From 618dc23cff42b546d70e875875794b22306df4fc Mon Sep 17 00:00:00 2001
From: Ben Frederickson <ben@benfrederickson.com>
Date: Wed, 17 May 2023 16:06:44 -0700
Subject: [PATCH] Learn heuristic to pick fastest select_k algorithm (#1523)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This uses the select_k dataset from https://github.com/rapidsai/raft/pull/1497 to learn a heuristic of the fastest select_k variant based off the rows/ cols/k of the input. This heuristic is modelled as a DecisionTree, which is automatically exported in C++ code that is compiled into RAFT. This lets us learn a function to pick the fastest select_k method - which requires only a few if statements in C++ code, making it very cheap to evaluate.

Authors:
  - Ben Frederickson (https://github.com/benfred)

Approvers:
  - Corey J. Nolet (https://github.com/cjnolet)

URL: https://github.com/rapidsai/raft/pull/1523
---
 cpp/bench/prims/matrix/select_k.cu            |  14 +-
 .../raft/matrix/detail/select_k-inl.cuh       | 120 +++-
 .../raft_internal/matrix/select_k.cuh         |   1 +
 .../select_k/algorithm_selection.ipynb        | 443 +++++++++++++
 .../select_k/generate_heuristic.ipynb         | 587 ++++++++++++++++++
 .../heuristics/select_k/generate_plots.ipynb  | 352 +++++++++++
 .../heuristics/select_k/select_k_dataset.py   | 114 ++++
 7 files changed, 1620 insertions(+), 11 deletions(-)
 create mode 100644 cpp/scripts/heuristics/select_k/algorithm_selection.ipynb
 create mode 100644 cpp/scripts/heuristics/select_k/generate_heuristic.ipynb
 create mode 100644 cpp/scripts/heuristics/select_k/generate_plots.ipynb
 create mode 100644 cpp/scripts/heuristics/select_k/select_k_dataset.py

diff --git a/cpp/bench/prims/matrix/select_k.cu b/cpp/bench/prims/matrix/select_k.cu
index 22ec998f4f..1bff66cac4 100644
--- a/cpp/bench/prims/matrix/select_k.cu
+++ b/cpp/bench/prims/matrix/select_k.cu
@@ -41,7 +41,7 @@ using namespace raft::bench;  // NOLINT
 template <typename KeyT, typename IdxT, select::Algo Algo>
 struct selection : public fixture {
   explicit selection(const select::params& p)
-    : fixture(true),
+    : fixture(p.use_memory_pool),
       params_(p),
       in_dists_(p.batch_size * p.len, stream),
       in_ids_(p.batch_size * p.len, stream),
@@ -193,7 +193,8 @@ SELECTION_REGISTER(double, int64_t, kWarpDistributedShm);     // NOLINT
     using SelectK = selection<KeyT, IdxT, select::Algo::A>;                               \
     std::stringstream name;                                                               \
     name << "SelectKDataset/" << #KeyT "/" #IdxT "/" #A << "/" << input.batch_size << "/" \
-         << input.len << "/" << input.k << "/" << input.use_index_input;                  \
+         << input.len << "/" << input.k << "/" << input.use_index_input << "/"            \
+         << input.use_memory_pool;                                                        \
     auto* b = ::benchmark::internal::RegisterBenchmarkInternal(                           \
       new raft::bench::internal::Fixture<SelectK, select::params>(name.str(), input));    \
     b->UseManualTime();                                                                   \
@@ -266,5 +267,14 @@ void add_select_k_dataset_benchmarks()
     SELECTION_REGISTER_INPUT(float, int64_t, input);
     SELECTION_REGISTER_INPUT(float, uint32_t, input);
   }
+
+  // also try again without a memory pool to see if there are significant differences
+  for (auto input : inputs) {
+    input.use_memory_pool = false;
+    SELECTION_REGISTER_INPUT(double, int64_t, input);
+    SELECTION_REGISTER_INPUT(double, uint32_t, input);
+    SELECTION_REGISTER_INPUT(float, int64_t, input);
+    SELECTION_REGISTER_INPUT(float, uint32_t, input);
+  }
 }
 }  // namespace raft::matrix
diff --git a/cpp/include/raft/matrix/detail/select_k-inl.cuh b/cpp/include/raft/matrix/detail/select_k-inl.cuh
index 20c2fb119d..dba2d1d841 100644
--- a/cpp/include/raft/matrix/detail/select_k-inl.cuh
+++ b/cpp/include/raft/matrix/detail/select_k-inl.cuh
@@ -21,11 +21,101 @@
 
 #include <raft/core/nvtx.hpp>
 
+#include <raft/neighbors/detail/selection_faiss.cuh>
 #include <rmm/cuda_stream_view.hpp>
 #include <rmm/mr/device/device_memory_resource.hpp>
 
 namespace raft::matrix::detail {
 
+// this is a subset of algorithms, chosen by running the algorithm_selection
+// notebook in cpp/scripts/heuristics/select_k
+enum class Algo { kRadix11bits, kWarpDistributedShm, kFaissBlockSelect };
+
+/**
+ * Predict the fastest select_k algorithm based on the number of rows/cols/k
+ *
+ * The body of this method is automatically generated, using a DecisionTree
+ * to predict the fastest algorithm based off of thousands of trial runs
+ * on different values of rows/cols/k. The decision tree is converted to c++
+ * code, which is cut and paste below.
+ *
+ * NOTE: The code to generate is in cpp/scripts/heuristics/select_k, running the
+ * 'generate_heuristic' notebook there will replace the body of this function
+ * with the latest learned heuristic
+ */
+inline Algo choose_select_k_algorithm(size_t rows, size_t cols, int k)
+{
+  if (k > 134) {
+    if (k > 256) {
+      if (k > 809) {
+        return Algo::kRadix11bits;
+      } else {
+        if (rows > 124) {
+          if (cols > 63488) {
+            return Algo::kFaissBlockSelect;
+          } else {
+            return Algo::kRadix11bits;
+          }
+        } else {
+          return Algo::kRadix11bits;
+        }
+      }
+    } else {
+      if (cols > 678736) {
+        return Algo::kWarpDistributedShm;
+      } else {
+        return Algo::kRadix11bits;
+      }
+    }
+  } else {
+    if (cols > 13776) {
+      if (rows > 335) {
+        if (k > 1) {
+          if (rows > 546) {
+            return Algo::kWarpDistributedShm;
+          } else {
+            if (k > 17) {
+              return Algo::kWarpDistributedShm;
+            } else {
+              return Algo::kFaissBlockSelect;
+            }
+          }
+        } else {
+          return Algo::kFaissBlockSelect;
+        }
+      } else {
+        if (k > 44) {
+          if (cols > 1031051) {
+            return Algo::kWarpDistributedShm;
+          } else {
+            if (rows > 22) {
+              return Algo::kWarpDistributedShm;
+            } else {
+              return Algo::kRadix11bits;
+            }
+          }
+        } else {
+          return Algo::kWarpDistributedShm;
+        }
+      }
+    } else {
+      if (k > 1) {
+        if (rows > 188) {
+          return Algo::kWarpDistributedShm;
+        } else {
+          if (k > 72) {
+            return Algo::kRadix11bits;
+          } else {
+            return Algo::kWarpDistributedShm;
+          }
+        }
+      } else {
+        return Algo::kFaissBlockSelect;
+      }
+    }
+  }
+}
+
 /**
  * Select k smallest or largest key/values from each row in the input data.
  *
@@ -77,15 +167,27 @@ void select_k(const T* in_val,
 {
   common::nvtx::range<common::nvtx::domain::raft> fun_scope(
     "matrix::select_k(batch_size = %zu, len = %zu, k = %d)", batch_size, len, k);
-  // TODO (achirkin): investigate the trade-off for a wider variety of inputs.
-  const bool radix_faster = batch_size >= 64 && len >= 102400 && k >= 128;
-  if (k <= select::warpsort::kMaxCapacity && !radix_faster) {
-    select::warpsort::select_k<T, IdxT>(
-      in_val, in_idx, batch_size, len, k, out_val, out_idx, select_min, stream, mr);
-  } else {
-    select::radix::select_k<T, IdxT, (sizeof(T) >= 4 ? 11 : 8), 512>(
-      in_val, in_idx, batch_size, len, k, out_val, out_idx, select_min, true, stream, mr);
+
+  auto algo = choose_select_k_algorithm(batch_size, len, k);
+  switch (algo) {
+    case Algo::kRadix11bits:
+      return detail::select::radix::select_k<T, IdxT, 11, 512>(in_val,
+                                                               in_idx,
+                                                               batch_size,
+                                                               len,
+                                                               k,
+                                                               out_val,
+                                                               out_idx,
+                                                               select_min,
+                                                               true,  // fused_last_filter
+                                                               stream);
+    case Algo::kWarpDistributedShm:
+      return detail::select::warpsort::
+        select_k_impl<T, IdxT, detail::select::warpsort::warp_sort_distributed_ext>(
+          in_val, in_idx, batch_size, len, k, out_val, out_idx, select_min, stream);
+    case Algo::kFaissBlockSelect:
+      return neighbors::detail::select_k(
+        in_val, in_idx, batch_size, len, out_val, out_idx, select_min, k, stream);
   }
 }
-
 }  // namespace raft::matrix::detail
diff --git a/cpp/internal/raft_internal/matrix/select_k.cuh b/cpp/internal/raft_internal/matrix/select_k.cuh
index ac9a0bb717..013a61886f 100644
--- a/cpp/internal/raft_internal/matrix/select_k.cuh
+++ b/cpp/internal/raft_internal/matrix/select_k.cuh
@@ -32,6 +32,7 @@ struct params {
   bool select_min;
   bool use_index_input       = true;
   bool use_same_leading_bits = false;
+  bool use_memory_pool       = true;
 };
 
 inline auto operator<<(std::ostream& os, const params& ss) -> std::ostream&
diff --git a/cpp/scripts/heuristics/select_k/algorithm_selection.ipynb b/cpp/scripts/heuristics/select_k/algorithm_selection.ipynb
new file mode 100644
index 0000000000..674ce3c906
--- /dev/null
+++ b/cpp/scripts/heuristics/select_k/algorithm_selection.ipynb
@@ -0,0 +1,443 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6bd615f3-0e28-4ea8-8878-49fd62d833cd",
+   "metadata": {},
+   "source": [
+    "# Select the selection algorithms\n",
+    "\n",
+    "The idea here is that we can simplify the decision logic, reduce the binary size\n",
+    "and speed up the compilation time by only including a subset of selection algorithms.\n",
+    "We're aiming to get algorithms that perform well in different situations, and complement\n",
+    "each other - so to do this, we're iteratively removing the worst performing algorithm,\n",
+    "after which algorithms are re-evaluated on their speedups relative to the remaining\n",
+    "algorithms. This gets us a minimum spanning set of selection algorithms that performs\n",
+    "well over diverse inputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fbf08b4e-7a91-4c4b-8320-ded040d3f827",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key_type</th>\n",
+       "      <th>index_type</th>\n",
+       "      <th>algo</th>\n",
+       "      <th>row</th>\n",
+       "      <th>col</th>\n",
+       "      <th>k</th>\n",
+       "      <th>use_index_input</th>\n",
+       "      <th>use_memory_pool</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>double</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix8bits</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>double</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>double</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix11bitsExtraPass</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>double</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kWarpImmediate</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>double</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kWarpFiltered</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179963</th>\n",
+       "      <td>float</td>\n",
+       "      <td>uint32_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>2042</td>\n",
+       "      <td>8175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.001018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179964</th>\n",
+       "      <td>float</td>\n",
+       "      <td>uint32_t</td>\n",
+       "      <td>kRadix11bitsExtraPass</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>2042</td>\n",
+       "      <td>8175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.001018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179965</th>\n",
+       "      <td>float</td>\n",
+       "      <td>uint32_t</td>\n",
+       "      <td>kRadix8bits</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>2042</td>\n",
+       "      <td>8175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179966</th>\n",
+       "      <td>float</td>\n",
+       "      <td>uint32_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>2042</td>\n",
+       "      <td>8175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179967</th>\n",
+       "      <td>float</td>\n",
+       "      <td>uint32_t</td>\n",
+       "      <td>kRadix11bitsExtraPass</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>2042</td>\n",
+       "      <td>8175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000072</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>179968 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       key_type index_type                   algo   row   col     k  \\\n",
+       "0        double    int64_t            kRadix8bits     1  1024     1   \n",
+       "1        double    int64_t           kRadix11bits     1  1024     1   \n",
+       "2        double    int64_t  kRadix11bitsExtraPass     1  1024     1   \n",
+       "3        double    int64_t         kWarpImmediate     1  1024     1   \n",
+       "4        double    int64_t          kWarpFiltered     1  1024     1   \n",
+       "...         ...        ...                    ...   ...   ...   ...   \n",
+       "179963    float   uint32_t           kRadix11bits  1075  2042  8175   \n",
+       "179964    float   uint32_t  kRadix11bitsExtraPass  1075  2042  8175   \n",
+       "179965    float   uint32_t            kRadix8bits  1075  2042  8175   \n",
+       "179966    float   uint32_t           kRadix11bits  1075  2042  8175   \n",
+       "179967    float   uint32_t  kRadix11bitsExtraPass  1075  2042  8175   \n",
+       "\n",
+       "        use_index_input  use_memory_pool      time  \n",
+       "0                     0                0  0.000050  \n",
+       "1                     0                0  0.000033  \n",
+       "2                     0                0  0.000033  \n",
+       "3                     0                0  0.000022  \n",
+       "4                     0                0  0.000024  \n",
+       "...                 ...              ...       ...  \n",
+       "179963                0                0  0.001018  \n",
+       "179964                0                0  0.001018  \n",
+       "179965                0                1  0.000059  \n",
+       "179966                0                1  0.000072  \n",
+       "179967                0                1  0.000072  \n",
+       "\n",
+       "[179968 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from select_k_dataset import load_dataframe, get_dataset\n",
