diff --git a/configs/datasets/UniProt.yaml b/configs/datasets/UniProt.yaml
new file mode 100644
index 00000000..0480e5ca
--- /dev/null
+++ b/configs/datasets/UniProt.yaml
@@ -0,0 +1,20 @@
+data_domain: pointcloud
+data_type: UniProt
+data_name: UniProt
+data_dir: datasets/${data_domain}/${data_type}
+#data_split_dir: ${oc.env:PROJECT_ROOT}/datasets/data_splits/${data_name}
+
+# Some parameters to do the query
+query: "length:[95 TO 155]" # number of residues per protein
+format: "tsv"
+fields: "accession,length"
+size: 20 # number of proteins to load
+
+# Dataset parameters
+num_features: 20
+num_classes: 1
+task: regression
+loss_type: mse
+monitor_metric: mae
+task_level: graph
+
diff --git a/configs/datasets/manual_prot_pointcloud.yaml b/configs/datasets/manual_prot_pointcloud.yaml
new file mode 100755
index 00000000..805a6ef2
--- /dev/null
+++ b/configs/datasets/manual_prot_pointcloud.yaml
@@ -0,0 +1,12 @@
+data_domain: pointcloud
+data_type: toy_dataset
+data_name: manual_prot
+data_dir: datasets/${data_domain}/${data_type}
+
+# Dataset parameters
+num_features: 1
+num_classes: 2
+task: classification
+loss_type: cross_entropy
+monitor_metric: accuracy
+task_level: node
diff --git a/configs/models/graph/graphsage.yaml b/configs/models/graph/graphsage.yaml
new file mode 100644
index 00000000..cd119b97
--- /dev/null
+++ b/configs/models/graph/graphsage.yaml
@@ -0,0 +1,6 @@
+in_channels_0: null # This will be set by the dataset
+in_channels_1: null # This will be set by the dataset
+in_channels_2: null # This will be set by the dataset
+hidden_channels: 32
+out_channels: null # This will be set by the dataset
+n_layers: 2
\ No newline at end of file
diff --git a/configs/transforms/liftings/pointcloud2graph/knn_lifting.yaml b/configs/transforms/liftings/pointcloud2graph/knn_lifting.yaml
new file mode 100644
index 00000000..c2a49fb9
--- /dev/null
+++ b/configs/transforms/liftings/pointcloud2graph/knn_lifting.yaml
@@ -0,0 +1,8 @@
+transform_type: 'lifting'
+transform_name: "PointCloudKNNLifting"
+max_cell_length: null
+preserve_edge_attr: False
+feature_lifting: ProjectionSum
+
+k_value: 10
+loop: False
\ No newline at end of file
diff --git a/modules/data/load/loaders.py b/modules/data/load/loaders.py
index 8ccafb11..d38bfdfb 100755
--- a/modules/data/load/loaders.py
+++ b/modules/data/load/loaders.py
@@ -1,8 +1,12 @@
 import os
+import random
 
 import numpy as np
+import requests
 import rootutils
+import torch
 import torch_geometric
+from Bio import PDB
 from omegaconf import DictConfig
 
 from modules.data.load.base import AbstractLoader
@@ -12,6 +16,7 @@
     load_cell_complex_dataset,
     load_hypergraph_pickle_dataset,
     load_manual_graph,
+    load_manual_prot_pointcloud,
     load_simplicial_dataset,
 )
 
@@ -204,3 +209,294 @@ def load(
             torch_geometric.data.Dataset object containing the loaded data.
         """
         return load_hypergraph_pickle_dataset(self.parameters)
+
+
+class PointCloudLoader(AbstractLoader):
+
+    def __init__(self, parameters: DictConfig):
+        super().__init__(parameters)
+        self.parameters = parameters
+
+#######################################################################
+############## Auxiliar functions for loading UniProt data ############
+#######################################################################
+
+    def fetch_uniprot_ids(self) -> list[dict]:
+        r"""Fetch UniProt IDs by its API under the parameters specified in the configuration file."""
+        query_url = "https://rest.uniprot.org/uniprotkb/search"
+        params = {
+            "query": self.parameters.query,
+            "format": self.parameters.format,
+            "fields": self.parameters.fields,
+            "size": self.parameters.size
+        }
+
+        response = requests.get(query_url, params=params)
+        if response.status_code != 200:
+            print(f"Failed to fetch data from UniProt. Status code: {response.status_code}")
+            return []
+
+        data = response.text.strip().split("\n")[1:]
+        proteins = [{"uniprot_id": row.split("\t")[0], "sequence_length": int(row.split("\t")[1])} for row in data]
+
+        # Ensure we have at least the required proteins to sample from
+        if len(proteins) >= self.parameters.size:
+            sampled_proteins = random.sample(proteins, self.parameters.size)
+        else:
+            print(f"Only found {len(proteins)} proteins within the specified length range. Returning all available proteins.")
+            sampled_proteins = proteins
+
+        # save sampled proteins to a csv file
+        # create directory if not exist
+        os.makedirs(self.data_dir, exist_ok=True)
+        with open(self.data_dir + "/uniprot_ids.csv", "w") as file:
+            for protein in sampled_proteins:
+                file.write(f"{protein}\n")
+
+        return sampled_proteins
+
+    def fetch_protein_mass(
+            self, uniprot_id : str
+        ) -> float:
+        r"""Returns the mass of a protein given its UniProt ID.
+        This will be used as our target variable.
+
+        Parameters
+        ----------
+        uniprot_id : str
+            The UniProt ID of the protein.
+
+        Returns
+        -------
+        float
+            The mass of the protein.
+        """
+        url = f"https://www.ebi.ac.uk/proteins/api/proteins/{uniprot_id}"
+        response = requests.get(url, headers={"Accept": "application/json"})
+        if response.status_code == 200:
+            data = response.json()
+            return data.get("sequence", {}).get("mass")
+        return None
+
+    def fetch_alphafold_structure(
+            self, uniprot_id : str
+        ) -> str:
+        r"""Fetches the AlphaFold structure for a given UniProt ID.
+        Not all the proteins have a structure available.
+        This ones will be descarded.
+
+        Parameters
+        ----------
+        uniprot_id : str
+            The UniProt ID of the protein.
+
+        Returns
+        -------
+        str
+            The path to the downloaded PDB file.
+        """
+        pdb_dir = self.data_dir + "/pdbs"
+        os.makedirs(pdb_dir, exist_ok=True)
+        file_path = os.path.join(pdb_dir, f"{uniprot_id}.pdb")
+
+        if os.path.exists(file_path):
+            print(f"PDB file for {uniprot_id} already exists.")
+        else:
+            url = f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.pdb"
+            response = requests.get(url)
+            if response.status_code == 200:
+                with open(file_path, "w") as file:
+                    file.write(response.text)
+                print(f"PDB file for {uniprot_id} downloaded successfully.")
+            else:
+                print(f"Failed to fetch the structure for {uniprot_id}. Status code: {response.status_code}")
+                return None
+        return file_path
+
+    def parse_pdb(
+            self, file_path : str
+        ) -> PDB.Structure:
+        r"""Parse a PDB file and return a BioPython structure object.
+
+        Parameters
+        ----------
+        file_path : str
+            The path to the PDB file.
+
+        Returns
+        -------
+        PDB.Structure
+            The BioPython structure object.
+        """
+
+        return PDB.PDBParser(QUIET=True).get_structure("alphafold_structure", file_path)
+
+    def residue_mapping(
+            self, uniprot_ids : list[str]
+        ) -> dict:
+        r"""Create a mapping of residue types to unique integers.
+        Each residue type will be represented as a one unique integer.
+        There are 20 standard amino acids, so we will have 20 unique integers (at maximum).
+
+        Parameters
+        ----------
+        uniprot_ids : list[str]
+            The list of UniProt IDs to process.
+
+        Returns
+        -------
+        dict
+            The mapping of residue types to unique integers.
+        """
+
+        residue_map = {}
+        residue_counter = 0
+
+        # First pass: determine unique residue types
+        for uniprot_id in uniprot_ids:
+            pdb_file = self.fetch_alphafold_structure(uniprot_id)
+            if pdb_file:
+                structure = self.parse_pdb(pdb_file)
+                residues = [residue for model in structure for chain in model for residue in chain]
+                for residue in residues:
+                    residue_type = residue.get_resname()
+                    if residue_type not in residue_map:
+                        residue_map[residue_type] = residue_counter
+                        residue_counter += 1
+        return residue_map
+
+    def calculate_residue_ca_distances_and_vectors(
+            self, structure : PDB.Structure
+        ):
+        r"""Calculate the distances between the alpha carbon atoms of the residues.
+        Also, calculate the vectors between the alpha carbon and beta carbon atoms of each residue.
+
+        Parameters
+        ----------
+        structure : PDB.Structure
+            The BioPython structure object.
+
+        Returns
+        -------
+        list
+            The list of residues.
+        dict
+            The dictionary of alpha carbon coordinates.
+        dict
+            The dictionary of beta carbon vectors.
+        np.ndarray
+            The matrix of distances between the residues.
+        """
+
+        residues = [residue for model in structure for chain in model for residue in chain]
+        ca_coordinates = {}
+        cb_vectors = {}
+        residue_keys = []
+
+        for residue in residues:
+            if "CA" in residue:
+                ca_coord = residue["CA"].get_coord()
+                residue_type = residue.get_resname()
+                residue_number = residue.get_id()[1]
+                key = f"{residue_type}_{residue_number}"
+                ca_coordinates[key] = ca_coord
+                cb_vectors[key] = residue["CB"].get_coord() - ca_coord if "CB" in residue else None
+                residue_keys.append(key)
+
+        return ca_coordinates, cb_vectors, residue_keys
+
+    def save_point_cloud(self, ca_coordinates, cb_vectors, file_path):
+        data = []
+        for key, ca_coord in ca_coordinates.items():
+            cb_vector = cb_vectors[key] if key in cb_vectors else np.zeros(3)
+            if cb_vector is None:
+                cb_vector = np.zeros(3)
+            data.append({
+                "residue_id": key,
+                "x": ca_coord[0],
+                "y": ca_coord[1],
+                "z": ca_coord[2],
+                "cb_x": cb_vector[0],
+                "cb_y": cb_vector[1],
+                "cb_z": cb_vector[2]
+            })
+
+        # Save data
+        if not os.path.exists(os.path.dirname(file_path)):
+            os.makedirs(os.path.dirname(file_path))
+        with open(file_path, "w") as file:
+            file.write("residue_id,x,y,z,cb_x,cb_y,cb_z\n")
+            for row in data:
+                file.write(f"{row['residue_id']},{row['x']},{row['y']},{row['z']},{row['cb_x']},{row['cb_y']},{row['cb_z']}\n")
+
+
+    def load(
+        self,
+    ) -> torch_geometric.data.Dataset:
+        r"""Load point cloud dataset.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        torch_geometric.data.Dataset
+            torch_geometric.data.Dataset object containing the loaded data.
+        """
+
+        root_folder = rootutils.find_root()
+        root_data_dir = os.path.join(root_folder, self.parameters["data_dir"])
+
+        self.data_dir = os.path.join(root_data_dir, self.parameters["data_name"])
+        if self.parameters.data_name in ["UniProt"]:
+            datasets = []
+            protein_data = self.fetch_uniprot_ids()
+            uniprot_ids = [protein["uniprot_id"] for protein in protein_data]
+            residue_map = self.residue_mapping(uniprot_ids)
+
+            for uniprot_id in uniprot_ids:
+                pdb_file = self.fetch_alphafold_structure(uniprot_id)
+                y = self.fetch_protein_mass(uniprot_id)
+
+                if pdb_file and y:
+                    structure = self.parse_pdb(pdb_file)
+                    ca_coordinates, cb_vectors, residue_keys = self.calculate_residue_ca_distances_and_vectors(structure)
+                    point_cloud_file = os.path.join(self.data_dir, "point_cloud", f"{uniprot_id}.csv")
+                    self.save_point_cloud(ca_coordinates, cb_vectors, point_cloud_file)
+
+                    # Create one-hot residues
+                    one_hot_residues = []
+                    for res_id in residue_keys:
+                        res_type = res_id.split("_")[0]
+                        one_hot = torch.zeros(len(residue_map))
+                        one_hot[residue_map[res_type]] = 1
+                        one_hot_residues.append(one_hot)
+
+                    x = torch.stack(one_hot_residues)
+                    pos_np = np.array([ca_coordinates[res_id] for res_id in residue_keys])
+                    pos = torch.tensor(pos_np, dtype=torch.float)
+
+                    node_attr = [None if cb_vectors[res_id] is None else cb_vectors[res_id] for res_id in residue_keys]
+
+                    data = torch_geometric.data.Data(
+                        x=x,
+                        pos=pos,
+                        node_attr=node_attr,
+                        y=y,
+                        uniprot_id=uniprot_id
+                    )
+
+                    datasets.append(data)
+
+            dataset = CustomDataset(datasets, self.data_dir)
+
+        elif self.parameters.data_name in ["manual_prot"]:
+            data = load_manual_prot_pointcloud()
+            dataset = CustomDataset([data], self.data_dir)
+        else:
+            raise NotImplementedError(
+                f"Dataset {self.parameters.data_name} not implemented"
+            )
+        return dataset
+
diff --git a/modules/data/utils/utils.py b/modules/data/utils/utils.py
index 93ab5021..449e6d62 100755
--- a/modules/data/utils/utils.py
+++ b/modules/data/utils/utils.py
@@ -333,6 +333,246 @@ def load_manual_graph():
         y=torch.tensor(y),
     )
 
+def load_manual_prot_pointcloud():
+    """Create a manual graph for testing protein data.
+    The graph corresponds to the representation of the
+    protein with uniprotid: P0DJJ1"""
+    y = [2005]
+
+    x = torch.tensor([[1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.],
+    [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+        0., 0.]])
+
+    node_attr = [
+        [ 1.3890,  0.6190, -0.1820],
+        [-0.9270, -0.9870,  0.7330],
+        [-0.2270,  1.4300, -0.5240],
+        [ 1.2680,  0.3270,  0.8000],
+        [ 0.1190,  1.1460, -1.0170],
+        [ 0.4530, -0.8660, -1.1890],
+        [ 1.4150, -0.3400,  0.5030],
+        [ 0.2660,  1.5060, -0.0980],
+        [-0.2630,  0.4330, -1.4480],
+        [ 1.0150, -0.7640, -0.8760],
+        [ 1.0040,  0.6400,  0.9630],
+        [-0.7060,  1.3490, -0.2130],
+        [ 0.8990, -1.1560, -0.4560],
+        [-1.0300,  1.1430,  0.0910],
+        [ 0.9240, -1.0550,  0.6270],
+        [-1.0380,  0.6640, -0.9280]
+    ]
+
+    pos = [
+        [  7.5210,   0.0560,  -6.7320],
+        [  4.8200,   1.0530,  -4.2620],
+        [  6.1700,   4.2550,  -2.6770],
+        [  6.0640,   4.2840,   1.1930],
+        [  3.1770,   6.8050,   0.7350],
+        [  0.8630,   3.9710,  -0.5950],
+        [  1.4180,   1.9200,   2.6160],
+        [ -0.2780,   4.7140,   4.6930],
+        [ -3.6400,   3.8990,   3.0330],
+        [ -3.5740,   0.0960,   3.6640],
+        [ -3.8460,  -0.2580,   7.5040],
+        [ -7.6510,   0.2670,   7.8800],
+        [ -8.4770,  -3.4030,   7.2390],
+        [-11.1830,  -3.1590,   9.8940],
+        [-12.1290,  -6.7670,  10.4510],
+        [-15.7920,  -5.9970,  11.1970]
+    ]
+
+
+    return torch_geometric.data.Data(
+        x=x,
+        y=torch.tensor(y),
+        pos=torch.tensor(pos),
+        node_attr=torch.tensor(node_attr),
+    )
+
+
+def load_manual_prot():
+    """Create a manual graph for testing protein data.
+    The graph corresponds to the representation of the
+    protein with uniprotid: P0DJJ1
+    """
+
+    # Define the vertices
+    vertices = [i for i in range(16)]
+    y = [2005]
+
+    # Define the edges
+    edges = [
+            [0, 1],
+            [0, 2],
+            [1, 2],
+            [2, 3],
+            [2, 4],
+            [3, 4],
+            [3, 6],
+            [4, 5],
+            [4, 7],
+            [5, 6],
+            [5, 8],
+            [6, 7],
+            [6, 9],
+            [7, 8],
+            [8, 9],
+            [9, 10],
+            [10, 11],
+            [11, 12],
+            [11, 13],
+            [12, 13],
+            [12, 14],
+            [13, 14],
+            [13, 15],
+            [14, 15]
+        ]
+
+    node_attr = [
+        [ 1.3890,  0.6190, -0.1820],
+        [-0.9270, -0.9870,  0.7330],
+        [-0.2270,  1.4300, -0.5240],
+        [ 1.2680,  0.3270,  0.8000],
+        [ 0.1190,  1.1460, -1.0170],
+        [ 0.4530, -0.8660, -1.1890],
+        [ 1.4150, -0.3400,  0.5030],
+        [ 0.2660,  1.5060, -0.0980],
+        [-0.2630,  0.4330, -1.4480],
+        [ 1.0150, -0.7640, -0.8760],
+        [ 1.0040,  0.6400,  0.9630],
+        [-0.7060,  1.3490, -0.2130],
+        [ 0.8990, -1.1560, -0.4560],
+        [-1.0300,  1.1430,  0.0910],
+        [ 0.9240, -1.0550,  0.6270],
+        [-1.0380,  0.6640, -0.9280]
+    ]
+
+    pos = [
+        [  7.5210,   0.0560,  -6.7320],
+        [  4.8200,   1.0530,  -4.2620],
+        [  6.1700,   4.2550,  -2.6770],
+        [  6.0640,   4.2840,   1.1930],
+        [  3.1770,   6.8050,   0.7350],
+        [  0.8630,   3.9710,  -0.5950],
+        [  1.4180,   1.9200,   2.6160],
+        [ -0.2780,   4.7140,   4.6930],
+        [ -3.6400,   3.8990,   3.0330],
+        [ -3.5740,   0.0960,   3.6640],
+        [ -3.8460,  -0.2580,   7.5040],
+        [ -7.6510,   0.2670,   7.8800],
+        [ -8.4770,  -3.4030,   7.2390],
+        [-11.1830,  -3.1590,   9.8940],
+        [-12.1290,  -6.7670,  10.4510],
+        [-15.7920,  -5.9970,  11.1970]
+    ]
+
+    edge_attr = [
+        [3.7934558, 149.50481451169998],
+        [5.9916463, 73.61527190033692],
+        [3.8193624, 131.95556949748686],
+        [3.8715599, 95.81568896389793],
+        [5.2059865, 24.99968085162557],
+        [3.8600485, 97.01387095949963],
+        [5.403586, 28.039804503840163],
+        [3.892949, 83.4287695658028],
+        [5.6546497, 37.945642805792026],
+        [3.850344, 81.81569949178396],
+        [5.7831287, 58.674074660579485],
+        [3.872568, 94.49543679831403],
+        [5.4171343, 58.10693508314127],
+        [3.8370392, 72.06193427289432],
+        [3.8555577, 73.54177428094899],
+        [3.8658636, 97.62340401695313],
+        [3.8594074, 91.23113911037706],
+        [3.8160264, 152.7860125877105],
+        [5.316831, 18.30498413525865],
+        [3.7988148, 165.50490996053213],
+        [5.9135895, 41.512625041705014],
+        [3.771317, 152.5153703231938],
+        [5.56731, 42.83026706257669],
+        [3.816672, 161.06592876960846]
+        ]
+
+    # Create a graph
+    G = nx.Graph()
+    # Add vertices
+    G.add_nodes_from(vertices)
+    # Add edges
+    G.add_edges_from(edges)
+    G.to_undirected()
+    edge_list = torch.Tensor(list(G.edges())).T.long()
+
+    x = torch.tensor([[1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.],
+        [1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0.,
+         0., 0.]])
+
+    return torch_geometric.data.Data(
+        x=x,
+        edge_index=edge_list,
+        num_nodes=len(vertices),
+        y=torch.tensor(y),
+        edge_attr=torch.tensor(edge_attr),
+        node_attr=torch.tensor(node_attr),
+        pos=torch.tensor(pos)
+    )
 
 def get_Planetoid_pyg(cfg):
     r"""Loads Planetoid graph datasets from torch_geometric.
diff --git a/modules/models/graph/graphsage.py b/modules/models/graph/graphsage.py
new file mode 100644
index 00000000..b6e69777
--- /dev/null
+++ b/modules/models/graph/graphsage.py
@@ -0,0 +1,47 @@
+import torch
+from torch_geometric.nn import global_mean_pool
+from torch_geometric.nn.models import GraphSAGE
+
+
+class GraphSAGEModel(torch.nn.Module):
+    r"""A GraphSAGE model that performs graph classification.
+
+    Parameters
+    ----------
+    model_config : Dict | DictConfig
+        Model configuration.
+    dataset_config : Dict | DictConfig
+        Dataset configuration.
+    """
+
+    def __init__(self, model_config, dataset_config):
+        in_channels = (
+            dataset_config["num_features"]
+            if isinstance(dataset_config["num_features"], int)
+            else dataset_config["num_features"][0]
+        )
+        hidden_channels = model_config["hidden_channels"]
+        out_channels = dataset_config["num_classes"]
+        n_layers = model_config["n_layers"]
+        super().__init__()
+        self.base_model = GraphSAGE(
+            in_channels=in_channels,
+            hidden_channels=hidden_channels,
+            out_channels=out_channels,
+            num_layers=n_layers,
+        )
+
+    def forward(self, data):
+        r"""Forward pass of the model.
+
+        Parameters
+        ----------
+        data : torch_geometric.data.Data
+            Input data.
+        Returns
+        -------
+        torch.Tensor
+            Output tensor.
+        """
+        z = self.base_model(data.x, data.edge_index)
+        return torch.nn.functional.softmax(global_mean_pool(z, None))
diff --git a/modules/transforms/data_transform.py b/modules/transforms/data_transform.py
index 59253ecf..7eddaf1a 100755
--- a/modules/transforms/data_transform.py
+++ b/modules/transforms/data_transform.py
@@ -15,6 +15,9 @@
 from modules.transforms.liftings.graph2simplicial.clique_lifting import (
     SimplicialCliqueLifting,
 )
+from modules.transforms.liftings.pointcloud2graph.knn_lifting import (
+    PointCloudKNNLifting,
+)
 
 TRANSFORMS = {
     # Graph -> Hypergraph
@@ -23,6 +26,8 @@
     "SimplicialCliqueLifting": SimplicialCliqueLifting,
     # Graph -> Cell Complex
     "CellCycleLifting": CellCycleLifting,
+    # PointCloud -> Graph
+    "PointCloudKNNLifting": PointCloudKNNLifting,
     # Feature Liftings
     "ProjectionSum": ProjectionSum,
     # Data Manipulations
diff --git a/modules/transforms/liftings/lifting.py b/modules/transforms/liftings/lifting.py
index ddb72781..a833c81f 100644
--- a/modules/transforms/liftings/lifting.py
+++ b/modules/transforms/liftings/lifting.py
@@ -60,7 +60,7 @@ def forward(self, data: torch_geometric.data.Data) -> torch_geometric.data.Data:
         """
         initial_data = data.to_dict()
         lifted_topology = self.lift_topology(data)
-        lifted_topology = self.feature_lifting(lifted_topology)
+        # lifted_topology = self.feature_lifting(lifted_topology)
         return torch_geometric.data.Data(**initial_data, **lifted_topology)
 
 
diff --git a/modules/transforms/liftings/pointcloud2graph/knn_lifting.py b/modules/transforms/liftings/pointcloud2graph/knn_lifting.py
new file mode 100644
index 00000000..222cf278
--- /dev/null
+++ b/modules/transforms/liftings/pointcloud2graph/knn_lifting.py
@@ -0,0 +1,87 @@
+import numpy as np
+import torch
+from torch_geometric.data import Data
+from torch_geometric.nn import knn_graph
+
+from modules.transforms.liftings.pointcloud2graph.base import PointCloud2GraphLifting
+
+
+class PointCloudKNNLifting(PointCloud2GraphLifting):
+    r"""Lifts graphs to graph domain by considering k-nearest neighbors."""
+
+    def __init__(self, k_value=10, loop=False, **kwargs):
+        self.k = k_value
+        self.loop = loop
+
+    def calculate_vector_angle(self, v1, v2):
+        if v1 is None or v2 is None:
+            return None
+        v1 = torch.tensor(np.copy(v1), dtype=torch.float)
+        v2 = torch.tensor(np.copy(v2), dtype=torch.float)
+        norm_v1 = torch.linalg.norm(v1)
+        norm_v2 = torch.linalg.norm(v2)
+        if norm_v1 == 0 or norm_v2 == 0:
+            return 0.0
+        cos_theta = torch.dot(v1, v2) / (norm_v1 * norm_v2)
+        return torch.acos(torch.clamp(cos_theta, -1.0, 1.0)) * 180 / torch.pi
+
+
+    def lift_topology(self, data: Data, k=10):
+        """Lifts the topology of the graph.
+        Takes the point cloud data and lifts it to a graph domain by considering k-nearest neighbors
+        and sequential edges.
+        Moreover, as edge attributes, the distance and angle between the nodes are considered.
+
+        Parameters
+        ----------
+        data : Data
+            The input data containing the point cloud.
+        k : int
+            The number of nearest neighbors to consider.
+
+        Returns
+        -------
+        dict
+            The lifted topology.
+        """
+
+        coordinates = data["pos"]
+        cb_vectors = data["node_attr"]
+
+        # Sequential edges
+        seq_edge_index = []
+        seq_edge_attr = []
+        for i in range(len(coordinates) - 1):
+            seq_edge_index.append([i, i + 1])
+            dist = torch.linalg.norm(coordinates[i] - coordinates[i + 1])
+            angle = self.calculate_vector_angle(cb_vectors[i], cb_vectors[i + 1])
+            seq_edge_attr.append([dist, angle])
+
+        seq_edge_index = torch.tensor(seq_edge_index, dtype=torch.long).t().contiguous()
+
+        # KNN edges
+        knn_edge_index = knn_graph(coordinates, k=k)
+        knn_edge_attr = []
+        existing_edges = set(tuple(edge) for edge in seq_edge_index.t().tolist())
+
+        for i, j in knn_edge_index.t().tolist():
+            if (i, j) not in existing_edges and (j, i) not in existing_edges:
+                dist = torch.linalg.norm(coordinates[i] - coordinates[j])
+                angle = self.calculate_vector_angle(cb_vectors[i], cb_vectors[j])
+                knn_edge_attr.append([dist, angle])
+                existing_edges.add((i, j))
+                existing_edges.add((j, i))
+
+        knn_edge_index = torch.tensor([list(edge) for edge in existing_edges if edge not in seq_edge_index.t().tolist()], dtype=torch.long).t().contiguous()
+
+        # Combine KNN and sequential edges
+        edge_index = torch.cat([seq_edge_index, knn_edge_index], dim=1)
+        edge_attr = seq_edge_attr + knn_edge_attr
+        lifted_data = Data(edge_index=edge_index, edge_attr=edge_attr)
+
+        return {
+            "num_nodes": lifted_data.edge_index.unique().shape[0],
+            "edge_index": lifted_data.edge_index,
+            "edge_attr": edge_attr,
+        }
+
diff --git a/pyproject.toml b/pyproject.toml
index af67ad7c..47b9e2eb 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -42,6 +42,7 @@ dependencies=[
     "rich",
     "rootutils",
     "pytest",
+    "Bio",
     "toponetx @ git+https://github.com/pyt-team/TopoNetX.git",
     "topomodelx @ git+https://github.com/pyt-team/TopoModelX.git",
     "topoembedx @ git+https://github.com/pyt-team/TopoEmbedX.git",
diff --git a/test/transforms/liftings/pointcloud2graph/test_knn_lifting.py b/test/transforms/liftings/pointcloud2graph/test_knn_lifting.py
new file mode 100644
index 00000000..afc8b839
--- /dev/null
+++ b/test/transforms/liftings/pointcloud2graph/test_knn_lifting.py
@@ -0,0 +1,55 @@
+import torch
+
+from modules.data.utils.utils import load_manual_prot_pointcloud
+from modules.transforms.liftings.pointcloud2graph.knn_lifting import (
+    PointCloudKNNLifting,
+)
+
+
+class TestPointCloudKNNLifting:
+    """Test the PointCloudKNNLifting class."""
+
+    def setup_method(self):
+        # Load the graph
+        self.data = load_manual_prot_pointcloud()
+
+        # Initialise the CellCyclesLifting class
+        self.lifting = PointCloudKNNLifting()
+
+    def test_lift_topology(self):
+        # Test the lift_topology method
+        lifted_data = self.lifting.forward(self.data.clone())
+
+        expected_num_nodes = 16
+
+        assert expected_num_nodes == lifted_data.num_nodes, "Something is wrong with the number of nodes."
+
+        expected_edge_index = torch.tensor([
+        [ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14,  4,  4,  5,
+          8,  5,  8, 10,  0, 11, 11, 13, 15,  6,  7,  6,  7,  4,  3,  5,  8,  9,
+         14,  5,  8,  9,  0,  2, 11,  1, 13,  6, 15,  7,  6,  7,  3, 14,  8,  9,
+          5,  9,  0, 11,  1, 15,  7,  7,  3, 12,  3,  5, 14,  9,  0,  9,  1, 13,
+         10, 13, 15, 12,  5, 12, 14,  5,  9, 10,  1, 13,  6,  7, 15,  7, 12,  3,
+          5, 14,  9,  5, 10,  8,  1, 13,  2, 15,  7,  7,  6, 12,  3, 14,  4,  9,
+          5,  8, 10,  8, 10,  1, 13,  2,  7,  6,  3,  3,  5,  4, 14, 10,  1,  0,
+         10, 11,  2,  6, 12,  4, 12, 14,  4,  8,  8, 10,  1,  0,  2, 11, 11,  6,
+          6, 12,  3,  8, 10,  8,  1,  0,  2, 11,  9, 15,  6,  7, 12, 14,  4,  8,
+         10,  9,  0,  5,  8, 10,  9,  0,  2, 15,  6,  7,  4,  5, 10,  9,  0,  2,
+         10,  9, 11,  2, 13,  6, 13, 15,  6,  7],
+        [ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,  0,  9,  1,
+          0, 10,  9,  6,  5,  5, 14,  8,  5,  2,  1, 11, 10,  2,  6,  3,  2,  1,
+         15, 12, 11, 10,  7,  4,  7,  8, 10,  4,  7,  3, 13, 12,  8,  8,  4,  3,
+         14, 12,  9,  9, 10,  9,  5, 14,  1, 13, 10,  7, 10,  5,  2, 14,  3,  5,
+         15, 14, 11,  6,  0, 15, 12,  9,  7,  8,  5,  7,  1,  0, 13,  9,  8,  5,
+          2,  5,  0, 11,  1, 13,  7,  9,  6,  6,  2, 11, 15, 10,  7,  7,  6,  2,
+         13,  6,  3, 15, 12,  9, 11,  8,  4,  8,  0,  9,  6,  8,  9,  5,  2,  4,
+         14, 13, 10, 10,  5,  1, 14, 11, 10,  1, 10,  7,  4,  6,  3,  6, 15,  3,
+         12,  7,  4,  3,  0, 12,  6,  8,  5,  8, 11,  8, 14, 13,  9,  6,  5,  5,
+          2,  4,  1, 15, 14, 11, 13, 10,  7, 10,  7, 15,  7,  8,  4,  6,  3,  0,
+         13, 15, 12,  9,  6,  0, 15, 12,  9,  8]])
+
+
+        assert (
+            expected_edge_index == lifted_data.edge_index.to_dense()
+        ).all(), "Something is wrong with edge_index."
