg2bRq6dOmCJUuWoF+/ftiwYQPOnTuH1atXAwCsra1hbW2t8h56enpwcHCAh4cHAKBZs2bo3bs3JkyYgFWrVqGwsBCTJ0/GyJEjy9wyhoiIqCq84mmHfSGd8dWOq9h47p5KW26BHHP+uYyd0Yn4dkhzuNho/n5zqht0LgMIACNGjMDixYsxe/ZstGzZEhcvXsSePXuKF3rEx8cjMTGx+Pj27dtj/fr1WL16NVq0aIEtW7YgLCwMPj4+lXrfdevWwdPTE927d0ffvn3RsWPH4iCSiIioupgZ6mHh0Ob4/a22qG+ung08c+cRei8/il+P34Gc2UB6ATqZAQSAyZMnY/LkyWW2HT58WK1u2LBhGDZsWIX7j4uLU6uzsrLC+vXrK9yHVnCDUCKiOqNLU1vsndEZC3bHYv3peJW2vEIF5u+4gl3Rifh2aHO42tbT0iipJtLZAJCekZ0KyYbXEZhwDqLT9oCDL+Dgo/yvvS9g1QTgkn8iolrH1FAPXw/yRT9fR3y8NQr3H6tuEn3+7mP0XX4MIT2b4s1A53J6IVLFALCmOLkcovunIQKArAfKcmNvSbueCWDv9TQg9AEcmiu/1uf9IUREtUEHNxvsnd4Z3+6Jxe8Rd1Xa8mUKLNgdi13RiXjNTQEuXaT/wgCwppAXPr+9MAe4f1ZZiokAa9enAaFvSTF1BDS4HyIREVUPEwMp5r3qgz5Ps4F3H+aqtF+6n4HLD4AUvdt4r6sbpBJeGaKyMQCsKQLfh3A3AqKkS5V4kQA8vKksV8JKqo2snl4+bl4SHNo0BaTqDygnIiLd066JNXZP64TFe69j7ck7KreHyxTAkvAbCL+agkVDW8DDwVR7AyWdxQCwprBwhnz8fkQd3IIW9mJIUq8ASdHKkpNaub6ePALuHFWWImI9wM5TeT9h0f2F9j6AsZVmz4OIiDTCWF+K2QO80NfXAR9ticLtNNVHg0Xdz0D/749hSjd3vNfVFXrMBlIpDABrEpEYT+o1guATAJTeATwrGUh+GgwmxSj/+/AGICgq3reisCSgLJ1kNGv4zIITH8CyMRecEBHpCH8XK+ya1glLw6/jl2O3UXpXmEK5gKXh17EnJgmLhjWHd31z7Q2UdAoDwNrA1F5Z3HqU1BU+AVKulASEyTHK/y/IqlzfmfeV5frukjr9eoC9t+q9hXZegP7zH09ERERVw1BPgk/7NkOvZraYuu4sHmSrJgCuJGbi1RUnMOkVN0x+xQ36Uv4RX9cxAKyt9IyABn7KUkShANLvqgaESdFARnz5/ZSlIBu4d1pZiojEgJVrqWzh0/sLTR244ISIqJq0dLLAV51NcSrbCj8fu6OSDZQpBHx34Ab2XU7CoqEt4NuQ2cC6jAFgXSIWA1aNlcVrYEn9k8dA8uVS2cJoIOUqIC+oeN+CQnnZ+eEN4PK2knpjm5L7CR2aK//fpikg0dPceRERUTF9iQgzezVFv+b1MXNzFK4lq175iU3KQvAPJ/Bu5yaY1sMdBlKJlkZK2sQAkAAjS8Clo7IUkRcCaTdKAsKi4DA3rXJ956YBtw8rSxGJPmDrWRIQFt1baGShgZMhIiIAaN7QAv9M6YAVB2/ih8O3VB4ZJ1cI+OHwLYRfScaiYS3Q0slCewMlrWAASGWT6Ck3krb3AjBCWScIQHZyyWKRokvJaTcAVOIRdfICIClKWUozd1YNCB18AAsXLjghInpBBlIJPujlgSBvB8zcEoWriZkq7TdSsjH4hxOY0KkJZvRsCkM9ZgPrCgaAVHEikfKePlMHwL1nSX1BrvKScVLU03sLo5WXlAuyK9d/RryyXNtVUqdvqlxwUnolsp2X8h5HIiKqEJ8G5vj7/Q748fAtfH/wBmSlsoEKAfjp6G2EX03GoqHN4deI23/VBQwA6eXpGwMN/ZSliEIBPL6jutgkOQbIuFe5vguygHunlKWISAxYu6s+C9nBV7kSmoiIyqQvFWNaD3f08rbHzC2XEJOgmg28nZqDoasi8FaHxviwlweM9JkNrM0YAFLVEIuVj6GzdgW8Xi2pz330dMFJ0UrkKCAlVrkPYUUJCiDtmrLEbC2pN7FVfRayg48yUJTw25yIqEgzRzNsn9QBq4/exvL9N1AgL9kyRhCAX4/fwYGryVg4pDkCmlhrcaRUlfibkaqXsRXQuJOyFJEVAGnXSy4fF5UnjyrXd04qcOugshSRGAB2zZ559J0PYMjtD4io7tKTiPH+K27o6WWPmZsv4dL9DJX2uIe5GLH6FN5s74KPenvAWJ/hQm3DGSXtk+o/DdB8gBYjlXWCAGQlPr18XOrewoe3ULkFJ/lA4kVlKc3CWfVZyA4+gEUj7llIRHVKU3tTbH2vPX45fgdLw6+jQKa6gfRvJ+NwIFaZDWzvaqOlUVJVYABIukkkAszqK0vTXiX1BTlA8hXVR98lXwYKc8rvqyzp8coSu6OkzsBcdcGJvc/TBSeGmjknIiIdJJWIMbGLK3o0s8dHWy4hMj5dpf3eoyd47efTeKOdMz7p0wz1DBg61AacRapZ9E0ApzbKUqRowUnprWmSooHMhMr1nZ8BxJ9UliIiCWDjXurewqcLTgy5So6Iahc3u3rYPLE91p64g0V7ryH/mWzgX6ficSg2Fd8M8UUnd1stjZI0hQEg1XylF5x4B5fU5z5SDQiTYoDUyi44kStfkxoLRG8urpbUs4eXQUOIczoAji2UQaGVKxecEFGNJhGL8HanJujezB4fb4nCmTjVe7ET0p9g9K9nMLKNEz7t1wxmhnyqU03F31ZUexlbAU26KEsRWYFy9XBRQFh0f+GTx5XqWpSdDMvsZODk+ZJKqaHykrGDT8nWNPbegKGZhk6IiKh6NLYxwYZ32uGPiDgs3HMNTwrlKu0bzt7Dkeup+HqwL17xsNPSKOllMACkukWqX3IZt4ggKC8XJ8Wo3lv46DYqteBElgc8iFSW0ixdVLemcfAFzJ244ISIdJpYLMKbHRqjm6c9Ptp6Caduq2YDEzPyMG7tWQz1a4gv+nnB3JjZwJqEASCRSASYN1QWj94l9fnZQMqVp4+te3oZOeUKUJhbuf4fxylL6QUnhuZPs4SlHn1n1wyQGmjijIiINMbZ2hjr326HdWfi8c2uq8gpUM0Gbjl/H0evp+LrQb7o4cUN+WsKBoBE5TGoBzi1VZYiCjnw6DbkDy7hwYV9aCB5BHHyZSDrQeX6zssA7h5XliJiKWDTVHVrGofmgAm3XiAi7RKLRRjdrhG6NrXFrG3ROH4zTaU9JSsfb/9xDoNaNcCcAV6wMNbX0kipohgAElWGWLkqWLBojPgsRzgGBEAslQI5D1UvHydFK+81VMgq3rdCpswwplwBojeV1NdzUH0Wsr2vcsGLmI9pIqLq5WRljD/Ht8WGs/fw1c6ryM5X/Tdu+4UEHLuRhi+DfdDbx0FLo6SKYABIpAkm1kCTrspSRJavXD1c+lnISVHK7F9lZCcBN5OAm+EldVIjwN5L9dF39l6AgakmzoaIqFwikQij2jqjy9Ns4JHrqSrtadn5mPjXefRv7oh5A71hXY+3tugiBoBEVUVqoNwixrFFSZ0gABn31R979/hO5fqWPQESzitLaZaNSxa5FAWH5g254ISINK6+hRF+G9cGW87fx//tuIKsPNVs4I6oRETceoj/e9UH/Zo7ammUVB4GgETVSSQCLJyUxaNPSX1+lvKJJqU3s06+ogz0KuPxHWW5+k9JnaGFakDo4AvYenDBCRG9NJFIhGH+TujkbovPtkfjQGyKSvvDnAK8vz4SO6Ic8H+v+sDWlP/u6AoGgES6wMAUcG6nLEUUcuWzj4ufhfw0a5idVLm+89KBuGPKUkQsBWw81O8tNLHWyOkQUd3iYG6IX8b6I+xiAub+cwUZT1Q33N8dk4RTtx9i7kBvDGxRHyJeldA6BoBEukosAWybKovv0JL67NSnC05K3VuYek351JKKUsiAlMvKElWq3rS+6tY0Ds0Bq8ZccEJE/0kkEmFQq4bo4GaDz7fHYN+VZJX2x7mFmLbhInZEJeKrYB/YmfE569rEAJCopqlnC9TrBrh2K6krzHu64OSZR9/lV3LBSdYDZbmxr6ROz1j5RJPSz0K281Juk0NE9Aw7U0P8NNoP/0YlYs7fMXicq5oNDL+SjDN3HmHOAC8MatWA2UAtYQBIVBvoGQL1WypLEUEAMu6pP/bucVzl+i7MBe6fVZZiIsCqierlYwdfwKw+F5wQEUQiEQa2qI/2rtaY8/dl7IxOVGnPeFKIkE2XsCMqEV8P8oWDObOB1Y0BIFFtJRIBFs7K4tmvpD4vs2TBSdHehSlXlY+yqzABeHRLWa78XVJtZKkaEDr4KO81lHJTWKK6yKaeAVa+3hr9ohPxRVgMHuYUqLQfjE1Bz2VH8EU/Lwzzb8hsYDViAEhU1xiaAY0ClaWIXAY8vFmyV2FSjPL/s5PL76csTx4Dd44qSxGxHmDr+cyCEx/A2Eoz50NEOq+vryPaNbHG3H8u459Lqk9OysqT4aOtUdgRnYgFg33RwMJIS6OsWxgAEhEgkQJ2nsqisuAkRXVrmqRoIO1GJRecFCozjcnRwKVS9WYNVLemcfBV7mMoFmvstIhId1iZ6OO7Ua3Qr7kjPtseg7TsfJX2o9dTEbTsKD7t2wyj2joxG1jFGAASUfnq2QFu3ZWlSOET5SXj0otNkmOA/MzK9Z2ZoCzX95TU6ZkoF5yUfhayXTNA30Qz50NEWhfk7YCAxlb4v3+vYNuFBJW27HwZPt0ejZ3RD/DN4OZwsjLW0ihrPwaARFQ5ekZAg9bKUkQQgPS76o+9S4+vXN+FOcD9M8pSTARYuykDwqKtaRx8AFM+WYCoprIw1sfSES3Rr7kjPt0ejeRM1WzgiZsPERR6FJ/08cQbAY0gFjMbqGkMAIno5YlEgKWLsjTrX1L/JF254KT0vYUpVwF5fjkdlUUAHt5QlsvbS6qNrSG284aLYAORURzQoCVg0xSQ6GnijIioGnRvZo99Llb4cscVbD5/X6Utt0CO2X9fxs6oRHw7tDkaWfNKgCYxACSiqmNkAbh0UJYicpkymCv9LOTkGCAntdxuypT7EOK4o2gAAHe3Kesk+qUWnBTdX+ijXJ1MRDrJ3EgPi4a1QL/mjpi1LRqJGao7Epy+8wi9Q49hZpAH3mzvwmyghjAAJKLqJZEq7+uzawY0H15Sn5WsujVNUowyUBQUFe9bXvA00xilWm/uVGqxydP/WrhwwQmRDunqYYd9Mzrj612x+N8Z1dtHnhTK8X87rmB3TCK+HdoCjW2YDXxZDACJSDeY2iuLe4+SuoJcIPVqqc2so5WXlAuyKtd3xj1lub67pE6/XkmGsGjvQrtmgD5vOifSFlNDPSwY7It+vo74eGsUEtKfqLSfjXuM3qFH8WEvD7zVsTEkzAa+MAaARKS79I2BBn7KUkSheLrgJBqKxCg8jj0Gq4IEiDLuVa7vgmzg3illKSISP11w8sz2NPXs+YQTomrU0d0Ge2d0xsLdsfjz1F2VtnyZAl/tuopdMYlYNLQ53OxMtTTKmo0BIBHVLGIxYNUYsGoMRdO+iDV8BQEBAZAWZpU84aTo0XepscrLwhUlKIC068oSs7Wk3thGdWsaex/Axp0LToiqUD0DKeYH+6CPrwM+2RqN+Ee5Ku0X4tPR97vjmNGjKSZ0agyphLd0VAYDQCKqHYwsAZeOylJEXqgM5ko/CzkpGsh9WLm+c9OA24eUpYjEQLlxdulH39l7Kxe+EJHGtHe1wZ7pnfDtnmv4PSIOglDSViBTYOGeWOx5em+ghwOzgRWlk+HyypUr4eLiAkNDQwQEBODMmTPPPX7z5s3w9PSEoaEhfH19sWvXLpX2uXPnwtPTEyYmJrC0tESPHj1w+vRplWOuX7+OV199FTY2NjAzM0PHjh1x6NAhEFENJtFTBmUtRgBBXwFj/gZm3gJCYoHXtwDdZwPegwFrdwCVvMQrzwcSLwEX/gL2fAz81hdY2AgI9QX+9xpwaAFw9V/g0R2o/MYiokoz1pdi7kBvbHo3sMwFIJfuZ6D/98ew4uANFMorsXCsDtO5DODGjRsREhKCVatWISAgAKGhoQgKCsK1a9dgZ2endvzJkycxatQoLFiwAP3798f69esRHByMyMhI+Pj4AACaNm2KFStWoEmTJnjy5AmWLVuGXr164ebNm7C1tQUA9O/fH+7u7jh48CCMjIwQGhqK/v3749atW3BwcKjWz4CIqpBIBJg5Kot7z5L6ghzlHoUqj76LUW5OXRnp8cpybWdJnYFZyRNOiu4ttGum3FSbiCqsjYsVdk3thCX7ruHXE3dU/rYqlAtYvO86dsckYdHQFvCqb6a9gdYAIkHQrT9NAwIC0KZNG6xYsQIAoFAo4OTkhClTpuCTTz5RO37EiBHIycnBjh07iuvatWuHli1bYtWqVWW+R2ZmJszNzbF//350794daWlpsLW1xdGjR9GpUycAQFZWFszMzBAeHo4ePXqU2U95/WZkZMDMTPPfeDKZDKdPn1be7yTVudi9TuFc6IYqnweFAnh8RzUgTIoGMu//92v/i0is3LhaZXua5srH79VA/JnQDXVpHs7ffYyPtlzCrVT1P9KkYhHef8UN77/iBn1p9V/srI55eNmYQ6e+OwoKCnD+/HnMmjWruE4sFqNHjx6IiIgo8zUREREICQlRqQsKCkJYWFi577F69WqYm5ujRYsWAABra2t4eHjgjz/+QOvWrWFgYICffvoJdnZ28PPzK7MfIqoDxGLA2lVZvINL6nMfqQaEydFASiygKKx434JCuUglNRaI2VJSb2JXEhAW3Vto7abcP5GIivk1ssTOqZ0Quv8GVh+9BUWpdJZMIWD5gRvYezkJi4e1gE8Dc+0NVEfp1L8oaWlpkMvlsLe3V6m3t7dHbGxsma9JSkoq8/ikpCSVuh07dmDkyJHIzc2Fo6MjwsPDYWNjAwAQiUTYv38/goODYWpqCrFYDDs7O+zZsweWluU/QSA/Px/5+SWPtMrMzASgjPxlMlnFT7yC5HI5BEGAXC7XeN9UOZwL3aC1edA3A5zaK0vxYAqAtBsQJUdD9PTxd6LkGIiePKpc3zkpwK0DyvKUIDUEbD0h2PsUF9h5A4a6c4mLPxO6oa7Ng1QEfNjTDT2b2eDjbTG4maKaDYxNysKrK0/g3U6N8f4rrjCopmxgdczDy8YZOhUAVqVXXnkFFy9eRFpaGn7++WcMHz4cp0+fhp2dHQRBwPvvvw87OzscO3YMRkZG+OWXXzBgwACcPXsWjo5lP3R+wYIFmDdvnlr9uXPnYGKi+V3KFQoFsrKycObMGYj5BAOt4lzoBt2ch8aAWWPArD/gJkA//yFMsm7DJPsOTLJuwSTrDgxzH0CEit99I5LlAYkXIUq8qFKfZ+SAHNMmyKnXWPlf0ybIN7TTyp6FujkXdU9dnofP/aXYfsMA/97MV8kGyhUCfjhyG/9ExuGdlsZwtaj60Kc65iEnp5L3Jz9DpwJAGxsbSCQSJCcnq9QnJyeXuxDDwcGhQsebmJjAzc0Nbm5uaNeuHdzd3fHrr79i1qxZOHjwIHbs2IHHjx8XX0f/4YcfEB4ejt9//73Mew8BYNasWSqXnzMzM+Hk5AR/f/8quQdQLpfj7NmzaNOmDSQSicb7p4rjXOiGmjoP8oJsIOWqMkP4tCDlCkSFuf/94lIMnyTB8EkSrFNOFtcJBmaAvQ8Ee28I9r4Q7L2Vz0eWGmr6NFTU1Lmober6PHRsD4xLyMDH22JwLTlbpe1+lgJzj2fj7Y6NMa2bKwz0qu7zqY55KLrq+KJ0KgDU19eHn58fDhw4gODgYADKKPrAgQOYPHlyma8JDAzEgQMHMH369OK68PBwBAYGPve9FApF8eXb3FzlP7rPRulisRgKRfnLyQ0MDGBgYKBWL5VKq+ymT5FIBIlEUutv7q0JOBe6oUbOg9QCcAlUliIKuXLLmOTSj72LATITKtW1KD8TiD8JUfzJUpUS5YKT0s9CtvcF6tlq5nyK3qYmzkUtVNfnoWUja/w7pRNWHrqJlYduQlYqHagQgNXH7uBAbAq+HdoCfo3Kv83rZVX1PLxsvzr33RESEoKxY8fC398fbdu2RWhoKHJycjBu3DgAwJgxY9CgQQMsWLAAADBt2jR06dIFS5YsQb9+/bBhwwacO3cOq1evBqBMkX711VcYOHAgHB0dkZaWhpUrVyIhIQHDhg0DoAwiLS0tMXbsWMyePRtGRkb4+eefcefOHfTr1087HwQR1S1iCWDjpizeg0rqcx89szVNtHLhiKIS9/8IcuUzlVOvAtGbSurrOTxdbFLqsXfWbsqxENVg+lIxZvRsiiBvB8zccgmXH6hmy26l5mDoqpN4u2NjfNDLA4ZVmA3UVToXAI4YMQKpqamYPXs2kpKS0LJlS+zZs6d4oUd8fLxKpq59+/ZYv349Pv/8c3z66adwd3dHWFhY8R6AEokEsbGx+P3335GWlgZra2u0adMGx44dg7e3NwDlpec9e/bgs88+Q7du3VBYWAhvb2/8/fffxSuFiYi0wtgKaNJFWYrI8oHUayUBYVHJS69c39lJwM0k4Ob+kjqpkXKPwqKAsOgJJwZ8wgLVPF71zRD2fgesOnwL3x28gUJ5STZQEICfj93B/qsp+HZoc7RxsdLiSKufzu0DWJNxH8C6g3OhGzgPpQiC8nJx0bOQk58GhY9ua6Z/y8aqz0J28AXMGxYvOOFc6AbOQ/muJWVh5pZLiLqfodYmEgFjA13wUW8PGOu//OfGfQCJiKh6iETKgMy8IeDRp6Q+PwtIvlISECbFAMmXAdmTyvX/+I6yXP23pM7QvHivQpFtM5hkigBZS4CBB+kgDwdTbHuvPX4+dgfL9l9HgazkHn9BAH47GYeDsSlYOKQ5Al2ttTjS6sGfUiKi2szAFHAOUJYiCrkyM5gU9TQgfHopOSuxcn3nZQB3jwN3j0MCoCUA4ewMwMaj1GKTp/81sdHgSRG9GKlEjPe6uqKnlx1mbonChfh0lfb4R7kY9fMpjG7XCJ/08YSJQe0Nk2rvmRERUdnEEsDGXVl8hpTU56