diff --git a/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml b/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml
new file mode 100755
index 00000000..07c17e15
--- /dev/null
+++ b/configs/transforms/liftings/graph2hypergraph/node_centrality_lifting.yaml
@@ -0,0 +1,11 @@
+transform_type: 'lifting'
+transform_name: "HypergraphNodeCentralityLifting"
+network_type: 'weighted'
+alpha: 0.85
+th_percentile: 0.05
+n_most_influential: 2
+do_weight_hyperedge_influence: False
+do_hyperedge_node_assignment_feature_lifting_passthrough: False
+max_iter: 100
+tol: 1e-06
+feature_lifting: ProjectionSum
\ No newline at end of file
diff --git a/modules/transforms/data_transform.py b/modules/transforms/data_transform.py
index 59253ecf..e84c1dd9 100755
--- a/modules/transforms/data_transform.py
+++ b/modules/transforms/data_transform.py
@@ -12,6 +12,9 @@
 from modules.transforms.liftings.graph2hypergraph.knn_lifting import (
     HypergraphKNNLifting,
 )
+from modules.transforms.liftings.graph2hypergraph.node_centrality_lifting import (
+    HypergraphNodeCentralityLifting,
+)
 from modules.transforms.liftings.graph2simplicial.clique_lifting import (
     SimplicialCliqueLifting,
 )
@@ -19,6 +22,7 @@
 TRANSFORMS = {
     # Graph -> Hypergraph
     "HypergraphKNNLifting": HypergraphKNNLifting,
+    "HypergraphNodeCentralityLifting": HypergraphNodeCentralityLifting,
     # Graph -> Simplicial Complex
     "SimplicialCliqueLifting": SimplicialCliqueLifting,
     # Graph -> Cell Complex
diff --git a/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py b/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py
new file mode 100644
index 00000000..8bc68596
--- /dev/null
+++ b/modules/transforms/liftings/graph2hypergraph/node_centrality_lifting.py
@@ -0,0 +1,162 @@
+import networkx as nx
+import numpy as np
+import torch
+import torch_geometric
+
+from modules.transforms.liftings.graph2hypergraph.base import Graph2HypergraphLifting
+
+
+class HypergraphNodeCentralityLifting(Graph2HypergraphLifting):
+    r"""Lifts graphs to hypergraph domain using node centrality.
+
+    This lifting creates hyperedges based on central, i.e. highly influential, nodes in the network. Mapping a connection between individual nodes to specific nodes in the network architecture that have a specific and potentially competing influence on them is a very convenient scenario to be modelled via hyperedges. Using shortest path distance to identify the most influential nodes on any given node even allows for placing weights on the hyperedge connection to individual, connected nodes (i.e. the inverse shortest path distance to the corresponding most influential node that the hyperedge represents). To define and identify influential nodes in the network, we refer to the variant of the Eigenvector Centrality with an additional jump probability (i.e. PageRank)
+
+    Parameters
+    ----------
+    network_type : str
+        Network type may be weighted or unweighted. Default is "weighted".
+    alpha: float
+        jump probability, called dampening factor, which decides whether to continue following the transition matrix or teleport to random positions, default=0.85.
+    th_percentile: float
+        Fraction of most influential nodes in the network to consider, default=0.05.
+    n_most_influential: integer
+        Number of most influential nodes to assign a node to. default=2.
+    do_weight_hyperedge_influence: bool
+        add a weight to the hyperedge connections per node based on the inverse spath distance to influential node. default=False.
+    do_hyperedge_node_assignment_feature_lifting_passthrough: bool
+        assign features of most influential nodes to corresponding hyperedges and pass through feature lifting. default=False.
+    max_iter: integer
+        Maximum number of iterations in power method eigenvalue solver.
+    tol: float
+        Error tolerance used to check convergence in power method solver. The iteration will stop after a tolerance of len(G) * tol is reached.
+
+    **kwargs : optional
+        Additional arguments for the class.
+    """
+
+    def __init__(
+        self,
+        network_type: str = "weighted",
+        alpha: float = 0.85,
+        th_percentile: float = 0.05,
+        n_most_influential: float = 2,
+        do_weight_hyperedge_influence: bool = False,
+        do_hyperedge_node_assignment_feature_lifting_passthrough: bool = False,
+        max_iter: int = 100,
+        tol: float = 1e-06,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.network_type = network_type
+        self.alpha = alpha
+        self.max_iter = max_iter
+        self.tol = tol
+        self.th_percentile = th_percentile
+        self.n_most_influential = n_most_influential
+        self.do_weight_hyperedge_influence = do_weight_hyperedge_influence
+        self.do_hyperedge_node_assignment_feature_lifting_passthrough = (
+            do_hyperedge_node_assignment_feature_lifting_passthrough
+        )
+
+    def lift_topology(self, data: torch_geometric.data.Data) -> dict:
+        r"""Lifts the topology of a graph to hypergraph domain using node centrality.
+
+        Parameters
+        ----------
+        data : torch_geometric.data.Data
+            The input data to be lifted.
+
+        Returns
+        -------
+        dict
+            The lifted topology.
+        """
+
+        edge_list = data.edge_index.t().numpy()
+
+        # for unweighted graphs or higher-dimensional edge or node features revert to unweighted network structure
+        if (
+            data.edge_attr is None
+            or self.network_type == "unweighted"
+            or data.edge_attr.shape[1] > 1
+        ):
+            edge_attr = np.ones(shape=(len(edge_list), 1))
+        elif isinstance(data.edge_attr, torch.Tensor):
+            edge_attr = data.edge_attr.numpy()
+        else:
+            edge_attr = data.edge_attr
+
+        if data.x is None or self.network_type == "unweighted" or data.x.shape[1] > 1:
+            node_attr = np.ones(shape=(data.num_nodes, 1))
+        elif isinstance(data.x, torch.Tensor):
+            node_attr = data.x.numpy()
+        else:
+            node_attr = data.x
+
+        # create directed networkx graph from pyg data
+        G = nx.Graph()
+        for v in range(len(node_attr)):
+            G.add_node(v)
+            G.nodes[v]["w"] = node_attr[v][0]
+
+        for e in range(len(edge_list)):
+            v1 = edge_list[e][0]
+            v2 = edge_list[e][1]
+            G.add_edge(v1, v2, w=edge_attr[e][0])
+
+        assert self.n_most_influential >= 1
+
+        # estimate distance between all nodes
+        if self.network_type == "unweighted":
+            sp = dict(nx.all_pairs_shortest_path_length(G))
+        elif self.network_type == "weighted":
+            sp = dict(nx.all_pairs_dijkstra_path_length(G))
+        else:
+            raise NotImplementedError(
+                f"network type {self.network_type} not implemented"
+            )
+
+        # estimate node centrality for all nodes
+        pr = nx.pagerank(
+            G, alpha=self.alpha, max_iter=self.max_iter, tol=self.tol, weight="w"
+        )
+
+        # estimate fraction of most influential nodes in the network to consider, i.e. the hyperedges
+        th_cutoff = np.quantile(list(pr.values()), (1 - self.th_percentile))
+        nodes_most_influential = [n for n, v in pr.items() if v >= th_cutoff]
+        num_hyperedges = len(nodes_most_influential)
+        hyperedge_map = {v: e for e, v in enumerate(nodes_most_influential)}
+
+        incidence_hyperedges = torch.zeros(data.num_nodes, num_hyperedges)
+
+        # assign each node to the hyeredges corresponding to the top "n_most_influential" most influential nodes
+        for v in list(G.nodes()):
+            if v in nodes_most_influential:
+                incidence_hyperedges[v, hyperedge_map[v]] = 1
+            else:
+                sp_v_influencial = {
+                    k: v for k, v in sp[v].items() if k in nodes_most_influential
+                }
+                v_influencial = [
+                    (k, v)
+                    for i, (k, v) in enumerate(sp_v_influencial.items())
+                    if i < self.n_most_influential
+                ]
+                for k_infl, v_infl in v_influencial:
+                    w = 1
+                    if self.do_weight_hyperedge_influence:
+                        w = max(1 / v_infl, 0.0001)
+                    incidence_hyperedges[v, hyperedge_map[k_infl]] = w
+
+        incidence_hyperedges = incidence_hyperedges.to_sparse_coo()
+        lifted_data = {
+            "incidence_hyperedges": incidence_hyperedges,
+            "num_hyperedges": num_hyperedges,
+            "x_0": data.x,
+        }
+
+        if self.do_hyperedge_node_assignment_feature_lifting_passthrough:
+            # assign features of most influential nodes to corresponding hyperedges and pass through feature lifting.
+            lifted_data["x_hyperedges"] = data.x[nodes_most_influential]
+
+        return lifted_data
diff --git a/modules/utils/utils.py b/modules/utils/utils.py
index 1dfcdc2e..54e955d5 100644
--- a/modules/utils/utils.py
+++ b/modules/utils/utils.py
@@ -233,7 +233,7 @@ def sort_vertices_ccw(vertices):
         n_hyperedges = incidence.shape[1]
         vertices += [i + n_vertices for i in range(n_hyperedges)]
         indices = incidence.indices()
-        edges = np.array([indices[1].numpy(), indices[0].numpy() + n_vertices]).T
+        edges = np.array([indices[0].numpy(), indices[1].numpy() + n_vertices]).T
         pos_n = [[i, 0] for i in range(n_vertices)]
         pos_he = [[i, 1] for i in range(n_hyperedges)]
         pos = pos_n + pos_he
diff --git a/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py b/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py
new file mode 100644
index 00000000..ac6c1c55
--- /dev/null
+++ b/test/transforms/liftings/graph2hypergraph/test_node_centrality_lifting.py
@@ -0,0 +1,98 @@
+"""Test Page Rank Lifting."""
+
+import pytest
+import torch
+
+from modules.data.utils.utils import load_manual_graph
+from modules.transforms.liftings.graph2hypergraph.node_centrality_lifting import (
+    HypergraphNodeCentralityLifting,
+)
+
+
+class TestHypergraphNodeCentralityLifting:
+    """Test the HypergraphNodeCentralityLifting class."""
+
+    def setup_method(self):
+        self.data = load_manual_graph()
+
+        self.lifting = HypergraphNodeCentralityLifting(
+            network_type="weighted",
+            th_percentile=0.2,
+            n_most_influential=1,
+        )
+
+    def test_lift_topology(self):
+        # Test the lift_topology method
+        lifted_data = self.lifting.forward(self.data.clone())
+
+        expected_n_hyperedges = 2
+
+        expected_incidence_1 = torch.tensor(
+            [
+                [1.0, 0.0],
+                [1.0, 0.0],
+                [0.0, 1.0],
+                [0.0, 1.0],
+                [1.0, 0.0],
+                [0.0, 1.0],
+                [0.0, 1.0],
+                [1.0, 0.0],
+            ]
+        )
+
+        assert (
+            expected_incidence_1 == lifted_data.incidence_hyperedges.to_dense()
+        ).all(), "Something is wrong with incidence_hyperedges."
+        assert (
+            expected_n_hyperedges == lifted_data.num_hyperedges
+        ), "Something is wrong with the number of hyperedges."
+
+        self.lifting.network_type = "unweighted"
+        lifted_data = self.lifting.forward(self.data.clone())
+
+        assert (
+            expected_incidence_1 == lifted_data.incidence_hyperedges.to_dense()
+        ).all(), "Something is wrong with incidence_hyperedges."
+        assert (
+            expected_n_hyperedges == lifted_data.num_hyperedges
+        ), "Something is wrong with the number of hyperedges."
+
+        expected_incidence_1 = torch.tensor(
+            [
+                [1.0, 0.0],
+                [1.0, 0.0],
+                [0.0, 1.0],
+                [0.0, 1.0],
+                [1.0, 0.0],
+                [0.0, 1.0],
+                [0.0, 0.5],
+                [1.0, 0.0],
+            ]
+        )
+
+        self.lifting.network_type = "unweighted"
+        self.lifting.do_weight_hyperedge_influence = True
+        lifted_data = self.lifting.forward(self.data.clone())
+
+        assert (
+            expected_incidence_1 == lifted_data.incidence_hyperedges.to_dense()
+        ).all(), "Something is wrong with incidence_hyperedges."
+        assert (
+            expected_n_hyperedges == lifted_data.num_hyperedges
+        ), "Something is wrong with the number of hyperedges."
+
+        assert (
+            lifted_data.x_hyperedges.to_dense() == torch.tensor([[5106.0], [1060.0]])
+        ).all(), "Something is wrong with x_hyperedges."
+
+        self.lifting.do_hyperedge_node_assignment_feature_lifting_passthrough = True
+        lifted_data = self.lifting.forward(self.data.clone())
+
+        assert (
+            lifted_data.x_hyperedges.to_dense() == torch.tensor([[1.0], [10.0]])
+        ).all(), "Something is wrong with x_hyperedges."
