diff --git a/dianna/visualization/__init__.py b/dianna/visualization/__init__.py
index bf4e6613..0e08ae8c 100644
--- a/dianna/visualization/__init__.py
+++ b/dianna/visualization/__init__.py
@@ -1,5 +1,6 @@
 # flake8: noqa: F401
 """Tools for visualization of model explanations."""
 from .image import plot_image
+from .tabular import plot_tabular
 from .text import highlight_text
 from .timeseries import plot_timeseries
diff --git a/dianna/visualization/tabular.py b/dianna/visualization/tabular.py
new file mode 100644
index 00000000..d11dba5d
--- /dev/null
+++ b/dianna/visualization/tabular.py
@@ -0,0 +1,52 @@
+"""Visualization module for tabular data."""
+from typing import List
+from typing import Optional
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def plot_tabular(
+    x: np.ndarray,
+    y: List[str],
+    x_label: str = "Importance score",
+    y_label: str = "Features",
+    num_features: Optional[int] = None,
+    show_plot: Optional[bool] = True,
+    output_filename: Optional[str] = None,
+) -> plt.Figure:
+    """Plot feature importance with segments highlighted.
+
+    Args:
+        x (np.ndarray): Array of feature importance scores
+        y (List[str]): List of feature names
+        x_label (str): Label for the x-axis
+        y_label (str): Label or list of labels for the y-axis
+        num_features (Optional[int]): Number of most salient features to display
+        show_plot (bool, optional): Shows plot if true (for testing or writing
+            plots to disk instead).
+        output_filename (str, optional): Name of the file to save
+            the plot to (optional).
+
+    Returns:
+        plt.Figure
+    """
+    if not num_features:
+        num_features = len(x)
+    fig, ax = plt.subplots()
+    abs_values = [abs(i) for i in x]
+    top_values = [x for _, x in sorted(zip(abs_values, x), reverse=True)][:num_features]
+    top_features = [x for _, x in sorted(zip(abs_values, y), reverse=True)][
+        :num_features
+    ]
+
+    colors = ["r" if x >= 0 else "b" for x in top_values]
+    ax.barh(top_features, top_values, color=colors)
+    ax.set_xlabel(x_label)
+    ax.set_ylabel(y_label)
+
+    if show_plot:
+        plt.show()
+    if output_filename:
+        plt.savefig(output_filename)
+
+    return fig
diff --git a/dianna/visualization/timeseries.py b/dianna/visualization/timeseries.py
index 78b6984b..54c545a9 100644
--- a/dianna/visualization/timeseries.py
+++ b/dianna/visualization/timeseries.py
@@ -16,7 +16,7 @@ def plot_timeseries(
     x_label: str = 't',
     y_label: Union[str, Iterable[str]] = None,
     cmap: Optional[str] = None,
-    show_plot: bool = False,
+    show_plot: Optional[bool] = False,
     output_filename: Optional[str] = None,
 ) -> plt.Figure:
     """Plot timeseries with segments highlighted.
diff --git a/tests/test_visualization.py b/tests/test_visualization.py
index 619bfe48..ddb9e0f9 100644
--- a/tests/test_visualization.py
+++ b/tests/test_visualization.py
@@ -1,16 +1,29 @@
+"""Unit tests for visualization modules."""
 from pathlib import Path
 import numpy as np
 import pytest
+from dianna.visualization import plot_tabular
 from dianna.visualization import plot_timeseries
 
 
+def test_plot_tabular(tmpdir):
+    """Test plot tabular data."""
+    x = np.linspace(-5, 5, 3)
+    y = [f"Feature {i}" for i in range(len(x))]
+    output_path = Path(tmpdir) / "temp_visualization_test_tabular.png"
+
+    plot_tabular(x=x, y=y, show_plot=False, output_filename=output_path)
+
+    assert output_path.exists()
+
+
 def test_plot_timeseries_univariate(tmpdir, random):
     """Test plot univariate time series."""
     x = np.linspace(0, 10, 20)
     y = np.sin(x)
     segments = get_test_segments(data=np.expand_dims(y, 0))
 
-    output_path = Path(tmpdir) / 'temp_visualization_test_univariate.png'
+    output_path = Path(tmpdir) / "temp_visualization_test_univariate.png"
 
     plot_timeseries(x=x,
                     y=y,
@@ -26,7 +39,7 @@ def test_plot_timeseries_multivariate(tmpdir, random):
     x = np.linspace(start=0, stop=10, num=20)
     ys = np.stack((np.sin(x), np.cos(x), np.tan(0.4 * x)))
     segments = get_test_segments(data=ys)
-    output_path = Path(tmpdir) / 'temp_visualization_test_multivariate.png'
+    output_path = Path(tmpdir) / "temp_visualization_test_multivariate.png"
 
     plot_timeseries(x=x,
                     y=ys.T,
@@ -48,13 +61,13 @@ def get_test_segments(data):
     for i_segment in range(n_segments):
         for i_channel in range(n_channels):
             segment = {
-                'index': i_segment + i_channel * n_segments,
-                'start': i_segment,
-                'stop': i_segment + 1,
-                'weight': data[i_channel, factor * i_segment],
+                "index": i_segment + i_channel * n_segments,
+                "start": i_segment,
+                "stop": i_segment + 1,
+                "weight": data[i_channel, factor * i_segment],
             }
             if n_channels > 1:
-                segment['channel'] = i_channel
+                segment["channel"] = i_channel
             segments.append(segment)
 
     return segments
diff --git a/tutorials/lime_tabular_penguin.ipynb b/tutorials/lime_tabular_penguin.ipynb
index 6c1049c2..f95730d6 100644
--- a/tutorials/lime_tabular_penguin.ipynb
+++ b/tutorials/lime_tabular_penguin.ipynb
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -368,7 +368,7 @@
    "metadata": {},
    "source": [
     "#### 4. Visualization\n",
-    "(TODO:) The output can be visualized with the DIANNA built-in visualization function. It shows the importance of each feature contributing to the prediction.\n",
+    "The output can be visualized with the DIANNA built-in visualization function. It shows the importance of each feature contributing to the prediction.\n",
     "\n",
     "The prediction is \"Gentoo\", so let's visualize the feature importance scores for \"Gentoo\".\n",
     "\n",
@@ -379,27 +379,10 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get the scores for the target class\n",
-    "explanation = explanation[np.argmax(predictions)]\n",