SpLjZJigHSrlVqwYlIIQNSLitL1MaSBlNH1YDQwRewasIFJ6QVbnam2DKxPdYcv4PF+64hX6a648efp+7iYKzy3sAObrXzjxcGgEREpGRiA7i+oixFZPnKVcdJ0aqPvstTv4/qubISleXGvpI6PWPAzkt1axp7b8CgnmbOh+g5JGIRJnRugu7N7PDRliicu/tYpT0h/Qle/+U0RrV1xqd9PWFqqKelkVYNBoBERFQ+qQHg2EJZiggCkHGv1OXjKAhJMRA9vlO5vgtzgYRzylJMBFg1LgkIi/YuNGuglSecUO3XxLYeNr4biN9PxuHbvbHIK1TNBv7vTDyOXEvBgiHN0aWpZvfO1CYGgEREVDkiEWDhrCyefQEAcpkM504cgr+zMaSpV0q2pkm5AsjyKtG5oLw/8dFt4MrfJdVGlk8vHzcvyRjaeABSfc2eG9VJErEIb3VsXJwNPH1H9RneDzLyMHbNGQzza4jP+3vB3KjmZwMZABIRkUbIpcaAUwDQuEOpShnw6Jb6vYXZSZXr/MljIO6YshQR6wG2Hur3FhrXrf3cSHMaWZvgfxPaYd3pu1iwOxa5BXKV9s3n7+PojVQsGOyLbp72WhqlZjAAJCKiqiORKoM0Ww/Ad2hJfXaq6tY0SdFA2nXlU0sqSlGoDCqTY1TrzRqUCgifZg0tGwPPPO6TqCxisQijA13Q1cMOH2+NwslbD1XakzPz8dZv5zC4VQPMHuAFC+OamYVmAEhERNWvni1Qrxvg2q2krjBP+bi60s9CTooG8iv50PvMBGW5sbekTs8EsPcqlS1srvxa30Qz50O1jpOVMda9HYD/nbmHr3ddRXa+6mr4bRcScOxmGr4K9kEvbwctjfLFMQAkIiLdoGcI1G+lLEUEAUiPV3/sXfrdyvVdmAPcP6ssxUSAtavq5WMHX+WWNVxwQgBEIhFeC3BGFw9bfLI1CsdupKm0p2bl450/z2Ngi/qYO9AbViY1JxvIAJCIiHSXSARYNlIWz34l9XkZyieaJClXISsvBV8B5PmV6FwAHt5UlithJdVGVs88C9lHeQlbUvNv/KcX08DCCH+81Rabz93H/J1XkJWnmg3859IDnLyVhv971Qd9fR21NMrKYQBIREQ1j6E50Ki9shSRy5TBXNFehUX3F+akVK7vJ4+AO0eUpYhYD7DzVN2axt6HC07qEJFIhOFtnNC5qS0+3R6Ng7Gq31dp2QWYtC4S/XwdMbufh5ZGWXEMAImIqHaQSJVBmp0ngGEl9VnJTwPCUvcWpl0HBEW5XalRFJZcfr5Uqt6sYanFJk+zhVxwUqs5mBvi17H+2BaZgHn/XkbmM9nAndGJOHkrDa976qGtIGhplP+NASAREdVupvbK4tajpK7wCZByVX17moKsyvWdeV9Zru8uqdOvp3yiSentaey8AH1jzZwPaZ1IJMIQv4bo5G6DT7fHYP/VZJX2x7mFWBFZiEQhBouHt4RIB+8pZQBIRER1j54R0KC1shRRKJSLS5JjVB97lx5fub4LsoF7p5WliEgMWLmqbk1j7wOYOnDBSQ1mZ2aIn8f44Z9LDzDnn8tIzy1Uad964QGCfBx1cpUwA0AiIiJAednWqrGyNBtQUv8k/emCk1L3FqbEVm7BiaAAHt5QlsvbSuqNbVSfhezgC9i4c8FJDSISifBqywZo72qD2X/HYHeM6ibnz24mrSsYABIRET2PkQXg0kFZisgLgbQbxc9CLs4Y5qaV202ZctOA24eVpYhEH7Br9jQgLHVvoZHFy58LVRlbUwP8+IYfdkYl4sudV5CYkYfAJlY6uyqYASAREVFlSfSUG0nbewHNhyvrBAHITlbdmiYpWrkyuTILTuQFQOIlZSnN3Fk1IHTwBSwaccGJjunX3BG9mtlg39FTCOrSBlKpbs4PA0AiIiJNEImU9/SZOgDupRacFOQqF5yU3pomOUZ5r2BlZMQry7VdJXX6piVb0jj4ArbNIK7UXohUFUQiESwMdTPwK8IAkIiIqCrpGwMN/ZSliEIBpMepP/Yu417l+i7IAuIjlAXKX+rtIAai3JQLTRx8Su4tNLXX2ClRzccAkIiIqLqJxYBVE2XxGlhS/+RxSYawaN/B1FjlZeEKEkGh3Ocw7ToQs6WkwcRW9VnIDj6Atbty/0SqczjrREREusLIEmjcSVmKyAuVwVzpZyEnxwC5DyvXd04qcOugshSRGCgXnKg8+s5b+aQVqtUYABIREekyiZ4yKLP3BlqMVNYJApCVpPbYO+HhTYhQiadPyPOBxIvKUppFI9XFJg4+yjruWVhrMAAkIiKqaUQiwMxRWZr2Kq6W52bg8uEt8LEWIEm98jRAvAwU5lSu//S7yhK7o6TOwLzkCScOJYtOoGeooZOi6sQAkIiIqLbQN0G2uScEvwBA+vRXvEIBPL6j/ti7zPuV6zs/A4g/qSxFRBLApqnqSmQHX6CenebOiaoEA0AiIqLaTCwGrF2VxTu4pD73kWpAWLTgRFFYbldqBDmQelVZojeX1NezVw0IHXwBazdALNHYadHLYQBIRERUFxlbAY07K0sRWQGQdk31WchJ0crVyZWRnawstw6U1EkNATsv1Wch23sDhmaaOR+qFAaAREREpCTVL8nYYZSyThCAzAfqj717dBuozIITWR7wIFJZSrN0Ud2axsEXMHfigpMqxgCQiIiIyicSAeYNlKVpUEl9fjaQckV1a5rky0BhbuX6fxynLKUXnBiaqz8L2a4ZIDXQxBkRGAASERHRizCoBzi1VZYiCjnw6I7qs5CTYoCsB5XrOy8DuHtcWYqIpU8XnPiq3l9oYqOZ86ljGAASERGRZoglgI2bsvgMLqnPefj0fsJSj75LjQUUsor3rZApM44pVwBsLKmv56C6NY29r3LBCxecPBcDQCIiIqpaJtZAk67KUkSWD6Ree2Z7mmggL71yfWcnATeTgJvhJXVSI8DeS/XRd/ZegIGpBk6mdmAASERERNVPagA4NleWIoIAZCaU2prm6aXkR7cr17fsCZBwXllKs2qiuuDE3gcwb1gnF5wwACQiIiLdIBIpAzLzhoBHn5L6/Cwg+YrqvYXJV5SBXmU8uq0sV/8pqTO0KPUc5KInnHgqV0TXYgwAiYiISLcZmALOAcpSRCEHHt5SeRYykmOArMTK9Z2XDsQdU5YiYqkyCCz9LGR7X+Wl7FqCASARERHVPGIJYNtUWXyGlNTnpKluTZMUrbzXUJBXvG+F7Om2NjFA1IaSetP6qlvTODRXXlYWizV3XtWEASARERHVHiY2gOsrylKkME+56rj01jTJ0crtZioj64Gy3NhXUqdnrHyiSemtaaybauZcqhADQCIiIqrd9AyB+i2VpYggABn31B979ziucn0X5gL3zyrLUxKI0Mq4AUTSGUDA25o4A41jAEhERER1j0gEWDgri2ffkvq8TOUTTUo/+i7livJRdhXtGgKMc+8Duz8AHLyARu2r4AReDgNAIiIioiKGZkCjQGUpIpcBj26p31uYnfzf/T2+ywCQiIiIqMaRSAFbD2XxHVpSn53yzEbWMUDa9eIFJ4KVK0Ru3bU06OdjAEhERET0IurZAW7dlaVIYR5kSTG4cikSXq8MhdTESnvjew4GgERERESaomcIOLZEVnw+YGCm7dGUSyc3rlm5ciVcXFxgaGiIgIAAnDlz5rnHb968GZ6enjA0NISvry927dql0j537lx4enrCxMQElpaW6NGjB06fPq3Wz86dOxEQEAAjIyNYWloiODhYk6dFREREpBN0LgDcuHEjQkJCMGfOHERGRqJFixYICgpCSkpKmcefPHkSo0aNwvjx43HhwgUEBwcjODgYMTExxcc0bdoUK1asQHR0NI4fPw4XFxf06tULqampxcds3boVo0ePxrhx43Dp0iWcOHECr732WpWfLxEREVF107kAcOnSpZgwYQLGjRsHLy8vrFq1CsbGxlizZk2Zxy9fvhy9e/fGzJkz0axZM8yfPx+tW7fGihUrio957bXX0KNHDzRp0gTe3t5YunQpMjMzERUVBQCQyWSYNm0aFi1ahIkTJ6Jp06bw8vLC8OHDq+WciYiIiKqTTgWABQUFOH/+PHr06FFcJxaL0aNHD0RERJT5moiICJXjASAoKKjc4wsKCrB69WqYm5ujRYsWAIDIyEgkJCRALBajVatWcHR0RJ8+fVSyiERERES1hU4tAklLS4NcLoe9vb1Kvb29PWJjY8t8TVJSUpnHJyUlqdTt2LEDI0eORG5uLhwdHREeHg4bGxsAwO3btwEo7xVcunQpXFxcsGTJEnTt2hXXr1+HlVXZK3jy8/ORn59f/HVmZiYAZUZRJpNV4swrRi6XQxAEyOWVeJ4hVQnOhW7gPOgOzoVu4DzohuqYh5eNM3QqAKxKr7zyCi5evIi0tDT8/PPPGD58OE6fPg07OzsoFAoAwGeffYYhQ5QPlF67di0aNmyIzZs349133y2zzwULFmDevHlq9efOnYOJiYnGz0GhUCArKwtnzpyBuAY+eLo24VzoBs6D7uBc6AbOg26ojnnIycl5qdfrVABoY2MDiUSC5GTVnbWTk5Ph4OBQ5mscHBwqdLyJiQnc3Nzg5uaGdu3awd3dHb/++itmzZoFR0dHAICXl1fx8QYGBmjSpAni4+PLHe+sWbMQEhJS/HVmZiacnJzg7+8PMzPNL/2Wy+U4e/Ys2rRpA4lEovH+qeI4F7qB86A7OBe6gfOgG6pjHoquOr4onQoA9fX14efnhwMHDhRvwaJQKHDgwAFMnjy5zNcEBgbiwIEDmD59enFdeHg4AgMDyzy+iEKhKL586+fnBwMDA1y7dg0dO3YEABQWFiIuLg6NGjUqtw8DAwMYGBio1UulUkilVfPRikQiSCSSKuufKo5zoRs4D7qDc6EbOA+6oarn4WX71bnvjpCQEIwdOxb+/v5o27YtQkNDkZOTg3HjxgEAxowZgwYNGmDBggUAgGnTpqFLly5YsmQJ+vXrhw0bNuDcuXNYvXo1AGWK9KuvvsLAgQPh6OiItLQ0rFy5EgkJCRg2bBgAwMzMDBMnTsScOXPg5OSERo0aYdGiRQBQfAwRERFRbaFzAeCIESOQmpqK2bNnIykpCS1btsSePXuKF3rEx8erXE9v37491q9fj88//xyffvop3N3dERYWBh8fHwCARCJBbGwsfv/9d6SlpcHa2hpt2rTBsWPH4O3tXdzPokWLIJVKMXr0aDx58gQBAQE4ePAgLC0tq/cDICIiIqpiOhcAAsDkyZPLveR7+PBhtbphw4aVm6kzNDTEtm3b/vM99fT0sHjxYixevLhSYyUiIiKqabhEiIiIiKiOYQBIREREVMfo5CXgmkoQBAAvvzS7PDKZDDk5OcjMzOTqLi3jXOgGzoPu4FzoBs6DbqiOeSiKNYpij8rid4cGZWVlAQCcnJy0PBIiIiKqC7KysmBubl7p14mEFw0dSY1CocCDBw9gamoKkUik8f6LNpq+d+9elWw0TRXHudANnAfdwbnQDZwH3VAd8yAIArKyslC/fv0XetoIM4AaJBaL0bBhwyp/HzMzM/5g6wjOhW7gPOgOzoVu4DzohqqehxfJ/BXhIhAiIiKiOoYBIBEREVEdwwCwBjEwMMCcOXPKfP4wVS/OhW7gPOgOzoVu4DzohpowD1wEQkRERFTHMANIREREVMcwACQiIiKqYxgAEhEREdUxDAB1yNGjRzFgwADUr18fIpEIYWFh//maw4cPo3Xr1jAwMICbmxt+++23Kh9nbVfZedi2bRt69uwJW1tbmJmZITAwEHv37q2ewdZyL/IzUeTEiROQSqVo2bJllY2vrniRecjPz8dnn32GRo0awcDAAC4u/9/e3cc0df1hAH94EVonYJiCXdGhURBQaozKsFsaJo7EhoS/2vkSmBU3IiSgEzZx0yFLumxjyYib08ShJLwENGSbRSO+oSJGITQRJIDiuxMm8w1hxtnz+8Osv3UMtMxypff5JDcph3PLczi098u5t20ofvzxR9eHdWPDmYeSkhJoNBqMHTsWKpUKJpMJPT09rg/rxsxmM+bPnw8/Pz8EBQUhKSkJbW1tz9yvsrISM2fOhEKhwOzZs1FdXT0CaQfHAvAl8vDhQ2g0Gnz33XfP1f/SpUvQ6/WIi4uD1WpFVlYWUlNTWXz8R87Ow/Hjx7F48WJUV1ejsbERcXFxSExMRFNTk4uTuj9n5+Ivd+/eRXJyMhYtWuSiZPIynHkwGAw4fPgwdu7ciba2NpSVlSE8PNyFKd2fs/NQV1eH5ORkrFq1Ci0tLaisrMSZM2ewevVqFyd1b7W1tUhPT8fp06dRU1ODx48f45133sHDhw8H3efUqVNYunQpVq1ahaamJiQlJSEpKQnNzc0jmPwfBL2UAIiqqqoh++Tk5IioqCiHNqPRKBISElyYTF6eZx7+TWRkpMjLy3vxgWTMmbkwGo3ik08+EZs3bxYajcalueTmeeZh//79IiAgQPT09IxMKBl6nnn46quvxLRp0xzaCgsLhVqtdmEy+enu7hYARG1t7aB9DAaD0Ov1Dm0xMTHigw8+cHW8QXEFcBSrr69HfHy8Q1tCQgLq6+slSkTA08+EfvDgAQIDA6WOIktFRUXo7OzE5s2bpY4iWz///DPmzZuHL7/8Emq1GmFhYVi/fj36+/uljiYrsbGxuHbtGqqrqyGEQFdXF/bs2YMlS5ZIHc2t3Lt3DwCGfM5/GY/X/CzgUezWrVsIDg52aAsODsb9+/fR398PpVIpUTJ5+/rrr9Hb2wuDwSB1FNnp6OjAxx9/jBMnTsDbm09vUuns7MTJkyehUChQVVWF27dvY82aNejp6UFRUZHU8WRDq9WipKQERqMRf/zxB/78808kJiY6fUkFDc5msyErKwtarRazZs0atN9gx+tbt265OuKguAJI9AKVlpYiLy8PFRUVCAoKkjqOrDx58gTLli1DXl4ewsLCpI4jazabDR4eHigpKcGCBQuwZMkSfPPNN9i9ezdXAUfQ+fPnkZmZiU2bNqGxsREHDhzA5cuXkZaWJnU0t5Geno7m5maUl5dLHcVp/Bd5FJs0aRK6uroc2rq6uuDv78/VPwmUl5cjNTUVlZWVA5b6yfUePHiAhoYGNDU1ISMjA8DTQkQIAW9vbxw8eBBvv/22xCnlQaVSQa1WIyAgwN4WEREBIQSuX7+OGTNmSJhOPsxmM7RaLbKzswEA0dHReOWVV/DWW2/h888/h0qlkjjh6JaRkYF9+/bh+PHjCAkJGbLvYMfrSZMmuTLikLgCOIrFxsbi8OHDDm01NTWIjY2VKJF8lZWVYeXKlSgrK4Ner5c6jiz5+/vj3LlzsFqt9i0tLQ3h4eGwWq2IiYmROqJsaLVa3Lx5E729vfa29vZ2eHp6PvNASS9OX18fPD0dD/NeXl4AAMFPgR02IQQyMjJQVVWFI0eOYOrUqc/c52U8XnMF8CXS29uLCxcu2L++dOkSrFYrAgMDMWXKFGzYsAE3btxAcXExACAtLQ1bt25FTk4OTCYTjhw5goqKClgsFqmG4BacnYfS0lKkpKTg22+/RUxMjP2aDqVS6bACQs5zZi48PT0HXIMTFBQEhUIx5LU59GzOPiaWLVuG/Px8rFy5Enl5ebh9+zays7NhMpl4duI/cHYeEhMTsXr1amzbtg0JCQn49ddfkZWVhQULFuC1116TahijXnp6OkpLS/HTTz/Bz8/P/pwfEBBg//tOTk6GWq2G2WwGAGRmZkKn06GgoAB6vR7l5eVoaGjAjh07JBsH3wbmJXL06FEBYMCWkpIihBAiJSVF6HS6AfvMmTNH+Pj4iGnTpomioqIRz+1unJ0HnU43ZH8avuE8Jv6ObwPzYgxnHlpbW0V8fLxQKpUiJCRErFu3TvT19Y18eDcynHkoLCwUkZGRQqlUCpVKJZYvXy6uX78+8uHdyL/NAQCH469OpxtwDKioqBBhYWHCx8dHREVFCYvFMrLB/8FDCK4DExEREckJrwEkIiIikhkWgEREREQywwKQiIiISGZYABIRERHJDAtAIiIiIplhAUhEREQkMywAiYiIiGSGBSARERGRzLAAJCIahY4dOwYPDw/cvXtX6ihENAqxACQiIiKSGRaARERERDLDApCIaBhsNhvMZjOmTp0KpVIJjUaDPXv2APj/6VmLxYLo6GgoFAq88cYbaG5udriPvXv3IioqCr6+vggNDUVBQYHD9x89eoSPPvoIkydPhq+vL6ZPn46dO3c69GlsbMS8efMwduxYLFy4EG1tba4dOBG5BRaARETDYDabUVxcjB9++AEtLS1Yu3YtVqxYgdraWnuf7OxsFBQU4OzZs5g4cSISExPx+PFjAE8LN4PBgHfffRfnzp3DZ599hk8//RS7du2y75+cnIyysjIUFhaitbUV27dvx7hx4xxybNy4EQUFBWhoaIC3tzdMJtOIjJ+IRjcPIYSQOgQR0Wjy6NEjBAYG4tChQ4iNjbW3p6amoq+vD++//z7i4uJQXl4Oo9EIAPj9998REhKCXbt2wWAwYPny5fjtt99w8OBB+/45OTmwWCxoaWlBe3s7wsPDUVNTg/j4+AEZjh07hri4OBw6dAiLFi0CAFRXV0Ov16O/vx8KhcLFvwUiGs24AkhE5KQLFy6gr68Pixcvxrhx4+xbcXExLl68aO/39+IwMDAQ4eHhaG1tBQC0trZCq9U63K9Wq0VHRweePHkCq9UKLy8v6HS6IbNER0fbb6tUKgBAd3f3fx4jEbk3b6kDEBGNNr29vQAAi8UCtVrt8D1fX1+HInC4lErlc/UbM2aM/baHhweAp9cnEhENhSuAREROioyMhK+vL65evYrp06c7bJMnT7b3O336tP32nTt30N7ejoiICABAREQE6urqHO63rq4OYWFh8PLywuzZs2Gz2RyuKSQielG4AkhE5CQ/Pz+sX78ea9euhc1mw5tvvol79+6hrq4O/v7+eP311wEAW7Zswauvvorg4GBs3LgREyZMQFJSEgDgww8/xPz585Gfnw+j0Yj6+nps3boV33//PQAgNDQUKSkpMJlMKCwshEajwZUrV9Dd3Q2DwSDV0InITbAAJCIahvz8fEycOBFmsxmdnZ0YP3485s6di9zcXPsp2C+++AKZmZno6OjAnDlz8Msvv8DHxwcAMHfuXFRUVGDTpk3Iz8+HSqXCli1b8N5779l/xrZt25Cbm4s1a9agp6cHU6ZMQW5urhTDJSI3w1cBExG9YH+9QvfOnTsYP3681HGIiAbgNYBEREREMsMCkIiIiEhmeAqYiIiISGa4AkhEREQkMywAiYiIiGSGBSARERGRzLAAJCIiIpIZFoBEREREMsMCkIiIiEhmWAASERERyQwLQCIiIiKZYQFIREREJDMsAImIiIhkhgUgERERkcywACQiIiKSGRaARERERDLDApCIiIhIZv4Haup1g4aS1mEAAAAASUVORK5CYII=" alt="learning_curves_temperature_loss.png"></div>