+
+    def test_validations(self):
+        with pytest.raises(NotImplementedError):
+            self.lifting.network_type = "mixed"
+            self.lifting.forward(self.data.clone())
diff --git a/tutorials/graph2hypergraph/knn_lifting.ipynb b/tutorials/graph2hypergraph/knn_lifting.ipynb
index 40bf15b9..21494321 100644
--- a/tutorials/graph2hypergraph/knn_lifting.ipynb
+++ b/tutorials/graph2hypergraph/knn_lifting.ipynb
@@ -339,7 +339,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/graph2hypergraph/node_centrality_lifting.ipynb b/tutorials/graph2hypergraph/node_centrality_lifting.ipynb
new file mode 100644
index 00000000..36c1ee27
--- /dev/null
+++ b/tutorials/graph2hypergraph/node_centrality_lifting.ipynb
@@ -0,0 +1,714 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Graph-to-Hypergraph Node Centrality Lifting Tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "This notebook shows how to import a dataset, with the desired lifting, and how to run a neural network using the loaded data.\n",
+    "\n",
+    "The notebook is divided into sections:\n",
+    "\n",
+    "- [Loading the dataset](#loading-the-dataset) loads the config files for the data and the desired tranformation, createsa a dataset object and visualizes it.\n",
+    "- [Loading and applying the lifting](#loading-and-applying-the-lifting) defines a simple neural network to test that the lifting creates the expected incidence matrices.\n",
+    "- [Create and run a simplicial nn model](#create-and-run-a-simplicial-nn-model) simply runs a forward pass of the model to check that everything is working as expected.\n",
+    "\n",
+    "***\n",
+    "***\n",
+    "\n",
+    "Note that for simplicity the notebook is setup to use a simple graph. However, there is a set of available datasets that you can play with.\n",
+    "\n",
+    "To switch to one of the available datasets, simply change the *dataset_name* variable in [Dataset config](#dataset-config) to one of the following names:\n",
+    "\n",
+    "* cocitation_cora\n",
+    "* cocitation_citeseer\n",
+    "* cocitation_pubmed\n",
+    "* MUTAG\n",
+    "* NCI1\n",
+    "* NCI109\n",
+    "* PROTEINS_TU\n",
+    "* AQSOL\n",
+    "* ZINC\n",
+    "***\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports and utilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# With this cell any imported module is reloaded before each cell execution\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "from modules.data.load.loaders import GraphLoader\n",
+    "from modules.data.preprocess.preprocessor import PreProcessor\n",
+    "from modules.utils.utils import (\n",
+    "    describe_data,\n",
+    "    load_dataset_config,\n",
+    "    load_model_config,\n",
+    "    load_transform_config,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we just need to spicify the name of the available dataset that we want to load. First, the dataset config is read from the corresponding yaml file (located at `/configs/datasets/` directory), and then the data is loaded via the implemented `Loaders`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Dataset configuration for manual_dataset:\n",
+      "\n",
+      "{'data_domain': 'graph',\n",
+      " 'data_type': 'toy_dataset',\n",
+      " 'data_name': 'manual',\n",
+      " 'data_dir': 'datasets/graph/toy_dataset',\n",
+      " 'num_features': 1,\n",
+      " 'num_classes': 2,\n",
+      " 'task': 'classification',\n",
+      " 'loss_type': 'cross_entropy',\n",
+      " 'monitor_metric': 'accuracy',\n",
+      " 'task_level': 'node'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_name = \"manual_dataset\"\n",
+    "dataset_config = load_dataset_config(dataset_name)\n",
+    "loader = GraphLoader(dataset_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then access to the data through the `load()`method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Dataset only contains 1 sample:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Graph with 8 vertices and 13 edges.\n",
+      " - Features dimensions: [1, 0]\n",
+      " - There are 0 isolated nodes.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = loader.load()\n",
+    "describe_data(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "modules.data.utils.custom_dataset.CustomDataset"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading and Applying the Lifting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Motivation**\n",
+    "In this section, we will instantiate the lifting we want to apply to the data. This is a novel lifting that creates hyperedges based on central, i.e. highly influential, nodes in the network. Mapping a connection between individual nodes to specific nodes in the network architecture that have a specific and potentially competing influence on them is a very convenient scenario to be modelled via hyperedges. Using shortest path distance to identify the most influential nodes on any given node even allows for placing weights on the hyperedge connection to individual, connected nodes (i.e. the inverse shortest path distance to the corresponding most influential node that the hyperedge represents). To define and identify influential nodes in the network, we refer to the variant of the Eigenvector Centrality with an additional jump probability (i.e. PageRank).\n",
+    "\n",
+    "**Background**\n",
+    "Eigenvector Centrality is an algorithm that measures the transitive influence of nodes. Relationships originating from high-scoring nodes contribute more to the score of a node than connections from low-scoring nodes. A high eigenvector score means that a node is connected to many nodes who themselves have high scores.\n",
+    "\n",
+    "The algorithm computes the eigenvector associated with the largest absolute eigenvalue. To compute that eigenvalue, the algorithm applies the power iteration approach. Within each iteration, the centrality score for each node is derived from the scores of its incoming neighbors. In the power iteration method, the eigenvector is L2-normalized after each iteration, leading to normalized results by default.\n",
+    "\n",
+    "The PageRank [[1]](https://doi.org/10.1137/140976649) variant of Eigenvector Centrality utilises, at any step of the power iteration, an additional jump probability, called dampening factor $\\alpha$, which decides whether to continue following the transition matrix or teleport to random positions in the process.These random teleportations have shown to be an effective way to ensure that the matrix and corresponding Markov chain exhibit ergodicity which makes them easier to analyze and to guarantee convergence.\n",
+    "\n",
+    "**Method**\n",
+    "Our approach is applicable to both directed and undirected as well as weighted and unweighted networks. It works as follows: (1) calculate the node centrality of all nodes in the graph, (2) select the top $n$ most influential nodes in the graph as hyperedges based on a given quantile. (3) Assign all nodes in the network to $m >= 1$ most influential nodes (with $m <= n$), i.e. their respective hyperedges, based on their shortest path distance $d$ to each influential node. (4, optional) model individual connection weights per node to a hyperedge via the inverse shortest path distance (i.e. $1/d$) to the hyperedge's corresponding most influential node. \n",
+    "\n",
+    "**Remarks on feature liftings**\n",
+    "Note that the algorithm currently supports the utilization of the ProjectionSum feature lifting to model the inverse relationship between all nodes towards their shared most influential node. In order to model the direct influence of the influential node on all individual nodes via the hyperedge, we have, however, further implemented a straight-forward feature lifting via assignment of the hyperedge's corresponding node's features, thereby bypassing the ProjectionSum feature lifting.\n",
+    "\n",
+    "\n",
+    "***\n",
+    "[[1]](https://doi.org/10.1137/140976649) Gleich, D.F. (2015) PageRank Beyond the Web. SIAM Review, 57, 321-363.\n",
+    "***\n",
+    "\n",
+    "\n",
+    "For hypergraphs, creating a lifting involves creating the `incidence_hyperedges` matrix.\n",
+    "\n",
+    "Similarly to before, we can specify the transformation we want to apply through its type and id --the corresponding config files located at `/configs/transforms.` \n",
+    "\n",
+    "Note that the *tranform_config* dictionary generated below can contain a sequence of transforms if it is needed.\n",
+    "\n",
