From 9d1e94331667652fc196ba314040a1d84b369287 Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Wed, 27 Dec 2023 16:52:37 -0500
Subject: [PATCH 01/24] refactor treatment of neighbors

---
 CHANGELOG.md                                  |   4 +
 docs/api.md                                   |   2 +-
 docs/notebooks/large_scale.ipynb              | 942 +++++++++---------
 pyproject.toml                                |  58 +-
 src/scib_metrics/_graph_connectivity.py       |   9 +-
 src/scib_metrics/_kbet.py                     |  52 +-
 src/scib_metrics/_lisi.py                     |  22 +-
 src/scib_metrics/_nmi_ari.py                  |  27 +-
 src/scib_metrics/benchmark/_core.py           |  27 +-
 .../nearest_neighbors/__init__.py             |   4 +-
 .../nearest_neighbors/_dataclass.py           |  61 +-
 src/scib_metrics/nearest_neighbors/_jax.py    |   6 +-
 .../nearest_neighbors/_pynndescent.py         |   6 +-
 src/scib_metrics/utils/_diffusion_nn.py       |  22 +-
 tests/test_metrics.py                         |  25 +-
 tests/utils/data.py                           |  28 +-
 16 files changed, 669 insertions(+), 626 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 4a793b5..a4e503c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,10 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## 0.5.0 (2024-MM-DD)
+
+-   Refactor all relevant metrics to use `NeighborsResults` as input instead of sparse distance/connectivity matrices.
+
 ## 0.4.1 (2023-10-08)
 
 -   Fix KMeans. All previous versions had a bug with KMeans and ARI/NMI metrics are not reliable with this clustering. ([#115][])
diff --git a/docs/api.md b/docs/api.md
index 59c40c2..9dc003d 100644
--- a/docs/api.md
+++ b/docs/api.md
@@ -82,7 +82,7 @@ scib_metrics.ilisi_knn(...)
 
     nearest_neighbors.pynndescent
     nearest_neighbors.jax_approx_min_k
-    nearest_neighbors.NeighborsOutput
+    nearest_neighbors.NeighborsResults
 ```
 
 ## Settings
diff --git a/docs/notebooks/large_scale.ipynb b/docs/notebooks/large_scale.ipynb
index dbcc076..6d781b3 100644
--- a/docs/notebooks/large_scale.ipynb
+++ b/docs/notebooks/large_scale.ipynb
@@ -1,474 +1,474 @@
 {
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Benchmarking large-scale integration"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we walkthrough applying the integration benchmarking metrics on a dataset of non-small cell lung cancer from the following paper:\n",
-    "\n",
-    "Salcher, S., Sturm, G., Horvath, L., Untergasser, G., Kuempers, C., Fotakis, G., ... & Trajanoski, Z. (2022). High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer Cell.\n",
-    "\n",
-    "This dataset contains ~900,000 cells, so here we demonstrate the scalbility of the metrics here on large data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Global seed set to 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import scanpy as sc\n",
-    "from scvi.data import cellxgene\n",
-    "\n",
-    "from scib_metrics.benchmark import Benchmarker, BioConservation\n",
-    "\n",
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load and preprocess data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[34mINFO    \u001b[0m File data/luca.h5ad already downloaded                                                                    \n"
-     ]
-    }
-   ],
-   "source": [
-    "url = \"https://cellxgene.cziscience.com/e/232f6a5a-a04c-4758-a6e8-88ab2e3a6e69.cxg/\"\n",
-    "adata = cellxgene(url, filename=\"luca.h5ad\", save_path=\"data/\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AnnData object with n_obs × n_vars = 892296 × 17811\n",
-       "    obs: 'sample', 'uicc_stage', 'ever_smoker', 'age', 'donor_id', 'origin', 'dataset', 'ann_fine', 'cell_type_predicted', 'doublet_status', 'leiden', 'n_genes_by_counts', 'total_counts', 'total_counts_mito', 'pct_counts_mito', 'ann_coarse', 'cell_type_tumor', 'tumor_stage', 'EGFR_mutation', 'TP53_mutation', 'ALK_mutation', 'BRAF_mutation', 'ERBB2_mutation', 'KRAS_mutation', 'ROS_mutation', 'origin_fine', 'study', 'platform', 'cell_type_major', 'suspension_type', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'is_primary_data', 'organism_ontology_term_id', 'sex_ontology_term_id', 'tissue_ontology_term_id', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage'\n",
-       "    var: 'is_highly_variable', 'mito', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype'\n",
-       "    uns: '_scvi', 'ann_fine_colors', 'batch_condition', 'cell_type_major_colors', 'cell_type_ontology_term_id_colors', 'cell_type_predicted_colors', 'dataset_colors', 'default_embedding', 'hvg', 'leiden_colors', 'neighbors', 'origin_colors', 'platform_colors', 'schema_version', 'title', 'umap'\n",
-       "    obsm: 'X_scANVI', 'X_scVI', 'X_umap'\n",
-       "    layers: 'count', 'counts_length_scaled'\n",
-       "    obsp: 'connectivities', 'distances'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "adata"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "33"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Number of unique cell types\n",
-    "adata.obs[\"cell_type\"].nunique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "adata.var[\"highly_variable\"] = np.asarray(adata.var[\"is_highly_variable\"].astype(bool))\n",
-    "sc.tl.pca(adata)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## \"Run\" methods"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The authors used scVI and scANVI in their manuscript, and these embeddings are already stored in the AnnData object. We can use these to run the metrics."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### scVI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "adata.obsm[\"scVI\"] = adata.obsm[\"X_scVI\"]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### scANVI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "adata.obsm[\"scANVI\"] = adata.obsm[\"X_scANVI\"]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Perform the benchmark"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we use a custom nearest neighbor function to speed up the computation of the metrics. This is not necessary, but can be useful for large datasets.\n",
-    "\n",
-    "In particular we use [faiss](https://github.com/facebookresearch/faiss), which can be accelerated with a GPU.\n",
-    "\n",
-    "This can be installed as\n",
-    "\n",
-    "```bash\n",
-    "conda install -c conda-forge faiss-gpu\n",
-    "```\n",
-    "\n",
-    "When using approximate nearest neighbors, an issue can arise where each cell does not get a unique set of K neighbors. This issue happens with faiss hnsw below, so we use the brute force method instead, which is still faster than pynndescent approximate nearest neighbors on CPU."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import faiss\n",
-    "\n",
-    "from scib_metrics.nearest_neighbors import NeighborsOutput\n",
-    "\n",
-    "\n",
-    "def faiss_hnsw_nn(X: np.ndarray, k: int):\n",
-    "    \"\"\"Gpu HNSW nearest neighbor search using faiss.\n",
-    "\n",
-    "    See https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md\n",
-    "    for index param details.\n",
-    "    \"\"\"\n",
-    "    X = np.ascontiguousarray(X, dtype=np.float32)\n",
-    "    res = faiss.StandardGpuResources()\n",
-    "    M = 32\n",
-    "    index = faiss.IndexHNSWFlat(X.shape[1], M, faiss.METRIC_L2)\n",
-    "    gpu_index = faiss.index_cpu_to_gpu(res, 0, index)\n",
-    "    gpu_index.add(X)\n",
-    "    distances, indices = gpu_index.search(X, k)\n",
-    "    del index\n",
-    "    del gpu_index\n",
-    "    # distances are squared\n",
-    "    return NeighborsOutput(indices=indices, distances=np.sqrt(distances))\n",
-    "\n",
-    "\n",
-    "def faiss_brute_force_nn(X: np.ndarray, k: int):\n",
-    "    \"\"\"Gpu brute force nearest neighbor search using faiss.\"\"\"\n",
-    "    X = np.ascontiguousarray(X, dtype=np.float32)\n",
-    "    res = faiss.StandardGpuResources()\n",
-    "    index = faiss.IndexFlatL2(X.shape[1])\n",
-    "    gpu_index = faiss.index_cpu_to_gpu(res, 0, index)\n",
-    "    gpu_index.add(X)\n",
-    "    distances, indices = gpu_index.search(X, k)\n",
-    "    del index\n",
-    "    del gpu_index\n",
-    "    # distances are squared\n",
-    "    return NeighborsOutput(indices=indices, distances=np.sqrt(distances))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Computing neighbors: 100%|██████████| 3/3 [06:48<00:00, 136.22s/it]\n",
-      "Embeddings: 100%|\u001b[32m██████████\u001b[0m| 3/3 [35:58<00:00, 719.52s/it]\n"
-     ]
+    "cells": [
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# Benchmarking large-scale integration"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Here we walkthrough applying the integration benchmarking metrics on a dataset of non-small cell lung cancer from the following paper:\n",
+                "\n",
+                "Salcher, S., Sturm, G., Horvath, L., Untergasser, G., Kuempers, C., Fotakis, G., ... & Trajanoski, Z. (2022). High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer Cell.\n",
+                "\n",
+                "This dataset contains ~900,000 cells, so here we demonstrate the scalbility of the metrics here on large data."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 1,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Global seed set to 0\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import numpy as np\n",
+                "import scanpy as sc\n",
+                "from scvi.data import cellxgene\n",
+                "\n",
+                "from scib_metrics.benchmark import Benchmarker, BioConservation\n",
+                "\n",
+                "%matplotlib inline"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Load and preprocess data"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 2,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "\u001b[34mINFO    \u001b[0m File data/luca.h5ad already downloaded                                                                    \n"
+                    ]
+                }
+            ],
+            "source": [
+                "url = \"https://cellxgene.cziscience.com/e/232f6a5a-a04c-4758-a6e8-88ab2e3a6e69.cxg/\"\n",
+                "adata = cellxgene(url, filename=\"luca.h5ad\", save_path=\"data/\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "AnnData object with n_obs × n_vars = 892296 × 17811\n",
+                            "    obs: 'sample', 'uicc_stage', 'ever_smoker', 'age', 'donor_id', 'origin', 'dataset', 'ann_fine', 'cell_type_predicted', 'doublet_status', 'leiden', 'n_genes_by_counts', 'total_counts', 'total_counts_mito', 'pct_counts_mito', 'ann_coarse', 'cell_type_tumor', 'tumor_stage', 'EGFR_mutation', 'TP53_mutation', 'ALK_mutation', 'BRAF_mutation', 'ERBB2_mutation', 'KRAS_mutation', 'ROS_mutation', 'origin_fine', 'study', 'platform', 'cell_type_major', 'suspension_type', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'is_primary_data', 'organism_ontology_term_id', 'sex_ontology_term_id', 'tissue_ontology_term_id', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage'\n",
+                            "    var: 'is_highly_variable', 'mito', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype'\n",
+                            "    uns: '_scvi', 'ann_fine_colors', 'batch_condition', 'cell_type_major_colors', 'cell_type_ontology_term_id_colors', 'cell_type_predicted_colors', 'dataset_colors', 'default_embedding', 'hvg', 'leiden_colors', 'neighbors', 'origin_colors', 'platform_colors', 'schema_version', 'title', 'umap'\n",
+                            "    obsm: 'X_scANVI', 'X_scVI', 'X_umap'\n",
+                            "    layers: 'count', 'counts_length_scaled'\n",
+                            "    obsp: 'connectivities', 'distances'"
+                        ]
+                    },
+                    "execution_count": 3,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "adata"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 4,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "33"
+                        ]
+                    },
+                    "execution_count": 4,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "# Number of unique cell types\n",
+                "adata.obs[\"cell_type\"].nunique()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 5,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "adata.var[\"highly_variable\"] = np.asarray(adata.var[\"is_highly_variable\"].astype(bool))\n",
+                "sc.tl.pca(adata)"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## \"Run\" methods"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "The authors used scVI and scANVI in their manuscript, and these embeddings are already stored in the AnnData object. We can use these to run the metrics."
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### scVI"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 6,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "adata.obsm[\"scVI\"] = adata.obsm[\"X_scVI\"]"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### scANVI"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 7,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "adata.obsm[\"scANVI\"] = adata.obsm[\"X_scANVI\"]"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Perform the benchmark"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Here we use a custom nearest neighbor function to speed up the computation of the metrics. This is not necessary, but can be useful for large datasets.\n",
+                "\n",
+                "In particular we use [faiss](https://github.com/facebookresearch/faiss), which can be accelerated with a GPU.\n",
+                "\n",
+                "This can be installed as\n",
+                "\n",
+                "```bash\n",
+                "conda install -c conda-forge faiss-gpu\n",
+                "```\n",
+                "\n",
+                "When using approximate nearest neighbors, an issue can arise where each cell does not get a unique set of K neighbors. This issue happens with faiss hnsw below, so we use the brute force method instead, which is still faster than pynndescent approximate nearest neighbors on CPU."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 8,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "import faiss\n",
+                "\n",
+                "from scib_metrics.nearest_neighbors import NeighborsResults\n",
+                "\n",
+                "\n",
+                "def faiss_hnsw_nn(X: np.ndarray, k: int):\n",
+                "    \"\"\"Gpu HNSW nearest neighbor search using faiss.\n",
+                "\n",
+                "    See https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md\n",
+                "    for index param details.\n",
+                "    \"\"\"\n",
+                "    X = np.ascontiguousarray(X, dtype=np.float32)\n",
+                "    res = faiss.StandardGpuResources()\n",
+                "    M = 32\n",
+                "    index = faiss.IndexHNSWFlat(X.shape[1], M, faiss.METRIC_L2)\n",
+                "    gpu_index = faiss.index_cpu_to_gpu(res, 0, index)\n",
+                "    gpu_index.add(X)\n",
+                "    distances, indices = gpu_index.search(X, k)\n",
+                "    del index\n",
+                "    del gpu_index\n",
+                "    # distances are squared\n",
+                "    return NeighborsResults(indices=indices, distances=np.sqrt(distances))\n",
+                "\n",
+                "\n",
+                "def faiss_brute_force_nn(X: np.ndarray, k: int):\n",
+                "    \"\"\"Gpu brute force nearest neighbor search using faiss.\"\"\"\n",
+                "    X = np.ascontiguousarray(X, dtype=np.float32)\n",
+                "    res = faiss.StandardGpuResources()\n",
+                "    index = faiss.IndexFlatL2(X.shape[1])\n",
+                "    gpu_index = faiss.index_cpu_to_gpu(res, 0, index)\n",
+                "    gpu_index.add(X)\n",
+                "    distances, indices = gpu_index.search(X, k)\n",
+                "    del index\n",
+                "    del gpu_index\n",
+                "    # distances are squared\n",
+                "    return NeighborsResults(indices=indices, distances=np.sqrt(distances))"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 9,
+            "metadata": {},
+            "outputs": [
+                {
+                    "name": "stderr",
+                    "output_type": "stream",
+                    "text": [
+                        "Computing neighbors: 100%|██████████| 3/3 [06:48<00:00, 136.22s/it]\n",
+                        "Embeddings: 100%|\u001b[32m██████████\u001b[0m| 3/3 [35:58<00:00, 719.52s/it]\n"
+                    ]
+                },
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Time: 42 min 47 sec\n"
+                    ]
+                }
+            ],
+            "source": [
+                "import time\n",
+                "\n",
+                "adata.obsm[\"Unintegrated\"] = adata.obsm[\"X_pca\"]\n",
+                "\n",
+                "biocons = BioConservation(isolated_labels=False)\n",
+                "\n",
+                "start = time.time()\n",
+                "bm = Benchmarker(\n",
+                "    adata,\n",
+                "    batch_key=\"sample\",\n",
+                "    label_key=\"cell_type\",\n",
+                "    embedding_obsm_keys=[\"Unintegrated\", \"scANVI\", \"scVI\"],\n",
+                "    pre_integrated_embedding_obsm_key=\"X_pca\",\n",
+                "    bio_conservation_metrics=biocons,\n",
+                "    n_jobs=-1,\n",
+                ")\n",
+                "bm.prepare(neighbor_computer=faiss_brute_force_nn)\n",
+                "bm.benchmark()\n",
+                "end = time.time()\n",
+                "print(f\"Time: {int((end - start) / 60)} min {int((end - start) % 60)} sec\")"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Visualize the results"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 10,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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urzkJCQlQKBRo2rSptKxcuXKoXr26UR24urqiatWqRnVg2H/GGHsaRTkXrFmzBi1atEBgYCDc3d0xbty4x7Z18fHxqFChAqpVq1ZgmuK2bfHx8WjZsiWUSqXJuvT0dNy4ccPonALknqfyn1Pyt/kAUKdOHahUKun/p0+fxuXLl+Hh4SGdP3x9fZGTk4OkpCTcvn0bN27cKPAcVxTFKXNh50HGmG1JTEzE0aNH0bt3bwCAQqFAr169sGjRIqM0TZo0MfrcM888Y5JP/mX5/w8AoaGh8Pf3l/5/+vRpZGRkoFy5ckZ94OTkZKmfXJS8b926hcGDByMiIgJeXl7w9PRERkbGY9v/06dPY8qUKUbbHjx4MFJTU5GVlWWS3tfXF/3790dUVBS6dOmC7777zui268LGE0XtS6tUKqN29K+//oJer0e1atWMyrl3716jsQQrGWXricGMMSNubm4IDw+X/r9w4UJ4eXnhp59+wrRp00pkGy4uLiWST95BhiHwZXgo4KRJk/DGG2/gt99+w++//46JEydi9erV6N69OzIyMvDOO+9gxIgRJnlWqlRJ+rebm5vJ+j59+mDEiBGYM2cOVq5ciTp16qBOnToAgMzMTERFRSEqKgorVqyAv78/rl27hqioKIs81DH/IEsQBBBRiW+HMVb2PO5cYAi6T548GVFRUfDy8sLq1asxY8aMQvMtSvtf3LatpM4p5tr8/MsyMjLQqFEj6QJEXv7+/pDJSvf6bWHnQcaYbVm0aBF0Oh2Cg4OlZUQEJycnfP/99/Dy8irR7Zlrv4KCgsw+ryjv86EeJzo6Gvfu3cN3332H0NBQODk5oVmzZo/t62ZkZGDy5Mno0aOHyTrDM6jyW7x4MUaMGIHt27djzZo1GDduHHbt2oVnn322RNp+FxcXo4vnGRkZkMvlOHHiBORyuVFad3f3p94eM8YznhhjEkEQIJPJkJ2dbXZ9ZGQkDh06ZDQoiIuLg4eHBypUqGD2M3Xr1i3wtdFVq1aFSqVCXFyctEyr1eLYsWOoWbNmscperVo1fPDBB9i5cyd69OiBxYsXAwAaNmyI8+fPIzw83OQv75Vtc7p27YqcnBxs374dK1euNJrtdOHCBdy7dw/Tp09Hy5YtUaNGDZMrz4b89Xp9gduIjIyETqfDkSNHpGX37t1DYmJiseuAMcZKQv5zwcGDBxEaGopPP/0UjRs3RkREhMnMWJVKZdLW1a1bF3///TcuXrxYYmWrW7cu9u/fb/ZNdJ6enggODjY6pwC556knaU8bNmyIS5cuoXz58ibnDy8vL3h4eCAsLKzAcxyQGywq7BxQ0mVmjFmfTqfD0qVLMWPGDGk2aXx8PE6fPo3g4GCsWrUKAFC9enWjl+EAMHrBjiFN/mX5/29Ow4YNcfPmTSgUCpP2y8/Pr8h5x8XFYcSIEXjppZeklyjcvXvXKI25dq5hw4ZITEw02/8uLGjfoEEDjB07FgcPHkTt2rWxcuVKAIWPJ560L92gQQPo9Xrcvn3bpIyBgYEFfo49GQ48MVaGqdVq3Lx5Ezdv3kRCQgKGDx+OjIwMdOnSxWz6IUOG4Pr16xg+fDguXLiATZs2YeLEifjwww8LPImMHTsWx44dw5AhQ3DmzBlcuHABP/zwA+7evQs3Nze8++67+Pjjj7F9+3acP38egwcPRlZWFgYOHFikfcjOzsawYcMQGxuLq1evIi4uDseOHUNkZCSA3DcxHTx4EMOGDUN8fDwuXbqETZs2SW8dKoybmxu6deuG8ePHIyEhQZouDeTOllKpVJgzZw6uXLmCzZs3Y+rUqUafDw0NhSAI2Lp1K+7cuYOMjAyTbURERKBr164YPHgwDhw4gNOnT+PNN99ESEgIunbtWqQ6YIyxp/G4c0FERASuXbuG1atXIykpCbNnz8aGDRuM8ggLC0NycjLi4+Nx9+5dqNVqtG7dGq1atULPnj2xa9cuJCcn4/fffzd6Y1txDRs2DOnp6Xj99ddx/PhxXLp0CcuWLZPedPrxxx/jyy+/xJo1a5CYmIgxY8YgPj4e7733XrG31adPH/j5+aFr167Yv38/kpOTERsbixEjRki3l0+aNAkzZszA7NmzcenSJZw8eRJz5swxqpfdu3fj5s2b0ttc8yvJMjPGrG/r1q1IS0vDwIEDUbt2baO/nj17SrfbvfPOO7hw4QJGjx6Nixcv4pdffpHefGeYmTN8+HBs27YN3377LS5duoQff/wRv//+u9HMHXPat2+PZs2aoVu3bti5cydSUlJw8OBBfPrpp1Kwa/jw4Vi0aBGWLFmCS5cuYdq0aThz5oxR3hEREVi2bBkSEhJw5MgR9OnTx2T2kbl2bsKECVi6dCkmT56Mc+fOISEhAatXr8a4cePMljc5ORljx47FoUOHcPXqVezcuROXLl2S+vMTJ07EqlWrMHHiRCQkJOCvv/7Cl19+KZXxSfrS1apVQ58+fdCvXz+sX78eycnJOHr0KL744gv89ttvhdYvewLWfcQUY8xaoqOjCYD05+HhQU2aNKFff/1VSpP/4eJERLGxsdSkSRNSqVQUGBhIo0ePfuxbdWJjY6l58+bk5ORE3t7eFBUVJT00MTs7m4YPH05+fn7k5ORELVq0oKNHj0qfNfeA7lOnThEASk5OJrVaTa+//jpVrFiRVCoVBQcH07Bhw6QHhxMRHT16lDp06EDu7u7k5uZGdevWNXqQa2hoqPQWovy2bdtGAKhVq1Ym61auXElhYWHk5OREzZo1o82bN5vU15QpUygwMJAEQaDo6GgiMn2r3b///kt9+/YlLy8vcnFxoaioKKO3aSxevNjoIZJERBs2bCBuwhljT6so5wIioo8//pjKlStH7u7u1KtXL5o5c6ZRu5STk0M9e/Ykb29v6Q1HRLkPjB0wYACVK1eOnJ2dqXbt2rR161YievK27fTp0/TCCy+Qq6sreXh4UMuWLSkpKYmIch98O2nSJAoJCSGlUkn16tWj33//XfqsufOaoR7yvt3PIDU1lfr16yedo6pUqUKDBw+mBw8eSGnmz59P1atXJ6VSSUFBQTR8+HBp3ebNmyk8PJwUCgWFhoYSkenDxZ+kzGlpaQTA6EHFjDHb0LlzZ3rppZfMrjty5AgBkN48t2nTJgoPDycnJydq06YN/fDDD0Yv0CEiWrBgAYWEhJCLiwt169aNpk2bRoGBgdJ6cy93ICJKT0+n4cOHU3BwMCmVSqpYsSL16dOHrl27JqWZMmUK+fn5kbu7O7311ls0YsQIevbZZ6X1J0+epMaNG5OzszNFRETQ2rVrTfrN5to5IqLt27dT8+bNycXFhTw9PemZZ56hBQsWmK2XmzdvUrdu3SgoKIhUKhWFhobShAkTjF5etG7dOqpfvz6pVCry8/OjHj16SOuepC9NlPtSowkTJlBYWJjUhnfv3p3OnDljtpzsyQlE/JAQxhhjjDHGGGPMmj777DPMnz8f169fLzDN4MGDceHCBezfv7/Et9+hQwcEBgZi2bJlJZ43K9v44eKMMcYYY4wxxlgpmzdvHpo0aYJy5cohLi4OX3/9tcnjIL755ht06NABbm5u+P3337FkyRLMmzfvqbedlZWF+fPnIyoqCnK5HKtWrcIff/yBXbt2PXXejOXHM54YY4wxxhhjjLFS9sEHH2DNmjX4999/UalSJfTt2xdjx46FQvFofshrr72G2NhYPHz4EFWqVMHw4cPxv//976m3nZ2djS5duuDUqVPIyclB9erVMW7cOLNvomPsaXHgiTHGGGOMMcYYY4xZBL/VjjHGGGOMMcYYY4xZBAeeGGOMMcYYY4wxxphFcOCJMcYYY4wxxhhjjFkEv9WOlTqNRoNr165ZuxiMMVZiKlWqBJVKZe1iGOG2ljHGGGAb5yg+JzFmu0qjjeAZT4wxxhhjjDHGGGPMIvitdowxxhhjjDHGGGPMInjGE2OMMcYYY4wxxhizCA48McYYY4wxxhhjjDGL4MATY4wxxhhjjDHGGLMIDjwxxhhjjDHGGGOMMYvgwBNjjDHGGGOMMcYYswgOPDHGGGOMMcYYY4wxi+DAE2OMMcYYY4wxxhizCA48McYYY4wxxhhjjDGL4MATY4wxxhhjjDHGGLMIDjwxxhhjjDHGGGOMMYvgwBNjjDHGGGOMMcYYswgOPDHGGGOMMcYYY4wxi+DAE2OMMcYYY4wxxhizCA48McbYYwiCgI0bNwIAUlJSIAgC4uPjAQCxsbEQBAH379+3WvkYy4uPV8aYrWrTpg3ef/99axeDsQLFxMTA29vb2sWwafn7FowVBQeemFX0798f3bp1M1r266+/wtnZGTNmzED//v0hCAL+97//mXx26NChEAQB/fv3L53C2hGu1ydz584dvPvuu6hUqRKcnJwQGBiIqKgoxMXFAQBSU1Px4osvWrmUj2euQ8+BBsfDxytj7EndvHkT7733HsLDw+Hs7IyAgAC0aNECP/zwA7KysqxdPIdj6HcJggCVSoXw8HBMmTIFOp0OAEBEWLBgAZo2bQp3d3d4e3ujcePGmDVrlvR9TJo0ScpDLpejYsWKePvtt/Hvv/9ac9csLm/dCYKAcuXKoWPHjjhz5kyR85g0aRLq169vuUI6qMfVfcWKFZGamoratWtbuaTMnnDgidmEhQsXok+fPvjhhx/w0UcfAcht1FavXo3s7GwpXU5ODlauXIlKlSpZq6h2heu1aHr27IlTp05hyZIluHjxIjZv3ow2bdrg3r17AIDAwEA4OTlZuZSM5eLjlTH2JK5cuYIGDRpg586d+Pzzz3Hq1CkcOnQIo0aNwtatW/HHH3+Y/ZxWqy3lkjqWjh07IjU1FZcuXcJHH32ESZMm4euvvwYA9O3bF++//z66du2KPXv2ID4+HuPHj8emTZuwc+dOKY9atWohNTUV165dw+LFi7F9+3a8++671tqlUmOou9TUVOzevRsKhQKdO3e2drHKhMLqXi6XIzAwEAqFwsqlZHaFGLOC6Oho6tq1KxERffnll+Ts7Ezr1683WV+7dm1avny5tHzFihVUt25d6tq1K0VHR0vL9Xo9ff755xQWFkbOzs5Ut25dWrt2rbRep9PRW2+9Ja2vVq0azZo1y2yZvv76awoMDCRfX18aMmQIaTQaKc3cuXMpPDycnJycqHz58tSzZ88Srpmnw/VafGlpaQSAYmNjC0wDgDZs2EBERMnJyQSATp06RUREe/bsIQD0xx9/UKNGjcjFxYWaNWtGFy5cMMpj3rx5VKVKFVIqlVStWjVaunSptC5/nnnLtWfPHmnZX3/9RR07diQ3NzcqX748vfnmm3Tnzh0iyq1nAEZ/hnzz/hm+38d9t8w28fHKxytjTyoqKooqVKhAGRkZZteLokhEuW3IvHnzqEuXLuTq6koTJ04s1vl+0qRJ5OfnRx4eHvTOO++QWq2W0rRu3ZqGDx9OH3/8Mfn4+FBAQABNnDjRYvtsbXn7ZQYdOnSgZ599ltasWUMAaOPGjSafE0WR7t+/T0REEydOpHr16hmt//DDD8nHx8dSxbYJ5upu//79BIBu375NRESjRo2iiIgIcnFxocqVK9O4ceOk/uXixYtNzimLFy8motxz1ttvv03ly5cnJycnqlWrFm3ZskX6nJeXF23fvp1q1KhBbm5uFBUVRTdu3Ci1fbe2x9W9uX5AbGwsNWnShFQqFQUGBtLo0aNJq9WWbsGZTeMZT8yqRo8ejalTp2Lr1q3o3r27yfq33noLixcvlv7/888/Y8CAASbpvvjiCyxduhTz58/HuXPn8MEHH+DNN9/E3r17AQCiKKJChQpYu3Ytzp8/jwkTJuCTTz7BL7/8YpTPnj17kJSUhD179mDJkiWIiYlBTEwMAOD48eMYMWIEpkyZgsTERGzfvh2tWrUqwdooOVyvRefu7g53d3ds3LgRarX6ifP59NNPMWPGDBw/fhwKhQJvvfWWtG7Dhg1477338NFHH+Hs2bN45513MGDAAOzZs6fI+d+/fx/t2rVDgwYNcPz4cWzfvh23bt3Ca6+9BgD47rvv0KxZMwwePFi6QlWxYkWsW7cOAJCYmIjU1FR89913AB7/3TLbxMcrH6+MPYl79+5h586dGDp0KNzc3MymEQRB+vekSZPQvXt3/PXXX3jrrbeKfL7fvXs3EhISEBsbi1WrVmH9+vWYPHmyUZolS5bAzc0NR44cwVdffYUpU6Zg165dJb/TNsrFxQUajQYrVqxA9erV0bVrV5M0giDAy8vL7OdTUlKwY8cOqFQqSxfVpmRkZGD58uUIDw9HuXLlAAAeHh6IiYnB+fPn8d133+Gnn37CzJkzAQC9evXCRx99JM0WS01NRa9evSCKIl588UXExcVh+fLlOH/+PKZPnw65XC5tKysrC9988w2WLVuGffv24dq1axg5cqRV9tsWmKv7vP755x+89NJLaNKkCU6fPo0ffvgBixYtwrRp06xQWmazrB35YmVTdHQ0qVQqAkC7d+82u75r1650+/ZtcnJyopSUFEpJSSFnZ2e6c+eO0cycnJwccnV1pYMHDxrlMXDgQOrdu3eBZRg6dKjRzJro6GgKDQ0lnU4nLXv11VepV69eRES0bt068vT0pPT09KfZdYvien0yv/76K/n4+JCzszM1b96cxo4dS6dPn5bWo4gzSAx+++03AkDZ2dlERNS8eXMaPHiw0TZfffVVeumll8zmSWQ6g2Tq1Kn0wgsvGOVx/fp1AkCJiYlElHsl+b333jNKYyhfWlqatOxJv1tmG/h4zcXHK2NFd/jwYQJgNAuaiKhcuXLk5uZGbm5uNGrUKCLKbUPef//9x+Zp7nzv6+tLmZmZ0rIffviB3N3dSa/XE1Hu7/65554zyqdJkyY0evToJ943W5Z35ogoirRr1y5ycnKikSNHUmRkJL388suPzWPixIkkk8nIzc2NnJ2dpdk73377rYVLb13R0dEkl8ul4xMABQUF0YkTJwr8zNdff02NGjWS/m9uttiOHTtIJpNJ56L8DDOlLl++LC2bO3cuBQQEPN0O2ZHH1X3+fsAnn3xC1atXl2ZNEuXWWd7fPmM844lZTd26dREWFoaJEyciIyPDbBp/f3906tQJMTExWLx4MTp16gQ/Pz+jNJcvX0ZWVhY6dOggzQZwd3fH0qVLkZSUJKWbO3cuGjVqBH9/f7i7u2PBggW4du2aUV61atUyuuIRFBSE27dvAwA6dOiA0NBQVKlSBX379sWKFSts8kGcXK/F17NnT9y4cQObN29Gx44dERsbi4YNG0qzsoqibt260r+DgoIAQNrHhIQEtGjRwih9ixYtkJCQUOT8T58+jT179hh9FzVq1AAAo++jKIr63TLbxMcrH6+MlZSjR48iPj4etWrVMppF2bhxY5O0RTnf16tXD66urtL/mzVrhoyMDFy/fl1alrf9AYz7BI5o69atcHd3h7OzM1588UX06tULkyZNAhEVOY/q1asjPj4ex44dw+jRoxEVFYXhw4dbsNS2oW3btoiPj0d8fDyOHj2KqKgovPjii7h69SoAYM2aNWjRogUCAwPh7u6OcePGmRyT+cXHx6NChQqoVq1agWlcXV1RtWpV6f+Ofoya87i6zyshIQHNmjUzmjXZokULZGRk4O+//y7NYjMbxk8EY1YTEhKCX3/9FW3btkXHjh3x+++/w8PDwyTdW2+9hWHDhgHI7fTkZwiu/PbbbwgJCTFaZ3jA7urVqzFy5EjMmDEDzZo1g4eHB77++mscOXLEKL1SqTT6vyAIEEURQO503pMnTyI2NhY7d+7EhAkTMGnSJBw7dsymXrvK9fpknJ2d0aFDB3To0AHjx4/HoEGDMHHixCK/5S/vPhpOvIZ9fByZLPcaQN5OaP6HuWZkZKBLly748ssvTT5vCBwUVVG+W2bb+HjNxccrY0UTHh4OQRCQmJhotLxKlSoAcm//yiv/7XhFPd8XRWF9AkfUtm1b/PDDD1CpVAgODpYeyFytWjVcuHChSHkY3ogHANOnT0enTp0wefJkTJ061WLltgVubm7SfgO5L83x8vLCTz/9hE6dOqFPnz6YPHkyoqKi4OXlhdWrV2PGjBmF5pn/WDfH3DFanEChIyis7gcNGmTFkjF75dAzngyvgCzOVeDiiImJkbaRkpJikW04utDQUOzduxc3b95Ex44d8fDhQ5M0HTt2hEajgVarRVRUlMn6mjVrwsnJCdeuXUN4eLjRX8WKFQEAcXFxaN68OYYMGYIGDRogPDz8ia6UKxQKtG/fHl999RXOnDmDlJQU/Pnnn8XfcQvjen16NWvWRGZmZonkFRkZKb3q3iAuLg41a9YEkDsDDQBSU1Ol9fHx8UbpGzZsiHPnziEsLMzk+zAMEFQqFfR6vdHnDM+AyLu8KN8tsy98vNqeNm3a4P3337d2MRgDAJQrVw4dOnTA999//0RtRVHP96dPnzZ6a+7hw4fh7u5u079VSzMM4CtVqmT0FrA33ngDFy9exKZNm0w+Q0R48OBBgXmOGzcO33zzDW7cuGGRMtsqQRAgk8mQnZ2NgwcPIjQ0FJ9++ikaN26MiIgIk9k45s4zdevWxd9//42LFy+WZtHtXt66zy8yMhKHDh0yCs7FxcXBw8MDFSpUKM1illmCIGDjxo3WLkahSi3w1KZNGylIU69ePaN19+7dg4uLi7R+zJgxRc43b/CH2aeKFSsiNjYWt2/fRlRUFNLT043Wy+VyJCQk4Pz580a3axl4eHhg5MiR+OCDD7BkyRIkJSXh5MmTmDNnDpYsWQIAiIiIwPHjx7Fjxw5cvHgR48ePx7Fjx4pVzq1bt2L27NmIj4/H1atXsXTpUoiiiOrVqz/5zlsQ12vR3Lt3D+3atcPy5ctx5swZJCcnY+3atfjqq6/MPvDzSXz88ceIiYnBDz/8gEuXLuHbb7/F+vXrpQdVuri44Nlnn8X06dORkJCAvXv3Yty4cUZ5DB06FP/++y969+6NY8eOISkpCTt27MCAAQOkTlVYWBiOHDmClJQU3L17F6IoIjQ0FIIgYOvWrbhz5w4yMjKK9N3ak8JOtrGxsRAEAffv35eW/fTTT6hXrx7c3d3h7e2NBg0a4IsvvpDWT5o0CfXr17dsoZ8QH6/2c7yuX79emo3wuCBU/mN47969aNeuHXx9feHq6oqIiAhER0dDo9EAMH9cO6r+/fujW7duRst+/fVXODs7Y8aMGejfv7/UDxQEAeXKlUPHjh1x5swZo8/kTZP3b/Xq1SZ55P8LCwsrvR22oHnz5kGn06Fx48ZYs2YNEhISkJiYiOXLl+PChQtm+wIGRT3fazQaDBw4EOfPn8e2bdswceJEDBs2TJopyR557bXX0KtXL/Tu3Ruff/45jh8/jqtXr2Lr1q1o3759oS90aNasGerWrYvPP/+8FEtc+tRqNW7evImbN28iISEBw4cPl2bURkRE4Nq1a1i9ejWSkpIwe/ZsbNiwwejzYWFhSE5ORnx8PO7evQu1Wo3WrVujVatW6NmzJ3bt2oXk5GT8/vvv2L59u5X20jYVVvf5DRkyBNevX8fw4cNx4cIFbNq0CRMnTsSHH35Y5n77hZ1LBEHApEmTCvxsSkoKBEEwuZjnKKxyJJw5cwb79u2T/r9w4ULk5ORYoyjMRlSoUAGxsbG4e/eu2SCJp6cnPD09C/z81KlTMX78eHzxxReIjIxEx44d8dtvv6Fy5coAgHfeeQc9evRAr1690LRpU9y7dw9DhgwpVhm9vb2xfv16tGvXDpGRkZg/fz5WrVqFWrVqFX+HSwnX6+O5u7ujadOmmDlzJlq1aoXatWtj/PjxGDx4ML7//vsS2Ua3bt3w3Xff4ZtvvkGtWrXw448/YvHixWjTpo2U5ueff4ZOp0OjRo3w/vvvm7wJJDg4GHFxcdDr9XjhhRdQp04dvP/++/D29pZO6iNHjoRcLkfNmjXh7++Pa9euISQkBJMnT8aYMWMQEBAg3V75uO/WUf388894//33MWLECMTHxyMuLg6jRo0q8HlotoaPV/s5Xn19fc3e5vw458+fR8eOHdG4cWPs27cPf/31F+bMmWP2yn1ZtHDhQvTp0wc//PADPvroIwC5M3gNb63avXs3FAoFOnfubPLZxYsXS+kMf4bjPe+y/GmLe0HFVlWtWhWnTp1C+/btMXbsWNSrVw+NGzfGnDlzMHLkyEJv2yrq+f75559HREQEWrVqhV69euHll18udKBVlgmCgJUrV+Lbb7/Fxo0b0bp1a9StWxeTJk1C165dzc5Gz+uDDz7AwoULjZ6f5Wi2b9+OoKAgBAUFoWnTpjh27BjWrl2LNm3a4OWXX8YHH3yAYcOGoX79+jh48CDGjx9v9PmePXuiY8eOaNu2Lfz9/bFq1SoAwLp169CkSRP07t0bNWvWxKhRo7h9zaewus8vJCQE27Ztw9GjR1GvXj3873//w8CBA00uSpUFec8ls2bNgqenp9Gysvx2xFJ7q13r1q0JACmVSgJAr7zyChER6XQ6qlSpkrQcgPRmi/v379OIESOk9SEhIfTBBx9Ib8uIjo6WPpP3b+LEiUS5c/0IAM2aNYv69+9PHh4eFBwcTFOnTjUq271792jIkCFUoUIFUigUVL58eerTpw9dvXrVKN3s2bMpODiYXF1d6Y033qBZs2ZJ20hOTrZsBTLGWBmm1+vpyy+/pKpVq5JKpaKKFSvStGnTiMj4LW755X9LWteuXal///6FbsvcW3AYK668b+0z9wa/vPIewzNnzqSwsLBC8zb39j9HlfetYF9++SU5OzsbvZkt73qD/fv3EwC6ffu2tKywdiK/4qRlj5j7LhhjrKxavHgxeXl5Sf/X6/U0efJkCgkJIZVKRfXq1aPff/9dWp8/ptG6dWsiIjp69Ci1b9+eypUrR56entSqVSuTtzvaw3mr1Gc81a9fH1WqVMHGjRvx999/Y/Pmzbh27RpeeeUVo3QajQZt2rTB7Nmzcfv2bURGRuLevXuYOXMmunTpAiJC1apVpYciAkDTpk3RtGlTk3tJx44dix07dsDJyQk3btzA+PHjsWvXLgBATk4OWrdujXnz5uHmzZuoVq0a0tPTsWLFCjRr1gx37twBAGzZsgUjRozAjRs34Obmhv379+PTTz+1cG0xxhgDctvx6dOnY/z48Th//jxWrlyJgICAYucTGBiIw4cPm30rC2O2IDAwEKmpqUYzwxkwevRoTJ06FVu3bkX37t0LTJeRkYHly5cjPDwc5cqVK8USMsYYYwX77rvvMGPGDHzzzTc4c+YMoqKi8PLLL+PSpUsAct8yCgB//PEHUlNTsX79egDAw4cPER0djQMHDuDw4cOIiIjASy+9ZPYZvras1ANPMpkMQ4cOhU6nww8//IA5c+YAgMkrQVetWoX4+HioVCqcOXMGp0+fxuHDhwEAf/75J/7880+MHz/eaErl4cOHcfjwYZMn7devXx8pKSlISEiQ3lKwe/duaTtnz54FAKxduxbnzp1DXFwcZDIZbty4Id268NVXXwHInaZ85coVJCcno0mTJiVdPYwxxvJ5+PAhvvvuO3z11VeIjo5G1apV8dxzzz3RW1UmTpwIb29vhIWFoXr16ujfvz9++eUXh36jErMvr776Knr37o3WrVsjKCgI3bt3x/fff29yq3RZ8vvvv+Orr77Cpk2b8Pzzz5usN7yu3t3dHR4eHti8eTPWrFlj8myR3r17S+kMf4979TpjjDFWEr755huMHj0ar7/+OqpXr44vv/wS9evXx6xZswA8enlLuXLlEBgYCF9fXwBAu3bt8Oabb6JGjRqIjIzEggULkJWVhb1791prV56IVZ7x9NZbb8HNzQ1z5szBnj170KhRIzRr1swojSHip9FoUK1aNQiCYPSwV0MQqihee+01qFQq+Pn5oXz58gCAW7duAYB0376rq6v08MqGDRtKDzY+fvw4AODcuXMAgKioKLi7u0Mul6NHjx7F3HPGGGPFlZCQALVabXbAWVxBQUE4dOgQ/vrrL7z33nvQ6XSIjo5Gx44dOfjEbIJcLsfixYvx999/46uvvkJISAg+//xz1KpVy+hNgmVJ3bp1ERYWhokTJ5p9Hlvbtm0RHx+P+Ph4HD16FFFRUXjxxRdNZjbOnDlTSmf4Cw4OLq3dcHgxMTE2/1YlxhizhvT0dNy4cQMtWrQwWt6iRQskJCQU+tlbt25h8ODBiIiIgJeXFzw9PZGRkWF3F06sEnjy9vbGm2++KU0Pyz/bKS+VSiXdQpf3z8fHp1jbMzC8xpTyvO6RMcaY7XJxcSnxPGvXro0hQ4Zg+fLl2LVrF3bt2mV3V46YYwsJCUHfvn3x/fff49y5c8jJycH8+fOtXSyrCAkJQWxsLP755x907NjR5PYCw+vqw8PD0aRJEyxcuBCZmZn46aefjNIFBgZK6Qx/eV9vzxhjjNma6OhoxMfH47vvvsPBgwcRHx+PcuXKSW+6tRdWe7+h4U01/v7+eP31103WG25j0+v1mDdvnnQbXWxsLD7++GO88cYbAHJnKhlkZmYWuxyG7WRlZUlXaU6ePInExEQAQOPGjQFAesPWzp07kZmZCb1eb/LKTsZY2fa4V6bnVVKvQw8LC5Om6DqqiIgIuLi4SLdIl7SaNWsCeLJziD0pzvFZUhz91cClxcfHB0FBQQ5/jBYmNDQUe/fuxc2bN80Gn/ISBAEymQzZ2dmlWEJWFJMmTTK6g+FxYmJijC4gM1NloR9gTYIg8Ew+K3GkPoSnp6f0xt+84uLipH6oSqUCAJM3LMbFxWHEiBF46aWXUKtWLTg5OeHu3bulU/ASZLXAU+3atXHv3j1cvnwZTk5OJut79+6NunXrQq/Xo0mTJqhduzaqV68Ob29vvPLKK9JgrUaNGtJnatasiWeffdbkCy1M7969Ubt2bQC5z1WoVasWWrRoAVEUERwcLAXIDK8+vHz5MqpUqYIqVarg4MGDT7r77An1798fgiBg+vTpRss3btwIQRAAPBrQ+/j4ICcnxyjdsWPHIAiClDZv+qcNADiKQ4cOQS6Xo1OnTkbLDY2/4c/X1xetW7fG/v37jdIVt1PJ2OM4Oztj9OjRGDVqFJYuXYqkpCQcPnwYixYtktIkJyeb3EJjbpD+7rvvYurUqYiLi8PVq1dx+PBh9OvXD/7+/ia3fDNj3FY+nTt37pgco4bb/vP68ccf8e6772Lnzp1ISkrCuXPnMHr0aJw7dw5dunSxQsltR8WKFREbG4vbt28jKipKeu6VWq3GzZs3cfPmTSQkJGD48OHIyMgwqa/79+9L6Qx/ZTmYZ2nmBuwjR44s1kWEXr164eLFi9L/uY9h6tixY3j77betXQy7V9CxlZqaihdffLH0C1TG9O/fX3rsjUHFihWRmpoqjdXt3ccff4wvv/wSa9asQWJiIsaMGYP4+Hi89957AIDy5cvDxcUF27dvx61bt/DgwQMAuRdgly1bhoSEBBw5cgR9+vSxyN0Alma1wBMA+Pr6wtPT0+w6Jycn7N27FyNGjEDFihVx8eJFpKWloXHjxvjss8+ktxnVrVsX48ePR0BAAK5du4YjR44gLS2tyGVwdnbG3r17MWTIEAQGBuLixYvw8PBAnz59cOjQIekhX127dsXMmTMRGBiIhw8fonHjxpg2bdrTVwIrNmdnZ3z55ZeP/Z49PDxMZqUtWrQIlSpVsmTx7N6iRYswfPhw7Nu3Dzdu3DBZb3jTwr59+xAcHIzOnTubHTwxVpLGjx+Pjz76CBMmTEBkZCR69eqF27dvS+s//PBDNGjQwOjv1KlTJvm0b98ehw8fxquvvopq1aqhZ8+ecHZ2xu7du/kNWMyiVq5caXKM5r8VDACeeeYZZGRk4H//+x9q1aqF1q1b4/Dhw9i4cSNat25thZLblgoVKiA2NhZ3796Vgk/bt29HUFAQgoKC0LRpUxw7dgxr165FmzZtjD47YMAAKZ3hz/CSG1Y63N3di9XWuri4SM9nZcYMt9n4+/sb3QHiyAq6tUir1Vpsm4GBgWYnSbDcR9fodDqL5S+XyxEYGOgwt0SPGDECH374IT766CPUqVMH27dvx+bNmxEREQEg95FAs2fPxo8//ojg4GB07doVQO7YLC0tDQ0bNkTfvn0xYsQI+2wXiTE7Eh0dTZ07d6YaNWrQxx9/LC3fsGEDGQ7nPXv2EAAaN24ctW/fXkqTlZVFXl5eNH78eMp76BvSp6Wlldp+2KqHDx+Su7s7XbhwgXr16kWfffaZtC45OZkA0KlTp6RlZ86cIQC0adMmadnEiROpXr16pVhq29G6dWt67733iIho6dKl1KhRI3J3d6eAgADq3bs33bp1S0prOO62bt1KderUIScnJ2ratCn99ddfRnnu37+fnnvuOXJ2dqYKFSrQ8OHDKSMjQ1ofGhpKM2fOLI3dY3audevWNHToUBo6dCh5enpSuXLlaNy4cSSKIhEVfswafv95/6Kjo4mISK/X05dffklVq1YllUpFFStWpGnTphl9bt26ddSmTRtycXGhunXr0sGDB61SB4zZi8J+V2fOnKG2bduSs7Mz+fr60uDBg+nhw4fSZ6Ojo6lr16709ddfU2BgIPn6+tKQIUNIo9FIaUJDQ+mzzz6jAQMGkLu7O1WsWJF+/PFHozJcu3aNXn31VfLy8iIfHx96+eWXKTk52SjNokWLqGbNmqRSqSgwMJCGDh0q5Z+3vQgNDSUi4z7Cjh07yMnJyaT/NWLECGrbti0RES1evJi8vLykf+dvhxYvXkwDBgygTp06GeWh0WjI39+fFi5caJG6/eyzz6h8+fLk5eVFkydPJq1WSyNHjiQfHx8KCQmhn3/+WfqMoR1ctWoVNWvWjJycnKhWrVoUGxsrpdHpdPTWW29RWFgYOTs7U7Vq1WjWrFlG5TZse9q0aRQUFERhYWFSXRv6AaIo0sSJE6lixYqkUqkoKCiIhg8fLuXx77//Ut++fcnb25tcXFyoY8eOdPHiRWm9ob63b99ONWrUIDc3N4qKiqIbN26Y1GNRlUR9591nQ32uXr2aWrVqRU5OTrR48WIiIvrpp5+oRo0a5OTkRNWrV6e5c+caleX69ev0+uuvk4+PD7m6ulKjRo3o8OHDBR5bREQAaMOGDVIeJfH7s3R9FbbtuXPnUnh4ODk5OVH58uWpZ8+eRtv+/PPPpeOwbt26tHbtWmm9oe+6bds2atiwISmVSvrxxx8JACUkJBjtx7fffktVqlQhoscf3xMnTjSp/z179pgde8TGxlKTJk2kNmf06NGk1Wql9a1bt6bhw4fTxx9/TD4+PhQQEEATJ04sUt0zy+LAE7MrhgZ1/fr15OzsTNevXyci84GnxMREcnJyoqtXrxIR0bJly6hevXpGafOm58BTbgeycePGRES0ZcsWqlq1qjQozd/4Z2Vl0ciRIwkA/f7771IeHHh6j4hy63Lbtm2UlJREhw4dombNmtGLL74opTUcd5GRkbRz5046c+YMde7cmcLCwqQOwuXLl8nNzY1mzpxJFy9epLi4OGrQoAH1799fyocDT6yoWrduTe7u7vTee+/RhQsXaPny5eTq6koLFiwgosKPWZ1OR+vWrZPa1tTUVLp//z4REY0aNYp8fHwoJiaGLl++TPv376effvqJiB61GzVq1KCtW7dSYmIivfLKKxQaGmrUUWSMGSvod5WRkUFBQUHUo0cP+uuvv2j37t1UuXJlKRBMlNtX8vT0pP/973+UkJBAW7ZsMfqtE+WeO3x9fWnu3Ll06dIl+uKLL0gmk9GFCxeIKDdwExkZSW+99RadOXOGzp8/T2+88QZVr16d1Go1ERHNmzePnJ2dadasWZSYmEhHjx6Vzke3b9+WBu+pqal0+/ZtIjLuI+h0OgoICDAKDuVfljfwlJWVRR999BHVqlWLUlNTKTU1lbKysiguLo7kcrlRcGT9+vXk5uZmNCgvqbr18PCgoUOH0oULF2jRokUEgKKiouizzz6jixcv0tSpU0mpVEp9VEM7WKFCBfr111/p/PnzNGjQIPLw8KC7d+9K9T1hwgQ6duwYXblyRWqf16xZY7Rtd3d36tu3L509e5bOnj0rfZeGel+7di15enrStm3b6OrVq3TkyBGj7/3ll1+myMhI2rdvH8XHx1NUVBSFh4dL/Y7FixeTUqmk9u3b07Fjx+jEiRMUGRlJb7zxRkGH6mM9bX3n32dDfYaFhdG6devoypUrdOPGDVq+fDkFBQVJy9atW0e+vr4UExNDRLkXV6tUqUItW7ak/fv306VLl2jNmjV08ODBAo8tIuPAU0n9/ixZX4Vt+9ixYySXy2nlypWUkpJCJ0+epO+++076/LRp06hGjRq0fft2SkpKosWLF5OTk5MUJDX0XevWrUs7d+6ky5cv071796hx48Y0btw4o/1o1KiRtOxxx/fDhw/ptddeo44dO0r1r1arTcYef//9N7m6utKQIUMoISGBNmzYQH5+fkaBpdatW5OnpydNmjSJLl68SEuWLCFBEGjnzp1Fqn9mORx4YnbFEHgiInr22WfprbfeIiLzgae0tDTq1q0bTZ48mYiI2rZtS9999x0HngrRvHlz6QqEVqslPz8/2rNnDxE96ji5uLiQm5sbCYJAAKhRo0ZGV1I48PSe2XXHjh0jAFIn2HDcrV69Wkpz7949cnFxkU7EAwcOpLffftson/3795NMJqPs7Gwi4sATK7rWrVtTZGSkFEwmIho9ejRFRkaaTV/QMZu3rUxPTycnJycp0JSfod3IO7A8d+6c2aujjLFchf2uFixYQD4+PkYzX3/77TeSyWR08+ZNIsrtK4WGhpJOp5PSvPrqq9SrVy/p/6GhofTmm29K/xdFkcqXL08//PADEeVerKtevbpRe6FWq8nFxYV27NhBRETBwcH06aefFrgf+WeKEJn2Ed577z1q166d9P/8s6DyBp7Mfd6gZs2a9OWXX0r/79Kli9FFGoOSqlu9Xi+lqV69OrVs2VL6v06nIzc3N1q1ahURPWoHp0+fLqXRarVUoUIFozLnN3ToUKPZKNHR0RQQECAF/gzy9gNmzJhB1apVMzu75uLFiwSA4uLipGV3794lFxcX+uWXX4jo0ayyy5cvS2nmzp1LAQEBBZazMCVR3/n32VCf+WeEVa1alVauXGm0bOrUqdSsWTMiIvrxxx/Jw8OD7t27Z7asBR1beY/jkvr9FcTSv/1169aRp6cnpaenm+Sfk5NDrq6uJjOSBw4cSL179yaiR/2AjRs3GqWZOXMmVa1aVfp/YmLiY8/z5o5vwxjPIH/g6ZNPPjFpl+bOnUvu7u7Sb7J169b03HPPGeXTpEkTGj16dIFlYaXDqs94YuxpfPnll1iyZAkSEhIKTPPWW28hJiYGV65cwaFDh9CnT59SLKF9SUxMxNGjR9G7d28AufcZ9+rVy+gBzgCwZs0anDp1CuvWrUN4eDhiYmKgVCqtUWSbduLECXTp0gWVKlWCh4eH9GyWa9euGaXL+0BrX19fVK9eXTqmT58+jZiYGLi7u0t/UVFREEURycnJpbczzGE8++yzRi9XaNasGS5dugS9Xl/kYzavhIQEqNVqPP/884Vut27dutK/g4KCAMDoGV2MsUcK+10lJCSgXr16cHNzk5YZXopjeCMzkPs2ZrlcLv0/KCjI5DeX93cpCAICAwOlNKdPn8bly5fh4eEhnX98fX2Rk5ODpKQk3L59Gzdu3Hjsb/9x+vTpg9jYWOmZkitWrECnTp2K/Sa7QYMGYfHixQCAW7du4ffff8dbb71lkq6k6lYmezSECggIQJ06daT/y+VylCtXzqS+857vFQoFGjdubNSHnTt3Lho1agR/f3+4u7tjwYIFJu1vnTp1pDdfmfPqq68iOzsbVapUweDBg7FhwwbpGTwJCQlQKBRo2rSplL5cuXJG/Q4g943hVatWlf5v7tgpqpKo74L22fDmcSD3rbRJSUkYOHCgUZ9p2rRpSEpKAgDEx8ejQYMG8PX1faJ9KU6Zi/L7Kyh/S/72O3TogNDQUFSpUgV9+/bFihUrkJWVBSD3BVpZWVno0KGDUR0aXuqSV966B4DXX38dKSkpOHz4MIDc33HDhg2NXgJWlOO7KPXTrFkzo35MixYtkJGRgb///ltalrdty18HzHoc40ldrExq1aoVoqKiMHbsWPTv399smhdffBFvv/02Bg4ciC5duvDDgwuxaNEi6HQ6BAcHS8uICE5OTvj++++lZRUrVkRERAQiIiKg0+nQvXt3nD17lh+8mEdmZiaioqIQFRWFFStWwN/fH9euXUNUVFSBD8Y0JyMjA++88w5GjBhhso4fks9KUk5OzhMds0V9q0re4LShwyiK4tMVmjEHVRJvK8p/QUgQBJPfXGFpMjIy0KhRI6xYscIkb39/f6PAy9No0qQJqlatitWrV+Pdd9/Fhg0bEBMTU+x8+vXrhzFjxuDQoUM4ePAgKleujJYtW5qks1TdFqW+C7N69WqMHDkSM2bMQLNmzeDh4YGvv/4aR44cMUqXN+hgTsWKFZGYmIg//vgDu3btwpAhQ/D1119j7969RS6LuX0hoiJ/Pq+SqO+C9jnv8oyMDADATz/9ZBRYAyAFYUrzLWBPejxY+rfv4eGBkydPIjY2Fjt37sSECRMwadIkHDt2TKrD3377DSEhIUZ55O/j5/9OAgMD0a5dO6xcuRLPPvssVq5ciXfffVdaX9Tju6Q87e+RWQbPeGJ2bfr06diyZQsOHTpkdr1CoUC/fv0QGxtr9soXy6XT6bB06VLMmDHD6FXfp0+fRnBwMFatWmX2c6+88goUCgXmzZtXyiW2bRcuXMC9e/cwffp0tGzZEjVq1CjwSovh6hAApKWl4eLFi4iMjAQANGzYEOfPn0d4eLjJX2FXPBkrSP5O3uHDhxEREVGkY9ZwzOn1emlZREQEXFxcivV6dMZY4Qr7XUVGRuL06dPIzMyUlsXFxUEmk6F69eolVoaGDRvi0qVLKF++vMn5x8vLCx4eHggLCyv0t69UKo3ai4L06dMHK1aswJYtWyCTydCpU6cC06pUKrN5litXDt26dcPixYsRExODAQMGmP28Nes27/lep9PhxIkT0vk+Li4OzZs3x5AhQ9CgQQOEh4ebzDIpKhcXF3Tp0gWzZ89GbGwsDh06hL/++guRkZHQ6XRG54F79+4hMTERNWvWfLqdK0Bp1XdAQACCg4Nx5coVk+O1cuXKAHJnwcTHx+Pff/81m0dBx5alymxOadSXQqFA+/bt8dVXX+HMmTNISUnBn3/+iZo1a8LJyQnXrl0zqcOKFSs+Nt8+ffpgzZo1OHToEK5cuYLXX3/dqJyPO76LWv+HDh0yCoTGxcXBw8MDFSpUKHIdMOvgwBOza3Xq1EGfPn0we/bsAtNMnToVd+7cQVRUVCmWzL5s3boVaWlpGDhwIGrXrm3017NnT5Pb7QwEQcCIESMwffp0aaouy52NpFKpMGfOHFy5cgWbN2/G1KlTzaadMmUKdu/ejbNnz6J///7w8/NDt27dAACjR4/GwYMHMWzYMMTHx+PSpUvYtGkThg0bVop7wxzJtWvX8OGHHyIxMRGrVq3CnDlz8N577xXpmA0NDYUgCNi6dSvu3LmDjIwMODs7Y/To0Rg1apQ0Hf/w4cMFthmMsccr7HfVp08fODs7Izo6GmfPnsWePXswfPhw9O3bFwEBASVWhj59+sDPzw9du3bF/v37kZycjNjYWIwYMUK6pWXSpEmYMWMGZs+ejUuXLuHkyZOYM2eOlIchMHXz5k2kpaUVuq2TJ0/is88+wyuvvFLoDOqwsDAkJycjPj4ed+/ehVqtltYNGjRIegRDdHS02c9bs27nzp2LDRs24MKFCxg6dCjS0tKki6IRERE4fvw4duzYgYsXL2L8+PE4duxYsbcRExODRYsW4ezZs7hy5QqWL18OFxcXhIaGIiIiAl27dsXgwYNx4MABnD59Gm+++SZCQkKk17aXtNKs78mTJ+OLL77A7NmzcfHiRfz1119YvHgxvv32WwBA7969ERgYiG7duiEuLg5XrlzBunXrpIvXhR1bBpY+RixdX1u3bsXs2bMRHx+Pq1evYunSpRBFEdWrV4eHhwdGjhyJDz74AEuWLEFSUpL0m16yZMlj8+7RowcePnyId999F23btjW6g6Iox3dYWBjOnDmDxMRE3L17F1qt1mQbQ4YMwfXr1zF8+HBcuHABmzZtwsSJE/Hhhx+W2CxMZjn8DTG7N2XKlEKnT6pUKvj5+RndD8yMLVq0CO3bt4eXl5fJup49e+L48eNIT083+9no6GhotVqj2/HKOn9/f8TExGDt2rWoWbMmpk+fjm+++cZs2unTp+O9995Do0aNcPPmTWzZskWaWVK3bl3s3bsXFy9eRMuWLdGgQQNMmDDB6GTOWHH069cP2dnZeOaZZzB06FC89957ePvtt4t0zIaEhGDy5MkYM2YMAgICpADo+PHj8dFHH2HChAmIjIxEr169+FkKjD2lgn5Xrq6u2LFjB/799180adIEr7zyCp5//vkSPwe7urpi3759qFSpEnr06IHIyEgMHDgQOTk58PT0BJB7/p81axbmzZuHWrVqoXPnzrh06ZKUx4wZM7Br1y5UrFgRDRo0KHBb4eHheOaZZ3DmzJnHPouzZ8+e6NixI9q2bQt/f3+jGdnt27dHUFAQoqKiCj1PWqtup0+fjunTp6NevXo4cOAANm/eDD8/PwDAO++8gx49eqBXr15o2rQp7t27hyFDhhR7G97e3vjpp5/QokUL1K1bF3/88Qe2bNkiPWpi8eLFaNSoETp37oxmzZqBiLBt2zaLPquztOp70KBBWLhwIRYvXow6deqgdevWiImJkWY8qVQq7Ny5E+XLl8dLL72EOnXqYPr06dKteIUdWwal8fuzZH15e3tj/fr1aNeuHSIjIzF//nysWrUKtWrVApB7sX78+PH44osvEBkZiY4dO+K3336T6rAwHh4e6NKlC06fPm3yOy7K8T148GBUr14djRs3hr+/P+Li4ky2ERISgm3btuHo0aOoV68e/ve//2HgwIEYN25ckeuAWY9AT3rTLmOMMcYYY4zZgIyMDISEhGDx4sXo0aOHtYsjSUlJQeXKlXHq1CnUr1/f2sVhjDGr4IeLM8YYY4wxxuySKIq4e/cuZsyYAW9vb7z88svWLhJjjLF8OPDEGGOMMcYYs0vXrl1D5cqVUaFCBcTExECh4OENY4zZGr7VjjHGGGOMMcYYY4xZBD9cnDHGGGOMMcYYY4xZBAeeGGOMMcYYY4wxxphFcOCJMcYYY4wxxhhjjFkEB54YY4wxxhhjjDHGmEVw4IkxxhhjjDHGGGOMWQQHnhhjjDHGGGOMMcaYRXDgiTHGGGOMMcYYY4xZBAeeGGOMMcYYY4wxxphFcOCJMcYYY4wxxhhjjFkEB54YY4wxxhhjjDHGmEVw4IkxxhhjjDHGGGOMWQQHnhhjjDHGGGOMMcaYRXDgiTHGGGOMMcYYY4xZhKI4ibOysnDhwgVLlYUxxhhjjDHGmA3Lzs5GSkoKwsLC4OLiYu3iMMaspEaNGnB1dS1S2mIFni5cuIBGjRo9UaEYY4wxxhhjjDHGmP07ceIEGjZsWKS0AhFRUTPmGU+MMcYYY4wxVnYlJCTgzTffxPLlyxEZGWnt4jDGrMRiM55cXV2LHNFijDHGGGOMMeaYIiMjeWzIGCsSfrg4Y4wxxhhjjDHGGLMIDjwxxhhjjDHGGGOMMYvgwBNjjDHGGGOMMcYYswgOPDHGGGOMMcYYY4wxi+DAE2OMMcYYY4wxxhizCA48McYYY4wxxhhjjDGLUFi7AIyVFCICEUEkfe6/AQgAAAEymQwyQQZBEKxbSDtERMjSZeKhNh1aUQuCCAEyKGQKeCg94aZw53p9AobjlURC7tGaSxAEoz9WPLltgAgSyWi5IOS2A1ynT4aIIIoiiPLVq0zgtvUJSecsUTRuAyDw8foUHvUFRCDv4SpAOla5XosvbxuQ/3g1HKtcr8XH5yzGWFnBgSdm14gIOr0WOr0OelH/2PSCIINCJodCoYRcJi+FEtontT4H/2Rex92c20jXpkOkgutWBhk8VJ4o5+SPCm6V4KxwKcWS2hdRFKHX6c0O4M0RZALkcjnkcjl3Pgtg6LTr9XqIeuMBfEFkMhkUcgV36gthGGTq9DqIovjY9AIEyOQyyOVyDkQVIvecpYNery9SGwD8d7wqFFyvhShuvQpCbtuqkCu4TgtgaFt1uqK1AQDXa1EY2la9Xm8SdC4In7MYY46AA0/MLomiCK1OA61eW6zPEYnQ6kVo9VrIBBmUChV3kPJIU/+LaxnJuJWdWuTPiBDxQHMfDzT3ceXhJfg7ByDUvTJ8nf0sWFL7IQ3gdTqTK5qP/axI0Ik66LS63ACUQg6ZjO+QBnLrVa/XQ6fXFXkAbyCKIjSiBgCgkCugUHAbYEBE0Ol00Ol1xfsccr8PvV4PQRCgkCs4YJpH7jlLW+QBfP7PajQarlczDG1AcetVOs51Oimwxxejcj1N25q/XpUKJZ+z/lNi5yyFgvutjDG7xIEnZleICFqdBhqd5qnzEkmEWpsDrU4GZ5UzZGW406kVNbhw/zxSs/6GgKfrzNzNuY07ObcQ4BKESO86UMlVJVRK+yOKIrQabbE7meYYBvVyhbzMB0r0oh4azdO3AQCg0+cGWZQKZZke0BMR9KIeWm3xgvkF5aXVaaHVaaFSqiCXl922lYig1WqLNCO3SHnpcmf4qpSqMj2gJyJotJonCuTlZwjsyWQyqJSqMtsGAP/VhVZTIucsURSh1qghl8uhVCjLdL3q9XpotCV0zvovsKdU5s7cL8v1yhizLxx4YnZDL+qh1uTkPrehBIkkIkudBZXCqUx2ju5k38K5tNPS1bSiTPsujOHzt7Nv4l/1XdT0qYsAl6CnLqc9ka5saos3a6Qo9LrcW8qUqrJ3Jdkw8Nbrn34Qn59WlxscKIsDz5IcxOen0WogF8vmwLMkB5t5ERHUGnWZna1nqXoVRRE56pwyGSx90pmORWG4aKJSqcrcrLKSDDznp9VqoZfZxjmrap9VVt2+tSSt6G3R/D0qD7Bo/o7uYfJis8urNPimlEtif66cGmmRfMvWqIXZLZ1eh2x1VokHnfLS6NTI0WSXyJU+e5HyMAmn7h2Tgk4liUDQilqcvncCSekXy0y9EhG0Gq1Fgk55t6FRaywSgLFVhsG2JffZMPC0RADGVpXGPuv1eqg16jLTBgC5sxIsERwx2oZeB42mZGan2AND4NnS9arRaqDVPf3MP3tBRNBoNBYJOuVVGtuwJdI5ywJBJ4OyeM5ijNkvDjwxm6fT65CjyS6VbelFfZkJPl1Jv4SLDxJKZVtJ6Rdx6cEFh69XQ9CptDqBWo0Wep3jB58MHfjSOn7UGnWZ6MgbboUpDaX9HVqTTqcrtcCFSGKZqledrnQCFzqdrkRuO7V1ht+lJS/q5aXVakvtO7QmPmcxxpgpDjwxm6YvxaCTtM0yEHy6lpGCy+mJpbrNlIwkpDxMKtVtlibDlPrS7vxptZa59cxWWCtg4egd+dIMOhmUheCTTl96QScDw62SjlyvhmdblSadvvQCXdZgmOlU2seNpW6XthVWPWeVUgCRMcaeBAeemM0iIuRocqyybb2oh66Yb8yzFw816Ui8f84q276UfgH31WlW2baliXoRot46nb6SeoC5LdLqrLdvjjqYNwQqrLVtR51JIpJotX0TRdFhb2MyvBXUGp70TYT2QKfXWS1Q4ahtK5B7Mcha++bIfQHGmP3jwBOzWWptzlM/6Prptu94Mx5EEvHXv6esWAIBZ9PioSfHutppC4NprcbxBvN6UW/VK+NE5JCDeb1eb9XBibW/V0sw3GZrTTqdzuHOWdYMkho4YpDEmsE8A0esV71eb9FnOj2OSKLDta2MMcfBgSdmkwyvOLe2HG2OQ3WMUh4mIUP30IoBPUKWLhNJ6RettH3LsPaAE8gdSDhSh9NwG4i1OdpgXhRFm3hwsqMNOvV6vU3c5uJo9arT6ay+P4Y3vjkKWwjmAf+ds6wYpClptlKvjjxLjzFm3zjwxGySRlu6zx4piCha9+pVSdKJWlx5eMnaxQAAXH14BRq99TtoJUEURZvp5Om01h+klRRbCqLZQqCmpNjSANqWvuOnYXjbmi0gIptpj56WLc041Okdp20VRdFm9sWat6WVNFs5VgHbKgtjjBlw4InZHL1oG1eODWxlQPG0bmT9YzP1SiD8k3Xd2sUoEbY0kHeUQactDTgB2xqoPQ0isqlAuqMM5m3tN2dLv52nYWv74SiBUls6ZwG29/t5EkRkU8eHtW+nZowxczjwxGyOVmdbM2H0ovUewFlSiAjXM5KtXQwj1zKS7b5jRERWe6B4QfQ62+n8PimRbC/QY2uD4Cdha/vgKIFSWxzI23u92tpAHnCMQKkoijbXn7G1dulJ2OLFCVv7/TDGmF0EnmJjYyEIAgRBwNKlS03W9+vXD4IgwMvLC1lZWZg0aZKUPiUlpfQLzJ5YQTMdDhyIw6s9X0NElWrwcPXEls1bH5vX/n378Vyzlijn7Yd6teth+bIVJmkWzF+AWjVqw8/HH21btcXxY8fN5qWz81lP6doHyNRlmiz/6/BZTBgwBb0bRSOqYhcc3H7osXmdPvQXhr74HjpX7Y7+z72Nnb/8YZJmc8xv6NdsIDqH98CILh/hwinTZzqp9TlIU997sh2yEYV17ObPn4/qNarD28cbLVu1xLFjxwpMq9Vq8fnnn6NmrZrw9vHGM02fwc6dO43SLFiwAE2eaYLyAeVRPqA8WrdpjR07dpjkZYsd4OIyV68HDhxAz549UaVyFbi6uGLz5s2PzWffvn1o1qwZvL28UbtWbSxbtswkzfz581Gjeg34ePugVctWBX5PjtCJL2gfiloHBuvXrUf9evXh4+2DJo2bYPv27UbrMzIy8MH7HyC8ajh8fXzRsEFD/PTTT2bzsvdBJxEVOJAvbr1+P+d71KtbD74+vogIj8Coj0chJ+fRm10fPnyIj0d+jOrVqsPXxxdt27TF8ePmz1m2NLPtSRQWfC5OvRra1lo1a8HH2wdNn2lq0rYWNU8icsi21aCk61Wv12Py5MmIrBEJXx9f1KpZC1988YVJHTrCOauwdqyk63XatGlwdXE1+qtfr36xyuRoHlzciWubRiB5dTT+2TEeOXcvF5j2xh9TcWXlGyZ/qbFfGaXTPPgHN/d+g+S1A5G8ZgD+2T4Ousy7lt4Vh6RJT0Dm9bXISFmKrBtboVffKTAtkQjN/Xhk/r0uN/0/m6DL+tskjTrtJDL//hUZV5ch8+910Nw/bfftSH7pd+Jw/dznSDk9FjcuzoY681qh6R/c3o+/E75CyumxuHZuGu79sxmi+Gj8KupzcO/vTbh27rP/8vwe6lK++8QuAk+tW7dG5cqVAcBk4JCZmYn169cDAF599VW4urqWevlYySmoA5+VmYk6dWpjxswZRconJSUFr/R4FS1bt0Tc4TgMGToEw4YMwx+7HgVJ1v26DmPHfIIxn4zBgYP7UbtOHXTv2gN3bps2iPbeiX+gSTO7PCc7B1UiK2PYtP8VKZ+b125ifPRk1G1WF/O2z0b3QS9j5qg5OB57UkoTu3k/FkxdiD7v98bcbbNQpWZlfNp3Au7fvW+mXKbL7ElBswrW/roWo8eMxqeffIpDBw+hbp26eLnry7h9+7bZ9JMmT8LCRQvx7YxvcerkKQwaOAi9Xu+F+Ph4KU1ISAimTpmKg3EHEXcgDm1at8Grr72K8+fPF7lc9sLcLLLMzEzUqVMHM2fNLFIeKSkp6NG9B1q3ao3DRw5j6LChGPLuEOzatUtK8+vaXzFm9Bh88uknOHjoIOrUrYOuL3c1+z3Z+6CzoPIXpw4A4PChw4iOjkZ0dDQOHT6Ezl06o9drvXDu3DkpzejRo7Fr1y78vPhnnIo/haHDhuLDDz7E1q2mFwxItN86BQr+rRW3XtesXoPx48fjk08+wan4U/hh/g/49ddfMXHCRCnNkHeH4M8//8Sinxfh2PFjeL798+jcqTP++eefIpfLXpRUvU6eNBmLFi7CjG9n4OSpkxg4aCBe7/W6UdtanDy5XnMVpV5nzJiBhT8txLczv8Wp+FOYNm0aZn47Ez/M+6HI5bIXpVmvAFCzZk1cSb4i/f2x2/QCoL2fs4oq4+oh3Du5HD61eyDkxc+g8q6Em3umQ5/zwGz6gJYfoFL3edJfhZe+AgQZ3Cs1ldJoH97CjV2TofQMRvDz41Hhpenwrt0dglxZWrvlMLSZydD8ewwq7/pwDX4ZMpUvsm/tgqjPNptek3YS2ocX4eTbFK4h3aH0qI6cO3ugz3OhWvvgLLQPE3PTBHeDk08jaB78Be3DhNLaLYvLSIvHvRtb4B3YAcHV34fKJRg3ryyEXptRQPpTSEvdBu/ADgip8TH8Kr6KzLTTSEv9XUpz9/qvyM64BP/Q3gip8RFcPKoh9fIC6DTmfyuWUKqBp23btuVeffb2hqurK8LDw9GrVy+kpeUOinft2oX27dvDy8sLzs7OqFGjBpYvXw5BENCvXz8AwJ9//mnUydqwYQMyM3NncvTv3780d4dZQEEBnheiXsCESRPwctcuRcpn0cKfERoWii+mf44aNarjnXffQbfuXTF3zlwpzfezv0f/AdHo2+9N1Iisge/mzIKLiwuWLjWdFWHvnaJ0zQMIEEyWN2nbGP1H9UWLF5sVKZ+ty7cjsGIA3pkwEJUiKqJr/85o+VILrF+4SUqz/qeN6Ng7ClG92iO0WiWM+GIInJydsGPNLpP87D3wVNCgefbs2RgwYAD69euHyMhIzJkzBy4uLliydInZ9CtXrsSoj0ehY8eOqFy5Mt5++21ERUXhu9nfSWk6deqEjh07Ijw8HBEREZg8eTLc3d1x9OjRIpfLHhCR2bcuRkVFYdKkSejatWuR8ln400KEhYVh+pfTUaNGDbz77rvo3r075syZI6Up6HtausR0Zi1g3+1AQUH94tbB3Llz0eGFDvjgww9Qo0YNTJw4EfXr18f8+fOlNEcOH0GfN/ugVatWCA0NxcCBA1Gnbh2zs3MI9j04KuiYKG69Hj58GM2aNUOv13shNDQU7du3x2uvvSbVWXZ2NjZu3Ihpn03Dc889h6pVq2LcuHGoUrWK2dlk9j6LpKTqdeXKlfh41Mcmbevs72Y/UZ723AYUNjvPEvV6+PBhdOrcCS+++CJCQ0PRvUd3PP/882bbAVu7/a84CvudWaJeAUCukCMwMFD68/PzM5ufPddrUT24sA2eVdvCo2obqLwqwO+ZgRAUTniYtNdsermTOxQu3tJf9s2/IMid4JYn8PTv6TVwDa6Pcg3egJNvGJQeAXCr0AhyZ6/S2i2HoX1wDkqPalB6RECm8oZTuWYQBAV0BbzsSJeZBKVXHShcK0Cm9IDSswbkLhWgTX90cUuvvg2FayUoXCtCpvSAwi0McpcQiGrHmZGWfmcfPMo1hUe5JlA5B6BchR4QZEo8/Ne0zw8AOZkpcHILg7tPAyidfOHqWR1uPvWlGU2iqEXm/b/gG9QJLu5VoHTyg0/QC1A6lUP6vcff7VJSSi3wdOfOHXTv3h2HDx+Gl5cXIiIicO/ePfzyyy948OAB1q5di6ioKOzevRtarRYRERFITU2VTlDR0dEQBAGiKGLFike3TBlmQIWHh+O5554rrd1hFiKW0Myio0eOom3bNkbLnm/fHkeP5k5x1mg0OHUqHm3atpXWy2QytGnXBkePmBnI2/ng6L4mzexgvrgSTlxAg5b1jZY1at0QCScvAAC0Gi0u/XUZDZ+rJ62XyWRo0LI+zp9INMnPngNPBV1NzD22TqFd23bSMplMhnbt2pk9tgyfcXZ2Nlrm4uKCgwcPmk2v1+vxy9pfkJmZiaZNm5qst+fBUUl1lI8cOYK2eX7fANC+Q3vpOzB8T23bGbcB7dq1w5GjRyxaNmswd0w8SR0cOXLE6NgGjOsVAJo+2xS/bf0N//zzD4gIe/fuxeVLl9G+ffsil81emDsmnqRen332WZw6dUq6DSc5ORk7duxAVMcoALnPkdLr9abthLMLDh0svU5jaTEXPH+Sen1c21rcPO35WC2oD2OJegVyj+nYPbG4dCl3gHnmzBkcOnQIL7zwgkl+9lyvBZXdUvUKAEmXk1ClchXUjKyJAf0H4Po187fL2PNFqKIgvQ7qf5PhElhbWiYIMrgE1kbO3aK9xTk9KRbuoc9CpsitdyIRWTfiofQIROqfXyBl3f/wz47xyLxe+K3SzBSRHqLmHuTOQdIyQRAgdw4q8HY7IhGCIDdaJghy6HNuSf+XO5WHPvsGRG3uTB295l+IObcgdwmxwF6UPhJ1UGf9Axf3CGmZIMjg4h4BdeZVs59xdguDJutv6XY8rfoestMvwNWzxn+Z6gGIEGQKo88JMiXUpfgMYMXjk5SMa9euQaPRwMPDAxcuXICLiwuICMePH4e/vz9Gjx4NIkLVqlVx4MABBAYGQqPRSCesypUro1WrVti7dy+WLVuGUaNG4ebNm9i9ezeA3MAUs38l1fm4desW/MuXN1pWvrw/0tPTkZ2djftp96HX61E+wD9fmvK4lGj6PCIgd4Ahz9cY2otsXVaJ5JN2Jw0+ft5Gy3z8vZH1MAvqbDUyHmRA1Ivw9vcxTuPnjeuXje/RBgC1mAORRMgEu7jr10hBnfi7d+/+d2zlP/7KIzHRNPgGAO3bt8fsObPx3HPPoUqVKtizZw82bdpk8jyOs2fPok3bNsjJyYG7uzvWrF6DyMjIIpfNHpRUR/nWrVtmvwNDG5CWlga9Xo+A8gEmaQr6nuy6Xs2U3XCsFqcObt26hfImbWt53Lr1qFP47bffYtjQYYgIj4BCoYBMJsPceXMLvDhk1/Vq5nh9knrt9Xov3Lt3D+2fb5/7rEOdDoMGD8KoUaMAAB4eHmjatCmmfzEd1atXR0BAAH755RccOXIEVatWNZunKIqQy+3vnFXQrMcnqdf27dtjzuw5Bbatxc2zJC7gWMvjzlklWa8AMHLkSDxMf4j69epDLpdDr9dj0uRJeL3366Zls+MASWnXa5MmTbBgwQJEVIvAzZs38flnn6N9+/Y4fuI4PDw8jPKz54slRaFXPwRINJmJJHf2gjb9xmM/n3P3MrQPrsO/6eBHeeakg3Q5uH9+C3zqvQrfBr2RfeMMbu2fhaDnx8ElwLTPxcwjvRoAQZC7GC0X5C5S0Cg/hUswtOnnIXcOhKDwgD4nFbqsq0Ce35nSqw5I1CDrnw0ABAAElXdDKN3NnwvtjV6fCUCEXOlutFyudIdWbf42XXefBtDrMnHj8rz/6kqER7ln4R3wPABAJneGk2so7t/8A0rn8pArPJCZdgrqzKtQOpmfMWkJpTbaq1WrFqpUqYKHDx+ifPnyaNiwIfr374/U1FRkZWUhOTk32jZgwAAEBgYCAFQqFWrVqiXlYbiV7uzZs4iPj8fKlSuh1+shk8mkW/EYsxR7HRwV1Im3FY7eMSqKb77+BlWrVkW9+vXg6eWJDz78AP369oNMZtxEV6tWDUcOH8G+vfswePBgDH57MBISTO9pt+Xv265xtRbJD/N+wNGjR7H217WIOxiHL6Z/gQ/e/wB//vmntYtW4krqt7Zv3z589fVXmPXdLBw8dBCrVq/C9t+344svvpDSLPp5EYgI4VXD4e3ljXlz5+G1114zaSfYI19/8zWqVq2K+vXqw8vTCx9+8CH69uv7VHVmr32BklSUel336zqsXr0aMTExOHjoIH5a+BO+m/Udli9fbpKfPZ+zSrLsRanXqKgo9OjZA3Xq1EGHDh2wYeMGPHjwAOvWrSuxcpQVD6/EQuVdEc5+4Y8W/vf7dq3QCN41XoKTTxi8a70M15AGSL9s+iwtVrKcfJtCUHgg658NyLy6FOp7h6F0jwCER48L0WUmQ5d5BU5+reAS/DKc/FpCk34O2oyCHyrv6LIfJuHBrd3wq9AdIdXfR/mwfshKv4C0m48ec+Ifmhv0v35uGlJOj8WDu3Fw86kPmHkUi6WU2ownZ2dnnDhxAsuWLcORI0dw/vx5LFu2DEuXLsUvv/xSpDxeeeUVDBs2DJmZmVi6dCn27NkDAGjbti0qVapkyeIzOxMQEIA7+R7eePv2HXh6esLFxQVyuRxyuRy3b93Jl+Y2ygcYX51ij/j4+yAt30PC0+7ch6uHK5xcnCCTyyCTy3D/jvHDzNPu3odPvllQj9hvh9McPz+//46t/MffbQQGBJr9jL+/P9b+shY5OTm4d+8egoODMW78OOmlCgYqlUqa3dCwYUOcOHECc+fOxffff2+ZnbFjAQEBZr+D/G3Ardu3TNIEBDpgG2DmZ2Y4VotTBwEBASYPxr19+zYC/ms3s7OzMXHiRKxesxovvvgiAKBOnTo4c+YMZs2ahXbt2pnk6WiepF6nTJ6CN3q/gQEDBgAAateujaysLAwbOgyjR4+GTCZDlSpVsHPXTmRmZiI9PR1BQUHo+2ZfhFUOs/Qu2YQnqVd/f3/8svYXo7Z1/LjxUtv6JHk6GkvUKwB88skn+GjkR3j1tVcB5B7T165dwzdff4M333zTcjtkIyxVr/l5e3sjPDwcV5KulGj57YHcyQMQZCYPEtfnPIDc2bvQz4q6HGRcPQTfOq+YyVMOlZfxbVtKzxDk3DE/U42ZJ8idAAigfA8SJ322ySyoR59xhkvA8yBRBxLVEOSu0KSdgEzxaDafJu04lF51oHSvAgCQq3xAugxo7p+B0j3cbL72RC53AyAzeZC4XpsBucLD7GfSbu6Au08jeJTLffyGyiUIJGpw9/o6eAc8D0GQQenkh6CIdyHqNRDFHCiUnridshwKJ19L75Kk1C6TpaenIyEhAcOGDcPy5ctx8uRJ6T7vffv2SY1qTEyM1KnVarVGb2xyd3fHK6/kNhA//fST9JYHfqg4y++Zps8gNtb4wYJ7/vwTzzzTBEDuAL5Bg/rYGxsrrRdFEXv37MUzTZ8xm6e5h3PbA0EQSqzskY1qIP7AaaNlJ/efQmTD3HuIlSolIuqE41TcGWm9KIqIP3AaNRtVN5unzE5vXyxI7rHVAHti90jLRFHEnj17Cjy2DJydnRESEgKdToeNGzeic6fOhaYXRRFqjdpkub0eqyWpadOmiM3z+waAP3f/KX0Hhu8pds+jNIbvqekzps/NAlCaF4VKnpmyP0kdNG3a1OjYBozrVavVQqvVmswokcvldn07TXE8Sb1mZWeZ1pkst23MP8PGzc0NQUFBSEtLwx9//IHOnQtvJxzFE/1m/5O/be3UudNT5+koLFGvQG4Q2lw7YO6RCvZ8ziqo7Jaq1/wyMjKQnJws3S1SlghyBZx8KyP71qMHTxOJyL55Ds5+EYV8Esi8dgTQ6+Be2fgWcEGugFO5KtCmpxot1z5MhcKt9G5JcgSCIIdMVQ76nEd1SUTQ56RC7uRfyCcBQaaATOEGgKDLugq5a8U8eejN/O7stw3JT5Ap4OQagpw8M7iIRGRnXIaTW6jZz5CoMZoV9l9GZtPK5CoolJ7Q67KQnZ4IV89aZtNZQqnNeLp9+zaaN28OHx8fVKhQARqNRrrHuW7dumjVqhV69eqFy5cvo3LlyqhatSquXbuG/v37Y9asWVI+/fv3x5IlS5CRkRsF9PT0RI8ePUprN5iFCYIMMHPrVUZGhtHVnKtXU3Dm9Bn4+PqgYsWKmDhhElJv3MCChQsAAAMHvYUF8xdg3Kfj0bdfX+yL3Yv16zbg1/VrpTyGjRiGdwb/Dw0aNkCjxo0x7/t5yMrKQt++5q/ECfl/0HZEJXeCWp9jsjw7Mxs3Uh6dEG5ev4Wkc1fg4e2O8iHl8fP0Jbh78x5GzfoQAND5zY7YHLMVCz9bjBd6tcfpuDPYt/UApsY8euV3j8Hd8M2HM1Gtbjiq16+GDYs2ISc7By+8ZvpgYYWgtNsOZ2HHw4gRIzB48GA0atgIjRs3xvfff4+srCz065t7S/DAQQMRHByMqVOmAgCOHj2KGzduoF69evjnxj/47LPPIIoiPvzwQynP8RPGI+qFKFSsWBEPHz7Eml/WYN++fdiyeUuxymbrCip7RkYGkpKSpP9fTbmK06dPw9fHFxUrVcSE8RNw48YNLFy0EAAwaPAgzJ8/H59+8in6RfdDbGws1q1bh/Ub1kt5GL6nho0aGn1Pffv1NV82Oz1WgYLr9XF1MGjgIAQHB2PK1CkAgKFDh+KFF17Ad7O+Q8cXO2Lt2rU4efIkvp+bO+vO09MTLVu2xKeffAoXFxdUqlQJ+/fvx8oVKzH9y+nFKps9EATB7K1Xxa3Xl156CXNmz0G9evXQ5JkmSEpKwpQpU/DSSy9Jz2natWsXiAjVqlVDUlISPvnkE1SrVq3ARw3Ya70WpW0tar3mbVtv/HPDbNta7HaA67VI9frSSy/hqy+/QsWKFVGzZk3Ex8djzuw5Zo9Xe61ToPTrdeyYsXip00uoVKkSUm+kYtq0aZDL5dLMsqKWzVF41XgJdw7Nh5NvFTiVq4oHib+DdDlwr9IaAHD74DwoXH3hW9/42WIPk2LhWqFR7gynfLwjO+NW3Gw4l68Bl4CayLpxGln/nETw8+NKZZ8cidKrFtR39kPm5Ae5yg+a9PMg0kHhkRsYzLmzH4LCFU4+jQAAevUdkC4LMpUvSJ8Fzf14AASV56MHyCtcKkDz4AwEhRtkSm+Imn+hST+Xe0ueg/D0b4W719ZA5VoBTq4VkX5nP0jUwMM3dwLFnaurIFd6wTf4JQCAq2dNPLizDyqXEDi5VoJOcxdpqTvg6lUzd2wNICs9EQBB6VQeOs1d/PvPViidy8OjXJNS269SCzyVK1cO/fv3x+HDh5GcnAxRFFGjRg3069cPgwYNgiAI8Pb2xvTp03H8+HFcvHgRoaGhaNy4sVE+rVu3RuXKlaVnQr366qtwdXUtrd1gFiaXyaA388ifUydP4aWOj674jB39CQDgjTffwI8L5uPmzZu4fv3Rw6vDwsLw6/q1GDNqLH6Y+wNCQoLx/bzv0b7Do+BHz1d64u6du/hs6ue4desW6tatg/Ub15k8kNjAnk/gXkpv3NbfNFl+8cxljHrtE+n/P05ZBADo8Eo7jJz5Af699S/u/PPodsTASoGYumQifpy8EBt/3gy/QD988NVwNG7TUErT5uWWePDvAyydsQJpd9JQpWYVfLZsstlb7bxU3nZbr4WV+9VXXsXdO3cxZeqU/46tuti0cZN0O9L169eNrgSr1WpMnjIZycnJcHd3R1RUFBYtXARvb28pzZ3bdzBw0EDcvHkTXl5eqF27NrZs3oLnn3/etGwy+6xTAAU+e+XkyZPoGNVR+v/o0aMBAG+++SYW/LTgvzbg0Zt9wsLCsH7DeowaNQpz585FSEgI5v0wDx06dJDSvPLqK7hz9w6mTpkqfU8bN22Uvqeils0eFPQA/8fVQf5j9dlmzyImJgaTJ0/GxIkTER4ejjW/rDF6HuOSpUswYcIEDOg/AGlpaahUqRImTZqEwYMHm2wfsPN6lclMXgIAFL9ex4wZA0EQMHnyZNy4cQN+fn54qdNLmDRpkpQm/UE6JkyYgH/++Qc+vj7o1rUbJk2eBKVSabZs9tq2Arn1am5mTHHrVa1WY8rkKUZt68JFC43a1uK0A/Z8rBZ2PFiiXmd8OwNTJk/B+++9jzt37iAoKAhvDXwLn3zySf7N23e9FnK+tUS9/vPPP4juF41///0Xfn5+aN68OWL3xsLf33QGiT2+uKW43EObQZ+TjrQzv0KXcx9OPqEIbDsGCpfcB47rsu6ZzPzQpN9Azp1EBLYdazZPt4pN4NdkIO6f24R7J5ZA6RGMgJbvw7l8DYvvj6NRulUG6XOgSTsF0mdDpvKFS0AHyP671U7UZRjffkV6qO+fBGkfAjIlFC4V4OLX8r/b9nI5lXsWmrSTUN87DBJzIMhdofSoDpV3PTgKd5/6EHWZSEvdAb3uIZxcghFQZRDkytxAqU5zH3lneXkHPg8IQFrqdui1DyBTuMPVKxI+gS9KaUR9DtJSt0GnfQC53BWu3nXgG9TR5C2CliQQPyWR2RCdXoccTfbjE5YyuUwOFyf7DXAmp1/GpfQL1i6GEQECwjyqIsLLfk/kGrXGJl8DrVQp7fJtVgbZObbXBgCAs5Oz3Q7miQg5atNZj7bAxdn8sx7sgU6vg1artXYxTAiCAGcn58cntFFarRY6vc7axTChUCigVJgP9NmDHHWOTT4cXalUQiEvtWvhJcqW21ZLnrNOnjyJRo0a4cSJE3h1Rtl89lHSit4Wzd+j8gCL5u/oHiYvNru8SoNvSrkk9ufKqZEWydfxQ+HMrhieaWFrZDZarqLyVHlbuwgmCAQvGyxXcdjqVVpbLVdR2Wr57TXoBNhu2W31uy4qW51RYKvn0qKy1ePCVr/vouJ6LXmCINhsvdpqu88YK5tss6VkZZYgCDbZYbbXK3EGPk6+UMpU1i6GEbmgQLnHPFzQ1snktteECoJg951NW/y92WKZissW98GeZ+YBtvt7s/d6tdWBvK2Wq6hs8biw5cBNUdlivdpie88YK9vsu6VnDkmpsK0AiUyQ2WQwrDhkggwVC3gTgjUIEFDBrZL916tMZnMdZoXC/jubtlangG0OLIrLFvfB3tsAQRBsboDnCAN5W6xXhVxhk0HG4pAJMpvbB1v7np+ELbZjcoXtlYkxVrbZd8+EOSS5TG5Tb4+ytUDYk6rgVsnaRZAQCBXdbScQ9jRsrXNni7OwisvWBp0ywfYCjE/C1gKljjCQB2wvoGdLv52nYWv1amtt/ZOwtbYVsL3v+UnYWr3KZDK7vn2RMeaYuFViNkcQBKiUthHssbXOxNNwVrgg2LWCtYsBAQLKuwTCVeFm7aKUCJnMdq4gKxSOMZAHbGvmli2V5WnZ0r7YUlmehq2dJxxhIA/YVqDUkQbytnR8OErwGbCtwKSjtK2MMcfiGGdR5nAUcqVNdPKclfb7FitzqnvXtPqznuSCHJHedaxahpIkCAKUKuu/5UgQBJvq+D4tQRBs4u1RcpncpgZqT0suk9vEbSFKhdKh2lZbCfqqlCqbKEdJsZWLULZSjpJgKxf3BEFwqACJTJDZxjlLbhttPGOM5Wf9kT1jZgiCAGeVdV+xrVQoIbehq9glQSlToZZPPauWIdKnDpzkTlYtQ0mTyWRWD/ooVY41kAdyO9DWnvGgVFp/IFHSrL1PMkHmUME8wDYG844WJAVso14dLZgH2EZwwlHr1doXTW0h+MUYY+Zw4InZLJlMBieldQIUgiCDSuFYwRGD8i4BCHG1zvOeAlyCEOgSbJVtW5o1ZzwolAqrB2gswdqDTkccGAH/1avKevXqiEFSIPecZa1b7gRBsHpA0VKsGYB2xGCegTWPF4XCcc9Z1qxXlcoxz1mMMcfgeK0+cyhKhQqqUn64twABLk4uDn3yjvSpDX/ngFLdpq+TH+r41nfYehUEASqn0u/0yRWOOzACcuvVSVX6QWClUunQ9SqXya0S1HNSOVl9RoAlKRQKq8wkcfQBp0qpKvVAhUwmc9hgHmC9tlUuk9vUM9FKmkwms0q9qpQqq89iY4yxwjhu7485jNIMPgmCABdnV4ceGAG5t7rUK9cI5V0CS2V7fs7+aODXBDLBsTtFpR18kivkNvNsGUsq7Y68Uql06IGRgVxeusEnJ5WTQ85yyMsw46E0B4COHswDHs1+LK3jRyaTOeyMx7xKu22Vy+RQKh1zxmNepV2vKqXKoS+UMMYcg2P3VJhDyO1wOsFJ6WzR7chlcrg6uTl8B95AJshQz7cRqnhE/LekpDuCufmFuVdB/XJNIHfwoJOBIfhk6QGSQqkoEx14A0NH3tL7q1KqykTQyUAul1v8tjvDzApHDzoZGIJPln5wskyQwdnJuUzVa2n8PhVyRZkIOhnIZP8dRxbu+ygUfM6yBJWKg06MMfsgEBFZuxCMFZVIItSaHOhFfYnm66R0dqjX+hZXuuYB/vr3FDJ1GSWWp4vcFXV8G8DbyafE8rQ3er0eWo22RPMUZLmD2rIy2MyPiKDT66DT6Uo037JyJb4gRAStVlvibatCoSjTbasoitBoNSjprlbuyy/kXK8lWK+lPavK1hBR7jlLV8LnLK5XhzxnnTx5Eo0aNcKJEyfQsGFDq5SBMWZfys5lXeYQZIIMzioX6PQ6aHUaiCQ+VX5KuRJKparMzHIqiKfKC80CWuFaRjKuZiRDrc9B7oyl4nTqc9OrZE6o5F4ZoR6Vy8wsp4LI5XLInGXQ6XTQ655uQG949bRMLiuzg03gv9kkitxbmbQ6LUTx6doAmSDLfS5PGb9ibHjguF6vh06ne+q2VSbLfbV4WR1sGhhmPZTUgF4uk+e+TKCMn7MM9VpSA/qyHiAF8pxjZLISaVsFQYBCrijTAVLAAucsmcxqz5JjjLGnwYEnZncMJ3GlQgm9qIdWp4FOX/SOp0zIHRAp5GV3doM5MkGGMI+qCHWvgrs5d3A9Mxn3cu6BkNtJEv67dY5ARv82rPNxKodK7mHwcy5f5gdFeeW97UbUi9DpdMW6Si+TyaBQ5g6I+Hh9xDDwNFxN1uv1xapXw4CorAdG8pPLcx9WL4pibhCqGG2rIAiQy+VlfgCfn2FAb6hXnV5XrMEn16t5hr6AQq7IrddiBkwNbyGUycp2MD+/p21buV7N43MWY6ys48ATs2tymRxylQuICEQEkfTQi+J/J/NHQRFBJoNckHNHqAgEQYC/S3n4u5SHSCIydRlI1zxAhjYdWlEHkfSQCXIoZAp4KD3gqfSGm9Kdg02PIQhC7hvoFPJHx6sogkQyCuBByA0CCjKBg01FkDcQbahTkf5rAwx9eiE3nUyQcRtQRDKZTHqrF9dryTAEkORy4zZAqldDOggQZFyvRVVYvQLIPV7/q8K8dcr1WrgC29Y85yxDOq7XouNzFmOsrOLAE3MIhs6ODDIoePZxiZEJMngoPeGh9LR2URyKdLzylcsSJQ1AwY1ASeJ6LXncBlgG16tlcBtgGVyvjLGyhM/MjDHGGGOMMcYYY8wiOPDEGGOMMcYYY4wxxiyCA0+MMcYYY4wxxhhjzCI48MQYY4wxxhhjjDHGLIIDT4wxxhhjjDHGGGPMIjjwxBhjjDHGGGOMMcYsggNPjDHGGGOMMcYYY8wiOPDEGGOMMcYYY4wxxiyi2IGn1NRU/PXXXwWuz87OxsmTJ5GRkVFgmvPnz+Pvv//mbfA2eBu8Dd4Gb4O3wdvgbfA2eBu8Dd6GHW3jzp07Ba4rqW04Sl3xNngbjrqNYqNimjhxIoWEhBS4/uzZswSADh48WGCamjVr0gcffMDb4G3wNngbvA3eBm+Dt8Hb4G3wNngbvA072sbbb79NAOjEiRMW24aj1BVvg7fhqNsormIHnm7cuEFnzpwpcH1WVhadOHGCHj58WGCac+fO0fXr13kbvA3eBm+Dt8Hb4G3wNngbvA3eBm+Dt2FH29i+fXuhgSd72Q/eBm+Dt/Hk2ygugYio5OZPMcYYY4wxxhhzVCdPnkSjRo1w4sQJNGzY0NrFYYzZAX64OGOMMcYYY4wxxhizCA48McYYY4wxxhhjjDGL4MATY4wxxhhjjDHGGLMIDjwxxhhjjDHGGGOMMYvgwBNjjDHGGGOMMcYYswgOPDHGGGOMMcYYY4wxi1BYuwCMlSQikv7ykslkEATBSqWyf1K9ggACIAACBAiCwPX6FIgIetIjt1pJqlOZwMfr0zA6XvHoWAXA9foUiAgkPmpfBUGAIOM24GkQEUTSQyTR0LRCgAC5TMH1+hQMbWvusZpbs4IgQC7IuV6fAvcFLINIhEh521ZAEGSQCTw/gDHmODjwxOwaEUEURYh6EaIomgSc8pPJZJDJZJAruPNZGEO96kX9Y+tVEITcOpXJOcD3GEQEjV4DnV4HvaiDSGKBaeUyORQyBZRyJQ9CH4OIoNPrctsCseA6Bf5rAwQZ5PLc45UVTBRF6DQ6iHoRep0eJJpvBwSZALlCDrlCDoWKj9XCEBF0ohYanRpaUQudqC0wrUyQQylXQilTwUnhBIEHoQUytK1avQY6UQu9qC8wrVymgEKmgEquglKu4uO1EEQEvV6f2xegwvsCMuG//pVczhdPHkMkETqdFjpRD1HUSxdJTAmQy2SQyxRQyhWQyeSlWk7GGCtJHHhidomIoNfpodPpivU5w8BUp9NBJpNBoVDwFfs8DAP44tSr1DHV53b0FXJFbr1ynUr0oh5qnRoanbpYnzF8TibI4KRwgkrhxPX6n9yZIrm/5ccFm/ISRREiROj0OsiE3DaAA6aPGNpWrVoLvbbgwbvRZ0SCTqODTqODOksNhUoBpZMScgUPkgxEEqHW5SBHm5U7y7FIn9FDrdNDjRxkaABnhQucla5QyLjrZpDbRuYgR5tdyOA9/2dyA/9qXQ4ECHBWusBZ4cyD+jxEMbeN1OmL3hcQKfcioE6vgyAIuX0BOfcFDIgIelEPjU4DvVjUeiWpL6DRqSGXyaFUqKDgi1GMMTvEvRdmVwxBDp22eAEnc0RRhEajgUwmg1KlLNMn8ScJOBXE0FlVKLjTSUTI1mRBo9c8VT4iicjWZiNbmw1XlRuU8rJ9vIokQqvRFjpjrKj5aLQaCIIApVIJeRkfeIp6ETmZORD1T1evhiCUXCmHs6szBFnZPVYBQK3LQYY6vciBkYLk6LKRo8uGk9wZbk4eZfo2HCJCtjYL2dqsp8sHj/JxVbrBWelSpttWIoJGqyl0xlhR89HqtNDqtFApVVDIy/ZwQxRF5Gizn7pe9aIeek02BEEGF5VLmT9nMcbsS9k+EzC7QmJuh6ig2z2elCiKUOeooVQqIZOXvZkPopg7+H7cbYrFpdPpoNfroVKqyuQtTVq9FlnqzKcebOaXpcmEQqaEq5NrmRt4GgLPWl3Btyc9ab4ajabMztYjImjVWmiyny5Amp9eq0dmeiacXZ2hUJW97oZIIjLU6dDoiz7TsSjU+hxoszVwd/KESu5UonnbA52oQ4Y6/akH8fllaTOh1qvh7uRRJmeV6fV6qLUle6wCyA1k/dcXKJNtq14LtTanhPMVkaXOhEqhsomZ0OEvL7Hq9m3B5c3RJZ5nlYbflniejuTKyQ+f6HPVnptfwiWxfxcP/K9UtlO2Ri3Mbol6EWq1usSDTnlptVpotdoSD8DYMr1eD7VGbbF9JiKoNepiTde3d0SEHG02MtUZJR50MtCJWjzMToeuyNP17Z/hSnxJB53y0ul1Fv092CIiQnZGdokHnR5tAMjJzEFOZk6ZqledXou0rLslHnQyEElEes59ZGkyylS9qnVqPMhOK/Ggk4Fe1OFBdlqxbou2d4a21RJBJwO9qEe2OrtYt0Xbu9wZz9klHnTKS6PTIEud+dSzfxljrDRw4InZPFGfe0tcaW1LqykbwSedXgeNtnTqVavVlshtfLYuN+iUgxwLdjSlbYGQkfOwTAT1DAOj0hi0GIKlZaENICJkP8yGqLN8veo0ujITfNLqtbifk2axwHNeWdpMZGnLRvBJrc29ZbE0PFSnQ62zfDtubYZb4krrPJKjySkTwSfDbfZFf5bTkxNJRJY6i4NPjDGbx4EnZtMMz2Eq7W06+swnvV4PrdZyM0fM0eq00gPIHZVal1Pqg5UM9UOLXf23BaUZdMq7TUcPPklBp6d8nlNx6LV6qLMcu151og7pOWlAKQSdDHKfUZRZatuzBrVOjQzNw1LdZob6ocPPfCrNoJOBowefHgWdSu+8TCQiW53l0G0rY8z+ceCJ2SwigkZdukEnA1EvluqArDQZBvLWUNoBhNKk1WtLZaaTOZlqx53xoNMX7611JcWav5PSoM5SW6WNMzx43BEREdJz7pfKTKf8srSZFrutz9r0or7UZjrl99ACz5KyFXq93mozZtVaxw1A5761rvSPGZFE5GiyS327jDFWVBx4YjarJN5c9zQccdaTLQymHbVeszTWm3EgUu4bcxyNKIpWvUXT8EpxR6PTWjf4o85SO2QAOkubAZGsF6TIUKc73O02RIQMdenOdMovU/3QIc9ZlnymU1G2b8nn9VmLXtRbdZacTtRBp3e8emWMOQYOPDGbJOpFm7gty9Ge96QX9VYf8IlkG99tScrWWH+Ku1rnWA9xt4UgKeB4gVIigjrT+jNj1JmONeNBq9ciW5tl1TKIJCJTk2HVMpQ0tS4HOtG6A2mtqHW45z3ZQttqrdmslkJENjHjKFtTNp6lxxizPxx4YjaptJ8/VBBRFB2mY0RENlOvWp3jDOb1oh4avfU78QCsPvAtSXpRbzPHiCNdmdfm2MZvT6/TQ69znAB0Zik/f6ggal22w7zt0tozSfPK0mTaxO+mJIiiaDO3D9pCAKyk6PRaG5lxSA7/bDLGmH3iwBOzOaIo2lQHz1EGR7bS0TRwlFlPttTB04v6UnmLTmmwpbcg6vW2EwR7GkQErdp2gmjaHNspy9PQiTqrz8rJy1Fuu1Xr1FZ5XpY5BHKYZ2jZ0sxYkRzn4p5GZztBNI1O4xDnLMaYY+HAE7M5tjTgBGwvEPakbK1edXqd3dcrEUFtQ4EnADZXnidhi785RwiU6rW2FUDT6/QO8RKHHBubaZijy7ap7/lJ5ehsq16zHSCgR0Q2FXgCbCsQ9qT0ot5GZjs94gj1yhhzLBx4YjaFiGxyIGLvs55scSBPRDZXpuLS2sgtdnk5wpVOW+ww22KZisuWZjsZaDW2V6biICLk6GwtIEF2/0winV5ne7N0RZ3d38ZoiwF0R7gIpbWh2U4GtjQDizHGADsMPBERqlSpAkEQIAgCBg0aZJJm0qRJ0voOHToYrQsMDIQgCOjfv79RXu3atTPJ588//5TyWb58OVJSUqT/T5o0yVK7WKaRWHDnY/78+aheozq8fbzRslVLHDt2rMC0Wq0Wn3/+OWrWqglvH2880/QZ7Ny50yTdP//8gwFvDUBIhRD4+PqgcZPGOHHihEk6e58KXlj558+fjxrVa8DH2wetWrYqtF6XLVsGVxdXoz8fbx+jNLdu3cLbg99GlcpVUM63HF5++WVcvny52OWyBwUNQuIOHMTrr/RGZNWa8HHzxW9bfntsXgf2HUDr5m0Q4BOIhnUaYeWylSZpfvpxIepG1kOgbxDat26PE8dNj1XA9m6rLC5zx8WBAwfQs2dPVKlcBa4urti8efNj89m3bx+aNWsGby9v1K5VG8uWLTNJU9Tj394DpURUYAB9wcIFqFWvFvyC/NC2fVscP3G80Lw2bNyAhk0bwi/ID01bNMWOXTtMtjXt82kIjwyHf7A/unTvgstJ5tsAvda+j9WC2oCDBw6hz6t9UTu8HvzdA7Fty++PzStuXxzateiAEN9KaFL3WaxavtokzaIff0bDmo1RoVwootq8iJPHT5rNSyva96BTW8CtiwcPHMIbr76JWuF14OdeHtu2bHtsXgf2xaFti+cR7FsBTeo+U0C9LkKDmo0QUq4iXmjTscB6tfc3hpk7Nxw4cACv9HwFVStXhZuLG7Zs3vLYfPbt24fmzZrDx8sHdWrVMdu2/jj/R0RWj4Svty9at2yN48cKblfsuW0FzLcDcQfi8FrPXqhWpTo8Xb2wdfPWx+azf99+tGzWEn7e/qhXuz5WLFthkmbB/J9Qu0Yd+PuUR9tW7XD8mPl+gEi2NcO1tDxI/gNX//gIV34bhL/3T0FO2pVC09+/sgPX/hyDK78NRsquD3H37EqI+S4qFjfPsib9zgFcPzcNKfGjcSPxO6gzrxWa/sHtffj7/HSkxI/GtbNTcO/vTRDztPmiPgf3/t6Ia2f/y/Pi7Mfm6Sjup8Yi+finuHxwOK6d/hI5D1MKTZ92YzdSTkzE5UMjkHzsE9y5staoLu9d24pLce8a/aWcnGTZnSiE3QWe9u7di+TkZOn/a9euRVZWwdOx//jjD/z5559m1wmCgH79+gEAYmNjcf36daP1hhOpp6cnevTo8bRFZ0VQUCBi7a9rMXrMaHz6yac4dPAQ6tapi5e7vozbt2+bTT9p8iQsXLQQ3874FqdOnsKggYPQ6/VeiI+Pl9KkpaWh3fPtoFQosXHDRpw6eQrTv5gOHx8fk/xsccZQcRQ0BfzXtb9izOgx+OTTT3Dw0EHUqVsHXV/uWmC9Arm/hyvJV6S/C4kXpHVEhF6v9UJycjJ+WfsLDh0+hEqVKqHTS52QmWn6kFi7DzwVcPU4KzMTtevUxtczvypSPldTrqJXz9fRstVz2HdoL/439H8YMfQ97N61W0qz/tf1GDdmHEaPHYXYuD2oXac2enZ9BXdu3zHJz56f81RQgCczMxN16tTBzFkzi5RPSkoKenTvgdatWuPwkcMYOmwohrw7BLt27ZLSFPf4t7VbKYqjoKD+uvXrMHbcWIwZNQYH9hxA7dq10f2V7rhzx/S4AoDDRw5jwOAB6NenHw7EHkDnlzqj95u9cf78eSnNzNkzMX/BfMyaMQt7du2Bq6srur/SHTk5prNwRL19t60FPdspKysLtWrXwpffflGkfK6mXMUbr7yJ51o1x56Df+CdIYPxwdCP8Ocfe6Q0G37diAljJ2Hk2I+w+8BO1KpdC6916222DbD/AEnB9Vq7di189e30IuWTW6998FyrFog9+CfeGfIO3h/6Af7841G/cMOvGzF+7ER8PHYk/jzwB2rVroVXu/UyX6923LYC5tuwJ2lbe3bviVatWuHQkUMYOmwohr471GzbOvbTsYg7FPf4ttWO+wIFn7OyULtObcyY+U2R8klJScGrPV5Dy9atEHf4AIYMHYJhQ4bjj11/SGnW/boOn4z5BGM+GY39B/ehTp3a6NG1u9ljFbDvc9aTyPjnCO6eXw2fat1QodVkqDwrIvXIN9Cp082mf/j3IfybsBY+1bqiYtvPUb7eW8i4cRT/Xlj3xHmWNRlpp3Dvn83wDnwBwdU/gMolGDeTFkCvNf/CjYx/TyLtxm/wDnwBIZGj4VepFzLT4pF249FFhLvXfkH2w4vwD+uNkMiP4eJRHamXf4RO86C0dssqHt45jrvJ6+BbsRMq1v8ETm4V/t/efcdHUe2NH//MbE/vCQmQhEAINYTQlV4FLCjqtSE27D7qvXbBdvV6bT8Fe0HlWlAUEAuodJAmCYSeUBJaSO89uzu/P5ZdSLKhZrMl5/168dzHydk5Z08mZ8585xSO756Nsc7+tVaWv4XCrMUEdZxEdNJzhHW+mfKCFAoP/9QgndarHbH9X7X969DrX63xdexq1cDTb7/9Znn7HBCAl5cXnTt35vrrr6e4uBiAP//8kzFjxuDv749erychIYGvvvqqwTm++OILAHr27IlOp6OsrIyFCxeeMd+nn3662Z/deuutSJKEoih8/fWpNwvV1dX8+KOl4bn22mvx8vK6kK8snKfmbpKzZ8/mtttuY9q0aXTr1o05c+ZgMBj4ct6XdtN/8803PP7Y40yYMIHY2FhmzJjB+PHjeWf2O7Y0b771Ju3bt+fjjz+mf//+xMTEMGbMGDp16uSQ7+ZMzXXqmqvXeV/Oa/ZckiQRERFh+xceHm772YEDB9iyZQvvzH6Hfv36ER8fz+zZs6mpqeH7779vWi437hQpioJZsR94Gjt+LM8+9wyTr5h8Tuea++nndIzuyL9f/TddE7oy4567uGLKFXzw7ge2NO/PeZ9pt03jpmk3kdAtgbdmv4WXwYuv5jV9I2p04xFPzQUhxo8fz/PPP8+VV155Tuf59JNPiYmJ4dX/vkpCQgL33nsvU6ZMYc6cObY053v9n2lEpqtrborNu++/y/Rp07nlpltISEjgnbfeweBlYN7X9uvgg48+YMzoMTz80MMkdE1g5jMzSeydyEeffgRYfn/vf/g+j/3zMSZPnEzPHj35+IOPOZFzgl9+tf/G353rtbnA05hxo3n6uSeZdMXEczrPl5/No2N0R178zwvEJ8Rz5z13cPlVk/nw3Y9taT589yNunn4TN95yA127deWN2a9hMBj45n9NR/CY3Hy0Q30zU1st9foUk66YdE7n+eKzL+kY3ZGX/vPiafV6OR+++5EtzQfvfsgt02+21eubs18/Wa/fNjmfOwf0mguQjB8/nueef44rrrzinM7z6SefEh0TbWtb77n3HqZMmcK7c961pZkze06DtnX2nNlnbFvduS/Q3AjjcePHMuv5mVx+5eXndJ65n84lOiaaV159ma4JXbn73hlcOeVK3pvzvi3Nu7Pf49bbbuXmaTeT0C2Bt+e8jcHgxf/mNR1xdqayeaqSQ7/j13E4fh2HovWNIrT3rUgqLeVH1tpNX1N8AH1QF3zbD0bjFYpXWE98ogZSe9qIpvM9Z1tTlrcW3+BB+AYPQGuIILjDNUiyhvLCLXbT11RmofOOwSeoLxpdEF5+XfEOTKK2yjKiyWyup7JkJ0GRkzH4xKHRhRDYbjwaXQhlBRta86u1uuLsFfiFX4J/+BB0Xu0Ii7sBSaWlLG+j3fQ1ZYfQ+8XhFzoAjT4Y78Du+Ib2azpKSlKh1vrb/qk0Po7/Ms1otcBTfn4+U6ZMYdOmTfj7+9OlSxcKCwv5/vvvKS0tZcGCBYwfP54VK1ZQX19Ply5dOHHiBFu3nhqaW1FRwQ8//ADAvffey6RJlo6HNRhlT1JSEps3b2bRokV2fx4bG8uwYcMAGgwVXrx4MeXllmjt9OnTL+arC+fB3gNIXV0d27ZtY9TIU9MhZVlm1KhRbNlsv2Grq6tDr9c3OGYwGNiw4VSj9euvv9K3b19uvOlGOkZ3ZNCgQcydO7fZsrn7G7nGrPU6ctRI2zFrvW7esrnZc1VUVNA1vitdOnfh2muvbTDSobbWsrD16XUvyzJarZaNG5o2nO48faklO8p/b/mbESOHNzg2evQotmyxTPuqq6tj+7a0BmlkWWb4yOH8vaXp1DB37my2VL1u3ryZkSNHNjg2ZuwYW5txIde/Oz8c2Vs7r66ujm1p2xgxfITtmCzLjBg+gi1/229bt/y9hZHDG9XrqDG29FmHs8jNzWXkiFNp/P386Zfcr9lzuuK6fueqpUbA/L05hWEjhzU4NnLMCLZusfSB6urqSNu2g+GnpZFlmWEjh9rSNGZqJjDu6s4U1D9ff2/e2qDOAEaNGcnfDeo1rUm9Dh85zJbmdO5ap9By09m2bN7SpG0dPXb0WdvWkaNGsmVLM22AG/evWuq+sGXz34wYOaLBsTFjRtvu8ZZ+wHZGnpZGlmVGjBrBls32p4i78z3rfClmI7WlWXiFdLcdkyQZQ0gPaooP2v2MPrAztSVZtqlz9ZV5VOXtwCu89wWfsy1RzEZqq45h8O1iOyZJMgbfeGqrDtv9jN47hrrqY7apc/W1hVSX7cXLr9vJk5oAM5KsbvA5SVZTW5mJp1LMRmorjuAVkGA7JkkyXv4J1JTbn9qp9+tEbcURW6CpviafyuLdeAf2aJCuvjqPQ1ueJHPrs+Skz6W+tshh3+NsWi3wdOTIEerq6vD19WXfvn2kpaVRVFTEli1bCA0N5YknnkBRFOLi4jh06BA7d+4kPz+fu+66y3aOH374gcrKSjQaDddffz0333wzAKtWrWoyTc7qlVdeAeDZZ59t9sZmDSzt2bPHtr6PdaRV586dufTSS1ukDoSzs7d1ckFBASaTibDwsAbHw8LCyMnNsXueMWPGMHvObA4cOIDZbGbFihX89NNP5OScSp+Zmcknn3xC57jOLPlpCXfddRf//Nc/m4yyc3fNdTat9RoeFt7geFhYGLk5uXY/E98lng8/+pDvF3zP3M/nYjabGTVyFMeOHQOga9eudOjQgVkzZ1FcXExdXR1vvvEmx48fb1D3nqAlt/nOy80jNKzh9R0aFkZ5WTnV1dUUFhZiMpkIDQttlCaUvFx7vyv3DOYBLVb03Nxcu21GWVkZ1dXVF3T9u3O12iu79boKC21UT6Fh5OXanxKTm5dLWFjTes3Ns9RZ7snr0d45rWmaFM1Ng88AtFDZ8/Ly7P59W9uAosIiu21AWFhos78rt67XFnK2ei1spl5DPbBeW+qedba2tbDgZLtir51opm1tyftp62vBem1yHTat19AmdR9qa3ebFs2d6/X8mOrKQTGj0vk3OK7W+WGqtT9Fy7f9YAK7Xs3xv17m4C93cGTl4xiCEwjscvkFn7MtMZkqATMqjW+D4yq1T7NT7XyC+hIQMYHs/e+Sue0xju15Bb1vHAERYwCQVXp03tGU5CzHWF+KopipKEqhtvIwpnrPnd5oqq/AUpd+DY6rtX7NTrXzCx1AcMfJHN35Bvs33E9Wyiy8/LsQ1OEyWxq9bwzhXaYR1eMBwuJupL62kGM738TspA1IWi3w1KNHDzp16kR5eTlhYWH07duX6dOnc+LECaqqqmzrNt12221EREQAoNVq6dHjVNTOOrJp4sSJBAcHM2nSJIKCgjCbzXz5pf0pVyNGjGDcuHHs2bPH7uKHAFOnTsXb2xuwjHrKy8uzLUR96623tsj3F85RC90j33j9DeLi4kjsk4ifvx+PPPoI026ZhiyfuuTNZjN9+vThxRdfpE+fPtxxxx3cdtttfPLpJy1TCA80cNBAbrrpJhITExk6dCjz588nJCSEzz77DACNRsO3879l/4H9REVGERwUzJq1axg3fhySLDm59C3MhftzLlw0QfAorv235tqlE1qZuBwcQ9Sr26ou2EvJgZ8J7TWN9sOeJ7zfg1TlplGU8dPZPyxckOryA5TmriCk/dVEJTxKWOx0qkr3Upxzao240OgbAYWju14ka/sTlOavwzswCfCw54iLVFWaQdGx3wnr9A86Jj5Nu4S7qSzaReHRU+tleQf2xDckGZ13e7wDuxPZ/X7MxirKC+1vSuBorRZ40uv1pKSkMHv2bNsaHf/73/+48sorWb169Vk/n5mZydq1lvm0v//+OwEBAYSFhVFaaok4Nxd4AsuoJ0mSeO6556ivbzo/38fHh6lTpwLw7bff8r///Q+j0Ygsy7bFx4VWYqdNCQkJQaVSNXn7mJeXR0R4hN3ThIaGsuD7BRQWFJK+L5207Wl4+3gTGxtrSxMREUG3hG4NPpfQNaHZ0XPuSpLsN9TWem08CiEvL4/wiHC7n2lMo9GQmJjIoYOnhoH27duXzZs3cyLnBIcyD7FkyRKKCosa1L1HaMH7X1h4GPmNFl3Nz8vD188Xg8FAcHAwKpWqyQKi+Xn5hIU3/V2JWzOEh4fbbTP8/PwwGAwXdv17WMVar6u8/Eb1lJ/XZESDVXhYeJMFgvPy8mwjx6xrvtk7Z+PRZTZuXK8tVfSwsDC7f9/WNiAoOMhuG5CXl9/s70py54ptIWer1+Bm6jX/DPXqtlrocjhb2xoccrJdsddOnGPfwq0008c6X+Hh4eQ1uQ6b1mt+k7rPb7DWZlul0vqCJDcZiWSsLWsyYsmqKH0RPu2H4Bc9HJ1fB3zaJRPUbSol+39FUcwXdM62RKXyBuQmo5tMxoomo6Csik8swycoGd+QQWgN7fAO6EVQ5ERKc1agnJwaqtGF0K7L/UT3foUOPWcS1fVhUEyodcEO/kbOY1l3SW4yqstYV4Za62f3M4VHluAbOgD/iEvReUfhE9yH4OgrKT62zFaXTfJRe6ExhFNfbX9DAkdrtcBTWVkZe/fu5YEHHuCrr74iNTWVcePGAZZtWa0PpV988YXtZlVfX29bP+bLL7+0DW+uqamhtLSU0tJS24KpBw4cYP369XbzTk5OZurUqRw+fJiiIvvzGq3T7fLy8nj++ecBGDlyJB07drz4Ly+cM3sdZa1WS1JSEqtWn9rdx2w2s2rVKgYMHHDG8+n1eqKiojAajSxevJjJk04t9jx48GAy9mc0SL//wP5mf+fNBXDclbVeV69abTtmrdeBAwae0zlMJhO7d++2jVI8nb+/P6GhoRw4cIDU1FQmTz63hbbdhdyCzWf/Af1Zs7rhQpWrVq5mwID+gOV31ScpsUEas9nM2tVr6H8yzekkye02LLVpqb+zgQMHNnmpsXLFSlubcSHXvzs/yNsbcajVaklKTGLN2jW2Y2azmTVr1jCgv/22dUD/Aaxeu7rBsZWrV9rSx0THEB4ezuo1p9KUlZWxNWVrs+d057ZVllQtcp7+A5NZt3pdg2NrVq6l34B+gOV3lZjUm7WnpTGbzaxbvd6WpjF3bQcs10PLXBP9B/ZrUGcAq1euoX+Dek1sUq9rV6+zpWlQNiS3vV5bqv0aMHDABbWtq1etZsAAz2sDWq5e+7Nm9ZoGx1auXGW7x1v6AX1YvbpRe71qDQMGNu0HgPu2ARdCktXo/GOoKji17qiimKku2IM+MM7uZ8ymWqTGfbnT6uxCztmWSLIanVd7asr3244pipnq8v3ovKLtfkYx19O0fbf/NySrdKg1fpiMVVSXp+Pl38NuOk8gyWp0Ph2pKk23HVMUM9Wl6eh97W96pZjqmvyNn+1v3myqob4mv9lglqO1WouUl5fHkCFDCA4Opnfv3iQkJPD7778D0Lt3b/773/8iSRIHDhwgNjaW3r17Exoayscff4yiKMybZ9kJY+rUqbYFiRVFwWg0EhISApx5kfGXXnoJlar5DuLw4cNtwa+KigpALCruDKdPhTvdQw89xOeff85XX33Fvn37eOihh6iqqmLaLZYRaXfceQczZ820pd+yZQuLFy8mMzOT9X+t54orr8BsNvPoo4/a0jz4wINs2bKF1157jYMHDzL/u/nMnTuXu+++224Z3LljdL71esu0WwC48447mTVzli39K6+8wvLly8nMzGTbtm3cftvtHDlyhOm3TbelWfjjQtauXUtmZiY///wzkydN5vLLL2fMmDFN8pckN+7ES1KzHc6Kigp2pu1kZ9pOwLKl9860nRw9alkL64VZL3LPnffa0t9+520czjrMrGeeIyM9g08//ozFCxdz7wOn0tz34H3M+3we3371Len70nn0//5JZVUVN91yY5P81XLLPAw7Q3NTMisqKkhLSyMtLQ2w1GlaWhpHj1hGKM6aOYs777jTlv7Ou+4kMzOTZ55+hvT0dD766CN+/PFHHnzwQVuas13/51o2dyCr7LcBD9z3AF/M+4Kvv/2afen7ePifD1vq4EZLHcy4dwbPvficLf29d9/L8hXLmf3ubNIz0nnl1VfYtn0bd99paTclSeK+e+7j9Tdf59elv7J7z25m3DeDdhHtGgT+T3eme7OrU6vUdo9XVFSyc8cudu7YBcCRw0fYuWMXx062AS899zL33/WALf2td0zjcNZhXnj2Rfan72fux5/z08Il3PPADFuaex64m6+++Jr5X39Hxr4MHvu/J6iqquKGm//RJH8JCdmNHzrVcnP1WsHOHTvZueNk23r4CDt37DytXv/NfXfdb0s//Y5bOZx1mOeffeFkvc7lp4U/cc8Dp+7z9z5wD//74ivmfz2fjH0Z/Ov/Hmu2Xpsrlzto7l7buG3Nyso6a9ualZlla1s//uhjFv64kAcePHU9P/jQgw3a1v976P/O2La687WqaqZ/VVFRwY60HexI2wFA1uHD7EjbYRtV//ys55lx56nr8PY7bycrM4uZz8wkIz2DTz76hEU/LuL+B++zpXngofv58vMv+fqrb0jfl84jDz1CVVUlN99y83mVzVMFdBpP+ZE1lB1dT115NgU75qGYavHtOBSA3G0fU7h3gS29d3gfSg+vpPz4Juqr8qnK30XRvoV4RfSxPcCf7ZxtnV/YMMoLN1Ne+Dd1NbkUHv0RxVyHb7AlyJyf9Q1F2b/a0nv5d6esYAMVxdtOLiyeTvGJZXj5d7fVeVXZPqrK9tl+nnPgAzS6MNs5PVVg5GjKctZTlreRuqoT5B38FrOpFr+wwQDkZHxBQdZiW3rvoN6U5qylPP9v6msKqCzZS+GRn/EO7G2ry/zMH6kqzaC+ppDqsoNk7/0ICRmfUPvBakdrtTtocHAw06dPZ9OmTWRmZmI2m0lISGDatGnceeedSJJEQEAAr776Klu3biUjI4Po6Gj69evHmjVrbGtAXX311Q3Oq1KpuOKKK5g7dy4LFixg9uzZdvPv2rUr06dPt61F05gkSUybNo0XXngBAD8/vyZ5CY4ny7Ldbb+vnXotBfkFvPjSi+Tm5tK7d29+WvyTbXjx0aNHGwRXamtreeHFF8jMzMTHx4fx48fz2aefERAQYEvTr18/vpv/HbOem8Ur/3mFmJgYXn/tdW74xw1N8nfnAAlYOnVmmg67nHrtVPIL8nnpxZds9br4p8XN1mtJcQn333c/ubm5BAQGkJSUxMpVK+nW7dSUxZycHJ544gnLVMiICG686Uaeeuopu+VSuXOARJJQySq7u1ptT93O5Zed2pr6mSefBeCGm27g/Y/fIzcn17YgO0B0TDTf/Tifp594ho/e/4jIqEhmv/cOo8eOtqW5eurVFBQU8sq//0Nebh69evfkh8UL7E4HUbnzw1EzwbzU1FQmjJ9g++8nnngCgJtvvpmPP/mYnJycBtNkY2JiWLhoIY8//jjvvfceUVFRvP/B+4wdO9aW5mzXf2Nu/XDUTHDnmquvoaCwgJf/8zK5ebn07tmbhQsW2hYGPnrsaIOA26CBg5j78VxefOVFXvj3C8R1iuPbr76le/dTu/488tAjVFVW8dAjD1FaWsrgQYNZuGBhk51G4WTb6sYBPbWssXs8LXU7V028xvbfM5+0BO+uv+k63v1otqUNOHrc9vPomGi++eErnn3yOT5+/1Mio9rx/957k1FjTu0MNmXqVRQWFPLff79GXm4+PXv34LtF3xIW3nBBYmu53PmepVapMZqbLo2wPTWNqyZOsf33zCctL0b+cdP1vPvRnGbq9WtmPjmTj9//hMiodrz93v9j1JhTu+Ra6/XVf79GXm4ePXv35PtF8+22rWqV/d+3O5AkSzCy8U5nqampXDb+1GK0Tz7xJAA33XyTrW21BvbA0rb+uOhHnnj8Cd5/732ioqJ474P3mrStBQUF/PvFf59b2+rGAZLmRhhsS93GpAmngu1PP/E0ADfefCMffvwBOTm5Tep1wcLveerxp/jgvQ+JjIrk3ffnMGbsqZd210y9hoL8Ql556RVyc3Pp1bsXPy5e2Oy0UNmN+1gXwidqIKa6corTF2GsLUXn15F2A/+J+uS0OGN1YYM+RmCXKwCJon0LMdUUo9L64hXRh6CEa875nG2dT2ASZmMlxSd+x2QsQ2eIIjzuLttUO2N9SYPpqNZFxIuzl2KqL0VW++Dl353AdhNtacymGoqzf8NYX4JK5YVXQG+CIi9DaqERxq7KN7QfJmMFhUd+wVRXhta7PVE9HrSNTjLWFjWoS+si4oVHfsZYV4JK7YN3UC+Co6+0pTHWFZOTPhezsRKVxge9Xxztez+OupmpkI4mKe66PYfgkcxmM3W1dc4uRhMqlQqN1n07nCaTibp616tXjUbT7IgBd1BdV02tk3aGOBNfvZ9bB/Vqa2tdchtovU7v1g/zlSWVLrcjl0qjwuBjcHYxLpjJbKK4usDZxWjCoPHCW+ucjmVLqDXWUFFrf1ckZ/LR+aFT65xdjAtWV1+H0dT0ZYmz6XV6tw7sV9VWYjI3fWnqXBK+Bse1AampqSQnJ5OSksJ1z+90WD7u4sCSlt+MqlPft1r8nJ7kUOqjZ09kR/ylH7ZwSdxfxvp7WiUf923lBY/kqiOLmpum4i5c9W2iOwdHADRq1wtGypLs1h14cM2pVypZ5ZJt0/lQa10vyOuKZTofsiSjcsG3sFpV09Fl7kSj0jq7CHZp3HjEE7hm22odieXOXPEFmsYFyyQIQtvm3i294HEkSUKldr2OkasGbs6VJEku1+H0iAd5We1yD506tc7t69XVrlUAtdr9O/EanYs9NEug1rh3vUqShF7j5exiNKCS1W4fIJElGZ3KtUYW6dTuPSoHLPXqavcHd79WwTUDpRq165VJEIS2zb3voIJHcrWHTrVa7XIdtQvham/kPOFBHkCnca2HI60bTwOxcrVAqSRJbh98BsvITVcK7Gt07r0OkZVe7Vqjiwxq1wqEXSi9xrWmYLpaeS6EJEku1xdwpbb+Qlnq1XUCaLKkcvsR5YIgeB7370kLHkeSJJea2uZKD2oXQ5ZdZwqWpzzIg+VNZ0ttp3yxPGG0k5UrPRx5SpAUQKN3nYcjlxuBdYEkScbgIqOeJCR0LhYIu1BqlcZldpFTy65TlovlUm2ryjNe7AFoXWiEkVbjOmURBEGw8ownP8HjaDSu8UCi1nhOpwhcp149qVMkSRJeOm9nF+PklB/3fyNvJcuySwR8ZFn2qDfHao3aJaa3aQ1ajwk+A3hpfFwisO+j8/eoe5aPzjUWSHeVcrQESZJc4h4sSZJLrpN4oVSyyiWCTypZ7TFBUkEQPIvze0mCYIckSU7fRU6SXWu6T0uQZdnpHT21Wu1RD5xgWaPC2Ws8eGm9PeqBE1zjbbhWo3V6GVqazsu5I+Nklewxo52sJEnCx8nba2tVerfecc0elazGS+vcwL6X1sejgs9gaVud/Z08sW3VqnVITg5A67XuvfuqIAiey7Oe/gSPolKpnDrlTqPxjPVHGlOpVE57M++K60u0FIPW4LQpd1qV1iMWaG3M2W/mPbUNkGQJnbfzAhR6b898MNKqtOjVzhl1KEmyR43KOZ1ebXDaCA61rHG5NbxaijPbVlcIfDmCJEkYtM4beazXGFxi5KUgCII9onUSXJpGo0GSW/8BRav1rGkgp5MkCa229d80SpKETus5axA1JksyPnrfVg8+qWUNBq1rrC/jCLIsO+UBSa1We2yQFCxT7nRerR98MvgYXGoNv5bmrfVF28q7sUlI+OsDPfaBU5IkfPX+yK28g6hKUuGr9/PYe5YkSei1rR9UU8kqp4+8diSVrELvhOCTVq3z6HoVBMH9eWYvRfAYtiBJKwafNFqNRz8YQesHgTw96GSlklWtGnxSyxq8dZ43xa4xlUrVqsEntcqzg05WGp2mVYNPeh89Ko3njXI4nSRJ+Or8Wy34ZA06efqaLrIk428IQNVKwSeVpMLPEOCxwTwrWZZbNfikklUeOcWuMY1K06prLmrVOpfbYVcQBKExz76jCh7BGnxqjWCQVqf1uHWdmmMNBjl6ZJcsy20i6GRlDT45+oFFq9a1iaCTVWsFnzRqjcdOsbNHo9Og93bsg6ckSxh8DS6xqHlrsAaf9GrHjkRUSWoCDEEutY27I8mSjJ8hAI3s2O+rUWnbRNDJyhp8cnSbp1ap20TQyUqjto5Gduz31Wv0IugkCIJbkBRFUZxdCEE4VyaTifq6+hY/r6yS29TD5ukURcFkNlFf3/L1qlFrUKlUbbZea4011NTXtOh5JUnCS+vtkWs6nQtFUaivr8dkNrXoea3rSXnqFNuzMZvN1FbWYjK2bL1qdBq0hrbzsNlYvamO8tpSzIq5Rc/rpfHGoGk7gefTWdrWWirrylv0vBIS3joftKq286LkdIqiUG+sx2gytuh5rW2rJ67pdC4UxUxNXQ1Gc8vWq0pWWdZ0ctI9KzU1leTkZFJSUujbt69TyiAIgntpG68fBY+hUqmQ9TL19fWYTRffkZckCbVG3WZGOdljXfBbJata7IHeui5PW+y8W0mShF5jQKPSUl1X1SKdTp1ah15jaPP1qtVqLUFoYz0t8e6kLQdIrWRZRu+jx1hvpK66DsV8cfUqq2V0Bh0qddttW8EyeibQEEJVfQXV9dXAxdWrRtbirfVpM6Oc7LG0rXo0Kg1VdZXUmWov+pxalQ5vrU+bDTzDqQCRWqWmrr6uRYKlapUajbptvtSzkiQZg86LelM9dfW1F12vEhJajQ6Nqm3XqyAI7kcEngS3Y33wVBQFk8mEyWg674dPWZZRayxbtYsbt8Xp9Wo0GTEazz9QYl2QWdTpKdapdyaziTpjLbXG83tIkiUZnUaPVtW2A3mNqVQqZFm2vaU3m8+vMy9LMmq1GlmWRb2eJEkSGq0GtUaN2WSmrqYOU/35BaI1Og0aneevk3c+JEnCW+uLl8aHWmMN1fVVmJRzb18lJHRqAwaNAZWHr+V0PlSyZfFvs2Kmtr6GamM1ynk81MuSjF5tQKfRt5lpdedClmX0Oj1ms9nSFzjPEVCSJKFRiWB+YxqVBrWstowwN9ad98solaxCq9aikkUfSxAE9yR6MILbkiQJtdoyWklRFBSzgtlsxmw2o6DYXixbg0uyLCPJknjQPAtJktCoNahVahRFwayYbXWrnPa2XkKy1acsySKIdxYqWYVB64VeY8CkmDCZrf+MWOKmCiAhSxIq2TICTS2L4OiZWOtGp9U1uVYbv1W2tgGyJIs24CwkSUKlVmHwMVjq1WTGbDJbgvxmxRLolyxtgKySLf/Uol7PxjoKUqfWY1bMGM31GM1GjKZ6zJixNASWa1otq0/+04gHzbOQJdnWtpoV08k6NWI0G1EU88mW1TLyRC1bXo6oZTWyJAIjZyLLMlpZi0atsbQDJ9vVJn0BSbK1q9Y2VrDPOsLc2r8ymU2YFRMmsxlzo9HmkiSjklW2f+JaFQTB3YnAk+D2bA/mMqho29M6WpItYGepWKGFSJKEWlJ7/C5UrU2SJMuOV+KZp0VZg1AqtQqNru1O72pJ1mtVJasQSwK3HEu9qlHJanSieW0xp7+8E1qONQglHsUEQWgrxF1EEARBEARBEARBEARBcAgReBIEQRAEQRAEQRAEQRAcQgSeBEEQBEEQBEEQBEEQBIcQgSdBEARBEARBEARBEATBIUTgSRAEQRAEQRAEQRAEQXAIEXgSBEEQBEEQBEEQBEEQHEIEngRBEARBEARBEARBEASHEIEnQRAEQRAEQRAEQRAEwSHOO/B04sQJdu7c2ezPq6urSU1NpaKiotk0e/bs4dixYyIPkYfIQ+Qh8hB5iDxEHiIPkYfIQ+Qh8nCjPPLz85v9WUvl4Sl1JfIQeXhqHudNOU/PPfecEhUV1ezPd+3apQDKhg0bmk3TvXt35ZFHHhF5iDxEHiIPkYfIQ+Qh8hB5iDxEHiIPkYcb5TFjxgwFUFJSUhyWh6fUlchD5OGpeZyv8w48ZWdnKzt27Gj251VVVUpKSopSXl7ebJrdu3crR48eFXmIPEQeIg+Rh8hD5CHyEHmIPEQeIg+RhxvlsWzZsjMGntzle4g8RB4ijwvP43xJiqIoLTd+ShAEQRAEQRAEQfBUqampJCcnk5KSQt++fZ1dHEEQ3IBYXFwQBEEQBEEQBEEQBEFwCBF4EgRBEARBEARBEARBEBxCBJ4EQRAEQRAEQRAEQRAEhxCBJ0EQBEEQBEEQBEEQBMEhROBJEARBEARBEARBEARBcAgReBIEQRAEQRAEQRAEQRAcQu3sAgiCo5hNZiRZQpIkZxfFYyiKgmJWRL22MFGvjqGYFRRFQVaJdywtQVEUao211JnqQQKdSotOrXN2sTyCaAMcQ9SrY4h6dQzFrKCgIMviniUIgucRgSfB7dVV13N453FyDxaQczCfnAP51FTUoSgKACq1TFBUAO3iw4joFEL77hEEtw90cqldX01lHYXHSijNq6A4t5yy/EpM9Sbbz1UaGb8QbwLCffEP8yW4vT8GH/EQejbF2aUc25dD7qFCcg7kUXi89FS9SqD30hIeF0pEXAhhscF07BmJzkvr3EK7OEVRyNmfT3ZGLjkHC8jel0tpXjlmkxkASZbwDjBY2oC4UNp1CaNDz3ao1Conl9y11dTX8PexbaQXHCQj/wD7Cw9RVV/dII2/3o+E0C7Eh8SRENqFpMheqGRRr2dSW13H0T055GUWknOogLysQmoqa8Fyy0KlkQlq509EXChhMcFEJYQT1M7fuYV2AxXFVRzbm0NuZiG5hwrIP1JEfa3R9nONXk1oxyAiOoUQFhNMh+4ReAd4ObHE7qHwWAnH9uaQczCfExl5FB0vwWS0tK1IoPfWEdE5lHadQwmPCyG6VyRag7hnnYmiKJzYn8+J/XnkHioge38epXnlKGZLI2C5Z3kR2SXU1h/o0L2deIkiCIJbkxTr07kguJnCY8VsW7aHnSszMNYakWTJEmxq5oqWVRJmk+WHkfFh9J3Yg/hBsag04iHJSlEUirLLyErL5sTBAlAsHSBrZ8ge288lCI8NJjaxHcHtA8Rb0NOYjGYObj3Mtt/3cHxvLtDwerTH+nOVRkWP4V1IHJtAaHRQaxXZLdRW1bFn9X5Sft1J0fFSkECSmr9eJdlyTSpmBYOfnqTLupM4thu+IT6tWWyXd6TkOL/u+51lGSupMdaiklSYFFOz6SUsox7MiplAQwBXdJvAhPhRBHmJAP/pCo4Vs2N5OnvWH8BYZ0KWJczncc/qM7YbcckdUanFw6eVoigc25vD9j/3cSj1CIpy7m2rJEvEJXckcUwC7RPCxT3rNKZ6Exmbs0j9bRfZ6XnAWepVwnI9mxTUOjW9RsbTZ3w3QjqKe9bpaipr2bNmP6nL9lCSU3b2e5YkgWS5Z3n5G+gzvhu9RyfgE+j8gGlqairJycmkpKTQt29fZxdHEAQ3IAJPgtspzSvnj4/Wk7X92FmDIs2xfk7vo2Pk9EH0GNGlzXc6i0+UkbZiPxVFVUgSXEjLYP2cd4CB3qO7EBzVtt/SK4pCxsZMVn2xiaqymou+Xtt3j2DsjEsJjPBzQGndh9lkZvPC7WxckIqxzgQSzT68n4kkS6BAj5FdGHX7EPRtfMReYVUxczZ8wsYjfyNLMmbFfEHnkSUJkLiy+2VMT74BfRufjleaV87yuRs4uifnwtsAyfJixeCrY/hNA+g6OLbN37OOp+eyfO4Gik+UXXTbGhTpz5g7hhDZJcwBJXUfiqKwZ80BVn2xkery2ouu1+jEKMbfMxT/MF8HlNZ9mIwmNi3czpafdmAyngziX1AfyxKI6jWqK8NvHuDU0dAi8CQIwvkSgSfBbSiKQtqf+1j1+UZMRvMFdYaaE5vUgfH3DsU32LvFzukuTEYz6ZsOcyj12AUHnJo4GQiISYwkYUgM6jY4qqyypJrln23g4N+HW+yckiwhyxJDb+xP0oTuthE8bUn+4SJ+fXsleZmFLXZOSZYw+Oq57MHhxPWLbrHzugtFUVh1aD1zNnxCjbH2ggNOjUlIhPqE8PiwB+kV0a1FzulOFLNC2op9rJu/FbNJaZl71sm2tVNSB0bfNhjvAMPFn9PN1Nca2fBDKtt+33vBgZHGrOfpe1l3hlyThFrb9laiqCiqZNkH68hMPdpi55RkCZVaZsStg+gzrlubvGflZRXy65zVFB4rvqBgkz2SLOHlr+ey+4YTk9i+ZU56nk4PPBk6ZV/UuboFTG6hUgnC2VUa19g97q0e3solaXtE4ElwC3XVdfz0+nKy0o475PySLKHWqrjin2Po1LeDQ/JwRZWl1Wz5aReVJTWOyUACg4+OgVf2xCfI+UPDW8vR3SdY8uYK6mrqWzRAerqobuFc+a8x6L3bzmiS7b/v4c+P1gO0eL1ag66J47ox9u5L28xaGnWmel5fM4e1WRsdcn7ryKmb+1zLzUnXtplROrVVdfz8zkqOnZxa29IkWUKjUzP5oZF07NHOIXm4ouITpSx6YznlBRUt85KkMQn8Q32Z8tgYAsLbzsjSzO3HWPLGcuprjQ67Z3XsFclVj49tU2sWpvy2i9XzNgOOuGdZRkH2vawHI28d1OpBPRF4EtyVCDw5T9voWQturaailvmzfuXwzou7sZ2JYlaorzWy8D+/k77xkMPycSXlRVX89X0aVaUOCjoBKJbf318L0ijNr3BcPi7kUOpRfnzld+qq6xzWgQfITs/ju+d+paqs+uyJPcCmH7fxxwfrLLv+OKBerQ+xaX/s5afX/zw1HcKD1RhrmfnHK6zL2uSwPKyjp77avoAPN39OW3jXVV1ew4KXl3L85No4jqCYFepq6ln8xp8cTD3isHxcScHRYr578TfKCysdE3QCUKCsoILvXvzNMkKlDcjYlMmPLy9z6IsSsLyQmT/rF6rLHdjncCF/fZ/Cqi82OfCeZTln6tLd/PruattmGoIgCK5KBJ4El1ZXU8+Cl5aSl1Xo0A4RAIqlM//zWys5mOLZHfmq0ho2/riD+pp6x3XgT1IUMNYZ2bRwJxXFVY7NzMmO7MpmyZvLMZvMjq9Xs0JRdik//HsZtVV1js3Mybb+vJO1/9vSavnt35zFb++scnyb40T1pnpeWvEGO3J2o7TU/I+zWLxnKXO3ft0qeTlLbXUdC1/7k8Ljpa1yzzKbFH6ds5ojuxz3YsYVFOeU8cMry6itdmxwBCxta01lHT/853dK88odmpezZW47ypI3V5xxY5aWopgV8g8XseClpdRV1zs2MyfbvDiNjT9sa7X89q0/yB8frW8TgX1BENyXCDwJLu2PD9eRe6igVR8AFUXhp9eXU3yitNXybE0mo5ktS3a1StDJyhp82vzTLkz1njmSpLyggp9eX96616pZofBYCUvfW+OxHc7DacdY+dmG1s1Ugb3rDrLpx9Z7cGhtX6R8S8rxNMuuaq3o+50/serg+lbNs7UoisIfH/9FwdHiVm0HzGaFJW+vpKzAM0eVGuuMLH5jeasEnayswadFbyy3bGDggUpyylj82p+tEnSyUswKeZmFLHt/betk6AQHU4+w7pu/Wz3fXaszSPl1V6vnKwiCcK5E4ElwWfu3ZLF33cHWH3WgWHbN+m3Oao8c8XDg7yNUFFe3WtDJSlGguryWfRuzWjfjVqAoCr9/tB5jvan169WscCjlKPv+8rwporXVdfz6zmqnLUi7/tut5Ge13CLmrmJPXjo/7vql1UY6nU5CYvaGTyiq8rxpTBmbsziYcsQp9yyT0cwfn/zlkQHoDT9uozS/vNXrVTErlOSWsWnx9lbNtzUoZoXf5qzGZDS3WtDp9LzTNxwiY1Nm62bcCmoqaln2/lqctZTd2m/+pii7xDmZC4IgnIUIPAkuqbq8ht/fX2fZwccJFLNCdnoeqb/tdk4BHKQkr5z9W1tux5rzpkDm9mwKj3vWaLJdq/ZzZGe28wKVEqz4bAOVJZ41lXH155uoLKlyagD4l7dXetR6T7XGWl5bM8dpi3wrKNQYa3jnr489KkhSWVrNyi82OvWedWxvDjtXZTinAA6SvT+P1KV7Wj04YqPA1l93kXMw30kFcIxty/ZwfF+uU+9Zv3+wjqoyz1rvaeUXG6mpqG31F1BWilnht3fXYDaL9Z4EQXA9IvAkuKQN36dSU1nrvM7mSWu+2uxRC2HuXHnA2UUACXas3O8xD5111fWsnue4xZnPiWLZYnzdN1udW44WlHMwn7Q/9jo16KSYFfKzitjxxz6nlaGlLd79GznlebZFv53BrJjZdHQrKcfTnFaGlrbhh1TqaoxOv2et/Warx6z5pigKK+ZudNqIRytJklj++UaPuWfVVNSy5n+bnVsIxbLz4/pvPeeedTw9lz1rDzj9npVzIJ/dq/c7rQyCIAjNEYEnweXUVdexY0W6S0xzMxnN7FzpGW+QS3LLKc2rcPqDEQpUFld7zKinvesOUF9jdHYxUMwK+/46RLWHvEFO/XUXksq5D5xWW3/e6REPnSaziZ/2LnXKFLvGZEnmpz1LnV2MFlFTWcvevw65xD3LWGdk718HnV2MFpGdkUfh8RKn16tiVig4UkzOoQKnlqOl7FqVgdEF1lpUzAq7V2VQW+kZgdLUpbudHiQFQIKU33Z5xD1LEATPIgJPgsvZveYAxlrnP8gDoEDqb7uc3vFtCVk7sp227kBjkgRZO044uxgXTVEUti3b4+xi2ChmM7vWuP+bzuryGsubY5Nr/N0Vnyjl6C73v163HEul0EXWVjIrZrYcSyWnPM/ZRbloe9YdcKmtzLf/sdcjHjrTVuxzjQd5QJIldix3/5GPilmxLCHgIpeHsd7Ebg+4Z1WWVJGxKdM1+ooKlkDpAc+aHioIgvtz28DTiBEjkCSJ6dOnO7sobmX69OlIkkRMTIyzi9Ks1KW7nbZOhj3lBZVkpR1zdjEuSn2tkePp+U5bd6AxRYGcgwXUuPmbzuz0XIqyXWfklqLA9mV73P6hc9eqDJd6kJdUEtuWuv96b0v2LEOWXOe2L0syS9OXO7sYF0VRFLb/uc9lHuTBMrr1eHqus4txUarKati/5bBrPMhzckHsTVluP/X+8M5sSvPKnV2MBjxhLc1dqzJcqg2QZIntv+91djEEQRAaULdWRiNGjGDNmjVER0eTlZVlOz59+nS+/PJLgPN6WOrevTs1NTXExcWdd1msi6p+/vnnLh24stbN8OHDWb16tbOL0yoqS6spOlZi92dJE7rT/8reeAcYyMsqYsVnG874Rid+cCyX3tAP/1Afik+UsearLWSmNlxY+5J/JNN7TAI6Ly3Z6bn88fF6Sk6UNUgjqyQO78gmNqnDRX8/ZynKLmu2A//ruoUsXDmf4rIiYqPiuPua/yM+unuz51q/bRVf/fYZeUU5RIZGMf3ye+jXY7Dt54qi8PXSufyx8WcqqyvoFtuL+659lMiwRvWnQNHxUiLjQ1vkOzrD4Z3ZSLJkt24Tx3Wj3+U98fY3kH+kmFWfbyTnYPNTNboMjOGS6/riF+pDSU4Z677ZSub2hgHPIdcm0XNUV/TeWo6n57Hisw2U5DS8XssLKynNKycg3K9lvqQTZG1vPtCbdFkPBkxJPNkOFLL8k7/I2d98O9B1SCcuvbEf/mG+FJ8oZc28zRxKadgOXHpDP3qPTUDnreP4vhz+/HAdxae1A4pJIXP7URRFcdqi3BfLZDaxM2dPk7WdeoZ349peV9AlpBPBXkE8v/w1Nh4581bgvSO6M2PArUQHdqCgspBvtv/InwdWN0hzebfxTO15BUGGAA4VH+b9jXNJL2i4xpxZMZNyPI3b+t3YIt/RGcoLKynLr7D7s96ju9JvYk+8/A0UHC1i1f+2kHuG6Vpd+kcz+Jok/EJ8KMktY/13KWTtON4gzaCr+9BrRBfLPWt/Hiu/2ERJbsNAgiRLHNl1gvYJERf/BZ3keHrzC187q17NJjPZ+/OI69vx4r+gkxzecRxZJWG2M5rUWX2s4hOlVBRX4RPo1TJf0gky0443+wzTZ3w3+l9uqdf8w0WsmLvxjIvVxw+K5ZLrky31mlPG2q+3kLmt4T3xkuv60mt0AjpvLdn7cvnz078a9AUUs0JmmhM3kmkB7733Hq+//jo5OTkkJiYyZ84cBgwYYDftJ598wrx589i1axcAycnJvPLKKw3SK4rCc889xyeffEJJSQmXXHIJH3zwAV26dGmV79NSzqdeABYsWMDMmTPJysqiS5cu/Pe//2XixIm2n3tKvThKS9d3RUUFTz75JIsXL6awsJDY2Fgeeugh7rnnntb4Ok7nOq8+z9P777/Ppk2bmDlzprOLcl7q6tx7hIej5TbzYN51SCdGTB/Ehu9TmffYIvIPF3LtzMvw8tPbTR/ZNYzLHxnFzhXpfPmvRezfksWUx8cS0iHQlmbAVYn0ndiDPz9az9dP/URdTT3XzrwMlUbV4Fxmk8KJA+49HaQ0r9zuNLt1qSv4dNF73DB+Om8/9imxkZ2Z9cG/KCm3Px1nb+ZOXp/3IuMGTeKdxz5lUK+hvPzZMxzOPmRL8+OKb/hl7Y/cd90/eeORj9Br9cz68F/U1dc2OJckS5Tk2X9gcxe5hwrsdjbjB8cy/JYBbPphO189tYT8w0Vc/dR4DM1cr+3iw5j00Ah2rcrgqyd/4sDWI1zxr9EEtw+wpel/RS/6TOjOik838M2zP1NfW8/VT41vcr0C5GUWtth3bG2KonBif57dt8cJl8Qx8vbB/DU/hS8f/ZH8rCKue24SXv7NtQPhXP7P0excns4Xj/7I/s1ZTHlyPCEdT2sHpiTSd3JP/vhwHV89voj6GiPXPjepSb3WVdVT5sbX65GSY9Sbm05h1mt0HCo6zLsbPzun84T7hPHS2KfYkbOb+xY/xqLdv/LIpfeQHJVoSzM8dggzBtzK19sXcP+SJzhUdJiXxz+Dv75pMDSz+Agms/PXm7lQeVn2/9biB8Yw7Mb+bFqcxjezfib/SDFTHhuDwbeZNqBzKJfdN4zda/fz9ayfOZh6hMsfHklwVIAtTb9JPUka240VX2xi/gu/UV9rZMpjY1FpGnblFEUhN9O91yPKyypEtrPGmzPrVZIl8rKKWvR7tracA/l2g07O7GMB5LrxroGKojQb+Ow6uBMjpg1i4w+p/O+JxeQdLmLqMxOar9f4MCb/30h2rUxn3hOLOfD3Ya56rFG9XtmbpMt68Ocn6/n66SXU1xqZ+syEJvVaVVrjtjvdfvfddzz66KM899xzpKamkpiYyPjx48nLs98XX716NTfccAOrVq1i48aNdOjQgXHjxnH8+KkA82uvvcbs2bP58MMP2bx5M97e3owfP56aGvcZxXi+9bJhwwZuuOEG7rjjDrZt28ZVV13FVVddZQvQgWfUi6M4or4fffRRli1bxldffcXevXt5+OGHeeCBB1iyZElrfS2ncrnAk3Uq2IgRI3jvvfeIiYnB19eXyZMnk5OTY0vXeKpdVlYWkiQhSRJffPEFkydPxsvLi9jYWD77zNKhXr16dYO31bfddluTaWdLly5l+PDh+Pr6YjAYGDp0KKtWrWpQxh07djB48GD0ej2JiYmsW7fOlvfzzz/fIC9JkliwYAEDBgxAq9XyzTffcOzYMSZOnEiHDh0wGAwYDAZ69uzJ22+/bXuIjYmJsY0EW7Nmje1c1pFP2dnZ3H777URGRqLVaunUqRMvvfQSRuOpB4va2lruvvtu/Pz8CAsL44UXXnD5KTi5h/LtrunQ7/Je7Fi+j12rMig8VsIfH62nvtZIz9Fd7Z4neVJPMrcd4++fdlB0vIS/5qeQm1lA0mU9TqWZ3JNNP2zjwN+HyT9cxG9zVuMT6EWXAdFNy3WwwGWG/F+IkrwKu9PsFq/+nvFDJjNm0EQ6RsRw33X/RKfV8+emX+2eZ8maH+ibMICrR99Ah4gYbp50J3Ht4/ll3ULA0gFbsmYB1427hUG9hhIbFccjNz9DUWkhm3aub3AuxaxQmutaQ/7PV86BfLsBkuRJPdm1Mp3da/ZTdLyE5Z/+hbHOSM8R8XbP0/ey7mSlHWPrL7soyi5lw/ep5GUW0mf8qZFnSZf1YPOiNA6mHKHgSDHL3luLT6CBzv0avn2XVdIZ3/67uoqiKmrKa+3+rN+Vvdjxx152rUyn8FgJv3+wlvpaI71GJ9hPf3kvMlOPsmVxGkXHSlj/zVZyDxXQd2LPBmk2fp/KgS2WduDXd1bhE+RFl4ExTc7nzluq7y88ZPf41mPb+TJ1PhsObzmn80xOGEtORR4fb5nH0dLjLNm7jHVZm7i6x2Rbmqt7TmZZ+gr+2L+aIyXHmP3Xx9Qa6xgfP6rJ+YxmI4dL3Hcqc3MBkr4TurNr9X72rDtAUXYpK77YiLHWRI/hne2eJ2l8N7J2Hiflt90UZ5ey8cft5GUVkTg2oUGazUt2cCj1KAVHi/n9o/V4B3g1HYGjQE4zQXF3kXOowG6AxJn1eqYAgztQFKXZNsyZfSxJltx64fbS3HLqa+rt/qzf5J7sXLGPXav3U3i8hD8/WU99nZGeI5vpC0zsSeb2Y/z9805LvX6XQu6hQvpM6N4gzaaF2zm49QgFR4r47V1LvXbub6fvesg9X0K99dZb3HXXXdx22210796dDz/8EC8vL+bOnWs3/ddff819991Hnz59SEhI4NNPP8VsNrNixQrAcu2//fbbPPvss1x55ZX07t2befPmkZ2dzeLFi1vxm12c862Xd955hwkTJvDYY4/RrVs3XnrpJfr27cu7774LeE69OEpL1zdYglO33norI0aMICYmhhkzZpCYmMiWLefWB3N3Lhd4stqwYQP/+te/0Gq1VFRU8Ouvv/LPf/7znD47Y8YMdu/ejUajISsrixkzZrBv3z78/PwYOHCgLV2nTp0YOHAgSUlJgCWyOWnSJNauXUtwcDDt2rVj/fr1jB071hZ8qq6uZuLEiWzatAmz2Ux9fT2TJ0+2Ww6rm2++mWPHjhEbG4skSRQUFLB0qWU3n27duuHn58fu3bt55JFHeP/99wFISkoiJCQEAF9fXwYOHMjAgQPx8/OjsLCQQYMG8fnnn1NRUUG3bt04evQos2bNYsaMGbZ8n376aT7++GPKy8vx9fXl7bff5scffzzH34Bz5GcVNXmQl9UyEXEhHD59aLxiGTIeGR9m9zyR8eEN02OZvhPZ1ZLeP9wXn0CvBmnqquo5sT+fyK7hTc5XX2ukNN99gyT2RmrUG+s5cDSDxPh+tmOyLNMnPpn0LPtrLuzL3E2frskNjiUlDGDfyfS5hScoLiuiz2nn9Db4EB/djX2Zu2istJkpKu6guqyGajsBElklEx4bzOGd2acOKpZpee2amVbYrktYw/RAVtqp69s/zHK9HjktTV11PTkH8mnX6G/AbFLc+q18fjMjSCztQGjDKTIKHE47ZvdvFixv5RtPqcncdiq9f7gvPkHejdqBOk5k5DU5p6ySmh3d4g4yiw6jlpuONDhf3cLi2Za9s8GxlOPb6RZmeZBSy2q6BHciNXuH7ecKCtuyd9A91P7D1qGirIsul7PkHyluEiCRVTJhMcEc3d2wDTiyJ5t2ne23ARGdQzm6u+EC9od3Hrel9wv1wTvAq8E566rryTmUb/ectZV1VJW67xvr/MNN2zCn16vS/Ag3d1BRVEldddMAidP7WIpCvhuP0s0/Yv9+K6tkwjuFNOkLHNl5nMj4Zu5Z8WEc3tmoXtOOEdmlYV+gQb1W13PiQH6T35UkS+Qfdr96raurIyUlhTFjxtiOybLMmDFj2Lhx4zmdo6qqivr6eoKCggDIzMwkJyenwTn9/f0ZOHDgOZ/T2S6kXjZu3NggPcD48eNt6T2hXhzFEfUNMGTIEJYsWcLx45bpuatWrSIjI4Nx48Y55ou4GJcNPJlMJjZt2kRGRgZTpkwBsEWuz+bKK6/k0KFDrFu3DgCz2czq1avp27cvmzZtsqWbOXMmmzZtYtGiRQA8+eSTKIrC7bffTmZmJgcPHmTKlCmYTCZmzZoFwDfffGMburlo0SL27NnDW2+9dcbyTJ06lWPHjpGens7NN99MbGwsmZmZHD16lNTUVE6cOMGwYcMAmD9/vu3ckyZNArCVe9OmTbbI6dGjRwkPD+fgwYOkpaXxww8/APDFF19w4MABKisree+99wD4xz/+wcGDB8nIyECn051THTpLTVVdk7e0Bl89skqmqqS6wfGq0mq8A+yvCeAdYKCytGH6ypJqvAMMtp9bjzVIU3oqTWP2Omzuwt7WyWWVpZjNJgJ9AxscD/ANorjcfkeqpLyIAN+gRukDKSmzpC8uL7QdO5dzmoyus4D0+aqttj9t1uCns1yvped3vTZ+SKwqrcbL33Itep28Jhufs7K0xu71WlvlvlN6m6tXr2bagcrSarwD7f/Negd4UdVoqkFlaZUtvfX3Ya8daLreiOTWbUBVfXWLbC4QaAiguLqkwbHi6lK8tV5oVVr8dL6oZBUl1aVN0gR6Bdg9Z3W9+wZIaiqbBp8NvifbgLLGf9M1ePs3c63622kDympsbYD1c5VN2gn7bQBAXTN/S+7A3ggSV6jXOlfZcfcC1DazmYez+1iKgltvNNLc/dbgZ6nXJnVQ0vzfrKUv0PzvwbuZvoC935UkSdS64T2roKAAk8lEeHjD4Fx4eHiDmS9n8sQTTxAZGWkLAlg/dzHndLYLqZecnJwzpveEenEUR9Q3wJw5c+jevTvt27dHq9UyYcIE3nvvPVscwNO1WuDpfBdk7dWrF4mJljUjune3DDHNzT23XVpuuukmJEmyfe5cPpufn29b9Hzu3LnIsowsy7ag1ObNmwHYvdsyssPLy8sWGLruuuvOeO4HH3wQWbZUtUqlQq1W89prrxEdHY1Go0GlUrF27VrAMoXubKzD8XJzcwkLC0OSJK666irAMmxy8+bNHDx4kNpaS4f46quvBiA0NJQRI0ac9fzOZLITIHEVZjcOkphddJqgYlbcdjqIK18Prvx3dDbmetetV7PRfeu13mREcaVtl06SJdnu2lPuwpWD5yYX2hnyfNmbZucKXGm3zfPl0teqG7etol5dy6uvvsr8+fNZtGgRer39tbQEwVnmzJnDpk2bWLJkCSkpKbz55pvcf//9LF/u3jv8nqtW29XO29sbgKKihqMeCgstIyR8fHwaHA8ICLD9/2r1+RXT+tnTP3c+D7edOnUiNLTpsO3TFwY/n0Ba4+jnww8/zKeffgpAly5dCAoK4uDBg7bo6rny9fVtEFyz8vJy351B1Nqmv+vq8hrMJrNt1IeVl7+h2YUTK0uqm7wB9Q4w2N48Wf/39GNgefvZ3FB6ewtiugtZlmjcNfLz9keWVRQ3Wki8pLyIwEajmqwCfIMoaTRyqaS8mAA/S/pA32DbsSD/kAbn7BTVdA0OSZbcdpew5q6H6rJay/Xqf37Xa+MFsr38T735tL6Jtpzj9OtVT56dKSlqrfteq83Va1Uz7YC3v4HK4mq7n6ksqcKr0Vtgb38vW3rr78M7wEBlcdVpaQzk2pn64c5tgEalQeLi/9aKq0sINAQ0OBZo8Keyroo6Ux1ltWZMZhMBBv8maYqrSpqcT1HMaORW64q0OLWda6K6/GQb4Nf4b1rfZJSIVWWpnTbAT29rA6yf8/bXNxjt4OWvtzstDUCldt/rVVZLmOob9ttcoV5VKpedKHBWzd6zXKCPpda4bxugUtu/JqrLLPXaeHSTd4C+ySgoK0tfoPnfQ2UzfQGv5urVDduAkJAQVCpVkwEDubm5RESceafON954g1dffZXly5fTu3dv23Hr53Jzc2nXrl2Dc/bp06flCu9AF1IvERERZ0zvCfXiKI6o7+rqap5++ukGs5p69+7N9u3beeONN5pM0/NErXYHtV7A5eXlfPrppxiNRlJSUmxrJ1lHN7UGg+HkEODKStux0NBQoqMtC/P17duX9evX26a3zZs3j5deegmtVkvPnj1tn/3jjz8A+P7778+YX+MHa+t0v3HjxpGRkcHq1auJiopq8jlrAOn0cgL0798fsATW5s+fbyvnn3/+yX333ceUKVOIi4uzTauzLhBXUFBgW5zcVem9dU0WFzcbzeQcLCC612l1JEF070iyM+zvLJCdkUvH3pENjkX3bk92uiV9aW45FcVVdDztnFqDhnZdQslOtz86TuelvZCv5BLUuqadOo1aQ+cO8ezISLEdM5vNpGWk0jWmR5P0AAmxPUjLSG1wbHv63yScTB8e3I5AvyDSTjtnVU0lGYf3khDbk8bsPbS5C20z14PZZCY3s5COPU+7/iTo2DOSExn2F3Y9sT+vYXoaXt+leSev19PSaA0aIjqHcsLO34De27Wn1J6JzruZejWayTmYT3Tvxu1AVLN/s9npeQ3TAzF9TqUvzS2noqiyQRqtQUO7+LAm51RQ3LoN8NF60QJxJ/bmZdCnXa8Gx/pGJrI3LwOwLBa+v/AQSZGn0khI9InsxZ78jCbnUwBvrfu+LDH46JrUq9lkJi+rkA49TnXkkaBD93acaGZ7+pwD+XTo3q7BsY49I23py/IrqCypapBGq9cQ0Sm02XO68/WqNTQtuyvUq9aguZCv4xL0PvbvC87uY0myhN7Xfe9ZzdaryUzuoQI7fYEosjOauWdl5DX8PXDyHre/YV8gunG9dg5t8rtSFKXZ+6kr02q1JCcnN1hexbpQ+ODBg5v93GuvvcZLL73EsmXL6NevX4OfxcbGEhER0eCcZWVlbN68+YzndCUXUi+DBw9uskzNn3/+aUvvCfXiKI6o7/r6eurr622zoKxUKhVms+uOnGxJrRZ4uueee2yLvN11111otVr69etHZWUlkiTx1FNPtVZRSEiw7Gby5JNPMmDAAJ5++mkAXnnlFQB++OEHIiMjSUpKIiIigq5du/L1118DcOONN9qCRJdffjk9evTgkUceOa/8rVH4P/74g65du9KhQweOHj3abDm3bt1Kr169GDRoENXV1dx///1ERUVRXFxM165d6dOnD3FxcQQHB3PrrbcClhFm9957L2BZl6pz587Ex8c3CWK5mrBY+yNttv68k95jutJjRBeCogIYN+NSNDoNu1ZaHmQmPjiCoTf1t6VP+XUXsX060O/yXgRF+TPkur5ExIWwbempRbNTftnF4KlJxPXrSEjHQCY+NIKK4ir2bzncJH+tQYNviHcLf9vWExDmY/eh86oR1/H7xl9YsWUpR3OyeH/Bm9TUVTNm4EQA3vrqZb78+SNb+iuGTyV172YWrZzP0dzDfLN0LgeOpjN5qGU6pyRJXDH8Wr77Yx6bd64nK/sgb331MkH+wQzqdWmT/P3DfJoccxcGH12z6zSk/LqLXqPi6T6sM0GR/oy5YwganZrdayzX64T7hnHpP04t0p66dA8xie1JntSTwEh/Bk9NIrxTCNt/32NLs23pbgZOSaRTcgdCOgQy4b5hVBRXc2DrkQZ5yyqJsNhgB3zj1nGmsm/9aSeJYxPoMTKeoPYBjLtnKBq9hp0r0gGY+H8jGXbzgFPpf95JbFJ7+l/Zm6CoAC75RzIRcaGk/rarQZrB1/alc/9oQqKDmPTwSCqKqti/OatB3opJISw2BHfVKSgGk7npiFq9Wk+noBg6BcUAEOEbRqegGEK9Ld/1tuQbeWzYA7b0v+z7k3a+YdzR72Y6+EcyOWEcw2IHs3D3L7Y0C3f9wmXxoxnTeTgd/KN4cMhd6NU6/shouEOsVdzJvN1RaHSQ3VGbqcv20HN4PN0ujSMw0p/Rtw5Co1OzZ+0BAMbNuJRLru1rS7/t971E94qi74TuBLbzY9CURMJjg0n7c1+DNAOu7E2npA4Etw9g/N2XUllSxcHUI03yN/jqm92y3R2ExwTbvWc5s14lSSLcjdtW7wBDs0ESZ/exwmLct17PVPatv+yi9+iu9Bhuqdexd16CRqdm1+r9AFx2/3CG3nAqSJL62y5iEtvTb3IvgiL9GXKtpV63L9vTIM2gq/sQl9yRkA6BXPbAcCqKqzjwd8N6VcyK29bro48+yieffMKXX37J3r17uffee6msrOS2224DYNq0aQ2eG//73/8yc+ZM5s6dS0xMDDk5OeTk5FBRYdnARpIkHn74Yf7973+zZMkSdu7cybRp04iMjLQtVeIOzrde/u///o9ly5bx5ptvsm/fPp5//nm2bt3KAw9Y7umeUi+O0tL17efnx/Dhw3nsscdYvXo1mZmZfPHFF8ybN8+2nrWna7Wxre3bt2fDhg0899xzrF69moKCAnx8fEhOTubxxx9n/PjxrVUUZs+ezX333Ud6ejp///03HTtatsy98cYb8ff35/XXXyclJYX09HSioqIYP348d955JwB6vZ7ffvuNGTNmsG3bNmRZ5ttvv7XtbGcdTXUmb731FpWVlaxYsYLy8nIee+wx9uzZw5dfftkg3e23387atWtZvnw5u3ZZHpRMJhOhoaFs2rSJWbNmsWzZMnbv3k1oaChDhw7l8ssvt33+P//5DxUVFcyfP5+SkhJmzJhBdnY2//vf/1qkHh0hvFMoip31iNI3HMLLX88l/0jGO8CLvMxCfvj3UtvQeN8Q7wbTKbPT8/jl7ZUMvaEfQ2/qT/GJUha99icFR09NK9uyOA2NXs34e4ai89ZyfF8uP7y0zO76OBGdQ912ShhAQJgvuYcKm6zwMrTvaEorSvj6t7kUlxXRqX1nXrjnDQJPTp3LL85t8L27xfbiX9Nm8dVvnzLvl0+IDG3PM3e8THRkJ1uaa0bfSE1dDe9+9waV1RV079SLF+55A62mYYdXkiUCwn0d9p1bQ0SXUA5tPdpkKm/Gxky8/PQMubYvXgEG8g8XsfDVP2yL3Da+Xk9k5PHbnNVccn0yl/wjmZKcMpa8sYLCYyW2NH8v2YlGp2bsXZeg89JyPD2Pha/+3uR6NZsUwju5b4DEO8ALrwBDk4VuAfb9dRCDv55Lb+iHd6AXeZkFLHjhN1s74Bfq06gdyOWXt1Yy9Kb+DL15AMXZpSx69XcKjpzWDixKQ6vXMO6+Yei9tRzbm8OCF39rth1wV11C4uwejw/pxOsTX7D99z0DpwPwx/7VvLnuPYK8Am1BKIDcijxm/vkf7h44nat6TKSgspD/t/5DUo6n2dKsydyAv96PaX2vJ9AQwKGiLJ7542VKahouOA6gVWlo7x/Z5Li7CIsJtnvPytichcFXz+Cr++Dlb6DgSBGLX19uWxjbL9ib01d7P3Egn2UfrGXw1CSGXNuXktwyfn57FYXHS2xptv66C7VOzejbBqPz0pK9P5dFbyzH1HhdNAki4ty3DQAsu4Htym7atjq5Xt25bZUkiYjOoWSlHWuye7Az+1iKWXHr69U32Budl9buIuPpGw/h5afnkuv64hXgRX5WIT+8suzUPSuk0T0rI49fZ6/i0n8kc+kN/Sg5Ucri1xvV60870OjUjLv7UktfYF8uP75iv+/qrtfr9ddfT35+PrNmzSInJ4c+ffqwbNky29IlR44caTBq5IMPPqCuro6pU6c2OM9zzz3H888/D8Djjz9OZWUlM2bMoKSkhEsvvZRly5a51TpQ51svQ4YM4ZtvvuHZZ5/l6aefpkuXLixevNg2ewc8o14cxRH1PX/+fJ566iluuukmioqKiI6O5uWXX+aee+5p9e/nDJLiriv7OtGBAweIi4uzPZB//fXX3HzzzQAsW7asVYNonqa6vIZ3p7tWYExWSfS/MpFhp73tczf5h4vZ/NOusydsZckTu9Gus3t2jAA2L9rOhgXb7D54OtNd716Hb4j7jiZb+PIyDm494lILz+t9dTw471a3DUCbFTNXzbuFWpNr7R7VLTSety9/2dnFuGCVJVV88tACZxejAUmWGHhVIoOuar0lDFrawdQj/Py2/RFyznTlP0cTm9je2cW4YOu/3crmRdtdbvH2+z+/xa1H6C3491IO7zju7GI04BPkxT0f3uiw86emppKcnExKSgqGTmffGOlMugVMbqFSCcLZVRrX2D3urR7eyiVpe9x3lUQn+te//kVcXBxXXHEFQ4cO5ZZbbgFg2LBhjBs3zsmlc28GXz3hnYJxpWc7s0khto/7djQBAtv5NbsAprNIskRwlP/ZE7qwmMT2Lhd0Cmznh0+w+04LBYhN7uhSQSdZlojrF+22QSew7B7XNyoRWXKddkBCYkCHvmdP6MK8A7wIivRvkfWzWopiVojp5b6jyACiuka43D1LpVERGR/m7GJclJik9i4VdJIkCI0JcuugE0CnpA4u1QZIskRcckdnF0MQBKEB17qru4kRI0ag0+lYvnw5f//9N127dmXmzJksXbrUrR9MXEXfiT1xmWdOCQIj/Wnf/cw7abg6tVZF++7hLhPQkySJqPhQt16oFSzD2MNiXStQmjShu9u3Q92Hd7a7IL6zmM0KfS+zv+C+O7mi2wTMiussYClJEhPiRzm7GBetz7huTaYuOY0EIR0C3XaKjZXeW0vXIZ2abDbiLJIs0f3SOHR2Fj13J1FdwwlqH+AyQRJFwSPa1h7Du7jUjoeKWbG0S4IgCC7EdVpJN/Lwww+zd+9eqqqqqKmpYe/evbz44ou2XeiEi9N1SCe0Xi4SkFCg78Qebv8gDxDTq53LBPQURSG6t3u/kbdKmtDdZepVpVHRbWhnZxfjoukMWnqN7uoSD52SZFnHJ6KL+67vZNUnsicRvq4xYkMlyVwSPYAgr0BnF+WiJQzuhFrrIjt0KpZAmCfcsxJHJ7jMiFLFrNB7dFdnF+OiSZJE8sQeLhMo1ejVHnHP0vvo6Da0s4vcsyTadQklNNo9FxYXBMFzicCT4HI0OjVJ411j1IZGr6bH8C7OLkaL8A32JjjK3+mjcyQJ/EK9CQh33zWIThc/OLbZnYJakyRL9BwZ79ZbqJ+u72U9XGK6naJAvyt6uUR7dLFkSebqHq6xloZJMXNV94nOLkaL0Bo09BwR7xIPnTovLV0HxTq7GC0iPDaYiE4hTq9XSZaI7BJGaEf7u+66m+7DOltGGzu7LyBLJI7rhsaFRrdejKQJ3V0iUKooCsmTep49oSAIQisTgSfBJQ26JgmfIC+nP+yNueMSj3mQB+g1sjPOjjwpQOKYeKf/bluKRqtmzJ1DnFsICQw+Oi65zr3XyzldcIdABlyV6NSHI0mWaN89gh4j4p1XiBY2KWEssUHRTl3rSZZkxnYeTs8Iz5kKMmhKHwy+Omc3r4yaPshjHuQBRt8+2NlFQJJcoxwtRWvQMubOS5w66kmSJbz9DQy51nPuWeGxIU6f6i7JEtG9Iuk6uNPZEwuCILQyEXgSXJLWoGHiQyOcNuJBkiVikzrQY6RnjHay8gnyImFIjFPL0KV/R/xDPWO0k1X8oFi6DIxx3pt5BcbdM9QlRl61pEtv6EdghL/T6lVWyUx8aKTTR1y0JLWs5olhDzotfwkJP50v9wy8zWllcAS9t5axd17itGm31sWE4wfGOKcADhLaMcjpu/MNvjqJ4KgAp5ahpXUf3plOyR2c1rYpZoXLHhzhUS/2AIbe2B/fEG+n1atao2L8vcM85sWeIAieRQSeBJfVsWckSZd1b/URD5Jkme43/t6hHnnz7tQnioBw31Z/My9J4BvsRZf+HVo341Yy+o7B6Ly0rd7hlCSJbsPi6NTX8+pVrVUz+ZFRTnszP/K2QQRE+DkncweKDYrmlqTrnJK3gsKjQ+/FR+feOy/aE5vYnh7DOrd+2ypLaPUaRk0f5JH3rH6TexHaMbD121ZZIiwm2LImkoeRJInx9w5Do9e0fpBEgsRxCcQkRrVuvq1Aq9dw2f3DnfbSdNRtg/EL8awXe4IgeA4ReBJc2shbBxGTGNVqnWlJsoxyuObZCfi6+Zb0zZFkif6Xd0ffitNCJMmy9siAK3oiu9DOLy3Jy8/ANU+PR6VRtV69yhKRXcMYe+clrZOhE7SLD2PiwyNbPd++k3qS5AG7LTXnH4lTGNnpUqRWjuzPGDCNgR2SWzXP1jRy2iCiEiJa7WFekiRUapkpj4/F29/QKnm2NpVa5sp/jsE7wNB69SpL+AZ5c+Wjoz32nuUT6MXUZycgq+RWvWdF94pi9O1Onp7uQB26t2P83UNbPd/+V/Sm1yj3XwBfEATP5Zl3U8FjqDQqrnp8HB17RTp85JMkS6g0Kq55ZgLtEyIcm5mT6by0DLkmEYOv3uEdTmvQafA1vTH4etZUsMbCO4VwzdPjUWvVDn9Asu5cM+WJcai1nrOmiz09hndhwv3DWy2/xPHdGH3HEI8cPWIlSzKPDXuAYbGtt3bN9OQbuKbn5a2WnzOotSqueGQUkfFhjm9bZQmVRmbKY2OJ6BTi2MyczCfQi2ufmYBPoJfj29aTQaepT4/HO8Azg3lWUV3DmfrsBMsLE4ffs6BDj3Zc9cRYVBoX2QXSQXqN6sroO1ovuJY8sSfDburfavkJgiBcCBF4ElyeRqfmmqfH02PYyfWWHNA3kmQJg5+e61+YRHRvzxv+bY/BV8cl1yXiH+br0Hx8g7255Lo+Ht+Bt4rqGs71z0+yvJ134JNn5wHRXPPMBMvuRG1A77EJXPn4WDQ6xwT1JFkCCYZcn8y4e4Z61LpOzVHJKp4Y/pBtdzlHXK+yJKOSVDww+E5uSLy6xc/virR6DVP+NZYuA2IclockSXj56bn2mcuI6hrusHxciX+oL9fPmujw3eXCooO4ftbENjNlqWPPSP7x4mS8/PSOafdOnjLh0jjLPUvfNu5ZSeO7M+mhkagdFNSTZAlJkhh6Qz9G3DrQo1+UCILgGSTFFfarFoRztH9zFr9/sJaayroW2bZWkizbpfccGc+o2waj8/ashS7PhWJWyNx+nL0bskChRdYmkCTL/+k6MJpOye2R28BDfGN1NfWs+/pv0v7cZ7vOLpZ1LZcxdw4hfnBsm+xoluVXsPTd1RxOO95yJ5UgIMKPyY+MIjK+bTzEN5ZyPI031r5HSU0pZsXcYuftHBzLY8MeJCbQ89YgOxcZW7JY8flG6qrrW+ieJaEoCr1GxjP0H/3aTOD5dGaTmZSlu9n44zYUhZap15MP8Zdcm0TShO7Ictt7L1tbWceqLzexc0W67Tq7WJIsofPSMv7eocQPim2BUrqfkrxylr23hmN7c1rupBIERQYw8cERThvtmJqaSnJyMikpKfTt6zm7EwqC4Dgi8CS4neryGlZ/uZnda/ZfcKBEkiUUs0JAhB9j7hxCbFLbfCg6XUVxFTtXH6TwaMkFB0qsnwuK9KPnyM74eeg6WefjyK5sVn6+kaLjpbbr7nxZA0xdh8QyYtpAvDx0LZdzpSgKO5ens/brLVSVVF9YvUqAYpka1e/yXgy5PtnjpyyeTWVdFXO3fs1v6ctRFAXlAlZ1l5BQUNCr9dyYeDVTe12BSvbsaTVnU1VWw9pv/iZ9U+ZF37MC2/kxctogOvZo54CSupfC4yWs/HITx/flXnjbevJz7btFMOrWQQRF+jugpO4lc/sxVnz6F8Unyi7uniVB92GdGTl9EAZfvQNK6j4Us0La8n389V0K1eU1F3XP0ujU9Lu8F4Ou7oNK7by2VQSeBEE4XyLwJLitiuIqdq5IZ9uyPVQWVwEgqyTMJjuXtASyJGE2K0iSRJeB0SRN6EGHnu3a5KiRM6koqiJr5wmO7s7BZDSfmtpor6U47WeyWqZDt3Cie7cTAadGFEUhOz2X7X/sJWNTFopZsXQ8FcVuvcoqy7WKAgY/PX3GdaPnqHh8g0S9ns5kNHHw78Ok/LqLo7tOAJbNAcwm+yN2Tv9ZUJQ/yZN70X1EF3SGtjfS8UxKqktZlrGSJXuXUlhVjIRlNEhzI6FUkgqTYgIgJqADV/WYxMhOl6DXtO2HzcYqiqvYtWY/acv3UV1WA9LJEUz2HkBP+5kkS3Tu15HEMQlEdQ0X96xGCo8Vs2NlOrvXHsBYZ7JMa2omwHf6z9Q6NT2Hdab3qK4ERQW0fsFdmKIoHN19gm1L97B/y9nvWZLq5HWsgHeAgaTLutNrdAI+gV6tX3gXZjKa2L/lMNuW7eb4vlzgDP3WRj8Lbh9A34k96XZpnEtMVxSBJ0EQzpcIPAluz2wyk3OwgNxDBeQezOfE/nyqyqox1pmQVTJqnZrQjoFEdA4jIi6EdvFhHrv7T0sy1psoySmnNL+CktxySvMqMNYaLcE7WUKtVREQ5ot/mA/+YT4ERvi2+REj56K6rIYT+/PIzSwk91ABeYeLLPVqMqPSqDD46IjoHEp4pxDbP5W67U37OF+leeWcyMgj50A+Jw7kUXS8FGOdERRQaVX4BnsTGR9GeFwoEZ1DCY0OEg/wZ2Eym9iTl8H+goPsLzzEnrwMiqqKMZrrkZBQqzS08w2jW2g8XULiSAjtTKegGFGvZ2EymsnLKiQvq5DczAJyDhZQXV6DyWhGliXUWjUhHQMJjw0hPDaYiLhQvPxEEO9s6mrqyTlYYKvX3MxC6qvrMZnMqFQyGoOGiE4hhMUEn6zXEDQ65z/Au7rK0mpL23qwgJyD+eQ3umd5+elpFx9GeKcQIuJCiIgL9didAFtSSV45OfvzyDlUQM6BfIpOlGKqN4GioNKo8QvxoV2XU32BkA6BLtW2isCTIAjnSwSeBEEQBEEQBEEQhHMiAk+CIJwv8UpCEARBEARBEARBEARBcAgReBIEQRAEQRAEQRAEQRAcQgSeBEEQBEEQBEEQBEEQBIcQgSdBEARBEARBEARBEATBIUTgSRAEQRAEQRAEQRAEQXAIEXgSBEEQBEEQBEEQBEEQHEIEngRBEARBEARBEARBEASHEIEnQRAEQRAEQRAEQRAEwSHU55O4qqqKffv2OaosgiAIgiAIgiAIggvbu3dvg/8VBKFtSkhIwMvL65zSSoqiKOd64tTUVJKTky+4YIIgCIIgCIIgCIIgCIJ7S0lJoW/fvueU9rwCT2LEkyAIgiAIgiAIQttVXV1NVlYWMTExGAwGZxdHEAQncdiIJ0EQBEEQBEEQBEEQBEE4V2JxcUEQBEEQBEEQBEEQBMEhROBJEARBEARBEARBEARBcAgReBIEQRAEQRAEQRAEQRAcQgSeBEEQBEEQBEEQBEEQBIcQgSdBEARBEARBEARBEATBIUTgSRAEQRAEQRAEQRAEQXAIEXgSBEEQBEEQBEEQBEEQHEIEngRBEARBEARBEARBEASHEIEnQRAEQRAEQRAEQRAEwSFE4EkQBEEQBEEQBEEQBEFwiP8PUYieZpbBs+kAAAAASUVORK5CYII=",
+                        "text/plain": [
+                            "<Figure size 1500x390 with 10 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "<plottable.table.Table at 0x7eff759c84c0>"
+                        ]
+                    },
+                    "execution_count": 10,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "bm.plot_results_table()"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 11,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "image/png": 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",
+                        "text/plain": [
+                            "<Figure size 1500x390 with 10 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "<plottable.table.Table at 0x7eff75b6f430>"
+                        ]
+                    },
+                    "execution_count": 11,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "bm.plot_results_table(min_max_scale=False)"
+            ]
+        },
+        {
+            "attachments": {},
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "We can also access the underlying dataframes to print the results ourselves."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 12,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/html": [