+    </div>
+    <div id="feature-tab" class="tabcontent">
+        <h2>Feature Importance</h2><p>Feature importance scores come from Ludwig's Integrated Gradients explainer. It interpolates between each example and a neutral baseline sample, summing the change in the model output along that path. Higher |importance| values indicate stronger influence. Plots share a common x-axis to make magnitudes comparable across labels, and the table columns can be sorted for quick scans.</p><div class='scroll-rows-30'><table class='feature-importance-table sortable-table'><thead><tr><th class='sortable'>label</th><th class='sortable'>feature</th><th class='sortable'>importance</th><th class='sortable'>abs importance</th></tr></thead><tbody><tr><td>temperature</td><td>temperature_feature</td><td>0.490821</td><td>0.490821</td></tr></tbody></table></div><div class="plot feature-importance-plot"><h3>Top features for temperature</h3><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA4QAAAHgCAYAAAD9tKNeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAABcSAAAXEgFnn9JSAAAwzUlEQVR4nO3deZxWdcH///eAoOKGCGjigqYzIKDI6gIZhpktpGnWrYKKZmVimVKQeqfl7m1UoHeWuWRaKandkrmQRGoqIqQoiFoYCIgoW4Bscv3+6DfzdZxBthkGOc/n48FDOedc5/qc68MM8+Kc61xlpVKpFAAAAAqnUUMPAAAAgIYhCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFBbNfQAgE3rueeey+rVq9O0adOGHgoAAOtgxYoVadSoUQ466KA637czhFAwq1evzurVqxt6GNSj5cuXZ/ny5Q09DOqROd7ymeMtnzne8tXlHJdKpXr7+c0ZQiiYpk2bZvny5enQoUNDD4V68uKLLyaJOd6CmeMtnzne8pnjLV9dznHlvuqDM4QAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKKitGnoAANStsrKyhh4C9cwcb/nM8ZbPHLO5EIRQQFtvvXVDD4F6dMABBzT0EKhn5njLZ463fOaYzYVLRgEAAArKGUIoqGNvnJxlK1c39DAAAD4UtmnSKPd9dcs7sysIoaCWrVydZatKDT0MAIAPiS3zH9JdMgoAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUI2WxNnz493/jGN9KzZ8+0a9cuFRUVefrppxt6WAAAsMXYJEH4+uuvp6KiIv37998UT1c499xzTyoqKjJ8+PCGHkqdWb16dc4999yMHj06bdu2Tb9+/XLcccelZcuWm2wMQ4YMEaEAAGzRtmroAUBtZs6cmSlTpqRbt2654447Gno4AACwRXLJKJulN954I0my5557NvBIAABgy1XvQTh8+PB84hOfSJKMGzcuFRUVVb+GDBlStd2CBQty3XXX5dOf/nQOPPDAdO3aNQMGDMiYMWNq7PO9l6AuXbo0V155ZY444ogceOCBOe644/Loo49WbfunP/0pX/ziF9O5c+ccdthhueyyy7Js2bIa+zzyyCNTUVGRUqmU2267LZ/+9KfTqVOn9O7dO5dddlkWLVpU6/GVSqWMGjUqAwYMSPfu3dOpU6ccc8wxGT58eN55550a2/fv3z8VFRV5/fXXc//99+fEE0/MwQcfnG7dulVt85e//CVDhw7NMcccky5duqRz587p169ffvazn2XFihU19jd06NAkyYgRI6q9vvfcc0+StV9S+t4x1fYaL168OFdeeWWOPPLIdOjQIZdffvkGzdu6qqioyCmnnJIkuffee6uO5/2XHP/jH//IkCFDcsQRR6Rjx4457LDDct555+WVV16psc/ly5fn7rvvzte//vV84hOfyIEHHphu3brl5JNPzh//+Mdax3DvvfcmSQYMGFDtda18ndZ2SWlFRUWOPPLIasveOxfTpk3Leeedl8MOOyzt2rXL6NGjN+jYAABgQ9X7JaPt27fP0UcfnYceeigtW7ZM7969q9Z17do1STJt2rScfvrpmT17dtq0aZNevXplyZIlee655/K1r30t3/nOd3LGGWfU2PfKlStz2mmn5fXXX0+3bt0yf/78jB8/Puecc05uuummvPzyy7n22mvTvXv39OrVK88880xuv/32zJ8/P9ddd12t4/3hD3+Yu+66Kz169Eh5eXnVY8aNG5c777wz22+/fdW2q1evzuDBgzNq1Kg0a9YsHTt2zE477ZQXXnghI0aMyF//+tfcfvvt2WabbWo8z89//vPcfffd6dKlS/r06ZPZs2dXrbvwwguzbNmy7L///qmoqMi///3vTJo0KcOGDcuTTz6Zm2++OY0bN06S9O7dO6tWrcqECRPSrl27tG/fvmo/e+2113rOVk3Lli3LKaecklmzZqV79+7p0KFDdtpppyQbPm9rc9xxx2Xu3Ll5/PHHs9dee1X9Odl3332rthk9enTOO++8rFixIu3bt89BBx2UN954I3/6058yZsyY/OIXv0j37t2rtn/99ddz0UUXpXXr1tlnn31y4IEH5q233srEiRMzfvz4/POf/8ygQYOqjeHZZ5/N9OnT06tXr7Rq1apqXbNmzdb7mN5v2rRpOeGEE9K8efP07NkzixYtylZbbbVBxwYAABuq3oOwb9++adeuXR566KHsu+++ueqqq6qtf/fdd3Puuedm9uzZGTx4cAYOHJhGjf5z4vJf//pXBg4cmOuuuy69e/dOeXl5tcdOnDgxhxxySEaPHl31Q/o999yToUOH5pJLLsmCBQvy29/+Np06dUqSzJkzJ8cdd1xGjRqVb33rW7VejviHP/whv/3tb9OxY8ckyZIlS3L22Wfnqaeeyk9+8pNceOGFVdvefPPNGTVqVHr06JEf/ehHVdGwYsWKXHrppRk5cmRGjBiRCy64oMbz3HfffbntttvSo0ePGusuvfTS9OrVq1pILl68OBdccEHGjBmT+++/P8cee2yS5KyzzkrLli0zYcKE9O3bt1rU1IXnn38+Bx98cEaPHp0dd9yxavnGzNvaXHXVVXn66afz+OOPp2vXrjX+zLz++usZPHhwttpqq9x444057LDDqtb99a9/zdlnn53Bgwfn4YcfTtOmTZMkLVq0yC233JJDDz00ZWVlVdvPmDEjp556am644YYcd9xx2WOPParGMGTIkEyfPj1nnXVWevbsuX4v3Fr88Y9/zCmnnJLvfe97VXG/occGAAAbqsHfQzhmzJi8/PLLOfroo3PmmWdWRUWS7L333hkyZEjefffd3HXXXTUe26hRo1xyySXVztgce+yx2XnnnfOvf/0rJ510UlUMJsmuu+6az33uc0mSZ555ptbxnHLKKVUxmCTbbbddLr744pSVlWXkyJFZvnx5kmTVqlW56aab0qxZswwbNqzaGaSmTZvm4osvTqtWrXLXXXdl9erVNZ7nhBNOqDUGk/9E9PvPKm6//fZVl4b++c9/rvVx9eXCCy+sFoPJxs3bxrrtttuydOnSnH/++dWCKUk+9rGP5ctf/nJmz56dsWPHVi3feeedc9hhh1WLweQ/71H8+te/ntWrV2/UZa7rq0WLFrnggguqxWCyYccGAMCmNXny5Lz44osf+Gv58uVZvnz5Wrdbl1+VDVIfGvwuo48//niS5Kijjqp1feXlgpMmTaqxrk2bNtlnn32qLWvUqFF23333zJ8/P7169arxmMqzgnPnzq31+T796U/XWLbffvulXbt2mTJlSiZPnpyDDz44kydPzvz583P44YfX+lEI22yzTTp06JC//OUvee2116pd7pikxnvL3u+1117L2LFjM3369CxdujSlUimlUqlq3abSqlWralFdaWPmbWM98cQTH/jc3bp1y+23357nn3++xjbjx4/PuHHjMmfOnKxYsSKlUqnqz8K//vWvOh/rmhx22GHZdtttayzfmGMDAID11eBBOHPmzCTJBRdcUOullZXmz59fY9muu+5a67bbbbfdGtdXnk18/81ZKrVp02aNy6dMmZI333wzSapuLPLEE0+koqJijeNe09g/8pGP1LptqVTK1VdfnVtvvbUqAN9vyZIlH/h8dWn33XevdfnGzNvGqnzuj33sYx+43Xuf+9///nfOOeecPPXUU2vcflO+rmua/w05NgAANq0DDjhgrdu8+OKLSZIOHTps9PNV7qs+NHgQVl5O2bt37w/80PGdd965xrL3XqZYm/dfHliXKmNt7733TpcuXT5w2+bNm9dYtvXWW9e67QMPPJBbbrklH/nIRzJ06NB07tw5LVq0SJMmTbJixYpaz9ZtrNouaV3bODdm3jZW5XMfd9xxH7jdQQcdVPX/1157bZ566qn06NEjgwYNyv77758dd9wxjRs3zuOPP54zzjhjjQG+MWNck7W9rutzbAAAsKEaPAh32223JMkXv/jFHH300Q08mv+coantjN+sWbOSJK1bt07y/84+1najnI3xyCOPJEkuueSSfPzjH6+2bsaMGRu0zyZNmiRJli5dWuv6997hdF015LzttttumT59er773e+uc3COHj06jRs3zv/+7/9Wu1NsUj+v64a8psmGHRsAAGyoTXJTmcofnFetWlVj3eGHH57k/4VQQ/vTn/5UY9k//vGPTJkyJc2aNav6WIcDDzwwO+ywQ8aNG5cFCxbU2fNXft5hZXCtbWzJB7++SapueDNt2rQa66ZNm7ZB8dKQ81Z5s5X3fm7f2ixatCjbb799jRhM1v66vvvuu7Wur3xda3tP59/+9rd1Htt7bcixAQDAhtokQbjzzjunSZMmmTFjRo0frj/5yU9mv/32y/3335/rr7++xnv7SqVSnn322Tz77LObYqj59a9/ncmTJ1f9/p133slll12WUqmU448/vurun02bNs2ZZ56ZJUuWZNCgQbWeZZozZ07uu+++9Xr+tm3bJkl+97vfVbuEcfz48fnlL39Z62Mqz1rWFnxJ0qlTp2y77bZ57LHH8sILL1QtnzdvXi666KK1Xt5Ym4act4EDB2abbbbJ1VdfnYcffrjG+hUrVuTBBx/MG2+8UbWsbdu2WbhwYR544IFq2956661r/GD5tb2ulZ8F+Jvf/Kbae/qmTJmSn/zkJ+t3UP+/DTk2AADYUJvkktGmTZumV69eGTNmTD7/+c/ngAMOSJMmTdKlS5ccf/zxuf7663PGGWfkpz/9ae64445UVFSkRYsWWbBgQaZMmZK33347Q4cOrbpzZX3q169fTjzxxPTs2TM77LBDxo8fn7lz52b//ffPN7/5zWrbnnXWWfnnP/+ZP/zhDznmmGNywAEHZI899sjKlSszbdq0vPrqq6moqKj6zMB10b9//9x777258847M27cuFRUVGTOnDl59tlnc/rpp+fmm2+u8ZjOnTtnl112yUMPPZT+/ftnjz32SKNGjXL88cenS5cu2W677TJw4MBcf/31Oemkk9K9e/eUlZXl+eefz7777puDDz44EydOXK/Xaauttmqwedt7771z3XXX5YILLsigQYOy9957Z999902zZs0yZ86cTJ48OUuXLs19991Xdab1rLPOyuDBg3PeeefljjvuyG677ZaXXnop//znP3Paaafl1ltvrfE8ffr0yfXXX5+rr746TzzxRNUlnBdccEF23nnnHHLIIenRo0fGjRuXz3zmM+nSpUvmz5+f5557Lv379691rurj2AAAYENtss8hvPzyy/P5z38+CxYsyKhRozJy5MiqzwJs27Zt7rvvvnzrW9/Kbrvtlr///e955JFHMm3atLRv3z7//d//nX79+m2ScV500UX59re/nVmzZlV93t/JJ5+cO+64IzvssEO1bRs1apRrrrkmN9xwQw4//PC8/vrrefjhh/Pss8+madOmOeOMM3LFFVes1/Pvs88+GTlyZPr06ZP58+fn0UcfzdKlS/ODH/wg3/3ud2t9zNZbb50bb7wxhx9+eKZMmZJ77703I0eOrHYp46BBg/Kd73wnu+22W55++um88sorOf7443PLLbdUXRq5vhpy3vr27Zv/+7//y0knnZSysrL87W9/y1/+8pe8/fbb6dOnT3784x/nox/9aNX2/fr1y89//vN07tw5U6ZMyV//+te0bt06t9122xo/AqRjx4659tprs99+++WJJ57IyJEjM3LkyKq7kZaVleWGG27Il7/85ZSVlWXs2LFZuHBhLrzwwjXOVX0cGwAAbKiyUl3eWvFD7Mgjj8zMmTMzderUhh4K1KvK2xafP6aUZat8+QMArItttirLg+d0XOft6+NjJ+piX++3yc4QAgAAsHkRhAAAAAXV4J9DyJbvH//4R37xi1+s07Zdu3bNF7/4xXoeEQAAkAjCKo8++mhDD2GL9dZbb+Xee+9d5+0FIQAAbBqCkHrXs2dPN+sBAIDNkPcQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApqq4YeANAwtmnSKMnqhh4GAMCHwn9+dtryCEIoqPu+ekBDDwEAgAa2ZWYuAAAAayUIoYCWL1/e0EOgHk2ePDmTJ09u6GFQj8zxls8cb/nMMZsLl4wCbGFKpVJDD4F6Zo63fOZ4y2eO2Vw4QwgAAFBQghAAAKCgBCEAAEBBCUIAAICCEoQAAAAFJQgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABVVWKpVKDT0IYNOZOHFiVq9enW222aahh0I9Wb58eZJk6623buCRUF/M8ZbPHG/5zPGWry7nePny5SkrK8vBBx+80ft6v63qfI/AZq1RIxcGbOn8cLHlM8dbPnO85TPHW766nOOysrJ6+xnOGUIAAICCcqoAAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAW1VUMPANg4y5Yty4033pgHHnggs2bNyk477ZTevXvnW9/6Vnbdddf12tfChQszfPjw/PnPf87cuXPTqlWr9O3bN4MGDcqOO+5YT0fA2tTVHI8bNy7jxo3L888/n+effz7z589PmzZt8uijj9bj6FkXdTHHixYtytixYzNmzJj8/e9/z5tvvpkmTZpkv/32y2c/+9mcdNJJadKkST0fCWtSF3O8atWq/O///m8mTZqUf/zjH5k3b15WrVqVj3zkIznssMPyla98JW3atKnnI2FN6vLv4/d67bXX0q9fvyxfvjyHHnpobr311robNOulrub4yCOPzMyZM9e4/oEHHshHP/rRuhjyOikrlUqlTfZsQJ1avnx5BgwYkL///e9p1apVunXrlpkzZ+b5559PixYtctddd2XPPfdcp33NmzcvX/7yl/Ovf/0re+65Zzp27JhXX301r7zyStq2bZvf/e53ad68ef0eEDXU5Rx//vOfz0svvVRtmSBseHU1x8OGDcvPfvazlJWVpX379mnbtm3mzZuXCRMmZMWKFenatWt++ctfZtttt90ER8V71dUcL1myJF26dEmzZs1SUVGR1q1bZ+XKlXnppZcya9asbL/99rn11lvTqVOnTXBUvFddfq9+v/79++eZZ55JqVQShA2oLue4MgiPO+64Wtd/+9vfTuvWrety+B+sBHxo/ehHPyqVl5eXvvSlL5UWL15ctfzmm28ulZeXl0455ZR13tf5559fKi8vL51zzjmllStXVi3/4Q9/WCovLy9997vfrdOxs27qco6vvvrq0g033FB67LHHSq+88kqpvLy81KdPn/oYNuuhrub4Zz/7Wemaa64pzZw5s9ryadOmlT7+8Y+XysvLS9ddd12djp11U1dzvHLlytL48eOrfY8ulUqlVatWlf7nf/6nVF5eXjruuOPqdOysm7r8Xv1ed911V6m8vLx08cUXl8rLy0unnnpqHY2Y9VWXc9ynT59SeXl5fQxzgwhC+JBavnx5qWvXrqXy8vLSiy++WGP95z73uVJ5eXlp0qRJa93XnDlzSu3atSt16NChNHfu3BrPc8ghh5Tat29feuutt+ps/KxdXc7x+7355puCcDNQn3P8Xvfff7/5biCbao5XrlxZ6tSpU6m8vLy0aNGijdoX66e+5nju3Lml7t27l04//fTSU089JQgbUF3P8eYWhG4qAx9SEyZMyL///e/stddeOeCAA2qsP/roo5MkY8aMWeu+HnvssaxevTrdunVLy5Ytq61r2rRp+vTpk3fffTdjx46tm8GzTupyjtk8bao5bteuXZLkzTff3Kj9sP421RyXlZWlcePGKSsr817RTay+5vjyyy/PsmXL8v3vf79OxsmG29L/PnZTGfiQqnwvWG3fmJKkQ4cOSZKpU6fWyb5+//vfr9O+qDt1OcdsnjbVHM+YMSNJ0qpVq43aD+tvU8xxqVTKL37xiyxdujSHHHJIttlmmw3eF+uvPuZ47NixeeCBB3Luuedm7733zhtvvLHxA2WD1dfX8U033ZTp06enadOm2X///XPUUUelRYsWGzfYDSAI4UNq9uzZSZLddtut1vWVy2fNmrXR+6q8c9a67Iu6U5dzzOZpU83xr371qyTJJz7xiY3aD+uvvub42muvzdtvv53Fixdn6tSpmT59ej760Y/msssu27gBs97qeo6XLl2aSy+9NPvss0++8pWv1M0g2Sj1+XX8XldeeWUuuuiinHDCCRswyg0nCOFDaunSpUmyxn8JrryT4JIlS9Z5X2u6+2CzZs3WeV/UnbqcYzZPm2KOf/Ob3+Rvf/tbdtxxRz9cNoD6muOHH34406dPr/p9RUVFrr322g2+kyUbrq7n+Mc//nFmzpyZX/3qV2natGndDJKNUtdzfOSRR6Znz57p0KFDWrRokRkzZuT3v/99fvWrX+Wiiy5K8+bN07dv37oZ/DrwHkIA2EKNHz8+l19+ecrKynLFFVds1GehsXl55JFHMnXq1Dz55JO56aabstVWW+X444/Pvffe29BDYyNMmjQpt99+e4499tj07NmzoYdDPbnoooty1FFHZffdd88222yT/fffP0OGDMkll1ySUqmU//mf/9mk4xGE8CFVedZu2bJlta5/5513kiTbbbfdOu+r8jHvV/kvY+uyL+pOXc4xm6f6nOOXX345Z599dlauXJkLL7wwRx111IYPlA1W31/HLVq0SO/evXPbbbelZcuWueSSS6oub2PTqKs5XrVqVS6++OLsuOOO+e53v1u3g2SjbKq/j0844YTssssumTZtWl5//fWN2tf6cMkofEh95CMfSZI1vtG8cvnuu+++0fuaM2fOOu+LulOXc8zmqb7meMaMGRk4cGAWLlyYQYMGpX///hs3UDbYpvo63mGHHdKnT5/ceeedeeKJJzb5e5CKrK7m+I033siUKVPSqlWrfPOb36y2btGiRUmSF198serr+fbbb9+ocbPuNtXXcaNGjbLXXnvl7bffzty5c7PHHnts1P7WlSCED6nK28hPnjy51vUvvvhikv+8r2RT7ou6Y162fPUxx2+++WYGDhyYuXPnZsCAATnnnHM2fqBssE35dbzzzjsnSebNm7fR+2Ld1fUcz507N3Pnzq113aJFizJu3LgNGCUbY1N+HS9cuDDJmu/rUB9cMgofUl26dMkOO+yQ6dOnZ8qUKTXWP/TQQ0mSPn36rHVfvXv3TqNGjTJ+/Pi8/fbb1datWLEiY8aMSePGjXPEEUfUzeBZJ3U5x2ye6nqOFy5cmDPOOCPTp0/PF77whXzve9+r0/Gy/jbl1/EzzzyTJNlrr702el+su7qa4z322CNTp06t9VflnYIPPfTQqmVsOpvq6/iVV17JtGnTsu2222bffffdqH2tD0EIH1JNmzbNySefnCS59NJLq97nlyS33HJLpk6dmh49eqRjx45Vy3/961/nU5/6VK677rpq+2rdunU+85nPZOXKlbn00kuzatWqqnXXXHNN5s2bl379+mWXXXap56Pivepyjtk81eUcv/POO/nqV7+al19+Occcc0wuu+yylJWVbZoDYY3qco7/8pe/ZMKECTWe45133smwYcMybty4tGrVKr17966no6E2vldv+epyjseOHZsnn3yyxnO89NJL+eY3v5lSqZQTTjhhk95h1iWj8CF29tln58knn8zEiRPzyU9+Mt26dcusWbPy3HPPpUWLFrniiiuqbT9//vxMmzat1ktRvve97+W5557LQw89lGOOOSYdO3bMq6++mpdffjlt27bNkCFDNtVh8R51Ocd333137r777iSpiv4333wzJ554YtU23//+96s+YJdNo67meNiwYZk4cWIaN26cxo0b58ILL6z1+a666qp6OxZqV1dzPGnSpIwYMSK77rpr2rdvn+233z5vvfVWXnrppSxYsCA77LBDfvzjH7vRVAOoy+/VbJ7qao6ff/75jBgxIm3atElFRUW23XbbzJgxI5MnT86qVavSo0ePXHDBBZvy0AQhfJhtvfXW+dWvfpUbb7wxo0aNyujRo9O8efN84QtfyDe/+c01foBqbVq0aJG77747I0aMyOjRo/PII4+kZcuW6d+/f84999zsuOOO9XgkrEldzvEbb7yR5557rtqylStXVlu2ePHiOhs766au5rjyphPvvvtuRo0atcbtBOGmV1dzfNRRR2XJkiUZP358Jk2alIULF2brrbfO3nvvnS996Us55ZRT0rp163o+GmpTl9+r2TzV1Rz36tUrs2fPzqRJkzJhwoQsXrw422+/fbp06ZJ+/frlC1/4Qho3blzPR1NdWalUKm3SZwQAAGCz4D2EAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAW5B77rknFRUVueeee6otr6ioyJFHHtlAo6I+Pf3006moqMjw4cMbeijAh5AgBAC2OJVhLJIAPthWDT0AAKD+PfDAA2nSpElDDwOAzYwgBIAC+OhHP9rQQwBgM+SSUQAogNreQ1j53rMhQ4bk7bffzve+970cfvjh6dy5c/7rv/4rEyZMqNr2N7/5TT73uc/lwAMPzBFHHJHhw4dn9erVa3yeFStW5Kc//Wn69u2bTp065ROf+ER+8pOfZPny5bWOb/78+bn66qvzyU9+Mp06dUqPHj1yxhln5PHHH//A41mxYkVGjBiRT33qU+nYsWPOPvvs9O/fP0OHDk2SjBgxIhUVFVW/Kt9bWSqVMmrUqJx33nk5+uij07lz5xx88ME54YQTcscdd9R6bMOHD6/ax9SpU/O1r30t3bt3T+fOnXPKKadUe73e77nnnst5552X3r17p2PHjunVq1dOPfXU3HXXXTW2feedd3LjjTfm2GOPzcEHH5yDDz44J554Yu6999417h9gQzlDCAAFt3DhwnzpS1/K6tWr06NHj8ycOTMTJkzIwIEDc/fdd+d3v/td7r777vTs2TNt2rTJuHHjMmLEiKxatSrnnXdejf2VSqWce+65efLJJ3PooYemffv2efLJJ3PDDTdk4sSJ+eUvf5nGjRtXbT9nzpycfPLJmTFjRnbffff07ds38+bNy5NPPpnHH388Q4cOzWmnnVbjeVavXp1vfOMbGT9+fLp3756Kioo0b948nTt3zqpVqzJhwoS0a9cu7du3r3rMXnvtlSRZsWJFzj///DRv3jz77bdfDjjggCxYsCATJ07MD37wg0yaNClXXXVVra/XCy+8kB/84AfZc88906tXr/zzn//MM888k9NOOy0jR45MeXl5te1vu+22XHXVVVm9enU6dOiQ7t27Z/78+Zk6dWquueaanHjiiVXbvv322zn99NMzderUtGrVKt27d0+pVMrEiRMzZMiQvPDCC7n44ovXa34BPoggBICCe/TRR9OvX79cccUVVe8zHD58eEaMGJFvfetbWbRoUe6///6qmHr11Vdz7LHH5rbbbstZZ52V7bbbrtr+Zs2aldWrV2fUqFHZc889kyTz5s3LqaeemieffDK33357tcD7/ve/nxkzZuSzn/1srrzyyjRt2jRJMn78+Jx55pm55ppr0rNnz2phlySzZ89O06ZN8+CDD2bXXXettq5ly5aZMGFC+vbtm0GDBtU45saNG+f666/PEUccUe29lfPmzctXvvKV3HvvvTn++OPTvXv3Go+94447cuGFF2bAgAFVy6644orcdtttuemmm3LNNddULX/mmWdy5ZVXplmzZrn++utz6KGHVq1btWpVnnjiiWr7Hjp0aKZOnZoBAwZk8ODBVa/FW2+9la9+9av59a9/nSOOOCIf+9jHaowLYEO4ZBQACm777bfPRRddVC2MTjvttJSVleXVV1/NueeeWxWDSbLffvvl4x//eN5555288MILte7zG9/4RlUMJkmLFi0yePDgJP8JqkozZszImDFj0qxZs1x88cVVAZQk3bp1y5e//OW8++671R7zXt/+9rdrxOC62GqrrdK3b98aN9pp0aJFzj///CTJn//851of26VLl2oxmCRf//rXk/wnYt/r5z//eUqlUr72ta9Vi8HKMRxxxBFVv58yZUrGjh2bTp06ZejQodVei5YtW+aHP/xhkv9cvgtQV5whBICC69ixY3baaadqy3bYYYfstNNOWbBgQXr16lXjMZWxN3fu3Fr3+elPf7rGso997GPZaaedMn369Lz55ptp3bp1nn322SRJ796907x58xqP+fznP59bbrmlRmglSVlZ2UZ/tuKUKVPy+OOPZ9asWVm2bFlKpVKWLFmSJHnttddqfczhhx9eY9nOO++c5s2b580336xatmrVqowbNy5J8qUvfWmtY6l8v2Tfvn3TqFHNf7M/4IAD0qxZs0yaNGmt+wJYV4IQAApuTWfYtttuuyxYsKDW9c2aNUvyn/fivd9OO+2U7bffvtZ97r777lm4cGFVEFYGVJs2bWrdvnL5nDlzaqzbZZddqp1FWx8rVqzI0KFDM2rUqDVuUxmG77fbbrvVurzy9aq0YMGCLFu2LM2bN68R3LWZOXNmkmTYsGEZNmzYB44doK4IQgAouNrORq3P+vpUVla2xnVbb731Bu/31ltvzahRo1JeXp7BgwenQ4cO2XHHHdOkSZNMmzYtn/rUp9b42Pp6PSrvbNq1a9dql+gC1CdBCADUqYULF2bx4sW1niWcPXt2kqR169bV/jtr1qxa91V51mxD3if4QR555JEkyY9+9KPsv//+1dbNmDGjTp5j5513zjbbbJMFCxZk0aJF2XHHHT9w+8ozj3379s3AgQPrZAwAa+OmMgBAnfvTn/5UY9njjz+eBQsWZM8996wKwa5duyZJHnvssSxatKjGY/7whz8k+c8NZtZH5c1iVq1aVev6yueq7fLP2sa+IRo3bpwePXokSX73u9+tdfvK9yZWxirApiAIAYA6d/311+f111+v+v28efOqPo7h5JNPrlq+55575uMf/3iWLFmSyy+/PCtXrqxaN3HixPz2t79N48aNqz1mXVQG57Rp02pd37Zt2yQ179j54IMPVkVoXfjKV76SsrKy/OxnP8tTTz1Vbd2qVasyduzYqt8fdNBBOfzwwzNhwoRceumlWbx4cY39vfTSS/nrX/9aZ+MDcMkoAFCndt9991RUVOSzn/1sDjnkkDRp0iRPPfVUFi1alJ49e6Z///7Vtv/BD36Qk046Kffdd1+eeeaZdO7cOfPmzcu4cePy7rvvZsiQITU+g3BtOnfunF122SUPPfRQ+vfvnz322CONGjXK8ccfny5duuTMM8/MY489luuuuy4PPvhg9tlnn7z22mt54YUXMnDgwNx888118lr06NEjgwcPzrXXXptTTz01HTt2TNu2bTN//vy89NJLWbFiRbU7qF577bU588wzc+edd2bUqFFp165dWrduncWLF2fq1KmZPXt2BgwY4HMIgTojCAGAOlVWVpaf/vSnuf7663P//fdX3VH0pJNOyte//vVstVX1Hz923XXXjBw5Mj//+c8zevToPPzww9l2221z6KGH5vTTT6/1Yy/WZuutt86NN96YYcOG5fnnn88zzzyTUqmUrl27pkuXLunevXvuvPPODBs2LFOmTMlrr72W8vLyDB8+PAcccECdBWGSnHHGGTnooINy6623ZsKECZk6dWqaN2+e8vLyfOYzn6m27S677JLf/va3ueuuu/LHP/4xU6ZMycSJE9OyZcvsueee6d+/f43HAGyMslKpVGroQQAAdeOee+7J0KFDc+WVV+YLX/jCJn/+ioqKtGnTJo8++ugmf+6ievrppzNgwICcc845GTRoUEMPB/iQ8R5CAACAghKEAAAABSUIAQAACspNZQCAOjN16tSGHgIA68EZQgDYgrRv3z7nnHPOen9MAx9ebdq0yTnnnJMePXo09FCADyF3GQUAACgoZwgBAAAKShACAAAUlCAEAAAoKEEIAABQUIIQAACgoAQhAABAQQlCAACAghKEAAAABSUIAQAACkoQAgAAFJQgBAAAKChBCAAAUFCCEAAAoKAEIQAAQEEJQgAAgIIShAAAAAUlCAEAAApKEAIAABSUIAQAACgoQQgAAFBQghAAAKCgBCEAAEBB/X9wbw0TlAIetAAAAABJRU5ErkJggg==" alt="Feature importance plot for temperature"></div>
+    </div>
+    
+        
+    <script>
+        function openTab(evt, tabId) {
+            var i, tabcontent, tablinks;
+            tabcontent = document.getElementsByClassName("tabcontent");
+            for (i = 0; i < tabcontent.length; i++) {
+                tabcontent[i].style.display = "none";
+                tabcontent[i].classList.remove("active");
+            }
+            tablinks = document.getElementsByClassName("tablink");
+            for (i = 0; i < tablinks.length; i++) {
+                tablinks[i].classList.remove("active");
+            }
+            var current = document.getElementById(tabId);
+            if (current) {
+                current.style.display = "block";
+                current.classList.add("active");
+            }
+            if (evt && evt.currentTarget) {
+                evt.currentTarget.classList.add("active");
+            }
+        }
+        document.addEventListener("DOMContentLoaded", function() {
+            openTab({currentTarget: document.querySelector(".tablink")}, "viz-tab");
+        });
+    </script>
+    
     
     </div>
     </body>
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/ludwig_experiment_report_test_breast.html	Sat Nov 22 01:15:52 2025 +0000
@@ -0,0 +1,249 @@
+
+    
+    <html>
+    <head>
+        <title>Galaxy-Ludwig Report</title>
+        <style>
+          body {
+              font-family: Arial, sans-serif;
+              margin: 0;
+              padding: 20px;
+              background-color: #f4f4f4;
+          }
+          .container {
+              max-width: 800px;
+              margin: auto;
+              background: white;
+              padding: 20px;
+              box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+              overflow-x: auto;
+          }
+          h1 {
+              text-align: center;
+              color: #333;
+          }
+          h2 {
+              border-bottom: 2px solid #4CAF50;
+              color: #4CAF50;
+              padding-bottom: 5px;
+          }
+          table {
+              border-collapse: collapse;
+              margin: 20px 0;
+              width: 100%;
+              table-layout: fixed; /* Enforces consistent column widths */
+          }
+          table, th, td {
+              border: 1px solid #ddd;
+          }
+          th, td {
+              padding: 8px;
+              text-align: center; /* Center-align text */
+              vertical-align: middle; /* Center-align content vertically */