+    "This can also be used to explore liftings from one topological domain to another; for example, using two liftings, it is possible to achieve a sequence such as graph -> simplicial complex -> hypergraph. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Transform configuration for graph2hypergraph/node_centrality_lifting:\n",
+      "\n",
+      "{'transform_type': 'lifting',\n",
+      " 'transform_name': 'HypergraphNodeCentralityLifting',\n",
+      " 'network_type': 'weighted',\n",
+      " 'alpha': 0.85,\n",
+      " 'th_percentile': 0.05,\n",
+      " 'n_most_influential': 2,\n",
+      " 'do_weight_hyperedge_influence': False,\n",
+      " 'do_hyperedge_node_assignment_feature_lifting_passthrough': False,\n",
+      " 'max_iter': 100,\n",
+      " 'tol': 1e-06,\n",
+      " 'feature_lifting': 'ProjectionSum'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define transformation type and id\n",
+    "transform_type = \"liftings\"\n",
+    "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n",
+    "transform_id = \"graph2hypergraph/node_centrality_lifting\"\n",
+    "\n",
+    "# Read yaml file\n",
+    "transform_config = {\n",
+    "    \"lifting\": load_transform_config(transform_type, transform_id)\n",
+    "    # other transforms (e.g. data manipulations, feature liftings) can be added here\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We than apply the transform via our `PreProcessor`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/1275153652\n",
+      "\n",
+      "Dataset only contains 1 sample:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Hypergraph with 8 vertices and 1 hyperedges.\n",
+      " - The nodes have feature dimensions 1.\n",
+      " - The hyperedges have feature dimensions 1.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n",
+    "describe_data(lifted_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given the stringent percentile threshold (i.e. 0.05), the algorithm detects one most influential node, hence one hyperedge."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/1661034459\n",
+      "\n",
+      "Dataset only contains 1 sample:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Hypergraph with 8 vertices and 2 hyperedges.\n",
+      " - The nodes have feature dimensions 1.\n",
+      " - The hyperedges have feature dimensions 1.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "transform_config[\"lifting\"][\"th_percentile\"] = 0.2\n",
+    "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n",
+    "describe_data(lifted_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A more lenient threshold leads to the selection of two most influential nodes. Further we observe the effect of passing through the feature lifting (based on projectiveSum) and assigning each influential node's features directly to the corresponding hyperedges. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_hyperedges=[8, 2], num_hyperedges=2, x_0=[8, 1], x_hyperedges=[2, 1])\n",
+      "tensor([[6656.],\n",
+      "        [6665.]])\n",
+      "Transform parameters are the same, using existing data_dir: /Users/michael/perelyn/research/topological_deep_learning/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/2688954955\n",
+      "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8, incidence_hyperedges=[8, 2], num_hyperedges=2, x_0=[8, 1], x_hyperedges=[2, 1])\n",
+      "tensor([[ 1.],\n",
+      "        [10.]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lifted_dataset.get(0))\n",
+    "print(lifted_dataset.get(0).x_hyperedges)\n",
+    "\n",
+    "transform_config[\"lifting\"][\n",
+    "    \"do_hyperedge_node_assignment_feature_lifting_passthrough\"\n",
+    "] = True\n",
+    "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n",
+    "\n",
+    "print(lifted_dataset.get(0))\n",
+    "print(lifted_dataset.get(0).x_hyperedges)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Dataset only contains 1 sample:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing...\n",
+      "Done!\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Hypergraph with 8 vertices and 2 hyperedges.\n",
+      " - The nodes have feature dimensions 1.\n",
+      " - The hyperedges have feature dimensions 1.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "transform_config[\"lifting\"][\"n_most_influential\"] = 1\n",
+    "transform_config[\"lifting\"][\"th_percentile\"] = 0.2\n",
+    "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n",
+    "describe_data(lifted_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assigning nodes only to one of the two most influential nodes leads to a uniqe assignment to hyperedges"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create and Run a Simplicial NN Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section a simple model is created to test that the used lifting works as intended. In this case the model uses the `incidence_hyperedges` matrix so the lifting should make sure to add it to the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Model configuration for hypergraph UNIGCN:\n",
+      "\n",
+      "{'in_channels': None,\n",
+      " 'hidden_channels': 32,\n",
+      " 'out_channels': None,\n",
+      " 'n_layers': 2}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from modules.models.hypergraph.unigcn import UniGCNModel\n",
+    "\n",
+    "model_type = \"hypergraph\"\n",
+    "model_id = \"unigcn\"\n",
+    "model_config = load_model_config(model_type, model_id)\n",
+    "\n",
+    "model = UniGCNModel(model_config, dataset_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_hat = model(lifted_dataset.get(0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If everything is correct the cell above should execute without errors. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's try another example, i.e. chemical structures from the MUTAG dataset, where node an edge features are > 1, hence the algorithm switches to the unweighted version automatically. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Dataset configuration for MUTAG:\n",
+      "\n",
+      "{'data_domain': 'graph',\n",
+      " 'data_type': 'TUDataset',\n",
+      " 'data_name': 'MUTAG',\n",
+      " 'data_dir': 'datasets/graph/TUDataset',\n",
+      " 'num_features': [7, 4],\n",
+      " 'num_classes': 2,\n",
+      " 'task': 'classification',\n",
+      " 'loss_type': 'cross_entropy',\n",
+      " 'monitor_metric': 'accuracy',\n",
+      " 'task_level': 'graph',\n",
+      " 'max_dim_if_lifted': 2,\n",
+      " 'preserve_edge_attr_if_lifted': False}\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_name = \"MUTAG\"\n",
+    "dataset_config = load_dataset_config(dataset_name)\n",
+    "loader = GraphLoader(dataset_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Dataset contains 188 samples.\n",
+      "\n",
+      "Providing more details about sample 0/188:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgcAAAIbCAYAAAB/tT3bAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAB84ElEQVR4nO3deXxc9X3v/9f3zK5dI2zwgg02OIBt4SVAHGpIAq5p4iYlBLKZplkwLU2XNA7c3tCb0NJfC02bW/IoN5CENolbiIE2SUUTY5zGdoEQx5jINhAHbCxkG2w0lkbL7Of7++NIssbWMpJGGi3v5+Phh6XRmXO+I9k67/kun6+x1lpEREREujmlboCIiIhMLAoHIiIikkfhQERERPIoHIiIiEgehQMRERHJo3AgIiIieRQOREREJI/CgYiIiORROBApsoMHD3LjjTdSW1tLbW0tN954IwcPHiz4+StXruTWW28ds+OHsmbNGu64446Cjr311lupra3FGMPChQu59dZbh/VaR2rNmjXce++9Y34dkelK4UCkiJ566ilWrlzJZZddxu7du9m9ezcLFixg5cqVPPXUUwWd48///M+58cYbC77mcI8vhoMHD7Jw4UIOHjzIo48+ysmTJ3nggQeIxWI89thj49oWESk+f6kbIDJVtLa2smbNGh599FE+9KEP9T5+zz33sHDhQm688UYOHTpETU3NoOfp+9xCDPf4YrjxxhtZsGABW7du7X3s2muv5dprrx33tohI8annQKRI7rjjDlasWNHvzXrDhg1Eo1H+5m/+pgQtK67HHnuM559/ngceeKDUTRGRMaJwIFIkTz311KDvnD/0oQ/lDS3ceOONPPjggzz44IMsXLiw92unj/m3trb2zmFYuXIld9xxBwsXLmTlypX9Hn/jjTdy77339s4H6Htu8G7uK1eu7J0nMNxhgO9973usWLGCBQsWDHnsQK9xqDbceuut3HHHHb2voba29oxjWlpaBnyNIjI6CgciRXLw4EEuu+yyAb++cOFCnn/++d7PW1tbeeCBB7jnnnu45557BgwWd9xxB9FolJMnT3Lrrbfy2GOP8eqrr7J79+5+j29tbeWOO+7oHcZYsWJF3oTFWCzGN77xDay1PPDAA9x444157Srkdb797W8v6NiBXuNQbTh48CAPPvhg72u46aabzpjYee+99w74GkVkdBQORIooFosN+LXW1tYzHjt48CC7d+8edN7A5s2be298GzZs4ODBg0OuCFixYgXXXnstNTU1Z6wg2LBhAytWrAC8eQILFiwY1rvu1tbWIedN9NXfayykDRs2bOh9DQ888AALFizIG8oY7DWKyOgoHIgUyYIFC3j11VcH/Pqrr756Rld8z81tuKLR6KBf7/vOvr9je96Vr1y5ctg31QULFpzR03DjjTdijMEYw5o1a/K+NtBrHG4brr322rzjhnqNIjJyCgciRXLttdcOOn6/efPmM4YOChm3v/baa3snMt57772sWLFiyEAx2NdXrlzJo48+yq233sru3bt738EXas2aNTz11FN5PSGPPvoo1lpuv/32M47v7zWOtg0w+GsUkdFROBApknvuuYeDBw/2W5znjjvuoLW1lXvuuWfY5+25CS9cuJCtW7eybdu2Ebfx4MGDPP/882zdunXEyw5vv/12FixYUHChpGK14amnnhp0ToeIFI/CgUiR1NTU8Oijj3LHHXdwxx139M4NuPXWW7n33nvZunXriN7tHjx4kA9/+MNs3bqVBx54YFTvmHu63x988EHg1LLE4Xr00UfZvHlz70TC1tZWnn/++YKGBwptw4MPPth77p7JiBs2bBh2W0Vk+BQORIroQx/6EK+++ioHDx5k5cqVrFy5klgsxquvvjrid+oLFizgxhtvZOHChSxcuBBjzIgrItbU1HD77bf3LgHsefc+3MCxYsUKDh06RDQa5ZZbbuktEw0M2aNQaBt6hlPOP//83kmNGkoQGR/GWmtL3QgR6d9jjz3GLbfckldZ8fnnn+eaa67hnnvumbLvpNesWcOKFStGNAwjIqOnngORCWzXrl1nvKtesWIFN91004B1DkRERkvhQGQC+/CHP8xTTz3FY4891jsx8bHHHusd7xcRGQvaeElkAluxYgWPPvoo99xzD7fccgvgzUH4xje+oU2ORGTMaM6BiIiI5NGwgoiIiORROBAREZE8CgciIiKSR+FARERE8igciIiISB6FAxEREcmjcCAiIiJ5FA5EREQkj8KBiIiI5FE4EBERkTwKByIiIpJH4UBERETyKByIiIhIHoUDERERyaNwICIiInkUDkRERCSPwoGIiIjkUTgQERGRPAoHIiIikkfhQERERPIoHIiIiEgehQMRERHJo3AgIiIieRQOREREJI/CgYiIiORROBAREZE8CgciIiKSR+FARERE8igciIiISB6FAxEREcnjL3UDpH85a2lJ5jieyHIikaMz65KzFp8xlPsdZkR8zIz4qQv78BlT6uaKiMgUonAwwcTTOfbHUjTGknRmLK61OMbgWtt7TM/njjGUBwz10TCLoyGqgr4StlxERKYKY22fu46UTCrn8vSxLhpjKXLWggW/Y3AA00/PgLUWF8i6Fgz4jKE+GuLKWWWEfBotEhGRkVM4mACa2jM82dxBWzqHg8Fv+g8EA7HWkrXgYqkJ+lgzt4J5lYExbLGIiExlCgcl1tiSZNuRTlxrCRiDM4r5A661ZLqHG66ZU059XbiILRURkelC/c8l1NiSZFtzJ65rCY4yGIA3FyFoDK5r2dbcSWNLskgtFRGR6UThoESa2jO9PQZBxwxrGGEwxhiCjjdhcduRTpraM0U5r4iITB8KByWQyrk82dxR9GDQo29A2NrcQSrnFvX8IiIytSkclMDTx7poS+cImOIHgx7GGALG0JrO8fSxrjG5hoiITE0KB+Msns7RGEvhMPo5BkNxjMHB0BhLEU/nxvRaIiIydSgcjLP93XUM/ONU1NBvvGqL+2Op8bmgiIhMegoH4yhnLY2xJNjh1TEYDWMMWGiMJb3iSiIiIkNQOBhHLckcnRmL3xnfvRD8jqEz4+3VICIiMhSFg3F0PJH19kQY4rhXdj/Lvu1b2NWwufexh+/6XN7nw+HgVVE8kciO6PkiIjK9KByMoxOJHM4QKxRiR1+nrLKa2Rdewo5HvtX7+Jy3LaHlaNOIrmu6r3k8oZ4DEREZmsLBOOrMunm7K/YndqyZ2YsuYd+OJ1m4YlXv45esvpbo7HNHfG3XWrqyqncgIiJD05bN46iQCYEXrPQCwd6f/oi1t3yeXC5Le0cHsZMxoudfRDaX5eX/2QZAor2N2lnn9j5nKFlNSBQRkQKo52Ac+QpcoZDoiHP01y9y9tuW8FZLC5lMhvjRJmrOmcPBl/byq13/w5Kr13LZupvY8cg3C76+f5xWSIiIyOSmcDCOyv3OkIWPLJZjr71C5Vnn0NXVRXl5OWfVnUU4FCYajXJk/wtYx08imQAgUlHFK7ufHfLajjGU+fXjFhGRoWlYYRzNiPhwrcUOUOcgnUnT3t5OxgXHcTjrrLPwOT72bd/CkqvXApBqa6EqWkdbWxu5XI5IZRXJjvig17XWYq1lZsQ3Jq9LRESmFoWDcTQz4scxBhfoe5vumVeQTCYJBAKcf/ES6t99Hc//1+NEulcu9BUOR6isrKS9vZ1UKkVXe+ug13XxwsiMiH7cIiIyNN0txlFd2Ed5wNCRdvH5DK516ezspKurC8dxqK6uJhwOYzBct2Fj/+eYPY9ERzvlZeX4fD4621oJVNTgWhfH9D9skHUtFUGHurB6DkREZGgahB5HPmOoj4YB6EomeOutt/LmFUTCEQyDz0lYuGIVR361D4BwKExX7DhzFi8n1tJCNndmkSNrLRioj4YLnhApIiLTm7FW69vG009/touduRnkXJeAgYrKSnzO8N7R79u+BfCWMkYqq7l49bWcPHkS13Wpqa4hGAz2HptxLcbApy6qoSqongMRERmawsE4aWpq4u6776ahoYEP/vk9zL9yLSG/r2jbNrvWpa21jXQmTVVVFZFwBNdaMtayvC7Me+ZWFOU6IiIy9WlYYRz813/9F6tXr+YXv/gFX/va1/i73/8Y0UiATPcqgmJwjENNbQ2RcIS2tjY6OtrJWEtN0MeVs8qKcg0REZke1HMwDh555BGam5u57bbbKCvzbtRN7RkePxTHdS1BZ/D9FobHkkilML4APp/DDedXMa8yUKRzi4jIdKBwMA46OzspLy8/4/HGliTbmjtxbfECgrWWtGtxjOGaueXU14VHfU4REZleFA5KrLElybYjXkAIGDOqOQg9cwwcY7hmjoKBiIiMjMLBBNDUnmFrcwet6RwOBr/pv4LiQKy1ZC24eHMM1syt0FCCiIiMmMLBcO3dCzt3wrx50NoK69cX5bSpnMvTx7pojKW83Rst+B2DQ/9BwVqLi1fgCNNTQyHElbPKCPk0z1REREZu0oeDnLW0JHMcT2Q5kcjRmXXJWYvPGMr9DjMiPmZG/NSFfaMvAhSPw403wpYtcPgwPPEE3HYbNDR4X3/hBbjzztFdIp1jfyxFYyxJZ8ZbzWCMwe3zY3KM6X28POAVVlocDamOgYiIFMWkDQen30Td7rH202+iPY8X5Sba0AA//CHcfLPXczB/vvdYdTWsXg333w9VVUXpTegJPScSWY4ncnRlXbLW4u/eXXFmxMeMYoUeERGRPibd3goDdb8HTM9s//wbpbXexkMdaZdn3uziueOJkXe/V1fDsmVeEACv92DdulNff+01LzgUgc8YZkb8zIz4WTzak43RUIiIiExNk2pwuqk9w3cPtLGnJYm1EDSGkM/BZwZeBmiMwdd9XNAYrIU9LUk2HWijqT0zvAasXg2xmNdb0NAATU2nvrZzJ9TXw9Klo3iFYyAeh40bveGPpUu9z8Fr/9q1+cf2vK677x7/doqIyIQxaYYVJvSSv717T/Ui7N07sQJCf0MhPT78Yfje904dNwbDIyIiMvlMip6D3mJBriU4ymAA3lyEoDG4rmVbcyeNLcm8rx8+fJgHH3ywsJMdPgwbNsB3v+u9E29tHVXbiq7vUMj8+V57+7Nu3anhktdeg0svHa8WiojIBDPh5xw0tWd6ewyKWWbYGEPQgbRr2Xakk5qgj1qSfO1rX+OBBx5g1apVbNiwYegTzZ8Pzz5blDaNidWrYfv2Uysqqqvzew9ON4LhkXFdMSIiImNuQoeDVM7lyeaOogeDHr0BwVoe3dfMv/3pRzl54jh//Md/zB/8wR8U9VolVejyyr17oa3NG04oYHikJCtGRERkzE3oOQc/ae5gT0ty1HMMBpPOpGnv6MD4gyR/vYfPrrmM2bNnj8m1JpSdO+H22+GLX/SGFA4fho985NSKhjvvPDXMcBoVbBIRmdombDiIp3M89HIr1kLAKX4wyOWytHd0kEwmCQQClFVWEfD7+dRFNXpXO4im9gxPNnfQplLPIiJT1oR927a/+12pv8i5wLUu7R3tvNXSQiaTobq6mmg0StjvJ2ct+2Op4l5wCmlsSfL4oTht6RwBYwiMYKjHdD8vYAyt6RyPH4qfMSFURERKa0LOOchZS2MsCRZMkXoNLJZkIkl7RzvWWsrLyykvKz91czOA61338rMjmjjXzXVdjDH84lic/zmRLdr8D8cYgnRPCG3uBNAukiIiE8SEDActyRydGYu/gGDwyu5nSXbESbS3cdm6mwB4+K7PccHKVb2fpzNp2tvbyWQyRMJhKior8TlnDh34HUNnxpt5PzMyIb81JbHpiac4WL2QYDhMeSg4pitGNMQgIlJ6E3JY4XjCe4c6VONiR1+nrLKa2Rdewo5HvtX7+Jy3LaHlaBO5XJbWtlZisRgA0WiU6uqafoMBeN8May0nEtkivZLJL2MhtfDthCIROttaicfjWIo3TcULCN6Khq3NHaRybtHOLSIiIzMhw8GJRA5nkJLIPWLHmpm96BL27XiShStW9T5+yeprKYvOPGNeQTAQHPR8pvuaxxO5oryOqeDpY120pXOUBYPU1tSQSqWIxWLk3OJ9j4w5NQfh6WNdRTuviIiMzIQMB51ZN2+t/EAuWOkFgr0//RFLrvb2CbBY4m1tnP22Jd68glCIXd//N3Y+8lBB13atpSurd6/grRhpjKVw8JaShkJhotEorusSi8XI5orXw+IYg4OhMZYinlY4ExEppQkZDnLDWF2Z6Ihz9Ncv9gaFdDrNkV+/yIKLl1JRXsHB55+lK35yWNfPTszVneOuvxUjAX+AaDSKYwyxlhipdPFWd/gNWjEiIjIBTMhwMJyVAiePNhOddW7v5z1DET1/L7l6LXWz5w3r+n6tVMhfMXLa98Pn+KiNRgkEA7SebKUrUZyhAGMMWGiMJYcVEEVEpLgmZDgo9zsFV0QMV1Tmff7ijq1c+I53MdLaTo4xlPkn5LdlXA21YsQxDjU1Nbx5YC+7n/whOx77Tu9ExYfv+hy7GjaP6Lp9V4yIiEhpTMi74IyID9fagm7w0dnnsviqNexq2My+7VuYdeHF3hdGEA5s9zVnRlQhsZAVIyePNnPW2bNYsGQ5zzz6L7S1tWGxvatFRkIrRkRESm9CLuafGfF7G/YAhdymr9uwsffjbC7LW2+9NaLFdi5e1/YM1TgoaMVI7FgzF6xcxY5HvsWFb39n70qGi3/jGl574ecjuq63YgSOJ3IsHmnjRURkVCZkz0Fd2Ed5wHgb9QxTz81sJMMKWddSHjDUhdVzUMiKkb6rRS59z/t6VzK0x9tZsOIdgDfEcPTAixw98CI/fvArBV1bK0ZEREprQoYDb9e+MJjh3+RPDwev7H62+88z7Nu+ZcDnWevtGFgfDat0MoWvGOm7WiTgD1BTU8ORX++nvG4mALFjr/OtjZ/kx9/4e67+2IaCr68VIyIipTNh+88XR0M8dzxB1kJgGPfq08PBBStX9b7DHUzWeqFkcTQ0ovZONYUGpNNXi/j9fowxuK73zv/qj97SW4NiOLRiRESkdCZsOKgK+qiPhtjTksS1FLx6oeeo4fQ4uNbiYlkeDWu75m6Frhg5fbXI/u1P8rZ3vgc35602aP7VXgAS7W0AvftdDEYrRkRESmvChgOAK2eVcag9Q2s6R5Az19v3r3sSXYHhwFpLxlpqgj6unFU2qvZOJadWjAz+fe+7WiTSvc8FjkOuu+eg72TRv1+/liXvuo5IRdWA59OKERGR0pvQ4SDkc1gzt4LHD8VJu5agU1hAMMYUtFrBWkvatTiOYc3cCkI+vVvtMZwVI30DAEBrWytuzmXf9i00/2pv79fDFZWcPNpMZNElA55LK0ZEREpvwt8N51UGuGZOOY4xpN3Cah8YY4Y8rjcYGMM1c8q1VfBpRrNixHEccm6O6KxzuWDllb2PJzvamT1IMACtGBERmQgmfDgAqK8Lc83cchzHkLZ2yCV2Q4UD11rS1usxuGZuOfV14WI3edIbzYoRn8+H67rMXnQJyY44+7Zv4ccPfoVP3vvNQZ+nFSMiIhODsSOtM1wCTe0ZtjZ30JrO4WDwm/6HGVpa3iIQDFJVmT+2ba0la8HFm2OwZm6FegwGEU/neOjlVqyFwABllPuTSCZoa2vj7LPPxlD48zKuxRj41EU1mhgqIlJCk6LnoMe8ygDrF1WzvC6MMZC2llTOJXdaqeW+PQfWWnLdx6Wtd/NZXhdm/aJqBYMh9KwYcRm6t6Yvn+P9s8rlCt8foWfFSH00pGAgIlJik27WV8jn8J65Fbx9ZoT9sRSNsSSdGUu2+8bvWosvXIbpM0fBGENF0KE+Gmaxbj7DMpIVI47P+/66rltQ/WutGBERmVgm1bBCf3LW28HvRCLL8USOrqzL/zzzLLg5fvNdVzEz4mNGxE9d2Kdx7BFqas/w+KE4rmsJOoPvtwDgWpfjx49TU11NOBwZ9Ni+K0ZuOL9KvTkiIhPApOs5OJ3PGGZG/MyM+Hs36vnBXz9Ge3s77/74b5W0bVNFz4qRbc2dBS0pdYyDMaa31sFAtGJERGRimlRzDgoViURIJBKlbsaUMtwVIz0rFgaiFSMiIhPXpO856E84HCaZTJa6GVNOfV2YmqDv1IoRy4ArRhzH6XdColaMiIhMfJN+zkF/Dh06RGtrK8uXLy91U6akVM7l6WNdNMZS3u6NFvyOweFUUGiLt5HL5YjWRrHW4uIVOML01FAIceWsMlWlFBGZgKZkOOh5SYXtxSAjFU/n8laM9KwMca0lk8mQy+Uoi0R6Hy8PGK0YERGZBKZkOJDx1d+KkQOvvMov9zzPJz56EzMjfq0YERGZRBQOZEw88cQT3HLLLezfv5/a2tpSN0dERIZBA74yJs455xwA3njjjRK3REREhkvhQMaEwoGIyOSlcCBjYubMmYDCgYjIZDQl6xxI6QUCAR5//HFmz55d6qaIiMgwTZ0JiXv3ws6dMG8etLbC+vWlbtG0pyWlIiKT09QYVojHYeNGuO02WLrU+7zv126/vXRtm8aMGXqTJhERmXimRjjYsQPmz/d6DsALCX2/1tpakmaJiIhMRlMjHFRXw7JlsHq1FxIOH/Yeb2iAdetK2jQREZHJZmpMSFy9GrZv98IAeGEhHveGGERERGRYps6ExNPt3XuqB+FrX4OvfEVhQUREpABTY1ihP0uXekMKra35ExRFRERkUFO350DGl5aSiohMGVO350DGz0BLSRsaYO3a/GM3bfJCxN13j387RUSkIFM2HFhrUafIOBloKem6dVBTc+q4ntCwejU0NZ2aEyIiIhPKlA0HTzzxBC+99FKpmzE9DLSU9HRVVd5ww6ZN3vDD/Pnj2kwRESnMlA0Hf/3Xf833v//9Ujdjeli9GmIxbxihocHrFRjM+vVeL4J6DkREJqSpUeegH+FwmGQyWepmTB933jn0MQ0NXi/D6tVw3nnwxBP51SxFRGRCmLI9B5FIhEQiUepmTG87d3q9CD3Fqa66CtravM9fe03BQERkglLPgYyd1avh2WdPfV5Vdaqctcpai4hMWFMqHOSspSWZ43giy3m/eRPh6lp+8FocnzGU+x1mRHzMjPipC/vwabdAERGRfk2JIkjxdI79sRSNsSSdGYtrLalUEmMcgsEgAI4xuNbiGEN5wFAfDbM4GqIq6Ctx66e2vv+8tH2ziMjkMKnDQSrn8vSxLhpjKXLWggW/Y3CAeLyNnOsSrY32Hm+txQWyrgUDPmOoj4a4clYZId+UnX5RUtlslieeeIIVK1Zw7rnnlro5IiJSgEl7R2xqz/DdA23saUliLQSNIeRz8BnjvUM15owiSMYYfN3HBY3BWtjTkmTTgTaa2jMleiVTm9/v54477uA///M/S90UEREp0KQMB40tSR4/FKctnSNgDAHHnNFlbYyBQTpFTPfzAsbQms7x+KE4jS2awDgWzjnnHI4dO1bqZoiISIEmXThobEmyrbkT17UEjcEZYBzb9NNz0B/HGILG4LqWbc2dCghj4Oyzz+aNN94odTNERKRAkyocNLVn2HakE9dagv30FvRljKHQyRTGGIKON2Fx25FODTEU2axZsxQOREQmkUkTDlI5lyebOwoKBlB4z0Hf43sCwtbmDlI5d7RNlm7qORARmVwmTZ2Dp4919c4xKGRJ3HDDQc9zAkBrOsfTx7p4z9yKEbZW4FTdieqLV7LgugANr8XJgepOiIhMcJMiHMTTORpjKRwGnmNwOkP3ts1YDIXfeBxjcCw0xlK8fWZEdRBG4PS6E5lzL2XxjEW81Jru/Umo7oSIyMQ1KcLB/u46BsHhvLscxTtRv4G0teyPpVh1TtmIzzPdDFR3AgfaE11UlkUI+AO9x1sLLtCRdnnmzS6eO55Q3QkRkQlgwoeDnLU0xpJgwTiF3/B7hh6stcOuzGeMAde77uVnR9TlXYCm9gxPNnfQls7h4K0A6f15Od6N3nXz53EYY/ABPp83BJTtrjtxqD3DmrkVzKsMICIi42/Cvz1rSebozFjvHeggXtn9LPu2b2FXw2bAu/E88dUv9X4+XH7H0JnxxsxlcEPVnXAcBwPkcgN/L1V3QkRk4pjw4eB4IuuNTQ9yTOzo65RVVjP7wkvY8ci3AO9mc/bCi2g50jSi6zp4vQ4nEtkRPX+6KKTuhMHg+Hxn9Bz0R3UnRERKb8KHgxOJHM4QKxRix5qZvegS9u14koUrVgFeOLjwiqupnTWyev6m+5rHE+o5GMhw6k44jjNoz0FfqjshIlJaEz4cdGZd3CGWJF6w0gsEe3/6I5ZcvRagd1b8eZdeBtA75PD9f/gyr+x+tqBru9bSlVW9g/4Mt+6E4zgF9Rz0UN0JEZHSmfDhIFdgrYJER5yjv36xNyj4/X5irx8kUFlD84F9AFy27ibWbvgzHr7rTwu+fnbyblo5poZbd8Ln8+EW2HPQw5hTcxCePtY10qaKiMgwTfhwUOhKgZNHm4nmDSEYysrKcF2XE0eP8MruZwCIVFRRVlXD0QMvFnRev1YqnGEkdSccxyE3jJ6D3ucZg4OhMZYintYQj4jIeJjwSxnL/U5BN6BwRWXe5/u2b+HSd7+XdCaNvWQZC1a8A7CAoSveyuxFlwx5TscYyvwTPj+Nu5HUnfB1T0gcblEqUN0JEZHxNuHDwYyID9darGXQ7uvo7HNZfNUadjVsJtK9cgEgGAhSVVVFW1sbfp+PrQ98hes//5dDXtdai7WWmRFV7OtrpHUnnD61DnzO8L6nqjshIjK+Jnw4mBnxe6V2gaFuKddt2Njv45FwhFwux/NPNTCvfmXvpMXBuHg3pRmRCf8tGlfDqTuR7IiTaG/jsnU34XMcnvjql7hk1dWs+sDHhn3dvnUnZupnIiIypiZ8n3ld2Ed5wJB1Rzcx8I2XG6mqrWNO/eUcfvGXxI6+PujxWddSHjDUhdVz0NdI6044Pp/qToiITBIT/i2Yz3ib8jzzZteISiGDd7N6+K7PAV63drKznbuf2jfg8dZaMFAfDasL+zSF1p24YOUqdjzyrd66E44xXPiOqzlxYP+IruvVnYDjiRyLR3QGEREp1IQPBwCLoyGeO54gayEwgnt1dPa5/MUPnwPAtS6xlhZOtrYSjUZxzJnvgbPWCyWLo6HRNn3KOVV3YuAfRN+6E2tv+TzQPRXUGOYsXp43KXHf9i2EK6p6nzMY1Z0QERkfE35YAaAq6KM+GsLFDlkQaSiOcaiprcV1XVpbW7Hkn8+1FhdLvbYP7tdI6k5YLPG2Nt589VdEas+ipaWFdDpFoiPO9oe/QbIjXvD1VXdCRGTsTYpwAHDlrDJqgj4y3asIRsPv81NTU0MmkyEej0N3QLDWkrGWmqCPK2dpyVx/hlt3oicYJJMpysvLOeuss3Ach9jJk/z8R//BkquvG9b1VXdCRGTsTZpwEPI5rJlbgWMMaXf0AaFniWMikaCzsxNrLWnX4hjDmrkVhHyT5lszroZbd6InGBzZ+3NWXPvb+H1+amtr6TpxlFkX19PZ2UEqnTqjB6c/qjshIjI+JtVv2nmVAa6ZU160gBAJR6ioqKC9o4OuTBbHGK6ZU868ykCRWjz1nKo7Mfj3vnb2XBZefhW7Gh7lyN6fc94ly3q/ZjB0tZzggsWX4vf76erq4uTJk2RzA69EUN0JEZHxMykmJPZVXxcGYNuRTtLWEoCCS/j2p6ysDNfxkezq5OKyNPV1dUVq6dRUSN0JiyUej7Pqw5+huqaacCic9/Udj3yL6Ky5vLhjKy2HXyX41ptEZ59LZv4FlJWVUVFRcUYVRdWdEBEZP5PyN219XZiaoI+tzR20pnM41iuxO5xljtZasta76cysiLDj3+7j0aeeoL6hgfPOO2/M2j7Z9dSd6Ei7+Hxnfr97gkEykaC6puaMYABw1Uc+3ftx86/2MvdtS7nksnfS2dlJZ2cnyWSSqqoqQsFTq0WyrqUi6KjuhIjIOJhUwwp9zasMsH5RNcvrwpju2vupnEtukC5vay257uPS1mIMLK8Lc/PbavjHL/0vqqurufnmm2lraxvnVzN59NSdwHDG9zkvGFT3Hwz6emX3s7z6/M/Y+9Mfc/JoMxXlFdTV1eHz+Th58iStba3k3JzqToiIjDNjRztwPwHE0zn2x1I0xpJ0ZmxvsaS+yx4dY3ofLw94N7jFpy1XPHToEO973/u45JJLePjhhwkENPegP/F0jodebsVaCHSXUT4jGIQHDwaDsySSSdrb28FCeVU1oVCQT11Uo+WlIiLjYEqEgx4569XeP5HIcjyRoyvrkrUWf/cs95kRHzMifurCvgHfgT733HPcdNNNXH/99Xz1q18dUUXG6eAnzR3saUkS6K5ceCoYVBMOR4pyDde6dHR0ksXw5i9+yh+8ZwVLliwpyrlFRGRgUyocFMt//Md/8Id/+Ifcfvvt/Omf/mmpmzMhpXIumw600ZrOkexoJ5lIUFVdTaRIwQC8YYu0tQRzaX74xc/w0t5GPv3pT/OFL3yBioqKol1HRETyTdo5B2Pp+uuvZ+PGjdx77718//vfL3VzJqSQz+GaOWWkEglyOFSORTDorjvxgQtn8F8//AH/+3//b7773e9y4MCBol1HRETOpJ6DAVhr+ZM/+RN+8IMf8Oijj3L55ZeXukkTiuu6fP7zn2dfLM2aP/oLfH4/QWfwDZkK1TcYXDO3vHf5KsDrr7/O3LlzNdwjIjKGJuVSxvFgjOErX/kKHR0dtLS0kMvl8Pk0GQ5OBYNHH32Uf/zHf+TC82uKVnfC7S5h7TheQaq+wQDg3HPPHWXrRURkKOo5GELfb4/erXrBYOPGjXzve9/jvvvu44YbbgCgqT1zqu4EZhR1J7y9LdbMrVClShGRElE4kIK5rssXvvAFHnnkkbxg0COVc3n6WBeNsZS3e6MFv2Nw6D8oWGtx8QocYXpqKIS4claZ9rYQESkhhQMpiOu63H777Tz88MP84z/+Ix/60IcGPLZYdSdERKQ0FA5kSNZa/vmf/5m/+Iu/4P/+3//LjTfeWNDzilF3omB798LOnTBvHrS2wvr1ozufiMg0pnAgQ+r5J7Jr166JuWojHocbb4QtW+DwYXjiCbjtNmhogK99zXv89OPvvhvuvbc07RURmeA0sCtD6pkvMCGDAcCOHTB/vtdzAF4wAFi3Dmpq+j++tXW8WiciMukoHAzX3r1w//3eu9JNm0rdmnEzoVdqVFfDsmWwerUXEg4fHvjYhgYvNIiIyIBU52A44nHYuDG/+xq8d6xtbdDU5I11V1WVtp3TzerVsH27d+MHLyzMn3/mcXv3wtKlRb10z7yK44ksJxI5OrPezqA+Yyj3O8yI+JhZrHkVIiLjROFgOPp2X8+b53VfHz7s3ZjuvNMLDwoGpXHnnYUdt3ev9+fw4VGFhdNXZLjWq+h4+oqMnse1IkNEJhMNKwxHf93XO3d6oaChAe67r9QtlL527vR6c3p6FJYu9YYUWlu9n9kIpHIuP2nu4KGXW3nmzS460i4+AyHHEHQMYZ/T+yfoGEKOwWegI+3yzJtdPPRyKz9p7iCVc4v3OkVEikyrFYbr7ru9gABeWDh82LvR3HbbqTkIk2EZnZb+naGn7sJAmtozPNncQZuqQIrIFKdhheE6vfu6pubULPmamskxC36guRM9waep6dSM/2nEGMMDDzzABz/4QWbMmJH3tcaWJNuOdOJaS8CYEe0fYYwhYMC10JrO8fiheL/7R4iIlJqGFUarZ8y6oQFeeGFyvAPvb+lfQwOcd57X7V5Vdepr00g8Hue+++5j9erVfPe738V1va7/xpYk25o7cV1LcITBoC/HGILG4LqWbc2dNLYki9F8EZGiUTgohttu826qhU6KK7X+5k6cPg6/d2+pWlcyVVVV7Ny5k9/6rd/ijjvu4AMf+AA797/S22NQrC2pwetFCDrehMVtRzppas8U5bwiIsWgcDAdrV4NsZjXW9DQcGoJZizm9Rg0Npa6hSUTjUb56le/yr//+7/Tlcrww1+fIJlKERjm/IJC9A0IWzVJUUQmEE1IHEM939oJXUCor56lmPffD+97X/+1AqaRrU1xdh/vpCsexxioqqwiFAoV/TqutWSsZXldmPfMrSj6+UVEhks9B2MonU6zdetWJkX+ise91RYNDd4KhmkeDOLpHPtbM4QCQaLRWvx+PydbT9La1krOzRX1Wo4xOBgaYyni6eKeW0RkJBQOxtCWLVv4vd/7Pf7hH/6h1E0ZWlXVqbkTKi/M/liKnLX4Dfh9fmpraqipriGdTtPy1lt0dXVhKV7o8xuv2uL+WKpo5xQRGSktZRxD73//+zl8+DB/8zd/w/z58/nQhz5U6iZJAXLW0hhLggXj9AwJGcLhMMFQkI6ODuLtcRLJBFVVVQT8o69VYIy3xrExluTysyMqtSwiJaVwMMY++9nPcujQIT7/+c8zd+5c3vGOd4x7G6y1uK6L4ziTZ/5DCbUkc3RmLH7nzO+VYxyqKqsIh8O0x+O88NMnsdk0NpPmit/+MAAP3/U5Lli5isvW3TSs6/odQ2fG26thZkT/NUWkdDSsMMaMMdxzzz1cccUVfPKTn+TVV18d1+tba/nyl7/Me9/7Xrq6usb12pPV8UTW2xNhkGOCgSCkuojOPIfqOfP57399gGQqCVjmvG0JLUebhn1dB+/ndSKRHWnTRUSKQuFgHAQCAb75zW8yc+ZMbr75ZmKx2KDH56zleCLLvliS/z7SScPhdn7wWpyGw+3895FO9sWSHE9kyQ0x0dFay1133cU3vvENPvrRj1JeXl7MlzVlnUjkcMzQNQ1OHjvCwvqVvLFvN+ddejmtra2cbG3l4t+4hrrZ84Z9XdN9zeMJTUoUkdLSUsZx1NTUxPve9z4WLFjA5s2bz1gWV8yd/qy1/OVf/iUPPPAAf/3Xf80nP/nJcXmNU0HD4XZePpki7CssO//T73+Itbf8GXOXrKC9vZ2OE29SUVnBjLnnAbDjkW8RnTUXgCVXrx30XMmcy8W1Id43v3JUr0FEZDTUczCO5s2bx7/8y7/Q2NjI5z73ud4ljsXe6c9ay1/91V8pGIzQUD0yfSU64hz99YtcsPKdhENhotEoR369n7KotzfDQ1/4NJetu5ElV69l+8PfKOicWeV1ESkxzXoaZytXruS+++7j1ltv5fzzz+fDv/+neTv9BY3pM0O+f8YYfIDPZ3p3+tvTkuRQe4Zr55bznf97D1//+tcVDEZoOCsFTh5tJjrr3FPPdXwE/AGSySRtzYeJVFQBcPTAi/zh1x8r6Jx+TRoVkRJTOCiB3/7t36apqYl/f24f9ldv4QsEirbT37+9eJyf/vqogsEolPudgn8W4Yr87v9927dw6TXvpbW1laaXG4kde52TR5sB+P4/fJnf+bMvD3o+xxjK/OrQE5HSUjgokd/48Kdoe3sLyXSacsfgBIKjOp9jIN3ZScaF3/yj/8PKBbVFaun0MyPiw7UWa4cufR2dfS6Lr1rDrobNRCqrmX3hJYRCIYwxdLTGvMcWXQLAkQP7OHrgxd7PT2etxVrLzIiv36+LiIwXhYMSaGrP8JOjXYRCYbLxVtpaE0SjUfy+kf44LO0dHXR2dlJZWYU/EGDbkU5qgj7mVY6+QM90MzPi9yZ+AoXcpq/bsPGMx0KhEGXRGXlDDpHKamLHXh8wHLh4YWSGahyISImp/3KcpXIuTzZ39G4BXFNTg+M4tJ48iWtHsivfqWBQVVlFeVmZdvobpbqwj/KAIeuOfGJgOBxmziXLaTl6uPexk8eaWbhy1YDPybqW8oChLqyeAxEpLS1lHGc/ae5gT0syb45Bzs3R0tLi1fCP1mIodO5B32BQSVnZqToG2ulvdJ59o4tn3uzyJoiOYC6IxXL8+HGa9vwMMikSHe1EZ80dcCmjtZa0tbzz7DJWnVM22uaLiIyK+i/HUTydozGWwiF/8qHP8VFbU0Ps5Ena2tqorq4uICD0HUrIDwbQvdOfhcZYirfPjJxRB0EGtzga4rnjCbIWAiNYPGAwhENhzn/7lZxVd9aQx2ett0picbT4W0KLiAyXhhXGUd+d/k4XCASprq4mmUzS2dk5xJnyg0F5Wf+VD7XT38hVBX3UR0O42LwiVMMRDofIZrNkc4OXQ3atxcVS308xKxGRUlA4GCd5O/0N0E0dDoWprKyko6ODRDIxwJksHQUEA+i+joXGWHJYhX3Ec+WsMmqCPjLdqwiGKxgK4RiHZDI54DG2e/inJujjylkaThCRiUHhYJwMttNfX+VlZZRFIsTjcdLp9Glf9YJBRwHBoEffnf5keEI+hzVzK3CMIe0OPyAYDKFwqDscnPlcay1p1yuFvWZuBaECyzWLiIw1/TYaJ4Xs9Afwyu6fcXjPs7z03/9Fa1sr2VyWh+/6HLsavjfsYADa6W+05lUGuGZO+YgDQjgcJpvNksnmf//7BoNr5pRryamITCgKB+OkkJ3+Ykdfp6yymjkXLmb3fz6C4zicPHmS2YsWc+y1V4cdDEA7/RVDfV2Ya+aW4ziGtB3eHIRgMIjjOKRSp+Z9uN0rExzHcM3ccurrwmPRbBGREVM4GCedWXfIm0rsWDOzF13Cvh1PsnDFKmpra7HWMmvpSiK1M4YdDHq41tKVVb2D0aivC3PD+VW9cxAyBfYiGAyhkDe0YK1Lxj01x+CG86sUDERkQlI4GCeFTAi8oLtAzt6f/oglV6/1ljjWenUPLly5ivKycvZt38LRAy+yq2Ezuxo2F3x97fQ3evMqA6xfVM3yujDGQNpaUjmX3CATFq21BMMRnECIZM7FGFheF2b9omoNJYjIhKVwME4K3env1BbAXlAI+APEmg5SMeMcEh1xtj/8DWYvuoQl77qO73/1ywVfXzv9FUfI5/CeuRV86qIa3nl2GRVBh5yFtOvNIUjm3N4/PY/5/H6SbTEyv9rFpy6q4T2afCgiE5yKII2TQnf6O30LYPDq9GezWXxVVb3b/p482szCFQOX4u1LO/0VX1XQx6pzyrj87AgtyRwnElmOJ3J0ZV2y1uLv/p7PjPiYEfFz76Z7+c//2cmff/i9pW66iMiQFA7GSaE7/fW3BfCya97LWy0tdHV1EawOsqthM6/sfpaPfumrQ15XO/2NLZ8xzIz4mRnxs3iQ4z7+sY/y0ov7eeWVV7jwwgvHrX0iIiOhvRXGyfFElk0H2vCZoYcYfvzgV6ibPa93C+Do7HPp6uqkvaODs846C5/j45Xdz7Jv+xZ+58++POi5ctaSs7B+UTUztdufiIgUQOFgnOSs5ZsvnaQj7Y5ovNm1LidOnMCHy1kzZwHwV++/go9+6f/2zk/oTyrnUhF0+MzFtQXPexARkelNA9HjxGcM9dEwGEZUitcxDi9v/zHb//Ub2O5qe2VVNZRVVg/4HGstGKiPhhUMRESkYOo5GEfxdI6HXm7FWggMUUa5P+2tMV7Y/iRnnT2bw40/p6yqlqs+8ukBj8+4FmPgUxfVaEMfEREpmMLBOPtJcwd7WpIEjClo9cLpWmItOI5DbU3toMe53Rv6LK8L8565FSNtroiITEMaVhhno93pryxSRiqVIjfINsDa6U9EREZD4WCcjXanv3A4jOM4dCX639JZO/2JiMhoaVihRBpbkmxr7sS1lqAz+IZMp4u3x0kmk8yYMQPDqefl7fSnDX0mhr17YedOmDcPWlth/fpSt0hEZEh6W1kio9npr6ysDNd1SSWTvY9pp78JKB6HjRvhtttg6VLvc4CGBli7Nv/YnTu9x++//9RxIiIlonBQQiPd6c/v8xMMBulKJLz5Bdrpb2LasQPmz/du/OCFBIB166Cm5tRxhw/D9u3e4+vXQ1XVuDdVRKQvhYMSG+lOf5GycvAFtNPfRFZdDcuWwerVXkg4fLj/43bu9HoLGhrgvvvGtYkiIv1ROJgARrTTXyBAMn6Srr1Pa6e/iWr1aojFvJt+QwM0NQ187HnneT0H550HmzaNVwtFRPqlYvsTyHB3+nvox9/inx96iI03rIFgeambL/25886hj7n00lNDDzU13sRFEZES0lvNCahnp7/F0TDvnlPO++ZX8oHzqnjf/ErePaecxdEwMyN+1n/843R0dPD973+/1E2W4di50+tFaGjwPl+61Pu7oQFeeEErGkSk5LSUcZL7xCc+wbFjx9iyZcuwlkOKiIgMRD0Hk9zv/u7vsm/fPl544YVSN0VERKYIhYNJ7l3vehdz587lO9/5TqmbIiIiU4TCwSTn8/m4+eab+f73v0+rJrJNOq7r0tXVRS6XK3VTRER6KRxMAR/96EdxXZfNmzeXuikyTMYYrr32Wr7whS+UuikiIr0UDqaAs846i/e+97185zvfGdFOj1I6xhje//7386Mf/YhMJlPq5oiIAAoHU8YnPvEJDh48yNNPP13qpsgwfeADH6CtrY2f7tjB8USWfbEk/32kk4bD7fzgtTgNh9v57yOd7IslOZ7IklMAFJExpqWMU4S1lne/+90sWrSIBx98sNTNkWFoS2X5k3u+xgVXvZey2rNwrbezZt/NuHo+d4yhPGCoj4ZZHA1RFfSVsOUiMlUpHEwhDz30EF/+8pfZtWsXZ599dqmbI0NI5VyePtZFYyxFMpUmk8lQWVGGj/638LbW4gJZ14LximXVR0NcOatMpbNFpKj0G2UK+dCHPkQgEODf/u3fSt0UGUJTe4bvHmhjT0sSayHk95Hq6iCXzgxYzMoYg88YQj6HoDFYC3takmw60EZTu+YriEjxKBxMIVVVVVx//fVs2rSJbDZb6ubIABpbkjx+KE5bOkfAGAKOIeD34/f7SaaSBZ3D9DzPGFrTOR4/FKexpbDniogMReFgiukpp7xt27ZSN0X60diSZFtzJ65rCRqD06eXIBwOk0omsRQ+0ucYQ9AYXNeyrblTAUFEikLhYIpZunQpy5cv59vf/napmyKnaWrPsO1IJ661BJ0z5xWEw2Fca0mlUsM6rzGGoONNWNx2pFNDDCIyagoHU9BXvvIV3vnOd6pi4gSSyrk82dwxYDAA8Pv8BPx+ksnhv/vvGxC2NneQyrnFaLaITFNarSAyDn7S3MGeliSB04YSTtfR2UFnZyczZ87EMPxdNl1ryVjL8row75lbMZomi8g0pp4DkTEWT+dojKVwGDwYgDe0YEcwtNDDMQYHQ2MsRTyt/RpEZGQUDkTG2P5Yipy1+AvoCPD7/AQCgRENLfSew0DOWvbHRhYwREQUDkTGUM5aGmNJsAxYv+B04XCYVCo14n0yjDFgoTGWVKllERkRhQORMdSSzNGZsfidwYPBK7ufZd/2Lexq2Ew4FMJay79+6U/Y1TCynTb9jqEzY2lJamhBRIZP4UBkDB1PZL09EQY5Jnb0dcoqq5l94SXseORb+LqHFmYsWETL0aYRXdfBK7d8IqFiWCIyfAoHImPoRCKHY/pfutgjdqyZ2YsuYd+OJ1m4YhXgDS2ct/Kd1M46d0TXNd3XPJ5Qz4GIDJ/CwVSydy/cfz80NMCmTaVujQCdWTdvd8X+XLDSCwR7f/ojlly9FvDCARbmX/p2AB6+63MkOuLDurZrLV1Z1TsQkeFTOJgq4nHYuBFuuw2WLvU+By8oNDTA3XefOrahAdauLU07p5lCJwQmOuIc/fWLvUEB4PihA9ScPYfY0dfZt2MLX/nYGv7q/VfwxWu84YdCZDUhUURGwF/qBkiR7NgB8+fDzp0wb54XEhoaoLoaVq+GpiavN2H9eli3Dr773VK3eFrwFbhC4eTRZqJ9hhDS6TQAgUCA2LFm7vzBz4hUVAGwq2Ezl627qaDz+gu8vohIXwoHU0V1NSxb5gUBgMOHvRDQ47XX4OabS9Gyaa3c7wxZ+AggXFGZ93njf/+Ii3/jWnyOL683YVfDZpa867qCru0YQ5lfnYMiMnwKB1PF6tWwfbvXWwBeWJg/3/t4506or/eGG2RczYj4cK3FDlHnIDr7XBZftYZdDZuJVFZTd+4CgsFg3jGxo6+T6Gjv7UEYjLUWay0zI75RvwYRmX4UDqaSO+8887G9e6GtzRtO2LtXAWGczYz4cYzBBYa6TV+3YSMAOTfHiRMnzggHP2/4HhesvLKg67p4YWRGRP/FRWT41Oc4lR0+DBs2ePML1q6Fnl0ad+705iD09DLImKkL+ygPGLJu4RMDe+YbnB4O9u/YSnTW3ILOkXUt5QFDXVg9ByIyfHpbMZXNnw/PPnvm46tX9/+4FJ3PGOqjYZ55swtrbUEllNPpNAG/H8fkZ/dwRSWRquohn2+tBQP10XDBEyJFRPrSls0iYyyezvHQy61YC4EhyiiD5cRbbxEOhaisHHpuQX8yrsUY+NRFNVQF1XMgIsOnYYVppmeimjLh+KkK+qiPhnCxQxZEyuZy5HI5gsHQiK7lWouLpT4aUjAQkRFTOJhmXNflV7/6FW+99VapmzKtXDmrjJqgj8wQwSyTTmOAQDAw7GtYa8lYS03Qx5WzykbRWhGZ7hQOphmfz8dDDz3E2rVryWa1Kc94Cfkc1sytwDGGtDtwQEin0/gDgTPmGwzFWkvatTjGsGZuBSGf/muLyMjpN8g09IlPfII33niDrVu3lrop08q8ygDXzCkfJCBY0pnMGasUhtI3GFwzp5x5lcPvdRAR6UvhYBpavHgxK1eu5Nvf/napmzLt1NeFuWZuOY5jSNv8OQin5hsUHg5ca0lbi+MYrplbTn1deCyaLSLTjMLBNPW7v/u77Nixg0OHDpW6KdNOfV2YG86v6p2DkOnuRUin0xhjCAaGfudvu5/XM8fghvOrFAxEpGgUDqap3/7t36ampobvagOmkphXGWD9omqW14UxBtLWkrWme5VC/8sdrbXkrCWVc0lbb7ni8row6xdVayhBRIpKdQ6msbvuuovvfe977Nmzh1BoZEvnZPTi6Rz7WpI07DlA1VlnEwgGMcbkDTk4xvQWUSoPeIWVFmu5ooiMEYWDaezQoUNceeWV3HfffXzoQx8qdXOmtZdeeok1v7mWbz3yOHMvXsrxRI6urEvWWvzduyvOjPiYEfFTF/ap8qGIjCmVT57Gzj//fFavXs23v/1thYMSe+aZZ/A5hqtXLiUcDrO41A0SkWlNcw6muU984hPs3r2bF198sdRNmdaefvpp3v72txMOa1KhiJSewsE0t2bNGs4++2wtayyhXC7HjBkz+MxnPlPqpoiIAJpzIMBXvvIVvv71r7Nnzx4qKytL3RwRESkx9RwIH//4x0mlUvz7v/97qZsiIiITgMKBMGvWLNasWcN3vvMd7dYoIiIKB+L5xCc+wUsvvcQvfvGLUjdFRERKTOFAAFi9ejXnnXeeJiaKiIgmJMop/+///T/+9m//lueff56aaJSWZI7jiSwnEjk6sy45a/EZQ7nfYUbEx0wV5BERmZIUDqRXLBZj9Zrr+Mxf/C1li5bRmfF2DXT6KeXb87hK+YqITD0KBwJAKufy9LEufvZ6DNcYIuEIfsfgAKafngFrLS6QdS0Y8BlDfTTElbPKCPk0WnWGvXth506YNw9aW2H9+lK3SERkQPotLjS1Z/jugTb2tCQJhEIkO9ohl8FnTL/BALzA4DOGkM8haAzWwp6WJJsOtNHUnhnnVzDBxeOwcSPcdhssXep9DtDQAGvX5h/b0OCFiPvvH/92ioh0UziY5hpbkjx+KE5bOkfAGCIBPwG/n0RXV8HnMMYQcAwBY2hN53j8UJzGluQYtnqS2bED5s/3bvrghQSAdeugpubUcYcPQ1MTrF7t9TAcPjzuTRURAYWDaa2xJcm25k5c1xI0BscYwBApKyOVSpFzc8M6n2MMQWNwXcu25k4FhB7V1bBsmXfTnz9/4Jv+/Pnwwgvw4Q97IWH+/PFspYhIL4WDaaqpPcO2I5241hJ08ocPwuEwGEMikRj2eY0xBB1vwuK2I50aYgAvFMRi3pBBQ4N34+9PPO6FiM9+Fr77XW+egohICWjL5mkolXN5srmj32AA4BiHSCRCIpGgvLwcw/CWKnoBAdLWsrW5g/WLqjVJ8c47hz7mhz+E973P6zF45BF44glvjoKIyDib5r+xp6enj3X1zjEYaMJhJBIhl8uRSqVGdA1jTs1BePpY4fMXppWdO71ehIYG7/P3v98LBDt3er0GWtEgIiWipYzTTDyd46GXW7EWAs7gPQKxWAwMRGujI75exrUYA5+6qEZ1EEREJgn1HEwz+2MpctbiL2CkIFIWIZ1Ok81lR3w9v4GcteyPjawHQkRExp/CwTSSs5bGWBJs/4WNThcOh3EcZ0QTE3sYY8BCYyxJTp1UgFdAquePiMhEpHAwjbQkc3RmLP4hhhNe2f0s+7Zv4RcNj/ZOTHz4rj9lV8PmEV3X7xg6M5aW5PCWRk5VHR0dvPXWW6VuhojIgBQOppHjiay3J8Igx8SOvk5ZZTWzL7yEHY98i0gkguu6zFx4ES1HB1iCNwQH793yicTIhyemko985CN86UtfKqj3RkSkFBQOppETiRzOICsUAGLHmpm96BL27XiShStW4ff5CYVCnLfindTNnjei65ruax5PqOcgHo/zy1/+kiuvvLLUTRERGZDqHEwjnVm3e3fFgcPBBStXAbD3pz9i7S2fB7xljdlclhkXXEJXootfPvkDKmpqOXnsCBesWMXsRZcMeW3XWrqyblFex2T23HPP4bquwoGITGjqOZhGCp0QmOiIc/TXL/YGhVAoxPGDByirm8mrv9zNi8/+lJkXL2fJb/4ODff/LclkontFw+Dnz2oCHs888wyzZ89mvkoji8gEpp6DacRX4Bj3yaPNRGed2/u5wRAIBAgEApx87QDnzF9ARUUFmUwG13U58MIuZp6/CMdxeo/r+eOYU/nTPw3G2HPWm3h5PJHlRCJHZ9YlZy0+Yyj3O7zSBVevux4XUNUHEZmoFA6mkXK/07250uDCFZV5n+/bvoWlV6+ls7OT2llzOPKrfZSXlQPQGTsByS5qa2vJZDJkMhm6urpwXW8Iwe/z4Q8E8IcjxN9qJXl2wNu7YYqJp3Psj6VojCXpzFhv4qcx3cM4HgMs+p1PEQwE+OZLJ6mPhlkcDak4lIhMOAoH08iMiA/XWuwQdQ6is89l8VVr2NWwmUj3yoVAMIjb0cFFV17Dvp9uIdER58iv9gNQVllNKBgiFAx1n8GSy+XIZLJkshky6QyZTJYHv/a33L5zCxdffDHLly/v/XPBBRfg803OG2Qq5/L0sS4au4tLYb2lm6dKU5/6PidTSVKd7VScNYOOtMszb3bx3PEE9dEQV84q0/4TIjJhqHzyNHI8kWXTgTZ8pvAhhh4Wy/Hjx6moqKC8rJyjB16kdvZcHtr4KT71lYeIVFQN+NycteSs5R3OW7yy5+fs2bOHF154gQMHDmCtpby8nEsvvZRly5axYsUKli9fzjnnnDPhl/o1tWd4srmDtnQOB4PfDB664u1x0qkUZ501A/CWd2YtuFhqgj7WzK1gXmVgvJovIjIghYNpJGct33zpJB1pd0TvUmMnY6S6OvjpN7/KR7/0VWJHX2fHI9/id/7sy4M+L5VzqQg6fObi2rxQ0tHRwS9/+UteeOEF9uzZw549ezh27BgAR44cmdDhoLEl2bvldcCYgoZr3mp5i0AgQHVVdd7jrrVkuochrplTTn3d1Bt2EZHJReFgmnn2jS6eebOL4BD1DvrT0dlBV1cXrz3335RV1hA71sxVH/n0oM+x1pK2lneeXcaqc8qGvMabb77Jnj17WLt27Znt27vX27Fw3jxobS3ZroWNLUm2NXcOuOV1f1zX5fiJ41RXVxMJR874urWWtNsdEOYqIIhIaWnOwTSzOBriueMJshYCw3xjHgwE6XA7WH7dBwn4C+v+zlpvCGNxNDT0wcDZZ5/Nddddd+YX4nHYuBG2bIHDh72tjeHUdscvvAB33jnwY0XS1J7p7TEoNBgApDNpAILBYL9fN8YQdCDtWrYd6aQm6NMQg4iUjGZATTNVQR/10RAuNm8mfSECwQDGGDLpTEHHu9biYqkvxoz8HTtg/nyv5wDgttu8EFBdDevWQTQKmzb1/1iRpHIuTzZ3DDsYAKTTafx+Pz5n4O+DFxC8FQ5bmztI5VQ0SkRKQz0H09CVs8o41J6hNZ0jSGE7NMKpegfpTJoyBh8isN3j6DVBH1fOGno4YUjV1bBsGaxe7X1++LAXAHq89hrcfDMsXXrmY6e1Cwp/zX09fayLtnSuz0qEwqXT6QF7DfoyxhAAWtM5nj7WxXvmVgy7nSIio6Weg2ko5HNYM7cCxxjS7vC2Dg4GAqTTaQarhth3/HzN3IriLNFbvRpiMa9noKEBmvpsArVzJ9TX5weD/h7r9sYbb7BmzRpuv/12Hn74YV566SVyucH3fYinczTGUjgUNvmwr5ybI5vNFhQOABxjcDA0xlLE09qPQkTGn3oOpql5lQGumVPOtuZO0q4l6BT2bjoQDOJ2dpLN5fD7zvznkzexbk55ccfN+5s/sHcvtLV5kxP37vXCQH+P9VFVVUV9fT2/+MUv+Nd//VestZSVlXHppZeyfPny3iWVs2bN6v2e7O+uYxAcQY+DF6YGnm/QH7+BtLXsj6UKmsgpIlJMWq0wzQ13SZ61LsePn6CyqpKySP5Na9yX5B0+DB/5yKnVC3fe6X18+mM9QxH96OjoYO/evb1LKffs2cPRo0cBmDlzJsuWLWPZihVw5Q1kfSHCI+gFaYu3kclkOKvurGE9b6AloCIiY03hQGhqz7C1uYPWAov5tMRa8Pl81FTXAFOvmM+bb77JL3/5S55//nn27NlD08kOrvs//0Quk8Yx5O0d4ff7Md1VEF/Z/SzJjjiJ9jYuW3cTAA/f9TlmLlrCZetupKpy4EJR/fGKR8H6RdXMjKiTT0TGj8KBAAOXAXY4Myi0d7STTCaJ1tWRdYHuiotTtQzw3pYEP27qwGbTZLv3j8hms1hrMcbg9/vpaHmTXCpJRXUt3/3z3+fzm7YAsP3hb3Di2BHW/eH/IhwaXk9KzxDNdfMqWBxV3QMRGT96OyKAN0nxPXMrePvMSN4GQllrMYa8ZY++YBg/DpmcpTLkm/IbCL2VdPE5DsFwBLoLGFmsFxSyWTKZDG81NzHnkmX8/IcPM+uiek62niQQCHDe268k/fOnhzXfoIcxBmPgeCLH4mK/KBGRQSgcSJ6qoI9V55Rx+dkRWpI5TiSyHE/k6Mq6ZK3Fbwx+N8M9d/4Fv/vBddz8wd+e8uPhnVm3Oxydep3ess4ggUAQIrDy3dfhWpdDu3byrk98FoCuri6SXUnmX/p2rOuy73+25p13ydVrh7y2ay1dWdU7EJHxNbX6f6VofMYwM+JncTTMu+eU8775lXzgvCreN7+StedHCbx1mOe3PzXlgwF4Y/+FSHV28MarL7P0yvdQW1PLzBkzSLQcI3rOXI41v07L0SaWXL2WJVev5ZXdzxZ8/axG/kRknCkcyIisWrWKZ58t/AY3mRUagE4ebSY669w+jxj8Pj+10Sih8gp+9oOHef1XewEIVxRe3Mg/DQKYiEwsCgcyIqtWreLIkSO8/vrrpW7KmCv3OwUVPgpXVOZ9vm/7FpZcvRaf4yNaW8tvfPz3uf/3b+Sfbr2Bqz+2oaBrO8ZQ5td/UxEZX/qtIyNy+eWXA0yL3oMZER+uHbqSZHT2uSy+ag27Gjazb/sWZl94Se/XfD4/bUde4+N/+018oQgPff6TQ17Xdl9zZmRqTvQUkYlLSxllxK655houvfRS/uEf/qHUTRlTxxNZNh1ow2cKH2I43b7tWwhXVHHessuIxWJs+8bf8/a17+fClVcO+BzVORCRUlHPgYzYO97xDn72s5+Vuhljri7sozxgyLojz9GJ9jbKKqu9OQi1tZy7ZAUZa3DtwCsRsq6lPGCoC6vnQETGl8KBjNiqVat47bXXeOONN0rdlDHlFXgKg2FYm1T1ddm6m3jl+WfZ1bCZF378H1RUVHDW/AtobW3t95zWWjBQHw1PixUhIjKxqK9SRuyKK64AvHkH119/fYlbM7YWR0M8dzxB1kJghPfqqz7y6bzP05k0J0+e5GTrSWpra3vLMANkrRdKFkdDo2m2iMiIqOdARmzGjBlccMEF02JooSrooz4awsXmVYscjWAgSG1tLZlMxutB6N4G27UWF0v9FK46KSITmyYkyqj0/PMpZLvnyS6Vc9l0oI3WdI6gMUV7zel0ipOtrQSDQaqrq8lYqAn6WL+oesrtUyEik4N+88iomCLeJCe6kM9hzdwKHGNIu0MvbSxUMBiipqaGdDpNRyKFg2HN3AoFAxEpGf32ERmGeZUBrplTXvyAEAhSWRMlk0lz9CePM6dM/zVFpHT0G0hkmOrrwlwztxzHMaTt6OcguNaStpZAwM+C9HEe+Ye/YuPGjbiuNlwSkdLQagWREaivC1MT9LG1uYPWdA7Hgt8Mb+6FtZasBRdLTdDHmrkVzKu/gor77uOzn/0swWCQv/3bv502wzYiMnEoHIiM0LzKAOsXVfP0sS4aYynS1oJr8TsGh/6DgrUWF6/AEd0VF5dHw1w5q6x3jsH1119POp3mc5/7HMFgkL/8y79UQBCRcaVwIDIKIZ/De+ZW8PaZEfbHUjTGknRmLFlrMYa8IQfHGKy1GGOoCDrUR8MsHmC54oc//GHS6TR33HEHoVCIL37xiwoIIjJuFA5kePbuhZ07Yd48aG2F9etL3aIJoSroY9U5ZVx+doSWZI4TiSzHEzm6si5Za/F37644M+JjRsRPXdg3ZOXDm2++mXQ6zV/8xV8QCoX4whe+ME6vRkSmO4UDKVw8Dhs3wpYtcPgwPPGE93hDg/f3Cy/AnXfmH3/33XDvvePe1FLxGcPMiJ+ZET+Li3C+T3/606RSKe6++26CwSB/8id/UoSziogMTkWQpHANDfDDH8LNN3s9B/Pne49VV8Pq1XD//VBVdao3oef4Bx8sbbungK6uLsLh8LSqKyEipaOljFK46mpYtswLAvPne70H69Z5nwO89hpceqn3cUOD9zUpikgkguM4CgYiMi40rCCFW70atm8/NYxQXe2FBPDmIdTXw9Kl3ryEpUtL184pSKFARMaThhVk9PbuPdWLsHev99jhw97fX/safOUrCgsiIpOIhhVkdA4fhg0b4LvfhbVrvRUMS5d6QaG11ZuUKCIik4p6DkRERCSPeg5EREQkjyYkikw2KkQlImNMPQdSVNba3j8yBnoKUd12mze3o2dOx6ZNXmC4++5TxzY0ePNARESGSeFAiurll19mzpw5PPPMM6VuytS0Y4e3fHTnTu/z2247FRBWr4amplMrRdatg5qakjRTRCY3hQMpqre97W1UV1fzs5/9rNRNmZr6K0TVU5Vy06ZTlStFREZB4UCKynEcrrjiCoWDsbJ6NcRi3pBBQ4PXU9Bj/XqvF6Gn50BEZIQUDqToVq1axS9+8QvS6XSpmzI13XmnN2TQU7q6oeHUMMN5553aEEtEZIQUDqToVq1aRSqV4oUXXih1U6aHq66CtjYvJLz2mjcPAbzA0NR0qty1iEiBVARJii6Xy3HxxRfz2c9+lj/+4z8udXNERGSY1HMgRefz+bj88st59tlnS90UEREZAYUDGROrVq1i165dZDKZUjdFRESGSeFAxsQ73vEOurq62LdvX6mbIpwqTpXNZhXYRGRICgcyJurr64lEIhpamCCMMTQ1NXHFFVdw7bXX8tZbb5W6SSIygWlCooyZD3/0o1TNmsef3HkXJxI5OrMuOWvxGUO532FGxMfMiJ+6sA+fMaVu7rRw8OBBPvjBD1JXV8djjz1GbW1tqZskIhOQwoEUXTydY38sxY5X