+
diff --git a/tutorials/graph2cell/cycle_lifting.ipynb b/tutorials/graph2cell/cycle_lifting.ipynb
deleted file mode 100644
index fe7834de..00000000
--- a/tutorials/graph2cell/cycle_lifting.ipynb
+++ /dev/null
@@ -1,351 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Graph-to-Cell Cycle 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",
-    "***"
-   ]
-  },
-  {
-   "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",
-    "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": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading and Applying the Lifting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section we will instantiate the lifting we want to apply to the data. For this example the cycle lifting was chosen. The algorithm finds a cycle base for the graph and creates a cell for each cycle in said base. This is a connectivity based deterministic lifting that preserves the initial connectivity of the graph. [[1]](https://arxiv.org/abs/2309.01632) combine two heuristics to design an algorithm that selects cycle basis in $O(m \\log m)$ time, where $m$ is the number of edges of the graph.\n",
-    "\n",
-    "***\n",
-    "[[1]](https://arxiv.org/abs/2309.01632) Hoppe, J., &amp; Schaub, M. T. (2024). Representing Edge Flows on Graphs via Sparse Cell\n",
-    "Complexes. In Learning on Graphs Conference (pp. 1-1). PMLR.\n",
-    "***\n",
-    "For cell complexes creating a lifting involves creating a `CellComplex` object from topomodelx and adding cells to it using the method `add_cells_from`. The `CellComplex` class then takes care of creating all the needed matrices.\n",
-    "\n",
-    "Similarly to before, we can specify the transformation we want to apply through its type and id --the correxponding config files located at `/configs/transforms`. \n",
-    "\n",
-    "Note that the *tranform_config* dictionary generated below can contain a sequence of tranforms 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 -> cell complex -> hypergraph. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Transform configuration for graph2cell/cycle_lifting:\n",
-      "\n",
-      "{'transform_type': 'lifting',\n",
-      " 'transform_name': 'CellCycleLifting',\n",
-      " 'max_cell_length': None,\n",
-      " 'preserve_edge_attr': False,\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 = \"graph2cell/cycle_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 `PreProcesor`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Transform parameters are the same, using existing data_dir: /challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/1820307683\n",
-      "\n",
-      "Dataset only contains 1 sample:\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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//rHO9sM1a9Zw+PBh5s6d22D7c+bMAajdUVDj17/+NcANn1tj0aJFbNu2jY8//vi6x4qLi3G5XLV9uVwu/vCHP9Q+7na7eeWVVxrto8Z3v/tdli9fzmuvvVa7K+TLevXqRXp6eu2tZgi/Kd/L4cOH07t3b1588cV6p9Ou/bfVFIWFhXW+DgoKIjU1FaUUTqezWW2J1k9GDgRvvPEGa9euve7+Rx99tFntDB8+nPXr1/PrX/+6dgh89OjRPPfcc2zatInRo0fz4IMPkpqaSlFREXv27GH9+vUUFRUB8OCDD/Lqq6/y1a9+ld27d5OQkMBbb71V+2bWmD59+vCPf/yDe+65h379+tVWSFRKcerUKf7xj39gMpnqXV/wZSkpKfTu3Zv/9//+HxcuXCAyMpLVq1c3OO8eHBzM2rVruffeexk9ejRr1qzhP//5D//93//d4Pz2zah54z969CjPPPNM7f2TJk1izZo1tXUTbiQkJITU1FSWL19O37596dChAwMGDLjh/HZTREZG8pvf/IYHHniAkSNHsmTJEmJiYti/fz+VlZX8/e9/x2q18qtf/Yqvfe1rTJ48mXvuuad2K2PPnj157LHHGmx/8ODB3HvvvfzpT3+iuLiYyZMns3PnTv7+97+zYMECpk6d2miM//Vf/8X777/PvHnzuO+++xg+fDgVFRUcOHCAVatWcfr0aTp27Mj8+fMZP348P/7xjzl9+jSpqam888471601acjLL7/Ma6+9xtixYwkNDeXtt9+u8/jtt99+w0JbTflemkwm/vKXv3DLLbeQlpbG1772Nbp27cqFCxfYtGkTkZGRfPDBB02KFzwJSefOnRk/fjydOnXi8OHDvPrqq8ydO7d2TY9oRwzYISECRM32poZu586da9ZWxiNHjqhJkyapkJAQBdTZ1pibm6u+/e1vq+7duyur1ao6d+6spk+frv70pz/VaePMmTPq1ltvVaGhoapjx47q0UcfVWvXrm10K+O1Pv/8c/Xwww+r5ORkFRwcrEJCQlRKSop66KGH1L59++pce++996qwsLB62/nss89Uenq6Cg8PVx07dlQPPvig2r9//3Xfj5o2Tpw4oWbOnKlCQ0NVp06d1C9+8Qvldrtrr6v5Xr7wwgvX9UUDWwvrEx8frwCVm5tbe192drYC1MSJE6+7/stbGZVSauvWrWr48OEqKCioTt8NfT++vK3wRt5//301btw4FRISoiIjI9WoUaPUP//5zzrXLF++XA0dOlTZbDbVoUMHtXTpUnX+/PlG+3Q6nep//ud/VFJSkrJarap79+7q8ccfV9XV1XWuS0xMVHPnzq03vrKyMvX444+r5ORkFRQUpDp27KjGjRunXnzxReVwOGqvKywsVF/5yldUZGSkioqKUl/5ylfU3r17m7SVsWZLaEO3a7cU3khTvpd79+5VCxcuVLGxscpms6nExES1aNEitWHDhtprmrKV8fXXX1eTJk2qbad3797qv/7rv1RJSUmTYhVti6ZUM8ZahRDXue+++1i1alW9Q7tCCNEayZoDIYQQQtQhyYEQQggh6pDkQAghhBB1yJoDIYQQQtQhIwdCCCGEqEOSAyGEEELUIcmBEEIIIeqQ5EAIIYQQdUhyIIQQQog6JDkQQgghRB2SHAghhBCiDkkOhBBCCFGHJAdCCCGEqEOSAyGEEELUIcmBEEIIIeqQ5EAIIYQQdUhyIIQQQog6JDkQQgghRB2SHAghhBCiDkkOhBBCCFGHJAdCCCGEqEOSAyGEEELUIcmBEEIIIeqQ5EAIIYQQdUhyIIQQQog6JDkQQgghRB2SHAghhBCiDkkOhBBCCFGHJAdCCCGEqEOSAyGEEELUIcmBEEIIIeqQ5EAIIYQQdUhyIIQQQog6JDkQQgghRB0WowMQQgjRPG6lKKx2k1flIr/KTYVLx60UZk0jzGIiLsRMfIiF2GAzZk0zOlzRCklyIIQQrUSpw82hIjs5RdVUOBW6Upg0DV2p2mtqvjZpGmFWjUEdgknrYCMyyGxg5KK10ZS65qdKCCFEwLG7dbZcqiSnyI5bKVBgMWmYAK2ekQGlFDrg0hVoYNY0BnWwMT4hFJtZZpNF4yQ5EEKIAHa2zMkn58spcbgxoWHR6k8IGqKUwqVARxEdZGZGt3B6RFh9GLFoCyQ5EEKIAJVTWM2GCxXoSmHVNEwtWD+gK4Xz6nTD9K5hDIoN9mKkoq2R8SUhhAhAOYXVbDhfga4rglqYGIBnLUKQpqHrig3nK8gprPZSpKItkuRACCECzNkyZ+2IQZBJa9Y0wo1omkaQybNgccOFCs6WOb3Srmh7JDkQQogAYnfrfHK+3OuJQY1rE4R158uxu3Wvti/aBkkOhBAigGy5VEmJw41V835iUEPTNKyaRrHDzZZLlT7pQ7RukhwIIUSAKHW4ySmyY6LlawwaY9I0TGjkFNkpdbh92pdofSQ5EEKIAHHoah0Di5+KGlo0T7XFQ0V2/3QoWg1JDoQQIgC4lSKnqBpUw3UM3vivr/Prr8yu97HXHrqLP3z77mb1qWkaKMgpqvYUVxLiKkkOhBAiABRWu6lwKiymhocNOvVM5srlC7icjjr3f5a9gQvHDzHja480u1+LSaPC6TmrQYgakhwIIUQAyKtyec5EuME18T2T0XU3BedP196nlGLDm6+QNGgkySPGNbtf09U28qtczX6uaLskORBCiACQX+XG1MgOhU49kwEoOHuq9r6Dm9dy+dQx0u9v/qgBeKYWNE0jr0pGDsQXJDkQQogAUOHS65yuWJ+4xN4A5F9NDnRdZ+P/vkafEePpnjakzrVnP9vHT9PT2PTWHxvtW1eKSpfUOxBfkORACCECQFMWBIaERxIZG0/+uZMA5Gz4kLyzJxh2+1cpyM2j6EoRlVWVuNwuPnrtV3TtN7DJ/btkQaK4hsXoAIQQQniOVW6K+J7JFJw9he52s/GtP9BvzBTieqZgRseq61SVlrJ13Xt07NkHt6MaRdPe9C0+rqsgWhcZORBCiAAQZjE1qfBRfGIyBedPs/eT97hy8RxjFt2PxWJD1yxYLRZs6OSsfYdJd30Np91ORXk5JaUl2B32BhMFk6YRapG3A/EFGTkQQogAEBdiRlcKdYM6B+BZlGivquDjP/+atMmzieycSFCQDbfLTLW9ks3/+xrjFy4jvnMCtqAggi0WsNspq6oCk4mg4GBCQkKwWiyAhlIKpRTxIWb/vVgR8CRVFEKIABAfYsGkaTS2LDA+ybNjoaqshAn3PACYCLJaCbLZOH/iOOePHmTk7IWAZ0TAYrEQFRlJdHg4oRYrelUVJYWFFBQUUF5RjtPtRtM04kLks6L4gqaUrEIRQgijuZXiL4evUO7QsZmb9rmtsKgQlI2w8HAAMv75Olv/9UeCw8IAqK4ox2w2kzYhnTu+/z+Ap6aBy+XC6XRidzghJBRXRSldz+5gwa23Eh8f75sXKFoVSQ6EECJAbLtcydbcSoKacCKjy+2ioOAKYaFRWIOCAKgoK6E8/yJREaGYzWb+8/oLxHTqwqRF9xMSHnFdG7pS2NHQPllN5itPc1HTSJ4wgdvvvJM5c+YQEXH9c0T7INMKQggRINI62DBrGq4mfGSrqqpC0yxYrNba+0LDIwmJ7UxQWCQRHTpiDbIRFBxab2IA4DaZsWoa3xjal3++/DIvLlnCwEuX+Ot3v8vUtDS++Y1vsHbtWhwOR73PF22XjBwIIUQA2Xi+nL2F1Vi1ho9tVigK8gsxW0IJDQ2t81hVVRXKXkZMVMQNRx90wKmZGVqRy7SSM3UeKywsZPuOHWTt2MGOc+eojIlh0m23sXDhQkaPHo3JJJ8r2zpJDoQQIoDY3TpvHyuh2OFucHrB4bBTdKWM8PBoLJa6Cwl1Xae89ApRIRZsNlu9fSjAoZmJdlWzLP8gNtXwMsgLFy6wbds2Nu7cycHCQvQuXZh9550sXLiQ/v37Nzr9IVonSQ6EECLAnC1zsvpUKbquCDJdnyCUlJZgt0NkZCRw/ZtzRUU5FncVUZHXTyfUJAYmFHcUHKWHo7RJMSml+Pzzz9m2bRvrd+3iWGUlkSkpzLvzTm6//Xa6d+9+My9VBChJDoQQIgDlFFaz4XwFuqqbICilyMsvwGaLIDg4uN7nupxOqsqLiYkIqTOycG1iML34NIMq828qNpfLxcGDB9m2fTsb9u3jjMtF91GjuO3OO5k/fz4dOnS4qXZF4JDkQAghAlROYTUbLngShJo1CFXVVZSUVBIZGY3J1FDhIkVZaSmhZhdhV7c11qwxaGli8GXV1dXs2bOHrdu3s+mzz8i1WEibNo3b77iDW2+9VdYntFKSHAghRAA7W+Zk3flyih1uTGiUlRSjuy2EN7LN0G6346wqISYyHN1sQUcj2lXNjOLTTZ5KaK7S0lJ27txJ1vbtbDl5krf37aNz164+6Uv4liQHQggR4OxunS2XKtlfUEVZZTUWSxBWk2e6QKtnzYHCUxLZ7qgmyKJhM5sYVJnP+NJzN1x86E15eXmEL1hAaGzs9Q+WlsIzz8ChQ1BcDJMnwze+AbfeCr17Q9eu8Lvf+SVOUT8Z7xFCiABnM5uY1i0c66cfsutfHxGkLCgNdE2hawq3ptfeau7DpOGudnNp+f9y78U9TCs547fEACA+Pp7QkJD6H3zoIZg9Gz74ADIy4OhR2LYNpk+H1aslMQgAkhwIIUQroJRixf/+E9dxF33zOpCUF0WXK+HEVAQTVWUjojqIqCobMRXBdLkSTlJeFJ1PWFn35scc3bHV6PC/kJ0Ne/bAU09BejrMnAlnz4Kuw+bNsGABvPOO0VG2e3LShhBCtAKHDh3iyJEyHnlkAhoawS4LwS4LVDX8nK4JXYhNncl/1v2HMWPG+C/YGzl4EJYtg5/+tO79DocnMdA0uOcemDoVYmIMCVHIyIEQQrQKK1euJDw8hQEDBjTredPSZ/HJ53ZOnz7tm8Caq0sXzwhBRYXn6+pqz7RCUBCEhEBwMIweDYESbz3cSpFX5eJgUTWbLlTw4Zky3jtdyodnyth0oYKDRdXkVblwt+IlfTJyIIQQAc7lcrFq1SeMGvUdzOaGti/Wb8iQIazqMIz169fzwAMP+CjCZpg7F3bs8EwphIV5koLvfx+6dfN8rRTs3w9f+5rRkV6n1OHmUJGdnKJqKpwKXSnPMdvXJAE1X5s0jTCrxqAOwaR1sBEZ1Ly/N6NJciCEEAEuIyODvDwbEyZMaPZzzWYz46bN5513H+fuu8sJv3q8s2HMZnj66evvz8iAZ58Fq9WTQHTqVO/T/+///o+BAwcycOBAv5VurtktklNk94wGKLCYNKy15a3rxqGUp65EuUNna24lO/KqGNTBxviE0CYfx2201hGlEEK0YytWrKRr11H06NHjpp4/efJkTtKTzMxML0fmRZMnw9q1nh0MDz3U4GV5GzbwzfR00idM4De/+Y3Pp0vOljl561gJewurUQqCNM/WUPMNjtXWNA3z1euCNA2lYG9hNW8fK+FsmdOn8XqLJAdCCBHASktL+eijXYwdO/mmPylHRkbSZ9Q83tu4Hbfb7eUI/euRBx/k9UcfZVGHDmx76SXuGj2aBXPn8sYbb1BQUODVvnIKq1l9qpQShxurpmGt55yLxmhXn2fVNIodblafKiWnsNqrcfqCJAdCCBHAPvzwQ6qrOzJ27LgWtTM9fQbb88M4cOCAlyJrAh8syDNrGoMGDeKb3/gGb738Mq888ACTqqt55/HHmT1oEMuWLGHVqlWUl5e3qJ/asy10RdANjs9uKpOmEaRp6Lpiw/mKgE8QZM2BEEIEsOXLV9O//1RiWritr3fv3gQnTWbt+k0MGTLEO8E1pqzMswPBm+crXPOmb7PZGDNmDGPGjOEb5eXs3LmT7O3bef3hh3kuNJSRt9zCwjvuYMqUKVit1iZ3cbbMWXumRX2nYt4sTdMIMoFDV2y4UEF0kJkeEU2Py5+kfLIQQgSos2fPMnLkHdx//28ZN65lIwcA2dnZbPnzd1j1/Hfo3MCCP69KSIARI7zTlq57Si1v2dLopQUFBWzfvp3MHTvYeeEC1bGxTFmwgIULFzJixIgbHgZld+u8dayEEoeboBusK2gJpRQOpYgOMrOsb1RALlKU5EAIIQLUb37zG156aSsvv/w2Nputxe05nU5++tj9/GSsk6VLl3ohwibo3Rv69fPsUmiJoiLYtctTLKkZzp07x7Zt29i4YwcHi4sxde/O7DvuYOHChaSkpFx3/cbz5ewtrK49BdNXdKVwKsXQ2GCmdTN4B0k9JDkQQogApJRizJipdOq0lAcffNBr7a5YsYLKjU/yj9/+wisJR5OYTBAdDZabmMlWyjOVUHWDUpBNoOs6x44dY9u2bWzYvZvjVVXEpKVx6113cdttt9G1a1dKHW7eOFKMUmA1+X6bpFNXaBrcnxIdcHUQJDkQQogAtHv3bm655RF+8IM/k5qa6rV2CwoKeOG/lvD7rw5g6tSpXmu3NXG5XBw4cICt27axft8+zuo6SWPHMu2hH1LZuS82L64zuJGa6YVxnUIZ2znU5/01hyxIFEKIALRy5UqiowfQr18/r7bbsWNHug65hQ/W/4MpU6b4rZBQILFYLAwdOpShQ4dyf1UVu3fvJnvnLi47g7BWVFCtuwgOCcFms9U5EvuN//o6xZcv8P231l7X5msP3YVmNvPw7//V5Dg0TQNdkVNUzahOIZgD6O8i8FZBCCFEO+dwOHjnnU2MHj292eWSm2J6+kwyz5s5evSo19tubUJCQpgwYQL3/+gnRCV0JdgEJpeLiuJiCvLyKCktwe6wo1B06pnMlcsXcDnrrnv4LHsDF44fYsbXHml2/xaTRoVTUVgdWPUnJDkQQogAs379eq5cCWP8+PE+aT81NRV3whjWb9jok/ZbozxrKEozEWy1EhkRQXREBOFBNrDbKbtyhcL8fCI6dUV3uyk4f7r2eUopNrz5CkmDRpI8ovk7SkxX28ivcnnvxXiBJAdCCBFgVqxYRWLiWLp27eqT9jVNY1L6PN7dfYErV674pI/WJt8aiglVO4lgNpsJCQkmKjKS6PBwQi1WYjt1Rdd1Th7cR3lFOS63i4Ob13L51DHS72/+qAF4/i40TSOvSkYOhBBCNKCoqIh16/Yzduxkn/Yzfvw4Llj7smnzZp/201pUmKzo1D/nb7FYCA0NIan/QEwmjbJL53CWV1CUl8fHb/yWXkPH0HPgcAB2vPdPfv/NO/j5zEFs+Pvvm9S3rhSVLt1rr8UbJDkQQogA8t577+FyJTBmzBif9hMSEsqA8bfy7ubduFyBNaRtBLfW+NthaEQkkR3iKMm9SHRUJGd3ZVF07jSjFn4Vl9vzPYyIjWfavd8hdeKMZvXvCrCNg5IcCCFEAFmx4t8MGDCNyMhIn/c1ffp0dhfH8Omnn/q8r0BnVk375B6f2JuCc6fR3W42/+sv9Bk5gc69U2p3faROmE7/cVMJCY9oVv+WANqpAJIcCCFEwPj888/Zvfsy48dP8Et/Xbt2JTZ1Jv9Zn+GX/gJZmO7EROOf3uMTe1Nw4TTb/rOKK5cvMHnpN9HMZsymm99VYtI0Qi2B9XYcWNEIIUQ7tmrVKoKDkxkyZKjf+pw6fSbrjldz5uxZv/UZiOKclehojaYHcT2SqK6sYPP//ZG0STOIT+yN+WYqP16llEIpRXxIYFVIlORACCECgK7rrFjxH0aMmNGsEwRbaujQoZTHDGXD+vV+6zMQxTsrMaEaXJQI4Ha7CY/rAkB1RTnTv/IwLre7RX9fOp4dC3EhgVWTUJIDIYQIANu3b+f8eY0JE3xT26AhZrOZcdNvZfXW45RfcxxyexPrrCLM7cTVwMJEl8tFeXk53VIG8sR/dvPEBzuJSeiOrhSWFowcuHRFmFUjNlhGDoQQQnzJypUr6dhxMMnJffze9+TJkzmhepKVleX3vgOFGcWgyjyA66YWnE4n5RUVmCwWQkJCak9r1HUdXak6Iwdulwunw47u1tHdNX+uv4aBUgo0GNQhOKBKJ4MkB0IIYbiqqireey+bMWOmGXLWQWRkJMmj5vHexu3oemDtt/entMoCzEqvM3pgtzuoqKzEYrUSHBJS5+9Hd+ugaZhNX1y/+f9e54lbhvLpmlW1f9677v16+3MpMGsaaR38dDpmMwTWJIcQQrRDa9eupawsymflkpsiPX0Gb259mwMHDjB48GDD4jBSpNvBoMp89oZ1Qgcc1dVU2+1Yg4IIstmuW42g6+6rixG/eGT6vd9m+r3fbrQvXSl0FEM7BAfccc0gIwdCCGG4FStW0bv3ROLj4w2LoVevXtiSJvHx+vZ93sL40nNEuaqpcutU2+0E2WxXT2e8nlvXsdzEYkSlFE6liA4yMz4hsI5qriHJgRBCGCg3N5fNm48ybtwkQ+PQNI3J02/hg5xCcnNzDY3FSCaHnYo3f4PL4cAaEYk1KKje65RSnuSgmYsRlVI4dIVJ05jRLRybOTDfhgMzKiGEaCf+/e9/A90YNWqU0aEwevRoCsIHsnFj+xw9qKyq4qVf/5ojWevpcWwnZpMJl8lSb+2D+hYjNubaxGB61zB6RPhvy2pzSXIghBAGWr78XQYPTicsLMzoUAgKCmLE5FtZnXkAu91udDh+VVxczHPPPcfFqiruXLqUUUFOBl06jEmpehMEz8JNDbO5aesFdKVwKIXJpDG9WxiDYoO9/hq8SZIDIYQwyGeffcahQ8WMG2fcQsQvmzp1KoeqOrNt2zajQ/Gb3Nxcnn7uOSqCg1m8bBndunUDILH4AmPO7iHMWYXLZMGlmWuTBLeuY7KY0W5QNAmuri/Qv1hjcEdSZMAnBiDJgRBCGGblypWEhaUwcOBAo0OpFRcXR9cht/D+ukzPPvw27tSpUzz17LNY4uO5e+lSOnbsWOfxjpVFTDq5naSis2h4RhGcJgtuaHAxolIKt1LY3ToOpdA0GBobzLK+UQE9lXAt2coohBAGcLlcrFr1CaNGPdyiCnu+MD19Ju+8sJzjx4/Tt29fo8PxmYMHD/LK66/TJTWV2xYsICQkpN7rrLqLgblHSS48w9noLpyJ7ordZMZiC8GhK/RrkiiTpqGUQtM0woNMDOoQTFoHW0BuV7yRwPqJFEKIdiIrK4vLly08+GDgTCnUSE1NZUXCWD5Zt77NJgfbtm3jr//3f/QdMYI58+ZhbUKCFuKqpl/BScIObuWVNRt4+OXfExTbmUqXjkspLFdPV4wPMRMXYiE22BxwlQ+bSpIDIYQwwIoVK0lIGEHPnj2NDuU6JpOJCdPm8O6/NrK0uJiY6GijQ/KqtWvXsvLDDxkyaRLT09PrVDhsisLcXC6f/Zxp/Xv49ZAsf5I1B0II4WdlZWX85z87GDt2qiHlkptiwoQJXLD0YfOmTUaH4jW6rrN8+XJWrVnDuNmzmTFzZrMTA4DcvDzievVqs4kBSHIghBB+99FHH1FV1ZGxY8caHUqDQkNDSRt/K//evBuXy2V0OC3mcrn481/+wvqdO0m//XYmTJjQyD6Dhl3KzaVvAC0i9QVJDoQQws+WL19NSsoUYmNjjQ7lhqZPn86e4mh2795tdCgtUl1dzW9efpm9J08yf9Eihg4ZctNtud1uTl26FFA7THxBkgMhhPCj8+fPs2XLScaNm2h0KI3q1q0bMSnpfLR+s9Gh3LSysjJ+9fzznC0t5Y4lS+jXr1+L2issLORKdbUkB0IIIbxn9erVWCw9GTFihNGhNMnU9Nl8fKyKs2fPGh1Ks+Xn5/PUM89QYjazaNkyevTo0eI28/LyKHG76d+/vxciDFySHAghhJ8opfjXv95j6NB0goMDv0oewLBhwyjvMJT1GzYYHUqznD17lieffRYtNpbFS5d67cTL/Px8ort1IzQ0ME9T9BZJDoQQwk/27dvHiRN2xo+fYHQoTWY2mxkzZT6rtxyjoqLC6HCa5PDhwzz74otE9+rF4iVLiPbiVsxLubn0buNTCiDJgRBC+M2qVauIjExtdUPSU6ZM4aRKJCsry+hQGrVz1y5eevVVug8ezF2LFnn1E76u65y8eLHNrzcASQ6EEMIvnE4nq1atZ8yY9Caf5BcooqKi6D1yHu9v2Hr1NMLAtH7DBl7/+98ZMGECC26/naCgIK+2f6W4mIKKCgYMGODVdgORJAdCCOEHGzZsoKgolPHjA69cclNMT5/B1rxQDhw4YHQo11FKsXr1av75/vuMnjGD2bNn31Rxo8bk5+VR4nLJyIEQQgjvWLFiFd27j6k9Dri16d27N0E9J/Hx+o1Gh1KH2+3mb3/7G2u2bGHarbcyafJkn1WdzMvLIzQ+nsjISJ+0H0gkORBCCB8rLi7mk0/2MW7cFKNDuWmapjElfQ4fHigiNzfX6HAAcDgc/O6VV9hx5Ahz77qLEcOH33TVw6bIzc8nqR1MKYAkB0II4XPvvfceTmcnxowZY3QoLTJ69GgKQlPZFADnLZSXl/OrF17g84ICbl+yhFQfL/JUwMnz5xk4aJBP+wkUkhwIIYSPrVjxb9LSphEVFWV0KC0SFBTEsMm3sTojB7vdblgchYWFPP3ssxTqOouWLSPJDydblpaUkF9e3i4WI4IkB0II4VMnT55k164LjBvXemob3Mi0adM4VNWJ7du3G9L/hQsXePLZZ3FFRXH3smV07tzZL/3m5eVR7HS2i8WIIMmBEEL41KpVqwgOTmbo0KFGh+IVcXFxdBl8Cx+sz0Ip5de+jx8/ztO/+hURPXpw99KlxMTE+K3v/Px8zFFRxMXF+a1PI0lyIIQQPqLrOitW/Ifhw2d4fc+9kabPmEXGWTj++ed+63Pv3r288Nvf0mXAAO5avJjwsDC/9Q2Qm5dHYlqaX/s0kiQHQgjhIzt37uTsWb3V1jZoSGpqKo5Oo/hk3Xq/9JeRkcGrf/kL/caMYeEdd2Cz2fzS77VOX7xIWjuZUgBJDoQQwmdWrlxJbOxg+vTpY3QoXmUymZiUPp93d53jSnGxz/pRSvH+++/zv6tWMWL6dObMmWNIdcnyigouXbnSbtYbgCQHQgjhE9XV1bz3XhZjxkzD5INqfUabMGECF6x9yNi82Sftu91u3n77bd7ftInJ8+YxdZpx38f8q4sR28tOBZDkQAghfOKTTz6hpCSS8ePHGR2KT4SGhpI2/jbe3bQLl8vl1badTiev/eEPZOXkMPuOOxg9erRPixs1Ji8vD1dISKutbnkzLEYHIIQQbdGKFavo1Ws8nTr5Z6udP4WFQVAQLF16C+9U7OJoQQFpqamNP1EpqKyEG9RIqKys5OXf/pbzZWUsuPtuevfu7cXIb05eXh7d+vf3WVnmQCTJgRBCeFleXh4bN37G4sX3GR2KV8XGwpAh8MUpyPHcfvurzW+oqAh274bq6jp3Xyku5qWXXqLUYuGuZcvo0qVLS0P2inOXLzNgxgyjw/ArmVYQQggve/fdd9H1LowaNcroULwmJARGj/b8v8Wio+FLpaQvX77MU888Q3VYGHcHUGJQXV3NuYKCdrUYEWTkQAghvG758n8zaNAthIeHGx2K13TpAiYTeGVk3WSCiAiIioKSEk6ePMmvX3mFqB49uP3OO4mIiPBCJ96Rn59PscPRrhYjgowcCCGEVx0+fJgDB64wfnzbKJdcIyLCs2TA243m5OTwq1//mrh+/Vh0zz0BlRiAZ4qo2mIhKSnJ6FD8SkYOhBDCi1avXk1oaF8GtbHT+3yxFu/zkyd55U9/os/IkcyZOxerJfDekvLy8ujUp48h9RWMJCMHQgjhJW63mxUr1jBq1AwsAfhGF2i27NzJoEmTmD9/fkAmBgDnc3NJbWOJXlME5t+GEEK0QpcuXeJb33qU0aNnERX1xc69y5evW5jfdpSWwjPPwKFDUFwMkyfD/Pnw5JOe4Ya5c+Ghh+p9avLAgcQOGGBoDYMbcTqdnL58mbvaYXIgIwdCCOElnTt35utfX8jgwZH07AlJSTBgAKSnQ0KC0dH5yEMPwezZ8MEHkJEBR4/ChQvw73977lu/Hqqq6n1q7969AzYxAMgvKKCkHS5GBBk5EEIIr2loKkEpGDoU8vLA7fZzUL6UnQ179sBTT3luAGVlEB8PVqvna5PJc2uF8vPyKFOKvn37Gh2K30lyIIQQPqZpYDZ73jMvXTI6Gi86eBCWLYOf/rT+xzMzoWdPMOAURW/IzcsjrlcvrDWJTjvSOtM5IYRoZZRqte+RDevSBTZvhooKz9fV1Z5pBfBkQa+8Ar/4hWHhtdTF3Fz6tcP1BiDJgRBCiJs1d66nbGJ6uue2cCGcOwcOBzz6KDz3nOcghlbI7XZz6uLFdrneAGRaQQghxM0ym+Hpp6+/f/lyOH4cfvhDz9e//z10bl0HUBUWFlJst7e7ssk1JDkQQojmqG/r3rRpnjdAgMOHYeVKSEszNExDLV7subVieXl5FLtcpDbltMk2SKYVhBCiOerbumezwerVsGoVJCZCO31DaUvy8vKI6d6dEK+cNNX6yMiBEEI0VUNb92oOHdi3DwYP9k2tYeFXl/PySG6nixFBkgMhhGi6xrbuffwxzJrl35j8xOHwfpsuI8pGmkwEx8RgusH2RF3XCe7alZkzZ/oxsMAiyYEQQjRVly6eqYPHHvOswq+uhjNnoF8/z+NbtsAPfmBsjD5y+TL06uWdtpRSKF2n8vJl7zTYRNEpKXQcMgRzE+oWPN5Gk7ymkuRACCGaau5c2LHDs20vLAyCguD73/ckB2fOeJKHNlowp7AQTp/21DTS9Zp7FfrVLzRNQ/vSdIpSCqV0TF9+TNO4vHUryuXyR+gAhHfvTqeRI/3WX2snyYEQQjRVQ1v3oE1PKdQ4cMBzbEKnTqBUNYcOHcRicdOzZyLBwfUv3Dtz+iQdtCv0SuyBUgpneTllZ8/iLC31a+yRvXujdB2tlZZy9jdJDoQQwhu+8Q2jI/CLoiLIyTnPyy+/SKdONu6663auXCkBSuq9/tix8+z/6DV+tGwmHTt29G+w17DFxEhi0AzynRJCCNFkR48e5YUXnqFHjwiWLFlMdHTMDa9PTu5NZVhv9u/f76cI6/flKQ9xY5IcCCGEn9TseGytdu/ezSuvvMjAgV1ZvPguwppQGtlkMpMyaCRbD1/Abrf7IUrhDTKtIIQQfqBpUF7u3z5NJoiLg9jYmz81uarKs1Phww83sXr124wdO4DZs2dhNpub3MaAAQNZtTOezw4fZuiQITcXiK/UV/HyySc921UPHfJUv3zkEaOj9DtJDoQQwsd03VMnoKjIf31arTB2LERFXbu7oPk0TdG/P6xde5rp00cyefJkTM3MNMLCwojvPZwd+9YxZPDgwBrif+ghz3qR557zfKMWL4Y//AEsFnj3XXjwQcjP92RZ7YhMKwghhI9VVsLWrf6dVujXDyIiPH82mW7+VvNG/txz93HLLdObnRjUGDJ0KEeKgzh77py3XmLLXVvxMj0dZs6Es2chJAQmTPBcM3Ys5OQYG6cBZORACCGa4Y033mDHjiq+9rWH0bTG3ygrK/0/nQCeQxC9tTjfkyAounQJ5+TJm9uCmJCQgCkujT379pPYo4d3Amuphipe/va3EB7u+XNYmGfqoZ2RkQMhhGgiu93Os8++TnV1J/LzTeTl0ejNiMQAPGdBeZNSEBLSks+TGgMGD2fnqRJKA+XNtksX2LwZKio8X1dXew7Sioz84i+uosLzdTsjyYEQQjTRJ598QnFxOOPHjzc6FEO0dKlASko/Sm092B8ow/Rz58Lo0Z4phfR0WLgQzp2DoUM9pbABtm+HdngAk0wrCCFEE61cuZqkpPEkJCQYHUqrZLFY6ZU2mm0HVzJ2zBgsFoPfgm5U8XLFCliwAKZMaXeLEUFGDoQQokkKCgpYv/4A48ZNMjqUlikthR//GObPh4kTPfPtNSsl//xnWLTIp90PHDiQM/ZIjh075tN+WuyZZzy7Fb73PaMjMYQkB0II0QTvvvsuut6F0aPHGB1Kyzz0EMyeDR98ABkZnjn2rVvB6fTs6/ex6OhoYnoOZ9e+g7TymlBtmiQHQgjRBCtWvMvAgdMJr1nF3ho1tHVPKVi92jOM7geDBg9hf57i8qVLfulPNJ8kB0II0YijR4+yb18+48dPNDqUlqnZurd+/Re3HTtg3DjPqv0pU/wSRmJiT1xRfdnnx/MWXFVVqNZev9qPJDkQQohGrFq1itDQvgxq7avWG9q699FHnlEEP9E0jdTBI9l2LJ/Kykq/9Fl+7lzrP9zCjyQ5EEKIG3C73axY8REjRqRjtVqNDqdlGtq6d+IELF8OS5Z41h384x8+DyUtLY18U2cOHDjg874Arhw5QlV+PgBK11Fud52b2+XC5XL5JZbWQLYyCiHEDWzdupVLl8zcd18bqG3Q0Na99HR49FHPnxct8iQJPmaz2ejRfzTbD7zPyJEjb7osc1Mpl4tz69cTlpBAaOfOmL60jfLwsWOY4+MZNWqUT+NoLWTkQAghbmDVqlXExQ2ld+/eRofiHytW+K2rQYMG83l5GCdOnPBPh7pOxYUL5O/eTe6OHbW389nZvPSjH3Hq1Cn/xNEKSHIghBANqKio4P33tzJ27LTAOkmwjYiLiyOkyyB25xw0NI78ggJKHA4GDhxoaByBRJIDIYRowJo1aygvj2bcuHFGh9