+              word-wrap: break-word; /* Break long words to avoid overflow */
+          }
+          th:first-child, td:first-child {
+              width: 5%; /* Smaller width for the first column */
+          }
+          th:nth-child(2), td:nth-child(2) {
+              width: 50%; /* Wider for the metric/description column */
+          }
+          th:last-child, td:last-child {
+              width: 25%; /* Value column gets remaining space */
+          }
+          th {
+              background-color: #4CAF50;
+              color: white;
+          }
+          /* feature importance layout tweaks */
+          table.feature-importance-table {
+              table-layout: auto;
+          }
+          table.feature-importance-table th,
+          table.feature-importance-table td {
+              white-space: nowrap;
+              word-break: normal;
+          }
+          /* sortable tables */
+          .sortable-table th.sortable {
+              cursor: pointer;
+              position: relative;
+              user-select: none;
+          }
+          .sortable-table th.sortable::after {
+              content: '⇅';
+              position: absolute;
+              right: 12px;
+              top: 50%;
+              transform: translateY(-50%);
+              font-size: 0.8em;
+              color: #eaf5ea;
+              text-shadow: 0 0 1px rgba(0,0,0,0.15);
+          }
+          .sortable-table th.sortable.sorted-none::after { content: '⇅'; color: #eaf5ea; }
+          .sortable-table th.sortable.sorted-asc::after  { content: '↑';  color: #ffffff; }
+          .sortable-table th.sortable.sorted-desc::after { content: '↓';  color: #ffffff; }
+          .scroll-rows-30 {
+              max-height: 900px;
+              overflow-y: auto;
+              overflow-x: auto;
+          }
+          .plot {
+              text-align: center;
+              margin: 20px 0;
+          }
+          .plot img {
+              max-width: 100%;
+              height: auto;
+          }
+        </style>
+        <script>
+          (function() {
+            if (window.__sortableInit) return;
+            window.__sortableInit = true;
+
+            function initSortableTables() {
+              document.querySelectorAll('table.sortable-table tbody').forEach(tbody => {
+                Array.from(tbody.rows).forEach((row, i) => { row.dataset.originalOrder = i; });
+              });
+
+              const getText = td => (td?.innerText || '').trim();
+              const cmp = (idx, asc) => (a, b) => {
+                const v1 = getText(a.children[idx]);
+                const v2 = getText(b.children[idx]);
+                const n1 = parseFloat(v1), n2 = parseFloat(v2);
+                if (!isNaN(n1) && !isNaN(n2)) return asc ? n1 - n2 : n2 - n1;
+                return asc ? v1.localeCompare(v2) : v2.localeCompare(v1);
+              };
+
+              document.querySelectorAll('table.sortable-table th.sortable').forEach(th => {
+                th.classList.remove('sorted-asc','sorted-desc');
+                th.classList.add('sorted-none');
+
+                th.addEventListener('click', () => {
+                  const table = th.closest('table');
+                  const headerRow = th.parentNode;
+                  const allTh = headerRow.querySelectorAll('th.sortable');
+                  const tbody = table.querySelector('tbody');
+
+                  const isAsc  = th.classList.contains('sorted-asc');
+                  const isDesc = th.classList.contains('sorted-desc');
+
+                  allTh.forEach(x => x.classList.remove('sorted-asc','sorted-desc','sorted-none'));
+
+                  let next;
+                  if (!isAsc && !isDesc) {
+                    next = 'asc';
+                  } else if (isAsc) {
+                    next = 'desc';
+                  } else {
+                    next = 'none';
+                  }
+                  th.classList.add('sorted-' + next);
+
+                  const rows = Array.from(tbody.rows);
+                  if (next === 'none') {
+                    rows.sort((a, b) => (a.dataset.originalOrder - b.dataset.originalOrder));
+                  } else {
+                    const idx = Array.from(headerRow.children).indexOf(th);
+                    rows.sort(cmp(idx, next === 'asc'));
+                  }
+                  rows.forEach(r => tbody.appendChild(r));
+                });
+              });
+            }
+
+            if (document.readyState === 'loading') {
+              document.addEventListener('DOMContentLoaded', initSortableTables);
+            } else {
+              initSortableTables();
+            }
+          })();
+        </script>
+    </head>
+    <body>
+    <div class="container">
+    
+        <h1>Ludwig Experiment</h1>
+        
+    <style>
+        .tabs {
+            display: flex;
+            border-bottom: 2px solid #ccc;
+            margin-top: 20px;
+            margin-bottom: 1rem;
+        }
+        .tablink {
+            padding: 9px 18px;
+            cursor: pointer;
+            border: 1px solid #ccc;
+            border-bottom: none;
+            background: #f9f9f9;
+            margin-right: 5px;
+            border-top-left-radius: 8px;
+            border-top-right-radius: 8px;
+            font-size: 0.95rem;
+            font-weight: 500;
+            font-family: Arial, sans-serif;
+            color: #4A4A4A;
+        }
+        .tablink.active {
+            background: #ffffff;
+            font-weight: bold;
+        }
+        .tabcontent {
+            border: 1px solid #ccc;
+            border-top: none;
+            padding: 20px;
+            display: none;
+        }
+        .tabcontent.active {
+            display: block;
+        }
+    </style>
+    
+        
+    <div class="tabs">
+        <button class="tablink active" onclick="openTab(event, 'viz-tab')">Visualizations</button>
+        <button class="tablink" onclick="openTab(event, 'feature-tab')">Feature Importance</button>
+    </div>
+    <div id="viz-tab" class="tabcontent active">
+        <h2>Visualizations</h2><div class="plot"><h3>confusion matrix  AGE CAT top2</h3><img src="data:image/png;base64,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" alt="confusion_matrix__AGE_CAT_top2.png"></div><div class="plot"><h3>confusion matrix entropy  AGE CAT top2</h3><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABIs0lEQVR4nO3deXwN9/7H8XdWJBFC7WoLSTSLJKKtrfaqoEKpUtHbhVarpdoiF21xW25LFy2XWltqreVaWhSt2mtfilpqSa1BCBKSnDO/P/wyt0eC5BCh83o+Hh6Sme985zufnDl5Z7bjYhiGIQAAAFiGa14PAAAAAHcXARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACKc0bNhQffv2zeth/G0EBgZq0KBBubqOvn37qmHDhrds17BhQ7388su5Ohbcvl9++UWtWrVSaGioAgMDlZSUlNdDuqHDhw/rhRdeUPXq1RUYGKhly5bd0f7//PNPBQYGas6cOXe03/tZbGysYmNj83oYuIe55/UAcG85evSoxo0bpzVr1uj06dPy8PBQQECAmjVrpvbt2yt//vx5PUQgS1u2bNGaNWv03HPPydfXN6+Hk6sSExPVs2dPValSRe+++648PT1VoECBmy6Tl/t237599eeff+rNN99UwYIFFRISkmvrutv69u2ruXPnytvbW2vXrs1Ux8OHD6tp06aSpN69e+vFF1/MUf+nTp3SzJkz1bhxY1WtWvWOjRsgAML0888/q0ePHvL09FSrVq0UEBCgtLQ0bd68WR9//LEOHDigwYMH5/UwgSxt3bpVX375pVq3bv23D4A7d+7U5cuX1aNHD9WqVeuW7fNy375y5Yq2bt2qV155RZ06dcqVdZQpU0Y7duyQu3ve/Epzd3fXlStXtGLFCkVHRzvMW7BggfLly6erV6861ffp06f15ZdfqkyZMjkKgOPHj3dqfbAOAiAkSfHx8XrzzTdVunRpff311ypevLg579lnn9WRI0f0888/590A7wHJycny8vLK62HgDrDb7UpLS1O+fPnyeihOOXfunCSpYMGCt2yb1/t2xlhzM5S7uLjk6c/S09NTkZGRWrRoUaYAuHDhQtWvX19Lliy5K2NJSUlRgQIF5OnpmavrSU9Pl91uz/X1IPdwDSAkSePGjVNycrI++OADh18QGcqXL6/nnnvuhsufP39e//73v9WyZUtFREQoMjJSL730kvbu3Zup7eTJk9W8eXNVq1ZNNWrUUJs2bbRgwQJz/qVLl/TBBx+oYcOGCgkJUc2aNfX888/rt99+c+hn+/btevHFF1W9enVVq1ZNnTp10ubNmx3aZLev633xxRcKDAzUgQMH9NZbb6lGjRrq2LGjJGnv3r3q27evGjVqpNDQUNWuXVtxcXFKTEzMso8jR46ob9++ioqKUvXq1RUXF6eUlJSbrl+SRo0apaCgIE2ePNmctnLlSnXs2FHh4eGKiIhQ165dtX///kzLLlu2TC1atFBoaKhatGihH3/88Zbru97q1avNa8yio6O1dOlSc158fLwCAwM1adKkTMtt2bJFgYGBWrhw4U37T01N1YgRI9SkSROFhISoXr16+uijj5SamurQLuP6yIxtCgkJUfPmzfXLL7+Ybb744gt99NFHkqRGjRopMDBQgYGB+vPPPx36mD9/vpo3b67Q0FCtWrVKkrR792699NJLioyMVEREhJ577jlt27bNYQxz5sxRYGCgNm7cqHfffVePPPKIIiMj1bt3b124cMFs16dPHz3yyCNKS0vLtL0vvPCCeSrwZn744Qe1adNGYWFheuSRR/T222/r1KlT5vzY2Fj16dNHktS2bVsFBgbe9HrcnO7b6enpGjlypBo3bqyQkBA1bNhQn3zySaafS8a1ops2bVLbtm0VGhqqRo0aad68eWabL774Qg0aNJAkffTRRwoMDDSvQ73RNakZ+81frVmzRh06dFBUVJQiIiLUtGlTffLJJ+b8G10DuG7dOnN/iYqKUrdu3XTw4MEs1+fsfpqhRYsW+uWXXxyuxdyxY4cOHz6sFi1aZGqfnffMDRs2qG3btpKkuLg483WdsZ2xsbFq0aKFdu3apWeffVbVqlUz63L9NYB9+vRRaGhopu1/8cUXVaNGDYfX2PUy6jt+/HhNmjRJjRs3dujrVnXeu3evAgMDtXz5cnParl27FBgYqNatWzus66WXXlK7du1uOBbcORwBhCTpp59+0oMPPqjIyEinlo+Pj9eyZcv0xBNPqGzZsjpz5oxmzJihTp06adGiRSpRooQkaebMmfrXv/6lpk2bqnPnzrp69ap+//13bd++XS1btpQkvffee1qyZIk6deokf39/nT9/Xps3b9bBgwcVHBws6dobTpcuXRQSEqLu3bvLxcVFc+bM0XPPPaepU6cqLCws233dTI8ePVS+fHm9+eabMgxDkrR27VrFx8erTZs2KlasmPbv36+ZM2fqwIEDmjlzplxcXBz66Nmzp8qWLatevXpp9+7dmjVrlooUKaJ33nnnhuv99NNPNWbMGA0aNEhPP/20JGnevHnq27ev6tSpo7ffflspKSmaNm2aOnbsqLlz56ps2bKSrgW3119/XZUrV9Zbb72lxMRExcXFqWTJktn+eR4+fFhvvvmmnnnmGbVu3VqzZ89Wjx49NG7cONWuXdt8rcyfP1//+Mc/HJZdsGCBvL291ahRoxv2b7fb1a1bN23evFlPP/20/P39tW/fPn399dc6fPiwRo0a5dB+8+bNWrp0qTp27Chvb29NnjxZb7zxhn766Sf5+fmpSZMmOnz4sBYuXKi4uDj5+flJkooUKWL2sX79ev3www969tln5efnpzJlymj//v169tln5e3trZdeeknu7u6aMWOGYmNjNWXKFFWrVs1hHIMGDZKvr6+6d++uQ4cOadq0aTp+/LgmT54sFxcXtWrVSvPmzdPq1avN4CNJCQkJWr9+vV577bWb1n3OnDmKi4tTaGioevXqpbNnz+qbb77Rli1bNG/ePPn6+uqVV15RxYoVNWPGDL3xxhsqW7asypUrd8M+c7pv9+/fX3PnzlXTpk31/PPPa8eOHRozZowOHjyokSNHOrQ9cuSIevToobZt25qvk759+yo4OFhVqlRRkyZNVLBgQQ0ZMkQtWrTQY489Jm9v72yNI8P+/fv18ssvKzAwUG+88YY8PT115MgRbdmy5abLrV27Vl26dFHZsmXVvXt3XblyRVOmTFGHDh00Z84cc3/J4Mx++ldNmjTRe++9p6VLl5qhbeHChapUqZIeeuihTO2z857p7++vN954QyNGjFD79u1VvXp1SXL4WZ4/f15dunRR8+bN9eSTT6po0aJZjq9fv35av369+vTpoxkzZsjNzU3Tp0/X6tWr9dFHH5nv0TczZ84cXb16VU8//bQ8PT1VqFChbNU5ICBAvr6+2rRpk/m+sGnTJrm6umrv3r26dOmSfHx8ZLfbtXXrVvM9D7nMgOVdvHjRCAgIMLp165btZRo0aGD06dPH/P7q1auGzWZzaBMfH2+EhIQYX375pTmtW7duRvPmzW/ad/Xq1Y2BAwfecL7dbjcef/xx44UXXjDsdrs5PSUlxWjYsKHx/PPPZ7uvGxkxYoQREBBg9OrVK9O8lJSUTNMWLlxoBAQEGBs3bszUR1xcnEPb1157zXj44YcdpgUEBJjjHDp0qBEUFGTMmTPHnH/p0iUjKirK6N+/v8NyCQkJRvXq1R2mt2rVyqhdu7aRlJRkTlu9erUREBBgNGjQ4Jbb3qBBAyMgIMBYsmSJOe3ixYtG7dq1jZiYGHPa9OnTjYCAAOPAgQPmtNTUVOORRx5xeG1kZd68eUZQUJBDvQzDMKZNm2YEBAQYmzdvNqcFBAQYwcHBxpEjR8xpe/bsMQICAozJkyeb08aNG2cEBAQY8fHxmdYXEBBgBAUFGfv373eY/uqrrxrBwcHG0aNHzWmnTp0yIiIijGeffdacNnv2bCMgIMBo3bq1kZqaak4fO3asERAQYCxbtswwDMOw2WzGY489ZvTs2dNhPRMnTjQCAwMd1nO91NRUo2bNmkaLFi2MK1eumNN/+uknIyAgwPj8888zjWfHjh037M8wcr5vZ9S1X79+DtOHDh1qBAQEGOvWrTOnZbxO/vozPHv2rBESEmIMHTrUnBYfH28EBAQY48aNc+izT58+Wb4eM/abDBMnTjQCAgKMs2fP3nDcGeuYPXu2Oa1Vq1ZGzZo1jcTERIftCwoKMnr37p1pfdnZT7PSp08fIzw83DAMw3j99deN5557zjCMa6+F2rVrG1988UWWNcjue+aOHTsybVuGTp06GQEBAca0adOynNepUyeHaatWrTICAgKMUaNGGUePHjXCw8ONV1999ZbbmDH+yMjITD+H7Na5a9euRtu2bc3vu3fvbnTv3t2oWrWqsXLlSsMwDOO3335z2J+QuzgFDF26dEmScvyX+V95enrK1fXay8lmsykxMVFeXl6qWLGidu/ebbbz9fXVyZMntWPHjhv25evrq+3bt9/wlMSePXt0+PBhtWzZUomJiTp37pzOnTun5ORk1axZUxs3bpTdbs9WX7fyzDPPZJr217v8rl69qnPnzplHirI6tXx9H1FRUTp//rxZ9wyGYWjQoEH65ptv9PHHHzucGlm7dq2SkpLUvHlzc3vPnTsnV1dXVatWTRs2bJB07YLxPXv2qHXr1g7Xh9WuXVuVK1fO9nYXL15cTZo0Mb/38fFRTEyMdu/erYSEBElSs2bNlC9fPofT96tXr1ZiYqKefPLJm/a/ePFi+fv7q1KlSg7b8+ijj0qSuT0ZatWq5XCUKygoSD4+PoqPj8/2NtWoUcOhBjabTWvWrFHjxo314IMPOmx7ixYttHnz5kw/o/bt28vDw8P8vkOHDnJ3d9fKlSslSa6urmrZsqVWrFjhsOz8+fMVERHhsJ7r7dq1S2fPnlWHDh0crmerX7++KlWq5NR1ejndtzO24/nnn3eY/sILLzjMz1C5cmVFRUWZ3xcpUkQVK1bM0c/lVjKuHVy+fLm5X9/KX/eDwoULm9ODgoJUq1atTNshZX8/vZmWLVvq119/NY/4JiQkmGc2rpfd98xb8fT0VJs2bbLVtk6dOmrfvr1Gjhyp119/Xfny5cvR46cef/xxh6PqOalz9erVtXv3biUnJ0u6dlT/scceU1BQkHnpzqZNm+Ti4mIe6UTu4hQw5OPjI0m6fPmy033Y7XZ98803mjp1qv7880/ZbDZz3l/fGLp06aK1a9eqXbt2Kl++vGrXrq0WLVo47PBvv/22+vbtq/r16ys4OFj16tVTTEyM+cvz8OHDkmReB5WVixcvqlChQrfs61auP00kXTvl8uWXX+r777/X2bNnM633eqVLl3b4PuMX2oULF8zaS9dO8SYnJ+v999/PdM1Qxjbf6DrMjH6OHz8u6dp1XdfLyS+W8uXLZzqVXaFCBUnSsWPHVKxYMfn6+qpBgwZauHChevbsKena6d8SJUqYQe5Gjhw5ooMHD6pmzZpZzr++rqVKlcrUplChQjl69t31P8tz584pJSVFFStWzNTW399fdrtdJ06cUJUqVczp19fV29tbxYoV07Fjx8xpMTExGjt2rJYtW6aYmBj98ccf+u233zRw4MCbji/jZ5fVeCpVqpTp+tbsyOm+fezYMbm6umY6pZzx8/7rdko3/rn89brI2xUdHa1Zs2apf//+Gj58uGrWrKkmTZroiSeeMAPU9W5WS39/f61evTrTTV3Z3U9vpl69evL29tb333+vvXv3KjQ0VOXLlzevRf2r7L5n3kqJEiVydCNGnz59tGLFCu3Zs0fDhw+/4SnjrFy/D+WkzlFRUUpPT9e2bdtUsmRJnT17VlFRUTpw4IA2bdok6VoArFy5co62H84jAEI+Pj4qXrx4ljcTZNfo0aP1+eef66mnnlKPHj1UqFAhubq66sMPPzSvnZOuvSksXrxYP//8s1atWqWlS5dq6tSpeu211/TGG29IuvaGHxUVpR9//FFr1qzR+PHjNXbsWH3xxReqV6+e2V/v3r1v+FiEjDf2W/V1K1ndWdizZ09t3bpVL774oqpWrSovLy/Z7Xa99NJLDtua4Ua/pK5vGxkZqb179+rbb79Vs2bNHN4EM9p+9NFHKlasWKa+3NzcbrktuSEmJkaLFy/Wli1bFBAQoBUrVqhDhw433OYMdrtdAQEBiouLy3L+9dcr3mj7sqr3jdytZ1hWrlxZwcHBmj9/vmJiYjR//nx5eHioWbNmd2X9f+Xsvn19+L+R23nd3Wgdfw1C0rWf27fffqsNGzaY7xvff/+9ZsyYoQkTJtyx135299Ob8fT0VJMmTTRv3jzFx8ere/fuN2yb3ffMW8np63rPnj3mH1j79u3L0bK3sw+FhIQoX7582rhxo0qXLq2iRYuqYsWKioqK0tSpU5WamqrNmzercePGTq8DOUMAhCSpQYMGmjFjhrZu3aqIiIgcL79kyRI98sgj+vDDDx2mJyUlmRfkZ/Dy8lJ0dLSio6OVmpqq119/XaNHj9bLL79sBq7ixYvr2Wef1bPPPquzZ8+qdevWGj16tOrVq2cevfPx8cnWM9Bu1ldOXbhwQevWrdPrr7/u8OaecYTudpQvX17vvPOOOnfurJdeekmTJk0yjzxkbHPRokVvus0ZRzGOHDmSad6hQ4eyPZYjR47IMAyHX9IZ21imTBlzWt26dVWkSBEtWLBA1apVU0pKilq1anXL/suVK6e9e/eqZs2a2Q4bt5LTfooUKaICBQpkWZc//vhDrq6umY5wHTlyxOHo5uXLl5WQkKDHHnvMoV1MTIyGDh2q06dPm48BKVSo0E3Hk/GzO3ToUKYjo4cOHcp0hCq7crJvlylTRna7XUeOHJG/v785/cyZM0pKSnL42d8uX1/fLI/gZhxV+itXV1fVrFlTNWvWVFxcnEaPHq1PP/1UGzZsyHJ/+Gstr/fHH3/Iz88v1x7p1LJlS82ePVuurq5q3rz5Ddtl9z3zTu0f0rVHWcXFxaly5cqKiIjQuHHj1LhxY/OmuZzKSZ09PT0VFhamTZs2qXTp0ualA9WrV1dqaqrmz5+vM2fOqEaNGk5uHXKKawAh6dqt915eXurfv7/OnDmTaf7Ro0f19ddf33B5Nze3TH+1/vDDD5muvbv+USmenp7y9/eXYRhKS0uTzWbLdBq1aNGiKl68uPkYipCQEJUrV04TJkzI8tRWxnPHstNXTt3oaMPNapMTQUFB+uqrr3Tw4EF169ZNV65ckXQtaPn4+GjMmDFZPmIkY5uLFy+uqlWrau7cuQ7bvmbNGh04cCDb4zh9+rTDo2MuXbqkefPmqWrVqg5HIN3d3dW8eXP98MMPmjNnjgICAhQUFHTL/ps1a2Z+wsH1rly5Yl4nlBMZn4SR1Wn4rLi5ual27dpavny5wym6M2fOaOHChapevXqmU38zZsxwqP+0adOUnp6eKQC2aNFCLi4u+uCDDxQfH3/LayKla6/rokWLavr06Q6vz5UrV+rgwYOqX79+trbrejnZtzP+KLr+9Txx4kSH+XdCuXLldPHiRYfHnlz/upOuXXJxvYwj/zfajzP2g3nz5jmEzH379mnNmjV3dDuu98gjj6hHjx4aMGBAlkfrM2T3PTPjdX0nPupv2LBhOnHihIYOHaq+ffuqTJky6tu3r9Pvhzmtc/Xq1bVjxw5t2LDBvOynSJEi8vf319ixYyXJ4ZpS5C6OAELStTfjYcOG6c0331R0dLT5aQGpqanaunWrFi9efNMLjevXr6+RI0cqLi5OERER2rdvnxYsWJDpWrsXX3xRDzzwgCIjI1W0aFH98ccfmjJliurVqycfHx8lJSWpXr16atq0qYKCguTl5aW1a9dq586d5rPOXF1d9a9//UtdunRRixYt1KZNG5UoUUKnTp3Shg0b5OPjo9GjR+vy5cu37CunfHx8VKNGDY0bN05paWkqUaKE1qxZk+U1Ps4KDw/XqFGj1LVrV73xxhsaOXKkfHx89P7776t3795q06aNoqOjVaRIER0/flwrV65UZGSk3n33XUlSr1699PLLL6tjx4566qmndP78eU2ZMkVVqlTJdrCqUKGC+vXrp507d6po0aKaPXu2zp49qyFDhmRqGxMTo8mTJ2vDhg16++23s9V/q1at9MMPP+i9997Thg0bFBkZKZvNpj/++EOLFy/WuHHjFBoamv2iSeZjfT799FNFR0fLw8NDDRo0uOmRnp49e2rt2rXq2LGjOnbsKDc3N82YMUOpqalZPv4jLS1N//jHP9SsWTMdOnRIU6dOVfXq1TM98qZIkSKqW7euFi9eLF9f32yFNw8PD7399tuKi4tTp06d1Lx5c/MxMGXKlMn0uJ3sysm+HRQUpNatW2vGjBlKSkpSjRo1tHPnTs2dO1eNGze+5bWdOREdHa1hw4ape/fuio2N1ZUrVzRt2jRVrFjR4WaqkSNHatOmTapXr57KlCmjs2fPaurUqSpZsuRNbxbo3bu3unTpovbt26tt27bm40kKFix401Ozt8vV1VWvvvrqLdtl9z2zXLly8vX11fTp0+Xt7S0vLy+FhYVl+zrmDOvWrdPUqVPVvXt3c18ZMmSIYmNj9dlnn6l379456i9DTuocFRWl0aNH68SJEw5BLyoqSjNmzFCZMmVy9Lgq3B4CIEyNGjXS/PnzNX78eC1fvlzTpk2Tp6en+aDZmz2b6ZVXXlFKSooWLFig77//Xg899JDGjBmj4cOHO7Rr3769FixYoIkTJyo5OVklS5ZUbGys+YaZP39+dejQQWvWrNHSpUtlGIbKlSun9957z3wQs3Ttr+wZM2Zo1KhRmjJlipKTk1WsWDGFhYWpffv2Oeorp4YPH67Bgwdr6tSpMgxDtWvX1tixY1W3bl2n+7xezZo19dlnn+mNN95Q7969NXz4cLVs2VLFixfXV199pfHjxys1NVUlSpRQVFSUQzh/7LHH9Pnnn+uzzz7T8OHDVa5cOQ0ZMkTLly/Xr7/+mq31V6hQQQMGDNBHH32kQ4cOqWzZsvr000+z3MaQkBBVqVJFBw8ezNaRLunaL8mRI0dq0qRJ+u9//6sff/xRBQoUUNmyZRUbG5vlReW3EhYWph49emj69OlatWqV7Ha7li9fftMAWKVKFX377bcaPny4xowZI8MwFBYWpo8//jjTMwAl6d1339WCBQs0YsQIpaWlqXnz5urfv3+Wp+latWqln376Sc2aNcv2Rfpt2rRR/vz5NXbsWA0bNkxeXl5q3Lix3nnnndv6JI2c7Nv/+te/VLZsWc2dO1fLli3TAw88oJdffvmOhyY/Pz99+eWXGjp0qD7++GPzGXxHjhxxCIANGzbUsWPHNHv2bCUmJsrPz08PP/ywXn/99Zt+EkqtWrU0btw4jRgxQiNGjJC7u7tq1Kihd955J8fhKTdk9z3Tw8NDQ4cO1SeffKL3339f6enpGjJkSI624dKlS+rXr58eeughvfLKK+b0qKgode7cWRMnTtTjjz+u8PDwHG9HTuocEREhNzc35c+f3+FMQUYA5Ojf3eVi5ORqUwDIQkxMjAoVKnTHToXfazIe0Pzdd99l+8jksmXL9Nprr+nbb7/lFxuAew7XAAK4LTt37tSePXsUExOT10O5p8yaNUsPPvggzzQDcE/iFDAAp+zbt0+//fabJkyYoGLFiik6Ojqvh3RPWLRokX7//Xf9/PPP6tev3x29ixMA7hQCIACnLFmyRCNHjlTFihX1ySefZPnMRCvq1auXvLy81LZt29u61hQAchPXAAIAAFgM1wACAABYDAEQAADAYv621wDa7Xalp6fL1dWVi7ABAMDfimEYstvtcnd3v+Xnr2flbxsA09PTtXPnzrweBgAAQK4JDQ3N9sPm/+pvGwAz0nBwcLA8PDzyeDT3D7vdroMHD8rf39+pvyisiro5j9o5h7o5j9o5h7o5J7fqZrPZtHPnTqf7/NsGwIzTvm5ubnJzc8vj0dw/XFxc5OLiIjc3N3bwHKBuzqN2zqFuzqN2zqFuzsntujl7mRs/QQAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYAiEzy5cuX10O4L1E351E751A351E751C3vw/3vB5AbnN1JePmhKurqypUqJDXw7jvUDfnUTvnUDfnUTvnULfssdkNubm65PUwbulvHwD7zN6uPScv5/UwAADA31zl4j76/JmIvB5GtvztA+AfCZf12/GkvB4GAADAPYPzowAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAABw1xiGXbaUi7KnXXVuebtNtpSLMtLTbtjGnnZF9qvJMgzjxm1Sr8iWfEGG7cb9/J255/UAAADA31/q6UNK2jhXyb+vlZF2RZLkUayCCkY2l09IY7m4e9xwWSM9VZf3/KKL235Q6on9kmGXJLkVKqECFcJVuG4nSVLS5oVK3vuL0hNPSJJcC/jK+6F6KhjVSh6FS+rKkR268OtspZ7YL3tKktm/m7ef8leKkm/Uk/IsXlGpZ44q+fc1Sj25X6mnD8tIT73WrkBBFYxqpYLhT+RKje4mAiAAAMhVSZsXKHH5WDO4ZUhLOKxzS0bq4tbvVeLpwXLzLpxp2bTzJ3V61vtKP/dnpnm2C6d0afsSXdq+RK4FfB1CnSTZU5J0cfMCXdq+RAUjWyjp1zlZjs92OVGXd/6oy7uWyyesiS7t+DHTWCXJnnxe55Z8qfzlQuVRpEwOKnDv4RQwAADINSmHtylx2RjJsOupp57SunXrlJqaqjNnzujTTz9VyZIllXb6kBLmf5TplK39arJOzxyg9HN/qnTp0hoyZIji4+OVlpamM2fOaOnSpSpUqNC1tilJCg8P14wZM3T+/HklJydr6dKlatiwoYz0VDP8RUdHa+PGjbpw4YIMw1BCQoJ+/vlnxcTESIZdl7YvkQy76tSpo88//1yrV6/Wzp07tXPnTkVFRUmSUhMO380S5gqOAAIAgFxz/pfJkqSXX35Zo0ePliQdOHBApUqVUs+ePRUdHa2IiAglH92hK4e3qUDFCHPZpF/nKj3xhMqXL6/169erZMmSunTpktavX698+fKpQYMG8vb21oULF1S5cmWtXr1a3t7e2rVrl06fPq0mTZqoUaNGatWqlRYuXChJeuihhxQaGqp9+/ZJkoKDg1WvXj3Vq1dPUVFR2rx5sySpY8eO6tatm8O2eHl5XfviJtcW3i/u6SOAGzZsUGBgoJKSkm7dGAAA3FNslxKVeuJ3ubi4aNCgQZKkqVOnqkqVKqpRo4YkKSAgQLGxsZKklIO/mssahqHLe36WJA0dOlQlS5bU77//rsqVK6tu3bp6+OGHVbp0aZ09e1aS9Nprr8nb21unTp1S9erV1ahRI82ZM0eurq4aOHCg2e+MGTNUpEgRhYWFKSwsTK1btzbnNWnSxPz6m2++0cMPP6wGDRrkTnHy2D0dAAEAwP3rytHtkqTIyEgVL15ckvTdd99Jkvbs2aPdu3dLkp544tpNFSmHtprLpp2NV3riCeXPn18tWrSQJH322WcKDw/Xs88+q6pVqyohIUFXr167m7hatWqSpMOHDys1NVWSi37//Xdz/SVLlpQkxcfHKzk52VyP3f6/a/127txpfr1+/Xpt3LhRV65cuUPVuLfc9ing2NhYValSRZL03//+V+7u7urQoYN69OghFxcXXbhwQR988IF++uknpaamqkaNGurfv78qVKggSTp27JgGDx6szZs3Ky0tTWXKlFHv3r3l7++vzp07S5L5V0Lr1q01dOjQ2x0yAAC4C9KTEiRJYWFh5rSTJ086