3yCFQ1l5BY4xuH3+qfV87hhDecBQHw2zOBqiKugrYcunhwMHDnDDDTcwe/ZsHn30UaqqqgY9PmctLckcxxNZhTyRaULhQIomlXN5+lgXjbEUOWvJ5Vw62lqpqakm6A+ccby1FhfIuhYM+IyhPhriylllhHwa8RpLL730EjfccAMLFizgkUceoaKi4oxjekJeYyxJZ8b2hjmFPJGpT+FAiqKpPcOTzR20pXM4GPwGMHD8+HEqKiooLysf9PnWWrIWXCw1QR9r5lYwr/LMQCHF09jYyE033cRFF13Ev/3bv1FWVgacGfKw4HcMDt7chdMp5IlMPQoHMmqNLUm2HenEtZaAMTh9biCxkzEc41BT4O6ArrVkut+JXjOnnPq68Bi1WgB2797NRz7yEZYvX853vvMdjmd8Z4S8/gLBQBTyRKYGhQMZlcaWJNuavWAQdMwZN5KOzg66urqYMWMGhsJuMtZa0m53QJirgDDWnnvuOT72sY/xW5/5Exa+92O4cEbIGy6FPJHJTeFARqypPcPjh+K4bv/BACCdThE7eZKz6urw9zPvYCC9AcEx3HB+ld59jrHHf7aXfdkK/IEAlZEwjhn9cIBCnsjkpQFBGZFUzuXJ5o4Bewx6BIJBjDGkh1l4xxhD0PEmu21t7iCVc4vRbOlHU3uGprLZRMrKSHa0E4/HsYz+PUPfn+G2I500tav4kshkoXAgI/L0sS7a0jkCZuBgAGAwBPyBEW3fbIwhYAyt6RxPH+saTXNlAH1DXsTvp6a6mlQqRbytregBQSFPZPJQOJBhi6dzNMZSOBQ2Lh0I9oSD4d9sHGNwMDTGUsTTuRG0VgZzesgLhcJUV1eTTCaL2oOgkCcyuSgcyLDt717i5i9wvlowGMR1XbK5kd3c/cYrxLM/lhrR86V/A4W8cChMVXU1iUSC9vZ2RhLqTqeQJzK5KBzIsOSspTGWBFv4ErdAIIDBjGhoAbqvY6ExlvTW3UtRDBbyIuEI1dXVdHV1ES9SQFDIE5k8FA5kWFqSOTozFr9T+DI3xzj4A34yIwwH4BXh6cx4ZXxl9AoJeZFwhKqqKrq6umjv6GC0AUEhT2TyUDiQYTmeyHrlcvv52iu7n2Xf9i3satjc+9jDd32OXQ2bCQaD3SsWRnZTcPCWxp1IZEf0fMlXaMgri5RRVVlJZ2cnHZ2do76uQp7I5KBwIMNyIpHD6WeFQuzo65RVVjP7wkvY8ci3eh+f87YltBxtIhgIksvlyI1w3oHpvubxhG4qxTBYyIP8oFdWVk5lZSXf+6vP8z+Pf2dU11XIE5kcFA5kWDqzbt7GOz1ix5qZvegS9u14koUrVvU+vuSq36Ru9jwCQa+IUSKRGPG1XWvpymopXDEMFPKg/6BXXlbOvEsu5Y3DB+nsGnkPgkKeyOSgcCDDMtBY8QUrvUCw96c/YsnVa/O+tnDFKhzjEHDgpw9/k63fvj9vidy+7Vt636W+svvZQa+f1Vh1UQwU8mDgoLfy2nWcPW8B7e3tdCVGviRRIU9k4lM4kGHxDbJCIdER5+ivX+wNCgBHf/0i0dnneh+/+AJuKkk6nSYWi5HNZYkdfZ1Xdj/LkqvXctm6m9jxyDcHvb5/FPX+5ZTBJgQOHPQMi9/5LsrLy2l96wQ/+dcH8oaQwAt6Rw+8yK6GzXlzT06nkCcysSkcyLCU+50BCx+dPNpMdNa5Az536dXXMeu8hZSXl2Fdl1hLCy89t51wRUXvMZGKqgF7DxxjKPPrn2wxDBbyYLCgN4/Kigqa9v6Ck8ffwFo37znbH/4GsxddwpJ3Xcf3v/rlAc+vkCcysek3rQzLjIgP11r6268rXFGZ9/m+7VvOGGIAcBwf0bo6QqEQxw69ghMM995kIpXVJDviZzzHdl9zZsRXpFcyvQ0W8mCooGe4/Leup+acuSQSyd4hokhFFX/49cd6n993SKIvhTyRic9f6gbI5DIz4scxBhc4/TYdnX0ui69aw66GzUS6J7QNxDEO1dXVhENhMpksLbEY1dXVACTa28443sWbzDYjon+yxXAq5PVf52CooOcYh7KyCLGOdjo6Oqjsc3zP3JGPfumrZ5xXIU9kctBvWhmWurCP8oChI+3i8515U7luw8ZhnM1w9vwFdLa3YYwhFovRfvItamfNPePIrGupCDrUhXVTKYbBQh4UFvT8Pj+hUIjOzk6CwSChYAiAy9bdRO2sc9ny4D/wO3/25bznKOSJTA7q25Nh8RlDfTQMhn6HFoZr4YpVHDvwItFolLKyMt5qPkzdwotx3VNL3ay1YKA+Gh5yrFwK0xPysu7AP8PrNmzksnU3seTqtb2TSk8XDAYJhUK0tbWRc3MkuoeELli5ir0//dEZ80eyrqU8YBTyRCY4hQMZtsXRED5jyA4zG7yy+9nuP8+wb/sWwHuHuvRd17F/+5O8/NMf8a71t5LJZHirpYVU2qvBn7VeKFkcDRX7pUxbxQx51dXVGGPYsfk7bP+3B3sfL6uqoayyuvdzhTyRycPYYrz9k2nnJ80d7GlJEjCFbds8HDk3RzweJ5VKUV5eQTBSxvKzwrxnbsXQT5aCxdM5Hnq5FWshMIy9MsALersaNpPoiHP5uptY9M5380bz67x5YC+1Z53NK7ufpqyqlqs+8une52RcizHwqYtqqAqq50BkIlM4kBFJ5Vw2HWijNZ0jOEClvdGwWDq7ukjnLOm2Fj5xUS0Xnn9eUa8hxQ15nV2dtLe3U1tb2zv/oIdrLRlrWV6nkCcyGWhYQUYk5HNYM7cCxxjSbv9LG0fFQiAUoSwS5pcP3897f3MNjz32WHGvIVw5q4yaoI/MAMtTh6O8rCxv/kEP2x0MaoI+rpxVNtomi8g4UDiQEZtXGeCaOeVFDwjWWtKuxTGG35xXxcP3f5X3vve9/PEf/zF/9Ed/REdHR1GuI8UOeaZ3/kFbaxsWm/ezXDO3gpBPv3JEJgMNK8ioNbYk2XakE9faUXdP93Q/O8ZwzZxy6uvCvV97/PHH+V//638xY8YM7r//fpYtW1aE1gt0/wybvZ9h0BndMFE6k+bkyZOUlZURjHjh8Zq5+T9LEZnYFONl1OrrwtxwflVv93RmBO9Abffzerqfbzi/6oybyQ033MDWrVupqanh/e9/P/fffz+uqw18iqG+Lsw1c8txHEPa2gE3ZSpEMBCkvKKCjDW4uayCgcgkpHAgRTGvMsD6RdUsrwtjDKStJZVzyQ0ylm2tJdd9XNp6M9mX14VZv6iaeZWBfp9z3nnn8YMf/IANGzZw991387GPfYzjx4+P5UubNooZ8gLhCG5nK//115/jrFRsjFosImNFwwpSdPF0jv2xFI2xJJ0Z7wZjjMl7N+oY0/t4ecBbc784GhrWErft27fzx3/8x/zd3/0da9asKfqKiekqlXN5+lgXjbGUt3ujBb9jcOi/1LK1FhevwBGmp4ZCiCVlGd639jeZNWsWjz/+OIFA/4FPRCYehQMZMzlraUnmOJHIcjyRoyvrkrUWf/fGOzMjPmZE/NSFfSMuitPe3k5lZeXQB8qwFSPk7d69m+uvv54NGzZw5513luqliMgwKRzIpNdzc5KxMdqQ9/Wvf52//Mu/5Dvf+Q7XXnttCV6BiAyXwoGIjClrLZs2beLw4cN89rOfpaamptRNEpEhKByIiIhIHq1WEBERkTwKByIiIpLHX+oGiBTd3r2wcyfMmwetrbB+falbJCIyqSgcyNQSj8PGjbBlCxw+DE884T3e0OD9/cIL0LOkbudOaGuDpiYvQFRVlaTJIiITjYYVZGrZsQPmz/du/AC33eYFg+pqWLcOolHYtMkLDtu3e48pGIiI5FE4kKmluhqWLYPVq72QcPiwFwBWr/a+/tprcOmlXniIx73gcN99pWyxiMiEo3AgU8vq1RCLeTf9hgZvyKDHzp1QXw9Ll3qfn3eeFxzOO8/rTZCxtXcv3H+/93PR91tkQtOcA5l6+ivTu3evN79g/Xrv457eA4CaGm/iooydgeaCbNrk9fBs337q59bf/BARGVfqOZCp7/Bh2LABvvtdWLvWCwI9vQcNDd5NSCsaxlZ/c0Hice/j1au9Hp7Dh/ufHyIi404VEkVk7O3c6fXY3Hab9/nhw15YAC8AvPbamb0Et98ON998KsiJyLhRz4FIP6y1pNPpUjdj6hhsLsj69V4vwuHDpx47fX6IiIwr9RyI9CMej/Nbv/VbfPnLX2bNmjUjOkfPbobHE1lOJHJ0Zl1y1uIzhnK/w4yIj5mj3LJ6UusZQli92puoCF7Pwt69p1aZ7N2rgCBSAgoHIv2IxWJ87nOfY+vWrXzyk5/k//yf/0MoFCroufF0jv2xFI2xJJ0Zi2stjjG4ff6r9XzuGEN5wFAfDbM4GqIq6BurlzTxxOPeXATw/r73Xi8UfOQjp6pb3nnnqWWoIhPEdAj+CgciA7DW8i//8i/cddddLFy4kK9//etceOGFAx6fyrk8fayLxliKnLVgwe8YHMD08wvCWosLZF0LBnzGUB8NceWsMkI+jfiJTDTTKfgrHIgM4aWXXuL3f//3ef311/mrv/orPvaxj51xs29qz/Bkcwdt6RwOBr/pPxAMxFpL1oKLpSboY83cCuZVBor9UkRkBKZj8Fc4EClAIpHgS1/6Eps2bWLdunX83d/9HdXV1QA0tiTZdqQT11oCxuCMohvRtZZM97uOa+aUU18XLtZLmFRO/7U0nKAlUkzTNfgrHIgMQ0NDAxs3bqSyspL777+f0IKlbGv2gkHQMUW5iVlrSbvdAWHu9A0IqVSKEydOUFlZ2RvERMbTdA7+k6N/Q2SCWLduHU899RSzZ8/mD//ibv7r1ZaiBgPw3pUEHW/cctuRTpraM0U572QTCoV44oknuPjii9m6dWupmyPTTGNL0gv+riU4ymAA3lyEoDG4rmVbcyeNLckitXRsqOdAZAQ6Uxm+9vPXSPtCkE1TXV2NzynuhCNrLWnrdUWuX1Q9acYqi8lay+/93u+xa9cutm7dypw5c844ZjrMHJfx1dSe4fFDcS8YFDH4Q5+eQcdww/lVE3aIQeFAZAR+0tzBnpYk5LLE29qw1lJVVUU4VNyuwp6uyOV1Yd4zt6Ko554sWltbWbNmDeeccw7//u//TiDg/TKdTjPHZfykci7fPdBGWzpH0BQ3GPSYDMFf4UBkmOLpHA+93Iq1EHAMrnWJt8VJppKUlZVRWVmJoXi/UDKuxRj41EU10/amtnv3bq6//npuueUWbv/fX5x2M8dl/PQE/9HOMRjKRA/++p8hMkz7u29K/u7fG45xqKmppqqqikQiQUtLC9lctmjX8xuv63x/LFW0c042K1eu5Itf/CI/3Plz/t/zR9jTksRaCBpDyOfgG+QdnjEGX/dxQWOwFva0JNl0oG3azueQ/sXTORpjKRzGNhiA16vlYGiMpYinc2N6rZFQOBAZhpy1NMaSYE9/l2ooi5RRF40C0NLSQleiCxh9x5wxBiw0xpLeO+Vp6h03/C6/c9c/0Z4Fn7UERjAWbIwh4BgCxtCazvH4ofiEnxgm4+f04D/WJnLwVzgQGYaWZI7OjMXv9P/bw+8PUFdXRyQSIR6P09rWhmvdUV/X7xg6M97Eu+mosSXJT450EYqUkU12EW9rxY4ieE22meMy9gYO/mNnIgd/hQORYTieyHoT3Ab4+iu7n2X/9if51fYfU1NTQzqd5tv/+zae/cG/jeq6Dt7Y+YlE8YYrJoum9kzvWvOQY6iuriaTzdLR0TGq82rJqPQ1WPB/Zfez7Nu+hV0Nm3sfe/iuz+V9PlITNfgrHIgMw4lEDmeA8e3Y0dcpq6xm9oWXsOORbxEOhamrq2PWhYs58uoBOjo7Rvxu13Rf83hiYv0CGWupnMuTzR15tSSCgSAVFRV0dnaSSo2uO7ZvQNja3EEqN/peHpmcBgr+p/+/7jHnbUtoOdrEaE3U4K9wIDIMnVk3b6lcX7FjzcxedAn7djzJwhWrAPA5Pi677gOcPX8BnR0dnDx5knh7nI7ODjq7OkkkE6TSKTLZDDk31z0E0f/5XWvpyk6vm9fTx7poS+cInBbIysvKCIVCtMXbyLmjC0zGnJqD8PSxrtE2WSapgYJ/f/+vAZZc9ZvUzZ436utO1ODvL3UDRCaTwcYFL1jp/eLY+9MfsfaWz/c+bjAsXvVuKqJR2mJv8dx/PIa1lpW//ZHePQRSnR3s3fafAFz2gY/hOI73i8pxvI8dBycY5sArzaSfP0RtbS3RaLT3TyQSmXL7Dww+c9wbXmhpaaGttZXaaHRUy0cdY3AsNMZSvH1mZNouGZ3OTgX//H9HA/2/Bjh/+RV0dnWS6uxg37YGAK76yKfzjtnxyLeIzpoLwJKr1/Z77YkY/BUORIZhqAp7iY44R3/9Yu8vFICjv36x95fCmy/vxWTTlFfVcvbMs7FYXNdl7/7dmEyaSFU1lZWVuK7b+8e6LplcDp/j51cv7OHv/v6LZ1w3FAqdERj6+1NbW8vixYtxnInfadgzczw4wPfcMQ411dXETp6ko6ODyorKUV3PbyDdPXN81TllozqXTD6DBf/T/1+71uXVvc8zf8UqOjo6aNrzLF3xk5RV1eY976EvfJqPfumrRCqq+Kff/9CA4QAgO8EmJCociAxDud8ZdP3zyaPNRGedO+DXl1y9lkR7G4mOdsDrVfA5Ppa9531kujpJdLRTFun/xpR2Lb/7kZu4/7aPEIvFOHnyJLFYLO/PyZMnaWlpIRaL8corr/Q+nk6ne8/zq1/9isrK026ke/fCzp0wbx60tsL69YV/U8ZA3szxAVaGAAS65x+0t7cTDAQJhUIjvqYxBlzvupefHVGp5WlmsJ93z//rnJujq6uLrq4uUqkk4XCY8vJyzl77O+xq2Nz7/xrg6IEXiVRU9X78h19/bNDr+yfYvzeFA5FhmBHx4VqLHWC5U/i0d6/7tm8Z9N1Coay1WGuZGfERiYSZM2dOv/sMDPTcRCLRGxQikUj+AfE4bNwIW7bA4cPwxBPe4w1eNykvvAB33nnqsepqL0zcdlu/13r66afzeipGcsMeauZ4siNOor2Ny9bdRHlZGY/+9ReYu3gF7/7IJ0e1x0XfmeMzI/r1OJ0MFvwDZWXkcjneeustDIamPc/yznU3DtoDd+TAPmLHXufk0WYAvv8PX+Z3/uzL/R7rGEOZf2L15ulfv8gwzIz4vXr9QH+3oOjsc1l81Rp2NWwm0j3DuRhcvDAyYwQ3LGMMZWVllJWVMXfu3DMP2LED5s8/1XNw222nQsDq1dDUBJs2nfr4ttugrc0LEvPn553KWstNN92U91hZWdkZQxv9DXfU1dX1fnw84fZuk9tXz8zx6Ky5/PPtn+GydTcBhvOXruCtY0dHPf/AwevePZHIKhxMM/0F/2wuS2dnJ24wwsLLV3Ng5xaq62aycOnKIYfmEh3t3u+ARd7vgCMH9nH0wIu9n/foG/wnEv3rFxmGurCP8oChI+3i8/V/A7puw8aiXzfrWiqCDnXhMfgFUl0Ny5Z5N3/wbvrr1p36+muvwc03e0HghRfgwx+Gq6/OP6abMYaf/exn/Q539Hz8xhtv8OKLL/Y+ls2euYTrXRtu5+K1H6QznTo1IdMYjhz6NQtXvIOfPf4dzlt2OZlsBsdxWHLVb3Jg1//01j8Y6fwDb+Y4HE/kWDyiM8hk1Tf4u9kMnZ2dJJNJfD4fFRUVfPBP/8+wJv1GZ83NG2KMVFYTO/b6GeFgNMF/LE2s1ohMcN6mPWGeebMLa+24rBCw1tswqD4aHptx8NWrYfv2U8MI1dWnegR27oT6eli61Bt+WLbM+/j2273nLV2adypjDPPmzWPevMKWeFlraW9vPyNAvF59PqlAAOu6uNYll8uRcV3OXrSE9ng7v9z2BFd+/PdpaWkBoO2tt6hbcDEALW++wTOPfRu/38+7PvoZfD4fYNi3fQuJ9jaOHPAmiPadNNrXRJw5LmOvLuzDl0vTkcmRaI/j9/moqqryVgKNoCdq4cpV/LxPkaSTx5pZ2M+/uTEN/qOgcCAyTIujIZ47niBrITDM3xmv7H6WV3Y/S6IjTnTW3N75CAM9DpC1XihZHB35ZLsh9cwp6GvvXm/4YP167+Nf/hLe9z4vODzyiDc34bRwMFzGGKqqqqiqquK8887rffwHr8X5dWuacD/zFbo64rzV9Cor3r22ezWH5UjjLi668j24rstru5+mszVGsLySE2+9hc/no6XpIKl0imXXXc/id13H339sDX/xw+cGbNdEmzkuY8day44dO7jvvvvIzL2Eyz96K1XV1UTC4YJDQX//fyMVVVy+7qbeiYprb/mz3gmKfa89psF/FBQORIapKuijPhpiT0sS1zKs3dsuWLmq33esAz3uWouLZXk0PL5r7w8fhg0bvDkIX/uaFx7e/35v7sHSpadCwxgZ7Bdla/fMcb/P3zvxIxQMUl5WDsAVv/VBduWydHXEqa2pJZ1Jk4if5FfP7WRO/eU4jkOwvIJXG3dz7sVLCQQCZ9wEJtrMcSk+ay0//vGPue+++/jlL3/JsmXL+IN17+bVsog372AYvQUD/f8dajLyuAT/EVI4EBmBK2eVcag9Q2s6R5Cx2ajFdu/3XhP0ceWscV53P38+PPvsmY/3s0JhLAw2c