+    "\n",
+    "df = load_dataframe(\"select_k_times.json\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c0d9fed5-35c3-4b0b-987a-973e93e0c59c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "\n",
+    "def rank_algos(df, use_relative_speedup=False):\n",
+    "    _, y, weights = get_dataset(df)\n",
+    "    times = Counter()\n",
+    "    for algo, speedup in zip(y, weights):\n",
+    "        times[algo] += speedup if use_relative_speedup else 1\n",
+    "    return sorted(times.items(), key=lambda x:-x[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "56c5dd8e-6f44-4ef3-b825-1d5a5d6698a2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('kRadix11bits', 7267),\n",
+       " ('kWarpDistributedShm', 6861),\n",
+       " ('kFaissBlockSelect', 3620),\n",
+       " ('kRadix8bits', 3229),\n",
+       " ('kWarpDistributed', 2619),\n",
+       " ('kWarpImmediate', 2584),\n",
+       " ('kRadix11bitsExtraPass', 2260),\n",
+       " ('kWarpFiltered', 490)]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# show the number of times each algorithm is fastest for a given k/# of rows/# of cols / dtype / memory pool etc\n",
+    "rank_algos(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ec63f794-0bdf-4afe-92a8-3fa8ab7a1648",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('kRadix11bits', 3591),\n",
+       " ('kWarpDistributedShm', 3589),\n",
+       " ('kFaissBlockSelect', 2006),\n",
+       " ('kWarpImmediate', 1552),\n",
+       " ('kWarpDistributed', 1448),\n",
+       " ('kRadix11bitsExtraPass', 1338),\n",
+       " ('kRadix8bits', 460),\n",
+       " ('kWarpFiltered', 290)]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# kRadix8bits seems to have a performance issue with 64 bit index types, it is one\n",
+    "# of the worst performing algorithms for 64bit indices, but one of the top 3 for 32 bit\n",
+    "rank_algos(df[df.index_type == \"int64_t\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9b7de19f-ddb6-4fa6-b423-db384428d701",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('kRadix11bits', 3676),\n",
+       " ('kWarpDistributedShm', 3272),\n",
+       " ('kRadix8bits', 2769),\n",
+       " ('kFaissBlockSelect', 1614),\n",
+       " ('kWarpDistributed', 1171),\n",
+       " ('kWarpImmediate', 1032),\n",
+       " ('kRadix11bitsExtraPass', 922),\n",
+       " ('kWarpFiltered', 200)]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rank_algos(df[df.index_type == \"uint32_t\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "bc0a10ea-652b-4822-8587-514c8f0348c3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "selected {'kRadix11bits', 'kWarpDistributedShm', 'kFaissBlockSelect'}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[('kRadix11bits', 12736),\n",
+       " ('kWarpDistributedShm', 12317),\n",
+       " ('kFaissBlockSelect', 3877)]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# do an algorithm selection pass, repeatedly remove the lowest performing algorithm\n",
+    "#\n",
+    "# The idea here is that we can simplify the decision logic, reduce the binary size\n",
+    "# and speed up the compilation time by only including a subset of selection algorithms.\n",
+    "# we're aiming to get algorithms that perform well in different situations, and complement\n",
+    "# each other - so to do this, we're iteratively removing the worst performing algorithm,\n",
+    "# after which algorithms are re-evaluated on their speedups relative to the remaining\n",
+    "# algorithms. This gets us a minimum spanning set of selection algorithms that performs\n",
+    "# well over diverse inputs.\n",
+    "#\n",
+    "# note: the lowest performing algorithm here might actually be pretty good, but\n",
+    "# just not provide much benefit over another similar algorithm. \n",
+    "# As an example, kWarpDistributed  is an excellent selection algorithm, but in testing \n",
+    "# kWarpDistributedShm is slightly faster than it in situations where it does well, \n",
+    "# meaning that it gets removed early on in this loop\n",
+    "current = df[df.use_memory_pool == True]\n",
+    "algos = set(df.algo)\n",
+    "\n",
+    "# we're arbitrarily getting this down to 3 selection algorithms\n",
+    "while len(algos) > 3:\n",
+    "    times = rank_algos(current, use_relative_speedup=False)\n",
+    "    algo, speedup = times[-1]\n",
+    "    algos.remove(algo)\n",
+    "    current = df[df.algo.isin(algos)]\n",
+    "\n",
+    "print(\"selected\", algos)\n",
+    "rank_algos(current)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "028ebbb1-5289-4104-a13c-494c74742087",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# experimenting with different subsets of index type / dtype / use memory seems\n",
+    "# to pretty consistently show that kRadix11bits / kWarpDistributedShm / kFaissBlockSelect\n",
+    "# all get selected here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cpp/scripts/heuristics/select_k/generate_heuristic.ipynb b/cpp/scripts/heuristics/select_k/generate_heuristic.ipynb
new file mode 100644
index 0000000000..12b06bdd47
--- /dev/null
+++ b/cpp/scripts/heuristics/select_k/generate_heuristic.ipynb
@@ -0,0 +1,587 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7e298e01-190d-4839-9d44-1360c9cd2b75",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Learning to select the fastest `select_k` algorithm\n",
+    "\n",
+    "Using the dataset of algorithm times generated by `generate_dataset.py` here - this notebook trains a\n",
+    "DecisionTree model that will predict the fastest algorithm based of the number of rows and columns in the input\n",
+    "array, as well as the `k` value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "40fef43f-194f-4b06-9eba-fdd8af42d214",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from select_k_dataset import load_dataframe, get_dataset\n",
+    "import sklearn.tree\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "56765f40-96ce-46c6-bce8-ab782cd72b6e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key_type</th>\n",
+       "      <th>index_type</th>\n",
+       "      <th>algo</th>\n",
+       "      <th>row</th>\n",
+       "      <th>col</th>\n",
+       "      <th>k</th>\n",
+       "      <th>use_index_input</th>\n",
+       "      <th>use_memory_pool</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>double</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>double</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kWarpDistributedShm</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>double</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kFaissBlockSelect</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>double</td>\n",
+       "      <td>uint32_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>double</td>\n",
+       "      <td>uint32_t</td>\n",
+       "      <td>kWarpDistributedShm</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179942</th>\n",
+       "      <td>float</td>\n",
+       "      <td>uint32_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>7586</td>\n",
+       "      <td>162460</td>\n",
+       "      <td>8149</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.021265</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179948</th>\n",
+       "      <td>double</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>2042</td>\n",
+       "      <td>8175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179954</th>\n",
+       "      <td>double</td>\n",
+       "      <td>uint32_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>2042</td>\n",
+       "      <td>8175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179960</th>\n",
+       "      <td>float</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>2042</td>\n",
+       "      <td>8175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179966</th>\n",
+       "      <td>float</td>\n",
+       "      <td>uint32_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>2042</td>\n",
+       "      <td>8175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000072</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>34909 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       key_type index_type                 algo   row     col     k  \\\n",
+       "9        double    int64_t         kRadix11bits     1    1024     1   \n",
+       "14       double    int64_t  kWarpDistributedShm     1    1024     1   \n",
+       "15       double    int64_t    kFaissBlockSelect     1    1024     1   \n",
+       "25       double   uint32_t         kRadix11bits     1    1024     1   \n",
+       "30       double   uint32_t  kWarpDistributedShm     1    1024     1   \n",
+       "...         ...        ...                  ...   ...     ...   ...   \n",
+       "179942    float   uint32_t         kRadix11bits  7586  162460  8149   \n",
+       "179948   double    int64_t         kRadix11bits  1075    2042  8175   \n",
+       "179954   double   uint32_t         kRadix11bits  1075    2042  8175   \n",
+       "179960    float    int64_t         kRadix11bits  1075    2042  8175   \n",
+       "179966    float   uint32_t         kRadix11bits  1075    2042  8175   \n",
+       "\n",
+       "        use_index_input  use_memory_pool      time  \n",
+       "9                     0                1  0.000024  \n",
+       "14                    0                1  0.000010  \n",
+       "15                    0                1  0.000005  \n",
+       "25                    0                1  0.000008  \n",
+       "30                    0                1  0.000010  \n",
+       "...                 ...              ...       ...  \n",
+       "179942                0                1  0.021265  \n",
+       "179948                0                1  0.000317  \n",
+       "179954                0                1  0.000154  \n",
+       "179960                0                1  0.000138  \n",
+       "179966                0                1  0.000072  \n",
+       "\n",
+       "[34909 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# load up the timings from the MATRIX_BENCH script into a pandas dataframe\n",
+    "df = load_dataframe(\"select_k_times.json\")\n",
+    "\n",
+    "# we're limiting down to 3 different select_k methods - chosen by \n",
+    "# the 'algorithm_selection.ipynb' script here\n",
+    "df = df[df.algo.isin([\"kRadix11bits\", \"kWarpDistributedShm\", \"kFaissBlockSelect\"])]\n",
+    "\n",
+    "# we're also assuming we have a memory pool for now\n",
+    "df = df[(df.use_memory_pool == True)]\n",
+    "# df = df[(df.index_type == 'int64_t') & (df.key_type == 'float')]\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1cc0bbbc-47a7-4e41-acf1-3bbcee91e1b1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((12295, 4), (2170, 4))"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# break down into a train/set set\n",
+    "X, y, weights = get_dataset(df)\n",
+    "train_test_sets = sklearn.model_selection.train_test_split(X, y, weights, test_size=0.15, random_state=1)\n",
+    "X_train, X_test, y_train, y_test, weights_train, weights_test = train_test_sets\n",
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a46cfff4-97db-46da-8c00-89c2b0557b91",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=6, max_leaf_nodes=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=6, max_leaf_nodes=20)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeClassifier(max_depth=6, max_leaf_nodes=20)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = sklearn.tree.DecisionTreeClassifier(max_depth=6, max_leaf_nodes=20)\n",
+    "model.fit(X_train, y_train) #, weights_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "32dc14a0-2c9e-414f-9443-c39ad5ab2b9a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9952052831917203"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_train, y_train, weights_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "19ffa733-fda7-4758-92da-8e9ea5d8ef9c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9998545056217218"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(X_test, y_test, weights_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a55279d6-7b7e-4197-afb0-ff10efcb1d32",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# print(sklearn.tree.export_text(model, feature_names=[\"k\", \"rows\", \"cols\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "058e488c-f0dd-40c8-a217-e8767d6439ec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.figure(figsize=(12,12))\n",
+    "viz = sklearn.tree.plot_tree(model, fontsize=8, label='all', class_names=model.classes_, feature_names=[\"k\", \"rows\", \"cols\", \"use_memory_pool\"], impurity=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "57cf8617-9dd6-4e82-aeb5-ccc860ba0d51",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inline Algo choose_select_k_algorithm(size_t rows, size_t cols, int k)\n",
+      "{\n",
+      "  if (k > 134) {\n",
+      "    if (k > 256) {\n",
+      "      if (k > 809) {\n",
+      "        return Algo::kRadix11bits;\n",
+      "      } else {\n",
+      "        if (rows > 124) {\n",
+      "          if (cols > 63488) {\n",
+      "            return Algo::kFaissBlockSelect;\n",
+      "          } else {\n",
+      "            return Algo::kRadix11bits;\n",
+      "          }\n",
+      "        } else {\n",
+      "          return Algo::kRadix11bits;\n",
+      "        }\n",
+      "      }\n",
+      "    } else {\n",
+      "      if (cols > 678736) {\n",
+      "        return Algo::kWarpDistributedShm;\n",
+      "      } else {\n",
+      "        return Algo::kRadix11bits;\n",
+      "      }\n",
+      "    }\n",
+      "  } else {\n",
+      "    if (cols > 13776) {\n",
+      "      if (rows > 335) {\n",
+      "        if (k > 1) {\n",
+      "          if (rows > 546) {\n",
+      "            return Algo::kWarpDistributedShm;\n",
+      "          } else {\n",
+      "            if (k > 17) {\n",
+      "              return Algo::kWarpDistributedShm;\n",
+      "            } else {\n",
+      "              return Algo::kFaissBlockSelect;\n",
+      "            }\n",
+      "          }\n",
+      "        } else {\n",
+      "          return Algo::kFaissBlockSelect;\n",
+      "        }\n",
+      "      } else {\n",
+      "        if (k > 44) {\n",
+      "          if (cols > 1031051) {\n",
+      "            return Algo::kWarpDistributedShm;\n",
+      "          } else {\n",
+      "            if (rows > 22) {\n",
+      "              return Algo::kWarpDistributedShm;\n",
+      "            } else {\n",
+      "              return Algo::kRadix11bits;\n",
+      "            }\n",
+      "          }\n",
+      "        } else {\n",
+      "          return Algo::kWarpDistributedShm;\n",
+      "        }\n",
+      "      }\n",
+      "    } else {\n",
+      "      if (k > 1) {\n",