-    "\n",
-    "# rank the results based on the absolute values\n",
-    "# and pick up the top 10 of them\n",
-    "num_features = 10\n",
-    "abs_values = [abs(i) for i in explanation]\n",
-    "top_values = [x for _, x in sorted(zip(abs_values, explanation), reverse=True)][:num_features]\n",
-    "top_features = [x for _, x in sorted(zip(abs_values, X_test.columns), reverse=True)][:num_features]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -409,14 +392,12 @@
     }
    ],
    "source": [
-    "from matplotlib import pyplot as plt\n",
+    "from dianna.visualization import plot_tabular\n",
     "\n",
-    "colors = ['b' if x >= 0 else 'r' for x in top_values]\n",
+    "# get the scores for the target class\n",
+    "explanation = explanation[np.argmax(predictions)]\n",
     "\n",
-    "plt.barh(top_features, top_values, color=colors)\n",
-    "plt.xlabel(\"Features\")\n",
-    "plt.ylabel(\"Importance scores\")\n",
-    "plt.show()"
+    "_ = plot_tabular(explanation, X_test.columns, num_features=10)"
    ]
   }
  ],
diff --git a/tutorials/lime_tabular_weather.ipynb b/tutorials/lime_tabular_weather.ipynb
index b597f25f..b7da342f 100644
--- a/tutorials/lime_tabular_weather.ipynb
+++ b/tutorials/lime_tabular_weather.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -149,7 +149,7 @@
        "array([[3.0719438]], dtype=float32)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -218,31 +218,17 @@
    "metadata": {},
    "source": [
     "#### 4. Visualization\n",
-    "(TODO:) The output can be visualized with the DIANNA built-in visualization function. It shows the top 10 importance of each feature contributing to the prediction."
+    "The output can be visualized with the DIANNA built-in visualization function. It shows the top 10 importance of each feature contributing to the prediction."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# rank the results based on the absolute values\n",
-    "# and pick up the top 10 of them\n",
-    "num_features = 10\n",
-    "abs_values = [abs(i) for i in explanation]\n",
-    "top_values = [x for _, x in sorted(zip(abs_values, explanation), reverse=True)][:num_features]\n",
-    "top_features = [x for _, x in sorted(zip(abs_values, X_test.columns), reverse=True)][:num_features]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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Unbe3t8e+fftw48aNQvs2MzODmZkZtmzZggcPHpTZHIrSvXt3jBo1Cj4+PkhJSUFKSgq6d+8OAOjatSvS0tKwa9cunDx5Eg0aNEDLli1x69Yt5fqkpCTs2rULu3fvRnR0NCIjI/Hmm2/i6tWrOHDgAObOnYuJEyfi2LFjqnEnTZqEzp07IyEhAb1790aPHj2QmJhY7LgjIiIwceJEnDp1Cnp6eujVqxfGjh2Lzz77DIcOHcKFCxcwefJkpf3atWsxefJkzJw5E4mJiZg1axYmTZqE1atXK23Mzc0RFRWFs2fP4rPPPsPy5cuxcOFC1bhJSUnYsmULtm/fju3bt+PAgQPKlp3CPHjwABkZGapCREREldhzSPorvLCwMDEwMBAAEhMTU+B8enq6GBsbK6u7cXFxYmZmJpmZmUqbM2fOiJeXl+jo6Iivr6+Eh4fLzp07Vf1s3LhRrKysxMjISBo3bizjx4+XhIQEVRsXFxcxMDAQU1NTVTl48KCIlH4lW+R/K7SPO3TokFhYWEh2draqvnbt2rJs2TLlOhMTE9VKbkhIiLi6ukpubq5S5+npKbNnz1aOAciQIUNU/QYEBMjQoUOfGmv+PFesWKHURUdHF/gezZ49Wzw9PVVxf/PNN6q+pk+fLoGBgUWONX/+fGnYsKFyXNh8x4wZIwEBAUX2ERERIQAKFK5kV27lvdrKwpVsItIurmSXAT8/P7i6uiIiIgJZWVmqc9HR0ahduzbq1q0LAKhXrx5cXFywfv16pY23tzd+++03HD16FAMGDEBaWhrat2+PQYMGKW06d+6Ma9euYdu2bQgNDUVsbCwaNGiAqKgo1XhjxoxBfHy8qvj7+5fJvBMSEpCVlQUbGxtltd3MzAzJyclISkpS2rm6uqr2hdvZ2cHb2xs6OjqqurS0NFX/gYGBBY5LspLt5+en6h8AfH19Cx3z7t27SEpKwsCBA1VzmTFjhmou69evR5MmTWBvbw8zMzNMnDgRly9fVo377/k6ODgUmNvjxo8fj/T0dKVcuXKl2HMkIiKiiocPPhaTo6MjNm7ciODgYISGhmLXrl1KkhUZGYkzZ85AT+9/tzMvLw8rV67EwIEDlTodHR00atQIjRo1wsiRI/Hf//4Xffr0wYQJE1CzZk0AgJGREVq3bo3WrVtj0qRJGDRoECIiItCvXz+ln6pVq8LNze25zDsrKwsODg6IjY0tcK5KlSrK1/r6+qpzGo2m0Lq8vDytxvf4GBqNptC6/DHzfzlavnw5AgICVP3o6uoCAI4cOYLevXtj6tSpCAkJgaWlJdatW6d6i8y/x/j3OIUxNDSEoaFhSadHREREFRST7BJwcXHBgQMHlER79+7duHjxIk6cOIHY2FhYW1srbW/duoUWLVrg3LlzqFOnTqH9eXt7A/hnhbUo3t7e2LJli1bnURQDAwPk5uaq6ho0aIDU1FTo6enB1dVV62MePXoUffv2VR3Xr19f6+MA/6xqV69eHX/++Sd69+5daJuff/4ZLi4umDBhglJ36dKlMomHiIiIKi8m2SXk7OyM2NhYBAcHIyQkBHXq1MGrr76K5s2bF2jbqFEjREZGYv78+ejSpQuaNGmCxo0bw97eHsnJyRg/fjw8PDxQp04d3Lx5E127dsWAAQPg5+cHc3NznDhxAvPmzUOHDh1U/WZmZiI1NVVVZ2JiAgsLC+X4/PnzBeLx8fEpsAL7OFdXVyQnJyM+Ph5OTk4wNzdHq1atEBgYiI4dO2LevHnw8PDAtWvXsGPHDnTq1OmZt6ls2LAB/v7+aNq0KdauXYvjx48XeGhUm6ZOnYoRI0bA0tISoaGhePDggfIqxQ8//BDu7u64fPky1q1bh0aNGmHHjh3YvHlzmcVDRERElRP3ZJeCk5MTYmNjkZqais2bN6NNmzaFtuvcuTPWrFmDR48eISQkBN9//z3at28PDw8PhIWFoU6dOvjxxx+hp6cHMzMzBAQEYOHChWjevDleeeUVTJo0CYMHD8aSJUtU/U6ePBkODg6qMnbsWFWbHj16oH79+qpy/fr1J86rc+fOCA0NRXBwMGxtbREdHQ2NRoOdO3eiefPm6N+/Pzw8PNCjRw9cunRJ2QP9LKZOnYp169bBz88Pa9asQXR0tLLCXxYGDRqEFStWYNWqVfD19UVQUBCioqKU7TpvvfUWPvjgAwwfPhz16tXDzz//jEmTJpVZPERERFQ5aUREyjsIejlpNBps3ry5wF/TfBlkZGTA0tIS6enpqn+BoMrl/z8mQJUQPzmJXk4l+fzmSjYRERERkZYxyX6J+Pj4qF5d93hZu3ZteYenMmvWrCJjLWp7DhEREdGLgttFXiKXLl3Co0ePCj1nZ2eneu9zebt165bqL0o+ztjYGI6Ojs85Iu3idpGXA7eLVF785CR6OZXk85tvF3mJuLi4lHcIxWZtba16JSIRERFRRcLtIkREREREWsYkm4iIiIhIy5hkExERERFpGfdkExGVET4cR0T08uJKNhERERGRljHJJiIiIiLSMibZRERERERaxiSbiIiIiEjLmGQTEREREWkZ3y5CRKQF/BPqLxe+OYaInoYr2UREREREWsYkm4iIiIhIy5hkExERERFpGZNsIiIiIiItY5JNRERERKRlTLKJiIiIiLSMSTYRERERkZYxySYiIiIi0jIm2UREREREWsYkm4iIiIhIy8o1ye7Xrx80Gg00Gg309fVhZ2eH1q1bY+XKlcjLy1PaaTQabNmypdDrO3bsqBwnJyejV69eqF69OoyMjODk5IQOHTrg3LlzSpsDBw7g9ddfh7W1NUxMTODu7o6wsDA8fPgQABAbG6vE9O+SmpoKAJgyZQrq1atX5LxatGihXGNoaAhHR0e0b98emzZtKrT99u3bERQUBHNzc5iYmKBRo0aIiopStbl48aIqFmtrawQFBeHQoUOqdlOmTCk09r179xYZb1H3k4iIiIhKp9xXskNDQ5GSkoKLFy9i165dCA4Oxvvvv4927dohJyen2P08evQIrVu3Rnp6OjZt2oTz589j/fr18PX1xZ07dwAAZ8+eRWhoKPz9/XHw4EGcPn0aixcvhoGBAXJzc1X9nT9/HikpKapSrVq1YsczePBgpKSkICkpCd999x28vb3Ro0cPvPPOO6p2ixcvRocOHdCkSRMcO3YMv/76K3r06IEhQ4Zg9OjRBfrdu3cvUlJScPDgQVSvXh3t2rXD9evXVW18fHwKxN68efNix05EREREz0jKUVhYmHTo0KFAfUxMjACQ5cuXi4gIANm8efMTr4+LixMAcvHixSLHW7hwobi6uj4xpv379wsAuX37dpFtIiIipG7dukWeDwoKkvfff79A/cqVKwWA7NmzR0RELl++LPr6+vLhhx8WaPv5558LADl69KiIiCQnJwsAiYuLU9r8+uuvAkC2bt1a7NieNCcAqrJ//34lzq5du4qlpaVYWVnJW2+9JcnJycq1+d+HmTNnSrVq1cTS0lKmTp0qjx49ktGjR4uVlZU4OjrKypUrlWvy5xMdHS2BgYFiaGgoPj4+EhsbW6x4879Pu3fvlnr16omRkZEEBwfL9evXZefOnVKnTh0xNzeXnj17yt27d5XrcnNzZdasWeLq6ipGRkbi5+cnGzZsUM7n5OTIgAEDlPMeHh6yaNEi1dj5850/f77Y29uLtbW1vPvuu/Lw4cMi483Ozpb09HSlXLlyRQBIenp6seZLLz6A5WUqRPRySk9PL/bnd7mvZBfm9ddfR926