+                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">                    KMeans NMI        KMeans ARI  Silhouette label  \\\n",
+                            "Embedding                                                            \n",
+                            "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.616205</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.35223</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.53138</span>   \n",
+                            "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.712114</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.423318</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.594688</span>   \n",
+                            "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.715713</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.472496</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.589948</span>   \n",
+                            "Metric Type   Bio conservation  Bio conservation  Bio conservation   \n",
+                            "\n",
+                            "                         cLISI  Silhouette batch             iLISI  \\\n",
+                            "Embedding                                                            \n",
+                            "Unintegrated           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.99968</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.865853</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.001673</span>   \n",
+                            "scANVI                     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.721484</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.008524</span>   \n",
+                            "scVI                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.732439</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.007782</span>   \n",
+                            "Metric Type   Bio conservation  Batch correction  Batch correction   \n",
+                            "\n",
+                            "                          KBET Graph connectivity    PCR comparison  \\\n",
+                            "Embedding                                                             \n",
+                            "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.337547</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.773024</span>               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>   \n",
+                            "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.459387</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.984871</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.436682</span>   \n",
+                            "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.446961</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.981359</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.414185</span>   \n",
+                            "Metric Type   Batch correction   Batch correction  Batch correction   \n",
+                            "\n",
+                            "             Batch correction Bio conservation            Total  \n",
+                            "Embedding                                                        \n",
+                            "Unintegrated         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.395619</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.624874</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.533172</span>  \n",
+                            "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.52219</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.68253</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.618394</span>  \n",
+                            "scVI                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.516545</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.694539</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.623342</span>  \n",
+                            "Metric Type   Aggregate score  Aggregate score  Aggregate score  \n",
+                            "</pre>\n"
+                        ],
+                        "text/plain": [
+                            "                    KMeans NMI        KMeans ARI  Silhouette label  \\\n",
+                            "Embedding                                                            \n",
+                            "Unintegrated          \u001b[1;36m0.616205\u001b[0m           \u001b[1;36m0.35223\u001b[0m           \u001b[1;36m0.53138\u001b[0m   \n",
+                            "scANVI                \u001b[1;36m0.712114\u001b[0m          \u001b[1;36m0.423318\u001b[0m          \u001b[1;36m0.594688\u001b[0m   \n",
+                            "scVI                  \u001b[1;36m0.715713\u001b[0m          \u001b[1;36m0.472496\u001b[0m          \u001b[1;36m0.589948\u001b[0m   \n",
+                            "Metric Type   Bio conservation  Bio conservation  Bio conservation   \n",
+                            "\n",
+                            "                         cLISI  Silhouette batch             iLISI  \\\n",
+                            "Embedding                                                            \n",
+                            "Unintegrated           \u001b[1;36m0.99968\u001b[0m          \u001b[1;36m0.865853\u001b[0m          \u001b[1;36m0.001673\u001b[0m   \n",
+                            "scANVI                     \u001b[1;36m1.0\u001b[0m          \u001b[1;36m0.721484\u001b[0m          \u001b[1;36m0.008524\u001b[0m   \n",
+                            "scVI                       \u001b[1;36m1.0\u001b[0m          \u001b[1;36m0.732439\u001b[0m          \u001b[1;36m0.007782\u001b[0m   \n",
+                            "Metric Type   Bio conservation  Batch correction  Batch correction   \n",
+                            "\n",
+                            "                          KBET Graph connectivity    PCR comparison  \\\n",
+                            "Embedding                                                             \n",
+                            "Unintegrated          \u001b[1;36m0.337547\u001b[0m           \u001b[1;36m0.773024\u001b[0m               \u001b[1;36m0.0\u001b[0m   \n",
+                            "scANVI                \u001b[1;36m0.459387\u001b[0m           \u001b[1;36m0.984871\u001b[0m          \u001b[1;36m0.436682\u001b[0m   \n",
+                            "scVI                  \u001b[1;36m0.446961\u001b[0m           \u001b[1;36m0.981359\u001b[0m          \u001b[1;36m0.414185\u001b[0m   \n",
+                            "Metric Type   Batch correction   Batch correction  Batch correction   \n",
+                            "\n",
+                            "             Batch correction Bio conservation            Total  \n",
+                            "Embedding                                                        \n",
+                            "Unintegrated         \u001b[1;36m0.395619\u001b[0m         \u001b[1;36m0.624874\u001b[0m         \u001b[1;36m0.533172\u001b[0m  \n",
+                            "scANVI                \u001b[1;36m0.52219\u001b[0m          \u001b[1;36m0.68253\u001b[0m         \u001b[1;36m0.618394\u001b[0m  \n",
+                            "scVI                 \u001b[1;36m0.516545\u001b[0m         \u001b[1;36m0.694539\u001b[0m         \u001b[1;36m0.623342\u001b[0m  \n",
+                            "Metric Type   Aggregate score  Aggregate score  Aggregate score  \n"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
+            "source": [
+                "from rich import print\n",
+                "\n",
+                "df = bm.get_results(min_max_scale=False)\n",
+                "print(df)"
+            ]
+        }
+    ],
+    "metadata": {
+        "kernelspec": {
+            "display_name": "scib-metrics",
+            "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": "3.10.6"
+        },
+        "vscode": {
+            "interpreter": {
+                "hash": "a039041fbbc3e5d1b9a7b0ef55a3584523fcf26d826fdb9093db3636ab3124e1"
+            }
+        }
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Time: 42 min 47 sec\n"
-     ]
-    }
-   ],
-   "source": [
-    "import time\n",
-    "\n",
-    "adata.obsm[\"Unintegrated\"] = adata.obsm[\"X_pca\"]\n",
-    "\n",
-    "biocons = BioConservation(isolated_labels=False)\n",
-    "\n",
-    "start = time.time()\n",
-    "bm = Benchmarker(\n",
-    "    adata,\n",
-    "    batch_key=\"sample\",\n",
-    "    label_key=\"cell_type\",\n",
-    "    embedding_obsm_keys=[\"Unintegrated\", \"scANVI\", \"scVI\"],\n",
-    "    pre_integrated_embedding_obsm_key=\"X_pca\",\n",
-    "    bio_conservation_metrics=biocons,\n",
-    "    n_jobs=-1,\n",
-    ")\n",
-    "bm.prepare(neighbor_computer=faiss_brute_force_nn)\n",
-    "bm.benchmark()\n",
-    "end = time.time()\n",
-    "print(f\"Time: {int((end - start) / 60)} min {int((end - start) % 60)} sec\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualize the results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x390 with 10 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<plottable.table.Table at 0x7eff759c84c0>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bm.plot_results_table()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x390 with 10 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<plottable.table.Table at 0x7eff75b6f430>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bm.plot_results_table(min_max_scale=False)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also access the underlying dataframes to print the results ourselves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">                    KMeans NMI        KMeans ARI  Silhouette label  \\\n",
-       "Embedding                                                            \n",
-       "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.616205</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.35223</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.53138</span>   \n",
-       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.712114</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.423318</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.594688</span>   \n",
-       "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.715713</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.472496</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.589948</span>   \n",
-       "Metric Type   Bio conservation  Bio conservation  Bio conservation   \n",
-       "\n",
-       "                         cLISI  Silhouette batch             iLISI  \\\n",
-       "Embedding                                                            \n",
-       "Unintegrated           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.99968</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.865853</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.001673</span>   \n",
-       "scANVI                     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.721484</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.008524</span>   \n",
-       "scVI                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.732439</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.007782</span>   \n",
-       "Metric Type   Bio conservation  Batch correction  Batch correction   \n",
-       "\n",
-       "                          KBET Graph connectivity    PCR comparison  \\\n",
-       "Embedding                                                             \n",
-       "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.337547</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.773024</span>               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>   \n",
-       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.459387</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.984871</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.436682</span>   \n",
-       "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.446961</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.981359</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.414185</span>   \n",
-       "Metric Type   Batch correction   Batch correction  Batch correction   \n",
-       "\n",
-       "             Batch correction Bio conservation            Total  \n",
-       "Embedding                                                        \n",
-       "Unintegrated         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.395619</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.624874</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.533172</span>  \n",
-       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.52219</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.68253</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.618394</span>  \n",
-       "scVI                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.516545</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.694539</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.623342</span>  \n",
-       "Metric Type   Aggregate score  Aggregate score  Aggregate score  \n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "                    KMeans NMI        KMeans ARI  Silhouette label  \\\n",
-       "Embedding                                                            \n",
-       "Unintegrated          \u001b[1;36m0.616205\u001b[0m           \u001b[1;36m0.35223\u001b[0m           \u001b[1;36m0.53138\u001b[0m   \n",
-       "scANVI                \u001b[1;36m0.712114\u001b[0m          \u001b[1;36m0.423318\u001b[0m          \u001b[1;36m0.594688\u001b[0m   \n",
-       "scVI                  \u001b[1;36m0.715713\u001b[0m          \u001b[1;36m0.472496\u001b[0m          \u001b[1;36m0.589948\u001b[0m   \n",
-       "Metric Type   Bio conservation  Bio conservation  Bio conservation   \n",
-       "\n",
-       "                         cLISI  Silhouette batch             iLISI  \\\n",
-       "Embedding                                                            \n",
-       "Unintegrated           \u001b[1;36m0.99968\u001b[0m          \u001b[1;36m0.865853\u001b[0m          \u001b[1;36m0.001673\u001b[0m   \n",
-       "scANVI                     \u001b[1;36m1.0\u001b[0m          \u001b[1;36m0.721484\u001b[0m          \u001b[1;36m0.008524\u001b[0m   \n",
-       "scVI                       \u001b[1;36m1.0\u001b[0m          \u001b[1;36m0.732439\u001b[0m          \u001b[1;36m0.007782\u001b[0m   \n",
-       "Metric Type   Bio conservation  Batch correction  Batch correction   \n",
-       "\n",
-       "                          KBET Graph connectivity    PCR comparison  \\\n",
-       "Embedding                                                             \n",
-       "Unintegrated          \u001b[1;36m0.337547\u001b[0m           \u001b[1;36m0.773024\u001b[0m               \u001b[1;36m0.0\u001b[0m   \n",
-       "scANVI                \u001b[1;36m0.459387\u001b[0m           \u001b[1;36m0.984871\u001b[0m          \u001b[1;36m0.436682\u001b[0m   \n",
-       "scVI                  \u001b[1;36m0.446961\u001b[0m           \u001b[1;36m0.981359\u001b[0m          \u001b[1;36m0.414185\u001b[0m   \n",
-       "Metric Type   Batch correction   Batch correction  Batch correction   \n",
-       "\n",
-       "             Batch correction Bio conservation            Total  \n",
-       "Embedding                                                        \n",
-       "Unintegrated         \u001b[1;36m0.395619\u001b[0m         \u001b[1;36m0.624874\u001b[0m         \u001b[1;36m0.533172\u001b[0m  \n",
-       "scANVI                \u001b[1;36m0.52219\u001b[0m          \u001b[1;36m0.68253\u001b[0m         \u001b[1;36m0.618394\u001b[0m  \n",
-       "scVI                 \u001b[1;36m0.516545\u001b[0m         \u001b[1;36m0.694539\u001b[0m         \u001b[1;36m0.623342\u001b[0m  \n",
-       "Metric Type   Aggregate score  Aggregate score  Aggregate score  \n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from rich import print\n",
-    "\n",
-    "df = bm.get_results(min_max_scale=False)\n",
-    "print(df)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "scib-metrics",
-   "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": "3.10.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "a039041fbbc3e5d1b9a7b0ef55a3584523fcf26d826fdb9093db3636ab3124e1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+    "nbformat": 4,
+    "nbformat_minor": 4
 }
diff --git a/pyproject.toml b/pyproject.toml
index ed48fdf..f171cb7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,13 +9,9 @@ version = "0.4.1"
 description = "Accelerated and Python-only scIB metrics"
 readme = "README.md"
 requires-python = ">=3.9"
-license = {file = "LICENSE"}
-authors = [
-    {name = "Adam Gayoso"},
-]
-maintainers = [
-    {name = "Adam Gayoso", email = "adamgayoso@berkeley.edu"},
-]
+license = { file = "LICENSE" }
+authors = [{ name = "Adam Gayoso" }]
+maintainers = [{ name = "Adam Gayoso", email = "adamgayoso@berkeley.edu" }]
 urls.Documentation = "https://scib-metrics.readthedocs.io/"
 urls.Source = "https://github.com/yoseflab/scib-metrics"
 urls.Home-page = "https://github.com/yoseflab/scib-metrics"
@@ -35,13 +31,14 @@ dependencies = [
     "matplotlib",
     "plottable",
     "tqdm",
+    "umap>=0.5.0",
 ]
 
 [project.optional-dependencies]
 dev = [
     # CLI for bumping the version number
     "bump2version",
-    "pre-commit"
+    "pre-commit",
 ]
 doc = [
     "sphinx>=4",
@@ -66,20 +63,18 @@ test = [
     "black",
     "numba>=0.57.1",
 ]
-parallel = [
-    "joblib"
-]
+parallel = ["joblib"]
 tutorial = [
     "rich",
     "scanorama",
     "harmony-pytorch",
     "scvi-tools",
     "pyliger",
-    "numexpr", # missing liger dependency
-    "plotnine", # missing liger dependency
-    "mygene", # missing liger dependency
-    "goatools", # missing liger dependency
-    "adjustText", # missing liger dependency
+    "numexpr",         # missing liger dependency
+    "plotnine",        # missing liger dependency
+    "mygene",          # missing liger dependency
+    "goatools",        # missing liger dependency
+    "adjustText",      # missing liger dependency
 ]
 
 [tool.hatch.build.targets.wheel]
@@ -87,9 +82,7 @@ packages = ['src/scib_metrics']
 
 [tool.coverage.run]
 source = ["scib_metrics"]
-omit = [
-    "**/test_*.py",
-]
+omit = ["**/test_*.py"]
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]
@@ -99,17 +92,17 @@ xfail_strict = true
 src = ["src"]
 line-length = 120
 select = [
-    "F",  # Errors detected by Pyflakes
-    "E",  # Error detected by Pycodestyle
-    "W",  # Warning detected by Pycodestyle
-    "I",  # isort
-    "D",  # pydocstyle
-    "B",  # flake8-bugbear
-    "TID",  # flake8-tidy-imports
-    "C4",  # flake8-comprehensions
-    "BLE",  # flake8-blind-except
-    "UP",  # pyupgrade
-    "RUF100",  # Report unused noqa directives
+    "F",      # Errors detected by Pyflakes
+    "E",      # Error detected by Pycodestyle
+    "W",      # Warning detected by Pycodestyle
+    "I",      # isort
+    "D",      # pydocstyle
+    "B",      # flake8-bugbear
+    "TID",    # flake8-tidy-imports
+    "C4",     # flake8-comprehensions
+    "BLE",    # flake8-blind-except
+    "UP",     # pyupgrade
+    "RUF100", # Report unused noqa directives
 ]
 ignore = [
     # line too long -> we accept long comment lines; black gets rid of long code lines
@@ -128,8 +121,7 @@ ignore = [
     "B008",
     # __magic__ methods are are often self-explanatory, allow missing docstrings
     "D105",
-    # first line should end with a period [Bug: doesn't work with single-line docstrings]
-    "D400",
+    # first line should end with a period [Bug: doesn't work with single-line docstrings]    "D400",
     # First line should be in imperative mood; try rephrasing
     "D401",
     ## Disable one in each pair of mutually incompatible rules
@@ -185,5 +177,5 @@ skip = [
     "docs/changelog.md",
     "docs/references.bib",
     "docs/references.md",
-    "docs/notebooks/example.ipynb"
+    "docs/notebooks/example.ipynb",
 ]
diff --git a/src/scib_metrics/_graph_connectivity.py b/src/scib_metrics/_graph_connectivity.py
index 6d2c5e9..3699ccf 100644
--- a/src/scib_metrics/_graph_connectivity.py
+++ b/src/scib_metrics/_graph_connectivity.py
@@ -1,10 +1,11 @@
 import numpy as np
 import pandas as pd
-from scipy.sparse import csr_matrix
 from scipy.sparse.csgraph import connected_components
 
+from scib_metrics.nearest_neighbors import NeighborsResults
 
-def graph_connectivity(X: csr_matrix, labels: np.ndarray) -> float:
+
+def graph_connectivity(X: NeighborsResults, labels: np.ndarray) -> float:
     """Quantify the connectivity of the subgraph per cell type label.
 
     Parameters
@@ -19,9 +20,11 @@ def graph_connectivity(X: csr_matrix, labels: np.ndarray) -> float:
     # TODO(adamgayoso): Utils for validating inputs
     clust_res = []
 
+    graph = X.knn_graph_distances
+
     for label in np.unique(labels):
         mask = labels == label
-        graph_sub = X[mask]
+        graph_sub = graph[mask]
         graph_sub = graph_sub[:, mask]
         _, comps = connected_components(graph_sub, connection="strong")
         tab = pd.value_counts(comps)
diff --git a/src/scib_metrics/_kbet.py b/src/scib_metrics/_kbet.py
index ca415a7..a1649df 100644
--- a/src/scib_metrics/_kbet.py
+++ b/src/scib_metrics/_kbet.py
@@ -8,9 +8,9 @@
 import numpy as np
 import pandas as pd
 import scipy
-from scipy.sparse import csr_matrix
 
-from scib_metrics.utils import convert_knn_graph_to_idx, diffusion_nn, get_ndarray
+from scib_metrics.nearest_neighbors import NeighborsResults
+from scib_metrics.utils import diffusion_nn, get_ndarray
 
 from ._types import NdArray
 
@@ -40,7 +40,7 @@ def _kbet(neigh_batch_ids: jnp.ndarray, batches: jnp.ndarray, n_batches: int) ->
     return test_statistics, p_values
 
 
-def kbet(X: csr_matrix, batches: np.ndarray, alpha: float = 0.05) -> float:
+def kbet(X: NeighborsResults, batches: np.ndarray, alpha: float = 0.05) -> float:
     """Compute kbet :cite:p:`buttner2018`.
 
     This implementation is inspired by the implementation in Pegasus:
@@ -57,8 +57,7 @@ def kbet(X: csr_matrix, batches: np.ndarray, alpha: float = 0.05) -> float:
     Parameters
     ----------
     X
-        Array of shape (n_cells, n_cells) with non-zero values
-        representing distances to exactly each cell's k nearest neighbors.
+        A :class:`~scib_metrics.utils.nearest_neighbors.NeighborsResults` object.
     batches
         Array of shape (n_cells,) representing batch values
         for each cell.
@@ -73,16 +72,10 @@ def kbet(X: csr_matrix, batches: np.ndarray, alpha: float = 0.05) -> float:
         Mean Kbet chi-square statistic over all cells.
     pvalue_mean
         Mean Kbet p-value over all cells.
-
-    Notes
-    -----
-    This function requires X to be cell-cell distances, not connectivies.
     """
-    if len(batches) != X.shape[0]:
+    if len(batches) != len(X.indices):
         raise ValueError("Length of batches does not match number of cells.")
-    _, knn_idx = convert_knn_graph_to_idx(X)
-    # Make sure self is included
-    knn_idx = np.concatenate([np.arange(knn_idx.shape[0])[:, None], knn_idx], axis=1)
+    knn_idx = X.indices
     batches = np.asarray(pd.Categorical(batches).codes)
     neigh_batch_ids = batches[knn_idx]
     chex.assert_equal_shape([neigh_batch_ids, knn_idx])
@@ -96,7 +89,7 @@ def kbet(X: csr_matrix, batches: np.ndarray, alpha: float = 0.05) -> float:
 
 
 def kbet_per_label(
-    X: csr_matrix,
+    X: NeighborsResults,
     batches: np.ndarray,
     labels: np.ndarray,
     alpha: float = 0.05,
@@ -113,8 +106,7 @@ def kbet_per_label(
     Parameters
     ----------
     X
-        Array of shape (n_cells, n_cells) with non-zero values
-        representing connectivies to exactly each cell's k nearest neighbors.
+        A :class:`~scib_metrics.utils.nearest_neighbors.NeighborsResults` object.
     batches
         Array of shape (n_cells,) representing batch values
         for each cell.
@@ -136,23 +128,25 @@ def kbet_per_label(
     -----
     This function requires X to be cell-cell connectivities, not distances.
     """
-    if len(batches) != X.shape[0]:
+    if len(batches) != len(X.indices):
         raise ValueError("Length of batches does not match number of cells.")
-    if len(labels) != X.shape[0]:
+    if len(labels) != len(X.indices):
         raise ValueError("Length of labels does not match number of cells.")
     # set upper bound for k0
     size_max = 2**31 - 1
     batches = np.asarray(pd.Categorical(batches).codes)
     labels = np.asarray(labels)
 
+    conn_graph = X.knn_graph_connectivities
+
     # prepare call of kBET per cluster
     kbet_scores = {"cluster": [], "kBET": []}
     for clus in np.unique(labels):
         # subset by label
         mask = labels == clus
-        X_sub = X[mask, :][:, mask]
-        X_sub.sort_indices()
-        n_obs = X_sub.shape[0]
+        conn_graph_sub = conn_graph[mask, :][:, mask]
+        conn_graph_sub.sort_indices()
+        n_obs = conn_graph_sub.shape[0]
         batches_sub = batches[mask]
 
         # check if neighborhood size too small or only one batch in subset
@@ -166,12 +160,12 @@ def kbet_per_label(
             if k0 * n_obs >= size_max:
                 k0 = np.floor(size_max / n_obs).astype("int")
 
-            n_comp, labs = scipy.sparse.csgraph.connected_components(X_sub, connection="strong")
+            n_comp, labs = scipy.sparse.csgraph.connected_components(conn_graph, connection="strong")
 
             if n_comp == 1:  # a single component to compute kBET on
                 try:
                     diffusion_n_comps = np.min([diffusion_n_comps, n_obs - 1])
-                    nn_graph_sub = diffusion_nn(X_sub, k=k0, n_comps=diffusion_n_comps).astype("float")
+                    nn_graph_sub = diffusion_nn(conn_graph, k=k0, n_comps=diffusion_n_comps).astype("float")
                     # call kBET
                     score, _, _ = kbet(
                         nn_graph_sub,
@@ -192,15 +186,17 @@ def kbet_per_label(
                 # check if 75% of all cells can be used for kBET run
                 if len(idx_nonan) / len(labs) >= 0.75:
                     # create another subset of components, assume they are not visited in a diffusion process
-                    X_sub_sub = X_sub[idx_nonan, :][:, idx_nonan]
-                    X_sub_sub.sort_indices()
+                    conn_graph_sub_sub_sub = conn_graph_sub[idx_nonan, :][:, idx_nonan]
+                    conn_graph_sub_sub_sub.sort_indices()
 
                     try:
-                        diffusion_n_comps = np.min([diffusion_n_comps, X_sub_sub.shape[0] - 1])
-                        nn_graph_sub_sub = diffusion_nn(X_sub_sub, k=k0, n_comps=diffusion_n_comps).astype("float")
+                        diffusion_n_comps = np.min([diffusion_n_comps, conn_graph_sub_sub_sub.shape[0] - 1])
+                        nn_results_sub_sub = diffusion_nn(
+                            conn_graph_sub_sub_sub, k=k0, n_comps=diffusion_n_comps
+                        ).astype("float")
                         # call kBET
                         score, _, _ = kbet(
-                            nn_graph_sub_sub,
+                            nn_results_sub_sub,
                             batches=batches_sub[idx_nonan],
                             alpha=alpha,
                         )
diff --git a/src/scib_metrics/_lisi.py b/src/scib_metrics/_lisi.py
index 3d4e85e..9e9c47a 100644
--- a/src/scib_metrics/_lisi.py
+++ b/src/scib_metrics/_lisi.py
@@ -1,18 +1,17 @@
 import numpy as np
 import pandas as pd
-from scipy.sparse import csr_matrix
 
-from scib_metrics.utils import compute_simpson_index, convert_knn_graph_to_idx
+from scib_metrics.nearest_neighbors import NeighborsResults
+from scib_metrics.utils import compute_simpson_index
 
 
-def lisi_knn(X: csr_matrix, labels: np.ndarray, perplexity: float = None) -> np.ndarray:
+def lisi_knn(X: NeighborsResults, labels: np.ndarray, perplexity: float = None) -> np.ndarray:
     """Compute the local inverse simpson index (LISI) for each cell :cite:p:`korsunsky2019harmony`.
 
     Parameters
     ----------
     X
-        Array of shape (n_cells, n_cells) with non-zero values
-        representing distances to exactly each cell's k nearest neighbors.
+        A :class:`~scib_metrics.utils.nearest_neighbors.NeighborsResults` object.
     labels
         Array of shape (n_cells,) representing label values
         for each cell.
@@ -26,7 +25,8 @@ def lisi_knn(X: csr_matrix, labels: np.ndarray, perplexity: float = None) -> np.
         Array of shape (n_cells,) with the LISI score for each cell.
     """
     labels = np.asarray(pd.Categorical(labels).codes)
-    knn_dists, knn_idx = convert_knn_graph_to_idx(X)
+    # Drop self edge
+    knn_dists, knn_idx = X.distances[:, 1:], X.indices[:, 1:]
 
     if perplexity is None:
         perplexity = np.floor(knn_idx.shape[1] / 3)
@@ -37,7 +37,7 @@ def lisi_knn(X: csr_matrix, labels: np.ndarray, perplexity: float = None) -> np.
     return 1 / simpson
 
 
-def ilisi_knn(X: csr_matrix, batches: np.ndarray, perplexity: float = None, scale: bool = True) -> np.ndarray:
+def ilisi_knn(X: NeighborsResults, batches: np.ndarray, perplexity: float = None, scale: bool = True) -> np.ndarray:
     """Compute the integration local inverse simpson index (iLISI) for each cell :cite:p:`korsunsky2019harmony`.
 
     Returns a scaled version of the iLISI score for each cell, by default :cite:p:`luecken2022benchmarking`.
@@ -45,8 +45,7 @@ def ilisi_knn(X: csr_matrix, batches: np.ndarray, perplexity: float = None, scal
     Parameters
     ----------
     X
-        Array of shape (n_cells, n_cells) with non-zero values
-        representing distances to exactly each cell's k nearest neighbors.
+        A :class:`~scib_metrics.utils.nearest_neighbors.NeighborsResults` object.
     batches
         Array of shape (n_cells,) representing batch values
         for each cell.
@@ -70,7 +69,7 @@ def ilisi_knn(X: csr_matrix, batches: np.ndarray, perplexity: float = None, scal
     return ilisi
 
 
-def clisi_knn(X: csr_matrix, labels: np.ndarray, perplexity: float = None, scale: bool = True) -> np.ndarray:
+def clisi_knn(X: NeighborsResults, labels: np.ndarray, perplexity: float = None, scale: bool = True) -> np.ndarray:
     """Compute the cell-type local inverse simpson index (cLISI) for each cell :cite:p:`korsunsky2019harmony`.
 
     Returns a scaled version of the cLISI score for each cell, by default :cite:p:`luecken2022benchmarking`.
@@ -78,8 +77,7 @@ def clisi_knn(X: csr_matrix, labels: np.ndarray, perplexity: float = None, scale
     Parameters
     ----------
     X
-        Array of shape (n_cells, n_cells) with non-zero values
-        representing distances to exactly each cell's k nearest neighbors.
+        A :class:`~scib_metrics.utils.nearest_neighbors.NeighborsResults` object.
     labels
         Array of shape (n_cells,) representing cell type label values
         for each cell.
diff --git a/src/scib_metrics/_nmi_ari.py b/src/scib_metrics/_nmi_ari.py
index 371b293..574c189 100644
--- a/src/scib_metrics/_nmi_ari.py
+++ b/src/scib_metrics/_nmi_ari.py
@@ -1,13 +1,14 @@
 import logging
 import warnings
 
+import igraph
 import numpy as np
-import scanpy as sc
 from scipy.sparse import spmatrix
 from sklearn.metrics.cluster import adjusted_rand_score, normalized_mutual_info_score
 from sklearn.utils import check_array
 
-from .utils import KMeans, check_square
+from .nearest_neighbors import NeighborsResults
+from .utils import KMeans
 
 logger = logging.getLogger(__name__)
 
@@ -19,14 +20,15 @@ def _compute_clustering_kmeans(X: np.ndarray, n_clusters: int) -> np.ndarray:
 
 
 def _compute_clustering_leiden(connectivity_graph: spmatrix, resolution: float) -> np.ndarray:
-    g = sc._utils.get_igraph_from_adjacency(connectivity_graph)
+    # The connectivity graph with the umap method is symmetric
+    g = igraph.Graph.Weighted_Adjacency(connectivity_graph, mode="undirected")
     clustering = g.community_leiden(objective_function="modularity", weights="weight", resolution_parameter=resolution)
     clusters = clustering.membership
     return np.asarray(clusters)
 
 
 def _compute_nmi_ari_cluster_labels(
-    X: np.ndarray,
+    X: spmatrix,
     labels: np.ndarray,
     resolution: float = 1.0,
 ) -> tuple[float, float]:
@@ -67,7 +69,7 @@ def nmi_ari_cluster_labels_kmeans(X: np.ndarray, labels: np.ndarray) -> dict[str
 
 
 def nmi_ari_cluster_labels_leiden(
-    X: spmatrix, labels: np.ndarray, optimize_resolution: bool = True, resolution: float = 1.0, n_jobs: int = 1
+    X: NeighborsResults, labels: np.ndarray, optimize_resolution: bool = True, resolution: float = 1.0, n_jobs: int = 1
 ) -> dict[str, float]:
     """Compute nmi and ari between leiden clusters and labels.
 
@@ -78,9 +80,7 @@ def nmi_ari_cluster_labels_leiden(
     Parameters
     ----------
     X
-        Array of shape (n_cells, n_cells) representing a connectivity graph.
-        Values should represent weights between pairs of neighbors, with a higher weight
-        indicating more connected.
+        A :class:`~scib_metrics.utils.nearest_neighbors.NeighborsResults` object.
     labels
         Array of shape (n_cells,) representing label values
     optimize_resolution
@@ -99,23 +99,24 @@ def nmi_ari_cluster_labels_leiden(
     ari
         Adjusted rand index score
     """
-    X = check_array(X, accept_sparse=True, ensure_2d=True)
-    check_square(X)
+    conn_graph = X.knn_graph_connectivities
     if optimize_resolution:
         n = 10
         resolutions = np.array([2 * x / n for x in range(1, n + 1)])
         try:
             from joblib import Parallel, delayed
 
-            out = Parallel(n_jobs=n_jobs)(delayed(_compute_nmi_ari_cluster_labels)(X, labels, r) for r in resolutions)
+            out = Parallel(n_jobs=n_jobs)(
+                delayed(_compute_nmi_ari_cluster_labels)(conn_graph, labels, r) for r in resolutions
+            )
         except ImportError:
             warnings.warn("Using for loop over clustering resolutions. `pip install joblib` for parallelization.")
-            out = [_compute_nmi_ari_cluster_labels(X, labels, r) for r in resolutions]
+            out = [_compute_nmi_ari_cluster_labels(conn_graph, labels, r) for r in resolutions]
         nmi_ari = np.array(out)
         nmi_ind = np.argmax(nmi_ari[:, 0])
         nmi, ari = nmi_ari[nmi_ind, :]
         return {"nmi": nmi, "ari": ari}
     else:
-        nmi, ari = _compute_nmi_ari_cluster_labels(X, labels, resolution)
+        nmi, ari = _compute_nmi_ari_cluster_labels(conn_graph, labels, resolution)
 
     return {"nmi": nmi, "ari": ari}
diff --git a/src/scib_metrics/benchmark/_core.py b/src/scib_metrics/benchmark/_core.py
index f0c1fcb..fe85143 100644
--- a/src/scib_metrics/benchmark/_core.py
+++ b/src/scib_metrics/benchmark/_core.py
@@ -18,7 +18,7 @@
 from tqdm import tqdm
 
 import scib_metrics
-from scib_metrics.nearest_neighbors import NeighborsOutput, pynndescent
+from scib_metrics.nearest_neighbors import NeighborsResults, pynndescent
 
 Kwargs = dict[str, Any]
 MetricType = Union[bool, Kwargs]
@@ -82,15 +82,15 @@ class MetricAnnDataAPI(Enum):
     """Specification of the AnnData API for a metric."""
 
     isolated_labels = lambda ad, fn: fn(ad.X, ad.obs[_LABELS], ad.obs[_BATCH])
-    nmi_ari_cluster_labels_leiden = lambda ad, fn: fn(ad.obsp["15_connectivities"], ad.obs[_LABELS])
+    nmi_ari_cluster_labels_leiden = lambda ad, fn: fn(ad.obsp["15_neighbor_res"], ad.obs[_LABELS])
     nmi_ari_cluster_labels_kmeans = lambda ad, fn: fn(ad.X, ad.obs[_LABELS])
     silhouette_label = lambda ad, fn: fn(ad.X, ad.obs[_LABELS])
-    clisi_knn = lambda ad, fn: fn(ad.obsp["90_distances"], ad.obs[_LABELS])
-    graph_connectivity = lambda ad, fn: fn(ad.obsp["15_distances"], ad.obs[_LABELS])
+    clisi_knn = lambda ad, fn: fn(ad.obsp["90_neighbor_res"], ad.obs[_LABELS])
+    graph_connectivity = lambda ad, fn: fn(ad.obsp["15_neighbor_res"], ad.obs[_LABELS])
     silhouette_batch = lambda ad, fn: fn(ad.X, ad.obs[_LABELS], ad.obs[_BATCH])
     pcr_comparison = lambda ad, fn: fn(ad.obsm[_X_PRE], ad.X, ad.obs[_BATCH], categorical=True)
-    ilisi_knn = lambda ad, fn: fn(ad.obsp["90_distances"], ad.obs[_BATCH])
-    kbet_per_label = lambda ad, fn: fn(ad.obsp["50_connectivities"], ad.obs[_BATCH], ad.obs[_LABELS])
+    ilisi_knn = lambda ad, fn: fn(ad.obsp["90_neighbor_res"], ad.obs[_BATCH])
+    kbet_per_label = lambda ad, fn: fn(ad.obsp["50_neighbor_res"], ad.obs[_BATCH], ad.obs[_LABELS])
 
 
 class Benchmarker:
@@ -156,7 +156,7 @@ def __init__(
             "Batch correction": self._batch_correction_metrics,
         }
 
-    def prepare(self, neighbor_computer: Optional[Callable[[np.ndarray, int], NeighborsOutput]] = None) -> None:
+    def prepare(self, neighbor_computer: Optional[Callable[[np.ndarray, int], NeighborsResults]] = None) -> None:
         """Prepare the data for benchmarking.
 
         Parameters
@@ -164,7 +164,7 @@ def prepare(self, neighbor_computer: Optional[Callable[[np.ndarray, int], Neighb
         neighbor_computer
             Function that computes the neighbors of the data. If `None`, the neighbors will be computed
             with :func:`~scib_metrics.utils.nearest_neighbors.pynndescent`. The function should take as input
-            the data and the number of neighbors to compute and return a :class:`~scib_metrics.utils.nearest_neighbors.NeighborsOutput`
+            the data and the number of neighbors to compute and return a :class:`~scib_metrics.utils.nearest_neighbors.NeighborsResults`
             object.
         """
         # Compute PCA
@@ -183,18 +183,13 @@ def prepare(self, neighbor_computer: Optional[Callable[[np.ndarray, int], Neighb
         # Compute neighbors
         for ad in tqdm(self._emb_adatas.values(), desc="Computing neighbors"):
             if neighbor_computer is not None:
-                neigh_output = neighbor_computer(ad.X, max(self._neighbor_values))
+                neigh_result = neighbor_computer(ad.X, max(self._neighbor_values))
             else:
-                neigh_output = pynndescent(
+                neigh_result = pynndescent(
                     ad.X, n_neighbors=max(self._neighbor_values), random_state=0, n_jobs=self._n_jobs
                 )
-            indices, distances = neigh_output.indices, neigh_output.distances
             for n in self._neighbor_values:
-                sp_distances, sp_conns = sc.neighbors._compute_connectivities_umap(
-                    indices[:, :n], distances[:, :n], ad.n_obs, n_neighbors=n
-                )
-                ad.obsp[f"{n}_connectivities"] = sp_conns
-                ad.obsp[f"{n}_distances"] = sp_distances
+                ad.obsp[f"{n}_neighbor_res"] = neigh_result
 
         self._prepared = True
 
diff --git a/src/scib_metrics/nearest_neighbors/__init__.py b/src/scib_metrics/nearest_neighbors/__init__.py
index 9b0529c..f8b6df1 100644
--- a/src/scib_metrics/nearest_neighbors/__init__.py
+++ b/src/scib_metrics/nearest_neighbors/__init__.py
@@ -1,9 +1,9 @@
-from ._dataclass import NeighborsOutput
+from ._dataclass import NeighborsResults
 from ._jax import jax_approx_min_k
 from ._pynndescent import pynndescent
 
 __all__ = [
     "pynndescent",
     "jax_approx_min_k",
-    "NeighborsOutput",
+    "NeighborsResults",
 ]
diff --git a/src/scib_metrics/nearest_neighbors/_dataclass.py b/src/scib_metrics/nearest_neighbors/_dataclass.py
index 0e1d986..95ecdd9 100644
--- a/src/scib_metrics/nearest_neighbors/_dataclass.py
+++ b/src/scib_metrics/nearest_neighbors/_dataclass.py
@@ -1,19 +1,74 @@
 from dataclasses import dataclass
+from functools import cached_property
 
+import chex
 import numpy as np
+from scipy.sparse import coo_matrix, csr_matrix
+from sklearn.neighbors import NearestNeighbors
+from umap.umap_ import fuzzy_simplicial_set
 
 
 @dataclass
-class NeighborsOutput:
-    """Output of the nearest neighbors function.
+class NeighborsResults:
+    """Nearest neighbors results data store.
 
     Attributes
     ----------
     distances : np.ndarray
         Array of distances to the nearest neighbors.
     indices : np.ndarray
-        Array of indices of the nearest neighbors.
+        Array of indices of the nearest neighbors. Self should always
+        be included here.
     """
 
     indices: np.ndarray
     distances: np.ndarray
+
+    def __post_init__(self):
+        chex.assert_equal_shape([self.indices, self.distances])
+        # Guarantee that the first neighbor is self.
+        # This is a simple and lightweight way to ensure this.
+        nn_obj = NearestNeighbors(n_neighbors=self.n_neighbors, metric="precomputed").fit(self.knn_graph_distances)
+        self.distances, self.indices = nn_obj.kneighbors(self.knn_graph_distances)
+
+    @property
+    def n_samples(self):
+        return self.indices.shape[0]
+
+    @property
+    def n_neighbors(self):
+        return self.indices.shape[1]
+
+    @cached_property
+    def knn_graph_distances(self) -> csr_matrix:
+        """Return the sparse weighted adjacency matrix."""
+        n_samples, n_neighbors = self.indices.shape
+        # Efficient creation of row pointer
+        rowptr = np.arange(0, n_samples * n_neighbors + 1, n_neighbors)
+        # Create CSR matrix
+        return csr_matrix((self.distances.ravel(), self.indices.ravel(), rowptr), shape=(n_samples, n_samples))
+
+    @cached_property
+    def knn_graph_connectivities(self) -> csr_matrix:
+        """Compute connectivities using the UMAP approach.
+
+        This function computes connectivities (similarities) from distances
+        using the approach from the UMAP method, which is also used by scanpy.
+        """
+        conn_graph = coo_matrix(([], ([], [])), shape=(self.n_samples, 1))
+        connectivities = fuzzy_simplicial_set(
+            conn_graph,
+            n_neighbors=self.n_neighbors,
+            random_state=None,
+            metric=None,
+            knn_indices=self.indices,
+            knn_dists=self.distances,
+            set_op_mix_ratio=1.0,
+            local_connectivity=1.0,
+        )
+        return connectivities[0]
+
+    def subset_neighbors(self, n: int) -> "NeighborsResults":
+        if n > self.n_neighbors:
+            raise ValueError("n must be smaller than the number of neighbors")
+        return self.__class__(self.distances[:, :n], self.indices[:, :n])
diff --git a/src/scib_metrics/nearest_neighbors/_jax.py b/src/scib_metrics/nearest_neighbors/_jax.py
index 16be73b..9ac8f95 100644
--- a/src/scib_metrics/nearest_neighbors/_jax.py
+++ b/src/scib_metrics/nearest_neighbors/_jax.py
@@ -6,7 +6,7 @@
 
 from scib_metrics.utils import cdist, get_ndarray
 
-from ._dataclass import NeighborsOutput
+from ._dataclass import NeighborsResults
 
 
 @functools.partial(jax.jit, static_argnames=["k", "recall_target"])
@@ -18,7 +18,7 @@ def _euclidean_ann(qy: jnp.ndarray, db: jnp.ndarray, k: int, recall_target: floa
 
 def jax_approx_min_k(
     X: np.ndarray, n_neighbors: int, recall_target: float = 0.95, chunk_size: int = 2048
-) -> NeighborsOutput:
+) -> NeighborsResults:
     """Run approximate nearest neighbor search using jax.
 
     On TPU backends, this is approximate nearest neighbor search. On other backends, this is exact nearest neighbor search.
@@ -47,4 +47,4 @@ def jax_approx_min_k(
         dists.append(dist)
     neighbors = jnp.concatenate(neighbors, axis=0)
     dists = jnp.concatenate(dists, axis=0)
-    return NeighborsOutput(indices=get_ndarray(neighbors), distances=get_ndarray(dists))
+    return NeighborsResults(indices=get_ndarray(neighbors), distances=get_ndarray(dists))
diff --git a/src/scib_metrics/nearest_neighbors/_pynndescent.py b/src/scib_metrics/nearest_neighbors/_pynndescent.py
index 7fb3ee6..975aaf2 100644
--- a/src/scib_metrics/nearest_neighbors/_pynndescent.py
+++ b/src/scib_metrics/nearest_neighbors/_pynndescent.py
@@ -1,10 +1,10 @@
 import numpy as np
 from pynndescent import NNDescent
 
-from ._dataclass import NeighborsOutput
+from ._dataclass import NeighborsResults
 
 
-def pynndescent(X: np.ndarray, n_neighbors: int, random_state: int = 0, n_jobs: int = 1) -> NeighborsOutput:
+def pynndescent(X: np.ndarray, n_neighbors: int, random_state: int = 0, n_jobs: int = 1) -> NeighborsResults:
     """Run pynndescent approximate nearest neighbor search.
 
     Parameters
@@ -37,4 +37,4 @@ def pynndescent(X: np.ndarray, n_neighbors: int, random_state: int = 0, n_jobs:
     )
     indices, distances = knn_search_index.neighbor_graph
 
-    return NeighborsOutput(indices=indices, distances=distances)
+    return NeighborsResults(indices=indices, distances=distances)
diff --git a/src/scib_metrics/utils/_diffusion_nn.py b/src/scib_metrics/utils/_diffusion_nn.py
index 8c4c9a8..fbd6714 100644
--- a/src/scib_metrics/utils/_diffusion_nn.py
+++ b/src/scib_metrics/utils/_diffusion_nn.py
@@ -2,12 +2,15 @@
 from typing import Literal
 
 import numpy as np
-import pynndescent
 import scipy
 from scipy.sparse import csr_matrix, issparse
 
+from scib_metrics import nearest_neighbors
+
 logger = logging.getLogger(__name__)
 
+_EPS = 1e-8
+
 
 def _compute_transitions(X: csr_matrix, density_normalize: bool = True):
     """Code from scanpy.
@@ -88,7 +91,7 @@ def _get_sparse_matrix_from_indices_distances_numpy(indices, distances, n_obs, n
     return D
 
 
-def diffusion_nn(X: csr_matrix, k: int, n_comps: int = 100):
+def diffusion_nn(X: csr_matrix, k: int, n_comps: int = 100) -> nearest_neighbors.NeighborsResults:
     """Diffusion-based neighbors.
 
     This function generates a nearest neighbour list from a connectivities matrix.
@@ -110,20 +113,15 @@ def diffusion_nn(X: csr_matrix, k: int, n_comps: int = 100):
 
     Returns
     -------
-    Neighbors graph
+    Neighbors results
     """
     transitions = _compute_transitions(X)
     evals, evecs = _compute_eigen(transitions, n_comps=n_comps)
-    evals += 1e-8  # Avoid division by zero
+    evals += _EPS  # Avoid division by zero
     # Multiscale such that the number of steps t gets "integrated out"
     embedding = evecs
     scaled_evals = np.array([e if e == 1 else e / (1 - e) for e in evals])
     embedding *= scaled_evals
-    nn_obj = pynndescent.NNDescent(embedding, n_neighbors=k + 1)
-    neigh_inds, neigh_distances = nn_obj.neighbor_graph
-    # We purposely ignore the first neighbor as it is the cell itself
-    # It gets added back inside the kbet internal function
-    neigh_graph = _get_sparse_matrix_from_indices_distances_numpy(
-        neigh_inds[:, 1:], neigh_distances[:, 1:], X.shape[0], k
-    )
-    return neigh_graph
+    nn_result = nearest_neighbors.pynndescent(embedding, n_neighbors=k + 1)
+
+    return nn_result
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 72eb7e3..39d0e5f 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -4,7 +4,6 @@
 import pandas as pd
 from harmonypy import compute_lisi as harmonypy_lisi
 from scib.metrics import isolated_labels_asw
-from scipy.sparse import csr_matrix
 from scipy.spatial.distance import cdist as sp_cdist
 from scipy.spatial.distance import pdist, squareform
 from sklearn.cluster import KMeans as SKMeans
@@ -14,6 +13,7 @@
 from sklearn.neighbors import NearestNeighbors
 
 import scib_metrics
+from scib_metrics.nearest_neighbors import NeighborsResults
 from tests.utils.data import dummy_x_labels, dummy_x_labels_batch
 
 scib_metrics.settings.jax_fix_no_kernel_image()
@@ -65,11 +65,10 @@ def test_compute_simpson_index():
 
 def test_lisi_knn():
     X, labels = dummy_x_labels()
-    dist_mat = csr_matrix(scib_metrics.utils.cdist(X, X))
     nbrs = NearestNeighbors(n_neighbors=30, algorithm="kd_tree").fit(X)
-    knn_graph = nbrs.kneighbors_graph(X)
-    knn_graph = knn_graph.multiply(dist_mat)
-    lisi_res = scib_metrics.lisi_knn(knn_graph, labels, perplexity=10)
+    dists, inds = nbrs.kneighbors(X)
+    neigh_results = NeighborsResults(indices=inds, distances=dists)
+    lisi_res = scib_metrics.lisi_knn(neigh_results, labels, perplexity=10)
     harmonypy_lisi_res = harmonypy_lisi(
         X, pd.DataFrame(labels, columns=["labels"]), label_colnames=["labels"], perplexity=10
     )[:, 0]
@@ -77,7 +76,7 @@ def test_lisi_knn():
 
 
 def test_ilisi_clisi_knn():
-    X, labels, batches = dummy_x_labels_batch(x_is_neighbors_graph=True)
+    X, labels, batches = dummy_x_labels_batch(x_is_neighbors_results=True)
     scib_metrics.ilisi_knn(X, batches, perplexity=10)
     scib_metrics.clisi_knn(X, labels, perplexity=10)
 
@@ -91,7 +90,7 @@ def test_nmi_ari_cluster_labels_kmeans():
 
 
 def test_nmi_ari_cluster_labels_leiden_parallel():
-    X, labels = dummy_x_labels(return_symmetric_positive=True)
+    X, labels = dummy_x_labels(symmetric_positive=True, x_is_neighbors_results=True)
     out = scib_metrics.nmi_ari_cluster_labels_leiden(X, labels, optimize_resolution=True, n_jobs=2)
     nmi, ari = out["nmi"], out["ari"]
     assert isinstance(nmi, float)
@@ -99,7 +98,7 @@ def test_nmi_ari_cluster_labels_leiden_parallel():
 
 
 def test_nmi_ari_cluster_labels_leiden_single_resolution():
-    X, labels = dummy_x_labels(return_symmetric_positive=True)
+    X, labels = dummy_x_labels(symmetric_positive=True, x_is_neighbors_results=True)
     out = scib_metrics.nmi_ari_cluster_labels_leiden(X, labels, optimize_resolution=False, resolution=0.1)
     nmi, ari = out["nmi"], out["ari"]
     assert isinstance(nmi, float)
@@ -144,20 +143,20 @@ def test_kmeans():
 
 
 def test_kbet():
-    X, _, batch = dummy_x_labels_batch(x_is_neighbors_graph=True)
+    X, _, batch = dummy_x_labels_batch(x_is_neighbors_results=True)
     acc_rate, stats, pvalues = scib_metrics.kbet(X, batch)
     assert isinstance(acc_rate, float)
-    assert len(stats) == X.shape[0]
-    assert len(pvalues) == X.shape[0]
+    assert len(stats) == X.indices.shape[0]
+    assert len(pvalues) == X.indices.shape[0]
 
 
 def test_kbet_per_label():
-    X, labels, batch = dummy_x_labels_batch(x_is_neighbors_graph=True)
+    X, labels, batch = dummy_x_labels_batch(x_is_neighbors_results=True)
     score = scib_metrics.kbet_per_label(X, batch, labels)
     assert isinstance(score, float)
 
 
 def test_graph_connectivity():
-    X, labels = dummy_x_labels(return_symmetric_positive=True)
+    X, labels = dummy_x_labels(symmetric_positive=True, x_is_neighbors_results=True)
     metric = scib_metrics.graph_connectivity(X, labels)
     assert isinstance(metric, float)
diff --git a/tests/utils/data.py b/tests/utils/data.py
index 69eab0a..8517741 100644
--- a/tests/utils/data.py
+++ b/tests/utils/data.py
@@ -4,30 +4,32 @@
 from sklearn.neighbors import NearestNeighbors
 
 import scib_metrics
+from scib_metrics.nearest_neighbors import NeighborsResults
 
 
-def dummy_x_labels(return_symmetric_positive=False, x_is_neighbors_graph=False):
-    np.random.seed(1)
-    X = np.random.normal(size=(100, 10))
-    labels = np.random.randint(0, 2, size=(100,))
-    if return_symmetric_positive:
+def dummy_x_labels(symmetric_positive=False, x_is_neighbors_results=False):
+    rng = np.random.default_rng(seed=42)
+    X = rng.normal(size=(100, 10))
+    labels = rng.integers(0, 4, size=(100,))
+    if symmetric_positive:
         X = np.abs(X @ X.T)
-    if x_is_neighbors_graph:
+    if x_is_neighbors_results:
         dist_mat = csr_matrix(scib_metrics.utils.cdist(X, X))
-        nbrs = NearestNeighbors(n_neighbors=30, algorithm="kd_tree").fit(X)
-        X = nbrs.kneighbors_graph(X)
-        X = X.multiply(dist_mat)
+        nbrs = NearestNeighbors(n_neighbors=30, metric="precomputed").fit(dist_mat)
+        dist, ind = nbrs.kneighbors(dist_mat)
+        X = NeighborsResults(indices=ind, distances=dist)
     return X, labels
 
 
-def dummy_x_labels_batch(x_is_neighbors_graph=False):
-    X, labels = dummy_x_labels(x_is_neighbors_graph=x_is_neighbors_graph)
-    batch = np.random.randint(0, 2, size=(100,))
+def dummy_x_labels_batch(x_is_neighbors_results=False):
+    rng = np.random.default_rng(seed=43)
+    X, labels = dummy_x_labels(x_is_neighbors_results=x_is_neighbors_results)
+    batch = rng.integers(0, 4, size=(100,))
     return X, labels, batch
 
 
 def dummy_benchmarker_adata():
-    X, labels, batch = dummy_x_labels_batch(x_is_neighbors_graph=False)
+    X, labels, batch = dummy_x_labels_batch(x_is_neighbors_results=False)
     adata = anndata.AnnData(X)
     labels_key = "labels"
     batch_key = "batch"

From db622858c2af616fe8686226968cdf68c2ca1067 Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Wed, 27 Dec 2023 16:58:37 -0500
Subject: [PATCH 02/24] fix typing

---
 src/scib_metrics/nearest_neighbors/_dataclass.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/scib_metrics/nearest_neighbors/_dataclass.py b/src/scib_metrics/nearest_neighbors/_dataclass.py
index 95ecdd9..d7ecc21 100644
--- a/src/scib_metrics/nearest_neighbors/_dataclass.py
+++ b/src/scib_metrics/nearest_neighbors/_dataclass.py
@@ -49,7 +49,7 @@ def knn_graph_distances(self) -> csr_matrix:
         return csr_matrix((self.distances.ravel(), self.indices.ravel(), rowptr), shape=(n_samples, n_samples))
 
     @cached_property
-    def knn_graph_connectivities(self) -> csr_matrix:
+    def knn_graph_connectivities(self) -> coo_matrix:
         """Compute connectivities using the UMAP approach.
 
         This function computes connectivities (similarities) from distances

From 7edd23243d7e2b3504746b8dc2a2fec266398b85 Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Wed, 27 Dec 2023 17:00:57 -0500
Subject: [PATCH 03/24] use uns for neigh results

---
 src/scib_metrics/benchmark/_core.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/scib_metrics/benchmark/_core.py b/src/scib_metrics/benchmark/_core.py
index fe85143..16895cb 100644
--- a/src/scib_metrics/benchmark/_core.py
+++ b/src/scib_metrics/benchmark/_core.py
@@ -82,15 +82,15 @@ class MetricAnnDataAPI(Enum):
     """Specification of the AnnData API for a metric."""
 
     isolated_labels = lambda ad, fn: fn(ad.X, ad.obs[_LABELS], ad.obs[_BATCH])
-    nmi_ari_cluster_labels_leiden = lambda ad, fn: fn(ad.obsp["15_neighbor_res"], ad.obs[_LABELS])
+    nmi_ari_cluster_labels_leiden = lambda ad, fn: fn(ad.uns["15_neighbor_res"], ad.obs[_LABELS])
     nmi_ari_cluster_labels_kmeans = lambda ad, fn: fn(ad.X, ad.obs[_LABELS])
     silhouette_label = lambda ad, fn: fn(ad.X, ad.obs[_LABELS])
-    clisi_knn = lambda ad, fn: fn(ad.obsp["90_neighbor_res"], ad.obs[_LABELS])
-    graph_connectivity = lambda ad, fn: fn(ad.obsp["15_neighbor_res"], ad.obs[_LABELS])
+    clisi_knn = lambda ad, fn: fn(ad.uns["90_neighbor_res"], ad.obs[_LABELS])
+    graph_connectivity = lambda ad, fn: fn(ad.uns["15_neighbor_res"], ad.obs[_LABELS])
     silhouette_batch = lambda ad, fn: fn(ad.X, ad.obs[_LABELS], ad.obs[_BATCH])
     pcr_comparison = lambda ad, fn: fn(ad.obsm[_X_PRE], ad.X, ad.obs[_BATCH], categorical=True)
-    ilisi_knn = lambda ad, fn: fn(ad.obsp["90_neighbor_res"], ad.obs[_BATCH])
-    kbet_per_label = lambda ad, fn: fn(ad.obsp["50_neighbor_res"], ad.obs[_BATCH], ad.obs[_LABELS])
+    ilisi_knn = lambda ad, fn: fn(ad.uns["90_neighbor_res"], ad.obs[_BATCH])
+    kbet_per_label = lambda ad, fn: fn(ad.uns["50_neighbor_res"], ad.obs[_BATCH], ad.obs[_LABELS])
 
 
 class Benchmarker:
@@ -189,7 +189,7 @@ def prepare(self, neighbor_computer: Optional[Callable[[np.ndarray, int], Neighb
                     ad.X, n_neighbors=max(self._neighbor_values), random_state=0, n_jobs=self._n_jobs
                 )
             for n in self._neighbor_values:
-                ad.obsp[f"{n}_neighbor_res"] = neigh_result
+                ad.uns[f"{n}_neighbor_res"] = neigh_result
 
         self._prepared = True
 

From 3dffad5e0da0576b0986bacc1f48926a9e2c8df8 Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Wed, 27 Dec 2023 17:02:06 -0500
Subject: [PATCH 04/24] remove unnecessary astype

---
 src/scib_metrics/_kbet.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/scib_metrics/_kbet.py b/src/scib_metrics/_kbet.py
index a1649df..a16ef04 100644
--- a/src/scib_metrics/_kbet.py
+++ b/src/scib_metrics/_kbet.py
@@ -165,7 +165,7 @@ def kbet_per_label(
             if n_comp == 1:  # a single component to compute kBET on
                 try:
                     diffusion_n_comps = np.min([diffusion_n_comps, n_obs - 1])
-                    nn_graph_sub = diffusion_nn(conn_graph, k=k0, n_comps=diffusion_n_comps).astype("float")
+                    nn_graph_sub = diffusion_nn(conn_graph, k=k0, n_comps=diffusion_n_comps)
                     # call kBET
                     score, _, _ = kbet(
                         nn_graph_sub,
@@ -191,9 +191,7 @@ def kbet_per_label(
 
                     try:
                         diffusion_n_comps = np.min([diffusion_n_comps, conn_graph_sub_sub_sub.shape[0] - 1])
-                        nn_results_sub_sub = diffusion_nn(
-                            conn_graph_sub_sub_sub, k=k0, n_comps=diffusion_n_comps
-                        ).astype("float")
+                        nn_results_sub_sub = diffusion_nn(conn_graph_sub_sub_sub, k=k0, n_comps=diffusion_n_comps)
                         # call kBET
                         score, _, _ = kbet(
                             nn_results_sub_sub,

From de79a181a432fc546969ca80489dbea74410d035 Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Wed, 27 Dec 2023 17:14:52 -0500
Subject: [PATCH 05/24] actually subset neighbors in benchmarker

---
 src/scib_metrics/benchmark/_core.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/scib_metrics/benchmark/_core.py b/src/scib_metrics/benchmark/_core.py
index 16895cb..1f0facd 100644
--- a/src/scib_metrics/benchmark/_core.py
+++ b/src/scib_metrics/benchmark/_core.py
@@ -189,7 +189,7 @@ def prepare(self, neighbor_computer: Optional[Callable[[np.ndarray, int], Neighb
                     ad.X, n_neighbors=max(self._neighbor_values), random_state=0, n_jobs=self._n_jobs
                 )
             for n in self._neighbor_values:
-                ad.uns[f"{n}_neighbor_res"] = neigh_result
+                ad.uns[f"{n}_neighbor_res"] = neigh_result.subset_neighbors(n=n)
 
         self._prepared = True
 

From e55225051e7217ac750fbecf537438df4249bb2b Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Wed, 27 Dec 2023 17:18:04 -0500
Subject: [PATCH 06/24] fix guarantee over self as first neighbor

---
 src/scib_metrics/nearest_neighbors/_dataclass.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/src/scib_metrics/nearest_neighbors/_dataclass.py b/src/scib_metrics/nearest_neighbors/_dataclass.py
index d7ecc21..04300d1 100644
--- a/src/scib_metrics/nearest_neighbors/_dataclass.py
+++ b/src/scib_metrics/nearest_neighbors/_dataclass.py
@@ -29,7 +29,10 @@ def __post_init__(self):
         # Guarantee that the first neighbor is self.
         # This is a simple and lightweight way to ensure this.
         nn_obj = NearestNeighbors(n_neighbors=self.n_neighbors, metric="precomputed").fit(self.knn_graph_distances)
-        self.distances, self.indices = nn_obj.kneighbors(self.knn_graph_distances)
+        # When X=None, the query is not considered its own neighbor so we can concatenate the self edge manually.
+        distances_non_self, indices_non_self = nn_obj.kneighbors()
+        self.distances = np.concatenate([np.zeros((self.n_samples, 1)), distances_non_self], axis=-1)
+        self.indices = np.concatenate([np.arange(self.n_samples)[:, np.newaxis], indices_non_self], axis=-1)
 
     @property
     def n_samples(self):

From cb5142ad60ba3652d94a6c45fecc9645c4b7ad15 Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Wed, 27 Dec 2023 17:28:08 -0500
Subject: [PATCH 07/24] fix umap dep

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index f171cb7..2149d75 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,7 +31,7 @@ dependencies = [
     "matplotlib",
     "plottable",
     "tqdm",
-    "umap>=0.5.0",
+    "umap-learn>=0.5.0",
 ]
 
 [project.optional-dependencies]

From 667f4109e9fa179dc6ffe7e53c2209710d290ec8 Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Wed, 27 Dec 2023 18:16:04 -0500
Subject: [PATCH 08/24] fix lisi with self edges

---
 docs/notebooks/lung_example.ipynb             | 138 ++++++++----------
 src/scib_metrics/_lisi.py                     |   8 +-
 .../nearest_neighbors/_dataclass.py           |  11 +-
 src/scib_metrics/utils/_lisi.py               |   6 +
 4 files changed, 70 insertions(+), 93 deletions(-)

diff --git a/docs/notebooks/lung_example.ipynb b/docs/notebooks/lung_example.ipynb
index 598b490..43f2120 100644
--- a/docs/notebooks/lung_example.ipynb
+++ b/docs/notebooks/lung_example.ipynb
@@ -18,9 +18,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniforge/base/envs/scib-metrics/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import scanpy as sc\n",
@@ -40,9 +49,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniforge/base/envs/scib-metrics/lib/python3.10/site-packages/anndata/__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "adata = sc.read(\n",
     "    \"data/lung_atlas.h5ad\",\n",
@@ -76,46 +94,7 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n",
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/scanpy/preprocessing/_highly_variable_genes.py:478: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
-      "  hvg = hvg.append(missing_hvg, ignore_index=True)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "sc.pp.highly_variable_genes(adata, n_top_genes=2000, flavor=\"cell_ranger\", batch_key=\"batch\")\n",
     "sc.tl.pca(adata, n_comps=30, use_highly_variable=True)"
@@ -203,8 +182,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/adam/miniconda3/envs/scib-metrics/lib/python3.10/site-packages/pyliger/preprocessing/_scale.py:88: RuntimeWarning: divide by zero encountered in divide\n",
-      "100%|██████████| 30/30 [02:17<00:00,  4.59s/it]\n"
+      "/opt/homebrew/Caskroom/miniforge/base/envs/scib-metrics/lib/python3.10/site-packages/umap/plot.py:203: NumbaDeprecationWarning: \u001b[1mThe keyword argument 'nopython=False' was supplied. From Numba 0.59.0 the default is being changed to True and use of 'nopython=False' will raise a warning as the argument will have no effect. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
+      "/opt/homebrew/Caskroom/miniforge/base/envs/scib-metrics/lib/python3.10/site-packages/pyliger/preprocessing/_scale.py:88: RuntimeWarning: divide by zero encountered in divide\n",
+      "100%|██████████| 30/30 [04:16<00:00,  8.54s/it]\n"
      ]
     }
    ],
@@ -284,21 +264,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:lightning_fabric.utilities.seed:Global seed set to 0\n",
-      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
-      "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
-      "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
-      "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
-      "INFO:lightning_fabric.accelerators.cuda:You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
-      "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=246` reached.\n"
+      "[E thread_pool.cpp:110] Exception in thread pool task: mutex lock failed: Invalid argument\n"
      ]
     }
    ],
@@ -308,7 +281,7 @@
     "\n",
     "scvi.model.SCVI.setup_anndata(adata, layer=\"counts\", batch_key=\"batch\")\n",
     "vae = scvi.model.SCVI(adata, gene_likelihood=\"nb\", n_layers=2, n_latent=30)\n",
-    "vae.train()\n",
+    "vae.train(accelerator=\"cpu\")\n",
     "adata.obsm[\"scVI\"] = vae.get_latent_representation()"
    ]
   },
@@ -322,23 +295,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
-      "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
-      "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
-      "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
-      "INFO:lightning_fabric.accelerators.cuda:You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
-      "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
-      "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=20` reached.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%capture\n",
     "lvae = scvi.model.SCANVI.from_scvi_model(\n",
@@ -347,7 +306,7 @@
     "    labels_key=\"cell_type\",\n",
     "    unlabeled_category=\"Unknown\",\n",
     ")\n",
-    "lvae.train(max_epochs=20, n_samples_per_label=100)\n",
+    "lvae.train(max_epochs=20, n_samples_per_label=100, accelerator=\"cpu\")\n",
     "adata.obsm[\"scANVI\"] = lvae.get_latent_representation()"
    ]
   },
@@ -361,15 +320,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing neighbors: 100%|██████████| 6/6 [02:06<00:00, 21.05s/it]\n",
-      "Embeddings: 100%|\u001b[32m██████████\u001b[0m| 6/6 [01:46<00:00, 17.77s/it]\n"
+      "Computing neighbors:   0%|          | 0/6 [00:11<?, ?it/s]\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "91 neighbors per samples are required, but some samples have only 90 neighbors in precomputed graph matrix. Decrease number of neighbors used or recompute the graph with more neighbors.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[14], line 8\u001b[0m\n\u001b[1;32m      1\u001b[0m bm \u001b[38;5;241m=\u001b[39m Benchmarker(\n\u001b[1;32m      2\u001b[0m     adata,\n\u001b[1;32m      3\u001b[0m     batch_key\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbatch\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      6\u001b[0m     n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m6\u001b[39m,\n\u001b[1;32m      7\u001b[0m )\n\u001b[0;32m----> 8\u001b[0m \u001b[43mbm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbenchmark\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/software/scib-metrics/src/scib_metrics/benchmark/_core.py:205\u001b[0m, in \u001b[0;36mBenchmarker.benchmark\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    199\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    200\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe benchmark has already been run. Running it again will overwrite the previous results.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    201\u001b[0m         \u001b[38;5;167;01mUserWarning\u001b[39;00m,\n\u001b[1;32m    202\u001b[0m     )\n\u001b[1;32m    204\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepared:\n\u001b[0;32m--> 205\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprepare\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    207\u001b[0m num_metrics \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(\n\u001b[1;32m    208\u001b[0m     [\u001b[38;5;28msum\u001b[39m([v \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m asdict(met_col)]) \u001b[38;5;28;01mfor\u001b[39;00m met_col \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metric_collection_dict\u001b[38;5;241m.\u001b[39mvalues()]\n\u001b[1;32m    209\u001b[0m )\n\u001b[1;32m    211\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m emb_key, ad \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_emb_adatas\u001b[38;5;241m.\u001b[39mitems(), desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEmbeddings\u001b[39m\u001b[38;5;124m\"\u001b[39m, position\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, colour\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgreen\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
+      "File \u001b[0;32m~/software/scib-metrics/src/scib_metrics/benchmark/_core.py:188\u001b[0m, in \u001b[0;36mBenchmarker.prepare\u001b[0;34m(self, neighbor_computer)\u001b[0m\n\u001b[1;32m    186\u001b[0m     neigh_result \u001b[38;5;241m=\u001b[39m neighbor_computer(ad\u001b[38;5;241m.\u001b[39mX, \u001b[38;5;28mmax\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_neighbor_values))\n\u001b[1;32m    187\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 188\u001b[0m     neigh_result \u001b[38;5;241m=\u001b[39m \u001b[43mpynndescent\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    189\u001b[0m \u001b[43m        \u001b[49m\u001b[43mad\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_neighbors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_neighbor_values\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_n_jobs\u001b[49m\n\u001b[1;32m    190\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    191\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_neighbor_values:\n\u001b[1;32m    192\u001b[0m     ad\u001b[38;5;241m.\u001b[39muns[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_neighbor_res\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m neigh_result\u001b[38;5;241m.\u001b[39msubset_neighbors(n\u001b[38;5;241m=\u001b[39mn)\n",
+      "File \u001b[0;32m~/software/scib-metrics/src/scib_metrics/nearest_neighbors/_pynndescent.py:40\u001b[0m, in \u001b[0;36mpynndescent\u001b[0;34m(X, n_neighbors, random_state, n_jobs)\u001b[0m\n\u001b[1;32m     27\u001b[0m knn_search_index \u001b[38;5;241m=\u001b[39m NNDescent(\n\u001b[1;32m     28\u001b[0m     X,\n\u001b[1;32m     29\u001b[0m     n_neighbors\u001b[38;5;241m=\u001b[39mn_neighbors,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     36\u001b[0m     max_candidates\u001b[38;5;241m=\u001b[39mmax_candidates,\n\u001b[1;32m     37\u001b[0m )\n\u001b[1;32m     38\u001b[0m indices, distances \u001b[38;5;241m=\u001b[39m knn_search_index\u001b[38;5;241m.\u001b[39mneighbor_graph\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNeighborsResults\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistances\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdistances\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m<string>:5\u001b[0m, in \u001b[0;36m__init__\u001b[0;34m(self, indices, distances)\u001b[0m\n",
+      "File \u001b[0;32m~/software/scib-metrics/src/scib_metrics/nearest_neighbors/_dataclass.py:33\u001b[0m, in \u001b[0;36mNeighborsResults.__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     31\u001b[0m nn_obj \u001b[38;5;241m=\u001b[39m NearestNeighbors(n_neighbors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_neighbors, metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprecomputed\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mfit(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mknn_graph_distances)\n\u001b[1;32m     32\u001b[0m \u001b[38;5;66;03m# When X=None, the query is not considered its own neighbor so we can concatenate the self edge manually.\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m distances_non_self, indices_non_self \u001b[38;5;241m=\u001b[39m \u001b[43mnn_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkneighbors\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistances \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate([np\u001b[38;5;241m.\u001b[39mzeros((\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_samples, \u001b[38;5;241m1\u001b[39m)), distances_non_self], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate([np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_samples)[:, np\u001b[38;5;241m.\u001b[39mnewaxis], indices_non_self], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/scib-metrics/lib/python3.10/site-packages/sklearn/neighbors/_base.py:835\u001b[0m, in \u001b[0;36mKNeighborsMixin.kneighbors\u001b[0;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m    822\u001b[0m     results \u001b[38;5;241m=\u001b[39m ArgKmin\u001b[38;5;241m.\u001b[39mcompute(\n\u001b[1;32m    823\u001b[0m         X\u001b[38;5;241m=\u001b[39mX,\n\u001b[1;32m    824\u001b[0m         Y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_X,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    829\u001b[0m         return_distance\u001b[38;5;241m=\u001b[39mreturn_distance,\n\u001b[1;32m    830\u001b[0m     )\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m (\n\u001b[1;32m    833\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbrute\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetric \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprecomputed\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m issparse(X)\n\u001b[1;32m    834\u001b[0m ):\n\u001b[0;32m--> 835\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[43m_kneighbors_from_graph\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    836\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_neighbors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_neighbors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_distance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_distance\u001b[49m\n\u001b[1;32m    837\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    839\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbrute\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    840\u001b[0m     \u001b[38;5;66;03m# Joblib-based backend, which is used when user-defined callable\u001b[39;00m\n\u001b[1;32m    841\u001b[0m     \u001b[38;5;66;03m# are passed for metric.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    845\u001b[0m     \u001b[38;5;66;03m#   - DistanceMetrics which work on supposedly binary data\u001b[39;00m\n\u001b[1;32m    846\u001b[0m     \u001b[38;5;66;03m#   - CSR-dense and dense-CSR case if 'euclidean' in metric.\u001b[39;00m\n\u001b[1;32m    847\u001b[0m     reduce_func \u001b[38;5;241m=\u001b[39m partial(\n\u001b[1;32m    848\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_kneighbors_reduce_func,\n\u001b[1;32m    849\u001b[0m         n_neighbors\u001b[38;5;241m=\u001b[39mn_neighbors,\n\u001b[1;32m    850\u001b[0m         return_distance\u001b[38;5;241m=\u001b[39mreturn_distance,\n\u001b[1;32m    851\u001b[0m     )\n",
+      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/scib-metrics/lib/python3.10/site-packages/sklearn/neighbors/_base.py:308\u001b[0m, in \u001b[0;36m_kneighbors_from_graph\u001b[0;34m(graph, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m    306\u001b[0m row_nnz_min \u001b[38;5;241m=\u001b[39m row_nnz\u001b[38;5;241m.\u001b[39mmin()\n\u001b[1;32m    307\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_neighbors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m row_nnz_min \u001b[38;5;241m<\u001b[39m n_neighbors:\n\u001b[0;32m--> 308\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    309\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m neighbors per samples are required, but some samples have only\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    310\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m neighbors in precomputed graph matrix. Decrease number of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    311\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mneighbors used or recompute the graph with more neighbors.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    312\u001b[0m         \u001b[38;5;241m%\u001b[39m (n_neighbors, row_nnz_min)\n\u001b[1;32m    313\u001b[0m     )\n\u001b[1;32m    315\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mextract\u001b[39m(a):\n\u001b[1;32m    316\u001b[0m     \u001b[38;5;66;03m# if each sample has the same number of provided neighbors\u001b[39;00m\n\u001b[1;32m    317\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m row_nnz\u001b[38;5;241m.\u001b[39mmax() \u001b[38;5;241m==\u001b[39m row_nnz_min:\n",
+      "\u001b[0;31mValueError\u001b[0m: 91 neighbors per samples are required, but some samples have only 90 neighbors in precomputed graph matrix. Decrease number of neighbors used or recompute the graph with more neighbors."
      ]
     }
    ],
@@ -394,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -424,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -462,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -584,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -817,7 +793,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.13"
   },
   "vscode": {
    "interpreter": {
diff --git a/src/scib_metrics/_lisi.py b/src/scib_metrics/_lisi.py
index 9e9c47a..17c2168 100644
--- a/src/scib_metrics/_lisi.py
+++ b/src/scib_metrics/_lisi.py
@@ -25,15 +25,17 @@ def lisi_knn(X: NeighborsResults, labels: np.ndarray, perplexity: float = None)
         Array of shape (n_cells,) with the LISI score for each cell.
     """
     labels = np.asarray(pd.Categorical(labels).codes)
-    # Drop self edge
-    knn_dists, knn_idx = X.distances[:, 1:], X.indices[:, 1:]
+    knn_dists, knn_idx = X.distances, X.indices
+    row_idx = np.arange(X.n_samples)[:, np.newaxis]
 
     if perplexity is None:
         perplexity = np.floor(knn_idx.shape[1] / 3)
 
     n_labels = len(np.unique(labels))
 
-    simpson = compute_simpson_index(knn_dists, knn_idx, labels, n_labels, perplexity=perplexity)
+    simpson = compute_simpson_index(
+        knn_dists=knn_dists, knn_idx=knn_idx, row_idx=row_idx, labels=labels, n_labels=n_labels, perplexity=perplexity
+    )
     return 1 / simpson
 
 
diff --git a/src/scib_metrics/nearest_neighbors/_dataclass.py b/src/scib_metrics/nearest_neighbors/_dataclass.py
index 04300d1..f12b637 100644
--- a/src/scib_metrics/nearest_neighbors/_dataclass.py
+++ b/src/scib_metrics/nearest_neighbors/_dataclass.py
@@ -4,7 +4,6 @@
 import chex
 import numpy as np
 from scipy.sparse import coo_matrix, csr_matrix
-from sklearn.neighbors import NearestNeighbors
 from umap.umap_ import fuzzy_simplicial_set
 
 
@@ -18,7 +17,8 @@ class NeighborsResults:
         Array of distances to the nearest neighbors.
     indices : np.ndarray
         Array of indices of the nearest neighbors. Self should always
-        be included here.
+        be included here; however, some approximate algorithms may not return
+        the self edge.
     """
 
     indices: np.ndarray
@@ -26,13 +26,6 @@ class NeighborsResults:
 
     def __post_init__(self):
         chex.assert_equal_shape([self.indices, self.distances])
-        # Guarantee that the first neighbor is self.
-        # This is a simple and lightweight way to ensure this.
-        nn_obj = NearestNeighbors(n_neighbors=self.n_neighbors, metric="precomputed").fit(self.knn_graph_distances)
-        # When X=None, the query is not considered its own neighbor so we can concatenate the self edge manually.
-        distances_non_self, indices_non_self = nn_obj.kneighbors()
-        self.distances = np.concatenate([np.zeros((self.n_samples, 1)), distances_non_self], axis=-1)
-        self.indices = np.concatenate([np.arange(self.n_samples)[:, np.newaxis], indices_non_self], axis=-1)
 
     @property
     def n_samples(self):
diff --git a/src/scib_metrics/utils/_lisi.py b/src/scib_metrics/utils/_lisi.py
index 5ac76c9..9c0fb28 100644
--- a/src/scib_metrics/utils/_lisi.py
+++ b/src/scib_metrics/utils/_lisi.py
@@ -85,6 +85,7 @@ def _non_zero_H_simpson():
 def compute_simpson_index(
     knn_dists: NdArray,
     knn_idx: NdArray,
+    row_idx: NdArray,
     labels: NdArray,
     n_labels: int,
     perplexity: float = 30,
@@ -98,6 +99,8 @@ def compute_simpson_index(
         KNN distances of size (n_cells, n_neighbors).
     knn_idx
         KNN indices of size (n_cells, n_neighbors) corresponding to distances.
+    row_idx
+        Idx of each row (n_cells, 1).
     labels
         Cell labels of size (n_cells,).
     n_labels
@@ -115,7 +118,10 @@ def compute_simpson_index(
     knn_dists = jnp.array(knn_dists)
     knn_idx = jnp.array(knn_idx)
     labels = jnp.array(labels)
+    row_idx = jnp.array(row_idx)
     knn_labels = labels[knn_idx]
+    # Set self edges to infinity so they are not considered.
+    knn_dists = jnp.where(knn_idx != row_idx, knn_dists, jnp.inf)
     simpson_fn = partial(_compute_simpson_index_cell, n_batches=n_labels, perplexity=perplexity, tol=tol)
     out = jax.vmap(simpson_fn)(knn_dists, knn_labels)
     return get_ndarray(out)

From 9ce729d610038c5ac880351e683cc014eefed12e Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 28 Dec 2023 04:25:13 +0000
Subject: [PATCH 09/24] finalize and add test

---
 .pre-commit-config.yaml                       |   4 +
 docs/notebooks/lung_example.ipynb             | 488 ++++++++----------
 src/scib_metrics/_kbet.py                     |  12 +-
 .../nearest_neighbors/_dataclass.py           |   2 +-
 tests/test_metrics.py                         |   2 +-
 tests/test_neighbors.py                       |  22 +-
 6 files changed, 240 insertions(+), 290 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 868dd7a..ff907f0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -73,3 +73,7 @@ repos:
                     mdformat-myst,
                 ]
             args: [--nbqa-md]
+    - repo: https://github.com/kynan/nbstripout
+      rev: 0.6.1
+      hooks:
+          - id: nbstripout
diff --git a/docs/notebooks/lung_example.ipynb b/docs/notebooks/lung_example.ipynb
index 43f2120..73e4f9d 100644
--- a/docs/notebooks/lung_example.ipynb
+++ b/docs/notebooks/lung_example.ipynb
@@ -18,18 +18,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/homebrew/Caskroom/miniforge/base/envs/scib-metrics/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import scanpy as sc\n",
@@ -49,18 +40,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/homebrew/Caskroom/miniforge/base/envs/scib-metrics/lib/python3.10/site-packages/anndata/__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "adata = sc.read(\n",
     "    \"data/lung_atlas.h5ad\",\n",
@@ -70,29 +52,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AnnData object with n_obs × n_vars = 32472 × 15148\n",
-       "    obs: 'dataset', 'location', 'nGene', 'nUMI', 'patientGroup', 'percent.mito', 'protocol', 'sanger_type', 'size_factors', 'sampling_method', 'batch', 'cell_type', 'donor'\n",
-       "    layers: 'counts'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "adata"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -110,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,11 +117,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%capture\n",
     "import scanorama\n",
     "\n",
     "# List of adata per batch\n",
@@ -175,19 +143,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/homebrew/Caskroom/miniforge/base/envs/scib-metrics/lib/python3.10/site-packages/umap/plot.py:203: NumbaDeprecationWarning: \u001b[1mThe keyword argument 'nopython=False' was supplied. From Numba 0.59.0 the default is being changed to True and use of 'nopython=False' will raise a warning as the argument will have no effect. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
-      "/opt/homebrew/Caskroom/miniforge/base/envs/scib-metrics/lib/python3.10/site-packages/pyliger/preprocessing/_scale.py:88: RuntimeWarning: divide by zero encountered in divide\n",
-      "100%|██████████| 30/30 [04:16<00:00,  8.54s/it]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pyliger\n",
     "\n",
@@ -227,27 +185,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\tInitialization is completed.\n",
-      "\tCompleted 1 / 10 iteration(s).\n",
-      "\tCompleted 2 / 10 iteration(s).\n",
-      "\tCompleted 3 / 10 iteration(s).\n",
-      "\tCompleted 4 / 10 iteration(s).\n",
-      "\tCompleted 5 / 10 iteration(s).\n",
-      "\tCompleted 6 / 10 iteration(s).\n",
-      "\tCompleted 7 / 10 iteration(s).\n",
-      "\tCompleted 8 / 10 iteration(s).\n",
-      "\tCompleted 9 / 10 iteration(s).\n",
-      "\tCompleted 10 / 10 iteration(s).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from harmony import harmonize\n",
     "\n",
@@ -264,24 +204,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[E thread_pool.cpp:110] Exception in thread pool task: mutex lock failed: Invalid argument\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "%%capture\n",
     "import scvi\n",
     "\n",
     "scvi.model.SCVI.setup_anndata(adata, layer=\"counts\", batch_key=\"batch\")\n",
     "vae = scvi.model.SCVI(adata, gene_likelihood=\"nb\", n_layers=2, n_latent=30)\n",
-    "vae.train(accelerator=\"cpu\")\n",
+    "vae.train()\n",
     "adata.obsm[\"scVI\"] = vae.get_latent_representation()"
    ]
   },
@@ -295,18 +226,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%capture\n",
     "lvae = scvi.model.SCANVI.from_scvi_model(\n",
     "    vae,\n",
     "    adata=adata,\n",
     "    labels_key=\"cell_type\",\n",
     "    unlabeled_category=\"Unknown\",\n",
     ")\n",
-    "lvae.train(max_epochs=20, n_samples_per_label=100, accelerator=\"cpu\")\n",
+    "lvae.train(max_epochs=20, n_samples_per_label=100)\n",
     "adata.obsm[\"scANVI\"] = lvae.get_latent_representation()"
    ]
   },
@@ -320,32 +250,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "keep_output"
+    ]
+   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Computing neighbors:   0%|          | 0/6 [00:11<?, ?it/s]\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "91 neighbors per samples are required, but some samples have only 90 neighbors in precomputed graph matrix. Decrease number of neighbors used or recompute the graph with more neighbors.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[14], line 8\u001b[0m\n\u001b[1;32m      1\u001b[0m bm \u001b[38;5;241m=\u001b[39m Benchmarker(\n\u001b[1;32m      2\u001b[0m     adata,\n\u001b[1;32m      3\u001b[0m     batch_key\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbatch\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      6\u001b[0m     n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m6\u001b[39m,\n\u001b[1;32m      7\u001b[0m )\n\u001b[0;32m----> 8\u001b[0m \u001b[43mbm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbenchmark\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/software/scib-metrics/src/scib_metrics/benchmark/_core.py:205\u001b[0m, in \u001b[0;36mBenchmarker.benchmark\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    199\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    200\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe benchmark has already been run. Running it again will overwrite the previous results.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    201\u001b[0m         \u001b[38;5;167;01mUserWarning\u001b[39;00m,\n\u001b[1;32m    202\u001b[0m     )\n\u001b[1;32m    204\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepared:\n\u001b[0;32m--> 205\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprepare\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    207\u001b[0m num_metrics \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(\n\u001b[1;32m    208\u001b[0m     [\u001b[38;5;28msum\u001b[39m([v \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m asdict(met_col)]) \u001b[38;5;28;01mfor\u001b[39;00m met_col \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metric_collection_dict\u001b[38;5;241m.\u001b[39mvalues()]\n\u001b[1;32m    209\u001b[0m )\n\u001b[1;32m    211\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m emb_key, ad \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_emb_adatas\u001b[38;5;241m.\u001b[39mitems(), desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEmbeddings\u001b[39m\u001b[38;5;124m\"\u001b[39m, position\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, colour\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgreen\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
-      "File \u001b[0;32m~/software/scib-metrics/src/scib_metrics/benchmark/_core.py:188\u001b[0m, in \u001b[0;36mBenchmarker.prepare\u001b[0;34m(self, neighbor_computer)\u001b[0m\n\u001b[1;32m    186\u001b[0m     neigh_result \u001b[38;5;241m=\u001b[39m neighbor_computer(ad\u001b[38;5;241m.\u001b[39mX, \u001b[38;5;28mmax\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_neighbor_values))\n\u001b[1;32m    187\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 188\u001b[0m     neigh_result \u001b[38;5;241m=\u001b[39m \u001b[43mpynndescent\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    189\u001b[0m \u001b[43m        \u001b[49m\u001b[43mad\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_neighbors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mmax\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_neighbor_values\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_n_jobs\u001b[49m\n\u001b[1;32m    190\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    191\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_neighbor_values:\n\u001b[1;32m    192\u001b[0m     ad\u001b[38;5;241m.\u001b[39muns[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_neighbor_res\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m neigh_result\u001b[38;5;241m.\u001b[39msubset_neighbors(n\u001b[38;5;241m=\u001b[39mn)\n",
-      "File \u001b[0;32m~/software/scib-metrics/src/scib_metrics/nearest_neighbors/_pynndescent.py:40\u001b[0m, in \u001b[0;36mpynndescent\u001b[0;34m(X, n_neighbors, random_state, n_jobs)\u001b[0m\n\u001b[1;32m     27\u001b[0m knn_search_index \u001b[38;5;241m=\u001b[39m NNDescent(\n\u001b[1;32m     28\u001b[0m     X,\n\u001b[1;32m     29\u001b[0m     n_neighbors\u001b[38;5;241m=\u001b[39mn_neighbors,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     36\u001b[0m     max_candidates\u001b[38;5;241m=\u001b[39mmax_candidates,\n\u001b[1;32m     37\u001b[0m )\n\u001b[1;32m     38\u001b[0m indices, distances \u001b[38;5;241m=\u001b[39m knn_search_index\u001b[38;5;241m.\u001b[39mneighbor_graph\n\u001b[0;32m---> 40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mNeighborsResults\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistances\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdistances\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m<string>:5\u001b[0m, in \u001b[0;36m__init__\u001b[0;34m(self, indices, distances)\u001b[0m\n",
-      "File \u001b[0;32m~/software/scib-metrics/src/scib_metrics/nearest_neighbors/_dataclass.py:33\u001b[0m, in \u001b[0;36mNeighborsResults.__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     31\u001b[0m nn_obj \u001b[38;5;241m=\u001b[39m NearestNeighbors(n_neighbors\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_neighbors, metric\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprecomputed\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mfit(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mknn_graph_distances)\n\u001b[1;32m     32\u001b[0m \u001b[38;5;66;03m# When X=None, the query is not considered its own neighbor so we can concatenate the self edge manually.\u001b[39;00m\n\u001b[0;32m---> 33\u001b[0m distances_non_self, indices_non_self \u001b[38;5;241m=\u001b[39m \u001b[43mnn_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkneighbors\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistances \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate([np\u001b[38;5;241m.\u001b[39mzeros((\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_samples, \u001b[38;5;241m1\u001b[39m)), distances_non_self], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate([np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_samples)[:, np\u001b[38;5;241m.\u001b[39mnewaxis], indices_non_self], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/scib-metrics/lib/python3.10/site-packages/sklearn/neighbors/_base.py:835\u001b[0m, in \u001b[0;36mKNeighborsMixin.kneighbors\u001b[0;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m    822\u001b[0m     results \u001b[38;5;241m=\u001b[39m ArgKmin\u001b[38;5;241m.\u001b[39mcompute(\n\u001b[1;32m    823\u001b[0m         X\u001b[38;5;241m=\u001b[39mX,\n\u001b[1;32m    824\u001b[0m         Y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_X,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    829\u001b[0m         return_distance\u001b[38;5;241m=\u001b[39mreturn_distance,\n\u001b[1;32m    830\u001b[0m     )\n\u001b[1;32m    832\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m (\n\u001b[1;32m    833\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbrute\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetric \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprecomputed\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m issparse(X)\n\u001b[1;32m    834\u001b[0m ):\n\u001b[0;32m--> 835\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[43m_kneighbors_from_graph\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    836\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_neighbors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_neighbors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_distance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_distance\u001b[49m\n\u001b[1;32m    837\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    839\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbrute\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    840\u001b[0m     \u001b[38;5;66;03m# Joblib-based backend, which is used when user-defined callable\u001b[39;00m\n\u001b[1;32m    841\u001b[0m     \u001b[38;5;66;03m# are passed for metric.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    845\u001b[0m     \u001b[38;5;66;03m#   - DistanceMetrics which work on supposedly binary data\u001b[39;00m\n\u001b[1;32m    846\u001b[0m     \u001b[38;5;66;03m#   - CSR-dense and dense-CSR case if 'euclidean' in metric.\u001b[39;00m\n\u001b[1;32m    847\u001b[0m     reduce_func \u001b[38;5;241m=\u001b[39m partial(\n\u001b[1;32m    848\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_kneighbors_reduce_func,\n\u001b[1;32m    849\u001b[0m         n_neighbors\u001b[38;5;241m=\u001b[39mn_neighbors,\n\u001b[1;32m    850\u001b[0m         return_distance\u001b[38;5;241m=\u001b[39mreturn_distance,\n\u001b[1;32m    851\u001b[0m     )\n",
-      "File \u001b[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/scib-metrics/lib/python3.10/site-packages/sklearn/neighbors/_base.py:308\u001b[0m, in \u001b[0;36m_kneighbors_from_graph\u001b[0;34m(graph, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m    306\u001b[0m row_nnz_min \u001b[38;5;241m=\u001b[39m row_nnz\u001b[38;5;241m.\u001b[39mmin()\n\u001b[1;32m    307\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_neighbors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m row_nnz_min \u001b[38;5;241m<\u001b[39m n_neighbors:\n\u001b[0;32m--> 308\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    309\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m neighbors per samples are required, but some samples have only\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    310\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m neighbors in precomputed graph matrix. Decrease number of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    311\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mneighbors used or recompute the graph with more neighbors.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    312\u001b[0m         \u001b[38;5;241m%\u001b[39m (n_neighbors, row_nnz_min)\n\u001b[1;32m    313\u001b[0m     )\n\u001b[1;32m    315\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mextract\u001b[39m(a):\n\u001b[1;32m    316\u001b[0m     \u001b[38;5;66;03m# if each sample has the same number of provided neighbors\u001b[39;00m\n\u001b[1;32m    317\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m row_nnz\u001b[38;5;241m.\u001b[39mmax() \u001b[38;5;241m==\u001b[39m row_nnz_min:\n",
-      "\u001b[0;31mValueError\u001b[0m: 91 neighbors per samples are required, but some samples have only 90 neighbors in precomputed graph matrix. Decrease number of neighbors used or recompute the graph with more neighbors."