+      "        if (rows > 188) {\n",
+      "          return Algo::kWarpDistributedShm;\n",
+      "        } else {\n",
+      "          if (k > 72) {\n",
+      "            return Algo::kRadix11bits;\n",
+      "          } else {\n",
+      "            return Algo::kWarpDistributedShm;\n",
+      "          }\n",
+      "        }\n",
+      "      } else {\n",
+      "        return Algo::kFaissBlockSelect;\n",
+      "      }\n",
+      "    }\n",
+      "  }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "def convert_model_to_code(model):\n",
+    "    classes = model.classes_\n",
+    "    tree = model.tree_\n",
+    "    feature_names = [\"k\", \"rows\", \"cols\", \"use_memory_pool\"]\n",
+    "    \n",
+    "    def _get_label(nodeid):\n",
+    "        \"\"\" returns the most frequent class name for the node \"\"\"\n",
+    "        return classes[np.argsort(tree.value[nodeid, 0])[-1]]\n",
+    "    \n",
+    "    def _is_leaf_node(nodeid):\n",
+    "        \"\"\" returns whether or not the node is a leaf node in the tree\"\"\"\n",
+    "        # negative values here indicate we're a leaf\n",
+    "        if tree.feature[nodeid] < 0:\n",
+    "            return True\n",
+    "        \n",
+    "        # some nodes have both branches with the same label, combine those\n",
+    "        left, right = tree.children_left[nodeid], tree.children_right[nodeid]    \n",
+    "        if (_is_leaf_node(left) and \n",
+    "            _is_leaf_node(right) and \n",
+    "            _get_label(left) == _get_label(right)):\n",
+    "            return True\n",
+    "    \n",
+    "        return False\n",
+    "    \n",
+    "    code = []\n",
+    "    def _convert_node(nodeid, indent):\n",
+    "        if _is_leaf_node(nodeid):\n",
+    "            # we're a leaf node, just output the label of the most frequent algorithm\n",
+    "            class_name = _get_label(nodeid)\n",
+    "            code.append(\" \" * indent + f\"return Algo::{class_name};\")\n",
+    "        else:             \n",
+    "            feature = feature_names[tree.feature[nodeid]]\n",
+    "            threshold = int(np.floor(tree.threshold[nodeid]))\n",
+    "            code.append(\" \" * indent + f\"if ({feature} > {threshold}) \" + \"{\")\n",
+    "            _convert_node(tree.children_right[nodeid], indent + 2)\n",
+    "            code.append(\" \" * indent + \"} else {\")\n",
+    "            _convert_node(tree.children_left[nodeid], indent + 2)\n",
+    "            code.append(\" \" * indent + \"}\")\n",
+    "    \n",
+    "    code.append(\"inline Algo choose_select_k_algorithm(size_t rows, size_t cols, int k)\")\n",
+    "    code.append(\"{\")\n",
+    "    _convert_node(0, indent=2)\n",
+    "    code.append(\"}\")\n",
+    "    return \"\\n\".join(code)\n",
+    "\n",
+    "code = convert_model_to_code(model)\n",
+    "print(code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "412548a6-53ce-40c3-bdc2-4a69e000a7e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6750"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# also update the source code in raft/matrix/detail/select_k.cuh\n",
+    "import pathlib\n",
+    "select_k_path = pathlib.Path.cwd() / \"..\" / \"..\" / \"..\" / \"include\" / \"raft\" / \"matrix\" / \"detail\" / \"select_k-inl.cuh\"\n",
+    "source_lines = open(select_k_path.resolve()).read().split(\"\\n\")\n",
+    "\n",
+    "# figure out the location of the code snippet in the file, and splice it in\n",
+    "code_lines = code.split(\"\\n\")\n",
+    "first_line = source_lines.index(code_lines[0])\n",
+    "last_line =  source_lines.index(code_lines[-1], first_line)\n",
+    "new_source = source_lines[:first_line] + code_lines + source_lines[last_line+1:]\n",
+    "\n",
+    "open(select_k_path.resolve(), \"w\").write(\"\\n\".join(new_source))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cpp/scripts/heuristics/select_k/generate_plots.ipynb b/cpp/scripts/heuristics/select_k/generate_plots.ipynb
new file mode 100644
index 0000000000..1ed1f432d3
--- /dev/null
+++ b/cpp/scripts/heuristics/select_k/generate_plots.ipynb
@@ -0,0 +1,352 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0d0564e1-137c-4458-b0b4-d4aa01301942",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from collections import defaultdict\n",
+    "import pandas as pd\n",
+    "import json\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "sns.set_theme()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f91d6f1d-e198-46c8-9ac6-955995f058d1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>key_type</th>\n",
+       "      <th>index_type</th>\n",
+       "      <th>algo</th>\n",
+       "      <th>row</th>\n",
+       "      <th>col</th>\n",
+       "      <th>k</th>\n",
+       "      <th>use_index_input</th>\n",
+       "      <th>use_memory_pool</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>float</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix8bits</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>float</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>float</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix11bitsExtraPass</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>float</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kWarpImmediate</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>float</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kWarpFiltered</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179936</th>\n",
+       "      <td>float</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>7586</td>\n",
+       "      <td>162460</td>\n",
+       "      <td>8149</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.104029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179937</th>\n",
+       "      <td>float</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix11bitsExtraPass</td>\n",
+       "      <td>7586</td>\n",
+       "      <td>162460</td>\n",
+       "      <td>8149</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.104047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179959</th>\n",
+       "      <td>float</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix8bits</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>2042</td>\n",
+       "      <td>8175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000201</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179960</th>\n",
+       "      <td>float</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix11bits</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>2042</td>\n",
+       "      <td>8175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179961</th>\n",
+       "      <td>float</td>\n",
+       "      <td>int64_t</td>\n",
+       "      <td>kRadix11bitsExtraPass</td>\n",
+       "      <td>1075</td>\n",
+       "      <td>2042</td>\n",
+       "      <td>8175</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000138</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22913 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       key_type index_type                   algo   row     col     k  \\\n",
+       "40        float    int64_t            kRadix8bits     1    1024     1   \n",
+       "41        float    int64_t           kRadix11bits     1    1024     1   \n",
+       "42        float    int64_t  kRadix11bitsExtraPass     1    1024     1   \n",
+       "43        float    int64_t         kWarpImmediate     1    1024     1   \n",
+       "44        float    int64_t          kWarpFiltered     1    1024     1   \n",
+       "...         ...        ...                    ...   ...     ...   ...   \n",
+       "179936    float    int64_t           kRadix11bits  7586  162460  8149   \n",
+       "179937    float    int64_t  kRadix11bitsExtraPass  7586  162460  8149   \n",
+       "179959    float    int64_t            kRadix8bits  1075    2042  8175   \n",
+       "179960    float    int64_t           kRadix11bits  1075    2042  8175   \n",
+       "179961    float    int64_t  kRadix11bitsExtraPass  1075    2042  8175   \n",
+       "\n",
+       "        use_index_input  use_memory_pool      time  \n",
+       "40                    0                1  0.000024  \n",
+       "41                    0                1  0.000013  \n",
+       "42                    0                1  0.000013  \n",
+       "43                    0                1  0.000009  \n",
+       "44                    0                1  0.000010  \n",
+       "...                 ...              ...       ...  \n",
+       "179936                0                1  0.104029  \n",
+       "179937                0                1  0.104047  \n",
+       "179959                0                1  0.000201  \n",
+       "179960                0                1  0.000138  \n",
+       "179961                0                1  0.000138  \n",
+       "\n",
+       "[22913 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from select_k_dataset import load_dataframe, get_dataset\n",
+    "df = load_dataframe(\"select_k_times.json\")\n",
+    "df = df[(df.use_memory_pool == True)]\n",
+    "df = df[(df.index_type == 'int64_t') & (df.key_type == 'float')]\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e1b124af-1630-4139-a1e7-ed9ea96c384d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def generate_plot(df, x_axis=\"col\", title=\"\"):\n",
+    "    fig, ax = plt.subplots()\n",
+    "    for algo in sorted(set(df.algo)):\n",
+    "        current = df[(df.algo == algo) & (df.time < np.inf)]\n",
+    "        ax.plot(current[x_axis], current[\"time\"], label=algo)\n",
+    "    ax.set_xscale('log', base=2)\n",
+    "    ax.set_yscale('log', base=2)\n",
+    "    ax.set_xlabel(x_axis)\n",
+    "    ax.set_ylabel(\"time(s)\")\n",
+    "    ax.set_title(title)\n",
+    "    fig.set_dpi(200)\n",
+    "    ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=4)\n",
+    "#    fig.legend()\n",
+    "    plt.show()\n",
+    "\n",
+    "def generate_k_plot(df, col, row):\n",
+    "    return generate_plot(df[(df.col == col) & (df.row == row)], \"k\", f\"#cols={col}, #rows={row}\")\n",
+    "\n",
+    "def generate_col_plot(df, row, k):\n",
+    "    return generate_plot(df[(df.row == row) & (df.k == k)], \"col\", f\"#rows={row}, k={k}\")\n",
+    "\n",
+    "def generate_row_plot(df, col, k):\n",
+    "    return generate_plot(df[(df.col == col) & (df.k == k)], \"row\", f\"#cols={col}, k={k}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "13742a23-09a2-4ca1-aeb9-8e3914c7fc4d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1280x960 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "generate_col_plot(df, 256, 32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8401ef27-8733-4ab3-a561-e915a783a196",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Pz9WpUyfj9nO5XBo/fpImT57aI9cWBAAAALoKgkEAAAAAAGARCAR08OA+5efnqaSkOG4/j8ejiROzNHFilhITEzuwQgAAAABng2AQAAAAAABIkvx+v/bu3aWCgs0qLy+L2y8xMUmTJ0/V+PGT5PF4OrBCAAAAAOeCYBAAAAAAgB7O6/Vq9+7tKijYourqqrj9UlN7acqUaRo7drycTt5SAAAAALoa/ooHAAAAAKCHqq2t0Y4dBdq+favq6mrj9ktL66OsrByNGjVWDoejAysEAAAA0JYIBgEAAAAA6GGqqqq0ffsW7dy5XT6fN26//v0HKjs7R8OGjZRhGB1YIQAAAID2QDAIAAAAAEAPUV5epoKCLdqzZ6cCAX/cfoMHZygrK0dDhmQSCAIAAADdCMEgAAAAAADdXEnJGW3dmqf9+/fINM24/YYOHa7s7OkaODC9A6sDAAAA0FEIBgEAAAAA6KZOnTqprVvzdOjQgbh9DMPQiBGjlZ2do759+3dccQAAAAA6HMEgAAAAAADdiGmaOnHimPLzc3Xs2JG4/RwOh8aMGa8pU6apd++0DqwQAAAAgF0IBgEAAAAA6AZM01Rh4SHl5+fq1KmTcfu5XC6NGzdJkydnKyUltQMrBAAAAGA3gkEAAAAAALqwQCCggwf3KT8/TyUlxXH7eTweTZyYpYkTs5SYmNiBFQIAAADoLAgGAQAAAADogvx+v/bu3aWCgs0qLy+L2y8xMUmTJ2dr/PjJ8ng8HVghAAAAgM6GYBAAAAAAgC7E6/Vq9+7tKijYourqqrj9UlJSlZWVozFjxsvl4uU/AAAAAIJBAAAAAAC6hNraWu3cWaDt2/NVW1sbt19aWh9lZeVo1KixcjgcHVghAAAAgM6OYBAAAAAAgE6surpK27bla+fObfL5vHH79e8/QNnZ0zVs2EgZhtGBFQIAAADoKggGAQAAAADohMrLy1RQsEV79uxUIOCP2y89fYiys6dryJBMAkEAAAAATSIYBAAAAACgEykpOaOtW/O0f/8emaYZt9/QocOVlZWjQYMGd2B1AAAAALoygkEAAAAAADqBU6dOauvWPB06dCBuH8MwNGLEaGVl5ahfv/4dVxwAAACAboFgEAAAAAAAm5imqRMnjik/P1fHjh2J28/hcGjMmPGaMmWaevdO68AKAQAAAHQnBIMAAAAAAHQw0zR15Mgh5efnqajoRNx+LpdL48ZN0uTJ2UpJSe3ACgEAAAB0RwSDAAAAAAB0kEAgoIMH92nr1jydOVMct5/H49HEiVmaODFLiYmJHVghAAAAgO6MYBAAAAAAgHbm9/u1d+8uFRRsVnl5Wdx+iYlJmjw5W+PHT5bH4+nACgEAAAD0BASDAAAAAAC0E6/Xq927t6ugYIuqq6vi9ktJSdWUKdM0duwEuVy8VAcAAADQPni1AQAAAABAG6utrdXOnQXavj1ftbW1cfulpfVRVlaORo0aK4fD0YEVAgAAAOiJCAYBAAAAAGgj1dVV2rYtXzt3bpPP543br3//AcrOnq5hw0bKMIwOrBAAAABAT0YwCAAAAADAOaqoKFdBwWbt3r1TgYA/br/09CHKzp6uIUMyCQQBAAAAdDiCQQAAAAAAzlJJyRlt3Zqn/fv3yDTNuP2GDh2urKwcDRo0uAOrAwAAAAArgkEAAAAAAFrp1Kkibd2aq0OHDsTtYxiGRowYraysHPXr17/jigMAAACAOAgGAQAAAABoAdM0deLEMeXn5+nYscK4/RwOh0aPHq+srGnq3TutAysEAAAAgKYRDAIAAAAA0ATTNLVv3z6tW/eJiopOxO3ncrk0btxETZ48VSkpqR1YIQAAAAC0DMEgAAAAAABx7NmzW+vXr1dRUVHcPh6PRxMnZmnixCwlJiZ2YHUAAAAA0DoEgwAAAAAARDBNU5s2faqCgs1x+yQmJmny5GyNHz9ZHo+nA6sDAAAAgLNDMAgAAAAAQBjTNJWXtyFuKJiSkqopU6Zp7NgJcrl4WQ0AAACg6+AVDAAAAAAAYbZs2aT8/Nyo9rS0PsrKytGoUWPlcDhsqAwAAAAAzg3BIAAAAAAADfLzc7V588ao9osvXqShQ8fIMAwbqgIAAACAtsFHHAEAAAAAkFRQsFm5uZ9FtV966aWaNm0aoSAAAACALo9gEAAAAADQ423blq+NG9dHtS9atEjZ2dk2VAQAAAAAbY9gEAAAAADQo+3YUaANG9ZFtZ933nxNnTrNhooAAAAAoH0QDAIAAAAAeqxdu7br008/jmqfNWuuJk3KsqEiAAAAAGg/BIMAAAAAgB5p9+4d+uST1VHtM2bM1uTJU22oCAAAAADaF8EgAAAAAKDH2bt3l9atWxXVnpMzS1lZOR1fEAAAAAB0AIJBAAAAAECPsn//Hq1d+1FU+9SpMzR16gwbKgIAAACAjkEwCAAAAADoMQ4c2Kc1a1bINE1Le1ZWjqZNm2lTVQAAAADQMQgGAQAAAAA9wqFDB7R69QdRoeDkyVM1ffp5MgzDpsoAAAAAoGMQDAIAAAAAur3CwoNatWp5VCg4aVKWZs6cQygIAAAAoEcgGAQAAAAAdGtHjhzWypXLFAgELO0TJkzWrFnzCAUBAAAA9BgEgwAAAACAbuvo0UKtWPF+VCg4btxEzZ59PqEgAAAAgB6FYBAAAAAA0C0dP35UK1YsVSDgt7SPGTNec+deQCgIAAAAoMchGAQAAAAAdDsnThzThx++J7/fGgqOHj1W8+ZdSCgIAAAAoEciGAQAAAAAdCsnTx7XBx+8J5/PZ2kfOXKM5s9fKIeDl8IAAAAAeiZeDQEAAAAAuo1Tp07qgw/elc/ntbQPHz5KCxZcTCgIAAAAoEfjFREAAAAAoFs4fbpIy5a9I6/XGgoOGzZCF154CaEgAAAAgB6PV0UAAAAAgC6vuPh0QyhYZ2nPzByuCy9cTCgIAAAAACIYBAAAAAB0cWfOFGvZsrdVV1drac/IGKqFCxfL6XTaVBkAAAAAdC4EgwAAAACALquk5IyWLXtbtbU1lvbBgzO1cOGlcjpdNlUGAAAAAJ0PwSAAAAAAoEsqLS3RsmVvqaam2tKenj5EixZdJpeLUBAAAAAAwhEMAgAAAAC6nLKyUr3//luqrraGgoMGDdaiRZcTCgIAAABADASDAAAAAIAupby8rCEUrLK0DxgwSJdccrncbrdNlQEAAABA50YwCAAAAADoMioqKvT++2+pqqrS0t6//0AtXnyl3G6PTZUBAAAAQOdHMAgAAAAA6BIqKyv0/vtvqrKywtLer98ALV58pTweQkEAAAAAaArBIAAAAACg06uqqtT777+tiopyS3vfvv20ePGVSkhIsKkyAAAAAOg6CAYBAAAAAJ1adXWVli17W+XlpZb2Pn36asmSq5SYmGhTZQAAAADQtRAMAgAAAAA6rZqaar3//tsqLS2xtKel9WkIBZPsKQwAAAAAuiCCQQAAAABAp1RTU6Nly95WaekZS3uvXmlasuRqJSUl21QZAAAAAHRNBIMAAAAAgE6ntrZWy5e/rTNnii3tqam9dOmlVyk5mVAQAAAAAFqLYBAAAAAA0KnU1dVp+fJ3VFx82tJeHwp+TikpqTZVBgAAAABdG8EgAAAAAKDTCIaCp08XWdqTk1O0ZMlVSk0lFAQAAACAs0UwCAAAAADoFLxerz744F2dOnXS0p6UlKxLL71avXr1tqkyAAAAAOgeCAYBAAAAALbzer368MP3VFR0wtKelJSkyy67Wr17p9lUGQAAAAB0HwSDAAAAAABb+Xw+rVixVCdOHLO0JyYmacmSq9W7dx97CgMAAACAboZgEAAAAABgG7/fpxUr3tfx40ct7QkJiVqy5Cr16dPXpsoAAAAAoPshGAQAAAAA2MLv92vlyuU6dqzQ0u7xJGjJkqvUt28/myoDAAAAgO6JYBAAAAAA0OECgYBWrVquI0cOWdo9Ho+WLLlK/fr1t6kyAAAAAOi+CAYBAAAAAB2qPhT8QIcPH7S0u91uLV58pfr3H2BTZQAAAADQvREMAgAAAAA6TCAQ0Jo1H+rQof2WdperPhQcMGCQTZUBAAAAQPdHMAgAAAAA6BCBQEAff7xSBw7ss7S7XC5dcsnlGjgw3abKAAAAAKBncNldAACg9crLy/Xss89q+fLlKiwslCSlp6dr5syZuuuuu5SezptqAACgczFNU+vWrdL+/Xss7U6nU4sWXa709CE2VQYA3YffH1DAlByG3ZUAAIDOimAQALqYPXv26Otf/7pOnjypESNG6IILLpDX69WhQ4f06quv6vrrrycYBAAAnYppmvrkk9Xau3eXpd3pdOriiy/T4MEZNlUGAN3Hqs1H9cqKPXK7HPr2DVM1PqO33SUBAIBOiGAQALqQsrIy3XrrrSopKdHvfvc7fe5zn7PsP3TokFJTU22qDgAAIJppmlq//mPt3r3D0u5wOLRw4aXKyBhqU2UA0H2cKq3WC+/tVMA0JUn//cZWPfrt+TZXBQAAOiOCQQDoQp555hmdOHFCP/7xj6NCQUkaPny4DVUBAADEZpqmPvtsnXbt2mZprw8Flygzc5hNlQFA97Jq89FQKAgAANAUh90FAABapra2Vv/85z+VlJSkL37xi3aXAwAA0CTTNLVx43rt2LHV0m4Yhi68cLGGDh1hU2UA0L34/AGt3nzM0jZnymCbqgEAAJ0dIwYBoAkVFRX6+OOPtX79em3btk0HDhxQeXm5EhISNGjQIE2dOlVXX321LrjgAhlG+67uvnXrVpWXl2vmzJlKSkrSunXrtHr1alVUVGjo0KFavHixRo8e3a41AAAAtIRpmsrN/Uzbtm2xtNeHgpdo+PCR9hQGAN1Q3u5TKq2ss7RdNpcPXwAAgNgIBgEgjr/+9a96/PHHVVtbG7XP5/Np//792r9/v9544w3NmjVLjz76qDIyMtqtnj179kiS+vfvr7vuuktLly617H/88cf1rW99S9/73vfarQYAAICW2Lx5o7ZuzbO0GYahBQsu1ogRfJAJANrSyrwjlu3Jo/ppxODeKi6utKkiAADQmREMAkAc+/fvD4WC6enpmj9/vqZMmaL+/furtrZWeXl5+te//qWqqipt2LBBX/nKV/TKK6+of//+7VJPaWmpJGnFihWSpHvuuUef+9zn5HQ69e677+qRRx7RH/7wB2VkZOjGG29slxoAAACas2XLJm3Zsimq/fzzF2rUqLE2VAQA3deJM1XaduCMpe3yeSPtKQYAAHQJBIMAEEf9p9oX6NZbb9W8efPkcFiXZb3++ut1++2367bbbtP+/ftVWFio3/3ud3r44YejznXvvfdqy5YtUe1NWbJkie6+++7QdiAQkCR5vV7deeed+sY3vhHa95WvfEU+n0+/+c1v9Ic//IFgEAAA2GLr1jzl5W2Iap8//yKNHj3OhooAoHv7KO+oZbtXslvnT22/mWwAAEDXRzAIAHH84Ac/UJ8+fZrsk5mZqSeeeELXXnutJOndd9/VL37xCyUlJVn6HTt2TPv372/V9YuKiizbycnJoduxgr8vfOEL+s1vfqOjR4/q8OHDGjZsWKuuBwAAcC62bduiTZs+jWqfO/cCjR07wYaKAKB78/oCWrPlmKVt0axh8rid8vsDNlUFAAA6O4JBAIijuVAwaOLEiRo1apT279+v6upqHTx4UBMnTrT0efHFF8+5nszMTEmSx+NRenp61P6UlBT169dPxcXFKioqIhgEAAAdZvv2rdqw4ZOo9tmzz9f48ZNsqAgAur+NO0+qotprabt0zgibqgEAAF2Fo/kuAIDmpKamhm4H1yVsa5MnT5Yk1dXVqbIyehF5v9+v8vJySdbRhQAAAO1p585t+uyztVHt5503TxMnTrGhIgDoGVZGTCM6aURfZQ5MjdMbAACgHsEgAJyjuro6HThwILSdkdE+6zkMGTJEU6bUv7m2fv36qP0bNmyQ1+tVUlKSRo8e3S41AAAAhNu9e4fWr18T1T5z5hxNmpRtQ0UA0DMcOVWpXYdLLG0Lp2faUwwAAOhSCAYB4By99dZboZF6U6ZM0cCBA9vtWrfffrsk6ZFHHlFhYWGo/cSJE/r1r38tSfq3f/s3eTyedqsBAABAkvbu3aV161ZFtU+ffp6mTJlmQ0UA0HN8lHvEst072a3p4wbYVA0AAOhKDNM0TbuLAICuqri4WFdffbVOnz4tSXrmmWe0ZMmSdr3m/fffr5dfflnJycmaMWOGHA6HcnNzVV5erpycHD333HNKSkpq1xriYYH77sfhMGQYhkzTVCDAnwxoOzy20B54XHWcHTu267333otqnzt3nubOnWtDRe2HxxXaC48tnK3aOp++/qtlqqrxhdo+f/FYfeWKSTyuujmnkzEeAIBz57K7AADoqurq6nTnnXeGQsHFixe3eygo1QeDM2fO1EsvvaTc3Fz5fD6NHDlSV199tW655RYlJCS0ew3x8CKl+zIMQ06nYXcZ6IZ4bKE98LhqXzt27NDSpUuj2ufMmaP58+fJMLrn957HFdoLjy201tr845ZQ0DCky+eNtLwe43EFAADiYcQgAJyFQCCge++9V2+++aYkafjw4Xr11VeVlpZmc2X2YsRg98MnjtFeeGyhPfC4an+7d+/WO++8rciXkTNnztKCBQu6ZSjI4wrthccWzta9z6zWrkMloe0ZEwbpF7fNkcTjqrvjw7gAgLbAiEEAaCXTNHXfffeFQsGMjAz99a9/7fGhoCSVldXI5/PbXQbaUL9+KXI6DQUCpoqLK+0uB90Ijy20Bx5X7evQoQP66KNlUaHgpEnZmjx5us6cqbKpsvbF4wrthccWzsahE+WWUFCSzp+SHnoM8bjqvlwup/r2Tba7DABAN8DHTACgFUzT1P33369XXnlFkjR48GA9//zzGjp0qM2VAQAAtJ/CwkNatWp5VCg4ceIUzZo1t1uOFASAzmhl3lHLdt9eCZo6tr9N1QAAgK6IYBAAWsg0Tf3yl7/U3//+d0lSenq6XnjhBQ0fPtzmygAAANrP0aOFWrlymQIB65Th48dP0nnnzScUBIAOUl3r07qC45a2C6dlyOng7T0AANBy/OUAAC0QDAVffvllSdKgQYP0wgsvaMSIETZXBgAA0H6OHTuiFSuWKhCwThU+duwEzZnTPdcUBIDOav22E6qta3w+NgzpgqlDbKwIAAB0RQSDANCMyFBw4MCBeuGFFzRy5Eh7CwMAAGhHx48f1Ycfvie/3xoKjh49TvPmXUgoCAAdyDRNrcw7YmnLGTtA/Xon2lQRAADoqggGAaAZDzzwQFQoOGrUKJurAgAAaD8nThyPGQqOGjVW8+dfRCgIAB1s/7FyHTpRYWlbOD3TpmoAAEBXRjAIAE148MEH9be//U1SYyg4evRom6sCAABoP0VFJ/Thh+/K5/NZ2keMGK3zz18oB2tZAUCHW5lrHS04IC1RU0b1s6kaAADQlbnsLgAAOqvHH39c//M//yNJMgxDX/3qV7Vv3z7t27evyeMmT56sjIyMjigRAACgTZ06VaTly9+R1+u1tA8fPlIXXLCIUBAAbFBV49Wn209Y2i7KyZCD0dsAAOAsEAwCQBybNm0K3TZNU4899liLjnv44Yd1ww03tFdZAAAA7eL06VNavvztqFBw6NDhuuCCSwgFAcAma7ceV50vENp2OgwtmMqHUQEAwNnhlR0AAAAA9HBnzpzWsmVvq66uztKemTlMF120RE6n06bKAKBnM01TK/OOWtqmjx+otBSPTRUBAICujhGDABDHiy++aHcJAAAA7a6kpFjvv/+26upqLe1DhmRq4UJCQQCw0+7CUh09VWlpuziH0YIAAODsMWIQAAAAAHqo0tISvf/+26qtrbG0Dx6coYsvvkxOJ58lBQA7rcw7YtlO75esiSP62lQNAADoDggGAQAAAKAHKisr1fvvv6WammpLe3r6EF188WVyuQgFAcBO5VV12rDjpKXtomkZMgzDpooAAEB3QDAIAAAAAD1MeXmZ3n//LVVXV1naBw5M16JFl8ntdttUGQAg6OP84/L5zdC2y+nQ+dmDbawIAAB0B3wEFAAAAAB6kBMnjmnNmhWqqrKuWTVgwCBdcskVcrs9NlUGAAgKmKY+iphG9LyJA9UrmedoAABwbggGAQAAAKCbM01TR48WKj8/VydPHo/a37//AC1efIU8Ht5wBoDOYMfBMzpxxjrV80U5mTZVAwAAuhOCQQAAAADopkzT1KFD+5Wfn6fi4lMx+/Tt21+LF18pjyehg6sDAMSzMtc6WjBzQIrGDU2zqRoAANCdEAwCAAAAQDcTCAS0f/8ebd2ap9LSkrj9+vXrr8WLr1JCQmLHFQcAaFJpRa1yd1s/zLFweqYMw7CpIgAA0J0QDAIAAABAN+H3+7Rnzy4VFGxWRUV53H7JySnKypqmceMmyunkZSEAdCartxyTP2CGtj1uh+ZNGWxjRQAAoDvhFSAAAAAAdHFer1e7dm3Xtm1bVF1dFbdfr15pysqaptGjx8npdHZghQCAlggETH2Ud9TSNntSupITeQsPAAC0Df6qAAAAAIAuqra2Vjt2bNX27VtVV1cbt1/fvv2UlTVdI0aMksPh6MAKAQCtsXV/sU6X1VjaLp6eaVM1AACgOyIYBAAAAIAuprq6Stu25WvXrm3yer1x+w0YMEhTp05XZuZw1qYCgC5gZe4Ry/aI9F4aObiXTdUAAIDuiGAQAAAAALqIiooKFRRs1p49O+T3++P2Gzw4U9nZORo8OINAEAC6iOKyGm3ee8rSdtF0nscBAEDbIhgEAAAAgE6urKxE+fl52rdvt0zTjNtv6NARys7O0cCB6R1YHQCgLazafFThT/GJHqfmTOL5HAAAtC2CQQAAAADopIqLTys/P1cHD+6L28cwDI0YMVrZ2Tnq27d/B1YHAGgr/kBAqzYftbTNmzJYSQm8dQcAANoWf10AAAAAQCdTVHRC+fm5Kiw8FLePw+HQ6NHjlJWVo9690zqwOgBAW9u857RKKuosbRflZNhUDQAA6M4IBgEAAACgEzBNU8ePH1V+fq6OHz8at5/T6dS4cZM0ZcpUpaSkdmCFAID2sjL3iGV7TEZvDU/vZVM1AACgOyMYBAAAAAAbmaapwsKDys/P1alTRXH7ud1uTZgwRZMmZSspKakDKwQAtKeTJdUq2F9saVs4PdOmagAAQHdHMAgAAAAANggEAjp4cJ/y8/NUUlIct19CQoImTcrWxIlT5PEkdGCFAICOsCrvqMyw7eQEl86bOMi2egAAQPdGMAgAAAAAHcjv92vfvt3aujVP5eVlcfslJSVrypSpGjduktxudwdWCADoKD5/QGu2WKePnp89WB6306aKAABAd0cwCAAAAAAdwOfzaffuHSoo2Kyqqsq4/VJTeykrK0djxoyX08kbwwDQnW3aVaSyKq+lbWEO04gCAID2QzAIAAAAAO2orq5OO3cWaPv2fNXU1MTtl5bWV9nZORo5cowcDkcHVggAsMvK3COW7fHD+ihjQIpN1QAAgJ6AYBAAAAAA2kFNTY22b8/Xjh0F8nrr4vbr33+AsrOna9iwkTIMowMrBADY6djpSu04VGJpWzg9w55iAABAj0EwCAAAAABtqKqqUgUFW7R793b5fL64/dLThyg7e7qGDMkkEASAHuijPOvagqlJbs0cP8imagAAQE9BMAgAAAAAbaC8vExbt+Zp795dCgQCcftlZg5TVtZ0pacP7sDqAACdSZ3Xr4/zj1naFkwdIreLqaQBAED7IhgEAAAAgHNQUlKs/Pw8HTiwV6Zpxu03YsQoZWVNV//+AzqwOgBAZ7Rh50lV1lhHlV+UwzSiAACg/REMAgAAAMBZOHWqSPn5uTp8+EDcPoZhaPTocZoyZZr69OnbccUBADq1lbnWaUSnjOyr9L7JNlUDAAB6EoJBAAAAAGgh0zR14sQx5efn6dixwrj9HA6nxo6doKysaUpN7dWBFQIAOrvCkxXac6TU0rZweqZN1QAAgJ6GYBAAAAAAmmGapo4cOaz8/FwVFZ2I28/lcmn8+MmaPHmqkpMZ+QEAiLYy74hlOy3Fo2ljmWYaAAB0DIJBAAAAAIgjEAjo8OEDys/PVXHx6bj9PJ4ETZw4RRMnZikxMbEDKwQAdCW1dX6tKzhuabtgWoZcTodNFQEAgJ6GYBAAAAAAIgQCAe3fv0dbt+aptLQkbr/ExCRNnjxVEyZMktvt6bgCAQBd0vrtJ1Rd6w9tG4Z00bQMGysCAAA9DcEgAAAAADTw+33avXunCgo2q7KyIm6/lJRUTZkyTWPHTpDLxcsqAEDLrMy1TiOaPbq/+qcx0hwAAHQcXsECAAAA6PG83jrt3Lld27dvUXV1ddx+vXunKSsrR6NHj5PDwbRvAICWO3C8TAeOl1vaFk7PtKkaAADQUxEMAgAAAOixamtrtGNHgbZv36q6utq4/fr27a/s7OkaPnwkgSAA4KyszD1q2e7XO0FTR/e3qRoAANBTEQwCAAAA6HGqq6u0bdsW7dy5XT6fN26/gQPTlZ09XZmZw2QYRgdWCADoTqpqfFq/7YSl7cJpGXI4+LcFAAB0LIJBAAAAAD1GRUW5Cgo2a/funQoE/HH7DRmSqezs6UpPH0IgCAA4Z59sO65ab+O/Ow7D0AVTM2ysCAAA9FQEgwAAAAC6vdLSEm3dmqd9+3bLNM24/YYNG6Hs7OkaMGBQB1YHAOjOTNPUytwjlrbp4waob68EmyoCAAA9GcEgAAAAgG7r9OlT2ro1VwcP7o/bxzAMjRw5RllZOerbt18HVgcA6An2Hi1TYVGlpe2i6YwWBAAA9iAYBAAAANDtnDx5XPn5uTpy5HDcPg6HQ2PGjNeUKdPUu3daB1YHAOhJIkcLDuyTqMkj+SAKAACwB8EgAAAAgG7BNE0dOHBAn3yyXkeOFMbt53Q6NX78JE2ePFUpKakdWCEAoKepqPbq0+0nLW0LczLlYP1aAABgE4JBAAAAAF3emTOntXTpv3TixPG4fdxutyZOnKJJk7KVmJjUgdUBAHqqtVuPy+cPhLadDkPnZw+xsSIAANDTEQwCAAAA6NKKi0/p/fffUl1dXcz9CQmJmjw5WxMmTJHH4+ng6gAAPZVpmlHTiM6cMFC9U/i3CAAA2IdgEAAAAECXVVJyRsuWvRMzFExOTtGUKVM1duxEud1uG6oDAPRkOw+V6HhxlaXt4umZNlUDAABQj2AQAAAAQJdUVlaqZcveVm1tjaU9LS1NkyZN1Zgx4+V0Om2qDgDQ063Ms44WHNI/WeOH9bGnGAAAgAYEgwAAAAC6nIqKCi1b9raqq60jMYYNG6Zrr71OZWW1NlUGAIBUVlmnjTuLLG0X5WTKMAybKgIAAKjnsLsAAAAAAGiNqqoqLVv2liorKyztgwcP0XXXXSeXi88/AgDstSb/mPwBM7Ttdjk0P2uwjRUBAADUIxgEAAAA0GXU1NRo2bK3VV5eZmnv16+/rrvuOnk8HpsqAwCgXsA09VHENKKzJw5SahLr3QIAAPsRDAIAAADoEurqarV8+dsqLT1jaU9L66vFi69SYmKiTZUBANBo24FiFZVY17+9aHqmTdUAAABYEQwCAAAA6PS8Xq8++OA9FReftrT36tVbS5YQCgIAOo+VuUct20MHpmpMRm+bqgEAALAiGAQAAADQqfl8Pq1YsVRFRScs7SkpqVqy5GolJyfbVBkAAFZnymuVt/uUpe3i6RkyDMOmigAAAKwIBgEAAAB0Wn6/Xx99tEzHj1tHXyQlJevSS69WamqqTZUBABBt9ZajCphmaDvB7dTcKYNtrAgAAMCKYBAAAABApxQIBLR69Qc6cuSwpT0hIVFLllylXr2Ylg0A0HkEAqZWbbZ+kGXO5HQlJbhsqggAACAawSAAAACATicQCOjjj1fq0KEDlnaPx6MlS65Unz597SkMAIA4tuw7reKyWkvbwukZNlUDAAAQG8EgAAAAgE7FNE2tX79G+/fvsbS7XG5dcsmV6tdvgE2VAQAQ38rcI5btkYN7aeRgRrcDAIDOhWAQAAAAQKdhmqY2bFin3bt3WNqdTqcWLbpMAwcOsqkyAADiO1Varfy9py1tC6dn2lQNAABAfASDAAAAADqNvLzPtH37Vkubw+HQwoWXavBgpmMDAHROqzYflRm2nZTg1JxJ6bbVAwAAEA/BIAAAAIBOYcuWTcrPz7O0GYahCy9crMzMYfYUBQBAM3z+gFZvPmZpmzdlsBI8TpsqAgAAiI9gEAAAAIDttm3LV17ehqj2BQsu1vDhIzu+IAAAWihv9ymVVtZZ2hbmMI0oAADonAgGAQAAANhq167t2rBhXVT7vHkXatSosTZUBABAy63MO2LZHjs0TUMHpdpUDQAAQNMIBgEAAADYZt++3frkk9VR7bNnz9e4cRNtqAgAgJY7caZK2w6csbQtzGFNXAAA0HkRDAIAAACwxcGD+/Txxyuj2mfMmK2JE7M6viAAAFrpo7yjlu2URJdmTRhkUzUAAADNIxgEAAAA0OEKCw9p9eoPZZqmpX3q1BnKysqxpygAAFrB6wtozZZjlrbzs4fI43baVBEAAEDzCAYBAAAAdKhjx45o5cplCgQClvbJk7M1bdpMm6oCAKB1Nu46qYpqr6XtIqYRBQAAnRzBIAAAAIAOc/Lkca1YsVSBgN/SPn78JM2cOVeGYdhUGQAArbMy1zqN6MThfTSkf4pN1QAAALQMwSAAAACADnH6dJE++OBd+Xw+S/uYMeM1Z84CQkEAQJdx5FSldh0usbQtnJ5pTzEAAACtQDAIAAAAoN2dOVOsZcvekddrnXJtxIjRmjfvQkJBAECX8lHeEct272S3ZowfaFM1AAAALUcwCAAAAKBdlZWVaNmyt1VXV2tpHzp0uBYsuFgOBy9LAABdR63Xr7X5xy1tC6ZmyOXk3zMAAND58RcLAAAAgHZTUVGu999/WzU11Zb2IUMyddFFi+V0Om2qDACAs/PZ9pOqqm2cFtuQdGFOhn0FAQAAtALBIAAAAIB2UVVVqffff0tVVZWW9kGDBmvhwkvldLpsqgwAgLO3MmIa0Smj+2lQnySbqgEAAGgdgkEAAAAAba66ulrLlr2tiopyS3v//gO1aNHlcrvdNlUGAMDZO3SiXPuOllnaFuZk2lQNAABA6xEMAgAAAGhTtbU1Wr78HZWWllja+/Tpp8WLr5DH47GnMAAAztHKvKOW7T6pHk0b29+magAAAFqPYBAAAABAm6mrq9MHH7yrM2dOW9p7907TkiVXKiEh0abKAAA4N9W1Pq0rOG5pu3BahpwO3l4DAABdB3+5AAAAAGgTPp9PH374nk6dKrK0p6b20pIlVykpKdmmygAAOHfrt59QbZ0/tG0Y9cEgAABAV0IwCAAAAOCc+f0+rVixVCdPWkdSJCenaMmSq5SSkmpTZQAAnDvTNLUy94ilbdqYAerXm5HwAACgayEYBAAAAHBOAoGAPvpouY4ds75hmpiYpCVLrlKvXr1tqgwAgLax/1i5Dp2osLQtnJ5pUzUAAABnj2AQAAAAwFkLBAJas+ZDFRYesrR7PAlasuRKpaX1sacwAADa0Mo864df+vdOVNaofjZVAwAAcPYIBgEAAACcFdM0tW7dKh04sM/S7na7tXjxFerbt79NlQEA0Haqarz6dNsJS9tFORlyOAybKgIAADh7BIMAAAAAWs00TX366cfau3eXpd3lcmnRois0YMAgmyoDAKBtrd16XHW+QGjb6TB0wdQhNlYEAABw9ggGAQAAALSKaZratGm9du7cZml3OJxauPBSpacPtqkyAADalmma+ijvqKVt+rgBSktNsKkiAACAc0MwCAAAAKBVtmzZpIKCLZY2wzC0cOESZWQMtakqAADa3u7CUh05VWlpWzg906ZqAAAAzh3BIAAAAIAWKyjYrM2bN1raDMPQBRdcoqFDh9tUFQAA7WNl3hHLdnrfJE0c0demagAAAM4dwSAAAACAFtmxo0AbN66Pap8//yKNHDnahooAAGg/5VV12rDjpKXtopxMOQzDpooAAADOHcEgAAAAgGbt2bNTn376cVT7nDkLNGbMeBsqAgCgfX2cf1w+vxnadjkNnZ/NOroAAKBrIxgEAAAA0KT9+/dq3bpVUe2zZs3VhAmTbagIAID2FTBNfRQxjeisiYPUK9ljU0UAAABtg2AQAAAAQFyHDx/UmjUfyjRNS3tOzixNnjzVpqoAAGhfOw6e0Ykz1Za2hTmZNlUDAADQdggGAQAAAMR09GihPvpoWVQoOGXKNGVnT7epKgAA2t/KvKOW7YwBKRo3NM2magAAANoOwSAAAACAKCdOHNOKFUsVCAQs7RMnTtGMGbNlGIZNlQEA0L5KK2qVu6vI0rYwJ4N/+wAAQLdAMAgAAADA4tSpk/rww/fk9/st7WPHTtB5583njVEAQLe2essx+QONo+U9LofmZw22sSIAAIC2QzAIAAAAIOTMmdNavvxdeb1eS/vIkWM0d+4FhIIAgG4tEDC1arN1GtHZk9KVnOi2qSIAAIC2RTAIAAAAQJJUWlqiZcveVl1draV92LARWrDgYjkcvHwAAHRvW/cX61RpjaVt4fRMm6oBAABoe7yyBwAAAKDy8jK9//5bqqmxvhmakTFUF164mFAQANAjrMw9Ytkenp6qUUN62VQNAABA2+PVPQAAANDDVVZWaNmyt1VdXWVpT08fooULL5XT6bSpMgAAOk5xWY027z1laVuYk8k02gAAoFshGAQAAAB6sOrqKr3//tuqqCi3tA8YMEiLFl0ml8tlU2UAAHSsVZuPyjQbtxM8Ts2ZnG5fQQAAAO2AYBAAAADooWpqarRs2dsqLy+1tPft21+XXHKF3G6PTZUBANCx/IGAVm0+ammbN2WwkhL4gAwAAOheCAYBAACAHqiurk4ffPCOSkrOWNrT0vpoyZIrlZCQYFNlAAB0vM17Tqukos7StjAnw6ZqAAAA2g/BIAAAANDDeL1effDBuzp92rqOUq9evbVkydVKTEyyqTIAAOyxMu+IZXt0Rm8NT+9lUzUAAADth2AQAAAA6EF8Pp9WrFiqoqITlvbk5BQtWXKVkpOTbaoMAAB7nCypVsG+YkvbwpxMm6oBAABoXwSDAAAAQA/h9/v10UfLdPy4dQ2lpKQkXXrp1UpNZWQEAKDnWZV3VGbYdlKCS+dNGmRbPQAAAO2JYBAAAADoAQKBgFav/lBHjhy2tCckJGjJkqvUu3eaTZUBAGAfnz+gNVusH5g5P2uwEtxOmyoCAABoXy67CwAAAJ1HwAzIG/DJG/DK6/eqtrxSAfllmlJZZbUMGTIMQ4YcDV8NOQwjdFvBbTW2WfrJkAxDjoj9ANqXaZpau/YjHTq039Ludnu0ePFV6tOnn02VAQBgr027ilRW5bW0XTS9a04jWnB6p/756b9UVHlaTodTDjnldDjkMBxyGs76/xwOOQynnE21OZwNxzT2cTiCtyP7NLRZtoP9HHI4nBHHBa9Vfzu4L35b47kBAEDbIBgEAKCTCpgB1fm98jUEdXUNYZ034LWEd3WBsDZ/7Nt1/rqG7Tp5G27XNez3BXzymj7VBfzyK2DLfQ3FisGwUIYMo749MkSMHTo6rG2GI6pf43nC+yqinyPG9RTjOGtd4YGos+HND5flq0tOh0Muwymnw9Xw1Rl6UyVWuyvia30/V1h/3iBBy5imqfXr12jfvt2WdpfLpcWLr1D//gNsqgxAd2OapkpqS1VeVyGP06MEp0eehv9chpMPA6FTWpl7xLI9flgfZQ5IsamasxcwA3p+60uq9NdIkvz+gCSv5Le3rrZiyAgLGmOHh5Y2R1ioaTgitoPHODQwaYDmDJmp3h6mUwcA9BwEgwAAtIA/4I8RyDXejtznbSbM8wa8DaGftyG0q2vcH/DJa2NIZwdTZv26LqYZ3ohmGDJCIaGr4U0Pl6MhPGwIGh0tCh6DwaU1eGxZcOkKvRkT6/jg+V0EmbYwTVMbNnyiXbu2W9qdTqcWLbpcAwem21QZgO7on3ve0oeHV8fc5zAc9UGhozEwjPwavJ3giNyfELbtVoLDowRXQuhcTgdTPuLsHDtdqR2HSixtC3MyOuz6pmnKb/qjXieEXkvE+dBf+GuOukD9a4pT1cWhULA7MmXKZ/olf9snnRtPbtY9M7/LcwkAoMcgGAQA9CgBM6Dck/nadWaPakMvrsPDvOBoumBQV/9foAeFdOg6TJnymj55/T67S2mR+k96G3IaTrkdYSNHzOBeQzIa+wb3B6egjWxv2CM1jOxs7KvQSE4F/x/ZR7KMXDFinCtmTcFbhiLPFmoLa2loCFYTdmbLfYs+f8NhodbwEbXh0/HW93DIYTTeJ0fwaoahQGGVAkerI38Qco3rrbXlm2RU5MYYhRs5kjZ8JG/jdR0No2xjXdcR+jk4mjinGkf3ht0/6zTD1vsampI4eB0jdGY5DENnlCSX0ynTNFVeURv6vjoiz9nMKOQma41zf4CerrS2XCsOr4m7P2AGVO2rUbXaPrhwGs7GUDEYMkYEkFHhY0SfYACZ4HQ3hpAOQsfu7qO88LUFTaUkOzVpdKpKakvlC/iswVz4B/wCPvnCPizoC30g0Nf4+iJOmFfXMGNH8AOEJp+Gs93h8iOq9tUo1dP1RooCAHA2CAYBAD3KioMr9c9979ldBtAj1X/S25TPDKg24G3+AJyTIWV9NLzMOk2oKVO7+x3XmYo9UoVNhXVTkdMMK8Z27KmK4wSOcUPK5oLM4Dkc1gA5Ti0Oo3HtKcMwQlOw1bcboX2OhvMFp19zOBq3G/sGp3Br3A6uUWUE15oK+y9yO16bQ9H7CGM7n6KqItsCDr/pV7WvWtW+6uY7t5LL4QqNYKwPDN2hEYyeiNGNMcPHsJAxsr2tmA0zLoR//82wWRjM8D2mpVfodn3/4HnC/m9aW0JHmBHXi9m/iXrMyCMbbpvhrWGVmPWjxbwRYZxlFo8mgrlQGBfxwcBTvkolzghIDr8MR/1HAX+y7p3Y3+gu6PbCM5IkvyEFDEN+NXw1pIAkv2EoYEh+NbQZUqDhdnBfsJ/12GCf+rZQn4b+wWuFzh/qE15HY7tp83N6Zo1XCV3kg3YAALQFgkEAQI+y/cBau0s4J4ZpymVKbtOUO2DKbZpymabcpuq/NrRFbgePcYXtd5umXIZTbsMpt8Mlt8Mtj8Mtl8Mtj8sjt9OjlMRkud1umQFTtXV1MgMBmWZAAdOUaQZkmn6ZodsBmYGAAobZ0K+hXWH7ZIb1Nxv21W8HpMZ2M9DwplEg9LaQKck0wm7LiNgO7jdi9K1/wyH2uWL0b2pf5LnC9gXU+OaHL+zNDp/R+AaLP+zNF394u6zHBd84Ac5GenlazFBwb78TOpNUaVNV3ZvZ8PwWqN9AOzNkxA0WrSGkMyzgtG47DGd9uOkI26fGfc6GgDMpKUEuh1OGHPLW+iNCyrCgNE6IGVwTK2a7Ea898n454+7vLMpKC+0uoV34Aj75Aj5V+qra/Nxuh1sOwwg9f0iNIVjwdlPhGtqAOzRwv9uZXVqtyXNvkSSZAb/k90kBvxTw128H/FLAF7Ydtj+yr9l4fOOxwf2+2Nv+huNaIBQahgWUjWFlY6gY/Ns6EPY3dcBQRFgZ7GMNNOvPaQ1DA4bUx+fXDK9bLndS+/wgAADohAgGAQA9yrg6aXsb/evnCA/lQoGbdTvefncguK8htAv2V/2bRB7DWR/QOd1yOzxyOd3yuBLkdLrlcCVIbo8Ml0dyuuu/ujwynA1fXR7J1dDu9ETsd8twJUgud/2xzbyh2K9fipxOh/z+gIqL7QkTTNNseIMsIAUC9bfNgNQQRioQkGQ27AtE7GvmONPabt0X3m62/LioN1zCt2O9yRL/DRoz4Jc/4Jff9Mtn+uUPBOQ367f9agwcA0bD7WYCx8bQsrE9+Glvv+X44L6GoLLhjRNfWHv98RHXIsjsFAZW9tLI0oFR7fv7Ful0MsME0T2E1ppq4ZvO3ZUhI2ZgGBk6Ro7YdLYodAwbAeoI2xcVftb3O3hym6W2YTVe3Xq0RHWGoTqHoTrDkNdhqLbhdp3DkNcwVOeQah0OeQ2F+sX8Gna7u3xwxsvoecThCL5uCCjsNUP9duNrjOjt4OuKgV6fZgybKff4Bbbej/q/48P+HvZHhoixAsqGbX+s0DFW3/q2mNv+2P3Dz+voM0CeaVfWv2YCAKCHIBgEAPQoi8ZdqV5r/z8dTHTLqYhQLiy0s7zAVv2IOo+zYTSd0yO30y2XK6ExcHNHB3H1t92NYZ0lpHNLroSo/Qpfdw2SGtZiMwxJDikix+zJ36n6ILIlb6w0sz/Gp79jhphN7W/4RLgZ8CkQ8MvX8J/f9NWHmgG//GYw1AzIZwbkV324Ggj4G6Y1Cx8FUb8+XfhYCDPeVyOyjxHdz4h9rPU8MY6LdduIt99obAsbRRqv/uj7Z71+5LXCj48c/Rp+PZ+vr/y1gxQl4YhG+4o1qjTW6Fvr/W9q5Kyi9kePno1bp+rD6fDvV2QdajguYDlP+P54I4WbqrMnP1OguzNlhj4w0tkipl5+Uyl9hynFVyfT75WCX2u95xzo+qRQsFjrsAaPdQ1t3rghY/wA0tuNQke0HVfYB/pifQAwclaO0GuIQNj+0Ewe1u3QTCBh/V2mqaiVLQ2j4bWDJ+yrp+EDf57GdneCklJT5Bk4VMmTFuhMmb3PDPV/x7skR/3bj/x2AQDQORAMAgB6FPeoGZrb/0Gdd3KvZDgbgrmEhqAuPNRrGFnndMtwdJ5puoAgw3BITofkdNdv21xPa0WORjWDoy/DR34GAo0BaNg+0/TXjwY1I9oj+qmhnxnRTwF/xOjPxmuYAeu2pS2ilsbagp+GDx892tAePG/oepE1R1+v/txhxwePa8ZRRz996h7WEKQ3muw9qAk1R9rrR9klmJH/NTtdsDWsNRvWTFLwmKbCyRj7gscFwvo2ntuIU1/sKYul+inRrGFq09ePd1+D5wlOuRacri04TVtwbalQe8NUbfHag98nf9S2EQqFg2tLmRHnIsDtfnon9FbK5x6Iuc8M+BuDQl+dTH+d5PNKfq9MX53kr5Pp80b0qf8qv1duX