fP3QQw+Z4c12MeF/y/7/TR8RERHy8fGRJP373/+Wr6+v2ea///2v2rdvr6tXr+rQoUNq0KCBgoKCVKRIEZ0/f16PPvqo2bZq1armusuVK6cpU6bI19dXoaGhunTpkgYMGKBFixZJkoo8/qou7/lFV+N33dF63EvuyBHAuXPnys3NTbNmzVK/fv00adIkzZo1S5LUt29f7dq1S//5z380Y8YMGYahrl27Ki3t2nN3Bg0apNTUVE2ZMkULFizQ22+/LS8vL5UqVUpffPGFJGnx4sVavXq1+vXrdyeGCwAA7oKMZ/V5enqa0zJ+/0v6/yN1/5tvpF3Vxa3f//+/HyTJDH+SzAyRcUq3VatW6tKliyRp5MiRSktLU6FChXTgwAEdPnzY4fStm5ub+XW+fPkUHBysqlWrytXVVT4+PmrXrp2KFStmtvGt8b9Twzllt9sd/hmGkWnanfh3O+7ITSClSpXSP//5T7m4uKhSpUrat2+fJk2apIcfflgrVqzQtGnTFBkZKUkaNmyY6tevr2XLlqlZs2Y6fvy4mjZtqsDAQEnSgw8+aPabcWdP0aJFHRI/AAC497nmvxbeTp8+bU4rWLCg+XXG7/ZTp06Z084tHeXQR8Y1ftK16wfHjh0rSXrqqacUEhKiJ598Ul9++aW2bNmi+vXrq1evXoqMjNSZM2e0YsUKPffcc5KkQ4cOmf3s379fRYsWlYuLi2JiYjRnzhzVqlVLcXFx6tWrl84t+0ruvg/ccLsSl4+Vq2cBFahUPcv5R48eNU9NS9KVK1d08ODBG/bnjJs95Do77kgArFatmlxcXMzvw8PDNXHiRB04cEDu7u7moV1J8vPzU8WKFc1CdO7cWe+//75Wr16tWrVq6fHHH1dQUNCdGBYAAMhD+Updu0Rs5cqVstlscnNzU1hYmH766Se5ubkpODhYkrR8+XJzmTZt2kiStm/froMHD2r79u1KTEyUn5/ftVDl6i7Z082A9degtXbtWq1du9b8/vPPP5ck7dq1K8sAZhiG5s6dq/Pnz6tw4cLmwSjZ05V+/mSm9hlsl87q9Kz3VOypAfKq/Eim+eXKlTO/ttvtOnDggPz9/eXqeuduvbDZbNqxY4fTy+f5Y2DatWunOnXq6Oeff9aaNWv01VdfqU+fPuYdQQAA4P7kWSpArvl9dO7cOS1ZskTR0dHq06ePzp07pwYNGphn+mbMmCFJcnFx0ezZsyVJPXr00IgRI2Sz2TRr1ix17dpV7dq105w5c1SmTBlFRFx7XEzGJWdFihTRgAED9OOPP+rq1at6/PHH9dprr0mSvvzyS3NMy5Yt0+rVq7V9+3a5u7urbdu2Kly4sCRpzZo1ZrvGjRurTp06DmcmX3jhBTVs2FDr1q3TkiVLlLJ/Q5YB8Pqg5+LiIldX1zsaAO+JI4DXJ9Dt27erfPnyqly5stLT07V9+3bzFHBiYqIOHTqkypUrm+1LlSqlDh06qEOHDho+fLhmzpyp2NhYeXhc+1gYm812J4YJAADuIhdXN/mENlHSxrnq2rWrFi5cqPDwcH3zzTeSrp0ajYuLM5+9dyO9e/dWYGCg6tWrp19++cWcPnXqVH377beSJA8PD/Xs2VM9e/Y056ekpGjYsGEaM2aMOS0wMFCNGjXKtI4ff/xRI0aMML9v2LCh4uLiHNpknE7+5JNPtGTJkvv6c4TvSAA8fvy4hgwZovbt22v37t2aMmWK+vTpowoVKqhRo0YaMGCABg4cKB8fHw0bNkwlSpQwi//BBx/oscceU4UKFZSUlKQNGzbI399fklSmTBm5uLjo559/Vr169ZQvXz55e3vfiSEDAIC7oFDtDko5tEXHjh1RRESEGjdurOrVq+vChQuaPXu2EhL+d+evYRhq2bKlJOm3334zp1+4cEH169dXrVq1VKdOHaWmpmrJkiXas2eP2SYhIUGPPfaYqlWrJl9fX508eVJz585VYmKiw3geffRR1apVSxUqVJCnp6dOnDihtWvXau/evQ7tvvnmG4fTyX/1xx9/SJJc892/mcTFuM1jiLGxsapcubLsdrsWLlwoNzc3dejQQT179nR4DMyKFSuUlpamqKgoDRgwwHwMzODBg/XLL7/o5MmT8vHxUd26dRUXFyc/Pz9J1+7qmTZtms6cOaOYmJhsPwbGZrNp27Zt+mB9srbE8yBpAADyii35gs4t+0rJe1dl+oxdN9/i8mvwgq4c2qJLO5Y6zHP1KqQSz3ygi1sW6tKOZZI93WG+i7unfB9uIxd3T51fNSXLz+91L1RChWp3lLtvMZ1Z8LFslxMztbm2MncVDH9C3g/V16mZA2Skptx0m1w88qlEh6HmdY6SFFzaV4veqOvQzm63a//+/apSpcodvwZw27ZtCg8Pd7jDObvuSAAMCgq65x7RQgAEAODeknb+pFL2r1d6UoJcPPIpX6lAFfCPkourmwy7TVfjf1P6hWs3X7h45Ff+cmFy8y4s6dqnilz+fbXSz5+Ui6ubPIpXlFflR+Sa79rHs6VfOKUrR3YoLfG4jLQrcs3no3xlqyp/hXC5uFwLXkZ6qq4e26vUUweVfumsZLfJNb+PPEtWUb6yD8nt/+9aTk86oyvxO6UbneJ181D+B4Pl7lvcYfL9FADz/CYQAABgDR6FS8qjRkyW81xc3ZS/fJiksCznu/n4ybd6yxv27V6ohHzCmtxwvnTtiGH+8mH/v54bc/d9QD7Bf8+PgMvAR8EBAABYzG0fAZw8efKdGAcAAADuEo4AAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMe55PYDcVqmYt67a8noUAADg765ycZ+8HkK2/e0D4L+fqiY3N7e8HgYAALAAm92Qm6tLXg/jlv72p4DtdnteD+G+YrfbdfjwYeqWQ9TNedTOOdTNedTOOdQte+6H8CdZIAAi565evZrXQ7gvUTfnUTvnUDfnUTvnULe/DwIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiLB8AL1++rMuXL+d4OcMwdOnSJSUnJ9+0nd1u1/nz55Wenn7Tdqmpqbpw4YIMw7jlOlNSUnI8XgAAgAyWDICnT5/Wu+++q7Jly8rHx0c+Pj4qV66cBg4cqDNnztx02WPHjqlPnz4qVaqUChYsKG9vb1WqVElDhw7V+fPnzXarV69WmzZt5O3tLT8/P+XLl0/169fXnDlzZLfbJUlXr17VuHHjFBkZqXz58qlw4cIqXLiwXnjhBf32228O6+zdu7e5Ti8vL/n7++vf//63wzoBAACyxfibSk9PNzZt2mSkpqY6TF+9erXh5+dnSMry3wMPPGD8+uuvWfa5dOlSo2DBgjdctnTp0saOHTuMXr163bCNJKNFixbG0aNHjYiIiBu2cXNzM0aNGmUsWbLE8PHxuWG7MmXKGLt27bpjdbPZbMbevXsNm812x/q0AurmPGrnHOrmPGrnHOrmnNyqW0bOSU9Pd2p5Sx0BPH36tFq1aqXExERFRERo1qxZunjxopKSkjR9+nSFhYXpzJkzatmypc6dO+ew7JEjR9SmTRtdvHhRjz76qObPn6/Lly/rwoUL+vrrrxUYGKjjx48rLCxMn3zyiVxcXNS1a1ft2LFDV69e1eHDh9WvXz95e3tr4cKFKleunLZu3aoSJUpo+PDhOnXqlFJSUrR69WrFxMTIZrPp1VdfVdOmTXXp0iXVrFlTCxYs0OXLl3X+/HlNmjRJAQEBOnbsmFq2bOnUaWwAAGBRdzSO3kOyOgLYu3dvQ5IRHh5uJCcnG4ZhGKdPnzZOnz5tGIZhXLp0yQgODjYkGQMGDHDor2vXroYko06dOmafx48fN86dO2cYhmEkJiYaFStWNI/MDRs2zFx2//795jJLliwx23h5eRl79+41DMMwkpOTjUOHDpnLvPjii2a7unXrZrnOc+fOGeXLlzckGZ9++ukdqRt/4TmHujmP2jmHujmP2jmHujmHI4D3gPnz50uS+vfvrwIFCujAgQMqX768KlasqMOHD8vb21txcXGSpAULFpjLGYZhfv/+++/Lw8ND27ZtU/ny5eXv76/Tp0+rcOHCevvttyVJxYoV05tvvilJeu+991SlShW1b99ekvT444+rXr16kqTOnTsrMDBQkhQVFaWKFStq2rRpkqR//etfcnFxkSQNHDhQHh4e2rJli8qXL6/KlSsrISFBfn5+euuttzKNFwAA4GbuqQCYmpqaa33/+eef2rt3r9zd3dW4cWNJ0qJFi5SSkqLLly9r8eLFkq4FNBcXF23btk0JCQmSpD179ujEiRPy9vZW3bp1JUn//e9/lZaWpsTERC1fvlyS9MQTT0iSmjRpIlfXa6X97rvvJEkLFy7UlStXHNpl/P/bb79p9+7dDu1LliypatWqycfHR3Xq1HFY57lz57RixQqHPlatWsXdwQAAIFvcc7Pz2NhYValSRdK18OLu7q4OHTqoR48ecnFxUcOGDfXUU0/pyJEjWrZsmR5//HENHTpUmzZt0ieffKJdu3bJz89PTZo0Ua9eveTl5eX0WI4ePSpJKl++vAoVKiRJOnHihDn/5MmTkq4dvStdurSOHTum+Ph4FStWTIcPH5YkBQUFydPTM9Oyp06dkiRVqlRJPj4+qlatmjkvo11GcCtdurQ5P+P/rPqSpPDwcHl4eMjDw+OG7apUqSIvLy8lJyfr1KlTqlChglP1AQAA1pGrAVCS5s6dq7Zt22rWrFnatWuX3n33XZUuXVpPP/20JGnChAl67bXX1L17d0nXglqXLl3Uo0cPffjhhzp37pwGDx6swYMHa8iQITlev91ul91uN4+O5cuXz5yXlpZmfv3Xo48ZbUaOHKmHH35YCxculCQz/N1q2Vu1y+g/o93t9JXRLjk5WSkpKeYjZpxlt9tlGMZt92M11M151M451M151M451M05uVW32+0v1wNgqVKl9M9//lMuLi6qVKmS9u3bp0mTJpkB8NFHH9ULL7xgtu/Xr59atmypf/zjH5KkChUqqF+/foqNjdX777/vEOCy49ixY0pLS1NSUpKka3cCZyhYsKD5ta+vr6RrBc14FuCECRM0YcIEs82tlr1y5YouXLiQqd2lS5cc2mUcvTt9+rRKly6dZV8Z7W61zpSUFHPbzp49q/3799+yJrdy5coVHTx48Lb7sRrq5jxq5xzq5jxq5xzq5pzcqJtxkw+OyI5cD4DVqlUzb2aQrp3WnDhxomw2myQpJCTEof3evXv1+++/Z7oJw263688//5S/v3+O1l+mTBl5eHioXLly8vLy0pkzZ7Rjxw6FhYUpLCzMbBcaGipJ2rRpkxmo6tatqyJFiujEiRP69ddfdfDgQR09elTlypXLctlVq1YpPT3dvD5PksLCwnTixAmVKVNGRYoUkSRz/ooVKxQeHq7g4GC5urrKbrebfdlsNq1cuVKJiYn6888/VbZs2SzXuXLlStlsNj344IOqWbOmQ62dYbfbdeDAAfn7+5vXMeLWqJvzqJ1zqJvzqJ1zqJtzcqtuNptNO3bscHr5XA+At1KgQAGH75OTk/XMM88oNjY2U9tSpUrluH9XV1e5urqqQIECql+/vr7//nvzmX9PPvmkevToIXd3dzVr1kySNH36dHPZDz/8UHXq1NHs2bPVtm1bc37v3r3VsWNHbdy4UQ888IBq167tsOzGjRt18OBB+fv7a+jQoSpcuLA6d+4s6dpHz2WcUp4xY4Z69eolPz8/jR49Wj///LN69+4tSVqyZIkSExPNft9++2116tRJmzZtUokSJVSzZk2HdTZt2lRubm45rk9WXFxczLoh+6ib86idc6ib86idc6ibc3Kjbrd7BDBXnwPYqVMnIzo62mHasGHDjGbNmhmGYRgNGjQwJk6c6DC/V69exnPPPXfb687qOYBz5841JBmenp7GzJkzMy3z7bffGu7u7ubz91atWmUYhmF89913Ds/uW7RoUaZlx4wZY7i4uJjtIiMjjfj4eIc258+fN5566imHT/J46623jCtXrji02759u1G2bFmHdf7www8ObWw2m/Gf//zHXOe6deucrtX1/fKcp5yjbs6jds6hbs6jds6hbs65V58DmOtHAI8fP64hQ4aoffv22r17t6ZMmaI+ffrcsH2XLl3Uvn17DRo0SO3atTOf17d27Vq9++67tzWWVq1aqWPHjpo6daqefvpphYWF6YknnpBhGPr+++8dPn9Xkv75z3+ap4AzJCcnq3nz5qpRo4YaNmyo9PR0zZ8/37z2rnXr1lq9erW2bNmiSpUqKSYmRgEBATpx4oRmzpypS5cuydPTUx06dNDXX3+t4cOHa8qUKWrbtq0KFSqkzZs3a+nSpTIMQ1WrVtUDDzygVatWqVmzZjdc51tvvaVHH330tmoDAACsI9cDYExMjK5cuaJ27drJzc1NnTt3Nh+KnJWgoCBNnjxZn332mTp27ChJevDBBxUdHX3bY3FxcdGkSZNUvnx5jRgxQjt27HA4f+7j46NevXqpbNmy6tq1q1atWmXOc3V11fTp07V27VqNHj1aGzdu1MaNG835hQsXVt++ffXOO+/owIEDeuWVV/TTTz9p1qxZDmMIDg7WqFGj9Nhjj+mJJ55Q7969FR8fr5EjRzqM8+mnn9YXX3yhggULKi4u7obrjIuL0zvvvHPbtQEAANbhYhi3exL5xmJjYxUUFKR+/frl1ipuyGazadu2bQoLCzOfo/dXiYmJmjlzpvbu3SsXFxdVrVpVTz/9tPmMwK1bt2rz5s0yDENubm6qWbOmqlatKklKSEjQzJkztX//frm5uSkkJETt2rWTj4+Pwzq2bt2qhQsX6tSpUypcuLAaNGighg0bOtyokZaWpoULF2r9+vVKTk5WuXLl1KZNm0w3uyQkJGjGjBk6cOCA3NzcFBoaqrZt22Za5+2y2+3av3+/qlSpwjUeOUDdnEftnEPdnEftnEPdnJNbdcvIOeHh4U7dA5DnN4HkFT8/P7388ss3nB8REaGIiIgs5xUrVkyvvfbaLddxsz4yeHh4qHXr1mrduvVN2xUrVsx8ViIAAMDtIMIDAABYTK4eAZw8eXJudg8AAAAncAQQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYtzzegC5xTAMSZLNZpOrKzk3u+x2uwzDkM1mM2uIW6NuzqN2zqFuzqN2zqFuzsmtutlsNklyuk8X42/6U0xNTdXOnTvzehgAAAC5JjQ0VJ6enjle7m8bAO12u9LT0+Xq6ioXF5e8Hg4AAMAdYxiG7Ha73N3dnTrT+bcNgAAAAMgaF8cBAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLua8D4LfffquGDRsqNDRU7dq1044dO27a/ocfftATTzyh0NBQtWzZUitXrrxLI7235KRu+/fv1+uvv66GDRsqMDBQkyZNunsDvcfkpG4zZ85Ux44dVaNGDdWoUUP/+Mc/bvn6/DvLSe2WLl2qNm3aKCoqSuHh4WrVqpXmzZt39wZ7D8npe1yGRYsWKTAwUK+++mouj/DelJO6zZkzR4GBgQ7/QkND7+Jo7y05fc0lJSVp4MCBqlOnjkJCQtS0aVNL/m7NSd1iY2MzveYCAwPVtWvXuzhiScZ9atGiRUZwcLDx3XffGfv37zf69+9vREVFGWfOnMmy/ebNm42qVasaY8eONQ4cOGB8+umnRnBwsPH777/f5ZHnrZzWbfv27cbQoUONhQsXGrVr1zYmTpx4dwd8j8hp3Xr16mVMmTLF2L17t3HgwAGjb9++RvXq1Y2TJ0/e5ZHnvZzWbv369cbSpUuNAwcOGEeOHDEmTZpkVK1a1fjll1/u8sjzVk7rliE+Pt6oW7eu0bFjR6Nbt253abT3jpzWbfbs2UZkZKRx+vRp819CQsJdHvW9Iae1u3r1qtGmTRujS5cuxqZNm4z4+Hhjw4YNxp49e+7yyPNWTuuWmJjo8Hrbt2+fUbVqVWP27Nl3ddz3bQBs27atMXDgQPN7m81m1KlTxxgzZkyW7Xv06GF07drVYVq7du2MAQMG5Oo47zU5rdtfNWjQwLIB8HbqZhiGkZ6ebkRERBhz587NpRHeu263doZhGDExMcann36aC6O7dzlTt/T0dKN9+/bGzJkzjT59+lgyAOa0brNnzzaqV69+t4Z3T8tp7aZOnWo0atTISE1NvVtDvCfd7nvcxIkTjYiICOPy5cu5NcQs3ZengFNTU/Xbb7+pVq1a5jRXV1fVqlVLW7duzXKZbdu2qWbNmg7T6tSpo23btuXmUO8pztQNd6ZuKSkpSk9PV6FChXJrmPek262dYRhat26dDh06pBo1auTmUO8pztZt5MiRKlq0qNq1a3c3hnnPcbZuycnJatCggerVq6du3bpp//79d2O49xRnardixQqFh4dr0KBBqlWrllq0aKHRo0fLZrPdrWHnuTvx+2H27Nlq3ry5vLy8cmuYWbovA2BiYqJsNpuKFi3qML1o0aI6c+ZMlsucOXNGDzzwQLbb/x05UzfcmboNGzZMxYsXd3iTsAJna3fx4kVFREQoJCREXbt2Vf/+/VW7du3cHu49w5m6bdq0Sd99950GDx58N4Z4T3KmbhUrVtSHH36oUaNG6eOPP5ZhGHrmmWd08uTJuzHke4YztYuPj9eSJUtks9n01Vdf6dVXX9XEiRP1n//8524M+Z5wu78fduzYoX379uXJH23ud32NgMV89dVX+v777/XNN98oX758eT2c+4K3t7fmzZun5ORkrVu3TkOHDtWDDz6oRx55JK+Hdk+6dOmSevfurcGDB6tIkSJ5PZz7SkREhCIiIhy+j46O1vTp09WzZ8+8G9h9wDAMFS1aVIMHD5abm5tCQkJ06tQpjR8/Xt27d8/r4d0XvvvuOwUEBCgsLOyur/u+DIB+fn5yc3PT2bNnHaafPXs201G+DA888ECmNH6z9n9HztQNt1e38ePH66uvvtLEiRMVFBSUm8O8JzlbO1dXV5UvX16SVLVqVR08eFBfffWVZQJgTusWHx+vY8eOqVu3buY0u90uSXrooYe0ePFilStXLncHfQ+4E+9xHh4eqlq1qo4ePZobQ7xnOVO7YsWKyd3dXW5ubua0SpUqKSEhQampqfL09MzVMd8Lbuc1l5ycrEWLFumNN97IzSHe0H15CtjT01PBwcFat26dOc1ut2vdunUOf8n9VXh4uNavX+8wbe3atQoPD8/Nod5TnKkbnK/b2LFjNWrUKI0bN86yj5W4U685u92u1NTU3BjiPSmndatUqZIWLFigefPmmf8aNmyoRx55RPPmzVPJkiXv5vDzzJ14vdlsNu3bt0/FihXLrWHek5ypXWRkpI4ePWr+sSFJhw8fVrFixSwR/qTbe80tXrxYqampevLJJ3N7mFm6L48AStLzzz+vPn36KCQkRGFhYfr666+VkpKiNm3aSJJ69+6tEiVK6K233pIkde7cWbGxsZowYYLq1aun77//Xrt27dKgQYPycjPuupzWLTU1VQcPHjS/PnXqlPbs2SMvLy/zCI0V5LRuX331lUaMGKHhw4erTJkySkhIkCR5eXnJ29s7z7YjL+S0dmPGjFFISIjKlSun1NRUrVy5UvPnz9f777+fh1tx9+Wkbvny5VNAQIDD8r6+vpKUafrfXU5fb19++aXCw8NVvnx5JSUlafz48Tp+/Lglb6TJae06dOigKVOm6IMPPlCnTp105MgRjRkzRrGxsXm5GXddTuuW4bvvvlPjxo3l5+eXF8O+fwNgdHS0zp07pxEjRighIUFVq1bVuHHjzEOuJ06ckKvr/w5wRkZGatiwYfrss8/0ySefqEKFCho5cqTl3hxzWrfTp08rJibG/H7ChAmaMGGCHn74YU2ePPluDz/P5LRu06dPV1paWqZD+927d9frr79+V8ee13Jau+TkZA0cOFAnT55U/vz5ValSJX388ceKjo7Oq03IEzmtG67Jad2SkpI0YMAAJSQkqFChQgoODtb06dNVuXLlvNqEPJPT2pUqVUrjx4/XkCFD9OSTT6pEiRLq3LmzunTpklebkCec2Vf/+OMPbd68WRMmTMiLIUuSXAzDMPJs7QAAALjr+PMRAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAADAYv4PXGyZiy4J0I4AAAAASUVORK5CYII=" alt="confusion_matrix_entropy__AGE_CAT_top2.png"></div><div