Jsvlh7p+veb3TQkOHsPldFYWGh19tuqrzcXMqUok+fPobFEGgkORBCiAYsX76KPn0mEdcKy+eWl+PVrXsmk0ZpqcNr7dVITu5NVVgv9vtxW+OX5eXnE9+7d+tfcOpFkhwIIUQ9Ll++TFbW8VZbLjkr67TXpkJ0XVFd7eLiRe9vOzSZzPQdOIqthy9gt9u93n5TXLh8mb4ypVCHJAdCCFGPd955B03rwciRI40Opdk+/vhjvv3tJ/ngg9243XqL2ysrc/DJJ+d8Mq0AMHDgAC66Y/ns8GGftH8jbreb05cuyXqDL5GtjEII8SVKKf71r3cZOvQ2QkNDjQ6nyZRSrFixgq1b1zF79jiKi8NYseJzOnQIxmxu/iiCUlBZ6aS01OmDaL8QFhZOfPJIdu5fx5DBg/26+LOgoIArdjsDBgzwW5+tgSQHQgjxJQcPHuTIkTIeeWSC0aE0mcvl4o03/srRo5+yYEE6Q4cOvXq/Ii+vyuDoGjdkyFA2r8jg7LlzJPbo4bd+8/PzKXG5SE1N9VufrYFMKwghxJesXLmSiIj+rebTpN1u57e/fZmTJ/eyaNH82sSgNenSJQFTXCp79+X4td+8vDyiu3cnJCTEr/0GOkkOhBDiGk6nk9Wr1zFqVDpms9nocBpVVlbGr371HCUlp1myZCH9+vUzOqSbpDFg8HB2nCymtLTUb71eysujT2s/M8MHJDkQQohrZGRkkJdnY8KEwC+XnJ+fzzPPPIXZXMLSpYvo0SPR6JBaJCUlhRJbd3Jy/DN6oOs6py5elMWI9ZDkQAghrrFy5Sq6dh1F9+7+m/e+GefOneO5554iNlZj6dLFdOrUyeiQWsxisdIrbTTbDp7yyyFIV65coaCiotVMH/mTJAdCCHFVaWkpH320i7FjpwR0ueQjR47w4ovPkpQUxZIli4mOjjY6JK8ZNGgQp6ujOHbsmM/7qlmMKMnB9SQ5EEKIqz744APs9jjGjRtrdCgN2rVrF6+++hKDBnVj0aK7WtVWy6aIjo4muucwdu07hG+qKnwhNy+P0E6diIyM9HFPrY8kB0IIcdWKFe/Qv/9UoqNjjA6lXhs2bODvf/8j48alsmDBAoKCgowOyScGDxlKTp5O7uXLPu0nNy+PXjJqUC9JDoQQAjhz5gzbt59l3LiJRodyHaUU77zzDu+//w9mzBjNLbfc0ip2UtysxMSeOKP6sHffPp/1oZTi1IULDJDFiPWS5EAIIYDVq1djtSYxfPhwo0Opw+128+abfyM7+yPmz5/G5MmTAno9hDdomkb/QSPZdiyfyirfFHAqKSkhr6xMdio0QJIDIUS7p5Ri+fL3GTZsBjabzehwajkcDl599XccPryDu+6ay4gRI4C2nRjUSEtLI9/UmYMHD/qk/bz8fIplMWKDJDkQQrR7u3fv5tQpJ+PHB05tg4qKCl588Xny8z9nyZLb6d+/fZX3DQ4OpnvKaHbkHEXXW3541Jfl5+Vhjopqlcdx+4MkB0KIdm/VqlVERw8kJSXF6FAAKCoq4tlnn8btLmDZskX07JlkdEiGGDx4MMfLwjh58qTX287LzydJRg0aJMmBEKJdczgcrF69kTFjpmMyGf8r8eLFizz77JNERjpZtuxuOnfubHRIhomLiyOky0D27D/g1XYVcPriRdJkvUGDjP+XIIQQBlq3bh3FxeEBMaVw/PhxnnvuKbp3D2fp0ruJiQnMLZX+NGDwcD49V0VhUZHX2qyoqODSlSuyGPEGJDkQQrRrK1asIjFxLF26dDE0jr179/K7373AgAFdWLz4LsLCwg2NJ1D06ZNMRWgSOfv3e63NvNxcip1OWYx4A5IcCCHarcLCQtavz2HcuCmGxpGZmclf//p7Ro/uxx13LAyoHRNGM5nM9Bs0iq2fncPucHilzfz8fNwhIXTt2tUr7bVFkhwIIdqt999/H5crgdGjRxvSv1KK999/n5Ur/860acOZM2dOmy5udLMGDhzIBXdHjhw+7JX28vLy6Nq/f5uvF9ESkhwIIdqt5cv/zcCB0w2pre92u3n77bfZtOl95s2bxLRpUwNiQWQgCgsLJz55JDu8dN7CmUuXGDh4sBdaarvkJ1EI0S4dP36cPXsuM27cBL/37XQ6+eMf/0BOThYLF85m9OgxtJfiRjdr8OAhfHYliHNnz7aonerqas4XFMhixEZIciCEaJdWrVpFcHAyQ4YM8Wu/lZWVvPTSi1y4cIi7714gb1JN1LVrF0wd+7NnX06L2snLy5PFiE0gyYEQot3RdZ0VK/7DyJEzsVqtfuu3uPgKzz77DHb7JZYtW0Tv3r391nfrp5E2eAQ7TxVTWlp6063k5edjt1pJSmqfhaWaSpIDIUS7s23bNi5cMPm1tkFu7mWeeeYpwsKqWbbsbsO3TrZG/funUBLUnZycmx89yMvNpXOfPrK+oxHy3RFCtDurVq2iY8chJCcn+6W/kydP8txzT5GQYGPp0ruJjY31S79tjcVipWfqKLYdPIXb7b6pNs7n5pI6aJCXI2t7JDkQQrQrlZWVvPdeNmPHTvPLVrYDBw7wm988T79+cdxzz2IiIiJ83mdbNnjwIE5XR3L02LFmP9fhcHAmN1fWeTSBJAdCiHZl7dq1lJVFMW7cOJ/3tXXrVl5//XeMGNGLO++8g+DgYJ/32dZFR8cQnTiM3fuaf5RzQUEBJQ6HJAdNIMmBEKJdWbFiNcnJk4iPj/dpP2vWrOEf//grkyYNYv78+Vgs/lv42NYNGjKMfbluLl++3Kzn5eXlUaYUffr08VFkbYckB0KIdiM3N5eMjKOMGzfJZ33ous6//vUvPv54NXPmTGDmzBmy+M3LEhMTcUb1Yd++5p23kJefT3zv3n7dodJayU+sEKLdeOedd4BujBo1yiftu1wu/vSn1/n0040sWDDj6tSFFDfyNpPJRP9Bo9h2LJfKqqomP+/C5cv0k8WITSLJgRCiXVBKsXz5uwwZMoPQ0FCvt19dXc1vfvNrTp/ez6JF8/1eXKm9SUtLI09L4ODBpq09cLvdnL50SYofNZEkB0KIduGzzz7js89KGDvW+wsRS0tL+dWvnqOs7BxLl95J3759vd6HqCs4OJhuKSPZkXMUXdcbvb6goIArdrssRmwiSQ6EEO3CqlWrCA/v7/U3h/z8fJ5++kms1lK+8pXFdO/e3avti4YNHjyE42WhnDp1qtFr8/LzKXG56N+/vx8ia/0kORBCtHkul4uVKz9m1Kh0LBaL19o9e/YMzz77JHFxZpYuvZu4uDivtS0aFx8fT3DCQHY34byF/Lw8Ynr0ICQkxA+RtX7e+1cihBABKjMzk9xcK9/8pvfKJX/22Wf88Y+/o1+/zixceBshId5fxyAaN3DICD5ds4v0oiKiO8RSZgunJDiC0uAI7JYgdM2ESekUDNEZOHgseVUuYoPNmP1QAKs1k+RACNHmrVixkoSEESQmJnqlvR07dvC///tnhg9PZv78ebI1zkB9+iSzd/8gdlg6YO4zEbslCKVpaAp0TUNDodAwjYyhQ5CNt4+VEGbVGNQhmLQONiKDzEa/hICkKaWU0UEIIYSvlJaWMmDAVObM+SVz585tcXvr16/n3Xf/yYQJg67WMJA3F6PoJo3SuBBKI8xomo7VYsGs3GhK1dlAqus6ZRUVhMfEYLFYcekKNDBrGoM62BifEIrNLLPs15KRAyFEm/bRRx9RVdWRsWPHtqgdpRSrV68mK2sNM2eOYeLEiX45m0HUzx5qobhLGG6rGbPSsZdXYQ1yYqpnFEfXdRRgtVgwaRpms4ZSCpeCvYXVnCpzMqNbOD0iZASohqRKQog2bfny1fTvP4UOHTrcdBtut5u//vUvbN26lltvnc6kSZMkMTBQRbSNwsQI3FYT6AqT0jCZrTicrnqvd+s6msmESfviLU/TNKwmDaumUexws/pUKTmF1f56CQFPkgMhRJt17tw5tm491aJyyXa7nd/97rccP76bu+6ax/Dhw70YoWiuimgbJQmhKDTQv6g/abFacSmt3qOcdV3H3MC6EJOmEaRp6Lpiw/kKSRCukmkFIUSbtXr1aiyWnjf9hl5eXs7LL7+E3Z7PPffcTs+ePb0boGgWe6iFks6exEDT664rMJtNYLLgdLowm+uuA3G73ViDghpsV9M0gkzg0BUbLlQQHWRu91MMMnIghGiTPOWS32fYsJk3dVRyYWEhzz77NHCFpUvvksTAYLpJo7hLGGhclxh4aFgtVhxuHf2adfa6ruPWFZZGdpR4EgQNXSnWnS/H7m686mJbJsmBEKJN2rt3LydO2Bk/vvm1Dc6fP8+zzz5JVJSbZcvupnPnzj6IUDRHaVwIbqu5zlTCl1msFhRmXE5n7X2exYgKaxOKX2naF2sQtlyq9FLkrZMkB0KINmnVqlVERaWRkpLSrOcdO3aMF154hh49Ili6dDHR0TE+ilA0lctqojLGBqq+EYNraZitVhwuNzVjB7qug8nU5C2nJk3DhEZOkZ1Sx/XrF9oLSQ6EEG2Ow+Fg9eoNjBmTft38843s2bOHV155kQEDurJ48V2EhYX7MErRVFVRQaBp0EBVnv/7yYP8/gFPDQurxYpbabhdnp0Lf/nBffzjvx9qVn8WDdxKcajI3qK4WzNJDoQQbc6GDRsoKgpl3Limn8C4efNm3njj94wZk8LChbdjs9l8GKFoKgVUxASjaHg6Ia5HMiW5F3E5HWgmE9rVbY2Ht23i8okjTP3qt5rVp3Y1EckpqsbdTusEym4FIUSbs3Llanr0GEu3bt0avVYpxXvvvcfGjR8wffpIJk+ejMkUuJ+boqODsNmaX5VRKaisdFFe7mz84gDispnRLRraDd6k4xJ7o+tuii6cIb5nH6xWK/YqOxvf+iPdUofQZ2Tz151YTBoVTkVhtZv4kPb3Vtn+XrEQotVyK88v67wqF/lVbipcOm6lMGsaYRYTcSFmgp2VrN9wgNtufbLx9txu3n77bfbty2LevCmMGjWShj+fGishIZSxYzsTFtayLXZXrtjJzLxAaWnrSBKcwWbPlIJ+o+QgGYDC86eI79kHs9nM0W0Z5J7+nMX/87ubOvvCBLiUIr/KJcmBEEIEolKHm0NFdnKKqqlwKnSlMGlanS1rNV+7XU7uefWv9LR2xam7sbrr/5TtdDr54x//wNmzB7njjlsYMGCAv15Os0VEWJk6tSsmU8sTl6ioIGbO7ME775xEv8EbbqBwBlsaXYjYsXtvAArOnQJA1xXbV/2VnoNH0bX/YMwmExXFRax+/r85tW8XkXGduPXRn9N72JgG29Q0DU2DvCo3ad58Qa2EJAdCiIBld+tsuVRJTpHdM/erPMO9Vk27Wr647luGUnClqpqIjrGUBrkpV8VEV9qIKw3FrL6YKqisrOR3v3uZsrLz3HPP7fTq1cvPr6x5EhMjrr5ZtTw5MJk0QkIsJCSEcuFChRei8y3donlOWWxoNSIKW1g44TFxFJw7icvl5OCm/1B47hQzv/UTz6gDGh/87inCYzry+DvZnNiznX/98jEe+9+1hEZGNdy3UlS62me9A0kOhBAB6WyZk0/Ol1PicGPCU+JWa+STs1t347A7CDWHYFIaSoOisGrKbU4SisMJc1gpLr7CSy+9hMVSyrJld5GQ0MVPr+jmRUd7d3GkriuiooICOjlQSlFVVUWVw4LSrbhcLpRSKF2hlGf0SCndM/qhFDFdE8k7c4KKsgqy/vk6vUZOomNiP0LDQrFXVvDZlg384O2PCQoOof+4qXRK6svhrRsZPvv2G8bhkgWJQggRGHIKq9lwoQJdKayahqmJn5irq6vRNAtWqxUNDc0z2IDD4uZsx1JCzjh57Znn6dDBxKJFd9OhQ6xvX4iX+OKMJ29MUdwMXXdTWVlFRUU5FRWVVFRUXL2VU15eQXl5OSUlZZSWluJ06kx4aBFJ4wbjqKwGTcNkMqFd/b/JZMFq9XzduVcK+z95h9OfZlCWf4k7/vs3aCaFzWYj98QxgoJDiIr7ophVp6Q+5J3+vNF4Le30gC1JDoQQASWnsJoN5z2JQZCp6UPpCkVVVTVWa2id52homBS4lU5RvIsRtw1nXGIK4eFSw8Cb3G537Rt9eXk5lZWVdd70S0vLKCsrp7S0DLdbx+VSuN0KXdcIDY0kIiKaiIhooqN7kpAQTXS05xbauxO6JYjgKNsNfxY6J/Xl0+pKNv7tN6ROnE1UQg8sFjdmkxlHVQW2L9WssIWFU1VafMPXZNI0Qi2Bu3PFlyQ5EEIEjLNlztoRg+YkBuBZYOh2Q0jI9QfsuJwuKisrsIUFM+COGVjPlkFl/cf7tiqlpfDMM3DoEBQXw+TJ8IMfwN13w4kT8Hnjn4wb43Q6a9/gv/yGX1FRTlmZ55N+eXkFbreO2w0ulwJMhIdHER7egcjIaKKjE+jZ0/OGHxUVRUxMDFFRUURERNywUFVxaDUXtfJG46zZsVBVVsLEpd/C7XYQEeFJCIJCwrBX1G3DXlFOUEhYg+0p5Zm+iA9p/rbRtkCSAyFEQLC7dT45X35TiQFAVVUVJpMVi6XuL3OHw05VVRVWqwWryQwaFHcJI+5kCabWvtbsoYfgG9+A554DXYfFi+HAAVi+3HP/DbhcLoqKCq8Z2i+/+qnf8+fS0nJKSkqprKy++qavcLnAbLYQHh5NeHg00dGxdOyYSHJyNNHRUURHe97wo6OjCQ8P98oCymCnBa0J20u79R/Cz9Z8BkBVZSVOV0VtIavYrj1wVFdRWpBLZMdOAOSd/pwhM29rsD0dz46FuHa4jREkORBCBIgtlyopcbiv2YnQdApFdbUDW1A4X+xgUFRX27HbqwkKsmKzBQEaSge31UxpXCjRua34cJ3sbNizB556ynMDKCsDkwmio2/4VF3XyczcwmuvfYDb7Rnet1pDrn7SjyY6uiNdu/YhNTWq9pN+zTB/SEiIV970m8rmNGNxazjNOmbVeL9KKRxOO6GhwbVJhS00jP7jprH+zVeZ/92fcGLPNi6fPEr/cdMabMelK8KDTMQGy8iBEEIYotThJqfIjommLz68lr3ajlIaQUFXpxSurnR3OB3YbEEEBVmpSRo0PG8glTE2IgqrMbeCrWr1Lpg/eBCWLYOf/rTZ7WmaxqBBo/nWtwbVvvEHarloDY3oymDyIypRqEZHEZxOJ0q5CAmJqHP/rY/+jFW/epynF4wjMq4Ti3/26wa3MSqlQINBHYIxy4JEIYQwxqGrdQyCbvIXcVV1FWazDZPZ7Hnjr6zE7XISEmzDUl91PAWYNCqjg4goqG5Z8C2g624qKiqvWcBXM5fvuZWVlVFSUkbHjkvp0WMEdeo6dOkCq1bBY49BWBhUV8OZM9CvX6P9apqJbt26U23cS2+W6EobheFVKA20RnYWOhx2gqxmLOa6b29h0R2499nXm9SfS4FZ00jrEJgJkz9IciCEMJRbKXKKqkHRYB2DN/7r6xRfvsD331p73WO/f+hOnC6dr/36nyilqKgoR9fdBIcEY7HU/ytOwzOnXBETTHhBtdcLJrtczmsW8F07l+/5v2fVfillZeW1w/o1i/hqVu5HRsYQE9OH7t2jSEjoef1Q/ty5sGMHpKd7koOgIPj+95uUHLQ2VreZ6EobRWHVVw9gqv9vTNfduFwOoqJCb7ovXSl0FEM7BBMZ1D6nFECSAyGEwQqr3VQ4FZYb7Lvv1DOZU/t24nI6sFi/2I3wWfYGLhw7xILHf4PFYqG8vAxQhISENHpUs6YUukXDZTNjtbubEKnCbnfUfqqvrKyoTQDKy8spL69ZuV9KZWUVLpd+9Y0fNM1cu4gvKqoDsbHd6N37i+16NfP64eHh9R76FBdXT60Dsxmefrr+UBct8kw7LFoEv/wlpKQ04fUFtrjSUMptThwWNyZVf4LgsDvQNJ1gW/BN9aGUwqkU0UFmxifcfILRFkhyIIQwVF6Vq7bYUUPieyaj624Kzp+mc1JfwPOLfMObr9Ct/xB6DZtARUU5Jg2CQ4IxmZrwie/q1IIz2Iy7pGZvfkWdT/mVlZ5P+WVl5RQXl1Bd7bhmjz5YLDbCw6OIjIwmMrIDnTv3IiXFs2r/2jd+fy/iY8UK//XlJ2ZlIqE4nLMdS9E1dX2CoBQOh52Q4BvXQ2iIUgqHrjCZNGZ0C8dmbp/1DWpIciCEMFR+lRtTIzsUOvX07GEvOHuqNjk4uHktl08eY+HPX8HtcmG2mAgJCUbTan6pq9q96rW3OqV3FaYgC9n797DjrQ+v2a6nCA4OJSIihrCwKGJiOtO9exQDB167Rz+aqKhogoNv7hOquDlhDiudi8O4HF1xXYLgdLnQlZOQ0Ohmt1ubGGga07uG0SOiZSdftgWSHAghDFXh0q+erthwchCX6Dl1L/9szal7Ohv+9/ckDh5Jl5QhKN3JJ6+9wJmcnVRXlBHbLYnJ9z5KQp8BXxzXo66etGcyoWkmTCYTZrOFpH4D6HVPYp0tezdzxK+vKeWbMsqtTUylJyH7coLgcNixWExYG1hn0hD96lSCyeRJDAbFSsIHkhwIIQzmbsLBNiHhkUTExpN75nPKK8rZt+59ck99zuKnf+hpw+kmvGNnFj/5FyLjOnN863re/9UP+dZf1xAcGn41Ibh+dMKt6XTumkD3kMBexGe3e7c9TfN+m/4UUxlMkMvMpehyHBY36Aqn005kRDA3SjKvpZTCpUDHs8ZgRrdwGTG4RvueVBFCGK6hfeQKhdPlpKKygivFV4jq3J1LJ49TVlrN1uVv0mf0VLr2ScNs0ojuGMfM+79Pl6RkwsPDGTpzAZYgGyW5FzBbLLWH9dTH1ITCOkbLzfXUNvJ2m61ZmMNKUn4UHSqCcblcBIUGY7KF4L46ZVQfpRRupbC7dRxKoWkwNDaYZX2jJDH4EkkOhBCGCrOYrhY+UrjcLiqrKikuKSY/v4DCwmLKyuy43Rbie/aj5PIFzuzKoDT/IuPu/iY2mwWrVcP+pY/BRRfOUFVWTIeE7jfsW0PDrAf+r8GCAjh92vNnXW/ZDTwVlh0Ow16O15iViU7FYbz/4z9Svj+LiCATbgUO3bOGoNqt195q7nMrCA8yMa5TKPenRDNNFh/WS6YVhBCGyc3N5czB49g79KKk3LPnH0yYzVas1nCsFgtmiwVN0+jSO4U9H1ay4W+/of/E2XTo2gObzYau65RV2FHKsyPAaa/m3y/8iPGLHiQ4PLLBvtXV/4IdrWMv+4EDcP48dOrkKWlwM6qq4NIlKG/8HKNW48SJE5w6vJEX0qYxvn8MhdVu8qtc5FW5qXTpuJTCcvV0xfgQM3EhFmKDze228mFTSXIghPCb0tJStm3bRlZWFpmZOzl6tIgOiYO55ze/xGoNIzjYguVqMvBl1566N+6uB7GYISjI6qmIWFWNw27HYjGz6pnHiEnozqSl32o0Hg2NYGfr+TV45YrnJr6QkZFB9+4wYcIETJpGfIiF+BALaUYH1sq1nn8VQohWp7q6ml27dl1NBrazf/95XK4YOnZMISXlbh54IJWU/inkW/WrB+s0PLxbe+qeUpSWlRIcbEbDs8gw2GaloqKKD197Ek3TuO0Hzza6113XFFa3CZurdYwciOtVV1fz6acf89hjt9VbPErcPEkOhBBe43K52L9/P1lZWWRnb2PHjs+prg4nMrIXffvOZunSNFJTU4mLi6vzPGdlZdMP1nG50HATFPRFBTubLZj3fvMTSovy+Mozf8XcyHY2dXWDY3RlcKP9icC1c+dOXK6T3H33b4wOpc2R5EAIcdN0Xefo0aNkZ2eTlbWFLVsOUVYWTHBwN/r0Gc2CBV8nNTWVrl273vCTfLMO1rHbsQWZsVxTHrmsIJeDGz/AbA3ixcXja+9f8uQf6TFgxHVtKM2zSyG6sv0erNMWZGZuYMqUfnTt2tXoUNocSQ6EEM1y5swZsrOzyczMIitrPwUFYDZ3Jjl5FNOn30Fqaio9e/Zs9GyDazX9YB0dt9tBeFhInftjOnXhibX7KCmtIDQsErO54V9tNQsRYyqDsbplSqG1unjxIidPZvHTnz5mdChtkiQHQogbysvLY8uWLWRnZ5ORsZuzZyuBjvTsOYIRI75PWloaycnJBN3sEvqrmnSwjsOB2Uy9fVmtVqwWz7bG0ND6f7UpFLqmCHKZiStt3wfrtHaZmZnExlYwa9Yso0NpkyQ5EELUUVpayvbt28nKyiIjYwdHjxai67F06TKIlJQHuPXWNPr160doqHffXJt6sE54SFC9iYMGBAfbKC2zo+sh1y1Qq0kMNDQSisNvuPhRBDaXy8W2bR/z1a/e0uKkVNRPkgMh2jnPiu9Pr+4o2Ma+fedxuaLp2DGFfv0W88ADaaSkpBAdHe3zWG50sI6rZiGiLaTB5wcF2bCYq3E47AQHf3HdtYlB5+IwwhxSDa8127t3L+Xln3HPPT82OpQ2S5IDIdqZmh0FnkWEW+vsKOjXbxZLlnh2FMTHxxsSX0MH69gdDoKsJiw3WE9g0jSCg4Mor7RjswWjadp1iUFN+6L1ysrKYMSIzvTrF9hnYrRmkhwI0cYppWp3FGRmZrN162eUlgZhs3Wjb9/R3Hbb/aSmptKtW7dGawP4S30H67hdDsIiGn9jt9lsVFY5cDqdWGxWFJ41BgnF4TJi0AYUFhZy6NB6Xn75q0aH0qZJciBEG3T27NmrIwNZZGbuJz9fYTLFk5w8mqlTF5KamkpSUlKzdhT4W83BOvmRleRZywgKs4E1CLfyHApTXx7jOfnZTHBYKC63jqY8iUZcaaisMWgjsrOzCQsr5NZbbzU6lDZNkgMh2oD8/Hy2bNlyNRnYzZkzNTsKhjNs2PdIS0ujT58+rW7xVs3BOn/79Uvc+Y2RhA0cRYUbXHjqIejA1b2PmL74I6Emxa5//4ch3abSuUcfA1+B8CZd18nO/oQFCyYQHh5udDhtmiQHQrRCNTsKPNsLd3DkSAG63oGEhEGkpHydefM8iwi9vaPACEePHqU6L4dbesykb2czhU7IdyrynIpKN7gUWDQINUO8VSPOqhFjCebEhd2s2+ug77clOWgrjhw5QlHRPpYsednoUNo8SQ6EaAXsdju7du26um7As6PA6YwiNrYf/frdxde/nkb//v39sqPA3zIyNjN2SCwpKSloaMQHQXyQ1sjBOmbmz53J5l+soLDwHmJjY/0UrfCljIxN9O0bwogR11e9FN4lyYEQAcjlcpGTk1Nblnj79uNUV0cQEZFEv34zufvu1NodBYGyiNAXysvL+fxwFg//Ym6zz0CYMGE8/ZNWsX79ehYvXuyjCIW/lJeXs3fvOn7+8zva9M98oJDkQIgAcO2OgpozCkpLgwgK6kLfvqO59davkZaWRteuXdvV6XNbt26lW3w1EyaMb/ziLwm2BbNg3mSefvUTFixYgM0m5yi0Ztu2bcNkOsddd91ldCjtgiQHQhjk3Llz1+wo2EdeXs2OglFMmbKAtLQ0evbsiaWREwbbKqUUW7ds5N75Q4iKjLqpNqZPT+eNtz9my5YtTJs2zcsRCn9RSpGZuY7Zs4fRsWNHo8NpF9rnbx0hDFBQUFC7oyAj41POnKkA4khMHMbQod8jNTWVPn36yCfcq06cOIFefZypU793023EdezI3BnDeOvDNUydOlWGo1up06dPc+HCDl566edGh9JuSHIghI+UlZXV2VFw+HD+1R0FA0lJ+Tpz56aSkpJCWFiY0aEGpM2bNzNyQDRpaTdeetiYWbNmsvz95zh06BADBgzwUnTCnzIzM+nSxcWUKVOMDqXdkORAiPqUlsIzz8ChQ1BcDJMnw5NPwttvwwcfgNsN//oXWOtW3FNKcfz4cR577Ifs3XsWpzOaDh36kpJyJ/ffn0pqan+io2OMeU2tSFVVJUcPZXL/f6dj0lq2xqJfv75MGtWddevWSnLQCtntdnbuXMt3vnNbQBftamskORCiPg89BN/4Bjz3HOg6LF4MK1fCkSOwYsUNn3r6dBm6PqPd7CjwhW3btpMQU86kSRNb3JaGxtw5M1n7+N+4dOkSCQkJXohQ+MuuXbuw24+zePFzRofSrrSfZc9CNFV2NuzZA089BenpMHMmnD0LdjtUVsJdd8FLLzX49IEDh/Gtb32bqVOn0qlTJ0kMbsKWrI3MnTGIGC+NsowZM4bB/UJZv369V9oT/pOVtYlJk3qTmJhodCjtiiQHQnzZwYOwbBmsX//FbccOuHLF8/jKlZ5k4dChep6sSTLQQqdPn6a6/DDTp03xWptWi4UF86ZzOGcdlZWVXmtX+FZu7mWOH89gyZK7jQ6l3ZHkQIgv69IFNm+GigrP19XVcPQoRETA2LGe+8aMgRMnDAuxLcvIyGBY/3AGDRro1XanTp1Cj/gKMjIyvNqu8J2MjExiYsqYM2eO0aG0O5IciHbN4XCg63rdO+fOhdGjPVMK6emwcCGcOwcjRnjWHAAcPgzduvk/4DbObrdzcP8mbp0zGbPJu4vPoqOiuXX2aLZmrcHtdnu1beF9brebbds+4a670mV7rwEkORDtisPhYOvWrTz//PPMnbuAf/7zvevfKMxmePpp2LbNM6Xw0UeeJGHgQHC54I47oKoKhg0z5kW0YTt37iQ+soRJkyb5pP1Zs2YSYT3L3r17fdK+8J6cnBxKSw+yZMkSo0Npl2S3gmjT3G43Bw4cuFprIJsdO45TVRVGWFgiKSnpJCePxWRqxj+DX/7Sd8EKsrI2cNu0NDrG+qYKXs/EnkyfkMyGDWvl8J4Al5m5mSFDOra4zoW4OZIciDalps6ApyxxNtnZhygpsRIUlECfPqOZN+9e0tJS6datOyaTia5dQdYPBobz589TXnSQ6dO/6dN+5s6ZxQcb/sCZM2dkBXyAKi6+woED63j+eTkwyyiSHIhW7/z587VliTMz93H5sguTKZ5evUYyadKtpKWlkpTUq92eUdBabN68mUF9Qxg6dKhP+xk2bBgj0qL45JOPefDBb/i0L3FzsrO3YLPlcvvttxsdSrslvy1Fq1NYWMiWLVuuThV8yqlTZSgVS2LicAYN+i733JPW5DMKXC7vxqaU99tsD5xOJzl7N/HE9yZgMfv215LZZObWeTP40dMfUFKymKiomzvUSfiGUors7HXceus4IiMjjQ6n3ZLkQAS88vLy2jMKMjN3cOhQLrregc6dB5KSch+zZ3vOKAgPD2922wUFkJzsvVg1DfLzvddee7Fr1y5iw4qYPHmKX/qbMmUyff/+bzZu3CifTgPM0aNHyc/fw9KlzxsdSrsmyYEIOA6Hg08//ZQtW7aQkbGVvXvP4nBEEhPTh5SUhXzta56yxDExLa+eV1DgucXGer6+2fUHSnn+X1oKly61OKx2JytrE7On9qNzp05+6S8sNIzb5k7k139dy7x587B+6YwMYZzMzAySksyMGTPG6FDaNUkOhOHcbjcHDx6ss6OgsjKUsLBE+vWbxqJFaaSlpRIf7/1SxErBzp3Qsyd07gzBwTeXINjtcPkynD7tOZNJNN3ly5e5kruX9OkP+LXfGTNm8Oa/NrJz507Gjx/v175F/SorK9m9+2Mef/wOqTRqMEkOhN8ppfj888+vThNkkZ198OqOgi706TOaOXO+QlpaGt27e3YU+Jrb7Sl2KAUPjbF582YGJNsYPny4X/vtkpDALdMGsHrjR4wbN07ejALA9u3bgbMsWrTI6FDaPUkODOZWisJqN3lVLvKr3FS4dNxKYdY0wiwm4kLMxIdYiA02Y27Fv7wuXLhAdnb21dGBvXV2FEycOJ+0tDR69ZIdBe2Ny+Vi7+5N/ORb47Fa/D+0P3vWTFZ/9DLHjx+nb9++fu9f1JWVtYH09AF08tP0kmiY/CY2SKnDzaEiOzlF1VQ4FbpSmDQNvWbyGmq/NmkaYVaNQR2CSetgIzIo8M80Lyoqqt1RsHnzTk6fLkfXY+nRYyiDBn2Xu+9OpW/fvlIWtZ3bs2cPUcF5TJky2ZD+BwwYwNih8XzyyceSHBjs7NmznDmzhaefftzoUASSHPid3a2z5VIlOUV23EqBAotJw6rVnOZXd3RAKdCBcofO1txKduRVMaiDjfEJodjMgVP9uqKionZHQUbG9mt2FAwgJeVrzJrVn/79+9/UjgLRdmVmbiJ9Qm+6dulqSP8mzcT8uTPY+PN/UVBwDx07+qYyo2hcRkYGnTs7mDZtmtGhCCQ58KuzZU4+OV9OicONCY0gTUMz3XiqQNM0zIDZrKGUwqVgb2E1p8qczOgWTo8IY1ZZOxwOdu/eXbujYM+eMzgckURHJ5OScjv33efZUdChQwdD4hOBLz8/n/zznzLzsXsNjWPChAmkJq1iw4YNLF4sFfmM4HQ62bHjY77xjbmycyRASHLgJzmF1Wy4UIGuFFZNw3QT6wc0TcOqga6g2OFm9alSpncNY1BssA8irsvtdnPo0KGriwiz2bbtKFVVYYSGJtKv31TuuiuVtLQ0OnXy/o4C0TZlZGSQ0svCqFGjDI3DFmRjwbwpPP3KWhYsWCBTXQbYvXs3VVVHueceObskUEhy4Ac5hdVsOO9JDIJMWovfPE2aRhDg0BUbzlcAeD1BUEpx4sQJsrKyrp5RcJDiYgtWa8LVHQVLSU1No0ePHn7ZUSDaFrfbze5dG/nhg2OxBQUZHQ7p6dN54+21ZGdnM336dKPDaXcyMzcxdmwPevXqZXQo4ipJDnzsbJmzdsTAG4lBDU3TCDJdTRAuVBAdZG7xFMPFixfJyspiy5YtbN68h8uXXWhaHL16jWTChHmkpqbSu3dv2VEgWmz//v2EWS4xefJDRocCQMfYjsybOYy/v7+GqVOnSsLrR/n5+Rw5spHXXpNzLgKJ/Jb3Ibtb55Pz5V5PDGrUJghKse58Ocv6RjVrkeKVK1fq7Cg4daoMXe9Ajx7DGDjwOyxe7NlREBzs+2kL0b5kZmxi6thEEnv0MDqUWrNmzeRf7z3HoUOHGDhwoNHhtBuZmZlERZUwb948o0MR15DkwIe2XKqkxOG+ZieC92mahhXPGoQtlyqZ1q3h3QAVFRXs2LGjzo4CtzuGTp3SSEm5l5kz+9O/f6rsKBA+VVRUxKWzO5n97XuMDqWOvn37Mnl0d9avWyPJgZ+43W62bv2EO+6YSkhIiNHhiGtIcuAjpQ43OUV2TNzc4sPmMGkaJgU5RXZGxIfU1kFwOp3s3r376iLCrezefQaHI4KoqGT691/Avfd6koHYmoMFhPCDzMxMkrvD6NGjjQ6lDg2NuXNmsibzb1y8eJEuXboYHVKbd/DgQYqLc1iy5I9GhyK+RJIDHzl0tY5BUAOJwRv/9XWKL1/g+2+tve6x1x66C81s5uHf/6vJ/Vk0z/TChsNnuLRlLVlZW9i27SiVlaGEhHSnX78p3HVXKqmpaXTu3Fl2FAhDuN1udu7YwGP3jiYkOPA+KY4ePYahKctZv349X/3qV40Op83LzMwgLS2KQYMGGR2K+BJJDnzArRQ5RdWgaLCOQaeeyZzatxOX04HF+sVq7c+yN3Dh+CG+9qu/NKEnhcvtxuFw4HA4UCYL2wvL+Mdzm+mVNIJbbllC//6p9OjRA7M58Ksqirbv4MGDBHOByZO/ZnQo9bJaLNw6dzo/e3EdFRV3EBYWZnRIbVZpaSn793/CM8/cJR9WApAkBz5QWO2mwqmw3KDAUXzPZHTdTcH503RO8pRtVUqx4c1XSBo0kuQR4+p9nlv/Ihmw253ougJMWMxBWIIsxHRO4IXX3iJMyV5tEXgyMzczcVQXevVKMjqUBk2bNpW//u8HZGRkMGfOHKPDabO2bNmC1XqJhQsXGh2KqIfs1/GBvCqX50yEG1zTqWcyAAVnT9Xed3DzWi6fOkb6/Y/U3qcrnWp7NaVlpRQUFJCfX0RJSSUOh4bVGkZYWDRRUR0Ij4jAZrOhmUw4g9V1/QlhtOLiYs6d3M6cWVPQCNxPilGRUdx6yxi2Za/BLedv+4RSiuzs9cybN4qYmBijwxH1kJEDH8ivcmNqZIdCXGJvz7VXkwNd19n4v6/RZ8QEEvoNoKy8jA9/+ySf79qCy15NZFwCk5Z9h35jp2G1WNDq2YetXf2VWx3khiqfvDQhblp2djZJXVyMG1f/qFggmTVrJm+veoI9e/YwcuRIo8Npcz7//HMuX97F0qVPGx2KaIAkBz5Q4dKvnq7YcHIQEh5JZGw8+edOApCz4UPyzp5g/vefoKSkAqVMjJh/L7Me+hnBoWFcOn6I//vvr9N76BiCIqMbbFehcJt0774gIVpI13V2bNvAt+4ZRWhIqNHhNCqxRyLTJySzfuPHkhz4QEZGBj16wPjx440ORTRAphV8wK2aNqwf3zOZgrOn0N1uNr71B1LHTyeqaxJmUxBRUVF075dGaHgEJpMJTdNwuZyUFeY22q6uybSCCCxHjhzBqp8x7GjmmzF3ziyqruzh9OnTRofSplRVVbF798csXXq7VKIMYPI34wPmJq68jU9MpuD8afZ+8h5XLp5j2n3fwel0Y7FaqRl1+OjVX/LMbUP566OLSBo8mviejZ85b1KBO58r2qfNmzcxblgn+vTpY3QoTTZ06DBGDIhm3bpPjA6lTdm5cycu10k5ATPASXLgA2EWU5MKH3XqmYy9qoKP//xrBkyZTYeuiYCG9ZqzC+Z85+f8+J1PWfbsG/QaNr7RLT8aGmZd/lpF4CgrK+PUsS3cMmtyQC9E/DKzycSt89I5dXQjxcXFRofTZmRlbWTatP5SZCrAybuID8SFmNGVQjUyvRCf5NmxUFVWwvT7vovd4cBksmD60vkIJrOZpCFjOLVvG8d3ZjTYnrr6X7BDahqIwLFlyxYSOzta5fzy5MmT6dNdZ9OmTUaH0iacP3+ekyezWLLkbqNDEY2Q5MAH4kMsmDSNxpYF9kgdwtMbPuPJdQfo2C0Rh8OJxRJEQwsZdZebK5fO3bBNDY1gp6wzFYFBKcXWLRuYP2sYEeERRofTbGGhYSyYO4FPt6/B6XQaHU6rl5mZSceOlcyaNcvoUEQjJDnwgdhgM2FWDZfe9IWBbt2Ny6XXHodcXV7KgU0f4qiqwO1y8VnmWk7n7KDHgBENtqFrCotbw+aSkQMRGI4fP47mOMm0qVOMDuWmzZgxg9jwPHbs2GF0KK2ay+Vi+/aPufvuW7BaW3a8vPA9+YjpA2ZNY1CHYLbmVqKUalJpUIfDAZhqkwM0jb1rV7Hm90+CUsR06cHtP3qBzr1T6n2+wpOIRFcGt6p5XdG2ZWRsZszgDqSk1P9z2xokdE5gzrSBrNzwEePHN77uR9Rv7949VFQc5p57fmJ0KKIJJDnwkbQONnbkVeFSYG3C7xKHw4HZbK3d2hMcFsFXf/Vmk/tTmmeXQnSllE0WgaGiooJjn2XyjZ/NxqS17kHK2bNnsuqj33Ds2DH69etndDitUmZmBiNHdqFv38Z3XAnjte5/sQEsMsjMoA42dNTVgkg3orDbnVgsNzfUVrMQMbrShtUtUwoiMGzbto1uHauYMGGC0aG02IABAxg7NJ716z42OpRWqbCwkEOH1rNsmWxfbC1k5MCHxieEcqrMSbHDTRA0OBzpcrvRdfXFlEIzKBS6pghymYkrDfzKc6J9UEqxJXsDS+cMIjoq2uhwWkxD49Z5M9n4s3+Sn38PcXFxRodkqNBQ6NYNoqOhKXWMCgp03n33KUaPHurz2IR3SHLgQzaziRndwll9qhSHrggy1Z8geNYbmJudHNQkBhoaCcXhmJUMBInAcPLkSVyVx5g+7VGjQ/Ga8ePHk9ZrJRs2bODuu9vvVryoKBg3zpMUaJrn1hhPMtW+E6rWRt5NfKxHhJXpXcMwaRoOvf7aB3a7HYvF2qyFTtcmBp2LwwhzyOpfETgyMjIYMSCatLQBRofiNbYgGwvmTSFn98dUV1cbHY5hUlM9iUFNciDaJkkO/GBQbDDTu4VhMmk4VN01CAqFw+Fq1nqDLycGMZXBvghbiJtSVVXFkYMZzJs9EXMbq52fnp5Ol9hStmzJNjoUQ5hMEBvbtKkE0brJX7GfDIoN5o6kSKKDzDiVwnl1FMHpdKKU1qQphZqkoGaNQY+CSEkMRMDZvn07naLLmDRpktGheF1sh1jmzRhK5uY16Hr7O/3UYpHRgvZCkgM/6hFhZVnfKIbGBqNp4FAKh1thtgRhNtf/V1GzE8Gt6Z7RAgUdKoJJyo+SqQQRkLZkb2LujIF0iOlgdCg+MWvWTII5ycGDB40ORQifkQWJfmYzm5jWLZwR8SEcKrKz9uAprGEx6CbQriYCNbSr92hoWN0moiuDZbuiCGhnz56hqvQQ06d+2+hQfKZP3z7MnNKHbVs3MmzYoCY9R9c9tzattBSeeQYOHYLiYpg8GRYuhCeeAKVgwgT40Y+MjlI0kSQHBokMMjMoQrH461/jrvt+zZCJI6gOcuM2eUYITMpzumKww0yw04LNZZbKhyLgbd6cwZCUcAYPHmx0KN4VbIOQIDCb0YAnXn6+2U2Ul8PJk3DmjPfDCwgPPQTf+AY895wnE1q82POi33/f8/iiRVBWBhGt74yN9kiSAwPt2LGDqsow+nXtTXRVMFQZHZEQN89ut3No/2Z++YNJmE1taHQrxAZhIZ5Pvy0QFgaDBnnm7E+f9k5oASM7G/bsgaee8tzAkwjUrFx0u6FTJwgJMS5G0SySHBgoKyuLmJg+dO7c2ehQhGixXbt2Eht+hcmT29hCxJCrJclbuBKv5um9e7fB5ODgQVi2DH760+sf+/e/4cUXYcoUz4pG0SrIgkQDbdq0jf79R8lBLqJNyMrcyC3T+hPXsQ0Vu6nZ0O9FoaEQFOTVJo3XpQts3gwVFZ6vq6vh6FHPn2+/HbKyIDcXDh82LETRPJIcGCQ/P5/Dh/NJTU01OhQhWuz8+fOUFh4gffoUo0PxLh/l7W2uTsDcuTB6NKSne24LF8K5c+BweB43mSA8HIJl63VrIWM8BtmyZQu6HivJgWgTMjIyGNw3hGHDhhkdijCC2QxPP339/R98AG++6VmgOGYMJCX5PTRxcyQ5MEhmZiZduw4mOjra6FCEaBGn00nO3k384tEJWMzt6FdKfVv3fvEL+M53oKAApk+Hb33L6CiNNX++5yZanbY2uNUqKKXYtGkXKSlDjA5FiBbbvXs3HUILmTx5stGh+NdDD8Hs2Z5PxxkZnjn2NWtgxAhYvRpycqCw0OgohbgpkhwY4PTp01y44CAtLc3oUIRosazMjcyY3JfOndrRrptrt+6lp8PMmXD2LJw6Bf37e67p0wf27TM0TCFuVjsaAwwcmZmZaFoc/fr1MzoUIVokN/cyhZf2MGP6140Oxb8a2rq3Zg1s2wbjx8OOHZ59i21Im6/yKGrJyIEBMjOz6NVrFCFSEES0cps3Z5CWHMTIkSOMDsW/Gtq6N3OmZyph8WLP8YUdOxoapre5XFBZaXQUwh8kOfAzt9tNZmYO/fsPNDoUIVrE5XKx99ON3HrLOKzNOHK8TWho657Z7CkfvHw5WK0wfLjRkXrdqVNGRyD8QaYV/OzAgQOUlAQxYICsNxCt2969e4mw5TFlyhSjQ/G/hrbuXbgAjzzi2df/4INtslzwyZOeao+9eknZgrZMkgM/y8rKIiioC0lJvYwORYgWyczcRPr4JLp17WZ0KIGja1fPToU27sQJz81m8+RBn3/+OX/+84/5y19+wtChQ40OT3iBTCv4WWZmNv36jcEiNcZFK5afn0/e+U+ZNXOq0aEIA9ntUFUFH364Hqu1kCFDhhgdkvASSQ78qKqqim3bjpOaOsDoUIRokaysLFJ6mhk1apTRoQiDVVZWsnv3x3zlK3fKOTFtiCQHfrRr1y7s9kgpmSxaNbfbzc7t67ltzhhsQTajw/GtFh7T3BCXyyfNGmLbtq1o2jnuuusuo0MRXiTJgR9lZmYSFdWbrl27Gh2KEDctJyeHcOul9lERUVfgdnstSVDKU2m5LSUHWVkbmTFjIPHx8UaHIrxIkgM/ysjYTkqKHNEsWrfMjE1MHdODnok9jQ7FPyqqPP9vYYKg654m2tKpxWfOnOHs2a0sW7bE6FCEl8mqOD8pKiriwIFL3HuvTCmI1uvKlStcOLODnzx8t9Gh+I/DBSXlYAsCi9mzj68RLreLc+cvYwvpQHh4BLoOV654KiyXlvohZj/JzMwkIcHJ1KmyMLWtkeTAT7Zs2YLbHUNqqtQ3EK1XZmYmyd0Uo0ePMToU/3K5wVXV5MstwIdvv83f/l3IL/7nRUymtjdI63A42L59DQ89NFd2X7VBbe8nNkBlZWXRufNAOnToYHQoQtwUXdfZuX0Dt90ymtA2WNzH22bNnEmIdooDBw4YHYpP7N69G7v9OPfcc4/RoQgfkHTPTzZv3kX//vcbHYYQddhsEBHhKWTTmNOnzzJ1YjxzbrsVrFd/dbi8t1ivrenTJ5kpYxLZsH4tgwcPNjocr8vM3MS4cT1JSkoyOhThA5Ic+MGZM2c4fbqCuXNlSkEEBrMZhg2DTp2aNIUOwOjRPVm8+EunECoFdieUy2k8X6ahMXfOTD7a/FcuXrxIly5djA7Ja3Jzczl6dBPf+943jQ5F+IhMK/hBdnY20JGUlBSjQxECgIEDm5cYNEjTwGaFMCmyX59Ro0YxpH8Y69atMzoUr8rKyiIqqoS5c+caHYrwEUkO/CAzM4ukpJGEhoYaHYoQmM2eE4e9tqNW0zwr+cV1LGYLC+ZN58iBdZSXlxsdjle43W62bv2Eu+6aLsfOt2GSHPiYrutkZOyjf/9BRociBOA5Sc9s9nKjJpMXs422ZerUqfTsVEVmZobRoXiF52TZA7IQsY2T5MDHPvvsM4qKzKSlyXoDERjkPdy/IiMiWTB3LFuz1uB2u40Op8WysjIYMCCagQMHGh2K8CFJDnwsMzMTqzWB3r17Gx2KEMIgM2fOJMp2nt27dxsdSouUlJSQk7OOr3xlkVR6beMkOfCxrKwt9OkzGqvVanQoQjSutBR+/GOYPx8mToSf/hSqq2HZMli4EB57zOgIW6Ue3XswY1I/Nm1cY3QoLbJlSzZW6yUWLlxodCjCxyQ58CGHw8HWrUfliGbRejz0EMyeDR98ABkZcPSo5/+jRsE774DFAkeOGB1lqzTnlllUF+/n1KlTRodyU5RSZGWtZ9680URFRRkdjvAxSQ586NNPP6WyMlTWG4jWITsb9uyBp56C9HSYOdNzGMC5c1B5tY5BRQVERhobZys1ZMgQRg3qwCeffGx0KDfl88+Pk5e3m6VL5ZCl9kCSAx/KysoiPDyJbt26GR2KEI07eNAzfbB+/Re3HTtg6VLP/ydN8qxmbEPFfPzJbDJx67x0zhzfTHHxFaPDabaMjEwSEzXGjRtndCjCDyQ58KGMjK2kpIxqk4euiDaoSxfYvNkzOgCetQZHj8KKFXDrrZCZCR06wKefGhpmazZp0iT6JSo2btxkdCjNUlVVyaefrmXp0tvl91k7IX/LPlJaWsrevedIS5P1BqKVmDsXRo/2TCmkp3sWIJ47B7oOMTGea6KjoaTE0DBbs9CQUG6bM4HdO9bidDqNDqfJduzYia6fZvHixUaHIvxEzlbwka1bt+JyxZCammp0KEI0jdkMTz99/f0lJfDNb8Jbb0FUFDzyiP9ja0NmzJjB3/6xge3btzNx4kSjw2mSrKyNTJ+eSkJCgtGhCD+R5MBHsrKyiI9PJS4uzuhQhGiZqCj417+MjqLN6NypM3PSB7P8k4+YMGFCwNcLOH/+PKdOZfHEE/9ldCjCj2RawUc2bdpBSspwo8MQQgSg2bNmYnYe5ejRo0aH0qiMjAzi4qqYMWOG0aEIP5LkwAcuXrzIiRMlpKXJlIIQ4nppaamMG9aZ9esCe1uj0+lk+/aPufvuOVLIrZ2R5MAHsrKy0PVY+veX5EAEHrsdlPJyo0r5oNG2S0Nj/tyZXDqTTX5+vtHhNGjv3r1UVh6RQ5baIUkOfCArK4vExOGEh4cbHYoQ13E64coVzyYEr1AKHC4vNdZ+jBs3jgHJVtavX290KA3KzNzEqFFd6dOnj9GhCD+T5MDLlFJs3ryH/v0HGx2KEA3at8+TJIAnSai5OZ1u3C73FyMBjd0AdAUVlYa9ltbKFhTEgnlTOLD3E6qqqowO5zoFBQUcPryBZctk+2J7JMmBlx09epS8PCUlk0VAq6iAjRs91ZJPnoTTp+HgwTKW/3MtBRcuQrWj8VuVHUor4EqpJ0EQzTZ9ejpdOpSwZcsWo0O5jqfC6xXmz59vdCjCALKV0cuysrIwmzvLMJwIeC4XXLjguQG89da7BNvXc/es0VAReJ9k26LYDh2YP3M4b/z7I6ZNmxYw1QfdbjdbtnzCwoWTCQsLMzocYYDA+ElsQzIzs0lOHkVQUJDRoQjRZA6HgwP7NjH/lglYzPKZwZ9mzZpFiHaKAwcOGB1KrcOHD3PlSo4sRGzHJDnwIqfTSXb2IVJTBxodihDNsmvXLjqGX2HSpElGh9LuJCf3ZsrYRNavW2N0KLUyMzNISQlj2LBhRociDCLJgRft3buXigo5olm0PpmZG5k9NYVO8Z2MDqXd0dCYe8ssSvJ2caFmjsdAZWVl7N37McuW3Rnw1RuF70hy4EVZWVmEhHSnR48eRociRJNdvHiRkvz9pE+fYnQo7daoUSMZ2j+cdevWGR0KW7duxWK5yJ133ml0KMJAkhx40ebNW0hJGYPZbDY6FCGaLCMjg0F9gxk2TMp9G8VitnDbvOkcPbie8vJyw+JQSpGdvYFbbhlGhw4dDItDGE+SAy8pKytj9+7TpKbKlIJoPZxOJ3s/3chtcyZgtchCRCNNnTqVpIQqMjIyDIvh5MmTXLiwnaVLlxgWgwgMkhx4yfbt23E6o2W9gWhV9uzZQ0xYIZMmTTY6lHYvMiKC224Zy7bsNbhcxlSczMzMoFs3vdUcJS18R5IDL8nKyiI2th/x8fFGhyJEk2VmbmTmpGS6JCQYHYoAZs6cSXTwBXbv3u33vu12O7t2fcLSpQtkalRIcuAtmzdvJyVlhKzuFa1Gbm4uhRd3M2O6jBoEih7de5A+sS+bNq71e987d+7E6fycxYulXLKQ5MArcnNzOXq0SKYURKuSkZFB/15WRo4caXQo4hpz58zCXrKfkydP+rXfzMyNTJrUh+7du/u1XxGYJDnwguzsbHQ9ltRUOaJZtA4ul4vduzZx25zxBFmlmmcgGTx4CKMGdWDduk/81ufFixc5cSKTZcukIqLwkOTAC7KysujWbQiRkZFGhyJEk+zfv59I22WmTJEphUBjNpm4dd4MzhzfTHHxFb/0mZWVRYcO5cyePdsv/YnAJ8lBCyml2LTpU1JThxodihBNlpGxienjkujeTYaQA9GkSRPpl6jYsGGjz/tyuVxs3bqWxYtnyZkwopYkBy104sQJLl1yyZSCaDUKCwvJPbeTmTOmGB2KaEBoSCi3zZnInp0f43Q6fdrX/v37KS//TA5ZEnVIctBCWVlZmEzx9OvXz+hQhGiSzMwM+vTQGD16lNGhiBuYMWMGcRH5bN++3af9ZGZuZtiwePr37+/TfkTrIslBC2VkZNK79yhsNpvRoQjRKLfbzc7tG1kwZwzBtmCjwxE30LlTJ+akD2LThv+glPJJH1euXOHgwXUsWybbF0Vdkhy0gMvlIjv7EP37yxHNonU4cOAAoeaLTJkyxehQRBPMnjUTs+sYR44c8Un7WVlZhIYWctttt/mkfdF6SXLQAvv376e01Cb1DUSrkZmxiSljutGzZ0+jQxFNkJqayvjhnVm/7mOvt63rOlu2rGPBgvFERER4vX3Rukly0AJZWVkEB3cjKSnJ6FCEaFRx8RXOn97OrBmT0ZBKnq2Bhsb8uTPJPZdNXl6eV9s+cuQIBQV7ZSGiqJckBy1i5lvfupfERDPdulHn1rUrxMQYHZ8QX8jKyqZ3Vxg7dqzRoYhmGDt2HAOSg1i/fr1X283MzKB37yBGjZKFqeJ6ckZrC3zve99t9JrqatixA0pL/RCQEA3QdZ3t2zbwyLKRhIaEGh2OaAZbUBC3zZ3Kk79bR9XttxMSEtLiNisqKtiz52N+9rM75TwYUS8ZOfCxoCAYMwbk358w0meffYZNnWXy5ClGhyJuQnr6dLrGlpCdne2V9rZt24amnePOO+/0Snui7ZHkwMdMJrDZIDbW6EhEe5aRsZlxwzqRnNzb6FDETegQ04F5M4aTnbkGXddb3F5W1gZmzRoiR8yLBkly4AdKQViY0VGI9qq0tJQzn29l7i1TZSFiKzZ79ixCtFPk5OS0qJ3Tp09z7tw2liy520uRibZIkgMh2rjs7GwSOzsZN2680aGIFujduzdTx/Zkw/o1LWonMzOThAQnU6dO9VJkoi2S5ECINkwpxfatG7l11jDCZfiqVdPQmDtnJiV5n3L+/PmbasPhcLBjx1qWLJmPxSLr0UXDJDloSGkp/PjHMH8+TJwIP/0pXLkCs2ZBcnLda3/yE1iwAH73O0NCFaIhR48eRXOeYurUKQZHIrxh5MiRDEsNZ926dTf1/E8//RS7/Th33y1TCuLGJDloyEMPwezZ8MEHkJEBR4/CgQOwfDkMG/bFdfv3g8UC777reTw/37CQhfiyjIzNjB0SS0pKitGhCC+wmC3cNm86xw6up7y8vNnPz8raxPjxSVIhUzRKkoP6ZGfDnj3w1FOQng4zZ8LZs56tB9HRda/dswcmTPD8eexYaOFiISG8pby8nM8PZzFn9hRZiNiGTJkylaQu1WzevLlZz8vNzeXo0U0sXSqHLInGyaRTfQ4ehGXLPFMJjSkthfBwz5/DwqTakQgYW7dupXunaiZOnGB0KMKLIiMiuO2Wcbz69hpmz56N2WLGbnVTbXVRbXXjNunomsKkNMy6iWCnmWCnhawt2URHlzJ37lyjX4JoBWTkoD5dusDmzVBR4fm6utozrVCfyEioGd6rqPB8LYTBlFJs3bKR+TOHEhkhP5NtzaxZM+nUuZKc8hN83ukKp+JKuBhdzpWwakpC7JQFOygJsXMlrJqL0eWciiumy51pLP3Z4zhMVqPDF62AJAf1mTsXRo/2TCmkp8PChXDuXP3XDh0KW7Z4/rx9Owwa5L84hWjAiRMn0KuPM23qFKNDEV5m1xXHw7ow5clfYu4TgdOsoykwKc0zWqBMtbea+9xONyHRoYQNHM8bR4rZeL4cu7vlxZRE2yXTCvUxm+Hpp+t/bNEiz7TDokXwy1/CkCGwYoVnt8KUKRAX58dAhajf5s2bGTkwhtTUVKNDEV50tlrxyRU3JW6wWq1Ul5RjCbWg3WBbooaGw24HZSfYHIZLwd7Cak6VOZnRLZweETKSIK4nyUFzrVhx/X3PPOP/OIRoQFVVJUcPZXL/f6dj0mRwsK3IKdfZUKyjA1ZAs1iwW03Y7fYb1izQdR2n005kZAiapmHVQFdQ7HCz+lQp07uGMSg22G+vQ7QO8ptDiDZm27btJMSUM2nSRKNDEV5SmxgoCAJMGmiAzWbD7Xbc8LwFh8OBhk5w8BcJgEnTCNI0dF2x4XwFOYXVvn8RolWR5ECINmZL1kbmzRxETHSM0aEILzhbrb5IDLS6J7zabEFYzGC32xt4tsLhsGMLtl43iqRpGkEmDV0pNlyo4GyZ03cvQrQ6khz4gaZBVZXRUYj24PTp09jLj8hCxDbCrnvWGOhcnxiAZz1BsC0Ip9OOUuq657tcbnTdSWhIaL3tX5sgrJNFiuIakhz4mFLgckFBgdGRiPYgIyODYanhDBo00OhQhBdsKdE9iw+5PjGoYbPZMGs6TofjusccdjtmM1iDGl506FmHoFHscLPlUqWXIhetnSQHTeRyua5m4TT5Bp7/79r1xddC+Irdbufg/k3Mv2USZpPZ6HBEC5W6FDkVChOeNQYNMZtM2GwW7A478MXogVIKh9NOSEhwoxUyTZqGCY2cIjulDrd3XoBo1WS3QhNUVVXxyitvMmTIHHr0SGzy8yorITfXM3IghK/t2LGD+MgSJk2aZHQowgsOVSrceBYgNsZmC6aquhyX04XF6hklcDgcgJuQkJAm9WfRwKEUh4rsjO1c/zSEaD8kOWiCjRs38sILq3nhhUU0uO5HCINlZ2/ktmlpdIztaHQoooXcSpFToYOChnajvvHDByi+fJHv/+9HWC0WgoKubmu0Wvnzd+9CV4qvvvDnJo8iaZpnj2NOUTWjOoVgbmgeQ/z/9u48Ks77vvf4+3lmYxcCAUIChGTEgFAk2dZibRYSQrajuG4cR3Yc5zq1E9e52eq4SdM0rZvNyb09ubm9OUmapb69zU1O0jbNaW9ybxpHMQK0SwxaLWtDCzCIfRtme57nd/8YhDUSIIRBMzDfl4/OkWd55gsHzXz4Ld9fQpBphQmoq6sjL6+C7OzsWJcixKiam5sZ7D7J9u2VsS5FTIGuMPjMyG/zY8krLqHnWgtGOLLWIMmVhGmFeLP+dbznT7F+1/MTHjW4zq5r+MKKroBMLSQ6CQcT8MYbhygvvz/WZQgxppqaGla6U1i1alWsSxFToD2ssBj/DTp3UQmWZdLZfBkAp9OBXYc3fvxtCpfdR/Gq1bhcrjt6XZ3IWoUOv8yFJjoJB7dx9epVmpoGqKioiHUpQowqHA5zovEN/uCRTdhtMlM4G3SEIwsRxxvZzysuAaDzahMQ2dZ48fAeOi6fY937PzKhhYg30zQNTdNo98vIQaKTd5LbqK+vB3IoKyuLdSlCjOrw4cNkpXSzZcuWWJcipojPhNttcMpZdA8AHVcuApE2yfU/+3uKV64l372M5ORkvrRzddRzwkE/D7/wp2za9UdjXtdSiiFDtlclOgkHt7FnTy3FxfeTmpoa61KEGFVd7e95eKubvNy8WJcipoipiOxKHOcX/+S0dDKycui42kTYMPD89t9ov3yBD7z4OVJSXNhtdl759ZGRx/d3tfM3T1WxbHP1bV/fGKWhkkgsMq0wDsuyqK1tpLx8ZaxLEWJUXq+XnvZGtldVxroUMYVsGuMGA4UiFA6RVVCM9+I5enq6qfnJ91m6bgul960jIyPjlucc+92vKFy2kqz8gtu+vl12KiQ8CQfjOHPmDJ2dmhx7K+LWnj17WF7i4v77ZcHsbJJqu/XN2bIsgsEgA4ODdPf00Nffz9yCRfR4r3LpSB39HW088sefweV0jbrWoPH1f+feHY/d9rV1TSPFLh8NiU5+AsZRW1uL3T6fpUuXxroUIW5hGAaNR9/gsXdvxGEfuz2umHlyHBoWEDYN/AE//QP99PT20D84gGGZJKekkJWdTXHZCsJBP7/7+7/lXZUPM39x6ajXa7v4Fp3Nl1m+5aFxX1cphVKK3GTpsJnoZM3BOGpr61m69AEcDnnjFfGnoaGBjKR2KisrY12KmCLBUJBTp05x6OwlgvdVY4SDWKaJ0+kkNT0dp9MZdbpi7uLIjgX/QB9VH/7kmNdtfP3/UL5hK8lpt0433MgismMhJ1k+GhKd/ASMIRQKsW/fGR555AOxLkWIUdXWvkH15ntYkL8g1qWId6Cnt5fGxkY8Hg91B/fT0t5ORk4OVSseJDktg2SHbcwtiUXLVvG13afHvb5lWRzb/Ssee+mV29ZiWIo0p052kowcJDoJB2M4evQoPl8KFRXLY12KELdob2+no/kI1S89G+tSxB1SKK5evYrH4+Hw0aMc8jTQ2d/HEncpDz3xODt27GDJkiXsbxti37Wh2+5auJ2LDQcwDYOlazaPX5dSoMGKrCRpnSwkHIylrq6O1NRFFBYWxroUIW5RW1tL2RI7a9eujXUpYgLChsFbb53B4/Gw7+BBTp8/x1A4xH3r1vLHL7/Etm3bmDt3btRzKrJcHGz3YyhwvIPP6sbf/Tsrtj6CzT7+272hwKZpVGTdWVdFMTtJOBjDnj37KC+vRtdlzaaIL6ZpcvTw7/mzFzbgck7kzD4RC4O+QY4dO0aDx0P9gf1c8XqxpybzYGUlX/nYC6xfv37c9UwZThsrslx4ugJYKrKLYDKe+Pw3bvsYSyksFPdmJZHhlCkFIeFgVP39/TQ0XOHpp6Vlsog/x44dI9XuZcuWF2NdirhJ27U2GhoaONrQwP4jh7nW082C4kVUPVTNKzt2UF5eHjn9cII25qfQNBCmN2TihDt67kQppQgrRabTxsZ8OapZREg4GMX+/fsxjLlynoKIS7V73qBqwyKKCotiXUrCMy2T8+fP4/E0cuDwIY6/eZo+/xDvuncVH3zxBbZv305e3uQ7V7psOtUFafyiqZ+QpXDqUxsQlFKELIWua1QXpOGyyUipiJBwMIra2lpycsrJycmJdSlCROnq6sJ75RA7Pi67aGLFH/Bz4sRJGhs91O3fT1PzVUy7zobNm/n8V77E5s2b7/io5PEUpTuoWpjK7mbflAaEkWCgaVQtTKUoXbZsi7dJOBhFTc1Byso+GOsyhLhFbW0tJYWwbt26WJeSULq6u/B4PDQ0eKg/dABvVydZeXlsrdrGSzv+klWrVk3r+qQV2UkA7G7xEVIKB5NfgwCRNQZhFRkxqFqYOnJ9Ia6TcHATr9fL+fN9bNsmUwoivpimyaGDv+czH15HctLU/WYaM7oGLic47OOfTXwnlALDhEAIrMmfLGgpi8uXL+PxeDh05AhHjjXS4xukZFk5jz3zNNXV1RQV3d1pnRXZSWQ6bbzePEhvyERXYNfubBRBKYWhwCKyxqC6IE1GDMSoJBzcpK6uDsvKory8PNalCBHl5MmTJGstVFaOfdzujKHrkJn2diiYyoV2Djsku6BvMBIUJigUDnH69Gk8Hg97Dx7gbNNFApbJmvXr+dQXPk9lZeWoBxrdTUXpDp4pncNe7xDHu4OElAJLYdc1dEYPCkopLCINjtAi2xXvzUpiY36KrDEQY5JwcJO6ujqKiu4jPT091qUIEaW2toYH1y1k8eLFsS7lnUtxRQLBdDTb0bTICEJKEvT7xn1oX38fx44d42hDA3sPHqC5/RrJczLYsnUrz730adasWRN37dNdNp1tBWmszk3mVHeQ490BfGGFoRSaFpkyuE7XNJRSaJpGmlNnRVYSFVku2a4obkvCwQ2UUtTUNLBy5adiXYoQUXp7e7l68QCfe/59Y7bSnVEcjukJBtdpWmQE4SYKRUtLKx6PhyNHj3Cg4Sidfb0UldzD9sceZceOHZSUlEzLlsGpluG0sX5+CmvzkukKmHT4Ddr9JkOGhaEU9uHTFXOTbeQk28lOsknnQzFhEg5ucO7cOa5ds2QLo4g79fX1LFlgsmHDhliXMjX0u/AhpWmgRU6vPHv2LB6Ph/2HDnHy7Bl8oRAr7r+P5z71Caqqqpg3b9701zNNbJpGbrKd3GQ78s4lpoqEgxvU1dVhs+VRWjr6sadCxIJlWRzY9zs+/vQaUpKlSc2d+MGPfsQbtXu43NqK5nKwubKSv3ruWTZu3IjLJW2ChRiLhIMb7NlTR0nJOpzSklbEkTNnzuBUV6isfDrWpUy//n549VU4dQp6e2HLFnj5ZXjqKbhwAc6fjzzu0CH48pcjowM7d8KLo3eL/I8D+9iwrZIv7NjB8uXLZ8R0gRDxQMLBsHA4zN69p6mqel+sSxEiSk3NG2y4L4+lS5fGupTp9+KL8MIL8I1vRLYiPvkknDgBP/955PbrFi2CX/4ysnbhiSfg2WdhlMZDv/jlv97F4oWYPWQfy7Bjx44xMJAk6w1EXBkYGKDp7F4eeahydixEHE99PTQ0wFe/Ctu3w44dcOXK8LbHzOjH5uVFggFE7pcD0oSYUjJyMKy2tpakpAIWLVoU61KEGLF3714WzQ+xceMsWYg4npMn4Zln4ItfnPhzamuhuBhk/YAQU0ri9rA9e/ZSVvYANpvs/xXxQSnFvr27efSh+0hPS4C+GwsWQE0N+IZ7EwQC8NZbYz/e64VvfxteeeWulCdEIpFwAPh8Po4cucSyZTKlIOLHuXPn0EIX2ba1Mtal3B07d8K6dZEphe3b4fHH4erV0R8bCsGnPx1Zm5CaenfrFCIByLQCcODAAUKhDJYvl3Ag4kdNzRs8sDKLsrKyWJdyd9hs8LWvjX7frl2RaYdduyK7FI4dg3Pn4HOfi9z/ne/A/Pl3r1YhZjkJB0T6G2RlucnNnfy560JMJZ/Px7k363jxrx5B12bPAJ+3zUtDg4fqp564sy3D//RP0f9fVhbZySCEmBYSDoCamgOUlT0he6BF3Ni/fz8F8/xs2rQp1qW8I6Zlcu7ceRobPew7eJATZ95kIOhn+5OyZViIeJbw4aC9vZ033+zkIx9ZHutShAAiCxH31u/mgztXMidjTqzLuWNDfj8nT57A4/FQf2A/F5uvgsPB+s2b+MKHvsKmTZukO6EQcS7hw8HevXvliGYRVy5evIgxdJaqrZ+OdSkT1tnVSWNjI0eOHmXf4UN4OzvIzp/P1qoqPrPjFVauXIkuvQiEmDESPhzU1tZSULCKOXNm3m9oYmZRKIIOk4DDIOAwMXULS1PoSsNm6SSFbSSF7dTW1bJ6eSYVFfE7mmUpi0uXLuHxeDh4+DANJ47TO+RjacUy/vBDH6S6uprCwsJYlymEmKSEDgeRI5qPUl7+sViXImaxsM2kNyVIb0oAw6ZQKDQ0FGrkMSP/r8D93AOUqBJ8lkZGHP2yHQqHOHXqNB6Ph70H9nPuchNBZbF2wwY+9YXPU1lZSUZGRqzLFEJMgYQOB01NTbS0hHjf+5bFuhQxC5maRUfGEL0pQSwtEgR0pXH9P25qh6yAUDhMalY6PUnzeO2ayYoUjY1zdFx344jjUfT29XLs2HEaGo5Sd+AALR3tpGRmsLWqio/86UusWbMGuz2h30aEmJUS+l91bW0tup6L2+2OdSlilvE5w3jnDhKymWhoI6FgPBoaoUCAZJeGS9MwFHh8iqagSXWmjaKk6Q8ICkVLSwsej4fDR45ysOEInf19FJcuZfvjj7Fjxw7uuece2dkjxCyX4OGgjiVL1pCUlBTrUsQs0pMSoC3Th0JNKBRcZ5oGShm4XKloGjgAS0GvAb/oNKnK1FmRNvXzDIZpcPbsWRoaGth38CCnz59lMBjk3rVreP5PPkVVVRXZ2dlT/rpCiPiVsOHANE3q6k6yZcsfxLoUMYtMNhgABIMhHHYNu90xcpuugVNBSMHuXgtgSgKCb8jH8ePHaWhooP7AAS57W9GTXDy4tZJXPvocGzZskO2GQiSwhA0Hx48fp6/PIUc0iynjc4YnHQyUUhjhIOlprluepWng5O2AkGnXJjXFcK39Gh5PI0cbItsNr3V3M7+okG3bq/iLHTuoqKiQ6QIhBJDA4aCurg6Xq4DFixfHuhQxC5iahXfu4KSCAUA4FELXFa4xWgqPBATg9V6TZ3Jtt12kaFoWFy9exONp4ODhw3hOnaRvyEfFqpU89dHnqa6uZr6cRyCEGEXChoM9e/bidssRzWJqdGQMEbKZkwoGAKFQCJfTPm6jIE0Dx/AahL19Ftvm3vqzGwgGOHXqFB6Ph7r9+7hw5QqmTeeBTRv53JdeYfPmzaTKKYZCiNtIyHDg9/s5ePAcjz/+kViXImaB630M3t6ieGdM08Sywrhct//Q1jXQFRwfUqxOV2TYNbp7umlsbIysHzh4kJbOdubm5lC5bRuf/OIXuPfeeyUECyHuSEKGg0OHDhEMZsh6AzElrvcx0NXoweB///nz9F5r4ROv/eaW+374yfejgA994wc4HI5bnzwKOxA0Lf7p8Jtc/PXPOHyske7BAUrKy9j5gV1UV1dTXFw8+S9ICJHwEjIc1NXVkZlZQn5+fqxLETOcQtGbEgAYc9QgZ1EJl44fwgiHsDveXlNwZt9uvOdP8d4v/HeSkpzjjjkowDDChMNhQqEwOBxcsqfTo8PH/+yzbN26VVqACyGmTEKGg5qa/ZSVPSYrs8U7FnSYGLaxRw0gEg6UZdLdconc4lIgsjthz4+/TVHF/SxetRrnKAsRLaUIh8MY4TDBcAjTtNB0DafTidPpJGVBAZ/65t+Sm5yQ/4yFENMojjq33x3d3d2cOOFl2TJpmSzeuYDDiDojYTQ5i0oA6LzaNHLb6drf0H7pLOuffAGX045Nj6wJMC2TQDDAwOAAPb099A/0EzTCuFwu5s7NZF52NhnpGbgcDpSCDr8xfV+cECJhJdyvHPX19VhWtqw3EFMi4DBvuxAxp+geALqGw4FlWdT+5LssuW8j+aUV2O02vv8n/4nmM8fRhkNC4bJVPPPqd3E5nej6rYsJNU1D06DdbyI/yUKIqZZw4aCuro78/Hcxd+7cWJciZgFTt4ZHDsYOB0lpGaRl5dLZfBHTNDj2u3+n48oFqj72F4CJb2gI0zR45BN/wf0P/yFOp3NCux4spRgyrKn7YoQQYljChYOamsOUlT0f6zLELHH9tMUbKaWwLAvTNIe3KZpkLlhEW9NZ+nq7qPvpd7lnzUYKy9w4HA5cLhdOp4uU5GRczjtrWWyo8ac0hBBiMhJqzcHly5e5fHmI5cuXx7oUMQsYhoHf58eyLAJ+Pz7fIP39ffT1dTMw0MPQUB/hsA9dCzN/8RL62q5ytWEPA51t7PzYZ5mbOZe01DQcw2cp/N/v/hdefe9GXvvs87RdfGtCNdhlUa0QYhok1MhBXV0dmiZHNIs7YxgG165do6WlZeSP13uBa9fOs/GP/oBVjz6EEQ5gt+skJdmx25Ox2+3Y7XZ0LZK/C0srOPKrn7P7tf/BuyofZv7i0qjXeOiFl8lddA+6zcb+X/6E//X5P+ZP/uFXuFLSxqxL1zRS7AmV74UQd0lChYPa2jqKi+8nJSUl1qWIOGSa5igh4CLXrp3HNHvRdR/z5rkoLy9g9eoS3O73M6f8fs45UnClp467NTZ3cWTHgn+gj6oPf/KW+wvLV4z8/cGnnufo//tXrp4+TsnqDaNeTymFUorcZOl8KISYegkTDizLYs+eRjZs+LNYlxI30tJgwQJITY307Z8KhgHt7ZE/VpyulTNNk87ODpqbW2hubqalpYW2tkt4vWdHQkBWlp2ysgJ27lxKael7cbvduN1usrKyoq7V7je4cLYPCxjvY7po2Sq+tvv0hGvUdX3cLZIWkR0LOdLjQAgxDRLmneXUqVP09Nilv8Gw/Hy4/364vp5tqsKBUrBoUSQcHD4c24BgWRYdHR20traOhACvtwmv9yyG0YOu+8jMtFFWVsBDD5Xgdj9GaWkpbrebefPmTeg1spNspDo0BkMWNtvkvon+wX5azpykeOVqNDQO/NtPGRroo6DsXWM+x7AUaU6d7CQZORBCTL2ECQd1dXU4HPmUlJTEupSY0zRYuTLy93EOAZz0tQFycmDhQrh6dWqvPxrLsujq6qK5uZnW1lZaWlpobY2EgHC4G00bZM4cHbd7AVVVJbjdO3G73ZSWlpKbm/uOOmXaNI0VWUnsuzaEUmpS17IMg9/+6Ft0Nl9Ct9nILynn2Vf/juS0jFEfr5QCDVZkJWGTBYlCiGmQMOGgpqaO0tIHsNsT5kseU1YWTPCMn3ckL29qw4FSiq6uruH1AM20tLTi9UamA4LBTjRtkPR0KCtbSGXlPbjdD41MB+Tl5U1bu+yKLBcH2/0YChyTeInUzCz+89/984Qfb6hIKKnIurNtj0IIMVEJ8UkZDAY5cOAsjz76bKxLiQujtPGfcpoGrkl+diml6O7uvmlh4GVaW98aCQFpaQq3ewGbNi3B7a4aCQH5+fl3/cyMDKeNFVkuPF0BLBXZRTBdLKWwUNyblUSGU6YUhBDTIyHCweHDh/H706RlcpxRStHX10tzcyQANDc34/Vewes9i9/fjqYNkpJi4nYvYMOGJZSWVo6EgAULFqBP9ZzIO7AxP4WmgTC9IRMnTEtAUUoRVopMp42N+bLjRggxfRIiHNTX15ORsYSFCxfGupSEFAkBfTctDLxMS8tb+P0daNogSUlhli6dz9q1S3C7N46sCSgsLIyrEDAWl02nuiCNXzT1E7IUTn1qA4JSipCl0HWN6oI0XLb4/54IIWauhAgHe/bsx+1+eEZ8yMRcfz+8+iqcOgW9vbBlC7z8Mjz1FFy4AOfPRx7X2wtPPhl92006Otr5+te/T2vrW/h8bWjaIC5XiJKSPO67bwkf+MD6kZGAwsJCbLaZPUxelO6gamEqu5t9UxoQRoKBplG1MJWi9LuwYEQIkdBmfTjo6+ujsbGZZ56RKYUJefFFeOEF+MY3IvsQn3wSTpyAn/88cvt1qam33nYTw+hi5cpWdu1aPRICioqKZvWi0BXZSQDsbvERUgoH72wNgjU8laDrkWBw/fpCCDGdZvy7tKkUXQGTdr9Bh9/EZ1iYSmHTNFLtOt6zb5FZuJJlFdLf4Lbq66GhAb761cgfgIGByH7HzMzoxzoct952k4qKcr73ve9NS6nxbEV2EplOG683D9IbMtEV2LU7G0VQSmEosIisMaguSJMRAyHEXTNjw0F/yORUd5Dj3QF8YYWlIsOu1g2n1OmaRjCtkA9868v0OHTU0BCZQy4c5swevp42J0/CM8/AF78Y60pmvKJ0B8+UzmGvd4jj3UFCSoGlsOsaOqMHBaUUFpEGR2iR7Yr3ZiWxMT9F1hgIIe6qGRcOgqY18oZrKgUK7LqGQ9OG33Cj33T7BwewOVIJ2yw60ofoSvOTOeQipz8Fm0q8N1y/f4i2tj4g/9Y7FyyAf/kXeOmlyLRBIACXL4McVDUpLpvOtoI0VucmRwVZQyk0jVuC7PUmSmlOnRVZSVRkuWS7ohAiJmZUOLgyEOa3zYP0hUx0NJyahqaPPVRrWiaGqXC5bNhUpFe90qA7NcCgK0x+bxqpodk5VBsIBIY7BbYO9wq4itd7ju7uyzz66Gre856v3fqknTvh4EHYvj0SDpxO+MxnJBy8QxlOG+vnp7A2L5mugEmH36DdbzJkWBhKYR8+XTE32UZOsp3sJJt0PhRCxJSmlBr7dJc4crwrwO4WH5ZSODRtQou8/AE/fX1DzJkzF017e5RAobA0hYbG/N5U5g7N3EVewWBwOABEOgZGQsB5urqa0DQfuu6jqGguZWWFlJVFFgWuWbOGoqKiO3uhXbsi0w7Ll8OXvwxlZaPfJoQQYsabEeHgeFeA3c2RYODUtQkv7Orr6yUU1klPv7VH/UwLCKFQ6IZRgBZaW5tpbT1PZ2cTmjaIpg1SUDCH8vIiyspKR3YHlJSUyBHVQggh7kjcTytcGQiPjBjcSTAARTAUxuFIG/VeDQ1dgaUp2jJ9OA1bXEwxhMNhvF7vDa2Dm/F6L9DefmEkBCxYkE55eSFbtpTidm+htLSU0tJSUlNTY12+EEKIWSCuw0HQtPht8+AkggGEDQPLAsc4e+pvDAjezEEWd8y5a4sUw+EwbW1to4YAGEDTBpk/P5Xy8kI2bSrF7d4wEgLS09PvSo1CCCESU1yHg73eIfpC5g07ESYuFAoBNmy3abhzPSCE7CYdGUPM7xt9pGGyDMMYCQGtrS00N7eMhADL6kPXfeTlpeB2L2T9+lLc7qdHWgdnZIx+ZK8QQggxneI2HPSHTI53B9GZ2OLDm4VCIex2x4RChYaGBvSmBMkeTJ5UHwTDMGhvb6elpZnm5sguAa/3Im1t50ZCwLx5LsrLC1i7dilu966REJB5m2ZCQgghxN0Ut+Hg1HAfA+cYH+6vffZ5etta+MyPf3PLfd958f0Yhsmz3/zphF9PG55e6E0JkjMw9gI+0zSHQ8DbJwm2tTXR1nYO0+xF131kZTkoKytg586llJY+PrI4MCsra8L1CCGEELESl+HAVIrj3QFQjNnHIK+4hKbGQxjhEHaHc+T20/W7aT17isf+/Jvjrje4WWTsQNGbEmDeQDKWadHZ2TkSACInCUZCgGH0oOs+MjNtlJUt5OGHl+J2/yGlpZFdAvPmzXun3wIhhBAiZuIyHHQFTHzhSKvZseQWl2BZJp3Nl5i/uBSItJ/d/Q/fpnD5vSxa+cDETvlTCsuyMC0LU1mENfjWD7/JmcN7R0JARoZOWdlCqqtLcLvfMzIdkJOTM6XH8gohhBDxIC7DQbvfGGl2NJa84hIAOq80jYSDkzW/oa3pLLu+/B3sdifc+HylsJSFaVqYpollmpiWiWkagAVEWtompaWxYUcWTz/67pHpgNzcXAkBQgghEkZchoMOv4l+mx0KOYvuiTz2ShMAlmXx+3/8LiWrN5JXUoGmaQQCAQ784n/S8OufEfANkDl/IU/89bdxpaRit9twOm3Y7S7sdjt2ux2brhOyYMN7n2TrQukZIIQQIjHFZTjwGdbwoTRjh4PktAwysnPpuHoRgOO7f0X7lQu893NfRdd1QiEfx/7jFzR56vnQf/0Bc+cvpLv5Enn5ebhcSWNe21IWQ4Y1DV+VEEIIMTPEZTgwJ9jRObe4hM4rTVimye9//D2WbayiqHwlhmmgTBPPr37GR7/1j2QvjJwjkOZePqHrGvHfUVoIIYSYNnF5ZvFET6TLXVRCZ/MlPL/9N3par1L1R58EwG6zM9jdSTjg51Tdb/n6+zbzrWffzeFf//OErmuX9QVCCCESWFyOHKTa9Qk1PsorLiHo9/EfP/xvLK98eGRhIsBAVzsB3wCdVy/xpz99na6Wy7z28nPkFC6meMXqMa+pDx+fK4QQQiSquPwUzEm2YSnF7Q6MzF0c2bHgH+ij6sOfjLrP7oycsrj1Qx/D4Upi/hI379r6CG8drB3zemr4NXOT77xDohBCCDFbxOXIQW6yHV3TsIDxPqaLlq3ia7tPj3rfvIJF2G5qn3y77YjW8GNykuPy2yKEEELcFXE5cpCdZCPVoWFYk18Y6ExOYfmDO6j5yQ8wwiHaL1/gRM1vcK97cMznGJYi1aGRnSQjB0IIIRKXpm43dh8j+9uG2HdtCOckTmS8zj/Yzy//5i85f3QfKRmZbHn6o6x5z65RH6uUIqQUG/JSWD9/7LMVhBBCiNkubsNBf8jktTO9KAWOcdooT5WwFemQ+FxZJhlOGTkQQgiRuOJyWgEgw2ljRZYLCzXcEGn6WEphoViR5ZJgIIQQIuHFbTgA2JifQqbTRngCOxcmSylFWCkynTY25st0ghBCCBHX4cBl06kuSEPXNELW1AcEpRQhS6FrGtUFabhscf3tEEIIIe6KuP80LEp3ULUwdcoDwo3BoGphKkXpjim5rhBCCDHTzYgN/SuyIw2Ndrf4CCmFAybUQXEs1vBUgq5HgsH16wshhBAijncrjObKQJjXmwfpDZnoaNi12zc2upFSCkOBRWSNQXVBmowYCCGEEDeZUeEAIGha7PUOcbw7GDm9UYFd19AZPSgopbCINDhCixzqtCLLxcb8FFljIIQQQoxixoWD6/pDJqe6gxzvDuALR9YiaJoWte1R17SR21MdGiuykqiQ7YpCCCHEuGZsOLjOVIqugEmH36DdbzJkWBhKYR8+XTE32UZOsp3sJNuEj4IWQgghEtmMDwdCCCGEmFoy6S6EEEKIKBIOhBBCCBFFwoEQQgghokg4EEIIIUQUCQdCCCGEiCLhQAghhBBRJBwIIYQQIoqEAyGEEEJEkXAghBBCiCgSDoQQQggRRcKBEEIIIaJIOBBCCCFEFAkHQgghhIgi4UAIIYQQUSQcCCGEECKKhAMhhBBCRJFwIIQQQogoEg6EEEIIEUXCgRBCCCGiSDgQQgghRBQJB0IIIYSIIuFACCGEEFEkHAghhBAiioQDIYQQQkSRcCCEEEKIKBIOhBBCCBFFwoEQQgghokg4EEIIIUQUCQdCCCGEiCLhQAghhBBRJBwIIYQQIoqEAyGEEEJEkXAghBBCiCgSDoQQQggRRcKBEEIIIaL8fwb1Qsw7LQifAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 500x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " - The complex has 8 0-cells.\n",
-      " - The 0-cells have features dimension 1\n",
-      " - The complex has 13 1-cells.\n",
-      " - The 1-cells have features dimension 1\n",
-      " - The complex has 6 2-cells.\n",
-      " - The 2-cells have features dimension 1\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n",
-    "describe_data(lifted_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create and Run a Cell 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 `x_0`, `x_1`, `x_2` which are the features of the nodes, edges and cells respectively. It also uses the `adjacency_1`, `incidence_1` and `incidence_2` matrices so the lifting should make sure to add them to the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Model configuration for cell CWN:\n",
-      "\n",
-      "{'in_channels_0': None,\n",
-      " 'in_channels_1': None,\n",
-      " 'in_channels_2': None,\n",
-      " 'hidden_channels': 32,\n",
-      " 'out_channels': None,\n",
-      " 'n_layers': 2}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from modules.models.cell.cwn import CWNModel\n",
-    "\n",
-    "model_type = \"cell\"\n",
-    "model_id = \"cwn\"\n",
-    "model_config = load_model_config(model_type, model_id)\n",
-    "\n",
-    "model = CWNModel(model_config, dataset_config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "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. "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "venv_topox",
-   "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": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/tutorials/graph2hypergraph/knn_lifting.ipynb b/tutorials/graph2hypergraph/knn_lifting.ipynb
index 40bf15b9..67ac3f41 100644
--- a/tutorials/graph2hypergraph/knn_lifting.ipynb
+++ b/tutorials/graph2hypergraph/knn_lifting.ipynb
@@ -48,9 +48,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "# With this cell any imported module is reloaded before each cell execution\n",
     "%load_ext autoreload\n",
@@ -81,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -119,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -132,7 +141,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -146,6 +155,8 @@
      "text": [
       " - Graph with 8 vertices and 13 edges.\n",
       " - Features dimensions: [1, 0]\n",
+      "tensor([[0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 3, 5, 5],\n",
+      "        [1, 2, 4, 7, 2, 4, 3, 5, 7, 4, 6, 6, 7]])\n",
       " - There are 0 isolated nodes.\n",
       "\n"
      ]
@@ -156,6 +167,26 @@
     "describe_data(dataset)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Data(x=[8, 1], edge_index=[2, 13], y=[8], num_nodes=8)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset[0]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -185,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -225,21 +256,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Transform parameters are the same, using existing data_dir: /Users/leone/Desktop/PhD-S/projects/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/557134810\n",
+      "Transform parameters are the same, using existing data_dir: /home/bmiquel/Documents/Projects/Topo/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/557134810\n",
       "\n",
       "Dataset only contains 1 sample:\n"
      ]
     },
     {
      "data": {
-      "image/png": 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9+cEHH2z552PlypUAuWNjivX+++8zYMAAbrjhBubPn8+YMWOYPn06p556Kueee26vmUs1YcKEXucthGC//fYr+v3mBwiAkSNH9jqQzPzve8wxx5Q9L/BmTjunZVTu57i5ubnk6Tht9OjRva77AQMG5PHklStX8p///IchQ4YUPQ3jvsbt+cTjcc4++2xuvfVW9uzZQ2NjI/fccw/19fV88pOf7HWahT8Pffr0YcSIEbnbkPHvbywxhRX+exa7z1q5ciXvv/8+Q4cOLXoa5vvZYpdpyJAhedev8b3HHntsr9OaPHlyr/O2cvnr6uq4/vrrufLKKxk2bBjHHHMMp59+Oueeey7Dhw8v+ndVSi8HNjJ+2/jsZz9b8o5n2rRprs+nX79+jBgxgtdee63XnxkPPKUezOrq6nod9Z3JZDjppJPYsWMHX/va15gyZQpNTU1s3LiR8847r9dvUXZKJBJFvy7KHOS3fv36Xg8OCxcuZM6cOY4vRxA5mdVtxnVz4403liRxxjNMZ555Jscffzx//etfeeyxx7jxxhu5/vrr+ctf/sIpp5xS8bzMv8kb5x2LxXjkkUeKzl7smS2rFTsv8GZOO6dlFPR1a+X8stkshxxyCD/5yU+Kfq/5YGQ35wNw7rnncuONN/K3v/2NT3/60/zhD3/g9NNPp1+/fhXPozDj3//uu+8u+iCZTOY/7BS7z8pmswwdOjTv2QxzpRYmL7Jz+a+44go+9KEP8be//Y1//vOffPvb3+a6666jtbWVww8/3LfLGER6OShR4bYNPT+Qffv2JZPJ5L0nQrHGjRvHa6+9hhAi77TeeOMNS+d/2mmnceedd7J06VKOPvpoexe+oFdffZU333yT3/3ud5x77rm5rz/++ONFvz+bzbJ69ercb30Ab775JtBz1L7bhg8f3uu8Dz300JLfX+y6AHLv7/DGG2/02vLfeOONXu//4PdcxmUqdh2vWLEi7zJb/fmYNGkS0PPbSqWfOYARI0ZwySWXcMkll7B161aOOOIIfvCDH1haDgqbNGkSQggmTJiQ929Wqk2bNvViaFb/fb2c0+5pWa3Uz6Hb7y3VpEmTeOWVV5g3b54np1eugw8+mMMPP5x77rmH0aNHs27dOn7xi18U/d6VK1cyd+7c3H/v3r2bt99+m1NPPTV3uQGGDh3q+N9/0qRJPPHEE8yaNavXImnOuD2tXLky7xmyd955p9ezl+PGjeOtt97qdRqFX7N7+SdNmsSVV17JlVdeycqVKznssMP48Y9/zO9///uKf1fm9DEHJWpqaur1NsqJRIIzzjiD+++/v+hv9eanyk899VQ2bdrEfffdl/vanj17Sr4cUdhVV11FY2MjF1xwAVu2bOn153Z+ozF+ezD/HSEEP/vZz0r+nZtvvjnve2+++WZqamqYN2+e5fMtVX19PSeeeGLe/wqfAjRnPNgUXh9HHnkkQ4cO5bbbbsujXY888gj/+c9/OO2003qdlp9zQc/1vnTpUp599tnc19rb27njjjsYP348U6dOzX2flZ+P6dOnM2nSJH70ox8VfZnJ+JnLZDK9Xh4aOnQoI0eOtMXezH384x8nkUhwzTXX9Pp5E0Kwffv2vK+l02luv/323H93dXVx++23M2TIEKZPn172vLyc0+pp2a3YfUK574XeP7N2OvPMM9m4cSP/7//9v15/1tHRQXt7u+PTLtY555zDY489xk033cSgQYNKLpR33HEH3d3duf++9dZbSafTue8/+eSTaW5u5tprr837PiMr//5nnnkmmUyG//3f/+31Z+l0OvfveuKJJ1JTU8MvfvGLvJ/Rm266qdffO/nkk3n22Wfz3rVyx44dvZ6dsHr59+zZQ2dnZ96fTZo0ib59+zq+zclU5J45+PWvf82jjz7a6+vFHG+5pk+fzhNPPMFPfvITRo4cyYQJE5gxYwY//OEPWbhwITNmzODCCy9k6tSp7NixgxdffJEnnniCHTt2AHDhhRdy8803c+655/LCCy8wYsQI7r77bhobGy2d/3777ccf/vAHPv3pT3PAAQfk3iFRCEFbWxt/+MMfiMfjRY8vKGzKlClMmjSJr3zlK2zcuJHm5mbuv//+kscF1NfX8+ijjzJ//nxmzJjBI488wkMPPcQ3vvENX5/OK9WkSZPo378/t912G3379qWpqYkZM2YwYcIErr/+es4//3xmz57Npz/9abZs2cLPfvYzxo8fz5e//OXA5/r617/OH//4R0455RS++MUvMnDgQH73u9/R1tbG/fffn3v61OrPRzwe58477+SUU07hoIMO4vzzz2fUqFFs3LiRhQsX0tzczD/+8Q927drF6NGj+cQnPsGhhx5Knz59eOKJJ1i2bBk//vGPHc0yadIkvv/97/M///M/rFmzho9+9KP07duXtrY2/vrXv3LRRRfxla98Jff9I0eO5Prrr2fNmjXsv//+/N///R8vv/wyd9xxR6/jEwrzck6rp2W3UvcJpf7tSv3MWu2cc87hz3/+M5///OdZuHAhs2bNIpPJsGLFCv785z/zz3/+kyOPPNL2HKX6zGc+w1VXXcVf//pXvvCFL5S8zrq6upg3bx5nnnkmb7zxBrfccgvHHXccH/7wh4GeZ2xuvfVWzjnnHI444gjOOusshgwZwrp163jooYeYNWtW3pJerNmzZ3PxxRdz3XXX8fLLL/OBD3yAmpoaVq5cyb333svPfvYzPvGJTzBkyBC+8pWvcN1113H66adz6qmn8tJLL/HII48wePDgvNO86qqr+P3vf89JJ53E5ZdfTlNTE3feeSdjx45lx44duWdnrF7+N998M/fvMHXqVJLJJH/961/ZsmULZ511lgfXSMgFaiN8zGA2pf63fv16W5RxxYoV4oQTThANDQ29SMyWLVvEpZdeKsaMGSNqamrE8OHDxbx588Qdd9yRdxpr164VH/7wh0VjY6MYPHiw+NKXvpSjOJXe58DorbfeEl/4whfE5MmTRX19vWhoaBBTpkwRn//858XLL7+c973z588XTU1NRU/n9ddfFyeeeKLo06ePGDx4sLjwwgvFK6+80uvfwziNVatW5Yz1sGHDxNVXX51jeELso4zFGA8grr76akvzWe2BBx4QU6dOFclkstdl/r//+z9x+OGHi7q6OjFw4EBx9tlniw0bNuT9fatzlWrcuHHitNNO6/X12bNn53EvIYRYtWqV+MQnPiH69+8v6uvrxdFHHy0efPDBXn/Xzs/HSy+9JD7+8Y+LQYMGibq6OjFu3Dhx5plnigULFggheojaV7/6VXHooYeKvn37iqamJnHooYeKW265peJsBucrxTnvv/9+cdxxx4mmpibR1NQkpkyZIi699FLxxhtv5P07HHTQQeL5558Xxx57rKivrxfjxo0TN998c95pGZTx3nvvLXpeXs5Z6bTKzW7cL5hNe7n7hGKV+pk1/q0Kmz9/vhg3blze17q6usT1118vDjroIFFXVycGDBggpk+fLq655hrx/vvvlz1/O+djdOqppwqg6Hs4GP8mTz31lLjooovEgAEDRJ8+fcTZZ58ttm/f3uv7Fy5cKE4++WTRr18/UV9fLyZNmiTOO+888fzzz+ddllL3WUIIcccdd4jp06eLhoYG0bdvX3HIIYeIq666SmzatCn3PZlMRlxzzTVixIgRoqGhQcyZM0e89tprYty4cb2uo5deekkcf/zxoq6uTowePVpcd9114uc//7kAxObNm21d/m3btolLL71UTJkyRTQ1NYl+/fqJGTNm5PFklYsJ4ePRVDrlOu+887jvvvuKPh2rclGdS5bmzJnDtm3bir7cplOnj33sY7z66qtFX5v/7W9/y/nnn8+yZcs8fcYi7K644gpuv/12du/eXfIAzmpMH3Og0+l0Ot5++20eeughzjnnnLAvim8Vvl389u3bufvuuznuuOP0YlBQ5I450Ol0Op312traWLRoEXfeeSc1NTV5b2QVtY499ljmzJnDgQceyJYtW/jVr37Fzp07+fa3vx32RZMuvRzodDpdFffUU09x/vnnM3bsWH73u99F4g18SnXqqady3333cccddxCLxTjiiCP41a9+xQknnBD2RZMufcyBTqfT6XS6vPQxBzqdTqfT6fLSy4FOp9PpdLq89HKg0+l0Op0uL70c6HQ6nU6ny0svBzqdTqfT6fLSy4FOp9PpdLq89HKg0+l0Op0uL70c6HQ6nU6ny0svBzqdTqfT6fLSy4FOp9PpdLq89HKg0+l0Op0uL70c6HQ6nU6ny0svBzqdTqfT6fLSy4FOp9PpdLq89HKg0+l0Op0uL70c6HQ6nU6ny0svBzqdTqfT6fLSy4FOp9PpdLq89HKg0+l0Op0uL70c6HQ6nU6ny0svBzqdTqfT6fLSy4FOp9PpdLq89HKg0+l0Op0uL70c6HQ6nU6ny0svBzqdTqfT6fLSy4FOp9PpdLq89HKg0+l0Op0uL70c6HQ6nU6ny0svBzqdTqfT6fLSy4FOp9PpdLq8kmFfALdlhGB7Z4atHWne6cjQns6SEYJELEZTMs6QhgRDG5IMqk+QiMXCvriu0rPqWVWfFaprXj2rnlXVWZVdDnZ2Zfj3jhTLd3TS3i3ICkE8FiMrRO57jP+Ox2I01cSYNrCegwbW0VybCPGS20/PqmdVfVaornn1rHpW1WeNCWGaSoFSmSyL3t7D8h0pMkKAgGQ8RhyIFdnQhBBkgXRWQAwSsRjTBtYxa0QjdQm5X1XRs+pZQe1Zobrm1bPqWUHtWY2UWg7W7ermsQ27eb8rQ5wYyVjxK6ZUQgjSArII+tcmOGl0H8b2rfHxEjtPz6pnLZZKs0J1zatn1bMWS6VZzSmzHCzf3smCje1khaAmFiPu4nWcrBB0733aZ96oJqYNqvfwkrpPz+osPatcVdO8elZn6VnlTYnlYPn2ThZs6LmCauMxW1tbqYQQdGX3XlGj5bmi9Kzu0rPKUTXNq2d1l55VzqR/8WPdru7c5ubVFQQ9TwvVxnsOHFmwsZ11u7o9OV036Vndp2cNv2qaV8/qPj2rnEm9HKQyWR7bsNvzK8jIfEU9vmE3qUzW09O3k57Vu/Ss4VVN8+pZvUvPKl9SLweL3t7D+10ZamLFr6DUnna+deJB/Ove3zo+j1gsRk0sxntdGRa9vcfFpXWXnnVfelZ7yTIrlJ/Xi1lBnnn1rD3pWe0ny6zlknY52NmVYfmOFHFKHwSyZc1KhBAMGz/Z1XnFYzHixFi+I8XOroyr03KSnjU/Pav9wp4VKs/r1awQ/rx61n3pWZ0V9qyVknY5+PdeT5os84zOlraVAAwdN8n1+SVjPe969e8dKdenZTc9a356VmeFOStUntfLWUHu61bP6jw9qxxJuRxkhGD5jk4Q5T3pltVvUt/Yh35DR7g+z1gsBgKW7+jseZOLgNKz9k7P6qywZgVr83o5K8h93epZnadnlSMp3z55e2eG9m5BMl7+QJAtbSsZMm4Sm958ncd//TPWvvYidY1NHP+pzzHzjHNsn28yHqO9u+c9s4c2BPNPo2ftnZ5VrVnB2rxezwryXrd6Vj2r1cK6zVZKymcOtnake96busL3bW57k9Sedu7+1iWM2O9APnjxV+g7cAgP3/pDNq9+w/b5xulxqO90pB1dbifpWXunZ1VrVrA2r9ezgrzXrZ5Vz2q1sG6zlZJnTTH1TkeGeImju412bt/Knp3vESPGpbffl3uaZ/y0I/nZBR/i7bdWMHziAbbONxaLEYvB1o4MB7mawHp61vz0rOrNCpXn9WNWkPO61bPqWe0U1m22UlI+c9CezuZ9ylWxtqx+E4B5512W9/pPItnzntWJGmfvXZ0Vgj3p4NypnjU/Pat6s0Llef2aFeS7bvWsela7hXGbrZSUzxxYOTBj894raupxJ+Z9feOqFWQzWZqHjgSg/b0d3H/DN2h7eRnNQ4bx4S99h0lHHFP2tNMBHwRTqVKzrl/5OtlMln7DRwOw5IE/8vzD97GlbSWzz76YefMvrXjaqsy67s1/k81k6T9iNOnuLh746TWsevFZOtt3M3TcJE695GuMnXpY2dNWZdb1plkB/vbjq1nx3JN0dXbQf+gIPvBfVzDl2LllTzvIWaHyvCVvr2/9h2w2S8PAIWSyGRLxno+1Xff6y9zxxbOZN/9y5p7z+YrnL9N1W2rWTatW5M36mysvYP1/XiGe6LkbHn/IdOb/8PaK56/SrPUDBueu16f/9Cue++s9dLbvYuDIMVx4013UNfYpe/pKzLr6jbxZv/+hGXl/3p3q4IMXfYXjzjy/7OkHfZutlJTLQcLCO1JtaVtJ86Bh9B00JP/rq98gnkjQZ1jPcvCPn3+fPgMG8z9/+RerXnyOP33vy3z5rkdpbO5X8rSTHr8jVrnczPr2WyuIJxIMGDkGgL6DhtIy/zJeWfCg5fNXZdaNb75OPJFgyLhJZNNpBgwfxUU/+z3NQ4bz2lOPcvc3L+Er9zxOXWNTydNWZtaV/yGeSDB47EQAZn1yPqd/8Zska2rZsOJVfvPVz3HlPY/R2Ny/5GkHOStUnrfUrBvefJ14PEHjoGFks1kS8QTZbJaHb7meUQccYvn8ZbpuS8/677xZAT525fc47KQP2zp/NWbtuV6bBg8nm82y7O9/YuWyf3HRz39Pv6Ej2NL2Joma2ornr8KsGwtmvfqh53N/tnP7Vm48ax5Tjz+p4vkHfZutlJQvKzQl4xU//WpL25sMn7R/r6+/s3YV/UeMRhAjtaed1xctYN55l1Fb38CBM+cybML+/Gdxa8nTjcdiNCaD+2dxNeuat+g/YjSxvb95TD1uHgfOnEtDn76WzludWQVb986arKmltqGRlnMvof+wkcTjcabNPZVkTS3bNqwpebrqzNpzJ9R/xGhqausAGDJ2IknjjjQWI93dzc5tW0qebtCzQuV5S8/6Jv1HjM57oFj24J8ZPeUQhu5djiol23VbatbNq1f2mtVuqsxqvl6zmQxP/eH/8dH/vob+w0YSi8UYPvGAfT/TJVJl1s2r3yh5vb7yxIOMmXooA/c+C1iqMG6zlZLr0uxtSEOCrBCU+sDIbCbD1rWrix78sXnVmwwaM5F0Os22jWuprW+g35DhuT8fNmE/tq55q+jpir3nObQh4c0gFnIz65a2nlkzGfvvrqXSrOlMhnfWvMWgMcUfLLZtWMuene8xcO8zKIWpNGsmky46699/9j2u/uDh3HrJmUw6YgbDJvS+k4JwZoXy85acNZth65q3GGJ6Q5k9O99j8f13M++8yyydr2zXbalZsyLL1jUrGTI2/81zHr7leq792Cx+/dXPVTzaXaVZt7StZPDeWXdu20JXZwf/fuYxrjvjeH46/1SWPXRv2fNVZVaByJu1sJcf/zuHf+AjZc83rNtspaRcDoY2JInHYpQ6PGP7xrWku1MMn5R/RXWnOnn37fUMHjcRBLTvep+6pvzXtOqa+tDV0V70dLP0HDk6JEBr6mbW9zZvZPA4Z8uBSrPu3vk+72/ZyOAi70rWnerk3uu+xgmfvpCGPs1FT1epWXftLDrrh7/0Ha5+6HkuuPHXTJ4+q+QR1WHMCuXnLTVr+66dvL95I0NNi87jv7qJmWecU/K6LEy267bsz/DmjQyduG/Wky+6kivveYyv/mkBk6fP5Hdfv5jUnt0lz1eVWY3rddjE/QDYtX0rne272LZ+DV/5w+N8+uqf8vidN7Fm+fNFTrUn1WYdOmG/Xn9n8+o32LZhLQfPPrns+YZ1m62UlMvBoPoETTUx0tniv3UZB4YML/jtaUvbSrKZTM8WF4NYIkmqPf/GlmrfTW1D8del01lBU02MQfXBbXBOZ93c9mbPrGMm5l6/tJNKs25c+R9ENpt7Dd4o093NH6/5MoNGjqHl3EtKnm8UZgWIJxJMOuIYVr34LG8891TR0w1jVig/b6VZh++9Y337rf+w4Y3XOOq0T1o+X9mu21Kzblq5ApHNMsz0IDLmwGnUNTZRU1fPCWd9jtqGJta/vrzk+aoyq3G9Gs9uJfe+PDb3nC9QU1fP8IkHcMjcU3hjydMlz1eVWTe8ufdneGLvZ/Jefvwfe1/mLb/ohnWbrZRcq8reErEY0wbWs3jLHoQQvX5LOmTOBzlkzgd7/b3RUw7hu4+9wrZt2wBoGjiUrs4Odm7bQvPgYQBsXfMWhxV5mkcIATGYNrDe0sFkXuV01hH7TeVL//ckQggymQwCQQxrl1ulWYXIMmjcZL74p4V535/NZrn3h18nFotxxteuK/mbtFKzIvbNWua6zGYy7Ni0vtfXw5oVys9bataBYyfxxT8tpG/fvuzevZs1y19g2/o1XP+pHonRuXsXiWSSHW+v54yrftDrPGW8bkvOOi5/1mLF43EExZdJVWbF9DPcp08f2tvbGTRqHIlkTd7fLff+H+rMCoP3Xq99mppo37Pv0xWz2SyvLHiQj3z56rLnGeZttlJSPnMAcNDAOhKxGGkHusO4Y43X1nHAsXN44rc3053qZMWzC9m8+g0OnNnS6++kRc8Px0ED69xedNs5mTWT7nkpwfhxymQyZNJpurtSZDNZshnj//d+yUGlWbu6ugB6PVg+8JPvsmvHO5x19U9JJEvvuKrNKoTomXTvuB27d/LKggdJ7Wknk07z6pOPsvrlJYyfdmSvvx/mrGBv3u7u7twdcCLR8xvT9FPP4L/veoTLbr+fy26/nwNntTDjw2dx6iVfK3oaqly36XSabDbbM+teqtm5exdvPb+YdHcXme5uFt33O/bsep/RU4oLDVVm7U6nyWQyeddrbUMjB5/wAZ685w7S3V1sXbuKV598lANmnFD0NFSZNZ1Jk85kiBEjnsj/rX/1i8+RSafZ76jjy59GyLfZckn5zAFAc22CaQPreGl7J1lBxSO/84rtezD5wOev4uGffY8ffHQmzUOG8alv/6QXY8wKQRbB4QPraa4N/qkdJ7OmM2ligNj7vZlMhn/98U5a7/pl7nuevOd2Pv7VHzD9gx/LfU21WVNdXcRisbzfqN7dvJHnH7mPZE0d135sVu7r86+7Le9BU7VZu1KpvbPuKxaL8fxD9/H3n/0vCMHAUWM585s3MmLylLy/G/asYG/elDGrEPseROobqDE9BVtTW0dtQ1PRp2XDntf2rIAA4ome38cy6W4eu/OnbNuwhngiwYjJBzL/2tuUn9VY5oHcIgTwoS99m7/e+G2u/dgsGpv7c+L5lxddcJWaNbV31hi5n2Gjl5/4O9PmnlL2F5ewZ61UTJQ6nFqCUpksv3/zfd7rylBb5m0tzXWnu9m+fTvQc4Ulk0kG9B9Q8vuFEHQJQf/aBJ/dvx91iXCeTLE7685dO0mlUrmDEZubm2lsaCz7d9SbVfDOtm09DyLZLJlslkGDBlGTrPxOZOrNCtu297wcls1mySo4K1ifd/v2bQh6Zh0wYADbt29Xbl7Ls+7YjhCCbCbDgIEDIz3rjnd3kM1kyGSzDIz4rO++927Ps0KZTM+sO3YoN2u55LtEpuoScU4a3Yd4LEZXtjQLK1Y8HieRSPQ8VVvmdbyurCAei3HS6D6hXkF2Z82kM7ltNZFIkE6X/9AOFWdNZzK5pygLn7Yrl4qzZjJp0ul03tOxVpJpVrA2byaboXvvrEkbs4Jc81qZNSuydHd3E4/Fyv4WWSxlZ40nbP0Mg3qzCgRdXV0kEome+yYbz2zLNGu55LxUpsb2rWHeqCbbC4LxwymEyHuqy8h8Bc0b1cTYvs7fF9ur7MyazqRzT9sl4omynFHVWYs99VwpZWc1Xj4RgkTc2s1Sxlmh8rxdXV25I0jsPGDKOG+lWVOpFNDzkoLTpU+VWY1jZoo9zV4upWcFkgm1f4ZLJf1yADBtUD3zRjcRj8foEqLiB9pAzw9nNpslkUjkbqBG2b1P6cTjMeaNbmLaoHq/LrrtrMwq6HmKMrF344wn4iWXA5VnTaVS1NbWkslkLN3ZqD5rTU0NmUzG0rMkMs8K5ec1z2r1QUTmecvN2pXqoqamhmw2a/lZElVnTaVSJJNJspkMSYtLn8qzJhIJRDZLIqn+9VosaQ9ILGzaoHr61yZ4fMNu3uvKEBeQjJUmMfF4zwNmQ0MDXakU9O3Z2tICsvS81nPS6D5Sbm6VZu2hi+QeRIwFyMwZVZ8VBN3d3TQ1NfXcEOOlb4DKzxrr+U2kqbEx91RlqVSZFYrPm4gJulJdNDQ20NXeXvG3LlXmLTVrqitFQ0MD7e3tFZ8lUXnWZKznGdq6ujo69nRUXPrUnrXn4OG6ujo6Ojqoj8ishSmzHEDPUz2f3b8fi97ew/IdKbqEgKwgGY8RJ39RSCQSCCFI1tTQmeqiI50hHo+RiMU4fGA9s0Y0SvtaD5SfNb2XMZqPORBCkE73/NaZzu59ak/hWTPpDLF4IrcAxQsuvxCCLERr1r0PHoV3rKrOCr3n7UxnSdY37PuY2yK/dak6b+GsqUyWZJ1p1iIPIlGZtTOTJVFbT7KmBsGeos+SRGnWuDHrnj1FF1xVZzUntVYo186uDP/ekWL5jk7au0XOTKezGVKdPU9Hpzo7qG9opGPPHhoTcNyEoRw0sE5KNlKuwlm7urvpSqWob2zoNWsy0fMuX9MG1is9a+ubG8gm66ipq6Orc9+sdfV1JOOJ3PUdhVkXrtxIJl5LbX197npMpaI1K/TMe/NfHmV3v5H0HzqCVFc3jQ0NZEQ2ktftbX9/gncbhzJgmDFrPRkhIjnrnQ8/yTs1AxgwfBSpri4aGurJRnTW3z22iI2xJgYNH01nVxcN9XVkIXKzKrscGGWEYHtnhnc60mztyLDxne0sWPgkc2efwD/uv5cPHDudfz36DzI7t/P7u+4K++K6ypj15t/ew5aONMe3nMSChU/SMmc2D/7lXmYfcQif+OA8BtUnpHu3LTsJITjm2Jmc/PEzSQ4cxrodu5jzgVNYsPBJ5s2dw6ghgxjakGBIQ1L5WQFOmD2HmSedQr/RE3jz7W3MO+X0yM564oknMvWggxk7dRpPvfAq/33193