52SIvvEODa8LWxRu7hM1Y+ODw8Z441abLKtIYD0NmzXNoSYPM7PXfisG+HBWvgIusYQz5SrmRF2kTN7BD80GD4Cr/lXAobkjhHaeTzRYZ47VqjXGO5Ftbnrv8rhavGHB/uG/Y2lTveRAQAA0BkQDAIAehxH70Fy9I4xogaAbQzDIRmMCm2KGRFuhoehR48e1WfrPrasPyVJU8aMUs64S+oDSEv46G8MN4OhpiXEDAskowLaOCGqpd2v6CA2XpAbHrZaawn1s4S9DbdbwVDEY8mMuGFG9DbCvhqGZDoabjc8QA0jdNuI0RarnwxHwymN0G3J0XgNxbiG6kdMGzHarNcIa5cRNtLaiN4ffjs4VbMZ4+cfN3QP+zlHPqZMM7pvcLrnZgR7NRc+Nra3PLBsUXtEH0uoqeBo14bpmyPPFb7mVQvPH97HH9Gnu4RH41xpcfcZDqfkSZKhjlvTywz4JF9EeNgQSEYGlKE+fq9lu75vnGPqGsLNUFBpDSPDQ8fwQNHyfYl124xsN2P3NZs4R+Rts/GAyD7R/Zo6l9lEndG349dpRvWNdX1HxP6WMaLCONPp0d4T1arxO1Upl7ymU0PS+2rYsAGhIC5WaBcM9yxt7oRWhXYAAACdAcEgAAAA0AWERomGt0k6fvyoPlq/ToGIUHDSpCzNmDWvW75ZaYav8RkWQPVJS5DDMBQwpTMlVfFDu1DwFxHQBUM1tKmoEcGRo3Yjw+HwNVtjjsL1hwXT1sC46RDTbLpvZAjesM/tUn1/v0/e2roY0ys3vWZWS0aqhYsMSS1hZAtCzpjhZVgffxPn8YeN7LT0jzpPw1ejcR3a4PmcpqnJlbXKGTe/jR5BbcNwuCSPS4anY8JIM/h49dfVB4vBUNHvDY1oTE1yyGFIgYCp8orqhtDMjHjMmI3t9ScOazfDL9jYHr4dMV13eHt0//APS8S6XrDd2q+xDLOZesPvU/x6rffdbGg2ZThdoekyw2f6qA/qYoR2wVlAIp7XV20+qud27QhtG5J+e9k8JaZ1XFANAABgJ4JBAAAAoIsqKjqhDz98T36/darR8eMnaVY3DQWl4JpFTklOhS/E5ExpnD7NUctLnc4i1ojgrvTIjJz6uLWi1oT1xwoSm1rX1RfzdnN9FfA1sd5sWH9/WH9/E/1bsS6gI22wPJMWtvp71Z0YhiE5XZLTJcOTHLNPcthjq+YsHls4OytzrdNrZ4/prwGEggAAoAfh1TIAAADQBZ0+fUrLl78rn89naR89epzmzFnQbUNBoKvp6mvCBpnBEZf+ZkJHh1OOtMGs0YxO6cDxMh04Xm5pW5iTaVM1AAAA9iAYBAAAALqYkpJiLV/+trzeOkv7iBGjNH/+RYSCANpc/Uhdl+RwSUrosgEneraVuUct2/16J2jqmP42VQMAAGAPPsIHAAAAdCFlZaVatuxt1dbWWtozM4drwYJFcjBKBwCAKNW1Pq3fdsLSduHUDDkcxNwAAKBn4V0DAAAAoIuoqCjX+++/perqakv74MEZWrhwsZxOZ5wjAQDo2T4pOK5ab+NamQ7D0AXTMmysCAAAwB4EgwAAAEAXUFVVqWXL3lZVVaWlfeDAdF188WVyOlklAACAWEzT1IrcI5a2nHED1LdXgk0VAQAA2IdgEAAAAOjkamqqtWzZ2yovL7O09+8/QJdccoXcbrdNlQEA0PntPVqmwiLrB2sW5jBaEAAA9EwEgwAAAEAnVltbq2XL3lFpaYmlvU+fvrrkkivl8XjsKQwAgC7io4jRggPSEjV5VD+bqgEAALAXwSAAAADQSXm9dfrgg3d15sxpS3vv3mlasuQqJSYm2lQZAABdQ0W1V5/uOGlpWzg9Uw7DsKkiAAAAexEMAgAAAJ2QaZpauXKZTp2yvpmZkpKqJUuuUlJSsk2VAQDQdazdelxeXyC07XQYWpA9xMaKAAAA7EUwCAAAAHRCO3du07Fj1qnPkpKSdemlVyslJdWmqgAA6DpM09TKiGlEZ04YqN4pTMMNAAB6LoJBAAAAoJOprKzQpk2fWtoSExN16aVXq1ev3jZVBQBA17LrcImOF1dZ2hbmZNpUDQAAQOdAMAgAAAB0IqZpav36NfL5vJb2Cy9crLS0PvYUBQBAF7QiYrTg4H7JmjC8jz3FAAAAdBIEgwAAAEAncuDAXhUWHrK0jR07UYMHZ9hUEQAAXU9ZZZ027iyytC3MyZBhGDZVBAAA0DkQDAIAAACdRE1NjT77bK2lLSkpSTNnzrGpIgAAuqaP84/JHzBD2y6nQ/Ozh9hYEQAAQOdAMAgAAAB0Ehs3fqKamhpL2+zZC5SQkGBTRQAAdD0B09TKPOs0orMnDVJqktumigAAADoPgkEAAACgEzh6tFB79+6ytA0fPlIjRoyyqSIAALqmbQeKVVRi/aDNwpxMm6oBAADoXAgGAQAAAJt5vV598slqS5vb7dHs2efbVBEAAF3XR7lHLdtDB6ZoTGZvm6oBAADoXAgGAQAAAJvl5W1QRUW5pW3mzDlKTk6xqSIAALqmM+W1yt19ytK2cHqmDMOwqSIAAIDOhWAQAAAAsNGpUye1Y8dWS1t6+hCNGzfRpooAAOi6Vm85qoBphrY9bofmTh5sY0UAAACdC8EgAAAAYJNAIKC1a1fJDHsD0+Fwat68CxjZAABAKwUCplZttk4jOndyupITXTZVBAAA0PkQDAIAAAA22bo1TyUlxZa2adNmqnfvPvYUBABAF7Zl32kVl9Va2hZOz7SpGgAAgM6JYBAAAACwQWlpibZs2WRp69u3v6ZMmWpTRQAAdG0rc49YtkcM7qWRg3vbVA0AAEDnRDAIAAAAdDDTNLVu3SoFAoFQm2EYmjfvQjkc/IkOAEBrnSqtVv7e05a2ixktCAAAEIV3HQAAAIAOtmvXdp08edzSNmlStgYMGGhTRQAAdG2rNh+TGbadlODU7EmDbKsHAACgsyIYBAAAADpQVVWlNm1ab2lLTe2lnJxZNlUEAEDX5vMHtHrzUUvb3CmDlehx2VQRAABA50UwCAAAAHQQ0zT1ySdr5PV6Le1z514gl4s3LwEAOBt5u0+ptLLO0rYwh2lEAQAAYiEYBAAAADrIwYP7VVh40NI2Zsx4ZWQMtakiAAC6vo/yjli2x2amadigVJuqAQAA6NwIBgEAAIAOUFtbo08//djSlpiYpFmz5tpUEQAAXd+JM1UqOHDG0rZweoZN1QAAAHR+BIMAAABAB9iw4RPV1FRb2mbPPl8JCYk2VQQAQNf3UZ51bcGURJdmTRhkUzUAAACdH8EgAAAA0M6OHi3U3r27LG1Dh47QiBGjbKoIAICuz+sLaM2WY5a287OHyON22lQRAABA50cwCAAAALQjn8+nTz5ZbWlzu92aM+d8GYZhU1UAAHR9G3edVEW119J2UQ7TiAIAADTFZXcBAICWWb9+vb761a8222/OnDl64YUXOqAiAEBL5OVtUEVFuaVtxow5SklJtakiAAC6h5W51mlEJw7voyH9U2yqBgAAoGsgGASALmLAgAG6/vrr4+5ftmyZKioqNHv27A6sCgDQlNOni7R9e76lbdCgwRo/fpJNFQEA0D0cPVWpXYdLLG0Lp2faUwwAAEAXQjAIAF3EmDFj9Jvf/CbmvmPHjumNN96QYRi69tprO7gyAEAsgUBAa9eukmmaoTaHw6F58y5kClEAAM7Ryrwjlu1eyW7NGD/QpmoAAAC6DtYYBIBu4I033lAgENCsWbM0bNgwu8sBAEgqKNiiM2dOW9qmTp2htLQ+9hQEAEA3Uev1a23+cUvbgqlD5HLyNhcAAEBz+IsJALqB//u//5MkXXfddbbWAQCoV1ZWqs2bN1ra+vTppylTptlUEQAA3cdn20+qqtZnabsoh2lEAQAAWoKpRAGgCRUVFfr444+1fv16bdu2TQcOHFB5ebkSEhI0aNAgTZ06VVdffbUuuOAC26aF27x5s/bv36+kpCRdfvnlttQAAGhkmqbWrVulQMAfajMMQ/PnXyin02ljZQAAdA8fRUwjmjWqnwb1SbKpGgAAgK6FYBAA4vjrX/+qxx9/XLW1tVH7fD6f9u/fr/379+uNN97QrFmz9OijjyojI6PD6wyOFlyyZIlSU1M7/PoAAKvdu3foxIljlraJE7M0YMAgmyoCAKD7OHSiXHuPllnaGC0IAADQcgSDABDH/v37Q6Fgenq65s+frylTpqh///6qra1VXl6e/vWvf6mqqkobNmzQV77yFb3yyivq379/h9VYV1end955R5J0/fXXd9h1AQCxVVVVauPG9Za21NReysmZZVNFAAB0Lyvzjlq2+6R6NG1sx70GAwAA6OoIBgEgDsMwtGDBAt16662aN2+eHA7rsqzXX3+9br/9dt12223av3+/CgsL9bvf/U4PP/xw1LnuvfdebdmypVXXX7Jkie6+++4m+6xYsUIlJSUaPHiw5s6d26rzAwDa3qeffiyvt87SNnfuBXK73TZVBABA91Fd69O6guOWtgunZcjldMQ5AgAAAJEIBgEgjh/84Afq06dPk30yMzP1xBNP6Nprr5Ukvfvuu/rFL36hpCTr+hbHjh3T/v37W3X9oqKiZvsEpxG99tpro4JLAEDHOnhwvw4dOmBpGz16nDIyhtpTEAAA3cz67SdUWxe+hm99MAgAAICWIxgEgDiaCwWDJk6cqFGjRmn//v2qrq7WwYMHNXHiREufF198sc3rKy4u1urVqyVJ1113XZufHwDQcnV1tfr00zWWtsTERM2aNc+migAA6F5M09TK3COWtmljBqhf70SbKgIAAOiaGF4CAG0gNTU1dDu4LmF7e+utt+T1epWTk6PRo0d3yDUBALFt2LBe1dXVlrbzzpuvxETerAQAoC0cOF6uQycqLG0LpzNaEAAAoLUIBgHgHNXV1enAgQOh7YyMjnlxGpxGlNGCAGCv48ePas+eHZa2zMzhGjlyjE0VAQDQ/ayIGC3Yv3eCskb1///Zu+/guNP7zvOfX3ejgUbOAAEQBHPOBEByOBxOpEZjnSXZstcnS5Z09m55dyWXymHrfD7ZssvW3Ule+2odyiuvJY20SvbZsqw0iTmCOXNIAgSJRCJndPz97g+QIB4wAUT4dQPvV9UU0Z9On9G0CKC//TyPS20AAAASF1uJAsAk/ehHP1JfX58kafXq1SooKJj257x+/bouXbokv9+vD37wg9P+fOOVyTY+s47HY438mZub5nIbzCaz5bUVjUb1wx+aW4gmJSVp9+5XlZmZ/ph7YbrMltcV4guvK0wXXlvj1z8UUc2Vu0b2gW0Vys/ne+1YvK4AAMDTMBgEgEno7OzUV77ylZHLv/mbvzkjz/sv//IvkqSXXnpJWVlZM/Kc4+H1shB9trIsS16v5XYNzEKJ/to6fPiYuru7jez5559XTk62K30wLNFfV4hPvK4wXXhtPd3Bs00KR+yRy16Ppde2VvD7xxPwugIAAI/DYBAAnlE4HNZnP/tZdXR0SJJeeeUVvfrqq9P+vLFYTP/2b/8mSfrIRz4y7c83EbGY/fQbIaF4PJYsy5LjOLJtx+06mEVmw2urtbVVJ0+eNLJ58+Zp7dp1/H3oktnwukL84XWF6cJra3wcx9FPj9QbWfXqYmWl+fl++wi8rmY3huEAgKnAYBAAnoFt2/r93//9kTeEy8vL9Wd/9mcz8txer1cHDx6ckeeaqN7eoKLRmNs1MIVyc9Pk9VqybUednQNu18EskuivLdu29bOfvSXHefCGm8fjUWXlDnV1DbrYbG5L9NcV4hOvK0wXXlvjc62hW7fv9hnZttVF/G/2GLyuZi+fz6ucnFS3awAAZgE+ZgIAE+Q4jv7wD/9wZNVeSUmJvva1r8XVlp4AgOl1+fIFdXa2G9natRuVnZ3jUiMAAGan/WebjMuFOQGtXMD3WwAAgGfFYBAAJsBxHP3RH/2Rvv/970uSiouL9Y1vfENlZWUuNwMAzJTe3h6dO2duIZqdnaM1aza4UwgAgFmqbzCsE1fbjGzXhlJ5LM7OAwAAeFYMBgFgnBzH0Re/+EV997vflSQVFRXpzTffVHl5ucvNAAAzxXEcHTt2ULGYuW3ytm075fV6XWoFAMDstO9Mk6KjzhH0eS09t7bYxUYAAACJj8EgAIzD/aHgd77zHUlSYWGh3nzzTS1YsMDlZgCAmXTjxvu6c6fZyFasWKOCgiKXGgEAMDtForbeO21uI1q5olAZqX6XGgEAAMwODAYB4CnGDgULCgr05ptvqqKiwt1iAIAZNTQ0qFOnjhlZWlq6Nm7c4lIjAABmr2OX76h3IGxkr1WyWwsAAMBkMRgEgKf44z/+44eGggsXLnS5FQBgptXUHFY4bL5BuXXrDiUlsXIBAICp5DiO3j7RYGQryrO1oDjDpUYAAACzh8/tAgDmNtu2df78eZ0/f15Xr15VU1OT2tvbNTQ0JEkKBALKz89XaWmpVqxYoXXr1mndunXyeGbmcw1/8id/om9/+9uSHgwFFy1aNCPPDQCIH7dv1+vWrZtGtnDhEpWWsnIBAICpdqm+U01tA0a2u4rvuQAAAFOBwSCAGReLxXTw4EH98Ic/1KFDh9TX1/fE29fV1RmXMzIytGPHDn3oQx/Szp075fV6p6XnX/zFX+hb3/qWJMmyLH3yk59UXV3dQ33GWrVqlUpKSqalEwBg5oXDYR0/fsjIkpOTVVm5zaVGAADMbm/VmKsFi3NTtXZxnkttAAAAZhcGgwBmTG9vr77zne/of/7P/6m2tjZJw1vEPMvj/PSnP9VPf/pT5efn61d/9Vf17/7dv1NWVtaU9j19+vTI147j6M///M/Hdb8vfelL+uhHPzqlXQAA7jl9+riGhgaNrLJyu1JSAi41AgBg9mps7delm51G9lrVfHksy6VGAAAAswuDQQDTbmhoSP/wD/+gr3/96+rv75f0YCBoWZYWLVqkFStWaPHixSoqKlJOTo4CgYAcx1EwGFRnZ6fu3r2ruro6Xb16VXV1dSP3b2tr01/+5V/qq1/9qj796U/r05/+tFJTU137dwUAzC5377bo2rUrRlZSMl8LFy5xqREAALPb2LMF0wNJ2r662KU2AAAAsw+DQQDT6kc/+pG+/OUvq7W1dWSYl5aWpl27dunVV19VdXW1cnJyJvSYXV1dOn78uN555x3t379f/f396u/v11/91V/pe9/7nv7Lf/kveuONNybd/Zvf/OakHwMAkLhisaiOHj1gZD6fT1u37pDFqgUAAKZcT39Ixy7fMbKXNpXKnzQ9x0cAAADMRQwGAUyr3/md3xn5et26dfqVX/kVvf7660pJSXnmx8zJydEHPvABfeADH1AoFNJPfvITfec739H58+fV2tqq3/3d352SwSAAYG47f/60ent7jGzjxkqlp2e41AgAgNntvdONisYeHDfh83r04qYyFxsBAADMPgwGAUy7qqoq/af/9J9UXV095Y+dnJysj3zkI/rIRz6i48eP66//+q914sSJKX8eAMDc0tnZoYsXzxlZfn6hli9f7VIjAABmt1Akpr2nm4xs2+oiZaX5XWoEAAAwOzEYBDCtvvrVr+r555+fkeeqrq5WdXW1Dh06NCPPBwCYnWzb1tGjB0a2wJaGz8Tdtm2nPB6Pi80AAJi9jlxo0UAwamSvVZW71AYAAGD24p0NANNqpoaCo+3YsWPGnxMAMHtcvXpRHR1tRrZ27Ubl5OS61AgAgNnNdhy9faLByNYuylNpfppLjQAAAGYvBoMAAADAPX19vTpzxtySOisrW2vXbnSpEQAAs9+5G+262zVkZK9VzXepDQAAwOzGYBAAAACQ5DiOjh07qFgsZuTbtu2U1+t1qRUAALPfWzXmasGygnStWpDjUhsAAIDZjcEggIQRiUTU3t6uaDT69BsDADBBdXXX1dLSZGTLl69SYWGxS40AAJj9brb06lpDt5Htrpovy7LcKQQAADDL+dwuAGBua2gY/mSo3+9XUVHRI29z69YtfelLX9Lhw4cVjUbl8Xi0bds2/Zf/8l+0dOnSmawLAJilhoYGdeLEUSNLTU3Txo1VLjUCAGBuGHu2YFa6X9WrHv27IQAAACaPFYMAXHP+/Hm99tpreu211/R3f/d3j7xNS0uLfvmXf1n79+9XJBKR4ziKxWI6dOiQfumXfknnzp2b4dYAgNnoxIkjCodDRrZ16w75/X6XGgEAMPt19AR14kqrkb2yuUw+L29XAQAATBd+0gLgmn379slxHEnSRz/60Ufe5ktf+pK6u7sfed3Q0JB+93d/V5FIZLoqAgDmgIaGW6qvrzOyiorFKitb4FIjAADmhndPNci+9zuhJPmTPHphQ6mLjQAAAGY/BoMAXHN/tV9OTo7WrFnz0PV3797VO++8I8uylJKSoi9/+cs6deqUfvSjH43cvqGhQT/96U9ntDcAYPYIh8M6fvyQkfn9yaqs3O5SIwAA5oahUFQHzjUb2Y6185QeSHKpEQAAwNzAYBCAaxoaGmRZllasWPHI6999992RFYW/8Ru/oQ996ENKS0vTkiVL9OUvf3nkdnv27JmRvgCA2efMmRoNDg4YWWXlNgUCAZcaAQAwNxw816yhUGzksiXp1cr57hUCAACYIxgMAnBNe3u7JKmo6NEHyx8/fnzk61/4hV8wrlu4cKHWrFkjx3F05cqV6SsJAJi1Wlvv6P33LxvZvHmlWrRoqUuNAACYG2K2rXdONhrZxmUFKspJdakRAADA3MFgEIBrQqGQJCklJeWR158+fVqWZWnJkiWPHB7Onz/8adL7A0YAAMYrFovp6NEDRubz+bR16/OyLMulVgAAzA2n3m9TR2/QyF5jtSAAAMCMYDAIwDV+v1+SNDg4+NB1t2/fHhn4bd68+ZH3z8zMlCQFg8FHXg8AwONcuHBGPT3dRrZhwxZlZGS6UwgAgDnCcRy9VdNgZAvnZWppWZZLjQAAAOYWBoMAXJOXlydJqq2tfei6gwcPjny9cePGR96/v79f0uNXHAIA8ChdXZ26cOGMkeXlFWjFijUuNQIAYO640dSjmy29Rra7aj4r9gEAAGYIg0EArlm5cuXIGYG3bt0yrvvBD34w8nV1dfUj79/YOHwmRWFh4bR1BADMLrZt6+jRA3IcZySzLEvbtu2Ux8OPxgAATLexqwXzMlO0eXmBS20AAADmHt79AOCaV155RdLwm7T/+T//Zx07dkzvv/++vvjFL+rChQuyLEvr1q1TcXHxQ/eNRCJ6//33ZVmWFi5cONPVAQAJ6v33L6m9vdXI1qxZr9zcPJcaAQAwd9ztGtSZa21G9uqWMnn5cA4AAMCM8bldAMDc9cYbb+jv/u7vdPPmTd24cUOf/vSnH7rNb/zGbzzyvkePHlUwGBwZHgIA8DT9/X06c+aEkWVmZmnduk0uNQIAYG5590SjnFGXA8lePb++xLU+AAAAcxEfyQLgGp/Pp7/+679WUVGRHMcx/pGkX/3VXx1ZVTjWv/7rv458/bitRgEAuM9xHB07dlDRaNTIt23bKa+Xz8oBADDd+ociOnih2ch2ri9RIJnvwwAAADOJn74AuGrhwoX68Y9/rP/v//v/dPLkSQ0MDKi4uFivv/66duzY8cj7dHV16eLFiyopKVFaWpo2bNgws6UBAAnn5s0bam5uNLJly1aqqGieS40AAJhb9p9tUjhij1z2WJZe2TzfxUYAAABzE4NBAK5LS0vTJz/5SX3yk58c1+1zcnL01ltvTXMrAMBsEQwO6cSJI0YWCKRq0yZWnAMAMBOiMVvvnjI/oFO5slB5WSkuNQIAAJi72EoUAAAAs9qJE0cVCoWMrLp6h/x+v0uNAACYW45fvque/rCRvVbJakEAAAA3MBgEAADArNXYeFs3b94wsgULFqq8vMKdQgAAzDGO4+itmgYjWzY/WwvnZbrUCAAAYG5jMAgAAIBZKRIJ69ixg0bm9/tVVfWcS40AAJh7rtzqUmNbv5HtrmK1IAAAgFsYDAKYVl//+tcViURm7PkikYi+/vWvz9jzAQDi15kzJzQ4OGBkmzdvVSCQ6lIjAADmnrGrBYtyAlq/JN+lNgAAAGAwCGBa/V//1/+l119/Xf/4j/84rQPCcDis733ve/rABz6g//v//r+n7XkAAImhre2url69ZGTFxSVasmS5S40AAJh7mtoHdKGuw8heq5wvj2W51AgAAAA+twsAmN0sy1JTU5O+8IUv6P/9f/9f/dIv/ZJ+4Rd+QaWlpVPy+E1NTfrHf/xH/dM//ZM6OjrkOI68Xu+UPDYAIDHFYjEdOXLAyLxer7Zt2ymLNyIBAJgx75y4bVxOS/Fp+9p5LrUBAACAxGAQwDT7x3/8R/3Jn/yJzp07p/b2dv3t3/6t/vZv/1br1q3Tyy+/rK1bt2rNmjXyeMa3gNm2bV28eFHHjh3Te++9pwsXLshxHDmOI0nasGGD/s//8/+czn8lAECcu3jxrHp6uoxs/fotysjIdKkRAABzT89AWEcu3jWyFzeVKjmJD3ICAAC4icEggGm1Zs0afe9739NPfvIT/dVf/ZXq6uokSefPn9f58+clSSkpKVq8eLEWLVqk4uJiZWdnKyUlRY7jKBQKqaurS3fu3NHNmzdVW1urYDA48vj3B4KLFi3SZz/7Wb3++usz/y8JAIgb3d1dunDhjJHl5uZr1aq1LjUCAGBu2nu6UdGYPXLZ57X08qYyFxsBAABAYjAIYIZ88IMf1Ac+8AG98847evPNN3Xq1KmR64aGhnTp0iVdunTpCY8w7P4g8L7Nmzfr137t1/Tqq6+yPRwAzHG2bevo0QOy7QdvQlqWpW3bdo57ZToAAJi8cCSmPaebjGzrqmJlpSe71AgAAAD3MRgEMGM8Ho92796t3bt3q76+Xv/2b/+mvXv36sqVKw8N/B7HsiytXLlSL774oj70oQ+poqJieksDABLGtWuX1dZmblm2atU65eXlu9QIAIC56cilO+ofihjZa5XzXWoDAACA0RgMAnBFRUWFPvvZz+qzn/2surq6dP78eb3//vtqbGxUR0eHhoaGJEmBQED5+fkqKyvT8uXLtXbtWuXk5LjcHgAQb/r7+3X69Akjy8jI1Pr1m11qBADA3GQ7jt6uaTCy1QtzVVaY7lIjAAAAjMZgEIDrcnJy9MILL+iFF15wuwoAIAE5jqPjxw8qGjVXJmzbtlM+Hz/uAgAwky7UduhO56CR7a5itSAAAEC84LAVAAAAJLT6+lo1NZkrE5YsWaHi4hKXGgEAMHe9VXPbuFxakKbVFbkutQEAAMBYDAYBAACQsILBoGpqjhhZIBDQli3VLjUCAGDuunWnT1dvdxvZa5XzZVmWO4UAAADwEAaDAAAASFgnTx5VKBQ0sqqqHfL7k11qBADA3PX2CXO1YGaaX1tXFbvUBgAAAI/CoSsA4sqVK1d06tQptbS0qLe3V7FYTH/2Z3/mdi0AQBxqampQXd11Iysvr9CCBQtdagQAwNzV2RtUzZVWI3t5U6mSfHwmHQAAIJ4wGAQQF372s5/pr/7qr1RbWzuSOY4jy7IeGgy2t7frwx/+sGKxmDZv3qy/+qu/mum6AACXRSIRHTt20MiSkvyqqnrOpUYAAMxt751qVMx2Ri77fR69uKnMxUYAAAB4FD62BcB1X/jCF/T5z39etbW1chxn5J/Hyc/P17Zt29TV1aU9e/bo7t27M9gWABAPzp49oYGBfiPbvLlaqalpLjUCAGDuCoaj2ne22cieWztP6YEklxoBAADgcRgMAnDVX/zFX+j73//+yDBwx44d+p3f+R1VV1c/8X4f/vCHJQ2vKty/f/8MNAUAxIu2tlZduXLRyIqK5mnp0hUuNQIAYG47eL5FQ6HoyGVL0quV890rBAAAgMdiMAjANfX19fof/+N/SJIyMzP1jW98Q3//93+vX//1X9eSJUueeN+tW7cqEAhIko4fPz7tXQEA8SEWi+noUfMDIR6PV9u27ZRlWS61AgBg7rJtR++caDCy9UvyVZyb6lIjAAAAPAmDQQCu+d73vqdoNCrLsvQnf/InT10lOJrX69Xy5cvlOI5u3LgxjS0BAPHk0qVz6u7uMrL16zcrMzPLpUYAAMxtp6+1qb0naGS7q1gtCAAAEK8YDAJwzbFjxyRJ5eXl2r1794TvX1paKkm6c+fOlPYCAMSnnp5unT9/2shycvK0evU6lxoBAIC3Ttw2Li8oztCy+dnulAEAAMBTMRgE4Jrm5mZZlqV1657tDd309HRJ0sDAwFTWAgDEIcdxdPToAdm2PZJZlqXt23fK4+FHWgAA3HCjqUe1Tb1GtrtqPtt7AwAAxDHeRQHgmsHBQUlSauqznT0RDA5vV5OcnDxlnQAA8enatStqbTVXiK9cuVZ5eQUuNQIAAG/XmKsFczOTtWV5oUttAAAAMB4MBgG4Jjs7W5LU1dX15Bs+xu3bw7+E5ubmTlUlAEAcGhjo1+nTx40sPT1DGzZscakRAABo6x7SqWttRvbK5vnyeXmrCQAAIJ7x0xoA15SXl8txHJ0/f37C9+3q6tLFixdlWZZWrFgxDe0AAPHAcRwdP35IkUjEyLdt2ymfz+dSKwAA8M7JBjnOg8spfq92ri9xrxAAAADGhcEgANc899xzkqS7d+/q3XffndB9//t//+8jbxJv3759yrsBAOLDrVt1amw0tylbvHiZ5s0rdakRAAAYDEZ08HyLke1cX6LUFD60AwAAEO8YDAJwzUc/+tGR8wG/+MUvqrGxcVz3+5d/+Rd9/etfl2VZyszM1M///M9PZ00AgEtCoaBqao4YWUpKQFu2bHWpEQAAkKT9Z5sVCsdGLluW9MrmMhcbAQAAYLwYDAJwTXFxsT7zmc/IcRy1t7frF3/xF/Wtb33rkWcOhkIhHT16VJ/73Of0+7//+3Lu7Vnzuc99TqmpqTNdHQAwA06ePKZgcMjIqqqeU3JyikuNAABANGbr3VPmhzq3LC9UfnbApUYAAACYCPZ4AOCqz33uc6qtrdXbb7+tnp4e/emf/qn+9E//VElJSSO3qaysVH9//8jl+0PBD3/4w/r4xz8+450BANOvublRtbXXjKysbIEWLFjoUiMAACBJJ662qqsvZGS7q8pdagMAAICJYsUgAFdZlqW//Mu/1G/+5m/K4/HIcRw5jqNIJCLLsiRJfX19I7njOPJ6vfrP//k/60tf+pLL7QEA0yESiejYsYNGlpSUpK1bd4x8bwAAADPPcRy9VWOe/bukLEuLSjJdagQAAICJYsUgANd5PB791m/9ln7xF39R3/jGN3TgwAHV19c/dLt58+Zp165d+sxnPqP58+fPfFEAwIw4d+6U+vv7jGzTpmqlpqa51AgAAEjS+7e7dftuv5HtrmS1IAAAQCJhMAggbpSWlur3f//39fu///vq7u5WW1ub+vr6lJqaqry8PBUUFLhdEQAwzdrb23TlygUjKyws1rJlK11qBAAA7hu7WrAwO6CNS/NdagMAAIBnwWAQQFzKzs5Wdna22zUAADPItm0dPbp/5CxZaXhV+bZtO9lCFAAAl7V0DOhcbYeRvVo5Xx4P36MBAAASCWcMAgAAIC5cunROXV2dRrZu3SZlZWW7UwgAAIx450SDcTktxacda+e51AYAAADPisEgAAAAXNfb261z504bWXZ2rlavXu9SIwAAcF/vYFiHL94xsl0bS5Xs97rUCAAAAM+KwSAAAABc5TiOjh49KNuOjWSWZWn79p3yennDEQAAt+073aRI1B657PVYemlTmYuNAAAA8Kw4YxBAXDh58qR+8pOf6Pz582psbFR/f79isdjT76jhN48vX748zQ0BANPl+vWrunu3xchWrFij/PxClxoBAID7ItGY9pxuNLLqVUXKyUh2qREAAAAmg8EgAFd1dXXp937v93To0KGRzHEcFxsBAGZSf3+/Tp06ZmTp6RnasGGLS40AAMBoRy/dVe9gxMheq5zvUhsAAABMFoNBAK4JhUL65Cc/qRs3bjAMBIA5yHEc7d27R5GI+Wbj1q3PKykpyaVWAADgPsdx9PaJBiNbuSBH5UUZLjUCAADAZDEYBOCar3/967p+/bosy5LX69VHP/pRvfHGG1q2bJmysrI4VwoAZrnr16+rtrbWyBYtWqqSEs4sAgAgHly82anm9gEj211V7lIbAAAATAUGgwBc89Of/nTk66985St6/fXXXWwDAJhJwWBQe/bsMbKUlBRt2bLNpUYAAGCst2puG5dL8tO0dlGuS20AAAAwFTxuFwAwd92+fVuWZWndunUMBQFgjjl48KAGBswVCJWV25WSkuJSIwAAMNrtu326XN9lZK9VzpdlWS41AgAAwFRgMAjANT7f8KLlZcuWudwEADCTWlqadOnSRSMrLS1XRcVilxoBAICx3hlztmBGapK2rS5yqQ0AAACmCoNBAK4pLS2VJIVCIZebAABmSiQS0bFjB43M50vS1q07WIEAAECc6OoL6djlu0b20qYyJfk4Bx4AACDRMRgE4Jrnn39ejuPo/PnzblcBAMyQmprD6uvrNbJNm6qUlpbuUiMAADDWntONitnOyOUkn0cvbip1sREAAACmCoNBAK75+Mc/Lr/fr1u3bundd991uw4AYJrV1l5Tbe01I5s3b56WL1/lUiMAADBWKBzTvjNNRrZ9TbEyU/0uNQIAAMBUYjAIwDVFRUX6whe+IMdx9L//7/+7ampq3K4EAJgmPT3dOn78kJH5/X7t3v0BthAFACCOHLrQooFg1Mheq5zvUhsAAABMNZ/bBQDMbb/4i7+o5ORk/eEf/qE+9alP6ZVXXtFrr72mJUuWKCMjY9xvFpeUlExzUwDAs4pGo9q//11Fo+abjK+++qqys7PV2TngUjMAADCabTt650SDka1bnKd5eWkuNQIAAMBUYzAIwHU/93M/p5aWFv3X//pf9c477+idd96Z0P0ty9Lly5enqR0AYLJOnjyq7u5OI1uzZq1WrFihWMx2