+      "Computing neighbors: 100%|██████████| 6/6 [00:47<00:00,  7.94s/it]\n",
+      "Embeddings: 100%|\u001b[32m██████████\u001b[0m| 6/6 [02:03<00:00, 20.57s/it]\n"
      ]
     }
    ],
@@ -371,11 +288,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "keep_output"
+    ]
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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ra6Snpz/3XBAREYGZM2dqbXvo0KFISEhAZmamTno7OzuEhIQgODgYXbt2xaJFi7SGgyjuvqik9wQymUzrmHru3DkQEXx9fbXKefjwYa17IlZ6Xq/R8BljlYK5ubnwig8ABAQEwNraGsuWLdP7Ki8RaQX+8pcB0Fmez9TUtMjtF/VdfdspeLOUvy5/gNvp06fjww8/xD///IPdu3dj2rRp+PPPP/Huu+9Co9Hgk08+wahRo3S2X7VqVeH/zc3Nddb37dsXo0ePxs8//4z169fD398f9evXB5A3/kmHDh3QoUMHrF27Fo6Ojrh9+zaCg4NfaLB1KvDaRHF1UPhmUSQSFfldxhh7XTzvPHby5Em8//77mDFjBoKDg2FtbY0///wTCxYsKDbf4s5d+V70uFySPEtyPtR3viq8TKPRICAgQHiAVZCjoyPE4tLrt/Cq53DGmPFavnw5VCoV3NzchGVEBKlUirS0NNja2hZ7f1Dw7+elAfQfy1xdXfWOX1dwvMDn5R0SEoLk5GT8+OOP8PT0hFwuR2Bg4HOv2TUaDWbMmKE1Ll8+hUKh9zuhoaEYNWoU9uzZg40bN2Ly5MnYv38/mjdvXqL7In3LC+6fqamp1t8ajQYSiQQRERFaQ1MAeZM0sdLHPf8YYxWeSCSCWCxGVlaW3vW1a9dGeHi41skpPDwclpaWWhcFBdWrVw8HDhzQu87HxwcymQzHjh0TlimVSpw9exa1atV6obL7+vriiy++wL59+9CjRw+EhoYCABo1aoTLly/Dx8dH51Owl4Q+3bt3R3Z2Nvbs2YP169ejX79+wrorV64gJSUFc+fORatWrVCzZk2dXgz5+eePb6hP7dq1oVKpcOrUKWFZamoqYmNjX7gOGGPsdVf4PHb8+HF4enpi0qRJaNy4MWrUqKHTu10mk+kcp+vVq4e7d+8iNja21MpWr149HD16VO8MllZWVqhSpYrW+RDIO8e+zLmgUaNGiIuLg5OTk865z9raGpaWlvDy8iry/AzkBeyKO3+VdpkZY8ZFpVJh9erVWLBggdDDOjIyEhcuXICnp6fwcKFmzZo644IWnHgvP83p06eLTaNPo0aNkJiYCBMTE51jmYODAwDAz8/vuXkfPXoUo0aNQufOnYVJjlJSUrTS6DvmNWrUCFevXtV7H1HcQ5SGDRti4sSJCA8PR506dbB+/XoAxd8Xvew9QcOGDaFWq5GUlKRTRhcXlyK/x14eB/8YYxVOTk4OEhMTkZiYiJiYGIwcORLp6eno2rWr3vTDhg3DnTt3MHLkSFy5cgV///03pk2bhi+//LLIE+DEiRNx5swZDBs2DFFRUbhy5QqWLl2KlJQUmJub47PPPsPYsWOxZ88eREdHY+jQocjMzMTgwYNLtA9ZWVkYMWIEwsLCcOvWLRw/fhxnzpwRTpLjx4/HiRMnMHz4cERGRiIuLg47duzAyJEjn5u3ubk5unXrhilTpiAmJgYffvihsK5q1aqQyWT4+eefcePGDezYsQOzZs3S+r6npydEIhF27dqF5ORkpKen62yjRo0a6NatG4YOHYpjx47hwoUL6NevH9zc3NCtW7cS1QFjjL2unnce8/Hxwe3bt/Hnn3/i+vXr+Omnn7B9+3atPLy8vBAfH4/IyEikpKQgJycHQUFBaN26NXr27In9+/cjPj4eu3fv1prJ9kWNGDECT548wfvvv4+zZ88iLi4Oa9asEWavHzt2LObNm4eNGzfi6tWrmDBhAiIjIzF69OgX3lbfvn3h4OCAbt264ejRo4iPj8fhw4cxevRoYZiO6dOnY8GCBfjpp58QFxeHc+fO4eeff9aqlwMHDiAxMRFpaWl6t1OaZWaMGZddu3YhLS0NgwcPRp06dbQ+vXr1wvLlywEAn3zyCa5cuYLx48cjNjYWmzZtEmYEzu+hNnLkSPz777/44YcfEBcXh99++w27d+8u8s2hfG+99RYCAwPRvXt37N27Fzdv3kR4eDgmT54sBPhGjhyJ5cuXY9WqVYiLi8Ps2bMRFRWllbePjw/WrFmDmJgYnDp1Cn379tXphafvmDd16lSsXr0a06dPx+XLlxETEyP05tMnPj4eEydOxIkTJ3Dr1i3s27dPK3g3bdo0bNiwAdOmTUNMTAwuXryI+fPnA3j5ewJfX1/07dsX/fv3x7Zt2xAfH48zZ85g3rx5+Pfff4utX/aSyneIQcYYezUDBgwgAMLH0tKSmjRpQlu2bBHSFJ7wg4goLCyMmjRpQjKZjFxcXGj8+PGkVCqL3VZYWBi1aNGC5HI52djYUHBwsDAAcFZWFo0cOZIcHBxILpdTy5Yt6fTp08J39U2acf78eQJA8fHxlJOTQ++//z55eHiQTCajKlWq0IgRI4TJPIjyJuJo3749WVhYkLm5OdWrV09rgHJPT09auHCh3rLnTy7SunVrnXXr168nLy8vksvlFBgYSDt27NCpr5kzZ5KLiwuJRCIaMGAAEWlP+EFE9PDhQ/roo4/I2tqaTE1NKTg4mGJjY4X1oaGhWgMiExFt376d+NTDGHudleQ8RkQ0duxYsre3JwsLC+rTpw8tXLhQ65ianZ1NPXv2JBsbGwJAoaGhRJQ3cPvAgQPJ3t6eFAoF1alTh3bt2kVEL39cvnDhAnXo0IHMzMzI0tKSWrVqRdevXyciIrVaTTNmzCA3NzeSSqVUv3592r17t/Bdfefk/Hro1q2bzrYSEhKof//+wvnV29ubhg4dSo8fPxbS/Prrr+Tn50dSqZRcXV1p5MiRwrodO3aQj48PmZiYkKenJxHpTvjxMmVOS0sjAHTo0KFi64oxZlhdunShzp07610XERFBAIQJlv7++2/y8fEhuVxObdq0oaVLl2pNzkdE9Pvvv5ObmxuZmppS9+7dafbs2eTi4iKs1zf5EhHRkydPaOTIkVSlShWSSqXk4eFBffv2pdu3bwtpZs6cSQ4ODmRhYUGDBg2iUaNGUfPmzYX1586do8aNG5NcLqcaNWrQ5s2bda7/9R3ziIj27NlDLVq0IFNTU7KysqKmTZvS77//rrdeEhMTqXv37uTq6koymYw8PT1p6tSpWhMjbt26lRo0aEAymYwcHByoR48ewrqXuScgypswcerUqeTl5UVSqZRcXFzo3XffpaioKL3lZK9GRMSDLzHGGGOMMcYYY+z1NWfOHPz666+4c+dOkWmGDh2KK1eu4OjRo6W+/fbt28PFxQVr1qwp9bwZ4wk/GGOMMcYYY4wx9lpZsmQJmjRpAnt7exw/fhzfffcdRowYoZXm+++/R/v27WFubo7du3dj1apVWLJkyStvOzMzE7/++iuCg4MhkUiwYcMG/Pfff9i/f/8r582YPtzzjzHGGGOMMcYYY6+VL774Ahs3bsTDhw9RtWpVfPTRR5g4cSJMTJ71kXrvvfcQFhaGp0+fwtvbGyNHjsSnn376ytvOyspC165dce7cOeTk5MDPzw+TJ0/WO0MvY6WBg3+MMcYYY4wxxhhjjFVSPNsvY4wxxhhjjDHGGGOVFAf/GGOMMcYYY4wxxhirpHjCD2Y0cnNzcfv2bUMXgzHGmJGoWrUqZDKZoYvxXHz+YowxVpixnMP4HMWY8SuP4wX3/GOMMcYYY4wxxhhjrJLiCT8YY4wxxhhjjDHGGKukuOcfY4wxxhhjjDHGGGOVFAf/GGOMMcYYY4wxxhirpDj4xxhjjDHGGGOMMcZYJcXBP8YYY4wxxhhjjDHGKikO/jHGGGOMMcYYY4wxVklx8I8xxhhjjDHGGGOMsUqKg3+MMcYYY4wxxhhjjFVSHPxjFd7NmzchEokQGRlpkO2HhISge/fuBtk2Y5WJSCTCX3/9BUD3dx0WFgaRSIRHjx4ZrHyMFcZtljFm7Nq0aYPPP//c0MVg7LlWrlwJGxsbQxfD6Bn63pdVXBz8YwZjyKBZZQ/Y6du/LVu2QKFQYP78+Zg+fTpEIhE6duyo89358+dDJBKhTZs25VPYCobr9uUlJSXhk08+QdWqVSGXy+Hi4oLg4GCcOHECAJCQkIBOnToZuJTPp+9GigM9lRO3WcbYq0pMTMTo0aPh4+MDhUIBZ2dnvPHGG/j111+RmZlp6OJVSiEhIRCJRBCJRJBKpfD29saYMWOQkZEhpNm6dSvatGkDa2trWFhYoF69epg5cyYePnwIIC8QlZ+HSCSCs7MzunbtisuXLxtqt8pNwfoTiUSwt7dHx44dERUVVeI8pk+fjgYNGpRdISux59W/h4cHEhISUKdOHQOXlFU0HPxj7DXwxx9/oG/fvvjll18wbtw4AICrqysOHTqEu3fvaqUNDQ1F1apVDVHMConrtuR69uyJCxcuYNWqVYiNjcWOHTvQpk0b4ULbxcUFcrncwKVk7Blus4yxV3Hjxg00bNgQ+/btwzfffIPz58/jv//+wxdffIGdO3fiv//+0/s9pVJZziWtfDp27IiEhATcuHEDs2fPxpIlSzBmzBgAwKRJk9CnTx80adIEu3fvxqVLl7BgwQJcuHABa9asEfKwsrJCQkIC7t+/j3/++QcZGRl4++23kZuba6jdKjf59ZeQkIADBw7AxMQEXbp0MXSxXhvF1b9EIoGLiwtMTEwMXEpW4RBjBjJgwADq1q0bERFt3ryZ6tSpQwqFguzs7Khdu3aUnp5ORERqtZpmzJhBbm5uJJPJqH79+rR7924hn/j4eAJA58+fJyIilUpFgwYNIi8vL1IoFOTr60s//vijkH7atGkEQOtz6NAhIiK6e/cuvffee2RjY0N2dnb0zjvvUHx8vPBdlUpFX3zxBVlbW5OdnR2NHTuW+vfvL+yHsShYt/PmzSO5XE5btmwR1k+bNo3q169PXbp0odmzZwvLjx8/Tg4ODvTZZ59RUFCQVp4rVqygmjVrklwuJz8/P1q8eLHW+nHjxlGNGjXI1NSUqlWrRpMnT6bc3Fydba5evZo8PT3JysqK+vTpQ0+ePBHSFNcOjAXX7ctJS0sjABQWFlZkGgC0fft2ItL9XR86dIgA0H///UcBAQFkampKgYGBdOXKFa08lixZQt7e3iSVSsnX15dWr14trCucZ8Fy5R8DiIguX75MnTp1InNzc3JycqJ+/fpRcnIyEeX9+xc+fuTnW/AzYMAAIiLSaDQ0b948qlatGikUCqpXrx5t3rz55SuSlRtus9xmGXtVwcHB5O7uXuT5VqPREFHesWTp0qX0zjvvkJmZGU2dOvW517NEz65Jpk+fTo6OjmRpaUkff/wx5eTkCGmCgoJo5MiRNHbsWLK1tSVnZ2eaNm1ame2zMSh4rZZvyJAh5OLiQqdOnSIAOnWZLy0tjYiIQkNDydraWmvdjh07CABFRUWVQamNh776O3LkCAGgpKQkIir+2jQ0NFTnHBMaGkpEefU7dOhQcnJyIrlcTv7+/rRz507he9bW1rRnzx6qWbMmmZubU3BwMN2/f7/c9t0YPK/+9V0bhIWFUZMmTUgmk5GLiwuNHz+elEpl+RacGT3u+ccMLiEhAR988AEGDRqEmJgYhIWFoUePHiAiAMCiRYuwYMECfP/994iKikJwcDDeeecdxMXF6c1Po9HA3d0dmzZtQnR0NKZOnYqvv/4amzZtAgCMGTMG7733ntYTlRYtWiAzMxNt27aFhYUFjhw5gmPHjsHCwgIdO3YUnvAtWLAAK1aswPLly3Hs2DE8fPgQ27dvL5+KegkTJkzArFmzsGvXLvTs2VNn/aBBg7By5Urh7xUrVqBv376QyWRa6ZYtW4ZJkyZhzpw5iImJwTfffIMpU6Zg1apVQhpLS0usXLkS0dHRWLRoEZYtW4aFCxdq5XP9+nX89ddf2LVrF3bt2oXDhw9j7ty5AJ7fDowN1+2LsbCwgIWFBf766y/k5OS8dD6TJk3CggULcPbsWZiYmGDQoEHCuu3bt2P06NH46quvcOnSJXzyyScYOHAgDh06VOL8ExISEBQUhAYNGuDs2bPYs2cPHjx4gPfeew9A3vEoMDAQQ4cOFY4fHh4e2Lp1KwDg6tWrSEhIwKJFiwAAkydPRmhoKJYuXYrLly/jiy++QL9+/XD48OGXrgNWPrjNcptl7FWkpqZi3759GD58OMzNzfWmEYlEwv9PmzYN3bp1w8WLFzFo0KDnXs/mO3DgAGJiYnDo0CFs2LAB27dvx4wZM7TSrFq1Cubm5jh16hTmz5+PmTNnYv/+/aW/00bM1NQUSqUS69atg4WFBYYNG6Y3XVFjzj169Ajr168HAEil0rIqplFKT0/HunXr4OPjA3t7ewDFX5v26dMHX331Ffz9/YXzTp8+faDRaNCpUyeEh4dj7dq1iI6Oxty5cyGRSIRtZWZm4vvvv8eaNWtw5MgR3L59W+ix+brSV/8F3bt3D507d0aTJk1w4cIFLF26FMuXL8fs2bMNUFpm1AwcfGSvsfynGhEREQSAbt68qTddlSpVaM6cOVrLmjRpQsOGDSMi/T0jChs2bBj17NlTZ9sFLV++nPz8/ISnsEREOTk5ZGpqSnv37iUiIldXV5o7d66wXqlUkru7u1H2/JPJZASADhw4oLM+v6dYbm4uOTk50eHDhyk9PZ0sLS3pwoULNHr0aK3eaR4eHrR+/XqtPGbNmkWBgYFFlmH+/PkUEBCgtU0zMzOt3mhjx46lZs2aERE9tx0YC67bl7dlyxaytbUlhUJBLVq0oIkTJ9KFCxeE9ShhL6p8//zzDwGgrKwsIiJq0aIFDR06VGubvXv3ps6dO+vNk0i3F9WUKVOoQ4cOWnncuXOHANDVq1eJKK8XxejRo7XS5Jcvv8cAEVF6ejopFAoKDw/XSjt48GD64IMPnl9hzOC4zebhNsvYizt58iQBoG3btmktt7e3J3NzczI3N6dx48YRUd6x5PPPP39unvquZ+3s7CgjI0NYtnTpUrKwsCC1Wk1Eeb//N954QyufJk2a0Pjx419634xd4ev8U6dOkb29Pb333nvUqVMnqlev3nPzyO+9Zm5uTmZmZkIPtnfeeacMS24cBgwYQBKJRGinAMjV1ZUiIiKK/I6+a9P69etrpdm7dy+JxWLh3FRYfp1fu3ZNWLZ48WJydnZ+tR2qYJ5X/4WvDb7++mude9jFixdrHQcYI+Kef8wI1K9fH+3atUPdunXRu3dvLFu2DGlpaQCAJ0+e4P79+2jZsqXWd1q2bImYmJgi8/z111/RuHFjODo6wsLCAsuWLcPt27eLLUdERASuXbsGS0tLoceHnZ0dsrOzcf36dTx+/BgJCQkIDAwUvmNiYoLGjRu/wt6XnXr16sHLywtTp07F06dP9aaRSqXo168fQkNDsXnzZvj6+qJevXpaaZKTk3Hnzh0MHjxYqBcLCwvMnj0b169fF9Jt2bIFb7zxBlxcXGBhYYEpU6bo1LmXlxcsLS2Fv11dXZGUlASg+HZgbLhuX07Pnj1x//597NixA8HBwQgLC0OjRo20ekg+T8E6dHV1BQBhP2NiYl74WFFYREQEDh06pPXvUbNmTQDQ+jcpiejoaGRnZ6N9+/Za+a1evfqF82KGwW2W2yxjr6pg7z4AOH36NCIjI+Hv76/Vq1jf9WRJrmfr168PMzMz4e/AwECkp6fjzp07wrLC1x8FrxEqq127dsHCwgIKhQKBgYFo3bo1fv75ZxCRzr9JUSwtLREZGYmIiAj8+uuvqF69On799dcyLrlxaNu2LSIjIxEZGYlTp06hQ4cO6NSpE27dugWgZNemhUVGRsLd3R2+vr5FpjEzM0P16tWFv1+HtqrP8+q/oJiYGAQGBmq165YtWyI9PV1n/HH2euNRIpnBSSQS7N+/H+Hh4di3bx9+/vlnTJo0CadOnRK6Nhc+SRd34t60aRO++OILLFiwAIGBgbC0tMR3332HU6dOFVsOjUaDgIAArFu3Tmedo6PjS+6d4bi5uWHr1q1o27YtOnbsiD179mgFh/INGjQIzZo1w6VLl7ReR8un0WgA5L2e2qxZM611+d30T548iffffx8zZsxAcHAwrK2t8eeff2LBggVa6Qu/JiESiYT8i2sH1apVe/mKKANcty9PoVCgffv2aN++PaZOnYohQ4Zg2rRpCAkJKdH3C+5n/jEgfz8LLstX8FghFouFZfkKD6qu0WjQtWtXzJs3T2fb+YGbksov1z///AM3NzetdTxJRMXBbTYPt1nGXoyPjw9EIhGuXLmitdzb2xtA3muoBRV+Nfhlr2fzFTy2FHeNUFm1bdsWS5cuhVQqRZUqVYQ68PX1xbFjx6BUKp/7+q5YLIaPjw8AoGbNmkhMTESfPn1w5MiRMi+/oZmbmwv7DgABAQGwtrbGsmXL0KVLlxJdmxZWuM3ro6+tkpEOAVSWiqv/IUOGaKXVd1+cX2clDXSz10Ol7vmXPz32izyhfxEFp4C/efNmmWzjdSESidCyZUvMmDED58+fh0wmw/bt22FlZYUqVarg2LFjWunDw8NRq1YtvXkdPXoULVq0wLBhw9CwYUP4+Pjo9FiQyWRQq9Vayxo1aoS4uDg4OTnBx8dH62NtbQ1ra2u4urri5MmTwndUKhUiIiJKqRZKX9WqVXH48GEkJSWhQ4cOePLkiU4af39/+Pv749KlS/jwww911js7O8PNzQ03btzQqZf8wNHx48fh6emJSZMmoXHjxqhRo4beJ1PPU1Q7MEZct6Wjdu3ayMjIKJW8atWqVeyxIj+In5CQIKyPjIzUSt+oUSNcvnwZXl5eOv8m+Tdm+o4f+WM5Flxeu3ZtyOVy3L59WycvDw+PUtlnVv64zRqnNm3a4PPPPzd0MRgT2Nvbo3379vjll19e6phRkutZALhw4QKysrKEv0+ePAkLCwu4u7u/Uvkruvzgiaenp1ZA6cMPP0R6ejqWLFmi93uPHj0qMs8vvvgCFy5cMIrrp/ImEokgFouRlZVVomtTfeedevXq4e7du4iNjS3PolcKBeu/sNq1ayM8PFwrSBoeHg5LS0udB3ms7Hh5eeHHH380dDGKVW7BvzZt2giBsvr162utS01NhampqbB+woQJJc63YACOVUynTp3CN998g7Nnz+L27dvYtm0bkpOThZufsWPHYt68edi4cSOuXr2KCRMmIDIyEqNHj9abn4+PD86ePYu9e/ciNjYWU6ZMwZkzZ7TSeHl5ISoqClevXkVKSgqUSiX69u0LBwcHdOvWDUePHkV8fDwOHz6M0aNHC12mR48ejblz52L79u24cuUKhg0bVuxFgjFwd3dHWFgYUlNT0aFDBzx+/FgnzcGDB5GQkFDkIMfTp0/Ht99+i0WLFiE2NhYXL15EaGgofvjhBwB5dX779m38+eefuH79On766acXvjB6XjswRly3JZeamoo333wTa9euRVRUFOLj47F582bMnz8f3bp1K5VtjB07FitXrsSvv/6KuLg4/PDDD9i2bZswULSpqSmaN2+OuXPnIjo6GkeOHMHkyZO18hg+fDgePnyIDz74AKdPn8aNGzewb98+DBo0SLiI9fLywqlTp3Dz5k2kpKRAo9HA09MTIpEIu3btQnJyMtLT02FpaYkxY8bgiy++wKpVq3D9+nWcP38eixcv1prQpaIQiUT466+/9K4LCwuDSCTSOh7+9ttvqF+/PszNzWFjY4OGDRtq9U6bPn06GjRoULaFfgXcZitWm922bRtmzZoF4PmBwMJt+dChQ2jbti3s7OxgZmaGGjVqYMCAAVCpVAD0t+/KKiQkBN27d9datmXLFigUCsyfPx/Tp08XrrtFIhGsra3RqlUrnQlhvLy8tNLlf+bOnauTh75PZXmovmTJEqhUKjRu3BgbN25ETEwMrl69irVr1+LKlStaEx0UVpLrWQDIzc3F4MGDER0djd27d2PatGkYMWKE0HOYaWvWrBnGjRuHr776CuPGjcOJEydw69YtHDhwAL179y72WGdlZSX0/q7svdFycnKQmJiIxMRExMTEYOTIkUhPT0fXrl1LdG3q5eWF+Ph4REZGIiUlBTk5OQgKCkLr1q3Rs2dP7N+/H/Hx8di9ezf27NljoL00XsXVf2HDhg3DnTt3MHLkSFy5cgV///03pk2bhi+//PK1PA487/zyvDc3irverfDKa3DBoKAgrem+Dx8+LKybO3eu1roXGYC24FTiheUvz59avLQV3HZ8fHyZbKMyyx+MNzo6moKDg8nR0ZHkcjn5+vrSzz//LKRTq9U0Y8YMcnNzI6lUSvXr16fdu3cL6wsPepqdnU0hISFkbW1NNjY29Nlnn9GECRO0Bp1NSkqi9u3bk4WFhdbA6QkJCdS/f39ycHAguVxO3t7eNHToUHr8+DER5U3wMXr0aLKysiIbGxv68ssvqX///kY54UfhMt2/f5/8/PyoSZMmNHr0aJ1BeAsqPCkFEdG6deuoQYMGJJPJyNbWllq3bq01iPXYsWPJ3t6eLCwsqE+fPrRw4UKytrYW1usb+HfhwoXk6elJRPTcdmAsuG5fTnZ2Nk2YMIEaNWpE1tbWZGZmRn5+fjR58mTKzMwkopJNnlBwcoLz58/rHH+XLFlC3t7eJJVKydfXl1avXq1VjujoaGrevDmZmppSgwYNaN++fVrHACKi2NhYevfdd8nGxoZMTU2pZs2a9PnnnwsDKV+9elXIo+D2Z86cSS4uLiQSiWjAgAFERKTRaGjRokXk5+dHUqmUHB0dKTg4WOscWFEU/PcprPC/zx9//EFmZmb0xx9/UFxcHF26dInWr19PkydPFr6jr90aE26zFbfN6pvgpKCC/26XLl0iuVxOY8eOpYsXL9K1a9do9+7dNHjwYMrJySEi/f+WlVXhc9yyZctIJpPRsmXLiCjvd+vv708JCQmUkJBAly9fpoEDB5KFhQU9evRI+J6npyfNnDlTSJf/SU9Pp6dPn2otc3d310mrUqnKe9fLzP3792nEiBFUrVo1kkqlZGFhQU2bNqXvvvtOmKhD3/G1JNez+f9eU6dOFa4ThgwZQtnZ2UIafb+Hbt26Cb/5ykjftVphGzdupNatW5OlpSWZm5tTvXr1aObMmcLvPDQ0VOtaK9+tW7fIxMSENm7cWPoFNxIDBgzQuje3tLSkJk2a0JYtW4Q0z7s2zc7Opp49e5KNjY3W/XhqaioNHDiQ7O3tSaFQUJ06dWjXrl1EpL/Ot2/frvc+vzJ7Xv3rmwwsLCyMmjRpQjKZjFxcXGj8+PGkVCoNtAeGVfBc8uOPP5KVlZXWsoLnKn2Ku94tjqenJy1cuPDlCl1Oyj34J5VKCQD16tWLiIhUKhVVrVpVWF4w+Pfo0SMaNWqUsN7NzY2++OIL4URZ+IeR/5k2bVrezv3/7x9//JEGDBhAlpaWVKVKFZo1a5ZW2VJTU2nYsGHk7u5OJiYm5OTkRP369aNbt25ppfvpp5+oSpUqZG5uTh9++CH9+OOPHPxjjDFWaajVapo7dy5Vr16dZDIZeXh40OzZs4noxYJ/3bp1o5CQkGK3ZezBP1axFAxwvEjwb+HCheTl5VVs3q9r8G/evHkkl8u1bvj1/W5v375NAOj06dPCshe5CaoIN0zGqiRBLsYYe13pCygX99DV09NTK66U35Hi2rVr9M4775CTkxOZm5tT48aNaf/+/Vr5VoRzWbn3A23QoAG8vb3x119/4e7du9ixYwdu376NXr16aaXLyclBmzZt8NNPPyEpKQm1atVCamoqFi5ciK5du4KIUL16dWHQXCCvG3ezZs10xriYOHEi9u3bB7lcjvv372PKlCnYv38/ACA7OxtBQUFYsmQJEhMT4evriydPnmDt2rUIDAxEcnIyAGDnzp0YNWoU7t+/DzMzMxw9ehSTJk0q49pijDHGys/EiRMxb948TJkyBdHR0Vi/fj2cnZ1fOB8XFxecPHnypcanZKw8ubi4ICEh4bUYwP9FTJgwAbNmzcKuXbvQs2fPItPl5ORg5cqVsLGxgZ+fXzmWkDHGGHsx27dvx+jRo/HVV1/h0qVL+OSTTzBw4EAcOnQIAIShFUJDQ5GQkCD8nZ6ejs6dO+O///7D+fPnERwcjK5duz53hmtjU+7BP7FYjOHDh0OlUmHp0qX4+eefAQAjR47USvfnn38iMjISMpkMUVFRuHDhgjDRwsGDB3Hw4EFMmTIFU6ZMEb5z8uRJnDx5UmcGnPr16+PmzZuIiYkRBnw9cOAAAGDDhg24dOkSAGDz5s24fPkyjh8/DrFYjPv37+OXX34BAMyfPx8AUL16ddy4cQPx8fFo0qRJaVcPY4wxZhBPnz7FokWLMH/+fAwYMADVq1fHG2+8oXNOLYlp06bBxsYGXl5e8PPzQ0hICDZt2lTpZ5dkFU/v3r3xwQcfICgoCK6urnj33Xfxyy+/6J3E6XWxe/duzJs3D3///TfeeustnfUXL16EhYUFLCwsYGpqiu+//x4bNmyAlZWVVrrx48cL6fI/YWFh5bQXjDHGmLbvv/8eISEhGDZsGHx9ffHll1+iR48e+P777wE8m2jNxsYGLi4uwt/169fHJ598grp166JGjRqYPXs2vL29sWPHDoPty8swyAiQgwYNgrm5OX7++WccOnQIAQEBCAwM1Epz+vRpAHmD2Pr6+kIkEmkNDF5wxtXn6dOnD2QyGRwcHODk5AQAePDgAYBn0V0zMzNhgONGjRoJTy/Pnj0LALh8+TIAIDg4GBYWFpBIJOjRo8cL7jljjDFmnGJiYpCTk4N27dq9cl6urq44ceIELl68iFGjRkGpVGLAgAHo2LEjBwCZUZFIJAgNDcXdu3cxf/58VKlSBXPmzIG/v7/WLMuvk3r16sHLywtTp07F06dPddb7+fkhMjISkZGRiIiIwGeffYbevXsL18z5xo4dK6TL/zRr1qy8duO1sHLlyso7MD1jjJWymJgYtGzZUmtZy5YtERMTU+z3MjIyMG7cONSuXRs2NjawsLDAlStXuOdfSdjY2KBfv37CBUXhXn8AhBmUZDKZ8DpvwY+tre0LbS+fiYmJVv4vquCswi+bB2OMMWZsTE1NSz3POnXqYPjw4Vi3bh3279+P/fv368wKypgxcHNzw0cffYTFixcjOjoa2dnZ+PXXXw1dLINwc3PD4cOHkZCQgI4dO+oEAGUyGXx8fODj44OGDRti7ty5cHNzw48//qiVzsHBQUiX/ymL4wxjjDFWUgXjOUBeTKfwssLGjh2LrVu3Ys6cOTh69CgiIyNRt25d5ObmlmVRS53B5n4eMWIEgLwLg/fff19nfdOmTQEAarUaS5YsEV7pDQsLw9ixY/Hhhx8CyOuxly8jI+OFy5H/6m5mZqbw5OzcuXO4evUqAKBx48YAAH9/fwDAvn37kJGRAbVazU/aylCbNm3w+eeflyhtWFgYRCIRHj169Erb9PLy0rlwZYwZFz42lJ0aNWrA1NRUGBajtNWuXRvAy52rK6IXaaul5ebNmxCJRIiMjCzX7VY2tra2cHV1fW3aqj5Vq1bF4cOHkZSUhA4dOjz3NWiJRIKsrKxyKh17EdOnT9d6e+p58sdwZMV7Xa4NDEkkEvH9toFU1uuJWrVq4dixY1rLwsPDUatWLeFvqVQKtVqtlebo0aMICQnBu+++i7p168LFxQU3b94sjyKXKoMF/+rUqYPU1FRcv34dcrlcZ/0HH3yAevXqQa1Wo0mTJqhTpw78/PxgY2ODXr16CTdzNWvWFL5Tu3ZtNG/eHMePHy9xOT744AMhsNe7d2/4+/ujZcuW0Gg0qFKlihCkHDNmDAAgLi4O3t7e8Pb2Rnh4+MvuPmNGKSQkBCKRCHPnztVa/tdffwlPRPIDKra2tsjOztZKd/r0aYhEIq2nJ6UVgKlMwsPDIZFI0LFjR63l+Sfa/I+1tTWaN2+OnTt3aqXjC3NWFhQKBcaPH49x48Zh9erVuH79Ok6ePInly5cLaeLj43Ve40tPT9fJ67PPPsOsWbNw/Phx3Lp1CydPnkT//v3h6OioM8wH04+Pna8mOTlZp60mJibqpPvtt9/w2WefYd++fbh+/TouX76M8ePH4/Lly+jatasBSm483N3dERYWhtTUVHTo0AGPHz8GAKhUKiQmJiIxMRFxcXGYPXs2oqOj0a1bN63vP336VEiX/3mdx1IsD/qCJWPGjHmhhzp9+vRBbGys8PeLBg9fF2fOnMHHH39s6GJUCkW1sYSEBHTq1Kn8C/SaCQkJEYY/y+fh4YGEhATUqVPHMIUqI2PHjsXKlSvx66+/Ii4uDj/88AO2bdsmxHqAvMD+gQMHkJiYiLS0NACAj48Ptm3bhsjISFy4cAEffvhhhRzGxmDBPwCws7PTGRw4n1wux+HDhzFq1Ch4eHggNjYWaWlpaNy4MebMmSPMPlivXj1MmTIFzs7OuH37Nk6dOiX8I5WEQqHAkSNHMGzYMLi4uCA2NhZWVlbo168fTpw4IQzy2K1bNyxcuBAuLi54+vQpGjdujNmzZ796JTBmZBQKBebNm/fc35GlpSW2b9+utWzFihWoWrVqWRavUlixYgVGjhyJY8eO6R0r4r///kNCQgJOnTqFpk2bomfPnsLERIyVpSlTpuCrr77C1KlTUatWLfTp0wdJSUnC+i+//BINGzbU+hQe5wsA3nrrLZw8eRK9e/eGr68vevbsCYVCgQMHDsDe3r48d4m9ptavX6/TVvW9xtu0aVOkp6fj008/hb+/P4KCgnDy5En89ddfCAoKMkDJjUv+K8CPHj1C+/bt8ejRI1y+fBmurq5wdXVFgwYNsGnTJixduhT9+/fX+u7UqVOFdPmfcePGGWhPXl8WFhYvdNw1NTUVxkhnuvJf83N0dNR6A62yK+r1RqVSWWbbdHFx0dtJiOW9qqpSqcosf4lEAhcXF2HItMqie/fuWLRoEb777jv4+/vjt99+Q2hoKNq0aSOkWbBgAfbv3w8PDw80bNgQALBw4ULY2tqiRYsW6Nq1K4KDg9GoUSMD7cUrIMaMUFBQEI0ePZqIiNasWUMBAQFkYWFBzs7O9MEHH9CDBw+EtIcOHSIAtGvXLqpXrx7J5XJq2rQpRUVFaeV5/PhxatWqFSkUCnJ3d6eRI0dSenq6sN7T05MWLlwo/D1t2jTy8PAgmUxGrq6uNHLkyDLdZ2MwYMAA6tKlC9WsWZPGjh0rLN++fTvlHy7y63vy5Mn01ltvCWkyMzPJ2tqapkyZQgUPLfnp09LSym0/jFl6ejpZWlrSlStXqE+fPjRjxgxhXXx8PAGg8+fPC8uePHlCAOinn34SloWGhpK1tXU5ltp4GMOxgbGSCAoKouHDh9Pw4cPJ2tqa7OzsaNKkSaTRaIio+Pabfywo+BkwYAAREanVapo7dy5Vr16dZDIZeXh40OzZs7W+t3XrVmrTpg2ZmppSvXr1KDw83CB1wFhFUtxvKyoqitq2bUsKhYLs7Oxo6NCh9PTpU+G7AwYMoG7dutF3331HLi4uZGdnR8OGDaPc3FwhjaenJ82ZM4cGDhxIFhYW5OHhQb/99ptWGe7evUvvvfce2djYkJ2dHb3zzjsUHx+vlWb58uVUu3Ztkslk5OLiQsOHDxfyL3jM8PT0JKK869n69esTEdGePXtILpfrXJONHDmSWrduTUTa1xihoaE6x6LQ0FAaOHAgvf3221p5KJVKcnZ2puXLl5dZ/c6ZM4ecnJzI2tqapk+fTkqlksaMGUO2trbk5uamte384+GGDRsoMDCQ5HI51a5dmw4dOiSkUalUNGjQIPLy8iKFQkG+vr70448/apU7f9vffPMNubq6CvX6IvcNDx8+pI8++ohsbGzI1NSUOnbsSLGxscL6/Drfs2cP1axZk8zNzSk4OJju37+vty5LqjTqvOB+59fpxo0bKSgoiORyOa1YsYKIiFasWEE1a9YkuVxOfn5+tHjxYq2y3Llzh/r06UO2trZkZmZGAQEBdPLkySLbGBERANq+fbuQR2n8Dsu6vorb9uLFi8nHx4fkcjk5OTlRz549hXUajYbmzZtH1apVI4VCQfXq1aPNmzcL6/Ovaffs2UMBAQEklUrp119/JQAUExOjtR8LFiwgT09P0mg0z23j06ZN06n/Q4cO6b0nCQsLoyZNmgjHnvHjx5NSqRTWBwUF0ciRI2ns2LFka2tLzs7ONG3atBLVPSsfHPxjRqngDf7y5cvp33//pevXr9OJEyeoefPm1KlTJyFt/sGwVq1atG/fPoqKiqIuXbqQl5eXcMCNiooiCwsLWrhwIcXGxtLx48epYcOGFBISIuRT8CS+efNmsrKyon///Zdu3bpFp06dot9//73c9t9Q8k9c27ZtI4VCQXfu3CEi/cG/q1evklwup1u3bhFR3o1s/fr1tdIWTM/BvzzLly+nxo0bExHRzp07ycvLSwgGFD7R5ubm0oIFCwgALV26VMiDg3+jicgwxwbGSiooKIgsLCxo9OjRdOXKFVq7di2ZmZkJ55Li2q9KpaKtW7cKx9qEhAR69OgRERGNGzeObG1taeXKlXTt2jU6evQoLVu2jIieHUNq1qxJu3btoqtXr1KvXr3I09NT6wKdMaarqN9WRkYGValShXr06EEXL16kAwcOULVq1YSAPFHe9ZOVlRV9+umnFBMTQzt37tT6vRPlnUvs7Oxo8eLFFBcXR99++y2JxWLhxj0jI4Nq1KhBgwYNoqioKIqOjqYPP/yQ/Pz8KCcnh4iIlixZQgqFgn788Ue6evUqnT59Wjg/JSUlCYGThIQESkpKIiLt4J9KpSJnZ2f6448/hHLlL8sPRBa8xsjMzKSvvvqK/P39KSEhgRISEigzM5OOHz9OEolEKzj1999/k7m5uVZApDTr19LSkoYPH05Xrlyh5cuXEwAKDg6mOXPmUGxsLM2aNYukUindvn2biJ4dD93d3WnLli0UHR1NQ4YMIUtLS0pJSSGivOusqVOn0unTp+nGjRvCcXrjxo1a27awsKCPPvqILl26RBcvXhT+PUt63/DOO+9QrVq16MiRIxQZGUnBwcHk4+MjXIuEhoaSVCqlt956i86cOUMRERFUq1Yt+vDDD/XWZUm9ap0X3u/8OvXy8qKtW7fSjRs36N69e/T777+Tq6ursGzr1q1kZ2dHK1euJCKip0+fkre3N7Vq1YqOHj1KcXFxtHHjRgoPDy+yjRFpB/9K63dYlvVV3LbPnDlDEomE1q9fTzdv3qRz587RokWLhO9//fXXVLNmTdqzZw9dv36dQkNDSS6XU1hYGBE9u6atV68e7du3j65du0YpKSkUEBBAkydP1tqPgIAAmjhxIhE9v40/ffqU3nvvPerYsaNQ/zk5OTr3JHfv3iUzMzMaNmwYxcTE0Pbt28nBwUEruBcUFERWVlY0ffp0io2NpVWrVpFIJKJ9+/aVqP5Z2ePgHzNKBW/wCzt9+jQBEC4u8g+Gf/75p5AmNTWVTE1NhQPbRx99RB9//LFWPkePHiWxWExZWVlEpH0SX7BgAfn6+pb4SVFlkR/8IyJq3rw5DRo0iIj0B//S0tKoe/fuQs+1tm3b0qJFizj49xwtWrQQnrgplUpycHCg/fv3E9GzC1VTU1MyNzcnsVgsXGSlpqYKeXDwb7TedeVxbGCspIKCgqhWrVpCcJ+IaPz48VSrVi296YtqvwWPnU+ePCG5XC4E+wrLP4YUvLG/fPmy3p4BjLFnivtt/f7772Rra6vVI/yff/4hsVhMiYmJRJR3/eTp6UkqlUpI07t3b+rTp4/wt6enJ/Xr10/4W6PRkJOTk/Bwb/ny5eTn56d1zMjJySFTU1Pau3cvERFVqVKFJk2aVOR+FO4pRaQd/CMiGjVqFL355pvC33v37iWZTEYPHz4kIt1rjMLfz1e7dm2aN2+e8Hf37t21HpwVVFr1q1arhTR+fn7UqlUr4W+VSkXm5ua0YcMGInp2PJw7d66QRqlUkru7u1a5Cxs2bJhWj6wBAwaQs7OzEIDNV9L7htjYWAJAx48fF5alpKSQqakpbdq0iYie9bC8du2akGbx4sXk7OxcZDmfpzTqvPB+59dp4d6RHh4etH79eq1ls2bNosDAQCIi+u2338jS0lLrWragotpYwfZcWr/DopT1MWDr1q1kZWVFT5480ck/PT2dFAqFTi/9wYMH0wcffEBEz64J/vrrL600P/zwA3l7ewt/X716lQDQ5cuXi9xXfW08//4vX+Hg39dff61zfFq8eDFZWFgIv8ugoCB64403tPJp0qQJjR8/vsiysPJl0DH/GCuJ8+fPo1u3bvD09ISlpaXwTn7hsdIKDiJvZ2cHPz8/xMTEAAAiIiKwcuVKWFhYCJ/g4GBoNBrEx8frbLN3797IysqCt7c3hg4diu3bt5fpuArGaN68eVi1ahWio6OLTDNo0CCsXLkSN27cwIkTJ9C3b99yLGHFc/XqVZw+fVqY4dzExAR9+vTBihUrtNJt3LgR58+fx44dO+Dj44M//vgDdnZ2hiiyUTPEsYGxF9G8eXOtCZACAwMRFxcHtVpd4vZbUExMDHJyctCuXbtit1uvXj3h/11dXQFAa+xGxpi24n5bMTExqF+/PszNzYVl+ZMDXr16VVjm7+8PiUQi/O3q6qrzuyv42xSJRHBxcRHSRERE4Nq1a7C0tBTOR3Z2dsjOzsb169eRlJSE+/fvP/f3/zx9+/ZFWFgY7t+/DwBYt24dOnfuDFtb2xfKZ8iQIQgNDQWQd3z5559/MGjQIL1pS6t+xeJnt67Ozs6oW7eu8LdEIoG9vb1OnRe8BjAxMUHjxo2FawAA+PXXX9G4cWM4OjrCwsICy5Yt0zkO161bFzKZrMi6KO6+ISYmBiYmJmjWrJmQ3t7eXutaBADMzMxQvXp14W997edFlEadF7XfjRs3Fv4/OTkZd+7cweDBg7WupWbPno3r168DACIjI9GwYcNXupYtzd9hUfmX5TGgffv28PT0hLe3Nz766COsW7cOmZmZAIDo6GhkZ2ejffv2WnWYP/laQQXrHgDef/99YWI1IO/33KBBA9SuXVtIU5I2XpL6CQwM1LqmadmyJdLT03H37l1hWcFjXOE6YIZXuUZwZJVORkYGOnTogA4dOmDt2rVwdHTE7du3ERwcXOTAswXlH6A0Gg0++eQTjBo1SieNvgkqPDw8cPXqVezfvx///fcfhg0bhu+++w6HDx+GVCp99R2rAFq3bo3g4GB8/fXXCAkJ0Zumc+fO+OSTTzB48GB07dqVB/J/juXLl0OlUsHNzU1YRkSQSqVaE6x4eHigRo0aqFGjBiwsLNCzZ09ER0fzANwFGOrYwFhpyM7Ofqn2a2pqWqL8C56nCrZ1xph+xf22iEjrhreggssLXx+KRCKd311xaTQaDQICArBu3Tqd7Tg6OmoFvl5F06ZNUb16dfz555/47LPPsH37diGI9yL69++PCRMm4MSJEzhx4gS8vLzQqlUrvWnLqn5LUufF5btp0yZ88cUXWLBgAQIDA2FpaYnvvvsOp06d0kpfMOijT3H3DUSk9zuF91vfvhT13ZIojTovar8LLs+v72XLlmkFOAEIgbCSnruKU5q/Q33K+hhgaWmJc+fOISwsDPv27cPUqVMxffp0nDlzRkjzzz//aN0jANCZ8KTwv4mrqyvatm2L9evXo3nz5tiwYQM++eQTYX1J2/jz6KuD/PZZGvXPygf3/GNG7cqVK0hJScHcuXPRqlUr1KxZs8inB/lPPAAgLS0NsbGxqFmzJgCgUaNGuHz5Mnx8fHQ+RT3JMzU1xTvvvIOffvoJYWFhOHHiBC5evFj6O2nE5s6di507dyI8PFzveolEgo8++ghhYWFFPu1leVQqFVavXo0FCxYgMjJS+Fy4cAGenp56L/YBICgoCHXq1MGcOXPKucTGzZDHBsZKqmDby/+7Ro0aJWq/+e1PrVYLy2rUqAFTU1McOHCg7AvP2GukuN9W7dq1ERkZiYyMDGHZ8ePHIRaL4evrW2plaNSoEeLi4uDk5KRzPrK2toalpSW8vLyK/f1LpVKtY0ZRPvzwQ6xbtw47d+6EWCzG22+/XWRamUymN097e3t0794doaGhCA0NxcCBA4vMw5D1W/A4rFKpEBERIVwDHD16FC1atMCwYcPQsGFD+Pj46PS0Kqmi7htq164NlUqlFWxJTU1FbGwsatWq9Wo7V4zyqnNnZ2e4ubnhxo0bOu22WrVqAPJ6g0VGRuLhw4d68yiqjZVVmfUpj/oyMTHBW2+9hfnz5yMqKgo3b97EwYMHUbt2bcjlcty+fVunDj08PJ6bb9++fbFx40acOHEC169fF94wAkrWxkta/+Hh4VoB6fDwcFhaWuoELJnx4uAfM2pVq1aFTCbDzz//jBs3bmDHjh2YNWuW3rQzZ87EgQMHcOnSJYSEhMDBwQHdu3cHAIwfPx4nTpzA8OHDERkZibi4OOzYsQMjR47Um9fKlSuxfPlyXLp0CTdu3MCaNWtgamoKT0/PstpVo1S3bl307dsXP//8c5FpZs2aheTkZAQHB5djySqeXbt2IS0tDYMHD0adOnW0Pr169cLy5cuL/O5XX32F3377Dffu3SvHEhs3Qx0bGHsRd+7cwZdffomrV69iw4YN+PnnnzF69OgStV9PT0+IRCLs2rULycnJSE9Ph0KhwPjx4zFu3DjhdaCTJ08We/xgjD1fcb+tvn37QqFQYMCAAbh06RIOHTqEkSNH4qOPPoKzs3OplaFv375wcHBAt27dcPToUcTHx+Pw4cMYPXq08Frd9OnTsWDBAvz000+Ii4vDuXPntK7R8oODiYmJWm8U6NvWuXPnMGfOHPTq1QsKhaLItF5eXoiPj0dkZCRSUlKQk5MjrBsyZAhWrVqFmJgYDBgwoMg8DFm/ixcvxvbt23HlyhUMHz4caWlpwgNrHx8fnD17Fnv37kVsbCymTJmCM2fOvPA2irtvqFGjBrp164ahQ4fi2LFjuHDhAvr16wc3Nzd069btlfevKOVZ59OnT8e3336LRYsWITY2FhcvXkRoaCh++OEHAMAHH3wAFxcXdO/eHcePH8eNGzewdetWnDhxAkDxbSxfWbeTsq6vXbt24aeffkJkZCRu3bqF1atXQ6PRwM/PD5aWlhgzZgy++OILrFq1CtevX8f58+exePFirFq16rl59+jRA0+ePMFnn32Gtm3bagXjStLGvby8EBUVhatXryIlJQVKpVJnG8OGDcOdO3cwcuRIXLlyBX///TemTZuGL7/8stR6JbOyx/9SzKg5Ojpi5cqV2Lx5M2rXro25c+fi+++/15t27ty5GD16NAICApCQkIAdO3YIPSfq1auHw4cPIy4uDq1atULDhg0xZcoUYSykwmxsbLBs2TK0bNkS9erVw4EDB7Bz587X8rXWWbNmFfvagUwmg4ODQ5Hd4Vme5cuX46233oK1tbXOup49exb7RLRLly7w8vLi3n8FGOrYwNiL6N+/P7KystC0aVMMHz4cI0eOxMcff1yi9uvm5oYZM2ZgwoQJcHZ2xogRIwAAU6ZMwVdffYWpU6eiVq1a6NOnD4+nw1gpKOq3ZWZmhr179+Lhw4do0qQJevXqhXbt2uGXX34p1e2bmZnhyJEjqFq1Knr06IFatWph0KBByMrKgpWVFQBgwIAB+PHHH7FkyRL4+/ujS5cuiIuLE/JYsGAB9u/fDw8PDzRs2LDIbdWoUQNNmjRBVFTUc8dr7tmzJzp27Ii2bdvC0dERGzZsENa99dZbcHV1RXBwMKpUqVJsPoaq37lz52LevHmoX78+jh49ir///hsODg4AgE8//RQ9evRAnz590KxZM6SmpmLYsGEvvI3n3TeEhoYiICAAXbp0QWBgIIgI//77b5kPJVRedT5kyBD88ccfWLlyJerWrYugoCCsXLlS6Pknk8mwb98+ODk5oXPnzqhbty7mzp0rvBZcXBvLVx6/w7KsLxsbG2zbtg1vvvkmatWqhV9//RUbNmyAv78/gLz7ralTp+Lbb79FrVq1EBwcjJ07dwp1WBwrKyt07doVFy5c0Pk9l6SNDx06FH5+fsK4gMePH9fZhpubG/7991+cPn0a9evXx6efforBgwdj8uTJJa4DZngiepXBBBhjjDHGGGOMvXYyMzNRpUoVrFixAj169DB0cbTcvHkT1apVw/nz59GgQQNDF4cxxgyOJ/xgjDHGGGOMMVYiGo0GiYmJWLBgAaytrfHOO+8YukiMMcaeg4N/jDHGGGOMMcZK5Pbt26hWrRrc3d2xcuVKmJjwLSVjjBk7fu2XMcYYY4wxxhhjjLFKiif8YIwxxhhjjDHGGGOskuLgH2OMMcYYY4wxxhhjlRQH/xhjjDHGGGOMMcYYq6Q4+McYY4wxxhhjjDHGWCXFwT/GGGOMMcYYY4wxxiqpF5qXPTMzE1euXCmrsjDGGGOMMcYYY0yPrKws3Lx5E15eXjA1NTV0cRhjBlazZk2YmZmVKO0LBf+uXLmCgICAlyoUY4wxxhhjjDHGGGPs1UVERKBRo0YlSisiIippxtzzjzHGGGOMMcYYK38xMTHo168f1q5di1q1ahm6OIwxAyuznn9mZmYljioyxhhjjDHGGGOsdNWqVYvvyxljL+SFgn+MGbtMZSbup9/Hvaf3kZyZDKUmFxrSwERsAnOpOVwtqsDNogoczR0hEUkMXdwKQ63R4P7TBFx/GI8bafFIy3qMXHUuxCIx5CYyVLF0RXW7aqhuWw02ptaGLm6F8iQzHVG3onHuxiVE341DRnYmlGoV5CYy2FpYo55nTTT0roPa7r6QS2WGLm6FQURIzkzBrce3cevxbTzMegilWgmIRJCJpXA0d4CndVV4WleFrcIWIpHI0EWuMDSkgVKTi1x1DnI1uSDSgEAQQQSRSAyZWAaZRA6pWAaxiOcVKykiAhFBTWpoNGpoSAPkv5shAsQiMcRiCSQiCUQiEbfZF0BE0Gg00KjzPgVfehGJRBCLxRBL8j5cry9GQ5q8utVo/t9m/1+3IhFEEEEizmu33GZfjFqjxv30BNx6lHcOS1emQ6lWQSISQ2Yig6uFK7ysq6KqdVVYyMwNXdwKRU3qvPOXOgdKTS4IGhCQ115FEsgkcsjEeecwbrOMscqEg3+swstUZuL8g/M4df80HmY/BJB3AhdBBA00z/4WifIuTAFIRBL42fmiWZXm8Lapxid3PYgIcanXsTtuP8LvnEauOhdAXt1p/n+zD0C4uc+vW3szO3So/ibaV28LW1Mbg5Td2KVnZ2BT+C4s27cel+5cBfD/G3uRCGrKuzEVi0SQiCVQqdUgEEzEErT2b45POvRFcIMgSMQcvNbn3pN7OHTrCE7eO41sVTYA3TZb+HhgKbNAS48WCPJsBUczB4OV3ZgRETJVGXia+xi5mpxi02YU+H+ZWA5LmTXMTMz5OFsEjUaDXHUulColnkX7nk8qkUJqIuNjQRGICGqVGspcJTRqTYm/JxaLYSIzgYnUhNtsETSkgUqlgkqlFI6rRVGpn/2/RGwCqVTKbbYIRIQrqVdxMD4MUUmXoKa8ytN73UX5ISvAycwRbbyC0NK9Ocw5EKiXmtTIUD7B09zHUJGq+MTK/P8RwVRiBiuZNeQSUz4eMMYqvBca848xY/IwKw1htw/hQlKU1kVRSYlFYmhIAzuFHd5wb4nGro25lwryLj6P3TqBrTE7cevRbaGeXkR+cKW5e2O8V6cHPG08yqi0FUvy41TM/2spVh/eisycLIhEIrzIIVgilkCtUcPF1gnDgvtjWMf+3Bvw/y4mXcKuuN24nnbjpdps/nfqOPqjq29nVLf1LqOSViwa0uBJ7iOk5z4RHqa8DDHEsJBZwUpmw8fZ/1OpVchV5UCtUT8/cTHEIgnkUjlMJPw8F8g7hylzlVDlql7o+KqPicwEMpkMIjHf9AOARqNGrjK3FNqsGFITKSQSDrACecfZw7eOYt+N/5CcmfLS110SsQTN3Zqia43OsDezL6PSVixKjRKPcx4iQ/X0lfIxEUlhJbOBhdTK4G323LlzCAgIeKFB/hljDODgH6uANKTBmYQz2H1jT95rJq9wQ1qQu6U7evn1hMNr3PMnNfMhFp9ehvMJURBB9MIB1cLyb/Lfr9sTPWp1fW2f9hMRtp3cjdErpiE9O/OVb5yAvFfVarhWwx/D5qNhtTqlUMqKKT03Axsub8Spe2dKrc1qSIP23u3Q3e8dyCWvb3A1S5WJh9nJQu+T0iARSWCvcILCxLTU8qxoiAg5yhwo/9+burSYiE2gkL3evVPUajVysnJAmtK9tJWbymEifX2Dq0QEpUoJpap026xYLIFcJn+tHwgkpCdi+fmVuPn4VqnkJxaJIRFJ8L5/b7Su+sZrezwgIqQrHyMtJ/WVrwsKkokVcDB1glRsuGsDDv4xxl4WB/9YhfIo+zG2XN2Cm49vlnreYuSN9RNcrQMC3QJfuwumsPhj+O1sKHLVuS/8xLkkvGyq4quWI+FuVaXU8zZmD9MfYcSyKdh5dv8L9/R7HolYAiLCV+98jEm9Rr52wdWoB5ew4sIqZCozS73NiiCCvakdPm40GN621Uo1b2OnIQ3SslNfuadEccxNLGGrsH/tbvrVahWycrNK9Wa0IBFEUMhMX7tegEQEZY4Sylzl8xO/JImJBHJT+Wt3baDRaJCdmw0qg+uCfHKpHCYm0jLL3xgREfbHH8DWmL9AoDK57qpp74vBDQa+dkOwqDRKpGQ/QI46u4y2IIKt3B6WUmuDHA84+McYe1kc/GMVRnJmMlZEhSIjN6PUevsVpXmVZuhcvfNrcWNKRNgavQProjaV6XbEIjEUJnJMbzsRNeyrl+m2jEViWhLe/iYE1xNvlUpvv6KIIMI7TdpjxYjvITN5PXqqHbsdjlVRawGgTAMpErEEIxp/ijpO/mWyDWOjIQ2SMhOeO65faZBLFHA0dXktjrMAoFIrkZWbVS7bUkgVkL4mxwIiQk5WDtSqsjvG5hOJRVCYKSAWvx5tVq1RIzunfNqs1EQG2WsyjIWGNFh/8U+E3T5aptsRi8SwklthbPMv4GzhVKbbMhZKTS4SM+9BU4o91otiKbWGrdyh3AOAHPxjjL2s1+PqhVV4qVmpWBb5BzKUZR/4A4CT909h17VdpdpLy1htufxXmQf+gLyL3WxVDqYcmI3rD+PLfHuGlvw4Fe1n9i3zwB+QF/zacXY/+v/0BVTq5wxkXQkcux2OlVFrQP//r6wQCGqNGj+dWYJLSdFlth1jUZ6BPwDIUWcjKTOhTHq8GJvyDPwBQLYyu9Rf0TRGRISczPIJ/AEAaQjZGdml/lqxMSrPwB8AKFW5yFW+Hm12TdT6Mg/8Af8fszXnCeaGf4fkjOQy356hKTW5SMy4Wy6BPwB4qnyMhznJr8W9AmOscuDgHzN6mcpMLL+wAtmq7HK9STydcAZht8PKbXuGsP/6Iay/uKXctqchDZQaFaYd/AYP0pPKbbvlLTs3B12/HYg7KffLPPCXj4jw77mD+CJ0Rrlsz1CiHlwUevyVBwKBiPDL2aW4+ah0xmQyRkSE5KzEcgv85cvV5CAl60GlvnlSa9TlGvjLl63MrvQPA3KycqBWl88xNh8RITszu1K3WY1GU66Bv3xKVf6s15XX37G7cPTO8XLbnoY0yFBm4rsTC5GRm/H8L1RQalLjQea9cukgUFC68gke56aV6zYZY+xlvV6DwrAKade1XXia+7RMe/gU5cCtg6hhVwPulu7lvu2ylvD0AZadXVXu29WQBlmqbPx08jfMajepUr7yN2frz4i+G1fuN4dEhJWHNqNTo7bo3OjNct12eUjPTceKyPJvswSCRqPBH+dDMa31JEgllW9sqnTlkzIcH6l42eospCufwlJmZZDtlyUiQlZupsG2n5WbBQuFRaUcp06lVJVbj7/CNBoNlDlKyBSV7zVVIkJOrmGOBQCQq8yBRCyplK9WX0+7gX/idpf7djWkwaPsR/jz8mYMbhhS7tsvD6U9OdWLeJz7EKYmZpBLFOW+7WrjdpX7Nstb/Pwu5b5NK+8h5b7NyubJjT/0LvdtvqScS1JxxJ4cVubbqHxnVlapRKdEIyr5okECf0DemF9brmyFSlO5ek9oSIOfTv5qsAslDWkQnXwF+64dNMj2y9LpuEgs+me5wXqFiEUiDP99MtIyHhtk+2Vp3cU/kakqu8kSiqOBBg8ykrAz7t9y33ZZU2qUSMt5aNAyPMpJhUpT+Xr85KhyDNxDjJBtgF6HZY00eeP8GZIyV1nuvQ7Lg0qtNPir+DnKytezMlediz/Or4Sh4vAaEE7cO4XIB1GGKUAZylSmI1OVbtAyVPYe7IyxyoGDf8xoZamy8Ffs3wYtA4GQkpVS6V7/3XvtAK6kxBr8Aj/0/FokZ6QYtAylSalSYujScQbtzaghQlrGI0xY863BylAWIhMv4ExChEHbLIGw+9pe3H58x2BlKG1EhIfZyYCBHrAI5QAhNbtyjUml1qiNYtw9lUYFlbpyBVZzsg0b+MuXk2Xo4G7p0mg0RjHunkajqXSvrO+M/RcpmSnQGLC9iCDCqgtrkKWsPA8ENKRGarbhh5FRkRKPcw37EI0xxp6Hg3/MaJ1LPI9MleFelyro+N1w5KiN42bjVak1Gmy5bNigaj6VRo1/Y/cZuhilZlfEAdx4cLvcxvkrilqjwYZjf+NeaqJBy1GadsXthgiGf3VRJBJhz/XK02ZzNTkGe923sBx1ttGUpTTkKI3nnJFjBAGd0qLRaAz2um9hpCGjKUtpMKbx9pTK3EoTWM1SZuG/+IMGe4slH4HwNDcd4XdPGrQcpSld+aTcx/krypPcRwZ/qM4YY8Xh4B8zShrS4OQ947k4UWqUiEqqHK9KnEuIxMMs4xicWEMa7Lt+EDlG0DumNPy6dw0kRjJOkVgkQuihsp/FuTzcenwbNx/fMviNE5DXZs8mnMPjnCeGLkqpeJprXK+HP82tHPWqIQ3URjRchIbUBn8oUVpUucZTr0De67+VAREZVQ/R/NnWK4OT905DaSTDGogAHIg/VCkCq0SEJ0Z0DiMQMpRPDV0MxhgrknHcpTJWyI1HN5CWYxwBqnzh905Uioulf2P3GdUkG5nKLITfOWXoYryyK3evIfxqBNQa43jqq9Zo8Md/G5BbCQKrYTePGFWbJSIcvV1+szWWFbVGjUyVcc3+mKlKN9hYpKXJmHpQ5TOGV5BfFREZXbBNo9ZAozaO4/6rMMbXbI3xd/SiiAj/xR8ydDEEBCApMxlXU2MNXZRXlq3OgpqMq90+VT6uFPcKjLHKyXjuphgr4EJSFMRG1jyTM5ORlGn4cUVexZOcp4hMvGhUryWIIEJY/FFDF+OVbT7xDyRiiaGLoSX1aRqORFfswKqGNDh177RRtVkCIfzOCUMX45UZW+AvX5bSOMv1IpRq4wu0KdXKCn9Taqyv2KqUxhWAeBnG1Osvn0ajrvBt9u7Te3iQ8cDQxdAiFolx8t5pQxfjlRljLzulJtdoenkyxlhhxhVdKQEigre3N0QiEUQiEQYOHKiTZvr06cL69u3ba61zcXGBSCRCSEgIiAjVqlWDSCTCm2++qZPP4cOHhXxWrVqFmzdvCn9Pnz69rHaRAbj95LbOGB63L9zCxgkbsajHj5gTNBtXj159bj63Im9h+dA/MLf9t1j8/i+I+DtCJ82VwzH4rf+vmPvWt/it/6+4cuRKkfnde3r/xXfGiNx4GK93eW3Hmvi69VdY3u0XbP9gHZq6BTw3L3/Hmvg+eDY2vheKpV0XItinnU6a5u5N8FPn+dj03kr81Hk+mrk31klDIFx7eKPCX+CfvXYBmkK9/lrWbIxNXy1F7C9H8HTdFXQJ0K2jwlrWbIIjs7ciOfQCohbux6B2fXTSvNOkA87M34WUlVE4M38XujZ+S29eErEE529cfrkdMhKJ6Q+Qq+dCuoadD0Y2+Qzfv/Ut/uiyFA2c6z83L1+7GpjyxkQs7fQTvm07C0FVW+mkaeTSEDODpmJpp58wM2gqGrrozzcpMxnZqoo9Pl2upugx6VYvW4uWdYPg61gLb7d+B6fDzxSb1/aNf6Nji7fh5+yPxjWaY8xn45CWqt17e/niULRt9BZ8nWqjea2WmDlhNrL1TNxQXLkqAiIq8ni27LdlqFurLhxtndC6RWuEHw8vUZ4nT5yEraUdWjZ7Q2t5THQM+n3QD3Vq1oWVmTUW/7LkOWUzniD6yyiuh93vy36Hf11/2DvZ443Wb+B4ePG9c3NycjB95nTUqlMLdo52qFu/LlavWS2s7/h2R1hYW+h8evbuqZNXRZ/1l4h0zl8F/f7bMvjXqgN7Wwe80aIVjh8vWc/nEydOwNrSBoHNWuisW/zLYjSs3xAOdo7wq1ET48dNQHa27jFVU8Ff/b356Jbe5YY8h2lIgxtp+q8HK5Lixohds2wd3qjbFr6O/ujSuvtzz2Grf1+Ldo2D4edUB2826oCt67drrY+NicOn/YajZZ028LKqgeWLQ4vMK1dTsa8NivLk/D+4+/tg3PyhB+6v/hzZd4u+vkz+dyFuftdV53NvxTCtdBlXj+PeimG4+cO7uLdiGDJiK/6DVWOS+yQa6Xc24unNlci49xdU2cWPBZ77JBoZd7fg6c2VSL+7BcqncbppHl9Cen6a238iO/UkyIiGOSkNjx4cQXzkVFw7PRq3L85F1pNrxaZ/knIaty5+g2tnPseNcxOReH0N1ErtGcjVqkwkxW/EjXMTce30aNy8MBMZjy6V5W7oVeGCf2FhYYiPf3bC2rJlCzIyiu4l8N9//+HgwYN614lEIvTv31/I984d7Rkc16xZAwCwsLBAr169XrXorIRy1bl4mKU7Y1ZulhLOPk4I/rxjifJ5lJCGjeP/hEe9qhiybCha9GuJfT/txZXDMUKau5fuYtuMbajToS6GLB+KOh3qYvv0bbgXfU8nP7FIjPvpussrkusP4/W+PqkwkeNm2m0si1hZonyczB0xuc1YRCdfwVd7JmHr5b8xuFF/NHdvIqTxs/fBmJYjEXbzGL7YPRFhN49hTMuRqGFfXSe/TGUWkjIq7kyfRISIGxd1xqQzk5vi4u0rGLNyVony8XR0w9axvyH8ylm8MeldLPj7N3zXfxLeadJBSNPUpwFWjfwBfx7bgRYTu+HPYzuwauRCNK5er8hyVWS3Ht/Wu1wukePOk3tYf2ljifJxMLXH6KbDEfvwGmYe/Qb/XtuDD+q8h0YuDYU03jbV8EmjwThx9xRmHJmDE3dP4ZNGQ1HNxktvnhV91t/cIm6cdm7dhZkTZmPEmGH459hONA1sggE9B+HeHf0PP86cOIsvPxmDPh/1xv5Te7B09S+4cC4K40dOFNJs3/g35k2fj9ETRuHAmX2Y/8tc7Nz2D+ZP/04nv4o+uVJR45Rt3bIVE8ZNxJhxY3DsxFEEtmyBnt176Vx7FPb48WN8POQTBLUN0lmXmZkJr2pemD5rGpydnUtQtood/CsqyLZl6xaMnzgeY8eMxfGjx9GiRQv06NWj2LrtH9Ifhw8fxpKfl+D82fMIXREKX19fYf36NetxPfa68Dl98jQkEgne7f6uTl4ajaZCP8AqruxbtmzF+HHjMXbcGBw/cQwtWrZAj+49S9xu27Rto7Nu458bMXXKNEz8eiIizp/FkqWLsXXLVkybOl0nbUVvs7ce34ZEpPtWgKHPYQnpicg1wh7KJaUhDVSkv4fdzq3/YOaEORgx5jP8e+xvNAlsjJCeQ4o8h635Yx3mz/gen08chf2n/sXnX4/C1DEz8N/uA0KarMwsVPXywPjpY+Do7Fhs2XIr+DlMn4wrR/Hw4B+wbv4eqgxYBLm7Px5smQ7VE/1vRNm3+xjun61+9vk0FGKFJcz8nj3Ayr53Bck758O8dltUGfATzGu3RfLOeci5//zOHez5lOk3kJN6CjKbBjCr0h0ShQuyEvdCo0rXmz73SQxyHp6FzKYRzN16QG7TENmpJ6DKvF0gz2vISTsLuU1DmLv1hMLhDagy4pGTdra8dqvMPU2NQPKtLbCrEoyqdSfC1MoH964uhjJH/2zeWU+v4cH11bB2DIRn3clwrTEYORm38CB+vZCGNCrcu/IzlLmpcK0xBJ71p8LZ+0OYSG3Kaa+eKdfg37///ovAwEDY2NjA1NQU1apVQ+/evZGWltc7YP/+/XjrrbdgbW0NhUKBmjVrYu3atVp5rFy5EgBQt25dyOVypKenY8uWLcVud+LEiUWuCwkJgUgkAhFh3bp1wvLs7Gwh3169esHc3Pxldpm9hIT0BL0D+/s090GbIW1Rs3XNEuVz7u9zsHKyQoeRHeDg5YCGXRqifucGOPnns4lETm85jWoB3mjZryUcPB3Qsl9LeAV44fRm3VclNaTBnSd3X37HjEDcwxuAnro9l3AB6y9uxsm7JTt4B/u0Q0pGKlacW4u7T+7jvxthOHjjMLrXeltI08WvEy4kXsK26B249zQB26J3IOrBZXT10x+8vV5Er8SK4E7KfTzO1H39ZP+Fo5i1eRF2nN1fonwGt3sfd1MTMGHtt7h6/wZWhW3BmsPbMPrtQUKaYZ364+ClcCzY8TtiE+KxYMfvCLt8EsM6DtDJT0ManL124eV3zAjcfHRL743TpeTL+OvqDpxLjCxRPkGerfAw6yE2Rm9GQnoijt45jmN3whFc/VmvyfbebyI65Qp2X9+LxIwH2H19L66kXMFb1XR7hosgKjIwWREQaYp8NemPX1agT//e+GBAH9Tw88G0eVPg6uaKtcvX6U1/7sx5uFd1x8DPQlDVywNNAhvjw4EfIOr8s8DzudPnEdA8AN3fewcenu5o3a4V3unVVStNPqWmYs/yWVRPpV9+Woz+Az7CgIED4FfTD/O+mws3dzcsX7a82PxGj/wcvd/rjaZNm+qsC2gcgNnfzEav3r0gl8ufW7aKPp5iUb3Tfln8C/p/1B8hA0JQ068m5s+dDzc3N/yx/A+96ff/tx/Hjh/D1s1b0bZtW3h6eqJxQGM0b9ZcSGNnZwdnZ2fhc+jQIZiZmekN/oGKD6AZu+J61/3y0y/oP6A/QgaGoGbNmpj/3Ty4ubvhj2X66zbfqJGji2y3p06dRvPA5nivz3vw9PREu7faofd7vXDu3LkXKltFcCMtXu/vztDnMALh7pOK+0C7uADbH7+swHv9e+H9Ae/Bx88H0+ZNhqubC9YuX683/fY//8aHA99H155vo2q1qninVxe891Ev/LpwmZCmfkA9fD17At7p1QUyuazYslWmWevzPT77FyzrtodlvWDI7D1g/+ZQmFg64Gnkbr3pxXJzmFjYCp+cxDhostNhWedZe30S8TdMvRrApnlvyOw9YNO8NxRV6+NJxI7y2q1KLffJJUgtfSGz9INEZgOFfXOITcyhfBKjN70y/RqkVjUhtfCGWGoFqUV1SC19kfvo2YSX6uwkSOROkFpUh1hqCRMzd5iYe0OTk1Jeu1Xm0hIOwNoxENZOLSEzdYGjZy+YyGzx+IH+Iaqy029CKreHjUtbSBUOMLX0gbXTG8jJeNbr+3HyCWhUmahS4xOYWlaHVG4PU0sfyM3dy2u3BOUW/EtOTsa7776LkydPwtraGr6+vnj06BG2bNmCx48fY/PmzQgODsaBAwegVCpRo0YNJCQk4OzZZ8GI9PR0bN26FQDw6aefomvXrgCeBQT1adiwIU6fPo3t27frXV+tWjW0apXXbT6/px8A/P3333j8OG8GqZCQkFfZdfaCHuWUzsxddy/fhXcTb61l3k28kXA1QRg36J7eNNVx97L+IN+jnEelUjZDeZCeBE0p3KD4OdRAZKL2Tfv5xChUt6smBGr8HHwQmag9Q3JkQhT8HHxRmFgkRnJm6iuXy1DupCaUSj5NazTAgYvar1MdiDqGhtX8YSIxyUvj0wAHo3TTNPNtoDfPB49TKvRN6cOsh6USsKhu643LKdoXPJeTo+Fp7QnJ/3vDett6Izo5WieNj632MQLIa7PGMmv2yyiqTnNzc3Ex8hJavan9emnrN99AxCndG3MACGjWCIn3E3Fwb94MkslJKdj99x68GdxWSNMkMACXIi8h8mxeMPp2/G0c2hemlaYk5asI9I1PmZubi8jzkXiznfZN+Jvt3sSpk0WPvbV29VrE34jHxEkTSqVsVIF7URGRvmdXyM3NxfnI82j3pvawCu3ebIeTp0/qfgHAP//+g4YNGuLHRT+iRs0aaNCoAb6e9DWysrKK3P6qNavQs0fPIh8Gk6biHmeLui7Izc3F+fPn0a5Qu23Xrh1Onix6PNk1q9cg/kY8vp6k/+F7YItARJ6PxNkzedf48fHx2Lt3Hzp2DNZJW5HPXwCQqudNlpdR2ucwAHrfsqkoiproIzc3F5ciL+ucw1oVcw7Lzc3VeXiiMFXgQkQUlMoXH79PZWSTkLwqUiuRm3gNCq+GWssVXg2RfU9/IKmw9Iv7ofCsDxNrJ2FZzv0rOnmaVmuI7Psly5MVjUgNTU4KJKZuWsslpm5Q5xQxfj2pISr0sF0kkkCdkywMGSJROEOdmwp1Tt7bWhrlE6iz7kBi5lH6O2EApFEhJ+MOzKxraS03t66F7PQber+jsPCGKvcRMh5dAhFBpXyC9IfnYWZTR0iTkRYFhUU1JN3ciBsRE3ArajYe3ttjkKFYTMprQ7dv30Zubi7MzMwQExMDMzOzvFfSIiLg6OiI8ePHg4hQvXp1HDt2DC4uLsjJycG1a8/esd68eTMyMjIglUrRp08fuLu7Y8uWLTh8+DBu3rwJLy8vne1+88036NSpEyZPnoxu3brpLVtISAiOHDmC6OhoREREICAgQAgEVqtWDa1bty6TOmH6qUppoNyMhxkwt9W+SLewM4dGrUHm40xY2lsi/WG6ThpzW3NkPNT/Krmqgo9pUFqveNgqrHE+WztI+yj7MUzEJrCSWyIt+xFsFDZ4lP2kUJonsFVY6+QnKsWyGUJ2buk85XW2dkTy42Nay5Iep0JqIoW9pS0ePEqGs40Dkp5oB0qTnqTC2bro11CylTkwlSlKpYzlTd94fy/DSm6FJznavTOf5DyFiVgCC5kFHuc8gbXcCo8LpXmc8xRWcqsiylZx22xRN9RpqWlQq9VwcHLQWu7g5IDkB/pfzW/cLAA//vEDRgwcjZzsHKhUKrTv/BZmfDdNSPNOr65ITXmIXsF98i6OVCr0G9wXw778VH/59EV5KrDUlFSo1Wo4OTtpLXdycsSDB/onA7h27TqmTZ2OPfv3wMSkdC7XKlu9AkBq6v/r1qlQ3To6IemB/pucmzdv4sTJE1AoFNiwbgNSU1PxxVdfIO1RGpYuXqqT/mzEWURHR2PJc8ZUrGyKa7dJRbbba5g6dRr27d9bZLvt3bsXUlJS0P6tDsLxYMjQIfhqzFc6aSt6my2tyR/K5hxWcSemKCpgnX8Ocyx0DnN0ckDKA/29k1q3a4U/V29Ghy7tUaeBPy6ev4TNa7ZAqVQiLTUNTi5Oer9XlIoesC5MnfUEIA0k5jZayyXmNlBnPHru91XpD5F1IwKOXcZo55vxCBKzQnma2UCdUXEfrBoLUmcDIIglplrLRRJTaNT6H3KZmLpD+fQqTMw8IZbZQ5ObAuXTWAAakDobIhMzSC2qg9TZyLy/C3lP4whSy5qQ2zx/zNKKQK1KB6CBRKp9zJRILaFSPtH7HVNLbzhXH4CEuBUgUgKkgblNXTh5viekUeakIutJLCwdmqBKzWFQZich6eYmEGlg7965LHdJR7n1/PP394e3tzcyMzPh5OSERo0aISQkBPfv30dmZqYwjt/AgQPh4uICAJDL5fD39xfyyO/h17lzZ9jb26NTp05wcHAAEWHVqlV6t9umTRt06NAB0dHRWj37Curdu7fwJHfNmjVITk7G3r17AQADBgyASCQqlTpgJVWK9V3o3y7/fCwqsA3df14q1SIYl9LbscKXNvl1WvBCvfBFu0jPssLfr4hK8xihU2f/z7rgxWThC0uRnmXa6ytu3ZYmffUGFG7L+utfn8pcr4X3jYiKbOexV+IwfdxMjBo/AruO/I3V20Jx59YdfP35FCHNiaMnsfj7JZj1wwz8c3QHflu3BAf3HsSieT+X6X4YHZ1zkv56VavVGBwyGF9PmogaNXzKq3QVWuF6JCr6XK7RaCASibB82XI0DmiM4A7B+HbOt1i7bq3e3n+rVq9C7dq10ThAd9Kq14Fu3ULvwVGtVmNQyGBMmjQJNWrUKDK/I0eO4rt532Hhjz/gWPgxrN+wDnt278Hcb+eVdtENrjTPE3wOe+b5l136jgf6vzRq3HC0ad8a77brDR+7Whj6wWfo2bcHAEAs0R125PVVqP6KOcYWlH7pAMQKc5jVaK67Uu+/ScVtlxVBUbUrs2kAiak7Mu/vQPrNUGQ9+A9Sy/8fx///76TKSkDu4wuQO7SAmVt3KJzaQZV5Bzlp58un8Aalv+ZyMhOQfGsL7N06oWqdCajiNxzKnFQk3dxQIBVBIrWEU7UPoTCvCkv7xrCrEozHSfpfJS5L5dbzT6FQICIiAmvWrMGpU6eEYNzq1auxadOm534/Pj4eR4/mVdDevXthY2MDAMJkH6tWrcLUqVP1XkR/88032L9/P6ZNm6a3+7aFhQV69uyJ1atXY8OGDfDw8IBKpdKaEISVHxNx6TRLcztzZDzUHtQ0Iy0DYokYptZ5T0Is7CyQXqiXX0Zapk5vwNIum6HIJNJSySct+7FODz5rhRVUGhWe5uTV+aPsR3rTFO4NCOQFvKSlVDZDkEufP9ZWSTx4nAwn60JPq63soVQp8TD9UV6aRylw1pMm6UnR423IpcWPT2PMZJLSKfuTnCewVmg/ybOUW0KlUSMjN6/N5vecKMhKZoknOfqf9knFFbfNFhXIs7W3hUQiQXKSdi+/1ORUnd6A+ZYs+BWNmwfg09EfAwBq1akJM3Mz9ArugzFTvoSzixMWzF6Id9/vjg8G5M1eXdPfD5kZWZg4ehJGjh0OsVj7WWRFvinVd4Fo72APiUSCpETt3lLJySk6PdYA4OnTpzh/7jyiLkRhzJdjATybVMLW0g5/7dyOoDa6E4A8v2QVuV71s7fPq9vCPSiTU5L11i0AuLi4oIprFVhbPztH+fn5gYhw7/49+FR/FnDNzMzE1m1bMenrSWWzA0Ysv90+0Gm3+uv26dOnOHfuHC5cuICvvszrxZffbq0tbfD3zr/Rpk0QZs2chQ8+fB8hA0MAAHXq+CMzMxMjR4zCuPFjtY4HFb3NmohNSmUSo7I5h1Xca9qi2kVR57CU5FQ4ONnr/Y7CVIHvlszFN4tmISUpBU4uTlgf+icsLM1hZ2/74mWrZJ1GJKZWgEis0yNPnflYp+deYUSE9Iv7YVG7LUSFrvPzeg4WzvORTg9D9uJEEgUAETTqLBQMX5M6C6JCvQGF74hNYOrYGuTwhpBO+fQqIJJCJM57eyg3LQImFj6QWfoBACQyO4BUyE45BplNgwrf9iUmFgDEUBfq5adWPoWJ1FLvd9Lu74WppTdsq7QHAMjN3CCWyHA3eiHs3bvCRGYNE6kVIJJAVGDSTZmpC9TKJyCNCqJyPBaXW8+/J0+e4MqVKxgxYgTWrl2Lc+fOoW3bvLF+jhw5gmrVqgHI692XlJT3moZSqUR0dLSwPP+JV3Z2Nh4/fozHjx9Dpcp7DTM+Ph5HjhzRu+2AgAD06tULt27dwsOH+se3yB/XLykpCdOnTwcABAUFCeVi5cdO8eInWn3c/d0Rf1Z7Eon4Mzfg6ucKiUneodDN3x3xZ7Xf4b9x5gbc/fUPwGmnsCuVshlKFUsXiEvhQvpqShzqu9TVWtbApS6uP3w2sPXVlGt60tTD1ZRYnfw0RHA2L372NGPm5Vg6A7aejovEm3VbaC17s25LnI+/DJU671h3+lok2hZOU68lTsVG6s2ziq1zhT4ZO5ja653w40VdT7uB2g7aY3j4O9TGrce3oP7/mBs30m6gtqN2mtqOtXEtTXecDw1p4GCm/0aiIpCI9F9oyGQy1G1QB0cPao8refTQcQQ0a6T3O1lZWRCLtduYcPP+//N2XhrtSw6JRAwi0ttrtTT+zQ1FrOf3JpPJ0KBhAxw8eEhr+aGDh9Csue6ECFZWVjh55gSOnzwmfAYNGYQavjVw/OQxNG7ycj3QROJyu+wrdSKRSO+Dd5lMhoYNGuLgoYNayw8eOojmTfX0NAHQvFlzJCQmID392QPCa9euQSwWw62K9hhJ27ZvQ05ODt7v837x5RNX3ONs4d9mPplMhoYNG+q024MHD6J582Y66a2srHDqzCmEnwwXPoOHDEYN3xoIPxmOJv9vt1mZuscDsUSi93hQkdssADiY6X9o8qJK+xxWmmUzhKIexstkMtRp4I9jhc5hx4o5h+WTSqVwdXOFRCLBzq3/4M3gtkX+NorNR1RxHwzqI5JIIXPxQfYt7d5d2bcioXCrVcS3/p/mziWoHiXAom4HnXXyKjWRfTNSO/3N81BUKT5P9nwikQRiuQPUWdqT+qiz7kMiL/41dpFIDLGJOUQiMVQZN2Bi5iHcRxCp9JyG9fdBrohEYhPIzT2Q+fiK1vLMx1egsNA/dqpGo4TuxUn+cSOvThSW3lBmPxs7EQBys5MgkVqXa+CvYMnKXFJSEgIDA2Fvb4969eqhZs2aOHgw70KtXr16mDdvHkQiEa5du4Zq1aqhXr16cHR0xO+//w4iEl7Z7dGjh3BxQERa47wUN/HHrFmzICmm63abNm2EMQPzLwZ5og/DcDF30ftELzczF4lxiUiMSwQAPEp4hMS4RDx+kDf23KHfD2LHnL+F9I26NcLjB4+x/5f9SLmZgsh/IhH5bySav//sZqBprya4cfYGwteHI+VWCsLXh+NmRDya9ta9qBWLxHC3Kv9ZeUpTdTtvvV3sFSZyeNl4wsvGEwDgbOEILxtPIbjRr34fjGr+bGyuvdcOwNHcHgMb9oW7VRW08w5CO+82+CvmHyHNrtg9aOBSF+/W6gI3S1e8W6sL6rn4Y+fVPUWUreIG2qvYOcPWXHcsQ3O5Gep61kRdz7wZqj0d3VHXsybc7V0BANP7fInfPp0rpF9+4E942FfBt30nwK+KNz4K6oH+bXpi0T8rhDRL96xBu7ot8UWXIfB1rYYvugxBW/9ALNmjO/SBRCxGY5+KPQ6Hp3VVvZM/yCVyeFi5w+P/v0lHM3t4WLkLDw961OyGQQ2ezYB8+NZR2Jva4b3aPeFq4YKWHoF4o2oL7L3+n5Dmv/hDqO1QCx2rd4CLuTM6Vu+AWg418V+8dlAByOut6mldtbR3t9yIRCJIxfp7VQ4ZMQgbV2/CxjWbEXf1GmZOmI37d++j76APAQDzpn+HLz5+NjbXW53exJ4d+7Dmj3W4HX8bZ06exfRxM9EgoD6cXZ3z0nRsh7XL12PHlp24ffMOjh48hgWzF6J953Y652apWFahA9Zisf5rjRGjhmP1ytVYs2oNrl65ignjJuLunbsYNCRvNu/pU6fj4yGf/D8PMWr719b6ODo6QiFXoLZ/bWGoktzcXERdiELUhSjk5uYi4f59RF2IwvXr1/WWoSIHVQEUeR03YvgIrFq9CqvXrMaVq1cwfuJ43L17F4MHDQYATJs+DUM/GSqkf6/3e7Czs8Onwz5FzJUYHDt+DJOnTEb/fv1haqrdK2LVmlXo8nYX2NsVE+wXFR1AqwjEoqLLPmLUCKxauQqrV63GlStXMH7cBNy9cxeDh/y/bqdOw9Aheb1+xWIx/P1ra33y261/gXbbqXMn/LFsOTZv3oKbN2/i4IGDmD1zNjq/3Vnn31hSTNkqAm8bL72/O0Ofw8QiMdwsq5T27pYbqbjoNy7yzmGbsWnNZly7eg0zJ8zB/bsJ6DvoAwDAvOnf48uPxwrpb8TFY/uffyP+2k1Enr2AESGfIzY6DmOnPTvP5ebm4nJUNC5HRUOZq8SDhAe4HBWNm9dv6WxfJimdt0GMiXXj7ngatR9PL+5HbuodPDy4DKonybCs3wkAkHZkFZL/+UHne+kX90Hm6geZo6fOOquAd5B18zwen9qC3NQ7eHxqC7JuXYBVwDtlvj+vA5lVHSifxkL5NBbq3EfITj0JjSodUsu8e5Kch2eQlXxYSK9RPoYy/Ro0ysdQ5yQjK+kg1LlpkNs+e9hoYlYVuU+uQJl+HRrlU6iy7iEnLQImZlW1erVVZLau7fA4ORyPk8KRm5WI5FtboMx9CGvnvEmEUm7/jcTrz+65LGzrID0tEo8eHIEyOwVZT68j+dZmyM09YSKzAQBYO7WGWpWB5FtbkJv1ABlpl5B2by9snMt/XolyCzXa29sjJCQEp06dQnx8PDQaDWrWrIn+/ftjyJAhEIlEsLGxwdy5c3H27FnExsbC09MTjRs3xuHDh4UxAXv16qWVr1gsRvfu3fH7779jy5Yt+OWXX/Ru38/PDyEhIVi+fLne9SKRCAMGDMCMGTMAAObm5jrbYuVDKpHCwcwByZnaXfYTrt7H2s/XCn//t3g/AKBex3roOvEdpKem43HSs0kobFxt0Wfe+9j/y35E/HUWFvYW6DAqGDWDnj1Rcq/jgXen9sDh5WE4vDwMtlVs8e70HnCrrf3kH8jr6VPFouJeKAGAj101vTNRVrfzxux2k4W/BzX6CABw8MYR/HzqN9gqbOBYoJdTUkYyZod9h4GN+qFTjfZ4mJWG5edW4+TdM0KaqylxWBD+Cz6s1xsf1O2NB+kPsOD4z4hL1b0pNZeaVeheVCKRCI196uG/qGNavRYaetfB7smrhb/nfpQ38+G6I9vx6W8T4WLjCA/7Z23qVvI99PzuE8ztNwFD23+IhLQkjF09BzvO7BPSnIo7j5BfvsLU3qMxufcoxD+4g5Cfv8TZ69ozKwtlqOavd3lF4WWjP8DmZVMVYwO/FP7u498bAHD8zgmEXlgNa7k17E2f9dRNyUrFotOL0ce/F9p6BuFRzmNsuLQJ5xKfPcm+nnYDv59fju5+76C7X1ckZyTj93N/IP7RTZ3tiyCCh3XFnt1MLpFDqWfSkq49uyDt4SP8NO9nJCUmw7d2DazcshzuVfOOi0mJSbh/99kM17379kL60wys+n0NZk/6BlbWVmjROhATZ44T0owcNxwikQjfz/oBiQkPYO9gh3Yd22HsVN0B/uWSijk5TT5JEcG/nr164mHqQ8z7dj4SExNRu3YtbNm+GVWr5rXxxMQHuHtH/0zzRUlISMAbga2Ev3/68Wf89OPPeKPVG/h37z866YsqW0UhloihVuk+DOjVsxcePnyIufPn5tVtrdrYunnrs7p9kIg7d+8I6S0sLLDjrx0YM3YMWrdpDTs7O/R4twemTp6qlW/ctTicOHECO7bvKLZcFb1eiwu29/p/u5377bz/t9va2Lp9S4F2m4g7d+4U+X19xk8YB5FIhFkzZuH+/ftwcHBAp86dMG36VJ20FTmoCgBeNp44dOuwnuWGPYe5WrhU6OFWxCIxTERSqEh3OKeuPd/Go4ePsGjeYiQnJsG3ti9CtyzTOofdu3tfSK/RqLHsl+W4ERcPqdQEzVs1x9b/NsLD89kD/wcJSXj7jWcTR/7+03L8/tNyNHujKTb+u05r+7IKfg7Tx7xmK6iznuBR+J9QZzyEzMETzj2nCbP3qtIfQvVU+75Nk5OBzNhw2L35sd48FW614Nh1HNKOrUHasXWQ2rjAses4yKv4lfn+vA6kFt4gTTZyHp0HqTIhltnC1LkDxP9/fVWjzgKpnvV+JyLkPr4IjfIxADFMTF1h7tpFSA/kjQsIADlpESB1JkRiBUzMqkJuG1Ceu1amLO0DoFZl4OG93VArn0Bm6go3v2GQyvPuU1XKx1DlPHtd3coxEBp1Dh4/OIyU29sglpjBzMoXDlW7C2mkclu41RyBlFtbcTvpG5jIbGDj0ga2VXR7xJY1EVW2KYlYpbA99i+cf3Beb6DKkEY3HgVHs4r7emp6bgb6b/3EqGbPE0GEBq51MbXNeEMX5ZV8s/VnzP/rV6g1ujemhrRzYija1Ak0dDFemoY0GLnny1IZM6k0uVq4YFabac9PaMTSc5/gYU7RY0Uaip3CERZFjK1SUaRnPzXKGR8tFJYVulelSqVCTqZxHQsAQCqXQiavuGOrAkBWTiY0GuO65gIAM4V5hW6z957ex7TDswxdDC1ikRitPFrio3ofGrooryQl+wEylE+fn7CcVTGvWmTP+tJw7tw5BAQEICIiAj3/vP/8L1Rw8fO7lPs2rbyHlPs2K5snN/7Qu9y3+ZJyLknFEXtyWJlvo2I/TmOVVgOn+kYV+BNBBFdz1wod+AMAC5k5Aqo0KPYVn/JGILStVv7dnkvbey27Gl3gz9naAW/UamLoYrwSsUiMFu7NjKrNiiBCS4+KG1DNZybVP7GRYYlgZmKM5Xox0lKaqKY0SSUV+3Vq4P+v/RrhLphIK+7ECflMjLAXmEQsqfBttoqFK9wsqxjVxCUa0qC5u+7wNhWNhYnxPSSSiuVlGvhjjLFXYTx3U4wV4GXtBXtT43kNlEAIdNM/cHhF09m3g1EFVi1kFmjuXrEDVADg4+KFNv6BRvP6l1gkxsft+8JEUvFvStt4tjaqNisWidHSo8XzExo5sUgCcxMLQxdDi4XUwqgCvS9LamJ8gRRjLNOLEolEkEqNaz/EEnGFfzUVgFGeKypLm21Xra3RvHEhggiuFq7wsdU/eH1FIpeYwsTIJtewkumO/8wYY8ai4l+tsEpJJBIh0M14etbIJXLUdaz7/IQVQH2XOnA0khnexCIxOtZoB6kR3nS8jE869DOa3n8iETCgbeUYt9TNyg0+ttWNoueEWCRGU7fGsJQZV9DsZVka2Y2KhdTK0EUoFWKRuMjZKA1BIpYYzYOJV2UiM556BQCpzLiCDy9LJBIZVbBNJBIVOXlORdOsShPIjWQSCALhrWptK3yPSiCvjRjTOUwEMcyM7IEaY4wVxME/ZrQaOjWApczSKG74W3m0qtADIxckFonRp24PQxcDACCTyNCpRntDF6PUdGwYhNoeNQx+ky0WiRHS9j0421Ts19QL6ur7ttH0nOhYvfwH6C0rMokcConp8xOWA4XEtFLNkiiTGs++yEyMpyyvSiwWQyI1jqCQSCyCxMQ4ylIaTIwo+Cc1qfivqeeTm8jRsXp7g1/NiiCCjcIGzd2aGrgkpcdCagWxkcxibi2zqRQ91xljlRcfoZjRkpvI0cP3XYPe8Ishhou5C1q5v2GwMpSFN6u1RgOXuga/SBkaMAB2prYGLUNpMpGYYNmn8wFDtlmRGC42jpj1wRiDlaEs+DvWwhseLSA28O3TO75d4GZZsWf9LsxO4WjwhywiiGCnqDzBaiCvt53MxPBjP5lIpEb5SuerkCvkRhEYUpgqjKIcpUUsEhtF0FosllS6NtvJJxiuFq4Gve4iEAY3GAB5ZXoYIBLDQeFk6GJAKpbBSlZ5rmcZY5UTB/+YUathVwMBLgEGvTHtVbOnwXtylTaRSIThzYYarDejWCRGI9f6aFutlUG2X5bqedXC+O7DDNZmNaTBb5/OhaVp5Xv15L3avWApN0xvYLFIDHdLt0rV6y+fidgEtnLDjrFqq3AwqtdkS4vMRG7Qm30RRFBIFQbbflkRiUSQmRo2sCqVSyGWVL7LaBOJicFft5XLjCO4W5pMxCYY0nAgYKBZwEUQIahqK9RyqGmQ7ZclUxNzmBt48g8HhXOla7OMscqn8l1ps0qnk3dHxD+Kx6PsR9CgfAf9D/buABdzl3LdZnlxMLPH8CZD8MOJxeW6XbFIDEuZBYY1HVJpL5TGdPsE+y4cwfn4y+U+BuDwjgPQpo7xjJdZmsykphjScCAWnvoZVI43UCKIIBWbYGijQTCpZA8C8plLLZGlykSWOrPct51341b5gtVAXpBKITNFZk6GQbavkJlW2uOsiYkJ1FI1VEpVuW9bLBFXmrH+ChOJRJBL5cjKKf9jAQDIpYYNmJelqtYeeLdmd2y9sr1ctysWieFo5oDetY1jyJeyYKdwQE5GFlRU/scDW7mDwYasiJ/fxSDbreye3PjD0EWotGJPDjN0EV5rlfPsyioVhYkCg+oNgrnMHOJybLKt3N9AS/eW5bY9Q2jl1QKDGvYrt+2JRWIoTOSY2W4S7M3sym275U1qIsW28ctQw7VaufUaFUGE91p2xTd9x5fL9gyllkNNDG04qNx6/4kggkQsweimIyrd674FiUQiOJg6QS4p315icokCDgqnShugAvJe/zWVmZX7dhUy00r36mRhMoWs3MfcE4lFUJhVrtd9CxOLxTCVl/9YoDKpzKjGHSwLHau3Rwfvt8pte2KRGNZyK4xp/gUUJpWvF3A+sUgCZzM3SMp5/D8rmQ2sZDbluk3GGHtZHPxjFYKNwhofN/gY1grrchnzq03VIHSoVvle79Ona81OGBowAADKNKAiFolhITPHN29NQ1Vr9zLbjrGwNbfGnilrUKeqHyTisj/U9v0fe+cdH0W1NuBnZmt67wWSQOhdOiJNBKWLYheVq9ferr1+VuyIih0UsKF0GyKIgvTQey8hvfdtM98fSwLL7gYIm2xhnt/d35XZs+d9d3L2zDnveUv/sXz238mITSDL3XSP78ZdXSchCmKjj1mtSsMjPR8gPaJlo8nxFARBJMovtskKgOhVfkT5xfq0EaUWtUrdpAZAvdbPZ4pU1YcgCOj8dE1mABRFEb8A3/WmPB1RVDWpAVCr0aHxgByZjY0gCFzTZhzD065odFmiIBLhF8FTfR8jzC+00eW5G7WoIdY/EZXQNIceIdowQrXuTZmhoKCgcD4IclPGTikoXCAVxgrm71vA3qK9Lu9bREQlqhjRYgTdYru6vH9PZ13mRj5c9zlVpiok2fXh1W0iW/FQ77uJDvStpP5no7y6gsdnvsbsf+YhCqJL761KVCEKAs9f+zAPXnX7RbEhPZ19hfv5YvMMimtKGqUwUEJQPP/pcjuJwQku79uTkWWZEkMR5abSRpMRrA0hRBt+0Y1Zi2ShxljdKHMsnMzxdxF4/J2JLMuYjCZMBlOjyVBpVB5TaKQpkSQJg7Gm0cYsgE6rv+jGLMDKY//y3c45mCWzS++vgLXsWOeYTtza6SaCtL6ZVsEZFslMYU1eo6WxqC1QFagJbpT+z8amTZvo1q0bGRkZdO168e1XFBQUGo5i/FPwOmRZZmveNhYfWIzRYrzgTb+AgIxMWmgaY9PHEqoPcZGm3keZoZzPN37FqmNrXWKoEgURlaBiYpcbGNZyiM/m8TkXlm5dyd2fPkV+eRGSdGH3tXbMdklpz2d3v0HrhDQXael91JhrmLdnIcuPrHDZmEWWGZF+JVe2GO6zOf7OBYOlhsLqPJfmUFILaiLcEF7sSciyjNFswGg2urRfjUqLTnPxGadOR7JIGKoNFzzHnk5tcRG1+uIzTtUiyzJmiwmjybVjViWq0Gl9O4T6bBRWFzFjy0z2FO6te7ZfCKIgolPpuLnD9XSPv+SivbeyLFNprqCoJs+lh4N6lR8R+hi3FqhSjH8KCgoNRTH+KXgt5YZyVmauZGN2BkbJeN6LplpDQXxgPH0T+9IxqsNFu0g6k40nNjN/98/syt9z3gaV2jBMtahmQEo/xrUdRWxgdGOp6lWUVpUz9ZfpfPHndxRVlJz3vVWLKsyShRaxzbn/yoncOvAan6tE3VD2Fx3g1wNL2J63o0FGQAEBQRC4JK4rw1tcQVKw74emnwuSLFFhLKPcVIpFbnjxGpWgIkgbQpAmGOEiPgQ4HYtkwWgyYJYuzLiqEtVo1dqL0nPKEbIsYzaZMRlMF1YYSACNRoNGp1HWBieRJAmj2YjFcmFjVhRFNMqYrUOWZdad2MCSQ0s5XpbZoHWXjIxepePS5H4MazGUEJ17vNI8DbNkpsxYTIWp7IKMgFpRR7A2FH91oNvnA8X4p6Cg0FAU45+C12O0GNmWt5312evJrsiue7jXepnJslz3oK5dTOlVetpGtqFnfE8Sgi6ukL7z4XhpJksOLOOfo2soN5QD1OVZq73PAgKSLCEjIyCQGBzP0BaDGJhyKQHaAHeq77EYzUYWb/iTz5Z+w/oDWzGf3EhpVGpk2Xpna8ds7XsBOn+GdrqUO4feRN/WF+9p/tkoqCrk76MrWZO5lhKDNWzVOmapW/afPmYBovwjuTS5L32T+igbJifIskyNpYpyYxk1lho4p02UgF6lJ0gbgl51ceRJawiSLGEymzBZjOdsrBIQ0Kg1aFTaiyLPZ0OQZRnJImEymrCYz91wXVvJV6VWKWPWCbWegCbz+RlY1SoNGrVGGbP1cLjkCH8d+ZvNOVupNlcDjp9htYcxoiCSFJzEwGb96Z5wCTqV7+dNbAiSLFFpKqfCVIZRMpzTZwRE/NUBBGlDPMpbXTH+KSgoNBTF+KfgU5gkE7kVuZyoyCK/Kg+TZMYiWVCLagI0AcQHxREfmECoLkRZ1J8nRVXFHCw+zMGiwxTXlGCymBAFEY1KQ3xQHC3CU0gJa+bT1eQaA6PZyO7MA2w+vJPdmfuprKnGaDGi1+gI8Q+mU/M2dE5pR2p0srJhOk/KDeUcLT3OkdKjFNcUY7KYEAQBjaghyj+SZiHJJIck4a9p+kqs3owsy5gkE0bJgMliREICWQZBQEREq9KiFXWoRcVj6nyRZRmLZEGSLCcN1FYErAVZVKIKURQv6hQKDUGWZWRJRpIkJItka7ASrJ5ookpEFEVlzJ4nsnzyvsoWJEnm9IMBQRAQlTHbIGRZprC6iKOlRzlaepwKYwUmyYRKUKFVaYkLjKVZSDOSghMuiuI+rkSWJYySEaPFgEmypg+qdRQQBRU6UYdWpUMlqD1yPlCMfwoKCg1FMf4pKCgoKCgoKCgoKCgoKHg4ivFPQUGhoSjHcAoKCgoKCgoKCgoKCgoKCgoKCj7KeRv/srOz2b59u9P3q6ur2bRpExUVFU7b7Nq1i8zMTEWGIkORochQZCgyFBmKDEWGIkORochQZCgyFBmKDEWGIkOR0UAZ54R8nrzwwgtyQkKC0/d37NghA/Lq1audtmnbtq388MMPKzIUGYoMRYYiQ5GhyFBkKDIUGYoMRYYiQ5GhyDgHGRkZGTIgZ2RkePX3UGQoMhQZrpVxLpy38S8rK0vetm2b0/erqqrkjIwMuby83GmbnTt3ysePH1dkKDIUGYoMRYYiQ5GhyFBkKDIUGYoMRYYiQ5FxDjIcGf+88XsoMhQZigzXyjgXlIIfCgoKCgoKCgoKCgoKCgoejlLwQ0FBoaGo3a2AgoKrMEsmqsyVVFuqqDZXYbBUIyEhAwICakGNvzoAvdofP5X1JQiCu9X2eGRZprCmkILqPPJr8smryqXKXIVFMiMI1vsaqgsjyj+aKH0U0f4xBGgC3a22VyDLMhbJgiRJSJIFSZY4/TxGEARUogpRFBEF6/8rY/bsyFavdiRZOnlvbe8rgCiI1vspCnX/rXB2LLLFOs+aK6k6+bLIFjg506oEFf7qAPzVAfid/H+VoHK32l5BVlkOe/IOsK/gILvz9pNVlo3RYkKWZbRqLTGBUbSJbkl6ZBqto1uQFJKgzAfngGSRqCiupvLkq6KoCrPJgizJ1jlWLeIf6kdgmB8BYX4Ehvuj1ipj9lyQzBJmoxmz0YLZaMZitlinAut0gKgSUWvVqLUqVFoVKo1KGbPngCzJVJZUW1/F1VQWVWMymuvGrKgS8QvWERjuj3+onsBwfzQ6ZUt3LhgqjRTllFGSU0ZxTjllBRVYTNY1gqgS0PlrCYsLJjQmiLCYYEJiApX1gYKCgs+geP4peDWyLFNpLqfQkE+5qfS8PqsW1ITrogjTRaIRNY2kofdSY65hX8kedhRup8JUDoCIiITksL2AiHzyvYSARNpFdCA5qBmioCyaTqfWKGUymbBI5vP+vEatQa3SKItRB8iyjMViwWwx2xn7zoYgCKjValSisjl1RLW5ioKaPIoMBXW/83NBQCRCF0mEPho/tX8jauidGC0m/jm0mnk7f2VP3n4AVILqpEHVHrWowixZ30sJS2Zs+6sY3OJS/DT6JtPZW6ipMJJ7qJC8w0VYTCfHrIDVMOWIk+8JAkQkhxKbFkFguDJmz0SWZUw1JmrKDZgN5/cME1Ui+iAd2gCt8gxzgLHaRP6RYnIPFWI2nJwDzmHMAoTFBxOTFk5QZIDyDDsDWZbJP1bMoc2ZZB8sOPk7F5yuEwRRQJas7+kCtKR2TqB5x3j0AbqmVNspiuefgoJCQ1GMfwpeS5mxhJzqTIyS8YL7CtGEEeefiFoxAmK0GFmfu5Y9xbuQ5HPf5J+OgICMjL/an+4xvUgPbaUsRgGLxYLBZEBu4H09HZWoQqvRKRsorAt7s9mM2XL+xlRHaNQaVCrFCAhWo19m5REqzc6rk50rAepAEgOaK0ZAwCJZ+Gn7Yr7dMo9yQwWiIJ73fFs7z/qp9YzvOJIbu4xHq1KeYTUVRo5sOUFJTkX9hpP6OPk5/xA9zbvEExwZ4GItvQ9ZljFWGqkqra4zjFwIukAt/iH+CKIyzxprTBzblkPRidKGjVeoG7O6AC3JHWMJiwt2pYpeS9b+fLav2E9VaU29Br96Eay3N7FNLB0ua4EuQOtyPc8HxfinoKDQUBTjn4LXYZbMZFcdp9RU7NJ+RUFFgn8SwZqwi3bTn1l+jBUnllNtrkZu8ArUnsTAJC5LGHjRhgPLsozRZMRsMbm8b61Gh1qlvmjHrCRJGE3Ghi3o60EQBLSai9c7RZYlcquzyak+4fK+Y/0SiPGLQ7hIvYKPlWTy2vL32Vdw0GV9CggkhsTz9KAHaRXVwmX9ehOyLJN7sJCj23Ks84ELp4TYFhEktY9Fpb44x6zFLFFZVHnenn5nQ1AJBIYHoNFfnEZrWZYpyizlyOYsLBbJpWM2IimEZp3iL9oQdkO1iW3L9pG5J9dlfQoCqLVqugxtTUKraJf1e74oxj8FBYWGohj/FLyKClM5xysPOQ2JcgXBmlASA5pfVOGqkmzh36xV7C7eWedN4koEBFSimoEJg0gJSXNp356ORbJgMNS4/J6ejiiK6LV+F5UB0NXefs7QqDWo1RdXLiWDxcDh8n3UWKobTYZe5UdqUDpalWeEUTUVC3f+xkdrpltD1F3gAXw6oiAiI3Nr1wnc3PWai2o+MBnM7FtzlPKCqkaTofPXkN6nGQGhfo0mwxMxVhmpKKp0qWHqTHQBWvzDLq48zBazxKGNxynOKm8cAQKoNSpa9Eq+6DxX848VsX7RTkwGE421y41Pj6Lb8LaoNU1vXFWMfwoKCg3l4rFuKHg9ZcYSjlTsb1TDH0CZqYTD5Y0vx1MwS2b+OPo7u4t3AjSKkUpGxiyZWHp8CXuKdrm8f0/FYrFQY3CtF6UjJEmi2lDV4DBtb0OWZUwmU6Mb/gBMZhMms+s9Nj2VGnM1+0t3NqrhD6DGUs2+0l2NLsdTkGWZrzZ+z/v/fo5Zsrjc8AfUFQz6KuN7pqz69KKZDwxVJnYsP0h5YeMZ/sDqSbRzxSHKCysbVY4nUVNhoKKwcQ1/YC3CUFFQ6XIPbk/FbLSwZ+XhxjP8AchWOXtXHqY4u6zx5HgYWQfy+fenrRgb0fAH1nDif3/cjMnF3rAKCgoKjYli/FPwCspNpRyrPNRk8qotlRwpP+DzmydJlvjz+B8crzjWZDL/yVrBvuK9TSbPXVgkCzXGpjNsyLJsNTT6+Oap1vBnkZrOOG82my8KA6DBUsP+st2Y5abZzJhlEwdKd2OwGJpEnjuZvfknZm6a02TyFu/+gw/+/cLn5wNTjZldfx/CUGVsdAMVsrW67e5/DlNZ7PtGa0OFgarixjWono6pxkRFoe8bAC1mC3v/PUJlSdOMIVmGA2uPUZp74XlbPZ2cQ4WsX7jdmpOyCeaD4uwyVs/dgtl0cTgLKCgoeD9K2K+Cx1NjqeZg2Z5G955yRLAmlKSAFJ8NRfk3ayU7i7Y3uVwBgZEpY4gNiGty2U2BJEtU1zTdpul0REFEr/PdEGCT2YTZ7J6Tdo1Gg1rlmyHAFtnC3pLtLimgdL5oRR2tQ9sjCr6Zm2rZgZW8uvw9t8i+u9dtXNNxpFtkNzayJLPjr4NWI0pTLg9OhlN2GpqORu+b84GpxkR5vnuMRfpAHf5hvlkUSJZl9q89RklOedOOWawVbNsPboFfkG+mWigrqOSvWeuRLE19YyG+ZRQ9R3VoMpGnh/3+YHFt/vPz5Y3ug90qv7FI+ehvd6vgMg7fe5m7VbCh7/xV7lbhnPl3bD93q+ByFM8/BY9GlmUyK4+4xfAH1hDgMhcXFvEUsipOuMXwV8vyzD8xS77nTSXLMkaj+zyZJFnC7KNeapIkuc3wB2AymXzWKyWr8rhbDH8ARslAVlWmW2Q3NoVVRby38hO3yf98/SyOlbi+aIsnkLUv3+qB19Q/SRnMJguHNp3wyflAlmRrjj83UVNhwGTwzWdY4fFSSrKb3vAH1rXJoY2ZPjlmJUli4687XVKF+ryRrXORKwuLKCgoKDQWivFPwaMpMOS6PSfUiarjPmekMllM/HViGQLu8Q6TkakwVbA+d51b5DcmFou5SUNSHWE0G5Ek3wpZr62Y7G4ao7Kwuyk3lVFoyHOrDgU1uVSYGjH/lRuQZZl3/vmEGrP7DgNkWWLyX++7fU5yNVWlNWTudONmW4birDIKM0vdp0MjUVVShdzU3lNnUFlY5R5DTiNirDZxZEuW+xSQobK4mpwDhe7ToZHYv+EYpXkVjZrj72xsWboXQ6X71ygKCgoK9aEY/xQ8FqPFQG61GxdKJ5FkC9k+5pWSkb+BKlOl2zwqrcjsKNxGfrV7jQ6uRJZlDCbPyF9mMNa4WwWXYrFYPMLoJkmSTxlWZVnmWEXT5VOtj2MVhzzib+wqVh5Zx9pjG92aO9YiS+zJP8Di3X+4TYfG4FBGplufXrUc3nQCiw/l+zIbzB5hwJAsEtVlvvUMO7YtG8ni/mdH5s5ca45MH6GypJrd/x52txqYjWa2rdjvbjUUFBQU6kUx/il4LEWGAnerUEepqRiTm0LiXI1JMrGrcIebDX9WBAS2F2xztxouw2zxHA9RSZZ8xttHlmWPKrjhztBjV1Nq9Jy5zSgZKDOVuFsNlzFn6wJEwTOWWXO2LfSZAlYVRVVUFLkh3NcBFpNE/rESd6vhMqrLPcfgVlNR4zPef8YqE0UnyjxizMqyTN5h30lnc3jrCdzq8ncSWYYTe3KpqfCMA2AFBQUFR3jGqlRB4QwkWaLI6DnGP4Big2+EShwo2d9k1TzPhozMwdID1Ji9v3KipxmoAI/Tp6F4mqedJPuO919BjWflKfI0fRrKwcIj7Mrb5zEGt5zyPDad8I2DlpyDhbgpY4VDcg4U+oTHqmSRMFV70DNDBqOPeKjlHSnynDErQ96hIo/wQrxQLCYLh7ee8ATbH2C17R7Z7v6IJQUFBQVnKMY/BY+k1FiMJHuW11KhId/rF/iybA219SRkJPYU73G3GheMJHlGWOrpWCxmj9OpIZgtnmGsPh1P1Ol8qbFUU2H2rDx75aYyDBbP8T5qKIt2LUHlIV5/ACpBZMHO39ytxgVjMpgpPF7qER5UtdSUGygvcE91d1diqPQ8jyVf8KKSJIm8Q0UeNWYtJgvFWWXuVuOCObEvD7PRg/YKMhzanOkzh4MKCgq+R5OtTI1GI6+//jpt27YlICCA4OBgWrRowdixY9m6dWtTqaHgJdQX+vX9l3MY1nkk3eJ6c+3AG8lYs7nevowGI1Nf+YihHa+ia2wvhncdxfzZC+vev23knXQI72b3umfCAzb9WGQz1RbvXuBXmCooNhQ5fG/F9//w9LDnubfbg7x67WT2Zxyot691P6/n5atf477uD/HYwKf46tlZVJRU2LTZtHQzL45+mXu7PsiLo19m87ItDvs6XHawQd/HkzBbnC9AP/v0c9q1aU9EWCT9+lzKv//+67TtwgULGTliFM2SmxMXE8+gAYP4c+mfNm127drNDdffSNvW7Qj0D+KjDz9y2p+3h/7Ksux0If3pp5/SpnUbwkLD6NOnD/+ucn5fT2fN6jUEBQbRs2dPm+vTp09nyOAhxMfFEx8Xz1VXXsWGDRsc9mGp5+/tLZQZS5y+N2f6T1zVdQw9Ey7lhkG3sOks8+wPX/7IuN4T6JXYnzE9r2HxD7/avD9p1N10iexp97r/uoft+iqtRy9v4Z/Dq7Gc4fXXMbYtr17xFHNu/ILld86jb7MeZ+2nY1xbPhn7Fr/f/j2zr5vGyDZD7dpcmtKL6de8z+93/MD0a96nX/Oedm0sssS6YxmYPCg1QUMoza1wGgo6d8n3jLtnGJfdcAkTH5/Alt0ZTvvZunsTdz57C1fcdimX3dCdCQ+O4rufZ9m0ueeF2+l9TUe716Ov3WvTThDwCUOKscr52Phy5pd06duZ+JZxDLpyIGvWrXHadu36tQwfO4wWHdNIaBlPz4E9+fiLaTZtvv3xWyKSw+1eNTW2hn+LyYJk9m5DSmVRtVMD1U+/f8+Yu4dx6XXduOWxa9m8y/mYBdi0cwO3PHYtl17XjbF3D2Pekjl2bZavWcqEB0fTb0JXJjw4mhXrltl3JECRD4zZrP35Tj0qf1u9kP++diMTnhrG/6b8l12HnB98r92+khc/e4yJL47jxmdH8uQH97F5r+NnP8CqLcsZ99hgJn/1nN17hioTJbmedajW1EybNo2UlBT0ej3dunVj5cqVTtuuWrWKvn37EhERgZ+fH61bt+a9996zaWMymXjppZdIS0tDr9fTqVMnfv/998b+Gi6n7J8FZL5wHUceupysN+6k5kD9zhiyyUjxoi84/twEjjx0OZkv3kD5mlNrq/J/fyb7vfs59tgIjj02gpwPHsFwZHdjfw23kP3bfDL+ey1rJgxm6//uoGyXc9tR6Y7NrB53qd2rKvNoXZvCtX+z9bFJrLtpOGuvv5wtj9xG3grvG1MNQd1Ugh577DGmTp0KQMuWLdHr9Rw5coQFCxZw44030qlTp6ZSxSUYjUa0Wq271fBZqs2OjWy/z/uDN55+h2ffepIuPTvz41dzufva+1m45kfiEuMcfubR25+kKK+Q/5v6PMmpSRTlF2Exn1qITZn5FibjqUVvSVEp4/tfz9DRQxzq5a8OuMBv5z4KnBTX2PB7BnPe+Ikbnp1AWpc0/vlxFR/c/REvLnyO8Lhwu/YHNh1gxjMzufbxq+l4WQdK8kr45uXvmfXCt9z9/p0AHNxyiM8fm86o+0bQZVAnNi/fymf/+5LHv36ElI4pNv0V1hQiyZLH5MdqCJITI9tPP83licef4L0p79Krdy+mfzmDcWOuZuOmDSQlJdm1//ff1QwaNJAXX3yBkNAQZs+czTXjr2XF33/RqbN1nqyuqiIlpTljx43lycefPLteqiab6l2OM8/Fn378iccfe5wp70+hd+/efPnFl4wZM4ZNmzaRlGx/X2spLS1l0qRJDBw4kNw82xDTlf+s5Jprr6FXr17o9XreffddRo0cxcaMjSQkJDjUTRA8JZbr/KkyVzq8vmT+Ut565j2eevNxOvfsyNyv53PfdQ8z99/viUuMtWs/Z/pcPnh5Gs+99zTturRlx6advPzw6wSHBHHZsEsBeOfryZiMp7wlS4tLmXDZTVw+erBdf9VO9PIWCiqLKK2x3/zpNToOFh7h973L+b+hT5y1n9igaF4f9iy/7vmT1/6aQvuYNjzY7z+U1JSx8vBaANpGp/P84EeZvvE7Vh1eR7+Unjw/5FEeWPgMe/Jtk89bZInDxcdIj0xzzRd1A5XF1QiCfYqvP//9nSkz3uSx/zxDx1ZdmL/0Rx559R6+fW8BsVH2awO93o/xw66jRbN0/HR+bN2zmTc+ewk/nR9jLh8PwOv/ew/zaakTSitKuOV/1zCot60BVpateQi9GVmWnRYumb9oHs/839O89cpb9LikJ19/8xUTbr2W1cvWkJiQaNfe39+fSRP/Q7vWbfH3D2DthrU8+tQj+Pv5c+uNE+vaBQUFse6v9Taf1ev1dv2ZTWa0au9db1eWOE5rsvTf33lvxhs8/p9n6di6C/P/+JGHX72b76csdDhms3IzefjVexk95Gr+78HJbNuzmTc/f4XQ4DAG9b4cgO17t/Dsu49x5/X3MaDHIFasX87T7/yPz175mvbpHU91JluNkt5OcbbjPIqrtvzFjEXT+M/YB2jTvD1L1v7MK18+xfv/m05UWIxd+52HttGpZTduHH4HAfpAlm/8nddnPMvk+z8kNaGlTdu84ly++vlT2qZ0cKpXSU454XEhF/z9vJEffviBhx56iGnTptG3b18+/fRThg8fzq5du0hOTrZrHxAQwH333UfHjh0JCAhg1apV3HXXXQQEBHDnndb9xLPPPsvs2bP5/PPPad26NUuWLGHs2LGsXr2aLl26NPVXbBCVGcspmvshERMeQpfagfJVi8id9jgJz36NOtx+TALkTf8/LOVFRN74GOqoBKTyEuTT9ho1+7cQ0G0wumvaIai1lP35PTkf/Y+EZ75CHRrVVF+t0SlYtYwjM6aS+p9HCGrTgdwli9j1ymN0eX8WuijH9w6gy4ffoPI7tWfXBIfW/bc6MJjEq2/BLzEZUa2haONqDnw4GU1IGGFd7A9QfYkm22n/8MMPADz//PPs27ePbdu2UVpayqpVq2wMf7IsM23aNLp06YKfnx9BQUH06NGDLVu2APD7779z6aWXEh0djVarJTg4mP79+9ucABw5cgRBEBAEga+++ooRI0bg7+9PSkoKX375pY1ex44d45ZbbiE2NhatVktSUhL33HMPRUWnvKMmTpyIIAgMGDCAN998k8TExLoFyqxZs+jRoweRkZFoNBrCwsK44oorWL/+1IJmxYoVdfp88cUX9O/fHz8/P/r06cPBgwdZuHAh6enphISEcN1111FWduo07u2336Zz586Eh4ej0WiIjo5m3Lhx7Nu3z3V/HA/DLJkxy45PoGdOm824m0Zz9S1jSW2VwhOv/4/Y+Bh+mP6Tw/ar/lxNxr8ZTJszld4DepKQHE+Hbu3p3PPUmAsJCyEyJrLutWbFOvR+eoaOvtyuP2/3/MuvyUdw8LP/c+Yy+o7rTb+r+xKXGsuEJ8YTFhvG3z84Pq07tO0IEfERDLpxIJGJkbTo2oJLx/fj6M5TpyrLZv9Fm16tGT7pCmJTYxk+6Qpa92zFstl/2fUnyRZKDN6bgFqWZae5vT6c+iG33HoLE2+bSOvWrXnzrTdISEzgi8+/cNj+zbfe4OFHHqbbJd1o0aIFL770Imkt0vj111Mhe90u6carr73KNdeMR6fT1aubt3v+OfP6mzp1KrdOvJXbbruN1q1b89bbb5GYmMjnn39eb3/333c/1064lh497b2uZnw1g7vuuotOnTrRqlUrpk2bhiRJrFix4rx08xYqzRUOr8/++DvG3DiKcTePJjU9hcdefYTY+Bh+nDHXYftffvyNq28dyxVjLyexeQLDxg1lzE0j+eqDmXVtrPNsRN1r7Yp16P10XD7K3vjnTC9vYV+BY0/m9cc3M33jd6w8su6c+hnZ5gryKgr4aM10jpWc4Ne9f/Lb3uVc23F0XZurO4xkY+ZWvtsyj+OlJ/huyzw2ndjO+A4jHOuW7xmVnRtKRXGVw/xe3/08k5GDxjJq8NU0T0zl4dueIDoylnl/2HtGAbRKacPQfleSmtSCuOgEhvUfQc9Ofdm6e1Ndm5CgECLCIute67etQafT1xlaTqeypNqrUyzUV7F42hfTuHHCTdx8/S20atmK1158nfj4eKbPmu6wfcf2Hbl69NW0btWG5KRkrh13LQMvG8Sa9Wtt2gmCQEx0jM3LER4V1tkAKktqHHqnfbd4JqMGjWP0kKtJSUzlkdufICYilrlLfnDYz7w/5hAbGcsjtz9BSmIqo4dczchBY/lm0Vd1bb7/eTY9OvVi4rhJNE9MZeK4SXTv0JPvf55t15+x2uTV99ZQZaTGSWXqxf/8xODuw7m851UkxjTjjtH3EhEazZI1ix22v2P0vYwdeB0tk1oTH5XITcMnEReZwMZdth6uFsnClG9f47qhtxIT7tjhQBCFi9rz79133+WOO+5g0qRJtGnThilTppCUlMTHH3/ssH2XLl24/vrradeuHc2bN+emm27iiiuusPEWnDVrFk8//TRXXnklqamp3H333VxxxRW88847TfW1LpjS5T8S1PtKgvqMQBvbjIjx96MOi6Z85UKH7at2raPmwBZi7n4Dv9aXoImIQ9e8DfrU9nVtoiY+S3D/MegSW1r7vOF/IMvU7N3ksE9vJWvxD0QPvoqYy0fin9iclDseQBcRTc6S+fV+ThMShjYsou4lqFR174W070JEr/74JzZHH5tA/IhrCGiWSvnu7Y39ddxOkxn/ajdJf/zxB4sXLyY3NxdBEOjbty8tW546VXnggQe499572bJlCwEBATRr1oytW7dy5MgRAHbs2MG6desICgqiffv2yLLMypUrGTlypMPw4TvvvJOdO3ei0Wg4cuQId955J3v2WPOL5eXl0bt3b2bNmkVJSQktW7YkJyeHjz/+mMsuu8wu/GDNmjU89dRTBAcHExkZCcC6devYvn07ERERtGvXjurqav744w+GDBlCTk6OnT733Xcfubm5SJLEmjVrGDZsGBMmTEClUlFeXs4PP/zA66+/Xtd+xYoVHDhwgNjYWFq3bk1RURHz589nyJAhdvr5CjVODGwmo4ldW/fQZ2Avm+t9BvZiy3rHrtMrfv+btl3aMn3q1wxuN4wR3cfy9nPvUVPt/N7Nm72AYeOG4h/gZ/ee13ukVOUjY2uwMJvMHNt1nLZ92thcb9unDQe3ON4opnVOpSS3hO3/7ECWZcoKyti0dDMd+p96KB3aetiuz3Z92jrts6A6vyFfySNwtukzGo1s3ryZwYMH2VwfPHgwa9eemwFAkiQqyisICwtrkG7ebqByZFQ9dV9tDUfW+7rWrn0tM2fO5NChQzzzzDPnJLuqqgqTyeT03ntKMYeGYJEsDqv8mowmdm/dQ++BtiefvQb2YOt6x4sik8GIVm/rmaPT69ixaRcmk+PciAu+WcwVYy/Hz8E8a5QMWDws5+v5sL/gkEvy/bWLSWdj5habaxszt9AqKg2VYF3Eto1JJ+PEmW020y6mtV1/KlHFfieGSW9AlmUqi+29lUwmE3sP7aZHpz4213t27M32vVvOqe+9h3ezfe8WurTr5rTN4mXzubzPMPz0/nbvSRaZmgrvLU5hcWIEMhqNbN2+lYH9B9pcH3jpQDZkrHf4mTPZtmMbGzLW07eX7d+nsrKSTr070r5HO66feB3bdjhex1mM3p1ftbKoys47zWQysefgLnp2tr0nPTr1cTpmt+/dajfGe3Xuy+6Du+o8VLfv20pPuzbO+6xy4pXoDZTkOTawmcwmDp7YR6f0S2yud07vxp6jO8+pb0mSqDZUE+gfbHP9x6WzCA4IYUiPK51+VpZkirK9P6S6IRiNRjIyMhg61NY7eujQoaxevfqc+ti8eTOrV6/msssuq7tmMBjsvIL9/PxYtWrVhSvdBMhmE8bje9G36W5zXd+mOzWHHY/J6u2r0SW3ovTP7zj+zHgy/+8miuZNQzI6z4MqGw1gMSP6B7lUf3cimUxUHNxHaCfbA/vQzt0p37Oj3s9uffQONtw+mp0vPEjpducGUVmWKdm2keqs4wS39a5I1IbQZMa/e+65B4C1a9cyatSoOmPWyy+/XGfEOnLkCB99ZM1dNW7cOLKystixYweZmZl062ZdkI0fP568vDwOHjzIpk2bOHbsGEFBQZjNZn76yd77a9SoURw6dKjuBOF0L46PPvqIrKwsRFFk9erV7Ny5kx9//BGwGhm/++47m76MRiMLFy5k165dZGdnA3D//fdTWFjI3r172bJlCzt2WAdieXk5v/zyi50+N998M3v37uWxxx4D4MCBAzz33HPs3r2bG2+8EYC//jrlGfXGG29QXFzMrl272L59e52H4/Hjx+vNG+bNmCXHC73iwhIsFgsRURE21yOiIyjMc1yJN/PICTav3cKB3QeZMvNtHn/tUZYuWsarj73hsP32jB0c2H2Qq28e41g3Jx6J3oKjML+K4goki0RwhO0iJygiiLJCxwuYtM6p3D75Vj5/bDr3dH2AxwY+hX+QH9c9dW1dm7KCMoIjbB9AwRFBlBXYL9gEBKot3rsIdWb8KywoxGKxEB0TbXM9OjqKvNxzq2o69f2pVFVVMu7qcS7XzxtwpHtBQQEWi8XOUyQ6JppcJ/f1wIEDPP/c88z4agZq9bmFQT/33HPEx8czaNAgh+978311NpfVzrPhUbbh/hFRzufZ3oN6sWDWInZt2Y0sy+zcvJuF3y7GbDJTUlhi137Hpp0c2H2QsTePtu/sJBYnzwFvoKS6zCXh4GF+YRRXl9pcK64uQS2qCdFb5+twv1CKqmzbFFWVEuYfatefJEmU1njvplSWZSSL/W+upLwYi2QhPNR2bRAWGkFRSUG9fY66awj9r+/G7U9cz9XDrmPU4Ksdttu5fzuHjh9g5GDn87DZ4L1jVnKSR7Gw6OQzLNI2hCwqKprcfMdpRGpp36MdcS1iGTxiEHfccgc3X39L3Xst01ry4Tsf8c2X3/L5B1+g0+m4ctxwDh62N047+pt7EyYH46JuzIacsZ4NjaCwxPE8W1hSSMQZYzw8JAKLxUxJecnJNgV2v4Pw0AgKnfwOTF7t+ef4GVZeWYokSYQG2R7ahQSGUVLuOOf1mSz650dqjNX06XTKALX78A7+3PAb91zz6Dno5r0HARdC3dosxnZtFhMT49Ah5nQSExPR6XRccskl3HvvvUyaNKnuvSuuuIJ3332X/fv3I0kSS5cuZeHChXV7cU/HUlEKkoTqjDGpCgrDUuZ4TJoKsqg5uB1T1mGi//My4ePvo3LL3xTNmeJUTvHCz1CFRKJv7fwQy9swl5eCZEETanvvNCFhGEsc3zttWARpdz9Gq8depvUTr6KPT2bniw9RunOLbd+VFay9YShrrx3I7lefIGXSQ4R27u6wT1+iyRJBvfjii3Tq1IkZM2bw999/U1ZWxt69e3n++ec5ePAgX331FRs2bKjbTD3yyCN1OfWiok4tOoxGIxMnTmT16tUUFhbaeLVkZdmXV7/pppsQBIG2bdvWXavdHNYmcm/VqhVdu3YFYMyYMfj7+1NVVcXGjRu57bbb6j6Xnp7OiBHWUBrVSdfR0tJS7rvvPjIyMigpKbHZDDrSZ+TIkQA0b97c7lpqaqqNfmANS77rrrvYtm0bFRUVZ+3fF5A4izfNGZsqWZadJvyVJAlBEJj82SsEBVsNUcZXHuGRiY/zzFtPoPezPUmaN3shLdqk0aFbe0fdefVmH8B8Pt40Mji7sVkHs/lh8k9c9d/htOvTltKCUua+M59vXv6OW1666VTDc/5bCV4dniqfpYzfmYYAWcbu3jhizpwfee3V1/lhzvdER/tO/o7zop5ba39fHefgs1gsTLx1Is88+4yNp3l9vPvOu/w450d+X/K7wzxU3s7Z5rJzvbcA/3n0dgrzCrl12B3IMoRHhTPquhF89cEsVCr7M8YFsxfRok0a7bu2cyr/rM8BD8ZkMToMTW0I9nOL4OC6bRvBUVK8k5+pMXvvpvRsRiDhzIeLLJ91nv3kpa+oqqli5/5tTPvmfRJjkxjaz96rZ/Hy+aQmtaBdS+d5vrzZSOXK+aCWX376lcqqSjZu2sBLk18ipXkqV4+2Gle7d+1O966nNlk9u/dk4JUD+HzG50x+afJ56ebpOCtQA/bDU5ZlZ8tZhx+onQdsx/65/60ki/fOs2crBOPoG9vNEQ5YuXk5P/wxkycnvkRooNXgUF1Txfvfvc494x8hOODsufy8+b66gobMFytXrqSiooK1a9fy5JNP0qJFC66//noA3n//ff7zn//QunVrBEEgLS2N2267jRkzZjTad2gcHD2jnDQ9ec+iJj6L6BdovTTuXvK/fIHwax9C1Nqm/Cld+h2VGcuIfXAKoqb+dEDeiKPx42xM+SUk45dwKr9kUKv2GAvzyFr4PSHtOtddV/n50+md6Ug11ZRsy+DIjA/Rx8QT0t478kg2lCbNAj927FjGjh2LJElkZGRwxx13sH37dhYudBzv7ogRI0awf/9+1Go1HTp0QK/Xs3nzZoxGo8Pqi6GhoQA2nh5nLiQcTVKOiI21TXReUVHBFVdcQUlJCXq9ni5duqDRaFi3zhrO50if4OBgO31qr9XqUSv/0KFDjBkzBqPRSFBQEN26dcNsNtflP/SFapOOcPZwDosIRaVSUZhne4JZlF9k5w1YS1RsJNFxUXWGP4DU9BRkWSY3K49maacmh+qqan6ft4R7n/pvPcp5b3J/ANGBs29gWCCiSrTz8isvKrfz3Kvl9y+WkNY5lStus+Y+SmyVgM5Py1u3vsfo+0cSEhVCcGQwZQVn9lnhpE/Zq4t9OBsVEZERqFQqcnNsvdHy8/OJjo528ikrP/00l3vvvpdZs2cycNDAetv6NAJ2BsDIyEhUKhU5ubYnyfl5ju9reXk5mzZtYuvWrTzy8COA9WBAlmWCAoNY/PNiBgwYUNd+yntTeOutt/j5l5/p0MH5Zt+rcTJoT82ztt4nRQVFdt6Atej99Lw49TmeeecpivILiYyJZO7XCwgI9Cc0ItSmbXVVDUvmL+XuJ+88i3reO9eqRJXDcXu+FFcXE+4XanMtzC8Es2Sm7GRBkaLqEsL9z2wTbOcxWItG9N7iP84W+qFBYahElZ13U3FpkZ1n1ZnEx1gLVrRolk5RaRFfzvnYzvhXY6jmz39/5z8T7qlfP9F7x6zTZ1j4yWfYGV5+BQX5dt6AZ9IsuRkAbVu3Ja8gnzfee6PO+HcmoijSpWMXDh2x9/zz5qJKwMl1o+1kcGrMnjHPlhbZee7VEhEaQWGx/RhXqdSEBIWcbBNp5+1a3+/Aq8esE92DAkIQRZHicts80qUVxYSc4Xl1Jqu2/MVHP77N/25+nk7pp7yncgqzyCvO4bUZz9Zdq92rjX/icj587GtiI+PPqpuvU7c2O8PLLy8vz84b8ExSUqyFADt06EBubi4vvvhinfEvKiqKBQsWUFNTQ2FhIfHx8Tz55JN1n/F0VIEhIIpYzvA8tVSUoApyvK5SBUegComsM/wBaGKbgSxjKclHjD5VbKn0z+8p+WM2sfe9gzbBewt6OUIdFAKiCmOx7b0zlRajCTn3dEhB6e3I//sPm2uCKOIXZ72PASktqc48wol5s3ze+Ndku+3HHnuszigmiiLdu3cnPT0dOGX86t69e91DfsqUKRiN1hPqwsJCMjMzKSwsZP9+a/W6l156iS1btvD99983eGHQvbv11HHPnj1s2mSNBV+wYAHV1dbww0susc0XcaacvXv3UlJSAsD06dPJyMhgypQpDdLFEbVGTYAlS5awYcMGnnji7BUCvR1nRiCNVkPbTq1Zs8I2V9qaFevo3KOjw8907tGZ/Jx8qipO5RE8cvAooigSE29rJFiyYClGo4kR1zrP5eHIeOZNqB1s/NQaNcltk9i9Zo/N9d1r9pDWOdVhP8Yak93iRhCt96Z2QZTaKYXda2xLzu9avdthnzKyQ928B8dzkFarpUuXLixfblvkZPny5fTq5bya1Jw5P/LfO//L9BlfMmz4MJdq6m04MgKduq/Lba5b72svu/bBwcFs2LiBtevW1r0m/WcS6enprF23tu5ZAPDeu+8xefJkFi5cWJdu4nx08xZEVA6va7Qa2nRqzdoVtvm81q5YT6ce9RtCNRo1MfExqFQqlsxfyqVD+yGKtnPm0oV/YjSauPKa4fXr58WHATq1ziUjY2fuProl2uafuSSxE3vzD9blRNyVu49uCbZtuiV0Zmeu7XwO1nvqzVVTRZXju6rRaGiV2oYN22yT86/ftpYOrTqfc/+yLGM024cSLlv9ByazkWH9HRdROZt+XoETY4VWq6VTh06sWLnC5vqKlSvo3s2+aJJTZBljfbmqZJntu3Y4LvrhxbcVHI8LjUZD67S2rN965phd43TMdmjVifVnjPF1W1bTJq0tarXG2ia9E+vO6HPd1tVO+zxzfvYmVGonewW1hrSEdLbuz7C5vnVfBq2bOfc2X7l5OR/+8CYP3/A0l7SxXUckRCfz3qNf8M7Dn9W9urftTfu0zrzz8GdEnFFZ1Zluvo5Wq6Vbt24sXbrU5vrSpUvp06ePk0/ZI8syBoP9fKHX60lISMBsNjN37lxGj3aeOsSTENQatEmtqNmz0eZ6zZ6N6FMcj0l9ansspYVIhlP7V3PecRBEVKeNt9I/v6fk91nE3PMmumb2uX69HVGjITAtnZKtG2yul2zdQFBrx1F6jqg4tA9NWP2HgWDNMejrNNnsNGvWLHr16kVQUBCdOnUiKSmJuXOtlQNvuOEGwBoKe++99wLw008/kZCQQIcOHUhISGDjxo2Eh4eTmGi10L7wwgt06NCBrl271oXgni/33nsvcXFxSJJEnz59aN++Pddccw0A7du3rztxcEZqaioBAdYS0nfccQcdO3ZkzJgxDdLFEe3atav7bsOGDaNDhw7cf//9LuvfU9GpnIfY3XLPTcydtYD5sxdyaO9h3nj6HbJP5HDtbeMBmPLSBzx99/N17a8aP4yQsFCeve9FDu45xMbVm3j3hfcZe+Mou5Df+bMXMujKAYSGhzZIN28gXB/h0GAx5JbBrJq7mn/nryb7UA5z3viJouwi+l/bD4D5UxYy4+mv69p3vKw9m5dt4e8f/iH/eAEHNh/kh8k/0rxDM0KjQwEYfNNAdq3Zw+9f/kHOoRx+//IPdq/bw+CbHHuxheoaVtDCE6hvAX3fA/fx9VdfM/PrmezZs4cnHn+SzOOZ3DHpDgBeeP4F/jPplBfUnDk/cuekO3nt9dfo0aMHuTm55ObkUlp6ypPHaDSybes2tm3dhtFoJCsri21bt3HwoGOvCW/2nHCm+wMPPMBXM77i66+/Zs+ePTz+2OMcP368Lk/M8889z6Q7rP8tiiLt2rWzeUVFRaHT62jXrl3dPP7uO+/yf//3f3zyySckN0smJyeHnJwcKiocV5/15vuqETUOK38D3HT39cyfvZAF3yzi0L7DvP3Me+ScyGX8RGu+s6kvf8Sz97xY1/7ogWP8Muc3jh48xo5NO3li0jMc3HOQ+5+9267vBd8sYsDw/oSGOw+dEhFRC5oL+4JuJDk0AbODNAZ6tZ60iOakRTQHIC44mrSI5kQHWAuITep+I08OeKCu/eLdS4gJjOLuXhNJDk1gWKtBDG81mDnbTkVLzNvxM5ckdua6TmNJCknguk5j6ZbYkZ+2/+xQt2ahiQ6vewOCIKALcGy8vH7ELSxaNo/Fy+dzJPMQU756k9yCbMYOta7ppn3zPv/3wdN17X/6/XtWblzB8eyjHM8+ys9/LeDbRV8z7NKr7PpevHwe/bsPIiQotF799EHeG2qlUjtfR98z6R5mfz+Lb36Yzd79e3nm/57mRNYJbrvJmhbnpckvcfdDp37rX3z9Bb8v/Z2Dhw9y8PBBvpnzDR9+9iHXjD2VE/jN995g+d/LOHL0CNt3bueBx+5nx67tTLzpNjv5ak3D1viegl+w43Xj9SNvYeGyuSxaNp/DmYd4b8Yb5BZkM26o9T59NHsKL049NWbHDb2WnPxspsx4k8OZh1i0bD6Lls/jxlET69pMuOom1m9dw8z5X3Ik8xAz53/J+m3ruG7ETWeKP6mb947ZoIgAp++N7D+eZet/Zdn638jMPcr0RdMoKMljaG9rmqXZv37B+9+dCi9fuXk5U7+fzK0j/0t6cluKy4ooLiuistr67NdqtDSLTbF5BegD8dP50Sw2BY3a9nkVHBnIxcojjzzCF198wfTp09m9ezcPP/wwx44d47//tUZVPfXUU9xyy6n8nx999BGLFy9m//797N+/nxkzZvD2229z002nxuy6deuYN29eXQ7/YcOGIUkSjz/+eJN/v4YSMugaylf/QvmaXzHmHKVo7oeYi3IJunQUYM3Xlz/ztbr2Ad0HIwYEUzD7DYzZR6g5sJWi+Z8Q2Ht4Xchv6dLvKP75SyJvfBx1RCzmskLMZbYGQ18gfuQE8pb9TO6yX6jKPMLh6VMxFOQRM3QMAEdnf8L+91+pa5+1eA6F6/6hOus4VccOc3T2JxSt/Zu4K0/l7M2cO4uSLRuoycmiKvMoWYu+J3/F70T1H3qmeJ+jyVxtXnnlFX7++We2bt3KgQMHMJvNtGrViuuuu45nnz3lRj116lTatGnD559/zp49ezh8+DAdO3akefPmCILA3Llzuffee9mxYwcWi4VvvvmGu+++m6NHj563TtHR0axdu5ZnnnmGP/74g7179xITE8OoUaN45ZVXzprnKSwsjB9//JH//e9/HDx4EK1Wy+LFi+nR4zxOQ+uhdevWTJ8+nRdffJHs7GwiIyN57733uPzyy13Sv6eiE/UICA7zqA0bN5SS4hI+eetz8nMLaNEmjWk/TCU+KQ6A/NwCsjNPuZv7B/rz2byPeP2Jt7hu8E2EhIVyxZgh3P+MbfjOkQNH2bR2C5/O/ahe3fzVzhcb3kCUXxT7Suw9QroP60ZlSSW/fPIbpfllxLeI475p9xARbz0lKc0vpSj7VAhFnzG9qak08Nd3f/Pj2/PwD/KnVY90xj08pq5NWudUJr15Gws/+JlFH/5MVFIk/3nrDlI6OnbTj9R7b047QRCcjtnx46+mqLCIya+/QU5ODm3btmXu/J9ITraGnOfk5HD8+PG69tO/nI7ZbOaRhx+pC1EFuPGmG/j0s08ByM7Opk/vvnXvvT9lKu9PmUq/S/vx+5LfbOSrRO/eOImiCA4yHIy/ZjyFRYW8/trr1vvari3zF8wnuZnj+3oufPbZZxiNxroDqVqefuZpm+eUjW5eiiAI+Kv9qTTbGzavGHs5pcWlfPb2dApyC2jROpUPvnuvbp4tyC0kJ/NUKLtFsjDr4285euAoarWaS/p146tfvyA+Od6m36MHjrF57VY+/mlqvbr5qwO82rCaHuk47KZVVBrvjXy57t/39L4dgN/3LufNvz8k3D+M6MDIuvdzyvN46vdXuLf37YxuN5zCyiI+XP0lKw+fqmi9M3cvLy97l9u7X89tl1xHVlkuL//5Dnvy99vJl2SJ9CjH3tzeQmC4nzWZ/hlT7ZC+wyitKGH6T59SWJxPalIL3nn6I+KirGOwsDif3IJTawNZlvjk2/fJyjuBSlSTEJvIPTc+yJjLr7Hp91jWEbbu2cz7z35ar146f41XG6nUWue6jx01jqKSYt56/y1y83Jpk96G77/+gaTEJABy83I5kZVZ116SJF5+4yWOHT+GSq0ipVkKzz/5AhNvnFjXprSslIeffJi8/DyCg4Lp0K4DP//4M90623tbq7TeHBUAgWF+VBRU2qXhvLzvMErLS5j+4ycUFOeTmtyC956eRlz06WP2VEGD+JhE3nvmI6bMeIuffv+eyPBoHr39KQb1PrUn6Ni6My8/8iaffvsBn37/IYkxSbz6yFu0T7ePjhFVAvpA7/UEDorwR1QJDnNt9us8kPKqMub8OYvisiKSY5vzzB2vEx1m9SwtLiukoORUKPsfa3/GIln4fP5UPp9/6vk0sNtQ7r/u/CKuBFEgLDb47A19lAkTJlBYWMhLL71EdnY27du359dff6VZM2sagOzsbI4dO1bXXpIknnrqKQ4fPoxarSYtLY3Jkydz11131bWpqanh2Wef5dChQwQGBnLllVcya9asutRe3kBAt0FYKsso+e1rLGVFaONSiLnnDdTh1pRi5rJCzEWn1lWizp+Y+96m6MepZL95F2JAMAFdBxI64o66NmUrF4DZRP6XL9jIChl+K2FX2R+keCuR/QZjKi8jc85XGIsL8U9Ooc0zb6KPtt47Y3EhhoJT9042mzj69TSMRfmIWh1+Sdb2Yd1617WRDDUc+vxdjIV51jYJzWj54HNE9hvc5N+vqRFkb8+kq+CTHCzbQ7XF804ukgNSCdaGuluNBpNblcPCQ/PcrYYd/uoAbmp9q7vVuCBqDNUeWbREq9Gi8eJQP1mWqTHUuFsNh+h1eq82Up2oPEp+TR4XnJzOpQhE62OID0g+e1MPxWgxcdX067HInpf0/aebviTc33u9rLP35XN0W/1VI5scASISQ2jZ03vHLEDxiZJ6i1O4i+CYINRebAAsyizlwPrzO4hqCgLD/Wg7wLvzg/01awMlueXuVsOOnqM7EN+y8Q60N23aRLdu3cjIyOAHS/HZP9CIvNHdN40lKR/97W4VXMbhey87e6MmpO/8Ve5W4Zz5d2w/d6vgcrzXbUHBpwlQe6bLvJ+Xe/5F6CNRCZ7lnSAgEB8Qf/aGHk5D0w80NqKXe/55Kt4eTg0QoA7Cswx/ADIBGseFhrwFrUpDy8g0j8sJGR0Q6dWGP4DAekL93IZcfwiit6DWeaCBTQCVF3tUAgSG+7tbBXsECIr0/jEbmRTqkc/h8LiL1/NPQUHBs1GMfwoeSZgu8uyNmphAdTAa0XvzUIG14EfL0FYetSmVkWkd1tbdalwwapXnjQ1BEL26cAJYjWynV0f3FDxRp/MlWBvqcYcBKkFNsMZ5PkBvYWTboQ7TALgLAYFRba9wtxoXTGC4n8fl1hNEgcjkUHerccHoAz3rvgLoAnQeadw5H7T+GoKjAz2rcIkMUc29+yAAoFmHeDwpgE0QIDY1wiN/SwoKCgqgGP8UPBSdSo+/h3n/RXhxTrrTaRve3qM2pcHaEOJ8wPNPEARUHlaxWKPWeP3GCUCt8qz7Ct6fSxGs1V8j9dFnb9iEROqjEbzcYA0wMK0f/ho/d6tRhygIDG/t/eFZgiAQ2+LsFfuaDAEik0PqzZnnLah1akSVZ/32dAG+YUSJSQv3HCdrAYKjAnzCQBUcEUBEYqjHGFZlGVK7eG9RJQUFBd/Hs57yCgqnEaHzHGObRtAQqPYNN/5Iv0ii/aI9xvuvfURHnzBQAWg0nuX954lGs4YgCIJHFddQq9Q+M2YjdJ5l/PM0fRqKXq3jqtZDPMLzViWIXJbahzC/UHer4hKikkMRVR7y+5MhJs3zIhUagiAIHuVVqdaqfMKoChAaG4RG7yHPYxli0jzIgH6BpHVJ9BjDql+wnujm4e5WQ0FBQcEp7l+VKig4IVgTip/KM3KlxPon+sxmH6BnbB+3e/8JCARpgmgd1tqtergSURA9xiNMq9H61JjVqD3HsOoLIb+1aFU6IvUx7lYDgCh9DFqV9xanOZMJncbip9G7/aBFEERu7XadW3VwJSqNisS2HjBmBQhPCCYwzHM8PC8UXYDOY7z//EI9Y/3nCgRBILlDrLvVAAECwvwIjfPuvKqnE98yitCYIDxhudPhshY+te5SUFDwPTzjCa+g4ABBEEgMaI67/fmDNaGEaL0/N8rpxAXE0z6iA+68tzIyAxOHoPbyPIqnIwgCOq37PSdEQfTIHIQXgiiKHmF08zWjKkCcfyIa0b1GN62oI87ft8Klwv1DeajfXW4/aPlPj5tJCvX+1AqnE5ceSUCYn1uXByq1ipSuCe5ToBEQRIEADyhQoQ/UofHEAiQXQHhiiNXo5sYxKwgCad196zBbEAUuubIt7rT+CQLEp0eR0Mo3PNcVFBR8F8X4p+DR6FR6Yv3ct2kRBRXx/kluk9+YdI/pRaAm0G1eKR0iOhEbEOcW2Y2JIIjoNO41AOq0ep9a3Nfi7nBbURQ9tqrzhaASVDQLTHWrDsmBqYgeVnzEFQxK60ffZj3cEv6rEkTaRKczrv2VTS67sREEgRbdE916NJjaLcHnDFQAGr0GnRvzwYkqEb8Q3/GmrEUQBJp3iUflRs/KpPYxPpHr70yCIgJo2899zzC1Tk3nIa3cJl9BQUHhXBFkTyqTpKDgAFmWOVpxkApzWZPLbhbYgiCNb+T6c0R+dR6LDi3AIltoqqQpAgJRflGMSBmD2sMKZLgKWZYxmgyYLeYml63V6DwqRNbVSJKEwWhocrm1Xp2+aFStJafqBDnVJ5pcbpx/IjFuPORpbEqqS7l3wZPkVeRjkaUmkSkKIsG6QD4a8wZxwR4QIttI5B0p4tDGph+z0anhpPqY19/pyJJMWV45FpOlaQULEBwdhFrrm2sDgJKccvatOdrkeepC44Jo2SvZZ59hsiSzZsE2cg8XNum9FQSBPld3atJcf5s2baJbt25kZGTQtWvXJpOroKDg/SiefwoejyAIJAemEtDE1X+TAlJ82vAHEOUXzfBmVyEKYpN4AAoIhOnCGd58pM8a/sA6ZrUaXZPn/9NqtD5t+AOr9507Qqt93fAHEOMXT5S+afNSRevjiNb7ngfw6YT6hfDuyJcI8wtF1QQegKIg4q/x450R/+fThj+A6ObhNOvYtOMnIjmElC6+a6wGayhlUFQgorpptwlBkYE+bfgDa/GPtO5NG1ESHBVAix5JPv0ME0SBniPbE5kQ2nSh1QJ0H9FOKfKhoKDgNSjGPwWvQBREmgW2aKKKuwLJAak+l+fPGfGBCYxoPgqVqG50A2CUXzQjU8egU/le2MmZWD3F9KiaqOKu1ePPd4ol1EdTGgAFQUCv880w6jMRBIF4/6Qm88KL9UsgzseKKTkjJjCKj8ZMJjYoulFDgEVBJEQfzIdjXiclvFmjyfEk4tIjm8wYF50STovuvm1EqUVUiQTHBKFqioq7AgRFB6HR+/bhVS0RiSEnvfBodENVaFwQ6X2aeUwhl8ZEpVHR5+pOxKQ0bjVjQRAQRIFeYzoqef4UFBS8CiXsV8GrkGWZAkMeudVZNIZfv07UkxTQHL3a/Qmvm5oyYykrMpeTU5Xt0n4FBGRkOkd2pVt0d4+phttUyLKM2WLGaGqcUFVBENBp9D6Zi+5sWMOrjUhS44RSqkQVGo3motjon0mJsZjjFYexyK4PXVcJapIDUy6aA5bTqTBW8vGar/ht7zJEQUBy0RKsdp7t17wnD196F2F+oS7p15soy6/gwIZMjFUm13YsWA1hKZ3jiWwWetHNB7IsU11aTU154zzD1Do1AeH+qNQX3zOssqSaQxszqS5z8b0VThbNaxdDbIuIi27MSpLEgY3H2bXqEMgyrt7pBkcFcsmVbQmJatqIpFqUsF8FBYWGohj/FLwSg6WG45VHqLFUuazPaH0cUfrYi26RdDqyLLOraAdrc1YjydIFVqkUAJkQbSgDEwcT7e/b4WdnQ5IkDCYDkuS6HEpqlcYnq8+eD7IsY5EsmEyu3fBrNdqL0qB6OmbJRGblUUqMRS7rM0wbQUJAM58O+z8XNhzfzBsrPqCouuSC+xIQCND688il/2VAWt8LV86LsZglju/IIedAYe0jqOGc/HxITCBplySi9bs4vNKcYTaYqSiqRDK77rDFP8wfXcDF/QyTJInsvQWc2JNnveCCMRsQ5kfqJYn4Bfl+lEV9lBVWkvHrLkpyyy94Pqgdo236ptCye7JbPSkV45+CgkJDUYx/Cl6LLMuUmUooNORTZa5oUB8iImG6SMJ1kehUehdr6L1UmCrYXbSTXUU7MVhq6rxKzoXatpH6SNpHdCQtpOVF5+3njFpDldlswnIBRkC1So1arVHu62nUeliazQ33VBMEAbVKjUqluqg3o2dSYSqnoCb3goyAodpwovQxBGiCXKiZd1NlrGbJvr+Yt+MXTpRloxLEcy4IUts2KiCCse2uZHjrIYTolXtbS2VJNTkHCik4VoIsnecy96SRIDQuiNi0CEJiApX54CSyLGOsMlJTbmhwMRBBFNAF6tAH6i6KUNRzpabCQO6hIvIPFyNZztPAenLMBkUGEJMWTlh8sDJmTyJJEtn7Czi4+TiFmaUIgsD5bn3VWhXNO8ST0jmBwDD3RwYpxj8FBYWGohj/FHyCGks1xYZCKs0VGCzV9RqqVIIaf1UAQdoQQrXhjZp/yduxSBaOlB/iUOlBcqtyqTJXOm1bW8wjNiCOVmGtifJT8qDUhyRJmC0mLBYL0jls+EVRhVqlQq26OMNQzxVZlk/eWzOyJJ/VaC0IAqIoolKprIVvlHvrFJNkoqgmn3JTKVXmSiScj1sREX+1dZ4N10WhES9ur6n6kGWZrdk7+WPfCnbm7iGzNLvecRsXFEO7mFYManEp3RM7K4cA9WA2Wsg/WkxJTjkVRVVYTPWMWZWAf6gfIVEBRKeEowu4OHKoNhSzwYyh0ojZaKr3vgIIKgG1Vo3WX4vWT3mG1YfFLFF4vISSbOuYNRudG1kFUcA/WEdQVABRzcMvek+/s1FWUMnR7VkUZJZQll+BVM/BgM5fQ1hcMHEtokhsHYNa4znzrGL8U1BQaCiK8U/B55BlGYOlmhqpBvlk6KqAiFpU46fyR61sQhtMjbmGgpp8qkxVWGQzAgJqUU2ILpRwXYSyCW0gsixbjVayZHMiLQhCnUFK2Sw1jFpjoIx8KuRHsL23CuePLMsYJQPV5iossuXkPCugElT4qf3Rir5fHbmxqDbVcLDwCDnluRgsRmQZdGot0QGRtIhMIUDrfs8Tb0SWZYzVJipLarAYLUiShCAKqNQq/IN16IOUMdtQZFnGYrJYjYCy1XQtnMyVqNKoFA+/C8BYY6KqpAazwYxkkRFEAVEt4hekwy9IhyAqY7YhSBaJ8sJKyouqMJssyJKMSi2i9dMSGhOE3oON/4rxT0FBoaEoxj8FBQUFBQUFBQUFBQUFBQ9HMf4pKCg0FOUoTkFBQUFBQUFBQUFBQUFBQUFBwUc5b+NfdnY227dvd/p+dXU1mzZtoqLCeQGGXbt2kZmZqchQZCgyFBmKDEWGIkORochQZCgyFBmKDEWGIkORochQZCgyGijjnJDPkxdeeEFOSEhw+v6OHTtkQF69erXTNm3btpUffvhhRYYiQ5GhyFBkKDIUGYoMRYYiQ5GhyFBkKDIUGecgIyMjQwbkjIwMr/4eigxFhiLDtTLOhfM2/mVlZcnbtm1z+n5VVZWckZEhl5eXO22zc+dO+fjx44oMRYYiQ5GhyFBkKDIUGYoMRYYiQ5GhyFBkKDLOQYYj4583fg9FhiJDkeFaGeeCUvBDwSeRZZlKUyUmyYQkS9ZKv2o/tCrPrd7lLUgWCYtJQpZkEEAUBVRalVIl8QKRZfm0irQyIChVfl2EZJEwnayUCCCqBLR6jVIl0QVIlpNzwUkEUVAqe7oAWZYxy6a66t+CIKAW1AiCcm8vFPlkdfXaedb6P2WuvVBk2fr8kjltPlCeYS7BYDZQZarCLJkRRRGtqCVQG6jc2wvEOs+akWQJaivWi2pUgsrdqtWLUvBDQUGhoajdrYCCgiuQZIn9xfs5Xn6crPIssiqzMFqMdu3CdGEkBiUSFxhHelg64X7hbtDWuzAZzFQUVFJTbqC63ICxymTXRhAF9IFa9MF6/IJ1BEb4I4rKJrU+ZFnGIlmwSOaT/29x2M66GFUhiirUKjUq0bMXpZ6AyWCm8HgpFUVVlBVWUl1aw5nHXIIoEBCqJygigKAIf8ITQ1BrlHtbH7IsYzaYMVWbMBnMmI1mZIv9+aGgElDr1Gi0ajR+GtQ6tbJJPQsW2UK5sZRqSxXV5koMlhobI0otOpUf/ip/9Gp/gjQhqEVlGXc2LJIFs8WMdHKedXRfQUAliqhElXXzLyoHWmdDlmUsFgnJYkGSJCTJsS+BIFgPBERRRKVSISqHLmclvzKfnQW7OF6WyZHSIxRWF9m10av0JAUnkRySRGpoCq0jWinrg7MgyzJlphIqTRVUmSuoMlciIdm104o6AtSB+KkDCNGGolPp3aCtgoKCgutRPP8UvJpyYzmbcjexMWcjlaZKREE8eYLnHAHrwlNGJiUkhR5xPWgZ1hJR8aioQ5ZlqkpqKD5RSkVh1bl/UABkENUioXFBhMYFo/XTNJqe3ogsy5jMRowWIw2ZfkVBhVatRa1SDCqnI8syFYVVZO0voOBoCbIsIwjYGf3OpLaNqBKISY0gtmUEASF+TaO0lyBLMoYKA9VlNVhMjo3U9aHSqPAL1qML1CnelmdQY6mm2FBAiaHIiVGqPgRCNKGE6yPxUwc0in7eiizLmC1mjGbDWdcEjhAEAa1ai0alVebZM5AkCbPZgtlkbtDnRZWIRqNGFEXl3p6GRbKwq2A3K4+vYl/R/jpv1LON39p1b5A2iEuT+tIroSfBuuAm0to7MElGCmvyKajJxSyf/7gN0gQTqY8hWBPqEWNW8fxTUFBoKIrxT8ErMUtm/jr2F2uz1gI0YNNkRUBARiZUF8qoFqNoHtLchVp6JzUVBrL35GOoNNYZ8y6EkNhAotMiUakvbuOqLMsYzUaMZoNL+hMQ0Gn1aFSKcbW63MD+dccoy6+8sDF78rPhiSG0uCTxojdcy7LV6FdRWHnB8wBYDa0BEQFWI6AHbKDciVkykV11nHJTmUv681cHEO+fjFalc0l/3ozJYqLGWINLBi2gU+vQqBUjoCzLGA0mLJbzPwBwhCAI6HRaJU0AsL/oAN/u/J7immJEBKQLWNMCDGo+kGGpQy/69YEkS2RXZZJfk+OS/rSijuTAVAI1QS7pr6Eoxj8FBYWGohj/FLyOzPJMFuxfQHFNcYONfmdSawTsEduDQc0GXZS5AWVJpuBYMYVHS1zet0qrIq5VFIHh/i7v2xuwSBZqjNUN8kA5G2pRjV6rvyhzgcmyTPb+Ag5vzqrLN+USBFCpRVpckkRkM8846W9qLGYLFfkVmGoa5t1THxq9hsCoAFTqiy9ETZZlyozFZFdlOgw3uxAEBGL84gnTRV6UY1aSJQzGGsyS68esKIjotX4XbVilxWzBYLBPpeIKNBo1as3F6cluMBtYvP9nVmWurluHugIBgUj/SG5qfwPNQpJd0qe3UWmq4GjFQYySaw5cTydSH0Ocf6LbcgMqxj8FBYWGohj/FLyK1SdW8+fRP126SDodAYEQXQg3t7uZMH2Yy/v3VMxGM8e35Vi9/RqR8MQQolLDL6pFvslspMZU08hSBPx1/hfVxtRssrB75WFKcysaVU5UszBa9kq+qPJUGauMlOWVu86Y6ggBgmOC0PpdPActsixxovIYZaaSRpUToA4iKbA5oocnrXclFslClaGKxh20oNf4oVFfPN5UsixjMpoxm11vUD0dQRTQ6y8uj+D8qgKmZXxCSU1Jo6xnRUFElmVGp49kQLPLXN6/J5NXnU1W1fFGlaEVdaQFt3JLPkDF+KegoNBQLj5XEQWvRJZllh9bzp9H/7T+u5EW+DIyZYYyvtz2JflV+Y0iw9MwGcwc3ZyFoapxDX8ARZml5OzLb1CuO2/E2CSGPwCZKkMlFkvjbtA8BbPRzPZlByjNa1zDH0D+0WJ2/3MIyeJ6r01PxFBpoCy3kQ1/ADKU5ZQ3+oGDpyDJEscqDje64Q+g0lzOkfIDWGTXhGd6OmaLmSpDJY0/aKHGVI3RfHGM2dow38Y2/IE18qCm2uC0aIivkV2Rw5T171NqKG209awkS8jILNi3iN8OLmkUGZ6GLMtkVx1vdMMfgFEysL90FzXm6kaXpaCgoOAqFOOfglfw74l/WZW5qklkSUhUm6uZuXMmJTUlTSLTXZiNFo5tybKG9jXRmrs0p4LcA4U+bwA0mY0YmsTwd4oqY5XTqsG+gsVsYceKQ1SWVDfZmC3OLmfP6qPIPr4xNVYZKW8Cg+rplOeVY6z2bWOKLMtkVh6h0lzeZDJrLNUcKz/YKKkGPAmLZKHaeB5FqVyAwVSDyWxf9d6XkGUZo9F1+f3OVaahxuDza4OCqgI+2jiNalNNk/0+lxz6g+VH/moSWe4krzqb3OrsJpNnls0cKNuDweL60GIFBQWFxkDtbgUUFM7G/uL9LD+2vEllyshUmar4fs/3/Kfjf3wynFKWZbJ25zZKTq+zUZJVhj5QS2icb1aks0iWJvL4s6faUEWAPtBnw6cObsykoqiqyQx/tRRllnJ8Zy7JHWKbVnATYTFbrKG+bqAst5ywxFCfzQGYX5NDhYsKe5wP1ZYqcqoyiQ/wzZxfsixTbWhaw18tNaZqRFH0ybUBWOcDi7npD5KsBkAjOr1vFlgxS2Y+3/IlVeZql+f8PBuL9v9MfFA8rSNaNancpqLMWEJ2dWaTyzXLJg6X76NVSDu35F5Ou21Ok8t0NwdnXNvoMl7Y9Gejy3DG/3Ud4jbZTUG/hU3jzOMKVo3u524VXI7i+afg0dSYa1h0YFFdBbOmREYmryqPVSe8Z5I6H0pzyqkqcY+BCiD3QCEmg++FqcqyTI3RfWEgMrLbDI+NTVFWGXmHi5vc8FfLsZ05VBT7XoiPLMtU5Fe47b4iQ0V+pU96/FSbqyioyXWb/BJjkVsMj01Bjam60UImz0m+sdonx6wkSRiN7vNslCTJLYbHpuCPQ0vJrcxzi0eugMC3O76nxux76wOzZOZYxSG3ya+xVDepx6GCgoJCQ1GMfwoezZLDS6gyVbl1gf/P8X/Iqcxxm/zGwFRjJvdAoVt1kCWZ7L2+l//PaDa4PdTObDFh9rH8f2ajmf1rj7lbDfatOepzeakM5Qa3eACfjqnGhKHCt0KnrAU+jrpbDbIqj/lc/j9PmOMkWfK5/H+1ef7cjdFo8rl59nhZJksPL3ObfBmZcmM5C/YtcpsOjcWJymOYZffOBznVJ6g2u8cTWUFBQeFcUYx/Ch7LifITbM3f6lbDXy2/HfrN3Sq4lLxDnpFzr6q4mopC31ksSZLnbAZrTL7llXJ8Zy4mo5sNmjJUldaQc6DAvXq4EEmSqSiqdLcaAFQWVvpUXsUiQwFGyf0GTbNspqDafd6HrsbqXe0Z3kuecNjjSiwWCUnyjO9jMnrGs9RV/Lh7rluiWE5HRmbtiXUcL2v8ghhNRaWpgmKjZzyTMyuPuFsFBQUFhXpRjH8KHsuGnA2IHjBEZWSOlx8nt9I3Nk9mg5ny/Er3hfidQfGJUner4DJMFs/ZrMiy7DPFPyxmiZwDhR4zZrN8yGPVUGHwmPsqy/iM958syxQZPGNDClBsLPAZI5VFMnvEoWAtJg858HEFZpPneIxbLJLPzLOZZZkcKzvW5Hn+HCEKIquOr3a3Gi7DnWkVzqTSXKF4/ykoKHg07resKCg4oMpUxY6CHR6xUAIQEcnIyXC3Gi6hJMc9Sf2dUVVSg7HK+zdPsix7jNdfLUazbxhSCo4VYzF7xlwAUFNhpLSJq+I2BrIsU1PmWTkMq8tqfGLDX2muwCR5znwgyRJlxhJ3q+ESPG+eNfnEmJUkz/H6q8WTjJEXwqrM1YhuKAbhCEmW2JidQZXJ+41UZslEsbHI3WrYUFiT524VFBQUFJziGU8iBYUz2JK3xaMW0xISW/K3YLB4tzFFlmWKT3he8vfiLM8ySDYEd+efcoRFsnjcZq4hZO3zHA8qAEGAbA/TqSGYDWYsJs8aHxaTBbMPFAIqNuS7WwU7ijxQp/NFkiQP9GiWMUveP2Y90dBmMps9ai3YEGrMNWzMzvAoz1tJlliftcHdalwwVu9qzxofhYYCn8uxqqCg4Dt4hfHvr7/+QhAEBEFg5syZdu/fdtttCIJAUFAQlZWVvPjii3Xtjxw50vQKK1wwh0oOOQ3rWT9vPe+Nf4+XB77MJ7d/wtEt9SdUXzd3HR/c8AEvD3yZqddNZctvW+zarPlhDVOvm8rLA1/mnbHv8Nv7v2E6I+m1WTJzovxEg7+TJ2CsMmExOV6UfLtgNkOuu4xOl7fh6jtHsXFb/QvD9VvWcfWdo+h0eRsuv34A3y/81mnbX5Ytps2ANO575i6H71cWe/8JtKWezd/nn35OhzYdiAqLpn+f/qz+13nIzaIFixg9YjQpyakkxCQyeMAQ/lz6p12by/peRlJcMrGRcfTt2Y/vvv3+vPXyBsxGM5VOKuzOXzqHax+8isG39uSOp29g655N9fa1efdG7nj6Bgbf2pNrHxzBgj9/tHl/0fJ53Pt/tzN8Un+GT+rPQ6/exa4DO+z6kWWrB623b0qN1c4T+0+fNZ1u/buS2DqBwaMGsWb9mnr7MhgMvPr2q3Tp15mE1vF0H3AJ38z5pu79Pfv2MPHuiXS9tAtRqZF8Mv0Tp32Z6tHLG5BlmQqTY8/Q776cw9DOV9ElrifXDLyBjDXOx+z6VRtpF97F7nVo3+G6NhNHTnLY5u4J99v1V2Op9vpNaX1GtvOZZ/97590E+4fYvXp061nX5rzmWQ88/DlfLBbnxqnPPvuUtu3aEB4RRt9+ffj333/Pqc81a9YQHBJEr949ba7Pmj2LgEB/u1dNzRm5HGW8fp49XHLE6bjdumgLM27+gg+vep/v7pnNie2Z9fa1ddEWZt7xFR+OeJ+vb5/B7qW77NoYKmr464NlfH7dp3x41fvMvOMrDq+3rYQrI7OncF/Dv5SHUGa0TxuTsXozD97wKJe3u4oukT3569e/z9rPxn83ccOgW+iZcCkjuo3lxxnz7Nr8uXg54/pMoEd8P8b1mcDyX1Y47EtGosrs/ZEB50LpniUc++k+Ds+8iczFT1Kdu9tp27yV0zj01QS71/EFjzpsX3HoXw59NYGcZW81lvpexbRp00hJSUGv19OtWzdWrlzptO2qVavo27cvERER+Pn50bp1a9577z27dlOmTKFVq1b4+fmRlJTEww8/bD8HewjZv85n453XsvqawWx55A5Kd2512rZ0+2b+HXOp3asq85S9IOePRWx/6l7W3jictTcOZ8fzD1G+z34+9UXU7lbgXBgwYADNmzfnyJEjzJo1i1tuuaXuverqaubOnQvA+PHjCQgIcJeaCi5ClmVOVDg2su34cwe/v/87Vz16Fckdk9m4YCOz/zebe2ffS2hsqF379fPXs+yTZYx6YhTxbeI5sfsEiyYvwi/Ij1b9WgGwbck2/vzkT0Y/NZqkDkkUHitkwasLABj+4PC6vgQEsiuySQ1Ndfl3bipqyh17Lv66/Gcmf/gKzz30f3Tt0I0fFn3HXY/fzuKvlxAfE2/XPjP7OP998g7GXzWBN595l03bM3h5yguEh4Yz9LJhNm1P5JzgrY8n061jd6d6GatMSBYJUeUV5xEOceaNMvenuTz5+FO8O+UdevXuxfQvZ3D1mPGs37SOpKQku/b//ruagYMG8vyLzxMSGso3M2czYfx1LP97GZ06dwIgLDyM/z3+P9JbpaPRavj9tyXcc9c9REVFMuTyIXZ6aVz/dZuMiiLHhr9la5YwdeZbPHL7U3RI78yiZXN57I37mPXWXGIi4+zaZ+Wd4PE372fkwHE8d+8rbN+3hXenv05ocBgDeljv2ZZdGxnSZxjtW3ZCq9Hy7c9f8+jku5n55lyiwqNt+rOYJQyVRvSBOtd/6SbCmYfd/J/n8+wrz/DGS2/Ss1tPvv72a667/Tr+XfIviQmJDj8z6f47yC/IZ8rk90lpnkJBYQFm86n+q6uraJ7cjNFXjuLZV56tVy+3F3a5QIySAdlByorf5i1h8tNv8dxbT9GlZ2fmfDWXu669j0Vr5hKfaD9ma/ll/QICgk6tbcIjw+r+e8rMdzAZTxlLS4tKGdd/AkNHX+6wrxpzNQGawIZ8LY9ActE8+8Zbk/m/l16s+7fZbKZPr76MGTum7tr5zrPejCzLTo1sP/30E48/8ThT3ptCr169+XL6l4wdN4aMjZsc3ttaSktL+c+dkxgwYCB5efZ52YKDg9m8eYvNNb1eb9dOkiRE0XvXBsfLMhER7dLY7Fuxl38+WcHA+wcT3y6e7b9sY+Ez87npi1sJjg6262fb4q2snr6KwQ9dTkyrGHL25LBsylJ0gTpSe6cBVs/peU/OxT/Un6ueG0FgZBDl+eVo/bR2/R0rO4YsywiCe4uQNBRZlqmy2Berqq6qJr19S0bdMIL/TXzyrP2cOJrF/dc/zLibR/PKJ//HlnXbeP3xNwmLDGXIyEEAbN2wnScnPcvdT93JoKsGsPyXFTxxx9NM/+UzOnRrb9dnlbmKIE3IhX9JD6bi8GoK139NZK870Ee3omzvn+QsfZ2kMe+iDoy0ax/ZcyLh3W44dUG2kLnocQKa9bJra6rIp3DjbPQxrRvzK3gNP/zwAw899BDTpk2jb9++fPrppwwfPpxdu3aRnJxs1z4gIID77ruPjh07EhAQwKpVq7jrrrsICAjgzjvvBOCbb77hySefZPr06fTp04d9+/YxceJEAIeGQneSv2oZh6dPJfWuRwhu3YGcJYvY9fJjdP1gFrqoGKef6/rRN6j8T62dNMGhdf9dumMLkZcOIaV1e0StlhPzv2Xni4/S5YOZ6CKiGvPruJ0mfZr++uuv9O7dm9DQUPz8/EhJSeGaa66huLgYgKVLlzJkyBBCQkLQ6/W0bt2a2bNnIwhCncFv+fLlnDhxyjC0YMECysutIYO1g1bBuyk1lDoNr139w2q6jOhCt1HdiGoexfCHhhMcHcyG+Y691Lb9vo1uo7vRfkh7whPC6TCkA11HdGXVN6vq2hzfcZykDkl0HNqRsLgwWvRsQfvL25O1J8uuv6xK+2veRE2FAUfF5r7+cTrjrryGa0ZMIK1ZC56+/zlio+P4fuE39o2B7xd9S1x0PE/f/xxpzVpwzYgJjBs+nuk/fGHTzmKx8PgrD3PfbQ+SFOd8kwBgqPSsPE7ngyzLTkN6Ppz6EbfcejO33nYrrVq34o23JpOQmMCXn3/psP0bb03moUceotsl3WjRIo0XXnqBtBZp/Pbr73VtLu1/KSNHj6RV61akpqZyz7130759O9asXmvXn7dvSiuKqx2O2R9+nc1VA8YwcuA4miek8sAtjxEdEcv8M7z5alm47CdiIuJ44JbHaJ6QysiB47hqwGi+//mUN/nz973G2MuvpWXzVjRLSOHx/zyHJMtk7FjnWDcnhklvwZnx75MvP+bGa27k5gk3k94inVeff5WEuHhmfDPDYftlfy9j9brVfDf9ey7rdxnJicl07dSVHt161LXp0qkrLz71f4wdOQ6dtn6DqbeH/dZYHI+Lr6fN5uqbxjD+lnGktUrlqdcfIy4+lh+mOx6ztYRHhRMVE1n3UqlUde+FhoXYvLd6xVr0fnqucGb8s3i3l7Wz+ex859mQkBBiYmPqXps3baakuISbbrmxrs35zLOS7N3FKepLD/HBh1O59ZZbmTjxNlq3bs1bb75FYkIin3/xeb19PvDA/Vx7zbX07NHD4fuCIBAbE2vzcqibxXvvK8DxsuMOI1k2zc2g3bD2tB/egfDkCC67eyCBUUFsX+zYo2XPsl20v7ID6QNaERIXSquBrWk3rD0b55xa/+5csgNDeQ0jXhxFfLsEgmOCSWifQFSa/Wa2ylRFqcHz0sCcK0bJiOTAk7nfkD7c+/R/GTxi4Dn189NX84hLiOWxVx8hNT2FcTePZvQNI5n50an177effE/PAT2446GJpLRszh0PTaRH/+5884ljT+CLwfOvdOcvBLUcRHD6YLShiUT2nIg6IIKyvX84bC9q/VH7h9a9DAWHkAyVBLUcYNNOliTy/vmAsM7XoA50bti5mHj33Xe54447mDRpEm3atGHKlCkkJSXx8ccfO2zfpUsXrr/+etq1a0fz5s256aabuOKKK2y8BdesWUPfvn254YYbaN68OUOHDuX6669n48aNTfW1zpmshT8QM+QqYi8fiX9Sc1InPYAuMprs3+fX+zlNSBjasIi6l3Da2qnVI88Td+VYAlNb4p/YjBb3PA6yROk238jvXx9NZvzLz89n7NixrF27lpCQENLT0ykpKeGnn36itLSUH3/8kSuuuIJly5ZhMplo2bIl2dnZdYNw4sSJCIKAJEl8882pCbk2DDglJYX+/fs31ddRaERyqxxX7jKbzGTvzaZFjxY219N6pHF8x3Gnn1FrbR1c1To1J3adwGK2LhqSOyWTvTebzF3WcIuiE0XsX7Of9D7pNp+TkcmuyG7Qd/IUasrtK3saTUZ27t1B3+79bK737d6PzTsdh6Rt2bnZvn2PS9m5dzsm8ykvlGkzPyAsNJzxV117dt0qvNf458zwZzQa2bJ5C4MGD7K5PmjwINatXX9ufUsSFeUVhIWFOXxflmVW/LWC/fsP0Ldfn3PWzVtwFPJrMpvYd3g3PTr2trnevUMvduxzvHHauX8r3TvYnjD36NiHPYd3YzY7DjM1GGowm80EBdqf4AsCVJZ4r/FPskjIkv2G1Gg0snXHVgZcartxGnDpQDZscjxml/z5O507dOaDTz+gQ+/29BzUgxdee57qmobdH9kiI9UTgujp1Jjtv7fRaGLX1t30GWg7ZvsM7MWW9c7DVwDGX3Ydl7W5nNvH3MW6lfWnY5g3ewHDx12Bf4CfY92cGCa9AWeHLK6YZ2d+PYsBgwY49KKolV3fPAvePddKDuYCsN7bzZs3M3jwYJvrgwYPZt1aeyNoLTNnzeTQ4UM8/fQzTttUVFTQuk0rWqa34Orx49iydYsT3bz3voLV8+9M45/FZCFvfy7JXZvZXG/WrRnZuxwfMluMFvv1rFZN7t6cuvXsoTUHiW0Tx4oPlvPZtZ8w+z9fs/67dU7n06xy7z3QdtVBxtYN2+k10NZA3WdQL3Zv2Y3pZB7MbRu303uAbeh674G92Lphu8M+fb3ir2wxYyg8hH98R5vrfvGdqMk7t3Dy8v3L8YvvgCbQ1jBdvPUnVPpggtMHOfnkxYXRaCQjI4OhQ4faXB86dCirV59b1e7NmzezevVqLrvssrpr/fr1IyMjg/Xrrc/IQ4cO8euvv3LVVVe5TnkXIJlMVBzcR2hn299oaOfulO+xT8tzOlseuYP1t41mx3MPUrK9/rRAFqMB2WJGHRh0wTp7Ok0W9nvs2DGMRiP+/v7s3r0bf39/ZFkmIyODqKgonnjiCWRZJi0tjVWrVhEbG4vBYODAgQOA1bh36aWX8s8//zBr1iwef/xxcnNzWbp0KQC33nqr17quK9hicFKhtKqkCskiERBuG9odGBZIRaHjU7YWPVqw6edNtOnfhrhWcWTtyWLzL5uxmC1UlVQRFBlEhyEdqCyuZPrd062bC4tE97HdufTmS+118/KCH44qppaUFmORLESG2brpR4RFUlDkOEF8QVE+EWe0jwyLxGwxU1xaTHRENJu2b2TuLz8y/4vF56Sb5EHVXM8XZx4fhQWFWCwWomNsQ0ajo6PIzXVs5D6TD97/gMqqSsZdPdbmemlpKa1btMFgMKBSqXh3yjt2m9/T9fPW+dFsstgZrEvLrWM2LCTc5npYSARFpYUO+yksKaRHx4gz2odjsZgpKS8hMszeM+KT76cSFR7NJe172r1Xp5uX4sjwB1BUbB2zUZG29yMqIoq8fMdVDI8cP8q6jevQ6XR8/clMCosKeeL5xykuKWHqm1Mbrp/q7O08EQn7cVFSWIzFYiEiynbMRkRHUJDneMxGxUTy4nvP0a5zG4wGI4vm/MIdY+7iq8Wfc0mfbnbtt2XsYP/uA7w09QXnunmxgcoZFzrP5mTnsPSPpXz51Rd2753vPOu1OHuGFRZY7220rQdOTHQ0fzoI5QU4cOAAzz//PEv/WIpa7Xib0Sq9FZ9++hnt2rWjvKycadM+YsiQwaxds44WLWwPeL36vgI1FvscWtVl1ciSjH+Y7XrWL8zfaQ7k5Euas+P3HaT2aUF0y2jy9ueyc8lOJLNETWk1ARGBlGWXkrnlOK0GtWb0K2MpOVHMig+XI1sket7U265PR7p5C67KX1qYV0hElO3aIDwqHLPZQklhCVGxkRTkFdrP3VHhFDqZu709t+rZsBjKQJZQ+dkejKr8QrBUl5z18+aqYqpObCG6/wM212ty91C+/y8SR73hSnW9moIC6xwcE3PGHBwTQ05OTr2fTUxMJD8/H7PZzIsvvsikSZPq3rvuuuvIz8+nX79+yLKM2Wzm7rvv5sknzx4q35SYyktBsqAJtXWA0ISEYSx2XOlbEx5B2j2PEZjWCslkIn/FEnY+/xDtX5lKSLvODj9zdOYnaMOjCO10iau/gsfRZMa/du3akZqayqFDh4iOjiY9PZ0OHTpw9dVX06xZMw4ftiawvu2224iNtbr+63Q62rVrV9fHxIkT+eeff9ixYwdbtmzhr7/+wmKx2IQFK3g/Z3tonmnEkHFu2LjstsuoKKrg8zut4SkBYQF0vrIz/37zL4LK+pnDmw6zcuZKrnr0KhLbJVKUWcRv7//GihkrGHDbAFvdvDyEst5F9Jn3VZYRHMVb1jW3bw/W3IiVVRU8/uqjvPTYq4SFhjv6+Bmdef8Cv14c3dtzMMb9OOcnXn91Mt/N+ZaoaFtjTFBQEKvWrqSyopK/V/zN008+Q/OU5lza395o7c3I9XiA2Y/Ps43ZM1rLtdftP/PN4q/4c/XvTH3uc6dhqrIXh6Od7fd2PvOsLEkIgsAn731KcLA1V5XhmZe5/d7beOOlN/DTO/ZCq1+/8/6Ix1DfvXU0bzq7ryktm5PSsnndvzv36ETOiVxmfDjTofFv3uwFtGzTgo4OclDV4ovGvzoaOM9+M/sbQkJDGDFyhN17F808e5b3HY5bB3OtxWLhttsn8uwzz9CyZUun/fXo0YMep4UD9+7dmz59e/PJJx/z9tvvnJfunk5960a74Sk7zHIBQM8be1JVXMmcB79DlmX8w/xpO7QtGXM2IpzMiSjLMn6h/gx+6HJElUhMegyVhZVk/LTRofHPm6tUy66cy+z+DifXs6f/gezWD/U8E315nrXB0aLq7HNu+YEViNoAApJP5QKXTNXkrfyQqD53otLb57y82DmftUMtK1eupKKigrVr1/Lkk0/SokULrr/+egBWrFjBq6++yrRp0+jZsycHDhzgwQcfJC4ujueee67RvkfDsf+uzr6/f0Iy/gmnvPiDW7fHUJDHiQXfOzT+Zc77hoKVf9L+lamIZ0lL4ws0mfFPr9eTkZHBrFmzWLduHbt27WLWrFnMnDmTOXPmnFMf11xzDffffz+VlZXMnDmTv/76C4DLLruMlJSUxlRfoQlRCY7dPfxD/RFVop2XX2VxpZ03YC0anYYxT49h5OMjqSiqICgiiI2LNqLz1+Ef4g/A8s+X0/GKjnQbZd1MxaTFYKwxsviNxfS/tb9NommV6KWuKCdxNFGGhoShElV2Xn5FJYVEhNsn7QWIDI+ya19YUohapSY0JJQDh/dzIieTe566s+792k1n+0Hp/DprKckJp4W7yM4ncW8mIjIClUpFXo6th0R+fgHR0dFOPmVl7k9zue/u+/h69tcMHGSfu0YURdLSrEm+O3bqyN49e3nn7Xd9blMqOCgCExJkHbNnevkVlxbZeQPWEhEaQVGJbfuSsiJUKjUhZ4T1fvfzTGYv/JL3nv6EFsm24f+2unnvmHX2ewsPOzlmz/DyKygssPMGrCU6OobY2Lg6wx9Aeot0ZFkmKzuLtJS0Buh33h/xGBwZRUIjwlCpVHZefkX5RXYeJfXR6ZIO/DznV7vr1VXV/DZvCfc9dXe9nxcF7y2c4IwLmWdlWWbWzNlcd/11aLX2RREumnnWyfWICGuOydxcWw+TvPx8h/e2vLycTZs2sXXrVh559BHAGrYryzLBIUEsWriYAQMG2H1OFEW6devGgYMHLvSreBwqUYVJsk0t4RfshyAKVBbZFqyoLqnCP8zfYT9qnYbLH72CQQ8Ooaq4ioDwAHb8uh2tvxa/EOsBS0B4AKJaZVM8LTw5nKqiSiwmCyqN7RrW2XrbGxBcNJdFREfYefAVFRSjVqsICbeuDSKjIyjMK7JrE+5k7naVbp6KShcMgmjn5WepKbPzBjwTWZYp37+CwLRLEVSnzBCmslzMFfnkLHvz9MYAHPr6epLGvocm2HFeUF8mMtI6B5/p5ZeXl2fnDXgmtbaRDh06kJuby4svvlhn/Hvuuee4+eab67wBO3ToQGVlJXfeeSfPPPOMxxRZ0gSFgKjCVGL7+zOVFtt5A9ZHUKt25P9tn4/yxILvyPxpNu1eeo+A5i0cfNL3aLK/bFlZGXv27OG+++5j9uzZbNq0iYEDrRvaf/75p26AfvXVV+TlWTcdJpOJXbtOlV0ODAzk6quvBuCLL75gy5YtgFLow9fQqR1b3dUaNXGt4ji44aDN9UMbDpHUvv5iEiq1ipDoEESVyI4/d5DeN71uYjMZTAii7dJXFEWr98YZx+Falf3mwJtQqe1/8lqNlnat2rN6478211dv/Jcu7bo67Kdzuy527f/dsIp2rTqgUWtITU5j4fRfmffF4rrXoD6D6dmlF/O+WExstH1lS9GBbt6CM0OKVqulc5fOLF/+l831v5b/Rc9ejpOgg9Xj7+477+HLGV8wbPgV56SDLMsYDY7zJnqzYVWtUdntTDVqDekpbdiw3Tbn1IYda2mf3slhP+1admLDDtv267etoXVKG9TqU/WQv138NV/P/5y3n/iI1qntzuzGXjcv5cw5rxatVkun9p34e9UKm+t/r1pB966Ox2zPbj3Izc2hovLUwczBwwcQRZH4OPtq4ReinzcgOthQa7Ua2nZqw+oVtmNw9Yq1dO7heMw6Yve2PUTG2h/K/L5gKUajkZHXXnkW3bx3nnVGQ+dZgFUrV3Ho4CFuufXmc5Llq/OsM2u7VqulS5cuLF++3Ob6X8uX07OXfZXO4OBg1q/bwJrVa+tek+6YRHrLdNasXkv37t3tPgPW+7pt27a6yB9b1bz4vgI6lf2aVqVREd0yhmObjtlcP7bpKHFt658zVWoVQVFBiCqRfSv20Lxn9X17hQAA3T1JREFUSt18GdcugZKsEpu0DsUnigkID7Az/AHo1fbVlb0FR/NsQ+jUvQNrV9jmBl3z1zradG6DRmM1TnW8pANr/7Yt/LVmxTo6de/gsE+VD86zpyOo1OgiUqnO2mZzvTprG/po5wemADU5uzCX5xDc0vZQWxMST+Lot0gc9Ubdyz+pG/q4diSOegN1gGNnBF9Hq9XSrVu3ujRntSxdupQ+fRznn3WELMsYDKdSV1VVVdkZ+FQqVb2V392BqNEQmJZOyRbbfMclWzYQ1Np5lMOZVB7ahzbMNrw/c/63HJ/zNe1eeJugFhdPZekm8/zLy8ujd+/ehIWFkZiYiNFoZO/evQB07NiR/v37M2HCBA4cOEBKSgppaWkcO3aMiRMnMmXKlLp+Jk6cyMyZM+sq/AYGBjJ+/Pim+hoKTUCMv/OTjD4T+jDv5XnEt44nqX0SGxdupDS3lO5jrQvKpR8vpbygnHHPjQOg4FgBJ3afILFtItXl1az5fg15h/IY++yp/Gmt+rZizfdriEuPI7GtNex3+efLadWvlc3pqYBAfEDDNrKegj5IR7WDoh+3XnM7T772P9q36kDndl2Ys/h7snOzmDDqBgDe/ewtcgtyeONpazjOdaNu4Nv5s5j80atcM2ICW3ZuZt6vP/L2c1MAa8h+emorGxlBgVavoDOv1+kW4L2G1fo21Pc9cC933nEXXbt2oUfPHsyY/hWZxzO5fdLtALz4/ItkZWXz2RefAlbD312T7uKNtybTvUd3ck96s+j99ISEWE9U33nrHbp07UJKagomo4k/lvzBd99+z3vvv3teunkDAaF68o/ZX59w5U28Mu1ZWqe2pV3LjixaPo+8ghzGDLY+Dz75fioFRXk8e88rAIwePJ55f3zPB7PeZuSgcezcv41fVizghftfr+vzm8Vf8eWP03j+vteIjYqnsKQAAD+9P/56W28MWQb/UO/dOAmigCAKDnP//feOu7n30Xvo1KEz3bt2Z+Z3X5OZdYKJN04E4OU3XyYnN5uP3pkGwLhRV/POh+/wwOMP8MRDT1BYXMiLr7/IDdfcUBfyazQa2XvA+sw3mozk5Gazfdd2AvwDSG2eaqeb6MDj01vQq/3AQXrYW++5iSfvfpb2ndvSqXtHfvx6Htkncphwm3XMvvfSVPKy83j9Y+uYnfnxNyQkx9OidSomo5nFP/7C0sXLmPL123Z9z5u9gMFXDiA0PLR+3VTnH4LtKQiCgCiIDkOXz3eerWXm17O4pPsltG3X1q7P85lnwbvnWrEeY/v99z3ApP/cQZeuXenZoyfTZ0zneOZxJt1h9Rh5/oXnycrK4ovPv0AURZt0PQBRUVHo9LZpfF577VW69+hBi7QWlJWX8fHH09i2bRvvvfveeenmDSQFJ1KWX2ZX9KPr1d1Y8uZvxKTHENc2ju2/bKc8r5wOI6yHAf9+uZKKwgqueHw4AMWZxeTuySamTRyG8ho2z91E4ZFChj42rK7PjiM6sXXhZv7++C86je5CyYliNny3ns5jujjULSHIe9e0firHHpJVFVUcP5xZ9+8TR7PYu30fwWHBxCXGMvXlj8jLzueVaS8CMH7iOL7/8kfefnYK424ZzbYN21nwzSJe/+zluj6uv2sCk0b+lxlTZzJgeH9W/PYP6/9ez/RfPnOsm9pxNJIvEdLuKvJWfog2Mg19VEvK9i3DXFlAUCtrpfmijG8xVxURfel9Np8r3/8XusgWaMNsiyuJaq39Na31Pp55/WLjkUce4eabb+aSSy6hd+/efPbZZxw7doz//ve/ADz11FOcOHGirgjqRx99RHJyMq1bWw1aq1at4u233+b++++v63PkyJG8++67dOnSpS7s97nnnmPUqFGoVJ51sB0/egL7p7xCYIvWBLVqR84fizAU5BF7xRgAjsz6BGNhAekPPQtA1qI56KJj8U9OQTabyft7CYVr/qb1E6/U9Zk57xuOffsl6Y88jy46FmOx1ftXpfdD5ed4bvEVmsz4FxERwcSJE1m3bh2HDx9GkiRat27NLbfcwqRJkxAEgdDQUCZPnszGjRvZt28fzZo145JLbBMvDhgwgJSUlLocgePHjycgwPcn2YuJEF0IOpXOYXGN9kPaU1VWxd8z/qa8sJzo1GhufPtGQmNDAagorKA0t7SuvSzJrP5uNYXHChHVIildU5j0ySTC4k65Cve/tT8IsPyz5ZTllxEQFkB633QG3zn4TPHEBdp7rHkT+kCdw+Q+Vw4aQUlZCdO+/oD8onxaprTkkze+JCE2AYD8wjyyc09VOk6MS+KTyV8y+aNX+XbBbKIjonn6/ucZetkw+87PEV2g9xr/6tuUXj3+aooKi3jj9TfJycmhbds2/DT/x7qqkjk5uWQeP7VQnfHlDMxmM48+/D8effh/dddvuOkGPvnsYwAqK6t45KFHyTqRhd5PT3p6Op9P/4yrx19tJ9/bQ9UDw/0djtnBva+grKKUr+Z9RmFJASmJLXjz8Q+IjbJuZgpLCsgtPBUmER+dwJuPf8AHs95h/tI5RIZF8eCtjzOgx5C6NguWzsFkNvHclMdsZN027i5uH/9fe92chGd5A4IgoNapMVXbVzoeO2IsxcXFvPPB2+Tm59I6vTXfTf+OpASrh3Vufi6ZWafGbGBAID/NnMtTLz7J5aOHEBYWxugrR/PUo0/XtcnJy2HQiFMn/R99/hEfff4RfXr2YeF3i2zkq3VNtjRpFJwZ2IaPu4KS4lI+fusz8nMLaNmmBZ/88AHxSdYxm59bQHbmqTFrMpl46/n3yMvOQ6fX0aJ1Gh//MJX+l9uGnB45cJRNazfz+dyPz0E37x2zYJ3PHFUuPd95FqzFPBYtWMQbb012KOt85llREL3aQ62+EK/x48dTVFTI5Mmvn7y3bZk3d/5p9zaHzOPHz0teSWkp999/H7m5uQQHh9CpUyf+WLKUSy6x9wz05oMAgOTgJHYV7Lbzpkkf0IrqsmrWfbOWqqJKIppFMPqVsQTHWA9KK4sqKc8rr2svSxKb5mZQnFmMqBJJ7JTEtVOuIzj2VJhlUHQQY1+/mn8+WcE3d80kMDKQzmO7cMm19vfVX+1PiK7+EE1PRitqERGRsJ0Pdm3ZzX/G3FP373dOHkqPvO4qXvrweQpyC8nJPJUiIKFZPB989x7vPDuFOdN/Iio2ksdfe5QhI08V9uncoyOvf/4y0177lGmvf0pS80Qmf/EqHZzkV/W/CIx/gSl9sBjKKdkyF3N1MdqwJGKHPFlXvddcVYK5wjacWjJWUXl0HRE9J7pBY+9lwoQJFBYW8tJLL5GdnU379u359ddfadbMmj4pOzubY8dOnZJLksRTTz3F4cOHUavVpKWlMXnyZO666666Ns8++yyCIPDss89y4sQJoqKiGDlyJK+++mqTf7+zEdVvMOayMo7/8BXG4kL8k1No+9yb6KOtnuKmokIM+ad+05LZxJGvpmEsykfU6vBPSqHNs28SfsmpvKc5vy1ANpvY+6ZtfsOkCbeRfP3tTfPF3IQge5Jvp4LCSWbtnMXh0sPuVsOOG9veSFro+eev8hQMlUYOb8w8e8MmRuunIbVH/aHbnk61sRqzxd6Q4m70Gj0atfcaVs1GM2vn7nC3Gnao1CK9xnfw6g1/ZXEV1SXV7lbDDv9QP6d5r7wBWZbZU7LNztPHE2gV2sGr83wZzUYMJs+rUKpRadBrvderEqC6qsajwr1q0fvpPCb/VEPYVbCbzzbbV5J2N60jWvHfrneevaEHc6B0NxXm8rM3bGLSgloRpG08w+qmTZvo1q0bGRkZXPOB7+XJPBsHZ1zb6DJe2PRno8twxv91HXL2Rl5Mv4Wr3K3CObNqdD93q+ByvPdpquDTpIak1lu10x2oBBWJgYnuVuOC0PprUGk872cfEO7dmyYAteiZ3koqD9XrXFFr1Z4XXitASGyQVxv+ALR6zdkbuQGNn2fqda4IgkCAOsjdatihV/l5teEPlHm2MVF5ooed4P05/5qHNEftYb87AYFW4fXnZvMGGtPA1lAEBPw1ge5WQ0FBQcEhHvikV1CAztGdPWrBJyLSKbqT02Ik3oIgCITGB5+9YRPjiTqdL2qV523+VKLKqz0maolPd1xl1m3IEN/S+5NPq/Vqjyu0o9KovD7sFyBc73njI1zneTqdL6IoemQqA0+c/88XtcbzvoNGrfaotWBD8Nf40S2um0flhBQFkR7xjouveBPhOg9bG2CdZ739kEVBQcF38ZwnkYLCaQRoA2gb0RbRQ4aohMQlsZecvaEXEBrnWYY2vxA9On/vDUutRRAEtB4WXutp+jSUqGahHmWk0gdoCYnx/pN9QRDwC/Esr0p9sN7rN/sAAeog1ILneDCKiARrw87e0AvwtDQGWrXWJ8asKIoeV1xDrfY8g2RD6JfUx2FOYHcgCiJdY7sQoPX+vHQaUUOoNtzdatgQqXdetFBBQUHB3XjObkpB4Qy6x3W3S+TrDgQEEgITiA2IdbcqLkGjUxMY6Tn5tMITPC9so6FoVJ6zKRUQfCIUDUClVhGbFoGnZAKIbxXlE5t9AF2gzmPuK8JJfXwAQRCI0HuOV0qYLsKjPI8uBLWo9qi0IJ40718onuT9p1KJCB5mjGwoScFJJAUlesS4lWSJfkl93a2Gy4jyIGObvzoQP7XnrK8VFBQUzsQ3VoIKPklSUBIdIjt4xGJpeOpwd6vgUqJTI9y/qBbAP1TvUYbIC0UURY/xttNrfcODqpakdjGotW4OpRHAL1hHbIsI9+rhQkRRJCDcMzxAAsIDPM7z6EII00WiFd1vzFQJap/yRhEEAb3WMzxWtWqtT6RWqEWl8pxUERqt53jOuoLxbeyrRDc1AgLd4y6hWUiyu1VxGQGaIMK0nvFMTgxo5m4VFBQUFOrFM57wCgpOGJYyDD+1e4tB9EvsR3xgvFt1cDVaPw3Rqe4NlRAEgTgf8qCqRavWIbjZw0atUqNW+dbGSaNT07KHmzcsMrTq3QzRExPjXwD6IJ3b8+xp9Gr0Qe43lLkSURBJCHD/Jjs+INlnvIBrUas0bi/+IQgiWi/PA3wmgiCg1bn/2aHVajzGCOkqmoUkM6j5QLcdaAsIBGoDGdtqjFvkNyYJAc1QCe6dD2L84vFXe8ZBmoKCgoIzfOvJquBz+Gn8GNlipFtkCwhE+kXSP7G/W+Q3NqHxwW7N9xWTFoHGQ6uNXgiCIOCndZ/BWkBAr/H+6smOiEgMIapZmNvCVJPaxRAY7jueqrUIgkBQVKD7wn8FCIwM9LmDAAA/dQAR+mi3yQ/RhhGk8aw8r65Cr/XDnTHrflo/nxyzoii61etOFEVUat8smDAsdSiR/pFuyWctI3N92wn4++D6QC2qSQ5McZt8nUpPjJ9vOQkoKCj4JoIsy7K7lVBQOBt/H/+bv4//3WTyBAT81H7c3uF2wv08K5mwKzEbzRzdnIWpxtykckPigohtGemTG6dajGYjBlNNk8v11wV4ZDVMV2E2Wdi+7ACVJdXQhE+vsPhg2lya4lNhqWdirDJSllve5HKDY4LQ+kDRH2fIssyxikNUmpv23upV/jQPSkP04cqTFslMlaGqyeXqNXqPKzziSmRZxmgwYbFYmlSuIAjo/XQ+vTbIq8xnyvr3qTbXIDfhQ+zKtOEMTR3SZPLcQXZVJrnVWU0qUyWoSQ9pi07VdIfpmzZtolu3bmRkZNC1a9cmk6ugoOD9KJ5/Cl5B/8T+9I7v3SSyBAR0ah03t7vZpw1/AGqtmuROcah1qiZzoAiODvR5wx9Yc0HpNE0bEuan9fdpwx+AWqOi/cA0/IP1TTZmQ2ICadOvuU8b/gC0/lqrB2ATEhQd6NOGP7AaNJICU/BXN9291an0NAtM9WnDH4BKVOOnbVpvXJ2PG/7gVPivqglTHAgC6PS+UTm5PqIDorj3knvQq/VNVoRncPNBXJ4yuElkuZNYvwSi9E1XnE8lqGgR3LpJDX8KCgoKF4Ji/FPwCgRBYEizIQxMHmj9dyPt+gUEgrRB3NHhDmICfCdBen1o9BqadUlA69f4YT6h8cHEtfa9PH/O0Kp16DVNsyj01/mjVvlWXi9naHRqOg5pSXBk4+fXiUgKpd1lqT6X588ZukAdwTFBjW9YFawef7oA38qZ5gxREEkOTCVI0/jVzf3VATQPaulzef6coVap8W8iA6Beo/eYok6NjdUAqG2SEFxBENDr9T6X588ZCUHxPNTjfoK0QY26ngUY0eIqRra86qJYdwmCQLx/ErF+CY0uSyNqSQ9pp1T3VVBQ8CqUsF8Fr+No2VEW7F9AmaHMZSETAgIyMl1junJ588vRqS6ODenpSJJEwZESio6XuLxvlUYkNj2KoCYw1ngiFslCjbEaSZZc3rdKVKPXNp0HgSchSzIn9uZzdGu2dS5w1dNMAFElktYtkeiUsIti03QmFpOF8vwKzAbXpwRQ69UERQai0vi2V5ojZFmm1FhEdtUJZFw7HwgIRPvFEa67eA5YTkeSJWqMNVgk149ZURDRa/183rPaGWazBaPB2Ch9qzVqNBr1RTlmq03VLNy3mLVZ6+rWoa5AQCDML4yb2t9Aaqj7cuG5kwpTGUcrDmGSXD9uI3RR1kJKbvKsVsJ+FRQUGopi/FPwSowWI8uPLWd99voLWjDVfjZIG8ToFqNJDU11sabeR3VZDdl78jFWm1zSX3B0IDEtIi7Kjf7pyLKM0WzEaDa4rE+91g+Nj1X1bQhVZTXsW3uMisIqq8daQ59qJz8bFhdEix5J6Hw8HPVsyLJMTbmByqJK1xhWBQgID0Af5Ns5vc4Fk2Qkq/K4y/IA+qn8iQ9IvujDz2RZxmwxU2OqdlmfWrUOrdr3w1HPhjUPoBGLxTVG6zrPwovEq7o+9hTu5dud31NmKLvgNS3AZcn9ubLFMLSqi/sZZpEtZFdlUlCT65L+NKKW5MCUJvHgrg/F+KegoNBQFOOfgldTaiglIzeDjJwMqs3ViIJ4Vu+q2sWRjExycDI9YnvQKrzVRXui7whZlqksqqY4q5TKovPYRJ00nogqgZDYYMLifTuRf0OQZAmT2YTJbGzQAl8URLRqLWqV5qLfjJ6OLMuU5VeSva+AgswSq7HqHAyBggCyDIIoEJ0STlyLCJ+s6HshSJKEocJAdWkNkvn8N/4qtYg+xA9doPaiCes7V6rNVRQZCigzFjdoPgjWhBKuj8RPFaDMB6dhNQKaMJqNDfK4FhDQqLVo1JqL0qu6PiRJwmwyYzY3rBiIqBLRqNWIKlEZs6dhkSxsz9/BP8dWcqjksM1atT5q170BGn/6Jvald2JPwvRhTaGy12C0GCg05FNQk4tFtnC+p4QB6iCi9DGEaEMRPGA+UIx/CgoKDUUx/in4BBbJwr7ifRwrO0ZWRRY5lTmYJHvPtWBtMIlBicQFxJEenk6Uf5QbtPUujNUmKgorqSk3Ul1W47AysCBYc4Xpg3T4BesIigy4aHKkNRRZlrFIZiySpe7lCAEBUVShElWoVWrFSH0OGKtNFBwvoaKomvLCSqrL7L0tBQH8Q/wIivQnMNyfyKQQ/p+98wyPqtoa8HvO1PTeCITQe+8gRUFB7IogiL1x1Wv7xA6o2LtYLlZUVOy9V0R6772H9N4m0845348hA8PMhCQk03Le+8xz5cyevdbs7Nll7bXX0upbRoy0xqIoCnazHavZht1ix26xo8juSwhBFNAatGgNWvRGHVpjy7zS1xAk2U6FrZwau4kaqRqL5DlTuF40EK6NwKgJJ1ofg1ZUPX9PhiRL2CXHWCvLkldjiuboOKsRHeOs2mfrRlEUJElClmRkWUb2MBaAw8NPFEVEjYhGI6oHAPUgtyqPbYXbyarI4lD5YcosZW5ldKKONtGtyYjOoF1sJj2SuqNtIXE+G4usyFRYy6i2V2KyV2OymzyGX9CJesK1EYRrI4nRxWLUhvlBW++oxj8VFZXGohr/VEISRVGosFZgk23IsuwICK4Nx6ht2VeimgLJLiPZJMemXwDx6EZf3SidGoqiHI1bpxx1WhMQBALilDnYkewyNrMNWXJMd6JGQB+mUw3Up4iiKCjy0ZeiIAgCgnj0pY4Hp4SiyNhkm9NQJSCgFVUvtKZAVmQcIUIVh2+VIBwdb9U+eyooimMcOB5BUNu1KTDZajDZqrHJNkRBg0GjJ9oQrY4Hp4iiKNhkKzIyiqIgCiIaQRvwRlTV+KeiotJYAnt0U1FpJIIgEGPwb0yOUEWjFdFo1QVnUyMc3YA2e5bVFohGK6KJbHlJfJobQRAQNAKozqhNjiCI6Ftg4ilfIAqiOs42A6qhr/kI14URrgss77NQQBAEdZxVUVFpUTR4B5+bm8uWLVu8vl9TU8P69eupqqryWmb79u0cOXJElaHKUGWoMlQZqgxVhipDlaHKUGWoMlQZqgxVhipDlaHKUGU0Uka9UBrInDlzlPT0dK/vb926VQGU5cuXey3TvXt35c4771RlqDJUGaoMVYYqQ5WhylBlqDJUGaoMVYYqQ5VRDxnr1q1TAGXdunVB/T1UGaoMVUbTyqgPDTb+5eTkKJs3b/b6vslkUtatW6dUVlZ6LbNt2zYlKytLlaHKUGWoMlQZqgxVhipDlaHKUGWoMlQZqgxVRj1keDL+BeP3UGWoMlQZTSujPqgJP1RUVFRUVFRUVFRUVFRUAhw14YeKikpjURN+qIQciqJgt9ixm+3IsgIKCKKAqBUdGT7VZBWNRpZk7BY7sl2m9thAEAW0eg0avUYN9t1IlKNZEmVJdv5bEBzJPzSiBkFU27WxKMqxbLROhGPB6dU+2zgURUG2y9itEorsyJ6KAIIoojVoEDWi2raNRFEUZFl26be1fVVt11NDUiTskg2Z4/osIlpRi0ZQ57DGIisyhaZCSswl2CRHlmqtqCVSF0lqZCoGNalCo3GOB44U1Uf7rKDOYaeIoihINhnJanesZ2sz1muOzWEqKioqoYZq/FMJCewWO9XFJixVFmwmG3X5s2p0IrpwPWGxRsJjw1TDSh0oioK1yoq50oLNbEO2yd4LC6DVa9GF6wiLMaI1qMNLXciyjN1mR7JLTqOfN2o3/RqtBq1Oqy7266B2oyRLsmPDdBLndkEQEEVH26obqbqRJRlzhRlrjQ272Y4ie29bQRTQGrXow/QYow3qRqoOag3/dpsdSZLqbFdwtK1G4xgLVGNg3ciKjNleg0UyHzVKeR9rBQS0og69xkCYNhyNqKaxrou86jw2FWzicMVhCkwFSIrktWysIZY2UW3oltCNzvGdHRmXVTyiKAqSLCFJErJc99oAHHOYRtSg0WgQRbVd60KySZgrzNhq7Nitdocx1QuiRnTMYRF6DJF6dZxVUVEJCdRrvypBi6IomCssVBVWY6m0NKoOQSMQmRBBRGK4aqw6DskuYy6rwVRWgyI1bojQGrWEx4VhiDKoi6aj1G7ybVYbkt37RulkaHVadHqdalA5DkVRkCQJyS6d1ODnDUEQ0Ggdmyi1zx7DZrZRU25u9DgLYIgyEBZjRGfUNaFmwY2iKNhtdmxW20kNft4QBAGdXodWrx4KHI9dtmGyVVNjNzW6DoPGSLguAp2obvxrkWSJ7cXbWZ27muyqbEREhxdlPRAQUFCI1EUyMHUg/VP6E6mPbGaNgwdZlpEkCbtkb3Qdoiii1WgRRfVQoBZFUbCabJjLzdhqbI2qQxAFjNEGjNFGNDr/Hwqo135VVFQai2r8UwlK7BY7JYdKsVY3biJ3Q4DotCiikiNb9IJJURTM5WYqC6rqPBFtCBq9hui0aHTGlm1clWUZq9l6Ska/E9HpdegMuhbdZwEkScJmbaKx4Ch6vb7FG1dlSaaqqPGHK54wRBmITIxo8W0r2SUsNZZGG6rdEMBgNKDVtexxVlEUqmwVmGzVTVanXmMgWh/b4j0Bc6ty+XrP1xTVFDkNeY3F4WWpZUK7CfRN7tui5zBFUbBLduz2xhv9TkQQBPQ6fYv3BJRsEpX5VdgtTde24fHhhMUa/dpnVeOfiopKY1GNfypBhaIoVBVWU55T0WTGqePRhemIz4xtkd4pkk2iIq8Sm6lpjSi1hCeEE5EQ3uIW+YqiODf6zYEgCBjCDGi0LW9jqigKNqutXlejGkPt1cqW1mcBLNVWKgsqG+35WxeCRiA6OQp9hL7J6w50FEXBarZitzXdZvR4NFoNBqOhRYazsEpWKiyldV4/bSwCAlGGGIyasBY3HkiyxJIjS1h6ZCnAKRn9PNE+pj3ndTyPGENMk9YbDMiyjNVmbbpDgBPQaXUt0pO99iC7urjxnr91odFriEqJQqv3z7pLNf6pqKg0lpZ9JKQSVCiyQvGBUsqzm8fwB2CrsZG/sxBzhbl5BAQothobJQdLm83wB2AqNlGWVd5shppApHaj31yGv1oZZpO5yT3fAh1ZlrGYLc3anyRJwmppvo1ZIKIoCtUlJipyK5rF8AegSArluRWYSptnYxaoyLJMTXVNsxn+wOFRWFNdc9I4oqFGjc1EqbmoWQx/4DB4VVjKqLRWtKjxwGw38/629/n3yL8oR//X1BwoP8D8jfPJrsxu8roDGUmSsFib0PvXAza7rVmNi4GIoihU5lU2m+EPQLJKlB0pw2qyNpsMFRUVleZANf6pBAWKrFC0vwRzuQ+McgoU7Suhpqym+WUFAFaTjdKsskbHnGoIthobZYfLW8TGVFEULDWWZt3oH4/VbG0xBkBZlrFafLPoVhSlRRkAq0tMmEp8Y5SrLjZRXdx01zMDGVmWMVebfTLOKopCjanlGABNtmoqrGU+kVVjr6bCUtYixgOz3cz7W99vdqOcgoJVsvL+tvfJqshqVlmBgiRJWG2+mcOa27swkFAUhYrcSqzNeJB9TBhU5FZiqVYNgCoqKsGDavxTCXgURaH4YGmTxp2qD8UHSjH7WKavsZntlB0pazZPSk/YLXbKsst9sgn2J00d36++Mn1lbPQXvjT81dJSDICmUhM1pb499DCV1oS8B6AiKw7Dny/7j4LDABjintY1dhOV1nKfyjRLNVRZK3wq09fYJBsfbf+IAlNBs3j7nYiCI8PtR9s/Ir86v9nl+RNfGv5qaQkGwFqPv8Ym9WgslXmVWH0sU0VFRaWxtOzI0CpBQVVhtW88/jxQcqCElO4paLShZydXZIXynHKfGv5qsdfYqS6uJjIpNDP92W12vxnhLDUWRI0YkoG+a2P8+VO23hCacepsNbZmvSZVF9XFJnRhupCNtWoxN+/VPq8ojvHAGO7f4PTNhV22U2Ep84tsk70anUaPURvmF/nNzV+H/yKnKscnhr9aFBRsso3Pd33OjL4z0Iqht0VRFMXnhr9aarMJa7Wh164A5nKzbzz+PFCZV0lcRqxfEll1+N8Sn8tsDvb9Z5S/VXDhiwO/NLuMSe0mNLuMQGTGsr/9rYKT+SNO97cKPif0docqIYXNbHck9/ATsqRQllXmN/nNSVVRNbLNf14hppIan5/Q+gJFVpo1xl99aNIsogGEJEl+/V61m6dQQ5EVKvIr/apDRX5lSHoD2212n3sAH48sySHpDawoCuWWUr/qUGEpQ1ZCz7PycMVhVuWu8qnhrxYFhVJzKYsPL/a5bF/gL8NfLTZ78yXI8ieSVfLb4RU45tCqopYRwkJFRSW4UY1/KgGLoiiUHir1i2fa8dSUmUMu/p+txubz632eqMitCLkNv8Xs/6viobjhl+XA+E42qy3kDKvVJdXIdv9uCGWbTLWPYg36ikA4CABHOIBQ2/Cb7NXYZf8eHtUmAQklbJKNb/Z8g4D/PEUVFJbnLOdI5RG/6dAcSJIUEL/DULv+qygKlQX+PbwCsFZZ1fh/KioqAY9q/FMJWPzpwn8iZdmhleGvsqDK3yoAINlkavx0pbs5kCTJr14+x2M1h9YCPxAMf7XY7YGjy6ki2SVqygLjN1hTVhMwv5+mIJAS8FjNobMplRWZaqv/N/sAFsmMTQqcv/Opsr5gPWWWMr94/R2PgMAfB//wqw5NiT+v+56IoigBYYRsKqzVVuyWwJg3qouqQ2rdpaKiEnqoxj+VgKWqMHBc6CWr5POEI82F3WLHbg4c40VNWU3ILJZslsDaBAaSwexUCLTNimT37/XjpsRf8VS9Ya4IjXHWn/EpPSHZpZDxsjbba/xunDqeGnvgrFVOBUVRWJ2z2t9qAA7vv8OVhyk0FfpblSYhkOYvALsUGmsDCKw5TLbLIRnORkVFJXRQjX8qAYnNbMdSFRinpLWESjyPQLvCLFmlkFgsKYoScF5LdmtoLPADrV3BcbU62FEUJeA8b2vKQ+MwIBAN7zZbaIyzJltgzcU1dlNIxP47WHGQUj/HUTweEZF1eev8rUaTEGjGNlmWA84g2RjsVglbAB1mQ2AZI1VUVFRORDX+qQQk/gzc6w1zuQXJFnhGiIagyIG32QcC5trhqRCIm/1QSVARiNdsA20z1xis1daA8wZTJAWrKbAOfhpDII4HoXAYYJdtSErgfQ+zPbAO1RrDurx1fo31dyIyMhsKNiDJwT2HBZrnei2hsDawVAbe2tFqsvk9hq6KioqKN3xm/BszZgyCIJCZmenyfPHixQiCgCAIvPfee75SRyXAsVZ5v/r1/mfvMfzcoXQc2p6J0yawav0qr2XzC/O59YFbGH3RSDIGtObhZ2e7lbn0hkm06Z/u9rrqtivc9QryYL52i91rApUFH7/LwLEDyejdhjMvHsfKtSu91pNfkM+M/5vB8AnDSO2WwkNPPORW5pOvPiGla7Lby2xxX6yFgudfXd5pb771Jj169SAhOYHTRp3GsuXLvJbNy8vjmuuuod+AfkTFRnHPffe4lZlwzgQiYyLdXpdceolb2WD3UKvLC+yNN96ga7euxMbFMnz4cJYuW1qvOpevWE5kVCRDhgxxeb59+3Yum3oZXbp2ISw8jFdefcW7XrIS9B5qdXlMvPvRuww8fQBterRm3IVjWblmRZ11WSwWnnjhcfqP7kfr7ukMOmMQH3/+kUuZ73/5ntMmjKB193ROmzCCH3/70bNeNYFn3GkIiqJ4/d01ZCxYvmI5484aR0ZmBokpifQb2I9XX3vVa/nPv/icyJhILpt2mVe9As3Y21Cssvc5eMGb7zGox1DaJrTnrNMmsHKZ97XBbTfdQWpkuttr1MDTnWVsNhvPP/kiQ3oNp21Ce84YOo6/fv/bY302KbjXBuDI8uvtOvXqr1bz4qQXmXv6XOZfO59DGw/VWdeqL1fxyrRXmHv6XOZdNo+NP290eX/74u28ce0bPDn+SR4b+xj/u+p/bPplk1s9NtlGgamg0d8pEKjL8PfGG2/QrWs34mLjGD58OMuW1jEeLFvOGaefQev01sTHxdO3T19emec6R9lsNp544gl6dO9BXGwcQwYP4bfffvNYX7AbVaHuteO7H77LgDH9ad09nbEXnMGKOuawZSuXktQx0e21Z9+eY7JsNp575VkGnT6Q1t3TGXPuaP7850/PelmCdw4r/+drDs+awoHbzuTIkzdQs9f9d3k8is1Kybdvcfihyey/bRyHZ0+lYvmxud2ac4C8N2dx+KEp7L95NOV/fd7cXyGoef3112nXrh1Go5EBAwbw77//ei27dOlSRowYQUJCAmFhYXTt2pUXX3zRrdyXX35J9+7dMRgMdO/ena+//ro5v0JQ05D2P55ly5ah1Wrp27evy/O33nqLkSNHEhcXR1xcHOPGjWP1av+G1wgpzz+rNfgXXypHAyN7mdC/+/VbHnnuYf573W38/PGvDO43mCv/O53s3GyP5a02KwlxCfz3utvo3rm7xzJvPvcW637b4Hz98flfaDQazhl3rnt9AZKApLF42+x/89M3zHpyFnfMuIM/vv6TIQOHMvXGyziS4znbnsVqISE+gTtm3EGPrj28youKjGLLv1tcXkaD0a2cbJeD3kjlTf8vvvyCe++/l5l3z2TZv8sYPnw4F0+6mKysLI/lLRYLiYmJzLx7Jr169vJY5uOFH7Nv9z7na/XK1Wg0Gi668KJ66xUseNs4ff7F58y8Zyb33nMvK1esZPiI4Vx44YUczjpcZ33l5eVcf/31nH766W7vmUwm2rVrx9y5c0lNST2pbsFu/LObPY9n3/z4NbMef4g7/nMHf377F0MHDuWy672PBwA33H49/y7/lxefeInlv63gjRffoGOHTs7312xYw4133MClF17K398v5tILL+WG269n3Ub3a332IN44gfc+29CxICI8ghtvvJFffv6FdavXce/Me3n0sUd5d8G7bmUPHz7Mg7MeZPjw4XXqFuzePnYvyTW++eJbZt/7MHfMvI3fl/3KkOGDmXbxdI5keV4bPPbMo2zet8H5Wr9rDXHxsZx30bF5/6lHn2Hhux/y+HNzWbL2b6687gqunXo9WzZtdavPVodRMhiotlVTZfOcCGzrH1v55eVfGHXlKGYsmEHb3m358O4PKcsr81h+9der+XP+n5x+7enc8uEtnH796fz4/I/sWrrLWSYsOoxRV43i+jeu5+b3b6bvOX355olv2Ltqr1t9OVU5TfId/YXX8eDzL7hn5j3cc+89rFi5ghHDR3DhhReSddjzeBAeEc6MGTP47fff2LBxA/fedy+PPPII77zzjrPMIw8/wjtvv8PzLzzP+g3rue7667hsymVs3LjRrT5FCe4DLEVRsFs9j2df//g1Dz3+IHf8507++u5vhg4axmXX1T2HAaz8fSVbV2xzvtpntne+9+SLT/D+J+/zxJwnWfrLMq6aehVX33wVm7dtdqsnWOewqrV/UfzFq8ROuIL0+9/C2LE3ea/di70k3+tn8t95mJpd60mcfg9t5iwk+drZ6FPaOt+XrWZ0ia2Iv/BGNNHxvvgaQcunn37KHXfcwYMPPsiGDRsYOXIkZ599NocPe17XRkREcOutt7JkyRJ27NjBQw89xEMPPcSbb77pLLNixQqmTJnCFVdcwaZNm7jiiiuYPHkyq1Z5PxxrqTS0/WspLy/nyiuvZOzYsW7vLV68mKlTp/L333+zYsUKMjIyOOuss8jO9rw28QUBZ/yrrq7mwgsvpF27dkRERGAwGOjUqROzZ892Me7VehJeccUVzJw5k+TkZLp06QJAZmam87277rqLmJgY0tPTWbBgAbm5uZxzzjlERETQp08fli1zPWVbunQpZ511FjExMRgMBrp168Zzzz3nsmCurf/ee+/l1ltvJSEhgeTkZG6//Xbn9bTTTjsNQRCYPn2683OSJJGcnIwgCDz11FPN2YxBjd3s3TvtrY/eYsqFlzH1oml0at+Jh2c+SquUViz84gOP5du0asMjMx9l0rmXEhUZ7bFMXEwcyYnJzte/K5cQZgzj3DPPcysb7MY/bwuS+e/NZ9ol05h+6XQ6d+jMYw88RnpqOu8tes9j+YzWGTz+4ONMvnAK0V7aFUAQBJKTUlxeXnULsLgtDaGuRfSrr73KlVdcydVXXU3XLl155qlnSE9P5+133vZYvm3btjz79LNMmzqN6BjPbRsfH09KSorz9ffffxMeHu7R+Bfsm31vG6d58+Zx9VVXc80119C1a1eee/Y5WrduzVtvvVVnfbf+91amTJ7CkMFD3N4bOHAgTz7xJJMvnYzeoG+0bsGAoihevRPmvzufaZMuZ/rkK+jcsTOPPfS4Yzz4eIHH8n8t+ZPlq5fz8duLGD1iNBmtM+jfpz+D+w92lnnzvTcYPWI0t8+4g04dOnH7jDsYOWwkb773hlt9drM9qDel3gzuDR0L+vTpw+RJk+nerTtt27blsimXMfaMsSxfsdylnCRJXHfDdTx4/4O0y2xXt25B3GfBu5HtjVffYuqVl3H51dPo3LUTc595lPT0Vrz/tue1QXRMNMkpyc7XpvWbKSst57IrpjjLfLHoS267+7+MGz+Wtu3acvUNVzFm3Gjmz3Pvs5IiBXXcv9yqXK/vLf90Of3O7ceA8weQlJnE2XecTXRyNGu+XuOx/OZfNjPgggH0HNeT+PR4eo3rRf9z+7P0o2Oe2e36t6Pb6G4kZSYR3zqeYZOHkdIhhUObXD0KRUEkt9q7bsFAXXPYVVdf5ZzDnn3u2TrnsL59+zJ5ymS6d3eMB1OnTmXcuHEsX3ZsPPj444+Zec9MJkyYQLt27bjxxhsZN24c816e57HOYB5nJavkda8w/93/cfmll3PFFMcc9vhDj5Oe1ooFH3mew2pJTEgiJSnF+dJoNM73PvvmM+6YcSdnjjmTzIxMrrn8Wk4feTr/e+d1t3qCdT1b/tdnRA2fSPSIc9GnZZJ46X/RxiZRseRbj+VN21Zh3rOJ1FueJrzrQHQJaRgzu2Hs0NNZxpjZjYSL/0PkwLEI2pOvq1oyL7zwAtdddx3XX3893bp146WXXqJNmzb873//81i+X79+TJ06lR49epCZmcn06dMZP368i7faSy+9xJlnnsn9999P165duf/++xk7diwvvfSSj75V8NDQ9q/lpptuYtq0aQwbNsztvY8++oibb76Zvn370rVrV9566y1kWebPPz17DfuCgDP+1dTU8O2331JTU0Pnzp1JTk5m7969zJ07lwcffNCt/GeffcbLL79MSkoKsbGxLu99/vnnLFy4kPDwcHJycrjhhhsYM2YMmzZtQq/Xs3nzZqZOneoMgr148WJOP/10fv/9dzQaDW3btmXnzp3MnDmTGTNmuMl+8cUXWbRoEWFhYRQWFjJv3jwWLHBMLDfffDMAX331FeXl5QD8+++/FBYWIoqii1FQxRVv3mlWm5UtOzYzauhol+ejho1m7aa1TSb/k28/4fyzLiA8LNyDbqFn/LNarWzetokxI8a4PB89YgxrN3he3NeXalM1A87oT9/Rfbj8psvZsn2Ld928nOAGA942+1arlQ0bNzD2DNfToLFnjGXlau/XqhvK+wvf55KLLyEiIsLtvWC/nupJd6vVyoYNG9xO2caOHcvKld7b9YMPPmD//v0e55Km0i1YkCXZ48bJarWyadsmxpw2xuX5mNPGsGa95/Hglz9/pU+vvrz61iv0Pq0XQ88cwpyn5lBjPhYHbe2GtYw5zdXb8vSRZ7DGwxijKAqKFMRt62Gz3xRjwaZNm1i1ehWnjTjN5fmTTz9JQmICV1151cl1C2JPYEVRkBT3ecJqtbJ5w2bGjHVdG4weO5o1K+u3Nvj4g0WMOn0kbTJaH1evBaPR4FLOaDSyaoXnKzuSHJwbfoCimiKP8f7sNju5u3LpOLijy/MOgzuQtdWzh5rdZker17o80xq0ZG/P9hgeQ1EU9q/dT9HhIjL7Zrq8Jyty8F/79WAUbuwcdjwbN25k5aqVnDby2HhgtVoxGl1vV4SFhbF8+fITPw4E9xzmLQa31Wpl09ZNbvPNmNNOZ836uq/bnXH+6fQY1p2Lr7iIpStcr/tZrVYMBg/jwTp3DyopCOOrKnYblsO7Ce82yOV5WLdBmPe7ezsDmDYvw5DRhbLfF3Ho/kvIevhyir98HdnqPXSTimesVivr1q3jrLPOcnl+1llnef39nsiGDRtYvnw5o0cfmwtXrFjhVuf48ePrXWdLobHtv2DBAvbt28ecOXPqJcdkMmGz2YiP958XrM+Nf4cOHXLG+BMEwe3qVXR0NNu2bSMvL48NGzaQlZXlNJR98sknbvUpisKqVavYsmULa9eudatrz549Tgu4JEnodDr27dvHF198AUBWVhb79u0DYM6cOdjtdtq2bcv+/fvZvXs3t99+OwDvvPMO+/fvd6m/devW7N+/n71799KqVSsApyV30qRJJCUlUVNTw6JFiwDHnXuAM844g9atW6PiGW8xiUrKSpAkiaSERJfnifGJFBY3zeJww9YN7Nq7k8sumtog3YIFT/qXlNa2a5LL86SEJAqKGt+uHdt3ZN6T8/jg9Q+Y//wbGA0Gzpt2LvsP7vdYPtjb1hPFxcVOj9/jSU5KpiC/afrs2nVr2b59O1dfdXWT1BdweOgWRUVFjnZNcW3XlOQU8vM9X0/Zu3cvs2bP4r0F76HVaj2WaQrdggVvTkrO8SDxhPEg0ft4cCjrIKvXrmLn7p2899r7PPbgY/zwy/fc9/C9zjIFRQWex5hCz3UG86bUU784lbGgc7fOxCfFM3LMSG684UaX3/qKlSv4YOEHvDrPeyzA0MHL2qD4aJ9Ndl0bJCUnUlhw8nE2Py+fv377m2lXuc77Y8aOYf4rb7J/735kWeafv5bw64+/UpDnpc8G8YBglawIgrvxz1RmQpZkIuJdD5Yi4yKpKvZ8Tbjj4I6s/2E9OTtzUBSF7B3ZbPhxA5JdwlR2LJmbucrM4+Me59HRj/LRzI+YeOdEOgzu4FG3UKN2DktJdr0NkZyS7HUOq6Vjh47ExsRy2ojTuOmmm7jmmmuc740bN45X5r3C3r17nd4lP/zwA3l5eR7rCuY+63WvUFrseQ6rY02bkpzCC4+9wLuvLuC9196nY7uOXHzlxSxffWzTf/rI05n/7v/Yd3AfsiyzeOlifvnjF/IL3P9ewTh9SVXlIEtoolyNEproOKSKEo+fsRXnYt63BVvOAVJueoyESf+lesM/FH3qHndOpW6cY0KK65iQkpLi9fdbS+vWrTEYDAwcOJBbbrmF66+/3vleXl5eo+psaTSm/ffs2cN9993HRx99VO99xX333Ud6ejrjxo07ZZ0bSxPtgOqPXq+nX79+zn9XVFSwY8cO5781Gg0ffvghX3zxBYcOHXK56puT4x734/TTT3fWd7x7Njiu3sbGxhIZGel8dtZZZ2EwGGjf/lgch/z8fLp27cqaNQ4PhIkTJzq9CKdNm8bLL7+MoiisW7fO5XPnn38+MTExALRr146cnBznpK3X67n22mt5+umneffdd7npppucATavuurkp/Mtm7pnzRNPpxVF8bhobQyffrOILh270q9nP88FgnBCd6Eu/U9oQ4VTa9eBfQcysO9A578H9x/MuIvH8vaHb/PEQ0940C14G/dkC+gT21FRFJoqqeL7H7xP9+7dGThg4MkLByF1ta2ndvXUZyVJ4qqrr+KhBx+iU6dObu+3TBreZ72NB7LseO9/L8wnOspxVf2R+y1c999reerhpwkzhjW4zqA2/tVBY8aC337+jarqKtasWcOch+fQvn17Jk+aTGVlJdffcD2vznuVxBMOxbwRzO16Ms099q96DLSffvgZMTHRnH3eBJfnc595lLv/O5PT+o92JKxr35Yp06fw6YefNkq/QOZkV5bd2raO9cHoa0ZTVVLFWzc6rq9GxEXQd2Jfln20DEFz7DP6cD0z3puB1WRl/7r9/PrKr8S1iqNdf9er68F8nfpkNGRMrOWPP/+gqqqK1atXM3vWbDq078DkKZMBePa5Z7nl5lvo26cvgiDQvn17rrjyChZ+sLDZvkOg4rZXqKPPdmzfiY7tj60NBvUfRHZuNq+//RrDBzviqD7+0BPc9eCdDD9rmGM8yMjkskum8smXi9wrDOJx1m3IVHDbHziRZRAg+ZqHEMMce+2ES24h/+3ZJE65E1Fv8Pw5Fa80Zkz4999/qaqqYuXKldx333107NiRqVOPHWY1ps6WSkP2FdOmTeORRx6hc+fO9ar7mWeeYdGiRSxevNjNQ9uX+Nz4l5aW5uLSXnvVtpannnqKJ598EnDEvkpNTeXIkSNkZ2d7vEqTmuo9KHt0tGMTcrw1tvbZ8X/IExfD9f1BHH/NuFbG8XXNmDGDZ599ljVr1vD222+TnZ1NZGQkF198cb3qb6l4a//42Hg0Gg0FxYUuz4tLi0mMT/L4mYZQU1PDd799x//NuLvBugUNHtSPj3O0a+EJJ6JFxUVunjqngiiK9O3VjwOHPHv+CWLwtq23DWZCQgIajcbtJL+wqNDNA6gxmEwmvvzqSx58oGmusQYiAoKbATAxMdHRrnmu7VpQWOCxXSsrK1m/fj2bNm3izrvuBBxXMxVFITIqkh++/4ExY8Y023cIRLyOs0fHgxM98uoaD1KSk0lNSXUa/gA6d+iMoijk5uXQPrMDyYnJbl4XRSVFbt4ZJ9MvWDmVsSAzMxOAnj16UlBYwJNPPsnkSZM5cOAAhw4f4tIplzrL1q6TYuJj2LB2g8uBJQR3u3obZ+MTjvbZfNe1QVFhMYnJdc9hiqKwaOEnTJp6CXq9azyqxKQE3vvkXcxmM6UlpaSmpfLY7Cdo0zbDi37Bi1b0vB0Ijw1H1IhuXn7VpdVu3oC16Aw6LnzgQs675zyqSqqISohi7XdrMYQbCI85Fk5FFEUSWicAkNY5jaKDRfy78F8345833YKZ2jksL9/Vo6SwoAHjQc+eFBQU8PjjjzuNf0lJSXz2+WeYzWaKi4tp1aoVsx6a5fxMSOF1DnOMtW7zTQPXtAP6DeSLb49lpk1MSOSD+QsxW8yUlpaSmpLK3GcfJaO1h/EgCMdZTWQMiBo3Lz+pshRNVJznz8QkoIlNchr+AHSpGaAoSGWFiMnqLbf64hwTTvAyKygocPNGO5F27RxjZq9evcjPz+fhhx92Gv9SU1MbVWdLo6HtX1lZydq1a9mwYQO33norcGxfodVq+e233zjjjDOc5Z977jmeeOIJ/vjjD3r37t28X+YkBFzMv1rDYOfOnTl48CDLly+nT58+Xss35UJ20CBHnIMff/yRsrIyAOeVXUEQGDBgQIPqy8zMZMIEx0nynXc6NpyTJk0iPNw9lpzKMUSt526p1+np1a03/65a4vL835VLGNjn1L2evv/9O6xWKxdP9G6c9aZbsOBJf71eT+8effhn+T8uz5cs/4eB/Qa5lW8siqKwbcdWr0k/BE3wtq23cUiv19Ovbz/++vsvl+d//f0XQwcPPWW5X339FRaLhcumXNYo/YIBT7rXepD/9dcJ7frXXwwd6t6u0dHRrF2zllUrVzlfN1x/A507d2bVylXOsb8pdAsWvBnb9Xo9fXr04Z9lruPBP8v+YVB/z+00uP8Q8gvyqao+ZiDYd3AfoiiSluoIiTGw30D+WbbY5XOLl/7NIC9jjBhi40FTjQWKomA5Gk+pc+fOrFqxiuVLlztf50w8h1EjR7F86XKP4UWCuc868Ny2vfv15p+/XNcG//y1hEFD614bLP93BQf2HWTqlZ5DfYAjrldaqzTsdjs/fvsTE849y2M5UdB4fB4MhOvCPXrYaXVa0rqksW/NPpfn+9fsp03PNnXWqdFqiEmOQdSIbP1jK51HdEYUvf+uFRSPcdwidJ6NjMGCJ6N1Q+cwbyiKgsXiHl/NaDSSnp6O3W7nm2++4Zxzz6m3bsGCqKljDuvZh3+WLnZ5/s/SxQw6LgnVydiybQspHtarRoORtFTHePD9Lz8wYdzZ9dYtkBG0OgwZnanZ4RpCq2bnWozte3r8jLFDT6SyImTzsev8toIjIIhoYpvOeaAloNfrGTBgAL///rvL899//53hw4fXu54Tx4Rhw4a51fnbb781qM6WQEPbPzo6mi1btrBx40bna8aMGXTp0oWNGzcyZMixpILPPvssc+fO5ZdffmHgQP/f0gq447TevXvzww8/sHv3btq1a4fNZqOmpubkH2wCHnnkEc4880wOHTpE+/btSUxMZM+ePQBcd911bifo9eE///kPP/30E9XV1YB65bc+6MJ0Xt+74fIbuGPW7fTu1ocBvQfw0Vcfkp2XzfRLrgDgqVeeJK8gl5fmHststm2XI1Bttama4rIStu3aik6np3N7VzfdT775hLPGjCcu1nsQTn2Ed92CAZ1Ri81DxuIZV8/g1ntvoU/PvgzsO5CFny3kSO4RrrrM0V8fe/4x8gpyefXp15yf2brDkbyj2lRNcUkRW3dsQafT06WjI+v2c68+y4A+A2iX2Z6qqkreWvg2W3du5cnZnjNd6wwBNxzVm7q8Fm+95VZuuOkG+vfrz+DBg1nw3gKOHDnCdddeB8Cch+eQk5vDW28cy/C3efNmAKqrqikqKmLz5s3o9Dq6de3mUvf7C9/n3HPOJSE+wav8YDaiwNG29RDX+7bbbuO6666jf//+DBkyhHfefYesrCxnrJNZs2eRk5PDO2+/gyiK9OjRw+XzSUlJGA1Gl+dWq9UZhsJqtZKTk8OmTZuIjIykQwf3WFTB7K0qakRErYhsd9/wz7h2BrfMvIW+PfswsN8gFn76gWM8mHo1AI89N5fc/Dxee9YxHlx83sW88Nrz3H7fbdxz272UlBbzyNMPM23SNOeV3xuuupELpp3PvDfmcfa4s/n5j59ZsnwJ3y/6wV03rRj0beuJho4Fb7z1Bm1at3FeKVmxYgXzXpnHTTfeBDg2+D26u/br2lAkJz4/mW7BgCAI6EQtNtl9Drvp1hv47w2306d/HwYOHsCHCz4k+0g2V17nWBs8PudJcnNyefUt16yniz5YRP9B/ejWo6tbnevXrCc3J4+evXuQm5PHc088jyzL3HLHzR710wSx8S8tIs3re8OnDOeruV/Rqmsr2vRsw9pv11KeX86gixyG+9//9zuVRZVcPMtxcFp0uIjsHdm07t6amsoaVnyygoL9BVz00LFs9Es+WEJ613Ti0uOQ7BJ7Vuxh08+bOPfuc11ki4i0imzVDN/Yd4iiiCS7T2InzmHvvvOuyxw2e9ZscnJynNnA58+fT5s2bejSxbHGWr58OS+/9DIz/nMsIeHq1avJycmhT58+5GTn8PjjjyPLMnfddZdX3YKVE5PKHM+Ma//DLXffTJ9efRnUbxAffPI+R3KzuXra1QDMfXYuefm5vPacI1Pv/AXzyWidQZdOXbBZbXz+7ef88Ov3LHjtPWed6zauIzc/l57depKbn8uz855BUWT+e+N/3XUzBudeIeaMyRS8/zj6tl0wtutBxbIfsJcWEDXyfABKvnkTe1khyVc7bptEDhxH6U8fULjwKeLOvRapqpySr/9H1PCznVd+FbsNa+5Bx39LNuxlRViy9iAawtCpnoEu3HXXXVxxxRUMHDiQYcOG8eabb3L48GFn0tH777+f7OxsPvjAkcX+tddeIyMjg65dHfPX0qVLee655/jvf4/1ydtvv51Ro0bx9NNPc8EFF/Dtt9/yxx9/sHTpUncFWjgNaX9RFOnZ09UonpycjNFodHn+zDPPMGvWLD7++GMyMzOdnoWRkZEuYel8ScDtth944AGOHDnCd999R0VFBddccw1hYWE89thjzS57zJgx/PXXX8ydO5eVK1dy8OBBunbtyrXXXut14jwZEydOJDMzk4MHD9K2bVuXDDwqntHoNF43peePv4DS8lJefutFCooK6NKhC+/PW0jrVo4JJL8on+w819iQE6aOd/73lh2b+ebnr2md1poVPx7L0LX/0D7WbFzNR697iN1xHPrw4JzQa3EsSNyN6RdOvJDSshJeeO158gvz6dqpKx+/sYg26Y6T/YLCfLJzsl0+M/aiY1nqNm3bxFc/fEWbVm1Y+9c6AMorK7h7zt0UFBYQFRVNr249+Wbht/Tv3d+jbhpD8G6cBEFAFEWPoQkmXTKJkpISnnrmKfLy8ujerTtffv4lGRmOqyJ5+XlkHXHNmjh85LFTpg0bN/DZ55+RkZHB9i3bnc/37N3DihUr+O7r7+rU7cRYqMGGt83JpZMupaS4hCeefIK8vDx6dO/BN19/Q9uMtoAjyHFWludslN7Izc1l6LBjXhcvvfQSL730EiNHjuS3X3+rt27BgtagxWp3D6Z/4TkXUVJWyvOvPU9+QT5dO3dl0VvHxoP8gnyyc444y0dGRPL5e19w/6P3c9bFZxIXG8f5Ey/g/jvvd5YZ3H8wb774Jk++9CRPv/wUmW0yefOltxjQ192jXhekG6davBnYGjoWyLLMnEfmcOjQIbRaLe0y2/HInEecxsKm1C1Y0Il6j8a/CyddQGlJKS889SIFeQV07d6Fj75c6Mzem5+XT3aW69qgoryCH7/9ibnPPOpRltls4alHn+HwwcNERIRzxvgzePXtecTExnjQSxfUXpXJ4cmIgujR+6/nuJ6YKkz8s+AfKosrSW6fzOXPXU5saiwAVcVVlOeXO8srssLyRcspPlyMqBVp178d18+/nri0Y1cHbWYbPzz/AxUFFegMOhLbJnLJ7EvoOc51MyUj12mYDAa8Gf8mXTqJ4pJinnziScd40KM7X3/zNRlHr5WfOIcpssKc2XM4ePCgYzxo345H5z7qEtzfYrHw6COPcuDAASIjIxk/fjxvv/O2S5ii4wnmPitqRQSN4DEz/EXnXERpaSnPv/rcsTns7ePmsMJ8jhw3h9lsVuY8OYe8/FyMRiNdOnXl47cXceaYM51lzBYzT77wBIeyDhEREcG40eN4/bnXiYl2Hw+0QbqejRx4BlJ1OWU/fYC9ohh9WjtSb34aXYIjxJa9ohh76bHr1KIxnLTbnqf4s5fJfupGxIhoIgecTtx5x/qkvbyI7CeP/bv8j08o/+MTjJ360urOl3335YKAKVOmUFxczKOPPkpubi49e/bkp59+om1bx7o2NzeXw4cPO8vLssz999/PgQMH0Gq1dOjQgaeeeoqbbrrJWWb48OF88sknPPTQQ8yaNYsOHTrw6aefunimqThoaPvXh9dffx2r1cqkSZNcns+ZM4eHH364qVRvEIISzNGfg4QJEybw66+/MmvWLB591PMiU8WVov3FmMsDL1V8UudEDBH6kxcMUCSbRPF+z1m7/InWoCU+03NMkWDBYrZgt9r9rYYbhjADWl3AnfPUG0VRsJgDbywAMIb5L2BvU2AqNVFdbDp5QR8TkRBBeFyYv9VoNIqiYKoyBWQGiPCo8KDe8NfYTVRYyvythhvh2giiDO5GgGDijY1vkG+qO9OsP7hjwB1EG6JPXjBAkWTJJXlhoCCKIoYgT8hQnlvh8TaLv4lJj27WQ6z169czYMAA1q1bx6WrPGfdDjb2/WeUv1Vw4YsDvzS7jEntJpy8UAgyY9nf/lbByfwRp5+8UIgR3EfAAc7jjz/O+PHj+fXXXwkPD+eWW27xt0pBQ1hM4G38RI0Y9J5/olZEow+8E0lDVPAaVGupb5p3X6PRBt7fuyHUelUGGsHuUQmgD9CDjEDVq74IghCQvztRIwa14Q/AoAlMY4VBG9wHAQBdE7oGXAy45PBkovRR/lbjlBCFwJu/IDTmsEA8jBdEAW0Qh7FRUVEJbQJzRgoRfv/9d37//Xc6duzIZ599pmbWaQDhcWEBF/MpIim4PSbAsSkNRI+aQDT2NpRA3FhrdJqA06kxBKIhJRB1aihavRadMbA2KbowHdoAPKBoKDp94B0U6QyBp1NDEQUNRk1gzRcaQYNODDwjREPpn+I5JIc/GZw2OOjnMEEQ0GoCa5wF0IjBP84aIg0Bl1jXGGMM+j6roqISugTebBRCLF682N8qBC2CKBCRGE5VQbW/VXESkRDcGedqMUQbqCyoCpgraYYoQ9BnUYajwej1OqyWwLneE4gGiMYQaJ5/geqN2BjCYsOw5VX6Ww0nYTHB70EFDq8ab3FA/YEgCCHh6QMQpovALPkmEVx9CNdFhsRmP0ofRdeEruwq3oWM//utXtTTM9FzltFgQ6PVYJcCJyyIVqMNiT4riAKGaCPmcrO/VXFijA5M72QVFRUVUD3/VAKYyMTAMbaFxRpDwhsFHIaUsNjA8ZwIRE/ExlJX9jlfI2rEkNnsC4IQUNeqgzmG4onoI/QBkwRC1IpBf+X3eALJ006nD+6EFMejE3VoxcBoWwEBozZ05rAhaUMCwvAnIDAgdQB6TWiMB6IgBtSBUaisDSCwDoz0EfqQuBWgoqISugTOTKSicgJag5boNP/HehFEgdjWwR3I+0QiEsMDwtvOGGNEFxYYm7imQBAE9MbA2KwYjKF1+qzRBsYVZlEMrE3cqSIIAlEpkf5WA4Co5NDwoKpFo9UEhGFVFMWAOpg4VQRBINoQ6281AIgyxARsTLfGkBGdQf/k/n6N/ScgEKWPYnSb0X7ToTnQ6wJjbaDVakNqDtPoNITH+98ALwhCQDktqKioqHgidEZ/lZAkKiUSXZh/Ny2xbWLQ6ELrJE8URaJT/WtYFbUikcmBYXRoSrQ6rd83/DqDzu86NDW116r9TSh5UNWiD9f7/aqSMdqAPjwwNsdNhSAIGML8b4Q3hBlCrs/qRB0ROv/OYXqNIeDiDzYFZ2aeSaQ+0m8GQAWFCztdGDJef7UIgoBO6985LFDjD54qYbFhfk9mF5EUERCH6ioqKip1oY5SKgGNIAjEt43DX4fQxmhDSF1LPR59hB6jH69LRKdFIQZYUpemwN8bflEUA8JI1hyIoujX6786XegZ/mqJSIzwm8FY1IpEJobeQQA4+qw/vYFD8SCglghdJFrRP+OBgEC0PjYkxwOD1sAFHS9A8VNg4EGpg8iMyfSL7OamNhaov9Dr9CHZZwVBIMqPh8n6cB2GyNAyVquoqIQmgqIoARL2X0XFOzXlZor3l/hUpi5cR1LHhJDdOAEoikL5kXKsJptP5UalRoZEht+6kOwSZpNvg1ALgkBYROBlym5KFEXBZrMhS76NS6XVakMq1p8n7FY7ZUfKUWTfLQscYRViQyamqjesZis2q2/HWY1Og8EYel5/xyPJEiXmImRF8qnceGMiuhDzTDuRdXnr+HH/jz6TJyDQMa4jk7tMDolMtN5QFAWL1YKvt196nT6kYv15wlJtpdLHCaw0Bg2xrWJ8uu5av349AwYMYN26dfTvH3hZulVUVAKX0LVqqIQUYTFG4jPjfCZPF64jqUNoG/7AYSyKSY9BH+47T7HI5NA3/IEj3pcx3HeelYIgYIwwhrThD45endLpfOo9odFqWkQQb61eS2y67zYxgigQmx4T8oY/cHjg+dIjV6MNfcMfgEbUEG9MQBR81YcE4owJIW/4AxiQOoAJ7Sb4TF6H2A5M6jwppA1/cPR2gN63v82WYPgDMETofRrDVmPQEJMWHfLrLhUVldAhtC0bKiFFeFwYiR3iETTNO8kaow0Oj78WErtDEAViWsc0f8wvwXHVN1SvUXtCo9U4DHLNvMgXNSJhEWEhFcS7Lmrj//nCIKfVaUP6uu+JaA1aYlvHNnucU41OQ1ybWLSG0PamrKU2GZDe0PxGI51eF5Jx/ryhEbXEGxObPQOwKIjEGxPQa/wfx9FXDE4bzEWdLkIrahGbYctQG1ewX3I/pnSdgk4TmiErTqTWAOiLObulGP5qMUQaiE6LanaDnC5cR2yrmJB3ElBRUQktWsaqWyVkMEYbSe2WTGlWOebypr1SKYgCsW1iCI8LazGbploEQSA6LRpDlIWKvEoUqWmvo+jCdESnRYVc4pT6oNFoCIsMw2q2YrfZm7x+vUGPVq9tkX1Wp9OhETXYbLYmv0JVa2BsKQbV49HqNcRlxGIqMWEqrWny+sPjwgiPD29xfRYcHoAarQZzjbnJr1fXxhttCV6qJ+LwAEzEZK+mylrR5PWHacOJ1EeHVGbf+tIrqRdtotrw7d5vOVRxqMnqFRAI14Vzfsfz6RTXqcnqDRYEQUCv0yPJEjZb04cEEEUxZGP8nQx9uJ64NrFUFVVhrW7athUEgYikCAyRLbNtVVRUghs15p9K0GIqraE8uxzJduqxv8JijcS2Dr2svo1BlmSqCqowV1hOuS5BIxCZGIExpvm934IByS5hMVuaZNMvakQMRoN66owjhpLdbkeyN03cL61Wi0arUfssYDPbqCqswm459bbVGrREJkWiM6rnjoqiYLPasFmaZmOq0+vQGVqOh2pd2GU7lZYyrLL1lOvSCFqiDTEtytvPG4qisC5/HYsPL8ZkNyEgNCopiICAIAj0TerLuMxxGLX+SzwWKCiKgtVmRZabJpZtS/P2qwtLlYXqIlOTxAnWR+iJTPR/Vl815p+KikpjUY1/KkGNoiiYKyxUF1U32FglagQiEiOISIxoETGnGopsl6kpN1NTVoNsb9iiSRemIywuTD0Z9YCiKMiSjM1qa5SxSqt3XENVjX7uKIqCJElIdqnBnoCCIKDVahE1otpnPWAz26kpr8FSZaFB+30BjFEGjDFh6FrIFd+GoCgKdpsdu9Xe4I2/IDq8U7W6luf5Wx/sso0am4kau6nBRiqDxki4LgKdqM5hJyLJErtKdrE6dzWHKw8DjivRsuK5/4qIyDjei9ZHMyh1EH1T+hKhi/CZzsGCrMhIdgm71PBbAqIootVoEUV1DjsRRVGwmWzUVJixNTDBnSAKGGOMGKMDx6taNf6pqKg0FtX4pxIySFYJi8mKzWTDWm3FZrY7PKwUQASNVkQfrkcXrkMfrkMfrleD9NYDRVGwm+3YzHbsFju2GhuyXUZRFMcCUzhqkDJq0Rq16Iw6NKoxtV4ossNYJUsykiShyIqL0UoQBESNiKgR0Wg0qmGqniiKox1lWUaRj/7/CVOdIAiIoujYKIlCi7ze2xhkSXaMBxbHeGC32J39VhAEBFFAa9CiNWjRGRxjgmqorh+144Asy8h22WFMqe22gsPAUjseiBpR3eTXE0WRsck2bJINm2zFJttQFBkFEDhq+Bd06DQ6dKIenUbnwwQiwU2puZTsymxyqnPIqcyhqKYIu2JHURS0opZIXSSto1qTFplGWkQaaZFpLfLqdENRFAVZOTZ/yYr7HCYKonMOU8eC+iPZJexmO3aLhN1sw26TQAaFo3OYRnDMXbUvY+AdrqjGPxUVlcaiHsOrhAwavYZwfRjEtpyEEr5AEAR0YTp0YS0jELcvEUQBragFtWmbFEEQnMY9laZF1IjoI/ToI0I/26mvqTXqqTQtgiCi1xjUq7vNQJwxjjhjHD2TevpblZBCEAQ0gkZNy9gMaLQaNJEaDL5LCqyioqISMKjTioqKioqKioqKioqKioqKioqKSojSYONfbm4uW7Zs8fp+TU0N69evp6qqymuZ7du3c+TIEVWGKkOVocpQZagyVBmqDFWGKkOVocpQZagyVBmqDFWGKkOV0UgZ9UJpIHPmzFHS09O9vr9161YFUJYvX+61TPfu3ZU777xTlaHKUGWoMlQZqgxVhipDlaHKUGWoMlQZqgxVRj1krFu3TgGUdevWBfX3UGWoMlQZTSujPjTY+JeTk6Ns3rzZ6/smk0lZt26dUllZ6bXMtm3blKysLFWGKkOVocpQZagyVBmqDFWGKkOVocpQZagyVBn1kOHJ+BeM30OVocpQZTStjPqgZvtVUVFRUVFRUVFRUVFRUQlw1Gy/KioqjUXN9qsSUiiygsVkxVxlxWqyIksKoCAIIhqdiDHSgDFKj1avdv2GIsl2LLIZi2RGUiQURQYEREFAJ+rRa4zoRQOioOYRagiKwwMbWZKRFQUUBQUQAI5mrBVFwZnBVqX+WCUrZdYSSiwl1NirkWQJBNAIWiK0kcQb4ok1xDsyLqvUG0VRMEtmqu2VmOzVSIodRVGOZqjUEq6NIFIbhUFjVPtsA5HsMqZyM9VlNVirrciyAgoIGgF9mI6I2DDCY41odRp/qxpUKIpCWW4F2dtzydmZT02FGckmIWpFDOF6Ujomkd49laTMBDXjcgOR7BLlhdWU5VdQVVqDZJdAcWSvNkbqiU2JIjYlGkOYmta+oSiKgizLKLJjneBEwJnRXl0bNBzJLlF4sITc3fkUHSrFZrYhSzJavZbw2DBSOyWR1jmFqIQIf6uqoqKi0qSoOx6VoEeWZSoLqynLrcRcZYHa9ZGntdDR9zQ6kciECOJaRWOI0PtK1aDDJlupsJZRZatAUuz1+oxeNBCtjyVSF40oqBtUTyiKgiTJ2G12ZFmu9+c0GhGtTuc0Bqq4Y7Kb2F+xh0OV+6m2VzufCycMCArHNlLRuhjaRXcgM6oDBo3BZ7oGE4qiUG4tJb8ml3JrKTKOfntiu8KxthURidHHkRreimhdrNpnvWCz2Ck6XEbRoVLMVVbn8xOb6/i9vyFcR0JGLElt49CrRhWPKIrC4U3ZrPh0HbuW7HWsD3AYpRDg2CkLyHZHf9YatGT2b8PQyf3pOqoTGq1qCPSEzWona3seBzfnUFFY5eybgujeaWvfM0YaaN01hXZ9WxEZG+5bhYMIWZaR7BKSJNX7M4IgoNVqETWiOs56wW61s23xHtZ/v4WcXfnO37yoFV33DYpy1HEAwmPD6DaqIwPO701K+0T/KK6ioqLShKjXflWCFrtVojS7nLLcSmSp/gYUJ0cX/8YoA/GtY4hMCFcXTUcx2aspsxRjlkyNrkNAIEoXQ6whAa2obk7BsRm12+zYbPUzpHpDEAR0Oi0arUbts0cpNhexs2wb2dVZCAguxr36IiKSEZlJ17geROtjmkHL4ENWZPJrcsgzZWORLY2owTHQGkQjqeHppISlqd7BR6mpMJO7p4iSI+WcykosLi2K1M5JRMaFNZ1yQYwsyaz7djNLP1xNwb4iRI3YoDWCIAooskJkYgTDpgxgxPTBGMLVQ0IAU3kNu9cc5vDWXCR7w9ddguAwYidnxtNpUAbJbeObQcvgo9bDz26zc6rbMo1Wg1arVdcGR6kuM7Hi0/Ws/2ELlmqr8/ddXwSNgCIptO6RxrDJ/elyWge/t6167VdFRaWxqMY/laCksrCavL1FzpO7piAiPozUTokt+kqwpEgU1+RTZa9osjoFBBKMKUTpYvy+YPInkiRhsVhphE3KK6IooDfoEcWWa0yxy3a2lmxid/mORhv9jqfWk61nfB+6xHZv0YaqKlsleyt2YpZqmqzOME04HaO7EqGLbLI6gw1FVsjdU0T2zoKjD06xwqMHWakdE0jvltyir60WHizm81nfk7U555h33ykgiALRyVFMfuw82g9q2yQ6BiOKonBwUw5bFu9BlpRTNlDVGgEzeqbRa0xH9MaWe0CoKAo2q61BtwDqg06vQ6Np2bcvti/ew48v/InFZG2Qwc8TtUbDzsPacc7/jSUy3n9XglXjn4qKSmNRjX8qQYVkk8jbW0RVUeM90upC1AikdEwkOrnlbUxNtioKzLnISv2vmjQEoyac5LC0FucFqCgKVqvNEQepmdDpdWhboBdgsbmQlfnLqLZXNUv9sfp4hqaMaHFegLIik119iGxTVrPJSA/PoHVE2xbXZ2sqzOxbe4SaisZ4UZ4cQ4SO9gNaExnfsq5VKorCso/W8MtLf6PIsvPaXlNQu+kfOqU/Z985tsVdszZVmFn303aKjpQ1ed2CAPowPQPO7kZKu4Qmrz/QkewSNput2eoXRRGdXtfixllTeQ0/vvAnO//d1ySHAMcjaAT0Bh0T7zyDnmO7NF3FDUA1/qmoqDQW1finEjTYLHayNudiM5/alcn6kNA2loQ2LSdGVYW1jCJzXrPL0Qga0sIz0LeQuGqKomAxWxxB+5sZjVaDvgUt8rOrs1ietwTglL39vCEgoBE0jGo1lkRjUrPICDRkRWZ3+XbKrCXNLitOH0+nmJbjXVlZXM3u5YecSTyaBcGx1+04OIPYtKhmEhJYyJLM13N/Zu3Xm5pVjiAKtOnVimtem4IxytissgKFiqJqln62HmvNqV9H9cpR40y/8V3J7NWqeWQEGIqiYLfbm/VQsBZBcNwQaClrg4rCSj6460vKcitO2dvPK0f77JhrhnHa9EE+b1vV+KeiotJYWsaKWyXosVvsHN6U4xPDH0DxoTKKD5X5RJa/qbCW+sTwB45rxTnVh7BKzeP1EkgoioLZR4Y/cHgQWC225tugBRBHqg6zLO8flKP/ay4UFOyKxOKcPygyFzabnEBBVmR2lW31ieEPoNRawq7ybchK0153C0Qqi6vZteyQwyOtOX+ijoTh7Fl1mNLcpgvfEKjIssIXs39odsMfOK5rZ23J4a0bPsJSHfpzWEVxNUsWrcNa08zzytGqN/y6kwObsptPTgDhK8MfHL19YLG2iLVBZVEVC279jPK8ZjT8gbPPLl6wgn/eX9l8clRUVFSaGNX4pxLwyHaZw1tysVt8s1CqpTirjNKc0N48VdkqKDLn+1SmjEyu6TB22TeGXH9Q6/HXrItPD0iSdMrJRAKdwpp8VuT/60OJCrIisSTnTyqs5T6U61sURWFvxU7KbWU+lVtuLWVfxa6Q3pjWVJjZvfywz8eDfauzqCxunhAZgcLPL/7Jhh+2+kyeIivk7irg/f9+3qiEF8FCTZWFpZ+ux261n1Iymoay8fdd5OwJ7YMWXxr+amkJBkCLycrC//uKypLqJr32fzL+/WA1a75p/sMHFRUVlaag5WY2UAkaCg6UYKvxj0GjYH8x4TFGDBGhl+nPLtsorPGNx9+JSIpEkTmPlLD0kLyKYrfZfebx50m2RiOGZKBvm2xjZf7SZvX284akSKzKX8bY1hNC8ppqkTmfEkuRX2QXWwqJs8STaEzxi/zmRJYV9q090uTB/OuDosD+tVn0HNsRjTb0xoO9Kw+w9IPVPperyAoH1h1m6QerGH3tMJ/Lb24URWH9LzuOevz5Xv66n7cT32ooxojQCw9Sm9HXHyiKgt1mR6cPzZiVf8z/l5LsMp8fsgD89voSMvu1IckP2aurbH95fS9Sd4YPNQktTvt2qb9VqDdLLzjN3yo0iBe3/u5TeXf2PNOn8gKd0NvBqIQU1aU1lOdV+k8BBXJ3FYbcaamiKBTW5KHgP88Fk72qSbMKBwqyLPvd+y5UT/g3Fa2jRjL7RbaC4rimWrbDL/KbE6tk4UDVXr/qcKByb0iGA8jbU+RI7uGnn6O1xs6RbQX+Ed6MmKssfP7Q9wii/w6PfnvtH/L3hZ6X2uFteRQcLPGL4Q8cid02/LYz5Oaw2qy+/kSSJCTJt16HvmD/usOs/2GrXwx/AMgK3z75K7IUut7AKioqoYFq/FMJWGRJJm+3/xfWlmorJUdC67pfla2CGqna32pQVJMfUtd/FUXBYrH6Ww0UBax+3mQ0NfmmPPZX7sVvVpSjbC3ZGHLXf/dX7vZ73D1JkThQucevOjQ1pgoz2Tv9b3grOFBCRZH/x/um5OeX/qKyuNp/m30AReGzB7/zm5d3c2CusrDpz91+1UFRIG9fMUd2+jYkSXMj2aWAMGjarKEVG9haY+W7p37z60GALCvk7i5g5ecb/KaDioqKSn1QjX8qAUtFQRV2a2CcUJZklfvl2lZzoCgKpX663nciCjIV1lJ/q9FkyJLs383ocQTKRqOp2F66GYHAuCK+s2y7v1VoMqptVZQFyG+w1FqCyR46Rqq83YExziJAzk7/H6Q1FRUFlaz5cqPfx1pZUsjZkc+eFfv9qkdTsm/DEWQfx6Pzxo7lB0JmDqvN7hsohJL33+bfdvr/IOAoyz5eg90aOH9nFRUVlRNRjX8qAYmiKJRmB86VUFmSqSwMjU1pjWTCrgSOV1iFrSxkFvj+vu57Iv6KLdTUVFjLKTQX+CXW34koKByuPIAlRK6o5tfk+FsFFwJNn8Zis9gpyS73t6OqAwUqi6oxV4VGn13z1UZ/q+BE1AisWLTO32o0CZJd5sCmbL9d9z2R6tIairLK/K1GkxBoxrZQORxUFIXVX28kQM4FMVdZ2P6Pf0NoqKioqNSFavxTCUhqKixYawLHQAWETObfQPO0kxWJarsf4zo2EbIsB5x3qM1uD4kF/t6K3QHj9QeOjNUHK/f5W41Txi7bKfRxtu+TURgioQCKDpcFjBEFAAEKDgTW2N8YJJvEik/XBYSXDzi8/3Yt3Utpdpm/VTllcvYUYDMHzm9PEAT2bzzibzWaBF9n9z0ZiqKExNoga0sOxYdLA+OQBRBEgTVfb/S3GioqKipeCVrj35gxYxAEgauvvtrfqgQVV199NYIgkJmZ6W9V6qSioMrfKrhhqbIGnEGyoUiKhMkeeG1bGQIx1AJtcQ+AQsAZJBuKoigcrNgfEF5/x3OgIviNfyWWooBrVxmZUkuxv9U4ZYoOBZihTXHoFOwb/n1rDlFdYvK3Gi4IgsDGn7f5W41T5tDW3IDxoALH2J+7pxBbkF+jlGU5IH93AblmaSCbf9uBqAmcTqvICjk78ykJgcMAFRWV0MRnxr9aY92JRqdaY5QgNGzw7t69O0OGDKFDhw4N1qVW3nvvvdfgz/qS2rYZM2aMv1XxOTUV3jN6fvz1QsZOHkXvcV25+PrzWbtptdeyBUUF/N+jtzPh8rF0G92BJ+Y96rHcr4t/5pwrzqLX2K6cc8VZ/L7kV4/lzJXBfW3KWkem1A/f+pjRvcbRLbkP54+6hDXL13otu/Lf1XSI6eb22rf7WOyjX7/7jQtGT6JvxmB6pvXn3NMu4utPvvVYn0WqCcjFcUOQ6sjy9uabb9C9RzfiE+IYcdpwli1b5rVsbl4uV19zNX379SEyKoKZ98z0WO7V116lb78+JCTG07lLJ+659x7MZve/b7Bnn6uyVXq9pv7rh39w65g7md79Wu67YBY71uyqs65/v13GzHMf4Iqe13HTsFt5/d43qSw95nWatfsIz9/yMreOvpMpHa/gxwW/eK2rwlYe9B5qVbZKjx6VG1Zs4u7p93Fe74sZljKaf37696R1rV++kavPvIHRGWdyyaDL+Op999/63z/8w9SRVzKqzTimjrySxT8tcSsjIFAV5J7Akk3CXOU58c8XP3/CBTeN57TJ/bny/yazYXvdV0bXb13Dlf83mdMm9+fCGRP48pdP3cpUVlfwzBuPcfa1Yzhtcn8m33oey9a5t61kl7GYgvsA68jWHAQvm/2hk/sz86ebeXT1Pdy66Boy+7Wps652AzK4ddE1PLr6Hmb++B8GX9rPrUyPsV2446sbmbvmHu746ka6n9HZY11ZW4L7urqiKJTmVnj1oPp5+bfMeOJyptw/gbtfmsH2/ZvrrG/bvk3c/dIMptw/gf88OZ1fV3zvVmbF5iXc9uw1TL5vArc9ew0rtyz1oBeU5wf3eFCXl+obb7xB125diY2LZfjw4Sxd5t4GtSxbvozTzzid9NbpxMXH0advH+a9Ms+t3CuvvkLvPr2Ji4+jY6eOzLxnpue1QZAfDAJkbc1Fljy374Dze3PrR1dz/y+3cP38y2jTq1WddWX0Tuf6+Zdx/y+3cOuHV9P/vF5uZbqO7MiMd6dz/y+3MOPd6XQ5zfMeNHtnXsO/jI94/fXXadeuHUajkQEDBvDvv3XP7//88w8DBgzAaDTSvn175s+f7/K+zWbj0UcfpUOHDhiNRvr06cMvv3hfO4UyuT99zdobJ7P80rFsvOs6yrdt8lq2fMsGll040u1lOnLIWSbvt+/Ycv8trLz8bFZefjZbZ99B5e7QiTntTxryO1i6dCkjRowgISGBsLAwunbtyosvvuhS5r333nPal45/eRp7/U3Qev69/vrrrFy5klmzZvlblQZhtfo/E2igI8syVi8blJ/+/IEnX3mMGVfewtdv/8DA3gO58Z5rycnP9ljearMSH5PAjCtuoWvHbh7LbNi6nrseuY3zx1/It+/+yPnjL+TOOf9l0/aNrgUFgj5mksWL8e+HL3/isfuf4ua7b+L7f79i0PABXDvpJnKy6t7Q/LHuJ1buXuJ8ZXZo63wvJi6Wm+++iS9+X8SPy77hkssv4t6bH2TJH+6LWxkZSQluQ4q3hfQXX3zBPffewz0z72H5shUMHz6Ciy6+kKysLI/lrRYriYmJzJx5D716uS8+AT759BNmz57F/fc/wPp1G3j99f/x5ZdfMHvO7HrrFSx48wJb/uNK3n/8Qy76zwU89d1cug7qwpPXPUtRjuckCzvX7uK1mW9wxqWjef7nJ7nzlf+yb/N+3njgHWcZi9lKSptkps6cTGxSzEl1C5REGY2lyl7h0fPPbKqhU4+O/N+Td9SrnpxDufzftHvpM6Q37//xFlfdPp0XH5zH3z/84yyzZc1WZt34CBMmncUHf73DhEln8dAND7NtnetCVkGhyhbcIRZM5Z7H2d+X/swL7z7FNZNuYOHzn9O3e3/umDuDvMJcj+Wz849wx2M307d7fxY+/zlXX3I9z7/zJH+t+N1ZxmazcevDN5BbmM1TM1/g81d/4IGbHyEpPtmzbmU1p/4F/ciR7bkeDVS9xnfjnHvO5O+3lvHKlHc4uD6Lq1+fQkxqtMd64tJjuPq1yRxcn8UrU97h77eXc969Z9FjbBdnmYze6Ux95iI2/LCFeZe+w4YftjDtmYvcjAiKrAS98c9UbvaaYG3pxr9Z8N3rXDJ2Gs/f8Qbd2vXisXfup7DUc8iA/JJcHnvnAbq168Xzd7zBxWdM5Z1vX2XF5mMG6V0Ht/H8R3MZPeBMXrjrTUYPOJPnP3yU3Yd3uFYmQFmQG/+8zcGff/E5M++Zyb333MvKFSsZPmI4F154IYezDnssHxEewYwZM/j9t9/ZuGEj9917H4888gjvvHNsDlv0ySJmzZrFAw88wMYNG5n/v/l88cUXzJrtvlcK9qu/Noud4iOe5+DuYzox/pZRLP1oDW/d+DGHt+Qw7akLiE6O8lg+NjWaqU9ewOEtObx148cs/XgNE24dTdeRHZ1l0runcsnss9ny+07evOFjtvy+k0tmn02rrikudYkakbzd/s/y7olPP/2UO+64gwcffJANGzYwcuRIzj77bA4f9tznDhw4wMSJExk5ciQbNmzggQce4LbbbuPLL790lnnooYd44403eOWVV9i+fTszZszgoosuYsOGlpX5uHDpnxx4dx6tL72Cvi+8Q3T3PmyfOxNLYd2hVfq/9hGDFnzjfIWltXa+V751I4kjx9Fz7jx6Pz0fQ1IK2x7+PyzFoZPAyx809HcQERHBrbfeypIlS9ixYwcPPfQQDz30EG+++aZLuejoaHJzc11eRqPRF1+pQQSc8e94b7dXX32VzMxMoqKiOPfcc8nLO3aScuK134MHD7p49J177rmEh4fTrl0758S4ePFiFw/Da665xs0b8eeff2b06NFERUURFhbGyJEj+fvvv1103Lx5M8OGDXOecPz7779O2Q8//LCLLEEQ+Pzzzxk8eDB6vZ6PP/6YQ4cOcfbZZ9OmTRvCwsIICwujZ8+evPTSS86JODMzk/fffx9wnLrU1rV48WIAcnJyuPbaa2nVqhV6vZ727dszd+5cl2xiFouFm266iejoaJKTk3nkkUeCYqK3VHv3THjvs3e45JxLufTcKXTI7MgDt80mNSmNRd985LF867TWPHj7bC6ccDGREZ4n/Q8+X8Dwgadx0/Sbad+2AzdNv5mhA4bz/ufvuhZUHLEIgxlvxr93X3ufS6+4mClXXUrHLh2Y9dQDpKWn8tE7n9RZX0JiAkkpSc6XRqNxvjd05GDGn3cmHbt0oG37DK75z5V07dGZtSs9e7p40y0YkOs42X/l1XlcdeVVXH31NXTt2pVnn3mW1umteevttzyWb9u2Lc89+xyXT7ucmGjPBqjVq1YxdOgwpkyeQtu2bRk3dhyXXjqZDevXe9AtuI1/JZYSj95pP777M2dcOpqxU8bQumM6Vz80nYS0BH776E+P9ezZuJfk9CTOvmo8yW2S6TqwC+OmnsH+LQecZTr2bs/0+6Yy4txh6PS6k+pWailp/BfzM7IiU2P3fH1y2Nih3HT/9Yw5Z1S96vr6g29JaZ3MnY/9l8zOmZw//VzOnTqRj18/Nn58+uYXDBo9gKtun05mp7Zcdft0Bo4cwKdvfu5Wn8leHRRzlTeqyzyPZR9/9wHnj72YC8+cRLs2HbjruvtISUjly188j7Nf/foZqYmp3HXdfbRr04ELz5zEeWdcxIffvOcs892fX1FRWc6z982jT7f+pCW3om/3/nRu19WtPkGA6iA3/mVtyfHoSTXyisGs/XoTa7/eROGBYn549g/K8yoYOrm/x3qGXNqfstwKfnj2DwoPFLP2602s+2YTo64a4iwzYvog9q48wD/vrqDwYDH/vLuCfasPMuLyQW71VRVXU1kceCE16ktdBrbvl3zB2EFnc+aQc2id0pbrLriFhNhkj958AL+u+J7EuGSuu+AWWqe05cwh53DGoAl8+89nx+pc+hV9Og3gkjOm0To5g0vOmEavjv354d8vXeoSgNIQNf7NmzePq6+6mmuucawNnnv2OVq3bs1bb3leG/Tt25cpk6fQvXt32rZty9SpUxk3bhzLlh+7SbBq1SqGDRvGZVMuc6wNxo1j8uTJrPewNgCCepwt2F/k1aty6KX92fDzNjb+tI2iw6X89toSKgqqGHi+5wPVAef1oqKgkt9eW0LR4VI2/rSNjT9vZ9hx48eQS/qxf+1hli1aS3FWKcsWreXA+iyGTHL1GJYlmewdgen598ILL3Dddddx/fXX061bN1566SXatGnD//73P4/l58+fT0ZGBi+99BLdunXj+uuv59prr+W5555zllm4cCEPPPAAEydOpH379vznP/9h/PjxPP/88776WgFBzrefkjLuHFLPPI/wNpm0v/42DInJ5P7ydZ2f08XEoY9LcL6E4/ZRXe6aTdrEi4hs34nw1m3pePM9oMiUbw6NJFP+oqG/g379+jF16lR69OhBZmYm06dPZ/z48W7egoIgkJqa6vIKRALO+FfL8uXLmTlzJnq9nqqqKn788Uf+7//+r16fvfHGG9m2bRs6nY6DBw9y4403snPnTqKjoxky5NjCrn379gwZMoR+/RwD96effso555zDkiVLSEhIIC0tjaVLl3LmmWc6DYA1NTVMnDiRlStXIssyNpuNc889t059pk+fTlZWFh06dEAQBAoLC50u0d26dSM6Oppt27Zx55138vrrrwOOjpaYmAhAVFQUQ4YMYciQIURHR1NUVMTQoUNZsGABVVVVdOvWjaysLGbPns2NN97olPvAAw/w5ptvUllZSVRUFC+99JLLaU2gYvMSV89qs7Jt91ZGDBrp8nzEoJFs2Op5YVMfNm5bz4hBp7k8O23wSDZ6qNNmDu4rUzbZ3fPUarWydeM2TjtjhMvz084YwfrVdZ/cnTfyYoZ2Hsn0865hxZJVXsspisKyxSvYv/cgg4cP9KJb8Latonhe3FutVjZs2MDYsWNdnp8xdiyrVq5stLxhw4azceMG1q5dAzhOZ3/79VfGT5jgQbfgXuBX26vcvNPsVjv7tx6k92muC/k+p/Vk9/o9Huvp3L8TxXklbFi8EUVRKCsqZ9XPq+l3et9G6SUgUm0L3s2+VbY2Wby/rWu3MWS0q0FkyOmD2LFplzPj9NZ12xh8Ypkxg9iy1j1WmoLicawKFiwmKydGMrHZbOzct50hfYe7PB/Sdzibd3q+GrRl1ya38kP7jWDHvm3Y7Y7x8t81i+nVpQ/PvPk4E64exWW3XciCL970mF1UUeo+XAt0JLtMVVG123ONVqRVtzT2rNjv8nzPigNk9GntVh4cXn17VhxwebZ7+X7Su6chasXjyux3K+OtztLs4I1dW11e49ZnAWx2G/uyd9Ons+u83bfzAHYe8hzncPeh7fTtPOCE8oPYd2Q3dsl+XBnXOvt1GcjOg651KgpUBViMx4biaf71tjYYO3YsK+u5Nti4cSOrVq5i5GnH1sTDhw9nw4YNrFlzbG3w66+/MsHD2sCbbsFCaY7n35uoFUnrnMz+ta5ePPvWHqJ1jzSPn0nvkca+tYfcyqd1SUbUOMaD1t3T2L/Otc79aw97rLMkAMcCq9XKunXrOOuss1yen3XWWSxfvtzjZ1asWOFWfvz48axduxabzTGXWCwWN++msLAwli71foU91JBtNqr27Sa272CX57F9B1G5c2udn91413WsvuYCts66nbItde9lJasFRbKjjfTszKJychrzOziRDRs2sHz5ckaPHu3yvKqqirZt29K6dWvOPffcgPV+DVjjnyRJrFixgt27d3PRRRcB8Oefnj06TuT8889n//79TousLMssXryY/v37u0yqs2bNYuXKlXz9tcMqf99996EoCtdeey0HDhxg3759XHTRRUiSxOzZjut0H3/8MdnZjiumX3/9Ndu3b+eFF16oU5+LLrqII0eOsGPHDqZPn07Hjh05cOAAWVlZrF+/ntzcXEaNcnhYfPLJJ866zznnHACn3itXrqR///689tprZGVlkZKSwr59+9i0aRNffPEF4LhzvnfvXqqrq3nttdcAuOyyy9i3bx+7d+/GYDDUqw39ibeTvNLyUiRJIiEu0eV5QnwCRSWNd4EuKilyrzMukcIS9+uDgZJhsLEouBupSovLkCSJxGTXNkhMSqAw3/MVyuTUJB5/+RFeW/gyr384j/adMrni/GtYvWyNS7nK8kp6tRpA18TeXD95BnOeedDNyFiXbsFOcXERkiSRnOx6LSQlOZn8gsZnWb300kuZ9dBsxp05jpjYaHr26sGoUaO4+//uPlWVAw7JQ1y9itJKZEkmJtH1Sl9MQgxlRZ4X3V36d+a/L/yHl25/jcu7XcNNQ28lIjqCa2Zf0UjNFCQleAOmezNYN4bighLikuJcnsUnxSPZJcpKyp1l4t3KxFFc4Nl7Um5C/XyNIrmbVcsqS5FkiYTYBJfn8bEJFJd5HmeLS4uIP6F8QmwCkmSnrKIMcFwN/mvF70iyxIuz/se1l97IR9++z4Iv3vRQY3B7Atu9JH4IjwtHoxWpKnY1DFYVVxOVGOHxM1GJER7La3QaImLDAIhMjGxQnXZL8IaukO0ynqx/ldXlyLJMbJTrbzcmMo6ySs+/3dLKEmIiXcvHRsUhyRIV1Y7xoKyyhJgT64yKo6zS/RqnZA/ePuuNoqKja4MU1+v5Kckp5OfXvTbo0LEDMbExjDhtBDfddBPXXHON873Jl05m9uzZjB03lqjoKLr36M7oUaOZebfn2MEBlu+pQXgdD2LCEDUi1aWuRuPq0hoi4z3/diPjwqkurTmhvAmNVkN4jMOwFRkf7pZsqLrERGRcuLtutsBbG9T2uZSUE9ajKSkuN+uOJy8vz2N5u91OUZFj3ho/fjwvvPACe/bsQZZlfv/9d7799ltycz2HswhFbJXlIEvoYl3HNF1MHNZSz+OkLj6BDjfPpOu9c+l67+OEpWewbfYdlG/b6FXOoQ/mo49PIraPZycKlZPTmN9BLa1bt8ZgMDBw4EBuueUWrr/+eud7Xbt25b333uO7775j0aJFGI1GRowYwZ49nh0S/InWV4IamtCjV69e9O3bF3Ak9/j6668pKKhfDIXp06cjCALdu3d3PjvZZFpYWMjBgwcBePfdd3n3Xdcrn6tWObyatm1znEqGh4c7jXOTJ0926QAncvvttzuvQ2o0GnQ6Hc888ww//vgjOTk5Lld1c3JOHjdm9erVzu+UnOy6cFAUhVWrVtGrVy8sFscV1YsvvhiApKQkxowZw1dffXVSGYGMW19SGt6/Tl6n4vEUPNipa53n3gSK13Zt36kd7Tu1c/67/+B+5Gbn8fYrCxg84ph3T0RUBN//+xWmahPL/1nJ4w8+TZvMNgwdOdhTtcHLSRbQJ7ajoiger7LWlyVLlvDMs0/z0osvMXDgIPbv38fMe2aS+lQq9913f6PrDUTq7LMntKGC9z57ZE82781dyCW3Xkifkb0oLSjjo6c/4e1ZC5jx1A3NoF3LwlMfdzz3XqausTuYW7Zuj0oPY0Edk423dq1tWFmWiYuJ54H/PIxGo6Fbhx4UlhTy4bcLuH7KfzwpF7yczEvpxLeFuj9yotdTbVu7PD7h84IgeK0zmA8HT6a5px5a1xzmdTw47jNu47fXdVfwtuvJ8Lg2OMni888//qSqqorVq1cza/Ys2ndoz5TJU4Cja4Onn+Hll15m0KBB7Nu3j7tn3k1qair33x9ia4OTjAfuv++TfMbLe8c/dhvbvf2pAtijsqF9zvvc7nj+8ssvc8MNN9C1a1cEQaBDhw5cc801LFiwoIk1Dwbc29Fb24anZxCenuH8d3TXnliKCsj+5hNievR1K3/kq48o+vcPej42D1Ef+I48gU5jxt5///2XqqoqVq5cyX333UfHjh2ZOnUqAEOHDmXo0KHOsiNGjKB///688sorzJvnnpjJn/jM+BcR4ThtKSlxtYAXFzuCuUdGRro8j42Ndf63VutQs77u6bWfrf1cfT57/Pvt27cnKSnJrczxyToaYmw68c73HXfcwdtvvw1Ap06diI+PZ9++fU5r9Mmo1TUqKsrFwFlLeHi4y/c5XtdgcPEXRM9tGxcTh0ajcfPyKy4tdvPcawiJ8YnudZYVk+ihTm+6BQuiB2ffuIRYNBqNm5dfcVEJickJbuW90XdgH779zDUGkCiKziQg3Xt3Y9+ufcx/4U2Pxj8hcB2RT46XbpGQkIhGoyE/3/U0qaCw0M1w3xDmzn2UqVOncfXVjhP/nj17Um0y8d//3so999yLKAZxW56AVnSfpqLjohA1opuXX0VxBTEJngP8fzP/ezr378T5NzgObdp2zcAYbmDOZY8x5a5LiUuObbBuGkFz8kIByqkemBxPQnI8JSd48JUWlaLRaoiJi3GWOdHLr6So1M0bsBZRCN4+LIoiAq4mi9ioODSixs3Lr7S8hPgYz+NsQlwixaWu5UvKS9BotMRGOdo1MS4JrVbrEm+1Xev2FJcWYbPZ0OlcY1fWXmELRrR6z0tWU6kJyS4TeYJHXmS8u3dfLZVF1UQluq47I+LDkWwSpnKHB1BVUZVbnRHx4V7r1Bp9tqRucjRa0aPBIioiBlEUKT3BI6+8qtTNc6+WuKh4N6/A8qoyNKKGqAjH+BzroUxFVZmbx6BDt+AdZ72RmHh0bZDn6phQUFhw0rVBbZzynj17UlBQwOOPP+40/j3y6CNMnTbV6Q3Ys2dPTCYTt9x6C/fe62FtEMRLWq3By3hQXoMsyW5efuGxYW7egLVUlZqIiHf14IuIC0eyS9RUOGK4VpWY3OqMiAunykOd3sYqf1Lb5070biooKHDzgqolNTXVY3mtVktCgmPeSkpK4ptvvsFsNlNcXEyrVq247777aNeunacqQxJdVAyIGmxlrmOarbzUzRuwLqK69KDwn9/cnmd/s4gjX3xIj0dfJCKzo4dPqtSXxvwOaqnt07169SI/P5+HH37Yafw7EVEUGTRoUEB6/vlsFVjrxVdZWcnbb7+N3W5n3bp1zlh6ffr08ZUqhIU5rnRUVx9bwCUnJ9O2rcNI0b9/f5YuXeq8avvBBx8wd+5c9Ho9PXv2dH72t98cP9DPPvuMujhxk1V79fiss85i9+7dLF68mPT0dLfPhYeHu+kJMHiww3Ci1Wr55JNPnHr+/vvv3HzzzVx00UV07NjRecW39lpzUVER//zzD4GOtwldr9PTo3NPlq91jSOxfO1S+vX0HNS7PvTt0Z/la1zrXLZmKX091OlNt2BBK7onMdDr9fTs24Nlf7vGOlj293L6D+7nVt4b2zfvICnF3Wh+PIqieM147cnIEyx4M6To9Xr69evHX3/95fL877/+YshxJ0QNxVRjclvEa0SN5+x9QtMaenxNmDbczUNEq9fSvmcmm5e6xlLZvHQrnft38liPxWxxa7PafzfmUERBIUzr+QpRMKAX9U1WV8+BPVi9ZK3Ls9WL19CtTxe0OsfvuueAHqw5scw/a+g1sIdbfQICOg9jVbCgD9e52VF0Oh1dO3Rn9aYVLs9Xb1pB766e1z+9uvRxK79q43K6deiBVutonz7d+nIk97DLdd7DOQdJjEtyM/wJAujDgrddRa1IeEyY23PJLpOzI5dOQ103mx2HtuPwpiMe6zq8OZuOJ5TvNKw92dtzHVdgj5Y5sc5Ow9p7rTPWS2bhYCAsyuDRWUmn1dEhvTOb9rgGmN+0ex1d27r/dgE6t+3Opt0nll9Lh9ad0Wq0x8qcUOfG3WvpmnlCnQLOa5fBiqf519va4K+//nLxHjkZiqI4b/kA1Jhq3Oc5jeg1s28wrw1ikjzHPZPtMrm7C2g/IMPlefsBGRzZ5iWz+rZc9/IDM8jdVYAsOcaDI9tzaVfPOmO8ZBX2J3q9ngEDBvD777+7PP/9998ZPny4x88MGzbMrfxvv/3GwIED3eYXo9FIeno6drudL7/8kgsuuKBpv0AAI+p0RHboTNlG19BHZRvXENW1Z73rqd6/G32c62Hgka8/Juuz9+kx5zmiOron8lJpGI35HXjixLHX0/sbN24kLc1znFF/4jPj34wZM4iPjwfghhtuQK/XM3DgQKqrqxEEwafu6F27On489913H4MHD+aBBx4A4IknngDgiy++oFWrVvTr14/U1FS6dOnCRx85sslOmzbNaag777zz6NGjB3feeWeD5Pfu3RtwDKBdunShTZs2ZGVledVz7dq19OrVi6FDh1JTU8Mtt9xCeno6paWldOnShb59+9KhQwcSEhK46qqrAIen5X/+47jy8/HHH9OxY0c6d+7sZkgMRIyR3jelV0++ji9++Iwvf/yMfQf38uQrc8ktyOGyCy4H4Pk3nuHex10Tw+zYs50de7ZjqqmmpKyEHXu2s/fgMUv8FZOuZtnapbz10Xz2H9rHWx/NZ8XaZVx16bWuwgXHAjmYMWg8L6KvveUqPvvgSz5f+CV7d+3jsfufJOdILtOudZwmP/vwC/zfTfc6yy94/X1+++EPDuw7yO4de3j24Rf45bvfuPLGac4y/3v+TZb+tYzDB7LYt3s/77z6Hl9/8h0XTD6vQboFA3UtoP9762289/57vP/B++zcuZN77r2HrCNZXH+dI1TA7Dmzuf4G17ABmzZvYtPmTVRVV1FUVMSmzZvYsWOH8/2JZ0/k7bff4vPPP+fgwYP8+defzH3sUc6ZeI6LBxAQ9F6A8fp4j9coz7n2bP76fDF/f/4PR/Zm8/5jH1KUW8yZ0xwB1D9+9lNevXu+s/yAM/qx+re1/PbRH+QfLmDnut0smLuQjn3aE5/iOJm1W+0c3H6Ig9sPYbfZKc0v5eD2Q+Qd9Bw2It4Q3wzf2DeIgoYwjXusIgBTtYndW/ewe6tjnMw5nMvurXvIO+Joh9cfe5NHbn3cWf6iKy8gLyufl2e/ysHdB/n+4x/5/uOfmHbzZc4yk2+cxOrFa1n4yscc3HOIha98zJol65hy46Vu8sO04UHt+RcR63ksm3b+lXz7x5d898dXHMjaxwvvPk1eUS4Xj3eMs68tfJE5Lx9bC108fjK5hbm8+O4zHMjax3d/fMV3f37F9Auvdpa5ZMIUyivLeP6dpziUfZCla//hvS/fYtLZl50oHkXxrlswIAgCrXumebwa+u/C1Qy8uC8DLuxNUrsEzrl7HLFp0az63BFAffxtY7j0sWNzz6rP1xPXKppz7h5LUrsEBlzYm4EX9WHJ+8cSVy37aA0dh7Vn1DVDScpMYNQ1Q+k4JJNlH61xkx8WYyQ6ADf89SU2xbvh8rxRk/hz9U/8ufpnjuQf4t3vXqeorICzhjna88Of3ublRU85y48fdh6FpQUs+O51juQf4s/VP/Pnmp+5YPRkZ5lzT7uYjbvX8tXfizhScJiv/l7E5j3rOXfkJW7y4+rQLRjwNgffdtttLHhvAe+/71gbzLxnJllZWc4wQrNmz+K6669zlp8/fz4//vgje/fuZe/evXzwwQe89PJLTL3smOfJxIkTeeutt/js888ca4M//+TRRx/lnHPc1wYQ3Ma/1E7eD5tXfr6efhN70GdCdxIz4jjz5lHEpESx7vstAJxx/XAuuO9YwP91328hJiWaM/8zksSMOPpM6E6/s3uw4rNjCRhWf7WRDgMzGH7ZABLaxDH8sgG0G9CGVV+4BvUXNSLp3QIzy+ddd93F22+/zbvvvsuOHTu48847OXz4MDNmzADg/vvv58orr3SWnzFjBocOHeKuu+5ix44dvPvuu7zzzjvcffex+NKrVq3iq6++csbanzBhArIsc8899/j8+/mTVhdMIf+PH8j/40dMWQfZ/848LEUFpI6/EICDC+ez+6XHnOVzvvuM4pVLqMnJwnT4AAcXzqd4xT+kTbzYWebIVx9x+KO36XjrfRiSU7GWFmMtLUaqCe4kSP6mob+D1157je+//549e/awZ88eFixYwHPPPcf06dOdZR555BF+/fVX9u/fz8aNG7nuuuvYuHGjs85AwmeuNq1bt2b58uXMmTOHxYsXU1RURGRkJAMGDOCee+5h/PjxvlKFefPmcfPNN7Nr1y7WrFlDRobjJGfatGnExMTw7LPPsm7dOnbt2kV6ejrjx493TsZGo5GffvqJG2+8kQ0bNiCKIosWLXJm/K31KqyLF154gerqav78808qKyuZOXMm27dv5/3333cpd+2117JkyRL++OMPtm51eLhIkkRSUhIrV65k9uzZ/PLLL2zbto2kpCRGjhzJeecdW9w++eSTVFVV8cknn1BWVsaNN95ITk4OCxcubJJ2bC5EjYjOqMVmdg/mO3HsuZRVlPLa+69QWFxIp3adeePpd0lPdRhkC4sLycl3jZt40XXHsjFv27WVH/74jlap6fz1mSMhTP9eA3h+zsu8/PYLzHvnRdq0yuCFh+fRp3tfV+EKGCND0/h37iUTKSsp45VnXqcwr5BO3TrxzufzSc9wtGtBfiG5R46dblqtNp586Fnyc/MxGo106taRtz+fz+lnHct8ZDKZmP1/j5KX4yjTvnM7nn/zac69ZKKbfAERrRC8HimCICCKosdg+pMmTaKkpJinnnqSvLw8unfvzldffu0cd/Ly8jhygvF/+PBhzv/esGEDn332KRkZGezYvhOAe++9D0EQeHTuI+Tk5JCYmMjEsycyZ87DbvI1QW78izN4vhI5/JyhVJZW8eWr31BaUEabzq257+27SUp3XNcvKyyjOKfYWX7MJaOoqTbz68I/WPjkIiKiw+kxtDuX3zPFWaakoJR7z3/I+e/v3/6J79/+ie6DuzLn4wfddIgNYuMfQKQuihqphhNjau3cuItbLr7D+e95cxzJoyZOmcCsefdTXFBMfvaxGLyt2qbx/MdP8/LsV/lywTckpiRw5+O3cfq5x8aD3oN68ugbs3njqXd48+l3SM9sxWNvPkyPAa6hKwQEIrXBa0QBPHqnAZx52tmUV5bzzmfzKSotpENGJ1586H+kJbcCoKi0iPzCY+NsekprXnrodV5c8Axf/LyIxPhk/u+6+zlj2JnOMimJacyb8yYvLXiGy++8mKT4ZKacO50rL7rOTT44rr4FM617pLF35UEUyXWs3fLrDiJiwhh742lEJUWSv7eQ9275lLLcCgCiEiNdPPNKs8t575bPOGfmOIZOGUBFYRXfP/0b2/7c5SxzeFM2n9z7DWfeOpozbxlNSVYpi+79hqwtJ8RmFqB1j1ZBbUiJjAtDoxU9Jtc4re/pVJoq+OyPhZRWlJCRmsmD1z1JcpzjilRpRTFFZcfGg5T4NB667gne/f51fl7+HfHRCVx3wa0M6z3KWaZrZg/uuvwhFv2ygE9+fY+UhFb83/RZdM7o5ipcgdjU4B4PBFEADxF9Lp10KSXFJTzx5BPk5eXRo3sPvvn6G9pmOG4h5eXluTgGyIrM7DmzOXjwIFqtlvbt2jP30bkuMcfvu8+xNnjkkWNrg3MmnsPDDz/srpcgBHWf1YfpiWsV4zHr7/bFewiLDmPUlUOIjA+n8GAxi+7/lvL8SsAREuB4Y31ZXgWL7v+Ws24ZxcALelNZXM0vr/7Dzn/3Ossc2ZbLV3N/Zsy1wxhzzTBKc8r5au7P5Ox0PRyUJZm0zo0P69KcTJkyheLiYh599FFyc3Pp2bMnP/30k/PmW25uLocPH8to3K5dO3766SfuvPNOXnvtNVq1asW8efO45JJjRnqz2cxDDz3E/v37iYyMZOLEiSxcuNAlfFdLIOm0sdgrKsj69D2spcWEZ7Sj+6xnMCY7DMG2kmIshcf6imy3cfC917GWFCLqDYS3aUe3h54hfuCx9X/ez9+g2G3semaWi6w2U64hY+oJDioq9aahvwNZlrn//vs5cOAAWq2WDh068NRTT3HTTTc5y9TaWfLy8oiJiaFfv34sWbLEeVszkBCUYAgCF2Ds3buXDh06OCfNjz76yGn9/eWXX3xqyAxVcncVUlFQ5W813Mjsn44houmuy/kaSbZzqGrvyQv6GKMmnFYRGScvGMBYrTbstsDL9mgw6IM6ZpKsyHx94NOAy6wboY3knLYX+luNUyLPlMPBABwP2kV1IiUs8K5KNIRNv+7GWmPztxouiBqB/ud2C+oN/47Fe/jg9s/9rYYLokZgzHXDOfOW0ScvHMAsWbSO4mzP2dL9yTm3jAzq6+qyLGO1eA534k80Gg06ffC2K8A3T/7K1r92oUiBtZW96Z3LSW7X+FjkJ2P9+vUMGDCAdevW0blXmddykbozmk2HUOe0b5eevFCAsPSC0/ytQoN4cevvJy/UhNzZ88yTF2pBBLdLiJ+4++676dChA+effz4jR47kiiuuAGDUqFGcddZZJ/m0Sn2ITgq8WFr6MB368OBeKGlELUYvV/38SZQuuK/1AGgD1MAWzAH+wZH4oU1E21PKjtz0CGRGtfe3EqdMvLH5NieNRUAg3hB4ejWUxIxYf6vgigAJrWOD2vAH0HFYOwwB5oEvSwp9zvYc/y6YaNM9sK4qCoJAcmZ8UBv+IHA97IJ9bQDQ84wugWX4EyAxI46kzPony1NRUVHxJcE/8vuBMWPGYDAY+OOPP1izZg1dunRh1qxZ/PzzzwE5wQcj4XFhaA2BZUyJbRUdEn/fGH39M0/5AgGRiBAw/omiiBhg2aC1Om1I9NmOMV08xv3zFwLQPjr4M67pRT0JhiQCJ+WjQIIhKaiTfdSSlBlY4ywKJLcLMJ0agc6gZcikfo6rlAGAIAq0G9CG5PbBb7Bu0y0FjS5wtgWKotC+X2t/q3HKCIIQcN73taFKgp0Og9oSnRR58oK+QoHBF/cNiXWXiopKaBL8I78fuOOOO9ixYwcmkwmz2cyOHTt49NFHndl5VU4dQRCIaxXjbzWcCKJATHIALTBOgXBtJBohcBai0fqYoA7ufzy1mU0DhUD1Rmwo8cYEYgPEaC0gkB7RhjBtaIz3KWGtODHmn/9QSAlv5W8lmgR9mM4RqyxA9oARsWFBH++vlsGT+jUqQ3dzoMgKw6YO9LcaTYJWr6Vtz8CJXWiMNJDaLjQ8qDwl2/AnGq0mYP7Op4IgCgy6qG/AjLM6g5ae49SMrCoqKoFLaOy4VUKSmJRIRG1gdNG4VtEBo8upIggCsV6SKPgegegAMeo0BRpN4CyoNRoxJE72a+ke18vfKgCgoNA1Nviv+NUSpYs+mmDD//02Shsd9Mk+jietc2LA2FXTugS/Z1otCW3i6HVmNwSNf/usIArEt4ml+5jOftWjKekwoLXHbMr+oMuQtgHj4XmqBJr3X6AZI0+FfhN7YIww+H0KEwQYfElfDOHBGxdcRUUl9AmdnaFKyKHRaUjt6P8Ni86oJSHQ4jedItG6OK+Zf32J44pf6CyUBEFAbwiMK4t6Q+i0K0DryAxaR2T4PfZfl9juxBsDxXh+6giCQIfoLv7eNyFwVI9AsTw0AZHx4aR08HNfESA+PZq4tOAPrXA85993FoZw/274FUVhyhMXoNGFjiElMjac7qM6+FUHQRCIbxVDu77pftWjqdFqAyMMh06vCwg9moqwaCPn3DXWrwctgigQ2yqGUVcO8Z8SKioqKvVANf6pBDRRSRFEJvr3el1al6SQCIx8PIIgkGRshT93TgZNWEh5/dWi0Wj8ft1Wb9CH1OK+lv6Jg9H6KR6cgECkNoqecX38Ir85CdOGkxHZzq86ZES2x6gNjWupx5PeLdmviaI0Wg0ZvYM7c7InIhMiuGjWBL9t+AUBRl01lIzeoWWgAujYvw1xaf6LcSyIAgMmBndWak8IgoBO59/DQVEUQ8rrr5buYzrRdWQHv3mKKorCRQ9MQKsPrNAvKioqKieijlIqAU9Kh0RqyrORbJLPZSe0iSUs2v8ecs2BXqMn0ZhMkTnf57IFRJLD0kJucV+LTq9DkmS/xKUKBONjc2HUGhmcPIxlef/4QbrAkJQRaMTQbNvUsHRKLMVU2sp9LjtaF0tqWGjE+jsRjVakw8DW7Pz3AP4IU9e+fzo6Q2gu9XqP7872xXvY/PM2n7atqBFIzExg3M2jfCfUhwiiwICzu/P3wjVIdsnnBtbeZ3QiMjY0YqqeiKgR0Wg1jnb1Azp9YNxMaA4m3nEGWdtyMZXVoMi+7bQjpw8mvZt/smVH6s7wi9xQZ+kFp/lbhZDlzp5n+luFFk1ouTOphCRavYaM3qk+j7kXnRJJQttYn8r0NdH6OGL1vr2WJiCQFt4mpK77noggCBiNBp/HThJFMWCuHTcX6RFt6J842KcyBQRGpI4iwej/MATNhSAIdInpQbg2wqdyI7SRdInpHrIHAeC4/tthcBufy83sm0ZsWujEUPTEpEfPpcPQdj7z+BE1AjEp0Vz/1rSQNaoCRMWHM2JSX0fcWB/+NLsOy6Rdn9DzpjwerVbrl9skoXojoJaIuHCufOESDOF6n3oA9p3Yg9FXD/WZPBUVFZVTQTX+qQQF+nA9Gb3T0Oh802VjUiNJ7ZQY0gulWuIMiT4zAAqIpIW3CcnrfSciiAIGo9FnfUjUiBiMob24r6VjTGefGQAFREakjqZVRGufyPMnWlFL99jeRPgo6UakNopusb3RiKFrRKklLi2ajoPb+OxAILNvK5Iy430jzI9odRqufGkSnYe3b/a2FTUC8a3juOm9K4lKiGxeYQFAQnoMIy7ti0Yr+qTfdhvRjq7D/Rt+wBfUXv/1pQFQb9CHVAIwbyRmxHPVy5cSFm30SUKg/uf14pw7z2gR6y4VFZXQIPRnApWQwRChp23fVhijDM0nRICkdnGkdGwZhj84GlzbmESiMbVZkynoRD3pEW0xakPzOo8nRFHAGGZo9kW+VqfFEOKn+ifSMaYzI1JHoxP1zdRvBcI04ZzealyLMPzVohV1dI/rTaIhpVnlJBlT6BbXG20LMPzVEtcqmi4jMtEZm+k7C45EWZ2GZpCUGXrxVL2hM+q44qVJnHbFEBBoeq+fo9V1Pq0jMz64kpiU0PamPJ7E1rGMnjaQiLjmmbcFATQ6kf4TutF1WLsWM4fVGgC12uYd/wRBwGAwtAjDXy3J7RK49rUptOrcPHOYKAqIWpFxN53GxDtOD7mY4CoqKqGNoPgjKJWKyimgKAqlORUUHShp0jg/hgg9aV2SMESE7nXUk2GTbRTW5GKWTE1ab6w+gThDyzGonoiiKEh2CavV1qT1OrIL69G04MWnWTKzvnA1R6oPN0l9AgIKCh2jO9MroR86PyUYCQRKLcXsq9iNXWm6fqsTdXSI6kKsIfS90rwh2SSytuVTeLC0SeuNT4+mbZ+0Fh10/tDGI3z24HeUZpc3ScxVUSOgC9Nz4YMT6HN2aF9PrwvJLrNr5UF2rTqIAE229kpqG0f/8d0ID9HYyvVBlmVsVluTxwjW6rRoNJoW22dlSWb1Vxv5661lyIqCIjVN+6Z1TuaC+8eT1NZ/c9j69esZMGAA69ato3///n7TQ0VFJfhQjX8qQYu1xkbRoVIqi6pPKSC11qAhPj2G2Fb+y24XSCiKQpWtnDJrCTbZekp1hWsjiTMkYtC03IX98Siygs1mw94Ewb51Oi1anVbts0fJrs5ie8kWSq0lTgNeQ6j9TLIxhe7xvUkOa17Pt2DBLtvJNh2moCYXSWl8v9UIGpLD0kgPz2hR3n51UVFYTc6uAiqLTA7vsobOY0c/ExEbRlqXROLSoptBy+DDZrax5L2VLF+0FlNZDYIoNCwBwNEhVafX0v/8XoydMZKoxNC/5lsfyvIr2bF8P3n7ihvVZwVBQFEUouLD6TykLW26p6pzGEcPCCUJu81+ynWJoohWp21R3n51UZxVypIPVrF98R4UWWmwkbV2/IhJiWLopf0ZeEFvv3v7qcY/FRWVxqIa/1SCHrtVojy/krLcCuyWo5tTT4vS458Jjg1TXKtowuPC1MWnBxRFwSzVUGEtpdpeRX1X+aKgIVoXS7Q+Fm0L9pqqC0VRsNsdC/2GDMG1i3qNRlT7rBdKLMXsK9/N4aqDTmOVJ2Pg8c90oo52UR3oEN2ZKL1qQPGErEgUmQvJq8nGZK+u9+citJGkhrUiwZiEKIRmpuRTpabSQuGBEooOlyHZZcBxHfLEoeH4Z6JGIKF1LEnt4oiIDf0Yqo1Bskls/3s3yxet5dCGLBTF0YaCKCJLsrNc7TXhWgNhUmYCw6YOoN+5vTBGNmOYkSDGVGHm4OZsDmzKwVrj8AwWRIdh7/ih9njDq6gRaNUxiXb9WpOQHqPOYR5QFAVZlpHsErIsn/wDx6HVatFoW66n38moLjWx8edtrP1uCxUFlYCjTyoKLocDoubY+CBqBDoMasugi/rSfkCGTxOJ1IVq/FNRUWksqvFPJaSwW+2Yq6yYKy1YTTZkWUZRHDE6NDoNxkg9xkgD+gg9YoBM4sGAoihYZQsWyYxVMmNXjhmtREFEJ+oxaIwYNEY0guqN1hAURUGWZGRZRpYVQHFuUhEERFF0xJgRVYNfQ5AVmSpbJaWWYkosJdTYTdhluyPGlKAlQhdJvCGBOEM8EdpItW0bgF22U22votpehclehSRLyIqMKIhoRA0R2kgitJGEayNVL78GoCgKFpON6tIaTGU1WEy24zahIvowLeGxYUTEhmGMbFkxPk8Vq8lK7u4CsrfnkrMzn5oKMzaLHa1egz5cT2rHJNK7p9GqWwoRsS0nLu2poigKNZUWyvIrKcuvoKqkxuHZLitodBoMEXriUqKITY0mKj7c7x5TwYSiKE5jYK3HmoLiiHErONZegro2aBRVJdXk7i4gZ1c+RYdLsZltSHYJnUFHRGwYqZ2SSeucTHK7hIAMo6Aa/1RUVBqLavxTUVFRUVFRUVFRUVFRUQlwVOOfiopKY1GP4FRUVFRUVFRUVFRUVFRUVFRUVEKUBhv/cnNz2bJli9f3a2pqWL9+PVVVVV7LbN++nSNHjqgyVBmqDFWGKkOVocpQZagyVBmqDFWGKkOVocpQZagyVBmqjEbKqBdKA5kzZ46Snp7u9f2tW7cqgLJ8+XKvZbp3767ceeedqgxVhipDlaHKUGWoMlQZqgxVhipDlaHKUGWoMuohY926dQqgrFu3Lqi/hypDlaHKaFoZ9aHBxr+cnBxl8+bNXt83mUzKunXrlMrKSq9ltm3bpmRlZakyVBmqDFWGKkOVocpQZagyVBmqDFWGKkOVocqohwxPxr9g/B6qDFWGKqNpZdQHNeGHioqKioqKioqKioqKikqAoyb8UFFRaSyBl79cRaWRyJJMdZmZ6tIaqstqMJWbkewyKAqCKKAzaomMCyciLoyIuDAM4ToEQfC32kGBzWzDVG6mpsKCqdyMzWxDlhUEQUDUCBgjDYRFGwiLNhIWbUSjVXMJ1QdFVrCZbVhNNmwmG9YaG4qsgKKAICBqRfRhOvThOnTherQGjdpn64m1xkZliYmqYhOVxSbMVVZkSQYBRI1IeLSByIRwouLDiUoIR6tXp8P6IEsyBfuLOLI1lyPbcsnekYu50ozdKqHVawiLCiO9eyrpPdJo3SON5PaJiBp1PKgPNoudqhITlSUmKotM1FRaHH1WAVErYozQE5Xo6LORCeHojTp/qxwUKIpCtb2KcmsZFdYyyq1lWCUrMjIiIlpRS5Q+hhh9LDH6WKJ00YiC2mfrg9lu5nB5FgfLD3Oo/DC5lbnOttWJOuKNcWTGtqVtTAaZsW2JNcb6W+WgQFEUbGY75ioL5krHy26VUJSj6y5RwBCpxxhpwBhlwBCpRxTVPlsfZEXGKlmwyhYskhmrZEE5+j8BAY2gwaAxYtAY0WuMaAWtuu5SUVEJGVTPP5Wgp7qshvz9JRQeLHUYTgAEwEPPFgSHXQUgLMpAascEEjNi0eg0PtM3WJAlmbK8SooOlVJTYXE89NKuLs8FiE2LIjEjjvAYo7po8oDdYqeqsJrqYtOxPuuN49pWo9cQmRhBeEK4amD1gCIrFB0pI3tnIeX5RwPq1tVnOfZeQpsY0rskEZsapfZZD5TnV7Dqs/Ws+HQd1SUmwGGQku2yW9njn0fGRzBs6gAGX9qfmOQon+ocDCiKQmluJTm7Cig+UuF4eELfdOG4/hybGkV6lyQSWscgiGqfPRGrZOFI9WEOVu7HLNUAICCgeGjY459rBS0ZkZm0iWxHhC7CpzoHA4qisK90P38f/Ie1ueuQFBkBAUEQkBXX8eDE55mxmYzNHMPAtP7oNKrx+kQku0xFQRWlR8qx1tgcD+ux7hJEgZjUKGLTojFG6n2kbXBhkSxUWsuoslV4HAO8oRP1ROtjiQygQwHV809FRaWxqMY/laCloqiaw5tzqSqp8b44qgeiRiC5fTxtuqeoRkBAlmUKjhpTPW3s68XRv4cxUk9a5ySikyObVMdgxVZjoyy7Akul5dQqEiA8LoyYVtFqn8Vh9MveVcjhrXnYzPZGjQe1BwPGSD2ZfdJIbhevGgGBkiOl/PDsH2z7cxfAyY3VHqg1TPUc15VzZ44jLj22KVUMShRFIX9/CQc35WKptjZuDjv6GZ1RS9teqbTqnKQaAXFs8neVbSO7OqtBm/zjqTUGJhqT6Rbbkyh9dBNrGZxszN/MVzu+IacqF1EQ3Yx9J6O2XcO1YZzZfiwTOpylGgFxGP2KDpZQllvZqDEWOLbuijaQ3D6B8Bhjk+oYrJjtJkosRVgk8ynVIyAQpY8lzpDgdyOgavxTUVFpLKrxTyXokOwyWVvzyNtbfEpGvxPRhWnpOLANMSkt11BlKjdzeHMOlmpbk9Yb1yqaVt2S0bZQQ5WiKFTmV1GRW9mk9Qoagbg2sYTHhTVpvcGEqdzMzmUHqSw2NWm98a2i6TwsA0N4y/SikGWFVZ+t4/unf0e2y47rp6eIqBHR6ETOu/cshkzu32KNqxaTlV0rDlOaU9Gk9UYlhtN1RCbh0S1z068oCrmmbLaWbERSpEYb/o5HOOqG2SmmK+2jO/l90+8vqqzVLNr2Kauy13j1nmwMqZGpXN/3ajJj2zZJfcFIdYmJ3F2F2K1Sk9Yb1zqGpMy4Fht2QVZkSi1FVFjLmrRejaAlKSyVMG14k9bbEFTjn4qKSmNRjX8qQUV1aQ27VxzCYmpa49TxJLePJ7NvK8QW5EGhKAr5e4vJ31fcbDK0eg0ZfdKISmhZ16jsFjvF+0scHmnNRFiskbiM2Ba3yM/eVci+tUdAUWjymUwAjUaky/C2JLWNa+LKA5vKoio++r+v2L/mULPJaD+4LdOfv4TIFjYeFBwsZfeKQ8iS3Cx9VhAEOg5sTasuSU1ceWBjl21sKl5Pfk1us8mI0kUzIGkI4dqW1We3F+7gzQ3vYrKZGuzpdzJEQURRFM7rPJHzOp3Tog4EFFkhb28R5U18KHg8OqOW9B4pGCMNzSYjELFIZgpMOdiV5lt3ReliSDAm+6XPqsY/FRWVxtKydooqQU1FYRXbFu/DUtN8hj+Agv0l7Fp2sEk8XYIBRVbI2pLXrIY/ALtVYv/aI5TlNd9CN9Cw1dgo2FXYrIY/gJoyM4V7ixt/TTvIUBSF/Ruy2bs6C0VuBsMfgOLwMt6+5AA5uwqbQUBgUppdxmvTFnBw/eFmlXNw3WFem7aAstzyZpUTSGTvLGDHvweQ7M1g+ANQHOP5ntVZHNiYQ0s527VKVlbmLyW/Jq9Z5VTZKlme9w+V1qb12Axk1uas56XVr1JtrW5ywx84vLMUFL7b/SPvb/6wWWQEIrLkuMHSnIY/AJvZzqENOZjKappVTiBRYzeRW53VrIY/gEpbOfmm7BbTZ1VUVEID1finEhRUFlWz49+DyJLSZNd866I8v4pdyw8hNzb2SpCgKApZW/Oa/PqZd4FwaGMOFQVVvpHnR2xmGwV7ihx91hfyTDaHAbAFGK0Pbswla2u+z+TtWZ1F7p4in8nzFxWFlfzvyvcpyy1v9n4rSwqlOWX878r3qSwM/fEgZ1che9cc8Zm8w1vyOLS5+bzgAgWbbGNVwVIqbRU09+JAQcEm21hZ8C9VttDvsxvyNvLG+redBrrmZmnWcj7Y/FHIG60VWeHI1jxMpb4xyNUe8NZUnFrMu2DAbK8h35Ttk/4KUCOZKDC1nIMWFRWV4Ee99qsS8JirrWz+fY9fvJqSMuPoMLC1z+X6irw9Rc3u8ecJQYCOw9qGbGwq2S6Tt6PAL33WEGUgsUPoJqvI3VPE7pXN65XmjV5ndCA+PcYvspsbu9XOy5PepvCA7wzWcDThUockbvvserT60IwJWnyknK1/7/OL7C7D2pLaMcEvspsbRVFYXbCMYksxPjkVPIqAgF5jYFTaGejE0IwJeqDsIE8uexZFkX3Ysg4u6Hwe53We6GOpviNnZwEV+b43HosagXYD26Azan0u2xfYZBvZVQd9Zvg7nkhdNElhqT6Td/y13ylrgv8gYs9No/ytgooPWVP4Y5PWNyjpnCatL9RRPf9UAhpFUdi35ojfvJkKD5ZSmhuaV3xM5Wa/GP7AkVX18KbckPWsLD1S7rcruJZKC9VNnPwiUKiptLB3TZbf5P9/e+cdH1WxPfDv3ZJseu+QhEDoPZQA0gWUpoKAiFjAwhPxZ0XxiQ95KuoTHyqgSFFB6SiIoGB7SEc6hJ7QSe992/39sSRhs5sgMckW5utnP364e/bM3LmTuTNnzpxzatdF9Nq6PUpkK36e9wdpien1avgDkwdgytk0fl2wvV7LrS90pXpO77pgs/LP7rtMSaHWZuXXJZcKzpNZmkF9Gv7A5AFYaijhRPbxei23vtAZdCw69IXpGLkNyt945gcu5dpunK9L8jMKbWL4A9NYm3w6zSm91GRZJr04xSaGP4ACXR5FukKblC0QCAS3gjD+CeyatKQs8jMKbTMDvU7i/iu1noXN1hiNRi4dvWbTOpQWakmzkfGxLinOLaG4no7zVEXu1TynM1LJsmzzo/i6Uj3n9tff0c364vKxa/xv0c66iUP3V5DhtwU7uHLC+Y6pnvvzMjobvj9ko5HTuy463YK/SF/IyRzbGt+uFl4irY7jDNqCjWc3kVaYjhEbhZCQJBYd+gK90bneYQadgZQzto0fW5RTQq4Txl3O1+VSarDtvCujJAWD7FxrBYFA4HwI45/AbtEW6+wiZpFea+DSMdvXozZJS8qitLBuE6f8FVITMynJL7V1NWoNo8FI9qUcW1cD2SiTfcm5EimkJGaSm1Zg040AZEhNzHIqb2CjUWblq+vBxtnNJQlWvbrBqbyBs67lkXY+26Z9VpYhJyWf1KQs21WiDjiWedguDJpHMw9hMDrPgv9y3hV+PLfVZh5UYEoCklyQzE+JP9usDnVBWlImBp3tY/Kmnst0qs1BvVFPVontk3IZZAPZJc4fG1ggEDg2wvgnsFtSk7LsYyEom47/6kqdY7JkNBjJuJBt62qYkCD9op3UpRYozi62m4y7pfml6Epsb+CtDUyJaeovwUe1SHA5wU7qUguc3ZlI+vlM5Ho+7lsZo0Em9Vw6iXvO27QetcnlhFSwk9CblxNS7cJYVhvkaXPJLE23qYGqDK2xlOQi5/EG/iXpN7uIFysDPyf96jTef3qtgVwbHfetjGyUyanjLMP1Sb42xy7GAjB5IDrTZoBAIHA+hPFPYJcYjTKpSZm29fK5Afm6AdAZyE3Nx2AnBipkyL6Wh0Hn+JMlWZbJT7evmC+FGc4R+y83tYBie/EQlSE7Od9+6vM32bn8TxRK2y/2ARRKBbuW/2nratQKRXkl5KTk2807rCi3hDw7G59qyqWCC0j2YlUFLuQn2boKtUKBtpA9V/dhlO1jflCoK+Rg8iFbV6NWyE3Js5uxACD7ap5TbAbIskyezr5OORTYWX0EAoHgRoTxT2CXZF/LQ19qXwahlHOZTjFZSr+YY+sqmCEbZbKuOf4xSm2RDn2JfXkpFGYW2SxZTm1y9XS63XhQASDBNRvHbqoNsq7mcOqPc/We5KMqjAYjJ34/Q06K448H185k2FWflSS4esrx+6zOqONK4UW78fQByNPlklPq+JuDu67sthvDH5iyKv964X+2rsbfRpZlsq/a15hm0BkocILEYEX6Aox2FmcvV5vjFGsFgUDgnNSb8a9Pnz5IkkR0dLTV76Ojo5EkiT59+phd12q1zJs3j169euHv74+LiwsRERH07NmTt956i4ICkxv9//73PyRJsvpp3769RT3KPiqVitDQUEaNGsX5885z3MjRybySW+XCad2WlYx4+i56P9iJR6eO4fDJA9XqOpiwn0enjqH3g50YOfluvt262kLm9z0/M/a5e+k1No6xz93L//b+aiGjLdZRaONEDn8XXYme4twSq9+t+mE5gx/tT5fhbRk7ZQQHj++vVtf+o/sYO2UEXYa3Zchjd7Jm00oLmV92bGHEk0PoPKwNI54cwm87rcfwyXGCGGrFOVX3ja9Wf0n3ofE0iY9h8IN3sffg3mp17T6wm8EP3kWT+Bh6DOvGsrVLLWQ2/7qJfiP70LhrI/qN7MOPv/1oISMbZUod3EPNaDCSeTnHqtfE+l9W88ALwxgwsRtPvjGOo6er9xI5fOoAT74xjgETuzH2xeFs+G2thcy2P3/lkVfvZ8CEeB559X627//NUpGTeAIn/Hq6yiN+3R6I49Wtz/D2oWk8u+ZxouMaVqsrplMkz655nLcPTeOVLc8QP6ajhUzrAc15ceMk3jk8jRc3TqJV/2ZWNEkk/Hq6JrdjV6RfyLKrPivLkHE5B9keQmn8DTJK0qwaqI7vTeDNCW8zvvMEhkTdx+4t1Y+xAMf2HOfZIS9yb9PRTLhjEpu//slCZufm3UzqP4V7Ykcxqf8Udv20x0JGQiKlyLYJtGqDfdf2WxhVY/2bMKXzP/jgzlksGvop7UPa3VRPU/9Ypt8xjU/v/phZff9N78ieFjIdQzsws/cbfHr3x8zs/QYdQi31ysgkZieRW+rY84OS/NIqk8YtX/81dz7Qm3YDWjDyyeHsP1q95/O+w3sZ+eRw2g1owYCxfVi5YbmFzFdrvuDu8XfSfmBL+o7qway5b1FaajkPsLeTCjWhQFf18eVlC7+hV5t+NA9uw/BeI9i3q/o57dKF3zCg8920CGlL/7hBfLtivdn3K79czei7HqR9ZGfaR3bmoeGPcuTAUQs9BlmP1uiY867c/33HhX+OIfGZAVx+5wmKzx6pVl7Waclcv5ALr43m3DN3cuH1seTt3GQmU3BwGxdnPMy5Z+7k4oyHKTj0R13eQp0yf/58GjVqhEajIS4uju3bt1crv23bNuLi4tBoNMTExPDZZ59ZyKxbt46WLVvi6upKy5Yt+e677+qq+rcNt/Kcvv32WwYMGEBQUBDe3t5069aNLVu2VCm/cuVKJEni3nvvrYOa1w927fmXk5ND9+7deeaZZ9i+fTvFxcU0bdoUjUbDvn37mD59OleuWMZaiYmJoWvXruWftm3bWsi4uLjQtWtXYmJiSE1NZe3atQwbNqw+bkvwFyjILLK6cPpl50/M+eJ9Hh35BF+9v5p2LTrywttPk5JuPSHHtdQrvDjradq16MhX76/mkRGP898l7/L7ngoj1LHTR5j+36nc1XsoSz9Yy129h/L6f18m4azlS72wGgOPI1CUZ93wt2XbZv6zYBaPPzCJlXO/o0OrTkye/iTJadYXNFdTrvDMG0/RoVUnVs79joljnuK9z97mlx0VA+aRk4d4ZdYLDOk/nNXzNzCk/3CmznqeY6csJxPFeaUOv1OqLdRavf79lg28+cEMpkx8lh+Xb6FLhy48POUhriZftSp/6eolHpkyni4duvDj8i08M2EK/3r/DTb/WjGhOnBkP0+/+g9GDBnJlpU/M2LISJ5+dRKHjh20rFeRY8f9K8wpsZqF9rc9W5n7zWweGj6BRTOX06ZpB6Z+MIXUDOtjQXL6VV794FnaNO3AopnLGTfsMT5Z9h+2/Vlh6E84e5Q3501jYI/BLHprBQN7DGbGvFc5kXjMQl9pkc7h44BeOX4Na7a/dne1ZNi0Qfy2YAcfjVzI+QOXmLjgQXzDvK3q8YvwZcJnYzl/4BIfjVzI75/vYPhrd9F6QPNymch2EYybPZKD3x/jv/d9zsHvj/HQhyNp2DbcTJekkLia4NgJlrTFOrTFln3D1n1WNsoU5jr2OyxXm2P1yG9JUQmNWkQzaeYTf0lPyqVU/vXoW7Tu0pKPN81mzOSRLJixmJ2bd5fLnDxwinef+YB+I/ow98f/0m9EH96d/AGnDp0x0yUjk6N17M0Ag9HAlTzLd5Kr0pXLeVdZfnzVX9IT6BbA/3WZzJmsc8zc/g6bz/3E2Naj6RjaoVwmxrcRT3WcyO4re3nzj7fZfWUvT3V8gka+0VZ1Xsy5VKN7sheqSmq2+bcfeHfuWzz10NN8u2gjcW0689TUCVxLtT7vupJ8mUmvTiSuTWe+XbSRJ8f9g3c+mcnWbRVG640/b+DDz99n8iPPsumrrbw19V1+/H0THy78j4W+4irmg45EqcH6PfywbjNvTZvF5Jf+wQ/b19OpexwT7n+Cq5ett+3Xi5bzwZuz+b9Xp7Blzyaem/Ys/3rpTX79sWIjZc+OvQwbOYTlPyxl3S8rCW8YxsP3TSDlmmUM4KrqZc/k7/+N9DVz8bt7PA3/uRBNk7Zcm/sKuqyqYxynLJxB0emDBI+fStSMZYROfAN1aFT598VJx0lZ9CZe8QOJfH0xXvEDSVk4g5LzJ+rjlmqVVatW8dxzz/HPf/6TQ4cO0bNnT+6++24uXbI+Pp0/f57BgwfTs2dPDh06xGuvvcazzz7LunXrymV2797NmDFjGD9+PEeOHGH8+PGMHj2avXtvvnklsM6tPqc//viDAQMGsHnzZg4cOEDfvn0ZNmwYhw5ZbsxevHiRl156iZ49LTe0HAm7Nv4988wzHDhg8uqaPHkymZmZHD9+nMTERHJycli7di2BgYEWv5s+fTp79uwp/yxdauk1ExYWxp49ezhz5gzjx48HICEhgaws58qI54jotQa0xdYNFit+WMqwfvcxvP9IohvE8PxjrxAcGGrVmw/gu5/XEBIYxvOPvUJ0gxiG9x/J0H73sfz7r8plVm36ms5t43nkvseJjmjEI/c9TqfWXVi16WszXZKEw3v+FeeWWPWoXPbdl9w3cCQj7hpFTGRjpk56jdCgUNZsWmFVz5pNKwkLDmPqpNeIiWzMiLtGce/AESxdt6Rc5pv1S4nv2J2JY56iUcMYJo55ii7t4/lm/VcW+mSjTGkVxjNHQJZldFYW+wALv1nImHsfYOx9DxIbE8uMl2cSHhJu1ZsP4Ou1y4gIjWDGyzOJjYll7H0PMuaeMSxYWrFjuGj5Inp27cUzE6bQpFETnpkwhR6d72DR8kUW+hzd+FeQZf1o0pqfvmZw73sY2uc+oiIaMeWhlwj2D7HqGQXw/W/rCA4IZcpDLxEV0Yihfe7j7l73sGrzsnKZtVuW06l1V8YNm0BUeCPGDZtAx5ZdWLvF+t9BVXVzFC4duWr1yG/PR+P5c90h9q07TFpSBhvf3UpOch7xD3Syqid+TBzZyXlsfHcraUkZ7Ft3mP3fHqb3Y90qdD7clbO7k/h94U7Sz2fy+8KdnNtznp7ju5rpMhqMXDzq2AkU8u25z2Y69jsstzTb6pHfTn3jePjlcfS4u5uVX1my+ZstBIUH8uS/JhIZ25BBYwcwYHQ/vv18fbnMhiU/0OGOdoyePJKGTRowevJI2vVoy4YlGy3r5eBH/VIKUqwm1ziensD6099zMOXwX9LTO6onWcVZrDqxhuSCFLZf3smOy7sY1PjOcpkBMf04kXGKHxO3kFKYyo+JWziVcYo7G/Wz0KeQFFzMdXDjX4F1499Xa5YwYvAoRg0dQ+OoJrw2ZTqhwWGs3PCNVfmV3y8nLDic16ZMp3FUE0YNHcOIu+9nyaqK9/7hhEN0bBPH0DuHExHWgB6dezKk/zASTltuBuhK9PYT/7kGGGQDBtn6vGvxvC8YNX4kYx4ZRZNmjXnj3X8SFhHKN4utj4vrV33P2EfHMHTkYCIbNWTY/UMYNf5+PpuzsFxmzqLZjH9iHC3btqBx08bM+vgtZKORXdt2W+jTGhzP8y/nl9V49xiMzx1DcQmLJmj0FFR+QeRu22BVvjBhL8VnjxD+zHu4t+iEOjAMTaMWuDVuXS6T++ta3FvE4X/XQ7iERuF/10O4N48j59c19XVbtcaHH37IxIkTefzxx2nRogVz5syhYcOGfPrpp1blP/vsMyIjI5kzZw4tWrTg8ccfZ8KECXzwwQflMnPmzGHAgAFMmzaN5s2bM23aNPr378+cOXPq6a6cj1t9TnPmzGHq1Kl07tyZ2NhY3nnnHWJjY9m40fw9bzAYGDduHG+++SYxMTH1cSt1ht0a/3Jzc1m1yrTT2LZtWz7++GPc3d3Lv3dzc2PkyJFWjX+3StmEzdvbGy8vr7+tT/D3qMq7TqfTcTrpJF3adTe73rVtN46dPmz1N8fPHKFrW/PFQNd23TmZdAK9XlcuY6GzfQ+OnTb3UJPlqhd1jkJRbomFR6VOp+Xk2QS6dexhdj2+Yw+OnLB+JO3oqcPEV5Lv3vEOTpxNQHe9XY+etJTpFncHR04etqqzOM/xJktl6EsNVo/TaXVajp08Sq/43mbXe3Xrzf4j1o+gHDh6gF7dKsv34ejJo+h0prY9eOwAveJ7mcn07tabA1Z0aosc16gKkJ9ZZOGdptPrOH3hFJ1bx5td79wm3qrHLkDCuaN0bmMu36VNPKcvVIwFCeeOWujs0qYbCdaOvkimujkqpYVaMq9Yeisp1QoiWoZxZqd5EoOzuxKJbt/Aqq6o9hGc3ZVodu30jkQatApDoTJNMyLbN+BsJZ1ndiYR1cFSZ8aFLIfOVF2QWWSxyWIPfVaSHPsdJssyudqcWtF16uBpOvZqb3atY68OnD2WiF6nL5fpYCHTnpMHLI+lG2Q9xQbHbdsLtWRga+wXQ0LGSbNrCekniPKJQimZxoIYvxhOpJ+wkGniZ7mgkmWZC7kXa6VutsLa3Ear05Jw+jg9Ot9hdr1H5zs4lGDpwQ8mw56FfJeeJJw+Vj7v6tgmjoTTxzl60vT3f/naJf7Y8z96x/e1qrO0CsOkI1CVgU2r1XL8cAI9+5m3Vc9+PTi4z/qcVluqxVXjanZNo9Fw9MCx8nlXZYqLitHp9Pj4+Vh8V2pwrE0WWa+j9NIZ3Ft0Nrvu3qIzJUnHrf6m8MhOXKOakb11BedfGcnFN8aRsXY+Rm3FcylJSrDU2bIzJUkJtX8TdYhWq+XAgQMMHDjQ7PrAgQPZtWuX1d/s3r3bQn7QoEHs37+/vE9VJVOVTkH11OQ5VcZoNJKfn4+/v7/Z9ZkzZxIUFMTEiRNrrb62wm6Nf2fOnEGvN03AevXqhUJhquqkSZPMYva9+uqrFr997LHHzGSee+45C5nk5GTi4+Np1qwZX3/9NX5+fixevBi1Wl2n9yW4Oboqkibk5GdjMBrw9w0wu+7nG0BWTobV32TmZOJXSd7fNwCDQU9Ofs51mQz8fcz/yP19/Mm0orOqujkK1uqfnXe9Xf3M2ynAN4CMbOvtmpGdTkDldvULQG/Qk5OXfV0mw0ImwDeAjCzrQecd+QilUW89nk9WThYGg4GgAPNNikD/QNIz06z+Jj0zjUB/c/mggED0ej1ZOSbP5PSMdAIDgsx1BgSRnmnZtrJBdmiPFG2xzuLYb25+DkajAT+fSmOBdwBZuZlW9WTlZOLnXUneJwCDwUBuQY5JJjcTP2/zscDP29+qTkmSrB7tdBQKsgqthlbw8HVHqVJQkGkeDyo/sxCvQE+rurwCPcmvJF+QWYhSrcTD171Kmap0ykaZwmzHNaRoS/QWsRTtoc/KMlV61TsCMjL6Kjx9bpXs9Gx8A33NrvkG+mLQG8jLyrsuk4NfJRm/QF+y060f8XXEo35l5JXmoZD+/pLA29WbvFLzOGx5pfmoFEo8XUx/6z6u3uRWksktzcfb1TKsgIxMdoljH6m2Fu8vJ9c07wr0M3/XB/gFVjlHyshKJ6CSfKBfIHqDnuxcUxsN6T+MZyc+z0NTxtCmfzMGPtiXLh3ieWLcpL9cN0ehKq+/7MxsDAYDgcGV5p9BgaSnWm/bnv3vYNXStRw7dBxZljl68Bhrvl6HTqcjO9N6/3t/xmxCw0K4o093i+/0dpaE5GYYCnLBaEBZ6V2i9PbDkGf9RJw+I5mSc8fQXjtP2KS3CBw1hYKD20hf+d8KmbwslN5+Fjr1Vei0VzIyMjAYDISEhJhdDwkJISUlxepvUlJSrMrr9XoyMjKqlalKp6B6avKcKjN79mwKCwsZPXp0+bWdO3eyePFiFi5cWM0vHQeVrStQFTcuVm+cRJfF86vuPHxMTAxBQRULY2tJRrRarZmONm3a0Lt3bws5Qf1zs+ykFvF+ZBmrgavK5Ct9V9a3btRjLei9tWuynWTFrClGY9Vta9lO1tugOnmovl2r1Cnh0IHob1b3yn1WluWata1UTZ+tRufNyrNnqhsPLNoVudoMq5ZNVvbcqumzVDG+yLJDZ1LW38TYXtlgLElS9Ubkyl9JZZdv+MJCp8Wlchx6M8BgrPLGbNpnufn71Z4x1PKC2toYev2LG4UqiVQ9lhrsKFPuraI16KzGUqwJFmNH2XVzKXOZaorWGRzXYA03mR9Y61/VPIebzWf3HdrDgmXzmf7cm7Rr2Z6LVy8w65N/Mz8giKcfnnJrdbNzbrapeStzpClTnyY9LZ2Rd45BlmUCgwMY+eB9fP7RIhRKpYX8gjkL2bh2E8s3LbXwGPwrdbNbKjePbO3i9a9kI0gQMuF1lG4mw76sn0zK528Q9MDzKFyut4vFc7CyjnMQrP393dpcXra4fqs6BTenpm26YsUKZsyYwYYNGwgODgYgPz+fhx56iIULF9bKaVN7wG6Nf82aNUOlUqHX69m5c2f5g5s6dSpTp06t9iFOnz6dRx99tFr9UVFRJCUl8fPPP3PPPffwxx9/MHHiRL7//vtavhPBLVPFs/X18kOpUFp45GXnZuFfyZuijADfALKyLeWVShU+Xj7XZQLJzDH3ksiqSqeDj8fW/m78vK+3a5Z5O2XlZlp47pUR6Bdk4RWYnZOJSqnCx9v3ukyghUxWbqbFzrVzYL1j+Pv6o1QqSavkkZeZnUmgf5DV3wQFBFt48GVkZaBSqfDzMe2gBgUGkZ6RZiFT2WOwonaO23Gt9VkfL18UCiVZueb9KycvC39v633W39fSwyonLwulUomPp2ks8PexJpONf6Xd8Iq6/eXbsDskpfXKF+YUYdAbLTzyPP3dLbwBy8jPKMAr0KOSvAcGnYGi62EcTDKVdXpQkFlgVadC4biNa9991oHbtRbHMb8gPwsPvpzMXJQqJd5+XtdlLL38cjJzLTwG66J+9U1teP2ByYPQR2Puwefl6oXeaKBQa/pbzy3Nw6eSl5+3ixd5VWT1ra262Qwr3cLXxzTvquzll5WTSUAV7/FA/yAL+czr8y5fH18APl7yX4YPvJdRQ8cA0DSmGcXFxfxr9j+Z9NDk8lNU1dXN0fEL8EOpVJKeaj7WZmZkEhhsvW01bhrenzeLt+fMJCMtk+DQIFZ8sQpPLw/8A8w91xZ+vJj5Hy5g2fovaNG6uVV9jtasSk8fUCgx5Jp75Bnysy0898pQ+QSg8g0qN/wBuIRGgiyjz07HJaQBKm//W9JprwQGBqJUKi28x9LS0iy8zMoIDQ21Kq9SqQgICKhWpiqdguqpyXMqY9WqVUycOJE1a9Zw550VMWoTExO5cOGCWVLYMkcalUrF6dOnady4cS3eRd1T729UWZYpKSkx+xgMlru5Pj4+5S6XBw8eZNq0aWi1tRu7SqFQMGjQICZPngzAxo0b2bNnT62WIbh1lFUsStVqNc1iWvDnUfPguvuO7qFNs/ZWf9O6aTv2HTV/pvuO7KJFTEtUKnW5jIXOI7to06ydhT6LiZODobDStmq1Cy1iW7H7kHk8hL0Hd9GuZQcLeYC2zduz96C5/O6DO2kZ2wr19XZt26I9eyrJ7Dm4k3Yt2lsqlK3XzVGQqjBUuKhdaNOiLdv3/mF2ffueP+jUznryhLi2cWzfYy7/x55ttG3RtjwsQcc2cWzfu72SzB/EVaHT4WaiN1AWM+5G1Co1zaKbs/+4uQf4/uN7aRVrmd0doFWTthbyfx7fQ7PoirGgKplWsZZjAZJktW6OgtrVeogLg87I1RPJxHY3j78V2z2GC4etJ+K4ePiqhXzTHjFcSUjGeD2g/KXDV4jt3shcZ48YLh6yrlOlcdwQHAqlZGEZto8+69jjrFKy9MCpKc07NuPQdvO4iIe2Hya2TWNUalW5zOHKMn8cpkVcsyrq57jjgYvSBWMteC4mZifRMrCF2bVWgS25mHux3DMyKTuJlkHmMi2DWnIu2zwm6I11c2SsbWS4qF1o1aw1u/bvNLu+a/9OOrTqaFVP+1YdLOR3/rmDVs3alM+7ikuLkSrNU5VKBbJsPfxHVXMXR0Cq4u/NxcWF1u1bseN387ba8fsuOnaxPqctQ61WExYRilKp5IdvN9N3UF+zef/nHy3ik//M58t1i2jbsU01dXOsdpVUalwjm1J00jxudNHJ/WhiWlv9jaZxa/Q5GRhLKkJ0aFOvgKRA5Wfa3NbEtLKi8080Ma1q+Q7qFhcXF+Li4vj555/Nrv/8889072557BugW7duFvJbt26lU6dO5XP5qmSq0imonpo8JzB5/D366KMsX76cIUOGmH3XvHlzjh07xuHDh8s/w4cPp2/fvhw+fJiGDRvWyb3UJfU+U7l06RJubm5mn08++cSq7Ny5c+nQwTRQv/feewQEBNC+fXuioqKsypfx73//m/j4+PLPgAEDqpV/8cUXcXExTS5mzZpVg7sS1CYaL0sX+jLGDn2Y73/9lo2/fceFK0nM+fJ9UjOSuW/gKADmf/MRb37yWrn8fQNGkZJxjY++/A8XriSx8bfv2Pjbdzw4/JFymdFDxrHvyG6WrV/ChavnWbZ+CX8e28uYIQ9ZlO/mXXXdHAGNp6tVQ9D4+x7luy1rWb9lHUmXEvnPglkkpydz/+AHAPj4i9m8/sEr5fKjhjzAtbRrfPD5LJIuJbJ+yzq+27qOh0dOKJd58J7x7Dm4ky9WL+T85SS+WL2QvYd2M+7eRyzKB3D1cNwJvkpT9aL0iXFPsPK7Faxcv5KzSWeZ8cG/uJpylYdGmrKMv/vJLJ6b/my5/EP3j+dK8hXenD2Ds0lnWbl+JavWr+Sphyti9kx8cCJ/7NnG/C/nce78OeZ/OY8d+7bz+IOPW5SvdFE63ET0Rty8Xa162I266yE2bVvP5m0buHj1PHO/mU1qZgrD+90PwOerP+GdBW+Uyw/vN5LUjGTmffMhF6+eZ/O2DWzetoExg8eXy4wcNJY/j+9h+Q9fcvHaeZb/8CUHEvZy/6CxFuXLRhk3L03t33A94RPihcrFer/d/uUeutzfgU4j2hEcE8iwVwbgG+bDnlUHALjr+X6MmXVPufyeVQfwC/Nh6NQBBMcE0mlEOzqP7MC2Lyo2VXYs20ds98b0mdidoEYB9JnYndj4RmxfZhnCQ61RVxlf0BFw89ZYPU5n6z4rXa+boyJJEhqlm9XviguLSUw4T2LCeQBSLqeSmHCetKsmT6kv31vG7Oc/KpcfPG4QaVfTWThzCZfOXmbrql/YuupXRjx5b7nM8MeGcnD7YdZ8+i2Xz11hzaffcnjnUe6ZMAxreKgdt8+GeARZzaLsqnSloXcDGnqbEvMEuQfQ0LsB/hqT586I5vcwoX3FO33bxe0EuPkzuuVIwjxD6dGwG3dEdmdL4i/lMr+c/52WgS24q/FAQj1CuKvxQFoENueX879ZlK+QFIR7hdX27dYrLu7W5zaPjJrAuk2rWbd5DYkXzzFr7lskp15jzPAHAfjw8//wyjsvlss/MPxBrqVe5d15b5N48RzrNq/h281rmDCm4r3ft1t/Vm5YzqZfN3Il+TI79+/g48X/pW+P/iitHF91raJujoBaUfUG0cTJj7F66VpWL1vLudOJ/HvaO1y7ksy4CaY57fszZvPiU1PL5ZPOnWf9qg2cT7zAkQNHefax5zlz4iwvv/F8ucyCOQv58K05vDf3HRpERpCemk56ajqFBZYe8WqF47Wr752jydu5ibydm9AmXyB99Vz02Wn49BoOQMZ3n5P6xdvl8l6d70Tp6U3q0nfRXrtA8dkjZH77Kd7d7y4/8uvT736KTu4ne8tytCkXyd6ynKKTB/DtP8om9/h3eOGFF1i0aBFLlizh5MmTPP/881y6dIlJk0xz82nTpvHwww+Xy0+aNImLFy/ywgsvcPLkSZYsWcLixYt56aWXymX+7//+j61bt/Lee+9x6tQp3nvvPX755ReruQoEf41bfU4rVqzg4YcfZvbs2cTHx5OSkkJKSgq5ubmAKfFP69atzT6+vr54eXnRunXrcvuRI2G3x34B/Pz82L17N/PmzWP16tWcOHGCkydPEhISQu/evRk2bBhjx1pOcJOSkkhKqthB9PGxzMR0I+Hh4YwfP57FixezceNGjh8/TuvW1nc6BHWPm7emylhQd/a4i9yCHJasXUBmdjoxDZsw+7V5hAWFA5CZnU5qRoW7b3hIA2ZPm89HX73Pui0rCfQL4vkJr9I3vsIg3LZZe2Y+9x4LVs7l85VziQhtyFvPv2/hjSFJpqNvjoybt4bsa5ZHawb1HkxOfg4Lls8jIyudJtGxzJ25gPCQCADSs9JJTrtWLh8R2oC5MxfwwefvsmrjcoICgnll0j+5845B5TLtW3bk3VdnM2/pR8xb9jENwxry3rQPadPcikcKjr0oVaqUKFSKci+nGxk+6B6yc7P5aOF/SctIo1njZnz18TIahJsWU6kZqVxNqWjbyIhIvvpkGTNnz2Dp6q8ICQrhzakzGdy/YjeqU7vOzJs1n//Mf58P5v+HqAZRzJ/1KR3aWHoMOLJRFcDL393qWNAvfiB5BTl8tWEhWTkZNGrQmPde/JjQQNNCMTMng9TMirEgLCiCd1/6mHnfzGb9r6sJ8A1iyviX6d25f7lM69h2vPH0OyxeN58l6z4lPLgB/3r6XVo2tr7D7xXguOOBQqkgrFkIl49ds/juyE8ncPd1485/9MI7yJOUs+kseWoFOddMkyHvQE98wyqO7WVfzWHJpBUMe3Ug3R/sRF5aPt+/8xPHfz5VLnPx8BWWv/Qtg57tw8Bn+5B5KZtvXvyWy0ctyw9vEeLQx369qnhP2LrPyrJj91kAXxc/UootM2mePZrItAeml/970b+/AKD//X15YfazZKVlk36t4shkaGQIb375OgtnfsEPy34kINifp2ZMpMfgbuUyLTs155VPXmTZ7OV8PXsFoZEhvDL3RZp3aGpRvkbp5pAL/jKifKxvqkf7RvJytxfK/z2mlWnRvvPybr44shQfVx8C3CqOmGcUZ/LRvnmMaXU/faN6k1Oay4rjqzmYUpFlNTE7ic8PLebeZsO5t9kw0gvT+fzgIs7nXLAoX5Zlonwia+kubYOblyvFeSUWcVEH9xtKTl4O87/6hPSsdGIbxfLZe4uJCL0+78pMIzk1uVy+QVhDPnt3Me/Oe5vl678mOCCY16a8wcDed5XLTBo/GUmS+Hjxh6RmpOLv60+f7v15buKLVEaSwMXdcT2sXRRVb8YPHTmY7KxsPnl/PukpaTRt0ZQlaz4nIvJ626amc+1KRdsaDUYWffIFSefOo1Kr6NazK2t/XkGDqIps9F8vXoFWq2Pyw8+alfXsq8/w3DTzeIquSsebz3p16oexIJesTUvR52XiGt6I8GfeQx0QCoAhNxNdVkW4GYXGnfD/m036yo+4POtJlJ7eeMb1xX94hTHarXFrQie+Qeb3i8n8fjHqoHBCn5iBplHLer+/v8uYMWPIzMxk5syZJCcn07p1azZv3lzukJScnMylSxVZ0xs1asTmzZt5/vnnmTdvHuHh4Xz88ceMHDmyXKZ79+6sXLmS119/nenTp9O4cWNWrVpF165d6/3+nIVbfU4LFixAr9czefLk8pOgAI888ghffvllfVe/XpBkh41KKnBmjv5ylqIc+8ucFxsfSUCD6o3J9kxBdhGJey/buhoWqFyUtOrXxNbV+FtkJGVSkltq62pY4BPhjVew43qklBSUsve7BFtXwwJJgjvGtkehdNyjfhve/ondqw5YNVrbCoVKQY9xnRn2ykBbV6XGGPRGdqw4bOtqWCV+RGuH3hBIzDvL6Rz7Gw9C3cLpGNTF1tWoMbIs89zWlynUWY/raUum9XiZxn4xNxe0U/LSCrh2Mu3mgvWMxsuV6I4Rtq7G3+JqwUW0RvubdwW7heGh9qoz/QcPHiQuLo4DBw4w5k/rcXMdibNP9bJ1FQT1yJ/pm2pVX+egITcXEpTjuKsWgVPjHehhl3HKPP2tHzlyFNy8NPaXpEACDz/HblcAVw/7PBLu4sALfTB5Lqpday/WV23h4efu0IY/gMj2DezK8Adg1BuJbOvYC1KlSmGXY5pao3JoTx8APxf7DBTv62o9wYqjIEkSTfwb211yDZWkLD9y7KhUF8rGZkiOfdqiDI3K/sZZcEzPP4FAcHtgX295geA6QdF+FkckbIoE3kEeDh0fBUyLUt8wb/syrMrg38DX1rX427jboWFY5ap0+MW+JEmExQbaV58Fwps6ftbqVv2a2Z0XmMbLlZb9rCdUcCTCY+2sf0imPuvI8T8B/FwDcFPa19FlCYkID8cL+l2ZnpE9aiXpR22hkBR0jeji8Ak/XNzUuPnYmTFIBt+wuvNMqy881d43F6pnNEo3VNXEIxQIBAJbIox/ArvEw9cND3sypsgQ2iTA1rWoFQIife3KsKrWqPAKtK/FXE1QqpW4+drXBN8zyNPhF/uAyfhnR31WoVIQHG2fHki3goubmi73d7SbDLCSQiJ+dEfUrnYdjvgvERzjbzftCoAMYU3szCBZAyRJItrLfo6ASkiEukfgqrRD765bpG1wa3xd7SesiVE20je6t62rUSv4RdiXkcrN29XuNn5qgqtSU23sP1vgbafeyQKBQADC+CewY8LsyNim1qjwC7OvyVtNcffRoPGyn0lfYJSfUxioADwDPWxdhQok+/RGrAkaT1f8I+zEY1UyjU1Ktf0dRa4J8WM6YjTYh2VVlmW6jo6zdTVqBZVaSWjjAPsIsyBBQEMfp1jsAzTwjERhJ9NXGZkor0a2rkatoJAU9GvUB8kOBloJiUjvhkT7Wk9E4mh4BXigVNtHnwXwi7AfI+/fxceOjG1KSYm7yo7mgQKBQFAJ+3kTCQSV8G/gg5uXq10s+Bu2CkFy4OyTNyJJEmFNg2xdDcCU6MORE6hUxsXTBRdP+1hge4d4OXxMuhuJbhduF95/CqWChi1DbF2NWiMoOoC4e9vafHyTFBKd7mtPQEP7Wcj9XezpvRHdNszWVag11AoXYrxtnyBKQiJAE4Sfi2PH+7uRPlG98HTxtLkBUEZmRPN7bVqH2kRSSARF20c/cfFQ42VPG5V/Ew+1F2o7OWbr5+r4oRUEAoFz4zwrQ4HToVAoaNKloW0X/BL4BHuaYhA6Ed5BnviF296TsWGbMKfxoAKTYdU/ytfmC36VRoVXqONm+LWGV4A7kW1CbV0NmnRu4DQeVGUMf3UQHn7uNlu0SAoJT38Phr0ywCbl1xUaT1cad7J9soKoNmF4+jt+aIUbaezTDA+VbY1UCklBW/8OTrXYd1e781i78cg2nHhJSPRs2IPWwS1tVoe6wCfMC3dfjc03tMObB9t8jlKbSJJEkJvtNzc0Sne7jEEoEAgENyKMfwK7xsPPjYgWwTYrX6GQiOkU4VST+zIiWgSjcrGd4c0vwhvvIOfZfS5D5aLCx8bxffyd6Cj1jUS1CcXdx0aLJwl8Q70IbWw/4QhqCzdvDaPeGoYs22bBLxtlRr8zHDcv+4qZWRuExQbiG+Jpm+O/kinMQ2Rr5/FULUMpKWkf2MmmRqqWfm1wUzmXURWgbUgbujXoisIGA62EhLerF6Najqz3susaSZIIbRZk03dzYJQfGk/7ipFXG7gqNfja0ANXQiLILcQp510CgcC5cPyo2gKnJ6JFEHlpBeRnFdW7F2Djzg0dPsNvVSjVSqLahZO4/3L9tqsEru4uRDS3nVG3rvEIcKc0v5TinJJ6L9snwtvhM/xWhUKpoEXPRhz+6TQGff1lpZQkU9zP5j2inHZy36J3LHc83JUdS/fWe9m9HutGszsa13u59YEkSTTrEc3BzafQlerrb6yVQKlU0LJXI6c6/n8jPi6+NPdtxamchHovO9Q9ggYezhGPzhpjW40mKfs86UUZ9ZoBWCEpeKrj47irnSNebWVcNGrCmgVx7WRavZft5qMxJXxzUnxd/Sk2FFFqqP95V5BbqM0y/J59qpdNyhUIakrnoCG2rsJtjXPOCAVOhUKhoNkd0fXu8dOoY4RTxaOzhmeAO9Htw+uvwOtGlMZdGjrVcd/KmI7/+uHqVb877F6hnngFO9dx38p4+rnRpn8TFAqpfsYDyWQobzegqdNuBJQxdOoAOt7Ttl7L7HRfO4a81L9ey6xvNB4utBsYi0qtrLc+q1BItLmzCR6+zmlEKSPGO5bG3k3rtcxATTDtA+KcdiMATMd/X+72PL4aXxRS3S8VJCQUKPhH3BM0DYit8/JsiXewJyH1nNDO1dOFBq1Dneq4b2UkSUGoe0S9Z/8N0ATjofaq1zIFAoGgpgjjn8AhUKmVtOwdg6df/SxkYjo1ICTGPoIz1zU+IV406hhhOpZWx/NCV3cXYrtGoXZ1fqdjSSERGOOPxqd+JqLeYV74OElG6pvhE+xJ24GxKJWKuu2zkslTo8PdzUybD06OQiEx+q1hxI/pWC/ldRvbifv/PcypjShlePi40eGuZqaxrw5vV5JAqVLQbmBTfIKceyOgjKY+LYj1aV4vZYW4hREX1LVeDGK2xlfjy2s9XibIPbBOjwArJAVKhZIpXf5B+9B2dVaOPeEX4UNo08B6KcvN25XIduEoVc7fZxWSklCPBrgq6+d9HagJwdvFt17KEggEgtpAkm0V5EcgqAFGg5HLCakkn8kwLaBqufe6uKlp0qUB3rfJoulGivJKuHQ0mdICbZ3o94vwJqJ5sFN7/FlDlmXyUwvIS86vE/0KpYRfpC9uTu7hY43ivBJO7bpIXnphnegPaOBD0/hIXNyc8xh1VciyzL61h/h+1lYMOgNGQ+0d+1MoJVQuKoa/NojOI9rfFoa/Gykt0nFmz0WyrubViX7vIA+a94jGrZ69ju2BlKJrHMs6hN6or9VYgGVJRZr5tqSRV5Pbrs8W6YpYmbCGXVf2ICHVepzFCK9wHu/wGA29bZ8cp74pzC4m+XQa+lJDnej3b+hDYLQfCoXzG/5uxCgbyS7NJE+bXSf6lZKKILdQm8X8PHjwIHFxcRw4cICOHetns04gEDgHwvgncEjyMws5t+8KpYW1YKi6bkQMaRxAZJvQ22J3tCqMRpm0xExSEzNrzbiqclHSsE2YUyb3uBV0xTqyLuagK9bVmk43Pw2+DXxv6z4ryzJXT6WTdPCqKWHF3+2z1z2nmnaNJCjaOROn/FWyr+Wy5vXvObfnApJCQjbWvHElpYRskInt3ohR/x6Gb5hzh1SoDlmWSTufxdl9l02xK2uhz0qSROO4CMJtnFDA1mgNpRzPOkJK8bVaM1T5uPjSLiAOz9v8aN/R1ON8cWQpBdqCv92uCkkBsszQpoMZ3ORuVIrba1PwRowGI2mJmeQk59favEvtpiK8eTBu3s7vsV4dJfpi0otT0Mu1N+/ydvHFzzXQpt6/wvgnEAhqijD+CRwWo8FI2oVsUs5lUJKvrdGkSZLAv4EPYbGBePo7X9a+mlKSX0r6xWyyr+Xd+oL/+nNQa1QERvkR0MDntvP2qwpZlinKKqYgvfBvGQE13q54BnuiuQ29e6qipKCUq6fSST6bcevJQMr6rKuK8GaBhDcNuu28/apClmUSfj3Njq/3kbTvIgqlhNHw18eEMvnGXaPpMa4zrfo3u62NUzeiLdZx9XQ6yWfS0ZUaavQOU6oVhMUGEtEsyCmzeNaUjJI0LuQlkVaSwq02bJnR0MfFl2ivGMLdG4o+e50iXRHbLu7gtwu/k12Sg0JS3HJCELVCTfcG8fRv1Jdwr7A6qqnjUZxXQtaVXPIzCm/dAHi9i7u4qfFr4INPqOdt5+1XFUbZSIEuj1xtNnpjzeddHiovfFz96u1IcXUI459AIKgpwvgncHhkWSY/o4jU85nkpxehvdGocuN8/XpPlxQS7j4a/CO8CY72R61x/vhzNcWgM5B1LY+c5DyK80rNDYFlbXvDJZWLEg8/N/wb+OAV6CEWTNWgLdJSmFFESX4pBu3Nj/yoNSo0Pho8Aj1QuQhjalUY9EbSLmSRlpRFfmZRhSFQqtRlr/dblYsS70APQpsEENDQ15RIRGCVtKQM9qw6wOkdiWRcyDJ5Wl5PLoEkgSxjNJq8LyWFRGC0P817NqHr6I4EN6qf+FaOiNFgJONyLqmJmeRlFKK/YTwoG0Jv7LNKtQKvAA9CYvwJivK7rT1/b0axvohLBRdIK06hQJdf7rEm3TA5uNGLzU3lTqAmmEjPaHxELK8qMcpGjqedYMflnZzNSiRfawprISFVvPdlMGIaf9UKNVE+kXQJ70R8g65Om823NtBr9eQk51OQUUhpoRazVZq1eZerEncfN3zDvHDz0Yh5VxXIskyJoZh8bS4lhmIMsv6mv3FRuOKh9sJL7Y1SYT9rBWH8EwgENUUY/wROh16rpzC7hKK8EowGGVmWUSgk1K4qPPzccPNydeqMZ3WFLMuUFmopzitFV6o3GQKvL/xdPVxw89bcFok86gKjwYiuSIeuRI9sNCLLpmN8CqWE2l2NWqMWfbYGyLJMSYGW/MwiSou0GA1GU1ZJpYTGyxUvf3dc3NVisVQDtMU6kk+ncu1UKqWFpRh0BpRqJa4eroS3CCGsaYjwnqwBsixTWqSjILOIkoJS0zsMGYVSgcbDBU9/dzSeLqLP1gCDbKBAl0+eNgedUYdRNiJJCpSSEm+1N94uPqgUos/WhNzSPC7mXCK1MBWdUY9RNqJWqPFx9SbaN5Jgj+DbIklKbVM27yop0GLQGZCNMpIkISklXN3VaDxdxcmKGmIwGtAaS9AatMjX/1MgoZCUuCo1qBX2O84K459AIKgpwvgnEAgEAoFAIBAIBAKBnSOMfwKBoKaIbTiBQCAQCAQCgUAgEAgEAoHASbmlM3pFRUWcOnWqruoiEAgEAoFAIBAIBAKBwAonT540+79AILi9ad68Oe7ufy1x6S0d+y1zMxYIBAKBQCAQCAQCgUAgEAgEtuFWQgDckvFPeP4JBAKBQCAQCAQCgUBQ/xQXF3PhwgWio6NxcxOZswWC25068/wTCAQCgUAgEAgEAoFAIBAIBI6DSPghEAgEAoFAIBAIBAKBQCAQOCnC+CcQCAQCgUAgEAgEAoFAIBA4KcL4JxAIBAKBQCAQCAQCgUAgEDgpwvgnEAgEAoFAIBAIBAKBQCAQOCnC+CcQCAQCgUAgEAgEAoFAIBA4KcL4JxAIBAKBQCAQCAQCgUAgEDgpwvgnEAgEAoFAIBAIBAKBQCAQOCnC+CcQCAQCgUAgEAgEAoFAIBA4KcL4JxAIBAKBQCAQCAQCgUAgEDgp/w9jFv4qiPAlBQAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 1625x480 with 19 Axes>"
       ]
@@ -386,10 +307,10 @@
     {
      "data": {
       "text/plain": [
-       "<plottable.table.Table at 0x7fbc6842dd20>"
+       "<plottable.table.Table at 0x7f477571fee0>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -401,11 +322,15 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "keep_output"
+    ]
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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MjIyMV14LAgICsGDBAoVtjx07FjExMcjKylJKb2ZmBh8fH3h7e6N3795YtWqVwnAQpd0XlfWeQEtLS+GceuvWLRARXFxcFMp5/vx5hXsiVn7erdHwGWM1gr6+vvCKDwA0b94cxsbG2Lhxo8pXeYlIIfBXuAyA0vJCurq6JW6/pO+q2k7Rm6XCdYUD3M6bNw9DhgzBsWPHcPz4ccydOxd//fUXPv74Y8jlcowfPx6TJ09W2n7t2rWF/9fX11daP3ToUEyZMgVr1qzBrl274O7ujsaNGwMoGP+kW7du6NatG3bs2AFLS0tER0fD29v7tQZbpyKvTZRWB8VvFkUiUYnfZYyxd8WrrmNXr17Fp59+ivnz58Pb2xvGxsb466+/sHz58lLzLe3aVeh1z8tlybMs10NV16viy+RyOZo3by48wCrK0tISYnH59Vt422s4Y6zq2rRpE6RSKezs7IRlRARNTU0kJyfD1NS01PuDop9flQZQfS6ztbVVOX5d0fECX5W3j48P4uPjsXLlSjg6OkJbWxuenp6vbLPL5XLMnz9fYVy+Qjo6Oiq/4+vri8mTJ+PEiRPYs2cPZs2ahdOnT6NNmzZlui9Stbzo/unq6ip8lsvlkEgkCAgIUBiaAiiYpImVP+75xxir9kQiEcRiMbKzs1Wub9iwIfz9/RUuTv7+/jA0NFRoFBTl4eGBM2fOqFzn7OwMLS0tXLp0SViWn5+PmzdvokGDBq9VdhcXF3z99dc4deoU+vXrB19fXwBAs2bNcP/+fTg7Oyv9Fe0loUrfvn2Rk5ODEydOYNeuXRg2bJiw7sGDB0hISMCSJUvQvn17uLm5KfViKMy/cHxDVRo2bAipVIpr164JyxITExEWFvbadcAYY++64texy5cvw9HRETNnzkSLFi1Qv359pd7tWlpaSudpDw8PPH36FGFhYeVWNg8PD1y8eFHlDJZGRkaoVauWwvUQKLjGvsm1oFmzZggPD4eVlZXStc/Y2BiGhoZwcnIq8foMFATsSrt+lXeZGWNVi1QqxbZt27B8+XKhh3VgYCDu3LkDR0dH4eGCm5ub0rigRSfeK0xz/fr1UtOo0qxZM8TGxkJDQ0PpXGZhYQEAcHV1fWXeFy9exOTJk9GzZ09hkqOEhASFNKrOec2aNUNoaKjK+4jSHqI0bdoU33//Pfz9/dGoUSPs2rULQOn3RW96T9C0aVPIZDLExcUpldHGxqbE77E3x8E/xli1k5ubi9jYWMTGxiIkJASTJk1CRkYGevfurTL9hAkT8OTJE0yaNAkPHjzAP//8g7lz5+Kbb74p8QL4/fff48aNG5gwYQKCgoLw4MEDrF+/HgkJCdDX18cXX3yBqVOn4sSJEwgODsbYsWORlZWF0aNHl2kfsrOz8eWXX8LPzw+PHz/G5cuXcePGDeEiOX36dFy5cgUTJ05EYGAgwsPDcfjwYUyaNOmVeevr66NPnz6YPXs2QkJCMGTIEGFd7dq1oaWlhTVr1uDRo0c4fPgwFi5cqPB9R0dHiEQiHD16FPHx8cjIyFDaRv369dGnTx+MHTsWly5dwp07dzBs2DDY2dmhT58+ZaoDxhh7V73qOubs7Izo6Gj89ddfiIiIwOrVq3Ho0CGFPJycnBAZGYnAwEAkJCQgNzcXXl5e6NChA/r374/Tp08jMjISx48fV5jJ9nV9+eWXSEtLw6effoqbN28iPDwc27dvF2avnzp1KpYuXYo9e/YgNDQUM2bMQGBgIKZMmfLa2xo6dCgsLCzQp08fXLx4EZGRkTh//jymTJkiDNMxb948LF++HKtXr0Z4eDhu3bqFNWvWKNTLmTNnEBsbi+TkZJXbKc8yM8aqlqNHjyI5ORmjR49Go0aNFP4GDBiATZs2AQDGjx+PBw8eYPr06QgLC8PevXuFGYELe6hNmjQJ//77L3799VeEh4fj999/x/Hjx0t8c6hQly5d4Onpib59++LkyZOIioqCv78/Zs2aJQT4Jk2ahE2bNmHr1q0IDw/HokWLEBQUpJC3s7Mztm/fjpCQEFy7dg1Dhw5V6oWn6pw3Z84cbNu2DfPmzcP9+/cREhIi9OZTJTIyEt9//z2uXLmCx48f49SpUwrBu7lz52L37t2YO3cuQkJCcPfuXSxbtgzAm98TuLi4YOjQoRg+fDgOHjyIyMhI3LhxA0uXLsW///5bav2yN1S5QwwyxtjbGTFiBAEQ/gwNDally5a0f/9+IU3xCT+IiPz8/Khly5akpaVFNjY2NH36dMrPzy91W35+ftS2bVvS1tYmExMT8vb2FgYAzs7OpkmTJpGFhQVpa2tTu3bt6Pr168J3VU2acfv2bQJAkZGRlJubS59++ik5ODiQlpYW1apVi7788kthMg+igok4unbtSgYGBqSvr08eHh4KA5Q7OjrSihUrVJa9cHKRDh06KK3btWsXOTk5kba2Nnl6etLhw4eV6mvBggVkY2NDIpGIRowYQUSKE34QESUlJdFnn31GxsbGpKurS97e3hQWFias9/X1VRgQmYjo0KFDxJcexti7rCzXMSKiqVOnkrm5ORkYGNCgQYNoxYoVCufUnJwc6t+/P5mYmBAA8vX1JaKCgdtHjhxJ5ubmpKOjQ40aNaKjR48S0Zufl+/cuUPdunUjPT09MjQ0pPbt21NERAQREclkMpo/fz7Z2dmRpqYmNW7cmI4fPy58V9U1ubAe+vTpo7StmJgYGj58uHB9rVu3Lo0dO5ZSU1OFNBs2bCBXV1fS1NQkW1tbmjRpkrDu8OHD5OzsTBoaGuTo6EhEyhN+vEmZk5OTCQCdO3eu1LpijKlXr169qGfPnirXBQQEEABhgqV//vmHnJ2dSVtbmzp27Ejr169XmJyPiOiPP/4gOzs70tXVpb59+9KiRYvIxsZGWK9q8iUiorS0NJo0aRLVqlWLNDU1ycHBgYYOHUrR0dFCmgULFpCFhQUZGBjQqFGjaPLkydSmTRth/a1bt6hFixakra1N9evXp3379im1/1Wd84iITpw4QW3btiVdXV0yMjKiVq1a0R9//KGyXmJjY6lv375ka2tLWlpa5OjoSHPmzFGYGPHAgQPUpEkT0tLSIgsLC+rXr5+w7k3uCYgKJkycM2cOOTk5kaamJtnY2NDHH39MQUFBKsvJ3o6IiAdfYowxxhhjjDHG2Ltr8eLF2LBhA548eVJimrFjx+LBgwe4ePFiuW+/a9eusLGxwfbt28s9b8Z4wg/GGGOMMcYYY4y9U9atW4eWLVvC3Nwcly9fxs8//4wvv/xSIc0vv/yCrl27Ql9fH8ePH8fWrVuxbt26t952VlYWNmzYAG9vb0gkEuzevRv//fcfTp8+/dZ5M6YK9/xjjDHGGGOMMcbYO+Xrr7/Gnj17kJSUhNq1a+Ozzz7D999/Dw2Nl32kPvnkE/j5+SE9PR1169bFpEmT8Pnnn7/1trOzs9G7d2/cunULubm5cHV1xaxZs1TO0MtYeeDgH2OMMcYYY4wxxhhjNRTP9ssYY4wxxhhjjDHGWA3FwT/GGGOMMcYYY4wxxmoonvCDVRl5eXmIjo5WdzEYY4xVEbVr14aWlpa6i/FKfP1ijDFWXFW5hvE1irGqrzLOF9zzjzHGGGOMMcYYY4yxGoon/GCMMcYYY4wxxhhjrIbinn+MMcYYY4wxxhhjjNVQHPxjjDHGGGOMMcYYY6yG4uAfY4wxxhhjjDHGGGM1FAf/GGOMMcYYY4wxxhiroTj4xxhjjDHGGGOMMcZYDcXBP8YYY4wxxhhjjDHGaigO/jHGGGOMMcYYY4wxVkNx8I9Ve1FRURCJRAgMDFTL9n18fNC3b1+1bJuxmkQkEuHvv/8GoPy79vPzg0gkQkpKitrKx1hxfMwyxqq6jh074quvvlJ3MRh7pS1btsDExETdxajy1H3vy6ovDv4xtVFn0KymB+xU7d/+/fuho6ODZcuWYd68eRCJROjevbvSd5ctWwaRSISOHTtWTmGrGa7bNxcXF4fx48ejdu3a0NbWho2NDby9vXHlyhUAQExMDHr06KHmUr6aqhspDvTUTHzMMsbeVmxsLKZMmQJnZ2fo6OjA2toa77//PjZs2ICsrCx1F69G8vHxgUgkgkgkgqamJurWrYvvvvsOmZmZQpoDBw6gY8eOMDY2hoGBATw8PLBgwQIkJSUBKAhEFeYhEolgbW2N3r174/79++rarUpTtP5EIhHMzc3RvXt3BAUFlTmPefPmoUmTJhVXyBrsVfXv4OCAmJgYNGrUSM0lZdUNB/8Yewf8+eefGDp0KH777TdMmzYNAGBra4tz587h6dOnCml9fX1Ru3ZtdRSzWuK6Lbv+/fvjzp072Lp1K8LCwnD48GF07NhRaGjb2NhAW1tbzaVk7CU+Zhljb+PRo0do2rQpTp06hR9//BG3b9/Gf//9h6+//hpHjhzBf//9p/J7+fn5lVzSmqd79+6IiYnBo0ePsGjRIqxbtw7fffcdAGDmzJkYNGgQWrZsiePHj+PevXtYvnw57ty5g+3btwt5GBkZISYmBs+fP8exY8eQmZmJDz/8EHl5eerarUpTWH8xMTE4c+YMNDQ00KtXL3UX651RWv1LJBLY2NhAQ0NDzaVk1Q4xpiYjRoygPn36EBHRvn37qFGjRqSjo0NmZmbUuXNnysjIICIimUxG8+fPJzs7O9LS0qLGjRvT8ePHhXwiIyMJAN2+fZuIiKRSKY0aNYqcnJxIR0eHXFxcaOXKlUL6uXPnEgCFv3PnzhER0dOnT+mTTz4hExMTMjMzo48++ogiIyOF70qlUvr666/J2NiYzMzMaOrUqTR8+HBhP6qKonW7dOlS0tbWpv379wvr586dS40bN6ZevXrRokWLhOWXL18mCwsL+uKLL8jLy0shz82bN5Obmxtpa2uTq6srrV27VmH9tGnTqH79+qSrq0t16tShWbNmUV5entI2t23bRo6OjmRkZESDBg2itLQ0IU1px0FVwXX7ZpKTkwkA+fn5lZgGAB06dIiIlH/X586dIwD033//UfPmzUlXV5c8PT3pwYMHCnmsW7eO6tatS5qamuTi4kLbtm0T1hXPs2i5Cs8BRET379+nHj16kL6+PllZWdGwYcMoPj6eiAr+/YufPwrzLfo3YsQIIiKSy+W0dOlSqlOnDuno6JCHhwft27fvzSuSVRo+ZvmYZexteXt7k729fYnXW7lcTkQF55L169fTRx99RHp6ejRnzpxXtmeJXrZJ5s2bR5aWlmRoaEjjxo2j3NxcIY2XlxdNmjSJpk6dSqampmRtbU1z586tsH2uCoq21QqNGTOGbGxs6Nq1awRAqS4LJScnExGRr68vGRsbK6w7fPgwAaCgoKAKKHXVoar+Lly4QAAoLi6OiEpvm/r6+ipdY3x9fYmooH7Hjh1LVlZWpK2tTe7u7nTkyBHhe8bGxnTixAlyc3MjfX198vb2pufPn1favlcFr6p/VW0DPz8/atmyJWlpaZGNjQ1Nnz6d8vPzK7fgrMrjnn9M7WJiYjB48GCMGjUKISEh8PPzQ79+/UBEAIBVq1Zh+fLl+OWXXxAUFARvb2989NFHCA8PV5mfXC6Hvb099u7di+DgYMyZMwc//PAD9u7dCwD47rvv8Mknnyg8UWnbti2ysrLQqVMnGBgY4MKFC7h06RIMDAzQvXt34Qnf8uXLsXnzZmzatAmXLl1CUlISDh06VDkV9QZmzJiBhQsX4ujRo+jfv7/S+lGjRmHLli3C582bN2Po0KHQ0tJSSLdx40bMnDkTixcvRkhICH788UfMnj0bW7duFdIYGhpiy5YtCA4OxqpVq7Bx40asWLFCIZ+IiAj8/fffOHr0KI4ePYrz589jyZIlAF59HFQ1XLevx8DAAAYGBvj777+Rm5v7xvnMnDkTy5cvx82bN6GhoYFRo0YJ6w4dOoQpU6bg22+/xb179zB+/HiMHDkS586dK3P+MTEx8PLyQpMmTXDz5k2cOHECL168wCeffAKg4Hzk6emJsWPHCucPBwcHHDhwAAAQGhqKmJgYrFq1CgAwa9Ys+Pr6Yv369bh//z6+/vprDBs2DOfPn3/jOmCVg49ZPmYZexuJiYk4deoUJk6cCH19fZVpRCKR8P9z585Fnz59cPfuXYwaNeqV7dlCZ86cQUhICM6dO4fdu3fj0KFDmD9/vkKarVu3Ql9fH9euXcOyZcuwYMECnD59uvx3ugrT1dVFfn4+du7cCQMDA0yYMEFlupLGnEtJScGuXbsAAJqamhVVzCopIyMDO3fuhLOzM8zNzQGU3jYdNGgQvv32W7i7uwvXnUGDBkEul6NHjx7w9/fHjh07EBwcjCVLlkAikQjbysrKwi+//ILt27fjwoULiI6OFnpsvqtU1X9Rz549Q8+ePdGyZUvcuXMH69evx6ZNm7Bo0SI1lJZVaWoOPrJ3WOFTjYCAAAJAUVFRKtPVqlWLFi9erLCsZcuWNGHCBCJS3TOiuAkTJlD//v2Vtl3Upk2byNXVVXgKS0SUm5tLurq6dPLkSSIisrW1pSVLlgjr8/Pzyd7evkr2/NPS0iIAdObMGaX1hT3F8vLyyMrKis6fP08ZGRlkaGhId+7coSlTpij0TnNwcKBdu3Yp5LFw4ULy9PQssQzLli2j5s2bK2xTT09PoTfa1KlTqXXr1kRErzwOqgqu2ze3f/9+MjU1JR0dHWrbti19//33dOfOHWE9ytiLqtCxY8cIAGVnZxMRUdu2bWns2LEK2xw4cCD17NlTZZ5Eyr2oZs+eTd26dVPI48mTJwSAQkNDiaigF8WUKVMU0hSWr7DHABFRRkYG6ejokL+/v0La0aNH0+DBg19dYUzt+JgtwMcsY6/v6tWrBIAOHjyosNzc3Jz09fVJX1+fpk2bRkQF55KvvvrqlXmqas+amZlRZmamsGz9+vVkYGBAMpmMiAp+/++//75CPi1btqTp06e/8b5VdcXb+deuXSNzc3P65JNPqEePHuTh4fHKPAp7r+nr65Oenp7Qg+2jjz6qwJJXDSNGjCCJRCIcpwDI1taWAgICSvyOqrZp48aNFdKcPHmSxGKxcG0qrrDOHz58KCxbu3YtWVtbv90OVTOvqv/ibYMffvhB6R527dq1CucBxoi45x+rAho3bozOnTvjvffew8CBA7Fx40YkJycDANLS0vD8+XO0a9dO4Tvt2rVDSEhIiXlu2LABLVq0gKWlJQwMDLBx40ZER0eXWo6AgAA8fPgQhoaGQo8PMzMz5OTkICIiAqmpqYiJiYGnp6fwHQ0NDbRo0eIt9r7ieHh4wMnJCXPmzEF6errKNJqamhg2bBh8fX2xb98+uLi4wMPDQyFNfHw8njx5gtGjRwv1YmBggEWLFiEiIkJIt3//frz//vuwsbGBgYEBZs+erVTnTk5OMDQ0FD7b2toiLi4OQOnHQVXDdftm+vfvj+fPn+Pw4cPw9vaGn58fmjVrptBD8lWK1qGtrS0ACPsZEhLy2ueK4gICAnDu3DmFfw83NzcAUPg3KYvg4GDk5OSga9euCvlt27bttfNi6sHHLB+zjL2tor37AOD69esIDAyEu7u7Qq9iVe3JsrRnGzduDD09PeGzp6cnMjIy8OTJE2FZ8fZH0TZCTXX06FEYGBhAR0cHnp6e6NChA9asWQMiUvo3KYmhoSECAwMREBCADRs2oF69etiwYUMFl7xq6NSpEwIDAxEYGIhr166hW7du6NGjBx4/fgygbG3T4gIDA2Fvbw8XF5cS0+jp6aFevXrC53fhWFXlVfVfVEhICDw9PRWO63bt2iEjI0Np/HH2buNRIpnaSSQSnD59Gv7+/jh16hTWrFmDmTNn4tq1a0LX5uIX6dIu3Hv37sXXX3+N5cuXw9PTE4aGhvj5559x7dq1Usshl8vRvHlz7Ny5U2mdpaXlG+6d+tjZ2eHAgQPo1KkTunfvjhMnTigEhwqNGjUKrVu3xr179xReRyskl8sBFLye2rp1a4V1hd30r169ik8//RTz58+Ht7c3jI2N8ddff2H58uUK6Yu/JiESiYT8SzsO6tSp8+YVUQG4bt+cjo4Ounbtiq5du2LOnDkYM2YM5s6dCx8fnzJ9v+h+Fp4DCvez6LJCRc8VYrFYWFao+KDqcrkcvXv3xtKlS5W2XRi4KavCch07dgx2dnYK63iSiOqDj9kCfMwy9nqcnZ0hEonw4MEDheV169YFUPAaalHFXw1+0/ZsoaLnltLaCDVVp06dsH79emhqaqJWrVpCHbi4uODSpUvIz89/5eu7YrEYzs7OAAA3NzfExsZi0KBBuHDhQoWXX9309fWFfQeA5s2bw9jYGBs3bkSvXr3K1DYtrvgxr4qqY5Wq6BBAFam0+h8zZoxCWlX3xYV1VtZAN3s31Oief4XTY7/OE/rXUXQK+KioqArZxrtCJBKhXbt2mD9/Pm7fvg0tLS0cOnQIRkZGqFWrFi5duqSQ3t/fHw0aNFCZ18WLF9G2bVtMmDABTZs2hbOzs1KPBS0tLchkMoVlzZo1Q3h4OKysrODs7KzwZ2xsDGNjY9ja2uLq1avCd6RSKQICAsqpFspf7dq1cf78ecTFxaFbt25IS0tTSuPu7g53d3fcu3cPQ4YMUVpvbW0NOzs7PHr0SKleCgNHly9fhqOjI2bOnIkWLVqgfv36Kp9MvUpJx0FVxHVbPho2bIjMzMxyyatBgwalnisKg/gxMTHC+sDAQIX0zZo1w/379+Hk5KT0b1J4Y6bq/FE4lmPR5Q0bNoS2tjaio6OV8nJwcCiXfWaVj4/Zqqljx4746quv1F0MxgTm5ubo2rUrfvvttzc6Z5SlPQsAd+7cQXZ2tvD56tWrMDAwgL29/VuVv7orDJ44OjoqBJSGDBmCjIwMrFu3TuX3UlJSSszz66+/xp07d6pE+6myiUQiiMViZGdnl6ltquq64+HhgadPnyIsLKwyi14jFK3/4ho2bAh/f3+FIKm/vz8MDQ2VHuSxiuPk5ISVK1equxilqrTgX8eOHYVAWePGjRXWJSYmQldXV1g/Y8aMMudbNADHqqdr167hxx9/xM2bNxEdHY2DBw8iPj5euPmZOnUqli5dij179iA0NBQzZsxAYGAgpkyZojI/Z2dn3Lx5EydPnkRYWBhmz56NGzduKKRxcnJCUFAQQkNDkZCQgPz8fAwdOhQWFhbo06cPLl68iMjISJw/fx5TpkwRukxPmTIFS5YswaFDh/DgwQNMmDCh1EZCVWBvbw8/Pz8kJiaiW7duSE1NVUpz9uxZxMTElDjI8bx58/DTTz9h1apVCAsLw927d+Hr64tff/0VQEGdR0dH46+//kJERARWr1792g2jVx0HVRHXbdklJibigw8+wI4dOxAUFITIyEjs27cPy5YtQ58+fcplG1OnTsWWLVuwYcMGhIeH49dff8XBgweFgaJ1dXXRpk0bLFmyBMHBwbhw4QJmzZqlkMfEiRORlJSEwYMH4/r163j06BFOnTqFUaNGCY1YJycnXLt2DVFRUUhISIBcLoejoyNEIhGOHj2K+Ph4ZGRkwNDQEN999x2+/vprbN26FREREbh9+zbWrl2rMKFLdSESifD333+rXOfn5weRSKRwPvz999/RuHFj6Ovrw8TEBE2bNlXonTZv3jw0adKkYgv9FviYrV7H7MGDB7Fw4UIArw4EFj+Wz507h06dOsHMzAx6enqoX78+RowYAalUCkD18V1T+fj4oG/fvgrL9u/fDx0dHSxbtgzz5s0T2t0ikQjGxsZo37690oQwTk5OCukK/5YsWaKUh6q/mvJQfd26dZBKpWjRogX27NmDkJAQhIaGYseOHXjw4IHCRAfFlaU9CwB5eXkYPXo0goODcfz4ccydOxdffvml0HOYKWrdujWmTZuGb7/9FtOmTcOVK1fw+PFjnDlzBgMHDiz1XGdkZCT0/q7pvdFyc3MRGxuL2NhYhISEYNKkScjIyEDv3r3L1DZ1cnJCZGQkAgMDkZCQgNzcXHh5eaFDhw7o378/Tp8+jcjISBw/fhwnTpxQ015WXaXVf3ETJkzAkydPMGnSJDx48AD//PMP5s6di2+++eadPA+86vryqjc3SmvvVnuVNbigl5eXwnTf58+fF9YtWbJEYd3rDEBbdCrx4gqXF04tXt6KbjsyMrJCtlGTFQ7GGxwcTN7e3mRpaUna2trk4uJCa9asEdLJZDKaP38+2dnZkaamJjVu3JiOHz8urC8+6GlOTg75+PiQsbExmZiY0BdffEEzZsxQGHQ2Li6OunbtSgYGBgoDp8fExNDw4cPJwsKCtLW1qW7dujR27FhKTU0looIJPqZMmUJGRkZkYmJC33zzDQ0fPrxKTvhRvEzPnz8nV1dXatmyJU2ZMkVpEN6iik9KQUS0c+dOatKkCWlpaZGpqSl16NBBYRDrqVOnkrm5ORkYGNCgQYNoxYoVZGxsLKxXNfDvihUryNHRkYjolcdBVcF1+2ZycnJoxowZ1KxZMzI2NiY9PT1ydXWlWbNmUVZWFhGVbfKEopMT3L59W+n8u27dOqpbty5pamqSi4sLbdu2TaEcwcHB1KZNG9LV1aUmTZrQqVOnFM4BRERhYWH08ccfk4mJCenq6pKbmxt99dVXwkDKoaGhQh5Ft79gwQKysbEhkUhEI0aMICIiuVxOq1atIldXV9LU1CRLS0vy9vZWuAZWF0X/fYor/u/z559/kp6eHv35558UHh5O9+7do127dtGsWbOE76g6bqsSPmar7zGraoKToor+u927d4+0tbVp6tSpdPfuXXr48CEdP36cRo8eTbm5uUSk+t+ypip+jdu4cSNpaWnRxo0biajgd+vu7k4xMTEUExND9+/fp5EjR5KBgQGlpKQI33N0dKQFCxYI6Qr/MjIyKD09XWGZvb29UlqpVFrZu15hnj9/Tl9++SXVqVOHNDU1ycDAgFq1akU///yzMFGHqvNrWdqzhf9ec+bMEdoJY8aMoZycHCGNqt9Dnz59hN98TaSqrVbcnj17qEOHDmRoaEj6+vrk4eFBCxYsEH7nvr6+Cm2tQo8fPyYNDQ3as2dP+Re8ihgxYoTCvbmhoSG1bNmS9u/fL6R5Vds0JyeH+vfvTyYmJgr344mJiTRy5EgyNzcnHR0datSoER09epSIVNf5oUOHVN7n12Svqn9Vk4H5+flRy5YtSUtLi2xsbGj69OmUn5+vpj1Qr6LXkpUrV5KRkZHCsqLXKlVKa++WxtHRkVasWPFmha4klR7809TUJAA0YMAAIiKSSqVUu3ZtYXnR4F9KSgpNnjxZWG9nZ0dff/21cKEs/sMo/Js7d27Bzv3/88qVK2nEiBFkaGhItWrVooULFyqULTExkSZMmED29vakoaFBVlZWNGzYMHr8+LFCutWrV1OtWrVIX1+fhgwZQitXruTgH2OMsRpDJpPRkiVLqF69eqSlpUUODg60aNEiInq94F+fPn3Ix8en1G1V9eAfq16KBjheJ/i3YsUKcnJyKjXvdzX4t3TpUtLW1la44Vf1u42OjiYAdP36dWHZ69wEVYcbpqqqLEEuxhh7V6kKKJf20NXR0VEhrlTYkeLhw4f00UcfkZWVFenr61OLFi3o9OnTCvlWh2tZpfcDbdKkCerWrYu///4bT58+xeHDhxEdHY0BAwYopMvNzUXHjh2xevVqxMXFoUGDBkhMTMSKFSvQu3dvEBHq1asnDJoLFHTjbt26tdIYF99//z1OnToFbW1tPH/+HLNnz8bp06cBADk5OfDy8sK6desQGxsLFxcXpKWlYceOHfD09ER8fDwA4MiRI5g8eTKeP38OPT09XLx4ETNnzqzg2mKMMcYqz/fff4+lS5di9uzZCA4Oxq5du2Btbf3a+djY2ODq1atvND4lY5XJxsYGMTEx78QA/q9jxowZWLhwIY4ePYr+/fuXmC43NxdbtmyBiYkJXF1dK7GEjDHG2Os5dOgQpkyZgm+//Rb37t3D+PHjMXLkSJw7dw4AhKEVfH19ERMTI3zOyMhAz5498d9//+H27dvw9vZG7969XznDdVVT6cE/sViMiRMnQiqVYv369VizZg0AYNKkSQrp/vrrLwQGBkJLSwtBQUG4c+eOMNHC2bNncfbsWcyePRuzZ88WvnP16lVcvXpVaQacxo0bIyoqCiEhIcKAr2fOnAEA7N69G/fu3QMA7Nu3D/fv38fly5chFovx/Plz/PbbbwCAZcuWAQDq1auHR48eITIyEi1btizv6mGMMcbUIj09HatWrcKyZcswYsQI1KtXD++//77SNbUs5s6dCxMTEzg5OcHV1RU+Pj7Yu3dvjZ9dklU/AwcOxODBg+Hl5QVbW1t8/PHH+O2331RO4vSuOH78OJYuXYp//vkHXbp0UVp/9+5dGBgYwMDAALq6uvjll1+we/duGBkZKaSbPn26kK7wz8/Pr5L2gjHGGFP0yy+/wMfHBxMmTICLiwu++eYb9OvXD7/88guAlxOtmZiYwMbGRvjcuHFjjB8/Hu+99x7q16+PRYsWoW7dujh8+LDa9uVNqGUEyFGjRkFfXx9r1qzBuXPn0Lx5c3h6eiqkuX79OoCCQWxdXFwgEokUBgYvOuPqqwwaNAhaWlqwsLCAlZUVAODFixcAXkZ39fT0hAGOmzVrJjy9vHnzJgDg/v37AABvb28YGBhAIpGgX79+r7nnjDHGWNUUEhKC3NxcdO7c+a3zsrW1xZUrV3D37l1MnjwZ+fn5GDFiBLp3784BQFalSCQS+Pr64unTp1i2bBlq1aqFxYsXw93dXWGW5XeJh4cHnJycMGfOHKSnpyutd3V1RWBgIAIDAxEQEIAvvvgCAwcOFNrMhaZOnSqkK/xr3bp1Ze3GO2HLli01d2B6xhgrZyEhIWjXrp3Csnbt2iEkJKTU72VmZmLatGlo2LAhTExMYGBggAcPHnDPv7IwMTHBsGHDhAZF8V5/AIQZlLS0tITXeYv+mZqavtb2CmloaCjk/7qKzir8pnkwxhhjVY2urm6559moUSNMnDgRO3fuxOnTp3H69GmlWUEZqwrs7Ozw2WefYe3atQgODkZOTg42bNig7mKphZ2dHc6fP4+YmBh0795dKQCopaUFZ2dnODs7o2nTpliyZAns7OywcuVKhXQWFhZCusK/ijjPMMYYY2VVNJ4DFMR0ii8rburUqThw4AAWL16MixcvIjAwEO+99x7y8vIqsqjlTm1zP3/55ZcAChoGn376qdL6Vq1aAQBkMhnWrVsnvNLr5+eHqVOnYsiQIQAKeuwVyszMfO1yFL66m5WVJTw5u3XrFkJDQwEALVq0AAC4u7sDAE6dOoXMzEzIZDJ+0laBOnbsiK+++qpMaf38/CASiZCSkvJW23RyclJquDLGqhY+N1Sc+vXrQ1dXVxgWo7w1bNgQwJtdq6uj1zlWy0tUVBREIhECAwMrdbs1jampKWxtbd+ZY1WV2rVr4/z584iLi0O3bt1e+Rq0RCJBdnZ2JZWOvY558+YpvD31KoVjOLLSvSttA3USiUR8v60mNbU90aBBA1y6dElhmb+/Pxo0aCB81tTUhEwmU0hz8eJF+Pj44OOPP8Z7770HGxsbREVFVUaRy5Xagn+NGjVCYmIiIiIioK2trbR+8ODB8PDwgEwmQ8uWLdGoUSO4urrCxMQEAwYMEG7m3NzchO80bNgQbdq0weXLl8tcjsGDBwuBvYEDB8Ld3R3t2rWDXC5HrVq1hCDld999BwAIDw9H3bp1UbduXfj7+7/p7jNWJfn4+EAkEmHJkiUKy//++2/hiUhhQMXU1BQ5OTkK6a5fvw6RSKTw9KS8AjA1ib+/PyQSCbp3766wvPBCW/hnbGyMNm3a4MiRIwrpuGHOKoKOjg6mT5+OadOmYdu2bYiIiMDVq1exadMmIU1kZKTSa3wZGRlKeX3xxRdYuHAhLl++jMePH+Pq1asYPnw4LC0tlYb5YKrxufPtxMfHKx2rsbGxSul+//13fPHFFzh16hQiIiJw//59TJ8+Hffv30fv3r3VUPKqw97eHn5+fkhMTES3bt2QmpoKAJBKpYiNjUVsbCzCw8OxaNEiBAcHo0+fPgrfT09PF9IV/r3LYylWBlXBku++++61HuoMGjQIYWFhwufXDR6+K27cuIFx48apuxg1QknHWExMDHr06FH5BXrH+Pj4CMOfFXJwcEBMTAwaNWqknkJVkKlTp2LLli3YsGEDwsPD8euvv+LgwYNCrAcoCOyfOXMGsbGxSE5OBgA4Ozvj4MGDCAwMxJ07dzBkyJBqOYyN2oJ/AGBmZqY0OHAhbW1tnD9/HpMnT4aDgwPCwsKQnJyMFi1aYPHixcLsgx4eHpg9ezasra0RHR2Na9euCf9IZaGjo4MLFy5gwoQJsLGxQVhYGIyMjDBs2DBcuXJFGOSxT58+WLFiBWxsbJCeno4WLVpg0aJFb18JjFUxOjo6WLp06St/R4aGhjh06JDCss2bN6N27doVWbwaYfPmzZg0aRIuXbqkcqyI//77DzExMbh27RpatWqF/v37CxMTMVaRZs+ejW+//RZz5sxBgwYNMGjQIMTFxQnrv/nmGzRt2lThr/g4XwDQpUsXXL16FQMHDoSLiwv69+8PHR0dnDlzBubm5pW5S+wdtWvXLqVjVdVrvK1atUJGRgY+//xzuLu7w8vLC1evXsXff/8NLy8vNZS8ail8BTglJQVdu3ZFSkoK7t+/D1tbW9ja2qJJkybYu3cv1q9fj+HDhyt8d86cOUK6wr9p06apaU/eXQYGBq913tXV1RXGSGfKCl/zs7S0VHgDraYr6fXG/Pz8CtumjY2Nyk5CrOBVValUWmH5SyQS2NjYCEOm1RR9+/bFqlWr8PPPP8Pd3R2///47fH190bFjRyHN8uXLcfr0aTg4OKBp06YAgBUrVsDU1BRt27ZF79694e3tjWbNmqlpL94CMVYFeXl50ZQpU4iIaPv27dS8eXMyMDAga2trGjx4ML148UJIe+7cOQJAR48eJQ8PD9LW1qZWrVpRUFCQQp6XL1+m9u3bk46ODtnb29OkSZMoIyNDWO/o6EgrVqwQPs+dO5ccHBxIS0uLbG1tadKkSRW6z1XBiBEjqFevXuTm5kZTp04Vlh86dIgKTxeF9T1r1izq0qWLkCYrK4uMjY1p9uzZVPTUUpg+OTm50vajKsvIyCBDQ0N68OABDRo0iObPny+si4yMJAB0+/ZtYVlaWhoBoNWrVwvLfH19ydjYuBJLXXVUhXMDY2Xh5eVFEydOpIkTJ5KxsTGZmZnRzJkzSS6XE1Hpx2/huaDo34gRI4iISCaT0ZIlS6hevXqkpaVFDg4OtGjRIoXvHThwgDp27Ei6urrk4eFB/v7+aqkDxqqT0n5bQUFB1KlTJ9LR0SEzMzMaO3YspaenC98dMWIE9enTh37++WeysbEhMzMzmjBhAuXl5QlpHB0dafHixTRy5EgyMDAgBwcH+v333xXK8PTpU/rkk0/IxMSEzMzM6KOPPqLIyEiFNJs2baKGDRuSlpYW2djY0MSJE4X8i54zHB0diaigPdu4cWMiIjpx4gRpa2srtckmTZpEHTp0ICLFNoavr6/SucjX15dGjhxJH374oUIe+fn5ZG1tTZs2baqw+l28eDFZWVmRsbExzZs3j/Lz8+m7774jU1NTsrOzU9h24flw9+7d5OnpSdra2tSwYUM6d+6ckEYqldKoUaPIycmJdHR0yMXFhVauXKlQ7sJt//jjj2RrayvU6+vcNyQlJdFnn31GJiYmpKurS927d6ewsDBhfWGdnzhxgtzc3EhfX5+8vb3p+fPnKuuyrMqjzovud2Gd7tmzh7y8vEhbW5s2b95MRESbN28mNzc30tbWJldXV1q7dq1CWZ48eUKDBg0iU1NT0tPTo+bNm9PVq1dLPMaIiADQoUOHhDzK43dY0fVV2rbXrl1Lzs7OpK2tTVZWVtS/f39hnVwup6VLl1KdOnVIR0eHPDw8aN++fcL6wjbtiRMnqHnz5qSpqUkbNmwgABQSEqKwH8uXLydHR0eSy+WvPMbnzp2rVP/nzp1TeU/i5+dHLVu2FM4906dPp/z8fGG9l5cXTZo0iaZOnUqmpqZkbW1Nc+fOLVPds8rBwT9WJRW9wd+0aRP9+++/FBERQVeuXKE2bdpQjx49hLSFJ8MGDRrQqVOnKCgoiHr16kVOTk7CCTcoKIgMDAxoxYoVFBYWRpcvX6amTZuSj4+PkE/Ri/i+ffvIyMiI/v33X3r8+DFdu3aN/vjjj0rbf3UpvHAdPHiQdHR06MmTJ0SkOvgXGhpK2tra9PjxYyIquJFt3LixQtqi6Tn4V2DTpk3UokULIiI6cuQIOTk5CcGA4hfavLw8Wr58OQGg9evXC3lw8G8KEann3MBYWXl5eZGBgQFNmTKFHjx4QDt27CA9PT3hWlLa8SuVSunAgQPCuTYmJoZSUlKIiGjatGlkampKW7ZsoYcPH9LFixdp48aNRPTyHOLm5kZHjx6l0NBQGjBgADk6Oio00Bljykr6bWVmZlKtWrWoX79+dPfuXTpz5gzVqVNHCMgTFbSfjIyM6PPPP6eQkBA6cuSIwu+dqOBaYmZmRmvXrqXw8HD66aefSCwWCzfumZmZVL9+fRo1ahQFBQVRcHAwDRkyhFxdXSk3N5eIiNatW0c6Ojq0cuVKCg0NpevXrwvXp7i4OCFwEhMTQ3FxcUSkGPyTSqVkbW1Nf/75p1CuwmWFgciibYysrCz69ttvyd3dnWJiYigmJoaysrLo8uXLJJFIFIJT//zzD+nr6ysERMqzfg0NDWnixIn04MED2rRpEwEgb29vWrx4MYWFhdHChQtJU1OToqOjiejl+dDe3p72799PwcHBNGbMGDI0NKSEhAQiKmhnzZkzh65fv06PHj0SztN79uxR2LaBgQF99tlndO/ePbp7967w71nW+4aPPvqIGjRoQBcuXKDAwEDy9vYmZ2dnoS3i6+tLmpqa1KVLF7px4wYFBARQgwYNaMiQISrrsqzets6L73dhnTo5OdGBAwfo0aNH9OzZM/rjjz/I1tZWWHbgwAEyMzOjLVu2EBFReno61a1bl9q3b08XL16k8PBw2rNnD/n7+5d4jBEpBv/K63dYkfVV2rZv3LhBEomEdu3aRVFRUXTr1i1atWqV8P0ffviB3Nzc6MSJExQREUG+vr6kra1Nfn5+RPSyTevh4UGnTp2ihw8fUkJCAjVv3pxmzZqlsB/Nmzen77//nohefYynp6fTJ598Qt27dxfqPzc3V+me5OnTp6Snp0cTJkygkJAQOnToEFlYWCgE97y8vMjIyIjmzZtHYWFhtHXrVhKJRHTq1Kky1T+reBz8Y1VS0Rv84q5fv04AhMZF4cnwr7/+EtIkJiaSrq6ucGL77LPPaNy4cQr5XLx4kcRiMWVnZxOR4kV8+fLl5OLiUuYnRTVFYfCPiKhNmzY0atQoIlId/EtOTqa+ffsKPdc6depEq1at4uDfK7Rt21Z44pafn08WFhZ0+vRpInrZUNXV1SV9fX0Si8VCIysxMVHIg4N/U1Suq4xzA2Nl5eXlRQ0aNBCC+0RE06dPpwYNGqhMX9LxW/TcmZaWRtra2kKwr7jCc0jRG/v79++r7BnAGHuptN/WH3/8Qaampgo9wo8dO0ZisZhiY2OJqKD95OjoSFKpVEgzcOBAGjRokPDZ0dGRhg0bJnyWy+VkZWUlPNzbtGkTubq6KpwzcnNzSVdXl06ePElERLVq1aKZM2eWuB/Fe0oRKQb/iIgmT55MH3zwgfD55MmTpKWlRUlJSUSk3MYo/v1CDRs2pKVLlwqf+/btq/DgrKjyql+ZTCakcXV1pfbt2wufpVIp6evr0+7du4no5flwyZIlQpr8/Hyyt7dXKHdxEyZMUOiRNWLECLK2thYCsIXKet8QFhZGAOjy5cvCsoSEBNLV1aW9e/cS0cselg8fPhTSrF27lqytrUss56uUR50X3+/COi3eO9LBwYF27dqlsGzhwoXk6elJRES///47GRoaKrRliyrpGCt6PJfX77AkFX0OOHDgABkZGVFaWppS/hkZGaSjo6PUS3/06NE0ePBgInrZJvj7778V0vz6669Ut25d4XNoaCgBoPv375e4r6qO8cL7v0LFg38//PCD0vlp7dq1ZGBgIPwuvby86P3331fIp2XLljR9+vQSy8Iql1rH/GOsLG7fvo0+ffrA0dERhoaGwjv5xcdKKzqIvJmZGVxdXRESEgIACAgIwJYtW2BgYCD8eXt7Qy6XIzIyUmmbAwcORHZ2NurWrYuxY8fi0KFDFTquQlW0dOlSbN26FcHBwSWmGTVqFLZs2YJHjx7hypUrGDp0aCWWsPoJDQ3F9evXhRnONTQ0MGjQIGzevFkh3Z49e3D79m0cPnwYzs7O+PPPP2FmZqaOIldp6jg3MPY62rRpozABkqenJ8LDwyGTycp8/BYVEhKC3NxcdO7cudTtenh4CP9va2sLAApjNzLGFJX22woJCUHjxo2hr68vLCucHDA0NFRY5u7uDolEIny2tbVV+t0V/W2KRCLY2NgIaQICAvDw4UMYGhoK1yMzMzPk5OQgIiICcXFxeP78+St//68ydOhQ+Pn54fnz5wCAnTt3omfPnjA1NX2tfMaMGQNfX18ABeeXY8eOYdSoUSrTllf9isUvb12tra3x3nvvCZ8lEgnMzc2V6rxoG0BDQwMtWrQQ2gAAsGHDBrRo0QKWlpYwMDDAxo0blc7D7733HrS0tEqsi9LuG0JCQqChoYHWrVsL6c3NzRXaIgCgp6eHevXqCZ9VHT+vozzqvKT9btGihfD/8fHxePLkCUaPHq3Qllq0aBEiIiIAAIGBgWjatOlbtWXL83dYUv4VeQ7o2rUrHB0dUbduXXz22WfYuXMnsrKyAADBwcHIyclB165dFeqwcPK1oorWPQB8+umnwsRqQMHvuUmTJmjYsKGQpizHeFnqx9PTU6FN065dO2RkZODp06fCsqLnuOJ1wNSvZo3gyGqczMxMdOvWDd26dcOOHTtgaWmJ6OhoeHt7lzjwbFGFJyi5XI7x48dj8uTJSmlUTVDh4OCA0NBQnD59Gv/99x8mTJiAn3/+GefPn4empubb71g10KFDB3h7e+OHH36Aj4+PyjQ9e/bE+PHjMXr0aPTu3ZsH8n+FTZs2QSqVws7OTlhGRNDU1FSYYMXBwQH169dH/fr1YWBggP79+yM4OJgH4C5CXecGxspDTk7OGx2/urq6Zcq/6HWq6LHOGFOttN8WESnc8BZVdHnx9qFIJFL63ZWWRi6Xo3nz5ti5c6fSdiwtLRUCX2+jVatWqFevHv766y988cUXOHTokBDEex3Dhw/HjBkzcOXKFVy5cgVOTk5o3769yrQVVb9lqfPS8t27dy++/vprLF++HJ6enjA0NMTPP/+Ma9euKaQvGvRRpbT7BiJS+Z3i+61qX0r6blmUR52XtN9FlxfW98aNGxUCnACEQFhZr12lKc/foSoVfQ4wNDTErVu34Ofnh1OnTmHOnDmYN28ebty4IaQ5duyYwj0CAKUJT4r/m9ja2qJTp07YtWsX2rRpg927d2P8+PHC+rIe46+iqg4Kj8/yqH9WObjnH6vSHjx4gISEBCxZsgTt27eHm5tbiU8PCp94AEBycjLCwsLg5uYGAGjWrBnu378PZ2dnpb+SnuTp6urio48+wurVq+Hn54crV67g7t275b+TVdiSJUtw5MgR+Pv7q1wvkUjw2Wefwc/Pr8SnvayAVCrFtm3bsHz5cgQGBgp/d+7cgaOjo8rGPgB4eXmhUaNGWLx4cSWXuGpT57mBsbIqeuwVfq5fv36Zjt/C408mkwnL6tevD11dXZw5c6biC8/YO6S031bDhg0RGBiIzMxMYdnly5chFovh4uJSbmVo1qwZwsPDYWVlpXQ9MjY2hqGhIZycnEr9/WtqaiqcM0oyZMgQ7Ny5E0eOHIFYLMaHH35YYlotLS2VeZqbm6Nv377w9fWFr68vRo4cWWIe6qzfoudhqVSKgIAAoQ1w8eJFtG3bFhMmTEDTpk3h7Oys1NOqrEq6b2jYsCGkUqlCsCUxMRFhYWFo0KDB2+1cKSqrzq2trWFnZ4dHjx4pHbd16tQBUNAbLDAwEElJSSrzKOkYq6gyq1IZ9aWhoYEuXbpg2bJlCAoKQlRUFM6ePYuGDRtCW1sb0dHRSnXo4ODwynyHDh2KPXv24MqVK4iIiBDeMALKdoyXtf79/f0VAtL+/v4wNDRUCliyqouDf6xKq127NrS0tLBmzRo8evQIhw8fxsKFC1WmXbBgAc6cOYN79+7Bx8cHFhYW6Nu3LwBg+vTpuHLlCiZOnIjAwECEh4fj8OHDmDRpksq8tmzZgk2bNuHevXt49OgRtm/fDl1dXTg6OlbUrlZJ7733HoYOHYo1a9aUmGbhwoWIj4+Ht7d3JZas+jl69CiSk5MxevRoNGrUSOFvwIAB2LRpU4nf/fbbb/H777/j2bNnlVjiqk1d5wbGXseTJ0/wzTffIDQ0FLt378aaNWswZcqUMh2/jo6OEIlEOHr0KOLj45GRkQEdHR1Mnz4d06ZNE14Hunr1aqnnD8bYq5X22xo6dCh0dHQwYsQI3Lt3D+fOncOkSZPw2WefwdrautzKMHToUFhYWKBPnz64ePEiIiMjcf78eUyZMkV4rW7evHlYvnw5Vq9ejfDwcNy6dUuhjVYYHIyNjVV4o0DVtm7duoXFixdjwIAB0NHRKTGtk5MTIiMjERgYiISEBOTm5grrxowZg61btyIkJAQjRowoMQ911u/atWtx6NAhPHjwABMnTkRycrLwwNrZ2Rk3b97EyZMnERYWhtmzZ+PGjRuvvY3S7hvq16+PPn36YOzYsbh06RLu3LmDYcOGwc7ODn369Hnr/StJZdb5vHnz8NNPP2HVqlUICwvD3bt34evri19//RUAMHjwYNjY2KBv3764fPkyHj16hAMHDuDKlSsASj/GClX0cVLR9XX06FGsXr0agYGBePz4MbZt2wa5XA5XV1cYGhriu+++w9dff42tW7ciIiICt2/fxtq1a7F169ZX5t2vXz+kpaXhiy++QKdOnRSCcWU5xp2cnBAUFITQ0FAkJCQgPz9faRsTJkzAkydPMGnSJDx48AD//PMP5s6di2+++abceiWzisf/UqxKs7S0xJYtW7Bv3z40bNgQS5YswS+//KIy7ZIlSzBlyhQ0b94cMTExOHz4sNBzwsPDA+fPn0d4eDjat2+Ppk2bYvbs2cJYSMWZmJhg48aNaNeuHTw8PHDmzBkcOXLknXytdeHChaW+dqClpQULC4sSu8OzAps2bUKXLl1gbGystK5///6lPhHt1asXnJycuPdfEeo6NzD2OoYPH47s7Gy0atUKEydOxKRJkzBu3LgyHb92dnaYP38+ZsyYAWtra3z55ZcAgNmzZ+Pbb7/FnDlz0KBBAwwaNIjH02GsHJT029LT08PJkyeRlJSEli1bYsCAAejcuTN+++23ct2+np4eLly4gNq1a6Nfv35o0KABRo0ahezsbBgZGQEARowYgZUrV2LdunVwd3dHr169EB4eLuSxfPlynD59Gg4ODmjatGmJ26pfvz5atmyJoKCgV47X3L9/f3Tv3h2dOnWCpaUldu/eLazr0qULbG1t4e3tjVq1apWaj7rqd8mSJVi6dCkaN26Mixcv4p9//oGFhQUA4PPPP0e/fv0waNAgtG7dGomJiZgwYcJrb+NV9w2+vr5o3rw5evXqBU9PTxAR/v333wofSqiy6nzMmDH4888/sWXLFrz33nvw8vLCli1bhJ5/WlpaOHXqFKysrNCzZ0+89957WLJkifBacGnHWKHK+B1WZH2ZmJjg4MGD+OCDD9CgQQNs2LABu3fvhru7O4CC+605c+bgp59+QoMGDeDt7Y0jR44IdVgaIyMj9O7dG3fu3FH6PZflGB87dixcXV2FcQEvX76stA07Ozv8+++/uH79Oho3bozPP/8co0ePxqxZs8pcB0z9RPQ2gwkwxhhjjDHGGHvnZGVloVatWti8eTP69eun7uIoiIqKQp06dXD79m00adJE3cVhjDG14wk/GGOMMcYYY4yViVwuR2xsLJYvXw5jY2N89NFH6i4SY4yxV+DgH2OMMcYYY4yxMomOjkadOnVgb2+PLVu2QEODbykZY6yq49d+GWOMMcYYY4wxxhiroXjCD8YYY4wxxhhjjDHGaigO/jHGGGOMMcYYY4wxVkNx8I8xxhhjjDHGGGOMsRqKg3+MMcYYY4wxxhhjjNVQHPxjjDHGGGOMMcYYY6yGeq152bOysvDgwYOKKgtjjDHGGGOMMcZUyM7ORlRUFJycnKCrq6vu4jDG1MzNzQ16enplSvtawb8HDx6gefPmb1QoxhhjjDHGGGOMMcbY2wsICECzZs3KlFZERFTWjLnnH2OMMcYYY4wxVvlCQkIwbNgw7NixAw0aNFB3cRhjalZhPf/09PTKHFVkjDHGGGOMMcZY+WrQoAHflzPGXstrBf8Yq+rSctIRlhCBsIRHiE5+ihxpDmRyObQkmjDWNUJ9i7pwsagHJ1MHSMQSdRe32pCTHLEZLxCV+hiPU6KRmpuGPFkexCIxtCRasDGwhqNxbTiZOMJY20jdxa1WsvOzEZ32BFEp0XiW/hy5slzI5DJoiDWgr6WH2kYOcDSuDTvDWtCUaKq7uNUGEeF5WixC4yMQGv8QcRkJyJXmQQRAW0MbtYys4WLpDFfLerAysIBIJFJ3kasNOcmRJ8tBrjwHebJcyEkOAkEE0f/PCTrQFutAS6INsYjnFSsrIgIRQU4yyORyEMlR+G6GSASIRGJIxGKIRRKIRCI+Zl8DEUGWLyv4y5MJdS2CCCKxCGINMTS0NCDRlEAk5np9HSQnyOVyyGVyyOVyhXUikQhiiRhisRgiMR+zr0Mml+Fx8hOEJUQgPPER0nLSkSfLg0QkgY6mDmqb2MHFoh6czevCSMdQ3cWtVuQkQ54sD/nyPEjlUhD9/7gViSARiaEp1oKmWAsaYk0+ZhljNQoH/1i1l5aTjpNh5/BP8Ak8T4sFAIhFYogAyIle3pSKxZDJZQAADbEG2tRujr7uPdG0ViO+uKtARHiUEoWzUedw8/kt5MnzAQASkUS42QcAMcSAqCAgAABmOqbwcmwPr9rvw1jHWG3lr8pypDm49uwGzkWdx9P0ZwBQcBMqEhXclBYJpMio4JgVi8RwM3fFB3U6wsOqEQdVSvAo8TEO3fsXp8L8kJWfDQCQiCX/D6oUHKMF5weRULcmOkb4sEFXfOTujVpGNmore1VGJEemNB2pecnIleWUnjg/VfhfbYkOjLXMoK9hyOfZEsjlcuTL8pAvzX9l2qIpNCWa0NDQ5AdZJSAiSHOkyM3MhTRXWubviTXE0DbQhpauFgcCS0BygjRfCmm+FK8cPajIQSvRkBQEWSV8zKpCRLj9/C4OBx/HtSe3IJUXHLcSsQRy+cuHLIXn0sJrmp2RLXo38EY3l04w1DZQW/mrMjnJkJWficz8DMio7OcDHYku9DUNoSXR5msYY6zae60x/xirSmLSXmD7rX347+EFyOQyIRhVVhKRGDKSo5aRDT7x6IMP3brwTRQKGp/Xnt/AsfCTeJr+FGKRWGhglpXo//81s22CPi69YG9kV0GlrV7SctNwNPw4Lj3xR54sDyKIXuu4Lfy3MNE2Ruc6H6BLnU7cG/D/rj4OwNaAPbgX+0D4bb+OwrptXbsZfFp8ikY2bhVU0upFTjKk5CYhLS8ZcrxenRYlhhhGWqYw0TaDWMTnWQCQyaTIk+YJD6XelFgkhpamNjQk/DwXKLiG5WbkIjcjFyR/iyauCNDS04KOoQ7EEn7YAgBymRz5efmQSd/umBWJRdDU0oREQ8IBFRT08vv3wWnsu3sYMekv3rjdpSGWoLOzF4Y1HQBrQ6sKKm31IpVLkZ6Ximxp5lvlIxFpwEDLCHoa+mo/Zm/duoXmzZu/1iD/jDEGcPCPVUNykuNI8Emsv7oVMrn0tW/yiysMwDSwcsGMjpPhYFKrnEpa/SRnJ2Pzne24F3//tQNTqhT2Tuvr0hs9nb3f2eAqEeFGTAC2B+1C7v9fk3xbIohgbWCNMU184GTiWA6lrJ7SctKx8uIfOB1+/o1umIorzGNQ4z4Y02oYdDS1y6mk1U+WNBPx2TGv1UviVSQiDVjp2kJXQ7/c8qxuiAh5+bnIl726p9/r0BBrQFtLR+03puokzZMiKzkLcunbn2MLiUQi6JrqQktXq9zyrG6ICNI8KfLzyveYFUvE0NLRglj87gZXo1OeYpnfaoQmRJRLfhKRGBpiDXzeZiQ+dOv6zp4PiAhZ+RlIzUsB3rItW5SmWAumOubQEKvvwSsH/xhjb4qDf6xaictIwI9nVyIoNrjc85aIxBCJxBjX+jP0b9TrnWsw+T+9im1Bu5Avzy+X4FRxDkb2+KLZWNgavluvVWbkZWJr0A7cjg0sl4BqUWKIQSD0dPZGH9fe79yrwFce38TiMyuRnptR7sesCCLYGFphbrfv4G7tWq55V3VykiMx5wXSi7y+W94MNY1hrmP9zh2zMpkMOXnZ5XoeKEoEEbS1dN65XoBEhJy0HORm5FbYNjR1NKFrqvvOBarkMjlyc96yF+UraOloQUPz3TtmD9w7gk03dhSMSVkB7a4mto0wveNkWOibl3veVZlMLkVyTiLy5BV3PjDSMoW+poFa7hU4+McYe1Mc/GPVRnTKM3x7dA5SslMrpJFU1MfuPTGx7ah34saUiHDs4XEcePBPhW5HLBJDW6KN7zy/Ql0TpwrdVlWRkpOKX66uRFxmXIUEVItqZtMU45qNgob43biBOhZyGkvP/QYAFRZIEYvEkIgk+KnnD2hdu3mFbKOqkZMMMVlPXj2uXznQkejCRs/+nXkNWCqTIicvu1K2pa2pA02Nd2NIACJCZlImpDnl10O1JGINMQwsDN6Z14BlMhlysyougFKUppYmNLXfjWNWTnKsufwnjj44WaHbkYjEMNU1wc8fzoe98bvxVotUno+E7BcV3uYCAH1NAxhpmVZ6AJCDf4yxN/VutF5YtfcsNQZTDs+slMAfABy6/y9WX9r46oGsa4Aj4f9WeOAPKGjs5kpzsNR/OaJSHlf49tQtLTcNS/1/qZTAHwDcjg3EhoCNbz1+WHVwLOQ0lpxbA/r/fxVFTnJI5VJMP7YQ16JvVdh2qgo5yRGTWTmBPwDIkWUjJvNJpfw+1K0yA38AkJufU6YJRKo7IkJmYuUE/gBALpUjIz4DclnNP2YrM/AHAPl5+cjPfTeO2ZWXNlR44A8AZCRHUnYKvjoyEzH/nxCvJpPK8xGfVTmBPwDIzM9Aam7yO3GvwBirGTj4x6q81Jx0fHN0DtJzMyol8FfocMhJ7Li9r9K2pw7nH1/CodDDlbY9OQj5snz8fGUF4rMSKm27lS1flo9fr65GYnZSpTVCCYTAF0HYcW93pWxPXfyjbgg9/ioDgSAnwvf/LkZo3MNK225lIyLEZj1FrrxyAn+FcuU5eJH1rEbfPMnkskoN/BXKzc+BVFY5QTF1yUrOeq2ZfMuDXCZHRmJGjT5m5XJ5pQb+CuXn5UOaX7OP2a23/sLx0DOVtj05yZGRm4Hvjs1Fem5GpW23sslJhoTsONBbTEz1JrKkGcjIT6vUbTLG2Jt6N94PY9XamssbkZiVrJbeIVtu7kFL+6Zws6pf6duuaHGZ8diphkARgZAjy8Wm21swre03NfLV6n/CjuJZ+vMK7ZVWkovRl9HY2gNNrD0qfdsVLTUnDT+eXVnp2yUQZCTDgv+Ww3fQamjVwBmW0/KTkSPLUsu2s2WZSM9PgZGWqVq2X5GISC2Bv0I5ednQ11HPuFQVLS87D/nZ6ukpJs+XIyctB7rGumrZfkUiIuRl56lt+3k5eRBLxDVybMXguDDsun2g0rcrIzkSspKw7somTO84pdK3XxlScpIhJ/W8+ZCelwptiQ60JJU/QZjLxguVvs3yFja2g7qLoKTuuP3qLkKle/THgErZjtOs45WyHXWJWtRD3UUoVc27srIa5VLUNZyNuKS218JEIhF+OrcKeeU8K6O6yUmOP2/7VmpPyuLbD00Kh9/ji2rZfkWKSH6EkxGn1RL4AwoG/N96Zzsy8zLVsv2K9OuF35Gem6mWupWTHE9SnmPLjb8qfdsVLV+eh6SceLWWITEnDvly9QUcKkqeNFftPcRy8yq3N2dlkMvkyE5WX1AVAHIzciHNq3m91KT5Usjl6n2tOS8nT+2/m/KWK83F0nOr1BaIl5Mc/z28gCuPb6hl+xUpW5qltodXhZJzEmvcMcsYq3k4+MeqrIzcTPxyfh1EUF+PBTnJ8TQ1Bjtu1azXf/0eX0B4coTax9r66/4+JGYlqbUM5Ukql2HT7S1q7WVDIGTmZWJPcM16ankp8hrOPryo1mOWQNhxez/C4iPUVobyRkSIz45RW7BaKAcI8dk1a0wqmVxWJcbdk8qlNe713+zU7Cpxo52VnFUlylFe5HJ5lRh3Ty6TQ5Zfs8av3XF7H2LSK288OlVEEGH5xXXIzFNvoKw8yUmOlBz1tyNlJEV6Xqq6i8EYY6Xi4B+rso6HnkV6bkaVuCndf/cIsvPV28ugvMhJjiNh/6q7GAAAGclwJuqcuotRbgJjAxGXFa/2oKochCtPryEpO1mt5ShPW2/ugbgKvLoohgi7bh9UdzHKTa4sBzmyqnFuy5FlVZmylIe8/MofM60kVaksb0smlantdd/i5FI58nOqRlnKQ1XqyZifl19jAquZeVk4dO9YlWjPpuWk43S4n1rLUZ6y8jMqfZy/kmTmp6u9/ccYY6Xh4B+rkuQkx6H76m8oFcqR5uLMw5rximrQi7tIya0aTyflJIff4wvIk9WM1/3ORPlBrMaeqkWJAFyIvqTuYpSL0PgIPIh/CHkVuBGUkRx+EZeRlFUzAqupeVVrP9Jyq1Z53pSc5FVq5u2qVp63kZdZta4XeRlVqzxvioiq1GQbRFRjZlU+8/ACcqtQO+fQ/WM1IrBKRMjMT1d3MQQEQra05vSqZIzVPBz8Y1XSrWdBiE2PU3cxBCKIcODe0RrRWPovyq9KTbKRLc3BjecB6i7GW3ueHoPwpIeQV5GAtRwEv8cXIJVXnZu5N/X3vX8hEUnUXQwBgXA0+LS6i/HWZHIpMqVVa5bCDGlajQhSSavA677FVYVXkN8WESE3s2r1YpTmSWvEK6pVcR/y82rGMXvo/jG1DmFTFIHwPC0Wd2Luqbsoby1XlgOZmib5KElmfnqNuFdgjNVMVScCwFgRZx5ehKQKBagIhMfJTxGV/ETdRXkrGXkZuB8fXKVeSxBBBP+nV9VdjLd2/fmNKhVUBQr+vR8khKm7GG9FJpfhdPj5KtXAlxPheOgZdRfjrWVKq06PiaKqarleR34VnCRKKqv+r1FKc6WoIs9XFKhzdtzyIpVWvQdFcpm82h+zkUmP8TT1eZV5kwUAJCJJjXibpSr2spPK8yGlqvdbYowxANBQdwFeFxGhXr16iIyMBAD4+PjA19dXIc28efMwf/58AECXLl1w+vTLHho2NjZ48eIFRowYAV9fX9StWxdRUVHo1KkTzp49q5DP+fPn0bFjRwDAli1b4OXlhTp16gAA5s6di3nz5lXQXrL7L0JLnIn2o4bdMcijD8z1TBGV/ARrr2zG3dgQlWmneX2J7q4fKC2PSorGqP1fAQCcTB3g0+JTuFjUg42hFdb6b8aBe0dV5heWEIE6ZrXfbKeqgKiU6BLXdXL0Qg/nbjDRNsaz9OfYdX8vwpMeqkw7uskIvO/QVmn5s/TnmOVX8NtrZ++JMU19lNKMPTZRoTcagRCZEgUiUutEGW/rUXJkiUHVjo4d4F2vK0y0jfE8PQZ/Be8rsW5dzetjquc3SstnnZuH2MwXAACJSIwezt3R1r4NTHVMEJv5AvtDDuF+fLDCd8QiMaJSH6ORVcO33Dv1iU55hlyp6hvrvu49MLhpv4JzQVI0Vl/+E0ExwSrTAoCmWAM+LT9FN5eOMNMzRXxGArYF7MO/D/4DUHAuGN1qKFwt68HWyBqrL/2JfUGHVeb1LDUGWXlZ0NPSe/udVJNcmepZYK9fvoGNqzfjXuB9xMXGY/3ONejWq0upeV27dB2Lf1iK8AcPYW1jhXFTRmPI6E8V0pz45xRWLF6N6Mho1K5TG9/MngLv3l3LXK7qgohKDFhs/H0jVq1cjdjYWDRo0ABLfl6Cdu2Uz6WF9vy1BytXrELEwwgYGRmhS9cuWPzTIpibmwtp1v62Fn9u3ISnT57C3NwcfT/ug3kL5kFHR0dl2arzeba0Men+3Pon1mxYgxdxL+Dm4oYf5/2Itq1V123si1jMWjALd+7eQURkBMaPGo+f5v+klO7wscP48ZcfEfk4EnUc62DWtFno1aOXUjpZXtV5OPEmXvWK7R8b/8DK1SuF43bZkmVo17adyrT+V/wxe85shIWFISs7C7UdamPUyFGY9OUklen37d8Hn1E+6PVhL+zZvUdpvVwmh0Sj6vT8fl2hCaqv9QDQu4E3Bnr0gbmuKaJSnmD9FV/ce6G6PQsAHzXojj7uPWBtYIm4jATsCjyA/x6eF9a/79Qagxv3Qy0jW0jEEjxPi8H+u0cU0gAF4y2HxIW+/c6pWZ6s5F7AWzZuw4ZVvyMuNh4uDepj/pK5aN2ulcq0X43/Fvt2KU+S5uJWH+duFLQP/v3nONYsX4uoR4+Rn5+POvXqYPyksRgwuJ/S9/JludAUa77hXqlPit8hJJ/8C7LUJGjVcoLloC+hW79xienl+XlIOroV6ddOQ5aWBA0TS5j2HAbj9z8U0qQHnEfS4U3Ij38OTctaMO87BgZNO1TG7lRLacEnkXrnH8iyU6Bpag/zNiOhY9tAZdp4v9+QEX5eabmmiT3sB64AAGRGXkNK4EFI02JBchk0jWxg5NEbhvW9KnQ/1C395lGkXj0AWUYStCwdYdp1HHRqNyoxPUnzkXJxFzLvnYUsMxkahhYwbvcpDJp0E9KkXf8b6QHHIEuLh1jXCHoN3odpJx+INLQqY5fKTbUL/vn5+QmBPwDYv38/fvvtN+jr66tM/99//+Hs2bP44APlAJBIJMLw4cOxYMEC+Pn54cmTJ3BwcBDWb9++HQBgYGCAAQMGID4+vpz3hqmSnZ+DZ6kxKtd1rNsOEz1HYtWljbj3IgS9G3hjSY9ZGLl3CuIyE5TSr/XfjI3XdwifJWIJNvb/FecjrwjLtDW0EZP2Aucf+WOC56gSyyURSRAa/xDeLp3eYu/UKyr1McQQQ15scORWtVpgSKNPsP3uLoQnRaCjYwd803oSZvrNUzlpxK57e7Av5JDwWSISY4HXbKXXd7Pys/H9uTkKy1S9hpotzUFCdiIs9SzeZvfUhogQmfJY5bqWts3xqftA7Lz7Fx4mR6BD7faY0moi5vgtQFJOyeObzTw3F9nSl0GQ9NyXvaH6un6ENvatsS1oB2IyXqCRZUNMbDEeP13+GU/SniqUK6qEclUXoSXMrPuB8/uY/P4Y/HphA+7GhuCjht3xc6+5+Gz3RMRlKJ8LAGC+93SY6Zpgybk1eJYaA1NdY0jEL28qdTS1EZMWC7+Iy5jUbnSp5SIA4QmRaFzL/Y33Td1KmlwjKysbbo1cMWDox5jw2ZRX5vMk6ilGD/wcg0YMwK8blyHg6i3M/XYhzCzM0L1PQcPp1vXbmDzyG3w9czK69e6CU0f+w2Sfb7Dn5A40aaF4c5FbzSf9KOm15QP7D2D6tBn4deWvaOPZBr6bNqN/3/64ceu6QtujkL//FYwbMx5Llv2EHj174PnzGHw1+St8OWESdu/ZBaAgODh39jys27AWrdu0xsPwh/h83BcAgCXLlqgsm1hctXoov46SgmwHDx/ED/N+wC+Lf0Hrlq2xZccWfPLZJ7hy7goc7JTrNi8vDxbmFvh28rdYt3GdyjyvB1zHqAmj8MPUH9Crey8cPXEUI78YieMHj6NFsxZK5arOgdXSetftP7Af02ZMw8pfV6JNmzbYtHkTPu7/MQKuB6g8bvX09DB+3Hg0atQI+nr68L/ij8lfTYa+vj5GjVRsY0VHR+OHWT+UGEgEqn/wLzzhESRiidJ5watuW3zRZiTW+G/E/RcP8KFbN/zYfSZG7/8K8Sras70aeGNUy6FYcWk9QuMj4GbpjK/f/wIZeZm4Gn0TAJCWm4FdgQfwJPUZ8mVStKndAt91mIiU7FTcfBaokN+T1GfIleZCW0O7wva9IslJDlkJPez+OXAE86YvwI+/LkTLNi2w3XcXhvUfAb8b/8HOwU4p/YJlc/HDgunCZ6lUhq6e3dHr45dBLBMzE0z+7ks4u9SDppYW/jtxBt988R0sLM3RsYtiICVfXv16AqffOIv4Pb/BasjX0HVuhNQLR/Bs9XQ4ztsKTXNrld+J/WMeZGnJsB4+DZpWdpClp4CKHOfZEfcQu3E+zPuMgkGT9sgIvIiY3+fBYdpv0KlbfR9KV5SMiMtIvOILi3ZjoW3tivQHpxF7YjHsB66AhoGlUnrztiNh2mroywVyOZ4d/A76dT2FRWJtA5g06QdNEzuIJBrIig5Awvl1kOgYQ8+hSSXsVeXLDD6PpNN/wKz7BOg4NET6reOI+2sOao3fAA1jK5XfiT/4E2SZyTDv9RU0TWtBlpkCKvLGUca9c0g+6wuLXl9B274h8pOeIfHIrwAAs67jKmW/ykultgD//fdfeHp6wsTEBLq6uqhTpw4GDhyI5OSCG+DTp0+jS5cuMDY2ho6ODtzc3LBjxw6FPLZs2QIAeO+996CtrY2MjAzs36/8tKao77//vsR1Pj4+EIlEICLs3LlTWJ6TkyPkO2DAgBKDi6z8RSRGlvh6xECP3jgeegb/hv6H6JRnWHtlM+IyEvFRQ2+V6TPzs5CcnSL8uVrUg6G2Pk6EvuzlGRr/EL9f24ZzEZdLfVWr4Elp+NvtnJpFpkSprNtudbvgQvRlXIi+jJiMWOy+vxdJ2cn4wFH1k6FsaQ7SctOEPycTR+hp6uHSE/9iKUkhXVpuyeOLVecgVVJ2ErKlqgMWXet2xqVof1x8UlC3e4L3ITk7GR2dSn/ymZabrlBvRf/dPO1b49/wE7gbdx8JWQnwe3wB9+OD4V1XsXdWQa/KyOJZVyuhcQ+hIVa+8RvUuA+OhfyHoyGn8Tj5KdZc/hNxGQn4uFFPlfm0cmiGJrXcMfXYfAQ8vYPY9DiExIXjXuwDIc2DuIdYd2ULzjy8iLxXvLYpggih8SX36Kjq5CQv8QalY9cO+Hb2V/D+qJvK9cXt2vwXatnbYvaSH+DsWg+DRgzEgGH98OeazUIa33Xb0K5TW3zx7TjUc6mLL74dB0+vNvBdt00pvzx5brV+1U8uV92D6rfVv2H4iOHwGTkCbm6uWPrzUtjZ2+HPjZtUpr9x/QYcHWvjiwlfwMnJCW3bemLU6JG4feu2kOb6teto49kGnwz6BI6OjujcpTMGfDIAt4qkUShbFXp9/k2UNC7duj/WYdinwzB8yHC41nfFT/N/gl0tO2zetlll+toOtbFkwRJ8OuBTGBkaqUyz4c8N6Ni+I7758hu4OLvgmy+/gVc7L6zftF4pLRGB5NX4mC2l19+a39ZgxPAR8BnhAzdXN/y89GfY29lj46aNKtM3adwEnwz8BA0bNISjoyMGfzoYXTp3wWX/ywrpZDIZRo0ZhVk/zIKTk1PJZSvh91RdhMSFqXwg0L9Rb5wIO4vjoWcQnfIM66/6Ij4zEb0bqG7PdnHugGMPTuP8I3/Epr+A36PLOBF2BoM8+gppgmLu4/Lj64hOeYaY9Bc4dP8YHiU9hruNm1J+ciI8Sqq+7a7SAmwbf/sTnw4fhCE+g1HfrT4WLJ2LWna22PbnDpXpjYyNYGVtJfwF3QpCakoqBg0bKKRp294TPT7qjvpu9eFU1xFjJoxCg0ZuuH7lhlJ+1XESu+TTe2H8fk8Yt+8FLVsnWA6aBA1TS6Se/0dl+sx715Addge1Ji+FXsMW0LSwhU6dBtCt97J3VcqZ/dBr0BxmPYZBy9YRZj2GQa9BcySf2VdZu1WtpN09CkPXD2Do1hlapvYw9xwJDQMLpAWfUplerKUPDT1T4S83IQLy3EwYFOmkolvLHfp1WkPL1B6aRjYwbvQhtMwckfvigco8a4K0a4dg0KQbDJt2h6ZFbZh1Gw+JkSXSbx1TmT474iZyou/C6tMF0K3TFBom1tC2c4WO/csAdd7TEOg4NIR+o07QMLGGbt1m0HP3Ql5M9YsLVFrwLz4+Hh9//DGuXr0KY2NjuLi4ICUlBfv370dqair27dsHb29vnDlzBvn5+ahfvz5iYmJw8+ZNIY+MjAwcOHAAAPD555+jd+/eAF4GBFVp2rQprl+/jkOHDqlcX6dOHbRv3x7Ay55+APDPP/8gNbVgRlQfH5+32XX2ml6U0GtHQ6wBF4t6uPn0jsLym08D4W6t3LBRpadbZ9x6FoQXGW/Wi/NFevXu/RmflaAU/JOIJHAyrq30yuj9+GDUM6tXpnw7OLyP4IQHSMxOUliuLdHGz51/xPIuSzCl1UTUNlLuJQAAYoiQVOy71Unx/S4kEUngaFwb9xOK1W1CCOqZ1i01zzkdfsAvXZbg2zZT4GruorBOQ6yBfLlicCpPlg9nM2elfFJz06p1IOVFRjykxW6cNMQacLF0xvUnigGOG09uo1EJ54L367RCaNxDDGnaDweH+2LXkPWY0HYktCRv1l1fIhaXeK6qDkrqMfEmbt8IxPsfKPbcad+5He7evo/8/Pz/p7mD9h8ovoLZoXM73LquOkhVnuWrbKTi9f+8vDzcvh2IDzorvoXQufMHuHb1msp8WrdpjWfPnuPkiZMgIsS9iMPfh/6Bd/eXwQHPtp4IvB2ImzcK2kqRkZE4dfKUQpqi5NU4QFVSgC0vLw+BdwPxQQfFuu3UoROu37z+xtu7HnAdH3gp5vlBxw9KzFMurb5BqpICl3l5ebgdeBudP+issPyDDz7AtWuqj9viAu8E4uq1q2j/fnuF5T8t+QkWFhYYMXxEqd+v7sE/Ve3NwvZswNNAheUBT+/A3dpVZT6aEk2loFKuNA+uls4lTojVtNZ7sDeuhbuxqofDKKmXfHVQUg/rvLw8BN2+C68PFI83r84dcPNa2SaX271tD9p3eh/2te1VriciXPS7hIjwR2jTrrVy2arZ9Yuk+ciNDoNew5YKy/UbtkROhOqJYTLvXIa2oyuST+7Go2n9ETVrKOL3rYM87+Wr2DkR95Xy1GvYEjkR98t/J6o5kuUjN+ERdO0U34TQtfNA7ouyvaKfHnoWOnbvQdNQuZcgUHDcZj+7i/zU59CxUf0qcXVHsnzkxTyEbp1mCst16zZF7lPVQypkhV2Dtm19pF3Zj6erPsOz9WOQ/N+fkOe/PJa1HdyRG/MQuc8K/i3yk2OQ/fAmdJ1bqsyzKqu0136jo6ORl5cHPT09hISEQE9PD0SEgIAAWFpaYvr06cJ4fpcuXYKNjQ1yc3Px8OHLnhX79u1DZmYmNDU1MWjQINjb22P//v04f/48oqKiVD45/PHHH9GjRw/MmjULffr0UVk2Hx8fXLhwAcHBwQgICEDz5s2FQGCdOnXQoQOPTVCZ8koY38tYxxASsQTJ2SkKy5OzU2GmZ/LKfM10TdHKoRkWnV3x5mWrhk/zilJVfkMtA0jEEqVeeam56WikrbpHRFHG2kZ4z8odv99S7L0SkxGLTYFb8TT9GXQ0dNC1zgf44f1pmHt+IV5kFpvJWSR6ZU+rqqx4IK6QgVC3ihMYpOWmw1jbWOV3UnLSsDVoBx6nRENDrAFP+9b4ts0U/HxlhTBO4P34EHSt2xlhSeGIz0xAAwtXNLFpDHEJswnmy/PfOMilbrlS5TF9jHWMoKHqXJCVCjMHE5X51DKywXu2DZEny8fMEz/CWMcI33T4HEbahlhybnW5la26KM+AcPyLBFhYmisss7CygFQqRXJiMqxsrJDwIgEWVhZKaRJeqL75rM4Ba1UlT0xIhEwmg5W14isnllZWePHihcp82rRpjU2+f8Jn+Ejk5ORAKpWi54c98cuvPwtpBgwcgISEBHTr4g0iglQqxZixo/Htd8rjhpZcumqihKInJhXUraWl4g2PpaUl4uLjVH+pDOLi42BpUSxPi5LzrEoTOpSXxMT/H7dWisettZU1/nvxX6nfre9WHwkJCZBKpZj5/Uz4jPAR1l25egVbt2/FlctXSs6ghlD1RsnL9myqwvLk7BSY6pqozCfgaSB6uHaBf9R1hCc+gotFPXR3/QCaEk0Y6xgi6f/XQz1NPfw15A9oSjQhl8ux2n8jbj0LUplndW7TlvR7S0pMhkwmU77eWFog7sWrH+C/iH2Bc6f98Ntm5XZBWmoamru2Rl5uHiQSCX78dSE6FAsyAtXv+iXLSAXkMkiMzBSWS4xMIU1T/XA7PyEGOQ/vQqSphVpfLIIsIxVxu1ZAnpkGa58ZAABpWhIkRqZKecpKyPNdJstJB0gOSbH7WYmuCWTF2rqqSLOSkf3kNiw7KQ/VIs/LRPTO8SCZFCKxGObtxkDXvuSxHKszWVYaQHKIDUwUlkv0TSHLUD3UkjQlFjlP7kOkoQnLAbMgz05D4om1kGWnw6L31wAAfXcvyLJSEbttKgAC5DIYNPsQxm0/qeA9Kn+V1vPP3d0ddevWRVZWFqysrNCsWTP4+Pjg+fPnyMrKEsbxGzlyJGxsbAAA2tracHd/OZ5SYQ+/nj17wtzcHD169ICFhQWICFu3blW53Y4dO6Jbt24IDg5W6NlX1MCBA4XXerdv3474+HicPHkSADBixIhqO4ZLtfWK6i5+URWJytbo9nbthIy8TFyOevOeANX9WBCVUrnFa7Csu/q+Q1tkSbNxKzZQYfmjlEhceXYNT9KeIjzpIdYHbMSLjBfo7KR6zMTqXbWlF17pmEXJx+yLzBe4GH0Z0WlP8CglEjvv/YW7cffgXfflxAi77+9FXGYcFnWchw0912BIo09x+ckVyEvIs7R/96qu1GO2eANbVHJoo+C3S1jw33KExIXjanQAfvPfjB5uH7xxYLQ612t5K35uLPy3UViuKk1JP/waWrVKu1vKOHEPQh5g6rfTMP376bh4+QIO/XMQjx8/xpRJXwlpLl64iJ+X/oJfV/6KS/4XsXP3Tpw4fhJLf1pacTtRRak6Bt/2ml0ReVZHxc91ZamH0ydO4+L5i1i9cjXWrluLvfv2AgDS09Mxeuxo/Lb6N1iYV89xfl9Pae2u4u1ZUYnXsB239+PGk1tY3ecnnBi1F/O7TsepsHMAoDDZWHZ+Nj4/9B2+/Hs6fAN24fPWPvCwVT02bXW+hr2q5Eq/XZTtt7t3x34YGRuhey/lYS8MDA1w6vJxHDt/GNPmfIf5PyyC/8UaHMAmoMSalssBEWAzehZ06jSA/nttYDlwItKunFDo/ae6zqvvcVf5CGWpr4wwP4i19KHvpNwTTaSpC7t+P6PWxz/BtMVgJF3diuznNbv3pdK5rbT2JskhEolg0WcatO1coevcEmZdxiIz6D+h91/O4yCkXt4Ds+4TYDt6NSz7z0L2w+tIubirgvek/FVazz8dHR0EBARg+/btuHbtmhCM27ZtG/bu3fvK70dGRuLixYJp6U+ePAkTExMAQGZmJgBg69atmDNnjsqTzI8//ojTp09j7ty5witIRRkYGKB///7Ytm0bdu/eDQcHB0ilUmFCEFa5SroRT81Jh0wug5me4lMkEx1jJGelqvxOUT1cP8Dp8PMqJ5woq+o4e1dRmhLl8qfnZUAml8G4WC8/Iy1DpJYyRl+h9g5t4f/0KmSvGEuqcFZfawPlwVaJqFrXraZY9ak0o7BudRTr1lDbsNTxD4t7lByJNnYvZ6nLyMvA2pu/Q0OsAQMtfaTkpKK/W18kZJX8ynx1pa1iFq3UnDRIVZwLTHWNkZyVojKfxMxkxGcmITMvS1j2OPkJxCIxrAzM8bSESYZKo1XNZvgqqjwDGJbWFoiPUzz2EuMToaGhARMzEwCAhbUFEor1ukiMT4SFlWKPQaF81fjmQFXJzS3MIZFI8CJWsddYfHy8Uq+qQst/WY42nm3w1dcFT/IbvdcIevr68O7ijTlzZ8PG1gYLFyzCp0M+hc/Iglcn3Ru5IysrE5O/nIKp06eqmNyj+tZrSUU3Nyuo27g4xbpNSEhQ6rn3OqwsrZR6+SUklpxndT5mS2Ju/v/jNk6xd2pcfFyJx22hwrdxGrk3QlxcHH786Ud8MvATPIp8hMePH2PgoJfjqRW+2mtkaoTAgEDUrVv6sBjViaZEAznFmp1Ce7ZYLz8THWOklNDLJ0+Wh+UX12Hlpd9hqmuMpOwU9HTrisy8LKTmvHy7gEB4nhYLAIhIikJtE3sMbtwPQTHKN/uq2oTVh+rfm5m5KSQSCeJVXG8srUoPNhMR/tqxF/0H94OWlvL1XSwWo049JwBAIw93PAx9iN+Wr0Pb9p4K6arbAwKJgTEglij1yJOlJ0OjWM894Tsm5tAwsYREz0BYpmVbGyCCNDkeWtb20DAygzS1WJ5pyUq9ARkg0TEERGLIirVhZdmpkOiqflOoEBEhPfQsDOp3gEjFb1okEkPT2BYAoG1eB3kpT5EaeAi61XjCupJI9IwK6rFYLz9ZVgok+iaqv2NgBomhOcQ6L+d30LRwAECQpSdAbGaHlPPbYfDeBzBs2h0AoGVVB/L8HCT9uwbG738Kkaj6TKRWaSVNS0vDgwcP8OWXX2LHjh24desWOnUq6AF04cIF1KlTB0BB777CBlx+fj6Cg4OF5YU9CXJycpCamorU1FRIpQVX1MjISFy4cEHltps3b44BAwbg8ePHSEpS3dW4cFy/uLg4zJs3DwDg5eUllItVHltD1Q1KqVyKsIQINC82HkJz+8a4/4qBSxvbusPeuBb+fXDmrcpWy0j1jFfVhbW+ldINioxkiEqNhrul4vgPDS0bICJJ9UyrhVzNXWBtYI2L0ZdLTVeotrEDUnOUA7UEgkU1nekXQIlll5EMj1Oj0dCiWN1aNEBE8qMy5+9g7IAUFcFCqVyKlJxUSERiNLdtisBY5Vd7THRMql1DtCgbI2uFGXmB/58L4h+iZbGZylraN8G9Es4Fd2NDYKFnBl0NHWGZg7EdZHIZ4jISX7tcMrkctobV93ygISq/gHDTlk1w+ZziZD+Xzl7Ge03doamp+f80jXGpWJqLZ/3RrFVTFTmKICnH8lU2VY1ALS0tNG3aBOfOnlVYfvbsObRuozxmFFAw63Lx4J1EUvC5sD2UrTKNpGB8PBWvnonF1fdcIBKJIFJRfi0tLTR5rwnOXTynsNzvoh9atWillL6sWjVvhXMXFPM8e/5siXmKNapP4784VfUK/P+4bdIUZ4sdt+fOnUPr1qqPW1WICLn/7w3k6uKK61ev48rlK8Lfhz0/RIcOHXDl8hXY2yuOs1adZ6cGoPI6UdiebVasPdvMzgP3XzG+l4xkSMhKgpzk6FS3Ha5FB7zi7RcRNCWqz6c2JbS3q4OSHmpqaWnBo+l7uHDuosLyC2cvokXr5qXmeeXSVURFRGHw8EFlKgMRIS9X+dXp6nb9EmloQru2C7KCbyoszwq5CZ0iE3gUpVuvEaQpCZDnvHygmvfiKSASQ8O04AGJTj13ZIUUyzP4BnTq1byg09sSSTShbVEX2cVe0c9+FgTtEsYBLZQTEwxpWiwMXT8oNZ2ACFTCcEXVnUiiCS1bZ2RHKo4nnRN5G9r2qsc51LZvCFl6EuR5LyduzE98BojEkBgW3N9Rfq5Sz0GRWAyACnoVViOVdkWNi4uDp6cnzM3N4eHhATc3N6Ex4eHhgaVLl0IkEuHhw4eoU6cOPDw8YGlpiT/++ANEJLyy269fP6FRS0QKY5GUNvHHwoULIZGoHhAXKHg9uPApZUZGBgCe6ENd6pk7QVxCsGJf0BH0dOuM7q4foLaJHSZ4joS1gQWOhBTMhDSm5VDM6DhZ6Xs93Toj+EUYopKjldZpiDVQz9wJ9cydoCHWgIW+GeqZO6GWkY1COolYAjer+uWwh+rjZOKocvmpR/+hQ+330d6hLWwNbPCp+0CY65rh3OOCgPoAt74Y08RH6XsdardDRPIjPEt/rrSuj0svNLJsCEs9CzgY2WNU4+FwMHIQ8lQqm7HqslUHpjom0NfUU7nu9KMzaF+7Hdo5eMLWwAaDGg6Ama4p/B4XNEz7ufXBqCYvBzzvUucDNLFuDCt9S9QysEU/tz5oYdsM56L8hDR1TJzQzKYJLPQsUN/MGV+1ngQRxDgRoTgjmAgi1DVxKvf9rUyulvVUDuy9584/6NWgK3q6dYGjqT0mtRsNK0NL/H3vOABgfJvhmNn5KyH9f2HnkZqbhu8/mAInUwc0tnXHhLY++PfBf8K4RxpiDTib14GzeR1oSjRgqW8GZ/M6sDOyVdo+geBqWbYJcaoikUgMLbG2ynWZGZkIDgpBcFDB4MhPHz9FcFAInj8p+J3/PO9XfDt+upB+yKhP8ezJcyz+YQkehkZg3/YD2Lf9IMZMGiWk8fliOC6d9cfvKzYiIuwRfl+xEf5+VzBygnLvei2xdrUOWEtKCFZ8OflLbN2yDdu2bseDB6GYMW0Gnj55itFjCupp7px5GDdmnJC+R88eOPzPYfz5x5+IjIzElStXMe3baWjRojlsa9n+P013bNq4Cfv37UdUVBTOnjmLRQsWoeeHPVW2ecQlTAxQXUi0VJd/wrgJ2L57O3b8tQOh4aH4Yd4PePrsKUZ+NhIAMP+n+fh8yucK37l7/y7u3r+LzKxMJCQm4O79u3gQ9vLhwfjR43HuwjmsXLsSYQ/DsHLtSpy/dB5fjP5CafsisQhiSfUNUpVW9klfTsKWbVuwdftWPAh9gGkzpuHJ0ycYM2oMAGDOvDkYM26MkP73P37Hv8f/xcOHD/Hw4UNs27ENq9aswqeffAqg4E0g94buCn/GxsYwNDCEe0N3pR5X1bleAcDNqr7SAywAOHDvCHq4doa3S0F79vPWPrAysMDRBwXX8VEthmKa1yQhvZ2RLTo7d4CdkS1cLZ3xQ6ev4WRaG5tv7hTSfNr4YzSz84CNoTUcjO3Qv1FvdK3vhTMPldtdEpEYdcyqb7tLo5S3RcZ+OQa7t+7BX9v2IPxBOObOWIBnT5/js9FDAQA/zV2KyeO+Vvre7m170LRFU7g1VA62rPllLS6cvYjHkdF4GPoQv6/ZiP27D6LfoL5KabUkqq+tVZlp10+QeukYUi8dQ15MFOL3/Ib8pDgYe30EAEg4+AdiNy8W0hu26gKJgRFebFmC3OdRyA67g4T962HUrgfEWgX7b9J5ALKCbyLpxC7kxTxG0oldyAoJgGnngSrL8K4zeq8X0kPPID30LPKSnyLxyhZIMxJg2KDgFfSk6zsRf26N0vfSQ89A26o+tMxqK61LCTyE7Kd3kJ/2Ankpz5AadAQZ4Rdg4Fxz5zMwav0xMgJPIiPwFPITopF0+g9IU+Nh2KwnACD5nC8SDv8ipNdv1BFiXUMkHlmBvPho5ETfRfLZTTBo3BVizYJjWbd+K6QHHEPm/fPIT4lF9qNbSDm/Hbr1W0Ok4vxelVXaowlzc3P4+Pjg2rVriIyMhFwuh5ubG4YPH44xY8ZAJBLBxMQES5Yswc2bNxEWFgZHR0e0aNEC58+fF8YEHDBggEK+YrEYffv2xR9//IH9+/fjt99+U7l9V1dX+Pj4YNOmTSrXi0QijBgxAvPnzwcA6OvrK22LVQ5tDW3YG9shOuWp0jq/R5dhpGOI4c0+gZmeKaKSovH98cXCbGpmeqawMlDshaWvqYf2dTyx1l/1v725nik29v9V+DyocV8MatwXgc/v4Zujc4TlMrkMLhbV92YfAOoYO6p8Qnz9+U3oa+rjI5cPYaxtjGfpz7Hi2m/CLLbGOsYw11UcCFhXQwfNbZth1709Krelq6mLER7DYKxthGxpNqJTn2CJ/y+ITIlSSqunoQsz3er7GoBIJEIdEyfcjw9Rqt8bMQHQ19JH7/ofwljbCM/TY7Dq+lphdmNjbcW61RBL8EnDfjDRMUG+LB/P0mOw6vpvuBv38pUdTbEm+rp+BEs9C+TIcnE37h7+vL0F2dJshW2LRKISA77VhZul8gzGAHD24SUYaRvCp8UgmOubITLxMaYdXSCcC8z1TGFt8PL1vGxpDr45PAdftR+PjQN+RWpuGs49vIyN13YIaSz0zeA7aJXweXDTfhjctB9uP7uLyf/MVNi+CCLUt6jer6ZpS3SRJ1eetOTu7fsY2utlQHrxDwXjx/Ub0hc/r/8JcS/iEfP05WvSDk722LRvAxZ/vwQ7Nu6ClY0V5iz9Ad37vBwzqXnrpli1eTl+XbQKKxavQe06DljtuxxNWigPOq0j0VFaVp2IS2gI9h/QH0mJSVj601LExsaiYcOG2H9oP2rXLmiwx8bG4smTl9e9YZ8NRUZGOn7//Q/88P1MGBsbw6tjByxYuEBIM23GNIhEIiycvxDPn8fAwsICPXp2x5x5c5S2D0BlEKI60dDUgLT4O5QA+n3UD0nJSVi2chlexL1AA9cG2LNtD2rbF9Tti7gXePpMsU3RwfvlzU9gUCD2/70fDvYOCLpa0POidYvW2LR2Exb/vBg//vIj6jjWweZ1m9GiWQul7Us0q3e9lhZsH9B/AJKSkrBk6RLhuD24/6DCcfv06cu6lcvlmDNvDh4/fgwNDQ3UqVMHC+YtwOhRo9+obNW955+LRT0clp9QWn7+kT+MtA0xrOnAgvZscjRmnvwRcUWuYUXbsxKxGAMa9Ya9iR1kcikCn9/HlCM/KMwmrKOhg8ltx8FC3wy50jw8SX2GJX6rcP6Rv9L2a5vYQ6sav/YrFomhIdKAVMXMun3690ZyUjJWLF2NuNg4uDZ0wfb9W4TZe1/ExgkPswqlpabh33+OY8HSeSq3l5WVhe+/mYXYZzHQ0dVBvfr1sPrPlejTv7dSWk1x9RsSxLDlB5BlpiLp2DbIUhOhVasO7CYthaZ5QUcIaWoipEkvh0EQ6+jB7qvliPtrFZ4sHgeJgREMWnSCeZ+XDwJ06zWCzdg5SPx7ExL/2QRNy1qwHTcPOnUbVvr+VQcG9dpBnpuBlFv7Ic1KhpaZA6y7/yDM3ivLSoY0U3GIFXleJrIir8Gs7UiVecrzc5Bw+U/IMhMh0tCCprEdLDtNgkG9dhW+P+qi39AL8qx0pFzaBVlGErQsnWD16XxoGBf0wpZlJEOa+vK8KdbShfWQxUg6tR6xm6dArGsIvYbtYeL18uG08fuDAYiQcn4bZOmJEOsZQ7d+K5h2LH22+qpIRNVtSiL2Tvjl/FqcDDsHWZFBjKuCLZ+sQW0TO3UX441l5mVi0slvq9SshCKI4G7ZEN+2Ue6xWZ38E3oUxx4eVxh4uyr4ts0UNLBwU3cx3pic5Oj+56fIzs9Rd1EUOJraY8fgdeouxltJy0tBQk6suouhxFLHBoZaJuouxlvJzMmokjM+6usYVOtelfk5+chMzFR3MZToGOpAx6h6B61zsnIgl1Wt6xcA6BroVutjNio5GmMPKPcyUyeJSILurp3x1fvj1V2Ut5KSk4gsadU7H1jp2ZbaM/Ft3bp1C82bN0dAQAA+DciosO1UlrCxVa8XWt1x+9VdhEr36I/K6fTkNOt4pWxHXaIW9VB3EUpVvR+nsRqra/2OVSrwJ4II9c3rVOvAHwDoa+nDw7oRxFVoYFIC4X0Hz1cnrOLa2LWqcoE/I20juJhV71fVxSIxurt+AEkVel1RBBF6unVRdzHemv7/2Dvv8Ciqtg/fM7Mtm95DQkgIvfcOImDvCthRLK/dT331xd4rNizYBRFsKFbELiiCFAm9SW/pyaZvn5nvjyWBsLuhJdnC3NeVS7N79jxnDydnzvzmKfpogq0AhICwf1yhTTAm0tdL+pAWUQB0Rp3f/HSBRG8Ovn/vo0WnD748ZaIkhvyazYrLpG18m6AqCCOrMqe0HxnoYRw3EbrIwzdqYfSivlmFPw0NDY3jIXgUAA2Ng+jZqiutY9OD5rCkonJh97MDPYwmYUz2qKASqSL1kfRr5Svpf2iRGpVCl6TOQSOsCgiMzh4Z8mF+ABd0O/Ow1aRbEkkUw0L8kwSJKH3M4Ru2IFH62JDPSweg0wXfzZ8+CMd0tAiCgCEyuELqdEYdki7012wwfge9ITzW7AXdzg6aiAsBgay41nQ7TBGBUMAgGYOuuEY4PLzS0NAIX4LjLlVD4xAEQeCi7sFzWDLrIxgVJvkRuiV38crfFyhEBEZln+S3aluoMTr75KARVgUERmSGx5rNScyiR1qXoBBWJUHklA4nERcRXKLZsRJrCK5cmzEhHu5bhyiISEG0r4mi5DcXYahhjAyuZPrGqOAaz7EiCAI6Q/CsWUEI7SIqBzOq3fAGleYDiYrKBd3ODnmPSvCskaggEtsEBCJ0vou/aWhoaAQD4XFV1QhLTutwMonmeL+Vf1sKAbis94UYdeFxwBcFkfM7nhPoYQCglwyMyR4V6GE0GT1TupMRnR5wkUpA4KQ2w4g1xQZ0HE3JNQMuDRph9fI+YwM9hCbDKJmIkILjZiVCisQY4sU+DsagDx4PNYMueMZyvIiSGDRhtqJORGcMHsHseAmm0F+9MfTD1OuI0JsY3/P8gEeziIJIkjmBMWFU6TNCHxnwM1cdUYYYhCAZi4aGhoYvtB1KI2gxGyKYNPI2lAAmTRcFkbYJWVzS64KAjaE5GJ45lG7JXQN+YLqyx2XEhZFAJYkS1/WeCAFcswICsaZYxnW9KGBjaA4GZPbh7M6nBHzNXjvgctomtAnoGJqa5IhWAb8pFRBIjkgL6BiaGkmU0AeB6KaTdOik4BF1mgJzrDkocv9FJkSGjUAFnsq6BmPg16woiUEZhnw8XNrrQtrEtw7oNUxRFf438nYi9OHzkEUUROKMiYEeBjpRH3RpNDQ0NDQORRP/NIKaAZl9OKtT4G74BeCB0XeGTVhqHYIgcG2vq9AH6HuJgkiP5G4Maz04IPabkzaxmZzT8ayA2VdRua731ZiCJMSoKblt2HXERcQGxBtYEkTaJWZzeZ/wElXBc9OSaEoN6BiSTGlhmSTdoDME9GZfQMAYRjf6dQiigDk+sB6rphgTkj68BCoASS8FPNzWYDKElagKnoI79518R8Dsi4LIOZ1Po29Gz4CNobkw6SICXvwj3pgYdmtWQ0Mj/AgvRUMjLLl5yERWF6ynqLq4xSsA3zDoKnISslrUZkuREBHPxF4TeGfltBa1KwoiUfpIruk1IWwPSme1P5N1xRvYXbmnxUNVT2k7mi5JnVvUZksRZYzkkVP+y91zH0VowYygoiCilww8euo9YedBVUe0Pharuwaru6bFbUfqosPWY0IQBIwGEzaHNSD2TQZT2O6zepMeQ6QBZ62zxW1LBilscv0diiAIGEwG7LX2gNg3mAyIYnj6JrRPbMu1/a/g/X9mtahdSRBJi07lPwOvalG7LUmsMR6n7EBW3S1uO8YQh14KjMfslv+ETwh3MLHj3XGBHkLYsuupMwM9hBOa8Ly6aoQVkQYzL5/zBHERcUgt6EFxaa8LGN/zvBazFwgGZwzksm7jW8yeKIgYJSOThv6X+IjgKjTQlOhEiTsH3kZaZGqLev0MyhjAxV3DJx+dL/q17sUjp94DLRSoKgoCkiDx4jmPhl2478EIgkBKRDomKaJF7ZokMykRrcJWoAJP+K/J0LLzCh7hTwpTsbqOiNgI9BEt6zEq6kQiE8Mr3PdQRFHEZG55j1G9UR9UeQebg4t7ns+4Hi13thQFkXhzPC+e/TjmAOxDLYUoiCRFpLR4tfgofQxRhvB8eKWhoRF+aOKfRkiQEpXE6+c/Q0pUUouIKVf2Gcd/Bk5odjvBwGk5p3BF90uB5pVTREEkUm/mgWH/IyM6vdnsBAuRhkgmDf0vraMzWkSmGtZ6CNf1nhjwnHgtwej2w3n89EmIgtis31cURAySgSnnPUGv9G7NZidYEAWRNHNmixUAiZAiSTO3PiESpOskXYsKgCaDCZ0UfmHUhyIInvDflhIAJb1EVHJU2HqmHYwotawAqDfq0RtOjDV7w8CruLTXhc1uSxRE0qJSePXcZ0iKDHxevOZGEnUkRaQitZAAGKWPIdoQPnmrNTQ0wh9BVQOYmV5D4ygpt1Xw4p9vsmTPCoQmDvyTBBG9pOf2YddzZqcxTdZvqLCycDXTV8/E5rY1S6hqh4T23NDnGpLMSU3edzBjd9v5dP3nLN63pMnXrCiICAhc2Pk8Ts85Naw9UXyxJn8DT/z2EqW1liZfswLQNiGLR069m3aJ2U3ad7CjqioWRwmVTkuz2Yg1JJBgTD7h1qysyDic9mZLByAgnBAef4eiqiqOGgf2quYLVdWb9UFTaKQlURQFp82JojRfCgtDhAGd7sRaswA//vsbbyyZjlt2NWlam7qzxtCsAfx3xC3Emk4szzRZkalwlOGQm2c/EBCINcZj1kc1S/+HY+XKlfTr14/c3Fz69u0bkDFoaGiEJpr4pxFyqKrK79sW8sqid7G7Hcd9E1V3SOqX0Yv/jbyVlKgTS5w6mBpnDbPWfcry/BWICCjHKVSJgogkiFzcdRyjs0eeEF5p/lhfvIEP1sykylHdZAJgVmwbrus9kfToVk3SXyhiddl4Z+lMvlo3D1EQj3s/kAQRFZjY/xIm9B0ftjn+jgS720axLR+36mqyPnWC3hNerAvf8LPDoaoqTrcTl7tpc9XpJT0GvfGEE1QPRnbJ1JbXoriaUEjZX1xEbwp/rzR/qKqK2+XG5Wi6vQBA0klhWdzjaCiuKeHFhW+wKn9dkzwgFAWRCL2JO4bdyMk5w07YuVVVFZvbSqXD0qQPXY2SiThjAlIACwFq4p+Ghsaxool/GiFLmdXCZ6u/4YfNv2Fz24/60CQJErIq0yEph4t7ns/odsNP2EPSoawpWscP235mi2XrUQsqAqACelHH0NaDOav9GaREJjfbWEMJq8vGLzt+ZcGuhdS6ao96zdb9W6RGpnBazimMaDPshBZUD2ZtwUY+WjmHJbtXHJMIKAgCIgKj2g3nir5jaZ/UtplGGlooqkKVs5xKZ/lxJVKXBB2xhnhiDPHamt2PrMg4XU5k5fgS1EuihEFnOOG8/fyhqirOWieOGgeKfOwioCAKGCINmKJMJ5y3nz8URcHlcCG75ePqR5RE9AY9ki78qiUfC6qqMn/7X3yx9ju2W3YiCeJReQLWnSUi9CbO6nQKl/S8kHhzXPMNOISQFZkaVxVWV81xiYB60UCUPhqTzhzwewVN/NPQ0DhWNPFPI+Sxuews2L6I7zb+zLaynSiqgoCAJIqoKqio9TnXZNVzYI00mBmRPZjzu51Bp+T2gRx+UJNXnc8fuxeyNO8fapyeKqCeUFPqj1ACAoqq1M9zq6g0Ts46iWGZgzHrWyZ3WKjhVtysLFzNgl1/sqN8R/0hXxKkBodTAerfM0pGuqd0Y0z2yXRIaB/ww2ewUlBVxLcbfuLnLQsorfWErYqCiCAIqKqKgEfoU1QFZf/lLz0mjXO6nMrZXU4hwRy+hWiOB1VVsbprqXKWY5etR3QTJSBgkszEGuKJ0IV3gYTjQVEV3G4XLtnFkR7JBAR0Oj16SX9C5J87FlRVxe1046xxejzWjvC0KxkkjJFG9BF6bc36oc4T0O1yoypHOLEC6HQ6dHodoqStWX/8W7KN7zb+xOLdy6h1eqqE1+WwO/g8q6gyKp7rW/vEtpzb5XRObjccky48q1AfL4qqYHNbsbqqcSlH5sEqIGDSmYnUR2MIUDVfX2jin4aGxrGiiX8aYYXT7WS7ZTdbSrezu3wvTrcTtyJjkPTERsTSISmHjkk5pEadeLmmjpdyewW7K3azq3IPlY4qXLILURDRSzpSI1NpG5dFm5hMjNrB86hwK27yqwvYVbmb/Op8HG4nbtWNXtRj1ptpE9OarNgskiNbpthNOFFuq2RLyXb+Ld5GcW0pDrcTURAwSAbSY9LolNKODkk5RBsDk7cnVFFVFZfixCHbcSr2BuK/KIgYRBNGyYRePLHD+Y4FVVWRFRlFUfZ7rx54zCIKAqIoIYniCVEkpSlRVRVFVpCdMrJLRlVUz8MAQQDBU8ij7kdbs0dH3dwqiuKZV1RQOTC3koQoipr35FGiqipFNSVsLd3O1tIdVDmqccouJEHCpDPSJi6DDkntyEnIwqALHmEqFKi7hrkUJ27F89BFhfprmF4yoBcNSEJw7gea+KehoXGsaOKfhoaGhoaGhoaGhoaGhkaQo4l/Ghoax4r26FhDQ0NDQ0NDQ0NDQ0NDQ0NDQyNMOWrxr6CggHXr1vl932azsXLlSmpqavy22bhxI/v27dNsaDY0G5oNzYZmQ7Oh2dBsaDY0G5oNzYZmQ7Oh2dBsaDY0G8do44hQj5JHH31UzcjI8Pv++vXrVUD9+++//bbp2rWretddd2k2NBuaDc2GZkOzodnQbGg2NBuaDc2GZkOzodk4Ahu5ubkqoObm5ob099BsaDY0G01r40g4avEvPz9fXbt2rd/3rVarmpubq1ZXV/tts2HDBnXv3r2aDc2GZkOzodnQbGg2NBuaDc2GZkOzodnQbGg2jsCGL/EvFL+HZkOzodloWhtHglbwQ0NDQ0NDQ0NDQ0NDQ0MjyNEKfmhoaBwrukAPQEOjqVAUBcWtIMsyiltBkRVUPNq2gAACSDoJSSchSiKiJCIIQoBHHfyoqoqqqCiygqJ45ldVVfZPLYIgIIiCZ05Fsf6/GodHVtxYZSs2txWbXItdtqGoMiqeNSsJEmZdJBGSGZPOTIRkRhS0uT0cqqpiq3ZQa7FRU26lxmLDYXWiyJ5FK0oCpigjUQkRRMWbiUqIwBRlDPCoQwNFUXHbXbjsbtx2F267G0XZvx8IIIoCOpMOvUlf/19B1PbZI8GlOLHLNhyyHbtsw6W4UFUF8OyzOlGPSYrAKEZgkkzoRYN2DTsCVFXFpThxya76/6pq3flAQNw/t3rJgH7/f7V99shQVAVFkZEVz38VpeG5SxAERFFCEkVEUUIUtHPXkSArMnk1+eRV57Gveh97qvZS66zFrboRETFIetIi08iMySQjOoM2MZlEGaICPeyQwKk4qXFVUeOqpsZVjdVdW3/uEgUBvWAgSh9NlD6aSH00UbooBG0/0NDQCBM08U8jpFFVFdkt43K4kF2y/3Z4bk7dTjdupxvw3EzpjXp0Rp0mVvlAVVXcLs98NeYgrKoqquwRB+sQJRGdQYckSdpB/xBUVcUmWymzF1PpqqBeRfWBrLpxOh1UYAFAFCQSDEkkGJMwSJpYdSiyW6Z0dwUF20qxVTkAEAQ4dPnKLnDZ3dSU1da/F5VoplX7RBIyYhElbT84FLfDja3Chq3S3tiSRZZVZJcTR7XT84IAEbEmIuIi0Bm1I8ehqKpCjbuaCocFh2JvpCHIsoxDtgPlABhEI7GGeKL1sZpY5QO34sbmqsXqqq0XpLxRkRvMrQeTLgKzPgqDZGiZwYYQqqoiK25cbhey0vi5S1U9ZwP3/maCIKDXGdBLeu1s4INKRxXL85ezJG8JNa5aAERBRFGVBu2sbk/bLeVbUVQFAYFuSV0Z2noo7eJytLk9BFVVqXSWU2DLo9xRtv9VgUMvZrIKLlxYZSvF9kIA9KKBtIh0UiNaaecuDQ2NkEcL+9UIWdxONw6bA1U5/iUs6SWMZqMmAuI5JDkdzkbF1CNGAIPRgKTTRECAWlc1+dZ9OBTbcfcVpYsh3ZypHUYBRVbYt6mYgi2lDUToY0FnkMjokkKrDknamsUjklYXV+O2uY+7L32EjujUaE0ExLPPVjgtlDtKUTi+NSsgEGdIJMGYqHmo4BH9qhwVOGXHcfelE3XEGOO0fZb9DwRlN06XoxEx9cjRSXqMeqO2zwLVjmrmbvueNcVrAY5pfkVEFBQSIxI5t/05dE3q0tTDDEnK7CXsqtm+X9z3FvyOhmRTKlnR7TCIgX0ooIX9amhoHCua+KcRcqiKit1qbxpx6mAEMJqN6PS6E/YwKrtlHHbH8ZyNfCJKIgaT4YQVVxVVpsiWT5mjpEn7FRBIM7cmwXDiClXVFivblu3BXuNs0n6jEiJoPzCTiGhTk/YbKqiqirXMSm2Ztcn7jkwyY04wn7Br1ik7KLLlN+7pdwzoRQOpEemYpIgm7TdUUFUVq6uWamdlk/dt1kcSbYg5YcVVRVFwuOyNevodCwICRoMJnXRiPhBQVZXVxWv4+t9vcMrO434QAJ45VVHpm9qH8zqci1lvboKRhh4uxcXOqq2UOoqbtF9J0NEupiNJppQm7fdo0MQ/DQ2NY0UT/zRCCrfLjb228bCz40XSS5giTSfUjamqqjjtTmR3Ewuqh2AwGdDpT6xDvs1tZU/tDlxK04pTB2PWRdEmMgedeOLMraqq7NtYxL6Nxcf7MN83gidErW3vdFLbJTZx58GN7JSpyKtEdjbffiAZJeIyYpH0UrPZCEYqnRZK7EXNaiPBmET8CfZAQFFlym2WZt1nJUEizpSIXtI3m41gxC27sDubVqg+lBPRC9ApO/l042w2lG5olv4FBMx6MxO6X0FOXE6z2AhWKp3l/FuxAbd6/B7r/kgwJtMhtjOS0PLXME3809DQOFZOzEeYGiGJ2+nGXtO8wh+A7JKxVdsazXMXTqiqisPmaHbhD8Bpd9bnXDwRqHXXsLN6S7PekAJY3TXsqP632e0EC6qqsiN3n0f4g+bZE1SPl/GOlXns29i8Yk0w4Xa4sewpb1bhD0B2yFh2l+N2nBj7gaqqlNlLml34A7A4SimxF54w1zBZkSmzljT7/ierMhZbSZOEE4cKLrez2YU/qBMYT5xzl81l451V77KxdGOz2VBRsbqsvLd6GhtLNzWbnWDDYi9lY/naZhX+ACyOEjZa1uBWToxrmIaGRnigiX8aIUG9x18LocjKCSEA1gl/x5sn7WhwOpy4XeF/WLK5a9lVvbVJwniOBKfiYGf11rA/iKqqR5Ar3lneYjb3bigib3PThg4FI26nTPneClS5ZfY9VVYp31vR7EJjMFDuLKPcWdpi9qpcFSeEACgrHkFOVltmDamolNvKcMnh/6DF5XbhcLWc0Ckr8gkhADrcDt5fM4191XlNkjuxMVRUZFVm5vpZbLFsbVZbwUC5o4zNleubfV7rqHZXsal8bYvtPxoaGhrHy4kTI6YRsiiy4vH4C4TdWntYhwA7Hc4WFf7q7dqdCIKApAvPkD+X4mJnzbYWO4DW4VQc7K7ZRk50p7Bds/lbSijeYWlxu3vWFWKMNJCUGdfitlsCRVGpaEHhrw5VVinfV0FidgKCGJ5rttpViaWJ830eCVWuCgyigThjeIatq6pKub2sxW+8VVQstlKSzKlIYnhew9yyG4er5c9dsiLjdDsw6sMz16qqqny6aXaLCH+H2p2x7kPuHHAHKebkFrPbkljdtWyuWN/idqvdVWyt3EznuG4tbhugzat/BsRuc7DnjpGBHkI92U/+EughNBu7Hj4toPazJv8eUPvNwe57xwR6CEeM5vmnEdSoqtqiHn+HIrvksPVSk91y0xdNOQqcdmdYPuFXVZX82t0oAXoSbJOtlDVxgutgwVplZ++6woDZ35G7D6fdFTD7zUlNcQ2Ku+UfBAAoLoWa0tqA2G5u3IqLYlvg1mypI3zDVGtdNbiVwPw9qqhUOSrC9hrmaIFQX3+43C5kOTzPXauKVrOxdGOLPxhUUVFUhdkbZ6OogdnnmxNVVdhauanF57UOi6OEUnt4nrs0NDTCC0380whqXA5XQDzTDsZhdaAo4XVYqivwEegxuBzhJ6RUOsupdlcFdAxFtnwccuBu3poDVVHZtmxvgI72HmS3wo7cvLC74XdandgrA7tebOU2nNbwCqVUVZVieyFqC4X++xkFRbb8sFuzLtlFjTOw+6xDtmN32wI6hubA4XIETESpw+6yh92arXJU8fWWbwJmX1EV9lbv46+9iwI2huYiz7qXWndNQMewvWoLzhMk77KGhkboool/GkGLIis4bUFwIVU9AmA44XK4guJg7Xa5keXwyZUiqzL51j2BHgYqKnm1uwM9jCalcHsZtRW2Zi/40ygqlOdXUV4QWNGhKVFVlaqC6kAPA4Cqwuqg2Jeailp3NdYA35ACOBQ7la6Wy5HZElQ5guP7VDkqwsqTSlZk3HLgH8qpqorTHQTnvybku63f4wqCuf1px8+U2ysCPYwmwy7b2FuzK9DDQFbd7KraFuhhaGhoaDSKJv5pBC3B5BUmu+Sw8f5TVTWoQpnDqfpvhaOsxQp8HA6rXIvNbQ30MJoEVVXJ/7flc6b5I5jGcrw4a5wBC/c9FMWl4KwNnxv+CmfL56b0R4XDEjbCqkt24gpQuO+hqKjYw2SfBXC6gufvz+UOn9QgFfZK1pWsC4rzgaIqLMtfFuhhNBmF1vyAe6rWUeooDts0CxoaGuGBJv5pBCWqquJyBsfhvg63IzxEqkDm+fOF7JZRleA4uB0PqqpSFoCk/o0RiCIDzUFFYTVOW/DsB9WlVqxV4RFWbS0PrrDFYBvPseKQ7djl4PkubtWFTQ6PvIq1rsB7Ux5MrbM2LEQqRVWQg6xavDtMcv8Fk9imorI0bynuIPu3PhZkVabIlh/oYTSgyFYQ6CFoaGho+EUT/zSCErfTHdjwPh8ES6js8aCqKi5X8IgodQSTJ+KxUuuuwakE1xPfCqcl6G7mjoXCbWUQTIVgBSjaXhboURw3bqcbVxCJqgAuqwu3M7geUBwLlc7gCEs9mGAc09GiqHLQ5dmTVTeuMMj15XYH114AHu+/UMetuFmavzRovNMArG4b60pavjJuU1NmL2nxat+Ho9CWhxpGqQA0NDTCi5AQ/xYsWIAgCAiCwMyZM73ev+aaaxAEgejoaGpra3nsscfq2+/atavlB6xx3DQmBr037T269+5OUqskRowaweIlixvty+Fw8PhTj9O1Z1cS0xLp2bcnMz86sI4++PADTjvrNDLbZpLZNpNzLzyXFbkrvPpRVTXgxUeOF1VV/XrZvfveu3Tt0ZWE5ASGnTSMxX8ffl4fe+IxOnfrTHxSPN17dufDWR/Wvz/r41lExkR6/djt3h5Tsju4Dm/HQrWrwu97n037nDN6n0u/VkO4eNQV5C5Z5bftP4tW0COhn9fPji0769ts27Sdu676H6f3OoceCf2Y9dYnPvtSUalxB0c+t2NFkRUqiqp9Pgz48ufZjL31TE6+YgDX3Hspqzet9NvPyg3/MPTiXl4/u/J2+mz/6+IfGXpxL+59/k7vN1Uo21t5jN8oeHDU+L+x/uCT6fQf0582PTM59aJTWLpiaaN9Tf94GsPPGkZWrzYMPWMIn38zu8H7LpeLl954kYGnDqBNz0xGnX8y8/+a77MvZ01wiejHgr+/u4/f+5RRPU6lW0pvLjhpHP/87X2tOZiP3vuE0wecQ/fUPpzW7yy+/vRbrzY/ffsLZww8h67JvThj4Dn8Mvc3n33VumtC/gGWw+1/bcx470MGdh9CdlI7ThtxJksX+/e2Kios4pZrb2V4n5NIj8nk4Xsf9Wrz0Qcfc/5pF9E5sxudM7tx8bmXsmqF773b7g59T+DGvOzee+c9unfpQVJ8MiOGnsTixX/7bfv330s4ZfSptGmdRXJCCn1792Pq61MbtPlo1sdEm2O8fg49HyiqEvI5FfdW7aPW5Ts0fNW3K3n38rd5+fQXmXnjDPat3dtoXyu/Wcm0ie8x5YyXeP+q91j/i7eA9+/Cf5l+zfu8fPqLTL/mfbb8tcWrjYDAhpINx/aFggiLo9Tve1998A3jBlzGqKzTuPa0G1i9dG2jff385a9cPfo6Rrc9g/N6juXpOyZTaTlwnb/twjsZljbK6+eeK+5r0I9LcVETBLlej4fqRd+Q/8Sl7L3nVApfvAHH9sbnTnU7qZj3PvmPX8Leu08l/8nLqVn6Q/371jULKXzpBvbddzb7Jp1B4fPXUfvPL839NYKe6uVz2ffyVex+4hwK3roV+651jbZX3U7Kf/uAfS9NYPfj55A3ZSI1K39u0Eax1VD2/VT2PX8Zu584h7zXrse2ZXlzfo2gonrJt+RNvpI9D51Jwes3Y995+Dmt+Hk6ec9dzp4HzyT/+QnU/PNj/fs1K35mz32neP2oQZQi42jRBXoAR8LJJ59MdnY2u3btYtasWVx11VX179lsNr788ksAxo0bR2RkZKCGqdGE+MtB9eVXX3LvA/fy8gsvM3jQYD6Y8QFjLx7LP0v+IbN1ps/PXHXtVRQXF/PGa2+Qk5NDSUkJbveBQ+6ixYsYP3Y8gwYOwmg08sprr3DB2AtY/vdy0tPTG/Qlu2UkndR0X7SF8SdezvlyDpPum8QrL7/C4MGDmTZ9GheOvZDc5blkZvqe1wlXT6C4uJg3p75Ju5x2nnk95OYhJiaGVbkNb5ZMJpP3uBQFVVURhGBy7zo6rG7fIXU/ffULkx94iYdeuI8+g3rzxYwvufni2/l2yRe0at3Kb39zl39FVPSB/Sw+Kb7+/+02O62zMzjt/FN4/qGXGh2XTbYSS3yjbYKZ2kq7T+Hvt79/4tUZz3PP9Q/Ss1NvvvltDnc/cwsfT/matCT/8/rZK98SaY6q/z0uxntuCkrymTrrZXp16eu3H5fDjcvuRm8KicuoT9x2354+3/zwDQ8/+zDPPTKZgX0HMnP2TC674VL++n4RrdNbe7Wf8ekHPP3y07z05Mv07tGbVWtXcffD/yU2Jo7TR58OwHOvPsuc7+bw0pMv0z6nPX8sWsA1t03k+0/n0aNrjwb9ueyh7a3qVlwoPrxR5n35I0/f/yyPvfQIfQf34bMPPuf6cTfy47K5pGeme7X/+P3PePHxKTz96hP06NudtbnreOiOR4iJi2HMmaMAWLV8NXdeczd3Png7p557Cr/O/Y07Jv6XT3+eRe/+vbz6dCh2TFJE03/pFsJfrr9vv/yOR+59jGdffpoBgwcw64OPuGLsBP78ZwGtMzO82jsdThKSEvm///0f773xns8+/160hAvHn0//Qf0xGo28+cpbXHrBFfyx/HdapTfcY1xy6N4IwP6Hm35Eti/nfMm9k+7j5VdeZvCQwXwwbTpjLxjLPyuX+zwfRJrN3HjTDXTv3h1zpJklfy/hjtvvxGyO5NrrrqlvFxMTw8rVuQ0+6+98IEoh4avgk7zqfQgIXp5/mxdsYv4bv3PqHaeR0T2DNXNXM+e+L7j2g+uJSY3x6mfVt6v46/0/Of3uM0jr1IqCzfn8/NLPmKJMtB/a3mNrQx5zn/iW4deOoMPwjmxdtIW5T3zLZa9dQXqXA3uMisqeqsaFxlCg2uW7+NZv38zn1Ufe4O7n7qTngO58M2su91x+Lx8tnEFa61Sv9muWreOp25/j/564hWGnDqWksJQXJr3Mc3e/yLMfPAnAM9OfwHWQY0KlpZKJY65n1Lkne/VX46omWu/9bxgKWFfOp+LrqcSPuxNj2x7U/P0dJe9MIu3+D9HFe88dQOmMx1GqLSRc+j90SRnINRWgHLgGiuZoYk6dgD6lDYJOh23DEiyfPocYFUdEl4Et9M2Ci9p1f2D58W0SzrkNU5tuVP8zj+KPHiL9tvfQxaX4/EzJ508j11SQeMFd6BPSkWsrUA+aZ9XtoujD+5Ei40i65CF0sUm4K0sQjaF7zT8aatcsoPz7t0g4//8wZnejZtk8Sj64n1b/nYYuzs/a/fhJ5JpyEsfejS4xA7m2osHaBRCMZtLvmdHwNb2hmb5F89OiV9MffviBIUOGEBcXR0REBG3btmX8+PGUl3tCUX799VdOOeUUYmNjMZlMdO7cmY8++ghBEOoFv/nz55OXl1ff5zfffEN1tecJ+8SJE1vy62g0E6qi+vVOmPrmVK668iomXjWRzp06M/nZyWSkZ/D+9Pd9tv/1t19ZvHgxX37+JaNOHkVWmyz69+vP4EGD69tMe3ca/7nuP/Ts0ZNOHTsx9dWpKIrCHwv/8Oov1D3//BUteX3q61x91dVMvNozry9MfoHWGa15b5rvm6Jffv2FRYsX8dWcrxg9ajRZWVn0799wXgEEQSAtNa3Bjz9COe+fqqp+83vNfPMjLrryfMZedSE5ndpy77P3kJaeyuzpcxrtMyE5gaTUpPofSTogOnfv2427n7iTM8eejsHQ+AUo1It+1PrJAffZ97M4d/SFnDfmIrJb53DnxEmkJKXx9S+fN9pffGwCiXFJ9T+S2FDMlxWZx1+7n+svvpmMFG+h62BqykN7bl023yLb2zPe5vKxl3Pl+Cvp2K4jTz3wFBlpGcz4dIbP9l98+wVXXXIVF5x1AdmZ2Vx49oVcPu4Kpr7/eoM2d9x4B6eMPIXszGwmXnYNJw8/mbc+eNOrP3+iZKjgkH17gU1/YwbjJozl4qvH0b5TOx567n7SMlrxybTPfLb/dvZ3XDrxYs4eeyZt2mZyzrizGDdhLO+9Mq2+zYw3ZzJs1BBuuvsG2nXM4aa7b2DIyMHMeHPWUY0tVPAnsr0z9V0uu+pSrph4OR07d+DJyY+TnpHOh+97R4sAZGZl8tTzT3Dx5eOIjon22ebNaVOZ+J+r6d6zGx06tefFqc+jKAp//eHtFe9SQjstSGPedVNfm8pVV1/FxGuupnPnTkx+YTIZrTN4/71pPtv36t2L8RePp0vXLmRlZXHpZZcy5pQxLPm7obegIAikpqU2+PGFrIR2ZMC+6jyfDzZXfPEPPc7sSc+ze5GYlcTo204hOiWa1d/59i7d+Ot6ep3Tm86juhCXHkeX0V3peWYPln92wCs798sVZPfPZvDlQ0hsk8jgy4fQpm8WuXO8PYwrHBVY/XgkhgIuxek33H72O19wzmVncd4VZ5PdMYs7n7yNlIwUvv7wO5/tN+RuJC0zjfHXjyU9qxW9BvXg/AnnsnnNv/VtYuJjSExJqP/5Z2EuxggTo88deUhvArWu0I24qP7jCyIHnUXUkHPQp2URf9HtSHEp1Czy9joHsG1ahmPbapJumIypU390ia0wZnXB2LZ7fRtThz6Ye45An5aFLimD6JHj0Ke3w3kYr6xwpurvr4jqezrR/c5En9yGhLNuRopJpvqf7322t239B/uudaRc+SQR7fqii0/D2Lozpjbd6tvUrPoZxVZN8uWPYsrqhi4uFVNWdwxp7VrqawWU6kVfEtX/DKIGnoU+JYv4c29Bik2hZulcn+1t/y7HvnMtydc8g6lDP3QJaRgzO2PM6tawoSAgRSc0+AllWkz8Kykp4cILL2Tp0qXExsbSsWNHKioqmDNnDpWVlXzxxRecfvrp/P7777hcLjp06EBBQQErVnguWBMnTkQQBBRF4eOPP67vty4MuG3btpx00kkt9XU0mhFZ9n3QczqdrFqzitGjRjd4fcyoMSxb7ju854effqBPnz688tordOzWkd4DevPAww9gs/nPGWS1WnG5XcTHe3sEhXp4qi/x0ul0smr1KsaMHtPg9dGjR7NsmZ95/cEzr1NenUL7Tu3p1acX9z94v9e81tTU0LlbZzp07sDY8WNZvWa1/7GFcDVlh2L3mc/H5XSxcc1mho5qKIoOHTWY1csbD6O4eOTljOpyGtdfcBPL//rnmMdm8+ORGCrUltu88v253C7+3bGJgb2GNHh9YM8hrPt3TaP9TZx0CefeMIbbn/gPueu9QyE+mPMOcTHxnDv6osYHJkBtRXDlHjsaFFnx6WHtdDpZu2ENJw87ucHrI4edzIpVvteh0+nEaGzosWMymli1blV9jlHfbSJYnuv9byC7lJB+GOBQvAU2p9PJhtUbGT56WIPXh48eysrlq33243Q4MZqMDV4zmYyszV1bP6+r/lnt1eeIMcNYtdy3gBDK4p+qqrh9eP45nU7WrlrHyNENz4Ajx5zEimWNh1UfDTarDbfLRXx8nK/RIauh67Hq7/rrdDpZtWo1o8cccu4aM5plS4+siMWa1WtYtnQZw4YPb/B6TU0NXTt1o1P7zoy7aDxrVvveu5UQF//2VO31Eldll0zhlkKy+7dt8Hp2/7bkbcjDF7JLRmdo+LBKZ9RTsLmg/myavzGP7H4N+2zbvy35fvrMrwmuYhlHg7/CPy6ni3/XbmHgyf0bvD5wZH/W/+M7z2GPAd0oKSjh79+WoqoqlhILf3z/J0NOGeyzPcD3n/zAKReMIiLyUK8q1a9HYrCjul049/2LqfOABq+bOg/Auct3mLht/d8Y2nSiev6n5D86joKnr6Ti2zdRnL5TNKiqin1LLu7ivRjbeXunnwiobhfOgq1EtOvX4PWI9v1w7Nno8zPWzUsxpnegatEX7HvhcvJevZbyn95FcTkatsnsguX7qeydfAn5U2+g8s9PG3gHhiuq24UzbwumDg3/7k0d+uHY7XtObZuWYGjdkeo/Z5P3zCXkv3g15fPeaTCnAKrTRt5zl5P3zKUUz3gQZ97WZvseLUGLxSvt2bMHp9OJ2Wxm06ZNmM1mVFUlNzeX5ORk7r33XlRVpV27dixatIi0tDQcDgfbtm0DPOLeiBEjWLhwIbNmzWLSpEkUFRXx66+/AnD11VeHdMigxgH83fSVlZUhyzIpyQ3doZNTkikqLvL5mV27drFk6RKMRiOfzPyEMksZ/73nv5SXl/PW1Ld8fubRJx4lvVU6o0aO8h5bCD/ZB9/jr5/XlIbzmpqSym9FvnNH7dy1kyVLlmAymvjs488oLSvlrrvvory8nLfffBuATh068c5b79CtWzeqq6t58603OeW0U1i6eCnt27c/orGFCv6KapSXVSDLMonJiQ1eT0xJpKzYd8GIpNQkHp3yIF17d8HpcPH95/O4/oKbmT73XfoP9R+G6g+F0A6pdjm8i/9UVJUjKzIJsQ3nNSE2EUuF7xxAifHJ3HvDI3TO6YrT7eSnhd/zf0/ewNRHp9Gnq+cAtnbzKubO/5oPn2/cexA8OZNcjtA9UKmy7783S7kFWZZJTkxu8HpyYjLFpcU+P3Py8FF8POcjzhxzJj279WTN+jV8+tUnuFwuLOUWUlNSOXn4KN6Z8TZD+g8hu002fy1ZyM/zf/L7sEeRFS+vzFDBl6dS3V6QlNJwzSYlJ1Ja5HvNjhgzjC9mzuHUs8fQrXdX1q/awJyPvsblclNeVkFKWjKlRaUkpnjvLyV++gy25PhHi6+HLJay/Ws25ZA1m5xMSVHTVTx/+tFnSUtPY8So4T7fD+XcdP4KFJSV7j8fpB567kqhqMj3uauOTu07U1paitvt5oEH72fiNVfXv9exUwfefvctunXrRlV1NW+98RanjjmNv5ct9jofhPLZAKDW5f0AzlZpRVVUIuPNDV6PjI+k1uL7gV32gLas/WEt7Yd3JLVDKkVbCln301oUt4Kt0kZUYhS1llrMCQ3TH5kTIqkt992nv1yEoYA/r78KSyWyrJCQ3PABfnxyPGUlvose9RjQnUffeJBHbnwCp8OJ7JYZfvpQ/vv0//lsv3HlJnZs3sn9L//Pz9hC03tdqa0ERUGKbjh3UnQ89iqLz8/IZfk4dqxD0BlIvPZJlNpKyr+YglJbTcLl9x7o21ZD/qPjUN0uEEXix92FqVN/n32GO7K1ChQFMSquwetSZBxyje816i4vwL5nA4LOQPJlj6BYqyj7fiqyrZqkC+8+0GbnaiJ7jiZlwlO4y/KwfD8VVZGJG3Vlc3+tgCJbG1m7W3yvXbelAMeu9Qg6A0kTHkeprcTyzWso1ioSx3v+tvUpmSSOn4Q+tS2Kw0r14q8oevtO0u54B31S49FBwUqLiX/dunUjJyeHHTt2kJKSQseOHenRowdjx44lKyuLnTs9SdevueYa0tI8oYFGo5Fu3Q64Xk6cOJGFCxeyfv16Vq9ezYIFC5BluUFYsEb44yViqD5e24+iKAiCwLR3pxEbEwvAM089w4SJE3j5hZeJiGj4xG7Ka1OY8+Ucfpj7g8/cM0FUrO3YaGT8wiHuVY0JRnXzOv396cTGeubV4XBwxYQrmPLSFCIiIhg4cCADBx7I5TFk8BCGjhjK2++8zYsvvHhUYwt2lMMNXvCeW3/Va9t2yKZth+z633sP7ElhXhEfTp11TOIfgIqCQGgKKY2G2h86r57NwGfTrPRsstKz63/v0bEXxaWFfDr3Q/p07UetrZbHX3+A+2581GceQG9CuwDQYW+ofcytv/3gv7f8l+LSYs669ExUVSU5MZlLL7yUqe9Prc/V9dSDT3H3w/9l2FlDEQSB7MxsLr3oUj77ynfIayjf8Kv4XxeHzmFj++ytk26mpLiU8adchqqqJKUkctHlF/Deq9OQDsqBdjR9hnIVysNVS/Wah0b2g6PljSlv8s2cb/jyhy98nw0I9TXbOF7TeAQPlH7+7Sdqa2pZvvwfHn3kUXLa5TD+4vEA3ueDIYMZPmQE77z1Di+89MJRjS3Ycft5OAj4PBv4m9YhE4ZSa6nl41tnoaoqkfGRdD+9B8s/W4YgHviQ18cbWZcuOTRFKji82O77XsF3253/7mLKQ69zzX+vYtCoAZQVlfHGE+/wwqSXuX/KJK/233/6Azmd29K1bxef/TV2DQgNDp07/2fWuutN4oSHECM8+ZTVC26lbMajxI27E9Hg8V4XjGZS//c+qsOGfetKKr55A11iK0wd+jTnFwlqDr3v8ixSP41VFQGBpHH3IZo8An/CGTdQMvspEs65DVFvBFVFiowj8bw7EEQJY3oH5KoyqhbPCXvx7wC+1q7fxeuZ00vvRzR51m78OTdR+vETxF/wf4h6I8Y2XTG26Vr/EWNWNwpfv5nqv78h4bzbmutLNCstJv6ZTCZyc3OZNWsWy5YtY+PGjcyaNYuZM2fy+eeH97IAGD9+PLfffju1tbXMnDmTBQsWADBy5Ejatm17mE9rhDqJiYlIkuTl5VdSUuLlDVhHaloq6a3S64U/gE4dO6GqKnn5ebRvd+AJ86uvv8pLL7/Ed19/R/du3X11539TDhUEvE7S/ua1uKTYyxuwjrS0NM+8xh40r532z2tenk/PPlEU6de3H9u2bzvurxFs+FsW8YlxSJJEWXFDLxxLicXLG7Axevbvwfef/3D4hkc9wuDH1w1mXEw8kih5efmVV1q8vAEbo1vHnvz81zwA8or2UlCSz6TJB570191cjLi0L5++8i2t0xomtw9Vb0rA75JIiE9AkiRKDvHyKy0r9fIGrCPCFMGrz7zKi4+/SElZCanJqcz6fCZRkVEkxnv+PZISkvjwjZnYHXbKK8pJS0njqZeepE3rNn6GF8Jz62PsdXvBoR55ZaUWL8+9OkwRJp5742mefOUxSovLSElL5rMPviAyOpL4RI9AnZSa5OU5aCmxeHkY1o8shNesv5EnJHrWbHHRIWu2pJTklKTjtvvWq2/z2ktTmf3dp3Tt3tVvu3Cc28Sk/eeDwoZzW1JS4vd8UEd2djYA3bp3o6S4mGeefrZe/DsUURTp268v27dtP+KxhQqi4J1hKSLWjCAKXl5+1gor5njfhQv1Rj1nTjqL0/57OtbyWiIToljz/RoMZgPmWI8HYWSCt+egtdxKpJ8+Q9W7Gvz/vcUlxCJJImXFDb19ykvLSTiocNrBzHr9E3oO6M4Vt14KQPuu7TCZI7jl/P/jP/ddR1Lqgf3UbrXz2zcLuH7SRP9jC9FVK0bGgigiVzecO7mmwm+eMykmESk2qV74A9CnZoGqIleWICZ7vKMEUUS///8NrTvgLtpN9W+fnJDin2SO8czzIV5+cm0lUqTvNSpFJyDFJNYLfwD65Daeea4qRUzMQIpKQJAkhIP+rvXJbZBrLKhuF4JO3zxfKAiQzI2s3ahG5jQ2qV74A9CntDmwdn149gmiiKF1R9ylvlMphAItlvOvqqqKzZs3c9ttt/HRRx+xcuVKRo3yhFUuXLiwXrybMWMGxcWeQ4bL5WLjxgNx2lFRUYwdOxaA999/n9WrVwNaoY9ww98F3WAw0KdXHxb8saDB6/P/mM+ggYN8fmbwwMEUFBZQU3MgN8i27dsQRZGM9AMVAF957RWef/F5vvriK/r28e9dFaoX9MYwGAz06d2H+fPnN3h9wYIFDBrke16HDB7iPa/b9s9rhndlRfA8HVy7dm29Z68XITy1vg73AHqDnq69OrPkj4a5kZb8sYzeA3secf+b1/5Lctqx38SG8rqVdN5zq9fp6ZTTheVrlzZ4/Z+1S+nR6chzyGzZuZnEOM+8ZqW3ZdaLc5jx/Oz6n+H9TqZvtwHMeH42qUne69bX2EKFxvbZnt168efffzZ4feHff9K/zwCfn6lDr9eTnpaOJEl8M+8bTj35NESx4RyZjCZapbbC7Xbz/S/fc/roM3x3JobumhV87AcGg4FuvbuyeEHDogeLF/xN34G9G+1Pr9fTKiMNSZKY99UPjDr95Pp57TOgt1efi+Yvps9A3zdUQsvWeWtiGlmzfXqwcMFfDV5fOP8v+g86vrCyN195iynPv8onX82id9/G95ZQ3mcbm9s+fXqz4JDzwfz5Cxg02Pf5wBeqquJ0+K+IrKoq69auJdXf+SCE0YvefhaSXiKtYxq7c3c1eH137i4yuvk+Q9V/VicRnRyDKIlsXrCJdoPb1Xv+pXfNYNchfe7K3Um6nz59jS1UEAXfwqXeoKdTz47882fDfJ///JlL9wG+H+zbbfYG3pNAvXf1oR69v3/3By6nk9PHntrI2EJznxV0egytO2H/t+Hc2f9dgSG7m8/PGNt2R64sQ3EcCCF3l+wFQUSK9f3AEAAVVHdoV0k/VgSdHkOrDti2r2zwun37ygZeZgdjbNMNudqC4jiQa9pVus8zzzFJ+9t0xWUpQD0oh6urbB9SdEJYC3+wf04zOmLf1rCCvH1bLsYsP3Oa1Q25qqzhnJbsa3TtqqqKq2B7SBf9aLFdv7i4mCFDhhAfH0/r1q1xOp38+6+nilLPnj056aSTuOSSS9i2bRtt27alXbt27Nmzh4kTJ/LKK6/U9zNx4kRmzpxZX+E3KiqKcePGtdTX0GgBBMn/Afq2W27jPzf/hz69+zBwwEBmfDiDfXn7uO6a6wBPvr6CggLefetdAC4edzHPv/g8N992Mw/e9yClllIeevQhJlwxoT7kd8prU3jqmaeY/u50stpk1eexiYyMJCoqqoF9UQrNC3odoij6zEd1+223c/0N19Onbx8GDRzE9A+ms3ffXq6/9noAHnnsEfLz83n/XU9V5YvHX8xzzz/HTbfcxIMPPEhZWRkPPvQgV024qn5en3n2GQYMGED7du2pqq7irbffYu26tUx5aYrfsYUqRtF3GBjAVbdcyf03P0y33l3pNaAnX3z4FQV5hVx8jWffeuWJ1ykuKOGZt54AYNZbn5DephXtO7fD5XTx/Rc/8Ovc35ny4YFQKJfTxfZ/d3j+3+WiuKCYzev+xRxppk1OQ+80g2gMaY8UU7TRp8fqpedM4InXH6RLTle6d+zFt799SVFpARec6vEseeuTVymxFPPIbU8DMHveR6Qlp5OT2Q6X28XPf83jj2W/8czdLwFgNBhp16ZDAxvRkZ4qoIe+Dp5Igohoo9froYKoE33OK8BNE2/itntvpVf33vTv3Z9Zn89iX8E+rr7Uk7PrqZeeorC4gKmT3wBg+87trFq3kr49+1FRVcHbM95m89bNvPbcgWq/uWtyKSwqoFuX7hQWFfLC1BdQFIXbrvcOmxBEAbGR60CwYxB9V+C+9taJ/O/Ge+nepxt9BvZm9owvKNhXwGXXXgLAi4+9TFFBMS+88xwAO7ftYm3uWnr170llRRUfTP2QrRu38vxbz9b3efXNE7j8zKt4Z8r7nHL2aH6bN5+//1jKpz/7rvZrkBqvDh7MCIKAJEg+8xbeeNsN3P6fO+jVpyf9Bvbjoxkfk7cvj6uumwB48vUVFhTy+ruv1n9m/VpPAvvaWitlpWWsX7vBIxx07gh4Qn2ff+pF3pj+OplZmfWehZGRkURGeXtSSaEspDRy/b3t/27jP9fdQJ++fRk4aCAzpn/Avr37uO76awF49JHHKMjP5933Peeud99+l9aZmXTs5Nk3l/y9lNdefZ0bb7qhvs9nn36WAQMH0K59O6qrqnnrzbdZu3YdL015ycfYQtc7DSA1Mo2aCm+Pxv7jBzDv2e9J65RGetd01ny/hqqiKnqd2xuAhe/9SXVpNWfffw4Alr0WCjYXkN6lFfZqOyvm/EPprhLOuu/s+j77XdSPT+/8hGWfLqX9sA5sW7yV3bm7uey1K3yOLcXcuPdmMBMhmf2+d8mN43ny9mfp3KsT3ft349uPvqcor4gLrzoXgLeefo/SghIenvoAAMNOHcrke17k6xnfMnB/2O+rD79B1z6dvR68fv/pD4w4YzixCbFedusw63x7WoYC0SePp+zjZzBkdsKY3Y2aJXORy4uIGnYeABVz30WuLCXxSs/cmfuNoeqXmVg+mUzsmdcg11ZS8d3bRA46sz7kt+rXjzG06YQuMR1VdmHfuIzaf34mfvxdAfuegSZm6EWUfvUCxoyOGDO7UL3iB9yVxUQP8Pw9l/86HbmqlKSxnrDzyB6jqPzjY8q+eYnYURNQrFWU//I+UX1P84T8AtEDz6F62XeU//gW0YPOx1WWR+XCz4gZfH7AvmdLEj18LGWfT8aQ0RFjVldqls1DrigmapDn777ip/dxV5aSdMl9AJh7j6Fy/seUzXmBuFOu9qzdH98lsv/p9XNa+dtMDG26oE/M2J/z72uc+duJP993PtBQoMVOKomJiUycOJFly5axc+dOFEWhc+fOXHXVVVx//fUIgkBcXBzPPfccK1asYMuWLWRlZdG/f8OntieffDJt27atzxE4btw4IiNDd5PV8KaxQ+jYi8ZiKbcw+YXJFBYV0rVLV+bMnkObTE/oWGFRIXv37a1vHxUVxbdffcv/7v0fJ405iYT4BC684EIeefCR+jbvT3sfp9PJlRMb5kO4f9L9PHDfAw3HFsKePuARL31VLB43dhwWi4XnJj9HYWEhXbt25as5X9Gmzf55LSxk37599e2joqKY++1c7v7f3YwYOYKEhAQuuvAiHn340fo2FZUV3H7H7RQVFRETE0Ovnr345cdfvP6mDx5bqCKJOvSCHpfqnT/njItOo6K8grdfeI+SolLad2nHm7NfIz2zFQAlRaUU7Cusb+9yuXjpkVcoLijBaDLSvnMOb8x+lZNOPZBkvriwhPEjL6//fcbUWcyYOov+w/rxwdx3G9gP5UMoQFR8hE+B6pShZ1BZXcn0L9+lrLyEnMz2vHj/G7RKTgegrLyUotKD5tXtYuqslymxFGM0GMnJbMeL901laN8Rxzy2yPhDq/yFDoIgoDPpcNu881FdcNYFlFdYePmNlygqKaJzh8588s6nZGZ4hOXikiLy8g+EPMiKzFsfvMX2ndvR6XQMGzSM7z+d1yCk1+Fw8Nyrz7F7724izZGMGTmGNya/0SAlQx06oy6kBWuj5PthwNljz6TCUsEbz79FcWEJHbt04L0v3iGjjccrp7iolPx9BfXtZVlm2usz2LltFzq9jsEjBjL7109onXXAi6fvoD5Mmf4irzz1Gq8+/RqZbdvwygcv0bu/by+1xh5UhAJ6yYDs9q6yff7Y8yi3lPPy5FcoLiymU9dOfDRnJpltPGE7xYXF5O1tGKZz6rDT6/9/7aq1fP35N7Ru05p/Nng8ime8PxOn08l/rryxwefuvv8u7nng7gavSYIUst4+cJhz17ixWMosTH52cv35YM7XcxqcD/buPXA+UFSFxx59jN27dqPT6Wjbti2PP/EY1+4XCwEqKyv5v9vu8JwPYmPo1asnP/36I/0HeJ8PpBB+MAiQGdOanZU7vXLUdR7VBVuVjb9nLqbWUktSdhJjnx1PbJpnT6yx1FBdfKBqrKoorPhiOZa9FkSdSJveWVzx2pX17QEyurfm3IfPY9H0v1j0wV/Epcdx7sPnkd4l3WtcelFPkvn4w+IDhVlnRkDwmQv0lAtGU1VexQcvz6Ss2EJO52xe/Pg50jI9nqVlRWUU5R0IZT/70jOw1liZM/1rXn/8LaJioug3vA+3PHRDg373bN/L2mXrmDK7YV7KhghE6WOa5DsGAnPf0cjWKqp+/hC5yoK+VVuSbpyMLsEzd3JVGXL5gTRBotFM8s0vUv7laxS9dCNiZAwRvUcRe9Z19W1Up43yL6YgV5Yg6I3oUtqQeOWDmPuO9rJ/ohDZ42QUWzUVf3yMXG3BkJJFypVPoYtLBUCutuCuPFCwSjRGkHr1s1jmvUnhO7cjRkRj7n4ScWMm1rfRxaaQetUzWH56h+o3b0IXnUTM4AuIGXFxS3+9gBDZaxSKtYrK3z9CrragT8smeeIz6OL3z2mVBbniwN+9aIwg5brJlH83lcKptyCaYzD3GEns6dfUt1FsNVi+moJcXY5oisSQ3o7UG6dgzOzc4t+vqRDUUM5QrBG2WKusQZlM3xRpQmcI3af7sizjsDoO37CFEQSBiKjQFVIA9tTsoMpVEehheNEqojWJptB9uu+wOlk5b3Ogh+GFIAgMvKg7YgiHp1YX12Ar9xZSAo05PoKolKjDNwxSVFVhe/W/gR6GT7KjOqALYQ+1Wmc11c6qwzdsYUy6COJMoRsGBFBjqyEYy2tEGM0hnZtubfFaPtrwSaCH4UVWTBa39rs50MM4LtaU5VLrrg70MLzoFNuNRFMjIa/HycqVK+nXrx+5ublc8Ffwff9jZc8dIwM9hHqyn/wl0ENoNnY9fFpA7WdN/j2g9puD3feOCfQQjpjQfpymEbZIuuA86IW851+QPkEPZa+/Osy64BQrgnVcR4ohQo/eFHxiRVRCREgLfwD6iODMAROs4zpSBEEMSg87naALaeEPQC8FZ6i93k+odygRrAJbKHtUArSJyQr0ELwQEcmJC/1CibGGWIIxYXR0CHv+aWhohDehfUXVCFv0xuC7+ZN0UtCKZ0eKIAjo9MF38xeMYzpa4gwJBNsh1CiaMEmh7VEpCAJp7Y68gm9LkRqEYzpajJEGryTngUaQBAxRoS+kxBp8V5cLJDFBOKajRS/qkYTgu15E6P3nHwsV9EGYEF4v6UM6BQBAnCmWjvEdEIPolktBoX+r4yuGEwykRLQi2LxV4w2JGIL0IYWGhoZG8FyJNDQOQpTEoPOy05uC72B8LASb0CYIQlh4/ulEHbH64Lq5TjSlhPyNE0BK24Sg0lUlvURia//JvkMFQRSIiAsucTgiLiIs1myUPiboKuvG6OMCPYTjRhAEIg3BlcfUpDOHvHcaeDz/gu1vTxeEguSxMLT1UBSCI5WNgED7+PYkh3C+vzrMukhi9MF1LU4zN16tWUNDQyOQhP5pRSNsMRiDx/tDEISgDUU+WkRJDCoPRp0htJP7H0xz5ng5WkRE4sLA0wc8ob8JGbHBIQAKkJqTEBaCNUBEXHCFp0bEBtd4jhVREIk1xAV6GPVE6WJCPuS3DpPOTHBsBh4i9cElRh4rgiCg1wXPuUsUxKANRT5aOid2IsYQHKGgKirDMoYEehhNRjCJbUbRGDbnLg0NjfAkPO5eNMISSS8FzQ220WwMG4EKgiesOljDkI+VCMlMdJA8hU6JaIUohMeNE0CbbqlBcbsv6STSOwWPyHu8SHopaLz/IuIjkPThs2bjDIlBEuonkGAMfS+fOkRBJMoQHehhAJ7KznopeASz40Uv6RGCYqcFoz58QidFQeTs9mcFehiIiLSJaUOXpC6BHkqTkWhMJjJIchtnRbcLq3sFDQ2N8CMYTqUaGj4RBAFTZOC9QCS9FNIVfn0h6aSgEN0MJkNYHZQEQSDD3CbgoluEZCbRGLoVfn0REWMis0daoIdBu/4Z6I2B/9tpSqKSIwOeZkHUi0QlhYcHVR06UUdyRODXbJIxOexyUEXqo9CJgX2IJSAQa4oL6BiaGkEQMBkCf+7S6/RIUnjts71TetE1qWtAHwgIgsAlXS4OizD1OgRBoENsl4CL1onGZJJM4XXu0tDQCD/CZ/fXCEtEScQQEcCn6gKYzIE/CDcHemNgE2nr9LqwCaU+GJ2oJ92cGTD7AgKtI7PDSlStI71jMpHxEYGJ+BMgISOWxNZxATDevAiiQEyrwHpSxaTFBF3xkaYgShcTUK8Uo2Qi1pAQMPvNhSAIxJkCG14Xa4oP+IOe5kCSdOilwAmrgiBg0IWXWA2e7zW244UYAugpela7M8Mi19+hmHWRtIkKXPVinaAjJ6ZDwOxraGhoHCmCqqrBVSZJQ+MQVFXFXmNHdsstbtsUZQoKD7nmQpEV7FZ7i9sVRTHsQqkPRlVV8q17KHeWtbjtDHMW8cbQr0TrD3uNg3W/b8PtkluuyJ8ApkgD3Ue3Dzuvv4OpLaulttTa4nYjkyKJTAz9aqn+kBU3e2t34VZdLWpXEiRaR2ajF8MnLPVQrK5aqhwVLW43QmcmNsDiY3Oiqio2hxVFbfkiFRFGc9jk+vPF5rJ/+WDtDNQWrFIrINAlsTNX9ZgQVl5/B6OqKpsr1lHutLSwZYGucT2IM7bcQ5aVK1fSr18/cnNz6du3b4vZ1dDQCH3C8wqgEVYIgoApytTiYWmmyPAW/sDjWWmMaNkn7IIohLXwB541m25u0+LVf1tFtA5r4Q/AFGWk68gcJJ3YMh6AAhhMerqOzAlr4Q/AnGAmIr5l8/+ZEyIwJwRHzsHmQhJ1ZERmIQktt35ERNLNbcJa+AMw6yOJNrRsnlWTFEGMMa5FbbY0giAQYWz5KsYRhoiwFv7AU/zjsq6XtliYqoBAu7gcruh2edgKf+BZsx3jurV49d+OsV1aVPjT0NDQOB7C9yqgEVYIgkBEVESLhYmaIk1hl+fPH5JOajEBUBRFTGZTWAt/dQiCJ/w2ztAyYly6uQ2JJ0i+mci4CI8XnkFqXgFwv8dfjzHtMZrDW0QBz5qNSo7E3EJeeJGJZiKTIk+I/UAv6smMzEYvNH84ZZ3Hn1EKz5QVhxJpiGoxMa7O4+9EWLMtLQBGGMxhl+fPH71TezGh+5WIgtjsOQC7JHbmmp4TAxrK3VJIgkSX+J7EN3uqAwEBgc5x3bU8fxoaGiGFFvarEVKoqorL4cJpczZL/6IoYow0hmUuusOhKApOmxNFaZ4wH51Bh94Q2DyDgUBVVSqcZeRb96HS9HOrFw20NmcTqQ+OanctidPuYkduHuX5Vc3Sf0rbBLJ6tUIXRhVojxRHtYOqwmpUpemPCIIkEJMWjTEq/PJ6HQ5ZlSm1F1HtqmyW/iN10SSb0tCJJ4aIcjBO2UGFvRxFbfoUIQICMcZYTDrzCXkNc7qduNzNde6SMOlNiOKJ54+QX53Pp5tmU1Rb1KT9ioKIgMCZOWcwPHNYWHv8+UJVFfKt+9hTs7NZwqvNukg6xHQJ2LlLC/vV0NA4VjTxTyMkUWQFe60dRW46McVgMqA3nXji1MGoqorb5cblaLrcVIIgYIgwIEknnoByME7FSV7tbmrd1U3WZ6IxhdSI9BPuYH8wqqpStreCHSvzkF1Nsx/oTTraD8wkLjWwRTACjSIrVBfV4Kh2NFmfxmgj0alRiNKJu2YBat01FNvykZtIqBIRSY5oRbQ+pkn6C1UUVaHGWYXVVdtkfRokI7HG+LAPRz0csiJjd9poytsGo96ITjqxz11uxc2C3X/w267fERBQjuMhoYCAikpmTCaXdLmYFHNyE4409LC6a9lauYlad00T9SjQJjKb9MjMgJ67NPFPQ0PjWNHEP42QRVVVZJeM0+FEcR/7YUlv1KM36k/4m9GDURQFt8uN2+k+5j4EUUBv0CPppBP6YH8wqqpS466izF5CjfvYvNUEBOIMiSQYk4jQhW+RhKPF5XBTvNNC4bZSnDa3Jxz4SK9u+9uaoo20ap9Icnb8Cen96w+n1YWtwnZcIqAx2og5PgJ9RPiHnh0piipT5aqk0mHBdYzFQHSCjlhDAjH6WKQT0NvPHy7ZidVVi8197AVsjJIJsz4SgxTeOWqPBlVVcctuXG7ncRUD0esM6HX6E/rB1aGUWEtZkreE5QX/4JSd9ULekSAKIoqqkBOXw7CMIXRL7qbN7X5UVaHMUUqhNY+qY/S4FgWJ1IhWpEWkB8W5SxP/NDQ0jhVN/NMICxRZweVwIbvlw3sDCiBJEjqDDp1Bpx3qG0FVVWS3jNvlRlXUwz7xF0TBM7d6nSamHgan7KDcWUaNqxq7bG30kC8KEmbJTLQ+ljhjIpKgCVP+UFWVioJqSvaUU2Ox4qhtXFQxRRuJTjSTkh1P9AmSf+5YUdwKtkobzloXbru70f1AEAR0Jh2GSAMRsS1fsCmUUFUVm2yl2lWJXbbhUhoPr9QJeky6CKJ1sZh12pptDEVVsLmsOGQ7Ltl5GDFFQC/qMUhGzHqzJqYeBlmRcbldyIqMehghUEBAFEV0kh6dpJ27GsMpO1lVtJqNpRvZU7WX2ka8WCVBolVUGjlxOQxsNYCUSC3/XGNY3bUU2QqoclZgddc2uh/oBT1R+hgSTEkkmVKC6tyliX8aGhrHiib+aYQdqqqiyIond91Bq1sQBURJPCHzyjQVdXOrquqBuRX2z60oagf6Y0RVVRyKHYdsR1EVVBQERCRBwqQzoxdO7LCo48Htkqktt+G0ueofDIiSiCnKgDk2wlM1WOOo8XheK7gdngcDqCoIgsfj16hD1Gv7wbGiqAoO2Y5bddV7V4mI6EQdRsmEGEQ3oaGEqqooqoxLcaGqSv0lTBQ8cysJmih1rHjmdv+566DDgSAISKLm/X88VDuq2VeTh9VVi0t2I4kietFAamQKKeaUEz4c/VhRVAWb24pNrkVWPedaURDRi3oiddEYpOAt8qWJfxoaGseK9lhTI+wQBAFJJyGhHYiamrq51WhaBEHAJEVgkiICPZSwQ6eXiE058YqhNDeCIKAzSOgM2n7Q1IiCGBShZeGGIAhIgk7z6GsGPHMraUJUMxBtjKaLsXOghxF2iIJIpD7qhCyWpqGhceKiuTxoaGhoaGhoaGhoaGhoaGhoaGiEKUct/hUUFLBu3Tq/79tsNlauXElNjf/KShs3bmTfvn2aDc2GZkOzodnQbGg2NBuaDc2GZkOzodnQbGg2NBuaDc2GZuMYbRwR6lHy6KOPqhkZGX7fX79+vQqof//9t982Xbt2Ve+66y7NhmZDs6HZ0GxoNjQbmg3NhmZDs6HZ0GxoNjQbR2AjNzdXBdTc3NyQ/h6aDc2GZqNpbRwJRy3+5efnq2vXrvX7vtVqVXNzc9Xq6mq/bTZs2KDu3btXs6HZ0GxoNjQbmg3NhmZDs6HZ0GxoNjQbmg3NxhHY8CX+heL30GxoNjQbTWvjSNCq/WqEJXXL+uDlLQiCVnGuCXC7ZBxWJ4qs1BcAMUUZtLk9TlRVxV7rxO10o8gKkk7CEKHHYNIHemghj6KouJ1uVFkFAURRQGfQIYjamj1erJU27DV23C4ZnV7CFGXCHKsVrjleFEX1qlBtMOkQJS1V8/HiUpw4ZSeKqtRX+jWIRu0adpwoqkK1oxqX4qlSrRP1RBkig7pqaqigyAqKW/FUVt9fVV3SKqofN6qqIqtuFFUF1PqiQKIQ3PusVu1XQ0PjWNFKnmmEBaqqIssKiqJ4DkmK4rOdIAiIoogoiUiSiCgG9wU+GLBW2cnbUowlv5LSvEqqLbVwyCMDSS8SnxZDYnosSa3jSO+QrFUFPgyqopK/rZiC7aUU77FQsseCw+ryahcZF0FqdiJJreNp0yWNhPTYAIw2tHA7ZSqLqrFVObBW2LDXOr3WrCAKmKIMmOMiMMeaiEmJQtJp+0FjqKrKrlV7+XfJNvaszWPXmr1Ul3rnLolJjiKrVyZtemTQaWh7sntnajeph8HtlCnZU05VaS1VJbVUW6yeG/2DEASByHgTsclRxCRFktwmHr1JO8YdjgpHOQXWfModZZTZS7HJVq82BtFAgjGJBFMiyaZUUiJStTV7GFyyi3Ul69lRvpNdlXvYV7UPp+Js0EZAIDUyhbZx2bSJbUOvlB7EmeICM+AQwmVzUVthw1nrxF7txO1we7URJAFjpAFTlBFjtBFznElbs4dBVVVq3NXY3LXYZSt2tw0F7/sFvWggQjJjkiKI0sdgkIwBGK2GhoZG06N5/mmENKqi4na7cbm8D0ZHgiiK6PQ6JEl7gnowqqpStMvC1hV7yNtSDKpHLDn0ZvRQ6troTTra982kXZ/WRMWbW2jUoYG91smWf3ax/q9t1FisRzavAoCAqqqkZCfQfUR72vbM0ATWg1BVFVulndI9FVQUVnvEPgEv0c+L/W0EUSAhI4bENnGYorSD/sE4ah2smLuGPz/8m4KtxYiSgKrS6LoVRAFBAEVWadUxlZFXDaX/eb0wmjUvoIOpKbeyb3MxBVtLUWT1CPcDz14giAKpbRNo3SWF2OSoFhpxaCCrMvtq9rC1cjMWRxkCwv6t4DBzi4CKSqQuig6xnciOztFu/A+h1FrKwr2LWbz3b2xuG5IgIatyo58RBbE+EqNXSg9GZp1Ex4QO2rnrIFRVxVpuo7KwGnuV48g/uP8aJulFYlKjiU6JQmfQzgYH41JcVDotlDtKD7tWfWHWRRFvTCJKFx0Ua1bz/NPQ0DhWNPFPIyRRVRWXy437GEW/QxEEAYNRjyRpB6bywiqWfreOyuKaI7oR9YcggKpCTu8Mep/S6YQPX1VkhbV/bGHFTxs9oXzHuPPW3fibooycdHE/snukN+1AQxCH1cnedUVYK2xHJvj5Y/9nY1KiyOiagt54YntVqarKks9X8NXT83BYnfV/00dL3Zo1RhoZ+9DZDB7XLyhuoAKJw+pi85JdlO6pOOZ5hQP7bFxaNF2HZxMRbWragYYg+2r2kFuyDIfiqBfzjhVREOke34uOcV2CPhSwubG6rHy5+WuW5C1DFEQU1XeExeGo+2xGdDpX95hA65iMJh5p6GGrtFOyvQy38+iFKV/EpccQ1zoW8QRPbaGoCqX2QiyO0ibpTy/qaWVug1kX2ST9HSua+KehoXGsaOKfRsghywpOh5PmWLo6nYTeoD8hb0xlWWHjoh1sXLSdOi+zpkAQwGg2MOi8HrRql9QkfYYa5UVVLPhoOaX7Kpqu0/1CVbs+mQwb2xtT5InnnaKqKmV7KijYUupZr021JQieHGsZXVOISwuOJ/0tjSW/go/v+5Itf29v8r47D2/P5c+OJb7ViRfCrqoqRTssbF6yy5PDq4nWrCB4vC3bD8ikdeeUE3LNOmQ7K0v+YW/t7ibvO96YyKCUocQYTrw1C7ChZCMz131MrbMGpYk22jpvwLPbn8HpOachiSfew1dFVijbU0F1kXf6hONFb9KR3D7xhPVkt7lrybfuxXVIKHpTEG9IJDmiVcAeCGjin4aGxrGiiX8aIYXL5cLlbBpvP38IgoDRZDih8gHaaxz88WkuFUXVzWNgv1DVeUg2vUZ3PKFuTLeu2M2fn604bJjksSIIAkaznrNuHEFSZnyT9x+syG6FXavyqLXYmtVOXKtoMrunnVDFQTYt3ML7t36M2ynXF5xoSkRJRGfU8Z83r6Dz8A5N3n+wosgKG//aSdFOS7PaSUiPocfo9uj0J46YYrGXsrBgAS7FeVyefv4QEACBgSlDyIpu2+T9ByuKqvD1v9/y+64Fx+1F2RiZMa25rd/NRBujm6X/YMRld1GwsbjJvP38kZAVR1yrmGa1EWyU2YspsRc2qw29aCAzsm1A0gJo4p+GhsaxcuKoGxohjaqqOJ3NL/zV2bLbHH6LhoQb1io7v85YRmVJ0z95rmf//cLmJbv4Z96GZvHaDEY2Lt7Ogo//QZHVZhH+wLNeHVYX3039g8KdTRPaEuzILpkdy/c2u/AHUFFQza5VeSfMfrD65/W8/Z+ZuOzuZhH+wCOCuWwu3rr+Q9b+urFZbAQbslthzW9bm134A7AUVLHyp824W+B6GQyU2IpZkP9rswl/ACoqKgrLihezvWprs9gINhRV4aP1n/D7rgUAzTa3AHnV+bywdAoV9spmsxFMOK1O8tYXNbvwB2DZXYFlb0Wz2wkGVFWlxFbY7MIfeKqG767ZhkO2N7stDQ0NjaZCE/80QgJ3E+b3O1JOBAHQXutk/qx/sFbZm02cOpQdq/NY+fPmsBcAtyzfxaI5q1rElqqquF0yP7y9iNK8ihaxGSgUt8KO3Dxs1UeREP04qS61smdNYYv9jQSKjX9u4YP/+wxFUZr971NVVRRZYdrtn7B58bZmtRVoFEVl/R/bseRXtYxBFarLrKz+dSuyu/nFhUBisZexsOB3ZFVuVnHqYHJLlrG7emeL2AoUqqoye+MXLM1b3iL2FFXBYrfwyvLXqXHWtojNQOGyu8jfWIzibrnzZUVeFRUttf8EkDJHCWWO4hazJ6sye2p24JSbPrRYQ0NDoznQwn41gh7ZLeNwBObCKggCpghjWIapqqrKgo9XULK7PCBC3MBzupHTu3WL220JSveV8/XL81t8XgVRICLKyMX3nY4hIjwLrOxdV0h5gG5iUtslkto+MSC2mxtLfgVPnz4Fl93doutWEAX0Jj0P/XQn8elxLWa3Jdm+Mo9da/Jb3rAArdon0XV4eIapOmUnP+79DqfsaDHhrw4BgVNbn0WcMTxTLSzau5hPNsxucbuiINIpoSO39b85PM9disq+dQW4bIHxyk3rnIw5LiIgtpubGlcV+2p3BcS2UTSRHd1y1asPDvu9NLcZo3ZaiC3/OSnQQ/Ai580/Az2EJmHHLSMDPQQv2r+9MNBDaBK23RR86/ZI0Dz/NIIaVVUDJvzV2W9pj8OWYseqfRTvsgTMAy/3581Yq8IvXEJ2Kyz46B9PnsMWRlVUbNV2ln63puWNtwBVJTUBE/4AiraXYatqOY/DlkJVVT6+90vcTrnF9wNVUXE73HzywFdh6Q1cVVobGOEPQIWCraWU5YVnKOXq0hUBEf7qWFa8+Jgr3gYzZTYLX2z6KiC2FVVhU9lmluYtC4j95qY8rzJgwh9AyfayFvU4bClkRabAujdg9h2KvUU9DjU0NDSOFU380whqnA5XoIeAy9V8ua8CRW2ljZW/bg7oGBS3wvLv14fdDf+qXzdRXlQVsBBRVYXNS3exb3Pz57xpSWSXzL71RYEdhAB71xWEXfjv37P/YcuS7QHb5xRZYfOibSydkxsQ+82FIitsWLgjIA8C6hFg4187wy7/X37tPnbV7AiY8KeiUumsYFP5+oDYby5UVWXWuo+R1cCGi8/eNIdye3lAx9DUOGqdVOQFNvRWdimU7Q6veQUosuUHfM2W2ouwy82fh1hDQ0PjeNDEP42gRZYVZDk48hU5neGVz2P1b/+iyIEVMFRVpXBHGXlbSgI6jqak2lLLqgCLqgCCAAu/WBlWIlXRDkuLJEdvFBXsNU7K9oWPJ5W9xsFXz/wQ6GEA8OVT83BYw2ev3be5GGulnQDpUx5UcNpd7FpbEMBBNC2KqpBb0jK56A7HxvJ1WN3hk6NuVdFqtli2Btyj0a24+ebf7wI6hqamdEfzF/s5EqpLanHUhM8+a3NbqXIFh6BZZM0L9BA0NDQ0GkUT/zSCFrcr8F5/dSiKGjbFP2zVDvZtLgoKYUgQYMs/uwM9jCZj05LgSAKvqlBjsbLv3wB7yjURiqxg2Rs8gltpgPJkNgf/fLsKpy04bgQdtQ5WhEnIuqqq7N0YJH9/KuRtLkEOk3C/Qms+Ntka6GHUs6MqfArWLNi9ECGgrqoeFFUht3AV1Y7qQA+lSXDUOnHUBsc+C1BZFB7zClDuKA30EOqxyVat+q+GhkZQo4l/GkGJqqrIQRZqGy65/7av3hdQR5SDUVUo3mWhqiz0PSdkt8ymv3cEjSgkiAIbFoXHTWlFYXVQhd47rS5qLaEf3qOqKn9+uCTQw6hHEODPmX8Hzd/Q8WDJr8IeRN41bpdM8a7g8Dw6XrZW/hsUAhV4wn+3VW4JuKdcU1BQU8D28u0BC6U+FFVVWbwvePan46GqMLjEtprS2rB4GOBW3FS5KgI9jAaUO8oCPQQNDQ0Nv2jin0ZQ4nYHn9Dmdrd8MvymRlFUtq3YE9gwtEMQBIHtKwOXqLmp2Lk2P6hCFlVFZc/GQqotoS+slu6uCPQQGiJA6Z6KQI/iuNmRu5uiHSVBsx+oKhRsKWLXqtDfD/ZtLibYipUGjSficVDjqqbIVhA0AhWAU3GQVxv6a/avPYsRheC5LVBR+XPPXyEvrCpuhZrSILsOq1BdEvpVYiudwfdAo9JpQQlw/kENDQ0Nf+haytDJJ5/Mn3/+SVZWFrt27fJ6Pzs7m927dzNy5Ej++OOP+tedTifvvfces2fPZv369dTU1JCcnExOTg6nn346d955J1FRUfzxxx+MGjXKp+1evXqxevXqBuOoQ5IkkpKSGDFiBM8//zxt27Ztyq+tcYw09kTy3Xff4ZVXX6GwsJAuXbrw/OQXGDZsmN/2DoeDZ599hs9mf0ZRUREZGRn873+TuPqqqwH44IPpfPLJJ2zctBGA3r378Phjj9G//wCvvhRFQZKk4/x2gaO6rBa7n9CT9v0y6Twkm4goI5UlNaz6ZTMleyt8tk3Jimf0hIFer897axHV+734Rk8YQEpWgleb/K0lLJy9sv53VVXJ31ZKn1OP4QsFEXlbihBEwWc4dddhOfQc1QlzjInywiqWfLOGwh2+Q1VS2yYy6NwexKVEo9PrqCmvZdOSnaz7c2t9m+we6fQ5tTMxSVGIokhlaQ3r/tjC1hV7vPrL31ZCp4GRTfdFWxjZJWOv9l1hd/bcj5kxZxqllhLaZXVg0k0P0Ld7f59t/1mzjOvvvcrr9W/e+4G2me28Xv/xj3nc99x/GTVkDK88+mbDN1WoKbOiqipCsCk8R8G/i7chSqJPr8oRVwxizPUjiEmJpmBrMV89NY/tK3b57WvElYM56crBJLSOpzy/gl/e/IPl36yqf1/UiZx208kMvKgPcakxFO8o5dsXfmLTwq0N+hElkX+XbKNt3zZN9j1bGlVVseRX4etZ0bfz5/DFT7MoqygjOyOHWy67ix4d+/jt6/clPzH7x5nkFe8lMiKKAd2HcMMl/0dsVBwAPy/6nhemP+H1uR/e+QuD3tjgteoyK26XjE4futewYpv/QkY/f/Qbc9+fR0VxJa07ZHD1Q1fSZUAnn23LiyuY9ewn7Fi/k8JdRZxx9WlMfOhKr3a1VbV89tIXLP9lBbWVVpIzk7nq/svoc3Lv+jYCAkW2AjKjso77+wWSDaUb/Qpt679fy+o5q7BaaonPSmDYjSNI755x2D4LNuTz7aSvSMhO5OI3LmvwnqPGwbIPl7Bz8XYcNQ6i02IYev1wsgZm17epdFRSUltCalTqcX23QGKvcfjcCwA++nIm7338DsVlJXRo24GH73yUAb29z1Z1OJwOXp/+Kt/+/A2lZSWkpaRxy9W3Mf7cSwBwuV28/eGbfPXjHApLishpk8OkW+5j5JCTvfqyVdiJaxXTFF8xYNS6/QuYn7w/m+mvz6CkqJT2ndtx/zOT6D+0r9/2cz+fx7TXZrB7xx6iYqIYMWYo/3vybuIT4gDYumkbrz/7JhtWbyJ/bz73PfM/rr7Ze89QUbG5rUTqo4/7+7U0FX98TfnPnyFXWjCkZ5N8yW1EdOjlt73icmL5/kOql/2KXGVBF5dM/FlXEjv87Po21bl/YvluGq6SfPTJ6SRecD1RfU5qia8TVFQt/IbK3z5DrixD36otieNuw9S+p9/2qstJ+Y8zqVn+K3K1Z27jTr+S6KFnAeDM30n5vA9w7vkXt6WIhLG3Ejt6fEt9naCh8s+vqfjVs2b1rbJJGt/4mlVdTiw/fEjN8l9x163ZM68kZuiBNVuz8k8sc6fhKs1Hn5ROwvnXE9U7fNZsi4l/x0JFRQWnnHIKubmeCoAmk4mOHTtis9lYvnw5ixYtYty4cXTu3LnB53JyckhOTq7/vWPHjl59GwwG+vTpg8ViYevWrcyZM4dNmzaxfn14VW4LRVTVf369OXPmMOneSbwy5RUGDx7CtOnTuPCiC8hdsZLMzEyfn5lw1ZUUFxfz5ptv0S6nHSUlxbjdB57KLfzrL8aPH8+gwYMxGU1MeeVlzjv/PFb8s4L09IaHW0UObfHPUuA7b1pm1zT6nNaZ3B83Urq3gnZ9Mznpsn78+PZirFX+85fMe/MvXI4DXpoHe74t+mI1onRAGDFE6DnjhqHs3eR981ZtqQ39m9JdFp/CX07v1gy5oDeL5qykaGcZXYbmcOYNw/n8uZ+prfAOHXU7ZTb8tR1LQSUuh5u0nCRGjO+Ly+lm8/6cgg6ri1W/bqaiqBpZVsjq1oqRl/b35HM8KM+fKAqU7i2n00E3U6GGrcq38PfTnz/w/DvP8uCtj9K7W1/m/PAZtzz0H75+dx6tUtL99vft+z8RZY6q/z0+1odAXZTHy+9P9iskgmcvcNncGMz6o/g2wcXutft8rtm+Z/XgogfP5vPHvmNH7m6GXTqQm6ddzdNnvEK5jz1k+OWDOPee0/jsga/ZvW4fWT0zuezpC7FW2Vg/31MA55y7TmXA+b359MGvKdpRQpcRHbn+zSuZcvHb7Nt4oBiFqqrsWRfaSdNtVQ4UHw+wFiz/lbc+fZn/mzCJbu17Me+Pr7l/yp1Me2o2qYlpXu3XbVnN5Pcf4+ZL72Jw7+GUlpfw6qznePmDp3n89hfq25kjIpnxzBcNPnuo8FdHdZmV+LTQuymtw+KwICB4ef79PW8pHz79Edc9NpFO/Trw22cLePa6F3j5p+dISk/y6sfldBGTEM2Ft5zPD9N/8mnL7XTz1NWTiU2M4a6p/0diWgJlBWWYIk0N2qmolNlDO9TP7nZQYvX9QGrbn1tY/M5fjLj1ZFp1bcWGH9Yz7+G5XPrOFUSn+F9LjloH81/8lda9M7FWNMzRKLtk5j7wDRFxEZz24JlEJUVRU1KD3sd+uqdqb0iLf/5y/X3/21yeeuUJHv/fk/Tr2Z9Pv/6Ea/97NT9/8hvpab6F1f976FZKLaU8d//zZGVmUWYpwy0fOIO9/M6LfPvT1zx9/3O0y2rPX8v+5Ob7buCLd7+iW6fuXuMK5QdYqqpid/vO/fnDVz/x3APP8/CLD9J3UG9mz5jDjRffwtwlX5Oe2cqrfe6Sldx380Pc98w9jDpjJEX5xTz236d4+P8eY+pHrwBgt9nJzGrN6eefynMPvtjo2OyyLeTEv+p/5lMyeyopl99FRPvuVC6cS95r95L12IfoE33//RW++xhyVTmpV01Cn5KBXF2Bqhy4v7JtX0/he4+TeP61RPUeQc3qvyh45zEyJ03FlNO1pb5awKnJnU/ZnKkkXXInxnY9qF70HYVvTKL1wx+iS/A9t8XTHkeutpB85f/QJWegHDK3qsuBPrEVkX1GYvnyjZb6KkFFzYr5lH4xleRL78LUrjtVf82l4I17yXzkQ/R+5rXwfc+aTb7ywJrloOKi9h3rKZr2OAnnXktk7xHUrv6LovceQ3fPVExtw2PNBo9/vw9uu+22euHv1ltvpaysjPXr17N9+3YqKiqYM2cOSUneh7qHH36YpUuX1v/MnDnTq02rVq1YunQpW7ZsYcKECQBs2LABiyX4XMhPNBoLrX196mtcfdXVTJx4DZ07d+aF51+gdUZr3nv/PZ/tf/n1FxYtWsRXX37N6FGjycrKon//AQwePLi+zQfTP+CGG26kV89edOrUiTemvomiKCw4yAO1jlAv+lFeUIUgeh/0Og/KYsfqfexYnUdVmadirbXKTvt+vgXVOuy1zgY/B//TOe2uBu+l5SQhuxT2bPIReqZCRQgnoHa7ZCqKfY+/58kd+XfZTv5dtouK4mqWfLOGmgorXYd5e5sBlOVVsH3VXsoLq6gpt7Itdw/7/i2iVc6Bva5gewm71uVTUVxNdVkt6xduw1JQSVpOw/1QUVSK94T2nuZPfJ711QdcePpYLjpzPDlt2jHppgdJS07j8+8/bbS/hLhEkhKS638OFfNlWeb+yfdw85W30zqt8fXfmDAeCuxeu8/nfjvq2uEs+SKXJZ+voGh7CV89PY/ygkqGXzHIZz8DLujN4k+Xs/KHdZTtLWflvLUs+WIFp9xw4EnpwAv68Mvbf7Lxzy2U7S1n0SfL2PzXVkZfN7xBX6qisntNaIdQ+sth+uXPn3DGiPM466QLyEpvyy2X/5eUhFTmLvjSZ/tNO9aTmtSKC0+9hFbJGfTo2JuzR17Ill2bGrQTEEiITWrw44/qEM+varGX+gz5nTf9R0aPH8mYS06mdfsMJj50JYmtEvnl49999pPSOpmJD09g5IXDMUdH+GyzYM6f1FbUcs9bd9K5X0eSM5Lo3L8T2V28PfyqnBXIIRzqt696n9/31ny9ms6ndaXrGd2Ib5PA8JtOIio5ig3z1jXa58LXFtBhVCdSu3gL25t/2Yij2s4Zj5xNq27pRKfG0Kp7Okk5yQ3aiYLInqrQ3g/8Vdad/un7jD/3Ei457zLaZ3fg4bsepVVKKz7+6iOf7f9c8gfLVi1j2kszGDZwOK1bZdKrW2/69TzwkOqbn77i5qtvZdTQ0bTJaMMVF01gxOCRTPvU+4ysuBVkV+iuWZfiQsH3mfzDN2dx0ZUXMv6qi2jXKYcHnp1EWkYan03/3Gf7NSvWkdEmnQk3XkHrrNb0G9KXS64Zx4ZVG+vb9Ojbnf89+V/OHnsmBoOh0bHZ5dDLCVz+6+fEDj+L2BHnYGiVTfIlt6OLT6byz299tq9dvwzbljWk/99kzF37o09qhaltFyLaHRCZK36fg7lLPxLOvBJDqywSzrwSc5d+lP/+hc8+w5Wq378geshZRA87B0NaFonjbkcXn0LVX77n1rphGfZtq0m9ZTIRnfujT2yFMbsLppwDc2vM6kzCRTcT1X8Mgi50H0IfDxW/f07M0LOIGe5Zs0kXe9Zs1cJG5nXrGlrdNhlzF8+8mrK7YDp4zc6fQ0TnfsSfcSWGtCziz7iSiM79qJwfPms2aMW/yspKZs+eDUDPnj157bXXMJvN9e9HREQwduxYn+Lf0VJ38xMTE0N0dGg9qQlHFD9VaJ1OJ6tWrWLMmDENXh89ZgzLli71+Zkf5s2jT5++THllCu07tKNX757c/8D92Gz+L8xWqxWXy0VCfPwRjy1UsBRWeXn6iKJAfKsYCnc09Fwo3FFGUuu4Rvs7/T9DOP+Okxl1RX+fIb4Hk9M7gz0bCvweNkNZ/KsorPIpooiSQFLrOK+qu/v+LSI1O/GI+k7MiCM1O5H8bSV+26R3SCE2OZqC7d5tLPnBUyX3WPAV8utyOdm0dQND+jYUjob0HcaaTau82h/MJbdewJjLhvOf+65m+RrvfeOdT94gPi6Bi844TPiE4HtsoUJ1WQ215d5eE5JeIrN7OpsXNQzH3bxoG237+g5r1Bl0uB0N87S6HC6yerZG1In1bVyOhhXcXXYXOf2yvfqrKqnBWhl6N0911FisXt40LreLLbs3079bQwG1X7dBbNy21mc/3dr3pLS8mGVrF6OqKuWVZfy1Yj6DejVMc2Fz2Lj8f+dx6d3n8OArd7F1978++xMEz9hCFVVVqXRWeL3udrrZsX4XPYf3aPB6r+Hd2bJyq1f7I2XF7yvp0Kc90x/7kBsG3crdZ97H129+5zNMXkWl2ll1zLYCTV5Vns8SKrJLpmRrMZmHhOFn9m1D4UEeu4ey+ZeNVBZU0v8K3yGsu5buJLVLK/56409mXPY+n930Mbmf/eM1t4qqsLfKvzAZCvjy/HO6nKz/dx3DB45o8PrwQSexcl2uz35+X/QrPTr34N2P32bouQMZc/HJPPPaU9jtBx5COZ1OjIaGXr8mo4kVa1b47NNZ6/L5eijgUHxfI5xOFxtWb2LY6CENXh82agirlvuuJt9nYC8K84v485e/UFWV0uIyfv72N0aeNsJn+8MRauKf6nbh2LMFc9eG6Y4iuw7Avt13RFztmsUYszpR/vOn7Jg0ll0PXUHJF2+iOA+ci+zbN3j1ae46APv2DU3/JYIU1e3CsfdfIro0nIeILgNw7PA9D9Z1f2No04nKXz9lzwPj2Pv4lZR91XBuT3Tq1mzEoeurywDsO/ys2bWLMbbpRMUvn7LrvrHsefQKSr9sOK+OHX7WrJ9/q1AkaMN+t2zZUl/04aSTTkIUPTcQN910E++88059u3vvvZfnnnuuwWevueYarrnmmvrf77jjDl555ZUGbQoKChg8eDDl5eVs2bKF+Ph43n33XfT6E1M9Dyr8eP6VlZUiyzIpKQ1deVNTUvit2Hci8527drJkyd+YTEY++/QzSsvKuOuuOykvt/D2W+/4/MwjjzxMeno6o0aN9jG00Bb/nDbvg57BbEAURa9cgI5aB6Yo3+K6rdrB8nkbKC+oRJREsnukM+rK/syf9Q8le8q92iekxxKXEs3y731vnoIgNAgfDjWcdt8HaFOkEVESsR0iEtmqHZhjTD4/U8flj55FRJQRQRTJ/Wkj/y7b1eB9vUnHlY+dg6QTURSVxXNWkbel2Ksf2a2gyAqiFLTPehrFV/7P8qpyZEUmMb6hgJoYn0SpxbdImpyQzCN3PEnX9t1wupx8P/9bbrhvItOen0W/Hp4L/aoNuXz98xw+f+ObYx5bqOBPuIyMNyPpJKpLG+ZSqi6rJiapg8/PbP5rK0Mu7s/aXzeyd0M+md0zGDyuPzqDjqj4SKpKqtn011ZGXzuc7ct3UbrHQseh7ehxShcEP+vSVm3HHOvbIyvYcbtkDq2iUlldgaLIxMc2XLPxMQlYKn2HjHZr35P7b3iCp956EKfbgSzLDOl9Erdd/r/6Npmtsph03SO0zWiH1V7LV7/O5s5nr+edxz+mdWpDwUZVPWkFQhVFVXx6/VWVe6qBxyY1zF8WmxhLRemxP/wo3lvChiWbGH7eEO6bdg8FuwqZ/thMZFlm3O0XerV3KaErpNjcdgRBRD0k55+9yoaqqJjjzQ1ej4iLwOrj4QFARV4FSz/4mwteGOv3ulNVWEn1mn10GNWJs584j4q8Cv56809UWfUSDK2u0BWsAVQfYnF5RTmyLJOU0PCMlRSfRImfa9ievL2sWLsCo8HIW8+9i6XSwqMvPExlVQWTH/KEoY4YdBLTP3ufAX0GkZWRxd8rFvPbwl/8Rq34ErJDBX/5KSvK9s9t8iHng+RESot9h7b3GdSbF959lv9eNwmn3Ynb7Wb0mSfz4PP3HePYQmuflWsqQZGRYho+xJdi4nFX+Y4ecZUWYN+2DkFvIP3mp5BrKin+ZApKbRWpEz3z5q6yIMU0dKSQYuKR/fQZjnjmVvGeh2j/8+Auzcex3TO3qTc8iVxTSdnsKSi11SRPuLclhh301K1ZXfQhazY6HrmykTW7f17TbvKs2dJPPWs25aqD1my097+Vv7+DUCRoxb+DRZaDn6Dn5OQwaNAgli1b5vezh+b8y87O9mrjdDob9NGjRw9Gjhx5nKPWaAkO9ahQVRXB5zNrj6eeIAhMn/YBsbGxADiefY4rrryCKS+/QkREw5vLl6e8zBdzvuDHH3/CZGpcnAlFFLkR8dJL2BT8CrHVFivVB3mQlOVVYo4x0Xlwtk/xL6d3BhXF1f690ASQQ/gQerixHyoaC54XG/3M3Nf/QGfUkZqVyMBzulNVWsP2g6qguhxuvnzxV/QGHekdUxh8QU+qymp9ev/J7tAV/9RGQu0P/btvLH9RdmYO2Zk59b/36tqHwpJCPpwzjX49BlBrreGB5//Ho3c86TMPoO+xhe7DALezcbHdW2QR/FYF/mnqfKKTorh7zs0gQHVpDcu+zOXUG0fW31x++dT3XPb0hTz0y10ez4o9FpZ+uZLBY30nYD/c+IKZxvbZQ1dnY2t2d94O3vj4Ja487zoGdB9MWWUp737+Oq/MfJZ7rn0YgK7tetC13QGPt27te3Hz4xP45rfPue2Ke7zHFsJr9nBhtV77AceXz0xVVGISY7jh6esQJZGc7m0pL6pg7vvzfIp/oXbDfzBuxe33HAX4WLj4rGatyAq/Tf6ZAVcOIq61d/RE/cdVj4A48v9GIUoiyR1SsFpqWT1npZf451JCdy+Axi/1XudZ/J9nVVVBAKY8/irRUR6h23mHg1sfuJnH73kKk8nEw3c9xgPP3cdpl45GEATaZGQx7uzxzJnnO2QtlB9oHypUe+HrXsHPfrBt83aevm8yt/zvRoaPHkpJUQkvPDKFx/77FE+//vgxjC1057UBKnj/8e9HUUCAtOseQtqfRzl5/K0UvPMIyZffhbjfA9X3nIdmnsnjw9cm6rulqno22JSJDyFGeOZWdd9K8fuPknjJnfVzq4HvOfR33Vc9azbl2oeQ6uZ13K0UvfcISZceWLO+Pt/o9THECFrxr1OnTuh0OtxuN4sXL67ftCdNmsSkSZMaPdA9/PDDTJw4sdH+s7Ky2LFjB7/++ivnn38+Cxcu5LrrruO7775r4m+i0VQkJiYhSRJFRQ0LRhSXlJCSkuLzM2lpaaSnp9cLfwCdOnVGVVXy8vJo3759/euvvPoKL774At/P/Z4e3Xv46i7kObgARx1OqxNFUTBFNbyYGCMNfisD+6Isr5KsHt6JlCWdSJuuaaz/c5v/D6sgjuDnGwAA8KtJREFUhag4Bf7Hbq91oMiKl5efKdqI9TAho3XianlBFRHRRvqd0bWB+IcKVaWe/F1l+ZXEp8bQ+5ROPsU/SRe6cysI3mOPj4lHEiVKyxs+xbdUlJEYf+SpIHp27sW8+Z49f2/BXvKL8vi/R2+uf7/Os6DvWV359v2fyExv6EnlK39mqCAZfBfXqS23IrtlYpIapsCIToyiqsx3ZUWXw80n93/FZw9/Q0xSFJXF1Qy7dCC2Gnt9aHGNpZb3bv4InUFHZLyZyqIqzvvf6ZTt835YAJ4w4VBF9LEuYqPjEEXJy8uvorqc+BjfYvOnP3xItw49ueRMT17inMwOmAwR3PXcDVxz0c0kxnmvdVEU6di2K3lFvvOk+RpbqCD62AsAYuKjESXRy8uvqqyK2MRjr2YalxyLpNc1eHCS0T6dipJK3E631xoVhdAtWKUTdT69Kk0xEQiigPWQcHFbpY2IOLNXe5fNRcnWYkq3l/DXm38C+29kVXj77Kmc8/T5tO6diTnejKgTG8xtXGY81nIrsktGOqj4l04M3b0APPeRh85sfFw8kiRRUtbwel1WXublDVhHcmIKqclp9cIfQLvs9qiqSkFJAW0z25IYn8g7k9/D4bBTXllBanIqz7/5HJnpvvPXhmqxD/B9NgCIS/TM7aFefpZSC4nJvtOtvDtlGn0H9ea6/5sIQKfuHYkwR3DlWddwx4O3kZKW7PNz/scWWvMqRcWCKHl5osnV5ehifIv4UlwiurjkeuEPwNCqDagq7vISDKmt0cUk4D7EC0uuKvfyggtnPHMr+pjbCqRo39d+XWwi7tikeuEPQJ+WBaqKXFGCmNK6WcccCtSt2UM98uRq/+tLiklEikuuF/4ADGn712xFCYYUz5r19XcQTmu2xe8IVVXFbrc3+JFl76elsbGxXHzxxQCsXLmS+++/H6fzyIWII0EURU4//XRuvfVWAObOnctSP7njNFoQPxfNugrN8+fPb/D6gvnzGXRQAY+DGTJ4MAUFBdTUHLhp3bZtK6IokpFxoJralFemMHnyc3zz9bf07duvkaGF1gX9UAwm77B2RVEpL6girW3DQ1Fa20RK91Uccd/xadHYa7wFrTZd05B0IrvW+88PpKoqemPoHvD1PuYVPB5ApfsqyOjYMFS9dcdUinYdTXVI4YgEPEnnffN56A1WqCHpvceu1xvo0qEbS1ctbvD60lV/06tLnyPue/P2TSQleA71bTNzmPP2XGa/+U39z8mDRzOg1yBmv/kNacneSetDWVSNiPbt2Sy7ZPauz6fz8PYNXu80vD07V+5utE/FrXjyXyoqfc/pyYb5/3p5QLidbiqLqhB1Ir3P6M663zb57OvQhxGhhM4gcejjaL1OT8eszuRuXN7g9dwNy+navqfPfuxOu9cNrrQ/BYo/zxJVVdm+ZwsJPoRBQaCBqBJqSILk8+m7zqAjp3s2axc1zPOzdtF6Ovb1Hap+JHTq15Gi3UUNQiYLdhYSnxLnU5zWi6GbNsakM/lcU5JeIrlDCvtWNRST963cQ1pX74d9BrOBi9+6nPFvXFb/0+2sHsS1jmP8G5eR2tmzj6Z1a0VVfmUD7+nKvArMCZFea9SsD83w/zp8pTYw6A1079SDxf/81eD1xcv/om8P32fQfj37U1xaRK31QNGenXt2IooirZIb/lsYjSbSUtJwy25+WvAjp4w4zWefoXw2EP3cwhoMerr17sLfCxrez/39x1L6DOzl8zN2m93rYZ5YVwzsGLz4/I0tWBF0eoxtOmLd2DA3pHXTigbFEA4mol133BWlKPYDDwacRftAENHFe85VpnbdsG46pM+N/2Bq162Jv0HwIuj0GDM7YdvccB5sm1dgzPE9D8ac7siVZQ3m1lW0FwQRKe7ohOhwpW7N2g5dX5tWNCiMcjAR7bojH7JmXcX71+z+eTXmdPPuc+M/mPz8W4UiLb477dmzh4iIiAY/r7/+us+2U6dOpU8fz43c5MmTSUxMpHfv3mRl+U46XseTTz7J4MGD639OPfXURtvffffd9ZWbnn322WP4VhpNSWOeCbff9n/M+HAGH878kM2bNzPp3kns3beX66+7HoBHHn2E6/9zfX37iy++hISEBG666UY2bdrEokWLePChB7nqqqvqQ35fnvIyTzzxOG+9+TZtstpQWFRIYVFhA8GwjlD29AGIbxXj8ztsXrabnD6tadsrg5jESPqc2glzrIltKz0H/p6jOjDovAObaceBWWR0TCEq3kxMUiQ9R3Ugs0saW//Z49V3Tu/W7Pu32Ge+wYOJSw3dYjvxqTF+vczX/rGFzoPb0mlgNnEp0Qy5oBdR8WY2/b0DgAFnd+fkyw8kl+06rB1turUiJimKmKQoOg7MoteojmxdcWBue4/pREbHFKITI4lNiabHyA50HJDF1hXe4kxCq1iv10IJU7RvEWjCRdfw1U9z+PrnOezYs50X3nmGguICxp99KQCvTn+JB1+YVN/+o69nMP/v39idt4ttu7by6vSX+G3Rz1x67pUAGA1GOmR3bPATHRlDZEQkHbI7otcfUt1P9T+2UCAqIZLIOD9VTqcvYsj4/gwe14/Udslc9OBZJLSKZdEnHuHq3HtOY8IL4+rbJ2cn0v/83iRnJZLVszUTX7mU9A6pzH3p5/o2Wb1a0+u0biRmxtOufza3TL8GQRD47d2FXvajEyOJ9OFVFCpExZt9CiljT7+cHxd+y49/fcfu/J28+enLFFsKOffkiwB4f84bPPfeo/Xth/QawaKVC/huwRzyi/NYv3UNUz95ic5tu5G0/+Zq5rfv8c/6JeQX57FtzxZe/OAptu/dUt/nwagqRCeE7rwKgkCMIc7ne2dfeybzv/iDBV/8yb5teXz41EeUFpRx6uWeAmGfvDCbqfe83eAzuzbuZtfG3ditDqosVezauJt9W/Pq3z/18jFUV9Qw48mPyN9ZwMoFq/nmre847cpTvMeGQLTh2L0MA01GdLpPzz+AXhf2ZtPPG9j080bK91hY/M5fVJfU0O0sz5lg6Qd/8/uLvwCeM1JidmKDn4i4CCSDjsTsxPoHZd3P7oG92s6itxdSsa+c3ct3snL2Crqf0zDqQhJEMqND28vFGOm7Muy1l13P59/N5ou5s9m2aytPvfIE+UX5XH7hFQC88OZk7n78rvr25512PnGx8dz71D1s3bmF5auW8dzUZxh3zsX1aWpWb1jFz3/8yJ68PfyzejnX3HkVqqpww5U3+hyDwRy6grVR8i8KX33LBL6c9RVffvQ12//dwbMPvEDBvgIuucZTyOvlx1/l3pserG8/6oyR/DZ3Pp9O+5y9u/axcukqnrlvMj37dSellSeyyOl0sWndZjat24zL5aI4v5hN6zaze4f3udekC719Nv7Ui6lcNI/KRfNwFuyiZPZUXJZiYkeeB0DpV+9SOP3p+vbRA09BioqhaMZzOPJ3YduyhtI5bxEz7Mz68Mm4MeOwblyB5adPcBbsxvLTJ1g35RI/5jAF1cKMmDHjqf57HtV//4CzcDdlc6bithQRPdwzt5Zv36Xkw2fq20f1H4MYGUPJR5NxFuzCtnUNlm/eJmrIgbn1FBLZimPvVlTZjVxRimPvVo+YdYIQN+ZiqhbPo+pvz5ot/WIq7vJiYkZ45rXsm3cpmnFgzUYNOAUxKobiWc/Vz2vZV28RPfSgNTtqHNZNKyj/+ROchbsp//kTbJtziR0dPms2qF1t4uPjWbJkCW+88Qaff/45GzduZNOmTaSmpjJy5EjOPfdcLrvsMq/P7dixgx07dtT/fnDIpy/S09OZMGEC06ZNY+7cuaxfv57u3X2rxhrNT2PedePGjcNiKeO5556lsLCQrl278tWXX9OmjSccr7CwkH17DzyhjoqKYu5333P3PXcz4qThJCQkcNFFY3n0kQM3WO+99y5Op5Mrrry8ga0H7n+ABx98qMFrdV4XoUpCWozPPGV7NxZijNDTfUQ7TFFGKkuqWfjZSqyVnipyEVFGIg9Kvi9KAr1P6UREtBHZrVBVUsOfn+ZSsL1hmEV0gpnkNvEs+Nh3pbmDCWXxT2eQiE2J9lmxeMfqfZgiDfQ9vQvmGBOWgip+fHcRNfvDIc0xJqIOSqYuiDDw7O5EJ0SiKipVZTUs+34dm5bsOMiejuHj+hAZa8btkqkormb+R8vZsbrhRV8UhcNWYQ52IvwURjlj5FlUVpXz7sdvUlJeTPusjrzx5Lukp3o8ekstJRQWH/A2dbldvPzeZIrLijAaTLTLas/UJ95lxMBjz/UaERO64p8gCLTp2ZrNf231cmxY+cM6IuPNnHHbaGJSoinYUsRb139IeX4FALHJ0cSnx9W3FyWR0dcNJ7VtErJbYevSHbx88dtY8irq2+iNes7+76kkZcbjqHWy8c9/mXnP59iq7Q1sC4JAVi/fIWqhQnSS7xu/UQNPpaqmko++m4alspTsjHY8c+cUUpM8XjuWylKKLQeKV50+/Bys9lq+/f0L3pn9KlER0fTu0p//jL+tvk2NtZopHz5LeWUZkRFRtGvTkSn3vkNnP0+p/Y0tVEg0JVLlrPASqoaePZjq8hq+nPoN5cUVZHZszX3v30NyhscDsqKkgrL8ht7W95534Pq+Y/1OFn+3hOSMJKb+OQWApPREHvxgEh8+/TGTzn6QhNR4zrz6dM6/8RyvccUYYpFCOOw3M8a/wNZ+ZEfs1XZyP1lOraWWhOxEzn7iXKJTPWKn1VJLTbHvlAD+iEqO5pynz2fxO3/x+S2fEpkYSY/ze9FnfEOvN1lVaBMb2vuBMdKAtdy7+us5p5xLRWU5r09/jZKyYjrkdGTaSzPIaOX5tyguK6agKL++faQ5kpmvfsTjLz/KBdecS1xsPGePOZv/3nCgAJDD4eDld15kT/5eIiPMjBwyipcefYWYaO/7IFES/aZ/CAX0oh4REQXv3H9nXXQGFZZK3nz+XUqKSujQpT1vz36DjDbpAJQUlVKw70AKoQsvP5/amlo+fv9Tnn/4JaJjoxk8YgB3P3ZnfZuSwmIuOumS+t+nT/2Q6VM/ZMCw/sz8floD+6ZGhMlgJXrAaOTaSizzZiJXlmFIb0vG7ZPRJ3q8dd2VZbgtB4rKiSYzGXe+RPFnr7L36RuQomKI6j+KxPMPOGBEtOtO2n8eoeybaZR9Ow19cjqtbngMU07XFv9+gSSq32iU2ioqfvwQd5UFQ6u2pN5yYG7lyjLc5Qeu/aLJTNrtL1L2xWvkT74RMTKGyL6jiD/3uvo27spS8p/7T/3vlb/PpvL32Zg69KLVna+23JcLIFH9PWu2fN5M3FVlGFq1pdWth8zrIWs2/f9eonT2q+x79gbEqBii+o4i4bwDa9bUrjup1z2C5btpWOZ61mzq9Y9hahs+a1ZQwyYrqUY4Ybc5/FYnCyRGkwFJCt3DUkVxNT+9+3egh+FFdKKZs28eEehhHBd/fLqCrSt2B10RiJGX9qfToOxAD+OYcbtkNs7fHuhheCFKAt3GtA/pVADzXvmVX976M6gqPoqSyBm3jeLM28cEeijHjKqq/DFrZVDNax0jr+i7Pyw5NNletZXcEv8F3wKBgEDb6Hb0T/GdfiRUeOTPxym1HU06ipbhkeEPkhaVeviGQYq13Ebhv74r+AaSiFgTrbr4zpcdKuyp2Y7VXXv4hi1MZmRbIvXN90B75cqV9OvXj9zcXC7NPTrhPRjZ8p+TAj0EL3L25ywNdXbcEnzFTNu/7R3xEYpsuyn41u2RENpuTBphS7AWfxBD3PMvJikKY5CFeQiiQKt2R16kIVjJ6JgSdMIfQHqH0M4PotNLmKJ8h00FDAGiEs0hLfwBdBzSLugEKkVW6DS0/eEbBjGCIJDQKjroChpGJ5pDWvgDSInwzr0ZaFRUUszBN66jpWtyF79FVQJFjCGalMjQvoYZo41BtxeAR/wLdSJ1wRcxIiCEZNivhobGiUFwXeU1NPaj0wdfRLpOJ4X8zb4oCrTv18ZvfrpAoCoq7fuGdlgPQNueGRgigkdYFUSB1p1TiU6IDPRQjpvErCCrsqVCYpu4QI/iuGk/sC3J2YlBc2MqCJDaLpm2fdscvnGQ07pLqneJzwDTukvoek/VEa2PJsWU6rPwR6AwiAYyIkP/GjYic3h9hfNgQEBgZNZJQSdIHi2STiQqKciuwwJEJwfZmI6BWEPwpTWJNcSHdAoADQ2N8Ca0r6gaYYsgCEHn/afTBZ8geSy069M6aO5JBQGS28QTkxR1+MZBjk4v0WVITtAUhVEVle7DQ9uDqo74tGhEKTjmFcAQoScqhAsn1CEIAiOvGhroYdSjqnDy1UND/iELQEJGjN9E/4FA0ouktg2+G+VjoUNcZ7/FKVoaAYF2MR3D4mY/IzqdnLi2QSWsDm09JNBDaBJigyyncVSiOaQrf9ehE3VE64OrqFmcMTHQQ9DQ0NDwS3CpKxoaBxFM3n+iKCAGmRh5rJhjTLTulBIUIpWqeioHhwtdhubgVT0hEAgQGRdB6y6hH4oGIOpEEloHzwE/KSsuLAQq/p+9s46TqmoD8HPv5HZ3scTu0t0NBqKiKHa3YgfYqJiIgZgooJioKBYGfipSinR3L9udU/fe749hF4aZWWp3J/Y+329+n9w5c94zZ8+ceM8bQJ+x3dEZvMNiVR+op9eYbp5uRqMgCAIpHbzE0k6ApMxYNFr/WMMSApO8JqC+gkLr0HaebkajMSxtqFcoVkVBpEd8N8IMvptB+WgMwXr0XnQZEBrvXcrI0yHC4D1hY4yaQK+Zm1RUVFRc4R87QRW/RKPReI31n17vPZu2xqDbyExEDyv/BMGeiTY507cDTh9NaFQQXUdmet6NUoHBl/Tw+N+4MYltHeUVlgqGID2RKeGebkajERBi5MJHzvF0MwAY++g5GIN9N4PysaS0jyUw1Ojx+UBn0NKqS4JnG9GIiIJIz5g+nm4GAO0jOhGk8333yTp6xHejbUQbj7vaagUtF2Ze4NE2NDbR6d4RviI4OtCv5tlAbRChunBPNwOA+IBETzdBRUVFpUG8Q7OiouIGvcHzSjetTus3Vn91BEcE0u2MTI+2QdQK9D2/k99YUNXR8+wOhEUHe+x7CYJARu80Ujv4z2EfQKvXkNzJ85ZUKV3i/UqpCjDoyj606d3KY/OcqBFp1681Ay/3DoVOYyFqRDoMSfd47L8Og9LRGbzHkr4xSApKITW4lcdcVAUEQnRhdIjo7BH5TYUoiFzb+SqPuzGPaz+WqAD/cFOvwxhsIDzRs5aMGp1IVCv/6leAuIBEj4/ZKEOsmuhDRUXF6/EvjYaK3yEIgkcVgIIgoPMi9+PGpG3PFGJSIzympOp+ZhZB4f7nHqHRahh+dR88ceIXBAFjkJ7+Y7s2u+zmICw2mPAEz7krxbaOtFty+RmiKHL1y+PsbqHNPB0IAmh0Gq6ecrHfXQQAhMUEk9bZQ+73AsS3jSLajyxVj6Z7dG/0oh5PmFYqKPSLG+BxhUNTEB0YzUUesroTBZGMyHYMTPaeWKSNSXhyGDqj5/aUMa2j/Mb9/2g0opb4wGSPydeLBqKN/uPFoqKi4r8IiuINAapUVBrGarFitdqaXa4xwIAo+t9GqQ5TlZnfP1xBTYWJ5pwK2nRPptfoDn552K9j2797Wfzl6uYTKNgzUp9/11BiUv3vZr8OySazZ+VBaivNzapfDYkJolW3RK+IldlUbPpzG+/f/gmK3EwdK9gV1rd9cC0dh3rWErkpkWWZDf/bRfGh8uYTKtjDEPQYlekV7vJNRbGpkL9y/oesSM0qt3dMf9JD2zSrzOZEURQ+3fQF/xz6t9lkioJIpDGSCf3uJ8TgPzHpjsVSayVnUz6y1LyZlSNSwohI8p7YuU1BYW0exeaCZpWpETSkBbdFr2k+V+o1a9bQs2dPVq9eTY8ePZpNroqKiu/jv1oNFb9Cq9M2ewIQf1f8gd0NZcQ1vQkIMTSbUiOtUwI9z/FvxR9AVr90+l/YPBZ4ggAarcioWwf5teIP7N8zvWcyxmYMnh4cFUha1wS/VvwBdBqRxXWvX4YgCjT1z1M4rPi74Y3L/VrxB3bLys4j2hLRXEH2BQiOCKDbWRl+rfgDiDLGMDh++OEYdc3z++we3duvFX9g/21e1elyeiX0bBZ5oiASbgjnvj53+7XiD+zZ4hM6xDZrBvvwxFCPuxw3B9HGOCKbMQGIKGhIDW7TrIo/FRUVldPBvzUbKn5Dnfttc7jgCkLLUPzVERQewJk39CUksgljlRze47brnUq/Czr7Xcw0d3Qe2o4hl/e0K1Oa6DsLooDOqOP8O4eS2DamSWR4G1q9hjZ9UghsBrfxsLhgWvVI9Lu4n+7oeW4Xbn3vGrT6pot1KmpEtHott71/Ld3P8a+Yae7QaEW6nZVBTFp4k8sKjwuh5znt/S7OnzviAuMZlngmOlHbZDEAhcP/6xPTn3Zh/q2srkMURK7vcg1DUgYDNGl8xfigOCb0f4DIAO9IitHUGIL0JHaKbxblfGRKOJGp/pOhviEEQSDGmEC0senjA2sFHa2C22LQ+F8oEBUVFf9FdftV8TkkScJittAUI1ej1aDX61rEJulYJJvEpsW72bp8L4JAo/WvINhvuvuc14mkjJYZE6U4p4y/Pv2PktyKRq87vUsSg8Z1JyCk5W1AFUWhaF8puTuLDj9opIoFEEWBxPaxRCSGtsj5oOhACZ8+PI/dK/c1et1t+6Zz9ZRxRCW3jIP+0SiKQu6uInb8ewBZkhtvHTtsSdm2VzIp7eP83krVFSZbLasKV5BTk93odYfpI+gXO5AwQ3ij1+0LrM/fwKebvqDWWoPcSBOtiIiCwtmtz+SctmejE3WNUq8vIdtkivaXUlVY3eh1aw0aYttGYwxpmVZpNbYqcqoPYlOsjV53mD6S2IAEj8X8VN1+VVRUThVV+afikyiKgtVixWZrpDg/AhgMejQa/3aROhGKc8r59/sNVBbXnJYSUBAEFEWxu/me3R59QMvb2B+NLMms+2Mbq3/bCopy6od+AVDAEKhj8CU9ad3Nc0GuvQVTlYWDm/KoLTfV988pcfizIdGBJHeMQ2ds4WNWlln6+X9899IvWM2HD1Cn0reH9VB6o46xj45m4BV9WqRC9WhM1Ra2LttLyaGK05xn7Z8NjQmi4+DWBIa1vEuAo1EUhYNV+1ld9B9W2XLa9QmIdIzsTFZ4x8OuxS2Xaks1X22dx8rc1YiCiKycWsy6us/GB8VxXZdrSAtLbeSW+h41ZbUU7i5BsjbOnjYsIYSI5LAWY7HuDlmRKazNpdRS3Cj1aQUdCYHJBOk865quKv9UVFROFVX5p+LTyLKMzSZhO8VkIKIootVp0WjEFn8YPRpFUcjdXcTOlQfI3V1ktyrh+AdUQRRQZMXultk9mTY9UgiNCmqWNvsKtZUmtq/Yx6alu6gpNyGKAvLxEiwctupRZIXo5HA6DW5L624paPWqsroORVGoKaul6EAZ5XlV9ocnogg8XEYQBSISQ4lKCSPADzP6ng61lSb+m7+Wvz9eTuG+YkSNiCI3bLUmCCCIIrIkE5sezdBr+9P7wpZpodoQFUXVZG8rIG93MYqsnJAisK6MIEBsq0iS28cSFhusrmFHYZNtHKzaz47ybZRbShEQUE5Ac11XLkATSLuwTFqFtsGouvU5kFeVz5KDS1me/S9myYxGEJGOowgUBRFFUVBQ6BTTkWGpQ8iKzmzxCtWjUWSF6tJaKvIqMVWaT/yDh9cwUSsSGhdMaGww2hbi8n+iWGULZeYSSi3Fp5QcKEATRIQhmhCdd3gCqMo/FRWVU0VV/qn4BYqiIEkysiQhy7JbZYog2BV+oiii0WpaTFy/06GqtIZDOwooya2g+FAZVaW1TmVEjUh4bDBRSeFEJYWRnBWH1s8DzZ8usqyQvS2P3N1FFB4oofBgKVazsxI7IMRIbFokMSkRpGTF+X1Cj8bAarZRnl9FbbmJmvJazNUu3H4Ee8KbwDAjgWFGwuKC/T45wumiKAq7/tvLjn92s39DNgc2ZFNd5jwfBIUHkNYlhdQuSWQOaEub3q284sDkzVjNNgr2l1JRWE1FYRXVZS4ysAsQGGokLDaY0KggYlpFYGjhFtUnQompiNyaHErNJRSbizBLJqcyWkFHpCGSSGM0MQFxxAXEq4qp42C2mVlXsIF9ZfvYV76fQ5U52GTnNSw6IJr08FakhqXQLbYLUYFRHmitb2GpsVBTasJcbcZUZUGyOCusBFFAH6TDGGzAGGIgMDygRbr7nwyKIlNpraRWqsZkq8Ek1bq8FNAKOgK0gRg1AQTrQr0urp+q/FNRUTlVVOWfil+iKIrTwUkQBPUA2ghYzTbMNRYkm3w4y6yGgNCWkyClqVAUhZoKE1azDVmS0Wg1GAJ1GINaZryexkSWZGxm+8UAHE44YdC2mMQzTYWiKFQWV2GqMmOz2NDqtRiDDYREqVZop4ssyZhrrPa4gIBGI6IP0KHRqvPs6WKWTFhkK7IsIQoiWlGLUROgjtnTRJIlyszlWCUrsiKj1+gI1odg1Kpr2Oki2WRkm4Qi201+RVFAo9eoY/Y0URQFm2Ktd2EXENAIWjSid18Eqso/FRWVU0W1C1fxS1RFX9OhM2hbTBbJ5kQQBILCmj57bUtE1IjoA1WlSWMjCAKh0SGERns2/pE/ImpEAlpooP6mxqAxep0ljz+gETVEBaiW6U2BRiuqiv8mQBAEdILe081QUVFRaTZOeiXJzc1l48aNbt+vra1lzZo1VFVVuS2zZcsWsrPdZ2NTZagyVBmqDFWGKkOVocpQZagyVBmqDFWGKkOVocpQZagyVBkNyzghlJPkqaeeUpKSkty+v2nTJgVQli9f7rZMhw4dlPvvv1+VocpQZagyVBmqDFWGKkOVocpQZagyVBmqDFXGCchYvXq1AiirV6/26e+hylBlqDIaV8aJcNLKv5ycHGXDhg1u36+pqVFWr16tVFZWui2zefNm5eDBg6oMVYYqQ5WhylBlqDJUGaoMVYYqQ5WhylBlqDJOQIYr5Z8vfg9VhipDldG4Mk4ENeGHioqKioqKioqKioqKioqXoyb8UFFROVXUqP0qfoeiKFQWV1NRVI3NKiHLChqtiCFQT0R8CIYANbjvqSLZZEyV9syesqQgCHXJFHQYAvUIavbUU0JRFGSbjGS1Z/NT7An9EDQCWr0WUaMG+j5VFEVBVmSH7N8CQn1SIDUx0KmhKAqSVcZaa0WR5KPGrIguQIdGJ6p9e4ooioIsy/bMnkchiAKiqPbr6SDJNiyyxT4noCAAgiCiF/VoBK3at6eIrMiUmUupsFQgKTYUFDSChgBtIFGGKHQadd91qtTPB4oCCiAA2DP+qmvYqeOw76rbH9RlUtZp1H2XioqKX6Iq/1T8gsqSavasO0ThgVJK8yqQbLLbsgEhBqKSwknOjCWlfRwaraYZW+pbKLJCZVE1FQVV1JSbsJpsbssKgoAhWE9QuJHwxFCMwWqmyoaQbDLmKjPWWis2i82+qXeDIApoDVq7kjXIoCpZG6DuoCTJ0pEDUwMIgl2hotVo1YPUcZBsMjXFNZgqzVhqLCiS+74VNAL6QD3GUANBkYGIaqZKt9SPWauEJElOSr9jEQQBUSui1dovBtQx6x5Zkam2VlFrq8Eim5AV93sDAQG9xoBRE0CQLgStqG6RG6LEVMyu8h3k1+ZRai5psG+DdSHEBsSRFpJOSnAqoqDOB+5QFAXJJmGTJBTZfrHSEKIoIGo0aLUaRFHt14aQrBLmags2sxWbVWp433X48lUfoEMfqFfnWRUVFb9AdftV8VkURSFnZyE7Vh4gf28xgiAc96BfjwAooDNqadsjhTY9UggOD2jS9voSVrON0pwKSrLLkSxSfX+dEIfLBoQaiUoJIzQ2WFVWHUZRFGwmG7WVJqw11lOrRABjsAFDiBGtXlVc16EoCpIkYZNsJz4PHIMgCGg1WjQajbrRPwpLtYWqwmpqympPfB44GgECIwIIjglCH6haANVht56UsFqtx1X4uUMQ7AdUrU61Wjsaq2Sh0lpBtbUS5ZQGLQRoAgnRh2HQGNW+PYykSOyv2MvW0s0UmgoQEE64f+vKBmgCyIroQEZ4FgHawCZuse8gyzI2m4TN6v6S9XiIoohOp14KHI2iKFhNVrvXivnU+tZ+uW3AEKz3CoMB1e1XRUXlVFGVfyo+SWVJNSt+2ERRdhmCwHFvRhtCsPv90GVYWzL7tWrRN6eKolCaU0HejqJTPoweiyFIT1LHOAJCWrYloGyTqSquxlp7iko/FxhDjQSGB7R45aokSVislkatU6/XoxE9v8n3JLJNpuxQOTUltY1WZ2BkAOHJYS3epUqySVhMllNWVLtCb9Sj1bVsazVZkSk3l1JpLW+0OgM0gUQao9G0cEvAYlMRi3P+otxSdlJKP9cIaAUNfeL60y4ss0UrqhRFwWa1YT0Npd+xiKKA3qBv0ftZsFv6VZdUY7NIjVZnQFgAxhCDR8esqvxTUVE5VVTln4pPoSgKO1YeYP0fO1AUpdEUVHVExIfS78LOhEUHN2q9voDFZCVnSwHVpY130D+amPQIoltFIrYwRZWiKFiqLVQXV5+WktodolYkOCYYnaHlHUwVRcFitSDL7t3NTgeNRoNOq2uRB9PachOlB8qQGwihcKqIWpHItHCMocZGr9vbURQFi9mCZG28w+jRaLQa9MaW6aJmlkwU1xZgUxpPiVKHgECkMYZAbVCL61tJkdhQtJb1xesOG/Y37kKWGJjEwIQhBOla3r5LlmXMZkuj72Xr0Ol1aLUtz5JdURTMVWa7tXoToNFpCI4KQqPzzAWhqvxTUVE5VVTln4rPINlk/pm/nuztBU0mQzgcQHnwpd1IaBPTZHK8jZpyE/vXHkKWlVNz6ztBAsOMpHZLRNNC4n8pikJ1SQ3mSnOTywqKDmpRcRZlWcZsafp+FQQBg96zt/zNiaIoVOZXUZFb2eSyQhNCCI0PaXI53oIsy5hrzI1q7ecSAYyBxhZl9VNlraDEVNTkcoJ1oUQYolrMfGCRLPx+8BcKTU2470JAK+o4O2U00QEtZ98l2STM5sa1WHeFRiOiN7ScCwFFURrdy8IdwdHB6AN0TS7nWFTln4qKyqnScnaGKj6NZJNZ/OUasnc03QYU7AkuZElm8dy1Tapk9Caqy2rZt+YQstS0ij+wKxn3rTnUYEIWf0FRFKqKqptF8QdQXVSNqdLULLI8TXMp/uCwBYGlGRQ2XkJFbmWzKP7qZJXnVjSLLE/TbIo/AAVM1SZkyf/nWYBKS3mzKP6gTslY2CLmA7Nk5tcDP1JkKmxSOQoKNtnKLwd+pKAmv0lleQu2ZlL8AUiSjLmRQwx4K4qiUFlY1SyKP4Cqoiostc3zd1RRUVFpDFTln4rXoygKy+evJ39fcZMrp46WueybdeTvLW4egR6ittLM/nU5TeZy4grTYZn+fjCtLqnBUt28m8Lq4hrMVc2jFPMUzan4q6OlKAAr8iupzK9qVpmVeVXNLrO5URSl+RR/R2GqNTWZS7y3UG2tpNTcvOt0ta2KsmaW2dzYZBv/O/grpebSRnfzdYWCgqTILDz4MyUm/+5bSZKwNJPirw5ZlrGY/VsBWHfheqpJPU6VqqJqrKbmUTaqqKionC4tL0iUis+x47/9HPKAFZ6iKCz7dj3n3jEIgx9mqJQlmYMbclGk5t8M1pabKNhbQnzb6GaX3RyYqy3NZvF3LFVF1WgNWo/FomlK6mL8eVK2Qe+frtXmKjMVOc1j8Xcs5TkV6IP1GIL8b54FGj2xxwmjgKXWgiHQP93WrbKF4ia2SnNHpbUCgyaAQF2QR+Q3NWsKVzapq69rFCRF4q9D/+OC9IvR+mGCFUVRMJs8s4ZJkj2bsM5PkwKZq8weU8JVFVcTFh/qkURWvb9a2uwyT4eVlw7ydBNOiNTXF3m6CY3KgfuHeboJ9WR8sNjTTThtdtwyxNNNOGVUyz8Vr6aypJr1f+7wjHAFrCYrq3/d6hn5TUzBnhKspua9IT2a4v1l1JT7n5uqLMlUF1d7tA1VRdV+ecMvSZJHv5csy0hS0yRq8CSKrFCyv8yjbSjZX9qsFsjNhc1qQ7J5bszIsoytEbOIeguKolBc6xnFXx0lpkIkxf/mg/yaPLaUbvKIbAWFSmsF64pWe0R+U9PcFn/HYrVY/dIaWLJKTZbc40RQZIWa0hqPyVdRUVE5UVTln4rXoigK/36/EcWD+xRFgQNb8sje5l9xaGrKaik+UObpZnBoc77fuf9WF1d7XIlhM9swecjysKmQZRmrzfOuNRar/7lOledUIFk8q8SQzBLlzRRrsLlQZAWLh6x8jsZq9r8Df6W1HIvs2TlORqa0mWINNhc22caS3EUIeNZSdFPJBgpr/Svuss0mIXnBfsff3H8VRaGqxLMXrgCWWiuWGs/P9yoqKioNoSr/VLyWQzsKKD5U7hWblLX/2+4V7Wgs8nZ5R0wdS62VMj868NvMNiw1nldQAdSU1nhcCdmYeIPirw6bzX8sqWwWiapCzx+cAKoKqpCs/mNJZbV4z5i1mr2nLaeLrMiUm0s93QwAamzVWCT/uWjZUbaNKmtls8T5awgBgVUFKzzahsZEURSsFu9QDMmy4hVKyMbCWmv1+OVVHTVltX51VlBRUfE/VOWfiteyY+UBvCVMUXVZrd8k/zBVman1Infb4oNlfrNZqq3wnn5Fscce9AcURfEqyyWbZPObMetpF/VjqS7yD9cpRVG8yt1Wskl+cxlQY63yuHLqaCqt/pGxWlEUtpZu9nQzALv7b35tHmVeouQ9XWRJxpuWDG+am04XkxclOZMludkTjqioqKicDKryT8UrqSiupmBfiddslgRBYOeqA55uRqNQkl2Ohz16HLDUWKkp8yKl2SkiS3KzZ/c9HiZvUkaeBjbJ+zbTkuwdlgangyIrVBd6l7LNX+JVejLOnzv84cCvKAoV1nJPN8OBamsVsh/E/suryfEqRaaAwPYy/4i5bPWy354sy151oXaqSFbJ65Rt/hZyRUVFxb9QlX8qXsmeddlelZ1QURQO7Sik1otuGE8FWZLtbrbedLYWoDTHuw5zp4I3Wtl548b4VPBGN1tvVO6cLKYKk9fF3JRtMqYK355nAWwW7xuz/qD8s8hmbLK3uTArVFu9y4L2VNhets3jsf6ORkFhZ9l2n0+qIsveZbleh80P1jBztfetFVaT1evWVRUVFZU6mk35N2zYMARBoFWrVg7PFy1ahCAICILARx991FzNUfFyCg+UurX+aNszhfPuGswlj5zBWTf1IyYl3G09sWkRXP7E2U6vkKig+jLpXRJdlhE1zj+P4kO+raQyVVncun7N/f4zzr56OD3O6cild1zI6o0rG6zLYjHzxuzXOPPKoXQ/pwOjrhnBt798Xf/+rn07ue/pOznrqmF0OqMdn3zzoeuKFKgu9X0LNZvJ/YF09iez6DG4O0mZiYw4fwT//PfPCdW5YtUK4trGMmz0UIfnP/36IyPHjKB1l3RSO6QwbPRQvvr2S9ft8nHlX0NWYDNmzKB9VnsiwiMYMGAAy5Yuc1t28eLFBAYEOr22b9/uUK6srIz77ruP9PR0IsIj6N6tO7/++qtTfbIi+7yFmrna4tYKeM5XHzHgvH607dea0VeOYsWahuNvffTlRwy/aCht+7dh6NjBzPvpa4f3rVYr095/nYFjBtC2X2vOuuwM/lr2l8u6LFXep0g/GRpyU3//g/fp0LkDkTGRDBwykGXL3Y9ZgLlfzqXvgL5Ex0XTul1rbrvjNoqLHUNQvPX2W3Tr0Y2o2Cgy2mcw8ZGJmEzOc6qiKD7v+mtuIL7exx98ysDOQ8mIac+5Q8bw33L3a1h+XgF333gfw3ucQauwtjzz8LNOZX754TfOG3oBnVO6kRXfiXMGnse3X8x30y7fX8Pya3LdulP/+fkiJpzxKLd0Hc/TFz/HjlU7T6jOnWt2cVOn25k0drLD81UL1/DMuOcZ3+debutxF5PGTmb5987rok2x+bzrb0OKv/ffn0GHju2JjIpg4KABLFvmfj649bZbCQoOdHr16tXToVxZWRn3338frdukExkVQY8e3fn1NxdrmB9krrc2sL+Z9fEsug/sRmK7BEaMHs4/K9zvu5b+s5So1Ein145dO+rLfPz5HM69eDStO6XTulM6Y68Yy+p1rrNSe+Plz7EU/DafDXddyuqrR7LlkZuo3LrebdmKzWtZddlgp1ftof0uy5cs+x+rLhvMrqmPNlXzfZbKZd+R89wVHJx4Fnmv3Yp5z4YGyys2C2U/zyTn2cs5OOEscp6/iqoVP9e/X7NhMXmv3Ub2Y+eR/cg55L1yM9WrFjb11/AayhbNZ++jl7Fr/JkceO4Wane6H8cAstVC0fwP2PvIpewafwb7HruC8qULHMpUrv6b/U9dy67xZ7D/qWupWru4Kb9Cs6P1dAMaE4vFgl6v93QzVE4TWVYozXOdBCKlQzzdz8pi9S9bKDpYRpseKQy5oie/vLeMmgZcHBe8s8Rhk2A+JiOXxWTl53eXOrbjmJs7QRQoyS0nOTP2ZL+S1+DOHeGXvxbw0rvP88Q9T9O9Yw++XjCX2x+9mR9m/UJCXKLLzzz47L0UlxYx+cEXSE1Ko6SsGNtRm8laUy3JCSmcNfQcXn73hQbbZTPbsFkltDrNqX85D2Mzu95Iz/9pPo8/+zgvT55K3159mPP5HC6/4TKWLVxOclKy2/oqKiq488HxDBkwhMKiQof3wsMjeODOB2jXph06nZ6Ffy7k7ol3Ex0Vw4ihIxzb5QOb0IZwd3Ca9/U8Jk6YyLQ3ptG/f39mzZzFhRdeyJo1a0hJTXFb3/oN6wkJCan/d0xMTP1/WywWzjv3PGJiY/j8s89JSkoiOzub4JBgl3UpiuJVFsoni6Xa6tIK+IffvueZV57m+UdfoFfX3nz2zSdce/fV/DlvEUkJSU7lP/56DlPeepEpT7xM147dWLd5HQ8/O4GwkDDOHHoWAFPfeZlvf/6Wl598mTat2vL3P4u45aGb+e7D7+mU1cmxXT6eMdGdgm3eN/OY+MhEpr02jX79+jFr9izGXjyW1f+tJiXFecwu/2c5t9x2C1NenMLoc0aTk5vDPffdw51338ncz+cCduXgpKcn8e7b79Kvbz927trJbXfcBsDLL73sVKcsy2hE351n3SXX+PGbn5j8yHM8+9oz9OrXk89nf8F1F9/I//77jaQU5zXMYrYQFR3JXQ+NZ+Y7ri+mwiPCuOuh8bTJaINep+OPX//kofEPExUTxdAzhhzTLt9W/plstdRKtS7fW/HzSj5/6UuuefJK2vVoy6IvF/PabdN5/seniUqMcltnTWUNHzwym/b9sqgodnQnDg4P4rzbRpPQOh6tTsO6RRuZ9fgcQqJC6Tyoo0PZIlMhUcbo0/+SHsLtGjZvHhMfnsi016fRr19/+3xw0YWsXrXG5Xww9eWpTJ58RIkq2Wz069+PsWPH1j+zWCycP+Y8YmJi+OzThtcwWVZ8eg1TFMVtoo/5P3zL4888xtTnptKnV1/mfPYRl113Kcv/+KfBfdeKRf8REnxkfxAddWTcLft3GRddcDF9evbBYDDw5nvTGXf1xSz733IS4x3nGJtFQh9wml+wCSlZ/gcH50wn9aYHCM7sTOH/fmDnixPo+NonGKLj3H6u0+ufoQk8YjyhDQ13KmMuzOPgp+8QnNW1KZru09Ss/ZOy794m4uL7MKR3omr5jxS+/zDxD3+ENsJ1vxfNeQa5spTIyyagjU5CqiyFo8LOiIGhhJ5xNbq4VASNltot/1AydwpicDgBWX2a66t5hMqVf1L45VvEXnk/AW07Ub74Rw5Nf5i0p+egi3Ldn3nvP41UUUrctRPRxSYhVZahHNWftbs3kffBM0RdcCPB3QZTtW4JuTOeJmXiWxhbd2iur9akeJ3bb3V1NRdeeCHp6ekEBQVhMBho164dkyZNwnJUpqw6S8JrrrmGCRMmEBsbS2ZmJgCtWrWqf++BBx4gLCyMpKQkPvzwQ3Jzczn33HMJCgqia9euTrdsS5cu5ayzziIsLAyDwUD79u155ZVXkI5SatTV//DDD3PXXXcRFRVFbGws9957b7172qBBgxAEgauvvrr+c5IkERsbiyAIvPTSS03ZjT5NZXG1W5P5rL5p7FmXzZ51h6gormbt79uoqTDRtqf7wz6Aqdri8HJlsHNsmWNRZIUSH3dPra0wubT0+fib2Vw0ahzjRl9Km7S2PDL+CeJj45n74+cu61n632JWbfiPd1+YSf+eA0mKT6ZzVle6d+xRX6ZzVhceuu0RRg8/D73u+Ep5X46TIkuy2zH77sx3uOrSq7jm8mvIaJvJ85NeIDEhkQ8/m91gnQ88/gAXj7mYXj16O703qN8gzj37PDLaZpKels5tN9xGh6yOrFj1r1PZhm7GfQF3B6fp06dz3fXXccMNN5CVlcXUV6aSnJzMBx980GB9MTExxMfH1780miOKkDlz5lBaWspXX31F/wH9SU1LZcDAAXTp0uWk2uYLKIqCtda1teoHn33AZRdezhVjr6Rd63Y8PWEyiXGJfDLvY5flv13wDVdddDVjzr6AtOQ0Ljj7Ai6/4HLenfNOfZlvFnzDXTfezYhBI0lLTuPaS65jaP+hvP/JDKf6LDVWn7aqdDcXvPnWm1x37XVcf931ZGVmMXXKVJKTkvlglusx+9/K/0hLTWP8HeNp1aoVA/oP4KYbbmLN2jVHyvz3H/369eOySy8jLS2NM0aewSXjLmHt2rUn1TZfwZ2SbeZbs7ns2ku44rrLaJfZlqemPElCUgKfzvrMZfmUtGSefnkSF195EaGhIS7L9B/cj1Hnn027zLaktU7jxvE3kNUpi5X/rHIqa1NsyIrv9m2Rqcjtewvn/M6QiwYx9JLBJLZJ4MrHLiMyPoI/5/7dYJ1znvqUfuf2pW231k7vZfXJpOeZ3Ulsk0BsaixnXTuS5Iwkdq7e5VBOQKC4gbb5Au7ng+n2+eD6w2vYy4fng5mu54OwsDDi4+LrX2vWrKG0tJRrrrm2vszHH9vXsC/nfkX//v1JTU1lwIABdOnseg3z5XlWsrr/vb0z8x2uuuxqrrniWjLbZfLC0y+SmJjI7E8a3nfFRMUQFxtX/zp6fzBj+vvcdO1NdO7YmYy2GUyb8gayLLN4qbNVkLdfuuYv+JLoEecSM/J8ApJbkXr9PeijYilc6NqyuQ5tWAS68Kj6l3DMRZIiS+x9czKJl9yIIS6hKb+CT1L599cE9R1NcL9z0cWlETH2LjThsVQt+8Fl+dqt/2HevZ7oW17CmNETbWQ8hrT2GNKPXJga23YjsMtgdHFpaKOTCBkyDl1CGyx7NzXX1/IYpb9/Rdig0YQNPg99QitiLrsbbUQM5X9/77J89aYV1O5YT+I9Uwjs0AtddALG9PYEtDnSn2V/zCOwfU8iz7kafUIakedcTWD7npT+8bXLOn0Rr1P+1dbW8v3331NbW0tGRgaxsbHs2rWLZ599lscff9yp/FdffcUbb7xBXFwc4eHhDu99/fXXfPLJJwQGBpKTk8Mtt9zCsGHDWL9+PXq9ng0bNnDFFVdgtdoPQIsWLWL48OH8/vvvaDQa0tLS2LZtGxMmTOD22293kv3666/zxRdfEBAQQGFhIdOnT+fDD+03yOPHjwfg22+/pbzcrjBasmQJhYWFiKLooBRUcaSiqMrlc1EUiEgIJW+Po8tT3p5iopPDG6zz7Fv6c8G9wxh+VS9i0yKd3tfqNZx/9xDG3DOUwZd1JzzO9WGgvNB123wFU7XFydLHarWwZcdmBvQa5PB8QM9BrN+yBlf89c8fdMzozOwvP2DEZYM497ozmTrjJUzmU7d+8MaYeSeKu02oxWJh/ab1DB883OH58MHD+W+1e5e0z7/+jH0H9jLh3onHla0oCouX/c3uPbvo36e/0/uy1bfdU1213WKxsHbtWkaOHOnwfOTIkfz7r7MC9Gj69+tPeno6o88Zzd9/Ox5eFyxYQN++fbnvvvtoldaKXj178fLLLztc/hyvbb6CZJVdWqhZrBY2bt3AkH6OruZD+g9l1XpnpQfY/x4Gg8HhmdEYwLpN6+rXV4vVjPHYMgYjK9f951SfIivINt9VpLhSClssFtauW8vIEY5jdsSIEaxY4dqlul/ffhzKOcSvv/2KoijkF+Tz3fffMersUfVl+vfvz7p161i1yv632bt3LwsXLuTss88+4bb5CoqiYFOcD9UWi4WN6zYxeITjGjZkxCBWr3C9hp2K7KWLlrFn5x76DnRtTWH1uliEJ065pcxlvD+bxca+zQfoONDR4qHjwA7sXrvbbX1Lvl1GwcFCLrjzvOPKVhSFLf9sJW9fPpm92jm+h0KpqeQEv4V34nY+cLGGjRg5khXHWcPqmPPxHIYPH0Fqamr9swU/L6BPn77cf/99tEpvRa/evZg61f0aJvtwGADZTcxCi8XC+o3rGT7Eed+1crXzenM0w0YPpUPP9lx4+YUsWb6kwbI1tTXYrDYiwiOc3pOs3utSLdusVO/ZQWgXx3kstGtvqnY0rDDa8vBNrL/tArY/ey8Vm5zn1px5H6ENDSdmxPF/9y0NxWbFkr0DY0Yvh+fGzF5Y9rnu99rNy9CnZFL511xynr6E3BevoeyHd5Etro0lFEXBtGM1tsKDGFq7Vvj7C4rNivnADgI7OBpIBHXojWm36/6sXr8MQ1ompb99wZ6JF7Pviaso/Podh/407d7sVGdgh96Ydm9u/C/hIZpd+bd///76GH+CIDB8uOPkHBoayubNm8nLy2Pt2rUcPHiwXlE2d+5cp/oURWHFihVs3LixfvN7dF07d+5kyRL7BC5JEjqdjt27dzNv3jwADh48yO7d9g3MU089hc1mIy0tjT179rBjxw7uvfdeAGbNmsWePXsc6k9OTmbPnj3s2rWLxES7yfcff/wBwLhx44iJiaG2tpYvvvgCgG+++Qawb/aTk92bnbd0bG7M+PWBekRRdLLKM1ebMQYbXH6mttLMfws2s2zeOpbOW0tFcTXDr+5FTOqRxbqiuJoVP2xiyZdr+Wf+emSbzBnX9yU4ItC5bV68oJ8Irg7UpeWlSLJEVISjW01URDRFJa5v3LNzD7Jm0yp27tvBG8+8zcPjH+f3xb/y3PSnT61hAsiS725C3aWlLi4tRpIkYqIdXcVjomMoKMx3+Znde3fz7JRnee/1GWi17iMzVFRUkNYxlYSMeK648QpefPolhh2jZPQHXMWgKioqQpIk4mIdzfpj42LJz3fdr/Hx8bz19lt8/sXnzP1iLu0y2jH6nNEsXXrE3X/f3n3Mnz8fSZL4dv63PPzww0x/YzpTpkw54bb5Cu5cU0vKSuxjNspxPoiOjKawuMDlZ4b0H8rc775gw5YNKIrC+i3r+fL7uVhtVkrK7Af3of2H8cGn77P3wB67tcS/i1n4928UFLmu09dj0x1LcXFxvfX/0cTFxrkds/369mP2zNlcd8N1hEeF07pta8LCwnh16qv1ZS4ZdwlPPv4kZ5x9BmGRYXTq2okhg4fw0AMPNen38QTufm+lxaVIkkR07DFjNjaawvxCl585USrKK2mf0Jm2UVnceMnNPDP1KSclY337fNjyz10SlcqyKmRJJjQ61OF5WFQo5UWuMwPn7ctn3mvfctvLN6PRuncxr6ms4faed3NLlzt4/fY3uerxy52UjIBLha+vU1xcdHg+cFzD4mJjyS9wPR8cTW5eLgsXLuT66653eL5v7z6++24+kiwx/9tveXjiw0x/czovv+x6DfOu7G8nh7vLt+KSw3NtdIzD85iYWPILXa83cbFxvP7S63z03hzmvD+Htm3aMvaKC1m+Yrlb+ZNfmkxCfAJDBw11ftOLpwJbRTnIErowR6WlLiwCa5lrRbs+Ioq0WyfQ5oFnafPg8xgTUtnx3H1UbllXX6Zy2waK/lpA2q3Hv7RuicjV5SDLaEIc+10TEmF35XWBVJyLee9GrHl7ibphMuEX3EnN+r8p+3aaY921VWQ/cg7ZE86kcOajhI+9G2NmL5d1+gtSlX0ca0IdDXo0oRHYKlyPY2tRLqZdGzEf2kviHc8Rc9ndVK35m8LPX68vY6soQRN6zN8oNALJTZ2+SLPH/NPr9XTv3r3+3xUVFWzdurX+3xqNhk8//ZR58+axf/9+B1ffnJwcp/qGDx9eX9/R5tlgd70NDw8nOPhIrIuzzjoLg8FA69ZH3BDy8/PJyspi5Uq7Jc7o0aPrrQivvPJK3njjDRRFYfXq1Q6fGzNmDGFhYQCkp6eTk5NTv4nX6/XceOONTJkyhdmzZ3Pbbbcxf77dnPq66647iR5reRz3JtJpwRfcKl8qS2qoLKmp/3fxoXICQ41k9WtF4YHS+mdHJ/IoPFjG2Tf3J6N3KmsWbnMU7esH0gaaf2zcl4ZiwciKjCAITHn0tfr4KBNuf5QHJt/NE/c8jdFgPPmm+bAV1fGUQCfat5Ikcdt9tzLx/odp27ptg3UGBwfz14JFVNdUs3jZYp587gnSUtMY1M/FwVTBbWIHr6eRxmxGRgYZGRn1/+7bry/Z2dlMmzaNQYPsfSbLMjExMbz99ttoNBp69OhBbm4ur097nccee+z0v4s3cZzf27FWQA317b233EdhcSEXXH8+iqIQHRnDJedfyrtz3qlfl5+ZMJmJz05g2EVDEQSBtOQ0Lj3/Mr760XWiGh+eDhqcD06mX7du28pDEx/ikYcf4YyRZ5CXl8fjTz7OPffdw7tvvwvA4iWLefmVl5n22jR69erFnj17mPDwBOKnxPPIw484t82XO/Y4nEzfnijBIUH8svRHqqtrWPb3cp577HlSW6XQf3C/06rX25CVukXC9fg4thcVRXG5psiSzIwJM7nwrjHEp7uPHQZgDDLyzLdPYq4xs+Xfrcyd8jWxKTFk9ck8pm1erEk5TVyuYSewWH/66aeEh4Vz/vnnOzyXFfsa9tab9jWse/ce5OblMm3a6zz6qIs1zIeng+NNZSezP2jXph3t2hyxOu3dsw85OYd4e8ZbDOg7wKn89Hen8+333/DDVz9iNDrvd33iYtCpf5z7rA5jYirGxCMWpsEZnbAUF5D301xCOnRDqq1h71vP0erWiehcxAFUOYpj+7iB/XndfBB11eOIAXZdhmIbT/Gcpwm/6D5Evd3wRTAEEvfgTBRLLaadayj7/h20UYkY23Zruu/hrSjgtkNlGQSIv+kJNIH2/oy55E5yZ0wi5sr7j/Sny9+Brx6inGl25V9CQoKDW1adq20dL730Ei+++CIAaWlpxMfHk52dzaFDh1yazsfHx7uVFRpqv6k82nqm7tnRf9hjN8Mnulk82s24TsbRdd1+++1MnTqVlStXMnPmTA4dOkRwcDAXXXTRCdXfUtFoXRukWmosyLLsZOVnCNK7jNHnjuJD5aR1bjgWRUluBcGRzpZ/GhcZgH0JQXQe2xFhEWhEDUUljhYSJWXFREW4DuYdExlLbHScQ2Dk1qlt7K5phXmkJbc6uYYpdrduX8XdnBEVEYVGo3Gy8isqLnKyBgSoqq5i3YZ1bNy8kUeeehiwK6QURSGubSxffzyPIQPsgeZFUaR1K/tlROcOndmxawdvvDPNtfLPd7vW5Xk0OjoajUZDXn6ew/PCgkIny6qG6NOnD3O/OGJRHh8fj06nc7hIyszKJD8v3+8SSrmaCwAiwyPtY7bYcT4oLi0mOjLG5WcCjAG8+vRrvPT4FIpKComNjuOzbz8lOCiYyHD7rWxURBSzXpuNyWyitLyU+Jh4Xpz+AqlHHSgc23caX87DuDq8R0XZ54JjrXoKCgvcjtlXXn2F/v36c/+99wPQuVNngoKCOPPsM5n05CQS4hN49rlnueLyK+otgDp17ER1dTV333s3EydMRBQdO9JXg/uD634FiIiKQKPRUFhwzJgtLHayBjxZRFGkVZtWAHTs0oFd23fzzqvvuVT+nYjSxluxJ4FxVliEhAcjakQnK7+KkkrCokKdypuqTezbtJ8DWw/y6XN2rxflcGKJmzrdzoMz76NDvyzA3rdxafaxn9o+hZzdefz0/i9Oyj+N4LsJatwRFWVfw/KPWcMKCo+/himKwieffMzlV1zhtCbFx8WjPXYNy8wkP9/NGua7Q9ZJh1JHVOThufYYK7+iokIna8CG6NmjF1/P/8rp+Vsz3uT1t1/j28/m07F9Rxef9O55VhsaBqLGycrPVlGK9hhrwIYIateRkiX2rLLm/ENYCnPZ+fJRF06HlfarrhhGp9c/wxjvnCysJSEGhYEoOlmQSVWlaIJd97smNBJNWHS94g9AF5cGioJUXogYY/ciFEQRXYy9f/VJbbHl76fyj8/8WvmnCbaPY6f+rCxFG+qmP8Oj0IbH1Cv+APQJqaAo2EoL0cclow2NxFZ+TJ0VpU7WgL6M122v6xSDGRkZ7Nu3j+XLl9O1q/uMQY05wfbubffxXrBgAWVlZQD1LruCINCzZ8+Tqq9Vq1aMGmWPzXP//fbN+7hx4wgMdFYqqRzBEOj6gC3LCqW5FcSnOyqk4tOjKMouO+H6I+JDMFU1nFwiPM51Gb2btvkKWr3zJlqn09MhoyP/rHZMfvPP6mV07dDDqTxA9449KCwuoKa2uv7Z/ux99s18jHuFfENofDjTrztFil6vp2unrixausjh+aKli+jT0zmRR0hwCEt+XcqiBX/Xv66/6nratm7LogV/07NbQ3OQgtniQgkuePdG9Hi4anudBfmff/7p8PzPP/+kX78Tt8hZv269wwVSv/792L17t8NF066du4iPj3ep+PPlfhXdXGTodXo6t+/CkhWOQcyX/LuYXl0bdiPR6XQkxCWi0Wj44bcfGDn4DCflk9FgJCE2AZvNxs9//FyfDfhE2+cTuBgWer2e7t2cx+xff/1F3759XVZTW1vr1H/1mXoP62lqamucy2g0KIri0srPl8csuFaw6fV6OnfrxJI/HdewJX8to2df12vYqaIoioNHytEc+3fwJYwao0trJa1eS6uOqWxevsXh+ZblW2nTvY1zPcFGnv3+KZ759sn617DLhhCfHscz3z5Jmy7p7huhKC4TJQRovTht6glwMmvYX3/+Sd/jrGFLlixh9+7dXHetsxdRv/792LOnZaxhgpvfm16vp2vnrixassjh+aIli+jd88Szn27ctIG4WMf97JvvTeeV6a/w1cdf071rdzefdL8n9AZErY6g1hlUbHCMO12xYSXBGZ3cfMqZ2n070B02EDAmptJx6hw6Tpld/wrvOZCQjt3pOGU2eheX3S0NQatDn5yBaYdjiDLTjtXoW7nud0N6J6SKYmTzkUzstsKDIIhowhpQZCsKis13Y9CeCIJWhyE1g5otjv1Zs3UVxjau+zOgTSdsZUXIpiMegZb8bBBEtBH2/jS26UjN1mPq3LISYxvXin5fpNkt/45Hly5d+Omnn9ixYwfp6elYrVZqa2uP/8FG4JlnnuHMM89k//79tG7dmujoaHbu3AnATTfd5ODye6Lccccd/Pzzz1RX25Ukqsvv8YmId75NrmPbiv30u6AzJbkVFGeX0aZHMoFhRnatOQhAl+HtCAgxsOIHe7DPjD5pVJfVUl5YhagRaNU5kZT28Sz9+kgmxI6D21B8qIzKkhp0Bi0ZvVOJiAth9a9bHWQLokBUUlgTfOPmIyDUQHVZrdMF/7UX38ijUybQMaMTXTt0Z96CL8ktyOWy868A4PWZr1BQlM+Lj0wF4NyR5/PeZ2/zxNRHuPO6eygtL+XV96cw9uxx9S6/VquF3fvtmfusNiv5Rfls27WFwIAgUpPSnNsW4jpuoy/QkOLyjpvHM/6BO+jWuTu9e/RizhcfcyjnENdfeQMAz748mdy8XN557V1EUaR9ZnuHz0dHxWA0GB2eT3vndbp17kartHSsVgu///U/vvz2S6Y++4qTfK3e66b5k0IURCScY23ec8893HTTTfTo0YO+ffsye9ZsDh48yM033wzApCcnkZOTw8xZMwF46823SE1LpUOHDlgsFuZ+MZfvvvuOz784ktH61ltu5b133+OhBx/ijvF3sGvXLqZOncod4+9w2zZfRdSKaHSiy2Q1t1x1C/c9eS9d2nelZ5eefPbtpxzKO8TVF18DwEtvvkheQS7Tnp0OwJ79u1m3aR3dO3enrKKcDz59n+27t/H65Gn1da7duIa8gjw6ZHYkryCP12e8iqLI3HH9eCf5Gr3Gp5V/7pRAd991NzffejPde3Snb5++zP5wNgezD3LzjYfH7NOHx+z79jF7zjnncNfdd/HBzA/sbr/5eUx8eCK9evYiIcFuvT561GjefPtNunbpSu9evdm9ZzfPPvcs544+1ykUCnj3ofR4CIKATtRjkZ0v5m6+60buv/UhuvToTI8+3fniw7nkZOdw1Y1XAjDl6ank5eTx+vtH4iVu3mBXaFVXV1NcVMLmDVvQ6XVkZNnd/95+9V26dO9MWnoqFquVvxYu4tsv5vPc65Od24aAVtA1xdduFqKM7i0kz7ruTD54ZDatOqXRtlsb/v5qMcW5JQy/zB7r7OvXvqUsv4xbptyIKIokZzha+IRGhaAz6Bye//T+L6R3TCMmNQbJamPD4k0s/+Efrpl0lcNnBQSijCdureWNiKLoMuHG3Xfdw8233ET3Hj0c54ObDs8HTx2eDz6Y6fC5OR/PoXfv3nTs6HwYveXmW3nvvfeYMOEhbr/9Dnbv3sXUV6Yy/g43a5gPK6xdXWbXMf7m8dxx/x1079KNXj168/HncziUc4gbrrbvuya/ZN93vTvNHj7hvZnvkpKSSlZGFlaLha/mf82Pv/zIRzPm1Nc5/d3pvPjqC8yY/j6pyan1VtxBQUEEBwU7yPf2fVfcuZex963nCGqTRVC7jhT+8QOWogJizrwQgOzP38NaUkT6XU8AkL/gK/Sx8QQkp6PYbBQv+Y3SFX/T5oHnABD1BgJSHc/ImsN9cuzzlkzI0Eso/vxF9CmZGFp1pOqfn5BK8wkeYHffL/vpA6SKQqKutLvoB/Y4g4rfP6Fk7hTCzr4eqbqcsh9nENTnnHoX1Yr/fYY+JRNtdCKKzYZp679Ur1pIxLj7PfY9m4uIMy8lb/bzGNIyCWjTkfLFP2EtKSBs6BgAir59H1tZIfE32hPGhvQ5g5IFH5P/0UtEjrkRuaqconnvEjrwSH+GjxxH9tR7KPn1c4K7DqRq/TJqtq4mZeJbHvuejY3XzU6PPfYY2dnZ/PDDD1RUVHDDDTcQEBDAc8891+Syhw0bxp9//smzzz7Lv//+y759+8jKyuLGG2/kgQceOKU6R48eTatWrdi3bx9paWkMHeoiMKyKA8YgPcZgg0vLu4Nb8jAE6Og0uA3GYAPlhZUsnruGmnJ7ltmAYANBYUduiUWNQLczMgkIMSDZZCoKq/j7i9Xk7j6SyEJv1NL73I4YgwxYzVZK8yr54+P/KMkpd5CtyAqRCe4Vk75AQKjRZYyXc4afS3lFGe99+jaFJQW0a5XBuy98QGKcfbNeVFJAbsGRmJuBAUF8MOUjXnhrMpeNv4iw0HBGDR3N3TccWWwKigsYd/sF9f/+6OtZfPT1LHp16cNHr33mIF8Q7O7bvoogCGj0GiQXyWrGnjeW0tISXpk+lfzCfLIy2vPF7LmkJKcAkF+QT3bOoZOSV1NTw4RJE8nNzcFoNNKuTTveff09xp431qms1uB10/xJ4e5wMu6ScRSXFPPiCy+Sl5dHh44dmP/dfFLT7G6keXl5HDx4sL68xWLhsUcfIycnh4CAANq3b8+387+tt84GSE5J5scff2TixIn06d2HxMRExt85ngcffPCk2uYr6IP01JY5Z+gec/YFlJaX8sYHr1NQVEBmm0zmTP+E5ES7i0l+UT6H8o7MB5Ik8/6nM9i9fzc6rY7+vQbw3Yffk5KYUl/GZDEz9Z2XOXDoAIGBgYwYOIJpz00nLMT5QkXvw3MBuLdaHHfxOEpKSnhpykv2MduhA9/O+7Y+W2deXh7Z2dn15a+56hqqKquY8f4MHn38UcLCwhg6ZCjPTT6yH3p44sMIgsDkZyeTk5tDdHQ0o0eN5qlJT51U23wFg8boUvl3/sXnUVpSxvQpb1KQV0hGh3Z8NG8Wyan2Nawgr4Cc7FyHz4wedCRe2sa1m/j+6x9ITk1i2Sa71WtNdQ1PPDCJ3Jw8jEYjbTJaM+2DVzn/YudMljpR79NWVOGGCAQEl9Z/fUf3prqsmh/eWUB5YTlJ7RK5/727iU6yW/2UF5ZTnHtywdDNNWY+nvw5pfml6I064tPjuWXKTfQd7WgRr6A0qJj0BUSNa+XfuHHjKCkp5qWXXjwyH3wz33E+OGoNAygvL+f7779j6stTXcpKTk7mh+9/5OFHJtK3n30Nu3P8eB54wHkNE3zcK0DUiAii4DIW99gxF1FSVsrUN6aSX5BP+4z2zJ3zpcO+61DOkbnWYrXy1HOTyM3LxWg0kpWRxdyPvuTMEWfWl5n9ySwsFgs33H69g6yJ903k4Qcc46s2pJj0BiIHjMRWWUHONx9hLS0mICWddo+8jOGw5461rBhz8ZEQFbLNSvYn72ApKbQr+lLSafvIy4R37++pr+CTBHYfgVRTQcXCj5EqStAltCL6lpfQRtr7XaosRio94q4uGgKIue0VSudPJ//12xGDQgnoOoywc26qL6NYTJR+Mw2prBBBZ0Abl0rUVY8R2H1Es3+/5iak9wik6nJKFnyMVF6MPjGdpLunoIuy96etvBhbyVH9aQwk6b5XKZj7BgefvxVNcCjBvYYTdcHN9WUC2nQi/pZJFH83i+LvZ6GLSSTh1qcxtnZORuWrCIo/R3/2EkaNGsVvv/3Gk08+yeTJzjfGKs4s+Woth3YWeF0w4jOu70t0crinm3HKWGqt7Fy+39PNcMIYYqBNn5TjF/RiqourMVU27E7uCYJjgn1asaooCiazs4LKGwgw+rY7WmV+JeU5lZ5uhhNhSaGExAYfv6CXoigKtVXN47FwsgQEB/j0gb/aWkWxyXXGTk8Sogsjwug6Rq6v8P3ebyg1e19Gw0vaXEGQznfnA0mSMJtOPC51c6HRiBiMvutxAVBZWIXV5H3ujaGxIU168bpmzRp69uzJ6tWruW1XzfE/4EWsvNR1tnRvI/X1RZ5uQqNy4P5hnm5CPRkfLD5+IS9nxy1DPN2EU8a3r4C9nOeff56zzz6b3377jcDAQO68805PN8lnSMqM9TrFnyFQ5/OWfzqjFn2g97kmhcYGeboJp43OS+NB6oy+bfknCIJXWti5cqn0NYxhJ5+Vuzkwhnpnu04UQRDQaL1vfIga0acVfwBGjXcq3AO0vh/LOS0k3euSloTrIwjU+vb+wBvXL/CPNUwf4H37WUG0e4KoqKioeCPeuSL5Cb///ju///47bdu25auvviIuLs7TTfIZUjvEe5XZvCBA256pPu8yJQgCUSnhnm6GIwJEJPq2UhXsSjbRTaZqT6EP0vv8mAXQarxPgemNbTpZdEad17nYGoL1Pq+wBtDpve9Q6o1tOlk0ooZArXdZgWkFLQaNbyusATLCMo9fqJnpENnJ5xXWgiCg1XnfnOaNFxQniz5Q73UZiw3BBp8fsyoqKv6L758KvZhFixYhyzI7d+7k3HPP9XRzfAqtTkObHiles4AqQJvuyZ5uRqMQFh/iPUHfBQiNDfb64MgngiAIGEO86wAY4OMWVHV4m+WEt1ojngrBMd5lVeNt7TlVRI3oVWNEEAS/uAgACNF712VRiD7Ma/Yqp0OgLojUkFZeY/2nFbSkhzpnFPZFtF6maNPqtH4xZgVRwBjkXa7L3tYeFRUVlaPxj52gil/SrmeKV9zoCYJASlYcgX6iSNFoRSKTvOTwpOB9loingSHYe26htQaNzyf7qEMQBLRa7/kuOq3vW1DVERBuRKPzjq2ARid6rSvyqeBNlxpavX8c9gH0ogG96B0WqwICQboQTzej0egQ0cll0o/mRkAgK6IDOtE/5lpRFL1K+e5tysjTwRDiPco2XYDO6zxAVFRUVI5GnaFUvJbgiEA6DfH8ra9GJ9LjrPaebkajEtM6Cq3B85u/iKRQAv3osC9qRIKivMNyKSjKu1zjThetxjuUF6LoXRZdp4sgCESkRni6GQBEpEV4xd+4sdBoNV5x4BdE73Q7PFUEQSDSGOvpZgAQaYxGFDz/N24s4gLjyQjL8rD1n0CgNpCu0T082IbGR6/3DoW1Tqf1qzVMo9V4h5eDAEERvh/7U0VFxb/xn9lfxS9pPyCd8DjPuqn2OqcDAV50s9gYaLQiSR08G4NSa9AS1zbao21oCgxBenQeDkIdGB7gVTEzGwNBENDrPH940uv0fqWgAjCGGgiM8uyhJSg6EKOfzbOCIKA3en7MGoz+F4NKr9ETpves0tqoCfS6+IONQa/YvgRoAzyoAFQYlDDMb6z+6hBFAb2H427620VAHcZQz1uwB0UEesVlj4qKikpDqLOUilcjiiL9LujsEdmCAIkZMaR1SvCI/KYmODKQCA+6/yZ3iEXjh+4RgiAQFBXkscO2Rq/xK9fJoxFF0aPuvzqdzu+UKHWEJ4V67PCk0YmE+UHSH1eIouhRBaBOr/PbA2moPhydh9x/BUSijNF+OR/oNXoGJQzzmPtvVngHEoISPSK7qdFoNR61ujMY/O/yCg7vuyI9p4jXGXX25CMqKioqXo6gKIrng3uoqByHQzsKWPL1WpprLyoIAuHxIYy4pjc6L4rb1NjIssKB9blUl9Q0q9zE9rF+keG3IawmGxX5Fc02ZgFErUhYQqjfHvYBFEXBarUiyVKzytVqtX4V688VVpONgh2FKFLzDVpRIxCTEeMXGX4bwmK2YLPYmlWmRqtBb/TPw34dkmwjryYHSWm+vhUQiA1M8IsMvw2xvWwr/+QtbTZ5AgJJQcmMSD7Lr1ypj0VRFEwmM4rcvMcvg0HvFxl+G8JSa6GqqLpZZWp0GkJjm9dDac2aNfTs2ZPVq1fTo4d/ucerqKg0Lf67uqr4FUkZsQy8qGuzHGIEAcLjQxh+ZS+/VvyB3Q0ltUs8QZEBzSYzITPG7xV/ADqjltC4kGZLACJqRULjQ/xa8Qd2xbxOp2tW6wmtRotW499zAdjHbEy7aERN8wxaUSMS0y7a7xV/YLfAa84EIC1B8QegEbXEBSagEZqnbwUEYgLi/V7xB5AZ3p6+cQOaTV5iUDLDks7wa8Uf2Ncwo9HQrMoifQtQ/AHoA/QEN2PcZY1OQ0hssEdDE6moqKicDP69wqr4FSnt4xlyeQ90xqYN/J/QJoYR1/RG7+G4bc2FqBFJ7ZpIeEITZiwU7LFmkjvGEZkc1nRyvAydUUdYfChCEytTtAYtYQmhLWJzD0fi/zWHQk6n1fm1u++x6AN0xGTENHlCIK1BS2xmtMfjYzYXgiCgN+jRGZr++2r12hah+KtDK+qID0xELzZtzEiNoCEuMBGjtvkuyzxN+4iODE4YjkbQNGkMwHZhmYxMPgut6P8XAXBEAdgcl1gGo96vsvseD32gnuDo4Caf/3RGHaGxIX6VPEVFRcX/Ud1+VXwOU7WZVb9sIXtbQaPVKQgCGp1Iz1EdaNU5ocUcmo6lsqiaQ1sKkKyN61IZFBFAYvvYFqNQPRZFVqguqcZcZWn0ugMjAjGG+l9A/xNFkiWsViuNvZTVKRhb6sZekRUq8iqpzK9q9LpD4oIJjfdsIidPIksy5iZw+xMEAX2AHo2m5Rz0j0ZRFCqt5ZSZSxq97mBdCOGGKL+3SnNHpaWSZbl/k1eb22h1CggYNAYGJQwlOTi10er1JRRFQbJJWCzWRq9boxHR+2mMvxNBlmSqS2uw1jZy3x7O6qsP9Fzfqm6/Kioqp4qq/FPxWQ5syWPd/7ZTU2FCEOBURnLd51Lax9HjrCwCQvzfled4SFaJvJ1FlOVVnna8Oo1OQ2zrSCKSQlvsBvRorLVWqkqqka3yadelM2oJigpCo2uZB/2jURQFm82GTWqcuF9ard3NVx2zYKmxUHqgvFEOULpAHREpYWpgdA6PWYsNayMd+rV6LTp9y7FQbQirbKHEVIRZMp12XVpBR6QxukVZ+7lDURS2l21lbdFqzJIJAeGUk4IICLQLy6RnbB8MGv/K8n0qyLKCxWJBlk5/byAIoNO3LGu/hjDXWKgpq2mUWLa6AJ1XZPVVlX8qKiqniqr8U/FpFEUhd3cRO1ceIHd3EWB3L3VnUSEIhzerit29rW3PFNp0TyYoTN3YH4vNYqM0p5KS7DJs5sOWgALuFYJHvRcYbiQyOZzQmKAWa93jDkVRsJltmCpMWGpO8uAvgDHEgDHEqCr9XKAoCpIkYZNsJ20JKAgCWq0WjahRFSgusNRYqCqspqa09uQuBQQIigwkKDoIfWDLtPxtiDrLH6vFetKWgIIgoNVr0epURbUrrJKFKmsFVdbKk1ZSBWqDCNaFYdC0XKtqd8iKzIHKfWwt3Ux+bR5Ag4rAo98L1AbRPqID7cIyVYWqC2RZxmaTsFlP/iJL1IjotFpEjaiO2WNQFAWryYa5yoTVdHJ9K4gChmADhiADGq13WP6qyj8VFZVTRVX+qfgNNRUmig+VUZJbQUluOeUFVdhsEoqsoNGIGAL1RCWFE5EQSmR8KJFJYWj8PDlCY6AoCrUVZkyVZmorTNRWmLCZJWRZQRBAEEUMQToCQo0EhBgICDO2WPfek0WWZGxmm/1lkezZQGVQUBAEAUEU0Oo1aA1a+yHfoFWVqSeAoigoioIsy8iKjCzLTspAQRAQRdH+EsQW6957ssiSjKXagqXGiqXGgrXGiiwrIAOiPYmQLlCPPlBnfwXpPW4l4SvIkn2sytLhlyI7KFoFUUCj0djHrEZEEAX1kH8CyIqMRTJjkc2YJTMWyWzvWxTA3od6UY9eY0CvMWAQjWhE9XLlRKi0VFBoKqDYVERRbSHlljIkRUJRFDSChgBtIDEBsUQZo+tfLdV1+mSoX79kpX5eOHYNq5sH6tcxdW9wQsg2GZvlyJ5Lskr1fSsgIGgOX6roNWgO/7+3zbOq8k9FReVUUZV/KioqKioqKioqKioqKipejqr8U1FROVXU6zcVFRUVFRUVFRUVFRUVFRUVFRU/5aSVf7m5uWzcuNHt+7W1taxZs4aqKvcZArds2UJ2drYqQ5WhylBlqDJUGaoMVYYqQ5WhylBlqDJUGaoMVYYqQ5WhyjhFGSeEcpI89dRTSlJSktv3N23apADK8uXL3Zbp0KGDcv/996syVBmqDFWGKkOVocpQZagyVBmqDFWGKkOVoco4ARmrV69WAGX16tU+/T1UGaoMVUbjyjgRTlr5l5OTo2zYsMHt+zU1Ncrq1auVyspKt2U2b96sHDx4UJWhylBlqDJUGaoMVYYqQ5WhylBlqDJUGaoMVcYJyHCl/PPF76HKUGWoMhpXxomgJvxQUVFRUVFRUVFRUVFRUfFy1IQfKioqp4rW0w1QUWlMFEVBVmRkWUZWZKjXbQsIgoAoivaXoOa6OVkskoVKazmV1goskhlJkREQ0AgigbogQnRhBOmC0QgaTzfVp1AUBZtsxSKbsUpWZGRAQUBAFDToRD16jQGNoEEQBE8316dQFAVZllFkhWPvuQRRQBREBFFQ+/UkqRuzZtmERTIjKxLKUWNWrzFg0BjRCjq1b0+SWquJ3cV72V64m9zKfCw2Cwpg0OqJCYoiM6YtbaPTCdYHebqpPoWiKFhNNmrKTdRWmpGsEoqs2PcFWhFjsJ7AMCOGIL06Zk8SRVGQLBI2iw3JKsGRbReiRkSr16LRaxA16r7rZLGZbZiqLJiqzEhWGUWRAQFRI6AP1GMM1mMI1COI6pg9GWRJprK4hrKCSqpKarBZJRRFQaMR0QfoCIsNITwuGGOQwdNNVVFRUWlUVOWfis+jKAo2yYbNZrUr/E4QrUaLTqtDFFVllTtqbNUcqj5Ifk0uFtlc/1zAcaOpcESxEqILJTEombiARLSiOsW4QlEUam01VFkrMEtm4PgG2AIiAdoAgvWh6EWDekB1g6Io2Gw2JElquFulI/8pCAIajQaNVlWwukNRFGqlGiospdTaqh1+8+4QEAjQBhGqjyBAE6j2rRvKTRX8sv1Pft3+BwfLcuoVqRpRrL+/EgTsF1uHHySExHFWxjDOzTqT6KBID7bee1EUhZoyE0UHSqkorEa2Hd4fuBqGdf0sCgRFBBCdGk5oTLCqVHGDIiuYq82YDytSTwRBI2AIMmAINqDRqfsud5iqzJTlVlJVVI10AmMWAYzBBsITQwmJDkJUx6xLJJtMzo4C9m3MpSy/EkW2d6DTb1yh/rJQH6AjsV0MrbokEBod3NxNVlFRUWl0VLdfFZ9FURSsNgtWm/W06hEFEZ1Oj0ZUD/51lJiK2F+1l1JzMfZd58lPE6IgkhCYTFpwOkZtQKO30ReRFZlKSzlV1oqTUlQfi07UEawPI0gbrI7Zw8iyjM1qQ5ZPvV/hsKWKVosoqlYqAIoiU2Epo9xSik059blWK+gI00cQqo9Qx+xh9pUe5It13/LX7qVIsnxCCtWjEQURBYVBrfpyRdexZMW2a6KW+haKolByqJyifaWYqiynuoSh1WuITosgOi0CjVadDwAkm4Sp3IS52nxKfVqH1qglIDQAXYCu8RrnwyiKQlVRDSWHyjFVmo//ATeIWpHw+BAiksPQqgpWAMw1FnatPsj+TbnYzNJJzweCYHciikgIpW3PFOLbRHl8DVPdflVUVE4VVfmn4pPYJBtmi6lR69SIGvR6Q4t2CbbKVnaWbSWvNodTPjEdhXDY3bpdWHsSA5M9vmHyJCZbLSWmQiTlxKwkTgS9aCAyIAad2HIPUPWWfrbG61cArVbb4i0BzZKJgtocrLKl0erUiXpiAxIxaIyNVqevIckSX6ybz5zVc+3/Po2LAACNICIrCpd0GcMNvS7HoG25rmrmagsHNuRSU954+wOdUUtq5wSCowIbrU5fQ1EUzFVmakprTndb4IA+SE9gZGCLvmyxmW3k7iyiprS20eoUtSLx7aIJiW7Z4QFydhSy/o8d2Cw2Tvu0e3hLHJceRdczMjAG6RujiaeEqvxTUVE5VVTln4pPoSgKZosJSW7cg/7RGHQGtNqWp0wpNhWypXRjox70jybCEEn7iC4YW9ihX1ZkykwlVNsqm0xGuCGSYF1oi1NUybKM1WJ1iufXWAiCgE6va3EHU0VRKDUXUWYpbjIZ4fooIgzRLW7M7is9yPN/vs7u4n2NXreAQEJoHI+PuI/2sRmNXr83oygKRftLyd1eaNdNNcGUEJUSRmJWbIuLXSfZJKqLqrGZbU1SvyAKBEUHoQ/wnDLFU5TnV5K/uxhFapo1LCQ6iLi2US3OzdpSa2X9HzvI3VXU6HULAmh0GrqMyCA5K7bR6z8RVOWfiorKqdKydjAqPo2syNSaa5pU8QdgtpqxWC1NplDwRg5VH2R98eomU/wBlJlLWVWwnGprVZPJ8DZkRaKwJq9JFX8AZeYSSk3FLWrMSpKExdy0v1NFUbCYLcjS6Vlm+RKyIpNfk92kij+AMksx+bWHTsv93dfYmLeV8fMnsrfkQJPUr6CQV1nAvT88zvL9K5tEhjeiKArZm/LI2VZot+5poimh+GA5u1cePOEYd/6AZJGoyK1oMsUf2OMHVhVUYT4Nd1dfo05ZnbejqMkUfwCVRdXsX5/bpH8/b6O20sySL9eQt7vxFX9gdwG2WSTW/LqVHSv2t6h9l4qKiu+jKv9UfAJZkTGZapttkbXHEmw6RZg3kV11gO1lm5tcjoKCVbayunBFi1AAyopMQU2eQ6KUpqTaVkmJqahFbEQlScJqOb1YnyeDxdIyFICKIpNXk02NVN0s8mpsVeTXZLeIMbsxbysP/fQ0ZpulSRWesiIjyTJPLnyJZfv+azI53oKiKBzcmEfJoYpmkVdTZrIrAG3+Px9IVomK/Ir6xAhNTXVJNabKxg3n4q0U7S+l+EBZs8iymqx2BaDF/xWApiozS79cS0256fTdfE+Abf/sY/u/+5tekIqKikojoSr/VLweRVEwmWtPOhj66WK1Wf1eAVhQm8eO8i3NJk9BQVJsrC1aeTjLrX+iKAqFNXlNaknpihpbFeWW0maV2dzIktysir86LBbLaScT8WYURaGgNheTVNOscmulGgpqc/xaAbiv9CCP/PwskiI1yzqmoIACz/xvKpvytja5PE+Ss72Q0pzmUfzVUVthZu/q7GZTinkC2SZTkdd8ir86akpqsNT4976r5FA5JQfLm0+gYo8reHBDnl9fYtksNpZ9sx5TtblZFH917Fixn73rDzWfQBUVFZXTQOvpBqioHA+L1eyxg6HFakEj+mfmT7NkYmvpxmaXq6BgkS1sK9tEl8gefhnzq9JS3mwWf65kGzUBfplhWVEULBbPHQytFit6g94vx2yVtaLJ3dPdUW2rpMpWQYguzCPymxKbbOO5P17DLDWtxd+xKCjIisyzf7zOR5e+QYDO/+aDyqJqivZ55rKjurSWwn0lxLaO8oj8pkRRFKqKqzym3KwuqkabpPXL2IrmaguFe0o8IttSa6Vwbylxbf1vzAJsXryH6rLaJnP7b4hNf+8mOiWckMjmT7By/eK/m11mQ3w0ZKinm9AotHriF083oUnZ99w5HpPdYfZij8luDLbcOMTTTTgt/G9lVfErJMmGTfKsq4LZYvI7qxRFUdhausmD8bYUik2F5Nfmekh+02GRLB63visxFfplLDWrtfkt/o5GUZRGzyrsDdhkK0WmPI+2oag2H5vsf25pX6ybz96S/R75PcqKQlFNMe+v+LTZZTc1kk3iwEbPrh+5O4swVfmfBbul2oLN5LnfoqIoVBdX+9++S1bI3V7g0TaU5VZQU9Z4WYW9hcIDpezflOsRxR8AisKaX7ch+7E1sIqKin+gKv9UvBZ7Zl/Pb6xlRcZq86zSobHJq82hxFzU7K7Ux7K9bDMWP3L/VRSFElOhp5uBpEiUmz1jXdBUSJLkFS5LNpvN79x/C2vzPD4XKMgeV0A2NntL9vPx6i892rOKovD9ll9Yn9P0cV2bk5zthdjMnlfEH9iQ61dKKtkmU13SPDE/G8Jaa/U799+S7HLM1Z7fS+buKPSKtbSxsFkk1v62DTxokK8oUF5QxZ412Z5rhIqKisoJoCr/VLwWm2Tz+IG0DqvNf7L/KorC3opdnm4GYFdSZVc3TeZLT2CSaps9zp87qqyVTZ4Zuzmx