O32ajNe/rppzNqzBgOnnEcDz39LF/73x+y8Z0dkZz1jDPOoE9zMzNPPIX7/tnK/1z7IzZti+asZ599NhkhOPnjn+Luvz7EN677EZt3vBe5WdV4fqNMiViMoQ1JDhpYz9xRTRyaeJ9/3vh1Dq/ZxdrH72XrK88y85ApLHrmGTo7O8O+uK4yZn3zqUdI/3txbtbDkjt5+6m/8/aL/2JoQ1LZH0ajt956i/Xr1jLnyENZ9cw/6Vz+TG7WQxPvM3dUEwcNrI/ErOvWreOtlW8ye/o01jy7gN0vLozsrFu2bOH111+nZe4c1v37ZXj7rbzbbJTm3bFjBy+99BItc3pmzW54k7mj+kRy1l27drFs2TJa5sxh/X+W07X2P7SMjuasHR0dLF68mJY5c9i44lXa33qFE8c2R3JW5ZeDck2YMIG2tjZaWlpIpVIsXrw47IvkSW1tbUyYMCHva8asUai1tZW6ujpmzpxZdNYo1draSjKZ5LjjjmP16tWRnnXhwoXE43Fmz55NW1sbEycW/wjuKPTUU08hhGDOnDmRn/WZZ54hnU7T0tIS+dvr4sWLSaVSVTFrpJeDiRMn0tbWxuTJkxk9ejStra1hXyTXdXV1sXHjxl53NlFaDhYsWMDMmTOpqalh7dq1kb4Btra2cvTRR9OnTx/WrFnD+PHjw75IvrVgwQIOP/xwBgwYQFtbW+RnPeiggxg2bFhVzDp58mTGjBkT+QfM1tZWxowZw6RJk1izZk2kl75ILwfjx49nzZo1ALS0tERiOVi3bh3ZbLbXnc2ECRNYv359xXdKlL329naWLFlCS0sLmzZtoru7O7J3NqlUin/961+0tLTwzjvv0N7eHtk7m+7ubp5++mnmzp3Lzp072bFjR2RnzWazPPnkk7S0tNDR0cHbb78d2Z9hIQQLFy6kpaWF7u5u1q9fH9lZgdysQojIL32RXg4mTpxIR0cHW7ZsoaWlhTVr1uSWBVUznh0ovGOdOHEi6XSaDRs2hHGxPGvRokV0d3fnri8gsnc2zz33HJ2dncydOzfys77wwgvs2rUr93QsRHfWV155hR07dtDS0sLatWuB3rfXqLRixQo2b95MS0sLGzduJJPJRHbWtrY21qxZQ0tLC1u2bKGzszOyP8MQ8eXA2Ora2tqYNWsWNTU1yj970NbWRn19PUOHDs37unlWlWttbWX8+PFMmDCB1atXk0wmGT16dNgXy5daW1sZPnw4U6ZMyV1v48aNC/lS+VNrayuDBg1i2rRpuVmj+ltXa2srzc3NTJ8+vSpmbWho4JhjjmH16tVAtGetqalh1qxZkV9wIeLLwbhx44jFYrS1tdHU1MQxxxwTieVgwoQJxAvef3/kyJHU1NQovRwIIWhtbaWlpQXomXXMmDGW34pVtVpbW5k3bx6xWIzVq1czcuRI6uvlfktVp7W2tjJ37lzi8TirV69m0KBBNDf3/njiKLRw4UJmz55NMpmkra2NPn36MHjw4LAvli+1trZy3HHHUVtbS1tbG7W1tYwcOTLsi+VLra2tHHvssTQ2NtLW1kY8Hmfs2LFhXyzfivRyUFtby6hRo3IPmC0tLSxatEhp0ljqda5EIsG4ceOUXg7eeustNmzYkLccRHUzX7duHatWrcrNumbNmsjOmiOMplmj+ttljjCafobHjx9v6ePmVStHGE2zjhs3rtcvLlEoRxhNs44aNYra2tqQL5l/Re9aLMh8FH8USGO5B0zVxYKZMEK0lwMzYQQizRjNhBGINO0zE0aI9qxmwgjRvr2aCSNEe1ajyC8HBmcElCeNpRijkerLgUEY6+vryWQykWaMBmHs27cvQohI/zZtJoxQ+tmvKGQmjBD9WQ3CCNF+wDQTRiDyjBGqYDkwOKPxXtcqk8ZSjNFIZc5oJoxApBmjmTACkWaMZsIIRJoxmgkjEGnGaCaMQOQZozFrLBYjm81GeukzivxyYOaMgNKksRRjNFKZM5oJIxBp2mcmjBDtWc2EEYj0Ud5mwghEmjGaCSMQacZoJoxAVTBGqILloJD4qUwaSzFGI5U5o5kwApFmjGbCCPuurygyRjNhhH2zRvG3LjNhhOjPahBGINKM0UwYIdoLrrnILwdmzggoTRpLMUYjVTljIWGEaDNGM2EEIs0YzYQRiDRjNBNGINKM0UwYgUgzRjNhBKqCMUIVLAeFnBHUJY2VXudSlTMWEkaI7sFNhYQRossYCwkjRJcxFhJGiC5jLCSMEF3GWEgYoToYI1TBcgC9j+JXlTRaecBUUSwUEkaI7nJQSBghuoyxkDBCdGlfIWGE6M5aSBghurfXQsII0Z21sKpYDsycEdQkjZUYo5GKy4GZMAKRZoxmwghEmjEWEkaILu0rJIwQ7VnNhBGi+4BZSBihOhgjVMlyYOaMgJKksRJjNFKNMxYSRoguYywkjBBdxlhIGCG6jLGQMEJ0GWMhYYRoM0YzYQSqhjFClSwHhZwR1CONlRijkWqcsZAwQnRpXyFhhOjOWkgYIbpHeRcSRoguYywkjBBdxlhIGKF6GCNUyXJQjPipRhorMUYj1ThjIWGE6DLGQsII0WWMhYQRokv7CgkjRHtWM2GE6DLGQsII0V1wi1UVy0EhZwT1SGMlxmikEmcsRhghuoyxkDBCdBljIWGE6DLGQsII0WWMhYQRossYCwkjVA9jhCpZDopxRlCLNFp9nUslzliMMEI0D24qRhghmoyxGGGEaDLGYoQRoskYixFGiCZjLEYYoXoYI1TJcgDFj+JXiTTaecBURSwUI4wQzeWgGGGEaDLGYoQRokn7ihFGiOasxQgjRPP2WowwQjRnLVXVLAeFnBHUIY1WGaORKstBIWGE6DLGQsII0WWMxQgjRJP2FSOMEN1ZCwkjRPMBsxhhhOphjFBFy0EhZwR1SKNVxmikAmcsRhghmoyxGGGEaDLGYoQRoskYixFGiCZjLEYYIbqMsZAwQnUxRqii5aAYZwQ1SKNVxmikAmcsRhghmrSvGGGEaM5ajDBCNI/yLkYYIZqMsRhhhGgyxmKEEaqLMUIVLQeliJ8KpNEqYzRSgTMWI4wQTcZYjDBCNBljMcII0aR9xQgjRHfWQsII0WSMxQgjRHPBLVfVLAfFOCOoQRqtMkYj2TljKcII0WSMxQgjRJMxFiOMEE3GWIwwQjQZYzHCCNFkjMUII1QXY4QqWg5KcUaQnzTafZ1Lds5YijBC9A5uKkUYIXqMsRRhhOgxxlKEEaLHGEsRRogeYyxFGKG6GCNU0XIApY/il500OnnAlFkslCKMEL3loBRhhOgxxlKEEaJH+0oRRojerKUII0Tv9lqKMEL0Zq1UVS0HxTgjyE0a7TJGI5mXg2KEEaLJGIsRRogmYyxFGCF6tK8UYYRozlqMMEL0HjBLEUaoLsYIVbYcFOOMIDdptMsYjWTljKUII0SPMZYijBA9xliKMEL0GGMpwgjRY4ylCCNEkzEWI4xQfYwRqmw5KMUZQV7SaJcxGsnKGUsRRoge7StFGCF6s5YijBC9o7xLEUaIHmMsRRgheoyxFGGE6mOMUGXLQTniJytptMsYjWTljKUII0SPMZYijBA9xliKMEL0aF8pwgjRnLUYYYToMcZShBGit+BaqaqWg1KcEeQljXYZo5GMnLEcYYToMcZShBGixxhLEUaIHmMsRRgheoyxFGGE6DHGUoQRqo8xQpUtB+U4I8hJGp2+ziUjZyxHGCFaBzeVI4wQLcZYjjBCtBhjOcII0WKM5QgjRIsxliOMUH2MEapsOYDyR/HLSBrdPGDKJhbKEUaI1nJQjjBCtBhjOcII0aJ95QgjRGvWcoQRonV7LUcYIVqzWq3qloNSnBHkI41OGaORbMtBKcII0WOMpQgjRI8xliOMEC3aV44wQvRmLUUYIVoPmOUII1QfY4QqXA5KcUaQjzQ6ZYxGMnHGcoQRosUYyxFGiBZjLEcYIVqMsRxhhGgxxnKEEaLHGEsRRqhOxghVuByU44wgF2l0yhiNZOKM5QgjRIv2lSOMEK1ZyxFGiNZR3uUII0SLMZYjjBAtxliOMEJ1MkaowuWgEvGTiTQ6ZYxGMnHGcoQRosUYyxFGiBZjLEcYIVq0rxxhhOjNWoowQrQYYznCCNFacO1UdctBOc4IcpFGp4zRSBbOWIkwQrQYYznCCNFijOUII0SLMZYjjBAtxliOMEK0GGM5wgjVyRihCpeDSpwR5CGNbl/nkoUzViKMEJ2DmyoRRogOY6xEGCE6jLESYYToMMZKhBGiwxgrEUaoTsYIVbgcQOWj+GUhjV48YMogFioRRojOclCJMEJ0GGMlwgjRoX2VCCNEZ9ZKhBGic3utRBghOrParSqXg3KcEeQgjW4Zo5EMy0E5wgjRYozlCCNEizFWIowQHdpXiTBCtGYtRxghOg+YlQgjVCdjhCpdDspxRpCDNLpljEZhc8ZKhBGiwxgrEUaIDmOsRBghOoyxEmGE6DDGSoQRosUYyxFGqF7GCFW6HFTijBA+aXTLGI3C5oyVCCNEh/ZVIowQnVkrEUaIzlHelQgjRIcxViKMEB3GWIkwQvUyRqjS5cAK8QubNLpljEZhc8ZKhBGiwxgrEUaIDmOsRBghOrSvEmGEaM1ajjBCdBhjJcII0VlwnVSVy0Elzgjhk0a3jNEoTM5ohTBCdBhjJcII0WGMlQgjRIcxViKMEB3GWIkwQnQYYyXCCNXLGKFKlwMrnBHCJY1evc4VJme0QhghGgc3WSGMEA3GaIUwQjQYoxXCCNFgjFYII0SDMVohjFC9jBGqdDkAa0fxh0kavXzADEssWCGMEI3lwAphhGgwRiuEEaJB+6wQRojGrFYII0Tj9mqFMEI0ZnVa1S4HlTgjhEcavWKMRmEtB5UII0SHMVYijBAdxmiFMEI0aJ8VwgjRmbUSYYRoPGBaIYxQvYwRqng5qMQZITzS6BVjNAqDM1ohjBANxmiFMEI0GKMVwgjRYIxWCCNEgzFaIYwQHcZYiTBCdTNGqOLlwApnhHBIo1eM0SgMzmiFMEI0aJ8VwgjRmNUKYYRoHOVthTBCNBijFcII0WCMVggjVDdjhCpeDqwSvzBIo1eM0SgMzmiFMEI0GKMVwgjRYIxWCCNEg/ZZIYwQnVkrEUaIBmO0QhghGguum6p2OTCOtq30gBkGafSKMRoFzRmtEkaIBmO0QhghGozRCmGEaDBGK4QRosEYrRBGiAZjtEIYYR9jVHmZd1PVLgdWOSMETxq9PuAnaM5olTCC+gc3WSWMoD5jtEoYQX3GaJUwgvqM0SphBPUZo1XCCD2zjh49mpqamgAumXypeQ171Pjx4y0vB0GSRj8OgglSLFgljKD+cmCVMIL6jNEqYQT1aZ9Vwgjqz2qVMIL6t1erhBGiIVDcVNXLgRXOCMGSRq8Zo1GQy4EVwgjRYIxWCCNEgzFaJYyg/h2rVcII0ZjVCmEE9ZcDq4QR1F/63FbVy4HxzEE5zgjBkkavGaNRUJzRKmEE9RmjVcII6jNGq4QR1GeMVgkjqM8YrRJGiAZjtEIYoednQPVl3m1VvRxMnDiRzs7OipwRgiONXjNGo6A4o1XCCOrTPquEEdSf1SphBPWP8rZKGEF9xmiVMIL6jNEqYQTNGKHKlwM7xC8o0ug1YzQKijNaJYygPmO0ShhBfcZolTCC+rTPKmGEaMxqhTCC+ozRKmEE9RdcL6rq5cAqZ4TgSKPXjNEoCM5ohzCC+ozRKmEE9RmjVcII6jNGq4QR1GeMVgkjqM8YrRJG0IwRqnw5sMMZIRjS6NcBP0FwRjuEEdQ+uMkOYQS1GaMdwghqM0Y7hBHUZox2CCOozRjtEEbQjBGqfDkA65wRgiGNfh757LdYsEMYQe3lwA5hBLUZox3CCGof5W2HMILas9ohjKD27dUOYQT1BYoXVf1yYJUzgv+k0S/GaOT3cmCVMIL6jNEqYQT1GaMdwghq37HaIYyg/qxWCSOovRzYIYyg9tLnVVW/HFjljOA/afSLMRr5yRntEEZQmzHaIYygNmO0QxhBbcZohzCC2ozRDmEE9RmjVcIImjEaVf1yYIczgr+k0S/GaOQnZzQI47x58yx9v8q0zw5hBLVntUMYQe2jvO0QRlCbMdohjKA2Y7RDGEEzRqOqXw7sEj8/SaNfjNHIT85oEEar27bKjNEOYQS1GaMdwghq0z47hBHUn9UqYQS1GaMdwghqL7heVvXLgR3OCP6SRr8Yo5FfnNEuYQS1GaMdwghqM0Y7hBHUZox2CCOozRjtEEZQmzHaIYygGaNR1S8Hdjkj+Eca/T7gxy/OaJcwgroHN9kljKAuY7RLGEFdxmiXMIK6jNEuYQR1GWNHRweLFi2yPWu1M0bQywFgjzOCf6QxiCOf/RALdgkjqLsc2CWMoC5jtEsYQd2jvO0SRlB3VruEEdS9vS5evJiuri5HS1+1p5cD7HFG8Ic0+s0YjfxYDuwQRlCbMdohjKA2Y7RLGEHdO1a7hBHUntUOYQR1lwO7hBHUXfq8Ti8H2OOM4A9p9JsxGnnNGe0SRlCXMdoljKAuY7RLGEFdxmiXMIK6jNEuYQS1GaMdwgiaMZrTywH2OSN4Txr9ZoxGXnNGu4QR1KV9dgkjqDurXcII6h7lbZcwgrqM0S5hBHUZo13CCJoxmtPLAc6In9ek0W/GaOQ1Z7RLGEFdxmiXMIK6jNEuYQR1aZ9dwghqz2qHMIK6jNEuYQR1F1w/0ssB9jkjeE8a/WaMRl5yRieEEdRljHYJI6jLGO0SRlCXMdoljKAuY7RLGEFdxmiXMIJmjOb0coAzzgjeksagDvjxkjM6IYyg5sFNTggjqMkYnRBGUJMxOiGMoCZjdEIYQU3G6IQwgmaM5tS5tn3OLmcEb0ljkEc+eyUWnBBGUHM5cEIYQU3G6IQwgppHeTshjKDmrE4II6h5e3VCGEFdgeJHejnYm13OCN6RxqAYo5FXy4FdwgjqMka7hBHUZYxOCCOoecfqhDCCurPaJYyg5nLghDCCmkufX+nlYG92OSN4RxqDYoxGXnBGJ4QR1GSMTggjqMkYnRBGUJMxOiGMoCZjdEIYQV3GaJcwgmaMhenlYG9OOCN4QxqDYoxGXnBGJ4QR1KR9TggjqDmrE8IIah7l7YQwgpqM0QlhBDUZoxPCCJoxFqaXg705JX5ekMagGKORF5zRCWEENRmjE8IIajJGJ4QR1KR9TggjqDurXcIIajJGJ4QR1Fxw/UwvB3tzwhnBG9IYFGM0cssZnRJGUJMxOiGMoCZjdEIYQU3G6IQwgpqM0QlhBDUZoxPCCJoxFqaXg7055YzgnjQGfcCPW87olDCCegc3OSWMoB5jdEoYQT3G6JQwgnqM0SlhBPUYo1PCCJoxFqbGNR5QTjgjuCeNYRz57EYsOCWMoN5y4JQwgnqM0SlhBPWO8nZKGEG9WZ0SRlDv9uqUMIKaAsXP9HJgyglnBHekMWjGaORmOXBCGEFNxuiEMIKajNEpYQT17lidEkZQc1YnhBHUWw6cEkZQb+nzO70cmHLCGcEdaQyaMRo55YxOCSOoxxidEkZQjzE6JYygHmN0ShhBPcbolDCCmozRCWEEzRiLpZcDU045IzgnjUEzRiOnnNEpYQT1aJ9TwgjqzeqUMIJ6R3k7JYygHmN0ShhBPcbolDCCZozF0suBKTfEzylpDJoxGjmd1SlhBPUYo1PCCOoxRqeEEdSjfU4JI6g5qxPCCOoxRqeEEdRbcINILwemnHJGcE4ag2aMRk44oxvCCOoxRqeEEdRjjE4JI6jHGJ0SRlCPMToljKAeY3RKGEEzxmLp5cCUG84IzkhjWAf8OOGMbggjqHVwkxvCCGoxRjeEEdRijG4II6jFGN0QRlCLMbohjKAZY7Hkv9YDzilnBGekMcwjn+2KBTeEEdRaDtwQRlCLMbohjKDWUd5uCCOoNasbwghq3V7dEEZQT6AEkV4OCnLKGcE+aQyLMRrZXQ6cEkZQjzE6JYygHmN0QxhBrTtWN4QR1JvVKWEEtZYDN4QR1Fr6gkovBwU55YxgnzSGxRiN7HBGN4QR1GKMbggjqMUY3RBGUIsxuiGMoBZjdEMYQT3G6JQwgmaMpdLLQUFuOCPYI41hMUYjO5zRDWEEtWifG8IIas3qhjCCWkd5uyGMoBZjdEMYQS3G6IYwgmaMpdLLQUFuP7HQDmkMizEa2ZnVDWEEtRijG8IIajFGN4QR1KJ9bggjqDerU8IIajFGN4QR1Fpwg0wvBwW54YxgjzSGxRiNrHJGt4QR1GKMbggjqMUY3RBGUIsxuiGMoBZjdEMYQS3G6IYwgmaMpdLLQUFuOSNYJ41hH/BjlTO6JYwQ/qxWc0sYQR3G6JYwgjqM0S1hBHUYo1vCCOowRreEETRjLJXc13xIueGMYJ00ynDksxWx4JYwgjrLgVvCCOowRreEEdQ5ytstYQR1ZnVLGEGd26tbwghy3A/LmF4OiuSGM4I10hg2YzSyshy4IYygFmN0QxhBLcboljCCOnesbgkjqDWrG8II6iwHbgkjqLP0BZ1eDorkhjOCNdIYNmM0qsQZ3RJGUIcxuiWMoA5jdEsYQR3G6JYwgjqM0S1hBLUYoxvCCJoxlksvB0VyyxmhMmkMmzEaVeKMbgkjqEP73BJGUGdWt4QR1DnK2y1hBHUYo1vCCOowRreEETRjLJdeDorkljNCZdIYNmM0qjSrW8II6jBGt4QR1GGMbgkjqEP73BJGUGtWN4QR1GGMbgkjqLPghpFeDorkljNCZdIYNmM0KscZvSCMoA5jdEsYQR3G6JYwgjqM0S1hBHUYo1vCCOowRreEETRjLJdeDorkBWeE8qRRlgN+ynFGLwgjyDNrubwgjKAGY/SCMIIajNELwghqMEYvCCOowRi9IIygGWO55L32Q84tZ4TypFGmI59LiQUvCCOosRx4QRhBDcboBWEENY7y9oIwghqzekEYQY3bqxeEEeS6H5YtvRyUyC1nhNKkURbGaFRqOXBLGEEdxuiWMII6jNELwghq3LF6QRhBnVndEkZQYznwgjCCGktfWOnloERuOSOUJo2yMEajYpzRC8IIajBGLwgjqMEYvSCMoAZj9IIwghqM0QvCCOowRreEETRjrJReDkrkBWeE4qRRFsZoVIwzekEYQQ3a5wVhBDVm9YIwghpHeXtBGEENxugFYQQ1GKMXhBE0Y6yUXg5K5AVnhOKkURbGaFRsVi8II6jBGL0gjKAGY/SCMIIatM8LwgjqzOqWMIIajNELwghqLLhhppeDEnnBGaE4aZSFMRoVckavCCOowRi9IIygBmP0gjCCGozRC8IIajBGLwgjqMEYvSCMoBljpeR4dJIwrzgj9CaNsh3wU8gZvSKMIN+shXlFGEF+xugVYQT5GaNXhBHkZ4xeEUaQnzF6RRhBM8ZKyfkTIElecEboTRplPPLZLBa8Iowg/3LgFWEE+RmjV4QR5D/K2yvCCPLP6hVhBPlvr14RRpDzflim9HJQJi84I+STRtkYo5F5OfCCMIIajNELwghqMEavCCPIf8fqFWEENWb1gjCC/MuBV4QR5F/6wk4vB2XygjNCPmmUjTEaGZzx/fff94QwgvyM0SvCCPIzRq8II8jPGL0ijCA/Y/SKMIIajNELwgiaMVpJLwdl8oozwj7S+Oyzz+ZOW6YMzvjAAw94QhhBftrnFWEE+Wf1ijCC/Ed5e0UYQX7G6BVhBPkZo1eEETRjtJJeDsrkFWeEfaTxiSeekIoxGhmzPvLII54QRpCfMXpFGEF+xugVYQT5aZ9XhBHUmNULwgjyM0avCCPIv+DKkF4OyuQVZ4R9pPGFF16QijEaGZzxueee82QzB/kZo1eEEeRnjF4RRpCfMXpFGEF+xugVYQT5GaNXhBE0Y7SSXI9QkuUlZ4R9Ly14ceCQ1yUSCYYOHcq2bds8XQ5k3cy9JIwgN2P0kjCC3IzRS8IIcjNGLwkjyM0YvSSMoBmjleT7KZAsrzgj9CwHXV1dUt74AJLJJEIITwgjyL0ceEkYQW7G6CVhBLmP8vaSMILcs3pJGEHu26uXhBHkFygyJOejlER5xRkBxo4dC/T8diNjO3fupKGhwZOnxmVnjF4RRpCfMXpJGEHuO1YvCSPIP6tXhBHkXg68JIwg99InS3o5qJBXnBFg/fr11NbWsmrVKg8umbe1t7ezefNmstls3qczOk1mxuglYQS5GaOXhBHkZoxeEkaQmzF6SRhBfsboFWEEzRitppeDCnnJGdva2qirq+Odd97J+5RGGVq0aBGxWIxkMpn36YxOk5n2eUkYQe5ZvSSMIPdR3l4SRpCbMXpJGEFuxuglYQTNGK2ml4MKGT9AXry00NbWRt++famrq8v7ICYZam1tZdy4cSQSCU9mlZkxekkYQW7G6CVhBLlpn5eEEeSf1SvCCHIzRi8JI+y7XmVchGRKLwcVGjt2rGecsa2tjUmTJvX6lMawMz6F8eSTT877dEY3ycwYvSSMIDdj9JIwgtyM0UvCCHIzRi8JI8jNGL0kjLCPMRrHgOmKp5eDChmc0dis3WQc0V74KY1hZ3wK44knnpj36YxukvXgJq8JI8jLGL0mjCAvY/SaMIK8jNFrwgjyMkavCSNoxmg1uX4SJG38+PGeHCNg3LEWfkpj2Jk/hdH8AUxuknU58JowgryM0WvCCPIe5e01YQR5Z/WaMIK8t1evCSPILVBkSi8HFvKCM5o/jdH8KY0yZP4URi+WA5kZo5eEEeRmjF4TRpD3jtVrwghyz+olYQR5lwOvCSPIu/TJll4OLOQFZzR/GqP5UxrDrr29Pe9TGI1PZ3TDGWVljF4TRpCXMXpNGEFexug1YQR5GaPXhBHkZoxeEkbQjNFOejmwkBecsfAIWeOtlMMmjYsWLcr7FEbj0xndcEZZaZ/XhBHkndVrwgjyMkavCSPIyxi9JowgL2P0mjCCZox20suBhbzgjG1tbdTX1+ee9jQ+pTHsZw9aW1vzPoXRi0+ilJUxek0YQV7G6DVhBHlpn9eEEeSe1UvCCPIyRq8JI2jGaCe9HFjIC85ovKZnPD1mfEpjmMuBQRjNm7nx6YxuZ5WRMXpNGEFexug1YQR5GaPXhBHkZYxeE0aQlzF6TRhBM0Y76eXAQl5wxmJHtIdNGg3CaF4OEomEa84o48FNfhBGkJMx+kEYQU7G6AdhBDkZox+EEeRkjH4QRtCM0U7y/DRInlvOWOyONWzSaCaM5tyKBRmXAz8II8jJGP0gjCDnUd5+EEaQc1Y/CCPIeXv1gzCCvAJFxvRyYDE3nNHMGM2FTRrNhNGcm+VAVsboNWEEeRmjH4QR5Lxj9YMwgryzek0YQc7lwA/CCHIufbKmlwOLueGMZsZoLkzSWEgYzbnhjDIyRj8II8jJGP0gjCAnY/SDMIKcjNEPwgjyMkavCSNoxmg3vRxYzA1nLHeEbFiksZAwmnPDGWWkfX4QRpBzVj8II8jJGP0gjCAnY/SDMIKcjNEPwgiaMdpNLwcWc8MZCxmjubBIYyFhNOeGM8rIGP0gjCAnY/SDMIKctM8Pwgjyzuo1YQQ5GaMfhBE0Y7SbXg4s5oYzFjJGc2GQxmKE0ZwbzigjY/SDMIKcjNEPwghyMkY/CCPIyRj9IIwgJ2P0gzCCZox208uBxdxwxkpHtAdNGosRRnNuOKNsBzf5RRhBPsboF2EE+RijX4QR5GOMfhFGkI8x+kUYQTNGu8nxE6FITjljpTvWoEljKcJozqlYkG058IswgnyM0S/CCPId5e0XYQT5ZvWLMIJ8t1e/CCPIKVBkTi8HNnLCGUsxRnNBk8ZShNGck+VARsboB2EEORmjX4QR5Ltj9Yswgpyz+kEYQb7lwC/CCPItfbKnlwMbOeGMpRijuSBJYznCaM4JZ5SNMfpFGEE+xugXYQT5GKNfhBHkY4x+EUaQkzH6QRhBM0Yn6eXARk44o9UjZIMijeUIozknnFE22ucXYQT5ZvWLMIJ8jNEvwgjyMUa/CCPIxxj9IoygGaOT9HJgIyecsRxjNBcUaSxHGM054YyyMUa/CCPIxxj9IowgH+3zizCCnLP6QRhBPsboF2EEzRidpJcDGznhjOUYo7kgSGMlwmjOCWeUjTH6RRhBPsboF2EE+RijX4QR5GOMfhFGkI8x+kUYQTNGJ+nlwEZOOKOdI9r9Jo2VCKM5J5xRpoOb/CSMIBdj9JMwglyM0U/CCHIxRj8JI8jFGP0kjKAZo5PC/6lQLLuc0c4dq9+k0QphNGdXLMi0HPhJGEEuxugnYQS5jvL2kzCCXLP6SRhBrturn4QR5BMoKqSXA5vZ4YxWGKM5v0mjFcJozs5yIBtj9IswgnyM0U/CCHLdsfpJGEG+Wf0ijCDXcuAnYQS5lj5V0suBzexwRiuM0ZyfpNEgjJWUgjk7nFEmxugnYQS5GKOfhBHkYox+EkaQizH6SRhBPsboF2EEzRidppcDm9nhjE6OkPWLNBqE0c6djR3OKBPt85Mwglyz+kkYQS7G6CdhBLkYo5+EEeRijH4SRtCM0Wl6ObCZHc5olTGa84s0WiWM5uxwRpkYo5+EEeRijH4SRpCL9vlJGEG+Wf0ijCAXY/STMIJmjE7Ty4HN7HBGq4zRnEEaFyxY4OZi5mWHMJqzwxllYox+EkaQizH6SRhBLsboJ2EEuRijn4QR5GKMCxYs8I0wgmaMTtPLgc3scEanR7S3tLSwePFiz0ijHcJozg5nlOXgJr8JI8jDGP0mjCAPY/SbMII8jNFvwgjyMMaOjg4WL17s+6yaMdpPLwcOssoZnd6xek0a7RJGc1bFgizLgd+EEeRhjH4TRpDnKG+/CSPIM6vfhBHkub36TRhBLoGiUno5cJAVzmiXMZrzmjTaJYzmrCwHMjFGPwkjyMUY/SaMIM8dq9+EEeSa1U/CCPIsB34TRpBn6VMtvRw4yApntMsYzXlJGp0QRnNWOKMsjNFvwgjyMEa/CSPIwxj9JowgD2P0mzCCXIzRT8IImjG6SS8HDrLCGd0eIesVaXRCGM1Z4Yyy0D6/CSPIM6vfhBHkYYx+E0aQhzH6TRhBHsboN2EEzRjdpJcDB1nhjE4YozmvSKMTwmjOCmeUhTH6TRhBHsboN2EEeWif34QR5JrVT8II8jBGvwkjaMboJr0cOMgKZ3TCGM15QRqdEkZzVjijLIzRb8II8jBGvwkjyMMY/SaMIA9j9JswgjyM0W/CCJoxukkvBw6ywhm9OKLdLWl0ShjNWeGMMhzcFARhBDkYYxCEEeRgjEEQRpCDMQZBGEEOxhgEYQTNGN2klwOHVeKMXtyxuiWNbgijuUpiQYblIAjCCHIwxiAII8hxlHcQhBHkmDUIwghy3F6DIIwgj0BRMb0cOKwcZ3TDGM25JY1uCKO5csuBLIzRb8II8jDGIAgjyHHHGgRhBHlm9ZswghzLQRCEEeRY+lRNLwcOK8cZ3TBGc25Io1vCaK4cZ5SBMQZBGEEOxhgEYQQ5GGMQhBHkYIxBEEaQhzH6TRhBM0a36eXAYeU4o5dHyDoljW4Jo7lynFEG2hcEYQQ5Zg2CMIIcjDEIwghyMMYgCCPIwRiDIIygGaPb9HLgsHKc0S1jNOeUNLoljObKcUYZGGMQhBHkYIxBEEaQg/YFQRhBnln9JowgB2MMgjCCZoxu08uBw8pxRreM0ZwT0ugFYTRXjjPKwBiDIIwgB2MMgjCCHIwxCMIIcjDGIAgjyMEYgyCMoBmj2/Ry4LBynNHrI9rtkkYvCKO5cpwx7IObgiKMED5jDIowQviMMSjCCOEzxqAII4TPGIMijKAZo9v0cuCiUpzR6ztWu6TRK8JorpRYCHs5CIowQviMMSjCCOEf5R0UYYTwZw2KMEL4t9egCCPIIVBUTi8HLirGGb1ijObskkavCKO5YsuBDIwxCMIIcjDGoAgjhH/HGhRhBDlmDYIwQvjLQVCEEcJf+lRPLwcuKsYZvWKM5uyQRi8Jo7linDFsxhgUYYTwGWNQhBHCZ4xBEUYInzEGRRhBDsYYBGEEzRi9SC8HLirGGf06QtYqafSSMJorxhnDpn1BEUYIf9agCCOEzxiDIowQPmMMijBC+IwxKMIImjF6kV4OXFSMM3rJGM1ZJY1eEkZzxThj2IwxKMII4TPGoAgjhE/7giKMIMesQRBGCJ8xBkUYQTNGL9LLgYuKcUYvGaM5K6TRa8JorhhnDJsxBkUYIXzGGBRhhPAZY1CEEcJnjEERRgifMQZFGEEzRi/Sy4GLinFGP49or0QavSaM5opxxjAPbgqSMEK4jDFIwgjhMsYgCSOEyxiDJIwQLmMMkjCCZoxepJcDlxVyRj/vWCuRRj8Io7lCsRDmchAkYYRwGWOQhBHCPco7SMII4c4aJGGEcG+vQRJGCF+gRCG9HLjMzBn9YIzmKpFGPwijOfNyEDZjDIowQviMMUjCCOHesQZJGCH8WYMijBD+Mh8UYQTNGL1ILwcuM3NGPxijuXKk0S/CaM7MGcNkjEESRgiXMQZJGCFcxhgkYYRwGWOQhBHCZ4xBEUbQjNGr9HLgMjNnDOII2VKk0S/CaM7MGcOkfUESRgiXMQZJGCFcxvjGG28ERhghXMbY1tYWGGGEcBmjcX8R1KyaMXqTXg5cZuaMfjFGc6VIo1+E0ZyZM4bJGIMkjBAuYwySMEK4tG/p0qWBEUYId9YlS5YERhghXMa4dOnSwAgjaMboVXo5cJmZM/rFGM0VI41+EkZzZs4YJmMMkjBCuIwxSMII4TLGpUuXBkYYIVzGuHTp0sAII4TLGJcsWRIYYQTNGL1KLwcuM3PGoI5oN0hjKpUCelifX4TRnJkzhnVw09tvvx0oYYTwGOP27dsDJYwQHmPMZrOsWLEi0FnDYoxCCF577bXAZw2DMQohePnllwOfVTNG9+nlwIMMzhjUHatBGl9++WWg57cQPwmjOUMshLUcLFmyJFDCCOExxqVLlwZKGCG8o7y7uroCJYwQ7qyZTCbwB8wwfoa7u7t9PxaqMM0YvUkvBx40ceJEVq1a5StjNGeQxqVLlwI9D5h+EkZzEyZMYPXq1aExxqVLlwZGGCFcxrhkyZJACSOEd8eaSqWYPHlyYIQRwp117NixgRFGCG85SKVSDB8+PDDCCJoxepVeDjxo/PjxvPXWW2QymUDubAzSuHTpUoQQLF++3FfCaG7ChAmsXbuWrq6uUO5sgnz3PAiXMb7wwguBiQwIjzFms1m6uro4+uijAzvPsBijEIJUKhXorGEyRmPWoF660YzRu/Ry4EETJ05kz549ZLPZwO5YW1pa2LRpEx0dHYG+y9rEiRNJpVJkMpnA72y6urpIpVKBPmCGxRi7u7tpb28P/OlYCH7WN954g2w2G+gDZliMsa2tLfBZw2KMGzZsIJPJMGPGjMDOUzNG79LLgQdNmDCBTCZDIpEI7GnRWbNmkUwm6ezsZOTIkYFtyuPHjyeTyQAEzhhTqRSDBw8OjDBCeIwxlUrRv3//wAgjhEf7li5dSiwWY+rUqYGdZ1izLlmyhFgsxqGHHhrYeYbFGI3r9bDDDgvsPDVj9C69HHjQ2LFjyWazDBgwILCnz5qamjjkkEPo6uoKdDMfOXIkQgiam5sDZ4zGrEEeXR4WY0ylUhx11FGBHl0eFmM0DqgN8ucpLMa4dOlSamtrAz2SPizGuGTJEmpqamhoaAjsPDVj9C69HHiQcWMP+gFk8uTJCCE4/PDDAzvPRCJBXV1doDd46CGM6XQ60KdjIRzGuH37dtLpdKBLH4TDGHfs2MGKFSuoq6sL9HzDYIy7du3itddeC2XWoBljR0cHL7/8ciizasboTXo58LAwPva1WlqyZAkARxxxRKDnGwZjNBTKkUceGej5hnGUt/EpjEG9GZBRGLM+88wzZDKZUGYN+md48eLFdHd3h7b06dynlwMP6urqoquri87OzkDPd+XKlcRiMV566aXAzjOTydDZ2Rn4rMuWLaO2tpampqbAzjMsxmi83WzQT++Hccfa2trK5MmTA39znrBmHTt2LIlEItDzDWM5MN7iPIxZ9fEG3qSXAw9at24diUSCd999FyFEIOfZ3t7Oq6++Sm1tbe43zSDatGkTsViMnTt3kk6nAznPVCrFiy++GPhvIWEwxu7ubp5//vnAZw2DMWazWRYuXBj4S0VhMEbjLc6DnjUsxhjG9WowRi0VvEkvBx7U1tZGIpEgk8mwZcuWQM5z0aJFpNNp6uvr2bRpU69PafSrNWvW5A4c27BhQyDn+dxzz5FKpQJ/OjYMxvjCCy/Q3t4eylPPEOysr7zyCjt27Aj8QSQMxrhixQo2b94c+KxhMMa2tjbWrFkT+DEzBmPULyt4k14OPGj16tU0NjYSj8dzbMjvWltbc0fRJ5PJXp/S6FerV6+mtraWRCKRe0Dxu9bWVgYPHhy4jgiDMba2ttK/f//AD6gKg/a1trbS3NwcKGGE8GZtaGgIlDBCOIyxtbWVmpqaQAkj7JtVv6zgTXo58KA1a9bkXjcN4jd44ynKGTNmEI/HOeSQQ/I+pdHPjNdqjU9nDKIwno6FcBhja2tr4AciQjiMceHChZxwwgmhLH1BM8bW1laOO+64UJa+oBnjggULOOaYYwIXTWvWrNGM0cP0cuBBq1evZtKkSblPZ/S7t956iw0bNuQeMGfMmMHixYsDOUjQOODH+HRGv1u3bh2rVq0K/ClKCJ4xbtmyhddff51jjjkmsPM0CvrAyx07dvDSSy8F9rbf5oJmjLt27WLZsmWBvtulUdCMsaOjg8WLF4d2vWrG6F16OfAg447V+HRGv2ttbaWuri73tN3RRx9NKpVi8eLFvp+3ceSz8emMftfa2koymQycMELwjHHhwoXE4/FQnjkI+ihvgzAG+SmMRkHP+swzzwT6FufmgpYKixcvpqurK7RZ9fEG3qWXA5d1dXXlPo1x4sSJgT1gzpw5M3dE+9ixYxk9erTvxx1kMpncpzEGtRwYRz0HSRghHMbY2trK4YcfHjhhhODvWFtbWznooIMC/RRGozBmnTx5cqCfwmgU9HLQ2trKmDFjAv0URiPNGL1NLwcuW7duHdlsNu8B00/O2N7eznPPPZf3tJ3xKY1+LwebNm2iu7s7N+v69et95YypVIpnnnkmlN9CgmaM3d3dPPXUU4F+qJRR0IzRIIxhXK9BM0bj+KAwZg2DMRrXa9BvCKcZo/fp5cBlZgI2YcIEOjs7feWMixYtoru7u9edTUtLC2vWrPH1ZQ0z7Zs4cSLpdNpXzvjcc8/R2dkZygNm0IzxhRdeYNeuXaE9HQvBzWoQxjBmDZoxGoQxjFmDZowGYQxjVs0YvU8vBy5bvXo19fX1DBs2LHfn6udBia2trbnjG8zNmjWLmpoaX589WL16NclkktGjR+fO38+XFox3WQvyUxiNgmaMra2tDBo0KNBPYTQKmvYZhHH69OmBnJ+5MGZtaGgI5SDToBmjQRhnzZoVyPmZ04zR+/Ry4DLjqaxYLMbYsWN95YzlnqJsampixowZvpLGtrY2xowZQzKZZOTIkb5zRmPWMD6zImjG2Nraypw5cwJ/G2EInjGGRRgheMZoEMag39QKgmeMBmFsbGwM5PzMacbofXo5cJn5iPba2lpfOaNBGEs9bTdv3jxfSaP54KZEIuErZzQIYxgkCoJljAZhDHPWoH67DJMwQrCMMUzCCMEyxjAJI2jG6Ed6OXBZ4R2rn5zRIIwzZ84s+uctLS2+ksbCI5/9FAsGYTzuuON8Of1KBckYDcI4e/bsQM6vsCCP8g6TMEKws4ZJGCFYqRAmYQTNGP1ILwcuMjNGIz85o0EYSz3VPXnyZN9Io5kxGvm5HBiEsW/fvr6cfrmCZowGYRwwYEAg51dYkHesYRJGCH7WsAgjBLschEkYQTNGP9LLgYvMjNHIL85YjDAW5idpNDNGI784Y5iEEYJljGESRgiWMYZJGCFYxhgmYYTgGWNYhBE0Y/QrvRy4qBgB84szliKMhflFGovRPr84Y5iEEYJljGESRgiWMYZJGCFYxhgmYYRgGWOYhBE0Y/QrvRy4yMwYjfzijKUIY2F+kUYzYzTyizOGSRghWMYYJmGEYGlfmIQRgp81LMIIwTLGMAkjaMboV3o5cJGZMRr5wRntPEXpF2k0M0YjvzhjmIQRgmWMYRJGCJYxhkkYIVjGGCZhhGAZY5iEETRj9Cu9HLio2BHtfnDGSoSxMD9IY7GDm/zgjGETRgiOMYZNGCE4xhg2YYTgGGPYhBGCY4xhE0bQjNGv9HLgolJ3rF5zxkqEsTA/SGOpI5+9FgthE0YIjjGGTRghuKO8wyaMENysYRNGCE4qhE0YQTNGv9LLgcOKMUYjrzljJcJYmNeksRhjNPJ6OQiTMEKwjDFswgjB3bGGTRgh2FnDJIwQ3HIQNmEEzRj9Si8HDivGGI285IxWCGNhXpPGYozRyEvOGDZhhOAYY9iEEYJjjGETRgiOMYZNGCFYxhgmYQTNGP1MLwcOK0fAvOSMVgljYV6SxnK0z0vOGDZhhOAYY9iEEYJjjGETRgiOMYZNGCE4xhg2YQTNGP1MLwcOK8YYjbzkjFYJY2FeksZijNHIS84YNmGE4Bhj2IQRgqN9YRNGCHbWMAkjBMcYwyaMoBmjn+nlwGHFGKORV5zRzVOUXpLGYozRyEvOGDZhhOAYY9iEEYJjjGETRgiOMYZNGCE4xhg2YQTNGP1MLwcOK3dEu1ec0S5hLMwr0lju4CavOKMMhBGCYYwyEEYIhjHKQBghGMYoA2GEYBijDIQRNGP0M70cOKzSHasXnNEuYSzMK9JY6chnL8SCDIQRgmGMMhBGCOYobxkIIwQzqwyEEYKRCjIQRtCM0c/0cuCgcozRyAvOaJcwFuYFaSzHGI28WA7CJowQHGOUgTBCMHesMhBGCG7WsAkjBLMcyEAYQTNGP9PLgYPKMUYjt5zRCWEszAvSWI4xGrnljDIQRgiGMcpAGCEYxigDYYRgGKMMhBGCY4xhE0bQjNHv9HLgICsEzC1ndEoYC3NLGq3QPrecUQbCCMEwRhkIIwTDGGUgjBAMY5SBMEIwjFEGwgiaMfqdXg4cVI4xGrnljE4JY2FuSWM5xmjkljPKQBghGMYoA2GEYGifDIQRgps1bMIIwTBGGQgjaMbod3o5cFA5xmjkhjN6+RSlW9JYjjEaueWMMhBGCIYxykAYIRjGKANhhGAYowyEEYJhjDIQRtCM0e/0cuAgK0e0u+GMbgljYW5Io5WDm9xwRlkII/jPGGUhjOA/Y5SFMIL/jFEWwgj+M0ZZCCNoxuh3ejlwkNU7Vqec0S1hLMwNabR65LNTsSALYQT/GaMshBH8P8pbFsII/s8qC2EE/6WCLIQRNGP0O70c2MwKYzRyyhndEsbCnJJGK4zRyOlyIANhhGAYoyyEEfy/Y5WFMEIws8pAGMH/5UAWwgiaMfqdXg5sZoUxGjnhjF4QxsKckkYrjNHICWeUhTCC/4xRFsII/jNGWQgj+M8YZSGMEAxjlIEwgmaMQaSXA5vZIWBOOKNXhLEwJ6TRDu1zwhllIYzgP2OUhTCC/4xRFsII/jNGWQgj+M8YZSGMoBljEOnlwGZWGKORE87oFWEszAlptMIYjZxwRlkII/jPGGUhjOA/7ZOFMEIws8pAGMF/xigLYQTNGINILwc2s8IYjexyRj+fonRCGq0wRiMnnFEWwgj+M0ZZCCP4zxhlIYzgP2OUhTCC/4xRFsIImjEGUfj3VIpl54h2u5zRa8JYmF3SaOfgJrucUSbCCP4yRpkII/jLGGUijOAvY5SJMIK/jFEmwgiaMQaRXg5sZveO1Q5n9JowFmaXNNo98tmOWJCJMIK/jFEmwgj+HuUtE2EEf2eViTCCv1JBJsIImjEGkV4ObGSHMRrZ4Yytra3MmjXLt6e27ZBGO4zRyM5yIAthBP8Zo0yEEfy9Y5WJMIL/s8pCGMHf5UAmwgiaMQaRXg5sZIcxGlnljAZh9HMzt0Ma7TBGI6ucUSbCCP4yRpkII/jLGGUijOAvY5SJMIL/jFEWwgiaMQaVXg5s5ISAWeWMfhHGwgzSWOk3fCe0zypnlIkwgr+MUSbCCP4yRpkII/jLGGUijOAvY5SJMIJmjEGllwMb2WGMRlY5o1+EsTCrpNEOYzSyyhllIozgL2OUiTCCv7RPJsII/s8qC2EEfxnjggULpCGMoBljUOnlwEZ2GKORFc4Y5FOUBmmstBzYYYxGVjmjTIQR/GWMMhFG8JcxykQYwV/GKBNhBH8ZY2trqzSEETRjDCo57rEUyckR7VY4o9+EsTArpNHJwU1WOKNshBH8Y4yyEUbwjzHKRhjBP8YoG2EE/xijbIQRNGMMKr0c2MjpHWslzug3YSzMCml0euRzJbEgG2EE/xijbIQR/DvKWzbCCP7NKhthBP+kgmyEETRjDCq9HFjMCWM0qsQZ/SaMhVUijU4Yo1Gl5UAmwgj+MkbZCCP4d8cqG2EEf2eViTCCf8uBbIQRNGMMKr0cWMwJYzQqxxmDIIyFVSKNThijUTnOKBthBP8Yo2yEEfxjjLIRRvCPMcpGGMFfxigTYQTNGINMLwcWc0PAynHGoAhjYeVIoxvaV44zykYYwT/GKBthBP8Yo2yEEfxjjLIRRvCPMcpGGEEzxiDTy4HFnDBGo3KcMSjCWFg50uiEMRqV44yyEUbwjzHKRhjBP9onG2EEf2eViTCCf4xRNsIImjEGmV4OLOaEMRqV4oxhPkVZjjQ6YYxG5TijbIQR/GOMshFG8I8xykYYwT/GKBthBP8Yo2yEETRjDDJ57rkkz80R7aU4Y9CEsbBSpNHNwU2lOKOMhBH8YYwyEkbwhzHKSBjBH8YoI2EEfxijjIQRNGMMMr0cWMztHWsxzhg0YSysFGl0e+RzMbEgI2EEfxijjIQR/DnKW0bCCP7MKiNhBH+kgoyEETRjDDK9HFjIDWM0KsYZgyaMhRUjjW4Yo1Gx5UA2wgj+MUYZCSP4c8cqI2EE/2aVjTCCP8uBjIQRNGMMMr0cWMgNYzQq5IxhEMbCipFGN4zRqJAzykgYwR/GKCNhBH8Yo4yEEfxhjDISRvCPMcpGGEEzxqDTy4GFvCBghZwxLMJYWCFp9IL2FXJGGQkj+MMYZSSM4A9jlJEwgj+MUUbCCP4wRhkJI2jGGHR6ObCQG8ZoVMgZwyKMhRWSRjeM0aiQM8pIGMEfxigjYQR/aJ+MhBH8m1U2wgj+MEYZCSNoxhh0ejmwkBvGaGTmjDI9RVlIGt0wRqNCzigjYQR/GKOMhBH8YYwyEkbwhzHKSBjBH8YoI2EEzRiDTq57MEnz4oh2M2cMmzAWZiaNXhzcZOaMshJG8J4xykoYwXvGKCthBO8Zo6yEEbxnjLISRtCMMej0cmAhr+5YDc4YNmEszEwavTry2TgAU1bCCN4zRlkJI3h/lLeshBG8n1VWwgjeSwVZCSNoxhh0ejmokBeM0cjgjGETxsIM0vjEE0+4ZoxGxnIgI2EEfxijrIQRvL9jlZUwgj+zykgYwfvlQFbCCJoxBp1eDirkBWM0mjBhAqtXr+bZZ5+VajM3SONjjz3mmjEaTZgwgXXr1vH0009LNauR14xRVsII3jNGWQkjeM8YZTo+qDA/GKOMhBE0YwwjvRxUyEsCNmHCBHbu3EkqlZLuzsYgjel02pNZJ06cSEdHB+3t7VI+YHrNGGUljOA9Y5SVMIL3jFFWwgjeM0ZZCSNoxhhGejmokBeM0WjChAl0dXUxdOhQ6X7IDbaUTqddMUaj8ePHk0ql6Nevn3SEEbxnjLISRvCe9slKGMGfWWUkjOA9Y5SVMIJmjGGkl4MKecEYjcaMGUMqlZLy9bympiaGDRtGIpHwhKaNHDmS7u5uJk6cKN1TlOA9Y5SVMIL3jFFWwgjeM0ZZCSN4zxhlJYygGWMYyXdPJlleHtG+bt06AAYOHOjJ6Xldc3MzHR0dvT6l0UkbN25ECCHtrF6+fikzYQRvGaPMhBG8ZYwyE0bwljHKTBhBM8Yw0stBhbx8EGltbaWmpib3+QqylU6nicVivT6l0UkGYcxmsx5cMu/zcumTmTCCt0d5y0wYwdtZZSaM4K1UkJkwgj8fLqUrn14OymQwRi+Xg/Hjx+eeQZCpTCbDli1bGDJkSN4HMTlt4cKFjBs3Lvf5CjLlNWOUmTCCt7RPZsII3s8qK2EEbx8wZSaMoJeDMNLLQZnWrl3rGWM0PoXxyCOPpK2tTbrfqDdt2kQ6nc57K2WnGZ/CeOSRR+Z9OqMseckYZSaM4C1jlJkwgreMUWbCCN4zRlkJI/T83OnlIPj0clAmL7mb8SmMJ554Ip2dnWzdutX1aXqZMespp5yS9ymNTjI+hfGkk07K+3RGWfLyepWZMIK3jFFmwgjeMkaZCSN4yxhlJozQc0xPKpWSTnhFPb0clMlLxmi8pHDsscfmTlumjE9j/MhHPpL3KY1OMj6F0XgN3s2i4UdeMkaZCSN4S/tkJozg/ayyEkbwljHKTBhBM8aw0stBmbxijOanKM2fzihTxqcx9uvXz/VLC8aso0aNyvt0RlnykjHKTBjBW8YoM2EEbxmjzIQRvGWMMhNG0IwxrOS8R5Mkr45oN38Ko/nTGWXK/Jqe+VMa7Wb+FEbzpzPKlFcCRXbCCN4xRtkJI3jHGGUnjOAdY5SdMIJmjGGll4MyefUgUvgpjBMmTJDymQNjVvOnNNqt8FMYjQ9gkimvlj7ZCSN4R/tkJ4zg3ayyE0bw7uh92QkjaKkQVno5KJGXjLHwUxhle8DMZDJ5n8ZofEqjk5cWCj+FUbZZvWSMshNG8I72yU4YwdtZZSaM4N0DpuyEEfRyEFZ6OSiRV4zRIIzmzdx4wJSFM27atCnv0xiNT2m0uxwYhLFwVpk4o1eMUXbCCN4xRtkJI3jHGGUnjOAtY5SZMIJmjGGml4MSecXdDMJY+IApE2csNqvxKY12fus3CKP5AXPixIlScUavrlfZCSN4xxhlJ4zgHWOUnTCCd4xRdsIImjGGmV4OSuQVYzQIo/mH27izluWgRIMxmj+NcdasWbZJo0EYzZ/CaMwty0sLXjFG2QkjeEf7ZCeM4O2sMhNG8I4xyk4YQTPGMNPLQYm8YIylnqKUjTMajNFM1JqammyTRmNW87/ZyJEjpeKMXjFG2QkjeMcYZSeM4B1jlJ0wgneMUXbCCJoxhpm892wh58UR7WbCaE42zljqNT07pNFMGM3Jxhm9ECgqEEbwhjGqQBjBG8aoAmEEbxijCoQRNGMMM70clMiLB5FCwmhOJs5YajmwQxoLCaM5mcSCF0ufCoQRvKF9KhBG8GZWFQgjeHP0vgqEEbRUCDO9HBTJK8ZYSBjNyfKAWcgYzdkhjYWE0Zwss3rFGFUgjOAN7VOBMIJ3s8pOGMGbB0wVCCPo5SDM9HJQJC8YYzHCaE4WzljIGM1ZJY3FCKM5WTijF4xRBcII3jBGFQgjeMMYVSCM4B1jlJ0wgmaMYaeXgyJ5wd2KEUZzsnDGSrNaIY3FCKM5WTijF9erCoQRvGGMKhBG8IYxqkAYwRvGqAJhBM0Yw04vB0XygjEWI4zmZOGMxRijOSuksRhhNCcLZ/SCMapAGMEb2qcCYQTvZpWdMII3jFEFwgiaMYadXg6K5JYxWnmKUhbOWIwxmrNCGosRRnOycEYvGKMKhBG8YYwqEEbwhjGqQBjBG8aoAmEEzRjDTu57uJBye0R7KcJoThbOaOU1vXKksRRhNCcLZ3QrUFQhjOCeMapCGME9Y1SFMIJ7xqgKYQTNGMNOLwdFcvsgUo4wmpOBM1pZDsqRxnKE0ZwMYsHt0qcKYQT3tE8VwgjuZ1WFMIL7o/dVIYygpULY6eWgIC8YYznCaC7sB8xyjNFcOdJYjjCaC3tWLxijKoQR3NM+VQgjeDOrCoQR3D9gqkIYQS8HYaeXg4LcMsZKhNFc2JyxHGM0V4o0ViKM5sLmjG4ZoyqEEdwzRlUII7hnjKoQRvCGMapAGEEzRhnSy0FBbrlbJcJoLmzOaGfWYqSxEmE0FzZndHu9qkIYwT1jVIUwgnvGqAphBPeMURXCCJoxypBeDgpyyxgrEUZzYXPGSozRXDHSWIkwmgubM7pljKoQRnBP+1QhjODNrCoQRnDPGFUhjKAZowzp5aAgN4zR7lOUYXPGSozRXDHSWIkwmgubM7pljKoQRnDPGFUhjOCeMapCGME9Y1SFMIJmjDIk/z1dwLk5ot0KYTQXNme0+5qemTRaIYzmwuaMbgSKSoQR3DFGlQgjuGOMKhFGcMcYVSKMoBmjDOnloCA3DyJWCaO5MDmj3eXATBqtEkZzYYoFN0ufSoQR3NE+lQgjuJtVJcII7o7eV4kwgpYKMqSXA1NuGaNVwmgurAdMq4zRnJk0WiWM5sKa1S1jVIkwgjvapxJhBPezqkIYwd0DpkqEEfRyIEN6OTDlhjHaIYzmwuKMVhmjOYM0LliwwDJhNBcWZ3TDGFUijOCOMapEGMEdY1SJMIJ7xqgKYQTNGGVJLwem3HA3O4TRXFic0emsLS0trFy5kt27d9t+wAyLM7q5XlUijOCOMapEGMEdY1SJMII7xqgSYQTNGGVJLwem3DBGO4TRXFic0Q5jNDdr1izS6TQNDQ2WCKO5sDijG8aoEmEEd7RPJcII7mdVhTCCO8aoEmEEzRhlSS8HppwyRjdPUYbFGe0wRnNNTU3U1NTQ2Nho+98pLM7ohjGqRBjBHWNUiTCCO8aoEmEEd4xRJcIImjHKkhr3eAHl9Ih2u4TRXFic0elreuvWraOrq4t333236Kc0lisszuhUoKhGGME5Y1SNMIJzxqgaYQTnjFE1wgiaMcqSXg5MOX0QcUIYzYXBGZ0uB62trblnDYp9SmOlwhALTpc+1QgjOKd9qhFGcD6raoQRnN9eVSOMoKWCLOnlYG9uGKMTwmgu6AdMJ4zRaOHChcycOZMxY8YU/ZTGSgU9qxvGqBphBOe0TzXCCO5mVYkwgrtlXiXCCHo5kCW9HOzNKWN0ShjNBc0ZnTBG2PcpjPPmzSv6KY1WCpozOmWMqhFGcM4YVSOM4JwxqkYYwR1jVIkwgmaMMqWXg7055W5OCaO5oDmj01nNn8JY7FMarRQ0Z3Q6q2qEEZwzRtUIIzhnjKoRRnDOGFUjjKAZo0zp5WBvThmjU8JoLmjO6JQxmj+FsdinNFopaM7olDGqRhjBOe1TjTCCu1lVIozgnDGqRhhBM0aZ0svB3pwwRq+eogyaMzpljOZPYSz2KY1WCpozOmWMqhFGcM4YVSOM4JwxqkYYwTljVI0wgmaMMqXOPZ/POTmi3Q1hNBc0Z3Tyml6xT2E0f0qj1YLmjE4EioqEEZwxRhUJIzhjjCoSRnDGGFUkjKAZo0zp5WBvTh5E3BJGc0FyRifLQbFPYTR/SqOdghQLTpY+FQkjOKN9KhJGcDarioQRnN1eVSSMoKWCTOnlAOeM0S1hNBfUA6ZTxljsUxjNn9Jop6BmdcoYVSSM4Iz2qUgYwfmsqhFGcL7Mq0YYQS8HMqWXA5wxRi8Io7mgOKMTxmgQxsJZjU9pdLIcBMEZnTBGFQkjOGOMKhJGcMYYVSSM4JwxqkYYQTNG2dLLAc64mxeE0VxQnNHJrGbCWJgT0hgUZ3Qyq4qEEZwxRhUJIzhjjCoSRnDGGFUkjKAZo2zp5QBnjNELwmguKM7ohDGaCWNhTkhjUJzRCWNUkTCCM9qnImEE57OqRhjBGWNUkTCCZoyypZcD7DNGP56iDIozOmGMZsJYmBPSGBRndMIYVSSM4IwxqkgYwRljVJEwgjPGqCJhBM0YZUute0CfsntEu1eE0VxQnNHua3rFCGNhdkljUJzRrkBRlTCCfcaoKmEE+4xRVcII9hmjqoQRNGOULb0cYP9BxEvCaC4Izmh3OShGGAtzQhqDEAt2lz5VCSPYp32qEkawP6uqhBHs315VJYygpYJsVf1y4IQxekkYzfn9gOmEMRYjjIU5IY1+z+qEMapKGME+7VOVMIKzWVUkjOBsmVeRMIJeDmSr6pcDu4zRa8Jozm/OaJcxliKMhTkhjX5zRruMUVXCCPYZo6qEEewzRlUJIzhjjCoSRtCMUcaqfjmwy928Jozm/OaMdmctRxgLs0sa/eaMdmdVlTCCfcaoKmEE+4xRVcII9hmjqoQRNGOUsapfDuwyRq8Jozm/OaNdxliOMBZmlzT6zRntMkZVCSPYp32qEkZwNquKhBHsM0ZVCSNoxihjVb8c2GGMfj9F6TdntMsYyxHGwuySRr85o13GqCphBPuMUVXCCPYZo6qEEewzRlUJI2jGKGPq3RN6nJ0j2v0gjOb85ox2XtOzQhgLs0Ma/eaMdgSKyoQR7DFGlQkj2GOMKhNGsMcYVSaMoBmjjFX9cmDnQcQvwmjOT85oZzmwQhgLs0sa/RQLdpY+lQkj2KN9KhNGsDeryoQR7N1eVSaMoKWCjFX1cmCXMfpFGM359YBplzFaIYyF2SWNfs1qlzGqTBjBHu1TmTCC/VlVJYxgf5lXlTCCXg5krKqXAzuM0U/CaM4vzmiHMVoljIXZJY1+cUY7jFFlwgj2GKPKhBHsMUaVCSPYY4zmWVUjjKAZo6xV9XJgh7v5SRjN+cUZ7cxqhzAWZoc0+sUZ7cyqMmEEe4xRZcII9hijyoQR7DHGtrY21q5dq+ysmjHKWVUvB3YYo5+E0ZxfnNEOY7RDGAuzQxr94ox2GKPKhBHs0T6VCSPYn1VVwgj2GGNra6uyhBE0Y5S1ql4OrDLGIJ+i9Isz2mGMbp6itEMa/eKMdhijyoQR7DFGlQkj2GOMKhNGsMcYVSaMoBmjrKl5j+hRVo9o95swmvOLM1p9Tc8JYSzMKmn0izNaFSiqE0awzhhVJ4xgnTGqThjBOmNUnTCCZoyyVtXLgdUHkSAIozk/OKPV5cAJYSzMDmn0QyxYXfpUJ4xgnfapThjB+qyqE0awfntVnTCClgqyVrXLgR3GGARhNOf1A6YdxuiEMBZmhzR6Pasdxqg6YQTrtE91wgj2ZlWZMIK9ZV5lwgh6OZC1ql0OrDLGoAijOa85o1XG6JQwFmaHNHrNGa0yRtUJI1hnjKoTRrDOGFUnjGCdMapOGEEzRpmr2uXAKncLijCa85ozWp3VDWEszCpp9JozWp1VdcII1hmj6oQRrDNG1QkjWGeMqhNG0IxR5qp2OVi9ejUNDQ0Vn2YNijCa85ozWmWMbghjYVZJo9ec0SpjVJ0wgnXapzphBHuzqkwYwTpjVJ0wgmaMMle1y4HxunS5p+PCeorSa85olTF6+RSlVdLoNWe0yhhVJ4xgnTGqThjBOmNUnTCCdcaoOmEEzRhlTt17RpdZOaI9SMJozmvOaOU1PS8IY2FWSKPXnNGKQIkCYQRrjDEKhBGsMcYoEEawxhijQBhBM0aZq9rlwMoDZtCE0ZyXR/FbndUtYSzMKmn0clYrS18UCCNYo31RIIxgbdYoEEawdnuNAmEEe5+eqgu2qlwOrDLGoAmjOa/e68AqY/SCMBZmlTR6tRxYZYxRIIxgjfZFgTCC9VlVJ4xgfZlXnTCC9fea0QVfVS4Ha9euRQhR9ocyDMJozivOaIUxekUYC7NKGr3ijFYYYxQII1hjjFEgjGCNMUaBMII1xhgFwgiaMcpeVS4HVrhbGITRnFec0cqsXhLGwqyQRq84o5VZo0AYwRpjjAJhBGuMMQqEEawxxigQRtCMUfaqcjmwwhjDIIzmvOKMVhijl4SxMCuk0SvOaIUxRoEwgjXaFwXCCNZnVZ0wgjXGGAXCCJoxyl5VLgeVGKMMT1F6xRmtMEY/n6K0Qhq94oxWGGMUCCNYY4xRIIxgjTFGgTCCNcYYBcIImjHKntr3kA6rdIRsWITRnFecsdJren4QxsIqkUavOGOlg5uiQhihMmOMCmGEyowxKoQRKjPGqBBG0IxR9qpyOaj0gBkmYTTnxVH8Vmb1mjAWZoU0ejFrpaUvKoQRKtO+qBBGqDxrVAgjVL69RoUwgmaMsld1y4EVxhgmYTTnljNaYYx+EMbCrJBGt8uBFcYYFcIIlWlfVAgjWJs1CoQRrC3zUSCMoBmj7FXdclCJMYZNGM255YyVGKNfhLEwK6TRLWesxBijQhihMmOMCmGEyoxRhuODvKoSY4wKYQTNGFWo6paDStwtbMJozi1nrDSrn4SxsEqk0S1nrDRrVAgjVGaMUSGMUJkxRoUwQmXGGBXCCJoxqlDVLQeVGGPYhNGcW85YiTH6SRgLq0Qa3XLGSowxKoQRKtO+qBBGsDZrFAgjVGaMUSGMoBmjClXdclCOMcr2FKVbzliJMQb5FGUl0uiWM1ZijFEhjFCZMUaFMEJlxhgVwgiVGWNUCCNoxqhC6t9T2qzcEbIyEEZzbjljudf0giCMhZUjjW45Y7mDm6JEGKE8Y4wSYYTyjDFKhBHKM8YoEUbQjFGFqm45KPeAKQthNOfmKP5Ks/pNGAurRBrdzFpu6YsSYYTytC9KhBHKzxolwgjlb69RIoygGaMKVdVyUIkxykIYzTnljJUYYxCEsbBKpNHpclCJMUaJMEJ52hclwgiVZ40KYYTKy3xUCCNoxqhCVbUclGOMMhFGc045YznGGBRhLKwSaXTKGcsxxigRRijPGKNEGKE8Y5Tt+CC3lWOMUSKMoBmjKlXVclCOu8lEGM055YzlZg2SMBZWjjQ65YzlZo0SYYTyjDFKhBHKM8YoEUYozxijRBhBM0ZVqqrloBxjlIkwmnPKGcsxxiAJY2HlSKNTzliOMUaJMEJ52hclwgiVZ40KYYTyjDFKhBE0Y1SlqloOSjFGmZ+idMoZyzHGMJ+iLEcanXLGcowxSoQRyjPGKBFGKM8Yo0QYoTxjjBJhBM0YVSka95gWK3WErGyE0ZxTzljqNb0wCGNhpUijU85Y6uCmqBFGKM0Yo0YYoTRjjBphhNKMMWqEETRjVKWqWg5KPWDKSBjNOTmKv9ysQRPGwsqRRiezllr6okYYoTTtixphhNKzRo0wQunba9QII2jGqEpVsxyUY4wyEkZzdjljOcYYBmEsrBxptLsclGOMUSOMUJr2RY0wQvlZo0QYofwyHyXCCJoxqlLVLAelGKOshNGcXc5YijGGRRgLK0ca7XLGUowxaoQRSjPGqBFGKM0YZT4+yGmlGGPUCCNoxqhSVbMclOJushJGc3Y5Y6lZwySMhZUijXY5Y6lZo0YYoTRjjBphhNKMMWqEEUozxqgRRtCMUaWqZjkoxRhlJYzm7HLGUowxTMJYWCnSaJczlmKMUSOMUJr2RY0wQvlZo0QYoTRjjBphBM0YVapqloNijFGVpyjtcsZSjFGmpyhLkUa7nLEUY4waYYTSjDFqhBFKM8aoEUYozRijRhhBM0aVis49Z4WKHSErM2E0Z5czFntNTwbCWFgx0miXMxY7uCmKhBGKM8YoEkYozhijSBihOGOMImEEzRhVqmqWg2IPmLITRnN2juIvNWvYhLGwUqTRzqzFlr4oEkYoTvuiSBih+KxRJIxQ/PYaRcIImjGqVFUsB6UYo+yE0ZxVzliKMcpAGAsrRRqtLgelGGMUCSMUp31RJIxQetaoEUYovcxHjTCCZowqVRXLQTHGqAJhNGeVMxZjjLIQxsJKkUarnLEYY4wiYYTijDGKhBGKM0ZVjg+yWzHGGEXCCJoxqlZVLAfFuJsKhNGcVc5YbFaZCGNhxUijVc5YbNYoEkYozhijSBihOGOMImGE4owxioQRNGNUrapYDooxRhUIozmrnLEYY5SJMBZWjDRa5YzFGGMUCSMUp31RJIxQetaoEUYozhijSBhBM0bVqorloJAxqvgUpVXOWIwxyvwUZTHSaJUzFmOMUSSMUJwxRpEwQnHGGEXCCMUZYxQJI2jGqFrRugctUeERsqoQRnNWOWPha3oyEsbCCkmjVc5YeHBTVAkj9GaMUSWM0JsxRpUwQm/GGFXCCJoxqlZVLAeFD5gqEUZzVo7iLzarbISxsGKk0cqshUtfVAkj9KZ9USWM0HvWqBJG6H17jSphBM0YVSvyy0ExxqgSYTRXiTMWY4wyEsbCipHGSstBMcYYVcIIvWlfVAkjFJ81ioQRii/zUSSMoBmjakV+OShkjKoRRnOVOGMhY5SVMBZWjDRW4oyFjDGqhBF6M8aoEkbozRhVPD7IaoWMMaqEETRjVLHILweF3E01wmiuEmcsnFVmwlhYIWmsxBkLZ40qYYTejDGqhBF6M8aoEkbozRijShhBM0YVi/xyUMgYVSOM5ipxxkLGKDNhLKyQNFbijIWMMaqEEXrTvqgSRig+axQJI/RmjFEljKAZo4pFfjkwM0bVn6KsxBkLGaNKT1EWksZKnLGQMUaVMEJvxhhVwgi9GWNUCSP0ZoxRJYygGaOKRe+etCDzEbIqEkZzlTij+TU9FQhjYWbSWIkzmg9uijJhhHzGGGXCCPmMMcqEEfIZY5QJI2jGqGKRXw7MD5iqEkZz5Y7iL5xVdsJYWCFpLDereemLMmGEfNoXZcII+bNGmTBC/u01yoQRNGNUsUgvB4WMUVXCaK4UZyxkjCoQxsIKSWOp5aCQMUaZMEI+7YsyYYTes0aVMELvZT6qhBE0Y1SxSC8HZsaoMmE0V4ozmhmjKoSxsELSWIozmhljlAkj5DPGKBNGgM7OzhxjVP34oEqZGWOUCSNoxqhqkV4OzNxNZcJorhRnNM+qEmEszEwaS3FG86xRJoyQzxijTBgB3n77baDniPYoE0aArVu35hhjlAkjwPbt2zVjVLBILwdmxqgyYTRXijOaGaNKhLEwM2ksxRnNjDHKhBHyaV+UCSOQWwKNWaNKGKH3rFEljLBvVs0Y1SrSy0Hh69JR2MxLcUYzY1T5KUozaSzFGc2MMcqEEfIZY5QJI/S8KZDBGKNMGKFnVoMxRpkwQs9Lnpoxqlc071H3ZhwhqzphNFeKMxqv6alIGAszSGN3d3dRzmgc3BR1wgj7FtyoE0boecAcP348u3fvjjRhhJ5Zx40bl9M5Ub9eNWNUr0gvB8YDZhQIo7liR/GbZ1WNMBZmJo3FZjWWvqgTRthH+6JOGKHnQWTixImRJ4xATlFFnTBCz8sK+mBE9YrsctDd3Z27AUaBMJor5IxmxqgiYSzMTBoLlwMzY4w6YYR9tC/qhBH2PXMQdcII5N03RZkwAr0+FVenRsq/eJkRgu2dGbZ2pHmnI8PGTD9O/uoPWbqngWPP+xLJsVN48+1fcdl5nw37orrOmHXkkScwpGYIL6ebOfmrP2RZRwNHffZSGiYewr/v/hPnn/mxsC+qq2KxGC3zTuT5Fav5yDkXMCrdh5f2zrq0o4FDz7yQ5gOOYPlfH+STp54U9sV1Xamf4edTTUz5yLkMOOgoXnpsIadF5KnnUvOmu1I0Tz+c5/7yf8z76CfJCEFCweNmzJWaNdPdxcBDD+Hph//GiR87kyyQCPvCuqzUrNl0N0MPOpDXdnQytCHJoPpEZK/XVzL92LaxnSENCeVnjQkhRNgXwkk7uzL8e0eK5Ts6ae8WZIUgHouRzmZIdaaoqamhK9VJXX0DnR0dDOrbyPRhfThoYB3NtWrdDAtnTWfSdHZ0UNfQSFcqRU1tLV2dHblZB/ap58jhzUrP+tz6HexMpalvaCC1d7auri5qa2tz/93Z0cGApjqOGtFP6VlL/QwXztq/oZajR/VXclaoPC9AbU0NXd3d1NfV0lxfw7SB9UrOa2fWutpa+jVEe9a62lriiQTxWIymmlhkZ62rryMZT+S+rvKsyi0HqUyWRW/vYfmOFBkhQEAyHiNOz2+c3elutm/fTmNjIx0dHTQ0NNCdTtOnuT/EIBGLMW1gHbNGNFKXkPtVlVKzZjMZtm/fRnNzMzt37qSpqYk9e/bQ0NBAVzpN34jMuvO9d2mor2P37t00921m566d9OnTh927d9PU1ESqq5u+/fpHYtZiP8NRmBUszrttG8Ri9Glqon3PHgYNGUImi3LzWpt1O8SgT58+tLe3M2jwEDKCaM66fTsAgwcPJhFPkAXSWRHZWQcNGkRNsgYhhLKzGin1ssK6Xd08tmE373dliBOjNhYjFi/+lE02myWRSJBKpairq6MuEUcIQVrAS9s7advVzUmj+zC2r5xH0JabNZ5MEAOymQzQc8xBIpGgq6uLmpqayMyaiMfo7u4mFouRyRbMmkpFatZiZTMZ4vE4XV1dJBMJ5WYF6/MKIB6L0dXdTW1tLclYnGQCpea1PqsgRs/Pdm1NDcl4nCQRnVUIYrEYiUSCGDESQCIRi+Ss5mIxNWc1p8YKAyzf3sn9bTt5vytDTSxGTTxW1vFn9t6xZjIZ6urqgJ4rrCYeoyYW472uDPe37WT59s6gRrBcpVljxIgnEmQyPW+hnM1kicfjpNPpSM1aV1dHV1cXiUSCjHkRisfpTqep22vgozBrsTJ7F9zu7m7lrlewP288Hs+bFdSZ1+6sxjJfWwWzxuNxYuT/eVRnLZYqsxamxHKwfHsnCza0k80KamMx4hauHOPBJBaL9XojlXisZ/vLZgULNrRLdUVZnTWZSO77bTqbARG9Wevq6nK/eRjXp/FsSQzy7lhB7VmLZcwMKHW9goN59/78CiHylgMjmed1NCtUx6z0LEKlitqs5ZJ51mJJvxys29XNgo3tZIWg1sbWls1myWQy1NbW9tpaYe8DaTxGVggWbGxn3a5ury+67ezMmkjm/zadFVlqamqIx3pfparOmkwkSCQSPa/fGbNms2SFiNysxcpkMoi9sybive9gZZwVnM0rEAghSCaTRWcFOed1PCs9D5rJEg+ckZlViLLLAURnVivJOGuppF4OUpksj23Y7fgKMr+kUCzzFfX4ht2kMtmS3+t3dmdNJBK5B0wg7yWFYqk5a4y6ujoymQyZvZ9CKYQgk073etYg728pOWvvhBC5BbdUMs0KzuY1jomudHsFueZ1dN3unTWbze6dtfTfUX7WvSUsvLV5VGa1kkyzlkvq5WDR23tyr/XYuoLEvjucSu/NHovtey1o0dt73FxcV9mdNZlIIgCEiPSsdbW1PfMJcrMKsPQgotqshYm986oyKzibN2ta/Kx8loIs8zqZ1Vhys9ksdbXlr1dQe1bjeo1XeObASOVZ7SbLrOWSdjnY2ZVh+Y4Ucey/1tPz8NHztHQyURlkxGMx4sRYviPFzq5Mxe/3OiezJpI9NzjDoSYSCWosfCCParMaDxYid632HOBk5cOHVJs1r70LXzwWs/Se9GHPCs7nLXd8UKnCntfprNkqmtW4Xiu9rGBO1VmdFPaslZJ2Ofj3Xk+aLHH9/Pqrn+Mn53yw6J/96RsX83/f+kLZp54LS8Z63vXq3ztSTi6uq5zMatzg/vQ/e2etraXcU5TmVJo1Fov3zCb2zVpXV1f0OJJiqTSruT+afoZVmBXKz1tu1l996bP8+duXUFtT/PigUsl63Zab9c69s9bU1Nj6rVTJWb94Nn/+9iWWXlYwp+Kst1/6af70zS/YPr+wb7PlknI5yAjB8h2duSOYizVs/GTe3byRdHdX3tf/s6iVravf5NgzP1fx6VhzsVgMBCzf0dnzJhcB5XTWGDFWP7+IrW1vcuyZF0R61rq6Ot5a9gxb297kmDMvyBFGK6k2K+z7GT5GkesVKs9batbX/7WAzW+t4JgzL6Cm1p79lvW6tTJrrc1PKFR5Vmz+Bq7irG+vfJ2Zn/qc7fMM8zZbKSmXg+2dGdq7BckybzQxdPxkstkM2zasyX1NCMGTd93CqAMPZey0I21/FnwyHqO9u+c9s4PKzazP3fvrvbMeFelZa2trWXLfbxl14KGMm3aUrWeEQK1ZzT/D4xS5XqHyvKVmXfDbXzBq6qGMPeRIamvszQpyXrdWZq2xeb2CerOOPugwxh1ylKPzVW3WcdOOZOy0Ix2db1i32UpJuRxs7Uj3vDd1me8ZNn4yANvW7fvEvteefJQtbSs55swLSCaTtp6ihJ5/DCEE73SkHVxqZ7mZddva1Rxz5gUkEomirK9cKs264pnH2b6uZ9Z4PB7pWc0/w/F4vCTrK1UYs0LleUvNurntTY755AUAxB28rayM123ZWc/smdXO6/BGqs0681P/ZfWVzl6pNmvLeZc6Pt+wbrOVkvLtk9/pyBCvcKTokHE9H3H6zt4rKpvN0nrXLUw84hhGTjmEmpoa2t/bwf03fIO2l5fRPGQYH/7Sd5h0xDElTzMWixGLwdaODAd5O1LJ3Mw64fAZuVmXPPBHnn/4Pra0rWT22Rczb375H1a1Zr2V8YcdzcgphxBDcP8N32TVi8/S2b6boeMmceolX2Ps1MNKnqZas97CpOkze67XZJK//fhqVjz3JF2dHfQfOoIP/NcVTDl2bsnTDGNWqDxvqVn3O3IWIw44OO8xZN3rL3PHF89m3vzLmXvO58uer4zXbdlZ9++Z9Tdf+RwbVywnvveA6fGHTGf+D28ve76qzTp8/4NykurpP/2K5/56D53tuxg4cgwX3nQXdY19Sp6vWrMex7iDj2D79u384MPH5P397lQHH7zoKxx35vklzzes22ylpFwO2tM9b3RTbu1s6NNM86ChvLN+NQDLFzzI1nWrOO2Kq4Ge16kfuOGb9BkwmP/5y79Y9eJz/Ol7X+bLdz1KY3O/kqebFYI96eDcqZtZz/vit4Cep937DhpKy/zLeGXBg5bPW6VZz7nh10CPQBkwfBQX/ez3NA8ZzmtPPcrd37yEr9zzOHWNTSVPV6VZP/Tf3wV6foZnfXI+p3/xmyRratmw4lV+89XPceU9j9HY3L/k6QY9K1Set9SsH7vq+ySTyX3vgJnN8vAt1zPqgEMsn7ds122pWT9eMOvHrvweh530YVvnrcqsZ3z9Wurq6kilUix94I+sXPYvLvr57+k3dARb2t4kYeElJHVmvS73Pd/8+3PUJHuOJ9m5fSs3njWPqcdX/lj5MG6zlZLyZQWrB2YMHT+ZbevayGYytN59K1NnzWPCIYfT3NyMyHTz+qIFzDvvMmrrGzhw5lyGTdif/yxurXi66YAPgrFSsVknHnokzc3N1NfXM/W4eRw4cy4NffraOn9VZt3v8KNobm6m34CBtJx7Cf2HjSQejzNt7qkka2rzXgcslSqzjjvosJ7rtaGBIWMnkjTuSGMx0t3d7Ny2peLpBjkrWJu32KxjDzyUQYMG0bdvX+LxOMse/DOjpxzC0LETbZ2/bNdtsVnHmGZ1o+RUmHX0AYfQ3NxMY0MDz/zpV3z0v6+h/7CRxGIxhk88YN/PdIXUmPVg4vE4ffr0IW6SGa888SBjph7KwBGjLZ1/0LfZSkm5HCQs3nKGjpvMtg1reOmxB3h303rmnX85iXiCPk19eG/TRmrrG+g3ZHju+4dN2I+ta96qeLpJn32rOS9mtfu6tDnVZ922YS17dr7HwJFjKp6uqrP+/Wff4+oPHs6tl5zJpCNmMGzC/hVPN8hZwdq8xWYFcvOmdu9i8f13M++8y2yfv2zXbaVZY8R4+JbrufZjs/j1Vz/H5tVvWD5/lWbN7NlNd6qTfz/zGNedcTw/nX8qyx661/L5qzRr4f3Ty4//ncM/8BHL5x/0bbZSUr6s0JSMW3oDimHjJ5PqaOef/+8nHDzngww33Wl2dbRT15T/mlZdUx86dr5X9jTjsRiNyeB2Ji9mdZrqs3anOrn3uq9xwqcvpKFPc9nTVHnWD3/pO5x+2Tdpe2UZW9pWVvTxQc8K1uatNOvjv7qJmWecU/G6LEzG67bSrCdfdCVDx00inkjw7F/v4Xdfv5grfvtg2dfhQb1Zd23fSmf7LratX8NX/vA42zeu5ddXXsCQMRMYX+HoftVmNbd59Rts27CWg2efbOm8w7jNVkquS7O3IQ0Jsqa3BS7V0Ak9R4927HqfeeddnvdntQ1NpNp3530t1b6b2obSr0sbb1c7tMH5b+J282JWJ6k+a6a7mz9e82UGjRxDy7mXlD091WeFnregnXTEMax68VneeO6pkt8Xxqxgbd5ys25a+R82vPEaR532SVvnK+t1W+l6HXPgNOoam6ipq+eEsz5HbUMT619fXvZ8VZw1WVsPwNxzvkBNXT3DJx7AIXNP4Y0lT5c9XxVnNffy4//Y+zJv5UU3rNtspaR85mBoQ5J4LEYWKPfPNXbqYfxgwetF/2zQqLF0dXawc9sWmgcPA2Drmrc4rMzTPFl6jhwd0hDcP4sXszpJ5Vmz2Sz3/vDrxGIxzvjadRV/k1Z51sKymQw7Nq0v/ecEPytYm7fcrG2vLGPb+jVc/6keidG5exeJZJIdb6/njKt+UPJ8Zb1u7V6v8Xjc9AbhxVNx1sGjx5FI5r8bpJV3hlRxVqNsNssrCx7kI1++2tL5hnWbrZSUzxwMqk/QVBMjnXV+gEZdYxMHzmzhid/eTHeqkxXPLmTz6jc4cGZLyb+TzgqaamIMqg9ug/NiVoBMOk13V4psJks2Y/z/0m+qofKsD/zku+za8Q5nXf1TEhY+Y0HVWTt27+SVBQ+S2tNOJp3m1ScfZfXLS8o+HRvGrOB+3qNO/yT/fdcjXHb7/Vx2+/0cOKuFGR8+i1Mv+VrZv6fidduxeydvPb+YdHcXme5uFt33O/bsep/RU8oLDRVnrW1o5OATPsCT99xBuruLrWtX8eqTj3LAjBPK/j0VZzVa/eJzZNJp9jvqeEvfH9ZttlJyrSp7S8RiTBtYz+ItexBCOP5krA9/6dvcd/3/8IOPzqR5yDA+9e2flGSMQgiIwbSB9ZYPJvMir2Z98p7bab3rl3n//fGv/oDpH/xYr+9VedZ3N2/k+UfuI1lTx7Ufm5X7+vzrbiv6oKnyrLFYjOcfuo+//+x/QQgGjhrLmd+8kRGTpxT9/rBmBffz1tY3UFvfkPvvmto6ahuayj4tq+p1m02neezOn7JtwxriiQQjJh/I/Gtvi+SsAB/60rf5643f5tqPzaKxuT8nnn952QVX5VkBXn7i70ybe4qlX1zCvM1WKiYqvSgaUju7Mvx6xXsIATVl3oLWq7qzglgMLpjSn+baYDc4Pat/6VmDq5rm1bP6l55VjqR8WQGguTbBtIF1ZBF735jCv7JCkEUwbWBdKFeQntWf9KzBVk3z6ln9Sc8qT9IuBwCzRjTSvzZBt4Wjvp0mhKBbCPrXJpg1otGX87CSntXb9KzhVE3z6lm9Tc8qV1IvB3WJOCeN7kM8FqMr6/0VJYSgKyuIx2KcNLoPdQ4++MWr9KzepWcNr2qaV8/qXXpW+ZLzUpka27eGeaOaPL+izFfQvFFNjO1r7/PV/UjP6j49a/hV07x6VvfpWeVM2gMSC1u+vZMFG9vJCkFNLGbp3edKld37lI5xBU0bVO/hJXWfntVZela5qqZ59azO0rPKmzLLAcC6Xd08vmE373VliBMjGbP2hhpGQgjSArL0vNZz0ug+0m5uelY9a7FUmhWqa149q561WCrNak6p5QAglcmy6O09LN+R6vkULQHJeIw4xa8wIQRZet5ogphhWeuYNaJR2td6jPSselZQe1aornn1rHpWUHtWI+WWA6OdXRn+vSPF8h2dtHeL3JtWmPlJPBbLfb2ppucNLg6SlI2US8+qZ1V9VqiuefWselbVZ1V2OTDKCMH2zgzvdKTZ2pFhTzpLWgiSsZ5PuRrakGBIQ5JB9Qnp3oHKbnpWPavqs0J1zatn1bOqOqvyy4FOp9PpdDpvU+PFD51Op9PpdIGllwOdTqfT6XR56eVAp9PpdDpdXno50Ol0Op1Ol5deDnQ6nU6n0+WllwOdTqfT6XR56eVAp9PpdDpdXno50Ol0Op1Ol5deDnQ6nU6n0+WllwOdTqfT6XR56eVAp9PpdDpdXno50Ol0Op1Ol5deDnQ6nU6n0+WllwOdTqfT6XR56eVAp9PpdDpdXno50Ol0Op1Ol5deDnQ6nU6n0+WllwOdTqfT6XR56eVAp9PpdDpdXno50Ol0Op1Ol5deDnQ6nU6n0+WllwOdTqfT6XR56eVAp9PpdDpdXno50Ol0Op1Ol5deDnQ6nU6n0+WllwOdTqfT6XR56eVAp9PpdDpdXno50Ol0Op1Ol5deDnQ6nU6n0+WllwOdTqfT6XR56eVAp9PpdDpdXno50Ol0Op1Ol5deDnQ6nU6n0+WllwOdTqfT6XR56eVAp9PpdDpdXv8f6QNukue2RgoAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -279,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -308,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/tutorials/graph2simplicial/clique_lifting.ipynb b/tutorials/graph2simplicial/clique_lifting.ipynb
deleted file mode 100644
index 4d551516..00000000
--- a/tutorials/graph2simplicial/clique_lifting.ipynb
+++ /dev/null
@@ -1,382 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Graph-to-Simplicial Clique 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",
-    "***"
-   ]
-  },
-  {
-   "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",
-    "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": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'data' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "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": 8,
-   "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": 9,
-   "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": [
-      " - 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": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading and Applying the Lifting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section we will instantiate the lifting we want to apply to the data. For this example the clique lifting was chosen. For a clique of n nodes the algorithm for $m=3,...,max(n, complex\\_dim)$ will create simplicials for every possible combinations containing m nodes of the clique. $complex\\_dim$ is a parameter of the lifting. This is a deterministic lifting, based on connectivity, that does not modify the initial connectivity of the graph. The problem of extracting all the cliques in a graph is NP-hard, on in some formulaitons NP-complete (clique decision problem). The computational complexity of this algorithm is $O(n^k k^2)$[[1]](https://www.sciencedirect.com/science/article/pii/S0019995885800413), where $n$ is the number of nodes in the graph and $k$ is the highest clique dimension considered.\n",
-    "\n",
-    "***\n",
-    "[[1]](https://www.sciencedirect.com/science/article/pii/S0019995885800413) Cook, S. A. (1985). A taxonomy of problems with fast parallel algorithms. Information and control, 64(1-3), 2-22.\n",
-    "***\n",
-    "\n",
-    "For simplicial complexes creating a lifting involves creating a `SimplicialComplex` object from topomodelx and adding simplices to it using the method `add_simplices_from`. The `SimplicialComplex` class then takes care of creating all the needed matrices.\n",
-    "\n",
-    "Similarly to before, we can specify the transformation we want to apply through its type and id --the correxponding config files located at `/configs/transforms`. \n",
-    "\n",
-    "Note that the *tranform_config* dictionary generated below can contain a sequence of tranforms 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": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Transform configuration for graph2simplicial/clique_lifting:\n",
-      "\n",
-      "{'transform_type': 'lifting',\n",
-      " 'transform_name': 'SimplicialCliqueLifting',\n",
-      " 'complex_dim': 3,\n",
-      " 'preserve_edge_attr': False,\n",
-      " 'signed': True,\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 = \"graph2simplicial/clique_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 `PreProcesor`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Transform parameters are the same, using existing data_dir: /Users/leone/Desktop/PhD-S/projects/challenge-icml-2024/datasets/graph/toy_dataset/manual/lifting/2744620725\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": [
-      " - The complex has 8 0-cells.\n",
-      " - The 0-cells have features dimension 1\n",
-      " - The complex has 13 1-cells.\n",
-      " - The 1-cells have features dimension 1\n",
-      " - The complex has 6 2-cells.\n",
-      " - The 2-cells have features dimension 1\n",
-      " - The complex has 1 3-cells.\n",
-      " - The 3-cells have features dimension 1\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n",
-    "describe_data(lifted_dataset)"
-   ]
-  },
-  {
-   "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 `up_laplacian_1` and the `down_laplacian_1` so the lifting should make sure to add them to the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Model configuration for simplicial SAN:\n",
-      "\n",
-      "{'in_channels': None,\n",
-      " 'hidden_channels': 32,\n",
-      " 'out_channels': None,\n",
-      " 'n_layers': 2,\n",
-      " 'n_filters': 2,\n",
-      " 'order_harmonic': 5,\n",
-      " 'epsilon_harmonic': 0.1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from modules.models.simplicial.san import SANModel\n",
-    "\n",
-    "model_type = \"simplicial\"\n",
-    "model_id = \"san\"\n",
-    "model_config = load_model_config(model_type, model_id)\n",
-    "\n",
-    "model = SANModel(model_config, dataset_config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "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. "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "venv_topox",
-   "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": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/tutorials/pointcloud2graph/knn_lifting.ipynb b/tutorials/pointcloud2graph/knn_lifting.ipynb
new file mode 100644
index 00000000..82d3c6da
--- /dev/null
+++ b/tutorials/pointcloud2graph/knn_lifting.ipynb
@@ -0,0 +1,403 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PointCloud to Graph Protein Lifting Tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "This notebook shows how to import UniProt protein data and convert it to a graph using the `PointCloudToGraph` class. Proteins are represented as point clouds where each point is a residue in the protein, setting CarbonAlpha as its centers. The graph is created by connecting residues that are close to each other in the 3D space or that appear in a sequential order.\n",
+    "\n",
+    "The target is the mass of each protein.\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, creates a dataset object and visualizes it.\n",
+    "- [Loading and applying the lifting](#loading-and-applying-the-lifting) definding the edges by the following way:\n",
+    "    - **Sequentialwise**: Connecting residues that appear in a sequential order (one after another). This approach is based on the presence of peptide bonds, which link the amino acids in a protein chain in a specific sequence.\n",
+    "    - **KNN**: Connecting residues that are close to each other in the 3D space. This approach is based on the physical proximity of the residues in the protein structure.\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 point cloud. \n",
+    "\n",
+    "With this submission, **UniProt** protein dataset is available and loaded as a point cloud, based on PDB files.\n",
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports and utilities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "\n",
+    "sys.path.append(\"../..\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "# With this cell any imported module is reloaded before each cell execution\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "from modules.data.load.loaders import PointCloudLoader\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 specify 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": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Dataset configuration for UniProt:\n",
+      "\n",
+      "{'data_domain': 'pointcloud',\n",
+      " 'data_type': 'UniProt',\n",
+      " 'data_name': 'UniProt',\n",
+      " 'data_dir': 'datasets/pointcloud/UniProt',\n",
+      " 'query': 'length:[95 TO 155]',\n",
+      " 'format': 'tsv',\n",
+      " 'fields': 'accession,length',\n",
+      " 'size': 20,\n",
+      " 'num_features': 20,\n",
+      " 'num_classes': 1,\n",
+      " 'task': 'regression',\n",
+      " 'loss_type': 'mse',\n",
+      " 'monitor_metric': 'mae',\n",
+      " 'task_level': 'graph'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_name = \"UniProt\"\n",
+    "dataset_config = load_dataset_config(dataset_name)\n",
+    "loader = PointCloudLoader(dataset_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Dataset configuration for UniProt:\n",
+      "\n",
+      "{'data_domain': 'pointcloud',\n",
+      " 'data_type': 'UniProt',\n",
+      " 'data_name': 'UniProt',\n",
+      " 'data_dir': 'datasets/pointcloud/UniProt',\n",
+      " 'query': 'length:[95 TO 155]',\n",
+      " 'format': 'tsv',\n",
+      " 'fields': 'accession,length',\n",
+      " 'size': 20,\n",
+      " 'num_features': 20,\n",
+      " 'num_classes': 1,\n",
+      " 'task': 'regression',\n",
+      " 'loss_type': 'mse',\n",
+      " 'monitor_metric': 'mae',\n",
+      " 'task_level': 'graph'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_name = \"UniProt\"\n",
+    "dataset_config = load_dataset_config(dataset_name)\n",
+    "loader = PointCloudLoader(dataset_config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then access to the data through the `load()`method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PDB file for O14960 already exists.\n",
+      "PDB file for O14907 already exists.\n",
+      "PDB file for O14519 already exists.\n",
+      "PDB file for O60519 already exists.\n",
+      "PDB file for O75379 already exists.\n",
+      "PDB file for A6NNB3 already exists.\n",
+      "PDB file for O60814 already exists.\n",
+      "PDB file for C9JLW8 already exists.\n",
+      "PDB file for O43914 already exists.\n",
+      "PDB file for A2RU14 already exists.\n",
+      "PDB file for O75956 already exists.\n",
+      "PDB file for O15116 already exists.\n",
+      "PDB file for O14933 already exists.\n",
+      "PDB file for O00453 already exists.\n",
+      "PDB file for A6NFY7 already exists.\n",
+      "PDB file for O00422 already exists.\n",
+      "PDB file for O15540 already exists.\n",
+      "PDB file for O15511 already exists.\n",
+      "PDB file for O95139 already exists.\n",
+      "PDB file for A8MQ03 already exists.\n",
+      "PDB file for O14960 already exists.\n",
+      "PDB file for O14907 already exists.\n",
+      "PDB file for O14519 already exists.\n",
+      "PDB file for O60519 already exists.\n",
+      "PDB file for O75379 already exists.\n",
+      "PDB file for A6NNB3 already exists.\n",
+      "PDB file for O60814 already exists.\n",
+      "PDB file for C9JLW8 already exists.\n",
+      "PDB file for O43914 already exists.\n",
+      "PDB file for A2RU14 already exists.\n",
+      "PDB file for O75956 already exists.\n",
+      "PDB file for O15116 already exists.\n",
+      "PDB file for O14933 already exists.\n",
+      "PDB file for O00453 already exists.\n",
+      "PDB file for A6NFY7 already exists.\n",
+      "PDB file for O00422 already exists.\n",
+      "PDB file for O15540 already exists.\n",
+      "PDB file for O15511 already exists.\n",
+      "PDB file for O95139 already exists.\n",
+      "PDB file for A8MQ03 already exists.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading and Applying the Lifting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section we will instantiate the lifting we want to apply to the data. For this example the knn lifting was chosen. The algorithm takes the k nearest neighbors for each node and creates a hyperedge with them. Moreover, the algorithm also creates an edge for each sequential pair of residues.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Transform configuration for pointcloud2graph/knn_lifting:\n",
+      "\n",
+      "{'transform_type': 'lifting',\n",
+      " 'transform_name': 'PointCloudKNNLifting',\n",
+      " 'max_cell_length': None,\n",
+      " 'preserve_edge_attr': False,\n",
+      " 'feature_lifting': 'ProjectionSum',\n",
+      " 'k_value': 10,\n",
+      " 'loop': False}\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 = \"pointcloud2graph/knn_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 `PreProcesor`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Transform parameters are the same, using existing data_dir: /home/bmiquel/Documents/Projects/Topo/challenge-icml-2024/datasets/pointcloud/UniProt/UniProt/lifting/1540663474\n",
+      "\n",
+      "Dataset contains 20 samples.\n",
+      "\n",
+      "Providing more details about sample 0/20:\n",
+      " - Graph with 151 vertices and 1730 edges.\n",
+      " - Features dimensions: [20, 0]\n",
+      " - There are 0 isolated nodes.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "lifted_dataset = PreProcessor(dataset, transform_config, loader.data_dir)\n",
+    "describe_data(lifted_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create and Run a Cell NN Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section a simple model is created to test that the used lifting works as intended. A graph neural network from torch_geometric is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Model configuration for graph GRAPHSAGE:\n",
+      "\n",
+      "{'in_channels_0': None,\n",
+      " 'in_channels_1': None,\n",
+      " 'in_channels_2': None,\n",
+      " 'hidden_channels': 32,\n",
+      " 'out_channels': None,\n",
+      " 'n_layers': 2}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from modules.models.graph.graphsage import GraphSAGEModel\n",
+    "\n",
+    "model_type = \"graph\"\n",
+    "model_id = \"graphsage\"\n",
+    "model_config = load_model_config(model_type, model_id)\n",
+    "\n",
+    "model = GraphSAGEModel(model_config, dataset_config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/bmiquel/Documents/Projects/Topo/challenge-icml-2024/tutorials/pointcloud2graph/../../modules/models/graph/graphsage.py:47: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
+      "  return torch.nn.functional.softmax(global_mean_pool(z, None))\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_hat = model(lifted_dataset.get(0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If everything is correct the cell above should execute without errors. "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv_topox",
+   "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}