qRUAABjr7I12tXYPGdnuqnKX2gAAAGA6MBgE4KqWlhb9xm/8hmpra0dWBzqO85R7AQASxc2btbp27YqRZWfnaNeuXe4UAgAAj/VWzW3jcnlhulaUZ7tTBgAAANOCwSAA1/T09OjjH/+4mpubjTwQCCgzM1Ner9elZgCAqdDb26Njxw4Ymc/n086dr8jn48dQAADiSV1zr6439hjZ7qpyzgIGAACYZXhHBoBr/v7v/17Nzc2yLEspKSn6D//hP+iNN97Q/PkcbA8AiS4Wi+nAgfcUiUSMvLp6h7Kzc1xqBQAAHuftE+ZqwZyMZFWuLHSpDQAAAKYLg0EArnn33XclDZ8R+NWvflVbtmxxuREAYKqcOnVMnZ3tRrZ48TItXrzMpUYAAOBx2nuGdPJqm5G9vLlMPq/HpUYAAACYLvyEB8A1TU1NsixLmzdvZigIALPI7ds3dfXqJSPLyspWVdVzLjUCAABP8u7JRtmjznpPTvLqhQ0lLjYCAADAdGEwCMA1gUBAkrRw4UKXmwAApkp/f5+OHNlvZF6vVzt3vqKkpCSXWgEAgMcZDEZ14Jx57vvz6+YpLYXv2wAAALMRg0EArikpGf4E6sDAgMtNAABTwbZtHTjwnsLhsJFXVm5XTk6uS60AAMCTHDjXrGA4NnLZsqRXKjn3HQAAYLZiMAjANS+//LIcx9Hp06fdrgIAmAKnT9eovb3VyCoqFmvp0hUuNQIAAE8Ss229e6rByDYtK1BhdsClRgAAAJhuDAYBuOaXf/mXlZWVpTt37ui73/2u23UAAJPQ2Hhbly+fN7KMjExt3fq8LMtyqRUAAHiSk1fb1NkbMrLdVeUutQEAAMBMYDAIwDUFBQX68z//cyUnJ+tP//RP9c///M9uVwIAPIOBgX4dPrzXyDwej3bufEV+v9+lVgAA4Ekcx9FbNbeNbHFJppaUZrnUCAAAADPB53YBAHPXiRMnlJycrM9//vP68z//c/0f/8f/oW9+85vavXu3li5dqoyMjHGvMqmsrJzmtgCAR7FtWwcP7lEoZK422LJlq/Ly8l1qBQAAnuZ6Y4/q7/QZGasFAQAAZj8GgwBc84lPfMIY/DmOo6tXr+rq1asTehzLsnT58uWprgcAGIdz506ptfWOkZWXV2j58tUuNQIAAOMxdrVgflaKNi0rcKkNAAAAZgqDQQCuchzniZcBAPGrublRFy6cMbL09Axt3/4C5woCABDH7nYO6uz1diN7dct8eTx8/wYAAJjtGAwCcA3bfwJA4hoaGtShQ+a5gpZl6fnnX5bfn+xSKwAAMB5vn2zQ6I9kBpJ92rFunmt9AAAAMHMYDAJwzTe/+U23KwAAnsH9cwWDwSEj37SpWgUFhS61AgAA49E/FNHh8y1GtmtDiQLJvEUEAAAwF3jcLgAAAIDEcvHiWd2502xkZWXlWrVqrUuNAADAeO0906Rw1B657PVYenlzmYuNAAAAMJMYDAIAAGDc7txp1rlzp4wsNTVN27fv4lxBAADiXCRqa8+pRiOrXFmo3MwUlxoBAABgpjEYBAAAwLgEg0M6eHCPHOfBqUT3zxVMSeENRQAA4t3xy3fVMxA2st2V5S61AQAAgBsYDAIAAOCpHMfRoUP7NDQ0aOQbNmxRUVGxS60AAMB4OY6jt0/cNrIV5dlaUJzhUiMAAAC4gZOlAUyr5mbzDKqSkpLHXjcZox8XADD1Ll06p+bmBiObN69Ua9ZscKcQAACYkMv1XWpsGzCy16pYLQgAADDXMBgEMK1eeumlkTOnLMvS5cuXH3ndZIx9XADA1GptvaMzZ04YWSAQ0I4dL3KuIAAACeKtGnO1YHFuqtYtznOpDQAAANzCYBDAjBh9HtVErgMAuCsUCurAgfce+rt6x46XFAikutQKAABMRGNbvy7e7DSy1yrny8MHfAAAAOYcBoMAptWTtvhk+08AiG+O4+jw4f0aHDS3HVu3bpPmzSt1qRUAAJiot0+Y24GnB5K0fQ1nBAMAAMxFDAYBTKs9e/Y803UAAPddvXpRjY23jKyoaJ7WrdvkUiMAADBRPf0hHbt0x8he2lQqf5LXpUYAAABwk8ftAgAAAIg/7e2tOnXquJGlpKTo+edfksfDj5AAACSK9043KRp7sCW4z+vRi5vKXGwEAAAAN7FiEIBrTpw4IUkqKipSeXn5hO/f0NCgO3eGP/laWVk5pd0AYC4Lh8M6cOA92bZt5M8996JSU9NcagUAACYqFIlp35kmI9u2ukhZaX6XGgEAAMBtDAYBuOYTn/iELMvSxz/+cf3BH/zBhO//7W9/W1//+tdlWZYuX748DQ0BYO5xHEdHj+5Xf3+fka9Zs0GlpfNdagUAAJ7FkYt31D8UMbLXKvl+DgAAMJcxGASQ0BzHefqNAADjdu3aFd26ddPICgqKtGHDFpcaAQCAZ2E7jt4+0WBkaxblqrQg3aVGAAAAiAccEAMAAABJUmdnu06cOGpkfn+ynn/+Zc4VBAAgwZy/0aG7nYNGtrtq4kc4AAAAYHbhHR4ACSsYDEqSkpOTXW4CAIkvErl/rmDMyJ97bpfS01lZAABAonmr5rZxuawgXasW5LjUBgAAAPGCwSCAhHX/XMGcHH65BYDJcBxHx44dUm9vj5GvXLlW8+cvcKkVAAB4VvV3evV+Q7eR7a6aL8uy3CkEAACAuMEZgwBmRHNz82OvGxgYeOL1o0WjUd29e1c/+9nPdO7cOVmWpRUrVkxVTQCYk27ceF83b94wsry8Am3aVOVSIwAAMBlv15hnC2al+1W9qsilNgAAAIgnDAYBzIiXXnrpkZ9OdRxHP/jBD/SDH/zgmR/7537u5ybRDADmtu7uTtXUHDaypCS/du58WV6v16VWAADgWXX2BlVzpdXIXtlcJp+XTaMAAADAYBDADHMcZ1zZeL3xxht64403JlMJAOasaDSq/fvfUyxmniu4fftOZWRkutQKAABMxrsnG2WP+h3Ln+TRCxtKXWwEAACAeMJgEMCMKCkpeShrbm6WZVlKTU1VVlbWUx/DsiwlJycrOztbS5cu1e7du7V9+/bpqAsAc0JNzWH19HQZ2fLlq7RgwSKXGgEAgMkYCkW1/1yTke1YO0/pgSSXGgEAACDeMBgEMCP27NnzUHb/bMCPfOQj+oM/+IOZrgQAc1pd3XXduPG+keXk5GnLlq0uNQIAAJN18HyLhkIPdgKwJL1aOd+9QgAAAIg7bDAPwFWT2UYUAPBsenu7dezYQSPz+ZL0wgsvy+vlc2MAACSimG3rnRMNRrZhab6KclJdagQAAIB4xDs/AFzz3nvvSZLS09NdbgIAc0csNnyuYDQaNfKtW59XZma2O6UAAMCknb7Wro7eoJHtrip3qQ0AAADiFYNBAK4pLS11uwIAzDknThxTV1eHkS1ZslyLFi1xqREAAJgsx3H0Vs1tI1s4L1NLy55+ljsAAADmFrYSBQAAmCPq6+t07dplI8vOzlFV1XMuNQIAAFOhtqlXdc29Rra7ar4sy3KpEQAAAOIVg0EAAIA5oK+vV0eP7jcyr9ernTtfkc/HJhIAACSysasF8zJTtHl5gUttAAAAEM8YDAIAAMxysVhMBw68p0gkYuTV1TuUnZ3jUisAADAVWrsGdfpam5G9uqVMXg9v+QAAAOBh/JQIAAAwy50+fVwdHeYbhosWLdXixctcagQAAKbKOycb5Yy6HEj26vn1Ja71AQAAQHxjMAgAADCLNTTU68qVi0aWmZml6uodnDsEAECCGwhGdOh8i5HtXF+iQDLbhAMAAODRGAwCAADMUv39fTp82DxX0OMZPlcwKSnJpVYAAGCq7DvTpFAkNnLZY1l6ZfN8FxsBAAAg3jEYBAAAmIVs29bBg3sUDoeMvLJym3Jz81xqBQAApko0Zuu9U41GtmVFgfKyUlxqBAAAgETAYBAAAGAWOnPmhNra7hrZggWLtGzZSpcaAQCAqVRz5a66+8NGtruq3KU2AAAASBQMBgEAAGaZpqbbunTpnJGlp2do27adnCsIAMAs4DiO3qppMLJl87O1cF6mS40AAACQKBgMAgAAzCKDgwM6dGifkXk8Hu3c+Yr8fr87pQAAwJS6eqtLDa39Rra7krMFAQAA8HQMBgEAAGaJ++cKhkJBI9+8eavy8wtcagUAAKbaWyfM1YJFOQGtX5rvUhsAAAAkEgaDAAAAs8T586d1926Lkc2fv0ArVqx2qREAAJhqze0DOl/bYWSvVc6Xh+3CAQAAMA4MBgEAAGaBlpYmnT9/2sjS0tK1ffsLnCsIAMAs8vaY1YJpKT5tXzvPpTYAAABINAwGAQAAEtzQ0KAOHtxjZJZlaefOl5WcnOJSKwAAMNV6B8I6cvGOkb24qVTJSV6XGgEAACDRMBgEAABIYI7j6NChvQoGh4x848YqFRQUudQKAABMhz2nGxWN2SOXfV5LL28qc7ERAAAAEg2DQQAAgAR24cIZtbQ0GVlp6XytXr3OpUYAAGA6hCMx7T1jfs+vXlWkrPRklxoBAAAgETEYBAAASFB377bo3LlTRhYIpOq553ZxriAAALPM0Ut31DcYMbLdleUutQEAAECiYjAIAACQgILBoA4e3CPHcUay++cKpqQEXGwGAACmmu04evtEg5GtXpirssJ0lxoBAAAgUTEYBAAASDCO4+jw4X0aHBww8vXrN6uoaJ5LrQAAwHS5WNehlo5BI9tdOd+lNgAAAEhkDAYBAAASzOXL59XUdNvIiotLtWbNBncKAQCAafVWjblasLQgTasX5rrUBgAAAImMwSAAAEACaWu7q9Ona4wsJSWg559/UR4PP9oBADDb3L7bpyu3uozstcr5nCcMAACAZ8K7RwAAAAkiFArpwIH3jHMFJWnHjhcVCKS61AoAAEynsasFM9P82rqq2KU2AAAASHQMBgEAABKA4zg6cmS/Bgb6jXzt2o0qKSlzqRUAAJhOXX0h1Vy5a2QvbypVko+3cwAAAPBs+EkSAAAgAVy9ekkNDfVGVlhYrPXrN7tTCAAATLt3TzUoZj/YKcDv82jXxlIXGwEAACDRMRgEAACIcx0dbTp16piRJScna+fOlzlXEACAWSoYjmr/mWYj2752njJS/S41AgAAwGzAO0kAAABxLBwO68CB92TbtpE/99yLSk1Nc6kVAACYbofOt2gwFDWy1yrnu9QGAAAAswWDQQAAgDjlOI6OHj2gvr5eI1+9ep3KyspdagUAAKabbTt652SDkW1Ykq/i3FSXGgEAAGC2YDAIAAAQp65fv6pbt+qMLD+/UBs3VrnUCAAAzIQz19vU1h00st1VrBYEAADA5DEYBAAAiENdXR06ceKIkfn9fs4VBABgDnirxlwtuKA4Q8vmZ7tTBgAAALMK7yoBAADEmUgkov3731UsFjPy7dt3KT09w6VWAABgJtQ29ehGU4+R7a6cL8uyXGoEAACA2cTndgEAwMTU1dXpq1/9qo4fP67W1lb5fD6Vl5frtdde06c//WmlpaW5XRHAJB0/fki9veYbgitXrlF5eYU7hQAAwIx564S5WjAnI1lbVhS61AYAAACzDSsGASCBnDx5Uh/5yEf0z//8z0pKStJLL72kyspKNTU16b/9t/+mj33sY+rp6Xn6AwGIW7W111RXd93I8vLytWlTtUuNAADATGnrHtKp91uN7NUt8+Xz8vYNAAAApgYrBgEggfzRH/2RgsGg/uN//I/63Oc+N7KdUHd3tz7zmc/o0qVL+vu//3v99m//tstNATyL7u4uHT9+yMiSkpK0c+cr8nq9LrUCAODpgkMRtbb0KRSMqqgkQ5nZAbcrJaR3TzbKcR5cTvZ7tXP9PPcKAQAAYNZhMAgACaKrq0vXr19XUlKSfvM3f9M4YyQ7O1uf+cxn9Nu//ds6d+6ciy0BPKtoNKoDB95VNBo18m3bdiojI9OlVgAAPCwSjqntTp9aWx7809cTHLnen+zVhz++QXmF6S62TDyDwYgOnG82sp3rSpSakuRSIwAAAMxGDAYBIEEkJY3vDYGcnJxpbgJgOpw4cUTd3V1GtmzZSlVULHapEQAAUixqq721X233h4B3+tTVPvjE+4RDMdXf6GAwOEH7zzUrFI6NXLYs6dUtZS42AgAAwGzEYBAAnqC/v1+HDx/W8ePHdfnyZdXX16uvr0/JyckqLCzUunXr9HM/93N6/vnnjRV80yE9PV0bN27UmTNn9Ld/+7cPbSX6D//wD5Kkj33sY9PaA8DUu3nzhq5fv2pkOTm52rJlm0uNAABzkW076uoYfDAEbOlTR2u/bNt5+p3HKCjOmIaGs1c0Zuvdk41Gtnl5ofLZkhUAAABTjMEgADzG1772Nf3FX/yFQqHQQ9dFo1HdvHlTN2/e1L/+679qy5Yt+vKXv6ySkpJp7fSnf/qn+vVf/3X9zd/8jX7yk59o+fLlCgaDOnXqlAKBgP6f/+f/0Y4dO6a1A4Cp1dvbo6NHDxqZz+fTzp2vyOfjRzUAwPRwHEe93UG1tvSNDALb7vYpGrGf+TF9Po/yi9K1ZnOp5i9kF4uJOHm1VV195u8du6vmu9QGAAAAsxnvNgHAY9y8eXNkKFhUVKTt27dr9erVysvLUygU0tmzZ/XDH/5Qg4ODOnnypD7xiU/o+9//vvLy8qat0+LFi/Wd73xHv/Vbv6WzZ8+qvr5+5Lrt27dryZIl0/bcAKZeLBbV/v3vKhqNGPnWrc8rKyvbnVIAgFlpoC80shXo/UFgKBh9+h0fw+OxlFuQpsJ5GSqYl6HC4gzlFqTJ45neXTRmI8dx9FZNg5EtKcvS4pIslxoBAABgNmMwCACPYVmWduzYoc985jPatm2bPB6Pcf1HPvIR/ft//+/1v/1v/5tu3rypxsZGfeUrX9GXvvSlhx7r937v93T+/PkJPf+rr76q3/7t3zayY8eO6XOf+5zy8/P193//91q/fr2Ghoa0b98+ffnLX9b+/fv1N3/zN6waBBLEyZPH1dXVYWSLFy/TokVLXWoEAJgNgkMRtd15sB1oW0ufBvrDk3rM7NyACudljgwC8wvT5EvyTlHjue1SXYdu3e0zst2VrBYEAADA9GAwCACP8fnPf17Z2dlPvE1paan+8i//Uj//8z8vSfrpT3+qL3zhCwoEzLNAWlpadPPmzQk9f1tbm3G5u7tbv/Vbv6VwOKyvfvWrKi0tlSRlZmbql3/5l5WRkaHPf/7z+sM//EO9/fbb8np5owaIZ7du1en99y8ZWVZWjqqqnnOpEQAgEUXCMbXf7TdWA/Z0DU3qMdMzk1U4L2N4CFg8/E9yCm8fTJd/PVBnXC7MDmjj0gKX2gAAAGC24yd7AHiMpw0F71uxYoUWLlyomzdvamhoSLdu3dKKFSuM23zzm9+cdJ99+/apu7tb27ZtGxkKjvbaa68pKSlJjY2NamhoUEVFxaSfE8D06Ovr1ZEjB4zM6/XqhRdeVlJSkkutAADxLhaz1dk2MLISsLWlT13tA3KcZ3/MlNSk4SFgccbIasDUNP/UlcYTNbb26cSVu0b2auV8tmQFAADAtGEwCABTID09feTr++cSTrW7d4ffMMjIyHjk9T6fT6mpqerp6VFPT8+0dAAwebFYTAcPvqdIxNzSrarqOWVn57rUCgAQb2zbUXfn4MhWoK0tfepo7Vcs9uxTwCS/VwX3BoD3/0nPTJZlMYRyyw/HrBZMTfbpubXFLrUBAADAXMBgEAAmKRwOq76+fuRySUnJtDxPQcHwdkKXLl1SNBqVz2f+FV5fXz8yEHzUikIA8eHMmRq1t5tbBS9cuERLlix3qREAwG2O46ivJ6TWlt6RQWDb3X5FwrFnfkyv11JeUfrISsDCeRnKzktlCBhHegdCeu/EbSPbtbFUKX7eqgEAAMD04adNAJikH/3oR+rr65MkrV69emSAN9V27typlJQUNTU16Stf+Yp+53d+Z2Q42NnZqT/4gz+QJFVVVSk/P39aOjxNZmaKK8+L6XN/GyuPx1JubprLbRJfXV2tLl++YGTZ2dl6/fXd8vvn1rZtvLYwHXhdYTpMx+uqvy+kloZuNTf2qKWhRy2NPRocCD/9jo9heSwVFKVrXlmWSuZna15ZlgqLM+T1eaakL6bH99+7pnDUHrns81r6xZeXKTeLn6nx7PheCAAAnobBIABMQmdnp77yla+MXP7N3/zNaXuu/Px8/cEf/IG+8IUv6Gtf+5reeustrVq1SsFgUOfOnVNfX5/y8/P1x3/8x9PW4Wm8Xt58mq0sy5LXywqDyejt7dXbb79tZF6vVx/60IcUCMzdNwB5bWE68LrCdHjW11VwKKLmhh41N3SP/NPbHZxUl9z8NJXMz1JJefbwILA0S0l+76QeEzMrHInpJ4frjez5DaUqyE11pxBmHb4XAgCAx2EwCADPKBwO67Of/aw6OjokSa+88opeffXVaX3Oj33sY1q2bJm+8Y1v6PTp09q/f7+8Xq/Kysr0sY99TL/+67+uvLy8ae3wJLGY/fQbIaF4PJYsy5LjOLLtZz/TaK6zbVs//vGPFQyabwTv3PmC8vLy5+T/d3htYTrwusJ0mMjrKhKJ6W5Tr5obu9XS0KPmhh51tg9M6vkzslLurQTM0ryyLM0ry1YgNemh283F7yWJbO+pBnX3m2eT/y/PL+K/IyaN74WzGx/GBQBMBctxHH5KAIAJsm1bv/d7v6d/+7d/kySVl5frn/7pn5SVleVyM3d1dQ0qGn32s3AQf3Jz0+T1ehSL2ersnNwbm3PZ6dM1unjxrJEtWLBQO3e+MmfPeuK1henA6wrT4XGvq1jMVmfbgNru9Km1ZfifzrYBTeY37OQU38h5gAX3/kxLT56CfwvEg56BsJra+tXUNqB3TjaovefBB4ZWLsjR7/7KRhfbYbbge+Hs5fN5lZPDqmIAwOSxYhAAJshxHP3hH/7hyFCwpKREX/va1+b8UBDAozU1NTw0FExPz9C2bTvn7FAQABKNYzvqaO3X9aut94aAvWpvHVAs+uyru3xJHhUUZ4wMAgvnZSgjK4XvDbPAUCiqprYBNbYPDwGb2vrV1D6gvsHIY++zu2r+DDYEAADAXMZgEAAmwHEc/dEf/ZG+//3vS5KKi4v1jW98Q2VlZS43AxCPBgcHdfjwXiPzeDzaufNl+f2sAAGAeNbbPaRbtZ1qud2jxltdCgWjz/xYHo+lvMJ0YwiYnZcqj4chYCKLRGNq6RhU471VgE3tw0PAjt7Q0+88SllhutYscu84AAAAAMwtDAYBYJwcx9EXv/hFffe735UkFRUV6c0331R5ebnLzQDEI9u2dejQnofOFdy0qUr5+YUutQIAPE4sZquloUe3azt1q65T3R2Dz/xYOfmpxhAwryBdXh/nQiWqmG2rtWtoeBXgvdV/TW0Duts1OKmtYyXJsqRP/9wqeVgpCgAAgBnCYBAAxuH+UPA73/mOJKmwsFBvvvmmFixY4HIzAPHqwoUzunOn2cjKysq1cuValxoBAMbq7wvpdm2nbtd2qPFWtyLhiZ+VnJmdMnwm4L1tQQuKM5Tk905DW0w3x3HU2RtSU3u/Gu9vAdo2oOaOQUVjz75t7Gh+n0cl+WkqLUjT0vJcbV07T8W5qZwFBwAAgBnDYBAAnmLsULCgoEBvvvmmKioq3C0GIG7dudOsc+dOGVlqapqee24XZ0cBgIts29Hdpl7dquvQ7dpOdbRObBiTmuZXwTzzXMCUQNI0tcV06h0MP1gB2DagpnvnAQafYTj8KF6PpeLcVJUWpKk0P01lBekqLUhTfnZgZHVgbm6avF6PYlM0dAQAAADGg8EgADzFH//xHz80FFy4cKHLrQDEq6GhIR08uMfILMvSzp0vKzk5xaVWADB3DQ6E1VDXqdt1nbpd16VwaPxnBaYEkrR0ZaGWrixU6YJsRWIxPuCRYIZCUTW3D5//19jaP3IOYO9gZMqeoyA7RaX5w4O/0oLhIWBxbqp8XraPBQAAQPxhMAgAT/Anf/In+va3vy3pwVBw0aJFLrcCEK8cx9GhQ3s1NGSeS7VhQ6UKC4tdagUAc4vjOGq706dbtZ26Xdup1pa+Cd2/cF6Gyhflqnxx7vD5gHnpI6u62O4xfkWitlo6BkbO/7u/ErCjN/j0O49TVppfZQVpKi1IV2n+8J8l+alK8fPWCgAAABIHP70CwGP8xV/8hb71rW9JGl7t88lPflJ1dXWqq6t74v1WrVqlkpKSmagIIM5cvHhOLS2NRlZSUqY1a9a71AgA5oZQMKKGm13Dw8C6TgUnsBrMn+zV/IXDg8DyRblKTfNPY1NMlm07au0eGjn/r/HeCsC7nUOyHWdKniOQ7BtZ+Te8DWiaSvLTlJHKawMAAACJj8EgADzG6dOnR752HEd//ud/Pq77felLX9JHP/rR6aoFIE61tt7R2bMnjCwQSNVzz73ItnMAMMUcx1Fn28DIqsA7TT2ayEworyBteBC4OFfFpVnyePh7Ot44jqOuvpAaR53/19Q2oOaOAUWiU3MmX5LPo5K8tAerAO+dB5iTkcz3bgAAAMxaDAYBAAAmKRQK6sCB9+SMelfasiw9//xLCgQCLjYDgNkjEo6psf7+qsAODfSFx31fX5JHZRU5WrA4V+WL8pSemTyNTTFRfYPh4cHfvdV/jfe2Ax2awHmQT+KxLBXlBlRakK6ye1uAlhWkqSA7wFAYAAAAcw6DQQB4jG9+85tuVwCQIM6cOaHBQfPcqXXrNqm4mG2FAeBZOY6j7s4h3b43CGxu6JEdG/+ywOzcgMoX52rB4jzNK8uS1+eZxrYYj2A4OnIGYNOolYA9A+Mf8j5NflbKyPl/97cDLc5NVRL//QEAAABJDAYBAAAmpb29TdeuXTGy4uISrV270aVGAJC4opGYmht6dOtGh27Xdaq3Ozju+3q9lkoWZGvBojyVL85VVg4rtt0Sjdm60zGoxrb+kUFgY1u/2nvG/9/zaTLT/PcGgA/OAizJT1Mgmbc5AAAAgCfhJ2YAAIBn5DiOamoOG5nP59P27bvk8bAyAQDGo7c7qNt1Hbpd26mmW92KTuD8uIzMZJUvydOCRbkqWZCtpCTvNDbF01xr6NY/7ruh+pY+xewJHPr4BIFkr0rzH5z/d38lYGaqf0oeHwAAAJhrGAwCAAA8oxs33ld7e6uRrV27Uenp6S41AoD4F4vZutPYM3xWYG2nujoGx31fj8dScVnW8FmBi3OVk5cqy+KMuHgQidr6mx9cVO8zbgvq83pUkpc6cv7f8CAwXbmZyfw3BgAAAKYQg0EAAIBnEAoFdfp0jZFlZGRp1ap1LjUCgPg10BfS7bpO3artVGN9lyLh2Ljvm5ruV/miXC1YnKuyihz52SoyLjW29Y9rKGhZUlFOqrEFaGlBmgpzAvKy2h4AAACYdvxGBQAA8AzOnj2pUMg8K6mqaru8XraxAwDbdtTa3DuyKrC9tX/c97UsqagkU+WLc1W+KFf5RemsGEsAfYMPDwXzMpOHt/7MvzcELEjTvLxUJfn4XgkAAAC4hcEgAADABHV0tOvatStGVl5eodLS+S41AgD3DQ2G1VDXpVt1nWqo61QoGB33fVMCPpUvylX54jzNX5ijlEDSNDbFdOgbjBiX5xem64ufqXKpDQAAAIDHYTAIAAAwAY7jqKbmsBzHGcm8Xq+2bNnmYisAmHmO46j9br9u1XbqVm2HWpv7JnT/guL04S1Cl+SpoDhDHg+rAhNFKBJTZ29QHT1Btd/788qtLuM2makMdwEAAIB4xGAQAABgAmprr6mt7a6RrV27UenpGS41AoCZEwpG1VjfpVu1Hbpd16mhgcjT73SPP9mr+QuHtwctX5Sr1HT/NDbFs3IcRwPBqDp6guq4N/TrGDME7B96+n/3jDT++wIAAADxiMEgAADAOIXDIZ0+fdzIMjIytXr1OpcaAcD0chxHne2Dul3boVu1nbrT2KNRC6afKrcgbXhV4OJcFZVmyuv1TF9ZjIvtOOrpDz8Y+D1i+BcKxyb9PPlZKVPQFgAAAMBUYzAIAAAwTmfPnlQwGDSyysrt8nr5kQrA7BEJx9R4q0u3azt1u65T/b2hcd/Xl+RR2YIclS8eXhWYwXBoxkVj9kPbfI4e/nX2hhSzJzDdfQYZqUnasXbetD4HAAAAgGfDu1gAAADj0NnZofffv2xkZWULVFZW7lIjAJg63Z2Dul3bqVu1nWpu6JYdG//gKCsnoAWLc1W+OFfz5mfL52NV4HQaCkUfWuU3euVfT39Y0zv2eyA9kKS8zBTlZaWM/FmQlaIVC3IUSObtBgAAACAe8ZM6AADAUziOo5qaw3JG7Z/n9XpVWbnNxVYA8OyiUVvNt7tHVgX2dA2N+75er6WS8uyRVYHZuanT2HRucRxHfUOR4WHfI8746+gNaiAYnZEulqTsjOSHBn8P/kxWip+3FAAAAIBEw0/xAAAAT1FXd12trXeMbM2aDcrIyHSpEQA8u6ZbXXr7B1cUHIqM+z7pmclasDhP5YtzVVqerSS/dxobzl4x21Z3X9g4z6+jJzi89ee9r8NRe0a6+LyWcjMfDPryxwz+cjKS5eNMSAAAAGDWYTAIAADwBOFwWKdOHTey9PQMrV693qVGAPDsIuGY3vu3q08dCno8lorLMlW+OE8LFuUqJz9VlmXNUMvEFYnG1NEbGlnd1z5m5V9XX0i2MzMbfQaSvcNDvses+MtM88vDf1MAAABgzmEwCAAA8ATnzp1UMGhusVdVtV0+Hz9GAUg852oaNNAffuR1qWn+ke1ByypylJzC33NjDQYjw8O+R53x1xNU7+D4V2FOVmZq0mO2+ExRflaKUlOSZqyLJNnhmIaONSra0i/fvHSlbiuTlcTKUgAAACDe8JseAADAY3R1derq1UtGVlZWrrKyBS41AoBnN9Af0pnjDUaWlRPQ8jVFKl+cq/yi9Dm7KtC2HfUNhtXdH1ZXf0jdfSF19w//0x+MqbM3qNauQQ3O0Pl+HstSTkayMfDLv/d1bubwuX/+OBq6Obajwf23FG3ukyRFbnYrPC9dyUvzXG4GAAAAYCwGgwAAAI/gOI5qag7LGbXlm8fjVWXldhdbAcCzqzlQr2jEPL/utQ+vUn5RukuNpp/jOBoMRe8N+sLqGjXw67qXdfeH1NMfnrEtPiXJ7/MMn++X9egz/rIz/PJ6Eud8v+DZOyNDwfucyMyclQgAAABgYhgMAgAAPMLNm7W6e7fFyNasWa+MjEyXGgHAs+to7dfV83eMbMW64oQeCoYiseEhX1/o3iq/8MjQr3vU0C8cnfkBVVqK77Fn++VlpSgjkDRrVmeG67sVutBqZFaKT/5FOS41AgAAAPAkDAYBAADGCIfDOnXqmJGlp2dozZoN7hQCgEk6urfOuOxL8qjq+Qp3yjxFNGard2D0lp5hcwDYH1Z3X0iDoZnZ1nMsS1JWuv/BsO8RA8BA8tz4VTvWNaTBw+b2tLKktF0L5OGMSgAAACAu8ZM6AADAGOfPn9bQ0KCRbdmyTT4fPzoBSDy36zrVcLPLyDZUz1daRvKM9rAdR/2DkUdu5flgi8+w+gbCmrlNPU0ey1JWul/Z6X5lpycrOyNZJQXpys8OKC8rIL/lKCcjRUm+xNnmc7rYoagG9tZLY1ZkBqpK5UvglagAAADAbMe7WwAAAKN0d3fpypULRlZaOl/z5y9wqREAPDvbdnRkT62RpaX7taFq/pQ9h+M4GgrFxgz8HqzsG9nesz+smO3WyE9KDyQpOz1ZORnJxuAvJz1Z2RnDlzNT/fJ4zC0+c3PT5PV6FIvZ6uwccKl9fHFsR4MHbsvuCxu5f0mO/MvzXGoFAAAAYDwYDAIAANzjOI5qag7LcR68ce3xeFRZuX3WnAUFYG65er5FXe3mCuiqnQuV5PeO6/7hSEzdA6MGfPdW+T3Y5nP4cigSm47645Li9z488Bu5PJxlpSezym8KBc/cUbS5z8i8+akKbC3j+yUAAAAQ5xgMAgAA3FNfX6c7d5qNbPXq9crMzHKpEQA8u3AoqpoD9UaWV5impasLFY7ENBCMjlnhF1J3372h373B30DQnXP8JMnntUaGfNn3hn45I18/GALOlfP84kW4vluhi61GZqX4lLZrgSwvw1cAAAAg3vEbFAAAgKRIJKJTp44ZWVpautau3ehSIwBzneM4CkdthSIxhcL3/onEFIzEFA4P//mk62J3BpQ0GDEe82TXgN76yj457u3oKcuSMtOGh3o5jxn65WQkKy3Fx+qzOBPrGtLg4QYztKS0XQvkSfO7UwoAAADAhDAYBAAAkHT+/GkNDppnR23Zsk0+Hz8uAXgy23EUidiPHtSFYwrfG9jdzx553WMGfM86v/NLWiuPpAeDtW45ao/YU/Gv/FhpKb4H5/bdO7svx1j1l6zMtCR5PawsSzR2KKqBPfVS1HwNBapK5StKd6cUAAAAgAnjnS4AADDn9fR06/Ll80Y2b16Zyssr3CkEYFrYjqPQowZ1975+5BAvYisUjo65zlYoEr13XczV8/Uep1SWPKOGgo4cNejZh4L+JI9yjHP7Hqz0u/91TrpfSb7xnV2IxOLYjgYP3JbdHzZy/5Jc+ZfnudQKAAAAwLNgMAgAAOY0x3FUU3NYzqh99Twej6qrt7OFHTAJjuPIdhxFY45iMVtR21Es5igasxWN2cNf2/aDzL53u5ijmD3mdiP5g/tF7+WxUffzej0KR20Fw1H1D4QfGv6Fp3m1XLxIlZQvc0VemxwFH3Fbr8cyhnv3z+4bPQDMyUhWit/L34lzWPDMHUWb+4zMm5+qwNZSXhcAAABAgmEwCAAA5rTbt2+qpaXJyFatWqfMzGx3CgGP4cagbfTtRx7jEY87+vb3e8Vi9jNvg4nHS07yKtnvVXKSR8lJPiX7PUpJ8irZ71Nykkf+JK/CtV2K9j1Y2eXxebT7taVKz0gevn+SVyl+r1KSfUoPJMnDYAdPEK7vVuhiq5FZKT6l7Vogy8uWsAAAAECiYTAIAADmrEgkohMnjhpZamqa1qzZoGhseGXR8DBGkjO8DaGR6V7mDGeOhk/zsixLsiSPZenel7JGvr73p/WojDfnJ8txhodVMduRbT/4OnZvsGXbjqIj190fgo2+/cNZ1LbHPNbw7Yzs/v1jox5j1PPFYo+5/b3BXMwZvq/R4f7jOQzaEo0lye/33hvYeUcN8x6d3f/6kdeNul9SkuepQ7yb19v1s7N3jazyuQXatK5kGv+NMVvFuoY0eLjBDC0pbdcCedL87pQCAAAAMCkMBgEAwGMdu9ii0++3DZ+lFYyODMaGB2WOHGNY9mA4NnZYNnLdozKNvm5M9tj7jrq9HvO49/6833f0fYeHfFJ5WpvK0weMf+cTjRn6yX89NBP/8z6SMUR8zPBw+Dajb2cOID3W8CNZ94aTskbnDwaW0nD2YJA5+vEfMdS0xnYYz21Gdbj3fLKklJQkOY4UjdoaDEbuDdXsMQOyh4ds9wd293Nj0BdzRl6PwHhYlpTi98r/mIFdSpL3iQO+x13n93lcGfTHYraO7q0zsvTMZK3bUjrjXZD47FBUA3vqpai5BW+gqlS+onR3SgEAAACYNAaDAADgkY5dvqP//sPLbteYNgFvWGVpXUbWFQqoLZjmUqNho4eXDxJgdvJ5PfJ6Lfk8lnxej3xeS16vZzj3WA8u37vee/9P76jbex5czkhLlt/vVbLPo1g09ugh3v3VeX6vfF53BnjT5fLZFvV0DhlZ9QsL5UvyutQIicqxHQ0euCW7P2zk/iW58i/Pc6kVAAAAgKnAYBAAADzEcRz92+F6t2tMI0dLs9pHVtBJwysIr/cUaHj9G5CY7g/LHhqs3RuueccM2u7fzhi0eT3yjQzbrFFfP2ow9+gB3sOP+2AA6L33PMMrV6f2/2+5uWnyej2KxWx1dg48/Q6zSCgY1clD9UZWUJyhpasK3SmEhBY806Joc7+RefNTFdhaOquG6QAAAMBcxGAQAAA85P3b3WrpGHS7xrTJTxlQbor579c4kK3BKOclzRWWpeGVZh5LXo8lz/2hmef+P4+6bkx2b8g1OvONXOcxHs8z9nG9DzKfxzPq+tHXPXjs+4O1+18/GLQ9GOBNx6ANieP00VsKDkWNbPvLi3hNYMLC9d0KXWwzMivFp7RdFbK8nifet6u1Ue1NdSooW6LsAs61BAAAAOIRg0EAAPCQvWeajMsF2QFVrih88hl3Y7OnnYk3OtMjztXTJG4/9r6j+sTsqM4c/alCwQf/fv7kgH71lVeVlJQkPeosPsuSR7p3Dt+D4Yvn3nUj5+jdY98741COM3K+4f1cjmRr9FmH985D1JizFI3zEx91vuKTz2wcPgdy1Ncyz4R80HF0hyef1/hQTz2iy6jbPPS89/5dbMdRSkqSvF6PPJYUDkVHBmLesUOyUYMyY4DmteS1hgdzD9/eM2ZI9/BAbvR/LyDR9XYP6fxJ8+/thcvyVTI/251CSFixriENHm4wQ0tK27VAnrSkJ9636cZ5HfnR12TbMXm8Xr387z6v3KLyaWwLAAAA4FkwGAQAAIaegbBOXzNXCnzo+UXasbrIpUZT68yZEwoFzdWC1VXbVFGS41KjuWkub/kITLXj+2/Kjj04j9TjsbR110IXGyER2aGoBvbUS1HbyANVpfIVpT/xvgO9nTr+1v+UbceGHysWU0vdJQaDAAAAQBx68j4gAABgzjl4rlkx+8EbzP4kr17aXOZio6nT29ujS5fOGVlxcYkqKha71AgAJudOU69uXDE/zLF6U4myc1NdaoRE5NiOBg/ckt0fNnL/0lz5l+c98b62HdOxn7ypSGjIyPNLF015TwAAAACTx2AQAACMsG1H+882G9nODaVKT038s/ccx9GJE0dk2w9WQliWpaqq7ZzBBSAhOY6jI3tqjcyf7NOW5xa41AiJKnimRdHmfiPz5qcqUF361O+Rl47+TO3NdUZWvnyTCucvm/KeAAAAACaPwSAAABhxoa5DHb1BI3t9e4U7ZaZYY+MtNTWZ5yatXLlG2dm5LjUCgMmpe79dd5t6jWzz9nKlBJ58FhwwWri+W6GL5qpTK8WntF0VsrxPfsvg7u1runz8bSNLy8rT5ld+mQ/dAAAAAHGKwSAAABix70yTcXlRaZaWzs92p8wUikajOnHiqJEFAgGtW7fZpUYAMDmxqK1j+8xVWpnZKVq7udSlRkhEsc4hDR42PzQjS0rbtUCetCcPmIODfTr20zclPdh+3PJ4tO2Dn5I/OTANbQEAAABMBQaDAABAktTeM6TztR1G9oGtC2bFJ/4vXjyr/v4+I9u8eav8/sTfIhXA3HTxdJN6u80V3lt3LZLXx694GB87FNXA3nopaht5oKpUvqL0J97XcWzVvPU/FRwwV6yue+5DypvHVrYAAABAPOO3RgAAIEk6cK551Gf+pUCyV89vSPyVJ319vbp48ZyRFRXN08KFS1xqBACTExyK6OTh20ZWXJqpRcvzXWqEROPYjgYP3JLdHzZy/9Jc+ZfnPfX+107vV8vNy0ZWvGCFlm95cUp7AgAAAJh6DAYBAICiMVsHzrUY2bbVxQok+1xqNHVOnDgi246NXLYsS1VVz82KlZAA5qaTh28pHIoa2baXFvP3GsYteKZF0eZ+I/PmpypQXfrU11Hn3ds6f/CHRpaSmqHqD/yqLIu3GAAAAIB4x0/tAABAZ663q3fAXDWwa2PirxZsbLylxkZzVc2KFauVk5PrUiMAmJzuzkFdOt1sZEtWFqi4NNOlRkg04fpuhS62GZkV8CltV4Us75PfIoiEgzr6428YH7iRpOrXP6GUNF6DAAAAQCJgMAgAALT3dKNxeWlZlsoKnny+ULyLxaKqqTliZCkpAa1fv8WlRgAwecf23ZRtP9j42eO1VP3CQhcbIZHEOoc0eLjBDD2W0nZVyJOW9NT7n3rv++rvNoeKKypfUfGCFVNZEwAAAMA0YjAIAMAc19IxoKu3u43sxVmwWvDixXPq7+8zss2bq+X3+11qBACT03y7WzevtRvZui2lyswOuNQIicQORTWwt16K2kYeqCqRrzDtqfe/eblGt66cNLLc4gVau/2NqawJAAAAYJoxGAQAYI7bd8bcki49kKTNywtdajM1+vp6dfHiWSMrKCjSokVL3SkEAJPkOI6O7KkzspSAT5u2LXCpERKJYzsaPHBLdr+5bbh/aa78y/Keev++rladfu/7RpbkT9G2Nz4lj9c7pV0BAAAATC8GgwAAzGHhSEyHL7QY2fPr5inJl9g/Ipw8eVSx2IPzjyzLUnX1DlmW5WIrAHh21y+3qu2OuQp6y44KJaf4XGqERBI806Joc7+RefNTFagufer3xlg0oiM/+pqiEXOoWPnaryg96+lDRQAAAADxJbHf9QMAAJNSc6VVg6Gokb2wocSlNlOjqem2GhpuGdmyZauUm8ublwASUzQS0/H9N40sKzegVRvmudQIiSR8s0uhi+a5gFbAp7RdFbK8T39L4NzBH6q7rcnIFq3drvnLNk5pTwAAAAAzg8EgAABz2N4z5ht9axbmqjAn1aU2kxeLxVRTc8TIUlJStHHjFpcaAcDknT/ZpP7ekJFt27VI3nEMdTC3xTqHNHik0Qw9ltJ2VciTlvTU+zfVXtD1M/uNLDOvWBt3fXQqawIAAACYQfwmCQDAHHXrTp9utvQa2a6NpS61mRqXLp1TX5/577RpU7X8/mSXGgHA5AwOhHX66G0jK5mfpYqlrILGk9mhqAb21ktR28gDVSXyFaY99f6DfV2qeevbRub1JmnbBz8lX5J/KqsCAAAAmEEMBgEAmKPGrhbMyUjW+iWJ+0Zzf3+fLlw4Y2T5+YVavHiZS40AYPJOHrqlSDhmZNtfXsyZqXgix3Y0eOCW7H7zXED/0lz5lz39e71t2zr2028qHBww8g27PqLsgsTechwAAACY6xgMAgAwBw0Gozp++a6R7VxfIq8ncX80OHnyqGIx883z6uodvHkOIGF1tQ/o8tlmI1u2ukgFxRkuNUKiCJ5pUbS538i8BakKVJeO6/vileNvqa3xhpGVLV2vxeuem9KeAAAAAGZe4r77BwAAntnRS3cUijwYonksSzvXJ+4KgObmRt2+XW9ky5atUl5evjuFAGAKHN1bJ8d5cNnr86j6hQrX+iAxhG92KXSxzcisgE9puypkjeNcyrbGWl069jMjS83IUeWrv8KHbQAAAIBZgMEgAABzjOM42nfW3EZ0w9J85WQk5jl8sVhMNTWHjSw5OVkbN25xqREATF5jfZdu1XYa2fqqMqVnprjUCIkg1jmkwSONZuixlLarQp7UpKfePzQ0oKM/+YacURNpy/Jo2xu/Jn9K6lTXBQAAAOACBoMAAMwx1xt71NRmnhn04sZSl9pM3uXLF9Tb22NkmzZVKzmZN88BJCbbdnRkT62RBdKStLF6vkuNkAjsYFQDe+ulqG3kgaoS+QrTnnp/x3F04u1va6i/28jXbP+g8ksWTWFTAAAAAG5iMAgAwBwzdrVgYXZAKytyXGozOQMD/bpw4bSR5ecXaMmS5S41AoDJu3bxrjpazQ9wVD1fIX+yz6VGiHeO7WjwwC3Z/WEj9y/NlX9Z3rge48bZg2qqvWBkhfOXaUXlK1PWEwAAAID7GAwCADCH9A6GdfJqq5Ht2lgqT4KeGXTy5DFFo1Ejq6rawRlIABJWJBzT8QM3jSwnP1Ur1s1zqRESQfBMi6It/UbmLUhVoLp0XN8Tu9qadPbAD4wsOZCmra9/Qh4PbxsAAAAAswk/4QMAMIccvtCiaOzBuUE+r0fPrS12sdGza25u1K1bdUa2dOkK5ecXuNQIACbvbE2DBses+tr+0mJ5PHzgAY8Wvtml0MU2I7MCPqXtqpDlffqv/NFISEd/9DXZsTEftNn9qwqkZ01pVwAAAADuYzAIAMAcYTuO9p9pNrLKFQXKSPW71OjZxWIxnThxxMj8/mRt3FjlUiMAmLyBvpDOHm8wsrKKHM1fmJjbPWP6xTqHNHik0Qw9ltJ2VciTmjSuxzi955/U12XuJrBs84sqWbR6qmoCAAAAiCMMBgEAmCMu13eqtXvIyF7cWOZSm8m5cuWienq6jWzTpkqlpKS4UwgApkDNwXpFI7aRbX9pEdsj45HsYFQDe+ulqPmaCVSVyleYNq7HuHX1lG5eOm5kOUXztW7Hh6aqJgAAAIA4w2AQAIA5Yu/pJuNyWUGaFpdmutTm2Q0ODuj8+VNGlpeXryVLVrjUCAAmr6O1X1fP3zGyFeuKlVeY7lIjxDPHdjR44JbsMdvO+pfmKnl53rgeo7+7TSff/a6R+ZKSte2Dn5LX65uyrgAAAADiC4NBAADmgM7eoM7d6DCyFzeWJuQqlJMnjykaHXMOUtUOeTz8WAMgMTmOoyN7zDNTfUkeVe2scKcQ4l7wdIuiLf1G5i1IVaC6dFz3j8WiOvLjbygaDhn55ld+SRk5nNULAAAAzGa8gwYAwBxw4FyzbMcZuZyc5NXW1cUuNno2d+40q76+1siWLFmugoJClxoBwOTdrutUY32XkW2snq+09GSXGiGehW92KXSpzcisgE9puypkecf3K/6FQz9S193bRlaxuloVKyunrCcAAACA+MRgEACAWS5m2zpwrtnItq0uUiA5sbYJs21bx48fNjK/369Nm6pcagQAk2fbjo7uNVcLpqX7tb5qvkuNEM9inUMaPNxghh5Labsq5ElNGtdjtNy8rPdP7TGyjJxCbXrxF6eqJgAAAIA4xmAQAIBZ7uz1DnWPOYNo18bxbTUWT65evaieHnNFzYYNlUpJCbjUCAAm78q5FnW1DxpZ1c6FSvJ7XWqEeGUHoxrYWy/FHCMPVJXKV5g2rscY6u/R8Z99y8g8Xq+2vfEpJflZoQoAAADMBQwGAQCY5fadbTIuLy7JVHlRhkttns3g4KDOnTtlZLm5eVq2bKVLjQBg8sKhqE4crDey/MJ0LV9b5E4hxC3HdjR44JbsMR/08S/NVfLyvPE9hmPr+M++qdCQeTbh+p0fVk5h2ZR1BQAAABDfGAwCADCL3e0a1KWbnUaWiKsFT506pkgkYmTV1Tvk8fCjDIDEdeZYg4YGzb/btr20SJZludQI8Sp4ukXRFnOg5y1IVaB6/N/Tr5x4V3dvXzOyksVrtHTDzinpCAAAACAx8G4aAACz2P6z5tmCaSk+Va4odKnNs7l7t0U3b94wssWLl6mggBU1ABJXf29Q5040GtmCxbkqq8hxqRHiVfhml0KX2ozMCviUtqtClnd8v9K3N9/UxcM/MbJAepaqXvs4g2gAAABgjmEwCADALBWJxnTofIuRPbd2nvxJiXNulW3bOn78sJElJfm1aVO1S40AYGoc339Tsag9ctmypG0vLnKxEeJRrHNIg4cbzNBjKW1XhTypSeN6jHBwUEd/8g05zujXm6WtH/w1JQfGdzYhAAAAgNmDwSAAALPUyffb1D9kblH3woYSl9o8m/ffv6TubnMr1A0btigQCLjUCAAmr7WlT9cutRrZqg0lyslnSIMH7GBUA3vrpZhj5IHqUvkKx/dacRxHJ975rgZ7ze+lq6p3q7BsyVRVBQAAAJBAGAwCADBL7T3TZFxeuSBH8/IS503noaFBnT170shycnK1fPkqlxoBwOQ5jqOje2qNLMnv1ZYdC1xqhHjk2I4GD9yS3R82cv/SXCUvyxv349RdOKLG62eNrKB0sVZt3T0VNQEAAAAkIAaDAADMQo2t/brR2GNkL24sdanNszl16rgiEXPFY1XVDnk8/PgCIHHVX+9Qc4P59/OmbeVKTfO71AjxKHi6RdGWfiPzFqQqUD3+7+Xd7c06s/efjcyfkqqtH/ykPJ7E2VYcAAAAwNTinTUAAGahvWfN1YJZaX5tWJrvUpuJa229o7q660a2aNFSFRUVu9QIACYvFrN1dF+dkaVnJmtdZZlLjRCPwje7FLrUZmRWwKe0XRWyvOP7FT4aCevoj7+hWGzMB2xe+1+VmpEzZV0BAAAAJB4GgwAAzDLBcFRHL94xsufXl8g3zjcT3Wbbto4fP2xkSUlJ2ry52qVGADA1Lp9pUU/nkJFVv7BQPl9i/P2M6RfrHNLg4QYz9FhK21UhT2rSuB/n7P5/UW9Hi5Et3bBTpUvWTUVNAAAAAAmM30ABAJhljl2+q2A4NnLZsqQX1pe42Ghirl27rK6uDiNbv36LAoFUlxoBwOSFglGdPFxvZIXzMrR0VaE7hRB37GBUA3vrpZhj5IHqUvkKx39GcMO1M6o9b37AJrugVOt3/vxU1AQAAACQ4BgMAgAwiziOo31nzG1E1y/OV15WikuNJmZoaEhnzpw0suzsHK1YsdqlRgAwNU4duaXgUNTItr+0WJZludQI8cSxHQ0euCW7P2zk/mW5Sl6WN+7HGejp0Il3vmtkviS/tr3xKXl9419xCAAAAGD2YjAIAMAsUtfSq9t3+41s18bEWS14+vRxRSLmm6LV1Tvk8fAjC4DE1ds9pAunzA9tLFyWr3nzs1xqhHgTPN2iaIv5/dtbkKpAVem4H8OOxXT0J99QJGRuV7vppY8pM7doSnoCAAAASHy8ywYAwCwydrVgflaK1iwc/0oDN7W13VVt7TUjW7hwiYqK5rnUCACmxrF9N2WP2h7S47G07cVFLjZCPAnXdSl0qc3IrIBPabsqZE3gfOCLR3+ijpZ6IytfsVkVq6qmoiYAAACAWYLBIAAAs0T/UEQ1V1qN7IUNJfJ44n+bOtu2dfy4eR6Sz5ekzZurXWoEAFPjTlOPaq+aQ581m0qUlRNwqRHiSbRzSINHGszQYyltV4U8qePf+vPOrfd1peZdI0vPyteWl3+J7WoBAAAAGBgMAgAwSxy50KJI1B657PVY2rEuMbYRvX79qjo7241s/frNSk1Nc6kRAEye4zg68l6tkfmTfdr83AKXGiGe2MGoBvfWS6NWk0pSoLpUvsLxf/8LDvbp+M++KWn0qlSvtr3xKSUlM4AGAAAAYGIwCADALOA4jvaebTayzcsLlJXmd6nR+AWDQZ05c8LIsrJytHLlGpcaAcDUqL3aprvNfUa25blypQTGvxIMs5NjOxo8cEt2v3murn9ZrpKXjX8LcMexdfxn31JwoNfI1z3/IeUWl09JVwAAAACzC4NBAABmgau3unS3c9DIXtxY6lKbiTl9ukbhcMjIqqufk8fDjykAElcsauvYvptGlpmdojWbEuPvZkyv4OkWRVv6jcxbkKpA1cReH++f2qc79VeMbF7FKi3btGuyFQEAAADMUrzjBgDALDB2teC8vFQtm5/tTpkJaG9v1Y0bV42somKRiosTYwtUAHicC6ea1NcTNLKtuxbJ6+NXsLkuXNel0CXz3Ekr4FPargpZ3vG/Pjru3NL5Qz80spS0TFV94OOyLF5nAAAAAB6N3xYAAEhw3f0hnblmvsG4a2OpLMtyqdH42Lat48cPG5nP59PmzVtdagQAUyM4FNGpI7eNrLgsU4uW57vUCPEi2jmkwSMNZuixlLarQp7U8W8xGwkN6eiPvy7Htkellra+/gmlpGZMTVkAAAAAsxKDQQAAEtzB8y2K2c7IZb/Po+fWFLvYaHxu3HhfHR3mQHPduk1KS0t3qREATI2Th24pHIoa2faXFsf9BzYwvexgVIN766WYY+SB6lL5CtPG/TiO4+jku9/XQE+Hka+selVF5cunoioAAACAWYzBIAAACcy2HR0422RkVauKlJoy/lUHbggGgzp9usbIsrKytXLlWpcaAcDU6O4c1KUz5vbOS1YWqKgk06VGiAeO7WjwwC3Z/WEj9y/LU/KyvAk91s1Lx3X7/VNGljevQmu2vT7pngAAAABmPwaDAAAksPN1HeroDRnZixtLXWozfmfPnlA4bPaurNwur9frUiMAmBrH9tbJHrWK2+u1VP3CIhcbIR4ET7Uo2tJvZN6CVAWqJnambm/nXZ3e809GlpQc0LYP/po8fA8FAAAAMA4MBgEASGD7zpirBRcUZ2jhvPheldLe3qZr164Y2YIFC1VSUuZSIwCYGs23u3Xzurm949otZcrMTnGpEeJBuK5Locvm1tlWwKe0XRWyvOP/lTwWjejIj76mWNRcdVj56q8oLWtiqw4BAAAAzF0MBgEASFDt3UO6UGu+AR3vqwUdx1FNzWEj8/l82rJlm0uNAGBqOI6jI3tqjSwlkKRN28pdaoR4EO0c0uCRBjP0WErbVSFP6sS2/T574F/V025uU7t43XOav2zDJFsCAAAAmEsYDAIAkKD2n2uWM+pyINmn6pVFrvUZjxs33ld7e6uRrV27UWlp6S41AoCpcf1Sq9rumFtFVu5YoOQUn0uN4DY7GNXg3nop5hh5oLpUvsK0CT1W043zunH2gJFl5c3Thhc+MtmaAAAAAOYYBoMAACSgaMzWwXPmqoHta4qV7I/f84VCoaBOn64xsoyMLK1atc6lRgAwNaKRmI4fuGlk2bkBrdwwz6VGcJtjOxo8cEt2v7ntp39ZnpKXTWzbz8G+LtW89W0j8/qStO2NT8mX5J90VwAAAABzC4NBAAAS0OlrbeodjBjZrg0lLrUZn7NnTyoUChpZVdV2eb3xO8wEgPE4f7JJ/b0hI9v64iJ5J3B+HGaX4KkWRVvMFaTeglQFqib2vdq2Yzr2kzcVDg0a+cZdv6CsfAbPAAAAACaO31QBAEhA+840GZeXzc9WaUH8bsfZ0dGua9euGFl5eYVKS+e71AgApsbgQFinj942spLyLFUsmdiqMMwe4bouhS63GZkV8CltV4WsCQ6LLx97S21N5tmV85dt0KK1nM0LAAAA4NkwGAQAIME0tw/o6u1uI9u1MX5XCzqOo5qaw3KcB2cseb1ebdnCm5oAEt+JQ/WKhGNGtv2lxbIsy6VGcFO0c0iDRxrM0GMp7cUKeVKTJvRYrQ3Xdfn4W0aWlpmrLa/8O15fAAAAAJ4Zg0EAABLMvrPmasGM1CRtXlboUpunq629pra2u0a2du1GpadnuNQIAKZGZ/uArpxtMbJla4pUUMzfb3ORHYxqYM9NKeYYeaC6VL6CtAk9VmioX8d++qbxoRrL8mjrB39N/pTUKekLAAAAYG5iMAgAQAIJRWI6cuGOke1YN09Jvvj8lh4Oh3T69HEjy8jI1OrV61xqBABT5+jeOo2a28jn86h650L3CsE1ju1o8MAtOQPm+b/+ZXlKXjaxbWUdx1HNW9/WUH+Pka997g3ll/D6AgAAADA58fkuIgAAeKSaK3c1GIqOXLYkvbCh1L1CT3H27EkFg0Ejq6zcLq/X51IjAJgajfVdul3baWTrq8qUnpnsUiO4KXiqRdGWfiPzFqQqUDXxrb6vnzmg5rqLRlZUvlwrKl+eVEcAAAAAkBgMAgCQUPadMbcRXb0oV4XZAZfaPFlnZ4fef/+ykZWVLVBZWblLjQBgati2oyPv1RpZIC1JG6rnu9QIbgrXdSl0uc3IrIBPabsqZHkn9it3V2uDzh38gZElp2ao+vVPyLL49R0AAADA5PGbBQAACaL+Tq9utvQZ2Ysb43O1oOM4qqk5bJyN5PV6VVm5zcVWADA13r9wRx1tA0ZW9fxC+ZNZDT3XRDuHNHikwQw9ltJerJAnNWlCjxUJh3TkR1+XHYsZefUHflWBtMzJVgUAAAAASQwGAQBIGGNXC+ZkJGvd4omdWzRT6uquq7XVPAtxzZoNysjgjU0AiS0SjqnmYL2R5RakacW6YncKwTV2MKqBPTelmGPkgepS+QrSJvx4p/f8o/q7zZWHK7a8rHkVKyfVEwAAAABGYzAIAEACGAxGdezyXSN7YUOJvJ74+1YeDod16tRxI0tPz9Dq1etdagQAU+fs8QYN9oeNbNuLi+TxWC41ghsc29Hg/ltyBiJG7l+Wp+RlE//QTv3lE6q/XGNkuUXlWvPcG5PqCQAAAABjxd+7iQAA4CFHL91ROGKPXPZYlp5fV+Jio8c7d+6kgsEhI6us3C6fjy32ACS2gb6QztaY20bOX5ij8kW5LjWCW4KnWhS9029k3sJUBaom/r25r6tVp977vpEl+VO07Y1PyevleycAAACAqcVgEACAOOc4zkPbiG5clq+cjGSXGj1eV1enrl69ZGSlpeWaP3+BS40AYOrUHKxXdNSHNCxreLUg5pZwXZdCl80tP62AT2m7KmR5J/Yrdiwa0dEff13RSMjIt7zyy0rPzp90VwAAAAAYi8EgAABx7npjj5raB4xs18ZSl9o8nuM4qqk5LMd5cNaSx+NVVdV2F1sBwNRov9uvq+fNs1NXrCtWXmG6S43ghmjHoAaPmKtG5bGU9mKFPIGkCT/e+UP/pq7WRiNbuHqryldsnkxNAAAAAHgsBoMAAMS5sasFi3ICWrkgx6U2j3fzZq3u3m0xsjVr1isjI9OlRgAwNRzH0dG9tUbmS/Ko6vmFLjWCG2JDEQ3srZdijpEHqkvlK0ib8OM1113StdP7jCwjt0ibXvqFSbQEAAAAgCdjMAgAQBzrHQzr5PutRvbChlJ5LMulRo8WDod16tQxI0tPz9CaNRvcKQQAU+h2Xaca67uNbOPWcqWm+90phBnn2I7u/vianIGIkfuX5Sl5Wd6EH2+ov0c1b33LyDxen7a/8Sn5kuJvq3AAAAAAsweDQQAA4tjh8y2KjlqZ4PN6tGPdPBcbPdr586c1NDRoZFu2bJPP53OpEQBMDdt2dHRPnZGlZfi1vqrMpUZwQ/v+mwo29BiZtzBVgaqSCT+Wbds69tM3FRoytwnf8MJHlF0Qf1uFAwAAAJhdGAwCABCnbMfRvrPmNqKVKwqV/gxnGE2n7u4uXblywchKS+dr/vwFLjUCgKlz5VyLujrMDz5U7VyopCSvS40w0/qvtqn7VLORWQGf0nZVyPJO/FfqqyfeUWvDdSMrXbJOS9bvmFRPAAAAABgPPsYPAECcunyzU23dQSN7cVN8rSRwHEc1NYflOA9WNXo8HlVWbpcVZ9udAsBEhUNR1RysN7L8onQtX1PkTiFMO8d2ZPcEFe0YUqxjULGOIcU6hswbeSylvVghzzN8UKetqU4Xj/zUyFIzclT56q/wfRMAAADAjGAwCABAnNp7xlwtOL8wXYtLMl1q82j19XW6c8dcRbF69XplZma51AgAps7pY7cVHDTPlNv+0iIGOLOEYzuye0OKdQwp2jE4PAjsDEpR+4n3C2wtla8gbcLPFw4O6thPviHHefD4lmVp6wc/qeTAxB8PAAAAAJ4Fg0EAAOJQZ29QZ2+0G9mujaVx9WZ0JBLRqVPHjCwtLV1r1250qREATJ2+nqDOnzA/oLFgSZ5KF+S41AiT4TiO7N7wyCrA6L0/nzYEHMu/PE/JS/Oe6flPvP0dDfZ1Gfnqba+roHTxhB8PAAAAAJ4Vg0EAAOLQgXPNGrU7p5L9Xm1dFV9b150/f1qDgwNGtmXLNvl8/HgBIPEdP3BTsejolV3SthcXudgI4+U4juz+8PA2oO0PBoGKTGwIOFb6inx5t8x7pvvWnj+kxhvnjKywbIlWVr02qU4AAAAAMFG8cwcAQJyJxmztP2duz7ltdbECyfHzbbunp1uXL583snnzylReXuFOIQCYQq0tfbp+qdXIVm8sUU5eqkuN8DiO48gZiAyvAGx/cC6gE45N6nGttCT58lLlzQ8ouyJXqfMyJL9XnZ0DT7/zGN1tzTqz71+MzJ+SpurXPymPxzOpngAAAAAwUfHzDiMAAJAknbvRrp7+sJHt2lDiUpuHOY6jmprDckYtafR4PKqu3h5XW50CwLNwHEdH9tQamT/Zqy07FrjUCPc5jiNnMKLoqJWAsY5BOaFJDgFTk+TNC4wMAr15qfKkPPhVOTU3TV6vR7HYxFccRiMhHf3x12THokZe/YGPKzUje1K9AQAAAOBZMBgEACDO7D1jnmm1uDRT5UUZLrV52O3bN9XSYnZctWqdMjOz3SkEAFOo/nqHWhp6jGzTtnIFUv0uNZq77MGIYh2DxiDQCUaffscnsFJ88uanypc3PAD05gXkSU2aosYPO7P3n9XbedfIlm3apZJFa6btOQEAAADgSRgMAgAQR+52DupyfZeRvbix1KU2D4tEIjpx4qiRpaamae3ajS41AoCpE4vZOrq3zsgyMpO1dkuZS43mDnsoMrIC8P4g0Bma5BAw2StvfuqD1YB5qbJSfTO2uv32+6dVd9H8npldWKZ1Oz40I88PAAAAAI/CYBAAgDiy76y5Ei8txafKFYUutXnYhQtnNDhonq+0Zcs2JSVN32oLAJgpl840q6dryMiqdy2Sz8c5cFPJDkZHzgKMdQwp2jEoZyAyqce0kr3yjloF6MtLlZWW5NoW1/09HTr5zneNzJfk1/Y3PiWvj++ZAAAAANzDYBAAgDgRicZ06HyLke1YN09JPq9LjUy9vd26fPm8kRUXl2rBgoUuNQKAqRMKRnTy0C0jK5yXoSUrC1xqNDvYoejIAPD+MNAec47uhCV55Lu3EnBkO9B0f9ycc2vHYjr6468rEg4a+eaXf0kZOfHzYR8AAAAAcxODQQAA4sSJq60aGHN20q4N8bGNqOM4qqk5Itu2RzLLslRVtT1u3ogFgMk4deS2QmP+Dt7+8mL+jpsAJxwb3gZ0ZDXgoOy+yQ8Bvbn3tgLNHx4EejLiZwj4KBeO/Fidd8wh84KVlapYVeVSIwAAAAB4gMEgAABxYt+ZZuPyqoocFeWmutTGVFtbq+bmRiNbtWqtsrNzXGoEAFOnt3tIF06ZWzkvWp6veWVZLjWKf04kdm8b0FErAXtDk3tQ3/0hYGDkbEBPZnJcDwHHulN/RVdPvGtk6dkF2vzyx1xqBAAAAAAmBoMAAMSBhtZ+3WjqMbJ4WS0YiUS0f/8+IwsEUrVu3SZ3CgHAFDu276bsmDNy2eOxtHXXIhcbxRcnaivWOTwAjLbfWwnYM8khoNeSN3d4BaDv/krAzGRZnsQZAo4VHOjV8Z99y8g8Hq+2vfEpJflTXGoFAAAAACYGgwAAxIF9Z8yVKlnpfm1Ymu9SG1NNTY36+vqMbMuWrUpK8rvUCACmzp3GHtVebTOyNZtLlJUTcKmRu5yorVjXvTMB2wcV7RiS3ROUnKff97E894eADwaBnqyUhB4CjuU4to7/7FsKDprfL9ft/F+UWzTfpVYAAAAA8DAGgwAAuGwoFNWRS3eMbOe6Evm8HpcaPdDd3a0TJ04YWXFxiSoqFrvUCACmjuM4OrKn1siSU3zavH2BS41mlhOzFesKjmwFGm0flN09BUPAnBR584a3AvXmp8qbPbuGgI9y9eQe3bl11chKFq3Wso27XOkDAAAAAI/DYBAAAJcdv3xXoXBs5LJlSS9sKHGx0TDHcbRv3z7FYqO7Waqq2p5Q5z0BwOPUXm3T3WZzhdfm5xYoJZDkUqOp49iOnKGI7MGo7MGInMGI7KHIg68HI7L7wpI9iSmgpVFDwFR58wPDQ8A4+GDLTOpoqdeFwz8yskBalqp2f5zvlwAAAADiDoNBAABc5DiO9o7ZRnT94nzlZrp7FpHjOLp06Zzq628a+cqVa5SdnetSKwCYOrGorWP7zL/jMrNTtGaT+x/MeBLHceSEYw+Ge4MROfeGf/Zg5N4wMCJnKDq1T2xJnuwU+e5tB+rNS5U3d+4NAccKh4Z09Mdfl2Pbo1JL1a9/QsmBdNd6AQAAAMDjMBgEAMBFdc29amjtN7IXN5W61GZYLBbV0aMHVVd33cgDgYDWrdvsUisAmFoXTjWprydoZFt3LZLXxUGXE7WN4d5DQ797q/4Um8xen+PjyUqWNz/1wSAwNyDLN7eHgGM5jqOT735PA72dRr6q+jUVlS9zqRUAAAAAPBmDQQAAXDR2tWB+VopWL3RvRd7Q0KD27n1b7e2tD123Zcs2+f1+F1oBwNQaGozo1JFbRlZclqlFy/On5fkc25ETjJrbeA6N+nowKmcwImfUttIzyZOZPLwNaG6qfPmB4SFgkteVLonk5sVjanj/tJHlly7S6m0fcKkRAAAAADwdg0EAAFzSPxRRzRVzALdrY6k8Lp1H1NHRrr1739Lg4MBD1z333HNauHCJC60AYOqdOnxL4ZA5hNv+0uIJnwfnOI4Usc0VfcbqvujICkBN/yK/R/NYsgI+eVKT5ElNknXvT09qkjxpSfLmBGT5GQJOVE9Hi07v/Scj8yenauvrn5THw/+eAAAAAOIXg0EAAFxy+EKLorEHZxJ5PZZ2rJ3nSpebN2t15Mg+xWLmG+VJSUn64Ac/qIULF6mz8+GBIQAkmu7OQV0602xkS1YVqqgk08icmC17KPrwsM/Y6jMqRW25xUrxyZPqGx72BR4e/FmpSbKSvRMeeOLJopGwjv74G4pFI0Zeuft/VVom5/ACAAAAiG8MBgEAcIHjONo3ZhvRLSsKlZk2s1t1Oo6js2dP6sKFMw9dl56eoQ9/+MMqKipULObeG98AMJWO7qlVkqRUn1cBr6U0n0eVhRkaPNJgDACdkDvbekqSfJ5Rw717q/0CY4Z+AZ8sF89DnMvOHfiBetrHDJfX71DZknUuNQIAAACA8WMwCACAC67c6tLdriEj27WhZEY7RCIRHT68V7dv1z90XVHRPL3wwivKz5+e87YAYCo5tiPFbMWGIoqFYwr3hhS+0/fQWX6R3pCqgjF5i7LM+7/fofBMFLUkK5D08NDv/kq/+6v+2NozbjVeP6cb5w4ZWVZ+iTa88BGXGgEAAADAxDAYBADABWNXC5bmp2nZ/OwZe/7+/j7t3fuWuro6H7pu2bKVqqzcLq+XN6YBPJ3jOMPn58VsOTFnHH86cmL24/+0n3T9ox/3/vl9PU/p6pGkadpW00r2PljRN7K6z2ds72kl+2R52NYzUfV1tavm7W8bmdfn1/Y3PiWvL8mlVgAAAAAwMQwGAQCYYd39IZ253m5kuzaWztgZUHfvtmjfvncUCgWN3LIsVVZu1/LlqziPCkggjuNI9tiB22MGc/Z4BncTGeCZg7lZyWs9vKLvUUM/tvWc1exYTO/+41cVCZmr/Te9+AvKzCt2qRUAAAAATByDQQAAZtjBc82K2Q/eRfcnebRt9cy8qXj9+lUdP35Itm2eGej3J+uFF17RvHmlM9IDiCcjK97s4QHbyKDNdoYHafa965z7l8fe7t5Wls7o+ziPuM/Tbifzecfe7gkDO0yc4zjmNp4jZ/fd/9on6962nnxYAjXv/avu3L5hZOXLN2nhmq0uNQIAAACAZ8NgEACAGWTbjvafazayrauKlJoyvd+SbdvWyZPHdPXqxYeuy8rK1osv7lZmZtYj7gk82uMGXI//Wkbe3zYkS5IdtRXqCz56iPbQgG4CQ7RH3W7s440aBmJ28SR7pZQHK/paOwdVW9+lwZijIdvWUMzWro+sUsVSzlHF0zXVXdXJfT8ysrSsPG1+5ZcZGgMAAABIOAwGAQCYQedrO9TZGzKyXRund5VeKBTSgQPvqqWl6aHrSkvn6/nnX5bf75/WDnjgodVpjxukjd4e8jEDstGr2Z7tvqMGaLEnDN0edZ9JGpz0IyAheK3hLTaNPy3J63nwp2fM5Uf+OZ7beJSTl6aklCQ5XkudnQOSpMGBsN77uxpFwrGRWiXl2VqwJM+t/1UQ58LBQfV03FFvR4t6Ou6o8dqZ4Q8f3GN5PNr2wU/JnxxwsSUAAAAAPBsGgwAAzKC9Z8zh3MJ5Gaoozpy25+vp6daePT9TX1/vQ9etXr1OGzdWyeOJr3OxRg+y5DxiZdqjtny8fzvjthr5+sFKNI2538Mr0x5cL3MF2/3bjnoOY4g2dpvJsV+zOg1ueORg7nF/jm/4Zg7yHv948lgzvprKl+aXx+tRLPZgu+QTB+uNoaAkbX9pESu9oEg4qN6OO+ppb1FPR8vw1x0tGurveeL91j33IeXNWzBDLQEAAABgajEYBABghrR1D+liXYeR7dowfasFGxtu6+Ch9xSJRIzc4/Goak21FhUvVOzOgGKjzy2z759b5ki2rU6/T5Yt2TFbQ4Phxw/IbGd4McXYAdm97JHDvtG3ZWiGRGBJsu4NvDzDf8qSefn+MMwz+na6d9sxt7uXyaNRj2feZuTyRAZ39/6UpTk//OpsG9CVcy1GtnxNkQqKM1xqBDdEIyH1dtx9MPy7Nwgc7Oua8GMVL1ih5VtenIaWAAAAADAzGAwCADCFHOf+gM2RYveHbMN/njnRoIokn3yWJZ8lpSV5tSk1WaHrnVLMvncfZ9TX9sj2jk5s1MBu1G2dmP1gy8h7X9tRW7V2sy47t4eHFqMkO0mqjC1V7jlb/edqn/rvE5ym/50wR1h6MKAaMxDzJnmHB1iWpZjtTO0QzTPqdmMHeSO305jh3SMez2PJskbdbo4P2RLR0b11o3eAlM/nUdULC90rhGkVi0bU23lXPe0tI9uA9nS0aKCnU8OfQJmcrLxCVX/gV2VZ8bXSHgAAAAAmgsEgAGDOsfvDit7tlxN9xJDtEavmRlbTjR3CjRoAjpzP9oQVb1WSqvKzjCxypFGRR9/8mcRk67xuqsFqf2gomOWkqUpLFVDyFD4jps3YodXowZlljQzVHn+7R3z92Ns/5vksy9wScgLP9bTVarm5afLe2/Lx/llwwFRquNmp23WdRra+qkzpGfwdmOhisaj6OlsfDP/urQAc6Gkf/oDOFLA8HmXkFCkrr1jF8xeoYN58lSxaqb7+6JQ8PgAAAAC4hcEgAGBOiTT2auC9m27XmBZBhXVC19Vl9T90XYmTqw1aJJ+8LjSbJqO2dTRWpFnmAM0caslcFXb/67Grwkbfd+xKNWv0fTXytTFIG91hPIO0R62OA/DMbNvRkT11Rpaa5tfGreUuNcKzsGMx9Xe3qaej5d7w7456O1rU19Umx7Gf/gDjYFkepecUKCuvWFl585SZV6ys/HnKyC6Uxzv8PXP0BxkkBoMAAAAAEhuDQQBIMLdv39bf/u3f6siRI+ro6FB2draqq6v1H//jf9TixYvdrhf3Qu+3u11hWnRrQDW6pqAVfui6FU6ZlqpE1v0lhPe2d7TurwTzeh6sCrv39fCZZh4lB5Lk8XnkWFI4EnvyQOz+EGzUyrKRgd3o1WaPuu2jBm+jt3E0hnmj7gsAj3D+ZKM628yVqJU7K5Tkn0UfjphFbNvWQE+7MfzraW9RX1erbDs2Rc9iKT07f3gAmD9PmXnzlJVXrIycQnl9SVP0HAAAAAAQ/xgMAkACOXnypP79v//3GhgYUHl5uV588UU1NTXpRz/6kd5991199atfVVVVlds149r/z959h0dV5X8c/8xMeu8Jofem9I5IWXvHirq6rh3LrosuKqIgNlx1ZX+uylqwoIJdEUE6SFF679KTEEjvbcrvj0mGDOnJTALM+/U8eTJz65nJyT333u8932MK9pVZOY2+X6vNJrMks80ms03y9/eSr5+3UxDOYDKUvi8NzpkMkvHU9FPBPOdlj6YnaO3e3bKcdvPUy+SlIf2GqVXLNs77qUNvNNI9AjjbFBeZtXzBPqdpEdGB6nJ+XBOVCGVsNqvystLtPQDTjis71T4GYE76SVksrkusHRgS4RT8C4lsppCIWHl5+7hsHwAAWYwntgAAxaZJREFUAABwtiIwCABnicLCQj322GPKy8vTPffcoyeeeEJGo1GS9OOPP2r8+PEaN26cFi5cqICAgCYu7ZnLr08zyWCQOSWvtDdbWRDOWNpjzh48qyww5xSwKwuwOQXsyve4O7XsrGV/aPGmREcZQgK89fpfhsrLZGzQZ7HZbNq6daO27d5UYV5gYJBGjbpU4eGRDdoHAJxt1iw7oLycIqdpQ0a1k5EUvY3GZrMpPydDWanHlZ2WXBoITFZ2WrIs5oo92+srIDjcnvozsllpIDBOIRFx8vZhHEkAAAAAqAqBQQA4SyxcuFApKSlq06aNHn/8cUdQUJKuvfZaLVmyRAsWLND333+v22+/vQlLemYzeBnl3z++0fZXVGzR6p0nnKYN6xnf4KBgSUmJVq9epqNHD1eYFxMTpxEjLpafn3+D9gEAZ5vsrEKtWX7AaVrLduFq2TaiiUp0brPZbCrIzbKn/kxLLk0FelzZ6ckyFxfVvIFa8gsMcQr+lY0F6ONLOwcAAAAAdUVgEADOEjt27JAk9e/fXyZTxTGSBg0apAULFmjx4sUEBs8ga3efUEGR2fHeIGl4z4YFJnNzc7Rs2QJlZKRXmNexYxcNGDC00joCAOe6FQv2yVxidbw3GKTBIxl/t6FsNpuK8nPsPf9K03+W9QQsKSpw2X58/YOcUoCGRjVTSEScfP0DXbYPAAAAAPB0BAYBoBq5ublavXq11q5dq127dunw4cPKycmRr6+vYmJi1KNHD1111VUaNmyYDAb3pigrKLDfeAsNDa10flhYmCRp165dbi0H6mb55kSn9+e3j1RUWP17OJw4kawVKxaqsLDQabrBYFC/foPVpUt3t9dFADjTlJRYlHQkU9s2JjhN79KjmSKjCSrVRVFBbmnPv9IAYOnr4kLXjTPr4xfg6PUXGtXM8dovINhl+wAAAAAAVI7AIABU4aOPPtKbb76poqKKqbDMZrMOHTqkQ4cO6ccff1S/fv302muvKT7efSkqIyLsadASEhIqnV82PTMzU3l5eQoM5EZoUzt0PFuHk3Ocpo3o1bze29u/f4/Wrl0lq9XqNN3Hx1cXXvgnxce3qPe2AZvNJpvttNelvyubZyudcGrZ8sudtp7NptJfjm0WF1oUGuYv/wDvxv6oOMsVFZqVeiK39CdHKSdylZmW76ijZbx9TBowrE2TlPFMZLVaZbWYZTGX2H9bzCrIzXIK/mWlHVdRfk7NG6slbx+/0uBfvEIj4+w9AaOayS8gmIdYAAAAAKCJEBgEgCocOnTIERSMjY3VkCFD1L17d0VGRqqoqEhbtmzRnDlzlJ+frw0bNuiOO+7QV199pcjISLeUZ+DAgZo+fbpWrFihlJQURUdHO+aZzWZ9++23jvcEBs8Mp/cWjAjxVY/2da8fVqtVGzf+rt27d1SYFxoappEjL1VISOU9SeFeJSUWZablKz0lT+mp+crOLJDVYrMHxCSnYNnpgbRT70tf2057XS64Zn9ffrmqA26n9l3dfirOayoBgT4KjwpQRHSgIqMDFREdqIioQHn7kA4XUn5esSMImJKco9QTucrOLKx5RUm9B7ZUQJCPm0tYM5vNKovZXCEoZ7GYZTWXOP8uXcY+rXQZS8mp9U/bxunrO08rt77FLNtpD5W4kpe3b+nYf6eCf6GRcfIPCiMACAAAAABnGAKDAFAFg8GgCy64QHfffbcGDx4so9HoNH/06NG6//77dc899+jQoUNKSEjQ66+/rldeeaXCtsaPH69t27bVaf8XX3yxHn/8ccf7wYMHq1evXtqyZYvuvvtuTZo0SV27dlViYqLeeOMNJSaeCkKdXlY0vvzCEq3ddcJp2vCe8TIa63aDtKioSL/+uljHjydWmNe8eUsNG/Yn+fg0/Y3vc525xKKMtHxlpOYrPdUeBMxIzat1gAJVy88rVn5esRKPZDpNDw33twcJSwOGkTGBCgnzr/P/EM4ONptNudlFTgHA1BO5ysstrtf2wiL8dX7/eHuQ7bSgm/13WaCt5FQwrsK00wJ1Vax/KhhnKf3tvIzVanHxt9V0TF7eComILRf8swcAA0LCZTBw7gEAAAAAZwODzdaUz4gDwJkrMzPTMW5fdfbs2aNrr71WkuTv76/ffvtN/v7OY8jdcccdWrduXZ32P3r0aE2dOtVp2smTJzV27Fjt2OHcc8zX11cTJ07Us88+K4PBoG3btjVJsCgjI19m87lzA7QhFm84pi8W73e8NxkNeu2hIQoL8q31NrKyMrV06QLl5GRVmNetWw/16TPA7UHgiIhAmUxGWSxWpae7bnypM5XFbFVGmj34VxYEzCjtCcgZU9Pz8jKW610YpMgYe+AwIJDg+NnEZrMpM73AkQrUHgzMVVGhuR5bM8vLkCmTIUPexnT5eGXJoCIZDPa0magfo8mkkIi40l6A9uBfaFQzBYREeuTDR57WFqLxULfgDtSrc5eXl0nh4QFNXQwAwDmAHoMAUIXaBAUlqUuXLmrbtq0OHTqkgoICHTlyRF26dHFaZubMmS4pU0xMjL7++mstX75c69evV15enpo3b64rrrhCFos9INe6dWt6kDUxm82mZaelEe3dMapOQcHExGP69dclKilx7i1jNBo1ePCFat++k0vK6qksFqsy0wuUkZrnSAOakZqnrAwCgNWzSrLKIIvzb4NVkkWGKqad+m2VwWCxz7NZZbX5yWwLk8UWJptq/v8wm61KSbYHkaRTPXL9ArztvQrLehjGBCo8KlDe3qQjbWoWi1UZqfmn0oGeyFHayTyVFNfnIRKbTIZsmQzp8jZmyNc7S7JkyJ5ot5S1NO2ui8p/rjMYjQoOj7UH/krHAgyJjFNQWJSMRv5/AAAAAOBcRGAQAFwgKCjI8bpsXEJ3MRqNGjVqlEaNGuU0/bvvvpMkDRo0yK37R832HcvU8bR8p2kjezev1bo2m027d2/Xxo1rdXqnfj8/f40ceYmio2NdVtZzncViVVZGgb33X0qeowdgVkaBrFbXhQ5CwvwUERWo8KgA+fjaT68MBntKYvvwWgbHexlqmKfyyxkkWWWzWWWzmmWzmksDambZLOVeWy32+TaLrBaLbDaLY3mr1SKbxSyrrfS31b68tZLXjmkWiyxW+z4cY5lZzW4djNBgCpJVYSooCpbFFiazLUw2+cv+jVSvML9EiUcyq0xHWj5gSDpS9zGXWJSWkqeU5FxHb8C0lDxZLfWpNzYZlS+TIUM+Xhny88mSLOmyWUtOLXKWd1A3GAwymrxl8vKS0eQlk5e3/bfp1HuTyUtGr7JpZe/tv+3rnbZM2TzTadss24fJ+7Rp3h7ZAxAAAAAAPBmBQQBooOLiYh0+fNjxPj4+vtHLYLFYNHPmTBkMBo0ZM6bR9w9np/cWjI0IUJfW4TWuZ7FY9PvvK3XgwL4K8yIiojRy5CUKDAyqZE1YrbbSAOCp3n9pKXnKSndtADA41E8RUQEKj7IHmiKiAhQWGSCruVBZqUnKyUiWxVx8aswxs8U+3tjpY5dZzLKa7b/Lxiw7fbq1dOwym9XqsvKfyWyWXBmUq4ByZ6dGk5/kFaGi4lAVlQTLbA2TVUGqTbBQkrIyCpSVUaBD+1Id0+zpSJ2DhaQjrbuiQrPSTuY6goApJ3KUmZZf79ixQUXyMmTIxztL/r5ZkiVVVnOBY76tpJqV67qvsoBcJQG1ssCcycu7hoCcPcjmFHQrv41KAnIVptMjDwAAAADQBAgMAkADzZ07Vzk5OZKk7t27Kzo62m372rdvn1q2bOk0hmFubq4mT56sXbt26bbbblPXrl3dtv+ahIT4Ndm+zxSZuUXatC/FadqVQ9ooMrL6gF5eXp7mzp2n48ePV5jXsWMnXXLJJfL29nZpWWujrGeV0WhQRERgo+//dFarTZlp+Uo5kaOU0h5JKSdylXYyTxaL6wJoIWF+io4NVnRskKLiSn/HBMlotCkjJUnpJxKVdmK39q9PVHpygvJyMl22b5xitRRKliR5K0nepWetRpOPvPyiZLGGKTc/UMXmEFlsIZJq1+vJno40RynJOU7TA4N8FB0XrJi4YEU3K/0dGyxvH4I3eblFSk7MVnJilk4kZSs5MVsZp/WKrhuLvAyZCvDLlr9ftmRJVUlhpmOutY4d7w0GgyJimyumeVvFtmqnyJjmMnl5y1AW5PP2lpcjqOcto4m/KermTGsLce6gbsEdqFcAAKAmBAYBoAHS09P1+uuvO96PHTvWrfubMWOGFixYoO7duysmJkY5OTnatGmTcnNzddlll2nChAlu3X9NTCbSkS3dcEzmcmnzfLyMumhg62q/mxMnTujHH390BJjLGzp0qAYOHFiaUrLpGAwGmUyNVwab1aaM9Hx7AOdEriOQk3oyV2azCwOAoX6Kjgu2/8QGlf4Olo+PUdmZqUpLTlBa8gHtXJOgtOQEZaad8JgefHVhNJpk8irtMeVVmq7Q+1TvqrLpXl7ejl5VMhiUmZKs1ORjsphr3x3MailWcV6SpCT5GyR/b8lgNMk3IFoyRaiwKEjZuQEyW0NVl1PdvNxi5f2RpsN/pJ2aaJAiIgMV0yxYMc2CFdssRDHNghUeGXhOpiO12WzKzizU8YQsJSdm6XhilpITs5WTVdiQrcpkyFZwUK4C/HJksKaqMDdFNptVskgleXXfYnB4lGJbtFVsy3aKbdFO0c1by9un9mO4AvXV2G0hPAd1C+5AvQIAAFUhMAgA9VRcXKxHH31UaWn2m8gXXXSRLr74Yrfu86KLLlJqaqr27t2rLVu2KDAwUD179tRNN92kyy+/3K37rg1X9tg6G1mtNv3y22GnaUN7xivA16vK72b//n1asGCBzGaz03Rvb29deull6tChQ2kqTPeN7VYdo9Egg8Egm83m0pScZWxWm7IyC5x6/zkCgCWuq09Bwb6KjgtSVOypAGBUTJD8/L1VkJdT2gNwj/auT9CaE4nKOJmokmL3jhfaYAaDvErTEnqVBt6MZcE3k/ep116nUhyeCtJ5VzLt1PhjztO8SoN83hWCfI7UiPUYo6ysblnMZqWdTFJq0lGlHj/q+F1cVFDzRkrZrBYV5iZLSpYkhXjZvx//wEiZfKNVYglVTq6/8vKDZFMdUobapPRU+9iUe7YnOyZ7eRvtPUrjghXbzP47plmwAoPOnuCUzWpTelqekhOzS3sB2oOABfkNydlpk9FQoPDQfAX424OABTn29Loqkgrr8S/lFxCkmBZtFdO8rWJatFF087YKCAqpsFzZMdbdxyx4JuoV3IW6BXegXp3beBgXAOAKBputviOBAIDnslqtGj9+vH766SdJUqtWrfTNN98oNDS0iUvWtDIy8mU2W5q6GE1m24E0Tft6q9O0Z+7sq/bxFeuFzWbT1q0btW3bpgrzAgODNHLkpYqIiHRbWWsrIiJQJpNRFotV6en16NpTymazKTe7SOmpecpIzXf67coAoH+gtyKiAhURFajwqAD76+gA+fp5y2IuUVZasrJSk5SVelxZqUnKTE1SYV62S/ZtNJkUHB4rX//ACmOPVRx/zHncsbKxzCqMdeZ1aswzY2lgrmy8M4PR2OQ9SRuiurpls1mVl5WmjJMJyjiZqMyTCco4eUyF+RV71daVb0CYvP2jZVO4CoqClZXtrxKzj2o7bmF1/AO8HeMWRkYHKSLaXg+9vZs2daXFYrWn4C03HmDayTyVFDfseG0yligsLF8Bfjky2tJUmJesksLc+m/P5K3w2JaKiGulyLjWiohrrcDQyDrVc1cds4DyqFdwF+oW3IF6de7y8jIpPDygqYsBADgH0GMQAOrIZrNp0qRJjqBgfHy8PvroI48PCkJavjnR6X2rmCC1a1axZ0tJSYlWr16uo0cPVZgXExOn4cMvdhpH8mxis9mUl1usjNQ8pafkKb1cELChQYjy/Py9FREdUBoADFREVIAiogPl5+/tCCplph5XeuI2HdqapMyUJOVmpshVz0MFhkQoNCpeYdHxCo2KV2hUMwWHxTB2mYsYDEYFhUUrKCxaLTv1dkwvyM0qDRYm2IOFKQnKy0qrZksVFeVnqig/0/E+2Cj5hATJLzhWRq9IFZtDlJMbqKwsk+oaLCzIL1HikUwlHsl0mh4a7l8uYBioiOhAhYT5uyUdqbnEorSUPKWeKA0CJucqPSVXFkvD6r6Xl03hEUUK9MuWUekqyktWQU6abLlSXj1igQaDQSGRzZyCgKGRzfgfAgAAAADAzQgMAkAd2Gw2TZ48WV999ZUkKS4uTp988olatGjRxCVDU0vLKtTWA6lO00b0aV6hp0tubq6WLVugjIyKwYwOHbpo4MChMp0FN8ZtNpvy84rtvf5KA4AZpSkXi4tcFwD09fOyB/9Kg4ARUQEKjwpUQKA9HWRRQa4yU5KUlbpPCXvsPQGz047LXFLskv37+AWUBv7iFRbVzBEE9Pbxc8n2UTf+QaHyDwpVfLvujmnFhfn2QGFKgjJO2IOFOekn6hQELi7MVXFhrqQDkiSTpJggPwWGxsnbL1oWhSm/IEgZGd4qLKh7/c7KKFBWRoEO7Tt1jPDyMiqiNEgYEX0qYFhWt2tV7iJzaQ/AXKUm5yr1ZK4yUvPU0Pi3j49REZEWBQZky1QaBMzNTJYl06r69q8NCIlQZFwrRZQGAcNjWjIuIAAAAAAATYDAIADUks1m0/PPP6/Zs2dLkmJjY/Xpp5+qVatWTVwynAl+3ZrkdDPez8ekQd1inZY5eTJZy5cvVGFhodN0g8Ggfv0Gq0uX7mdUakibzSaL2aqC/BKdSMrSkYPpjvHWMlLzVVRornkjteTj61WuB2CAIx2of6C3DAaDzCXFyk5PVlbqPu09nORIB+rKNKAhEXGlAUB78C8surn8AkPOqL8JKvLxC1Bsq06KbdXJMc1cUqzMlER7sPBkojJOHlNWapKsltoH9czFhcpKOSzpsGNasMlb8c3j5BsYI5kiVFQSouwcP2WkFctirltKXLPZqpPHc3TyuHN6VP9Ab0eQsHw60pJii6MXYNlPVkbtx2Gsin+AlyKjjAoMzJHJlqbi/BPKTktUcVqRiuvWGdPBxzdAEaVBwMhmrRUR20p+gRV7TwMAAAAAgMZHYBAAaqEsKDhr1ixJUkxMjD799FO1bt26iUuGM4HZYtWv25Kcpg0+L05+Pqea2f3792jt2lWyWp2DBz4+Prpg6CjFxMSrsMAsi8Uqq8Uqi8VW+tv+2mK2ls6zOf22ON5Xsk755czO61hrua6refuYHL3+IqJLU4BGBSogyEcGg0E2m1W5mWnKSj2qQ9uTlFk6FqBL04CGRpYG/0oDgFHxCgqPltF45vfURO14efsoKr6touLbOqZZLRZlpyc7pyI9mSBzSVGtt2uxlCgz5ZiUcswxzWAwqmV0jILCmsnoEyWzNUz5BYHKSDPXK3BXkFeihLxMJRzOrPO6NQkO9VN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",
+      "text/plain": [
+       "<Figure size 1280x960 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "generate_k_plot(df, 2**20, 256)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba761181-2f7d-4890-8819-f12a8483cb17",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/cpp/scripts/heuristics/select_k/select_k_dataset.py b/cpp/scripts/heuristics/select_k/select_k_dataset.py
new file mode 100644
index 0000000000..6d174febe5
--- /dev/null
+++ b/cpp/scripts/heuristics/select_k/select_k_dataset.py
@@ -0,0 +1,114 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from collections import defaultdict
+import json
+
+import pandas as pd
+import numpy as np
+
+
+def load_dataframe(filename):
+    """Loads up the select_k benchmark times as a pandas dataframe
+
+    This loads up the timings from the MATRIX_BENCH script into a pandas dataframe
+    This file is generated by running:
+
+    ./cpp/build/MATRIX_BENCH --benchmark_filter=SelectKDataset \
+        --benchmark_out_format=json \
+        --benchmark_out=select_k_times.json \
+        --select_k_dataset
+
+    Note running these MATRIX_BENCH tests takes over 24 hours right now
+    """
+    benchmarks = json.load(open(filename))["benchmarks"]
+    df = pd.DataFrame(benchmarks, columns=["real_time", "run_name"])
+    run_info = [
+        run[1:4] + list(map(int, run[4:9]))
+        for run in df.run_name.str.split("/").tolist()
+    ]
+    df[
+        [
+            "key_type",
+            "index_type",
+            "algo",
+            "row",
+            "col",
+            "k",
+            "use_index_input",
+            "use_memory_pool",
+        ]
+    ] = pd.DataFrame(run_info, index=df.index)
+    df["time"] = df["real_time"] / 1000
+    df = df.drop(["run_name", "real_time"], axis=1)
+    df = df.sort_values(
+        by=[
+            "k",
+            "row",
+            "col",
+            "key_type",
+            "index_type",
+            "use_index_input",
+            "use_memory_pool",
+        ]
+    )
+    df = df.reset_index(drop=True)
+    return df
+
+
+def get_dataset(df):
+    """Returns the training features, labels and sample weights from a dataframe"""
+    # group the dataframe by the input features
+    feature_algo_time = defaultdict(list)
+    for row in df.itertuples():
+        feature_algo_time[
+            (
+                row.k,
+                row.row,
+                row.col,
+                row.use_memory_pool,
+                row.key_type,
+                row.index_type,
+            )
+        ].append((row.algo, row.time))
+
+    # get the features (x), labels (y) and sample_weights from the grouped times
+    X, y, weights = [], [], []
+    for feature, algo_times in feature_algo_time.items():
+        # we can't yet handle the dtype values in training, remove
+        feature = feature[:-2]
+
+        # figure out the fastest algorithm for this set of features
+        algo_times = sorted(algo_times, key=lambda x: x[1])
+        best_algo, best_time = algo_times[0]
+
+        # set the sample_weight to the absolute speed increase above the
+        # time of the next fastest algorithm. the idea here is that
+        # we really want to capture the 2x or 10x speedups - but
+        # the 1% speedups might just be noise (and this is especially
+        # true for the faster runs)
+        if len(algo_times) == 1:
+            # no other algorithm handles this K value,
+            second_best_time = np.inf
+        else:
+            second_best_time = algo_times[1][1]
+
+        # sample_weight = min((second_best_time / best_time) - 1, 10)
+        sample_weight = min((second_best_time - best_time), 10)
+
+        X.append(feature)
+        y.append(best_algo)
+        weights.append(sample_weight)
+
+    return np.array(X), np.array(y), np.array(weights)