2bzHKsyb2nK6mCUTtc2U4ON41NiqsEj+E/D/i3XzPd0EAERB5OM1X3m6GY2G1WRr3php7lDs8f8qi5o3TmZTYqo0ec566hhqy5ovyVtTI0syJdllnm4GADazREWhd8z5jcHBrfmYqi1eMW53rjzQIpIBqaio+C6q8k/FK1EUxeuSbXja/bixKDUXY5K8x+3jUPUBv3FRrbJ4wYH0KKqtnonh1tjIsuxVwfVlSfabQ2mFlyWIqbCUeboJjUK5qYK/di9D8oK5TVZk1uZs5GBZjqeb0igUe4kSBQABig5412/oVFEUBbMXuTHLNhmb2T/2XZWF1ciS96wZpYfK/WINUxSFveu8x9rOaraRs9Pz3h8qKioq7lCVfypeiSx73+Ha5ieuv9nVBxA86R9xDFbZSpHJs3FwGgObbMXkZVZLldYKr/sdnQreGGfPG9t0skiKRJW1eTOlHo9Kazmy4vt9+8u2P7zqUkMURH7c+punm3HaKLJC8YEyTzfjCIpdsWOp8f39gaXG4lWXLHDYEtEPaO6M1MfDUmPFVOk9it5TpSSnnKpS77nMRoC969TMvyoqKt6LqvxT8UpskvdtpGVF9vlYX3WKNm9xp64jt9p7bm5PlWprlaeb4ISsSJi9TCF5siiKgiR5nzLIG9t0slRbK71uLlBQvPK3dLL8vP0Pr+pbWZH5ZfsfPn8ZUFVSg83ifb+90lzvUu6cCt5k9VeHtcbqdQrJk8VcY8Fc7V2eLAhQUeD78+zBrfkIgvdcZqNAWX4lVX6YVEVFRcU/aDbln8Vi4cUXX6RDhw4EBQURGhpK27ZtGTt2LOvXr2+uZqj4CA3FKnt/xgd0bN+JqIhoBg0YzLJlyxqsy2w28/RTz9A+swOR4VF07tiFj+d8XP/+9999z+CBQ0hKSCY2Oo7+fQfwxedfuKxL9vEYapUNWPl8P+cnrup/PaPajOH2c+5mw4pNbstOuf9VRiaf4/S6ccRtDuUWL1jKDcNvZVTr87lh+K0s/cX136rc4vsuKA0lLvn4g08Y2HkI7WKyGD1kDCuW/3dCda78dxXpEe0YNfBch+dWq5VpL01nUJdhtIvJ4uwBo1n0+98n3S5foKFxMWPGDLLaZxEeEc6AAQNYumyp27KLFy8mIDDA6bV9+3aHcmVlZdx3332kp6cTHhFOt+7d+PXXX122y9fHrDvF8H/LVnLLZXfQP3MIbcLas/Cn/x23rhVL/2PMkItpH9uVYV3O5PNZc53K/Pr9Qs7ucx7tY7pwdp/z+O3H3920y7cPTjWWWrLLXbvYjukwis8uf5dfb5zLe2On0jm+fYN1XdBhFB9eMp1fbvyCOZe+yZnthjm8P7hVX94d+zI/XPcJC274nPcvepUz2w11WVe1pYbcyvxT+k7eQk25yW1g/y9/+pzR14+kz5guXHH3RazZtKrBulZt+I8r7r6IPmO6cO4NZ/D1AucxW1FVwQtvT+aMKwfTZ0wXxt46miX/Oc+1NeW+PWYVRWnQxXbWJ7PoPqg7iRmJjDhvBP/894/bsv+u/JdzLj6Htt3akpSZRN8RfXl35rsOZbbt2MZ1t19Ht4HdiGoVxXuz3nNbn83i266/DVnYfT7/E0ZeOoQuZ2Rx0c1jWLW+4b2BxWLm9Q9eYcQlg+g8MoszLx/GNwuOxPO02qy8/dF0zrx8GF3OyOKCG0azZIWLvcHheJW+TkmOe++Gn5d+xy3PXs64CWfywKu3snn3Brf1/LNhMZPefZBrnriAyx8ZzcRp41mzzflvsXz939z50nVc/NCZ3PnSdfyzYYnL+sryfP8yoDF55513SE9Px2g00rNnT5Yscd1vx7Js2TK0Wi3dunVzW2bu3LkIgsCFF17YOI1tZipX/UT2Wzew/6ULyJ11D6YD7s9eAIrNSulfc8h+8zr2vzSGQ2/fSNW6hQ5lKv77jkPv3sKBKReSPf1aSn5/H8XLQmk1BiV/zmfXw5ex7bYz2Tv5Fmp2uNcnVW9by9abhjq9zLn7Hev8/Wt2P3Y1224/k50PjSN/7lvIVt+fK49G21yCJkyYwPTp0wFo164dRqORffv28d1333HVVVfRtWvX5mpKo2CxWNDr9Z5uhl/S0KF63rxveHjiw7w+7TX69e/H7FkfctGFF7NqzUpSUlJcfubaq6+loKCAd959m9ZtWlNYUOgQvy8iMpIJEx8iIzMDvV7PL7/8yu233UFMTAxnnHmGQ12SLDffj6YJqLS43pD89cPfvPP0DO55/k469e7AT5/+zKPXPMnsv2YQlxTrVP7OZ27nlkdvqP+3ZJO45aw7GXru4Ppnm1dv5dnxL3LDhGsZNGoAS39dzuQ7XuSNb1+hfY8sh/psihWzbMaoMTbSN21+3CnZfvjmJ5555Dmee20yvfr15LPZn3PdxTfyx3+/kZSS5La+ivIK7r/1IQYOHUBRYZHDe1OffZX5X37PlOkv0CajDYv/WMwtV93O/N/n0alrR8d2yb69aLmz+vh63tdMmDiBN6a9Qf/+/Zk5ayYXXngha9asITUl1W19G9ZvICQkpP7fMTEx9f9tsVg497xziY2J5bPPPyMpKYns7GxCgkNcVYUiKwgaL7I6OEncKdlqamrJ6pTJuKvGMv6ae49bz8F92dx0ye1cdt04XvvgZVb/u4anHnyWyOhIRl1wFgBr/lvLPTc8wP2P38NZ55/Bwh//xz3XP8CXv31Kt16O67+3uc+fLLuK97p8Pqz1QO7sfwNvLP2ATflbOb/92bx0zhPc8NW9FFQXOZUf0/5sbu5zNa8ufpfthbvIim3Lg4PHU2Wu4p8DdsVWhbmKz9Z+w4GybGySjX5pvZg49C5Ka8tZlb3Oqc4dRXtIDI1v1O/bnNSU17oM7P/b3z8zdcaLPHbnJLp16MG8n7/kzidv5dsZP5EQm+hU/lBeNndNuo2LRl3C8xOmsm7LGl54ezIRYRGcMehsAKxWC7c/diOR4VFMffwN4qLjyC/MIzAwyLldZb49ZmWb7DZhwvwf5/P45MeZ+uxU+vTqw5zP5nDZ9Zex/PflJCclO5UPDAjk5mtvpmP7jgQGBPLvqn958LEHCQwM5LorrwOgpraGVqmtuGD0BTzx7BMNtk2ySOiMutP+jp7CVGWxK6yP6d+f//iJF998jkkPTKZHp558+cPn3DrxRn76+DcS41zvDe576m6KS4t47uGXSE1qRUlpscN+9o0PXuWH37/n2Qkv0DqtDUv/W8xdj9/OF+/Mo0OG497AXGNBlhVE0TfXMMkmUVXqOtnOkrV/Muu7t7ht3H20T+/Mb8t/YPL7E3nrkTnERMQ5ld+8ez3dMnpxzbm3EBQQzB///cLzMx9j6n3v0jq5HQDb9m1m6sfPcNU5N9Gv8yD+3biUqXOe5sV73iQzrUN9XYIoUFZQRXKWs5yWyJdffsl9993HO++8w8CBA5kxYwbnnHMOW7ZsITXV/V6tvLyca6+9lpEjR5Kf7/rSav/+/Tz00EMMHjzY5fveTvWWvyn5/X0iR43HmNKByjW/UDB3Eom3vYc2zPnsBVD47YtI1aVEnXcfuohEpOoylKNCpVRt+ovSPz8k+rz7MCR3wFpyiOIfXwMg8sxbm+V7NQcV//1J/ty3iL/6fgLbdqL07x85MO1h2jw7B12U+99e6+c/RRMQWP9vTUh4/X+X//s7BfPeJ+GGiQS07YQlL5vc2S8CEHf5XU32XZqbZrP8+/LLLwGYNGkSO3bsYMOGDZSXl7N06VIHxZ+iKLzzzjt0796dgIAAQkJC6NOnD+vWrQPg119/ZfDgwcTGxqLX6wkNDWXIkCEOlhn79u1DEAQEQeCjjz7ivPPOIzAwkPT0dGbNmuXQrgMHDnDttdcSHx+PXq8nJSWF8ePHU1JSUl/m+uuvRxAEhg0bxssvv0xycjJGo11J8cknn9CnTx+io6PR6XRERERw9tln899/R26MFi1aVN+emTNnMmTIEAICAhgwYAC7d+/m+++/JyMjg7CwMC6//HIqKo4oaF555RW6detGZGQkOp2O2NhYLrroInbs2NF4fxwvo6E4SW9Nf4trr7uW62+4nqysLF6eOoWk5CRmfjDTZfnfF/7O0qXL+Gb+NwwfMZy0tDR69e5Fv3796ssMGTKYMReMISsri9atW3PnnePp1KkTy5c732z7vOWfm4QU896fzzmXn8W5V44irV0qdz5zO7GJMfz48QKX5YNDg4iMjax/bd+wk6ryKkZddmZ9mW9nfkfPwT248q7LSG2bwpV3XUaPgd34ZtZ3J9U2X0CSbci4Hrcz35rFZddewhXXXUa7zLY8PWUSiUkJfDLrswbrfPTeJ7jwkjH06NPd6b1v537HXQ/ewYizh5OWnso1N1/N0JFD+OBN59+Br1v+uZsPpk+fzvXXXc8NN9xAVlYWr0x9heTkZD744IMG64uJiSE+Pr7+pdFo6t+bM2cOpaWlfPXVVwzoP4C01DQGDhhIly5dTqptvoCiKG4Vw8POHMKDT97H2WPOOqG6Pp89l8TkBJ586THaZrbhsusuYdzVFzHzzdn1ZT5852MGDh/AHQ/eSpuM1tzx4K30H9qPD9/52Kk+i2z2aavKHUW7XcZVvaTL+fyy/Q9+3v4/DpQd4u1/ZlNQVcyYDme7rOfMdkP5aetCFu1ZRm5lPn/tXsYv2//g8m5j68usz93M0n0rOFB2iJzKfL7dtIA9JftdWhRqRA07Cr0j0/up4k7J9sn8jxh71sVcNOoSWqe2YeLtjxEfE8/XC1xb8X+9YC4JsQlMvP0xWqe24aJRl3DhWRfx8TdHxux3C7+lorKc1ye9RfeOPUiMS6J7p55kts5yqs9mkbD6cHIKqQFX6ndmvsNVl17FNZdfQ2bbTF546gUSExKZ/elsl+W7dOrCxRdcTFZGFqkpqVw69lKGDxnOPyuP7Kl6dO3BM489w0VjLjruRbqvJ/0wVbjOoPzRV7O4+NxLuOS8y2jTqi2P3TOJ+JgEvvjO9d5gyYq/Wbl+BTNens2AXoNITkimS4eu9Ojcs77M9wu/47ar72Bo/+GkJKZyxYVXM6jPED780sUeWQGLt7kjnwQVhdVuFdbfL/qaM/qO5qx+55ESl8bNY+8mOjyWX5Z977L8zWPv5qKRV9AuNYvEmGSuOfcWEqKT+W/z8voyP/w9j24ZvRh3xlUkx6Ux7oyr6JLRgx//nudQlyIrquXfUbz22mvcdNNN3HzzzbRv355p06aRkpLCu+++2+DnbrvtNq688kr69+/v8n1Jkrjqqqt45plnaN26dVM0vcmpWDGf4G5nEdJ9FLroVCLPug1NaAyVa1yfvWp3r8J0YCOxl08mIL072vA4DEmZGJOPKJ8t2VsxpnQgqNNwtOFxBLTuQWDHoVhydzbX12oWihd+Rfjg0UQMOQ9DYivir7gbXWQMpYtc/8br0IaGow2Lqn8J4pEzQO3uzQS07URYvzPRRycQ3Kk3oX1HUrtvW1N/nWal2ZR/dbHSFi5cyI8//kh+vj1Ow8CBA2nXrl19uXvuuYc777yTdevWERQURFpaGuvXr2ffvn0AbNq0iRUrVhASEkKnTp1QFIUlS5Zw/vnnu3QfvvXWW9m8eTM6nY59+/Zx6623sm2b/Y9YUFBA//79+eSTTygrK6Ndu3bk5eXx7rvvMnToUEwmx03mP//8w6OPPkpoaCjR0dEArFixgo0bNxIVFUXHjh2pra1l4cKFnHHGGeTl5Tm156677iI/Px9Zlvnnn38YNWoUl112GRqNhsrKSr788ktefPHF+vKLFi1i165dxMfHk5WVRUlJCfPnz+eMM85wap+/oLiJq2exWFi7di0jR45weD5y5Ej+/XeFy88sWPAz3Xt0Z9rr02jXJoNuXbrx2KOPUVvr2uJFURT++msRO3fuZNCggU7vyz58IAWokaqdnlktVnZs3EmvIT0cnvcc0oPNq7acUL2/zP2NHoO7EZd85LZly+qt9BrqWGevYT3ZvGqryzpqfdjVzya7PpxYLBY2rtvEkBGOt5KDRwxm9Yo1buv76tOv2b93P/c9eo/res0WDEaDwzOj0cDKf53d3CRF8mlFiivLvyNzwUiH5/a54N8G6+vXvx/p6emcM/oc/v7b0R1qwYIF9O3bl/vuu4+0Vmn07NWTl19+2W18P1/uV5vSeHFV165cx6ARjvPl4JED2bh2M1ar9XCZ9QweMcChzJCRA1nz31oXNSpIiu8e+HMr8tGIjtsrraglI7oNq7Id9ymrstfRMc5ZmQSg0+iwHBP/1mwzkxXTFo2gcfmZ7omdSQ5LZEOu89wtyzK5lb6bXEmRFZfx/qxWC1t3bqZ/D8cx2K/HQNZvcTW+YMO2dfQ7pvyAHoPYsnMz1sPJvRb9+ydd2nfjxbcnM+KKgVx8+/nMnPue2/nAUut9sYpPFHcJjCwWC+s3rWf44OEOz4cPHs7K1StPqO4NmzawcvVKBvZ13lOdTtt8BavJeS6zWC1s3rGJgb0d9wYDew9m7SbXe4M/l/2PTpmdmfX5DIZc1J+zrxzBlLdfwGQ+chawWC0Y9I57A4PBwOqNrl3gXbXNV6gud30Gstqs7M7eTrfM3g7Pu2X2Ztu+zSdUtyzL1JprCAk8YvW/fd9mpzq7Z/ZxWWe1GvMPsM8fq1ev5qyzHC8SzzrrLJYvX+7mU/Dhhx+ye/dunnrqKbdlJk+eTExMDDfddFOjtbc5USQrltxdBKQ7npMCWnfHnO36nFSzYwWGhHZU/DOP7Deu4dC7N1P6v5kObqmGlI6Yc3dhPmQPaWMtzaV21yoC2vZ2WacvotismPbvIKij43cK6tCb2l0Nu03vfeZmdjwwlv1T76d6m+NcG9C2M6b9O6jdY+9/S2EOVRv/JbiLawW0r9Jsyr/x48cD8O+//zJmzJh6Zdazzz5br8Tat28fb7/9NgAXXXQROTk5bNq0iezsbHr2tN9sjRs3joKCAnbv3s2aNWs4cOAAISEh2Gw25s2b5yR3zJgx7Nmzpz6+gCzLLFq0CIC3336bnJwcRFFk+fLlbN68ma+//hqwKxm/+MLxxthisfD999+zZcsWcnNzAbj77rspLi5m+/btrFu3jk2b7IOusrKSBQucNffXXHMN27dvZ8KECQDs2rWLJ598kq1bt3LVVVcB8Ndff9WXnzJlCqWlpWzZsoWNGzfWWzgePHjwuLHufBV3x+niomIkSSI2ztEUOjY2hgI3JuH79u7jn+X/sGXzFr6Y+zlTXp7Cd/O/54H7HnAoV15eTlxMPBFhkYy7aByvvDqVEccoGRtunW8guciiWV5SgSzJRMREODyPiAmnpLD0uHUW55fw31+rGH3FKIfnJYWlRESHO9YZHU5pYQnHIiD4dIZPd4H9S4pLkSSJ6Nhoh+cxsVEU5he6/MzeXXt56amXmT7zdbRa107mQ0cO5oO3ZrN3115kWWbxn0tY+PP/KMhzXaevj9tjKSoqcjkXxMXGuXUPiY+P5+233uaLz7/gi7lfkNEug3NGn8PSpUfiBO7dt5f58+cjSRLzv53Pww8/zBvT32DKlCmuG+LD3dqYisvC/CKiY6IcnkXHRmOz2Sgtts8hRflFTr+D6NhoivKd3V3B/W/KF7BIVo7t3jBjCBpRQ2ltmcPz0tpyIgPDXdazMnsdo7POoF203aohI7oNozJHotPoCDOG1pcL0gWy4IbPWHjzV7w46nHeXD6T1YecL0MVFCw+HPdHdhMCoLSiFEmWiIxwHINR4VEUlboeX0WlhUSFO5aPjIjCJtkoq7CP2UN5B/nf0t+QZZm3Js/glstv55NvP2TmXNfx6RTJd8esu59bcenhfVeM41wbExNDflHD8SM79etEQkYCI8eM5KZrb+Kay685tab58CULuB63peX2vUFUhOOcGBUZRVGJ63X8YM5BVm9cxY69O3jrufd47O4n+e3vX5j8+qT6MoP6DOajr2az76B9b7Bs5RL+XPo/Cotd1+nLF9qyGyV8RXU5siwTHuK4pw0PiaC0wnn/6YrvFn2F2WJiYLcjSu+yypITrlOSfNcroDGp26vFxTm6YcbFxbk0kAHYuXMnjzzyCJ999pnbPfCyZcuYNWvWcT09vBmppgIUGTE43OG5JigCqcr12ctWlofp4GashfuIGfcEkWfeRvW2pZT8+k59maCOQwkfeg15H09g/4vnk/POTRjTuhA24NKm/DrNiq2yHGQJbWikw3NtWAS2cte/cW14FPHXPkTS+GdJHv8s+vgUDrzyADXbj+yVwvqOJGbsjex76S623jqC3Y9cQWBmd6JHX9Wk36e5abbwZU8//TRdu3blww8/5O+//6aiooLt27czadIkdu/ezUcffcTKlSvrF/kHHnig3hXg2JhM119/PcuXL6e4uNgh+2pOjnOA7auvvhpBEOjQ4YhJbN3hcOVK+61lZmYmPXrYNe8XXnghgYGB1NTUsGrVKm644Uhcs4yMDM477zyAelex8vJy7rrrLlavXk1ZWZnDJsVVe84//3wAWrVq5fSszmz56MPrgQMHuO2229iwYQNVVVXHrb8lcGxmL0UB3GT7khUZQRCY9eEswsLCAHjxpRe4+qpreG3aawQEBAAQEhLC8n+XUV1VzaJFi3j0kcdolZ7OkCG+GUfCLQ3t847tQ0U5oSxqv339O8GhwQw82/lmxPlvpbj9W/k2DW+gj3UBtHeDcz9IksTdN93H/Y/dR+t27t0Ynn55Eg/f/RjDe52JIAikpady6VXj+Ooz5wuQutb5aq83pARyNb7cjdmMjAwyMjLq/92vbz+ys7OZNm0agwYNAuyXQzExMbz99ttoNBp69OhBbm4u016fxmOPPdYI38Z/cflbP/b5ycwHvnsmbXDMHqvIEAT35T9Z8zWRgeG8feFLCAiU1pbx246/uKLbWAeX8xprLbd88yABOiM9Erswvt8N5Fbksz7X2SLFlw/7x8PV3qChNczlXoIj87WsyESGR/HkPZPRaDR0aNeJwpIC5sybzW1X3elUny8rrI+H8xqmuHRtP5oFXy+gurqaVWtXMXnKZNLT0rn4goubspk+h9P4bGDMyrKMgMArT75OSLBd+f+I5XHunXQnk+6fjNFg5PF7JvHky48x+hr73iAlMZWLzhnHt7+43hs43VL4EMdrutPvG+WEtp+L1/zB3N8+4rEbn3NS9p1wnb7brU3Cie7VJEniyiuv5JlnnnHYrx1NZWUlV199NR988EG9F54v4zSPNrgvsp9poy+YiGi0x56NPOMWCr95gchR4xF1Bkz7N1C+7EsiR43HkJSJrSSXkt9nULYkgvDBVzbxt/EwDegDDPGpGOKPxJgMbNsJW2kBxb/NJTDTHn6uettain76lPir7yegdXssBYfI/+JNCn+cQ8z51zXHN2gWmjV3wdixYxk7diyyLLN69WpuuukmNm7cyPffN+yffTTnnXceO3fuRKvV0rlzZ4xGI2vXrsVisbh0xQgPDwdwuD1w3ny7PrQcS3y8Y5Dsqqoqzj77bMrKyjAajXTv3h2dTseKFXYXVFftCQ0NdWpP3bO6dtTJ37NnDxdeeCEWi4WQkBB69uyJzWarj3/ozvXE13G3NkdFR6HRaMjPc7xtLiwsJDbWdWDU+Pg4EhMT6xV/AJlZmSiKwqFDh2jbti0AoijSpk0bALp07cL2bdt59ZVX/U7558pVLCwyFFEjUlrgeFtSWlTuZLl3LIqi8OvchZx58Qh0eseA3JExEU6Wg2XF5UREO26mwL6BEpvPELnREdy0PTIqAo1GQ2GB4617UWGxkxUUQFVlNRvWbmTzhi1MeuhpwL7hVxSF9Ih2fPrdHAYOHUBUdBQzv5iByWSmrKSUuIQ4XnxqCilprpPeHO+Q5s0ICE6H6ujoaJdzQUFhgdu5wBV9+vThi7lHLLzj4+PR6XQOcQCzMrPIy8/zuyRPJ6LYP1Fi4qIpLHC0sCouLEar1RIeGQ5AdFw0RcdYuxYXFhMd62h91RTta270Gp19/3nUsC03Vdqt0wKPsRwxhlFa4zreqUWyMPXvt3lt8XtEBIZTUlPKeVlnUm2podx0JKaUgkJOhd2KYnfxPlIjkrmy20VOyj8BAYPWd8ewu8QEEaERaEQNxSWOY7CkvNjJuq+O6IgYJ6vA0rJitBotYaHhAMRExKDVOs4H6SltKCotxGq1oNM59qUo+u4a5m6JiIo4vO8qdJxri4qKiI1ueK5NS0kDoENWBwqKCpjyxpRTUv758lwA9gQQx4aviAiz7w2OtfIrLi12sgasIyYqlriY+HrFH0CbtLYoikJeQS6tUtKJDI/i7RdmYDabKasoJTY6jlffm0Jyguu9gS+PWY3WddtDg8IQRdHJIq+8sozwkEiXn6ljydo/eXPuyzx83dN0y+zl8F54SOQJ1+mubS2Nur3asVZ+BQUFTtaAYFfsrVq1irVr13LXXfYEC3V7YK1Wy8KFC4mMjGTfvn31hjN1ZcB+tt6+fXv9ec6b0QSGgiA6WflJNWVogsJdfyY4Ek1IVL3iD0AXnQIoSJVFiJFJlP39CcGdRxDS3e6NpY9NR7aaKPn5TcIGXY4g+P7Y1IaEgajBdszv0VZRijbU+YzpjoDWHSn/90im5MLvZhHW/ywihtgNvYzJbVDMJnI/foXoc69B8OH58mia7VtMmDChXikmiiK9e/eu1+rXKb969+5dv8hPmzYNi8XunlJcXEx2djbFxcXs3GkPWDl58mTWrVtXn+L7VOjd2+4rvm3bNtassft9f/fdd/Xx4Hr1cpz4j5Wzfft2ysrKAJg9ezarV69m2rRpp9QWV9QpNQF+++03Vq5cycMPP9xo9Xsr7iYmvV5P9+7d+fPPvxye//nnn/Tr19flZ/r160dubi5VVVX1z3bt3IUoiiQluc+0qigKZrNzMHzRxydNozbA6ZlOryOjcztWL3GMjbR6yRo69urgVP5o1v+zkUP7cjjncueA9R16tmf1Ysd4Cqv+XkPHXs6B6N21zVfQiK7jb+n1ejp368SSP5c6PF/y11J69u3hVD4kNJjf//2FX5f9VP+6+sYradOuNb8u+4nuvbo5lDcaDcQnxmOz2fjl+98469wznOoUBY1PH55ctf3IXPCnw3P7XNDPqbw71q1f53Cp079ff3bv3u1gUb5z1876hFBObfPRLIkAGqHx7v669+7Gsr8c4/cs/XMZnbt3RKfTHS7TlaXHlFny53KXCW0au33NTWxQtJOFnU22saNoNz2THDMb90zuyub8hoNJS4pEUXUxsiIzvM0g/j2wqmGLWOzxAo9FFEVig33YUkIAjc55Ddbp9LRv15F/1jqOrxVrltO1g+vx1SWrGyvWOJb/Z80yOrTriE5r77uuHXtwIGe/w3yw/9A+YiJjnBR/ALoA3x2zohtlhV6vp2unrixausjh+aKli+jd8yRiSCn2WLWN2TZfQWdwHhd6nZ6OGZ1Yvspxb7B81VK6d3LeGwD06NyTgqJ8qmuOxG7ed3AvoigSH5vgUNZgMBAXE49NsrFw8W+MGOS8NwDQGlzvXXyBgBCDy+c6rY42yZms3+EY53DdjlVktero8jNgt/ib/sVLPHjNE/Tq6OzJktmqo3Od21e6rDMgxHgiX8Hv0ev19OzZk99//93h+e+//86AAQOcyoeGhrJx40bWrVtX/7r99tvJzMxk3bp19O3bl6ysLKcyY8aMYfjw4axbt46UFNeKbm9D0OjQJ7Sldq/j2cu0dy2GZNfnJENyB6TKEmTLkZiS1uJDIIhoQuxru2I1O1m/2ZVWik9b+h6NoNVhTMugerPj77F6yyoC2nY64XpMB3aiDTtyQahYzM5nDlE83G/+0XfQjMq/Tz75hH79+hESEkLXrl1JSUnhm2++AeDKK+1mqK1ateLOO+2uFPPmzSMpKYnOnTuTlJTEqlWriIyMJDk5GYCnnnqKzp0706NHD4db2ZPhzjvvJCEhAVmWGTBgAJ06deKSSy4BoFOnTlxxxRUNfr5169YEBdm17zfddBNdunThwgsvPKW2uKJjx471323UqFF07tyZu+++u9Hq91Yauom86567mPPRHD6e8zHbtm3j4YmPkH0wm5tutgd8fWrSU9xy85FU5pdedimRkZHcftsdbN26jaVLl/LE409w7bXX1Lv8vjL1Ff7840/27t3L9u3beXP6m3z++RdcfvllLtrmuxslgFB9mMvn424dy89f/MYvc39j/84DvPP0DAoOFXL+NaMBmPnih7x07ytOn/tl7m+0755JelYrp/cuuukCVi1ewxdvf8WBXQf54u2vWLN0LRffdKHLNoToQl0+9wU0gtatdd3Nd93E3I+/4stPvmLn9l0888iz5GTncPWN9hgSLz39Mvfd+iBgH/uZHTIdXlExURiMBjI7ZBIYZE9Pv3blOn754Vf27z3AiuX/cc1F1yMrMrffe5uTfL3ou5Y+4H4+uOeee/jwow+ZM2cO27ZtY8LECRw8eJCbb74ZgCcnPVk/LwC8+dab/PDDD+zatYstW7bw5KQn+e6777j9ttvry9xy6y2UlJTw4EMPsnPnTn755RemTp3qUMahbT58GSAKIjo3Y6O6qpotG7ayZYM96HH2/my2bNhKzkF7qImpT7/Gg7cduYi68sbLOXQwh+cfe4ld23fz9Sff8PUn33Lz3TfWl7n+jmtZ+udyZrz+Abt37GHG6x+wfNE/3DD+Wif5etHg0wrrjJg2LjNBf73hR0ZnjWRU5ghSw5MY3/8G4oKj+XGr/eb55t5X8ciwI0l+ksMSOKPtEJJCE8iKacsTIx+gVWQqM/87kg30im4X0TOpKwkhcaSEJTGu8/mclTGM/+1c7CRfkiUyon0zKyLYLwICw1xfEl0z9nrm/zaP7377hj0HdjN1xovkFuYybvTlAEz/8FWeeOXImL3k3MvJKcjhlfdfZM+B3Xz32zfMX/gN1158ZMxeeu4VlFeW8fJ7z7M/ey+L/1vErC9ncOl5zvF/NDrRpZLHV9Dq3bd9/M3j+fTLT/nsq8/Yvms7j09+nEM5h7jhKntYnMlTJnPHA3fUl5/58Ux+/d+v7N67m917d/PZV5/x1gdvccnYS+rLWCwWNm7eyMbNG7FYLeTm57Jx80b27NtzUm3zBYwhBpeWlddfehPzfvqKbxZ8xe59u3jxzWfJLcjh8gvs4+vVGS/z8PMP1pc/74wxhIdG8NhLE9m1bycr1/3Hy+++yMWjL8FosCub1m9Zx8K/f+VgzgFWrf+PWx66HlmWufkK570BgCHId/cHYTEhbt+7YNgl/P7vAv634mcO5u9n5vy3KCrNZ9SAMQB8/NP7vP7ZC/XlF6/5g2mfvcANY8aTmdaB0opiSiuKqa49Yjhw/pCLWbt9Jd/88TnZ+fv55o/PWb9jNecPHecgWxAFIuLdt62l8cADDzBz5kxmz57N1q1buf/++zlw4AC3327fVz366KNce619HyCKIp06dXJ4xcbGYjQa6dSpE0FBQfX/ffQrPDy8PhGoL3lohPYdS9W636hatxBr0QFKfn8fW3khIT3sZ6/Svz6k6IcjZ6+gTsMQA0Io/vF1LIUHMB3YSOmfswjueiaizq4MD2jXh8rVC6je/DfWsjxq96yh7O9PCGjX1yGzra8TddallC1ZQNmSBZhz9pE/9y2sJQVEDLX/xgu+eZ+cmc/Xly/5/Wsq1yzBkp+N+dBeCr55n8rVfxMx4qL6MsFdB1C66HvKV/yBpTCXqs0rKfxuNsHdBvpV3zXbivrcc8/x008/sX79enbt2oXNZiMzM5PLL7+cJ554or7c9OnTad++PR988AHbtm1j7969dOnShVatWiEIAt988w133nknmzZtQpIkPvvsM+644w72799/0m2KjY3l33//5fHHH2fhwoVs376duLg4xowZw3PPPYfR2PDNTUREBF9//TUPPfQQu3fvRq/X8+OPP9KnT5+TbosrsrKymD17Nk8//TS5ublER0fz+uuvc+aZZzZK/d6KIAgIguDS/XrcuIspKS7hpRenkJeXR4cOHfhm/jxSU+1+/Hl5eRw8eLC+fHBwMD/89D0PPTiBIYOGEBkZyUUXj2XSU0cCJFdX13D/fQ9w6NAhAgICyMhox8zZMxk3ztk9xZddJMC9gm34mKFUlFbyybTPKSkooVVmK178eHJ99t7ighIKDjlmiayqqGbJz8u48xnXm8qOvTrwxNuP8OHUj/nolU9ITEvgyXcepX0P58yWGkGDUeO7ln+CIKDXGDBLztnnxlx8HmUlpbwx5U0K8grJ6JDBnHmzSU61W54W5BWSk31y8TvNZjNTn32Ng/sOEBgUxPCzhjHt/dcIC3f+++o1rm/HfQV31nWXjLuEkuISXnjxBfLy8ujYoSPfzf+OtFS7q9mxc4HFYuHRxx4lJyeHgIAA2rdvz/xv5zNq1JFENSnJKfz4449MnDiR3n16k5iYyJ3j7+TBBx90kt9Q23wFgyYAq+xsjbNx7WauOu9IfJPnH7MnPLnoyguZ+u6LFOQXkpudW/9+SqtkZn39Hs8/+hKffvA5sfGxTJryGKMuOJLhr2ff7rwx+1Vee+4NXn/+TVLTU5j+4at06+VoCVfXLl+mnRsF26I9ywg1hnBtj0uJDIxgX8kBHv3lefKr7K5/kYERDpZ5oiBySZcxpIQnYZNtrMvZxD3fP1pfHiBAa+DeQbcQExSF2WbhYNkhXvjzDRbtcZ0QLCPG+92hGiIwzEhlcbXTJfzZQ0dTVlnGjM/fpqikkLat2vHW5Bkkxtnn2cKSQnILjsyzSfHJvDV5Bq+8/xJf/vg5MVGxPHz745wx6IgVe3xMAu8+P4tXZrzEJeMvIDYqjisvuIYbLrnFqV0BYUafVliLWtGuoHJh3DD2/LGUlJUw9Y2p5Bfm0z6jPXM/nEtKst3CJr8gn0OHDtWXl2WZZ19+lgMHD6DRakhPTWfSxElcf9X19WXy8vMYdu6w+n+/9f5bvPX+WwzsO5AfvvzBQb7PK/+C9ZS7yG0weuR5lFWU8vacNyksLqRdegYzpswmKf7wmC0uJCf/yJgNCgxi9msf89wbTzPulgsID41g1PDR3HfLkfXJbDHzxszXOJh7gMCAIIb2G8aUJ14jNMTF3iBAh6jx3T2tVq8hMMxIjYusv4O7j6CyuoIvf5tDSUUJaQnpTLp1CrGRdkv/0opiikqPuLL/tvwHJFlixjfTmPHNtPrnI3qfzb1XPgpA+/ROPHTNJD77ZRaf/zKb+KhEJlz3FJlpjh4yiqwQFqsq/+q47LLLKC4uZvLkyeTm5tKpUyd+/vln0tLse7Xc3FwOHDjg4VZ6hqAOQ5FrKilb+jlSVQn6mFbEXv4M2jD72UuqKsVWfmS9F/UBxF35PCUL3yVv9r2IASEEdhhM+NAjl6hhg64ABMr+/hipshgxMIyAdn2IGOY/MesAQvuMwFZVTtGPH2MrL8aQlE7qvVPQRdt/47ayYqwlR86tis1K/tfvYistRNAZMCS1IuXeKQR3OeItFH3eNYBA4XezsJUWogkJJ6TrAGIuurm5v16TIii+nkZLxS8xW0zYJJunm+FEgCHAp63/LJKFpXl/Hr9gMxOuj6RHTOMozT1FmbmESovr2F2eJDogjgBtoKebccooioLZ5OyC72kEQcBg9G3FaoWllCJTwxk7PUG0MZ5Qfbinm3FaXPH5bQ5KOm/AoDWw4IbPfNpitbygin1rDh2/YHMiQFzrKOLb+bBLNVCRV4HN7H37rvDkcJ9WUpmqzOxf630J+kLjgknIiDl+QS9mzW/bOLQt3+u8GYdd3YvQ6KDjFzxF1qxZQ8+ePVm9ejXTqyqbTM6p8NGQoZ5uQqPQ6olfPN2EJmXfc+d4THaH2c6eCb7ElhuHeLoJp4XvrqYqfo1G4303vXaLRN/+yeg1eiIMDQc89gTxgYmebsJpE6gN9nQTnBARfdqiEuy/O288+J1quAlvIlDrndYJQV74WzpZzsoY5lVKNo0gckbbIV7VplMhJCrQ+2LAKRCe4J2/pZNB74UuoFqj1ivn/5PBEKRHZ/S+PW1ojO/Ps0kZMV6n+AuODCQkyncvXFVUVPwb315RVfwWjajxugylOq3ep9166kgOSvN0ExzQCBriAhKOX9DL0Wv06EXvsgQL0of4xZg9Oju6t6DR+r7yTytqCfIyBWCwNhSN6H1/75PlvKwzG0zK0dxIiswFHUYdv6CXI2pEolJcx671FEERARiDvWvuPxUMQa5j03kSox8kThAEgYhE74pprDNqCQz3/b6NbRVJgJf99lp3S/KLfZeKiop/oir/VLwSQRDqM+55C1ovtEY8FaKMMV6TBEJAIDEoxW22XF8jRO9dG/xgnXcpdk4VURS9ajPtbe05HcL0EZ5uggOhXtaeUyUmOJoBqb3ReIGlnSgIZMW0pW10uqeb0ihEpYR7ugkORKf5x5gVRAGDFylSBI2ALsC79oGnSmicd13EhSeGelV7ThVBEEjvluTpZtSj0YokZ8V6uhkqKioqbvH8rlRFxQ1aL1L+6bQ6v9gogT2IfFqItwR9F0gOSvV0IxqNAG0QGsE7lMSB2iC0ovf8hk4Xrc47+hW8qy2ni0ETgEHjHRYg3tSWxuCKbmORXGT9bW5kReGq7uOOX9BHMATqCYsP8QorNX2AjrBY33efrMObLO0CwgL8Zt+l0YqEe4n1n6gVCYvznzGb2ineazJtt+6e5PMJalRUVPwbVfmn4rUIgoBB7/mNqN0K0Tss5RqL5KBUQnVhePr01DYsw6eTURyLIAhEGT0fQFtEJNwQ5elmNCoajcYrsm1rtN7RjsZCEARijIl4ei4AgdiABL857AN0iMtkXOfzPfqdREFkWOuBDGzl2wmVjiWpfSwaL4gFl9o1weezfh+NRqchIMLzcWK1Bq1XWSE2BtFp4V6hpIpvF+0XYSvq0Bt1dB2Z4dE2CAIEhRvJ6NvKo+1QUVFROR6e3zmpqDSAVqP1uEuoQW/0qwMp2A/8HSK6eOy4LyAQpg/3uviDjYFBayRY59kb/ghjtMd/N02BTu9ZS0ZBELwy/uDpotfoiTR4VmkdZYhB5yXhCBqTG3tfSXxwjEcSbQgIBOsDuXfQLc0uu6nRGbQkd4zzaBti0iMICve8oqyxMYYY0eg9u34ERQX53b5L1IjEZ3p2ng2JCSKkCbPQeorEjBgS2kbjqSGjKNDj7PZovC0ZkYqKisox+N8pRsXvMOiN1JpqPBI8/f/tnXd8VFXagJ977/T03kgIJBB674ggKthQsbuydrF+Lupa115WxV11ddW1oWLD3hVFEREQ6b339N4zmXbv98eQwJAJkJBM4zz+5ofcOXPeM5cz577nPW/R6/QhaUQBsOjD6BHVm23Vm3wuW5ZkescMCDnlvokoYwyNTitOzeFz2RZdOBZ96Cn3sN8L16DHYff9fQW38TFk56whhgZnLY0uq89lmxRLyOT6OxSTzsh9E29jxtf3IyH59DmmoXHXhP8jyhQY4YYdTXRKJNUldVQV1vpWsASmMAPJ2fG+lesjJEkiPD6c6sJq/FGzxhJrQdGHqN4VZSK2SxQVedU+l60YFJKyQisi4GAGTOxBRUE1dqvD5xWAe47MICbFP+vs2yeO94vcUGfP46f7ewghy6arT/T3EI5rxBGFIOCRJAmT0fen64qiC7lw30PpEp5Bpo/z/8nIDIofjiWEwn0PRZZkEi3JKJJvNzAmxUysKTQ3pE0oiuKXnHt6gz6kwn0PRZIkki1dfF6x2iAbSbaEdnXEvkk5PHzqnSDh0yr2t4+7kTFdh/tMnj9I759CeJwPnyWSu1Jq9+HpyAEQdtxZKHqFiETfF4wyRZkCKu9gZxCfGUOkj/NEyjqZjAEpIWtUBXcu0DEXDEJn0PnUAzCjbzI5ozJ9J1AgEAiOgdDVXAQhhSzLmI1mfJWXSqfoMOqNIb0hbaJbRLaPDIASiqQwOH44UYZoH8jzL4qsI9GSis5HBUBMipl4c+JxMWd1Op1PDYB6gx5FCd1NUxOypJASloFR9s3m26iYSAnLQPaxkdwfjM0cwcOn3IksyZ1qAGzq+44Tb+Ss3qd2mpxAQZYlug1J800oowRGs54eIzMCIndbZ6M36YlI8l1hFXOUGXNU6IVRH4okSST3jCfSR0U3FL1CxsAUDCFSOflwRMRaGHvRIPQmvU8MgF37pzDw5J7Hhd4lEAhCA2H8EwQNsqxgNpo7PXeSXmfAcJwY/sCtiHaP7EGv6H6dujG16MIYmjCKKGNohvd5Q7ffAGhSOndDE6GPIt6chOSHvGL+QqfT+SQHoMFgOC4Mf00o+w2A4brODWEK10eRYsnwuXesPxnXbRT/PusR4sNiO+U5Jksy4cYwnph8H2f2Cn3DXxOyItNtSBoJmZ37bImMDyN7VFf0ptA3ojShN+mJTI5E7sxcZpI7x585OnSq+x4JSZJI7hFPfCfPWXOkka6DUjFaQjuK5WAi48IYd8lgopM6x3NVkkCSJfqM686AiT1CquCPQCAIfSRN83VmBIHg2NA0DafLgd1h79B+ZUnGaDCFdGjfkbA6rWyuXE+VvaKDepQAjcyILDIjsvyS9D4Q0DSNBmcdlY3lHZrzS5F0xJkTMCqhHSZ1ODRNw2F3oKpqh/YrKzJ6fejm+DsaGhx1lDQWomquDutTkRQSzClYdL4NewskGuxWXvtzNl9v/hFZklG1Y5u7siShahonZY3l1rHTiTL5PlwzUKivtLJvXSF2awflBZXca0GXPklEp0Qct+uBpmlYq600Vjd2aL86k46wuLCQqj7bVmz1dgq3lmKr7zidVpIkErrFEJ0aefzOWVVj15p8Ni3aBZrWYXkAoxLDGXJaLyJi/ZdbedWqVQwdOpSVK1cyZMgQv41DIBAEH8L4JwhaVFXF7rDhUo9tYyohodfr0SnH90a/CU3TKGrIZ2/dbhqc9ceUpD7elEi3iGwiDKGZcL6tuFQn1fYq6h11HEsmdVmSCddHEmGIOm4NqoficrlwOpwc6yNNkiX0On1I5/NqCy7NRZWtnBp7FRrtN1JJyEQaookxxh0XYb5Hw+qC9cxe+TFrCzeiSDKuNhoBmz7TKyGbywZfwNjMEZ000uBCdamU7K6gbG8VLkf79QNJlohNiyQpO/64CPM9Gpw2J9ZqK45jNK7KehlzpBlDmEHoXbj1rsqCGirzq3HajkGnlSAiPoz4rjHHRZjv0VBX2cDWpXsp2F7q1g/aqCJIkruarznCSNbQdDIHpCL72dtPGP8EAkF7EcY/QdCjaSoOpxOn09EmI5UsK83VfIXy2RJN06i2V5JXv49Sa3HzvfVmDDz4ml42kBaWTqqlCyZd6OfvaQ+qplLvqKPOXo1Tcx715wyKkQh9FGadRczZVlBVFafTiepqoyFFUVB0ynHt+Xs43HO2hmp7JXbVdtSfM8gmogwxhOkjhKG6FfZV5fH1ph/5ceuv1DsaAFBkBVVVPdZdWZabD7uMOiOnZJ/I2X0m0yO+u9/GHshoqkZ1SR1leyuprzyoirXbIb1VjGEG4jOiiUmLPK490g6Hy+nCVmvDVmdDU49e79Jb9JgiTOiMOvEM84KmadRXWqksqKGhDXNWZ1SITokkKikCnUHMWW/YGuzs21jEnnUFWGvdzzBJ2q+7HnRvJVlqntOSJJGYGUO3gWkkdI0JmDkrjH8CgaC9COOfIKRQNRVV3f/SVLerP269SZIkZNm9uZclOWAe4sGAqqnUO+uotddQ66jB7rLh0lxISCiygllnIVIfRYQ+EqNiEve2Dbg0Fw6XDbvLjkO1o6KhaRqyJCEjo1cMGBQjetkgjCdtQNPc91FTNbcR5ZBHnSRL7nVAlpAkSczZNqBqLmyuRmyuRuyqDVVz319Jct9Tg2zCqJgwKkbh5dcGNE2joLaYbaU72Fa6i8LaYmxOGxoaJp2RhLB4eiZkkZOQRVpkCoos7u3R4nKqNNbaaKhpxFrTiMuhorlUJEVGViRMEUYskSbMkSZhPGkDmqahulRcdhdOmxPVqaKpGhqae42VZXRGHYpBQdGLg9a2oKoqtnoHtjobjXV2XA4Xqqoh7Q9FN5j1mCKMmMIN6AzCM7UtNNbbqS6ppaq4ltqKhuZ7q+gUjGY9UYnhRCdFEBEXhtKZ+S7biTD+CQSC9iKMfwKBQCAQCAQCgUAgEAQ4wvgnEAjaS+AdZwgEAoFAIBAIBAKBQCAQCASCDqHNxr/CwkLWr1/f6vtWq5VVq1ZRV1fXaptNmzaRl5cnZAgZQoaQIWQIGUKGkCFkCBlChpAhZAgZQoaQIWQIGUJGO2UcFVobeeihh7S0tLRW39+wYYMGaEuWLGm1TZ8+fbTbbrtNyBAyhAwhQ8gQMoQMIUPIEDKEDCFDyBAyhIyjkLFy5UoN0FauXBnU30PIEDKEjI6VcTS02fhXUFCgrVu3rtX3GxoatJUrV2q1tbWtttm4caOWm5srZAgZQoaQIWQIGUKGkCFkCBlChpAhZAgZQsZRyPBm/AvG7yFkCBlCRsfKOBpEwQ+BQCAQCAQCgUAgEAgCHFHwQyAQtBdRG14QMrg0F/WOOuoctdQ5a2lw1OHSXGhoyJKMQTYQro8kXB9BuC4Co2JCkiR/DzsoUDUVVXXhUt1/qpoGuM8NJCRkWUaWFRTJ/ae4r0eHpmk4bS6cNmfzS1P331tJQpYldCYdOqMenVGHopfFvT1K7C4btY4aah211NqraXRZUTUVAFlSsOgsROgjiTBEEq6PRC/r/Tzi4EDTNOyqjUaXFZvLSqOrEXX/OishIUsKJsWEUTFjUswYZKOYs0eJ0+6ivspKQ5WVukortjo7qss9ZyVZwhhmIDzGjCXaTFiMGb1RqHBHg6ZqlOdVUrithIJtJRRuK6ahyorT4ULRKRjDDCRnJZCSk0hqThKJ3eJQdIq/hx0UOGxOKgqrqSiopjy/murSWlwOF5oGik7GEmkiLi2a2NQo4tKisUSa/D3koEBzR2YdpHu5ONhX42C9S5ZlZEnoBkeLw+akZFcZhdtLKNxRSunuMuyNTlSXik6vEBYbRmqPBJJ7JJLSI5GopAhxbwUCQcggPP8EQU+do5aihnxKrEWoqPuvSjQZpw5GQkLbf92sWEgNSyfBlIROFpuoQ9E0DafLicNpbzaaHC06WYdebxAKaSu47C6sNVYaq220ZQmWdTLmKBOmSBOyIoq1H4qqqZQ3lpJfv48qe2WbPhtnTCAtPIMYQ6yYs15wqA6q7RVU2ytwaa6j/pwiKUQb4ogyxKATBtYWaJpGTUkdxTsrqCqqdV/0/vhq8V5kQhhJWXFEJ0cgyWLOHkpDlZXVczey/PO1VJe4762syM0G1YM5+LoxzMDQKf0ZOqU/sWnRvhxyUKBpGmW5lWz9cw97NxS6D6yk/frVoc8zCSRJ2n+oBXFp0eSMyqRr3xQUvTCwHsrBeldbt2c6RYdep0eWxX31RvGuMlZ+s571v2zBaXeBBLIsobpa3ueD14P4jBiGnzOQfhN7YjAbfD1srwjPP4FA0F6E8U8QtFTbq9hTu4NaRw2H3y0dHhmZZEsaGeHdhBEQt/LpcNqxO+3H3JcsyRj0RnSKuK8ATpuTurJ6HFbHMfdljDASHh8mjIC452x+/T721u3GobZv3jYdDJgUM5kRWSSZU4QREHCodkqtRdQ5a465r3BdJAnmZPRyYGyg/ImmaZTvqyJvcwn2Bkf7HmH7P6M36kjplUBSd2G4BqivbODn1xaxbt4WNJdKe7RcSXYbrLKGd2XSjeNI7B7f8QMNQvK2FLN63maqS+qa71Gb2D9nDSYdvcZ0p++4LOFlyX6Paocdp+vYdYMmvUtRxH0F2LehgF/eWEzBlmJkxbux77ActM4OOasfJ04b4XcjoDD+CQSC9iKMf4Kgw6W52Fu7i4KG3A7t1yAb6RnVm2hjbIf2G0y4VBeNdmubT5yPhE7WYTQcv2HWmqbRUGmloaKhQ/uVZImIxHCM4cYO7TeYaHDWs7lyA7WO6g7tN9YYT050H4zK8Rmmpmka1fYKShuLmr2lOwIJiQRTClGGmON2PbBbHexelU91cV2H9hsWYyZreBdMx+l6oGkaG3/dxnfPzsfWYG+7YcoLkiIhITHhqlGMvWQYsu74PGyxNdhZ/t1G9qzLP5az1hZExocz9oJBxB3HHpYulxOb3dah6yyATqfHoDMct+uso9HBr28vZfmXa9tnqPaCJEuEx4Zx9t9PIXNQlw4YZfsQxj+BQNBehPFPEFTUOWrZXLUem6ux02Qkm9PoHtkDWTp+lHxN07A77Tg6wNuvNSQkjAbTcecF6HK4qC6swWU/+lDJtmIIMxCZdPyF/uXX72NH9VaADt84gYQiyeRE9yXRnNzBfQc2TtVJYcM+rK6ONVYfjFkJI8WSftx5W5fnVbN7ZT6qqnaYAaWZ/SGWGQOSSeoe18GdBza2BjtfPfUTmxfu6FDj1MEkZSdw8aNnEZMa1fGdBzCFO0tZ9PFq7FZHhx8MSpLb43rAhJ70P6nHcWWocnv72XC6nJ0mQ5LcepdynIUCF24v4bPHfqCmtK7j5+x+Q+LQKf059foT/OK5Kox/AoGgvRw/1g1B0FNtr2Rd+cpONfwBFFnz2Vy5rk15rYIZTdOwORo71fAHbuNMo93aIWEtwYLT5qQyt6pTDX8A9no7VflVXnNZhSKaprGrZjvbq7eg7f+vE6Tg0lxsqlxHfn3HehkHMg7Vzr66nZ1q+AOwuurJrd/V7jDtYKR4Zzk7l+W6f6edM2XRVI29awrJ21jc4ZveQKWh2so7f/uELYt2ui900tcu2V3GGzfNoWR3eecICED2bihg/uxl2Kxtz0F3NGiaBhqs+3UbS79ch9oB3lnBgKZpNNobO9Xw1yzHZsXlOj70WYC9a/OY/ffPqSnreMMf0OxBuPLb9Xz66Pc4bJ37bygQCAQdiTD+CYKCGns1GyrWHlTQo3OptFewpXJ9mwtdBBtNhr/OVkAPxhcKbyDgtDupyq/ukFCTo5Jnc1FdUO32KApxdtfuYF/dbp/J2169mYL6PJ/J8xdO1UFu3W6cmm8M9A7V7panhv6BQPGucvauLfSZvIKtpeRvLvGZPH9hq7cx+47PKdpZ1ulrrebSsNY08vbfPqE8t20FhYKR3E1F/P7xKvd99cFjbOeqXP78al3IG62bDH+q6juDXKPdisuH8vxF3qZCPrz/G1x2V+frXhrsXL6Pzx77AZcz9O+tQCAIDUTYryDgaXRaWV2+zC+eeEnmFHpE9fa5XF9hc9g63eOvNcxGS8iGoqgulcp9lW1PLN0B6M16olIjQzZ8qqA+j23Vm/wiu3/sEOJMoZn4X9VU9tXtxK7afC7bIBvpGp6FFKKpFqoKa9n2x16/yO42JI2EzBi/yO5sNFXj3b9/zp41eT47ZAF3HsDwmDBuemsapojQzAlallfFj68v9ul9bWLAxJ4MOKmnz+X6ikZ7Iy4/HYCajRZkOUTX2aIaXr/xQxw2p2/nrQSDTuvDmX+b6DORB4f9fi9V+Ezuwdw/+BS/yPUVmY/86O8hdBp7HprsV/nD5vzuV/ntYcUl4/w9hA4jNJ8AgpBB0zS21WzG5ScPvGJrIRW2Mr/I7mxcqstvhj9wK8ChevZQV1rnF8MfgMPqoLHG9wYcX2B1NrCjeovf5G+p2oAjRL3UKmwlfjH8AdhVG+W2Ur/I7mycdie7VvrPa3Tv2gJsDaEZWr3i63XsXpXrcwOV5tKoq6jnx5cW+lSur3A5XCz+dDXtKpPcAaz/dRsVBR1bwClQcLqcfjP8Adgcoal3aarGN//+2feGPwAN1vywiR3L/XPAIxAIBG1BGP8EAU2RtYAaexU+iTlphe3Vm0MuLM0ddmL18xhU7H40PnYWtnobtjr/fq+6sjpcjtAKQ9E0jS1VG1H9uBY4VDs79xcYCSUanQ1+P+SosJXS6PLvmtQZ7F1biNOPv0VV1di9Mj/kNvyVhdX89LL/jG+aqrFm7ia2L/Vd+gFfsW7BdmrL6/1l+wNJYvGnq3E5QyuFhaZp2Oydm7P6SKiqGpJpV1Z9v4F96wv84qkK7sIq3z77M411oXnwKhAIQgdh/BMELDaXjd012/09DByqg921O/09jA7F4eyc5N3tGUco5aHRVI3akjp/DwM0qC0NgHF0IEXWAqrtlfjzIKBpHJW20En4r2kahdZ8fw8DgKKGvIBYlzqKquJaynOr/TtlNagpradsX5UfB9HxfPuvX/xe4EiSJL56el5IJfyvLKph48Idfh2DpmpUl9axaVFo6V02R2AYhuwOW0jls64tr+Pn1xb5dQyaptFQ3cj8WUv8Og6BQCA4EsL4JwhYihryfVbg40iUWAtDpiqlpmkB5XHncIaOV2VjrQ3NT+G+h+JocOC0h8amVNM09tUGioeNxL66Pf4eRIfR4KzD4adw30OxqzYaXPX+HkaHUbitDAIk9WbhtrKQMawW7ypj18p9fkut0ISmadRXNrBxfuh4A29esgtJDoxJu3nJrpDx/tM01a/hvofidAbOWI6VVd9vDIh5oqkaa3/aTENN6HmwCwSC0EEY/wQBiaqpFAWINwqAhkax1XeVGjuTQAv5cLocIbEp1TQNa3VgKX2N1f4NMeooquyVWF0N/h7GfjQqbeVYnYEynmOj0h5YXoxVIeJVaa21UVta729H1WYaa23UlYfGnF3x1TpkJTAMVJIs8efna/w9jA7B1mBnz7p8v4VOHord6iB3U2joXY4AM7Y5naGhd7mcLlZ+sz5g5qzmUln302Z/D0MgEAhaJWiNfxMmTECSJK688kp/DyWouPLKK5EkiczMTH8P5bBU2MoCLrF+QX1ohKT5s8hHazhcgfVv3R6cNicue2CFMFtrGgNGKT4W8uv3ETAuVPspqPdfEYeOwqHaaXAGVnh4vbM24Nb+9lCyuyKwpqwExbv8UxWyI7HV21gzd5Pfvf6a0FSNou2l5G8p8vdQjpmdq/MC5r4CSBJsWbrH38M4ZjRNwxlgEQ4aWkikXNm2ZDfWmsA55NQ0WP7VupDQuwQCQWjiM+Nfk7HuUKNTkzFKktqmJffp04eRI0eSlZXV5rE0yXv77bfb/Flf0nRvJkyY4O+h+Jwya0mr733x9ldcNOIyTs48jWsm3cDapesO25fdZue1J9/kgmGXMrHraVw8ahrfffiDR5va6jqevfc/nDPwQk7OPI1p467ij1/+9OxHtVHnrG3/lwoAVE1tNdfL66++Tr/e/YmPSWDcmBNZvPjwuUtsNhuPPPQofXL6Ehcdz4C+A5j9zrsebaqqqrh9xu1kd+tBfEwCQwcP48e5P7boK9AU4/ZwuCIfs96fxbCThpLetwunnHsyS5f/cVR9/rnyT1J6JXPSlAke17/98VtOnXoK2UOyyBzQlZOmTODjLz9u2YEG9iCv9KlqKuWNpXhzofry7W+4dNTlTOp+FtNPu5l1f65vtZ81S9ZyUtrkFq99O/Y1t3E6nLzz3HtcNuZKJnU/i2tOuYFlvy732l9JCHgC1zlqvF5fvngFN1x8Cyf0mkhOdH9+/vaXI/a1bNFyzht/Ef2ThnLywNP4cFbL+fjjV/M4Y+Q59Escwhkjz2HeN977bW1cwURFK7n+Pp07h3NvPI1xlwzl8jsvYvWmlYftZ+7Cb7ns9vM58dLhnHHNSTz63/uprq3yaFNbX8PM1x/njGtOYtwlQ7n41rNZvPKQYhgaVBbUBP2mdOfyfThbybE37JwB3PrhVfzjp1u47tVLyeifeti+ug5M47pXL+UfP93CrR9cxdCz+3u8n5AZy4WPnMnf5lzNQwtmMPKCwV77kRWJTb/5Pz/xsbJ3fUGr7/Uc0ZVzb5/IpQ+ezuk3nEBC19hW26b3SebkK0ZywT2nctE/JjP5urGkZCd4tMkemsGka0Zz4X2TuPC+SZx85Uji0qI92mgalOVWYg3yIgqqpqK14gL82quv07d3P+Ji4jlhzDgWL17caj/XT7+ecEtEi9ewocM92lVVVXHbjNvJ6pZNXEw8QwYP9a53BVgUSHvYvGhHq2HqQ8/qx81vX87dX9/A1S9eRHrflMP2ldE/latfvIi7v76Bm976K0PO6NuiTc7YLKa/+hfu/vpGpr/6F3LGdG/Rpqa0jqKdoVm9/ki8/PLLdOvWDZPJxNChQ/n9999bbbto0SLGjh1LXFwcZrOZXr168dxzz3m0abIdHPo688wzO/ur+ITaZd+Q9/wV7H1sCoWv3kLj3g2Hba857VT+8jZ5z13O3semkP+fq6hbdeC3XfTWnex9+LQWr5L3H+jsr+IzSn76gvW3XsSqy09h833XUrtlbattazetZuWlJ7Z4NeYfqMxd9tsPXtuo9uB+7hwOnb8H0F5efvllfw+hXdjtdgwGg7+HEfDUOqq9Xv/lq1954cGXuf3JW+k/vB9fv/std152L+/+NoukLkleP/PQ9Y9RUVrJPc/+nbRuaVSWVeFyHjjxdNgd3H7xXUTHR/PY6w+RmJJASUEJlnBLi77qHLVE6CM75kv6AVX1bvj77NPPuPuue3j2+WcZNXoUb705i/PPPZ/lq5aRnp7u9TOXT7uCkpISXnrlv3TP6k5pSSlO14H7arfbOeesc4hPSODdD94lLS2VvLx8IsLDW45LU9E0rc2HAIGEo9G7AfPL777ggSfu5+mHn2bEkJHMnvMOl1x7CYt+WEyX1C6t9ldTW8Mtd97CuNHjKC3zVCRjomOYceNt9OjeA71ez7xff+Jv99xKfFw8E8dN9GjrtDkxhhuP/Qv6iXpHndeN0/yvFvDSw/9jxj9vod/wvnzz7nfcPe1+3l7wOklpia32N3vhm4RFHPhtR8VFNf//mzPf5ufP53PHzBlkZKezfMEKHrj2Uf771XP06Jft0Y9NteFQHehlfQd8S//QWnXdhgYrOf17ct5l5/J/l992xH5y9+Qx/aKbufDy83nmtadY9edqHrnjcWLjYph8zqkArF62htuuvpO//eMWTjlrIj9/O58ZV/2dD+a+w8BhAzz6swV51V9Ho9NrEYh5i+fy3FtPc9d19zOg12C++OkTbnviRuY8/xXJCS03pms2r+KRF//BjCvvYtyw8ZRUlPD0q4/xxMsPMfPu/7hlORz83yPTiYmK5ck7nyUxNoni8iIs5rAW/WmqhrXWhiXK1PFf2kcUbC1GVuQWxT76ntST024Zz3fPzyd3fQFDzx7AZTPP5aUr3qWmpOWhXXRyJH956lxWfbeBL56YS3r/VM6cMZGGKiub9xe80Bv1VBVWs+m37Uy+eXyrY1JdGgWbizv2i/oY1aVSWeTd6N61XwpDT+/L8m/XU7Kvkh7DMpj41xF88+ICGryklkjsGkvhzjLW/LwFu9VJ1pAuTLhsOHNfW0RloVtGUrc49qwvoPS7SlxOF31OyOLkK0byzYu/Ya317LOioIq0nt71u2BAbcXD7tNPP+Puu+7muf1616w33+K8c89nxarlXvWumc/M5NFHH23+u9PpZPSo0UydOrX5mt1u5+yzziEhIZ73PniXtLQ08vLyiAiPOOpxBRP5m4u8Hmj0PjGbU68fx9yXfiN3YyFDzujLJY9P4dXpH1DjpRhaVFIEFz82hTU/bOSrmfNI75vCaTePp766ka2L3YVn0nonc959k/lt9p9sXbyTnLFZTL1vMrPv+JyCrZ6//8LtJaT0aF0PCUU++ugjZsyYwcsvv8zYsWN59dVXOf3009m0aRMZGRkt2oeFhXHLLbcwYMAAwsLCWLRoEddffz1hYWFMnz4dgM8//xy7/cABdnl5OQMHDuTCCy/02ffqLOo3/EbF3FeJPfNmTBl9qV3xPSXv3U/qza+hi/Y+d0o/+Seuuirizp6BPjYVV3012kG/44SLH4SDIqlc1hoKX7kJS59xnf59fEHFH7+QN/tFMq6+nbCcfpT9/DU7nrqLvv+ajSG+9WdE32ffRzEf0Pt1kdEe78vmMPo9+57nNUPw7puORMCF/R7s7fbf//6XzMxMIiIiOOussygqOhBWcWjY7549ezw8+s466ywsFgvdunXjzTffBGDBggUexoWrrrqqhTfiDz/8wPjx44mIiMBsNjNu3Dh+/fVXjzGuW7eO0aNHYzKZGDhwIL///nuz7IcffthDliRJfPLJJ4wYMQKDwcAHH3zA3r17Of3000lPT8dsNmM2m+nXrx/PP/98c1hpZmYm77zzDgC//fZbc18LFiwAoKCggKuvvprU1FQMBgPdu3fnscce80jia7PZuP7664mMjCQxMZFHHnkkKMJWnaoDWysJ6D969VPOvPR0plx2Jpk9u3LrYzeTmJrIF+9847X9n/OXseaPtTzz3j8ZduJQUtKT6TO4F/2HHzjR++7DudRU1fDkW48yYEQ/ktOTGDCyP9l9Pb1KJSTqHMHt+ddamMd/X/gvl19xOVdedQW9euXw9DNPk9YljTdef9Nr+3k/zWPxosV89sWnnDTxJLp27cqw4cMYNWpkc5t333mXyspK5nz8IaNHjyIjI4MxY0bTf0B/r31qQVx9TtO0Vr1R/jfrf/zlgsuYdtFf6Zndk8fvf4K05DTe/uCtw/b59wfu4Pwp5zFs8PAW740dOZYzJ51Jz+yedOvajelXXk+fnD4sW/Fni7bBXomythUvsE9e/5wzLpnMmX85na49Mrjl0RtJTE3g69nfHra/mPhoYhNjm1+KojS/N++zX/jL/13CqJNHkNo1hXOumMLw8UP5+NXP2jS2YKE149/4U8dx2/23MunsU46qnzlvfUxKl2T+8dTdZOV058LLz+e8aVOZ9d+3m9u888p7jDlpFNfffi1ZPbtz/e3XMmr8SN555b0W/QVOfsf2UV/l/b5++M1szp54Hueccj7dunTn9qvvJikumc9+/Mhr+w3b1pGSkMrFZ15GalIXBvUewtRJF7B558bmNt/M/4Kaumqeufs/DOw1mJTEVAb1HkLPzBzvY6sMbsNqwZZir1V+R104hNXfb2T1dxsp21fJj//9jeqSOoafM8BLLzDs7AFUl9Ty439/o2xfJau/28jqHzYy+uKhB2RtLWbe/xaxcf42XI7DG0oKtpUEhX7VGjVlda1WT+49pjs7V+1jx8pcakrrWPnDJhpqrPQckem1/cofNrFp0U7K86uprahnzc9bqa2op0vOgc3Z4k9Xs23ZXiqLaqgpq+fPr9aBBMlZcR59SbJEeb73w+BgobVD1wN615X06tWLmc161xte20dFRZGUnNT8WrVqFZWVVfz18mnNbWY3611zGD169H69a4xXvUvTtKCes9baRq+GPICR5w1izY+bWDN3E+W5lcx7dRE1pXUMOcu7/jnkzH7UlNQy79VFlOdWsmbuJtb+tJlRB3n7jjh3ILtX5bLko5WU51Wx5KOV7FmTx4ipAz36khWZou3Hn+ffs88+yzXXXMO1115L7969ef7550lPT+eVV17x2n7w4MFceuml9O3bl8zMTKZNm8bkyZM9vAVjY2NJTk5ufs2bNw+LxRISxr+aPz4nfMhkIoaejj4hg9jTb0CJSqB2hXcd1rp9BY171pN42WOYs4agi0nG2CUHU0af5jaKJQIlIrb51bhzNZLehKXvib76Wp1K8XcfE3fSmcRPPAtzWibpV9yKIS6B0nlfHvZzusho9NFxzS9JVjzelyTJ4319dFwrPYUGAWf8a2LJkiXceeedGAwG6urq+O6777jjjjuO6rPTp09n48aN6PV69uzZw/Tp09myZQuRkZGMHHnAONG9e3dGjhzJ4MHuxf2jjz7izDPPZOHChcTFxZGSksKiRYs49dRTmw2AVquVM844g6VLl6KqKg6Hg7POOuuw45k2bRq5ublkZWUhSRKlpaXMnTsXgN69exMZGcnGjRu57bbbmj0aBw8eTHx8PAARERGMHDmSkSNHEhkZSVlZGaNGjeKtt96irq6O3r17k5uby4MPPth8WgJw33338dprr1FbW0tERATPP/88n33mfRMbSLRmYHPYHWxbt40R44d5XB8+figbVmz0+plFP/1BzsAcPnj5I6YOvohLx17OS4/8D5v1gHFx8U9L6Du0D8/e+wJn9z+fyydcw+z/vI/L5ansa2iteiQGC95Oeu12O6tXr2HiyZ4eYyefPJE/l7Y0JgF8/933DB4ymOef+w89s3IYNGAw9937D6xWq0ebESNHcPuMO+iemcWIYSN5Zua/WtzXJlytKMjBgMuheg3xs9vtrN24lgknTPC4PuGECSxf5T2cFODDTz9gz749/P3/7jyibE3TWLhkITt372TU8NEt3nc2Br/xTzokeZp7LdjOsPFDPa4PGz+UDSs2Hba/6ybfxPmDL+X2i+5m9eI1nv3aHBiMnp7ZRpOR9cu8ry919uA1/qmaq8MqmK9ZtpaxJ43xuDZu4lg2rN6Ew+E+hV6zfC0ntGgzhtV/rmnRn0O1t5qeIBior7K2yPfncDjYsnMTIwd53oMRA8ewfusar/0MyBlESXkxi1cuRNM0yqvKmP/HPMYOPaDIL1z+K/1zBjLz9Sc47erxXDpjKm9/9rrXdVaSWjdMBgOaplGwraWHnayTSc1JZOfyvR7Xdy3fS5dWQv269E1m1yHtdy7bS2pOIrLSdtXY3mCnqjB414PyAu+6jaxIxKZGUbijzON64Y4yEtJjjq5zCfQGHTZr6+k9FL2CrMjYGzzbaJpGRZAb/7wdurr1rtWc3ELvOpmlrehdhzL7ndmcNPEkD6+qJr3rthm30y2zO8OHjeCZmc+0qne1ZpgMBop2eDewyTqZlB6J7F6V63F916pcuvRO9vqZLr2T2XVo+5X7SOmR0LwepPVOZteqfS3aHNqn6lJDIgdoW7Db7axcuZJJkyZ5XJ80aRJLlhw+hVATq1evZsmSJYwf37qX9Ztvvskll1xCWFhLz/ZgQnM6sBdsx5w1xOO6OWsItlzvBWMati7FmNqDmsWfkPfvy8h/4Roqf3wd1dF6eGrd6h8J6zce2RC83v5NqE4HDbu3ETnA0yEicsBw6rYdPlx6873XsPbGc9n2+AxqN65q8b6r0cr6/7uQdTefz46Zd9Owe1uHjj3QCFjjn8vl4o8//mDbtm3NLu2//HLkvEMAZ599Nrt27Wo+PVBVlQULFjBkyBCWLl3a3O6BBx5g6dKlfPHFFwDcc889aJrG1Vdfze7du9m5cydTp07F5XLx4IMPAvDBBx+Qn++uQvvFF1+wadMmnn322cOOZ+rUqeTl5bF582amTZtGdnY2u3fvJjc3l1WrVlFYWMiJJ7qV+Tlz5jT33ZTToGncS5cuZciQIbz00kvk5uaSlJTEzp07Wbt2LZ9++ikAb7/9Njt27KC+vp6XXnoJgEsuuYSdO3eybds2jMbAd2O1t7Ihra6oxuVSiUnwVDhjEmKoKPWeyLxgbyHrl61n15bdPDHrUW599GYWfLuQZ+97waPNb98tRFVdPPPek1w+4zI+evUTZv/n/Rb9OVzBnT/N2ylveVk5LpeLxCRPN/OExESKi72HMu3ZvYc/lvzBpo2b+GDO+zw98ym++uIrbp9xwEC/e88evvziK1wuF599/il33X0nL77wIs88/cxRjy1YaM1joqKyApfLRUK8Z76jhPgESsq857XctWcnj/3rcV759//Q6VrPzFBTW0PmwK6k9Unlsuv+wj8f+GcLIyO4Q/2C+d7aVVuLsN/qihpUl0pMfLTH9Zj4aCpLKr32E5sYyx0z/8Yjrz/Ao68/QHpWF+64+B7WLj2QJ3DYhKF88tpn5O3KR1VVVixcyeIf/6CipOX6IiFhb8VDORhwah0X8lVWUk58oudJaVxiHE6nk8ryKneb4jLivLQpLfE0KjTh0oLXaO1odLao9VFVW4lLdREbdcg9iI6jvMp7heMBvQbxyIynuP/ZOxl78RDOuOYkwsMi+Ps19za3KSjOY/4f81BVlef+8TJXXTCd979+h7c+e61Ff5rmHluwojpVbPUtn8GWKDOyIlNX6ekxWlfZQHhsy/QdAOGxYV7bKzql3WHRdRX17fpcINBYa/OaO81oMSArcou8e9Y6G+aIo9Mn+4zpjs6gsHdD6zkFB0/qjbWmkcJdh6wHGjQEUEGH9tAWvSsxMYGSVvSugykqLOKnn+ZxxZVXeFzfvWc3X37xJarLxeeff8Zdd9/Fiy+8yMxW9a7gNf7VV3r3ELdEel8P6g+zHoTFhLXo79D1IDzG0sJzur7SSlhMS0PUobJDnbKyMlwuF0lJnqGXSUlJHlF73ujSpQtGo5Fhw4Zx8803c+2113ptt2zZMjZs2NDq+8GEq6EGNBU5zHM/q4TF4Krzvp91VhbSuG8jjpI9JFz8ILGnXU/9pt+p+O4lr+1teVtxlOwhfMhpHT5+f+CsqQbVhT7K857pomJxVHu/Z/roODKuvZOs2x4j67bHMaVksO2J26jdvKa5jSk1g8wb7iXr70/S7f8eRNIb2PLwzTQW5nrtMxTwWc6/tuby6t+/P4MGDQLcxT2++OILSkpaLwJxMNOmTUOSJPr0OeAK25oRo4nS0lL27NkDwKxZs5g1a5bH+3/+6T6J27jR7QFisViajXMXXXTRYRejv/3tb82hZYqioNfrmTlzJt999x0FBQUeoboFBa0rR00sW7as+TslJnoqDpqm8eeff9K/f39sNreydt555wGQkJDAhAkT+Pzzz48ow58cyeOjxVTSaOEZ1PyWqoIk8eBL9xEe6c41d8vDN/LAdY9w+z9vxWg2omoq0XEx3PnM7SiKQs7AnpQVlfPhKx9z1e2Xe46N4FWUgFaTToO3+9p6Dj5VU5EkiTffeoOoKHfOtH8+ZeOvl/2VZ5//N2azGVVVSUhI4MWXXkBRFAYPGUxhYRH/ee4/3HPfPV5HF7QcIYn+ofextfyGLpeLG26/gbtuvYusbocvZhQeFs78r3+lvr6e3//4nQeffJCuGZmMHTm2ZWONwKo82gYOtx60uIea1ur3zMhOJyP7QB6lvsP6UFpQysf/+5SBo9yhQP/36I38687nuWL8tSBBWtdUTrt4EnM/+slrn64g3jh19KbP2xw/9PrR/g4O/nwworlaX2lbTlmt1Z/mrtydPPvmU1x94Q2MGjSG8soyXpz9b5569THuv9md+0vVNGKiYrn3hodQFIXeWX0pqyjhva/e5tqLbmzRZ2sHFcGA034Ew+UhN/2Iamcr7ds785wBVu29LTidRxq7512RJPdyeyQy+6cyYGJPFry/wqvhFqDPCVlk9k9l3qw/UJ0t56friGMLXlquiRzFxIX33nuPqOgopkzxjDzSmvWuF5v1rqLCQp5/7j/c60XvCt5Vtu0pTY52zh7cHjw/02Jll9xXD8UVxGvBsdCWZ3wTv//+O3V1dSxdupR77rmH7OxsLr300hbt3nzzTfr168eIESM6dMz+pOWtOYyyvv9exp9/N7LJbXCOdU6n9OMniD3zZmS952FM3eq56BMzMXbxngIkePGm93u/Z6bUDEypBzyjw3v2w15RQvG3c4joPch9rUdfwnv0PahNfzbfdy0lP35OxpV/6/DRBwI+M/41uehWVHhaZ8vL3Sfe4YcUAYiOjm7+/ybvl6PdDDR99mCvmSN99uD3u3fvTkJCQos2BycdbYsxMznZ0yV8xowZvPGGO6dHjx49iI2NZefOnc0nJ0eiaawREREeBs4mLBaLx/c5eKzBsKFqzZAXFRuFoshUHOLZU1lW2cIbsIm4pFgSkuObDX8AXXtkoGkaJYWlpHfvQlxiHDq9ziP3V2aPDCpKKnDYHegNByf0D1ILymGIi49DURSKizyN66WlpS2My00kJSeTmprSbPgDyOmVg6Zp5Ofnk52dTXJyMnq93uO+5uT0pLi4OPQK37QyLWJj3DnlSko9721ZeRkJcS3XmLr6OtasX8P6Teu591G3oq6q7mIoKb2S+fitTxg32p24V5Zlund1V5rr36c/23du4z//e9678S+Ip623oUfFRiIrMhWlh6wF5dWtrgXe6DOkF/M+n9/89+i4aB6f9TD2RjvVlTXEJ8fx2j/fJDnDeyLh1taqYKAjxx6fGEdpsafHTkVpBTqdjuhY9xoRnxRPmZc28QkhmFtFcs/bg5+20RExKLLSwsuvorqC2Fbyy7zz+RsM6DWIv557FQA9MnMwmcxcf/8V3PCX/yM+JoH4mHh0yiHPry7dKa8qw+FwoNd7FqRprTJmMCDJ3oNVGqqtqC61hVdPWLSFugrvHjh1FfVe27ucLqxeilgc3fiC+N62otPaGuyoLhVzuKc3pCnMSOMRqvB27ZfCqHMH8vtHKyk61KNvP73Hdqffidn8/PZSqoq9p3wJ5vvaGgf0Lk/HhMPpXU1omsa7s9/j0ksvbaFHJXnVu3Ja1buC+c7KivfRN9TsXw9iPH/flmhLq96C9ZX1hMW0sh7s9zytq2xo0WdYtNlrn6E4Zw9HfHw8iqK08PIrKSlp4Q14KN26dQPcTj/FxcU8/PDDLYx/DQ0NzJkzx6PgTTCjWCJBknHVeeqwrvoqlHDvOqw7j19cs+EPQJ+QAWi4asqQ49Kar6v2Ruo3/Eb0SZd76Sk40UVGgay08PJz1lSijzx6vT8suw8Vi7wf6INbzwjr3gtbUV67xxro+Czst8mLr7a2ljfeeAOn08nKlSubc+kNHDjwMJ/uWMxmMwD19QdCNBITE+natSvgDrNdtGhRc6jt7NmzeeyxxzAYDPTr16/5sz/95J48H3/88WHlHapUNYUeT5o0iW3btrFgwQLS0tJafM5isbQYJ9B86qHT6ZgzZ07zOOfNm8dNN93E1KlTyc7Obg7xbQprLisr47fffjvS7fE7suR9WuoNenoO6MnyhSs9ri9fuJJ+w/p6/Uz/4f0oKyqnof6Aq37urjxkWSYxJWF/m77k7873yH2SuyuPuKS4Qwx/rY8tWPC24TcYDAwePIhf58/3uD5//q+MPKiAx8GMGjWKwsIi6uoOJFvesX0Hsiw3z+VRo0axa+cuj/u6Y8cOkpOTvRv+grjSb2sbJ4PBwMC+A/ltsefv7rfFvzF8SMtCHhHhEfz23ULmf/1r8+uKS68ku3s287/+lSEDh7T4TBOapnkcUIQKsqS0uOZeC3qwYqFn7o6VC1fRb1jLA5HW2L5hJ3GJsS2uG0wGElLicTldLPx+EWMntcyl6B5b8K4HHVlZe9CIgSxZ8IfHtUW/LqHf4D7NxqdBwwey2EubwSMHee0zmO+tt5xxer2eXll9WLbW8x4sW/cH/XMGee2n0dbY4j4o+w1gTQd5A3oNJq8o12Od3Vewl/iYhBaGPwA5iDelOkPLtQDc4cAFW0voPsyzomT3YRnkbSz0+pm8jUUt2mcN70rB1pJ2e0e2Nr5gQGdQvLpFqS6NioJqkrPiPa4nZ8VTmus9xQK4Pf5GnzeIRZ+uIn+b96idPmO7039CD+bP/pOKVnIOgjsfYKjh1rsGM3++Z0HB+fPnexRO88bvvy9i586dXH5Fy439aC961/YQ1bv0xpbrG7jXg8LtJXQb7FkxudvgdPI2ew9Bzdtc1LL9kHQKt5c2rwf5Xtp0H5Lhtc9gXgvag8FgYOjQocybN8/j+rx58xgzZkwrn2qJpmnNUWsH8/HHH2Oz2Zg2bZqXTwUfkk6PIbUH1p2rPa437lyNMb23188Y0/vgqq1AtR3YzzrK80GSUSI91+eGjQvRnA7CBkw8tJugRdbpsXTrSe26FR7Xa9avILxnv6Pup2HP9sMW9NA0jYa9O0K66IfPtOsbbriB2Fj3Juu6667DYDAwbNgw6uvrkSSJe++99wg9dBy9evUC3Dn+RowYwX333QfAP//5TwA+/fRTUlNTGTx4MMnJyeTk5PD+++78b3/5y1+ajRtTpkyhb9++3HbbbW2SP2CAu/rcTz/9RE5ODunp6eTmtowtbxrnihUr6N+/P6NGjcJqtXLzzTeTlpZGZWUlOTk5DBo0iKysLOLi4rjiCnf+j7CwMG680R3y88EHH5CdnU3Pnj1bGBIDEbPOe04OgIuvv4BvP/ie7z78gT3b9vLCgy9Tkl/CuZdPAeB/T7zB4//3VHP7U847majYSJ6cMZPdW/ew5o91vPzYq5xxyWkYzW7j6LlXnE11ZQ3/eeAl9u3MZcnPS3n3hQ8478pzWsi3HGZswYDciufELbfewjtvz2b2O++yZctW7rnrHvJy87jm2qsBeOjBh5l+7YFiMhddfCGxsbHceP1NbNm8hUWLFnP/P+7nr5f/tdm4fu30a6ioqOCuv9/F9u3bmfvDXP71zL+Zfv113scWxJt95TCK3g1X38D7n7zHB5+8z7Yd23jgifvJK8zjikuvBODxfz3GzXfeDLj/fXr37O3xio+Lx2gw0rtnb8Is7hO///zveRYsWsCefXvYvnM7r8x6hY+//JgLzrmghXxZJ3eoocfXWHRhXo3WF153Ht9/OJfv5/zI3u37eOmh/1GcX8KUv7rTMbz+5Cz+eevM5vafvv45i+YuIW9XPru37uH1J2ex8PtFnHvV2c1tNq3awsLvF1Gwt5B1f67nrsv+gaZqXHrTRS3ka2hYdMGbdFon6Vv1/quva2Dzui1sXrcFgLy9+Wxet4WCXLch5d+PPM9d19/X3P6Sqy6iILeQJ++byc6tu/j03S/47N3PufqWK5vbXH7DNBbP/4PXnn+Tndt28drzb/LHgj+54saWyryEhCL5LDChwzGFG7yGl1065XK++uUzvv7lC3bn7eK5t56muKyQ8ya559dL7z3Pwy8cuK/jho3n1z9/4bO5H5FflMvaLav595tP0bdHfxJi3d5B50++mOraKp6d9RT7CvawaOVC3v78dS447ZKWA5DAdJR52gIRWZGJSozw+t7ST1Yx5Mx+DDq9D/EZMUy++USikiJY8fU6AE6+bizn3nsgGf2Kr9cRlRTJpJtOJD4jhkGn92HwGX3546MDh4uyTiYpO4Gk7AQUnUxkfBhJ2QnEpEW1kA8Qd7QFMAKQiNiwVkMiNy/ZRfbQDLKGpBOZEM7Q0/sQFmVm+zJ3wZRBp/ZizPmDmttn9k9lzPmDWDV3E2W5VZjCjZjCjeiNB37TfU7IYuApOfzxxVrqqqzNbQ41mkiyRHQr/+bBwuH1rneY/c5stmzZwt3Netc1ADz04ENcd5De1cTsd2YzfPgw+vZtedB17fRrqaio4E4PvetfTL++ZT8AchDrBrFp0a2+9+fnaxh0Wh8GTupNXHoMp0w/gajEcFZ95y4MMOGq0Uz5+4Fq9qu+20BUUgSnTD+BuPQYBk7qzaDJfVj66QHjzLIv19J9aAajLxxCXJdoRl84hMzBXVj2xdoW8uMzWh4qhjq33347b7zxBrNmzWLz5s3cdttt7Nu3jxtuuAGAe++9l8svP2Cwfumll/jmm2/Yvn0727dv56233uJf//qXVwPfm2++ybnnnktcXOgYZCJHn0fdqrnUrfoRR+k+Kua+irO6hIhhbh228udZlH1+IFdnWP+TkC0RlH/1b+wle2ncs57Kn94gfPAkLyG/P2LpNcbtYRhCJJ15EWW/fkvZr99hzd9D7uwXsZeVEH+Ke7+e/+Gr7H75ieb2xd9/TNXy32kszMWau5v8D1+latlvJEw+r7lNwadvUb12GbbiAhr2bGfvq0/TsHc7Caec3UJ+qOAz7bpLly4sWbKEhx56iAULFlBWVkZ4eDhDhw7lrrvuYvLkyb4aCi+88AI33XQTW7duZfny5c2Vsv7yl78QFRXFM888w8qVK9m6dStpaWlMnjy5OaefyWTi+++/Z/r06axevRpZlvnwww+bK/42GT4Ox7PPPkt9fT2//PILtbW13HnnnWzatIl33nnHo93VV1/NwoUL+fnnn9mwwf3AcrlcJCQksHTpUh588EHmzp3Lxo0bSUhIYNy4cUyZMqX5808++SR1dXXMmTOHqqoqpk+fTkFBAe+++26H3MfOommz7y1r0snnnERNZQ1vP/su5SUVdMvJZOZ7T5Kc7nYrLy8ppzj/wCmzJczMs3Nm8vz9L3LdaTcRFRvJSVPGc93dVze3SUpL5Nk5T/PiQ69w1cnXEZ8czwXXnsdlt3huniQkIvTBvZDKsgKulnlSzr/gfCrKK3j6yacpKiqiT58+fPrFp82/jaKiInJzD7hAh4eH89W3X3HnHXdy4gnjiY2NZer5U3nwoQea23Tp0oUvv/mCe+66l9EjxpCamsKNN93I7Xd4N5a3piAHA7IiIysSqqvlnD33zKlUVFXy75f+TXFJMb169uLD1z8kPc19glxcUkx+QdvcyxsaGrj74bsoLCrEZDKR3b0HL//rZc49c2qLtnqT99PxYCFCH+l1LZh4zgRqKmuZ/dz7VJRUkJnTlafefZzkLvvXguIKSgoOVAN0OJy88thrlBWVYzQZyOzZlSdnP8aokw/kj7Hb7Mya+Q4F+woxW8yMnDic+164i/Co8Bbym8YWrEiShFEx0ehqWf11w+qNXD7lwBr55D/cCujUS8/mqVeeoLSolMK8Ax5V6ZldeO3jl3jyvmd4/405JCYn8o+n72XyOac2txkychDPzprJ84+/yAtP/Jf0buk8N+sZBg4b0EK+UTEHtcE6LMa7HnDq2NOorq1i1if/o6yylO4Z2Tx338ukJKYCUF5ZSnHZgft61sRzaWis55MfPuQ/7/yLiLAIhvUfwc3TDqyhSfHJvPDgqzz31jNcdvv5JMQmcsmZ0/jruVe3kI/mDlMLZtL6JFNTVod2SJ7Vjb9uwxxpYvwVowiPtVCyu5z37/6K6v2hpOFxYUQlHfi9VhXV8ME9XzL55vEMP3cAteX1/PDiAjYv3NHcJiI+nBveuKz572MuGcaYS4axZ00e78z41EN+ZGI45ojgragY14pBE2DvhkKMFgP9J/TAHGGkqriWX99dRn21e+0whxsJizowr3oM74qsyIyY0p8RU/o3X9+5Kpc/9htKeo7oiqJTGH/pMA9Z6+ZvY92vByotaqpGbGrrYwsGZFnxWlX3gv1611MH6V2ftdC7PB0Dqqur+erLr5j5zNNeZXXp0oWvvvmSe+66h1EjRpOamspNN93I7Xfc7rW9FMSHrgldY5EV2aun7uaFO7BEmjjhsuGEx4RRurecOQ98S03J/vUg1uJxkFBdXMtHD3zDqdefwNCz+lNXUc9Pryxk6+KdzW3yNxfxxZM/Mv6KUYy/fCSVhdV88eSPFGz1DN2WFYnUnocP3Q5FLr74YsrLy3n00UcpLCykX79+fP/9981RdYWFhezbd6Basqqq3HvvvezevRudTkdWVhZPPfUU119/vUe/27ZtY9GiRc3RdqFCWL/xqA01VP32Pq66SgyJXUm87DF00W4d1lVbgbP6wH5WNppJ+uuTVPzwMkWv3YpsicDS90SiJ3oW/XGU5WHbt5HEv/7Tp9/HF8SOPhlnbQ2Fn7+Do6occ3o3su9+GmOCO72ao6oce9mB36PmdJL3/svYK0qRDUbMXbqRfdfTRA0+EM3jaqhj3xvP4KiqQLGEYcnsQc6DLxKWffRRRMGGpAVDErgAY8eOHWRlZTVvTt5///3mk4q5c+f61JAZqqwuW0a9s+7IDX1Mr+h+xJuC96Hucjmx2ltu9v2NhESY2buBJVioLqzB3kpCc38SFh+GJYg3/FanlT9Lfvf3MFogITEu5eSg9lgtsRZQZfdeJc2fRBviSDSn+HsY7UZ1qaz4apO/h+GVQaflYLAE74HA4g9X8Mvri1sY//yJJEv0HpfNhY+c6e+htBtN0/jkyZ+wWx3+HkoLJk8fS0IQe1U6nQ5sjsCrDC9LMmZTcEezvHHTHIpbySfpT877x2n0Hpfdaf2vWrWKoUOHsnLlSr6X/PMMv3/wKUduFMRkPvKjv4fQaex5yL92imFzAk+nPxIrLhnn7yF0GMEbV+NH/v73v7Nu3Tr69etHZWUlixcvBuDEE09k0qRJR/i04GiINETT4Kw/bHVafxAexJ4+sN/zLwAJZq+/JvQmXUAa/w4OtQpGTIoJvazHoQbWpjRMHxHUhj8Ak2IBAs/4Z1aCe0MqKzKWKBMN7Swc0VnojDr05uBeD7r0TQkowx8AmntcwYwkSSR2jSV/a0lAFYaTFYmYZKF3dQaBOq62kN4vldK95V6jLvxJWq/DF7kQCAQCfxHcOxc/MWHCBIxGIz///DPLly8nJyeHBx54gB9++CGoQ5UCiSRzSsAZ/qIMMZiU4A3rAbeCr5MDb/On1wV/5V9jAIZ8yXoZnSnw/r3bgiRJpFi6+HsYLUgNwDG1lXB9JHKAqQEyMmH64M7xBZDQLcA8lSRI7BYT9DpKRr9UolMCyxgkyTDg1F7+HsYxkz00I6AMf5Is0W1gGrogL/ghy3JAHnDqdcGtGwAMnNw7oAx/kiyROagLkQnB/wwTCAShSeA9jYKAGTNmsHnzZhoaGmhsbGTz5s08+uijzdV5BcdOuD4i4LzsQmGzD6DXB5ahTUJCCYETaEUnYwgLrHtriQru3GlNBNpvT5YUkszJ/h7GMSNLMlGGwDJSRRlig96jEiA+PRpZCaDfngaJmcGfhF6SJUacN4hWatX4HFmR6DOhJ2Exwa//pfZMxBxABWE0VaPniEx/D6NDCLQDTlmSQ8LzLzkrgZSeiQGj52iqxrBzWuaxFQgEgkAh+DVsQcgSSBt+vWwg1hgaVaZkSQ6ozbVeZwgYxe1YMQdSbj0JjAG0kTsWTDozccZ4AmPHL5FiSUMJQA/a9hBlCCyDUJQxsMbTXhS9QnzXmECZskSnRAR1rr+DGXRaHxRdYBguVJfG8HMH+nsYHYIsS+SM7BYwczY2JZK4w1R0DSbcB5yBcGPd6HWhsRYADD9nYMB4rIbHWugRIgZrgUAQmgSOBUAgOIR4U2LA5H7qGt49qKuiHYwkSRj0gWEUkpBCSgnVm3TozYHxfSwxFmQlNOYsQGZENgRAKgBZkkkPz/T3MDoMg2IkQh/t72EAEKmPxiAHlofMsZDSMx5ZDowNf5fewVuo6lDMESbGXDLU77YUSZHoPjSD9H7Bne/vYHqO6IrJYvD7vUWDQSEQSt2EW+8KjLVNkmQUJTQOrwD6nJhNXHoMUgCsteOvGBVSepdAIAg9xAolCFhkSSYnuq+fRyERZYghKYgrT3pDp+jQBYDyZzSYQsbrD9wKfkRiuN83TopBwRITQF6IHUCEIZKu4d39PQx6RPUK+tyfh5JoTkGR/OtJpUg6EkJsnTVaDGT09/93SuuVGNQVv71x4l9HEO/nDb9Or+Psu08NqWeYwaxn1NSBfj1nkSTIGppOao/QMViDW+8KhNx/RoMxpOasolc4565T/TtnZYluQ9IZOKm3/wYhEAgER4H/n0ICwWEI10eQHpbpN/myJNEzqndIKUpNGPUmJD9aqQLFANnRKHqF8Phwv44hMikiJOds14juWHRhfpIuEWOMJdmc6if5nYciKSSZ/ZtmIdmc5ncDZGeQ0C2GiPgw/xwISGCONJKSE+8H4Z2LzqBj6j9O8+uG/7T/G09UYugl9u+Sk0T3QV3wyyNEAmOYkaGn9fGD8M5FkiSMev8eHOl1hpDIsXwoKT0S/eoNrDMonHXbySGpdwkEgtAi9HbegpAjPTyTKnsltY5qn8vuGdUHY4h5+TQhSRJGg4lGu9U/sv2sBHcmpkgj9gY79nq7z2WHxYehM4bm0i5LMn1iBrC6bBkuzeUzuRISetlAr+h+Iavch+sjiDbEUWUv97nsGEN8SFT49YYkSXQflsbGX3fhtDt9aqySFZmsEekB4W3UGaTmJHHKDScw75XffSpXkqDPhJ4MPsPfkQmdx7Az+lKaW0ldZQOa6rtJK0sS4y4egsEUGOkzOhpZljHqjdgcNr/IDqU0K4dywqXD2bs2j/wtxT6dswBT7jiFyAT/HPreP/gUv8gNdfY8NNnfQwhZVlwyzt9DOK4JTY1QEFLIkkzfmIGE6Xz7YM2OzCHeFFphJ4eiU3SYDL41wkmShNloCVkjCri/Y2RyhM/z/1lizCEX3nco4foIBsQOQfbZ40tCkXQMih8WsgcBTSSYkn2e/y9SH028KcmnMn2N0WKg97hMFJ3sM88USZbIGZuJJTK05+yYi4dywmXDfSZPkqD78K5MvW9ySD/DDGY9p149GkuEyWeh1ZIE4y4ZSlJmaBRXaw2dTu/zvMuyJGMymEN6zuoMChc/NoXEzDifpgM4428n0Xtcts/kCQQCwbEgjH+CoEAn6+gfO4QIfaRP5PWI7E2yJc0nsvyNTtFjMvjGYNRk+AukasOdhSRJRKVGYgjzTZLvsDgLYXH+Con1LVHGGAbGD9sfJtqZSr6EQTYwJGGEH8ONfYckSSSb04jSx/hEXpQ+liRzWkhvSJswR5roMyELvUHX2VMWRSfT+8RuRMQFRsGszmbitWOYcPVo9186eSr1GpfNJY9PQdGHXujkoVgiTUyePpbwGEunhgBLkoSsyEyYNoL03smdJyiA0PvQAChLMiZjaBv+mjCFGZk2cyqpPRM7d87KEkgw5Y6TGXx66HoACwSC0EPSAqU+ukBwFKiai711u8mv39cp/RtlIz2j+xBl8M3mN5BwqS5s9kZUTe2U/nWKzp1n8DhQQA9G0zSsVVbqyxs6pX9JdhcZMYYHRgVnX9LgbGBL5XpqOiklQJwxgZ7RfTAqx9e91TSNakclpdZCtE6IU5WQSDSnEKmPOe7WA7vVwZ7V+VQV1XVK/+FxFroP64LJR4cOgcTmhTv4+pl52OrtHRr2JykSkiQx8doxjL5wyHFXzdNudbDih43sWp3nNq528JIQnRjB2AsGE5Pim8PdQMLlcmKz2zplnQW3kVGvMxx366zD5uS32Uv587M1SLLUseuBJBGREMY5d55KRn//OAmsWrWKoUOHsnLlSoYMGeKXMQgEguBEGP8EQUmNvZpt1RtpdDV2QG9ubTbF0oXM8KyQTIZ8tGiaht1px+HsuFx1Eu7cgqFY3KMtOG1Oaoprcdk7LledMdxAeEL4cbcZPRhN08iv38fOmm37t0/H/khTJB09o3qTaE4+7jZNB+NQ7RQ15GN11XdYnxYljCRLGnr5+DNONaFpGuW51exZU4DqUo99ykruDWlG/2QSu8ce13O2ocrKd8/PZ9OC7ciKhOpq/81tMhqk5CQy9b7TSOga24EjDT7ytxXzx+draWywH/OclWQJNI3+E3rSb3z2cf8MsztsOF3ODuuzKafz8azPAuRtKuSrmfOoKqo5ZsN103oy/JwBTLhqtF/zUgrjn0AgaC/C+CcIWlyaixJrIQX1eVhdDbTnyS4hEW9KJNWSToTh+Dt1bg2X6sLhdOB0Odrdh4SEXmdAr9Mf15vRg9E0DVutDWu1Faet/UZAQ5gec5QZg+X4NaAcitVpJb9+H4UNee0uBqKX9aRa0kkLS8dwnHn7tYamadQ5a6mylR+TEdCshBFjjCNMF5qVqNuDvdFBya4KSnZV4GznoYCsk0nsFkNSVhxGsR40s2vFPv78bDXblu5Gktrm+dO0yU/NSWLE+YPof3LOcW2cOhi71cH2FXvZunQPDTWN7nvbxm2EopPpPrgLvUZ1C8lqye3FpbpwOOy41PbrBpLk1rt0ik6ss/tx2Jys/Wkzy79cS0V+VZsPBSQJJFmmz4nZDD93IKk5/s9RK4x/AoGgvQjjnyDo0TSNGkcVRQ0FVNursKsHV1GT9psENY8rYbpw4kwJJJlTMShiw9QamqbhcDlwOh1HFQ4sIe2vKGdAkRWhfB4GR6OTxhor9gYHqvPI91YxKBjDDJgiTcdFvqn24lJdlDQWUdxQSK2j2sMQKO1PCHbweqCT9EQaoki2pBJvSjwu8lG2F7vLRpW9gnpnLQ71yN7BetlAmC6CaEOsMKYeBlVVqSyopWxvJXUVVlyOgzb/By+h+6etrJMJjzETlxFNXJcoYZg6DFVFNaz8dj3bluymbG9586ZfkqX9zycNVdWa721MahTdhqYzbMoAUnqGdsGvY0FVNQp3lLBjRS6l+ypobKpsv98LFQBNo2mHoehlYlOiyOyfSrdBXUK2mm9HoGkqDqcTl8vZBr1LQa/TI8uy0LtaQdM0cjcUsPr7jexdX0BtmTv1gtu4JwFuQ3bTQYGik0nsFk+vE7IYOLkPYQFUTE0Y/wQCQXsRxj9ByOFQHdQ7amlw1qNqKioqsqSgl/WE6yKw6CxIYoPfZjRNQ9NUXKq6/6R//9IhSciSjCzLwnDSTlRVxWlz4bI599/nA6fNOqMOnVEYUtuDpmk0uqzUOmqwuQ7ks5QlBbNiJtwQiVE2invbDlRNxeZqdN9XXGia5k7cj4JRMWFUTGI9aAeapmG3OmioasRWb99vmNKQFBmjWY8lxozRIryp24PT7qRkdzlF20ux1jXisrtQ9Ap6k56k7nEk90gU3pPtxFpno6KgipqyelxOFU3VUPQy5nAjsanRRMaF+bQCa6hwsN6FduDYSpLYr3cJ3aC9NNRYKdpRSvm+Shx2J6pTRWdQsESZSc5OID4jNmAPVoTxTyAQtBdh/BMIBAKBQCAQCAQCgSDAEcY/gUDQXgLzSEMgEAgEAoFAIBAIBAKBQCAQHDNtKr/Z0NDAli1bOmssAoFAIBAIBAKBQCAQCLywefNmjz8FAsHxTa9evbBYLEfVtk1hv01uxgKBQCAQCAQCgUAgEAgEAoHAP7QlBUCbjH/C808gEAgEAoFAIBAIBALfY7Va2bNnD5mZmZjNgVOFWCAQ+IdO8/wTCAQCgUAgEAgEAoFAIBAIBMGDKPghEAgEAoFAIBAIBAKBQCAQhCjC+CcQCAQCgUAgEAgEAoFAIBCEKML4JxAIBAKBQCAQCAQCgUAgEIQowvgnEAgEAoFAIBAIBAKBQCAQhCjC+CcQCAQCgUAgEAgEAoFAIBCEKML4JxAIBAKBQCAQCAQCgUAgEIQowvgnEAgEAoFAIBAIBAKBQCAQhCjC+CcQCAQCgUAgEAgEAoFAIBCEKML4JxAIBAKBQCAQCAQCgUAgEIQo/w/iZMhgx8lYsAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1625x480 with 19 Axes>"
       ]
@@ -416,10 +341,10 @@
     {
      "data": {
       "text/plain": [
-       "<plottable.table.Table at 0x7fbc3473a800>"
+       "<plottable.table.Table at 0x7f47749a5930>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -439,111 +364,115 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "keep_output"
+    ]
+   },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">               Isolated labels        KMeans NMI        KMeans ARI  \\\n",
        "Embedding                                                            \n",
-       "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.464394</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.654386</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.442916</span>   \n",
-       "Scanorama             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.504315</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.685718</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.450299</span>   \n",
-       "LIGER                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.45722</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.593406</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.408749</span>   \n",
-       "Harmony                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.46885</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.610796</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.440707</span>   \n",
-       "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.526255</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.597475</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.452404</span>   \n",
-       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.527354</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.709471</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.602122</span>   \n",
+       "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.661278</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.659168</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.427279</span>   \n",
+       "Scanorama             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.626327</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.704883</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.490931</span>   \n",
+       "LIGER                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.621488</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.570878</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.329819</span>   \n",
+       "Harmony               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.554801</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.606546</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.432499</span>   \n",
+       "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.617493</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.650634</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.509873</span>   \n",
+       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.712235</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.747422</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.587875</span>   \n",
        "Metric Type   Bio conservation  Bio conservation  Bio conservation   \n",
        "\n",
        "              Silhouette label             cLISI  Silhouette batch  \\\n",
        "Embedding                                                            \n",
        "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.585415</span>               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.855258</span>   \n",
-       "Scanorama             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.564586</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.999625</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.932109</span>   \n",
-       "LIGER                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.542106</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.985223</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.850703</span>   \n",
-       "Harmony                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.56432</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.994074</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.878664</span>   \n",
-       "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.543061</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.994309</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.897465</span>   \n",
-       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.595713</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.998238</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.884396</span>   \n",
+       "Scanorama             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.564498</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.99961</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.932364</span>   \n",
+       "LIGER                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.542106</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.98501</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.850703</span>   \n",
+       "Harmony               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.564499</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.993888</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.879939</span>   \n",
+       "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.538018</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.994553</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.895502</span>   \n",
+       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.613172</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.999632</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.884228</span>   \n",
        "Metric Type   Bio conservation  Bio conservation  Batch correction   \n",
        "\n",
        "                         iLISI              KBET Graph connectivity  \\\n",
        "Embedding                                                             \n",
-       "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.007811</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.223815</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.764953</span>   \n",
-       "Scanorama             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.064213</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.308119</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.675554</span>   \n",
-       "LIGER                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.161164</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.463056</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.694717</span>   \n",
-       "Harmony                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.12853</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.44529</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.765538</span>   \n",
-       "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.119576</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.331022</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.899629</span>   \n",
-       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.110701</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.339256</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.907899</span>   \n",
+       "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.008071</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.223882</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.764953</span>   \n",
+       "Scanorama              <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.06491</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.313843</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.689019</span>   \n",
+       "LIGER                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.16332</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.463284</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.694717</span>   \n",
+       "Harmony               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.130195</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.432907</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.756294</span>   \n",
+       "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.124561</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.347675</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.896865</span>   \n",
+       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.095614</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.337137</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.932888</span>   \n",
        "Metric Type   Batch correction  Batch correction   Batch correction   \n",
        "\n",
        "                PCR comparison Batch correction Bio conservation  \\\n",
        "Embedding                                                          \n",
-       "Unintegrated                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.370367</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.629422</span>   \n",
-       "Scanorama              <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.21128</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.438255</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.640908</span>   \n",
-       "LIGER                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.810099</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.595948</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.597341</span>   \n",
-       "Harmony               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.522324</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.548069</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.61575</span>   \n",
-       "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.879229</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.625384</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.622701</span>   \n",
-       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.798182</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.608087</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.686579</span>   \n",
+       "Unintegrated                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.370433</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.666628</span>   \n",
+       "Scanorama             <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.204786</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.440984</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.67725</span>   \n",
+       "LIGER                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.810099</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.596425</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.60986</span>   \n",
+       "Harmony               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.521998</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.544267</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.630447</span>   \n",
+       "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.871396</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6272</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.662114</span>   \n",
+       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.734378</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.596849</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.732067</span>   \n",
        "Metric Type   Batch correction  Aggregate score  Aggregate score   \n",
        "\n",
        "                        Total  \n",
        "Embedding                      \n",
-       "Unintegrated           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.5258</span>  \n",
-       "Scanorama            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.559847</span>  \n",
-       "LIGER                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.596784</span>  \n",
-       "Harmony              <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.588678</span>  \n",
-       "scVI                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.623774</span>  \n",
-       "scANVI               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.655182</span>  \n",
+       "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.54815</span>  \n",
+       "Scanorama            <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.582743</span>  \n",
+       "LIGER                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.604486</span>  \n",
+       "Harmony              <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.595975</span>  \n",
+       "scVI                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.648148</span>  \n",
+       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.67798</span>  \n",
        "Metric Type   Aggregate score  \n",
        "</pre>\n"
       ],
       "text/plain": [
        "               Isolated labels        KMeans NMI        KMeans ARI  \\\n",
        "Embedding                                                            \n",
-       "Unintegrated          \u001b[1;36m0.464394\u001b[0m          \u001b[1;36m0.654386\u001b[0m          \u001b[1;36m0.442916\u001b[0m   \n",
-       "Scanorama             \u001b[1;36m0.504315\u001b[0m          \u001b[1;36m0.685718\u001b[0m          \u001b[1;36m0.450299\u001b[0m   \n",
-       "LIGER                  \u001b[1;36m0.45722\u001b[0m          \u001b[1;36m0.593406\u001b[0m          \u001b[1;36m0.408749\u001b[0m   \n",
-       "Harmony                \u001b[1;36m0.46885\u001b[0m          \u001b[1;36m0.610796\u001b[0m          \u001b[1;36m0.440707\u001b[0m   \n",
-       "scVI                  \u001b[1;36m0.526255\u001b[0m          \u001b[1;36m0.597475\u001b[0m          \u001b[1;36m0.452404\u001b[0m   \n",
-       "scANVI                \u001b[1;36m0.527354\u001b[0m          \u001b[1;36m0.709471\u001b[0m          \u001b[1;36m0.602122\u001b[0m   \n",
+       "Unintegrated          \u001b[1;36m0.661278\u001b[0m          \u001b[1;36m0.659168\u001b[0m          \u001b[1;36m0.427279\u001b[0m   \n",
+       "Scanorama             \u001b[1;36m0.626327\u001b[0m          \u001b[1;36m0.704883\u001b[0m          \u001b[1;36m0.490931\u001b[0m   \n",
+       "LIGER                 \u001b[1;36m0.621488\u001b[0m          \u001b[1;36m0.570878\u001b[0m          \u001b[1;36m0.329819\u001b[0m   \n",
+       "Harmony               \u001b[1;36m0.554801\u001b[0m          \u001b[1;36m0.606546\u001b[0m          \u001b[1;36m0.432499\u001b[0m   \n",
+       "scVI                  \u001b[1;36m0.617493\u001b[0m          \u001b[1;36m0.650634\u001b[0m          \u001b[1;36m0.509873\u001b[0m   \n",
+       "scANVI                \u001b[1;36m0.712235\u001b[0m          \u001b[1;36m0.747422\u001b[0m          \u001b[1;36m0.587875\u001b[0m   \n",
        "Metric Type   Bio conservation  Bio conservation  Bio conservation   \n",
        "\n",
        "              Silhouette label             cLISI  Silhouette batch  \\\n",
        "Embedding                                                            \n",
        "Unintegrated          \u001b[1;36m0.585415\u001b[0m               \u001b[1;36m1.0\u001b[0m          \u001b[1;36m0.855258\u001b[0m   \n",
-       "Scanorama             \u001b[1;36m0.564586\u001b[0m          \u001b[1;36m0.999625\u001b[0m          \u001b[1;36m0.932109\u001b[0m   \n",
-       "LIGER                 \u001b[1;36m0.542106\u001b[0m          \u001b[1;36m0.985223\u001b[0m          \u001b[1;36m0.850703\u001b[0m   \n",
-       "Harmony                \u001b[1;36m0.56432\u001b[0m          \u001b[1;36m0.994074\u001b[0m          \u001b[1;36m0.878664\u001b[0m   \n",
-       "scVI                  \u001b[1;36m0.543061\u001b[0m          \u001b[1;36m0.994309\u001b[0m          \u001b[1;36m0.897465\u001b[0m   \n",
-       "scANVI                \u001b[1;36m0.595713\u001b[0m          \u001b[1;36m0.998238\u001b[0m          \u001b[1;36m0.884396\u001b[0m   \n",
+       "Scanorama             \u001b[1;36m0.564498\u001b[0m           \u001b[1;36m0.99961\u001b[0m          \u001b[1;36m0.932364\u001b[0m   \n",
+       "LIGER                 \u001b[1;36m0.542106\u001b[0m           \u001b[1;36m0.98501\u001b[0m          \u001b[1;36m0.850703\u001b[0m   \n",
+       "Harmony               \u001b[1;36m0.564499\u001b[0m          \u001b[1;36m0.993888\u001b[0m          \u001b[1;36m0.879939\u001b[0m   \n",
+       "scVI                  \u001b[1;36m0.538018\u001b[0m          \u001b[1;36m0.994553\u001b[0m          \u001b[1;36m0.895502\u001b[0m   \n",
+       "scANVI                \u001b[1;36m0.613172\u001b[0m          \u001b[1;36m0.999632\u001b[0m          \u001b[1;36m0.884228\u001b[0m   \n",
        "Metric Type   Bio conservation  Bio conservation  Batch correction   \n",
        "\n",
        "                         iLISI              KBET Graph connectivity  \\\n",
        "Embedding                                                             \n",
-       "Unintegrated          \u001b[1;36m0.007811\u001b[0m          \u001b[1;36m0.223815\u001b[0m           \u001b[1;36m0.764953\u001b[0m   \n",
-       "Scanorama             \u001b[1;36m0.064213\u001b[0m          \u001b[1;36m0.308119\u001b[0m           \u001b[1;36m0.675554\u001b[0m   \n",
-       "LIGER                 \u001b[1;36m0.161164\u001b[0m          \u001b[1;36m0.463056\u001b[0m           \u001b[1;36m0.694717\u001b[0m   \n",
-       "Harmony                \u001b[1;36m0.12853\u001b[0m           \u001b[1;36m0.44529\u001b[0m           \u001b[1;36m0.765538\u001b[0m   \n",
-       "scVI                  \u001b[1;36m0.119576\u001b[0m          \u001b[1;36m0.331022\u001b[0m           \u001b[1;36m0.899629\u001b[0m   \n",
-       "scANVI                \u001b[1;36m0.110701\u001b[0m          \u001b[1;36m0.339256\u001b[0m           \u001b[1;36m0.907899\u001b[0m   \n",
+       "Unintegrated          \u001b[1;36m0.008071\u001b[0m          \u001b[1;36m0.223882\u001b[0m           \u001b[1;36m0.764953\u001b[0m   \n",
+       "Scanorama              \u001b[1;36m0.06491\u001b[0m          \u001b[1;36m0.313843\u001b[0m           \u001b[1;36m0.689019\u001b[0m   \n",
+       "LIGER                  \u001b[1;36m0.16332\u001b[0m          \u001b[1;36m0.463284\u001b[0m           \u001b[1;36m0.694717\u001b[0m   \n",
+       "Harmony               \u001b[1;36m0.130195\u001b[0m          \u001b[1;36m0.432907\u001b[0m           \u001b[1;36m0.756294\u001b[0m   \n",
+       "scVI                  \u001b[1;36m0.124561\u001b[0m          \u001b[1;36m0.347675\u001b[0m           \u001b[1;36m0.896865\u001b[0m   \n",
+       "scANVI                \u001b[1;36m0.095614\u001b[0m          \u001b[1;36m0.337137\u001b[0m           \u001b[1;36m0.932888\u001b[0m   \n",
        "Metric Type   Batch correction  Batch correction   Batch correction   \n",
        "\n",
        "                PCR comparison Batch correction Bio conservation  \\\n",
        "Embedding                                                          \n",
-       "Unintegrated                 \u001b[1;36m0\u001b[0m         \u001b[1;36m0.370367\u001b[0m         \u001b[1;36m0.629422\u001b[0m   \n",
-       "Scanorama              \u001b[1;36m0.21128\u001b[0m         \u001b[1;36m0.438255\u001b[0m         \u001b[1;36m0.640908\u001b[0m   \n",
-       "LIGER                 \u001b[1;36m0.810099\u001b[0m         \u001b[1;36m0.595948\u001b[0m         \u001b[1;36m0.597341\u001b[0m   \n",
-       "Harmony               \u001b[1;36m0.522324\u001b[0m         \u001b[1;36m0.548069\u001b[0m          \u001b[1;36m0.61575\u001b[0m   \n",
-       "scVI                  \u001b[1;36m0.879229\u001b[0m         \u001b[1;36m0.625384\u001b[0m         \u001b[1;36m0.622701\u001b[0m   \n",
-       "scANVI                \u001b[1;36m0.798182\u001b[0m         \u001b[1;36m0.608087\u001b[0m         \u001b[1;36m0.686579\u001b[0m   \n",
+       "Unintegrated                 \u001b[1;36m0\u001b[0m         \u001b[1;36m0.370433\u001b[0m         \u001b[1;36m0.666628\u001b[0m   \n",
+       "Scanorama             \u001b[1;36m0.204786\u001b[0m         \u001b[1;36m0.440984\u001b[0m          \u001b[1;36m0.67725\u001b[0m   \n",
+       "LIGER                 \u001b[1;36m0.810099\u001b[0m         \u001b[1;36m0.596425\u001b[0m          \u001b[1;36m0.60986\u001b[0m   \n",
+       "Harmony               \u001b[1;36m0.521998\u001b[0m         \u001b[1;36m0.544267\u001b[0m         \u001b[1;36m0.630447\u001b[0m   \n",
+       "scVI                  \u001b[1;36m0.871396\u001b[0m           \u001b[1;36m0.6272\u001b[0m         \u001b[1;36m0.662114\u001b[0m   \n",
+       "scANVI                \u001b[1;36m0.734378\u001b[0m         \u001b[1;36m0.596849\u001b[0m         \u001b[1;36m0.732067\u001b[0m   \n",
        "Metric Type   Batch correction  Aggregate score  Aggregate score   \n",
        "\n",
        "                        Total  \n",
        "Embedding                      \n",
-       "Unintegrated           \u001b[1;36m0.5258\u001b[0m  \n",
-       "Scanorama            \u001b[1;36m0.559847\u001b[0m  \n",
-       "LIGER                \u001b[1;36m0.596784\u001b[0m  \n",
-       "Harmony              \u001b[1;36m0.588678\u001b[0m  \n",
-       "scVI                 \u001b[1;36m0.623774\u001b[0m  \n",
-       "scANVI               \u001b[1;36m0.655182\u001b[0m  \n",
+       "Unintegrated          \u001b[1;36m0.54815\u001b[0m  \n",
+       "Scanorama            \u001b[1;36m0.582743\u001b[0m  \n",
+       "LIGER                \u001b[1;36m0.604486\u001b[0m  \n",
+       "Harmony              \u001b[1;36m0.595975\u001b[0m  \n",
+       "scVI                 \u001b[1;36m0.648148\u001b[0m  \n",
+       "scANVI                \u001b[1;36m0.67798\u001b[0m  \n",
        "Metric Type   Aggregate score  \n"
       ]
      },
@@ -561,7 +490,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "keep_output"
+    ]
+   },
    "outputs": [
     {
      "data": {
@@ -596,132 +529,132 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Isolated labels</th>\n",
-       "      <td>0.464394</td>\n",
-       "      <td>0.504315</td>\n",
-       "      <td>0.45722</td>\n",
-       "      <td>0.46885</td>\n",
-       "      <td>0.526255</td>\n",
-       "      <td>0.527354</td>\n",
+       "      <td>0.661278</td>\n",
+       "      <td>0.626327</td>\n",
+       "      <td>0.621488</td>\n",
+       "      <td>0.554801</td>\n",
+       "      <td>0.617493</td>\n",
+       "      <td>0.712235</td>\n",
        "      <td>Bio conservation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KMeans NMI</th>\n",
-       "      <td>0.654386</td>\n",
-       "      <td>0.685718</td>\n",
-       "      <td>0.593406</td>\n",
-       "      <td>0.610796</td>\n",
-       "      <td>0.597475</td>\n",
-       "      <td>0.709471</td>\n",
+       "      <td>0.659168</td>\n",
+       "      <td>0.704883</td>\n",
+       "      <td>0.570878</td>\n",
+       "      <td>0.606546</td>\n",
+       "      <td>0.650634</td>\n",
+       "      <td>0.747422</td>\n",
        "      <td>Bio conservation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KMeans ARI</th>\n",
-       "      <td>0.442916</td>\n",
-       "      <td>0.450299</td>\n",
-       "      <td>0.408749</td>\n",
-       "      <td>0.440707</td>\n",
-       "      <td>0.452404</td>\n",
-       "      <td>0.602122</td>\n",
+       "      <td>0.427279</td>\n",
+       "      <td>0.490931</td>\n",
+       "      <td>0.329819</td>\n",
+       "      <td>0.432499</td>\n",
+       "      <td>0.509873</td>\n",
+       "      <td>0.587875</td>\n",
        "      <td>Bio conservation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Silhouette label</th>\n",
        "      <td>0.585415</td>\n",
-       "      <td>0.564586</td>\n",
+       "      <td>0.564498</td>\n",
        "      <td>0.542106</td>\n",
-       "      <td>0.56432</td>\n",
-       "      <td>0.543061</td>\n",
-       "      <td>0.595713</td>\n",
+       "      <td>0.564499</td>\n",
+       "      <td>0.538018</td>\n",
+       "      <td>0.613172</td>\n",
        "      <td>Bio conservation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cLISI</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.999625</td>\n",
-       "      <td>0.985223</td>\n",
-       "      <td>0.994074</td>\n",
-       "      <td>0.994309</td>\n",
-       "      <td>0.998238</td>\n",
+       "      <td>0.99961</td>\n",
+       "      <td>0.98501</td>\n",
+       "      <td>0.993888</td>\n",
+       "      <td>0.994553</td>\n",
+       "      <td>0.999632</td>\n",
        "      <td>Bio conservation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Silhouette batch</th>\n",