7zQFSEGQygUIhQKsfxd17HsXWvJZDKk02mS7XEqzplLLBbzKiQGAgSDQS8o+AITbua4jI2NGzdy8cUX873vfY/f+I3fwBjDT5o7RhT8h6tkwb9ACgciIxDyOayZW8Hjh+KkXUvQKW5AsNaSdi2OY1gzt4KQb3rdrAZbMjrSFSEGb1+G//ra/8cNX7ibmTNnks1mSafTZDIZurq6cF2XUHkl/3zfP/HfoRxXXHEFV1xxBWedddZYvEwpIWMMmzdvZvHi/KmnUz74F8hYq8E1kZFqbEmyrbkzb2Og0eoNBsZwzdxy6uvCRWjp5HI8kWXTgTZ8ZuiqlP3pGec9vZTtvu1bgIG6ey3pbI5MLseb//ktnv7xf/L6668DsGDBgt6gcMUVVzBv3rwpV65aTmlqz/D4oTiuW7z/1z36Bv8bzq9iXmWgaOcuJoUDkVFqbEn2bikcMP3tA1A4t/sdhWMM18yZnsEAvAqJ33zpJB1pd0S9Jv2Fg1d2e8MkF6xcxdEDLxKuqCQ6O7+aZSrnUhF0+MzFtfiM4dixYzz33HO9f15++WUAzj777N6g8I53vIO3ve1tk6IktRRuugd/hQORImhqz7C1uYPWdA4Hg98MrzvSWkvWgovX1bhmbsWEfUcxXp59o4tn3uwiOMAW2QN5Zfez3eEgzuXrbmLJ1WuJHX2df/r9D/Uek+xs56+3vZj3PGstaWt559llA+6t0Nrayq5du3rDwi9/+Uuy2SxVVVVcfvnlvYHh0ksvJRCY3j+/MTOOpb6nc/BXOBApklTO5eljXTR2bxiE9crxOvQfFKy1uHjrnDE9NRRCXDmrbNrNMehPPJ3joZdbsRYCg+yvUCwZ12IMfOqimoIniCUSCfbs2dMbFn7xi1/Q1dVFKBRixYoVvWHhqquumrLDEDnrlZs+nshyIpGjM+uSsxafMZT7HWZEfMyM+L06AqP9HsTjcOON+aW+b7vNW0Uzf75Xr+POO73j7r7b+7iqaujzDmK6Bn+FA5Eii6dz7I+laIwl6czY3mJJbp//ao4xvY+XB7zCSoujoQk5a7mUemaOj/Zd21B63tUtrwvznrkVIz5PNptl//79eUMRsViMI0eOTLlwcPq/c7f7XfHp/857Hi/Kv/OGBvjhD72KnfPmeYEgHvceW7/eW377xS+e2i+kx+rV/dfyKNB0DP4KByJjpOcd1YlEluOJHF1Zl6y1+Ls3WZkZ8TGjWO+opqhUzmXTgTZv5vgwhxcK1TOcUBP0sX5RdVF/eVtrefXVV1m4cOGUCQclvVHu3Jm/6Vff/T02bfJKfd95Z/7jDQ39lvoeiekU/BUORGRCm7IzxyfYNtmFaGrP8GRzB22l7GK/+26vjDec2hysx+23wx/+YX5geP/7Rz20cLrpEPwVDkRkwptyM8cLHTuH/h8rgQk7Oa/vDqL33+891tOzsGEDPPjgyM89jU2OwQ8Rmdbq68JcM7ccxzGkrc3rxh0Jt3sowXFKtKSs7zbZ4N3M4nHv456tsQ8f7v+xEugNZ64lWIT5H44xBI3BdS3bmjtpbEn2e1zPdsaDuuoqr5x3Q4M3rNATDOJxqKkZVTunM/UciMikMWVmjhc6dt6jv8c4tavl//zP/3D55ZePSSXHUgzr5HI5GhoaWLRoERdddNGUma8xmajnQEQmjXmVAdYvqmZ5XRhjIG0tqZy3dG6g9znWWnLdx6Wtt1xxeV2Y9YuqS7ekbPVqiMW8d7sNDV6vQI/16713vX17Cfp7rFsqleIzn/kM9fX1rF69ms9//vNs3ryZw4cPD/2uewipnMuTzR1FHc7pyxhD0PEm9G1t7qAzmebhhx9m9erV/MEf/AHHjh0r6vWkcOo5EJFJacrNHO9v7HzevIHH0/sYq0qO47WUNGddEuksv9r6H2z52l/x3ve+lz/6oz/i0ksvHbNryuAUDkRkUpsyM8fjcW8uAnh/33tv/48VoBiVHMejCJVrXRJdXXR2deELhAj4fby3NsWyiy4ck+tJ4RQORESmuEQiwfPPP8/Pf/7zQSs5rly5kooKrwjUSMtXF8K1Ll2dnXQlElhriUQilEXKyDnOoOWrZfwoHIiITDMDVXJ0HIclS5ZwxTtWUf2+T+H6I4T9xZualnNzdHV10dXVBUBZWRllZWX4HG+Y5/SNr6R0FA5ERKa5nkqOPUHhpeY3+Y0/u4dcJo1jIBAIEAwGCQYCHHrh5yQ72km0t3HZupsAePiuz3HBylW9n58um8vS1dlFIpnAGNMbChyTHzxy1pKzsH5RNTMj/jF/3TIwffdFRKY5YwwXXHABF1xwAR//+MfZF0vyo8PtkA2QyWRIp9MkEgna3jxKJudScc5cfvTAV1h23fX4/X7mvG0JLUebzjhvNpels6OTZDKBcRwqKiqIRCJnhIIeDpC1lhOJrMJBiem7LyIieU4kcvgch2A4QiQcAbx5AicPHWD+kuU8vfmfmXPJMlpaWnAch3OXXcGR/XtIZ9IEAgGy2e5QkEri8/morKoiEolgGHyowBiDMXA8kWPxeLxQGZDCgYiI5OnMut1LQk/dzB3jcPE7rgLg1Z/vYO0tf0a0Nko6k6bzLcuMRYuJxWKkuzpp3PpDfD6Hd318A+FwOC8U7Nu+hXBFFResXNXvtV1r6cq6Y/r6ZGgqgiQiInlyg0xFS3TEOfrrF7lg5TsJBoNUlFfQefwoi5Yup66ujjcP7CPZ0QaA3+/PCwaJjjjbH/4GyY74oNfPaipcySkciIhInsFWCpw82kx01rlnPG4wBPwBLlv7AeZecBEAsViMrkQX4N3s9/30xyx9128NeX2/ViqUnMKBiIjkKfc7A1ZEDFdU5n2+b/sWlly9Nu8xn+NQXu5NPozH47S2tdF8YB8LV/Q/lNCX0128SkpLPwEREckzI+LDHWC/iujsc1l81Rp2NWxm3/YtzL7wkgHPU1VZRU11DelUiqZfvUjlzHMGvW7PLowzIxOwvPU0owmJIiKSZ2bEj2MMLtDfbfq6DRsLPlc4HOZn//FdgpW1PPdf/87h/S8QO9pEdNa5zF6UHyxcvBULM7SMseT0ExARkTx1YR/lAUNH2sXnG/34/7s+egsWb3vpN155iVkX1XPOhRedcVzWtVQEHerC6jkoNQ0riIhIHp/xdrDEMOxtn1/Z/Wz3n2fYt31L7+MGw/ED+zn2ciP7dz7Jq/sbyWTSvV+31oKB+mhYpZMnAJVPFhGRM4zlrow5N0drayvZbJaKigrKysrIumAMfOqimom5pfY0o54DERE5Q1XQR300hIvtLohUPD7HRzQapaysjPb2dlrb2shZS300pGAwQSgciIhIv66cVUZN0EdmgJULo2EwVFZUUlNbCz4/b71+kNCRF4t6DRk5hQMREelXyOewZm4FjjGk3eIHBGstxhegPBLh6LbH+crf/g3pdHroJ8qY05wDEREZVGNLkm3NnbjWEnQMpggTBq21pF2LYwzXzC3nkmo/juN0b76kCYmlpqWMIiIyqPq6MADbjnSStpYADFhBsRCutWSsxXEM18wp7z2/TBzqORARkYI0tWfY2txBazqHg8FvGNa7fGstWQsulpqgjzVzK5hXGRjDFstIKRyIiEjBUjmXp4910RhLebs3WvA7Bof+g4K1FhevwBGmp4ZCiCtnlRHyadrbRKVwICIiwxZP59gfS9EYS9KZ8SYrGmPylj06xvQ+Xh7wCist1nLFSUHhQERERixnLS3JHCcSWY4ncnRlXbLW4u/eXXFmxMeMiJ+6sE+VDycRhQMREZm49u6FnTth3jxobYX160vdomlBAz4iIjIxxeOwcSPcdhssXep9DrBpkxcY7r771LE7d0JDA9x//6njZMQUDkREZGLasQPmz/du/OCFhJ4b/+rV0NQEhw97f7Zvh3XrvJ6FqqrStXmKUDgQEZGJqboali3zgsD8+V4IqKryAsCmTd5QQ094iMe9noP77it1q6cEhQMREZmYVq+GWMy76Tc0eD0FPdav9wLB4cPe5+ed5/UcnHeeFxxkVFQhUUREJq4778z/vKHB61FYvdoLAk884X3cM/RQU+NNXByBnpUXxxNZTiRydGZdctbiM4Zyv8OMiI+Z02TlhVYriIjI5BGPe3MRwPv73nu9j++/3xtmeOGFMwPFUKc8rWaDa709H06v2dDz+HSo2aBwICIi05KqPQ5M4UBERKasnlvc6Tf7pvYMTzZ30KZ9Ivo1taKOiIhIt55g8LWvfY2XXnqp9/HGliSPH4rTls4RMIbACLahNt3PCxhDazrH44fiNLYki9r+UlI4EBGRKckYw5EjR/jBD37Ae9/7XjZt2sQv30qwrbkT17UEjRnV1tPgzUUIGoPrWrY1d06ZgKBhBRERmdKSySRf/vKX2fb8fn7nL+8nFI4QGkFvwWCstaRdi+MYbji/atIPMSgciIjIlJfKudz/i9fpcA25ZILqmhoC/uLewK21pK03B2H9oupJPUlx8rZcRESkQE8f68INl1MRDmMch1gsRldXF1C898fGnJqD8PSxrqKdtxQUDkREZEqLp3M0xlI4GAJ+P9FolLJIhHh7nNbWNlzrFu1ajjE4GBpjKeLpXNHOO94UDkREZErb313HwN89xcBgqKysoqamhnQmTUtLC+lMumjX8xuv2uL+WKpo5xxvCgciIjJl5aylMZYEe2Ydg3AoTF1dHT7H4eTJk3R2dWKLMMxgjAELjbGkV1xpElI4EBGRKaslmaMzY/E7Z65MeGX3s7y08yleeeYpysrKaG9v59tf/EOe+8/vjfq6fsfQmfH2apiMFA5ERGTKOp7IensinPZ47OjrlFVWM/vCS9j5yENUVlRSW1vLzPMX0fzKr0Y9zODgrV44kciO6jylonAgIiJT1olEDsecWdMgdqyZ2YsuYd+OJ1m4YhUAoWCIy677HaKzz+VkLEZHZ8eIhxlM9zWPJ9RzICIiMqF0Zt283RV7XLDSCwR7f/ojlly9tvdxn+OjfvU1lFdU0NbyFjsf+RY7HvlW3nMfvutzHD3wIkcPvMiPH/zKgNd2raUrW7yVEONJ4UBERKaswSYEJjriHP31i71BAeDor1+kbvY8KsoriB16ma546xnPix17nW9t/CQ//sbfc/XHNgx6/ewknZDoL3UDRERExopvkBLJJ482E5117oBfX3r1dSTb4yQ62vMev/qjt+T1NgzGX8QSzeNJ4UBERKascr8z4OZK4YrKvM/3bd9S0E2/+Vd7AUi0twFw2bqb+j3OMYYy/+TsoFc4EBGRKWtGxIdrLbafOgfR2eey+Ko17GrYTKR75UIhrtuwsffjv1+/liXvuo5IRVXeMdZarLXMjPhG/yJKQOFARESmrJkRP44xuEB/t+m+N/pC7Nu+heZf7e19XriikpNHm4ksyg8WLl4YmRGZnLfZydlqERGRAtSFfZQHDB1pF59v9OP/0VnnEu7TS5DsaGf2ojN7HLKupSLoUBeenD0Hk3MwREREpAA+Y6iPhsF4Xf3D8cruZ7v/PMO+7VsAmL3oEpIdcfZt38KPH/wKn7z3m2c8z1oLBuqj4UEnRE5kxg73uyUiIjKJxNM5Hnq5FWsh0E8Z5WLLuBZj4FMX1VAVVM+BiIjIhFMV9FEfDeFi+y2IVEyutbhY6qOhSRsMQOFARESmgStnlVET9JHpXkUwFqy1ZKylJujjylllY3KN8aJwICIiU17I57BmbgWOMaTd4gcEay1p1+IYw5q5FYR8k/v2OrlbLyIiUqB5lQGumVNe9IDQNxhcM6eceZWBopy3lLSUUUREpo36ujAA2450kraWAAxYQbEQbvdQguN4waDn/JOdViuIiMi009SeYWtzB63pHA4GvzmzguJgrLVkLbh4cwzWzK2YEj0GPRQORERkWkrlXJ4+1kVjLOXt3mjB7xgc+g8K1lpcvAJHmJ4aCiGunFU26ecYnE7hQEREprV4Osf+WIrGWJLOjDcXwRiTt+zRMab38fKAV1hp8SRfrjgYhQMREREgZy0tyRwnElmOJ3J0ZV2y1uLv3l1xZsTHjIifurBv0lY+LJTCgYiIiOSZWoMkIiIiMmoKByIiIpJH4UBERETyKByIiIhIHoUDERERyaNwICIiInkUDkRERCSPwoGIiIjkUTgQERGRPAoHIiIikkfhQERERPIoHIiIiEgehQMRERHJo3AgIiIieRQOREREJI/CgYiIiORROBAREZE8CgciIiKSR+FARERE8igciIiISB6FAxEREcmjcCAiIiJ5FA5EREQkj8KBiIiI5FE4EBERkTwKByIiIpJH4UBERETyKByIiIhIHoUDERERyaNwICIiInkUDkRERCSPwoGIiIjkUTgQERGRPAoHIiIikkfhQERERPL8/0zD1Y4IysWcAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Graph with 17 vertices and 38 edges.\n",
+      " - Features dimensions: [7, 4]\n",
+      " - There are 0 isolated nodes.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = loader.load()\n",
+    "describe_data(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Transform configuration for graph2hypergraph/node_centrality_lifting:\n",
+      "\n",
+      "{'transform_type': 'lifting',\n",
+      " 'transform_name': 'HypergraphNodeCentralityLifting',\n",
+      " 'network_type': 'weighted',\n",
+      " 'alpha': 0.85,\n",
+      " 'th_percentile': 0.05,\n",
+      " 'n_most_influential': 2,\n",
+      " 'do_weight_hyperedge_influence': False,\n",
+      " 'do_hyperedge_node_assignment_feature_lifting_passthrough': False,\n",
+      " 'max_iter': 100,\n",
+      " 'tol': 1e-06,\n",
+      " 'feature_lifting': 'ProjectionSum'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define transformation type and id\n",
+    "transform_type = \"liftings\"\n",
+    "# If the transform is a topological lifting, it should include both the type of the lifting and the identifier\n",
+    "transform_id = \"graph2hypergraph/node_centrality_lifting\"\n",
+    "\n",
+    "# Read yaml file\n",
+    "transform_config = {\n",
+    "    \"lifting\": load_transform_config(transform_type, transform_id)\n",
+    "    # other transforms (e.g. data manipulations, feature liftings) can be added here\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Dataset contains 188 samples.\n",
+      "\n",
+      "Providing more details about sample 0/188:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Done!\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - Hypergraph with 17 vertices and 4 hyperedges.\n",
+      " - The nodes have feature dimensions 7.\n",
+      " - The hyperedges have feature dimensions 7.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "transform_config[\"lifting\"][\"n_most_influential\"] = 1\n",
+    "transform_config[\"lifting\"][\"th_percentile\"] = 0.2\n",
+    "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n",
+    "describe_data(lifted_dataset)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".topox_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}