dYvcXlEYW1tb6OjoYOPGjQVWpfPZ29srq8DlISwsDFZWVsq8Nm7ciEePHhW6Yh0eHg4zMzNER0cX2tf9+/exZs0aAICBgcEzxzZ69Gh069ZN+ZeFlJQUNG7cGI8ePUJISAjMzc1x6NAhHD58GGZmZggNDVW22ADAvn37cO3aNRw8eBCffvopIiIi0K5dO1hZWeHYsWMYMmQIwsPDcfXqVdW4Y8aMwahRoxAXF4fAwEC0b98eN2/eLHbcU6ZMwZIlS/Dzzz/jypUr6NatGxYtWoRvvvkGO3bswI8//ojFixcr7WfPno01a9Zg6dKlOHPmDD744AO8/fbbOHDgAAAgLy8PTk5O2LBhA86ePYvJkyfj448/xrfffqsad//+/UhKSsL+/fuxevVqREVFFdji87jZs2fD0tJSKc7OzsWeIxEREVVAzyHpL1JRK9kiIt27dxcvLy8REQGevpItIrJkyRIxMTERc3NzCQ4OlmnTpklSUpJyPicnR/r16ycAxN7eXjp27CiLFy9W/TaSv0JqamqqKt7e3kqb0q5ki4gEBARImzZtRERkyJAhYmlpWWQ/fn5+Stv8lV9jY2MxNTUVjUYjAKRhw4aqFdSIiAjR0dFRxd6oUaMix3hcYd+Pr7/+Wjw9PSUvL0+pe/DggRgbG8sPP/ygXOfi4iK5ublKG09PT2nWrJlynJOTI6amphIdHa2az5w5c5Q2jx49EicnJ5k7d+5TY83/Pu3du1epmz17tgBQfc/Dw8MlJCRERP5ZTTYxMZGff/5Z1dfAgQOlZ8+eRY41bNgw6dy5s3KcP9+cnBylrmvXrtK9e/ci++BKduVX3iurLM+3ENHLqSQr2Xrlldw/jYhAo9GU6Jphw4ahb9++iI2NxdGjR7FhwwbMmjUL27ZtQ+vWraGrq4tVq1ZhxowZ2LdvH44dO4ZZs2Zh7ty5OH78OBwcHJS+Dh06BHNzc+VYX1+/3Ob1uPXr16NOnTr47bffMHbsWERFRRWIzdPTE9u2bVOODQ0NSz1eQkICLly4oLoXAJCdnY2kpCTl2MfHBzo6//uHETs7O7zyyivKsa6uLmxsbJCWlqbqJzAwUPlaT08P/v7+SExMLHZ8fn5+qjFNTExQq1YtVd3x48cBABcuXMC9e/fQunVrVR8PHz5E/fr1leMvvvgCK1euxOXLl3H//n08fPiwwIOuPj4+0NXVVY4dHBxw+vTpIuM0NDR8pu8DERERVSwvbJKdmJiImjVrAgDMzc2Rnp5eoM2dO3dgaWmpqjM3N0f79u3Rvn17zJgxAyEhIZgxY4YqsXJ0dESfPn3Qp08fTJ8+HR4eHli6dCmmTp2qtKlZsyaqVKmi1Tnl5ubijz/+QKNGjQAAHh4eSE9Px7Vr11C9enVV24cPHyIpKQnBwcGqemdnZ7i7u8Pd3R05OTno1KkTfvvtN1UCZ2BgADc3N63EnJWVhYYNG2Lt2rUFztna2ipf/zvRz39jzL/rHn9rjDY8PsbTxszKygIA7NixA46Ojqp2+fdv3bp1GD16NBYsWIDAwECYm5tj/vz5OHbsWJHj/nscIiIiohdyT/a+fftw+vRpdO7cGcA/K7MnT55UtcnNzUVCQgI8PDyK7Eej0aBOnTq4e/dukW2srKzg4ODwxDbasnr1aty+fVuZV+fOnaGvr48FCxYUaLt06VLcvXsXPXv2LLK/Ll26QE9PD19++aVW4ivsLSsNGjTAH3/8gWrVqsHNzU1V/v0LTmkcPXpU+TonJwcnT56El5fXM/dbGG9vbxgaGuLy5csF5pK/R/rw4cNo3Lgx3n33XdSvXx9ubm6qFXsiIiKi4ij3lewHDx4gNTUVubm5uH79Onbv3o3Zs2ejXbt26Nu3LwDgww8/xMCBA1GnTh20bt0ad+/exeLFi3H79m0MGjQIABAfH4+IiAj06dMH3t7eMDAwwIEDB7By5UqMGzcOALBs2TLEx8ejU6dOqF27NrKzs7FmzRqcOXNG9XAcAKSlpSE7O1tVZ2Njo6xg3r9/H/Hx8arz5ubmqF27NgDg3r17SE1NRU5ODq5evYrNmzdj4cKFGDp0qLI6XaNGDcybNw+jRo2CkZER+vTpA319fWzduhUff/wxRo0ahYCAgCLvnUajwYgRIzBlyhSEh4fDxMSklN+Ff7i6uuKHH37A+fPnYWNjA0tLS/Tu3Rvz589Hhw4dMG3aNDg5OeHSpUvYtGkTxo4dCycnp2ca84svvoC7uzu8vLywcOFC3L59GwMGDHimPotibm6O0aNH44MPPkBeXh6aNm2K9PR0HD58GBYWFggLC4O7uzvWrFmDH374ATVr1sTXX3+NX375RflXFSIiIqLiKPcke/fu3XBwcICenh6srKxQt25dfP755wgLC1P2+Pbs2RMigk8//RQfffQRTExM0LBhQxw8eBB2dnYAACcnJ7i6umLq1KnKH27JP/7ggw8AAK+++ip++uknDBkyBNeuXYOZmRl8fHywZcsWBAUFqeLy9PQsEOuRI0fw2muvAQB+//131T5eAGjZsqXyR1+WL1+O5cuXw8DAADY2NmjYsCHWr1+PTp06qa4ZOXIkatWqhU8++QSfffYZcnNz4ePjg6+++gr9+/d/6v0LCwvDhAkTsGTJEowdO7Y4t7xIgwcPRmxsLPz9/ZGVlYX9+/ejRYsWOHjwIMaNG4f//Oc/yMzMhKOjI1q2bAkLC4tnGg8A5syZgzlz5iA+Ph5ubm7Ytm0bqlat+sz9FmX69OmwtbXF7Nmz8eeff6JKlSpo0KABPv74YwD/vNUlLi4O3bt3h0ajQc+ePfHuu+9i165dZRYTERERVT4aEZHyDoJePhcvXkTNmjURFxf3xL+eWVllZGTA0tIS6enpWvllhcrfMzzPTBUQPzmJXk4l+fx+IfdkExERERFVZEyyXyJmZmZFlkOHDpV3eCpDhgwpMtYhQ4aUd3hERERET8TtIi+RCxcuFHnO0dERxsbGzzGaJ0tLS0NGRkah5ywsLFCtWrXnHJF2cbtI5cPtIi8XfnISvZxK8vld7g8+0vOjrXdnPw/VqlWr8Ik0ERERvby4XYSIiIiISMuYZBMRERERaRm3ixARaQH36BIR0eO4kk1EREREpGVMsomIiIiItIxJNhERERGRljHJJiIiIiLSMibZRERERERaxiSbiIiIiEjL+Ao/IiKiktJoyjsCqgz47s9KjSvZRERERERaxiSbiIiIiEjLmGQTEREREWkZk2wiIiIiIi1jkk1EREREpGVMsomIiIiItIxJNhERERGRljHJJiIiIiLSMibZRE8xZcoU1KtXr7zDICIiogqESXY56devHzQajVJsbGwQGhqKX3/9tUDb8PBw6OrqYsOGDQXO3bt3D+PHj0ft2rVhZGQEW1tbBAUFYevWrUqbFi1aqMbKL0OGDFHaaDQabNmypcTziIqKQpUqVUp8XUUyevRoxMTElHcYREREVIEwyS5HoaGhSElJQUpKCmJiYqCnp4d27dqp2ty7dw/r1q3D2LFjsXLlygJ9DBkyBJs2bcLixYtx7tw57N69G126dMHNmzdV7QYPHqyMlV/mzZtXpvOrLMzMzGBjY1PeYRAREVEFwiS7HBkaGsLe3h729vaoV68ePvroI1y5cgU3btxQ2mzYsAHe3t746KOPcPDgQVy5ckXVx7Zt2/Dxxx+jbdu2cHV1RcOGDfHee+9hwIABqnYmJibKWPnFwsLimeKPjY1F//79kZ6erqyOT5kyBQDw4MEDjB49Go6OjjA1NUVAQABiY2OVa/NXwLdv3w5PT0+YmJigS5cuuHfvHlavXg1XV1dYWVlhxIgRyM3NVa5zdXXF9OnT0bNnT5iamsLR0RFffPFFsWPWaDRYtmwZ2rVrBxMTE3h5eeHIkSO4cOECWrRoAVNTUzRu3BhJSUnKNf/eLtKvXz907NgRn3zyCRwcHGBjY4Nhw4bh0aNHRY774MEDZGRkqAoRERFVXkyyXxBZWVn473//Czc3N9WqaWRkJN5++21YWlqiTZs2iIqKUl1nb2+PnTt3IjMz8zlHDDRu3BiLFi2ChYWFsjo+evRoAMDw4cNx5MgRrFu3Dr/++iu6du2K0NBQ/PHHH8r19+7dw+eff45169Zh9+7diI2NRadOnbBz507s3LkTX3/9NZYtW4aNGzeqxp0/fz7q1q2LuLg4fPTRR3j//fexZ8+eYsc9ffp09O3bF/Hx8ahTpw569eqF8PBwjB8/HidOnICIYPjw4U/sY//+/UhKSsL+/fuxevVqREVFFfjePG727NmwtLRUirOzc7HjJSIiogpIqFyEhYWJrq6umJqaiqmpqQAQBwcHOXnypNLm999/F319fblx44aIiGzevFlq1qwpeXl5SpsDBw6Ik5OT6Ovri7+/v4wcOVJ++ukn1VhBQUGir6+vjJVf/vvf/yptAMjmzZtLPI9Vq1aJpaWlqu7SpUuiq6srf/31l6q+ZcuWMn78eOU6AHLhwgXlfHh4uJiYmEhmZqZSFxISIuHh4cqxi4uLhIaGqvrt3r27tGnTpljxApCJEycqx0eOHBEAEhkZqdRFR0eLkZGRchwRESF169ZVjsPCwsTFxUVycnKUuq5du0r37t2LHDc7O1vS09OVcuXKFQEg6enpxYqbiF4wAAvLsxeqcNLT06W4n99cyS5HwcHBiI+PR3x8PI4fP46QkBC0adMGly5dAgCsXLkSISEhqFq1KgCgbdu2SE9Px759+5Q+mjdvjj///BMxMTHo0qULzpw5g2bNmmH69OmqsXr37q2MlV/eeuutMpnX6dOnkZubCw8PD5iZmSnlwIEDqm0YJiYmqF27tnJsZ2cHV1dXmJmZqerS0tJU/QcGBhY4TkxMLHZ8fn5+qv4BwNfXV1WXnZ39xC0dPj4+0NXVVY4dHBwKxPk4Q0NDWFhYqAoRERFVXnrlHcDLzNTUFG5ubsrxihUrYGlpieXLl2Pq1KlYvXo1UlNToaf3v29Tbm4uVq5ciZYtWyp1+vr6aNasGZo1a4Zx48ZhxowZmDZtGsaNGwcDAwMAgKWlpWqsspSVlQVdXV2cPHlSlYgCUCXQ+vr6qnMajabQury8PK3G9/gYGo2myLonjfs84iQiIqKKi0n2C0Sj0UBHRwf3799X9lnHxcWpEtXffvsN/fv3x507d4p8dZ63tzdycnKQnZ2tJNllxcDAQPVgIgDUr18fubm5SEtLQ7NmzbQ+5tGjRwsce3l5aX0cIiIiotJikl2OHjx4gNTUVADA7du3sWTJEmRlZaF9+/ZYtGgR3nzzTdStW1d1jbe3Nz744AOsXbsWw4YNQ4sWLdCzZ0/4+/vDxsYGZ8+exccff4zg4GDVloR79+4pY+UzNDSElZWVcpycnIz4+HhVG3d3d5iamhY5B1dXV2RlZSEmJgZ169aFiYkJPDw80Lt3b/Tt2xcLFixA/fr1cePGDcTExMDPzw9vvvlmaW8ZAODw4cOYN28eOnbsiD179mDDhg3YsWPHM/VJREREpE3ck12Odu/eDQcHBzg4OCAgIAC//PILNmzYAC8vL+zYsQOdO3cucI2Ojg46deqEyMhIAEBISAhWr16NN954A15eXnjvvfcQEhKCb7/9VnXd8uXLlbHyS8+ePVVtPvzwQ9SvX19V4uLinjiHxo0bY8iQIejevTtsbW2Vd2+vWrUKffv2xahRo+Dp6YmOHTvil19+QY0aNZ7llgEARo0ahRMnTqB+/fqYMWMGPv30U4SEhDxzv0RERETaohERKe8giIrL1dUVI0eOxMiRI8s7lGeSkZEBS0tLpKen8yFIooro/z+7QfRMmIJVOCX5/OZKNhERERGRljHJpidq06aN6jV8j5dZs2aVd3gqa9euLTJWHx+f8g6PiIiIXiLcLkJP9Ndff+H+/fuFnrO2toa1tfVzjqhomZmZuH79eqHn9PX14eLi8pwjKhq3ixBVcNwuQtrAFKzCKcnnN98uQk/k6OhY3iEUm7m5OczNzcs7DCIiIiJuFyEiIiIi0jYm2UREREREWsbtIkRERCXFvbRE9BRcySYiIiIi0jIm2UREREREWsYkm4iIiIhIy5hkExERERFpGZNsIiIiIiItY5JNRERERKRlfIUfERFRSfHPqlNZ4eshKw2uZBMRERERaRmTbCIiIiIiLWOSTURERESkZUyyiYiIiIi0TGtJ9p07d7TVFRERERFRhVaqJHvu3LlYv369ctytWzfY2NjA0dERCQkJWguOiIiIiKgiKlWSvXTpUjg7OwMA9uzZgz179mDXrl1o06YNxowZo9UAiYiIiIgqmlK9Jzs1NVVJsrdv345u3brhjTfegKurKwICArQaIBERERFRRVOqlWwrKytcuXIFALB79260atUKACAiyM3N1V50RC+Afv36oWPHjuUdBhEREVUgpUqy//Of/6BXr15o3bo1bt68iTZt2gAA4uLi4ObmptUAK6rCErONGzfCyMgICxYsUOpmz54NXV1dzJ8/v0Afubm5mDNnDurUqQNjY2NYW1sjICAAK1asUNrcuHEDQ4cORY0aNWBoaAh7e3uEhITg8OHDShtXV1doNJoCZc6cOQCAixcvQqPRID4+vsTznDJlCurVq1fi6yqSzz77DFFRUeUdBhEREVUgpdousnDhQri6uuLKlSuYN28ezMzMAAApKSl49913tRpgZbFixQoMGzYMS5cuRf/+/ZX6lStXYuzYsVi5cmWB/exTp07FsmXLsGTJEvj7+yMjIwMnTpzA7du3lTadO3fGw4cPsXr1atSqVQvXr19HTEwMbt68qepr2rRpGDx4sKrO3Ny8DGZa+VhaWpZ3CERERFTRCJWJsLAw6dChg4iIzJ07V4yMjGTTpk2qNrGxseLo6CgPHz6U6tWry+HDh1Xn69atK1OmTClyjNu3bwsAiY2NfWIsLi4usnDhwiLPJycnCwCJi4t7Yj//tmrVKgGgKqtWrVJiGzhwoFStWlXMzc0lODhY4uPjlWsjIiKkbt26EhkZKc7OzmJqaipDhw6VnJwcmTt3rtjZ2Ymtra3MmDFDNSYA+fLLLyU0NFSMjIykZs2asmHDhmLFmz/P9evXS9OmTcXIyEj8/f3l/Pnzcvz4cWnYsKGYmppKaGiopKWlKdc9/r0UEQkKCpL33ntPxowZI1ZWVmJnZycRERElunfp6ekCQNLT00t0HRG9IAAWlrIp9EIryed3qd+T/fXXX6Np06aoXr06Ll26BABYtGgRtm7d+oxpf+Uybtw4TJ8+Hdu3b0enTp1U5yIjI9GzZ0/o6+ujZ8+eiIyMVJ23t7fHvn37cOPGjUL7NjMzg5mZGbZs2YIHDx6U2RyK0r17d4waNQo+Pj5ISUlBSkoKunfvDgDo2rUr0tLSsGvXLpw8eRINGjRAy5YtcevWLeX6pKQk7Nq1C7t370Z0dDQiIyPx5ptv4urVqzhw4ADmzp2LiRMn4tixY6pxJ02ahM6dOyMhIQG9e/dGjx49kJiYWOy4IyIiMHHiRJw6dQp6enro1asXxo4di88++wyHDh3ChQsXMHny5Cf2sXr1apiamuLYsWOYN28epk2bhj179hTZ/sGDB8jIyFAVIiIiqsRKk8V/+eWXUrVqVZkxY4YYGxtLUlKSiPyzstmiRYvSdFnphIWFiYGBgQCQmJiYAufT09PF2NhYWd2Ni4sTMzMzyczMVNqcOXNGvLy8REdHR3x9fSU8PFx27typ6mfjxo1iZWUlRkZG0rhxYxk/frwkJCSo2ri4uIiBgYGYmpqqysGDB0Wk9CvZIv9bkX7coUOHxMLCQrKzs1X1tWvXlmXLlinXmZiYSEZGhnI+JCREXF1dJTc3V6nz9PSU2bNnK8cAZMiQIap+AwICZOjQoU+NNX+eK1asUOqio6MLfI9mz54tnp6eynFhK9lNmzZV9d2oUSMZN25ckWNHREQIgAKFK9lEFVR5r3ayVN5CL7QyX8levHgxli9fjgkTJkBXV1ep9/f3x+nTp58t669E/Pz84OrqioiICGRlZanORUdHo3bt2qhbty4AoF69enBxcVH9kR9vb2/89ttvOHr0KAYMGIC0tDS0b98egwYNUtp07twZ165dw7Zt2xAaGorY2Fg0aNCgwIN6Y8aMQXx8vKr4+/uXybwTEhKQlZUFGxsbZbXdzMwMycnJSEpKUtq5urqq9oXb2dnB29sbOjo6qrq0tDRV/4GBgQWOS7KS7efnp+ofAHx9fZ845pP6AAAHB4cnXjN+/Hikp6crJf/tPERERFQ5lerBx+TkZNSvX79AvaGhIe7evfvMQVUWjo6O2LhxI4KDgxEaGopdu3YpSWVkZCTOnDkDPb3/fQvy8vKwcuVKDBw4UKnT0dFBo0aN0KhRI4wcORL//e9/0adPH0yYMAE1a9YEABgZGaF169Zo3bo1Jk2ahEGDBiEiIgL9+vVT+qlatepze/NLVlYWHBwcEBsbW+BclSpVlK/19fVV5zQaTaF1eXl5Wo3v8TE0Gk2hdU8bs6RxGhoawtDQsDThEhERUQVUqpXsmjVrFvq6t927d8PLy+tZY6pUXFxccODAAaSmpiI0NBSZmZk4ffo0Tpw4gdjYWNXKcmxsLI4cOYJz584V2Z+3tzcAPPGXGW9v7+f2y46BgUGBd6M3aNAAqamp0NPTg5ubm6pUrVr1mcc8evRogWP+3BEREdGLpFQr2R9++CGGDRuG7OxsiAiOHz+O6OhozJ49W/UOZ/qHs7MzYmNjERwcjJCQENSpUwevvvoqmjdvXqBto0aNEBkZifnz56NLly5o0qQJGjduDHt7eyQnJ2P8+PHw8PBAnTp1cPPmTXTt2hUDBgyAn58fzM3NceLECcybNw8dOnRQ9ZuZmYnU1FRVnYmJCSwsLJTj8+fPF4jHx8enwKrt41xdXZGcnIz4+Hg4OTnB3NwcrVq1QmBgIDp27Ih58+bBw8MD165dw44dO9CpU6dn3qayYcMG+Pv7o2nTpli7di2OHz9e4KFRIiIiovJUqiR70KBBMDY2xsSJE3Hv3j306tUL1atXx2effYYePXpoO8ZKwcnJCbGxsWjWrBkSExMxcuTIQtt17twZCxYswKxZsxASEqL88pKeng57e3u8/vrrmDJlCvT09GBmZoaAgAAsXLgQSUlJePToEZydnTF48GB8/PHHqn4nT55c4I0Z4eHhWLp0qXJc2PfuypUrcHJyKnJenTt3xqZNmxAcHIw7d+5g1apV6NevH3bu3IkJEyagf//+uHHjBuzt7dG8eXNlD/SzmDp1KtatW4d3330XDg4OiI6OVlb4iYiIiF4EGhGRklyQk5ODb775BiEhIbCzs8O9e/eQlZWFatWqlVWMRAqNRoPNmzdX+D9znpGRAUtLS6Snp6v+NYGIKoj//zwHkdaVLC2j56wkn98l3pOtp6eHIUOGIDs7G8A/Ww6YYBMRERER/U+pHnx89dVXERcXp+1Y6AXk4+Ojeg3f42Xt2rXlHZ7KrFmzioy1TZs25R0eERERvURKtSf73XffxahRo3D16lU0bNgQpqamqvP/focwVVw7d+7Eo0ePCj2njf3VJfWk3U1DhgxBt27dCj1nbGxcViERERERFVDiPdkAVH8sROlIo4GIQKPRFHilGxGpcU82UQXHPdlUVrgn+4VWks/vUv8xGiIiIiIiKlypkmwXFxdtx0FEREREVGmUKsles2bNE8/37du3VMEQERFVCPwnfSJ6ilLtybayslIdP3r0CPfu3YOBgQFMTExw69YtrQVIVBlxTzYREVHFU6bvyQaA27dvq0pWVhbOnz+Ppk2bIjo6ulRBExERERFVFqVKsgvj7u6OOXPm4P3339dWl0REREREFZLWkmzgn78Gee3aNW12SURERERU4ZTqwcdt27apjkUEKSkpWLJkCZo0aaKVwIiIiIiIKqpSJdkdO3ZUHWs0Gtja2uL111/HggULtBEXEREREVGFVaokOy8vT9txENFLin84jyoivsGPiJ6mVHuyp02bhnv37hWov3//PqZNm/bMQRERERERVWSlek+2rq4uUlJSUK1aNVX9zZs3Ua1aNeTm5motQKLKiO/J/h+uZFNFxJVsopdTmb8nW0SgKeSTMSEhAdbW1qXpkoiIiIio0ijRnmwrKytoNBpoNBp4eHioEu3c3FxkZWVhyJAhWg+SiIiIiKgiKVGSvWjRIogIBgwYgKlTp8LS0lI5Z2BgAFdXVwQGBmo9SCIiIiKiiqRESXZYWBgAoGbNmmjcuDH09fXLJCgiIiIiooqsVK/wCwoKUr7Ozs7Gw4cPVedf9ge5iIiIiOjlVqoHH+/du4fhw4ejWrVqMDU1hZWVlaoQEREREb3MSpVkjxkzBvv27cNXX30FQ0NDrFixAlOnTkX16tWxZs0abcdIRERERFShlGq7yPfff481a9agRYsW6N+/P5o1awY3Nze4uLhg7dq16N27t7bjJCIiIiKqMEq1kn3r1i3UqlULwD/7r2/dugUAaNq0KQ4ePKi96CqIfv36Ka821Gg0sLGxQWhoKH799dcCbcPDw6Grq4sNGzYUOHfv3j2MHz8etWvXhpGREWxtbREUFIStW7cqbVq0aKEaK788/upEjUaDLVu2lHgeUVFRqFKlSomvIyIiIiK1UiXZtWrVQnJyMgCgTp06+PbbbwH8s8L9siZpoaGhSElJQUpKCmJiYqCnp4d27dqp2ty7dw/r1q3D2LFjsXLlygJ9DBkyBJs2bcLixYtx7tw57N69G126dMHNmzdV7QYPHqyMlV/mzZtXpvMjIiIiouIrVZLdv39/JCQkAAA++ugjfPHFFzAyMsIHH3yAMWPGaDXAisLQ0BD29vawt7dHvXr18NFHH+HKlSu4ceOG0mbDhg3w9vbGRx99hIMHD+LKlSuqPrZt24aPP/4Ybdu2haurKxo2bIj33nsPAwYMULUzMTFRxsovz/pGl9jYWPTv3x/p6enK6viUKVMAAA8ePMDo0aPh6OgIU1NTBAQEIDY2Vrk2fwV8+/bt8PT0hImJCbp06YJ79+5h9erVcHV1hZWVFUaMGIHc3FzlOldXV0yfPh09e/aEqakpHB0d8cUXXxQ7Zo1Gg2XLlqFdu3YwMTGBl5cXjhw5ggsXLqBFixYwNTVF48aNkZSUpLpu69ataNCgAYyMjFCrVi1MnToVOTk5yvlPP/0Uvr6+MDU1hbOzM959911kZWUVmO8PP/wALy8vmJmZKb9kFeXBgwfIyMhQFSIiIqrERAsuXrwo3333nSQkJGijuwonLCxMOnTooBxnZmZKeHi4uLm5SW5urlLfrFkzWbJkiYiIdO7cWaZNm6bqx9PTU7p16yYZGRlFjhUUFCTvv//+E+MBIJs3by7RHB48eCCLFi0SCwsLSUlJkZSUFMnMzBQRkUGDBknjxo3l4MGDcuHCBZk/f74YGhrK77//LiIiq1atEn19fWndurWcOnVKDhw4IDY2NvLGG29It27d5MyZM/L999+LgYGBrFu3ThnTxcVFzM3NZfbs2XL+/Hn5/PPPRVdXV3788cdixQxAHB0dZf369XL+/Hnp2LGjuLq6yuuvvy67d++Ws2fPymuvvSahoaHKNQcPHhQLCwuJioqSpKQk+fHHH8XV1VWmTJmitFm4cKHs27dPkpOTJSYmRjw9PWXo0KHK+fz5tmrVSn755Rc5efKkeHl5Sa9evYqMNSIiQgAUKOnp6cX7BlViAAtLxStE9HJKT0+X4n5+P/P/Ku7fv/+sXVR4YWFhoqurK6ampmJqaioAxMHBQU6ePKm0+f3330VfX19u3LghIiKbN2+WmjVrSl5entLmwIED4uTkJPr6+uLv7y8jR46Un376STVWUFCQ6OvrK2Pll//+979Km9Ik2SL/JI+WlpaqukuXLomurq789ddfqvqWLVvK+PHjlesAyIULF5Tz4eHhYmJioiTqIiIhISESHh6uHLu4uKgSYBGR7t27S5s2bYoVLwCZOHGicnzkyBEBIJGRkUpddHS0GBkZqeKeNWuWqp+vv/5aHBwcihxnw4YNYmNjoxwXNt8vvvhC7OzsiuwjOztb0tPTlXLlypVi/0da2ZV3ssTCUppCRC+nkiTZpdoukpubi+nTp8PR0RFmZmb4888/AQCTJk1CZGTkM62sV1TBwcGIj49HfHw8jh8/jpCQELRp0waXLl0CAKxcuRIhISGoWrUqAKBt27ZIT0/Hvn37lD6aN2+OP//8EzExMejSpQvOnDmDZs2aYfr06aqxevfurYyVX956660ymdfp06eRm5sLDw8PmJmZKeXAgQOqbRgmJiaoXbu2cmxnZwdXV1eYmZmp6tLS0lT9BwYGFjhOTEwsdnx+fn6q/gHA19dXVZedna1sz0hISMC0adNUc8nf437v3j0AwN69e9GyZUs4OjrC3Nwcffr0wc2bN5Xzhc3XwcGhwNweZ2hoCAsLC1UhIiKiyqtUr/CbOXMmVq9ejXnz5mHw4MFK/SuvvIJFixZh4MCBWguwojA1NYWbm5tyvGLFClhaWmL58uWYOnUqVq9ejdTUVOjp/e+W5+bmYuXKlWjZsqVSp6+vj2bNmqFZs2YYN24cZsyYgWnTpmHcuHEwMDAAAFhaWqrGKktZWVnQ1dXFyZMnoaurqzr3eAKtr6+vOqfRaAqty8vL02p8j4+h0WiKrMsfNysrC1OnTsV//vOfAn0ZGRnh4sWLaNeuHYYOHYqZM2fC2toaP/30EwYOHIiHDx/CxMSkwBj544iIVudGREREFVepkuw1a9bg//7v/9CyZUvVq+Pq1q2Lc+fOaS24ikyj0UBHRwf379/Hzp07kZmZibi4OFWi+ttvv6F///64c+dOkW9l8fb2Rk5ODrKzs5Uku6wYGBioHkwEgPr16yM3NxdpaWlo1qyZ1sc8evRogWMvLy+tj5OvQYMGOH/+fJG/pJw8eRJ5eXlYsGABdHT++Yee/LfnEBERERVXqZLsv/76q9AkJS8vD48ePXrmoCqiBw8eIDU1FQBw+/ZtLFmyBFlZWWjfvj0WLVqEN998E3Xr1lVd4+3tjQ8++ABr167FsGHD0KJFC/Ts2RP+/v6wsbHB2bNn8fHHHyM4OFi1veDevXvKWPkMDQ1Vf9I+OTkZ8fHxqjbu7u4wNTUtcg6urq7IyspCTEwM6tatCxMTE3h4eKB3797o27cvFixYgPr16+PGjRuIiYmBn58f3nzzzdLeMgDA4cOHMW/ePHTs2BF79uzBhg0bsGPHjmfq80kmT56Mdu3aoUaNGujSpQt0dHSQkJCA3377DTNmzICbmxsePXqExYsXo3379jh8+DCWLl1aZvEQERFR5VSqPdne3t44dOhQgfqNGzeifv36zxxURbR79244ODjAwcEBAQEB+OWXX7BhwwZ4eXlhx44d6Ny5c4FrdHR00KlTJ2Ufe0hICFavXo033ngDXl5eeO+99xASElJgJXX58uXKWPmlZ8+eqjYffvgh6tevrypxcXFPnEPjxo0xZMgQdO/eHba2tsq7t1etWoW+ffti1KhR8PT0RMeOHfHLL7+gRo0az3LLAACjRo3CiRMnUL9+fcyYMQOffvopQkJCnrnfooSEhGD79u348ccf0ahRI7z22mtYuHAhXFxcAPzzrzGffvop5s6di1deeQVr167F7NmzyyweIiIiqpw0UoqNpFu3bkVYWBjGjx+PadOmYerUqTh//jzWrFmD7du3o3Xr1mURK1Uyrq6uGDlyJEaOHFneoTx3GRkZsLS0RHp6+kv/EOT/3zZPVKHwEQyil1NJPr9LtJL9559/QkTQoUMHfP/999i7dy9MTU0xefJkJCYm4vvvv2eCTUREREQvvRIl2e7u7spfMGzWrBmsra1x+vRp3Lt3Dz/99BPeeOONMgmStKNNmzaqV9c9XmbNmlXe4amsXbu2yFh9fHzKOzwiIiKiJyrRg4//3lmya9cu3L17V6sBUdlZsWIF7t+/X+g5a2vr5xwNcPHixSLPvfXWWwgICCj03L9fn0dERET0oinV20Xy8b3AFYujo2N5h1Bs5ubmMDc3L+8wiIiIiEqlRNtFNBqN8sc9Hq8jIiIiIqL/KfF2kX79+sHQ0BAAkJ2djSFDhhR49/KmTZu0FyERERERUQVToiQ7LCxMdfz2229rNRgievlw1xkREVVGJUqyV61aVVZxEBERERFVGqX6i49ERERERFQ0JtlERERERFrGJJuIiIiISMuYZBMRERERaRmTbCIiIiIiLXumv/hIRPQ0/HtVVBnx1ZNE9DRcySYiIiIi0jIm2UREREREWsYkm4iIiIhIy5hkExERERFpGZNsIiIiIiItY5JNRERERKRlTLKJiIiIiLSMSTYRERERkZYxySYiIiIi0rIXPsnu168fNBoNNBoN9PX1YWdnh9atW2PlypXIy8tT2mk0GmzZsqXQ6zt27KgcJycno1evXqhevTqMjIzg5OSEDh064Ny5c0qbAwcO4PXXX4e1tTVMTEzg7u6OsLAwPHz4EAAQGxurxPTvkpqaCgCYMmUK6tWrV+S8WrRooVxjaGgIR0dHtG/fHps2bSq0/fbt2xEUFARzc3OYmJigUaNGiIqKUrW5ePGiKhZra2sEBQXh0KFDqnZTpkwpNPa9e/cWGW9R95OIiIiICnrhk2wACA0NRUpKCi5evIhdu3YhODgY77//Ptq1a4ecnJxi9/Po0SO0bt0a6enp2LRpE86fP4/169fD19cXd+7cAQCcPXsWoaGh8Pf3x8GDB3H69GksXrwYBgYGyM3NVfV3/vx5pKSkqEq1atWKHc/gwYORkpKCpKQkfPfdd/D29kaPHj3wzjvvqNotXrwYHTp0QJMmTXDs2DH8+uuv6NGjB4YMGYLRo0cX6Hfv3r1ISUnBwYMHUb16dbRr1w7Xr19XtfHx8SkQe/PmzYsdOxERERE9gbzgwsLCpEOHDgXqY2JiBIAsX75cREQAyObNm594fVxcnACQixcvFjnewoULxdXV9Ykx7d+/XwDI7du3i2wTEREhdevWLfJ8UFCQvP/++wXqV65cKQBkz549IiJy+fJl0dfXlw8//LBA288//1wAyNGjR0VEJDk5WQBIXFyc0ubXX38VALJ169Zix/akOQFQlf379ytxdu3aVSwtLcXKykreeustSU5OVq7N/z7MnDlTqlWrJpaWljJ16lR59OiRjB49WqysrMTR0VFWrlypXJM/n+joaAkMDBRDQ0Px8fGR2NjYYsWb/33avXu31KtXT4yMjCQ4OFiuX78uO3fulDp16oi5ubn07NlT7t69q1y3a9cuadKkiVhaWoq1tbW8+eabcuHCBeX86tWrxdTUVH7//XelbujQoeLp6anq53HZ2dmSnp6ulCtXrggASU9PL9ZcKjKAhaXyFSJ6OaWnp0txP78rxEp2YV5//XXUrVu3yO0VhbG1tYWOjg42btxYYFU6n729vbIKXB7CwsJgZWWlzGvjxo149OhRoSvW4eHhMDMzQ3R0dKF93b9/H2vWrAEAGBgYPHNso0ePRrdu3ZR/WUhJSUHjxo3x6NEjhISEwNzcHIcOHcLhw4dhZmaG0NBQZYsNAOzbtw/Xrl3DwYMH8emnnyIiIgLt2rWDlZUVjh07hiFDhiA8PBxXr15VjTtmzBiMGjUKcXFxCAwMRPv27XHz5s1ixz1lyhQsWbIEP//8M65cuYJu3bph0aJF+Oabb7Bjxw78+OOPWLx4sdL+7t27+PDDD3HixAnExMRAR0cHnTp1UrYn9e3bF23btkXv3r2Rk5ODHTt2YMWKFVi7di1MTEwKjWH27NmwtLRUirOzc0luPREREVU0zyHpfyZFrWSLiHTv3l28vLxERAR4+kq2iMiSJUvExMREzM3NJTg4WKZNmyZJSUnK+ZycHOnXr58AEHt7e+nYsaMsXrxY9RtL/gqpqampqnh7eyttSruSLSISEBAgbdq0ERGRIUOGiKWlZZH9+Pn5KW3zV36NjY3F1NRUNBqNAJCGDRvKw4cPVbHp6OioYm/UqFGRYzyusO/H119/LZ6enpKXl6fUPXjwQIyNjeWHH35QrnNxcZHc3FyljaenpzRr1kw5zsnJEVNTU4mOjlbNZ86cOUqbR48eiZOTk8ydO/epseZ/n/bu3avUzZ49WwCovufh4eESEhJSZD83btwQAHL69Gml7tatW+Lk5CRDhw4VOzs7mTlz5hNj4Uo2C0vlKkT0cnopVrIBQESg0WhKdM2wYcOQmpqKtWvXIjAwEBs2bICPjw/27NkDANDV1cWqVatw9epVzJs3D46Ojpg1a5ayh/lxhw4dQnx8vFJ27txZbvN63Pr16xEXF4fvvvsObm5uiIqKgr6+vqqNp6enKvbvvvuu1OMlJCTgwoULMDc3h5mZGczMzGBtbY3s7GwkJSUp7Xx8fKCj878fOTs7O/j6+irHurq6sLGxQVpamqr/wMBA5Ws9PT34+/sjMTGx2PH5+fmpxjQxMUGtWrVUdY+P+ccff6Bnz56oVasWLCws4OrqCgC4fPmy0sbKygqRkZH46quvULt2bXz00UdPjMHQ0BAWFhaqQkRERJWXXnkH8CwSExNRs2ZNAIC5uTnS09MLtLlz5w4sLS1Vdebm5mjfvj3at2+PGTNmICQkBDNmzEDr1q2VNo6OjujTpw/69OmD6dOnw8PDA0uXLsXUqVOVNjVr1kSVKlW0Oqfc3Fz88ccfaNSoEQDAw8MD6enpuHbtGqpXr65q+/DhQyQlJSE4OFhV7+zsDHd3d7i7uyMnJwedOnXCb7/9BkNDQ6WNgYEB3NzctBJzVlYWGjZsiLVr1xY4Z2trq3z970Q//40x/657/K0x2vD4GMUZs3379nBxccHy5ctRvXp15OXl4ZVXXlFtfQGAgwcPQldXFykpKbh79y7Mzc21GjcRERFVXBV2JXvfvn04ffo0OnfuDOCfldmTJ0+q2uTm5iIhIQEeHh5F9qPRaFCnTh3cvXu3yDZWVlZwcHB4YhttWb16NW7fvq3Mq3PnztDX18eCBQsKtF26dCnu3r2Lnj17Ftlfly5doKenhy+//FIr8RX2lpUGDRrgjz/+QLVq1eDm5qYq//4FpzSOHj2qfJ2Tk4OTJ0/Cy8vrmfstzM2bN3H+/HlMnDgRLVu2hJeXF27fvl2g3c8//4y5c+fi+++/h5mZGYYPH14m8RAREVHFVCFWsh88eIDU1FTk5ubi+vXr2L17N2bPno127dqhb9++AIAPP/wQAwcORJ06ddC6dWvcvXsXixcvxu3btzFo0CAAQHx8PCIiItCnTx94e3vDwMAABw4cwMqVKzFu3DgAwLJlyxAfH49OnTqhdu3ayM7Oxpo1a3DmzBnVw3EAkJaWhuzsbFWdjY2NslJ6//59xMfHq86bm5ujdu3aAIB79+4hNTUVOTk5uHr1KjZv3oyFCxdi6NChyup0jRo1MG/ePIwaNQpGRkbo06cP9PX1sXXrVnz88ccYNWoUAgICirx3Go0GI0aMwJQpUxAeHl7kg3nF5erqih9++AHnz5+HjY0NLC0t0bt3b8yfPx8dOnTAtGnT4OTkhEuXLmHTpk0YO3YsnJycnmnML774Au7u7vDy8sLChQtx+/ZtDBgw4Jn6LIqVlRVsbGzwf//3f3BwcMDly5cLbAXJzMxEnz59MGLECLRp0wZOTk5o1KgR2rdvjy5dupRJXERERFTBlPkO8WcUFhYmwD+vi9PT0xNbW1tp1aqVrFy5UvUQnYjI2rVrpWHDhmJubi52dnbStm1bSUhIUM7fuHFDRowYIa+88oqYmZmJubm5+Pr6yieffKL0derUKXn77belZs2aYmhoKDY2NtK8eXPZtm2b0k/+A3WFlSNHjohI4a+7AyAtW7YUkX8efMyvMzAwEAcHB2nXrp1s2rSp0PuwdetWadasmZiamoqRkZE0bNhQ9bo7kcJf4ScicvfuXbGyslIeFiztK/xERNLS0qR169ZiZmYmwP9e4ZeSkiJ9+/aVqlWriqGhodSqVUsGDx6sPBhQ2AOThT386eLiIgsXLlTN55tvvpFXX31VDAwMxNvbW/bt21esWAt71eKqVasKPEj67/uxZ88e8fLyEkNDQ/Hz85PY2FgB/vdgbf/+/cXX11eys7OVaxYsWCDW1tZy9erVYsVWkgcnKrryfkCNhaUsChG9nEry+a0REXmeST1RcV28eBE1a9ZEXFzcE/96ZkWUkZEBS0tLpKenV/qHIJ/hGV6iFxY/OYleTiX5/K6we7KJiIiIiF5UTLJJJf8VfIWVQ4cOlXd4KkOGDCky1iFDhpR3eERERPQS43YRUrlw4UKR5xwdHWFsbPwco3mytLQ0ZGRkFHrOwsIC1apVe84RFR+3ixBVbPzkJHo5leTzu0K8XYSeH229O/t5qFat2gudSBMREdHLi9tFiIiIiIi0jEk2EREREZGWcbsIEZUp7l0lIqKXEVeyiYiIiIi0jEk2EREREZGWMckmIiIiItIyJtlERERERFrGJJuIiIiISMuYZBMRERERaRlf4UdERFRSGk15R0BUtvj+1WfGlWwiIiIiIi1jkk1EREREpGVMsomIiIiItIxJNhERERGRljHJJiIiIiLSMibZRERERERaxiSbiIiIiEjLmGQTEREREWkZk2wiIiIiIi2rkEn2jRs3MHToUNSoUQOGhoawt7dHSEgIDh8+rLT5+eef0bZtW1hZWcHIyAi+vr749NNPkZubq+pLo9HAyMgIly5dUtV37NgR/fr1U4779esHjUaDOXPmqNpt2bIFmsf+8ldsbCw0Go1SjI2N4ePjg//7v/9TXdevXz907NixyDm6urpi0aJFqrq4uDh07doVdnZ2MDIygru7OwYPHozff/8dAHDx4kVoNBrEx8cr12RmZiI4OBje3t64evVqkeM9HvudO3ee2I6IiIiInqxCJtmdO3dGXFwcVq9ejd9//x3btm1DixYtcPPmTQDA5s2bERQUBCcnJ+zfvx/nzp3D+++/jxkzZqBHjx6Qf/2pUI1Gg8mTJz91XCMjI8ydOxe3b99+atvz588jJSUFZ8+eRXh4OIYOHYqYmJjSTRjA9u3b8dprr+HBgwdYu3YtEhMT8d///heWlpaYNGlSodfcuHEDwcHBuHv3Lg4dOgQnJ6dSj09EREREJSAVzO3btwWAxMbGFno+KytLbGxs5D//+U+Bc9u2bRMAsm7dOqUOgIwePVp0dHTk9OnTSn2HDh0kLCxMOQ4LC5N27dpJnTp1ZMyYMUr95s2b5fHbuH//fgEgt2/fVo1du3ZtmTdvnqq/Dh06FDlPFxcXWbhwoYiI3L17V6pWrSodO3YstG3+WMnJyQJA4uLi5PLly+Lp6Smvv/66ZGZmFjlOvvxrHy/588/NzZVZs2aJq6urGBkZiZ+fn2zYsKHAnHfv3i316tUTIyMjCQ4OluvXr8vOnTulTp06Ym5uLj179pS7d+8q1wUFBcmwYcNk2LBhYmFhITY2NjJx4kTJy8t7arz592j69OnSp08fMTU1lRo1asjWrVslLS1N3nrrLTE1NRVfX1/55ZdfVNcdOnRImjZtKkZGRuLk5CTvvfeeZGVlKefXrFkjDRs2FDMzM7Gzs5OePXvK9evXC8x379690rBhQzE2NpbAwEA5d+5cseIWEUlPTxcAkp6eXuxriOgFArCwVO5ChSrJ53eFW8k2MzODmZkZtmzZggcPHhQ4/+OPP+LmzZsYPXp0gXPt27eHh4cHoqOjVfVNmjRBu3bt8NFHHz1xbF1dXcyaNQuLFy9+6taLfCKC3bt34/LlywgICCjWNf/2ww8/4O+//8bYsWMLPV+lShXV8fnz59GkSRN4e3tj586dMDMze+oYzs7O+O6775TrU1JS8NlnnwEAZs+ejTVr1mDp0qU4c+YMPvjgA7z99ts4cOCAqo8pU6ZgyZIl+Pnnn3HlyhV069YNixYtwjfffIMdO3bgxx9/xOLFi1XXrF69Gnp6ejh+/Dg+++wzfPrpp1ixYkVxbw0WLlyIJk2aIC4uDm+++Sb69OmDvn374u2338apU6dQu3Zt9O3bFyICAEhKSkJoaCg6d+6MX3/9FevXr8dPP/2E4cOHK30+evQI06dPR0JCArZs2YKLFy+qtg7lmzBhAhYsWIATJ05AT08PAwYMKDLOBw8eICMjQ1WIiIioEivzlL8MbNy4UaysrMTIyEgaN24s48ePl4SEBBERmTNnjgAFV5LzvfXWW+Ll5aUcA5DNmzfLmTNnRFdXVw4ePCgiha9k5688v/baazJgwAARKXol29TUVExNTUVPT090dHRkxowZqjhKspI9d+5cASC3bt164n3JX402MDCQ4OBgycnJeWL7fytsFT47O1tMTEzk559/VrUdOHCg9OzZU3Xd3r17lfOzZ88WAJKUlKTUhYeHS0hIiHIcFBQkXl5eqpXrcePGqb4/T+Li4iJvv/22cpySkiIAZNKkSUrdkSNHBICkpKQocb/zzjuqfg4dOiQ6Ojpy//79Qsf55ZdfBIDyLwKFzXfHjh0CoMg+IiIiBECBwpVsogqqvFcZWVjKulChKvVKNvDPnuxr165h27ZtCA0NRWxsLBo0aICoqCiljYiUqE9vb2/07dv3qavZADB37lysXr0aiYmJRbY5dOgQ4uPjER8fjxUrVmDWrFn46quvShRTvpLO5a233sKhQ4ewadOmUo33uAsXLuDevXto3bq18q8IZmZmWLNmDZKSklRt/fz8lK/t7OxgYmKCWrVqqerS0tJU17z22muqB0cDAwPxxx9/FHhAtSj/HhMAfH19C9Tlj5uQkICoqCjVXEJCQpCXl4fk5GQAwMmTJ9G+fXvUqFED5ubmCAoKAgBcvny5yLEdHBxU4/zb+PHjkZ6erpQrV64Ua35ERERUMemVdwClZWRkhNatW6N169aYNGkSBg0ahIiICOWNHImJiWjcuHGB6xITE+Ht7V1on1OnToWHhwe2bNnyxLGbN2+OkJAQjB8/vtBtBABQs2ZNZRuHj48Pjh07hpkzZ2Lo0KHFnaLCw8MDAHDu3DkEBgY+tf2ECRPg5+eHXr16QUTQrVu3Eo+ZLysrCwCwY8cOODo6qs4ZGhqqjvX19ZWvNRqN6ji/Li8vr9SxFObfYxZVlz9uVlYWwsPDMWLEiAJ91ahRA3fv3kVISAhCQkKwdu1a2Nra4vLlywgJCcHDhw+fOnZR8zM0NCxwv4iIiKjyqrBJ9r95e3tjy5YteOONN2BtbY0FCxYUSLK3bduGP/74A9OnTy+0D2dnZwwfPhwff/wxateu/cTx5syZg3r16sHT07NY8enq6uL+/fvFm8y/vPHGG6hatSrmzZuHzZs3Fzh/586dAvuyJ02aBB0dHfTu3Rsigu7duz91HAMDAwBQrSJ7e3vD0NAQly9fVlZ0tenYsWOq46NHj8Ld3R26urpaHwsAGjRogLNnz8LNza3Q86dPn8bNmzcxZ84cODs7AwBOnDhRJrEQERFR5VXhkuybN2+ia9euGDBgAPz8/GBubo4TJ05g3rx56NChA0xNTbFs2TL06NED77zzDoYPHw4LCwvExMRgzJgx6NKlyxNXdsePH4/ly5cjOTn5iYmpr68vevfujc8//7zQ82lpacjOzsaDBw9w/PhxfP311+jSpYuqTXp6uuqd1gBgY2OjJHf5TE1NsWLFCnTt2hVvvfUWRowYATc3N/z999/49ttvcfnyZaxbt65ADBMmTICuri569+6NvLw89OzZs8j5AICLiws0Gg22b9+Otm3bwtjYGObm5hg9ejQ++OAD5OXloWnTpkhPT8fhw4dhYWGBsLCwJ/b5NJcvX8aHH36I8PBwnDp1CosXL8aCBQueqc8nGTduHF577TUMHz4cgwYNgqmpKc6ePYs9e/ZgyZIlqFGjBgwMDLB48WIMGTIEv/32W5G/lBEREREVpcIl2WZmZggICMDChQuRlJSER48ewdnZGYMHD8bHH38MAOjSpQv279+PmTNnolmzZsjOzoa7uzsmTJiAkSNHqvYA/5u1tTXGjRun9PUk06ZNw/r16ws9l7/CraenB2dnZ4SHh2PKlCmqNrGxsahfv76qbuDAgYW+XaNDhw74+eefMXv2bPTq1QsZGRlwdnbG66+/jhkzZhQZ40cffQQdHR306dMHIoJevXoV2dbR0RFTp07FRx99hP79+6Nv376IiorC9OnTYWtri9mzZ+PPP/9ElSpV0KBBg2Ldo6fp27cv7t+/j1dffRW6urp4//338c477zxzv0Xx8/PDgQMHMGHCBDRr1gwigtq1ayu/UNna2iIqKgoff/wxPv/8czRo0ACffPIJ3nrrrTKLiYiIiCofjZT0qToiLWnRogXq1atX4C9bvgwyMjJgaWmJ9PR0WFhYlHc4RFRST1isIaoUmB4WqiSf3xXy7SJERERERC8yJtkvkSFDhqheXfd4GTJkSHmHp3Lo0KEiYy3OH9chIiIiKk/cLvISSUtLK/IvDVpYWKBatWrPOaKi3b9/H3/99VeR54t6O0hFwe0iRBUct4tQZcf0sFAl+fyucA8+UulVq1bthUqkn8TY2LjCJ9JERET08uJ2ESIiIiIiLWOSTURERESkZdwuQkREVFLcr0pET8GVbCIiIiIiLWOSTURERESkZUyyiYiIiIi0jEk2EREREZGWMckmIiIiItIyJtlERERERFrGV/gRVUL8i89EZYtv8COip+FKNhERERGRljHJJiIiIiLSMibZRERERERaxiSbiIiIiEjLmGQTEREREWkZk2wiIiIiIi1jkk1EREREpGVMsomIiIiItIxJNhERERGRllXKJLtfv37o2LFjgfrY2FhoNBrcuXNH+bqwkpqaqrru6tWrMDAwwCuvvKLUTZkypcjr80txY3n8OL/Y2tqibdu2OH36dIFrr1y5ggEDBqB69eowMDCAi4sL3n//fdy8eVNp89FHH6FOnTqq686dOweNRoN+/fqp6qOiomBoaIj79+8/6bbi4sWL0Gg0iI+Pf2I7IiIiopddpUyyS+L8+fNISUlRlWrVqqnaREVFoVu3bsjIyMCxY8cAAKNHj1Zd4+TkhGnTpqnqniWeH374AQ8ePMCbb76Jhw8fKuf//PNP+Pv7448//kB0dDQuXLiApUuXIiYmBoGBgbh16xYAIDg4GOfPn1f9wrB//344OzsjNjZWNeb+/fvx2muvwdjYuFQxExEREZHaS59kV6tWDfb29qqio/O/2yIiWLVqFfr06YNevXohMjISAGBmZqa6RldXF+bm5qq6Z4mnQYMGGDlyJK5cuYJz584p54cNGwYDAwP8+OOPCAoKQo0aNdCmTRvs3bsXf/31FyZMmAAAaNq0KfT19VUJdWxsLIYNG4Zbt27h4sWLqvrg4OCnxlazZk0AQP369aHRaNCiRQvl3IoVK+Dl5QUjIyPUqVMHX375pXIufwX822+/RbNmzWBsbIxGjRrh999/xy+//AJ/f3+YmZmhTZs2uHHjhnJd/r8CTJ06Fba2trCwsMCQIUNUv3Q8SYsWLfDee+9h5MiRsLKygp2dHZYvX467d++if//+MDc3h5ubG3bt2qVck5ubi4EDB6JmzZowNjaGp6cnPvvsM+V8dnY2fHx88M477yh1SUlJMDc3x8qVK4sVFxEREVV+L32S/TT79+/HvXv30KpVK7z99ttYt24d7t69W+bjpqenY926dQAAAwMDAMCtW7fwww8/4N133y2w6mxvb4/evXtj/fr1EBGYmpqiUaNG2L9/v9ImNjYWLVu2RJMmTZT6P//8E5cvXy5Wkn38+HEAwN69e5GSkoJNmzYBANauXYvJkydj5syZSExMxKxZszBp0iSsXr1adX1ERAQmTpyIU6dOQU9PD7169cLYsWPx2Wef4dChQ7hw4QImT56suiYmJgaJiYmIjY1FdHQ0Nm3ahKlTpxb7Pq5evRpVq1bF8ePH8d5772Ho0KHo2rUrGjdujFOnTuGNN95Anz59cO/ePQBAXl4enJycsGHDBpw9exaTJ0/Gxx9/jG+//RYAYGRkhLVr12L16tXYunUrcnNz8fbbb6N169YYMGBAkXE8ePAAGRkZqkJERESVmFRCYWFhoqurK6ampqpiZGQkAOT27duyf/9+AVCgjbe3t6qvXr16yciRI5XjunXryqpVqwqM6eLiIgsXLixVLCJSIB4AAkDeeustpa+jR48KANm8eXOh8/70008FgFy/fl1ERCZMmCAeHh4iInLmzBmxsLCQnJwcmTVrlvTt21dERCIjI8XIyEiys7Ofel+Tk5MFgMTFxanqa9euLd98842qbvr06RIYGKi6bsWKFcr56OhoASAxMTFK3ezZs8XT01N176ytreXu3btK3VdffSVmZmaSm5v71HiDgoKkadOmynFOTo6YmppKnz59lLqUlBQBIEeOHCmyn2HDhknnzp1VdfPmzZOqVavK8OHDxcHBQf7+++8nxhIREaF8Tx8v6enpT51HaQAsLCxlWYjo5ZSeni7F/fzWe95J/fMSHByMr776SlV37NgxvP3226q6Q4cOwdzcXDnW19dXvr5z5w42bdqEn376Sal7++23ERkZWeDhQW3Ekh+PiYkJjh49ilmzZmHp0qUF2ohIscZt0aIFZs6ciZSUFMTGxqJp06bQ1dVFUFCQ0m9sbCwaN24MQ0PDYs/ncXfv3kVSUhIGDhyIwYMHK/U5OTmwtLRUtfXz81O+trOzAwD4+vqq6tLS0lTX1K1bFyYmJspxYGAgsrKycOXKFbi4uDw1vsfH1NXVhY2NTYExAajG/eKLL7By5UpcvnwZ9+/fx8OHD1GvXj1Vv6NGjcKWLVuwZMkS7Nq1CzY2Nk+MY/z48fjwww+V44yMDDg7Oz81fiIiIqqYKm2SbWpqCjc3N1Xd1atXC7SrWbMmqlSpUmgf33zzDbKzsxEQEKDUiQjy8vLw+++/w8PDQ6uxPB6Pp6cn0tLS0L17dxw8eBAA4ObmBo1Gg8TERHTq1KnAtYmJibCysoKtrS0AoEmTJjAwMMD+/fuxf/9+BAUFAQAaNWqEv//+G3/++SdiY2MRHh5erHkUJisrCwCwfPly1X0C/klqH/f4LzD5b1/5d11eXl6pYynM4/3nj1FYHPnjrlu3DqNHj8aCBQsQGBgIc3NzzJ8/X3ngNV9aWhp+//136Orq4o8//kBoaOgT4zA0NCz1LzJERERU8XBP9hNERkZi1KhRiI+PV0pCQgKaNWv2XB5yGzZsGH777Tds3rwZAGBjY4PWrVvjyy+/LPC6vdTUVKxduxbdu3dXEkdjY2MEBAQgNjYWBw4cUB5U1NfXx2uvvYbIyEhcuXKlWPuxgf/tDc/NzVXq7OzsUL16dfz5559wc3NTlfwHJZ9FQkKCaq5Hjx6FmZlZma0CHz58GI0bN8a7776L+vXrw83NDUlJSQXaDRgwAL6+vli9ejXGjRuHxMTEMomHiIiIKqaXPslOS0tDamqqqjx69Ajx8fE4deoUBg0ahFdeeUVVevbsidWrVyMnJ6dMYzMxMcHgwYMRERGhbBFZsmQJHjx4gJCQEBw8eBBXrlzB7t270bp1azg6OmLmzJmqPoKDg7Fu3TpkZ2ejQYMGSn1QUBAWL16sPCBZHNWqVYOxsTF2796N69evIz09HQAwdepUzJ49G59//jl+//13nD59GqtWrcKnn376zPfg4cOHGDhwIM6ePYudO3ciIiICw4cPV70BRpvc3d1x4sQJ/PDDD/j9998xadIk/PLLL6o2X3zxBY4cOYLVq1ejd+/e6NixI3r37l3st54QERFR5ffSJ9menp5wcHBQlZMnTyIyMhLe3t4F/qALAHTq1AlpaWnYuXNnmcc3fPhwJCYmYsOGDQD+lwTWqlUL3bp1Q+3atfHOO+8gODgYR44cgbW1ter64OBgZGZmokmTJtDT+9/uoKCgIGRmZiqv+isOPT09fP7551i2bBmqV6+ODh06AAAGDRqEFStWYNWqVfD19UVQUBCioqK0spLdsmVLuLu7o3nz5ujevTveeustTJky5Zn7LUp4eDj+85//oHv37ggICMDNmzfx7rvvKufPnTuHMWPG4Msvv1RW07/88kv8/fffmDRpUpnFRURERBWLRor7FB3Rc9avXz/cuXMHW7ZsKe9QtC4jIwOWlpZIT0+HhYWF1vv//zuGiKiM8JOT6OVUks/vl34lm4iIiIhI25hkk2LWrFkwMzMrtLRp06a8w1O5fPlykbGamZnh8uXL5R0iERERvcS4XYQUt27dwq1btwo9Z2xsDEdHx+ccUdFycnJUfxr+31xdXVV70F803C5CVLHxk5Po5VSSz+8XNwuh587a2rrAg5MvKj09vQLvHiciIiJ6UXC7CBERERGRljHJJiIiIiLSMibZRERERERaxj3ZRJUQH8oiIiIqX1zJJiIiIiLSMibZRERERERaxiSbiIiIiEjLmGQTEREREWkZk2wiIiIiIi3j20WIiIhKSqMp7wiI6GnK+VVbXMkmIiIiItIyJtlERERERFrGJJuIiIiISMuYZBMRERERaRmTbCIiIiIiLWOSTURERESkZUyyiYiIiIi0jEk2EREREZGWMckmIiIiItIyJtlERERERFrGJFtL+vXrh44dO6rqNm7cCCMjIyxYsECpmz17NnR1dTF//vwCfeTm5mLOnDmoU6cOjI2NYW1tjYCAAKxYsUJpc+PGDQwdOhQ1atSAoaEh7O3tERISgsOHDyttXF1dodFoCpQ5c+YAAC5evAiNRoP4+PgSz3PKlCmoV69eia8jIiIiepnolXcAldWKFSswbNgwLF26FP3791fqV65cibFjx2LlypUYM2aM6pqpU6di2bJlWLJkCfz9/ZGRkYETJ07g9u3bSpvOnTvj4cOHWL16NWrVqoXr168jJiYGN2/eVPU1bdo0DB48WFVnbm5eBjMlIiIiogKEtCIsLEw6dOggIiJz584VIyMj2bRpk6pNbGysODo6ysOHD6V69epy+PBh1fm6devKlClTihzj9u3bAkBiY2OfGIuLi4ssXLiwyPPJyckCQOLi4p7Yz7+tWrVKAKjKqlWrlNgGDhwoVatWFXNzcwkODpb4+Hjl2oiICKlbt65ERkaKs7OzmJqaytChQyUnJ0fmzp0rdnZ2YmtrKzNmzFCNCUC+/PJLCQ0NFSMjI6lZs6Zs2LChWPHmz3P9+vXStGlTMTIyEn9/fzl//rwcP35cGjZsKKamphIaGippaWnKdcePH5dWrVqJjY2NWFhYSPPmzeXkyZPK+f3794u+vr4cPHhQqZs7d67Y2tpKampqobFkZ2dLenq6Uq5cuSIAJD09vVhzIaIXDMDCwvKilzKQnp4uxf385nYRLRs3bhymT5+O7du3o1OnTqpzkZGR6NmzJ/T19dGzZ09ERkaqztvb22Pfvn24ceNGoX2bmZnBzMwMW7ZswYMHD8psDkXp3r07Ro0aBR8fH6SkpCAlJQXdu3cHAHTt2hVpaWnYtWsXTp48iQYNGqBly5a4deuWcn1SUhJ27dqF3bt3Izo6GpGRkXjzzTdx9epVHDhwAHPnzsXEiRNx7Ngx1biTJk1C586dkZCQgN69e6NHjx5ITEwsdtwRERGYOHEiTp06BT09PfTq1Qtjx47FZ599hkOHDuHChQuYPHmy0j4zMxNhYWH46aefcPToUbi7u6Nt27bIzMwEALRo0QIjR45Enz59kJ6ejri4OEyaNAkrVqyAnZ1doTHMnj0blpaWSnF2di52/ERERFQBlUma/xIKCwsTAwMDASAxMTEFzqenp4uxsbGyuhsXFydmZmaSmZmptDlz5ox4eXmJjo6O+Pr6Snh4uOzcuVPVz8aNG8XKykqMjIykcePGMn78eElISFC1cXFxEQMDAzE1NVWV/JXX0q5ki/xvRfpxhw4dEgsLC8nOzlbV165dW5YtW6ZcZ2JiIhkZGcr5kJAQcXV1ldzcXKXO09NTZs+erRwDkCFDhqj6DQgIkKFDhz411vx5rlixQqmLjo4u8D2aPXu2eHp6FtlPbm6umJuby/fff6/UPXjwQOrVqyfdunUTb29vGTx48BNj4Uo2USVT3it0LCwsTy9lgCvZ5cTPzw+urq6IiIhAVlaW6lx0dDRq166NunXrAgDq1asHFxcXrF+/Xmnj7e2N3377DUePHsWAAQOQlpaG9u3bY9CgQUqbzp0749q1a9i2bRtCQ0MRGxuLBg0aICoqSjXemDFjEB8fryr+/v5lMu+EhARkZWXBxsZGWW03MzNDcnIykpKSlHaurq6qfeF2dnbw9vaGjo6Oqi4tLU3Vf2BgYIHjkqxk+/n5qfoHAF9f3yLHvH79OgYPHgx3d3dYWlrCwsICWVlZuHz5stLGwMAAa9euxXfffYfs7GwsXLjwiTEYGhrCwsJCVYiIiKjy4oOPWuTo6IiNGzciODgYoaGh2LVrl5JURkZG4syZM9DT+98tz8vLw8qVKzFw4EClTkdHB40aNUKjRo0wcuRI/Pe//0WfPn0wYcIE1KxZEwBgZGSE1q1bo3Xr1pg0aRIGDRqEiIgI9OvXT+mnatWqcHNzey7zzsrKgoODA2JjYwucq1KlivK1vr6+6pxGoym0Li8vT6vxPT6GRqMptO7xMcPCwnDz5k189tlncHFxgaGhIQIDA/Hw4UNVvz///DMA4NatW7h16xZMTU21GjcRERFVXFzJ1jIXFxccOHAAqampCA0NRWZmJk6fPo0TJ04gNjZWtbIcGxuLI0eO4Ny5c0X25+3tDQC4e/fuE9s86bw2GRgYIDc3V1XXoEEDpKamQk9PD25ubqpStWrVZx7z6NGjBY69vLyeud+iHD58GCNGjEDbtm3h4+MDQ0ND/P3336o2SUlJ+OCDD7B8+XIEBAQgLCxM678cEBERUcXFlewy4OzsjNjYWAQHByMkJAR16tTBq6++iubNmxdo26hRI0RGRmL+/Pno0qULmjRpgsaNG8Pe3h7JyckYP348PDw8UKdOHdy8eRNdu3bFgAED4OfnB3Nzc5w4cQLz5s1Dhw4dVP1mZmYiNTVVVWdiYqLapnD+/PkC8fj4+BRYXX6cq6srkpOTER8fDycnJ5ibm6NVq1YIDAxEx44dMW/ePHh4eODatWvYsWMHOnXq9MzbVDZs2AB/f380bdoUa9euxfHjxws8NKpN7u7u+Prrr5XXKI4ZMwbGxsbK+dzcXLz99tsICQlB//79ERoaCl9fXyxYsKDAaxmJiIjo5cSV7DLi5OSE2NhYpKamYvPmzWjTpk2h7Tp37ow1a9bg0aNHCAkJwffff4/27dvDw8MDYWFhqFOnDn788Ufo6enBzMwMAQEBWLhwIZo3b45XXnkFkyZNwuDBg7FkyRJVv5MnT4aDg4OqjB07VtWmR48eqF+/vqpcv379ifPq3LkzQkNDERwcDFtbW0RHR0Oj0WDnzp1o3rw5+vfvDw8PD/To0QOXLl0q8m0bJTF16lSsW7cOfn5+WLNmDaKjo5UV/rIQGRmJ27dvo0GDBujTpw9GjBiBatWqKednzpyJS5cuYdmyZQAABwcH/N///R8mTpyIhISEMouLiIiIKg7NPw9JE72YNBoNNm/eXOCvaVZ0GRkZsLS0RHp6Oh+CJKqI/v/zHUT0AiuDFLckn99cySYiIiIi0jIm2aTi4+Ojeg3f42Xt2rXlHZ7KrFmzioy1qO05RERERM8Dt4uQyqVLl/Do0aNCz9nZ2anec13e8l+dVxhjY2M4Ojo+54iKj9tFiCo4bhchevGV83YRvl2EVFxcXMo7hGKztraGtbV1eYdBREREVAC3ixARERERaRmTbCIiIiIiLeN2ESIiopLi40xE9BRcySYiIiIi0jIm2UREREREWsYkm4iIiIhIy5hkExERERFpGZNsIiIiIiItY5JNRERERKRlTLKJiIiIiLSMSTYRERERkZYxySYiIiIi0jIm2UREREREWsYkm4iIiIhIy5hkExERERFpGZNsIiIiIiItY5JNRERERKRlTLKJiIiIiLRMr7wDIHoZiQgAICMjo5wjISIiouLK/9zO/xx/EibZROUgMzMTAODs7FzOkRAREVFJZWZmwtLS8oltNFKcVJyItCovLw/Xrl2Dubk5NBpNke0yMjLg7OyMK1euwMLC4jlGWHHxnpUM71fJ8Z6VHO9ZyfGeldzzuGcigszMTFSvXh06Ok/edc2VbKJyoKOjAycnp2K3t7Cw4P9kS4j3rGR4v0qO96zkeM9Kjves5Mr6nj1tBTsfH3wkIiIiItIyJtlERERERFrGJJvoBWZoaIiIiAgYGhqWdygVBu9ZyfB+lRzvWcnxnpUc71nJvWj3jA8+EhERERFpGVeyiYiIiIi0jEk2EREREZGWMckmIiIiItIyJtlERERERFrGJJvoBTNz5kw0btwYJiYmqFKlSrGu6devHzQajaqEhoaWbaAviNLcLxHB5MmT4eDgAGNjY7Rq1Qp//PFH2Qb6Arl16xZ69+4NCwsLVKlSBQMHDkRWVtYTr2nRokWBn7EhQ4Y8p4ifvy+++AKurq4wMjJCQEAAjh8//sT2GzZsQJ06dWBkZARfX1/s3LnzOUX64ijJPYuKiirw82RkZPQcoy1fBw8eRPv27VG9enVoNBps2bLlqdfExsaiQYMGMDQ0hJubG6Kioso8zhdJSe9ZbGxsgZ8xjUaD1NTU5xMwmGQTvXAePnyIrl27YujQoSW6LjQ0FCkpKUqJjo4uowhfLKW5X/PmzcPnn3+OpUuX4tixYzA1NUVISAiys7PLMNIXR+/evXHmzBns2bMH27dvx8GDB/HOO+889brBgwerfsbmzZv3HKJ9/tavX48PP/wQEREROHXqFOrWrYuQkBCkpaUV2v7nn39Gz549MXDgQMTFxaFjx47o2LEjfvvtt+ccefkp6T0D/vmrfI//PF26dOk5Rly+7t69i7p16+KLL74oVvvk5GS8+eabCA4ORnx8PEaOHIlBgwbhhx9+KONIXxwlvWf5zp8/r/o5q1atWhlFWAghohfSqlWrxNLSslhtw8LCpEOHDmUaz4uuuPcrLy9P7O3tZf78+UrdnTt3xNDQUKKjo8swwhfD2bNnBYD88ssvSt2uXbtEo9HIX3/9VeR1QUFB8v777z+HCMvfq6++KsOGDVOOc3NzpXr16jJ79uxC23fr1k3efPNNVV1AQICEh4eXaZwvkpLes5L8/62yAyCbN29+YpuxY8eKj4+Pqq579+4SEhJShpG9uIpzz/bv3y8A5Pbt288lpsJwJZuokoiNjUW1atXg6emJoUOH4ubNm+Ud0gspOTkZqampaNWqlVJnaWmJgIAAHDlypBwjez6OHDmCKlWqwN/fX6lr1aoVdHR0cOzYsSdeu3btWlStWhWvvPIKxo8fj3v37pV1uM/dw4cPcfLkSdXPh46ODlq1alXkz8eRI0dU7QEgJCTkpfh5Akp3zwAgKysLLi4ucHZ2RocOHXDmzJnnEW6F9LL/jD2LevXqwcHBAa1bt8bhw4ef69h6z3U0IioToaGh+M9//oOaNWsiKSkJH3/8Mdq0aYMjR45AV1e3vMN7oeTvx7Ozs1PV29nZPde9euUlNTW1wD+X6unpwdra+onz79WrF1xcXFC9enX8+uuvGDduHM6fP49NmzaVdcjP1d9//43c3NxCfz7OnTtX6DWpqakv7c8TULp75unpiZUrV8LPzw/p6en45JNP0LhxY5w5cwZOTk7PI+wKpaifsYyMDNy/fx/GxsblFNmLy8HBAUuXLoW/vz8ePHiAFStWoEWLFjh27BgaNGjwXGJgkk30HHz00UeYO3fuE9skJiaiTp06peq/R48eyte+vr7w8/ND7dq1ERsbi5YtW5aqz/JU1verMiruPSutx/ds+/r6wsHBAS1btkRSUhJq165d6n7p5RQYGIjAwEDluHHjxvDy8sKyZcswffr0coyMKgtPT094enoqx40bN0ZSUhIWLlyIr7/++rnEwCSb6DkYNWoU+vXr98Q2tWrV0tp4tWrVQtWqVXHhwoUKmWSX5f2yt7cHAFy/fh0ODg5K/fXr11GvXr1S9fkiKO49s7e3L/AwWk5ODm7duqXcm+IICAgAAFy4cKFSJdlVq1aFrq4url+/rqq/fv16kffH3t6+RO0rm9Lcs3/T19dH/fr1ceHChbIIscIr6mfMwsKCq9gl8Oqrr+Knn356buMxySZ6DmxtbWFra/vcxrt69Spu3rypSiIrkrK8XzVr1oS9vT1iYmKUpDojIwPHjh0r8RtdXiTFvWeBgYG4c+cOTp48iYYNGwIA9u3bh7y8PCVxLo74+HgAqLA/Y0UxMDBAw4YNERMTg44dOwIA8vLyEBMTg+HDhxd6TWBgIGJiYjBy5Eilbs+ePaqV2sqsNPfs33Jzc3H69Gm0bdu2DCOtuAIDAwu8FvJl+hnTlvj4+Of7/6xye+SSiAp16dIliYuLk6lTp4qZmZnExcVJXFycZGZmKm08PT1l06ZNIiKSmZkpo0ePliNHjkhycrLs3btXGjRoIO7u7pKdnV1e03huSnq/RETmzJkjVapUka1bt8qvv/4qHTp0kJo1a8r9+/fLYwrPXWhoqNSvX1+OHTsmP/30k7i7u0vPnj2V81evXhVPT085duyYiIhcuHBBpk2bJidOnJDk5GTZunWr1KpVS5o3b15eUyhT69atE0NDQ4mKipKzZ8/KO++8I1WqVJHU1FQREenTp4989NFHSvvDhw+Lnp6efPLJJ5KYmCgRERGir68vp0+fLq8pPHclvWdTp06VH374QZKSkuTkyZPSo0cPMTIykjNnzpTXFJ6rzMxM5f9VAOTTTz+VuLg4uXTpkoiIfPTRR9KnTx+l/Z9//ikmJiYyZswYSUxMlC+++EJ0dXVl9+7d5TWF566k92zhwoWyZcsW+eOPP+T06dPy/vvvi46Ojuzdu/e5xcwkm+gFExYWJgAKlP379yttAMiqVatEROTevXvyxhtviK2trejr64uLi4sMHjxY+XCr7Ep6v0T+eY3fpEmTxM7OTgwNDaVly5Zy/vz55x98Obl586b07NlTzMzMxMLCQvr376/6pSQ5OVl1Dy9fvizNmzcXa2trMTQ0FDc3NxkzZoykp6eX0wzK3uLFi6VGjRpiYGAgr776qhw9elQ5FxQUJGFhYar23377rXh4eIiBgYH4+PjIjh07nnPE5a8k92zkyJFKWzs7O2nbtq2cOnWqHKIuH/mvl/t3yb9HYWFhEhQUVOCaevXqiYGBgdSqVUv1/7SXQUnv2dy5c6V27dpiZGQk1tbW0qJFC9m3b99zjVkjIvLcls2JiIiIiF4CfE82EREREZGWMckmIiIiItIyJtlERERERFrGJJuIiIiISMuYZBMRERERaRmTbCIiIiIiLWOSTURERESkZUyyiYiIiIi0jEk2EREREZGWMckmIqLnol+/fujYsWN5h1GkixcvQqPRID4+vrxDIaJKgEk2ERG99B4+fFjeIbzQeH+ISo5JNhERlYsWLVrgvffew8iRI2FlZQU7OzssX74cd+/eRf/+/WFubg43Nzfs2rVLuSY2NhYajQY7duyAn58fjIyM8Nprr+G3335T9f3dd9/Bx8cHhoaGcHV1xYIFC1TnXV1dMX36dPTt2xcWFhZ45513ULNmTQBA/fr1odFo0KJFCwDAL7/8gtatW6Nq1aqwtLREUFAQTp06pepPo9FgxYoV6NSpE0xMTODu7o5t27ap2pw5cwbt2rWDhYUFzM3N0axZMyQlJSnnV6xYAS8vLxgZGaFOnTr48ssvn3j/Nm7cCF9fXxgbG8PGxgatWrXC3bt3lfMrV65U7oGDgwOGDx+unLt8+TI6dOgAMzMzWFhYoFu3brh+/bpyfsqUKahXrx5WrFiBmjVrwsjICABw584dDBo0CLa2trCwsMDrr7+OhISEJ8ZJ9LJikk1EROVm9erVqFq1Ko4fP4733nsPQ4cORdeuXdG4cWOcOnUKb7zxBvr06YN79+6prhszZgwWLFiAX375Bba2tmjfvj0ePXoEADh58iS6deuGHj164PTp05gyZQomTZqEqKgoVR+ffPIJ6tati7i4OEyaNAnHjx8HAOzduxcpKSnYtGkTACAzMxNhYWH46aefcPToUbi7u6Nt27bIzMxU9Td16lR069YNv/76K9q2bYvevXvj1q1bAIC//voLzZs3h6GhIfbt24eTJ09iwIAByMnJAQCsXbsWkydPxsyZM5GYmIhZs2Zh0qRJWL16daH3LSUlBT179sSAAQOQmJiI2NhY/Oc//4GIAAC++uorDBs2DO+88w5Onz6Nbdu2wc3NDQCQl5eHDh064NatWzhw4AD27NmDP//8E927d1eNceHCBXz33XfYtGmTsoWma9euSEtLw65du3Dy5Ek0aNAALVu2VOZJRI8RIiKi5yAsLEw6dOigHAcFBUnTpk2V45ycHDE1NZU+ffoodSkpKQJAjhw5IiIi+/fvFwCybt06pc3NmzfF2NhY1q9fLyIivXr1ktatW6vGHjNmjHh7eyvHLi4u0rFjR1Wb5ORkASBxcXFPnEdubq6Ym5vL999/r9QBkIkTJyrHWVlZAkB27dolIiLjx4+XmjVrysOHDwvts3bt2vLNN9+o6qZPny6BgYGFtj958qQAkIsXLxZ6vnr16jJhwoRCz/3444+iq6srly9fVurOnDkjAOT48eMiIhIRESH6+vqSlpamtDl06JBYWFhIdnZ2gdiXLVtW6FhELzOuZBMRUbnx8/NTvtbV1YWNjQ18fX2VOjs7OwBAWlqa6rrAwEDla2tra3h6eiIxMREAkJiYiCZNmqjaN2nSBH/88Qdyc3OVOn9//2LFeP36dQwePBju7u6wtLSEhYUFsrKycPny5SLnYmpqCgsLCyXu+Ph4NGvWDPr6+gX6v3v3LpKSkjBw4ECYmZkpZcaMGartJI+rW7cuWrZsCV9fX3Tt2hXLly/H7du3Afxzr65du4aWLVsWem1iYiKcnZ3h7Oys1Hl7e6NKlSrKPQQAFxcX2NraKscJCQnIysqCjY2NKs7k5OQi4yR6memVdwBERPTy+nfSqdFoVHUajQbAP1sctM3U1LRY7cLCwnDz5k189tlncHFxgaGhIQIDAws8DFjYXPLjNjY2LrL/rKwsAMDy5csREBCgOqerq1voNbq6utizZw9+/vln/Pjjj1i8eDEmTJiAY8eOoWrVqsWa19P8+/5kZWXBwcEBsbGxBdpWqVJFK2MSVSZcySYiogrn6NGjyte3b9/G77//Di8vLwCAl5cXDh8+rGp/+PBheHh4FJm0AoCBgQEAqFa7868dMWIE2rZtqzxI+Pfff5coXj8/Pxw6dEjZN/44Ozs7VK9eHX/++Sfc3NxUJf9hzMJoNBo0adIEU6dORVxcHAwMDLB582aYm5vD1dUVMTExhV7n5eWFK1eu4MqVK0rd2bNncefOHXh7exc5XoMGDZCamgo9Pb0CcWorsSeqTLiSTUREFc60adNgY2MDOzs7TJgwAVWrVlXewT1q1Cg0atQI06dPR/fu3XHkyBEsWbLkqW/rqFatGoyNjbF79244OTnByMgIlpaWcHd3x9dffw1/f39kZGRgzJgxT1yZLszw4cOxePFi9OjRA+PHj4elpSWOHj2KV199FZ6enpg6dSpGjBgBS0tLhIaG4sGDBzhx4gRu376NDz/8sEB/x44dQ0xMDN544w1Uq1YNx44dw40bN5RfNKZMmYIhQ4agWrVqaNOmDTIzM3H48GG89957aNWqFXx9fdG7d28sWrQIOTk5ePfddxEUFPTELTStWrVCYGAgOv6/du5QRbkggOL4EcyCCAajYYpa7u33IlgUwbVZBAWzvoAYTCIIYhBERaPaRDSK+Bomi9huse8X9sNNu+zChF32/3uAYaYdDod5eVG/35cxRrfbTfv9XuVy+cvzG+CvoMkGAPw6vV5PrVZLruvqfr9rt9s9m2jHcbTZbLRarZROp9XpdNTtdlWr1T49MxwOazQaaTKZKJFIqFQqSZLm87mCIJDjOKpWq2o2m4rH49+6bywW0/F41OPxkO/7cl1X0+n0OTFpNBqazWZaLBbKZDLyfV/L5fLDJjsSieh8PqtQKMgYo3a7rcFgoHw+L+lt4jIcDjUej5VKpVQsFnW5XCS9NeDb7VbRaFSe5ymXyymZTGq9Xn/6hlAopMPhIM/zVK/XZYxRpVLR9Xp9bucBvAu9vv7/7wcAgB/udDopm80qCAJ2wAB+NJpsAAAAwDJCNgAAAGAZcxEAAADAMppsAAAAwDJCNgAAAGAZIRsAAACwjJANAAAAWEbIBgAAACwjZAMAAACWEbIBAAAAywjZAAAAgGX/AMyFNj5t3d8wAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -252,14 +238,9 @@
     }
    ],
    "source": [
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "colors = ['b' if x >= 0 else 'r' for x in top_values]\n",
+    "from dianna.visualization import plot_tabular\n",
     "\n",
-    "plt.barh(top_features, top_values, color=colors)\n",
-    "plt.xlabel(\"Importance scores\")\n",
-    "plt.ylabel(\"Features\")\n",
-    "plt.show()"
+    "_ = plot_tabular(explanation, X_test.columns, num_features=10)"
    ]
   }
  ],