        "      <td>0.855258</td>\n",
-       "      <td>0.932109</td>\n",
+       "      <td>0.932364</td>\n",
        "      <td>0.850703</td>\n",
-       "      <td>0.878664</td>\n",
-       "      <td>0.897465</td>\n",
-       "      <td>0.884396</td>\n",
+       "      <td>0.879939</td>\n",
+       "      <td>0.895502</td>\n",
+       "      <td>0.884228</td>\n",
        "      <td>Batch correction</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>iLISI</th>\n",
-       "      <td>0.007811</td>\n",
-       "      <td>0.064213</td>\n",
-       "      <td>0.161164</td>\n",
-       "      <td>0.12853</td>\n",
-       "      <td>0.119576</td>\n",
-       "      <td>0.110701</td>\n",
+       "      <td>0.008071</td>\n",
+       "      <td>0.06491</td>\n",
+       "      <td>0.16332</td>\n",
+       "      <td>0.130195</td>\n",
+       "      <td>0.124561</td>\n",
+       "      <td>0.095614</td>\n",
        "      <td>Batch correction</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KBET</th>\n",
-       "      <td>0.223815</td>\n",
-       "      <td>0.308119</td>\n",
-       "      <td>0.463056</td>\n",
-       "      <td>0.44529</td>\n",
-       "      <td>0.331022</td>\n",
-       "      <td>0.339256</td>\n",
+       "      <td>0.223882</td>\n",
+       "      <td>0.313843</td>\n",
+       "      <td>0.463284</td>\n",
+       "      <td>0.432907</td>\n",
+       "      <td>0.347675</td>\n",
+       "      <td>0.337137</td>\n",
        "      <td>Batch correction</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Graph connectivity</th>\n",
        "      <td>0.764953</td>\n",
-       "      <td>0.675554</td>\n",
+       "      <td>0.689019</td>\n",
        "      <td>0.694717</td>\n",
-       "      <td>0.765538</td>\n",
-       "      <td>0.899629</td>\n",
-       "      <td>0.907899</td>\n",
+       "      <td>0.756294</td>\n",
+       "      <td>0.896865</td>\n",
+       "      <td>0.932888</td>\n",
        "      <td>Batch correction</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PCR comparison</th>\n",
        "      <td>0</td>\n",
-       "      <td>0.21128</td>\n",
+       "      <td>0.204786</td>\n",
        "      <td>0.810099</td>\n",
-       "      <td>0.522324</td>\n",
-       "      <td>0.879229</td>\n",
-       "      <td>0.798182</td>\n",
+       "      <td>0.521998</td>\n",
+       "      <td>0.871396</td>\n",
+       "      <td>0.734378</td>\n",
        "      <td>Batch correction</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Batch correction</th>\n",
-       "      <td>0.370367</td>\n",
-       "      <td>0.438255</td>\n",
-       "      <td>0.595948</td>\n",
-       "      <td>0.548069</td>\n",
-       "      <td>0.625384</td>\n",
-       "      <td>0.608087</td>\n",
+       "      <td>0.370433</td>\n",
+       "      <td>0.440984</td>\n",
+       "      <td>0.596425</td>\n",
+       "      <td>0.544267</td>\n",
+       "      <td>0.6272</td>\n",
+       "      <td>0.596849</td>\n",
        "      <td>Aggregate score</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Bio conservation</th>\n",
-       "      <td>0.629422</td>\n",
-       "      <td>0.640908</td>\n",
-       "      <td>0.597341</td>\n",
-       "      <td>0.61575</td>\n",
-       "      <td>0.622701</td>\n",
-       "      <td>0.686579</td>\n",
+       "      <td>0.666628</td>\n",
+       "      <td>0.67725</td>\n",
+       "      <td>0.60986</td>\n",
+       "      <td>0.630447</td>\n",
+       "      <td>0.662114</td>\n",
+       "      <td>0.732067</td>\n",
        "      <td>Aggregate score</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Total</th>\n",
-       "      <td>0.5258</td>\n",
-       "      <td>0.559847</td>\n",
-       "      <td>0.596784</td>\n",
-       "      <td>0.588678</td>\n",
-       "      <td>0.623774</td>\n",
-       "      <td>0.655182</td>\n",
+       "      <td>0.54815</td>\n",
+       "      <td>0.582743</td>\n",
+       "      <td>0.604486</td>\n",
+       "      <td>0.595975</td>\n",
+       "      <td>0.648148</td>\n",
+       "      <td>0.67798</td>\n",
        "      <td>Aggregate score</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -730,37 +663,37 @@
       ],
       "text/plain": [
        "Embedding          Unintegrated Scanorama     LIGER   Harmony      scVI  \\\n",
-       "Isolated labels        0.464394  0.504315   0.45722   0.46885  0.526255   \n",
-       "KMeans NMI             0.654386  0.685718  0.593406  0.610796  0.597475   \n",
-       "KMeans ARI             0.442916  0.450299  0.408749  0.440707  0.452404   \n",
-       "Silhouette label       0.585415  0.564586  0.542106   0.56432  0.543061   \n",
-       "cLISI                       1.0  0.999625  0.985223  0.994074  0.994309   \n",
-       "Silhouette batch       0.855258  0.932109  0.850703  0.878664  0.897465   \n",
-       "iLISI                  0.007811  0.064213  0.161164   0.12853  0.119576   \n",
-       "KBET                   0.223815  0.308119  0.463056   0.44529  0.331022   \n",
-       "Graph connectivity     0.764953  0.675554  0.694717  0.765538  0.899629   \n",
-       "PCR comparison                0   0.21128  0.810099  0.522324  0.879229   \n",
-       "Batch correction       0.370367  0.438255  0.595948  0.548069  0.625384   \n",
-       "Bio conservation       0.629422  0.640908  0.597341   0.61575  0.622701   \n",
-       "Total                    0.5258  0.559847  0.596784  0.588678  0.623774   \n",
+       "Isolated labels        0.661278  0.626327  0.621488  0.554801  0.617493   \n",
+       "KMeans NMI             0.659168  0.704883  0.570878  0.606546  0.650634   \n",
+       "KMeans ARI             0.427279  0.490931  0.329819  0.432499  0.509873   \n",
+       "Silhouette label       0.585415  0.564498  0.542106  0.564499  0.538018   \n",
+       "cLISI                       1.0   0.99961   0.98501  0.993888  0.994553   \n",
+       "Silhouette batch       0.855258  0.932364  0.850703  0.879939  0.895502   \n",
+       "iLISI                  0.008071   0.06491   0.16332  0.130195  0.124561   \n",
+       "KBET                   0.223882  0.313843  0.463284  0.432907  0.347675   \n",
+       "Graph connectivity     0.764953  0.689019  0.694717  0.756294  0.896865   \n",
+       "PCR comparison                0  0.204786  0.810099  0.521998  0.871396   \n",
+       "Batch correction       0.370433  0.440984  0.596425  0.544267    0.6272   \n",
+       "Bio conservation       0.666628   0.67725   0.60986  0.630447  0.662114   \n",
+       "Total                   0.54815  0.582743  0.604486  0.595975  0.648148   \n",
        "\n",
        "Embedding             scANVI       Metric Type  \n",
-       "Isolated labels     0.527354  Bio conservation  \n",
-       "KMeans NMI          0.709471  Bio conservation  \n",
-       "KMeans ARI          0.602122  Bio conservation  \n",
-       "Silhouette label    0.595713  Bio conservation  \n",
-       "cLISI               0.998238  Bio conservation  \n",
-       "Silhouette batch    0.884396  Batch correction  \n",
-       "iLISI               0.110701  Batch correction  \n",
-       "KBET                0.339256  Batch correction  \n",
-       "Graph connectivity  0.907899  Batch correction  \n",
-       "PCR comparison      0.798182  Batch correction  \n",
-       "Batch correction    0.608087   Aggregate score  \n",
-       "Bio conservation    0.686579   Aggregate score  \n",
-       "Total               0.655182   Aggregate score  "
+       "Isolated labels     0.712235  Bio conservation  \n",
+       "KMeans NMI          0.747422  Bio conservation  \n",
+       "KMeans ARI          0.587875  Bio conservation  \n",
+       "Silhouette label    0.613172  Bio conservation  \n",
+       "cLISI               0.999632  Bio conservation  \n",
+       "Silhouette batch    0.884228  Batch correction  \n",
+       "iLISI               0.095614  Batch correction  \n",
+       "KBET                0.337137  Batch correction  \n",
+       "Graph connectivity  0.932888  Batch correction  \n",
+       "PCR comparison      0.734378  Batch correction  \n",
+       "Batch correction    0.596849   Aggregate score  \n",
+       "Bio conservation    0.732067   Aggregate score  \n",
+       "Total                0.67798   Aggregate score  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -768,13 +701,6 @@
    "source": [
     "df.transpose()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/src/scib_metrics/_kbet.py b/src/scib_metrics/_kbet.py
index a16ef04..fdb450d 100644
--- a/src/scib_metrics/_kbet.py
+++ b/src/scib_metrics/_kbet.py
@@ -160,12 +160,12 @@ def kbet_per_label(
             if k0 * n_obs >= size_max:
                 k0 = np.floor(size_max / n_obs).astype("int")
 
-            n_comp, labs = scipy.sparse.csgraph.connected_components(conn_graph, connection="strong")
+            n_comp, labs = scipy.sparse.csgraph.connected_components(conn_graph_sub, connection="strong")
 
             if n_comp == 1:  # a single component to compute kBET on
                 try:
                     diffusion_n_comps = np.min([diffusion_n_comps, n_obs - 1])
-                    nn_graph_sub = diffusion_nn(conn_graph, k=k0, n_comps=diffusion_n_comps)
+                    nn_graph_sub = diffusion_nn(conn_graph_sub, k=k0, n_comps=diffusion_n_comps)
                     # call kBET
                     score, _, _ = kbet(
                         nn_graph_sub,
@@ -186,12 +186,12 @@ def kbet_per_label(
                 # check if 75% of all cells can be used for kBET run
                 if len(idx_nonan) / len(labs) >= 0.75:
                     # create another subset of components, assume they are not visited in a diffusion process
-                    conn_graph_sub_sub_sub = conn_graph_sub[idx_nonan, :][:, idx_nonan]
-                    conn_graph_sub_sub_sub.sort_indices()
+                    conn_graph_sub_sub = conn_graph_sub[idx_nonan, :][:, idx_nonan]
+                    conn_graph_sub_sub.sort_indices()
 
                     try:
-                        diffusion_n_comps = np.min([diffusion_n_comps, conn_graph_sub_sub_sub.shape[0] - 1])
-                        nn_results_sub_sub = diffusion_nn(conn_graph_sub_sub_sub, k=k0, n_comps=diffusion_n_comps)
+                        diffusion_n_comps = np.min([diffusion_n_comps, conn_graph_sub_sub.shape[0] - 1])
+                        nn_results_sub_sub = diffusion_nn(conn_graph_sub_sub, k=k0, n_comps=diffusion_n_comps)
                         # call kBET
                         score, _, _ = kbet(
                             nn_results_sub_sub,
diff --git a/src/scib_metrics/nearest_neighbors/_dataclass.py b/src/scib_metrics/nearest_neighbors/_dataclass.py
index f12b637..47a61df 100644
--- a/src/scib_metrics/nearest_neighbors/_dataclass.py
+++ b/src/scib_metrics/nearest_neighbors/_dataclass.py
@@ -67,4 +67,4 @@ def knn_graph_connectivities(self) -> coo_matrix:
     def subset_neighbors(self, n: int) -> "NeighborsResults":
         if n > self.n_neighbors:
             raise ValueError("n must be smaller than the number of neighbors")
-        return self.__class__(self.distances[:, :n], self.indices[:, :n])
+        return self.__class__(indices=self.indices[:, :n], distances=self.distances[:, :n])
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 39d0e5f..bac2fbc 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -72,7 +72,7 @@ def test_lisi_knn():
     harmonypy_lisi_res = harmonypy_lisi(
         X, pd.DataFrame(labels, columns=["labels"]), label_colnames=["labels"], perplexity=10
     )[:, 0]
-    assert np.allclose(lisi_res, harmonypy_lisi_res)
+    assert np.testing.assert_allclose(lisi_res, harmonypy_lisi_res)
 
 
 def test_ilisi_clisi_knn():
diff --git a/tests/test_neighbors.py b/tests/test_neighbors.py
index 00e1c24..e59f709 100644
--- a/tests/test_neighbors.py
+++ b/tests/test_neighbors.py
@@ -1,4 +1,8 @@
-from scib_metrics.nearest_neighbors import jax_approx_min_k
+import numpy as np
+import pytest
+import scanpy as sc
+
+from scib_metrics.nearest_neighbors import jax_approx_min_k, pynndescent
 from tests.utils.data import dummy_benchmarker_adata
 
 
@@ -6,3 +10,19 @@ def test_jax_neighbors():
     ad, emb_keys, _, _ = dummy_benchmarker_adata()
     output = jax_approx_min_k(ad.obsm[emb_keys[0]], 10)
     assert output.distances.shape == (ad.n_obs, 10)
+
+
+@pytest.mark.parametrize("n", [5, 10, 20, 21])
+def test_neighbors_results(n):
+    adata, embedding_keys, *_ = dummy_benchmarker_adata()
+    neigh_result = pynndescent(adata.obsm[embedding_keys[0]], n_neighbors=n)
+    neigh_result = neigh_result.subset_neighbors(n=n)
+    new_connect = neigh_result.knn_graph_connectivities
+    new_dist = neigh_result.knn_graph_distances
+
+    sc_dist, sc_connect = sc.neighbors._compute_connectivities_umap(
+        neigh_result.indices[:, :n], neigh_result.distances[:, :n], adata.n_obs, n_neighbors=n
+    )
+
+    np.testing.assert_allclose(new_dist.A, sc_dist.A)
+    np.testing.assert_allclose(new_connect.A, sc_connect.A)

From 1a5d0f90a7811ab808dbec13c376c6184fc449ba Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Thu, 28 Dec 2023 04:25:33 +0000
Subject: [PATCH 10/24] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 docs/notebooks/large_scale.ipynb | 776 ++++++++++++-------------------
 1 file changed, 304 insertions(+), 472 deletions(-)

diff --git a/docs/notebooks/large_scale.ipynb b/docs/notebooks/large_scale.ipynb
index 6d781b3..72ef8a9 100644
--- a/docs/notebooks/large_scale.ipynb
+++ b/docs/notebooks/large_scale.ipynb
@@ -1,474 +1,306 @@
 {
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Benchmarking large-scale integration"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Here we walkthrough applying the integration benchmarking metrics on a dataset of non-small cell lung cancer from the following paper:\n",
-                "\n",
-                "Salcher, S., Sturm, G., Horvath, L., Untergasser, G., Kuempers, C., Fotakis, G., ... & Trajanoski, Z. (2022). High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer Cell.\n",
-                "\n",
-                "This dataset contains ~900,000 cells, so here we demonstrate the scalbility of the metrics here on large data."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Global seed set to 0\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import numpy as np\n",
-                "import scanpy as sc\n",
-                "from scvi.data import cellxgene\n",
-                "\n",
-                "from scib_metrics.benchmark import Benchmarker, BioConservation\n",
-                "\n",
-                "%matplotlib inline"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Load and preprocess data"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "\u001b[34mINFO    \u001b[0m File data/luca.h5ad already downloaded                                                                    \n"
-                    ]
-                }
-            ],
-            "source": [
-                "url = \"https://cellxgene.cziscience.com/e/232f6a5a-a04c-4758-a6e8-88ab2e3a6e69.cxg/\"\n",
-                "adata = cellxgene(url, filename=\"luca.h5ad\", save_path=\"data/\")"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "AnnData object with n_obs × n_vars = 892296 × 17811\n",
-                            "    obs: 'sample', 'uicc_stage', 'ever_smoker', 'age', 'donor_id', 'origin', 'dataset', 'ann_fine', 'cell_type_predicted', 'doublet_status', 'leiden', 'n_genes_by_counts', 'total_counts', 'total_counts_mito', 'pct_counts_mito', 'ann_coarse', 'cell_type_tumor', 'tumor_stage', 'EGFR_mutation', 'TP53_mutation', 'ALK_mutation', 'BRAF_mutation', 'ERBB2_mutation', 'KRAS_mutation', 'ROS_mutation', 'origin_fine', 'study', 'platform', 'cell_type_major', 'suspension_type', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'is_primary_data', 'organism_ontology_term_id', 'sex_ontology_term_id', 'tissue_ontology_term_id', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage'\n",
-                            "    var: 'is_highly_variable', 'mito', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype'\n",
-                            "    uns: '_scvi', 'ann_fine_colors', 'batch_condition', 'cell_type_major_colors', 'cell_type_ontology_term_id_colors', 'cell_type_predicted_colors', 'dataset_colors', 'default_embedding', 'hvg', 'leiden_colors', 'neighbors', 'origin_colors', 'platform_colors', 'schema_version', 'title', 'umap'\n",
-                            "    obsm: 'X_scANVI', 'X_scVI', 'X_umap'\n",
-                            "    layers: 'count', 'counts_length_scaled'\n",
-                            "    obsp: 'connectivities', 'distances'"
-                        ]
-                    },
-                    "execution_count": 3,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "adata"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "33"
-                        ]
-                    },
-                    "execution_count": 4,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "# Number of unique cell types\n",
-                "adata.obs[\"cell_type\"].nunique()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "adata.var[\"highly_variable\"] = np.asarray(adata.var[\"is_highly_variable\"].astype(bool))\n",
-                "sc.tl.pca(adata)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## \"Run\" methods"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "The authors used scVI and scANVI in their manuscript, and these embeddings are already stored in the AnnData object. We can use these to run the metrics."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### scVI"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "adata.obsm[\"scVI\"] = adata.obsm[\"X_scVI\"]"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### scANVI"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "adata.obsm[\"scANVI\"] = adata.obsm[\"X_scANVI\"]"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Perform the benchmark"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Here we use a custom nearest neighbor function to speed up the computation of the metrics. This is not necessary, but can be useful for large datasets.\n",
-                "\n",
-                "In particular we use [faiss](https://github.com/facebookresearch/faiss), which can be accelerated with a GPU.\n",
-                "\n",
-                "This can be installed as\n",
-                "\n",
-                "```bash\n",
-                "conda install -c conda-forge faiss-gpu\n",
-                "```\n",
-                "\n",
-                "When using approximate nearest neighbors, an issue can arise where each cell does not get a unique set of K neighbors. This issue happens with faiss hnsw below, so we use the brute force method instead, which is still faster than pynndescent approximate nearest neighbors on CPU."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import faiss\n",
-                "\n",
-                "from scib_metrics.nearest_neighbors import NeighborsResults\n",
-                "\n",
-                "\n",
-                "def faiss_hnsw_nn(X: np.ndarray, k: int):\n",
-                "    \"\"\"Gpu HNSW nearest neighbor search using faiss.\n",
-                "\n",
-                "    See https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md\n",
-                "    for index param details.\n",
-                "    \"\"\"\n",
-                "    X = np.ascontiguousarray(X, dtype=np.float32)\n",
-                "    res = faiss.StandardGpuResources()\n",
-                "    M = 32\n",
-                "    index = faiss.IndexHNSWFlat(X.shape[1], M, faiss.METRIC_L2)\n",
-                "    gpu_index = faiss.index_cpu_to_gpu(res, 0, index)\n",
-                "    gpu_index.add(X)\n",
-                "    distances, indices = gpu_index.search(X, k)\n",
-                "    del index\n",
-                "    del gpu_index\n",
-                "    # distances are squared\n",
-                "    return NeighborsResults(indices=indices, distances=np.sqrt(distances))\n",
-                "\n",
-                "\n",
-                "def faiss_brute_force_nn(X: np.ndarray, k: int):\n",
-                "    \"\"\"Gpu brute force nearest neighbor search using faiss.\"\"\"\n",
-                "    X = np.ascontiguousarray(X, dtype=np.float32)\n",
-                "    res = faiss.StandardGpuResources()\n",
-                "    index = faiss.IndexFlatL2(X.shape[1])\n",
-                "    gpu_index = faiss.index_cpu_to_gpu(res, 0, index)\n",
-                "    gpu_index.add(X)\n",
-                "    distances, indices = gpu_index.search(X, k)\n",
-                "    del index\n",
-                "    del gpu_index\n",
-                "    # distances are squared\n",
-                "    return NeighborsResults(indices=indices, distances=np.sqrt(distances))"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stderr",
-                    "output_type": "stream",
-                    "text": [
-                        "Computing neighbors: 100%|██████████| 3/3 [06:48<00:00, 136.22s/it]\n",
-                        "Embeddings: 100%|\u001b[32m██████████\u001b[0m| 3/3 [35:58<00:00, 719.52s/it]\n"
-                    ]
-                },
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Time: 42 min 47 sec\n"
-                    ]
-                }
-            ],
-            "source": [
-                "import time\n",
-                "\n",
-                "adata.obsm[\"Unintegrated\"] = adata.obsm[\"X_pca\"]\n",
-                "\n",
-                "biocons = BioConservation(isolated_labels=False)\n",
-                "\n",
-                "start = time.time()\n",
-                "bm = Benchmarker(\n",
-                "    adata,\n",
-                "    batch_key=\"sample\",\n",
-                "    label_key=\"cell_type\",\n",
-                "    embedding_obsm_keys=[\"Unintegrated\", \"scANVI\", \"scVI\"],\n",
-                "    pre_integrated_embedding_obsm_key=\"X_pca\",\n",
-                "    bio_conservation_metrics=biocons,\n",
-                "    n_jobs=-1,\n",
-                ")\n",
-                "bm.prepare(neighbor_computer=faiss_brute_force_nn)\n",
-                "bm.benchmark()\n",
-                "end = time.time()\n",
-                "print(f\"Time: {int((end - start) / 60)} min {int((end - start) % 60)} sec\")"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Visualize the results"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1500x390 with 10 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "<plottable.table.Table at 0x7eff759c84c0>"
-                        ]
-                    },
-                    "execution_count": 10,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "bm.plot_results_table()"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1500x390 with 10 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "<plottable.table.Table at 0x7eff75b6f430>"
-                        ]
-                    },
-                    "execution_count": 11,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "bm.plot_results_table(min_max_scale=False)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We can also access the underlying dataframes to print the results ourselves."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/html": [
-                            "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">                    KMeans NMI        KMeans ARI  Silhouette label  \\\n",
-                            "Embedding                                                            \n",
-                            "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.616205</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.35223</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.53138</span>   \n",
-                            "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.712114</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.423318</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.594688</span>   \n",
-                            "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.715713</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.472496</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.589948</span>   \n",
-                            "Metric Type   Bio conservation  Bio conservation  Bio conservation   \n",
-                            "\n",
-                            "                         cLISI  Silhouette batch             iLISI  \\\n",
-                            "Embedding                                                            \n",
-                            "Unintegrated           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.99968</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.865853</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.001673</span>   \n",
-                            "scANVI                     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.721484</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.008524</span>   \n",
-                            "scVI                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.732439</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.007782</span>   \n",
-                            "Metric Type   Bio conservation  Batch correction  Batch correction   \n",
-                            "\n",
-                            "                          KBET Graph connectivity    PCR comparison  \\\n",
-                            "Embedding                                                             \n",
-                            "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.337547</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.773024</span>               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>   \n",
-                            "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.459387</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.984871</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.436682</span>   \n",
-                            "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.446961</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.981359</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.414185</span>   \n",
-                            "Metric Type   Batch correction   Batch correction  Batch correction   \n",
-                            "\n",
-                            "             Batch correction Bio conservation            Total  \n",
-                            "Embedding                                                        \n",
-                            "Unintegrated         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.395619</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.624874</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.533172</span>  \n",
-                            "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.52219</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.68253</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.618394</span>  \n",
-                            "scVI                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.516545</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.694539</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.623342</span>  \n",
-                            "Metric Type   Aggregate score  Aggregate score  Aggregate score  \n",
-                            "</pre>\n"
-                        ],
-                        "text/plain": [
-                            "                    KMeans NMI        KMeans ARI  Silhouette label  \\\n",
-                            "Embedding                                                            \n",
-                            "Unintegrated          \u001b[1;36m0.616205\u001b[0m           \u001b[1;36m0.35223\u001b[0m           \u001b[1;36m0.53138\u001b[0m   \n",
-                            "scANVI                \u001b[1;36m0.712114\u001b[0m          \u001b[1;36m0.423318\u001b[0m          \u001b[1;36m0.594688\u001b[0m   \n",
-                            "scVI                  \u001b[1;36m0.715713\u001b[0m          \u001b[1;36m0.472496\u001b[0m          \u001b[1;36m0.589948\u001b[0m   \n",
-                            "Metric Type   Bio conservation  Bio conservation  Bio conservation   \n",
-                            "\n",
-                            "                         cLISI  Silhouette batch             iLISI  \\\n",
-                            "Embedding                                                            \n",
-                            "Unintegrated           \u001b[1;36m0.99968\u001b[0m          \u001b[1;36m0.865853\u001b[0m          \u001b[1;36m0.001673\u001b[0m   \n",
-                            "scANVI                     \u001b[1;36m1.0\u001b[0m          \u001b[1;36m0.721484\u001b[0m          \u001b[1;36m0.008524\u001b[0m   \n",
-                            "scVI                       \u001b[1;36m1.0\u001b[0m          \u001b[1;36m0.732439\u001b[0m          \u001b[1;36m0.007782\u001b[0m   \n",
-                            "Metric Type   Bio conservation  Batch correction  Batch correction   \n",
-                            "\n",
-                            "                          KBET Graph connectivity    PCR comparison  \\\n",
-                            "Embedding                                                             \n",
-                            "Unintegrated          \u001b[1;36m0.337547\u001b[0m           \u001b[1;36m0.773024\u001b[0m               \u001b[1;36m0.0\u001b[0m   \n",
-                            "scANVI                \u001b[1;36m0.459387\u001b[0m           \u001b[1;36m0.984871\u001b[0m          \u001b[1;36m0.436682\u001b[0m   \n",
-                            "scVI                  \u001b[1;36m0.446961\u001b[0m           \u001b[1;36m0.981359\u001b[0m          \u001b[1;36m0.414185\u001b[0m   \n",
-                            "Metric Type   Batch correction   Batch correction  Batch correction   \n",
-                            "\n",
-                            "             Batch correction Bio conservation            Total  \n",
-                            "Embedding                                                        \n",
-                            "Unintegrated         \u001b[1;36m0.395619\u001b[0m         \u001b[1;36m0.624874\u001b[0m         \u001b[1;36m0.533172\u001b[0m  \n",
-                            "scANVI                \u001b[1;36m0.52219\u001b[0m          \u001b[1;36m0.68253\u001b[0m         \u001b[1;36m0.618394\u001b[0m  \n",
-                            "scVI                 \u001b[1;36m0.516545\u001b[0m         \u001b[1;36m0.694539\u001b[0m         \u001b[1;36m0.623342\u001b[0m  \n",
-                            "Metric Type   Aggregate score  Aggregate score  Aggregate score  \n"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "from rich import print\n",
-                "\n",
-                "df = bm.get_results(min_max_scale=False)\n",
-                "print(df)"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "scib-metrics",
-            "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": "3.10.6"
-        },
-        "vscode": {
-            "interpreter": {
-                "hash": "a039041fbbc3e5d1b9a7b0ef55a3584523fcf26d826fdb9093db3636ab3124e1"
-            }
-        }
-    },
-    "nbformat": 4,
-    "nbformat_minor": 4
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Benchmarking large-scale integration"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we walkthrough applying the integration benchmarking metrics on a dataset of non-small cell lung cancer from the following paper:\n",
+    "\n",
+    "Salcher, S., Sturm, G., Horvath, L., Untergasser, G., Kuempers, C., Fotakis, G., ... & Trajanoski, Z. (2022). High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. Cancer Cell.\n",
+    "\n",
+    "This dataset contains ~900,000 cells, so here we demonstrate the scalbility of the metrics here on large data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import scanpy as sc\n",
+    "from scvi.data import cellxgene\n",
+    "\n",
+    "from scib_metrics.benchmark import Benchmarker, BioConservation\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load and preprocess data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://cellxgene.cziscience.com/e/232f6a5a-a04c-4758-a6e8-88ab2e3a6e69.cxg/\"\n",
+    "adata = cellxgene(url, filename=\"luca.h5ad\", save_path=\"data/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Number of unique cell types\n",
+    "adata.obs[\"cell_type\"].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata.var[\"highly_variable\"] = np.asarray(adata.var[\"is_highly_variable\"].astype(bool))\n",
+    "sc.tl.pca(adata)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## \"Run\" methods"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The authors used scVI and scANVI in their manuscript, and these embeddings are already stored in the AnnData object. We can use these to run the metrics."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### scVI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata.obsm[\"scVI\"] = adata.obsm[\"X_scVI\"]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### scANVI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata.obsm[\"scANVI\"] = adata.obsm[\"X_scANVI\"]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Perform the benchmark"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we use a custom nearest neighbor function to speed up the computation of the metrics. This is not necessary, but can be useful for large datasets.\n",
+    "\n",
+    "In particular we use [faiss](https://github.com/facebookresearch/faiss), which can be accelerated with a GPU.\n",
+    "\n",
+    "This can be installed as\n",
+    "\n",
+    "```bash\n",
+    "conda install -c conda-forge faiss-gpu\n",
+    "```\n",
+    "\n",
+    "When using approximate nearest neighbors, an issue can arise where each cell does not get a unique set of K neighbors. This issue happens with faiss hnsw below, so we use the brute force method instead, which is still faster than pynndescent approximate nearest neighbors on CPU."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import faiss\n",
+    "\n",
+    "from scib_metrics.nearest_neighbors import NeighborsResults\n",
+    "\n",
+    "\n",
+    "def faiss_hnsw_nn(X: np.ndarray, k: int):\n",
+    "    \"\"\"Gpu HNSW nearest neighbor search using faiss.\n",
+    "\n",
+    "    See https://github.com/nmslib/hnswlib/blob/master/ALGO_PARAMS.md\n",
+    "    for index param details.\n",
+    "    \"\"\"\n",
+    "    X = np.ascontiguousarray(X, dtype=np.float32)\n",
+    "    res = faiss.StandardGpuResources()\n",
+    "    M = 32\n",
+    "    index = faiss.IndexHNSWFlat(X.shape[1], M, faiss.METRIC_L2)\n",
+    "    gpu_index = faiss.index_cpu_to_gpu(res, 0, index)\n",
+    "    gpu_index.add(X)\n",
+    "    distances, indices = gpu_index.search(X, k)\n",
+    "    del index\n",
+    "    del gpu_index\n",
+    "    # distances are squared\n",
+    "    return NeighborsResults(indices=indices, distances=np.sqrt(distances))\n",
+    "\n",
+    "\n",
+    "def faiss_brute_force_nn(X: np.ndarray, k: int):\n",
+    "    \"\"\"Gpu brute force nearest neighbor search using faiss.\"\"\"\n",
+    "    X = np.ascontiguousarray(X, dtype=np.float32)\n",
+    "    res = faiss.StandardGpuResources()\n",
+    "    index = faiss.IndexFlatL2(X.shape[1])\n",
+    "    gpu_index = faiss.index_cpu_to_gpu(res, 0, index)\n",
+    "    gpu_index.add(X)\n",
+    "    distances, indices = gpu_index.search(X, k)\n",
+    "    del index\n",
+    "    del gpu_index\n",
+    "    # distances are squared\n",
+    "    return NeighborsResults(indices=indices, distances=np.sqrt(distances))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "adata.obsm[\"Unintegrated\"] = adata.obsm[\"X_pca\"]\n",
+    "\n",
+    "biocons = BioConservation(isolated_labels=False)\n",
+    "\n",
+    "start = time.time()\n",
+    "bm = Benchmarker(\n",
+    "    adata,\n",
+    "    batch_key=\"sample\",\n",
+    "    label_key=\"cell_type\",\n",
+    "    embedding_obsm_keys=[\"Unintegrated\", \"scANVI\", \"scVI\"],\n",
+    "    pre_integrated_embedding_obsm_key=\"X_pca\",\n",
+    "    bio_conservation_metrics=biocons,\n",
+    "    n_jobs=-1,\n",
+    ")\n",
+    "bm.prepare(neighbor_computer=faiss_brute_force_nn)\n",
+    "bm.benchmark()\n",
+    "end = time.time()\n",
+    "print(f\"Time: {int((end - start) / 60)} min {int((end - start) % 60)} sec\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bm.plot_results_table()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bm.plot_results_table(min_max_scale=False)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also access the underlying dataframes to print the results ourselves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rich import print\n",
+    "\n",
+    "df = bm.get_results(min_max_scale=False)\n",
+    "print(df)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "scib-metrics",
+   "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": "3.10.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "a039041fbbc3e5d1b9a7b0ef55a3584523fcf26d826fdb9093db3636ab3124e1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }

From 78aaca2ab5f9e8df287090b028eea4354a6ab62e Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 28 Dec 2023 04:32:39 +0000
Subject: [PATCH 11/24] fix test

---
 docs/notebooks/large_scale.ipynb | 38 ++++++++++++++++++++++++++------
 tests/test_metrics.py            |  5 +++--
 2 files changed, 34 insertions(+), 9 deletions(-)

diff --git a/docs/notebooks/large_scale.ipynb b/docs/notebooks/large_scale.ipynb
index 72ef8a9..87ea804 100644
--- a/docs/notebooks/large_scale.ipynb
+++ b/docs/notebooks/large_scale.ipynb
@@ -56,7 +56,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "keep_output"
+    ]
+   },
    "outputs": [],
    "source": [
     "adata"
@@ -65,7 +69,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "keep_output"
+    ]
+   },
    "outputs": [],
    "source": [
     "# Number of unique cell types\n",
@@ -205,7 +213,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "keep_output"
+    ]
+   },
    "outputs": [],
    "source": [
     "import time\n",
@@ -241,7 +253,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "keep_output"
+    ]
+   },
    "outputs": [],
    "source": [
     "bm.plot_results_table()"
@@ -250,7 +266,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "keep_output"
+    ]
+   },
    "outputs": [],
    "source": [
     "bm.plot_results_table(min_max_scale=False)"
@@ -267,7 +287,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "keep_output"
+    ]
+   },
    "outputs": [],
    "source": [
     "from rich import print\n",
@@ -293,7 +317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.10.13"
   },
   "vscode": {
    "interpreter": {
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index bac2fbc..a46da0a 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -58,8 +58,9 @@ def test_compute_simpson_index():
     D = scib_metrics.utils.cdist(X, X)
     nbrs = NearestNeighbors(n_neighbors=30, algorithm="kd_tree").fit(X)
     D, knn_idx = nbrs.kneighbors(X)
+    row_idx = np.arange(X.shape[0])[:, None]
     scib_metrics.utils.compute_simpson_index(
-        jnp.array(D), jnp.array(knn_idx), jnp.array(labels), len(np.unique(labels))
+        jnp.array(D), jnp.array(knn_idx), jnp.array(row_idx), jnp.array(labels), len(np.unique(labels))
     )
 
 
@@ -72,7 +73,7 @@ def test_lisi_knn():
     harmonypy_lisi_res = harmonypy_lisi(
         X, pd.DataFrame(labels, columns=["labels"]), label_colnames=["labels"], perplexity=10
     )[:, 0]
-    assert np.testing.assert_allclose(lisi_res, harmonypy_lisi_res)
+    np.testing.assert_allclose(lisi_res, harmonypy_lisi_res)
 
 
 def test_ilisi_clisi_knn():

From 578e5c18f25039682d637b79d4797ea5477f607e Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 28 Dec 2023 04:35:52 +0000
Subject: [PATCH 12/24] fix test

---
 tests/test_metrics.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index a46da0a..d5bfa54 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -73,7 +73,10 @@ def test_lisi_knn():
     harmonypy_lisi_res = harmonypy_lisi(
         X, pd.DataFrame(labels, columns=["labels"]), label_colnames=["labels"], perplexity=10
     )[:, 0]
-    np.testing.assert_allclose(lisi_res, harmonypy_lisi_res)
+    # Slight numerical differences arise due to how self edges are handled. With approximate nearest
+    # neighbors methods, there is no guarantee that the self edge is the first edge. To accomodate this,
+    # we mask out self edges internally in lisi computation which causes slight numerical differences.
+    np.testing.assert_allclose(lisi_res, harmonypy_lisi_res, rtol=5e-5, atol=5e-5)
 
 
 def test_ilisi_clisi_knn():

From 125ef849f7803e0fb0dec5fe40abf898d1eefa5d Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 28 Dec 2023 04:50:34 +0000
Subject: [PATCH 13/24] finalize

---
 src/scib_metrics/nearest_neighbors/_dataclass.py | 7 +++++--
 tests/test_metrics.py                            | 2 +-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/src/scib_metrics/nearest_neighbors/_dataclass.py b/src/scib_metrics/nearest_neighbors/_dataclass.py
index 47a61df..f7119db 100644
--- a/src/scib_metrics/nearest_neighbors/_dataclass.py
+++ b/src/scib_metrics/nearest_neighbors/_dataclass.py
@@ -28,11 +28,13 @@ def __post_init__(self):
         chex.assert_equal_shape([self.indices, self.distances])
 
     @property
-    def n_samples(self):
+    def n_samples(self) -> np.ndarray:
+        """Number of samples (cells)."""
         return self.indices.shape[0]
 
     @property
-    def n_neighbors(self):
+    def n_neighbors(self) -> np.ndarray:
+        """Number of neighbors."""
         return self.indices.shape[1]
 
     @cached_property
@@ -65,6 +67,7 @@ def knn_graph_connectivities(self) -> coo_matrix:
         return connectivities[0]
 
     def subset_neighbors(self, n: int) -> "NeighborsResults":
+        """Subset down to `n` neighbors."""
         if n > self.n_neighbors:
             raise ValueError("n must be smaller than the number of neighbors")
         return self.__class__(indices=self.indices[:, :n], distances=self.distances[:, :n])
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index d5bfa54..c327a06 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -74,7 +74,7 @@ def test_lisi_knn():
         X, pd.DataFrame(labels, columns=["labels"]), label_colnames=["labels"], perplexity=10
     )[:, 0]
     # Slight numerical differences arise due to how self edges are handled. With approximate nearest
-    # neighbors methods, there is no guarantee that the self edge is the first edge. To accomodate this,
+    # neighbors methods, there is no guarantee that the self edge is the first edge. To accommodate this,
     # we mask out self edges internally in lisi computation which causes slight numerical differences.
     np.testing.assert_allclose(lisi_res, harmonypy_lisi_res, rtol=5e-5, atol=5e-5)
 

From b241ec766c8bea80f8aecc381ca5d9ccfe5b26c4 Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 28 Dec 2023 04:54:33 +0000
Subject: [PATCH 14/24] docstring

---
 src/scib_metrics/nearest_neighbors/_dataclass.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/scib_metrics/nearest_neighbors/_dataclass.py b/src/scib_metrics/nearest_neighbors/_dataclass.py
index f7119db..14e5a26 100644
--- a/src/scib_metrics/nearest_neighbors/_dataclass.py
+++ b/src/scib_metrics/nearest_neighbors/_dataclass.py
@@ -50,7 +50,7 @@ def knn_graph_distances(self) -> csr_matrix:
     def knn_graph_connectivities(self) -> coo_matrix:
         """Compute connectivities using the UMAP approach.
 
-        This function computes connectivities (similarities) from distances
+        Connectivities (similarities) are computed from distances
         using the approach from the UMAP method, which is also used by scanpy.
         """
         conn_graph = coo_matrix(([], ([], [])), shape=(self.n_samples, 1))

From 96becb47b28073f64fea6bf8de53614d682e6cb5 Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 28 Dec 2023 16:10:39 +0000
Subject: [PATCH 15/24] finalize

---
 docs/notebooks/large_scale.ipynb | 187 ++++++++++++++++++++++++++++++-
 src/scib_metrics/utils/_pcr.py   |  13 +--
 tests/test_metrics.py            |  11 +-
 3 files changed, 192 insertions(+), 19 deletions(-)

diff --git a/docs/notebooks/large_scale.ipynb b/docs/notebooks/large_scale.ipynb
index 87ea804..46e9743 100644
--- a/docs/notebooks/large_scale.ipynb
+++ b/docs/notebooks/large_scale.ipynb
@@ -61,11 +61,37 @@
      "keep_output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 892296 × 17811\n",
+       "    obs: 'sample', 'uicc_stage', 'ever_smoker', 'age', 'donor_id', 'origin', 'dataset', 'ann_fine', 'cell_type_predicted', 'doublet_status', 'leiden', 'n_genes_by_counts', 'total_counts', 'total_counts_mito', 'pct_counts_mito', 'ann_coarse', 'cell_type_tumor', 'tumor_stage', 'EGFR_mutation', 'TP53_mutation', 'ALK_mutation', 'BRAF_mutation', 'ERBB2_mutation', 'KRAS_mutation', 'ROS_mutation', 'origin_fine', 'study', 'platform', 'cell_type_major', 'suspension_type', 'assay_ontology_term_id', 'cell_type_ontology_term_id', 'development_stage_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'is_primary_data', 'organism_ontology_term_id', 'sex_ontology_term_id', 'tissue_ontology_term_id', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage'\n",
+       "    var: 'is_highly_variable', 'mito', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype'\n",
+       "    uns: '_scvi', 'ann_fine_colors', 'batch_condition', 'cell_type_major_colors', 'cell_type_ontology_term_id_colors', 'cell_type_predicted_colors', 'dataset_colors', 'default_embedding', 'hvg', 'leiden_colors', 'neighbors', 'origin_colors', 'platform_colors', 'schema_version', 'title', 'umap'\n",
+       "    obsm: 'X_scANVI', 'X_scVI', 'X_umap'\n",
+       "    layers: 'count', 'counts_length_scaled'\n",
+       "    obsp: 'connectivities', 'distances'"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "adata"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pp.subsample(adata, n_obs=500_000)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -74,7 +100,18 @@
      "keep_output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Number of unique cell types\n",
     "adata.obs[\"cell_type\"].nunique()"
@@ -218,7 +255,30 @@
      "keep_output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Computing neighbors:   0%|          | 0/3 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Computing neighbors: 100%|██████████| 3/3 [00:25<00:00,  8.57s/it]\n",
+      "Embeddings: 100%|\u001b[32m██████████\u001b[0m| 3/3 [20:19<00:00, 406.57s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time: 20 min 45 sec\n"
+     ]
+    }
+   ],
    "source": [
     "import time\n",
     "\n",
@@ -258,7 +318,28 @@
      "keep_output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x390 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<plottable.table.Table at 0x7f2642db39a0>"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bm.plot_results_table()"
    ]
@@ -271,7 +352,28 @@
      "keep_output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x390 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<plottable.table.Table at 0x7f2714881870>"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bm.plot_results_table(min_max_scale=False)"
    ]
@@ -292,13 +394,86 @@
      "keep_output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">                    KMeans NMI        KMeans ARI  Silhouette label  \\\n",
+       "Embedding                                                            \n",
+       "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.630967</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.372678</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.531423</span>   \n",
+       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.744273</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.485717</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.594908</span>   \n",
+       "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.722253</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.435955</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.590104</span>   \n",
+       "Metric Type   Bio conservation  Bio conservation  Bio conservation   \n",
+       "\n",
+       "                         cLISI  Silhouette batch             iLISI  \\\n",
+       "Embedding                                                            \n",
+       "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.999528</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.861078</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.002044</span>   \n",
+       "scANVI                     <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.710921</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.009262</span>   \n",
+       "scVI                       <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.721547</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.008491</span>   \n",
+       "Metric Type   Bio conservation  Batch correction  Batch correction   \n",
+       "\n",
+       "                          KBET Graph connectivity    PCR comparison  \\\n",
+       "Embedding                                                             \n",
+       "Unintegrated          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.350416</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.733284</span>               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>   \n",
+       "scANVI                <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.504855</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.981208</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.428348</span>   \n",
+       "scVI                  <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.490899</span>           <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.980226</span>          <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.403148</span>   \n",
+       "Metric Type   Batch correction   Batch correction  Batch correction   \n",
+       "\n",
+       "             Batch correction Bio conservation            Total  \n",
+       "Embedding                                                        \n",
+       "Unintegrated         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.389365</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.633649</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.535935</span>  \n",
+       "scANVI               <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.526919</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.706225</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.634502</span>  \n",
+       "scVI                 <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.520862</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.687078</span>         <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.620592</span>  \n",
+       "Metric Type   Aggregate score  Aggregate score  Aggregate score  \n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "                    KMeans NMI        KMeans ARI  Silhouette label  \\\n",
+       "Embedding                                                            \n",
+       "Unintegrated          \u001b[1;36m0.630967\u001b[0m          \u001b[1;36m0.372678\u001b[0m          \u001b[1;36m0.531423\u001b[0m   \n",
+       "scANVI                \u001b[1;36m0.744273\u001b[0m          \u001b[1;36m0.485717\u001b[0m          \u001b[1;36m0.594908\u001b[0m   \n",
+       "scVI                  \u001b[1;36m0.722253\u001b[0m          \u001b[1;36m0.435955\u001b[0m          \u001b[1;36m0.590104\u001b[0m   \n",
+       "Metric Type   Bio conservation  Bio conservation  Bio conservation   \n",
+       "\n",
+       "                         cLISI  Silhouette batch             iLISI  \\\n",
+       "Embedding                                                            \n",
+       "Unintegrated          \u001b[1;36m0.999528\u001b[0m          \u001b[1;36m0.861078\u001b[0m          \u001b[1;36m0.002044\u001b[0m   \n",
+       "scANVI                     \u001b[1;36m1.0\u001b[0m          \u001b[1;36m0.710921\u001b[0m          \u001b[1;36m0.009262\u001b[0m   \n",
+       "scVI                       \u001b[1;36m1.0\u001b[0m          \u001b[1;36m0.721547\u001b[0m          \u001b[1;36m0.008491\u001b[0m   \n",
+       "Metric Type   Bio conservation  Batch correction  Batch correction   \n",
+       "\n",
+       "                          KBET Graph connectivity    PCR comparison  \\\n",
+       "Embedding                                                             \n",
+       "Unintegrated          \u001b[1;36m0.350416\u001b[0m           \u001b[1;36m0.733284\u001b[0m               \u001b[1;36m0.0\u001b[0m   \n",
+       "scANVI                \u001b[1;36m0.504855\u001b[0m           \u001b[1;36m0.981208\u001b[0m          \u001b[1;36m0.428348\u001b[0m   \n",
+       "scVI                  \u001b[1;36m0.490899\u001b[0m           \u001b[1;36m0.980226\u001b[0m          \u001b[1;36m0.403148\u001b[0m   \n",
+       "Metric Type   Batch correction   Batch correction  Batch correction   \n",
+       "\n",
+       "             Batch correction Bio conservation            Total  \n",
+       "Embedding                                                        \n",
+       "Unintegrated         \u001b[1;36m0.389365\u001b[0m         \u001b[1;36m0.633649\u001b[0m         \u001b[1;36m0.535935\u001b[0m  \n",
+       "scANVI               \u001b[1;36m0.526919\u001b[0m         \u001b[1;36m0.706225\u001b[0m         \u001b[1;36m0.634502\u001b[0m  \n",
+       "scVI                 \u001b[1;36m0.520862\u001b[0m         \u001b[1;36m0.687078\u001b[0m         \u001b[1;36m0.620592\u001b[0m  \n",
+       "Metric Type   Aggregate score  Aggregate score  Aggregate score  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from rich import print\n",
     "\n",
     "df = bm.get_results(min_max_scale=False)\n",
     "print(df)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/src/scib_metrics/utils/_pcr.py b/src/scib_metrics/utils/_pcr.py
index 57860e2..77782de 100644
--- a/src/scib_metrics/utils/_pcr.py
+++ b/src/scib_metrics/utils/_pcr.py
@@ -1,6 +1,5 @@
 from typing import Optional
 
-import jax
 import jax.numpy as jnp
 import numpy as np
 import pandas as pd
@@ -74,12 +73,8 @@ def _pcr(
     var
         Array of shape (n_components,) containing the explained variance of each PC.
     """
+    residual_sum = jnp.linalg.lstsq(covariate, X_pca)[1]
+    total_sum = jnp.sum((X_pca - jnp.mean(X_pca, axis=0, keepdims=True)) ** 2, axis=0)
+    r2 = jnp.maximum(0, 1 - residual_sum / total_sum)
 
-    def r2(pc, batch):
-        residual_sum = jnp.linalg.lstsq(batch, pc)[1]
-        total_sum = jnp.sum((pc - jnp.mean(pc)) ** 2)
-        return jnp.maximum(0, 1 - residual_sum / total_sum)
-
-    # Index PCs on axis = 1, don't index batch
-    r2_ = jax.vmap(r2, in_axes=(1, None))(X_pca, covariate)
-    return jnp.dot(jnp.ravel(r2_), var) / jnp.sum(var)
+    return jnp.dot(jnp.ravel(r2), var) / jnp.sum(var)
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index c327a06..021aec1 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -2,6 +2,7 @@
 import jax.numpy as jnp
 import numpy as np
 import pandas as pd
+import pytest
 from harmonypy import compute_lisi as harmonypy_lisi
 from scib.metrics import isolated_labels_asw
 from scipy.spatial.distance import cdist as sp_cdist
@@ -64,14 +65,16 @@ def test_compute_simpson_index():
     )
 
 
-def test_lisi_knn():
+@pytest.mark.parametrize("n_neighbors", [12, 21, 30])
+def test_lisi_knn(n_neighbors):
+    perplexity = n_neighbors // 3
     X, labels = dummy_x_labels()
-    nbrs = NearestNeighbors(n_neighbors=30, algorithm="kd_tree").fit(X)
+    nbrs = NearestNeighbors(n_neighbors=n_neighbors, algorithm="kd_tree").fit(X)
     dists, inds = nbrs.kneighbors(X)
     neigh_results = NeighborsResults(indices=inds, distances=dists)
-    lisi_res = scib_metrics.lisi_knn(neigh_results, labels, perplexity=10)
+    lisi_res = scib_metrics.lisi_knn(neigh_results, labels, perplexity=perplexity)
     harmonypy_lisi_res = harmonypy_lisi(
-        X, pd.DataFrame(labels, columns=["labels"]), label_colnames=["labels"], perplexity=10
+        X, pd.DataFrame(labels, columns=["labels"]), label_colnames=["labels"], perplexity=perplexity
     )[:, 0]
     # Slight numerical differences arise due to how self edges are handled. With approximate nearest
     # neighbors methods, there is no guarantee that the self edge is the first edge. To accommodate this,

From c0d39d7ddd36437f8366cb71ac1ea30355d00dfc Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 28 Dec 2023 16:15:37 +0000
Subject: [PATCH 16/24] fix test

---
 tests/test_metrics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 021aec1..67ad6d2 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -65,7 +65,7 @@ def test_compute_simpson_index():
     )
 
 
-@pytest.mark.parametrize("n_neighbors", [12, 21, 30])
+@pytest.mark.parametrize("n_neighbors", [30, 60, 72])
 def test_lisi_knn(n_neighbors):
     perplexity = n_neighbors // 3
     X, labels = dummy_x_labels()

From 6c1f706f60209d9e095a13ab6689f96f9223cd11 Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 28 Dec 2023 16:22:52 +0000
Subject: [PATCH 17/24] test 3.11

---
 .github/workflows/test.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 4357e2f..d5469b6 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -20,7 +20,7 @@ jobs:
         strategy:
             fail-fast: false
             matrix:
-                python: ["3.9", "3.10"]
+                python: ["3.9", "3.10", "3.11"]
                 os: [ubuntu-latest]
 
         env:

From e866f3f8b07b086e70714d2ffe236467b54bf60e Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 28 Dec 2023 16:29:21 +0000
Subject: [PATCH 18/24] kmeans test tolerance due to flakiness

---
 tests/test_metrics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 67ad6d2..52a1f35 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -146,7 +146,7 @@ def test_kmeans():
     sk_means_labels = pairwise_distances_argmin(X, sk_means_cluster_centers)
 
     accuracy = (k_means_labels == sk_means_labels).sum() / len(k_means_labels)
-    assert accuracy > 0.999
+    assert accuracy > 0.997
 
 
 def test_kbet():

From a6e51a78be74ec021dcf3e882613de893260cc08 Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 28 Dec 2023 16:34:42 +0000
Subject: [PATCH 19/24] kmeans test tolerance due to flakiness

---
 tests/test_metrics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 52a1f35..6e129b2 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -146,7 +146,7 @@ def test_kmeans():
     sk_means_labels = pairwise_distances_argmin(X, sk_means_cluster_centers)
 
     accuracy = (k_means_labels == sk_means_labels).sum() / len(k_means_labels)
-    assert accuracy > 0.997
+    assert accuracy > 0.995
 
 
 def test_kbet():

From 7cceca9b0ff0d9630b75ff626a4bf084eb135465 Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 28 Dec 2023 17:09:32 +0000
Subject: [PATCH 20/24] fix leiden

---
 src/scib_metrics/_nmi_ari.py |  6 ++++--
 tests/test_metrics.py        | 12 ++++++++++++
 2 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/src/scib_metrics/_nmi_ari.py b/src/scib_metrics/_nmi_ari.py
index 574c189..13c75f9 100644
--- a/src/scib_metrics/_nmi_ari.py
+++ b/src/scib_metrics/_nmi_ari.py
@@ -20,8 +20,10 @@ def _compute_clustering_kmeans(X: np.ndarray, n_clusters: int) -> np.ndarray:
 
 
 def _compute_clustering_leiden(connectivity_graph: spmatrix, resolution: float) -> np.ndarray:
-    # The connectivity graph with the umap method is symmetric
-    g = igraph.Graph.Weighted_Adjacency(connectivity_graph, mode="undirected")
+    # The connectivity graph with the umap method is symmetric, but we need to first make it directed
+    # to have both sets of edges as is done in scanpy. See test for more details.
+    g = igraph.Graph.Weighted_Adjacency(connectivity_graph, mode="directed")
+    g.to_undirected(mode="each")
     clustering = g.community_leiden(objective_function="modularity", weights="weight", resolution_parameter=resolution)
     clusters = clustering.membership
     return np.asarray(clusters)
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 6e129b2..cc1333c 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -1,8 +1,10 @@
 import anndata
+import igraph
 import jax.numpy as jnp
 import numpy as np
 import pandas as pd
 import pytest
+import scanpy as sc
 from harmonypy import compute_lisi as harmonypy_lisi
 from scib.metrics import isolated_labels_asw
 from scipy.spatial.distance import cdist as sp_cdist
@@ -112,6 +114,16 @@ def test_nmi_ari_cluster_labels_leiden_single_resolution():
     assert isinstance(ari, float)
 
 
+def test_leiden_graph_construction():
+    X, _ = dummy_x_labels(symmetric_positive=True, x_is_neighbors_results=True)
+    conn_graph = X.knn_graph_connectivities
+    g = igraph.Graph.Weighted_Adjacency(conn_graph, mode="directed")
+    g.to_undirected(mode="each")
+    sc_g = sc._utils.get_igraph_from_adjacency(conn_graph, directed=False)
+    assert g.isomorphic(sc_g)
+    np.testing.assert_equal(g.es["weight"], sc_g.es["weight"])
+
+
 def test_isolated_labels():
     X, labels, batch = dummy_x_labels_batch()
     pred = scib_metrics.isolated_labels(X, labels, batch)

From 890387f0151742b7274e8f55d461c3e96dfb4add Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 28 Dec 2023 17:14:19 +0000
Subject: [PATCH 21/24] bump version

---
 .bumpversion.cfg | 2 +-
 pyproject.toml   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 2579f1c..fd40ffb 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.4.1
+current_version = 0.5.0
 tag = True
 commit = True
 
diff --git a/pyproject.toml b/pyproject.toml
index 2149d75..3894a70 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ requires = ["hatchling"]
 
 [project]
 name = "scib-metrics"
-version = "0.4.1"
+version = "0.5.0"
 description = "Accelerated and Python-only scIB metrics"
 readme = "README.md"
 requires-python = ">=3.9"

From a9a4bd80f484ea067169928a1cdbd77173858141 Mon Sep 17 00:00:00 2001
From: Adam Gayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 4 Jan 2024 10:15:01 -0500
Subject: [PATCH 22/24] lisi test

---
 tests/test_metrics.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index cc1333c..00f057d 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -78,10 +78,7 @@ def test_lisi_knn(n_neighbors):
     harmonypy_lisi_res = harmonypy_lisi(
         X, pd.DataFrame(labels, columns=["labels"]), label_colnames=["labels"], perplexity=perplexity
     )[:, 0]
-    # Slight numerical differences arise due to how self edges are handled. With approximate nearest
-    # neighbors methods, there is no guarantee that the self edge is the first edge. To accommodate this,
-    # we mask out self edges internally in lisi computation which causes slight numerical differences.
-    np.testing.assert_allclose(lisi_res, harmonypy_lisi_res, rtol=5e-5, atol=5e-5)
+    np.testing.assert_allclose(lisi_res, harmonypy_lisi_res)
 
 
 def test_ilisi_clisi_knn():

From 1201b1b130b830b1e6ce4b5f78cffa80732684ce Mon Sep 17 00:00:00 2001
From: Adam Gayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 4 Jan 2024 10:28:52 -0500
Subject: [PATCH 23/24] Update _lisi.py

---
 src/scib_metrics/_lisi.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/scib_metrics/_lisi.py b/src/scib_metrics/_lisi.py
index 17c2168..e646fa1 100644
--- a/src/scib_metrics/_lisi.py
+++ b/src/scib_metrics/_lisi.py
@@ -29,7 +29,8 @@ def lisi_knn(X: NeighborsResults, labels: np.ndarray, perplexity: float = None)
     row_idx = np.arange(X.n_samples)[:, np.newaxis]
 
     if perplexity is None:
-        perplexity = np.floor(knn_idx.shape[1] / 3)
+        # -1 to account for this implementation masking out self edge later
+        perplexity = np.floor((knn_idx.shape[1] - 1) / 3)
 
     n_labels = len(np.unique(labels))
 

From a22c3e70cbc29b98b3b3b7925a18ec1debb708cd Mon Sep 17 00:00:00 2001
From: adamgayoso <adamgayoso@users.noreply.github.com>
Date: Thu, 4 Jan 2024 17:22:42 +0000
Subject: [PATCH 24/24] fix lisi

---
 src/scib_metrics/_lisi.py       |  3 +--
 src/scib_metrics/utils/_lisi.py | 26 ++++++++++++++++----------
 tests/test_metrics.py           |  2 +-
 3 files changed, 18 insertions(+), 13 deletions(-)

diff --git a/src/scib_metrics/_lisi.py b/src/scib_metrics/_lisi.py
index e646fa1..17c2168 100644
--- a/src/scib_metrics/_lisi.py
+++ b/src/scib_metrics/_lisi.py
@@ -29,8 +29,7 @@ def lisi_knn(X: NeighborsResults, labels: np.ndarray, perplexity: float = None)
     row_idx = np.arange(X.n_samples)[:, np.newaxis]
 
     if perplexity is None:
-        # -1 to account for this implementation masking out self edge later
-        perplexity = np.floor((knn_idx.shape[1] - 1) / 3)
+        perplexity = np.floor(knn_idx.shape[1] / 3)
 
     n_labels = len(np.unique(labels))
 
diff --git a/src/scib_metrics/utils/_lisi.py b/src/scib_metrics/utils/_lisi.py
index 9c0fb28..9726bc3 100644
--- a/src/scib_metrics/utils/_lisi.py
+++ b/src/scib_metrics/utils/_lisi.py
@@ -23,8 +23,10 @@ class _NeighborProbabilityState:
 
 
 @jax.jit
-def _Hbeta(knn_dists_row: jnp.ndarray, beta: float) -> tuple[jnp.ndarray, jnp.ndarray]:
+def _Hbeta(knn_dists_row: jnp.ndarray, row_self_mask: jnp.ndarray, beta: float) -> tuple[jnp.ndarray, jnp.ndarray]:
     P = jnp.exp(-knn_dists_row * beta)
+    # Mask out self edges to be zero
+    P = jnp.where(row_self_mask, P, 0)
     sumP = jnp.nansum(P)
     H = jnp.where(sumP == 0, 0, jnp.log(sumP) + beta * jnp.nansum(knn_dists_row * P) / sumP)
     P = jnp.where(sumP == 0, jnp.zeros_like(knn_dists_row), P / sumP)
@@ -33,12 +35,12 @@ def _Hbeta(knn_dists_row: jnp.ndarray, beta: float) -> tuple[jnp.ndarray, jnp.nd
 
 @jax.jit
 def _get_neighbor_probability(
-    knn_dists_row: jnp.ndarray, perplexity: float, tol: float
+    knn_dists_row: jnp.ndarray, row_self_mask: jnp.ndarray, perplexity: float, tol: float
 ) -> tuple[jnp.ndarray, jnp.ndarray]:
     beta = 1
     betamin = -jnp.inf
     betamax = jnp.inf
-    H, P = _Hbeta(knn_dists_row, beta)
+    H, P = _Hbeta(knn_dists_row, row_self_mask, beta)
     Hdiff = H - jnp.log(perplexity)
 
     def _get_neighbor_probability_step(state):
@@ -55,7 +57,7 @@ def _get_neighbor_probability_step(state):
             jnp.where(betamax == jnp.inf, beta * 2, (beta + betamax) / 2),
             jnp.where(betamin == -jnp.inf, beta / 2, (beta + betamin) / 2),
         )
-        new_H, new_P = _Hbeta(knn_dists_row, new_beta)
+        new_H, new_P = _Hbeta(knn_dists_row, row_self_mask, new_beta)
         new_Hdiff = new_H - jnp.log(perplexity)
         return _NeighborProbabilityState(
             H=new_H, P=new_P, Hdiff=new_Hdiff, beta=new_beta, betamin=new_betamin, betamax=new_betamax, tries=tries + 1
@@ -63,7 +65,7 @@ def _get_neighbor_probability_step(state):
 
     def _get_neighbor_probability_convergence(state):
         Hdiff, tries = state.Hdiff, state.tries
-        return jnp.logical_and(jnp.abs(Hdiff) > tol, tries < 50)
+        return jnp.logical_and(jnp.abs(Hdiff) >= tol, tries < 50)
 
     init_state = _NeighborProbabilityState(H=H, P=P, Hdiff=Hdiff, beta=beta, betamin=betamin, betamax=betamax, tries=0)
     final_state = jax.lax.while_loop(_get_neighbor_probability_convergence, _get_neighbor_probability_step, init_state)
@@ -71,9 +73,14 @@ def _get_neighbor_probability_convergence(state):
 
 
 def _compute_simpson_index_cell(
-    knn_dists_row: jnp.ndarray, knn_labels_row: jnp.ndarray, n_batches: int, perplexity: float, tol: float
+    knn_dists_row: jnp.ndarray,
+    knn_labels_row: jnp.ndarray,
+    row_self_mask: jnp.ndarray,
+    n_batches: int,
+    perplexity: float,
+    tol: float,
 ) -> jnp.ndarray:
-    H, P = _get_neighbor_probability(knn_dists_row, perplexity, tol)
+    H, P = _get_neighbor_probability(knn_dists_row, row_self_mask, perplexity, tol)
 
     def _non_zero_H_simpson():
         sumP = jnp.bincount(knn_labels_row, weights=P, length=n_batches)
@@ -120,8 +127,7 @@ def compute_simpson_index(
     labels = jnp.array(labels)
     row_idx = jnp.array(row_idx)
     knn_labels = labels[knn_idx]
-    # Set self edges to infinity so they are not considered.
-    knn_dists = jnp.where(knn_idx != row_idx, knn_dists, jnp.inf)
+    self_mask = knn_idx != row_idx
     simpson_fn = partial(_compute_simpson_index_cell, n_batches=n_labels, perplexity=perplexity, tol=tol)
-    out = jax.vmap(simpson_fn)(knn_dists, knn_labels)
+    out = jax.vmap(simpson_fn)(knn_dists, knn_labels, self_mask)
     return get_ndarray(out)
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 00f057d..81a2a6c 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -78,7 +78,7 @@ def test_lisi_knn(n_neighbors):
     harmonypy_lisi_res = harmonypy_lisi(
         X, pd.DataFrame(labels, columns=["labels"]), label_colnames=["labels"], perplexity=perplexity
     )[:, 0]
-    np.testing.assert_allclose(lisi_res, harmonypy_lisi_res)
+    np.testing.assert_allclose(lisi_res, harmonypy_lisi_res, rtol=5e-5, atol=5e-5)
 
 
 def test_ilisi_clisi_knn():