class="plot"><h3>frequency vs f1  AGE CAT</h3><img src="data:image/png;base64,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" alt="frequency_vs_f1__AGE_CAT.png"></div><div class="plot"><h3>learning curves AGE CAT accuracy</h3><img src="data:image/png;base64,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" alt="learning_curves_AGE_CAT_accuracy.png"></div><div class="plot"><h3>learning curves AGE CAT loss</h3><img src="data:image/png;base64,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" alt="learning_curves_AGE_CAT_loss.png"></div><div class="plot"><h3>roc curves</h3><img src="data:image/png;base64,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" alt="roc_curves.png"></div>
+    </div>
+    <div id="feature-tab" class="tabcontent">
+        <h2>Feature Importance</h2><p>Feature importance scores come from Ludwig's Integrated Gradients explainer. It interpolates between each example and a neutral baseline sample, summing the change in the model output along that path. Higher |importance| values indicate stronger influence. Plots share a common x-axis to make magnitudes comparable across labels, and the table columns can be sorted for quick scans.</p><div class='scroll-rows-30'><table class='feature-importance-table sortable-table'><thead><tr><th class='sortable'>label</th><th class='sortable'>feature</th><th class='sortable'>importance</th><th class='sortable'>abs importance</th></tr></thead><tbody><tr><td>post</td><td>RP11-465B22_8</td><td>-0.007552</td><td>0.007552</td></tr><tr><td>post</td><td>RP11-206L10_9</td><td>-0.004579</td><td>0.004579</td></tr><tr><td>post</td><td>RPL23AP24</td><td>0.003386</td><td>0.003386</td></tr><tr><td>pre</td><td>RP11-465B22_8</td><td>0.007552</td><td>0.007552</td></tr><tr><td>pre</td><td>RP11-206L10_9</td><td>0.004579</td><td>0.004579</td></tr><tr><td>pre</td><td>RPL23AP24</td><td>-0.003386</td><td>0.003386</td></tr></tbody></table></div><div class="plot feature-importance-plot"><h3>Top features for post</h3><img 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" alt="Feature importance plot for post"></div><div class="plot feature-importance-plot"><h3>Top features for pre</h3><img src="data:image/png;base64,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" alt="Feature importance plot for pre"></div>
+    </div>
+    
+        
+    <script>
+        function openTab(evt, tabId) {
+            var i, tabcontent, tablinks;
+            tabcontent = document.getElementsByClassName("tabcontent");
+            for (i = 0; i < tabcontent.length; i++) {
+                tabcontent[i].style.display = "none";
+                tabcontent[i].classList.remove("active");
+            }
+            tablinks = document.getElementsByClassName("tablink");
+            for (i = 0; i < tablinks.length; i++) {
+                tablinks[i].classList.remove("active");
+            }
+            var current = document.getElementById(tabId);
+            if (current) {
+                current.style.display = "block";
+                current.classList.add("active");
+            }
+            if (evt && evt.currentTarget) {
+                evt.currentTarget.classList.add("active");
+            }
+        }
+        document.addEventListener("DOMContentLoaded", function() {
+            openTab({currentTarget: document.querySelector(".tablink")}, "viz-tab");
+        });
+    </script>
+    
+    
+    </div>
+    </body>
+    </html>
+    
+    
\ No newline at end of file
--- a/utils.py	Sat Sep 06 01:52:48 2025 +0000
+++ b/utils.py	Sat Nov 22 01:15:52 2025 +0000
@@ -59,6 +59,39 @@
               background-color: #4CAF50;
               color: white;
           }
+          /* feature importance layout tweaks */
+          table.feature-importance-table {
+              table-layout: auto;
+          }
+          table.feature-importance-table th,
+          table.feature-importance-table td {
+              white-space: nowrap;
+              word-break: normal;
+          }
+          /* sortable tables */
+          .sortable-table th.sortable {
+              cursor: pointer;
+              position: relative;
+              user-select: none;
+          }
+          .sortable-table th.sortable::after {
+              content: '⇅';
+              position: absolute;
+              right: 12px;
+              top: 50%;
+              transform: translateY(-50%);
+              font-size: 0.8em;
+              color: #eaf5ea;
+              text-shadow: 0 0 1px rgba(0,0,0,0.15);
+          }
+          .sortable-table th.sortable.sorted-none::after { content: '⇅'; color: #eaf5ea; }
+          .sortable-table th.sortable.sorted-asc::after  { content: '↑';  color: #ffffff; }
+          .sortable-table th.sortable.sorted-desc::after { content: '↓';  color: #ffffff; }
+          .scroll-rows-30 {
+              max-height: 900px;
+              overflow-y: auto;
+              overflow-x: auto;
+          }
           .plot {
               text-align: center;
               margin: 20px 0;
@@ -68,6 +101,69 @@
               height: auto;
           }
         </style>
+        <script>
+          (function() {
+            if (window.__sortableInit) return;
+            window.__sortableInit = true;
+
+            function initSortableTables() {
+              document.querySelectorAll('table.sortable-table tbody').forEach(tbody => {
+                Array.from(tbody.rows).forEach((row, i) => { row.dataset.originalOrder = i; });
+              });
+
+              const getText = td => (td?.innerText || '').trim();
+              const cmp = (idx, asc) => (a, b) => {
+                const v1 = getText(a.children[idx]);
+                const v2 = getText(b.children[idx]);
+                const n1 = parseFloat(v1), n2 = parseFloat(v2);
+                if (!isNaN(n1) && !isNaN(n2)) return asc ? n1 - n2 : n2 - n1;
+                return asc ? v1.localeCompare(v2) : v2.localeCompare(v1);
+              };
+
+              document.querySelectorAll('table.sortable-table th.sortable').forEach(th => {
+                th.classList.remove('sorted-asc','sorted-desc');
+                th.classList.add('sorted-none');
+
+                th.addEventListener('click', () => {
+                  const table = th.closest('table');
+                  const headerRow = th.parentNode;
+                  const allTh = headerRow.querySelectorAll('th.sortable');
+                  const tbody = table.querySelector('tbody');
+
+                  const isAsc  = th.classList.contains('sorted-asc');
+                  const isDesc = th.classList.contains('sorted-desc');
+
+                  allTh.forEach(x => x.classList.remove('sorted-asc','sorted-desc','sorted-none'));
+
+                  let next;
+                  if (!isAsc && !isDesc) {
+                    next = 'asc';
+                  } else if (isAsc) {
+                    next = 'desc';
+                  } else {
+                    next = 'none';
+                  }
+                  th.classList.add('sorted-' + next);
+
+                  const rows = Array.from(tbody.rows);
+                  if (next === 'none') {
+                    rows.sort((a, b) => (a.dataset.originalOrder - b.dataset.originalOrder));
+                  } else {
+                    const idx = Array.from(headerRow.children).indexOf(th);
+                    rows.sort(cmp(idx, next === 'asc'));
+                  }
+                  rows.forEach(r => tbody.appendChild(r));
+                });
+              });
+            }
+
+            if (document.readyState === 'loading') {
+              document.addEventListener('DOMContentLoaded', initSortableTables);
+            } else {
+              initSortableTables();
+            }
+          })();
+        </script>
     </head>
     <body>
     <div class="container">